Prebuild all our kernels.

Cargo.toml

@@ -19,7 +19,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.4.2"
+version = "0.4.0"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -31,18 +31,17 @@ license = "MIT OR Apache-2.0"
 accelerate-src = { version = "0.3.2" }
 anyhow = { version = "1", features = ["backtrace"] }
 byteorder = "1.4.3"
-candle = { path = "./candle-core", package = "candle-core", version = "0.4.2" }
-candle-datasets = { path = "./candle-datasets", version = "0.4.2" }
-candle-flash-attn = { path = "./candle-flash-attn", version = "0.4.2" }
-candle-kernels = { path = "./candle-kernels", version = "0.4.2" }
-candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.4.2" }
-candle-nn = { path = "./candle-nn", version = "0.4.2" }
-candle-onnx = { path = "./candle-onnx", version = "0.4.2" }
-candle-transformers = { path = "./candle-transformers", version = "0.4.2" }
+candle = { path = "./candle-core", package = "candle-core", version = "0.4.0" }
+candle-datasets = { path = "./candle-datasets", version = "0.4.0" }
+candle-flash-attn = { path = "./candle-flash-attn", version = "0.4.0" }
+candle-kernels = { path = "./candle-kernels", version = "0.4.0" }
+candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.4.0" }
+candle-nn = { path = "./candle-nn", version = "0.4.0" }
+candle-onnx = { path = "./candle-onnx", version = "0.4.0" }
+candle-transformers = { path = "./candle-transformers", version = "0.4.0" }
 clap = { version = "4.2.4", features = ["derive"] }
 criterion = { version = "0.5.1", default-features=false }
 cudarc = { version = "0.10.0", features = ["f16"] }
-fancy-regex = "0.13.0"
 gemm = { version = "0.17.0", features = ["wasm-simd128-enable"] }
 hf-hub = "0.3.0"
 half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }

README.md

@@ -63,8 +63,6 @@ We also provide a some command line based examples using state of the art models
 - [LLaMA and LLaMA-v2](./candle-examples/examples/llama/): general LLM, includes
   the SOLAR-10.7B variant.
 - [Falcon](./candle-examples/examples/falcon/): general LLM.
-- [Gemma](./candle-examples/examples/gemma/): 2b and 7b general LLMs from Google
-  Deepmind.
 - [Phi-1, Phi-1.5, and Phi-2](./candle-examples/examples/phi/): 1.3b and 2.7b general LLMs with performance on par with LLaMA-v2 7b.
 - [StableLM-3B-4E1T](./candle-examples/examples/stable-lm/): a 3b general LLM
   pre-trained on 1T tokens of English and code datasets. Also supports
@@ -76,10 +74,9 @@ We also provide a some command line based examples using state of the art models
 - [Mixtral8x7b-v0.1](./candle-examples/examples/mixtral/): a sparse mixture of
   experts 8x7b general LLM with better performance than a Llama 2 70B model with
   much faster inference.
-- [StarCoder](./candle-examples/examples/bigcode/) and
-  [StarCoder2](./candle-examples/examples/starcoder2/): LLM specialized to code generation.
+- [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code generation.
 - [Qwen1.5](./candle-examples/examples/qwen/): Bilingual (English/Chinese) LLMs.
-- [RWKV v5 and v6](./candle-examples/examples/rwkv/): An RNN with transformer level LLM
+- [RWKV v5](./candle-examples/examples/rwkv/): An RNN with transformer level LLM
   performance.
 - [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
 - [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
@@ -110,12 +107,7 @@ 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">
 
-- [SegFormer](./candle-examples/examples/segformer/): transformer based semantic segmantation model.
 - [Whisper](./candle-examples/examples/whisper/): speech recognition model.
-- [EnCodec](./candle-examples/examples/encodec/): high-quality audio compression
-  model using residual vector quantization.
-- [MetaVoice](./candle-examples/examples/metavoice/): foundational model for
-  text-to-speech.
 - [T5](./candle-examples/examples/t5), [Bert](./candle-examples/examples/bert/),
   [JinaBert](./candle-examples/examples/jina-bert/) : useful for sentence embeddings.
 - [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
@@ -175,7 +167,6 @@ And then head over to
 - [`kalosm`](https://github.com/floneum/floneum/tree/master/interfaces/kalosm): A multi-modal meta-framework in Rust for interfacing with local pre-trained models with support for controlled generation, custom samplers, in-memory vector databases, audio transcription, and more.
 - [`candle-sampling`](https://github.com/EricLBuehler/candle-sampling): Sampling techniques for Candle.
 - [`gpt-from-scratch-rs`](https://github.com/jeroenvlek/gpt-from-scratch-rs): A port of Andrej Karpathy's _Let's build GPT_ tutorial on YouTube showcasing the Candle API on a toy problem.
-- [`candle-einops`](https://github.com/tomsanbear/candle-einops): A pure rust implementation of the python [einops](https://github.com/arogozhnikov/einops) library.
 
 If you have an addition to this list, please submit a pull request.
 
@@ -196,10 +187,9 @@ If you have an addition to this list, please submit a pull request.
     - Language Models.
         - LLaMA v1 and v2 with variants such as SOLAR-10.7B.
         - Falcon.
-        - StarCoder, StarCoder2.
+        - StarCoder.
         - Phi 1, 1.5, and 2.
         - Mamba, Minimal Mamba
-        - Gemma 2b and 7b.
         - Mistral 7b v0.1.
         - Mixtral 8x7b v0.1.
         - StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
@@ -207,7 +197,7 @@ If you have an addition to this list, please submit a pull request.
         - Bert.
         - Yi-6B and Yi-34B.
         - Qwen1.5.
-        - RWKV v5 and v6.
+        - RWKV.
     - Quantized LLMs.
         - Llama 7b, 13b, 70b, as well as the chat and code variants.
         - Mistral 7b, and 7b instruct.
@@ -217,22 +207,18 @@ If you have an addition to this list, please submit a pull request.
     - Text to text.
         - T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
         - Marian MT (Machine Translation).
+    - Whisper (multi-lingual support).
     - Text to image.
         - Stable Diffusion v1.5, v2.1, XL v1.0.
         - Wurstchen v2.
     - Image to text.
         - BLIP.
         - TrOCR.
-    - Audio.
-        - Whisper, multi-lingual speech-to-text.
-        - EnCodec, audio compression model.
-        - MetaVoice-1B, text-to-speech model.
     - Computer Vision Models.
         - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
-          ConvNeXTv2, MobileOne, EfficientVit (MSRA).
+          ConvNeXTv2.
         - yolo-v3, yolo-v8.
         - Segment-Anything Model (SAM).
-        - SegFormer.
 - 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.

candle-core/examples/cuda_basics.rs

@@ -5,32 +5,25 @@ extern crate accelerate_src;
 extern crate intel_mkl_src;
 
 use anyhow::Result;
-use candle_core::{Device, Module, Tensor};
-
-use candle_core::quantized::{QMatMul, QTensor};
+use candle_core::{Device, Tensor};
 
 fn main() -> Result<()> {
     let device = Device::new_cuda(0)?;
-    let q = Tensor::randn(0f32, 1.0, (72, 256), &device)?;
-    let q_cpu = q.to_device(&Device::Cpu)?;
-    let q = QTensor::quantize(&q, candle_core::quantized::GgmlDType::Q8K)?;
-    let q = QMatMul::from_qtensor(q)?;
-    let x = Tensor::randn(0f32, 1.0, (5, 256), &device)?;
-    let res_q_cuda = q.forward(&x)?;
-    println!("{res_q_cuda}");
-
-    let q_cpu = QTensor::quantize(&q_cpu, candle_core::quantized::GgmlDType::Q8K)?;
-    let q_cpu_tensor = q_cpu.dequantize(&Device::Cpu)?;
-    let q_cpu = QMatMul::from_qtensor(q_cpu)?;
-    let x_cpu = x.to_device(&Device::Cpu)?;
-    let res_q_cpu = q_cpu.forward(&x_cpu)?;
-    println!("{res_q_cpu}");
-
-    let res_mm = x_cpu.matmul(&q_cpu_tensor.t()?)?;
-    let diff = (res_mm - res_q_cuda.to_device(&Device::Cpu))?
-        .abs()?
-        .flatten_all()?
-        .max(0)?;
+    let in_t = Tensor::rand(-1f32, 1f32, (1, 3, 12, 7), &device)?;
+    let k_t = Tensor::rand(-1f32, 1f32, (6, 3, 1, 1), &device)?;
+    let out_t = in_t.conv2d(&k_t, 0, 1, 1, 1)?;
+    println!("{out_t}");
+    let in_t = in_t.to_device(&Device::Cpu)?;
+    let k_t = k_t.to_device(&Device::Cpu)?;
+    let out_t2 = in_t.conv2d(&k_t, 0, 1, 1, 1)?;
+    let diff = (out_t.to_device(&Device::Cpu)? - out_t2)?
+        .sqr()?
+        .sum_all()?;
     println!("{diff}");
+
+    let t = Tensor::randn(0f32, 1f32, (2, 4, 96, 96), &device)?;
+    let w = Tensor::randn(0f32, 1f32, (320, 4, 3, 3), &device)?;
+    let res = t.conv2d(&w, 1, 1, 1, 1)?;
+    println!("{res:?}");
     Ok(())
 }

candle-core/src/backend.rs

@@ -98,19 +98,6 @@ pub trait BackendStorage: Sized {
     ) -> Result<Self>;
 
     fn copy_strided_src(&self, _: &mut Self, _: usize, _: &Layout) -> Result<()>;
-
-    #[allow(clippy::too_many_arguments)]
-    // Similar to cudaMemcpy2D, though values are in elements and not in bytes.
-    fn copy2d(
-        &self,
-        _: &mut Self,
-        _d1: usize,
-        _d2: usize,
-        _src_stride1: usize,
-        _dst_stride1: usize,
-        _src_offset: usize,
-        _dst_offset: usize,
-    ) -> Result<()>;
 }
 
 pub trait BackendDevice: Sized + std::fmt::Debug + Clone {

candle-core/src/backprop.rs

@@ -113,7 +113,7 @@ impl Tensor {
                     | Op::Unary(_node, UnaryOp::Floor)
                     | Op::Unary(_node, UnaryOp::Round) => nodes,
                     Op::Reshape(node)
-                    | Op::UpsampleNearest1D { arg: node, .. }
+                    | Op::UpsampleNearest1D(node)
                     | Op::UpsampleNearest2D { arg: node, .. }
                     | Op::AvgPool2D { arg: node, .. }
                     | Op::MaxPool2D { arg: node, .. }
@@ -250,7 +250,6 @@ impl Tensor {
                             out_padding,
                             *stride,
                             *dilation,
-                            /* groups */ 1,
                         )?;
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
@@ -348,18 +347,9 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
                     }
-                    Op::UpsampleNearest1D { arg, target_size } => {
-                        let (_n, c, size) = arg.dims3()?;
-                        if target_size % size != 0 {
-                            crate::bail!("backward not supported for non integer upscaling factors")
-                        }
-                        let scale = target_size / size;
-
-                        let kernel = Tensor::ones((c, 1, scale), arg.dtype(), arg.device())?;
-                        let conv_sum = grad.conv1d(&kernel, 0, scale, 1, c)?;
-                        let sum_grad = grads.or_insert(arg)?;
-                        *sum_grad = conv_sum;
-                    }
+                    Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
+                        op: "upsample-nearest1d",
+                    })?,
                     Op::UpsampleNearest2D {
                         arg,
                         target_h,

candle-core/src/conv.rs

@@ -187,16 +187,36 @@ impl Tensor {
         }
     }
 
-    fn conv_transpose1d_single_group(
+    /// Applies a 1D transposed convolution over the input tensor.
+    pub fn conv_transpose1d(
         &self,
         kernel: &Self,
-        params: &ParamsConvTranspose1D,
+        padding: usize,
+        output_padding: usize,
+        stride: usize,
+        dilation: usize,
     ) -> Result<Self> {
+        let (b_size, c_in, l_in) = self.dims3()?;
+        let (c_in_k, c_out, k_size) = kernel.dims3()?;
+        if c_in != c_in_k {
+            crate::bail!("in_channel mismatch between input ({c_in}) and kernel ({c_in_k})")
+        }
+        let params = ParamsConvTranspose1D {
+            b_size,
+            l_in,
+            k_size,
+            c_out,
+            c_in,
+            padding,
+            output_padding,
+            stride,
+            dilation,
+        };
         let storage = self.storage().conv_transpose1d(
             self.layout(),
             &kernel.storage(),
             kernel.layout(),
-            params,
+            &params,
         )?;
         let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::ConvTranspose1D {
             arg,
@@ -210,49 +230,6 @@ impl Tensor {
         Ok(crate::tensor::from_storage(storage, out_dims, op, false))
     }
 
-    /// Applies a 1D transposed convolution over the input tensor.
-    pub fn conv_transpose1d(
-        &self,
-        kernel: &Self,
-        padding: usize,
-        output_padding: usize,
-        stride: usize,
-        dilation: usize,
-        groups: usize,
-    ) -> Result<Self> {
-        let (c_in_k, c_out, k_size) = kernel.dims3()?;
-        let (b_size, c_in, l_in) = self.dims3()?;
-        if c_in != c_in_k {
-            crate::bail!("in_channel mismatch between input ({c_in}) and kernel ({c_in_k})")
-        }
-        if c_in % groups != 0 {
-            crate::bail!("in_channel {c_in} is not divisible by the number of groups")
-        }
-        let params = ParamsConvTranspose1D {
-            b_size,
-            l_in,
-            k_size,
-            c_out,
-            c_in: c_in / groups,
-            padding,
-            output_padding,
-            stride,
-            dilation,
-        };
-        if groups == 1 {
-            self.conv_transpose1d_single_group(kernel, &params)
-        } else {
-            let blocks = self.chunk(groups, 1)?;
-            let kernel = kernel.chunk(groups, 0)?;
-            let blocks = blocks
-                .iter()
-                .zip(&kernel)
-                .map(|(block, kernel)| block.conv_transpose1d_single_group(kernel, &params))
-                .collect::<Result<Vec<_>>>()?;
-            Tensor::cat(&blocks, 1)
-        }
-    }
-
     fn conv2d_single_group(&self, kernel: &Self, params: &ParamsConv2D) -> Result<Self> {
         let storage =
             self.storage()

candle-core/src/cpu_backend.rs

@@ -5,7 +5,6 @@ use half::{bf16, f16};
 use rayon::prelude::*;
 
 const USE_IM2COL_CONV1D: bool = true;
-const USE_IM2COL_CONV1D_TR: bool = true;
 const USE_IM2COL_CONV2D: bool = true;
 
 // TODO: Maybe we should not implement [Clone] here and instead have an explicit allocator +
@@ -1023,26 +1022,6 @@ impl<'a, I: IntDType> Map2 for IndexAdd<'a, I> {
     }
 }
 
-#[allow(clippy::too_many_arguments)]
-fn copy2d_<T: Copy>(
-    src: &[T],
-    dst: &mut [T],
-    d1: usize,
-    d2: usize,
-    src_stride1: usize,
-    dst_stride1: usize,
-    src_offset: usize,
-    dst_offset: usize,
-) {
-    for i1 in 0..d1 {
-        let dst_idx = i1 * dst_stride1 + dst_offset;
-        let src_idx = i1 * src_stride1 + src_offset;
-        let dst = &mut dst[dst_idx..dst_idx + d2];
-        let src = &src[src_idx..src_idx + d2];
-        dst.copy_from_slice(src)
-    }
-}
-
 fn copy_strided_src_<T: Copy>(src: &[T], dst: &mut [T], dst_offset: usize, src_l: &Layout) {
     match src_l.strided_blocks() {
         crate::StridedBlocks::SingleBlock { start_offset, len } => {
@@ -1277,34 +1256,6 @@ impl Map1 for Im2Col {
     }
 }
 
-struct Col2Im1D {
-    stride: usize,
-}
-
-impl Map1 for Col2Im1D {
-    fn f<T: WithDType>(&self, col: &[T], l: &Layout) -> Result<Vec<T>> {
-        let (b_size, l_in, c_out, k_size) = l.shape().dims4()?;
-        let stride = self.stride;
-        let l_out = (l_in - 1) * stride + k_size;
-        let mut im = vec![T::zero(); b_size * c_out * l_out];
-        let (dst_s0, dst_s1) = (c_out * l_out, l_out);
-        let (src_s0, src_s1, src_s2) = (c_out * k_size * l_in, c_out * k_size, k_size);
-        for l_in_i in 0..l_in {
-            for k_i in 0..k_size {
-                let l_out_i = l_in_i * stride + k_i;
-                for b_i in 0..b_size {
-                    for c_i in 0..c_out {
-                        let dst_idx = b_i * dst_s0 + c_i * dst_s1 + l_out_i;
-                        let src_idx = b_i * src_s0 + l_in_i * src_s1 + c_i * src_s2 + k_i;
-                        im[dst_idx] += col[src_idx]
-                    }
-                }
-            }
-        }
-        Ok(im)
-    }
-}
-
 struct ConvTranspose1D<'a>(&'a crate::conv::ParamsConvTranspose1D);
 
 impl<'a> Map2 for ConvTranspose1D<'a> {
@@ -1312,7 +1263,6 @@ impl<'a> Map2 for ConvTranspose1D<'a> {
     fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
         let p = self.0;
         let inp = &inp[inp_l.start_offset()..];
-        let k = &k[k_l.start_offset()..];
         let (inp_s0, inp_s1, inp_s2) = crate::shape::dims3(inp_l.stride())?;
         let (k_s0, k_s1, k_s2) = crate::shape::dims3(k_l.stride())?;
         let l_out = p.l_out();
@@ -2472,48 +2422,6 @@ impl BackendStorage for CpuStorage {
         }
     }
 
-    fn copy2d(
-        &self,
-        dst: &mut Self,
-        d1: usize,
-        d2: usize,
-        src_s: usize,
-        dst_s: usize,
-        src_o: usize,
-        dst_o: usize,
-    ) -> Result<()> {
-        match (self, dst) {
-            (Self::U8(src), Self::U8(dst)) => copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o),
-            (Self::U32(src), Self::U32(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::I64(src), Self::I64(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::BF16(src), Self::BF16(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::F16(src), Self::F16(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::F32(src), Self::F32(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::F64(src), Self::F64(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (_, dst) => {
-                return Err(Error::DTypeMismatchBinaryOp {
-                    lhs: self.dtype(),
-                    rhs: dst.dtype(),
-                    op: "copy2d",
-                }
-                .bt());
-            }
-        }
-        Ok(())
-    }
-
     fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
         match (self, dst) {
             (Self::U8(src), Self::U8(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
@@ -2602,52 +2510,7 @@ impl BackendStorage for CpuStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConvTranspose1D,
     ) -> Result<Self> {
-        let can_use_col2im = kernel_l.is_contiguous()
-            && params.dilation == 1
-            && params.padding == 0
-            && params.output_padding == 0;
-        if USE_IM2COL_CONV1D_TR && can_use_col2im {
-            let (b_size, c_in, l_in) = l.shape().dims3()?;
-            let (c_in2, c_out, k_size) = kernel_l.shape().dims3()?;
-            if !kernel_l.is_contiguous() {
-                crate::bail!(
-                    "convtr1d: the second argument (kernel) has to be contiguous {kernel_l:?}"
-                )
-            }
-            if c_in != c_in2 {
-                crate::bail!(
-                    "convtr1d: shape mismatch on c_in {:?} {:?}",
-                    l.shape(),
-                    kernel_l.shape()
-                )
-            }
-            let col = {
-                // This merges the last two dimensions of the kernel together.
-                let kernel_l_mm = Layout::new(
-                    (b_size, c_in, k_size * c_out).into(),
-                    vec![0, k_size * c_out, 1],
-                    kernel_l.start_offset(),
-                );
-                self.matmul(
-                    kernel,
-                    (
-                        b_size,
-                        /* m */ l_in,
-                        /* n */ c_out * k_size,
-                        /* k */ c_in,
-                    ),
-                    &l.transpose(1, 2)?,
-                    &kernel_l_mm,
-                )?
-            };
-            let col_l = Layout::contiguous((b_size, l_in, c_out, k_size));
-            Col2Im1D {
-                stride: params.stride,
-            }
-            .map(&col, &col_l)
-        } else {
-            ConvTranspose1D(params).map(self, l, kernel, kernel_l)
-        }
+        ConvTranspose1D(params).map(self, l, kernel, kernel_l)
     }
 
     fn conv2d(
@@ -2711,7 +2574,7 @@ impl BackendStorage for CpuStorage {
             Self::U8(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
             Self::U32(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
             Self::I64(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
-            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-select").bt()),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-select")),
         }
     }
 
@@ -2720,7 +2583,7 @@ impl BackendStorage for CpuStorage {
             Self::U8(ids) => Gather { ids, ids_l, dim }.map(self, l),
             Self::U32(ids) => Gather { ids, ids_l, dim }.map(self, l),
             Self::I64(ids) => Gather { ids, ids_l, dim }.map(self, l),
-            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "gather").bt()),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "gather")),
         }
     }
 
@@ -2737,7 +2600,7 @@ impl BackendStorage for CpuStorage {
             Self::U8(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
             Self::U32(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
             Self::I64(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
-            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "scatter-add").bt()),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "scatter-add")),
         }
     }
 

candle-core/src/cuda_backend.rs

@@ -608,34 +608,6 @@ impl Map1 for Elu {
     }
 }
 
-struct Col2Im1D {
-    stride: usize,
-}
-
-impl Map1 for Col2Im1D {
-    fn f<T: DeviceRepr + WithDType>(
-        &self,
-        src: &CudaSlice<T>,
-        dev: &CudaDevice,
-        layout: &Layout,
-    ) -> Result<CudaSlice<T>> {
-        let (b_size, l_in, c_out, k_size) = layout.shape().dims4()?;
-        let stride = self.stride;
-        let l_out = (l_in - 1) * stride + k_size;
-
-        let dst_el = b_size * c_out * l_out;
-        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
-        let src = &src.slice(layout.start_offset()..);
-        let func = dev.get_or_load_func(&kernel_name::<T>("col2im1d"), kernels::CONV)?;
-        // SAFETY: Set later by running the kernel.
-        let dst = unsafe { dev.alloc::<T>(dst_el) }.w()?;
-        let params = (l_in, l_out, c_out, k_size, b_size, stride, src, &dst);
-        // SAFETY: ffi.
-        unsafe { func.launch(cfg, params) }.w()?;
-        Ok(dst)
-    }
-}
-
 struct Im2Col1D {
     l_k: usize,
     stride: usize,
@@ -1893,54 +1865,8 @@ impl BackendStorage for CudaStorage {
         params: &crate::conv::ParamsConvTranspose1D,
     ) -> Result<Self> {
         let device = self.device().clone();
-        const USE_COL2IM_CONV1D_TR: bool = true;
-
-        let can_use_col2im = kernel_l.is_contiguous()
-            && params.dilation == 1
-            && params.padding == 0
-            && params.output_padding == 0;
-        if !can_use_col2im || !USE_COL2IM_CONV1D_TR {
-            let slice =
-                ConvTranspose1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
-            return Ok(Self { slice, device });
-        }
-
-        let (b_size, c_in, l_in) = l.shape().dims3()?;
-        let (c_in2, c_out, k_size) = kernel_l.shape().dims3()?;
-        if !kernel_l.is_contiguous() {
-            crate::bail!("convtr1d: the second argument (kernel) has to be contiguous {kernel_l:?}")
-        }
-        if c_in != c_in2 {
-            crate::bail!(
-                "convtr1d: shape mismatch on c_in {:?} {:?}",
-                l.shape(),
-                kernel_l.shape()
-            )
-        }
-        let col = {
-            // This merges the last two dimensions of the kernel together.
-            let kernel_l_mm = Layout::new(
-                (b_size, c_in, k_size * c_out).into(),
-                vec![0, k_size * c_out, 1],
-                kernel_l.start_offset(),
-            );
-            self.matmul(
-                kernel,
-                (
-                    b_size,
-                    /* m */ l_in,
-                    /* n */ c_out * k_size,
-                    /* k */ c_in,
-                ),
-                &l.transpose(1, 2)?,
-                &kernel_l_mm,
-            )?
-        };
-        let col_l = Layout::contiguous((b_size, l_in, c_out, k_size));
-        let slice = Col2Im1D {
-            stride: params.stride,
-        }
-        .map(&col.slice, &device, &col_l)?;
+        let slice =
+            ConvTranspose1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
         Ok(Self { slice, device })
     }
 
@@ -2219,67 +2145,6 @@ impl BackendStorage for CudaStorage {
         Ok(Self { slice, device })
     }
 
-    fn copy2d(
-        &self,
-        dst: &mut Self,
-        d1: usize,
-        d2: usize,
-        src_s: usize,
-        dst_s: usize,
-        src_o: usize,
-        dst_o: usize,
-    ) -> Result<()> {
-        let dev = &self.device;
-        let d1 = d1 as u32;
-        let d2 = d2 as u32;
-        let dst_s = dst_s as u32;
-        let src_s = src_s as u32;
-        let (src, dst, kname) = match (&self.slice, &mut dst.slice) {
-            (S::U8(s), S::U8(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_u8",
-            ),
-            (S::U32(s), S::U32(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_u32",
-            ),
-            (S::I64(s), S::I64(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_i64",
-            ),
-            (S::BF16(s), S::BF16(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_bf16",
-            ),
-            (S::F16(s), S::F16(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_f16",
-            ),
-            (S::F32(s), S::F32(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_f32",
-            ),
-            (S::F64(s), S::F64(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_f64",
-            ),
-            _ => Err(CudaError::InternalError("dtype mismatch in copy2d"))?,
-        };
-        let func = dev.get_or_load_func(kname, kernels::FILL)?;
-        let cfg = LaunchConfig::for_num_elems(d1 * d2);
-        let params = (src, dst, d1, d2, src_s, dst_s);
-        // SAFETY: ffi.
-        unsafe { func.launch(cfg, params) }.w()?;
-        Ok(())
-    }
-
     fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
         let src_shape = src_l.shape();
         let dims = src_shape.dims();

candle-core/src/display.rs

@@ -65,13 +65,12 @@ impl std::fmt::Debug for Tensor {
 }
 
 /// Options for Tensor pretty printing
-#[derive(Debug, Clone)]
 pub struct PrinterOptions {
-    pub precision: usize,
-    pub threshold: usize,
-    pub edge_items: usize,
-    pub line_width: usize,
-    pub sci_mode: Option<bool>,
+    precision: usize,
+    threshold: usize,
+    edge_items: usize,
+    line_width: usize,
+    sci_mode: Option<bool>,
 }
 
 static PRINT_OPTS: std::sync::Mutex<PrinterOptions> =
@@ -90,10 +89,6 @@ impl PrinterOptions {
     }
 }
 
