From 9c61b0fc9b9062b347c176b5f0f86b97b6804a1b Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 3 Sep 2023 21:33:50 +0200
Subject: [PATCH 001/150] Proper log buckets for t5. (#727)

* Proper log buckets for t5.

* Properly pass the position bias.
---
 candle-examples/examples/musicgen/t5_model.rs | 111 ++++++++++++------
 1 file changed, 74 insertions(+), 37 deletions(-)

diff --git a/candle-examples/examples/musicgen/t5_model.rs b/candle-examples/examples/musicgen/t5_model.rs
index 607b5c93..22f0a4f5 100644
--- a/candle-examples/examples/musicgen/t5_model.rs
+++ b/candle-examples/examples/musicgen/t5_model.rs
@@ -202,7 +202,11 @@ impl T5Attention {
         })
     }
 
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+    fn forward(
+        &self,
+        xs: &Tensor,
+        position_bias: Option<&Tensor>,
+    ) -> Result<(Tensor, Option<Tensor>)> {
         // TODO: Apply the mask(s)?
         // TODO: kv caching.
         let (b_sz, seq_len) = (xs.dim(0)?, xs.dim(1)?);
@@ -223,34 +227,57 @@ impl T5Attention {
             .contiguous()?;
         let scores = q.matmul(&k.t()?)?;
 
-        let scores = match &self.relative_attention_bias {
-            None => scores,
-            Some(relative_attention_bias) => {
-                let query_length = seq_len;
-                let key_length = seq_len;
-                // This only handles the bidirectional case.
-                let num_buckets = self.relative_attention_num_buckets / 2;
-                let relative_position = (0..query_length as u32)
-                    .map(|i| {
-                        (0..key_length as u32)
-                            .map(|j| {
-                                if i < j {
-                                    j - i + num_buckets as u32
-                                } else {
-                                    i - j
-                                }
-                            })
-                            .collect::<Vec<u32>>()
-                    })
-                    .collect::<Vec<Vec<_>>>();
-                let relative_buckets = Tensor::new(relative_position, q.device())?;
-                let position_bias = relative_attention_bias
-                    .forward(&relative_buckets)?
-                    .permute((2, 0, 1))?
-                    .unsqueeze(0)?;
-                (scores + position_bias)?
-                // TODO: position_bias_masked?
-            }
+        let (scores, position_bias) = match position_bias {
+            Some(position_bias) => ((scores + position_bias)?, Some(position_bias.clone())),
+            None => match &self.relative_attention_bias {
+                None => (scores, None),
+                Some(relative_attention_bias) => {
+                    let query_length = seq_len;
+                    let key_length = seq_len;
+                    // This only handles the bidirectional case.
+                    let num_buckets = self.relative_attention_num_buckets as u32 / 2;
+                    let max_exact = num_buckets / 2;
+                    let relative_position = (0..query_length as u32)
+                        .map(|i| {
+                            (0..key_length as u32)
+                                .map(|j| {
+                                    if i < j {
+                                        if j - i < max_exact {
+                                            j - i + num_buckets
+                                        } else {
+                                            let b = f32::log(
+                                                (j - i) as f32 / max_exact as f32,
+                                                self.relative_attention_max_distance as f32
+                                                    / max_exact as f32,
+                                            ) * (num_buckets - max_exact) as f32;
+                                            u32::min(
+                                                max_exact + num_buckets + b as u32,
+                                                self.relative_attention_num_buckets as u32 - 1,
+                                            )
+                                        }
+                                    } else if i - j < max_exact {
+                                        i - j
+                                    } else {
+                                        let b = f32::log(
+                                            (i - j) as f32 / max_exact as f32,
+                                            self.relative_attention_max_distance as f32
+                                                / max_exact as f32,
+                                        ) * (num_buckets - max_exact) as f32;
+                                        max_exact + b as u32
+                                    }
+                                })
+                                .collect::<Vec<u32>>()
+                        })
+                        .collect::<Vec<Vec<_>>>();
+                    let relative_buckets = Tensor::new(relative_position, q.device())?;
+                    let position_bias = relative_attention_bias
+                        .forward(&relative_buckets)?
+                        .permute((2, 0, 1))?
+                        .unsqueeze(0)?;
+                    ((scores + &position_bias)?, Some(position_bias))
+                    // TODO: position_bias_masked?
+                }
+            },
         };
 
         let attn_weights = candle_nn::ops::softmax(&scores, D::Minus1)?;
@@ -259,7 +286,7 @@ impl T5Attention {
             .transpose(1, 2)?
             .reshape((b_sz, seq_len, self.inner_dim))?;
         let attn_output = self.o.forward(&attn_output)?;
-        Ok(attn_output)
+        Ok((attn_output, position_bias))
     }
 }
 
@@ -280,11 +307,15 @@ impl T5LayerSelfAttention {
         })
     }
 
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+    fn forward(
+        &self,
+        xs: &Tensor,
+        position_bias: Option<&Tensor>,
+    ) -> Result<(Tensor, Option<Tensor>)> {
         let normed_xs = self.layer_norm.forward(xs)?;
-        let ys = self.self_attention.forward(&normed_xs)?;
+        let (ys, position_bias) = self.self_attention.forward(&normed_xs, position_bias)?;
         let ys = (xs + ys)?;
-        Ok(ys)
+        Ok((ys, position_bias))
     }
 }
 
@@ -326,8 +357,12 @@ impl T5Block {
         })
     }
 
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = self.self_attn.forward(xs)?;
+    fn forward(
+        &self,
+        xs: &Tensor,
+        position_bias: Option<&Tensor>,
+    ) -> Result<(Tensor, Option<Tensor>)> {
+        let (mut xs, position_bias) = self.self_attn.forward(xs, position_bias)?;
         // TODO: clamp for f16?
         if let Some(cross_attn) = &self.cross_attn {
             xs = cross_attn.forward(&xs)?;
@@ -335,7 +370,7 @@ impl T5Block {
         }
         let xs = self.ff.forward(&xs)?;
         // TODO: clamp for f16?
-        Ok(xs)
+        Ok((xs, position_bias))
     }
 }
 
@@ -368,8 +403,10 @@ impl T5Stack {
         let (_b_sz, _seq_len) = (input_embeds.dim(0)?, input_embeds.dim(1)?);
 
         let mut hidden_states = input_embeds;
+        let mut position_bias = None;
         for block in self.block.iter() {
-            hidden_states = block.forward(&hidden_states)?
+            (hidden_states, position_bias) =
+                block.forward(&hidden_states, position_bias.as_ref())?
         }
         let hidden_states = self.final_layer_norm.forward(&hidden_states)?;
         Ok(hidden_states)

From 20512ba408f9840828e902b7dd824be5a0969feb Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 4 Sep 2023 08:07:00 +0200
Subject: [PATCH 002/150] Return the metadata in the gguf pyo3 bindings. (#729)

* Return the metadata in the gguf pyo3 bindings.

* Read the metadata in the quantized llama example.

* Get inference to work on gguf files.
---
 candle-pyo3/quant-llama.py | 39 ++++++++++++++++++++++++++++++++----
 candle-pyo3/src/lib.rs     | 41 ++++++++++++++++++++++++++++++++++----
 2 files changed, 72 insertions(+), 8 deletions(-)

diff --git a/candle-pyo3/quant-llama.py b/candle-pyo3/quant-llama.py
index 7d74c25e..0f7a51c6 100644
--- a/candle-pyo3/quant-llama.py
+++ b/candle-pyo3/quant-llama.py
@@ -111,7 +111,8 @@ class QuantizedLlama:
         self.norm = RmsNorm(all_tensors["norm.weight"])
         self.output = all_tensors["output.weight"]
         self.layers = []
-        cos_sin = precompute_freqs_cis(hparams, 10000.)
+        rope_freq = hparams.get("rope_freq", 10000.)
+        cos_sin = precompute_freqs_cis(hparams, rope_freq)
         for layer_idx in range(hparams["n_layer"]):
             layer = QuantizedLayer(layer_idx, hparams, all_tensors, cos_sin)
             self.layers.append(layer)
@@ -133,15 +134,45 @@ class QuantizedLlama:
         x = self.output.matmul_t(x)
         return x
 
+def gguf_rename(tensor_name):
+    if tensor_name == 'token_embd.weight': return 'tok_embeddings.weight'
+    if tensor_name == 'output_norm.weight': return 'norm.weight'
+    tensor_name = tensor_name.replace('blk.', 'layers.')
+    tensor_name = tensor_name.replace('.attn_q.', '.attention.wq.')
+    tensor_name = tensor_name.replace('.attn_k.', '.attention.wk.')
+    tensor_name = tensor_name.replace('.attn_v.', '.attention.wv.')
+    tensor_name = tensor_name.replace('.attn_output.', '.attention.wo.')
+    tensor_name = tensor_name.replace('.ffn_gate.', '.feed_forward.w1.')
+    tensor_name = tensor_name.replace('.ffn_down.', '.feed_forward.w2.')
+    tensor_name = tensor_name.replace('.ffn_up.', '.feed_forward.w3.')
+    tensor_name = tensor_name.replace('.attn_norm.', '.attention_norm.')
+    return tensor_name
+
 def main():
     if len(sys.argv) < 2:
         raise ValueError("missing weight file argument")
     filename = sys.argv[1]
     print(f"reading model file {filename}")
     if filename.endswith("gguf"):
-        all_tensors = candle.load_gguf(sys.argv[1])
-        hparams = None
-        vocab = None
+        all_tensors, metadata = candle.load_gguf(sys.argv[1])
+        vocab = metadata["tokenizer.ggml.tokens"]
+        for i, v in enumerate(vocab):
+            vocab[i] = '\n' if v == '<0x0A>' else v.replace('▁', ' ')
+        hparams = {k: v for (k, v) in metadata.items() if not k.startswith("tokenizer")}
+        print(hparams)
+        hparams = {
+            'n_vocab': len(vocab),
+            'n_embd': metadata['llama.embedding_length'],
+            'n_mult': 256,
+            'n_head': metadata['llama.attention.head_count'],
+            'n_head_kv': metadata['llama.attention.head_count_kv'],
+            'n_layer': metadata['llama.block_count'],
+            'n_rot': metadata['llama.rope.dimension_count'],
+            'rope_freq': metadata['llama.rope.freq_base'],
+            'ftype': metadata['general.file_type'],
+        }
+        all_tensors = { gguf_rename(k): v for k, v in all_tensors.items() }
+
     else:
         all_tensors, hparams, vocab = candle.load_ggml(sys.argv[1])
     print(hparams)
diff --git a/candle-pyo3/src/lib.rs b/candle-pyo3/src/lib.rs
index 79f86479..f71970d5 100644
--- a/candle-pyo3/src/lib.rs
+++ b/candle-pyo3/src/lib.rs
@@ -746,10 +746,35 @@ fn load_ggml(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject, PyObje
 }
 
 #[pyfunction]
-fn load_gguf(path: &str, py: Python<'_>) -> PyResult<PyObject> {
+fn load_gguf(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject)> {
+    use ::candle::quantized::gguf_file;
+    fn gguf_value_to_pyobject(v: &gguf_file::Value, py: Python<'_>) -> PyResult<PyObject> {
+        let v: PyObject = match v {
+            gguf_file::Value::U8(x) => x.into_py(py),
+            gguf_file::Value::I8(x) => x.into_py(py),
+            gguf_file::Value::U16(x) => x.into_py(py),
+            gguf_file::Value::I16(x) => x.into_py(py),
+            gguf_file::Value::U32(x) => x.into_py(py),
+            gguf_file::Value::I32(x) => x.into_py(py),
+            gguf_file::Value::U64(x) => x.into_py(py),
+            gguf_file::Value::I64(x) => x.into_py(py),
+            gguf_file::Value::F32(x) => x.into_py(py),
+            gguf_file::Value::F64(x) => x.into_py(py),
+            gguf_file::Value::Bool(x) => x.into_py(py),
+            gguf_file::Value::String(x) => x.into_py(py),
+            gguf_file::Value::Array(x) => {
+                let list = pyo3::types::PyList::empty(py);
+                for elem in x.iter() {
+                    list.append(gguf_value_to_pyobject(elem, py)?)?;
+                }
+                list.into()
+            }
+        };
+        Ok(v)
+    }
     let mut file = std::fs::File::open(path)?;
-    let gguf = ::candle::quantized::gguf_file::Content::read(&mut file).map_err(wrap_err)?;
-    let res = gguf
+    let gguf = gguf_file::Content::read(&mut file).map_err(wrap_err)?;
+    let tensors = gguf
         .tensor_infos
         .keys()
         .map(|key| {
@@ -758,7 +783,15 @@ fn load_gguf(path: &str, py: Python<'_>) -> PyResult<PyObject> {
         })
         .collect::<::candle::Result<Vec<_>>>()
         .map_err(wrap_err)?;
-    Ok(res.into_py_dict(py).to_object(py))
+    let tensors = tensors.into_py_dict(py).to_object(py);
+    let metadata = gguf
+        .metadata
+        .iter()
+        .map(|(key, value)| Ok((key, gguf_value_to_pyobject(value, py)?)))
+        .collect::<PyResult<Vec<_>>>()?
+        .into_py_dict(py)
+        .to_object(py);
+    Ok((tensors, metadata))
 }
 
 #[pyfunction]

From d0cdea95a5ec8f53b24c6de19f6029060339ed98 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 4 Sep 2023 08:50:52 +0200
Subject: [PATCH 003/150] Add back the bf16 flash-attn kernels. (#730)

---
 candle-flash-attn/build.rs             | 18 +++++++++---------
 candle-flash-attn/kernels/flash_api.cu | 26 +++++++++++++-------------
 candle-flash-attn/src/ffi.rs           |  1 +
 candle-flash-attn/src/lib.rs           |  2 ++
 4 files changed, 25 insertions(+), 22 deletions(-)

diff --git a/candle-flash-attn/build.rs b/candle-flash-attn/build.rs
index 773c5638..2a3b7eb1 100644
--- a/candle-flash-attn/build.rs
+++ b/candle-flash-attn/build.rs
@@ -6,7 +6,7 @@ use rayon::prelude::*;
 use std::path::PathBuf;
 use std::str::FromStr;
 
-const KERNEL_FILES: [&str; 9] = [
+const KERNEL_FILES: [&str; 17] = [
     "flash_api.cu",
     "flash_fwd_hdim128_fp16_sm80.cu",
     "flash_fwd_hdim160_fp16_sm80.cu",
@@ -16,14 +16,14 @@ const KERNEL_FILES: [&str; 9] = [
     "flash_fwd_hdim32_fp16_sm80.cu",
     "flash_fwd_hdim64_fp16_sm80.cu",
     "flash_fwd_hdim96_fp16_sm80.cu",
-    // "flash_fwd_hdim128_bf16_sm80.cu",
-    // "flash_fwd_hdim160_bf16_sm80.cu",
-    // "flash_fwd_hdim192_bf16_sm80.cu",
-    // "flash_fwd_hdim224_bf16_sm80.cu",
-    // "flash_fwd_hdim256_bf16_sm80.cu",
-    // "flash_fwd_hdim32_bf16_sm80.cu",
-    // "flash_fwd_hdim64_bf16_sm80.cu",
-    // "flash_fwd_hdim96_bf16_sm80.cu",
+    "flash_fwd_hdim128_bf16_sm80.cu",
+    "flash_fwd_hdim160_bf16_sm80.cu",
+    "flash_fwd_hdim192_bf16_sm80.cu",
+    "flash_fwd_hdim224_bf16_sm80.cu",
+    "flash_fwd_hdim256_bf16_sm80.cu",
+    "flash_fwd_hdim32_bf16_sm80.cu",
+    "flash_fwd_hdim64_bf16_sm80.cu",
+    "flash_fwd_hdim96_bf16_sm80.cu",
 ];
 
 fn main() -> Result<()> {
diff --git a/candle-flash-attn/kernels/flash_api.cu b/candle-flash-attn/kernels/flash_api.cu
index d928bcb6..72991257 100644
--- a/candle-flash-attn/kernels/flash_api.cu
+++ b/candle-flash-attn/kernels/flash_api.cu
@@ -1,20 +1,19 @@
 #include "flash_fwd_launch_template.h"
 
-// TODO: Switch back to handling bf16.
-void run_mha_fwd(Flash_fwd_params &params, cudaStream_t stream) {
-    FWD_HEADDIM_SWITCH(params.d, [&] {
-        run_mha_fwd_<cutlass::half_t, kHeadDim>(params, stream);
-    });
-}
-
 // void run_mha_fwd(Flash_fwd_params &params, cudaStream_t stream) {
-//     FP16_SWITCH(!params.is_bf16, [&] {
-//         FWD_HEADDIM_SWITCH(params.d, [&] {
-//             run_mha_fwd_<elem_type, kHeadDim>(params, stream);
-//         });
+//     FWD_HEADDIM_SWITCH(params.d, [&] {
+//         run_mha_fwd_<cutlass::half_t, kHeadDim>(params, stream);
 //     });
 // }
 
+void run_mha_fwd(Flash_fwd_params &params, cudaStream_t stream) {
+  FP16_SWITCH(!params.is_bf16, [&] {
+      FWD_HEADDIM_SWITCH(params.d, [&] {
+          run_mha_fwd_<elem_type, kHeadDim>(params, stream);
+          });
+      });
+}
+
 extern "C" void run_mha(
     void *q_ptr,
     void *k_ptr,
@@ -52,7 +51,8 @@ extern "C" void run_mha(
     uint32_t seqlen_q_rounded,
     uint32_t seqlen_k_rounded,
 
-    int is_causal
+    int is_causal,
+    int is_bf16
 ) {
     Flash_fwd_params params;
     // Reset the parameters
@@ -102,7 +102,7 @@ extern "C" void run_mha(
     params.p_dropout_in_uint8_t = uint8_t(std::floor(params.p_dropout * 255.0));
     params.rp_dropout = 1.f / params.p_dropout;
     params.scale_softmax_rp_dropout = params.rp_dropout * params.scale_softmax;
-    params.is_bf16 = 0;
+    params.is_bf16 = is_bf16;
     params.cu_seqlens_q = cu_seqlens_q_ptr;
     params.cu_seqlens_k = cu_seqlens_k_ptr;
     params.p_ptr = nullptr; // used for `return_softmax`.
diff --git a/candle-flash-attn/src/ffi.rs b/candle-flash-attn/src/ffi.rs
index ae61c405..90f34e43 100644
--- a/candle-flash-attn/src/ffi.rs
+++ b/candle-flash-attn/src/ffi.rs
@@ -38,6 +38,7 @@ extern "C" {
         seqlen_k_rounded: u32,
 
         is_causal: c_int,
+        is_bf16: c_int,
     );
 
 }
diff --git a/candle-flash-attn/src/lib.rs b/candle-flash-attn/src/lib.rs
index 3c5fd455..cdb4b083 100644
--- a/candle-flash-attn/src/lib.rs
+++ b/candle-flash-attn/src/lib.rs
@@ -146,6 +146,7 @@ impl candle::CustomOp3 for FlashAttn {
                 /* seqlen_q_rounded */ seqlen_q_rounded as u32,
                 /* seqlen_k_rounded */ seqlen_k_rounded as u32,
                 /* is_causal */ causal,
+                /* is_bf16 */ 0,
             )
         }
 
@@ -354,6 +355,7 @@ impl candle::CustomOp3 for FlashAttnVarLen {
                 /* seqlen_q_rounded */ seqlen_q_rounded as u32,
                 /* seqlen_k_rounded */ seqlen_k_rounded as u32,
                 /* is_causal */ causal,
+                /* is_bf16 */ 0,
             )
         }
 

From e2f9f60ac2e4ab6b39b8275442f7ad4e76995707 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 4 Sep 2023 09:18:32 +0200
Subject: [PATCH 004/150] Avoid some redundant clone. (#731)

---
 candle-nn/tests/batch_norm.rs              | 4 ++--
 candle-wasm-examples/llama2-c/src/bin/m.rs | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/candle-nn/tests/batch_norm.rs b/candle-nn/tests/batch_norm.rs
index 209fc10a..5bbaf238 100644
--- a/candle-nn/tests/batch_norm.rs
+++ b/candle-nn/tests/batch_norm.rs
@@ -59,8 +59,8 @@ fn batch_norm() -> Result<()> {
     );
     let bn2 = BatchNorm::new(
         5,
-        running_mean.clone(),
-        running_var.clone(),
+        running_mean,
+        running_var,
         Tensor::new(&[0.5f32], &Device::Cpu)?.broadcast_as(5)?,
         Tensor::new(&[-1.5f32], &Device::Cpu)?.broadcast_as(5)?,
         1e-8,
diff --git a/candle-wasm-examples/llama2-c/src/bin/m.rs b/candle-wasm-examples/llama2-c/src/bin/m.rs
index d014e38a..da71f071 100644
--- a/candle-wasm-examples/llama2-c/src/bin/m.rs
+++ b/candle-wasm-examples/llama2-c/src/bin/m.rs
@@ -80,7 +80,7 @@ impl Model {
         let tokens = self
             .inner
             .tokenizer
-            .encode(prompt.to_string(), true)
+            .encode(prompt, true)
             .map_err(|m| JsError::new(&m.to_string()))?
             .get_ids()
             .to_vec();

From 8395152d20c7c72fb866ca3f8cbcab8859bfed57 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radam=C3=A9s=20Ajna?= <radamajna@gmail.com>
Date: Mon, 4 Sep 2023 07:59:22 -0700
Subject: [PATCH 005/150] Llama2c WASM UI improvements  (#732)

* pass seed, expose model seq_len

* wip new llama2.c ui

* final new UI example

* small coppy

* copy
---
 candle-wasm-examples/llama2-c/README.md       |  47 +++
 candle-wasm-examples/llama2-c/build-lib.sh    |   2 +
 .../llama2-c/lib-example.html                 | 311 ++++++++++++++++++
 .../llama2-c/llama2cWorker.js                 |  96 ++++++
 candle-wasm-examples/llama2-c/src/bin/m.rs    |   8 +-
 candle-wasm-examples/llama2-c/src/worker.rs   |   2 +-
 6 files changed, 464 insertions(+), 2 deletions(-)
 create mode 100644 candle-wasm-examples/llama2-c/README.md
 create mode 100644 candle-wasm-examples/llama2-c/build-lib.sh
 create mode 100644 candle-wasm-examples/llama2-c/lib-example.html
 create mode 100644 candle-wasm-examples/llama2-c/llama2cWorker.js

diff --git a/candle-wasm-examples/llama2-c/README.md b/candle-wasm-examples/llama2-c/README.md
new file mode 100644
index 00000000..0b41e064
--- /dev/null
+++ b/candle-wasm-examples/llama2-c/README.md
@@ -0,0 +1,47 @@
+## Running [llama2.c](https://github.com/karpathy/llama2.c) Examples
+
+Here, we provide two examples of how to run [llama2.c](https://github.com/karpathy/llama2.c) written in Rust using a Candle-compiled WASM binary and runtimes.
+
+### Pure Rust UI
+
+To build and test the UI made in Rust you will need [Trunk](https://trunkrs.dev/#install)
+From the `candle-wasm-examples/llama2-c` directory run:
+
+Download assets:
+
+```bash
+# Model and tokenizer
+
+wget -c https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/model.bin
+wget -c https://huggingface.co/spaces/lmz/candle-llama2/resolve/main/tokenizer.json
+
+```
+
+Run hot reload server:
+
+```bash
+trunk serve --release --public-url / --port 8080
+```
+
+### Vanilla JS and WebWorkers
+
+To build and test the UI made in Vanilla JS and WebWorkers, first we need to build the WASM library:
+
+```bash
+sh build-lib.sh
+```
+
+This will bundle the library under `./build` and we can import it inside our WebWorker like a normal JS module:
+
+```js
+import init, { Model } from "./build/m.js";
+```
+
+The full example can be found under `./lib-example.html`. All needed assets are fetched from the web, so no need to download anything.
+Finally, you can preview the example by running a local HTTP server. For example:
+
+```bash
+python -m http.server
+```
+
+Then open `http://localhost:8000/lib-example.html` in your browser.
diff --git a/candle-wasm-examples/llama2-c/build-lib.sh b/candle-wasm-examples/llama2-c/build-lib.sh
new file mode 100644
index 00000000..b0ebb182
--- /dev/null
+++ b/candle-wasm-examples/llama2-c/build-lib.sh
@@ -0,0 +1,2 @@
+cargo build --target wasm32-unknown-unknown --release
+wasm-bindgen ../../target/wasm32-unknown-unknown/release/m.wasm --out-dir build --target web
diff --git a/candle-wasm-examples/llama2-c/lib-example.html b/candle-wasm-examples/llama2-c/lib-example.html
new file mode 100644
index 00000000..bc519e4b
--- /dev/null
+++ b/candle-wasm-examples/llama2-c/lib-example.html
@@ -0,0 +1,311 @@
+<html>
+  <head>
+    <meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
+    <title>Candle Llama.c Rust/WASM</title>
+  </head>
+  <body></body>
+</html>
+
+<!doctype html>
+<html>
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <style>
+      @import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
+      html,
+      body {
+        font-family: "Source Sans 3", sans-serif;
+      }
+      code,
+      output,
+      select,
+      pre {
+        font-family: "Source Code Pro", monospace;
+      }
+    </style>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <script type="module">
+      // base url for audio examples
+      const MODELS_BASE_URL =
+        "https://huggingface.co/karpathy/tinyllamas/resolve/main";
+
+      // models base url
+      const MODELS = {
+        stories15M: {
+          url: "stories15M.bin",
+          seq_len: 256,
+        },
+        stories42M: {
+          url: "stories42M.bin",
+          seq_len: 256,
+        },
+        stories110M: {
+          url: "stories110M.bin",
+          seq_len: 256,
+        },
+      };
+
+      const llamaWorker = new Worker("./llama2cWorker.js", {
+        type: "module",
+      });
+      async function generateSequence(controller) {
+        const getValue = (id) => document.querySelector(`#${id}`).value;
+        const modelID = getValue("model");
+        const model = MODELS[modelID];
+        const weightsURL = `${MODELS_BASE_URL}/${model.url}`;
+        const prompt = getValue("prompt");
+        const temperature = getValue("temperature");
+        const repeatPenalty = getValue("repeat_penalty");
+        const seed = getValue("seed");
+        const maxSeqLen = getValue("max-seq");
+
+        function updateStatus({ status, message, prompt, sentence }) {
+          const outStatus = document.querySelector("#output-status");
+          const outGen = document.querySelector("#output-generation");
+
+          switch (status) {
+            case "loading":
+              outStatus.hidden = false;
+              outStatus.textContent = message;
+              outGen.hidden = true;
+              break;
+            case "generating":
+              outStatus.hidden = true;
+              outGen.hidden = false;
+              outGen.innerHTML = `<span class="font-semibold">${prompt}</span>${sentence.replace(
+                /\<s\>|\<\/s\>/g,
+                ""
+              )}`;
+              break;
+            case "complete":
+              outStatus.hidden = true;
+              outGen.hidden = false;
+              break;
+          }
+        }
+
+        return new Promise((resolve, reject) => {
+          llamaWorker.postMessage({
+            weightsURL,
+            modelID,
+            tokenizerURL: "tokenizer.json",
+            prompt,
+            temp: temperature,
+            repeatPenalty,
+            seed: BigInt(seed),
+            maxSeqLen,
+            command: "start",
+          });
+
+          const handleAbort = () => {
+            llamaWorker.postMessage({ command: "abort" });
+          };
+          const handleMessage = (event) => {
+            const { status, error, message, prompt, sentence } = event.data;
+            if (status) updateStatus(event.data);
+            if (error) reject(new Error(error));
+            if (status === "complete") resolve(event.data);
+          };
+
+          controller.signal.addEventListener("abort", handleAbort);
+          llamaWorker.addEventListener("message", handleMessage);
+        });
+      }
+
+      const form = document.querySelector("#form");
+      const prompt = document.querySelector("#prompt");
+      const clearBtn = document.querySelector("#clear-btn");
+      const runBtn = document.querySelector("#run");
+      let runController = new AbortController();
+      let isRunning = false;
+
+      form.addEventListener("submit", async (e) => {
+        e.preventDefault();
+        if (isRunning) {
+          stopRunning();
+        } else {
+          startRunning();
+          await generateSequence(runController);
+          stopRunning();
+        }
+      });
+
+      function startRunning() {
+        isRunning = true;
+        runBtn.textContent = "Stop";
+      }
+
+      function stopRunning() {
+        runController.abort();
+        runController = new AbortController();
+        runBtn.textContent = "Run";
+        isRunning = false;
+      }
+      clearBtn.addEventListener("click", (e) => {
+        e.preventDefault();
+        prompt.value = "";
+        clearBtn.classList.add("invisible");
+        runBtn.disabled = true;
+        stopRunning();
+      });
+      prompt.addEventListener("input", (e) => {
+        runBtn.disabled = false;
+        if (e.target.value.length > 0) {
+          clearBtn.classList.remove("invisible");
+        } else {
+          clearBtn.classList.add("invisible");
+        }
+      });
+    </script>
+  </head>
+  <body class="container max-w-4xl mx-auto p-4 text-gray-800">
+    <main class="grid grid-cols-1 gap-8 relative">
+      <span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
+      <div>
+        <h1 class="text-5xl font-bold">Candle Llama2.c</h1>
+        <h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
+        <p class="max-w-lg">
+          <a
+            href="https://github.com/karpathy/llama2.c"
+            target="_blank"
+            class="underline hover:text-blue-500 hover:no-underline"
+            target="_blank"
+            >Llama2.c</a
+          >
+          is Andrey Karpathy's C implementation of the Llama 2 LLM model in C.
+          This demo uses
+          <a
+            href="https://github.com/huggingface/candle/"
+            target="_blank"
+            class="underline hover:text-blue-500 hover:no-underline"
+            >Candle
+          </a>
+          to run Llama2.c in the browser using rust/wasm.
+        </p>
+      </div>
+
+      <div>
+        <label for="model" class="font-medium">Models Options: </label>
+        <select
+          id="model"
+          class="border-2 border-gray-500 rounded-md font-light"
+        >
+          <option value="stories15M" selected>stories 15M (60.8 MB)</option>
+          <option value="stories42M">stories 42M (167 MB)</option>
+          <option value="stories110M">stories 110M (438 MB)</option>
+        </select>
+      </div>
+      <form
+        id="form"
+        class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center"
+      >
+        <input type="submit" hidden />
+        <input
+          type="text"
+          id="prompt"
+          class="font-light w-full px-3 py-2 mx-1 resize-none outline-none"
+          placeholder="Add your prompt here..."
+        />
+        <button class="invisible" id="clear-btn">
+          <svg
+            fill="none"
+            xmlns="http://www.w3.org/2000/svg"
+            width="40"
+            viewBox="0 0 70 40"
+          >
+            <path opacity=".5" d="M39 .2v40.2" stroke="#1F2937" />
+            <path
+              d="M1.5 11.5 19 29.1m0-17.6L1.5 29.1"
+              opacity=".5"
+              stroke="#1F2937"
+              stroke-width="2"
+            />
+          </svg>
+        </button>
+        <button
+          id="run"
+          disabled
+          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed"
+        >
+          Run
+        </button>
+      </form>
+      <div class="grid grid-cols-3 max-w-md items-center gap-3">
+        <label class="text-sm font-medium" for="max-seq">Maximum length </label>
+        <input
+          type="range"
+          id="max-seq"
+          name="temperature"
+          min="1"
+          max="256"
+          step="1"
+          value="200"
+          oninput="this.nextElementSibling.value = Number(this.value)"
+        />
+        <output
+          class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
+        >
+          200</output
+        >
+        <label class="text-sm font-medium" for="temperature">Temperature</label>
+        <input
+          type="range"
+          id="temperature"
+          name="temperature"
+          min="0"
+          max="2"
+          step="0.01"
+          value="0.50"
+          oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
+        />
+        <output
+          class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
+        >
+          0.50</output
+        >
+
+        <label class="text-sm font-medium" for="repeat_penalty"
+          >Repeat Penalty</label
+        >
+
+        <input
+          type="range"
+          id="repeat_penalty"
+          name="repeat_penalty"
+          min="-2"
+          max="2"
+          step="0.01"
+          value="1.10"
+          oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
+        />
+        <output
+          class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
+          >1.10</output
+        >
+        <label class="text-sm font-medium" for="seed">Seed</label>
+        <input
+          type="number"
+          id="seed"
+          name="seed"
+          value="299792458"
+          class="font-light border border-gray-700 text-right rounded-md p-2"
+        />
+      </div>
+      <div>
+        <h3 class="font-medium">Generation:</h3>
+
+        <div
+          class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md grid"
+        >
+          <p hidden id="output-generation"></p>
+          <span
+            id="output-status"
+            class="justify-self-center self-center font-light"
+            >No output yet</span
+          >
+        </div>
+      </div>
+    </main>
+  </body>
+</html>
diff --git a/candle-wasm-examples/llama2-c/llama2cWorker.js b/candle-wasm-examples/llama2-c/llama2cWorker.js
new file mode 100644
index 00000000..ba303aaa
--- /dev/null
+++ b/candle-wasm-examples/llama2-c/llama2cWorker.js
@@ -0,0 +1,96 @@
+import init, { Model } from "./build/m.js";
+
+async function fetchArrayBuffer(url) {
+  const res = await fetch(url, {
+    cache: "force-cache",
+  });
+  const data = await res.arrayBuffer();
+  return new Uint8Array(data);
+}
+
+class Llama2C {
+  static instance = {};
+
+  static async getInstance(weightsURL, modelID, tokenizerURL) {
+    // load individual modelID only once
+    if (!this.instance[modelID]) {
+      await init();
+
+      self.postMessage({ status: "loading", message: "Loading Model" });
+
+      const [weightsArrayU8, tokenizerArrayU8] = await Promise.all([
+        fetchArrayBuffer(weightsURL),
+        fetchArrayBuffer(tokenizerURL),
+      ]);
+
+      this.instance[modelID] = new Model(weightsArrayU8, tokenizerArrayU8);
+    }
+    return this.instance[modelID];
+  }
+}
+
+let controller = null;
+self.addEventListener("message", (event) => {
+  if (event.data.command === "start") {
+    controller = new AbortController();
+    generate(event.data);
+  } else if (event.data.command === "abort") {
+    controller.abort();
+  }
+});
+
+async function generate(data) {
+  const {
+    weightsURL,
+    modelID,
+    tokenizerURL,
+    prompt,
+    temp,
+    repeatPenalty,
+    seed,
+    maxSeqLen,
+  } = data;
+  try {
+    self.postMessage({ status: "loading", message: "Starting llama2.c" });
+    const model = await Llama2C.getInstance(weightsURL, modelID, tokenizerURL);
+
+    self.postMessage({ status: "loading", message: "Initializing model" });
+    model.init_with_prompt(prompt, temp, repeatPenalty, seed);
+
+    const seq_len = model.get_seq_len();
+
+    let sentence = "";
+    let max_tokens = maxSeqLen ? maxSeqLen : seq_len - prompt.length - 1;
+
+    while (max_tokens--) {
+      await new Promise(async (resolve) => {
+        if (controller && controller.signal.aborted) {
+          self.postMessage({
+            status: "aborted",
+            message: "Aborted",
+            output: prompt + sentence,
+          });
+          return;
+        }
+        const token = await model.next_token();
+
+        sentence += token;
+        self.postMessage({
+          status: "generating",
+          message: "Generating token",
+          token: token,
+          sentence: sentence,
+          prompt: prompt,
+        });
+        setTimeout(resolve, 0);
+      });
+    }
+    self.postMessage({
+      status: "complete",
+      message: "complete",
+      output: prompt + sentence,
+    });
+  } catch (e) {
+    self.postMessage({ error: e });
+  }
+}
diff --git a/candle-wasm-examples/llama2-c/src/bin/m.rs b/candle-wasm-examples/llama2-c/src/bin/m.rs
index da71f071..62b1bdf7 100644
--- a/candle-wasm-examples/llama2-c/src/bin/m.rs
+++ b/candle-wasm-examples/llama2-c/src/bin/m.rs
@@ -58,6 +58,11 @@ impl Model {
             Err(e) => Err(JsError::new(&e.to_string())),
         }
     }
+    #[wasm_bindgen]
+    pub fn get_seq_len(&mut self) -> usize {
+        let seq_len = self.inner.config.seq_len;
+        seq_len
+    }
 
     #[wasm_bindgen]
     pub fn init_with_prompt(
@@ -65,6 +70,7 @@ impl Model {
         prompt: String,
         temp: f64,
         repeat_penalty: f32,
+        seed: u64,
     ) -> Result<String, JsError> {
         // First reset the cache.
         {
@@ -74,7 +80,7 @@ impl Model {
             }
         }
         let temp = if temp <= 0. { None } else { Some(temp) };
-        self.logits_processor = LogitsProcessor::new(299792458, temp);
+        self.logits_processor = LogitsProcessor::new(seed, temp);
         self.repeat_penalty = repeat_penalty;
         self.tokens.clear();
         let tokens = self
diff --git a/candle-wasm-examples/llama2-c/src/worker.rs b/candle-wasm-examples/llama2-c/src/worker.rs
index 3d187fcc..7e97b5da 100644
--- a/candle-wasm-examples/llama2-c/src/worker.rs
+++ b/candle-wasm-examples/llama2-c/src/worker.rs
@@ -51,7 +51,7 @@ fn read_tensor<R: std::io::Read, S: Into<Shape>>(
 
 pub struct Model {
     pub cache: Cache,
-    config: Config,
+    pub config: Config,
     pub llama: Llama,
     pub tokenizer: Tokenizer,
 }

From 0d00c06a83b98c55d146564b96d913de4cec71c7 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 4 Sep 2023 17:09:19 +0200
Subject: [PATCH 006/150] Fix clippy lint. (#736)

---
 candle-wasm-examples/llama2-c/src/bin/m.rs | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/candle-wasm-examples/llama2-c/src/bin/m.rs b/candle-wasm-examples/llama2-c/src/bin/m.rs
index 62b1bdf7..6628ab7e 100644
--- a/candle-wasm-examples/llama2-c/src/bin/m.rs
+++ b/candle-wasm-examples/llama2-c/src/bin/m.rs
@@ -58,10 +58,10 @@ impl Model {
             Err(e) => Err(JsError::new(&e.to_string())),
         }
     }
+
     #[wasm_bindgen]
     pub fn get_seq_len(&mut self) -> usize {
-        let seq_len = self.inner.config.seq_len;
-        seq_len
+        self.inner.config.seq_len
     }
 
     #[wasm_bindgen]

From f80fd44201a61833781131b4fdc83d2e58e1b559 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 4 Sep 2023 17:35:43 +0200
Subject: [PATCH 007/150] BF16 support for flash-attn. (#737)

---
 candle-flash-attn/src/lib.rs | 122 +++++++++++++++++++++++------------
 1 file changed, 81 insertions(+), 41 deletions(-)

diff --git a/candle-flash-attn/src/lib.rs b/candle-flash-attn/src/lib.rs
index cdb4b083..b610915b 100644
--- a/candle-flash-attn/src/lib.rs
+++ b/candle-flash-attn/src/lib.rs
@@ -4,7 +4,7 @@ use candle::backend::BackendStorage;
 use candle::cuda_backend::cudarc::driver::DevicePtr;
 use candle::cuda_backend::WrapErr;
 use candle::{CpuStorage, Layout, Result, Shape, Tensor};
-use half::f16;
+use half::{bf16, f16};
 
 pub struct FlashAttn {
     pub softmax_scale: f32,
@@ -15,24 +15,10 @@ fn round_multiple(x: usize, m: usize) -> usize {
     (x + m - 1) / m * m
 }
 
-impl candle::CustomOp3 for FlashAttn {
-    fn name(&self) -> &'static str {
-        "flash-attn"
-    }
-
-    fn cpu_fwd(
-        &self,
-        _: &CpuStorage,
-        _: &Layout,
-        _: &CpuStorage,
-        _: &Layout,
-        _: &CpuStorage,
-        _: &Layout,
-    ) -> Result<(CpuStorage, Shape)> {
-        candle::bail!("no cpu support for flash-attn")
-    }
-
-    fn cuda_fwd(
+impl FlashAttn {
+    fn cuda_fwd_t<
+        T: candle::cuda_backend::CudaDType + candle::cuda_backend::cudarc::driver::DeviceRepr,
+    >(
         &self,
         q: &candle::CudaStorage,
         q_l: &Layout,
@@ -46,9 +32,9 @@ impl candle::CustomOp3 for FlashAttn {
         let out_shape = q_l.shape().clone();
         let out_l = Layout::contiguous(&out_shape);
 
-        let q = q.as_cuda_slice::<f16>()?;
-        let k = k.as_cuda_slice::<f16>()?;
-        let v = v.as_cuda_slice::<f16>()?;
+        let q = q.as_cuda_slice::<T>()?;
+        let k = k.as_cuda_slice::<T>()?;
+        let v = v.as_cuda_slice::<T>()?;
         let q = q.slice(q_l.start_offset()..);
         let k = k.slice(k_l.start_offset()..);
         let v = v.slice(v_l.start_offset()..);
@@ -104,7 +90,7 @@ impl candle::CustomOp3 for FlashAttn {
         let seqlen_k_rounded = round_multiple(seqlen_k, 128);
 
         let elem_count = out_shape.elem_count();
-        let dst = unsafe { dev.alloc::<f16>(elem_count) }.w()?;
+        let dst = unsafe { dev.alloc::<T>(elem_count) }.w()?;
         let softmax_lse = dev.alloc_zeros::<f32>(b_sz * num_heads * seqlen_q).w()?;
 
         let causal = if self.causal { 1 } else { 0 };
@@ -155,6 +141,40 @@ impl candle::CustomOp3 for FlashAttn {
     }
 }
 
+impl candle::CustomOp3 for FlashAttn {
+    fn name(&self) -> &'static str {
+        "flash-attn"
+    }
+
+    fn cpu_fwd(
+        &self,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+    ) -> Result<(CpuStorage, Shape)> {
+        candle::bail!("no cpu support for flash-attn")
+    }
+
+    fn cuda_fwd(
+        &self,
+        q: &candle::CudaStorage,
+        q_l: &Layout,
+        k: &candle::CudaStorage,
+        k_l: &Layout,
+        v: &candle::CudaStorage,
+        v_l: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        match q.dtype() {
+            candle::DType::F16 => self.cuda_fwd_t::<f16>(q, q_l, k, k_l, v, v_l),
+            candle::DType::BF16 => self.cuda_fwd_t::<bf16>(q, q_l, k, k_l, v, v_l),
+            dt => candle::bail!("flash-attn is only supported for f16/bf16 ({dt:?})"),
+        }
+    }
+}
+
 /// Flash-attention v2 layer.
 ///
 /// This implements scaled dot-product attention, `softmax(Q @ K^T . softmax_scale) @ V`.
@@ -191,24 +211,10 @@ struct FlashAttnVarLen {
     seqlens_k: Tensor,
 }
 
-impl candle::CustomOp3 for FlashAttnVarLen {
-    fn name(&self) -> &'static str {
-        "flash-attn-varlen"
-    }
-
-    fn cpu_fwd(
-        &self,
-        _: &CpuStorage,
-        _: &Layout,
-        _: &CpuStorage,
-        _: &Layout,
-        _: &CpuStorage,
-        _: &Layout,
-    ) -> Result<(CpuStorage, Shape)> {
-        candle::bail!("no cpu support for flash-attn")
-    }
-
-    fn cuda_fwd(
+impl FlashAttnVarLen {
+    fn cuda_fwd_t<
+        T: candle::cuda_backend::CudaDType + candle::cuda_backend::cudarc::driver::DeviceRepr,
+    >(
         &self,
         q: &candle::CudaStorage,
         q_l: &Layout,
@@ -364,6 +370,40 @@ impl candle::CustomOp3 for FlashAttnVarLen {
     }
 }
 
+impl candle::CustomOp3 for FlashAttnVarLen {
+    fn name(&self) -> &'static str {
+        "flash-attn-varlen"
+    }
+
+    fn cpu_fwd(
+        &self,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+        _: &CpuStorage,
+        _: &Layout,
+    ) -> Result<(CpuStorage, Shape)> {
+        candle::bail!("no cpu support for flash-attn")
+    }
+
+    fn cuda_fwd(
+        &self,
+        q: &candle::CudaStorage,
+        q_l: &Layout,
+        k: &candle::CudaStorage,
+        k_l: &Layout,
+        v: &candle::CudaStorage,
+        v_l: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        match q.dtype() {
+            candle::DType::F16 => self.cuda_fwd_t::<f16>(q, q_l, k, k_l, v, v_l),
+            candle::DType::BF16 => self.cuda_fwd_t::<bf16>(q, q_l, k, k_l, v, v_l),
+            dt => candle::bail!("flash-attn is only supported for f16/bf16 ({dt:?})"),
+        }
+    }
+}
+
 #[allow(clippy::too_many_arguments)]
 /// Flash-attention v2 layer with variable-length batching.
 ///

From ab0d9fbdd1db6a586c0cd6ca9ee1a31203db3684 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 4 Sep 2023 17:45:26 +0200
Subject: [PATCH 008/150] Properly set the is_bf16 flag. (#738)

---
 candle-flash-attn/src/lib.rs | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/candle-flash-attn/src/lib.rs b/candle-flash-attn/src/lib.rs
index b610915b..61980a58 100644
--- a/candle-flash-attn/src/lib.rs
+++ b/candle-flash-attn/src/lib.rs
@@ -26,6 +26,7 @@ impl FlashAttn {
         k_l: &Layout,
         v: &candle::CudaStorage,
         v_l: &Layout,
+        is_bf16: bool,
     ) -> Result<(candle::CudaStorage, Shape)> {
         // https://github.com/Dao-AILab/flash-attention/blob/b252072409e69c25f2b9d473cc534e49b24decd2/csrc/flash_attn/flash_api.cpp#L187
         let dev = q.device();
@@ -94,6 +95,7 @@ impl FlashAttn {
         let softmax_lse = dev.alloc_zeros::<f32>(b_sz * num_heads * seqlen_q).w()?;
 
         let causal = if self.causal { 1 } else { 0 };
+        let is_bf16 = if is_bf16 { 1 } else { 0 };
 
         unsafe {
             let q_ptr = *q.device_ptr() as *const core::ffi::c_void;
@@ -132,7 +134,7 @@ impl FlashAttn {
                 /* seqlen_q_rounded */ seqlen_q_rounded as u32,
                 /* seqlen_k_rounded */ seqlen_k_rounded as u32,
                 /* is_causal */ causal,
-                /* is_bf16 */ 0,
+                /* is_bf16 */ is_bf16,
             )
         }
 
@@ -168,8 +170,8 @@ impl candle::CustomOp3 for FlashAttn {
         v_l: &Layout,
     ) -> Result<(candle::CudaStorage, Shape)> {
         match q.dtype() {
-            candle::DType::F16 => self.cuda_fwd_t::<f16>(q, q_l, k, k_l, v, v_l),
-            candle::DType::BF16 => self.cuda_fwd_t::<bf16>(q, q_l, k, k_l, v, v_l),
+            candle::DType::F16 => self.cuda_fwd_t::<f16>(q, q_l, k, k_l, v, v_l, false),
+            candle::DType::BF16 => self.cuda_fwd_t::<bf16>(q, q_l, k, k_l, v, v_l, true),
             dt => candle::bail!("flash-attn is only supported for f16/bf16 ({dt:?})"),
         }
     }
@@ -222,6 +224,7 @@ impl FlashAttnVarLen {
         k_l: &Layout,
         v: &candle::CudaStorage,
         v_l: &Layout,
+        is_bf16: bool,
     ) -> Result<(candle::CudaStorage, Shape)> {
         // https://github.com/Dao-AILab/flash-attention/blob/184b992dcb2a0890adaa19eb9b541c3e4f9d2a08/csrc/flash_attn/flash_api.cpp#L327
         let dev = q.device();
@@ -321,6 +324,7 @@ impl FlashAttnVarLen {
             .w()?;
 
         let causal = if self.causal { 1 } else { 0 };
+        let is_bf16 = if is_bf16 { 1 } else { 0 };
 
         unsafe {
             let q_ptr = *q.device_ptr() as *const core::ffi::c_void;
@@ -361,7 +365,7 @@ impl FlashAttnVarLen {
                 /* seqlen_q_rounded */ seqlen_q_rounded as u32,
                 /* seqlen_k_rounded */ seqlen_k_rounded as u32,
                 /* is_causal */ causal,
-                /* is_bf16 */ 0,
+                /* is_bf16 */ is_bf16,
             )
         }
 
@@ -397,8 +401,8 @@ impl candle::CustomOp3 for FlashAttnVarLen {
         v_l: &Layout,
     ) -> Result<(candle::CudaStorage, Shape)> {
         match q.dtype() {
-            candle::DType::F16 => self.cuda_fwd_t::<f16>(q, q_l, k, k_l, v, v_l),
-            candle::DType::BF16 => self.cuda_fwd_t::<bf16>(q, q_l, k, k_l, v, v_l),
+            candle::DType::F16 => self.cuda_fwd_t::<f16>(q, q_l, k, k_l, v, v_l, false),
+            candle::DType::BF16 => self.cuda_fwd_t::<bf16>(q, q_l, k, k_l, v, v_l, true),
             dt => candle::bail!("flash-attn is only supported for f16/bf16 ({dt:?})"),
         }
     }

From 000487c36fc6da3a8d645fffbd8c24e23bcdeed1 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 4 Sep 2023 21:32:14 +0200
Subject: [PATCH 009/150] Add a python function to save as safetensors. (#740)

---
 candle-pyo3/src/lib.rs | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/candle-pyo3/src/lib.rs b/candle-pyo3/src/lib.rs
index f71970d5..eddc0fda 100644
--- a/candle-pyo3/src/lib.rs
+++ b/candle-pyo3/src/lib.rs
@@ -1,5 +1,4 @@
 #![allow(clippy::redundant_closure_call)]
-// TODO: Handle negative dimension indexes.
 use pyo3::exceptions::{PyTypeError, PyValueError};
 use pyo3::prelude::*;
 use pyo3::types::{IntoPyDict, PyTuple};
@@ -714,6 +713,18 @@ fn load_safetensors(path: &str, py: Python<'_>) -> PyResult<PyObject> {
     Ok(res.into_py_dict(py).to_object(py))
 }
 
+#[pyfunction]
+fn save_safetensors(
+    path: &str,
+    tensors: std::collections::HashMap<String, PyTensor>,
+) -> PyResult<()> {
+    let tensors = tensors
+        .into_iter()
+        .map(|(s, t)| (s, t.0))
+        .collect::<std::collections::HashMap<_, _>>();
+    ::candle::safetensors::save(&tensors, path).map_err(wrap_err)
+}
+
 #[pyfunction]
 fn load_ggml(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject, PyObject)> {
     let mut file = std::fs::File::open(path)?;
@@ -867,6 +878,7 @@ fn candle(py: Python<'_>, m: &PyModule) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(rand, m)?)?;
     m.add_function(wrap_pyfunction!(randn, m)?)?;
     m.add_function(wrap_pyfunction!(tensor, m)?)?;
+    m.add_function(wrap_pyfunction!(save_safetensors, m)?)?;
     m.add_function(wrap_pyfunction!(stack, m)?)?;
     m.add_function(wrap_pyfunction!(zeros, m)?)?;
     Ok(())

From 4698eb5cb6cb068fb053d5b6b2b11b1eb1d324d5 Mon Sep 17 00:00:00 2001
From: Masato Mori <13274198+spica314@users.noreply.github.com>
Date: Tue, 5 Sep 2023 17:25:11 +0900
Subject: [PATCH 010/150] Fix typo in the nll function document (#742)

---
 candle-nn/src/loss.rs | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/candle-nn/src/loss.rs b/candle-nn/src/loss.rs
index cddf278e..72451f83 100644
--- a/candle-nn/src/loss.rs
+++ b/candle-nn/src/loss.rs
@@ -1,6 +1,6 @@
 use candle::{Result, Tensor};
 
-/// The negative loss likelihodd loss.
+/// The negative log likelihood loss.
 ///
 /// Arguments
 ///

From cda45a7443a5f2cd14e4c48ef08733029a68f689 Mon Sep 17 00:00:00 2001
From: Gonzalo <456459+grzuy@users.noreply.github.com>
Date: Tue, 5 Sep 2023 05:27:32 -0300
Subject: [PATCH 011/150] Let outside CustomOp2 implementations use
 binary_map/binary_map_vec (#741)

---
 candle-core/src/cpu_backend.rs | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index ed3dd3fc..c3ec8249 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -445,7 +445,7 @@ pub fn unary_map_vec<T: Copy, U: Copy, F: FnMut(T) -> U, FV: FnMut(&[T], &mut [U
 }
 
 // This function maps over two strided index sequences.
-fn binary_map<T: Copy, U: Copy, F: FnMut(T, T) -> U>(
+pub fn binary_map<T: Copy, U: Copy, F: FnMut(T, T) -> U>(
     lhs_l: &Layout,
     rhs_l: &Layout,
     lhs: &[T],
@@ -525,7 +525,7 @@ fn binary_map<T: Copy, U: Copy, F: FnMut(T, T) -> U>(
 }
 
 // Similar to binary_map but with vectorized variants.
-fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>(
+pub fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>(
     lhs_l: &Layout,
     rhs_l: &Layout,
     lhs: &[T],

From a8410bf35ea3ad8eb973f48d301e65309d232377 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 5 Sep 2023 10:51:12 +0200
Subject: [PATCH 012/150] Add some documentation. (#743)

---
 candle-core/src/dtype.rs  | 11 +++++++++++
 candle-core/src/shape.rs  |  6 ++++++
 candle-core/src/tensor.rs |  1 +
 3 files changed, 18 insertions(+)

diff --git a/candle-core/src/dtype.rs b/candle-core/src/dtype.rs
index adfc4a3c..c7a1567f 100644
--- a/candle-core/src/dtype.rs
+++ b/candle-core/src/dtype.rs
@@ -1,15 +1,24 @@
+//! Types for elements that can be stored and manipulated using tensors.
 #![allow(clippy::redundant_closure_call)]
 use crate::backend::BackendStorage;
 use crate::{CpuStorage, Error, Result};
 
+/// The different types of elements allowed in tensors.
 #[derive(Debug, Copy, Clone, PartialEq, Eq, Hash)]
 pub enum DType {
+    // Unsigned 8 bits integer.
     U8,
+    // Unsigned 32 bits integer.
     U32,
+    // Signed 64 bits integer.
     I64,
+    // Brain floating-point using half precision (16 bits).
     BF16,
+    // Floating-point using half precision (16 bits).
     F16,
+    // Floating-point using single precision (32 bits).
     F32,
+    // Floating-point using double precision (64 bits).
     F64,
 }
 
@@ -33,6 +42,7 @@ impl std::str::FromStr for DType {
 }
 
 impl DType {
+    /// String representation for dtypes.
     pub fn as_str(&self) -> &'static str {
         match self {
             Self::U8 => "u8",
@@ -45,6 +55,7 @@ impl DType {
         }
     }
 
+    /// The size used by each element in bytes, i.e. 1 for `U8`, 4 for `F32`.
     pub fn size_in_bytes(&self) -> usize {
         match self {
             Self::U8 => 1,
diff --git a/candle-core/src/shape.rs b/candle-core/src/shape.rs
index aea8b887..db0fe98a 100644
--- a/candle-core/src/shape.rs
+++ b/candle-core/src/shape.rs
@@ -1,3 +1,4 @@
+//! The shape of a tensor is a tuple with the size of each of its dimensions.
 #![allow(clippy::redundant_closure_call)]
 use crate::{Error, Result};
 
@@ -119,6 +120,7 @@ impl Shape {
         Self(dims.to_vec())
     }
 
+    /// The rank is the number of dimensions, 0 for a scalar value, 1 for a vector, etc.
     pub fn rank(&self) -> usize {
         self.0.len()
     }
@@ -127,10 +129,12 @@ impl Shape {
         self.0
     }
 
+    /// The dimensions as a slice of `usize`.
     pub fn dims(&self) -> &[usize] {
         &self.0
     }
 
+    /// The total number of elements, this is the product of all dimension sizes.
     pub fn elem_count(&self) -> usize {
         self.0.iter().product()
     }
@@ -182,6 +186,8 @@ impl Shape {
         true
     }
 
+    /// Modifies the shape by adding a list of additional dimensions at the end of the existing
+    /// dimensions.
     pub fn extend(mut self, additional_dims: &[usize]) -> Self {
         self.0.extend(additional_dims);
         self
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index e181f240..1eca694c 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -1,3 +1,4 @@
+//! Tensors are N-dimenional matrixes of elements using a single data type.
 #![allow(clippy::redundant_closure_call)]
 use crate::backend::{BackendDevice, BackendStorage};
 use crate::op::{

From 1c9e5394a5056aadc948f9330ea31fea4972e65e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 5 Sep 2023 15:20:23 +0200
Subject: [PATCH 013/150] Add a custom softmax implementation. (#744)

* Add a custom softmax implementation.

* Add softmaxlastdim to the benchmarks.

* And add a test.

* Support more dtypes.

* Polish the code.

* Use the slow implementation on cuda.

* Add a todo for the cuda kernel.
---
 .../examples/stable-diffusion/attention.rs    |  2 +-
 candle-nn/Cargo.toml                          |  4 ++
 .../examples/cpu_benchmarks.rs                | 42 ++++++-----
 candle-nn/src/ops.rs                          | 69 ++++++++++++++++++-
 candle-nn/tests/ops.rs                        | 10 +++
 5 files changed, 109 insertions(+), 18 deletions(-)
 rename {candle-core => candle-nn}/examples/cpu_benchmarks.rs (80%)

diff --git a/candle-examples/examples/stable-diffusion/attention.rs b/candle-examples/examples/stable-diffusion/attention.rs
index 1ae1bfc3..000cd2fe 100644
--- a/candle-examples/examples/stable-diffusion/attention.rs
+++ b/candle-examples/examples/stable-diffusion/attention.rs
@@ -198,7 +198,7 @@ impl CrossAttention {
             let xs = query.matmul(&(key.t()? * self.scale)?)?;
             let xs = {
                 let _enter = self.span_softmax.enter();
-                nn::ops::softmax(&xs, D::Minus1)?
+                nn::ops::softmax_last_dim(&xs)?
             };
             xs.matmul(&value)?.to_dtype(in_dtype)?
         };
diff --git a/candle-nn/Cargo.toml b/candle-nn/Cargo.toml
index aa055583..db0f6a8f 100644
--- a/candle-nn/Cargo.toml
+++ b/candle-nn/Cargo.toml
@@ -12,12 +12,16 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
+half = { workspace = true }
 thiserror = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
+num-traits = { workspace = true }
+rayon = { workspace = true }
 safetensors = { workspace = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
+clap = { workspace = true }
 
 [features]
 default = []
diff --git a/candle-core/examples/cpu_benchmarks.rs b/candle-nn/examples/cpu_benchmarks.rs
similarity index 80%
rename from candle-core/examples/cpu_benchmarks.rs
rename to candle-nn/examples/cpu_benchmarks.rs
index 13175ac1..20c92dbb 100644
--- a/candle-core/examples/cpu_benchmarks.rs
+++ b/candle-nn/examples/cpu_benchmarks.rs
@@ -5,19 +5,10 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-use candle_core::quantized::GgmlType;
-use candle_core::{Device, Result, Tensor, D};
+use candle::quantized::GgmlType;
+use candle::{Device, Result, Tensor, D};
 use clap::{Parser, Subcommand};
 
-fn softmax<D: candle_core::shape::Dim>(xs: &Tensor, dim: D) -> Result<Tensor> {
-    let dim = dim.to_index(xs.shape(), "softmax")?;
-    let max = xs.max_keepdim(dim)?;
-    let diff = xs.broadcast_sub(&max)?;
-    let num = diff.exp()?;
-    let den = num.sum_keepdim(dim)?;
-    num.broadcast_div(&den)
-}
-
 trait Benchmark {
     type PreProcessData;
     type RunResult;
@@ -86,12 +77,12 @@ impl Benchmark for Matmul {
 // https://github.com/ggerganov/llama.cpp/blob/master/examples/benchmark/benchmark-matmult.cpp
 struct QMatMul;
 impl Benchmark for QMatMul {
-    type PreProcessData = (candle_core::quantized::QMatMul, Tensor);
+    type PreProcessData = (candle::quantized::QMatMul, Tensor);
     type RunResult = Tensor;
     fn preprocess() -> Result<Self::PreProcessData> {
-        let zeros = vec![candle_core::quantized::k_quants::BlockQ4_0::zeros(); 4096 * 11008 / 32];
-        let mm = candle_core::quantized::QTensor::new(zeros, (4096, 11008))?;
-        let mm = candle_core::quantized::QMatMul::from_qtensor(mm);
+        let zeros = vec![candle::quantized::k_quants::BlockQ4_0::zeros(); 4096 * 11008 / 32];
+        let mm = candle::quantized::QTensor::new(zeros, (4096, 11008))?;
+        let mm = candle::quantized::QMatMul::from_qtensor(mm);
         let arg = Tensor::randn(0f32, 1., (128, 11008), &Device::Cpu)?;
         Ok((mm, arg))
     }
@@ -114,7 +105,24 @@ impl Benchmark for Softmax {
     }
 
     fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
-        softmax(d, D::Minus1)
+        candle_nn::ops::softmax(d, D::Minus1)
+    }
+
+    const ITERS: usize = 100;
+}
+
+struct SoftmaxLastDim;
+impl Benchmark for SoftmaxLastDim {
+    type PreProcessData = Tensor;
+    type RunResult = Tensor;
+    fn preprocess() -> Result<Self::PreProcessData> {
+        // Typical whisper tiny size.
+        let x = Tensor::randn(0f32, 1., (1, 6, 200, 1500), &Device::Cpu)?;
+        Ok(x)
+    }
+
+    fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
+        candle_nn::ops::softmax_last_dim(d)
     }
 
     const ITERS: usize = 100;
@@ -140,6 +148,7 @@ enum Task {
     Matmul,
     Qmatmul,
     Softmax,
+    SoftmaxLastDim,
 }
 
 #[derive(Parser, Debug)]
@@ -160,6 +169,7 @@ fn main() -> Result<()> {
         Task::Conv2d => run::<Conv2d>(args.iters)?,
         Task::Matmul => run::<Matmul>(args.iters)?,
         Task::Softmax => run::<Softmax>(args.iters)?,
+        Task::SoftmaxLastDim => run::<SoftmaxLastDim>(args.iters)?,
         Task::Qmatmul => run::<QMatMul>(args.iters)?,
     }
     Ok(())
diff --git a/candle-nn/src/ops.rs b/candle-nn/src/ops.rs
index c3b6ffa2..55da46f8 100644
--- a/candle-nn/src/ops.rs
+++ b/candle-nn/src/ops.rs
@@ -1,4 +1,5 @@
-use candle::{Result, Tensor};
+use candle::{CpuStorage, Layout, Result, Shape, Tensor};
+use rayon::prelude::*;
 
 /// Applies the softmax function to the input tensor, rescaling the element so that elements on
 /// a slice of fixed index on dimension `dim` are between 0 and 1 and sum to 1.
@@ -77,3 +78,69 @@ impl Dropout {
         }
     }
 }
+
+struct SoftmaxLastDim;
+
+impl candle::CustomOp1 for SoftmaxLastDim {
+    fn name(&self) -> &'static str {
+        "softmax-last-dim"
+    }
+
+    fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)> {
+        fn softmax<T: candle::WithDType + num_traits::Float>(
+            src: &[T],
+            layout: &Layout,
+        ) -> Result<(CpuStorage, Shape)> {
+            let src = match layout.contiguous_offsets() {
+                None => candle::bail!("input has to be contiguous"),
+                Some((o1, o2)) => &src[o1..o2],
+            };
+            let el_count = layout.shape().elem_count();
+            let dims = layout.shape().dims();
+            let dim_m1 = dims[dims.len() - 1];
+            let mut dst = vec![T::zero(); el_count];
+            src.par_chunks(dim_m1)
+                .zip(dst.par_chunks_mut(dim_m1))
+                .for_each(|(src, dst)| {
+                    let mut max = T::neg_infinity();
+                    for &s in src.iter() {
+                        max = T::max(s, max)
+                    }
+                    let mut sum_exp = T::zero();
+                    for (s, d) in src.iter().zip(dst.iter_mut()) {
+                        *d = (*s - max).exp();
+                        sum_exp += *d
+                    }
+                    for d in dst.iter_mut() {
+                        *d /= sum_exp
+                    }
+                });
+            let storage = candle::WithDType::to_cpu_storage_owned(dst);
+            Ok((storage, Shape::from_dims(dims)))
+        }
+
+        match storage {
+            CpuStorage::BF16(slice) => softmax::<half::bf16>(slice, layout),
+            CpuStorage::F16(slice) => softmax::<half::f16>(slice, layout),
+            CpuStorage::F32(slice) => softmax::<f32>(slice, layout),
+            CpuStorage::F64(slice) => softmax::<f64>(slice, layout),
+            _ => candle::bail!("unsupported dtype for softmax {:?}", storage),
+        }
+    }
+
+    fn cuda_fwd(
+        &self,
+        _storage: &candle::CudaStorage,
+        _layout: &Layout,
+    ) -> Result<(candle::CudaStorage, Shape)> {
+        candle::bail!("TODO: implement a cuda kernel")
+    }
+}
+
+pub fn softmax_last_dim(xs: &Tensor) -> Result<Tensor> {
+    if xs.device().is_cpu() {
+        xs.apply_op1_no_bwd(&SoftmaxLastDim)
+    } else {
+        softmax(xs, candle::D::Minus1)
+    }
+}
diff --git a/candle-nn/tests/ops.rs b/candle-nn/tests/ops.rs
index 4ba8cfcc..5ca01b37 100644
--- a/candle-nn/tests/ops.rs
+++ b/candle-nn/tests/ops.rs
@@ -41,6 +41,16 @@ fn softmax() -> Result<()> {
             [[0.2, 0.1, 0.7], [0.4444, 0.1111, 0.4444]]
         ]
     );
+    let t2 = candle_nn::ops::softmax_last_dim(&tensor.log()?)?;
+    assert_eq!(
+        to_vec3_round(&t2, 4)?,
+        &[
+            // (3, 1, 4) / 8, (1, 5, 9) / 15
+            [[0.375, 0.125, 0.5], [0.0667, 0.3333, 0.6]],
+            // (2, 1, 7) / 10, (8, 2, 8) / 18
+            [[0.2, 0.1, 0.7], [0.4444, 0.1111, 0.4444]]
+        ]
+    );
     Ok(())
 }
 

From 6615daf2425bbf33f0e1f97d2a18534e6bdb9fc3 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 5 Sep 2023 16:22:27 +0200
Subject: [PATCH 014/150] Tweaks to softmax. (#745)

---
 candle-core/src/cpu/kernels.rs | 95 +++++++++++++++++++++++++++++-----
 candle-nn/src/ops.rs           |  8 ++-
 2 files changed, 84 insertions(+), 19 deletions(-)

diff --git a/candle-core/src/cpu/kernels.rs b/candle-core/src/cpu/kernels.rs
index 97e195ef..527646d6 100644
--- a/candle-core/src/cpu/kernels.rs
+++ b/candle-core/src/cpu/kernels.rs
@@ -1,4 +1,7 @@
-pub trait VecOps: num_traits::NumAssign + PartialOrd + Copy {
+pub trait VecOps: num_traits::NumAssign + Copy {
+    fn min(self, rhs: Self) -> Self;
+    fn max(self, rhs: Self) -> Self;
+
     /// Dot-product of two vectors.
     ///
     /// # Safety
@@ -37,10 +40,7 @@ pub trait VecOps: num_traits::NumAssign + PartialOrd + Copy {
     unsafe fn vec_reduce_max(xs: *const Self, res: *mut Self, len: usize) {
         *res = *xs;
         for i in 1..len {
-            let x = *xs.add(i);
-            if x > *res {
-                *res = x
-            }
+            *res = (*res).max(*xs.add(i))
         }
     }
 
@@ -54,15 +54,22 @@ pub trait VecOps: num_traits::NumAssign + PartialOrd + Copy {
     unsafe fn vec_reduce_min(xs: *const Self, res: *mut Self, len: usize) {
         *res = *xs;
         for i in 1..len {
-            let x = *xs.add(i);
-            if x < *res {
-                *res = x
-            }
+            *res = (*res).min(*xs.add(i))
         }
     }
 }
 
 impl VecOps for f32 {
+    #[inline(always)]
+    fn min(self, other: Self) -> Self {
+        Self::min(self, other)
+    }
+
+    #[inline(always)]
+    fn max(self, other: Self) -> Self {
+        Self::max(self, other)
+    }
+
     #[inline(always)]
     unsafe fn vec_dot(lhs: *const Self, rhs: *const Self, res: *mut Self, len: usize) {
         super::vec_dot_f32(lhs, rhs, res, len)
@@ -75,6 +82,16 @@ impl VecOps for f32 {
 }
 
 impl VecOps for half::f16 {
+    #[inline(always)]
+    fn min(self, other: Self) -> Self {
+        Self::min(self, other)
+    }
+
+    #[inline(always)]
+    fn max(self, other: Self) -> Self {
+        Self::max(self, other)
+    }
+
     #[inline(always)]
     unsafe fn vec_dot(lhs: *const Self, rhs: *const Self, res: *mut Self, len: usize) {
         let mut res_f32 = 0f32;
@@ -83,11 +100,61 @@ impl VecOps for half::f16 {
     }
 }
 
-impl VecOps for f64 {}
-impl VecOps for half::bf16 {}
-impl VecOps for u8 {}
-impl VecOps for u32 {}
-impl VecOps for i64 {}
+impl VecOps for f64 {
+    #[inline(always)]
+    fn min(self, other: Self) -> Self {
+        Self::min(self, other)
+    }
+
+    #[inline(always)]
+    fn max(self, other: Self) -> Self {
+        Self::max(self, other)
+    }
+}
+impl VecOps for half::bf16 {
+    #[inline(always)]
+    fn min(self, other: Self) -> Self {
+        Self::min(self, other)
+    }
+
+    #[inline(always)]
+    fn max(self, other: Self) -> Self {
+        Self::max(self, other)
+    }
+}
+impl VecOps for u8 {
+    #[inline(always)]
+    fn min(self, other: Self) -> Self {
+        <Self as Ord>::min(self, other)
+    }
+
+    #[inline(always)]
+    fn max(self, other: Self) -> Self {
+        <Self as Ord>::max(self, other)
+    }
+}
+impl VecOps for u32 {
+    #[inline(always)]
+    fn min(self, other: Self) -> Self {
+        <Self as Ord>::min(self, other)
+    }
+
+    #[inline(always)]
+    fn max(self, other: Self) -> Self {
+        <Self as Ord>::max(self, other)
+    }
+}
+impl VecOps for i64 {
+    #[inline(always)]
+    fn min(self, other: Self) -> Self {
+        <Self as Ord>::min(self, other)
+    }
+
+    #[inline(always)]
+    fn max(self, other: Self) -> Self {
+        <Self as Ord>::max(self, other)
+    }
+}
 
 #[inline(always)]
 pub fn par_for_each(n_threads: usize, func: impl Fn(usize) + Send + Sync) {
diff --git a/candle-nn/src/ops.rs b/candle-nn/src/ops.rs
index 55da46f8..73214077 100644
--- a/candle-nn/src/ops.rs
+++ b/candle-nn/src/ops.rs
@@ -103,14 +103,12 @@ impl candle::CustomOp1 for SoftmaxLastDim {
                 .zip(dst.par_chunks_mut(dim_m1))
                 .for_each(|(src, dst)| {
                     let mut max = T::neg_infinity();
-                    for &s in src.iter() {
-                        max = T::max(s, max)
-                    }
-                    let mut sum_exp = T::zero();
+                    unsafe { T::vec_reduce_max(src.as_ptr(), &mut max, dim_m1) };
                     for (s, d) in src.iter().zip(dst.iter_mut()) {
                         *d = (*s - max).exp();
-                        sum_exp += *d
                     }
+                    let mut sum_exp = T::zero();
+                    unsafe { T::vec_reduce_sum(dst.as_ptr(), &mut sum_exp, dim_m1) };
                     for d in dst.iter_mut() {
                         *d /= sum_exp
                     }

From 94c6a8d3d3381f10afd41f6a2f98d5fad4a249ac Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 5 Sep 2023 17:53:20 +0200
Subject: [PATCH 015/150] Add a dedicated cuda kernel for softmax. (#746)

---
 candle-kernels/src/reduce.cu | 55 ++++++++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)

diff --git a/candle-kernels/src/reduce.cu b/candle-kernels/src/reduce.cu
index 271502c5..fca6865e 100644
--- a/candle-kernels/src/reduce.cu
+++ b/candle-kernels/src/reduce.cu
@@ -49,6 +49,50 @@ fast_sum(const size_t src_numel, const size_t el_to_sum_per_block,
     dst[dst_id] = shr[0];
 }
 
+// Softmax implementation adapted from ggml.
+// https://github.com/ggerganov/llama.cpp/blob/d59bd97065cd7ded6c4ecab54b1d5e0b1b11e318/ggml-cuda.cu#L4159
+template <typename T, typename ACC>
+__device__ void softmax(const T * x, T * dst, const int ncols) {
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int block_size = blockDim.y;
+    const int tid = threadIdx.y;
+
+    T max_val = -INFINITY;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const int i = row*ncols + col;
+        max_val = maxg(max_val, x[i]);
+    }
+
+    // find the max value in the block
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        max_val = maxg(max_val, __shfl_xor_sync(0xffffffff, max_val, mask, 32));
+    }
+
+    ACC tmp = 0.;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const int i = row*ncols + col;
+        const T val = expg(x[i] - max_val);
+        tmp += static_cast<ACC>(val);
+        dst[i] = val;
+    }
+
+    // sum up partial sums
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
+    }
+
+    const ACC inv_tmp = 1. / tmp;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const int i = row*ncols + col;
+        dst[i] *= inv_tmp;
+    }
+}
+
 template <typename T>
 __device__ void
 fast_max(const size_t src_numel, const size_t el_to_sum_per_block,
@@ -290,12 +334,21 @@ fast_argmax(const size_t src_numel, const size_t el_to_sum_per_block,
     }                                                                          \
   }
 
+#define SOFTMAX_OP(TYPENAME, ACC_TYPENAME, FN_NAME) \
+  extern "C" __global__ void FN_NAME(                                          \
+      const TYPENAME *src, TYPENAME *dst,                                      \
+      const int n_cols) {                                                      \
+    softmax<TYPENAME, ACC_TYPENAME>(src, dst, n_cols);                         \
+  }                                                                            \
+
 #if __CUDA_ARCH__ >= 800
+SOFTMAX_OP(__nv_bfloat16, float, softmax_bf16)
 SUM_OP(__nv_bfloat16, sum_bf16)
 FAST_OP(__nv_bfloat16, fast_min_bf16, fast_max_bf16, fast_argmin_bf16, fast_argmax_bf16, fast_sum_bf16)
 #endif
 
 #if __CUDA_ARCH__ >= 530
+SOFTMAX_OP(__half, float, softmax_f16)
 SUM_OP(__half, sum_f16)
 FAST_OP(__half, fast_min_f16, fast_max_f16, fast_argmin_f16, fast_argmax_f16, fast_sum_f16)
 #endif
@@ -303,6 +356,8 @@ FAST_OP(__half, fast_min_f16, fast_max_f16, fast_argmin_f16, fast_argmax_f16, fa
 SUM_OP(float, sum_f32)
 SUM_OP(double, sum_f64)
 SUM_OP(uint32_t, sum_u32)
+SOFTMAX_OP(float, float, softmax_f32)
+SOFTMAX_OP(double, double, softmax_f64)
 
 FAST_OP(float, fast_min_f32, fast_max_f32, fast_argmin_f32, fast_argmax_f32, fast_sum_f32)
 FAST_OP(double, fast_min_f64, fast_max_f64, fast_argmin_f64, fast_argmax_f64, fast_sum_f64)

From a0d65585db0323747f71b4f33831a165b56a759b Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 5 Sep 2023 19:38:03 +0200
Subject: [PATCH 016/150] Softmax implementation for cuda. (#747)

---
 candle-core/src/cuda_backend.rs | 20 +++++------
 candle-nn/src/ops.rs            | 59 ++++++++++++++++++++++++++++-----
 2 files changed, 61 insertions(+), 18 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 663f2319..2180be5e 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -1,7 +1,7 @@
 use crate::backend::{BackendDevice, BackendStorage};
 use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
 use crate::{CpuStorage, DType, Layout, Result, Shape, WithDType};
-use candle_kernels as kernels;
+pub use candle_kernels as kernels;
 pub use cudarc;
 use cudarc::cublas::{Gemm, GemmConfig, StridedBatchedConfig};
 use cudarc::driver::{
@@ -383,7 +383,7 @@ impl BackendDevice for CudaDevice {
 }
 
 #[derive(Debug)]
-enum CudaStorageSlice {
+pub enum CudaStorageSlice {
     U8(CudaSlice<u8>),
     U32(CudaSlice<u32>),
     I64(CudaSlice<i64>),
@@ -394,7 +394,7 @@ enum CudaStorageSlice {
 }
 type S = CudaStorageSlice;
 
-trait Map1 {
+pub trait Map1 {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
         &self,
         src: &CudaSlice<T>,
@@ -416,7 +416,7 @@ trait Map1 {
     }
 }
 
-trait Map2 {
+pub trait Map2 {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
         &self,
         src1: &CudaSlice<T>,
@@ -441,7 +441,7 @@ trait Map2 {
     }
 }
 
-trait Map2InPlace {
+pub trait Map2InPlace {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
         &self,
         dst: &mut CudaSlice<T>,
@@ -472,7 +472,7 @@ trait Map2InPlace {
     }
 }
 
-trait Map1Any {
+pub trait Map1Any {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits, W: Fn(CudaSlice<T>) -> S>(
         &self,
         src: &CudaSlice<T>,
@@ -495,7 +495,7 @@ trait Map1Any {
     }
 }
 
-trait Map2Any {
+pub trait Map2Any {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
         &self,
         src1: &CudaSlice<T>,
@@ -532,7 +532,7 @@ impl Map1 for Clone {
     }
 }
 
-fn kernel_name<T: WithDType>(root: &str) -> String {
+pub fn kernel_name<T: WithDType>(root: &str) -> String {
     let dtype = T::DTYPE.as_str();
     format!("{root}_{dtype}")
 }
@@ -1310,8 +1310,8 @@ fn slice_src_and_dst<'a, T>(
 
 #[derive(Debug)]
 pub struct CudaStorage {
-    slice: CudaStorageSlice,
-    device: CudaDevice,
+    pub slice: CudaStorageSlice,
+    pub device: CudaDevice,
 }
 
 pub trait CudaDType: Sized {
diff --git a/candle-nn/src/ops.rs b/candle-nn/src/ops.rs
index 73214077..adf1451c 100644
--- a/candle-nn/src/ops.rs
+++ b/candle-nn/src/ops.rs
@@ -126,19 +126,62 @@ impl candle::CustomOp1 for SoftmaxLastDim {
         }
     }
 
+    #[cfg(feature = "cuda")]
     fn cuda_fwd(
         &self,
-        _storage: &candle::CudaStorage,
-        _layout: &Layout,
+        storage: &candle::CudaStorage,
+        layout: &Layout,
     ) -> Result<(candle::CudaStorage, Shape)> {
-        candle::bail!("TODO: implement a cuda kernel")
+        use candle::cuda_backend::cudarc::driver::{
+            CudaSlice, DeviceRepr, LaunchAsync, LaunchConfig,
+        };
+        use candle::cuda_backend::{kernel_name, kernels, Map1, WrapErr};
+        use candle::{CudaDevice, WithDType};
+
+        struct S;
+        impl Map1 for S {
+            fn f<T: DeviceRepr + WithDType>(
+                &self,
+                src: &CudaSlice<T>,
+                dev: &CudaDevice,
+                layout: &Layout,
+            ) -> Result<CudaSlice<T>> {
+                let src = match layout.contiguous_offsets() {
+                    None => candle::bail!("input has to be contiguous"),
+                    Some((o1, o2)) => src.slice(o1..o2),
+                };
+                let el = layout.shape().elem_count();
+                let dims = layout.shape().dims();
+                let dim_m1 = dims[dims.len() - 1];
+                let (n_rows, n_cols) = (el / dim_m1, dim_m1);
+
+                let cfg = LaunchConfig {
+                    grid_dim: (n_rows as u32, 1, 1),
+                    block_dim: (1, 32, 1),
+                    shared_mem_bytes: 0,
+                };
+                let src = &src.slice(layout.start_offset()..);
+                let func = dev.get_or_load_func(&kernel_name::<T>("softmax"), kernels::REDUCE)?;
+                // SAFETY: Set later by running the kernel.
+                let dst = unsafe { dev.alloc::<T>(el) }.w()?;
+                let params = (src, &dst, n_cols as i32);
+                // SAFETY: ffi.
+                unsafe { func.launch(cfg, params) }.w()?;
+                Ok(dst)
+            }
+        }
+
+        use candle::backend::BackendStorage;
+        let dev = storage.device();
+        let slice = S.map(&storage.slice, dev, layout)?;
+        let dst = candle::cuda_backend::CudaStorage {
+            slice,
+            device: dev.clone(),
+        };
+        Ok((dst, layout.shape().clone()))
     }
 }
 
 pub fn softmax_last_dim(xs: &Tensor) -> Result<Tensor> {
-    if xs.device().is_cpu() {
-        xs.apply_op1_no_bwd(&SoftmaxLastDim)
-    } else {
-        softmax(xs, candle::D::Minus1)
-    }
+    xs.apply_op1_no_bwd(&SoftmaxLastDim)
 }

From 6a40decc76246b23f7f1c6ada539c93afadfffba Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radam=C3=A9s=20Ajna?= <radamajna@gmail.com>
Date: Tue, 5 Sep 2023 11:24:43 -0700
Subject: [PATCH 017/150] Minor WASM UI improvements  (#748)

* add stats

* random seed btn

* minor ui improvoments
---
 .../llama2-c/lib-example.html                 | 38 ++++++---
 .../llama2-c/llama2cWorker.js                 | 12 ++-
 candle-wasm-examples/whisper/lib-example.html | 17 ++--
 candle-wasm-examples/yolo/lib-example.html    | 78 +++++++++++++------
 4 files changed, 101 insertions(+), 44 deletions(-)

diff --git a/candle-wasm-examples/llama2-c/lib-example.html b/candle-wasm-examples/llama2-c/lib-example.html
index bc519e4b..5995f003 100644
--- a/candle-wasm-examples/llama2-c/lib-example.html
+++ b/candle-wasm-examples/llama2-c/lib-example.html
@@ -60,23 +60,30 @@
         const seed = getValue("seed");
         const maxSeqLen = getValue("max-seq");
 
-        function updateStatus({ status, message, prompt, sentence }) {
+        function updateStatus(data) {
           const outStatus = document.querySelector("#output-status");
           const outGen = document.querySelector("#output-generation");
+          const outCounter = document.querySelector("#output-counter");
 
-          switch (status) {
+          switch (data.status) {
             case "loading":
               outStatus.hidden = false;
-              outStatus.textContent = message;
+              outStatus.textContent = data.message;
               outGen.hidden = true;
+              outCounter.hidden = true;
               break;
             case "generating":
+              const { message, prompt, sentence, tokensSec, totalTime } = data;
               outStatus.hidden = true;
+              outCounter.hidden = false;
               outGen.hidden = false;
               outGen.innerHTML = `<span class="font-semibold">${prompt}</span>${sentence.replace(
                 /\<s\>|\<\/s\>/g,
                 ""
               )}`;
+              outCounter.innerHTML = `${(totalTime / 1000).toFixed(
+                2
+              )}s (${tokensSec.toFixed(2)} tok/s)`;
               break;
             case "complete":
               outStatus.hidden = true;
@@ -206,8 +213,9 @@
           id="prompt"
           class="font-light w-full px-3 py-2 mx-1 resize-none outline-none"
           placeholder="Add your prompt here..."
+          value="Once upon a time"
         />
-        <button class="invisible" id="clear-btn">
+        <button id="clear-btn">
           <svg
             fill="none"
             xmlns="http://www.w3.org/2000/svg"
@@ -225,7 +233,6 @@
         </button>
         <button
           id="run"
-          disabled
           class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed"
         >
           Run
@@ -291,17 +298,26 @@
           value="299792458"
           class="font-light border border-gray-700 text-right rounded-md p-2"
         />
+        <button
+          id="run"
+          onclick="document.querySelector('#seed').value = BigInt(Math.floor(Math.random() * 2**64-1))"
+          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-1 w-[50px] rounded disabled:bg-gray-300 disabled:cursor-not-allowed text-sm"
+        >
+          Rand
+        </button>
       </div>
       <div>
         <h3 class="font-medium">Generation:</h3>
-
         <div
-          class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md grid"
+          class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md flex flex-col gap-2"
         >
-          <p hidden id="output-generation"></p>
-          <span
-            id="output-status"
-            class="justify-self-center self-center font-light"
+          <div
+            id="output-counter"
+            hidden
+            class="ml-auto font-semibold grid-rows-1 text-sm"
+          ></div>
+          <p hidden id="output-generation" class="grid-rows-2"></p>
+          <span id="output-status" class="m-auto font-light"
             >No output yet</span
           >
         </div>
diff --git a/candle-wasm-examples/llama2-c/llama2cWorker.js b/candle-wasm-examples/llama2-c/llama2cWorker.js
index ba303aaa..e4229055 100644
--- a/candle-wasm-examples/llama2-c/llama2cWorker.js
+++ b/candle-wasm-examples/llama2-c/llama2cWorker.js
@@ -60,9 +60,10 @@ async function generate(data) {
     const seq_len = model.get_seq_len();
 
     let sentence = "";
-    let max_tokens = maxSeqLen ? maxSeqLen : seq_len - prompt.length - 1;
-
-    while (max_tokens--) {
+    let maxTokens = maxSeqLen ? maxSeqLen : seq_len - prompt.length - 1;
+    let startTime = performance.now();
+    let tokensCount = 0;
+    while (tokensCount < maxTokens) {
       await new Promise(async (resolve) => {
         if (controller && controller.signal.aborted) {
           self.postMessage({
@@ -73,6 +74,8 @@ async function generate(data) {
           return;
         }
         const token = await model.next_token();
+        const tokensSec =
+          ((tokensCount + 1) / (performance.now() - startTime)) * 1000;
 
         sentence += token;
         self.postMessage({
@@ -80,10 +83,13 @@ async function generate(data) {
           message: "Generating token",
           token: token,
           sentence: sentence,
+          totalTime: performance.now() - startTime,
+          tokensSec,
           prompt: prompt,
         });
         setTimeout(resolve, 0);
       });
+      tokensCount++;
     }
     self.postMessage({
       status: "complete",
diff --git a/candle-wasm-examples/whisper/lib-example.html b/candle-wasm-examples/whisper/lib-example.html
index a8c49785..ad48072b 100644
--- a/candle-wasm-examples/whisper/lib-example.html
+++ b/candle-wasm-examples/whisper/lib-example.html
@@ -141,7 +141,9 @@
             const { output } = result;
             const text = output.map((segment) => segment.dr.text).join(" ");
             console.log(text);
-            document.getElementById("output").textContent = text;
+            document.querySelector("#output-status").hidden = true;
+            document.querySelector("#output-generation").hidden = false;
+            document.querySelector("#output-generation").textContent = text;
           })
           .catch((error) => {
             console.error(error);
@@ -295,18 +297,21 @@
         <button
           id="detect"
           disabled
-          class="bg-orange-900 hover:bg-orange-800 text-white font-normal py-2 px-4 rounded disabled:opacity-75 disabled:cursor-not-allowed"
+          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 px-4 rounded disabled:bg-gray-300 disabled:cursor-not-allowed"
         >
           Transcribe Audio
         </button>
       </div>
       <div>
         <h3 class="font-medium">Transcription:</h3>
-
         <div
-          id="output"
-          class="min-h-[100px] bg-slate-500 text-white p-4 rounded-md"
-        ></div>
+          class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md flex flex-col gap-2"
+        >
+          <p hidden id="output-generation" class="grid-rows-2"></p>
+          <span id="output-status" class="m-auto font-light"
+            >No transcription results yet</span
+          >
+        </div>
       </div>
     </main>
   </body>
diff --git a/candle-wasm-examples/yolo/lib-example.html b/candle-wasm-examples/yolo/lib-example.html
index bab2ec13..8f7d07c7 100644
--- a/candle-wasm-examples/yolo/lib-example.html
+++ b/candle-wasm-examples/yolo/lib-example.html
@@ -145,6 +145,10 @@
         }
       });
 
+      document.querySelector("#clear-btn").addEventListener("click", () => {
+        drawImageCanvas();
+      });
+
       function drawImageCanvas(imgURL) {
         const canvas = document.querySelector("#canvas");
         const canvasResult = document.querySelector("#canvas-result");
@@ -153,21 +157,28 @@
           .clearRect(0, 0, canvas.width, canvas.height);
         const ctx = canvas.getContext("2d");
         ctx.clearRect(0, 0, canvas.width, canvas.height);
-        document.querySelector("#share-btn").hidden = true;
+        document.querySelector("#share-btn").classList.add("invisible");
+        document.querySelector("#clear-btn").classList.add("invisible");
+        document.querySelector("#detect").disabled = true;
+        hasImage = false;
+        canvas.parentElement.style.height = "auto";
 
-        const img = new Image();
-        img.crossOrigin = "anonymous";
+        if (imgURL && imgURL !== "") {
+          const img = new Image();
+          img.crossOrigin = "anonymous";
 
-        img.onload = () => {
-          canvas.width = img.width;
-          canvas.height = img.height;
-          ctx.drawImage(img, 0, 0);
+          img.onload = () => {
+            canvas.width = img.width;
+            canvas.height = img.height;
+            ctx.drawImage(img, 0, 0);
 
-          canvas.parentElement.style.height = canvas.offsetHeight + "px";
-          hasImage = true;
-          document.querySelector("#detect").disabled = false;
-        };
-        img.src = imgURL;
+            canvas.parentElement.style.height = canvas.offsetHeight + "px";
+            hasImage = true;
+            document.querySelector("#detect").disabled = false;
+            document.querySelector("#clear-btn").classList.remove("invisible");
+          };
+          img.src = imgURL;
+        }
       }
 
       async function classifyImage(
@@ -310,7 +321,7 @@
           button.classList.add("bg-blue-950");
           button.classList.remove("bg-blue-700");
           button.textContent = "Predict";
-          document.querySelector("#share-btn").hidden = false;
+          document.querySelector("#share-btn").classList.remove("invisible");
         }
       }
       document.querySelector("#share-btn").addEventListener("click", () => {
@@ -372,8 +383,37 @@
           <option value="yolov8x_pose">yolov8x_pose (139 MB)</option>
         </select>
       </div>
+      <div>
+        <button
+          id="detect"
+          disabled
+          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 px-4 rounded disabled:bg-gray-300 disabled:cursor-not-allowed"
+        >
+          Predict
+        </button>
+      </div>
       <!-- drag and drop area -->
       <div class="relative">
+        <div class="py-1">
+          <button
+            id="clear-btn"
+            class="text-xs bg-white rounded-md disabled:opacity-50 flex gap-1 items-center ml-auto invisible"
+          >
+            <svg
+              class=""
+              xmlns="http://www.w3.org/2000/svg"
+              viewBox="0 0 13 12"
+              height="1em"
+            >
+              <path
+                d="M1.6.7 12 11.1M12 .7 1.6 11.1"
+                stroke="#2E3036"
+                stroke-width="2"
+              />
+            </svg>
+            Clear image
+          </button>
+        </div>
         <div
           id="drop-area"
           class="flex flex-col items-center justify-center border-2 border-gray-300 border-dashed rounded-xl relative aspect-video w-full overflow-hidden"
@@ -422,8 +462,7 @@
         <div class="text-right py-2">
           <button
             id="share-btn"
-            hidden
-            class="bg-white rounded-md hover:outline outline-orange-200 disabled:opacity-50"
+            class="bg-white rounded-md hover:outline outline-orange-200 disabled:opacity-50 invisible"
           >
             <img
               src="https://huggingface.co/datasets/huggingface/badges/raw/main/share-to-community-sm.svg"
@@ -489,15 +528,6 @@
           >
         </div>
       </div>
-      <div>
-        <button
-          id="detect"
-          disabled
-          class="bg-blue-950 hover:bg-blue-700 text-white font-normal py-2 px-4 rounded disabled:opacity-75 disabled:hover:bg-blue-950"
-        >
-          Predict
-        </button>
-      </div>
     </main>
   </body>
 </html>

From a4f40f3dc881802daa973c8f4f89d133cfa25e2b Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 5 Sep 2023 21:26:15 +0200
Subject: [PATCH 018/150] Use rayon directly rather than constraining the
 number of threads. (#749)

---
 candle-core/src/cpu_backend.rs | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index c3ec8249..01ccfde7 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -2,6 +2,7 @@ use crate::backend::{BackendDevice, BackendStorage};
 use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
 use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType};
 use half::{bf16, f16};
+use rayon::prelude::*;
 
 // TODO: Maybe we should not implement [Clone] here and instead have an explicit allocator +
 // intercept the oom errors to avoid panicking and provide a proper error.
@@ -1052,10 +1053,8 @@ impl<'a> Map2 for Conv1D<'a> {
             }
         }
 
-        let num_threads = crate::utils::get_num_threads();
-
         for offset in 0..p.k_size {
-            crate::cpu::kernels::par_range(0, p.c_out, num_threads, |dst_c_idx| {
+            (0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
                 let dst_idx = dst_c_idx * l_out;
                 let k_cont = (0..p.c_in)
                     .map(|c_in_idx| k[dst_c_idx * k_s0 + c_in_idx * k_s1 + offset * k_s2])
@@ -1123,11 +1122,9 @@ impl<'a> Map2 for Conv2D<'a> {
             }
         }
 
-        let num_threads = crate::utils::get_num_threads();
-
         for offset_h in 0..p.k_h {
             for offset_w in 0..p.k_w {
-                crate::cpu::kernels::par_range(0, p.c_out, num_threads, |dst_c_idx| {
+                (0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
                     let dst_idx = dst_c_idx * out_w * out_h;
                     let k_cont = (0..p.c_in)
                         .map(|c_in_idx| {
@@ -1216,11 +1213,10 @@ impl<'a> Map2 for ConvTranspose2D<'a> {
                 }
             }
         }
-        let num_threads = crate::utils::get_num_threads();
 
         for k_y in 0..p.k_h {
             for k_x in 0..p.k_w {
-                crate::cpu::kernels::par_range(0, p.c_out, num_threads, |dst_c_idx| {
+                (0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
                     let k_cont = (0..p.c_in)
                         .map(|c_in_idx| {
                             k[c_in_idx * k_s0 + dst_c_idx * k_s1 + k_y * k_s2 + k_x * k_s3]

From 16bf44f6e9ed89d68260baa7914277fa269dcaee Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radam=C3=A9s=20Ajna?= <radamajna@gmail.com>
Date: Tue, 5 Sep 2023 20:53:31 -0700
Subject: [PATCH 019/150] force model cache (#751)

---
 .../llama2-c/lib-example.html                 | 12 ++++++++++--
 .../llama2-c/llama2cWorker.js                 | 16 ++++++++++------
 candle-wasm-examples/whisper/whisperWorker.js | 19 ++++++++++---------
 candle-wasm-examples/yolo/yoloWorker.js       | 17 ++++++++++++++---
 4 files changed, 44 insertions(+), 20 deletions(-)

diff --git a/candle-wasm-examples/llama2-c/lib-example.html b/candle-wasm-examples/llama2-c/lib-example.html
index 5995f003..b5033c54 100644
--- a/candle-wasm-examples/llama2-c/lib-example.html
+++ b/candle-wasm-examples/llama2-c/lib-example.html
@@ -38,11 +38,11 @@
         },
         stories42M: {
           url: "stories42M.bin",
-          seq_len: 256,
+          seq_len: 1024,
         },
         stories110M: {
           url: "stories110M.bin",
-          seq_len: 256,
+          seq_len: 1024,
         },
       };
 
@@ -124,9 +124,17 @@
       const prompt = document.querySelector("#prompt");
       const clearBtn = document.querySelector("#clear-btn");
       const runBtn = document.querySelector("#run");
+      const modelSelect = document.querySelector("#model");
       let runController = new AbortController();
       let isRunning = false;
 
+      modelSelect.addEventListener("change", (e) => {
+        const model = MODELS[e.target.value];
+        document.querySelector("#max-seq").max = model.seq_len;
+        document.querySelector("#max-seq").nextElementSibling.value =
+          model.seq_len;
+      });
+
       form.addEventListener("submit", async (e) => {
         e.preventDefault();
         if (isRunning) {
diff --git a/candle-wasm-examples/llama2-c/llama2cWorker.js b/candle-wasm-examples/llama2-c/llama2cWorker.js
index e4229055..abaf3401 100644
--- a/candle-wasm-examples/llama2-c/llama2cWorker.js
+++ b/candle-wasm-examples/llama2-c/llama2cWorker.js
@@ -1,13 +1,17 @@
 import init, { Model } from "./build/m.js";
 
 async function fetchArrayBuffer(url) {
-  const res = await fetch(url, {
-    cache: "force-cache",
-  });
-  const data = await res.arrayBuffer();
-  return new Uint8Array(data);
+  const cacheName = "llama2c-candle-cache";
+  const cache = await caches.open(cacheName);
+  const cachedResponse = await cache.match(url);
+  if (cachedResponse) {
+    const data = await cachedResponse.arrayBuffer();
+    return new Uint8Array(data);
+  }
+  const res = await fetch(url, { cache: "force-cache" });
+  cache.put(url, res.clone());
+  return new Uint8Array(await res.arrayBuffer());
 }
-
 class Llama2C {
   static instance = {};
 
diff --git a/candle-wasm-examples/whisper/whisperWorker.js b/candle-wasm-examples/whisper/whisperWorker.js
index 2598adde..d2ad8e0b 100644
--- a/candle-wasm-examples/whisper/whisperWorker.js
+++ b/candle-wasm-examples/whisper/whisperWorker.js
@@ -2,16 +2,17 @@
 import init, { Decoder } from "./build/m.js";
 
 async function fetchArrayBuffer(url) {
-  const res = await fetch(url, {
-    cache: "force-cache",
-    headers: {
-      "Cache-Control": "public, max-age=31536000",
-    },
-  });
-  const data = await res.arrayBuffer();
-  return new Uint8Array(data);
+  const cacheName = "whisper-candle-cache";
+  const cache = await caches.open(cacheName);
+  const cachedResponse = await cache.match(url);
+  if (cachedResponse) {
+    const data = await cachedResponse.arrayBuffer();
+    return new Uint8Array(data);
+  }
+  const res = await fetch(url, { cache: "force-cache" });
+  cache.put(url, res.clone());
+  return new Uint8Array(await res.arrayBuffer());
 }
-
 class Whisper {
   static instance = {};
   // Retrieve the Whisper model. When called for the first time,
diff --git a/candle-wasm-examples/yolo/yoloWorker.js b/candle-wasm-examples/yolo/yoloWorker.js
index 93097372..8b5ef8b9 100644
--- a/candle-wasm-examples/yolo/yoloWorker.js
+++ b/candle-wasm-examples/yolo/yoloWorker.js
@@ -1,6 +1,19 @@
 //load the candle yolo wasm module
 import init, { Model, ModelPose } from "./build/m.js";
 
+async function fetchArrayBuffer(url) {
+  const cacheName = "yolo-candle-cache";
+  const cache = await caches.open(cacheName);
+  const cachedResponse = await cache.match(url);
+  if (cachedResponse) {
+    const data = await cachedResponse.arrayBuffer();
+    return new Uint8Array(data);
+  }
+  const res = await fetch(url, { cache: "force-cache" });
+  cache.put(url, res.clone());
+  return new Uint8Array(await res.arrayBuffer());
+}
+
 class Yolo {
   static instance = {};
   // Retrieve the YOLO model. When called for the first time,
@@ -11,9 +24,7 @@ class Yolo {
       await init();
 
       self.postMessage({ status: `loading model ${modelID}:${modelSize}` });
-      const modelRes = await fetch(modelURL);
-      const yoloArrayBuffer = await modelRes.arrayBuffer();
-      const weightsArrayU8 = new Uint8Array(yoloArrayBuffer);
+      const weightsArrayU8 = await fetchArrayBuffer(modelURL);
       if (/pose/.test(modelID)) {
         // if pose model, use ModelPose
         this.instance[modelID] = new ModelPose(weightsArrayU8, modelSize);

From 7299a683534a84e13ec3ff8a92b2fff77102d7e7 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 6 Sep 2023 08:06:49 +0200
Subject: [PATCH 020/150] img2img pipeline for stable diffusion. (#752)

* img2img pipeline for stable diffusion.

* Rename the arguments + fix.

* Fix for zero strength.

* Another fix.

* Another fix.

* Revert.

* Include the backtrace.

* Noise scaling.

* Fix the height/width.
---
 candle-core/src/error.rs                      |  4 +-
 .../examples/stable-diffusion/ddim.rs         | 11 +++
 .../examples/stable-diffusion/main.rs         | 85 ++++++++++++++++---
 candle-examples/src/lib.rs                    |  8 +-
 4 files changed, 91 insertions(+), 17 deletions(-)

diff --git a/candle-core/src/error.rs b/candle-core/src/error.rs
index 1cf20a84..d030fab1 100644
--- a/candle-core/src/error.rs
+++ b/candle-core/src/error.rs
@@ -207,11 +207,11 @@ pub type Result<T> = std::result::Result<T, Error>;
 
 impl Error {
     pub fn wrap(err: impl std::error::Error + Send + Sync + 'static) -> Self {
-        Self::Wrapped(Box::new(err))
+        Self::Wrapped(Box::new(err)).bt()
     }
 
     pub fn msg(err: impl std::error::Error + Send + Sync + 'static) -> Self {
-        Self::Msg(err.to_string())
+        Self::Msg(err.to_string()).bt()
     }
 
     pub fn bt(self) -> Self {
diff --git a/candle-examples/examples/stable-diffusion/ddim.rs b/candle-examples/examples/stable-diffusion/ddim.rs
index f2e021ce..260a4965 100644
--- a/candle-examples/examples/stable-diffusion/ddim.rs
+++ b/candle-examples/examples/stable-diffusion/ddim.rs
@@ -163,6 +163,17 @@ impl DDIMScheduler {
         }
     }
 
+    pub fn add_noise(&self, original: &Tensor, noise: Tensor, timestep: usize) -> Result<Tensor> {
+        let timestep = if timestep >= self.alphas_cumprod.len() {
+            timestep - 1
+        } else {
+            timestep
+        };
+        let sqrt_alpha_prod = self.alphas_cumprod[timestep].sqrt();
+        let sqrt_one_minus_alpha_prod = (1.0 - self.alphas_cumprod[timestep]).sqrt();
+        (original * sqrt_alpha_prod)? + (noise * sqrt_one_minus_alpha_prod)?
+    }
+
     pub fn init_noise_sigma(&self) -> f64 {
         self.init_noise_sigma
     }
diff --git a/candle-examples/examples/stable-diffusion/main.rs b/candle-examples/examples/stable-diffusion/main.rs
index 8372edcd..70e1e92c 100644
--- a/candle-examples/examples/stable-diffusion/main.rs
+++ b/candle-examples/examples/stable-diffusion/main.rs
@@ -96,6 +96,15 @@ struct Args {
 
     #[arg(long)]
     use_f16: bool,
+
+    #[arg(long, value_name = "FILE")]
+    img2img: Option<String>,
+
+    /// The strength, indicates how much to transform the initial image. The
+    /// value must be between 0 and 1, a value of 1 discards the initial image
+    /// information.
+    #[arg(long, default_value_t = 0.8)]
+    img2img_strength: f64,
 }
 
 #[derive(Debug, Clone, Copy, clap::ValueEnum)]
@@ -306,6 +315,26 @@ fn text_embeddings(
     Ok(text_embeddings)
 }
 
+fn image_preprocess<T: AsRef<std::path::Path>>(path: T) -> anyhow::Result<Tensor> {
+    let img = image::io::Reader::open(path)?.decode()?;
+    let (height, width) = (img.height() as usize, img.width() as usize);
+    let height = height - height % 32;
+    let width = width - width % 32;
+    let img = img.resize_to_fill(
+        width as u32,
+        height as u32,
+        image::imageops::FilterType::CatmullRom,
+    );
+    let img = img.to_rgb8();
+    let img = img.into_raw();
+    let img = Tensor::from_vec(img, (height, width, 3), &Device::Cpu)?
+        .permute((2, 0, 1))?
+        .to_dtype(DType::F32)?
+        .affine(2. / 255., -1.)?
+        .unsqueeze(0)?;
+    Ok(img)
+}
+
 fn run(args: Args) -> Result<()> {
     use tracing_chrome::ChromeLayerBuilder;
     use tracing_subscriber::prelude::*;
@@ -328,9 +357,15 @@ fn run(args: Args) -> Result<()> {
         tracing,
         use_f16,
         use_flash_attn,
+        img2img,
+        img2img_strength,
         ..
     } = args;
 
+    if !(0. ..=1.).contains(&img2img_strength) {
+        anyhow::bail!("img2img-strength should be between 0 and 1, got {img2img_strength}")
+    }
+
     let _guard = if tracing {
         let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
         tracing_subscriber::registry().with(chrome_layer).init();
@@ -382,25 +417,53 @@ fn run(args: Args) -> Result<()> {
     println!("Building the autoencoder.");
     let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
     let vae = sd_config.build_vae(&vae_weights, &device, dtype)?;
+    let init_latent_dist = match &img2img {
+        None => None,
+        Some(image) => {
+            let image = image_preprocess(image)?.to_device(&device)?;
+            Some(vae.encode(&image)?)
+        }
+    };
     println!("Building the unet.");
     let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
     let unet = sd_config.build_unet(&unet_weights, &device, 4, use_flash_attn, dtype)?;
 
+    let t_start = if img2img.is_some() {
+        n_steps - (n_steps as f64 * img2img_strength) as usize
+    } else {
+        0
+    };
     let bsize = 1;
     for idx in 0..num_samples {
-        let mut latents = Tensor::randn(
-            0f32,
-            1f32,
-            (bsize, 4, sd_config.height / 8, sd_config.width / 8),
-            &device,
-        )?
-        .to_dtype(dtype)?;
-
-        // scale the initial noise by the standard deviation required by the scheduler
-        latents = (latents * scheduler.init_noise_sigma())?;
+        let timesteps = scheduler.timesteps();
+        let latents = match &init_latent_dist {
+            Some(init_latent_dist) => {
+                let latents = (init_latent_dist.sample()? * 0.18215)?.to_device(&device)?;
+                if t_start < timesteps.len() {
+                    let noise = latents.randn_like(0f64, 1f64)?;
+                    scheduler.add_noise(&latents, noise, timesteps[t_start])?
+                } else {
+                    latents
+                }
+            }
+            None => {
+                let latents = Tensor::randn(
+                    0f32,
+                    1f32,
+                    (bsize, 4, sd_config.height / 8, sd_config.width / 8),
+                    &device,
+                )?;
+                // scale the initial noise by the standard deviation required by the scheduler
+                (latents * scheduler.init_noise_sigma())?
+            }
+        };
+        let mut latents = latents.to_dtype(dtype)?;
 
         println!("starting sampling");
-        for (timestep_index, &timestep) in scheduler.timesteps().iter().enumerate() {
+        for (timestep_index, &timestep) in timesteps.iter().enumerate() {
+            if timestep_index < t_start {
+                continue;
+            }
             let start_time = std::time::Instant::now();
             let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
 
diff --git a/candle-examples/src/lib.rs b/candle-examples/src/lib.rs
index 395162eb..f9581b02 100644
--- a/candle-examples/src/lib.rs
+++ b/candle-examples/src/lib.rs
@@ -35,14 +35,14 @@ pub fn load_image_and_resize<P: AsRef<std::path::Path>>(
 }
 
 /// Saves an image to disk using the image crate, this expects an input with shape
-/// (c, width, height).
+/// (c, height, width).
 pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<()> {
     let p = p.as_ref();
-    let (channel, width, height) = img.dims3()?;
+    let (channel, height, width) = img.dims3()?;
     if channel != 3 {
-        candle::bail!("save_image expects an input of shape (3, width, height)")
+        candle::bail!("save_image expects an input of shape (3, height, width)")
     }
-    let img = img.transpose(0, 1)?.t()?.flatten_all()?;
+    let img = img.permute((1, 2, 0))?.flatten_all()?;
     let pixels = img.to_vec1::<u8>()?;
     let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
         match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {

From dcf708559da9f5d9f07a1394200236aec261e820 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 6 Sep 2023 08:49:28 +0200
Subject: [PATCH 021/150] Fix for cudnn to work with img2img. (#753)

---
 candle-examples/examples/stable-diffusion/unet_2d_blocks.rs | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/candle-examples/examples/stable-diffusion/unet_2d_blocks.rs b/candle-examples/examples/stable-diffusion/unet_2d_blocks.rs
index 26a1035b..be258acb 100644
--- a/candle-examples/examples/stable-diffusion/unet_2d_blocks.rs
+++ b/candle-examples/examples/stable-diffusion/unet_2d_blocks.rs
@@ -754,6 +754,7 @@ impl UpBlock2D {
         let mut xs = xs.clone();
         for (index, resnet) in self.resnets.iter().enumerate() {
             xs = Tensor::cat(&[&xs, &res_xs[res_xs.len() - index - 1]], 1)?;
+            xs = xs.contiguous()?;
             xs = resnet.forward(&xs, temb)?;
         }
         match &self.upsampler {
@@ -855,6 +856,7 @@ impl CrossAttnUpBlock2D {
         let mut xs = xs.clone();
         for (index, resnet) in self.upblock.resnets.iter().enumerate() {
             xs = Tensor::cat(&[&xs, &res_xs[res_xs.len() - index - 1]], 1)?;
+            xs = xs.contiguous()?;
             xs = resnet.forward(&xs, temb)?;
             xs = self.attentions[index].forward(&xs, encoder_hidden_states)?;
         }

From bdc9d46fe38e550dd0b332a26f14711947e6c0d9 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 6 Sep 2023 16:29:09 +0200
Subject: [PATCH 022/150] Use an arc in the varbuilder rather than rc. (#757)

* Use an arc in the varbuilder rather than rc.

* Require the backends to be send.

* Request send and sync.
---
 candle-nn/src/var_builder.rs | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/candle-nn/src/var_builder.rs b/candle-nn/src/var_builder.rs
index bf5d5b43..4ccbaf17 100644
--- a/candle-nn/src/var_builder.rs
+++ b/candle-nn/src/var_builder.rs
@@ -5,14 +5,14 @@ use crate::VarMap;
 use candle::{safetensors::Load, DType, Device, Error, Result, Shape, Tensor};
 use safetensors::{slice::IndexOp, tensor::SafeTensors};
 use std::collections::HashMap;
-use std::rc::Rc;
+use std::sync::Arc;
 
 /// A structure used to retrieve variables, these variables can either come from storage or be
 /// generated via some form of initialization.
 ///
 /// The way to retrieve variables is defined in the backend embedded in the `VarBuilder`.
 pub struct VarBuilderArgs<'a, B: Backend> {
-    data: Rc<TensorData<B>>,
+    data: Arc<TensorData<B>>,
     path: Vec<String>,
     _phantom: std::marker::PhantomData<&'a B>,
 }
@@ -43,7 +43,7 @@ struct TensorData<B: Backend> {
 /// Note that there is a speciliazed version of this trait (`SimpleBackend`) that can be used most
 /// of the time. The main restriction is that it doesn't allow for specific args (besides
 /// initialization hints).
-pub trait Backend {
+pub trait Backend: Send + Sync {
     type Hints: Default;
 
     /// Retrieve a tensor with some target shape.
@@ -59,7 +59,7 @@ pub trait Backend {
     fn contains_tensor(&self, name: &str) -> bool;
 }
 
-pub trait SimpleBackend {
+pub trait SimpleBackend: Send + Sync {
     /// Retrieve a tensor based on a target name and shape.
     fn get(
         &self,
@@ -99,7 +99,7 @@ impl<'a, B: Backend> VarBuilderArgs<'a, B> {
             device: dev.clone(),
         };
         Self {
-            data: Rc::new(data),
+            data: Arc::new(data),
             path: vec![],
             _phantom: std::marker::PhantomData,
         }
@@ -333,7 +333,7 @@ impl<'a> VarBuilder<'a> {
             device,
         };
         Self {
-            data: Rc::new(data),
+            data: Arc::new(data),
             path: vec![],
             _phantom: std::marker::PhantomData,
         }

From 7b1f2da828302ff938af76311ecf68a4ce0d7c2e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 6 Sep 2023 18:39:39 +0200
Subject: [PATCH 023/150] Cudnn fix. (#758)

---
 candle-core/src/cudnn.rs | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/candle-core/src/cudnn.rs b/candle-core/src/cudnn.rs
index 235ad6e3..dd466ba2 100644
--- a/candle-core/src/cudnn.rs
+++ b/candle-core/src/cudnn.rs
@@ -54,8 +54,8 @@ pub(crate) fn launch_conv2d<
     let x_shape = [
         params.b_size as i32,
         params.c_in as i32,
-        params.i_w as i32,
         params.i_h as i32,
+        params.i_w as i32,
     ];
     // Note that `src` already starts at the proper offset.
     let x = if src_l.is_contiguous() {
@@ -75,14 +75,14 @@ pub(crate) fn launch_conv2d<
         [
             params.c_out as i32,
             params.c_in as i32,
-            params.k_w as i32,
             params.k_h as i32,
+            params.k_w as i32,
         ],
     )?;
     let (w_out, h_out) = (params.out_w() as i32, params.out_h() as i32);
     let y = cudnn.create_4d_tensor(
         cudarc::cudnn::sys::cudnnTensorFormat_t::CUDNN_TENSOR_NCHW,
-        [params.b_size as i32, params.c_out as i32, w_out, h_out],
+        [params.b_size as i32, params.c_out as i32, h_out, w_out],
     )?;
     let conv2d = Conv2dForward {
         conv: &conv,

From 6527ab81a3a6f26dae37f6c56eecf0f4bb826f02 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 7 Sep 2023 06:34:05 +0200
Subject: [PATCH 024/150] Sketch the segment anything model. (#759)

* Sketch the segment anything model.

* Fix some clippy lint.

* Add the mask decoder.
---
 candle-core/src/shape.rs                      |  11 +
 .../examples/segment-anything/main.rs         | 446 ++++++++++++++++++
 2 files changed, 457 insertions(+)
 create mode 100644 candle-examples/examples/segment-anything/main.rs

diff --git a/candle-core/src/shape.rs b/candle-core/src/shape.rs
index db0fe98a..578e8ac9 100644
--- a/candle-core/src/shape.rs
+++ b/candle-core/src/shape.rs
@@ -425,6 +425,17 @@ impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim> Dims for (D1, D2, D3, D4) {
     }
 }
 
+impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim, D5: Dim> Dims for (D1, D2, D3, D4, D5) {
+    fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
+        let d0 = self.0.to_index(shape, op)?;
+        let d1 = self.1.to_index(shape, op)?;
+        let d2 = self.2.to_index(shape, op)?;
+        let d3 = self.3.to_index(shape, op)?;
+        let d4 = self.4.to_index(shape, op)?;
+        Ok(vec![d0, d1, d2, d3, d4])
+    }
+}
+
 extract_dims!(dims0, 0, |_: &[usize]| (), ());
 extract_dims!(dims1, 1, |d: &[usize]| d[0], usize);
 extract_dims!(dims2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
new file mode 100644
index 00000000..a53cff8b
--- /dev/null
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -0,0 +1,446 @@
+//! SAM: Segment Anything Model
+//! https://github.com/facebookresearch/segment-anything
+#![allow(unused)]
+
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use clap::Parser;
+
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+const IMG_SIZE: usize = 518;
+const PATCH_SIZE: usize = 14;
+const NUM_CLASSES: usize = 1000;
+
+fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
+    if bias {
+        candle_nn::linear(in_dim, out_dim, vb)
+    } else {
+        candle_nn::linear_no_bias(in_dim, out_dim, vb)
+    }
+}
+
+#[derive(Debug)]
+struct MlpBlock {
+    lin1: Linear,
+    lin2: Linear,
+}
+
+impl MlpBlock {
+    fn new(embedding_dim: usize, mlp_dim: usize, vb: VarBuilder) -> Result<Self> {
+        let lin1 = candle_nn::linear(embedding_dim, mlp_dim, vb.pp("lin1"))?;
+        let lin2 = candle_nn::linear(mlp_dim, embedding_dim, vb.pp("lin2"))?;
+        Ok(Self { lin1, lin2 })
+    }
+}
+
+impl Module for MlpBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.lin1)?.gelu()?.apply(&self.lin2)
+    }
+}
+
+#[derive(Debug)]
+struct PatchEmbed {
+    proj: candle_nn::Conv2d,
+}
+
+impl PatchEmbed {
+    fn new(
+        in_chans: usize,
+        embed_dim: usize,
+        k_size: usize,
+        stride: usize,
+        padding: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let cfg = candle_nn::Conv2dConfig {
+            stride,
+            padding,
+            ..Default::default()
+        };
+        let proj = candle_nn::conv2d(in_chans, embed_dim, k_size, cfg, vb.pp("proj"))?;
+        Ok(Self { proj })
+    }
+}
+
+impl Module for PatchEmbed {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.proj)?.permute((0, 2, 3, 1))
+    }
+}
+
+#[derive(Debug)]
+struct Attention {
+    qkv: Linear,
+    proj: Linear,
+    num_heads: usize,
+    scale: f64,
+    use_rel_pos: bool,
+    rel_pos_hw: Option<(Tensor, Tensor)>,
+}
+
+impl Attention {
+    fn new(
+        dim: usize,
+        num_heads: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let qkv = linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
+        let proj = linear(vb.pp("proj"), dim, dim, true)?;
+        let head_dim = dim / num_heads;
+        let scale = 1. / (head_dim as f64).sqrt();
+        let rel_pos_hw = if use_rel_pos {
+            let h = vb.get((2 * window_size - 1, head_dim), "rel_pos_h")?;
+            let w = vb.get((2 * window_size - 1, head_dim), "rel_pos_w")?;
+            Some((h, w))
+        } else {
+            None
+        };
+        Ok(Self {
+            qkv,
+            proj,
+            num_heads,
+            scale,
+            use_rel_pos,
+            rel_pos_hw,
+        })
+    }
+}
+
+impl Module for Attention {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (b, h, w, c) = xs.dims4()?;
+        let qkv = self
+            .qkv
+            .forward(xs)?
+            .reshape((b, h * w, 3, self.num_heads, c / self.num_heads))?
+            .permute((2, 0, 3, 1, 4))?
+            .reshape((3, b * self.num_heads, h * w, c / self.num_heads))?;
+        let q = qkv.i(0)?;
+        let k = qkv.i(1)?;
+        let v = qkv.i(2)?;
+        let attn = (q * self.scale)?.matmul(&k.t()?)?;
+        if self.use_rel_pos {
+            todo!()
+        }
+        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
+        let attn = attn
+            .matmul(&v)?
+            .reshape((b, self.num_heads, h, w, c / self.num_heads))?
+            .permute((0, 2, 3, 1, 4))?
+            .reshape((b, h, w, c / self.num_heads))?;
+        self.proj.forward(&attn)
+    }
+}
+
+#[derive(Debug)]
+struct Block {
+    norm1: LayerNorm,
+    attn: Attention,
+    norm2: LayerNorm,
+    mlp: MlpBlock,
+    window_size: usize,
+}
+
+impl Block {
+    fn new(
+        dim: usize,
+        num_heads: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
+        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
+        let attn = Attention::new(
+            dim,
+            num_heads,
+            qkv_bias,
+            use_rel_pos,
+            window_size,
+            vb.pp("attn"),
+        )?;
+        let mlp = MlpBlock::new(dim, dim * 4, vb.pp("mlp"))?;
+        Ok(Self {
+            norm1,
+            attn,
+            norm2,
+            mlp,
+            window_size,
+        })
+    }
+}
+
+impl Module for Block {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let shortcut = xs;
+        let xs = self.norm1.forward(xs)?;
+        if self.window_size > 0 {
+            todo!()
+        }
+        let xs = self.attn.forward(&xs)?;
+        if self.window_size > 0 {
+            todo!()
+        }
+        let xs = (xs + shortcut)?;
+        &xs + xs.apply(&self.norm2)?.apply(&self.mlp)?
+    }
+}
+
+#[derive(Debug)]
+struct ImageEncoderViT {
+    img_size: usize,
+    patch_embed: PatchEmbed,
+    blocks: Vec<Block>,
+    neck_conv1: candle_nn::Conv2d,
+    neck_ln1: LayerNorm,
+    neck_conv2: candle_nn::Conv2d,
+    neck_ln2: LayerNorm,
+    pos_embed: Option<Tensor>,
+}
+
+impl ImageEncoderViT {
+    #[allow(clippy::too_many_arguments)]
+    fn new(
+        img_size: usize,
+        patch_size: usize,
+        in_chans: usize,
+        embed_dim: usize,
+        depth: usize,
+        num_heads: usize,
+        out_chans: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        use_abs_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let patch_embed = PatchEmbed::new(
+            in_chans,
+            embed_dim,
+            patch_size,
+            patch_size,
+            0,
+            vb.pp("patch_embed"),
+        )?;
+        let mut blocks = Vec::with_capacity(depth);
+        let vb_b = vb.pp("blocks");
+        for i in 0..depth {
+            let block = Block::new(
+                embed_dim,
+                num_heads,
+                qkv_bias,
+                use_rel_pos,
+                window_size,
+                vb_b.pp(i),
+            )?;
+            blocks.push(block)
+        }
+        let neck_conv1 = candle_nn::conv2d_no_bias(
+            embed_dim,
+            out_chans,
+            1,
+            Default::default(),
+            vb.pp("neck.0"),
+        )?;
+        let neck_ln1 = layer_norm(out_chans, 1e-6, vb.pp("neck.1"))?;
+        let cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
+        let neck_ln2 = layer_norm(out_chans, 1e-6, vb.pp("neck.3"))?;
+        let pos_embed = if use_abs_pos {
+            let p = vb.get(
+                (1, img_size / patch_size, img_size / patch_size, embed_dim),
+                "pos_embed",
+            )?;
+            Some(p)
+        } else {
+            None
+        };
+        Ok(Self {
+            img_size,
+            patch_embed,
+            blocks,
+            neck_conv1,
+            neck_ln1,
+            neck_conv2,
+            neck_ln2,
+            pos_embed,
+        })
+    }
+}
+
+impl Module for ImageEncoderViT {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.patch_embed.forward(xs)?;
+        let mut xs = match &self.pos_embed {
+            Some(pos_embed) => (xs + pos_embed)?,
+            None => xs,
+        };
+        for block in self.blocks.iter() {
+            xs = block.forward(&xs)?
+        }
+        xs.permute((0, 3, 1, 2))?
+            .apply(&self.neck_conv1)?
+            .apply(&self.neck_ln1)?
+            .apply(&self.neck_conv2)?
+            .apply(&self.neck_ln2)
+    }
+}
+
+#[derive(Debug)]
+struct MlpMaskDecoder {
+    layers: Vec<Linear>,
+    sigmoid_output: bool,
+}
+
+impl MlpMaskDecoder {
+    fn new(
+        input_dim: usize,
+        hidden_dim: usize,
+        output_dim: usize,
+        num_layers: usize,
+        sigmoid_output: bool,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let mut layers = Vec::with_capacity(num_layers);
+        let vb = vb.pp("layers");
+        for i in 0..num_layers {
+            let in_dim = if i == 0 { input_dim } else { hidden_dim };
+            let out_dim = if i + 1 == num_layers {
+                output_dim
+            } else {
+                hidden_dim
+            };
+            let layer = linear(vb.pp(i), in_dim, out_dim, true)?;
+            layers.push(layer)
+        }
+        Ok(Self {
+            layers,
+            sigmoid_output,
+        })
+    }
+}
+
+impl Module for MlpMaskDecoder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.clone();
+        for (i, layer) in self.layers.iter().enumerate() {
+            xs = layer.forward(&xs)?;
+            if i + 1 < self.layers.len() {
+                xs = xs.relu()?
+            }
+        }
+        if self.sigmoid_output {
+            candle_nn::ops::sigmoid(&xs)
+        } else {
+            Ok(xs)
+        }
+    }
+}
+
+#[derive(Debug)]
+struct MaskDecoder {
+    iou_tokens: candle_nn::Embedding,
+    mask_tokens: candle_nn::Embedding,
+    iou_prediction_head: MlpMaskDecoder,
+}
+
+impl MaskDecoder {
+    fn new(
+        transformer_dim: usize,
+        num_multimask_outputs: usize,
+        iou_head_depth: usize,
+        iou_head_hidden_dim: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let num_mask_tokens = num_multimask_outputs - 1;
+        let iou_prediction_head = MlpMaskDecoder::new(
+            transformer_dim,
+            iou_head_hidden_dim,
+            num_mask_tokens,
+            iou_head_depth,
+            false,
+            vb.pp("iou_prediction_head"),
+        )?;
+        let iou_tokens = candle_nn::embedding(1, transformer_dim, vb.pp("iou_tokens"))?;
+        let mask_tokens =
+            candle_nn::embedding(num_mask_tokens, transformer_dim, vb.pp("mask_tokens"))?;
+        Ok(Self {
+            iou_tokens,
+            mask_tokens,
+            iou_prediction_head,
+        })
+    }
+}
+
+/*
+    fn interpolate_pos_encoding(&self, xs: &Tensor, w: usize, h: usize) -> Result<Tensor> {
+        let npatch = xs.dim(1)? - 1;
+        let n = self.pos_embed.dim(1)? - 1;
+        let sqrt_n = (n as f64).sqrt();
+        if npatch == n && w == h {
+            return Ok(xs.clone());
+        }
+        let class_pos_embed = self.pos_embed.i((.., ..1))?;
+        let patch_pos_embed = self.pos_embed.i((.., 1..))?;
+        let dim = xs.dim(D::Minus1)?;
+        let (w0, h0) = ((w / PATCH_SIZE) as f64 + 0.1, (h / PATCH_SIZE) as f64 + 0.1);
+        let patch_pos_embed = patch_pos_embed
+            .reshape((1, sqrt_n as usize, sqrt_n as usize, dim))?
+            .transpose(2, 3)?
+            .transpose(1, 2)?;
+        // This uses bicubic interpolation in the original implementation.
+        let patch_pos_embed = patch_pos_embed.upsample_nearest2d(h0 as usize, w0 as usize)?;
+        let el_count = patch_pos_embed.shape().elem_count();
+        let patch_pos_embed =
+            patch_pos_embed
+                .transpose(1, 2)?
+                .transpose(2, 3)?
+                .reshape((1, el_count / dim, dim))?;
+        Tensor::cat(&[&class_pos_embed, &patch_pos_embed], 1)
+    }
+
+    fn prepare_tokens_with_mask(&self, xs: &Tensor) -> Result<Tensor> {
+        let (_b, _nc, w, h) = xs.dims4()?;
+        let xs = self.patch_embed.forward(xs)?;
+        let xs = Tensor::cat(&[&self.cls_token, &xs], 1)?;
+        &xs + &self.interpolate_pos_encoding(&xs, w, h)?
+    }
+*/
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    #[arg(long)]
+    image: String,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+
+    let _device = candle_examples::device(args.cpu)?;
+
+    let image = candle_examples::imagenet::load_image224(args.image)?;
+    println!("loaded image {image:?}");
+
+    Ok(())
+}

From a17a7c42c1b95f4d710d95be86efcc2665eadb19 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 7 Sep 2023 06:47:28 +0200
Subject: [PATCH 025/150] Add a nn layer for conv-transpose2d. (#760)

---
 candle-nn/src/conv.rs | 51 ++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 50 insertions(+), 1 deletion(-)

diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index dbf23aa5..f985cfd6 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -80,7 +80,6 @@ impl Default for Conv2dConfig {
     }
 }
 
-#[allow(dead_code)]
 #[derive(Debug)]
 pub struct Conv2d {
     weight: Tensor,
@@ -122,6 +121,56 @@ impl crate::Module for Conv2d {
     }
 }
 
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub struct ConvTranspose2dConfig {
+    pub padding: usize,
+    pub output_padding: usize,
+    pub stride: usize,
+    pub dilation: usize,
+    // TODO: support groups.
+}
+
+#[derive(Debug)]
+pub struct ConvTranspose2d {
+    weight: Tensor,
+    bias: Option<Tensor>,
+    config: ConvTranspose2dConfig,
+}
+
+impl ConvTranspose2d {
+    pub fn new(weight: Tensor, bias: Option<Tensor>, config: ConvTranspose2dConfig) -> Self {
+        Self {
+            weight,
+            bias,
+            config,
+        }
+    }
+
+    pub fn config(&self) -> &ConvTranspose2dConfig {
+        &self.config
+    }
+}
+
+impl crate::Module for ConvTranspose2d {
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let x = x.conv_transpose2d(
+            &self.weight,
+            self.config.padding,
+            self.config.output_padding,
+            self.config.stride,
+            self.config.dilation,
+        )?;
+        match &self.bias {
+            None => Ok(x),
+            Some(bias) => {
+                let b = bias.dims1()?;
+                let bias = bias.reshape((1, b, 1, 1))?;
+                Ok(x.broadcast_add(&bias)?)
+            }
+        }
+    }
+}
+
 pub fn conv1d(
     in_channels: usize,
     out_channels: usize,

From 000fa00e3171205c217ab7f25602a70d6473f8e9 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 7 Sep 2023 07:04:52 +0200
Subject: [PATCH 026/150] Expose the conv2d-transpose layers. (#761)

---
 candle-nn/src/conv.rs | 41 +++++++++++++++++++++++++++++++++++++++++
 candle-nn/src/lib.rs  |  5 ++++-
 2 files changed, 45 insertions(+), 1 deletion(-)

diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index f985cfd6..fe44c153 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -240,3 +240,44 @@ pub fn conv2d_no_bias(
     )?;
     Ok(Conv2d::new(ws, None, cfg))
 }
+
+pub fn conv_transpose2d(
+    in_channels: usize,
+    out_channels: usize,
+    kernel_size: usize,
+    cfg: ConvTranspose2dConfig,
+    vs: crate::VarBuilder,
+) -> Result<ConvTranspose2d> {
+    let bound = 1. / (out_channels as f64).sqrt() / kernel_size as f64;
+    let init = crate::Init::Uniform {
+        lo: -bound,
+        up: bound,
+    };
+    let ws = vs.get_with_hints(
+        (in_channels, out_channels, kernel_size, kernel_size),
+        "weight",
+        init,
+    )?;
+    let bs = vs.get_with_hints(out_channels, "bias", init)?;
+    Ok(ConvTranspose2d::new(ws, Some(bs), cfg))
+}
+
+pub fn conv_transpose2d_no_bias(
+    in_channels: usize,
+    out_channels: usize,
+    kernel_size: usize,
+    cfg: ConvTranspose2dConfig,
+    vs: crate::VarBuilder,
+) -> Result<ConvTranspose2d> {
+    let bound = 1. / (out_channels as f64).sqrt() / kernel_size as f64;
+    let init = crate::Init::Uniform {
+        lo: -bound,
+        up: bound,
+    };
+    let ws = vs.get_with_hints(
+        (out_channels, in_channels, kernel_size, kernel_size),
+        "weight",
+        init,
+    )?;
+    Ok(ConvTranspose2d::new(ws, None, cfg))
+}
diff --git a/candle-nn/src/lib.rs b/candle-nn/src/lib.rs
index 6e268f4e..8e5580df 100644
--- a/candle-nn/src/lib.rs
+++ b/candle-nn/src/lib.rs
@@ -16,7 +16,10 @@ pub mod var_map;
 
 pub use activation::Activation;
 pub use batch_norm::{batch_norm, BatchNorm, BatchNormConfig};
-pub use conv::{conv1d, conv2d, conv2d_no_bias, Conv1d, Conv1dConfig, Conv2d, Conv2dConfig};
+pub use conv::{
+    conv1d, conv2d, conv2d_no_bias, conv_transpose2d, conv_transpose2d_no_bias, Conv1d,
+    Conv1dConfig, Conv2d, Conv2dConfig, ConvTranspose2d, ConvTranspose2dConfig,
+};
 pub use embedding::{embedding, Embedding};
 pub use func::{func, Func};
 pub use group_norm::{group_norm, GroupNorm};

From 8c991df3945a7c86ae86a7a52a74639ec321cef2 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 7 Sep 2023 08:28:30 +0200
Subject: [PATCH 027/150] More segment-anything. (#763)

* More segment-anything.

* Split the model in multiple files.

* Start adding the transformer.

* Add the attention block.

* Move the MLP Block.
---
 .../examples/segment-anything/main.rs         | 356 +-----------------
 .../segment-anything/model_image_encoder.rs   | 257 +++++++++++++
 .../segment-anything/model_mask_decoder.rs    | 222 +++++++++++
 .../segment-anything/model_transformer.rs     |  77 ++++
 candle-nn/src/conv.rs                         |  11 +
 5 files changed, 574 insertions(+), 349 deletions(-)
 create mode 100644 candle-examples/examples/segment-anything/model_image_encoder.rs
 create mode 100644 candle-examples/examples/segment-anything/model_mask_decoder.rs
 create mode 100644 candle-examples/examples/segment-anything/model_transformer.rs

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index a53cff8b..de16f70c 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -8,16 +8,15 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-use clap::Parser;
+mod model_image_encoder;
+mod model_mask_decoder;
+mod model_transformer;
 
 use candle::{DType, IndexOp, Result, Tensor, D};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use clap::Parser;
 
-const IMG_SIZE: usize = 518;
-const PATCH_SIZE: usize = 14;
-const NUM_CLASSES: usize = 1000;
-
-fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
+pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
     if bias {
         candle_nn::linear(in_dim, out_dim, vb)
     } else {
@@ -26,13 +25,13 @@ fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<L
 }
 
 #[derive(Debug)]
-struct MlpBlock {
+pub struct MlpBlock {
     lin1: Linear,
     lin2: Linear,
 }
 
 impl MlpBlock {
-    fn new(embedding_dim: usize, mlp_dim: usize, vb: VarBuilder) -> Result<Self> {
+    pub fn new(embedding_dim: usize, mlp_dim: usize, vb: VarBuilder) -> Result<Self> {
         let lin1 = candle_nn::linear(embedding_dim, mlp_dim, vb.pp("lin1"))?;
         let lin2 = candle_nn::linear(mlp_dim, embedding_dim, vb.pp("lin2"))?;
         Ok(Self { lin1, lin2 })
@@ -45,347 +44,6 @@ impl Module for MlpBlock {
     }
 }
 
-#[derive(Debug)]
-struct PatchEmbed {
-    proj: candle_nn::Conv2d,
-}
-
-impl PatchEmbed {
-    fn new(
-        in_chans: usize,
-        embed_dim: usize,
-        k_size: usize,
-        stride: usize,
-        padding: usize,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let cfg = candle_nn::Conv2dConfig {
-            stride,
-            padding,
-            ..Default::default()
-        };
-        let proj = candle_nn::conv2d(in_chans, embed_dim, k_size, cfg, vb.pp("proj"))?;
-        Ok(Self { proj })
-    }
-}
-
-impl Module for PatchEmbed {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        xs.apply(&self.proj)?.permute((0, 2, 3, 1))
-    }
-}
-
-#[derive(Debug)]
-struct Attention {
-    qkv: Linear,
-    proj: Linear,
-    num_heads: usize,
-    scale: f64,
-    use_rel_pos: bool,
-    rel_pos_hw: Option<(Tensor, Tensor)>,
-}
-
-impl Attention {
-    fn new(
-        dim: usize,
-        num_heads: usize,
-        qkv_bias: bool,
-        use_rel_pos: bool,
-        window_size: usize,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let qkv = linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
-        let proj = linear(vb.pp("proj"), dim, dim, true)?;
-        let head_dim = dim / num_heads;
-        let scale = 1. / (head_dim as f64).sqrt();
-        let rel_pos_hw = if use_rel_pos {
-            let h = vb.get((2 * window_size - 1, head_dim), "rel_pos_h")?;
-            let w = vb.get((2 * window_size - 1, head_dim), "rel_pos_w")?;
-            Some((h, w))
-        } else {
-            None
-        };
-        Ok(Self {
-            qkv,
-            proj,
-            num_heads,
-            scale,
-            use_rel_pos,
-            rel_pos_hw,
-        })
-    }
-}
-
-impl Module for Attention {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let (b, h, w, c) = xs.dims4()?;
-        let qkv = self
-            .qkv
-            .forward(xs)?
-            .reshape((b, h * w, 3, self.num_heads, c / self.num_heads))?
-            .permute((2, 0, 3, 1, 4))?
-            .reshape((3, b * self.num_heads, h * w, c / self.num_heads))?;
-        let q = qkv.i(0)?;
-        let k = qkv.i(1)?;
-        let v = qkv.i(2)?;
-        let attn = (q * self.scale)?.matmul(&k.t()?)?;
-        if self.use_rel_pos {
-            todo!()
-        }
-        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
-        let attn = attn
-            .matmul(&v)?
-            .reshape((b, self.num_heads, h, w, c / self.num_heads))?
-            .permute((0, 2, 3, 1, 4))?
-            .reshape((b, h, w, c / self.num_heads))?;
-        self.proj.forward(&attn)
-    }
-}
-
-#[derive(Debug)]
-struct Block {
-    norm1: LayerNorm,
-    attn: Attention,
-    norm2: LayerNorm,
-    mlp: MlpBlock,
-    window_size: usize,
-}
-
-impl Block {
-    fn new(
-        dim: usize,
-        num_heads: usize,
-        qkv_bias: bool,
-        use_rel_pos: bool,
-        window_size: usize,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
-        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
-        let attn = Attention::new(
-            dim,
-            num_heads,
-            qkv_bias,
-            use_rel_pos,
-            window_size,
-            vb.pp("attn"),
-        )?;
-        let mlp = MlpBlock::new(dim, dim * 4, vb.pp("mlp"))?;
-        Ok(Self {
-            norm1,
-            attn,
-            norm2,
-            mlp,
-            window_size,
-        })
-    }
-}
-
-impl Module for Block {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let shortcut = xs;
-        let xs = self.norm1.forward(xs)?;
-        if self.window_size > 0 {
-            todo!()
-        }
-        let xs = self.attn.forward(&xs)?;
-        if self.window_size > 0 {
-            todo!()
-        }
-        let xs = (xs + shortcut)?;
-        &xs + xs.apply(&self.norm2)?.apply(&self.mlp)?
-    }
-}
-
-#[derive(Debug)]
-struct ImageEncoderViT {
-    img_size: usize,
-    patch_embed: PatchEmbed,
-    blocks: Vec<Block>,
-    neck_conv1: candle_nn::Conv2d,
-    neck_ln1: LayerNorm,
-    neck_conv2: candle_nn::Conv2d,
-    neck_ln2: LayerNorm,
-    pos_embed: Option<Tensor>,
-}
-
-impl ImageEncoderViT {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        img_size: usize,
-        patch_size: usize,
-        in_chans: usize,
-        embed_dim: usize,
-        depth: usize,
-        num_heads: usize,
-        out_chans: usize,
-        qkv_bias: bool,
-        use_rel_pos: bool,
-        use_abs_pos: bool,
-        window_size: usize,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let patch_embed = PatchEmbed::new(
-            in_chans,
-            embed_dim,
-            patch_size,
-            patch_size,
-            0,
-            vb.pp("patch_embed"),
-        )?;
-        let mut blocks = Vec::with_capacity(depth);
-        let vb_b = vb.pp("blocks");
-        for i in 0..depth {
-            let block = Block::new(
-                embed_dim,
-                num_heads,
-                qkv_bias,
-                use_rel_pos,
-                window_size,
-                vb_b.pp(i),
-            )?;
-            blocks.push(block)
-        }
-        let neck_conv1 = candle_nn::conv2d_no_bias(
-            embed_dim,
-            out_chans,
-            1,
-            Default::default(),
-            vb.pp("neck.0"),
-        )?;
-        let neck_ln1 = layer_norm(out_chans, 1e-6, vb.pp("neck.1"))?;
-        let cfg = candle_nn::Conv2dConfig {
-            padding: 1,
-            ..Default::default()
-        };
-        let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
-        let neck_ln2 = layer_norm(out_chans, 1e-6, vb.pp("neck.3"))?;
-        let pos_embed = if use_abs_pos {
-            let p = vb.get(
-                (1, img_size / patch_size, img_size / patch_size, embed_dim),
-                "pos_embed",
-            )?;
-            Some(p)
-        } else {
-            None
-        };
-        Ok(Self {
-            img_size,
-            patch_embed,
-            blocks,
-            neck_conv1,
-            neck_ln1,
-            neck_conv2,
-            neck_ln2,
-            pos_embed,
-        })
-    }
-}
-
-impl Module for ImageEncoderViT {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let xs = self.patch_embed.forward(xs)?;
-        let mut xs = match &self.pos_embed {
-            Some(pos_embed) => (xs + pos_embed)?,
-            None => xs,
-        };
-        for block in self.blocks.iter() {
-            xs = block.forward(&xs)?
-        }
-        xs.permute((0, 3, 1, 2))?
-            .apply(&self.neck_conv1)?
-            .apply(&self.neck_ln1)?
-            .apply(&self.neck_conv2)?
-            .apply(&self.neck_ln2)
-    }
-}
-
-#[derive(Debug)]
-struct MlpMaskDecoder {
-    layers: Vec<Linear>,
-    sigmoid_output: bool,
-}
-
-impl MlpMaskDecoder {
-    fn new(
-        input_dim: usize,
-        hidden_dim: usize,
-        output_dim: usize,
-        num_layers: usize,
-        sigmoid_output: bool,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let mut layers = Vec::with_capacity(num_layers);
-        let vb = vb.pp("layers");
-        for i in 0..num_layers {
-            let in_dim = if i == 0 { input_dim } else { hidden_dim };
-            let out_dim = if i + 1 == num_layers {
-                output_dim
-            } else {
-                hidden_dim
-            };
-            let layer = linear(vb.pp(i), in_dim, out_dim, true)?;
-            layers.push(layer)
-        }
-        Ok(Self {
-            layers,
-            sigmoid_output,
-        })
-    }
-}
-
-impl Module for MlpMaskDecoder {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = xs.clone();
-        for (i, layer) in self.layers.iter().enumerate() {
-            xs = layer.forward(&xs)?;
-            if i + 1 < self.layers.len() {
-                xs = xs.relu()?
-            }
-        }
-        if self.sigmoid_output {
-            candle_nn::ops::sigmoid(&xs)
-        } else {
-            Ok(xs)
-        }
-    }
-}
-
-#[derive(Debug)]
-struct MaskDecoder {
-    iou_tokens: candle_nn::Embedding,
-    mask_tokens: candle_nn::Embedding,
-    iou_prediction_head: MlpMaskDecoder,
-}
-
-impl MaskDecoder {
-    fn new(
-        transformer_dim: usize,
-        num_multimask_outputs: usize,
-        iou_head_depth: usize,
-        iou_head_hidden_dim: usize,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let num_mask_tokens = num_multimask_outputs - 1;
-        let iou_prediction_head = MlpMaskDecoder::new(
-            transformer_dim,
-            iou_head_hidden_dim,
-            num_mask_tokens,
-            iou_head_depth,
-            false,
-            vb.pp("iou_prediction_head"),
-        )?;
-        let iou_tokens = candle_nn::embedding(1, transformer_dim, vb.pp("iou_tokens"))?;
-        let mask_tokens =
-            candle_nn::embedding(num_mask_tokens, transformer_dim, vb.pp("mask_tokens"))?;
-        Ok(Self {
-            iou_tokens,
-            mask_tokens,
-            iou_prediction_head,
-        })
-    }
-}
-
 /*
     fn interpolate_pos_encoding(&self, xs: &Tensor, w: usize, h: usize) -> Result<Tensor> {
         let npatch = xs.dim(1)? - 1;
diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
new file mode 100644
index 00000000..c8b6fd7b
--- /dev/null
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -0,0 +1,257 @@
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+#[derive(Debug)]
+struct PatchEmbed {
+    proj: candle_nn::Conv2d,
+}
+
+impl PatchEmbed {
+    fn new(
+        in_chans: usize,
+        embed_dim: usize,
+        k_size: usize,
+        stride: usize,
+        padding: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let cfg = candle_nn::Conv2dConfig {
+            stride,
+            padding,
+            ..Default::default()
+        };
+        let proj = candle_nn::conv2d(in_chans, embed_dim, k_size, cfg, vb.pp("proj"))?;
+        Ok(Self { proj })
+    }
+}
+
+impl Module for PatchEmbed {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.proj)?.permute((0, 2, 3, 1))
+    }
+}
+
+#[derive(Debug)]
+struct Attention {
+    qkv: Linear,
+    proj: Linear,
+    num_heads: usize,
+    scale: f64,
+    use_rel_pos: bool,
+    rel_pos_hw: Option<(Tensor, Tensor)>,
+}
+
+impl Attention {
+    fn new(
+        dim: usize,
+        num_heads: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let qkv = crate::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
+        let proj = crate::linear(vb.pp("proj"), dim, dim, true)?;
+        let head_dim = dim / num_heads;
+        let scale = 1. / (head_dim as f64).sqrt();
+        let rel_pos_hw = if use_rel_pos {
+            let h = vb.get((2 * window_size - 1, head_dim), "rel_pos_h")?;
+            let w = vb.get((2 * window_size - 1, head_dim), "rel_pos_w")?;
+            Some((h, w))
+        } else {
+            None
+        };
+        Ok(Self {
+            qkv,
+            proj,
+            num_heads,
+            scale,
+            use_rel_pos,
+            rel_pos_hw,
+        })
+    }
+}
+
+impl Module for Attention {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (b, h, w, c) = xs.dims4()?;
+        let qkv = self
+            .qkv
+            .forward(xs)?
+            .reshape((b, h * w, 3, self.num_heads, c / self.num_heads))?
+            .permute((2, 0, 3, 1, 4))?
+            .reshape((3, b * self.num_heads, h * w, c / self.num_heads))?;
+        let q = qkv.i(0)?;
+        let k = qkv.i(1)?;
+        let v = qkv.i(2)?;
+        let attn = (q * self.scale)?.matmul(&k.t()?)?;
+        if self.use_rel_pos {
+            todo!()
+        }
+        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
+        let attn = attn
+            .matmul(&v)?
+            .reshape((b, self.num_heads, h, w, c / self.num_heads))?
+            .permute((0, 2, 3, 1, 4))?
+            .reshape((b, h, w, c / self.num_heads))?;
+        self.proj.forward(&attn)
+    }
+}
+
+#[derive(Debug)]
+struct Block {
+    norm1: LayerNorm,
+    attn: Attention,
+    norm2: LayerNorm,
+    mlp: crate::MlpBlock,
+    window_size: usize,
+}
+
+impl Block {
+    fn new(
+        dim: usize,
+        num_heads: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
+        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
+        let attn = Attention::new(
+            dim,
+            num_heads,
+            qkv_bias,
+            use_rel_pos,
+            window_size,
+            vb.pp("attn"),
+        )?;
+        let mlp = crate::MlpBlock::new(dim, dim * 4, vb.pp("mlp"))?;
+        Ok(Self {
+            norm1,
+            attn,
+            norm2,
+            mlp,
+            window_size,
+        })
+    }
+}
+
+impl Module for Block {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let shortcut = xs;
+        let xs = self.norm1.forward(xs)?;
+        if self.window_size > 0 {
+            todo!()
+        }
+        let xs = self.attn.forward(&xs)?;
+        if self.window_size > 0 {
+            todo!()
+        }
+        let xs = (xs + shortcut)?;
+        &xs + xs.apply(&self.norm2)?.apply(&self.mlp)?
+    }
+}
+
+#[derive(Debug)]
+struct ImageEncoderViT {
+    img_size: usize,
+    patch_embed: PatchEmbed,
+    blocks: Vec<Block>,
+    neck_conv1: candle_nn::Conv2d,
+    neck_ln1: LayerNorm,
+    neck_conv2: candle_nn::Conv2d,
+    neck_ln2: LayerNorm,
+    pos_embed: Option<Tensor>,
+}
+
+impl ImageEncoderViT {
+    #[allow(clippy::too_many_arguments)]
+    fn new(
+        img_size: usize,
+        patch_size: usize,
+        in_chans: usize,
+        embed_dim: usize,
+        depth: usize,
+        num_heads: usize,
+        out_chans: usize,
+        qkv_bias: bool,
+        use_rel_pos: bool,
+        use_abs_pos: bool,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let patch_embed = PatchEmbed::new(
+            in_chans,
+            embed_dim,
+            patch_size,
+            patch_size,
+            0,
+            vb.pp("patch_embed"),
+        )?;
+        let mut blocks = Vec::with_capacity(depth);
+        let vb_b = vb.pp("blocks");
+        for i in 0..depth {
+            let block = Block::new(
+                embed_dim,
+                num_heads,
+                qkv_bias,
+                use_rel_pos,
+                window_size,
+                vb_b.pp(i),
+            )?;
+            blocks.push(block)
+        }
+        let neck_conv1 = candle_nn::conv2d_no_bias(
+            embed_dim,
+            out_chans,
+            1,
+            Default::default(),
+            vb.pp("neck.0"),
+        )?;
+        let neck_ln1 = layer_norm(out_chans, 1e-6, vb.pp("neck.1"))?;
+        let cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
+        let neck_ln2 = layer_norm(out_chans, 1e-6, vb.pp("neck.3"))?;
+        let pos_embed = if use_abs_pos {
+            let p = vb.get(
+                (1, img_size / patch_size, img_size / patch_size, embed_dim),
+                "pos_embed",
+            )?;
+            Some(p)
+        } else {
+            None
+        };
+        Ok(Self {
+            img_size,
+            patch_embed,
+            blocks,
+            neck_conv1,
+            neck_ln1,
+            neck_conv2,
+            neck_ln2,
+            pos_embed,
+        })
+    }
+}
+
+impl Module for ImageEncoderViT {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.patch_embed.forward(xs)?;
+        let mut xs = match &self.pos_embed {
+            Some(pos_embed) => (xs + pos_embed)?,
+            None => xs,
+        };
+        for block in self.blocks.iter() {
+            xs = block.forward(&xs)?
+        }
+        xs.permute((0, 3, 1, 2))?
+            .apply(&self.neck_conv1)?
+            .apply(&self.neck_ln1)?
+            .apply(&self.neck_conv2)?
+            .apply(&self.neck_ln2)
+    }
+}
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
new file mode 100644
index 00000000..55a006c4
--- /dev/null
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -0,0 +1,222 @@
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+#[derive(Debug)]
+struct MlpMaskDecoder {
+    layers: Vec<Linear>,
+    sigmoid_output: bool,
+}
+
+impl MlpMaskDecoder {
+    fn new(
+        input_dim: usize,
+        hidden_dim: usize,
+        output_dim: usize,
+        num_layers: usize,
+        sigmoid_output: bool,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let mut layers = Vec::with_capacity(num_layers);
+        let vb = vb.pp("layers");
+        for i in 0..num_layers {
+            let in_dim = if i == 0 { input_dim } else { hidden_dim };
+            let out_dim = if i + 1 == num_layers {
+                output_dim
+            } else {
+                hidden_dim
+            };
+            let layer = crate::linear(vb.pp(i), in_dim, out_dim, true)?;
+            layers.push(layer)
+        }
+        Ok(Self {
+            layers,
+            sigmoid_output,
+        })
+    }
+}
+
+impl Module for MlpMaskDecoder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.clone();
+        for (i, layer) in self.layers.iter().enumerate() {
+            xs = layer.forward(&xs)?;
+            if i + 1 < self.layers.len() {
+                xs = xs.relu()?
+            }
+        }
+        if self.sigmoid_output {
+            candle_nn::ops::sigmoid(&xs)
+        } else {
+            Ok(xs)
+        }
+    }
+}
+
+#[derive(Debug)]
+struct MaskDecoder {
+    iou_token: candle_nn::Embedding,
+    mask_tokens: candle_nn::Embedding,
+    iou_prediction_head: MlpMaskDecoder,
+    output_upscaling_conv1: candle_nn::ConvTranspose2d,
+    output_upscaling_ln: LayerNorm,
+    output_upscaling_conv2: candle_nn::ConvTranspose2d,
+    num_mask_tokens: usize,
+    output_hypernetworks_mlps: Vec<MlpMaskDecoder>,
+}
+
+impl MaskDecoder {
+    fn new(
+        transformer_dim: usize,
+        num_multimask_outputs: usize,
+        iou_head_depth: usize,
+        iou_head_hidden_dim: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let num_mask_tokens = num_multimask_outputs - 1;
+        let iou_prediction_head = MlpMaskDecoder::new(
+            transformer_dim,
+            iou_head_hidden_dim,
+            num_mask_tokens,
+            iou_head_depth,
+            false,
+            vb.pp("iou_prediction_head"),
+        )?;
+        let iou_token = candle_nn::embedding(1, transformer_dim, vb.pp("iou_token"))?;
+        let mask_tokens =
+            candle_nn::embedding(num_mask_tokens, transformer_dim, vb.pp("mask_tokens"))?;
+        let cfg = candle_nn::ConvTranspose2dConfig {
+            stride: 2,
+            ..Default::default()
+        };
+        let output_upscaling_conv1 = candle_nn::conv_transpose2d(
+            transformer_dim,
+            transformer_dim / 4,
+            2,
+            cfg,
+            vb.pp("output_upscaling.0"),
+        )?;
+        let output_upscaling_ln =
+            layer_norm(transformer_dim / 4, 1e-6, vb.pp("output_upscaling.1"))?;
+        let output_upscaling_conv2 = candle_nn::conv_transpose2d(
+            transformer_dim / 4,
+            transformer_dim / 8,
+            2,
+            cfg,
+            vb.pp("output_upscaling.3"),
+        )?;
+        let mut output_hypernetworks_mlps = Vec::with_capacity(num_mask_tokens);
+        let vb_o = vb.pp("output_hypernetworks_mlps");
+        for i in 0..num_mask_tokens {
+            let mlp = MlpMaskDecoder::new(
+                transformer_dim,
+                transformer_dim,
+                transformer_dim / 8,
+                3,
+                false,
+                vb_o.pp(i),
+            )?;
+            output_hypernetworks_mlps.push(mlp)
+        }
+        Ok(Self {
+            iou_token,
+            mask_tokens,
+            iou_prediction_head,
+            output_upscaling_conv1,
+            output_upscaling_ln,
+            output_upscaling_conv2,
+            num_mask_tokens,
+            output_hypernetworks_mlps,
+        })
+    }
+
+    fn forward(
+        &self,
+        image_embeddings: &Tensor,
+        image_pe: &Tensor,
+        sparse_prompt_embeddings: &Tensor,
+        dense_prompt_embeddings: &Tensor,
+        multimask_output: bool,
+    ) -> Result<(Tensor, Tensor)> {
+        let (masks, iou_pred) = self.predict_masks(
+            image_embeddings,
+            image_pe,
+            sparse_prompt_embeddings,
+            dense_prompt_embeddings,
+        )?;
+        let masks = if multimask_output {
+            masks.i((.., 1..))?
+        } else {
+            masks.i((.., 0..1))?
+        };
+        let iou_pred = if multimask_output {
+            iou_pred.i((.., 1..))?
+        } else {
+            iou_pred.i((.., 0..1))?
+        };
+        Ok((masks, iou_pred))
+    }
+
+    fn predict_masks(
+        &self,
+        image_embeddings: &Tensor,
+        image_pe: &Tensor,
+        sparse_prompt_embeddings: &Tensor,
+        dense_prompt_embeddings: &Tensor,
+    ) -> Result<(Tensor, Tensor)> {
+        // Concatenate ouput tokens.
+        let output_tokens = Tensor::cat(
+            &[self.iou_token.embeddings(), self.mask_tokens.embeddings()],
+            0,
+        )?;
+        let (d1, d2) = output_tokens.dims2()?;
+        let output_tokens =
+            output_tokens
+                .unsqueeze(0)?
+                .expand((sparse_prompt_embeddings.dim(0)?, d1, d2))?;
+        let tokens = Tensor::cat(&[&output_tokens, sparse_prompt_embeddings], 1)?;
+
+        // Expand per-image data in batch direction to be per mask
+        let src = repeat_interleave(image_embeddings, tokens.dim(0)?, 0)?;
+        let src = (src + dense_prompt_embeddings)?;
+        let pos_src = repeat_interleave(image_pe, tokens.dim(0)?, 0)?;
+        let (b, c, h, w) = src.dims4()?;
+
+        // Run the transformer
+        let (hs, src) = run_transformer(&src, &pos_src, &tokens)?;
+        let iou_token_out = hs.i((.., 0))?;
+        let mask_tokens_out = hs.i((.., 1, 1 + self.num_mask_tokens))?;
+
+        // Upscale mask embeddings and predict masks using the masks tokens.
+        let src = src.transpose(1, 2)?.reshape((b, c, h, w))?;
+        let upscaled_embedding = self
+            .output_upscaling_conv1
+            .forward(&src)?
+            .apply(&self.output_upscaling_ln)?
+            .gelu()?
+            .apply(&self.output_upscaling_conv2)?
+            .gelu()?;
+        let mut hyper_in_list = Vec::with_capacity(self.num_mask_tokens);
+        for (i, mlp) in self.output_hypernetworks_mlps.iter().enumerate() {
+            let h = mlp.forward(&mask_tokens_out.i((.., i))?)?;
+            hyper_in_list.push(h)
+        }
+        let hyper_in = Tensor::stack(hyper_in_list.as_slice(), 1)?;
+        let (b, c, h, w) = upscaled_embedding.dims4()?;
+        let masks = hyper_in
+            .matmul(&upscaled_embedding.reshape((b, c, h * w))?)?
+            .reshape((b, 0, h, w))?;
+
+        // Generate mask quality predictions.
+        let iou_pred = self.iou_prediction_head.forward(&iou_token_out)?;
+        Ok((masks, iou_pred))
+    }
+}
+
+// Equivalent to torch.repeat_interleave
+fn repeat_interleave(_img: &Tensor, _repeats: usize, _dim: usize) -> Result<Tensor> {
+    todo!()
+}
+
+fn run_transformer(_src: &Tensor, _pos: &Tensor, _tokens: &Tensor) -> Result<(Tensor, Tensor)> {
+    todo!()
+}
diff --git a/candle-examples/examples/segment-anything/model_transformer.rs b/candle-examples/examples/segment-anything/model_transformer.rs
new file mode 100644
index 00000000..10f7f4e5
--- /dev/null
+++ b/candle-examples/examples/segment-anything/model_transformer.rs
@@ -0,0 +1,77 @@
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+#[derive(Debug)]
+struct Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    out_proj: Linear,
+    internal_dim: usize,
+    num_heads: usize,
+}
+
+impl Attention {
+    fn new(
+        embedding_dim: usize,
+        num_heads: usize,
+        downsample_rate: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let internal_dim = embedding_dim / downsample_rate;
+        let q_proj = candle_nn::linear(embedding_dim, internal_dim, vb.pp("q_proj"))?;
+        let k_proj = candle_nn::linear(embedding_dim, internal_dim, vb.pp("k_proj"))?;
+        let v_proj = candle_nn::linear(embedding_dim, internal_dim, vb.pp("v_proj"))?;
+        let out_proj = candle_nn::linear(internal_dim, embedding_dim, vb.pp("out_proj"))?;
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            out_proj,
+            internal_dim,
+            num_heads,
+        })
+    }
+
+    fn separate_heads(&self, x: &Tensor) -> Result<Tensor> {
+        let (b, n, c) = x.dims3()?;
+        x.reshape((b, n, self.num_heads, c / self.num_heads))?
+            .transpose(1, 2)
+    }
+
+    fn recombine_heads(&self, x: &Tensor) -> Result<Tensor> {
+        let (b, n_heads, n_tokens, c_per_head) = x.dims4()?;
+        x.transpose(1, 2)?
+            .reshape((b, n_tokens, n_heads * c_per_head))
+    }
+
+    fn forward(&self, q: &Tensor, k: &Tensor, v: &Tensor) -> Result<Tensor> {
+        let q = self.q_proj.forward(q)?;
+        let k = self.k_proj.forward(k)?;
+        let v = self.v_proj.forward(v)?;
+
+        let q = self.separate_heads(&q)?;
+        let k = self.separate_heads(&k)?;
+        let v = self.separate_heads(&v)?;
+
+        let (_, _, _, c_per_head) = q.dims4()?;
+        let attn = (q.matmul(&k.t()?)? / (c_per_head as f64).sqrt())?;
+        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
+
+        let out = attn.matmul(&v)?;
+        self.recombine_heads(&out)?.apply(&self.out_proj)
+    }
+}
+
+#[derive(Debug)]
+struct TwoWayAttentionBlock {
+    self_attn: Attention,
+    norm1: LayerNorm,
+    cross_attn_token_to_image: Attention,
+    norm2: LayerNorm,
+    mlp: crate::MlpBlock,
+    norm3: LayerNorm,
+    norm4: LayerNorm,
+    cross_attn_image_to_token: Attention,
+    skip_first_layer_pe: bool,
+}
diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index fe44c153..b2483058 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -130,6 +130,17 @@ pub struct ConvTranspose2dConfig {
     // TODO: support groups.
 }
 
+impl Default for ConvTranspose2dConfig {
+    fn default() -> Self {
+        Self {
+            padding: 0,
+            output_padding: 0,
+            stride: 1,
+            dilation: 1,
+        }
+    }
+}
+
 #[derive(Debug)]
 pub struct ConvTranspose2d {
     weight: Tensor,

From 7b50f3e106b3d3a333e1c67f2006cbfd60c8a55a Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 7 Sep 2023 13:06:55 +0200
Subject: [PATCH 028/150] More segment-anything again. (#764)

* More segment-anything again.

* Transformer block forward.

* Two-ways transformer.

* Position embeddings.

* Sketch the prompt encoder.

* More prompt-encoder.

* More prompt-encoder.

* Add the main sam module.

* Embed the transformer.

* And hook the transformer forward step.

* Build the model.

* Handle the global attn indexes.

* Get the model to load.
---
 .../examples/segment-anything/main.rs         |  19 +-
 .../segment-anything/model_image_encoder.rs   |  25 ++-
 .../segment-anything/model_mask_decoder.rs    |  23 ++-
 .../segment-anything/model_prompt_encoder.rs  | 192 ++++++++++++++++++
 .../examples/segment-anything/model_sam.rs    |  72 +++++++
 .../segment-anything/model_transformer.rs     | 143 +++++++++++++
 6 files changed, 454 insertions(+), 20 deletions(-)
 create mode 100644 candle-examples/examples/segment-anything/model_prompt_encoder.rs
 create mode 100644 candle-examples/examples/segment-anything/model_sam.rs

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index de16f70c..368b5a33 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -8,9 +8,11 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-mod model_image_encoder;
-mod model_mask_decoder;
-mod model_transformer;
+pub mod model_image_encoder;
+pub mod model_mask_decoder;
+pub mod model_prompt_encoder;
+pub mod model_sam;
+pub mod model_transformer;
 
 use candle::{DType, IndexOp, Result, Tensor, D};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
@@ -82,7 +84,7 @@ impl Module for MlpBlock {
 #[derive(Parser)]
 struct Args {
     #[arg(long)]
-    model: Option<String>,
+    model: String,
 
     #[arg(long)]
     image: String,
@@ -95,10 +97,15 @@ struct Args {
 pub fn main() -> anyhow::Result<()> {
     let args = Args::parse();
 
-    let _device = candle_examples::device(args.cpu)?;
+    let device = candle_examples::device(args.cpu)?;
 
-    let image = candle_examples::imagenet::load_image224(args.image)?;
+    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device);
     println!("loaded image {image:?}");
 
+    let weights = unsafe { candle::safetensors::MmapedFile::new(args.model)? };
+    let weights = weights.deserialize()?;
+    let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
+    let _sam = model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)?; // sam_vit_b
+
     Ok(())
 }
diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
index c8b6fd7b..cfcdbb38 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -47,7 +47,7 @@ impl Attention {
         num_heads: usize,
         qkv_bias: bool,
         use_rel_pos: bool,
-        window_size: usize,
+        input_size: (usize, usize),
         vb: VarBuilder,
     ) -> Result<Self> {
         let qkv = crate::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
@@ -55,8 +55,8 @@ impl Attention {
         let head_dim = dim / num_heads;
         let scale = 1. / (head_dim as f64).sqrt();
         let rel_pos_hw = if use_rel_pos {
-            let h = vb.get((2 * window_size - 1, head_dim), "rel_pos_h")?;
-            let w = vb.get((2 * window_size - 1, head_dim), "rel_pos_w")?;
+            let h = vb.get((2 * input_size.0 - 1, head_dim), "rel_pos_h")?;
+            let w = vb.get((2 * input_size.1 - 1, head_dim), "rel_pos_w")?;
             Some((h, w))
         } else {
             None
@@ -114,16 +114,22 @@ impl Block {
         qkv_bias: bool,
         use_rel_pos: bool,
         window_size: usize,
+        input_size: (usize, usize),
         vb: VarBuilder,
     ) -> Result<Self> {
         let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
         let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
+        let input_size_attn = if window_size == 0 {
+            input_size
+        } else {
+            (window_size, window_size)
+        };
         let attn = Attention::new(
             dim,
             num_heads,
             qkv_bias,
             use_rel_pos,
-            window_size,
+            input_size_attn,
             vb.pp("attn"),
         )?;
         let mlp = crate::MlpBlock::new(dim, dim * 4, vb.pp("mlp"))?;
@@ -154,7 +160,7 @@ impl Module for Block {
 }
 
 #[derive(Debug)]
-struct ImageEncoderViT {
+pub struct ImageEncoderViT {
     img_size: usize,
     patch_embed: PatchEmbed,
     blocks: Vec<Block>,
@@ -167,7 +173,7 @@ struct ImageEncoderViT {
 
 impl ImageEncoderViT {
     #[allow(clippy::too_many_arguments)]
-    fn new(
+    pub fn new(
         img_size: usize,
         patch_size: usize,
         in_chans: usize,
@@ -179,6 +185,7 @@ impl ImageEncoderViT {
         use_rel_pos: bool,
         use_abs_pos: bool,
         window_size: usize,
+        global_attn_indexes: &[usize],
         vb: VarBuilder,
     ) -> Result<Self> {
         let patch_embed = PatchEmbed::new(
@@ -192,12 +199,18 @@ impl ImageEncoderViT {
         let mut blocks = Vec::with_capacity(depth);
         let vb_b = vb.pp("blocks");
         for i in 0..depth {
+            let window_size = if global_attn_indexes.contains(&i) {
+                0
+            } else {
+                window_size
+            };
             let block = Block::new(
                 embed_dim,
                 num_heads,
                 qkv_bias,
                 use_rel_pos,
                 window_size,
+                (img_size / patch_size, img_size / patch_size),
                 vb_b.pp(i),
             )?;
             blocks.push(block)
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
index 55a006c4..cf3879cd 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -1,6 +1,8 @@
 use candle::{DType, IndexOp, Result, Tensor, D};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
 
+use crate::model_transformer::TwoWayTransformer;
+
 #[derive(Debug)]
 struct MlpMaskDecoder {
     layers: Vec<Linear>,
@@ -53,7 +55,7 @@ impl Module for MlpMaskDecoder {
 }
 
 #[derive(Debug)]
-struct MaskDecoder {
+pub struct MaskDecoder {
     iou_token: candle_nn::Embedding,
     mask_tokens: candle_nn::Embedding,
     iou_prediction_head: MlpMaskDecoder,
@@ -62,17 +64,18 @@ struct MaskDecoder {
     output_upscaling_conv2: candle_nn::ConvTranspose2d,
     num_mask_tokens: usize,
     output_hypernetworks_mlps: Vec<MlpMaskDecoder>,
+    transformer: TwoWayTransformer,
 }
 
 impl MaskDecoder {
-    fn new(
+    pub fn new(
         transformer_dim: usize,
         num_multimask_outputs: usize,
         iou_head_depth: usize,
         iou_head_hidden_dim: usize,
         vb: VarBuilder,
     ) -> Result<Self> {
-        let num_mask_tokens = num_multimask_outputs - 1;
+        let num_mask_tokens = num_multimask_outputs + 1;
         let iou_prediction_head = MlpMaskDecoder::new(
             transformer_dim,
             iou_head_hidden_dim,
@@ -117,6 +120,13 @@ impl MaskDecoder {
             )?;
             output_hypernetworks_mlps.push(mlp)
         }
+        let transformer = TwoWayTransformer::new(
+            /* depth */ 2,
+            /* embedding_dim */ transformer_dim,
+            /* num_heads */ 8,
+            /* mlp_dim */ 2048,
+            vb.pp("transformer"),
+        )?;
         Ok(Self {
             iou_token,
             mask_tokens,
@@ -126,6 +136,7 @@ impl MaskDecoder {
             output_upscaling_conv2,
             num_mask_tokens,
             output_hypernetworks_mlps,
+            transformer,
         })
     }
 
@@ -182,7 +193,7 @@ impl MaskDecoder {
         let (b, c, h, w) = src.dims4()?;
 
         // Run the transformer
-        let (hs, src) = run_transformer(&src, &pos_src, &tokens)?;
+        let (hs, src) = self.transformer.forward(&src, &pos_src, &tokens)?;
         let iou_token_out = hs.i((.., 0))?;
         let mask_tokens_out = hs.i((.., 1, 1 + self.num_mask_tokens))?;
 
@@ -216,7 +227,3 @@ impl MaskDecoder {
 fn repeat_interleave(_img: &Tensor, _repeats: usize, _dim: usize) -> Result<Tensor> {
     todo!()
 }
-
-fn run_transformer(_src: &Tensor, _pos: &Tensor, _tokens: &Tensor) -> Result<(Tensor, Tensor)> {
-    todo!()
-}
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
new file mode 100644
index 00000000..7ac4c66d
--- /dev/null
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -0,0 +1,192 @@
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+#[derive(Debug)]
+struct PostionEmbeddingRandom {
+    positional_encoding_gaussian_matrix: Tensor,
+}
+
+impl PostionEmbeddingRandom {
+    fn new(num_pos_feats: usize, vb: VarBuilder) -> Result<Self> {
+        let positional_encoding_gaussian_matrix =
+            vb.get((2, num_pos_feats), "positional_encoding_gaussian_matrix")?;
+        Ok(Self {
+            positional_encoding_gaussian_matrix,
+        })
+    }
+
+    fn pe_encoding(&self, coords: &Tensor) -> Result<Tensor> {
+        let coords = coords.affine(2., -1.)?;
+        let coords = coords.matmul(&self.positional_encoding_gaussian_matrix)?;
+        let coords = (coords * (2. * std::f64::consts::PI))?;
+        Tensor::cat(&[coords.sin()?, coords.cos()?], D::Minus1)
+    }
+
+    fn forward(&self, h: usize, w: usize) -> Result<Tensor> {
+        let device = self.positional_encoding_gaussian_matrix.device();
+        let grid = Tensor::ones((h, w), DType::F32, device)?;
+        // TODO: cumsum
+        let x_embed = (&grid - 0.5)?;
+        // TODO: cumsum
+        let y_embed = (&grid - 0.5)?;
+        let x_embed = (x_embed / w as f64)?;
+        let y_embed = (y_embed / h as f64)?;
+        let coords = Tensor::stack(&[&x_embed, &y_embed], D::Minus1)?;
+        self.pe_encoding(&coords)?.permute((2, 0, 1))
+    }
+
+    fn forward_with_coords(
+        &self,
+        coords_input: &Tensor,
+        image_size: (usize, usize),
+    ) -> Result<Tensor> {
+        let coords0 = (coords_input.narrow(D::Minus1, 0, 1)? / image_size.1 as f64)?;
+        let coords1 = (coords_input.narrow(D::Minus1, 1, 1)? / image_size.0 as f64)?;
+        let c = coords_input.dim(D::Minus1)?;
+        let coords_rest = coords_input.narrow(D::Minus1, 2, c - 2)?;
+        let coords = Tensor::cat(&[&coords0, &coords1, &coords_rest], D::Minus1)?;
+        self.pe_encoding(&coords)
+    }
+}
+
+#[derive(Debug)]
+pub struct PromptEncoder {
+    pe_layer: PostionEmbeddingRandom,
+    point_embeddings: Vec<candle_nn::Embedding>,
+    not_a_point_embed: candle_nn::Embedding,
+    mask_downscaling_conv1: candle_nn::Conv2d,
+    mask_downscaling_ln1: LayerNorm,
+    mask_downscaling_conv2: candle_nn::Conv2d,
+    mask_downscaling_ln2: LayerNorm,
+    mask_downscaling_conv3: candle_nn::Conv2d,
+    no_mask_embed: candle_nn::Embedding,
+    image_embedding_size: (usize, usize),
+    input_image_size: (usize, usize),
+}
+
+impl PromptEncoder {
+    pub fn new(
+        embed_dim: usize,
+        image_embedding_size: (usize, usize),
+        input_image_size: (usize, usize),
+        mask_in_chans: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let num_points_embeddings = 4;
+        let pe_layer = PostionEmbeddingRandom::new(embed_dim / 2, vb.pp("pe_layer"))?;
+        let not_a_point_embed = candle_nn::embedding(1, embed_dim, vb.pp("not_a_point_embed"))?;
+        let no_mask_embed = candle_nn::embedding(1, embed_dim, vb.pp("no_mask_embed"))?;
+        let cfg = candle_nn::Conv2dConfig {
+            stride: 2,
+            ..Default::default()
+        };
+        let mask_downscaling_conv1 =
+            candle_nn::conv2d(1, mask_in_chans / 4, 2, cfg, vb.pp("mask_downscaling.0"))?;
+        let mask_downscaling_conv2 = candle_nn::conv2d(
+            mask_in_chans / 4,
+            mask_in_chans,
+            2,
+            cfg,
+            vb.pp("mask_downscaling.3"),
+        )?;
+        let mask_downscaling_conv3 = candle_nn::conv2d(
+            mask_in_chans,
+            embed_dim,
+            1,
+            Default::default(),
+            vb.pp("mask_downscaling.6"),
+        )?;
+        let mask_downscaling_ln1 =
+            layer_norm(mask_in_chans / 4, 1e-6, vb.pp("mask_downscaling.1"))?;
+        let mask_downscaling_ln2 = layer_norm(mask_in_chans, 1e-6, vb.pp("mask_downscaling.4"))?;
+        let mut point_embeddings = Vec::with_capacity(num_points_embeddings);
+        let vb_e = vb.pp("point_embeddings");
+        for i in 0..num_points_embeddings {
+            let emb = candle_nn::embedding(1, embed_dim, vb_e.pp(i))?;
+            point_embeddings.push(emb)
+        }
+        Ok(Self {
+            pe_layer,
+            point_embeddings,
+            not_a_point_embed,
+            mask_downscaling_conv1,
+            mask_downscaling_ln1,
+            mask_downscaling_conv2,
+            mask_downscaling_ln2,
+            mask_downscaling_conv3,
+            no_mask_embed,
+            image_embedding_size,
+            input_image_size,
+        })
+    }
+
+    fn embed_masks(&self, masks: &Tensor) -> Result<Tensor> {
+        masks
+            .apply(&self.mask_downscaling_conv1)?
+            .apply(&self.mask_downscaling_ln1)?
+            .gelu()?
+            .apply(&self.mask_downscaling_conv2)?
+            .apply(&self.mask_downscaling_ln2)?
+            .gelu()?
+            .apply(&self.mask_downscaling_conv3)
+    }
+
+    fn embed_points(&self, points: &Tensor, labels: &Tensor, pad: bool) -> Result<Tensor> {
+        let points = (points + 0.5)?;
+        let points = if pad { todo!() } else { points };
+        let point_embedding = self
+            .pe_layer
+            .forward_with_coords(&points, self.input_image_size)?;
+        // TODO: tweak based on labels.
+        Ok(point_embedding)
+    }
+
+    fn embed_boxes(&self, boxes: &Tensor) -> Result<Tensor> {
+        let boxes = (boxes + 0.5)?;
+        let coords = boxes.reshape((boxes.elem_count() / 4, 2, 2))?;
+        let corner_embedding = self
+            .pe_layer
+            .forward_with_coords(&coords, self.input_image_size)?;
+        let ce1 = corner_embedding.i((.., 0))?;
+        let ce2 = corner_embedding.i((.., 1))?;
+        let ce1 = (ce1 + self.point_embeddings[2].embeddings())?;
+        let ce2 = (ce2 + self.point_embeddings[3].embeddings())?;
+        Tensor::cat(&[&ce1, &ce2], 1)
+    }
+
+    fn forward(
+        &self,
+        points: Option<(&Tensor, &Tensor)>,
+        boxes: Option<&Tensor>,
+        masks: Option<&Tensor>,
+    ) -> Result<(Tensor, Tensor)> {
+        let se_points = match points {
+            Some((coords, labels)) => Some(self.embed_points(coords, labels, boxes.is_none())?),
+            None => None,
+        };
+        let se_boxes = match boxes {
+            Some(boxes) => Some(self.embed_boxes(boxes)?),
+            None => None,
+        };
+        let sparse_embeddings = match (se_points, se_boxes) {
+            (Some(se_points), Some(se_boxes)) => Tensor::cat(&[se_points, se_boxes], 1)?,
+            (Some(se_points), None) => se_points,
+            (None, Some(se_boxes)) => se_boxes,
+            (None, None) => Tensor::zeros(1, DType::F32, &candle::Device::Cpu)?,
+        };
+
+        let dense_embeddings = match masks {
+            None => {
+                let emb = self.no_mask_embed.embeddings();
+                emb.reshape((1, emb.elem_count(), 1, 1))?.expand((
+                    1,
+                    0,
+                    self.image_embedding_size.0,
+                    self.image_embedding_size.1,
+                ))?
+            }
+            Some(masks) => self.embed_masks(masks)?,
+        };
+        Ok((sparse_embeddings, dense_embeddings))
+    }
+}
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-examples/examples/segment-anything/model_sam.rs
new file mode 100644
index 00000000..5a0d7e8f
--- /dev/null
+++ b/candle-examples/examples/segment-anything/model_sam.rs
@@ -0,0 +1,72 @@
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+use crate::model_image_encoder::ImageEncoderViT;
+use crate::model_mask_decoder::MaskDecoder;
+use crate::model_prompt_encoder::PromptEncoder;
+
+#[derive(Debug)]
+pub struct Sam {
+    image_encoder: ImageEncoderViT,
+    prompt_encoder: PromptEncoder,
+    mask_decoder: MaskDecoder,
+    pixel_mean: Tensor,
+    pixel_std: Tensor,
+}
+
+impl Sam {
+    pub fn new(
+        encoder_embed_dim: usize,
+        encoder_depth: usize,
+        encoder_num_heads: usize,
+        encoder_global_attn_indexes: &[usize],
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        const PROMPT_EMBED_DIM: usize = 256;
+        const IMAGE_SIZE: usize = 1024;
+        const VIT_PATCH_SIZE: usize = 16;
+
+        let image_embedding_size = IMAGE_SIZE / VIT_PATCH_SIZE;
+
+        let image_encoder = ImageEncoderViT::new(
+            IMAGE_SIZE,
+            VIT_PATCH_SIZE,
+            3,
+            encoder_embed_dim,
+            encoder_depth,
+            encoder_num_heads,
+            PROMPT_EMBED_DIM,
+            /* qkv_bias */ true,
+            /* use_rel_pos */ true,
+            /* use_abs_pos */ true,
+            /* window_size */ 14,
+            /* global_attn_indexes */ encoder_global_attn_indexes,
+            vb.pp("image_encoder"),
+        )?;
+        let prompt_encoder = PromptEncoder::new(
+            PROMPT_EMBED_DIM,
+            (image_embedding_size, image_embedding_size),
+            (IMAGE_SIZE, IMAGE_SIZE),
+            16,
+            vb.pp("prompt_encoder"),
+        )?;
+        let mask_decoder = MaskDecoder::new(
+            PROMPT_EMBED_DIM,
+            /* num_multitask_outputs */ 3,
+            /* iou_head_depth */ 3,
+            /* iou_head_hidden_dim */ 256,
+            vb.pp("mask_decoder"),
+        )?;
+        let pixel_mean =
+            Tensor::new(&[123.675f32, 116.28, 103.53], vb.device())?.reshape((3, 1, 1))?;
+        let pixel_std =
+            Tensor::new(&[58.395f32, 57.12, 57.375], vb.device())?.reshape((3, 1, 1))?;
+        Ok(Self {
+            image_encoder,
+            prompt_encoder,
+            mask_decoder,
+            pixel_std,
+            pixel_mean,
+        })
+    }
+}
diff --git a/candle-examples/examples/segment-anything/model_transformer.rs b/candle-examples/examples/segment-anything/model_transformer.rs
index 10f7f4e5..a845085d 100644
--- a/candle-examples/examples/segment-anything/model_transformer.rs
+++ b/candle-examples/examples/segment-anything/model_transformer.rs
@@ -75,3 +75,146 @@ struct TwoWayAttentionBlock {
     cross_attn_image_to_token: Attention,
     skip_first_layer_pe: bool,
 }
+
+impl TwoWayAttentionBlock {
+    fn new(
+        embedding_dim: usize,
+        num_heads: usize,
+        mlp_dim: usize,
+        skip_first_layer_pe: bool,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let self_attn = Attention::new(embedding_dim, num_heads, 1, vb.pp("self_attn"))?;
+        let norm1 = layer_norm(embedding_dim, 1e-5, vb.pp("norm1"))?;
+        let norm2 = layer_norm(embedding_dim, 1e-5, vb.pp("norm2"))?;
+        let norm3 = layer_norm(embedding_dim, 1e-5, vb.pp("norm3"))?;
+        let norm4 = layer_norm(embedding_dim, 1e-5, vb.pp("norm4"))?;
+        let self_attn = Attention::new(embedding_dim, num_heads, 1, vb.pp("self_attn"))?;
+        let cross_attn_token_to_image = Attention::new(
+            embedding_dim,
+            num_heads,
+            2,
+            vb.pp("cross_attn_token_to_image"),
+        )?;
+        let cross_attn_image_to_token = Attention::new(
+            embedding_dim,
+            num_heads,
+            2,
+            vb.pp("cross_attn_image_to_token"),
+        )?;
+        // TODO: use relu in this mlp
+        let mlp = crate::MlpBlock::new(embedding_dim, mlp_dim, vb.pp("mlp"))?;
+        Ok(Self {
+            self_attn,
+            norm1,
+            cross_attn_image_to_token,
+            norm2,
+            mlp,
+            norm3,
+            norm4,
+            cross_attn_token_to_image,
+            skip_first_layer_pe,
+        })
+    }
+
+    fn forward(
+        &self,
+        queries: &Tensor,
+        keys: &Tensor,
+        query_pe: &Tensor,
+        key_pe: &Tensor,
+    ) -> Result<(Tensor, Tensor)> {
+        // Self attention block
+        let queries = if self.skip_first_layer_pe {
+            self.self_attn.forward(queries, keys, queries)?
+        } else {
+            let q = (queries + query_pe)?;
+            let attn_out = self.self_attn.forward(&q, &q, queries)?;
+            (queries + attn_out)?
+        };
+        let queries = self.norm1.forward(&queries)?;
+
+        // Cross attention block, tokens attending to image embedding
+        let q = (&queries + query_pe)?;
+        let k = (keys + key_pe)?;
+        let attn_out = self.cross_attn_token_to_image.forward(&q, &k, keys)?;
+        let queries = (&queries + attn_out)?;
+        let queries = self.norm2.forward(&queries)?;
+
+        // MLP block
+        let mlp_out = self.mlp.forward(&queries);
+        let queries = (queries + mlp_out)?;
+        let queries = self.norm3.forward(&queries)?;
+
+        // Cross attention block, image embedding attending to tokens
+        let q = (&queries + query_pe)?;
+        let k = (keys + key_pe)?;
+        let attn_out = self.cross_attn_image_to_token.forward(&k, &q, &queries)?;
+        let keys = (keys + attn_out)?;
+        let keys = self.norm4.forward(&keys)?;
+
+        Ok((queries, keys))
+    }
+}
+
+#[derive(Debug)]
+pub struct TwoWayTransformer {
+    layers: Vec<TwoWayAttentionBlock>,
+    final_attn_token_to_image: Attention,
+    norm_final_attn: LayerNorm,
+}
+
+impl TwoWayTransformer {
+    pub fn new(
+        depth: usize,
+        embedding_dim: usize,
+        num_heads: usize,
+        mlp_dim: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let vb_l = vb.pp("layers");
+        let mut layers = Vec::with_capacity(depth);
+        for i in 0..depth {
+            let layer =
+                TwoWayAttentionBlock::new(embedding_dim, num_heads, mlp_dim, i == 0, vb_l.pp(i))?;
+            layers.push(layer)
+        }
+        let final_attn_token_to_image = Attention::new(
+            embedding_dim,
+            num_heads,
+            2,
+            vb.pp("final_attn_token_to_image"),
+        )?;
+        let norm_final_attn = layer_norm(embedding_dim, 1e-5, vb.pp("norm_final_attn"))?;
+        Ok(Self {
+            layers,
+            final_attn_token_to_image,
+            norm_final_attn,
+        })
+    }
+
+    pub fn forward(
+        &self,
+        image_embedding: &Tensor,
+        image_pe: &Tensor,
+        point_embedding: &Tensor,
+    ) -> Result<(Tensor, Tensor)> {
+        let (bs, c, h, w) = image_embedding.dims4()?;
+        let image_embedding = image_embedding.flatten_from(2)?.permute((0, 2, 1))?;
+        let image_pe = image_pe.flatten_from(2)?.permute((0, 2, 1))?;
+
+        let mut queries = point_embedding.clone();
+        let mut keys = image_embedding;
+
+        for layer in self.layers.iter() {
+            (queries, keys) = layer.forward(&queries, &keys, point_embedding, &image_pe)?
+        }
+
+        let q = (&queries + point_embedding)?;
+        let k = (&keys + image_pe)?;
+        let attn_out = self.final_attn_token_to_image.forward(&q, &k, &keys)?;
+        let queries = (queries + attn_out)?.apply(&self.norm_final_attn)?;
+
+        Ok((queries, keys))
+    }
+}

From 7396b8ed1a5394c58fcc772e5f6e6038577505b8 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 7 Sep 2023 19:22:45 +0100
Subject: [PATCH 029/150] Segment Anything - process images (#766)

* Start processing images.

* Add LayerNorm2d.

* Properly use LayerNorm2d.

* Tweak eps.

* Use LayerNorm on inputs with a rank different from 3.

* Window partitioning.

* Fix a couple todos.

* More todos.

* Hard-code the einsums.

* More padding support.

* Some sizes tweaks.

* Use the hub to get the weights.

* Use a batch matmul.

* Tweaks.

* More fixes.

* Get some predictions to be generated.
---
 candle-core/src/error.rs                      |   2 +-
 candle-core/src/shape.rs                      |   8 +
 .../examples/segment-anything/main.rs         | 106 +++++++-----
 .../segment-anything/model_image_encoder.rs   | 153 +++++++++++++++---
 .../segment-anything/model_mask_decoder.rs    |  22 +--
 .../segment-anything/model_prompt_encoder.rs  |  54 +++++--
 .../examples/segment-anything/model_sam.rs    |  37 ++++-
 .../segment-anything/model_transformer.rs     |  13 +-
 candle-nn/src/layer_norm.rs                   |   8 +-
 candle-nn/src/linear.rs                       |   5 +-
 10 files changed, 303 insertions(+), 105 deletions(-)

diff --git a/candle-core/src/error.rs b/candle-core/src/error.rs
index d030fab1..be8f7b07 100644
--- a/candle-core/src/error.rs
+++ b/candle-core/src/error.rs
@@ -30,7 +30,7 @@ pub enum Error {
     UnsupportedDTypeForOp(DType, &'static str),
 
     // === Dimension Index Errors ===
-    #[error("{op}: dimension index {dim} out of range for {shape:?}")]
+    #[error("{op}: dimension index {dim} out of range for shape {shape:?}")]
     DimOutOfRange {
         shape: Shape,
         dim: i32,
diff --git a/candle-core/src/shape.rs b/candle-core/src/shape.rs
index 578e8ac9..9617d1ac 100644
--- a/candle-core/src/shape.rs
+++ b/candle-core/src/shape.rs
@@ -73,6 +73,14 @@ impl From<(usize, usize, usize, usize, usize)> for Shape {
     }
 }
 
+impl From<(usize, usize, usize, usize, usize, usize)> for Shape {
+    fn from(d123456: (usize, usize, usize, usize, usize, usize)) -> Self {
+        Self(vec![
+            d123456.0, d123456.1, d123456.2, d123456.3, d123456.4, d123456.5,
+        ])
+    }
+}
+
 impl From<Vec<usize>> for Shape {
     fn from(dims: Vec<usize>) -> Self {
         Self(dims)
diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 368b5a33..a2722270 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -15,7 +15,7 @@ pub mod model_sam;
 pub mod model_transformer;
 
 use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use candle_nn::{Linear, Module, VarBuilder};
 use clap::Parser;
 
 pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
@@ -26,65 +26,74 @@ pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Resu
     }
 }
 
+#[derive(Debug)]
+pub struct LayerNorm2d {
+    weight: Tensor,
+    bias: Tensor,
+    num_channels: usize,
+    eps: f64,
+}
+
+impl LayerNorm2d {
+    pub fn new(num_channels: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
+        let weight = vb.get(num_channels, "weight")?;
+        let bias = vb.get(num_channels, "bias")?;
+        Ok(Self {
+            weight,
+            bias,
+            num_channels,
+            eps,
+        })
+    }
+}
+
+impl Module for LayerNorm2d {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let u = xs.mean_keepdim(1)?;
+        let xs = xs.broadcast_sub(&u)?;
+        let s = xs.sqr()?.mean_keepdim(1)?;
+        let xs = xs.broadcast_div(&(s + self.eps)?.sqrt()?)?;
+        xs.broadcast_mul(&self.weight.reshape((1, self.num_channels, 1, 1))?)?
+            .broadcast_add(&self.bias.reshape((1, self.num_channels, 1, 1))?)
+    }
+}
+
 #[derive(Debug)]
 pub struct MlpBlock {
     lin1: Linear,
     lin2: Linear,
+    activation: candle_nn::Activation,
 }
 
 impl MlpBlock {
-    pub fn new(embedding_dim: usize, mlp_dim: usize, vb: VarBuilder) -> Result<Self> {
+    pub fn new(
+        embedding_dim: usize,
+        mlp_dim: usize,
+        activation: candle_nn::Activation,
+        vb: VarBuilder,
+    ) -> Result<Self> {
         let lin1 = candle_nn::linear(embedding_dim, mlp_dim, vb.pp("lin1"))?;
         let lin2 = candle_nn::linear(mlp_dim, embedding_dim, vb.pp("lin2"))?;
-        Ok(Self { lin1, lin2 })
+        Ok(Self {
+            lin1,
+            lin2,
+            activation,
+        })
     }
 }
 
 impl Module for MlpBlock {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        xs.apply(&self.lin1)?.gelu()?.apply(&self.lin2)
+        xs.apply(&self.lin1)?
+            .apply(&self.activation)?
+            .apply(&self.lin2)
     }
 }
 
-/*
-    fn interpolate_pos_encoding(&self, xs: &Tensor, w: usize, h: usize) -> Result<Tensor> {
-        let npatch = xs.dim(1)? - 1;
-        let n = self.pos_embed.dim(1)? - 1;
-        let sqrt_n = (n as f64).sqrt();
-        if npatch == n && w == h {
-            return Ok(xs.clone());
-        }
-        let class_pos_embed = self.pos_embed.i((.., ..1))?;
-        let patch_pos_embed = self.pos_embed.i((.., 1..))?;
-        let dim = xs.dim(D::Minus1)?;
-        let (w0, h0) = ((w / PATCH_SIZE) as f64 + 0.1, (h / PATCH_SIZE) as f64 + 0.1);
-        let patch_pos_embed = patch_pos_embed
-            .reshape((1, sqrt_n as usize, sqrt_n as usize, dim))?
-            .transpose(2, 3)?
-            .transpose(1, 2)?;
-        // This uses bicubic interpolation in the original implementation.
-        let patch_pos_embed = patch_pos_embed.upsample_nearest2d(h0 as usize, w0 as usize)?;
-        let el_count = patch_pos_embed.shape().elem_count();
-        let patch_pos_embed =
-            patch_pos_embed
-                .transpose(1, 2)?
-                .transpose(2, 3)?
-                .reshape((1, el_count / dim, dim))?;
-        Tensor::cat(&[&class_pos_embed, &patch_pos_embed], 1)
-    }
-
-    fn prepare_tokens_with_mask(&self, xs: &Tensor) -> Result<Tensor> {
-        let (_b, _nc, w, h) = xs.dims4()?;
-        let xs = self.patch_embed.forward(xs)?;
-        let xs = Tensor::cat(&[&self.cls_token, &xs], 1)?;
-        &xs + &self.interpolate_pos_encoding(&xs, w, h)?
-    }
-*/
-
 #[derive(Parser)]
 struct Args {
     #[arg(long)]
-    model: String,
+    model: Option<String>,
 
     #[arg(long)]
     image: String,
@@ -99,13 +108,24 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device);
+    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
     println!("loaded image {image:?}");
 
-    let weights = unsafe { candle::safetensors::MmapedFile::new(args.model)? };
+    let model = match args.model {
+        Some(model) => std::path::PathBuf::from(model),
+        None => {
+            let api = hf_hub::api::sync::Api::new()?;
+            let api = api.model("lmz/candle-sam".to_string());
+            api.get("sam_vit_b_01ec64.safetensors")?
+        }
+    };
+    let weights = unsafe { candle::safetensors::MmapedFile::new(model)? };
     let weights = weights.deserialize()?;
     let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
-    let _sam = model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)?; // sam_vit_b
+    let sam = model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)?; // sam_vit_b
 
+    let (mask, iou_predictions) = sam.forward(&image, false)?;
+    println!("mask: {mask:?}");
+    println!("iou_predictions: {iou_predictions:?}");
     Ok(())
 }
diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
index cfcdbb38..f5db2830 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -70,6 +70,60 @@ impl Attention {
             rel_pos_hw,
         })
     }
+
+    fn add_decomposed_rel_pos(
+        &self,
+        attn: Tensor,
+        q: &Tensor,
+        (q_h, q_w): (usize, usize),
+        (k_h, k_w): (usize, usize),
+    ) -> Result<Tensor> {
+        match &self.rel_pos_hw {
+            Some((rel_pos_h, rel_pos_w)) => {
+                let r_h = get_rel_pos(q_h, k_h, rel_pos_h)?;
+                let r_w = get_rel_pos(q_w, k_w, rel_pos_w)?;
+                let (b, _, dim) = q.dims3()?;
+                let r_q = q.reshape((b, q_h, q_w, dim))?;
+                // rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
+                let rel_h = r_q.matmul(&r_h.broadcast_left(b)?.t()?.contiguous()?)?;
+                // rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
+                let rel_w = r_q
+                    .transpose(1, 2)? // -> bwhc
+                    .contiguous()?
+                    .matmul(&r_w.broadcast_left(b)?.t()?.contiguous()?)? // bwhc,bwck -> bwhk
+                    .transpose(1, 2)?;
+                (attn.reshape((b, q_h, q_w, k_h, k_w))?
+                    + rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
+                .reshape((b, q_h * q_w, k_h * k_w))
+            }
+            None => Ok(attn),
+        }
+    }
+}
+
+fn get_rel_pos(q_size: usize, k_size: usize, rel_pos: &Tensor) -> Result<Tensor> {
+    let max_rel_dist = 2 * usize::max(q_size, k_size) - 1;
+    let dev = rel_pos.device();
+    let rel_pos_resized = if rel_pos.dim(0)? != max_rel_dist {
+        todo!("interpolation")
+    } else {
+        rel_pos
+    };
+    let q_coords = Tensor::arange(0u32, q_size as u32, dev)?
+        .reshape((q_size, 1))?
+        .to_dtype(DType::F32)?;
+    let k_coords = Tensor::arange(0u32, k_size as u32, dev)?
+        .reshape((1, k_size))?
+        .to_dtype(DType::F32)?;
+    let q_coords = (q_coords * f64::max(1f64, k_size as f64 / q_size as f64))?;
+    let k_coords = (k_coords * f64::max(1f64, q_size as f64 / k_size as f64))?;
+    let relative_coords = (q_coords.broadcast_sub(&k_coords)?
+        + (k_size as f64 - 1.) * f64::max(1f64, q_size as f64 / k_size as f64))?;
+    let (d1, d2) = relative_coords.dims2()?;
+    let relative_coords = relative_coords.to_dtype(DType::U32)?;
+    rel_pos_resized
+        .index_select(&relative_coords.reshape(d1 * d2)?, 0)?
+        .reshape((d1, d2, rel_pos_resized.dim(1)?))
 }
 
 impl Module for Attention {
@@ -77,24 +131,22 @@ impl Module for Attention {
         let (b, h, w, c) = xs.dims4()?;
         let qkv = self
             .qkv
-            .forward(xs)?
+            .forward(&xs.flatten_to(1)?)?
             .reshape((b, h * w, 3, self.num_heads, c / self.num_heads))?
             .permute((2, 0, 3, 1, 4))?
             .reshape((3, b * self.num_heads, h * w, c / self.num_heads))?;
         let q = qkv.i(0)?;
         let k = qkv.i(1)?;
         let v = qkv.i(2)?;
-        let attn = (q * self.scale)?.matmul(&k.t()?)?;
-        if self.use_rel_pos {
-            todo!()
-        }
+        let attn = (&q * self.scale)?.matmul(&k.t()?)?;
+        let attn = self.add_decomposed_rel_pos(attn, &q, (h, w), (h, w))?;
         let attn = candle_nn::ops::softmax_last_dim(&attn)?;
+        let attn = attn.matmul(&v)?;
         let attn = attn
-            .matmul(&v)?
             .reshape((b, self.num_heads, h, w, c / self.num_heads))?
             .permute((0, 2, 3, 1, 4))?
-            .reshape((b, h, w, c / self.num_heads))?;
-        self.proj.forward(&attn)
+            .reshape((b, h * w, c))?;
+        self.proj.forward(&attn)?.reshape((b, h, w, c))
     }
 }
 
@@ -117,8 +169,8 @@ impl Block {
         input_size: (usize, usize),
         vb: VarBuilder,
     ) -> Result<Self> {
-        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
-        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
+        let norm1 = layer_norm(dim, 1e-6, vb.pp("norm1"))?;
+        let norm2 = layer_norm(dim, 1e-6, vb.pp("norm2"))?;
         let input_size_attn = if window_size == 0 {
             input_size
         } else {
@@ -132,7 +184,7 @@ impl Block {
             input_size_attn,
             vb.pp("attn"),
         )?;
-        let mlp = crate::MlpBlock::new(dim, dim * 4, vb.pp("mlp"))?;
+        let mlp = crate::MlpBlock::new(dim, dim * 4, candle_nn::Activation::Gelu, vb.pp("mlp"))?;
         Ok(Self {
             norm1,
             attn,
@@ -143,17 +195,76 @@ impl Block {
     }
 }
 
+fn window_partition(xs: Tensor, window_size: usize) -> Result<(Tensor, (usize, usize))> {
+    let (b, h, w, c) = xs.dims4()?;
+    let pad_h = (window_size - h % window_size) % window_size;
+    let pad_w = (window_size - w % window_size) % window_size;
+    let xs = if pad_h > 0 {
+        xs.pad_with_zeros(1, 0, pad_h)?
+    } else {
+        xs
+    };
+    let xs = if pad_w > 0 {
+        xs.pad_with_zeros(2, 0, pad_w)?
+    } else {
+        xs
+    };
+    let (h_p, w_p) = (h + pad_h, w + pad_w);
+    let windows = xs
+        .reshape((
+            b,
+            h_p / window_size,
+            window_size,
+            w_p / window_size,
+            window_size,
+            c,
+        ))?
+        .transpose(2, 3)?
+        .contiguous()?
+        .flatten_to(2)?;
+    Ok((windows, (h_p, w_p)))
+}
+
+fn window_unpartition(
+    windows: Tensor,
+    window_size: usize,
+    (h_p, w_p): (usize, usize),
+    (h, w): (usize, usize),
+) -> Result<Tensor> {
+    let b = windows.dim(0)? / (h_p * w_p / window_size / window_size);
+    let xs = windows
+        .reshape((
+            b,
+            h_p / window_size,
+            w_p / window_size,
+            window_size,
+            window_size,
+            windows.elem_count() / b / h_p / w_p,
+        ))?
+        .transpose(2, 3)?
+        .contiguous()?
+        .reshape((b, h_p, w_p, windows.elem_count() / b / h_p / w_p))?;
+    let xs = if h_p > h { xs.narrow(1, 0, h)? } else { xs };
+    let xs = if w_p > w { xs.narrow(2, 0, w)? } else { xs };
+    Ok(xs)
+}
+
 impl Module for Block {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let shortcut = xs;
         let xs = self.norm1.forward(xs)?;
-        if self.window_size > 0 {
-            todo!()
-        }
+        let hw = (xs.dim(1)?, xs.dim(2)?);
+        let (xs, pad_hw) = if self.window_size > 0 {
+            window_partition(xs, self.window_size)?
+        } else {
+            (xs, (0, 0))
+        };
         let xs = self.attn.forward(&xs)?;
-        if self.window_size > 0 {
-            todo!()
-        }
+        let xs = if self.window_size > 0 {
+            window_unpartition(xs, self.window_size, pad_hw, hw)?
+        } else {
+            xs
+        };
         let xs = (xs + shortcut)?;
         &xs + xs.apply(&self.norm2)?.apply(&self.mlp)?
     }
@@ -165,9 +276,9 @@ pub struct ImageEncoderViT {
     patch_embed: PatchEmbed,
     blocks: Vec<Block>,
     neck_conv1: candle_nn::Conv2d,
-    neck_ln1: LayerNorm,
+    neck_ln1: crate::LayerNorm2d,
     neck_conv2: candle_nn::Conv2d,
-    neck_ln2: LayerNorm,
+    neck_ln2: crate::LayerNorm2d,
     pos_embed: Option<Tensor>,
 }
 
@@ -222,13 +333,13 @@ impl ImageEncoderViT {
             Default::default(),
             vb.pp("neck.0"),
         )?;
-        let neck_ln1 = layer_norm(out_chans, 1e-6, vb.pp("neck.1"))?;
+        let neck_ln1 = crate::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.1"))?;
         let cfg = candle_nn::Conv2dConfig {
             padding: 1,
             ..Default::default()
         };
         let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
-        let neck_ln2 = layer_norm(out_chans, 1e-6, vb.pp("neck.3"))?;
+        let neck_ln2 = crate::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.3"))?;
         let pos_embed = if use_abs_pos {
             let p = vb.get(
                 (1, img_size / patch_size, img_size / patch_size, embed_dim),
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
index cf3879cd..1ef46eeb 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -1,5 +1,5 @@
 use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use candle_nn::{Linear, Module, VarBuilder};
 
 use crate::model_transformer::TwoWayTransformer;
 
@@ -60,7 +60,7 @@ pub struct MaskDecoder {
     mask_tokens: candle_nn::Embedding,
     iou_prediction_head: MlpMaskDecoder,
     output_upscaling_conv1: candle_nn::ConvTranspose2d,
-    output_upscaling_ln: LayerNorm,
+    output_upscaling_ln: crate::LayerNorm2d,
     output_upscaling_conv2: candle_nn::ConvTranspose2d,
     num_mask_tokens: usize,
     output_hypernetworks_mlps: Vec<MlpMaskDecoder>,
@@ -99,7 +99,7 @@ impl MaskDecoder {
             vb.pp("output_upscaling.0"),
         )?;
         let output_upscaling_ln =
-            layer_norm(transformer_dim / 4, 1e-6, vb.pp("output_upscaling.1"))?;
+            crate::LayerNorm2d::new(transformer_dim / 4, 1e-6, vb.pp("output_upscaling.1"))?;
         let output_upscaling_conv2 = candle_nn::conv_transpose2d(
             transformer_dim / 4,
             transformer_dim / 8,
@@ -140,7 +140,7 @@ impl MaskDecoder {
         })
     }
 
-    fn forward(
+    pub fn forward(
         &self,
         image_embeddings: &Tensor,
         image_pe: &Tensor,
@@ -195,7 +195,7 @@ impl MaskDecoder {
         // Run the transformer
         let (hs, src) = self.transformer.forward(&src, &pos_src, &tokens)?;
         let iou_token_out = hs.i((.., 0))?;
-        let mask_tokens_out = hs.i((.., 1, 1 + self.num_mask_tokens))?;
+        let mask_tokens_out = hs.i((.., 1..1 + self.num_mask_tokens))?;
 
         // Upscale mask embeddings and predict masks using the masks tokens.
         let src = src.transpose(1, 2)?.reshape((b, c, h, w))?;
@@ -213,9 +213,8 @@ impl MaskDecoder {
         }
         let hyper_in = Tensor::stack(hyper_in_list.as_slice(), 1)?;
         let (b, c, h, w) = upscaled_embedding.dims4()?;
-        let masks = hyper_in
-            .matmul(&upscaled_embedding.reshape((b, c, h * w))?)?
-            .reshape((b, 0, h, w))?;
+        let masks = hyper_in.matmul(&upscaled_embedding.reshape((b, c, h * w))?)?;
+        let masks = masks.reshape((b, masks.elem_count() / b / h / w, h, w))?;
 
         // Generate mask quality predictions.
         let iou_pred = self.iou_prediction_head.forward(&iou_token_out)?;
@@ -224,6 +223,9 @@ impl MaskDecoder {
 }
 
 // Equivalent to torch.repeat_interleave
-fn repeat_interleave(_img: &Tensor, _repeats: usize, _dim: usize) -> Result<Tensor> {
-    todo!()
+fn repeat_interleave(img: &Tensor, repeats: usize, dim: usize) -> Result<Tensor> {
+    let img = img.unsqueeze(dim + 1)?;
+    let mut dims = img.dims().to_vec();
+    dims[dim + 1] = repeats;
+    img.broadcast_as(dims)?.flatten(dim, dim + 1)
 }
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
index 7ac4c66d..c6ffffd2 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -1,5 +1,5 @@
 use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use candle_nn::{Linear, Module, VarBuilder};
 
 #[derive(Debug)]
 struct PostionEmbeddingRandom {
@@ -17,7 +17,7 @@ impl PostionEmbeddingRandom {
 
     fn pe_encoding(&self, coords: &Tensor) -> Result<Tensor> {
         let coords = coords.affine(2., -1.)?;
-        let coords = coords.matmul(&self.positional_encoding_gaussian_matrix)?;
+        let coords = coords.broadcast_matmul(&self.positional_encoding_gaussian_matrix)?;
         let coords = (coords * (2. * std::f64::consts::PI))?;
         Tensor::cat(&[coords.sin()?, coords.cos()?], D::Minus1)
     }
@@ -25,12 +25,14 @@ impl PostionEmbeddingRandom {
     fn forward(&self, h: usize, w: usize) -> Result<Tensor> {
         let device = self.positional_encoding_gaussian_matrix.device();
         let grid = Tensor::ones((h, w), DType::F32, device)?;
-        // TODO: cumsum
-        let x_embed = (&grid - 0.5)?;
-        // TODO: cumsum
-        let y_embed = (&grid - 0.5)?;
-        let x_embed = (x_embed / w as f64)?;
-        let y_embed = (y_embed / h as f64)?;
+        let x_embed = (Tensor::arange(0u32, w as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
+        let y_embed = (Tensor::arange(0u32, h as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
+        let x_embed = (x_embed / w as f64)?
+            .reshape((1, w))?
+            .broadcast_as((h, w))?;
+        let y_embed = (y_embed / h as f64)?
+            .reshape((h, 1))?
+            .broadcast_as((h, w))?;
         let coords = Tensor::stack(&[&x_embed, &y_embed], D::Minus1)?;
         self.pe_encoding(&coords)?.permute((2, 0, 1))
     }
@@ -55,13 +57,14 @@ pub struct PromptEncoder {
     point_embeddings: Vec<candle_nn::Embedding>,
     not_a_point_embed: candle_nn::Embedding,
     mask_downscaling_conv1: candle_nn::Conv2d,
-    mask_downscaling_ln1: LayerNorm,
+    mask_downscaling_ln1: crate::LayerNorm2d,
     mask_downscaling_conv2: candle_nn::Conv2d,
-    mask_downscaling_ln2: LayerNorm,
+    mask_downscaling_ln2: crate::LayerNorm2d,
     mask_downscaling_conv3: candle_nn::Conv2d,
     no_mask_embed: candle_nn::Embedding,
     image_embedding_size: (usize, usize),
     input_image_size: (usize, usize),
+    embed_dim: usize,
 }
 
 impl PromptEncoder {
@@ -97,8 +100,9 @@ impl PromptEncoder {
             vb.pp("mask_downscaling.6"),
         )?;
         let mask_downscaling_ln1 =
-            layer_norm(mask_in_chans / 4, 1e-6, vb.pp("mask_downscaling.1"))?;
-        let mask_downscaling_ln2 = layer_norm(mask_in_chans, 1e-6, vb.pp("mask_downscaling.4"))?;
+            crate::LayerNorm2d::new(mask_in_chans / 4, 1e-6, vb.pp("mask_downscaling.1"))?;
+        let mask_downscaling_ln2 =
+            crate::LayerNorm2d::new(mask_in_chans, 1e-6, vb.pp("mask_downscaling.4"))?;
         let mut point_embeddings = Vec::with_capacity(num_points_embeddings);
         let vb_e = vb.pp("point_embeddings");
         for i in 0..num_points_embeddings {
@@ -117,9 +121,16 @@ impl PromptEncoder {
             no_mask_embed,
             image_embedding_size,
             input_image_size,
+            embed_dim,
         })
     }
 
+    pub fn get_dense_pe(&self) -> Result<Tensor> {
+        self.pe_layer
+            .forward(self.image_embedding_size.0, self.image_embedding_size.1)?
+            .unsqueeze(0)
+    }
+
     fn embed_masks(&self, masks: &Tensor) -> Result<Tensor> {
         masks
             .apply(&self.mask_downscaling_conv1)?
@@ -133,7 +144,16 @@ impl PromptEncoder {
 
     fn embed_points(&self, points: &Tensor, labels: &Tensor, pad: bool) -> Result<Tensor> {
         let points = (points + 0.5)?;
-        let points = if pad { todo!() } else { points };
+        let dev = points.device();
+        let (points, labels) = if pad {
+            let padding_point = Tensor::zeros((points.dim(0)?, 1, 2), DType::F32, dev)?;
+            let padding_label = (Tensor::ones((labels.dim(0)?, 1), DType::F32, dev)? * (-1f64))?;
+            let points = Tensor::cat(&[&points, &padding_point], 1)?;
+            let labels = Tensor::cat(&[labels, &padding_label], 1)?;
+            (points, labels)
+        } else {
+            (points, labels.clone())
+        };
         let point_embedding = self
             .pe_layer
             .forward_with_coords(&points, self.input_image_size)?;
@@ -154,7 +174,7 @@ impl PromptEncoder {
         Tensor::cat(&[&ce1, &ce2], 1)
     }
 
-    fn forward(
+    pub fn forward(
         &self,
         points: Option<(&Tensor, &Tensor)>,
         boxes: Option<&Tensor>,
@@ -172,7 +192,9 @@ impl PromptEncoder {
             (Some(se_points), Some(se_boxes)) => Tensor::cat(&[se_points, se_boxes], 1)?,
             (Some(se_points), None) => se_points,
             (None, Some(se_boxes)) => se_boxes,
-            (None, None) => Tensor::zeros(1, DType::F32, &candle::Device::Cpu)?,
+            (None, None) => {
+                Tensor::zeros((1, 0, self.embed_dim), DType::F32, &candle::Device::Cpu)?
+            }
         };
 
         let dense_embeddings = match masks {
@@ -180,7 +202,7 @@ impl PromptEncoder {
                 let emb = self.no_mask_embed.embeddings();
                 emb.reshape((1, emb.elem_count(), 1, 1))?.expand((
                     1,
-                    0,
+                    emb.elem_count(),
                     self.image_embedding_size.0,
                     self.image_embedding_size.1,
                 ))?
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-examples/examples/segment-anything/model_sam.rs
index 5a0d7e8f..1c8e9a59 100644
--- a/candle-examples/examples/segment-anything/model_sam.rs
+++ b/candle-examples/examples/segment-anything/model_sam.rs
@@ -5,6 +5,10 @@ use crate::model_image_encoder::ImageEncoderViT;
 use crate::model_mask_decoder::MaskDecoder;
 use crate::model_prompt_encoder::PromptEncoder;
 
+const PROMPT_EMBED_DIM: usize = 256;
+const IMAGE_SIZE: usize = 1024;
+const VIT_PATCH_SIZE: usize = 16;
+
 #[derive(Debug)]
 pub struct Sam {
     image_encoder: ImageEncoderViT,
@@ -22,10 +26,6 @@ impl Sam {
         encoder_global_attn_indexes: &[usize],
         vb: VarBuilder,
     ) -> Result<Self> {
-        const PROMPT_EMBED_DIM: usize = 256;
-        const IMAGE_SIZE: usize = 1024;
-        const VIT_PATCH_SIZE: usize = 16;
-
         let image_embedding_size = IMAGE_SIZE / VIT_PATCH_SIZE;
 
         let image_encoder = ImageEncoderViT::new(
@@ -69,4 +69,33 @@ impl Sam {
             pixel_mean,
         })
     }
+
+    pub fn forward(&self, img: &Tensor, multimask_output: bool) -> Result<(Tensor, Tensor)> {
+        let img = self.preprocess(img)?.unsqueeze(0)?;
+        let img_embeddings = self.image_encoder.forward(&img)?;
+        let image_pe = self.prompt_encoder.get_dense_pe()?;
+        let (sparse_prompt_embeddings, dense_prompt_embeddings) =
+            self.prompt_encoder.forward(None, None, None)?;
+        let (low_res_mask, iou_predictions) = self.mask_decoder.forward(
+            &img_embeddings,
+            &image_pe,
+            &sparse_prompt_embeddings,
+            &dense_prompt_embeddings,
+            multimask_output,
+        )?;
+        // TODO: post-processing.
+        Ok((low_res_mask, iou_predictions))
+    }
+
+    fn preprocess(&self, img: &Tensor) -> Result<Tensor> {
+        let (c, h, w) = img.dims3()?;
+        let img = img
+            .broadcast_sub(&self.pixel_mean)?
+            .broadcast_div(&self.pixel_std)?;
+        if h > IMAGE_SIZE || w > IMAGE_SIZE {
+            candle::bail!("image is too large ({w}, {h}), maximum size {IMAGE_SIZE}")
+        }
+        let img = img.pad_with_zeros(1, 0, IMAGE_SIZE - h)?;
+        img.pad_with_zeros(2, 0, IMAGE_SIZE - w)
+    }
 }
diff --git a/candle-examples/examples/segment-anything/model_transformer.rs b/candle-examples/examples/segment-anything/model_transformer.rs
index a845085d..044dce9b 100644
--- a/candle-examples/examples/segment-anything/model_transformer.rs
+++ b/candle-examples/examples/segment-anything/model_transformer.rs
@@ -36,7 +36,8 @@ impl Attention {
     fn separate_heads(&self, x: &Tensor) -> Result<Tensor> {
         let (b, n, c) = x.dims3()?;
         x.reshape((b, n, self.num_heads, c / self.num_heads))?
-            .transpose(1, 2)
+            .transpose(1, 2)?
+            .contiguous()
     }
 
     fn recombine_heads(&self, x: &Tensor) -> Result<Tensor> {
@@ -102,8 +103,12 @@ impl TwoWayAttentionBlock {
             2,
             vb.pp("cross_attn_image_to_token"),
         )?;
-        // TODO: use relu in this mlp
-        let mlp = crate::MlpBlock::new(embedding_dim, mlp_dim, vb.pp("mlp"))?;
+        let mlp = crate::MlpBlock::new(
+            embedding_dim,
+            mlp_dim,
+            candle_nn::Activation::Relu,
+            vb.pp("mlp"),
+        )?;
         Ok(Self {
             self_attn,
             norm1,
@@ -126,7 +131,7 @@ impl TwoWayAttentionBlock {
     ) -> Result<(Tensor, Tensor)> {
         // Self attention block
         let queries = if self.skip_first_layer_pe {
-            self.self_attn.forward(queries, keys, queries)?
+            self.self_attn.forward(queries, queries, queries)?
         } else {
             let q = (queries + query_pe)?;
             let attn_out = self.self_attn.forward(&q, &q, queries)?;
diff --git a/candle-nn/src/layer_norm.rs b/candle-nn/src/layer_norm.rs
index 08e2f628..d2e80a82 100644
--- a/candle-nn/src/layer_norm.rs
+++ b/candle-nn/src/layer_norm.rs
@@ -28,7 +28,7 @@
 //! ```
 //!
 //! [`Layer Normalization`]: https://arxiv.org/abs/1607.06450
-use candle::{DType, Result, Tensor};
+use candle::{DType, Result, Tensor, D};
 
 #[derive(Debug, Clone, Copy, PartialEq)]
 pub struct LayerNormConfig {
@@ -104,15 +104,15 @@ impl crate::Module for LayerNorm {
             DType::F16 | DType::BF16 => DType::F32,
             d => d,
         };
-        let (_bsize, _seq_len, hidden_size) = x.dims3()?;
+        let hidden_size = x.dim(D::Minus1)?;
         let x = x.to_dtype(internal_dtype)?;
         let x = if self.remove_mean {
-            let mean_x = (x.sum_keepdim(2)? / hidden_size as f64)?;
+            let mean_x = (x.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
             x.broadcast_sub(&mean_x)?
         } else {
             x
         };
-        let norm_x = (x.sqr()?.sum_keepdim(2)? / hidden_size as f64)?;
+        let norm_x = (x.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
         let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
         let x = x_normed.to_dtype(x_dtype)?.broadcast_mul(&self.weight)?;
         match &self.bias {
diff --git a/candle-nn/src/linear.rs b/candle-nn/src/linear.rs
index 7028f68c..de335964 100644
--- a/candle-nn/src/linear.rs
+++ b/candle-nn/src/linear.rs
@@ -41,8 +41,9 @@ impl Linear {
 
 impl super::Module for Linear {
     fn forward(&self, x: &Tensor) -> candle::Result<Tensor> {
-        let w = match x.dims() {
-            &[bsize, _, _] => self.weight.broadcast_left(bsize)?.t()?,
+        let w = match *x.dims() {
+            [b1, b2, _, _] => self.weight.broadcast_left((b1, b2))?.t()?,
+            [bsize, _, _] => self.weight.broadcast_left(bsize)?.t()?,
             _ => self.weight.t()?,
         };
         let x = x.matmul(&w)?;

From 79c27fc489f2eece486fa433a0ae75c66a398e6f Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 7 Sep 2023 21:45:16 +0100
Subject: [PATCH 030/150] Segment-anything fixes: avoid normalizing twice.
 (#767)

* Segment-anything fixes: avoid normalizing twice.

* More fixes for the image aspect ratio.
---
 .../examples/segment-anything/main.rs         |  5 ++--
 .../examples/segment-anything/model_sam.rs    |  3 +-
 candle-examples/src/lib.rs                    | 28 +++++++++++++++++++
 3 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index a2722270..03ebe346 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -108,7 +108,8 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
+    let image =
+        candle_examples::load_image(args.image, Some(model_sam::IMAGE_SIZE))?.to_device(&device)?;
     println!("loaded image {image:?}");
 
     let model = match args.model {
@@ -125,7 +126,7 @@ pub fn main() -> anyhow::Result<()> {
     let sam = model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)?; // sam_vit_b
 
     let (mask, iou_predictions) = sam.forward(&image, false)?;
-    println!("mask: {mask:?}");
+    println!("mask:\n{mask}");
     println!("iou_predictions: {iou_predictions:?}");
     Ok(())
 }
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-examples/examples/segment-anything/model_sam.rs
index 1c8e9a59..acba7ef4 100644
--- a/candle-examples/examples/segment-anything/model_sam.rs
+++ b/candle-examples/examples/segment-anything/model_sam.rs
@@ -6,7 +6,7 @@ use crate::model_mask_decoder::MaskDecoder;
 use crate::model_prompt_encoder::PromptEncoder;
 
 const PROMPT_EMBED_DIM: usize = 256;
-const IMAGE_SIZE: usize = 1024;
+pub const IMAGE_SIZE: usize = 1024;
 const VIT_PATCH_SIZE: usize = 16;
 
 #[derive(Debug)]
@@ -90,6 +90,7 @@ impl Sam {
     fn preprocess(&self, img: &Tensor) -> Result<Tensor> {
         let (c, h, w) = img.dims3()?;
         let img = img
+            .to_dtype(DType::F32)?
             .broadcast_sub(&self.pixel_mean)?
             .broadcast_div(&self.pixel_std)?;
         if h > IMAGE_SIZE || w > IMAGE_SIZE {
diff --git a/candle-examples/src/lib.rs b/candle-examples/src/lib.rs
index f9581b02..66cd2f99 100644
--- a/candle-examples/src/lib.rs
+++ b/candle-examples/src/lib.rs
@@ -16,6 +16,34 @@ pub fn device(cpu: bool) -> Result<Device> {
     }
 }
 
+pub fn load_image<P: AsRef<std::path::Path>>(
+    p: P,
+    resize_longest: Option<usize>,
+) -> Result<Tensor> {
+    let img = image::io::Reader::open(p)?
+        .decode()
+        .map_err(candle::Error::wrap)?;
+    let img = match resize_longest {
+        None => img,
+        Some(resize_longest) => {
+            let (height, width) = (img.height(), img.width());
+            let resize_longest = resize_longest as u32;
+            let (height, width) = if height < width {
+                let h = (resize_longest * height) / width;
+                (h, resize_longest)
+            } else {
+                let w = (resize_longest * width) / height;
+                (resize_longest, w)
+            };
+            img.resize_exact(width, height, image::imageops::FilterType::CatmullRom)
+        }
+    };
+    let (height, width) = (img.height() as usize, img.width() as usize);
+    let img = img.to_rgb8();
+    let data = img.into_raw();
+    Tensor::from_vec(data, (height, width, 3), &Device::Cpu)?.permute((2, 0, 1))
+}
+
 pub fn load_image_and_resize<P: AsRef<std::path::Path>>(
     p: P,
     width: usize,

From 3898e500debd632b520054ebfa42f8333323a20e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 05:53:08 +0100
Subject: [PATCH 031/150] Generate a mask image + the scaled input image.
 (#769)

* Also round-trip the original image.

* Make it possible to use a safetensors input.
---
 .../examples/segment-anything/main.rs         | 27 +++++++++++++++++--
 .../examples/segment-anything/model_sam.rs    | 10 ++++++-
 2 files changed, 34 insertions(+), 3 deletions(-)

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 03ebe346..89d5b56c 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -108,8 +108,20 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let image =
-        candle_examples::load_image(args.image, Some(model_sam::IMAGE_SIZE))?.to_device(&device)?;
+    let image = if args.image.ends_with(".safetensors") {
+        let mut tensors = candle::safetensors::load(&args.image, &device)?;
+        match tensors.remove("image") {
+            Some(image) => image,
+            None => {
+                if tensors.len() != 1 {
+                    anyhow::bail!("multiple tensors in '{}'", args.image)
+                }
+                tensors.into_values().next().unwrap()
+            }
+        }
+    } else {
+        candle_examples::load_image(args.image, Some(model_sam::IMAGE_SIZE))?.to_device(&device)?
+    };
     println!("loaded image {image:?}");
 
     let model = match args.model {
@@ -128,5 +140,16 @@ pub fn main() -> anyhow::Result<()> {
     let (mask, iou_predictions) = sam.forward(&image, false)?;
     println!("mask:\n{mask}");
     println!("iou_predictions: {iou_predictions:?}");
+
+    // Save the mask as an image.
+    let mask = mask.ge(&mask.zeros_like()?)?;
+    let mask = (mask * 255.)?.squeeze(0)?;
+    let (_one, h, w) = mask.dims3()?;
+    let mask = mask.expand((3, h, w))?;
+    candle_examples::save_image(&mask, "sam_mask.png")?;
+
+    let image = sam.preprocess(&image)?;
+    let image = sam.unpreprocess(&image)?.to_dtype(DType::U8)?;
+    candle_examples::save_image(&image, "sam_input_scaled.png")?;
     Ok(())
 }
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-examples/examples/segment-anything/model_sam.rs
index acba7ef4..237163a3 100644
--- a/candle-examples/examples/segment-anything/model_sam.rs
+++ b/candle-examples/examples/segment-anything/model_sam.rs
@@ -87,7 +87,15 @@ impl Sam {
         Ok((low_res_mask, iou_predictions))
     }
 
-    fn preprocess(&self, img: &Tensor) -> Result<Tensor> {
+    pub fn unpreprocess(&self, img: &Tensor) -> Result<Tensor> {
+        let img = img
+            .broadcast_mul(&self.pixel_std)?
+            .broadcast_add(&self.pixel_mean)?;
+        img.maximum(&img.zeros_like()?)?
+            .minimum(&(img.ones_like()? * 255.)?)
+    }
+
+    pub fn preprocess(&self, img: &Tensor) -> Result<Tensor> {
         let (c, h, w) = img.dims3()?;
         let img = img
             .to_dtype(DType::F32)?

From cfcbec9fc70aca2b0e08f382dec8634f88b61bce Mon Sep 17 00:00:00 2001
From: Zsombor <gzsombor@gmail.com>
Date: Fri, 8 Sep 2023 09:15:14 +0200
Subject: [PATCH 032/150] Add small customization to the build  (#768)