-pub fn print_options() -> &'static std::sync::Mutex<PrinterOptions> {
-    &PRINT_OPTS
-}
-
 pub fn set_print_options(options: PrinterOptions) {
     *PRINT_OPTS.lock().unwrap() = options
 }
@@ -122,26 +117,6 @@ pub fn set_print_options_full() {
     }
 }
 
-pub fn set_line_width(line_width: usize) {
-    PRINT_OPTS.lock().unwrap().line_width = line_width
-}
-
-pub fn set_precision(precision: usize) {
-    PRINT_OPTS.lock().unwrap().precision = precision
-}
-
-pub fn set_edge_items(edge_items: usize) {
-    PRINT_OPTS.lock().unwrap().edge_items = edge_items
-}
-
-pub fn set_threshold(threshold: usize) {
-    PRINT_OPTS.lock().unwrap().threshold = threshold
-}
-
-pub fn set_sci_mode(sci_mode: Option<bool>) {
-    PRINT_OPTS.lock().unwrap().sci_mode = sci_mode
-}
-
 struct FmtSize {
     current_size: usize,
 }

candle-core/src/dtype.rs

@@ -23,15 +23,7 @@ pub enum DType {
 }
 
 #[derive(Debug, PartialEq, Eq)]
-pub struct DTypeParseError(String);
-
-impl std::fmt::Display for DTypeParseError {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "cannot parse '{}' as a dtype", self.0)
-    }
-}
-
-impl std::error::Error for DTypeParseError {}
+pub struct DTypeParseError;
 
 impl std::str::FromStr for DType {
     type Err = DTypeParseError;
@@ -44,7 +36,7 @@ impl std::str::FromStr for DType {
             "f16" => Ok(Self::F16),
             "f32" => Ok(Self::F32),
             "f64" => Ok(Self::F64),
-            _ => Err(DTypeParseError(s.to_string())),
+            _ => Err(DTypeParseError),
         }
     }
 }

candle-core/src/dummy_cuda_backend.rs

@@ -154,19 +154,6 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    fn copy2d(
-        &self,
-        _: &mut Self,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-    ) -> Result<()> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
     fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
         Err(Error::NotCompiledWithCudaSupport)
     }

candle-core/src/dummy_metal_backend.rs

@@ -166,19 +166,6 @@ impl crate::backend::BackendStorage for MetalStorage {
         Err(Error::NotCompiledWithMetalSupport)
     }
 
-    fn copy2d(
-        &self,
-        _: &mut Self,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-    ) -> Result<()> {
-        Err(Error::NotCompiledWithMetalSupport)
-    }
-
     fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
         Err(Error::NotCompiledWithMetalSupport)
     }

candle-core/src/layout.rs

@@ -70,7 +70,7 @@ impl Layout {
         self.shape.is_fortran_contiguous(&self.stride)
     }
 
-    pub fn narrow(&self, dim: usize, start: usize, len: usize) -> Result<Self> {
+    pub(crate) fn narrow(&self, dim: usize, start: usize, len: usize) -> Result<Self> {
         let dims = self.shape().dims();
         if dim >= dims.len() {
             Err(Error::DimOutOfRange {
@@ -99,7 +99,7 @@ impl Layout {
         })
     }
 
-    pub fn transpose(&self, dim1: usize, dim2: usize) -> Result<Self> {
+    pub(crate) fn transpose(&self, dim1: usize, dim2: usize) -> Result<Self> {
         let rank = self.shape.rank();
         if rank <= dim1 || rank <= dim2 {
             Err(Error::UnexpectedNumberOfDims {
@@ -120,7 +120,7 @@ impl Layout {
         })
     }
 
-    pub fn permute(&self, idxs: &[usize]) -> Result<Self> {
+    pub(crate) fn permute(&self, idxs: &[usize]) -> Result<Self> {
         let is_permutation =
             idxs.len() == self.shape.rank() && (0..idxs.len()).all(|i| idxs.contains(&i));
         if !is_permutation {

candle-core/src/lib.rs

@@ -67,7 +67,6 @@ pub mod shape;
 mod storage;
 mod strided_index;
 mod tensor;
-mod tensor_cat;
 pub mod test_utils;
 pub mod utils;
 mod variable;
@@ -130,15 +129,6 @@ impl<T: Fn(&Tensor) -> Result<Tensor>> Module for T {
     }
 }
 
-impl<M: Module> Module for Option<&M> {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        match self {
-            None => Ok(xs.clone()),
-            Some(m) => m.forward(xs),
-        }
-    }
-}
-
 // A trait defining a module with forward method using a single tensor argument and a flag to
 // separate the training and evaluation behaviors.
 pub trait ModuleT {

candle-core/src/metal_backend.rs

@@ -9,7 +9,7 @@ use metal::{Buffer, CommandBuffer, CommandQueue, MTLResourceOptions, NSUInteger}
 use std::collections::HashMap;
 use std::ffi::c_void;
 use std::path::Path;
-use std::sync::{Arc, Mutex, RwLock, RwLockWriteGuard, TryLockError};
+use std::sync::{Arc, Mutex, RwLock, TryLockError};
 
 /// Simple way to catch lock error without
 /// depending on T
@@ -60,8 +60,7 @@ impl From<String> for MetalError {
     }
 }
 
-type BufferMap = HashMap<(NSUInteger, MTLResourceOptions), Vec<Arc<Buffer>>>;
-type AllocatedBuffers = Arc<RwLock<BufferMap>>;
+type AllocatedBuffers = Arc<RwLock<HashMap<(NSUInteger, MTLResourceOptions), Vec<Arc<Buffer>>>>>;
 
 #[derive(Clone)]
 pub struct MetalDevice {
@@ -69,7 +68,7 @@ pub struct MetalDevice {
     device: metal::Device,
 
     /// Single command queue for the entire device.
-    command_queue: CommandQueue,
+    command_queue: metal::CommandQueue,
     /// One command buffer at a time.
     /// The scheduler works by allowing multiple
     /// [ComputeCommandEncoder](https://developer.apple.com/documentation/metal/mtlcomputecommandencoder?language=objc)
@@ -79,7 +78,7 @@ pub struct MetalDevice {
     /// Despite what the documentation says, command buffers are NOT ordered. They are ordered
     /// for their START time, but there's no guarantee that command buffer1 will finish before
     /// command buffer2 starts (or there are metal bugs there)
-    command_buffer: Arc<RwLock<CommandBuffer>>,
+    command_buffer: Arc<RwLock<metal::CommandBuffer>>,
     /// Keeps track of the current amount of compute command encoders on the current
     /// command buffer
     /// Arc, RwLock because of the interior mutability.
@@ -88,7 +87,7 @@ pub struct MetalDevice {
     compute_per_buffer: usize,
     /// Simple keeper struct to keep track of the already compiled kernels so we can reuse them.
     /// Heavily used by [`candle_metal_kernels`]
-    kernels: Arc<Kernels>,
+    kernels: Arc<candle_metal_kernels::Kernels>,
     /// Simple allocator struct.
     /// The buffers are stored in size buckets since ML tends to use similar shapes over and over.
     /// We store the buffers in [`Arc`] because it's much faster than Obj-c internal ref counting
@@ -100,7 +99,7 @@ pub struct MetalDevice {
     /// operation, so that this buffer is not being used by another kernel at the same time.
     /// Arc is the CPU reference count, it doesn't mean anything on the GPU side of things.
     ///
-    /// Whenever we actually allocate a new buffer, we make a full sweep to clean up unused buffers
+    /// Whenever we actually allocate a new buffer, we make a full sweep to cleanup unused buffers
     /// (strong_count = 1).
     buffers: AllocatedBuffers,
     /// Seed for random number generation.
@@ -146,8 +145,6 @@ impl MetalDevice {
             command_buffer = self.command_queue.new_command_buffer().to_owned();
             *command_buffer_lock = command_buffer.clone();
             *index = 0;
-
-            self.drop_unused_buffers()?;
         }
         *index += 1;
         Ok(command_buffer)
@@ -166,7 +163,6 @@ impl MetalDevice {
         command_buffer.commit();
         command_buffer.wait_until_completed();
         *command_buffer = self.command_queue.new_command_buffer().to_owned();
-
         Ok(())
     }
 
@@ -203,25 +199,39 @@ impl MetalDevice {
     }
 
     /// Creates a new buffer from data.
-    /// The buffer is [MTLManaged](https://developer.apple.com/documentation/metal/mtlstoragemode)
+    /// The buffer is [MTLPrivate](https://developer.apple.com/documentation/metal/mtlstoragemode)
     ///
-    /// Does not require synchronization, as [newBufferWithBytes](https://developer.apple.com/documentation/metal/mtldevice/1433429-newbufferwithbytes)
-    /// allocates the buffer and copies over the existing data before returning the MTLBuffer.
+    /// This method will block the computation because of the
+    /// lack of lifetime management through the GPU.
+    /// Internal comment for technical details.
     pub fn new_buffer_with_data<T>(&self, data: &[T]) -> Result<Arc<Buffer>> {
         let size = core::mem::size_of_val(data) as NSUInteger;
-        let new_buffer = self.device.new_buffer_with_data(
-            data.as_ptr() as *const c_void,
+        let tmp = self.device.new_buffer_with_data(
+            data.as_ptr() as *const core::ffi::c_void,
             size,
-            MTLResourceOptions::StorageModeManaged,
+            metal::MTLResourceOptions::StorageModeManaged,
         );
-        let mut buffers = self.buffers.try_write().map_err(MetalError::from)?;
-        let subbuffers = buffers
-            .entry((size, MTLResourceOptions::StorageModeManaged))
-            .or_insert(vec![]);
+        let real = self.allocate_buffer(
+            size,
+            metal::MTLResourceOptions::StorageModePrivate,
+            "with_data",
+        )?;
+        let command_buffer = self.command_buffer()?;
+        command_buffer.set_label("with_data");
+        let blit = command_buffer.new_blit_command_encoder();
+        blit.set_label("with_data_blit");
+        blit.copy_from_buffer(&tmp, 0, &real, 0, tmp.length());
+        blit.end_encoding();
 
-        let new_buffer = Arc::new(new_buffer);
-        subbuffers.push(new_buffer.clone());
-        Ok(new_buffer)
+        // This is necessary, for mmaped safetensors
+        // Because of the unsafe slice cast we're doing.
+        // The slice might not live long enough for metal
+        // To actually fill the GPU buffer.
+        // Putting this wait forces the GPU buffer to be filled
+        // with the actual data allowing the CPU storage to do
+        // deallocate properly.
+        self.wait_until_completed()?;
+        Ok(real)
     }
 
     pub fn allocate_zeros(&self, size_in_bytes: usize) -> Result<Arc<Buffer>> {
@@ -245,40 +255,6 @@ impl MetalDevice {
         Ok(buffer)
     }
 
-    fn find_available_buffer(
-        &self,
-        size: NSUInteger,
-        option: MTLResourceOptions,
-        buffers: &RwLockWriteGuard<BufferMap>,
-    ) -> Option<Arc<Buffer>> {
-        let mut best_buffer: Option<&Arc<Buffer>> = None;
-        let mut best_buffer_size: NSUInteger = NSUInteger::MAX;
-        for ((buffer_size, buffer_option), subbuffers) in buffers.iter() {
-            if buffer_size >= &size && buffer_size < &best_buffer_size && buffer_option == &option {
-                for sub in subbuffers {
-                    if Arc::strong_count(sub) == 1 {
-                        best_buffer = Some(sub);
-                        best_buffer_size = *buffer_size;
-                    }
-                }
-            }
-        }
-        return best_buffer.map(|b| b.clone());
-    }
-
-    fn drop_unused_buffers(&self) -> Result<()> {
-        let mut buffers = self.buffers.try_write().map_err(MetalError::from)?;
-        for subbuffers in buffers.values_mut() {
-            let newbuffers = subbuffers
-                .iter()
-                .filter(|s| Arc::strong_count(*s) > 1)
-                .map(Arc::clone)
-                .collect();
-            *subbuffers = newbuffers;
-        }
-        Ok(())
-    }
-
     /// The critical allocator algorithm
     fn allocate_buffer(
         &self,
@@ -287,18 +263,24 @@ impl MetalDevice {
         _name: &str,
     ) -> Result<Arc<Buffer>> {
         let mut buffers = self.buffers.try_write().map_err(MetalError::from)?;
-        if let Some(b) = self.find_available_buffer(size, option, &buffers) {
-            // Cloning also ensures we increment the strong count
-            return Ok(b.clone());
-        }
-
-        let size = buf_size(size);
         let subbuffers = buffers.entry((size, option)).or_insert(vec![]);
 
+        for sub in &mut *subbuffers {
+            if Arc::strong_count(sub) == 1 {
+                return Ok(sub.clone());
+            }
+        }
         let new_buffer = self.device.new_buffer(size as NSUInteger, option);
         let new_buffer = Arc::new(new_buffer);
         subbuffers.push(new_buffer.clone());
-
+        for subbuffers in buffers.values_mut() {
+            let newbuffers = subbuffers
+                .iter()
+                .filter(|s| Arc::strong_count(s) > 1)
+                .map(Arc::clone)
+                .collect();
+            *subbuffers = newbuffers;
+        }
         Ok(new_buffer)
     }
 
@@ -323,8 +305,6 @@ pub struct MetalStorage {
     buffer: Arc<metal::Buffer>,
     /// a reference to the device owning this buffer
     device: MetalDevice,
-    /// The count of allocated elements in the buffer
-    count: usize,
     /// The dtype is kept since buffers are untyped.
     dtype: DType,
 }
@@ -406,7 +386,7 @@ impl BackendStorage for MetalStorage {
             )
             .map_err(MetalError::from)?;
         }
-        Ok(Self::new(buffer, device.clone(), el, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     fn powf(&self, layout: &Layout, pow: f64) -> Result<Self> {
@@ -422,7 +402,6 @@ impl BackendStorage for MetalStorage {
             let name = match self.dtype {
                 DType::F32 => "powf_f32",
                 DType::F16 => "powf_f16",
-                DType::BF16 => "powf_bf16",
                 dtype => crate::bail!("Metal contiguous powf {dtype:?} not implemented"),
             };
             candle_metal_kernels::call_powf(
@@ -440,7 +419,6 @@ impl BackendStorage for MetalStorage {
             let name = match self.dtype {
                 DType::F32 => "powf_f32_strided",
                 DType::F16 => "powf_f16_strided",
-                DType::BF16 => "powf_bf16_strided",
                 dtype => crate::bail!("Metal strided powf {dtype:?} not implemented"),
             };
             candle_metal_kernels::call_powf_strided(
@@ -457,7 +435,7 @@ impl BackendStorage for MetalStorage {
             )
             .map_err(MetalError::from)?;
         }
-        Ok(Self::new(buffer, device.clone(), el, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     fn elu(&self, layout: &Layout, alpha: f64) -> Result<Self> {
@@ -473,7 +451,6 @@ impl BackendStorage for MetalStorage {
             let name = match self.dtype {
                 DType::F32 => "elu_f32",
                 DType::F16 => "elu_f16",
-                DType::BF16 => "elu_bf16",
                 dtype => crate::bail!("Metal contiguous elu {dtype:?} not implemented"),
             };
             candle_metal_kernels::call_elu(
@@ -491,7 +468,6 @@ impl BackendStorage for MetalStorage {
             let name = match self.dtype {
                 DType::F32 => "elu_f32_strided",
                 DType::F16 => "elu_f16_strided",
-                DType::BF16 => "elu_bf16_strided",
                 dtype => crate::bail!("Metal strided elu {dtype:?} not implemented"),
             };
             candle_metal_kernels::call_elu_strided(
@@ -508,7 +484,7 @@ impl BackendStorage for MetalStorage {
             )
             .map_err(MetalError::from)?;
         }
-        Ok(Self::new(buffer, device.clone(), el, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     fn reduce_op(&self, op: ReduceOp, layout: &Layout, sum_dims: &[usize]) -> Result<Self> {
@@ -586,7 +562,7 @@ impl BackendStorage for MetalStorage {
         )
         .map_err(MetalError::from)?;
 
-        Ok(Self::new(buffer, device, dst_el, dtype))
+        Ok(Self::new(buffer, device, dtype))
     }
 
     fn cmp(&self, op: CmpOp, rhs: &Self, lhs_l: &Layout, rhs_l: &Layout) -> Result<Self> {
@@ -609,41 +585,29 @@ impl BackendStorage for MetalStorage {
         let command_buffer = device.command_buffer()?;
         if layout.is_contiguous() && layout.start_offset() == 0 {
             let kernel_name = match (self.dtype, dtype) {
-                (DType::U32, DType::BF16) => "cast_u32_bf16",
-                (DType::U32, DType::F16) => "cast_u32_f16",
                 (DType::U32, DType::F32) => "cast_u32_f32",
-                (DType::U32, DType::I64) => "cast_u32_i64",
                 (DType::U32, DType::U8) => "cast_u32_u8",
+                (DType::U32, DType::I64) => "cast_u32_i64",
+                (DType::U32, DType::F16) => "cast_u32_f16",
+                (DType::U32, DType::BF16) => "cast_u32_bf16",
 
-                (DType::U8, DType::BF16) => "cast_u8_bf16",
-                (DType::U8, DType::F16) => "cast_u8_f16",
+                (DType::U8, DType::U32) => "cast_u8_u32",
                 (DType::U8, DType::F32) => "cast_u8_f32",
                 (DType::U8, DType::I64) => "cast_u8_i64",
-                (DType::U8, DType::U32) => "cast_u8_u32",
+                (DType::U8, DType::BF16) => "cast_u8_bf16",
 
-                (DType::F32, DType::BF16) => "cast_f32_bf16",
                 (DType::F32, DType::F16) => "cast_f32_f16",
-                (DType::F32, DType::I64) => "cast_f32_i64",
-                (DType::F32, DType::U32) => "cast_f32_u32",
-                (DType::F32, DType::U8) => "cast_f32_u8",
+                (DType::F32, DType::BF16) => "cast_f32_bf16",
 
-                (DType::I64, DType::BF16) => "cast_i64_bf16",
-                (DType::I64, DType::F16) => "cast_i64_f16",
                 (DType::I64, DType::F32) => "cast_i64_f32",
-                (DType::I64, DType::U32) => "cast_i64_u32",
-                (DType::I64, DType::U8) => "cast_i64_u8",
 
                 (DType::F16, DType::BF16) => "cast_f16_bf16",
                 (DType::F16, DType::F32) => "cast_f16_f32",
-                (DType::F16, DType::I64) => "cast_f16_i64",
-                (DType::F16, DType::U32) => "cast_f16_u32",
-                (DType::F16, DType::U8) => "cast_f16_u8",
 
+                (DType::BF16, DType::U8) => "cast_bf16_u8",
+                (DType::BF16, DType::U32) => "cast_bf16_u32",
                 (DType::BF16, DType::F16) => "cast_bf16_f16",
                 (DType::BF16, DType::F32) => "cast_bf16_f32",
-                (DType::BF16, DType::I64) => "cast_bf16_i64",
-                (DType::BF16, DType::U32) => "cast_bf16_u32",
-                (DType::BF16, DType::U8) => "cast_bf16_u8",
 
                 (left, right) => {
                     crate::bail!("Metal contiguous to_dtype {left:?} {right:?} not implemented")
@@ -691,7 +655,7 @@ impl BackendStorage for MetalStorage {
             .map_err(MetalError::from)?;
         }
         command_buffer.set_label("to_dtype");
-        Ok(Self::new(buffer, device.clone(), el_count, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     fn unary_impl<B: UnaryOpT>(&self, layout: &Layout) -> Result<Self> {
@@ -775,7 +739,6 @@ impl BackendStorage for MetalStorage {
                 ("ufloor", DType::F32) => strided::floor::FLOAT,
                 ("urelu", DType::F32) => strided::relu::FLOAT,
                 ("uround", DType::F32) => strided::round::FLOAT,
-                ("utanh", DType::F32) => strided::tanh::FLOAT,
                 ("ucos", DType::F16) => strided::cos::HALF,
                 ("usin", DType::F16) => strided::sin::HALF,
                 ("usqr", DType::F16) => strided::sqr::HALF,
@@ -792,7 +755,6 @@ impl BackendStorage for MetalStorage {
                 ("ufloor", DType::F16) => strided::floor::HALF,
                 ("urelu", DType::F16) => strided::relu::HALF,
                 ("uround", DType::F16) => strided::round::HALF,
-                ("utanh", DType::F16) => strided::tanh::HALF,
                 (name, dtype) => {
                     crate::bail!("Metal strided unary {name} {dtype:?} not implemented")
                 }
@@ -811,7 +773,7 @@ impl BackendStorage for MetalStorage {
             )
             .map_err(MetalError::from)?;
         }
-        Ok(Self::new(buffer, device.clone(), el_count, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     fn binary_impl<B: BinaryOpT>(
@@ -866,13 +828,13 @@ impl BackendStorage for MetalStorage {
                 layout.start_offset() * self.dtype.size_in_bytes(),
             ),
             &t.buffer,
-            (t_l.stride(), t_l.start_offset() * t.dtype.size_in_bytes()),
+            (&t_l.stride(), t_l.start_offset() * t.dtype.size_in_bytes()),
             &f.buffer,
-            (f_l.stride(), f_l.start_offset() * f.dtype.size_in_bytes()),
+            (&f_l.stride(), f_l.start_offset() * f.dtype.size_in_bytes()),
             &buffer,
         )
         .map_err(MetalError::from)?;
-        Ok(Self::new(buffer, device, el, dtype))
+        Ok(Self::new(buffer, device, dtype))
     }
 
     fn conv1d(
@@ -917,7 +879,6 @@ impl BackendStorage for MetalStorage {
         let col = Self {
             buffer: dst,
             device,
-            count: dst_el,
             dtype: self.dtype,
         };
         let l_out = params.l_out();
@@ -1002,7 +963,6 @@ impl BackendStorage for MetalStorage {
         let col = Self {
             buffer: dst,
             device,
-            count: dst_el,
             dtype: self.dtype,
         };
         let h_out = params.out_h();
@@ -1088,7 +1048,7 @@ impl BackendStorage for MetalStorage {
             &buffer,
         )
         .map_err(MetalError::from)?;
-        Ok(Self::new(buffer, self.device.clone(), dst_el, self.dtype))
+        Ok(Self::new(buffer, self.device.clone(), self.dtype))
     }
 
     fn gather(&self, src_l: &Layout, ids: &Self, ids_l: &Layout, dim: usize) -> Result<Self> {
@@ -1122,7 +1082,7 @@ impl BackendStorage for MetalStorage {
             &buffer,
         )
         .map_err(MetalError::from)?;
-        Ok(Self::new(buffer, device.clone(), dst_el, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     fn scatter_add(
@@ -1145,15 +1105,7 @@ impl BackendStorage for MetalStorage {
             None => Err(crate::Error::RequiresContiguous { op: "scatter-add" }.bt())?,
         };
         let name = match (ids.dtype, self.dtype) {
-            (DType::U8, DType::F32) => "sa_u8_f32",
-            (DType::U8, DType::F16) => "sa_u8_f16",
-            (DType::U8, DType::BF16) => "sa_u8_bf16",
             (DType::U32, DType::F32) => "sa_u32_f32",
-            (DType::U32, DType::F16) => "sa_u32_f16",
-            (DType::U32, DType::BF16) => "sa_u32_bf16",
-            (DType::I64, DType::F32) => "sa_i64_f32",
-            (DType::I64, DType::F16) => "sa_i64_f16",
-            (DType::I64, DType::BF16) => "sa_i64_bf16",
             _ => Err(MetalError::UnexpectedDType {
                 msg: "scatter-add ids should be u8/u32/i64",
                 expected: DType::U32,
@@ -1219,7 +1171,7 @@ impl BackendStorage for MetalStorage {
             &buffer,
         )
         .map_err(MetalError::from)?;
-        Ok(Self::new(buffer, device.clone(), dst_el, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     fn index_add(
@@ -1301,73 +1253,7 @@ impl BackendStorage for MetalStorage {
             &buffer,
         )
         .map_err(MetalError::from)?;
-        Ok(Self::new(
-            buffer,
-            self.device.clone(),
-            b * m * n,
-            self.dtype(),
-        ))
-    }
-
-    fn copy2d(
-        &self,
-        dst: &mut Self,
-        d1: usize,
-        d2: usize,
-        src_s: usize,
-        dst_s: usize,
-        src_o: usize,
-        dst_o: usize,
-    ) -> Result<()> {
-        if self.dtype() != dst.dtype() {
-            crate::bail!(
-                "copy2d with inconsistent dtypes {:?} {:?}",
-                self.dtype(),
-                dst.dtype()
-            )
-        }
-        let command_buffer = self.device.command_buffer()?;
-        if src_s == d2 && dst_s == d2 {
-            command_buffer.set_label("copy2d_contiguous");
-            let blit = command_buffer.new_blit_command_encoder();
-            blit.set_label("copy2d_contiguous");
-            let src_offset = (src_o * self.dtype.size_in_bytes()) as NSUInteger;
-            let length = (d1 * d2 * self.dtype.size_in_bytes()) as NSUInteger;
-            let dst_offset = (dst_o * dst.dtype().size_in_bytes()) as NSUInteger;
-            blit.copy_from_buffer(&self.buffer, src_offset, dst.buffer(), dst_offset, length);
-            blit.end_encoding();
-        } else {
-            let el_count = d1 * d2;
-            if el_count == 0 {
-                return Ok(());
-            }
-            let kernel_name = match self.dtype {
-                DType::F32 => candle_metal_kernels::copy2d::FLOAT,
-                DType::F16 => candle_metal_kernels::copy2d::HALF,
-                DType::BF16 => candle_metal_kernels::copy2d::BFLOAT,
-                DType::I64 => candle_metal_kernels::copy2d::I64,
-                DType::U32 => candle_metal_kernels::copy2d::U32,
-                DType::U8 => candle_metal_kernels::copy2d::U8,
-                dtype => crate::bail!("Metal copy2d {dtype:?} not implemented"),
-            };
-            candle_metal_kernels::call_copy2d(
-                &self.device.device,
-                &command_buffer,
-                &self.device.kernels,
-                kernel_name,
-                &self.buffer,
-                &dst.buffer,
-                d1,
-                d2,
-                src_s,
-                dst_s,
-                src_o * self.dtype.size_in_bytes(),
-                dst_o * self.dtype.size_in_bytes(),
-            )
-            .map_err(MetalError::from)?;
-            command_buffer.set_label("copy2d");
-        }
-        Ok(())
+        Ok(Self::new(buffer, self.device.clone(), self.dtype()))
     }
 
     fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
@@ -1379,7 +1265,7 @@ impl BackendStorage for MetalStorage {
             let src_offset = (src_l.start_offset() * self.dtype.size_in_bytes()) as NSUInteger;
             let length = (src_l.shape().elem_count() * self.dtype.size_in_bytes()) as NSUInteger;
             let dst_offset = (dst_offset * dst.dtype().size_in_bytes()) as NSUInteger;
-            blit.copy_from_buffer(&self.buffer, src_offset, dst.buffer(), dst_offset, length);
+            blit.copy_from_buffer(&self.buffer, src_offset, &dst.buffer(), dst_offset, length);
             blit.end_encoding();
         } else {
             let src_shape = src_l.shape();
@@ -1416,11 +1302,10 @@ impl BackendStorage for MetalStorage {
 }
 
 impl MetalStorage {
-    pub fn new(buffer: Arc<Buffer>, device: MetalDevice, count: usize, dtype: DType) -> Self {
+    pub fn new(buffer: Arc<Buffer>, device: MetalDevice, dtype: DType) -> Self {
         Self {
             buffer,
             device,
-            count,
             dtype,
         }
     }
@@ -1635,23 +1520,29 @@ impl MetalStorage {
             (buffer, dtype)
         };
         command_buffer.set_label("binary");
-        Ok(Self::new(buffer, device.clone(), el_count, dtype))
+        Ok(Self::new(buffer, device.clone(), dtype))
     }
 
     pub(crate) fn to_cpu<T: Clone>(&self) -> Result<Vec<T>> {
-        let size = (self.count * self.dtype.size_in_bytes()) as NSUInteger;
-
-        let buffer = self.device.new_buffer_managed(size)?;
+        let length = self.buffer.length() as usize;
+        let size = self.dtype.size_in_bytes();
+        if length % size != 0 {
+            crate::bail!(
+                "The Metal buffer length is not aligned with dtype {:?}",
+                self.dtype
+            );
+        }
+        let buffer = self.device.new_buffer_managed(self.buffer.length())?;
         {
             let command_buffer = self.device.command_buffer()?;
             command_buffer.set_label("to_cpu");
             let blit = command_buffer.new_blit_command_encoder();
             blit.set_label("blit_to_cpu");
-            blit.copy_from_buffer(&self.buffer, 0, &buffer, 0, size);
+            blit.copy_from_buffer(&self.buffer, 0, &buffer, 0, self.buffer.length());
             blit.end_encoding();
         }
         self.device.wait_until_completed()?;
-        Ok(read_to_vec(&buffer, self.count))
+        Ok(read_to_vec(&buffer, length / size))
     }
 }
 