* Add ability to override the compiler used by NVCC from an environment variable

* Allow relative paths in CANDLE_FLASH_ATTN_BUILD_DIR

* Add the compilation failure to the readme, with a possible solution

* Adjust the error message, and remove the special handling of the relative paths
---
 README.md                  | 11 +++++++++++
 candle-flash-attn/build.rs | 24 ++++++++++++++++++++----
 2 files changed, 31 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 140382c7..076f2363 100644
--- a/README.md
+++ b/README.md
@@ -243,6 +243,17 @@ authentication token. See issue
 git submodule update --init
 ```
 
+#### Compiling with flash-attention fails
+
+```
+/usr/include/c++/11/bits/std_function.h:530:146: error: parameter packs not expanded with ‘...’:
+```
+
+This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a different, supported gcc version - for example gcc-10, and specify the path to the compiler in the CANDLE_NVCC_CCBIN environment variable.
+```
+env CANDLE_NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
+```
+
 #### Tracking down errors
 
 You can set `RUST_BACKTRACE=1` to be provided with backtraces when a candle
diff --git a/candle-flash-attn/build.rs b/candle-flash-attn/build.rs
index 2a3b7eb1..4cc7e5fb 100644
--- a/candle-flash-attn/build.rs
+++ b/candle-flash-attn/build.rs
@@ -57,9 +57,17 @@ fn main() -> Result<()> {
             #[allow(clippy::redundant_clone)]
             out_dir.clone()
         }
-        Ok(build_dir) => PathBuf::from(build_dir),
+        Ok(build_dir) =>
+        {
+            let path = PathBuf::from(build_dir);
+            path.canonicalize().expect(&format!("Directory doesn't exists: {} (the current directory is {})", &path.display(), std::env::current_dir()?.display()))
+        }
     };
     set_cuda_include_dir()?;
+
+    let ccbin_env = std::env::var("CANDLE_NVCC_CCBIN");
+    println!("cargo:rerun-if-env-changed=CANDLE_NVCC_CCBIN");
+
     let compute_cap = compute_cap()?;
 
     let out_file = build_dir.join("libflashattention.a");
@@ -95,14 +103,21 @@ fn main() -> Result<()> {
                     .args(["--default-stream", "per-thread"])
                     .arg("-Icutlass/include")
                     .arg("--expt-relaxed-constexpr")
-                    .arg(cu_file);
+                    .arg("--verbose");
+                if let Ok(ccbin_path) = &ccbin_env {
+                    command
+                        .arg("-allow-unsupported-compiler")
+                        .args(["-ccbin", ccbin_path]);
+                }
+                command.arg(cu_file);
                 let output = command
                     .spawn()
                     .context("failed spawning nvcc")?
                     .wait_with_output()?;
                 if !output.status.success() {
                     anyhow::bail!(
-                        "nvcc error while compiling:\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
+                        "nvcc error while executing compiling: {:?}\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
+                        &command,
                         String::from_utf8_lossy(&output.stdout),
                         String::from_utf8_lossy(&output.stderr)
                     )
@@ -122,7 +137,8 @@ fn main() -> Result<()> {
             .wait_with_output()?;
         if !output.status.success() {
             anyhow::bail!(
-                "nvcc error while linking:\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
+                "nvcc error while linking: {:?}\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
+                &command,
                 String::from_utf8_lossy(&output.stdout),
                 String::from_utf8_lossy(&output.stderr)
             )

From 0e250aee4fcff8991c086ba0606a90db92b4e488 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 08:38:13 +0100
Subject: [PATCH 033/150] Shape with holes (#770)

* Shape with holes.

* rustfmt.
---
 candle-core/src/shape.rs   | 168 +++++++++++++++++++++++++++++++++++++
 candle-core/src/tensor.rs  |  13 ++-
 candle-flash-attn/build.rs |   9 +-
 3 files changed, 184 insertions(+), 6 deletions(-)

diff --git a/candle-core/src/shape.rs b/candle-core/src/shape.rs
index 9617d1ac..b1f56817 100644
--- a/candle-core/src/shape.rs
+++ b/candle-core/src/shape.rs
@@ -482,3 +482,171 @@ mod tests {
         assert_eq!(shape.stride_contiguous(), [458 * 792, 458, 1]);
     }
 }
+
+pub trait ShapeWithOneHole {
+    fn into_shape(self, el_count: usize) -> Result<Shape>;
+}
+
+impl<S: Into<Shape>> ShapeWithOneHole for S {
+    fn into_shape(self, _el_count: usize) -> Result<Shape> {
+        Ok(self.into())
+    }
+}
+
+impl ShapeWithOneHole for ((),) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        Ok(el_count.into())
+    }
+}
+
+impl ShapeWithOneHole for ((), usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let ((), d1) = self;
+        if el_count % d1 != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d1}")
+        }
+        Ok((el_count / d1, d1).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, ()) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, ()) = self;
+        if el_count % d1 != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d1}")
+        }
+        Ok((d1, el_count / d1).into())
+    }
+}
+
+impl ShapeWithOneHole for ((), usize, usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let ((), d1, d2) = self;
+        let d = d1 * d2;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((el_count / d, d1, d2).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, (), usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, (), d2) = self;
+        let d = d1 * d2;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, el_count / d, d2).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, usize, ()) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, d2, ()) = self;
+        let d = d1 * d2;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, d2, el_count / d).into())
+    }
+}
+
+impl ShapeWithOneHole for ((), usize, usize, usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let ((), d1, d2, d3) = self;
+        let d = d1 * d2 * d3;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((el_count / d, d1, d2, d3).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, (), usize, usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, (), d2, d3) = self;
+        let d = d1 * d2 * d3;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, el_count / d, d2, d3).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, usize, (), usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, d2, (), d3) = self;
+        let d = d1 * d2 * d3;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, d2, el_count / d, d3).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, usize, usize, ()) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, d2, d3, ()) = self;
+        let d = d1 * d2 * d3;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, d2, d3, el_count / d).into())
+    }
+}
+
+impl ShapeWithOneHole for ((), usize, usize, usize, usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let ((), d1, d2, d3, d4) = self;
+        let d = d1 * d2 * d3 * d4;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((el_count / d, d1, d2, d3, d4).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, (), usize, usize, usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, (), d2, d3, d4) = self;
+        let d = d1 * d2 * d3 * d4;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, el_count / d, d2, d3, d4).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, usize, (), usize, usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, d2, (), d3, d4) = self;
+        let d = d1 * d2 * d3 * d4;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, d2, el_count / d, d3, d4).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, usize, usize, (), usize) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, d2, d3, (), d4) = self;
+        let d = d1 * d2 * d3 * d4;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, d2, d3, el_count / d, d4).into())
+    }
+}
+
+impl ShapeWithOneHole for (usize, usize, usize, usize, ()) {
+    fn into_shape(self, el_count: usize) -> Result<Shape> {
+        let (d1, d2, d3, d4, ()) = self;
+        let d = d1 * d2 * d3 * d4;
+        if el_count % d != 0 {
+            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
+        }
+        Ok((d1, d2, d3, d4, el_count / d).into())
+    }
+}
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 1eca694c..6bb3d740 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -1685,12 +1685,15 @@ impl Tensor {
         Ok(from_storage(storage, shape, BackpropOp::none(), true))
     }
 
-    // TODO: Do we want to allow target shape using -1 on some dimensions?
     /// Reshape returns a tensor with the target shape provided that the number of elements of the
     /// original tensor is the same.
     /// If the input tensor is contiguous, this is a view on the original data. Otherwise this uses
     /// a new storage and copies the data over, the returned tensor is always contiguous.
     ///
+    /// The shape can be specified using a tuple of `usize` and at most one `()` in which case
+    /// the behavior is the same as when using `-1` in PyTorch: this dimension size is adjusted so
+    /// as to match the number of elements in the tensor.
+    ///
     /// ```rust
     /// # use candle_core::{Tensor, DType, Device, D};
     /// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
@@ -1700,10 +1703,14 @@ impl Tensor {
     ///
     /// let c = a.reshape((3, 2))?;
     /// assert_eq!(c.shape().dims(), &[3, 2]);
+    ///
+    /// let c = a.reshape((2, (), 1))?;
+    /// assert_eq!(c.shape().dims(), &[2, 3, 1]);
+    ///
     /// # Ok::<(), candle_core::Error>(())
     /// ```
-    pub fn reshape<S: Into<Shape>>(&self, shape: S) -> Result<Tensor> {
-        let shape = shape.into();
+    pub fn reshape<S: crate::shape::ShapeWithOneHole>(&self, s: S) -> Result<Tensor> {
+        let shape = s.into_shape(self.elem_count())?;
         if shape.elem_count() != self.elem_count() {
             return Err(Error::ShapeMismatchBinaryOp {
                 lhs: self.shape().clone(),
diff --git a/candle-flash-attn/build.rs b/candle-flash-attn/build.rs
index 4cc7e5fb..64275fda 100644
--- a/candle-flash-attn/build.rs
+++ b/candle-flash-attn/build.rs
@@ -57,10 +57,13 @@ fn main() -> Result<()> {
             #[allow(clippy::redundant_clone)]
             out_dir.clone()
         }
-        Ok(build_dir) =>
-        {
+        Ok(build_dir) => {
             let path = PathBuf::from(build_dir);
-            path.canonicalize().expect(&format!("Directory doesn't exists: {} (the current directory is {})", &path.display(), std::env::current_dir()?.display()))
+            path.canonicalize().expect(&format!(
+                "Directory doesn't exists: {} (the current directory is {})",
+                &path.display(),
+                std::env::current_dir()?.display()
+            ))
         }
     };
     set_cuda_include_dir()?;

From 989a4807b151f08c651b5027cc1b547a59adf966 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 08:50:27 +0100
Subject: [PATCH 034/150] Use shape with holes. (#771)

---
 .../examples/segment-anything/model_image_encoder.rs      | 4 ++--
 .../examples/segment-anything/model_mask_decoder.rs       | 2 +-
 .../examples/segment-anything/model_prompt_encoder.rs     | 8 ++++----
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
index f5db2830..79e52d47 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -123,7 +123,7 @@ fn get_rel_pos(q_size: usize, k_size: usize, rel_pos: &Tensor) -> Result<Tensor>
     let relative_coords = relative_coords.to_dtype(DType::U32)?;
     rel_pos_resized
         .index_select(&relative_coords.reshape(d1 * d2)?, 0)?
-        .reshape((d1, d2, rel_pos_resized.dim(1)?))
+        .reshape((d1, d2, ()))
 }
 
 impl Module for Attention {
@@ -243,7 +243,7 @@ fn window_unpartition(
         ))?
         .transpose(2, 3)?
         .contiguous()?
-        .reshape((b, h_p, w_p, windows.elem_count() / b / h_p / w_p))?;
+        .reshape((b, h_p, w_p, ()))?;
     let xs = if h_p > h { xs.narrow(1, 0, h)? } else { xs };
     let xs = if w_p > w { xs.narrow(2, 0, w)? } else { xs };
     Ok(xs)
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
index 1ef46eeb..acbfeeea 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -214,7 +214,7 @@ impl MaskDecoder {
         let hyper_in = Tensor::stack(hyper_in_list.as_slice(), 1)?;
         let (b, c, h, w) = upscaled_embedding.dims4()?;
         let masks = hyper_in.matmul(&upscaled_embedding.reshape((b, c, h * w))?)?;
-        let masks = masks.reshape((b, masks.elem_count() / b / h / w, h, w))?;
+        let masks = masks.reshape((b, (), h, w))?;
 
         // Generate mask quality predictions.
         let iou_pred = self.iou_prediction_head.forward(&iou_token_out)?;
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
index c6ffffd2..aab0c4fd 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -28,10 +28,10 @@ impl PostionEmbeddingRandom {
         let x_embed = (Tensor::arange(0u32, w as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
         let y_embed = (Tensor::arange(0u32, h as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
         let x_embed = (x_embed / w as f64)?
-            .reshape((1, w))?
+            .reshape((1, ()))?
             .broadcast_as((h, w))?;
         let y_embed = (y_embed / h as f64)?
-            .reshape((h, 1))?
+            .reshape(((), 1))?
             .broadcast_as((h, w))?;
         let coords = Tensor::stack(&[&x_embed, &y_embed], D::Minus1)?;
         self.pe_encoding(&coords)?.permute((2, 0, 1))
@@ -163,7 +163,7 @@ impl PromptEncoder {
 
     fn embed_boxes(&self, boxes: &Tensor) -> Result<Tensor> {
         let boxes = (boxes + 0.5)?;
-        let coords = boxes.reshape((boxes.elem_count() / 4, 2, 2))?;
+        let coords = boxes.reshape(((), 2, 2))?;
         let corner_embedding = self
             .pe_layer
             .forward_with_coords(&coords, self.input_image_size)?;
@@ -200,7 +200,7 @@ impl PromptEncoder {
         let dense_embeddings = match masks {
             None => {
                 let emb = self.no_mask_embed.embeddings();
-                emb.reshape((1, emb.elem_count(), 1, 1))?.expand((
+                emb.reshape((1, (), 1, 1))?.expand((
                     1,
                     emb.elem_count(),
                     self.image_embedding_size.0,

From c1453f00b11c9dd12c5aa81fb4355ce47d22d477 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 09:39:10 +0100
Subject: [PATCH 035/150] Improve the safetensor loading in the
 segment-anything example. (#772)

* Improve the safetensor loading in the segment-anything example.

* Properly handle the labels when embedding the point prompts.
---
 .../examples/segment-anything/main.rs         |  7 +++++-
 .../segment-anything/model_prompt_encoder.rs  | 23 ++++++++++++++++++-
 2 files changed, 28 insertions(+), 2 deletions(-)

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 89d5b56c..c53c1010 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -110,7 +110,7 @@ pub fn main() -> anyhow::Result<()> {
 
     let image = if args.image.ends_with(".safetensors") {
         let mut tensors = candle::safetensors::load(&args.image, &device)?;
-        match tensors.remove("image") {
+        let image = match tensors.remove("image") {
             Some(image) => image,
             None => {
                 if tensors.len() != 1 {
@@ -118,6 +118,11 @@ pub fn main() -> anyhow::Result<()> {
                 }
                 tensors.into_values().next().unwrap()
             }
+        };
+        if image.rank() == 4 {
+            image.get(0)?
+        } else {
+            image
         }
     } else {
         candle_examples::load_image(args.image, Some(model_sam::IMAGE_SIZE))?.to_device(&device)?
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
index aab0c4fd..e4291ebb 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -157,7 +157,28 @@ impl PromptEncoder {
         let point_embedding = self
             .pe_layer
             .forward_with_coords(&points, self.input_image_size)?;
-        // TODO: tweak based on labels.
+        let zeros = point_embedding.zeros_like()?;
+        let point_embeddings = labels.lt(&labels.zeros_like()?)?.where_cond(
+            &self
+                .not_a_point_embed
+                .embeddings()
+                .broadcast_as(zeros.shape())?,
+            &point_embedding,
+        )?;
+        let labels0 = labels.eq(&labels.zeros_like()?)?.where_cond(
+            &self.point_embeddings[0]
+                .embeddings()
+                .broadcast_as(zeros.shape())?,
+            &zeros,
+        )?;
+        let point_embedding = (point_embedding + labels0)?;
+        let labels1 = labels.eq(&labels.ones_like()?)?.where_cond(
+            &self.point_embeddings[1]
+                .embeddings()
+                .broadcast_as(zeros.shape())?,
+            &zeros,
+        )?;
+        let point_embedding = (point_embedding + labels1)?;
         Ok(point_embedding)
     }
 

From 28c87f6a34e594aca5f558bceebc4c0a9c95911a Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 12:26:56 +0100
Subject: [PATCH 036/150] Automatic mask generator + point base mask (#773)

* Add more to the automatic mask generator.

* Add the target point.

* Fix.

* Remove the allow-unused.

* Mask post-processing.
---
 .../examples/segment-anything/main.rs         |  58 ++++--
 .../segment-anything/model_image_encoder.rs   |   6 +-
 .../segment-anything/model_mask_decoder.rs    |   4 +-
 .../segment-anything/model_prompt_encoder.rs  |   6 +-
 .../examples/segment-anything/model_sam.rs    | 181 +++++++++++++++++-
 .../segment-anything/model_transformer.rs     |   6 +-
 candle-examples/src/lib.rs                    |  30 ++-
 7 files changed, 249 insertions(+), 42 deletions(-)

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index c53c1010..0f0c0482 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -1,6 +1,5 @@
 //! SAM: Segment Anything Model
 //! https://github.com/facebookresearch/segment-anything
-#![allow(unused)]
 
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
@@ -14,7 +13,7 @@ pub mod model_prompt_encoder;
 pub mod model_sam;
 pub mod model_transformer;
 
-use candle::{DType, IndexOp, Result, Tensor, D};
+use candle::{DType, Result, Tensor};
 use candle_nn::{Linear, Module, VarBuilder};
 use clap::Parser;
 
@@ -101,6 +100,15 @@ struct Args {
     /// Run on CPU rather than on GPU.
     #[arg(long)]
     cpu: bool,
+
+    #[arg(long)]
+    generate_masks: bool,
+
+    #[arg(long)]
+    point_x: Option<f64>,
+
+    #[arg(long)]
+    point_y: Option<f64>,
 }
 
 pub fn main() -> anyhow::Result<()> {
@@ -108,7 +116,7 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let image = if args.image.ends_with(".safetensors") {
+    let (image, initial_h, initial_w) = if args.image.ends_with(".safetensors") {
         let mut tensors = candle::safetensors::load(&args.image, &device)?;
         let image = match tensors.remove("image") {
             Some(image) => image,
@@ -119,13 +127,16 @@ pub fn main() -> anyhow::Result<()> {
                 tensors.into_values().next().unwrap()
             }
         };
-        if image.rank() == 4 {
+        let image = if image.rank() == 4 {
             image.get(0)?
         } else {
             image
-        }
+        };
+        let (_c, h, w) = image.dims3()?;
+        (image, h, w)
     } else {
-        candle_examples::load_image(args.image, Some(model_sam::IMAGE_SIZE))?.to_device(&device)?
+        let (image, h, w) = candle_examples::load_image(args.image, Some(model_sam::IMAGE_SIZE))?;
+        (image.to_device(&device)?, h, w)
     };
     println!("loaded image {image:?}");
 
@@ -142,19 +153,30 @@ pub fn main() -> anyhow::Result<()> {
     let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
     let sam = model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)?; // sam_vit_b
 
-    let (mask, iou_predictions) = sam.forward(&image, false)?;
-    println!("mask:\n{mask}");
-    println!("iou_predictions: {iou_predictions:?}");
+    if args.generate_masks {
+        // Default options similar to the Python version.
+        sam.generate_masks(
+            &image,
+            /* points_per_side */ 32,
+            /* crop_n_layer */ 0,
+            /* crop_overlap_ratio */ 512. / 1500.,
+            /* crop_n_points_downscale_factor */ 1,
+        )?
+    } else {
+        let point = args.point_x.zip(args.point_y);
+        let (mask, iou_predictions) = sam.forward(&image, point, false)?;
+        println!("mask:\n{mask}");
+        println!("iou_predictions: {iou_predictions:?}");
 
-    // Save the mask as an image.
-    let mask = mask.ge(&mask.zeros_like()?)?;
-    let mask = (mask * 255.)?.squeeze(0)?;
-    let (_one, h, w) = mask.dims3()?;
-    let mask = mask.expand((3, h, w))?;
-    candle_examples::save_image(&mask, "sam_mask.png")?;
+        // Save the mask as an image.
+        let mask = (mask.ge(&mask.zeros_like()?)? * 255.)?;
+        let (_one, h, w) = mask.dims3()?;
+        let mask = mask.expand((3, h, w))?;
+        candle_examples::save_image_resize(&mask, "sam_mask.png", initial_h, initial_w)?;
 
-    let image = sam.preprocess(&image)?;
-    let image = sam.unpreprocess(&image)?.to_dtype(DType::U8)?;
-    candle_examples::save_image(&image, "sam_input_scaled.png")?;
+        let image = sam.preprocess(&image)?;
+        let image = sam.unpreprocess(&image)?.to_dtype(DType::U8)?;
+        candle_examples::save_image(&image, "sam_input_scaled.png")?;
+    }
     Ok(())
 }
diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
index 79e52d47..f1b76e23 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -1,4 +1,4 @@
-use candle::{DType, IndexOp, Result, Tensor, D};
+use candle::{DType, IndexOp, Result, Tensor};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
 
 #[derive(Debug)]
@@ -37,7 +37,6 @@ struct Attention {
     proj: Linear,
     num_heads: usize,
     scale: f64,
-    use_rel_pos: bool,
     rel_pos_hw: Option<(Tensor, Tensor)>,
 }
 
@@ -66,7 +65,6 @@ impl Attention {
             proj,
             num_heads,
             scale,
-            use_rel_pos,
             rel_pos_hw,
         })
     }
@@ -272,7 +270,6 @@ impl Module for Block {
 
 #[derive(Debug)]
 pub struct ImageEncoderViT {
-    img_size: usize,
     patch_embed: PatchEmbed,
     blocks: Vec<Block>,
     neck_conv1: candle_nn::Conv2d,
@@ -350,7 +347,6 @@ impl ImageEncoderViT {
             None
         };
         Ok(Self {
-            img_size,
             patch_embed,
             blocks,
             neck_conv1,
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
index acbfeeea..598af1f6 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -1,4 +1,4 @@
-use candle::{DType, IndexOp, Result, Tensor, D};
+use candle::{IndexOp, Result, Tensor};
 use candle_nn::{Linear, Module, VarBuilder};
 
 use crate::model_transformer::TwoWayTransformer;
@@ -188,7 +188,7 @@ impl MaskDecoder {
 
         // Expand per-image data in batch direction to be per mask
         let src = repeat_interleave(image_embeddings, tokens.dim(0)?, 0)?;
-        let src = (src + dense_prompt_embeddings)?;
+        let src = src.broadcast_add(dense_prompt_embeddings)?;
         let pos_src = repeat_interleave(image_pe, tokens.dim(0)?, 0)?;
         let (b, c, h, w) = src.dims4()?;
 
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
index e4291ebb..b401a900 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -1,5 +1,5 @@
 use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn::{Linear, Module, VarBuilder};
+use candle_nn::VarBuilder;
 
 #[derive(Debug)]
 struct PostionEmbeddingRandom {
@@ -24,7 +24,6 @@ impl PostionEmbeddingRandom {
 
     fn forward(&self, h: usize, w: usize) -> Result<Tensor> {
         let device = self.positional_encoding_gaussian_matrix.device();
-        let grid = Tensor::ones((h, w), DType::F32, device)?;
         let x_embed = (Tensor::arange(0u32, w as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
         let y_embed = (Tensor::arange(0u32, h as u32, device)?.to_dtype(DType::F32)? + 0.5)?;
         let x_embed = (x_embed / w as f64)?
@@ -157,8 +156,9 @@ impl PromptEncoder {
         let point_embedding = self
             .pe_layer
             .forward_with_coords(&points, self.input_image_size)?;
+        let labels = labels.unsqueeze(2)?.broadcast_as(point_embedding.shape())?;
         let zeros = point_embedding.zeros_like()?;
-        let point_embeddings = labels.lt(&labels.zeros_like()?)?.where_cond(
+        let point_embedding = labels.lt(&labels.zeros_like()?)?.where_cond(
             &self
                 .not_a_point_embed
                 .embeddings()
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-examples/examples/segment-anything/model_sam.rs
index 237163a3..884559af 100644
--- a/candle-examples/examples/segment-anything/model_sam.rs
+++ b/candle-examples/examples/segment-anything/model_sam.rs
@@ -1,5 +1,5 @@
-use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use candle::{DType, IndexOp, Result, Tensor};
+use candle_nn::{Module, VarBuilder};
 
 use crate::model_image_encoder::ImageEncoderViT;
 use crate::model_mask_decoder::MaskDecoder;
@@ -70,12 +70,30 @@ impl Sam {
         })
     }
 
-    pub fn forward(&self, img: &Tensor, multimask_output: bool) -> Result<(Tensor, Tensor)> {
+    pub fn forward(
+        &self,
+        img: &Tensor,
+        point: Option<(f64, f64)>,
+        multimask_output: bool,
+    ) -> Result<(Tensor, Tensor)> {
+        let (_c, original_h, original_w) = img.dims3()?;
         let img = self.preprocess(img)?.unsqueeze(0)?;
         let img_embeddings = self.image_encoder.forward(&img)?;
         let image_pe = self.prompt_encoder.get_dense_pe()?;
+        let points = match point {
+            None => None,
+            Some((x, y)) => {
+                let points = Tensor::new(
+                    &[[[x as f32 * original_w as f32, y as f32 * original_h as f32]]],
+                    img.device(),
+                )?;
+                let labels = Tensor::ones((1, 1), DType::F32, img.device())?;
+                Some((points, labels))
+            }
+        };
+        let points = points.as_ref().map(|(x, y)| (x, y));
         let (sparse_prompt_embeddings, dense_prompt_embeddings) =
-            self.prompt_encoder.forward(None, None, None)?;
+            self.prompt_encoder.forward(points, None, None)?;
         let (low_res_mask, iou_predictions) = self.mask_decoder.forward(
             &img_embeddings,
             &image_pe,
@@ -83,8 +101,11 @@ impl Sam {
             &dense_prompt_embeddings,
             multimask_output,
         )?;
-        // TODO: post-processing.
-        Ok((low_res_mask, iou_predictions))
+        let mask = low_res_mask
+            .upsample_nearest2d(IMAGE_SIZE, IMAGE_SIZE)?
+            .get(0)?
+            .i((.., ..original_h, ..original_w))?;
+        Ok((mask, iou_predictions))
     }
 
     pub fn unpreprocess(&self, img: &Tensor) -> Result<Tensor> {
@@ -96,7 +117,7 @@ impl Sam {
     }
 
     pub fn preprocess(&self, img: &Tensor) -> Result<Tensor> {
-        let (c, h, w) = img.dims3()?;
+        let (_c, h, w) = img.dims3()?;
         let img = img
             .to_dtype(DType::F32)?
             .broadcast_sub(&self.pixel_mean)?
@@ -107,4 +128,150 @@ impl Sam {
         let img = img.pad_with_zeros(1, 0, IMAGE_SIZE - h)?;
         img.pad_with_zeros(2, 0, IMAGE_SIZE - w)
     }
+
+    fn process_crop(&self, img: &Tensor, cb: CropBox, point_grids: &[(f64, f64)]) -> Result<()> {
+        // Crop the image and calculate embeddings.
+        let img = img.i((.., cb.y0..cb.y1, cb.x0..cb.x1))?;
+        let img = self.preprocess(&img)?.unsqueeze(0)?;
+        let img_embeddings = self.image_encoder.forward(&img)?;
+
+        let crop_w = cb.x1 - cb.x0;
+        let crop_h = cb.y1 - cb.y0;
+
+        // Generate masks for this crop.
+        let image_pe = self.prompt_encoder.get_dense_pe()?;
+        let points = point_grids
+            .iter()
+            .map(|&(x, y)| vec![x as f32 * crop_w as f32, y as f32 * crop_h as f32])
+            .collect::<Vec<_>>();
+        for points in points.chunks(64) {
+            let points_len = points.len();
+            let in_points = Tensor::new(points.to_vec(), img.device())?.unsqueeze(1)?;
+            let in_labels = Tensor::ones((points_len, 1), DType::F32, img.device())?;
+            let (sparse_prompt_embeddings, dense_prompt_embeddings) =
+                self.prompt_encoder
+                    .forward(Some((&in_points, &in_labels)), None, None)?;
+            let (_low_res_mask, iou_predictions) = self.mask_decoder.forward(
+                &img_embeddings,
+                &image_pe,
+                &sparse_prompt_embeddings,
+                &dense_prompt_embeddings,
+                /* multimask_output */ true,
+            )?;
+
+            println!("{cb:?} {iou_predictions}");
+        }
+
+        // Remove duplicates within this crop.
+
+        // Return to the original image frame.
+        Ok(())
+    }
+
+    pub fn generate_masks(
+        &self,
+        img: &Tensor,
+        points_per_side: usize,
+        crop_n_layer: usize,
+        crop_overlap_ratio: f64,
+        crop_n_points_downscale_factor: usize,
+    ) -> Result<()> {
+        let (_c, h, w) = img.dims3()?;
+        let point_grids = build_all_layer_point_grids(
+            points_per_side,
+            crop_n_layer,
+            crop_n_points_downscale_factor,
+        );
+        let crop_boxes = generate_crop_boxes((h, w), crop_n_layer, crop_overlap_ratio);
+        for crop_box in crop_boxes.into_iter() {
+            let layer_idx = crop_box.layer_idx;
+            self.process_crop(img, crop_box, &point_grids[layer_idx])?
+        }
+        // TODO: remove duplicates
+        Ok(())
+    }
+}
+
+#[derive(Debug)]
+struct CropBox {
+    x0: usize,
+    y0: usize,
+    x1: usize,
+    y1: usize,
+    layer_idx: usize,
+}
+
+impl CropBox {
+    fn new(x0: usize, y0: usize, x1: usize, y1: usize, layer_idx: usize) -> Self {
+        Self {
+            x0,
+            y0,
+            x1,
+            y1,
+            layer_idx,
+        }
+    }
+}
+
+fn generate_crop_boxes(
+    (im_h, im_w): (usize, usize),
+    n_layers: usize,
+    overlap_ratio: f64,
+) -> Vec<CropBox> {
+    fn crop_len(orig_len: usize, n_crops: usize, overlap: usize) -> usize {
+        f64::ceil((overlap * (n_crops - 1) + orig_len) as f64 / n_crops as f64) as usize
+    }
+
+    let short_side = usize::min(im_h, im_w);
+
+    let mut crop_boxes = Vec::new();
+
+    // Original image.
+    crop_boxes.push(CropBox::new(0, 0, im_w, im_h, 0));
+
+    for layer_idx in 1..=n_layers {
+        let n_crops_per_side = 1 << layer_idx;
+        let overlap = (overlap_ratio * short_side as f64 * 2. / n_crops_per_side as f64) as usize;
+        let crop_w = crop_len(im_w, n_crops_per_side, overlap);
+        let crop_h = crop_len(im_w, n_crops_per_side, overlap);
+
+        for i_x in 0..n_crops_per_side {
+            let x0 = (crop_w - overlap) * i_x;
+            for i_y in 0..n_crops_per_side {
+                let y0 = (crop_h - overlap) * i_y;
+                let x1 = usize::min(im_w, x0 + crop_w);
+                let y1 = usize::min(im_h, y0 + crop_h);
+                crop_boxes.push(CropBox::new(x0, y0, x1, y1, layer_idx));
+            }
+        }
+    }
+
+    crop_boxes
+}
+
+// Generates a 2D grid of points evenly spaced in [0,1]x[0,1].
+fn build_point_grid(n_per_side: usize) -> Vec<(f64, f64)> {
+    let offset = 1f64 / (2 * n_per_side) as f64;
+    let mut points = Vec::with_capacity(n_per_side * n_per_side);
+    for i_x in 0..n_per_side {
+        let x = offset + i_x as f64 / n_per_side as f64;
+        for i_y in 0..n_per_side {
+            let y = offset + i_y as f64 / n_per_side as f64;
+            points.push((x, y))
+        }
+    }
+    points
+}
+
+fn build_all_layer_point_grids(
+    n_per_side: usize,
+    n_layers: usize,
+    scale_per_layer: usize,
+) -> Vec<Vec<(f64, f64)>> {
+    let mut points_by_layer = Vec::with_capacity(n_layers + 1);
+    for i in 0..=n_layers {
+        let n_points = n_per_side / scale_per_layer.pow(i as u32);
+        points_by_layer.push(build_point_grid(n_points))
+    }
+    points_by_layer
 }
diff --git a/candle-examples/examples/segment-anything/model_transformer.rs b/candle-examples/examples/segment-anything/model_transformer.rs
index 044dce9b..e4de27cb 100644
--- a/candle-examples/examples/segment-anything/model_transformer.rs
+++ b/candle-examples/examples/segment-anything/model_transformer.rs
@@ -1,4 +1,4 @@
-use candle::{DType, IndexOp, Result, Tensor, D};
+use candle::{Result, Tensor};
 use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
 
 #[derive(Debug)]
@@ -7,7 +7,6 @@ struct Attention {
     k_proj: Linear,
     v_proj: Linear,
     out_proj: Linear,
-    internal_dim: usize,
     num_heads: usize,
 }
 
@@ -28,7 +27,6 @@ impl Attention {
             k_proj,
             v_proj,
             out_proj,
-            internal_dim,
             num_heads,
         })
     }
@@ -85,7 +83,6 @@ impl TwoWayAttentionBlock {
         skip_first_layer_pe: bool,
         vb: VarBuilder,
     ) -> Result<Self> {
-        let self_attn = Attention::new(embedding_dim, num_heads, 1, vb.pp("self_attn"))?;
         let norm1 = layer_norm(embedding_dim, 1e-5, vb.pp("norm1"))?;
         let norm2 = layer_norm(embedding_dim, 1e-5, vb.pp("norm2"))?;
         let norm3 = layer_norm(embedding_dim, 1e-5, vb.pp("norm3"))?;
@@ -204,7 +201,6 @@ impl TwoWayTransformer {
         image_pe: &Tensor,
         point_embedding: &Tensor,
     ) -> Result<(Tensor, Tensor)> {
-        let (bs, c, h, w) = image_embedding.dims4()?;
         let image_embedding = image_embedding.flatten_from(2)?.permute((0, 2, 1))?;
         let image_pe = image_pe.flatten_from(2)?.permute((0, 2, 1))?;
 
diff --git a/candle-examples/src/lib.rs b/candle-examples/src/lib.rs
index 66cd2f99..c14b2d6b 100644
--- a/candle-examples/src/lib.rs
+++ b/candle-examples/src/lib.rs
@@ -19,10 +19,11 @@ pub fn device(cpu: bool) -> Result<Device> {
 pub fn load_image<P: AsRef<std::path::Path>>(
     p: P,
     resize_longest: Option<usize>,
-) -> Result<Tensor> {
+) -> Result<(Tensor, usize, usize)> {
     let img = image::io::Reader::open(p)?
         .decode()
         .map_err(candle::Error::wrap)?;
+    let (initial_h, initial_w) = (img.height() as usize, img.width() as usize);
     let img = match resize_longest {
         None => img,
         Some(resize_longest) => {
@@ -41,7 +42,8 @@ pub fn load_image<P: AsRef<std::path::Path>>(
     let (height, width) = (img.height() as usize, img.width() as usize);
     let img = img.to_rgb8();
     let data = img.into_raw();
-    Tensor::from_vec(data, (height, width, 3), &Device::Cpu)?.permute((2, 0, 1))
+    let data = Tensor::from_vec(data, (height, width, 3), &Device::Cpu)?.permute((2, 0, 1))?;
+    Ok((data, initial_h, initial_w))
 }
 
 pub fn load_image_and_resize<P: AsRef<std::path::Path>>(
@@ -80,3 +82,27 @@ pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<()> {
     image.save(p).map_err(candle::Error::wrap)?;
     Ok(())
 }
+
+pub fn save_image_resize<P: AsRef<std::path::Path>>(
+    img: &Tensor,
+    p: P,
+    h: usize,
+    w: usize,
+) -> Result<()> {
+    let p = p.as_ref();
+    let (channel, height, width) = img.dims3()?;
+    if channel != 3 {
+        candle::bail!("save_image expects an input of shape (3, height, width)")
+    }
+    let img = img.permute((1, 2, 0))?.flatten_all()?;
+    let pixels = img.to_vec1::<u8>()?;
+    let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
+        match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
+            Some(image) => image,
+            None => candle::bail!("error saving image {p:?}"),
+        };
+    let image = image::DynamicImage::from(image);
+    let image = image.resize_to_fill(w as u32, h as u32, image::imageops::FilterType::CatmullRom);
+    image.save(p).map_err(candle::Error::wrap)?;
+    Ok(())
+}

From 98172d46fa866ecaad2afd52777e9ac9a5f15b52 Mon Sep 17 00:00:00 2001
From: zmlcc <zmlcc@outlook.com>
Date: Fri, 8 Sep 2023 20:29:40 +0800
Subject: [PATCH 037/150] Fix some errors about BlockQ8_1 (#776)

* use int8 type instead of uint8 for BlockQ8_1.qs

The uint8 type of BlockQ8_1.qs causes great loss for negative weights
Ref: https://github.com/ggerganov/llama.cpp/blob/ebc96086af49fe70108cafcea6ab4bebd658a41a/ggml.c#L904

Signed-off-by: Zhang Miaolei <zmlcc@outlook.com>

* fix sum error in vec_dot of BlockQ4_1

Ref: https://github.com/ggerganov/llama.cpp/blob/ebc96086af49fe70108cafcea6ab4bebd658a41a/ggml.c#L2840

Signed-off-by: Zhang Miaolei <zmlcc@outlook.com>

* fix sum error in vec_dot of BlockQ5_1

Ref: https://github.com/ggerganov/llama.cpp/blob/ebc96086af49fe70108cafcea6ab4bebd658a41a/ggml.c#L3490

Signed-off-by: Zhang Miaolei <zmlcc@outlook.com>

---------

Signed-off-by: Zhang Miaolei <zmlcc@outlook.com>
---
 candle-core/src/quantized/k_quants.rs | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/candle-core/src/quantized/k_quants.rs b/candle-core/src/quantized/k_quants.rs
index 65fd6a6e..a0fe455c 100644
--- a/candle-core/src/quantized/k_quants.rs
+++ b/candle-core/src/quantized/k_quants.rs
@@ -85,7 +85,7 @@ const _: () = assert!(std::mem::size_of::<BlockQ8_0>() == 34);
 pub struct BlockQ8_1 {
     pub(crate) d: f16,
     pub(crate) s: f16,
-    pub(crate) qs: [u8; QK8_1],
+    pub(crate) qs: [i8; QK8_1],
 }
 const _: () = assert!(std::mem::size_of::<BlockQ8_1>() == 36);
 
@@ -278,6 +278,7 @@ impl GgmlType for BlockQ4_1 {
             }
 
             sumf += sumi as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
+                + f16::to_f32(xs.m) * f16::to_f32(ys.s)
         }
         Ok(sumf)
     }
@@ -471,6 +472,7 @@ impl GgmlType for BlockQ5_1 {
             }
 
             sumf += sumi as f32 * f16::to_f32(xs.d) * f16::to_f32(ys.d)
+                + f16::to_f32(xs.m) * f16::to_f32(ys.s)
         }
         Ok(sumf)
     }
@@ -652,8 +654,8 @@ impl GgmlType for BlockQ8_1 {
             for j in 0..Self::BLCK_SIZE / 2 {
                 let v0 = xs[j] * id;
                 let v1 = xs[j + Self::BLCK_SIZE / 2] * id;
-                ys.qs[j] = f32::round(v0) as u8;
-                ys.qs[j + Self::BLCK_SIZE / 2] = f32::round(v1) as u8;
+                ys.qs[j] = f32::round(v0) as i8;
+                ys.qs[j + Self::BLCK_SIZE / 2] = f32::round(v1) as i8;
                 sum += ys.qs[j] as i32 + ys.qs[j + Self::BLCK_SIZE / 2] as i32;
             }
             ys.s = f16::from_f32(sum as f32) * ys.d;

From e5703d2f56ce24652e7ae85dc74484681e4dbcb9 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 14:04:34 +0100
Subject: [PATCH 038/150] Draw the mask on a merged image. (#775)

* Draw the mask on a merged image.

* Clippy fix.

* Enable the target point by default.

* Add to the readme.
---
 README.md                                     |  3 +
 .../examples/segment-anything/main.rs         | 59 ++++++++++++++++---
 2 files changed, 53 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 076f2363..9e5f938a 100644
--- a/README.md
+++ b/README.md
@@ -64,6 +64,8 @@ Check out our [examples](./candle-examples/examples/):
 - [yolo-v3](./candle-examples/examples/yolo-v3/) and
   [yolo-v8](./candle-examples/examples/yolo-v8/): object detection and pose
   estimation models.
+  [segment-anything](./candle-examples/examples/segment-anything/): image
+  segmentation model with prompt.
 Run them using the following commands:
 ```
 cargo run --example whisper --release
@@ -76,6 +78,7 @@ cargo run --example dinov2 --release -- --image path/to/myinput.jpg
 cargo run --example quantized --release
 cargo run --example yolo-v3 --release -- myimage.jpg
 cargo run --example yolo-v8 --release -- myimage.jpg # for pose estimation, add --task pose 
+cargo run --example segment-anything --release -- --image myimage.jpg
 ```
 
 In order to use **CUDA** add `--features cuda` to the example command line. If
diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 0f0c0482..c5095c0e 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -104,11 +104,11 @@ struct Args {
     #[arg(long)]
     generate_masks: bool,
 
-    #[arg(long)]
-    point_x: Option<f64>,
+    #[arg(long, default_value_t = 0.5)]
+    point_x: f64,
 
-    #[arg(long)]
-    point_y: Option<f64>,
+    #[arg(long, default_value_t = 0.5)]
+    point_y: f64,
 }
 
 pub fn main() -> anyhow::Result<()> {
@@ -135,7 +135,7 @@ pub fn main() -> anyhow::Result<()> {
         let (_c, h, w) = image.dims3()?;
         (image, h, w)
     } else {
-        let (image, h, w) = candle_examples::load_image(args.image, Some(model_sam::IMAGE_SIZE))?;
+        let (image, h, w) = candle_examples::load_image(&args.image, Some(model_sam::IMAGE_SIZE))?;
         (image.to_device(&device)?, h, w)
     };
     println!("loaded image {image:?}");
@@ -163,7 +163,7 @@ pub fn main() -> anyhow::Result<()> {
             /* crop_n_points_downscale_factor */ 1,
         )?
     } else {
-        let point = args.point_x.zip(args.point_y);
+        let point = Some((args.point_x, args.point_y));
         let (mask, iou_predictions) = sam.forward(&image, point, false)?;
         println!("mask:\n{mask}");
         println!("iou_predictions: {iou_predictions:?}");
@@ -174,9 +174,50 @@ pub fn main() -> anyhow::Result<()> {
         let mask = mask.expand((3, h, w))?;
         candle_examples::save_image_resize(&mask, "sam_mask.png", initial_h, initial_w)?;
 
-        let image = sam.preprocess(&image)?;
-        let image = sam.unpreprocess(&image)?.to_dtype(DType::U8)?;
-        candle_examples::save_image(&image, "sam_input_scaled.png")?;
+        if !args.image.ends_with(".safetensors") {
+            let mut img = image::io::Reader::open(&args.image)?
+                .decode()
+                .map_err(candle::Error::wrap)?;
+            let mask_pixels = mask.permute((1, 2, 0))?.flatten_all()?.to_vec1::<u8>()?;
+            let mask_img: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
+                match image::ImageBuffer::from_raw(w as u32, h as u32, mask_pixels) {
+                    Some(image) => image,
+                    None => anyhow::bail!("error saving merged image"),
+                };
+            let mask_img = image::DynamicImage::from(mask_img).resize_to_fill(
+                img.width(),
+                img.height(),
+                image::imageops::FilterType::CatmullRom,
+            );
+            for x in 0..img.width() {
+                for y in 0..img.height() {
+                    let mask_p = imageproc::drawing::Canvas::get_pixel(&mask_img, x, y);
+                    if mask_p.0[0] > 100 {
+                        let mut img_p = imageproc::drawing::Canvas::get_pixel(&img, x, y);
+                        img_p.0[2] = 255 - (255 - img_p.0[2]) / 2;
+                        img_p.0[1] /= 2;
+                        img_p.0[0] /= 2;
+                        imageproc::drawing::Canvas::draw_pixel(&mut img, x, y, img_p)
+                    }
+                }
+            }
+            match point {
+                Some((x, y)) => {
+                    let (x, y) = (
+                        (x * img.width() as f64) as i32,
+                        (y * img.height() as f64) as i32,
+                    );
+                    imageproc::drawing::draw_filled_circle(
+                        &img,
+                        (x, y),
+                        3,
+                        image::Rgba([255, 0, 0, 200]),
+                    )
+                    .save("sam_merged.jpg")?
+                }
+                None => img.save("sam_merged.jpg")?,
+            };
+        }
     }
     Ok(())
 }

From 158ff3c609b22ed998dea5283738cc1ed13aa592 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 15:31:29 +0100
Subject: [PATCH 039/150] Add tracing to segment-anything (#777)

* Tracing support for segment-anything.

* More tracing.

* Handle the empty slice case.
---
 candle-core/src/cuda_backend.rs               |  3 ++
 .../examples/segment-anything/main.rs         | 47 ++++++++++++++++---
 .../segment-anything/model_image_encoder.rs   | 39 ++++++++++++---
 .../segment-anything/model_mask_decoder.rs    | 12 ++++-
 .../segment-anything/model_prompt_encoder.rs  |  4 ++
 candle-examples/examples/whisper/main.rs      |  1 -
 6 files changed, 90 insertions(+), 16 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 2180be5e..cb00441f 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -1889,6 +1889,9 @@ impl BackendStorage for CudaStorage {
         let src_shape = src_l.shape();
         let dims = src_shape.dims();
         let el_count = src_shape.elem_count();
+        if el_count == 0 {
+            return Ok(());
+        }
         let cfg = LaunchConfig::for_num_elems(el_count as u32);
         let dev = &self.device;
         let ds = dev.htod_copy([dims, src_l.stride()].concat()).w()?;
diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index c5095c0e..a749ba2a 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -14,15 +14,17 @@ pub mod model_sam;
 pub mod model_transformer;
 
 use candle::{DType, Result, Tensor};
-use candle_nn::{Linear, Module, VarBuilder};
+use candle_nn::{Module, VarBuilder};
 use clap::Parser;
 
 pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
-    if bias {
-        candle_nn::linear(in_dim, out_dim, vb)
+    let inner = if bias {
+        candle_nn::linear(in_dim, out_dim, vb)?
     } else {
-        candle_nn::linear_no_bias(in_dim, out_dim, vb)
-    }
+        candle_nn::linear_no_bias(in_dim, out_dim, vb)?
+    };
+    let span = tracing::span!(tracing::Level::TRACE, "linear");
+    Ok(Linear { inner, span })
 }
 
 #[derive(Debug)]
@@ -62,6 +64,7 @@ pub struct MlpBlock {
     lin1: Linear,
     lin2: Linear,
     activation: candle_nn::Activation,
+    span: tracing::Span,
 }
 
 impl MlpBlock {
@@ -71,24 +74,40 @@ impl MlpBlock {
         activation: candle_nn::Activation,
         vb: VarBuilder,
     ) -> Result<Self> {
-        let lin1 = candle_nn::linear(embedding_dim, mlp_dim, vb.pp("lin1"))?;
-        let lin2 = candle_nn::linear(mlp_dim, embedding_dim, vb.pp("lin2"))?;
+        let lin1 = linear(vb.pp("lin1"), embedding_dim, mlp_dim, true)?;
+        let lin2 = linear(vb.pp("lin2"), mlp_dim, embedding_dim, true)?;
+        let span = tracing::span!(tracing::Level::TRACE, "mlp-block");
         Ok(Self {
             lin1,
             lin2,
             activation,
+            span,
         })
     }
 }
 
 impl Module for MlpBlock {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         xs.apply(&self.lin1)?
             .apply(&self.activation)?
             .apply(&self.lin2)
     }
 }
 
+#[derive(Debug)]
+pub struct Linear {
+    inner: candle_nn::Linear,
+    span: tracing::Span,
+}
+
+impl Module for Linear {
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(x)
+    }
+}
+
 #[derive(Parser)]
 struct Args {
     #[arg(long)]
@@ -109,10 +128,24 @@ struct Args {
 
     #[arg(long, default_value_t = 0.5)]
     point_y: f64,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
 }
 
 pub fn main() -> anyhow::Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
     let args = Args::parse();
+    let _guard = if args.tracing {
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
 
     let device = candle_examples::device(args.cpu)?;
 
diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
index f1b76e23..f997170d 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -1,9 +1,10 @@
 use candle::{DType, IndexOp, Result, Tensor};
-use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use candle_nn::{layer_norm, LayerNorm, Module, VarBuilder};
 
 #[derive(Debug)]
 struct PatchEmbed {
     proj: candle_nn::Conv2d,
+    span: tracing::Span,
 }
 
 impl PatchEmbed {
@@ -21,23 +22,28 @@ impl PatchEmbed {
             ..Default::default()
         };
         let proj = candle_nn::conv2d(in_chans, embed_dim, k_size, cfg, vb.pp("proj"))?;
-        Ok(Self { proj })
+        let span = tracing::span!(tracing::Level::TRACE, "patch-embed");
+        Ok(Self { proj, span })
     }
 }
 
 impl Module for PatchEmbed {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         xs.apply(&self.proj)?.permute((0, 2, 3, 1))
     }
 }
 
 #[derive(Debug)]
 struct Attention {
-    qkv: Linear,
-    proj: Linear,
+    qkv: crate::Linear,
+    proj: crate::Linear,
     num_heads: usize,
     scale: f64,
     rel_pos_hw: Option<(Tensor, Tensor)>,
+    span: tracing::Span,
+    span_rel_pos: tracing::Span,
+    span_softmax: tracing::Span,
 }
 
 impl Attention {
@@ -49,6 +55,9 @@ impl Attention {
         input_size: (usize, usize),
         vb: VarBuilder,
     ) -> Result<Self> {
+        let span = tracing::span!(tracing::Level::TRACE, "attention");
+        let span_rel_pos = tracing::span!(tracing::Level::TRACE, "attn-rel-pos");
+        let span_softmax = tracing::span!(tracing::Level::TRACE, "attn-sm");
         let qkv = crate::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
         let proj = crate::linear(vb.pp("proj"), dim, dim, true)?;
         let head_dim = dim / num_heads;
@@ -66,6 +75,9 @@ impl Attention {
             num_heads,
             scale,
             rel_pos_hw,
+            span,
+            span_rel_pos,
+            span_softmax,
         })
     }
 
@@ -126,6 +138,7 @@ fn get_rel_pos(q_size: usize, k_size: usize, rel_pos: &Tensor) -> Result<Tensor>
 
 impl Module for Attention {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let (b, h, w, c) = xs.dims4()?;
         let qkv = self
             .qkv
@@ -137,8 +150,14 @@ impl Module for Attention {
         let k = qkv.i(1)?;
         let v = qkv.i(2)?;
         let attn = (&q * self.scale)?.matmul(&k.t()?)?;
-        let attn = self.add_decomposed_rel_pos(attn, &q, (h, w), (h, w))?;
-        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
+        let attn = {
+            let _enter = self.span_rel_pos.enter();
+            self.add_decomposed_rel_pos(attn, &q, (h, w), (h, w))?
+        };
+        let attn = {
+            let _enter = self.span_softmax.enter();
+            candle_nn::ops::softmax_last_dim(&attn)?
+        };
         let attn = attn.matmul(&v)?;
         let attn = attn
             .reshape((b, self.num_heads, h, w, c / self.num_heads))?
@@ -155,6 +174,7 @@ struct Block {
     norm2: LayerNorm,
     mlp: crate::MlpBlock,
     window_size: usize,
+    span: tracing::Span,
 }
 
 impl Block {
@@ -183,12 +203,14 @@ impl Block {
             vb.pp("attn"),
         )?;
         let mlp = crate::MlpBlock::new(dim, dim * 4, candle_nn::Activation::Gelu, vb.pp("mlp"))?;
+        let span = tracing::span!(tracing::Level::TRACE, "ie-block");
         Ok(Self {
             norm1,
             attn,
             norm2,
             mlp,
             window_size,
+            span,
         })
     }
 }
@@ -249,6 +271,7 @@ fn window_unpartition(
 
 impl Module for Block {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let shortcut = xs;
         let xs = self.norm1.forward(xs)?;
         let hw = (xs.dim(1)?, xs.dim(2)?);
@@ -277,6 +300,7 @@ pub struct ImageEncoderViT {
     neck_conv2: candle_nn::Conv2d,
     neck_ln2: crate::LayerNorm2d,
     pos_embed: Option<Tensor>,
+    span: tracing::Span,
 }
 
 impl ImageEncoderViT {
@@ -346,6 +370,7 @@ impl ImageEncoderViT {
         } else {
             None
         };
+        let span = tracing::span!(tracing::Level::TRACE, "image-encoder-vit");
         Ok(Self {
             patch_embed,
             blocks,
@@ -354,12 +379,14 @@ impl ImageEncoderViT {
             neck_conv2,
             neck_ln2,
             pos_embed,
+            span,
         })
     }
 }
 
 impl Module for ImageEncoderViT {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let xs = self.patch_embed.forward(xs)?;
         let mut xs = match &self.pos_embed {
             Some(pos_embed) => (xs + pos_embed)?,
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
index 598af1f6..1f6d62a4 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -1,12 +1,13 @@
 use candle::{IndexOp, Result, Tensor};
-use candle_nn::{Linear, Module, VarBuilder};
+use candle_nn::{Module, VarBuilder};
 
 use crate::model_transformer::TwoWayTransformer;
 
 #[derive(Debug)]
 struct MlpMaskDecoder {
-    layers: Vec<Linear>,
+    layers: Vec<crate::Linear>,
     sigmoid_output: bool,
+    span: tracing::Span,
 }
 
 impl MlpMaskDecoder {
@@ -30,15 +31,18 @@ impl MlpMaskDecoder {
             let layer = crate::linear(vb.pp(i), in_dim, out_dim, true)?;
             layers.push(layer)
         }
+        let span = tracing::span!(tracing::Level::TRACE, "mlp-mask-decoder");
         Ok(Self {
             layers,
             sigmoid_output,
+            span,
         })
     }
 }
 
 impl Module for MlpMaskDecoder {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let mut xs = xs.clone();
         for (i, layer) in self.layers.iter().enumerate() {
             xs = layer.forward(&xs)?;
@@ -65,6 +69,7 @@ pub struct MaskDecoder {
     num_mask_tokens: usize,
     output_hypernetworks_mlps: Vec<MlpMaskDecoder>,
     transformer: TwoWayTransformer,
+    span: tracing::Span,
 }
 
 impl MaskDecoder {
@@ -127,6 +132,7 @@ impl MaskDecoder {
             /* mlp_dim */ 2048,
             vb.pp("transformer"),
         )?;
+        let span = tracing::span!(tracing::Level::TRACE, "mask-decoder");
         Ok(Self {
             iou_token,
             mask_tokens,
@@ -137,6 +143,7 @@ impl MaskDecoder {
             num_mask_tokens,
             output_hypernetworks_mlps,
             transformer,
+            span,
         })
     }
 
@@ -148,6 +155,7 @@ impl MaskDecoder {
         dense_prompt_embeddings: &Tensor,
         multimask_output: bool,
     ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
         let (masks, iou_pred) = self.predict_masks(
             image_embeddings,
             image_pe,
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
index b401a900..40cc6e36 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -64,6 +64,7 @@ pub struct PromptEncoder {
     image_embedding_size: (usize, usize),
     input_image_size: (usize, usize),
     embed_dim: usize,
+    span: tracing::Span,
 }
 
 impl PromptEncoder {
@@ -108,6 +109,7 @@ impl PromptEncoder {
             let emb = candle_nn::embedding(1, embed_dim, vb_e.pp(i))?;
             point_embeddings.push(emb)
         }
+        let span = tracing::span!(tracing::Level::TRACE, "prompt-encoder");
         Ok(Self {
             pe_layer,
             point_embeddings,
@@ -121,6 +123,7 @@ impl PromptEncoder {
             image_embedding_size,
             input_image_size,
             embed_dim,
+            span,
         })
     }
 
@@ -201,6 +204,7 @@ impl PromptEncoder {
         boxes: Option<&Tensor>,
         masks: Option<&Tensor>,
     ) -> Result<(Tensor, Tensor)> {
+        let _enter = self.span.enter();
         let se_points = match points {
             Some((coords, labels)) => Some(self.embed_points(coords, labels, boxes.is_none())?),
             None => None,
diff --git a/candle-examples/examples/whisper/main.rs b/candle-examples/examples/whisper/main.rs
index 5dd8ee20..dbe9cc8d 100644
--- a/candle-examples/examples/whisper/main.rs
+++ b/candle-examples/examples/whisper/main.rs
@@ -431,7 +431,6 @@ fn main() -> Result<()> {
 
     let args = Args::parse();
     let _guard = if args.tracing {
-        println!("tracing...");
         let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
         tracing_subscriber::registry().with(chrome_layer).init();
         Some(guard)

From 0906acab9186fbb14a2268e12dd66c13b0877f3e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 19:11:34 +0100
Subject: [PATCH 040/150] Automatic mask generation (#779)

* A few more contiguous fixes for cuda.

* Mask generation.

* Generic bbox.

* Generate all the masks.
---
 .../examples/segment-anything/main.rs         |  16 ++-
 .../segment-anything/model_mask_decoder.rs    |   2 +-
 .../examples/segment-anything/model_sam.rs    | 103 ++++++++++++++++--
 .../segment-anything/model_transformer.rs     |   6 +-
 candle-examples/examples/yolo-v3/main.rs      |   4 +-
 candle-examples/examples/yolo-v8/main.rs      |  12 +-
 candle-examples/src/object_detection.rs       |   8 +-
 7 files changed, 125 insertions(+), 26 deletions(-)

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index a749ba2a..4627248c 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -188,13 +188,25 @@ pub fn main() -> anyhow::Result<()> {
 
     if args.generate_masks {
         // Default options similar to the Python version.
-        sam.generate_masks(
+        let bboxes = sam.generate_masks(
             &image,
             /* points_per_side */ 32,
             /* crop_n_layer */ 0,
             /* crop_overlap_ratio */ 512. / 1500.,
             /* crop_n_points_downscale_factor */ 1,
-        )?
+        )?;
+        for (idx, bbox) in bboxes.iter().enumerate() {
+            println!("{bbox:?}");
+            let mask = (&bbox.data.to_dtype(DType::U8)? * 255.)?;
+            let (h, w) = mask.dims2()?;
+            let mask = mask.broadcast_as((3, h, w))?;
+            candle_examples::save_image_resize(
+                &mask,
+                format!("sam_mask{idx}.png"),
+                initial_h,
+                initial_w,
+            )?;
+        }
     } else {
         let point = Some((args.point_x, args.point_y));
         let (mask, iou_predictions) = sam.forward(&image, point, false)?;
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-examples/examples/segment-anything/model_mask_decoder.rs
index 1f6d62a4..c02b44a7 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-examples/examples/segment-anything/model_mask_decoder.rs
@@ -219,7 +219,7 @@ impl MaskDecoder {
             let h = mlp.forward(&mask_tokens_out.i((.., i))?)?;
             hyper_in_list.push(h)
         }
-        let hyper_in = Tensor::stack(hyper_in_list.as_slice(), 1)?;
+        let hyper_in = Tensor::stack(hyper_in_list.as_slice(), 1)?.contiguous()?;
         let (b, c, h, w) = upscaled_embedding.dims4()?;
         let masks = hyper_in.matmul(&upscaled_embedding.reshape((b, c, h * w))?)?;
         let masks = masks.reshape((b, (), h, w))?;
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-examples/examples/segment-anything/model_sam.rs
index 884559af..ade976c1 100644
--- a/candle-examples/examples/segment-anything/model_sam.rs
+++ b/candle-examples/examples/segment-anything/model_sam.rs
@@ -8,6 +8,11 @@ use crate::model_prompt_encoder::PromptEncoder;
 const PROMPT_EMBED_DIM: usize = 256;
 pub const IMAGE_SIZE: usize = 1024;
 const VIT_PATCH_SIZE: usize = 16;
+const PRED_IOU_THRESH: f32 = 0.88;
+const STABILITY_SCORE_OFFSET: f32 = 1.0;
+const STABILITY_SCORE_THRESHOLD: f32 = 0.95;
+const MODEL_MASK_THRESHOLD: f32 = 0.0;
+const CROP_NMS_THRESH: f32 = 0.7;
 
 #[derive(Debug)]
 pub struct Sam {
@@ -129,7 +134,12 @@ impl Sam {
         img.pad_with_zeros(2, 0, IMAGE_SIZE - w)
     }
 
-    fn process_crop(&self, img: &Tensor, cb: CropBox, point_grids: &[(f64, f64)]) -> Result<()> {
+    fn process_crop(
+        &self,
+        img: &Tensor,
+        cb: CropBox,
+        point_grids: &[(f64, f64)],
+    ) -> Result<Vec<candle_examples::object_detection::Bbox<Tensor>>> {
         // Crop the image and calculate embeddings.
         let img = img.i((.., cb.y0..cb.y1, cb.x0..cb.x1))?;
         let img = self.preprocess(&img)?.unsqueeze(0)?;
@@ -144,28 +154,86 @@ impl Sam {
             .iter()
             .map(|&(x, y)| vec![x as f32 * crop_w as f32, y as f32 * crop_h as f32])
             .collect::<Vec<_>>();
+
+        let mut bboxes = Vec::new();
         for points in points.chunks(64) {
+            // Run the model on this batch.
             let points_len = points.len();
             let in_points = Tensor::new(points.to_vec(), img.device())?.unsqueeze(1)?;
             let in_labels = Tensor::ones((points_len, 1), DType::F32, img.device())?;
             let (sparse_prompt_embeddings, dense_prompt_embeddings) =
                 self.prompt_encoder
                     .forward(Some((&in_points, &in_labels)), None, None)?;
-            let (_low_res_mask, iou_predictions) = self.mask_decoder.forward(
+
+            let (low_res_mask, iou_predictions) = self.mask_decoder.forward(
                 &img_embeddings,
                 &image_pe,
                 &sparse_prompt_embeddings,
                 &dense_prompt_embeddings,
                 /* multimask_output */ true,
             )?;
+            let low_res_mask = low_res_mask.flatten(0, 1)?;
+            let iou_predictions = iou_predictions.flatten(0, 1)?.to_vec1::<f32>()?;
+            let dev = low_res_mask.device();
 
-            println!("{cb:?} {iou_predictions}");
+            for (i, iou) in iou_predictions.iter().enumerate() {
+                // Filter by predicted IoU.
+                if *iou < PRED_IOU_THRESH {
+                    continue;
+                }
+                let low_res_mask = low_res_mask.get(i)?;
+
+                // Calculate stability score.
+                let bound = Tensor::new(MODEL_MASK_THRESHOLD + STABILITY_SCORE_OFFSET, dev)?
+                    .broadcast_as(low_res_mask.shape())?;
+                let intersections = low_res_mask
+                    .ge(&bound)?
+                    .to_dtype(DType::F32)?
+                    .sum_all()?
+                    .to_vec0::<f32>()?;
+                let bound = Tensor::new(MODEL_MASK_THRESHOLD - STABILITY_SCORE_OFFSET, dev)?
+                    .broadcast_as(low_res_mask.shape())?;
+                let unions = low_res_mask
+                    .ge(&bound)?
+                    .to_dtype(DType::F32)?
+                    .sum_all()?
+                    .to_vec0::<f32>()?;
+                let stability_score = intersections / unions;
+                if stability_score < STABILITY_SCORE_THRESHOLD {
+                    continue;
+                }
+
+                // Threshold masks and calculate boxes.
+                let low_res_mask = low_res_mask
+                    .ge(&Tensor::new(0f32, dev)?.broadcast_as(low_res_mask.shape())?)?
+                    .to_dtype(DType::U32)?;
+                let low_res_mask_per_x = low_res_mask.sum(0)?.to_vec1::<u32>()?;
+                let low_res_mask_per_y = low_res_mask.sum(1)?.to_vec1::<u32>()?;
+                let min_max_x = min_max_indexes(&low_res_mask_per_x);
+                let min_max_y = min_max_indexes(&low_res_mask_per_y);
+                if let Some(((x0, x1), (y0, y1))) = min_max_x.zip(min_max_y) {
+                    let bbox = candle_examples::object_detection::Bbox {
+                        xmin: x0 as f32,
+                        ymin: y0 as f32,
+                        xmax: x1 as f32,
+                        ymax: y1 as f32,
+                        confidence: *iou,
+                        data: low_res_mask,
+                    };
+                    bboxes.push(bbox);
+                }
+                // TODO:
+                // Filter boxes that touch crop boundaries
+                // Compress to RLE.
+            }
         }
 
+        let mut bboxes = vec![bboxes];
         // Remove duplicates within this crop.
+        candle_examples::object_detection::non_maximum_suppression(&mut bboxes, CROP_NMS_THRESH);
 
-        // Return to the original image frame.
-        Ok(())
+        // TODO: Return to the original image frame.
+        Ok(bboxes.remove(0))
     }
 
     pub fn generate_masks(
@@ -175,7 +243,7 @@ impl Sam {
         crop_n_layer: usize,
         crop_overlap_ratio: f64,
         crop_n_points_downscale_factor: usize,
-    ) -> Result<()> {
+    ) -> Result<Vec<candle_examples::object_detection::Bbox<Tensor>>> {
         let (_c, h, w) = img.dims3()?;
         let point_grids = build_all_layer_point_grids(
             points_per_side,
@@ -183,12 +251,31 @@ impl Sam {
             crop_n_points_downscale_factor,
         );
         let crop_boxes = generate_crop_boxes((h, w), crop_n_layer, crop_overlap_ratio);
+        let mut bboxes = Vec::new();
         for crop_box in crop_boxes.into_iter() {
             let layer_idx = crop_box.layer_idx;
-            self.process_crop(img, crop_box, &point_grids[layer_idx])?
+            let b = self.process_crop(img, crop_box, &point_grids[layer_idx])?;
+            bboxes.extend(b)
         }
         // TODO: remove duplicates
-        Ok(())
+        Ok(bboxes)
+    }
+}
+
+// Return the first and last indexes i for which values[i] > 0
+fn min_max_indexes(values: &[u32]) -> Option<(usize, usize)> {
+    let (mut min_i, mut max_i) = (usize::MAX, usize::MIN);
+    for (i, &s) in values.iter().enumerate() {
+        if s == 0 {
+            continue;
+        }
+        min_i = usize::min(i, min_i);
+        max_i = usize::max(i, max_i);
+    }
+    if max_i < min_i {
+        None
+    } else {
+        Some((min_i, max_i))
     }
 }
 
diff --git a/candle-examples/examples/segment-anything/model_transformer.rs b/candle-examples/examples/segment-anything/model_transformer.rs
index e4de27cb..e12aac08 100644
--- a/candle-examples/examples/segment-anything/model_transformer.rs
+++ b/candle-examples/examples/segment-anything/model_transformer.rs
@@ -45,9 +45,9 @@ impl Attention {
     }
 
     fn forward(&self, q: &Tensor, k: &Tensor, v: &Tensor) -> Result<Tensor> {
-        let q = self.q_proj.forward(q)?;
-        let k = self.k_proj.forward(k)?;
-        let v = self.v_proj.forward(v)?;
+        let q = self.q_proj.forward(&q.contiguous()?)?;
+        let k = self.k_proj.forward(&k.contiguous()?)?;
+        let v = self.v_proj.forward(&v.contiguous()?)?;
 
         let q = self.separate_heads(&q)?;
         let k = self.separate_heads(&k)?;
diff --git a/candle-examples/examples/yolo-v3/main.rs b/candle-examples/examples/yolo-v3/main.rs
index 5e388921..20021b45 100644
--- a/candle-examples/examples/yolo-v3/main.rs
+++ b/candle-examples/examples/yolo-v3/main.rs
@@ -46,7 +46,7 @@ pub fn report(
     let (npreds, pred_size) = pred.dims2()?;
     let nclasses = pred_size - 5;
     // The bounding boxes grouped by (maximum) class index.
-    let mut bboxes: Vec<Vec<Bbox>> = (0..nclasses).map(|_| vec![]).collect();
+    let mut bboxes: Vec<Vec<Bbox<()>>> = (0..nclasses).map(|_| vec![]).collect();
     // Extract the bounding boxes for which confidence is above the threshold.
     for index in 0..npreds {
         let pred = Vec::<f32>::try_from(pred.get(index)?)?;
@@ -65,7 +65,7 @@ pub fn report(
                     xmax: pred[0] + pred[2] / 2.,
                     ymax: pred[1] + pred[3] / 2.,
                     confidence,
-                    keypoints: vec![],
+                    data: (),
                 };
                 bboxes[class_index].push(bbox)
             }
diff --git a/candle-examples/examples/yolo-v8/main.rs b/candle-examples/examples/yolo-v8/main.rs
index d5c5ac1c..2017b5be 100644
--- a/candle-examples/examples/yolo-v8/main.rs
+++ b/candle-examples/examples/yolo-v8/main.rs
@@ -64,7 +64,7 @@ pub fn report_detect(
     let (pred_size, npreds) = pred.dims2()?;
     let nclasses = pred_size - 4;
     // The bounding boxes grouped by (maximum) class index.
-    let mut bboxes: Vec<Vec<Bbox>> = (0..nclasses).map(|_| vec![]).collect();
+    let mut bboxes: Vec<Vec<Bbox<Vec<KeyPoint>>>> = (0..nclasses).map(|_| vec![]).collect();
     // Extract the bounding boxes for which confidence is above the threshold.
     for index in 0..npreds {
         let pred = Vec::<f32>::try_from(pred.i((.., index))?)?;
@@ -83,7 +83,7 @@ pub fn report_detect(
                     xmax: pred[0] + pred[2] / 2.,
                     ymax: pred[1] + pred[3] / 2.,
                     confidence,
-                    keypoints: vec![],
+                    data: vec![],
                 };
                 bboxes[class_index].push(bbox)
             }
@@ -176,7 +176,7 @@ pub fn report_pose(
                 xmax: pred[0] + pred[2] / 2.,
                 ymax: pred[1] + pred[3] / 2.,
                 confidence,
-                keypoints,
+                data: keypoints,
             };
             bboxes.push(bbox)
         }
@@ -204,7 +204,7 @@ pub fn report_pose(
                 image::Rgb([255, 0, 0]),
             );
         }
-        for kp in b.keypoints.iter() {
+        for kp in b.data.iter() {
             if kp.mask < 0.6 {
                 continue;
             }
@@ -219,8 +219,8 @@ pub fn report_pose(
         }
 
         for &(idx1, idx2) in KP_CONNECTIONS.iter() {
-            let kp1 = &b.keypoints[idx1];
-            let kp2 = &b.keypoints[idx2];
+            let kp1 = &b.data[idx1];
+            let kp2 = &b.data[idx2];
             if kp1.mask < 0.6 || kp2.mask < 0.6 {
                 continue;
             }
diff --git a/candle-examples/src/object_detection.rs b/candle-examples/src/object_detection.rs
index c7c60136..ce579316 100644
--- a/candle-examples/src/object_detection.rs
+++ b/candle-examples/src/object_detection.rs
@@ -1,12 +1,12 @@
 /// A bounding box around an object.
 #[derive(Debug, Clone)]
-pub struct Bbox {
+pub struct Bbox<D> {
     pub xmin: f32,
     pub ymin: f32,
     pub xmax: f32,
     pub ymax: f32,
     pub confidence: f32,
-    pub keypoints: Vec<KeyPoint>,
+    pub data: D,
 }
 
 #[derive(Debug, Clone, Copy, PartialEq)]
@@ -17,7 +17,7 @@ pub struct KeyPoint {
 }
 
 /// Intersection over union of two bounding boxes.
-pub fn iou(b1: &Bbox, b2: &Bbox) -> f32 {
+pub fn iou<D>(b1: &Bbox<D>, b2: &Bbox<D>) -> f32 {
     let b1_area = (b1.xmax - b1.xmin + 1.) * (b1.ymax - b1.ymin + 1.);
     let b2_area = (b2.xmax - b2.xmin + 1.) * (b2.ymax - b2.ymin + 1.);
     let i_xmin = b1.xmin.max(b2.xmin);
@@ -28,7 +28,7 @@ pub fn iou(b1: &Bbox, b2: &Bbox) -> f32 {
     i_area / (b1_area + b2_area - i_area)
 }
 
-pub fn non_maximum_suppression(bboxes: &mut [Vec<Bbox>], threshold: f32) {
+pub fn non_maximum_suppression<D>(bboxes: &mut [Vec<Bbox<D>>], threshold: f32) {
     // Perform non-maximum suppression.
     for bboxes_for_class in bboxes.iter_mut() {
         bboxes_for_class.sort_by(|b1, b2| b2.confidence.partial_cmp(&b1.confidence).unwrap());

From acf8f10ae17d7f472dc1a634fbd7358a79d7b4d4 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 20:13:29 +0100
Subject: [PATCH 041/150] Get the comparison operation to work on scalar
 values. (#780)

* Get the comparison operation to work on scalar values.

* Add some time measurement.
---
 candle-core/src/lib.rs                        |  1 +
 candle-core/src/scalar.rs                     | 23 ++++++++++++++++++
 candle-core/src/tensor.rs                     | 24 ++++++++++++-------
 .../examples/segment-anything/main.rs         |  7 +++++-
 .../segment-anything/model_prompt_encoder.rs  |  6 ++---
 5 files changed, 49 insertions(+), 12 deletions(-)
 create mode 100644 candle-core/src/scalar.rs

diff --git a/candle-core/src/lib.rs b/candle-core/src/lib.rs
index a0347416..3504b0a6 100644
--- a/candle-core/src/lib.rs
+++ b/candle-core/src/lib.rs
@@ -59,6 +59,7 @@ mod op;
 pub mod pickle;
 pub mod quantized;
 pub mod safetensors;
+pub mod scalar;
 pub mod shape;
 mod storage;
 mod strided_index;
diff --git a/candle-core/src/scalar.rs b/candle-core/src/scalar.rs
new file mode 100644
index 00000000..43e1f4c8
--- /dev/null
+++ b/candle-core/src/scalar.rs
@@ -0,0 +1,23 @@
+use crate::{Result, Tensor, WithDType};
+
+pub enum TensorScalar {
+    Tensor(Tensor),
+    Scalar(Tensor),
+}
+
+pub trait TensorOrScalar {
+    fn to_tensor_scalar(self) -> Result<TensorScalar>;
+}
+
+impl TensorOrScalar for &Tensor {
+    fn to_tensor_scalar(self) -> Result<TensorScalar> {
+        Ok(TensorScalar::Tensor(self.clone()))
+    }
+}
+
+impl<T: WithDType> TensorOrScalar for T {
+    fn to_tensor_scalar(self) -> Result<TensorScalar> {
+        let scalar = Tensor::new(self, &crate::Device::Cpu)?;
+        Ok(TensorScalar::Scalar(scalar))
+    }
+}
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 6bb3d740..8ad9322b 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -4,6 +4,7 @@ use crate::backend::{BackendDevice, BackendStorage};
 use crate::op::{
     BackpropOp, BinaryOp, CmpOp, CustomOp1, CustomOp2, CustomOp3, Op, ReduceOp, UnaryOp,
 };
+use crate::scalar::TensorOrScalar;
 use crate::shape::{Dim, Dims};
 use crate::{storage::Storage, DType, Device, Error, Layout, Result, Shape};
 use std::sync::{Arc, RwLock};
@@ -776,8 +777,15 @@ impl Tensor {
     /// comparison operation is specified by the `op` argument.
     ///
     /// The returned tensor has the same shape as the original tensors and uses `u8` elements.
-    pub fn cmp(&self, rhs: &Self, op: CmpOp) -> Result<Self> {
-        let shape = self.same_shape_binary_op(rhs, "cmp")?;
+    pub fn cmp<T: TensorOrScalar>(&self, rhs: T, op: CmpOp) -> Result<Self> {
+        let rhs = match rhs.to_tensor_scalar()? {
+            crate::scalar::TensorScalar::Tensor(rhs) => rhs,
+            crate::scalar::TensorScalar::Scalar(rhs) => rhs
+                .to_dtype(self.dtype())?
+                .to_device(self.device())?
+                .broadcast_as(self.shape())?,
+        };
+        let shape = self.same_shape_binary_op(&rhs, "cmp")?;
         let storage = self
             .storage()
             .cmp(op, &rhs.storage(), self.layout(), rhs.layout())?;
@@ -786,36 +794,36 @@ impl Tensor {
     }
 
     /// Element-wise equality.
-    pub fn eq(&self, rhs: &Self) -> Result<Self> {
+    pub fn eq<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
         self.cmp(rhs, CmpOp::Eq)
     }
 
     /// Element-wise non-equality.
-    pub fn ne(&self, rhs: &Self) -> Result<Self> {
+    pub fn ne<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
         self.cmp(rhs, CmpOp::Ne)
     }
 
     /// Element-wise comparison with lower-than, the returned tensor uses value 1 where `self <
     /// rhs` and 0 otherwise.
-    pub fn lt(&self, rhs: &Self) -> Result<Self> {
+    pub fn lt<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
         self.cmp(rhs, CmpOp::Lt)
     }
 
     /// Element-wise comparison with greater-than, the returned tensor uses value 1 where `self >
     /// rhs` and 0 otherwise.
-    pub fn gt(&self, rhs: &Self) -> Result<Self> {
+    pub fn gt<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
         self.cmp(rhs, CmpOp::Gt)
     }
 
     /// Element-wise comparison with greater-equal, the returned tensor uses value 1 where `self >=
     /// rhs` and 0 otherwise.
-    pub fn ge(&self, rhs: &Self) -> Result<Self> {
+    pub fn ge<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
         self.cmp(rhs, CmpOp::Ge)
     }
 
     /// Element-wise comparison with lower-equal, the returned tensor uses value 1 where `self <=
     /// rhs` and 0 otherwise.
-    pub fn le(&self, rhs: &Self) -> Result<Self> {
+    pub fn le<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
         self.cmp(rhs, CmpOp::Le)
     }
 
diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 4627248c..ce8e3bb4 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -209,12 +209,17 @@ pub fn main() -> anyhow::Result<()> {
         }
     } else {
         let point = Some((args.point_x, args.point_y));
+        let start_time = std::time::Instant::now();
         let (mask, iou_predictions) = sam.forward(&image, point, false)?;
+        println!(
+            "mask generated in {:.2}s",
+            start_time.elapsed().as_secs_f32()
+        );
         println!("mask:\n{mask}");
         println!("iou_predictions: {iou_predictions:?}");
 
         // Save the mask as an image.
-        let mask = (mask.ge(&mask.zeros_like()?)? * 255.)?;
+        let mask = (mask.ge(0f32)? * 255.)?;
         let (_one, h, w) = mask.dims3()?;
         let mask = mask.expand((3, h, w))?;
         candle_examples::save_image_resize(&mask, "sam_mask.png", initial_h, initial_w)?;
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
index 40cc6e36..7bbe8419 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_prompt_encoder.rs
@@ -161,21 +161,21 @@ impl PromptEncoder {
             .forward_with_coords(&points, self.input_image_size)?;
         let labels = labels.unsqueeze(2)?.broadcast_as(point_embedding.shape())?;
         let zeros = point_embedding.zeros_like()?;
-        let point_embedding = labels.lt(&labels.zeros_like()?)?.where_cond(
+        let point_embedding = labels.lt(0f32)?.where_cond(
             &self
                 .not_a_point_embed
                 .embeddings()
                 .broadcast_as(zeros.shape())?,
             &point_embedding,
         )?;
-        let labels0 = labels.eq(&labels.zeros_like()?)?.where_cond(
+        let labels0 = labels.eq(0f32)?.where_cond(
             &self.point_embeddings[0]
                 .embeddings()
                 .broadcast_as(zeros.shape())?,
             &zeros,
         )?;
         let point_embedding = (point_embedding + labels0)?;
-        let labels1 = labels.eq(&labels.ones_like()?)?.where_cond(
+        let labels1 = labels.eq(1f32)?.where_cond(
             &self.point_embeddings[1]
                 .embeddings()
                 .broadcast_as(zeros.shape())?,

From 057f7909bc5544b0dcb0e6d5360288633dbf443f Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 8 Sep 2023 21:58:56 +0100
Subject: [PATCH 042/150] Accelerate support for gelu. (#782)

---
 candle-core/src/accelerate.rs | 32 ++++++++++++++++++++++++++++++++
 candle-core/src/op.rs         | 18 ++++++++++++++++++
 2 files changed, 50 insertions(+)

diff --git a/candle-core/src/accelerate.rs b/candle-core/src/accelerate.rs
index 87e0ee8d..1cb34e19 100644
--- a/candle-core/src/accelerate.rs
+++ b/candle-core/src/accelerate.rs
@@ -370,6 +370,38 @@ pub fn vd_sqr(a: &[f64], y: &mut [f64]) {
     y.iter_mut().zip(a.iter()).for_each(|(y, a)| *y = *a * *a)
 }
 
+#[inline]
+pub fn vs_tanh_inplace(y: &mut [f32]) {
+    unsafe { ffi::vvtanhf(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
+}
+
+#[inline]
+pub fn vd_tanh_inplace(y: &mut [f64]) {
+    unsafe { ffi::vvtanh(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
+}
+
+#[inline]
+pub fn vs_gelu(vs: &[f32], ys: &mut [f32]) {
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = (2.0f32 / std::f32::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
+    }
+    vs_tanh_inplace(ys);
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = 0.5 * v * (1.0 + *y)
+    }
+}
+
+#[inline]
+pub fn vd_gelu(vs: &[f64], ys: &mut [f64]) {
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = (2.0f64 / std::f64::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)
+    }
+    vd_tanh_inplace(ys);
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = 0.5 * v * (1.0 + *y)
+    }
+}
+
 macro_rules! binary_op {
     ($fn_name:ident, $ty:ty, $accelerate_name:ident) => {
         #[inline]
diff --git a/candle-core/src/op.rs b/candle-core/src/op.rs
index fbfc9c1a..9382b217 100644
--- a/candle-core/src/op.rs
+++ b/candle-core/src/op.rs
@@ -600,6 +600,24 @@ impl UnaryOpT for Gelu {
     fn f64_vec(xs: &[f64], ys: &mut [f64]) {
         crate::mkl::vd_gelu(xs, ys)
     }
+
+    #[cfg(feature = "accelerate")]
+    const F32_VEC: bool = true;
+
+    #[cfg(feature = "accelerate")]
+    #[inline(always)]
+    fn f32_vec(xs: &[f32], ys: &mut [f32]) {
+        crate::accelerate::vs_gelu(xs, ys)
+    }
+
+    #[cfg(feature = "accelerate")]
+    const F64_VEC: bool = true;
+
+    #[cfg(feature = "accelerate")]
+    #[inline(always)]
+    fn f64_vec(xs: &[f64], ys: &mut [f64]) {
+        crate::accelerate::vd_gelu(xs, ys)
+    }
 }
 
 impl UnaryOpT for Relu {

From c88d6fd4b9615307c7689ec3133d09f16a6b7d77 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 9 Sep 2023 08:27:37 +0100
Subject: [PATCH 043/150] Remove set_training. (#784)

---
 candle-core/src/lib.rs | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/candle-core/src/lib.rs b/candle-core/src/lib.rs
index 3504b0a6..0a64a5a6 100644
--- a/candle-core/src/lib.rs
+++ b/candle-core/src/lib.rs
@@ -112,12 +112,6 @@ impl ToUsize2 for (usize, usize) {
 // A simple trait defining a module with forward method using a single argument.
 pub trait Module: std::fmt::Debug {
     fn forward(&self, xs: &Tensor) -> Result<Tensor>;
-
-    /// Change the module to use training mode vs eval mode.
-    ///
-    /// The default implementation does nothing as this is only used for a couple modules such as
-    /// dropout or batch-normalization.
-    fn set_training(&mut self, _training: bool) {}
 }
 
 impl Module for quantized::QMatMul {

From 976a1086ee8c30398582df1727ffb27679b2c2d7 Mon Sep 17 00:00:00 2001
From: YangNianYi <y790174683@163.com>
Date: Sat, 9 Sep 2023 15:55:35 +0800
Subject: [PATCH 044/150] feat: u32 from_be_bytes (#765)

---
 candle-datasets/src/vision/mnist.rs | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/candle-datasets/src/vision/mnist.rs b/candle-datasets/src/vision/mnist.rs
index 30b0d01f..7a8240cf 100644
--- a/candle-datasets/src/vision/mnist.rs
+++ b/candle-datasets/src/vision/mnist.rs
@@ -9,12 +9,9 @@ use std::fs::File;
 use std::io::{self, BufReader, Read};
 
 fn read_u32<T: Read>(reader: &mut T) -> Result<u32> {
-    let mut b = vec![0u8; 4];
+    let mut b = [0u8; 4];
     reader.read_exact(&mut b)?;
-    let (result, _) = b.iter().rev().fold((0u64, 1u64), |(s, basis), &x| {
-        (s + basis * u64::from(x), basis * 256)
-    });
-    Ok(result as u32)
+    Ok(u32::from_be_bytes(b))
 }
 
 fn check_magic_number<T: Read>(reader: &mut T, expected: u32) -> Result<()> {

From 722c50bb0ce18d749edcf86268238e5d9c9ee57e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 9 Sep 2023 09:03:59 +0100
Subject: [PATCH 045/150] Use byteorder in mnist. (#785)

---
 candle-datasets/src/vision/mnist.rs | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/candle-datasets/src/vision/mnist.rs b/candle-datasets/src/vision/mnist.rs
index 7a8240cf..2dac883c 100644
--- a/candle-datasets/src/vision/mnist.rs
+++ b/candle-datasets/src/vision/mnist.rs
@@ -8,10 +8,9 @@ use parquet::file::reader::{FileReader, SerializedFileReader};
 use std::fs::File;
 use std::io::{self, BufReader, Read};
 
-fn read_u32<T: Read>(reader: &mut T) -> Result<u32> {
-    let mut b = [0u8; 4];
-    reader.read_exact(&mut b)?;
-    Ok(u32::from_be_bytes(b))
+fn read_u32<T: Read>(reader: &mut T) -> std::io::Result<u32> {
+    use byteorder::ReadBytesExt;
+    reader.read_u32::<byteorder::BigEndian>()
 }
 
 fn check_magic_number<T: Read>(reader: &mut T, expected: u32) -> Result<()> {

From 3cd7e7b51dc7bf49215b136702f5bc3cd4642144 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 9 Sep 2023 10:46:09 +0100
Subject: [PATCH 046/150] Fuse the rel-pos additions via a custom-op. (#786)

* Fuse the rel-pos additions via a custom-op.

* Run with rayon.

* Add more tracing.
---
 candle-examples/Cargo.toml                    |  1 +
 .../segment-anything/model_image_encoder.rs   | 91 +++++++++++++++++--
 2 files changed, 86 insertions(+), 6 deletions(-)

diff --git a/candle-examples/Cargo.toml b/candle-examples/Cargo.toml
index 9035eae0..6f8792a3 100644
--- a/candle-examples/Cargo.toml
+++ b/candle-examples/Cargo.toml
@@ -24,6 +24,7 @@ intel-mkl-src = { workspace = true, optional = true }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true }
+rayon = { workspace = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-examples/examples/segment-anything/model_image_encoder.rs
index f997170d..76cd15d0 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-examples/examples/segment-anything/model_image_encoder.rs
@@ -34,6 +34,70 @@ impl Module for PatchEmbed {
     }
 }
 
+// A custom op to make add_decomposed_rel_pos faster. Most of the time is spent on the final
+// addition in the case where b = 12, q_h = q_w = 4096, k_h = k_w = 4096
+//   (attn.reshape((b, q_h, q_w, k_h, k_w))?
+//       + rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
+//   .reshape((b, q_h * q_w, k_h * k_w))
+// Ideally we would perform this operation in place but this is not supported in candle at the
+// moment. We should also investigate using f16 rather than f32.
+struct Add3(usize, usize, usize, usize, usize);
+impl candle::CustomOp3 for Add3 {
+    fn name(&self) -> &'static str {
+        "add3"
+    }
+
+    fn cpu_fwd(
+        &self,
+        s1: &candle::CpuStorage,
+        l1: &candle::Layout,
+        s2: &candle::CpuStorage,
+        l2: &candle::Layout,
+        s3: &candle::CpuStorage,
+        l3: &candle::Layout,
+    ) -> Result<(candle::CpuStorage, candle::Shape)> {
+        use rayon::prelude::*;
+
+        let Add3(b, q_h, q_w, k_h, k_w) = *self;
+        let s1 = s1.as_slice::<f32>()?;
+        let s1 = match l1.contiguous_offsets() {
+            None => candle::bail!("input1 has to be contiguous"),
+            Some((o1, o2)) => &s1[o1..o2],
+        };
+        let s2 = s2.as_slice::<f32>()?;
+        let s2 = match l2.contiguous_offsets() {
+            None => candle::bail!("input2 has to be contiguous"),
+            Some((o1, o2)) => &s2[o1..o2],
+        };
+        let s3 = s3.as_slice::<f32>()?;
+        let s3 = match l3.contiguous_offsets() {
+            None => candle::bail!("input3 has to be contiguous"),
+            Some((o1, o2)) => &s3[o1..o2],
+        };
+        let mut dst = vec![0f32; b * q_h * q_w * k_h * k_w];
+        dst.par_chunks_exact_mut(k_h * k_w)
+            .enumerate()
+            .for_each(|(b_idx, dst)| {
+                let s1_idx = b_idx * k_h * k_w;
+                let s2_idx = b_idx * k_h;
+                let s3_idx = b_idx * k_w;
+                for h_idx in 0..k_h {
+                    let s1_idx = s1_idx + h_idx * k_w;
+                    let s2_idx = s2_idx + h_idx;
+                    let dst_idx = h_idx * k_w;
+                    for w_idx in 0..k_w {
+                        let s1_idx = s1_idx + w_idx;
+                        let s3_idx = s3_idx + w_idx;
+                        let dst_idx = dst_idx + w_idx;
+                        dst[dst_idx] = s1[s1_idx] + s2[s2_idx] + s3[s3_idx]
+                    }
+                }
+            });
+        let dst = candle::WithDType::to_cpu_storage_owned(dst);
+        Ok((dst, (b, q_h * q_w, k_h * k_w).into()))
+    }
+}
+
 #[derive(Debug)]
 struct Attention {
     qkv: crate::Linear,
@@ -42,6 +106,7 @@ struct Attention {
     scale: f64,
     rel_pos_hw: Option<(Tensor, Tensor)>,
     span: tracing::Span,
+    span_matmul: tracing::Span,
     span_rel_pos: tracing::Span,
     span_softmax: tracing::Span,
 }
@@ -56,6 +121,7 @@ impl Attention {
         vb: VarBuilder,
     ) -> Result<Self> {
         let span = tracing::span!(tracing::Level::TRACE, "attention");
+        let span_matmul = tracing::span!(tracing::Level::TRACE, "attn-matmul");
         let span_rel_pos = tracing::span!(tracing::Level::TRACE, "attn-rel-pos");
         let span_softmax = tracing::span!(tracing::Level::TRACE, "attn-sm");
         let qkv = crate::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
@@ -76,6 +142,7 @@ impl Attention {
             scale,
             rel_pos_hw,
             span,
+            span_matmul,
             span_rel_pos,
             span_softmax,
         })
@@ -101,10 +168,16 @@ impl Attention {
                     .transpose(1, 2)? // -> bwhc
                     .contiguous()?
                     .matmul(&r_w.broadcast_left(b)?.t()?.contiguous()?)? // bwhc,bwck -> bwhk
-                    .transpose(1, 2)?;
-                (attn.reshape((b, q_h, q_w, k_h, k_w))?
-                    + rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
-                .reshape((b, q_h * q_w, k_h * k_w))
+                    .transpose(1, 2)?
+                    .contiguous()?;
+                if attn.device().is_cpu() {
+                    let op = Add3(b, q_h, q_w, k_h, k_w);
+                    attn.apply_op3_no_bwd(&rel_h, &rel_w, &op)
+                } else {
+                    (attn.reshape((b, q_h, q_w, k_h, k_w))?
+                        + rel_h.unsqueeze(4)?.broadcast_add(&rel_w.unsqueeze(3)?)?)?
+                    .reshape((b, q_h * q_w, k_h * k_w))
+                }
             }
             None => Ok(attn),
         }
@@ -149,7 +222,10 @@ impl Module for Attention {
         let q = qkv.i(0)?;
         let k = qkv.i(1)?;
         let v = qkv.i(2)?;
-        let attn = (&q * self.scale)?.matmul(&k.t()?)?;
+        let attn = {
+            let _enter = self.span_matmul.enter();
+            (&q * self.scale)?.matmul(&k.t()?)?
+        };
         let attn = {
             let _enter = self.span_rel_pos.enter();
             self.add_decomposed_rel_pos(attn, &q, (h, w), (h, w))?
@@ -158,7 +234,10 @@ impl Module for Attention {
             let _enter = self.span_softmax.enter();
             candle_nn::ops::softmax_last_dim(&attn)?
         };
-        let attn = attn.matmul(&v)?;
+        let attn = {
+            let _enter = self.span_matmul.enter();
+            attn.matmul(&v)?
+        };
         let attn = attn
             .reshape((b, self.num_heads, h, w, c / self.num_heads))?
             .permute((0, 2, 3, 1, 4))?

From b7cd58473b4621268480d8bdba8812a38d510d5b Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 9 Sep 2023 15:10:06 +0100
Subject: [PATCH 047/150] TinyViT backbone for segment-anything. (#787)

* TinyViT.

* More TinyViT.

* Add more to the tinyvit backbone.

* Proper padding.

* Plus ViT.

* Add the tiniest vit spec.
---
 .../examples/segment-anything/main.rs         |   1 +
 .../segment-anything/model_tiny_vit.rs        | 563 ++++++++++++++++++
 candle-nn/src/conv.rs                         |  26 +-
 3 files changed, 577 insertions(+), 13 deletions(-)
 create mode 100644 candle-examples/examples/segment-anything/model_tiny_vit.rs

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index ce8e3bb4..3ef5762e 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -11,6 +11,7 @@ pub mod model_image_encoder;
 pub mod model_mask_decoder;
 pub mod model_prompt_encoder;
 pub mod model_sam;
+pub mod model_tiny_vit;
 pub mod model_transformer;
 
 use candle::{DType, Result, Tensor};
diff --git a/candle-examples/examples/segment-anything/model_tiny_vit.rs b/candle-examples/examples/segment-anything/model_tiny_vit.rs
new file mode 100644
index 00000000..36e4c578
--- /dev/null
+++ b/candle-examples/examples/segment-anything/model_tiny_vit.rs
@@ -0,0 +1,563 @@
+// Adapted from:
+// https://github.com/ChaoningZhang/MobileSAM/blob/master/mobile_sam/modeling/tiny_vit_sam.py
+#![allow(unused)]
+use candle::{DType, IndexOp, Result, Tensor, D};
+use candle_nn::{Conv2dConfig, Module, VarBuilder};
+
+const MBCONV_EXPAND_RATIO: usize = 4;
+const MLP_RATIO: usize = 4;
+const LOCAL_CONV_SIZE: usize = 3;
+const IMG_SIZE: usize = 224;
+const IN_CHANNELS: usize = 3;
+
+#[derive(Debug)]
+struct Conv2dBN {
+    c: candle_nn::Conv2d,
+    bn: candle_nn::BatchNorm,
+}
+
+impl Conv2dBN {
+    fn new(in_: usize, out: usize, ks: usize, cfg: Conv2dConfig, vb: VarBuilder) -> Result<Self> {
+        let c = candle_nn::conv2d(in_, out, ks, cfg, vb.pp("c"))?;
+        let bn = candle_nn::batch_norm(out, 1e-5, vb.pp("bn"))?;
+        Ok(Self { c, bn })
+    }
+}
+
+impl Module for Conv2dBN {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.c)?.apply(&self.bn)
+    }
+}
+
+#[derive(Debug)]
+struct PatchEmbed {
+    conv1: Conv2dBN,
+    conv2: Conv2dBN,
+}
+
+impl PatchEmbed {
+    fn new(in_chans: usize, embed_dim: usize, vb: VarBuilder) -> Result<Self> {
+        let cfg = candle_nn::Conv2dConfig {
+            stride: 2,
+            padding: 1,
+            ..Default::default()
+        };
+        let conv1 = Conv2dBN::new(in_chans, embed_dim / 2, 3, cfg, vb.pp("seq.0"))?;
+        let conv2 = Conv2dBN::new(embed_dim / 2, embed_dim, 3, cfg, vb.pp("seq.2"))?;
+        Ok(Self { conv1, conv2 })
+    }
+}
+
+impl Module for PatchEmbed {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.conv1)?.gelu()?.apply(&self.conv2)
+    }
+}
+
+#[derive(Debug)]
+struct MBConv {
+    conv1: Conv2dBN,
+    conv2: Conv2dBN,
+    conv3: Conv2dBN,
+}
+
+impl MBConv {
+    fn new(in_: usize, out: usize, expand_ratio: usize, vb: VarBuilder) -> Result<Self> {
+        let hidden = in_ * expand_ratio;
+        let cfg2 = candle_nn::Conv2dConfig {
+            padding: 1,
+            groups: hidden,
+            ..Default::default()
+        };
+        let conv1 = Conv2dBN::new(in_, hidden, 1, Default::default(), vb.pp("conv1"))?;
+        let conv2 = Conv2dBN::new(hidden, hidden, 3, cfg2, vb.pp("conv2"))?;
+        let conv3 = Conv2dBN::new(hidden, out, 1, Default::default(), vb.pp("conv3"))?;
+        Ok(Self {
+            conv1,
+            conv2,
+            conv3,
+        })
+    }
+}
+
+impl Module for MBConv {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let shortcut = xs;
+        let xs = xs
+            .apply(&self.conv1)?
+            .gelu()?
+            .apply(&self.conv2)?
+            .gelu()?
+            .apply(&self.conv3)?;
+        (xs + shortcut)?.gelu()
+    }
+}
+
+#[derive(Debug)]
+struct PatchMerging {
+    conv1: Conv2dBN,
+    conv2: Conv2dBN,
+    conv3: Conv2dBN,
+    input_resolution: (usize, usize),
+}
+
+impl PatchMerging {
+    fn new(
+        input_resolution: (usize, usize),
+        dim: usize,
+        out: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let stride = if [320, 448, 576].contains(&out) { 1 } else { 2 };
+        let cfg2 = candle_nn::Conv2dConfig {
+            padding: 1,
+            stride,
+            groups: out,
+            ..Default::default()
+        };
+        let conv1 = Conv2dBN::new(dim, out, 1, Default::default(), vb.pp("conv1"))?;
+        let conv2 = Conv2dBN::new(out, out, 3, cfg2, vb.pp("conv2"))?;
+        let conv3 = Conv2dBN::new(out, out, 1, Default::default(), vb.pp("conv3"))?;
+        Ok(Self {
+            conv1,
+            conv2,
+            conv3,
+            input_resolution,
+        })
+    }
+}
+
+impl Module for PatchMerging {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = if xs.rank() == 3 {
+            let (h, w) = self.input_resolution;
+            let b = xs.dim(0)?;
+            xs.reshape((b, h, w, ()))?.permute((0, 3, 1, 2))?
+        } else {
+            xs.clone()
+        };
+        xs.apply(&self.conv1)?
+            .gelu()?
+            .apply(&self.conv2)?
+            .gelu()?
+            .apply(&self.conv3)?
+            .flatten_from(2)?
+            .transpose(1, 2)
+    }
+}
+
+#[derive(Debug)]
+struct ConvLayer {
+    blocks: Vec<MBConv>,
+    downsample: Option<PatchMerging>,
+}
+
+impl ConvLayer {
+    fn new(
+        dim: usize,
+        out: usize,
+        input_resolution: (usize, usize),
+        depth: usize,
+        downsample: bool,
+        conv_expand_ratio: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let vb_b = vb.pp("blocks");
+        let mut blocks = Vec::with_capacity(depth);
+        for index in 0..depth {
+            let block = MBConv::new(dim, dim, conv_expand_ratio, vb_b.pp(index))?;
+            blocks.push(block)
+        }
+        let downsample = if downsample {
+            let downsample = PatchMerging::new(input_resolution, dim, out, vb.pp("downsample"))?;
+            Some(downsample)
+        } else {
+            None
+        };
+        Ok(Self { blocks, downsample })
+    }
+}
+
+impl Module for ConvLayer {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.clone();
+        for block in self.blocks.iter() {
+            xs = block.forward(&xs)?
+        }
+        match &self.downsample {
+            None => Ok(xs),
+            Some(downsample) => downsample.forward(&xs),
+        }
+    }
+}
+
+#[derive(Debug)]
+struct Mlp {
+    norm: candle_nn::LayerNorm,
+    fc1: candle_nn::Linear,
+    fc2: candle_nn::Linear,
+}
+
+impl Mlp {
+    fn new(in_: usize, hidden: usize, vb: VarBuilder) -> Result<Self> {
+        let norm = candle_nn::layer_norm(in_, 1e-5, vb.pp("norm"))?;
+        let fc1 = candle_nn::linear(in_, hidden, vb.pp("fc1"))?;
+        let fc2 = candle_nn::linear(hidden, in_, vb.pp("fc2"))?;
+        Ok(Self { norm, fc1, fc2 })
+    }
+}
+
+impl Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.norm)?
+            .apply(&self.fc1)?
+            .gelu()?
+            .apply(&self.fc2)
+    }
+}
+
+#[derive(Debug)]
+struct Attention {
+    norm: candle_nn::LayerNorm,
+    qkv: candle_nn::Linear,
+    proj: candle_nn::Linear,
+    attention_biases: Tensor,
+    ab: Tensor,
+    key_dim: usize,
+    num_heads: usize,
+    d: usize,
+    dh: usize,
+    scale: f64,
+}
+
+impl Attention {
+    fn new(
+        dim: usize,
+        key_dim: usize,
+        num_heads: usize,
+        attn_ratio: usize,
+        resolution: (usize, usize),
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let d = attn_ratio * key_dim;
+        let dh = d * num_heads;
+        let nh_kd = key_dim * num_heads;
+        let h = dh + nh_kd * 2;
+        let norm = candle_nn::layer_norm(dim, 1e-5, vb.pp("norm"))?;
+        let qkv = candle_nn::linear(dim, h, vb.pp("qkv"))?;
+        let proj = candle_nn::linear(dh, dim, vb.pp("proj"))?;
+
+        let points = (0..resolution.0)
+            .flat_map(|x| (0..resolution.1).map(move |y| (x as i64, y as i64)))
+            .collect::<Vec<_>>();
+        let mut idxs = Vec::with_capacity(points.len() * points.len());
+        let mut attention_offsets = std::collections::HashMap::new();
+        for &(x1, y1) in points.iter() {
+            for &(x2, y2) in points.iter() {
+                let offset = ((x2 - x1).abs(), (y2 - y1).abs());
+                let l = attention_offsets.len();
+                let idx = attention_offsets.entry(offset).or_insert(l);
+                idxs.push(*idx as u32)
+            }
+        }
+        let attention_biases = vb.get((num_heads, attention_offsets.len()), "attention_biases")?;
+        let idxs = Tensor::new(idxs, attention_biases.device())?;
+        let ab = attention_biases.index_select(&idxs, 1)?;
+        Ok(Self {
+            norm,
+            qkv,
+            proj,
+            attention_biases,
+            ab,
+            key_dim,
+            num_heads,
+            d,
+            dh,
+            scale: 1f64 / (key_dim as f64).sqrt(),
+        })
+    }
+}
+
+impl Module for Attention {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (b, n, _) = xs.dims3()?;
+        let xs = xs.apply(&self.norm)?;
+        let qkv = xs.apply(&self.qkv)?.reshape((b, n, self.num_heads, ()))?;
+        let q = qkv
+            .narrow(D::Minus1, 0, self.key_dim)?
+            .permute((0, 2, 1, 3))?;
+        let k = qkv
+            .narrow(D::Minus1, self.key_dim, self.key_dim)?
+            .permute((0, 2, 1, 3))?;
+        let v = qkv
+            .narrow(D::Minus1, 2 * self.key_dim, self.d)?
+            .permute((0, 2, 1, 3))?;
+        let attn = (q.matmul(&k.t()?)? * self.scale)?;
+        let attn = (attn + &self.ab)?;
+        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
+        attn.matmul(&v)?
+            .transpose(1, 2)?
+            .reshape((b, n, self.dh))?
+            .apply(&self.proj)
+    }
+}
+
+#[derive(Debug)]
+struct TinyViTBlock {
+    attn: Attention,
+    local_conv: Conv2dBN,
+    mlp: Mlp,
+    window_size: usize,
+    input_resolution: (usize, usize),
+}
+
+impl TinyViTBlock {
+    fn new(
+        dim: usize,
+        input_resolution: (usize, usize),
+        num_heads: usize,
+        window_size: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let head_dim = dim / num_heads;
+        let attn = Attention::new(
+            dim,
+            head_dim,
+            num_heads,
+            1,
+            (window_size, window_size),
+            vb.pp("attn"),
+        )?;
+        let mlp = Mlp::new(dim, dim * MLP_RATIO, vb.pp("mlp"))?;
+        let cfg = candle_nn::Conv2dConfig {
+            padding: LOCAL_CONV_SIZE / 2,
+            ..Default::default()
+        };
+        let local_conv = Conv2dBN::new(dim, dim, LOCAL_CONV_SIZE, cfg, vb.pp("local_conv"))?;
+        Ok(Self {
+            attn,
+            local_conv,
+            mlp,
+            window_size,
+            input_resolution,
+        })
+    }
+}
+
+impl Module for TinyViTBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (h, w) = self.input_resolution;
+        let (b, l, c) = xs.dims3()?;
+        let res_x = xs;
+        let xs = if h == self.window_size && w == self.window_size {
+            self.attn.forward(xs)?
+        } else {
+            let xs = xs.reshape((b, h, w, c))?;
+            let pad_b = (self.window_size - h % self.window_size) % self.window_size;
+            let pad_r = (self.window_size - w % self.window_size) % self.window_size;
+
+            let xs = if pad_b > 0 {
+                xs.pad_with_zeros(D::Minus2, 0, pad_b)?
+            } else {
+                xs
+            };
+            let xs = if pad_r > 0 {
+                xs.pad_with_zeros(D::Minus1, 0, pad_r)?
+            } else {
+                xs
+            };
+            let (p_h, p_w) = (h + pad_b, w + pad_r);
+            let n_h = p_h / self.window_size;
+            let n_w = p_w / self.window_size;
+            let xs = xs
+                .reshape((b, n_h, self.window_size, n_w, self.window_size, c))?
+                .transpose(2, 3)?
+                .reshape((b * n_h * n_w, self.window_size * self.window_size, c))?;
+            let xs = self.attn.forward(&xs)?;
+            let xs = xs
+                .reshape((b, n_h, n_w, self.window_size, self.window_size, c))?
+                .transpose(2, 3)?
+                .reshape((b, p_h, p_w, c))?;
+            let xs = if pad_r > 0 {
+                xs.i((.., .., ..w))?.contiguous()?
+            } else {
+                xs
+            };
+            let xs = if pad_b > 0 {
+                xs.i((.., ..h, ..))?.contiguous()?
+            } else {
+                xs
+            };
+            xs.reshape((b, l, c))?
+        };
+        let xs = (xs + res_x)?;
+        let xs = xs
+            .transpose(1, 2)?
+            .reshape((b, c, h, w))?
+            .apply(&self.local_conv)?
+            .reshape((b, c, l))?
+            .transpose(1, 2)?;
+        &xs + self.mlp.forward(&xs)?
+    }
+}
+
+#[derive(Debug)]
+struct BasicLayer {
+    blocks: Vec<TinyViTBlock>,
+    downsample: Option<PatchMerging>,
+}
+
+impl BasicLayer {
+    #[allow(clippy::too_many_arguments)]
+    fn new(
+        dim: usize,
+        input_resolution: (usize, usize),
+        depth: usize,
+        num_heads: usize,
+        window_size: usize,
+        downsample: bool,
+        out: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let vb_b = vb.pp("blocks");
+        let mut blocks = Vec::with_capacity(depth);
+        for index in 0..depth {
+            let block = TinyViTBlock::new(
+                dim,
+                input_resolution,
+                num_heads,
+                window_size,
+                vb_b.pp(index),
+            )?;
+            blocks.push(block)
+        }
+        let downsample = if downsample {
+            let downsample = PatchMerging::new(input_resolution, dim, out, vb.pp("downsample"))?;
+            Some(downsample)
+        } else {
+            None
+        };
+        Ok(Self { blocks, downsample })
+    }
+}
+
+impl Module for BasicLayer {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.clone();
+        for block in self.blocks.iter() {
+            xs = block.forward(&xs)?
+        }
+        match &self.downsample {
+            None => Ok(xs),
+            Some(downsample) => downsample.forward(&xs),
+        }
+    }
+}
+
+#[derive(Debug)]
+pub struct TinyViT {
+    patch_embed: PatchEmbed,
+    layer0: ConvLayer,
+    layers: Vec<BasicLayer>,
+    norm_head: candle_nn::LayerNorm,
+    head: candle_nn::Linear,
+    neck_conv1: candle_nn::Conv2d,
+    neck_ln1: crate::LayerNorm2d,
+    neck_conv2: candle_nn::Conv2d,
+    neck_ln2: crate::LayerNorm2d,
+}
+
+impl TinyViT {
+    pub fn new(
+        embed_dims: &[usize],
+        depths: &[usize],
+        num_heads: &[usize],
+        window_sizes: &[usize],
+        num_classes: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let patch_embed = PatchEmbed::new(IN_CHANNELS, embed_dims[0], vb.pp("patch_embed"))?;
+        let patches_resolution = IMG_SIZE / 4;
+
+        let vb_l = vb.pp("layers");
+        let layer0 = ConvLayer::new(
+            /* dim */ embed_dims[0],
+            /* out */ embed_dims[1],
+            /* input_resolution */ (patches_resolution, patches_resolution),
+            /* depth */ depths[0],
+            /* downsample */ true,
+            /* conv_expand_ratio */ MBCONV_EXPAND_RATIO,
+            vb_l.pp(0),
+        )?;
+
+        let num_layers = embed_dims.len();
+        let mut layers = Vec::with_capacity(num_layers - 1);
+        for i_layer in 1..num_layers {
+            let patches_resolution = patches_resolution / (1 << usize::min(i_layer, 2));
+            let layer = BasicLayer::new(
+                /* dim */ embed_dims[i_layer],
+                /* input_resolution */ (patches_resolution, patches_resolution),
+                /* depth */ depths[i_layer],
+                /* num_heads */ num_heads[i_layer],
+                /* window_size */ window_sizes[i_layer],
+                /* downsample */ i_layer < num_layers - 1,
+                /* out */ embed_dims[usize::min(i_layer + 1, num_layers - 1)],
+                vb_l.pp(i_layer),
+            )?;
+            layers.push(layer)
+        }
+
+        let last_embed_dim = embed_dims[embed_dims.len() - 1];
+        let norm_head = candle_nn::layer_norm(last_embed_dim, 1e-5, vb.pp("norm_head"))?;
+        let head = candle_nn::linear(last_embed_dim, num_classes, vb.pp("head"))?;
+        let neck_conv1 =
+            candle_nn::conv2d_no_bias(last_embed_dim, 256, 1, Default::default(), vb.pp("neck.0"))?;
+        let neck_ln1 = crate::LayerNorm2d::new(256, 1e-6, vb.pp("neck.1"))?;
+        let cfg = candle_nn::Conv2dConfig {
+            padding: 1,
+            ..Default::default()
+        };
+        let neck_conv2 = candle_nn::conv2d_no_bias(256, 256, 3, cfg, vb.pp("neck.2"))?;
+        let neck_ln2 = crate::LayerNorm2d::new(256, 1e-6, vb.pp("neck.3"))?;
+
+        Ok(Self {
+            patch_embed,
+            layer0,
+            layers,
+            norm_head,
+            head,
+            neck_conv1,
+            neck_ln1,
+            neck_conv2,
+            neck_ln2,
+        })
+    }
+}
+
+impl Module for TinyViT {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = self.patch_embed.forward(xs)?;
+        for layer in self.layers.iter() {
+            xs = layer.forward(&xs)?
+        }
+        let (b, _, c) = xs.dims3()?;
+        xs.reshape((b, 64, 64, c))?
+            .permute((0, 3, 1, 2))?
+            .apply(&self.neck_conv1)?
+            .apply(&self.neck_ln1)?
+            .apply(&self.neck_conv2)?
+            .apply(&self.neck_ln2)
+    }
+}
+
+pub fn tiny_vit_5m_224(vb: VarBuilder) -> Result<TinyViT> {
+    TinyViT::new(
+        /* embed_dims */ &[64, 128, 160, 320],
+        /* depths */ &[2, 2, 6, 2],
+        /* num_heads */ &[2, 4, 5, 10],
+        /* window_sizes */ &[7, 7, 14, 7],
+        /* num_classes */ 1000,
+        vb,
+    )
+}
diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index b2483058..cfe86bfa 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -187,10 +187,10 @@ pub fn conv1d(
     out_channels: usize,
     kernel_size: usize,
     cfg: Conv1dConfig,
-    vs: crate::VarBuilder,
+    vb: crate::VarBuilder,
 ) -> Result<Conv1d> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
-    let ws = vs.get_with_hints(
+    let ws = vb.get_with_hints(
         (out_channels, in_channels / cfg.groups, kernel_size),
         "weight",
         init_ws,
@@ -200,7 +200,7 @@ pub fn conv1d(
         lo: -bound,
         up: bound,
     };
-    let bs = vs.get_with_hints(out_channels, "bias", init_bs)?;
+    let bs = vb.get_with_hints(out_channels, "bias", init_bs)?;
     Ok(Conv1d::new(ws, Some(bs), cfg))
 }
 
@@ -209,10 +209,10 @@ pub fn conv2d(
     out_channels: usize,
     kernel_size: usize,
     cfg: Conv2dConfig,
-    vs: crate::VarBuilder,
+    vb: crate::VarBuilder,
 ) -> Result<Conv2d> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
-    let ws = vs.get_with_hints(
+    let ws = vb.get_with_hints(
         (
             out_channels,
             in_channels / cfg.groups,
@@ -227,7 +227,7 @@ pub fn conv2d(
         lo: -bound,
         up: bound,
     };
-    let bs = vs.get_with_hints(out_channels, "bias", init_bs)?;
+    let bs = vb.get_with_hints(out_channels, "bias", init_bs)?;
     Ok(Conv2d::new(ws, Some(bs), cfg))
 }
 
@@ -236,10 +236,10 @@ pub fn conv2d_no_bias(
     out_channels: usize,
     kernel_size: usize,
     cfg: Conv2dConfig,
-    vs: crate::VarBuilder,
+    vb: crate::VarBuilder,
 ) -> Result<Conv2d> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
-    let ws = vs.get_with_hints(
+    let ws = vb.get_with_hints(
         (
             out_channels,
             in_channels / cfg.groups,
@@ -257,19 +257,19 @@ pub fn conv_transpose2d(
     out_channels: usize,
     kernel_size: usize,
     cfg: ConvTranspose2dConfig,
-    vs: crate::VarBuilder,
+    vb: crate::VarBuilder,
 ) -> Result<ConvTranspose2d> {
     let bound = 1. / (out_channels as f64).sqrt() / kernel_size as f64;
     let init = crate::Init::Uniform {
         lo: -bound,
         up: bound,
     };
-    let ws = vs.get_with_hints(
+    let ws = vb.get_with_hints(
         (in_channels, out_channels, kernel_size, kernel_size),
         "weight",
         init,
     )?;
-    let bs = vs.get_with_hints(out_channels, "bias", init)?;
+    let bs = vb.get_with_hints(out_channels, "bias", init)?;
     Ok(ConvTranspose2d::new(ws, Some(bs), cfg))
 }
 
@@ -278,14 +278,14 @@ pub fn conv_transpose2d_no_bias(
     out_channels: usize,
     kernel_size: usize,
     cfg: ConvTranspose2dConfig,
-    vs: crate::VarBuilder,
+    vb: crate::VarBuilder,
 ) -> Result<ConvTranspose2d> {
     let bound = 1. / (out_channels as f64).sqrt() / kernel_size as f64;
     let init = crate::Init::Uniform {
         lo: -bound,
         up: bound,
     };
-    let ws = vs.get_with_hints(
+    let ws = vb.get_with_hints(
         (out_channels, in_channels, kernel_size, kernel_size),
         "weight",
         init,

From 74ad4deb42da71d4c47220e9595f58445f6f7298 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 9 Sep 2023 16:21:44 +0100
Subject: [PATCH 048/150] Get the MobileSAM TinyViT based version to work.
 (#789)

* More TinyViT support in SA.

* More mobilesam work.

* Add the mobile-sam weights to the hub.
---
 .../examples/segment-anything/main.rs         | 17 ++++++-
 .../examples/segment-anything/model_sam.rs    | 51 ++++++++++++++++++-
 .../segment-anything/model_tiny_vit.rs        | 47 +++++++++--------
 3 files changed, 89 insertions(+), 26 deletions(-)

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 3ef5762e..9ce2f158 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -133,6 +133,10 @@ struct Args {
     /// Enable tracing (generates a trace-timestamp.json file).
     #[arg(long)]
     tracing: bool,
+
+    /// Use the TinyViT based models from MobileSAM
+    #[arg(long)]
+    use_tiny: bool,
 }
 
 pub fn main() -> anyhow::Result<()> {
@@ -179,13 +183,22 @@ pub fn main() -> anyhow::Result<()> {
         None => {
             let api = hf_hub::api::sync::Api::new()?;
             let api = api.model("lmz/candle-sam".to_string());
-            api.get("sam_vit_b_01ec64.safetensors")?
+            let filename = if args.use_tiny {
+                "mobile_sam-tiny-vitt.safetensors"
+            } else {
+                "sam_vit_b_01ec64.safetensors"
+            };
+            api.get(filename)?
         }
     };
     let weights = unsafe { candle::safetensors::MmapedFile::new(model)? };
     let weights = weights.deserialize()?;
     let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
-    let sam = model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)?; // sam_vit_b
+    let sam = if args.use_tiny {
+        model_sam::Sam::new_tiny(vb)? // tiny vit_t
+    } else {
+        model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)? // sam_vit_b
+    };
 
     if args.generate_masks {
         // Default options similar to the Python version.
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-examples/examples/segment-anything/model_sam.rs
index ade976c1..b1a81af6 100644
--- a/candle-examples/examples/segment-anything/model_sam.rs
+++ b/candle-examples/examples/segment-anything/model_sam.rs
@@ -4,6 +4,7 @@ use candle_nn::{Module, VarBuilder};
 use crate::model_image_encoder::ImageEncoderViT;
 use crate::model_mask_decoder::MaskDecoder;
 use crate::model_prompt_encoder::PromptEncoder;
+use crate::model_tiny_vit::{tiny_vit_5m, TinyViT};
 
 const PROMPT_EMBED_DIM: usize = 256;
 pub const IMAGE_SIZE: usize = 1024;
@@ -14,9 +15,24 @@ const STABILITY_SCORE_THRESHOLD: f32 = 0.95;
 const MODEL_MASK_THRESHOLD: f32 = 0.0;
 const CROP_NMS_THRESH: f32 = 0.7;
 
+#[derive(Debug)]
+enum ImageEncoder {
+    Original(ImageEncoderViT),
+    TinyViT(TinyViT),
+}
+
+impl Module for ImageEncoder {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        match self {
+            Self::Original(vit) => vit.forward(xs),
+            Self::TinyViT(vit) => vit.forward(xs),
+        }
+    }
+}
+
 #[derive(Debug)]
 pub struct Sam {
-    image_encoder: ImageEncoderViT,
+    image_encoder: ImageEncoder,
     prompt_encoder: PromptEncoder,
     mask_decoder: MaskDecoder,
     pixel_mean: Tensor,
@@ -67,7 +83,38 @@ impl Sam {
         let pixel_std =
             Tensor::new(&[58.395f32, 57.12, 57.375], vb.device())?.reshape((3, 1, 1))?;
         Ok(Self {
-            image_encoder,
+            image_encoder: ImageEncoder::Original(image_encoder),
+            prompt_encoder,
+            mask_decoder,
+            pixel_std,
+            pixel_mean,
+        })
+    }
+
+    pub fn new_tiny(vb: VarBuilder) -> Result<Self> {
+        let image_embedding_size = IMAGE_SIZE / VIT_PATCH_SIZE;
+
+        let image_encoder = tiny_vit_5m(vb.pp("image_encoder"))?;
+        let prompt_encoder = PromptEncoder::new(
+            PROMPT_EMBED_DIM,
+            (image_embedding_size, image_embedding_size),
+            (IMAGE_SIZE, IMAGE_SIZE),
+            16,
+            vb.pp("prompt_encoder"),
+        )?;
+        let mask_decoder = MaskDecoder::new(
+            PROMPT_EMBED_DIM,
+            /* num_multitask_outputs */ 3,
+            /* iou_head_depth */ 3,
+            /* iou_head_hidden_dim */ 256,
+            vb.pp("mask_decoder"),
+        )?;
+        let pixel_mean =
+            Tensor::new(&[123.675f32, 116.28, 103.53], vb.device())?.reshape((3, 1, 1))?;
+        let pixel_std =
+            Tensor::new(&[58.395f32, 57.12, 57.375], vb.device())?.reshape((3, 1, 1))?;
+        Ok(Self {
+            image_encoder: ImageEncoder::TinyViT(image_encoder),
             prompt_encoder,
             mask_decoder,
             pixel_std,
diff --git a/candle-examples/examples/segment-anything/model_tiny_vit.rs b/candle-examples/examples/segment-anything/model_tiny_vit.rs
index 36e4c578..b3941ee1 100644
--- a/candle-examples/examples/segment-anything/model_tiny_vit.rs
+++ b/candle-examples/examples/segment-anything/model_tiny_vit.rs
@@ -1,13 +1,12 @@
 // Adapted from:
 // https://github.com/ChaoningZhang/MobileSAM/blob/master/mobile_sam/modeling/tiny_vit_sam.py
-#![allow(unused)]
-use candle::{DType, IndexOp, Result, Tensor, D};
+use candle::{IndexOp, Result, Tensor, D};
 use candle_nn::{Conv2dConfig, Module, VarBuilder};
 
 const MBCONV_EXPAND_RATIO: usize = 4;
 const MLP_RATIO: usize = 4;
 const LOCAL_CONV_SIZE: usize = 3;
-const IMG_SIZE: usize = 224;
+const IMG_SIZE: usize = 1024;
 const IN_CHANNELS: usize = 3;
 
 #[derive(Debug)]
@@ -18,7 +17,7 @@ struct Conv2dBN {
 
 impl Conv2dBN {
     fn new(in_: usize, out: usize, ks: usize, cfg: Conv2dConfig, vb: VarBuilder) -> Result<Self> {
-        let c = candle_nn::conv2d(in_, out, ks, cfg, vb.pp("c"))?;
+        let c = candle_nn::conv2d_no_bias(in_, out, ks, cfg, vb.pp("c"))?;
         let bn = candle_nn::batch_norm(out, 1e-5, vb.pp("bn"))?;
         Ok(Self { c, bn })
     }
@@ -222,7 +221,6 @@ struct Attention {
     norm: candle_nn::LayerNorm,
     qkv: candle_nn::Linear,
     proj: candle_nn::Linear,
-    attention_biases: Tensor,
     ab: Tensor,
     key_dim: usize,
     num_heads: usize,
@@ -263,12 +261,14 @@ impl Attention {
         }
         let attention_biases = vb.get((num_heads, attention_offsets.len()), "attention_biases")?;
         let idxs = Tensor::new(idxs, attention_biases.device())?;
-        let ab = attention_biases.index_select(&idxs, 1)?;
+        let ab =
+            attention_biases
+                .index_select(&idxs, 1)?
+                .reshape(((), points.len(), points.len()))?;
         Ok(Self {
             norm,
             qkv,
             proj,
-            attention_biases,
             ab,
             key_dim,
             num_heads,
@@ -286,15 +286,18 @@ impl Module for Attention {
         let qkv = xs.apply(&self.qkv)?.reshape((b, n, self.num_heads, ()))?;
         let q = qkv
             .narrow(D::Minus1, 0, self.key_dim)?
-            .permute((0, 2, 1, 3))?;
+            .permute((0, 2, 1, 3))?
+            .contiguous()?;
         let k = qkv
             .narrow(D::Minus1, self.key_dim, self.key_dim)?
-            .permute((0, 2, 1, 3))?;
+            .permute((0, 2, 1, 3))?
+            .contiguous()?;
         let v = qkv
             .narrow(D::Minus1, 2 * self.key_dim, self.d)?
-            .permute((0, 2, 1, 3))?;
+            .permute((0, 2, 1, 3))?
+            .contiguous()?;
         let attn = (q.matmul(&k.t()?)? * self.scale)?;
-        let attn = (attn + &self.ab)?;
+        let attn = attn.broadcast_add(&self.ab)?;
         let attn = candle_nn::ops::softmax_last_dim(&attn)?;
         attn.matmul(&v)?
             .transpose(1, 2)?
@@ -332,6 +335,7 @@ impl TinyViTBlock {
         let mlp = Mlp::new(dim, dim * MLP_RATIO, vb.pp("mlp"))?;
         let cfg = candle_nn::Conv2dConfig {
             padding: LOCAL_CONV_SIZE / 2,
+            groups: dim,
             ..Default::default()
         };
         let local_conv = Conv2dBN::new(dim, dim, LOCAL_CONV_SIZE, cfg, vb.pp("local_conv"))?;
@@ -358,12 +362,12 @@ impl Module for TinyViTBlock {
             let pad_r = (self.window_size - w % self.window_size) % self.window_size;
 
             let xs = if pad_b > 0 {
-                xs.pad_with_zeros(D::Minus2, 0, pad_b)?
+                xs.pad_with_zeros(1, 0, pad_b)?
             } else {
                 xs
             };
             let xs = if pad_r > 0 {
-                xs.pad_with_zeros(D::Minus1, 0, pad_r)?
+                xs.pad_with_zeros(2, 0, pad_r)?
             } else {
                 xs
             };
@@ -460,8 +464,8 @@ pub struct TinyViT {
     patch_embed: PatchEmbed,
     layer0: ConvLayer,
     layers: Vec<BasicLayer>,
-    norm_head: candle_nn::LayerNorm,
-    head: candle_nn::Linear,
+    // norm_head: candle_nn::LayerNorm,
+    // head: candle_nn::Linear,
     neck_conv1: candle_nn::Conv2d,
     neck_ln1: crate::LayerNorm2d,
     neck_conv2: candle_nn::Conv2d,
@@ -474,7 +478,7 @@ impl TinyViT {
         depths: &[usize],
         num_heads: &[usize],
         window_sizes: &[usize],
-        num_classes: usize,
+        _num_classes: usize,
         vb: VarBuilder,
     ) -> Result<Self> {
         let patch_embed = PatchEmbed::new(IN_CHANNELS, embed_dims[0], vb.pp("patch_embed"))?;
@@ -509,8 +513,8 @@ impl TinyViT {
         }
 
         let last_embed_dim = embed_dims[embed_dims.len() - 1];
-        let norm_head = candle_nn::layer_norm(last_embed_dim, 1e-5, vb.pp("norm_head"))?;
-        let head = candle_nn::linear(last_embed_dim, num_classes, vb.pp("head"))?;
+        // let norm_head = candle_nn::layer_norm(last_embed_dim, 1e-5, vb.pp("norm_head"))?;
+        // let head = candle_nn::linear(last_embed_dim, num_classes, vb.pp("head"))?;
         let neck_conv1 =
             candle_nn::conv2d_no_bias(last_embed_dim, 256, 1, Default::default(), vb.pp("neck.0"))?;
         let neck_ln1 = crate::LayerNorm2d::new(256, 1e-6, vb.pp("neck.1"))?;
@@ -525,8 +529,6 @@ impl TinyViT {
             patch_embed,
             layer0,
             layers,
-            norm_head,
-            head,
             neck_conv1,
             neck_ln1,
             neck_conv2,
@@ -537,7 +539,8 @@ impl TinyViT {
 
 impl Module for TinyViT {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = self.patch_embed.forward(xs)?;
+        let xs = self.patch_embed.forward(xs)?;
+        let mut xs = self.layer0.forward(&xs)?;
         for layer in self.layers.iter() {
             xs = layer.forward(&xs)?
         }
@@ -551,7 +554,7 @@ impl Module for TinyViT {
     }
 }
 
-pub fn tiny_vit_5m_224(vb: VarBuilder) -> Result<TinyViT> {
+pub fn tiny_vit_5m(vb: VarBuilder) -> Result<TinyViT> {
     TinyViT::new(
         /* embed_dims */ &[64, 128, 160, 320],
         /* depths */ &[2, 2, 6, 2],

From 31936c08fe26ad0bc401fb1ea5b4eac869491637 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 9 Sep 2023 17:26:39 +0100
Subject: [PATCH 049/150] ViT tracing. (#790)

---
 .../segment-anything/model_tiny_vit.rs        | 101 +++++++++++++++---
 1 file changed, 84 insertions(+), 17 deletions(-)

diff --git a/candle-examples/examples/segment-anything/model_tiny_vit.rs b/candle-examples/examples/segment-anything/model_tiny_vit.rs
index b3941ee1..ff076773 100644
--- a/candle-examples/examples/segment-anything/model_tiny_vit.rs
+++ b/candle-examples/examples/segment-anything/model_tiny_vit.rs
@@ -13,18 +13,21 @@ const IN_CHANNELS: usize = 3;
 struct Conv2dBN {
     c: candle_nn::Conv2d,
     bn: candle_nn::BatchNorm,
+    span: tracing::Span,
 }
 
 impl Conv2dBN {
     fn new(in_: usize, out: usize, ks: usize, cfg: Conv2dConfig, vb: VarBuilder) -> Result<Self> {
         let c = candle_nn::conv2d_no_bias(in_, out, ks, cfg, vb.pp("c"))?;
         let bn = candle_nn::batch_norm(out, 1e-5, vb.pp("bn"))?;
-        Ok(Self { c, bn })
+        let span = tracing::span!(tracing::Level::TRACE, "conv2d-bn");
+        Ok(Self { c, bn, span })
     }
 }
 
 impl Module for Conv2dBN {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         xs.apply(&self.c)?.apply(&self.bn)
     }
 }
@@ -33,6 +36,7 @@ impl Module for Conv2dBN {
 struct PatchEmbed {
     conv1: Conv2dBN,
     conv2: Conv2dBN,
+    span: tracing::Span,
 }
 
 impl PatchEmbed {
@@ -44,12 +48,14 @@ impl PatchEmbed {
         };
         let conv1 = Conv2dBN::new(in_chans, embed_dim / 2, 3, cfg, vb.pp("seq.0"))?;
         let conv2 = Conv2dBN::new(embed_dim / 2, embed_dim, 3, cfg, vb.pp("seq.2"))?;
-        Ok(Self { conv1, conv2 })
+        let span = tracing::span!(tracing::Level::TRACE, "patch-embed");
+        Ok(Self { conv1, conv2, span })
     }
 }
 
 impl Module for PatchEmbed {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         xs.apply(&self.conv1)?.gelu()?.apply(&self.conv2)
     }
 }
@@ -59,6 +65,7 @@ struct MBConv {
     conv1: Conv2dBN,
     conv2: Conv2dBN,
     conv3: Conv2dBN,
+    span: tracing::Span,
 }
 
 impl MBConv {
@@ -72,16 +79,19 @@ impl MBConv {
         let conv1 = Conv2dBN::new(in_, hidden, 1, Default::default(), vb.pp("conv1"))?;
         let conv2 = Conv2dBN::new(hidden, hidden, 3, cfg2, vb.pp("conv2"))?;
         let conv3 = Conv2dBN::new(hidden, out, 1, Default::default(), vb.pp("conv3"))?;
+        let span = tracing::span!(tracing::Level::TRACE, "mb-conv");
         Ok(Self {
             conv1,
             conv2,
             conv3,
+            span,
         })
     }
 }
 
 impl Module for MBConv {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let shortcut = xs;
         let xs = xs
             .apply(&self.conv1)?
@@ -99,6 +109,7 @@ struct PatchMerging {
     conv2: Conv2dBN,
     conv3: Conv2dBN,
     input_resolution: (usize, usize),
+    span: tracing::Span,
 }
 
 impl PatchMerging {
@@ -118,17 +129,20 @@ impl PatchMerging {
         let conv1 = Conv2dBN::new(dim, out, 1, Default::default(), vb.pp("conv1"))?;
         let conv2 = Conv2dBN::new(out, out, 3, cfg2, vb.pp("conv2"))?;
         let conv3 = Conv2dBN::new(out, out, 1, Default::default(), vb.pp("conv3"))?;
+        let span = tracing::span!(tracing::Level::TRACE, "patch-merging");
         Ok(Self {
             conv1,
             conv2,
             conv3,
             input_resolution,
+            span,
         })
     }
 }
 
 impl Module for PatchMerging {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let xs = if xs.rank() == 3 {
             let (h, w) = self.input_resolution;
             let b = xs.dim(0)?;
@@ -150,6 +164,7 @@ impl Module for PatchMerging {
 struct ConvLayer {
     blocks: Vec<MBConv>,
     downsample: Option<PatchMerging>,
+    span: tracing::Span,
 }
 
 impl ConvLayer {
@@ -174,12 +189,18 @@ impl ConvLayer {
         } else {
             None
         };
-        Ok(Self { blocks, downsample })
+        let span = tracing::span!(tracing::Level::TRACE, "conv-layer");
+        Ok(Self {
+            blocks,
+            downsample,
+            span,
+        })
     }
 }
 
 impl Module for ConvLayer {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let mut xs = xs.clone();
         for block in self.blocks.iter() {
             xs = block.forward(&xs)?
@@ -194,21 +215,29 @@ impl Module for ConvLayer {
 #[derive(Debug)]
 struct Mlp {
     norm: candle_nn::LayerNorm,
-    fc1: candle_nn::Linear,
-    fc2: candle_nn::Linear,
+    fc1: crate::Linear,
+    fc2: crate::Linear,
+    span: tracing::Span,
 }
 
 impl Mlp {
     fn new(in_: usize, hidden: usize, vb: VarBuilder) -> Result<Self> {
         let norm = candle_nn::layer_norm(in_, 1e-5, vb.pp("norm"))?;
-        let fc1 = candle_nn::linear(in_, hidden, vb.pp("fc1"))?;
-        let fc2 = candle_nn::linear(hidden, in_, vb.pp("fc2"))?;
-        Ok(Self { norm, fc1, fc2 })
+        let fc1 = crate::linear(vb.pp("fc1"), in_, hidden, true)?;
+        let fc2 = crate::linear(vb.pp("fc2"), hidden, in_, true)?;
+        let span = tracing::span!(tracing::Level::TRACE, "mlp");
+        Ok(Self {
+            norm,
+            fc1,
+            fc2,
+            span,
+        })
     }
 }
 
 impl Module for Mlp {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         xs.apply(&self.norm)?
             .apply(&self.fc1)?
             .gelu()?
@@ -219,14 +248,17 @@ impl Module for Mlp {
 #[derive(Debug)]
 struct Attention {
     norm: candle_nn::LayerNorm,
-    qkv: candle_nn::Linear,
-    proj: candle_nn::Linear,
+    qkv: crate::Linear,
+    proj: crate::Linear,
     ab: Tensor,
     key_dim: usize,
     num_heads: usize,
     d: usize,
     dh: usize,
     scale: f64,
+    span: tracing::Span,
+    span_matmul: tracing::Span,
+    span_softmax: tracing::Span,
 }
 
 impl Attention {
@@ -243,8 +275,8 @@ impl Attention {
         let nh_kd = key_dim * num_heads;
         let h = dh + nh_kd * 2;
         let norm = candle_nn::layer_norm(dim, 1e-5, vb.pp("norm"))?;
-        let qkv = candle_nn::linear(dim, h, vb.pp("qkv"))?;
-        let proj = candle_nn::linear(dh, dim, vb.pp("proj"))?;
+        let qkv = crate::linear(vb.pp("qkv"), dim, h, true)?;
+        let proj = crate::linear(vb.pp("proj"), dh, dim, true)?;
 
         let points = (0..resolution.0)
             .flat_map(|x| (0..resolution.1).map(move |y| (x as i64, y as i64)))
@@ -265,6 +297,9 @@ impl Attention {
             attention_biases
                 .index_select(&idxs, 1)?
                 .reshape(((), points.len(), points.len()))?;
+        let span = tracing::span!(tracing::Level::TRACE, "attention");
+        let span_matmul = tracing::span!(tracing::Level::TRACE, "attn-matmul");
+        let span_softmax = tracing::span!(tracing::Level::TRACE, "attn-sm");
         Ok(Self {
             norm,
             qkv,
@@ -275,12 +310,16 @@ impl Attention {
             d,
             dh,
             scale: 1f64 / (key_dim as f64).sqrt(),
+            span,
+            span_matmul,
+            span_softmax,
         })
     }
 }
 
 impl Module for Attention {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let (b, n, _) = xs.dims3()?;
         let xs = xs.apply(&self.norm)?;
         let qkv = xs.apply(&self.qkv)?.reshape((b, n, self.num_heads, ()))?;
@@ -296,11 +335,20 @@ impl Module for Attention {
             .narrow(D::Minus1, 2 * self.key_dim, self.d)?
             .permute((0, 2, 1, 3))?
             .contiguous()?;
-        let attn = (q.matmul(&k.t()?)? * self.scale)?;
+        let attn = {
+            let _enter = self.span_matmul.enter();
+            (q.matmul(&k.t()?)? * self.scale)?
+        };
         let attn = attn.broadcast_add(&self.ab)?;
-        let attn = candle_nn::ops::softmax_last_dim(&attn)?;
-        attn.matmul(&v)?
-            .transpose(1, 2)?
+        let attn = {
+            let _enter = self.span_softmax.enter();
+            candle_nn::ops::softmax_last_dim(&attn)?
+        };
+        let attn = {
+            let _enter = self.span_matmul.enter();
+            attn.matmul(&v)?
+        };
+        attn.transpose(1, 2)?
             .reshape((b, n, self.dh))?
             .apply(&self.proj)
     }
@@ -313,6 +361,7 @@ struct TinyViTBlock {
     mlp: Mlp,
     window_size: usize,
     input_resolution: (usize, usize),
+    span: tracing::Span,
 }
 
 impl TinyViTBlock {
@@ -339,18 +388,21 @@ impl TinyViTBlock {
             ..Default::default()
         };
         let local_conv = Conv2dBN::new(dim, dim, LOCAL_CONV_SIZE, cfg, vb.pp("local_conv"))?;
+        let span = tracing::span!(tracing::Level::TRACE, "attention");
         Ok(Self {
             attn,
             local_conv,
             mlp,
             window_size,
             input_resolution,
+            span,
         })
     }
 }
 
 impl Module for TinyViTBlock {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let (h, w) = self.input_resolution;
         let (b, l, c) = xs.dims3()?;
         let res_x = xs;
@@ -410,6 +462,7 @@ impl Module for TinyViTBlock {
 struct BasicLayer {
     blocks: Vec<TinyViTBlock>,
     downsample: Option<PatchMerging>,
+    span: tracing::Span,
 }
 
 impl BasicLayer {
@@ -442,12 +495,18 @@ impl BasicLayer {
         } else {
             None
         };
-        Ok(Self { blocks, downsample })
+        let span = tracing::span!(tracing::Level::TRACE, "basic-layer");
+        Ok(Self {
+            blocks,
+            downsample,
+            span,
+        })
     }
 }
 
 impl Module for BasicLayer {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let mut xs = xs.clone();
         for block in self.blocks.iter() {
             xs = block.forward(&xs)?
@@ -470,6 +529,8 @@ pub struct TinyViT {
     neck_ln1: crate::LayerNorm2d,
     neck_conv2: candle_nn::Conv2d,
     neck_ln2: crate::LayerNorm2d,
+    span: tracing::Span,
+    span_neck: tracing::Span,
 }
 
 impl TinyViT {
@@ -525,6 +586,8 @@ impl TinyViT {
         let neck_conv2 = candle_nn::conv2d_no_bias(256, 256, 3, cfg, vb.pp("neck.2"))?;
         let neck_ln2 = crate::LayerNorm2d::new(256, 1e-6, vb.pp("neck.3"))?;
 
+        let span = tracing::span!(tracing::Level::TRACE, "tiny-vit");
+        let span_neck = tracing::span!(tracing::Level::TRACE, "neck");
         Ok(Self {
             patch_embed,
             layer0,
@@ -533,18 +596,22 @@ impl TinyViT {
             neck_ln1,
             neck_conv2,
             neck_ln2,
+            span,
+            span_neck,
         })
     }
 }
 
 impl Module for TinyViT {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let xs = self.patch_embed.forward(xs)?;
         let mut xs = self.layer0.forward(&xs)?;
         for layer in self.layers.iter() {
             xs = layer.forward(&xs)?
         }
         let (b, _, c) = xs.dims3()?;
+        let _enter = self.span_neck.enter();
         xs.reshape((b, 64, 64, c))?
             .permute((0, 3, 1, 2))?
             .apply(&self.neck_conv1)?

From 258ac32c3868d4103e90df19af99a3e13c805c4e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 9 Sep 2023 18:44:21 +0100
Subject: [PATCH 050/150] Fix cuda randn when generating an odd number of
 values. (#793)

---
 candle-core/src/cuda_backend.rs   | 11 +++++++++--
 candle-core/tests/tensor_tests.rs |  9 +++++++++
 2 files changed, 18 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index cb00441f..7cc85489 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -312,6 +312,13 @@ impl BackendDevice for CudaDevice {
         // cudarc changes.
         let elem_count = shape.elem_count();
         let curand = self.curand.lock().unwrap();
+        // curand can only generate an odd number of values.
+        // https://github.com/huggingface/candle/issues/734
+        let elem_count_round = if elem_count % 2 == 1 {
+            elem_count + 1
+        } else {
+            elem_count
+        };
         let slice = match dtype {
             DType::U8 | DType::U32 | DType::I64 | DType::F16 | DType::BF16 => {
                 Err(CudaError::UnsupportedDtype {
@@ -321,7 +328,7 @@ impl BackendDevice for CudaDevice {
                 .w()?
             }
             DType::F32 => {
-                let mut data = unsafe { self.alloc::<f32>(elem_count) }.w()?;
+                let mut data = unsafe { self.alloc::<f32>(elem_count_round) }.w()?;
                 curand
                     .0
                     .fill_with_normal(&mut data, mean as f32, std as f32)
@@ -329,7 +336,7 @@ impl BackendDevice for CudaDevice {
                 CudaStorageSlice::F32(data)
             }
             DType::F64 => {
-                let mut data = unsafe { self.alloc::<f64>(elem_count) }.w()?;
+                let mut data = unsafe { self.alloc::<f64>(elem_count_round) }.w()?;
                 curand.0.fill_with_normal(&mut data, mean, std).w()?;
                 CudaStorageSlice::F64(data)
             }
diff --git a/candle-core/tests/tensor_tests.rs b/candle-core/tests/tensor_tests.rs
index 6af43196..cd68908f 100644
--- a/candle-core/tests/tensor_tests.rs
+++ b/candle-core/tests/tensor_tests.rs
@@ -877,6 +877,14 @@ fn broadcasting(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn randn(device: &Device) -> Result<()> {
+    let tensor = Tensor::randn(0f32, 1f32, (5, 3), device)?;
+    assert_eq!(tensor.dims(), [5, 3]);
+    let tensor = Tensor::rand(0f32, 1f32, (5, 3), device)?;
+    assert_eq!(tensor.dims(), [5, 3]);
+    Ok(())
+}
+
 test_device!(zeros, zeros_cpu, zeros_gpu);
 test_device!(add_mul, add_mul_cpu, add_mul_gpu);
 test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu);
@@ -899,6 +907,7 @@ test_device!(index_select, index_select_cpu, index_select_gpu);
 test_device!(index_add, index_add_cpu, index_add_gpu);
 test_device!(gather, gather_cpu, gather_gpu);
 test_device!(scatter_add, scatter_add_cpu, scatter_add_gpu);
+test_device!(randn, randn_cpu, randn_gpu);
 
 // There was originally a bug on the CPU implementation for randn
 // https://github.com/huggingface/candle/issues/381

From d3f05eae8c4f2df186b46e433be101ac39fceca5 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 09:40:27 +0100
Subject: [PATCH 051/150] Move some models to candle-transformers so that it's
 easier to re-use. (#794)

* Move some models to candle-transformers so that they can be shared.

* Also move falcon.

* Move Llama.

* Move whisper (partial).
---
 candle-examples/Cargo.toml                    | 12 ++--
 candle-examples/examples/bert/main.rs         |  3 +-
 candle-examples/examples/bigcode/main.rs      |  3 +-
 candle-examples/examples/falcon/main.rs       |  3 +-
 candle-examples/examples/llama/main.rs        |  3 +-
 candle-examples/examples/whisper/main.rs      | 71 ++++++-------------
 .../examples/whisper/multilingual.rs          |  2 +-
 candle-transformers/Cargo.toml                |  4 ++
 .../src/models/bert.rs                        |  0
 .../src/models/bigcode.rs                     |  0
 .../src/models/falcon.rs                      | 11 ++-
 .../src/models/llama.rs                       |  2 +-
 candle-transformers/src/models/mod.rs         |  6 +-
 .../src/models}/whisper/audio.rs              | 10 +--
 candle-transformers/src/models/whisper/mod.rs | 26 +++++++
 .../src/models}/whisper/model.rs              |  0
 16 files changed, 78 insertions(+), 78 deletions(-)
 rename candle-examples/examples/bert/model.rs => candle-transformers/src/models/bert.rs (100%)
 rename candle-examples/examples/bigcode/model.rs => candle-transformers/src/models/bigcode.rs (100%)
 rename candle-examples/examples/falcon/model.rs => candle-transformers/src/models/falcon.rs (98%)
 rename candle-examples/examples/llama/model.rs => candle-transformers/src/models/llama.rs (99%)
 rename {candle-examples/examples => candle-transformers/src/models}/whisper/audio.rs (97%)
 create mode 100644 candle-transformers/src/models/whisper/mod.rs
 rename {candle-examples/examples => candle-transformers/src/models}/whisper/model.rs (100%)

diff --git a/candle-examples/Cargo.toml b/candle-examples/Cargo.toml
index 6f8792a3..eb552b88 100644
--- a/candle-examples/Cargo.toml
+++ b/candle-examples/Cargo.toml
@@ -13,18 +13,18 @@ readme = "README.md"
 accelerate-src = { workspace = true, optional = true }
 candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
 candle-datasets = { path = "../candle-datasets", version = "0.2.1" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.1", optional = true }
 candle-nn = { path = "../candle-nn", version = "0.2.1" }
 candle-transformers = { path = "../candle-transformers", version = "0.2.1" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.1", optional = true }
-safetensors = { workspace = true }
-serde = { workspace = true }
-serde_json = { workspace = true }
-num-traits = { workspace = true }
-intel-mkl-src = { workspace = true, optional = true }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true }
+intel-mkl-src = { workspace = true, optional = true }
+num-traits = { workspace = true }
 rayon = { workspace = true }
+safetensors = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
diff --git a/candle-examples/examples/bert/main.rs b/candle-examples/examples/bert/main.rs
index 6cee66ee..9d0eccdf 100644
--- a/candle-examples/examples/bert/main.rs
+++ b/candle-examples/examples/bert/main.rs
@@ -3,14 +3,13 @@ extern crate intel_mkl_src;
 
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
-mod model;
+use candle_transformers::models::bert::{BertModel, Config, DTYPE};
 
 use anyhow::{anyhow, Error as E, Result};
 use candle::Tensor;
 use candle_nn::VarBuilder;
 use clap::Parser;
 use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
-use model::{BertModel, Config, DTYPE};
 use tokenizers::{PaddingParams, Tokenizer};
 
 #[derive(Parser, Debug)]
diff --git a/candle-examples/examples/bigcode/main.rs b/candle-examples/examples/bigcode/main.rs
index 652cd47f..3540f75d 100644
--- a/candle-examples/examples/bigcode/main.rs
+++ b/candle-examples/examples/bigcode/main.rs
@@ -7,8 +7,7 @@ extern crate accelerate_src;
 use anyhow::{Error as E, Result};
 use clap::Parser;
 
-mod model;
-use model::{Config, GPTBigCode};
+use candle_transformers::models::bigcode::{Config, GPTBigCode};
 
 use candle::{DType, Device, Tensor};
 use candle_nn::VarBuilder;
diff --git a/candle-examples/examples/falcon/main.rs b/candle-examples/examples/falcon/main.rs
index 05507f08..c45fe545 100644
--- a/candle-examples/examples/falcon/main.rs
+++ b/candle-examples/examples/falcon/main.rs
@@ -14,8 +14,7 @@ use clap::Parser;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::Tokenizer;
 
-mod model;
-use model::{Config, Falcon};
+use candle_transformers::models::falcon::{Config, Falcon};
 
 struct TextGeneration {
     model: Falcon,
diff --git a/candle-examples/examples/llama/main.rs b/candle-examples/examples/llama/main.rs
index 6f8766d4..db3d216c 100644
--- a/candle-examples/examples/llama/main.rs
+++ b/candle-examples/examples/llama/main.rs
@@ -21,11 +21,10 @@ use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use std::io::Write;
 
-mod model;
+use candle_transformers::models::llama as model;
 use model::{Config, Llama, LlamaConfig};
 
 const EOS_TOKEN: &str = "</s>";
-const MAX_SEQ_LEN: usize = 4096;
 const DEFAULT_PROMPT: &str = "My favorite theorem is ";
 
 #[derive(Parser, Debug)]
diff --git a/candle-examples/examples/whisper/main.rs b/candle-examples/examples/whisper/main.rs
index dbe9cc8d..c71d562a 100644
--- a/candle-examples/examples/whisper/main.rs
+++ b/candle-examples/examples/whisper/main.rs
@@ -10,41 +10,16 @@ extern crate accelerate_src;
 extern crate intel_mkl_src;
 
 use anyhow::{Error as E, Result};
-use candle::{DType, Device, IndexOp, Tensor};
+use candle::{Device, IndexOp, Tensor};
 use candle_nn::{ops::softmax, VarBuilder};
 use clap::{Parser, ValueEnum};
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use rand::{distributions::Distribution, SeedableRng};
 use tokenizers::Tokenizer;
 
-mod audio;
-mod model;
-use model::{Config, Whisper};
 mod multilingual;
-
-const DTYPE: DType = DType::F32;
-
-// Audio parameters.
-const SAMPLE_RATE: usize = 16000;
-const N_FFT: usize = 400;
-const N_MELS: usize = 80;
-const HOP_LENGTH: usize = 160;
-const CHUNK_LENGTH: usize = 30;
-const N_SAMPLES: usize = CHUNK_LENGTH * SAMPLE_RATE; // 480000 samples in a 30-second chunk
-const N_FRAMES: usize = N_SAMPLES / HOP_LENGTH; // 3000 frames in a mel spectrogram input
-
-const NO_SPEECH_THRESHOLD: f64 = 0.6;
-const LOGPROB_THRESHOLD: f64 = -1.0;
-const TEMPERATURES: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0];
-const COMPRESSION_RATIO_THRESHOLD: f64 = 2.4;
-
-// Tokenizer dependent bits.
-const SOT_TOKEN: &str = "<|startoftranscript|>";
-const TRANSCRIBE_TOKEN: &str = "<|transcribe|>";
-const TRANSLATE_TOKEN: &str = "<|translate|>";
-const NO_TIMESTAMPS_TOKEN: &str = "<|notimestamps|>";
-const EOT_TOKEN: &str = "<|endoftext|>";
-const NO_SPEECH_TOKEN: &str = "<|nocaptions|>";
+use candle_transformers::models::whisper::{self as m, audio, model};
+use model::{Config, Whisper};
 
 #[allow(dead_code)]
 #[derive(Debug, Clone)]
@@ -94,7 +69,7 @@ impl Decoder {
         timestamps: bool,
         verbose: bool,
     ) -> Result<Self> {
-        let no_timestamps_token = token_id(&tokenizer, NO_TIMESTAMPS_TOKEN)?;
+        let no_timestamps_token = token_id(&tokenizer, m::NO_TIMESTAMPS_TOKEN)?;
         // Suppress the notimestamps token when in timestamps mode.
         // https://github.com/openai/whisper/blob/e8622f9afc4eba139bf796c210f5c01081000472/whisper/decoding.py#L452
         let suppress_tokens: Vec<f32> = (0..model.config.vocab_size as u32)
@@ -109,11 +84,11 @@ impl Decoder {
             })
             .collect();
         let suppress_tokens = Tensor::new(suppress_tokens.as_slice(), device)?;
-        let sot_token = token_id(&tokenizer, SOT_TOKEN)?;
-        let transcribe_token = token_id(&tokenizer, TRANSCRIBE_TOKEN)?;
-        let translate_token = token_id(&tokenizer, TRANSLATE_TOKEN)?;
-        let eot_token = token_id(&tokenizer, EOT_TOKEN)?;
-        let no_speech_token = token_id(&tokenizer, NO_SPEECH_TOKEN)?;
+        let sot_token = token_id(&tokenizer, m::SOT_TOKEN)?;
+        let transcribe_token = token_id(&tokenizer, m::TRANSCRIBE_TOKEN)?;
+        let translate_token = token_id(&tokenizer, m::TRANSLATE_TOKEN)?;
+        let eot_token = token_id(&tokenizer, m::EOT_TOKEN)?;
+        let no_speech_token = token_id(&tokenizer, m::NO_SPEECH_TOKEN)?;
         Ok(Self {
             model,
             rng: rand::rngs::StdRng::seed_from_u64(seed),
@@ -220,17 +195,17 @@ impl Decoder {
     }
 
     fn decode_with_fallback(&mut self, segment: &Tensor) -> Result<DecodingResult> {
-        for (i, &t) in TEMPERATURES.iter().enumerate() {
+        for (i, &t) in m::TEMPERATURES.iter().enumerate() {
             let dr: Result<DecodingResult> = self.decode(segment, t);
-            if i == TEMPERATURES.len() - 1 {
+            if i == m::TEMPERATURES.len() - 1 {
                 return dr;
             }
             // On errors, we try again with a different temperature.
             match dr {
                 Ok(dr) => {
-                    let needs_fallback = dr.compression_ratio > COMPRESSION_RATIO_THRESHOLD
-                        || dr.avg_logprob < LOGPROB_THRESHOLD;
-                    if !needs_fallback || dr.no_speech_prob > NO_SPEECH_THRESHOLD {
+                    let needs_fallback = dr.compression_ratio > m::COMPRESSION_RATIO_THRESHOLD
+                        || dr.avg_logprob < m::LOGPROB_THRESHOLD;
+                    if !needs_fallback || dr.no_speech_prob > m::NO_SPEECH_THRESHOLD {
                         return Ok(dr);
                     }
                 }
@@ -248,13 +223,13 @@ impl Decoder {
         let mut segments = vec![];
         while seek < content_frames {
             let start = std::time::Instant::now();
-            let time_offset = (seek * HOP_LENGTH) as f64 / SAMPLE_RATE as f64;
-            let segment_size = usize::min(content_frames - seek, N_FRAMES);
+            let time_offset = (seek * m::HOP_LENGTH) as f64 / m::SAMPLE_RATE as f64;
+            let segment_size = usize::min(content_frames - seek, m::N_FRAMES);
             let mel_segment = mel.narrow(2, seek, segment_size)?;
-            let segment_duration = (segment_size * HOP_LENGTH) as f64 / SAMPLE_RATE as f64;
+            let segment_duration = (segment_size * m::HOP_LENGTH) as f64 / m::SAMPLE_RATE as f64;
             let dr = self.decode_with_fallback(&mel_segment)?;
             seek += segment_size;
-            if dr.no_speech_prob > NO_SPEECH_THRESHOLD && dr.avg_logprob < LOGPROB_THRESHOLD {
+            if dr.no_speech_prob > m::NO_SPEECH_THRESHOLD && dr.avg_logprob < m::LOGPROB_THRESHOLD {
                 println!("no speech detected, skipping {seek} {dr:?}");
                 continue;
             }
@@ -492,8 +467,8 @@ fn main() -> Result<()> {
     let mut input = std::fs::File::open(input)?;
     let (header, data) = wav::read(&mut input)?;
     println!("loaded wav data: {header:?}");
-    if header.sampling_rate != SAMPLE_RATE as u32 {
-        anyhow::bail!("wav file must have a {} sampling rate", SAMPLE_RATE)
+    if header.sampling_rate != m::SAMPLE_RATE as u32 {
+        anyhow::bail!("wav file must have a {} sampling rate", m::SAMPLE_RATE)
     }
     let data = data.as_sixteen().expect("expected 16 bit wav file");
     let pcm_data: Vec<_> = data[..data.len() / header.channel_count as usize]
@@ -501,14 +476,14 @@ fn main() -> Result<()> {
         .map(|v| *v as f32 / 32768.)
         .collect();
     println!("pcm data loaded {}", pcm_data.len());
-    let mel = audio::pcm_to_mel(&pcm_data, &mel_filters)?;
+    let mel = audio::pcm_to_mel(&pcm_data, &mel_filters);
     let mel_len = mel.len();
-    let mel = Tensor::from_vec(mel, (1, N_MELS, mel_len / N_MELS), &device)?;
+    let mel = Tensor::from_vec(mel, (1, m::N_MELS, mel_len / m::N_MELS), &device)?;
     println!("loaded mel: {:?}", mel.dims());
 
     let weights = unsafe { candle::safetensors::MmapedFile::new(weights_filename)? };
     let weights = weights.deserialize()?;
-    let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &device);
+    let vb = VarBuilder::from_safetensors(vec![weights], m::DTYPE, &device);
     let config: Config = serde_json::from_str(&std::fs::read_to_string(config_filename)?)?;
     let mut model = Whisper::load(&vb, config)?;
 
diff --git a/candle-examples/examples/whisper/multilingual.rs b/candle-examples/examples/whisper/multilingual.rs
index bc0bae1f..a82b09ef 100644
--- a/candle-examples/examples/whisper/multilingual.rs
+++ b/candle-examples/examples/whisper/multilingual.rs
@@ -113,7 +113,7 @@ pub fn detect_language(model: &mut Whisper, tokenizer: &Tokenizer, mel: &Tensor)
         .iter()
         .map(|(t, _)| crate::token_id(tokenizer, &format!("<|{t}|>")))
         .collect::<Result<Vec<_>>>()?;
-    let sot_token = crate::token_id(tokenizer, crate::SOT_TOKEN)?;
+    let sot_token = crate::token_id(tokenizer, crate::m::SOT_TOKEN)?;
     let audio_features = model.encoder.forward(&mel, true)?;
     let tokens = Tensor::new(&[[sot_token]], device)?;
     let language_token_ids = Tensor::new(language_token_ids.as_slice(), device)?;
diff --git a/candle-transformers/Cargo.toml b/candle-transformers/Cargo.toml
index a05b9bb7..6b2087cb 100644
--- a/candle-transformers/Cargo.toml
+++ b/candle-transformers/Cargo.toml
@@ -14,7 +14,11 @@ accelerate-src = { workspace = true, optional = true }
 candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
 candle-nn = { path = "../candle-nn", version = "0.2.1" }
 intel-mkl-src = { workspace = true, optional = true }
+num-traits = { workspace = true }
 rand = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
+tracing = { workspace = true }
 wav = { workspace = true }
 
 [features]
diff --git a/candle-examples/examples/bert/model.rs b/candle-transformers/src/models/bert.rs
similarity index 100%
rename from candle-examples/examples/bert/model.rs
rename to candle-transformers/src/models/bert.rs
diff --git a/candle-examples/examples/bigcode/model.rs b/candle-transformers/src/models/bigcode.rs
similarity index 100%
rename from candle-examples/examples/bigcode/model.rs
rename to candle-transformers/src/models/bigcode.rs
diff --git a/candle-examples/examples/falcon/model.rs b/candle-transformers/src/models/falcon.rs
similarity index 98%
rename from candle-examples/examples/falcon/model.rs
rename to candle-transformers/src/models/falcon.rs
index b638dd51..6ede136a 100644
--- a/candle-examples/examples/falcon/model.rs
+++ b/candle-transformers/src/models/falcon.rs
@@ -1,5 +1,4 @@
-use anyhow::Result;
-use candle::{DType, Device, Tensor, D};
+use candle::{DType, Device, Result, Tensor, D};
 use candle_nn::{Embedding, LayerNorm, Linear, Module, VarBuilder};
 
 const MAX_SEQ_LEN: usize = 5000;
@@ -21,7 +20,7 @@ fn layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> {
             if let (Ok(weight), Ok(bias)) = (vb.get(size, "gamma"), vb.get(size, "beta")) {
                 (weight, bias)
             } else {
-                return Err(err.into());
+                return Err(err);
             }
         }
     };
@@ -82,13 +81,13 @@ impl Default for Config {
 impl Config {
     pub fn validate(&self) -> Result<()> {
         if self.alibi {
-            anyhow::bail!("alibi is not supported");
+            candle::bail!("alibi is not supported");
         }
         if self.new_decoder_architecture {
-            anyhow::bail!("new_decoder_architecture is not supported");
+            candle::bail!("new_decoder_architecture is not supported");
         }
         if self.n_head_kv.is_some() {
-            anyhow::bail!("n_head_kv is not supported");
+            candle::bail!("n_head_kv is not supported");
         }
         Ok(())
     }
diff --git a/candle-examples/examples/llama/model.rs b/candle-transformers/src/models/llama.rs
similarity index 99%
rename from candle-examples/examples/llama/model.rs
rename to candle-transformers/src/models/llama.rs
index 275856e0..eed4df5e 100644
--- a/candle-examples/examples/llama/model.rs
+++ b/candle-transformers/src/models/llama.rs
@@ -4,7 +4,7 @@ use serde::Deserialize;
 use std::collections::HashMap;
 use std::sync::{Arc, Mutex};
 
-use super::MAX_SEQ_LEN;
+pub const MAX_SEQ_LEN: usize = 4096;
 
 #[derive(Deserialize)]
 pub struct LlamaConfig {
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 8b137891..1b3dcf25 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -1 +1,5 @@
-
+pub mod bert;
+pub mod bigcode;
+pub mod falcon;
+pub mod llama;
+pub mod whisper;
diff --git a/candle-examples/examples/whisper/audio.rs b/candle-transformers/src/models/whisper/audio.rs
similarity index 97%
rename from candle-examples/examples/whisper/audio.rs
rename to candle-transformers/src/models/whisper/audio.rs
index 2ceed065..4e01de32 100644
--- a/candle-examples/examples/whisper/audio.rs
+++ b/candle-transformers/src/models/whisper/audio.rs
@@ -198,17 +198,13 @@ fn log_mel_spectrogram_<T: Float + std::fmt::Display>(
     mel
 }
 
-pub fn pcm_to_mel<T: Float + std::fmt::Display>(
-    samples: &[T],
-    filters: &[T],
-) -> anyhow::Result<Vec<T>> {
-    let mel = log_mel_spectrogram_(
+pub fn pcm_to_mel<T: Float + std::fmt::Display>(samples: &[T], filters: &[T]) -> Vec<T> {
+    log_mel_spectrogram_(
         samples,
         filters,
         super::N_FFT,
         super::HOP_LENGTH,
         super::N_MELS,
         false,
-    );
-    Ok(mel)
+    )
 }
diff --git a/candle-transformers/src/models/whisper/mod.rs b/candle-transformers/src/models/whisper/mod.rs
new file mode 100644
index 00000000..7dc8107b
--- /dev/null
+++ b/candle-transformers/src/models/whisper/mod.rs
@@ -0,0 +1,26 @@
+pub mod audio;
+pub mod model;
+
+pub const DTYPE: candle::DType = candle::DType::F32;
+
+// Audio parameters.
+pub const SAMPLE_RATE: usize = 16000;
+pub const N_FFT: usize = 400;
+pub const N_MELS: usize = 80;
+pub const HOP_LENGTH: usize = 160;
+pub const CHUNK_LENGTH: usize = 30;
+pub const N_SAMPLES: usize = CHUNK_LENGTH * SAMPLE_RATE; // 480000 samples in a 30-second chunk
+pub const N_FRAMES: usize = N_SAMPLES / HOP_LENGTH; // 3000 frames in a mel spectrogram input
+
+pub const NO_SPEECH_THRESHOLD: f64 = 0.6;
+pub const LOGPROB_THRESHOLD: f64 = -1.0;
+pub const TEMPERATURES: [f64; 6] = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0];
+pub const COMPRESSION_RATIO_THRESHOLD: f64 = 2.4;
+
+// Tokenizer dependent bits.
+pub const SOT_TOKEN: &str = "<|startoftranscript|>";
+pub const TRANSCRIBE_TOKEN: &str = "<|transcribe|>";
+pub const TRANSLATE_TOKEN: &str = "<|translate|>";
+pub const NO_TIMESTAMPS_TOKEN: &str = "<|notimestamps|>";
+pub const EOT_TOKEN: &str = "<|endoftext|>";
+pub const NO_SPEECH_TOKEN: &str = "<|nocaptions|>";
diff --git a/candle-examples/examples/whisper/model.rs b/candle-transformers/src/models/whisper/model.rs
similarity index 100%
rename from candle-examples/examples/whisper/model.rs
rename to candle-transformers/src/models/whisper/model.rs

From 35f72514f59b3fa4bd321e3e88a75f5b43cf060f Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 10:20:18 +0100
Subject: [PATCH 052/150] Move more models to candle-transformers (#796)

* Move dinov2.

* Move efficientnet.

* Move the quantized llama model.

* Move segment-anything.
---
 candle-examples/examples/dinov2/main.rs       | 283 +--------------
 candle-examples/examples/efficientnet/main.rs | 335 +-----------------
 candle-examples/examples/quantized/main.rs    |   2 +-
 .../examples/segment-anything/main.rs         | 109 +-----
 candle-examples/examples/yolo-v3/main.rs      |   2 +-
 candle-examples/examples/yolo-v8/main.rs      |   2 +-
 candle-examples/src/lib.rs                    |   1 -
 candle-transformers/Cargo.toml                |   1 +
 candle-transformers/src/lib.rs                |   1 +
 candle-transformers/src/models/dinov2.rs      | 279 +++++++++++++++
 .../src/models/efficientnet.rs                | 331 +++++++++++++++++
 candle-transformers/src/models/mod.rs         |   4 +
 .../src/models/quantized_llama.rs             |   0
 .../models/segment_anything/image_encoder.rs  |  20 +-
 .../models/segment_anything/mask_decoder.rs   |  10 +-
 .../src/models/segment_anything/mod.rs        | 100 ++++++
 .../models/segment_anything/prompt_encoder.rs |   8 +-
 .../src/models/segment_anything/sam.rs        |  16 +-
 .../src/models/segment_anything/tiny_vit.rs   |  24 +-
 .../models/segment_anything/transformer.rs    |   4 +-
 .../src/object_detection.rs                   |   0
 21 files changed, 773 insertions(+), 759 deletions(-)
 create mode 100644 candle-transformers/src/models/dinov2.rs
 create mode 100644 candle-transformers/src/models/efficientnet.rs
 rename candle-examples/examples/quantized/model.rs => candle-transformers/src/models/quantized_llama.rs (100%)
 rename candle-examples/examples/segment-anything/model_image_encoder.rs => candle-transformers/src/models/segment_anything/image_encoder.rs (96%)
 rename candle-examples/examples/segment-anything/model_mask_decoder.rs => candle-transformers/src/models/segment_anything/mask_decoder.rs (96%)
 create mode 100644 candle-transformers/src/models/segment_anything/mod.rs
 rename candle-examples/examples/segment-anything/model_prompt_encoder.rs => candle-transformers/src/models/segment_anything/prompt_encoder.rs (97%)
 rename candle-examples/examples/segment-anything/model_sam.rs => candle-transformers/src/models/segment_anything/sam.rs (96%)
 rename candle-examples/examples/segment-anything/model_tiny_vit.rs => candle-transformers/src/models/segment_anything/tiny_vit.rs (97%)
 rename candle-examples/examples/segment-anything/model_transformer.rs => candle-transformers/src/models/segment_anything/transformer.rs (99%)
 rename {candle-examples => candle-transformers}/src/object_detection.rs (100%)

diff --git a/candle-examples/examples/dinov2/main.rs b/candle-examples/examples/dinov2/main.rs
index e80c81e2..d3adb37c 100644
--- a/candle-examples/examples/dinov2/main.rs
+++ b/candle-examples/examples/dinov2/main.rs
@@ -9,285 +9,10 @@ extern crate accelerate_src;
 
 use clap::Parser;
 
-use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use candle::{DType, IndexOp, D};
+use candle_nn::{Module, VarBuilder};
+use candle_transformers::models::dinov2;
 
-const IMG_SIZE: usize = 518;
-const PATCH_SIZE: usize = 14;
-const NUM_CLASSES: usize = 1000;
-
-fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
-    if bias {
-        candle_nn::linear(in_dim, out_dim, vb)
-    } else {
-        candle_nn::linear_no_bias(in_dim, out_dim, vb)
-    }
-}
-
-#[derive(Debug)]
-struct Attention {
-    qkv: Linear,
-    proj: Linear,
-    num_heads: usize,
-    scale: f64,
-}
-
-impl Attention {
-    fn new(
-        vb: VarBuilder,
-        dim: usize,
-        num_heads: usize,
-        qkv_bias: bool,
-        proj_bias: bool,
-    ) -> Result<Self> {
-        let qkv = linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
-        let proj = linear(vb.pp("proj"), dim, dim, proj_bias)?;
-        let scale = 1. / ((dim / num_heads) as f64).sqrt();
-        Ok(Self {
-            qkv,
-            proj,
-            num_heads,
-            scale,
-        })
-    }
-}
-
-impl Module for Attention {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let (b, n, c) = xs.dims3()?;
-        let qkv = self
-            .qkv
-            .forward(xs)?
-            .reshape((b, n, 3, self.num_heads, c / self.num_heads))?
-            .transpose(1, 2)? // 02134
-            .transpose(0, 1)? // 20134
-            .transpose(2, 3)?; // 20314
-        let q = (qkv.i(0)? * self.scale)?;
-        let k = qkv.i(1)?;
-        let v = qkv.i(2)?;
-        let attn = candle_nn::ops::softmax(&q.matmul(&k.t()?)?, D::Minus1)?;
-        let attn = attn.matmul(&v)?.transpose(1, 2)?.reshape((b, n, c))?;
-        self.proj.forward(&attn)
-    }
-}
-
-#[derive(Debug)]
-struct LayerScale {
-    gamma: Tensor,
-}
-
-impl LayerScale {
-    fn new(vb: VarBuilder, dim: usize) -> Result<Self> {
-        let gamma = vb.get(dim, "gamma")?;
-        Ok(Self { gamma })
-    }
-}
-
-impl Module for LayerScale {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        xs.broadcast_mul(&self.gamma)
-    }
-}
-
-#[derive(Debug)]
-struct Mlp {
-    fc1: Linear,
-    fc2: Linear,
-}
-
-impl Mlp {
-    fn new(vb: VarBuilder, in_features: usize, hidden_features: usize, bias: bool) -> Result<Self> {
-        let out_features = in_features;
-        let fc1 = linear(vb.pp("fc1"), in_features, hidden_features, bias)?;
-        let fc2 = linear(vb.pp("fc2"), hidden_features, out_features, bias)?;
-        Ok(Self { fc1, fc2 })
-    }
-}
-
-impl Module for Mlp {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let xs = self.fc1.forward(xs)?.gelu()?;
-        self.fc2.forward(&xs)
-    }
-}
-
-#[derive(Debug)]
-struct Block {
-    norm1: LayerNorm,
-    attn: Attention,
-    ls1: LayerScale,
-    norm2: LayerNorm,
-    mlp: Mlp,
-    ls2: LayerScale,
-}
-
-impl Block {
-    fn new(vb: VarBuilder, dim: usize, num_heads: usize) -> Result<Self> {
-        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
-        let attn = Attention::new(vb.pp("attn"), dim, num_heads, true, true)?;
-        let ls1 = LayerScale::new(vb.pp("ls1"), dim)?;
-        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
-        let mlp = Mlp::new(vb.pp("mlp"), dim, dim * 4, true)?;
-        let ls2 = LayerScale::new(vb.pp("ls2"), dim)?;
-        Ok(Self {
-            norm1,
-            attn,
-            ls1,
-            norm2,
-            mlp,
-            ls2,
-        })
-    }
-}
-
-impl Module for Block {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let residual = xs;
-        let xs = self
-            .ls1
-            .forward(&self.attn.forward(&self.norm1.forward(xs)?)?)?;
-        let xs = (xs + residual)?;
-        let residual = &xs;
-        let xs = self
-            .ls2
-            .forward(&self.mlp.forward(&self.norm2.forward(&xs)?)?)?;
-        xs + residual
-    }
-}
-
-#[derive(Debug)]
-struct PatchEmbed {
-    proj: candle_nn::Conv2d,
-    patch_size: (usize, usize),
-    num_patches: usize,
-}
-
-impl PatchEmbed {
-    fn new(
-        vb: VarBuilder,
-        img_size: usize,
-        patch_size: usize,
-        in_chans: usize,
-        embed_dim: usize,
-    ) -> Result<Self> {
-        let config = candle_nn::Conv2dConfig {
-            stride: patch_size,
-            ..Default::default()
-        };
-        let proj = candle_nn::conv2d(in_chans, embed_dim, patch_size, config, vb.pp("proj"))?;
-        let num_patches = (img_size / patch_size) * (img_size / patch_size);
-        Ok(Self {
-            proj,
-            patch_size: (patch_size, patch_size),
-            num_patches,
-        })
-    }
-}
-
-impl Module for PatchEmbed {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let (_b, _c, h, w) = xs.dims4()?;
-        let (patch_h, patch_w) = self.patch_size;
-        if (h % patch_h) != 0 {
-            candle::bail!("image height {h} is not a multiple of patch height {patch_h}")
-        }
-        if (w % patch_w) != 0 {
-            candle::bail!("image width {w} is not a multiple of patch width {patch_w}")
-        }
-        let xs = self.proj.forward(xs)?;
-        let (b, c, h, w) = xs.dims4()?;
-        // flatten embeddings.
-        xs.reshape((b, c, h * w))?.transpose(1, 2)
-    }
-}
-
-#[derive(Debug)]
-pub struct DinoVisionTransformer {
-    patch_embed: PatchEmbed,
-    cls_token: Tensor,
-    pos_embed: Tensor,
-    blocks: Vec<Block>,
-    norm: LayerNorm,
-    head: Linear,
-}
-
-impl DinoVisionTransformer {
-    pub fn new(vb: VarBuilder, depth: usize, embed_dim: usize, num_heads: usize) -> Result<Self> {
-        let patch_embed =
-            PatchEmbed::new(vb.pp("patch_embed"), IMG_SIZE, PATCH_SIZE, 3, embed_dim)?;
-        let cls_token = vb.get((1, 1, embed_dim), "cls_token")?;
-        let num_tokens = 1;
-        let pos_embed = vb.get(
-            (1, patch_embed.num_patches + num_tokens, embed_dim),
-            "pos_embed",
-        )?;
-        let head = linear(vb.pp("head"), 2 * embed_dim, NUM_CLASSES, true)?;
-        let norm = layer_norm(embed_dim, 1e-5, vb.pp("norm"))?;
-        let vb_b = vb.pp("blocks");
-        let blocks = (0..depth)
-            .map(|i| Block::new(vb_b.pp(&i.to_string()), embed_dim, num_heads))
-            .collect::<Result<Vec<_>>>()?;
-        Ok(Self {
-            patch_embed,
-            cls_token,
-            pos_embed,
-            blocks,
-            norm,
-            head,
-        })
-    }
-
-    fn interpolate_pos_encoding(&self, xs: &Tensor, w: usize, h: usize) -> Result<Tensor> {
-        let npatch = xs.dim(1)? - 1;
-        let n = self.pos_embed.dim(1)? - 1;
-        let sqrt_n = (n as f64).sqrt();
-        if npatch == n && w == h {
-            return Ok(xs.clone());
-        }
-        let class_pos_embed = self.pos_embed.i((.., ..1))?;
-        let patch_pos_embed = self.pos_embed.i((.., 1..))?;
-        let dim = xs.dim(D::Minus1)?;
-        let (w0, h0) = ((w / PATCH_SIZE) as f64 + 0.1, (h / PATCH_SIZE) as f64 + 0.1);
-        let patch_pos_embed = patch_pos_embed
-            .reshape((1, sqrt_n as usize, sqrt_n as usize, dim))?
-            .transpose(2, 3)?
-            .transpose(1, 2)?;
-        // This uses bicubic interpolation in the original implementation.
-        let patch_pos_embed = patch_pos_embed.upsample_nearest2d(h0 as usize, w0 as usize)?;
-        let el_count = patch_pos_embed.shape().elem_count();
-        let patch_pos_embed =
-            patch_pos_embed
-                .transpose(1, 2)?
-                .transpose(2, 3)?
-                .reshape((1, el_count / dim, dim))?;
-        Tensor::cat(&[&class_pos_embed, &patch_pos_embed], 1)
-    }
-
-    fn prepare_tokens_with_mask(&self, xs: &Tensor) -> Result<Tensor> {
-        let (_b, _nc, w, h) = xs.dims4()?;
-        let xs = self.patch_embed.forward(xs)?;
-        let xs = Tensor::cat(&[&self.cls_token, &xs], 1)?;
-        &xs + &self.interpolate_pos_encoding(&xs, w, h)?
-    }
-}
-
-impl Module for DinoVisionTransformer {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = self.prepare_tokens_with_mask(xs)?;
-        for blk in self.blocks.iter() {
-            xs = blk.forward(&xs)?
-        }
-        let xs = self.norm.forward(&xs)?;
-        let xs_norm_clstoken = xs.i((.., 0))?;
-        let xs_norm_patchtokens = xs.i((.., 1..))?.mean(1)?;
-        let xs = Tensor::cat(&[xs_norm_clstoken, xs_norm_patchtokens], D::Minus1)?;
-        self.head.forward(&xs)
-    }
-}
-
-pub fn vit_small(vb: VarBuilder) -> Result<DinoVisionTransformer> {
-    DinoVisionTransformer::new(vb, 12, 384, 6)
-}
 #[derive(Parser)]
 struct Args {
     #[arg(long)]
@@ -320,7 +45,7 @@ pub fn main() -> anyhow::Result<()> {
     let weights = unsafe { candle::safetensors::MmapedFile::new(model_file)? };
     let weights = weights.deserialize()?;
     let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
-    let model = vit_small(vb)?;
+    let model = dinov2::vit_small(vb)?;
     println!("model built");
     let logits = model.forward(&image.unsqueeze(0)?)?;
     let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
diff --git a/candle-examples/examples/efficientnet/main.rs b/candle-examples/examples/efficientnet/main.rs
index cbe2c90a..1e45e301 100644
--- a/candle-examples/examples/efficientnet/main.rs
+++ b/candle-examples/examples/efficientnet/main.rs
@@ -8,340 +8,11 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
+use candle::{DType, IndexOp, D};
+use candle_nn::{Module, VarBuilder};
+use candle_transformers::models::efficientnet::{EfficientNet, MBConvConfig};
 use clap::{Parser, ValueEnum};
 
-use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn as nn;
-use nn::{Module, VarBuilder};
-
-// Based on the Python version from torchvision.
-// https://github.com/pytorch/vision/blob/0d75d9e5516f446c9c0ef93bd4ed9fea13992d06/torchvision/models/efficientnet.py#L47
-#[derive(Debug, Clone, Copy)]
-pub struct MBConvConfig {
-    expand_ratio: f64,
-    kernel: usize,
-    stride: usize,
-    input_channels: usize,
-    out_channels: usize,
-    num_layers: usize,
-}
-
-fn make_divisible(v: f64, divisor: usize) -> usize {
-    let min_value = divisor;
-    let new_v = usize::max(
-        min_value,
-        (v + divisor as f64 * 0.5) as usize / divisor * divisor,
-    );
-    if (new_v as f64) < 0.9 * v {
-        new_v + divisor
-    } else {
-        new_v
-    }
-}
-
-fn bneck_confs(width_mult: f64, depth_mult: f64) -> Vec<MBConvConfig> {
-    let bneck_conf = |e, k, s, i, o, n| {
-        let input_channels = make_divisible(i as f64 * width_mult, 8);
-        let out_channels = make_divisible(o as f64 * width_mult, 8);
-        let num_layers = (n as f64 * depth_mult).ceil() as usize;
-        MBConvConfig {
-            expand_ratio: e,
-            kernel: k,
-            stride: s,
-            input_channels,
-            out_channels,
-            num_layers,
-        }
-    };
-    vec![
-        bneck_conf(1., 3, 1, 32, 16, 1),
-        bneck_conf(6., 3, 2, 16, 24, 2),
-        bneck_conf(6., 5, 2, 24, 40, 2),
-        bneck_conf(6., 3, 2, 40, 80, 3),
-        bneck_conf(6., 5, 1, 80, 112, 3),
-        bneck_conf(6., 5, 2, 112, 192, 4),
-        bneck_conf(6., 3, 1, 192, 320, 1),
-    ]
-}
-
-impl MBConvConfig {
-    fn b0() -> Vec<Self> {
-        bneck_confs(1.0, 1.0)
-    }
-    fn b1() -> Vec<Self> {
-        bneck_confs(1.0, 1.1)
-    }
-    fn b2() -> Vec<Self> {
-        bneck_confs(1.1, 1.2)
-    }
-    fn b3() -> Vec<Self> {
-        bneck_confs(1.2, 1.4)
-    }
-    fn b4() -> Vec<Self> {
-        bneck_confs(1.4, 1.8)
-    }
-    fn b5() -> Vec<Self> {
-        bneck_confs(1.6, 2.2)
-    }
-    fn b6() -> Vec<Self> {
-        bneck_confs(1.8, 2.6)
-    }
-    fn b7() -> Vec<Self> {
-        bneck_confs(2.0, 3.1)
-    }
-}
-
-/// Conv2D with same padding.
-#[derive(Debug)]
-struct Conv2DSame {
-    conv2d: nn::Conv2d,
-    s: usize,
-    k: usize,
-}
-
-impl Conv2DSame {
-    fn new(
-        vb: VarBuilder,
-        i: usize,
-        o: usize,
-        k: usize,
-        stride: usize,
-        groups: usize,
-        bias: bool,
-    ) -> Result<Self> {
-        let conv_config = nn::Conv2dConfig {
-            stride,
-            groups,
-            ..Default::default()
-        };
-        let conv2d = if bias {
-            nn::conv2d(i, o, k, conv_config, vb)?
-        } else {
-            nn::conv2d_no_bias(i, o, k, conv_config, vb)?
-        };
-        Ok(Self {
-            conv2d,
-            s: stride,
-            k,
-        })
-    }
-}
-
-impl Module for Conv2DSame {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let s = self.s;
-        let k = self.k;
-        let (_, _, ih, iw) = xs.dims4()?;
-        let oh = (ih + s - 1) / s;
-        let ow = (iw + s - 1) / s;
-        let pad_h = usize::max((oh - 1) * s + k - ih, 0);
-        let pad_w = usize::max((ow - 1) * s + k - iw, 0);
-        if pad_h > 0 || pad_w > 0 {
-            let xs = xs.pad_with_zeros(2, pad_h / 2, pad_h - pad_h / 2)?;
-            let xs = xs.pad_with_zeros(3, pad_w / 2, pad_w - pad_w / 2)?;
-            self.conv2d.forward(&xs)
-        } else {
-            self.conv2d.forward(xs)
-        }
-    }
-}
-
-#[derive(Debug)]
-struct ConvNormActivation {
-    conv2d: Conv2DSame,
-    bn2d: nn::BatchNorm,
-    activation: bool,
-}
-
-impl ConvNormActivation {
-    fn new(
-        vb: VarBuilder,
-        i: usize,
-        o: usize,
-        k: usize,
-        stride: usize,
-        groups: usize,
-    ) -> Result<Self> {
-        let conv2d = Conv2DSame::new(vb.pp("0"), i, o, k, stride, groups, false)?;
-        let bn2d = nn::batch_norm(o, 1e-3, vb.pp("1"))?;
-        Ok(Self {
-            conv2d,
-            bn2d,
-            activation: true,
-        })
-    }
-
-    fn no_activation(self) -> Self {
-        Self {
-            activation: false,
-            ..self
-        }
-    }
-}
-
-impl Module for ConvNormActivation {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let xs = self.conv2d.forward(xs)?;
-        let xs = self.bn2d.forward(&xs)?;
-        if self.activation {
-            swish(&xs)
-        } else {
-            Ok(xs)
-        }
-    }
-}
-
-#[derive(Debug)]
-struct SqueezeExcitation {
-    fc1: Conv2DSame,
-    fc2: Conv2DSame,
-}
-
-impl SqueezeExcitation {
-    fn new(vb: VarBuilder, in_channels: usize, squeeze_channels: usize) -> Result<Self> {
-        let fc1 = Conv2DSame::new(vb.pp("fc1"), in_channels, squeeze_channels, 1, 1, 1, true)?;
-        let fc2 = Conv2DSame::new(vb.pp("fc2"), squeeze_channels, in_channels, 1, 1, 1, true)?;
-        Ok(Self { fc1, fc2 })
-    }
-}
-
-impl Module for SqueezeExcitation {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let residual = xs;
-        // equivalent to adaptive_avg_pool2d([1, 1])
-        let xs = xs.mean_keepdim(D::Minus2)?.mean_keepdim(D::Minus1)?;
-        let xs = self.fc1.forward(&xs)?;
-        let xs = swish(&xs)?;
-        let xs = self.fc2.forward(&xs)?;
-        let xs = nn::ops::sigmoid(&xs)?;
-        residual.broadcast_mul(&xs)
-    }
-}
-
-#[derive(Debug)]
-struct MBConv {
-    expand_cna: Option<ConvNormActivation>,
-    depthwise_cna: ConvNormActivation,
-    squeeze_excitation: SqueezeExcitation,
-    project_cna: ConvNormActivation,
-    config: MBConvConfig,
-}
-
-impl MBConv {
-    fn new(vb: VarBuilder, c: MBConvConfig) -> Result<Self> {
-        let vb = vb.pp("block");
-        let exp = make_divisible(c.input_channels as f64 * c.expand_ratio, 8);
-        let expand_cna = if exp != c.input_channels {
-            Some(ConvNormActivation::new(
-                vb.pp("0"),
-                c.input_channels,
-                exp,
-                1,
-                1,
-                1,
-            )?)
-        } else {
-            None
-        };
-        let start_index = if expand_cna.is_some() { 1 } else { 0 };
-        let depthwise_cna =
-            ConvNormActivation::new(vb.pp(start_index), exp, exp, c.kernel, c.stride, exp)?;
-        let squeeze_channels = usize::max(1, c.input_channels / 4);
-        let squeeze_excitation =
-            SqueezeExcitation::new(vb.pp(start_index + 1), exp, squeeze_channels)?;
-        let project_cna =
-            ConvNormActivation::new(vb.pp(start_index + 2), exp, c.out_channels, 1, 1, 1)?
-                .no_activation();
-        Ok(Self {
-            expand_cna,
-            depthwise_cna,
-            squeeze_excitation,
-            project_cna,
-            config: c,
-        })
-    }
-}
-
-impl Module for MBConv {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let use_res_connect =
-            self.config.stride == 1 && self.config.input_channels == self.config.out_channels;
-        let ys = match &self.expand_cna {
-            Some(expand_cna) => expand_cna.forward(xs)?,
-            None => xs.clone(),
-        };
-        let ys = self.depthwise_cna.forward(&ys)?;
-        let ys = self.squeeze_excitation.forward(&ys)?;
-        let ys = self.project_cna.forward(&ys)?;
-        if use_res_connect {
-            ys + xs
-        } else {
-            Ok(ys)
-        }
-    }
-}
-
-fn swish(s: &Tensor) -> Result<Tensor> {
-    s * nn::ops::sigmoid(s)?
-}
-
-#[derive(Debug)]
-struct EfficientNet {
-    init_cna: ConvNormActivation,
-    blocks: Vec<MBConv>,
-    final_cna: ConvNormActivation,
-    classifier: nn::Linear,
-}
-
-impl EfficientNet {
-    fn new(p: VarBuilder, configs: Vec<MBConvConfig>, nclasses: usize) -> Result<Self> {
-        let f_p = p.pp("features");
-        let first_in_c = configs[0].input_channels;
-        let last_out_c = configs.last().unwrap().out_channels;
-        let final_out_c = 4 * last_out_c;
-        let init_cna = ConvNormActivation::new(f_p.pp(0), 3, first_in_c, 3, 2, 1)?;
-        let nconfigs = configs.len();
-        let mut blocks = vec![];
-        for (index, cnf) in configs.into_iter().enumerate() {
-            let f_p = f_p.pp(index + 1);
-            for r_index in 0..cnf.num_layers {
-                let cnf = if r_index == 0 {
-                    cnf
-                } else {
-                    MBConvConfig {
-                        input_channels: cnf.out_channels,
-                        stride: 1,
-                        ..cnf
-                    }
-                };
-                blocks.push(MBConv::new(f_p.pp(r_index), cnf)?)
-            }
-        }
-        let final_cna =
-            ConvNormActivation::new(f_p.pp(nconfigs + 1), last_out_c, final_out_c, 1, 1, 1)?;
-        let classifier = nn::linear(final_out_c, nclasses, p.pp("classifier.1"))?;
-        Ok(Self {
-            init_cna,
-            blocks,
-            final_cna,
-            classifier,
-        })
-    }
-}
-
-impl Module for EfficientNet {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = self.init_cna.forward(xs)?;
-        for block in self.blocks.iter() {
-            xs = block.forward(&xs)?
-        }
-        let xs = self.final_cna.forward(&xs)?;
-        // Equivalent to adaptive_avg_pool2d([1, 1]) -> squeeze(-1) -> squeeze(-1)
-        let xs = xs.mean(D::Minus1)?.mean(D::Minus1)?;
-        self.classifier.forward(&xs)
-    }
-}
-
 #[derive(Clone, Copy, Debug, ValueEnum)]
 enum Which {
     B0,
diff --git a/candle-examples/examples/quantized/main.rs b/candle-examples/examples/quantized/main.rs
index a3f98d8e..c8179d33 100644
--- a/candle-examples/examples/quantized/main.rs
+++ b/candle-examples/examples/quantized/main.rs
@@ -12,7 +12,7 @@ use candle::quantized::{ggml_file, gguf_file};
 use candle::{Device, Tensor};
 use candle_transformers::generation::LogitsProcessor;
 
-mod model;
+use candle_transformers::models::quantized_llama as model;
 use model::ModelWeights;
 
 const DEFAULT_PROMPT: &str = "My favorite theorem is ";
diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 9ce2f158..21ba0415 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -7,108 +7,11 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-pub mod model_image_encoder;
-pub mod model_mask_decoder;
-pub mod model_prompt_encoder;
-pub mod model_sam;
-pub mod model_tiny_vit;
-pub mod model_transformer;
-
-use candle::{DType, Result, Tensor};
-use candle_nn::{Module, VarBuilder};
+use candle::DType;
+use candle_nn::VarBuilder;
+use candle_transformers::models::segment_anything::sam;
 use clap::Parser;
 
-pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
-    let inner = if bias {
-        candle_nn::linear(in_dim, out_dim, vb)?
-    } else {
-        candle_nn::linear_no_bias(in_dim, out_dim, vb)?
-    };
-    let span = tracing::span!(tracing::Level::TRACE, "linear");
-    Ok(Linear { inner, span })
-}
-
-#[derive(Debug)]
-pub struct LayerNorm2d {
-    weight: Tensor,
-    bias: Tensor,
-    num_channels: usize,
-    eps: f64,
-}
-
-impl LayerNorm2d {
-    pub fn new(num_channels: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
-        let weight = vb.get(num_channels, "weight")?;
-        let bias = vb.get(num_channels, "bias")?;
-        Ok(Self {
-            weight,
-            bias,
-            num_channels,
-            eps,
-        })
-    }
-}
-
-impl Module for LayerNorm2d {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let u = xs.mean_keepdim(1)?;
-        let xs = xs.broadcast_sub(&u)?;
-        let s = xs.sqr()?.mean_keepdim(1)?;
-        let xs = xs.broadcast_div(&(s + self.eps)?.sqrt()?)?;
-        xs.broadcast_mul(&self.weight.reshape((1, self.num_channels, 1, 1))?)?
-            .broadcast_add(&self.bias.reshape((1, self.num_channels, 1, 1))?)
-    }
-}
-
-#[derive(Debug)]
-pub struct MlpBlock {
-    lin1: Linear,
-    lin2: Linear,
-    activation: candle_nn::Activation,
-    span: tracing::Span,
-}
-
-impl MlpBlock {
-    pub fn new(
-        embedding_dim: usize,
-        mlp_dim: usize,
-        activation: candle_nn::Activation,
-        vb: VarBuilder,
-    ) -> Result<Self> {
-        let lin1 = linear(vb.pp("lin1"), embedding_dim, mlp_dim, true)?;
-        let lin2 = linear(vb.pp("lin2"), mlp_dim, embedding_dim, true)?;
-        let span = tracing::span!(tracing::Level::TRACE, "mlp-block");
-        Ok(Self {
-            lin1,
-            lin2,
-            activation,
-            span,
-        })
-    }
-}
-
-impl Module for MlpBlock {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let _enter = self.span.enter();
-        xs.apply(&self.lin1)?
-            .apply(&self.activation)?
-            .apply(&self.lin2)
-    }
-}
-
-#[derive(Debug)]
-pub struct Linear {
-    inner: candle_nn::Linear,
-    span: tracing::Span,
-}
-
-impl Module for Linear {
-    fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        let _enter = self.span.enter();
-        self.inner.forward(x)
-    }
-}
-
 #[derive(Parser)]
 struct Args {
     #[arg(long)]
@@ -173,7 +76,7 @@ pub fn main() -> anyhow::Result<()> {
         let (_c, h, w) = image.dims3()?;
         (image, h, w)
     } else {
-        let (image, h, w) = candle_examples::load_image(&args.image, Some(model_sam::IMAGE_SIZE))?;
+        let (image, h, w) = candle_examples::load_image(&args.image, Some(sam::IMAGE_SIZE))?;
         (image.to_device(&device)?, h, w)
     };
     println!("loaded image {image:?}");
@@ -195,9 +98,9 @@ pub fn main() -> anyhow::Result<()> {
     let weights = weights.deserialize()?;
     let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
     let sam = if args.use_tiny {
-        model_sam::Sam::new_tiny(vb)? // tiny vit_t
+        sam::Sam::new_tiny(vb)? // tiny vit_t
     } else {
-        model_sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)? // sam_vit_b
+        sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)? // sam_vit_b
     };
 
     if args.generate_masks {
diff --git a/candle-examples/examples/yolo-v3/main.rs b/candle-examples/examples/yolo-v3/main.rs
index 20021b45..ecf75bdf 100644
--- a/candle-examples/examples/yolo-v3/main.rs
+++ b/candle-examples/examples/yolo-v3/main.rs
@@ -4,7 +4,7 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-use candle_examples::object_detection::{non_maximum_suppression, Bbox};
+use candle_transformers::object_detection::{non_maximum_suppression, Bbox};
 mod darknet;
 
 use anyhow::Result;
diff --git a/candle-examples/examples/yolo-v8/main.rs b/candle-examples/examples/yolo-v8/main.rs
index 2017b5be..d48bac35 100644
--- a/candle-examples/examples/yolo-v8/main.rs
+++ b/candle-examples/examples/yolo-v8/main.rs
@@ -8,8 +8,8 @@ mod model;
 use model::{Multiples, YoloV8, YoloV8Pose};
 
 use candle::{DType, Device, IndexOp, Result, Tensor};
-use candle_examples::object_detection::{non_maximum_suppression, Bbox, KeyPoint};
 use candle_nn::{Module, VarBuilder};
+use candle_transformers::object_detection::{non_maximum_suppression, Bbox, KeyPoint};
 use clap::{Parser, ValueEnum};
 use image::DynamicImage;
 
diff --git a/candle-examples/src/lib.rs b/candle-examples/src/lib.rs
index c14b2d6b..5e0b44fb 100644
--- a/candle-examples/src/lib.rs
+++ b/candle-examples/src/lib.rs
@@ -1,6 +1,5 @@
 pub mod coco_classes;
 pub mod imagenet;
-pub mod object_detection;
 
 use candle::{Device, Result, Tensor};
 
diff --git a/candle-transformers/Cargo.toml b/candle-transformers/Cargo.toml
index 6b2087cb..86caf918 100644
--- a/candle-transformers/Cargo.toml
+++ b/candle-transformers/Cargo.toml
@@ -16,6 +16,7 @@ candle-nn = { path = "../candle-nn", version = "0.2.1" }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
 rand = { workspace = true }
+rayon = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
 tracing = { workspace = true }
diff --git a/candle-transformers/src/lib.rs b/candle-transformers/src/lib.rs
index a8890dc8..b83e5056 100644
--- a/candle-transformers/src/lib.rs
+++ b/candle-transformers/src/lib.rs
@@ -1,4 +1,5 @@
 pub mod generation;
 pub mod models;
+pub mod object_detection;
 pub mod pipelines;
 pub mod utils;
diff --git a/candle-transformers/src/models/dinov2.rs b/candle-transformers/src/models/dinov2.rs
new file mode 100644
index 00000000..0edc8494
--- /dev/null
+++ b/candle-transformers/src/models/dinov2.rs
@@ -0,0 +1,279 @@
+use candle::{IndexOp, Result, Tensor, D};
+use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+
+const IMG_SIZE: usize = 518;
+const PATCH_SIZE: usize = 14;
+const NUM_CLASSES: usize = 1000;
+
+fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
+    if bias {
+        candle_nn::linear(in_dim, out_dim, vb)
+    } else {
+        candle_nn::linear_no_bias(in_dim, out_dim, vb)
+    }
+}
+
+#[derive(Debug)]
+struct Attention {
+    qkv: Linear,
+    proj: Linear,
+    num_heads: usize,
+    scale: f64,
+}
+
+impl Attention {
+    fn new(
+        vb: VarBuilder,
+        dim: usize,
+        num_heads: usize,
+        qkv_bias: bool,
+        proj_bias: bool,
+    ) -> Result<Self> {
+        let qkv = linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
+        let proj = linear(vb.pp("proj"), dim, dim, proj_bias)?;
+        let scale = 1. / ((dim / num_heads) as f64).sqrt();
+        Ok(Self {
+            qkv,
+            proj,
+            num_heads,
+            scale,
+        })
+    }
+}
+
+impl Module for Attention {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (b, n, c) = xs.dims3()?;
+        let qkv = self
+            .qkv
+            .forward(xs)?
+            .reshape((b, n, 3, self.num_heads, c / self.num_heads))?
+            .transpose(1, 2)? // 02134
+            .transpose(0, 1)? // 20134
+            .transpose(2, 3)?; // 20314
+        let q = (qkv.i(0)? * self.scale)?;
+        let k = qkv.i(1)?;
+        let v = qkv.i(2)?;
+        let attn = candle_nn::ops::softmax(&q.matmul(&k.t()?)?, D::Minus1)?;
+        let attn = attn.matmul(&v)?.transpose(1, 2)?.reshape((b, n, c))?;
+        self.proj.forward(&attn)
+    }
+}
+
+#[derive(Debug)]
+struct LayerScale {
+    gamma: Tensor,
+}
+
+impl LayerScale {
+    fn new(vb: VarBuilder, dim: usize) -> Result<Self> {
+        let gamma = vb.get(dim, "gamma")?;
+        Ok(Self { gamma })
+    }
+}
+
+impl Module for LayerScale {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.broadcast_mul(&self.gamma)
+    }
+}
+
+#[derive(Debug)]
+struct Mlp {
+    fc1: Linear,
+    fc2: Linear,
+}
+
+impl Mlp {
+    fn new(vb: VarBuilder, in_features: usize, hidden_features: usize, bias: bool) -> Result<Self> {
+        let out_features = in_features;
+        let fc1 = linear(vb.pp("fc1"), in_features, hidden_features, bias)?;
+        let fc2 = linear(vb.pp("fc2"), hidden_features, out_features, bias)?;
+        Ok(Self { fc1, fc2 })
+    }
+}
+
+impl Module for Mlp {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.fc1.forward(xs)?.gelu()?;
+        self.fc2.forward(&xs)
+    }
+}
+
+#[derive(Debug)]
+struct Block {
+    norm1: LayerNorm,
+    attn: Attention,
+    ls1: LayerScale,
+    norm2: LayerNorm,
+    mlp: Mlp,
+    ls2: LayerScale,
+}
+
+impl Block {
+    fn new(vb: VarBuilder, dim: usize, num_heads: usize) -> Result<Self> {
+        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
+        let attn = Attention::new(vb.pp("attn"), dim, num_heads, true, true)?;
+        let ls1 = LayerScale::new(vb.pp("ls1"), dim)?;
+        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
+        let mlp = Mlp::new(vb.pp("mlp"), dim, dim * 4, true)?;
+        let ls2 = LayerScale::new(vb.pp("ls2"), dim)?;
+        Ok(Self {
+            norm1,
+            attn,
+            ls1,
+            norm2,
+            mlp,
+            ls2,
+        })
+    }
+}
+
+impl Module for Block {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let residual = xs;
+        let xs = self
+            .ls1
+            .forward(&self.attn.forward(&self.norm1.forward(xs)?)?)?;
+        let xs = (xs + residual)?;
+        let residual = &xs;
+        let xs = self
+            .ls2
+            .forward(&self.mlp.forward(&self.norm2.forward(&xs)?)?)?;
+        xs + residual
+    }
+}
+
+#[derive(Debug)]
+struct PatchEmbed {
+    proj: candle_nn::Conv2d,
+    patch_size: (usize, usize),
+    num_patches: usize,
+}
+
+impl PatchEmbed {
+    fn new(
+        vb: VarBuilder,
+        img_size: usize,
+        patch_size: usize,
+        in_chans: usize,
+        embed_dim: usize,
+    ) -> Result<Self> {
+        let config = candle_nn::Conv2dConfig {
+            stride: patch_size,
+            ..Default::default()
+        };
+        let proj = candle_nn::conv2d(in_chans, embed_dim, patch_size, config, vb.pp("proj"))?;
+        let num_patches = (img_size / patch_size) * (img_size / patch_size);
+        Ok(Self {
+            proj,
+            patch_size: (patch_size, patch_size),
+            num_patches,
+        })
+    }
+}
+
+impl Module for PatchEmbed {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (_b, _c, h, w) = xs.dims4()?;
+        let (patch_h, patch_w) = self.patch_size;
+        if (h % patch_h) != 0 {
+            candle::bail!("image height {h} is not a multiple of patch height {patch_h}")
+        }
+        if (w % patch_w) != 0 {
+            candle::bail!("image width {w} is not a multiple of patch width {patch_w}")
+        }
+        let xs = self.proj.forward(xs)?;
+        let (b, c, h, w) = xs.dims4()?;
+        // flatten embeddings.
+        xs.reshape((b, c, h * w))?.transpose(1, 2)
+    }
+}
+
+#[derive(Debug)]
+pub struct DinoVisionTransformer {
+    patch_embed: PatchEmbed,
+    cls_token: Tensor,
+    pos_embed: Tensor,
+    blocks: Vec<Block>,
+    norm: LayerNorm,
+    head: Linear,
+}
+
+impl DinoVisionTransformer {
+    pub fn new(vb: VarBuilder, depth: usize, embed_dim: usize, num_heads: usize) -> Result<Self> {
+        let patch_embed =
+            PatchEmbed::new(vb.pp("patch_embed"), IMG_SIZE, PATCH_SIZE, 3, embed_dim)?;
+        let cls_token = vb.get((1, 1, embed_dim), "cls_token")?;
+        let num_tokens = 1;
+        let pos_embed = vb.get(
+            (1, patch_embed.num_patches + num_tokens, embed_dim),
+            "pos_embed",
+        )?;
+        let head = linear(vb.pp("head"), 2 * embed_dim, NUM_CLASSES, true)?;
+        let norm = layer_norm(embed_dim, 1e-5, vb.pp("norm"))?;
+        let vb_b = vb.pp("blocks");
+        let blocks = (0..depth)
+            .map(|i| Block::new(vb_b.pp(&i.to_string()), embed_dim, num_heads))
+            .collect::<Result<Vec<_>>>()?;
+        Ok(Self {
+            patch_embed,
+            cls_token,
+            pos_embed,
+            blocks,
+            norm,
+            head,
+        })
+    }
+
+    fn interpolate_pos_encoding(&self, xs: &Tensor, w: usize, h: usize) -> Result<Tensor> {
+        let npatch = xs.dim(1)? - 1;
+        let n = self.pos_embed.dim(1)? - 1;
+        let sqrt_n = (n as f64).sqrt();
+        if npatch == n && w == h {
+            return Ok(xs.clone());
+        }
+        let class_pos_embed = self.pos_embed.i((.., ..1))?;
+        let patch_pos_embed = self.pos_embed.i((.., 1..))?;
+        let dim = xs.dim(D::Minus1)?;
+        let (w0, h0) = ((w / PATCH_SIZE) as f64 + 0.1, (h / PATCH_SIZE) as f64 + 0.1);
+        let patch_pos_embed = patch_pos_embed
+            .reshape((1, sqrt_n as usize, sqrt_n as usize, dim))?
+            .transpose(2, 3)?
+            .transpose(1, 2)?;
+        // This uses bicubic interpolation in the original implementation.
+        let patch_pos_embed = patch_pos_embed.upsample_nearest2d(h0 as usize, w0 as usize)?;
+        let el_count = patch_pos_embed.shape().elem_count();
+        let patch_pos_embed =
+            patch_pos_embed
+                .transpose(1, 2)?
+                .transpose(2, 3)?
+                .reshape((1, el_count / dim, dim))?;
+        Tensor::cat(&[&class_pos_embed, &patch_pos_embed], 1)
+    }
+
+    fn prepare_tokens_with_mask(&self, xs: &Tensor) -> Result<Tensor> {
+        let (_b, _nc, w, h) = xs.dims4()?;
+        let xs = self.patch_embed.forward(xs)?;
+        let xs = Tensor::cat(&[&self.cls_token, &xs], 1)?;
+        &xs + &self.interpolate_pos_encoding(&xs, w, h)?
+    }
+}
+
+impl Module for DinoVisionTransformer {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = self.prepare_tokens_with_mask(xs)?;
+        for blk in self.blocks.iter() {
+            xs = blk.forward(&xs)?
+        }
+        let xs = self.norm.forward(&xs)?;
+        let xs_norm_clstoken = xs.i((.., 0))?;
+        let xs_norm_patchtokens = xs.i((.., 1..))?.mean(1)?;
+        let xs = Tensor::cat(&[xs_norm_clstoken, xs_norm_patchtokens], D::Minus1)?;
+        self.head.forward(&xs)
+    }
+}
+
+pub fn vit_small(vb: VarBuilder) -> Result<DinoVisionTransformer> {
+    DinoVisionTransformer::new(vb, 12, 384, 6)
+}
diff --git a/candle-transformers/src/models/efficientnet.rs b/candle-transformers/src/models/efficientnet.rs
new file mode 100644
index 00000000..ab51c76d
--- /dev/null
+++ b/candle-transformers/src/models/efficientnet.rs
@@ -0,0 +1,331 @@
+use candle::{Result, Tensor, D};
+use candle_nn as nn;
+use nn::{Module, VarBuilder};
+
+// Based on the Python version from torchvision.
+// https://github.com/pytorch/vision/blob/0d75d9e5516f446c9c0ef93bd4ed9fea13992d06/torchvision/models/efficientnet.py#L47
+#[derive(Debug, Clone, Copy)]
+pub struct MBConvConfig {
+    expand_ratio: f64,
+    kernel: usize,
+    stride: usize,
+    input_channels: usize,
+    out_channels: usize,
+    num_layers: usize,
+}
+
+fn make_divisible(v: f64, divisor: usize) -> usize {
+    let min_value = divisor;
+    let new_v = usize::max(
+        min_value,
+        (v + divisor as f64 * 0.5) as usize / divisor * divisor,
+    );
+    if (new_v as f64) < 0.9 * v {
+        new_v + divisor
+    } else {
+        new_v
+    }
+}
+
+fn bneck_confs(width_mult: f64, depth_mult: f64) -> Vec<MBConvConfig> {
+    let bneck_conf = |e, k, s, i, o, n| {
+        let input_channels = make_divisible(i as f64 * width_mult, 8);
+        let out_channels = make_divisible(o as f64 * width_mult, 8);
+        let num_layers = (n as f64 * depth_mult).ceil() as usize;
+        MBConvConfig {
+            expand_ratio: e,
+            kernel: k,
+            stride: s,
+            input_channels,
+            out_channels,
+            num_layers,
+        }
+    };
+    vec![
+        bneck_conf(1., 3, 1, 32, 16, 1),
+        bneck_conf(6., 3, 2, 16, 24, 2),
+        bneck_conf(6., 5, 2, 24, 40, 2),
+        bneck_conf(6., 3, 2, 40, 80, 3),
+        bneck_conf(6., 5, 1, 80, 112, 3),
+        bneck_conf(6., 5, 2, 112, 192, 4),
+        bneck_conf(6., 3, 1, 192, 320, 1),
+    ]
+}
+
+impl MBConvConfig {
+    pub fn b0() -> Vec<Self> {
+        bneck_confs(1.0, 1.0)
+    }
+    pub fn b1() -> Vec<Self> {
+        bneck_confs(1.0, 1.1)
+    }
+    pub fn b2() -> Vec<Self> {
+        bneck_confs(1.1, 1.2)
+    }
+    pub fn b3() -> Vec<Self> {
+        bneck_confs(1.2, 1.4)
+    }
+    pub fn b4() -> Vec<Self> {
+        bneck_confs(1.4, 1.8)
+    }
+    pub fn b5() -> Vec<Self> {
+        bneck_confs(1.6, 2.2)
+    }
+    pub fn b6() -> Vec<Self> {
+        bneck_confs(1.8, 2.6)
+    }
+    pub fn b7() -> Vec<Self> {
+        bneck_confs(2.0, 3.1)
+    }
+}
+
+/// Conv2D with same padding.
+#[derive(Debug)]
+struct Conv2DSame {
+    conv2d: nn::Conv2d,
+    s: usize,
+    k: usize,
+}
+
+impl Conv2DSame {
+    fn new(
+        vb: VarBuilder,
+        i: usize,
+        o: usize,
+        k: usize,
+        stride: usize,
+        groups: usize,
+        bias: bool,
+    ) -> Result<Self> {
+        let conv_config = nn::Conv2dConfig {
+            stride,
+            groups,
+            ..Default::default()
+        };
+        let conv2d = if bias {
+            nn::conv2d(i, o, k, conv_config, vb)?
+        } else {
+            nn::conv2d_no_bias(i, o, k, conv_config, vb)?
+        };
+        Ok(Self {
+            conv2d,
+            s: stride,
+            k,
+        })
+    }
+}
+
+impl Module for Conv2DSame {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let s = self.s;
+        let k = self.k;
+        let (_, _, ih, iw) = xs.dims4()?;
+        let oh = (ih + s - 1) / s;
+        let ow = (iw + s - 1) / s;
+        let pad_h = usize::max((oh - 1) * s + k - ih, 0);
+        let pad_w = usize::max((ow - 1) * s + k - iw, 0);
+        if pad_h > 0 || pad_w > 0 {
+            let xs = xs.pad_with_zeros(2, pad_h / 2, pad_h - pad_h / 2)?;
+            let xs = xs.pad_with_zeros(3, pad_w / 2, pad_w - pad_w / 2)?;
+            self.conv2d.forward(&xs)
+        } else {
+            self.conv2d.forward(xs)
+        }
+    }
+}
+
+#[derive(Debug)]
+struct ConvNormActivation {
+    conv2d: Conv2DSame,
+    bn2d: nn::BatchNorm,
+    activation: bool,
+}
+
+impl ConvNormActivation {
+    fn new(
+        vb: VarBuilder,
+        i: usize,
+        o: usize,
+        k: usize,
+        stride: usize,
+        groups: usize,
+    ) -> Result<Self> {
+        let conv2d = Conv2DSame::new(vb.pp("0"), i, o, k, stride, groups, false)?;
+        let bn2d = nn::batch_norm(o, 1e-3, vb.pp("1"))?;
+        Ok(Self {
+            conv2d,
+            bn2d,
+            activation: true,
+        })
+    }
+
+    fn no_activation(self) -> Self {
+        Self {
+            activation: false,
+            ..self
+        }
+    }
+}
+
+impl Module for ConvNormActivation {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.conv2d.forward(xs)?;
+        let xs = self.bn2d.forward(&xs)?;
+        if self.activation {
+            swish(&xs)
+        } else {
+            Ok(xs)
+        }
+    }
+}
+
+#[derive(Debug)]
+struct SqueezeExcitation {
+    fc1: Conv2DSame,
+    fc2: Conv2DSame,
+}
+
+impl SqueezeExcitation {
+    fn new(vb: VarBuilder, in_channels: usize, squeeze_channels: usize) -> Result<Self> {
+        let fc1 = Conv2DSame::new(vb.pp("fc1"), in_channels, squeeze_channels, 1, 1, 1, true)?;
+        let fc2 = Conv2DSame::new(vb.pp("fc2"), squeeze_channels, in_channels, 1, 1, 1, true)?;
+        Ok(Self { fc1, fc2 })
+    }
+}
+
+impl Module for SqueezeExcitation {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let residual = xs;
+        // equivalent to adaptive_avg_pool2d([1, 1])
+        let xs = xs.mean_keepdim(D::Minus2)?.mean_keepdim(D::Minus1)?;
+        let xs = self.fc1.forward(&xs)?;
+        let xs = swish(&xs)?;
+        let xs = self.fc2.forward(&xs)?;
+        let xs = nn::ops::sigmoid(&xs)?;
+        residual.broadcast_mul(&xs)
+    }
+}
+
+#[derive(Debug)]
+struct MBConv {
+    expand_cna: Option<ConvNormActivation>,
+    depthwise_cna: ConvNormActivation,
+    squeeze_excitation: SqueezeExcitation,
+    project_cna: ConvNormActivation,
+    config: MBConvConfig,
+}
+
+impl MBConv {
+    fn new(vb: VarBuilder, c: MBConvConfig) -> Result<Self> {
+        let vb = vb.pp("block");
+        let exp = make_divisible(c.input_channels as f64 * c.expand_ratio, 8);
+        let expand_cna = if exp != c.input_channels {
+            Some(ConvNormActivation::new(
+                vb.pp("0"),
+                c.input_channels,
+                exp,
+                1,
+                1,
+                1,
+            )?)
+        } else {
+            None
+        };
+        let start_index = if expand_cna.is_some() { 1 } else { 0 };
+        let depthwise_cna =
+            ConvNormActivation::new(vb.pp(start_index), exp, exp, c.kernel, c.stride, exp)?;
+        let squeeze_channels = usize::max(1, c.input_channels / 4);
+        let squeeze_excitation =
+            SqueezeExcitation::new(vb.pp(start_index + 1), exp, squeeze_channels)?;
+        let project_cna =
+            ConvNormActivation::new(vb.pp(start_index + 2), exp, c.out_channels, 1, 1, 1)?
+                .no_activation();
+        Ok(Self {
+            expand_cna,
+            depthwise_cna,
+            squeeze_excitation,
+            project_cna,
+            config: c,
+        })
+    }
+}
+
+impl Module for MBConv {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let use_res_connect =
+            self.config.stride == 1 && self.config.input_channels == self.config.out_channels;
+        let ys = match &self.expand_cna {
+            Some(expand_cna) => expand_cna.forward(xs)?,
+            None => xs.clone(),
+        };
+        let ys = self.depthwise_cna.forward(&ys)?;
+        let ys = self.squeeze_excitation.forward(&ys)?;
+        let ys = self.project_cna.forward(&ys)?;
+        if use_res_connect {
+            ys + xs
+        } else {
+            Ok(ys)
+        }
+    }
+}
+
+fn swish(s: &Tensor) -> Result<Tensor> {
+    s * nn::ops::sigmoid(s)?
+}
+
+#[derive(Debug)]
+pub struct EfficientNet {
+    init_cna: ConvNormActivation,
+    blocks: Vec<MBConv>,
+    final_cna: ConvNormActivation,
+    classifier: nn::Linear,
+}
+
+impl EfficientNet {
+    pub fn new(p: VarBuilder, configs: Vec<MBConvConfig>, nclasses: usize) -> Result<Self> {
+        let f_p = p.pp("features");
+        let first_in_c = configs[0].input_channels;
+        let last_out_c = configs.last().unwrap().out_channels;
+        let final_out_c = 4 * last_out_c;
+        let init_cna = ConvNormActivation::new(f_p.pp(0), 3, first_in_c, 3, 2, 1)?;
+        let nconfigs = configs.len();
+        let mut blocks = vec![];
+        for (index, cnf) in configs.into_iter().enumerate() {
+            let f_p = f_p.pp(index + 1);
+            for r_index in 0..cnf.num_layers {
+                let cnf = if r_index == 0 {
+                    cnf
+                } else {
+                    MBConvConfig {
+                        input_channels: cnf.out_channels,
+                        stride: 1,
+                        ..cnf
+                    }
+                };
+                blocks.push(MBConv::new(f_p.pp(r_index), cnf)?)
+            }
+        }
+        let final_cna =
+            ConvNormActivation::new(f_p.pp(nconfigs + 1), last_out_c, final_out_c, 1, 1, 1)?;
+        let classifier = nn::linear(final_out_c, nclasses, p.pp("classifier.1"))?;
+        Ok(Self {
+            init_cna,
+            blocks,
+            final_cna,
+            classifier,
+        })
+    }
+}
+
+impl Module for EfficientNet {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = self.init_cna.forward(xs)?;
+        for block in self.blocks.iter() {
+            xs = block.forward(&xs)?
+        }
+        let xs = self.final_cna.forward(&xs)?;
+        // Equivalent to adaptive_avg_pool2d([1, 1]) -> squeeze(-1) -> squeeze(-1)
+        let xs = xs.mean(D::Minus1)?.mean(D::Minus1)?;
+        self.classifier.forward(&xs)
+    }
+}
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 1b3dcf25..76e13b2a 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -1,5 +1,9 @@
 pub mod bert;
 pub mod bigcode;
+pub mod dinov2;
+pub mod efficientnet;
 pub mod falcon;
 pub mod llama;
+pub mod quantized_llama;
+pub mod segment_anything;
 pub mod whisper;
diff --git a/candle-examples/examples/quantized/model.rs b/candle-transformers/src/models/quantized_llama.rs
similarity index 100%
rename from candle-examples/examples/quantized/model.rs
rename to candle-transformers/src/models/quantized_llama.rs
diff --git a/candle-examples/examples/segment-anything/model_image_encoder.rs b/candle-transformers/src/models/segment_anything/image_encoder.rs
similarity index 96%
rename from candle-examples/examples/segment-anything/model_image_encoder.rs
rename to candle-transformers/src/models/segment_anything/image_encoder.rs
index 76cd15d0..0b313830 100644
--- a/candle-examples/examples/segment-anything/model_image_encoder.rs
+++ b/candle-transformers/src/models/segment_anything/image_encoder.rs
@@ -100,8 +100,8 @@ impl candle::CustomOp3 for Add3 {
 
 #[derive(Debug)]
 struct Attention {
-    qkv: crate::Linear,
-    proj: crate::Linear,
+    qkv: super::Linear,
+    proj: super::Linear,
     num_heads: usize,
     scale: f64,
     rel_pos_hw: Option<(Tensor, Tensor)>,
@@ -124,8 +124,8 @@ impl Attention {
         let span_matmul = tracing::span!(tracing::Level::TRACE, "attn-matmul");
         let span_rel_pos = tracing::span!(tracing::Level::TRACE, "attn-rel-pos");
         let span_softmax = tracing::span!(tracing::Level::TRACE, "attn-sm");
-        let qkv = crate::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
-        let proj = crate::linear(vb.pp("proj"), dim, dim, true)?;
+        let qkv = super::linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
+        let proj = super::linear(vb.pp("proj"), dim, dim, true)?;
         let head_dim = dim / num_heads;
         let scale = 1. / (head_dim as f64).sqrt();
         let rel_pos_hw = if use_rel_pos {
@@ -251,7 +251,7 @@ struct Block {
     norm1: LayerNorm,
     attn: Attention,
     norm2: LayerNorm,
-    mlp: crate::MlpBlock,
+    mlp: super::MlpBlock,
     window_size: usize,
     span: tracing::Span,
 }
@@ -281,7 +281,7 @@ impl Block {
             input_size_attn,
             vb.pp("attn"),
         )?;
-        let mlp = crate::MlpBlock::new(dim, dim * 4, candle_nn::Activation::Gelu, vb.pp("mlp"))?;
+        let mlp = super::MlpBlock::new(dim, dim * 4, candle_nn::Activation::Gelu, vb.pp("mlp"))?;
         let span = tracing::span!(tracing::Level::TRACE, "ie-block");
         Ok(Self {
             norm1,
@@ -375,9 +375,9 @@ pub struct ImageEncoderViT {
     patch_embed: PatchEmbed,
     blocks: Vec<Block>,
     neck_conv1: candle_nn::Conv2d,
-    neck_ln1: crate::LayerNorm2d,
+    neck_ln1: super::LayerNorm2d,
     neck_conv2: candle_nn::Conv2d,
-    neck_ln2: crate::LayerNorm2d,
+    neck_ln2: super::LayerNorm2d,
     pos_embed: Option<Tensor>,
     span: tracing::Span,
 }
@@ -433,13 +433,13 @@ impl ImageEncoderViT {
             Default::default(),
             vb.pp("neck.0"),
         )?;
-        let neck_ln1 = crate::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.1"))?;
+        let neck_ln1 = super::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.1"))?;
         let cfg = candle_nn::Conv2dConfig {
             padding: 1,
             ..Default::default()
         };
         let neck_conv2 = candle_nn::conv2d_no_bias(out_chans, out_chans, 3, cfg, vb.pp("neck.2"))?;
-        let neck_ln2 = crate::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.3"))?;
+        let neck_ln2 = super::LayerNorm2d::new(out_chans, 1e-6, vb.pp("neck.3"))?;
         let pos_embed = if use_abs_pos {
             let p = vb.get(
                 (1, img_size / patch_size, img_size / patch_size, embed_dim),
diff --git a/candle-examples/examples/segment-anything/model_mask_decoder.rs b/candle-transformers/src/models/segment_anything/mask_decoder.rs
similarity index 96%
rename from candle-examples/examples/segment-anything/model_mask_decoder.rs
rename to candle-transformers/src/models/segment_anything/mask_decoder.rs
index c02b44a7..2a91cd44 100644
--- a/candle-examples/examples/segment-anything/model_mask_decoder.rs
+++ b/candle-transformers/src/models/segment_anything/mask_decoder.rs
@@ -1,11 +1,11 @@
 use candle::{IndexOp, Result, Tensor};
 use candle_nn::{Module, VarBuilder};
 
-use crate::model_transformer::TwoWayTransformer;
+use super::transformer::TwoWayTransformer;
 
 #[derive(Debug)]
 struct MlpMaskDecoder {
-    layers: Vec<crate::Linear>,
+    layers: Vec<super::Linear>,
     sigmoid_output: bool,
     span: tracing::Span,
 }
@@ -28,7 +28,7 @@ impl MlpMaskDecoder {
             } else {
                 hidden_dim
             };
-            let layer = crate::linear(vb.pp(i), in_dim, out_dim, true)?;
+            let layer = super::linear(vb.pp(i), in_dim, out_dim, true)?;
             layers.push(layer)
         }
         let span = tracing::span!(tracing::Level::TRACE, "mlp-mask-decoder");
@@ -64,7 +64,7 @@ pub struct MaskDecoder {
     mask_tokens: candle_nn::Embedding,
     iou_prediction_head: MlpMaskDecoder,
     output_upscaling_conv1: candle_nn::ConvTranspose2d,
-    output_upscaling_ln: crate::LayerNorm2d,
+    output_upscaling_ln: super::LayerNorm2d,
     output_upscaling_conv2: candle_nn::ConvTranspose2d,
     num_mask_tokens: usize,
     output_hypernetworks_mlps: Vec<MlpMaskDecoder>,
@@ -104,7 +104,7 @@ impl MaskDecoder {
             vb.pp("output_upscaling.0"),
         )?;
         let output_upscaling_ln =
-            crate::LayerNorm2d::new(transformer_dim / 4, 1e-6, vb.pp("output_upscaling.1"))?;
+            super::LayerNorm2d::new(transformer_dim / 4, 1e-6, vb.pp("output_upscaling.1"))?;
         let output_upscaling_conv2 = candle_nn::conv_transpose2d(
             transformer_dim / 4,
             transformer_dim / 8,
diff --git a/candle-transformers/src/models/segment_anything/mod.rs b/candle-transformers/src/models/segment_anything/mod.rs
new file mode 100644
index 00000000..c29db70a
--- /dev/null
+++ b/candle-transformers/src/models/segment_anything/mod.rs
@@ -0,0 +1,100 @@
+use candle::{Result, Tensor};
+use candle_nn::{Module, VarBuilder};
+
+pub mod image_encoder;
+pub mod mask_decoder;
+pub mod prompt_encoder;
+pub mod sam;
+pub mod tiny_vit;
+pub mod transformer;
+
+pub fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
+    let inner = if bias {
+        candle_nn::linear(in_dim, out_dim, vb)?
+    } else {
+        candle_nn::linear_no_bias(in_dim, out_dim, vb)?
+    };
+    let span = tracing::span!(tracing::Level::TRACE, "linear");
+    Ok(Linear { inner, span })
+}
+
+#[derive(Debug)]
+pub struct LayerNorm2d {
+    weight: Tensor,
+    bias: Tensor,
+    num_channels: usize,
+    eps: f64,
+}
+
+impl LayerNorm2d {
+    pub fn new(num_channels: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
+        let weight = vb.get(num_channels, "weight")?;
+        let bias = vb.get(num_channels, "bias")?;
+        Ok(Self {
+            weight,
+            bias,
+            num_channels,
+            eps,
+        })
+    }
+}
+
+impl Module for LayerNorm2d {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let u = xs.mean_keepdim(1)?;
+        let xs = xs.broadcast_sub(&u)?;
+        let s = xs.sqr()?.mean_keepdim(1)?;
+        let xs = xs.broadcast_div(&(s + self.eps)?.sqrt()?)?;
+        xs.broadcast_mul(&self.weight.reshape((1, self.num_channels, 1, 1))?)?
+            .broadcast_add(&self.bias.reshape((1, self.num_channels, 1, 1))?)
+    }
+}
+
+#[derive(Debug)]
+pub struct MlpBlock {
+    lin1: Linear,
+    lin2: Linear,
+    activation: candle_nn::Activation,
+    span: tracing::Span,
+}
+
+impl MlpBlock {
+    pub fn new(
+        embedding_dim: usize,
+        mlp_dim: usize,
+        activation: candle_nn::Activation,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let lin1 = linear(vb.pp("lin1"), embedding_dim, mlp_dim, true)?;
+        let lin2 = linear(vb.pp("lin2"), mlp_dim, embedding_dim, true)?;
+        let span = tracing::span!(tracing::Level::TRACE, "mlp-block");
+        Ok(Self {
+            lin1,
+            lin2,
+            activation,
+            span,
+        })
+    }
+}
+
+impl Module for MlpBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        xs.apply(&self.lin1)?
+            .apply(&self.activation)?
+            .apply(&self.lin2)
+    }
+}
+
+#[derive(Debug)]
+pub struct Linear {
+    inner: candle_nn::Linear,
+    span: tracing::Span,
+}
+
+impl Module for Linear {
+    fn forward(&self, x: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(x)
+    }
+}
diff --git a/candle-examples/examples/segment-anything/model_prompt_encoder.rs b/candle-transformers/src/models/segment_anything/prompt_encoder.rs
similarity index 97%
rename from candle-examples/examples/segment-anything/model_prompt_encoder.rs
rename to candle-transformers/src/models/segment_anything/prompt_encoder.rs
index 7bbe8419..9d0074b1 100644
--- a/candle-examples/examples/segment-anything/model_prompt_encoder.rs
+++ b/candle-transformers/src/models/segment_anything/prompt_encoder.rs
@@ -56,9 +56,9 @@ pub struct PromptEncoder {
     point_embeddings: Vec<candle_nn::Embedding>,
     not_a_point_embed: candle_nn::Embedding,
     mask_downscaling_conv1: candle_nn::Conv2d,
-    mask_downscaling_ln1: crate::LayerNorm2d,
+    mask_downscaling_ln1: super::LayerNorm2d,
     mask_downscaling_conv2: candle_nn::Conv2d,
-    mask_downscaling_ln2: crate::LayerNorm2d,
+    mask_downscaling_ln2: super::LayerNorm2d,
     mask_downscaling_conv3: candle_nn::Conv2d,
     no_mask_embed: candle_nn::Embedding,
     image_embedding_size: (usize, usize),
@@ -100,9 +100,9 @@ impl PromptEncoder {
             vb.pp("mask_downscaling.6"),
         )?;
         let mask_downscaling_ln1 =
-            crate::LayerNorm2d::new(mask_in_chans / 4, 1e-6, vb.pp("mask_downscaling.1"))?;
+            super::LayerNorm2d::new(mask_in_chans / 4, 1e-6, vb.pp("mask_downscaling.1"))?;
         let mask_downscaling_ln2 =
-            crate::LayerNorm2d::new(mask_in_chans, 1e-6, vb.pp("mask_downscaling.4"))?;
+            super::LayerNorm2d::new(mask_in_chans, 1e-6, vb.pp("mask_downscaling.4"))?;
         let mut point_embeddings = Vec::with_capacity(num_points_embeddings);
         let vb_e = vb.pp("point_embeddings");
         for i in 0..num_points_embeddings {
diff --git a/candle-examples/examples/segment-anything/model_sam.rs b/candle-transformers/src/models/segment_anything/sam.rs
similarity index 96%
rename from candle-examples/examples/segment-anything/model_sam.rs
rename to candle-transformers/src/models/segment_anything/sam.rs
index b1a81af6..c40473e3 100644
--- a/candle-examples/examples/segment-anything/model_sam.rs
+++ b/candle-transformers/src/models/segment_anything/sam.rs
@@ -1,10 +1,10 @@
 use candle::{DType, IndexOp, Result, Tensor};
 use candle_nn::{Module, VarBuilder};
 
-use crate::model_image_encoder::ImageEncoderViT;
-use crate::model_mask_decoder::MaskDecoder;
-use crate::model_prompt_encoder::PromptEncoder;
-use crate::model_tiny_vit::{tiny_vit_5m, TinyViT};
+use super::image_encoder::ImageEncoderViT;
+use super::mask_decoder::MaskDecoder;
+use super::prompt_encoder::PromptEncoder;
+use super::tiny_vit::{tiny_vit_5m, TinyViT};
 
 const PROMPT_EMBED_DIM: usize = 256;
 pub const IMAGE_SIZE: usize = 1024;
@@ -186,7 +186,7 @@ impl Sam {
         img: &Tensor,
         cb: CropBox,
         point_grids: &[(f64, f64)],
-    ) -> Result<Vec<candle_examples::object_detection::Bbox<Tensor>>> {
+    ) -> Result<Vec<crate::object_detection::Bbox<Tensor>>> {
         // Crop the image and calculate embeddings.
         let img = img.i((.., cb.y0..cb.y1, cb.x0..cb.x1))?;
         let img = self.preprocess(&img)?.unsqueeze(0)?;
@@ -259,7 +259,7 @@ impl Sam {
                 let min_max_x = min_max_indexes(&low_res_mask_per_x);
                 let min_max_y = min_max_indexes(&low_res_mask_per_y);
                 if let Some(((x0, x1), (y0, y1))) = min_max_x.zip(min_max_y) {
-                    let bbox = candle_examples::object_detection::Bbox {
+                    let bbox = crate::object_detection::Bbox {
                         xmin: x0 as f32,
                         ymin: y0 as f32,
                         xmax: x1 as f32,
@@ -277,7 +277,7 @@ impl Sam {
 
         let mut bboxes = vec![bboxes];
         // Remove duplicates within this crop.
-        candle_examples::object_detection::non_maximum_suppression(&mut bboxes, CROP_NMS_THRESH);
+        crate::object_detection::non_maximum_suppression(&mut bboxes, CROP_NMS_THRESH);
 
         // TODO: Return to the original image frame.
         Ok(bboxes.remove(0))
@@ -290,7 +290,7 @@ impl Sam {
         crop_n_layer: usize,
         crop_overlap_ratio: f64,
         crop_n_points_downscale_factor: usize,
-    ) -> Result<Vec<candle_examples::object_detection::Bbox<Tensor>>> {
+    ) -> Result<Vec<crate::object_detection::Bbox<Tensor>>> {
         let (_c, h, w) = img.dims3()?;
         let point_grids = build_all_layer_point_grids(
             points_per_side,
diff --git a/candle-examples/examples/segment-anything/model_tiny_vit.rs b/candle-transformers/src/models/segment_anything/tiny_vit.rs
similarity index 97%
rename from candle-examples/examples/segment-anything/model_tiny_vit.rs
rename to candle-transformers/src/models/segment_anything/tiny_vit.rs
index ff076773..cd2936ab 100644
--- a/candle-examples/examples/segment-anything/model_tiny_vit.rs
+++ b/candle-transformers/src/models/segment_anything/tiny_vit.rs
@@ -215,16 +215,16 @@ impl Module for ConvLayer {
 #[derive(Debug)]
 struct Mlp {
     norm: candle_nn::LayerNorm,
-    fc1: crate::Linear,
-    fc2: crate::Linear,
+    fc1: super::Linear,
+    fc2: super::Linear,
     span: tracing::Span,
 }
 
 impl Mlp {
     fn new(in_: usize, hidden: usize, vb: VarBuilder) -> Result<Self> {
         let norm = candle_nn::layer_norm(in_, 1e-5, vb.pp("norm"))?;
-        let fc1 = crate::linear(vb.pp("fc1"), in_, hidden, true)?;
-        let fc2 = crate::linear(vb.pp("fc2"), hidden, in_, true)?;
+        let fc1 = super::linear(vb.pp("fc1"), in_, hidden, true)?;
+        let fc2 = super::linear(vb.pp("fc2"), hidden, in_, true)?;
         let span = tracing::span!(tracing::Level::TRACE, "mlp");
         Ok(Self {
             norm,
@@ -248,8 +248,8 @@ impl Module for Mlp {
 #[derive(Debug)]
 struct Attention {
     norm: candle_nn::LayerNorm,
-    qkv: crate::Linear,
-    proj: crate::Linear,
+    qkv: super::Linear,
+    proj: super::Linear,
     ab: Tensor,
     key_dim: usize,
     num_heads: usize,
@@ -275,8 +275,8 @@ impl Attention {
         let nh_kd = key_dim * num_heads;
         let h = dh + nh_kd * 2;
         let norm = candle_nn::layer_norm(dim, 1e-5, vb.pp("norm"))?;
-        let qkv = crate::linear(vb.pp("qkv"), dim, h, true)?;
-        let proj = crate::linear(vb.pp("proj"), dh, dim, true)?;
+        let qkv = super::linear(vb.pp("qkv"), dim, h, true)?;
+        let proj = super::linear(vb.pp("proj"), dh, dim, true)?;
 
         let points = (0..resolution.0)
             .flat_map(|x| (0..resolution.1).map(move |y| (x as i64, y as i64)))
@@ -526,9 +526,9 @@ pub struct TinyViT {
     // norm_head: candle_nn::LayerNorm,
     // head: candle_nn::Linear,
     neck_conv1: candle_nn::Conv2d,
-    neck_ln1: crate::LayerNorm2d,
+    neck_ln1: super::LayerNorm2d,
     neck_conv2: candle_nn::Conv2d,
-    neck_ln2: crate::LayerNorm2d,
+    neck_ln2: super::LayerNorm2d,
     span: tracing::Span,
     span_neck: tracing::Span,
 }
@@ -578,13 +578,13 @@ impl TinyViT {
         // let head = candle_nn::linear(last_embed_dim, num_classes, vb.pp("head"))?;
         let neck_conv1 =
             candle_nn::conv2d_no_bias(last_embed_dim, 256, 1, Default::default(), vb.pp("neck.0"))?;
-        let neck_ln1 = crate::LayerNorm2d::new(256, 1e-6, vb.pp("neck.1"))?;
+        let neck_ln1 = super::LayerNorm2d::new(256, 1e-6, vb.pp("neck.1"))?;
         let cfg = candle_nn::Conv2dConfig {
             padding: 1,
             ..Default::default()
         };
         let neck_conv2 = candle_nn::conv2d_no_bias(256, 256, 3, cfg, vb.pp("neck.2"))?;
-        let neck_ln2 = crate::LayerNorm2d::new(256, 1e-6, vb.pp("neck.3"))?;
+        let neck_ln2 = super::LayerNorm2d::new(256, 1e-6, vb.pp("neck.3"))?;
 
         let span = tracing::span!(tracing::Level::TRACE, "tiny-vit");
         let span_neck = tracing::span!(tracing::Level::TRACE, "neck");
diff --git a/candle-examples/examples/segment-anything/model_transformer.rs b/candle-transformers/src/models/segment_anything/transformer.rs
similarity index 99%
rename from candle-examples/examples/segment-anything/model_transformer.rs
rename to candle-transformers/src/models/segment_anything/transformer.rs
index e12aac08..80efb38c 100644
--- a/candle-examples/examples/segment-anything/model_transformer.rs
+++ b/candle-transformers/src/models/segment_anything/transformer.rs
@@ -68,7 +68,7 @@ struct TwoWayAttentionBlock {
     norm1: LayerNorm,
     cross_attn_token_to_image: Attention,
     norm2: LayerNorm,
-    mlp: crate::MlpBlock,
+    mlp: super::MlpBlock,
     norm3: LayerNorm,
     norm4: LayerNorm,
     cross_attn_image_to_token: Attention,
@@ -100,7 +100,7 @@ impl TwoWayAttentionBlock {
             2,
             vb.pp("cross_attn_image_to_token"),
         )?;
-        let mlp = crate::MlpBlock::new(
+        let mlp = super::MlpBlock::new(
             embedding_dim,
             mlp_dim,
             candle_nn::Activation::Relu,
diff --git a/candle-examples/src/object_detection.rs b/candle-transformers/src/object_detection.rs
similarity index 100%
rename from candle-examples/src/object_detection.rs
rename to candle-transformers/src/object_detection.rs

From 6c58fc59fd828492021cfd0f4518ae5ae3b03f56 Mon Sep 17 00:00:00 2001
From: Ssslakter <67190162+Ssslakter@users.noreply.github.com>
Date: Sun, 10 Sep 2023 18:02:52 +0700
Subject: [PATCH 053/150] Little docs changes (#791)

* Little doc fixes

* change imports in lib

* rename candle_core to candle

* revert "rename candle_core to candle"
---
 candle-book/src/guide/hello_world.md | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/candle-book/src/guide/hello_world.md b/candle-book/src/guide/hello_world.md
index fc4af0e1..74a147e7 100644
--- a/candle-book/src/guide/hello_world.md
+++ b/candle-book/src/guide/hello_world.md
@@ -25,8 +25,8 @@ fn main() -> Result<()> {
     // Use Device::new_cuda(0)?; to use the GPU.
     let device = Device::Cpu;
 
-    let first = Tensor::zeros((784, 100), DType::F32, &device)?;
-    let second = Tensor::zeros((100, 10), DType::F32, &device)?;
+    let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
+    let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
     let model = Model { first, second };
 
     let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
@@ -110,15 +110,15 @@ fn main() -> Result<()> {
     let device = Device::cuda_if_available(0)?;
 
     // Creating a dummy model
-    let weight = Tensor::zeros((784, 100), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
     let bias = Tensor::zeros((100, ), DType::F32, &device)?;
     let first = Linear{weight, bias};
-    let weight = Tensor::zeros((100, 10), DType::F32, &device)?;
-    let bias = Tensor::zeros((10, ), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
     let second = Linear{weight, bias};
     let model = Model { first, second };
 
-    let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
 
     // Inference on the model
     let digit = model.forward(&dummy_image)?;
@@ -167,15 +167,15 @@ fn main() -> Result<()> {
     let device = Device::Cpu;
 
     // This has changed (784, 100) -> (100, 784) !
-    let weight = Tensor::zeros((100, 784), DType::F32, &device)?;
-    let bias = Tensor::zeros((100, ), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
     let first = Linear::new(weight, Some(bias));
-    let weight = Tensor::zeros((10, 100), DType::F32, &device)?;
-    let bias = Tensor::zeros((10, ), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
     let second = Linear::new(weight, Some(bias));
     let model = Model { first, second };
 
-    let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
 
     let digit = model.forward(&dummy_image)?;
     println!("Digit {digit:?} digit");
@@ -188,8 +188,8 @@ Feel free to modify this example to use `Conv2d` to create a classical convnet i
 
 Now that we have the running dummy code we can get to more advanced topics:
 
-- [For PyTorch users](./guide/cheatsheet.md)
-- [Running existing models](./inference/README.md)
-- [Training models](./training/README.md)
+- [For PyTorch users](../guide/cheatsheet.md)
+- [Running existing models](../inference/README.md)
+- [Training models](../training/README.md)
 
 

From 584171cae18923450e029eb04245f70c7e7e5fc4 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 12:29:37 +0100
Subject: [PATCH 054/150] Add a wasm module for the segment anything example.
 (#797)

---
 Cargo.toml                                    |   1 +
 .../src/models/segment_anything/sam.rs        |  28 ++++-
 .../segment-anything/Cargo.toml               |  29 +++++
 .../segment-anything/build-lib.sh             |   2 +
 .../segment-anything/src/bin/m.rs             | 113 ++++++++++++++++++
 .../segment-anything/src/lib.rs               |  19 +++
 6 files changed, 189 insertions(+), 3 deletions(-)
 create mode 100644 candle-wasm-examples/segment-anything/Cargo.toml
 create mode 100644 candle-wasm-examples/segment-anything/build-lib.sh
 create mode 100644 candle-wasm-examples/segment-anything/src/bin/m.rs
 create mode 100644 candle-wasm-examples/segment-anything/src/lib.rs

diff --git a/Cargo.toml b/Cargo.toml
index ce41876a..b45a2ab6 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -8,6 +8,7 @@ members = [
     "candle-pyo3",
     "candle-transformers",
     "candle-wasm-examples/llama2-c",
+    "candle-wasm-examples/segment-anything",
     "candle-wasm-examples/whisper",
     "candle-wasm-examples/yolo",
 ]
diff --git a/candle-transformers/src/models/segment_anything/sam.rs b/candle-transformers/src/models/segment_anything/sam.rs
index c40473e3..92756591 100644
--- a/candle-transformers/src/models/segment_anything/sam.rs
+++ b/candle-transformers/src/models/segment_anything/sam.rs
@@ -122,6 +122,11 @@ impl Sam {
         })
     }
 
+    pub fn embeddings(&self, img: &Tensor) -> Result<Tensor> {
+        let img = self.preprocess(img)?.unsqueeze(0)?;
+        self.image_encoder.forward(&img)
+    }
+
     pub fn forward(
         &self,
         img: &Tensor,
@@ -131,15 +136,32 @@ impl Sam {
         let (_c, original_h, original_w) = img.dims3()?;
         let img = self.preprocess(img)?.unsqueeze(0)?;
         let img_embeddings = self.image_encoder.forward(&img)?;
+        self.forward_for_embeddings(
+            &img_embeddings,
+            original_h,
+            original_w,
+            point,
+            multimask_output,
+        )
+    }
+
+    pub fn forward_for_embeddings(
+        &self,
+        img_embeddings: &Tensor,
+        original_h: usize,
+        original_w: usize,
+        point: Option<(f64, f64)>,
+        multimask_output: bool,
+    ) -> Result<(Tensor, Tensor)> {
         let image_pe = self.prompt_encoder.get_dense_pe()?;
         let points = match point {
             None => None,
             Some((x, y)) => {
                 let points = Tensor::new(
                     &[[[x as f32 * original_w as f32, y as f32 * original_h as f32]]],
-                    img.device(),
+                    img_embeddings.device(),
                 )?;
-                let labels = Tensor::ones((1, 1), DType::F32, img.device())?;
+                let labels = Tensor::ones((1, 1), DType::F32, img_embeddings.device())?;
                 Some((points, labels))
             }
         };
@@ -147,7 +169,7 @@ impl Sam {
         let (sparse_prompt_embeddings, dense_prompt_embeddings) =
             self.prompt_encoder.forward(points, None, None)?;
         let (low_res_mask, iou_predictions) = self.mask_decoder.forward(
-            &img_embeddings,
+            img_embeddings,
             &image_pe,
             &sparse_prompt_embeddings,
             &dense_prompt_embeddings,
diff --git a/candle-wasm-examples/segment-anything/Cargo.toml b/candle-wasm-examples/segment-anything/Cargo.toml
new file mode 100644
index 00000000..ab82ab1f
--- /dev/null
+++ b/candle-wasm-examples/segment-anything/Cargo.toml
@@ -0,0 +1,29 @@
+[package]
+name = "candle-wasm-example-sam"
+version.workspace = true
+edition.workspace = true
+description.workspace = true
+repository.workspace = true
+keywords.workspace = true
+categories.workspace = true
+license.workspace = true
+
+[dependencies]
+candle = { path = "../../candle-core", version = "0.2.1", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.1" }
+candle-transformers = { path = "../../candle-transformers", version = "0.2.1" }
+num-traits = { workspace = true }
+
+# App crates.
+anyhow = { workspace = true }
+byteorder = { workspace = true }
+getrandom = { version = "0.2", features = ["js"] }
+image = { workspace = true }
+log = { workspace = true }
+safetensors = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
+
+# Wasm specific crates.
+console_error_panic_hook = "0.1.7"
+wasm-bindgen = "0.2.87"
diff --git a/candle-wasm-examples/segment-anything/build-lib.sh b/candle-wasm-examples/segment-anything/build-lib.sh
new file mode 100644
index 00000000..b0ebb182
--- /dev/null
+++ b/candle-wasm-examples/segment-anything/build-lib.sh
@@ -0,0 +1,2 @@
+cargo build --target wasm32-unknown-unknown --release
+wasm-bindgen ../../target/wasm32-unknown-unknown/release/m.wasm --out-dir build --target web
diff --git a/candle-wasm-examples/segment-anything/src/bin/m.rs b/candle-wasm-examples/segment-anything/src/bin/m.rs
new file mode 100644
index 00000000..c4c79fe0
--- /dev/null
+++ b/candle-wasm-examples/segment-anything/src/bin/m.rs
@@ -0,0 +1,113 @@
+use candle::{DType, Device, Tensor};
+use candle_nn::VarBuilder;
+use candle_wasm_example_sam as sam;
+use wasm_bindgen::prelude::*;
+
+#[allow(unused)]
+struct Embeddings {
+    original_width: u32,
+    original_height: u32,
+    width: u32,
+    height: u32,
+    data: Tensor,
+}
+
+#[wasm_bindgen]
+pub struct Model {
+    sam: sam::Sam,
+    embeddings: Option<Embeddings>,
+}
+
+#[wasm_bindgen]
+impl Model {
+    #[wasm_bindgen(constructor)]
+    pub fn new(weights: &[u8], use_tiny: bool) -> Result<Model, JsError> {
+        console_error_panic_hook::set_once();
+        let dev = &Device::Cpu;
+        let weights = safetensors::tensor::SafeTensors::deserialize(weights)?;
+        let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, dev);
+        let sam = if use_tiny {
+            sam::Sam::new_tiny(vb)? // tiny vit_t
+        } else {
+            sam::Sam::new(768, 12, 12, &[2, 5, 8, 11], vb)? // sam_vit_b
+        };
+        Ok(Self {
+            sam,
+            embeddings: None,
+        })
+    }
+
+    pub fn set_image_embeddings(&mut self, image_data: Vec<u8>) -> Result<(), JsError> {
+        sam::console_log!("image data: {}", image_data.len());
+        let image_data = std::io::Cursor::new(image_data);
+        let image = image::io::Reader::new(image_data)
+            .with_guessed_format()?
+            .decode()
+            .map_err(candle::Error::wrap)?;
+        let (original_height, original_width) = (image.height(), image.width());
+        let (height, width) = (original_height, original_width);
+        let resize_longest = sam::IMAGE_SIZE as u32;
+        let (height, width) = if height < width {
+            let h = (resize_longest * height) / width;
+            (h, resize_longest)
+        } else {
+            let w = (resize_longest * width) / height;
+            (resize_longest, w)
+        };
+        let image_t = {
+            let img = image.resize_exact(width, height, image::imageops::FilterType::CatmullRom);
+            let data = img.to_rgb8().into_raw();
+            Tensor::from_vec(
+                data,
+                (img.height() as usize, img.width() as usize, 3),
+                &Device::Cpu,
+            )?
+            .permute((2, 0, 1))?
+        };
+        let data = self.sam.embeddings(&image_t)?;
+        self.embeddings = Some(Embeddings {
+            original_width,
+            original_height,
+            width,
+            height,
+            data,
+        });
+        Ok(())
+    }
+
+    // x and y have to be between 0 and 1
+    pub fn mask_for_point(&self, x: f64, y: f64) -> Result<String, JsError> {
+        let embeddings = match &self.embeddings {
+            None => todo!(),
+            Some(embeddings) => embeddings,
+        };
+        let (mask, iou_predictions) = self.sam.forward_for_embeddings(
+            &embeddings.data,
+            embeddings.height as usize,
+            embeddings.width as usize,
+            Some((x, y)),
+            false,
+        )?;
+        let iou = iou_predictions.to_vec1::<f32>()?[0];
+        let mask_shape = mask.dims().to_vec();
+        let mask_data = mask.ge(0f32)?.flatten_all()?.to_vec1::<u8>()?;
+        let mask = Mask {
+            iou,
+            mask_shape,
+            mask_data,
+        };
+        let json = serde_json::to_string(&mask)?;
+        Ok(json)
+    }
+}
+
+#[derive(serde::Serialize, serde::Deserialize)]
+struct Mask {
+    iou: f32,
+    mask_shape: Vec<usize>,
+    mask_data: Vec<u8>,
+}
+
+fn main() {
+    console_error_panic_hook::set_once();
+}
diff --git a/candle-wasm-examples/segment-anything/src/lib.rs b/candle-wasm-examples/segment-anything/src/lib.rs
new file mode 100644
index 00000000..0f4f96fd
--- /dev/null
+++ b/candle-wasm-examples/segment-anything/src/lib.rs
@@ -0,0 +1,19 @@
+use candle_transformers::models::segment_anything::sam;
+use wasm_bindgen::prelude::*;
+
+pub use sam::{Sam, IMAGE_SIZE};
+
+#[wasm_bindgen]
+extern "C" {
+    // Use `js_namespace` here to bind `console.log(..)` instead of just
+    // `log(..)`
+    #[wasm_bindgen(js_namespace = console)]
+    pub fn log(s: &str);
+}
+
+#[macro_export]
+macro_rules! console_log {
+    // Note that this is using the `log` function imported above during
+    // `bare_bones`
+    ($($t:tt)*) => ($crate::log(&format_args!($($t)*).to_string()))
+}

From 90e077e4093510917e5762de92384ae312162f23 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 13:03:02 +0100
Subject: [PATCH 055/150] Return the low res mask in the wasm segment-anything
 module. (#798)

* Return the low res mask.

* Add some validations.
---
 .../src/models/segment_anything/sam.rs         | 18 +++++++++---------
 .../segment-anything/src/bin/m.rs              | 12 +++++++++++-
 2 files changed, 20 insertions(+), 10 deletions(-)

diff --git a/candle-transformers/src/models/segment_anything/sam.rs b/candle-transformers/src/models/segment_anything/sam.rs
index 92756591..07e9a759 100644
--- a/candle-transformers/src/models/segment_anything/sam.rs
+++ b/candle-transformers/src/models/segment_anything/sam.rs
@@ -136,13 +136,18 @@ impl Sam {
         let (_c, original_h, original_w) = img.dims3()?;
         let img = self.preprocess(img)?.unsqueeze(0)?;
         let img_embeddings = self.image_encoder.forward(&img)?;
-        self.forward_for_embeddings(
+        let (low_res_mask, iou) = self.forward_for_embeddings(
             &img_embeddings,
             original_h,
             original_w,
             point,
             multimask_output,
-        )
+        )?;
+        let mask = low_res_mask
+            .upsample_nearest2d(IMAGE_SIZE, IMAGE_SIZE)?
+            .get(0)?
+            .i((.., ..original_h, ..original_w))?;
+        Ok((mask, iou))
     }
 
     pub fn forward_for_embeddings(
@@ -168,18 +173,13 @@ impl Sam {
         let points = points.as_ref().map(|(x, y)| (x, y));
         let (sparse_prompt_embeddings, dense_prompt_embeddings) =
             self.prompt_encoder.forward(points, None, None)?;
-        let (low_res_mask, iou_predictions) = self.mask_decoder.forward(
+        self.mask_decoder.forward(
             img_embeddings,
             &image_pe,
             &sparse_prompt_embeddings,
             &dense_prompt_embeddings,
             multimask_output,
-        )?;
-        let mask = low_res_mask
-            .upsample_nearest2d(IMAGE_SIZE, IMAGE_SIZE)?
-            .get(0)?
-            .i((.., ..original_h, ..original_w))?;
-        Ok((mask, iou_predictions))
+        )
     }
 
     pub fn unpreprocess(&self, img: &Tensor) -> Result<Tensor> {
diff --git a/candle-wasm-examples/segment-anything/src/bin/m.rs b/candle-wasm-examples/segment-anything/src/bin/m.rs
index c4c79fe0..b53f5b9b 100644
--- a/candle-wasm-examples/segment-anything/src/bin/m.rs
+++ b/candle-wasm-examples/segment-anything/src/bin/m.rs
@@ -77,8 +77,18 @@ impl Model {
 
     // x and y have to be between 0 and 1
     pub fn mask_for_point(&self, x: f64, y: f64) -> Result<String, JsError> {
+        if !(0. ..=1.).contains(&x) {
+            Err(JsError::new(&format!(
+                "x has to be between 0 and 1, got {x}"
+            )))?
+        }
+        if !(0. ..=1.).contains(&y) {
+            Err(JsError::new(&format!(
+                "y has to be between 0 and 1, got {y}"
+            )))?
+        }
         let embeddings = match &self.embeddings {
-            None => todo!(),
+            None => Err(JsError::new("image embeddings have not been set"))?,
             Some(embeddings) => embeddings,
         };
         let (mask, iou_predictions) = self.sam.forward_for_embeddings(

From 3dd58042991786e698573a95932a108a3cf1f7db Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 13:49:47 +0100
Subject: [PATCH 056/150] Fix typo in readme. (#799)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 9e5f938a..01c2bb8b 100644
--- a/README.md
+++ b/README.md
@@ -64,7 +64,7 @@ Check out our [examples](./candle-examples/examples/):
 - [yolo-v3](./candle-examples/examples/yolo-v3/) and
   [yolo-v8](./candle-examples/examples/yolo-v8/): object detection and pose
   estimation models.
-  [segment-anything](./candle-examples/examples/segment-anything/): image
+- [segment-anything](./candle-examples/examples/segment-anything/): image
   segmentation model with prompt.
 Run them using the following commands:
 ```

From 559944146fea5ba0baa0f23fcf67062f04ca22da Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 16:56:28 +0100
Subject: [PATCH 057/150] Add an im2col based benchmark. (#800)

* Add an im2col based benchmark.

* Reshape the final result.
---
 candle-nn/examples/cpu_benchmarks.rs | 73 +++++++++++++++++++++++++++-
 1 file changed, 71 insertions(+), 2 deletions(-)

diff --git a/candle-nn/examples/cpu_benchmarks.rs b/candle-nn/examples/cpu_benchmarks.rs
index 20c92dbb..012456ec 100644
--- a/candle-nn/examples/cpu_benchmarks.rs
+++ b/candle-nn/examples/cpu_benchmarks.rs
@@ -6,7 +6,7 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use candle::quantized::GgmlType;
-use candle::{Device, Result, Tensor, D};
+use candle::{CpuStorage, Device, Layout, Result, Shape, Tensor, D};
 use clap::{Parser, Subcommand};
 
 trait Benchmark {
@@ -19,6 +19,48 @@ trait Benchmark {
     const ITERS: usize;
 }
 
+struct Im2Col(usize, usize);
+impl candle::CustomOp1 for Im2Col {
+    fn name(&self) -> &'static str {
+        "im2col"
+    }
+
+    fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)> {
+        let &Self(h_k, w_k) = self;
+        let (b, c, h, w) = layout.shape().dims4()?;
+        let (h_out, w_out) = (h - h_k + 1, w - w_k + 1);
+        let slice = storage.as_slice::<f32>()?;
+        let src = match layout.contiguous_offsets() {
+            None => candle::bail!("input has to be contiguous"),
+            Some((o1, o2)) => &slice[o1..o2],
+        };
+        let mut dst = vec![0f32; b * h_out * w_out * c * h_k * w_k];
+        let (s_b, s_c, s_h) = (c * h * w, h * w, w);
+        for b_idx in 0..b {
+            let src_idx = b_idx * s_b;
+            let dst_idx = b_idx * h_out * w_out * c * h_k * w_k;
+            for h_idx in 0..h_out {
+                let dst_idx = dst_idx + h_idx * w_out * c * h_k * w_k;
+                for w_idx in 0..w_out {
+                    let dst_idx = dst_idx + w_idx * c * h_k * w_k;
+                    for c_idx in 0..c {
+                        let dst_idx = dst_idx + c_idx * h_k * w_k;
+                        let src_idx = c_idx * s_c + src_idx;
+                        for h_k_idx in 0..h_k {
+                            let src_idx = src_idx + (h_idx + h_k_idx) * s_h + w_idx;
+                            let dst_idx = dst_idx + h_k_idx * w_k;
+                            dst[dst_idx..dst_idx + w_k]
+                                .copy_from_slice(&src[src_idx..src_idx + w_k])
+                        }
+                    }
+                }
+            }
+        }
+        let storage = candle::WithDType::to_cpu_storage_owned(dst);
+        Ok((storage, (b * h_out * w_out, c * h_k * w_k).into()))
+    }
+}
+
 // Conv1d example as used in whisper.
 struct Conv1d;
 impl Benchmark for Conv1d {
@@ -53,7 +95,32 @@ impl Benchmark for Conv2d {
         d.0.conv2d(&d.1, 0, 1, 1, 1)
     }
 
-    const ITERS: usize = 1;
+    const ITERS: usize = 5;
+}
+
+// Conv2d example as used in stable-diffusion, im2col implementation.
+struct Conv2dIm2Col;
+impl Benchmark for Conv2dIm2Col {
+    type PreProcessData = (Tensor, Tensor);
+    type RunResult = Tensor;
+
+    fn preprocess() -> Result<Self::PreProcessData> {
+        let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
+        let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
+        Ok((inp, w))
+    }
+
+    fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
+        // d.0.conv2d(&d.1, 0, 1, 1, 1)
+        let (b, _, h, w) = d.0.dims4()?;
+        let (h_k, w_k) = (3, 3);
+        let (h_out, w_out) = (h - h_k + 1, w - w_k + 1);
+        let col = d.0.apply_op1_no_bwd(&Im2Col(h_k, w_k))?;
+        let res = col.matmul(&d.1.flatten_from(1)?.t()?)?;
+        res.reshape((b, (), h_out, w_out))
+    }
+
+    const ITERS: usize = 5;
 }
 
 struct Matmul;
@@ -145,6 +212,7 @@ fn run<B: Benchmark>(iters: Option<usize>) -> Result<()> {
 enum Task {
     Conv1d,
     Conv2d,
+    Conv2dIm2Col,
     Matmul,
     Qmatmul,
     Softmax,
@@ -167,6 +235,7 @@ fn main() -> Result<()> {
     match args.task {
         Task::Conv1d => run::<Conv1d>(args.iters)?,
         Task::Conv2d => run::<Conv2d>(args.iters)?,
+        Task::Conv2dIm2Col => run::<Conv2dIm2Col>(args.iters)?,
         Task::Matmul => run::<Matmul>(args.iters)?,
         Task::Softmax => run::<Softmax>(args.iters)?,
         Task::SoftmaxLastDim => run::<SoftmaxLastDim>(args.iters)?,

From 4f18180fc7564b9a02cab13c4acda0ac7b17a799 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 16:59:46 +0100
Subject: [PATCH 058/150] Bugfix so that im2col produce the same results as
 conv2d. (#801)

---
 candle-nn/examples/cpu_benchmarks.rs | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/candle-nn/examples/cpu_benchmarks.rs b/candle-nn/examples/cpu_benchmarks.rs
index 012456ec..3ba30f94 100644
--- a/candle-nn/examples/cpu_benchmarks.rs
+++ b/candle-nn/examples/cpu_benchmarks.rs
@@ -117,7 +117,11 @@ impl Benchmark for Conv2dIm2Col {
         let (h_out, w_out) = (h - h_k + 1, w - w_k + 1);
         let col = d.0.apply_op1_no_bwd(&Im2Col(h_k, w_k))?;
         let res = col.matmul(&d.1.flatten_from(1)?.t()?)?;
-        res.reshape((b, (), h_out, w_out))
+        let res = res
+            .reshape((b, h_out, w_out, ()))?
+            .permute((0, 3, 1, 2))?
+            .contiguous()?;
+        Ok(res)
     }
 
     const ITERS: usize = 5;

From 18d6db2180800dcc134ffabe8523a774c6a7f9a3 Mon Sep 17 00:00:00 2001
From: Ssslakter <67190162+Ssslakter@users.noreply.github.com>
Date: Mon, 11 Sep 2023 02:36:29 +0700
Subject: [PATCH 059/150] more doc fixes (#804)

---
 candle-book/src/SUMMARY.md                       |  4 ++--
 candle-book/src/guide/hello_world.md             | 16 ++++++++--------
 .../src/inference/{README.md => inference.md}    |  0
 .../src/training/{README.md => training.md}      |  0
 4 files changed, 10 insertions(+), 10 deletions(-)
 rename candle-book/src/inference/{README.md => inference.md} (100%)
 rename candle-book/src/training/{README.md => training.md} (100%)

diff --git a/candle-book/src/SUMMARY.md b/candle-book/src/SUMMARY.md
index 8228da22..e92f298f 100644
--- a/candle-book/src/SUMMARY.md
+++ b/candle-book/src/SUMMARY.md
@@ -10,10 +10,10 @@
 
 # Reference Guide
 
-- [Running a model](inference/README.md)
+- [Running a model](inference/inference.md)
     - [Using the hub](inference/hub.md)
 - [Error management](error_manage.md)
-- [Training](training/README.md)
+- [Training](training/training.md)
     - [MNIST](training/mnist.md)
     - [Fine-tuning]()
     - [Serialization]()
diff --git a/candle-book/src/guide/hello_world.md b/candle-book/src/guide/hello_world.md
index 74a147e7..b5b8d7b4 100644
--- a/candle-book/src/guide/hello_world.md
+++ b/candle-book/src/guide/hello_world.md
@@ -6,7 +6,7 @@ Open `src/main.rs` and fill in this content:
 
 ```rust
 # extern crate candle_core;
-use candle_core::{DType, Device, Result, Tensor};
+use candle_core::{Device, Result, Tensor};
 
 struct Model {
     first: Tensor,
@@ -29,7 +29,7 @@ fn main() -> Result<()> {
     let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
     let model = Model { first, second };
 
-    let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
 
     let digit = model.forward(&dummy_image)?;
     println!("Digit {digit:?} digit");
@@ -50,7 +50,7 @@ the classical `Linear` layer. We can do as such
 
 ```rust
 # extern crate candle_core;
-# use candle_core::{DType, Device, Result, Tensor};
+# use candle_core::{Device, Result, Tensor};
 struct Linear{
     weight: Tensor,
     bias: Tensor,
@@ -80,7 +80,7 @@ This will change the model running code into a new function
 
 ```rust
 # extern crate candle_core;
-# use candle_core::{DType, Device, Result, Tensor};
+# use candle_core::{Device, Result, Tensor};
 # struct Linear{
 #     weight: Tensor,
 #     bias: Tensor,
@@ -111,7 +111,7 @@ fn main() -> Result<()> {
 
     // Creating a dummy model
     let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
-    let bias = Tensor::zeros((100, ), DType::F32, &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
     let first = Linear{weight, bias};
     let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
     let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
@@ -146,7 +146,7 @@ And rewrite our examples using it
 ```rust
 # extern crate candle_core;
 # extern crate candle_nn;
-use candle_core::{DType, Device, Result, Tensor};
+use candle_core::{Device, Result, Tensor};
 use candle_nn::{Linear, Module};
 
 struct Model {
@@ -189,7 +189,7 @@ Feel free to modify this example to use `Conv2d` to create a classical convnet i
 Now that we have the running dummy code we can get to more advanced topics:
 
 - [For PyTorch users](../guide/cheatsheet.md)
-- [Running existing models](../inference/README.md)
-- [Training models](../training/README.md)
+- [Running existing models](../inference/inference.md)
+- [Training models](../training/training.md)
 
 
diff --git a/candle-book/src/inference/README.md b/candle-book/src/inference/inference.md
similarity index 100%
rename from candle-book/src/inference/README.md
rename to candle-book/src/inference/inference.md
diff --git a/candle-book/src/training/README.md b/candle-book/src/training/training.md
similarity index 100%
rename from candle-book/src/training/README.md
rename to candle-book/src/training/training.md

From 98d1242b8fd917baa95c9143252962f8fad3ebf7 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 10 Sep 2023 21:02:42 +0100
Subject: [PATCH 060/150] im2col based conv2d (#802)

* im2col implementation for conv2d.

* Fix for the im2col implementation to match the current conv2d.

* Small optimization.

* Add a cuda kernel.

* Handle arbitrary layouts.

* Im2Col cuda code.
---
 candle-core/src/cuda_backend.rs      | 52 ++++++++++++++++
 candle-kernels/src/conv.cu           | 89 ++++++++++++++++++++++++++++
 candle-nn/examples/cpu_benchmarks.rs | 85 +++++++++++++++++++++-----
 3 files changed, 210 insertions(+), 16 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 7cc85489..b4bdc6cf 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -600,6 +600,58 @@ impl Map1 for Elu {
     }
 }
 
+struct Im2Col {
+    h_k: usize,
+    w_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col {
+    fn hw_out(&self, h: usize, w: usize) -> (usize, usize) {
+        let h_out = (h + 2 * self.padding - self.dilation * (self.h_k - 1) - 1) / self.stride + 1;
+        let w_out = (w + 2 * self.padding - self.dilation * (self.w_k - 1) - 1) / self.stride + 1;
+        (h_out, w_out)
+    }
+}
+
+impl Map1 for Im2Col {
+    fn f<T: DeviceRepr + WithDType>(
+        &self,
+        src: &CudaSlice<T>,
+        dev: &CudaDevice,
+        layout: &Layout,
+    ) -> Result<CudaSlice<T>> {
+        let shape = layout.shape();
+        let dims = shape.dims();
+        let (h_out, w_out) = self.hw_out(dims[2], dims[3]);
+        let dst_el = dims[0] * h_out * w_out * dims[1] * self.h_k * self.w_k;
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
+        let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
+        let src = &src.slice(layout.start_offset()..);
+        let func = dev.get_or_load_func(&kernel_name::<T>("im2col"), kernels::CONV)?;
+        // SAFETY: Set later by running the kernel.
+        let dst = unsafe { dev.alloc::<T>(dst_el) }.w()?;
+        let params = (
+            dst_el,
+            h_out,
+            w_out,
+            self.h_k,
+            self.w_k,
+            self.stride,
+            self.padding,
+            self.dilation,
+            &ds,
+            src,
+            &dst,
+        );
+        // SAFETY: ffi.
+        unsafe { func.launch(cfg, params) }.w()?;
+        Ok(dst)
+    }
+}
+
 struct Powf(f64);
 impl Map1 for Powf {
     fn f<T: DeviceRepr + WithDType>(
diff --git a/candle-kernels/src/conv.cu b/candle-kernels/src/conv.cu
index ba2fa1ad..51c393cb 100644
--- a/candle-kernels/src/conv.cu
+++ b/candle-kernels/src/conv.cu
@@ -51,6 +51,71 @@ __device__ void conv1d(
   dst[dst_i] = static_cast<T>(d);
 }
 
+template <typename T>
+__device__ void im2col(
+    const size_t dst_numel,
+    const size_t h_out,
+    const size_t w_out,
+    const size_t h_k,
+    const size_t w_k,
+    const size_t stride,
+    const size_t padding,
+    const size_t dilation,
+    const size_t *info,
+    const T *src,
+    T *dst
+) {
+  const size_t dst_i = blockIdx.x * blockDim.x + threadIdx.x;
+  // dst: (b_size, h_out, w_out, c_in, h_k, w_k)
+  // src: (b_size, c_in, h_in, w_in)
+  if (dst_i >= dst_numel) {
+    return;
+  }
+  const size_t *src_dims = info;
+  const size_t *src_s = info + 4;
+  const size_t b_in = src_dims[0];
+  const size_t c_in = src_dims[1];
+  const size_t h_in = src_dims[2];
+  const size_t w_in = src_dims[3];
+
+  const size_t dst_s4 = w_k; 
+  const size_t dst_s3 = h_k * dst_s4;
+  const size_t dst_s2 = c_in * dst_s3;
+  const size_t dst_s1 = w_out * dst_s2;
+  const size_t dst_s0 = h_out * dst_s1;
+
+  size_t tmp_dst_i = dst_i;
+  const size_t b_idx = tmp_dst_i / dst_s0;
+  tmp_dst_i -= b_idx * dst_s0;
+  const size_t h_idx = tmp_dst_i / dst_s1;
+  tmp_dst_i -= h_idx * dst_s1;
+  const size_t w_idx = tmp_dst_i / dst_s2;
+  tmp_dst_i -= w_idx * dst_s2;
+  const size_t c_idx = tmp_dst_i / dst_s3;
+  tmp_dst_i -= c_idx * dst_s3;
+  const size_t h_k_idx = tmp_dst_i / dst_s4;
+  tmp_dst_i -= h_k_idx * dst_s4;
+  const size_t w_k_idx = tmp_dst_i;
+  size_t src_h_idx = h_idx * stride + h_k_idx * dilation;
+  size_t src_w_idx = w_idx * stride + w_k_idx * dilation;
+  if (src_h_idx < padding || src_h_idx >= h_in + padding) {
+    dst[dst_i] = static_cast<T>(0);
+  }
+  else if (src_w_idx < padding || src_w_idx >= w_in + padding) {
+    dst[dst_i] = static_cast<T>(0);
+  }
+  else {
+    src_h_idx -= padding;
+    src_w_idx -= padding;
+    const size_t src_i =
+      b_idx * src_s[0]
+      + c_idx * src_s[1]
+      + src_h_idx * src_s[2]
+      + src_w_idx * src_s[3];
+    dst[dst_i] = src[src_i];
+  }
+}
+
 // Naive implementation of conv2d.
 template <typename T, typename A>
 __device__ void conv2d(
@@ -363,6 +428,23 @@ extern "C" __global__ void FN_NAME(  \
   conv2d<TYPENAME, TYPEACC>(src_numel, w_out, h_out, stride, padding, dilation, info, src, kernel, dst); \
 } \
 
+#define IM2COL_OP(TYPENAME, FN_NAME) \
+extern "C" __global__ void FN_NAME(  \
+    const size_t dst_numel, \
+    const size_t h_out, \
+    const size_t w_out, \
+    const size_t h_k, \
+    const size_t w_k, \
+    const size_t stride, \
+    const size_t padding, \
+    const size_t dilation, \
+    const size_t *info, \
+    const TYPENAME *src, \
+    TYPENAME *dst \
+) {  \
+  im2col<TYPENAME>(dst_numel, h_out, w_out, h_k, w_k, stride, padding, dilation, info, src, dst); \
+} \
+
 #define CONVT2D_OP(TYPENAME, TYPEACC, FN_NAME) \
 extern "C" __global__ void FN_NAME(  \
     const size_t src_numel, \
@@ -428,6 +510,7 @@ CONVT2D_OP(__nv_bfloat16, float, conv_transpose2d_bf16)
 AVG_POOL2D_OP(__nv_bfloat16, float, avg_pool2d_bf16)
 MAX_POOL2D_OP(__nv_bfloat16, max_pool2d_bf16)
 UPSAMPLE_NEAREST2D_OP(__nv_bfloat16, upsample_nearest2d_bf16)
+IM2COL_OP(__nv_bfloat16, im2col_bf16)
 #endif
 
 #if __CUDA_ARCH__ >= 530
@@ -437,6 +520,7 @@ CONVT2D_OP(__half, float, conv_transpose2d_f16)
 AVG_POOL2D_OP(__half, float, avg_pool2d_f16)
 MAX_POOL2D_OP(__half, max_pool2d_f16)
 UPSAMPLE_NEAREST2D_OP(__half, upsample_nearest2d_f16)
+IM2COL_OP(__half, im2col_f16)
 #endif
 
 CONV1D_OP(float, float, conv1d_f32)
@@ -468,3 +552,8 @@ UPSAMPLE_NEAREST2D_OP(float, upsample_nearest2d_f32)
 UPSAMPLE_NEAREST2D_OP(double, upsample_nearest2d_f64)
 UPSAMPLE_NEAREST2D_OP(uint8_t, upsample_nearest2d_u8)
 UPSAMPLE_NEAREST2D_OP(uint32_t, upsample_nearest2d_u32)
+
+IM2COL_OP(float, im2col_f32)
+IM2COL_OP(double, im2col_f64)
+IM2COL_OP(uint8_t, im2col_u8)
+IM2COL_OP(uint32_t, im2col_u32)
diff --git a/candle-nn/examples/cpu_benchmarks.rs b/candle-nn/examples/cpu_benchmarks.rs
index 3ba30f94..5fb99625 100644
--- a/candle-nn/examples/cpu_benchmarks.rs
+++ b/candle-nn/examples/cpu_benchmarks.rs
@@ -9,6 +9,8 @@ use candle::quantized::GgmlType;
 use candle::{CpuStorage, Device, Layout, Result, Shape, Tensor, D};
 use clap::{Parser, Subcommand};
 
+const CHECK_CONV2D: bool = false;
+
 trait Benchmark {
     type PreProcessData;
     type RunResult;
@@ -19,25 +21,51 @@ trait Benchmark {
     const ITERS: usize;
 }
 
-struct Im2Col(usize, usize);
+struct Im2Col {
+    h_k: usize,
+    w_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col {
+    fn hw_out(&self, h: usize, w: usize) -> (usize, usize) {
+        let h_out = (h + 2 * self.padding - self.dilation * (self.h_k - 1) - 1) / self.stride + 1;
+        let w_out = (w + 2 * self.padding - self.dilation * (self.w_k - 1) - 1) / self.stride + 1;
+        (h_out, w_out)
+    }
+}
+
 impl candle::CustomOp1 for Im2Col {
     fn name(&self) -> &'static str {
         "im2col"
     }
 
     fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)> {
-        let &Self(h_k, w_k) = self;
+        let &Self {
+            h_k,
+            w_k,
+            stride,
+            dilation,
+            padding,
+        } = self;
         let (b, c, h, w) = layout.shape().dims4()?;
-        let (h_out, w_out) = (h - h_k + 1, w - w_k + 1);
+        let (h_out, w_out) = self.hw_out(h, w);
         let slice = storage.as_slice::<f32>()?;
-        let src = match layout.contiguous_offsets() {
-            None => candle::bail!("input has to be contiguous"),
-            Some((o1, o2)) => &slice[o1..o2],
-        };
+        let src = &slice[layout.start_offset()..];
         let mut dst = vec![0f32; b * h_out * w_out * c * h_k * w_k];
-        let (s_b, s_c, s_h) = (c * h * w, h * w, w);
+        let (src_s0, src_s1, src_s2, src_s3) = {
+            let s = layout.stride();
+            (s[0], s[1], s[2], s[3])
+        };
+        // TODO: provide specialized kernels for the common use cases.
+        // - h_k = w_k = 1
+        // - padding = 0
+        // - stride = 1
+        // - dilation = 1
         for b_idx in 0..b {
-            let src_idx = b_idx * s_b;
+            let src_idx = b_idx * src_s0;
             let dst_idx = b_idx * h_out * w_out * c * h_k * w_k;
             for h_idx in 0..h_out {
                 let dst_idx = dst_idx + h_idx * w_out * c * h_k * w_k;
@@ -45,12 +73,25 @@ impl candle::CustomOp1 for Im2Col {
                     let dst_idx = dst_idx + w_idx * c * h_k * w_k;
                     for c_idx in 0..c {
                         let dst_idx = dst_idx + c_idx * h_k * w_k;
-                        let src_idx = c_idx * s_c + src_idx;
+                        let src_idx = c_idx * src_s1 + src_idx;
                         for h_k_idx in 0..h_k {
-                            let src_idx = src_idx + (h_idx + h_k_idx) * s_h + w_idx;
+                            let src_h = h_idx * stride + h_k_idx * dilation;
+                            if padding != 0 && (src_h < padding || src_h >= h + padding) {
+                                continue;
+                            }
+                            let src_h = src_h - padding;
+                            let src_idx = src_idx + src_h * src_s2;
                             let dst_idx = dst_idx + h_k_idx * w_k;
-                            dst[dst_idx..dst_idx + w_k]
-                                .copy_from_slice(&src[src_idx..src_idx + w_k])
+                            for w_k_idx in 0..w_k {
+                                let src_w = w_idx * stride + w_k_idx * dilation;
+                                if padding != 0 && (src_w < padding || src_w >= h + padding) {
+                                    continue;
+                                }
+                                let src_w = src_w - padding;
+                                let src_idx = src_idx + src_w * src_s3;
+                                let dst_idx = dst_idx + w_k_idx;
+                                dst[dst_idx] = src[src_idx]
+                            }
                         }
                     }
                 }
@@ -113,14 +154,26 @@ impl Benchmark for Conv2dIm2Col {
     fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
         // d.0.conv2d(&d.1, 0, 1, 1, 1)
         let (b, _, h, w) = d.0.dims4()?;
-        let (h_k, w_k) = (3, 3);
-        let (h_out, w_out) = (h - h_k + 1, w - w_k + 1);
-        let col = d.0.apply_op1_no_bwd(&Im2Col(h_k, w_k))?;
+        let (_, _, h_k, w_k) = d.1.dims4()?;
+        let op = Im2Col {
+            h_k,
+            w_k,
+            stride: 1,
+            dilation: 1,
+            padding: 0,
+        };
+        let (h_out, w_out) = op.hw_out(h, w);
+        let col = d.0.apply_op1_no_bwd(&op)?;
         let res = col.matmul(&d.1.flatten_from(1)?.t()?)?;
         let res = res
             .reshape((b, h_out, w_out, ()))?
             .permute((0, 3, 1, 2))?
             .contiguous()?;
+        if CHECK_CONV2D {
+            let res2 = d.0.conv2d(&d.1, op.padding, op.stride, op.dilation, 1);
+            let diff = (&res - res2)?.sqr()?.mean_all()?;
+            println!("{diff}");
+        }
         Ok(res)
     }
 

From 1cd74129d47745a292c5f54ae3f9a1a1348cdf3e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 08:52:33 +0100
Subject: [PATCH 061/150] Add Im2Col support on the gpu side. (#808)

* Add Im2Col support on the gpu side.

* Actually enable.
---
 candle-core/src/cuda_backend.rs | 35 +++++++++++++++++++++++++++++++--
 1 file changed, 33 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index b4bdc6cf..dbfaa928 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -11,6 +11,8 @@ use cudarc::driver::{
 use half::{bf16, f16};
 use std::sync::{Arc, Mutex};
 
+const USE_IM2COL_CONV2D: bool = true;
+
 /// cudarc related errors
 #[derive(thiserror::Error, Debug)]
 pub enum CudaError {
@@ -1723,8 +1725,37 @@ impl BackendStorage for CudaStorage {
         params: &crate::conv::ParamsConv2D,
     ) -> Result<Self> {
         let device = self.device().clone();
-        let slice = Conv2D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
-        Ok(Self { slice, device })
+        if !USE_IM2COL_CONV2D {
+            let slice = Conv2D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
+            return Ok(Self { slice, device });
+        }
+
+        let col = Im2Col {
+            h_k: params.k_h,
+            w_k: params.k_w,
+            stride: params.stride,
+            dilation: params.dilation,
+            padding: params.padding,
+        }
+        .map(&self.slice, &device, l)?;
+        let col = Self { slice: col, device };
+        let h_out = params.out_h();
+        let w_out = params.out_w();
+        let b = params.b_size;
+        let n = params.c_out;
+        let k = params.k_h * params.k_w * params.c_in;
+        let m = h_out * w_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+            .transpose(1, 2)?
+            .broadcast_as((b, k, n))?;
+        let res = col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?;
+        let res_l = Layout::contiguous((b, h_out, w_out, n))
+            .transpose(1, 2)?
+            .transpose(1, 3)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
     }
 
     #[cfg(feature = "cudnn")]

From 6fb665004ca340808c049514e738ac1e814d9a23 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 09:28:13 +0100
Subject: [PATCH 062/150] Enable im2col on the cpu side. (#805)

* Enable im2col on the cpu side.

* Hook im2col on the cpu backend.

* Use the kernel offset.

* Avoid an unnecessary copy.

* Handle non-contiguous kernels.

* Add a const to select the conv2d kernel.
---
 candle-core/src/cpu_backend.rs | 115 ++++++++++++++++++++++++++++++++-
 1 file changed, 114 insertions(+), 1 deletion(-)

diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index 01ccfde7..3cdc538a 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -4,6 +4,8 @@ use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType};
 use half::{bf16, f16};
 use rayon::prelude::*;
 
+const USE_IM2COL_CONV2D: bool = true;
+
 // TODO: Maybe we should not implement [Clone] here and instead have an explicit allocator +
 // intercept the oom errors to avoid panicking and provide a proper error.
 #[derive(Debug, Clone)]
@@ -1089,6 +1091,81 @@ impl<'a> Map2 for Conv1D<'a> {
     }
 }
 
+struct Im2Col {
+    h_k: usize,
+    w_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col {
+    fn hw_out(&self, h: usize, w: usize) -> (usize, usize) {
+        let h_out = (h + 2 * self.padding - self.dilation * (self.h_k - 1) - 1) / self.stride + 1;
+        let w_out = (w + 2 * self.padding - self.dilation * (self.w_k - 1) - 1) / self.stride + 1;
+        (h_out, w_out)
+    }
+}
+
+impl Map1 for Im2Col {
+    fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>> {
+        let &Self {
+            h_k,
+            w_k,
+            stride,
+            dilation,
+            padding,
+        } = self;
+        let (b, c, h, w) = layout.shape().dims4()?;
+        let (h_out, w_out) = self.hw_out(h, w);
+        let src = &vs[layout.start_offset()..];
+        let mut dst = vec![T::zero(); b * h_out * w_out * c * h_k * w_k];
+        let (src_s0, src_s1, src_s2, src_s3) = {
+            let s = layout.stride();
+            (s[0], s[1], s[2], s[3])
+        };
+        // TODO: provide specialized kernels for the common use cases.
+        // - h_k = w_k = 1
+        // - padding = 0
+        // - stride = 1
+        // - dilation = 1
+        for b_idx in 0..b {
+            let src_idx = b_idx * src_s0;
+            let dst_idx = b_idx * h_out * w_out * c * h_k * w_k;
+            for h_idx in 0..h_out {
+                let dst_idx = dst_idx + h_idx * w_out * c * h_k * w_k;
+                for w_idx in 0..w_out {
+                    let dst_idx = dst_idx + w_idx * c * h_k * w_k;
+                    for c_idx in 0..c {
+                        let dst_idx = dst_idx + c_idx * h_k * w_k;
+                        let src_idx = c_idx * src_s1 + src_idx;
+                        for h_k_idx in 0..h_k {
+                            let src_h = h_idx * stride + h_k_idx * dilation;
+                            if padding != 0 && (src_h < padding || src_h >= h + padding) {
+                                continue;
+                            }
+                            let src_h = src_h - padding;
+                            let src_idx = src_idx + src_h * src_s2;
+                            let dst_idx = dst_idx + h_k_idx * w_k;
+                            for w_k_idx in 0..w_k {
+                                let src_w = w_idx * stride + w_k_idx * dilation;
+                                if padding != 0 && (src_w < padding || src_w >= h + padding) {
+                                    continue;
+                                }
+                                let src_w = src_w - padding;
+                                let src_idx = src_idx + src_w * src_s3;
+                                let dst_idx = dst_idx + w_k_idx;
+                                dst[dst_idx] = src[src_idx]
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        Ok(dst)
+    }
+}
+
 struct Conv2D<'a>(&'a crate::conv::ParamsConv2D);
 
 impl<'a> Map2 for Conv2D<'a> {
@@ -2237,7 +2314,43 @@ impl BackendStorage for CpuStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv2D,
     ) -> Result<Self> {
-        Conv2D(params).map(self, l, kernel, kernel_l)
+        if !USE_IM2COL_CONV2D {
+            return Conv2D(params).map(self, l, kernel, kernel_l);
+        }
+        let op = Im2Col {
+            h_k: params.k_h,
+            w_k: params.k_w,
+            padding: params.padding,
+            stride: params.stride,
+            dilation: params.dilation,
+        };
+        let col = op.map(self, l)?;
+        let b = params.b_size;
+        let n = params.c_out;
+        let (h_out, w_out) = (params.out_h(), params.out_w());
+        let k = op.h_k * op.w_k * params.c_in;
+        let m = h_out * w_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
+        let res_l = Layout::contiguous((b, h_out, w_out, params.c_out))
+            .transpose(1, 2)?
+            .transpose(1, 3)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
     }
 
     fn conv_transpose2d(

From df712ecf64daeb77eed4a368e670050617ed5d26 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 09:48:31 +0100
Subject: [PATCH 063/150] Handle the case where the kernel is not contiguous in
 the cuda backend. (#809)

---
 candle-core/src/cuda_backend.rs | 18 ++++++++++++++----
 1 file changed, 14 insertions(+), 4 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index dbfaa928..20cab125 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -1746,10 +1746,20 @@ impl BackendStorage for CudaStorage {
         let k = params.k_h * params.k_w * params.c_in;
         let m = h_out * w_out;
         let col_l = Layout::contiguous((b, m, k));
-        let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
-            .transpose(1, 2)?
-            .broadcast_as((b, k, n))?;
-        let res = col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?;
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
         let res_l = Layout::contiguous((b, h_out, w_out, n))
             .transpose(1, 2)?
             .transpose(1, 3)?;

From 84ee870efda4fbbadafa67ee8615c76a8947ad24 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 11:05:05 +0100
Subject: [PATCH 064/150] Use softmax-last-dim in whisper. (#810)

---
 candle-transformers/src/models/whisper/model.rs | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/candle-transformers/src/models/whisper/model.rs b/candle-transformers/src/models/whisper/model.rs
index e58ab2ca..d2eda796 100644
--- a/candle-transformers/src/models/whisper/model.rs
+++ b/candle-transformers/src/models/whisper/model.rs
@@ -1,5 +1,5 @@
 use candle::{Device, IndexOp, Result, Tensor, D};
-use candle_nn::{ops::softmax, Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder};
+use candle_nn::{Conv1d, Conv1dConfig, Embedding, LayerNorm, Module, VarBuilder};
 use serde::Deserialize;
 
 // The names in comments correspond to the original implementation:
@@ -166,7 +166,7 @@ impl MultiHeadAttention {
         }
         let w = {
             let _enter = self.softmax_span.enter();
-            softmax(&qk, D::Minus1)?
+            candle_nn::ops::softmax_last_dim(&qk)?
         };
         let wv = {
             let _enter = self.matmul_span.enter();

From d7b9fec849ca8ebc224410d409ea1ea5a366174d Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 11:45:57 +0100
Subject: [PATCH 065/150] Move the stable-diffusion modeling code so that it's
 easier to re-use. (#812)

---
 .../examples/stable-diffusion/main.rs         | 12 +----------
 candle-transformers/src/models/mod.rs         |  1 +
 .../src/models/stable_diffusion}/attention.rs |  0
 .../src/models/stable_diffusion}/clip.rs      |  0
 .../src/models/stable_diffusion}/ddim.rs      |  6 +++---
 .../models/stable_diffusion}/embeddings.rs    |  0
 .../src/models/stable_diffusion/mod.rs        | 21 +++++++++++++------
 .../src/models/stable_diffusion}/resnet.rs    |  2 +-
 .../models/stable_diffusion}/schedulers.rs    |  0
 .../src/models/stable_diffusion}/unet_2d.rs   |  6 +++---
 .../stable_diffusion}/unet_2d_blocks.rs       |  6 +++---
 .../src/models/stable_diffusion}/utils.rs     |  0
 .../src/models/stable_diffusion}/vae.rs       |  2 +-
 13 files changed, 28 insertions(+), 28 deletions(-)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/attention.rs (100%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/clip.rs (100%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/ddim.rs (97%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/embeddings.rs (100%)
 rename candle-examples/examples/stable-diffusion/stable_diffusion.rs => candle-transformers/src/models/stable_diffusion/mod.rs (95%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/resnet.rs (99%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/schedulers.rs (100%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/unet_2d.rs (99%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/unet_2d_blocks.rs (99%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/utils.rs (100%)
 rename {candle-examples/examples/stable-diffusion => candle-transformers/src/models/stable_diffusion}/vae.rs (99%)

diff --git a/candle-examples/examples/stable-diffusion/main.rs b/candle-examples/examples/stable-diffusion/main.rs
index 70e1e92c..6bce2917 100644
--- a/candle-examples/examples/stable-diffusion/main.rs
+++ b/candle-examples/examples/stable-diffusion/main.rs
@@ -4,17 +4,7 @@ extern crate accelerate_src;
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
-mod attention;
-mod clip;
-mod ddim;
-mod embeddings;
-mod resnet;
-mod schedulers;
-mod stable_diffusion;
-mod unet_2d;
-mod unet_2d_blocks;
-mod utils;
-mod vae;
+use candle_transformers::models::stable_diffusion;
 
 use anyhow::{Error as E, Result};
 use candle::{DType, Device, IndexOp, Tensor, D};
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index 76e13b2a..f33d01e6 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -6,4 +6,5 @@ pub mod falcon;
 pub mod llama;
 pub mod quantized_llama;
 pub mod segment_anything;
+pub mod stable_diffusion;
 pub mod whisper;
diff --git a/candle-examples/examples/stable-diffusion/attention.rs b/candle-transformers/src/models/stable_diffusion/attention.rs
similarity index 100%
rename from candle-examples/examples/stable-diffusion/attention.rs
rename to candle-transformers/src/models/stable_diffusion/attention.rs
diff --git a/candle-examples/examples/stable-diffusion/clip.rs b/candle-transformers/src/models/stable_diffusion/clip.rs
similarity index 100%
rename from candle-examples/examples/stable-diffusion/clip.rs
rename to candle-transformers/src/models/stable_diffusion/clip.rs
diff --git a/candle-examples/examples/stable-diffusion/ddim.rs b/candle-transformers/src/models/stable_diffusion/ddim.rs
similarity index 97%
rename from candle-examples/examples/stable-diffusion/ddim.rs
rename to candle-transformers/src/models/stable_diffusion/ddim.rs
index 260a4965..916b7349 100644
--- a/candle-examples/examples/stable-diffusion/ddim.rs
+++ b/candle-transformers/src/models/stable_diffusion/ddim.rs
@@ -7,7 +7,7 @@
 //!
 //! Denoising Diffusion Implicit Models, J. Song et al, 2020.
 //! https://arxiv.org/abs/2010.02502
-use crate::schedulers::{betas_for_alpha_bar, BetaSchedule, PredictionType};
+use super::schedulers::{betas_for_alpha_bar, BetaSchedule, PredictionType};
 use candle::{Result, Tensor};
 
 /// The configuration for the DDIM scheduler.
@@ -67,14 +67,14 @@ impl DDIMScheduler {
             .rev()
             .collect();
         let betas = match config.beta_schedule {
-            BetaSchedule::ScaledLinear => crate::utils::linspace(
+            BetaSchedule::ScaledLinear => super::utils::linspace(
                 config.beta_start.sqrt(),
                 config.beta_end.sqrt(),
                 config.train_timesteps,
             )?
             .sqr()?,
             BetaSchedule::Linear => {
-                crate::utils::linspace(config.beta_start, config.beta_end, config.train_timesteps)?
+                super::utils::linspace(config.beta_start, config.beta_end, config.train_timesteps)?
             }
             BetaSchedule::SquaredcosCapV2 => betas_for_alpha_bar(config.train_timesteps, 0.999)?,
         };
diff --git a/candle-examples/examples/stable-diffusion/embeddings.rs b/candle-transformers/src/models/stable_diffusion/embeddings.rs
similarity index 100%
rename from candle-examples/examples/stable-diffusion/embeddings.rs
rename to candle-transformers/src/models/stable_diffusion/embeddings.rs
diff --git a/candle-examples/examples/stable-diffusion/stable_diffusion.rs b/candle-transformers/src/models/stable_diffusion/mod.rs
similarity index 95%
rename from candle-examples/examples/stable-diffusion/stable_diffusion.rs
rename to candle-transformers/src/models/stable_diffusion/mod.rs
index cffc00d8..d9721532 100644
--- a/candle-examples/examples/stable-diffusion/stable_diffusion.rs
+++ b/candle-transformers/src/models/stable_diffusion/mod.rs
@@ -1,5 +1,14 @@
-use crate::schedulers::PredictionType;
-use crate::{clip, ddim, unet_2d, vae};
+pub mod attention;
+pub mod clip;
+pub mod ddim;
+pub mod embeddings;
+pub mod resnet;
+pub mod schedulers;
+pub mod unet_2d;
+pub mod unet_2d_blocks;
+pub mod utils;
+pub mod vae;
+
 use candle::{DType, Device, Result};
 use candle_nn as nn;
 
@@ -80,7 +89,7 @@ impl StableDiffusionConfig {
         sliced_attention_size: Option<usize>,
         height: Option<usize>,
         width: Option<usize>,
-        prediction_type: PredictionType,
+        prediction_type: schedulers::PredictionType,
     ) -> Self {
         let bc = |out_channels, use_cross_attn, attention_head_dim| unet_2d::BlockConfig {
             out_channels,
@@ -154,7 +163,7 @@ impl StableDiffusionConfig {
             sliced_attention_size,
             height,
             width,
-            PredictionType::VPrediction,
+            schedulers::PredictionType::VPrediction,
         )
     }
 
@@ -162,7 +171,7 @@ impl StableDiffusionConfig {
         sliced_attention_size: Option<usize>,
         height: Option<usize>,
         width: Option<usize>,
-        prediction_type: PredictionType,
+        prediction_type: schedulers::PredictionType,
     ) -> Self {
         let bc = |out_channels, use_cross_attn, attention_head_dim| unet_2d::BlockConfig {
             out_channels,
@@ -235,7 +244,7 @@ impl StableDiffusionConfig {
             height,
             width,
             // https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/scheduler/scheduler_config.json
-            PredictionType::Epsilon,
+            schedulers::PredictionType::Epsilon,
         )
     }
 
diff --git a/candle-examples/examples/stable-diffusion/resnet.rs b/candle-transformers/src/models/stable_diffusion/resnet.rs
similarity index 99%
rename from candle-examples/examples/stable-diffusion/resnet.rs
rename to candle-transformers/src/models/stable_diffusion/resnet.rs
index 4cfd386d..0d818115 100644
--- a/candle-examples/examples/stable-diffusion/resnet.rs
+++ b/candle-transformers/src/models/stable_diffusion/resnet.rs
@@ -4,7 +4,7 @@
 //!
 //! Denoising Diffusion Implicit Models, K. He and al, 2015.
 //! https://arxiv.org/abs/1512.03385
-use crate::utils::{conv2d, Conv2d};
+use super::utils::{conv2d, Conv2d};
 use candle::{Result, Tensor, D};
 use candle_nn as nn;
 use candle_nn::Module;
diff --git a/candle-examples/examples/stable-diffusion/schedulers.rs b/candle-transformers/src/models/stable_diffusion/schedulers.rs
similarity index 100%
rename from candle-examples/examples/stable-diffusion/schedulers.rs
rename to candle-transformers/src/models/stable_diffusion/schedulers.rs
diff --git a/candle-examples/examples/stable-diffusion/unet_2d.rs b/candle-transformers/src/models/stable_diffusion/unet_2d.rs
similarity index 99%
rename from candle-examples/examples/stable-diffusion/unet_2d.rs
rename to candle-transformers/src/models/stable_diffusion/unet_2d.rs
index 81bd9547..a3ed136e 100644
--- a/candle-examples/examples/stable-diffusion/unet_2d.rs
+++ b/candle-transformers/src/models/stable_diffusion/unet_2d.rs
@@ -2,9 +2,9 @@
 //!
 //! The 2D Unet models take as input a noisy sample and the current diffusion
 //! timestep and return a denoised version of the input.
-use crate::embeddings::{TimestepEmbedding, Timesteps};
-use crate::unet_2d_blocks::*;
-use crate::utils::{conv2d, Conv2d};
+use super::embeddings::{TimestepEmbedding, Timesteps};
+use super::unet_2d_blocks::*;
+use super::utils::{conv2d, Conv2d};
 use candle::{Result, Tensor};
 use candle_nn as nn;
 use candle_nn::Module;
diff --git a/candle-examples/examples/stable-diffusion/unet_2d_blocks.rs b/candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs
similarity index 99%
rename from candle-examples/examples/stable-diffusion/unet_2d_blocks.rs
rename to candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs
index be258acb..c53bd542 100644
--- a/candle-examples/examples/stable-diffusion/unet_2d_blocks.rs
+++ b/candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs
@@ -1,10 +1,10 @@
 //! 2D UNet Building Blocks
 //!
-use crate::attention::{
+use super::attention::{
     AttentionBlock, AttentionBlockConfig, SpatialTransformer, SpatialTransformerConfig,
 };
-use crate::resnet::{ResnetBlock2D, ResnetBlock2DConfig};
-use crate::utils::{conv2d, Conv2d};
+use super::resnet::{ResnetBlock2D, ResnetBlock2DConfig};
+use super::utils::{conv2d, Conv2d};
 use candle::{Result, Tensor, D};
 use candle_nn as nn;
 
diff --git a/candle-examples/examples/stable-diffusion/utils.rs b/candle-transformers/src/models/stable_diffusion/utils.rs
similarity index 100%
rename from candle-examples/examples/stable-diffusion/utils.rs
rename to candle-transformers/src/models/stable_diffusion/utils.rs
diff --git a/candle-examples/examples/stable-diffusion/vae.rs b/candle-transformers/src/models/stable_diffusion/vae.rs
similarity index 99%
rename from candle-examples/examples/stable-diffusion/vae.rs
rename to candle-transformers/src/models/stable_diffusion/vae.rs
index aa8e13a0..48155ada 100644
--- a/candle-examples/examples/stable-diffusion/vae.rs
+++ b/candle-transformers/src/models/stable_diffusion/vae.rs
@@ -4,7 +4,7 @@
 //! Auto-encoder models compress their input to a usually smaller latent space
 //! before expanding it back to its original shape. This results in the latent values
 //! compressing the original information.
-use crate::unet_2d_blocks::{
+use super::unet_2d_blocks::{
     DownEncoderBlock2D, DownEncoderBlock2DConfig, UNetMidBlock2D, UNetMidBlock2DConfig,
     UpDecoderBlock2D, UpDecoderBlock2DConfig,
 };

From 70f38c20693ad26c6fffa359e734bde92124c0dd Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 12:10:51 +0100
Subject: [PATCH 066/150] Proper error on unsupported dtypes when using gemm.
 (#813)

---
 candle-core/src/cpu_backend.rs | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index 3cdc538a..fa96ba18 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -1371,8 +1371,9 @@ impl Map2 for MatMul {
     ) -> Result<Vec<T>> {
         use gemm::{gemm, Parallelism};
 
-        if T::DTYPE == DType::BF16 {
-            return Err(Error::UnsupportedDTypeForOp(T::DTYPE, "matmul").bt())?;
+        match T::DTYPE {
+            DType::F16 | DType::F32 | DType::F64 => {}
+            _ => Err(Error::UnsupportedDTypeForOp(T::DTYPE, "matmul").bt())?,
         }
 
         let (b, m, n, k) = self.0;

From 5c35fbbb1316b6676c74e0889314802c76261bcf Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 13:06:29 +0100
Subject: [PATCH 067/150] Stable-Diffusion readme (#814)

* Stable Diffusion readme.

* Fix the image path.

* Move the assets.

* Resize the sample image.

* Lower resolution.
---
 .../examples/stable-diffusion/README.md       |  63 ++++++++++++++++++
 .../assets/stable-diffusion-xl.jpg            | Bin 0 -> 36573 bytes
 2 files changed, 63 insertions(+)
 create mode 100644 candle-examples/examples/stable-diffusion/README.md
 create mode 100644 candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg

diff --git a/candle-examples/examples/stable-diffusion/README.md b/candle-examples/examples/stable-diffusion/README.md
new file mode 100644
index 00000000..ee83b3f9
--- /dev/null
+++ b/candle-examples/examples/stable-diffusion/README.md
@@ -0,0 +1,63 @@
+# candle-stable-diffusion: A Diffusers API in Rust/Candle
+
+![rusty robot holding a candle](./assets/stable-diffusion-xl.jpg)
+
+_A rusty robot holding a fire torch in its hand_, generated by Stable Diffusion
+XL using Rust and [candle](https://github.com/huggingface/candle).
+
+The `stable-diffusion` example is a conversion of
+[diffusers-rs](https://github.com/LaurentMazare/diffusers-rs) using candle
+rather than libtorch. This implementation supports Stable Diffusion v1.5, v2.1,
+as well as Stable Diffusion XL 1.0.
+
+## Getting the weights
+
+The weights are automatically downloaded for you from the [HuggingFace
+Hub](https://huggingface.co/) on the first run. There are various command line
+flags to use local files instead, run with `--help` to learn about them.
+
+## Running some example.
+
+```bash
+cargo run --example stable-diffusion --release --features=cuda,cudnn \
+    -- --prompt "a cosmonaut on a horse (hd, realistic, high-def)"
+```
+
+The final image is named `sd_final.png` by default.
+The default scheduler is the Denoising Diffusion Implicit Model scheduler (DDIM). The
+original paper and some code can be found in the [associated repo](https://github.com/ermongroup/ddim).
+
+### Command-line flags
+
+- `--prompt`: the prompt to be used to generate the image.
+- `--uncond-prompt`: the optional unconditional prompt.
+- `--sd-version`: the Stable Diffusion version to use, can be `v1-5`, `v2-1`, or
+  `xl`.
+- `--cpu`: use the cpu rather than the gpu (much slower).
+- `--height`, `--width`: set the height and width for the generated image.
+- `--n-steps`: the number of steps to be used in the diffusion process.
+- `--num-samples`: the number of samples to generate.
+- `--final-image`: the filename for the generated image(s).
+
+### Using flash-attention
+
+Using flash attention makes image generation a lot faster and uses less memory.
+The downside is some long compilation time. You can set the
+`CANDLE_FLASH_ATTN_BUILD_DIR` environment variable to something like
+`/home/user/.candle` to ensures that the compilation artifacts are properly
+cached.
+
+Enabling flash-attention requires both a feature flag, `--feature flash-attn`
+and using the command line flag `--use-flash-attn`.
+
+## Image to Image Pipeline
+...
+
+## FAQ
+
+### Memory Issues
+
+This requires a GPU with more than 8GB of memory, as a fallback the CPU version can be used
+with the `--cpu` flag but is much slower.
+Alternatively, reducing the height and width with the `--height` and `--width`
+flag is likely to reduce memory usage significantly.
diff --git a/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg b/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..a6f7b6c65ec0e0a0343ef228180926441d780887
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From dbd4561416647a01e75709adf51fff04bc5db95c Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 14:40:09 +0100
Subject: [PATCH 068/150] im2col version of the conv1d kernel. (#815)

* im2col version of the cuda conv1d kernel.

* im2col version of the conv1d cpu kernel.
---
 candle-core/src/cpu_backend.rs  | 95 ++++++++++++++++++++++++++++++++-
 candle-core/src/cuda_backend.rs | 87 +++++++++++++++++++++++++++++-
 candle-kernels/src/conv.cu      | 71 +++++++++++++++++++++++-
 3 files changed, 249 insertions(+), 4 deletions(-)

diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index fa96ba18..ee227ef9 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -4,6 +4,7 @@ use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType};
 use half::{bf16, f16};
 use rayon::prelude::*;
 
+const USE_IM2COL_CONV1D: bool = true;
 const USE_IM2COL_CONV2D: bool = true;
 
 // TODO: Maybe we should not implement [Clone] here and instead have an explicit allocator +
@@ -1091,6 +1092,65 @@ impl<'a> Map2 for Conv1D<'a> {
     }
 }
 
+struct Im2Col1D {
+    l_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col1D {
+    fn l_out(&self, l: usize) -> usize {
+        (l + 2 * self.padding - self.dilation * (self.l_k - 1) - 1) / self.stride + 1
+    }
+}
+
+impl Map1 for Im2Col1D {
+    fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>> {
+        let &Self {
+            l_k,
+            stride,
+            dilation,
+            padding,
+        } = self;
+        let (b, c, l) = layout.shape().dims3()?;
+        let l_out = self.l_out(l);
+        let src = &vs[layout.start_offset()..];
+        let mut dst = vec![T::zero(); b * l_out * c * l_k];
+        let (src_s0, src_s1, src_s2) = {
+            let s = layout.stride();
+            (s[0], s[1], s[2])
+        };
+        // TODO: provide specialized kernels for the common use cases.
+        // - l_k = 1
+        // - padding = 0
+        // - stride = 1
+        // - dilation = 1
+        for b_idx in 0..b {
+            let src_idx = b_idx * src_s0;
+            let dst_idx = b_idx * l_out * c * l_k;
+            for l_idx in 0..l_out {
+                let dst_idx = dst_idx + l_idx * c * l_k;
+                for c_idx in 0..c {
+                    let dst_idx = dst_idx + c_idx * l_k;
+                    let src_idx = c_idx * src_s1 + src_idx;
+                    for l_k_idx in 0..l_k {
+                        let src_l = l_idx * stride + l_k_idx * dilation;
+                        if padding != 0 && (src_l < padding || src_l >= l + padding) {
+                            continue;
+                        }
+                        let src_l = src_l - padding;
+                        let src_idx = src_idx + src_l * src_s2;
+                        let dst_idx = dst_idx + l_k_idx;
+                        dst[dst_idx] = src[src_idx]
+                    }
+                }
+            }
+        }
+        Ok(dst)
+    }
+}
+
 struct Im2Col {
     h_k: usize,
     w_k: usize,
@@ -2305,7 +2365,40 @@ impl BackendStorage for CpuStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv1D,
     ) -> Result<Self> {
-        Conv1D(params).map(self, l, kernel, kernel_l)
+        if !USE_IM2COL_CONV1D {
+            return Conv1D(params).map(self, l, kernel, kernel_l);
+        }
+        let op = Im2Col1D {
+            l_k: params.k_size,
+            padding: params.padding,
+            stride: params.stride,
+            dilation: params.dilation,
+        };
+        let col = op.map(self, l)?;
+        let b = params.b_size;
+        let n = params.c_out;
+        let l_out = params.l_out();
+        let k = op.l_k * params.c_in;
+        let m = l_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
+        let res_l = Layout::contiguous((b, l_out, params.c_out)).transpose(1, 2)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
     }
 
     fn conv2d(
diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 20cab125..55443068 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -11,6 +11,7 @@ use cudarc::driver::{
 use half::{bf16, f16};
 use std::sync::{Arc, Mutex};
 
+const USE_IM2COL_CONV1D: bool = true;
 const USE_IM2COL_CONV2D: bool = true;
 
 /// cudarc related errors
@@ -602,6 +603,53 @@ impl Map1 for Elu {
     }
 }
 
+struct Im2Col1D {
+    l_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col1D {
+    fn l_out(&self, l: usize) -> usize {
+        (l + 2 * self.padding - self.dilation * (self.l_k - 1) - 1) / self.stride + 1
+    }
+}
+
+impl Map1 for Im2Col1D {
+    fn f<T: DeviceRepr + WithDType>(
+        &self,
+        src: &CudaSlice<T>,
+        dev: &CudaDevice,
+        layout: &Layout,
+    ) -> Result<CudaSlice<T>> {
+        let shape = layout.shape();
+        let dims = shape.dims();
+        let l_out = self.l_out(dims[2]);
+        let dst_el = dims[0] * l_out * dims[1] * self.l_k;
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
+        let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
+        let src = &src.slice(layout.start_offset()..);
+        let func = dev.get_or_load_func(&kernel_name::<T>("im2col1d"), kernels::CONV)?;
+        // SAFETY: Set later by running the kernel.
+        let dst = unsafe { dev.alloc::<T>(dst_el) }.w()?;
+        let params = (
+            dst_el,
+            l_out,
+            self.l_k,
+            self.stride,
+            self.padding,
+            self.dilation,
+            &ds,
+            src,
+            &dst,
+        );
+        // SAFETY: ffi.
+        unsafe { func.launch(cfg, params) }.w()?;
+        Ok(dst)
+    }
+}
+
 struct Im2Col {
     h_k: usize,
     w_k: usize,
@@ -1712,8 +1760,43 @@ impl BackendStorage for CudaStorage {
         params: &crate::conv::ParamsConv1D,
     ) -> Result<Self> {
         let device = self.device().clone();
-        let slice = Conv1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
-        Ok(Self { slice, device })
+        if !USE_IM2COL_CONV1D {
+            let slice = Conv1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
+            return Ok(Self { slice, device });
+        }
+
+        let col = Im2Col1D {
+            l_k: params.k_size,
+            stride: params.stride,
+            dilation: params.dilation,
+            padding: params.padding,
+        }
+        .map(&self.slice, &device, l)?;
+        let col = Self { slice: col, device };
+        let l_out = params.l_out();
+        let b = params.b_size;
+        let n = params.c_out;
+        let k = params.k_size * params.c_in;
+        let m = l_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
+        let res_l = Layout::contiguous((b, l_out, n)).transpose(1, 2)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
     }
 
     #[cfg(not(feature = "cudnn"))]
diff --git a/candle-kernels/src/conv.cu b/candle-kernels/src/conv.cu
index 51c393cb..9c8ce00f 100644
--- a/candle-kernels/src/conv.cu
+++ b/candle-kernels/src/conv.cu
@@ -51,6 +51,53 @@ __device__ void conv1d(
   dst[dst_i] = static_cast<T>(d);
 }
 
+template <typename T>
+__device__ void im2col1d(
+    const size_t dst_numel,
+    const size_t l_out,
+    const size_t l_k,
+    const size_t stride,
+    const size_t padding,
+    const size_t dilation,
+    const size_t *info,
+    const T *src,
+    T *dst
+) {
+  const size_t dst_i = blockIdx.x * blockDim.x + threadIdx.x;
+  // dst: (b_size, l_out, c_in, l_k)
+  // src: (b_size, c_in, l_in)
+  if (dst_i >= dst_numel) {
+    return;
+  }
+  const size_t *src_dims = info;
+  const size_t *src_s = info + 3;
+  const size_t b_in = src_dims[0];
+  const size_t c_in = src_dims[1];
+  const size_t l_in = src_dims[2];
+
+  const size_t dst_s2 = l_k;
+  const size_t dst_s1 = c_in * dst_s2;
+  const size_t dst_s0 = l_out * dst_s1;
+
+  size_t tmp_dst_i = dst_i;
+  const size_t b_idx = tmp_dst_i / dst_s0;
+  tmp_dst_i -= b_idx * dst_s0;
+  const size_t l_idx = tmp_dst_i / dst_s1;
+  tmp_dst_i -= l_idx * dst_s1;
+  const size_t c_idx = tmp_dst_i / dst_s2;
+  tmp_dst_i -= c_idx * dst_s2;
+  const size_t l_k_idx = tmp_dst_i;
+  size_t src_l_idx = l_idx * stride + l_k_idx * dilation;
+  if (src_l_idx < padding || src_l_idx >= l_in + padding) {
+    dst[dst_i] = static_cast<T>(0);
+  }
+  else {
+    src_l_idx -= padding;
+    const size_t src_i = b_idx * src_s[0] + c_idx * src_s[1] + src_l_idx * src_s[2];
+    dst[dst_i] = src[src_i];
+  }
+}
+
 template <typename T>
 __device__ void im2col(
     const size_t dst_numel,
@@ -78,7 +125,7 @@ __device__ void im2col(
   const size_t h_in = src_dims[2];
   const size_t w_in = src_dims[3];
 
-  const size_t dst_s4 = w_k; 
+  const size_t dst_s4 = w_k;
   const size_t dst_s3 = h_k * dst_s4;
   const size_t dst_s2 = c_in * dst_s3;
   const size_t dst_s1 = w_out * dst_s2;
@@ -428,6 +475,21 @@ extern "C" __global__ void FN_NAME(  \
   conv2d<TYPENAME, TYPEACC>(src_numel, w_out, h_out, stride, padding, dilation, info, src, kernel, dst); \
 } \
 
+#define IM2COL1D_OP(TYPENAME, FN_NAME) \
+extern "C" __global__ void FN_NAME(  \
+    const size_t dst_numel, \
+    const size_t l_out, \
+    const size_t l_k, \
+    const size_t stride, \
+    const size_t padding, \
+    const size_t dilation, \
+    const size_t *info, \
+    const TYPENAME *src, \
+    TYPENAME *dst \
+) {  \
+  im2col1d<TYPENAME>(dst_numel, l_out, l_k, stride, padding, dilation, info, src, dst); \
+} \
+
 #define IM2COL_OP(TYPENAME, FN_NAME) \
 extern "C" __global__ void FN_NAME(  \
     const size_t dst_numel, \
@@ -511,6 +573,7 @@ AVG_POOL2D_OP(__nv_bfloat16, float, avg_pool2d_bf16)
 MAX_POOL2D_OP(__nv_bfloat16, max_pool2d_bf16)
 UPSAMPLE_NEAREST2D_OP(__nv_bfloat16, upsample_nearest2d_bf16)
 IM2COL_OP(__nv_bfloat16, im2col_bf16)
+IM2COL1D_OP(__nv_bfloat16, im2col1d_bf16)
 #endif
 
 #if __CUDA_ARCH__ >= 530
@@ -521,6 +584,7 @@ AVG_POOL2D_OP(__half, float, avg_pool2d_f16)
 MAX_POOL2D_OP(__half, max_pool2d_f16)
 UPSAMPLE_NEAREST2D_OP(__half, upsample_nearest2d_f16)
 IM2COL_OP(__half, im2col_f16)
+IM2COL1D_OP(__half, im2col1d_f16)
 #endif
 
 CONV1D_OP(float, float, conv1d_f32)
@@ -557,3 +621,8 @@ IM2COL_OP(float, im2col_f32)
 IM2COL_OP(double, im2col_f64)
 IM2COL_OP(uint8_t, im2col_u8)
 IM2COL_OP(uint32_t, im2col_u32)
+
+IM2COL1D_OP(float, im2col1d_f32)
+IM2COL1D_OP(double, im2col1d_f64)
+IM2COL1D_OP(uint8_t, im2col1d_u8)
+IM2COL1D_OP(uint32_t, im2col1d_u32)

From 59e63d690c95e58526bd41144823c6e63f9f2916 Mon Sep 17 00:00:00 2001
From: Eric Buehler <65165915+EricLBuehler@users.noreply.github.com>
Date: Mon, 11 Sep 2023 11:01:11 -0400
Subject: [PATCH 069/150] Add weight, bias, and hidden_size methods (#816)

* Add weight, bias methods to Conv(1|2)

* Add hidden_size method to Embedding

* Expose hidden_size
---
 candle-nn/src/conv.rs      | 16 ++++++++++++++++
 candle-nn/src/embedding.rs |  5 +++++
 2 files changed, 21 insertions(+)

diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index cfe86bfa..309a5f37 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -39,6 +39,14 @@ impl Conv1d {
     pub fn config(&self) -> &Conv1dConfig {
         &self.config
     }
+
+    pub fn weight(&self) -> &Tensor {
+        &self.weight
+    }
+
+    pub fn bias(&self) -> Option<&Tensor> {
+        self.bias.as_ref()
+    }
 }
 
 impl crate::Module for Conv1d {
@@ -99,6 +107,14 @@ impl Conv2d {
     pub fn config(&self) -> &Conv2dConfig {
         &self.config
     }
+
+    pub fn weight(&self) -> &Tensor {
+        &self.weight
+    }
+
+    pub fn bias(&self) -> Option<&Tensor> {
+        self.bias.as_ref()
+    }
 }
 
 impl crate::Module for Conv2d {
diff --git a/candle-nn/src/embedding.rs b/candle-nn/src/embedding.rs
index d84f9f53..fccc8a17 100644
--- a/candle-nn/src/embedding.rs
+++ b/candle-nn/src/embedding.rs
@@ -18,6 +18,11 @@ impl Embedding {
     pub fn embeddings(&self) -> &Tensor {
         &self.embeddings
     }
+
+    /// Get the hidden size of the embedding matrix
+    pub fn hidden_size(&self) -> usize {
+        self.hidden_size
+    }
 }
 
 impl crate::Module for Embedding {

From c5a058b16954154a68899d26d681adfba003babf Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 20:40:07 +0100
Subject: [PATCH 070/150] Use the module trait in stable-diffusion. (#817)

---
 candle-core/src/safetensors.rs                    |  8 ++------
 candle-examples/examples/stable-diffusion/main.rs |  2 +-
 .../src/models/stable_diffusion/attention.rs      |  8 +++++---
 .../src/models/stable_diffusion/clip.rs           |  8 ++++----
 .../src/models/stable_diffusion/embeddings.rs     |  8 ++++----
 .../src/models/stable_diffusion/unet_2d_blocks.rs | 12 ++++++------
 .../src/models/stable_diffusion/vae.rs            | 15 ++++++++-------
 7 files changed, 30 insertions(+), 31 deletions(-)

diff --git a/candle-core/src/safetensors.rs b/candle-core/src/safetensors.rs
index f37bb8ef..d588ea67 100644
--- a/candle-core/src/safetensors.rs
+++ b/candle-core/src/safetensors.rs
@@ -78,11 +78,7 @@ impl st::View for &Tensor {
 }
 
 impl Tensor {
-    pub fn save_safetensors<P: AsRef<std::path::Path>>(
-        &self,
-        name: &str,
-        filename: P,
-    ) -> Result<()> {
+    pub fn save_safetensors<P: AsRef<Path>>(&self, name: &str, filename: P) -> Result<()> {
         let data = [(name, self.clone())];
         Ok(st::serialize_to_file(data, &None, filename.as_ref())?)
     }
@@ -267,7 +263,7 @@ impl MmapedFile {
     /// # Safety
     ///
     /// The unsafe is inherited from [`memmap2::MmapOptions`].
-    pub unsafe fn new<P: AsRef<std::path::Path>>(p: P) -> Result<Self> {
+    pub unsafe fn new<P: AsRef<Path>>(p: P) -> Result<Self> {
         let p = p.as_ref();
         let file = std::fs::File::open(p).map_err(|e| Error::from(e).with_path(p))?;
         let inner = memmap2::MmapOptions::new()
diff --git a/candle-examples/examples/stable-diffusion/main.rs b/candle-examples/examples/stable-diffusion/main.rs
index 6bce2917..c8b771a0 100644
--- a/candle-examples/examples/stable-diffusion/main.rs
+++ b/candle-examples/examples/stable-diffusion/main.rs
@@ -7,7 +7,7 @@ extern crate intel_mkl_src;
 use candle_transformers::models::stable_diffusion;
 
 use anyhow::{Error as E, Result};
-use candle::{DType, Device, IndexOp, Tensor, D};
+use candle::{DType, Device, IndexOp, Module, Tensor, D};
 use clap::Parser;
 use tokenizers::Tokenizer;
 
diff --git a/candle-transformers/src/models/stable_diffusion/attention.rs b/candle-transformers/src/models/stable_diffusion/attention.rs
index 000cd2fe..2b925cee 100644
--- a/candle-transformers/src/models/stable_diffusion/attention.rs
+++ b/candle-transformers/src/models/stable_diffusion/attention.rs
@@ -17,7 +17,7 @@ impl GeGlu {
     }
 }
 
-impl GeGlu {
+impl Module for GeGlu {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
         let hidden_states_and_gate = self.proj.forward(xs)?.chunk(2, D::Minus1)?;
@@ -53,7 +53,7 @@ impl FeedForward {
     }
 }
 
-impl FeedForward {
+impl Module for FeedForward {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
         let xs = self.project_in.forward(xs)?;
@@ -501,8 +501,10 @@ impl AttentionBlock {
         xs.reshape((batch, t, self.num_heads, h_times_d / self.num_heads))?
             .transpose(1, 2)
     }
+}
 
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+impl Module for AttentionBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
         let in_dtype = xs.dtype();
         let residual = xs;
diff --git a/candle-transformers/src/models/stable_diffusion/clip.rs b/candle-transformers/src/models/stable_diffusion/clip.rs
index d26c1c46..397a1cef 100644
--- a/candle-transformers/src/models/stable_diffusion/clip.rs
+++ b/candle-transformers/src/models/stable_diffusion/clip.rs
@@ -14,7 +14,7 @@ pub enum Activation {
     Gelu,
 }
 
-impl Activation {
+impl Module for Activation {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         match self {
             Activation::QuickGelu => xs * nn::ops::sigmoid(&(xs * 1.702f64)?)?,
@@ -129,7 +129,7 @@ impl ClipTextEmbeddings {
     }
 }
 
-impl ClipTextEmbeddings {
+impl Module for ClipTextEmbeddings {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let token_embedding = self.token_embedding.forward(xs)?;
         let position_embedding = self.position_embedding.forward(&self.position_ids)?;
@@ -328,8 +328,8 @@ impl ClipTextTransformer {
     }
 }
 
-impl ClipTextTransformer {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+impl Module for ClipTextTransformer {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let (bsz, seq_len) = xs.dims2()?;
         let xs = self.embeddings.forward(xs)?;
         let causal_attention_mask = Self::build_causal_attention_mask(bsz, seq_len, xs.device())?;
diff --git a/candle-transformers/src/models/stable_diffusion/embeddings.rs b/candle-transformers/src/models/stable_diffusion/embeddings.rs
index 97bc61f1..0de5f9a7 100644
--- a/candle-transformers/src/models/stable_diffusion/embeddings.rs
+++ b/candle-transformers/src/models/stable_diffusion/embeddings.rs
@@ -17,8 +17,8 @@ impl TimestepEmbedding {
     }
 }
 
-impl TimestepEmbedding {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+impl Module for TimestepEmbedding {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let xs = nn::ops::silu(&self.linear_1.forward(xs)?)?;
         self.linear_2.forward(&xs)
     }
@@ -41,8 +41,8 @@ impl Timesteps {
     }
 }
 
-impl Timesteps {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+impl Module for Timesteps {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let half_dim = (self.num_channels / 2) as u32;
         let exponent = (Tensor::arange(0, half_dim, xs.device())?.to_dtype(candle::DType::F32)?
             * -f64::ln(10000.))?;
diff --git a/candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs b/candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs
index c53bd542..29510cef 100644
--- a/candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs
+++ b/candle-transformers/src/models/stable_diffusion/unet_2d_blocks.rs
@@ -5,7 +5,7 @@ use super::attention::{
 };
 use super::resnet::{ResnetBlock2D, ResnetBlock2DConfig};
 use super::utils::{conv2d, Conv2d};
-use candle::{Result, Tensor, D};
+use candle::{Module, Result, Tensor, D};
 use candle_nn as nn;
 
 #[derive(Debug)]
@@ -43,7 +43,7 @@ impl Downsample2D {
     }
 }
 
-impl Downsample2D {
+impl Module for Downsample2D {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
         match &self.conv {
@@ -172,8 +172,8 @@ impl DownEncoderBlock2D {
     }
 }
 
-impl DownEncoderBlock2D {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+impl Module for DownEncoderBlock2D {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
         let mut xs = xs.clone();
         for resnet in self.resnets.iter() {
@@ -256,8 +256,8 @@ impl UpDecoderBlock2D {
     }
 }
 
-impl UpDecoderBlock2D {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+impl Module for UpDecoderBlock2D {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let _enter = self.span.enter();
         let mut xs = xs.clone();
         for resnet in self.resnets.iter() {
diff --git a/candle-transformers/src/models/stable_diffusion/vae.rs b/candle-transformers/src/models/stable_diffusion/vae.rs
index 48155ada..21709afe 100644
--- a/candle-transformers/src/models/stable_diffusion/vae.rs
+++ b/candle-transformers/src/models/stable_diffusion/vae.rs
@@ -132,14 +132,15 @@ impl Encoder {
 
 impl Encoder {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = self.conv_in.forward(xs)?;
+        let mut xs = xs.apply(&self.conv_in)?;
         for down_block in self.down_blocks.iter() {
-            xs = down_block.forward(&xs)?
+            xs = xs.apply(down_block)?
         }
-        let xs = self.mid_block.forward(&xs, None)?;
-        let xs = self.conv_norm_out.forward(&xs)?;
-        let xs = nn::ops::silu(&xs)?;
-        self.conv_out.forward(&xs)
+        let xs = self
+            .mid_block
+            .forward(&xs, None)?
+            .apply(&self.conv_norm_out)?;
+        nn::ops::silu(&xs)?.apply(&self.conv_out)
     }
 }
 
@@ -302,7 +303,7 @@ impl DiagonalGaussianDistribution {
     }
 
     pub fn sample(&self) -> Result<Tensor> {
-        let sample = Tensor::randn(0., 1f32, self.mean.shape(), self.mean.device());
+        let sample = self.mean.randn_like(0., 1.);
         &self.mean + &self.std * sample
     }
 }

From 871efc0307e39441236327643abb397b3ab200d0 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 11 Sep 2023 23:11:27 +0100
Subject: [PATCH 071/150] Bugfix for the conv2d cpu kernel. (#820)

---
 candle-core/src/cpu_backend.rs       | 2 +-
 candle-nn/examples/cpu_benchmarks.rs | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index ee227ef9..544ce32d 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -1209,7 +1209,7 @@ impl Map1 for Im2Col {
                             let dst_idx = dst_idx + h_k_idx * w_k;
                             for w_k_idx in 0..w_k {
                                 let src_w = w_idx * stride + w_k_idx * dilation;
-                                if padding != 0 && (src_w < padding || src_w >= h + padding) {
+                                if padding != 0 && (src_w < padding || src_w >= w + padding) {
                                     continue;
                                 }
                                 let src_w = src_w - padding;
diff --git a/candle-nn/examples/cpu_benchmarks.rs b/candle-nn/examples/cpu_benchmarks.rs
index 5fb99625..204a7109 100644
--- a/candle-nn/examples/cpu_benchmarks.rs
+++ b/candle-nn/examples/cpu_benchmarks.rs
@@ -84,7 +84,7 @@ impl candle::CustomOp1 for Im2Col {
                             let dst_idx = dst_idx + h_k_idx * w_k;
                             for w_k_idx in 0..w_k {
                                 let src_w = w_idx * stride + w_k_idx * dilation;
-                                if padding != 0 && (src_w < padding || src_w >= h + padding) {
+                                if padding != 0 && (src_w < padding || src_w >= w + padding) {
                                     continue;
                                 }
                                 let src_w = src_w - padding;

From 2257f4d475c676e33dbd8dbafabf95e821d27f62 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 12 Sep 2023 06:39:24 +0100
Subject: [PATCH 072/150] Bump the crate version + update the changelog. (#822)

---
 CHANGELOG.md                                  | 31 ++++++++++++++++++-
 Cargo.toml                                    |  2 +-
 candle-book/Cargo.toml                        | 10 +++---
 candle-core/Cargo.toml                        |  2 +-
 candle-datasets/Cargo.toml                    |  4 +--
 candle-examples/Cargo.toml                    | 10 +++---
 candle-flash-attn/Cargo.toml                  |  6 ++--
 candle-kernels/Cargo.toml                     |  2 +-
 candle-nn/Cargo.toml                          |  2 +-
 candle-pyo3/Cargo.toml                        |  4 +--
 candle-transformers/Cargo.toml                |  4 +--
 candle-wasm-examples/llama2-c/Cargo.toml      |  6 ++--
 .../segment-anything/Cargo.toml               |  6 ++--
 candle-wasm-examples/whisper/Cargo.toml       |  4 +--
 candle-wasm-examples/yolo/Cargo.toml          |  4 +--
 15 files changed, 63 insertions(+), 34 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a52429cf..a0275c57 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,13 +1,42 @@
 # Changelog
 This documents the main changes to the `candle` crate.
 
-## v0.2.1 - Unreleased
+## v0.2.2 - Unreleased
 
 ### Added
 
+### Modified
+
+## v0.2.1 - 2023-09-11
+
+### Added
+- Add some RNNs (GRU and LSTM) in `candle-nn`
+  [674](https://github.com/huggingface/candle/pull/674),
+  [688](https://github.com/huggingface/candle/pull/688).
+- gguf v2 support
+  [725](https://github.com/huggingface/candle/pull/725).
+- Quantized llama example in Python using the pyo3 api
+  [716](https://github.com/huggingface/candle/pull/716).
+- `candle-nn` layer for conv2d-transposed
+  [760](https://github.com/huggingface/candle/pull/760).
+- Add the Segment-Anything Model (SAM) as an example
+  [773](https://github.com/huggingface/candle/pull/773).
+- TinyViT backbone for the segemnt anything example
+  [787](https://github.com/huggingface/candle/pull/787).
+- Shape with holes support
+  [770](https://github.com/huggingface/candle/pull/770).
+
 ### Modified
 - Dilations are now supported in conv-transpose2d.
   [671](https://github.com/huggingface/candle/pull/671).
+- Interactive mode for the quantized model
+  [690](https://github.com/huggingface/candle/pull/690).
+- Faster softmax operation
+  [747](https://github.com/huggingface/candle/pull/747).
+- Faster convolution operations on CPU and CUDA via im2col
+  [802](https://github.com/huggingface/candle/pull/802).
+- Moving some models to a more central location
+  [796](https://github.com/huggingface/candle/pull/796).
 
 ## v0.2.0 - 2023-08-30
 
diff --git a/Cargo.toml b/Cargo.toml
index b45a2ab6..cb6bf3cb 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -19,7 +19,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.2.1"
+version = "0.2.2"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
diff --git a/candle-book/Cargo.toml b/candle-book/Cargo.toml
index 320fb887..ac179cb9 100644
--- a/candle-book/Cargo.toml
+++ b/candle-book/Cargo.toml
@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.2.1" }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
-candle-transformers = { path = "../candle-transformers", version = "0.2.1" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.1", optional = true }
+candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.2.2" }
+candle-nn = { path = "../candle-nn", version = "0.2.2" }
+candle-transformers = { path = "../candle-transformers", version = "0.2.2" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.2", optional = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
diff --git a/candle-core/Cargo.toml b/candle-core/Cargo.toml
index e7213919..fad6d0bd 100644
--- a/candle-core/Cargo.toml
+++ b/candle-core/Cargo.toml
@@ -12,7 +12,7 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-candle-kernels = { path = "../candle-kernels", version = "0.2.1", optional = true }
+candle-kernels = { path = "../candle-kernels", version = "0.2.2", optional = true }
 cudarc = { workspace = true, optional = true }
 gemm = { workspace = true }
 half = { workspace = true }
diff --git a/candle-datasets/Cargo.toml b/candle-datasets/Cargo.toml
index d69318e1..e34aa9be 100644
--- a/candle-datasets/Cargo.toml
+++ b/candle-datasets/Cargo.toml
@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 byteorder = { workspace = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
+candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.2.2" }
 hf-hub = { workspace = true}
 intel-mkl-src = { workspace = true, optional = true }
 memmap2 = { workspace = true }
diff --git a/candle-examples/Cargo.toml b/candle-examples/Cargo.toml
index eb552b88..a52ad88f 100644
--- a/candle-examples/Cargo.toml
+++ b/candle-examples/Cargo.toml
@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.2.1" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.1", optional = true }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
-candle-transformers = { path = "../candle-transformers", version = "0.2.1" }
+candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.2.2" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.2", optional = true }
+candle-nn = { path = "../candle-nn", version = "0.2.2" }
+candle-transformers = { path = "../candle-transformers", version = "0.2.2" }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true }
diff --git a/candle-flash-attn/Cargo.toml b/candle-flash-attn/Cargo.toml
index 0d130519..f92383ea 100644
--- a/candle-flash-attn/Cargo.toml
+++ b/candle-flash-attn/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.2.1"
+version = "0.2.2"
 edition = "2021"
 
 description = "Flash attention layer for the candle ML framework."
@@ -11,7 +11,7 @@ license = "MIT OR Apache-2.0"
 readme = "README.md"
 
 [dependencies]
-candle = { path = "../candle-core", features = ["cuda"], version = "0.2.1", package = "candle-core" }
+candle = { path = "../candle-core", features = ["cuda"], version = "0.2.2", package = "candle-core" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]
@@ -21,4 +21,4 @@ rayon = "1.7.0"
 
 [dev-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }
-candle-nn = { path = "../candle-nn", version = "0.2.1", features = ["cuda"] }
+candle-nn = { path = "../candle-nn", version = "0.2.2", features = ["cuda"] }
diff --git a/candle-kernels/Cargo.toml b/candle-kernels/Cargo.toml
index 576c52ea..d6924c76 100644
--- a/candle-kernels/Cargo.toml
+++ b/candle-kernels/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.2.1"
+version = "0.2.2"
 edition = "2021"
 
 description = "CUDA kernels for Candle"
diff --git a/candle-nn/Cargo.toml b/candle-nn/Cargo.toml
index db0f6a8f..5062a717 100644
--- a/candle-nn/Cargo.toml
+++ b/candle-nn/Cargo.toml
@@ -11,7 +11,7 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
+candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
 half = { workspace = true }
 thiserror = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
diff --git a/candle-pyo3/Cargo.toml b/candle-pyo3/Cargo.toml
index 97631b0a..1d431cfb 100644
--- a/candle-pyo3/Cargo.toml
+++ b/candle-pyo3/Cargo.toml
@@ -15,8 +15,8 @@ crate-type = ["cdylib"]
 doc = false
 
 [dependencies]
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
+candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.2.2" }
 half = { workspace = true }
 pyo3 = { version = "0.19.0", features = ["extension-module"] }
 
diff --git a/candle-transformers/Cargo.toml b/candle-transformers/Cargo.toml
index 86caf918..fd42060f 100644
--- a/candle-transformers/Cargo.toml
+++ b/candle-transformers/Cargo.toml
@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
+candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.2.2" }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
 rand = { workspace = true }
diff --git a/candle-wasm-examples/llama2-c/Cargo.toml b/candle-wasm-examples/llama2-c/Cargo.toml
index 51eac694..7209a648 100644
--- a/candle-wasm-examples/llama2-c/Cargo.toml
+++ b/candle-wasm-examples/llama2-c/Cargo.toml
@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.1" }
-candle-transformers = { path = "../../candle-transformers", version = "0.2.1" }
+candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.2" }
+candle-transformers = { path = "../../candle-transformers", version = "0.2.2" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 
diff --git a/candle-wasm-examples/segment-anything/Cargo.toml b/candle-wasm-examples/segment-anything/Cargo.toml
index ab82ab1f..b67de112 100644
--- a/candle-wasm-examples/segment-anything/Cargo.toml
+++ b/candle-wasm-examples/segment-anything/Cargo.toml
@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.1" }
-candle-transformers = { path = "../../candle-transformers", version = "0.2.1" }
+candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.2" }
+candle-transformers = { path = "../../candle-transformers", version = "0.2.2" }
 num-traits = { workspace = true }
 
 # App crates.
diff --git a/candle-wasm-examples/whisper/Cargo.toml b/candle-wasm-examples/whisper/Cargo.toml
index 47e7e094..10d3356e 100644
--- a/candle-wasm-examples/whisper/Cargo.toml
+++ b/candle-wasm-examples/whisper/Cargo.toml
@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.1" }
+candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.2" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 
diff --git a/candle-wasm-examples/yolo/Cargo.toml b/candle-wasm-examples/yolo/Cargo.toml
index b4daf6e6..9d9e60cc 100644
--- a/candle-wasm-examples/yolo/Cargo.toml
+++ b/candle-wasm-examples/yolo/Cargo.toml
@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.1" }
+candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.2" }
 num-traits = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }

From bb23b90b1df684471e21b9133a1008c2604e1738 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 12 Sep 2023 10:17:31 +0100
Subject: [PATCH 073/150] Add a small readme for the quantized example. (#823)

---
 candle-examples/examples/quantized/README.md | 35 ++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 candle-examples/examples/quantized/README.md

diff --git a/candle-examples/examples/quantized/README.md b/candle-examples/examples/quantized/README.md
new file mode 100644
index 00000000..f3159493
--- /dev/null
+++ b/candle-examples/examples/quantized/README.md
@@ -0,0 +1,35 @@
+# candle-quantized-llama: Fast Inference of quantized LLaMA models
+
+This example provides a quantized LLaMA model similar to
+[llama.cpp](https://github.com/ggerganov/llama.cpp). This is based on candle
+built-in quantization methods. Supported features include:
+
+- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support.
+- SIMD optimizations on Apple Silicon and x86.
+- Support using the `gguf` and `ggml` file formats.
+
+The weights are automatically downloaded for you from the [HuggingFace
+Hub](https://huggingface.co/) on the first run. There are various command line
+flags to use local files instead, run with `--help` to learn about them.
+
+## Running some example.
+
+```bash
+cargo run --example quantized --release -- --prompt "The best thing about coding in rust is "
+
+> avx: true, neon: false, simd128: false, f16c: true
+> temp: 0.80 repeat-penalty: 1.10 repeat-last-n: 64
+> loaded 291 tensors (3.79GB) in 2.17s
+> params: HParams { n_vocab: 32000, n_embd: 4096, n_mult: 256, n_head: 32, n_layer: 32, n_rot: 128, ftype: 2 }
+> The best thing about coding in rust is 1.) that I don’t need to worry about memory leaks, 2.) speed and 3.) my program will compile even on old machines.
+```
+
+### Command-line flags
+
+Run with `--help` to see all options.
+
+- `--which`: specify the model to use, e.g. `7b`, `13-chat`, `7b-code`.
+- `--prompt interactive`: interactive mode where multiple prompts can be
+  entered.
+- `--model mymodelfile.gguf`: use a local model file rather than getting one
+  from the hub.

From 7a62aad24a5d8b1d8cb351e5ef77a7b3457a74b8 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 12 Sep 2023 11:01:06 +0100
Subject: [PATCH 074/150] Add a readme for yolo-v8. (#824)

---
 candle-examples/examples/yolo-v8/README.md    |  47 ++++++++++++++++++
 .../examples/yolo-v8/assets/bike.jpg          | Bin 0 -> 182991 bytes
 .../examples/yolo-v8/assets/bike.od.jpg       | Bin 0 -> 179024 bytes
 .../examples/yolo-v8/assets/bike.pose.jpg     | Bin 0 -> 193397 bytes
 .../examples/yolo-v8/assets/peoples.pp.jpg    | Bin 0 -> 81845 bytes
 5 files changed, 47 insertions(+)
 create mode 100644 candle-examples/examples/yolo-v8/README.md
 create mode 100644 candle-examples/examples/yolo-v8/assets/bike.jpg
 create mode 100644 candle-examples/examples/yolo-v8/assets/bike.od.jpg
 create mode 100644 candle-examples/examples/yolo-v8/assets/bike.pose.jpg
 create mode 100644 candle-examples/examples/yolo-v8/assets/peoples.pp.jpg

diff --git a/candle-examples/examples/yolo-v8/README.md b/candle-examples/examples/yolo-v8/README.md
new file mode 100644
index 00000000..8590a8a9
--- /dev/null
+++ b/candle-examples/examples/yolo-v8/README.md
@@ -0,0 +1,47 @@
+# candle-yolo-v8: Object Detection and Pose Estimation
+
+This is a port of [Ultralytics
+YOLOv8](https://github.com/ultralytics/ultralytics). The implementation is based
+on the [tinygrad
+version](https://github.com/tinygrad/tinygrad/blob/master/examples/yolov8.py)
+and on the model architecture described in this
+[issue](https://github.com/ultralytics/ultralytics/issues/189). The supported
+tasks are object detection and pose estimation.
+
+You can try this model online on the [Candle YOLOv8
+Space](https://huggingface.co/spaces/lmz/candle-yolo). The model then fully runs
+in your browser using WebAssembly - if you use a custom image it will never
+leave your phone/computer!
+
+## Running some example.
+
+### Object Detection
+```bash
+cargo run --example yolo-v8 --release -- candle-examples/examples/yolo-v8/assets/bike.jpg
+```
+
+This prints details about the detected objects and generates a `bike.pp.jpg` file.
+
+![Leading group, Giro d'Italia 2021](./assets/bike.jpg)
+
+Image source:
+[wikimedia](https://commons.wikimedia.org/wiki/File:Leading_group,_Giro_d%27Italia_2021,_Stage_15.jpg).
+
+![Leading group, Giro d'Italia 2021](./assets/bike.od.jpg)
+
+### Pose Estimation
+```bash
+cargo run --example yolo-v8 --release -- \
+  candle-examples/examples/yolo-v8/assets/peoples.jpeg --task pose
+```
+
+![Leading group, Giro d'Italia 2021](./assets/bike.pose.jpg)
+
+### Command-line flags
+
+- `--which`: select the model variant to be used, `n`, `s` , `m`, `l`, or `x` by
+  increasing size and quality.
+- `--task`: `detect` for object detection and `pose` for pose estimation.
+- `--legend-size`: the size of the characters to print.
+- `--model`: use a local model file rather than downloading it from the hub.
+
diff --git a/candle-examples/examples/yolo-v8/assets/bike.jpg b/candle-examples/examples/yolo-v8/assets/bike.jpg
new file mode 100644
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From 25aacda28eff79d14be09d636275b0351d81069d Mon Sep 17 00:00:00 2001
From: Eric Buehler <65165915+EricLBuehler@users.noreply.github.com>
Date: Tue, 12 Sep 2023 06:06:21 -0400
Subject: [PATCH 075/150] Add useful libraries section (#825)

* Add useful libraries section

* Add link
---
 README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/README.md b/README.md
index 01c2bb8b..c3e35d33 100644
--- a/README.md
+++ b/README.md
@@ -101,6 +101,16 @@ trunk serve --release --port 8081
 And then head over to
 [http://localhost:8081/](http://localhost:8081/).
 
+<!--- ANCHOR: useful_libraries --->
+
+## Useful Libraries
+- `candle-lora`
+    - [`candle-lora`](https://github.com/EricLBuehler/candle-lora) provides a LoRA implementation that conforms to the official `peft` implementation.
+
+If you have an addition to this list, please submit a pull request.
+
+<!--- ANCHOR_END: useful_libraries --->
+
 <!--- ANCHOR: features --->
 
 ## Features

From 42da17694a4214a3e39e0d64afc22635ce83f557 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 12 Sep 2023 14:35:55 +0100
Subject: [PATCH 076/150] Segment Anything readme (#827)

* Add a readme for the segment-anything model.

* Add the original image.

* Clean-up the segment anything cli example.

* Also print the mask id in the outputs.
---
 .../examples/segment-anything/README.md       |  40 +++++++
 .../segment-anything/assets/sam_merged.jpg    | Bin 0 -> 160984 bytes
 .../examples/segment-anything/main.rs         | 108 +++++++-----------
 .../examples/yolo-v8/assets/peoples.pp.jpg    | Bin 81845 -> 0 bytes
 4 files changed, 81 insertions(+), 67 deletions(-)
 create mode 100644 candle-examples/examples/segment-anything/README.md
 create mode 100644 candle-examples/examples/segment-anything/assets/sam_merged.jpg
 delete mode 100644 candle-examples/examples/yolo-v8/assets/peoples.pp.jpg

diff --git a/candle-examples/examples/segment-anything/README.md b/candle-examples/examples/segment-anything/README.md
new file mode 100644
index 00000000..3c5b034f
--- /dev/null
+++ b/candle-examples/examples/segment-anything/README.md
@@ -0,0 +1,40 @@
+# candle-segment-anything: Segment-Anything Model
+
+This example is based on Meta AI [Segment-Anything
+Model](https://github.com/facebookresearch/segment-anything). This model
+provides a robust and fast image segmentation pipeline that can be tweaked via
+some prompting (requesting some points to be in the target mask, requesting some
+points to be part of the background so _not_ in the target mask, specifying some
+bounding box).
+
+The default backbone can be replaced by the smaller and faster TinyViT model
+based on [MobileSAM](https://github.com/ChaoningZhang/MobileSAM).
+
+## Running some example.
+
+```bash
+cargo run --example segment-anything --release -- \
+    --image candle-examples/examples/yolo-v8/assets/bike.jpg
+    --use-tiny
+    --point-x 0.4
+    --point-y 0.3
+```
+
+Running this command generates a `sam_merged.jpg` file containing the original
+image with a blue overlay of the selected mask. The red dot represents the prompt
+specified by `--point-x 0.4 --point-y 0.3`, this prompt is assumed to be part
+of the target mask.
+
+The values used for `--point-x` and `--point-y` should be between 0 and 1 and
+are proportional to the image dimension, i.e. use 0.5 for the image center.
+
+![Leading group, Giro d'Italia 2021](../yolo-v8/assets/bike.jpg)
+
+![Leading group, Giro d'Italia 2021](./assets/sam_merged.jpg)
+
+### Command-line flags
+- `--use-tiny`: use the TinyViT based MobileSAM backbone rather than the default
+  one.
+- `--point-x`, `--point-y`: specifies the location of the target point.
+- `--threshold`: sets the threshold value to be part of the mask, a negative
+  value results in a larger mask and can be specified via `--threshold=-1.2`.
diff --git a/candle-examples/examples/segment-anything/assets/sam_merged.jpg b/candle-examples/examples/segment-anything/assets/sam_merged.jpg
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AIsgCw

literal 0
HcmV?d00001

diff --git a/candle-examples/examples/segment-anything/main.rs b/candle-examples/examples/segment-anything/main.rs
index 21ba0415..3d9898b6 100644
--- a/candle-examples/examples/segment-anything/main.rs
+++ b/candle-examples/examples/segment-anything/main.rs
@@ -27,12 +27,19 @@ struct Args {
     #[arg(long)]
     generate_masks: bool,
 
+    /// The target point x coordinate, between 0 and 1 (0.5 is at the middle of the image).
     #[arg(long, default_value_t = 0.5)]
     point_x: f64,
 
+    /// The target point y coordinate, between 0 and 1 (0.5 is at the middle of the image).
     #[arg(long, default_value_t = 0.5)]
     point_y: f64,
 
+    /// The detection threshold for the mask, 0 is the default value, negative values mean a larger
+    /// mask, positive makes the mask more selective.
+    #[arg(long, default_value_t = 0.)]
+    threshold: f32,
+
     /// Enable tracing (generates a trace-timestamp.json file).
     #[arg(long)]
     tracing: bool,
@@ -57,28 +64,9 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let (image, initial_h, initial_w) = if args.image.ends_with(".safetensors") {
-        let mut tensors = candle::safetensors::load(&args.image, &device)?;
-        let image = match tensors.remove("image") {
-            Some(image) => image,
-            None => {
-                if tensors.len() != 1 {
-                    anyhow::bail!("multiple tensors in '{}'", args.image)
-                }
-                tensors.into_values().next().unwrap()
-            }
-        };
-        let image = if image.rank() == 4 {
-            image.get(0)?
-        } else {
-            image
-        };
-        let (_c, h, w) = image.dims3()?;
-        (image, h, w)
-    } else {
-        let (image, h, w) = candle_examples::load_image(&args.image, Some(sam::IMAGE_SIZE))?;
-        (image.to_device(&device)?, h, w)
-    };
+    let (image, initial_h, initial_w) =
+        candle_examples::load_image(&args.image, Some(sam::IMAGE_SIZE))?;
+    let image = image.to_device(&device)?;
     println!("loaded image {image:?}");
 
     let model = match args.model {
@@ -113,7 +101,7 @@ pub fn main() -> anyhow::Result<()> {
             /* crop_n_points_downscale_factor */ 1,
         )?;
         for (idx, bbox) in bboxes.iter().enumerate() {
-            println!("{bbox:?}");
+            println!("{idx} {bbox:?}");
             let mask = (&bbox.data.to_dtype(DType::U8)? * 255.)?;
             let (h, w) = mask.dims2()?;
             let mask = mask.broadcast_as((3, h, w))?;
@@ -135,56 +123,42 @@ pub fn main() -> anyhow::Result<()> {
         println!("mask:\n{mask}");
         println!("iou_predictions: {iou_predictions:?}");
 
-        // Save the mask as an image.
-        let mask = (mask.ge(0f32)? * 255.)?;
+        let mask = (mask.ge(args.threshold)? * 255.)?;
         let (_one, h, w) = mask.dims3()?;
         let mask = mask.expand((3, h, w))?;
-        candle_examples::save_image_resize(&mask, "sam_mask.png", initial_h, initial_w)?;
 
-        if !args.image.ends_with(".safetensors") {
-            let mut img = image::io::Reader::open(&args.image)?
-                .decode()
-                .map_err(candle::Error::wrap)?;
-            let mask_pixels = mask.permute((1, 2, 0))?.flatten_all()?.to_vec1::<u8>()?;
-            let mask_img: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
-                match image::ImageBuffer::from_raw(w as u32, h as u32, mask_pixels) {
-                    Some(image) => image,
-                    None => anyhow::bail!("error saving merged image"),
-                };
-            let mask_img = image::DynamicImage::from(mask_img).resize_to_fill(
-                img.width(),
-                img.height(),
-                image::imageops::FilterType::CatmullRom,
-            );
-            for x in 0..img.width() {
-                for y in 0..img.height() {
-                    let mask_p = imageproc::drawing::Canvas::get_pixel(&mask_img, x, y);
-                    if mask_p.0[0] > 100 {
-                        let mut img_p = imageproc::drawing::Canvas::get_pixel(&img, x, y);
-                        img_p.0[2] = 255 - (255 - img_p.0[2]) / 2;
-                        img_p.0[1] /= 2;
-                        img_p.0[0] /= 2;
-                        imageproc::drawing::Canvas::draw_pixel(&mut img, x, y, img_p)
-                    }
+        let mut img = image::io::Reader::open(&args.image)?
+            .decode()
+            .map_err(candle::Error::wrap)?;
+        let mask_pixels = mask.permute((1, 2, 0))?.flatten_all()?.to_vec1::<u8>()?;
+        let mask_img: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
+            match image::ImageBuffer::from_raw(w as u32, h as u32, mask_pixels) {
+                Some(image) => image,
+                None => anyhow::bail!("error saving merged image"),
+            };
+        let mask_img = image::DynamicImage::from(mask_img).resize_to_fill(
+            img.width(),
+            img.height(),
+            image::imageops::FilterType::CatmullRom,
+        );
+        for x in 0..img.width() {
+            for y in 0..img.height() {
+                let mask_p = imageproc::drawing::Canvas::get_pixel(&mask_img, x, y);
+                if mask_p.0[0] > 100 {
+                    let mut img_p = imageproc::drawing::Canvas::get_pixel(&img, x, y);
+                    img_p.0[2] = 255 - (255 - img_p.0[2]) / 2;
+                    img_p.0[1] /= 2;
+                    img_p.0[0] /= 2;
+                    imageproc::drawing::Canvas::draw_pixel(&mut img, x, y, img_p)
                 }
             }
-            match point {
-                Some((x, y)) => {
-                    let (x, y) = (
-                        (x * img.width() as f64) as i32,
-                        (y * img.height() as f64) as i32,
-                    );
-                    imageproc::drawing::draw_filled_circle(
-                        &img,
-                        (x, y),
-                        3,
-                        image::Rgba([255, 0, 0, 200]),
-                    )
-                    .save("sam_merged.jpg")?
-                }
-                None => img.save("sam_merged.jpg")?,
-            };
         }
+        let (x, y) = (
+            (args.point_x * img.width() as f64) as i32,
+            (args.point_y * img.height() as f64) as i32,
+        );
+        imageproc::drawing::draw_filled_circle(&img, (x, y), 3, image::Rgba([255, 0, 0, 200]))
+            .save("sam_merged.jpg")?
     }
     Ok(())
 }
diff --git a/candle-examples/examples/yolo-v8/assets/peoples.pp.jpg b/candle-examples/examples/yolo-v8/assets/peoples.pp.jpg
deleted file mode 100644
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GIT binary patch
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BjgSBU


From 805bf9ffa78119a1a7e047b4ddf6b2ea7df4d94f Mon Sep 17 00:00:00 2001
From: Juarez Bochi <jbochi@gmail.com>
Date: Tue, 12 Sep 2023 09:10:16 -0700
Subject: [PATCH 077/150] Implement top_p / nucleus sampling (#819)

* Implement top_p / nucleus sampling

* Update changelog

* rustfmt

* Add tests

* Fix clippy warning

* Fix another clippy error
---
 CHANGELOG.md                                  |  2 +
 candle-examples/examples/bigcode/main.rs      | 16 ++++-
 candle-examples/examples/falcon/main.rs       | 37 ++++++----
 candle-examples/examples/llama/main.rs        |  6 +-
 candle-examples/examples/llama2-c/main.rs     |  7 +-
 candle-examples/examples/quantized/main.rs    |  6 +-
 candle-transformers/src/generation/mod.rs     | 70 +++++++++++++++----
 candle-transformers/tests/generation_tests.rs | 29 ++++++++
 .../llama2-c/lib-example.html                 | 20 +++++-
 candle-wasm-examples/llama2-c/src/app.rs      | 23 ++++--
 candle-wasm-examples/llama2-c/src/bin/m.rs    | 10 ++-
 candle-wasm-examples/llama2-c/src/worker.rs   | 16 +++--
 12 files changed, 199 insertions(+), 43 deletions(-)
 create mode 100644 candle-transformers/tests/generation_tests.rs

diff --git a/CHANGELOG.md b/CHANGELOG.md
index a0275c57..06041294 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,8 @@ This documents the main changes to the `candle` crate.
 ## v0.2.2 - Unreleased
 
 ### Added
+- Support for `top_p` sampling
+  [819](https://github.com/huggingface/candle/pull/819).
 
 ### Modified
 
diff --git a/candle-examples/examples/bigcode/main.rs b/candle-examples/examples/bigcode/main.rs
index 3540f75d..5f17109e 100644
--- a/candle-examples/examples/bigcode/main.rs
+++ b/candle-examples/examples/bigcode/main.rs
@@ -28,9 +28,10 @@ impl TextGeneration {
         tokenizer: Tokenizer,
         seed: u64,
         temp: Option<f64>,
+        top_p: Option<f64>,
         device: &Device,
     ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp);
+        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
         Self {
             model,
             tokenizer,
@@ -94,6 +95,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -149,7 +154,14 @@ fn main() -> Result<()> {
     let model = GPTBigCode::load(vb, config)?;
     println!("loaded the model in {:?}", start.elapsed());
 
-    let mut pipeline = TextGeneration::new(model, tokenizer, args.seed, args.temperature, &device);
+    let mut pipeline = TextGeneration::new(
+        model,
+        tokenizer,
+        args.seed,
+        args.temperature,
+        args.top_p,
+        &device,
+    );
     pipeline.run(&args.prompt, args.sample_len)?;
     Ok(())
 }
diff --git a/candle-examples/examples/falcon/main.rs b/candle-examples/examples/falcon/main.rs
index c45fe545..b0973d64 100644
--- a/candle-examples/examples/falcon/main.rs
+++ b/candle-examples/examples/falcon/main.rs
@@ -25,17 +25,25 @@ struct TextGeneration {
     repeat_last_n: usize,
 }
 
+struct GenerationOptions {
+    temp: Option<f64>,
+    top_p: Option<f64>,
+    repeat_penalty: f32,
+    repeat_last_n: usize,
+}
+
 impl TextGeneration {
     fn new(
         model: Falcon,
         tokenizer: Tokenizer,
+        generation_options: GenerationOptions,
         seed: u64,
-        temp: Option<f64>,
         device: &Device,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
     ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp);
+        let logits_processor =
+            LogitsProcessor::new(seed, generation_options.temp, generation_options.top_p);
+        let repeat_penalty = generation_options.repeat_penalty;
+        let repeat_last_n = generation_options.repeat_last_n;
         Self {
             model,
             tokenizer,
@@ -118,6 +126,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -185,15 +197,14 @@ fn main() -> Result<()> {
     let model = Falcon::load(vb, config)?;
     println!("loaded the model in {:?}", start.elapsed());
 
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        &device,
-        args.repeat_penalty,
-        args.repeat_last_n,
-    );
+    let generation_options = GenerationOptions {
+        temp: args.temperature,
+        top_p: args.top_p,
+        repeat_penalty: args.repeat_penalty,
+        repeat_last_n: args.repeat_last_n,
+    };
+    let mut pipeline =
+        TextGeneration::new(model, tokenizer, generation_options, args.seed, &device);
     pipeline.run(&args.prompt, args.sample_len)?;
     Ok(())
 }
diff --git a/candle-examples/examples/llama/main.rs b/candle-examples/examples/llama/main.rs
index db3d216c..b2d7d938 100644
--- a/candle-examples/examples/llama/main.rs
+++ b/candle-examples/examples/llama/main.rs
@@ -42,6 +42,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -193,7 +197,7 @@ fn main() -> Result<()> {
 
     println!("starting the inference loop");
     print!("{prompt}");
-    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature);
+    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature, args.top_p);
     let start_gen = std::time::Instant::now();
     let mut index_pos = 0;
     let mut token_generated = 0;
diff --git a/candle-examples/examples/llama2-c/main.rs b/candle-examples/examples/llama2-c/main.rs
index e0ade322..e752a494 100644
--- a/candle-examples/examples/llama2-c/main.rs
+++ b/candle-examples/examples/llama2-c/main.rs
@@ -27,6 +27,10 @@ struct InferenceCmd {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     #[arg(long, default_value = "")]
     prompt: String,
 
@@ -133,6 +137,7 @@ fn main() -> anyhow::Result<()> {
         None => {
             let cmd = InferenceCmd {
                 temperature: None,
+                top_p: None,
                 prompt: "".to_string(),
                 config: None,
                 model_id: "karpathy/tinyllamas".to_string(),
@@ -256,7 +261,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
     let model = Llama::load(vb, &cache, config)?;
 
     println!("starting the inference loop");
-    let mut logits_processor = LogitsProcessor::new(299792458, args.temperature);
+    let mut logits_processor = LogitsProcessor::new(299792458, args.temperature, args.top_p);
     let mut index_pos = 0;
 
     print!("{}", args.prompt);
diff --git a/candle-examples/examples/quantized/main.rs b/candle-examples/examples/quantized/main.rs
index c8179d33..a80ad420 100644
--- a/candle-examples/examples/quantized/main.rs
+++ b/candle-examples/examples/quantized/main.rs
@@ -71,6 +71,10 @@ struct Args {
     #[arg(long, default_value_t = 0.8)]
     temperature: f64,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -310,7 +314,7 @@ fn main() -> anyhow::Result<()> {
             prompt_tokens
         };
         let mut all_tokens = vec![];
-        let mut logits_processor = LogitsProcessor::new(args.seed, temperature);
+        let mut logits_processor = LogitsProcessor::new(args.seed, temperature, args.top_p);
 
         let start_prompt_processing = std::time::Instant::now();
         let mut next_token = {
diff --git a/candle-transformers/src/generation/mod.rs b/candle-transformers/src/generation/mod.rs
index b1d20168..6c8c8ae4 100644
--- a/candle-transformers/src/generation/mod.rs
+++ b/candle-transformers/src/generation/mod.rs
@@ -4,32 +4,76 @@ use rand::{distributions::Distribution, SeedableRng};
 pub struct LogitsProcessor {
     rng: rand::rngs::StdRng,
     temperature: Option<f64>,
+    top_p: Option<f64>,
 }
 
 impl LogitsProcessor {
-    pub fn new(seed: u64, temperature: Option<f64>) -> Self {
+    pub fn new(seed: u64, temperature: Option<f64>, top_p: Option<f64>) -> Self {
         Self {
             rng: rand::rngs::StdRng::seed_from_u64(seed),
             temperature,
+            top_p,
         }
     }
 
+    fn sample_argmax(&mut self, logits: Tensor) -> Result<u32> {
+        let logits_v: Vec<f32> = logits.to_vec1()?;
+        let next_token = logits_v
+            .iter()
+            .enumerate()
+            .max_by(|(_, u), (_, v)| u.total_cmp(v))
+            .map(|(i, _)| i as u32)
+            .unwrap();
+        Ok(next_token)
+    }
+
+    fn sample_multi(&mut self, prs: &Vec<f32>) -> Result<u32> {
+        let distr = rand::distributions::WeightedIndex::new(prs).map_err(Error::wrap)?;
+        let next_token = distr.sample(&mut self.rng) as u32;
+        Ok(next_token)
+    }
+
+    fn sample_topp(&mut self, prs: &mut Vec<f32>, top_p: f32) -> Result<u32> {
+        // top-p sampling (or "nucleus sampling") samples from the smallest set of
+        // tokens that exceed probability top_p. This way we never sample tokens that
+        // have very low probabilities and are less likely to go "off the rails".
+        let mut argsort_indices = (0..prs.len()).collect::<Vec<_>>();
+
+        // Sort by descending probability.
+        argsort_indices.sort_by(|&i, &j| prs[j].partial_cmp(&prs[i]).unwrap());
+
+        // Clamp smaller probabilities to zero.
+        let mut cumsum = 0.;
+        for index in &argsort_indices {
+            if cumsum >= top_p {
+                prs[*index] = 0.0;
+            } else {
+                cumsum += prs[*index];
+            }
+        }
+
+        // Sample with clamped probabilities.
+        let next_token = self.sample_multi(prs)?;
+        Ok(next_token)
+    }
+
     pub fn sample(&mut self, logits: &Tensor) -> Result<u32> {
         let logits = logits.to_dtype(DType::F32)?;
         let temperature = self.temperature.unwrap_or(0.);
-        let next_token = if temperature > 0. {
-            let prs = candle_nn::ops::softmax(&(&logits / temperature)?, D::Minus1)?;
-            let prs: Vec<f32> = prs.to_vec1()?;
-            let distr = rand::distributions::WeightedIndex::new(prs).map_err(Error::wrap)?;
-            distr.sample(&mut self.rng) as u32
+        let top_p = self.top_p.unwrap_or(1.);
+        let next_token = if temperature == 0. {
+            self.sample_argmax(logits)?
         } else {
-            let logits_v: Vec<f32> = logits.to_vec1()?;
-            logits_v
-                .iter()
-                .enumerate()
-                .max_by(|(_, u), (_, v)| u.total_cmp(v))
-                .map(|(i, _)| i as u32)
-                .unwrap()
+            let logits = &(&logits / temperature)?;
+            let prs = candle_nn::ops::softmax(logits, D::Minus1)?;
+            let mut prs: Vec<f32> = prs.to_vec1()?;
+            if top_p <= 0.0 || top_p >= 1.0 {
+                // simply sample from the predicted probability distribution
+                self.sample_multi(&prs)?
+            } else {
+                // top-p (nucleus) sampling, clamping the least likely tokens to zero
+                self.sample_topp(&mut prs, top_p as f32)?
+            }
         };
         Ok(next_token)
     }
diff --git a/candle-transformers/tests/generation_tests.rs b/candle-transformers/tests/generation_tests.rs
new file mode 100644
index 00000000..76f994d0
--- /dev/null
+++ b/candle-transformers/tests/generation_tests.rs
@@ -0,0 +1,29 @@
+use candle::{Device, Result, Tensor};
+use candle_transformers::generation::LogitsProcessor;
+
+#[test]
+fn sample_with_zero_temperature() -> Result<()> {
+    let mut logits_process = LogitsProcessor::new(1337, None, None);
+    let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
+    let token = logits_process.sample(&logits)?;
+    assert_eq!(token, 3);
+    Ok(())
+}
+
+#[test]
+fn sample_with_temperature() -> Result<()> {
+    let mut logits_process = LogitsProcessor::new(42, Some(0.9), None);
+    let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
+    let token = logits_process.sample(&logits)?;
+    assert_eq!(token, 0);
+    Ok(())
+}
+
+#[test]
+fn sample_with_top_p() -> Result<()> {
+    let mut logits_process = LogitsProcessor::new(42, Some(1.0), Some(0.5));
+    let logits = Tensor::new(&[0.1, 0.2, 0.3, 0.4], &Device::Cpu)?;
+    let token = logits_process.sample(&logits)?;
+    assert_eq!(token, 2);
+    Ok(())
+}
diff --git a/candle-wasm-examples/llama2-c/lib-example.html b/candle-wasm-examples/llama2-c/lib-example.html
index b5033c54..22b12517 100644
--- a/candle-wasm-examples/llama2-c/lib-example.html
+++ b/candle-wasm-examples/llama2-c/lib-example.html
@@ -56,6 +56,7 @@
         const weightsURL = `${MODELS_BASE_URL}/${model.url}`;
         const prompt = getValue("prompt");
         const temperature = getValue("temperature");
+        const topP = getValue("top-p");
         const repeatPenalty = getValue("repeat_penalty");
         const seed = getValue("seed");
         const maxSeqLen = getValue("max-seq");
@@ -99,6 +100,7 @@
             tokenizerURL: "tokenizer.json",
             prompt,
             temp: temperature,
+            top_p: topP,
             repeatPenalty,
             seed: BigInt(seed),
             maxSeqLen,
@@ -251,7 +253,7 @@
         <input
           type="range"
           id="max-seq"
-          name="temperature"
+          name="max-seq"
           min="1"
           max="256"
           step="1"
@@ -279,6 +281,22 @@
         >
           0.50</output
         >
+        <label class="text-sm font-medium" for="top-p">Top-p</label>
+        <input
+          type="range"
+          id="top-p"
+          name="top-p"
+          min="0"
+          max="1"
+          step="0.01"
+          value="1.00"
+          oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)"
+        />
+        <output
+          class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
+        >
+          1.00</output
+        >
 
         <label class="text-sm font-medium" for="repeat_penalty"
           >Repeat Penalty</label
diff --git a/candle-wasm-examples/llama2-c/src/app.rs b/candle-wasm-examples/llama2-c/src/app.rs
index 782026a4..ea04a810 100644
--- a/candle-wasm-examples/llama2-c/src/app.rs
+++ b/candle-wasm-examples/llama2-c/src/app.rs
@@ -46,6 +46,7 @@ pub struct App {
     status: String,
     loaded: bool,
     temperature: std::rc::Rc<std::cell::RefCell<f64>>,
+    top_p: std::rc::Rc<std::cell::RefCell<f64>>,
     prompt: std::rc::Rc<std::cell::RefCell<String>>,
     generated: String,
     n_tokens: usize,
@@ -81,6 +82,7 @@ impl Component for App {
             status,
             n_tokens: 0,
             temperature: std::rc::Rc::new(std::cell::RefCell::new(0.)),
+            top_p: std::rc::Rc::new(std::cell::RefCell::new(1.0)),
             prompt: std::rc::Rc::new(std::cell::RefCell::new("".to_string())),
             generated: String::new(),
             current_decode: None,
@@ -122,10 +124,11 @@ impl Component for App {
                     self.n_tokens = 0;
                     self.generated.clear();
                     let temp = *self.temperature.borrow();
+                    let top_p = *self.top_p.borrow();
                     let prompt = self.prompt.borrow().clone();
-                    console_log!("temp: {}, prompt: {}", temp, prompt);
+                    console_log!("temp: {}, top_p: {}, prompt: {}", temp, top_p, prompt);
                     ctx.link()
-                        .send_message(Msg::WorkerInMsg(WorkerInput::Run(temp, prompt)))
+                        .send_message(Msg::WorkerInMsg(WorkerInput::Run(temp, top_p, prompt)))
                 }
                 true
             }
@@ -177,13 +180,21 @@ impl Component for App {
     fn view(&self, ctx: &Context<Self>) -> Html {
         use yew::TargetCast;
         let temperature = self.temperature.clone();
-        let oninput = ctx.link().callback(move |e: yew::InputEvent| {
+        let oninput_temperature = ctx.link().callback(move |e: yew::InputEvent| {
             let input: web_sys::HtmlInputElement = e.target_unchecked_into();
             if let Ok(temp) = f64::from_str(&input.value()) {
                 *temperature.borrow_mut() = temp
             }
             Msg::Refresh
         });
+        let top_p = self.top_p.clone();
+        let oninput_top_p = ctx.link().callback(move |e: yew::InputEvent| {
+            let input: web_sys::HtmlInputElement = e.target_unchecked_into();
+            if let Ok(top_p_input) = f64::from_str(&input.value()) {
+                *top_p.borrow_mut() = top_p_input
+            }
+            Msg::Refresh
+        });
         let prompt = self.prompt.clone();
         let oninput_prompt = ctx.link().callback(move |e: yew::InputEvent| {
             let input: web_sys::HtmlInputElement = e.target_unchecked_into();
@@ -201,9 +212,13 @@ impl Component for App {
                 </p>
                 </div>
                 {"temperature  \u{00a0} "}
-                <input type="range" min="0." max="1.2" step="0.1" value={self.temperature.borrow().to_string()} {oninput} id="temp"/>
+                <input type="range" min="0." max="1.2" step="0.1" value={self.temperature.borrow().to_string()} oninput={oninput_temperature} id="temp"/>
                 {format!(" \u{00a0} {}", self.temperature.borrow())}
                 <br/ >
+                {"top_p  \u{00a0} "}
+                <input type="range" min="0." max="1.0" step="0.05" value={self.top_p.borrow().to_string()} oninput={oninput_top_p} id="top_p"/>
+                {format!(" \u{00a0} {}", self.top_p.borrow())}
+                <br/ >
                 {"prompt: "}<input type="text" value={self.prompt.borrow().to_string()} oninput={oninput_prompt} id="prompt"/>
                 <br/ >
                 {
diff --git a/candle-wasm-examples/llama2-c/src/bin/m.rs b/candle-wasm-examples/llama2-c/src/bin/m.rs
index 6628ab7e..61de9d7f 100644
--- a/candle-wasm-examples/llama2-c/src/bin/m.rs
+++ b/candle-wasm-examples/llama2-c/src/bin/m.rs
@@ -47,7 +47,7 @@ impl Model {
             tokenizer,
             model: weights,
         });
-        let logits_processor = LogitsProcessor::new(299792458, None);
+        let logits_processor = LogitsProcessor::new(299792458, None, None);
         match model {
             Ok(inner) => Ok(Self {
                 inner,
@@ -69,6 +69,7 @@ impl Model {
         &mut self,
         prompt: String,
         temp: f64,
+        top_p: f64,
         repeat_penalty: f32,
         seed: u64,
     ) -> Result<String, JsError> {
@@ -80,7 +81,12 @@ impl Model {
             }
         }
         let temp = if temp <= 0. { None } else { Some(temp) };
-        self.logits_processor = LogitsProcessor::new(seed, temp);
+        let top_p = if top_p <= 0. || top_p >= 1. {
+            None
+        } else {
+            Some(top_p)
+        };
+        self.logits_processor = LogitsProcessor::new(seed, temp, top_p);
         self.repeat_penalty = repeat_penalty;
         self.tokens.clear();
         let tokens = self
diff --git a/candle-wasm-examples/llama2-c/src/worker.rs b/candle-wasm-examples/llama2-c/src/worker.rs
index 7e97b5da..79dd2f32 100644
--- a/candle-wasm-examples/llama2-c/src/worker.rs
+++ b/candle-wasm-examples/llama2-c/src/worker.rs
@@ -62,12 +62,18 @@ impl Model {
         link: &WorkerLink<Worker>,
         id: HandlerId,
         temp: f64,
+        top_p: f64,
         prompt: String,
     ) -> Result<()> {
         let dev = Device::Cpu;
         let temp = if temp <= 0. { None } else { Some(temp) };
-        console_log!("{temp:?} {prompt}");
-        let mut logits_processor = LogitsProcessor::new(299792458, temp);
+        let top_p = if top_p <= 0. || top_p >= 1.0 {
+            None
+        } else {
+            Some(top_p)
+        };
+        console_log!("temp: {temp:?} top_p: {top_p:?} prompt: {prompt}");
+        let mut logits_processor = LogitsProcessor::new(299792458, temp, top_p);
         let mut index_pos = 0;
         let mut tokens = self
             .tokenizer
@@ -268,7 +274,7 @@ pub struct Worker {
 #[derive(Serialize, Deserialize)]
 pub enum WorkerInput {
     ModelData(ModelData),
-    Run(f64, String),
+    Run(f64, f64, String),
 }
 
 #[derive(Serialize, Deserialize)]
@@ -301,7 +307,7 @@ impl yew_agent::Worker for Worker {
                 }
                 Err(err) => Err(format!("model creation error {err:?}")),
             },
-            WorkerInput::Run(temp, prompt) => match &mut self.model {
+            WorkerInput::Run(temp, top_p, prompt) => match &mut self.model {
                 None => Err("model has not been set yet".to_string()),
                 Some(model) => {
                     {
@@ -311,7 +317,7 @@ impl yew_agent::Worker for Worker {
                         }
                     }
                     let result = model
-                        .run(&self.link, id, temp, prompt)
+                        .run(&self.link, id, temp, top_p, prompt)
                         .map_err(|e| e.to_string());
                     Ok(WorkerOutput::GenerationDone(result))
                 }

From e82fcf1c594b54c105f1a3979a09f3d2e044a2e0 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 12 Sep 2023 18:21:24 +0200
Subject: [PATCH 078/150] Add more example readmes. (#828)

* Add more readmes.