@@ -1669,7 +1560,7 @@ impl BackendDevice for MetalDevice {
         let buffers = Arc::new(RwLock::new(HashMap::new()));
         let compute_per_buffer = match std::env::var("CANDLE_METAL_COMPUTE_PER_BUFFER") {
             Ok(val) => val.parse()?,
-            _ => 50,
+            _ => 10,
         };
         let seed = Arc::new(Mutex::new(device.new_buffer_with_data(
             [299792458].as_ptr() as *const c_void,
@@ -1701,12 +1592,7 @@ impl BackendDevice for MetalDevice {
     fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<MetalStorage> {
         let size = shape.elem_count() * dtype.size_in_bytes();
         let buffer = self.allocate_zeros(size)?;
-        Ok(MetalStorage::new(
-            buffer,
-            self.clone(),
-            shape.elem_count(),
-            dtype,
-        ))
+        Ok(MetalStorage::new(buffer, self.clone(), dtype))
     }
 
     fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<Self::Storage> {
@@ -1716,21 +1602,16 @@ impl BackendDevice for MetalDevice {
     }
 
     fn storage_from_cpu_storage(&self, storage: &CpuStorage) -> Result<Self::Storage> {
-        let (count, buffer) = match storage {
-            CpuStorage::U8(storage) => (storage.len(), self.new_buffer_with_data(storage)),
-            CpuStorage::U32(storage) => (storage.len(), self.new_buffer_with_data(storage)),
-            CpuStorage::I64(storage) => (storage.len(), self.new_buffer_with_data(storage)),
-            CpuStorage::BF16(storage) => (storage.len(), self.new_buffer_with_data(storage)),
-            CpuStorage::F16(storage) => (storage.len(), self.new_buffer_with_data(storage)),
-            CpuStorage::F32(storage) => (storage.len(), self.new_buffer_with_data(storage)),
-            CpuStorage::F64(storage) => (storage.len(), self.new_buffer_with_data(storage)),
-        };
-        Ok(Self::Storage::new(
-            buffer?,
-            self.clone(),
-            count,
-            storage.dtype(),
-        ))
+        let buffer = match storage {
+            CpuStorage::U8(storage) => self.new_buffer_with_data(storage),
+            CpuStorage::U32(storage) => self.new_buffer_with_data(storage),
+            CpuStorage::I64(storage) => self.new_buffer_with_data(storage),
+            CpuStorage::BF16(storage) => self.new_buffer_with_data(storage),
+            CpuStorage::F16(storage) => self.new_buffer_with_data(storage),
+            CpuStorage::F32(storage) => self.new_buffer_with_data(storage),
+            CpuStorage::F64(storage) => self.new_buffer_with_data(storage),
+        }?;
+        Ok(Self::Storage::new(buffer, self.clone(), storage.dtype()))
     }
 
     fn rand_uniform(
@@ -1756,17 +1637,12 @@ impl BackendDevice for MetalDevice {
             min as f32,
             max as f32,
             shape.elem_count(),
-            &self.seed.lock().unwrap(),
+            &*self.seed.lock().unwrap(),
             &buffer,
         )
         .map_err(MetalError::from)?;
 
-        Ok(Self::Storage::new(
-            buffer,
-            self.clone(),
-            shape.elem_count(),
-            dtype,
-        ))
+        Ok(Self::Storage::new(buffer, self.clone(), dtype))
     }
 
     fn rand_normal(
@@ -1792,17 +1668,12 @@ impl BackendDevice for MetalDevice {
             mean as f32,
             stddev as f32,
             shape.elem_count(),
-            &self.seed.lock().unwrap(),
+            &*self.seed.lock().unwrap(),
             &buffer,
         )
         .map_err(MetalError::from)?;
 
-        Ok(Self::Storage::new(
-            buffer,
-            self.clone(),
-            shape.elem_count(),
-            dtype,
-        ))
+        Ok(Self::Storage::new(buffer, self.clone(), dtype))
     }
 
     fn set_seed(&self, seed: u64) -> Result<()> {
@@ -1813,7 +1684,7 @@ impl BackendDevice for MetalDevice {
         let seed_buffer = self.seed.try_lock().map_err(MetalError::from)?;
         let contents = seed_buffer.contents();
         unsafe {
-            std::ptr::copy([seed].as_ptr(), contents as *mut u32, 1);
+            std::ptr::copy([seed].as_ptr(), contents as *mut u32, 4);
         }
         seed_buffer.did_modify_range(metal::NSRange::new(0, 4));
 
@@ -1821,10 +1692,6 @@ impl BackendDevice for MetalDevice {
     }
 }
 
-fn buf_size(size: NSUInteger) -> NSUInteger {
-    (size - 1).next_power_of_two() as NSUInteger
-}
-
 fn read_to_vec<T: Clone>(buffer: &Buffer, n: usize) -> Vec<T> {
     let ptr = buffer.contents() as *const T;
     assert!(!ptr.is_null());

candle-core/src/op.rs

@@ -132,10 +132,7 @@ pub enum Op {
         stride: (usize, usize),
     },
 
-    UpsampleNearest1D {
-        arg: Tensor,
-        target_size: usize,
-    },
+    UpsampleNearest1D(Tensor),
     UpsampleNearest2D {
         arg: Tensor,
         target_h: usize,

candle-core/src/pickle.rs

@@ -42,7 +42,7 @@ pub enum OpCode {
     Stop = b'.',
     NewObj = 0x81,
     EmptyList = b']',
-    BinFloat = b'G',
+    BinFloat = b'g',
     Append = b'a',
     Appends = b'e',
 }
@@ -462,10 +462,7 @@ impl Stack {
                 self.push(Object::Int(arg))
             }
             OpCode::BinFloat => {
-                // Somehow floats are encoded using BigEndian whereas int types use LittleEndian.
-                // https://github.com/python/cpython/blob/0c80da4c14d904a367968955544dd6ae58c8101c/Lib/pickletools.py#L855
-                // https://github.com/pytorch/pytorch/blob/372d078f361e726bb4ac0884ac334b04c58179ef/torch/_weights_only_unpickler.py#L243
-                let arg = r.read_f64::<byteorder::BigEndian>()?;
+                let arg = r.read_f64::<LittleEndian>()?;
                 self.push(Object::Float(arg))
             }
             OpCode::BinUnicode => {

candle-core/src/quantized/cuda.rs

@@ -1,343 +0,0 @@
-use super::{GgmlDType, QStorage};
-use crate::{backend::BackendDevice, cuda_backend::WrapErr};
-use crate::{CudaDevice, CudaStorage, Result};
-
-use cudarc::driver::{CudaSlice, DeviceSlice};
-
-pub struct QCudaStorage {
-    data: CudaSlice<u8>,
-    dtype: GgmlDType,
-    device: CudaDevice,
-}
-
-pub const WARP_SIZE: usize = 32;
-pub const MMQ_X_Q4_0_AMPERE: usize = 4;
-pub const MMQ_Y_Q4_0_AMPERE: usize = 32;
-pub const NWARPS_Q4_0_AMPERE: usize = 4;
-pub const GGML_CUDA_MMV_X: usize = 32;
-pub const GGML_CUDA_MMV_Y: usize = 1;
-pub const CUDA_DEQUANTIZE_BLOCK_SIZE: usize = 256;
-
-fn dequantize(
-    data: &CudaSlice<u8>,
-    dtype: GgmlDType,
-    elem_count: usize,
-    dev: &CudaDevice,
-) -> Result<CudaStorage> {
-    use cudarc::driver::LaunchAsync;
-
-    let nb = (elem_count + 255) / 256;
-    let (kernel_name, is_k, block_dim, num_blocks) = match dtype {
-        GgmlDType::Q4_0 => ("dequantize_block_q4_0", false, 32, nb),
-        GgmlDType::Q4_1 => ("dequantize_block_q4_1", false, 32, nb),
-        GgmlDType::Q5_0 => {
-            let nb = (elem_count + 2 * CUDA_DEQUANTIZE_BLOCK_SIZE - 1)
-                / (2 * CUDA_DEQUANTIZE_BLOCK_SIZE);
-            (
-                "dequantize_block_q5_0",
-                false,
-                CUDA_DEQUANTIZE_BLOCK_SIZE,
-                nb,
-            )
-        }
-        GgmlDType::Q5_1 => {
-            let nb = (elem_count + 2 * CUDA_DEQUANTIZE_BLOCK_SIZE - 1)
-                / (2 * CUDA_DEQUANTIZE_BLOCK_SIZE);
-            (
-                "dequantize_block_q5_1",
-                false,
-                CUDA_DEQUANTIZE_BLOCK_SIZE,
-                nb,
-            )
-        }
-        GgmlDType::Q8_0 => ("dequantize_block_q8_0", false, 32, nb),
-        GgmlDType::Q2K => ("dequantize_block_q2_K", true, 64, nb),
-        GgmlDType::Q3K => ("dequantize_block_q3_K", true, 64, nb),
-        GgmlDType::Q4K => ("dequantize_block_q4_K", true, 32, nb),
-        GgmlDType::Q5K => ("dequantize_block_q5_K", true, 64, nb),
-        GgmlDType::Q6K => ("dequantize_block_q6_K", true, 64, nb),
-        GgmlDType::Q8K => ("dequantize_block_q8_K", true, 32, nb),
-        _ => crate::bail!("unsupported dtype for dequantize {dtype:?}"),
-    };
-    let func = dev.get_or_load_func(kernel_name, candle_kernels::QUANTIZED)?;
-    let dst = dev.alloc_zeros::<f32>(elem_count).w()?;
-    // See e.g.
-    // https://github.com/ggerganov/llama.cpp/blob/cbbd1efa06f8c09f9dff58ff9d9af509cc4c152b/ggml-cuda.cu#L7270
-    let cfg = cudarc::driver::LaunchConfig {
-        grid_dim: (num_blocks as u32, 1, 1),
-        block_dim: (block_dim as u32, 1, 1),
-        shared_mem_bytes: 0,
-    };
-
-    if is_k {
-        let params = (data, &dst);
-        unsafe { func.launch(cfg, params) }.w()?;
-    } else {
-        let nb32 = match dtype {
-            GgmlDType::Q5_0 | GgmlDType::Q5_1 => elem_count,
-            _ => elem_count / 32,
-        };
-        let params = (data, &dst, nb32 as i32);
-        unsafe { func.launch(cfg, params) }.w()?;
-    }
-    Ok(CudaStorage::wrap_cuda_slice(dst, dev.clone()))
-}
-
-fn dequantize_mut_mal_vec(
-    data: &CudaSlice<u8>,
-    y: &cudarc::driver::CudaView<f32>,
-    dtype: GgmlDType,
-    ncols: usize,
-    nrows: usize,
-    dev: &CudaDevice,
-) -> Result<CudaStorage> {
-    use cudarc::driver::LaunchAsync;
-
-    let kernel_name = match dtype {
-        GgmlDType::Q4_0 => "dequantize_mul_mat_vec_q4_0_cuda",
-        GgmlDType::Q4_1 => "dequantize_mul_mat_vec_q4_1_cuda",
-        GgmlDType::Q5_0 => "dequantize_mul_mat_vec_q5_0_cuda",
-        GgmlDType::Q5_1 => "dequantize_mul_mat_vec_q5_1_cuda",
-        GgmlDType::Q8_0 => "dequantize_mul_mat_vec_q8_0_cuda",
-        GgmlDType::Q2K => "dequantize_mul_mat_vec_q2_k",
-        GgmlDType::Q3K => "dequantize_mul_mat_vec_q3_k",
-        GgmlDType::Q4K => "dequantize_mul_mat_vec_q4_k",
-        GgmlDType::Q5K => "dequantize_mul_mat_vec_q5_k",
-        GgmlDType::Q6K => "dequantize_mul_mat_vec_q6_k",
-        _ => crate::bail!("unsupported dtype for quantized matmul {dtype:?}"),
-    };
-    let func = dev.get_or_load_func(kernel_name, candle_kernels::QUANTIZED)?;
-    let dst = dev.alloc_zeros::<f32>(nrows).w()?;
-    let block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
-    let cfg = cudarc::driver::LaunchConfig {
-        grid_dim: (block_num_y as u32, 1, 1),
-        block_dim: (WARP_SIZE as u32, GGML_CUDA_MMV_Y as u32, 1),
-        shared_mem_bytes: 0,
-    };
-
-    let params = (data, y, &dst, ncols as i32, nrows as i32);
-    unsafe { func.launch(cfg, params) }.w()?;
-    Ok(CudaStorage::wrap_cuda_slice(dst, dev.clone()))
-}
-
-impl QCudaStorage {
-    pub fn zeros(device: &CudaDevice, el_count: usize, dtype: GgmlDType) -> Result<Self> {
-        let size_in_bytes = el_count * dtype.type_size() / dtype.block_size();
-        let data = device.alloc_zeros::<u8>(size_in_bytes).w()?;
-        Ok(QCudaStorage {
-            data,
-            device: device.clone(),
-            dtype,
-        })
-    }
-
-    pub fn dtype(&self) -> GgmlDType {
-        self.dtype
-    }
-
-    pub fn device(&self) -> &CudaDevice {
-        &self.device
-    }
-
-    pub fn dequantize(&self, elem_count: usize) -> Result<CudaStorage> {
-        let fast_kernel = matches!(
-            self.dtype,
-            GgmlDType::Q4_0
-                | GgmlDType::Q4_1
-                | GgmlDType::Q5_0
-                | GgmlDType::Q5_1
-                | GgmlDType::Q8_0
-                | GgmlDType::Q2K
-                | GgmlDType::Q3K
-                | GgmlDType::Q4K
-                | GgmlDType::Q5K
-                | GgmlDType::Q6K
-                | GgmlDType::Q8K
-        );
-        if fast_kernel {
-            return dequantize(&self.data, self.dtype, elem_count, self.device());
-        }
-        // Run the dequantization on cpu.
-        use crate::quantized::k_quants::GgmlType;
-
-        let buffer = self.device.dtoh_sync_copy(&self.data).w()?;
-        let mut out = vec![0.0; elem_count];
-        let block_len = elem_count / self.dtype.block_size();
-        match self.dtype {
-            GgmlDType::F32 => {
-                let slice =
-                    unsafe { std::slice::from_raw_parts(buffer.as_ptr() as *const f32, block_len) };
-                out.copy_from_slice(slice)
-            }
-            GgmlDType::F16 => {
-                let vec: Vec<half::f16> = read_to_vec(&buffer, block_len);
-                half::f16::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q4_0 => {
-                let vec: Vec<crate::quantized::BlockQ4_0> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ4_0::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q4_1 => {
-                let vec: Vec<crate::quantized::BlockQ4_1> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ4_1::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q5_0 => {
-                let vec: Vec<crate::quantized::BlockQ5_0> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ5_0::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q5_1 => {
-                let vec: Vec<crate::quantized::BlockQ5_1> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ5_1::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q8_0 => {
-                let vec: Vec<crate::quantized::BlockQ8_0> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ8_0::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q8_1 => {
-                let vec: Vec<crate::quantized::BlockQ8_1> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ8_1::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q2K => {
-                let vec: Vec<crate::quantized::BlockQ2K> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ2K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q3K => {
-                let vec: Vec<crate::quantized::BlockQ3K> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ3K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q4K => {
-                let vec: Vec<crate::quantized::BlockQ4K> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ4K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q5K => {
-                let vec: Vec<crate::quantized::BlockQ5K> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ5K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q6K => {
-                let vec: Vec<crate::quantized::BlockQ6K> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ6K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q8K => {
-                let vec: Vec<crate::quantized::BlockQ8K> = read_to_vec(&buffer, block_len);
-                crate::quantized::BlockQ8K::to_float(&vec, &mut out)?;
-            }
-        }
-
-        self.device
-            .storage_from_cpu_storage(&crate::CpuStorage::F32(out))
-    }
-
-    pub fn quantize(&mut self, src: &CudaStorage) -> Result<()> {
-        // Run the quantization on cpu.
-        let src = match &src.slice {
-            crate::cuda_backend::CudaStorageSlice::F32(data) => {
-                self.device.dtoh_sync_copy(data).w()?
-            }
-            _ => crate::bail!("only f32 can be quantized"),
-        };
-        let src_len = src.len();
-        let src = crate::Storage::Cpu(crate::CpuStorage::F32(src));
-        let mut qcpu_storage = crate::Device::Cpu.qzeros(src_len, self.dtype)?;
-        qcpu_storage.quantize(&src)?;
-        let data = qcpu_storage.data()?;
-        let data = self.device.htod_sync_copy(data.as_ref()).w()?;
-        self.data = data;
-        Ok(())
-    }
-
-    pub fn storage_size_in_bytes(&self) -> usize {
-        self.data.len()
-    }
-
-    pub fn fwd(
-        &self,
-        self_shape: &crate::Shape,
-        storage: &CudaStorage,
-        layout: &crate::Layout,
-    ) -> Result<(CudaStorage, crate::Shape)> {
-        if matches!(layout.shape().dims(), [1, 1, _] | [1, _]) {
-            self.dequantize_matmul_vec(self_shape, storage, layout)
-        } else {
-            self.dequantize_matmul(self_shape, storage, layout)
-        }
-    }
-}
-
-impl QCudaStorage {
-    fn dequantize_matmul_vec(
-        &self,
-        self_shape: &crate::Shape,
-        rhs: &CudaStorage,
-        rhs_l: &crate::Layout,
-    ) -> Result<(CudaStorage, crate::Shape)> {
-        let (nrows, ncols) = self_shape.dims2()?;
-        let rhs = rhs.as_cuda_slice::<f32>()?;
-        let rhs = match rhs_l.contiguous_offsets() {
-            Some((o1, o2)) => rhs.slice(o1..o2),
-            None => Err(crate::Error::RequiresContiguous { op: "dmmv" }.bt())?,
-        };
-        let (with_batch, k) = match rhs_l.shape().dims() {
-            [1, 1, k] => (true, k),
-            [1, k] => (false, k),
-            _ => crate::bail!("unexpected rhs shape in dmmv {:?}", rhs_l.shape()),
-        };
-        if ncols != *k {
-            crate::bail!("mismatch on matmul dim {self_shape:?} {:?}", rhs_l.shape())
-        }
-
-        let out =
-            dequantize_mut_mal_vec(&self.data, &rhs, self.dtype, ncols, nrows, self.device())?;
-        let out_shape = if with_batch {
-            vec![1, 1, nrows]
-        } else {
-            vec![1, nrows]
-        };
-        Ok((out, out_shape.into()))
-    }
-
-    fn dequantize_matmul(
-        &self,
-        self_shape: &crate::Shape,
-        storage: &CudaStorage,
-        layout: &crate::Layout,
-    ) -> Result<(CudaStorage, crate::Shape)> {
-        use crate::backend::BackendStorage;
-        let (n, k) = self_shape.dims2()?;
-        let (b, m, k2) = match layout.shape().dims() {
-            &[b, m, k2] => (b, m, k2),
-            &[m, k2] => (1, m, k2),
-            s => crate::bail!("unexpected shape for input {s:?}"),
-        };
-        if k2 != k {
-            crate::bail!("mismatch on matmul dim {self_shape:?} {:?}", layout.shape())
-        }
-
-        let data_f32 = self.dequantize(n * k)?;
-        let rhs_l = crate::Layout::new((k, n).into(), vec![1, k], 0).broadcast_as((b, k, n))?;
-        let out = storage.matmul(&data_f32, (b, m, n, k), layout, &rhs_l)?;
-        let mut out_shape = layout.shape().dims().to_vec();
-        out_shape.pop();
-        out_shape.push(n);
-        Ok((out, out_shape.into()))
-    }
-}
-
-fn read_to_vec<T: Clone>(buffer: &[u8], n: usize) -> Vec<T> {
-    let slice = unsafe { std::slice::from_raw_parts(buffer.as_ptr() as *const T, n) };
-    slice.to_vec()
-}
-
-pub fn load_quantized<T: super::GgmlType + Send + Sync + 'static>(
-    device: &CudaDevice,
-    data: &[T],
-) -> Result<super::QStorage> {
-    let data = unsafe {
-        std::slice::from_raw_parts(data.as_ptr() as *const u8, core::mem::size_of_val(data))
-    };
-    let data = device.htod_sync_copy(data).w()?;
-    Ok(QStorage::Cuda(QCudaStorage {
-        data,
-        device: device.clone(),
-        dtype: T::DTYPE,
-    }))
-}

candle-core/src/quantized/dummy_cuda.rs

@@ -1,50 +0,0 @@
-#![allow(unused)]
-use super::GgmlDType;
-use crate::{CudaDevice, CudaStorage, Error, Result};
-
-pub struct QCudaStorage {
-    dtype: GgmlDType,
-    device: CudaDevice,
-}
-
-impl QCudaStorage {
-    pub fn zeros(_: &CudaDevice, _: usize, _: GgmlDType) -> Result<Self> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
-    pub fn dtype(&self) -> GgmlDType {
-        self.dtype
-    }
-
-    pub fn device(&self) -> &CudaDevice {
-        &self.device
-    }
-
-    pub fn dequantize(&self, _elem_count: usize) -> Result<CudaStorage> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
-    pub fn quantize(&mut self, _src: &CudaStorage) -> Result<()> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
-    pub fn storage_size_in_bytes(&self) -> usize {
-        0
-    }
-
-    pub fn fwd(
-        &self,
-        _self_shape: &crate::Shape,
-        _storage: &CudaStorage,
-        _layout: &crate::Layout,
-    ) -> Result<(CudaStorage, crate::Shape)> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-}
-
-pub fn load_quantized<T: super::GgmlType + Send + Sync + 'static>(
-    _device: &CudaDevice,
-    _data: &[T],
-) -> Result<super::QStorage> {
-    Err(Error::NotCompiledWithCudaSupport)
-}

candle-core/src/quantized/dummy_metal.rs

@@ -41,10 +41,3 @@ impl QMetalStorage {
         Err(Error::NotCompiledWithMetalSupport)
     }
 }
-
-pub fn load_quantized<T: super::GgmlType + Send + Sync + 'static>(
-    _device: &MetalDevice,
-    _data: &[T],
-) -> Result<super::QStorage> {
-    Err(Error::NotCompiledWithMetalSupport)
-}

candle-core/src/quantized/ggml_file.rs

@@ -1,5 +1,7 @@
 //! Support for the GGML file format.
 