* Add a readme for dinov2.

* Add some skeleton files for a couple more examples.

* More whisper details.
---
 candle-examples/examples/bert/README.md      | 44 ++++++++++++++++++++
 candle-examples/examples/bigcode/README.md   |  7 ++++
 candle-examples/examples/dinov2/README.md    | 19 +++++++++
 candle-examples/examples/falcon/README.md    |  3 ++
 candle-examples/examples/quantized/README.md |  2 +-
 candle-examples/examples/whisper/README.md   | 39 +++++++++++++++++
 6 files changed, 113 insertions(+), 1 deletion(-)
 create mode 100644 candle-examples/examples/bert/README.md
 create mode 100644 candle-examples/examples/bigcode/README.md
 create mode 100644 candle-examples/examples/dinov2/README.md
 create mode 100644 candle-examples/examples/falcon/README.md
 create mode 100644 candle-examples/examples/whisper/README.md

diff --git a/candle-examples/examples/bert/README.md b/candle-examples/examples/bert/README.md
new file mode 100644
index 00000000..82ca5f40
--- /dev/null
+++ b/candle-examples/examples/bert/README.md
@@ -0,0 +1,44 @@
+# candle-bert
+
+Bert is a general large language model. In this example it can be used for two
+different tasks:
+- Compute sentence embeddings for a prompt.
+- Compute similarities between a set of sentences.
+
+
+## Sentence embeddings
+
+Bert is used to compute the sentence embeddings for a prompt. The model weights
+are downloaded from the hub on the first run.
+
+```bash
+cargo run --example bert --release -- --prompt "Here is a test sentence"
+
+> [[[ 0.0798, -0.0665, -0.0247, ..., -0.1082, -0.1000, -0.2751],
+>   [ 0.4218,  0.2690,  0.2740, ...,  0.3889,  1.3503,  0.9908],
+>   [ 0.0466,  0.3041, -0.1143, ...,  0.4427,  0.6926, -0.1515],
+>   ...
+>   [ 0.3396,  0.4320, -0.4408, ...,  0.9212,  0.2331, -0.6777],
+>   [ 0.2789,  0.7539,  0.4306, ..., -0.0095,  0.3375, -1.7529],
+>   [ 0.6737,  0.7882,  0.0548, ...,  0.1836,  0.7299, -0.6617]]]
+> Tensor[[1, 7, 384], f32]
+```
+
+## Similarities
+
+In this example, Bert is used to compute the sentence embeddings for a set of
+sentences (hardcoded in the examples). Then cosine similarities are computed for
+each sentence pair and they are reported by decreasing values, hence the first
+reported pair contains the two sentences that have the highest similarity score.
+The sentence embeddings are computed using average pooling through all the
+sentence tokens, including some potential padding.
+
+```bash
+cargo run --example bert --release
+
+> score: 0.85 'The new movie is awesome' 'The new movie is so great'
+> score: 0.61 'The cat sits outside' 'The cat plays in the garden'
+> score: 0.52 'I love pasta' 'Do you like pizza?'
+> score: 0.23 'The new movie is awesome' 'Do you like pizza?'
+> score: 0.22 'I love pasta' 'The new movie is awesome'
+```
diff --git a/candle-examples/examples/bigcode/README.md b/candle-examples/examples/bigcode/README.md
new file mode 100644
index 00000000..0b593674
--- /dev/null
+++ b/candle-examples/examples/bigcode/README.md
@@ -0,0 +1,7 @@
+# candle-starcoder: code generation model
+
+StarCoder/BigCode is a LLM model specialized to code generation.
+
+```bash
+cargo run --example bigcode --release -- --prompt "fn fact(n: u64) -> u64 "
+```
diff --git a/candle-examples/examples/dinov2/README.md b/candle-examples/examples/dinov2/README.md
new file mode 100644
index 00000000..10d4ac1f
--- /dev/null
+++ b/candle-examples/examples/dinov2/README.md
@@ -0,0 +1,19 @@
+# candle-dinov2
+
+[DINOv2](https://github.com/facebookresearch/dinov2) is a computer vision model.
+In this example, it is used as an ImageNet classifier: the model returns the
+probability for the image to belong to each of the 1000 ImageNet categories.
+
+## Running some example
+
+```bash
+cargo run --example dinov2 --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+
+> mountain bike, all-terrain bike, off-roader: 43.67%
+> bicycle-built-for-two, tandem bicycle, tandem: 33.20%
+> crash helmet            : 13.23%
+> unicycle, monocycle     : 2.44%
+> maillot                 : 2.42%
+```
+
+![Leading group, Giro d'Italia 2021](../yolo-v8/assets/bike.jpg)
diff --git a/candle-examples/examples/falcon/README.md b/candle-examples/examples/falcon/README.md
new file mode 100644
index 00000000..267c78c2
--- /dev/null
+++ b/candle-examples/examples/falcon/README.md
@@ -0,0 +1,3 @@
+# candle-falcon
+
+Falcon is a general large language model.
diff --git a/candle-examples/examples/quantized/README.md b/candle-examples/examples/quantized/README.md
index f3159493..ee4f3420 100644
--- a/candle-examples/examples/quantized/README.md
+++ b/candle-examples/examples/quantized/README.md
@@ -24,7 +24,7 @@ cargo run --example quantized --release -- --prompt "The best thing about coding
 > The best thing about coding in rust is 1.) that I don’t need to worry about memory leaks, 2.) speed and 3.) my program will compile even on old machines.
 ```
 
-### Command-line flags
+## Command-line flags
 
 Run with `--help` to see all options.
 
diff --git a/candle-examples/examples/whisper/README.md b/candle-examples/examples/whisper/README.md
new file mode 100644
index 00000000..124cd182
--- /dev/null
+++ b/candle-examples/examples/whisper/README.md
@@ -0,0 +1,39 @@
+# candle-whisper: speech recognition
+
+An implementation of [OpenAI Whisper](https://github.com/openai/whisper) using
+candle. Whisper is a general purpose speech recognition model, it can be used to
+convert audio files (in the `.wav` format) to text. Supported features include
+language detection as well as multilingual speech recognition.
+
+## Running some example
+
+If no audio file is passed as input, a [sample
+file](https://huggingface.co/datasets/Narsil/candle-examples/resolve/main/samples_jfk.wav) is automatically downloaded
+from the hub.
+
+```bash
+ cargo run --example whisper --release
+
+> No audio file submitted: Downloading https://huggingface.co/datasets/Narsil/candle_demo/blob/main/samples_jfk.wav
+> loaded wav data: Header { audio_format: 1, channel_count: 1, sampling_rate: 16000, bytes_per_second: 32000, bytes_per_sample: 2, bits_per_sample: 16 }
+> pcm data loaded 176000
+> loaded mel: [1, 80, 3000]
+> 0.0s -- 30.0s:  And so my fellow Americans ask not what your country can do for you ask what you can do for your country
+ ```
+
+ In order to use the multilingual mode, specify a multilingual model via the
+ `--model` flag, see the details below.
+
+## Command line flags
+
+- `--input`: the audio file to be converted to text, in wav format.
+- `--language`: force the language to some specific value rather than being
+  detected, e.g. `en`.
+- `--task`: the task to be performed, can be `transcribe` (return the text data
+  in the original language) or `translate` (translate the text to English). 
+- `--timestamps`: enable the timestamp mode where some timestamps are reported
+  for each recognized audio extracts.
+- `--model`: the model to be used. Models that do not end with `-en` are
+  multilingual models, other ones are English only models. The supported models
+  are `tiny`, `tiny.en`, `base`, `base.en`, `small`, `small.en`, `medium`,
+  `medium.en`, `large`, and `large-v2`.

From 9daa6dbe87a6cb11496941acb4d7d5fb785183f8 Mon Sep 17 00:00:00 2001
From: Juarez Bochi <juarez.bochi@grammarly.com>
Date: Tue, 12 Sep 2023 23:14:05 -0700
Subject: [PATCH 079/150] Extract T5 module and add main function to use it
 (#829)

* Extract t5 out of musicgen

* Add main for t5 module
---
 candle-examples/examples/musicgen/main.rs     |   1 -
 .../examples/musicgen/musicgen_model.rs       |  11 +-
 candle-examples/examples/t5/README.md         |  17 +++
 candle-examples/examples/t5/main.rs           | 134 ++++++++++++++++++
 candle-nn/Cargo.toml                          |   1 +
 candle-nn/src/activation.rs                   |   5 +-
 candle-transformers/src/models/mod.rs         |   1 +
 .../src/models/t5.rs                          |  35 +++--
 8 files changed, 184 insertions(+), 21 deletions(-)
 create mode 100644 candle-examples/examples/t5/README.md
 create mode 100644 candle-examples/examples/t5/main.rs
 rename candle-examples/examples/musicgen/t5_model.rs => candle-transformers/src/models/t5.rs (94%)

diff --git a/candle-examples/examples/musicgen/main.rs b/candle-examples/examples/musicgen/main.rs
index 3794c22d..df8c3135 100644
--- a/candle-examples/examples/musicgen/main.rs
+++ b/candle-examples/examples/musicgen/main.rs
@@ -13,7 +13,6 @@ extern crate accelerate_src;
 mod encodec_model;
 mod musicgen_model;
 mod nn;
-mod t5_model;
 
 use musicgen_model::{GenConfig, MusicgenForConditionalGeneration};
 
diff --git a/candle-examples/examples/musicgen/musicgen_model.rs b/candle-examples/examples/musicgen/musicgen_model.rs
index 7e272fd7..d6d8ae15 100644
--- a/candle-examples/examples/musicgen/musicgen_model.rs
+++ b/candle-examples/examples/musicgen/musicgen_model.rs
@@ -1,9 +1,10 @@
-use crate::{encodec_model, t5_model};
+use crate::encodec_model;
 use candle::{DType, Device, Result, Tensor, D};
 use candle_nn::{
     embedding, layer_norm, linear_no_bias, Activation, Embedding, LayerNorm, Linear, Module,
     VarBuilder,
 };
+use candle_transformers::models::t5;
 
 // https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/models/musicgen/configuration_musicgen.py#L83
 #[derive(Debug, Clone, PartialEq)]
@@ -370,7 +371,7 @@ impl MusicgenForCausalLM {
 
 #[derive(Debug)]
 pub struct MusicgenForConditionalGeneration {
-    pub text_encoder: crate::t5_model::T5EncoderModel,
+    pub text_encoder: t5::T5EncoderModel,
     pub audio_encoder: crate::encodec_model::EncodecModel,
     pub decoder: MusicgenForCausalLM,
     cfg: GenConfig,
@@ -379,7 +380,7 @@ pub struct MusicgenForConditionalGeneration {
 #[derive(Debug, Clone, PartialEq)]
 pub struct GenConfig {
     musicgen: Config,
-    t5: crate::t5_model::Config,
+    t5: t5::Config,
     encodec: crate::encodec_model::Config,
 }
 
@@ -387,7 +388,7 @@ impl GenConfig {
     pub fn small() -> Self {
         Self {
             musicgen: Config::musicgen_small(),
-            t5: t5_model::Config::musicgen_small(),
+            t5: t5::Config::musicgen_small(),
             encodec: encodec_model::Config::musicgen_small(),
         }
     }
@@ -399,7 +400,7 @@ impl MusicgenForConditionalGeneration {
     }
 
     pub fn load(vb: VarBuilder, cfg: GenConfig) -> Result<Self> {
-        let text_encoder = t5_model::T5EncoderModel::load(vb.pp("text_encoder"), &cfg.t5)?;
+        let text_encoder = t5::T5EncoderModel::load(vb.pp("text_encoder"), &cfg.t5)?;
         let audio_encoder =
             encodec_model::EncodecModel::load(vb.pp("audio_encoder"), &cfg.encodec)?;
         let decoder = MusicgenForCausalLM::load(vb.pp("decoder"), &cfg.musicgen)?;
diff --git a/candle-examples/examples/t5/README.md b/candle-examples/examples/t5/README.md
new file mode 100644
index 00000000..66952395
--- /dev/null
+++ b/candle-examples/examples/t5/README.md
@@ -0,0 +1,17 @@
+# candle-t5
+
+Generates embeddings using a T5 model. It doesn't support generation yet.
+
+```bash
+$ cargo run --example t5 -- --model-id t5-large --prompt 'how tall is obama' --n 1
+Loaded and encoded 2.014244792s
+[[[-0.3174, -0.1462,  0.0065, ..., -0.0579, -0.0581,  0.1387],
+  [-0.2905, -0.1945, -0.0685, ..., -0.2457, -0.5137, -0.1760],
+  [-0.0591, -0.0213, -0.0241, ..., -0.0210,  0.0491, -0.0300],
+  ...
+  [-0.4333,  0.0027, -0.0609, ...,  0.3069, -0.2252,  0.3306],
+  [-0.1458,  0.1323, -0.0138, ...,  0.3000, -0.4550, -0.0384],
+  [ 0.0397,  0.0485, -0.2373, ...,  0.2578, -0.2650, -0.4356]]]
+Tensor[[1, 9, 1024], f32]
+Took 2.1363425s
+```
\ No newline at end of file
diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
new file mode 100644
index 00000000..bcba846d
--- /dev/null
+++ b/candle-examples/examples/t5/main.rs
@@ -0,0 +1,134 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+use candle_transformers::models::t5;
+
+use anyhow::{anyhow, Error as E, Result};
+use candle::{DType, Tensor};
+use candle_nn::VarBuilder;
+use clap::Parser;
+use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+const DTYPE: DType = DType::F32;
+const DEFAULT_PROMPT: &str = "Translate English to German: That is good.";
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Run offline (you must have the files already cached)
+    #[arg(long)]
+    offline: bool,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    /// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    /// Compute embeddings for this prompt or use the DEFAULT_PROMPT.
+    #[arg(long)]
+    prompt: Option<String>,
+
+    /// The number of times to run the prompt.
+    #[arg(long, default_value = "1")]
+    n: usize,
+}
+
+impl Args {
+    fn build_model_and_tokenizer(&self) -> Result<(t5::T5EncoderModel, Tokenizer)> {
+        let device = candle_examples::device(self.cpu)?;
+        let default_model = "t5-small".to_string();
+        let default_revision = "refs/pr/15".to_string();
+        let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
+            (Some(model_id), Some(revision)) => (model_id, revision),
+            (Some(model_id), None) => (model_id, "main".to_string()),
+            (None, Some(revision)) => (default_model, revision),
+            (None, None) => (default_model, default_revision),
+        };
+
+        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
+        let (config_filename, tokenizer_filename, weights_filename) = if self.offline {
+            let cache = Cache::default().repo(repo);
+            (
+                cache
+                    .get("config.json")
+                    .ok_or(anyhow!("Missing config file in cache"))?,
+                cache
+                    .get("tokenizer.json")
+                    .ok_or(anyhow!("Missing tokenizer file in cache"))?,
+                cache
+                    .get("model.safetensors")
+                    .ok_or(anyhow!("Missing weights file in cache"))?,
+            )
+        } else {
+            let api = Api::new()?;
+            let api = api.repo(repo);
+            (
+                api.get("config.json")?,
+                api.get("tokenizer.json")?,
+                api.get("model.safetensors")?,
+            )
+        };
+        let config = std::fs::read_to_string(config_filename)?;
+        let config: t5::Config = serde_json::from_str(&config)?;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+        let weights = unsafe { candle::safetensors::MmapedFile::new(weights_filename)? };
+        let weights = weights.deserialize()?;
+        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &device);
+        let model = t5::T5EncoderModel::load(vb, &config)?;
+        Ok((model, tokenizer))
+    }
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        println!("tracing...");
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+    let start = std::time::Instant::now();
+
+    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
+    let device = &model.device;
+    let prompt = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
+    let tokenizer = tokenizer
+        .with_padding(None)
+        .with_truncation(None)
+        .map_err(E::msg)?;
+    let tokens = tokenizer
+        .encode(prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+    println!("Loaded and encoded {:?}", start.elapsed());
+    for idx in 0..args.n {
+        let start = std::time::Instant::now();
+        let ys = model.forward(&token_ids)?;
+        if idx == 0 {
+            println!("{ys}");
+        }
+        println!("Took {:?}", start.elapsed());
+    }
+    Ok(())
+}
diff --git a/candle-nn/Cargo.toml b/candle-nn/Cargo.toml
index 5062a717..2a8ec8ce 100644
--- a/candle-nn/Cargo.toml
+++ b/candle-nn/Cargo.toml
@@ -18,6 +18,7 @@ intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
 rayon = { workspace = true }
 safetensors = { workspace = true }
+serde = { workspace = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
diff --git a/candle-nn/src/activation.rs b/candle-nn/src/activation.rs
index 0db3edc9..6e442458 100644
--- a/candle-nn/src/activation.rs
+++ b/candle-nn/src/activation.rs
@@ -1,7 +1,10 @@
 use candle::Tensor;
+use serde::Deserialize;
 
-#[derive(Debug, Clone, Copy, PartialEq)]
+#[derive(Debug, Clone, Copy, PartialEq, Deserialize, Default)]
+#[serde(rename_all = "lowercase")]
 pub enum Activation {
+    #[default]
     Gelu,
     Relu,
     Elu(f64),
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index f33d01e6..e2e0bf81 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -7,4 +7,5 @@ pub mod llama;
 pub mod quantized_llama;
 pub mod segment_anything;
 pub mod stable_diffusion;
+pub mod t5;
 pub mod whisper;
diff --git a/candle-examples/examples/musicgen/t5_model.rs b/candle-transformers/src/models/t5.rs
similarity index 94%
rename from candle-examples/examples/musicgen/t5_model.rs
rename to candle-transformers/src/models/t5.rs
index 22f0a4f5..1454b7cc 100644
--- a/candle-examples/examples/musicgen/t5_model.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -1,11 +1,12 @@
 // T5 Text Encoder
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
 
-use candle::{DType, Result, Tensor, D};
+use candle::{DType, Device, Result, Tensor, D};
 use candle_nn::{embedding, linear_no_bias, Activation, Embedding, Linear, Module, VarBuilder};
+use serde::Deserialize;
 use std::sync::Arc;
 
-#[derive(Debug, Clone, PartialEq)]
+#[derive(Debug, Clone, PartialEq, Deserialize)]
 pub struct Config {
     vocab_size: usize,
     d_model: usize,
@@ -15,14 +16,15 @@ pub struct Config {
     num_decoder_layers: Option<usize>,
     num_heads: usize,
     relative_attention_num_buckets: usize,
-    relative_attention_max_distance: usize,
+    relative_attention_max_distance: Option<usize>,
     dropout_rate: f64,
     layer_norm_epsilon: f64,
     initializer_factor: f64,
+    #[serde(default)]
     feed_forward_proj: Activation,
-    is_decoder: bool,
+    is_decoder: Option<bool>,
     is_encoder_decoder: bool,
-    use_cache: bool,
+    use_cache: Option<bool>,
     pad_token_id: usize,
     eos_token_id: usize,
 }
@@ -38,14 +40,14 @@ impl Default for Config {
             num_decoder_layers: None,
             num_heads: 8,
             relative_attention_num_buckets: 32,
-            relative_attention_max_distance: 128,
+            relative_attention_max_distance: Some(128),
             dropout_rate: 0.1,
             layer_norm_epsilon: 1e-6,
             initializer_factor: 1.0,
             feed_forward_proj: Activation::Relu,
-            is_decoder: false,
+            is_decoder: Some(false),
             is_encoder_decoder: true,
-            use_cache: true,
+            use_cache: Some(true),
             pad_token_id: 0,
             eos_token_id: 1,
         }
@@ -63,16 +65,16 @@ impl Config {
             eos_token_id: 1,
             feed_forward_proj: Activation::Relu,
             initializer_factor: 1.0,
-            is_decoder: false,
+            is_decoder: Some(false),
             is_encoder_decoder: true,
             layer_norm_epsilon: 1e-6,
             num_decoder_layers: Some(12),
             num_heads: 12,
             num_layers: 12,
             pad_token_id: 0,
-            relative_attention_max_distance: 128,
+            relative_attention_max_distance: Some(128),
             relative_attention_num_buckets: 32,
-            use_cache: true,
+            use_cache: Some(true),
             vocab_size: 32128,
         }
     }
@@ -197,7 +199,7 @@ impl T5Attention {
             d_kv: cfg.d_kv,
             relative_attention_bias,
             relative_attention_num_buckets: cfg.relative_attention_num_buckets,
-            relative_attention_max_distance: cfg.relative_attention_max_distance,
+            relative_attention_max_distance: cfg.relative_attention_max_distance.unwrap_or(128),
             inner_dim,
         })
     }
@@ -343,7 +345,7 @@ impl T5Block {
     fn load(has_relative_attention_bias: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
         let vb = vb.pp("layer");
         let self_attn = T5LayerSelfAttention::load(has_relative_attention_bias, vb.pp("0"), cfg)?;
-        let cross_attn = if cfg.is_decoder {
+        let cross_attn = if cfg.is_decoder.unwrap_or(false) {
             Some(T5LayerCrossAttention::load(vb.pp("1"), cfg)?)
         } else {
             None
@@ -417,6 +419,7 @@ impl T5Stack {
 pub struct T5EncoderModel {
     shared: Arc<Embedding>,
     encoder: T5Stack,
+    pub device: Device,
 }
 
 impl T5EncoderModel {
@@ -424,7 +427,11 @@ impl T5EncoderModel {
         let shared = embedding(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
         let shared = Arc::new(shared);
         let encoder = T5Stack::load(vb.pp("encoder"), &shared, cfg)?;
-        Ok(Self { shared, encoder })
+        Ok(Self {
+            shared,
+            encoder,
+            device: vb.device().clone(),
+        })
     }
 
     pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {

From d801e1d564c5a6560680ff085e31dc4322627542 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 08:16:20 +0200
Subject: [PATCH 080/150] Clippy fix. (#830)

---
 candle-transformers/src/models/t5.rs | 2 --
 1 file changed, 2 deletions(-)

diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 1454b7cc..3700f1e0 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -417,7 +417,6 @@ impl T5Stack {
 
 #[derive(Debug)]
 pub struct T5EncoderModel {
-    shared: Arc<Embedding>,
     encoder: T5Stack,
     pub device: Device,
 }
@@ -428,7 +427,6 @@ impl T5EncoderModel {
         let shared = Arc::new(shared);
         let encoder = T5Stack::load(vb.pp("encoder"), &shared, cfg)?;
         Ok(Self {
-            shared,
             encoder,
             device: vb.device().clone(),
         })

From e4553fb355ffebe6781ea2d35ba0734a310cab9b Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 08:37:04 +0200
Subject: [PATCH 081/150] T5 tweaks (#831)

* Use default values rather than options.

* Avoid exposing the device field.

* More tweaks.
---
 candle-examples/examples/t5/main.rs  | 16 ++-------
 candle-transformers/src/models/t5.rs | 51 ++++++++++++++++++----------
 2 files changed, 35 insertions(+), 32 deletions(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index bcba846d..84be0204 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -30,7 +30,7 @@ struct Args {
     #[arg(long)]
     tracing: bool,
 
-    /// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
+    /// The model repository to use on the HuggingFace hub.
     #[arg(long)]
     model_id: Option<String>,
 
@@ -94,22 +94,10 @@ impl Args {
 }
 
 fn main() -> Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
     let args = Args::parse();
-    let _guard = if args.tracing {
-        println!("tracing...");
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
     let start = std::time::Instant::now();
 
     let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
-    let device = &model.device;
     let prompt = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
     let tokenizer = tokenizer
         .with_padding(None)
@@ -120,7 +108,7 @@ fn main() -> Result<()> {
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
-    let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+    let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
     println!("Loaded and encoded {:?}", start.elapsed());
     for idx in 0..args.n {
         let start = std::time::Instant::now();
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 3700f1e0..691817d1 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -6,6 +6,18 @@ use candle_nn::{embedding, linear_no_bias, Activation, Embedding, Linear, Module
 use serde::Deserialize;
 use std::sync::Arc;
 
+fn default_relative_attention_max_distance() -> usize {
+    128
+}
+
+fn default_is_decoder() -> bool {
+    false
+}
+
+fn default_use_cache() -> bool {
+    true
+}
+
 #[derive(Debug, Clone, PartialEq, Deserialize)]
 pub struct Config {
     vocab_size: usize,
@@ -16,15 +28,18 @@ pub struct Config {
     num_decoder_layers: Option<usize>,
     num_heads: usize,
     relative_attention_num_buckets: usize,
-    relative_attention_max_distance: Option<usize>,
+    #[serde(default = "default_relative_attention_max_distance")]
+    relative_attention_max_distance: usize,
     dropout_rate: f64,
     layer_norm_epsilon: f64,
     initializer_factor: f64,
     #[serde(default)]
     feed_forward_proj: Activation,
-    is_decoder: Option<bool>,
+    #[serde(default = "default_is_decoder")]
+    is_decoder: bool,
     is_encoder_decoder: bool,
-    use_cache: Option<bool>,
+    #[serde(default = "default_use_cache")]
+    use_cache: bool,
     pad_token_id: usize,
     eos_token_id: usize,
 }
@@ -40,14 +55,14 @@ impl Default for Config {
             num_decoder_layers: None,
             num_heads: 8,
             relative_attention_num_buckets: 32,
-            relative_attention_max_distance: Some(128),
+            relative_attention_max_distance: 128,
             dropout_rate: 0.1,
             layer_norm_epsilon: 1e-6,
             initializer_factor: 1.0,
             feed_forward_proj: Activation::Relu,
-            is_decoder: Some(false),
+            is_decoder: false,
             is_encoder_decoder: true,
-            use_cache: Some(true),
+            use_cache: true,
             pad_token_id: 0,
             eos_token_id: 1,
         }
@@ -65,16 +80,16 @@ impl Config {
             eos_token_id: 1,
             feed_forward_proj: Activation::Relu,
             initializer_factor: 1.0,
-            is_decoder: Some(false),
+            is_decoder: false,
             is_encoder_decoder: true,
             layer_norm_epsilon: 1e-6,
             num_decoder_layers: Some(12),
             num_heads: 12,
             num_layers: 12,
             pad_token_id: 0,
-            relative_attention_max_distance: Some(128),
+            relative_attention_max_distance: 128,
             relative_attention_num_buckets: 32,
-            use_cache: Some(true),
+            use_cache: true,
             vocab_size: 32128,
         }
     }
@@ -199,7 +214,7 @@ impl T5Attention {
             d_kv: cfg.d_kv,
             relative_attention_bias,
             relative_attention_num_buckets: cfg.relative_attention_num_buckets,
-            relative_attention_max_distance: cfg.relative_attention_max_distance.unwrap_or(128),
+            relative_attention_max_distance: cfg.relative_attention_max_distance,
             inner_dim,
         })
     }
@@ -345,7 +360,7 @@ impl T5Block {
     fn load(has_relative_attention_bias: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
         let vb = vb.pp("layer");
         let self_attn = T5LayerSelfAttention::load(has_relative_attention_bias, vb.pp("0"), cfg)?;
-        let cross_attn = if cfg.is_decoder.unwrap_or(false) {
+        let cross_attn = if cfg.is_decoder {
             Some(T5LayerCrossAttention::load(vb.pp("1"), cfg)?)
         } else {
             None
@@ -402,23 +417,20 @@ impl T5Stack {
 
     fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
         let input_embeds = self.shared.as_ref().forward(input_ids)?;
-        let (_b_sz, _seq_len) = (input_embeds.dim(0)?, input_embeds.dim(1)?);
-
         let mut hidden_states = input_embeds;
         let mut position_bias = None;
         for block in self.block.iter() {
             (hidden_states, position_bias) =
                 block.forward(&hidden_states, position_bias.as_ref())?
         }
-        let hidden_states = self.final_layer_norm.forward(&hidden_states)?;
-        Ok(hidden_states)
+        self.final_layer_norm.forward(&hidden_states)
     }
 }
 
 #[derive(Debug)]
 pub struct T5EncoderModel {
     encoder: T5Stack,
-    pub device: Device,
+    device: Device,
 }
 
 impl T5EncoderModel {
@@ -433,7 +445,10 @@ impl T5EncoderModel {
     }
 
     pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
-        let encoder_outputs = self.encoder.forward(input_ids)?;
-        Ok(encoder_outputs)
+        self.encoder.forward(input_ids)
+    }
+
+    pub fn device(&self) -> &Device {
+        &self.device
     }
 }

From 18d3c803a8d2b80a14e7e4f33c47a92c4a35fa6d Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 09:24:58 +0200
Subject: [PATCH 082/150] Scalar support in minimum/maximum. (#832)

* Scalar support in minimum/maximum.

* Add a clamp method to tensors.
---
 candle-core/src/tensor.rs         | 31 +++++++++++++++++++++++++++++--
 candle-core/tests/tensor_tests.rs | 12 ++++++++++++
 2 files changed, 41 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 8ad9322b..59a23c39 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -105,6 +105,28 @@ macro_rules! binary_op {
     };
 }
 
+macro_rules! binary_op_scalar {
+    ($fn_name:ident, $op_name:ident) => {
+        pub fn $fn_name<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
+            let rhs = match rhs.to_tensor_scalar()? {
+                crate::scalar::TensorScalar::Tensor(rhs) => rhs,
+                crate::scalar::TensorScalar::Scalar(rhs) => rhs
+                    .to_dtype(self.dtype())?
+                    .to_device(self.device())?
+                    .broadcast_as(self.shape())?,
+            };
+            let shape = self.same_shape_binary_op(&rhs, stringify!($fn_name))?;
+            let storage = self.storage().binary_impl::<crate::op::$op_name>(
+                &*rhs.storage(),
+                self.layout(),
+                rhs.layout(),
+            )?;
+            let op = BackpropOp::new2(self, &rhs, |t1, t2| Op::Binary(t1, t2, BinaryOp::$op_name));
+            Ok(from_storage(storage, shape.clone(), op, false))
+        }
+    };
+}
+
 macro_rules! broadcast_binary_op {
     ($fn_name:ident, $inner_fn_name:ident) => {
         pub fn $fn_name(&self, rhs: &Self) -> Result<Self> {
@@ -447,8 +469,8 @@ impl Tensor {
     binary_op!(mul, Mul);
     binary_op!(sub, Sub);
     binary_op!(div, Div);
-    binary_op!(maximum, Maximum);
-    binary_op!(minimum, Minimum);
+    binary_op_scalar!(maximum, Maximum);
+    binary_op_scalar!(minimum, Minimum);
     broadcast_binary_op!(broadcast_add, add);
     broadcast_binary_op!(broadcast_mul, mul);
     broadcast_binary_op!(broadcast_sub, sub);
@@ -827,6 +849,11 @@ impl Tensor {
         self.cmp(rhs, CmpOp::Le)
     }
 
+    /// Clamp the tensor values to be between `min` and `max`.
+    pub fn clamp<T1: TensorOrScalar, T2: TensorOrScalar>(&self, min: T1, max: T2) -> Result<Self> {
+        self.maximum(min)?.minimum(max)
+    }
+
     /// Upsample the input tensor to the `(target_h, target_w)` size, taking the value of the
     /// nearest element.
     ///
diff --git a/candle-core/tests/tensor_tests.rs b/candle-core/tests/tensor_tests.rs
index cd68908f..f1c204ea 100644
--- a/candle-core/tests/tensor_tests.rs
+++ b/candle-core/tests/tensor_tests.rs
@@ -33,6 +33,17 @@ fn tensor_2d(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn clamp(device: &Device) -> Result<()> {
+    let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
+    let tensor = Tensor::new(data, device)?;
+    let tensor = tensor.clamp(1.5, 6.2)?;
+    assert_eq!(
+        tensor.to_vec2::<f32>()?,
+        [[3.0, 1.5, 4.0, 1.5, 5.0], [2.0, 1.5, 6.2, 6.2, 2.0]],
+    );
+    Ok(())
+}
+
 fn binary_op(device: &Device) -> Result<()> {
     let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
     let tensor1 = Tensor::new(data, device)?;
@@ -908,6 +919,7 @@ test_device!(index_add, index_add_cpu, index_add_gpu);
 test_device!(gather, gather_cpu, gather_gpu);
 test_device!(scatter_add, scatter_add_cpu, scatter_add_gpu);
 test_device!(randn, randn_cpu, randn_gpu);
+test_device!(clamp, clamp_cpu, clamp_gpu);
 
 // There was originally a bug on the CPU implementation for randn
 // https://github.com/huggingface/candle/issues/381

From cbd36157acb9852b11ce94ab15317c2a1b2bd6c4 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 09:43:52 +0200
Subject: [PATCH 083/150] Add a gif to the quantized readme. (#833)

* Add a gif to the quantized readme.

* gif update.
---
 candle-examples/examples/quantized/README.md   |   2 ++
 .../examples/quantized/assets/aoc.gif          | Bin 0 -> 121923 bytes
 2 files changed, 2 insertions(+)
 create mode 100644 candle-examples/examples/quantized/assets/aoc.gif

diff --git a/candle-examples/examples/quantized/README.md b/candle-examples/examples/quantized/README.md
index ee4f3420..bed09243 100644
--- a/candle-examples/examples/quantized/README.md
+++ b/candle-examples/examples/quantized/README.md
@@ -12,6 +12,8 @@ The weights are automatically downloaded for you from the [HuggingFace
 Hub](https://huggingface.co/) on the first run. There are various command line
 flags to use local files instead, run with `--help` to learn about them.
 
+![Axiom of Choice](./assets/aoc.gif)
+
 ## Running some example.
 
 ```bash
diff --git a/candle-examples/examples/quantized/assets/aoc.gif b/candle-examples/examples/quantized/assets/aoc.gif
new file mode 100644
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GIT binary patch
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z{z$#<=%%kkR9LdyT=NF+^L*2KoK@YE9@_^YcEhYIF1)+kIB_{|qVymJ0f)^bTViB)
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literal 0
HcmV?d00001


From e6f040d6e36b29ad8ff643981e632238c0f78298 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 10:05:47 +0200
Subject: [PATCH 084/150] Readme gallery (#834)

* More readme tweaks.

* Update README.md
---
 README.md                                  | 46 +++++++++++++---------
 candle-examples/examples/bigcode/README.md | 14 ++++++-
 candle-examples/examples/yolo-v8/README.md |  2 +-
 3 files changed, 41 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index c3e35d33..533e7492 100644
--- a/README.md
+++ b/README.md
@@ -45,40 +45,48 @@ For more advanced examples, please have a look at the following section.
 
 ## Check out our examples
 
-Check out our [examples](./candle-examples/examples/):
+These online demos run entirely in your browser:
+- [yolo](https://huggingface.co/spaces/lmz/candle-yolo): pose estimation and
+  object recognition.
+- [whisper](https://huggingface.co/spaces/lmz/candle-whisper): text to speech.
+- [LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2): text generation.
+
+We also provide a some command line based examples using state of the art models:
 
-- [Whisper](./candle-examples/examples/whisper/): speech recognition model.
 - [LLaMA and LLaMA-v2](./candle-examples/examples/llama/): general LLM.
 - [Falcon](./candle-examples/examples/falcon/): general LLM.
-- [Bert](./candle-examples/examples/bert/): useful for sentence embeddings.
 - [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code
   generation.
-- [Stable Diffusion](./candle-examples/examples/stable-diffusion/): text to
-  image generative model, support for the 1.5, 2.1, and SDXL 1.0 versions.
-- [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
-  using self-supervision (can be used for imagenet classification, depth
-  evaluation, segmentation).
 - [Quantized LLaMA](./candle-examples/examples/quantized/): quantized version of
   the LLaMA model using the same quantization techniques as
   [llama.cpp](https://github.com/ggerganov/llama.cpp).
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/quantized/assets/aoc.gif" width="600">
+  
+- [Stable Diffusion](./candle-examples/examples/stable-diffusion/): text to
+  image generative model, support for the 1.5, 2.1, and SDXL 1.0 versions.
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg" width="200">
+
 - [yolo-v3](./candle-examples/examples/yolo-v3/) and
   [yolo-v8](./candle-examples/examples/yolo-v8/): object detection and pose
   estimation models.
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.od.jpg" width="200"><img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.pose.jpg" width="200">
 - [segment-anything](./candle-examples/examples/segment-anything/): image
   segmentation model with prompt.
-Run them using the following commands:
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/sam_merged.jpg" width="200">
+
+- [Whisper](./candle-examples/examples/whisper/): speech recognition model.
+- [Bert](./candle-examples/examples/bert/): useful for sentence embeddings.
+- [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
+  using self-supervision (can be used for imagenet classification, depth
+  evaluation, segmentation).
+
+Run them using commands like:
 ```
-cargo run --example whisper --release
-cargo run --example llama --release
-cargo run --example falcon --release
-cargo run --example bert --release
-cargo run --example bigcode --release
-cargo run --example stable-diffusion --release -- --prompt "a rusty robot holding a fire torch"
-cargo run --example dinov2 --release -- --image path/to/myinput.jpg
 cargo run --example quantized --release
-cargo run --example yolo-v3 --release -- myimage.jpg
-cargo run --example yolo-v8 --release -- myimage.jpg # for pose estimation, add --task pose 
-cargo run --example segment-anything --release -- --image myimage.jpg
 ```
 
 In order to use **CUDA** add `--features cuda` to the example command line. If
diff --git a/candle-examples/examples/bigcode/README.md b/candle-examples/examples/bigcode/README.md
index 0b593674..cb4e79b1 100644
--- a/candle-examples/examples/bigcode/README.md
+++ b/candle-examples/examples/bigcode/README.md
@@ -1,7 +1,19 @@
 # candle-starcoder: code generation model
 
-StarCoder/BigCode is a LLM model specialized to code generation.
+[StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
+model specialized to code generation. The initial model was trained on 80
+programming languages.
+
+## Running some example
 
 ```bash
 cargo run --example bigcode --release -- --prompt "fn fact(n: u64) -> u64 "
+
+> fn fact(n: u64) -> u64  {
+>     if n == 0 {
+>         1
+>     } else {
+>         n * fact(n - 1)
+>     }
+> }
 ```
diff --git a/candle-examples/examples/yolo-v8/README.md b/candle-examples/examples/yolo-v8/README.md
index 8590a8a9..938dea13 100644
--- a/candle-examples/examples/yolo-v8/README.md
+++ b/candle-examples/examples/yolo-v8/README.md
@@ -13,7 +13,7 @@ Space](https://huggingface.co/spaces/lmz/candle-yolo). The model then fully runs
 in your browser using WebAssembly - if you use a custom image it will never
 leave your phone/computer!
 
-## Running some example.
+## Running some example
 
 ### Object Detection
 ```bash

From 3e9432401206953e83d53d943e0dd06ce72d4ce8 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 11:44:02 +0200
Subject: [PATCH 085/150] Add some sentence similarity part to the t5 example.
 (#835)

* Add some sentence similarity part to the t5 example.

* Clippy fix.
---
 candle-examples/examples/t5/main.rs  | 106 ++++++++++++++++++++++-----
 candle-transformers/src/models/t5.rs |   7 +-
 2 files changed, 93 insertions(+), 20 deletions(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 84be0204..03c861c1 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -13,7 +13,6 @@ use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
 use tokenizers::Tokenizer;
 
 const DTYPE: DType = DType::F32;
-const DEFAULT_PROMPT: &str = "Translate English to German: That is good.";
 
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
@@ -37,13 +36,17 @@ struct Args {
     #[arg(long)]
     revision: Option<String>,
 
-    /// Compute embeddings for this prompt or use the DEFAULT_PROMPT.
+    /// Compute embeddings for this prompt, otherwise compute sentence similarities.
     #[arg(long)]
     prompt: Option<String>,
 
     /// The number of times to run the prompt.
     #[arg(long, default_value = "1")]
     n: usize,
+
+    /// L2 normalization for embeddings.
+    #[arg(long, default_value = "true")]
+    normalize_embeddings: bool,
 }
 
 impl Args {
@@ -95,28 +98,95 @@ impl Args {
 
 fn main() -> Result<()> {
     let args = Args::parse();
-    let start = std::time::Instant::now();
-
     let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
-    let prompt = args.prompt.unwrap_or_else(|| DEFAULT_PROMPT.to_string());
     let tokenizer = tokenizer
         .with_padding(None)
         .with_truncation(None)
         .map_err(E::msg)?;
-    let tokens = tokenizer
-        .encode(prompt, true)
-        .map_err(E::msg)?
-        .get_ids()
-        .to_vec();
-    let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
-    println!("Loaded and encoded {:?}", start.elapsed());
-    for idx in 0..args.n {
-        let start = std::time::Instant::now();
-        let ys = model.forward(&token_ids)?;
-        if idx == 0 {
-            println!("{ys}");
+    match args.prompt {
+        Some(prompt) => {
+            let tokens = tokenizer
+                .encode(prompt, true)
+                .map_err(E::msg)?
+                .get_ids()
+                .to_vec();
+            let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
+            for idx in 0..args.n {
+                let start = std::time::Instant::now();
+                let ys = model.forward(&token_ids)?;
+                if idx == 0 {
+                    println!("{ys}");
+                }
+                println!("Took {:?}", start.elapsed());
+            }
+        }
+        None => {
+            let sentences = [
+                "The cat sits outside",
+                "A man is playing guitar",
+                "I love pasta",
+                "The new movie is awesome",
+                "The cat plays in the garden",
+                "A woman watches TV",
+                "The new movie is so great",
+                "Do you like pizza?",
+            ];
+            let n_sentences = sentences.len();
+            if let Some(pp) = tokenizer.get_padding_mut() {
+                pp.strategy = tokenizers::PaddingStrategy::BatchLongest
+            } else {
+                let pp = tokenizers::PaddingParams {
+                    strategy: tokenizers::PaddingStrategy::BatchLongest,
+                    ..Default::default()
+                };
+                tokenizer.with_padding(Some(pp));
+            }
+            let tokens = tokenizer
+                .encode_batch(sentences.to_vec(), true)
+                .map_err(E::msg)?;
+            let token_ids = tokens
+                .iter()
+                .map(|tokens| {
+                    let tokens = tokens.get_ids().to_vec();
+                    Ok(Tensor::new(tokens.as_slice(), model.device())?)
+                })
+                .collect::<Result<Vec<_>>>()?;
+
+            let token_ids = Tensor::stack(&token_ids, 0)?;
+            println!("running inference on batch {:?}", token_ids.shape());
+            let embeddings = model.forward(&token_ids)?;
+            println!("generated embeddings {:?}", embeddings.shape());
+            // Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
+            let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
+            let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
+            let embeddings = if args.normalize_embeddings {
+                normalize_l2(&embeddings)?
+            } else {
+                embeddings
+            };
+            println!("pooled embeddings {:?}", embeddings.shape());
+
+            let mut similarities = vec![];
+            for i in 0..n_sentences {
+                let e_i = embeddings.get(i)?;
+                for j in (i + 1)..n_sentences {
+                    let e_j = embeddings.get(j)?;
+                    let sum_ij = (&e_i * &e_j)?.sum_all()?.to_scalar::<f32>()?;
+                    let sum_i2 = (&e_i * &e_i)?.sum_all()?.to_scalar::<f32>()?;
+                    let sum_j2 = (&e_j * &e_j)?.sum_all()?.to_scalar::<f32>()?;
+                    let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
+                    similarities.push((cosine_similarity, i, j))
+                }
+            }
+            similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
+            for &(score, i, j) in similarities[..5].iter() {
+                println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
+            }
         }
-        println!("Took {:?}", start.elapsed());
     }
     Ok(())
 }
+
+pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
+    Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
+}
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 691817d1..325eb752 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -245,7 +245,10 @@ impl T5Attention {
         let scores = q.matmul(&k.t()?)?;
 
         let (scores, position_bias) = match position_bias {
-            Some(position_bias) => ((scores + position_bias)?, Some(position_bias.clone())),
+            Some(position_bias) => (
+                scores.broadcast_add(position_bias)?,
+                Some(position_bias.clone()),
+            ),
             None => match &self.relative_attention_bias {
                 None => (scores, None),
                 Some(relative_attention_bias) => {
@@ -291,7 +294,7 @@ impl T5Attention {
                         .forward(&relative_buckets)?
                         .permute((2, 0, 1))?
                         .unsqueeze(0)?;
-                    ((scores + &position_bias)?, Some(position_bias))
+                    (scores.broadcast_add(&position_bias)?, Some(position_bias))
                     // TODO: position_bias_masked?
                 }
             },

From 1c0916402179f8d5da849065219cfcf657bf2714 Mon Sep 17 00:00:00 2001
From: Charles Lew <crlf0710@gmail.com>
Date: Wed, 13 Sep 2023 18:39:22 +0800
Subject: [PATCH 086/150] Add `CANDLE_NVCC_CCBIN` support for `candle-kernels`,
 and eliminate warning. (#836)

---
 candle-core/src/cuda_backend.rs |  1 +
 candle-kernels/build.rs         | 11 +++++++++--
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 55443068..07c5dfa8 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -12,6 +12,7 @@ use half::{bf16, f16};
 use std::sync::{Arc, Mutex};
 
 const USE_IM2COL_CONV1D: bool = true;
+#[cfg(not(feature = "cudnn"))]
 const USE_IM2COL_CONV2D: bool = true;
 
 /// cudarc related errors
diff --git a/candle-kernels/build.rs b/candle-kernels/build.rs
index 3c8e96a9..ad084671 100644
--- a/candle-kernels/build.rs
+++ b/candle-kernels/build.rs
@@ -164,6 +164,8 @@ mod cuda {
 
         println!("cargo:rustc-env=CUDA_COMPUTE_CAP=sm_{compute_cap}");
 
+        let ccbin_env = std::env::var("CANDLE_NVCC_CCBIN");
+        println!("cargo:rerun-if-env-changed=CANDLE_NVCC_CCBIN");
         let children = kernel_paths
                 .par_iter()
                 .flat_map(|p| {
@@ -188,8 +190,13 @@ mod cuda {
                             .args(["--output-directory", &out_dir])
                             // Flash attention only
                             // .arg("--expt-relaxed-constexpr")
-                            .args(&include_options)
-                            .arg(p);
+                            .args(&include_options);
+                        if let Ok(ccbin_path) = &ccbin_env {
+                            command
+                                .arg("-allow-unsupported-compiler")
+                                .args(["-ccbin", ccbin_path]);
+                        }
+                        command.arg(p);
                         Some((p,  command.spawn()
                         .expect("nvcc failed to start. Ensure that you have CUDA installed and that `nvcc` is in your PATH.").wait_with_output()))
                     }})

From b11a2a7b9db802d7b7e833cf25c5b1d86d70937c Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 12:56:53 +0200
Subject: [PATCH 087/150] Move the constant to avoid some unused warning.
 (#837)

---
 candle-core/src/cuda_backend.rs | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 07c5dfa8..4ec41b87 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -11,10 +11,6 @@ use cudarc::driver::{
 use half::{bf16, f16};
 use std::sync::{Arc, Mutex};
 
-const USE_IM2COL_CONV1D: bool = true;
-#[cfg(not(feature = "cudnn"))]
-const USE_IM2COL_CONV2D: bool = true;
-
 /// cudarc related errors
 #[derive(thiserror::Error, Debug)]
 pub enum CudaError {
@@ -1760,6 +1756,8 @@ impl BackendStorage for CudaStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv1D,
     ) -> Result<Self> {
+        const USE_IM2COL_CONV1D: bool = true;
+
         let device = self.device().clone();
         if !USE_IM2COL_CONV1D {
             let slice = Conv1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
@@ -1808,6 +1806,8 @@ impl BackendStorage for CudaStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv2D,
     ) -> Result<Self> {
+        const USE_IM2COL_CONV2D: bool = true;
+
         let device = self.device().clone();
         if !USE_IM2COL_CONV2D {
             let slice = Conv2D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;

From 31ab2ddaebe632f06f647c470259364eb53ca9e8 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 14:00:59 +0200
Subject: [PATCH 088/150] Remove the padding. (#838)

---
 candle-examples/examples/t5/main.rs | 67 +++++++++++++----------------
 1 file changed, 29 insertions(+), 38 deletions(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 03c861c1..1e182974 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -132,48 +132,39 @@ fn main() -> Result<()> {
                 "Do you like pizza?",
             ];
             let n_sentences = sentences.len();
-            if let Some(pp) = tokenizer.get_padding_mut() {
-                pp.strategy = tokenizers::PaddingStrategy::BatchLongest
-            } else {
-                let pp = tokenizers::PaddingParams {
-                    strategy: tokenizers::PaddingStrategy::BatchLongest,
-                    ..Default::default()
+            let mut all_embeddings = Vec::with_capacity(n_sentences);
+            for sentence in sentences {
+                let tokens = tokenizer
+                    .encode(sentence, true)
+                    .map_err(E::msg)?
+                    .get_ids()
+                    .to_vec();
+                let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
+                let embeddings = model.forward(&token_ids)?;
+                println!("generated embeddings {:?}", embeddings.shape());
+                // Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
+                let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
+                let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
+                let embeddings = if args.normalize_embeddings {
+                    normalize_l2(&embeddings)?
+                } else {
+                    embeddings
                 };
-                tokenizer.with_padding(Some(pp));
+                println!("pooled embeddings {:?}", embeddings.shape());
+                all_embeddings.push(embeddings)
             }
-            let tokens = tokenizer
-                .encode_batch(sentences.to_vec(), true)
-                .map_err(E::msg)?;
-            let token_ids = tokens
-                .iter()
-                .map(|tokens| {
-                    let tokens = tokens.get_ids().to_vec();
-                    Ok(Tensor::new(tokens.as_slice(), model.device())?)
-                })
-                .collect::<Result<Vec<_>>>()?;
-
-            let token_ids = Tensor::stack(&token_ids, 0)?;
-            println!("running inference on batch {:?}", token_ids.shape());
-            let embeddings = model.forward(&token_ids)?;
-            println!("generated embeddings {:?}", embeddings.shape());
-            // Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
-            let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
-            let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
-            let embeddings = if args.normalize_embeddings {
-                normalize_l2(&embeddings)?
-            } else {
-                embeddings
-            };
-            println!("pooled embeddings {:?}", embeddings.shape());
 
             let mut similarities = vec![];
-            for i in 0..n_sentences {
-                let e_i = embeddings.get(i)?;
-                for j in (i + 1)..n_sentences {
-                    let e_j = embeddings.get(j)?;
-                    let sum_ij = (&e_i * &e_j)?.sum_all()?.to_scalar::<f32>()?;
-                    let sum_i2 = (&e_i * &e_i)?.sum_all()?.to_scalar::<f32>()?;
-                    let sum_j2 = (&e_j * &e_j)?.sum_all()?.to_scalar::<f32>()?;
+            for (i, e_i) in all_embeddings.iter().enumerate() {
+                for (j, e_j) in all_embeddings
+                    .iter()
+                    .enumerate()
+                    .take(n_sentences)
+                    .skip(i + 1)
+                {
+                    let sum_ij = (e_i * e_j)?.sum_all()?.to_scalar::<f32>()?;
+                    let sum_i2 = (e_i * e_i)?.sum_all()?.to_scalar::<f32>()?;
+                    let sum_j2 = (e_j * e_j)?.sum_all()?.to_scalar::<f32>()?;
                     let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
                     similarities.push((cosine_similarity, i, j))
                 }

From 9a465e1b2601195b65f1422f16793d6825252231 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 13 Sep 2023 17:50:39 +0200
Subject: [PATCH 089/150] Add 1d upsampling. (#839)

* Add 1d upsampling.

* Add the interpolate functions.
---
 candle-core/src/backend.rs            |  1 +
 candle-core/src/backprop.rs           |  4 ++++
 candle-core/src/cpu_backend.rs        | 34 +++++++++++++++++++++++++++
 candle-core/src/cuda_backend.rs       |  4 ++++
 candle-core/src/dummy_cuda_backend.rs |  4 ++++
 candle-core/src/op.rs                 |  1 +
 candle-core/src/storage.rs            | 13 ++++++++++
 candle-core/src/tensor.rs             | 27 +++++++++++++++++++--
 8 files changed, 86 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/backend.rs b/candle-core/src/backend.rs
index 67a08714..03a07434 100644
--- a/candle-core/src/backend.rs
+++ b/candle-core/src/backend.rs
@@ -57,6 +57,7 @@ pub trait BackendStorage: Sized {
 
     fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
     fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
+    fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self>;
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self>;
 
     fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self>;
diff --git a/candle-core/src/backprop.rs b/candle-core/src/backprop.rs
index d2099df7..b930a9f4 100644
--- a/candle-core/src/backprop.rs
+++ b/candle-core/src/backprop.rs
@@ -91,6 +91,7 @@ impl Tensor {
                         }
                     }
                     Op::Reshape(node)
+                    | Op::UpsampleNearest1D(node)
                     | Op::UpsampleNearest2D(node)
                     | Op::AvgPool2D { arg: node, .. }
                     | Op::MaxPool2D { arg: node, .. }
@@ -262,6 +263,9 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
                     }
+                    Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
+                        op: "upsample-nearest1d",
+                    })?,
                     Op::UpsampleNearest2D { .. } => Err(Error::BackwardNotSupported {
                         op: "upsample-nearest2d",
                     })?,
diff --git a/candle-core/src/cpu_backend.rs b/candle-core/src/cpu_backend.rs
index 544ce32d..4e808b34 100644
--- a/candle-core/src/cpu_backend.rs
+++ b/candle-core/src/cpu_backend.rs
@@ -727,6 +727,36 @@ impl Map1 for MaxPool2D {
     }
 }
 
+struct UpsampleNearest1D(usize);
+
+impl Map1 for UpsampleNearest1D {
+    fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
+        // TODO: Specialized implementation for the case 2*sz?
+        let dst_sz = self.0;
+        let (b_sz, c, src_sz) = layout.shape().dims3()?;
+        let stride = layout.stride();
+        let stride_sz = stride[2];
+        let src_index = layout.start_offset();
+        let scale_sz = src_sz as f64 / dst_sz as f64;
+        let mut dst = vec![T::zero(); b_sz * c * dst_sz];
+        let src_idxs = (0..dst_sz)
+            .map(|idx| usize::min(src_sz - 1, (idx as f64 * scale_sz) as usize))
+            .collect::<Vec<_>>();
+        for b_idx in 0..b_sz {
+            let dst = &mut dst[b_idx * c * dst_sz..];
+            let src_index = src_index + b_idx * stride[0];
+            for c_idx in 0..c {
+                let dst = &mut dst[c_idx * dst_sz..];
+                let src_index = src_index + c_idx * stride[1];
+                for (idx, src_idx) in src_idxs.iter().enumerate() {
+                    dst[idx] = src[src_index + src_idx * stride_sz]
+                }
+            }
+        }
+        Ok(dst)
+    }
+}
+
 struct UpsampleNearest2D(usize, usize);
 
 impl Map1 for UpsampleNearest2D {
@@ -2137,6 +2167,10 @@ impl BackendStorage for CpuStorage {
         MaxPool2D(kernel_size, stride).map(self, layout)
     }
 
+    fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
+        UpsampleNearest1D(sz).map(self, layout)
+    }
+
     fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
         UpsampleNearest2D(h, w).map(self, layout)
     }
diff --git a/candle-core/src/cuda_backend.rs b/candle-core/src/cuda_backend.rs
index 4ec41b87..00fd1d04 100644
--- a/candle-core/src/cuda_backend.rs
+++ b/candle-core/src/cuda_backend.rs
@@ -1954,6 +1954,10 @@ impl BackendStorage for CudaStorage {
         Ok(Self { slice, device })
     }
 
+    fn upsample_nearest1d(&self, _: &Layout, _out_sz: usize) -> Result<Self> {
+        crate::bail!("upsample-nearest1d is not supported on cuda")
+    }
+
     fn upsample_nearest2d(&self, l: &Layout, out_w: usize, out_h: usize) -> Result<Self> {
         let device = self.device().clone();
         let slice = UpsampleNearest2D(out_w, out_h).map(&self.slice, &device, l)?;
diff --git a/candle-core/src/dummy_cuda_backend.rs b/candle-core/src/dummy_cuda_backend.rs
index 6c896653..5cc9c6d8 100644
--- a/candle-core/src/dummy_cuda_backend.rs
+++ b/candle-core/src/dummy_cuda_backend.rs
@@ -152,6 +152,10 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
+    fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithCudaSupport)
+    }
+
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {
         Err(Error::NotCompiledWithCudaSupport)
     }
diff --git a/candle-core/src/op.rs b/candle-core/src/op.rs
index 9382b217..7940739c 100644
--- a/candle-core/src/op.rs
+++ b/candle-core/src/op.rs
@@ -116,6 +116,7 @@ pub enum Op {
         stride: (usize, usize),
     },
 
+    UpsampleNearest1D(Tensor),
     UpsampleNearest2D(Tensor),
 
     Cat(Vec<Tensor>, usize),
diff --git a/candle-core/src/storage.rs b/candle-core/src/storage.rs
index 8bd14ea9..9bd1fed6 100644
--- a/candle-core/src/storage.rs
+++ b/candle-core/src/storage.rs
@@ -369,6 +369,19 @@ impl Storage {
         }
     }
 
+    pub(crate) fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
+        match self {
+            Storage::Cpu(storage) => {
+                let storage = storage.upsample_nearest1d(layout, sz)?;
+                Ok(Self::Cpu(storage))
+            }
+            Self::Cuda(storage) => {
+                let storage = storage.upsample_nearest1d(layout, sz)?;
+                Ok(Self::Cuda(storage))
+            }
+        }
+    }
+
     pub(crate) fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
         match self {
             Storage::Cpu(storage) => {
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 59a23c39..4388bf77 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -854,12 +854,30 @@ impl Tensor {
         self.maximum(min)?.minimum(max)
     }
 
-    /// Upsample the input tensor to the `(target_h, target_w)` size, taking the value of the
+    /// Interpolate the input tensor to the `target_size` size, taking the value of the nearest element.
+    ///
+    /// The input tensor should have three dimensions, `(batch, channels, l)`, the returned
+    /// tensor also has three dimensions, `(batch, channels, target_size)`.
+    pub fn interpolate1d(&self, target_size: usize) -> Result<Self> {
+        let (n, c, _l) = self.dims3()?;
+        let op = BackpropOp::new1(self, Op::UpsampleNearest1D);
+        let storage = self
+            .storage()
+            .upsample_nearest1d(self.layout(), target_size)?;
+        Ok(from_storage(storage, (n, c, target_size), op, false))
+    }
+
+    /// Alias for `interpolate1d`.
+    pub fn upsample_nearest1d(&self, target_size: usize) -> Result<Self> {
+        self.interpolate1d(target_size)
+    }
+
+    /// Interpolate the input tensor to the `(target_h, target_w)` size, taking the value of the
     /// nearest element.
     ///
     /// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned
     /// tensor also has four dimensions, `(batch, channels, target_h, target_w)`.
-    pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
+    pub fn interpolate2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
         let (n, c, _h, _w) = self.dims4()?;
         let op = BackpropOp::new1(self, Op::UpsampleNearest2D);
         let storage = self
@@ -868,6 +886,11 @@ impl Tensor {
         Ok(from_storage(storage, (n, c, target_h, target_w), op, false))
     }
 
+    /// Alias for `interpolate2d`.
+    pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
+        self.interpolate2d(target_h, target_w)
+    }
+
     /// 2D average pooling over an input tensor with multiple channels.
     ///
     /// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned

From 49d3f7f70814bd0e8b569f93bb76419306359251 Mon Sep 17 00:00:00 2001
From: Juarez Bochi <juarez.bochi@grammarly.com>
Date: Wed, 13 Sep 2023 10:27:20 -0700
Subject: [PATCH 090/150] Add support to flan-t5 (#840)

---
 candle-nn/src/activation.rs          |  6 ++++
 candle-transformers/src/models/t5.rs | 54 +++++++++++++++++++++++++---
 2 files changed, 55 insertions(+), 5 deletions(-)

diff --git a/candle-nn/src/activation.rs b/candle-nn/src/activation.rs
index 6e442458..22e062b0 100644
--- a/candle-nn/src/activation.rs
+++ b/candle-nn/src/activation.rs
@@ -6,6 +6,8 @@ use serde::Deserialize;
 pub enum Activation {
     #[default]
     Gelu,
+    #[serde(rename = "gated-gelu")]
+    NewGelu,
     Relu,
     Elu(f64),
 }
@@ -14,6 +16,10 @@ impl super::Module for Activation {
     fn forward(&self, xs: &Tensor) -> candle::Result<Tensor> {
         match self {
             Self::Gelu => xs.gelu(),
+            // TODO: This is "gelu_new", not the original "gelu".
+            // There's some small numerical difference:
+            // https://github.com/huggingface/transformers/blob/12f043eaeaabfef6f6efea411d98e6f6d3c094b7/src/transformers/activations.py#L49-L78
+            Self::NewGelu => xs.gelu(),
             Self::Relu => xs.relu(),
             &Self::Elu(alpha) => xs.elu(alpha),
         }
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 325eb752..de7de496 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -148,27 +148,71 @@ impl T5DenseActDense {
     }
 }
 
+#[derive(Debug)]
+struct T5DenseGatedActDense {
+    wi_0: Linear,
+    wi_1: Linear,
+    wo: Linear,
+    act: Activation,
+}
+
+impl T5DenseGatedActDense {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let wi_0 = linear_no_bias(cfg.d_model, cfg.d_ff, vb.pp("wi_0"))?;
+        let wi_1 = linear_no_bias(cfg.d_model, cfg.d_ff, vb.pp("wi_1"))?;
+        let wo = linear_no_bias(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
+        Ok(Self {
+            wi_0,
+            wi_1,
+            wo,
+            act: Activation::NewGelu,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let hidden_gelu = self.act.forward(&self.wi_0.forward(xs)?)?;
+        let hidden_linear = self.wi_1.forward(xs)?;
+        let xs = hidden_gelu.broadcast_mul(&hidden_linear)?;
+        let xs = self.wo.forward(&xs)?;
+        Ok(xs)
+    }
+}
+
 #[derive(Debug)]
 struct T5LayerFF {
-    dense_relu_dense: T5DenseActDense,
+    dense_act: Option<T5DenseActDense>,
+    gated_dense_act: Option<T5DenseGatedActDense>,
     layer_norm: T5LayerNorm,
 }
 
 impl T5LayerFF {
     fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        // is_gated_act is not supported.
-        let dense_relu_dense = T5DenseActDense::load(vb.pp("DenseReluDense"), cfg)?;
         let layer_norm =
             T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
+        let (dense_act, gated_dense_act) = if cfg.feed_forward_proj == Activation::NewGelu {
+            (
+                None,
+                Some(T5DenseGatedActDense::load(vb.pp("DenseReluDense"), cfg)?),
+            )
+        } else {
+            (
+                Some(T5DenseActDense::load(vb.pp("DenseReluDense"), cfg)?),
+                None,
+            )
+        };
         Ok(Self {
-            dense_relu_dense,
+            dense_act,
+            gated_dense_act,
             layer_norm,
         })
     }
 
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let ys = self.layer_norm.forward(xs)?;
-        let ys = self.dense_relu_dense.forward(&ys)?;
+        let ys = match &self.dense_act {
+            Some(dense_act) => dense_act.forward(&ys)?,
+            None => self.gated_dense_act.as_ref().unwrap().forward(&ys)?,
+        };
         let xs = (xs + ys)?;
         Ok(xs)
     }

From d6447ad635bc450ef1f15ca7a4424c0f86e7a90a Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 14 Sep 2023 08:47:07 +0200
Subject: [PATCH 091/150] Tensor based indexing. (#842)

---
 candle-core/src/indexer.rs | 39 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 38 insertions(+), 1 deletion(-)

diff --git a/candle-core/src/indexer.rs b/candle-core/src/indexer.rs
index 2b6d694b..7b84d316 100644
--- a/candle-core/src/indexer.rs
+++ b/candle-core/src/indexer.rs
@@ -46,19 +46,31 @@ impl Tensor {
                     current_dim += 1;
                     out
                 }
+                TensorIndexer::IndexSelect(indexes) => {
+                    if indexes.rank() != 1 {
+                        crate::bail!("multi-dimensional tensor indexing is not supported")
+                    }
+                    let out = x.index_select(&indexes.to_device(x.device())?, current_dim)?;
+                    current_dim += 1;
+                    out
+                }
+                TensorIndexer::Err(e) => crate::bail!("indexing error {e:?}"),
             };
         }
         Ok(x)
     }
 }
 
-#[derive(Debug, Clone)]
+#[derive(Debug)]
 /// Generic structure used to index a slice of the tensor
 pub enum TensorIndexer {
     /// This selects the elemnts for which an index has some specific value.
     Select(usize),
     /// This is a regular slice, purely indexing a chunk of the tensor
     Narrow(Bound<usize>, Bound<usize>),
+    /// Indexing via a 1d tensor
+    IndexSelect(Tensor),
+    Err(Error),
 }
 
 impl From<usize> for TensorIndexer {
@@ -67,6 +79,31 @@ impl From<usize> for TensorIndexer {
     }
 }
 
+impl From<&[u32]> for TensorIndexer {
+    fn from(index: &[u32]) -> Self {
+        match Tensor::new(index, &crate::Device::Cpu) {
+            Ok(tensor) => TensorIndexer::IndexSelect(tensor),
+            Err(e) => TensorIndexer::Err(e),
+        }
+    }
+}
+
+impl From<Vec<u32>> for TensorIndexer {
+    fn from(index: Vec<u32>) -> Self {
+        let len = index.len();
+        match Tensor::from_vec(index, len, &crate::Device::Cpu) {
+            Ok(tensor) => TensorIndexer::IndexSelect(tensor),
+            Err(e) => TensorIndexer::Err(e),
+        }
+    }
+}
+
+impl From<&Tensor> for TensorIndexer {
+    fn from(tensor: &Tensor) -> Self {
+        TensorIndexer::IndexSelect(tensor.clone())
+    }
+}
+
 macro_rules! impl_from_range {
     ($range_type:ty) => {
         impl From<$range_type> for TensorIndexer {

From 286f01db1471a48b6050d9eddb6cf4f28ed7d8cb Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 14 Sep 2023 11:56:07 +0200
Subject: [PATCH 092/150] Start adding the Wuerstchen diffusion pipeline (#843)

* Wuerstchen common bits.

* Add the prior layer.

* Start adding diffnext.
---
 candle-transformers/src/models/mod.rs         |   1 +
 .../src/models/wuerstchen/common.rs           | 126 ++++++++++++++++++
 .../src/models/wuerstchen/diffnext.rs         |  55 ++++++++
 .../src/models/wuerstchen/mod.rs              |   3 +
 .../src/models/wuerstchen/prior.rs            |  94 +++++++++++++
 5 files changed, 279 insertions(+)
 create mode 100644 candle-transformers/src/models/wuerstchen/common.rs
 create mode 100644 candle-transformers/src/models/wuerstchen/diffnext.rs
 create mode 100644 candle-transformers/src/models/wuerstchen/mod.rs
 create mode 100644 candle-transformers/src/models/wuerstchen/prior.rs

diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index e2e0bf81..a20254d9 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -9,3 +9,4 @@ pub mod segment_anything;
 pub mod stable_diffusion;
 pub mod t5;
 pub mod whisper;
+pub mod wuerstchen;
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
new file mode 100644
index 00000000..fc731a59
--- /dev/null
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -0,0 +1,126 @@
+use candle::{Module, Result, Tensor, D};
+use candle_nn::VarBuilder;
+
+// https://github.com/huggingface/diffusers/blob/19edca82f1ff194c07317369a92b470dbae97f34/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py#L22
+#[derive(Debug)]
+pub struct WLayerNorm {
+    inner: candle_nn::LayerNorm,
+}
+
+impl WLayerNorm {
+    pub fn new(size: usize, vb: VarBuilder) -> Result<Self> {
+        let cfg = candle_nn::layer_norm::LayerNormConfig {
+            eps: 1e-6,
+            remove_mean: true,
+            affine: false,
+        };
+        let inner = candle_nn::layer_norm(size, cfg, vb)?;
+        Ok(Self { inner })
+    }
+}
+
+impl Module for WLayerNorm {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.permute((0, 2, 3, 1))?
+            .apply(&self.inner)?
+            .permute((0, 3, 1, 2))
+    }
+}
+
+#[derive(Debug)]
+pub struct TimestepBlock {
+    mapper: candle_nn::Linear,
+}
+
+impl TimestepBlock {
+    pub fn new(c: usize, c_timestep: usize, vb: VarBuilder) -> Result<Self> {
+        let mapper = candle_nn::linear(c_timestep, c * 2, vb.pp("mapper"))?;
+        Ok(Self { mapper })
+    }
+
+    pub fn forward(&self, xs: &Tensor, t: &Tensor) -> Result<Tensor> {
+        let ab = self
+            .mapper
+            .forward(t)?
+            .unsqueeze(2)?
+            .unsqueeze(3)?
+            .chunk(2, 1)?;
+        xs.broadcast_mul(&(&ab[0] + 1.)?)?.broadcast_add(&ab[1])
+    }
+}
+
+#[derive(Debug)]
+pub struct GlobalResponseNorm {
+    gamma: Tensor,
+    beta: Tensor,
+}
+
+impl GlobalResponseNorm {
+    pub fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
+        let gamma = vb.get((1, 1, 1, 1, dim), "gamma")?;
+        let beta = vb.get((1, 1, 1, 1, dim), "beta")?;
+        Ok(Self { gamma, beta })
+    }
+}
+
+impl Module for GlobalResponseNorm {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let agg_norm = xs.sqr()?.sum_keepdim((1, 2))?;
+        let stand_div_norm =
+            agg_norm.broadcast_div(&(agg_norm.mean_keepdim(D::Minus1)? + 1e-6)?)?;
+        (xs.broadcast_mul(&stand_div_norm)?
+            .broadcast_mul(&self.gamma)
+            + &self.beta)?
+            + xs
+    }
+}
+
+#[derive(Debug)]
+pub struct ResBlock {
+    depthwise: candle_nn::Conv2d,
+    norm: WLayerNorm,
+    channelwise_lin1: candle_nn::Linear,
+    channelwise_grn: GlobalResponseNorm,
+    channelwise_lin2: candle_nn::Linear,
+}
+
+impl ResBlock {
+    pub fn new(c: usize, c_skip: usize, ksize: usize, vb: VarBuilder) -> Result<Self> {
+        let cfg = candle_nn::Conv2dConfig {
+            padding: ksize / 2,
+            groups: c,
+            ..Default::default()
+        };
+        let depthwise = candle_nn::conv2d(c + c_skip, c, ksize, cfg, vb.pp("depthwise"))?;
+        let norm = WLayerNorm::new(c, vb.pp("norm"))?;
+        let channelwise_lin1 = candle_nn::linear(c, c * 4, vb.pp("channelwise.0"))?;
+        let channelwise_grn = GlobalResponseNorm::new(c * 4, vb.pp("channelwise.2"))?;
+        let channelwise_lin2 = candle_nn::linear(c * 4, c, vb.pp("channelwise.4"))?;
+        Ok(Self {
+            depthwise,
+            norm,
+            channelwise_lin1,
+            channelwise_grn,
+            channelwise_lin2,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, x_skip: Option<&Tensor>) -> Result<Tensor> {
+        let x_res = xs;
+        let xs = match x_skip {
+            None => xs.clone(),
+            Some(x_skip) => Tensor::cat(&[xs, x_skip], 1)?,
+        };
+        let xs = xs
+            .apply(&self.depthwise)?
+            .apply(&self.norm)?
+            .permute((0, 2, 3, 1))?;
+        let xs = xs
+            .apply(&self.channelwise_lin1)?
+            .gelu()?
+            .apply(&self.channelwise_grn)?
+            .apply(&self.channelwise_lin2)?
+            .permute((0, 3, 1, 2))?;
+        xs + x_res
+    }
+}
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
new file mode 100644
index 00000000..82c973a1
--- /dev/null
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -0,0 +1,55 @@
+#![allow(unused)]
+use super::common::{GlobalResponseNorm, ResBlock, TimestepBlock, WLayerNorm};
+use candle::{DType, Module, Result, Tensor, D};
+use candle_nn::VarBuilder;
+
+#[derive(Debug)]
+pub struct ResBlockStageB {
+    depthwise: candle_nn::Conv2d,
+    norm: WLayerNorm,
+    channelwise_lin1: candle_nn::Linear,
+    channelwise_grn: GlobalResponseNorm,
+    channelwise_lin2: candle_nn::Linear,
+}
+
+impl ResBlockStageB {
+    pub fn new(c: usize, c_skip: usize, ksize: usize, vb: VarBuilder) -> Result<Self> {
+        let cfg = candle_nn::Conv2dConfig {
+            groups: c,
+            padding: ksize / 2,
+            ..Default::default()
+        };
+        let depthwise = candle_nn::conv2d(c, c, ksize, cfg, vb.pp("depthwise"))?;
+        let norm = WLayerNorm::new(c, vb.pp("norm"))?;
+        let channelwise_lin1 = candle_nn::linear(c + c_skip, c * 4, vb.pp("channelwise.0"))?;
+        let channelwise_grn = GlobalResponseNorm::new(4 * c, vb.pp("channelwise.2"))?;
+        let channelwise_lin2 = candle_nn::linear(c * 4, c, vb.pp("channelwise.4"))?;
+        Ok(Self {
+            depthwise,
+            norm,
+            channelwise_lin1,
+            channelwise_grn,
+            channelwise_lin2,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, x_skip: Option<&Tensor>) -> Result<Tensor> {
+        let x_res = xs;
+        let xs = xs.apply(&self.depthwise)?.apply(&self.norm)?;
+        let xs = match x_skip {
+            None => xs.clone(),
+            Some(x_skip) => Tensor::cat(&[&xs, x_skip], 1)?,
+        };
+        let xs = xs
+            .permute((0, 2, 3, 1))?
+            .apply(&self.channelwise_lin1)?
+            .gelu()?
+            .apply(&self.channelwise_grn)?
+            .apply(&self.channelwise_lin2)?
+            .permute((0, 3, 1, 2))?;
+        xs + x_res
+    }
+}
+
+#[derive(Debug)]
+pub struct WDiffNeXt {}
diff --git a/candle-transformers/src/models/wuerstchen/mod.rs b/candle-transformers/src/models/wuerstchen/mod.rs
new file mode 100644
index 00000000..81755dd1
--- /dev/null
+++ b/candle-transformers/src/models/wuerstchen/mod.rs
@@ -0,0 +1,3 @@
+pub mod common;
+pub mod diffnext;
+pub mod prior;
diff --git a/candle-transformers/src/models/wuerstchen/prior.rs b/candle-transformers/src/models/wuerstchen/prior.rs
new file mode 100644
index 00000000..a4e0300c
--- /dev/null
+++ b/candle-transformers/src/models/wuerstchen/prior.rs
@@ -0,0 +1,94 @@
+#![allow(unused)]
+use super::common::{ResBlock, TimestepBlock};
+use candle::{DType, Module, Result, Tensor, D};
+use candle_nn::VarBuilder;
+
+#[derive(Debug)]
+struct Block {
+    res_block: ResBlock,
+    ts_block: TimestepBlock,
+    // TODO: attn_block: super::common::AttnBlock,
+}
+
+#[derive(Debug)]
+pub struct WPrior {
+    projection: candle_nn::Conv2d,
+    cond_mapper_lin1: candle_nn::Linear,
+    cond_mapper_lin2: candle_nn::Linear,
+    blocks: Vec<Block>,
+    out_ln: super::common::WLayerNorm,
+    out_conv: candle_nn::Conv2d,
+    c_r: usize,
+}
+
+impl WPrior {
+    pub fn new(
+        c_in: usize,
+        c: usize,
+        c_cond: usize,
+        c_r: usize,
+        depth: usize,
+        _nhead: usize,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let projection = candle_nn::conv2d(c_in, c, 1, Default::default(), vb.pp("projection"))?;
+        let cond_mapper_lin1 = candle_nn::linear(c_cond, c, vb.pp("cond_mapper.0"))?;
+        let cond_mapper_lin2 = candle_nn::linear(c, c, vb.pp("cond_mapper.2"))?;
+        let out_ln = super::common::WLayerNorm::new(c, vb.pp("out.0"))?;
+        let out_conv = candle_nn::conv2d(c, c_in * 2, 1, Default::default(), vb.pp("out.1"))?;
+        let mut blocks = Vec::with_capacity(depth);
+        for index in 0..depth {
+            let res_block = ResBlock::new(c, 0, 3, vb.pp(format!("blocks.{}", 3 * index)))?;
+            let ts_block = TimestepBlock::new(c, c_r, vb.pp(format!("blocks.{}", 3 * index + 1)))?;
+            blocks.push(Block {
+                res_block,
+                ts_block,
+            })
+        }
+        Ok(Self {
+            projection,
+            cond_mapper_lin1,
+            cond_mapper_lin2,
+            blocks,
+            out_ln,
+            out_conv,
+            c_r,
+        })
+    }
+
+    pub fn gen_r_embedding(&self, r: &Tensor) -> Result<Tensor> {
+        const MAX_POSITIONS: usize = 10000;
+        let r = (r * MAX_POSITIONS as f64)?;
+        let half_dim = self.c_r / 2;
+        let emb = (MAX_POSITIONS as f64).ln() / (half_dim - 1) as f64;
+        let emb = (Tensor::arange(0u32, half_dim as u32, r.device())?.to_dtype(DType::F32)?
+            * -emb)?
+            .exp()?;
+        let emb = r.unsqueeze(1)?.broadcast_mul(&emb.unsqueeze(0)?)?;
+        let emb = Tensor::cat(&[emb.sin()?, emb.cos()?], 1)?;
+        let emb = if self.c_r % 2 == 1 {
+            emb.pad_with_zeros(D::Minus1, 0, 1)?
+        } else {
+            emb
+        };
+        emb.to_dtype(r.dtype())
+    }
+
+    pub fn forward(&self, xs: &Tensor, r: &Tensor, c: &Tensor) -> Result<Tensor> {
+        let x_in = xs;
+        let mut xs = xs.apply(&self.projection)?;
+        // TODO: leaky relu
+        let c_embed = c
+            .apply(&self.cond_mapper_lin1)?
+            .relu()?
+            .apply(&self.cond_mapper_lin2)?;
+        let r_embed = self.gen_r_embedding(r)?;
+        for block in self.blocks.iter() {
+            xs = block.res_block.forward(&xs, None)?;
+            xs = block.ts_block.forward(&xs, &r_embed)?;
+            // TODO: attn
+        }
+        let ab = xs.apply(&self.out_ln)?.apply(&self.out_conv)?.chunk(1, 2)?;
+        (x_in - &ab[0])? / ((&ab[1] - 1.)?.abs()? + 1e-5)
+    }
+}

From a0c6d5548cec20bc0313b1dc3597c1c94abd0896 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 14 Sep 2023 16:40:09 +0200
Subject: [PATCH 093/150] Add the attention block. (#846)

* Add the attention block.

* Add more to clipnext.
---
 .../src/models/stable_diffusion/attention.rs  |  6 +--
 .../src/models/wuerstchen/common.rs           | 41 ++++++++++++++++++
 .../src/models/wuerstchen/diffnext.rs         | 43 ++++++++++++++++++-
 .../src/models/wuerstchen/prior.rs            | 16 +++++--
 4 files changed, 98 insertions(+), 8 deletions(-)

diff --git a/candle-transformers/src/models/stable_diffusion/attention.rs b/candle-transformers/src/models/stable_diffusion/attention.rs
index 2b925cee..b3ea91f9 100644
--- a/candle-transformers/src/models/stable_diffusion/attention.rs
+++ b/candle-transformers/src/models/stable_diffusion/attention.rs
@@ -78,7 +78,7 @@ fn flash_attn(_: &Tensor, _: &Tensor, _: &Tensor, _: f32, _: bool) -> Result<Ten
 }
 
 #[derive(Debug)]
-struct CrossAttention {
+pub struct CrossAttention {
     to_q: nn::Linear,
     to_k: nn::Linear,
     to_v: nn::Linear,
@@ -94,7 +94,7 @@ struct CrossAttention {
 
 impl CrossAttention {
     // Defaults should be heads = 8, dim_head = 64, context_dim = None
-    fn new(
+    pub fn new(
         vs: nn::VarBuilder,
         query_dim: usize,
         context_dim: Option<usize>,
@@ -205,7 +205,7 @@ impl CrossAttention {
         self.reshape_batch_dim_to_heads(&xs)
     }
 
-    fn forward(&self, xs: &Tensor, context: Option<&Tensor>) -> Result<Tensor> {
+    pub fn forward(&self, xs: &Tensor, context: Option<&Tensor>) -> Result<Tensor> {
         let _enter = self.span.enter();
         let query = self.to_q.forward(xs)?;
         let context = context.unwrap_or(xs).contiguous()?;
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
index fc731a59..10e7b19f 100644
--- a/candle-transformers/src/models/wuerstchen/common.rs
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -124,3 +124,44 @@ impl ResBlock {
         xs + x_res
     }
 }
+use crate::models::stable_diffusion::attention::CrossAttention as Attention;
+#[derive(Debug)]
+pub struct AttnBlock {
+    self_attn: bool,
+    norm: WLayerNorm,
+    attention: Attention,
+    kv_mapper_lin: candle_nn::Linear,
+}
+
+impl AttnBlock {
+    pub fn new(
+        c: usize,
+        c_cond: usize,
+        nhead: usize,
+        self_attn: bool,
+        vb: VarBuilder,
+    ) -> Result<Self> {
+        let norm = WLayerNorm::new(c, vb.pp("norm"))?;
+        let attention = Attention::new(vb.pp("attention"), c, None, nhead, c / nhead, None, false)?;
+        let kv_mapper_lin = candle_nn::linear(c_cond, c, vb.pp("kv_mapper.1"))?;
+        Ok(Self {
+            self_attn,
+            norm,
+            attention,
+            kv_mapper_lin,
+        })
+    }
+
+    pub fn forward(&self, xs: &Tensor, kv: &Tensor) -> Result<Tensor> {
+        let kv = candle_nn::ops::silu(kv)?.apply(&self.kv_mapper_lin)?;
+        let norm_xs = self.norm.forward(xs)?;
+        let kv = if self.self_attn {
+            let (b_size, channel, _, _) = xs.dims4()?;
+            let norm_xs = norm_xs.reshape((b_size, channel, ()))?.transpose(1, 2)?;
+            Tensor::cat(&[&norm_xs, &kv], 1)?
+        } else {
+            kv
+        };
+        xs + self.attention.forward(&norm_xs, Some(&kv))
+    }
+}
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 82c973a1..8e5099f6 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -52,4 +52,45 @@ impl ResBlockStageB {
 }
 
 #[derive(Debug)]
-pub struct WDiffNeXt {}
+pub struct WDiffNeXt {
+    clip_mapper: candle_nn::Linear,
+    effnet_mappers: Vec<candle_nn::Conv2d>,
+    seq_norm: candle_nn::LayerNorm,
+    embedding_conv: candle_nn::Conv2d,
+    embedding_ln: WLayerNorm,
+}
+
+impl WDiffNeXt {
+    pub fn new(
+        c_in: usize,
+        c_out: usize,
+        vb: VarBuilder,
+        c_cond: usize,
+        clip_embd: usize,
+        patch_size: usize,
+    ) -> Result<Self> {
+        const C_HIDDEN: [usize; 4] = [320, 640, 1280, 1280];
+
+        let clip_mapper = candle_nn::linear(clip_embd, c_cond, vb.pp("clip_mapper"))?;
+        let effnet_mappers = vec![];
+        let cfg = candle_nn::layer_norm::LayerNormConfig {
+            ..Default::default()
+        };
+        let seq_norm = candle_nn::layer_norm(c_cond, cfg, vb.pp("seq_norm"))?;
+        let embedding_ln = WLayerNorm::new(C_HIDDEN[0], vb.pp("embedding.1"))?;
+        let embedding_conv = candle_nn::conv2d(
+            c_in * patch_size * patch_size,
+            C_HIDDEN[1],
+            1,
+            Default::default(),
+            vb.pp("embedding.2"),
+        )?;
+        Ok(Self {
+            clip_mapper,
+            effnet_mappers,
+            seq_norm,
+            embedding_conv,
+            embedding_ln,
+        })
+    }
+}
diff --git a/candle-transformers/src/models/wuerstchen/prior.rs b/candle-transformers/src/models/wuerstchen/prior.rs
index a4e0300c..eea70a02 100644
--- a/candle-transformers/src/models/wuerstchen/prior.rs
+++ b/candle-transformers/src/models/wuerstchen/prior.rs
@@ -1,5 +1,5 @@
 #![allow(unused)]
-use super::common::{ResBlock, TimestepBlock};
+use super::common::{AttnBlock, ResBlock, TimestepBlock};
 use candle::{DType, Module, Result, Tensor, D};
 use candle_nn::VarBuilder;
 
@@ -7,7 +7,7 @@ use candle_nn::VarBuilder;
 struct Block {
     res_block: ResBlock,
     ts_block: TimestepBlock,
-    // TODO: attn_block: super::common::AttnBlock,
+    attn_block: AttnBlock,
 }
 
 #[derive(Debug)]
@@ -28,7 +28,7 @@ impl WPrior {
         c_cond: usize,
         c_r: usize,
         depth: usize,
-        _nhead: usize,
+        nhead: usize,
         vb: VarBuilder,
     ) -> Result<Self> {
         let projection = candle_nn::conv2d(c_in, c, 1, Default::default(), vb.pp("projection"))?;
@@ -40,9 +40,17 @@ impl WPrior {
         for index in 0..depth {
             let res_block = ResBlock::new(c, 0, 3, vb.pp(format!("blocks.{}", 3 * index)))?;
             let ts_block = TimestepBlock::new(c, c_r, vb.pp(format!("blocks.{}", 3 * index + 1)))?;
+            let attn_block = AttnBlock::new(
+                c,
+                c,
+                nhead,
+                true,
+                vb.pp(format!("blocks.{}", 3 * index + 2)),
+            )?;
             blocks.push(Block {
                 res_block,
                 ts_block,
+                attn_block,
             })
         }
         Ok(Self {
@@ -86,7 +94,7 @@ impl WPrior {
         for block in self.blocks.iter() {
             xs = block.res_block.forward(&xs, None)?;
             xs = block.ts_block.forward(&xs, &r_embed)?;
-            // TODO: attn
+            xs = block.attn_block.forward(&xs, &c_embed)?;
         }
         let ab = xs.apply(&self.out_ln)?.apply(&self.out_conv)?.chunk(1, 2)?;
         (x_in - &ab[0])? / ((&ab[1] - 1.)?.abs()? + 1e-5)

From 0a647875ec7f9861ede7fa54713af50b4207ffb7 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 14 Sep 2023 18:29:24 +0200
Subject: [PATCH 094/150] Use softmax-last-dim in the quantized example. (#848)

---
 candle-transformers/src/generation/mod.rs     | 41 ++++++++++---------
 .../src/models/quantized_llama.rs             |  2 +-
 2 files changed, 23 insertions(+), 20 deletions(-)

diff --git a/candle-transformers/src/generation/mod.rs b/candle-transformers/src/generation/mod.rs
index 6c8c8ae4..b1a567c3 100644
--- a/candle-transformers/src/generation/mod.rs
+++ b/candle-transformers/src/generation/mod.rs
@@ -1,4 +1,4 @@
-use candle::{DType, Error, Result, Tensor, D};
+use candle::{DType, Error, Result, Tensor};
 use rand::{distributions::Distribution, SeedableRng};
 
 pub struct LogitsProcessor {
@@ -9,6 +9,11 @@ pub struct LogitsProcessor {
 
 impl LogitsProcessor {
     pub fn new(seed: u64, temperature: Option<f64>, top_p: Option<f64>) -> Self {
+        let temperature = if temperature.map_or(true, |v| v < 1e-7) {
+            None
+        } else {
+            temperature
+        };
         Self {
             rng: rand::rngs::StdRng::seed_from_u64(seed),
             temperature,
@@ -27,7 +32,7 @@ impl LogitsProcessor {
         Ok(next_token)
     }
 
-    fn sample_multi(&mut self, prs: &Vec<f32>) -> Result<u32> {
+    fn sample_multinomial(&mut self, prs: &Vec<f32>) -> Result<u32> {
         let distr = rand::distributions::WeightedIndex::new(prs).map_err(Error::wrap)?;
         let next_token = distr.sample(&mut self.rng) as u32;
         Ok(next_token)
@@ -51,28 +56,26 @@ impl LogitsProcessor {
                 cumsum += prs[*index];
             }
         }
-
         // Sample with clamped probabilities.
-        let next_token = self.sample_multi(prs)?;
-        Ok(next_token)
+        self.sample_multinomial(prs)
     }
 
     pub fn sample(&mut self, logits: &Tensor) -> Result<u32> {
         let logits = logits.to_dtype(DType::F32)?;
-        let temperature = self.temperature.unwrap_or(0.);
-        let top_p = self.top_p.unwrap_or(1.);
-        let next_token = if temperature == 0. {
-            self.sample_argmax(logits)?
-        } else {
-            let logits = &(&logits / temperature)?;
-            let prs = candle_nn::ops::softmax(logits, D::Minus1)?;
-            let mut prs: Vec<f32> = prs.to_vec1()?;
-            if top_p <= 0.0 || top_p >= 1.0 {
-                // simply sample from the predicted probability distribution
-                self.sample_multi(&prs)?
-            } else {
-                // top-p (nucleus) sampling, clamping the least likely tokens to zero
-                self.sample_topp(&mut prs, top_p as f32)?
+        let next_token = match self.temperature {
+            None => self.sample_argmax(logits)?,
+            Some(temperature) => {
+                let logits = &(&logits / temperature)?;
+                let prs = candle_nn::ops::softmax_last_dim(logits)?;
+                let mut prs: Vec<f32> = prs.to_vec1()?;
+                let top_p = self.top_p.unwrap_or(1.);
+                if top_p <= 0.0 || top_p >= 1.0 {
+                    // simply sample from the predicted probability distribution
+                    self.sample_multinomial(&prs)?
+                } else {
+                    // top-p (nucleus) sampling, clamping the least likely tokens to zero
+                    self.sample_topp(&mut prs, top_p as f32)?
+                }
             }
         };
         Ok(next_token)
diff --git a/candle-transformers/src/models/quantized_llama.rs b/candle-transformers/src/models/quantized_llama.rs
index da0bd0b0..2988b0fb 100644
--- a/candle-transformers/src/models/quantized_llama.rs
+++ b/candle-transformers/src/models/quantized_llama.rs
@@ -144,7 +144,7 @@ impl LayerWeights {
         let att = (q.matmul(&k.t()?)? / (self.head_dim as f64).sqrt())?;
         let mask = mask.broadcast_as(att.shape())?;
         let att = masked_fill(&att, &mask, f32::NEG_INFINITY)?;
-        let att = candle_nn::ops::softmax(&att, D::Minus1)?;
+        let att = candle_nn::ops::softmax_last_dim(&att)?;
         // Convert to contiguous as matmul doesn't support strided vs for now.
         let y = att.matmul(&v.contiguous()?)?;
         let y = y.transpose(1, 2)?.reshape(&[b_sz, seq_len, n_embd])?;

From 91ec546febee4c6333cd65d95e8fd09e94499024 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 14 Sep 2023 22:16:31 +0200
Subject: [PATCH 095/150] More DiffNeXt. (#847)

* More DiffNeXt.

* Down blocks.
---
 .../src/models/wuerstchen/diffnext.rs         | 146 +++++++++++++++++-
 1 file changed, 144 insertions(+), 2 deletions(-)

diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 8e5099f6..5e49437c 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -1,5 +1,5 @@
 #![allow(unused)]
-use super::common::{GlobalResponseNorm, ResBlock, TimestepBlock, WLayerNorm};
+use super::common::{AttnBlock, GlobalResponseNorm, ResBlock, TimestepBlock, WLayerNorm};
 use candle::{DType, Module, Result, Tensor, D};
 use candle_nn::VarBuilder;
 
@@ -51,6 +51,27 @@ impl ResBlockStageB {
     }
 }
 
+#[derive(Debug)]
+struct SubBlock {
+    res_block: ResBlockStageB,
+    ts_block: TimestepBlock,
+    attn_block: Option<AttnBlock>,
+}
+
+#[derive(Debug)]
+struct DownBlock {
+    layer_norm: Option<WLayerNorm>,
+    conv: Option<candle_nn::Conv2d>,
+    sub_blocks: Vec<SubBlock>,
+}
+
+#[derive(Debug)]
+struct UpBlock {
+    sub_blocks: Vec<SubBlock>,
+    layer_norm: Option<WLayerNorm>,
+    conv: Option<candle_nn::Conv2d>,
+}
+
 #[derive(Debug)]
 pub struct WDiffNeXt {
     clip_mapper: candle_nn::Linear,
@@ -58,18 +79,27 @@ pub struct WDiffNeXt {
     seq_norm: candle_nn::LayerNorm,
     embedding_conv: candle_nn::Conv2d,
     embedding_ln: WLayerNorm,
+    down_blocks: Vec<DownBlock>,
+    up_blocks: Vec<UpBlock>,
+    clf_ln: WLayerNorm,
+    clf_conv: candle_nn::Conv2d,
+    c_r: usize,
 }
 
 impl WDiffNeXt {
     pub fn new(
         c_in: usize,
         c_out: usize,
-        vb: VarBuilder,
+        c_r: usize,
         c_cond: usize,
         clip_embd: usize,
         patch_size: usize,
+        vb: VarBuilder,
     ) -> Result<Self> {
         const C_HIDDEN: [usize; 4] = [320, 640, 1280, 1280];
+        const BLOCKS: [usize; 4] = [4, 4, 14, 4];
+        const NHEAD: [usize; 4] = [0, 10, 20, 20];
+        const INJECT_EFFNET: [bool; 4] = [false, true, true, true];
 
         let clip_mapper = candle_nn::linear(clip_embd, c_cond, vb.pp("clip_mapper"))?;
         let effnet_mappers = vec![];
@@ -85,12 +115,124 @@ impl WDiffNeXt {
             Default::default(),
             vb.pp("embedding.2"),
         )?;
+
+        let mut down_blocks = Vec::with_capacity(C_HIDDEN.len());
+        for (i, &c_hidden) in C_HIDDEN.iter().enumerate() {
+            let vb = vb.pp("down_blocks").pp(i);
+            let (layer_norm, conv, start_layer_i) = if i > 0 {
+                let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1], vb.pp(0))?;
+                let cfg = candle_nn::Conv2dConfig {
+                    stride: 2,
+                    ..Default::default()
+                };
+                let conv = candle_nn::conv2d(C_HIDDEN[i - 1], c_hidden, 2, cfg, vb.pp(1))?;
+                (Some(layer_norm), Some(conv), 2)
+            } else {
+                (None, None, 0)
+            };
+            let mut sub_blocks = Vec::with_capacity(BLOCKS[i]);
+            let mut layer_i = start_layer_i;
+            for j in 0..BLOCKS[i] {
+                let c_skip = if INJECT_EFFNET[i] { c_cond } else { 0 };
+                let res_block = ResBlockStageB::new(c_hidden, c_skip, 3, vb.pp(layer_i))?;
+                layer_i += 1;
+                let ts_block = TimestepBlock::new(c_hidden, c_r, vb.pp(layer_i))?;
+                layer_i += 1;
+                let attn_block = if j == 0 {
+                    None
+                } else {
+                    let attn_block =
+                        AttnBlock::new(c_hidden, c_cond, NHEAD[i], true, vb.pp(layer_i))?;
+                    layer_i += 1;
+                    Some(attn_block)
+                };
+                let sub_block = SubBlock {
+                    res_block,
+                    ts_block,
+                    attn_block,
+                };
+                sub_blocks.push(sub_block)
+            }
+            let down_block = DownBlock {
+                layer_norm,
+                conv,
+                sub_blocks,
+            };
+            down_blocks.push(down_block)
+        }
+
+        // TODO: populate.
+        let up_blocks = Vec::with_capacity(C_HIDDEN.len());
+
+        let clf_ln = WLayerNorm::new(C_HIDDEN[0], vb.pp("clf.0"))?;
+        let clf_conv = candle_nn::conv2d(
+            C_HIDDEN[0],
+            2 * c_out * patch_size * patch_size,
+            1,
+            Default::default(),
+            vb.pp("clf.1"),
+        )?;
         Ok(Self {
             clip_mapper,
             effnet_mappers,
             seq_norm,
             embedding_conv,
             embedding_ln,
+            down_blocks,
+            up_blocks,
+            clf_ln,
+            clf_conv,
+            c_r,
         })
     }
+
+    fn gen_r_embedding(&self, r: &Tensor) -> Result<Tensor> {
+        const MAX_POSITIONS: usize = 10000;
+        let r = (r * MAX_POSITIONS as f64)?;
+        let half_dim = self.c_r / 2;
+        let emb = (MAX_POSITIONS as f64).ln() / (half_dim - 1) as f64;
+        let emb = (Tensor::arange(0u32, half_dim as u32, r.device())?.to_dtype(DType::F32)?
+            * -emb)?
+            .exp()?;
+        let emb = r.unsqueeze(1)?.broadcast_mul(&emb.unsqueeze(0)?)?;
+        let emb = Tensor::cat(&[emb.sin()?, emb.cos()?], 1)?;
+        let emb = if self.c_r % 2 == 1 {
+            emb.pad_with_zeros(D::Minus1, 0, 1)?
+        } else {
+            emb
+        };
+        emb.to_dtype(r.dtype())
+    }
+
+    fn gen_c_embeddings(&self, clip: &Tensor) -> Result<Tensor> {
+        clip.apply(&self.clip_mapper)?.apply(&self.seq_norm)
+    }
+
+    pub fn forward(
+        &self,
+        xs: &Tensor,
+        r: &Tensor,
+        effnet: &Tensor,
+        clip: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        const EPS: f64 = 1e-3;
+
+        let r_embed = self.gen_r_embedding(r)?;
+        let clip = match clip {
+            None => None,
+            Some(clip) => Some(self.gen_c_embeddings(clip)?),
+        };
+        let x_in = xs;
+
+        // TODO: pixel unshuffle.
+        let xs = xs.apply(&self.embedding_conv)?.apply(&self.embedding_ln)?;
+        // TODO: down blocks
+        let level_outputs = xs.clone();
+        // TODO: up blocks
+        let xs = level_outputs;
+        // TODO: pxel shuffle
+        let ab = xs.apply(&self.clf_ln)?.apply(&self.clf_conv)?.chunk(1, 2)?;
+        let b = ((candle_nn::ops::sigmoid(&ab[1])? * (1. - EPS * 2.))? + EPS)?;
+        (x_in - &ab[0])? / b
+    }
 }

From 130fe5a087715fc4d7bf9b581ca7c11378736ac5 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 14 Sep 2023 23:24:56 +0200
Subject: [PATCH 096/150] Add the upblocks. (#853)

---
 candle-core/src/lib.rs                        |  8 ++-
 candle-nn/src/ops.rs                          |  4 ++
 .../src/models/wuerstchen/diffnext.rs         | 53 ++++++++++++++++++-
 .../src/models/wuerstchen/prior.rs            |  3 +-
 4 files changed, 63 insertions(+), 5 deletions(-)

diff --git a/candle-core/src/lib.rs b/candle-core/src/lib.rs
index 0a64a5a6..52effdcf 100644
--- a/candle-core/src/lib.rs
+++ b/candle-core/src/lib.rs
@@ -110,7 +110,7 @@ impl ToUsize2 for (usize, usize) {
 }
 
 // A simple trait defining a module with forward method using a single argument.
-pub trait Module: std::fmt::Debug {
+pub trait Module {
     fn forward(&self, xs: &Tensor) -> Result<Tensor>;
 }
 
@@ -119,3 +119,9 @@ impl Module for quantized::QMatMul {
         self.forward(xs)
     }
 }
+
+impl<T: Fn(&Tensor) -> Result<Tensor>> Module for T {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        self(xs)
+    }
+}
diff --git a/candle-nn/src/ops.rs b/candle-nn/src/ops.rs
index adf1451c..c4055792 100644
--- a/candle-nn/src/ops.rs
+++ b/candle-nn/src/ops.rs
@@ -44,6 +44,10 @@ pub fn sigmoid(xs: &Tensor) -> Result<Tensor> {
     (xs.neg()?.exp()? + 1.0)?.recip()
 }
 
+pub fn leaky_relu(xs: &Tensor, negative_slope: f64) -> Result<Tensor> {
+    xs.relu()?.minimum(&(xs * negative_slope)?)
+}
+
 pub fn dropout(xs: &Tensor, drop_p: f32) -> Result<Tensor> {
     // This implementation is inefficient as it stores the full mask for the backward pass.
     // Instead we could just store the seed and have a specialized kernel that would both
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 5e49437c..7289a54d 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -161,8 +161,57 @@ impl WDiffNeXt {
             down_blocks.push(down_block)
         }
 
-        // TODO: populate.
-        let up_blocks = Vec::with_capacity(C_HIDDEN.len());
+        let mut up_blocks = Vec::with_capacity(C_HIDDEN.len());
+        for (i, &c_hidden) in C_HIDDEN.iter().enumerate().rev() {
+            let vb = vb.pp("up_blocks").pp(i);
+            let mut sub_blocks = Vec::with_capacity(BLOCKS[i]);
+            let mut layer_i = 0;
+            for j in 0..BLOCKS[i] {
+                let c_skip = if INJECT_EFFNET[i] { c_cond } else { 0 };
+                let c_skip_res = if i < BLOCKS.len() - 1 && j == 0 {
+                    c_hidden + c_skip
+                } else {
+                    c_skip
+                };
+                let res_block = ResBlockStageB::new(c_hidden, c_skip_res, 3, vb.pp(layer_i))?;
+                layer_i += 1;
+                let ts_block = TimestepBlock::new(c_hidden, c_r, vb.pp(layer_i))?;
+                layer_i += 1;
+                let attn_block = if j == 0 {
+                    None
+                } else {
+                    let attn_block =
+                        AttnBlock::new(c_hidden, c_cond, NHEAD[i], true, vb.pp(layer_i))?;
+                    layer_i += 1;
+                    Some(attn_block)
+                };
+                let sub_block = SubBlock {
+                    res_block,
+                    ts_block,
+                    attn_block,
+                };
+                sub_blocks.push(sub_block)
+            }
+            let (layer_norm, conv, start_layer_i) = if i > 0 {
+                let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1], vb.pp(layer_i))?;
+                layer_i += 1;
+                let cfg = candle_nn::Conv2dConfig {
+                    stride: 2,
+                    ..Default::default()
+                };
+                let conv = candle_nn::conv2d(C_HIDDEN[i - 1], c_hidden, 2, cfg, vb.pp(layer_i))?;
+                layer_i += 1;
+                (Some(layer_norm), Some(conv), 2)
+            } else {
+                (None, None, 0)
+            };
+            let up_block = UpBlock {
+                layer_norm,
+                conv,
+                sub_blocks,
+            };
+            up_blocks.push(up_block)
+        }
 
         let clf_ln = WLayerNorm::new(C_HIDDEN[0], vb.pp("clf.0"))?;
         let clf_conv = candle_nn::conv2d(
diff --git a/candle-transformers/src/models/wuerstchen/prior.rs b/candle-transformers/src/models/wuerstchen/prior.rs
index eea70a02..5dd03778 100644
--- a/candle-transformers/src/models/wuerstchen/prior.rs
+++ b/candle-transformers/src/models/wuerstchen/prior.rs
@@ -85,10 +85,9 @@ impl WPrior {
     pub fn forward(&self, xs: &Tensor, r: &Tensor, c: &Tensor) -> Result<Tensor> {
         let x_in = xs;
         let mut xs = xs.apply(&self.projection)?;
-        // TODO: leaky relu
         let c_embed = c
             .apply(&self.cond_mapper_lin1)?
-            .relu()?
+            .apply(&|xs: &_| candle_nn::ops::leaky_relu(xs, 0.2))?
             .apply(&self.cond_mapper_lin2)?;
         let r_embed = self.gen_r_embedding(r)?;
         for block in self.blocks.iter() {

From 5cefbba75777547f97abd92affcf9ef10ac36163 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radam=C3=A9s=20Ajna?= <radamajna@gmail.com>
Date: Thu, 14 Sep 2023 22:30:50 -0700
Subject: [PATCH 097/150] minor UI fixes (#856)

* fixes

* remove listener

* remove event listener
---
 candle-wasm-examples/llama2-c/lib-example.html | 12 +++++++++---
 candle-wasm-examples/whisper/lib-example.html  |  9 ++++++---
 candle-wasm-examples/yolo/lib-example.html     | 17 ++++++++++++-----
 3 files changed, 27 insertions(+), 11 deletions(-)

diff --git a/candle-wasm-examples/llama2-c/lib-example.html b/candle-wasm-examples/llama2-c/lib-example.html
index 22b12517..86fe9811 100644
--- a/candle-wasm-examples/llama2-c/lib-example.html
+++ b/candle-wasm-examples/llama2-c/lib-example.html
@@ -6,7 +6,7 @@
   <body></body>
 </html>
 
-<!doctype html>
+<!DOCTYPE html>
 <html>
   <head>
     <meta charset="UTF-8" />
@@ -113,8 +113,14 @@
           const handleMessage = (event) => {
             const { status, error, message, prompt, sentence } = event.data;
             if (status) updateStatus(event.data);
-            if (error) reject(new Error(error));
-            if (status === "complete") resolve(event.data);
+            if (error) {
+              llamaWorker.removeEventListener("message", handleMessage);
+              reject(new Error(error));
+            }
+            if (status === "complete") {
+              llamaWorker.removeEventListener("message", handleMessage);
+              resolve(event.data);
+            }
           };
 
           controller.signal.addEventListener("abort", handleAbort);
diff --git a/candle-wasm-examples/whisper/lib-example.html b/candle-wasm-examples/whisper/lib-example.html
index ad48072b..3cfd87a7 100644
--- a/candle-wasm-examples/whisper/lib-example.html
+++ b/candle-wasm-examples/whisper/lib-example.html
@@ -6,7 +6,7 @@
   <body></body>
 </html>
 
-<!doctype html>
+<!DOCTYPE html>
 <html>
   <head>
     <meta charset="UTF-8" />
@@ -51,18 +51,21 @@
             mel_filtersURL,
             audioURL,
           });
-          whisperWorker.addEventListener("message", (event) => {
+          function messageHandler(event) {
             console.log(event.data);
             if ("status" in event.data) {
               updateStatus(event.data);
             }
             if ("error" in event.data) {
+              whisperWorker.removeEventListener("message", messageHandler);
               reject(new Error(event.data.error));
             }
             if (event.data.status === "complete") {
+              whisperWorker.removeEventListener("message", messageHandler);
               resolve(event.data);
             }
-          });
+          }
+          whisperWorker.addEventListener("message", messageHandler);
         });
       }
 
diff --git a/candle-wasm-examples/yolo/lib-example.html b/candle-wasm-examples/yolo/lib-example.html
index 8f7d07c7..d9f18975 100644
--- a/candle-wasm-examples/yolo/lib-example.html
+++ b/candle-wasm-examples/yolo/lib-example.html
@@ -6,7 +6,7 @@
   <body></body>
 </html>
 
-<!doctype html>
+<!DOCTYPE html>
 <html>
   <head>
     <meta charset="UTF-8" />
@@ -199,17 +199,21 @@
             confidence,
             iou_threshold,
           });
-          yoloWorker.addEventListener("message", (event) => {
+          function handleMessage(event) {
+            console.log("message", event.data);
             if ("status" in event.data) {
               updateStatus(event.data.status);
             }
             if ("error" in event.data) {
+              yoloWorker.removeEventListener("message", handleMessage);
               reject(new Error(event.data.error));
             }
             if (event.data.status === "complete") {
+              yoloWorker.removeEventListener("message", handleMessage);
               resolve(event.data);
             }
-          });
+          }
+          yoloWorker.addEventListener("message", handleMessage);
         });
       }
       // add event listener to detect button
@@ -393,7 +397,7 @@
         </button>
       </div>
       <!-- drag and drop area -->
-      <div class="relative">
+      <div class="relative max-w-lg">
         <div class="py-1">
           <button
             id="clear-btn"
@@ -471,7 +475,10 @@
         </div>
       </div>
       <div>
-        <div class="flex gap-3 items-center" id="image-select">
+        <div
+          class="flex gap-3 items-center overflow-x-scroll"
+          id="image-select"
+        >
           <h3 class="font-medium">Examples:</h3>
 
           <img

From 39157346cb96d7610ddb6cdc686aed32d12bba3d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radam=C3=A9s=20Ajna?= <radamajna@gmail.com>
Date: Thu, 14 Sep 2023 22:31:58 -0700
Subject: [PATCH 098/150] Add SAM UI Demo (#854)

* fix tensor flattening

* send image data back

* sam ui worker example

* SAM example

* resize container

* no need for this
---
 .../segment-anything/README.md                |  26 ++
 .../segment-anything/lib-example.html         | 407 ++++++++++++++++++
 .../segment-anything/samWorker.js             | 156 +++++++
 .../segment-anything/src/bin/m.rs             |  22 +-
 4 files changed, 609 insertions(+), 2 deletions(-)
 create mode 100644 candle-wasm-examples/segment-anything/README.md
 create mode 100644 candle-wasm-examples/segment-anything/lib-example.html
 create mode 100644 candle-wasm-examples/segment-anything/samWorker.js

diff --git a/candle-wasm-examples/segment-anything/README.md b/candle-wasm-examples/segment-anything/README.md
new file mode 100644
index 00000000..04ff2033
--- /dev/null
+++ b/candle-wasm-examples/segment-anything/README.md
@@ -0,0 +1,26 @@
+## Running Segment Anything Example
+
+Here, we provide two examples of how to run Whisper using a Candle-compiled WASM binary and runtimes.
+
+### Vanilla JS and WebWorkers
+
+To build and test the UI made in Vanilla JS and WebWorkers, first we need to build the WASM library:
+
+```bash
+sh build-lib.sh
+```
+
+This will bundle the library under `./build` and we can import it inside our WebWorker like a normal JS module:
+
+```js
+import init, { Model } from "./build/m.js";
+```
+
+The full example can be found under `./lib-example.html`. All needed assets are fetched from the web, so no need to download anything.
+Finally, you can preview the example by running a local HTTP server. For example:
+
+```bash
+python -m http.server
+```
+
+Then open `http://localhost:8000/lib-example.html` in your browser.
diff --git a/candle-wasm-examples/segment-anything/lib-example.html b/candle-wasm-examples/segment-anything/lib-example.html
new file mode 100644
index 00000000..127b9152
--- /dev/null
+++ b/candle-wasm-examples/segment-anything/lib-example.html
@@ -0,0 +1,407 @@
+<html>
+  <head>
+    <meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
+    <title>Candle Segment Anything Model (SAM) Rust/WASM</title>
+  </head>
+  <body></body>
+</html>
+
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <style>
+      @import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
+      html,
+      body {
+        font-family: "Source Sans 3", sans-serif;
+      }
+    </style>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <script type="module">
+      // base url for audio examples
+      const MODEL_BASEURL =
+        "https://huggingface.co/lmz/candle-sam/resolve/main/";
+
+      // models base url
+      const MODELS = {
+        sam_mobile_tiny: {
+          url: "mobile_sam-tiny-vitt.safetensors",
+        },
+        sam_base: {
+          url: "sam_vit_b_01ec64.safetensors",
+        },
+      };
+      const samWorker = new Worker("./samWorker.js", { type: "module" });
+
+      async function segmentPoints(
+        modelURL, // URL to the weights file
+        modelID, // model ID
+        imageURL, // URL to the audio file
+        points // {x, y} points to prompt image
+      ) {
+        return new Promise((resolve, reject) => {
+          function messageHandler(event) {
+            console.log(event.data);
+            if ("status" in event.data) {
+              updateStatus(event.data);
+            }
+            if ("error" in event.data) {
+              samWorker.removeEventListener("message", messageHandler);
+              reject(new Error(event.data.error));
+            }
+            if (event.data.status === "complete-embedding") {
+              samWorker.removeEventListener("message", messageHandler);
+              resolve();
+            }
+            if (event.data.status === "complete") {
+              samWorker.removeEventListener("message", messageHandler);
+              resolve(event.data.output);
+            }
+          }
+          samWorker.addEventListener("message", messageHandler);
+          samWorker.postMessage({
+            modelURL,
+            modelID,
+            imageURL,
+            points,
+          });
+        });
+      }
+      function updateStatus(statusMessage) {
+        statusOutput.innerText = event.data.message;
+      }
+
+      const clearBtn = document.querySelector("#clear-btn");
+      const canvas = document.querySelector("#canvas");
+      const mask = document.querySelector("#mask");
+      const ctxCanvas = canvas.getContext("2d");
+      const ctxMask = mask.getContext("2d");
+      const fileUpload = document.querySelector("#file-upload");
+      const dropArea = document.querySelector("#drop-area");
+      const dropButtons = document.querySelector("#drop-buttons");
+      const imagesExamples = document.querySelector("#image-select");
+      const modelSelection = document.querySelector("#model");
+      const statusOutput = document.querySelector("#output-status");
+
+      //add event listener to file input
+      fileUpload.addEventListener("change", (e) => {
+        const target = e.target;
+        if (target.files.length > 0) {
+          const href = URL.createObjectURL(target.files[0]);
+          cleanImageCanvas();
+          drawImageCanvas(href);
+          setImageEmbeddings(href);
+        }
+      });
+      // add event listener to drop-area
+      dropArea.addEventListener("dragenter", (e) => {
+        e.preventDefault();
+        dropArea.classList.add("border-blue-700");
+      });
+      dropArea.addEventListener("dragleave", (e) => {
+        e.preventDefault();
+        dropArea.classList.remove("border-blue-700");
+      });
+      dropArea.addEventListener("dragover", (e) => {
+        e.preventDefault();
+      });
+      dropArea.addEventListener("drop", (e) => {
+        e.preventDefault();
+        dropArea.classList.remove("border-blue-700");
+        const url = e.dataTransfer.getData("text/uri-list");
+        const files = e.dataTransfer.files;
+
+        if (files.length > 0) {
+          const href = URL.createObjectURL(files[0]);
+          cleanImageCanvas();
+          drawImageCanvas(href);
+          setImageEmbeddings(href);
+        } else if (url) {
+          cleanImageCanvas();
+          drawImageCanvas(url);
+          setImageEmbeddings(url);
+        }
+      });
+
+      let hasImage = false;
+      let isSegmenting = false;
+      let isEmbedding = false;
+      let currentImageURL = "";
+      //add event listener to image examples
+      imagesExamples.addEventListener("click", (e) => {
+        if (isEmbedding || isSegmenting) {
+          return;
+        }
+        const target = e.target;
+        if (target.nodeName === "IMG") {
+          const href = target.src;
+          cleanImageCanvas();
+          drawImageCanvas(href);
+          setImageEmbeddings(href);
+        }
+      });
+      //add event listener to clear button
+      clearBtn.addEventListener("click", () => {
+        cleanImageCanvas();
+      });
+      //add click event to canvas
+      canvas.addEventListener("click", async (event) => {
+        if (!hasImage || isEmbedding || isSegmenting) {
+          return;
+        }
+        const targetBox = event.target.getBoundingClientRect();
+        const x = (event.clientX - targetBox.left) / targetBox.width;
+        const y = (event.clientY - targetBox.top) / targetBox.height;
+        isSegmenting = true;
+        const { maskURL } = await getSegmentationMask({ x, y });
+        isSegmenting = false;
+        drawMask(maskURL);
+      });
+
+      async function getSegmentationMask(points) {
+        const modelID = modelSelection.value;
+        const modelURL = MODEL_BASEURL + MODELS[modelID].url;
+        const imageURL = currentImageURL;
+        const { maskURL } = await segmentPoints(
+          modelURL,
+          modelID,
+          imageURL,
+          points
+        );
+        return { maskURL };
+      }
+      async function setImageEmbeddings(imageURL) {
+        if (isEmbedding) {
+          return;
+        }
+        canvas.classList.remove("cursor-pointer");
+        canvas.classList.add("cursor-wait");
+        clearBtn.disabled = true;
+        const modelID = modelSelection.value;
+        const modelURL = MODEL_BASEURL + MODELS[modelID].url;
+        isEmbedding = true;
+        await segmentPoints(modelURL, modelID, imageURL);
+        canvas.classList.remove("cursor-wait");
+        canvas.classList.add("cursor-pointer");
+        clearBtn.disabled = false;
+        isEmbedding = false;
+        currentImageURL = imageURL;
+      }
+
+      function cleanImageCanvas() {
+        ctxCanvas.clearRect(0, 0, canvas.width, canvas.height);
+        ctxMask.clearRect(0, 0, canvas.width, canvas.height);
+        hasImage = false;
+        isEmbedding = false;
+        isSegmenting = false;
+        currentImageURL = "";
+        clearBtn.classList.add("invisible");
+        canvas.parentElement.style.height = "auto";
+        dropButtons.classList.remove("invisible");
+      }
+      function drawMask(maskURL) {
+        if (!maskURL) {
+          throw new Error("No mask URL provided");
+        }
+
+        const img = new Image();
+        img.crossOrigin = "anonymous";
+
+        img.onload = () => {
+          mask.width = canvas.width;
+          mask.height = canvas.height;
+          ctxMask.drawImage(canvas, 0, 0);
+          ctxMask.globalCompositeOperation = "source-atop";
+          ctxMask.fillStyle = "rgba(255, 0, 0, 0.6)";
+          ctxMask.fillRect(0, 0, canvas.width, canvas.height);
+          ctxMask.globalCompositeOperation = "destination-in";
+          ctxMask.drawImage(img, 0, 0);
+        };
+        img.src = maskURL;
+      }
+      function drawImageCanvas(imgURL) {
+        if (!imgURL) {
+          throw new Error("No image URL provided");
+        }
+
+        ctxCanvas.clearRect(0, 0, canvas.width, canvas.height);
+        ctxCanvas.clearRect(0, 0, canvas.width, canvas.height);
+
+        const img = new Image();
+        img.crossOrigin = "anonymous";
+
+        img.onload = () => {
+          canvas.width = img.width;
+          canvas.height = img.height;
+          ctxCanvas.drawImage(img, 0, 0);
+          canvas.parentElement.style.height = canvas.offsetHeight + "px";
+          hasImage = true;
+          clearBtn.classList.remove("invisible");
+          dropButtons.classList.add("invisible");
+        };
+        img.src = imgURL;
+      }
+
+      const observer = new ResizeObserver((entries) => {
+        for (let entry of entries) {
+          if (entry.target === canvas) {
+            canvas.parentElement.style.height = canvas.offsetHeight + "px";
+          }
+        }
+      });
+      observer.observe(canvas);
+    </script>
+  </head>
+  <body class="container max-w-4xl mx-auto p-4">
+    <main class="grid grid-cols-1 gap-8 relative">
+      <span class="absolute text-5xl -ml-[1em]">🕯️</span>
+      <div>
+        <h1 class="text-5xl font-bold">Candle Segment Anything</h1>
+        <h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
+        <p class="max-w-lg">
+          Zero-shot image segmentation with
+          <a
+            href="https://segment-anything.com"
+            class="underline hover:text-blue-500 hover:no-underline"
+            target="_blank"
+            >Segment Anything Model (SAM)</a
+          >
+          and
+          <a
+            href="https://github.com/ChaoningZhang/MobileSAM"
+            class="underline hover:text-blue-500 hover:no-underline"
+            target="_blank"
+            >MobileSAM </a
+          >. It runs in the browser with a WASM runtime built with
+          <a
+            href="https://github.com/huggingface/candle/"
+            target="_blank"
+            class="underline hover:text-blue-500 hover:no-underline"
+            >Candle
+          </a>
+        </p>
+      </div>
+      <div>
+        <label for="model" class="font-medium">Models Options: </label>
+        <select
+          id="model"
+          class="border-2 border-gray-500 rounded-md font-light"
+        >
+          <option value="sam_mobile_tiny" selected>
+            Mobile SAM Tiny (40.6 MB)
+          </option>
+          <option value="sam_base">SAM Base (375 MB)</option>
+        </select>
+      </div>
+      <div>
+        <p class="text-xs italic max-w-lg">
+          <b>Note:</b>
+          The model's first run may take a few seconds as it loads and caches
+          the model in the browser, and then creates the image embeddings. Any
+          subsequent clicks on points will be significantly faster.
+        </p>
+      </div>
+      <div class="relative max-w-lg">
+        <div class="flex justify-between items-center">
+          <div class="px-2 rounded-md inline text-xs">
+            <span id="output-status" class="m-auto font-light"></span>
+          </div>
+          <button
+            id="clear-btn"
+            class="text-xs bg-white rounded-md disabled:opacity-50 flex gap-1 items-center invisible"
+          >
+            <svg
+              class=""
+              xmlns="http://www.w3.org/2000/svg"
+              viewBox="0 0 13 12"
+              height="1em"
+            >
+              <path
+                d="M1.6.7 12 11.1M12 .7 1.6 11.1"
+                stroke="#2E3036"
+                stroke-width="2"
+              />
+            </svg>
+            Clear image
+          </button>
+        </div>
+        <div
+          id="drop-area"
+          class="flex flex-col items-center justify-center border-2 border-gray-300 border-dashed rounded-xl relative p-20 w-full overflow-hidden"
+        >
+          <div
+            id="drop-buttons"
+            class="flex flex-col items-center justify-center space-y-1 text-center relative z-10"
+          >
+            <svg
+              width="25"
+              height="25"
+              viewBox="0 0 25 25"
+              fill="none"
+              xmlns="http://www.w3.org/2000/svg"
+            >
+              <path
+                d="M3.5 24.3a3 3 0 0 1-1.9-.8c-.5-.5-.8-1.2-.8-1.9V2.9c0-.7.3-1.3.8-1.9.6-.5 1.2-.7 2-.7h18.6c.7 0 1.3.2 1.9.7.5.6.7 1.2.7 2v18.6c0 .7-.2 1.4-.7 1.9a3 3 0 0 1-2 .8H3.6Zm0-2.7h18.7V2.9H3.5v18.7Zm2.7-2.7h13.3c.3 0 .5 0 .6-.3v-.7l-3.7-5a.6.6 0 0 0-.6-.2c-.2 0-.4 0-.5.3l-3.5 4.6-2.4-3.3a.6.6 0 0 0-.6-.3c-.2 0-.4.1-.5.3l-2.7 3.6c-.1.2-.2.4 0 .7.1.2.3.3.6.3Z"
+                fill="#000"
+              />
+            </svg>
+            <div class="flex text-sm text-gray-600">
+              <label
+                for="file-upload"
+                class="relative cursor-pointer bg-white rounded-md font-medium text-blue-950 hover:text-blue-700"
+              >
+                <span>Drag and drop your image here</span>
+                <span class="block text-xs">or</span>
+                <span class="block text-xs">Click to upload</span>
+              </label>
+            </div>
+            <input
+              id="file-upload"
+              name="file-upload"
+              type="file"
+              class="sr-only"
+            />
+          </div>
+          <canvas id="canvas" class="absolute w-full"></canvas>
+          <canvas
+            id="mask"
+            class="pointer-events-none absolute w-full"
+          ></canvas>
+        </div>
+        <div class="text-right py-2">
+          <button
+            id="share-btn"
+            class="bg-white rounded-md hover:outline outline-orange-200 disabled:opacity-50 invisible"
+          >
+            <img
+              src="https://huggingface.co/datasets/huggingface/badges/raw/main/share-to-community-sm.svg"
+            />
+          </button>
+        </div>
+      </div>
+      <div>
+        <div
+          class="flex gap-3 items-center overflow-x-scroll"
+          id="image-select"
+        >
+          <h3 class="font-medium">Examples:</h3>
+
+          <img
+            src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/sf.jpg"
+            class="cursor-pointer w-24 h-24 object-cover"
+          />
+          <img
+            src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/bike.jpeg"
+            class="cursor-pointer w-24 h-24 object-cover"
+          />
+          <img
+            src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/candle/examples/000000000077.jpg"
+            class="cursor-pointer w-24 h-24 object-cover"
+          />
+        </div>
+      </div>
+    </main>
+  </body>
+</html>
diff --git a/candle-wasm-examples/segment-anything/samWorker.js b/candle-wasm-examples/segment-anything/samWorker.js
new file mode 100644
index 00000000..b90498de
--- /dev/null
+++ b/candle-wasm-examples/segment-anything/samWorker.js
@@ -0,0 +1,156 @@
+//load the candle SAM Model wasm module
+import init, { Model } from "./build/m.js";
+
+async function fetchArrayBuffer(url, cacheModel = true) {
+  if (!cacheModel)
+    return new Uint8Array(await (await fetch(url)).arrayBuffer());
+  const cacheName = "sam-candle-cache";
+  const cache = await caches.open(cacheName);
+  const cachedResponse = await cache.match(url);
+  if (cachedResponse) {
+    const data = await cachedResponse.arrayBuffer();
+    return new Uint8Array(data);
+  }
+  const res = await fetch(url, { cache: "force-cache" });
+  cache.put(url, res.clone());
+  return new Uint8Array(await res.arrayBuffer());
+}
+class SAMModel {
+  static instance = {};
+  // keep current image embeddings state
+  static imageArrayHash = {};
+  // Add a new property to hold the current modelID
+  static currentModelID = null;
+
+  static async getInstance(modelURL, modelID) {
+    if (!this.instance[modelID]) {
+      await init();
+
+      self.postMessage({
+        status: "loading",
+        message: `Loading Model ${modelID}`,
+      });
+      const weightsArrayU8 = await fetchArrayBuffer(modelURL);
+      this.instance[modelID] = new Model(
+        weightsArrayU8,
+        /tiny|mobile/.test(modelID)
+      );
+    } else {
+      self.postMessage({ status: "loading", message: "Model Already Loaded" });
+    }
+    // Set the current modelID to the modelID that was passed in
+    this.currentModelID = modelID;
+    return this.instance[modelID];
+  }
+
+  // Remove the modelID parameter from setImageEmbeddings
+  static setImageEmbeddings(imageArrayU8) {
+    // check if image embeddings are already set for this image and model
+    const imageArrayHash = this.getSimpleHash(imageArrayU8);
+    if (
+      this.imageArrayHash[this.currentModelID] === imageArrayHash &&
+      this.instance[this.currentModelID]
+    ) {
+      self.postMessage({
+        status: "embedding",
+        message: "Embeddings Already Set",
+      });
+      return;
+    }
+    this.imageArrayHash[this.currentModelID] = imageArrayHash;
+    this.instance[this.currentModelID].set_image_embeddings(imageArrayU8);
+    self.postMessage({ status: "embedding", message: "Embeddings Set" });
+  }
+
+  static getSimpleHash(imageArrayU8) {
+    // get simple hash of imageArrayU8
+    let imageArrayHash = 0;
+    for (let i = 0; i < imageArrayU8.length; i += 100) {
+      imageArrayHash ^= imageArrayU8[i];
+    }
+    return imageArrayHash.toString(16);
+  }
+}
+
+async function createImageCanvas(
+  { mask_shape, mask_data }, // mask
+  { original_width, original_height, width, height } // original image
+) {
+  const [_, __, shape_width, shape_height] = mask_shape;
+  const maskCanvas = new OffscreenCanvas(shape_width, shape_height); // canvas for mask
+  const maskCtx = maskCanvas.getContext("2d");
+  const canvas = new OffscreenCanvas(original_width, original_height); // canvas for creating mask with original image size
+  const ctx = canvas.getContext("2d");
+
+  const imageData = maskCtx.createImageData(
+    maskCanvas.width,
+    maskCanvas.height
+  );
+  const data = imageData.data;
+
+  for (let p = 0; p < data.length; p += 4) {
+    data[p] = 0;
+    data[p + 1] = 0;
+    data[p + 2] = 0;
+    data[p + 3] = mask_data[p / 4] * 255;
+  }
+  maskCtx.putImageData(imageData, 0, 0);
+
+  let sx, sy;
+  if (original_height < original_width) {
+    sy = original_height / original_width;
+    sx = 1;
+  } else {
+    sy = 1;
+    sx = original_width / original_height;
+  }
+  ctx.drawImage(
+    maskCanvas,
+    0,
+    0,
+    maskCanvas.width * sx,
+    maskCanvas.height * sy,
+    0,
+    0,
+    original_width,
+    original_height
+  );
+
+  const blob = await canvas.convertToBlob();
+  return URL.createObjectURL(blob);
+}
+
+self.addEventListener("message", async (event) => {
+  const { modelURL, modelID, imageURL, points } = event.data;
+  try {
+    self.postMessage({ status: "loading", message: "Starting SAM" });
+    const sam = await SAMModel.getInstance(modelURL, modelID);
+
+    self.postMessage({ status: "loading", message: "Loading Image" });
+    const imageArrayU8 = await fetchArrayBuffer(imageURL, false);
+
+    self.postMessage({ status: "embedding", message: "Creating Embeddings" });
+    SAMModel.setImageEmbeddings(imageArrayU8);
+    if (!points) {
+      // no points only do the embeddings
+      self.postMessage({
+        status: "complete-embedding",
+        message: "Embeddings Complete",
+      });
+      return;
+    }
+
+    self.postMessage({ status: "segmenting", message: "Segmenting" });
+    const result = sam.mask_for_point(points.x, points.y);
+    const { mask, image } = JSON.parse(result);
+    const maskDataURL = await createImageCanvas(mask, image);
+    // Send the segment back to the main thread as JSON
+    self.postMessage({
+      status: "complete",
+      message: "Segmentation Complete",
+      output: { maskURL: maskDataURL },
+    });
+  } catch (e) {
+    self.postMessage({ error: e });
+  }
+});
diff --git a/candle-wasm-examples/segment-anything/src/bin/m.rs b/candle-wasm-examples/segment-anything/src/bin/m.rs
index b53f5b9b..949c18a0 100644
--- a/candle-wasm-examples/segment-anything/src/bin/m.rs
+++ b/candle-wasm-examples/segment-anything/src/bin/m.rs
@@ -98,7 +98,7 @@ impl Model {
             Some((x, y)),
             false,
         )?;
-        let iou = iou_predictions.to_vec1::<f32>()?[0];
+        let iou = iou_predictions.flatten(0, 1)?.to_vec1::<f32>()?[0];
         let mask_shape = mask.dims().to_vec();
         let mask_data = mask.ge(0f32)?.flatten_all()?.to_vec1::<u8>()?;
         let mask = Mask {
@@ -106,7 +106,13 @@ impl Model {
             mask_shape,
             mask_data,
         };
-        let json = serde_json::to_string(&mask)?;
+        let image = Image {
+            original_width: embeddings.original_width,
+            original_height: embeddings.original_height,
+            width: embeddings.width,
+            height: embeddings.height,
+        };
+        let json = serde_json::to_string(&MaskImage { mask, image })?;
         Ok(json)
     }
 }
@@ -117,6 +123,18 @@ struct Mask {
     mask_shape: Vec<usize>,
     mask_data: Vec<u8>,
 }
+#[derive(serde::Serialize, serde::Deserialize)]
+struct Image {
+    original_width: u32,
+    original_height: u32,
+    width: u32,
+    height: u32,
+}
+#[derive(serde::Serialize, serde::Deserialize)]
+struct MaskImage {
+    mask: Mask,
+    image: Image,
+}
 
 fn main() {
     console_error_panic_hook::set_once();

From 0633c85514891141f5d8ded4e1a935ca430c97fe Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 15 Sep 2023 08:05:38 +0200
Subject: [PATCH 099/150] Add leaky-relu in the activation enum. (#858)

---
 candle-nn/src/activation.rs | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/candle-nn/src/activation.rs b/candle-nn/src/activation.rs
index 22e062b0..17467b31 100644
--- a/candle-nn/src/activation.rs
+++ b/candle-nn/src/activation.rs
@@ -10,6 +10,7 @@ pub enum Activation {
     NewGelu,
     Relu,
     Elu(f64),
+    LeakyRelu(f64),
 }
 
 impl super::Module for Activation {
@@ -22,6 +23,7 @@ impl super::Module for Activation {
             Self::NewGelu => xs.gelu(),
             Self::Relu => xs.relu(),
             &Self::Elu(alpha) => xs.elu(alpha),
+            &Self::LeakyRelu(negative_slope) => crate::ops::leaky_relu(xs, negative_slope),
         }
     }
 }

From 81a36b8713721e2cfd098fad04972bf823ef0d5d Mon Sep 17 00:00:00 2001
From: Ssslakter <67190162+Ssslakter@users.noreply.github.com>
Date: Fri, 15 Sep 2023 13:25:10 +0700
Subject: [PATCH 100/150] Add link error info (#851)

* add link error info

* grammar fix
---
 README.md | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/README.md b/README.md
index 533e7492..a2bb371c 100644
--- a/README.md
+++ b/README.md
@@ -275,6 +275,24 @@ This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a d
 env CANDLE_NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
 ```
 
+#### Linking error on windows when running rustdoc or mdbook tests
+
+```
+Couldn't compile the test.
+---- .\candle-book\src\inference\hub.md - Using_the_hub::Using_in_a_real_model_ (line 50) stdout ----
+error: linking with `link.exe` failed: exit code: 1181
+//very long chain of linking
+ = note: LINK : fatal error LNK1181: cannot open input file 'windows.0.48.5.lib'
+```
+
+Make sure you link all native libraries that might be located outside a project target, e.g., to run mdbook tests, you should run:
+
+```
+mdbook test candle-book -L .\target\debug\deps\ `
+-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.42.2\lib `
+-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.48.5\lib
+```
+
 #### Tracking down errors
 
 You can set `RUST_BACKTRACE=1` to be provided with backtraces when a candle

From 2746f2c4bec61d8f2c7947a297c46a74a3f799db Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 15 Sep 2023 11:14:02 +0200
Subject: [PATCH 101/150] DiffNeXt/unet (#859)

* DiffNeXt/unet

* Start adding the vae.

* VAE residual block.

* VAE forward pass.

* Add pixel shuffling.

* Actually use pixel shuffling.
---
 candle-core/src/shape.rs                      |  12 ++
 candle-nn/src/ops.rs                          |  24 ++++
 .../src/models/wuerstchen/diffnext.rs         |  77 ++++++++++--
 .../src/models/wuerstchen/mod.rs              |   1 +
 .../src/models/wuerstchen/paella_vq.rs        | 111 ++++++++++++++++++
 5 files changed, 216 insertions(+), 9 deletions(-)
 create mode 100644 candle-transformers/src/models/wuerstchen/paella_vq.rs

diff --git a/candle-core/src/shape.rs b/candle-core/src/shape.rs
index b1f56817..4d500e7f 100644
--- a/candle-core/src/shape.rs
+++ b/candle-core/src/shape.rs
@@ -444,6 +444,18 @@ impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim, D5: Dim> Dims for (D1, D2, D3, D4, D5)
     }
 }
 
+impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim, D5: Dim, D6: Dim> Dims for (D1, D2, D3, D4, D5, D6) {
+    fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
+        let d0 = self.0.to_index(shape, op)?;
+        let d1 = self.1.to_index(shape, op)?;
+        let d2 = self.2.to_index(shape, op)?;
+        let d3 = self.3.to_index(shape, op)?;
+        let d4 = self.4.to_index(shape, op)?;
+        let d5 = self.5.to_index(shape, op)?;
+        Ok(vec![d0, d1, d2, d3, d4, d5])
+    }
+}
+
 extract_dims!(dims0, 0, |_: &[usize]| (), ());
 extract_dims!(dims1, 1, |d: &[usize]| d[0], usize);
 extract_dims!(dims2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
diff --git a/candle-nn/src/ops.rs b/candle-nn/src/ops.rs
index c4055792..16b2e924 100644
--- a/candle-nn/src/ops.rs
+++ b/candle-nn/src/ops.rs
@@ -189,3 +189,27 @@ impl candle::CustomOp1 for SoftmaxLastDim {
 pub fn softmax_last_dim(xs: &Tensor) -> Result<Tensor> {
     xs.apply_op1_no_bwd(&SoftmaxLastDim)
 }
+
+// https://pytorch.org/docs/stable/generated/torch.nn.PixelShuffle.html
+pub fn pixel_shuffle(xs: &Tensor, upscale_factor: usize) -> Result<Tensor> {
+    let (b_size, c, h, w) = xs.dims4()?;
+    let out_c = c / upscale_factor / upscale_factor;
+    xs.reshape((b_size, out_c, upscale_factor, upscale_factor, h, w))?
+        .permute((0, 1, 4, 2, 5, 3))?
+        .reshape((b_size, out_c, h * upscale_factor, w * upscale_factor))
+}
+
+pub fn pixel_unshuffle(xs: &Tensor, downscale_factor: usize) -> Result<Tensor> {
+    let (b_size, c, h, w) = xs.dims4()?;
+    let out_c = c * downscale_factor * downscale_factor;
+    xs.reshape((
+        b_size,
+        c,
+        h / downscale_factor,
+        downscale_factor,
+        w / downscale_factor,
+        downscale_factor,
+    ))?
+    .permute((0, 1, 3, 5, 2, 4))?
+    .reshape((b_size, out_c, h / downscale_factor, w / downscale_factor))
+}
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 7289a54d..33ca192e 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -75,7 +75,7 @@ struct UpBlock {
 #[derive(Debug)]
 pub struct WDiffNeXt {
     clip_mapper: candle_nn::Linear,
-    effnet_mappers: Vec<candle_nn::Conv2d>,
+    effnet_mappers: Vec<Option<candle_nn::Conv2d>>,
     seq_norm: candle_nn::LayerNorm,
     embedding_conv: candle_nn::Conv2d,
     embedding_ln: WLayerNorm,
@@ -84,6 +84,7 @@ pub struct WDiffNeXt {
     clf_ln: WLayerNorm,
     clf_conv: candle_nn::Conv2d,
     c_r: usize,
+    patch_size: usize,
 }
 
 impl WDiffNeXt {
@@ -102,6 +103,7 @@ impl WDiffNeXt {
         const INJECT_EFFNET: [bool; 4] = [false, true, true, true];
 
         let clip_mapper = candle_nn::linear(clip_embd, c_cond, vb.pp("clip_mapper"))?;
+        // TODO: populate effnet_mappers
         let effnet_mappers = vec![];
         let cfg = candle_nn::layer_norm::LayerNormConfig {
             ..Default::default()
@@ -232,6 +234,7 @@ impl WDiffNeXt {
             clf_ln,
             clf_conv,
             c_r,
+            patch_size,
         })
     }
 
@@ -273,14 +276,70 @@ impl WDiffNeXt {
         };
         let x_in = xs;
 
-        // TODO: pixel unshuffle.
-        let xs = xs.apply(&self.embedding_conv)?.apply(&self.embedding_ln)?;
-        // TODO: down blocks
-        let level_outputs = xs.clone();
-        // TODO: up blocks
-        let xs = level_outputs;
-        // TODO: pxel shuffle
-        let ab = xs.apply(&self.clf_ln)?.apply(&self.clf_conv)?.chunk(1, 2)?;
+        let mut xs = xs
+            .apply(&|xs: &_| candle_nn::ops::pixel_unshuffle(xs, self.patch_size))?
+            .apply(&self.embedding_conv)?
+            .apply(&self.embedding_ln)?;
+
+        let mut level_outputs = Vec::new();
+        for (i, down_block) in self.down_blocks.iter().enumerate() {
+            if let Some(ln) = &down_block.layer_norm {
+                xs = xs.apply(ln)?
+            }
+            if let Some(conv) = &down_block.conv {
+                xs = xs.apply(conv)?
+            }
+            let skip = match &self.effnet_mappers[i] {
+                None => None,
+                Some(m) => {
+                    let effnet = effnet.interpolate2d(xs.dim(D::Minus2)?, xs.dim(D::Minus1)?)?;
+                    Some(m.forward(&effnet)?)
+                }
+            };
+            for block in down_block.sub_blocks.iter() {
+                xs = block.res_block.forward(&xs, skip.as_ref())?;
+                xs = block.ts_block.forward(&xs, &r_embed)?;
+                if let Some(attn_block) = &block.attn_block {
+                    xs = attn_block.forward(&xs, clip.as_ref().unwrap())?;
+                }
+            }
+            level_outputs.push(xs.clone())
+        }
+        level_outputs.reverse();
+
+        for (i, up_block) in self.up_blocks.iter().enumerate() {
+            let skip = match &self.effnet_mappers[self.down_blocks.len() + i] {
+                None => None,
+                Some(m) => {
+                    let effnet = effnet.interpolate2d(xs.dim(D::Minus2)?, xs.dim(D::Minus1)?)?;
+                    Some(m.forward(&effnet)?)
+                }
+            };
+            for (j, block) in up_block.sub_blocks.iter().enumerate() {
+                let skip = if j == 0 && i > 0 {
+                    Some(&level_outputs[i])
+                } else {
+                    None
+                };
+                xs = block.res_block.forward(&xs, skip)?;
+                xs = block.ts_block.forward(&xs, &r_embed)?;
+                if let Some(attn_block) = &block.attn_block {
+                    xs = attn_block.forward(&xs, clip.as_ref().unwrap())?;
+                }
+            }
+            if let Some(ln) = &up_block.layer_norm {
+                xs = xs.apply(ln)?
+            }
+            if let Some(conv) = &up_block.conv {
+                xs = xs.apply(conv)?
+            }
+        }
+
+        let ab = xs
+            .apply(&self.clf_ln)?
+            .apply(&self.clf_conv)?
+            .apply(&|xs: &_| candle_nn::ops::pixel_shuffle(xs, self.patch_size))?
+            .chunk(1, 2)?;
         let b = ((candle_nn::ops::sigmoid(&ab[1])? * (1. - EPS * 2.))? + EPS)?;
         (x_in - &ab[0])? / b
     }
diff --git a/candle-transformers/src/models/wuerstchen/mod.rs b/candle-transformers/src/models/wuerstchen/mod.rs
index 81755dd1..435bdac2 100644
--- a/candle-transformers/src/models/wuerstchen/mod.rs
+++ b/candle-transformers/src/models/wuerstchen/mod.rs
@@ -1,3 +1,4 @@
 pub mod common;
 pub mod diffnext;
+pub mod paella_vq;
 pub mod prior;
diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
new file mode 100644
index 00000000..6301b7a1
--- /dev/null
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -0,0 +1,111 @@
+#![allow(unused)]
+use super::common::{AttnBlock, ResBlock, TimestepBlock};
+use candle::{DType, Module, Result, Tensor, D};
+use candle_nn::VarBuilder;
+
+#[derive(Debug)]
+struct MixingResidualBlock {
+    norm1: candle_nn::LayerNorm,
+    depthwise_conv: candle_nn::Conv2d,
+    norm2: candle_nn::LayerNorm,
+    channelwise_lin1: candle_nn::Linear,
+    channelwise_lin2: candle_nn::Linear,
+    gammas: Vec<f32>,
+}
+
+impl MixingResidualBlock {
+    pub fn new(inp: usize, embed_dim: usize, vb: VarBuilder) -> Result<Self> {
+        let cfg = candle_nn::LayerNormConfig {
+            affine: false,
+            eps: 1e-6,
+            remove_mean: true,
+        };
+        let norm1 = candle_nn::layer_norm(inp, cfg, vb.pp("norm1"))?;
+        let norm2 = candle_nn::layer_norm(inp, cfg, vb.pp("norm1"))?;
+        let cfg = candle_nn::Conv2dConfig {
+            groups: inp,
+            ..Default::default()
+        };
+        let depthwise_conv = candle_nn::conv2d(inp, inp, 3, cfg, vb.pp("depthwise.1"))?;
+        let channelwise_lin1 = candle_nn::linear(inp, embed_dim, vb.pp("channelwise.0"))?;
+        let channelwise_lin2 = candle_nn::linear(embed_dim, inp, vb.pp("channelwise.2"))?;
+        let gammas = vb.get(6, "gammas")?.to_vec1::<f32>()?;
+        Ok(Self {
+            norm1,
+            depthwise_conv,
+            norm2,
+            channelwise_lin1,
+            channelwise_lin2,
+            gammas,
+        })
+    }
+}
+
+impl Module for MixingResidualBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mods = &self.gammas;
+        let x_temp = xs
+            .permute((0, 2, 3, 1))?
+            .apply(&self.norm1)?
+            .permute((0, 3, 1, 2))?
+            .affine(1. + mods[0] as f64, mods[1] as f64)?;
+        // TODO: Add the ReplicationPad2d
+        let xs = (xs + x_temp.apply(&self.depthwise_conv)? * mods[2] as f64)?;
+        let x_temp = xs
+            .permute((0, 2, 3, 1))?
+            .apply(&self.norm2)?
+            .permute((0, 3, 1, 2))?
+            .affine(1. + mods[3] as f64, mods[4] as f64)?;
+        let x_temp = x_temp
+            .permute((0, 2, 3, 1))?
+            .apply(&self.channelwise_lin1)?
+            .gelu()?
+            .apply(&self.channelwise_lin2)?
+            .permute((0, 3, 1, 2))?;
+        xs + x_temp * mods[5] as f64
+    }
+}
+
+#[derive(Debug)]
+struct PaellaVQ {
+    in_block_conv: candle_nn::Conv2d,
+    out_block_conv: candle_nn::Conv2d,
+    down_blocks: Vec<(Option<candle_nn::Conv2d>, MixingResidualBlock)>,
+    down_blocks_conv: candle_nn::Conv2d,
+    down_blocks_bn: candle_nn::BatchNorm,
+    up_blocks_conv: candle_nn::Conv2d,
+    up_blocks: Vec<(MixingResidualBlock, Option<candle_nn::ConvTranspose2d>)>,
+}
+
+impl PaellaVQ {
+    pub fn encode(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = candle_nn::ops::pixel_unshuffle(xs, 2)?.apply(&self.in_block_conv)?;
+        for down_block in self.down_blocks.iter() {
+            if let Some(conv) = &down_block.0 {
+                xs = xs.apply(conv)?
+            }
+            xs = xs.apply(&down_block.1)?
+        }
+        xs.apply(&self.down_blocks_conv)?
+            .apply(&self.down_blocks_bn)
+        // TODO: quantizer
+    }
+
+    pub fn decode(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.apply(&self.up_blocks_conv)?;
+        for up_block in self.up_blocks.iter() {
+            xs = xs.apply(&up_block.0)?;
+            if let Some(conv) = &up_block.1 {
+                xs = xs.apply(conv)?
+            }
+        }
+        xs.apply(&self.out_block_conv)?
+            .apply(&|xs: &_| candle_nn::ops::pixel_shuffle(xs, 2))
+    }
+}
+
+impl Module for PaellaVQ {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        self.decode(&self.encode(xs)?)
+    }
+}

From 107d3d953070f7817b3aaac9ed8ca0fed7030d01 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 15 Sep 2023 11:38:38 +0200
Subject: [PATCH 102/150] Add the embed mapper convolutions. (#860)

---
 .../src/models/wuerstchen/diffnext.rs         | 33 +++++++++++++++++--
 1 file changed, 31 insertions(+), 2 deletions(-)

diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 33ca192e..74e1836c 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -101,10 +101,39 @@ impl WDiffNeXt {
         const BLOCKS: [usize; 4] = [4, 4, 14, 4];
         const NHEAD: [usize; 4] = [0, 10, 20, 20];
         const INJECT_EFFNET: [bool; 4] = [false, true, true, true];
+        const EFFNET_EMBD: usize = 16;
 
         let clip_mapper = candle_nn::linear(clip_embd, c_cond, vb.pp("clip_mapper"))?;
-        // TODO: populate effnet_mappers
-        let effnet_mappers = vec![];
+        let mut effnet_mappers = Vec::with_capacity(2 * INJECT_EFFNET.len());
+        let vb_e = vb.pp("effnet_mappers");
+        for (i, &inject) in INJECT_EFFNET.iter().enumerate() {
+            let c = if inject {
+                Some(candle_nn::conv2d(
+                    EFFNET_EMBD,
+                    c_cond,
+                    1,
+                    Default::default(),
+                    vb_e.pp(i),
+                )?)
+            } else {
+                None
+            };
+            effnet_mappers.push(c)
+        }
+        for (i, &inject) in INJECT_EFFNET.iter().rev().enumerate() {
+            let c = if inject {
+                Some(candle_nn::conv2d(
+                    EFFNET_EMBD,
+                    c_cond,
+                    1,
+                    Default::default(),
+                    vb_e.pp(i + INJECT_EFFNET.len()),
+                )?)
+            } else {
+                None
+            };
+            effnet_mappers.push(c)
+        }
         let cfg = candle_nn::layer_norm::LayerNormConfig {
             ..Default::default()
         };

From 30be5b6660ca86f8ddd2cca88890cf4e40e45e12 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 15 Sep 2023 15:06:21 +0200
Subject: [PATCH 103/150] Replication pad (#861)

* Add the embed mapper convolutions.

* Add the replication pad layer.

* Use the replication-pad op.

* Tweak a todo.
---
 candle-nn/src/ops.rs                              | 15 +++++++++++++++
 .../src/models/wuerstchen/paella_vq.rs            |  4 ++--
 2 files changed, 17 insertions(+), 2 deletions(-)

diff --git a/candle-nn/src/ops.rs b/candle-nn/src/ops.rs
index 16b2e924..1256a076 100644
--- a/candle-nn/src/ops.rs
+++ b/candle-nn/src/ops.rs
@@ -213,3 +213,18 @@ pub fn pixel_unshuffle(xs: &Tensor, downscale_factor: usize) -> Result<Tensor> {
     .permute((0, 1, 3, 5, 2, 4))?
     .reshape((b_size, out_c, h / downscale_factor, w / downscale_factor))
 }
+
+// https://pytorch.org/docs/stable/generated/torch.nn.ReplicationPad2d.html
+pub fn replication_pad2d(xs: &Tensor, pad: usize) -> Result<Tensor> {
+    match pad {
+        0 => Ok(xs.clone()),
+        1 => {
+            let (_b_size, _c, h, w) = xs.dims4()?;
+            let (first, last) = (xs.narrow(3, 0, 1)?, xs.narrow(3, w - 1, 1)?);
+            let xs = Tensor::cat(&[&first, xs, &last], 3)?;
+            let (first, last) = (xs.narrow(2, 0, 1)?, xs.narrow(2, h - 1, 1)?);
+            Tensor::cat(&[&first, &xs, &last], 2)
+        }
+        n => candle::bail!("replication-pad with a size of {n} is not supported"),
+    }
+}
diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
index 6301b7a1..6589a07d 100644
--- a/candle-transformers/src/models/wuerstchen/paella_vq.rs
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -49,7 +49,7 @@ impl Module for MixingResidualBlock {
             .apply(&self.norm1)?
             .permute((0, 3, 1, 2))?
             .affine(1. + mods[0] as f64, mods[1] as f64)?;
-        // TODO: Add the ReplicationPad2d
+        let x_temp = candle_nn::ops::replication_pad2d(&x_temp, 1)?;
         let xs = (xs + x_temp.apply(&self.depthwise_conv)? * mods[2] as f64)?;
         let x_temp = xs
             .permute((0, 2, 3, 1))?
@@ -88,10 +88,10 @@ impl PaellaVQ {
         }
         xs.apply(&self.down_blocks_conv)?
             .apply(&self.down_blocks_bn)
-        // TODO: quantizer
     }
 
     pub fn decode(&self, xs: &Tensor) -> Result<Tensor> {
+        // TODO: quantizer if we want to support `force_not_quantize=False`.
         let mut xs = xs.apply(&self.up_blocks_conv)?;
         for up_block in self.up_blocks.iter() {
             xs = xs.apply(&up_block.0)?;

From c2007ac88fb0dd6fa6f82f6624693a0095db2edb Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 15 Sep 2023 16:11:11 +0200
Subject: [PATCH 104/150] W fixes. (#862)

---
 .../src/models/wuerstchen/diffnext.rs         | 19 +++++++++++--------
 .../src/models/wuerstchen/paella_vq.rs        |  6 ++----
 .../src/models/wuerstchen/prior.rs            |  3 +--
 3 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 74e1836c..001b35d7 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -1,5 +1,4 @@
-#![allow(unused)]
-use super::common::{AttnBlock, GlobalResponseNorm, ResBlock, TimestepBlock, WLayerNorm};
+use super::common::{AttnBlock, GlobalResponseNorm, TimestepBlock, WLayerNorm};
 use candle::{DType, Module, Result, Tensor, D};
 use candle_nn::VarBuilder;
 
@@ -223,7 +222,7 @@ impl WDiffNeXt {
                 };
                 sub_blocks.push(sub_block)
             }
-            let (layer_norm, conv, start_layer_i) = if i > 0 {
+            let (layer_norm, conv) = if i > 0 {
                 let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1], vb.pp(layer_i))?;
                 layer_i += 1;
                 let cfg = candle_nn::Conv2dConfig {
@@ -231,10 +230,9 @@ impl WDiffNeXt {
                     ..Default::default()
                 };
                 let conv = candle_nn::conv2d(C_HIDDEN[i - 1], c_hidden, 2, cfg, vb.pp(layer_i))?;
-                layer_i += 1;
-                (Some(layer_norm), Some(conv), 2)
+                (Some(layer_norm), Some(conv))
             } else {
-                (None, None, 0)
+                (None, None)
             };
             let up_block = UpBlock {
                 layer_norm,
@@ -337,7 +335,7 @@ impl WDiffNeXt {
         level_outputs.reverse();
 
         for (i, up_block) in self.up_blocks.iter().enumerate() {
-            let skip = match &self.effnet_mappers[self.down_blocks.len() + i] {
+            let effnet_c = match &self.effnet_mappers[self.down_blocks.len() + i] {
                 None => None,
                 Some(m) => {
                     let effnet = effnet.interpolate2d(xs.dim(D::Minus2)?, xs.dim(D::Minus1)?)?;
@@ -350,7 +348,12 @@ impl WDiffNeXt {
                 } else {
                     None
                 };
-                xs = block.res_block.forward(&xs, skip)?;
+                let skip = match (skip, effnet_c.as_ref()) {
+                    (Some(skip), Some(effnet_c)) => Some(Tensor::cat(&[skip, effnet_c], 1)?),
+                    (None, Some(skip)) | (Some(skip), None) => Some(skip.clone()),
+                    (None, None) => None,
+                };
+                xs = block.res_block.forward(&xs, skip.as_ref())?;
                 xs = block.ts_block.forward(&xs, &r_embed)?;
                 if let Some(attn_block) = &block.attn_block {
                     xs = attn_block.forward(&xs, clip.as_ref().unwrap())?;
diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
index 6589a07d..1268047a 100644
--- a/candle-transformers/src/models/wuerstchen/paella_vq.rs
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -1,10 +1,8 @@
-#![allow(unused)]
-use super::common::{AttnBlock, ResBlock, TimestepBlock};
-use candle::{DType, Module, Result, Tensor, D};
+use candle::{Module, Result, Tensor};
 use candle_nn::VarBuilder;
 
 #[derive(Debug)]
-struct MixingResidualBlock {
+pub struct MixingResidualBlock {
     norm1: candle_nn::LayerNorm,
     depthwise_conv: candle_nn::Conv2d,
     norm2: candle_nn::LayerNorm,
diff --git a/candle-transformers/src/models/wuerstchen/prior.rs b/candle-transformers/src/models/wuerstchen/prior.rs
index 5dd03778..a9e3e793 100644
--- a/candle-transformers/src/models/wuerstchen/prior.rs
+++ b/candle-transformers/src/models/wuerstchen/prior.rs
@@ -1,6 +1,5 @@
-#![allow(unused)]
 use super::common::{AttnBlock, ResBlock, TimestepBlock};
-use candle::{DType, Module, Result, Tensor, D};
+use candle::{DType, Result, Tensor, D};
 use candle_nn::VarBuilder;
 
 #[derive(Debug)]

From 3e49f8fce52c6b8f361bfd37d541a99b5e1f8c63 Mon Sep 17 00:00:00 2001
From: Juarez Bochi <juarez.bochi@grammarly.com>
Date: Fri, 15 Sep 2023 13:05:12 -0700
Subject: [PATCH 105/150] Implement T5 decoding (#864)

* Load t5 decoder

* Run enc, dec, and lm head, but no cross attn

* Cross-attention over key_value_states

* New arg for decoder input ids

* Add mask, don't forward position biases through decoder

* Update t5 examples

* Clippy + rustfmt
---
 candle-examples/examples/t5/README.md |  34 +++--
 candle-examples/examples/t5/main.rs   | 106 ++++++++++++---
 candle-transformers/src/models/t5.rs  | 181 ++++++++++++++++++++++----
 3 files changed, 260 insertions(+), 61 deletions(-)

diff --git a/candle-examples/examples/t5/README.md b/candle-examples/examples/t5/README.md
index 66952395..c6ea2125 100644
--- a/candle-examples/examples/t5/README.md
+++ b/candle-examples/examples/t5/README.md
@@ -1,17 +1,25 @@
 # candle-t5
 
-Generates embeddings using a T5 model. It doesn't support generation yet.
+## Encoder-decoder example:
 
 ```bash
-$ cargo run --example t5 -- --model-id t5-large --prompt 'how tall is obama' --n 1
-Loaded and encoded 2.014244792s
-[[[-0.3174, -0.1462,  0.0065, ..., -0.0579, -0.0581,  0.1387],
-  [-0.2905, -0.1945, -0.0685, ..., -0.2457, -0.5137, -0.1760],
-  [-0.0591, -0.0213, -0.0241, ..., -0.0210,  0.0491, -0.0300],
-  ...
-  [-0.4333,  0.0027, -0.0609, ...,  0.3069, -0.2252,  0.3306],
-  [-0.1458,  0.1323, -0.0138, ...,  0.3000, -0.4550, -0.0384],
-  [ 0.0397,  0.0485, -0.2373, ...,  0.2578, -0.2650, -0.4356]]]
-Tensor[[1, 9, 1024], f32]
-Took 2.1363425s
-```
\ No newline at end of file
+$ cargo run --example t5 -- --model-id "t5-small" --prompt "translate to German: A beautiful candle." --decode
+...
+Running on CPU, to run on GPU, build this example with `--features cuda`
+ Eine schöne Kerze.
+9 tokens generated (2.42 token/s)
+```
+
+## Sentence embedding example:
+
+```bash
+$ cargo run --example t5 -- --model-id "t5-small" --prompt "A beautiful candle."
+...
+[[[ 0.0515, -0.0541, -0.0761, ..., -0.0392,  0.1511, -0.0265],
+  [-0.0974,  0.0998, -0.1659, ..., -0.2450,  0.1738, -0.0164],
+  [ 0.0624, -0.1024,  0.0430, ..., -0.1388,  0.0564, -0.2962],
+  [-0.0389, -0.1173,  0.0026, ...,  0.1064, -0.1065,  0.0990],
+  [ 0.1300,  0.0027, -0.0326, ...,  0.0026, -0.0317,  0.0851]]]
+Tensor[[1, 5, 512], f32]
+Took 303.766583ms
+```
diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 1e182974..00291609 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -3,18 +3,22 @@ extern crate intel_mkl_src;
 
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
+use std::io::Write;
+use std::path::PathBuf;
+
 use candle_transformers::models::t5;
 
 use anyhow::{anyhow, Error as E, Result};
-use candle::{DType, Tensor};
+use candle::{DType, Device, Tensor};
 use candle_nn::VarBuilder;
+use candle_transformers::generation::LogitsProcessor;
 use clap::Parser;
 use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
 use tokenizers::Tokenizer;
 
 const DTYPE: DType = DType::F32;
 
-#[derive(Parser, Debug)]
+#[derive(Parser, Debug, Clone)]
 #[command(author, version, about, long_about = None)]
 struct Args {
     /// Run on CPU rather than on GPU.
@@ -36,7 +40,11 @@ struct Args {
     #[arg(long)]
     revision: Option<String>,
 
-    /// Compute embeddings for this prompt, otherwise compute sentence similarities.
+    /// Enable decoding.
+    #[arg(long)]
+    decode: bool,
+
+    /// Use this prompt, otherwise compute sentence similarities.
     #[arg(long)]
     prompt: Option<String>,
 
@@ -49,12 +57,18 @@ struct Args {
     normalize_embeddings: bool,
 }
 
-impl Args {
-    fn build_model_and_tokenizer(&self) -> Result<(t5::T5EncoderModel, Tokenizer)> {
-        let device = candle_examples::device(self.cpu)?;
+struct T5ModelBuilder {
+    device: Device,
+    config: t5::Config,
+    weights_filename: PathBuf,
+}
+
+impl T5ModelBuilder {
+    pub fn load(args: &Args) -> Result<(Self, Tokenizer)> {
+        let device = candle_examples::device(args.cpu)?;
         let default_model = "t5-small".to_string();
         let default_revision = "refs/pr/15".to_string();
-        let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
+        let (model_id, revision) = match (args.model_id.to_owned(), args.revision.to_owned()) {
             (Some(model_id), Some(revision)) => (model_id, revision),
             (Some(model_id), None) => (model_id, "main".to_string()),
             (None, Some(revision)) => (default_model, revision),
@@ -62,7 +76,7 @@ impl Args {
         };
 
         let repo = Repo::with_revision(model_id, RepoType::Model, revision);
-        let (config_filename, tokenizer_filename, weights_filename) = if self.offline {
+        let (config_filename, tokenizer_filename, weights_filename) = if args.offline {
             let cache = Cache::default().repo(repo);
             (
                 cache
@@ -87,18 +101,36 @@ impl Args {
         let config = std::fs::read_to_string(config_filename)?;
         let config: t5::Config = serde_json::from_str(&config)?;
         let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+        Ok((
+            Self {
+                device,
+                config,
+                weights_filename,
+            },
+            tokenizer,
+        ))
+    }
 
-        let weights = unsafe { candle::safetensors::MmapedFile::new(weights_filename)? };
+    pub fn build_encoder(&self) -> Result<t5::T5EncoderModel> {
+        let weights =
+            unsafe { candle::safetensors::MmapedFile::new(self.weights_filename.clone())? };
         let weights = weights.deserialize()?;
-        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &device);
-        let model = t5::T5EncoderModel::load(vb, &config)?;
-        Ok((model, tokenizer))
+        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &self.device);
+        Ok(t5::T5EncoderModel::load(vb, &self.config)?)
+    }
+
+    pub fn build_conditional_generation(&self) -> Result<t5::T5ForConditionalGeneration> {
+        let weights =
+            unsafe { candle::safetensors::MmapedFile::new(self.weights_filename.clone())? };
+        let weights = weights.deserialize()?;
+        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &self.device);
+        Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
     }
 }
 
 fn main() -> Result<()> {
     let args = Args::parse();
-    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
+    let (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
     let tokenizer = tokenizer
         .with_padding(None)
         .with_truncation(None)
@@ -110,17 +142,51 @@ fn main() -> Result<()> {
                 .map_err(E::msg)?
                 .get_ids()
                 .to_vec();
-            let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
-            for idx in 0..args.n {
-                let start = std::time::Instant::now();
-                let ys = model.forward(&token_ids)?;
-                if idx == 0 {
-                    println!("{ys}");
+            let input_token_ids = Tensor::new(&tokens[..], &builder.device)?.unsqueeze(0)?;
+            if !args.decode {
+                let model = builder.build_encoder()?;
+                for idx in 0..args.n {
+                    let start = std::time::Instant::now();
+                    let ys = model.forward(&input_token_ids)?;
+                    if idx == 0 {
+                        println!("{ys}");
+                    }
+                    println!("Took {:?}", start.elapsed());
                 }
-                println!("Took {:?}", start.elapsed());
+            } else {
+                let model = builder.build_conditional_generation()?;
+                let mut output_token_ids = [builder.config.pad_token_id as u32].to_vec();
+                let mut logits_processor = LogitsProcessor::new(299792458, None, None);
+                let start = std::time::Instant::now();
+
+                for _index in 0.. {
+                    if output_token_ids.len() > 512 {
+                        break;
+                    }
+                    let decoder_token_ids =
+                        Tensor::new(&output_token_ids[..], &builder.device)?.unsqueeze(0)?;
+                    let logits = model.forward(&input_token_ids, &decoder_token_ids)?;
+                    let next_token_id = logits_processor.sample(&logits.flatten_to(1)?)?;
+                    if (next_token_id as usize) == builder.config.eos_token_id {
+                        break;
+                    }
+                    output_token_ids.push(next_token_id);
+                    if let Some(text) = tokenizer.id_to_token(next_token_id) {
+                        let text = text.replace('▁', " ").replace("<0x0A>", "\n");
+                        print!("{text}");
+                        std::io::stdout().flush()?;
+                    }
+                }
+                let dt = start.elapsed();
+                println!(
+                    "\n{} tokens generated ({:.2} token/s)\n",
+                    tokens.len(),
+                    tokens.len() as f64 / dt.as_secs_f64(),
+                );
             }
         }
         None => {
+            let model = builder.build_encoder()?;
             let sentences = [
                 "The cat sits outside",
                 "A man is playing guitar",
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index de7de496..c35dea0b 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -18,6 +18,21 @@ fn default_use_cache() -> bool {
     true
 }
 
+fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
+    let mask: Vec<_> = (0..size)
+        .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
+        .collect();
+    let result = Tensor::from_slice(&mask, (size, size), device)?;
+    Ok(result)
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
+    let shape = mask.shape();
+    let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
+    let m = mask.where_cond(&on_true, on_false)?;
+    Ok(m)
+}
+
 #[derive(Debug, Clone, PartialEq, Deserialize)]
 pub struct Config {
     vocab_size: usize,
@@ -40,8 +55,8 @@ pub struct Config {
     is_encoder_decoder: bool,
     #[serde(default = "default_use_cache")]
     use_cache: bool,
-    pad_token_id: usize,
-    eos_token_id: usize,
+    pub pad_token_id: usize,
+    pub eos_token_id: usize,
 }
 
 impl Default for Config {
@@ -233,13 +248,13 @@ struct T5Attention {
 }
 
 impl T5Attention {
-    fn load(h: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+    fn load(has_relative_attention_bias: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
         let inner_dim = cfg.num_heads * cfg.d_kv;
         let q = linear_no_bias(cfg.d_model, inner_dim, vb.pp("q"))?;
         let k = linear_no_bias(cfg.d_model, inner_dim, vb.pp("k"))?;
         let v = linear_no_bias(cfg.d_model, inner_dim, vb.pp("v"))?;
         let o = linear_no_bias(inner_dim, cfg.d_model, vb.pp("o"))?;
-        let relative_attention_bias = if h {
+        let relative_attention_bias = if has_relative_attention_bias {
             let emb = embedding(
                 cfg.relative_attention_num_buckets,
                 cfg.num_heads,
@@ -267,26 +282,46 @@ impl T5Attention {
         &self,
         xs: &Tensor,
         position_bias: Option<&Tensor>,
+        key_value_states: Option<&Tensor>,
+        mask: Option<&Tensor>,
     ) -> Result<(Tensor, Option<Tensor>)> {
-        // TODO: Apply the mask(s)?
+        // Performs Self-attention (if key_value_states is None) or attention
+        // over source sentence (provided by key_value_states).
         // TODO: kv caching.
-        let (b_sz, seq_len) = (xs.dim(0)?, xs.dim(1)?);
+        let kv_input = match key_value_states {
+            None => xs,
+            Some(key_value_states) => key_value_states,
+        };
+        let (b_sz, q_len) = (xs.dim(0)?, xs.dim(1)?);
+        let kv_len = kv_input.dim(1)?;
         let q = self.q.forward(xs)?;
-        let k = self.k.forward(xs)?;
-        let v = self.v.forward(xs)?;
+        let k = self.k.forward(kv_input)?;
+        let v = self.v.forward(kv_input)?;
         let q = q
-            .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
+            .reshape((b_sz, q_len, self.n_heads, self.d_kv))?
             .transpose(1, 2)?
             .contiguous()?;
         let k = k
-            .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
+            .reshape((b_sz, kv_len, self.n_heads, self.d_kv))?
             .transpose(1, 2)?
             .contiguous()?;
         let v = v
-            .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
+            .reshape((b_sz, kv_len, self.n_heads, self.d_kv))?
             .transpose(1, 2)?
             .contiguous()?;
+        // TODO: Use flash_attn.
         let scores = q.matmul(&k.t()?)?;
+        let scores = match mask {
+            None => scores,
+            Some(mask) => masked_fill(
+                &scores,
+                &mask
+                    .unsqueeze(0)?
+                    .unsqueeze(0)?
+                    .repeat((b_sz, self.n_heads))?,
+                f32::NEG_INFINITY,
+            )?,
+        };
 
         let (scores, position_bias) = match position_bias {
             Some(position_bias) => (
@@ -296,14 +331,12 @@ impl T5Attention {
             None => match &self.relative_attention_bias {
                 None => (scores, None),
                 Some(relative_attention_bias) => {
-                    let query_length = seq_len;
-                    let key_length = seq_len;
                     // This only handles the bidirectional case.
                     let num_buckets = self.relative_attention_num_buckets as u32 / 2;
                     let max_exact = num_buckets / 2;
-                    let relative_position = (0..query_length as u32)
+                    let relative_position = (0..q_len as u32)
                         .map(|i| {
-                            (0..key_length as u32)
+                            (0..kv_len as u32)
                                 .map(|j| {
                                     if i < j {
                                         if j - i < max_exact {
@@ -348,7 +381,7 @@ impl T5Attention {
         let attn_output = attn_weights.matmul(&v)?;
         let attn_output = attn_output
             .transpose(1, 2)?
-            .reshape((b_sz, seq_len, self.inner_dim))?;
+            .reshape((b_sz, q_len, self.inner_dim))?;
         let attn_output = self.o.forward(&attn_output)?;
         Ok((attn_output, position_bias))
     }
@@ -375,24 +408,49 @@ impl T5LayerSelfAttention {
         &self,
         xs: &Tensor,
         position_bias: Option<&Tensor>,
+        mask: Option<&Tensor>,
     ) -> Result<(Tensor, Option<Tensor>)> {
         let normed_xs = self.layer_norm.forward(xs)?;
-        let (ys, position_bias) = self.self_attention.forward(&normed_xs, position_bias)?;
+        let (ys, position_bias) =
+            self.self_attention
+                .forward(&normed_xs, position_bias, None, mask)?;
         let ys = (xs + ys)?;
         Ok((ys, position_bias))
     }
 }
 
 #[derive(Debug)]
-struct T5LayerCrossAttention {}
+struct T5LayerCrossAttention {
+    cross_attention: T5Attention,
+    layer_norm: T5LayerNorm,
+}
 
 impl T5LayerCrossAttention {
-    fn load(_vb: VarBuilder, _cfg: &Config) -> Result<Self> {
-        todo!()
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let cross_attention = T5Attention::load(false, vb.pp("EncDecAttention"), cfg)?;
+        let layer_norm =
+            T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
+        Ok(Self {
+            cross_attention,
+            layer_norm,
+        })
     }
 
-    fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
-        todo!()
+    fn forward(
+        &self,
+        hidden_states: &Tensor,
+        position_bias: Option<&Tensor>,
+        key_value_states: &Tensor,
+    ) -> Result<(Tensor, Option<Tensor>)> {
+        let normed_hidden_states = self.layer_norm.forward(hidden_states)?;
+        let (ys, position_bias) = self.cross_attention.forward(
+            &normed_hidden_states,
+            position_bias,
+            Some(key_value_states),
+            None,
+        )?;
+        let ys = (hidden_states + ys)?;
+        Ok((ys, position_bias))
     }
 }
 
@@ -425,11 +483,17 @@ impl T5Block {
         &self,
         xs: &Tensor,
         position_bias: Option<&Tensor>,
+        encoder_hidden_states: Option<&Tensor>,
     ) -> Result<(Tensor, Option<Tensor>)> {
-        let (mut xs, position_bias) = self.self_attn.forward(xs, position_bias)?;
+        // TODO: Cache masks
+        let mask = match self.cross_attn.is_some() {
+            true => Some(get_mask(xs.dim(1)?, xs.device())?),
+            false => None,
+        };
+        let (mut xs, position_bias) = self.self_attn.forward(xs, position_bias, mask.as_ref())?;
         // TODO: clamp for f16?
         if let Some(cross_attn) = &self.cross_attn {
-            xs = cross_attn.forward(&xs)?;
+            (xs, _) = cross_attn.forward(&xs, None, encoder_hidden_states.unwrap())?;
             // TODO: clamp for f16?
         }
         let xs = self.ff.forward(&xs)?;
@@ -462,13 +526,20 @@ impl T5Stack {
         })
     }
 
-    fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
+    fn forward(
+        &self,
+        input_ids: &Tensor,
+        encoder_hidden_states: Option<&Tensor>,
+    ) -> Result<Tensor> {
         let input_embeds = self.shared.as_ref().forward(input_ids)?;
         let mut hidden_states = input_embeds;
         let mut position_bias = None;
         for block in self.block.iter() {
-            (hidden_states, position_bias) =
-                block.forward(&hidden_states, position_bias.as_ref())?
+            (hidden_states, position_bias) = block.forward(
+                &hidden_states,
+                position_bias.as_ref(),
+                encoder_hidden_states,
+            )?
         }
         self.final_layer_norm.forward(&hidden_states)
     }
@@ -492,7 +563,61 @@ impl T5EncoderModel {
     }
 
     pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
-        self.encoder.forward(input_ids)
+        self.encoder.forward(input_ids, None)
+    }
+
+    pub fn device(&self) -> &Device {
+        &self.device
+    }
+}
+
+#[derive(Debug)]
+pub struct T5ForConditionalGeneration {
+    encoder: T5Stack,
+    decoder: T5Stack,
+    shared: Arc<Embedding>,
+    device: Device,
+}
+
+impl T5ForConditionalGeneration {
+    pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        assert!(cfg.is_encoder_decoder);
+        let shared = embedding(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
+        let shared = Arc::new(shared);
+
+        let mut encoder_cfg = cfg.clone();
+        encoder_cfg.is_decoder = false;
+        encoder_cfg.use_cache = false;
+        encoder_cfg.is_encoder_decoder = false;
+        let encoder = T5Stack::load(vb.pp("encoder"), &shared, &encoder_cfg)?;
+
+        let mut decoder_cfg = cfg.clone();
+        decoder_cfg.is_decoder = true;
+        decoder_cfg.is_encoder_decoder = false;
+        decoder_cfg.num_layers = cfg.num_decoder_layers.unwrap_or(cfg.num_layers);
+        let decoder = T5Stack::load(vb.pp("decoder"), &shared, &decoder_cfg)?;
+
+        Ok(Self {
+            encoder,
+            decoder,
+            shared,
+            device: vb.device().clone(),
+        })
+    }
+
+    pub fn forward(&self, input_ids: &Tensor, decoder_input_ids: &Tensor) -> Result<Tensor> {
+        let encoder_output = self.encoder.forward(input_ids, None)?;
+        let decoder_output = self
+            .decoder
+            .forward(decoder_input_ids, Some(&encoder_output))?;
+        let sequence_output = decoder_output
+            .narrow(1, decoder_output.dim(1)? - 1, 1)?
+            .squeeze(1)?;
+        // TODO: check cfg.tie_word_embeddings to load from model instead.
+        let lm_head_weights = self.shared.embeddings().t()?;
+        let output = sequence_output.matmul(&lm_head_weights)?;
+        // TODO: Rescale output before projecting on vocab? * (self.model_dim**-0.5)
+        Ok(output)
     }
 
     pub fn device(&self) -> &Device {

From 635012d770a75033081008a22044804d277fafa8 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Fri, 15 Sep 2023 23:15:40 +0200
Subject: [PATCH 106/150] Do not backprop through argmin/argmax. (#865)

---
 candle-core/src/backprop.rs | 4 +++-
 candle-core/src/tensor.rs   | 7 ++++++-
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/candle-core/src/backprop.rs b/candle-core/src/backprop.rs
index b930a9f4..9c8f685f 100644
--- a/candle-core/src/backprop.rs
+++ b/candle-core/src/backprop.rs
@@ -98,7 +98,7 @@ impl Tensor {
                     | Op::Copy(node)
                     | Op::Broadcast(node)
                     | Op::Cmp(node, _)
-                    | Op::Reduce(node, _, _)
+                    | Op::Reduce(node, ReduceOp::Min | ReduceOp::Sum | ReduceOp::Max, _)
                     | Op::ToDType(node)
                     | Op::ToDevice(node)
                     | Op::Transpose(node, _, _)
@@ -112,6 +112,7 @@ impl Tensor {
                         track_grad |= tg;
                         nodes
                     }
+                    Op::Reduce(_, ReduceOp::ArgMin | ReduceOp::ArgMax, _) => nodes,
                 }
             } else {
                 nodes
@@ -521,6 +522,7 @@ impl Tensor {
     }
 }
 
+#[derive(Debug)]
 pub struct GradStore(HashMap<TensorId, Tensor>);
 
 impl GradStore {
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 4388bf77..61f576cf 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -666,7 +666,12 @@ impl Tensor {
         let storage = self.storage().reduce_op(op, self.layout(), &[dim])?;
         let mut dims = self.dims().to_vec();
         dims[dim] = 1;
-        let op = BackpropOp::new1(self, |arg| Op::Reduce(arg, op, dims.to_vec()));
+        let op = match op {
+            ReduceOp::Sum | ReduceOp::Min | ReduceOp::Max => {
+                BackpropOp::new1(self, |arg| Op::Reduce(arg, op, dims.to_vec()))
+            }
+            ReduceOp::ArgMin | ReduceOp::ArgMax => BackpropOp::none(),
+        };
         let res = from_storage(storage, dims, op, false);
         if keepdim {
             Ok(res)

From 04ca2b9ebd75b065641512d4254850e065699030 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radam=C3=A9s=20Ajna?= <radamajna@gmail.com>
Date: Fri, 15 Sep 2023 22:34:13 -0700
Subject: [PATCH 107/150] Update README + SAM (#866)

* use serde-wasm-bindgen, faster serialization

* update readme with demos
---
 README.md                                          | 4 +++-
 candle-wasm-examples/segment-anything/Cargo.toml   | 1 +
 candle-wasm-examples/segment-anything/samWorker.js | 3 +--
 candle-wasm-examples/segment-anything/src/bin/m.rs | 5 ++---
 4 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index a2bb371c..610ef555 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,7 @@ These online demos run entirely in your browser:
   object recognition.
 - [whisper](https://huggingface.co/spaces/lmz/candle-whisper): text to speech.
 - [LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2): text generation.
+- [Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm): Image segmentation.
 
 We also provide a some command line based examples using state of the art models:
 
@@ -96,7 +97,8 @@ There are also some wasm examples for whisper and
 [llama2.c](https://github.com/karpathy/llama2.c). You can either build them with
 `trunk` or try them online:
 [whisper](https://huggingface.co/spaces/lmz/candle-whisper),
-[llama2](https://huggingface.co/spaces/lmz/candle-llama2).
+[llama2](https://huggingface.co/spaces/lmz/candle-llama2),
+[Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
 
 For LLaMA2, run the following command to retrieve the weight files and start a
 test server:
diff --git a/candle-wasm-examples/segment-anything/Cargo.toml b/candle-wasm-examples/segment-anything/Cargo.toml
index b67de112..a847424d 100644
--- a/candle-wasm-examples/segment-anything/Cargo.toml
+++ b/candle-wasm-examples/segment-anything/Cargo.toml
@@ -27,3 +27,4 @@ serde_json = { workspace = true }
 # Wasm specific crates.
 console_error_panic_hook = "0.1.7"
 wasm-bindgen = "0.2.87"
+serde-wasm-bindgen = "0.6.0"
diff --git a/candle-wasm-examples/segment-anything/samWorker.js b/candle-wasm-examples/segment-anything/samWorker.js
index b90498de..c1a152ef 100644
--- a/candle-wasm-examples/segment-anything/samWorker.js
+++ b/candle-wasm-examples/segment-anything/samWorker.js
@@ -141,8 +141,7 @@ self.addEventListener("message", async (event) => {
     }
 
     self.postMessage({ status: "segmenting", message: "Segmenting" });
-    const result = sam.mask_for_point(points.x, points.y);
-    const { mask, image } = JSON.parse(result);
+    const { mask, image } = sam.mask_for_point(points.x, points.y);
     const maskDataURL = await createImageCanvas(mask, image);
     // Send the segment back to the main thread as JSON
     self.postMessage({
diff --git a/candle-wasm-examples/segment-anything/src/bin/m.rs b/candle-wasm-examples/segment-anything/src/bin/m.rs
index 949c18a0..5140b979 100644
--- a/candle-wasm-examples/segment-anything/src/bin/m.rs
+++ b/candle-wasm-examples/segment-anything/src/bin/m.rs
@@ -76,7 +76,7 @@ impl Model {
     }
 
     // x and y have to be between 0 and 1
-    pub fn mask_for_point(&self, x: f64, y: f64) -> Result<String, JsError> {
+    pub fn mask_for_point(&self, x: f64, y: f64) -> Result<JsValue, JsError> {
         if !(0. ..=1.).contains(&x) {
             Err(JsError::new(&format!(
                 "x has to be between 0 and 1, got {x}"
@@ -112,8 +112,7 @@ impl Model {
             width: embeddings.width,
             height: embeddings.height,
         };
-        let json = serde_json::to_string(&MaskImage { mask, image })?;
-        Ok(json)
+        Ok(serde_wasm_bindgen::to_value(&MaskImage { mask, image })?)
     }
 }
 

From 7cafca835a4bb9a21f3c8111e2f61b7a6b1270fd Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sat, 16 Sep 2023 08:22:24 +0200
Subject: [PATCH 108/150] readme tweaks. (#867)

---
 README.md | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 610ef555..d897de89 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,9 @@ Candle is a minimalist ML framework for Rust with a focus on performance (includ
 and ease of use. Try our online demos: 
 [whisper](https://huggingface.co/spaces/lmz/candle-whisper),
 [LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2),
-[yolo](https://huggingface.co/spaces/lmz/candle-yolo).
+[yolo](https://huggingface.co/spaces/lmz/candle-yolo),
+[Segment
+Anything](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
 
 ## Get started
 
@@ -114,8 +116,7 @@ And then head over to
 <!--- ANCHOR: useful_libraries --->
 
 ## Useful Libraries
-- `candle-lora`
-    - [`candle-lora`](https://github.com/EricLBuehler/candle-lora) provides a LoRA implementation that conforms to the official `peft` implementation.
+- [`candle-lora`](https://github.com/EricLBuehler/candle-lora) provides a LoRA implementation that conforms to the official `peft` implementation.
 
 If you have an addition to this list, please submit a pull request.
 
@@ -133,10 +134,20 @@ If you have an addition to this list, please submit a pull request.
     - CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
     - WASM support, run your models in a browser.
 - Included models.
-    - LLMs: LLaMA v1 and v2, Falcon, StarCoder.
+    - Language Models.
+        - LLaMA v1 and v2.
+        - Falcon.
+        - StarCoder.
+        - T5.
+        - Bert.
     - Whisper (multi-lingual support).
-    - Stable Diffusion.
-    - Computer Vision: DINOv2, EfficientNet, yolo-v3, yolo-v8.
+    - Stable Diffusion v1.5, v2.1, XL v1.0.
+    - Computer Vision Models.
+        - DINOv2.
+        - EfficientNet.
+        - yolo-v3.
+        - yolo-v8.
+        - Segment-Anything Model (SAM).
 - File formats: load models from safetensors, npz, ggml, or PyTorch files.
 - Serverless (on CPU), small and fast deployments.
 - Quantization support using the llama.cpp quantized types.

From 8658df348527cabcd722bfe2e9e48aba3c7f8e96 Mon Sep 17 00:00:00 2001
From: Lukas Kreussel <65088241+LLukas22@users.noreply.github.com>
Date: Sat, 16 Sep 2023 18:23:38 +0200
Subject: [PATCH 109/150] Generate `*.pyi` stubs for PyO3 wrapper (#870)

* Begin to generate typehints.

* generate correct stubs

* Correctly include stubs

* Add comments and typhints to static functions

* ensure candle-pyo3 directory

* Make `llama.rope.freq_base` optional

* `fmt`
---
 candle-pyo3/.gitignore                       | 160 ++++++++++++
 candle-pyo3/Cargo.toml                       |   1 -
 candle-pyo3/README.md                        |  21 +-
 candle-pyo3/py_src/candle/__init__.py        |   1 +
 candle-pyo3/py_src/candle/__init__.pyi       | 248 +++++++++++++++++++
 candle-pyo3/py_src/candle/nn/__init__.py     |   5 +
 candle-pyo3/py_src/candle/nn/__init__.pyi    |  19 ++
 candle-pyo3/py_src/candle/typing/__init__.py |  16 ++
 candle-pyo3/py_src/candle/utils/__init__.py  |  11 +
 candle-pyo3/py_src/candle/utils/__init__.pyi |  63 +++++
 candle-pyo3/pyproject.toml                   |  30 +++
 candle-pyo3/quant-llama.py                   |   7 +-
 candle-pyo3/src/lib.rs                       |  89 ++++---
 candle-pyo3/stub.py                          | 217 ++++++++++++++++
 candle-pyo3/test.py                          |   9 +-
 15 files changed, 857 insertions(+), 40 deletions(-)
 create mode 100644 candle-pyo3/.gitignore
 create mode 100644 candle-pyo3/py_src/candle/__init__.py
 create mode 100644 candle-pyo3/py_src/candle/__init__.pyi
 create mode 100644 candle-pyo3/py_src/candle/nn/__init__.py
 create mode 100644 candle-pyo3/py_src/candle/nn/__init__.pyi
 create mode 100644 candle-pyo3/py_src/candle/typing/__init__.py
 create mode 100644 candle-pyo3/py_src/candle/utils/__init__.py
 create mode 100644 candle-pyo3/py_src/candle/utils/__init__.pyi
 create mode 100644 candle-pyo3/pyproject.toml
 create mode 100644 candle-pyo3/stub.py

diff --git a/candle-pyo3/.gitignore b/candle-pyo3/.gitignore
new file mode 100644
index 00000000..68bc17f9
--- /dev/null
+++ b/candle-pyo3/.gitignore
@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
diff --git a/candle-pyo3/Cargo.toml b/candle-pyo3/Cargo.toml
index 1d431cfb..c96681bd 100644
--- a/candle-pyo3/Cargo.toml
+++ b/candle-pyo3/Cargo.toml
@@ -12,7 +12,6 @@ readme = "README.md"
 [lib]
 name = "candle"
 crate-type = ["cdylib"]
-doc = false
 
 [dependencies]
 candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
diff --git a/candle-pyo3/README.md b/candle-pyo3/README.md
index 07dff468..be6d4f68 100644
--- a/candle-pyo3/README.md
+++ b/candle-pyo3/README.md
@@ -1,7 +1,26 @@
+## Installation 
+
 From the `candle-pyo3` directory, enable a virtual env where you will want the
 candle package to be installed then run.
 
 ```bash
-maturin develop
+maturin develop -r 
 python test.py
 ```
+
+## Generating Stub Files for Type Hinting
+
+For type hinting support, the `candle-pyo3` package requires `*.pyi` files. You can automatically generate these files using the `stub.py` script.
+
+### Steps:
+1. Install the package using `maturin`.
+2. Generate the stub files by running:
+   ```
+   python stub.py
+   ```
+
+### Validation:
+To ensure that the stub files match the current implementation, execute:
+```
+python stub.py --check
+```
diff --git a/candle-pyo3/py_src/candle/__init__.py b/candle-pyo3/py_src/candle/__init__.py
new file mode 100644
index 00000000..49c96122
--- /dev/null
+++ b/candle-pyo3/py_src/candle/__init__.py
@@ -0,0 +1 @@
+from .candle import *
\ No newline at end of file
diff --git a/candle-pyo3/py_src/candle/__init__.pyi b/candle-pyo3/py_src/candle/__init__.pyi
new file mode 100644
index 00000000..c21e6738
--- /dev/null
+++ b/candle-pyo3/py_src/candle/__init__.pyi
@@ -0,0 +1,248 @@
+# Generated content DO NOT EDIT
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
+from os import PathLike
+from candle.typing import _ArrayLike, Device
+
+class bf16(DType):
+    pass
+
+@staticmethod
+def cat(tensors: List[Tensor], dim: int):
+    """
+    Concatenate the tensors across one axis.
+    """
+    pass
+
+class f16(DType):
+    pass
+
+class f32(DType):
+    pass
+
+class f64(DType):
+    pass
+
+class i64(DType):
+    pass
+
+@staticmethod
+def ones(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None):
+    """ """
+    pass
+
+@staticmethod
+def rand(shape: Sequence[int], device: Optional[Device] = None):
+    """
+    Creates a new tensor with random values.
+    """
+    pass
+
+@staticmethod
+def randn(shape: Sequence[int], device: Optional[Device] = None):
+    """ """
+    pass
+
+@staticmethod
+def stack(tensors: List[Tensor], dim: int):
+    """
+    Stack the tensors along a new axis.
+    """
+    pass
+
+@staticmethod
+def tensor(data: _ArrayLike):
+    """
+    Creates a new tensor from a Python value. The value can be a scalar or array-like object.
+    """
+    pass
+
+class u32(DType):
+    pass
+
+class u8(DType):
+    pass
+
+@staticmethod
+def zeros(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None):
+    """ """
+    pass
+
+class DType:
+    pass
+
+class QTensor:
+    def dequantize(self):
+        """ """
+        pass
+    @property
+    def ggml_dtype(self):
+        """ """
+        pass
+    def matmul_t(self, lhs):
+        """ """
+        pass
+    @property
+    def rank(self):
+        """ """
+        pass
+    @property
+    def shape(self):
+        """ """
+        pass
+
+class Tensor:
+    def __init__(data: _ArrayLike):
+        pass
+    def argmax_keepdim(self, dim):
+        """ """
+        pass
+    def argmin_keepdim(self, dim):
+        """ """
+        pass
+    def broadcast_add(self, rhs):
+        """ """
+        pass
+    def broadcast_as(self, shape):
+        """ """
+        pass
+    def broadcast_div(self, rhs):
+        """ """
+        pass
+    def broadcast_left(self, shape):
+        """ """
+        pass
+    def broadcast_mul(self, rhs):
+        """ """
+        pass
+    def broadcast_sub(self, rhs):
+        """ """
+        pass
+    def contiguous(self):
+        """ """
+        pass
+    def copy(self):
+        """ """
+        pass
+    def cos(self):
+        """ """
+        pass
+    def detach(self):
+        """ """
+        pass
+    @property
+    def device(self):
+        """ """
+        pass
+    @property
+    def dtype(self):
+        """ """
+        pass
+    def exp(self):
+        """ """
+        pass
+    def flatten_all(self):
+        """ """
+        pass
+    def flatten_from(self, dim):
+        """ """
+        pass
+    def flatten_to(self, dim):
+        """ """
+        pass
+    def get(self, index):
+        """ """
+        pass
+    def index_select(self, rhs, dim):
+        """ """
+        pass
+    def is_contiguous(self):
+        """ """
+        pass
+    def is_fortran_contiguous(self):
+        """ """
+        pass
+    def log(self):
+        """ """
+        pass
+    def matmul(self, rhs):
+        """ """
+        pass
+    def max_keepdim(self, dim):
+        """ """
+        pass
+    def mean_all(self):
+        """ """
+        pass
+    def min_keepdim(self, dim):
+        """ """
+        pass
+    def narrow(self, dim, start, len):
+        """ """
+        pass
+    def powf(self, p):
+        """ """
+        pass
+    def quantize(self, quantized_dtype):
+        """ """
+        pass
+    @property
+    def rank(self):
+        """ """
+        pass
+    def recip(self):
+        """ """
+        pass
+    def reshape(self, shape):
+        """ """
+        pass
+    @property
+    def shape(self):
+        """
+        Gets the tensor shape as a Python tuple.
+        """
+        pass
+    def sin(self):
+        """ """
+        pass
+    def sqr(self):
+        """ """
+        pass
+    def sqrt(self):
+        """ """
+        pass
+    def squeeze(self, dim):
+        """ """
+        pass
+    @property
+    def stride(self):
+        """ """
+        pass
+    def sum_all(self):
+        """ """
+        pass
+    def sum_keepdim(self, dims):
+        """ """
+        pass
+    def t(self):
+        """ """
+        pass
+    def to_device(self, device):
+        """ """
+        pass
+    def to_dtype(self, dtype):
+        """ """
+        pass
+    def transpose(self, dim1, dim2):
+        """ """
+        pass
+    def unsqueeze(self, dim):
+        """ """
+        pass
+    def values(self):
+        """
+        Gets the tensor's data as a Python scalar or array-like object.
+        """
+        pass
+    def where_cond(self, on_true, on_false):
+        """ """
+        pass
diff --git a/candle-pyo3/py_src/candle/nn/__init__.py b/candle-pyo3/py_src/candle/nn/__init__.py
new file mode 100644
index 00000000..b8c5cfb7
--- /dev/null
+++ b/candle-pyo3/py_src/candle/nn/__init__.py
@@ -0,0 +1,5 @@
+# Generated content DO NOT EDIT
+from .. import nn
+
+silu = nn.silu
+softmax = nn.softmax
diff --git a/candle-pyo3/py_src/candle/nn/__init__.pyi b/candle-pyo3/py_src/candle/nn/__init__.pyi
new file mode 100644
index 00000000..821cd052
--- /dev/null
+++ b/candle-pyo3/py_src/candle/nn/__init__.pyi
@@ -0,0 +1,19 @@
+# Generated content DO NOT EDIT
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
+from os import PathLike
+from candle.typing import _ArrayLike, Device
+from candle import Tensor, DType
+
+@staticmethod
+def silu(tensor: Tensor):
+    """
+    Applies the Sigmoid Linear Unit (SiLU) function to a given tensor.
+    """
+    pass
+
+@staticmethod
+def softmax(tensor: Tensor, dim: int):
+    """
+    Applies the Softmax function to a given tensor.
+    """
+    pass
diff --git a/candle-pyo3/py_src/candle/typing/__init__.py b/candle-pyo3/py_src/candle/typing/__init__.py
new file mode 100644
index 00000000..ea85d2a3
--- /dev/null
+++ b/candle-pyo3/py_src/candle/typing/__init__.py
@@ -0,0 +1,16 @@
+from typing import TypeVar, Union, Sequence
+
+_T = TypeVar("_T")
+
+_ArrayLike  = Union[
+    _T,
+    Sequence[_T],
+    Sequence[Sequence[_T]],
+    Sequence[Sequence[Sequence[_T]]],
+    Sequence[Sequence[Sequence[Sequence[_T]]]],
+]
+
+CPU:str = "cpu"
+CUDA:str = "cuda"
+
+Device = TypeVar("Device", CPU, CUDA)
\ No newline at end of file
diff --git a/candle-pyo3/py_src/candle/utils/__init__.py b/candle-pyo3/py_src/candle/utils/__init__.py
new file mode 100644
index 00000000..2ead6d84
--- /dev/null
+++ b/candle-pyo3/py_src/candle/utils/__init__.py
@@ -0,0 +1,11 @@
+# Generated content DO NOT EDIT
+from .. import utils
+
+cuda_is_available = utils.cuda_is_available
+get_num_threads = utils.get_num_threads
+has_accelerate = utils.has_accelerate
+has_mkl = utils.has_mkl
+load_ggml = utils.load_ggml
+load_gguf = utils.load_gguf
+load_safetensors = utils.load_safetensors
+save_safetensors = utils.save_safetensors
diff --git a/candle-pyo3/py_src/candle/utils/__init__.pyi b/candle-pyo3/py_src/candle/utils/__init__.pyi
new file mode 100644
index 00000000..7a0a5231
--- /dev/null
+++ b/candle-pyo3/py_src/candle/utils/__init__.pyi
@@ -0,0 +1,63 @@
+# Generated content DO NOT EDIT
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
+from os import PathLike
+from candle.typing import _ArrayLike, Device
+from candle import Tensor, DType
+
+@staticmethod
+def cuda_is_available():
+    """
+    Returns true if the 'cuda' backend is available.
+    """
+    pass
+
+@staticmethod
+def get_num_threads():
+    """
+    Returns the number of threads used by the candle.
+    """
+    pass
+
+@staticmethod
+def has_accelerate():
+    """
+    Returns true if candle was compiled with 'accelerate' support.
+    """
+    pass
+
+@staticmethod
+def has_mkl():
+    """
+    Returns true if candle was compiled with MKL support.
+    """
+    pass
+
+@staticmethod
+def load_ggml(path: Union[str, PathLike]):
+    """
+    Load a GGML file. Returns a tuple of three objects: a dictionary mapping tensor names to tensors,
+    a dictionary mapping hyperparameter names to hyperparameter values, and a vocabulary.
+    """
+    pass
+
+@staticmethod
+def load_gguf(path: Union[str, PathLike]):
+    """
+    Loads a GGUF file. Returns a tuple of two dictionaries: the first maps tensor names to tensors,
+    and the second maps metadata keys to metadata values.
+    """
+    pass
+
+@staticmethod
+def load_safetensors(path: Union[str, PathLike]):
+    """
+    Loads a safetensors file. Returns a dictionary mapping tensor names to tensors.
+    """
+    pass
+
+@staticmethod
+def save_safetensors(path: Union[str, PathLike], tensors: Dict[str, Tensor]):
+    """
+    Saves a dictionary of tensors to a safetensors file.
+    """
+    pass
diff --git a/candle-pyo3/pyproject.toml b/candle-pyo3/pyproject.toml
new file mode 100644
index 00000000..b4e372d7
--- /dev/null
+++ b/candle-pyo3/pyproject.toml
@@ -0,0 +1,30 @@
+[project]
+name = 'candle-pyo3'
+requires-python = '>=3.7'
+authors = [
+    {name = 'Laurent Mazare', email = ''},
+]
+
+dynamic = [
+    'description',
+    'license',
+    'readme',
+]
+
+[project.urls]
+Homepage = 'https://github.com/huggingface/candle'
+Source = 'https://github.com/huggingface/candle'
+
+[build-system]
+requires = ["maturin>=1.0,<2.0"]
+build-backend = "maturin"
+
+[tool.maturin]
+python-source = "py_src"
+module-name = "candle.candle"
+bindings = 'pyo3'
+features = ["pyo3/extension-module"]
+
+[tool.black]
+line-length = 119
+target-version = ['py35']
\ No newline at end of file
diff --git a/candle-pyo3/quant-llama.py b/candle-pyo3/quant-llama.py
index 0f7a51c6..020d525d 100644
--- a/candle-pyo3/quant-llama.py
+++ b/candle-pyo3/quant-llama.py
@@ -1,6 +1,7 @@
 # This example shows how the candle Python api can be used to replicate llama.cpp.
 import sys
 import candle
+from candle.utils import load_ggml,load_gguf
 
 MAX_SEQ_LEN = 4096
 
@@ -154,7 +155,7 @@ def main():
     filename = sys.argv[1]
     print(f"reading model file {filename}")
     if filename.endswith("gguf"):
-        all_tensors, metadata = candle.load_gguf(sys.argv[1])
+        all_tensors, metadata = load_gguf(sys.argv[1])
         vocab = metadata["tokenizer.ggml.tokens"]
         for i, v in enumerate(vocab):
             vocab[i] = '\n' if v == '<0x0A>' else v.replace('▁', ' ')
@@ -168,13 +169,13 @@ def main():
             'n_head_kv': metadata['llama.attention.head_count_kv'],
             'n_layer': metadata['llama.block_count'],
             'n_rot': metadata['llama.rope.dimension_count'],
-            'rope_freq': metadata['llama.rope.freq_base'],
+            'rope_freq': metadata.get('llama.rope.freq_base', 10000.),
             'ftype': metadata['general.file_type'],
         }
         all_tensors = { gguf_rename(k): v for k, v in all_tensors.items() }
 
     else:
-        all_tensors, hparams, vocab = candle.load_ggml(sys.argv[1])
+        all_tensors, hparams, vocab = load_ggml(sys.argv[1])
     print(hparams)
     model = QuantizedLlama(hparams, all_tensors)
     print("model built, starting inference")
diff --git a/candle-pyo3/src/lib.rs b/candle-pyo3/src/lib.rs
index eddc0fda..1df78ec6 100644
--- a/candle-pyo3/src/lib.rs
+++ b/candle-pyo3/src/lib.rs
@@ -197,38 +197,40 @@ trait MapDType {
 #[pymethods]
 impl PyTensor {
     #[new]
+    #[pyo3(text_signature = "(data:_ArrayLike)")]
     // TODO: Handle arbitrary input dtype and shape.
-    fn new(py: Python<'_>, vs: PyObject) -> PyResult<Self> {
+    /// Creates a new tensor from a Python value. The value can be a scalar or array-like object.
+    fn new(py: Python<'_>, data: PyObject) -> PyResult<Self> {
         use Device::Cpu;
-        let tensor = if let Ok(vs) = vs.extract::<u32>(py) {
+        let tensor = if let Ok(vs) = data.extract::<u32>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<i64>(py) {
+        } else if let Ok(vs) = data.extract::<i64>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<f32>(py) {
+        } else if let Ok(vs) = data.extract::<f32>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<u32>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<u32>>(py) {
             let len = vs.len();
             Tensor::from_vec(vs, len, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<i64>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<i64>>(py) {
             let len = vs.len();
             Tensor::from_vec(vs, len, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<f32>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<f32>>(py) {
             let len = vs.len();
             Tensor::from_vec(vs, len, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<Vec<u32>>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<Vec<u32>>>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<Vec<i64>>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<Vec<i64>>>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<Vec<f32>>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<Vec<f32>>>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<Vec<Vec<u32>>>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<Vec<Vec<u32>>>>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<Vec<Vec<i64>>>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<Vec<Vec<i64>>>>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
-        } else if let Ok(vs) = vs.extract::<Vec<Vec<Vec<f32>>>>(py) {
+        } else if let Ok(vs) = data.extract::<Vec<Vec<Vec<f32>>>>(py) {
             Tensor::new(vs, &Cpu).map_err(wrap_err)?
         } else {
-            let ty = vs.as_ref(py).get_type();
+            let ty = data.as_ref(py).get_type();
             Err(PyTypeError::new_err(format!(
                 "incorrect type {ty} for tensor"
             )))?
@@ -236,7 +238,7 @@ impl PyTensor {
         Ok(Self(tensor))
     }
 
-    /// Gets the tensor data as a Python value/array/array of array/...
+    /// Gets the tensor's data as a Python scalar or array-like object.
     fn values(&self, py: Python<'_>) -> PyResult<PyObject> {
         struct M<'a>(Python<'a>);
         impl<'a> MapDType for M<'a> {
@@ -280,6 +282,7 @@ impl PyTensor {
     }
 
     #[getter]
+    /// Gets the tensor shape as a Python tuple.
     fn shape(&self, py: Python<'_>) -> PyObject {
         PyTuple::new(py, self.0.dims()).to_object(py)
     }
@@ -580,8 +583,9 @@ impl PyTensor {
     }
 }
 
-/// Concatenate the tensors across one axis.
 #[pyfunction]
+#[pyo3(text_signature = "(tensors:List[Tensor], dim:int )")]
+/// Concatenate the tensors across one axis.
 fn cat(tensors: Vec<PyTensor>, dim: i64) -> PyResult<PyTensor> {
     if tensors.is_empty() {
         return Err(PyErr::new::<PyValueError, _>("empty input to cat"));
@@ -593,6 +597,8 @@ fn cat(tensors: Vec<PyTensor>, dim: i64) -> PyResult<PyTensor> {
 }
 
 #[pyfunction]
+#[pyo3(text_signature = "(tensors:List[Tensor], dim:int)")]
+/// Stack the tensors along a new axis.
 fn stack(tensors: Vec<PyTensor>, dim: usize) -> PyResult<PyTensor> {
     let tensors = tensors.into_iter().map(|t| t.0).collect::<Vec<_>>();
     let tensor = Tensor::stack(&tensors, dim).map_err(wrap_err)?;
@@ -600,12 +606,15 @@ fn stack(tensors: Vec<PyTensor>, dim: usize) -> PyResult<PyTensor> {
 }
 
 #[pyfunction]
-fn tensor(py: Python<'_>, vs: PyObject) -> PyResult<PyTensor> {
-    PyTensor::new(py, vs)
+#[pyo3(text_signature = "(data:_ArrayLike)")]
+/// Creates a new tensor from a Python value. The value can be a scalar or array-like object.
+fn tensor(py: Python<'_>, data: PyObject) -> PyResult<PyTensor> {
+    PyTensor::new(py, data)
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, device=None))]
+#[pyo3(signature = (shape, *, device=None), text_signature = "(shape:Sequence[int], device:Optional[Device]=None)")]
+/// Creates a new tensor with random values.
 fn rand(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<PyTensor> {
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
     let tensor = Tensor::rand(0f32, 1f32, shape.0, &device).map_err(wrap_err)?;
@@ -613,7 +622,7 @@ fn rand(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<P
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, device=None))]
+#[pyo3(signature = (shape, *, device=None), text_signature = "(shape:Sequence[int], device:Optional[Device]=None)")]
 fn randn(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<PyTensor> {
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
     let tensor = Tensor::randn(0f32, 1f32, shape.0, &device).map_err(wrap_err)?;
@@ -621,7 +630,7 @@ fn randn(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, dtype=None, device=None))]
+#[pyo3(signature = (shape, *, dtype=None, device=None),text_signature = "(shape:Sequence[int], dtype:Optional[DType]=None, device:Optional[Device]=None)")]
 fn ones(
     py: Python<'_>,
     shape: PyShape,
@@ -638,7 +647,7 @@ fn ones(
 }
 
 #[pyfunction]
-#[pyo3(signature = (shape, *, dtype=None, device=None))]
+#[pyo3(signature = (shape, *, dtype=None, device=None), text_signature = "(shape:Sequence[int], dtype:Optional[DType]=None, device:Optional[Device]=None)")]
 fn zeros(
     py: Python<'_>,
     shape: PyShape,
@@ -704,6 +713,8 @@ impl PyQTensor {
 }
 
 #[pyfunction]
+#[pyo3(text_signature = "(path:Union[str,PathLike])")]
+/// Loads a safetensors file. Returns a dictionary mapping tensor names to tensors.
 fn load_safetensors(path: &str, py: Python<'_>) -> PyResult<PyObject> {
     let res = ::candle::safetensors::load(path, &Device::Cpu).map_err(wrap_err)?;
     let res = res
@@ -714,6 +725,8 @@ fn load_safetensors(path: &str, py: Python<'_>) -> PyResult<PyObject> {
 }
 
 #[pyfunction]
+#[pyo3(text_signature = "(path:Union[str,PathLike], tensors:Dict[str,Tensor])")]
+/// Saves a dictionary of tensors to a safetensors file.
 fn save_safetensors(
     path: &str,
     tensors: std::collections::HashMap<String, PyTensor>,
@@ -726,6 +739,9 @@ fn save_safetensors(
 }
 
 #[pyfunction]
+#[pyo3(text_signature = "(path:Union[str,PathLike])")]
+/// Load a GGML file. Returns a tuple of three objects: a dictionary mapping tensor names to tensors,
+/// a dictionary mapping hyperparameter names to hyperparameter values, and a vocabulary.
 fn load_ggml(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject, PyObject)> {
     let mut file = std::fs::File::open(path)?;
     let ggml = ::candle::quantized::ggml_file::Content::read(&mut file).map_err(wrap_err)?;
@@ -757,6 +773,9 @@ fn load_ggml(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject, PyObje
 }
 
 #[pyfunction]
+#[pyo3(text_signature = "(path:Union[str,PathLike])")]
+/// Loads a GGUF file. Returns a tuple of two dictionaries: the first maps tensor names to tensors,
+/// and the second maps metadata keys to metadata values.
 fn load_gguf(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject)> {
     use ::candle::quantized::gguf_file;
     fn gguf_value_to_pyobject(v: &gguf_file::Value, py: Python<'_>) -> PyResult<PyObject> {
@@ -806,21 +825,25 @@ fn load_gguf(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject)> {
 }
 
 #[pyfunction]
+/// Returns true if the 'cuda' backend is available.
 fn cuda_is_available() -> bool {
     ::candle::utils::cuda_is_available()
 }
 
 #[pyfunction]
+/// Returns true if candle was compiled with 'accelerate' support.
 fn has_accelerate() -> bool {
     ::candle::utils::has_accelerate()
 }
 
 #[pyfunction]
+/// Returns true if candle was compiled with MKL support.
 fn has_mkl() -> bool {
     ::candle::utils::has_mkl()
 }
 
 #[pyfunction]
+/// Returns the number of threads used by the candle.
 fn get_num_threads() -> usize {
     ::candle::utils::get_num_threads()
 }
@@ -830,19 +853,27 @@ fn candle_utils(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(get_num_threads, m)?)?;
     m.add_function(wrap_pyfunction!(has_accelerate, m)?)?;
     m.add_function(wrap_pyfunction!(has_mkl, m)?)?;
+    m.add_function(wrap_pyfunction!(load_ggml, m)?)?;
+    m.add_function(wrap_pyfunction!(load_gguf, m)?)?;
+    m.add_function(wrap_pyfunction!(load_safetensors, m)?)?;
+    m.add_function(wrap_pyfunction!(save_safetensors, m)?)?;
     Ok(())
 }
 
 #[pyfunction]
-fn softmax(t: PyTensor, dim: i64) -> PyResult<PyTensor> {
-    let dim = actual_dim(&t, dim).map_err(wrap_err)?;
-    let sm = candle_nn::ops::softmax(&t.0, dim).map_err(wrap_err)?;
+#[pyo3(text_signature = "(tensor:Tensor, dim:int)")]
+/// Applies the Softmax function to a given tensor.
+fn softmax(tensor: PyTensor, dim: i64) -> PyResult<PyTensor> {
+    let dim = actual_dim(&tensor, dim).map_err(wrap_err)?;
+    let sm = candle_nn::ops::softmax(&tensor.0, dim).map_err(wrap_err)?;
     Ok(PyTensor(sm))
 }
 
 #[pyfunction]
-fn silu(t: PyTensor) -> PyResult<PyTensor> {
-    let s = candle_nn::ops::silu(&t.0).map_err(wrap_err)?;
+#[pyo3(text_signature = "(tensor:Tensor)")]
+/// Applies the Sigmoid Linear Unit (SiLU) function to a given tensor.
+fn silu(tensor: PyTensor) -> PyResult<PyTensor> {
+    let s = candle_nn::ops::silu(&tensor.0).map_err(wrap_err)?;
     Ok(PyTensor(s))
 }
 
@@ -871,14 +902,10 @@ fn candle(py: Python<'_>, m: &PyModule) -> PyResult<()> {
     m.add("f32", PyDType(DType::F32))?;
     m.add("f64", PyDType(DType::F64))?;
     m.add_function(wrap_pyfunction!(cat, m)?)?;
-    m.add_function(wrap_pyfunction!(load_ggml, m)?)?;
-    m.add_function(wrap_pyfunction!(load_gguf, m)?)?;
-    m.add_function(wrap_pyfunction!(load_safetensors, m)?)?;
     m.add_function(wrap_pyfunction!(ones, m)?)?;
     m.add_function(wrap_pyfunction!(rand, m)?)?;
     m.add_function(wrap_pyfunction!(randn, m)?)?;
     m.add_function(wrap_pyfunction!(tensor, m)?)?;
-    m.add_function(wrap_pyfunction!(save_safetensors, m)?)?;
     m.add_function(wrap_pyfunction!(stack, m)?)?;
     m.add_function(wrap_pyfunction!(zeros, m)?)?;
     Ok(())
diff --git a/candle-pyo3/stub.py b/candle-pyo3/stub.py
new file mode 100644
index 00000000..b5b9256f
--- /dev/null
+++ b/candle-pyo3/stub.py
@@ -0,0 +1,217 @@
+#See: https://raw.githubusercontent.com/huggingface/tokenizers/main/bindings/python/stub.py
+import argparse
+import inspect
+import os
+from typing import Optional
+import black
+from pathlib import Path
+
+
+INDENT = " " * 4
+GENERATED_COMMENT = "# Generated content DO NOT EDIT\n"
+TYPING = """from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
+from os import PathLike
+"""
+CANDLE_SPECIFIC_TYPING = "from candle.typing import _ArrayLike, Device\n"
+CANDLE_TENSOR_IMPORTS = "from candle import Tensor,DType\n"
+
+
+
+def do_indent(text: Optional[str], indent: str):
+    if text is None:
+        return ""
+    return text.replace("\n", f"\n{indent}")
+
+
+def function(obj, indent:str, text_signature:str=None):
+    if text_signature is None:
+        text_signature = obj.__text_signature__
+
+    text_signature = text_signature.replace("$self", "self").lstrip().rstrip()
+    string = ""
+    string += f"{indent}def {obj.__name__}{text_signature}:\n"
+    indent += INDENT
+    string += f'{indent}"""\n'
+    string += f"{indent}{do_indent(obj.__doc__, indent)}\n"
+    string += f'{indent}"""\n'
+    string += f"{indent}pass\n"
+    string += "\n"
+    string += "\n"
+    return string
+
+
+def member_sort(member):
+    if inspect.isclass(member):
+        value = 10 + len(inspect.getmro(member))
+    else:
+        value = 1
+    return value
+
+
+def fn_predicate(obj):
+    value = inspect.ismethoddescriptor(obj) or inspect.isbuiltin(obj)
+    if value:
+        return obj.__text_signature__ and not obj.__name__.startswith("_")
+    if inspect.isgetsetdescriptor(obj):
+        return not obj.__name__.startswith("_")
+    return False
+
+
+def get_module_members(module):
+    members = [
+        member
+        for name, member in inspect.getmembers(module)
+        if not name.startswith("_") and not inspect.ismodule(member)
+    ]
+    members.sort(key=member_sort)
+    return members
+
+
+def pyi_file(obj, indent=""):
+    string = ""
+    if inspect.ismodule(obj):
+        string += GENERATED_COMMENT
+        string += TYPING
+        string += CANDLE_SPECIFIC_TYPING
+        if obj.__name__ != "candle.candle":
+            string += CANDLE_TENSOR_IMPORTS
+        members = get_module_members(obj)
+        for member in members:
+            string += pyi_file(member, indent)
+
+    elif inspect.isclass(obj):
+        indent += INDENT
+        mro = inspect.getmro(obj)
+        if len(mro) > 2:
+            inherit = f"({mro[1].__name__})"
+        else:
+            inherit = ""
+        string += f"class {obj.__name__}{inherit}:\n"
+
+        body = ""
+        if obj.__doc__:
+            body += f'{indent}"""\n{indent}{do_indent(obj.__doc__, indent)}\n{indent}"""\n'
+
+        fns = inspect.getmembers(obj, fn_predicate)
+
+        # Init
+        if obj.__text_signature__:
+            body += f"{indent}def __init__{obj.__text_signature__}:\n"
+            body += f"{indent+INDENT}pass\n"
+            body += "\n"
+
+        for (name, fn) in fns:
+            body += pyi_file(fn, indent=indent)
+
+        if not body:
+            body += f"{indent}pass\n"
+
+        string += body
+        string += "\n\n"
+
+    elif inspect.isbuiltin(obj):
+        string += f"{indent}@staticmethod\n"
+        string += function(obj, indent)
+
+    elif inspect.ismethoddescriptor(obj):
+        string += function(obj, indent)
+
+    elif inspect.isgetsetdescriptor(obj):
+        # TODO it would be interesing to add the setter maybe ?
+        string += f"{indent}@property\n"
+        string += function(obj, indent, text_signature="(self)")
+
+    elif obj.__class__.__name__ == "DType":
+        string += f"class {str(obj).lower()}(DType):\n"
+        string += f"{indent+INDENT}pass\n"
+    else:
+        raise Exception(f"Object {obj} is not supported")
+    return string
+
+
+def py_file(module, origin):
+    members = get_module_members(module)
+
+    string = GENERATED_COMMENT
+    string += f"from .. import {origin}\n"
+    string += "\n"
+    for member in members:
+        if hasattr(member, "__name__"):
+            name = member.__name__
+        else:
+            name = str(member)
+        string += f"{name} = {origin}.{name}\n"
+    return string
+
+
+def do_black(content, is_pyi):
+    mode = black.Mode(
+        target_versions={black.TargetVersion.PY35},
+        line_length=119,
+        is_pyi=is_pyi,
+        string_normalization=True,
+        experimental_string_processing=False,
+    )
+    try:
+        return black.format_file_contents(content, fast=True, mode=mode)
+    except black.NothingChanged:
+        return content
+
+
+def write(module, directory, origin, check=False):
+    submodules = [(name, member) for name, member in inspect.getmembers(module) if inspect.ismodule(member)]
+
+    filename = os.path.join(directory, "__init__.pyi")
+    pyi_content = pyi_file(module)
+    pyi_content = do_black(pyi_content, is_pyi=True)
+    os.makedirs(directory, exist_ok=True)
+    if check:
+        with open(filename, "r") as f:
+            data = f.read()
+            assert data == pyi_content, f"The content of {filename} seems outdated, please run `python stub.py`"
+    else:
+        with open(filename, "w") as f:
+            f.write(pyi_content)
+
+    filename = os.path.join(directory, "__init__.py")
+    py_content = py_file(module, origin)
+    py_content = do_black(py_content, is_pyi=False)
+    os.makedirs(directory, exist_ok=True)
+
+    is_auto = False
+    if not os.path.exists(filename):
+        is_auto = True
+    else:
+        with open(filename, "r") as f:
+            line = f.readline()
+            if line == GENERATED_COMMENT:
+                is_auto = True
+
+    if is_auto:
+        if check:
+            with open(filename, "r") as f:
+                data = f.read()
+                assert data == py_content, f"The content of {filename} seems outdated, please run `python stub.py`"
+        else:
+            with open(filename, "w") as f:
+                f.write(py_content)
+
+    for name, submodule in submodules:
+        write(submodule, os.path.join(directory, name), f"{name}", check=check)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--check", action="store_true")
+
+    args = parser.parse_args()
+
+    #Enable execution from the candle and candle-pyo3 directories
+    cwd = Path.cwd()
+    directory = "py_src/candle/"
+    if cwd.name != "candle-pyo3":
+        directory = f"candle-pyo3/{directory}"
+        
+    import candle
+
+    write(candle.candle, directory, "candle", check=args.check)
diff --git a/candle-pyo3/test.py b/candle-pyo3/test.py
index 7f24b49d..c78ffc41 100644
--- a/candle-pyo3/test.py
+++ b/candle-pyo3/test.py
@@ -1,4 +1,5 @@
 import candle
+from candle import Tensor, QTensor
 
 t = candle.Tensor(42.0)
 print(t)
@@ -9,7 +10,7 @@ t = candle.Tensor([3.0, 1, 4, 1, 5, 9, 2, 6])
 print(t)
 print(t+t)
 
-t = t.reshape([2, 4])
+t:Tensor = t.reshape([2, 4])
 print(t.matmul(t.t()))
 
 print(t.to_dtype(candle.u8))
@@ -20,7 +21,7 @@ print(t)
 print(t.dtype)
 
 t = candle.randn((16, 256))
-quant_t = t.quantize("q6k")
-dequant_t = quant_t.dequantize()
-diff2 = (t - dequant_t).sqr()
+quant_t:QTensor = t.quantize("q6k")
+dequant_t:Tensor = quant_t.dequantize()
+diff2:Tensor = (t - dequant_t).sqr()
 print(diff2.mean_all())

From 1a276b5da79a4bb2305dde7368b800d165599819 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 17 Sep 2023 09:00:45 +0200
Subject: [PATCH 110/150] Add a KV cache to T5. (#873)

* Add a KV cache to T5.

* Suggest using release mode.

* Use the kv cache in decoding.

* Add a comment.
---
 candle-examples/examples/musicgen/main.rs   |   2 +-
 candle-examples/examples/t5/README.md       |   4 +-
 candle-examples/examples/t5/main.rs         |  37 +-
 candle-examples/examples/wuerstchen/main.rs | 499 ++++++++++++++++++++
 candle-transformers/src/models/t5.rs        |  85 ++--
 5 files changed, 577 insertions(+), 50 deletions(-)
 create mode 100644 candle-examples/examples/wuerstchen/main.rs

diff --git a/candle-examples/examples/musicgen/main.rs b/candle-examples/examples/musicgen/main.rs
index df8c3135..0fae67b5 100644
--- a/candle-examples/examples/musicgen/main.rs
+++ b/candle-examples/examples/musicgen/main.rs
@@ -77,7 +77,7 @@ fn main() -> Result<()> {
     let model = model.deserialize()?;
     let vb = VarBuilder::from_safetensors(vec![model], DTYPE, &device);
     let config = GenConfig::small();
-    let model = MusicgenForConditionalGeneration::load(vb, config)?;
+    let mut model = MusicgenForConditionalGeneration::load(vb, config)?;
 
     let tokens = tokenizer
         .encode(args.prompt.as_str(), true)
diff --git a/candle-examples/examples/t5/README.md b/candle-examples/examples/t5/README.md
index c6ea2125..6a406467 100644
--- a/candle-examples/examples/t5/README.md
+++ b/candle-examples/examples/t5/README.md
@@ -3,7 +3,7 @@
 ## Encoder-decoder example:
 
 ```bash
-$ cargo run --example t5 -- --model-id "t5-small" --prompt "translate to German: A beautiful candle." --decode
+$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "translate to German: A beautiful candle." --decode
 ...
 Running on CPU, to run on GPU, build this example with `--features cuda`
  Eine schöne Kerze.
@@ -13,7 +13,7 @@ Running on CPU, to run on GPU, build this example with `--features cuda`
 ## Sentence embedding example:
 
 ```bash
-$ cargo run --example t5 -- --model-id "t5-small" --prompt "A beautiful candle."
+$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "A beautiful candle."
 ...
 [[[ 0.0515, -0.0541, -0.0761, ..., -0.0392,  0.1511, -0.0265],
   [-0.0974,  0.0998, -0.1659, ..., -0.2450,  0.1738, -0.0164],
diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 00291609..c432e004 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -48,10 +48,6 @@ struct Args {
     #[arg(long)]
     prompt: Option<String>,
 
-    /// The number of times to run the prompt.
-    #[arg(long, default_value = "1")]
-    n: usize,
-
     /// L2 normalization for embeddings.
     #[arg(long, default_value = "true")]
     normalize_embeddings: bool,
@@ -131,6 +127,7 @@ impl T5ModelBuilder {
 fn main() -> Result<()> {
     let args = Args::parse();
     let (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
+    let device = &builder.device;
     let tokenizer = tokenizer
         .with_padding(None)
         .with_truncation(None)
@@ -142,32 +139,32 @@ fn main() -> Result<()> {
                 .map_err(E::msg)?
                 .get_ids()
                 .to_vec();
-            let input_token_ids = Tensor::new(&tokens[..], &builder.device)?.unsqueeze(0)?;
+            let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
             if !args.decode {
-                let model = builder.build_encoder()?;
-                for idx in 0..args.n {
-                    let start = std::time::Instant::now();
-                    let ys = model.forward(&input_token_ids)?;
-                    if idx == 0 {
-                        println!("{ys}");
-                    }
-                    println!("Took {:?}", start.elapsed());
-                }
+                let mut model = builder.build_encoder()?;
+                let start = std::time::Instant::now();
+                let ys = model.forward(&input_token_ids)?;
+                println!("{ys}");
+                println!("Took {:?}", start.elapsed());
             } else {
-                let model = builder.build_conditional_generation()?;
+                let mut model = builder.build_conditional_generation()?;
                 let mut output_token_ids = [builder.config.pad_token_id as u32].to_vec();
                 let mut logits_processor = LogitsProcessor::new(299792458, None, None);
                 let start = std::time::Instant::now();
 
-                for _index in 0.. {
+                for index in 0.. {
                     if output_token_ids.len() > 512 {
                         break;
                     }
-                    let decoder_token_ids =
-                        Tensor::new(&output_token_ids[..], &builder.device)?.unsqueeze(0)?;
+                    let decoder_token_ids = if index == 0 || !builder.config.use_cache {
+                        Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
+                    } else {
+                        let last_token = *output_token_ids.last().unwrap();
+                        Tensor::new(&[last_token], device)?.unsqueeze(0)?
+                    };
                     let logits = model.forward(&input_token_ids, &decoder_token_ids)?;
                     let next_token_id = logits_processor.sample(&logits.flatten_to(1)?)?;
-                    if (next_token_id as usize) == builder.config.eos_token_id {
+                    if next_token_id as usize == builder.config.eos_token_id {
                         break;
                     }
                     output_token_ids.push(next_token_id);
@@ -186,7 +183,7 @@ fn main() -> Result<()> {
             }
         }
         None => {
-            let model = builder.build_encoder()?;
+            let mut model = builder.build_encoder()?;
             let sentences = [
                 "The cat sits outside",
                 "A man is playing guitar",
diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
new file mode 100644
index 00000000..c8b771a0
--- /dev/null
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -0,0 +1,499 @@
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+use candle_transformers::models::stable_diffusion;
+
+use anyhow::{Error as E, Result};
+use candle::{DType, Device, IndexOp, Module, Tensor, D};
+use clap::Parser;
+use tokenizers::Tokenizer;
+
+const GUIDANCE_SCALE: f64 = 7.5;
+
+#[derive(Parser)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// The prompt to be used for image generation.
+    #[arg(
+        long,
+        default_value = "A very realistic photo of a rusty robot walking on a sandy beach"
+    )]
+    prompt: String,
+
+    #[arg(long, default_value = "")]
+    uncond_prompt: String,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    /// The height in pixels of the generated image.
+    #[arg(long)]
+    height: Option<usize>,
+
+    /// The width in pixels of the generated image.
+    #[arg(long)]
+    width: Option<usize>,
+
+    /// The UNet weight file, in .safetensors format.
+    #[arg(long, value_name = "FILE")]
+    unet_weights: Option<String>,
+
+    /// The CLIP weight file, in .safetensors format.
+    #[arg(long, value_name = "FILE")]
+    clip_weights: Option<String>,
+
+    /// The VAE weight file, in .safetensors format.
+    #[arg(long, value_name = "FILE")]
+    vae_weights: Option<String>,
+
+    #[arg(long, value_name = "FILE")]
+    /// The file specifying the tokenizer to used for tokenization.
+    tokenizer: Option<String>,
+
+    /// The size of the sliced attention or 0 for automatic slicing (disabled by default)
+    #[arg(long)]
+    sliced_attention_size: Option<usize>,
+
+    /// The number of steps to run the diffusion for.
+    #[arg(long, default_value_t = 30)]
+    n_steps: usize,
+
+    /// The number of samples to generate.
+    #[arg(long, default_value_t = 1)]
+    num_samples: i64,
+
+    /// The name of the final image to generate.
+    #[arg(long, value_name = "FILE", default_value = "sd_final.png")]
+    final_image: String,
+
+    #[arg(long, value_enum, default_value = "v2-1")]
+    sd_version: StableDiffusionVersion,
+
+    /// Generate intermediary images at each step.
+    #[arg(long, action)]
+    intermediary_images: bool,
+
+    #[arg(long)]
+    use_flash_attn: bool,
+
+    #[arg(long)]
+    use_f16: bool,
+
+    #[arg(long, value_name = "FILE")]
+    img2img: Option<String>,
+
+    /// The strength, indicates how much to transform the initial image. The
+    /// value must be between 0 and 1, a value of 1 discards the initial image
+    /// information.
+    #[arg(long, default_value_t = 0.8)]
+    img2img_strength: f64,
+}
+
+#[derive(Debug, Clone, Copy, clap::ValueEnum)]
+enum StableDiffusionVersion {
+    V1_5,
+    V2_1,
+    Xl,
+}
+
+#[allow(unused)]
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+enum ModelFile {
+    Tokenizer,
+    Tokenizer2,
+    Clip,
+    Clip2,
+    Unet,
+    Vae,
+}
+
+impl StableDiffusionVersion {
+    fn repo(&self) -> &'static str {
+        match self {
+            Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
+            Self::V2_1 => "stabilityai/stable-diffusion-2-1",
+            Self::V1_5 => "runwayml/stable-diffusion-v1-5",
+        }
+    }
+
+    fn unet_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5 | Self::V2_1 | Self::Xl => {
+                if use_f16 {
+                    "unet/diffusion_pytorch_model.fp16.safetensors"
+                } else {
+                    "unet/diffusion_pytorch_model.safetensors"
+                }
+            }
+        }
+    }
+
+    fn vae_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5 | Self::V2_1 | Self::Xl => {
+                if use_f16 {
+                    "vae/diffusion_pytorch_model.fp16.safetensors"
+                } else {
+                    "vae/diffusion_pytorch_model.safetensors"
+                }
+            }
+        }
+    }
+
+    fn clip_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5 | Self::V2_1 | Self::Xl => {
+                if use_f16 {
+                    "text_encoder/model.fp16.safetensors"
+                } else {
+                    "text_encoder/model.safetensors"
+                }
+            }
+        }
+    }
+
+    fn clip2_file(&self, use_f16: bool) -> &'static str {
+        match self {
+            Self::V1_5 | Self::V2_1 | Self::Xl => {
+                if use_f16 {
+                    "text_encoder_2/model.fp16.safetensors"
+                } else {
+                    "text_encoder_2/model.safetensors"
+                }
+            }
+        }
+    }
+}
+
+impl ModelFile {
+    fn get(
+        &self,
+        filename: Option<String>,
+        version: StableDiffusionVersion,
+        use_f16: bool,
+    ) -> Result<std::path::PathBuf> {
+        use hf_hub::api::sync::Api;
+        match filename {
+            Some(filename) => Ok(std::path::PathBuf::from(filename)),
+            None => {
+                let (repo, path) = match self {
+                    Self::Tokenizer => {
+                        let tokenizer_repo = match version {
+                            StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 => {
+                                "openai/clip-vit-base-patch32"
+                            }
+                            StableDiffusionVersion::Xl => {
+                                // This seems similar to the patch32 version except some very small
+                                // difference in the split regex.
+                                "openai/clip-vit-large-patch14"
+                            }
+                        };
+                        (tokenizer_repo, "tokenizer.json")
+                    }
+                    Self::Tokenizer2 => {
+                        ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
+                    }
+                    Self::Clip => (version.repo(), version.clip_file(use_f16)),
+                    Self::Clip2 => (version.repo(), version.clip2_file(use_f16)),
+                    Self::Unet => (version.repo(), version.unet_file(use_f16)),
+                    Self::Vae => (version.repo(), version.vae_file(use_f16)),
+                };
+                let filename = Api::new()?.model(repo.to_string()).get(path)?;
+                Ok(filename)
+            }
+        }
+    }
+}
+
+fn output_filename(
+    basename: &str,
+    sample_idx: i64,
+    num_samples: i64,
+    timestep_idx: Option<usize>,
+) -> String {
+    let filename = if num_samples > 1 {
+        match basename.rsplit_once('.') {
+            None => format!("{basename}.{sample_idx}.png"),
+            Some((filename_no_extension, extension)) => {
+                format!("{filename_no_extension}.{sample_idx}.{extension}")
+            }
+        }
+    } else {
+        basename.to_string()
+    };
+    match timestep_idx {
+        None => filename,
+        Some(timestep_idx) => match filename.rsplit_once('.') {
+            None => format!("{filename}-{timestep_idx}.png"),
+            Some((filename_no_extension, extension)) => {
+                format!("{filename_no_extension}-{timestep_idx}.{extension}")
+            }
+        },
+    }
+}
+
+#[allow(clippy::too_many_arguments)]
+fn text_embeddings(
+    prompt: &str,
+    uncond_prompt: &str,
+    tokenizer: Option<String>,
+    clip_weights: Option<String>,
+    sd_version: StableDiffusionVersion,
+    sd_config: &stable_diffusion::StableDiffusionConfig,
+    use_f16: bool,
+    device: &Device,
+    dtype: DType,
+    first: bool,
+) -> Result<Tensor> {
+    let tokenizer_file = if first {
+        ModelFile::Tokenizer
+    } else {
+        ModelFile::Tokenizer2
+    };
+    let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
+    let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
+    let pad_id = match &sd_config.clip.pad_with {
+        Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
+        None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
+    };
+    println!("Running with prompt \"{prompt}\".");
+    let mut tokens = tokenizer
+        .encode(prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    while tokens.len() < sd_config.clip.max_position_embeddings {
+        tokens.push(pad_id)
+    }
+    let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
+
+    let mut uncond_tokens = tokenizer
+        .encode(uncond_prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
+        uncond_tokens.push(pad_id)
+    }
+    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
+
+    println!("Building the Clip transformer.");
+    let clip_weights_file = if first {
+        ModelFile::Clip
+    } else {
+        ModelFile::Clip2
+    };
+    let clip_weights = clip_weights_file.get(clip_weights, sd_version, false)?;
+    let clip_config = if first {
+        &sd_config.clip
+    } else {
+        sd_config.clip2.as_ref().unwrap()
+    };
+    let text_model =
+        stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
+    let text_embeddings = text_model.forward(&tokens)?;
+    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
+    let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?;
+    Ok(text_embeddings)
+}
+
+fn image_preprocess<T: AsRef<std::path::Path>>(path: T) -> anyhow::Result<Tensor> {
+    let img = image::io::Reader::open(path)?.decode()?;
+    let (height, width) = (img.height() as usize, img.width() as usize);
+    let height = height - height % 32;
+    let width = width - width % 32;
+    let img = img.resize_to_fill(
+        width as u32,
+        height as u32,
+        image::imageops::FilterType::CatmullRom,
+    );
+    let img = img.to_rgb8();
+    let img = img.into_raw();
+    let img = Tensor::from_vec(img, (height, width, 3), &Device::Cpu)?
+        .permute((2, 0, 1))?
+        .to_dtype(DType::F32)?
+        .affine(2. / 255., -1.)?
+        .unsqueeze(0)?;
+    Ok(img)
+}
+
+fn run(args: Args) -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let Args {
+        prompt,
+        uncond_prompt,
+        cpu,
+        height,
+        width,
+        n_steps,
+        tokenizer,
+        final_image,
+        sliced_attention_size,
+        num_samples,
+        sd_version,
+        clip_weights,
+        vae_weights,
+        unet_weights,
+        tracing,
+        use_f16,
+        use_flash_attn,
+        img2img,
+        img2img_strength,
+        ..
+    } = args;
+
+    if !(0. ..=1.).contains(&img2img_strength) {
+        anyhow::bail!("img2img-strength should be between 0 and 1, got {img2img_strength}")
+    }
+
+    let _guard = if tracing {
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+
+    let dtype = if use_f16 { DType::F16 } else { DType::F32 };
+    let sd_config = match sd_version {
+        StableDiffusionVersion::V1_5 => {
+            stable_diffusion::StableDiffusionConfig::v1_5(sliced_attention_size, height, width)
+        }
+        StableDiffusionVersion::V2_1 => {
+            stable_diffusion::StableDiffusionConfig::v2_1(sliced_attention_size, height, width)
+        }
+        StableDiffusionVersion::Xl => {
+            stable_diffusion::StableDiffusionConfig::sdxl(sliced_attention_size, height, width)
+        }
+    };
+
+    let scheduler = sd_config.build_scheduler(n_steps)?;
+    let device = candle_examples::device(cpu)?;
+
+    let which = match sd_version {
+        StableDiffusionVersion::Xl => vec![true, false],
+        _ => vec![true],
+    };
+    let text_embeddings = which
+        .iter()
+        .map(|first| {
+            text_embeddings(
+                &prompt,
+                &uncond_prompt,
+                tokenizer.clone(),
+                clip_weights.clone(),
+                sd_version,
+                &sd_config,
+                use_f16,
+                &device,
+                dtype,
+                *first,
+            )
+        })
+        .collect::<Result<Vec<_>>>()?;
+    let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
+    println!("{text_embeddings:?}");
+
+    println!("Building the autoencoder.");
+    let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
+    let vae = sd_config.build_vae(&vae_weights, &device, dtype)?;
+    let init_latent_dist = match &img2img {
+        None => None,
+        Some(image) => {
+            let image = image_preprocess(image)?.to_device(&device)?;
+            Some(vae.encode(&image)?)
+        }
+    };
+    println!("Building the unet.");
+    let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
+    let unet = sd_config.build_unet(&unet_weights, &device, 4, use_flash_attn, dtype)?;
+
+    let t_start = if img2img.is_some() {
+        n_steps - (n_steps as f64 * img2img_strength) as usize
+    } else {
+        0
+    };
+    let bsize = 1;
+    for idx in 0..num_samples {
+        let timesteps = scheduler.timesteps();
+        let latents = match &init_latent_dist {
+            Some(init_latent_dist) => {
+                let latents = (init_latent_dist.sample()? * 0.18215)?.to_device(&device)?;
+                if t_start < timesteps.len() {
+                    let noise = latents.randn_like(0f64, 1f64)?;
+                    scheduler.add_noise(&latents, noise, timesteps[t_start])?
+                } else {
+                    latents
+                }
+            }
+            None => {
+                let latents = Tensor::randn(
+                    0f32,
+                    1f32,
+                    (bsize, 4, sd_config.height / 8, sd_config.width / 8),
+                    &device,
+                )?;
+                // scale the initial noise by the standard deviation required by the scheduler
+                (latents * scheduler.init_noise_sigma())?
+            }
+        };
+        let mut latents = latents.to_dtype(dtype)?;
+
+        println!("starting sampling");
+        for (timestep_index, &timestep) in timesteps.iter().enumerate() {
+            if timestep_index < t_start {
+                continue;
+            }
+            let start_time = std::time::Instant::now();
+            let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
+
+            let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;
+            let noise_pred =
+                unet.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
+            let noise_pred = noise_pred.chunk(2, 0)?;
+            let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
+            let noise_pred =
+                (noise_pred_uncond + ((noise_pred_text - noise_pred_uncond)? * GUIDANCE_SCALE)?)?;
+            latents = scheduler.step(&noise_pred, timestep, &latents)?;
+            let dt = start_time.elapsed().as_secs_f32();
+            println!("step {}/{n_steps} done, {:.2}s", timestep_index + 1, dt);
+
+            if args.intermediary_images {
+                let image = vae.decode(&(&latents / 0.18215)?)?;
+                let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
+                let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
+                let image_filename =
+                    output_filename(&final_image, idx + 1, num_samples, Some(timestep_index + 1));
+                candle_examples::save_image(&image, image_filename)?
+            }
+        }
+
+        println!(
+            "Generating the final image for sample {}/{}.",
+            idx + 1,
+            num_samples
+        );
+        let image = vae.decode(&(&latents / 0.18215)?)?;
+        // TODO: Add the clamping between 0 and 1.
+        let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
+        let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
+        let image_filename = output_filename(&final_image, idx + 1, num_samples, None);
+        candle_examples::save_image(&image, image_filename)?
+    }
+    Ok(())
+}
+
+fn main() -> Result<()> {
+    let args = Args::parse();
+    run(args)
+}
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index c35dea0b..8b621f64 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -54,7 +54,7 @@ pub struct Config {
     is_decoder: bool,
     is_encoder_decoder: bool,
     #[serde(default = "default_use_cache")]
-    use_cache: bool,
+    pub use_cache: bool,
     pub pad_token_id: usize,
     pub eos_token_id: usize,
 }
@@ -245,10 +245,17 @@ struct T5Attention {
     relative_attention_num_buckets: usize,
     relative_attention_max_distance: usize,
     inner_dim: usize,
+    use_cache: bool,
+    kv_cache: Option<(Tensor, Tensor)>,
 }
 
 impl T5Attention {
-    fn load(has_relative_attention_bias: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+    fn load(
+        has_relative_attention_bias: bool,
+        decoder: bool,
+        vb: VarBuilder,
+        cfg: &Config,
+    ) -> Result<Self> {
         let inner_dim = cfg.num_heads * cfg.d_kv;
         let q = linear_no_bias(cfg.d_model, inner_dim, vb.pp("q"))?;
         let k = linear_no_bias(cfg.d_model, inner_dim, vb.pp("k"))?;
@@ -275,11 +282,13 @@ impl T5Attention {
             relative_attention_num_buckets: cfg.relative_attention_num_buckets,
             relative_attention_max_distance: cfg.relative_attention_max_distance,
             inner_dim,
+            use_cache: cfg.use_cache && decoder,
+            kv_cache: None,
         })
     }
 
     fn forward(
-        &self,
+        &mut self,
         xs: &Tensor,
         position_bias: Option<&Tensor>,
         key_value_states: Option<&Tensor>,
@@ -287,7 +296,6 @@ impl T5Attention {
     ) -> Result<(Tensor, Option<Tensor>)> {
         // Performs Self-attention (if key_value_states is None) or attention
         // over source sentence (provided by key_value_states).
-        // TODO: kv caching.
         let kv_input = match key_value_states {
             None => xs,
             Some(key_value_states) => key_value_states,
@@ -301,14 +309,22 @@ impl T5Attention {
             .reshape((b_sz, q_len, self.n_heads, self.d_kv))?
             .transpose(1, 2)?
             .contiguous()?;
-        let k = k
+        let mut k = k
             .reshape((b_sz, kv_len, self.n_heads, self.d_kv))?
             .transpose(1, 2)?
             .contiguous()?;
-        let v = v
+        let mut v = v
             .reshape((b_sz, kv_len, self.n_heads, self.d_kv))?
             .transpose(1, 2)?
             .contiguous()?;
+
+        if self.use_cache {
+            if let Some((kv_cache_k, kv_cache_v)) = &self.kv_cache {
+                k = Tensor::cat(&[kv_cache_k, &k], 2)?.contiguous()?;
+                v = Tensor::cat(&[kv_cache_v, &v], 2)?.contiguous()?;
+            };
+            self.kv_cache = Some((k.clone(), v.clone()));
+        };
         // TODO: Use flash_attn.
         let scores = q.matmul(&k.t()?)?;
         let scores = match mask {
@@ -394,8 +410,8 @@ struct T5LayerSelfAttention {
 }
 
 impl T5LayerSelfAttention {
-    fn load(h: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let self_attention = T5Attention::load(h, vb.pp("SelfAttention"), cfg)?;
+    fn load(h: bool, d: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let self_attention = T5Attention::load(h, d, vb.pp("SelfAttention"), cfg)?;
         let layer_norm =
             T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
         Ok(Self {
@@ -405,7 +421,7 @@ impl T5LayerSelfAttention {
     }
 
     fn forward(
-        &self,
+        &mut self,
         xs: &Tensor,
         position_bias: Option<&Tensor>,
         mask: Option<&Tensor>,
@@ -426,8 +442,8 @@ struct T5LayerCrossAttention {
 }
 
 impl T5LayerCrossAttention {
-    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let cross_attention = T5Attention::load(false, vb.pp("EncDecAttention"), cfg)?;
+    fn load(decoder: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let cross_attention = T5Attention::load(false, decoder, vb.pp("EncDecAttention"), cfg)?;
         let layer_norm =
             T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
         Ok(Self {
@@ -437,7 +453,7 @@ impl T5LayerCrossAttention {
     }
 
     fn forward(
-        &self,
+        &mut self,
         hidden_states: &Tensor,
         position_bias: Option<&Tensor>,
         key_value_states: &Tensor,
@@ -462,11 +478,17 @@ struct T5Block {
 }
 
 impl T5Block {
-    fn load(has_relative_attention_bias: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+    fn load(
+        has_relative_attention_bias: bool,
+        decoder: bool,
+        vb: VarBuilder,
+        cfg: &Config,
+    ) -> Result<Self> {
         let vb = vb.pp("layer");
-        let self_attn = T5LayerSelfAttention::load(has_relative_attention_bias, vb.pp("0"), cfg)?;
+        let self_attn =
+            T5LayerSelfAttention::load(has_relative_attention_bias, decoder, vb.pp("0"), cfg)?;
         let cross_attn = if cfg.is_decoder {
-            Some(T5LayerCrossAttention::load(vb.pp("1"), cfg)?)
+            Some(T5LayerCrossAttention::load(decoder, vb.pp("1"), cfg)?)
         } else {
             None
         };
@@ -480,19 +502,28 @@ impl T5Block {
     }
 
     fn forward(
-        &self,
+        &mut self,
         xs: &Tensor,
         position_bias: Option<&Tensor>,
         encoder_hidden_states: Option<&Tensor>,
     ) -> Result<(Tensor, Option<Tensor>)> {
         // TODO: Cache masks
         let mask = match self.cross_attn.is_some() {
-            true => Some(get_mask(xs.dim(1)?, xs.device())?),
+            true => {
+                let mask_len = xs.dim(1)?;
+                // If the input seq length is 1, no need for a mask, this is also helpful to avoid shape
+                // issues when using the KV cache in the decoder.
+                if mask_len <= 1 {
+                    None
+                } else {
+                    Some(get_mask(mask_len, xs.device())?)
+                }
+            }
             false => None,
         };
         let (mut xs, position_bias) = self.self_attn.forward(xs, position_bias, mask.as_ref())?;
         // TODO: clamp for f16?
-        if let Some(cross_attn) = &self.cross_attn {
+        if let Some(cross_attn) = &mut self.cross_attn {
             (xs, _) = cross_attn.forward(&xs, None, encoder_hidden_states.unwrap())?;
             // TODO: clamp for f16?
         }
@@ -510,9 +541,9 @@ struct T5Stack {
 }
 
 impl T5Stack {
-    fn load(vb: VarBuilder, shared: &Arc<Embedding>, cfg: &Config) -> Result<Self> {
+    fn load(decoder: bool, vb: VarBuilder, shared: &Arc<Embedding>, cfg: &Config) -> Result<Self> {
         let block = (0..cfg.num_layers)
-            .map(|i| T5Block::load(i == 0, vb.pp(&format!("block.{i}")), cfg))
+            .map(|i| T5Block::load(i == 0, decoder, vb.pp(&format!("block.{i}")), cfg))
             .collect::<Result<Vec<_>>>()?;
         let final_layer_norm = T5LayerNorm::load(
             cfg.d_model,
@@ -527,14 +558,14 @@ impl T5Stack {
     }
 
     fn forward(
-        &self,
+        &mut self,
         input_ids: &Tensor,
         encoder_hidden_states: Option<&Tensor>,
     ) -> Result<Tensor> {
         let input_embeds = self.shared.as_ref().forward(input_ids)?;
         let mut hidden_states = input_embeds;
         let mut position_bias = None;
-        for block in self.block.iter() {
+        for block in self.block.iter_mut() {
             (hidden_states, position_bias) = block.forward(
                 &hidden_states,
                 position_bias.as_ref(),
@@ -555,14 +586,14 @@ impl T5EncoderModel {
     pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
         let shared = embedding(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
         let shared = Arc::new(shared);
-        let encoder = T5Stack::load(vb.pp("encoder"), &shared, cfg)?;
+        let encoder = T5Stack::load(false, vb.pp("encoder"), &shared, cfg)?;
         Ok(Self {
             encoder,
             device: vb.device().clone(),
         })
     }
 
-    pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
+    pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
         self.encoder.forward(input_ids, None)
     }
 
@@ -589,13 +620,13 @@ impl T5ForConditionalGeneration {
         encoder_cfg.is_decoder = false;
         encoder_cfg.use_cache = false;
         encoder_cfg.is_encoder_decoder = false;
-        let encoder = T5Stack::load(vb.pp("encoder"), &shared, &encoder_cfg)?;
+        let encoder = T5Stack::load(false, vb.pp("encoder"), &shared, &encoder_cfg)?;
 
         let mut decoder_cfg = cfg.clone();
         decoder_cfg.is_decoder = true;
         decoder_cfg.is_encoder_decoder = false;
         decoder_cfg.num_layers = cfg.num_decoder_layers.unwrap_or(cfg.num_layers);
-        let decoder = T5Stack::load(vb.pp("decoder"), &shared, &decoder_cfg)?;
+        let decoder = T5Stack::load(true, vb.pp("decoder"), &shared, &decoder_cfg)?;
 
         Ok(Self {
             encoder,
@@ -605,7 +636,7 @@ impl T5ForConditionalGeneration {
         })
     }
 
-    pub fn forward(&self, input_ids: &Tensor, decoder_input_ids: &Tensor) -> Result<Tensor> {
+    pub fn forward(&mut self, input_ids: &Tensor, decoder_input_ids: &Tensor) -> Result<Tensor> {
         let encoder_output = self.encoder.forward(input_ids, None)?;
         let decoder_output = self
             .decoder

From eeb54716dd2635aa3dd9943fd3009eae863df71b Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 17 Sep 2023 11:05:15 +0200
Subject: [PATCH 111/150] Tweaks for the T5 example. (#874)

---
 README.md                           |  2 +-
 candle-examples/examples/t5/main.rs | 40 ++++++++++++++++++++++++++---
 2 files changed, 38 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index d897de89..3d1b10fe 100644
--- a/README.md
+++ b/README.md
@@ -82,7 +82,7 @@ We also provide a some command line based examples using state of the art models
 <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/sam_merged.jpg" width="200">
 
 - [Whisper](./candle-examples/examples/whisper/): speech recognition model.
-- [Bert](./candle-examples/examples/bert/): useful for sentence embeddings.
+- [T5](./candle-examples/examples/t5), [Bert](./candle-examples/examples/bert/): useful for sentence embeddings.
 - [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
   using self-supervision (can be used for imagenet classification, depth
   evaluation, segmentation).
diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index c432e004..72be23bc 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -51,6 +51,22 @@ struct Args {
     /// L2 normalization for embeddings.
     #[arg(long, default_value = "true")]
     normalize_embeddings: bool,
+
+    /// The temperature used to generate samples.
+    #[arg(long, default_value_t = 0.8)]
+    temperature: f64,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// The context size to consider for the repeat penalty.
+    #[arg(long, default_value_t = 64)]
+    repeat_last_n: usize,
 }
 
 struct T5ModelBuilder {
@@ -149,7 +165,12 @@ fn main() -> Result<()> {
             } else {
                 let mut model = builder.build_conditional_generation()?;
                 let mut output_token_ids = [builder.config.pad_token_id as u32].to_vec();
-                let mut logits_processor = LogitsProcessor::new(299792458, None, None);
+                let temperature = if args.temperature <= 0. {
+                    None
+                } else {
+                    Some(args.temperature)
+                };
+                let mut logits_processor = LogitsProcessor::new(299792458, temperature, args.top_p);
                 let start = std::time::Instant::now();
 
                 for index in 0.. {
@@ -162,8 +183,21 @@ fn main() -> Result<()> {
                         let last_token = *output_token_ids.last().unwrap();
                         Tensor::new(&[last_token], device)?.unsqueeze(0)?
                     };
-                    let logits = model.forward(&input_token_ids, &decoder_token_ids)?;
-                    let next_token_id = logits_processor.sample(&logits.flatten_to(1)?)?;
+                    let logits = model
+                        .forward(&input_token_ids, &decoder_token_ids)?
+                        .squeeze(0)?;
+                    let logits = if args.repeat_penalty == 1. {
+                        logits
+                    } else {
+                        let start_at = tokens.len().saturating_sub(args.repeat_last_n);
+                        candle_transformers::utils::apply_repeat_penalty(
+                            &logits,
+                            args.repeat_penalty,
+                            &tokens[start_at..],
+                        )?
+                    };
+
+                    let next_token_id = logits_processor.sample(&logits)?;
                     if next_token_id as usize == builder.config.eos_token_id {
                         break;
                     }

From 7f65af1f0dc518ef17623d718f3901f58c3aab06 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 17 Sep 2023 11:25:54 +0200
Subject: [PATCH 112/150] Avoid re-encoding the input in the T5 example. (#875)

---
 candle-examples/examples/t5/main.rs  |  3 ++-
 candle-transformers/src/models/t5.rs | 18 +++++++++++++++---
 2 files changed, 17 insertions(+), 4 deletions(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 72be23bc..36cbee7c 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -171,6 +171,7 @@ fn main() -> Result<()> {
                     Some(args.temperature)
                 };
                 let mut logits_processor = LogitsProcessor::new(299792458, temperature, args.top_p);
+                let encoder_output = model.encode(&input_token_ids)?;
                 let start = std::time::Instant::now();
 
                 for index in 0.. {
@@ -184,7 +185,7 @@ fn main() -> Result<()> {
                         Tensor::new(&[last_token], device)?.unsqueeze(0)?
                     };
                     let logits = model
-                        .forward(&input_token_ids, &decoder_token_ids)?
+                        .decode(&decoder_token_ids, &encoder_output)?
                         .squeeze(0)?;
                     let logits = if args.repeat_penalty == 1. {
                         logits
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 8b621f64..2ffc2ee1 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -636,11 +636,18 @@ impl T5ForConditionalGeneration {
         })
     }
 
-    pub fn forward(&mut self, input_ids: &Tensor, decoder_input_ids: &Tensor) -> Result<Tensor> {
-        let encoder_output = self.encoder.forward(input_ids, None)?;
+    pub fn encode(&mut self, input_ids: &Tensor) -> Result<Tensor> {
+        self.encoder.forward(input_ids, None)
+    }
+
+    pub fn decode(
+        &mut self,
+        decoder_input_ids: &Tensor,
+        encoder_output: &Tensor,
+    ) -> Result<Tensor> {
         let decoder_output = self
             .decoder
-            .forward(decoder_input_ids, Some(&encoder_output))?;
+            .forward(decoder_input_ids, Some(encoder_output))?;
         let sequence_output = decoder_output
             .narrow(1, decoder_output.dim(1)? - 1, 1)?
             .squeeze(1)?;
@@ -651,6 +658,11 @@ impl T5ForConditionalGeneration {
         Ok(output)
     }
 
+    pub fn forward(&mut self, input_ids: &Tensor, decoder_input_ids: &Tensor) -> Result<Tensor> {
+        let encoder_output = self.encode(input_ids)?;
+        self.decode(decoder_input_ids, &encoder_output)
+    }
+
     pub fn device(&self) -> &Device {
         &self.device
     }

From db3e9dae048db2272ec6e4478f8f503c4b6745b6 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 17 Sep 2023 13:46:38 +0200
Subject: [PATCH 113/150] Wuerstchen main (#876)

* Wuerstchen main.

* More of the wuerstchen cli example.

* Paella creation.

* Build the prior model.

* Fix the weight file names.
---
 candle-examples/examples/wuerstchen/main.rs   | 359 +++++-------------
 .../src/models/stable_diffusion/clip.rs       |  15 +
 .../src/models/wuerstchen/paella_vq.rs        | 112 +++++-
 3 files changed, 213 insertions(+), 273 deletions(-)

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index c8b771a0..32c7d158 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -1,3 +1,5 @@
+#![allow(unused)]
+
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
@@ -5,6 +7,7 @@ extern crate accelerate_src;
 extern crate intel_mkl_src;
 
 use candle_transformers::models::stable_diffusion;
+use candle_transformers::models::wuerstchen;
 
 use anyhow::{Error as E, Result};
 use candle::{DType, Device, IndexOp, Module, Tensor, D};
@@ -42,17 +45,21 @@ struct Args {
     #[arg(long)]
     width: Option<usize>,
 
-    /// The UNet weight file, in .safetensors format.
+    /// The decoder weight file, in .safetensors format.
     #[arg(long, value_name = "FILE")]
-    unet_weights: Option<String>,
+    decoder_weights: Option<String>,
 
     /// The CLIP weight file, in .safetensors format.
     #[arg(long, value_name = "FILE")]
     clip_weights: Option<String>,
 
-    /// The VAE weight file, in .safetensors format.
+    /// The prior weight file, in .safetensors format.
     #[arg(long, value_name = "FILE")]
-    vae_weights: Option<String>,
+    prior_weights: Option<String>,
+
+    /// The VQGAN weight file, in .safetensors format.
+    #[arg(long, value_name = "FILE")]
+    vqgan_weights: Option<String>,
 
     #[arg(long, value_name = "FILE")]
     /// The file specifying the tokenizer to used for tokenization.
@@ -73,138 +80,31 @@ struct Args {
     /// The name of the final image to generate.
     #[arg(long, value_name = "FILE", default_value = "sd_final.png")]
     final_image: String,
-
-    #[arg(long, value_enum, default_value = "v2-1")]
-    sd_version: StableDiffusionVersion,
-
-    /// Generate intermediary images at each step.
-    #[arg(long, action)]
-    intermediary_images: bool,
-
-    #[arg(long)]
-    use_flash_attn: bool,
-
-    #[arg(long)]
-    use_f16: bool,
-
-    #[arg(long, value_name = "FILE")]
-    img2img: Option<String>,
-
-    /// The strength, indicates how much to transform the initial image. The
-    /// value must be between 0 and 1, a value of 1 discards the initial image
-    /// information.
-    #[arg(long, default_value_t = 0.8)]
-    img2img_strength: f64,
 }
 
-#[derive(Debug, Clone, Copy, clap::ValueEnum)]
-enum StableDiffusionVersion {
-    V1_5,
-    V2_1,
-    Xl,
-}
-
-#[allow(unused)]
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
 enum ModelFile {
     Tokenizer,
-    Tokenizer2,
     Clip,
-    Clip2,
-    Unet,
-    Vae,
-}
-
-impl StableDiffusionVersion {
-    fn repo(&self) -> &'static str {
-        match self {
-            Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
-            Self::V2_1 => "stabilityai/stable-diffusion-2-1",
-            Self::V1_5 => "runwayml/stable-diffusion-v1-5",
-        }
-    }
-
-    fn unet_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
-                if use_f16 {
-                    "unet/diffusion_pytorch_model.fp16.safetensors"
-                } else {
-                    "unet/diffusion_pytorch_model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn vae_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
-                if use_f16 {
-                    "vae/diffusion_pytorch_model.fp16.safetensors"
-                } else {
-                    "vae/diffusion_pytorch_model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn clip_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
-                if use_f16 {
-                    "text_encoder/model.fp16.safetensors"
-                } else {
-                    "text_encoder/model.safetensors"
-                }
-            }
-        }
-    }
-
-    fn clip2_file(&self, use_f16: bool) -> &'static str {
-        match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
-                if use_f16 {
-                    "text_encoder_2/model.fp16.safetensors"
-                } else {
-                    "text_encoder_2/model.safetensors"
-                }
-            }
-        }
-    }
+    Decoder,
+    VqGan,
+    Prior,
 }
 
 impl ModelFile {
-    fn get(
-        &self,
-        filename: Option<String>,
-        version: StableDiffusionVersion,
-        use_f16: bool,
-    ) -> Result<std::path::PathBuf> {
+    fn get(&self, filename: Option<String>) -> Result<std::path::PathBuf> {
         use hf_hub::api::sync::Api;
         match filename {
             Some(filename) => Ok(std::path::PathBuf::from(filename)),
             None => {
+                let repo_main = "warp-ai/wuerstchen";
+                let repo_prior = "warp-ai/wuerstchen-prior";
                 let (repo, path) = match self {
-                    Self::Tokenizer => {
-                        let tokenizer_repo = match version {
-                            StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 => {
-                                "openai/clip-vit-base-patch32"
-                            }
-                            StableDiffusionVersion::Xl => {
-                                // This seems similar to the patch32 version except some very small
-                                // difference in the split regex.
-                                "openai/clip-vit-large-patch14"
-                            }
-                        };
-                        (tokenizer_repo, "tokenizer.json")
-                    }
-                    Self::Tokenizer2 => {
-                        ("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
-                    }
-                    Self::Clip => (version.repo(), version.clip_file(use_f16)),
-                    Self::Clip2 => (version.repo(), version.clip2_file(use_f16)),
-                    Self::Unet => (version.repo(), version.unet_file(use_f16)),
-                    Self::Vae => (version.repo(), version.vae_file(use_f16)),
+                    Self::Tokenizer => (repo_main, "tokenizer/tokenizer.json"),
+                    Self::Clip => (repo_main, "text_encoder/model.safetensors"),
+                    Self::Decoder => (repo_main, "decoder/diffusion_pytorch_model.safetensors"),
+                    Self::VqGan => (repo_main, "vqgan/diffusion_pytorch_model.safetensors"),
+                    Self::Prior => (repo_prior, "prior/diffusion_pytorch_model.safetensors"),
                 };
                 let filename = Api::new()?.model(repo.to_string()).get(path)?;
                 Ok(filename)
@@ -240,27 +140,17 @@ fn output_filename(
     }
 }
 
-#[allow(clippy::too_many_arguments)]
-fn text_embeddings(
+fn encode_prompt(
     prompt: &str,
     uncond_prompt: &str,
     tokenizer: Option<String>,
     clip_weights: Option<String>,
-    sd_version: StableDiffusionVersion,
-    sd_config: &stable_diffusion::StableDiffusionConfig,
-    use_f16: bool,
+    clip_config: stable_diffusion::clip::Config,
     device: &Device,
-    dtype: DType,
-    first: bool,
 ) -> Result<Tensor> {
-    let tokenizer_file = if first {
-        ModelFile::Tokenizer
-    } else {
-        ModelFile::Tokenizer2
-    };
-    let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
+    let tokenizer = ModelFile::Tokenizer.get(tokenizer)?;
     let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
-    let pad_id = match &sd_config.clip.pad_with {
+    let pad_id = match &clip_config.pad_with {
         Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
         None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
     };
@@ -270,7 +160,7 @@ fn text_embeddings(
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
-    while tokens.len() < sd_config.clip.max_position_embeddings {
+    while tokens.len() < clip_config.max_position_embeddings {
         tokens.push(pad_id)
     }
     let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
@@ -280,51 +170,21 @@ fn text_embeddings(
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
-    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
+    while uncond_tokens.len() < clip_config.max_position_embeddings {
         uncond_tokens.push(pad_id)
     }
     let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
 
     println!("Building the Clip transformer.");
-    let clip_weights_file = if first {
-        ModelFile::Clip
-    } else {
-        ModelFile::Clip2
-    };
-    let clip_weights = clip_weights_file.get(clip_weights, sd_version, false)?;
-    let clip_config = if first {
-        &sd_config.clip
-    } else {
-        sd_config.clip2.as_ref().unwrap()
-    };
+    let clip_weights = ModelFile::Clip.get(clip_weights)?;
     let text_model =
-        stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
+        stable_diffusion::build_clip_transformer(&clip_config, clip_weights, device, DType::F32)?;
     let text_embeddings = text_model.forward(&tokens)?;
     let uncond_embeddings = text_model.forward(&uncond_tokens)?;
-    let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?;
+    let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?;
     Ok(text_embeddings)
 }
 
-fn image_preprocess<T: AsRef<std::path::Path>>(path: T) -> anyhow::Result<Tensor> {
-    let img = image::io::Reader::open(path)?.decode()?;
-    let (height, width) = (img.height() as usize, img.width() as usize);
-    let height = height - height % 32;
-    let width = width - width % 32;
-    let img = img.resize_to_fill(
-        width as u32,
-        height as u32,
-        image::imageops::FilterType::CatmullRom,
-    );
-    let img = img.to_rgb8();
-    let img = img.into_raw();
-    let img = Tensor::from_vec(img, (height, width, 3), &Device::Cpu)?
-        .permute((2, 0, 1))?
-        .to_dtype(DType::F32)?
-        .affine(2. / 255., -1.)?
-        .unsqueeze(0)?;
-    Ok(img)
-}
-
 fn run(args: Args) -> Result<()> {
     use tracing_chrome::ChromeLayerBuilder;
     use tracing_subscriber::prelude::*;
@@ -340,22 +200,14 @@ fn run(args: Args) -> Result<()> {
         final_image,
         sliced_attention_size,
         num_samples,
-        sd_version,
         clip_weights,
-        vae_weights,
-        unet_weights,
+        prior_weights,
+        vqgan_weights,
+        decoder_weights,
         tracing,
-        use_f16,
-        use_flash_attn,
-        img2img,
-        img2img_strength,
         ..
     } = args;
 
-    if !(0. ..=1.).contains(&img2img_strength) {
-        anyhow::bail!("img2img-strength should be between 0 and 1, got {img2img_strength}")
-    }
-
     let _guard = if tracing {
         let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
         tracing_subscriber::registry().with(chrome_layer).init();
@@ -364,102 +216,75 @@ fn run(args: Args) -> Result<()> {
         None
     };
 
-    let dtype = if use_f16 { DType::F16 } else { DType::F32 };
-    let sd_config = match sd_version {
-        StableDiffusionVersion::V1_5 => {
-            stable_diffusion::StableDiffusionConfig::v1_5(sliced_attention_size, height, width)
-        }
-        StableDiffusionVersion::V2_1 => {
-            stable_diffusion::StableDiffusionConfig::v2_1(sliced_attention_size, height, width)
-        }
-        StableDiffusionVersion::Xl => {
-            stable_diffusion::StableDiffusionConfig::sdxl(sliced_attention_size, height, width)
-        }
-    };
-
-    let scheduler = sd_config.build_scheduler(n_steps)?;
     let device = candle_examples::device(cpu)?;
 
-    let which = match sd_version {
-        StableDiffusionVersion::Xl => vec![true, false],
-        _ => vec![true],
-    };
-    let text_embeddings = which
-        .iter()
-        .map(|first| {
-            text_embeddings(
-                &prompt,
-                &uncond_prompt,
-                tokenizer.clone(),
-                clip_weights.clone(),
-                sd_version,
-                &sd_config,
-                use_f16,
-                &device,
-                dtype,
-                *first,
-            )
-        })
-        .collect::<Result<Vec<_>>>()?;
-    let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
+    let text_embeddings = encode_prompt(
+        &prompt,
+        &uncond_prompt,
+        tokenizer.clone(),
+        clip_weights.clone(),
+        stable_diffusion::clip::Config::wuerstchen(),
+        &device,
+    );
     println!("{text_embeddings:?}");
 
-    println!("Building the autoencoder.");
-    let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
-    let vae = sd_config.build_vae(&vae_weights, &device, dtype)?;
-    let init_latent_dist = match &img2img {
-        None => None,
-        Some(image) => {
-            let image = image_preprocess(image)?.to_device(&device)?;
-            Some(vae.encode(&image)?)
-        }
+    println!("Building the prior.");
+    // https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
+    let _prior = {
+        let prior_weights = ModelFile::Prior.get(prior_weights)?;
+        let weights = unsafe { candle::safetensors::MmapedFile::new(prior_weights)? };
+        let weights = weights.deserialize()?;
+        let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
+        wuerstchen::prior::WPrior::new(
+            /* c_in */ 16, /* c */ 1536, /* c_cond */ 1280, /* c_r */ 64,
+            /* depth */ 32, /* nhead */ 24, vb,
+        )
     };
-    println!("Building the unet.");
-    let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
-    let unet = sd_config.build_unet(&unet_weights, &device, 4, use_flash_attn, dtype)?;
 
-    let t_start = if img2img.is_some() {
-        n_steps - (n_steps as f64 * img2img_strength) as usize
-    } else {
-        0
+    println!("Building the vqgan.");
+    let _vqgan = {
+        let vqgan_weights = ModelFile::VqGan.get(vqgan_weights)?;
+        let weights = unsafe { candle::safetensors::MmapedFile::new(vqgan_weights)? };
+        let weights = weights.deserialize()?;
+        let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
+        wuerstchen::paella_vq::PaellaVQ::new(vb)?
     };
-    let bsize = 1;
+
+    println!("Building the decoder.");
+
+    // https://huggingface.co/warp-ai/wuerstchen/blob/main/decoder/config.json
+    let _decoder = {
+        let decoder_weights = ModelFile::Decoder.get(decoder_weights)?;
+        let weights = unsafe { candle::safetensors::MmapedFile::new(decoder_weights)? };
+        let weights = weights.deserialize()?;
+        let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
+        wuerstchen::diffnext::WDiffNeXt::new(
+            /* c_in */ 4, /* c_out */ 4, /* c_r */ 64, /* c_cond */ 1024,
+            /* clip_embd */ 1024, /* patch_size */ 2, vb,
+        )?
+    };
+
+    let _bsize = 1;
     for idx in 0..num_samples {
+        /*
         let timesteps = scheduler.timesteps();
-        let latents = match &init_latent_dist {
-            Some(init_latent_dist) => {
-                let latents = (init_latent_dist.sample()? * 0.18215)?.to_device(&device)?;
-                if t_start < timesteps.len() {
-                    let noise = latents.randn_like(0f64, 1f64)?;
-                    scheduler.add_noise(&latents, noise, timesteps[t_start])?
-                } else {
-                    latents
-                }
-            }
-            None => {
-                let latents = Tensor::randn(
-                    0f32,
-                    1f32,
-                    (bsize, 4, sd_config.height / 8, sd_config.width / 8),
-                    &device,
-                )?;
-                // scale the initial noise by the standard deviation required by the scheduler
-                (latents * scheduler.init_noise_sigma())?
-            }
-        };
-        let mut latents = latents.to_dtype(dtype)?;
+        let latents = Tensor::randn(
+            0f32,
+            1f32,
+            (bsize, 4, sd_config.height / 8, sd_config.width / 8),
+            &device,
+        )?;
+        // scale the initial noise by the standard deviation required by the scheduler
+        let mut latents = latents * scheduler.init_noise_sigma()?;
 
         println!("starting sampling");
         for (timestep_index, &timestep) in timesteps.iter().enumerate() {
-            if timestep_index < t_start {
-                continue;
-            }
             let start_time = std::time::Instant::now();
             let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
 
             let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;
             let noise_pred =
-                unet.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
+                decoder.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
             let noise_pred = noise_pred.chunk(2, 0)?;
             let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
             let noise_pred =
@@ -467,28 +292,22 @@ fn run(args: Args) -> Result<()> {
             latents = scheduler.step(&noise_pred, timestep, &latents)?;
             let dt = start_time.elapsed().as_secs_f32();
             println!("step {}/{n_steps} done, {:.2}s", timestep_index + 1, dt);
-
-            if args.intermediary_images {
-                let image = vae.decode(&(&latents / 0.18215)?)?;
-                let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
-                let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
-                let image_filename =
-                    output_filename(&final_image, idx + 1, num_samples, Some(timestep_index + 1));
-                candle_examples::save_image(&image, image_filename)?
-            }
         }
+        */
 
         println!(
             "Generating the final image for sample {}/{}.",
             idx + 1,
             num_samples
         );
+        /*
         let image = vae.decode(&(&latents / 0.18215)?)?;
         // TODO: Add the clamping between 0 and 1.
         let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
         let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
         let image_filename = output_filename(&final_image, idx + 1, num_samples, None);
         candle_examples::save_image(&image, image_filename)?
+        */
     }
     Ok(())
 }
diff --git a/candle-transformers/src/models/stable_diffusion/clip.rs b/candle-transformers/src/models/stable_diffusion/clip.rs
index 397a1cef..31d025b3 100644
--- a/candle-transformers/src/models/stable_diffusion/clip.rs
+++ b/candle-transformers/src/models/stable_diffusion/clip.rs
@@ -99,6 +99,21 @@ impl Config {
             activation: Activation::Gelu,
         }
     }
+
+    // https://huggingface.co/warp-ai/wuerstchen/blob/main/text_encoder/config.json
+    pub fn wuerstchen() -> Self {
+        Self {
+            vocab_size: 49408,
+            embed_dim: 1024,
+            intermediate_size: 4096,
+            max_position_embeddings: 77,
+            pad_with: Some("!".to_string()),
+            num_hidden_layers: 24,
+            num_attention_heads: 16,
+            projection_dim: 1024,
+            activation: Activation::Gelu,
+        }
+    }
 }
 
 // CLIP Text Model
diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
index 1268047a..a60f8e8a 100644
--- a/candle-transformers/src/models/wuerstchen/paella_vq.rs
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -65,17 +65,121 @@ impl Module for MixingResidualBlock {
 }
 
 #[derive(Debug)]
-struct PaellaVQ {
+pub struct PaellaVQ {
     in_block_conv: candle_nn::Conv2d,
     out_block_conv: candle_nn::Conv2d,
     down_blocks: Vec<(Option<candle_nn::Conv2d>, MixingResidualBlock)>,
     down_blocks_conv: candle_nn::Conv2d,
     down_blocks_bn: candle_nn::BatchNorm,
     up_blocks_conv: candle_nn::Conv2d,
-    up_blocks: Vec<(MixingResidualBlock, Option<candle_nn::ConvTranspose2d>)>,
+    up_blocks: Vec<(Vec<MixingResidualBlock>, Option<candle_nn::ConvTranspose2d>)>,
 }
 
 impl PaellaVQ {
+    pub fn new(vb: VarBuilder) -> Result<Self> {
+        const IN_CHANNELS: usize = 3;
+        const OUT_CHANNELS: usize = 3;
+        const LATENT_CHANNELS: usize = 4;
+        const EMBED_DIM: usize = 384;
+        const BOTTLENECK_BLOCKS: usize = 12;
+        const C_LEVELS: [usize; 2] = [EMBED_DIM / 2, EMBED_DIM];
+
+        let in_block_conv = candle_nn::conv2d(
+            IN_CHANNELS * 4,
+            C_LEVELS[0],
+            1,
+            Default::default(),
+            vb.pp("in_block.1"),
+        )?;
+        let out_block_conv = candle_nn::conv2d(
+            C_LEVELS[0],
+            OUT_CHANNELS * 4,
+            1,
+            Default::default(),
+            vb.pp("out_block.0"),
+        )?;
+
+        let mut down_blocks = Vec::new();
+        let vb_d = vb.pp("down_blocks");
+        let mut d_idx = 0;
+        for (i, &c_level) in C_LEVELS.iter().enumerate() {
+            let conv_block = if i > 0 {
+                let cfg = candle_nn::Conv2dConfig {
+                    padding: 1,
+                    stride: 2,
+                    ..Default::default()
+                };
+                let block =
+                    candle_nn::conv2d_no_bias(C_LEVELS[i - 1], c_level, 4, cfg, vb_d.pp(d_idx))?;
+                d_idx += 1;
+                Some(block)
+            } else {
+                None
+            };
+            let res_block = MixingResidualBlock::new(c_level, c_level * 4, vb_d.pp(d_idx))?;
+            d_idx += 1;
+            down_blocks.push((conv_block, res_block))
+        }
+        let down_blocks_conv = candle_nn::conv2d_no_bias(
+            C_LEVELS[1],
+            LATENT_CHANNELS,
+            1,
+            Default::default(),
+            vb_d.pp(d_idx),
+        )?;
+        d_idx += 1;
+        let down_blocks_bn = candle_nn::batch_norm(LATENT_CHANNELS, 1e-5, vb_d.pp(d_idx))?;
+
+        let mut up_blocks = Vec::new();
+        let vb_u = vb.pp("up_blocks");
+        let mut u_idx = 0;
+        let up_blocks_conv = candle_nn::conv2d_no_bias(
+            LATENT_CHANNELS,
+            C_LEVELS[1],
+            1,
+            Default::default(),
+            vb_u.pp(u_idx),
+        )?;
+        u_idx += 1;
+        for (i, &c_level) in C_LEVELS.iter().rev().enumerate() {
+            let mut res_blocks = Vec::new();
+            let n_bottleneck_blocks = if i == 0 { BOTTLENECK_BLOCKS } else { 1 };
+            for _j in 0..n_bottleneck_blocks {
+                let res_block = MixingResidualBlock::new(c_level, c_level * 4, vb_u.pp(u_idx))?;
+                u_idx += 1;
+                res_blocks.push(res_block)
+            }
+            let conv_block = if i < C_LEVELS.len() - 1 {
+                let cfg = candle_nn::ConvTranspose2dConfig {
+                    padding: 1,
+                    stride: 2,
+                    ..Default::default()
+                };
+                let block = candle_nn::conv_transpose2d_no_bias(
+                    c_level,
+                    C_LEVELS[i - 1],
+                    4,
+                    cfg,
+                    vb_u.pp(u_idx),
+                )?;
+                u_idx += 1;
+                Some(block)
+            } else {
+                None
+            };
+            up_blocks.push((res_blocks, conv_block))
+        }
+        Ok(Self {
+            in_block_conv,
+            down_blocks,
+            down_blocks_conv,
+            down_blocks_bn,
+            up_blocks,
+            up_blocks_conv,
+            out_block_conv,
+        })
+    }
+
     pub fn encode(&self, xs: &Tensor) -> Result<Tensor> {
         let mut xs = candle_nn::ops::pixel_unshuffle(xs, 2)?.apply(&self.in_block_conv)?;
         for down_block in self.down_blocks.iter() {
@@ -92,7 +196,9 @@ impl PaellaVQ {
         // TODO: quantizer if we want to support `force_not_quantize=False`.
         let mut xs = xs.apply(&self.up_blocks_conv)?;
         for up_block in self.up_blocks.iter() {
-            xs = xs.apply(&up_block.0)?;
+            for b in up_block.0.iter() {
+                xs = xs.apply(b)?;
+            }
             if let Some(conv) = &up_block.1 {
                 xs = xs.apply(conv)?
             }

From 5f83c13f17a7b16955c9b649424aca276d5e930d Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 17 Sep 2023 15:03:01 +0100
Subject: [PATCH 114/150] Add the DDPM scheduler. (#877)

* Add the DDPM scheduler.

* Minor tweaks.
---
 candle-examples/examples/wuerstchen/main.rs   |  24 +-
 .../src/models/stable_diffusion/ddpm.rs       | 205 ++++++++++++++++++
 .../src/models/stable_diffusion/mod.rs        |   1 +
 3 files changed, 226 insertions(+), 4 deletions(-)
 create mode 100644 candle-transformers/src/models/stable_diffusion/ddpm.rs

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index 32c7d158..7e4d2360 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -15,6 +15,7 @@ use clap::Parser;
 use tokenizers::Tokenizer;
 
 const GUIDANCE_SCALE: f64 = 7.5;
+const RESOLUTION_MULTIPLE: f64 = 42.67;
 
 #[derive(Parser)]
 #[command(author, version, about, long_about = None)]
@@ -217,6 +218,8 @@ fn run(args: Args) -> Result<()> {
     };
 
     let device = candle_examples::device(cpu)?;
+    let height = height.unwrap_or(1024);
+    let width = width.unwrap_or(1024);
 
     let text_embeddings = encode_prompt(
         &prompt,
@@ -225,12 +228,12 @@ fn run(args: Args) -> Result<()> {
         clip_weights.clone(),
         stable_diffusion::clip::Config::wuerstchen(),
         &device,
-    );
+    )?;
     println!("{text_embeddings:?}");
 
     println!("Building the prior.");
     // https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
-    let _prior = {
+    let prior = {
         let prior_weights = ModelFile::Prior.get(prior_weights)?;
         let weights = unsafe { candle::safetensors::MmapedFile::new(prior_weights)? };
         let weights = weights.deserialize()?;
@@ -238,7 +241,7 @@ fn run(args: Args) -> Result<()> {
         wuerstchen::prior::WPrior::new(
             /* c_in */ 16, /* c */ 1536, /* c_cond */ 1280, /* c_r */ 64,
             /* depth */ 32, /* nhead */ 24, vb,
-        )
+        )?
     };
 
     println!("Building the vqgan.");
@@ -264,8 +267,21 @@ fn run(args: Args) -> Result<()> {
         )?
     };
 
-    let _bsize = 1;
+    let latent_height = (height as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
+    let latent_width = (width as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
+    let b_size = 1;
     for idx in 0..num_samples {
+        let latents = Tensor::randn(
+            0f32,
+            1f32,
+            (b_size, 4, latent_height, latent_width),
+            &device,
+        )?;
+        // TODO: latents denoising loop, use the scheduler values.
+        let ratio = Tensor::ones(1, DType::F32, &device)?;
+        let prior = prior.forward(&latents, &ratio, &text_embeddings)?;
+
+        let latents = ((latents * 42.)? - 1.)?;
         /*
         let timesteps = scheduler.timesteps();
         let latents = Tensor::randn(
diff --git a/candle-transformers/src/models/stable_diffusion/ddpm.rs b/candle-transformers/src/models/stable_diffusion/ddpm.rs
new file mode 100644
index 00000000..d393f39a
--- /dev/null
+++ b/candle-transformers/src/models/stable_diffusion/ddpm.rs
@@ -0,0 +1,205 @@
+use super::schedulers::{betas_for_alpha_bar, BetaSchedule, PredictionType};
+use candle::{Result, Tensor};
+
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub enum DDPMVarianceType {
+    FixedSmall,
+    FixedSmallLog,
+    FixedLarge,
+    FixedLargeLog,
+    Learned,
+}
+
+impl Default for DDPMVarianceType {
+    fn default() -> Self {
+        Self::FixedSmall
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct DDPMSchedulerConfig {
+    /// The value of beta at the beginning of training.
+    pub beta_start: f64,
+    /// The value of beta at the end of training.
+    pub beta_end: f64,
+    /// How beta evolved during training.
+    pub beta_schedule: BetaSchedule,
+    /// Option to predicted sample between -1 and 1 for numerical stability.
+    pub clip_sample: bool,
+    /// Option to clip the variance used when adding noise to the denoised sample.
+    pub variance_type: DDPMVarianceType,
+    /// prediction type of the scheduler function
+    pub prediction_type: PredictionType,
+    /// number of diffusion steps used to train the model.
+    pub train_timesteps: usize,
+}
+
+impl Default for DDPMSchedulerConfig {
+    fn default() -> Self {
+        Self {
+            beta_start: 0.00085,
+            beta_end: 0.012,
+            beta_schedule: BetaSchedule::ScaledLinear,
+            clip_sample: false,
+            variance_type: DDPMVarianceType::FixedSmall,
+            prediction_type: PredictionType::Epsilon,
+            train_timesteps: 1000,
+        }
+    }
+}
+
+pub struct DDPMScheduler {
+    alphas_cumprod: Vec<f64>,
+    init_noise_sigma: f64,
+    timesteps: Vec<usize>,
+    step_ratio: usize,
+    pub config: DDPMSchedulerConfig,
+}
+
+impl DDPMScheduler {
+    pub fn new(inference_steps: usize, config: DDPMSchedulerConfig) -> Result<Self> {
+        let betas = match config.beta_schedule {
+            BetaSchedule::ScaledLinear => super::utils::linspace(
+                config.beta_start.sqrt(),
+                config.beta_end.sqrt(),
+                config.train_timesteps,
+            )?
+            .sqr()?,
+            BetaSchedule::Linear => {
+                super::utils::linspace(config.beta_start, config.beta_end, config.train_timesteps)?
+            }
+            BetaSchedule::SquaredcosCapV2 => betas_for_alpha_bar(config.train_timesteps, 0.999)?,
+        };
+
+        let betas = betas.to_vec1::<f64>()?;
+        let mut alphas_cumprod = Vec::with_capacity(betas.len());
+        for &beta in betas.iter() {
+            let alpha = 1.0 - beta;
+            alphas_cumprod.push(alpha * *alphas_cumprod.last().unwrap_or(&1f64))
+        }
+
+        // min(train_timesteps, inference_steps)
+        // https://github.com/huggingface/diffusers/blob/8331da46837be40f96fbd24de6a6fb2da28acd11/src/diffusers/schedulers/scheduling_ddpm.py#L187
+        let inference_steps = inference_steps.min(config.train_timesteps);
+        // arange the number of the scheduler's timesteps
+        let step_ratio = config.train_timesteps / inference_steps;
+        let timesteps: Vec<usize> = (0..inference_steps).map(|s| s * step_ratio).rev().collect();
+
+        Ok(Self {
+            alphas_cumprod,
+            init_noise_sigma: 1.0,
+            timesteps,
+            step_ratio,
+            config,
+        })
+    }
+
+    fn get_variance(&self, timestep: usize) -> f64 {
+        let prev_t = timestep as isize - self.step_ratio as isize;
+        let alpha_prod_t = self.alphas_cumprod[timestep];
+        let alpha_prod_t_prev = if prev_t >= 0 {
+            self.alphas_cumprod[prev_t as usize]
+        } else {
+            1.0
+        };
+        let current_beta_t = 1. - alpha_prod_t / alpha_prod_t_prev;
+
+        // For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
+        // and sample from it to get previous sample
+        // x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
+        let variance = (1. - alpha_prod_t_prev) / (1. - alpha_prod_t) * current_beta_t;
+
+        // retrieve variance
+        match self.config.variance_type {
+            DDPMVarianceType::FixedSmall => variance.max(1e-20),
+            // for rl-diffuser https://arxiv.org/abs/2205.09991
+            DDPMVarianceType::FixedSmallLog => {
+                let variance = variance.max(1e-20).ln();
+                (variance * 0.5).exp()
+            }
+            DDPMVarianceType::FixedLarge => current_beta_t,
+            DDPMVarianceType::FixedLargeLog => current_beta_t.ln(),
+            DDPMVarianceType::Learned => variance,
+        }
+    }
+
+    pub fn timesteps(&self) -> &[usize] {
+        self.timesteps.as_slice()
+    }
+
+    ///  Ensures interchangeability with schedulers that need to scale the denoising model input
+    /// depending on the current timestep.
+    pub fn scale_model_input(&self, sample: Tensor, _timestep: usize) -> Tensor {
+        sample
+    }
+
+    pub fn step(&self, model_output: &Tensor, timestep: usize, sample: &Tensor) -> Result<Tensor> {
+        let prev_t = timestep as isize - self.step_ratio as isize;
+
+        // https://github.com/huggingface/diffusers/blob/df2b548e893ccb8a888467c2508756680df22821/src/diffusers/schedulers/scheduling_ddpm.py#L272
+        // 1. compute alphas, betas
+        let alpha_prod_t = self.alphas_cumprod[timestep];
+        let alpha_prod_t_prev = if prev_t >= 0 {
+            self.alphas_cumprod[prev_t as usize]
+        } else {
+            1.0
+        };
+        let beta_prod_t = 1. - alpha_prod_t;
+        let beta_prod_t_prev = 1. - alpha_prod_t_prev;
+        let current_alpha_t = alpha_prod_t / alpha_prod_t_prev;
+        let current_beta_t = 1. - current_alpha_t;
+
+        // 2. compute predicted original sample from predicted noise also called "predicted x_0" of formula (15)
+        let mut pred_original_sample = match self.config.prediction_type {
+            PredictionType::Epsilon => {
+                ((sample - model_output * beta_prod_t.sqrt())? / alpha_prod_t.sqrt())?
+            }
+            PredictionType::Sample => model_output.clone(),
+            PredictionType::VPrediction => {
+                ((sample * alpha_prod_t.sqrt())? - model_output * beta_prod_t.sqrt())?
+            }
+        };
+
+        // 3. clip predicted x_0
+        if self.config.clip_sample {
+            pred_original_sample = pred_original_sample.clamp(-1f32, 1f32)?;
+        }
+
+        // 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
+        // See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+        let pred_original_sample_coeff = (alpha_prod_t_prev.sqrt() * current_beta_t) / beta_prod_t;
+        let current_sample_coeff = current_alpha_t.sqrt() * beta_prod_t_prev / beta_prod_t;
+
+        // 5. Compute predicted previous sample µ_t
+        // See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+        let pred_prev_sample = ((&pred_original_sample * pred_original_sample_coeff)?
+            + sample * current_sample_coeff)?;
+
+        // https://github.com/huggingface/diffusers/blob/df2b548e893ccb8a888467c2508756680df22821/src/diffusers/schedulers/scheduling_ddpm.py#L305
+        // 6. Add noise
+        let mut variance = model_output.zeros_like()?;
+        if timestep > 0 {
+            let variance_noise = model_output.randn_like(0., 1.)?;
+            if self.config.variance_type == DDPMVarianceType::FixedSmallLog {
+                variance = (variance_noise * self.get_variance(timestep))?;
+            } else {
+                variance = (variance_noise * self.get_variance(timestep).sqrt())?;
+            }
+        }
+        &pred_prev_sample + variance
+    }
+
+    pub fn add_noise(
+        &self,
+        original_samples: &Tensor,
+        noise: Tensor,
+        timestep: usize,
+    ) -> Result<Tensor> {
+        (original_samples * self.alphas_cumprod[timestep].sqrt())?
+            + noise * (1. - self.alphas_cumprod[timestep]).sqrt()
+    }
+
+    pub fn init_noise_sigma(&self) -> f64 {
+        self.init_noise_sigma
+    }
+}
diff --git a/candle-transformers/src/models/stable_diffusion/mod.rs b/candle-transformers/src/models/stable_diffusion/mod.rs
index d9721532..c6f1b904 100644
--- a/candle-transformers/src/models/stable_diffusion/mod.rs
+++ b/candle-transformers/src/models/stable_diffusion/mod.rs
@@ -1,6 +1,7 @@
 pub mod attention;
 pub mod clip;
 pub mod ddim;
+pub mod ddpm;
 pub mod embeddings;
 pub mod resnet;
 pub mod schedulers;

From 06cc329e715cbb820343f9849a4a45c818cb8c5e Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 17 Sep 2023 15:59:27 +0100
Subject: [PATCH 115/150] Remove the parameters for the Wuerstchen layer-norm.
 (#879)

* Remove the parameters for the Wuerstchen layer-norm.

* Fixes.

* More fixes (including conv-transpose2d.

* More fixes.

* Again more fixes.
---
 candle-nn/src/conv.rs                         |  2 +-
 .../src/models/wuerstchen/common.rs           | 39 +++++++++++--------
 .../src/models/wuerstchen/diffnext.rs         | 23 +++++------
 .../src/models/wuerstchen/paella_vq.rs        | 24 +++++-------
 .../src/models/wuerstchen/prior.rs            |  2 +-
 5 files changed, 45 insertions(+), 45 deletions(-)

diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index 309a5f37..31bf9af0 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -302,7 +302,7 @@ pub fn conv_transpose2d_no_bias(
         up: bound,
     };
     let ws = vb.get_with_hints(
-        (out_channels, in_channels, kernel_size, kernel_size),
+        (in_channels, out_channels, kernel_size, kernel_size),
         "weight",
         init,
     )?;
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
index 10e7b19f..ee318d27 100644
--- a/candle-transformers/src/models/wuerstchen/common.rs
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -1,28 +1,35 @@
-use candle::{Module, Result, Tensor, D};
+use candle::{DType, Module, Result, Tensor, D};
 use candle_nn::VarBuilder;
 
 // https://github.com/huggingface/diffusers/blob/19edca82f1ff194c07317369a92b470dbae97f34/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py#L22
 #[derive(Debug)]
 pub struct WLayerNorm {
-    inner: candle_nn::LayerNorm,
+    eps: f64,
 }
 
 impl WLayerNorm {
-    pub fn new(size: usize, vb: VarBuilder) -> Result<Self> {
-        let cfg = candle_nn::layer_norm::LayerNormConfig {
-            eps: 1e-6,
-            remove_mean: true,
-            affine: false,
-        };
-        let inner = candle_nn::layer_norm(size, cfg, vb)?;
-        Ok(Self { inner })
+    pub fn new(_size: usize) -> Result<Self> {
+        Ok(Self { eps: 1e-6 })
     }
 }
 
 impl Module for WLayerNorm {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        xs.permute((0, 2, 3, 1))?
-            .apply(&self.inner)?
+        let xs = xs.permute((0, 2, 3, 1))?;
+
+        let x_dtype = xs.dtype();
+        let internal_dtype = match x_dtype {
+            DType::F16 | DType::BF16 => DType::F32,
+            d => d,
+        };
+
+        let hidden_size = xs.dim(D::Minus1)?;
+        let xs = xs.to_dtype(internal_dtype)?;
+        let mean_x = (xs.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
+        let xs = xs.broadcast_sub(&mean_x)?;
+        let norm_x = (xs.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
+        xs.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?
+            .to_dtype(x_dtype)?
             .permute((0, 3, 1, 2))
     }
 }
@@ -57,8 +64,8 @@ pub struct GlobalResponseNorm {
 
 impl GlobalResponseNorm {
     pub fn new(dim: usize, vb: VarBuilder) -> Result<Self> {
-        let gamma = vb.get((1, 1, 1, 1, dim), "gamma")?;
-        let beta = vb.get((1, 1, 1, 1, dim), "beta")?;
+        let gamma = vb.get((1, 1, 1, dim), "gamma")?;
+        let beta = vb.get((1, 1, 1, dim), "beta")?;
         Ok(Self { gamma, beta })
     }
 }
@@ -92,7 +99,7 @@ impl ResBlock {
             ..Default::default()
         };
         let depthwise = candle_nn::conv2d(c + c_skip, c, ksize, cfg, vb.pp("depthwise"))?;
-        let norm = WLayerNorm::new(c, vb.pp("norm"))?;
+        let norm = WLayerNorm::new(c)?;
         let channelwise_lin1 = candle_nn::linear(c, c * 4, vb.pp("channelwise.0"))?;
         let channelwise_grn = GlobalResponseNorm::new(c * 4, vb.pp("channelwise.2"))?;
         let channelwise_lin2 = candle_nn::linear(c * 4, c, vb.pp("channelwise.4"))?;
@@ -141,7 +148,7 @@ impl AttnBlock {
         self_attn: bool,
         vb: VarBuilder,
     ) -> Result<Self> {
-        let norm = WLayerNorm::new(c, vb.pp("norm"))?;
+        let norm = WLayerNorm::new(c)?;
         let attention = Attention::new(vb.pp("attention"), c, None, nhead, c / nhead, None, false)?;
         let kv_mapper_lin = candle_nn::linear(c_cond, c, vb.pp("kv_mapper.1"))?;
         Ok(Self {
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 001b35d7..70e4ba34 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -19,7 +19,7 @@ impl ResBlockStageB {
             ..Default::default()
         };
         let depthwise = candle_nn::conv2d(c, c, ksize, cfg, vb.pp("depthwise"))?;
-        let norm = WLayerNorm::new(c, vb.pp("norm"))?;
+        let norm = WLayerNorm::new(c)?;
         let channelwise_lin1 = candle_nn::linear(c + c_skip, c * 4, vb.pp("channelwise.0"))?;
         let channelwise_grn = GlobalResponseNorm::new(4 * c, vb.pp("channelwise.2"))?;
         let channelwise_lin2 = candle_nn::linear(c * 4, c, vb.pp("channelwise.4"))?;
@@ -75,7 +75,7 @@ struct UpBlock {
 pub struct WDiffNeXt {
     clip_mapper: candle_nn::Linear,
     effnet_mappers: Vec<Option<candle_nn::Conv2d>>,
-    seq_norm: candle_nn::LayerNorm,
+    seq_norm: WLayerNorm,
     embedding_conv: candle_nn::Conv2d,
     embedding_ln: WLayerNorm,
     down_blocks: Vec<DownBlock>,
@@ -98,7 +98,7 @@ impl WDiffNeXt {
     ) -> Result<Self> {
         const C_HIDDEN: [usize; 4] = [320, 640, 1280, 1280];
         const BLOCKS: [usize; 4] = [4, 4, 14, 4];
-        const NHEAD: [usize; 4] = [0, 10, 20, 20];
+        const NHEAD: [usize; 4] = [1, 10, 20, 20];
         const INJECT_EFFNET: [bool; 4] = [false, true, true, true];
         const EFFNET_EMBD: usize = 16;
 
@@ -133,24 +133,21 @@ impl WDiffNeXt {
             };
             effnet_mappers.push(c)
         }
-        let cfg = candle_nn::layer_norm::LayerNormConfig {
-            ..Default::default()
-        };
-        let seq_norm = candle_nn::layer_norm(c_cond, cfg, vb.pp("seq_norm"))?;
-        let embedding_ln = WLayerNorm::new(C_HIDDEN[0], vb.pp("embedding.1"))?;
+        let seq_norm = WLayerNorm::new(c_cond)?;
+        let embedding_ln = WLayerNorm::new(C_HIDDEN[0])?;
         let embedding_conv = candle_nn::conv2d(
             c_in * patch_size * patch_size,
-            C_HIDDEN[1],
+            C_HIDDEN[0],
             1,
             Default::default(),
-            vb.pp("embedding.2"),
+            vb.pp("embedding.1"),
         )?;
 
         let mut down_blocks = Vec::with_capacity(C_HIDDEN.len());
         for (i, &c_hidden) in C_HIDDEN.iter().enumerate() {
             let vb = vb.pp("down_blocks").pp(i);
             let (layer_norm, conv, start_layer_i) = if i > 0 {
-                let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1], vb.pp(0))?;
+                let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1])?;
                 let cfg = candle_nn::Conv2dConfig {
                     stride: 2,
                     ..Default::default()
@@ -223,7 +220,7 @@ impl WDiffNeXt {
                 sub_blocks.push(sub_block)
             }
             let (layer_norm, conv) = if i > 0 {
-                let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1], vb.pp(layer_i))?;
+                let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1])?;
                 layer_i += 1;
                 let cfg = candle_nn::Conv2dConfig {
                     stride: 2,
@@ -242,7 +239,7 @@ impl WDiffNeXt {
             up_blocks.push(up_block)
         }
 
-        let clf_ln = WLayerNorm::new(C_HIDDEN[0], vb.pp("clf.0"))?;
+        let clf_ln = WLayerNorm::new(C_HIDDEN[0])?;
         let clf_conv = candle_nn::conv2d(
             C_HIDDEN[0],
             2 * c_out * patch_size * patch_size,
diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
index a60f8e8a..faf2d2b4 100644
--- a/candle-transformers/src/models/wuerstchen/paella_vq.rs
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -1,11 +1,12 @@
+use super::common::WLayerNorm;
 use candle::{Module, Result, Tensor};
 use candle_nn::VarBuilder;
 
 #[derive(Debug)]
 pub struct MixingResidualBlock {
-    norm1: candle_nn::LayerNorm,
+    norm1: WLayerNorm,
     depthwise_conv: candle_nn::Conv2d,
-    norm2: candle_nn::LayerNorm,
+    norm2: WLayerNorm,
     channelwise_lin1: candle_nn::Linear,
     channelwise_lin2: candle_nn::Linear,
     gammas: Vec<f32>,
@@ -13,13 +14,8 @@ pub struct MixingResidualBlock {
 
 impl MixingResidualBlock {
     pub fn new(inp: usize, embed_dim: usize, vb: VarBuilder) -> Result<Self> {
-        let cfg = candle_nn::LayerNormConfig {
-            affine: false,
-            eps: 1e-6,
-            remove_mean: true,
-        };
-        let norm1 = candle_nn::layer_norm(inp, cfg, vb.pp("norm1"))?;
-        let norm2 = candle_nn::layer_norm(inp, cfg, vb.pp("norm1"))?;
+        let norm1 = WLayerNorm::new(inp)?;
+        let norm2 = WLayerNorm::new(inp)?;
         let cfg = candle_nn::Conv2dConfig {
             groups: inp,
             ..Default::default()
@@ -120,15 +116,15 @@ impl PaellaVQ {
             d_idx += 1;
             down_blocks.push((conv_block, res_block))
         }
+        let vb_d = vb_d.pp(d_idx);
         let down_blocks_conv = candle_nn::conv2d_no_bias(
             C_LEVELS[1],
             LATENT_CHANNELS,
             1,
             Default::default(),
-            vb_d.pp(d_idx),
+            vb_d.pp(0),
         )?;
-        d_idx += 1;
-        let down_blocks_bn = candle_nn::batch_norm(LATENT_CHANNELS, 1e-5, vb_d.pp(d_idx))?;
+        let down_blocks_bn = candle_nn::batch_norm(LATENT_CHANNELS, 1e-5, vb_d.pp(1))?;
 
         let mut up_blocks = Vec::new();
         let vb_u = vb.pp("up_blocks");
@@ -138,7 +134,7 @@ impl PaellaVQ {
             C_LEVELS[1],
             1,
             Default::default(),
-            vb_u.pp(u_idx),
+            vb_u.pp(u_idx).pp(0),
         )?;
         u_idx += 1;
         for (i, &c_level) in C_LEVELS.iter().rev().enumerate() {
@@ -157,7 +153,7 @@ impl PaellaVQ {
                 };
                 let block = candle_nn::conv_transpose2d_no_bias(
                     c_level,
-                    C_LEVELS[i - 1],
+                    C_LEVELS[C_LEVELS.len() - i - 2],
                     4,
                     cfg,
                     vb_u.pp(u_idx),
diff --git a/candle-transformers/src/models/wuerstchen/prior.rs b/candle-transformers/src/models/wuerstchen/prior.rs
index a9e3e793..93385a32 100644
--- a/candle-transformers/src/models/wuerstchen/prior.rs
+++ b/candle-transformers/src/models/wuerstchen/prior.rs
@@ -33,7 +33,7 @@ impl WPrior {
         let projection = candle_nn::conv2d(c_in, c, 1, Default::default(), vb.pp("projection"))?;
         let cond_mapper_lin1 = candle_nn::linear(c_cond, c, vb.pp("cond_mapper.0"))?;
         let cond_mapper_lin2 = candle_nn::linear(c, c, vb.pp("cond_mapper.2"))?;
-        let out_ln = super::common::WLayerNorm::new(c, vb.pp("out.0"))?;
+        let out_ln = super::common::WLayerNorm::new(c)?;
         let out_conv = candle_nn::conv2d(c, c_in * 2, 1, Default::default(), vb.pp("out.1"))?;
         let mut blocks = Vec::with_capacity(depth);
         for index in 0..depth {

From c2b866172abaf1d4b8d75273c4f4e28a16d872f0 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Sun, 17 Sep 2023 22:08:11 +0100
Subject: [PATCH 116/150] More Wuerstchen fixes. (#882)

* More Weurstchen fixes.