+#[cfg(feature = "metal")]
+use super::metal::load_quantized_metal;
 use super::{k_quants, GgmlDType, QStorage};
 use crate::{Device, Result};
 use byteorder::{LittleEndian, ReadBytesExt};
@@ -128,8 +130,13 @@ fn from_raw_data<T: super::GgmlType + Send + Sync + 'static>(
     let data = unsafe { std::slice::from_raw_parts(raw_data_ptr as *const T, n_blocks) };
     let data: QStorage = match device {
         Device::Cpu => QStorage::Cpu(Box::new(data.to_vec())),
-        Device::Metal(metal) => super::metal::load_quantized(metal, data)?,
-        Device::Cuda(cuda) => super::cuda::load_quantized(cuda, data)?,
+        #[cfg(feature = "metal")]
+        Device::Metal(metal) => load_quantized_metal(metal, data)?,
+        #[cfg(not(feature = "metal"))]
+        Device::Metal(_metal) => {
+            crate::bail!("Metal backend requires `metal` feature")
+        }
+        device => unimplemented!("Implement quantized tensor for device {device:?}"),
     };
     super::QTensor::new(data, dims)
 }

candle-core/src/quantized/metal.rs

@@ -34,8 +34,6 @@ impl QMetalStorage {
     }
 
     pub fn dequantize(&self, elem_count: usize) -> Result<MetalStorage> {
-        use crate::quantized::k_quants::GgmlType;
-
         let buffer = self.device.new_buffer_managed(self.buffer.length())?;
         let command_buffer = self.device.command_buffer()?;
         command_buffer.set_label("to_cpu");
@@ -45,73 +43,87 @@ impl QMetalStorage {
         blit.end_encoding();
         self.device.wait_until_completed()?;
         let mut out = vec![0.0; elem_count];
-        let block_len = elem_count / self.dtype.block_size();
         match self.dtype {
             GgmlDType::F32 => {
-                let vec: Vec<f32> = read_to_vec(&buffer, block_len);
+                let vec: Vec<f32> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 f32::to_float(&vec, &mut out)?;
             }
             GgmlDType::F16 => {
-                let vec: Vec<half::f16> = read_to_vec(&buffer, block_len);
+                let vec: Vec<half::f16> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 half::f16::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q4_0 => {
-                let vec: Vec<crate::quantized::BlockQ4_0> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ4_0> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ4_0::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q4_1 => {
-                let vec: Vec<crate::quantized::BlockQ4_1> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ4_1> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ4_1::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q5_0 => {
-                let vec: Vec<crate::quantized::BlockQ5_0> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ5_0> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ5_0::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q5_1 => {
-                let vec: Vec<crate::quantized::BlockQ5_1> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ5_1> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ5_1::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q8_0 => {
-                let vec: Vec<crate::quantized::BlockQ8_0> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ8_0> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ8_0::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q8_1 => {
-                let vec: Vec<crate::quantized::BlockQ8_1> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ8_1> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ8_1::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q2K => {
-                let vec: Vec<crate::quantized::BlockQ2K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ2K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ2K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q3K => {
-                let vec: Vec<crate::quantized::BlockQ3K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ3K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ3K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q4K => {
-                let vec: Vec<crate::quantized::BlockQ4K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ4K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ4K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q5K => {
-                let vec: Vec<crate::quantized::BlockQ5K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ5K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ5K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q6K => {
-                let vec: Vec<crate::quantized::BlockQ6K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ6K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ6K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q8K => {
-                let vec: Vec<crate::quantized::BlockQ8K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ8K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ8K::to_float(&vec, &mut out)?;
             }
         }
 
         let buffer = self.device.new_buffer_with_data(&out)?;
-        Ok(MetalStorage::new(
-            buffer,
-            self.device.clone(),
-            elem_count,
-            DType::F32,
-        ))
+        Ok(MetalStorage::new(buffer, self.device.clone(), DType::F32))
     }
 
     pub fn quantize(&mut self, src: &MetalStorage) -> Result<()> {
@@ -175,12 +187,12 @@ impl QMetalStorage {
             &dst,
         )
         .map_err(MetalError::from)?;
-        let dst_storage = crate::MetalStorage::new(dst, device, dst_shape.elem_count(), DType::F32);
+        let dst_storage = crate::MetalStorage::new(dst, device, DType::F32);
         Ok((dst_storage, dst_shape))
     }
 }
 
-pub fn load_quantized<T: super::GgmlType + Send + Sync + 'static>(
+pub fn load_quantized_metal<T: super::GgmlType + Send + Sync + 'static>(
     device: &MetalDevice,
     data: &[T],
 ) -> Result<QStorage> {

candle-core/src/quantized/mod.rs

@@ -4,7 +4,6 @@ use std::borrow::Cow;
 
 #[cfg(target_feature = "avx")]
 pub mod avx;
-mod dummy_cuda;
 mod dummy_metal;
 pub mod ggml_file;
 pub mod gguf_file;
@@ -15,13 +14,6 @@ pub mod metal;
 mod metal {
     pub use super::dummy_metal::*;
 }
-#[cfg(feature = "cuda")]
-pub mod cuda;
-#[cfg(not(feature = "cuda"))]
-mod cuda {
-    pub use super::dummy_cuda::*;
-}
-
 #[cfg(target_feature = "neon")]
 pub mod neon;
 #[cfg(target_feature = "simd128")]
@@ -47,9 +39,8 @@ impl Device {
                 let storage = metal::QMetalStorage::zeros(metal, elem_count, dtype)?;
                 Ok(QStorage::Metal(storage))
             }
-            Device::Cuda(cuda) => {
-                let storage = cuda::QCudaStorage::zeros(cuda, elem_count, dtype)?;
-                Ok(QStorage::Cuda(storage))
+            Device::Cuda(_cuda) => {
+                crate::bail!("Cuda ggml quantization not supported");
             }
         }
     }
@@ -58,7 +49,6 @@ impl Device {
 pub enum QStorage {
     Cpu(Box<dyn QuantizedType>),
     Metal(metal::QMetalStorage),
-    Cuda(cuda::QCudaStorage),
 }
 
 impl QStorage {
@@ -66,7 +56,6 @@ impl QStorage {
         match self {
             QStorage::Cpu(storage) => storage.block_size(),
             QStorage::Metal(storage) => storage.dtype().block_size(),
-            QStorage::Cuda(storage) => storage.dtype().block_size(),
         }
     }
 
@@ -74,7 +63,6 @@ impl QStorage {
         match self {
             QStorage::Cpu(storage) => storage.dtype(),
             QStorage::Metal(storage) => storage.dtype(),
-            QStorage::Cuda(storage) => storage.dtype(),
         }
     }
 
@@ -82,7 +70,6 @@ impl QStorage {
         match self {
             QStorage::Cpu(_storage) => Device::Cpu,
             QStorage::Metal(storage) => Device::Metal(storage.device().clone()),
-            QStorage::Cuda(storage) => Device::Cuda(storage.device().clone()),
         }
     }
 
@@ -90,7 +77,6 @@ impl QStorage {
         match self {
             QStorage::Cpu(storage) => storage.storage_size_in_bytes(),
             QStorage::Metal(storage) => storage.storage_size_in_bytes(),
-            QStorage::Cuda(storage) => storage.storage_size_in_bytes(),
         }
     }
 
@@ -100,7 +86,6 @@ impl QStorage {
                 storage.from_float(src.as_slice::<f32>()?)?;
             }
             (QStorage::Metal(storage), Storage::Metal(src)) => storage.quantize(src)?,
-            (QStorage::Cuda(storage), Storage::Cuda(src)) => storage.quantize(src)?,
             _ => crate::bail!("Invalid dequantize storage locations do not match"),
         }
         Ok(())
@@ -110,7 +95,6 @@ impl QStorage {
         match self {
             QStorage::Cpu(storage) => Ok(Storage::Cpu(storage.dequantize(elem_count)?)),
             QStorage::Metal(storage) => Ok(Storage::Metal(storage.dequantize(elem_count)?)),
-            QStorage::Cuda(storage) => Ok(Storage::Cuda(storage.dequantize(elem_count)?)),
         }
     }
 
@@ -122,7 +106,7 @@ impl QStorage {
                 let data = unsafe { std::slice::from_raw_parts(data_ptr, size_in_bytes) };
                 Ok(Cow::from(data))
             }
-            QStorage::Metal(_) | QStorage::Cuda(_) => {
+            QStorage::Metal(_storage) => {
                 crate::bail!("not implemented");
             }
         }
@@ -398,7 +382,7 @@ impl QMatMul {
             _ => DEQUANTIZE_ALL.with(|b| *b),
         };
         let t = if dequantize {
-            let tensor = qtensor.dequantize(&qtensor.device())?;
+            let tensor = qtensor.dequantize(&Device::Cpu)?;
             Self::Tensor(tensor)
         } else {
             Self::QTensor(qtensor)
@@ -440,7 +424,7 @@ impl crate::CustomOp1 for QTensor {
         #[allow(clippy::infallible_destructuring_match)]
         let self_storage = match &self.storage {
             QStorage::Cpu(storage) => storage,
-            QStorage::Metal(_) | QStorage::Cuda(_) => crate::bail!("Invalid storage"),
+            QStorage::Metal(_) => crate::bail!("Invalid storage"),
         };
         let slice = storage.as_slice::<f32>()?;
         let slice = &slice[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
@@ -460,18 +444,6 @@ impl crate::CustomOp1 for QTensor {
         };
         self_storage.fwd(&self.shape, storage, layout)
     }
-
-    fn cuda_fwd(
-        &self,
-        storage: &crate::CudaStorage,
-        layout: &crate::Layout,
-    ) -> Result<(crate::CudaStorage, Shape)> {
-        let self_storage = match &self.storage {
-            QStorage::Cuda(cuda) => cuda,
-            _ => unreachable!("Cannot call cuda matmul on non cuda QTensor"),
-        };
-        self_storage.fwd(&self.shape, storage, layout)
-    }
 }
 
 impl crate::Module for QMatMul {

candle-core/src/storage.rs

@@ -352,10 +352,6 @@ impl Storage {
                 let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
                 Ok(Self::Cuda(s))
             }
-            (Storage::Metal(inp), Storage::Metal(kernel)) => {
-                let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
-                Ok(Self::Metal(s))
-            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -701,32 +697,4 @@ impl Storage {
             .bt()),
         }
     }
-
-    #[allow(clippy::too_many_arguments)]
-    pub(crate) fn copy2d(
-        &self,
-        dst: &mut Self,
-        d1: usize,
-        d2: usize,
-        src_s: usize,
-        dst_s: usize,
-        src_o: usize,
-        dst_o: usize,
-    ) -> Result<()> {
-        match (self, dst) {
-            (Self::Cpu(src), Self::Cpu(dst)) => src.copy2d(dst, d1, d2, src_s, dst_s, src_o, dst_o),
-            (Self::Cuda(src), Self::Cuda(dst)) => {
-                Ok(src.copy2d(dst, d1, d2, src_s, dst_s, src_o, dst_o)?)
-            }
-            (Self::Metal(src), Self::Metal(dst)) => {
-                Ok(src.copy2d(dst, d1, d2, src_s, dst_s, src_o, dst_o)?)
-            }
-            (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
-                lhs: lhs.device().location(),
-                rhs: rhs.device().location(),
-                op: "copy2d",
-            }
-            .bt()),
-        }
-    }
 }

candle-core/src/tensor.rs

@@ -666,7 +666,7 @@ impl Tensor {
         Ok(from_storage(storage, self.shape(), op, false))
     }
 
-    pub(crate) fn check_dim(&self, dim: usize, op: &'static str) -> Result<()> {
+    fn check_dim(&self, dim: usize, op: &'static str) -> Result<()> {
         if dim >= self.dims().len() {
             Err(Error::DimOutOfRange {
                 shape: self.shape().clone(),
@@ -1015,7 +1015,7 @@ impl Tensor {
     /// 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, |arg| Op::UpsampleNearest1D { arg, target_size });
+        let op = BackpropOp::new1(self, Op::UpsampleNearest1D);
         let storage = self
             .storage()
             .upsample_nearest1d(self.layout(), target_size)?;
@@ -2149,6 +2149,152 @@ impl Tensor {
         Self::cat(&args, dim)
     }
 
+    /// Concatenates two or more tensors along a particular dimension.
+    ///
+    /// All tensors must of the same rank, and the output will have
+    /// the same rank
+    ///
+    /// ```rust
+    /// # use candle_core::{Tensor, DType, Device};
+    /// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
+    /// let b = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
+    ///
+    /// let c = Tensor::cat(&[&a, &b], 0)?;
+    /// assert_eq!(c.shape().dims(), &[4, 3]);
+    ///
+    /// let c = Tensor::cat(&[&a, &b], 1)?;
+    /// assert_eq!(c.shape().dims(), &[2, 6]);
+    /// # Ok::<(), candle_core::Error>(())
+    /// ```
+    pub fn cat<A: AsRef<Tensor>, D: Dim>(args: &[A], dim: D) -> Result<Self> {
+        if args.is_empty() {
+            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
+        }
+        let arg0 = args[0].as_ref();
+        if args.len() == 1 {
+            return Ok(arg0.clone());
+        }
+        let dim = dim.to_index(arg0.shape(), "cat")?;
+        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 {
+            // TODO: Avoid these transpositions and have an implementation that works
+            // for dim != 0...
+            let args: Vec<Tensor> = args
+                .iter()
+                .map(|a| a.as_ref().transpose(0, dim))
+                .collect::<Result<Vec<_>>>()?;
+            let cat = Self::cat0(&args)?;
+            cat.transpose(0, dim)
+        }
+    }
+
+    fn cat0<A: AsRef<Tensor>>(args: &[A]) -> Result<Self> {
+        if args.is_empty() {
+            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
+        }
+        let arg0 = args[0].as_ref();
+        if args.len() == 1 {
+            return Ok(arg0.clone());
+        }
+        let rank = arg0.rank();
+        let device = arg0.device();
+        let dtype = arg0.dtype();
+        let first_dims = arg0.shape().dims();
+        let mut cat_dims = first_dims.to_vec();
+        cat_dims[0] = 0;
+        let mut offsets = vec![0usize];
+        for (arg_idx, arg) in args.iter().enumerate() {
+            let arg = arg.as_ref();
+            if arg.dtype() != dtype {
+                Err(Error::DTypeMismatchBinaryOp {
+                    lhs: dtype,
+                    rhs: arg.dtype(),
+                    op: "cat",
+                }
+                .bt())?
+            }
+            if arg.device().location() != device.location() {
+                Err(Error::DeviceMismatchBinaryOp {
+                    lhs: device.location(),
+                    rhs: arg.device().location(),
+                    op: "cat",
+                }
+                .bt())?
+            }
+            if rank != arg.rank() {
+                Err(Error::UnexpectedNumberOfDims {
+                    expected: 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 == 0 {
+                    cat_dims[0] += v2;
+                }
+                if dim_idx != 0 && v1 != v2 {
+                    Err(Error::ShapeMismatchCat {
+                        dim: dim_idx,
+                        first_shape: arg0.shape().clone(),
+                        n: arg_idx + 1,
+                        nth_shape: arg.shape().clone(),
+                    }
+                    .bt())?
+                }
+            }
+            let next_offset = offsets.last().unwrap() + arg.elem_count();
+            offsets.push(next_offset);
+        }
+        let shape = Shape::from(cat_dims);
+        let op = BackpropOp::new(args, |args| Op::Cat(args, 0));
+        let mut storage = device.zeros(&shape, dtype)?;
+        for (arg, &offset) in args.iter().zip(offsets.iter()) {
+            let arg = arg.as_ref();
+            arg.storage()
+                .copy_strided_src(&mut storage, offset, arg.layout())?;
+        }
+        Ok(from_storage(storage, shape, op, false))
+    }
+
     /// Pad the input tensor using 0s along dimension `dim`. This adds `left` elements before the
     /// input tensor values and `right` elements after.
     pub fn pad_with_zeros<D: Dim>(&self, dim: D, left: usize, right: usize) -> Result<Self> {

candle-core/src/tensor_cat.rs

@@ -1,240 +0,0 @@
-use crate::{shape::Dim, Error, Result, Shape, Tensor};
-
-impl Tensor {
-    /// Concatenates two or more tensors along a particular dimension.
-    ///
-    /// All tensors must of the same rank, and the output will have
-    /// the same rank
-    ///
-    /// ```rust
-    /// # use candle_core::{Tensor, DType, Device};
-    /// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
-    /// let b = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
-    ///
-    /// let c = Tensor::cat(&[&a, &b], 0)?;
-    /// assert_eq!(c.shape().dims(), &[4, 3]);
-    ///
-    /// let c = Tensor::cat(&[&a, &b], 1)?;
-    /// assert_eq!(c.shape().dims(), &[2, 6]);
-    /// # Ok::<(), candle_core::Error>(())
-    /// ```
-    pub fn cat<A: AsRef<Tensor>, D: Dim>(args: &[A], dim: D) -> Result<Self> {
-        if args.is_empty() {
-            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
-        }
-        let arg0 = args[0].as_ref();
-        if args.len() == 1 {
-            return Ok(arg0.clone());
-        }
-        let dim = dim.to_index(arg0.shape(), "cat")?;
-        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 {
-            let all_contiguous = args.iter().all(|v| v.as_ref().is_contiguous());
-            if all_contiguous {
-                Self::cat_contiguous(args, dim)
-            } else {
-                let args: Vec<Tensor> = args
-                    .iter()
-                    .map(|a| a.as_ref().transpose(0, dim))
-                    .collect::<Result<Vec<_>>>()?;
-                let cat = Self::cat0(&args)?;
-                cat.transpose(0, dim)
-            }
-        }
-    }
-
-    fn cat0<A: AsRef<Tensor>>(args: &[A]) -> Result<Self> {
-        if args.is_empty() {
-            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
-        }
-        let arg0 = args[0].as_ref();
-        if args.len() == 1 {
-            return Ok(arg0.clone());
-        }
-        let rank = arg0.rank();
-        let device = arg0.device();
-        let dtype = arg0.dtype();
-        let first_dims = arg0.shape().dims();
-        let mut cat_dims = first_dims.to_vec();
-        cat_dims[0] = 0;
-        let mut offsets = vec![0usize];
-        for (arg_idx, arg) in args.iter().enumerate() {
-            let arg = arg.as_ref();
-            if arg.dtype() != dtype {
-                Err(Error::DTypeMismatchBinaryOp {
-                    lhs: dtype,
-                    rhs: arg.dtype(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if arg.device().location() != device.location() {
-                Err(Error::DeviceMismatchBinaryOp {
-                    lhs: device.location(),
-                    rhs: arg.device().location(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if rank != arg.rank() {
-                Err(Error::UnexpectedNumberOfDims {
-                    expected: 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 == 0 {
-                    cat_dims[0] += v2;
-                }
-                if dim_idx != 0 && v1 != v2 {
-                    Err(Error::ShapeMismatchCat {
-                        dim: dim_idx,
-                        first_shape: arg0.shape().clone(),
-                        n: arg_idx + 1,
-                        nth_shape: arg.shape().clone(),
-                    }
-                    .bt())?
-                }
-            }
-            let next_offset = offsets.last().unwrap() + arg.elem_count();
-            offsets.push(next_offset);
-        }
-        let shape = Shape::from(cat_dims);
-        let op = crate::op::BackpropOp::new(args, |args| crate::op::Op::Cat(args, 0));
-        let mut storage = device.zeros(&shape, dtype)?;
-        for (arg, &offset) in args.iter().zip(offsets.iter()) {
-            let arg = arg.as_ref();
-            arg.storage()
-                .copy_strided_src(&mut storage, offset, arg.layout())?;
-        }
-        Ok(crate::tensor::from_storage(storage, shape, op, false))
-    }
-
-    fn cat_contiguous<A: AsRef<Tensor>>(args: &[A], dim: usize) -> Result<Self> {
-        if args.is_empty() {
-            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
-        }
-        let arg0 = args[0].as_ref();
-        if args.len() == 1 {
-            return Ok(arg0.clone());
-        }
-        let rank = arg0.rank();
-        let device = arg0.device();
-        let dtype = arg0.dtype();
-        let first_dims = arg0.shape().dims();
-        let mut cat_dims = first_dims.to_vec();
-        cat_dims[dim] = 0;
-        for (arg_idx, arg) in args.iter().enumerate() {
-            let arg = arg.as_ref();
-            if arg.dtype() != dtype {
-                Err(Error::DTypeMismatchBinaryOp {
-                    lhs: dtype,
-                    rhs: arg.dtype(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if arg.device().location() != device.location() {
-                Err(Error::DeviceMismatchBinaryOp {
-                    lhs: device.location(),
-                    rhs: arg.device().location(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if rank != arg.rank() {
-                Err(Error::UnexpectedNumberOfDims {
-                    expected: 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 {
-                    cat_dims[dim] += v2;
-                }
-                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())?
-                }
-            }
-        }
-        let cat_target_dim_len = cat_dims[dim];
-        let block_size: usize = cat_dims.iter().skip(1 + dim).product();
-        let shape = Shape::from(cat_dims);
-        let op = crate::op::BackpropOp::new(args, |args| crate::op::Op::Cat(args, dim));
-        let mut storage = device.zeros(&shape, dtype)?;
-        let mut dst_o = 0;
-        for arg in args.iter() {
-            let arg = arg.as_ref();
-            let arg_dims = arg.shape().dims();
-            let d1: usize = arg_dims.iter().take(dim).product();
-            let d2 = block_size * arg_dims[dim];
-            let dst_s = block_size * cat_target_dim_len;
-            let src_o = arg.layout().start_offset();
-            arg.storage().copy2d(
-                &mut storage,
-                d1,
-                d2,
-                /* src_s */ d2,
-                dst_s,
-                src_o,
-                dst_o,
-            )?;
-            dst_o += d2;
-        }
-        Ok(crate::tensor::from_storage(storage, shape, op, false))
-    }
-}

candle-core/tests/conv_tests.rs

@@ -18,9 +18,6 @@ w_t = w.transpose(0, 1)
 res = torch.nn.functional.conv_transpose1d(t, w_t)
 print(res.shape)
 print(res)
-res = torch.nn.functional.conv_transpose1d(t, w_t, groups=2)
-print(res.shape)
-print(res)
 */
 fn conv1d(dev: &Device) -> Result<()> {
     let t = Tensor::new(
@@ -53,36 +50,15 @@ fn conv1d(dev: &Device) -> Result<()> {
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [2.4509, 2.6357, -1.3336, 4.1393, 0.5657, 1.8091, -1.1784, 3.5675, 0.5069, 3.3352]
     );
-
-    // conv-transposes are not implemented for metal.
-    if dev.is_metal() {
-        return Ok(());
-    }
-
-    let w = w.transpose(0, 1)?;
-    // The CPU kernels applied in the contiguous and non contiguous cases are different.
-    for w in [w.clone(), w.contiguous()?] {
-        let res = t.conv_transpose1d(&w, 0, 0, 1, 1, 1)?;
-        assert_eq!(res.dims(), [1, 2, 7]);
-        assert_eq!(
-            test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
-            [
-                0.0699, -1.2899, 8.3018, 5.5873, 2.4572, -2.6143, -0.0706, 1.8765, 4.8318, 1.1538,
-                4.7076, -5.9745, -0.8276, 1.621
-            ],
-        );
-        let res = t.conv_transpose1d(&w, 0, 0, 1, 1, 2)?;
-        assert_eq!(res.dims(), [1, 4, 7]);
-        assert_eq!(
-            test_utils::to_vec2_round(&res.squeeze(0)?, 4)?,
-            [
-                [-1.5596, -1.8099, 2.0407, 4.8764, -0.1743, -0.735, -0.7819],
-                [0.7816, 3.8152, -0.5926, 2.2515, -5.1844, -0.3157, 1.4721],
-                [1.6295, 0.52, 6.2611, 0.7109, 2.6315, -1.8793, 0.7113],
-                [1.0949, 1.0166, 1.7464, 2.4561, -0.79, -0.5119, 0.1488]
-            ]
-        );
-    }
+    let res = t.conv_transpose1d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
+    assert_eq!(res.dims(), [1, 2, 7]);
+    assert_eq!(
+        test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
+        [
+            0.0699, -1.2899, 8.3018, 5.5873, 2.4572, -2.6143, -0.0706, 1.8765, 4.8318, 1.1538,
+            4.7076, -5.9745, -0.8276, 1.621
+        ],
+    );
     Ok(())
 }
 
@@ -168,33 +144,31 @@ fn conv2d(dev: &Device) -> Result<()> {
             10.389, 3.6023, -4.2808, 0.2672, 5.3646, -5.2023, -2.1955, -9.4075
         ]
     );
-    if !dev.is_metal() {
-        let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
-        assert_eq!(res.dims(), [1, 2, 7, 7]);
-        assert_eq!(
-            test_utils::to_vec3_round(&res.i(0)?, 4)?,
+    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
+    assert_eq!(res.dims(), [1, 2, 7, 7]);
+    assert_eq!(
+        test_utils::to_vec3_round(&res.i(0)?, 4)?,
+        [
             [
-                [
-                    [-1.9918, 2.6797, -0.4599, -1.6037, 1.4131, -2.4012, 2.9277],
-                    [1.8016, -3.5361, 1.0757, 3.5395, -8.2168, -3.2023, 0.5375],
-                    [0.8243, 1.8675, 7.8929, -4.0746, -6.4415, 5.1139, 1.6889],
-                    [0.2722, 8.9679, 3.3477, 1.8514, -4.2896, -3.8228, -7.5632],
-                    [-8.5412, -5.8142, -7.1587, -1.6095, 0.4651, 0.2748, -2.0985],
-                    [2.0833, -0.6482, -12.1692, -4.1284, -2.9765, -0.0656, -4.5114],
-                    [5.307, 2.6957, 2.3087, 1.0478, 0.7808, -1.1519, -0.9579]
-                ],
-                [
-                    [1.089, 0.1872, -0.6408, -0.9897, 0.8503, 1.1019, -0.9211],
-                    [-0.1741, -0.2915, 4.2472, 1.9417, 1.65, 0.6303, -4.7131],
-                    [1.6555, 2.4026, -2.9293, 2.9953, 0.5328, 3.5873, -0.9621],
-                    [-1.4289, -3.2787, 4.1747, -6.0341, -4.6341, -5.7945, 4.142],
-                    [7.5973, 6.4431, 5.9872, 2.1639, -8.6566, 3.3143, -3.4059],
-                    [-0.8775, -3.048, 11.6543, 0.6442, 2.3218, -0.4765, 1.1516],
-                    [-5.5423, -2.5188, 1.0754, -0.0563, -2.9386, -1.1504, 1.0171]
-                ]
+                [-1.9918, 2.6797, -0.4599, -1.6037, 1.4131, -2.4012, 2.9277],
+                [1.8016, -3.5361, 1.0757, 3.5395, -8.2168, -3.2023, 0.5375],
+                [0.8243, 1.8675, 7.8929, -4.0746, -6.4415, 5.1139, 1.6889],
+                [0.2722, 8.9679, 3.3477, 1.8514, -4.2896, -3.8228, -7.5632],
+                [-8.5412, -5.8142, -7.1587, -1.6095, 0.4651, 0.2748, -2.0985],
+                [2.0833, -0.6482, -12.1692, -4.1284, -2.9765, -0.0656, -4.5114],
+                [5.307, 2.6957, 2.3087, 1.0478, 0.7808, -1.1519, -0.9579]
+            ],
+            [
+                [1.089, 0.1872, -0.6408, -0.9897, 0.8503, 1.1019, -0.9211],
+                [-0.1741, -0.2915, 4.2472, 1.9417, 1.65, 0.6303, -4.7131],
+                [1.6555, 2.4026, -2.9293, 2.9953, 0.5328, 3.5873, -0.9621],
+                [-1.4289, -3.2787, 4.1747, -6.0341, -4.6341, -5.7945, 4.142],
+                [7.5973, 6.4431, 5.9872, 2.1639, -8.6566, 3.3143, -3.4059],
+                [-0.8775, -3.048, 11.6543, 0.6442, 2.3218, -0.4765, 1.1516],
+                [-5.5423, -2.5188, 1.0754, -0.0563, -2.9386, -1.1504, 1.0171]
             ]
-        );
-    }
+        ]
+    );
     // Dilations.
     let res = t.conv2d(&w, 0, 1, 2, 1)?;
     assert_eq!(res.dims(), [1, 2, 1, 1]);
@@ -203,44 +177,36 @@ fn conv2d(dev: &Device) -> Result<()> {
         [2.45, -2.3504],
     );
 