* More shape fixes.

* Add more of the prior specific bits.

* Broadcast add.

* Fix the clip config.

* Add some masking options to the clip model.
---
 candle-examples/examples/wuerstchen/main.rs   | 57 ++++++++++++-------
 .../src/models/stable_diffusion/clip.rs       | 49 +++++++++++++---
 .../src/models/wuerstchen/common.rs           |  6 +-
 .../src/models/wuerstchen/diffnext.rs         | 25 ++++----
 4 files changed, 96 insertions(+), 41 deletions(-)

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index 7e4d2360..b3231360 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -16,6 +16,7 @@ use tokenizers::Tokenizer;
 
 const GUIDANCE_SCALE: f64 = 7.5;
 const RESOLUTION_MULTIPLE: f64 = 42.67;
+const PRIOR_CIN: usize = 16;
 
 #[derive(Parser)]
 #[command(author, version, about, long_about = None)]
@@ -54,6 +55,10 @@ struct Args {
     #[arg(long, value_name = "FILE")]
     clip_weights: Option<String>,
 
+    /// The CLIP weight file used by the prior model, in .safetensors format.
+    #[arg(long, value_name = "FILE")]
+    prior_clip_weights: Option<String>,
+
     /// The prior weight file, in .safetensors format.
     #[arg(long, value_name = "FILE")]
     prior_weights: Option<String>,
@@ -66,6 +71,10 @@ struct Args {
     /// The file specifying the tokenizer to used for tokenization.
     tokenizer: Option<String>,
 
+    #[arg(long, value_name = "FILE")]
+    /// The file specifying the tokenizer to used for prior tokenization.
+    prior_tokenizer: Option<String>,
+
     /// The size of the sliced attention or 0 for automatic slicing (disabled by default)
     #[arg(long)]
     sliced_attention_size: Option<usize>,
@@ -86,7 +95,9 @@ struct Args {
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
 enum ModelFile {
     Tokenizer,
+    PriorTokenizer,
     Clip,
+    PriorClip,
     Decoder,
     VqGan,
     Prior,
@@ -102,7 +113,9 @@ impl ModelFile {
                 let repo_prior = "warp-ai/wuerstchen-prior";
                 let (repo, path) = match self {
                     Self::Tokenizer => (repo_main, "tokenizer/tokenizer.json"),
+                    Self::PriorTokenizer => (repo_prior, "tokenizer/tokenizer.json"),
                     Self::Clip => (repo_main, "text_encoder/model.safetensors"),
+                    Self::PriorClip => (repo_prior, "text_encoder/model.safetensors"),
                     Self::Decoder => (repo_main, "decoder/diffusion_pytorch_model.safetensors"),
                     Self::VqGan => (repo_main, "vqgan/diffusion_pytorch_model.safetensors"),
                     Self::Prior => (repo_prior, "prior/diffusion_pytorch_model.safetensors"),
@@ -144,12 +157,11 @@ fn output_filename(
 fn encode_prompt(
     prompt: &str,
     uncond_prompt: &str,
-    tokenizer: Option<String>,
-    clip_weights: Option<String>,
+    tokenizer: std::path::PathBuf,
+    clip_weights: std::path::PathBuf,
     clip_config: stable_diffusion::clip::Config,
     device: &Device,
 ) -> Result<Tensor> {
-    let tokenizer = ModelFile::Tokenizer.get(tokenizer)?;
     let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
     let pad_id = match &clip_config.pad_with {
         Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
@@ -161,6 +173,7 @@ fn encode_prompt(
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
+    let tokens_len = tokens.len();
     while tokens.len() < clip_config.max_position_embeddings {
         tokens.push(pad_id)
     }
@@ -171,17 +184,17 @@ fn encode_prompt(
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
+    let uncond_tokens_len = uncond_tokens.len();
     while uncond_tokens.len() < clip_config.max_position_embeddings {
         uncond_tokens.push(pad_id)
     }
     let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
 
-    println!("Building the Clip transformer.");
-    let clip_weights = ModelFile::Clip.get(clip_weights)?;
+    println!("Building the clip transformer.");
     let text_model =
         stable_diffusion::build_clip_transformer(&clip_config, clip_weights, device, DType::F32)?;
-    let text_embeddings = text_model.forward(&tokens)?;
-    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
+    let text_embeddings = text_model.forward_with_mask(&tokens, tokens_len)?;
+    let uncond_embeddings = text_model.forward_with_mask(&uncond_tokens, uncond_tokens_len)?;
     let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?;
     Ok(text_embeddings)
 }
@@ -221,15 +234,19 @@ fn run(args: Args) -> Result<()> {
     let height = height.unwrap_or(1024);
     let width = width.unwrap_or(1024);
 
-    let text_embeddings = encode_prompt(
-        &prompt,
-        &uncond_prompt,
-        tokenizer.clone(),
-        clip_weights.clone(),
-        stable_diffusion::clip::Config::wuerstchen(),
-        &device,
-    )?;
-    println!("{text_embeddings:?}");
+    let prior_text_embeddings = {
+        let tokenizer = ModelFile::PriorTokenizer.get(args.prior_tokenizer)?;
+        let weights = ModelFile::PriorClip.get(args.prior_clip_weights)?;
+        encode_prompt(
+            &prompt,
+            &uncond_prompt,
+            tokenizer.clone(),
+            weights,
+            stable_diffusion::clip::Config::wuerstchen_prior(),
+            &device,
+        )?
+    };
+    println!("{prior_text_embeddings}");
 
     println!("Building the prior.");
     // https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
@@ -239,8 +256,8 @@ fn run(args: Args) -> Result<()> {
         let weights = weights.deserialize()?;
         let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
         wuerstchen::prior::WPrior::new(
-            /* c_in */ 16, /* c */ 1536, /* c_cond */ 1280, /* c_r */ 64,
-            /* depth */ 32, /* nhead */ 24, vb,
+            /* c_in */ PRIOR_CIN, /* c */ 1536, /* c_cond */ 1280,
+            /* c_r */ 64, /* depth */ 32, /* nhead */ 24, vb,
         )?
     };
 
@@ -274,12 +291,12 @@ fn run(args: Args) -> Result<()> {
         let latents = Tensor::randn(
             0f32,
             1f32,
-            (b_size, 4, latent_height, latent_width),
+            (b_size, PRIOR_CIN, latent_height, latent_width),
             &device,
         )?;
         // TODO: latents denoising loop, use the scheduler values.
         let ratio = Tensor::ones(1, DType::F32, &device)?;
-        let prior = prior.forward(&latents, &ratio, &text_embeddings)?;
+        let prior = prior.forward(&latents, &ratio, &prior_text_embeddings)?;
 
         let latents = ((latents * 42.)? - 1.)?;
         /*
diff --git a/candle-transformers/src/models/stable_diffusion/clip.rs b/candle-transformers/src/models/stable_diffusion/clip.rs
index 31d025b3..7f86cf31 100644
--- a/candle-transformers/src/models/stable_diffusion/clip.rs
+++ b/candle-transformers/src/models/stable_diffusion/clip.rs
@@ -107,13 +107,28 @@ impl Config {
             embed_dim: 1024,
             intermediate_size: 4096,
             max_position_embeddings: 77,
-            pad_with: Some("!".to_string()),
+            pad_with: None,
             num_hidden_layers: 24,
             num_attention_heads: 16,
             projection_dim: 1024,
             activation: Activation::Gelu,
         }
     }
+
+    // https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/text_encoder/config.json
+    pub fn wuerstchen_prior() -> Self {
+        Self {
+            vocab_size: 49408,
+            embed_dim: 1280,
+            intermediate_size: 5120,
+            max_position_embeddings: 77,
+            pad_with: None,
+            num_hidden_layers: 32,
+            num_attention_heads: 20,
+            projection_dim: 512,
+            activation: Activation::Gelu,
+        }
+    }
 }
 
 // CLIP Text Model
@@ -334,21 +349,39 @@ impl ClipTextTransformer {
     }
 
     // https://github.com/huggingface/transformers/blob/674f750a57431222fa2832503a108df3badf1564/src/transformers/models/clip/modeling_clip.py#L678
-    fn build_causal_attention_mask(bsz: usize, seq_len: usize, device: &Device) -> Result<Tensor> {
+    fn build_causal_attention_mask(
+        bsz: usize,
+        seq_len: usize,
+        mask_after: usize,
+        device: &Device,
+    ) -> Result<Tensor> {
         let mask: Vec<_> = (0..seq_len)
-            .flat_map(|i| (0..seq_len).map(move |j| if j > i { f32::MIN } else { 0. }))
+            .flat_map(|i| {
+                (0..seq_len).map(move |j| {
+                    if j > i || j > mask_after {
+                        f32::MIN
+                    } else {
+                        0.
+                    }
+                })
+            })
             .collect();
         let mask = Tensor::from_slice(&mask, (seq_len, seq_len), device)?;
         mask.broadcast_as((bsz, seq_len, seq_len))
     }
+
+    pub fn forward_with_mask(&self, xs: &Tensor, mask_after: usize) -> Result<Tensor> {
+        let (bsz, seq_len) = xs.dims2()?;
+        let xs = self.embeddings.forward(xs)?;
+        let causal_attention_mask =
+            Self::build_causal_attention_mask(bsz, seq_len, mask_after, xs.device())?;
+        let xs = self.encoder.forward(&xs, &causal_attention_mask)?;
+        self.final_layer_norm.forward(&xs)
+    }
 }
 
 impl Module for ClipTextTransformer {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let (bsz, seq_len) = xs.dims2()?;
-        let xs = self.embeddings.forward(xs)?;
-        let causal_attention_mask = Self::build_causal_attention_mask(bsz, seq_len, xs.device())?;
-        let xs = self.encoder.forward(&xs, &causal_attention_mask)?;
-        self.final_layer_norm.forward(&xs)
+        self.forward_with_mask(xs, usize::MAX)
     }
 }
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
index ee318d27..5337fdc6 100644
--- a/candle-transformers/src/models/wuerstchen/common.rs
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -75,9 +75,9 @@ impl Module for GlobalResponseNorm {
         let agg_norm = xs.sqr()?.sum_keepdim((1, 2))?;
         let stand_div_norm =
             agg_norm.broadcast_div(&(agg_norm.mean_keepdim(D::Minus1)? + 1e-6)?)?;
-        (xs.broadcast_mul(&stand_div_norm)?
-            .broadcast_mul(&self.gamma)
-            + &self.beta)?
+        xs.broadcast_mul(&stand_div_norm)?
+            .broadcast_mul(&self.gamma)?
+            .broadcast_add(&self.beta)?
             + xs
     }
 }
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 70e4ba34..664251ed 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -68,7 +68,7 @@ struct DownBlock {
 struct UpBlock {
     sub_blocks: Vec<SubBlock>,
     layer_norm: Option<WLayerNorm>,
-    conv: Option<candle_nn::Conv2d>,
+    conv: Option<candle_nn::ConvTranspose2d>,
 }
 
 #[derive(Debug)]
@@ -152,20 +152,20 @@ impl WDiffNeXt {
                     stride: 2,
                     ..Default::default()
                 };
-                let conv = candle_nn::conv2d(C_HIDDEN[i - 1], c_hidden, 2, cfg, vb.pp(1))?;
-                (Some(layer_norm), Some(conv), 2)
+                let conv = candle_nn::conv2d(C_HIDDEN[i - 1], c_hidden, 2, cfg, vb.pp("0.1"))?;
+                (Some(layer_norm), Some(conv), 1)
             } else {
                 (None, None, 0)
             };
             let mut sub_blocks = Vec::with_capacity(BLOCKS[i]);
             let mut layer_i = start_layer_i;
-            for j in 0..BLOCKS[i] {
+            for _j in 0..BLOCKS[i] {
                 let c_skip = if INJECT_EFFNET[i] { c_cond } else { 0 };
                 let res_block = ResBlockStageB::new(c_hidden, c_skip, 3, vb.pp(layer_i))?;
                 layer_i += 1;
                 let ts_block = TimestepBlock::new(c_hidden, c_r, vb.pp(layer_i))?;
                 layer_i += 1;
-                let attn_block = if j == 0 {
+                let attn_block = if i == 0 {
                     None
                 } else {
                     let attn_block =
@@ -190,7 +190,7 @@ impl WDiffNeXt {
 
         let mut up_blocks = Vec::with_capacity(C_HIDDEN.len());
         for (i, &c_hidden) in C_HIDDEN.iter().enumerate().rev() {
-            let vb = vb.pp("up_blocks").pp(i);
+            let vb = vb.pp("up_blocks").pp(C_HIDDEN.len() - 1 - i);
             let mut sub_blocks = Vec::with_capacity(BLOCKS[i]);
             let mut layer_i = 0;
             for j in 0..BLOCKS[i] {
@@ -204,7 +204,7 @@ impl WDiffNeXt {
                 layer_i += 1;
                 let ts_block = TimestepBlock::new(c_hidden, c_r, vb.pp(layer_i))?;
                 layer_i += 1;
-                let attn_block = if j == 0 {
+                let attn_block = if i == 0 {
                     None
                 } else {
                     let attn_block =
@@ -221,12 +221,17 @@ impl WDiffNeXt {
             }
             let (layer_norm, conv) = if i > 0 {
                 let layer_norm = WLayerNorm::new(C_HIDDEN[i - 1])?;
-                layer_i += 1;
-                let cfg = candle_nn::Conv2dConfig {
+                let cfg = candle_nn::ConvTranspose2dConfig {
                     stride: 2,
                     ..Default::default()
                 };
-                let conv = candle_nn::conv2d(C_HIDDEN[i - 1], c_hidden, 2, cfg, vb.pp(layer_i))?;
+                let conv = candle_nn::conv_transpose2d(
+                    c_hidden,
+                    C_HIDDEN[i - 1],
+                    2,
+                    cfg,
+                    vb.pp(layer_i).pp(1),
+                )?;
                 (Some(layer_norm), Some(conv))
             } else {
                 (None, None)

From 03e194123d743ca73b10797a315b8d47734735e8 Mon Sep 17 00:00:00 2001
From: Lukas Kreussel <65088241+LLukas22@users.noreply.github.com>
Date: Sun, 17 Sep 2023 23:11:01 +0200
Subject: [PATCH 117/150] Add return types to `*.pyi` stubs (#880)

* Start generating return types

* Finish tensor type hinting

* Add `save_gguf` to `utils`

* Typehint `quant-llama.py`
---
 candle-pyo3/py_src/candle/__init__.py        |   6 +-
 candle-pyo3/py_src/candle/__init__.pyi       | 435 ++++++++++++-------
 candle-pyo3/py_src/candle/nn/__init__.pyi    |   8 +-
 candle-pyo3/py_src/candle/utils/__init__.py  |   1 +
 candle-pyo3/py_src/candle/utils/__init__.pyi |  25 +-
 candle-pyo3/quant-llama.py                   |  31 +-
 candle-pyo3/src/lib.rs                       | 270 +++++++++++-
 candle-pyo3/stub.py                          |  23 +-
 candle-pyo3/test.py                          |   9 +-
 9 files changed, 611 insertions(+), 197 deletions(-)

diff --git a/candle-pyo3/py_src/candle/__init__.py b/candle-pyo3/py_src/candle/__init__.py
index 49c96122..951609cc 100644
--- a/candle-pyo3/py_src/candle/__init__.py
+++ b/candle-pyo3/py_src/candle/__init__.py
@@ -1 +1,5 @@
-from .candle import *
\ No newline at end of file
+from .candle import *
+
+__doc__ = candle.__doc__
+if hasattr(candle, "__all__"):
+    __all__ = candle.__all__
\ No newline at end of file
diff --git a/candle-pyo3/py_src/candle/__init__.pyi b/candle-pyo3/py_src/candle/__init__.pyi
index c21e6738..414f0bc4 100644
--- a/candle-pyo3/py_src/candle/__init__.pyi
+++ b/candle-pyo3/py_src/candle/__init__.pyi
@@ -7,7 +7,7 @@ class bf16(DType):
     pass
 
 @staticmethod
-def cat(tensors: List[Tensor], dim: int):
+def cat(tensors: List[Tensor], dim: int) -> Tensor:
     """
     Concatenate the tensors across one axis.
     """
@@ -26,31 +26,35 @@ class i64(DType):
     pass
 
 @staticmethod
-def ones(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None):
-    """ """
+def ones(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None) -> Tensor:
+    """
+    Creates a new tensor filled with ones.
+    """
     pass
 
 @staticmethod
-def rand(shape: Sequence[int], device: Optional[Device] = None):
+def rand(shape: Sequence[int], device: Optional[Device] = None) -> Tensor:
     """
     Creates a new tensor with random values.
     """
     pass
 
 @staticmethod
-def randn(shape: Sequence[int], device: Optional[Device] = None):
-    """ """
+def randn(shape: Sequence[int], device: Optional[Device] = None) -> Tensor:
+    """
+    Creates a new tensor with random values from a normal distribution.
+    """
     pass
 
 @staticmethod
-def stack(tensors: List[Tensor], dim: int):
+def stack(tensors: List[Tensor], dim: int) -> Tensor:
     """
     Stack the tensors along a new axis.
     """
     pass
 
 @staticmethod
-def tensor(data: _ArrayLike):
+def tensor(data: _ArrayLike) -> Tensor:
     """
     Creates a new tensor from a Python value. The value can be a scalar or array-like object.
     """
@@ -63,186 +67,309 @@ class u8(DType):
     pass
 
 @staticmethod
-def zeros(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None):
-    """ """
+def zeros(shape: Sequence[int], dtype: Optional[DType] = None, device: Optional[Device] = None) -> Tensor:
+    """
+    Creates a new tensor filled with zeros.
+    """
     pass
 
 class DType:
-    pass
+    """
+    A `candle` dtype.
+    """
 
 class QTensor:
-    def dequantize(self):
-        """ """
+    """
+    A quantized tensor.
+    """
+
+    def dequantize(self) -> Tensor:
+        """
+        Dequantizes the tensor.
+        """
         pass
     @property
-    def ggml_dtype(self):
-        """ """
+    def ggml_dtype(self) -> str:
+        """
+        Gets the tensors quantized dtype.
+        """
         pass
-    def matmul_t(self, lhs):
-        """ """
+    def matmul_t(self, lhs: Tensor) -> Tensor:
+        """
+        Performs a quantized matrix multiplication, with the quantized tensor as the right hand side.
+        """
         pass
     @property
-    def rank(self):
-        """ """
+    def rank(self) -> int:
+        """
+        Gets the rank of the tensor.
+        """
         pass
     @property
-    def shape(self):
-        """ """
+    def shape(self) -> Tuple[int]:
+        """
+        Gets the shape of the tensor.
+        """
         pass
 
 class Tensor:
-    def __init__(data: _ArrayLike):
+    """
+    A `candle` tensor.
+    """
+
+    def __init__(self, data: _ArrayLike):
         pass
-    def argmax_keepdim(self, dim):
-        """ """
-        pass
-    def argmin_keepdim(self, dim):
-        """ """
-        pass
-    def broadcast_add(self, rhs):
-        """ """
-        pass
-    def broadcast_as(self, shape):
-        """ """
-        pass
-    def broadcast_div(self, rhs):
-        """ """
-        pass
-    def broadcast_left(self, shape):
-        """ """
-        pass
-    def broadcast_mul(self, rhs):
-        """ """
-        pass
-    def broadcast_sub(self, rhs):
-        """ """
-        pass
-    def contiguous(self):
-        """ """
-        pass
-    def copy(self):
-        """ """
-        pass
-    def cos(self):
-        """ """
-        pass
-    def detach(self):
-        """ """
-        pass
-    @property
-    def device(self):
-        """ """
-        pass
-    @property
-    def dtype(self):
-        """ """
-        pass
-    def exp(self):
-        """ """
-        pass
-    def flatten_all(self):
-        """ """
-        pass
-    def flatten_from(self, dim):
-        """ """
-        pass
-    def flatten_to(self, dim):
-        """ """
-        pass
-    def get(self, index):
-        """ """
-        pass
-    def index_select(self, rhs, dim):
-        """ """
-        pass
-    def is_contiguous(self):
-        """ """
-        pass
-    def is_fortran_contiguous(self):
-        """ """
-        pass
-    def log(self):
-        """ """
-        pass
-    def matmul(self, rhs):
-        """ """
-        pass
-    def max_keepdim(self, dim):
-        """ """
-        pass
-    def mean_all(self):
-        """ """
-        pass
-    def min_keepdim(self, dim):
-        """ """
-        pass
-    def narrow(self, dim, start, len):
-        """ """
-        pass
-    def powf(self, p):
-        """ """
-        pass
-    def quantize(self, quantized_dtype):
-        """ """
-        pass
-    @property
-    def rank(self):
-        """ """
-        pass
-    def recip(self):
-        """ """
-        pass
-    def reshape(self, shape):
-        """ """
-        pass
-    @property
-    def shape(self):
+    def argmax_keepdim(self, dim: int) -> Tensor:
         """
-        Gets the tensor shape as a Python tuple.
+        Returns the indices of the maximum value(s) across the selected dimension.
         """
         pass
-    def sin(self):
-        """ """
+    def argmin_keepdim(self, dim: int) -> Tensor:
+        """
+        Returns the indices of the minimum value(s) across the selected dimension.
+        """
         pass
-    def sqr(self):
-        """ """
+    def broadcast_add(self, rhs: Tensor) -> Tensor:
+        """
+        Adds the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+        """
         pass
-    def sqrt(self):
-        """ """
+    def broadcast_as(self, shape: Sequence[int]) -> Tensor:
+        """
+        Broadcasts the tensor to the given shape.
+        """
         pass
-    def squeeze(self, dim):
-        """ """
+    def broadcast_div(self, rhs: Tensor) -> Tensor:
+        """
+        Divides the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+        """
+        pass
+    def broadcast_left(self, shape: Sequence[int]) -> Tensor:
+        """
+        Broadcasts the tensor to the given shape, adding new dimensions on the left.
+        """
+        pass
+    def broadcast_mul(self, rhs: Tensor) -> Tensor:
+        """
+        Multiplies the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+        """
+        pass
+    def broadcast_sub(self, rhs: Tensor) -> Tensor:
+        """
+        Subtracts the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+        """
+        pass
+    def contiguous(self) -> Tensor:
+        """
+        Makes the tensor contiguous in memory.
+        """
+        pass
+    def copy(self) -> Tensor:
+        """
+        Returns a copy of the tensor.
+        """
+        pass
+    def cos(self) -> Tensor:
+        """
+        Performs the `cos` operation on the tensor.
+        """
+        pass
+    def detach(self) -> Tensor:
+        """
+        Detach the tensor from the computation graph.
+        """
         pass
     @property
-    def stride(self):
-        """ """
+    def device(self) -> Device:
+        """
+        Gets the tensor's device.
+        """
         pass
-    def sum_all(self):
-        """ """
+    @property
+    def dtype(self) -> DType:
+        """
+        Gets the tensor's dtype.
+        """
         pass
-    def sum_keepdim(self, dims):
-        """ """
+    def exp(self) -> Tensor:
+        """
+        Performs the `exp` operation on the tensor.
+        """
         pass
-    def t(self):
-        """ """
+    def flatten_all(self) -> Tensor:
+        """
+        Flattens the tensor into a 1D tensor.
+        """
         pass
-    def to_device(self, device):
-        """ """
+    def flatten_from(self, dim: int) -> Tensor:
+        """
+        Flattens the tensor on the dimension indexes from `dim` (inclusive) to the last dimension.
+        """
         pass
-    def to_dtype(self, dtype):
-        """ """
+    def flatten_to(self, dim: int) -> Tensor:
+        """
+        Flattens the tensor on the dimension indexes from `0` to `dim` (inclusive).
+        """
         pass
-    def transpose(self, dim1, dim2):
-        """ """
+    def get(self, index: int) -> Tensor:
+        """
+        Gets the value at the specified index.
+        """
         pass
-    def unsqueeze(self, dim):
-        """ """
+    def index_select(self, rhs: Tensor, dim: int) -> Tensor:
+        """
+        Select values for the input tensor at the target indexes across the specified dimension.
+
+        The `indexes` is argument is an int tensor with a single dimension.
+        The output has the same number of dimension as the `self` input. The target dimension of
+        the output has length the length of `indexes` and the values are taken from `self` using
+        the index from `indexes`. Other dimensions have the same number of elements as the input
+        tensor.
+        """
         pass
-    def values(self):
+    def is_contiguous(self) -> bool:
+        """
+        Returns true if the tensor is contiguous in C order.
+        """
+        pass
+    def is_fortran_contiguous(self) -> bool:
+        """
+        Returns true if the tensor is contiguous in Fortran order.
+        """
+        pass
+    def log(self) -> Tensor:
+        """
+        Performs the `log` operation on the tensor.
+        """
+        pass
+    def matmul(self, rhs: Tensor) -> Tensor:
+        """
+        Performs a matrix multiplication between the two tensors.
+        """
+        pass
+    def max_keepdim(self, dim: int) -> Tensor:
+        """
+        Gathers the maximum value across the selected dimension.
+        """
+        pass
+    def mean_all(self) -> Tensor:
+        """
+        Returns the mean of the tensor.
+        """
+        pass
+    def min_keepdim(self, dim: int) -> Tensor:
+        """
+        Gathers the minimum value across the selected dimension.
+        """
+        pass
+    def narrow(self, dim: int, start: int, len: int) -> Tensor:
+        """
+        Returns a new tensor that is a narrowed version of the input, the dimension `dim`
+        ranges from `start` to `start + len`.
+        """
+        pass
+    def powf(self, p: float) -> Tensor:
+        """
+        Performs the `pow` operation on the tensor with the given exponent.
+        """
+        pass
+    def quantize(self, quantized_dtype: str) -> QTensor:
+        """
+        Quantize the tensor.
+        """
+        pass
+    @property
+    def rank(self) -> int:
+        """
+        Gets the tensor's rank.
+        """
+        pass
+    def recip(self) -> Tensor:
+        """
+        Get the `recip` of the tensor.
+        """
+        pass
+    def reshape(self, shape: Sequence[int]) -> Tensor:
+        """
+        Reshapes the tensor to the given shape.
+        """
+        pass
+    @property
+    def shape(self) -> Tuple[int]:
+        """
+        Gets the tensor's shape.
+        """
+        pass
+    def sin(self) -> Tensor:
+        """
+        Performs the `sin` operation on the tensor.
+        """
+        pass
+    def sqr(self) -> Tensor:
+        """
+        Squares the tensor.
+        """
+        pass
+    def sqrt(self) -> Tensor:
+        """
+        Calculates the square root of the tensor.
+        """
+        pass
+    def squeeze(self, dim: int) -> Tensor:
+        """
+        Creates a new tensor with the specified dimension removed if its size was one.
+        """
+        pass
+    @property
+    def stride(self) -> Tuple[int]:
+        """
+        Gets the tensor's strides.
+        """
+        pass
+    def sum_all(self) -> Tensor:
+        """
+        Returns the sum of the tensor.
+        """
+        pass
+    def sum_keepdim(self, dim: Union[int, List[int]]) -> Tensor:
+        """
+        Returns the sum of all elements in the input tensor. The sum is performed over all the input dimensions.
+        """
+        pass
+    def t(self) -> Tensor:
+        """
+        Transposes the tensor.
+        """
+        pass
+    def to_device(self, device: Union[str, Device]) -> Tensor:
+        """
+        Move the tensor to a new device.
+        """
+        pass
+    def to_dtype(self, dtype: Union[str, DType]) -> Tensor:
+        """
+        Convert the tensor to a new dtype.
+        """
+        pass
+    def transpose(self, dim1: int, dim2: int) -> Tensor:
+        """
+        Returns a tensor that is a transposed version of the input, the given dimensions are swapped.
+        """
+        pass
+    def unsqueeze(self, dim: int) -> Tensor:
+        """
+        Creates a new tensor with a dimension of size one inserted at the specified position.
+        """
+        pass
+    def values(self) -> _ArrayLike:
         """
         Gets the tensor's data as a Python scalar or array-like object.
         """
         pass
-    def where_cond(self, on_true, on_false):
-        """ """
+    def where_cond(self, on_true: Tensor, on_false: Tensor) -> Tensor:
+        """
+        Returns a tensor with the same shape as the input tensor, the values are taken from
+        `on_true` if the input tensor value is not zero, and `on_false` at the positions where the
+        input tensor is equal to zero.
+        """
         pass
diff --git a/candle-pyo3/py_src/candle/nn/__init__.pyi b/candle-pyo3/py_src/candle/nn/__init__.pyi
index 821cd052..01b30fce 100644
--- a/candle-pyo3/py_src/candle/nn/__init__.pyi
+++ b/candle-pyo3/py_src/candle/nn/__init__.pyi
@@ -2,18 +2,18 @@
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
 from os import PathLike
 from candle.typing import _ArrayLike, Device
-from candle import Tensor, DType
+from candle import Tensor, DType, QTensor
 
 @staticmethod
-def silu(tensor: Tensor):
+def silu(tensor: Tensor) -> Tensor:
     """
     Applies the Sigmoid Linear Unit (SiLU) function to a given tensor.
     """
     pass
 
 @staticmethod
-def softmax(tensor: Tensor, dim: int):
+def softmax(tensor: Tensor, dim: int) -> Tensor:
     """
-    Applies the Softmax function to a given tensor.
+    Applies the Softmax function to a given tensor.#
     """
     pass
diff --git a/candle-pyo3/py_src/candle/utils/__init__.py b/candle-pyo3/py_src/candle/utils/__init__.py
index 2ead6d84..62d85dc9 100644
--- a/candle-pyo3/py_src/candle/utils/__init__.py
+++ b/candle-pyo3/py_src/candle/utils/__init__.py
@@ -8,4 +8,5 @@ has_mkl = utils.has_mkl
 load_ggml = utils.load_ggml
 load_gguf = utils.load_gguf
 load_safetensors = utils.load_safetensors
+save_gguf = utils.save_gguf
 save_safetensors = utils.save_safetensors
diff --git a/candle-pyo3/py_src/candle/utils/__init__.pyi b/candle-pyo3/py_src/candle/utils/__init__.pyi
index 7a0a5231..61964ffc 100644
--- a/candle-pyo3/py_src/candle/utils/__init__.pyi
+++ b/candle-pyo3/py_src/candle/utils/__init__.pyi
@@ -2,38 +2,38 @@
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union, Sequence
 from os import PathLike
 from candle.typing import _ArrayLike, Device
-from candle import Tensor, DType
+from candle import Tensor, DType, QTensor
 
 @staticmethod
-def cuda_is_available():
+def cuda_is_available() -> bool:
     """
     Returns true if the 'cuda' backend is available.
     """
     pass
 
 @staticmethod
-def get_num_threads():
+def get_num_threads() -> int:
     """
     Returns the number of threads used by the candle.
     """
     pass
 
 @staticmethod
-def has_accelerate():
+def has_accelerate() -> bool:
     """
     Returns true if candle was compiled with 'accelerate' support.
     """
     pass
 
 @staticmethod
-def has_mkl():
+def has_mkl() -> bool:
     """
     Returns true if candle was compiled with MKL support.
     """
     pass
 
 @staticmethod
-def load_ggml(path: Union[str, PathLike]):
+def load_ggml(path: Union[str, PathLike]) -> Tuple[Dict[str, QTensor], Dict[str, Any], List[str]]:
     """
     Load a GGML file. Returns a tuple of three objects: a dictionary mapping tensor names to tensors,
     a dictionary mapping hyperparameter names to hyperparameter values, and a vocabulary.
@@ -41,7 +41,7 @@ def load_ggml(path: Union[str, PathLike]):
     pass
 
 @staticmethod
-def load_gguf(path: Union[str, PathLike]):
+def load_gguf(path: Union[str, PathLike]) -> Tuple[Dict[str, QTensor], Dict[str, Any]]:
     """
     Loads a GGUF file. Returns a tuple of two dictionaries: the first maps tensor names to tensors,
     and the second maps metadata keys to metadata values.
@@ -49,14 +49,21 @@ def load_gguf(path: Union[str, PathLike]):
     pass
 
 @staticmethod
-def load_safetensors(path: Union[str, PathLike]):
+def load_safetensors(path: Union[str, PathLike]) -> Dict[str, Tensor]:
     """
     Loads a safetensors file. Returns a dictionary mapping tensor names to tensors.
     """
     pass
 
 @staticmethod
-def save_safetensors(path: Union[str, PathLike], tensors: Dict[str, Tensor]):
+def save_gguf(path: Union[str, PathLike], tensors: Dict[str, QTensor], metadata: Dict[str, Any]):
+    """
+    Save quanitzed tensors and metadata to a GGUF file.
+    """
+    pass
+
+@staticmethod
+def save_safetensors(path: Union[str, PathLike], tensors: Dict[str, Tensor]) -> None:
     """
     Saves a dictionary of tensors to a safetensors file.
     """
diff --git a/candle-pyo3/quant-llama.py b/candle-pyo3/quant-llama.py
index 020d525d..46d9ff62 100644
--- a/candle-pyo3/quant-llama.py
+++ b/candle-pyo3/quant-llama.py
@@ -1,27 +1,28 @@
 # This example shows how the candle Python api can be used to replicate llama.cpp.
 import sys
+from typing import Dict, Tuple, Any
 import candle
-from candle.utils import load_ggml,load_gguf
+from candle import Tensor, QTensor, utils, nn
 
 MAX_SEQ_LEN = 4096
 
-def masked_fill(on_false, mask, on_true):
+def masked_fill(on_false:Tensor, mask:Tensor, on_true:Tensor):
     shape = mask.shape
     on_true = candle.tensor(on_true).broadcast_as(shape)
     return mask.where_cond(on_true, on_false)
 
 class RmsNorm:
-    def __init__(self, qtensor):
+    def __init__(self, qtensor:QTensor):
         self.weight = qtensor.dequantize()
 
-    def __call__(self, x):
+    def __call__(self, x:Tensor):
         b_size, seq_len, hidden_size = x.shape
         norm_x = x.sqr().sum_keepdim(2) / hidden_size
         x_normed = x.broadcast_div((norm_x + 1e-5).sqrt())
         return x_normed.broadcast_mul(self.weight)
 
 class QuantizedLayer:
-    def __init__(self, layer_idx, hparams, all_tensors, cos_sin):
+    def __init__(self, layer_idx:int, hparams:Dict[str,Any], all_tensors:Dict[str,QTensor], cos_sin:Tuple[Tensor,Tensor]):
         p = f"layers.{layer_idx}"
         self.attention_wq = all_tensors[f"{p}.attention.wq.weight"]
         self.attention_wk = all_tensors[f"{p}.attention.wk.weight"]
@@ -41,7 +42,7 @@ class QuantizedLayer:
         self.cos = cos_sin[0]
         self.sin = cos_sin[1]
 
-    def __call__(self, x, mask, index_pos):
+    def __call__(self, x:Tensor, mask:Tensor, index_pos:int):
         residual = x
         x = self.attn_norm(x)
         attn = self.forward_attn(x, mask, index_pos) 
@@ -51,11 +52,11 @@ class QuantizedLayer:
         x = self.ffn_norm(x)
         w1 = self.ffw1.matmul_t(x)
         w3 = self.ffw3.matmul_t(x)
-        mlp = self.ffw2.matmul_t(candle.nn.silu(w1) * w3)
+        mlp = self.ffw2.matmul_t(nn.silu(w1) * w3)
 
         return mlp + residual
 
-    def forward_attn(self, x, mask, index_pos):
+    def forward_attn(self, x:Tensor, mask:Tensor, index_pos:int):
         b_size, seq_len, n_embd = x.shape
         q = self.attention_wq.matmul_t(x)
         k = self.attention_wk.matmul_t(x)
@@ -80,12 +81,12 @@ class QuantizedLayer:
         att = q.matmul(k.t()) / self.head_dim**0.5
         mask = mask.broadcast_as(att.shape)
         att = masked_fill(att, mask, float("-inf"))
-        att = candle.nn.softmax(att, -1)
+        att = nn.softmax(att, -1)
         y = att.matmul(v.contiguous())
         y = y.transpose(1, 2).reshape((b_size, seq_len, n_embd))
         return self.attention_wo.matmul_t(y)
 
-    def apply_rotary_emb(self, x, index_pos):
+    def apply_rotary_emb(self, x:Tensor, index_pos:int):
         (b_size, n_head, seq_len, n_embd) = x.shape
         cos = self.cos.narrow(0, index_pos, seq_len).reshape((seq_len, n_embd//2, 1))
         sin = self.sin.narrow(0, index_pos, seq_len).reshape((seq_len, n_embd//2, 1))
@@ -107,7 +108,7 @@ def precompute_freqs_cis(hparams, freq_base):
     return (m.cos(), m.sin())
 
 class QuantizedLlama:
-    def __init__(self, hparams, all_tensors):
+    def __init__(self, hparams:Dict[str,Any], all_tensors:Dict[str,QTensor]):
         self.tok_embeddings = all_tensors["tok_embeddings.weight"].dequantize()
         self.norm = RmsNorm(all_tensors["norm.weight"])
         self.output = all_tensors["output.weight"]
@@ -118,7 +119,7 @@ class QuantizedLlama:
             layer = QuantizedLayer(layer_idx, hparams, all_tensors, cos_sin)
             self.layers.append(layer)
 
-    def __call__(self, token, index_pos):
+    def __call__(self, token:Tensor, index_pos:int):
         b_size, seq_len = token.shape
         vocab_size, hidden_size = self.tok_embeddings.shape
         token = token.reshape((b_size * seq_len,))
@@ -135,7 +136,7 @@ class QuantizedLlama:
         x = self.output.matmul_t(x)
         return x
 
-def gguf_rename(tensor_name):
+def gguf_rename(tensor_name:str):
     if tensor_name == 'token_embd.weight': return 'tok_embeddings.weight'
     if tensor_name == 'output_norm.weight': return 'norm.weight'
     tensor_name = tensor_name.replace('blk.', 'layers.')
@@ -155,7 +156,7 @@ def main():
     filename = sys.argv[1]
     print(f"reading model file {filename}")
     if filename.endswith("gguf"):
-        all_tensors, metadata = load_gguf(sys.argv[1])
+        all_tensors, metadata = utils.load_gguf(sys.argv[1])
         vocab = metadata["tokenizer.ggml.tokens"]
         for i, v in enumerate(vocab):
             vocab[i] = '\n' if v == '<0x0A>' else v.replace('▁', ' ')
@@ -175,7 +176,7 @@ def main():
         all_tensors = { gguf_rename(k): v for k, v in all_tensors.items() }
 
     else:
-        all_tensors, hparams, vocab = load_ggml(sys.argv[1])
+        all_tensors, hparams, vocab = utils.load_ggml(sys.argv[1])
     print(hparams)
     model = QuantizedLlama(hparams, all_tensors)
     print("model built, starting inference")
diff --git a/candle-pyo3/src/lib.rs b/candle-pyo3/src/lib.rs
index 1df78ec6..55b7a888 100644
--- a/candle-pyo3/src/lib.rs
+++ b/candle-pyo3/src/lib.rs
@@ -1,7 +1,7 @@
 #![allow(clippy::redundant_closure_call)]
 use pyo3::exceptions::{PyTypeError, PyValueError};
 use pyo3::prelude::*;
-use pyo3::types::{IntoPyDict, PyTuple};
+use pyo3::types::{IntoPyDict, PyDict, PyTuple};
 use pyo3::ToPyObject;
 use std::sync::Arc;
 
@@ -31,6 +31,7 @@ impl From<PyShape> for ::candle::Shape {
 
 #[derive(Clone, Debug)]
 #[pyclass(name = "Tensor")]
+/// A `candle` tensor.
 struct PyTensor(Tensor);
 
 impl std::ops::Deref for PyTensor {
@@ -43,6 +44,7 @@ impl std::ops::Deref for PyTensor {
 
 #[derive(Clone, Copy, Debug, PartialEq, Eq)]
 #[pyclass(name = "DType")]
+/// A `candle` dtype.
 struct PyDType(DType);
 
 #[pymethods]
@@ -197,7 +199,7 @@ trait MapDType {
 #[pymethods]
 impl PyTensor {
     #[new]
-    #[pyo3(text_signature = "(data:_ArrayLike)")]
+    #[pyo3(text_signature = "(self, data:_ArrayLike)")]
     // TODO: Handle arbitrary input dtype and shape.
     /// Creates a new tensor from a Python value. The value can be a scalar or array-like object.
     fn new(py: Python<'_>, data: PyObject) -> PyResult<Self> {
@@ -239,6 +241,7 @@ impl PyTensor {
     }
 
     /// Gets the tensor's data as a Python scalar or array-like object.
+    /// &RETURNS&: _ArrayLike
     fn values(&self, py: Python<'_>) -> PyResult<PyObject> {
         struct M<'a>(Python<'a>);
         impl<'a> MapDType for M<'a> {
@@ -282,27 +285,36 @@ impl PyTensor {
     }
 
     #[getter]
-    /// Gets the tensor shape as a Python tuple.
+    /// Gets the tensor's shape.
+    /// &RETURNS&: Tuple[int]
     fn shape(&self, py: Python<'_>) -> PyObject {
         PyTuple::new(py, self.0.dims()).to_object(py)
     }
 
     #[getter]
+    /// Gets the tensor's strides.
+    /// &RETURNS&: Tuple[int]
     fn stride(&self, py: Python<'_>) -> PyObject {
         PyTuple::new(py, self.0.stride()).to_object(py)
     }
 
     #[getter]
+    /// Gets the tensor's dtype.
+    /// &RETURNS&: DType
     fn dtype(&self) -> PyDType {
         PyDType(self.0.dtype())
     }
 
     #[getter]
+    /// Gets the tensor's device.
+    /// &RETURNS&: Device
     fn device(&self, py: Python<'_>) -> PyObject {
         PyDevice::from_device(self.0.device()).to_object(py)
     }
 
     #[getter]
+    /// Gets the tensor's rank.
+    /// &RETURNS&: int
     fn rank(&self) -> usize {
         self.0.rank()
     }
@@ -315,69 +327,117 @@ impl PyTensor {
         self.__repr__()
     }
 
+    /// Performs the `sin` operation on the tensor.
+    /// &RETURNS&: Tensor
     fn sin(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.sin().map_err(wrap_err)?))
     }
 
+    /// Performs the `cos` operation on the tensor.
+    /// &RETURNS&: Tensor
     fn cos(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.cos().map_err(wrap_err)?))
     }
 
+    /// Performs the `log` operation on the tensor.
+    /// &RETURNS&: Tensor
     fn log(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.log().map_err(wrap_err)?))
     }
 
+    /// Squares the tensor.
+    /// &RETURNS&: Tensor
     fn sqr(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.sqr().map_err(wrap_err)?))
     }
 
+    /// Calculates the square root of the tensor.
+    /// &RETURNS&: Tensor
     fn sqrt(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.sqrt().map_err(wrap_err)?))
     }
 
+    /// Get the `recip` of the tensor.
+    /// &RETURNS&: Tensor
     fn recip(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.recip().map_err(wrap_err)?))
     }
 
+    /// Performs the `exp` operation on the tensor.
+    /// &RETURNS&: Tensor
     fn exp(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.exp().map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, p:float)")]
+    /// Performs the `pow` operation on the tensor with the given exponent.
+    /// &RETURNS&: Tensor
     fn powf(&self, p: f64) -> PyResult<Self> {
         Ok(PyTensor(self.0.powf(p).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, rhs:Tensor, dim:int)")]
+    /// Select values for the input tensor at the target indexes across the specified dimension.
+    ///
+    /// The `indexes` is argument is an int tensor with a single dimension.
+    /// The output has the same number of dimension as the `self` input. The target dimension of
+    /// the output has length the length of `indexes` and the values are taken from `self` using
+    /// the index from `indexes`. Other dimensions have the same number of elements as the input
+    /// tensor.
+    /// &RETURNS&: Tensor
     fn index_select(&self, rhs: &Self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.index_select(rhs, dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, rhs:Tensor)")]
+    /// Performs a matrix multiplication between the two tensors.
+    /// &RETURNS&: Tensor
     fn matmul(&self, rhs: &Self) -> PyResult<Self> {
         Ok(PyTensor(self.0.matmul(rhs).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, rhs:Tensor)")]
+    /// Adds the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+    /// &RETURNS&: Tensor
     fn broadcast_add(&self, rhs: &Self) -> PyResult<Self> {
         Ok(PyTensor(self.0.broadcast_add(rhs).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, rhs:Tensor)")]
+    /// Subtracts the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+    /// &RETURNS&: Tensor
     fn broadcast_sub(&self, rhs: &Self) -> PyResult<Self> {
         Ok(PyTensor(self.0.broadcast_sub(rhs).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, rhs:Tensor)")]
+    /// Multiplies the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+    /// &RETURNS&: Tensor
     fn broadcast_mul(&self, rhs: &Self) -> PyResult<Self> {
         Ok(PyTensor(self.0.broadcast_mul(rhs).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, rhs:Tensor)")]
+    /// Divides the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
+    /// &RETURNS&: Tensor
     fn broadcast_div(&self, rhs: &Self) -> PyResult<Self> {
         Ok(PyTensor(self.0.broadcast_div(rhs).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, on_true:Tensor, on_false:Tensor)")]
+    /// Returns a tensor with the same shape as the input tensor, the values are taken from
+    /// `on_true` if the input tensor value is not zero, and `on_false` at the positions where the
+    /// input tensor is equal to zero.
+    /// &RETURNS&: Tensor
     fn where_cond(&self, on_true: &Self, on_false: &Self) -> PyResult<Self> {
         Ok(PyTensor(
             self.0.where_cond(on_true, on_false).map_err(wrap_err)?,
         ))
     }
 
+    /// Add two tensors.
+    /// &RETURNS&: Tensor
     fn __add__(&self, rhs: &PyAny) -> PyResult<Self> {
         let tensor = if let Ok(rhs) = rhs.extract::<Self>() {
             (&self.0 + &rhs.0).map_err(wrap_err)?
@@ -393,6 +453,8 @@ impl PyTensor {
         self.__add__(rhs)
     }
 
+    /// Multiply two tensors.
+    /// &RETURNS&: Tensor
     fn __mul__(&self, rhs: &PyAny) -> PyResult<Self> {
         let tensor = if let Ok(rhs) = rhs.extract::<Self>() {
             (&self.0 * &rhs.0).map_err(wrap_err)?
@@ -408,6 +470,8 @@ impl PyTensor {
         self.__mul__(rhs)
     }
 
+    /// Subtract two tensors.
+    /// &RETURNS&: Tensor
     fn __sub__(&self, rhs: &PyAny) -> PyResult<Self> {
         let tensor = if let Ok(rhs) = rhs.extract::<Self>() {
             (&self.0 - &rhs.0).map_err(wrap_err)?
@@ -419,6 +483,8 @@ impl PyTensor {
         Ok(Self(tensor))
     }
 
+    /// Divide two tensors.
+    /// &RETURNS&: Tensor
     fn __truediv__(&self, rhs: &PyAny) -> PyResult<Self> {
         let tensor = if let Ok(rhs) = rhs.extract::<Self>() {
             (&self.0 / &rhs.0).map_err(wrap_err)?
@@ -430,62 +496,102 @@ impl PyTensor {
         Ok(Self(tensor))
     }
 
+    #[pyo3(text_signature = "(self, shape:Sequence[int])")]
+    /// Reshapes the tensor to the given shape.
+    /// &RETURNS&: Tensor
     fn reshape(&self, shape: PyShape) -> PyResult<Self> {
         Ok(PyTensor(self.0.reshape(shape).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, shape:Sequence[int])")]
+    /// Broadcasts the tensor to the given shape.
+    /// &RETURNS&: Tensor
     fn broadcast_as(&self, shape: PyShape) -> PyResult<Self> {
         Ok(PyTensor(self.0.broadcast_as(shape).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, shape:Sequence[int])")]
+    /// Broadcasts the tensor to the given shape, adding new dimensions on the left.
+    /// &RETURNS&: Tensor
     fn broadcast_left(&self, shape: PyShape) -> PyResult<Self> {
         Ok(PyTensor(self.0.broadcast_left(shape).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    /// Creates a new tensor with the specified dimension removed if its size was one.
+    /// &RETURNS&: Tensor
     fn squeeze(&self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.squeeze(dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    /// Creates a new tensor with a dimension of size one inserted at the specified position.
+    /// &RETURNS&: Tensor
     fn unsqueeze(&self, dim: usize) -> PyResult<Self> {
         Ok(PyTensor(self.0.unsqueeze(dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, index:int)")]
+    /// Gets the value at the specified index.
+    /// &RETURNS&: Tensor
     fn get(&self, index: i64) -> PyResult<Self> {
         let index = actual_index(self, 0, index).map_err(wrap_err)?;
         Ok(PyTensor(self.0.get(index).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim1:int, dim2:int)")]
+    /// Returns a tensor that is a transposed version of the input, the given dimensions are swapped.
+    /// &RETURNS&: Tensor
     fn transpose(&self, dim1: usize, dim2: usize) -> PyResult<Self> {
         Ok(PyTensor(self.0.transpose(dim1, dim2).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int, start:int, len:int)")]
+    /// Returns a new tensor that is a narrowed version of the input, the dimension `dim`
+    /// ranges from `start` to `start + len`.
+    /// &RETURNS&: Tensor
     fn narrow(&self, dim: i64, start: i64, len: usize) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         let start = actual_index(self, dim, start).map_err(wrap_err)?;
         Ok(PyTensor(self.0.narrow(dim, start, len).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    /// Returns the indices of the maximum value(s) across the selected dimension.
+    /// &RETURNS&: Tensor
     fn argmax_keepdim(&self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.argmax_keepdim(dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    /// Returns the indices of the minimum value(s) across the selected dimension.
+    /// &RETURNS&: Tensor
     fn argmin_keepdim(&self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.argmin_keepdim(dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    /// Gathers the maximum value across the selected dimension.
+    /// &RETURNS&: Tensor
     fn max_keepdim(&self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.max_keepdim(dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    /// Gathers the minimum value across the selected dimension.
+    /// &RETURNS&: Tensor
     fn min_keepdim(&self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.min_keepdim(dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:Union[int, List[int]])")]
+    /// Returns the sum of all elements in the input tensor. The sum is performed over all the input dimensions.
+    /// &RETURNS&: Tensor
     fn sum_keepdim(&self, dims: PyObject, py: Python<'_>) -> PyResult<Self> {
         let dims = if let Ok(dim) = dims.extract::<usize>(py) {
             vec![dim]
@@ -497,10 +603,14 @@ impl PyTensor {
         ))
     }
 
+    /// Returns the sum of the tensor.
+    /// &RETURNS&: Tensor
     fn sum_all(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.sum_all().map_err(wrap_err)?))
     }
 
+    /// Returns the mean of the tensor.
+    /// &RETURNS&: Tensor
     fn mean_all(&self) -> PyResult<Self> {
         let elements = self.0.elem_count();
         let sum = self.0.sum_all().map_err(wrap_err)?;
@@ -508,54 +618,83 @@ impl PyTensor {
         Ok(PyTensor(mean))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    /// Flattens the tensor on the dimension indexes from `dim` (inclusive) to the last dimension.
+    /// &RETURNS&: Tensor
     fn flatten_from(&self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.flatten_from(dim).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dim:int)")]
+    ///Flattens the tensor on the dimension indexes from `0` to `dim` (inclusive).
+    /// &RETURNS&: Tensor
     fn flatten_to(&self, dim: i64) -> PyResult<Self> {
         let dim = actual_dim(self, dim).map_err(wrap_err)?;
         Ok(PyTensor(self.0.flatten_to(dim).map_err(wrap_err)?))
     }
 
+    /// Flattens the tensor into a 1D tensor.
+    /// &RETURNS&: Tensor
     fn flatten_all(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.flatten_all().map_err(wrap_err)?))
     }
 
+    /// Transposes the tensor.
+    /// &RETURNS&: Tensor
     fn t(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.t().map_err(wrap_err)?))
     }
 
+    /// Makes the tensor contiguous in memory.
+    /// &RETURNS&: Tensor
     fn contiguous(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.contiguous().map_err(wrap_err)?))
     }
 
+    /// Returns true if the tensor is contiguous in C order.
+    /// &RETURNS&: bool
     fn is_contiguous(&self) -> bool {
         self.0.is_contiguous()
     }
 
+    /// Returns true if the tensor is contiguous in Fortran order.
+    /// &RETURNS&: bool
     fn is_fortran_contiguous(&self) -> bool {
         self.0.is_fortran_contiguous()
     }
 
+    /// Detach the tensor from the computation graph.
+    /// &RETURNS&: Tensor
     fn detach(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.detach().map_err(wrap_err)?))
     }
 
+    /// Returns a copy of the tensor.
+    /// &RETURNS&: Tensor
     fn copy(&self) -> PyResult<Self> {
         Ok(PyTensor(self.0.copy().map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, dtype:Union[str,DType])")]
+    /// Convert the tensor to a new dtype.
+    /// &RETURNS&: Tensor
     fn to_dtype(&self, dtype: PyObject, py: Python<'_>) -> PyResult<Self> {
         let dtype = PyDType::from_pyobject(dtype, py)?;
         Ok(PyTensor(self.0.to_dtype(dtype.0).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, device:Union[str,Device])")]
+    /// Move the tensor to a new device.
+    /// &RETURNS&: Tensor
     fn to_device(&self, device: PyDevice) -> PyResult<Self> {
         let device = device.as_device()?;
         Ok(PyTensor(self.0.to_device(&device).map_err(wrap_err)?))
     }
 
+    #[pyo3(text_signature = "(self, quantized_dtype:str)")]
+    /// Quantize the tensor.
+    /// &RETURNS&: QTensor
     fn quantize(&self, quantized_dtype: &str) -> PyResult<PyQTensor> {
         use ::candle::quantized;
         let res = match quantized_dtype {
@@ -586,6 +725,7 @@ impl PyTensor {
 #[pyfunction]
 #[pyo3(text_signature = "(tensors:List[Tensor], dim:int )")]
 /// Concatenate the tensors across one axis.
+/// &RETURNS&: Tensor
 fn cat(tensors: Vec<PyTensor>, dim: i64) -> PyResult<PyTensor> {
     if tensors.is_empty() {
         return Err(PyErr::new::<PyValueError, _>("empty input to cat"));
@@ -599,6 +739,7 @@ fn cat(tensors: Vec<PyTensor>, dim: i64) -> PyResult<PyTensor> {
 #[pyfunction]
 #[pyo3(text_signature = "(tensors:List[Tensor], dim:int)")]
 /// Stack the tensors along a new axis.
+/// &RETURNS&: Tensor
 fn stack(tensors: Vec<PyTensor>, dim: usize) -> PyResult<PyTensor> {
     let tensors = tensors.into_iter().map(|t| t.0).collect::<Vec<_>>();
     let tensor = Tensor::stack(&tensors, dim).map_err(wrap_err)?;
@@ -608,6 +749,7 @@ fn stack(tensors: Vec<PyTensor>, dim: usize) -> PyResult<PyTensor> {
 #[pyfunction]
 #[pyo3(text_signature = "(data:_ArrayLike)")]
 /// Creates a new tensor from a Python value. The value can be a scalar or array-like object.
+/// &RETURNS&: Tensor
 fn tensor(py: Python<'_>, data: PyObject) -> PyResult<PyTensor> {
     PyTensor::new(py, data)
 }
@@ -615,6 +757,7 @@ fn tensor(py: Python<'_>, data: PyObject) -> PyResult<PyTensor> {
 #[pyfunction]
 #[pyo3(signature = (shape, *, device=None), text_signature = "(shape:Sequence[int], device:Optional[Device]=None)")]
 /// Creates a new tensor with random values.
+/// &RETURNS&: Tensor
 fn rand(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<PyTensor> {
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
     let tensor = Tensor::rand(0f32, 1f32, shape.0, &device).map_err(wrap_err)?;
@@ -623,6 +766,8 @@ fn rand(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<P
 
 #[pyfunction]
 #[pyo3(signature = (shape, *, device=None), text_signature = "(shape:Sequence[int], device:Optional[Device]=None)")]
+/// Creates a new tensor with random values from a normal distribution.
+/// &RETURNS&: Tensor
 fn randn(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<PyTensor> {
     let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
     let tensor = Tensor::randn(0f32, 1f32, shape.0, &device).map_err(wrap_err)?;
@@ -631,6 +776,8 @@ fn randn(_py: Python<'_>, shape: PyShape, device: Option<PyDevice>) -> PyResult<
 
 #[pyfunction]
 #[pyo3(signature = (shape, *, dtype=None, device=None),text_signature = "(shape:Sequence[int], dtype:Optional[DType]=None, device:Optional[Device]=None)")]
+/// Creates a new tensor filled with ones.
+/// &RETURNS&: Tensor
 fn ones(
     py: Python<'_>,
     shape: PyShape,
@@ -648,6 +795,8 @@ fn ones(
 
 #[pyfunction]
 #[pyo3(signature = (shape, *, dtype=None, device=None), text_signature = "(shape:Sequence[int], dtype:Optional[DType]=None, device:Optional[Device]=None)")]
+/// Creates a new tensor filled with zeros.
+/// &RETURNS&: Tensor
 fn zeros(
     py: Python<'_>,
     shape: PyShape,
@@ -663,8 +812,9 @@ fn zeros(
     Ok(PyTensor(tensor))
 }
 
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 #[pyclass(name = "QTensor")]
+/// A quantized tensor.
 struct PyQTensor(Arc<QTensor>);
 
 impl std::ops::Deref for PyQTensor {
@@ -678,16 +828,22 @@ impl std::ops::Deref for PyQTensor {
 #[pymethods]
 impl PyQTensor {
     #[getter]
+    ///Gets the tensors quantized dtype.
+    /// &RETURNS&: str
     fn ggml_dtype(&self) -> String {
         format!("{:?}", self.0.dtype())
     }
 
     #[getter]
+    ///Gets the rank of the tensor.
+    /// &RETURNS&: int
     fn rank(&self) -> usize {
         self.0.rank()
     }
 
     #[getter]
+    ///Gets the shape of the tensor.
+    /// &RETURNS&: Tuple[int]
     fn shape(&self, py: Python<'_>) -> PyObject {
         PyTuple::new(py, self.0.shape().dims()).to_object(py)
     }
@@ -700,11 +856,16 @@ impl PyQTensor {
         self.__repr__()
     }
 
+    /// Dequantizes the tensor.
+    /// &RETURNS&: Tensor  
     fn dequantize(&self) -> PyResult<PyTensor> {
         let tensor = self.0.dequantize(&Device::Cpu).map_err(wrap_err)?;
         Ok(PyTensor(tensor))
     }
 
+    #[pyo3(text_signature = "(self, lhs:Tensor)")]
+    /// Performs a quantized matrix multiplication, with the quantized tensor as the right hand side.
+    /// &RETURNS&: Tensor
     fn matmul_t(&self, lhs: &PyTensor) -> PyResult<PyTensor> {
         let qmatmul = ::candle::quantized::QMatMul::from_arc(self.0.clone());
         let res = qmatmul.forward(lhs).map_err(wrap_err)?;
@@ -715,6 +876,7 @@ impl PyQTensor {
 #[pyfunction]
 #[pyo3(text_signature = "(path:Union[str,PathLike])")]
 /// Loads a safetensors file. Returns a dictionary mapping tensor names to tensors.
+/// &RETURNS&: Dict[str,Tensor]
 fn load_safetensors(path: &str, py: Python<'_>) -> PyResult<PyObject> {
     let res = ::candle::safetensors::load(path, &Device::Cpu).map_err(wrap_err)?;
     let res = res
@@ -727,6 +889,7 @@ fn load_safetensors(path: &str, py: Python<'_>) -> PyResult<PyObject> {
 #[pyfunction]
 #[pyo3(text_signature = "(path:Union[str,PathLike], tensors:Dict[str,Tensor])")]
 /// Saves a dictionary of tensors to a safetensors file.
+/// &RETURNS&: None
 fn save_safetensors(
     path: &str,
     tensors: std::collections::HashMap<String, PyTensor>,
@@ -742,6 +905,7 @@ fn save_safetensors(
 #[pyo3(text_signature = "(path:Union[str,PathLike])")]
 /// Load a GGML file. Returns a tuple of three objects: a dictionary mapping tensor names to tensors,
 /// a dictionary mapping hyperparameter names to hyperparameter values, and a vocabulary.
+/// &RETURNS&: Tuple[Dict[str,QTensor], Dict[str,Any], List[str]]
 fn load_ggml(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject, PyObject)> {
     let mut file = std::fs::File::open(path)?;
     let ggml = ::candle::quantized::ggml_file::Content::read(&mut file).map_err(wrap_err)?;
@@ -776,6 +940,7 @@ fn load_ggml(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject, PyObje
 #[pyo3(text_signature = "(path:Union[str,PathLike])")]
 /// Loads a GGUF file. Returns a tuple of two dictionaries: the first maps tensor names to tensors,
 /// and the second maps metadata keys to metadata values.
+/// &RETURNS&: Tuple[Dict[str,QTensor], Dict[str,Any]]
 fn load_gguf(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject)> {
     use ::candle::quantized::gguf_file;
     fn gguf_value_to_pyobject(v: &gguf_file::Value, py: Python<'_>) -> PyResult<PyObject> {
@@ -824,26 +989,118 @@ fn load_gguf(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject)> {
     Ok((tensors, metadata))
 }
 
+#[pyfunction]
+#[pyo3(
+    text_signature = "(path:Union[str,PathLike], tensors:Dict[str,QTensor], metadata:Dict[str,Any])"
+)]
+/// Save quanitzed tensors and metadata to a GGUF file.
+fn save_gguf(path: &str, tensors: PyObject, metadata: PyObject, py: Python<'_>) -> PyResult<()> {
+    use ::candle::quantized::gguf_file;
+
+    fn pyobject_to_gguf_value(v: &PyAny, py: Python<'_>) -> PyResult<gguf_file::Value> {
+        let v: gguf_file::Value = if let Ok(x) = v.extract::<u8>() {
+            gguf_file::Value::U8(x)
+        } else if let Ok(x) = v.extract::<i8>() {
+            gguf_file::Value::I8(x)
+        } else if let Ok(x) = v.extract::<u16>() {
+            gguf_file::Value::U16(x)
+        } else if let Ok(x) = v.extract::<i16>() {
+            gguf_file::Value::I16(x)
+        } else if let Ok(x) = v.extract::<u32>() {
+            gguf_file::Value::U32(x)
+        } else if let Ok(x) = v.extract::<i32>() {
+            gguf_file::Value::I32(x)
+        } else if let Ok(x) = v.extract::<u64>() {
+            gguf_file::Value::U64(x)
+        } else if let Ok(x) = v.extract::<i64>() {
+            gguf_file::Value::I64(x)
+        } else if let Ok(x) = v.extract::<f32>() {
+            gguf_file::Value::F32(x)
+        } else if let Ok(x) = v.extract::<f64>() {
+            gguf_file::Value::F64(x)
+        } else if let Ok(x) = v.extract::<bool>() {
+            gguf_file::Value::Bool(x)
+        } else if let Ok(x) = v.extract::<String>() {
+            gguf_file::Value::String(x)
+        } else if let Ok(x) = v.extract::<Vec<PyObject>>() {
+            let x = x
+                .into_iter()
+                .map(|f| pyobject_to_gguf_value(f.as_ref(py), py))
+                .collect::<PyResult<Vec<_>>>()?;
+            gguf_file::Value::Array(x)
+        } else {
+            return Err(PyErr::new::<PyValueError, _>(format!(
+                "unsupported type {:?}",
+                v
+            )));
+        };
+        Ok(v)
+    }
+    let tensors = tensors
+        .extract::<&PyDict>(py)
+        .map_err(|_| PyErr::new::<PyValueError, _>("expected a dict"))?
+        .iter()
+        .map(|(key, value)| {
+            Ok((
+                key.extract::<String>()
+                    .map_err(|_| PyErr::new::<PyValueError, _>("keys must be strings"))?,
+                value.extract::<PyQTensor>()?.0,
+            ))
+        })
+        .collect::<PyResult<Vec<_>>>()?;
+
+    let metadata = metadata
+        .extract::<&PyDict>(py)
+        .map_err(|_| PyErr::new::<PyValueError, _>("expected a dict"))?
+        .iter()
+        .map(|(key, value)| {
+            Ok((
+                key.extract::<String>()
+                    .map_err(|_| PyErr::new::<PyValueError, _>("keys must be strings"))?,
+                pyobject_to_gguf_value(value, py)?,
+            ))
+        })
+        .collect::<PyResult<Vec<_>>>()?;
+
+    let converted_metadata: Vec<_> = metadata
+        .iter()
+        .map(|(name, value)| (name.as_str(), value))
+        .collect();
+
+    let converted_tensors: Vec<_> = tensors
+        .iter()
+        .map(|(name, tensor)| (name.as_str(), tensor.as_ref()))
+        .collect();
+
+    let mut file = std::fs::File::create(path)?;
+
+    gguf_file::write(&mut file, &converted_metadata, &converted_tensors).map_err(wrap_err)
+}
+
 #[pyfunction]
 /// Returns true if the 'cuda' backend is available.
+/// &RETURNS&: bool
 fn cuda_is_available() -> bool {
     ::candle::utils::cuda_is_available()
 }
 
 #[pyfunction]
 /// Returns true if candle was compiled with 'accelerate' support.
+/// &RETURNS&: bool
 fn has_accelerate() -> bool {
     ::candle::utils::has_accelerate()
 }
 
 #[pyfunction]
 /// Returns true if candle was compiled with MKL support.
+/// &RETURNS&: bool
 fn has_mkl() -> bool {
     ::candle::utils::has_mkl()
 }
 
 #[pyfunction]
 /// Returns the number of threads used by the candle.
+/// &RETURNS&: int
 fn get_num_threads() -> usize {
     ::candle::utils::get_num_threads()
 }
@@ -855,6 +1112,7 @@ fn candle_utils(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
     m.add_function(wrap_pyfunction!(has_mkl, m)?)?;
     m.add_function(wrap_pyfunction!(load_ggml, m)?)?;
     m.add_function(wrap_pyfunction!(load_gguf, m)?)?;
+    m.add_function(wrap_pyfunction!(save_gguf, m)?)?;
     m.add_function(wrap_pyfunction!(load_safetensors, m)?)?;
     m.add_function(wrap_pyfunction!(save_safetensors, m)?)?;
     Ok(())
@@ -862,7 +1120,8 @@ fn candle_utils(_py: Python<'_>, m: &PyModule) -> PyResult<()> {
 
 #[pyfunction]
 #[pyo3(text_signature = "(tensor:Tensor, dim:int)")]
-/// Applies the Softmax function to a given tensor.
+/// Applies the Softmax function to a given tensor.#
+/// &RETURNS&: Tensor
 fn softmax(tensor: PyTensor, dim: i64) -> PyResult<PyTensor> {
     let dim = actual_dim(&tensor, dim).map_err(wrap_err)?;
     let sm = candle_nn::ops::softmax(&tensor.0, dim).map_err(wrap_err)?;
@@ -872,6 +1131,7 @@ fn softmax(tensor: PyTensor, dim: i64) -> PyResult<PyTensor> {
 #[pyfunction]
 #[pyo3(text_signature = "(tensor:Tensor)")]
 /// Applies the Sigmoid Linear Unit (SiLU) function to a given tensor.
+/// &RETURNS&: Tensor
 fn silu(tensor: PyTensor) -> PyResult<PyTensor> {
     let s = candle_nn::ops::silu(&tensor.0).map_err(wrap_err)?;
     Ok(PyTensor(s))
diff --git a/candle-pyo3/stub.py b/candle-pyo3/stub.py
index b5b9256f..149715c2 100644
--- a/candle-pyo3/stub.py
+++ b/candle-pyo3/stub.py
@@ -13,8 +13,8 @@ TYPING = """from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 from os import PathLike
 """
 CANDLE_SPECIFIC_TYPING = "from candle.typing import _ArrayLike, Device\n"
-CANDLE_TENSOR_IMPORTS = "from candle import Tensor,DType\n"
-
+CANDLE_TENSOR_IMPORTS = "from candle import Tensor,DType,QTensor\n"
+RETURN_TYPE_MARKER = "&RETURNS&: "
 
 
 def do_indent(text: Optional[str], indent: str):
@@ -28,11 +28,26 @@ def function(obj, indent:str, text_signature:str=None):
         text_signature = obj.__text_signature__
 
     text_signature = text_signature.replace("$self", "self").lstrip().rstrip()
+    doc_string = obj.__doc__
+    if doc_string is None:
+        doc_string = ""
+
+    # Check if we have a return type annotation in the docstring 
+    return_type = None
+    doc_lines = doc_string.split("\n")
+    if doc_lines[-1].lstrip().startswith(RETURN_TYPE_MARKER):
+        # Extract the return type and remove it from the docstring
+        return_type = doc_lines[-1].lstrip()[len(RETURN_TYPE_MARKER):].strip()
+        doc_string = "\n".join(doc_lines[:-1])
+
     string = ""
-    string += f"{indent}def {obj.__name__}{text_signature}:\n"
+    if return_type:
+        string += f"{indent}def {obj.__name__}{text_signature} -> {return_type}:\n"
+    else:
+        string += f"{indent}def {obj.__name__}{text_signature}:\n"
     indent += INDENT
     string += f'{indent}"""\n'
-    string += f"{indent}{do_indent(obj.__doc__, indent)}\n"
+    string += f"{indent}{do_indent(doc_string, indent)}\n"
     string += f'{indent}"""\n'
     string += f"{indent}pass\n"
     string += "\n"
diff --git a/candle-pyo3/test.py b/candle-pyo3/test.py
index c78ffc41..7f24b49d 100644
--- a/candle-pyo3/test.py
+++ b/candle-pyo3/test.py
@@ -1,5 +1,4 @@
 import candle
-from candle import Tensor, QTensor
 
 t = candle.Tensor(42.0)
 print(t)
@@ -10,7 +9,7 @@ t = candle.Tensor([3.0, 1, 4, 1, 5, 9, 2, 6])
 print(t)
 print(t+t)
 
-t:Tensor = t.reshape([2, 4])
+t = t.reshape([2, 4])
 print(t.matmul(t.t()))
 
 print(t.to_dtype(candle.u8))
@@ -21,7 +20,7 @@ print(t)
 print(t.dtype)
 
 t = candle.randn((16, 256))
-quant_t:QTensor = t.quantize("q6k")
-dequant_t:Tensor = quant_t.dequantize()
-diff2:Tensor = (t - dequant_t).sqr()
+quant_t = t.quantize("q6k")
+dequant_t = quant_t.dequantize()
+diff2 = (t - dequant_t).sqr()
 print(diff2.mean_all())

From ef8cd8fea04240e9cd59b003236e33d584aaf768 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 18 Sep 2023 09:36:20 +0100
Subject: [PATCH 118/150] Update the candle-gemm version. (#885)

---
 Cargo.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Cargo.toml b/Cargo.toml
index cb6bf3cb..fb03d93b 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -34,7 +34,7 @@ byteorder = "1.4.3"
 clap = { version = "4.2.4", features = ["derive"] }
 cudarc = { version = "0.9.14", features = ["f16"] }
 # TODO: Switch back to the official gemm implementation once it has caught up.
-gemm = { version = "0.15.6", package = "candle-gemm" }
+gemm = { version = "0.16.0", package = "candle-gemm" }
 hf-hub = "0.3.0"
 half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
 image = { version = "0.24.7", default-features = false, features = ["jpeg", "png"] }

From 12696b7b2df829fee05231d40a4f00e480ad5376 Mon Sep 17 00:00:00 2001
From: Aaron Sun <aaronzs@users.noreply.github.com>
Date: Mon, 18 Sep 2023 16:41:50 +0800
Subject: [PATCH 119/150] Fix typos in SAM WASM example (#884)

---
 candle-wasm-examples/segment-anything/lib-example.html | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/candle-wasm-examples/segment-anything/lib-example.html b/candle-wasm-examples/segment-anything/lib-example.html
index 127b9152..5060f073 100644
--- a/candle-wasm-examples/segment-anything/lib-example.html
+++ b/candle-wasm-examples/segment-anything/lib-example.html
@@ -20,7 +20,7 @@
     </style>
     <script src="https://cdn.tailwindcss.com"></script>
     <script type="module">
-      // base url for audio examples
+      // base url for image examples
       const MODEL_BASEURL =
         "https://huggingface.co/lmz/candle-sam/resolve/main/";
 
@@ -38,7 +38,7 @@
       async function segmentPoints(
         modelURL, // URL to the weights file
         modelID, // model ID
-        imageURL, // URL to the audio file
+        imageURL, // URL to the image file
         points // {x, y} points to prompt image
       ) {
         return new Promise((resolve, reject) => {

From 7dd8e1247208e9a060d174830569c748599e0fe0 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 18 Sep 2023 12:14:03 +0100
Subject: [PATCH 120/150] Bump the crate versions to v0.2.3. (#886)

* Bump the crate version.

* Also update the python bindings.
---
 CHANGELOG.md                                     | 16 +++++++++++++++-
 Cargo.toml                                       |  2 +-
 candle-book/Cargo.toml                           | 10 +++++-----
 candle-core/Cargo.toml                           |  2 +-
 candle-datasets/Cargo.toml                       |  4 ++--
 candle-examples/Cargo.toml                       | 10 +++++-----
 candle-flash-attn/Cargo.toml                     |  6 +++---
 candle-kernels/Cargo.toml                        |  2 +-
 candle-nn/Cargo.toml                             |  2 +-
 candle-pyo3/Cargo.toml                           |  4 ++--
 candle-pyo3/pyproject.toml                       |  6 +++---
 candle-transformers/Cargo.toml                   |  4 ++--
 candle-wasm-examples/llama2-c/Cargo.toml         |  6 +++---
 candle-wasm-examples/segment-anything/Cargo.toml |  6 +++---
 candle-wasm-examples/whisper/Cargo.toml          |  4 ++--
 candle-wasm-examples/yolo/Cargo.toml             |  4 ++--
 16 files changed, 51 insertions(+), 37 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 06041294..df9574d5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,13 +1,27 @@
 # Changelog
 This documents the main changes to the `candle` crate.
 
-## v0.2.2 - Unreleased
+## v0.2.3 - Unreleased
+
+### Added
+
+### Modified
+
+## v0.2.2 - 2023-09-18
 
 ### Added
 - Support for `top_p` sampling
   [819](https://github.com/huggingface/candle/pull/819).
+- T5 model including decoding
+  [864](https://github.com/huggingface/candle/pull/864).
+- 1-d upsampling
+  [839](https://github.com/huggingface/candle/pull/839).
 
 ### Modified
+- Bugfix for conv2d
+  [820](https://github.com/huggingface/candle/pull/820).
+- Support tensor based indexing using `.i`
+  [842](https://github.com/huggingface/candle/pull/842).
 
 ## v0.2.1 - 2023-09-11
 
diff --git a/Cargo.toml b/Cargo.toml
index fb03d93b..3a5763a1 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -19,7 +19,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.2.2"
+version = "0.2.3"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
diff --git a/candle-book/Cargo.toml b/candle-book/Cargo.toml
index ac179cb9..8ec92e87 100644
--- a/candle-book/Cargo.toml
+++ b/candle-book/Cargo.toml
@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.2.2" }
-candle-nn = { path = "../candle-nn", version = "0.2.2" }
-candle-transformers = { path = "../candle-transformers", version = "0.2.2" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.2", optional = true }
+candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.2.3" }
+candle-nn = { path = "../candle-nn", version = "0.2.3" }
+candle-transformers = { path = "../candle-transformers", version = "0.2.3" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.3", optional = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
diff --git a/candle-core/Cargo.toml b/candle-core/Cargo.toml
index fad6d0bd..7af9b6fa 100644
--- a/candle-core/Cargo.toml
+++ b/candle-core/Cargo.toml
@@ -12,7 +12,7 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-candle-kernels = { path = "../candle-kernels", version = "0.2.2", optional = true }
+candle-kernels = { path = "../candle-kernels", version = "0.2.3", optional = true }
 cudarc = { workspace = true, optional = true }
 gemm = { workspace = true }
 half = { workspace = true }
diff --git a/candle-datasets/Cargo.toml b/candle-datasets/Cargo.toml
index e34aa9be..316f31c5 100644
--- a/candle-datasets/Cargo.toml
+++ b/candle-datasets/Cargo.toml
@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 byteorder = { workspace = true }
-candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.2.2" }
+candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.2.3" }
 hf-hub = { workspace = true}
 intel-mkl-src = { workspace = true, optional = true }
 memmap2 = { workspace = true }
diff --git a/candle-examples/Cargo.toml b/candle-examples/Cargo.toml
index a52ad88f..cf8f0021 100644
--- a/candle-examples/Cargo.toml
+++ b/candle-examples/Cargo.toml
@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.2.2" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.2", optional = true }
-candle-nn = { path = "../candle-nn", version = "0.2.2" }
-candle-transformers = { path = "../candle-transformers", version = "0.2.2" }
+candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.2.3" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.3", optional = true }
+candle-nn = { path = "../candle-nn", version = "0.2.3" }
+candle-transformers = { path = "../candle-transformers", version = "0.2.3" }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true }
diff --git a/candle-flash-attn/Cargo.toml b/candle-flash-attn/Cargo.toml
index f92383ea..808e0070 100644
--- a/candle-flash-attn/Cargo.toml
+++ b/candle-flash-attn/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.2.2"
+version = "0.2.3"
 edition = "2021"
 
 description = "Flash attention layer for the candle ML framework."
@@ -11,7 +11,7 @@ license = "MIT OR Apache-2.0"
 readme = "README.md"
 
 [dependencies]
-candle = { path = "../candle-core", features = ["cuda"], version = "0.2.2", package = "candle-core" }
+candle = { path = "../candle-core", features = ["cuda"], version = "0.2.3", package = "candle-core" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]
@@ -21,4 +21,4 @@ rayon = "1.7.0"
 
 [dev-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }
-candle-nn = { path = "../candle-nn", version = "0.2.2", features = ["cuda"] }
+candle-nn = { path = "../candle-nn", version = "0.2.3", features = ["cuda"] }
diff --git a/candle-kernels/Cargo.toml b/candle-kernels/Cargo.toml
index d6924c76..80b6aaab 100644
--- a/candle-kernels/Cargo.toml
+++ b/candle-kernels/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.2.2"
+version = "0.2.3"
 edition = "2021"
 
 description = "CUDA kernels for Candle"
diff --git a/candle-nn/Cargo.toml b/candle-nn/Cargo.toml
index 2a8ec8ce..a6629d33 100644
--- a/candle-nn/Cargo.toml
+++ b/candle-nn/Cargo.toml
@@ -11,7 +11,7 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
+candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
 half = { workspace = true }
 thiserror = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
diff --git a/candle-pyo3/Cargo.toml b/candle-pyo3/Cargo.toml
index c96681bd..7fd0ac28 100644
--- a/candle-pyo3/Cargo.toml
+++ b/candle-pyo3/Cargo.toml
@@ -14,8 +14,8 @@ name = "candle"
 crate-type = ["cdylib"]
 
 [dependencies]
-candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.2.2" }
+candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.2.3" }
 half = { workspace = true }
 pyo3 = { version = "0.19.0", features = ["extension-module"] }
 
diff --git a/candle-pyo3/pyproject.toml b/candle-pyo3/pyproject.toml
index b4e372d7..88793493 100644
--- a/candle-pyo3/pyproject.toml
+++ b/candle-pyo3/pyproject.toml
@@ -1,8 +1,8 @@
 [project]
-name = 'candle-pyo3'
+name = 'candle-nn'
 requires-python = '>=3.7'
 authors = [
-    {name = 'Laurent Mazare', email = ''},
+    {name = 'The Candle Team'},
 ]
 
 dynamic = [
@@ -27,4 +27,4 @@ features = ["pyo3/extension-module"]
 
 [tool.black]
 line-length = 119
-target-version = ['py35']
\ No newline at end of file
+target-version = ['py35']
diff --git a/candle-transformers/Cargo.toml b/candle-transformers/Cargo.toml
index fd42060f..2faadad9 100644
--- a/candle-transformers/Cargo.toml
+++ b/candle-transformers/Cargo.toml
@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.2", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.2.2" }
+candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.2.3" }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
 rand = { workspace = true }
diff --git a/candle-wasm-examples/llama2-c/Cargo.toml b/candle-wasm-examples/llama2-c/Cargo.toml
index 7209a648..601f5e34 100644
--- a/candle-wasm-examples/llama2-c/Cargo.toml
+++ b/candle-wasm-examples/llama2-c/Cargo.toml
@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.2" }
-candle-transformers = { path = "../../candle-transformers", version = "0.2.2" }
+candle = { path = "../../candle-core", version = "0.2.3", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.3" }
+candle-transformers = { path = "../../candle-transformers", version = "0.2.3" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 
diff --git a/candle-wasm-examples/segment-anything/Cargo.toml b/candle-wasm-examples/segment-anything/Cargo.toml
index a847424d..46b85615 100644
--- a/candle-wasm-examples/segment-anything/Cargo.toml
+++ b/candle-wasm-examples/segment-anything/Cargo.toml
@@ -9,9 +9,9 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.2" }
-candle-transformers = { path = "../../candle-transformers", version = "0.2.2" }
+candle = { path = "../../candle-core", version = "0.2.3", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.3" }
+candle-transformers = { path = "../../candle-transformers", version = "0.2.3" }
 num-traits = { workspace = true }
 
 # App crates.
diff --git a/candle-wasm-examples/whisper/Cargo.toml b/candle-wasm-examples/whisper/Cargo.toml
index 10d3356e..8f1df531 100644
--- a/candle-wasm-examples/whisper/Cargo.toml
+++ b/candle-wasm-examples/whisper/Cargo.toml
@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.2" }
+candle = { path = "../../candle-core", version = "0.2.3", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.3" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 
diff --git a/candle-wasm-examples/yolo/Cargo.toml b/candle-wasm-examples/yolo/Cargo.toml
index 9d9e60cc..71ef8049 100644
--- a/candle-wasm-examples/yolo/Cargo.toml
+++ b/candle-wasm-examples/yolo/Cargo.toml
@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.2.2" }
+candle = { path = "../../candle-core", version = "0.2.3", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.3" }
 num-traits = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }

From 5082954c523c82a76ce91622ae9c3966dce0dc89 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 18 Sep 2023 14:50:14 +0100
Subject: [PATCH 121/150] Fix the W clip embeddings. (#887)

* Fix the W clip embeddings.

* Add the specialized ddpm scheduler.
---
 candle-examples/examples/wuerstchen/main.rs   |  6 +-
 .../src/models/wuerstchen/ddpm.rs             | 97 +++++++++++++++++++
 .../src/models/wuerstchen/mod.rs              |  1 +
 3 files changed, 101 insertions(+), 3 deletions(-)
 create mode 100644 candle-transformers/src/models/wuerstchen/ddpm.rs

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index b3231360..12e4c10e 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -193,9 +193,9 @@ fn encode_prompt(
     println!("Building the clip transformer.");
     let text_model =
         stable_diffusion::build_clip_transformer(&clip_config, clip_weights, device, DType::F32)?;
-    let text_embeddings = text_model.forward_with_mask(&tokens, tokens_len)?;
-    let uncond_embeddings = text_model.forward_with_mask(&uncond_tokens, uncond_tokens_len)?;
-    let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?;
+    let text_embeddings = text_model.forward_with_mask(&tokens, tokens_len - 1)?;
+    let uncond_embeddings = text_model.forward_with_mask(&uncond_tokens, uncond_tokens_len - 1)?;
+    let text_embeddings = Tensor::cat(&[text_embeddings, uncond_embeddings], 0)?;
     Ok(text_embeddings)
 }
 
diff --git a/candle-transformers/src/models/wuerstchen/ddpm.rs b/candle-transformers/src/models/wuerstchen/ddpm.rs
new file mode 100644
index 00000000..f4f16bfb
--- /dev/null
+++ b/candle-transformers/src/models/wuerstchen/ddpm.rs
@@ -0,0 +1,97 @@
+use candle::{Result, Tensor};
+
+#[derive(Debug, Clone)]
+pub struct DDPMWSchedulerConfig {
+    scaler: f64,
+    s: f64,
+}
+
+impl Default for DDPMWSchedulerConfig {
+    fn default() -> Self {
+        Self {
+            scaler: 1f64,
+            s: 0.008f64,
+        }
+    }
+}
+
+pub struct DDPMWScheduler {
+    init_alpha_cumprod: f64,
+    init_noise_sigma: f64,
+    timesteps: Vec<f64>,
+    pub config: DDPMWSchedulerConfig,
+}
+
+impl DDPMWScheduler {
+    pub fn new(inference_steps: usize, config: DDPMWSchedulerConfig) -> Result<Self> {
+        let init_alpha_cumprod = (config.s / (1. + config.s) * std::f64::consts::PI)
+            .cos()
+            .powi(2);
+        let timesteps = (0..=inference_steps)
+            .map(|i| 1. - i as f64 / inference_steps as f64)
+            .collect::<Vec<_>>();
+        Ok(Self {
+            init_alpha_cumprod,
+            init_noise_sigma: 1.0,
+            timesteps,
+            config,
+        })
+    }
+
+    fn alpha_cumprod(&self, t: f64) -> f64 {
+        let scaler = self.config.scaler;
+        let s = self.config.s;
+        let t = if scaler > 1. {
+            1. - (1. - t).powf(scaler)
+        } else if scaler < 1. {
+            t.powf(scaler)
+        } else {
+            t
+        };
+        let alpha_cumprod =
+            ((t + s) / (1. + s) * std::f64::consts::PI * 0.5).powi(2) / self.init_alpha_cumprod;
+        alpha_cumprod.clamp(0.0001, 0.9999)
+    }
+
+    fn previous_timestep(&self, ts: f64) -> f64 {
+        let index = self
+            .timesteps
+            .iter()
+            .enumerate()
+            .map(|(idx, v)| (idx, (v - ts).abs()))
+            .min_by(|x, y| x.1.total_cmp(&y.1))
+            .unwrap()
+            .0;
+        self.timesteps[index + 1]
+    }
+
+    ///  Ensures interchangeability with schedulers that need to scale the denoising model input
+    /// depending on the current timestep.
+    pub fn scale_model_input(&self, sample: Tensor, _timestep: usize) -> Tensor {
+        sample
+    }
+
+    pub fn step(&self, model_output: &Tensor, ts: f64, sample: &Tensor) -> Result<Tensor> {
+        let prev_t = self.previous_timestep(ts);
+
+        let alpha_cumprod = self.alpha_cumprod(ts);
+        let alpha_cumprod_prev = self.alpha_cumprod(prev_t);
+        let alpha = alpha_cumprod / alpha_cumprod_prev;
+
+        let mu = (sample - model_output * ((1. - alpha) / (1. - alpha_cumprod).sqrt()))?;
+        let mu = (mu * (1. / alpha).sqrt())?;
+
+        let std_noise = mu.randn_like(0., 1.)?;
+        let std =
+            std_noise * ((1. - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt();
+        if prev_t == 0. {
+            Ok(mu)
+        } else {
+            mu + std
+        }
+    }
+
+    pub fn init_noise_sigma(&self) -> f64 {
+        self.init_noise_sigma
+    }
+}
diff --git a/candle-transformers/src/models/wuerstchen/mod.rs b/candle-transformers/src/models/wuerstchen/mod.rs
index 435bdac2..f499bc35 100644
--- a/candle-transformers/src/models/wuerstchen/mod.rs
+++ b/candle-transformers/src/models/wuerstchen/mod.rs
@@ -1,4 +1,5 @@
 pub mod common;
+pub mod ddpm;
 pub mod diffnext;
 pub mod paella_vq;
 pub mod prior;

From 82a98f6da0e0d8273bcb6cf5766bc25a58843f80 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 18 Sep 2023 16:51:38 +0100
Subject: [PATCH 122/150] Prior denoising. (#889)

---
 candle-examples/examples/wuerstchen/main.rs   | 26 +++++++++++++++----
 .../src/models/wuerstchen/ddpm.rs             |  4 +++
 2 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index 12e4c10e..d40cfbd9 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -14,7 +14,7 @@ use candle::{DType, Device, IndexOp, Module, Tensor, D};
 use clap::Parser;
 use tokenizers::Tokenizer;
 
-const GUIDANCE_SCALE: f64 = 7.5;
+const PRIOR_GUIDANCE_SCALE: f64 = 8.0;
 const RESOLUTION_MULTIPLE: f64 = 42.67;
 const PRIOR_CIN: usize = 16;
 
@@ -288,16 +288,32 @@ fn run(args: Args) -> Result<()> {
     let latent_width = (width as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
     let b_size = 1;
     for idx in 0..num_samples {
-        let latents = Tensor::randn(
+        let mut latents = Tensor::randn(
             0f32,
             1f32,
             (b_size, PRIOR_CIN, latent_height, latent_width),
             &device,
         )?;
-        // TODO: latents denoising loop, use the scheduler values.
-        let ratio = Tensor::ones(1, DType::F32, &device)?;
-        let prior = prior.forward(&latents, &ratio, &prior_text_embeddings)?;
 
+        let prior_scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
+        let timesteps = prior_scheduler.timesteps();
+        println!("prior denoising");
+        for (index, &t) in timesteps.iter().enumerate() {
+            let start_time = std::time::Instant::now();
+            if index == timesteps.len() - 1 {
+                continue;
+            }
+            let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
+            let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
+            let noise_pred = prior.forward(&latent_model_input, &ratio, &prior_text_embeddings)?;
+            let noise_pred = noise_pred.chunk(2, 0)?;
+            let (noise_pred_text, noise_pred_uncond) = (&noise_pred[0], &noise_pred[1]);
+            let noise_pred = (noise_pred_uncond
+                + ((noise_pred_text - noise_pred_uncond)? * PRIOR_GUIDANCE_SCALE)?)?;
+            latents = prior_scheduler.step(&noise_pred, t, &latents)?;
+            let dt = start_time.elapsed().as_secs_f32();
+            println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
+        }
         let latents = ((latents * 42.)? - 1.)?;
         /*
         let timesteps = scheduler.timesteps();
diff --git a/candle-transformers/src/models/wuerstchen/ddpm.rs b/candle-transformers/src/models/wuerstchen/ddpm.rs
index f4f16bfb..80640072 100644
--- a/candle-transformers/src/models/wuerstchen/ddpm.rs
+++ b/candle-transformers/src/models/wuerstchen/ddpm.rs
@@ -38,6 +38,10 @@ impl DDPMWScheduler {
         })
     }
 
+    pub fn timesteps(&self) -> &[f64] {
+        &self.timesteps
+    }
+
     fn alpha_cumprod(&self, t: f64) -> f64 {
         let scaler = self.config.scaler;
         let s = self.config.s;

From 1542e92629501030085108a3180befd574dccdb0 Mon Sep 17 00:00:00 2001
From: Juarez Bochi <juarez.bochi@grammarly.com>
Date: Mon, 18 Sep 2023 12:20:21 -0700
Subject: [PATCH 123/150] T5: Add option to override use_cache from config
 (#892)

* Add option to override use_cache from config

* Disable cache by default and cleanup code
---
 candle-examples/examples/t5/main.rs | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 36cbee7c..117aed13 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -44,6 +44,10 @@ struct Args {
     #[arg(long)]
     decode: bool,
 
+    // Enable/disable decoding.
+    #[arg(long, default_value = "false")]
+    use_cache: bool,
+
     /// Use this prompt, otherwise compute sentence similarities.
     #[arg(long)]
     prompt: Option<String>,
@@ -111,7 +115,8 @@ impl T5ModelBuilder {
             )
         };
         let config = std::fs::read_to_string(config_filename)?;
-        let config: t5::Config = serde_json::from_str(&config)?;
+        let mut config: t5::Config = serde_json::from_str(&config)?;
+        config.use_cache = args.use_cache;
         let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
         Ok((
             Self {

From 92db8cecd32df9f9a338b89160d3072586595e96 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Mon, 18 Sep 2023 21:16:09 +0100
Subject: [PATCH 124/150] Specialized attention module for Wuerstchen. (#890)

* Specialized attention module for Wuerstchen.

* Reshaping ops.

* Attention processor.

* Finish the forward pass.

* Hook the new attention processor.

* Get the prior forward pass to work.

* Make it contiguous.
---
 .../models/wuerstchen/attention_processor.rs  | 74 +++++++++++++++++++
 .../src/models/wuerstchen/common.rs           |  8 +-
 .../src/models/wuerstchen/mod.rs              |  1 +
 .../src/models/wuerstchen/prior.rs            |  2 +-
 4 files changed, 80 insertions(+), 5 deletions(-)
 create mode 100644 candle-transformers/src/models/wuerstchen/attention_processor.rs

diff --git a/candle-transformers/src/models/wuerstchen/attention_processor.rs b/candle-transformers/src/models/wuerstchen/attention_processor.rs
new file mode 100644
index 00000000..3f1a72eb
--- /dev/null
+++ b/candle-transformers/src/models/wuerstchen/attention_processor.rs
@@ -0,0 +1,74 @@
+use candle::{Module, Result, Tensor};
+use candle_nn::{linear, Linear, VarBuilder};
+
+// A simplified version of:
+// https://github.com/huggingface/diffusers/blob/119ad2c3dc8a8fb8446a83f4bf6f20929487b47f/src/diffusers/models/attention_processor.py#L38
+#[derive(Debug)]
+pub struct Attention {
+    to_q: Linear,
+    to_k: Linear,
+    to_v: Linear,
+    to_out: Linear,
+    heads: usize,
+    scale: f64,
+}
+
+impl Attention {
+    pub fn new(query_dim: usize, heads: usize, dim_head: usize, vb: VarBuilder) -> Result<Self> {
+        let inner_dim = dim_head * heads;
+        let scale = 1.0 / f64::sqrt(dim_head as f64);
+        let to_q = linear(query_dim, inner_dim, vb.pp("to_q"))?;
+        let to_k = linear(query_dim, inner_dim, vb.pp("to_k"))?;
+        let to_v = linear(query_dim, inner_dim, vb.pp("to_v"))?;
+        let to_out = linear(inner_dim, query_dim, vb.pp("to_out.0"))?;
+        Ok(Self {
+            to_q,
+            to_k,
+            to_v,
+            to_out,
+            scale,
+            heads,
+        })
+    }
+
+    fn batch_to_head_dim(&self, xs: &Tensor) -> Result<Tensor> {
+        let (b_size, seq_len, dim) = xs.dims3()?;
+        xs.reshape((b_size / self.heads, self.heads, seq_len, dim))?
+            .permute((0, 2, 1, 3))?
+            .reshape((b_size / self.heads, seq_len, dim * self.heads))
+    }
+
+    fn head_to_batch_dim(&self, xs: &Tensor) -> Result<Tensor> {
+        let (b_size, seq_len, dim) = xs.dims3()?;
+        xs.reshape((b_size, seq_len, self.heads, dim / self.heads))?
+            .permute((0, 2, 1, 3))?
+            .reshape((b_size * self.heads, seq_len, dim / self.heads))
+    }
+
+    fn get_attention_scores(&self, query: &Tensor, key: &Tensor) -> Result<Tensor> {
+        let attn_probs = (query.matmul(&key.t()?)? * self.scale)?;
+        candle_nn::ops::softmax_last_dim(&attn_probs)
+    }
+
+    pub fn forward(&self, xs: &Tensor, encoder_hidden_states: &Tensor) -> Result<Tensor> {
+        let (b_size, channel, h, w) = xs.dims4()?;
+        let xs = xs.reshape((b_size, channel, h * w))?.t()?;
+
+        let query = self.to_q.forward(&xs)?;
+        let key = self.to_k.forward(encoder_hidden_states)?;
+        let value = self.to_v.forward(encoder_hidden_states)?;
+
+        let query = self.head_to_batch_dim(&query)?;
+        let key = self.head_to_batch_dim(&key)?;
+        let value = self.head_to_batch_dim(&value)?;
+
+        let attn_prs = self.get_attention_scores(&query, &key)?;
+        let xs = attn_prs.matmul(&value)?;
+        let xs = self.batch_to_head_dim(&xs)?;
+
+        self.to_out
+            .forward(&xs)?
+            .t()?
+            .reshape((b_size, channel, h, w))
+    }
+}
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
index 5337fdc6..1eb0c2e7 100644
--- a/candle-transformers/src/models/wuerstchen/common.rs
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -131,7 +131,7 @@ impl ResBlock {
         xs + x_res
     }
 }
-use crate::models::stable_diffusion::attention::CrossAttention as Attention;
+use super::attention_processor::Attention;
 #[derive(Debug)]
 pub struct AttnBlock {
     self_attn: bool,
@@ -149,7 +149,7 @@ impl AttnBlock {
         vb: VarBuilder,
     ) -> Result<Self> {
         let norm = WLayerNorm::new(c)?;
-        let attention = Attention::new(vb.pp("attention"), c, None, nhead, c / nhead, None, false)?;
+        let attention = Attention::new(c, nhead, c / nhead, vb.pp("attention"))?;
         let kv_mapper_lin = candle_nn::linear(c_cond, c, vb.pp("kv_mapper.1"))?;
         Ok(Self {
             self_attn,
@@ -165,10 +165,10 @@ impl AttnBlock {
         let kv = if self.self_attn {
             let (b_size, channel, _, _) = xs.dims4()?;
             let norm_xs = norm_xs.reshape((b_size, channel, ()))?.transpose(1, 2)?;
-            Tensor::cat(&[&norm_xs, &kv], 1)?
+            Tensor::cat(&[&norm_xs, &kv], 1)?.contiguous()?
         } else {
             kv
         };
-        xs + self.attention.forward(&norm_xs, Some(&kv))
+        xs + self.attention.forward(&norm_xs, &kv)
     }
 }
diff --git a/candle-transformers/src/models/wuerstchen/mod.rs b/candle-transformers/src/models/wuerstchen/mod.rs
index f499bc35..7b076f06 100644
--- a/candle-transformers/src/models/wuerstchen/mod.rs
+++ b/candle-transformers/src/models/wuerstchen/mod.rs
@@ -1,3 +1,4 @@
+pub mod attention_processor;
 pub mod common;
 pub mod ddpm;
 pub mod diffnext;
diff --git a/candle-transformers/src/models/wuerstchen/prior.rs b/candle-transformers/src/models/wuerstchen/prior.rs
index 93385a32..168b70a6 100644
--- a/candle-transformers/src/models/wuerstchen/prior.rs
+++ b/candle-transformers/src/models/wuerstchen/prior.rs
@@ -94,7 +94,7 @@ impl WPrior {
             xs = block.ts_block.forward(&xs, &r_embed)?;
             xs = block.attn_block.forward(&xs, &c_embed)?;
         }
-        let ab = xs.apply(&self.out_ln)?.apply(&self.out_conv)?.chunk(1, 2)?;
+        let ab = xs.apply(&self.out_ln)?.apply(&self.out_conv)?.chunk(2, 1)?;
         (x_in - &ab[0])? / ((&ab[1] - 1.)?.abs()? + 1e-5)
     }
 }

From aaa9d4ed6cd422f97767452b02c2ed873eed7a7f Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 19 Sep 2023 07:13:44 +0100
Subject: [PATCH 125/150] W decoding. (#893)

* W decoding.

* Add the diffusion loop.

* Use the appropriate config.
---
 candle-examples/examples/wuerstchen/main.rs | 56 +++++++++++----------
 1 file changed, 29 insertions(+), 27 deletions(-)

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index d40cfbd9..b92fe8fd 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -248,6 +248,20 @@ fn run(args: Args) -> Result<()> {
     };
     println!("{prior_text_embeddings}");
 
+    let text_embeddings = {
+        let tokenizer = ModelFile::Tokenizer.get(tokenizer)?;
+        let weights = ModelFile::Clip.get(clip_weights)?;
+        encode_prompt(
+            &prompt,
+            &uncond_prompt,
+            tokenizer.clone(),
+            weights,
+            stable_diffusion::clip::Config::wuerstchen(),
+            &device,
+        )?
+    };
+    println!("{prior_text_embeddings}");
+
     println!("Building the prior.");
     // https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
     let prior = {
@@ -262,7 +276,7 @@ fn run(args: Args) -> Result<()> {
     };
 
     println!("Building the vqgan.");
-    let _vqgan = {
+    let vqgan = {
         let vqgan_weights = ModelFile::VqGan.get(vqgan_weights)?;
         let weights = unsafe { candle::safetensors::MmapedFile::new(vqgan_weights)? };
         let weights = weights.deserialize()?;
@@ -273,7 +287,7 @@ fn run(args: Args) -> Result<()> {
     println!("Building the decoder.");
 
     // https://huggingface.co/warp-ai/wuerstchen/blob/main/decoder/config.json
-    let _decoder = {
+    let decoder = {
         let decoder_weights = ModelFile::Decoder.get(decoder_weights)?;
         let weights = unsafe { candle::safetensors::MmapedFile::new(decoder_weights)? };
         let weights = weights.deserialize()?;
@@ -314,49 +328,37 @@ fn run(args: Args) -> Result<()> {
             let dt = start_time.elapsed().as_secs_f32();
             println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
         }
-        let latents = ((latents * 42.)? - 1.)?;
-        /*
-        let timesteps = scheduler.timesteps();
-        let latents = Tensor::randn(
+        let effnet = ((latents * 42.)? - 1.)?;
+        let mut latents = Tensor::randn(
             0f32,
             1f32,
-            (bsize, 4, sd_config.height / 8, sd_config.width / 8),
+            (b_size, PRIOR_CIN, latent_height, latent_width),
             &device,
         )?;
-        // scale the initial noise by the standard deviation required by the scheduler
-        let mut latents = latents * scheduler.init_noise_sigma()?;
 
-        println!("starting sampling");
-        for (timestep_index, &timestep) in timesteps.iter().enumerate() {
+        println!("diffusion process");
+        for (index, &t) in timesteps.iter().enumerate() {
             let start_time = std::time::Instant::now();
-            let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
-
-            let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;
-            let noise_pred =
-                decoder.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
-            let noise_pred = noise_pred.chunk(2, 0)?;
-            let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
-            let noise_pred =
-                (noise_pred_uncond + ((noise_pred_text - noise_pred_uncond)? * GUIDANCE_SCALE)?)?;
-            latents = scheduler.step(&noise_pred, timestep, &latents)?;
+            if index == timesteps.len() - 1 {
+                continue;
+            }
+            let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
+            let noise_pred = decoder.forward(&latents, &ratio, &effnet, Some(&text_embeddings))?;
+            latents = prior_scheduler.step(&noise_pred, t, &latents)?;
             let dt = start_time.elapsed().as_secs_f32();
-            println!("step {}/{n_steps} done, {:.2}s", timestep_index + 1, dt);
+            println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
         }
-        */
-
         println!(
             "Generating the final image for sample {}/{}.",
             idx + 1,
             num_samples
         );
-        /*
-        let image = vae.decode(&(&latents / 0.18215)?)?;
+        let image = vqgan.decode(&(&latents * 0.3764)?)?;
         // TODO: Add the clamping between 0 and 1.
         let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
         let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
         let image_filename = output_filename(&final_image, idx + 1, num_samples, None);
         candle_examples::save_image(&image, image_filename)?
-        */
     }
     Ok(())
 }

From 9cf26c5cff637a2764f1cc7926402bf60bfbd7a1 Mon Sep 17 00:00:00 2001
From: Ikko Eltociear Ashimine <eltociear@gmail.com>
Date: Tue, 19 Sep 2023 15:14:15 +0900
Subject: [PATCH 126/150] Fix typo in error_manage.md (#888)