-    if !dev.is_metal() {
-        // Transpose and dilations.
-        let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 2)?;
-        assert_eq!(res.dims(), [1, 2, 9, 9]);
-        assert_eq!(
-            test_utils::to_vec3_round(&res.i(0)?, 4)?,
+    // Transpose and dilations.
+    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 2)?;
+    assert_eq!(res.dims(), [1, 2, 9, 9]);
+    assert_eq!(
+        test_utils::to_vec3_round(&res.i(0)?, 4)?,
+        [
             [
-                [
-                    [-1.9918, 3.1652, -0.6778, -4.3442, 4.4351, 0.6652, -3.0124, -0.6031, 2.9277],
-                    [2.7036, -1.7156, -0.3969, 1.0516, 1.6381, -2.8886, -0.205, 2.4682, -1.0499],
-                    [-0.9459, 3.1631, 3.707, -4.8369, -8.5166, -1.4496, -2.7559, -3.2698, 1.4376],
-                    [-0.2157, 3.7786, -2.0252, -4.2633, 3.6731, -1.5142, 5.9391, -0.2622, -0.141],
-                    [-6.8121, -3.1744, 1.5945, 3.0637, -9.6088, 1.4446, 2.9489, -3.0082, -7.3822],
-                    [0.2371, 3.3303, 0.3861, 2.2646, -4.6784, 4.1235, -0.0109, 0.3176, -0.03],
-                    [
-                        -2.5339, -2.9564, -3.4518, -4.4594, -9.1873, -1.9709, -0.4676, 0.51,
-                        -3.5024
-                    ],
-                    [4.007, 0.3067, -2.2954, 1.1105, -0.1992, 1.6372, -2.9268, 0.2807, -1.2787],
-                    [5.307, 1.1317, 1.3518, 0.9049, 3.8116, -0.4075, -0.8874, -0.2241, -0.9579]
-                ],
-                [
-                    [1.089, -0.6483, 0.0726, -0.4752, -1.3283, 1.7103, 1.0703, 0.1076, -0.9211],
-                    [-0.8629, 0.1376, 0.3202, 2.0955, 0.9696, 2.8988, -1.0012, 1.5049, -0.1278],
-                    [1.9286, -1.5255, -2.9563, 2.4589, 3.3611, -0.6951, 0.3525, -1.7724, -5.9861],
-                    [1.1226, 2.1561, 3.6417, 4.7546, -0.692, 4.4126, -5.1902, 6.0805, 2.3185],
-                    [1.0111, 0.3604, 0.6432, -3.6605, 7.9517, -9.2955, -5.2988, -3.7803, -2.0642],
-                    [3.3172, -1.7967, -3.6576, -2.0942, 1.3158, 0.112, -1.7405, 2.9167, 0.7957],
-                    [5.1001, 1.8995, -1.8639, 1.1262, 9.9629, 2.683, -3.6319, -1.1607, 0.5856],
-                    [-4.8445, -0.5642, 4.2317, 0.0856, 1.2267, -0.5712, 1.736, 1.0997, 0.6908],
-                    [
-                        -5.5423, -1.1831, -1.2176, 0.0843, 0.0446, -0.7545, -2.4798, -0.0827,
-                        1.0171
-                    ]
-                ]
+                [-1.9918, 3.1652, -0.6778, -4.3442, 4.4351, 0.6652, -3.0124, -0.6031, 2.9277],
+                [2.7036, -1.7156, -0.3969, 1.0516, 1.6381, -2.8886, -0.205, 2.4682, -1.0499],
+                [-0.9459, 3.1631, 3.707, -4.8369, -8.5166, -1.4496, -2.7559, -3.2698, 1.4376],
+                [-0.2157, 3.7786, -2.0252, -4.2633, 3.6731, -1.5142, 5.9391, -0.2622, -0.141],
+                [-6.8121, -3.1744, 1.5945, 3.0637, -9.6088, 1.4446, 2.9489, -3.0082, -7.3822],
+                [0.2371, 3.3303, 0.3861, 2.2646, -4.6784, 4.1235, -0.0109, 0.3176, -0.03],
+                [-2.5339, -2.9564, -3.4518, -4.4594, -9.1873, -1.9709, -0.4676, 0.51, -3.5024],
+                [4.007, 0.3067, -2.2954, 1.1105, -0.1992, 1.6372, -2.9268, 0.2807, -1.2787],
+                [5.307, 1.1317, 1.3518, 0.9049, 3.8116, -0.4075, -0.8874, -0.2241, -0.9579]
+            ],
+            [
+                [1.089, -0.6483, 0.0726, -0.4752, -1.3283, 1.7103, 1.0703, 0.1076, -0.9211],
+                [-0.8629, 0.1376, 0.3202, 2.0955, 0.9696, 2.8988, -1.0012, 1.5049, -0.1278],
+                [1.9286, -1.5255, -2.9563, 2.4589, 3.3611, -0.6951, 0.3525, -1.7724, -5.9861],
+                [1.1226, 2.1561, 3.6417, 4.7546, -0.692, 4.4126, -5.1902, 6.0805, 2.3185],
+                [1.0111, 0.3604, 0.6432, -3.6605, 7.9517, -9.2955, -5.2988, -3.7803, -2.0642],
+                [3.3172, -1.7967, -3.6576, -2.0942, 1.3158, 0.112, -1.7405, 2.9167, 0.7957],
+                [5.1001, 1.8995, -1.8639, 1.1262, 9.9629, 2.683, -3.6319, -1.1607, 0.5856],
+                [-4.8445, -0.5642, 4.2317, 0.0856, 1.2267, -0.5712, 1.736, 1.0997, 0.6908],
+                [-5.5423, -1.1831, -1.2176, 0.0843, 0.0446, -0.7545, -2.4798, -0.0827, 1.0171]
             ]
-        );
-    }
+        ]
+    );
     Ok(())
 }
 
@@ -294,12 +260,6 @@ fn conv2d_small(dev: &Device) -> Result<()> {
             0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000
         ]
     );
-
-    // conv-transposes are not implemented for metal
-    if dev.is_metal() {
-        return Ok(());
-    }
-
     let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 1, 3, 3]);
     assert_eq!(
@@ -401,10 +361,6 @@ print(w.grad.shape)
 print(w.grad[0])
 */
 fn conv2d_grad(dev: &Device) -> Result<()> {
-    // conv-transposes are not implemented for metal
-    if dev.is_metal() {
-        return Ok(());
-    }
     use candle_core::Var;
     let t = Var::from_slice(
         &[

candle-core/tests/grad_tests.rs

@@ -1,4 +1,3 @@
-#![allow(clippy::approx_constant)]
 use anyhow::{Context, Result};
 use candle_core::{test_device, test_utils, Device, Shape, Tensor, Var};
 
@@ -97,24 +96,24 @@ fn unary_grad(device: &Device) -> Result<()> {
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(
-        test_utils::to_vec1_round(&y, 4)?,
-        [20.0855, 2.7183, 54.5982, 1.1618]
+        y.to_vec1::<f32>()?,
+        [20.085537, 2.7182817, 54.59815, 1.1618342]
     );
     assert_eq!(
-        test_utils::to_vec1_round(grad_x, 4)?,
-        [20.0855, 2.7183, 54.5982, 1.1618]
+        grad_x.to_vec1::<f32>()?,
+        [20.085537, 2.7182817, 54.59815, 1.1618342]
     );
     let y = x.exp()?.sqr()?;
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(
-        test_utils::to_vec1_round(&y, 3)?,
-        [403.429, 7.389, 2980.958, 1.35]
+        y.to_vec1::<f32>()?,
+        [403.4288, 7.3890557, 2980.9578, 1.3498588]
     );
     // exp(x)^2 = exp(2*x)
     assert_eq!(
-        test_utils::to_vec1_round(grad_x, 2)?,
-        [806.86, 14.78, 5961.92, 2.7]
+        grad_x.to_vec1::<f32>()?,
+        [806.8576, 14.778111, 5961.9155, 2.6997175]
     );
     let y = x.sin()?;
     let grads = y.backward()?;
@@ -262,7 +261,6 @@ fn unary_grad(device: &Device) -> Result<()> {
     let y = elu_x.elu(2.)?;
     let grads = y.backward()?;
     let grad_x = grads.get(&elu_x).context("no grad for x")?;
-
     assert_eq!(
         test_utils::to_vec1_round(&y, 4)?,
         [-1.2642, 0.0000, -1.7293, 3.0000]
@@ -285,38 +283,19 @@ fn unary_grad(device: &Device) -> Result<()> {
         [1.0881, 0.9277, 1.0527, 0.5747],
     );
 
-    if device.is_cpu() {
-        let x = Var::new(&[[[1f32, 2., 3.], [4., 5., 6.], [7., 8., 9.]]], device)?;
-        let y = x.interpolate1d(12)?.reshape(36)?;
-
-        let z = Tensor::new(
-            &[
-                1_f32, 02., 03., 04., 05., 06., 07., 08., 09., 10., 11., 12., 13., 14., 15., 16.,
-                17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32.,
-                33., 34., 35., 36.,
-            ],
-            device,
-        )?;
-
-        let loss = y.unsqueeze(1)?.transpose(0, 1)?.matmul(&z.unsqueeze(1)?)?;
-        let grads = loss.backward()?;
-        let grad_x = grads.get(&x).context("no grad for x")?;
-
-        assert_eq!(
-            test_utils::to_vec3_round(grad_x, 4)?,
-            [[[10_f32, 26., 42.], [58., 74., 90.], [106., 122., 138.]]]
-        );
-    }
-
     // manually checked: see comments
     let x = Var::new(&[[[[1f32, 2., 3.], [4., 5., 6.], [7., 8., 9.]]]], device)?;
     let y = x.interpolate2d(6, 6)?.reshape(36)?;
 
+    #[rustfmt::skip]
     let z = Tensor::new(
         &[
-            1_f32, 02., 03., 04., 05., 06., 07., 08., 09., 10., 11., 12., 13., 14., 15., 16., 17.,
-            18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34.,
-            35., 36.,
+            1_f32, 02., 03., 04., 05., 06.,
+            07.,   08., 09., 10., 11., 12.,
+            13.,   14., 15., 16., 17., 18.,
+            19.,   20., 21., 22., 23., 24.,
+            25.,   26., 27., 28., 29., 30.,
+            31.,   32., 33., 34., 35., 36.,
         ],
         device,
     )?;
@@ -347,11 +326,15 @@ fn unary_grad(device: &Device) -> Result<()> {
     let x = Var::new(&[[[[1f32, 2.], [4., 5.]]]], device)?;
     let y = x.interpolate2d(6, 6)?.reshape(36)?;
 
+    #[rustfmt::skip]
     let z = Tensor::new(
         &[
-            1_f32, 02., 03., 04., 05., 06., 07., 08., 09., 10., 11., 12., 13., 14., 15., 16., 17.,
-            18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34.,
-            35., 36.,
+            1_f32, 02., 03., 04., 05., 06.,
+            07.,   08., 09., 10., 11., 12.,
+            13.,   14., 15., 16., 17., 18.,
+            19.,   20., 21., 22., 23., 24.,
+            25.,   26., 27., 28., 29., 30.,
+            31.,   32., 33., 34., 35., 36.,
         ],
         device,
     )?;

candle-core/tests/pool_tests.rs

@@ -2,9 +2,6 @@ use candle_core::{test_device, test_utils, Device, IndexOp, Result, Tensor};
 
 // https://github.com/huggingface/candle/issues/364
 fn avg_pool2d(dev: &Device) -> Result<()> {
-    if dev.is_metal() {
-        return Ok(());
-    }
     let data: Vec<f32> = vec![
         1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
     ];
@@ -22,9 +19,6 @@ fn avg_pool2d(dev: &Device) -> Result<()> {
 }
 
 fn max_pool2d(dev: &Device) -> Result<()> {
-    if dev.is_metal() {
-        return Ok(());
-    }
     let data: Vec<f32> = vec![
         1., 2., 1., 3., 0., 0., 1., 1., 1., 1., 1., 1., 5., 1., 1., 1.,
     ];
@@ -49,9 +43,6 @@ res = torch.nn.functional.avg_pool2d(t, 2)
 print(res)
 */
 fn avg_pool2d_pytorch(dev: &Device) -> Result<()> {
-    if dev.is_metal() {
-        return Ok(());
-    }
     let t = Tensor::new(
         &[
             0.4056f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997, 3.0616,

candle-core/tests/quantized_tests.rs

@@ -178,6 +178,10 @@ test_device!(
 );
 
 fn quantize_q4_0(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
 
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
@@ -205,6 +209,10 @@ fn quantize_q4_0(device: &Device) -> Result<()> {
 }
 
 fn quantize_q4_1(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
     let quant = quantized::QTensor::quantize(&src, GgmlDType::Q4_1)?;
@@ -231,6 +239,10 @@ fn quantize_q4_1(device: &Device) -> Result<()> {
 }
 
 fn quantize_q5_0(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
     let quant = quantized::QTensor::quantize(&src, GgmlDType::Q5_0)?;
@@ -257,6 +269,10 @@ fn quantize_q5_0(device: &Device) -> Result<()> {
 }
 
 fn quantize_q5_1(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
     let quant = quantized::QTensor::quantize(&src, GgmlDType::Q5_1)?;
@@ -357,6 +373,10 @@ fn ggml_quantization_error_test(dtype: GgmlDType, device: &Device, max_error: f3
 }
 
 fn quantize_q2k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q2K;
 
     let src = get_test_vector2(0.5, 1024, device)?;
@@ -391,6 +411,10 @@ fn quantize_q2k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q3k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q3K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -424,6 +448,10 @@ fn quantize_q3k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q4k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q4K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -457,6 +485,10 @@ fn quantize_q4k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q5k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q5K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -490,6 +522,10 @@ fn quantize_q5k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q6k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q6K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -523,6 +559,10 @@ fn quantize_q6k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q8k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q8K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -738,6 +778,10 @@ macro_rules! quantized_matmul {
     // stable. https://github.com/rust-lang/rust/issues/29599
     ($fn_name: ident, $fn_name_cpu: ident, $fn_name_cuda: ident, $fn_name_metal: ident, $dtype: expr) => {
         fn $fn_name(device: &Device) -> Result<()> {
+            if device.is_cuda() {
+                // TODO Enable Cuda GGML sometime maybe.
+                return Ok(());
+            }
             test_matmul(device, (1, 3, 4, 256), $dtype)?;
             Ok(())
         }

candle-core/tests/tensor_tests.rs

@@ -672,31 +672,6 @@ fn cat(device: &Device) -> Result<()> {
             [2.0, 7.0, 1.0, 8.0, 2.0, 2.0, 7.0, 1.0, 8.0, 2.0]
         ]
     );
-
-    // 3D
-    let t1 = Tensor::arange(0, 48i64, device)?.reshape((2, 6, 4))?;
-    let t2 = Tensor::arange(100, 124i64, device)?.reshape((2, 3, 4))?;
-    let t3 = Tensor::arange(10000, 10032i64, device)?.reshape((2, 4, 4))?;
-
-    let t_cat = Tensor::cat(&[&t1, &t2, &t3], 1)?;
-
-    let t1 = t1.t()?.contiguous()?.t()?;
-    let t2 = t2.t()?.contiguous()?.t()?;
-    let t3 = t3.t()?.contiguous()?.t()?;
-    let t_cat2 = Tensor::cat(&[&t1, &t2, &t3], 1)?;
-
-    let diff = t_cat.eq(&t_cat2)?.to_dtype(DType::F32)?.sum_all()?;
-    assert_eq!(diff.to_vec0::<f32>()?, 104.0);
-    assert_eq!(t_cat.i((0, 0, 0))?.to_vec0::<i64>()?, 0);
-    assert_eq!(t_cat.i((0, 4, 0))?.to_vec0::<i64>()?, 16);
-    assert_eq!(t_cat.i((0, 5, 0))?.to_vec0::<i64>()?, 20);
-    assert_eq!(t_cat.i((1, 5, 0))?.to_vec0::<i64>()?, 44);
-    assert_eq!(t_cat.i((0, 6, 0))?.to_vec0::<i64>()?, 100);
-    assert_eq!(t_cat.i((1, 6, 0))?.to_vec0::<i64>()?, 112);
-    assert_eq!(t_cat.i((0, 6, 1))?.to_vec0::<i64>()?, 101);
-    assert_eq!(t_cat.i((0, 7, 1))?.to_vec0::<i64>()?, 105);
-    assert_eq!(t_cat.i((0, 12, 1))?.to_vec0::<i64>()?, 10013);
-    assert_eq!(t_cat.i((1, 12, 3))?.to_vec0::<i64>()?, 10031);
     Ok(())
 }
 
@@ -1105,33 +1080,8 @@ fn broadcasting(device: &Device) -> Result<()> {
 fn randn(device: &Device) -> Result<()> {
     let tensor = Tensor::randn(0f32, 1f32, (5, 3), device)?;
     assert_eq!(tensor.dims(), [5, 3]);
-    // Check that the seed gets updated by checking that
-    // a new series of numbers is generated each time
-    let tensor2 = Tensor::randn(0f32, 1f32, (5, 3), device)?;
-    assert_ne!(tensor.to_vec2::<f32>()?, tensor2.to_vec2::<f32>()?);
     let tensor = Tensor::rand(0f32, 1f32, (5, 3), device)?;
     assert_eq!(tensor.dims(), [5, 3]);
-    // Check that the seed gets updated by checking that
-    // a new series of numbers is generated each time
-    let tensor2 = Tensor::rand(0f32, 1f32, (5, 3), device)?;
-    assert_ne!(tensor.to_vec2::<f32>()?, tensor2.to_vec2::<f32>()?);
-    // We do not expect deterministic elements at any index.
-    // There once was a bug that had a deterministic zero element in evenly sized tensors.
-    const N: usize = 2;
-    let v = (0..100)
-        .map(|_| Tensor::randn(0f32, 1f32, N, device).and_then(|t| t.to_vec1::<f32>()))
-        .collect::<Result<Vec<_>>>()?;
-    assert!(
-        (0..N).all(|i| v.windows(2).any(|pair| pair[0][i] != pair[1][i])),
-        "There are deterministic values in the randn tensors"
-    );
-    let v = (0..100)
-        .map(|_| Tensor::rand(0f32, 1f32, N, device).and_then(|t| t.to_vec1::<f32>()))
-        .collect::<Result<Vec<_>>>()?;
-    assert!(
-        (0..N).all(|i| v.windows(2).any(|pair| pair[0][i] != pair[1][i])),
-        "There are deterministic values in the rand tensors"
-    );
     Ok(())
 }
 

candle-examples/Cargo.toml

@@ -12,7 +12,7 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 candle = { workspace = true }
-candle-datasets = { workspace = true, optional = true }
+candle-datasets = { workspace = true }
 candle-nn = { workspace = true }
 candle-transformers = { workspace = true }
 candle-flash-attn = { workspace = true, optional = true }
@@ -30,7 +30,7 @@ rayon = { workspace = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
-symphonia = { version = "0.5.3", features = ["all"], optional = true }
+symphonia = { version = "0.5.3", features = ["all"] }
 tokenizers = { workspace = true, features = ["onig"] }
 cpal= { version = "0.15.2", optional = true }
 
@@ -80,24 +80,6 @@ required-features = ["onnx"]
 name = "onnx_basics"
 required-features = ["onnx"]
 
-[[example]]
-name = "whisper"
-required-features = ["symphonia"]
-
 [[example]]
 name = "whisper-microphone"
 required-features = ["microphone"]
-
-[[example]]
-name = "mnist-training"
-required-features = ["candle-datasets"]
-
-[[example]]
-name = "llama2-c"
-required-features = ["candle-datasets"]
-
-[[example]]
-name = "encodec"
-required-features = ["symphonia"]
-
-

candle-examples/examples/convmixer/main.rs

@@ -28,7 +28,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/convnext/main.rs

@@ -93,7 +93,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/custom-ops/cuda_kernels.rs

@@ -1 +0,0 @@
-pub const LAYERNORM_KERNELS: &str = include_str!(concat!(env!("OUT_DIR"), "/layernorm_kernels.ptx"));

candle-examples/examples/dinov2/main.rs

@@ -31,7 +31,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/efficientnet/main.rs

@@ -47,7 +47,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/efficientvit/README.md

@@ -1,20 +0,0 @@
-# candle-efficientvit
-
-[EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention](https://arxiv.org/abs/2305.07027).
-
-This candle implementation uses a pre-trained EfficientViT (from Microsoft Research Asia) network for inference.
-The classification head has been trained on the ImageNet dataset and returns the probabilities for the top-5 classes.
-
-## Running an example
-
-```
-$ cargo run --example efficientvit --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which m1
-
-loaded image Tensor[dims 3, 224, 224; f32]
-model built
-mountain bike, all-terrain bike, off-roader: 69.80%
-unicycle, monocycle     : 13.03%
-bicycle-built-for-two, tandem bicycle, tandem: 9.28%
-crash helmet            : 2.25%
-alp                     : 0.46%
-```

candle-examples/examples/efficientvit/main.rs

@@ -1,99 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use clap::{Parser, ValueEnum};
-
-use candle::{DType, IndexOp, D};
-use candle_nn::{Module, VarBuilder};
-use candle_transformers::models::efficientvit;
-
-#[derive(Clone, Copy, Debug, ValueEnum)]
-enum Which {
-    M0,
-    M1,
-    M2,
-    M3,
-    M4,
-    M5,
-}
-
-impl Which {
-    fn model_filename(&self) -> String {
-        let name = match self {
-            Self::M0 => "m0",
-            Self::M1 => "m1",
-            Self::M2 => "m2",
-            Self::M3 => "m3",
-            Self::M4 => "m4",
-            Self::M5 => "m5",
-        };
-        format!("timm/efficientvit_{}.r224_in1k", name)
-    }
-
-    fn config(&self) -> efficientvit::Config {
-        match self {
-            Self::M0 => efficientvit::Config::m0(),
-            Self::M1 => efficientvit::Config::m1(),
-            Self::M2 => efficientvit::Config::m2(),
-            Self::M3 => efficientvit::Config::m3(),
-            Self::M4 => efficientvit::Config::m4(),
-            Self::M5 => efficientvit::Config::m5(),
-        }
-    }
-}
-
-#[derive(Parser)]
-struct Args {
-    #[arg(long)]
-    model: Option<String>,
-
-    #[arg(long)]
-    image: String,
-
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    #[arg(value_enum, long, default_value_t=Which::M0)]
-    which: Which,
-}
-
-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)?.to_device(&device)?;
-    println!("loaded image {image:?}");
-
-    let model_file = match args.model {
-        None => {
-            let model_name = args.which.model_filename();
-            let api = hf_hub::api::sync::Api::new()?;
-            let api = api.model(model_name);
-            api.get("model.safetensors")?
-        }
-        Some(model) => model.into(),
-    };
-
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
-    let model = efficientvit::efficientvit(&args.which.config(), 1000, vb)?;
-    println!("model built");
-    let logits = model.forward(&image.unsqueeze(0)?)?;
-    let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
-        .i(0)?
-        .to_vec1::<f32>()?;
-    let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
-    prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
-    for &(category_idx, pr) in prs.iter().take(5) {
-        println!(
-            "{:24}: {:.2}%",
-            candle_examples::imagenet::CLASSES[category_idx],
-            100. * pr
-        );
-    }
-    Ok(())
-}

candle-examples/examples/encodec/README.md

@@ -1,20 +0,0 @@
-# candle-endocec
-
-[EnCodec](https://huggingface.co/facebook/encodec_24khz) is a high-quality audio
-compression model using an encoder/decoder architecture with residual vector
-quantization.
-
-## Running one example
-
-```bash
-cargo run --example encodec --features symphonia --release -- code-to-audio \
-    candle-examples/examples/encodec/jfk-codes.safetensors \
-    jfk.wav
-```
-
-This decodes the EnCodec tokens stored in `jfk-codes.safetensors` and generates
-an output wav file containing the audio data. Instead of `code-to-audio` one
-can use:
-- `audio-to-audio in.mp3 out.wav`: encodes the input audio file then decodes it to a wav file.
-- `audio-to-code in.mp3 out.safetensors`: generates a safetensors file
-  containing EnCodec tokens for the input audio file.