occured -> occurred
---
 candle-book/src/error_manage.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/candle-book/src/error_manage.md b/candle-book/src/error_manage.md
index c1a16bd9..0623e0e3 100644
--- a/candle-book/src/error_manage.md
+++ b/candle-book/src/error_manage.md
@@ -29,7 +29,7 @@ After adding `RUST_BACKTRACE=1`:
 Error: WithBacktrace { inner: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }, backtrace: Backtrace [{ fn: "candle::error::Error::bt", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/error.rs", line: 200 }, { fn: "candle::tensor::Tensor::matmul", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/tensor.rs", line: 816 }, { fn: "myapp::main", file: "./src/main.rs", line: 29 }, { fn: "core::ops::function::FnOnce::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 250 }, { fn: "std::sys_common::backtrace::__rust_begin_short_backtrace", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/sys_common/backtrace.rs", line: 135 }, { fn: "std::rt::lang_start::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 166 }, { fn: "core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &F>::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 284 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::rt::lang_start", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 165 }, { fn: "main" }, { fn: "__libc_start_main" }, { fn: "_start" }] }
 ```
 
-Not super pretty at the moment, but we can see error occured on `{ fn: "myapp::main", file: "./src/main.rs", line: 29 }`
+Not super pretty at the moment, but we can see error occurred on `{ fn: "myapp::main", file: "./src/main.rs", line: 29 }`
 
 
 Another thing to note, is that since Rust is compiled it is not necessarily as easy to recover proper stacktraces

From 06e46d7c3bb4ee4ca3ae0a64e9c2add95f5e0fb3 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 19 Sep 2023 14:13:05 +0100
Subject: [PATCH 127/150] Only use classifier free guidance for the prior.
 (#896)

* Only use classifier free guidance for the prior.

* Add another specific layer-norm structure.

* Tweaks.

* Fix the latent shape.

* Print the prior shape.

* More shape fixes.

* Remove some debugging continue.
---
 candle-examples/examples/wuerstchen/main.rs   | 154 ++++++++++--------
 .../src/models/wuerstchen/common.rs           |  28 ++++
 .../src/models/wuerstchen/diffnext.rs         |   7 +-
 .../src/models/wuerstchen/paella_vq.rs        |  10 +-
 4 files changed, 125 insertions(+), 74 deletions(-)

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index b92fe8fd..4e4bce0b 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -16,7 +16,9 @@ use tokenizers::Tokenizer;
 
 const PRIOR_GUIDANCE_SCALE: f64 = 8.0;
 const RESOLUTION_MULTIPLE: f64 = 42.67;
+const LATENT_DIM_SCALE: f64 = 10.67;
 const PRIOR_CIN: usize = 16;
+const DECODER_CIN: usize = 4;
 
 #[derive(Parser)]
 #[command(author, version, about, long_about = None)]
@@ -156,7 +158,7 @@ fn output_filename(
 
 fn encode_prompt(
     prompt: &str,
-    uncond_prompt: &str,
+    uncond_prompt: Option<&str>,
     tokenizer: std::path::PathBuf,
     clip_weights: std::path::PathBuf,
     clip_config: stable_diffusion::clip::Config,
@@ -179,24 +181,30 @@ fn encode_prompt(
     }
     let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
 
-    let mut uncond_tokens = tokenizer
-        .encode(uncond_prompt, true)
-        .map_err(E::msg)?
-        .get_ids()
-        .to_vec();
-    let uncond_tokens_len = uncond_tokens.len();
-    while uncond_tokens.len() < clip_config.max_position_embeddings {
-        uncond_tokens.push(pad_id)
-    }
-    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
-
     println!("Building the clip transformer.");
     let text_model =
         stable_diffusion::build_clip_transformer(&clip_config, clip_weights, device, DType::F32)?;
     let text_embeddings = text_model.forward_with_mask(&tokens, tokens_len - 1)?;
-    let uncond_embeddings = text_model.forward_with_mask(&uncond_tokens, uncond_tokens_len - 1)?;
-    let text_embeddings = Tensor::cat(&[text_embeddings, uncond_embeddings], 0)?;
-    Ok(text_embeddings)
+    match uncond_prompt {
+        None => Ok(text_embeddings),
+        Some(uncond_prompt) => {
+            let mut uncond_tokens = tokenizer
+                .encode(uncond_prompt, true)
+                .map_err(E::msg)?
+                .get_ids()
+                .to_vec();
+            let uncond_tokens_len = uncond_tokens.len();
+            while uncond_tokens.len() < clip_config.max_position_embeddings {
+                uncond_tokens.push(pad_id)
+            }
+            let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
+
+            let uncond_embeddings =
+                text_model.forward_with_mask(&uncond_tokens, uncond_tokens_len - 1)?;
+            let text_embeddings = Tensor::cat(&[text_embeddings, uncond_embeddings], 0)?;
+            Ok(text_embeddings)
+        }
+    }
 }
 
 fn run(args: Args) -> Result<()> {
@@ -239,40 +247,72 @@ fn run(args: Args) -> Result<()> {
         let weights = ModelFile::PriorClip.get(args.prior_clip_weights)?;
         encode_prompt(
             &prompt,
-            &uncond_prompt,
+            Some(&uncond_prompt),
             tokenizer.clone(),
             weights,
             stable_diffusion::clip::Config::wuerstchen_prior(),
             &device,
         )?
     };
-    println!("{prior_text_embeddings}");
+    println!("generated prior text embeddings {prior_text_embeddings:?}");
 
     let text_embeddings = {
         let tokenizer = ModelFile::Tokenizer.get(tokenizer)?;
         let weights = ModelFile::Clip.get(clip_weights)?;
         encode_prompt(
             &prompt,
-            &uncond_prompt,
+            None,
             tokenizer.clone(),
             weights,
             stable_diffusion::clip::Config::wuerstchen(),
             &device,
         )?
     };
-    println!("{prior_text_embeddings}");
+    println!("generated text embeddings {text_embeddings:?}");
 
     println!("Building the prior.");
-    // https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
-    let prior = {
-        let prior_weights = ModelFile::Prior.get(prior_weights)?;
-        let weights = unsafe { candle::safetensors::MmapedFile::new(prior_weights)? };
-        let weights = weights.deserialize()?;
-        let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
-        wuerstchen::prior::WPrior::new(
-            /* c_in */ PRIOR_CIN, /* c */ 1536, /* c_cond */ 1280,
-            /* c_r */ 64, /* depth */ 32, /* nhead */ 24, vb,
-        )?
+    let b_size = 1;
+    let image_embeddings = {
+        // https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
+        let latent_height = (height as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
+        let latent_width = (width as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
+        let mut latents = Tensor::randn(
+            0f32,
+            1f32,
+            (b_size, PRIOR_CIN, latent_height, latent_width),
+            &device,
+        )?;
+
+        let prior = {
+            let prior_weights = ModelFile::Prior.get(prior_weights)?;
+            let weights = unsafe { candle::safetensors::MmapedFile::new(prior_weights)? };
+            let weights = weights.deserialize()?;
+            let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
+            wuerstchen::prior::WPrior::new(
+                /* c_in */ PRIOR_CIN, /* c */ 1536, /* c_cond */ 1280,
+                /* c_r */ 64, /* depth */ 32, /* nhead */ 24, vb,
+            )?
+        };
+        let prior_scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
+        let timesteps = prior_scheduler.timesteps();
+        println!("prior denoising");
+        for (index, &t) in timesteps.iter().enumerate() {
+            let start_time = std::time::Instant::now();
+            if index == timesteps.len() - 1 {
+                continue;
+            }
+            let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
+            let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
+            let noise_pred = prior.forward(&latent_model_input, &ratio, &prior_text_embeddings)?;
+            let noise_pred = noise_pred.chunk(2, 0)?;
+            let (noise_pred_text, noise_pred_uncond) = (&noise_pred[0], &noise_pred[1]);
+            let noise_pred = (noise_pred_uncond
+                + ((noise_pred_text - noise_pred_uncond)? * PRIOR_GUIDANCE_SCALE)?)?;
+            latents = prior_scheduler.step(&noise_pred, t, &latents)?;
+            let dt = start_time.elapsed().as_secs_f32();
+            println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
+        }
+        ((latents * 42.)? - 1.)?
     };
 
     println!("Building the vqgan.");
@@ -293,58 +333,40 @@ fn run(args: Args) -> Result<()> {
         let weights = weights.deserialize()?;
         let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
         wuerstchen::diffnext::WDiffNeXt::new(
-            /* c_in */ 4, /* c_out */ 4, /* c_r */ 64, /* c_cond */ 1024,
-            /* clip_embd */ 1024, /* patch_size */ 2, vb,
+            /* c_in */ DECODER_CIN,
+            /* c_out */ DECODER_CIN,
+            /* c_r */ 64,
+            /* c_cond */ 1024,
+            /* clip_embd */ 1024,
+            /* patch_size */ 2,
+            vb,
         )?
     };
 
-    let latent_height = (height as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
-    let latent_width = (width as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
-    let b_size = 1;
     for idx in 0..num_samples {
+        // https://huggingface.co/warp-ai/wuerstchen/blob/main/model_index.json
+        let latent_height = (image_embeddings.dim(2)? as f64 * LATENT_DIM_SCALE) as usize;
+        let latent_width = (image_embeddings.dim(3)? as f64 * LATENT_DIM_SCALE) as usize;
+
         let mut latents = Tensor::randn(
             0f32,
             1f32,
-            (b_size, PRIOR_CIN, latent_height, latent_width),
+            (b_size, DECODER_CIN, latent_height, latent_width),
             &device,
         )?;
 
-        let prior_scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
-        let timesteps = prior_scheduler.timesteps();
-        println!("prior denoising");
+        println!("diffusion process with prior {image_embeddings:?}");
+        let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
+        let timesteps = scheduler.timesteps();
         for (index, &t) in timesteps.iter().enumerate() {
             let start_time = std::time::Instant::now();
             if index == timesteps.len() - 1 {
                 continue;
             }
-            let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
-            let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
-            let noise_pred = prior.forward(&latent_model_input, &ratio, &prior_text_embeddings)?;
-            let noise_pred = noise_pred.chunk(2, 0)?;
-            let (noise_pred_text, noise_pred_uncond) = (&noise_pred[0], &noise_pred[1]);
-            let noise_pred = (noise_pred_uncond
-                + ((noise_pred_text - noise_pred_uncond)? * PRIOR_GUIDANCE_SCALE)?)?;
-            latents = prior_scheduler.step(&noise_pred, t, &latents)?;
-            let dt = start_time.elapsed().as_secs_f32();
-            println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
-        }
-        let effnet = ((latents * 42.)? - 1.)?;
-        let mut latents = Tensor::randn(
-            0f32,
-            1f32,
-            (b_size, PRIOR_CIN, latent_height, latent_width),
-            &device,
-        )?;
-
-        println!("diffusion process");
-        for (index, &t) in timesteps.iter().enumerate() {
-            let start_time = std::time::Instant::now();
-            if index == timesteps.len() - 1 {
-                continue;
-            }
-            let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
-            let noise_pred = decoder.forward(&latents, &ratio, &effnet, Some(&text_embeddings))?;
-            latents = prior_scheduler.step(&noise_pred, t, &latents)?;
+            let ratio = (Tensor::ones(1, DType::F32, &device)? * t)?;
+            let noise_pred =
+                decoder.forward(&latents, &ratio, &image_embeddings, Some(&text_embeddings))?;
+            latents = scheduler.step(&noise_pred, t, &latents)?;
             let dt = start_time.elapsed().as_secs_f32();
             println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
         }
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
index 1eb0c2e7..3cac2a59 100644
--- a/candle-transformers/src/models/wuerstchen/common.rs
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -34,6 +34,34 @@ impl Module for WLayerNorm {
     }
 }
 
+#[derive(Debug)]
+pub struct LayerNormNoWeights {
+    eps: f64,
+}
+
+impl LayerNormNoWeights {
+    pub fn new(_size: usize) -> Result<Self> {
+        Ok(Self { eps: 1e-6 })
+    }
+}
+
+impl Module for LayerNormNoWeights {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let x_dtype = xs.dtype();
+        let internal_dtype = match x_dtype {
+            DType::F16 | DType::BF16 => DType::F32,
+            d => d,
+        };
+        let hidden_size = xs.dim(D::Minus1)?;
+        let xs = xs.to_dtype(internal_dtype)?;
+        let mean_x = (xs.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
+        let xs = xs.broadcast_sub(&mean_x)?;
+        let norm_x = (xs.sqr()?.sum_keepdim(D::Minus1)? / hidden_size as f64)?;
+        xs.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?
+            .to_dtype(x_dtype)
+    }
+}
+
 #[derive(Debug)]
 pub struct TimestepBlock {
     mapper: candle_nn::Linear,
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 664251ed..60b799ae 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -1,4 +1,4 @@
-use super::common::{AttnBlock, GlobalResponseNorm, TimestepBlock, WLayerNorm};
+use super::common::{AttnBlock, GlobalResponseNorm, LayerNormNoWeights, TimestepBlock, WLayerNorm};
 use candle::{DType, Module, Result, Tensor, D};
 use candle_nn::VarBuilder;
 
@@ -75,7 +75,7 @@ struct UpBlock {
 pub struct WDiffNeXt {
     clip_mapper: candle_nn::Linear,
     effnet_mappers: Vec<Option<candle_nn::Conv2d>>,
-    seq_norm: WLayerNorm,
+    seq_norm: LayerNormNoWeights,
     embedding_conv: candle_nn::Conv2d,
     embedding_ln: WLayerNorm,
     down_blocks: Vec<DownBlock>,
@@ -133,7 +133,7 @@ impl WDiffNeXt {
             };
             effnet_mappers.push(c)
         }
-        let seq_norm = WLayerNorm::new(c_cond)?;
+        let seq_norm = LayerNormNoWeights::new(c_cond)?;
         let embedding_ln = WLayerNorm::new(C_HIDDEN[0])?;
         let embedding_conv = candle_nn::conv2d(
             c_in * patch_size * patch_size,
@@ -335,6 +335,7 @@ impl WDiffNeXt {
             level_outputs.push(xs.clone())
         }
         level_outputs.reverse();
+        let mut xs = level_outputs[0].clone();
 
         for (i, up_block) in self.up_blocks.iter().enumerate() {
             let effnet_c = match &self.effnet_mappers[self.down_blocks.len() + i] {
diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
index faf2d2b4..8cf33505 100644
--- a/candle-transformers/src/models/wuerstchen/paella_vq.rs
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -1,12 +1,12 @@
-use super::common::WLayerNorm;
+use super::common::LayerNormNoWeights;
 use candle::{Module, Result, Tensor};
 use candle_nn::VarBuilder;
 
 #[derive(Debug)]
 pub struct MixingResidualBlock {
-    norm1: WLayerNorm,
+    norm1: LayerNormNoWeights,
     depthwise_conv: candle_nn::Conv2d,
-    norm2: WLayerNorm,
+    norm2: LayerNormNoWeights,
     channelwise_lin1: candle_nn::Linear,
     channelwise_lin2: candle_nn::Linear,
     gammas: Vec<f32>,
@@ -14,8 +14,8 @@ pub struct MixingResidualBlock {
 
 impl MixingResidualBlock {
     pub fn new(inp: usize, embed_dim: usize, vb: VarBuilder) -> Result<Self> {
-        let norm1 = WLayerNorm::new(inp)?;
-        let norm2 = WLayerNorm::new(inp)?;
+        let norm1 = LayerNormNoWeights::new(inp)?;
+        let norm2 = LayerNormNoWeights::new(inp)?;
         let cfg = candle_nn::Conv2dConfig {
             groups: inp,
             ..Default::default()

From 4f91c8e1097fcbcd38aaf4a8ebf4f619d7598473 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 19 Sep 2023 15:09:47 +0100
Subject: [PATCH 128/150] Improve the error message on shape mismatch for cat.
 (#897)

* Improve the error message on shape mismatch for cat.

* Cosmetic tweak.
---
 candle-core/src/tensor.rs                     | 28 +++++++++++++++++++
 candle-examples/examples/wuerstchen/main.rs   |  8 ++----
 .../src/models/wuerstchen/diffnext.rs         |  3 +-
 .../src/models/wuerstchen/paella_vq.rs        |  1 +
 4 files changed, 33 insertions(+), 7 deletions(-)

diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 61f576cf..756fedb2 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -1907,6 +1907,34 @@ impl Tensor {
         for arg in args {
             arg.as_ref().check_dim(dim, "cat")?;
         }
+        for (arg_idx, arg) in args.iter().enumerate() {
+            let arg = arg.as_ref();
+            if arg0.rank() != arg.rank() {
+                Err(Error::UnexpectedNumberOfDims {
+                    expected: arg0.rank(),
+                    got: arg.rank(),
+                    shape: arg.shape().clone(),
+                }
+                .bt())?
+            }
+            for (dim_idx, (v1, v2)) in arg0
+                .shape()
+                .dims()
+                .iter()
+                .zip(arg.shape().dims().iter())
+                .enumerate()
+            {
+                if dim_idx != dim && v1 != v2 {
+                    Err(Error::ShapeMismatchCat {
+                        dim: dim_idx,
+                        first_shape: arg0.shape().clone(),
+                        n: arg_idx + 1,
+                        nth_shape: arg.shape().clone(),
+                    }
+                    .bt())?
+                }
+            }
+        }
         if dim == 0 {
             Self::cat0(args)
         } else {
diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index 4e4bce0b..8064f87f 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -295,12 +295,10 @@ fn run(args: Args) -> Result<()> {
         };
         let prior_scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
         let timesteps = prior_scheduler.timesteps();
+        let timesteps = &timesteps[..timesteps.len() - 1];
         println!("prior denoising");
         for (index, &t) in timesteps.iter().enumerate() {
             let start_time = std::time::Instant::now();
-            if index == timesteps.len() - 1 {
-                continue;
-            }
             let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
             let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
             let noise_pred = prior.forward(&latent_model_input, &ratio, &prior_text_embeddings)?;
@@ -358,11 +356,9 @@ fn run(args: Args) -> Result<()> {
         println!("diffusion process with prior {image_embeddings:?}");
         let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
         let timesteps = scheduler.timesteps();
+        let timesteps = &timesteps[..timesteps.len() - 1];
         for (index, &t) in timesteps.iter().enumerate() {
             let start_time = std::time::Instant::now();
-            if index == timesteps.len() - 1 {
-                continue;
-            }
             let ratio = (Tensor::ones(1, DType::F32, &device)? * t)?;
             let noise_pred =
                 decoder.forward(&latents, &ratio, &image_embeddings, Some(&text_embeddings))?;
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 60b799ae..501a2776 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -41,6 +41,7 @@ impl ResBlockStageB {
         };
         let xs = xs
             .permute((0, 2, 3, 1))?
+            .contiguous()?
             .apply(&self.channelwise_lin1)?
             .gelu()?
             .apply(&self.channelwise_grn)?
@@ -374,7 +375,7 @@ impl WDiffNeXt {
             .apply(&self.clf_ln)?
             .apply(&self.clf_conv)?
             .apply(&|xs: &_| candle_nn::ops::pixel_shuffle(xs, self.patch_size))?
-            .chunk(1, 2)?;
+            .chunk(2, 1)?;
         let b = ((candle_nn::ops::sigmoid(&ab[1])? * (1. - EPS * 2.))? + EPS)?;
         (x_in - &ab[0])? / b
     }
diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
index 8cf33505..6da7362c 100644
--- a/candle-transformers/src/models/wuerstchen/paella_vq.rs
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -52,6 +52,7 @@ impl Module for MixingResidualBlock {
             .affine(1. + mods[3] as f64, mods[4] as f64)?;
         let x_temp = x_temp
             .permute((0, 2, 3, 1))?
+            .contiguous()?
             .apply(&self.channelwise_lin1)?
             .gelu()?
             .apply(&self.channelwise_lin2)?

From 34f2ecbc3bc0ae8ba0666808db7de19fb3d907d4 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 19 Sep 2023 18:17:17 +0100
Subject: [PATCH 129/150] Fix the leaky relu. (#898)

---
 candle-nn/src/ops.rs | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/candle-nn/src/ops.rs b/candle-nn/src/ops.rs
index 1256a076..32de1af9 100644
--- a/candle-nn/src/ops.rs
+++ b/candle-nn/src/ops.rs
@@ -45,7 +45,8 @@ pub fn sigmoid(xs: &Tensor) -> Result<Tensor> {
 }
 
 pub fn leaky_relu(xs: &Tensor, negative_slope: f64) -> Result<Tensor> {
-    xs.relu()?.minimum(&(xs * negative_slope)?)
+    let zeros = xs.zeros_like()?;
+    xs.maximum(&zeros)? + xs.minimum(&zeros)? * negative_slope
 }
 
 pub fn dropout(xs: &Tensor, drop_p: f32) -> Result<Tensor> {

From d7e48234d4d94653894a33f7a6da31b0a740b060 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 19 Sep 2023 19:54:28 +0100
Subject: [PATCH 130/150] Add an erf based gelu op (#900)

* Erf based gelu.

* Add the erf backed gelu.

* Test the new gelu op (which is not gelu_new).
---
 candle-core/src/backprop.rs       |   3 +
 candle-core/src/cpu/erf.rs        | 763 ++++++++++++++++++++++++++++++
 candle-core/src/cpu/mod.rs        |   1 +
 candle-core/src/op.rs             |  36 ++
 candle-core/src/tensor.rs         |   1 +
 candle-core/tests/tensor_tests.rs |  23 +-
 candle-kernels/src/cuda_utils.cuh |   8 +
 candle-kernels/src/unary.cu       |  17 +
 8 files changed, 851 insertions(+), 1 deletion(-)
 create mode 100644 candle-core/src/cpu/erf.rs

diff --git a/candle-core/src/backprop.rs b/candle-core/src/backprop.rs
index 9c8f685f..3e2ae1ed 100644
--- a/candle-core/src/backprop.rs
+++ b/candle-core/src/backprop.rs
@@ -442,6 +442,9 @@ impl Tensor {
                         *sum_grad = sum_grad.add(&arg_grad)?
                     }
                     Op::Unary(_, UnaryOp::Gelu) => Err(Error::BackwardNotSupported { op: "gelu" })?,
+                    Op::Unary(_, UnaryOp::GeluErf) => {
+                        Err(Error::BackwardNotSupported { op: "gelu-erf" })?
+                    }
                     Op::Unary(arg, UnaryOp::Relu) => {
                         let sum_grad = grads.or_insert(arg)?;
                         let relu_grad = arg.ge(&arg.zeros_like()?)?.to_dtype(arg.dtype())?;
diff --git a/candle-core/src/cpu/erf.rs b/candle-core/src/cpu/erf.rs
new file mode 100644
index 00000000..ca6be53f
--- /dev/null
+++ b/candle-core/src/cpu/erf.rs
@@ -0,0 +1,763 @@
+#![allow(clippy::excessive_precision)]
+// Code taken from https://github.com/statrs-dev/statrs
+//! Provides the [error](https://en.wikipedia.org/wiki/Error_function) and
+//! related functions
+
+mod evaluate {
+    //! Provides functions that don't have a numerical solution and must
+    //! be solved computationally (e.g. evaluation of a polynomial)
+
+    /// evaluates a polynomial at `z` where `coeff` are the coeffecients
+    /// to a polynomial of order `k` where `k` is the length of `coeff` and the
+    /// coeffecient
+    /// to the `k`th power is the `k`th element in coeff. E.g. [3,-1,2] equates to
+    /// `2z^2 - z + 3`
+    ///
+    /// # Remarks
+    ///
+    /// Returns 0 for a 0 length coefficient slice
+    pub fn polynomial(z: f64, coeff: &[f64]) -> f64 {
+        let n = coeff.len();
+        if n == 0 {
+            return 0.0;
+        }
+
+        let mut sum = *coeff.last().unwrap();
+        for c in coeff[0..n - 1].iter().rev() {
+            sum = *c + z * sum;
+        }
+        sum
+    }
+}
+use std::f64;
+
+/// `erf` calculates the error function at `x`.
+pub fn erf(x: f64) -> f64 {
+    if x.is_nan() {
+        f64::NAN
+    } else if x >= 0.0 && x.is_infinite() {
+        1.0
+    } else if x <= 0.0 && x.is_infinite() {
+        -1.0
+    } else if x == 0. {
+        0.0
+    } else {
+        erf_impl(x, false)
+    }
+}
+
+/// `erf_inv` calculates the inverse error function
+/// at `x`.
+pub fn erf_inv(x: f64) -> f64 {
+    if x == 0.0 {
+        0.0
+    } else if x >= 1.0 {
+        f64::INFINITY
+    } else if x <= -1.0 {
+        f64::NEG_INFINITY
+    } else if x < 0.0 {
+        erf_inv_impl(-x, 1.0 + x, -1.0)
+    } else {
+        erf_inv_impl(x, 1.0 - x, 1.0)
+    }
+}
+
+/// `erfc` calculates the complementary error function
+/// at `x`.
+pub fn erfc(x: f64) -> f64 {
+    if x.is_nan() {
+        f64::NAN
+    } else if x == f64::INFINITY {
+        0.0
+    } else if x == f64::NEG_INFINITY {
+        2.0
+    } else {
+        erf_impl(x, true)
+    }
+}
+
+/// `erfc_inv` calculates the complementary inverse
+/// error function at `x`.
+pub fn erfc_inv(x: f64) -> f64 {
+    if x <= 0.0 {
+        f64::INFINITY
+    } else if x >= 2.0 {
+        f64::NEG_INFINITY
+    } else if x > 1.0 {
+        erf_inv_impl(-1.0 + x, 2.0 - x, -1.0)
+    } else {
+        erf_inv_impl(1.0 - x, x, 1.0)
+    }
+}
+
+// **********************************************************
+// ********** Coefficients for erf_impl polynomial **********
+// **********************************************************
+
+/// Polynomial coefficients for a numerator of `erf_impl`
+/// in the interval [1e-10, 0.5].
+const ERF_IMPL_AN: &[f64] = &[
+    0.00337916709551257388990745,
+    -0.00073695653048167948530905,
+    -0.374732337392919607868241,
+    0.0817442448733587196071743,
+    -0.0421089319936548595203468,
+    0.0070165709512095756344528,
+    -0.00495091255982435110337458,
+    0.000871646599037922480317225,
+];
+
+/// Polynomial coefficients for a denominator of `erf_impl`
+/// in the interval [1e-10, 0.5]
+const ERF_IMPL_AD: &[f64] = &[
+    1.0,
+    -0.218088218087924645390535,
+    0.412542972725442099083918,
+    -0.0841891147873106755410271,
+    0.0655338856400241519690695,
+    -0.0120019604454941768171266,
+    0.00408165558926174048329689,
+    -0.000615900721557769691924509,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [0.5, 0.75].
+const ERF_IMPL_BN: &[f64] = &[
+    -0.0361790390718262471360258,
+    0.292251883444882683221149,
+    0.281447041797604512774415,
+    0.125610208862766947294894,
+    0.0274135028268930549240776,
+    0.00250839672168065762786937,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [0.5, 0.75].
+const ERF_IMPL_BD: &[f64] = &[
+    1.0,
+    1.8545005897903486499845,
+    1.43575803037831418074962,
+    0.582827658753036572454135,
+    0.124810476932949746447682,
+    0.0113724176546353285778481,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [0.75, 1.25].
+const ERF_IMPL_CN: &[f64] = &[
+    -0.0397876892611136856954425,
+    0.153165212467878293257683,
+    0.191260295600936245503129,
+    0.10276327061989304213645,
+    0.029637090615738836726027,
+    0.0046093486780275489468812,
+    0.000307607820348680180548455,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [0.75, 1.25].
+const ERF_IMPL_CD: &[f64] = &[
+    1.0,
+    1.95520072987627704987886,
+    1.64762317199384860109595,
+    0.768238607022126250082483,
+    0.209793185936509782784315,
+    0.0319569316899913392596356,
+    0.00213363160895785378615014,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [1.25, 2.25].
+const ERF_IMPL_DN: &[f64] = &[
+    -0.0300838560557949717328341,
+    0.0538578829844454508530552,
+    0.0726211541651914182692959,
+    0.0367628469888049348429018,
+    0.00964629015572527529605267,
+    0.00133453480075291076745275,
+    0.778087599782504251917881e-4,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [1.25, 2.25].
+const ERF_IMPL_DD: &[f64] = &[
+    1.0,
+    1.75967098147167528287343,
+    1.32883571437961120556307,
+    0.552528596508757581287907,
+    0.133793056941332861912279,
+    0.0179509645176280768640766,
+    0.00104712440019937356634038,
+    -0.106640381820357337177643e-7,
+];
+
+///  Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [2.25, 3.5].
+const ERF_IMPL_EN: &[f64] = &[
+    -0.0117907570137227847827732,
+    0.014262132090538809896674,
+    0.0202234435902960820020765,
+    0.00930668299990432009042239,
+    0.00213357802422065994322516,
+    0.00025022987386460102395382,
+    0.120534912219588189822126e-4,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [2.25, 3.5].
+const ERF_IMPL_ED: &[f64] = &[
+    1.0,
+    1.50376225203620482047419,
+    0.965397786204462896346934,
+    0.339265230476796681555511,
+    0.0689740649541569716897427,
+    0.00771060262491768307365526,
+    0.000371421101531069302990367,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [3.5, 5.25].
+const ERF_IMPL_FN: &[f64] = &[
+    -0.00546954795538729307482955,
+    0.00404190278731707110245394,
+    0.0054963369553161170521356,
+    0.00212616472603945399437862,
+    0.000394984014495083900689956,
+    0.365565477064442377259271e-4,
+    0.135485897109932323253786e-5,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [3.5, 5.25].
+const ERF_IMPL_FD: &[f64] = &[
+    1.0,
+    1.21019697773630784832251,
+    0.620914668221143886601045,
+    0.173038430661142762569515,
+    0.0276550813773432047594539,
+    0.00240625974424309709745382,
+    0.891811817251336577241006e-4,
+    -0.465528836283382684461025e-11,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [5.25, 8].
+const ERF_IMPL_GN: &[f64] = &[
+    -0.00270722535905778347999196,
+    0.0013187563425029400461378,
+    0.00119925933261002333923989,
+    0.00027849619811344664248235,
+    0.267822988218331849989363e-4,
+    0.923043672315028197865066e-6,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [5.25, 8].
+const ERF_IMPL_GD: &[f64] = &[
+    1.0,
+    0.814632808543141591118279,
+    0.268901665856299542168425,
+    0.0449877216103041118694989,
+    0.00381759663320248459168994,
+    0.000131571897888596914350697,
+    0.404815359675764138445257e-11,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [8, 11.5].
+const ERF_IMPL_HN: &[f64] = &[
+    -0.00109946720691742196814323,
+    0.000406425442750422675169153,
+    0.000274499489416900707787024,
+    0.465293770646659383436343e-4,
+    0.320955425395767463401993e-5,
+    0.778286018145020892261936e-7,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [8, 11.5].
+const ERF_IMPL_HD: &[f64] = &[
+    1.0,
+    0.588173710611846046373373,
+    0.139363331289409746077541,
+    0.0166329340417083678763028,
+    0.00100023921310234908642639,
+    0.24254837521587225125068e-4,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [11.5, 17].
+const ERF_IMPL_IN: &[f64] = &[
+    -0.00056907993601094962855594,
+    0.000169498540373762264416984,
+    0.518472354581100890120501e-4,
+    0.382819312231928859704678e-5,
+    0.824989931281894431781794e-7,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [11.5, 17].
+const ERF_IMPL_ID: &[f64] = &[
+    1.0,
+    0.339637250051139347430323,
+    0.043472647870310663055044,
+    0.00248549335224637114641629,
+    0.535633305337152900549536e-4,
+    -0.117490944405459578783846e-12,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [17, 24].
+const ERF_IMPL_JN: &[f64] = &[
+    -0.000241313599483991337479091,
+    0.574224975202501512365975e-4,
+    0.115998962927383778460557e-4,
+    0.581762134402593739370875e-6,
+    0.853971555085673614607418e-8,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [17, 24].
+const ERF_IMPL_JD: &[f64] = &[
+    1.0,
+    0.233044138299687841018015,
+    0.0204186940546440312625597,
+    0.000797185647564398289151125,
+    0.117019281670172327758019e-4,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [24, 38].
+const ERF_IMPL_KN: &[f64] = &[
+    -0.000146674699277760365803642,
+    0.162666552112280519955647e-4,
+    0.269116248509165239294897e-5,
+    0.979584479468091935086972e-7,
+    0.101994647625723465722285e-8,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [24, 38].
+const ERF_IMPL_KD: &[f64] = &[
+    1.0,
+    0.165907812944847226546036,
+    0.0103361716191505884359634,
+    0.000286593026373868366935721,
+    0.298401570840900340874568e-5,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [38, 60].
+const ERF_IMPL_LN: &[f64] = &[
+    -0.583905797629771786720406e-4,
+    0.412510325105496173512992e-5,
+    0.431790922420250949096906e-6,
+    0.993365155590013193345569e-8,
+    0.653480510020104699270084e-10,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [38, 60].
+const ERF_IMPL_LD: &[f64] = &[
+    1.0,
+    0.105077086072039915406159,
+    0.00414278428675475620830226,
+    0.726338754644523769144108e-4,
+    0.477818471047398785369849e-6,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [60, 85].
+const ERF_IMPL_MN: &[f64] = &[
+    -0.196457797609229579459841e-4,
+    0.157243887666800692441195e-5,
+    0.543902511192700878690335e-7,
+    0.317472492369117710852685e-9,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [60, 85].
+const ERF_IMPL_MD: &[f64] = &[
+    1.0,
+    0.052803989240957632204885,
+    0.000926876069151753290378112,
+    0.541011723226630257077328e-5,
+    0.535093845803642394908747e-15,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [85, 110].
+const ERF_IMPL_NN: &[f64] = &[
+    -0.789224703978722689089794e-5,
+    0.622088451660986955124162e-6,
+    0.145728445676882396797184e-7,
+    0.603715505542715364529243e-10,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [85, 110].
+const ERF_IMPL_ND: &[f64] = &[
+    1.0,
+    0.0375328846356293715248719,
+    0.000467919535974625308126054,
+    0.193847039275845656900547e-5,
+];
+
+// **********************************************************
+// ********** Coefficients for erf_inv_impl polynomial ******
+// **********************************************************
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0, 0.5].
+const ERF_INV_IMPL_AN: &[f64] = &[
+    -0.000508781949658280665617,
+    -0.00836874819741736770379,
+    0.0334806625409744615033,
+    -0.0126926147662974029034,
+    -0.0365637971411762664006,
+    0.0219878681111168899165,
+    0.00822687874676915743155,
+    -0.00538772965071242932965,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0, 0.5].
+const ERF_INV_IMPL_AD: &[f64] = &[
+    1.0,
+    -0.970005043303290640362,
+    -1.56574558234175846809,
+    1.56221558398423026363,
+    0.662328840472002992063,
+    -0.71228902341542847553,
+    -0.0527396382340099713954,
+    0.0795283687341571680018,
+    -0.00233393759374190016776,
+    0.000886216390456424707504,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.5, 0.75].
+const ERF_INV_IMPL_BN: &[f64] = &[
+    -0.202433508355938759655,
+    0.105264680699391713268,
+    8.37050328343119927838,
+    17.6447298408374015486,
+    -18.8510648058714251895,
+    -44.6382324441786960818,
+    17.445385985570866523,
+    21.1294655448340526258,
+    -3.67192254707729348546,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.5, 0.75].
+const ERF_INV_IMPL_BD: &[f64] = &[
+    1.0,
+    6.24264124854247537712,
+    3.9713437953343869095,
+    -28.6608180499800029974,
+    -20.1432634680485188801,
+    48.5609213108739935468,
+    10.8268667355460159008,
+    -22.6436933413139721736,
+    1.72114765761200282724,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x less than 3.
+const ERF_INV_IMPL_CN: &[f64] = &[
+    -0.131102781679951906451,
+    -0.163794047193317060787,
+    0.117030156341995252019,
+    0.387079738972604337464,
+    0.337785538912035898924,
+    0.142869534408157156766,
+    0.0290157910005329060432,
+    0.00214558995388805277169,
+    -0.679465575181126350155e-6,
+    0.285225331782217055858e-7,
+    -0.681149956853776992068e-9,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x less than 3.
+const ERF_INV_IMPL_CD: &[f64] = &[
+    1.0,
+    3.46625407242567245975,
+    5.38168345707006855425,
+    4.77846592945843778382,
+    2.59301921623620271374,
+    0.848854343457902036425,
+    0.152264338295331783612,
+    0.01105924229346489121,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 3 and 6.
+const ERF_INV_IMPL_DN: &[f64] = &[
+    -0.0350353787183177984712,
+    -0.00222426529213447927281,
+    0.0185573306514231072324,
+    0.00950804701325919603619,
+    0.00187123492819559223345,
+    0.000157544617424960554631,
+    0.460469890584317994083e-5,
+    -0.230404776911882601748e-9,
+    0.266339227425782031962e-11,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 3 and 6.
+const ERF_INV_IMPL_DD: &[f64] = &[
+    1.0,
+    1.3653349817554063097,
+    0.762059164553623404043,
+    0.220091105764131249824,
+    0.0341589143670947727934,
+    0.00263861676657015992959,
+    0.764675292302794483503e-4,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 6 and 18.
+const ERF_INV_IMPL_EN: &[f64] = &[
+    -0.0167431005076633737133,
+    -0.00112951438745580278863,
+    0.00105628862152492910091,
+    0.000209386317487588078668,
+    0.149624783758342370182e-4,
+    0.449696789927706453732e-6,
+    0.462596163522878599135e-8,
+    -0.281128735628831791805e-13,
+    0.99055709973310326855e-16,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 6 and 18.
+const ERF_INV_IMPL_ED: &[f64] = &[
+    1.0,
+    0.591429344886417493481,
+    0.138151865749083321638,
+    0.0160746087093676504695,
+    0.000964011807005165528527,
+    0.275335474764726041141e-4,
+    0.282243172016108031869e-6,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 18 and 44.
+const ERF_INV_IMPL_FN: &[f64] = &[
+    -0.0024978212791898131227,
+    -0.779190719229053954292e-5,
+    0.254723037413027451751e-4,
+    0.162397777342510920873e-5,
+    0.396341011304801168516e-7,
+    0.411632831190944208473e-9,
+    0.145596286718675035587e-11,
+    -0.116765012397184275695e-17,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 18 and 44.
+const ERF_INV_IMPL_FD: &[f64] = &[
+    1.0,
+    0.207123112214422517181,
+    0.0169410838120975906478,
+    0.000690538265622684595676,
+    0.145007359818232637924e-4,
+    0.144437756628144157666e-6,
+    0.509761276599778486139e-9,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x greater than 44.
+const ERF_INV_IMPL_GN: &[f64] = &[
+    -0.000539042911019078575891,
+    -0.28398759004727721098e-6,
+    0.899465114892291446442e-6,
+    0.229345859265920864296e-7,
+    0.225561444863500149219e-9,
+    0.947846627503022684216e-12,
+    0.135880130108924861008e-14,
+    -0.348890393399948882918e-21,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x greater than 44.
+const ERF_INV_IMPL_GD: &[f64] = &[
+    1.0,
+    0.0845746234001899436914,
+    0.00282092984726264681981,
+    0.468292921940894236786e-4,
+    0.399968812193862100054e-6,
+    0.161809290887904476097e-8,
+    0.231558608310259605225e-11,
+];
+
+/// `erf_impl` computes the error function at `z`.
+/// If `inv` is true, `1 - erf` is calculated as opposed to `erf`
+fn erf_impl(z: f64, inv: bool) -> f64 {
+    if z < 0.0 {
+        if !inv {
+            return -erf_impl(-z, false);
+        }
+        if z < -0.5 {
+            return 2.0 - erf_impl(-z, true);
+        }
+        return 1.0 + erf_impl(-z, false);
+    }
+
+    let result = if z < 0.5 {
+        if z < 1e-10 {
+            z * 1.125 + z * 0.003379167095512573896158903121545171688
+        } else {
+            z * 1.125
+                + z * evaluate::polynomial(z, ERF_IMPL_AN) / evaluate::polynomial(z, ERF_IMPL_AD)
+        }
+    } else if z < 110.0 {
+        let (r, b) = if z < 0.75 {
+            (
+                evaluate::polynomial(z - 0.5, ERF_IMPL_BN)
+                    / evaluate::polynomial(z - 0.5, ERF_IMPL_BD),
+                0.3440242112,
+            )
+        } else if z < 1.25 {
+            (
+                evaluate::polynomial(z - 0.75, ERF_IMPL_CN)
+                    / evaluate::polynomial(z - 0.75, ERF_IMPL_CD),
+                0.419990927,
+            )
+        } else if z < 2.25 {
+            (
+                evaluate::polynomial(z - 1.25, ERF_IMPL_DN)
+                    / evaluate::polynomial(z - 1.25, ERF_IMPL_DD),
+                0.4898625016,
+            )
+        } else if z < 3.5 {
+            (
+                evaluate::polynomial(z - 2.25, ERF_IMPL_EN)
+                    / evaluate::polynomial(z - 2.25, ERF_IMPL_ED),
+                0.5317370892,
+            )
+        } else if z < 5.25 {
+            (
+                evaluate::polynomial(z - 3.5, ERF_IMPL_FN)
+                    / evaluate::polynomial(z - 3.5, ERF_IMPL_FD),
+                0.5489973426,
+            )
+        } else if z < 8.0 {
+            (
+                evaluate::polynomial(z - 5.25, ERF_IMPL_GN)
+                    / evaluate::polynomial(z - 5.25, ERF_IMPL_GD),
+                0.5571740866,
+            )
+        } else if z < 11.5 {
+            (
+                evaluate::polynomial(z - 8.0, ERF_IMPL_HN)
+                    / evaluate::polynomial(z - 8.0, ERF_IMPL_HD),
+                0.5609807968,
+            )
+        } else if z < 17.0 {
+            (
+                evaluate::polynomial(z - 11.5, ERF_IMPL_IN)
+                    / evaluate::polynomial(z - 11.5, ERF_IMPL_ID),
+                0.5626493692,
+            )
+        } else if z < 24.0 {
+            (
+                evaluate::polynomial(z - 17.0, ERF_IMPL_JN)
+                    / evaluate::polynomial(z - 17.0, ERF_IMPL_JD),
+                0.5634598136,
+            )
+        } else if z < 38.0 {
+            (
+                evaluate::polynomial(z - 24.0, ERF_IMPL_KN)
+                    / evaluate::polynomial(z - 24.0, ERF_IMPL_KD),
+                0.5638477802,
+            )
+        } else if z < 60.0 {
+            (
+                evaluate::polynomial(z - 38.0, ERF_IMPL_LN)
+                    / evaluate::polynomial(z - 38.0, ERF_IMPL_LD),
+                0.5640528202,
+            )
+        } else if z < 85.0 {
+            (
+                evaluate::polynomial(z - 60.0, ERF_IMPL_MN)
+                    / evaluate::polynomial(z - 60.0, ERF_IMPL_MD),
+                0.5641309023,
+            )
+        } else {
+            (
+                evaluate::polynomial(z - 85.0, ERF_IMPL_NN)
+                    / evaluate::polynomial(z - 85.0, ERF_IMPL_ND),
+                0.5641584396,
+            )
+        };
+        let g = (-z * z).exp() / z;
+        g * b + g * r
+    } else {
+        0.0
+    };
+
+    if inv && z >= 0.5 {
+        result
+    } else if z >= 0.5 || inv {
+        1.0 - result
+    } else {
+        result
+    }
+}
+
+// `erf_inv_impl` computes the inverse error function where
+// `p`,`q`, and `s` are the first, second, and third intermediate
+// parameters respectively
+fn erf_inv_impl(p: f64, q: f64, s: f64) -> f64 {
+    let result = if p <= 0.5 {
+        let y = 0.0891314744949340820313;
+        let g = p * (p + 10.0);
+        let r = evaluate::polynomial(p, ERF_INV_IMPL_AN) / evaluate::polynomial(p, ERF_INV_IMPL_AD);
+        g * y + g * r
+    } else if q >= 0.25 {
+        let y = 2.249481201171875;
+        let g = (-2.0 * q.ln()).sqrt();
+        let xs = q - 0.25;
+        let r =
+            evaluate::polynomial(xs, ERF_INV_IMPL_BN) / evaluate::polynomial(xs, ERF_INV_IMPL_BD);
+        g / (y + r)
+    } else {
+        let x = (-q.ln()).sqrt();
+        if x < 3.0 {
+            let y = 0.807220458984375;
+            let xs = x - 1.125;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_CN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_CD);
+            y * x + r * x
+        } else if x < 6.0 {
+            let y = 0.93995571136474609375;
+            let xs = x - 3.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_DN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_DD);
+            y * x + r * x
+        } else if x < 18.0 {
+            let y = 0.98362827301025390625;
+            let xs = x - 6.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_EN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_ED);
+            y * x + r * x
+        } else if x < 44.0 {
+            let y = 0.99714565277099609375;
+            let xs = x - 18.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_FN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_FD);
+            y * x + r * x
+        } else {
+            let y = 0.99941349029541015625;
+            let xs = x - 44.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_GN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_GD);
+            y * x + r * x
+        }
+    };
+    s * result
+}
diff --git a/candle-core/src/cpu/mod.rs b/candle-core/src/cpu/mod.rs
index 9a8e6317..50afb30f 100644
--- a/candle-core/src/cpu/mod.rs
+++ b/candle-core/src/cpu/mod.rs
@@ -1,3 +1,4 @@
+pub mod erf;
 pub mod kernels;
 
 trait Cpu<const ARR: usize> {
diff --git a/candle-core/src/op.rs b/candle-core/src/op.rs
index 7940739c..26dc6609 100644
--- a/candle-core/src/op.rs
+++ b/candle-core/src/op.rs
@@ -58,6 +58,7 @@ pub enum UnaryOp {
     Sqr,
     Sqrt,
     Gelu,
+    GeluErf,
     Relu,
     Tanh,
 }
@@ -325,6 +326,7 @@ pub(crate) struct Recip;
 pub(crate) struct Sqr;
 pub(crate) struct Sqrt;
 pub(crate) struct Gelu;
+pub(crate) struct GeluErf;
 pub(crate) struct Relu;
 pub(crate) struct Tanh;
 
@@ -621,6 +623,40 @@ impl UnaryOpT for Gelu {
     }
 }
 
+impl UnaryOpT for GeluErf {
+    const NAME: &'static str = "gelu_erf";
+    const KERNEL: &'static str = "ugelu_erf";
+    const V: Self = GeluErf;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        bf16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        f16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        Self::f64(v as f64) as f32
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        (crate::cpu::erf::erf(v / 2f64.sqrt()) + 1.) * 0.5 * v
+    }
+    #[inline(always)]
+    fn u8(_: u8) -> u8 {
+        0
+    }
+    #[inline(always)]
+    fn u32(_: u32) -> u32 {
+        0
+    }
+    #[inline(always)]
+    fn i64(_: i64) -> i64 {
+        0
+    }
+}
+
 impl UnaryOpT for Relu {
     const NAME: &'static str = "relu";
     const KERNEL: &'static str = "urelu";
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index 756fedb2..eafd7002 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -489,6 +489,7 @@ impl Tensor {
     unary_op!(sqr, Sqr);
     unary_op!(sqrt, Sqrt);
     unary_op!(gelu, Gelu);
+    unary_op!(gelu_erf, GeluErf);
     unary_op!(relu, Relu);
 
     /// Retrieves the single scalar value hold in the tensor. If the tensor contains multiple
diff --git a/candle-core/tests/tensor_tests.rs b/candle-core/tests/tensor_tests.rs
index f1c204ea..408f4c55 100644
--- a/candle-core/tests/tensor_tests.rs
+++ b/candle-core/tests/tensor_tests.rs
@@ -1,4 +1,4 @@
-use candle_core::{test_device, DType, Device, IndexOp, Result, Tensor};
+use candle_core::{test_device, test_utils, DType, Device, IndexOp, Result, Tensor};
 
 fn zeros(device: &Device) -> Result<()> {
     let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
@@ -44,6 +44,26 @@ fn clamp(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn unary_op(device: &Device) -> Result<()> {
+    let data = &[[-3f32, 1., 4., -0.1, 0.5], [2.7, -1.8, -0.28, 1.8, 2.8]];
+    let tensor = Tensor::new(data, device)?;
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.gelu()?, 4)?,
+        [
+            [-0.0036, 0.8412, 3.9999, -0.046, 0.3457],
+            [2.6911, -0.0647, -0.1091, 1.7353, 2.7933]
+        ]
+    );
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.gelu_erf()?, 4)?,
+        [
+            [-0.004, 0.8413, 3.9999, -0.046, 0.3457],
+            [2.6906, -0.0647, -0.1091, 1.7353, 2.7928]
+        ]
+    );
+    Ok(())
+}
+
 fn binary_op(device: &Device) -> Result<()> {
     let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
     let tensor1 = Tensor::new(data, device)?;
@@ -908,6 +928,7 @@ test_device!(max, max_cpu, max_gpu);
 test_device!(argmax, argmax_cpu, argmax_gpu);
 test_device!(argmin, argmin_cpu, argmin_gpu);
 test_device!(transpose, transpose_cpu, transpose_gpu);
+test_device!(unary_op, unary_op_cpu, unary_op_gpu);
 test_device!(binary_op, binary_op_cpu, binary_op_gpu);
 test_device!(embeddings, embeddings_cpu, embeddings_gpu);
 test_device!(cmp, cmp_cpu, cmp_gpu);
diff --git a/candle-kernels/src/cuda_utils.cuh b/candle-kernels/src/cuda_utils.cuh
index 4096d2d1..8e46a07c 100644
--- a/candle-kernels/src/cuda_utils.cuh
+++ b/candle-kernels/src/cuda_utils.cuh
@@ -129,6 +129,10 @@ __device__ __forceinline__ float powg(float a, float b) { return powf(a, b); }
 __device__ __forceinline__ double powg(double a, double b) { return pow(a, b); }
 __device__ __forceinline__ float tanhg(float a) { return tanhf(a); }
 __device__ __forceinline__ double tanhg(double a) { return tanh(a); }
+__device__ __forceinline__ float erfg(float a) { return erff(a); }
+__device__ __forceinline__ double erfg(double a) { return erf(a); }
+__device__ __forceinline__ float normcdfg(float a) { return normcdff(a); }
+__device__ __forceinline__ double normcdfg(double a) { return normcdf(a); }
 __device__ __forceinline__ float maxg(float a, float b) { return fmaxf(a, b); }
 __device__ __forceinline__ double maxg(double a, double b) { return fmax(a, b); }
 __device__ __forceinline__ float ming(float a, float b) { return fminf(a, b); }
@@ -157,6 +161,8 @@ __device__ __forceinline__ __half sing(__half a) { return hsin(a); }
 __device__ __forceinline__ __half recipg(__half a) { __half one = 1.0; return one / a; }
 __device__ __forceinline__ __half maxg(__half a, __half b) { return __hmax_nan(a, b); }
 __device__ __forceinline__ __half tanhg(__half a) { return __float2half(tanhf(__half2float(a))); }
+__device__ __forceinline__ __half erfg(__half a) { return __float2half(erff(__half2float(a))); }
+__device__ __forceinline__ __half normcdfg(__half a) { return __float2half(normcdff(__half2float(a))); }
 __device__ __forceinline__ __half ming(__half a, __half b) { return __hmin_nan(a, b); }
 __device__ __forceinline__ __half logg(__half a) { return hlog(a); }
 __device__ __forceinline__ __half expg(__half a) { return hexp(a); }
@@ -173,6 +179,8 @@ __device__ __forceinline__ __nv_bfloat16 sing(__nv_bfloat16 a) { return hsin(a);
 __device__ __forceinline__ __nv_bfloat16 recipg(__nv_bfloat16 a) { __nv_bfloat16 one = 1.0; return one / a; }
 __device__ __forceinline__ __nv_bfloat16 maxg(__nv_bfloat16 a, __nv_bfloat16 b) { return __hmax_nan(a, b); }
 __device__ __forceinline__ __nv_bfloat16 tanhg(__nv_bfloat16 a) { return __float2bfloat16(tanhf(__bfloat162float(a))); }
+__device__ __forceinline__ __nv_bfloat16 erfg(__nv_bfloat16 a) { return __float2bfloat16(erff(__bfloat162float(a))); }
+__device__ __forceinline__ __nv_bfloat16 normcdfg(__nv_bfloat16 a) { return __float2bfloat16(normcdff(__bfloat162float(a))); }
 __device__ __forceinline__ __nv_bfloat16 ming(__nv_bfloat16 a, __nv_bfloat16 b) { return __hmin_nan(a, b); }
 __device__ __forceinline__ __nv_bfloat16 logg(__nv_bfloat16 a) { return hlog(a); }
 __device__ __forceinline__ __nv_bfloat16 expg(__nv_bfloat16 a) { return hexp(a); }
diff --git a/candle-kernels/src/unary.cu b/candle-kernels/src/unary.cu
index c6142a03..105d8c3a 100644
--- a/candle-kernels/src/unary.cu
+++ b/candle-kernels/src/unary.cu
@@ -28,6 +28,11 @@ extern "C" __global__ void FN_NAME( \
     } \
 } \
 
+template<typename T>
+__device__ __forceinline__ T gelu_erf_fwd(T x) {
+  return x * normcdfg(x);
+}
+
 template<typename T>
 __device__ __forceinline__ T gelu_fwd(T x) {
     T x_sq = x * x;
@@ -86,10 +91,13 @@ UNARY_OP(__nv_bfloat16, ulog_bf16, logg(x))
 UNARY_OP(__nv_bfloat16, usin_bf16, sing(x))
 UNARY_OP(__nv_bfloat16, ucos_bf16, cosg(x))
 UNARY_OP(__nv_bfloat16, utanh_bf16, tanhg(x))
+UNARY_OP(__nv_bfloat16, uerf_bf16, erfg(x))
+UNARY_OP(__nv_bfloat16, unormcdf_bf16, normcdfg(x))
 UNARY_OP(__nv_bfloat16, uabs_bf16, absg(x))
 UNARY_OP(__nv_bfloat16, usqr_bf16, x*x)
 UNARY_OP(__nv_bfloat16, usqrt_bf16, sqrtg(x))
 UNARY_OP(__nv_bfloat16, ugelu_bf16, gelu_fwd(x))
+UNARY_OP(__nv_bfloat16, ugelu_erf_bf16, gelu_erf_fwd(x))
 UNARY_OP(__nv_bfloat16, urelu_bf16, relu_fwd(x))
 UNARY_OP1(__nv_bfloat16, uelu_bf16, elu_fwd(x, param))
 UNARY_OP1(__nv_bfloat16, upowf_bf16, powg(x, param))
@@ -104,10 +112,13 @@ UNARY_OP(__half, ulog_f16, logg(x))
 UNARY_OP(__half, usin_f16, sing(x))
 UNARY_OP(__half, ucos_f16, cosg(x))
 UNARY_OP(__half, utanh_f16, tanhg(x))
+UNARY_OP(__half, uerf_f16, erfg(x))
+UNARY_OP(__half, unormcdf_f16, normcdfg(x))
 UNARY_OP(__half, uabs_f16, absg(x))
 UNARY_OP(__half, usqr_f16, x*x)
 UNARY_OP(__half, usqrt_f16, sqrtg(x))
 UNARY_OP(__half, ugelu_f16, gelu_fwd(x))
+UNARY_OP(__half, ugelu_erf_f16, gelu_erf_fwd(x))
 UNARY_OP(__half, urelu_f16, relu_fwd(x))
 UNARY_OP1(__half, uelu_f16, elu_fwd(x, param))
 UNARY_OP1(__half, upowf_f16, powg(x, param))
@@ -131,6 +142,10 @@ UNARY_OP(float, ucos_f32, cosg(x))
 UNARY_OP(double, ucos_f64, cosg(x))
 UNARY_OP(float, utanh_f32, tanhg(x))
 UNARY_OP(double, utanh_f64, tanhg(x))
+UNARY_OP(float, uerf_f32, erfg(x))
+UNARY_OP(double, uerf_f64, erfg(x))
+UNARY_OP(float, unormcdf_f32, normcdfg(x))
+UNARY_OP(double, unormcdf_f64, normcdfg(x))
 UNARY_OP(float, uabs_f32, absg(x))
 UNARY_OP(double, uabs_f64, absg(x))
 UNARY_OP(float, usqr_f32, x*x)
@@ -139,6 +154,8 @@ UNARY_OP(float, usqrt_f32, sqrtg(x))
 UNARY_OP(double, usqrt_f64, sqrtg(x))
 UNARY_OP(float, ugelu_f32, gelu_fwd(x))
 UNARY_OP(double, ugelu_f64, gelu_fwd(x))
+UNARY_OP(float, ugelu_erf_f32, gelu_erf_fwd(x))
+UNARY_OP(double, ugelu_erf_f64, gelu_erf_fwd(x))
 UNARY_OP(float, urelu_f32, relu_fwd(x))
 UNARY_OP(double, urelu_f64, relu_fwd(x))
 UNARY_OP1(float, uelu_f32, elu_fwd(x, param))

From 8696f64baef3da878a6974b1fa82cba406789f13 Mon Sep 17 00:00:00 2001
From: Juarez Bochi <juarez.bochi@grammarly.com>
Date: Tue, 19 Sep 2023 12:36:15 -0700
Subject: [PATCH 131/150] Fix T5 kv cache (#899)

* Fix T5 kv cache

* Add argument for decoder prompt

* Fix range
---
 candle-examples/examples/t5/main.rs  | 26 ++++++++++++++++++++------
 candle-transformers/src/models/t5.rs |  7 ++++++-
 2 files changed, 26 insertions(+), 7 deletions(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 117aed13..f5972754 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -46,12 +46,16 @@ struct Args {
 
     // Enable/disable decoding.
     #[arg(long, default_value = "false")]
-    use_cache: bool,
+    disable_cache: bool,
 
     /// Use this prompt, otherwise compute sentence similarities.
     #[arg(long)]
     prompt: Option<String>,
 
+    /// If set along with --decode, will use this prompt to initialize the decoder.
+    #[arg(long)]
+    decoder_prompt: Option<String>,
+
     /// L2 normalization for embeddings.
     #[arg(long, default_value = "true")]
     normalize_embeddings: bool,
@@ -116,7 +120,7 @@ impl T5ModelBuilder {
         };
         let config = std::fs::read_to_string(config_filename)?;
         let mut config: t5::Config = serde_json::from_str(&config)?;
-        config.use_cache = args.use_cache;
+        config.use_cache = !args.disable_cache;
         let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
         Ok((
             Self {
@@ -170,6 +174,16 @@ fn main() -> Result<()> {
             } else {
                 let mut model = builder.build_conditional_generation()?;
                 let mut output_token_ids = [builder.config.pad_token_id as u32].to_vec();
+                if let Some(decoder_prompt) = &args.decoder_prompt {
+                    print!("{decoder_prompt}");
+                    output_token_ids.extend(
+                        tokenizer
+                            .encode(decoder_prompt.to_string(), false)
+                            .map_err(E::msg)?
+                            .get_ids()
+                            .to_vec(),
+                    );
+                }
                 let temperature = if args.temperature <= 0. {
                     None
                 } else {
@@ -195,11 +209,11 @@ fn main() -> Result<()> {
                     let logits = if args.repeat_penalty == 1. {
                         logits
                     } else {
-                        let start_at = tokens.len().saturating_sub(args.repeat_last_n);
+                        let start_at = output_token_ids.len().saturating_sub(args.repeat_last_n);
                         candle_transformers::utils::apply_repeat_penalty(
                             &logits,
                             args.repeat_penalty,
-                            &tokens[start_at..],
+                            &output_token_ids[start_at..],
                         )?
                     };
 
@@ -217,8 +231,8 @@ fn main() -> Result<()> {
                 let dt = start.elapsed();
                 println!(
                     "\n{} tokens generated ({:.2} token/s)\n",
-                    tokens.len(),
-                    tokens.len() as f64 / dt.as_secs_f64(),
+                    output_token_ids.len(),
+                    output_token_ids.len() as f64 / dt.as_secs_f64(),
                 );
             }
         }
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 2ffc2ee1..b1f3a3aa 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -348,9 +348,14 @@ impl T5Attention {
                 None => (scores, None),
                 Some(relative_attention_bias) => {
                     // This only handles the bidirectional case.
+                    let kv_len = k.dim(2)?;
+                    let (q_start, q_end) = match self.use_cache {
+                        true => ((kv_len - q_len) as u32, kv_len as u32),
+                        false => (0_u32, kv_len as u32),
+                    };
                     let num_buckets = self.relative_attention_num_buckets as u32 / 2;
                     let max_exact = num_buckets / 2;
-                    let relative_position = (0..q_len as u32)
+                    let relative_position = (q_start..q_end)
                         .map(|i| {
                             (0..kv_len as u32)
                                 .map(|j| {

From 7ad82b87e4c8dbbe8e6d84bcd05007c54675c310 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Radam=C3=A9s=20Ajna?= <radamajna@gmail.com>
Date: Tue, 19 Sep 2023 13:31:37 -0700
Subject: [PATCH 132/150] BERT Wasm (#902)

* implement wasm module

* add example to workspace

* add UI explore semantic similiarity

* change status messages

* formatting

* minor changes
---
 Cargo.toml                                 |   6 +-
 candle-wasm-examples/bert/Cargo.toml       |  33 ++
 candle-wasm-examples/bert/README.md        |  26 ++
 candle-wasm-examples/bert/bertWorker.js    |  77 +++++
 candle-wasm-examples/bert/build-lib.sh     |   2 +
 candle-wasm-examples/bert/lib-example.html | 368 +++++++++++++++++++++
 candle-wasm-examples/bert/src/bin/m.rs     |  92 ++++++
 candle-wasm-examples/bert/src/lib.rs       |  20 ++
 candle-wasm-examples/bert/utils.js         |  99 ++++++
 9 files changed, 719 insertions(+), 4 deletions(-)
 create mode 100644 candle-wasm-examples/bert/Cargo.toml
 create mode 100644 candle-wasm-examples/bert/README.md
 create mode 100644 candle-wasm-examples/bert/bertWorker.js
 create mode 100644 candle-wasm-examples/bert/build-lib.sh
 create mode 100644 candle-wasm-examples/bert/lib-example.html
 create mode 100644 candle-wasm-examples/bert/src/bin/m.rs
 create mode 100644 candle-wasm-examples/bert/src/lib.rs
 create mode 100644 candle-wasm-examples/bert/utils.js

diff --git a/Cargo.toml b/Cargo.toml
index 3a5763a1..6cbbf00f 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -11,11 +11,9 @@ members = [
     "candle-wasm-examples/segment-anything",
     "candle-wasm-examples/whisper",
     "candle-wasm-examples/yolo",
+    "candle-wasm-examples/bert",
 ]
-exclude = [
-    "candle-flash-attn",
-    "candle-kernels",
-]
+exclude = ["candle-flash-attn", "candle-kernels"]
 resolver = "2"
 
 [workspace.package]
diff --git a/candle-wasm-examples/bert/Cargo.toml b/candle-wasm-examples/bert/Cargo.toml
new file mode 100644
index 00000000..81a043de
--- /dev/null
+++ b/candle-wasm-examples/bert/Cargo.toml
@@ -0,0 +1,33 @@
+[package]
+name = "candle-wasm-example-bert"
+version.workspace = true
+edition.workspace = true
+description.workspace = true
+repository.workspace = true
+keywords.workspace = true
+categories.workspace = true
+license.workspace = true
+
+[dependencies]
+candle = { path = "../../candle-core", version = "0.2.2", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.2.2" }
+candle-transformers = { path = "../../candle-transformers", version = "0.2.2" }
+num-traits = { workspace = true }
+tokenizers = { workspace = true, features = ["unstable_wasm"] }
+
+# App crates.
+anyhow = { workspace = true }
+byteorder = { workspace = true }
+log = { workspace = true }
+rand = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
+safetensors = { workspace = true }
+
+# Wasm specific crates.
+console_error_panic_hook = "0.1.7"
+getrandom = { version = "0.2", features = ["js"] }
+gloo = "0.8"
+js-sys = "0.3.64"
+wasm-bindgen = "0.2.87"
+serde-wasm-bindgen = "0.6.0"
diff --git a/candle-wasm-examples/bert/README.md b/candle-wasm-examples/bert/README.md
new file mode 100644
index 00000000..c34d33cc
--- /dev/null
+++ b/candle-wasm-examples/bert/README.md
@@ -0,0 +1,26 @@
+## Running BERT with Candle and WASM
+
+Here, we provide two examples of how to run Bert using a Candle-compiled WASM binary and runtime.
+
+### Vanilla JS and WebWorkers
+
+To build and test the UI made in Vanilla JS and WebWorkers, first we need to build the WASM library:
+
+```bash
+sh build-lib.sh
+```
+
+This will bundle the library under `./build` and we can import it inside our WebWorker like a normal JS module:
+
+```js
+import init, { Model } from "./build/m.js";
+```
+
+The full example can be found under `./lib-example.html`. All needed assets are fetched from the web, so no need to download anything.
+Finally, you can preview the example by running a local HTTP server. For example:
+
+```bash
+python -m http.server
+```
+
+Then open `http://localhost:8000/lib-example.html` in your browser.
diff --git a/candle-wasm-examples/bert/bertWorker.js b/candle-wasm-examples/bert/bertWorker.js
new file mode 100644
index 00000000..fd796c2b
--- /dev/null
+++ b/candle-wasm-examples/bert/bertWorker.js
@@ -0,0 +1,77 @@
+//load Candle Bert Module wasm module
+import init, { Model } from "./build/m.js";
+
+async function fetchArrayBuffer(url) {
+  const cacheName = "bert-candle-cache";
+  const cache = await caches.open(cacheName);
+  const cachedResponse = await cache.match(url);
+  if (cachedResponse) {
+    const data = await cachedResponse.arrayBuffer();
+    return new Uint8Array(data);
+  }
+  const res = await fetch(url, { cache: "force-cache" });
+  cache.put(url, res.clone());
+  return new Uint8Array(await res.arrayBuffer());
+}
+class Bert {
+  static instance = {};
+
+  static async getInstance(weightsURL, tokenizerURL, configURL, modelID) {
+    if (!this.instance[modelID]) {
+      await init();
+
+      self.postMessage({ status: "loading", message: "Loading Model" });
+      const [weightsArrayU8, tokenizerArrayU8, mel_filtersArrayU8] =
+        await Promise.all([
+          fetchArrayBuffer(weightsURL),
+          fetchArrayBuffer(tokenizerURL),
+          fetchArrayBuffer(configURL),
+        ]);
+
+      this.instance[modelID] = new Model(
+        weightsArrayU8,
+        tokenizerArrayU8,
+        mel_filtersArrayU8
+      );
+    } else {
+      self.postMessage({ status: "ready", message: "Model Already Loaded" });
+    }
+    return this.instance[modelID];
+  }
+}
+
+self.addEventListener("message", async (event) => {
+  const {
+    weightsURL,
+    tokenizerURL,
+    configURL,
+    modelID,
+    sentences,
+    normalize = true,
+  } = event.data;
+  try {
+    self.postMessage({ status: "ready", message: "Starting Bert Model" });
+    const model = await Bert.getInstance(
+      weightsURL,
+      tokenizerURL,
+      configURL,
+      modelID
+    );
+    self.postMessage({
+      status: "embedding",
+      message: "Calculating Embeddings",
+    });
+    const output = model.get_embeddings({
+      sentences: sentences,
+      normalize_embeddings: normalize,
+    });
+
+    self.postMessage({
+      status: "complete",
+      message: "complete",
+      output: output.data,
+    });
+  } catch (e) {
+    self.postMessage({ error: e });
+  }
+});
diff --git a/candle-wasm-examples/bert/build-lib.sh b/candle-wasm-examples/bert/build-lib.sh
new file mode 100644
index 00000000..b0ebb182
--- /dev/null
+++ b/candle-wasm-examples/bert/build-lib.sh
@@ -0,0 +1,2 @@
+cargo build --target wasm32-unknown-unknown --release
+wasm-bindgen ../../target/wasm32-unknown-unknown/release/m.wasm --out-dir build --target web
diff --git a/candle-wasm-examples/bert/lib-example.html b/candle-wasm-examples/bert/lib-example.html
new file mode 100644
index 00000000..d10ea1db
--- /dev/null
+++ b/candle-wasm-examples/bert/lib-example.html
@@ -0,0 +1,368 @@
+<html>
+  <head>
+    <meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
+    <title>Candle Bert</title>
+  </head>
+  <body></body>
+</html>
+
+<!DOCTYPE html>
+<html>
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <style>
+      @import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
+      html,
+      body {
+        font-family: "Source Sans 3", sans-serif;
+      }
+    </style>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <script type="module" src="./code.js"></script>
+    <script type="module">
+      import { hcl } from "https://cdn.skypack.dev/d3-color@3";
+      import { interpolateReds } from "https://cdn.skypack.dev/d3-scale-chromatic@3";
+      import { scaleLinear } from "https://cdn.skypack.dev/d3-scale@4";
+      import {
+        getModelInfo,
+        getEmbeddings,
+        getWikiText,
+        cosineSimilarity,
+      } from "./utils.js";
+
+      const bertWorker = new Worker("./bertWorker.js", {
+        type: "module",
+      });
+
+      const inputContainerEL = document.querySelector("#input-container");
+      const textAreaEl = document.querySelector("#input-area");
+      const outputAreaEl = document.querySelector("#output-area");
+      const formEl = document.querySelector("#form");
+      const searchInputEl = document.querySelector("#search-input");
+      const formWikiEl = document.querySelector("#form-wiki");
+      const searchWikiEl = document.querySelector("#search-wiki");
+      const outputStatusEl = document.querySelector("#output-status");
+      const modelSelectEl = document.querySelector("#model");
+
+      const sentencesRegex =
+        /(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<![A-Z]\.)(?<=\.|\?)\s/gm;
+
+      let sentenceEmbeddings = [];
+      let currInputText = "";
+      let isCalculating = false;
+
+      function toggleTextArea(state) {
+        if (state) {
+          textAreaEl.hidden = false;
+          textAreaEl.focus();
+        } else {
+          textAreaEl.hidden = true;
+        }
+      }
+      inputContainerEL.addEventListener("focus", (e) => {
+        toggleTextArea(true);
+      });
+      textAreaEl.addEventListener("blur", (e) => {
+        toggleTextArea(false);
+      });
+      textAreaEl.addEventListener("focusout", (e) => {
+        toggleTextArea(false);
+        if (currInputText === textAreaEl.value || isCalculating) return;
+        populateOutputArea(textAreaEl.value);
+        calculateEmbeddings(textAreaEl.value);
+      });
+
+      modelSelectEl.addEventListener("change", (e) => {
+        if (currInputText === "" || isCalculating) return;
+        populateOutputArea(textAreaEl.value);
+        calculateEmbeddings(textAreaEl.value);
+      });
+
+      function populateOutputArea(text) {
+        currInputText = text;
+        const sentences = text.split(sentencesRegex);
+
+        outputAreaEl.innerHTML = "";
+        for (const [id, sentence] of sentences.entries()) {
+          const sentenceEl = document.createElement("span");
+          sentenceEl.id = `sentence-${id}`;
+          sentenceEl.innerText = sentence + " ";
+          outputAreaEl.appendChild(sentenceEl);
+        }
+      }
+      formEl.addEventListener("submit", async (e) => {
+        e.preventDefault();
+        if (isCalculating || currInputText === "") return;
+        toggleInputs(true);
+        const modelID = modelSelectEl.value;
+        const { modelURL, tokenizerURL, configURL, search_prefix } =
+          getModelInfo(modelID);
+
+        const text = searchInputEl.value;
+        const query = search_prefix + searchInputEl.value;
+        outputStatusEl.classList.remove("invisible");
+        outputStatusEl.innerText = "Calculating embeddings for query...";
+        isCalculating = true;
+        const out = await getEmbeddings(
+          bertWorker,
+          modelURL,
+          tokenizerURL,
+          configURL,
+          modelID,
+          [query]
+        );
+        outputStatusEl.classList.add("invisible");
+        const queryEmbeddings = out.output[0];
+        // calculate cosine similarity with all sentences given the query
+        const distances = sentenceEmbeddings
+          .map((embedding, id) => ({
+            id,
+            similarity: cosineSimilarity(queryEmbeddings, embedding),
+          }))
+          .sort((a, b) => b.similarity - a.similarity)
+          // getting top 10 most similar sentences
+          .slice(0, 10);
+
+        const colorScale = scaleLinear()
+          .domain([
+            distances[distances.length - 1].similarity,
+            distances[0].similarity,
+          ])
+          .range([0, 1])
+          .interpolate(() => interpolateReds);
+        outputAreaEl.querySelectorAll("span").forEach((el) => {
+          el.style.color = "unset";
+          el.style.backgroundColor = "unset";
+        });
+        distances.forEach((d) => {
+          const el = outputAreaEl.querySelector(`#sentence-${d.id}`);
+          const color = colorScale(d.similarity);
+          const fontColor = hcl(color).l < 70 ? "white" : "black";
+          el.style.color = fontColor;
+          el.style.backgroundColor = color;
+        });
+
+        outputAreaEl
+          .querySelector(`#sentence-${distances[0].id}`)
+          .scrollIntoView({
+            behavior: "smooth",
+            block: "center",
+            inline: "nearest",
+          });
+
+        isCalculating = false;
+        toggleInputs(false);
+      });
+      async function calculateEmbeddings(text) {
+        isCalculating = true;
+        toggleInputs(true);
+        const modelID = modelSelectEl.value;
+        const { modelURL, tokenizerURL, configURL, document_prefix } =
+          getModelInfo(modelID);
+
+        const sentences = text.split(sentencesRegex);
+        const allEmbeddings = [];
+        outputStatusEl.classList.remove("invisible");
+        for (const [id, sentence] of sentences.entries()) {
+          const query = document_prefix + sentence;
+          outputStatusEl.innerText = `Calculating embeddings: sentence ${
+            id + 1
+          } of ${sentences.length}`;
+          const embeddings = await getEmbeddings(
+            bertWorker,
+            modelURL,
+            tokenizerURL,
+            configURL,
+            modelID,
+            [query],
+            updateStatus
+          );
+          allEmbeddings.push(embeddings);
+        }
+        outputStatusEl.classList.add("invisible");
+        sentenceEmbeddings = allEmbeddings.map((e) => e.output[0]);
+        isCalculating = false;
+        toggleInputs(false);
+      }
+
+      function updateStatus(data) {
+        if ("status" in data) {
+          if (data.status === "loading") {
+            outputStatusEl.innerText = data.message;
+            outputStatusEl.classList.remove("invisible");
+          }
+        }
+      }
+      function toggleInputs(state) {
+        const interactive = document.querySelectorAll(".interactive");
+        interactive.forEach((el) => {
+          if (state) {
+            el.disabled = true;
+          } else {
+            el.disabled = false;
+          }
+        });
+      }
+
+      searchWikiEl.addEventListener("input", () => {
+        searchWikiEl.setCustomValidity("");
+      });
+
+      formWikiEl.addEventListener("submit", async (e) => {
+        e.preventDefault();
+        if ("example" in e.submitter.dataset) {
+          searchWikiEl.value = e.submitter.innerText;
+        }
+        const text = searchWikiEl.value;
+
+        if (isCalculating || text === "") return;
+        try {
+          const wikiText = await getWikiText(text);
+          searchWikiEl.setCustomValidity("");
+          textAreaEl.innerHTML = wikiText;
+          populateOutputArea(wikiText);
+          calculateEmbeddings(wikiText);
+          searchWikiEl.value = "";
+        } catch {
+          searchWikiEl.setCustomValidity("Invalid Wikipedia article name");
+          searchWikiEl.reportValidity();
+        }
+      });
+    </script>
+  </head>
+  <body class="container max-w-4xl mx-auto p-4">
+    <main class="grid grid-cols-1 gap-5 relative">
+      <span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
+      <div>
+        <h1 class="text-5xl font-bold">Candle BERT</h1>
+        <h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
+        <p class="max-w-lg">
+          Running sentence embeddings and similarity search in the browser using
+          the Bert Model written with
+          <a
+            href="https://github.com/huggingface/candle/"
+            target="_blank"
+            class="underline hover:text-blue-500 hover:no-underline"
+            >Candle
+          </a>
+          and compiled to Wasm. Embeddings models from are from
+          <a
+            href="https://huggingface.co/sentence-transformers/"
+            target="_blank"
+            class="underline hover:text-blue-500 hover:no-underline"
+          >
+            Sentence Transformers
+          </a>
+          and
+          <a
+            href="https://huggingface.co/intfloat/"
+            target="_blank"
+            class="underline hover:text-blue-500 hover:no-underline"
+          >
+            Liang Wang - e5 Models
+          </a>
+        </p>
+      </div>
+
+      <div>
+        <label for="model" class="font-medium block">Models Options: </label>
+        <select
+          id="model"
+          class="border-2 border-gray-500 rounded-md font-light interactive disabled:cursor-not-allowed w-full max-w-max"
+        >
+          <option value="intfloat_e5_small_v2" selected>
+            intfloat/e5-small-v2 (133 MB)
+          </option>
+          <option value="intfloat_e5_base_v2">
+            intfloat/e5-base-v2 (438 MB)
+          </option>
+          <option value="intfloat_multilingual_e5_small">
+            intfloat/multilingual-e5-small (471 MB)
+          </option>
+          <option value="sentence_transformers_all_MiniLM_L6_v2">
+            sentence-transformers/all-MiniLM-L6-v2 (90.9 MB)
+          </option>
+          <option value="sentence_transformers_all_MiniLM_L12_v2">
+            sentence-transformers/all-MiniLM-L12-v2 (133 MB)
+          </option>
+        </select>
+      </div>
+      <div>
+        <h3 class="font-medium">Examples:</h3>
+        <form
+          id="form-wiki"
+          class="flex text-xs rounded-md justify-between w-min gap-3"
+        >
+          <input type="submit" hidden />
+
+          <button data-example class="disabled:cursor-not-allowed interactive">
+            Pizza
+          </button>
+          <button data-example class="disabled:cursor-not-allowed interactive">
+            Paris
+          </button>
+          <button data-example class="disabled:cursor-not-allowed interactive">
+            Physics
+          </button>
+          <input
+            type="text"
+            id="search-wiki"
+            title="Search Wikipedia article by title"
+            class="font-light py-0 mx-1 resize-none outline-none w-32 disabled:cursor-not-allowed interactive"
+            placeholder="Load Wikipedia article..."
+          />
+          <button
+            title="Search Wikipedia article and load into input"
+            class="bg-gray-700 hover:bg-gray-800 text-white font-normal px-2 py-1 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"
+          >
+            Load
+          </button>
+        </form>
+      </div>
+      <form
+        id="form"
+        class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center"
+      >
+        <input type="submit" hidden />
+        <input
+          type="text"
+          id="search-input"
+          class="font-light w-full px-3 py-2 mx-1 resize-none outline-none interactive disabled:cursor-not-allowed"
+          placeholder="Search query here..."
+        />
+        <button
+          class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"
+        >
+          Search
+        </button>
+      </form>
+      <div>
+        <h3 class="font-medium">Input text:</h3>
+        <div class="flex justify-between items-center">
+          <div class="rounded-md inline text-xs">
+            <span id="output-status" class="m-auto font-light invisible"
+              >C</span
+            >
+          </div>
+        </div>
+        <div
+          id="input-container"
+          tabindex="0"
+          class="min-h-[250px] bg-slate-100 text-gray-500 rounded-md p-4 flex flex-col gap-2 relative"
+        >
+          <textarea
+            id="input-area"
+            hidden
+            value=""
+            placeholder="Input text to perform semantic similarity search..."
+            class="flex-1 resize-none outline-none left-0 right-0 top-0 bottom-0 m-4 absolute interactive disabled:invisible"
+          ></textarea>
+          <p id="output-area" class="grid-rows-2">
+            Input text to perform semantic similarity search...
+          </p>
+        </div>
+      </div>
+    </main>
+  </body>
+</html>
diff --git a/candle-wasm-examples/bert/src/bin/m.rs b/candle-wasm-examples/bert/src/bin/m.rs
new file mode 100644
index 00000000..f5521abd
--- /dev/null
+++ b/candle-wasm-examples/bert/src/bin/m.rs
@@ -0,0 +1,92 @@
+use candle::{DType, Device, Tensor};
+use candle_nn::VarBuilder;
+use candle_transformers::models::bert::{BertModel, Config};
+use candle_wasm_example_bert::console_log;
+use tokenizers::{PaddingParams, Tokenizer};
+use wasm_bindgen::prelude::*;
+
+#[wasm_bindgen]
+pub struct Model {
+    bert: BertModel,
+    tokenizer: Tokenizer,
+}
+
+#[wasm_bindgen]
+impl Model {
+    #[wasm_bindgen(constructor)]
+    pub fn load(weights: Vec<u8>, tokenizer: Vec<u8>, config: Vec<u8>) -> Result<Model, JsError> {
+        console_error_panic_hook::set_once();
+        console_log!("loading model");
+        let device = &Device::Cpu;
+        let weights = safetensors::tensor::SafeTensors::deserialize(&weights)?;
+        let vb = VarBuilder::from_safetensors(vec![weights], DType::F64, device);
+        let config: Config = serde_json::from_slice(&config)?;
+        let tokenizer =
+            Tokenizer::from_bytes(&tokenizer).map_err(|m| JsError::new(&m.to_string()))?;
+        let bert = BertModel::load(vb, &config)?;
+
+        Ok(Self { bert, tokenizer })
+    }
+
+    pub fn get_embeddings(&mut self, input: JsValue) -> Result<JsValue, JsError> {
+        let input: Params =
+            serde_wasm_bindgen::from_value(input).map_err(|m| JsError::new(&m.to_string()))?;
+        let sentences = input.sentences;
+        let normalize_embeddings = input.normalize_embeddings;
+
+        let device = &Device::Cpu;
+        if let Some(pp) = self.tokenizer.get_padding_mut() {
+            pp.strategy = tokenizers::PaddingStrategy::BatchLongest
+        } else {
+            let pp = PaddingParams {
+                strategy: tokenizers::PaddingStrategy::BatchLongest,
+                ..Default::default()
+            };
+            self.tokenizer.with_padding(Some(pp));
+        }
+        let tokens = self
+            .tokenizer
+            .encode_batch(sentences.to_vec(), true)
+            .map_err(|m| JsError::new(&m.to_string()))?;
+
+        let token_ids: Vec<Tensor> = tokens
+            .iter()
+            .map(|tokens| {
+                let tokens = tokens.get_ids().to_vec();
+                Tensor::new(tokens.as_slice(), device)
+            })
+            .collect::<Result<Vec<_>, _>>()?;
+
+        let token_ids = Tensor::stack(&token_ids, 0)?;
+        let token_type_ids = token_ids.zeros_like()?;
+        console_log!("running inference on batch {:?}", token_ids.shape());
+        let embeddings = self.bert.forward(&token_ids, &token_type_ids)?;
+        console_log!("generated embeddings {:?}", embeddings.shape());
+        // Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
+        let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
+        let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
+        let embeddings = if normalize_embeddings {
+            embeddings.broadcast_div(&embeddings.sqr()?.sum_keepdim(1)?.sqrt()?)?
+        } else {
+            embeddings
+        };
+        let embeddings_data = embeddings.to_vec2()?;
+        Ok(serde_wasm_bindgen::to_value(&Embeddings {
+            data: embeddings_data,
+        })?)
+    }
+}
+
+#[derive(serde::Serialize, serde::Deserialize)]
+struct Embeddings {
+    data: Vec<Vec<f64>>,
+}
+
+#[derive(serde::Serialize, serde::Deserialize)]
+pub struct Params {
+    sentences: Vec<String>,
+    normalize_embeddings: bool,
+}
+fn main() {
+    console_error_panic_hook::set_once();
+}
diff --git a/candle-wasm-examples/bert/src/lib.rs b/candle-wasm-examples/bert/src/lib.rs
new file mode 100644
index 00000000..1e3657be
--- /dev/null
+++ b/candle-wasm-examples/bert/src/lib.rs
@@ -0,0 +1,20 @@
+use candle_transformers::models::bert;
+use wasm_bindgen::prelude::*;
+
+pub use bert::{BertModel, Config, DTYPE};
+pub use tokenizers::{PaddingParams, Tokenizer};
+
+#[wasm_bindgen]
+extern "C" {
+    // Use `js_namespace` here to bind `console.log(..)` instead of just
+    // `log(..)`
+    #[wasm_bindgen(js_namespace = console)]
+    pub fn log(s: &str);
+}
+
+#[macro_export]
+macro_rules! console_log {
+    // Note that this is using the `log` function imported above during
+    // `bare_bones`
+    ($($t:tt)*) => ($crate::log(&format_args!($($t)*).to_string()))
+}
diff --git a/candle-wasm-examples/bert/utils.js b/candle-wasm-examples/bert/utils.js
new file mode 100644
index 00000000..9d8bd7bd
--- /dev/null
+++ b/candle-wasm-examples/bert/utils.js
@@ -0,0 +1,99 @@
+export async function getEmbeddings(
+  worker,
+  weightsURL,
+  tokenizerURL,
+  configURL,
+  modelID,
+  sentences,
+  updateStatus = null
+) {
+  return new Promise((resolve, reject) => {
+    worker.postMessage({
+      weightsURL,
+      tokenizerURL,
+      configURL,
+      modelID,
+      sentences,
+    });
+    function messageHandler(event) {
+      if ("error" in event.data) {
+        worker.removeEventListener("message", messageHandler);
+        reject(new Error(event.data.error));
+      }
+      if (event.data.status === "complete") {
+        worker.removeEventListener("message", messageHandler);
+        resolve(event.data);
+      }
+      if (updateStatus) updateStatus(event.data);
+    }
+    worker.addEventListener("message", messageHandler);
+  });
+}
+
+const MODELS = {
+  intfloat_e5_small_v2: {
+    base_url: "https://huggingface.co/intfloat/e5-small-v2/resolve/main/",
+    search_prefix: "query: ",
+    document_prefix: "passage: ",
+  },
+  intfloat_e5_base_v2: {
+    base_url: "https://huggingface.co/intfloat/e5-base-v2/resolve/main/",
+    search_prefix: "query: ",
+    document_prefix: "passage:",
+  },
+  intfloat_multilingual_e5_small: {
+    base_url:
+      "https://huggingface.co/intfloat/multilingual-e5-small/resolve/main/",
+    search_prefix: "query: ",
+    document_prefix: "passage: ",
+  },
+  sentence_transformers_all_MiniLM_L6_v2: {
+    base_url:
+      "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/resolve/refs%2Fpr%2F21/",
+    search_prefix: "",
+    document_prefix: "",
+  },
+  sentence_transformers_all_MiniLM_L12_v2: {
+    base_url:
+      "https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2/resolve/refs%2Fpr%2F4/",
+    search_prefix: "",
+    document_prefix: "",
+  },
+};
+export function getModelInfo(id) {
+  return {
+    modelURL: MODELS[id].base_url + "model.safetensors",
+    configURL: MODELS[id].base_url + "config.json",
+    tokenizerURL: MODELS[id].base_url + "tokenizer.json",
+    search_prefix: MODELS[id].search_prefix,
+    document_prefix: MODELS[id].document_prefix,
+  };
+}
+
+export function cosineSimilarity(vec1, vec2) {
+  const dot = vec1.reduce((acc, val, i) => acc + val * vec2[i], 0);
+  const a = Math.sqrt(vec1.reduce((acc, val) => acc + val * val, 0));
+  const b = Math.sqrt(vec2.reduce((acc, val) => acc + val * val, 0));
+  return dot / (a * b);
+}
+export async function getWikiText(article) {
+  // thanks to wikipedia for the API
+  const URL = `https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exlimit=1&titles=${article}&explaintext=1&exsectionformat=plain&format=json&origin=*`;
+  return fetch(URL, {
+    method: "GET",
+    headers: {
+      Accept: "application/json",
+    },
+  })
+    .then((r) => r.json())
+    .then((data) => {
+      const pages = data.query.pages;
+      const pageId = Object.keys(pages)[0];
+      const extract = pages[pageId].extract;
+      if (extract === undefined || extract === "") {
+        throw new Error("No article found");
+      }
+      return extract;
+    })
+    .catch((error) => console.error("Error:", error));
+}

From 67a486d18d47a276b5b5beb422fed2a0932e7ac9 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Tue, 19 Sep 2023 21:59:44 +0100
Subject: [PATCH 133/150] Line-up the wuerstchen model with the python
 implementation. (#901)

* Line-up the wuerstchen model with the python implementation.

* Missing cos.

* Fix the picture denormalization.
---
 candle-examples/examples/wuerstchen/main.rs             | 1 -
 candle-transformers/src/models/stable_diffusion/clip.rs | 6 ++++--
 candle-transformers/src/models/wuerstchen/common.rs     | 4 ++--
 candle-transformers/src/models/wuerstchen/ddpm.rs       | 6 ++++--
 4 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index 8064f87f..bce68114 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -373,7 +373,6 @@ fn run(args: Args) -> Result<()> {
         );
         let image = vqgan.decode(&(&latents * 0.3764)?)?;
         // TODO: Add the clamping between 0 and 1.
-        let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
         let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
         let image_filename = output_filename(&final_image, idx + 1, num_samples, None);
         candle_examples::save_image(&image, image_filename)?
diff --git a/candle-transformers/src/models/stable_diffusion/clip.rs b/candle-transformers/src/models/stable_diffusion/clip.rs
index 7f86cf31..e7a20270 100644
--- a/candle-transformers/src/models/stable_diffusion/clip.rs
+++ b/candle-transformers/src/models/stable_diffusion/clip.rs
@@ -12,6 +12,7 @@ use candle_nn::Module;
 pub enum Activation {
     QuickGelu,
     Gelu,
+    GeluErf,
 }
 
 impl Module for Activation {
@@ -19,6 +20,7 @@ impl Module for Activation {
         match self {
             Activation::QuickGelu => xs * nn::ops::sigmoid(&(xs * 1.702f64)?)?,
             Activation::Gelu => xs.gelu(),
+            Activation::GeluErf => xs.gelu_erf(),
         }
     }
 }
@@ -111,7 +113,7 @@ impl Config {
             num_hidden_layers: 24,
             num_attention_heads: 16,
             projection_dim: 1024,
-            activation: Activation::Gelu,
+            activation: Activation::GeluErf,
         }
     }
 
@@ -126,7 +128,7 @@ impl Config {
             num_hidden_layers: 32,
             num_attention_heads: 20,
             projection_dim: 512,
-            activation: Activation::Gelu,
+            activation: Activation::GeluErf,
         }
     }
 }
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
index 3cac2a59..8416a1f1 100644
--- a/candle-transformers/src/models/wuerstchen/common.rs
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -100,7 +100,7 @@ impl GlobalResponseNorm {
 
 impl Module for GlobalResponseNorm {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let agg_norm = xs.sqr()?.sum_keepdim((1, 2))?;
+        let agg_norm = xs.sqr()?.sum_keepdim((1, 2))?.sqrt()?;
         let stand_div_norm =
             agg_norm.broadcast_div(&(agg_norm.mean_keepdim(D::Minus1)? + 1e-6)?)?;
         xs.broadcast_mul(&stand_div_norm)?
@@ -152,7 +152,7 @@ impl ResBlock {
             .permute((0, 2, 3, 1))?;
         let xs = xs
             .apply(&self.channelwise_lin1)?
-            .gelu()?
+            .gelu_erf()?
             .apply(&self.channelwise_grn)?
             .apply(&self.channelwise_lin2)?
             .permute((0, 3, 1, 2))?;
diff --git a/candle-transformers/src/models/wuerstchen/ddpm.rs b/candle-transformers/src/models/wuerstchen/ddpm.rs
index 80640072..9e69b868 100644
--- a/candle-transformers/src/models/wuerstchen/ddpm.rs
+++ b/candle-transformers/src/models/wuerstchen/ddpm.rs
@@ -52,8 +52,10 @@ impl DDPMWScheduler {
         } else {
             t
         };
-        let alpha_cumprod =
-            ((t + s) / (1. + s) * std::f64::consts::PI * 0.5).powi(2) / self.init_alpha_cumprod;
+        let alpha_cumprod = ((t + s) / (1. + s) * std::f64::consts::PI * 0.5)
+            .cos()
+            .powi(2)
+            / self.init_alpha_cumprod;
         alpha_cumprod.clamp(0.0001, 0.9999)
     }
 

From 05626ef492300a4f99c87555304ec863071722d5 Mon Sep 17 00:00:00 2001
From: Juarez Bochi <juarez.bochi@grammarly.com>
Date: Tue, 19 Sep 2023 14:36:47 -0700
Subject: [PATCH 134/150] Flan T5: Read lm_head when word embeddings are not
 tied (#903)

* Read lm_head when word embeddings are not tied

* Fix formatting

* Address comments
---
 candle-transformers/src/models/t5.rs | 50 ++++++++++++++++++++++++----
 1 file changed, 43 insertions(+), 7 deletions(-)

diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index b1f3a3aa..fd2720d3 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -18,12 +18,15 @@ fn default_use_cache() -> bool {
     true
 }
 
+fn default_tie_word_embeddings() -> bool {
+    true
+}
+
 fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
     let mask: Vec<_> = (0..size)
         .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
         .collect();
-    let result = Tensor::from_slice(&mask, (size, size), device)?;
-    Ok(result)
+    Tensor::from_slice(&mask, (size, size), device)
 }
 
 fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
@@ -50,6 +53,8 @@ pub struct Config {
     initializer_factor: f64,
     #[serde(default)]
     feed_forward_proj: Activation,
+    #[serde(default = "default_tie_word_embeddings")]
+    tie_word_embeddings: bool,
     #[serde(default = "default_is_decoder")]
     is_decoder: bool,
     is_encoder_decoder: bool,
@@ -75,6 +80,7 @@ impl Default for Config {
             layer_norm_epsilon: 1e-6,
             initializer_factor: 1.0,
             feed_forward_proj: Activation::Relu,
+            tie_word_embeddings: true,
             is_decoder: false,
             is_encoder_decoder: true,
             use_cache: true,
@@ -94,6 +100,7 @@ impl Config {
             dropout_rate: 0.1,
             eos_token_id: 1,
             feed_forward_proj: Activation::Relu,
+            tie_word_embeddings: true,
             initializer_factor: 1.0,
             is_decoder: false,
             is_encoder_decoder: true,
@@ -611,6 +618,9 @@ impl T5EncoderModel {
 pub struct T5ForConditionalGeneration {
     encoder: T5Stack,
     decoder: T5Stack,
+    d_model: usize,
+    tie_word_embeddings: bool,
+    lm_head: Option<Linear>,
     shared: Arc<Embedding>,
     device: Device,
 }
@@ -618,6 +628,7 @@ pub struct T5ForConditionalGeneration {
 impl T5ForConditionalGeneration {
     pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
         assert!(cfg.is_encoder_decoder);
+        let d_model = cfg.d_model;
         let shared = embedding(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
         let shared = Arc::new(shared);
 
@@ -633,9 +644,23 @@ impl T5ForConditionalGeneration {
         decoder_cfg.num_layers = cfg.num_decoder_layers.unwrap_or(cfg.num_layers);
         let decoder = T5Stack::load(true, vb.pp("decoder"), &shared, &decoder_cfg)?;
 
+        let tie_word_embeddings = cfg.tie_word_embeddings;
+        let lm_head = if tie_word_embeddings {
+            None
+        } else {
+            Some(linear_no_bias(
+                cfg.d_model,
+                cfg.vocab_size,
+                vb.pp("lm_head"),
+            )?)
+        };
+
         Ok(Self {
             encoder,
             decoder,
+            d_model,
+            tie_word_embeddings,
+            lm_head,
             shared,
             device: vb.device().clone(),
         })
@@ -653,12 +678,23 @@ impl T5ForConditionalGeneration {
         let decoder_output = self
             .decoder
             .forward(decoder_input_ids, Some(encoder_output))?;
-        let sequence_output = decoder_output
+
+        let scaling_factor = if self.tie_word_embeddings {
+            // Rescale output before projecting on vocab
+            // See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+            (self.d_model as f64).sqrt()
+        } else {
+            1.0
+        };
+        let sequence_output = ((decoder_output
             .narrow(1, decoder_output.dim(1)? - 1, 1)?
-            .squeeze(1)?;
-        // TODO: check cfg.tie_word_embeddings to load from model instead.
-        let lm_head_weights = self.shared.embeddings().t()?;
-        let output = sequence_output.matmul(&lm_head_weights)?;
+            .squeeze(1)?)
+            * scaling_factor)?;
+        let output = match self.lm_head {
+            None => sequence_output.matmul(&self.shared.embeddings().t()?)?,
+            Some(ref lm_head) => lm_head.forward(&sequence_output)?,
+        };
+
         // TODO: Rescale output before projecting on vocab? * (self.model_dim**-0.5)
         Ok(output)
     }

From 098dd0d1e9cc2b1ca902e4e0d77a9abe3de72a9c Mon Sep 17 00:00:00 2001
From: Mahmoud <botchm@hotmail.com>
Date: Wed, 20 Sep 2023 00:54:56 -0700
Subject: [PATCH 135/150] fix: add missing`top_p` in llama_multiprocess (#905)

---
 candle-examples/examples/llama_multiprocess/main.rs | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/candle-examples/examples/llama_multiprocess/main.rs b/candle-examples/examples/llama_multiprocess/main.rs
index 17dc90e2..8a13ce6c 100644
--- a/candle-examples/examples/llama_multiprocess/main.rs
+++ b/candle-examples/examples/llama_multiprocess/main.rs
@@ -89,6 +89,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -222,7 +226,7 @@ fn main() -> Result<()> {
         .to_vec();
 
     println!("starting the inference loop");
-    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature);
+    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature, args.top_p);
     let mut new_tokens = vec![];
     let start_gen = std::time::Instant::now();
     let mut index_pos = 0;

From c0b49d5a50a23e7fc466deb8dd52e8a2b8266fa4 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 09:26:24 +0100
Subject: [PATCH 136/150] Wuerstchen parameter tweaks. (#907)

---
 candle-examples/examples/wuerstchen/main.rs | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index bce68114..aaa9b78a 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -14,7 +14,7 @@ use candle::{DType, Device, IndexOp, Module, Tensor, D};
 use clap::Parser;
 use tokenizers::Tokenizer;
 
-const PRIOR_GUIDANCE_SCALE: f64 = 8.0;
+const PRIOR_GUIDANCE_SCALE: f64 = 4.0;
 const RESOLUTION_MULTIPLE: f64 = 42.67;
 const LATENT_DIM_SCALE: f64 = 10.67;
 const PRIOR_CIN: usize = 16;
@@ -354,7 +354,7 @@ fn run(args: Args) -> Result<()> {
         )?;
 
         println!("diffusion process with prior {image_embeddings:?}");
-        let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
+        let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(12, Default::default())?;
         let timesteps = scheduler.timesteps();
         let timesteps = &timesteps[..timesteps.len() - 1];
         for (index, &t) in timesteps.iter().enumerate() {

From f685b2231cad71e98257b62618b364c255feacd7 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 10:14:51 +0100
Subject: [PATCH 137/150] Add some missing biases. (#908)

---
 candle-transformers/src/models/wuerstchen/paella_vq.rs | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/candle-transformers/src/models/wuerstchen/paella_vq.rs b/candle-transformers/src/models/wuerstchen/paella_vq.rs
index 6da7362c..4a69cca0 100644
--- a/candle-transformers/src/models/wuerstchen/paella_vq.rs
+++ b/candle-transformers/src/models/wuerstchen/paella_vq.rs
@@ -106,8 +106,7 @@ impl PaellaVQ {
                     stride: 2,
                     ..Default::default()
                 };
-                let block =
-                    candle_nn::conv2d_no_bias(C_LEVELS[i - 1], c_level, 4, cfg, vb_d.pp(d_idx))?;
+                let block = candle_nn::conv2d(C_LEVELS[i - 1], c_level, 4, cfg, vb_d.pp(d_idx))?;
                 d_idx += 1;
                 Some(block)
             } else {
@@ -130,7 +129,7 @@ impl PaellaVQ {
         let mut up_blocks = Vec::new();
         let vb_u = vb.pp("up_blocks");
         let mut u_idx = 0;
-        let up_blocks_conv = candle_nn::conv2d_no_bias(
+        let up_blocks_conv = candle_nn::conv2d(
             LATENT_CHANNELS,
             C_LEVELS[1],
             1,
@@ -152,7 +151,7 @@ impl PaellaVQ {
                     stride: 2,
                     ..Default::default()
                 };
-                let block = candle_nn::conv_transpose2d_no_bias(
+                let block = candle_nn::conv_transpose2d(
                     c_level,
                     C_LEVELS[C_LEVELS.len() - i - 2],
                     4,

From 7b1ddcff47b14f80c3d11e65aa9b452ecc17d59a Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 11:33:51 +0100
Subject: [PATCH 138/150] Add clone to various nn layers. (#910)

---
 candle-nn/src/batch_norm.rs | 2 +-
 candle-nn/src/conv.rs       | 6 +++---
 candle-nn/src/embedding.rs  | 2 +-
 candle-nn/src/group_norm.rs | 2 +-
 candle-nn/src/layer_norm.rs | 4 ++--
 candle-nn/src/linear.rs     | 2 +-
 candle-nn/src/rnn.rs        | 4 ++--
 7 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/candle-nn/src/batch_norm.rs b/candle-nn/src/batch_norm.rs
index 2dac0758..27ef15f7 100644
--- a/candle-nn/src/batch_norm.rs
+++ b/candle-nn/src/batch_norm.rs
@@ -38,7 +38,7 @@ impl From<f64> for BatchNormConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct BatchNorm {
     running_mean: Tensor,
     running_var: Tensor,
diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index 31bf9af0..89e9f42d 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -20,7 +20,7 @@ impl Default for Conv1dConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Conv1d {
     weight: Tensor,
     bias: Option<Tensor>,
@@ -88,7 +88,7 @@ impl Default for Conv2dConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Conv2d {
     weight: Tensor,
     bias: Option<Tensor>,
@@ -157,7 +157,7 @@ impl Default for ConvTranspose2dConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct ConvTranspose2d {
     weight: Tensor,
     bias: Option<Tensor>,
diff --git a/candle-nn/src/embedding.rs b/candle-nn/src/embedding.rs
index fccc8a17..52968bc2 100644
--- a/candle-nn/src/embedding.rs
+++ b/candle-nn/src/embedding.rs
@@ -1,7 +1,7 @@
 //! Embedding Layer.
 use candle::{Result, Tensor};
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Embedding {
     embeddings: Tensor,
     hidden_size: usize,
diff --git a/candle-nn/src/group_norm.rs b/candle-nn/src/group_norm.rs
index eb1b889f..5b80b970 100644
--- a/candle-nn/src/group_norm.rs
+++ b/candle-nn/src/group_norm.rs
@@ -4,7 +4,7 @@
 use candle::{DType, Result, Tensor};
 
 // This group norm version handles both weight and bias so removes the mean.
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct GroupNorm {
     weight: Tensor,
     bias: Tensor,
diff --git a/candle-nn/src/layer_norm.rs b/candle-nn/src/layer_norm.rs
index d2e80a82..7617fc6c 100644
--- a/candle-nn/src/layer_norm.rs
+++ b/candle-nn/src/layer_norm.rs
@@ -60,7 +60,7 @@ impl From<f64> for LayerNormConfig {
 }
 
 // This layer norm version handles both weight and bias so removes the mean.
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct LayerNorm {
     weight: Tensor,
     bias: Option<Tensor>,
@@ -143,7 +143,7 @@ pub fn layer_norm<C: Into<LayerNormConfig>>(
 }
 
 /// RmsNorm is a specialized version of the LayerNorm module.
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct RmsNorm(LayerNorm);
 
 impl RmsNorm {
diff --git a/candle-nn/src/linear.rs b/candle-nn/src/linear.rs
index de335964..94632296 100644
--- a/candle-nn/src/linear.rs
+++ b/candle-nn/src/linear.rs
@@ -19,7 +19,7 @@
 //! ```
 use candle::{Result, Tensor};
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Linear {
     weight: Tensor,
     bias: Option<Tensor>,
diff --git a/candle-nn/src/rnn.rs b/candle-nn/src/rnn.rs
index d52a9082..18a4a71c 100644
--- a/candle-nn/src/rnn.rs
+++ b/candle-nn/src/rnn.rs
@@ -85,7 +85,7 @@ impl LSTMConfig {
 ///
 /// <https://en.wikipedia.org/wiki/Long_short-term_memory>
 #[allow(clippy::upper_case_acronyms, unused)]
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct LSTM {
     w_ih: Tensor,
     w_hh: Tensor,
@@ -235,7 +235,7 @@ impl GRUConfig {
 ///
 /// <https://en.wikipedia.org/wiki/Gated_recurrent_unit>
 #[allow(clippy::upper_case_acronyms, unused)]
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct GRU {
     w_ih: Tensor,
     w_hh: Tensor,

From 728e16733406791c43bd545518393fae408c33ef Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 13:09:35 +0100
Subject: [PATCH 139/150] Add details on wuerstchen. (#911)

---
 README.md                                     |   6 ++++
 candle-examples/examples/wuerstchen/README.md |  27 ++++++++++++++++++
 .../examples/wuerstchen/assets/cat.jpg        | Bin 0 -> 38638 bytes
 3 files changed, 33 insertions(+)
 create mode 100644 candle-examples/examples/wuerstchen/README.md
 create mode 100644 candle-examples/examples/wuerstchen/assets/cat.jpg

diff --git a/README.md b/README.md
index 3d1b10fe..93a47082 100644
--- a/README.md
+++ b/README.md
@@ -71,6 +71,11 @@ We also provide a some command line based examples using state of the art models
 
 <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg" width="200">
 
+- [Wuerstchen](./candle-examples/examples/wuerstchen/): another text to
+  image generative model.
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/wuerstchen/assets/cat.jpg" width="200">
+
 - [yolo-v3](./candle-examples/examples/yolo-v3/) and
   [yolo-v8](./candle-examples/examples/yolo-v8/): object detection and pose
   estimation models.
@@ -142,6 +147,7 @@ If you have an addition to this list, please submit a pull request.
         - Bert.
     - Whisper (multi-lingual support).
     - Stable Diffusion v1.5, v2.1, XL v1.0.
+    - Wurstchen v2.
     - Computer Vision Models.
         - DINOv2.
         - EfficientNet.
diff --git a/candle-examples/examples/wuerstchen/README.md b/candle-examples/examples/wuerstchen/README.md
new file mode 100644
index 00000000..1b8accd1
--- /dev/null
+++ b/candle-examples/examples/wuerstchen/README.md
@@ -0,0 +1,27 @@
+# candle-wuerstchen: Efficient Pretraining of Text-to-Image Models
+
+![anthropomorphic cat dressed as a fire fighter](./assets/cat.jpg)
+
+The `wuerstchen` example is a port of the [diffusers
+implementation](https://github.com/huggingface/diffusers/tree/19edca82f1ff194c07317369a92b470dbae97f34/src/diffusers/pipelines/wuerstchen) for Würstchen v2.
+The candle implementation reproduces the same structure/files for models and
+pipelines. Useful resources:
+
+- [Official implementation](https://github.com/dome272/Wuerstchen).
+- [Arxiv paper](https://arxiv.org/abs/2306.00637).
+- Blog post: [Introducing Würstchen: Fast Diffusion for Image Generation](https://huggingface.co/blog/wuerstchen).
+
+## Getting the weights
+
+The weights are automatically downloaded for you from the [HuggingFace
+Hub](https://huggingface.co/) on the first run. There are various command line
+flags to use local files instead, run with `--help` to learn about them.
+
+## Running some example.
+
+```bash
+cargo run --example wuerstchen --release --features cuda,cudnn -- \
+  --prompt "Anthropomorphic cat dressed as a fire fighter"
+```
+
+The final image is named `sd_final.png` by default.
diff --git a/candle-examples/examples/wuerstchen/assets/cat.jpg b/candle-examples/examples/wuerstchen/assets/cat.jpg
new file mode 100644
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From fb1c2ac535236ef5c08ad241588057499d0b117d Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 14:07:55 +0100
Subject: [PATCH 140/150] Add flash-attn support. (#912)

* Add flash-attn support.

* Add the use-flash-attn flag.

* Re-enable flash-attn.
---
 candle-examples/Cargo.toml                    |  3 +-
 candle-examples/examples/wuerstchen/main.rs   | 14 +++++-
 candle-transformers/Cargo.toml                |  2 +
 .../models/wuerstchen/attention_processor.rs  | 50 +++++++++++++++++--
 .../src/models/wuerstchen/common.rs           |  3 +-
 .../src/models/wuerstchen/diffnext.rs         | 22 ++++++--
 .../src/models/wuerstchen/prior.rs            |  3 ++
 7 files changed, 85 insertions(+), 12 deletions(-)

diff --git a/candle-examples/Cargo.toml b/candle-examples/Cargo.toml
index cf8f0021..0e2e8093 100644
--- a/candle-examples/Cargo.toml
+++ b/candle-examples/Cargo.toml
@@ -13,7 +13,6 @@ readme = "README.md"
 accelerate-src = { workspace = true, optional = true }
 candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
 candle-datasets = { path = "../candle-datasets", version = "0.2.3" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.3", optional = true }
 candle-nn = { path = "../candle-nn", version = "0.2.3" }
 candle-transformers = { path = "../candle-transformers", version = "0.2.3" }
 cudarc = { workspace = true, optional = true }
@@ -51,7 +50,7 @@ default = []
 accelerate = ["dep:accelerate-src", "candle/accelerate", "candle-nn/accelerate", "candle-transformers/accelerate"]
 cuda = ["candle/cuda", "candle-nn/cuda", "candle-transformers/cuda"]
 cudnn = ["candle/cudnn"]
-flash-attn = ["cuda", "dep:candle-flash-attn"]
+flash-attn = ["cuda", "candle-transformers/flash-attn"]
 mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl", "candle-transformers/mkl"]
 nccl = ["cuda", "cudarc/nccl", "dep:half"]
 
diff --git a/candle-examples/examples/wuerstchen/main.rs b/candle-examples/examples/wuerstchen/main.rs
index aaa9b78a..95f3b8f4 100644
--- a/candle-examples/examples/wuerstchen/main.rs
+++ b/candle-examples/examples/wuerstchen/main.rs
@@ -41,6 +41,9 @@ struct Args {
     #[arg(long)]
     tracing: bool,
 
+    #[arg(long)]
+    use_flash_attn: bool,
+
     /// The height in pixels of the generated image.
     #[arg(long)]
     height: Option<usize>,
@@ -289,8 +292,14 @@ fn run(args: Args) -> Result<()> {
             let weights = weights.deserialize()?;
             let vb = candle_nn::VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
             wuerstchen::prior::WPrior::new(
-                /* c_in */ PRIOR_CIN, /* c */ 1536, /* c_cond */ 1280,
-                /* c_r */ 64, /* depth */ 32, /* nhead */ 24, vb,
+                /* c_in */ PRIOR_CIN,
+                /* c */ 1536,
+                /* c_cond */ 1280,
+                /* c_r */ 64,
+                /* depth */ 32,
+                /* nhead */ 24,
+                args.use_flash_attn,
+                vb,
             )?
         };
         let prior_scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
@@ -337,6 +346,7 @@ fn run(args: Args) -> Result<()> {
             /* c_cond */ 1024,
             /* clip_embd */ 1024,
             /* patch_size */ 2,
+            args.use_flash_attn,
             vb,
         )?
     };
diff --git a/candle-transformers/Cargo.toml b/candle-transformers/Cargo.toml
index 2faadad9..a3115c2b 100644
--- a/candle-transformers/Cargo.toml
+++ b/candle-transformers/Cargo.toml
@@ -12,6 +12,7 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 candle = { path = "../candle-core", version = "0.2.3", package = "candle-core" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.3", optional = true }
 candle-nn = { path = "../candle-nn", version = "0.2.3" }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
@@ -26,4 +27,5 @@ wav = { workspace = true }
 default = []
 accelerate = ["dep:accelerate-src", "candle/accelerate", "candle-nn/accelerate"]
 cuda = ["candle/cuda", "candle-nn/cuda"]
+flash-attn = ["cuda", "dep:candle-flash-attn"]
 mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl"]
diff --git a/candle-transformers/src/models/wuerstchen/attention_processor.rs b/candle-transformers/src/models/wuerstchen/attention_processor.rs
index 3f1a72eb..0b90cb9d 100644
--- a/candle-transformers/src/models/wuerstchen/attention_processor.rs
+++ b/candle-transformers/src/models/wuerstchen/attention_processor.rs
@@ -11,10 +11,33 @@ pub struct Attention {
     to_out: Linear,
     heads: usize,
     scale: f64,
+    use_flash_attn: bool,
+}
+
+#[cfg(feature = "flash-attn")]
+fn flash_attn(
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    softmax_scale: f32,
+    causal: bool,
+) -> Result<Tensor> {
+    candle_flash_attn::flash_attn(q, k, v, softmax_scale, causal)
+}
+
+#[cfg(not(feature = "flash-attn"))]
+fn flash_attn(_: &Tensor, _: &Tensor, _: &Tensor, _: f32, _: bool) -> Result<Tensor> {
+    unimplemented!("compile with '--features flash-attn'")
 }
 
 impl Attention {
-    pub fn new(query_dim: usize, heads: usize, dim_head: usize, vb: VarBuilder) -> Result<Self> {
+    pub fn new(
+        query_dim: usize,
+        heads: usize,
+        dim_head: usize,
+        use_flash_attn: bool,
+        vb: VarBuilder,
+    ) -> Result<Self> {
         let inner_dim = dim_head * heads;
         let scale = 1.0 / f64::sqrt(dim_head as f64);
         let to_q = linear(query_dim, inner_dim, vb.pp("to_q"))?;
@@ -28,6 +51,7 @@ impl Attention {
             to_out,
             scale,
             heads,
+            use_flash_attn,
         })
     }
 
@@ -62,8 +86,28 @@ impl Attention {
         let key = self.head_to_batch_dim(&key)?;
         let value = self.head_to_batch_dim(&value)?;
 
-        let attn_prs = self.get_attention_scores(&query, &key)?;
-        let xs = attn_prs.matmul(&value)?;
+        let xs = if self.use_flash_attn {
+            let init_dtype = query.dtype();
+            let q = query
+                .to_dtype(candle::DType::F16)?
+                .unsqueeze(0)?
+                .transpose(1, 2)?;
+            let k = key
+                .to_dtype(candle::DType::F16)?
+                .unsqueeze(0)?
+                .transpose(1, 2)?;
+            let v = value
+                .to_dtype(candle::DType::F16)?
+                .unsqueeze(0)?
+                .transpose(1, 2)?;
+            flash_attn(&q, &k, &v, self.scale as f32, false)?
+                .transpose(1, 2)?
+                .squeeze(0)?
+                .to_dtype(init_dtype)?
+        } else {
+            let attn_prs = self.get_attention_scores(&query, &key)?;
+            attn_prs.matmul(&value)?
+        };
         let xs = self.batch_to_head_dim(&xs)?;
 
         self.to_out
diff --git a/candle-transformers/src/models/wuerstchen/common.rs b/candle-transformers/src/models/wuerstchen/common.rs
index 8416a1f1..c89ec919 100644
--- a/candle-transformers/src/models/wuerstchen/common.rs
+++ b/candle-transformers/src/models/wuerstchen/common.rs
@@ -174,10 +174,11 @@ impl AttnBlock {
         c_cond: usize,
         nhead: usize,
         self_attn: bool,
+        use_flash_attn: bool,
         vb: VarBuilder,
     ) -> Result<Self> {
         let norm = WLayerNorm::new(c)?;
-        let attention = Attention::new(c, nhead, c / nhead, vb.pp("attention"))?;
+        let attention = Attention::new(c, nhead, c / nhead, use_flash_attn, vb.pp("attention"))?;
         let kv_mapper_lin = candle_nn::linear(c_cond, c, vb.pp("kv_mapper.1"))?;
         Ok(Self {
             self_attn,
diff --git a/candle-transformers/src/models/wuerstchen/diffnext.rs b/candle-transformers/src/models/wuerstchen/diffnext.rs
index 501a2776..64a48c8a 100644
--- a/candle-transformers/src/models/wuerstchen/diffnext.rs
+++ b/candle-transformers/src/models/wuerstchen/diffnext.rs
@@ -88,6 +88,7 @@ pub struct WDiffNeXt {
 }
 
 impl WDiffNeXt {
+    #[allow(clippy::too_many_arguments)]
     pub fn new(
         c_in: usize,
         c_out: usize,
@@ -95,6 +96,7 @@ impl WDiffNeXt {
         c_cond: usize,
         clip_embd: usize,
         patch_size: usize,
+        use_flash_attn: bool,
         vb: VarBuilder,
     ) -> Result<Self> {
         const C_HIDDEN: [usize; 4] = [320, 640, 1280, 1280];
@@ -169,8 +171,14 @@ impl WDiffNeXt {
                 let attn_block = if i == 0 {
                     None
                 } else {
-                    let attn_block =
-                        AttnBlock::new(c_hidden, c_cond, NHEAD[i], true, vb.pp(layer_i))?;
+                    let attn_block = AttnBlock::new(
+                        c_hidden,
+                        c_cond,
+                        NHEAD[i],
+                        true,
+                        use_flash_attn,
+                        vb.pp(layer_i),
+                    )?;
                     layer_i += 1;
                     Some(attn_block)
                 };
@@ -208,8 +216,14 @@ impl WDiffNeXt {
                 let attn_block = if i == 0 {
                     None
                 } else {
-                    let attn_block =
-                        AttnBlock::new(c_hidden, c_cond, NHEAD[i], true, vb.pp(layer_i))?;
+                    let attn_block = AttnBlock::new(
+                        c_hidden,
+                        c_cond,
+                        NHEAD[i],
+                        true,
+                        use_flash_attn,
+                        vb.pp(layer_i),
+                    )?;
                     layer_i += 1;
                     Some(attn_block)
                 };
diff --git a/candle-transformers/src/models/wuerstchen/prior.rs b/candle-transformers/src/models/wuerstchen/prior.rs
index 168b70a6..97ccf0e2 100644
--- a/candle-transformers/src/models/wuerstchen/prior.rs
+++ b/candle-transformers/src/models/wuerstchen/prior.rs
@@ -21,6 +21,7 @@ pub struct WPrior {
 }
 
 impl WPrior {
+    #[allow(clippy::too_many_arguments)]
     pub fn new(
         c_in: usize,
         c: usize,
@@ -28,6 +29,7 @@ impl WPrior {
         c_r: usize,
         depth: usize,
         nhead: usize,
+        use_flash_attn: bool,
         vb: VarBuilder,
     ) -> Result<Self> {
         let projection = candle_nn::conv2d(c_in, c, 1, Default::default(), vb.pp("projection"))?;
@@ -44,6 +46,7 @@ impl WPrior {
                 c,
                 nhead,
                 true,
+                use_flash_attn,
                 vb.pp(format!("blocks.{}", 3 * index + 2)),
             )?;
             blocks.push(Block {

From 9b24d89d2d637d72ff1bc52b9ea8bb7ccdd11c88 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 15:03:35 +0100
Subject: [PATCH 141/150] Tracing mode for T5. (#913)

* Tracing mode for T5.

* Tracing for the linear layer.
---
 candle-examples/examples/t5/main.rs  | 13 ++++
 candle-transformers/src/models/t5.rs | 90 +++++++++++++++++++++++-----
 2 files changed, 87 insertions(+), 16 deletions(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index f5972754..348e9a55 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -150,7 +150,20 @@ impl T5ModelBuilder {
 }
 
 fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
     let args = Args::parse();
+
+    let _guard = if args.tracing {
+        println!("tracing...");
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+
     let (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
     let device = &builder.device;
     let tokenizer = tokenizer
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index fd2720d3..efb2819b 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -1,11 +1,32 @@
 // T5 Text Encoder
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
 
-use candle::{DType, Device, Result, Tensor, D};
-use candle_nn::{embedding, linear_no_bias, Activation, Embedding, Linear, Module, VarBuilder};
+use candle::{DType, Device, Module, Result, Tensor, D};
+use candle_nn::{embedding, Activation, Embedding, VarBuilder};
 use serde::Deserialize;
 use std::sync::Arc;
 
+#[derive(Debug)]
+struct Linear {
+    inner: candle_nn::Linear,
+    span: tracing::Span,
+}
+
+impl Linear {
+    fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
+        let inner = candle_nn::linear_no_bias(d1, d2, vb)?;
+        let span = tracing::span!(tracing::Level::TRACE, "linear");
+        Ok(Self { inner, span })
+    }
+}
+
+impl Module for Linear {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(xs)
+    }
+}
+
 fn default_relative_attention_max_distance() -> usize {
     128
 }
@@ -121,6 +142,7 @@ impl Config {
 struct T5LayerNorm {
     weight: Tensor,
     variance_epsilon: f64,
+    span: tracing::Span,
 }
 
 impl T5LayerNorm {
@@ -129,10 +151,14 @@ impl T5LayerNorm {
         Ok(Self {
             weight,
             variance_epsilon: eps,
+            span: tracing::span!(tracing::Level::TRACE, "layer-norm"),
         })
     }
+}
 
+impl Module for T5LayerNorm {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let dtype = xs.dtype();
         let xs_f32 = xs.to_dtype(DType::F32)?;
         // variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
@@ -149,20 +175,25 @@ struct T5DenseActDense {
     wi: Linear,
     wo: Linear,
     act: Activation,
+    span: tracing::Span,
 }
 
 impl T5DenseActDense {
     fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let wi = linear_no_bias(cfg.d_model, cfg.d_ff, vb.pp("wi"))?;
-        let wo = linear_no_bias(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
+        let wi = Linear::new(cfg.d_model, cfg.d_ff, vb.pp("wi"))?;
+        let wo = Linear::new(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
         Ok(Self {
             wi,
             wo,
             act: Activation::Relu,
+            span: tracing::span!(tracing::Level::TRACE, "dense-act-dense"),
         })
     }
+}
 
+impl Module for T5DenseActDense {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let xs = self.wi.forward(xs)?;
         let xs = self.act.forward(&xs)?;
         let xs = self.wo.forward(&xs)?;
@@ -176,22 +207,27 @@ struct T5DenseGatedActDense {
     wi_1: Linear,
     wo: Linear,
     act: Activation,
+    span: tracing::Span,
 }
 
 impl T5DenseGatedActDense {
     fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let wi_0 = linear_no_bias(cfg.d_model, cfg.d_ff, vb.pp("wi_0"))?;
-        let wi_1 = linear_no_bias(cfg.d_model, cfg.d_ff, vb.pp("wi_1"))?;
-        let wo = linear_no_bias(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
+        let wi_0 = Linear::new(cfg.d_model, cfg.d_ff, vb.pp("wi_0"))?;
+        let wi_1 = Linear::new(cfg.d_model, cfg.d_ff, vb.pp("wi_1"))?;
+        let wo = Linear::new(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
         Ok(Self {
             wi_0,
             wi_1,
             wo,
             act: Activation::NewGelu,
+            span: tracing::span!(tracing::Level::TRACE, "dense-gated-act-dense"),
         })
     }
+}
 
+impl Module for T5DenseGatedActDense {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let hidden_gelu = self.act.forward(&self.wi_0.forward(xs)?)?;
         let hidden_linear = self.wi_1.forward(xs)?;
         let xs = hidden_gelu.broadcast_mul(&hidden_linear)?;
@@ -205,6 +241,7 @@ struct T5LayerFF {
     dense_act: Option<T5DenseActDense>,
     gated_dense_act: Option<T5DenseGatedActDense>,
     layer_norm: T5LayerNorm,
+    span: tracing::Span,
 }
 
 impl T5LayerFF {
@@ -226,10 +263,14 @@ impl T5LayerFF {
             dense_act,
             gated_dense_act,
             layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer-ff"),
         })
     }
+}
 
+impl Module for T5LayerFF {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let ys = self.layer_norm.forward(xs)?;
         let ys = match &self.dense_act {
             Some(dense_act) => dense_act.forward(&ys)?,
@@ -254,6 +295,7 @@ struct T5Attention {
     inner_dim: usize,
     use_cache: bool,
     kv_cache: Option<(Tensor, Tensor)>,
+    span: tracing::Span,
 }
 
 impl T5Attention {
@@ -264,10 +306,10 @@ impl T5Attention {
         cfg: &Config,
     ) -> Result<Self> {
         let inner_dim = cfg.num_heads * cfg.d_kv;
-        let q = linear_no_bias(cfg.d_model, inner_dim, vb.pp("q"))?;
-        let k = linear_no_bias(cfg.d_model, inner_dim, vb.pp("k"))?;
-        let v = linear_no_bias(cfg.d_model, inner_dim, vb.pp("v"))?;
-        let o = linear_no_bias(inner_dim, cfg.d_model, vb.pp("o"))?;
+        let q = Linear::new(cfg.d_model, inner_dim, vb.pp("q"))?;
+        let k = Linear::new(cfg.d_model, inner_dim, vb.pp("k"))?;
+        let v = Linear::new(cfg.d_model, inner_dim, vb.pp("v"))?;
+        let o = Linear::new(inner_dim, cfg.d_model, vb.pp("o"))?;
         let relative_attention_bias = if has_relative_attention_bias {
             let emb = embedding(
                 cfg.relative_attention_num_buckets,
@@ -291,6 +333,7 @@ impl T5Attention {
             inner_dim,
             use_cache: cfg.use_cache && decoder,
             kv_cache: None,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
         })
     }
 
@@ -303,6 +346,7 @@ impl T5Attention {
     ) -> Result<(Tensor, Option<Tensor>)> {
         // Performs Self-attention (if key_value_states is None) or attention
         // over source sentence (provided by key_value_states).
+        let _enter = self.span.enter();
         let kv_input = match key_value_states {
             None => xs,
             Some(key_value_states) => key_value_states,
@@ -419,6 +463,7 @@ impl T5Attention {
 struct T5LayerSelfAttention {
     self_attention: T5Attention,
     layer_norm: T5LayerNorm,
+    span: tracing::Span,
 }
 
 impl T5LayerSelfAttention {
@@ -429,6 +474,7 @@ impl T5LayerSelfAttention {
         Ok(Self {
             self_attention,
             layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "self-attn"),
         })
     }
 
@@ -438,6 +484,7 @@ impl T5LayerSelfAttention {
         position_bias: Option<&Tensor>,
         mask: Option<&Tensor>,
     ) -> Result<(Tensor, Option<Tensor>)> {
+        let _enter = self.span.enter();
         let normed_xs = self.layer_norm.forward(xs)?;
         let (ys, position_bias) =
             self.self_attention
@@ -451,6 +498,7 @@ impl T5LayerSelfAttention {
 struct T5LayerCrossAttention {
     cross_attention: T5Attention,
     layer_norm: T5LayerNorm,
+    span: tracing::Span,
 }
 
 impl T5LayerCrossAttention {
@@ -461,6 +509,7 @@ impl T5LayerCrossAttention {
         Ok(Self {
             cross_attention,
             layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "cross-attn"),
         })
     }
 
@@ -470,6 +519,7 @@ impl T5LayerCrossAttention {
         position_bias: Option<&Tensor>,
         key_value_states: &Tensor,
     ) -> Result<(Tensor, Option<Tensor>)> {
+        let _enter = self.span.enter();
         let normed_hidden_states = self.layer_norm.forward(hidden_states)?;
         let (ys, position_bias) = self.cross_attention.forward(
             &normed_hidden_states,
@@ -487,6 +537,7 @@ struct T5Block {
     self_attn: T5LayerSelfAttention,
     cross_attn: Option<T5LayerCrossAttention>,
     ff: T5LayerFF,
+    span: tracing::Span,
 }
 
 impl T5Block {
@@ -510,6 +561,7 @@ impl T5Block {
             self_attn,
             cross_attn,
             ff,
+            span: tracing::span!(tracing::Level::TRACE, "block"),
         })
     }
 
@@ -519,6 +571,7 @@ impl T5Block {
         position_bias: Option<&Tensor>,
         encoder_hidden_states: Option<&Tensor>,
     ) -> Result<(Tensor, Option<Tensor>)> {
+        let _enter = self.span.enter();
         // TODO: Cache masks
         let mask = match self.cross_attn.is_some() {
             true => {
@@ -550,6 +603,7 @@ struct T5Stack {
     block: Vec<T5Block>,
     shared: Arc<Embedding>,
     final_layer_norm: T5LayerNorm,
+    span: tracing::Span,
 }
 
 impl T5Stack {
@@ -566,6 +620,7 @@ impl T5Stack {
             block,
             shared: shared.clone(),
             final_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "stack"),
         })
     }
 
@@ -574,6 +629,7 @@ impl T5Stack {
         input_ids: &Tensor,
         encoder_hidden_states: Option<&Tensor>,
     ) -> Result<Tensor> {
+        let _enter = self.span.enter();
         let input_embeds = self.shared.as_ref().forward(input_ids)?;
         let mut hidden_states = input_embeds;
         let mut position_bias = None;
@@ -592,6 +648,7 @@ impl T5Stack {
 pub struct T5EncoderModel {
     encoder: T5Stack,
     device: Device,
+    span: tracing::Span,
 }
 
 impl T5EncoderModel {
@@ -602,10 +659,12 @@ impl T5EncoderModel {
         Ok(Self {
             encoder,
             device: vb.device().clone(),
+            span: tracing::span!(tracing::Level::TRACE, "encoder"),
         })
     }
 
     pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
         self.encoder.forward(input_ids, None)
     }
 
@@ -623,6 +682,7 @@ pub struct T5ForConditionalGeneration {
     lm_head: Option<Linear>,
     shared: Arc<Embedding>,
     device: Device,
+    span_decode: tracing::Span,
 }
 
 impl T5ForConditionalGeneration {
@@ -648,11 +708,7 @@ impl T5ForConditionalGeneration {
         let lm_head = if tie_word_embeddings {
             None
         } else {
-            Some(linear_no_bias(
-                cfg.d_model,
-                cfg.vocab_size,
-                vb.pp("lm_head"),
-            )?)
+            Some(Linear::new(cfg.d_model, cfg.vocab_size, vb.pp("lm_head"))?)
         };
 
         Ok(Self {
@@ -663,6 +719,7 @@ impl T5ForConditionalGeneration {
             lm_head,
             shared,
             device: vb.device().clone(),
+            span_decode: tracing::span!(tracing::Level::TRACE, "decode"),
         })
     }
 
@@ -675,6 +732,7 @@ impl T5ForConditionalGeneration {
         decoder_input_ids: &Tensor,
         encoder_output: &Tensor,
     ) -> Result<Tensor> {
+        let _enter = self.span_decode.enter();
         let decoder_output = self
             .decoder
             .forward(decoder_input_ids, Some(encoder_output))?;

From 3a0d3e05df74c214f21fe8c2188424e475542569 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 16:37:51 +0100
Subject: [PATCH 142/150] Add more t5 tracing. (#914)

* Add more t5 tracing.

* Rever the sm change.
---
 candle-transformers/src/models/t5.rs | 40 ++++++++++++++++++++++++----
 1 file changed, 35 insertions(+), 5 deletions(-)

diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index efb2819b..ffa2764b 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -2,10 +2,35 @@
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
 
 use candle::{DType, Device, Module, Result, Tensor, D};
-use candle_nn::{embedding, Activation, Embedding, VarBuilder};
+use candle_nn::{Activation, VarBuilder};
 use serde::Deserialize;
 use std::sync::Arc;
 
+#[derive(Debug)]
+struct Embedding {
+    inner: candle_nn::Embedding,
+    span: tracing::Span,
+}
+
+impl Embedding {
+    fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
+        let inner = candle_nn::embedding(d1, d2, vb)?;
+        let span = tracing::span!(tracing::Level::TRACE, "embedding");
+        Ok(Self { inner, span })
+    }
+
+    fn embeddings(&self) -> &Tensor {
+        self.inner.embeddings()
+    }
+}
+
+impl Module for Embedding {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(xs)
+    }
+}
+
 #[derive(Debug)]
 struct Linear {
     inner: candle_nn::Linear,
@@ -296,6 +321,7 @@ struct T5Attention {
     use_cache: bool,
     kv_cache: Option<(Tensor, Tensor)>,
     span: tracing::Span,
+    span_sm: tracing::Span,
 }
 
 impl T5Attention {
@@ -311,7 +337,7 @@ impl T5Attention {
         let v = Linear::new(cfg.d_model, inner_dim, vb.pp("v"))?;
         let o = Linear::new(inner_dim, cfg.d_model, vb.pp("o"))?;
         let relative_attention_bias = if has_relative_attention_bias {
-            let emb = embedding(
+            let emb = Embedding::new(
                 cfg.relative_attention_num_buckets,
                 cfg.num_heads,
                 vb.pp("relative_attention_bias"),
@@ -334,6 +360,7 @@ impl T5Attention {
             use_cache: cfg.use_cache && decoder,
             kv_cache: None,
             span: tracing::span!(tracing::Level::TRACE, "attention"),
+            span_sm: tracing::span!(tracing::Level::TRACE, "attention-sm"),
         })
     }
 
@@ -449,7 +476,10 @@ impl T5Attention {
             },
         };
 
-        let attn_weights = candle_nn::ops::softmax(&scores, D::Minus1)?;
+        let attn_weights = {
+            let _enter = self.span_sm.enter();
+            candle_nn::ops::softmax(&scores, D::Minus1)?
+        };
         let attn_output = attn_weights.matmul(&v)?;
         let attn_output = attn_output
             .transpose(1, 2)?
@@ -653,7 +683,7 @@ pub struct T5EncoderModel {
 
 impl T5EncoderModel {
     pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let shared = embedding(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
+        let shared = Embedding::new(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
         let shared = Arc::new(shared);
         let encoder = T5Stack::load(false, vb.pp("encoder"), &shared, cfg)?;
         Ok(Self {
@@ -689,7 +719,7 @@ impl T5ForConditionalGeneration {
     pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
         assert!(cfg.is_encoder_decoder);
         let d_model = cfg.d_model;
-        let shared = embedding(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
+        let shared = Embedding::new(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
         let shared = Arc::new(shared);
 
         let mut encoder_cfg = cfg.clone();

From ab1d40ea97b387f0dd05f77db37c840a4d624a08 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Wed, 20 Sep 2023 20:20:54 +0100
Subject: [PATCH 143/150] Add more t5 tracing. (#915)

---
 candle-transformers/src/models/t5.rs | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index ffa2764b..2b71fcda 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -321,6 +321,8 @@ struct T5Attention {
     use_cache: bool,
     kv_cache: Option<(Tensor, Tensor)>,
     span: tracing::Span,
+    span_cache: tracing::Span,
+    span_mm: tracing::Span,
     span_sm: tracing::Span,
 }
 
@@ -360,6 +362,8 @@ impl T5Attention {
             use_cache: cfg.use_cache && decoder,
             kv_cache: None,
             span: tracing::span!(tracing::Level::TRACE, "attention"),
+            span_cache: tracing::span!(tracing::Level::TRACE, "attention-cache"),
+            span_mm: tracing::span!(tracing::Level::TRACE, "attention-mm"),
             span_sm: tracing::span!(tracing::Level::TRACE, "attention-sm"),
         })
     }
@@ -397,6 +401,7 @@ impl T5Attention {
             .contiguous()?;
 
         if self.use_cache {
+            let _enter = self.span_cache.enter();
             if let Some((kv_cache_k, kv_cache_v)) = &self.kv_cache {
                 k = Tensor::cat(&[kv_cache_k, &k], 2)?.contiguous()?;
                 v = Tensor::cat(&[kv_cache_v, &v], 2)?.contiguous()?;
@@ -404,7 +409,10 @@ impl T5Attention {
             self.kv_cache = Some((k.clone(), v.clone()));
         };
         // TODO: Use flash_attn.
-        let scores = q.matmul(&k.t()?)?;
+        let scores = {
+            let _enter = self.span_mm.enter();
+            q.matmul(&k.t()?)?
+        };
         let scores = match mask {
             None => scores,
             Some(mask) => masked_fill(
@@ -713,6 +721,7 @@ pub struct T5ForConditionalGeneration {
     shared: Arc<Embedding>,
     device: Device,
     span_decode: tracing::Span,
+    span_decode_head: tracing::Span,
 }
 
 impl T5ForConditionalGeneration {
@@ -750,6 +759,7 @@ impl T5ForConditionalGeneration {
             shared,
             device: vb.device().clone(),
             span_decode: tracing::span!(tracing::Level::TRACE, "decode"),
+            span_decode_head: tracing::span!(tracing::Level::TRACE, "decode-head"),
         })
     }
 
@@ -778,9 +788,12 @@ impl T5ForConditionalGeneration {
             .narrow(1, decoder_output.dim(1)? - 1, 1)?
             .squeeze(1)?)
             * scaling_factor)?;
-        let output = match self.lm_head {
-            None => sequence_output.matmul(&self.shared.embeddings().t()?)?,
-            Some(ref lm_head) => lm_head.forward(&sequence_output)?,
+        let output = {
+            let _enter = self.span_decode_head.enter();
+            match self.lm_head {
+                None => sequence_output.matmul(&self.shared.embeddings().t()?)?,
+                Some(ref lm_head) => lm_head.forward(&sequence_output)?,
+            }
         };
 
         // TODO: Rescale output before projecting on vocab? * (self.model_dim**-0.5)

From 7b26e513f15a0c7cd55ccfe48525bda1079427ce Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 21 Sep 2023 06:19:10 +0100
Subject: [PATCH 144/150] Add the erf function. (#917)

---
 candle-core/src/backprop.rs       |  1 +
 candle-core/src/op.rs             | 36 +++++++++++++++++++++++++++++++
 candle-core/src/tensor.rs         |  1 +
 candle-core/tests/tensor_tests.rs |  7 ++++++
 4 files changed, 45 insertions(+)

diff --git a/candle-core/src/backprop.rs b/candle-core/src/backprop.rs
index 3e2ae1ed..a2548198 100644
--- a/candle-core/src/backprop.rs
+++ b/candle-core/src/backprop.rs
@@ -442,6 +442,7 @@ impl Tensor {
                         *sum_grad = sum_grad.add(&arg_grad)?
                     }
                     Op::Unary(_, UnaryOp::Gelu) => Err(Error::BackwardNotSupported { op: "gelu" })?,
+                    Op::Unary(_, UnaryOp::Erf) => Err(Error::BackwardNotSupported { op: "erf" })?,
                     Op::Unary(_, UnaryOp::GeluErf) => {
                         Err(Error::BackwardNotSupported { op: "gelu-erf" })?
                     }
diff --git a/candle-core/src/op.rs b/candle-core/src/op.rs
index 26dc6609..4882a205 100644
--- a/candle-core/src/op.rs
+++ b/candle-core/src/op.rs
@@ -59,6 +59,7 @@ pub enum UnaryOp {
     Sqrt,
     Gelu,
     GeluErf,
+    Erf,
     Relu,
     Tanh,
 }
@@ -327,6 +328,7 @@ pub(crate) struct Sqr;
 pub(crate) struct Sqrt;
 pub(crate) struct Gelu;
 pub(crate) struct GeluErf;
+pub(crate) struct Erf;
 pub(crate) struct Relu;
 pub(crate) struct Tanh;
 
@@ -623,6 +625,40 @@ impl UnaryOpT for Gelu {
     }
 }
 
+impl UnaryOpT for Erf {
+    const NAME: &'static str = "erf";
+    const KERNEL: &'static str = "uerf";
+    const V: Self = Erf;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        bf16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        f16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        Self::f64(v as f64) as f32
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        crate::cpu::erf::erf(v)
+    }
+    #[inline(always)]
+    fn u8(_: u8) -> u8 {
+        0
+    }
+    #[inline(always)]
+    fn u32(_: u32) -> u32 {
+        0
+    }
+    #[inline(always)]
+    fn i64(_: i64) -> i64 {
+        0
+    }
+}
+
 impl UnaryOpT for GeluErf {
     const NAME: &'static str = "gelu_erf";
     const KERNEL: &'static str = "ugelu_erf";
diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index eafd7002..9dccf2b5 100644
--- a/candle-core/src/tensor.rs
+++ b/candle-core/src/tensor.rs
@@ -490,6 +490,7 @@ impl Tensor {
     unary_op!(sqrt, Sqrt);
     unary_op!(gelu, Gelu);
     unary_op!(gelu_erf, GeluErf);
+    unary_op!(erf, Erf);
     unary_op!(relu, Relu);
 
     /// Retrieves the single scalar value hold in the tensor. If the tensor contains multiple
diff --git a/candle-core/tests/tensor_tests.rs b/candle-core/tests/tensor_tests.rs
index 408f4c55..edd0bd79 100644
--- a/candle-core/tests/tensor_tests.rs
+++ b/candle-core/tests/tensor_tests.rs
@@ -61,6 +61,13 @@ fn unary_op(device: &Device) -> Result<()> {
             [2.6906, -0.0647, -0.1091, 1.7353, 2.7928]
         ]
     );
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.erf()?, 4)?,
+        [
+            [-1.0, 0.8427, 1.0, -0.1125, 0.5205],
+            [0.9999, -0.9891, -0.3079, 0.9891, 0.9999]
+        ]
+    );
     Ok(())
 }
 

From c89b82b2d419bd2e99ffc64c90a2615e97d4ea66 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 21 Sep 2023 09:01:06 +0100
Subject: [PATCH 145/150] Add a clear cache function to the t5 model. (#919)

---
 candle-transformers/src/models/t5.rs | 30 ++++++++++++++++++++++++++++
 1 file changed, 30 insertions(+)

diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 2b71fcda..94cf5233 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -495,6 +495,10 @@ impl T5Attention {
         let attn_output = self.o.forward(&attn_output)?;
         Ok((attn_output, position_bias))
     }
+
+    fn clear_kv_cache(&mut self) {
+        self.kv_cache = None
+    }
 }
 
 #[derive(Debug)]
@@ -530,6 +534,10 @@ impl T5LayerSelfAttention {
         let ys = (xs + ys)?;
         Ok((ys, position_bias))
     }
+
+    fn clear_kv_cache(&mut self) {
+        self.self_attention.clear_kv_cache()
+    }
 }
 
 #[derive(Debug)]
@@ -568,6 +576,10 @@ impl T5LayerCrossAttention {
         let ys = (hidden_states + ys)?;
         Ok((ys, position_bias))
     }
+
+    fn clear_kv_cache(&mut self) {
+        self.cross_attention.clear_kv_cache()
+    }
 }
 
 #[derive(Debug)]
@@ -634,6 +646,11 @@ impl T5Block {
         // TODO: clamp for f16?
         Ok((xs, position_bias))
     }
+
+    fn clear_kv_cache(&mut self) {
+        self.self_attn.clear_kv_cache();
+        self.cross_attn.iter_mut().for_each(|c| c.clear_kv_cache());
+    }
 }
 
 #[derive(Debug)]
@@ -680,6 +697,10 @@ impl T5Stack {
         }
         self.final_layer_norm.forward(&hidden_states)
     }
+
+    fn clear_kv_cache(&mut self) {
+        self.block.iter_mut().for_each(|b| b.clear_kv_cache())
+    }
 }
 
 #[derive(Debug)]
@@ -709,6 +730,10 @@ impl T5EncoderModel {
     pub fn device(&self) -> &Device {
         &self.device
     }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.encoder.clear_kv_cache()
+    }
 }
 
 #[derive(Debug)]
@@ -808,4 +833,9 @@ impl T5ForConditionalGeneration {
     pub fn device(&self) -> &Device {
         &self.device
     }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.encoder.clear_kv_cache();
+        self.decoder.clear_kv_cache();
+    }
 }

From 2619c4307fe02db031d3a41cfbed91b12b97df31 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 21 Sep 2023 11:13:39 +0100
Subject: [PATCH 146/150] Add a quantized version of the t5 model. (#921)

---
 .gitignore                                    |   1 +
 candle-core/src/quantized/mod.rs              |   2 +-
 candle-transformers/src/models/mod.rs         |   1 +
 .../src/models/quantized_t5.rs                | 869 ++++++++++++++++++
 candle-transformers/src/models/t5.rs          |   2 +-
 5 files changed, 873 insertions(+), 2 deletions(-)
 create mode 100644 candle-transformers/src/models/quantized_t5.rs

diff --git a/.gitignore b/.gitignore
index 2748d37e..d0a8c320 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,6 +23,7 @@ flamegraph.svg
 *.dylib
 *.so
 *.swp
+*.swo
 trace-*.json
 
 candle-wasm-examples/*/build
diff --git a/candle-core/src/quantized/mod.rs b/candle-core/src/quantized/mod.rs
index 5c2bb2b2..f627f0f6 100644
--- a/candle-core/src/quantized/mod.rs
+++ b/candle-core/src/quantized/mod.rs
@@ -229,7 +229,7 @@ impl QTensor {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct QMatMul(std::sync::Arc<QTensor>);
 
 impl QMatMul {
diff --git a/candle-transformers/src/models/mod.rs b/candle-transformers/src/models/mod.rs
index a20254d9..d783a2c6 100644
--- a/candle-transformers/src/models/mod.rs
+++ b/candle-transformers/src/models/mod.rs
@@ -5,6 +5,7 @@ pub mod efficientnet;
 pub mod falcon;
 pub mod llama;
 pub mod quantized_llama;
+pub mod quantized_t5;
 pub mod segment_anything;
 pub mod stable_diffusion;
 pub mod t5;
diff --git a/candle-transformers/src/models/quantized_t5.rs b/candle-transformers/src/models/quantized_t5.rs
new file mode 100644
index 00000000..c14500ba
--- /dev/null
+++ b/candle-transformers/src/models/quantized_t5.rs
@@ -0,0 +1,869 @@
+// T5 Text Model, quantized version
+// https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
+
+use candle::quantized::QTensor;
+use candle::{DType, Device, Module, Result, Shape, Tensor, D};
+use candle_nn::Activation;
+use serde::Deserialize;
+use std::sync::Arc;
+
+// VarBuilder specialized for QTensors
+pub struct VarBuilder {
+    data: Arc<std::collections::HashMap<String, Arc<QTensor>>>,
+    path: Vec<String>,
+    device: Device,
+}
+
+impl VarBuilder {
+    fn pp<S: ToString>(&self, s: S) -> Self {
+        let mut path = self.path.clone();
+        path.push(s.to_string());
+        Self {
+            data: self.data.clone(),
+            path,
+            device: self.device.clone(),
+        }
+    }
+
+    fn path(&self, tensor_name: &str) -> String {
+        if self.path.is_empty() {
+            tensor_name.to_string()
+        } else {
+            [&self.path.join("."), tensor_name].join(".")
+        }
+    }
+
+    fn get<S: Into<Shape>>(&self, s: S, name: &str) -> Result<Arc<QTensor>> {
+        let path = self.path(name);
+        match self.data.get(&path) {
+            None => {
+                candle::bail!("cannot find tensor {name}")
+            }
+            Some(qtensor) => {
+                let shape = s.into();
+                if qtensor.shape() != &shape {
+                    candle::bail!(
+                        "shape mismatch for {name}, got {:?}, expected {shape:?}",
+                        qtensor.shape()
+                    )
+                }
+                Ok(qtensor.clone())
+            }
+        }
+    }
+}
+
+#[derive(Debug)]
+struct Embedding {
+    inner: candle_nn::Embedding,
+    span: tracing::Span,
+}
+
+impl Embedding {
+    fn new(d1: usize, d2: usize, vb: VarBuilder) -> Result<Self> {
+        let embeddings = vb.get((d1, d2), "weight")?.dequantize(&vb.device)?;
+        let inner = candle_nn::Embedding::new(embeddings, d2);
+        let span = tracing::span!(tracing::Level::TRACE, "embedding");
+        Ok(Self { inner, span })
+    }
+
+    fn embeddings(&self) -> &Tensor {
+        self.inner.embeddings()
+    }
+}
+
+impl Module for Embedding {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(xs)
+    }
+}
+
+// QMatMul wrapper adding some tracing.
+struct QMatMul {
+    inner: candle::quantized::QMatMul,
+    span: tracing::Span,
+}
+
+impl QMatMul {
+    fn new(out_dim: usize, in_dim: usize, vb: VarBuilder) -> Result<Self> {
+        let ws = vb.get((out_dim, in_dim), "weight")?;
+        let inner = candle::quantized::QMatMul::from_arc(ws);
+        let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
+        Ok(Self { inner, span })
+    }
+}
+
+impl Module for QMatMul {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.inner.forward(xs)
+    }
+}
+
+impl std::fmt::Debug for QMatMul {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "QMatMul")
+    }
+}
+
+fn default_relative_attention_max_distance() -> usize {
+    128
+}
+
+fn default_is_decoder() -> bool {
+    false
+}
+
+fn default_use_cache() -> bool {
+    true
+}
+
+fn default_tie_word_embeddings() -> bool {
+    true
+}
+
+fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
+    let mask: Vec<_> = (0..size)
+        .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
+        .collect();
+    Tensor::from_slice(&mask, (size, size), device)
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
+    let shape = mask.shape();
+    let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
+    let m = mask.where_cond(&on_true, on_false)?;
+    Ok(m)
+}
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct Config {
+    vocab_size: usize,
+    d_model: usize,
+    d_kv: usize,
+    d_ff: usize,
+    num_layers: usize,
+    num_decoder_layers: Option<usize>,
+    num_heads: usize,
+    relative_attention_num_buckets: usize,
+    #[serde(default = "default_relative_attention_max_distance")]
+    relative_attention_max_distance: usize,
+    dropout_rate: f64,
+    layer_norm_epsilon: f64,
+    initializer_factor: f64,
+    #[serde(default)]
+    feed_forward_proj: Activation,
+    #[serde(default = "default_tie_word_embeddings")]
+    tie_word_embeddings: bool,
+    #[serde(default = "default_is_decoder")]
+    is_decoder: bool,
+    is_encoder_decoder: bool,
+    #[serde(default = "default_use_cache")]
+    pub use_cache: bool,
+    pub pad_token_id: usize,
+    pub eos_token_id: usize,
+}
+
+impl Default for Config {
+    fn default() -> Self {
+        Self {
+            vocab_size: 32128,
+            d_model: 512,
+            d_kv: 64,
+            d_ff: 2048,
+            num_layers: 6,
+            num_decoder_layers: None,
+            num_heads: 8,
+            relative_attention_num_buckets: 32,
+            relative_attention_max_distance: 128,
+            dropout_rate: 0.1,
+            layer_norm_epsilon: 1e-6,
+            initializer_factor: 1.0,
+            feed_forward_proj: Activation::Relu,
+            tie_word_embeddings: true,
+            is_decoder: false,
+            is_encoder_decoder: true,
+            use_cache: true,
+            pad_token_id: 0,
+            eos_token_id: 1,
+        }
+    }
+}
+
+#[derive(Debug)]
+struct T5LayerNorm {
+    weight: Tensor,
+    variance_epsilon: f64,
+    span: tracing::Span,
+}
+
+impl T5LayerNorm {
+    fn load(h: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
+        let weight = vb.get(h, "weight")?.dequantize(&vb.device)?;
+        Ok(Self {
+            weight,
+            variance_epsilon: eps,
+            span: tracing::span!(tracing::Level::TRACE, "layer-norm"),
+        })
+    }
+}
+
+impl Module for T5LayerNorm {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let dtype = xs.dtype();
+        let xs_f32 = xs.to_dtype(DType::F32)?;
+        // variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        let variance = xs_f32.sqr()?.mean_keepdim(D::Minus1)?;
+        let xs = xs.broadcast_div(&(variance + self.variance_epsilon)?.sqrt()?)?;
+        let xs = xs.to_dtype(dtype)?;
+        let xs = xs.broadcast_mul(&self.weight)?;
+        Ok(xs)
+    }
+}
+
+#[derive(Debug)]
+struct T5DenseActDense {
+    wi: QMatMul,
+    wo: QMatMul,
+    act: Activation,
+    span: tracing::Span,
+}
+
+impl T5DenseActDense {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let wi = QMatMul::new(cfg.d_model, cfg.d_ff, vb.pp("wi"))?;
+        let wo = QMatMul::new(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
+        Ok(Self {
+            wi,
+            wo,
+            act: Activation::Relu,
+            span: tracing::span!(tracing::Level::TRACE, "dense-act-dense"),
+        })
+    }
+}
+
+impl Module for T5DenseActDense {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let xs = self.wi.forward(xs)?;
+        let xs = self.act.forward(&xs)?;
+        let xs = self.wo.forward(&xs)?;
+        Ok(xs)
+    }
+}
+
+#[derive(Debug)]
+struct T5DenseGatedActDense {
+    wi_0: QMatMul,
+    wi_1: QMatMul,
+    wo: QMatMul,
+    act: Activation,
+    span: tracing::Span,
+}
+
+impl T5DenseGatedActDense {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let wi_0 = QMatMul::new(cfg.d_model, cfg.d_ff, vb.pp("wi_0"))?;
+        let wi_1 = QMatMul::new(cfg.d_model, cfg.d_ff, vb.pp("wi_1"))?;
+        let wo = QMatMul::new(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
+        Ok(Self {
+            wi_0,
+            wi_1,
+            wo,
+            act: Activation::NewGelu,
+            span: tracing::span!(tracing::Level::TRACE, "dense-gated-act-dense"),
+        })
+    }
+}
+
+impl Module for T5DenseGatedActDense {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let hidden_gelu = self.act.forward(&self.wi_0.forward(xs)?)?;
+        let hidden_linear = self.wi_1.forward(xs)?;
+        let xs = hidden_gelu.broadcast_mul(&hidden_linear)?;
+        let xs = self.wo.forward(&xs)?;
+        Ok(xs)
+    }
+}
+
+#[derive(Debug)]
+struct T5LayerFF {
+    dense_act: Option<T5DenseActDense>,
+    gated_dense_act: Option<T5DenseGatedActDense>,
+    layer_norm: T5LayerNorm,
+    span: tracing::Span,
+}
+
+impl T5LayerFF {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let layer_norm =
+            T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
+        let (dense_act, gated_dense_act) = if cfg.feed_forward_proj == Activation::NewGelu {
+            (
+                None,
+                Some(T5DenseGatedActDense::load(vb.pp("DenseReluDense"), cfg)?),
+            )
+        } else {
+            (
+                Some(T5DenseActDense::load(vb.pp("DenseReluDense"), cfg)?),
+                None,
+            )
+        };
+        Ok(Self {
+            dense_act,
+            gated_dense_act,
+            layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer-ff"),
+        })
+    }
+}
+
+impl Module for T5LayerFF {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let ys = self.layer_norm.forward(xs)?;
+        let ys = match &self.dense_act {
+            Some(dense_act) => dense_act.forward(&ys)?,
+            None => self.gated_dense_act.as_ref().unwrap().forward(&ys)?,
+        };
+        let xs = (xs + ys)?;
+        Ok(xs)
+    }
+}
+
+#[derive(Debug)]
+struct T5Attention {
+    q: QMatMul,
+    k: QMatMul,
+    v: QMatMul,
+    o: QMatMul,
+    n_heads: usize,
+    d_kv: usize,
+    relative_attention_bias: Option<Embedding>,
+    relative_attention_num_buckets: usize,
+    relative_attention_max_distance: usize,
+    inner_dim: usize,
+    use_cache: bool,
+    kv_cache: Option<(Tensor, Tensor)>,
+    span: tracing::Span,
+    span_cache: tracing::Span,
+    span_mm: tracing::Span,
+    span_sm: tracing::Span,
+}
+
+impl T5Attention {
+    fn load(
+        has_relative_attention_bias: bool,
+        decoder: bool,
+        vb: VarBuilder,
+        cfg: &Config,
+    ) -> Result<Self> {
+        let inner_dim = cfg.num_heads * cfg.d_kv;
+        let q = QMatMul::new(cfg.d_model, inner_dim, vb.pp("q"))?;
+        let k = QMatMul::new(cfg.d_model, inner_dim, vb.pp("k"))?;
+        let v = QMatMul::new(cfg.d_model, inner_dim, vb.pp("v"))?;
+        let o = QMatMul::new(inner_dim, cfg.d_model, vb.pp("o"))?;
+        let relative_attention_bias = if has_relative_attention_bias {
+            let emb = Embedding::new(
+                cfg.relative_attention_num_buckets,
+                cfg.num_heads,
+                vb.pp("relative_attention_bias"),
+            )?;
+            Some(emb)
+        } else {
+            None
+        };
+        Ok(Self {
+            q,
+            k,
+            v,
+            o,
+            n_heads: cfg.num_heads,
+            d_kv: cfg.d_kv,
+            relative_attention_bias,
+            relative_attention_num_buckets: cfg.relative_attention_num_buckets,
+            relative_attention_max_distance: cfg.relative_attention_max_distance,
+            inner_dim,
+            use_cache: cfg.use_cache && decoder,
+            kv_cache: None,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+            span_cache: tracing::span!(tracing::Level::TRACE, "attention-cache"),
+            span_mm: tracing::span!(tracing::Level::TRACE, "attention-mm"),
+            span_sm: tracing::span!(tracing::Level::TRACE, "attention-sm"),
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        position_bias: Option<&Tensor>,
+        key_value_states: Option<&Tensor>,
+        mask: Option<&Tensor>,
+    ) -> Result<(Tensor, Option<Tensor>)> {
+        // Performs Self-attention (if key_value_states is None) or attention
+        // over source sentence (provided by key_value_states).
+        let _enter = self.span.enter();
+        let kv_input = match key_value_states {
+            None => xs,
+            Some(key_value_states) => key_value_states,
+        };
+        let (b_sz, q_len) = (xs.dim(0)?, xs.dim(1)?);
+        let kv_len = kv_input.dim(1)?;
+        let q = self.q.forward(xs)?;
+        let k = self.k.forward(kv_input)?;
+        let v = self.v.forward(kv_input)?;
+        let q = q
+            .reshape((b_sz, q_len, self.n_heads, self.d_kv))?
+            .transpose(1, 2)?
+            .contiguous()?;
+        let mut k = k
+            .reshape((b_sz, kv_len, self.n_heads, self.d_kv))?
+            .transpose(1, 2)?
+            .contiguous()?;
+        let mut v = v
+            .reshape((b_sz, kv_len, self.n_heads, self.d_kv))?
+            .transpose(1, 2)?
+            .contiguous()?;
+
+        if self.use_cache {
+            let _enter = self.span_cache.enter();
+            if let Some((kv_cache_k, kv_cache_v)) = &self.kv_cache {
+                k = Tensor::cat(&[kv_cache_k, &k], 2)?.contiguous()?;
+                v = Tensor::cat(&[kv_cache_v, &v], 2)?.contiguous()?;
+            };
+            self.kv_cache = Some((k.clone(), v.clone()));
+        };
+        // TODO: Use flash_attn.
+        let scores = {
+            let _enter = self.span_mm.enter();
+            q.matmul(&k.t()?)?
+        };
+        let scores = match mask {
+            None => scores,
+            Some(mask) => masked_fill(
+                &scores,
+                &mask
+                    .unsqueeze(0)?
+                    .unsqueeze(0)?
+                    .repeat((b_sz, self.n_heads))?,
+                f32::NEG_INFINITY,
+            )?,
+        };
+
+        let (scores, position_bias) = match position_bias {
+            Some(position_bias) => (
+                scores.broadcast_add(position_bias)?,
+                Some(position_bias.clone()),
+            ),
+            None => match &self.relative_attention_bias {
+                None => (scores, None),
+                Some(relative_attention_bias) => {
+                    // This only handles the bidirectional case.
+                    let kv_len = k.dim(2)?;
+                    let (q_start, q_end) = match self.use_cache {
+                        true => ((kv_len - q_len) as u32, kv_len as u32),
+                        false => (0_u32, kv_len as u32),
+                    };
+                    let num_buckets = self.relative_attention_num_buckets as u32 / 2;
+                    let max_exact = num_buckets / 2;
+                    let relative_position = (q_start..q_end)
+                        .map(|i| {
+                            (0..kv_len as u32)
+                                .map(|j| {
+                                    if i < j {
+                                        if j - i < max_exact {
+                                            j - i + num_buckets
+                                        } else {
+                                            let b = f32::log(
+                                                (j - i) as f32 / max_exact as f32,
+                                                self.relative_attention_max_distance as f32
+                                                    / max_exact as f32,
+                                            ) * (num_buckets - max_exact) as f32;
+                                            u32::min(
+                                                max_exact + num_buckets + b as u32,
+                                                self.relative_attention_num_buckets as u32 - 1,
+                                            )
+                                        }
+                                    } else if i - j < max_exact {
+                                        i - j
+                                    } else {
+                                        let b = f32::log(
+                                            (i - j) as f32 / max_exact as f32,
+                                            self.relative_attention_max_distance as f32
+                                                / max_exact as f32,
+                                        ) * (num_buckets - max_exact) as f32;
+                                        max_exact + b as u32
+                                    }
+                                })
+                                .collect::<Vec<u32>>()
+                        })
+                        .collect::<Vec<Vec<_>>>();
+                    let relative_buckets = Tensor::new(relative_position, q.device())?;
+                    let position_bias = relative_attention_bias
+                        .forward(&relative_buckets)?
+                        .permute((2, 0, 1))?
+                        .unsqueeze(0)?;
+                    (scores.broadcast_add(&position_bias)?, Some(position_bias))
+                    // TODO: position_bias_masked?
+                }
+            },
+        };
+
+        let attn_weights = {
+            let _enter = self.span_sm.enter();
+            candle_nn::ops::softmax(&scores, D::Minus1)?
+        };
+        let attn_output = attn_weights.matmul(&v)?;
+        let attn_output = attn_output
+            .transpose(1, 2)?
+            .reshape((b_sz, q_len, self.inner_dim))?;
+        let attn_output = self.o.forward(&attn_output)?;
+        Ok((attn_output, position_bias))
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.kv_cache = None
+    }
+}
+
+#[derive(Debug)]
+struct T5LayerSelfAttention {
+    self_attention: T5Attention,
+    layer_norm: T5LayerNorm,
+    span: tracing::Span,
+}
+
+impl T5LayerSelfAttention {
+    fn load(h: bool, d: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let self_attention = T5Attention::load(h, d, vb.pp("SelfAttention"), cfg)?;
+        let layer_norm =
+            T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
+        Ok(Self {
+            self_attention,
+            layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "self-attn"),
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        position_bias: Option<&Tensor>,
+        mask: Option<&Tensor>,
+    ) -> Result<(Tensor, Option<Tensor>)> {
+        let _enter = self.span.enter();
+        let normed_xs = self.layer_norm.forward(xs)?;
+        let (ys, position_bias) =
+            self.self_attention
+                .forward(&normed_xs, position_bias, None, mask)?;
+        let ys = (xs + ys)?;
+        Ok((ys, position_bias))
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.self_attention.clear_kv_cache()
+    }
+}
+
+#[derive(Debug)]
+struct T5LayerCrossAttention {
+    cross_attention: T5Attention,
+    layer_norm: T5LayerNorm,
+    span: tracing::Span,
+}
+
+impl T5LayerCrossAttention {
+    fn load(decoder: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let cross_attention = T5Attention::load(false, decoder, vb.pp("EncDecAttention"), cfg)?;
+        let layer_norm =
+            T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
+        Ok(Self {
+            cross_attention,
+            layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "cross-attn"),
+        })
+    }
+
+    fn forward(
+        &mut self,
+        hidden_states: &Tensor,
+        position_bias: Option<&Tensor>,
+        key_value_states: &Tensor,
+    ) -> Result<(Tensor, Option<Tensor>)> {
+        let _enter = self.span.enter();
+        let normed_hidden_states = self.layer_norm.forward(hidden_states)?;
+        let (ys, position_bias) = self.cross_attention.forward(
+            &normed_hidden_states,
+            position_bias,
+            Some(key_value_states),
+            None,
+        )?;
+        let ys = (hidden_states + ys)?;
+        Ok((ys, position_bias))
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.cross_attention.clear_kv_cache()
+    }
+}
+
+#[derive(Debug)]
+struct T5Block {
+    self_attn: T5LayerSelfAttention,
+    cross_attn: Option<T5LayerCrossAttention>,
+    ff: T5LayerFF,
+    span: tracing::Span,
+}
+
+impl T5Block {
+    fn load(
+        has_relative_attention_bias: bool,
+        decoder: bool,
+        vb: VarBuilder,
+        cfg: &Config,
+    ) -> Result<Self> {
+        let vb = vb.pp("layer");
+        let self_attn =
+            T5LayerSelfAttention::load(has_relative_attention_bias, decoder, vb.pp("0"), cfg)?;
+        let cross_attn = if cfg.is_decoder {
+            Some(T5LayerCrossAttention::load(decoder, vb.pp("1"), cfg)?)
+        } else {
+            None
+        };
+        let ff_i = if cross_attn.is_some() { 2 } else { 1 };
+        let ff = T5LayerFF::load(vb.pp(ff_i), cfg)?;
+        Ok(Self {
+            self_attn,
+            cross_attn,
+            ff,
+            span: tracing::span!(tracing::Level::TRACE, "block"),
+        })
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        position_bias: Option<&Tensor>,
+        encoder_hidden_states: Option<&Tensor>,
+    ) -> Result<(Tensor, Option<Tensor>)> {
+        let _enter = self.span.enter();
+        // TODO: Cache masks
+        let mask = match self.cross_attn.is_some() {
+            true => {
+                let mask_len = xs.dim(1)?;
+                // If the input seq length is 1, no need for a mask, this is also helpful to avoid shape
+                // issues when using the KV cache in the decoder.
+                if mask_len <= 1 {
+                    None
+                } else {
+                    Some(get_mask(mask_len, xs.device())?)
+                }
+            }
+            false => None,
+        };
+        let (mut xs, position_bias) = self.self_attn.forward(xs, position_bias, mask.as_ref())?;
+        // TODO: clamp for f16?
+        if let Some(cross_attn) = &mut self.cross_attn {
+            (xs, _) = cross_attn.forward(&xs, None, encoder_hidden_states.unwrap())?;
+            // TODO: clamp for f16?
+        }
+        let xs = self.ff.forward(&xs)?;
+        // TODO: clamp for f16?
+        Ok((xs, position_bias))
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.self_attn.clear_kv_cache();
+        self.cross_attn.iter_mut().for_each(|c| c.clear_kv_cache());
+    }
+}
+
+#[derive(Debug)]
+struct T5Stack {
+    block: Vec<T5Block>,
+    shared: Arc<Embedding>,
+    final_layer_norm: T5LayerNorm,
+    span: tracing::Span,
+}
+
+impl T5Stack {
+    fn load(decoder: bool, vb: VarBuilder, shared: &Arc<Embedding>, cfg: &Config) -> Result<Self> {
+        let block = (0..cfg.num_layers)
+            .map(|i| T5Block::load(i == 0, decoder, vb.pp(format!("block.{i}")), cfg))
+            .collect::<Result<Vec<_>>>()?;
+        let final_layer_norm = T5LayerNorm::load(
+            cfg.d_model,
+            cfg.layer_norm_epsilon,
+            vb.pp("final_layer_norm"),
+        )?;
+        Ok(Self {
+            block,
+            shared: shared.clone(),
+            final_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "stack"),
+        })
+    }
+
+    fn forward(
+        &mut self,
+        input_ids: &Tensor,
+        encoder_hidden_states: Option<&Tensor>,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        let input_embeds = self.shared.as_ref().forward(input_ids)?;
+        let mut hidden_states = input_embeds;
+        let mut position_bias = None;
+        for block in self.block.iter_mut() {
+            (hidden_states, position_bias) = block.forward(
+                &hidden_states,
+                position_bias.as_ref(),
+                encoder_hidden_states,
+            )?
+        }
+        self.final_layer_norm.forward(&hidden_states)
+    }
+
+    fn clear_kv_cache(&mut self) {
+        self.block.iter_mut().for_each(|b| b.clear_kv_cache())
+    }
+}
+
+#[derive(Debug)]
+pub struct T5EncoderModel {
+    encoder: T5Stack,
+    device: Device,
+    span: tracing::Span,
+}
+
+impl T5EncoderModel {
+    pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let shared = Embedding::new(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
+        let shared = Arc::new(shared);
+        let encoder = T5Stack::load(false, vb.pp("encoder"), &shared, cfg)?;
+        Ok(Self {
+            encoder,
+            device: vb.device.clone(),
+            span: tracing::span!(tracing::Level::TRACE, "encoder"),
+        })
+    }
+
+    pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+        self.encoder.forward(input_ids, None)
+    }
+
+    pub fn device(&self) -> &Device {
+        &self.device
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.encoder.clear_kv_cache()
+    }
+}
+
+#[derive(Debug)]
+pub struct T5ForConditionalGeneration {
+    encoder: T5Stack,
+    decoder: T5Stack,
+    d_model: usize,
+    tie_word_embeddings: bool,
+    lm_head: Option<QMatMul>,
+    shared: Arc<Embedding>,
+    device: Device,
+    span_decode: tracing::Span,
+    span_decode_head: tracing::Span,
+}
+
+impl T5ForConditionalGeneration {
+    pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        assert!(cfg.is_encoder_decoder);
+        let d_model = cfg.d_model;
+        let shared = Embedding::new(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
+        let shared = Arc::new(shared);
+
+        let mut encoder_cfg = cfg.clone();
+        encoder_cfg.is_decoder = false;
+        encoder_cfg.use_cache = false;
+        encoder_cfg.is_encoder_decoder = false;
+        let encoder = T5Stack::load(false, vb.pp("encoder"), &shared, &encoder_cfg)?;
+
+        let mut decoder_cfg = cfg.clone();
+        decoder_cfg.is_decoder = true;
+        decoder_cfg.is_encoder_decoder = false;
+        decoder_cfg.num_layers = cfg.num_decoder_layers.unwrap_or(cfg.num_layers);
+        let decoder = T5Stack::load(true, vb.pp("decoder"), &shared, &decoder_cfg)?;
+
+        let tie_word_embeddings = cfg.tie_word_embeddings;
+        let lm_head = if tie_word_embeddings {
+            None
+        } else {
+            Some(QMatMul::new(cfg.d_model, cfg.vocab_size, vb.pp("lm_head"))?)
+        };
+
+        Ok(Self {
+            encoder,
+            decoder,
+            d_model,
+            tie_word_embeddings,
+            lm_head,
+            shared,
+            device: vb.device.clone(),
+            span_decode: tracing::span!(tracing::Level::TRACE, "decode"),
+            span_decode_head: tracing::span!(tracing::Level::TRACE, "decode-head"),
+        })
+    }
+
+    pub fn encode(&mut self, input_ids: &Tensor) -> Result<Tensor> {
+        self.encoder.forward(input_ids, None)
+    }
+
+    pub fn decode(
+        &mut self,
+        decoder_input_ids: &Tensor,
+        encoder_output: &Tensor,
+    ) -> Result<Tensor> {
+        let _enter = self.span_decode.enter();
+        let decoder_output = self
+            .decoder
+            .forward(decoder_input_ids, Some(encoder_output))?;
+
+        let scaling_factor = if self.tie_word_embeddings {
+            // Rescale output before projecting on vocab
+            // See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
+            (self.d_model as f64).sqrt()
+        } else {
+            1.0
+        };
+        let sequence_output = ((decoder_output
+            .narrow(1, decoder_output.dim(1)? - 1, 1)?
+            .squeeze(1)?)
+            * scaling_factor)?;
+        let output = {
+            let _enter = self.span_decode_head.enter();
+            match self.lm_head {
+                None => sequence_output.matmul(&self.shared.embeddings().t()?)?,
+                Some(ref lm_head) => lm_head.forward(&sequence_output)?,
+            }
+        };
+
+        // TODO: Rescale output before projecting on vocab? * (self.model_dim**-0.5)
+        Ok(output)
+    }
+
+    pub fn forward(&mut self, input_ids: &Tensor, decoder_input_ids: &Tensor) -> Result<Tensor> {
+        let encoder_output = self.encode(input_ids)?;
+        self.decode(decoder_input_ids, &encoder_output)
+    }
+
+    pub fn device(&self) -> &Device {
+        &self.device
+    }
+
+    pub fn clear_kv_cache(&mut self) {
+        self.encoder.clear_kv_cache();
+        self.decoder.clear_kv_cache();
+    }
+}
diff --git a/candle-transformers/src/models/t5.rs b/candle-transformers/src/models/t5.rs
index 94cf5233..539ae89b 100644
--- a/candle-transformers/src/models/t5.rs
+++ b/candle-transformers/src/models/t5.rs
@@ -1,4 +1,4 @@
-// T5 Text Encoder
+// T5 Text Model
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
 
 use candle::{DType, Device, Module, Result, Tensor, D};

From 3b557765e8e1641d1289d33b177938abe10d24d2 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 21 Sep 2023 12:33:15 +0100
Subject: [PATCH 147/150] T5 quantized example (#922)

* Load gguf files for the quantized t5.

* Add the quantized t5 example.

* Allow for loading local files.

* Add some support for quantizing safetensor files.

* Transpose before quantizing.

* Quantized t5.

* Retrieve the weights from the hub.
---
 candle-core/examples/tensor-tools.rs          |  53 +++++
 .../examples/quantized-t5/README.md           |  17 ++
 candle-examples/examples/quantized-t5/main.rs | 186 ++++++++++++++++++
 .../src/models/quantized_t5.rs                |  17 +-
 4 files changed, 272 insertions(+), 1 deletion(-)
 create mode 100644 candle-examples/examples/quantized-t5/README.md
 create mode 100644 candle-examples/examples/quantized-t5/main.rs

diff --git a/candle-core/examples/tensor-tools.rs b/candle-core/examples/tensor-tools.rs
index 2bc1fa2e..c3459004 100644
--- a/candle-core/examples/tensor-tools.rs
+++ b/candle-core/examples/tensor-tools.rs
@@ -218,12 +218,65 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
     Ok(())
 }
 
+fn run_quantize_safetensors(
+    in_file: std::path::PathBuf,
+    out_file: std::path::PathBuf,
+    q: Quantization,
+) -> Result<()> {
+    let mut out_file = std::fs::File::create(out_file)?;
+    let tensors = candle_core::safetensors::load(in_file, &Device::Cpu)?;
+    println!("tensors: {}", tensors.len());
+
+    let quantize_fn = match q {
+        Quantization::Q4_0 => QTensor::quantize::<k_quants::BlockQ4_0>,
+        Quantization::Q4_1 => QTensor::quantize::<k_quants::BlockQ4_1>,
+        Quantization::Q5_0 => QTensor::quantize::<k_quants::BlockQ5_0>,
+        Quantization::Q5_1 => QTensor::quantize::<k_quants::BlockQ5_1>,
+        Quantization::Q8_0 => QTensor::quantize::<k_quants::BlockQ8_0>,
+        Quantization::Q8_1 => QTensor::quantize::<k_quants::BlockQ8_1>,
+        Quantization::Q2k => QTensor::quantize::<k_quants::BlockQ2K>,
+        Quantization::Q3k => QTensor::quantize::<k_quants::BlockQ3K>,
+        Quantization::Q4k => QTensor::quantize::<k_quants::BlockQ4K>,
+        Quantization::Q5k => QTensor::quantize::<k_quants::BlockQ5K>,
+        Quantization::Q6k => QTensor::quantize::<k_quants::BlockQ6K>,
+        Quantization::Q8k => QTensor::quantize::<k_quants::BlockQ8K>,
+        Quantization::F16 => QTensor::quantize::<half::f16>,
+        Quantization::F32 => QTensor::quantize::<f32>,
+    };
+
+    let qtensors = tensors
+        .into_par_iter()
+        .map(|(name, tensor)| {
+            println!("  quantizing {name} {tensor:?}");
+            let should_quantize = tensor.rank() == 2 && tensor.dim(0)? % 256 == 0;
+            let tensor = if should_quantize {
+                quantize_fn(&tensor)?
+            } else {
+                QTensor::quantize::<f32>(&tensor)?
+            };
+            Ok((name, tensor))
+        })
+        .collect::<Result<Vec<_>>>()?;
+    let qtensors = qtensors
+        .iter()
+        .map(|(k, v)| (k.as_str(), v))
+        .collect::<Vec<_>>();
+    gguf_file::write(&mut out_file, &[], &qtensors)?;
+    Ok(())
+}
+
 fn run_quantize(
     in_file: std::path::PathBuf,
     out_file: std::path::PathBuf,
     q: Quantization,
     qmode: QuantizationMode,
 ) -> Result<()> {
+    if let Some(extension) = in_file.extension() {
+        if extension == "safetensors" {
+            return run_quantize_safetensors(in_file, out_file, q);
+        }
+    }
+
     // Open the out file early so as to fail directly on missing directories etc.
     let mut out_file = std::fs::File::create(out_file)?;
     let mut in_ = std::fs::File::open(&in_file)?;
diff --git a/candle-examples/examples/quantized-t5/README.md b/candle-examples/examples/quantized-t5/README.md
new file mode 100644
index 00000000..1f6b99eb
--- /dev/null
+++ b/candle-examples/examples/quantized-t5/README.md
@@ -0,0 +1,17 @@
+# candle-quantized-t5
+
+This example uses a quantized version of the t5 model.
+
+```bash
+$ cargo run --example quantized-t5 --release -- --prompt "translate to German: A beautiful candle."
+...
+ Eine schöne Kerze.
+```
+
+The weight file is automatically retrieved from the hub. It is also possible to
+generate quantized weight files from the original safetensors file by using the
+`tensor-tools` command line utility via:
+
+```bash
+cargo run --example tensor-tools --release -- quantize --quantization q6k PATH/TO/T5/model.safetensors /tmp/model.gguf
+```
diff --git a/candle-examples/examples/quantized-t5/main.rs b/candle-examples/examples/quantized-t5/main.rs
new file mode 100644
index 00000000..86d3762e
--- /dev/null
+++ b/candle-examples/examples/quantized-t5/main.rs
@@ -0,0 +1,186 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+use std::io::Write;
+use std::path::PathBuf;
+
+use candle_transformers::models::quantized_t5 as t5;
+
+use anyhow::{Error as E, Result};
+use candle::{Device, Tensor};
+use candle_transformers::generation::LogitsProcessor;
+use clap::Parser;
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+#[derive(Parser, Debug, Clone)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Enable tracing (generates a trace-timestamp.json file).
+    #[arg(long)]
+    tracing: bool,
+
+    /// The model repository to use on the HuggingFace hub.
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    #[arg(long)]
+    weight_file: Option<String>,
+
+    // Enable/disable decoding.
+    #[arg(long, default_value = "false")]
+    disable_cache: bool,
+
+    /// Use this prompt, otherwise compute sentence similarities.
+    #[arg(long)]
+    prompt: String,
+
+    /// The temperature used to generate samples.
+    #[arg(long, default_value_t = 0.8)]
+    temperature: f64,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// The context size to consider for the repeat penalty.
+    #[arg(long, default_value_t = 64)]
+    repeat_last_n: usize,
+}
+
+struct T5ModelBuilder {
+    device: Device,
+    config: t5::Config,
+    weights_filename: PathBuf,
+}
+
+impl T5ModelBuilder {
+    pub fn load(args: &Args) -> Result<(Self, Tokenizer)> {
+        let device = Device::Cpu;
+        let default_model = "lmz/candle-quantized-t5".to_string();
+        let (model_id, revision) = match (args.model_id.to_owned(), args.revision.to_owned()) {
+            (Some(model_id), Some(revision)) => (model_id, revision),
+            (Some(model_id), None) => (model_id, "main".to_string()),
+            (None, Some(revision)) => (default_model, revision),
+            (None, None) => (default_model, "main".to_string()),
+        };
+
+        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
+        let api = Api::new()?;
+        let api = api.repo(repo);
+        let config_filename = api.get("config.json")?;
+        let tokenizer_filename = api.get("tokenizer.json")?;
+        let weights_filename = match &args.weight_file {
+            Some(filename) => std::path::PathBuf::from(filename),
+            None => api.get("model.gguf")?,
+        };
+        let config = std::fs::read_to_string(config_filename)?;
+        let mut config: t5::Config = serde_json::from_str(&config)?;
+        config.use_cache = !args.disable_cache;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+        Ok((
+            Self {
+                device,
+                config,
+                weights_filename,
+            },
+            tokenizer,
+        ))
+    }
+
+    pub fn build_model(&self) -> Result<t5::T5ForConditionalGeneration> {
+        let vb = t5::VarBuilder::from_gguf(&self.weights_filename)?;
+        Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
+    }
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+
+    let _guard = if args.tracing {
+        println!("tracing...");
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+
+    let (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
+    let device = &builder.device;
+    let tokenizer = tokenizer
+        .with_padding(None)
+        .with_truncation(None)
+        .map_err(E::msg)?;
+    let tokens = tokenizer
+        .encode(args.prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+    let mut model = builder.build_model()?;
+    let mut output_token_ids = [builder.config.pad_token_id as u32].to_vec();
+    let temperature = if args.temperature <= 0. {
+        None
+    } else {
+        Some(args.temperature)
+    };
+    let mut logits_processor = LogitsProcessor::new(299792458, temperature, args.top_p);
+    let encoder_output = model.encode(&input_token_ids)?;
+    let start = std::time::Instant::now();
+
+    for index in 0.. {
+        if output_token_ids.len() > 512 {
+            break;
+        }
+        let decoder_token_ids = if index == 0 || !builder.config.use_cache {
+            Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
+        } else {
+            let last_token = *output_token_ids.last().unwrap();
+            Tensor::new(&[last_token], device)?.unsqueeze(0)?
+        };
+        let logits = model
+            .decode(&decoder_token_ids, &encoder_output)?
+            .squeeze(0)?;
+        let logits = if args.repeat_penalty == 1. {
+            logits
+        } else {
+            let start_at = output_token_ids.len().saturating_sub(args.repeat_last_n);
+            candle_transformers::utils::apply_repeat_penalty(
+                &logits,
+                args.repeat_penalty,
+                &output_token_ids[start_at..],
+            )?
+        };
+
+        let next_token_id = logits_processor.sample(&logits)?;
+        if next_token_id as usize == builder.config.eos_token_id {
+            break;
+        }
+        output_token_ids.push(next_token_id);
+        if let Some(text) = tokenizer.id_to_token(next_token_id) {
+            let text = text.replace('▁', " ").replace("<0x0A>", "\n");
+            print!("{text}");
+            std::io::stdout().flush()?;
+        }
+    }
+    let dt = start.elapsed();
+    println!(
+        "\n{} tokens generated ({:.2} token/s)\n",
+        output_token_ids.len(),
+        output_token_ids.len() as f64 / dt.as_secs_f64(),
+    );
+    Ok(())
+}
diff --git a/candle-transformers/src/models/quantized_t5.rs b/candle-transformers/src/models/quantized_t5.rs
index c14500ba..a10c3b80 100644
--- a/candle-transformers/src/models/quantized_t5.rs
+++ b/candle-transformers/src/models/quantized_t5.rs
@@ -15,6 +15,21 @@ pub struct VarBuilder {
 }
 
 impl VarBuilder {
+    pub fn from_gguf<P: AsRef<std::path::Path>>(p: P) -> Result<Self> {
+        let mut file = std::fs::File::open(p)?;
+        let content = candle::quantized::gguf_file::Content::read(&mut file)?;
+        let mut data = std::collections::HashMap::new();
+        for tensor_name in content.tensor_infos.keys() {
+            let tensor = content.tensor(&mut file, tensor_name)?;
+            data.insert(tensor_name.to_string(), Arc::new(tensor));
+        }
+        Ok(Self {
+            data: Arc::new(data),
+            path: Vec::new(),
+            device: Device::Cpu,
+        })
+    }
+
     fn pp<S: ToString>(&self, s: S) -> Self {
         let mut path = self.path.clone();
         path.push(s.to_string());
@@ -87,7 +102,7 @@ struct QMatMul {
 
 impl QMatMul {
     fn new(out_dim: usize, in_dim: usize, vb: VarBuilder) -> Result<Self> {
-        let ws = vb.get((out_dim, in_dim), "weight")?;
+        let ws = vb.get((in_dim, out_dim), "weight")?;
         let inner = candle::quantized::QMatMul::from_arc(ws);
         let span = tracing::span!(tracing::Level::TRACE, "qmatmul");
         Ok(Self { inner, span })

From b43ca493f67a98aa6a6f53144ecb17a0a0d20fd0 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 21 Sep 2023 13:23:30 +0100
Subject: [PATCH 148/150] Add more quantized flan t5 variants (#923)

* Add the quantized flan-t5-large variant.

* Add more sizes.
---
 candle-examples/examples/quantized-t5/main.rs | 34 +++++++++++++++++--
 1 file changed, 31 insertions(+), 3 deletions(-)

diff --git a/candle-examples/examples/quantized-t5/main.rs b/candle-examples/examples/quantized-t5/main.rs
index 86d3762e..93a86309 100644
--- a/candle-examples/examples/quantized-t5/main.rs
+++ b/candle-examples/examples/quantized-t5/main.rs
@@ -11,10 +11,20 @@ use candle_transformers::models::quantized_t5 as t5;
 use anyhow::{Error as E, Result};
 use candle::{Device, Tensor};
 use candle_transformers::generation::LogitsProcessor;
-use clap::Parser;
+use clap::{Parser, ValueEnum};
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::Tokenizer;
 
+#[derive(Clone, Debug, Copy, ValueEnum)]
+enum Which {
+    T5Small,
+    FlanT5Small,
+    FlanT5Base,
+    FlanT5Large,
+    FlanT5Xl,
+    FlanT5Xxl,
+}
+
 #[derive(Parser, Debug, Clone)]
 #[command(author, version, about, long_about = None)]
 struct Args {
@@ -55,6 +65,10 @@ struct Args {
     /// The context size to consider for the repeat penalty.
     #[arg(long, default_value_t = 64)]
     repeat_last_n: usize,
+
+    /// The model size to use.
+    #[arg(long, default_value = "t5-small")]
+    which: Which,
 }
 
 struct T5ModelBuilder {
@@ -77,11 +91,25 @@ impl T5ModelBuilder {
         let repo = Repo::with_revision(model_id, RepoType::Model, revision);
         let api = Api::new()?;
         let api = api.repo(repo);
-        let config_filename = api.get("config.json")?;
+        let config_filename = match args.which {
+            Which::T5Small => api.get("config.json")?,
+            Which::FlanT5Small => api.get("config-flan-t5-small.json")?,
+            Which::FlanT5Base => api.get("config-flan-t5-base.json")?,
+            Which::FlanT5Large => api.get("config-flan-t5-large.json")?,
+            Which::FlanT5Xl => api.get("config-flan-t5-xl.json")?,
+            Which::FlanT5Xxl => api.get("config-flan-t5-xxl.json")?,
+        };
         let tokenizer_filename = api.get("tokenizer.json")?;
         let weights_filename = match &args.weight_file {
             Some(filename) => std::path::PathBuf::from(filename),
-            None => api.get("model.gguf")?,
+            None => match args.which {
+                Which::T5Small => api.get("model.gguf")?,
+                Which::FlanT5Small => api.get("model-flan-t5-small.gguf")?,
+                Which::FlanT5Base => api.get("model-flan-t5-base.gguf")?,
+                Which::FlanT5Large => api.get("model-flan-t5-large.gguf")?,
+                Which::FlanT5Xl => api.get("model-flan-t5-xl.gguf")?,
+                Which::FlanT5Xxl => api.get("model-flan-t5-xxl.gguf")?,
+            },
         };
         let config = std::fs::read_to_string(config_filename)?;
         let mut config: t5::Config = serde_json::from_str(&config)?;

From aa8ec06fd2b02c1039a46fcb518fd6d351487978 Mon Sep 17 00:00:00 2001
From: Laurent Mazare <laurent.mazare@gmail.com>
Date: Thu, 21 Sep 2023 14:48:13 +0100
Subject: [PATCH 149/150] Add the t5-xxl version. (#924)

---
 candle-examples/examples/t5/main.rs | 73 +++++++++++++++--------------
 1 file changed, 37 insertions(+), 36 deletions(-)

diff --git a/candle-examples/examples/t5/main.rs b/candle-examples/examples/t5/main.rs
index 348e9a55..55929c33 100644
--- a/candle-examples/examples/t5/main.rs
+++ b/candle-examples/examples/t5/main.rs
@@ -8,12 +8,12 @@ use std::path::PathBuf;
 
 use candle_transformers::models::t5;
 
-use anyhow::{anyhow, Error as E, Result};
+use anyhow::{Error as E, Result};
 use candle::{DType, Device, Tensor};
 use candle_nn::VarBuilder;
 use candle_transformers::generation::LogitsProcessor;
 use clap::Parser;
-use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
+use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::Tokenizer;
 
 const DTYPE: DType = DType::F32;
@@ -25,10 +25,6 @@ struct Args {
     #[arg(long)]
     cpu: bool,
 
-    /// Run offline (you must have the files already cached)
-    #[arg(long)]
-    offline: bool,
-
     /// Enable tracing (generates a trace-timestamp.json file).
     #[arg(long)]
     tracing: bool,
@@ -80,7 +76,7 @@ struct Args {
 struct T5ModelBuilder {
     device: Device,
     config: t5::Config,
-    weights_filename: PathBuf,
+    weights_filename: Vec<PathBuf>,
 }
 
 impl T5ModelBuilder {
@@ -95,28 +91,21 @@ impl T5ModelBuilder {
             (None, None) => (default_model, default_revision),
         };
 
-        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
-        let (config_filename, tokenizer_filename, weights_filename) = if args.offline {
-            let cache = Cache::default().repo(repo);
-            (
-                cache
-                    .get("config.json")
-                    .ok_or(anyhow!("Missing config file in cache"))?,
-                cache
-                    .get("tokenizer.json")
-                    .ok_or(anyhow!("Missing tokenizer file in cache"))?,
-                cache
-                    .get("model.safetensors")
-                    .ok_or(anyhow!("Missing weights file in cache"))?,
-            )
+        let repo = Repo::with_revision(model_id.clone(), RepoType::Model, revision);
+        let api = Api::new()?;
+        let api = api.repo(repo);
+        let config_filename = api.get("config.json")?;
+        let tokenizer_filename = api.get("tokenizer.json")?;
+        let weights_filename = if model_id == "google/flan-t5-xxl" {
+            vec![
+                api.get("model-00001-of-00005.safetensors")?,
+                api.get("model-00002-of-00005.safetensors")?,
+                api.get("model-00003-of-00005.safetensors")?,
+                api.get("model-00004-of-00005.safetensors")?,
+                api.get("model-00005-of-00005.safetensors")?,
+            ]
         } else {
-            let api = Api::new()?;
-            let api = api.repo(repo);
-            (
-                api.get("config.json")?,
-                api.get("tokenizer.json")?,
-                api.get("model.safetensors")?,
-            )
+            vec![api.get("model.safetensors")?]
         };
         let config = std::fs::read_to_string(config_filename)?;
         let mut config: t5::Config = serde_json::from_str(&config)?;
@@ -133,18 +122,30 @@ impl T5ModelBuilder {
     }
 
     pub fn build_encoder(&self) -> Result<t5::T5EncoderModel> {
-        let weights =
-            unsafe { candle::safetensors::MmapedFile::new(self.weights_filename.clone())? };
-        let weights = weights.deserialize()?;
-        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &self.device);
+        let weights = self
+            .weights_filename
+            .iter()
+            .map(|f| unsafe { candle::safetensors::MmapedFile::new(f) })
+            .collect::<candle::Result<Vec<_>>>()?;
+        let weights = weights
+            .iter()
+            .map(|w| w.deserialize())
+            .collect::<candle::Result<Vec<_>>>()?;
+        let vb = VarBuilder::from_safetensors(weights, DTYPE, &self.device);
         Ok(t5::T5EncoderModel::load(vb, &self.config)?)
     }
 
     pub fn build_conditional_generation(&self) -> Result<t5::T5ForConditionalGeneration> {
-        let weights =
-            unsafe { candle::safetensors::MmapedFile::new(self.weights_filename.clone())? };
-        let weights = weights.deserialize()?;
-        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &self.device);
+        let weights = self
+            .weights_filename
+            .iter()
+            .map(|f| unsafe { candle::safetensors::MmapedFile::new(f) })
+            .collect::<candle::Result<Vec<_>>>()?;
+        let weights = weights
+            .iter()
+            .map(|w| w.deserialize())
+            .collect::<candle::Result<Vec<_>>>()?;
+        let vb = VarBuilder::from_safetensors(weights, DTYPE, &self.device);
         Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
     }
 }

From a96878f2357fbcebf9db8747dcbb55bc8200d8ab Mon Sep 17 00:00:00 2001
From: Gonzalo <456459+grzuy@users.noreply.github.com>
Date: Thu, 21 Sep 2023 15:52:39 -0300
Subject: [PATCH 150/150] cuda cast i64 (#925)

---
 candle-kernels/src/cast.cu | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/candle-kernels/src/cast.cu b/candle-kernels/src/cast.cu
index ab2045a3..ee20fe5f 100644
--- a/candle-kernels/src/cast.cu
+++ b/candle-kernels/src/cast.cu
@@ -77,20 +77,30 @@ CAST_OP(double,   __half, cast_f64_f16)
 
 CAST_OP(uint32_t, uint32_t, cast_u32_u32)
 CAST_OP(uint32_t, uint8_t,  cast_u32_u8 )
+CAST_OP(uint32_t, int64_t,  cast_u32_i64 )
 CAST_OP(uint32_t, float,    cast_u32_f32)
 CAST_OP(uint32_t, double,   cast_u32_f64)
 
 CAST_OP(uint8_t, uint32_t, cast_u8_u32)
 CAST_OP(uint8_t, uint8_t,  cast_u8_u8 )
+CAST_OP(uint8_t, int64_t,  cast_u8_i64 )
 CAST_OP(uint8_t, float,    cast_u8_f32)
 CAST_OP(uint8_t, double,   cast_u8_f64)
 
+CAST_OP(int64_t, uint32_t, cast_i64_u32)
+CAST_OP(int64_t, uint8_t,  cast_i64_u8 )
+CAST_OP(int64_t, int64_t,  cast_i64_i64 )
+CAST_OP(int64_t, float,    cast_i64_f32)
+CAST_OP(int64_t, double,   cast_i64_f64)
+
 CAST_OP(float, uint8_t,  cast_f32_u8 )
 CAST_OP(float, uint32_t, cast_f32_u32)
+CAST_OP(float, int64_t,  cast_f32_i64 )
 CAST_OP(float, float,    cast_f32_f32)
 CAST_OP(float, double,   cast_f32_f64)
 
 CAST_OP(double, uint8_t,  cast_f64_u8 )
 CAST_OP(double, uint32_t, cast_f64_u32)
+CAST_OP(double, int64_t,  cast_f64_i64 )
 CAST_OP(double, float,    cast_f64_f32)
 CAST_OP(double, double,   cast_f64_f64)