candle-examples/examples/encodec/main.rs

@@ -1,142 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::Result;
-use candle::{DType, IndexOp, Tensor};
-use candle_nn::VarBuilder;
-use candle_transformers::models::encodec::{Config, Model};
-use clap::{Parser, ValueEnum};
-use hf_hub::api::sync::Api;
-
-fn conv<T>(samples: &mut Vec<f32>, data: std::borrow::Cow<symphonia::core::audio::AudioBuffer<T>>)
-where
-    T: symphonia::core::sample::Sample,
-    f32: symphonia::core::conv::FromSample<T>,
-{
-    use symphonia::core::audio::Signal;
-    use symphonia::core::conv::FromSample;
-    samples.extend(data.chan(0).iter().map(|v| f32::from_sample(*v)))
-}
-
-fn pcm_decode<P: AsRef<std::path::Path>>(path: P) -> anyhow::Result<(Vec<f32>, u32)> {
-    use symphonia::core::audio::{AudioBufferRef, Signal};
-
-    let src = std::fs::File::open(path)?;
-    let mss = symphonia::core::io::MediaSourceStream::new(Box::new(src), Default::default());
-    let hint = symphonia::core::probe::Hint::new();
-    let meta_opts: symphonia::core::meta::MetadataOptions = Default::default();
-    let fmt_opts: symphonia::core::formats::FormatOptions = Default::default();
-    let probed = symphonia::default::get_probe().format(&hint, mss, &fmt_opts, &meta_opts)?;
-    let mut format = probed.format;
-    let track = format
-        .tracks()
-        .iter()
-        .find(|t| t.codec_params.codec != symphonia::core::codecs::CODEC_TYPE_NULL)
-        .expect("no supported audio tracks");
-    let mut decoder = symphonia::default::get_codecs()
-        .make(&track.codec_params, &Default::default())
-        .expect("unsupported codec");
-    let track_id = track.id;
-    let sample_rate = track.codec_params.sample_rate.unwrap_or(0);
-    let mut pcm_data = Vec::new();
-    while let Ok(packet) = format.next_packet() {
-        while !format.metadata().is_latest() {
-            format.metadata().pop();
-        }
-        if packet.track_id() != track_id {
-            continue;
-        }
-        match decoder.decode(&packet)? {
-            AudioBufferRef::F32(buf) => pcm_data.extend(buf.chan(0)),
-            AudioBufferRef::U8(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::U16(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::U24(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::U32(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S8(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S16(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S24(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S32(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::F64(data) => conv(&mut pcm_data, data),
-        }
-    }
-    Ok((pcm_data, sample_rate))
-}
-
-#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
-enum Action {
-    AudioToAudio,
-    AudioToCode,
-    CodeToAudio,
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// The action to be performed, specifies the format for the input and output data.
-    action: Action,
-
-    /// The input file, either an audio file or some encodec tokens stored as safetensors.
-    in_file: String,
-
-    /// The output file, either a wave audio file or some encodec tokens stored as safetensors.
-    out_file: String,
-
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// The model weight file, in safetensor format.
-    #[arg(long)]
-    model: Option<String>,
-}
-
-fn main() -> Result<()> {
-    let args = Args::parse();
-    let device = candle_examples::device(args.cpu)?;
-    let model = match args.model {
-        Some(model) => std::path::PathBuf::from(model),
-        None => Api::new()?
-            .model("facebook/encodec_24khz".to_string())
-            .get("model.safetensors")?,
-    };
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? };
-    let config = Config::default();
-    let model = Model::new(&config, vb)?;
-
-    let codes = match args.action {
-        Action::CodeToAudio => {
-            let codes = candle::safetensors::load(args.in_file, &device)?;
-            codes.get("codes").expect("no codes in input file").clone()
-        }
-        Action::AudioToCode | Action::AudioToAudio => {
-            let (pcm, sample_rate) = pcm_decode(args.in_file)?;
-            if sample_rate != 24_000 {
-                println!("WARNING: encodec uses a 24khz sample rate, input uses {sample_rate}")
-            }
-            let pcm_len = pcm.len();
-            let pcm = Tensor::from_vec(pcm, (1, 1, pcm_len), &device)?;
-            println!("input pcm shape: {:?}", pcm.shape());
-            model.encode(&pcm)?
-        }
-    };
-    println!("codes shape: {:?}", codes.shape());
-
-    match args.action {
-        Action::AudioToCode => {
-            codes.save_safetensors("codes", &args.out_file)?;
-        }
-        Action::AudioToAudio | Action::CodeToAudio => {
-            let pcm = model.decode(&codes)?;
-            println!("output pcm shape: {:?}", pcm.shape());
-            let pcm = pcm.i(0)?.i(0)?;
-            let pcm = candle_examples::audio::normalize_loudness(&pcm, 24_000, true)?;
-            let pcm = pcm.to_vec1::<f32>()?;
-            let mut output = std::fs::File::create(&args.out_file)?;
-            candle_examples::wav::write_pcm_as_wav(&mut output, &pcm, 24_000)?;
-        }
-    }
-    Ok(())
-}

candle-examples/examples/gemma/README.md

@@ -1,27 +0,0 @@
-# candle-gemma: 2b and 7b LLMs from Google DeepMind
-
-[Gemma](https://ai.google.dev/gemma/docs) is a collection of lightweight open
-models published by Google Deepmind with a 2b and a 7b variant.
-
-In order to use the example below, you have to accept the license on the
-[HuggingFace Hub Gemma repo](https://huggingface.co/google/gemma-7b) and set up
-your access token via the [HuggingFace cli login
-command](https://huggingface.co/docs/huggingface_hub/guides/cli#huggingface-cli-login).
-
-## Running the example
-
-```bash
-$ cargo run --example gemma --release -- --prompt "fn count_primes(max_n: usize)"
-fn count_primes(max_n: usize) -> usize {
-    let mut primes = vec![true; max_n];
-    for i in 2..=max_n {
-        if primes[i] {
-            for j in i * i..max_n {
-                primes[j] = false;
-             }
-         }
-    }
-    primes.len()
-}
-```
-

candle-examples/examples/gemma/main.rs

@@ -1,256 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::{Error as E, Result};
-use clap::Parser;
-
-use candle_transformers::models::gemma::{Config, Model};
-
-use candle::{DType, Device, Tensor};
-use candle_examples::token_output_stream::TokenOutputStream;
-use candle_nn::VarBuilder;
-use candle_transformers::generation::LogitsProcessor;
-use hf_hub::{api::sync::Api, Repo, RepoType};
-use tokenizers::Tokenizer;
-
-struct TextGeneration {
-    model: Model,
-    device: Device,
-    tokenizer: TokenOutputStream,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            tokenizer: TokenOutputStream::new(tokenizer),
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        self.tokenizer.clear();
-        let mut tokens = self
-            .tokenizer
-            .tokenizer()
-            .encode(prompt, true)
-            .map_err(E::msg)?
-            .get_ids()
-            .to_vec();
-        for &t in tokens.iter() {
-            if let Some(t) = self.tokenizer.next_token(t)? {
-                print!("{t}")
-            }
-        }
-        std::io::stdout().flush()?;
-
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_token("<eos>") {
-            Some(token) => token,
-            None => anyhow::bail!("cannot find the <eos> token"),
-        };
-        let start_gen = std::time::Instant::now();
-        for index in 0..sample_len {
-            let context_size = if index > 0 { 1 } else { tokens.len() };
-            let start_pos = tokens.len().saturating_sub(context_size);
-            let ctxt = &tokens[start_pos..];
-            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = self.model.forward(&input, start_pos)?;
-            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            if let Some(t) = self.tokenizer.next_token(next_token)? {
-                print!("{t}");
-                std::io::stdout().flush()?;
-            }
-        }
-        let dt = start_gen.elapsed();
-        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
-            print!("{rest}");
-        }
-        std::io::stdout().flush()?;
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// The temperature used to generate samples.
-    #[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,
-
-    /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 10000)]
-    sample_len: usize,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long, default_value = "main")]
-    revision: String,
-
-    #[arg(long)]
-    tokenizer_file: Option<String>,
-
-    #[arg(long)]
-    config_file: Option<String>,
-
-    #[arg(long)]
-    weight_files: Option<String>,
-
-    /// 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,
-}
-
-fn main() -> 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
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let model_id = match &args.model_id {
-        Some(model_id) => match model_id.as_str() {
-            "7b-it" => "google/gemma-7b-it".to_string(),
-            "7b" => "google/gemma-7b".to_string(),
-            "2b-it" => "google/gemma-2b-it".to_string(),
-            "2b" => "google/gemma-2b".to_string(),
-            _ => model_id.to_string(),
-        },
-        None => "google/gemma-2b".to_string(),
-    };
-    let repo = api.repo(Repo::with_revision(
-        model_id,
-        RepoType::Model,
-        args.revision,
-    ));
-    let tokenizer_filename = match args.tokenizer_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("tokenizer.json")?,
-    };
-    let config_filename = match args.config_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("config.json")?,
-    };
-    let filenames = match args.weight_files {
-        Some(files) => files
-            .split(',')
-            .map(std::path::PathBuf::from)
-            .collect::<Vec<_>>(),
-        None => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-    let config: Config = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
-
-    let start = std::time::Instant::now();
-    let device = candle_examples::device(args.cpu)?;
-    let dtype = if device.is_cuda() {
-        DType::BF16
-    } else {
-        DType::F32
-    };
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-    let model = Model::new(&config, vb)?;
-
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

candle-examples/examples/llama/main.rs

@@ -120,7 +120,7 @@ fn main() -> Result<()> {
         Some(dtype) => bail!("Unsupported dtype {dtype}"),
         None => DType::F16,
     };
-    let (llama, tokenizer_filename, mut cache) = {
+    let (llama, tokenizer_filename, cache) = {
         let api = Api::new()?;
         let model_id = args.model_id.unwrap_or_else(|| match args.which {
             Which::V1 => "Narsil/amall-7b".to_string(),
@@ -146,7 +146,7 @@ fn main() -> Result<()> {
         let cache = model::Cache::new(!args.no_kv_cache, dtype, &config, &device)?;
 
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-        (Llama::load(vb, &config)?, tokenizer_filename, cache)
+        (Llama::load(vb, &cache, &config)?, tokenizer_filename, cache)
     };
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
     let eos_token_id = tokenizer.token_to_id(EOS_TOKEN);
@@ -172,7 +172,7 @@ fn main() -> Result<()> {
         };
         let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
         let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
-        let logits = llama.forward(&input, context_index, &mut cache)?;
+        let logits = llama.forward(&input, context_index)?;
         let logits = logits.squeeze(0)?;
         let logits = if args.repeat_penalty == 1. {
             logits

candle-examples/examples/llama2-c/main.rs

@@ -19,7 +19,7 @@ use candle_transformers::generation::LogitsProcessor;
 use std::io::Write;
 use tokenizers::Tokenizer;
 
-use model::{Cache, Config, Llama};
+use model::{Config, Llama};
 use qmodel::QLlama;
 use weights::TransformerWeights;
 
@@ -160,10 +160,10 @@ enum Model {
 }
 
 impl Model {
-    fn forward(&self, xs: &Tensor, pos: usize, cache: &mut Cache) -> anyhow::Result<Tensor> {
+    fn forward(&self, xs: &Tensor, pos: usize) -> anyhow::Result<Tensor> {
         match self {
-            Self::Llama(l) => Ok(l.forward(xs, pos, cache)?),
-            Self::QLlama(l) => Ok(l.forward(xs, pos, cache)?),
+            Self::Llama(l) => Ok(l.forward(xs, pos)?),
+            Self::QLlama(l) => Ok(l.forward(xs, pos)?),
         }
     }
 }
@@ -188,8 +188,8 @@ fn run_eval(args: &EvaluationCmd, common_args: &Args) -> Result<()> {
     let config = Config::from_reader(&mut file)?;
     let weights = TransformerWeights::from_reader(&mut file, &config, &device)?;
     let vb = weights.var_builder(&config, &device)?;
-    let mut cache = Cache::new(false, &config, vb.pp("rot"))?;
-    let model = Llama::load(vb, config)?;
+    let cache = model::Cache::new(false, &config, vb.pp("rot"))?;
+    let model = Llama::load(vb, &cache, config)?;
 
     let tokens = match &args.pretokenized_dir {
         None => {
@@ -235,7 +235,7 @@ fn run_eval(args: &EvaluationCmd, common_args: &Args) -> Result<()> {
     let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
     for inp_tgt in batch_iter {
         let (inp, tgt) = inp_tgt?;
-        let logits = model.forward(&inp, 0, &mut cache)?;
+        let logits = model.forward(&inp, 0)?;
         let loss = candle_nn::loss::cross_entropy(&logits.flatten_to(1)?, &tgt.flatten_to(1)?)?;
         println!("{}", loss.to_vec0::<f32>()?);
     }
@@ -261,7 +261,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
     let is_safetensors = config_path
         .extension()
         .map_or(false, |v| v == "safetensors");
-    let (model, config, mut cache) = if is_gguf {
+    let (model, config) = if is_gguf {
         let vb = qmodel::VarBuilder::from_gguf(config_path, &device)?;
         let (_vocab_size, dim) = vb
             .get_no_shape("model.embed_tokens.weight")?
@@ -298,15 +298,15 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
             &device,
         );
         let cache = model::Cache::new(true, &config, fake_vb)?;
-        let model = Model::QLlama(QLlama::load(vb, config.clone())?);
-        (model, config, cache)
+        let model = Model::QLlama(QLlama::load(vb, &cache, config.clone())?);
+        (model, config)
     } else if is_safetensors {
         let config = Config::tiny_15m();
         let tensors = candle::safetensors::load(config_path, &device)?;
         let vb = candle_nn::VarBuilder::from_tensors(tensors, candle::DType::F32, &device);
         let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
-        let model = Model::Llama(Llama::load(vb, config.clone())?);
-        (model, config, cache)
+        let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
+        (model, config)
     } else {
         let mut file = std::fs::File::open(config_path)?;
         let config = Config::from_reader(&mut file)?;
@@ -314,8 +314,8 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         let weights = TransformerWeights::from_reader(&mut file, &config, &device)?;
         let vb = weights.var_builder(&config, &device)?;
         let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
-        let model = Model::Llama(Llama::load(vb, config.clone())?);
-        (model, config, cache)
+        let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
+        (model, config)
     };
 
     println!("starting the inference loop");
@@ -338,7 +338,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         let context_size = if index > 0 { 1 } else { tokens.len() };
         let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
         let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
-        let logits = model.forward(&input, index_pos, &mut cache)?;
+        let logits = model.forward(&input, index_pos)?;
         let logits = logits.i((0, logits.dim(1)? - 1))?;
         let logits = if common_args.repeat_penalty == 1. || tokens.is_empty() {
             logits

candle-examples/examples/llama2-c/training.rs

@@ -8,7 +8,6 @@ fn valid_loss(
     model: &Llama,
     args: &crate::TrainingCmd,
     device: &Device,
-    cache: &mut Cache,
 ) -> Result<f64> {
     let iter = DatasetRandomIter::new(dataset, true, model.config.seq_len, device.clone());
     let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
@@ -16,7 +15,7 @@ fn valid_loss(
     let mut cnt = 0usize;
     for inp_tgt in batch_iter.take(50) {
         let (inp, tgt) = inp_tgt?;
-        let logits = model.forward(&inp, 0, cache)?;
+        let logits = model.forward(&inp, 0)?;
         let loss = candle_nn::loss::cross_entropy(&logits.flatten_to(1)?, &tgt.flatten_to(1)?)?;
         sum_ce += loss.to_vec0::<f32>()? as f64;
         cnt += 1;
@@ -38,8 +37,8 @@ pub fn run(args: &crate::TrainingCmd, common_args: &crate::Args) -> Result<()> {
     let iter = DatasetRandomIter::new(&dataset, false, config.seq_len, device.clone());
     let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
 
-    let mut cache = Cache::new(false, &config, vb.pp("rot"))?;
-    let model = Llama::load(vb, config)?;
+    let cache = Cache::new(false, &config, vb.pp("rot"))?;
+    let model = Llama::load(vb, &cache, config)?;
     let params = candle_nn::ParamsAdamW {
         lr: args.learning_rate,
         ..Default::default()
@@ -47,14 +46,14 @@ pub fn run(args: &crate::TrainingCmd, common_args: &crate::Args) -> Result<()> {
     let mut opt = candle_nn::AdamW::new(varmap.all_vars(), params)?;
     for (batch_index, batch) in batch_iter.enumerate() {
         let (inp, tgt) = batch?;
-        let logits = model.forward(&inp, 0, &mut cache)?;
+        let logits = model.forward(&inp, 0)?;
         let loss = candle_nn::loss::cross_entropy(&logits.flatten_to(1)?, &tgt.flatten_to(1)?)?;
         opt.backward_step(&loss)?;
 
         if batch_index > 0 && batch_index % 100 == 0 {
             // TODO: Add a way to deactivate the backprop graph tracking when computing the
             // validation loss.
-            let loss = valid_loss(&dataset, &model, args, &device, &mut cache)?;
+            let loss = valid_loss(&dataset, &model, args, &device)?;
             println!("{batch_index} {loss}");
         }
         if batch_index > 0 && batch_index % 1000 == 0 {

candle-examples/examples/metavoice/README.md

@@ -1,18 +0,0 @@
-# candle-metavoice
-
-MetaVoice-1B is a text-to-speech model trained on 100K hours of speech, more
-details on the [model
-card](https://huggingface.co/metavoiceio/metavoice-1B-v0.1).
-
-Note that the current candle implementation suffers from some limitations as of
-2024-03-02:
-- The speaker embeddings are hardcoded.
-- The generated audio file quality is weaker than the Python implementation,
-  probably because of some implementation discrepancies.
-
-## Run an example
-
-```bash
-cargo run --example metavoice --release -- \\
-  --prompt "This is a demo of text to speech by MetaVoice-1B, an open-source foundational audio model."
-```

candle-examples/examples/metavoice/main.rs

@@ -1,277 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::Result;
-use clap::Parser;
-use std::io::Write;
-
-use candle_transformers::generation::LogitsProcessor;
-use candle_transformers::models::encodec;
-use candle_transformers::models::metavoice::{adapters, gpt, tokenizers, transformer};
-use candle_transformers::models::quantized_metavoice::transformer as qtransformer;
-
-use candle::{DType, IndexOp, Tensor};
-use candle_nn::VarBuilder;
-use hf_hub::api::sync::Api;
-use rand::{distributions::Distribution, SeedableRng};
-
-pub const ENCODEC_NTOKENS: u32 = 1024;
-
-#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
-enum ArgDType {
-    F32,
-    F16,
-    Bf16,
-}
-
-enum Transformer {
-    Normal(transformer::Model),
-    Quantized(qtransformer::Model),
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// Use the quantized version of the model.
-    #[arg(long)]
-    quantized: bool,
-
-    /// The guidance scale.
-    #[arg(long, default_value_t = 3.0)]
-    guidance_scale: f64,
-
-    /// The temperature used to generate samples.
-    #[arg(long, default_value_t = 1.0)]
-    temperature: f64,
-
-    /// The seed to use when generating random samples.
-    #[arg(long, default_value_t = 299792458)]
-    seed: u64,
-
-    /// The maximum number of tokens to generate for the first stage.
-    #[arg(long, default_value_t = 2000)]
-    max_tokens: u64,
-
-    /// The output file using the wav format.
-    #[arg(long, default_value = "out.wav")]
-    out_file: String,
-
-    #[arg(long)]
-    first_stage_meta: Option<String>,
-
-    #[arg(long)]
-    first_stage_weights: Option<String>,
-
-    #[arg(long)]
-    second_stage_weights: Option<String>,
-
-    #[arg(long)]
-    encodec_weights: Option<String>,
-
-    #[arg(long)]
-    spk_emb: Option<String>,
-
-    #[arg(long, default_value = "f32")]
-    dtype: ArgDType,
-}
-
-fn main() -> 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
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    let device = candle_examples::device(args.cpu)?;
-    let api = Api::new()?;
-    let repo = api.model("lmz/candle-metavoice".to_string());
-    let first_stage_meta = match &args.first_stage_meta {
-        Some(w) => std::path::PathBuf::from(w),
-        None => repo.get("first_stage.meta.json")?,
-    };
-    let first_stage_meta: serde_json::Value =
-        serde_json::from_reader(&std::fs::File::open(first_stage_meta)?)?;
-    let first_stage_tokenizer = match first_stage_meta.as_object() {
-        None => anyhow::bail!("not a json object"),
-        Some(j) => match j.get("tokenizer") {
-            None => anyhow::bail!("no tokenizer key"),
-            Some(j) => j,
-        },
-    };
-    let fs_tokenizer = tokenizers::BPE::from_json(first_stage_tokenizer, 512)?;
-
-    let second_stage_weights = match &args.second_stage_weights {
-        Some(w) => std::path::PathBuf::from(w),
-        None => repo.get("second_stage.safetensors")?,
-    };
-    let encodec_weights = match args.encodec_weights {
-        Some(w) => std::path::PathBuf::from(w),
-        None => Api::new()?
-            .model("facebook/encodec_24khz".to_string())
-            .get("model.safetensors")?,
-    };
-    let dtype = match args.dtype {
-        ArgDType::F32 => DType::F32,
-        ArgDType::F16 => DType::F16,
-        ArgDType::Bf16 => DType::BF16,
-    };
-
-    let first_stage_config = transformer::Config::cfg1b_v0_1();
-    let mut first_stage_model = if args.quantized {
-        let filename = match &args.first_stage_weights {
-            Some(w) => std::path::PathBuf::from(w),
-            None => repo.get("first_stage_q4k.gguf")?,
-        };
-        let vb =
-            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
-        let first_stage_model = qtransformer::Model::new(&first_stage_config, vb)?;
-        Transformer::Quantized(first_stage_model)
-    } else {
-        let first_stage_weights = match &args.first_stage_weights {
-            Some(w) => std::path::PathBuf::from(w),
-            None => repo.get("first_stage.safetensors")?,
-        };
-        let first_stage_vb =
-            unsafe { VarBuilder::from_mmaped_safetensors(&[first_stage_weights], dtype, &device)? };
-        let first_stage_model = transformer::Model::new(&first_stage_config, first_stage_vb)?;
-        Transformer::Normal(first_stage_model)
-    };
-
-    let second_stage_vb =
-        unsafe { VarBuilder::from_mmaped_safetensors(&[second_stage_weights], dtype, &device)? };
-    let second_stage_config = gpt::Config::cfg1b_v0_1();
-    let second_stage_model = gpt::Model::new(second_stage_config.clone(), second_stage_vb)?;
-
-    let encodec_device = if device.is_metal() {
-        &candle::Device::Cpu
-    } else {
-        &device
-    };
-    let encodec_vb =
-        unsafe { VarBuilder::from_mmaped_safetensors(&[encodec_weights], dtype, encodec_device)? };
-    let encodec_config = encodec::Config::default();
-    let encodec_model = encodec::Model::new(&encodec_config, encodec_vb)?;
-
-    println!("prompt: '{}'", args.prompt);
-    let prompt_tokens = fs_tokenizer.encode(&args.prompt)?;
-    let mut tokens = prompt_tokens.clone();
-    println!("{tokens:?}");
-    let spk_emb_file = match &args.spk_emb {
-        Some(w) => std::path::PathBuf::from(w),
-        None => repo.get("spk_emb.safetensors")?,
-    };
-    let spk_emb = candle::safetensors::load(&spk_emb_file, &candle::Device::Cpu)?;
-    let spk_emb = match spk_emb.get("spk_emb") {
-        None => anyhow::bail!("missing spk_emb tensor in {spk_emb_file:?}"),
-        Some(spk_emb) => spk_emb.to_dtype(dtype)?,
-    };
-    let spk_emb = spk_emb.to_device(&device)?;
-    let mut logits_processor = LogitsProcessor::new(args.seed, Some(args.temperature), Some(0.95));
-
-    // First stage generation.
-    for index in 0..args.max_tokens {
-        let context_size = if index > 0 { 1 } else { tokens.len() };
-        let start_pos = tokens.len().saturating_sub(context_size);
-        let ctxt = &tokens[start_pos..];
-        let input = Tensor::new(ctxt, &device)?;
-        let input = Tensor::stack(&[&input, &input], 0)?;
-        let logits = match &mut first_stage_model {
-            Transformer::Normal(m) => m.forward(&input, &spk_emb, tokens.len() - context_size)?,
-            Transformer::Quantized(m) => {
-                m.forward(&input, &spk_emb, tokens.len() - context_size)?
-            }
-        };
-        let logits0 = logits.i((0, 0))?;
-        let logits1 = logits.i((1, 0))?;
-        let logits = ((logits0 * args.guidance_scale)? + logits1 * (1. - args.guidance_scale))?;
-        let logits = logits.to_dtype(DType::F32)?;
-        let next_token = logits_processor.sample(&logits)?;
-        tokens.push(next_token);
-        print!(".");
-        std::io::stdout().flush()?;
-        if next_token == 2048 {
-            break;
-        }
-    }
-    println!();
-    let fie2c = adapters::FlattenedInterleavedEncodec2Codebook::new(ENCODEC_NTOKENS);
-    let (text_ids, ids1, ids2) = fie2c.decode(&tokens);
-    println!("text ids len: {}", text_ids.len());
-    let mut rng = rand::rngs::StdRng::seed_from_u64(args.seed + 1337);
-    // TODO: Use the config rather than hardcoding the offset here.
-    let encoded_text: Vec<_> = prompt_tokens.iter().map(|v| v - 1024).collect();
-    let mut hierarchies_in1 =
-        [encoded_text.as_slice(), ids1.as_slice(), &[ENCODEC_NTOKENS]].concat();
-    let mut hierarchies_in2 = [
-        vec![ENCODEC_NTOKENS; encoded_text.len()].as_slice(),
-        ids2.as_slice(),
-        &[ENCODEC_NTOKENS],
-    ]
-    .concat();
-    hierarchies_in1.resize(second_stage_config.block_size, ENCODEC_NTOKENS);
-    hierarchies_in2.resize(second_stage_config.block_size, ENCODEC_NTOKENS);
-    let in_x1 = Tensor::new(hierarchies_in1, &device)?;
-    let in_x2 = Tensor::new(hierarchies_in2, &device)?;
-    let in_x = Tensor::stack(&[in_x1, in_x2], 0)?.unsqueeze(0)?;
-    let logits = second_stage_model.forward(&in_x)?;
-    println!("sampling from logits...");
-    let mut codes = vec![];
-    for logits in logits.iter() {
-        let logits = logits.squeeze(0)?;
-        let (seq_len, _) = logits.dims2()?;
-        let mut codes_ = Vec::with_capacity(seq_len);
-        for step in 0..seq_len {
-            let logits = logits.i(step)?.to_dtype(DType::F32)?;
-            let logits = &(&logits / 1.0)?;
-            let prs = candle_nn::ops::softmax_last_dim(logits)?.to_vec1::<f32>()?;
-            let distr = rand::distributions::WeightedIndex::new(prs.as_slice())?;
-            let sample = distr.sample(&mut rng) as u32;
-            codes_.push(sample)
-        }
-        codes.push(codes_)
-    }
-
-    let codes = Tensor::new(codes, &device)?.unsqueeze(0)?;
-    let codes = Tensor::cat(&[in_x, codes], 1)?;
-    println!("codes: {codes}");
-    let tilted_encodec = adapters::TiltedEncodec::new(ENCODEC_NTOKENS);
-    let codes = codes.i(0)?.to_vec2::<u32>()?;
-    let (text_ids, audio_ids) = tilted_encodec.decode(&codes);
-    println!("text_ids len: {:?}", text_ids.len());
-    let audio_ids = Tensor::new(audio_ids, encodec_device)?.unsqueeze(0)?;
-    println!("audio_ids shape: {:?}", audio_ids.shape());
-    let pcm = encodec_model.decode(&audio_ids)?;
-    println!("output pcm shape: {:?}", pcm.shape());
-    let pcm = pcm.i(0)?.i(0)?.to_dtype(DType::F32)?;
-    let pcm = candle_examples::audio::normalize_loudness(&pcm, 24_000, true)?;
-    let pcm = pcm.to_vec1::<f32>()?;
-    let mut output = std::fs::File::create(&args.out_file)?;
-    candle_examples::wav::write_pcm_as_wav(&mut output, &pcm, 24_000)?;
-    Ok(())
-}

candle-examples/examples/mobileone/main.rs

@@ -63,7 +63,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/musicgen/encodec_model.rs

@@ -0,0 +1,580 @@
+use crate::nn::conv1d_weight_norm;
+use candle::{DType, IndexOp, Module, Result, Tensor};
+use candle_nn::{conv1d, Conv1d, Conv1dConfig, VarBuilder};
+
+// Encodec Model
+// https://github.com/huggingface/transformers/blob/main/src/transformers/models/encodec/modeling_encodec.py
+
+#[derive(Debug, Clone, PartialEq)]
+enum NormType {
+    WeightNorm,
+    TimeGroupNorm,
+    None,
+}
+
+#[derive(Debug, Clone, PartialEq)]
+pub struct Config {
+    target_bandwidths: Vec<f64>,
+    sampling_rate: usize,
+    audio_channels: usize,
+    normalize: bool,
+    chunk_length_s: Option<usize>,
+    overlap: Option<usize>,
+    hidden_size: usize,
+    num_filters: usize,
+    num_residual_layers: usize,
+    upsampling_ratios: Vec<usize>,
+    norm_type: NormType,
+    kernel_size: usize,
+    last_kernel_size: usize,
+    residual_kernel_size: usize,
+    dilation_growth_rate: usize,
+    use_causal_conv: bool,
+    pad_mode: &'static str,
+    compress: usize,
+    num_lstm_layers: usize,
+    trim_right_ratio: f64,
+    codebook_size: usize,
+    codebook_dim: Option<usize>,
+    use_conv_shortcut: bool,
+}
+
+impl Default for Config {
+    fn default() -> Self {
+        Self {
+            target_bandwidths: vec![1.5, 3.0, 6.0, 12.0, 24.0],
+            sampling_rate: 24_000,
+            audio_channels: 1,
+            normalize: false,
+            chunk_length_s: None,
+            overlap: None,
+            hidden_size: 128,
+            num_filters: 32,
+            num_residual_layers: 1,
+            upsampling_ratios: vec![8, 5, 4, 2],
+            norm_type: NormType::WeightNorm,
+            kernel_size: 7,
+            last_kernel_size: 7,
+            residual_kernel_size: 3,
+            dilation_growth_rate: 2,
+            use_causal_conv: true,
+            pad_mode: "reflect",
+            compress: 2,
+            num_lstm_layers: 2,
+            trim_right_ratio: 1.0,
+            codebook_size: 1024,
+            codebook_dim: None,
+            use_conv_shortcut: true,
+        }
+    }
+}
+
+impl Config {
+    // https://huggingface.co/facebook/musicgen-small/blob/495da4ad086b3416a27c6187f9239f9fd96f3962/config.json#L6
+    pub fn musicgen_small() -> Self {
+        Self {
+            audio_channels: 1,
+            chunk_length_s: None,
+            codebook_dim: Some(128),
+            codebook_size: 2048,
+            compress: 2,
+            dilation_growth_rate: 2,
+            hidden_size: 128,
+            kernel_size: 7,
+            last_kernel_size: 7,
+            norm_type: NormType::WeightNorm,
+            normalize: false,
+            num_filters: 64,
+            num_lstm_layers: 2,
+            num_residual_layers: 1,
+            overlap: None,
+            pad_mode: "reflect",
+            residual_kernel_size: 3,
+            sampling_rate: 32_000,
+            target_bandwidths: vec![2.2],
+            trim_right_ratio: 1.0,
+            upsampling_ratios: vec![8, 5, 4, 4],
+            use_causal_conv: false,
+            use_conv_shortcut: false,
+        }
+    }
+
+    fn codebook_dim(&self) -> usize {
+        self.codebook_dim.unwrap_or(self.codebook_size)
+    }
+
+    fn frame_rate(&self) -> usize {
+        let hop_length: usize = self.upsampling_ratios.iter().product();
+        (self.sampling_rate + hop_length - 1) / hop_length
+    }
+
+    fn num_quantizers(&self) -> usize {
+        let num = 1000f64
+            * self
+                .target_bandwidths
+                .last()
+                .expect("empty target_bandwidths");
+        (num as usize) / (self.frame_rate() * 10)
+    }
+}
+
+// https://github.com/huggingface/transformers/blob/abaca9f9432a84cfaa95531de4c72334f38a42f2/src/transformers/models/encodec/modeling_encodec.py#L340
+#[derive(Debug)]
+struct EncodecEuclideanCodebook {
+    inited: Tensor,
+    cluster_size: Tensor,
+    embed: Tensor,
+    embed_avg: Tensor,
+}
+
+impl EncodecEuclideanCodebook {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let inited = vb.get(1, "inited")?;
+        let cluster_size = vb.get(cfg.codebook_size, "cluster_size")?;
+        let e_shape = (cfg.codebook_size, cfg.codebook_dim());
+        let embed = vb.get(e_shape, "embed")?;
+        let embed_avg = vb.get(e_shape, "embed_avg")?;
+        Ok(Self {
+            inited,
+            cluster_size,
+            embed,
+            embed_avg,
+        })
+    }
+
+    fn decode(&self, embed_ind: &Tensor) -> Result<Tensor> {
+        let quantize = self.embed.embedding(embed_ind)?;
+        Ok(quantize)
+    }
+}
+
+#[derive(Debug)]
+struct EncodecVectorQuantization {
+    codebook: EncodecEuclideanCodebook,
+}
+
+impl EncodecVectorQuantization {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let codebook = EncodecEuclideanCodebook::load(vb.pp("codebook"), cfg)?;
+        Ok(Self { codebook })
+    }
+
+    fn decode(&self, embed_ind: &Tensor) -> Result<Tensor> {
+        let quantize = self.codebook.decode(embed_ind)?;
+        let quantize = quantize.transpose(1, 2)?;
+        Ok(quantize)
+    }
+}
+
+#[derive(Debug)]
+struct EncodecResidualVectorQuantizer {
+    layers: Vec<EncodecVectorQuantization>,
+}
+
+impl EncodecResidualVectorQuantizer {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let vb = &vb.pp("layers");
+        let layers = (0..cfg.num_quantizers())
+            .map(|i| EncodecVectorQuantization::load(vb.pp(&i.to_string()), cfg))
+            .collect::<Result<Vec<_>>>()?;
+        Ok(Self { layers })
+    }
+
+    fn decode(&self, codes: &Tensor) -> Result<Tensor> {
+        let mut quantized_out = Tensor::zeros((), DType::F32, codes.device())?;
+        if codes.dim(0)? != self.layers.len() {
+            candle::bail!(
+                "codes shape {:?} does not match the number of quantization layers {}",
+                codes.shape(),
+                self.layers.len()
+            )
+        }
+        for (i, layer) in self.layers.iter().enumerate() {
+            let quantized = layer.decode(&codes.i(i)?)?;
+            quantized_out = quantized.broadcast_add(&quantized_out)?;
+        }
+        Ok(quantized_out)
+    }
+}
+
+// https://github.com/huggingface/transformers/blob/abaca9f9432a84cfaa95531de4c72334f38a42f2/src/transformers/models/encodec/modeling_encodec.py#L226
+#[derive(Debug)]
+struct EncodecLSTM {
+    layers: Vec<candle_nn::LSTM>,
+}
+
+impl EncodecLSTM {
+    fn load(dim: usize, vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let vb = &vb.pp("lstm");
+        let mut layers = vec![];
+        for layer_idx in 0..cfg.num_lstm_layers {
+            let config = candle_nn::LSTMConfig {
+                layer_idx,
+                ..Default::default()
+            };
+            let lstm = candle_nn::lstm(dim, dim, config, vb.clone())?;
+            layers.push(lstm)
+        }
+        Ok(Self { layers })
+    }
+}
+
+impl Module for EncodecLSTM {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        use candle_nn::RNN;
+        let mut xs = xs.clone();
+        for layer in self.layers.iter() {
+            let states = layer.seq(&xs)?;
+            xs = layer.states_to_tensor(&states)?;
+        }
+        Ok(xs)
+    }
+}
+
+#[derive(Debug)]
+struct EncodecConvTranspose1d {
+    weight_g: Tensor,
+    weight_v: Tensor,
+    bias: Tensor,
+}
+
+impl EncodecConvTranspose1d {
+    fn load(
+        in_c: usize,
+        out_c: usize,
+        k: usize,
+        _stride: usize,
+        vb: VarBuilder,
+        _cfg: &Config,
+    ) -> Result<Self> {
+        let vb = &vb.pp("conv");
+        let weight_g = vb.get((in_c, 1, 1), "weight_g")?;
+        let weight_v = vb.get((in_c, out_c, k), "weight_v")?;
+        let bias = vb.get(out_c, "bias")?;
+        Ok(Self {
+            weight_g,
+            weight_v,
+            bias,
+        })
+    }
+}
+
+impl Module for EncodecConvTranspose1d {
+    fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
+        todo!()
+    }
+}
+
+#[derive(Debug)]
+struct EncodecConv1d {
+    causal: bool,
+    conv: Conv1d,
+    norm: Option<candle_nn::GroupNorm>,
+}
+
+impl EncodecConv1d {
+    fn load(
+        in_c: usize,
+        out_c: usize,
+        kernel_size: usize,
+        stride: usize,
+        vb: VarBuilder,
+        cfg: &Config,
+    ) -> Result<Self> {
+        let conv = match cfg.norm_type {
+            NormType::WeightNorm => conv1d_weight_norm(
+                in_c,
+                out_c,
+                kernel_size,
+                Conv1dConfig {
+                    padding: 0,
+                    stride,
+                    groups: 1,
+                    dilation: 1,
+                },
+                vb.pp("conv"),
+            )?,
+            NormType::None | NormType::TimeGroupNorm => conv1d(
+                in_c,
+                out_c,
+                kernel_size,
+                Conv1dConfig {
+                    padding: 0,
+                    stride,
+                    groups: 1,
+                    dilation: 1,
+                },
+                vb.pp("conv"),
+            )?,
+        };
+        let norm = match cfg.norm_type {
+            NormType::None | NormType::WeightNorm => None,
+            NormType::TimeGroupNorm => {
+                let gn = candle_nn::group_norm(1, out_c, 1e-5, vb.pp("norm"))?;
+                Some(gn)
+            }
+        };
+        Ok(Self {
+            causal: cfg.use_causal_conv,
+            conv,
+            norm,
+        })
+    }
+}
+
+impl Module for EncodecConv1d {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        // TODO: padding, depending on causal.
+        let xs = self.conv.forward(xs)?;
+        match &self.norm {
+            None => Ok(xs),
+            Some(norm) => xs.apply(norm),
+        }
+    }
+}
+
+#[derive(Debug)]
+struct EncodecResnetBlock {
+    block_conv1: EncodecConv1d,
+    block_conv2: EncodecConv1d,
+    shortcut: Option<EncodecConv1d>,
+}
+
+impl EncodecResnetBlock {
+    fn load(dim: usize, dilations: &[usize], vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let h = dim / cfg.compress;
+        let mut layer = Layer::new(vb.pp("block"));
+        if dilations.len() != 2 {
+            candle::bail!("expected dilations of size 2")
+        }
+        // TODO: Apply dilations!
+        layer.inc();
+        let block_conv1 =
+            EncodecConv1d::load(dim, h, cfg.residual_kernel_size, 1, layer.next(), cfg)?;
+        layer.inc();
+        let block_conv2 = EncodecConv1d::load(h, dim, 1, 1, layer.next(), cfg)?;
+        let shortcut = if cfg.use_conv_shortcut {
+            let conv = EncodecConv1d::load(dim, dim, 1, 1, vb.pp("shortcut"), cfg)?;
+            Some(conv)
+        } else {
+            None
+        };
+        Ok(Self {
+            block_conv1,
+            block_conv2,
+            shortcut,
+        })
+    }
+}
+
+impl Module for EncodecResnetBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let residual = xs.clone();
+        let xs = xs.elu(1.)?;
+        let xs = self.block_conv1.forward(&xs)?;
+        let xs = xs.elu(1.)?;
+        let xs = self.block_conv2.forward(&xs)?;
+        let xs = match &self.shortcut {
+            None => (xs + residual)?,
+            Some(shortcut) => xs.add(&shortcut.forward(&residual)?)?,
+        };
+        Ok(xs)
+    }
+}
+
+struct Layer<'a> {
+    vb: VarBuilder<'a>,
+    cnt: usize,
+}
+
+impl<'a> Layer<'a> {
+    fn new(vb: VarBuilder<'a>) -> Self {
+        Self { vb, cnt: 0 }
+    }
+
+    fn inc(&mut self) {
+        self.cnt += 1;
+    }
+
+    fn next(&mut self) -> VarBuilder {
+        let vb = self.vb.pp(&self.cnt.to_string());
+        self.cnt += 1;
+        vb
+    }
+}
+
+#[derive(Debug)]
+struct EncodecEncoder {
+    init_conv: EncodecConv1d,
+    sampling_layers: Vec<(Vec<EncodecResnetBlock>, EncodecConv1d)>,
+    final_lstm: EncodecLSTM,
+    final_conv: EncodecConv1d,
+}
+
+impl EncodecEncoder {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let mut layer = Layer::new(vb.pp("layers"));
+        let init_conv = EncodecConv1d::load(
+            cfg.audio_channels,
+            cfg.num_filters,
+            cfg.kernel_size,
+            1,
+            layer.next(),
+            cfg,
+        )?;
+        let mut sampling_layers = vec![];
+        let mut scaling = 1;
+        for &ratio in cfg.upsampling_ratios.iter().rev() {
+            let current_scale = scaling * cfg.num_filters;
+            let mut resnets = vec![];
+            for j in 0..(cfg.num_residual_layers as u32) {
+                let resnet = EncodecResnetBlock::load(
+                    current_scale,
+                    &[cfg.dilation_growth_rate.pow(j), 1],
+                    layer.next(),
+                    cfg,
+                )?;
+                resnets.push(resnet)
+            }
+            layer.inc(); // ELU
+            let conv1d = EncodecConv1d::load(
+                current_scale,
+                current_scale * 2,
+                ratio * 2,
+                ratio,
+                layer.next(),
+                cfg,
+            )?;
+            sampling_layers.push((resnets, conv1d));
+            scaling *= 2;
+        }
+        let final_lstm = EncodecLSTM::load(cfg.num_filters * scaling, layer.next(), cfg)?;
+        layer.inc(); // ELU
+        let final_conv = EncodecConv1d::load(
+            cfg.num_filters * scaling,
+            cfg.hidden_size,
+            cfg.last_kernel_size,
+            1,
+            layer.next(),
+            cfg,
+        )?;
+        Ok(Self {
+            init_conv,
+            sampling_layers,
+            final_conv,
+            final_lstm,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.apply(&self.init_conv)?;
+        for (resnets, conv) in self.sampling_layers.iter() {
+            for resnet in resnets.iter() {
+                xs = xs.apply(resnet)?;
+            }
+            xs = xs.elu(1.0)?.apply(conv)?;
+        }
+        xs.apply(&self.final_lstm)?
+            .elu(1.0)?
+            .apply(&self.final_conv)
+    }
+}
+
+#[derive(Debug)]
+struct EncodecDecoder {
+    init_conv: EncodecConv1d,
+    init_lstm: EncodecLSTM,
+    sampling_layers: Vec<(EncodecConvTranspose1d, Vec<EncodecResnetBlock>)>,
+    final_conv: EncodecConv1d,
+}
+
+impl EncodecDecoder {
+    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let mut layer = Layer::new(vb.pp("layers"));
+        let mut scaling = usize::pow(2, cfg.upsampling_ratios.len() as u32);
+        let init_conv = EncodecConv1d::load(
+            cfg.hidden_size,
+            cfg.num_filters * scaling,
+            cfg.last_kernel_size,
+            1,
+            layer.next(),
+            cfg,
+        )?;
+        let init_lstm = EncodecLSTM::load(cfg.num_filters * scaling, layer.next(), cfg)?;
+        let mut sampling_layers = vec![];
+        for &ratio in cfg.upsampling_ratios.iter() {
+            let current_scale = scaling * cfg.num_filters;
+            layer.inc(); // ELU
+            let conv1d = EncodecConvTranspose1d::load(
+                current_scale,
+                current_scale / 2,
+                ratio * 2,
+                ratio,
+                layer.next(),
+                cfg,
+            )?;
+            let mut resnets = vec![];
+            for j in 0..(cfg.num_residual_layers as u32) {
+                let resnet = EncodecResnetBlock::load(
+                    current_scale / 2,
+                    &[cfg.dilation_growth_rate.pow(j), 1],
+                    layer.next(),
+                    cfg,
+                )?;
+                resnets.push(resnet)
+            }
+            sampling_layers.push((conv1d, resnets));
+            scaling /= 2;
+        }
+        layer.inc(); // ELU
+        let final_conv = EncodecConv1d::load(
+            cfg.num_filters,
+            cfg.audio_channels,
+            cfg.last_kernel_size,
+            1,
+            layer.next(),
+            cfg,
+        )?;
+        Ok(Self {
+            init_conv,
+            init_lstm,
+            sampling_layers,
+            final_conv,
+        })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.apply(&self.init_conv)?.apply(&self.init_lstm)?;
+        for (conv, resnets) in self.sampling_layers.iter() {
+            xs = xs.elu(1.)?.apply(conv)?;
+            for resnet in resnets.iter() {
+                xs = xs.apply(resnet)?
+            }
+        }
+        xs.elu(1.)?.apply(&self.final_conv)
+    }
+}
+
+#[derive(Debug)]
+pub struct EncodecModel {
+    encoder: EncodecEncoder,
+    decoder: EncodecDecoder,
+    quantizer: EncodecResidualVectorQuantizer,
+}
+
+impl EncodecModel {
+    pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
+        let encoder = EncodecEncoder::load(vb.pp("encoder"), cfg)?;
+        let decoder = EncodecDecoder::load(vb.pp("decoder"), cfg)?;
+        let quantizer = EncodecResidualVectorQuantizer::load(vb.pp("quantizer"), cfg)?;
+        Ok(Self {
+            encoder,
+            decoder,
+            quantizer,
+        })
+    }
+
+    pub fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
+        todo!()
+    }
+}

candle-examples/examples/musicgen/main.rs

@@ -10,7 +10,9 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
+mod encodec_model;
 mod musicgen_model;
+mod nn;
 
 use musicgen_model::{GenConfig, MusicgenForConditionalGeneration};
 

candle-examples/examples/musicgen/musicgen_model.rs

@@ -1,9 +1,10 @@
+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::{encodec, t5};
+use candle_transformers::models::t5;
 
 // https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/models/musicgen/configuration_musicgen.py#L83
 #[derive(Debug, Clone, PartialEq)]
@@ -371,7 +372,7 @@ impl MusicgenForCausalLM {
 #[derive(Debug)]
 pub struct MusicgenForConditionalGeneration {
     pub text_encoder: t5::T5EncoderModel,
-    pub audio_encoder: encodec::Model,
+    pub audio_encoder: crate::encodec_model::EncodecModel,
     pub decoder: MusicgenForCausalLM,
     cfg: GenConfig,
 }
@@ -380,42 +381,15 @@ pub struct MusicgenForConditionalGeneration {
 pub struct GenConfig {
     musicgen: Config,
     t5: t5::Config,
-    encodec: encodec::Config,
+    encodec: crate::encodec_model::Config,
 }
 
 impl GenConfig {
     pub fn small() -> Self {
-        // https://huggingface.co/facebook/musicgen-small/blob/495da4ad086b3416a27c6187f9239f9fd96f3962/config.json#L6
-        let encodec = encodec::Config {
-            audio_channels: 1,
-            chunk_length_s: None,
-            codebook_dim: Some(128),
-            codebook_size: 2048,
-            compress: 2,
-            dilation_growth_rate: 2,
-            hidden_size: 128,
-            kernel_size: 7,
-            last_kernel_size: 7,
-            norm_type: encodec::NormType::WeightNorm,
-            normalize: false,
-            num_filters: 64,
-            num_lstm_layers: 2,
-            num_residual_layers: 1,
-            overlap: None,
-            // This should be Reflect and not Replicate but Reflect does not work yet.
-            pad_mode: encodec::PadMode::Replicate,
-            residual_kernel_size: 3,
-            sampling_rate: 32_000,
-            target_bandwidths: vec![2.2],
-            trim_right_ratio: 1.0,
-            upsampling_ratios: vec![8, 5, 4, 4],
-            use_causal_conv: false,
-            use_conv_shortcut: false,
-        };
         Self {
             musicgen: Config::musicgen_small(),
             t5: t5::Config::musicgen_small(),
-            encodec,
+            encodec: encodec_model::Config::musicgen_small(),
         }
     }
 }
@@ -427,7 +401,8 @@ impl MusicgenForConditionalGeneration {
 
     pub fn load(vb: VarBuilder, cfg: GenConfig) -> Result<Self> {
         let text_encoder = t5::T5EncoderModel::load(vb.pp("text_encoder"), &cfg.t5)?;
-        let audio_encoder = encodec::Model::new(&cfg.encodec, vb.pp("audio_encoder"))?;
+        let audio_encoder =
+            encodec_model::EncodecModel::load(vb.pp("audio_encoder"), &cfg.encodec)?;
         let decoder = MusicgenForCausalLM::load(vb.pp("decoder"), &cfg.musicgen)?;
         Ok(Self {
             text_encoder,

candle-examples/examples/musicgen/nn.rs

@@ -0,0 +1,20 @@
+use candle::Result;
+use candle_nn::{Conv1d, Conv1dConfig, VarBuilder};
+
+// Applies weight norm for inference by recomputing the weight tensor. This
+// does not apply to training.
+// https://pytorch.org/docs/stable/generated/torch.nn.utils.weight_norm.html
+pub fn conv1d_weight_norm(
+    in_c: usize,
+    out_c: usize,
+    kernel_size: usize,
+    config: Conv1dConfig,
+    vb: VarBuilder,
+) -> Result<Conv1d> {
+    let weight_g = vb.get((out_c, 1, 1), "weight_g")?;
+    let weight_v = vb.get((out_c, in_c, kernel_size), "weight_v")?;
+    let norm_v = weight_v.sqr()?.sum_keepdim((1, 2))?.sqrt()?;
+    let weight = weight_v.broadcast_mul(&weight_g)?.broadcast_div(&norm_v)?;
+    let bias = vb.get(out_c, "bias")?;
+    Ok(Conv1d::new(weight, Some(bias), config))
+}

candle-examples/examples/quantized/main.rs

@@ -212,14 +212,6 @@ struct Args {
     #[arg(long)]
     verbose_prompt: bool,
 
-    /// Process prompt elements separately.
-    #[arg(long)]
-    split_prompt: bool,
-
-    /// Run on CPU rather than GPU even if a GPU is available.
-    #[arg(long)]
-    cpu: bool,
-
     /// Penalty to be applied for repeating tokens, 1. means no penalty.
     #[arg(long, default_value_t = 1.1)]
     repeat_penalty: f32,
@@ -369,7 +361,7 @@ fn main() -> anyhow::Result<()> {
     let model_path = args.model()?;
     let mut file = std::fs::File::open(&model_path)?;
     let start = std::time::Instant::now();
-    let device = candle_examples::device(args.cpu)?;
+    let device = candle_examples::device(false)?;
 
     let mut model = match model_path.extension().and_then(|v| v.to_str()) {
         Some("gguf") => {
@@ -495,20 +487,11 @@ fn main() -> anyhow::Result<()> {
         let mut logits_processor = LogitsProcessor::new(args.seed, temperature, args.top_p);
 
         let start_prompt_processing = std::time::Instant::now();
-        let mut next_token = if !args.split_prompt {
+        let mut next_token = {
             let input = Tensor::new(prompt_tokens.as_slice(), &device)?.unsqueeze(0)?;
             let logits = model.forward(&input, 0)?;
             let logits = logits.squeeze(0)?;
             logits_processor.sample(&logits)?
-        } else {
-            let mut next_token = 0;
-            for (pos, token) in prompt_tokens.iter().enumerate() {
-                let input = Tensor::new(&[*token], &device)?.unsqueeze(0)?;
-                let logits = model.forward(&input, pos)?;
-                let logits = logits.squeeze(0)?;
-                next_token = logits_processor.sample(&logits)?
-            }
-            next_token
         };
         let prompt_dt = start_prompt_processing.elapsed();
         all_tokens.push(next_token);

candle-examples/examples/repvgg/main.rs

@@ -78,7 +78,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/resnet/main.rs

@@ -45,7 +45,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/rwkv/README.md

@@ -2,8 +2,8 @@
 
 The [RWKV model](https://wiki.rwkv.com/) is a recurrent neural network model
 with performance on par with transformer architectures. Several variants are
-available, candle implements the v5 and v6 versions and can be used with
-Eagle 7B([blog post](https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers)).
+available, candle implements the v5 version and can be used with Eagle 7B([blog
+post](https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers)).
 
 ```bash
 $ cargo run --example rwkv --release -- --prompt "The smallest prime is "

candle-examples/examples/rwkv/main.rs

@@ -7,36 +7,13 @@ extern crate accelerate_src;
 use anyhow::Result;
 use clap::{Parser, ValueEnum};
 
-use candle_transformers::models::quantized_rwkv_v5::Model as Q5;
-use candle_transformers::models::quantized_rwkv_v6::Model as Q6;
-use candle_transformers::models::rwkv_v5::{Config, Model as M5, State, Tokenizer};
-use candle_transformers::models::rwkv_v6::Model as M6;
+use candle_transformers::models::rwkv_v5::{Config, Model, State, Tokenizer};
 
 use candle::{DType, Device, Tensor};
 use candle_nn::VarBuilder;
 use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 
-const EOS_TOKEN_ID: u32 = 261;
-
-enum Model {
-    M5(M5),
-    Q5(Q5),
-    M6(M6),
-    Q6(Q6),
-}
-
-impl Model {
-    fn forward(&self, xs: &Tensor, state: &mut State) -> candle::Result<Tensor> {
-        match self {
-            Self::M5(m) => m.forward(xs, state),
-            Self::Q5(m) => m.forward(xs, state),
-            Self::M6(m) => m.forward(xs, state),
-            Self::Q6(m) => m.forward(xs, state),
-        }
-    }
-}
-
 struct TextGeneration {
     model: Model,
     config: Config,
@@ -106,9 +83,6 @@ impl TextGeneration {
             let next_token = self.logits_processor.sample(&logits)?;
             tokens.push(next_token);
             generated_tokens += 1;
-            if next_token == EOS_TOKEN_ID || next_token == 0 {
-                break;
-            }
             print!("{}", self.tokenizer.decode(&[next_token])?);
             std::io::stdout().flush()?;
 
@@ -129,7 +103,6 @@ enum Which {
     Eagle7b,
     World1b5,
     World3b,
-    World6_1b6,
 }
 
 impl std::fmt::Display for Which {
@@ -141,10 +114,9 @@ impl std::fmt::Display for Which {
 impl Which {
     fn model_id(&self) -> &'static str {
         match self {
-            Self::Eagle7b => "RWKV/v5-Eagle-7B-HF",
+            Self::Eagle7b => "RWKV/HF_v5-Eagle-7B",
             Self::World1b5 => "RWKV/rwkv-5-world-1b5",
             Self::World3b => "RWKV/rwkv-5-world-3b",
-            Self::World6_1b6 => "paperfun/rwkv",
         }
     }
 
@@ -152,7 +124,6 @@ impl Which {
         match self {
             Self::Eagle7b => "refs/pr/1",
             Self::World1b5 | Self::World3b => "refs/pr/2",
-            Self::World6_1b6 => "main",
         }
     }
 }
@@ -205,9 +176,6 @@ struct Args {
     #[arg(long)]
     config_file: Option<String>,
 
-    #[arg(long)]
-    quantized: bool,
-
     /// Penalty to be applied for repeating tokens, 1. means no penalty.
     #[arg(long, default_value_t = 1.1)]
     repeat_penalty: f32,
@@ -268,27 +236,7 @@ fn main() -> Result<()> {
             .map(std::path::PathBuf::from)
             .collect::<Vec<_>>(),
         None => {
-            if args.quantized {
-                vec![match args.which {
-                    Which::World1b5 => api
-                        .model("lmz/candle-rwkv".to_string())
-                        .get("world1b5-q4k.gguf")?,
-                    Which::World3b => api
-                        .model("lmz/candle-rwkv".to_string())
-                        .get("world3b-q4k.gguf")?,
-                    Which::Eagle7b => api
-                        .model("lmz/candle-rwkv".to_string())
-                        .get("eagle7b-q4k.gguf")?,
-                    Which::World6_1b6 => repo.get("rwkv-6-world-1b6-q4k.gguf")?,
-                }]
-            } else {
-                vec![match args.which {
-                    Which::World1b5 | Which::World3b | Which::Eagle7b => {
-                        repo.get("model.safetensors")?
-                    }
-                    Which::World6_1b6 => repo.get("rwkv-6-world-1b6.safetensors")?,
-                }]
-            }
+            vec![repo.get("model.safetensors")?]
         }
     };
     println!("retrieved the files in {:?}", start.elapsed());
@@ -297,21 +245,8 @@ fn main() -> Result<()> {
     let start = std::time::Instant::now();
     let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
     let device = candle_examples::device(args.cpu)?;
-    let model = if args.quantized {
-        let filename = &filenames[0];
-        let vb =
-            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
-        match args.which {
-            Which::World1b5 | Which::World3b | Which::Eagle7b => Model::Q5(Q5::new(&config, vb)?),
-            Which::World6_1b6 => Model::Q6(Q6::new(&config, vb)?),
-        }
-    } else {
-        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
-        match args.which {
-            Which::World1b5 | Which::World3b | Which::Eagle7b => Model::M5(M5::new(&config, vb)?),
-            Which::World6_1b6 => Model::M6(M6::new(&config, vb)?),
-        }
-    };
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
+    let model = Model::new(&config, vb)?;
     println!("loaded the model in {:?}", start.elapsed());
 
     let mut pipeline = TextGeneration::new(

candle-examples/examples/segformer/README.md

@@ -1,28 +0,0 @@
-# candle-segformer
-
-- [HuggingFace Segformer Model Card][segformer]
-- [`mit-b0` - An encoder only pretrained model][encoder]
-- [`segformer-b0-finetuned-ade-512-512` - A fine tuned model for segmentation][ade512]
-
-## How to run the example
-
-If you want you can use the example images from this [pull request][pr], download them and supply the path to the image as an argument to the example.
-
-```bash
-# run the image classification task
-cargo run --example segformer classify <path-to-image>
-# run the segmentation task
-cargo run --example segformer segment <path-to-image>
-```
-
-Example output for classification:
-
-```text
-classification logits [3.275261e-5, 0.0008562019, 0.0008868563, 0.9977506, 0.0002465068, 0.0002241473, 2.846596e-6]
-label: hamburger
-```
-
-[pr]: https://github.com/huggingface/candle/pull/1617
-[segformer]: https://huggingface.co/docs/transformers/model_doc/segformer
-[encoder]: https://huggingface.co/nvidia/mit-b0
-[ade512]: https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512

candle-examples/examples/segformer/assets/labels.json

@@ -1,752 +0,0 @@
-[
-  {
-    "index": 1,
-    "color": "#787878",
-    "label": "wall"
-  },
-  {
-    "index": 2,
-    "color": "#B47878",
-    "label": "building;edifice"
-  },
-  {
-    "index": 3,
-    "color": "#06E6E6",
-    "label": "sky"
-  },
-  {
-    "index": 4,
-    "color": "#503232",
-    "label": "floor;flooring"
-  },
-  {
-    "index": 5,
-    "color": "#04C803",
-    "label": "tree"
-  },
-  {
-    "index": 6,
-    "color": "#787850",
-    "label": "ceiling"
-  },
-  {
-    "index": 7,
-    "color": "#8C8C8C",
-    "label": "road;route"
-  },
-  {
-    "index": 8,
-    "color": "#CC05FF",
-    "label": "bed"
-  },
-  {
-    "index": 9,
-    "color": "#E6E6E6",
-    "label": "windowpane;window"
-  },
-  {
-    "index": 10,
-    "color": "#04FA07",
-    "label": "grass"
-  },
-  {
-    "index": 11,
-    "color": "#E005FF",
-    "label": "cabinet"
-  },
-  {
-    "index": 12,
-    "color": "#EBFF07",
-    "label": "sidewalk;pavement"
-  },
-  {
-    "index": 13,
-    "color": "#96053D",
-    "label": "person;individual;someone;somebody;mortal;soul"
-  },
-  {
-    "index": 14,
-    "color": "#787846",
-    "label": "earth;ground"
-  },
-  {
-    "index": 15,
-    "color": "#08FF33",
-    "label": "door;double;door"
-  },
-  {
-    "index": 16,
-    "color": "#FF0652",
-    "label": "table"
-  },
-  {
-    "index": 17,
-    "color": "#8FFF8C",
-    "label": "mountain;mount"
-  },
-  {
-    "index": 18,
-    "color": "#CCFF04",
-    "label": "plant;flora;plant;life"
-  },
-  {
-    "index": 19,
-    "color": "#FF3307",
-    "label": "curtain;drape;drapery;mantle;pall"
-  },
-  {
-    "index": 20,
-    "color": "#CC4603",
-    "label": "chair"
-  },
-  {
-    "index": 21,
-    "color": "#0066C8",
-    "label": "car;auto;automobile;machine;motorcar"
-  },
-  {
-    "index": 22,
-    "color": "#3DE6FA",
-    "label": "water"
-  },
-  {
-    "index": 23,
-    "color": "#FF0633",
-    "label": "painting;picture"
-  },
-  {
-    "index": 24,
-    "color": "#0B66FF",
-    "label": "sofa;couch;lounge"
-  },
-  {
-    "index": 25,
-    "color": "#FF0747",
-    "label": "shelf"
-  },
-  {
-    "index": 26,
-    "color": "#FF09E0",
-    "label": "house"
-  },
-  {
-    "index": 27,
-    "color": "#0907E6",
-    "label": "sea"
-  },
-  {
-    "index": 28,
-    "color": "#DCDCDC",
-    "label": "mirror"
-  },
-  {
-    "index": 29,
-    "color": "#FF095C",
-    "label": "rug;carpet;carpeting"
-  },
-  {
-    "index": 30,
-    "color": "#7009FF",
-    "label": "field"
-  },
-  {
-    "index": 31,
-    "color": "#08FFD6",
-    "label": "armchair"
-  },
-  {
-    "index": 32,
-    "color": "#07FFE0",
-    "label": "seat"
-  },
-  {
-    "index": 33,
-    "color": "#FFB806",
-    "label": "fence;fencing"
-  },
-  {
-    "index": 34,
-    "color": "#0AFF47",
-    "label": "desk"
-  },
-  {
-    "index": 35,
-    "color": "#FF290A",
-    "label": "rock;stone"
-  },
-  {
-    "index": 36,
-    "color": "#07FFFF",
-    "label": "wardrobe;closet;press"
-  },
-  {
-    "index": 37,
-    "color": "#E0FF08",
-    "label": "lamp"
-  },
-  {
-    "index": 38,
-    "color": "#6608FF",
-    "label": "bathtub;bathing;tub;bath;tub"
-  },
-  {
-    "index": 39,
-    "color": "#FF3D06",
-    "label": "railing;rail"
-  },
-  {
-    "index": 40,
-    "color": "#FFC207",
-    "label": "cushion"
-  },
-  {
-    "index": 41,
-    "color": "#FF7A08",
-    "label": "base;pedestal;stand"
-  },
-  {
-    "index": 42,
-    "color": "#00FF14",
-    "label": "box"
-  },
-  {
-    "index": 43,
-    "color": "#FF0829",
-    "label": "column;pillar"
-  },
-  {
-    "index": 44,
-    "color": "#FF0599",
-    "label": "signboard;sign"
-  },
-  {
-    "index": 45,
-    "color": "#0633FF",
-    "label": "chest;of;drawers;chest;bureau;dresser"
-  },
-  {
-    "index": 46,
-    "color": "#EB0CFF",
-    "label": "counter"
-  },
-  {
-    "index": 47,
-    "color": "#A09614",
-    "label": "sand"
-  },
-  {
-    "index": 48,
-    "color": "#00A3FF",
-    "label": "sink"
-  },
-  {
-    "index": 49,
-    "color": "#8C8C8C",
-    "label": "skyscraper"
-  },
-  {
-    "index": 50,
-    "color": "#FA0A0F",
-    "label": "fireplace;hearth;open;fireplace"
-  },
-  {
-    "index": 51,
-    "color": "#14FF00",
-    "label": "refrigerator;icebox"
-  },
-  {
-    "index": 52,
-    "color": "#1FFF00",
-    "label": "grandstand;covered;stand"
-  },
-  {
-    "index": 53,
-    "color": "#FF1F00",
-    "label": "path"
-  },
-  {
-    "index": 54,
-    "color": "#FFE000",
-    "label": "stairs;steps"
-  },
-  {
-    "index": 55,
-    "color": "#99FF00",
-    "label": "runway"
-  },
-  {
-    "index": 56,
-    "color": "#0000FF",
-    "label": "case;display;case;showcase;vitrine"
-  },
-  {
-    "index": 57,
-    "color": "#FF4700",
-    "label": "pool;table;billiard;table;snooker;table"
-  },
-  {
-    "index": 58,
-    "color": "#00EBFF",
-    "label": "pillow"
-  },
-  {
-    "index": 59,
-    "color": "#00ADFF",
-    "label": "screen;door;screen"
-  },
-  {
-    "index": 60,
-    "color": "#1F00FF",
-    "label": "stairway;staircase"
-  },
-  {
-    "index": 61,
-    "color": "#0BC8C8",
-    "label": "river"
-  },
-  {
-    "index": 62,
-    "color": "#FF5200",
-    "label": "bridge;span"
-  },
-  {
-    "index": 63,
-    "color": "#00FFF5",
-    "label": "bookcase"
-  },
-  {
-    "index": 64,
-    "color": "#003DFF",
-    "label": "blind;screen"
-  },
-  {
-    "index": 65,
-    "color": "#00FF70",
-    "label": "coffee;table;cocktail;table"
-  },
-  {
-    "index": 66,
-    "color": "#00FF85",
-    "label": "toilet;can;commode;crapper;pot;potty;stool;throne"
-  },
-  {
-    "index": 67,
-    "color": "#FF0000",
-    "label": "flower"
-  },
-  {
-    "index": 68,
-    "color": "#FFA300",
-    "label": "book"
-  },
-  {
-    "index": 69,
-    "color": "#FF6600",
-    "label": "hill"
-  },
-  {
-    "index": 70,
-    "color": "#C2FF00",
-    "label": "bench"
-  },
-  {
-    "index": 71,
-    "color": "#008FFF",
-    "label": "countertop"
-  },
-  {
-    "index": 72,
-    "color": "#33FF00",
-    "label": "stove;kitchen;stove;range;kitchen;range;cooking;stove"
-  },
-  {
-    "index": 73,
-    "color": "#0052FF",
-    "label": "palm;palm;tree"
-  },
-  {
-    "index": 74,
-    "color": "#00FF29",
-    "label": "kitchen;island"
-  },
-  {
-    "index": 75,
-    "color": "#00FFAD",
-    "label": "computer;computing;machine;computing;device;data;processor;electronic;computer;information;processing;system"
-  },
-  {
-    "index": 76,
-    "color": "#0A00FF",
-    "label": "swivel;chair"
-  },
-  {
-    "index": 77,
-    "color": "#ADFF00",
-    "label": "boat"
-  },
-  {
-    "index": 78,
-    "color": "#00FF99",
-    "label": "bar"
-  },
-  {
-    "index": 79,
-    "color": "#FF5C00",
-    "label": "arcade;machine"
-  },
-  {
-    "index": 80,
-    "color": "#FF00FF",
-    "label": "hovel;hut;hutch;shack;shanty"
-  },
-  {
-    "index": 81,
-    "color": "#FF00F5",
-    "label": "bus;autobus;coach;charabanc;double-decker;jitney;motorbus;motorcoach;omnibus;passenger;vehicle"
-  },
-  {
-    "index": 82,
-    "color": "#FF0066",
-    "label": "towel"
-  },
-  {
-    "index": 83,
-    "color": "#FFAD00",
-    "label": "light;light;source"
-  },
-  {
-    "index": 84,
-    "color": "#FF0014",
-    "label": "truck;motortruck"
-  },
-  {
-    "index": 85,
-    "color": "#FFB8B8",
-    "label": "tower"
-  },
-  {
-    "index": 86,
-    "color": "#001FFF",
-    "label": "chandelier;pendant;pendent"
-  },
-  {
-    "index": 87,
-    "color": "#00FF3D",
-    "label": "awning;sunshade;sunblind"
-  },
-  {
-    "index": 88,
-    "color": "#0047FF",
-    "label": "streetlight;street;lamp"
-  },
-  {
-    "index": 89,
-    "color": "#FF00CC",
-    "label": "booth;cubicle;stall;kiosk"
-  },
-  {
-    "index": 90,
-    "color": "#00FFC2",
-    "label": "television;television;receiver;television;set;tv;tv;set;idiot;box;boob;tube;telly;goggle;box"
-  },
-  {
-    "index": 91,
-    "color": "#00FF52",
-    "label": "airplane;aeroplane;plane"
-  },
-  {
-    "index": 92,
-    "color": "#000AFF",
-    "label": "dirt;track"
-  },
-  {
-    "index": 93,
-    "color": "#0070FF",
-    "label": "apparel;wearing;apparel;dress;clothes"
-  },
-  {
-    "index": 94,
-    "color": "#3300FF",
-    "label": "pole"
-  },
-  {
-    "index": 95,
-    "color": "#00C2FF",
-    "label": "land;ground;soil"
-  },
-  {
-    "index": 96,
-    "color": "#007AFF",
-    "label": "bannister;banister;balustrade;balusters;handrail"
-  },
-  {
-    "index": 97,
-    "color": "#00FFA3",
-    "label": "escalator;moving;staircase;moving;stairway"
-  },
-  {
-    "index": 98,
-    "color": "#FF9900",
-    "label": "ottoman;pouf;pouffe;puff;hassock"
-  },
-  {
-    "index": 99,
-    "color": "#00FF0A",
-    "label": "bottle"
-  },
-  {
-    "index": 100,
-    "color": "#FF7000",
-    "label": "buffet;counter;sideboard"
-  },
-  {
-    "index": 101,
-    "color": "#8FFF00",
-    "label": "poster;posting;placard;notice;bill;card"
-  },
-  {
-    "index": 102,
-    "color": "#5200FF",
-    "label": "stage"
-  },
-  {
-    "index": 103,
-    "color": "#A3FF00",
-    "label": "van"
-  },
-  {
-    "index": 104,
-    "color": "#FFEB00",
-    "label": "ship"
-  },
-  {
-    "index": 105,
-    "color": "#08B8AA",
-    "label": "fountain"
-  },
-  {
-    "index": 106,
-    "color": "#8500FF",
-    "label": "conveyer;belt;conveyor;belt;conveyer;conveyor;transporter"
-  },
-  {
-    "index": 107,
-    "color": "#00FF5C",
-    "label": "canopy"
-  },
-  {
-    "index": 108,
-    "color": "#B800FF",
-    "label": "washer;automatic;washer;washing;machine"
-  },
-  {
-    "index": 109,
-    "color": "#FF001F",
-    "label": "plaything;toy"
-  },
-  {
-    "index": 110,
-    "color": "#00B8FF",
-    "label": "swimming;pool;swimming;bath;natatorium"
-  },
-  {
-    "index": 111,
-    "color": "#00D6FF",
-    "label": "stool"
-  },
-  {
-    "index": 112,
-    "color": "#FF0070",
-    "label": "barrel;cask"
-  },
-  {
-    "index": 113,
-    "color": "#5CFF00",
-    "label": "basket;handbasket"
-  },
-  {
-    "index": 114,
-    "color": "#00E0FF",
-    "label": "waterfall;falls"
-  },
-  {
-    "index": 115,
-    "color": "#70E0FF",
-    "label": "tent;collapsible;shelter"
-  },
-  {
-    "index": 116,
-    "color": "#46B8A0",
-    "label": "bag"
-  },
-  {
-    "index": 117,
-    "color": "#A300FF",
-    "label": "minibike;motorbike"
-  },
-  {
-    "index": 118,
-    "color": "#9900FF",
-    "label": "cradle"
-  },
-  {
-    "index": 119,
-    "color": "#47FF00",
-    "label": "oven"
-  },
-  {
-    "index": 120,
-    "color": "#FF00A3",
-    "label": "ball"
-  },
-  {
-    "index": 121,
-    "color": "#FFCC00",
-    "label": "food;solid;food"
-  },
-  {
-    "index": 122,
-    "color": "#FF008F",
-    "label": "step;stair"
-  },
-  {
-    "index": 123,
-    "color": "#00FFEB",
-    "label": "tank;storage;tank"
-  },
-  {
-    "index": 124,
-    "color": "#85FF00",
-    "label": "trade;name;brand;name;brand;marque"
-  },
-  {
-    "index": 125,
-    "color": "#FF00EB",
-    "label": "microwave;microwave;oven"
-  },
-  {
-    "index": 126,
-    "color": "#F500FF",
-    "label": "pot;flowerpot"
-  },
-  {
-    "index": 127,
-    "color": "#FF007A",
-    "label": "animal;animate;being;beast;brute;creature;fauna"
-  },
-  {
-    "index": 128,
-    "color": "#FFF500",
-    "label": "bicycle;bike;wheel;cycle"
-  },
-  {
-    "index": 129,
-    "color": "#0ABED4",
-    "label": "lake"
-  },
-  {
-    "index": 130,
-    "color": "#D6FF00",
-    "label": "dishwasher;dish;washer;dishwashing;machine"
-  },
-  {
-    "index": 131,
-    "color": "#00CCFF",
-    "label": "screen;silver;screen;projection;screen"
-  },
-  {
-    "index": 132,
-    "color": "#1400FF",
-    "label": "blanket;cover"
-  },
-  {
-    "index": 133,
-    "color": "#FFFF00",
-    "label": "sculpture"
-  },
-  {
-    "index": 134,
-    "color": "#0099FF",
-    "label": "hood;exhaust;hood"
-  },
-  {
-    "index": 135,
-    "color": "#0029FF",
-    "label": "sconce"
-  },
-  {
-    "index": 136,
-    "color": "#00FFCC",
-    "label": "vase"
-  },
-  {
-    "index": 137,
-    "color": "#2900FF",
-    "label": "traffic;light;traffic;signal;stoplight"
-  },
-  {
-    "index": 138,
-    "color": "#29FF00",
-    "label": "tray"
-  },
-  {
-    "index": 139,
-    "color": "#AD00FF",
-    "label": "ashcan;trash;can;garbage;can;wastebin;ash;bin;ash-bin;ashbin;dustbin;trash;barrel;trash;bin"
-  },
-  {
-    "index": 140,
-    "color": "#00F5FF",
-    "label": "fan"
-  },
-  {
-    "index": 141,
-    "color": "#4700FF",
-    "label": "pier;wharf;wharfage;dock"
-  },
-  {
-    "index": 142,
-    "color": "#7A00FF",
-    "label": "crt;screen"
-  },
-  {
-    "index": 143,
-    "color": "#00FFB8",
-    "label": "plate"
-  },
-  {
-    "index": 144,
-    "color": "#005CFF",
-    "label": "monitor;monitoring;device"
-  },
-  {
-    "index": 145,
-    "color": "#B8FF00",
-    "label": "bulletin;board;notice;board"
-  },
-  {
-    "index": 146,
-    "color": "#0085FF",
-    "label": "shower"
-  },
-  {
-    "index": 147,
-    "color": "#FFD600",
-    "label": "radiator"
-  },
-  {
-    "index": 148,
-    "color": "#19C2C2",
-    "label": "glass;drinking;glass"
-  },
-  {
-    "index": 149,
-    "color": "#66FF00",
-    "label": "clock"
-  },
-  {
-    "index": 150,
-    "color": "#5C00FF",
-    "label": "flag"
-  }
-]

candle-examples/examples/segformer/main.rs

@@ -1,155 +0,0 @@
-use candle::Device;
-use candle::Module;
-use candle_nn::VarBuilder;
-use candle_transformers::models::segformer::{
-    Config, ImageClassificationModel, SemanticSegmentationModel,
-};
-use clap::{Args, Parser, Subcommand};
-use image::Rgb;
-use imageproc::integral_image::ArrayData;
-use std::collections::HashMap;
-use std::path::PathBuf;
-
-#[derive(Parser)]
-#[clap(about, version, long_about = None)]
-struct CliArgs {
-    #[arg(long, help = "use cpu")]
-    cpu: bool,
-    #[command(subcommand)]
-    command: Commands,
-}
-#[derive(Args, Debug)]
-struct SegmentationArgs {
-    #[arg(
-        long,
-        help = "name of the huggingface hub model",
-        default_value = "nvidia/segformer-b0-finetuned-ade-512-512"
-    )]
-    model_name: String,
-    #[arg(
-        long,
-        help = "path to the label file in json format",
-        default_value = "candle-examples/examples/segformer/assets/labels.json"
-    )]
-    label_path: PathBuf,
-    #[arg(long, help = "path to for the output mask image")]
-    output_path: PathBuf,
-    #[arg(help = "path to image as input")]
-    image: PathBuf,
-}
-
-#[derive(Args, Debug)]
-struct ClassificationArgs {
-    #[arg(
-        long,
-        help = "name of the huggingface hub model",
-        default_value = "paolinox/segformer-finetuned-food101"
-    )]
-    model_name: String,
-    #[arg(help = "path to image as input")]
-    image: PathBuf,
-}
-
-#[derive(Subcommand, Debug)]
-enum Commands {
-    Segment(SegmentationArgs),
-    Classify(ClassificationArgs),
-}
-
-fn get_vb_and_config(model_name: String, device: &Device) -> anyhow::Result<(VarBuilder, Config)> {
-    println!("loading model {} via huggingface hub", model_name);
-    let api = hf_hub::api::sync::Api::new()?;
-    let api = api.model(model_name.clone());
-    let model_file = api.get("model.safetensors")?;
-    println!("model {} downloaded and loaded", model_name);
-    let vb =
-        unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], candle::DType::F32, device)? };
-    let config = std::fs::read_to_string(api.get("config.json")?)?;
-    let config: Config = serde_json::from_str(&config)?;
-    println!("{:?}", config);
-    Ok((vb, config))
-}
-
-#[derive(Debug, serde::Deserialize)]
-struct LabelItem {
-    index: u32,
-    color: String,
-}
-
-fn segmentation_task(args: SegmentationArgs, device: &Device) -> anyhow::Result<()> {
-    let label_file = std::fs::read_to_string(&args.label_path)?;
-    let label_items: Vec<LabelItem> = serde_json::from_str(&label_file)?;
-    let label_colors: HashMap<u32, Rgb<u8>> = label_items
-        .iter()
-        .map(|x| {
-            (x.index - 1, {
-                let color = x.color.trim_start_matches('#');
-                let r = u8::from_str_radix(&color[0..2], 16).unwrap();
-                let g = u8::from_str_radix(&color[2..4], 16).unwrap();
-                let b = u8::from_str_radix(&color[4..6], 16).unwrap();
-                Rgb([r, g, b])
-            })
-        })
-        .collect();
-
-    let image = candle_examples::imagenet::load_image224(args.image)?
-        .unsqueeze(0)?
-        .to_device(device)?;
-    let (vb, config) = get_vb_and_config(args.model_name, device)?;
-    let num_labels = label_items.len();
-
-    let model = SemanticSegmentationModel::new(&config, num_labels, vb)?;
-    let segmentations = model.forward(&image)?;
-
-    // generate a mask image
-    let mask = &segmentations.squeeze(0)?.argmax(0)?;
-    let (h, w) = mask.dims2()?;
-    let mask = mask.flatten_all()?.to_vec1::<u32>()?;
-    let mask = mask
-        .iter()
-        .flat_map(|x| label_colors[x].data())
-        .collect::<Vec<u8>>();
-    let mask: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
-        image::ImageBuffer::from_raw(w as u32, h as u32, mask).unwrap();
-    // resize
-    let mask = image::DynamicImage::from(mask);
-    let mask = mask.resize_to_fill(
-        w as u32 * 4,
-        h as u32 * 4,
-        image::imageops::FilterType::CatmullRom,
-    );
-    mask.save(args.output_path.clone())?;
-    println!("mask image saved to {:?}", args.output_path);
-    Ok(())
-}
-
-fn classification_task(args: ClassificationArgs, device: &Device) -> anyhow::Result<()> {
-    let image = candle_examples::imagenet::load_image224(args.image)?
-        .unsqueeze(0)?
-        .to_device(device)?;
-    let (vb, config) = get_vb_and_config(args.model_name, device)?;
-    let num_labels = 7;
-    let model = ImageClassificationModel::new(&config, num_labels, vb)?;
-    let classification = model.forward(&image)?;
-    let classification = candle_nn::ops::softmax_last_dim(&classification)?;
-    let classification = classification.squeeze(0)?;
-    println!(
-        "classification logits {:?}",
-        classification.to_vec1::<f32>()?
-    );
-    let label_id = classification.argmax(0)?.to_scalar::<u32>()?;
-    let label_id = format!("{}", label_id);
-    println!("label: {}", config.id2label[&label_id]);
-    Ok(())
-}
-
-pub fn main() -> anyhow::Result<()> {
-    let args = CliArgs::parse();
-    let device = candle_examples::device(args.cpu)?;
-    if let Commands::Segment(args) = args.command {
-        segmentation_task(args, &device)?
-    } else if let Commands::Classify(args) = args.command {
-        classification_task(args, &device)?
-    }
-    Ok(())
-}

candle-examples/examples/stable-diffusion/README.md

@@ -57,7 +57,7 @@ The downside is some long compilation time. You can set the
 `/home/user/.candle` to ensures that the compilation artifacts are properly
 cached.
 
-Enabling flash-attention requires both a feature flag, `--features flash-attn`
+Enabling flash-attention requires both a feature flag, `--feature flash-attn`
 and using the command line flag `--use-flash-attn`.
 
 Note that flash-attention-v2 is only compatible with Ampere, Ada, or Hopper GPUs

candle-examples/examples/stable-diffusion/main.rs

@@ -96,10 +96,6 @@ struct Args {
     /// information.
     #[arg(long, default_value_t = 0.8)]
     img2img_strength: f64,
-
-    /// The seed to use when generating random samples.
-    #[arg(long)]
-    seed: Option<u64>,
 }
 
 #[derive(Debug, Clone, Copy, clap::ValueEnum, PartialEq, Eq)]
@@ -378,7 +374,6 @@ fn run(args: Args) -> Result<()> {
         use_flash_attn,
         img2img,
         img2img_strength,
-        seed,
         ..
     } = args;
 
@@ -432,9 +427,6 @@ fn run(args: Args) -> Result<()> {
 
     let scheduler = sd_config.build_scheduler(n_steps)?;
     let device = candle_examples::device(cpu)?;
-    if let Some(seed) = seed {
-        device.set_seed(seed)?;
-    }
     let use_guide_scale = guidance_scale > 1.0;
 
     let which = match sd_version {

candle-examples/examples/stable-lm/README.md

@@ -10,6 +10,11 @@ order to be able to use it.
 
 Other available models are Stable-Code-3B, StableLM-2 and Zephyr variants.
 
+StableLM-2 uses a Tiktoken based GPT-3.5/GPT-4 tokenizer not supported by
+Candle, so to run it you can download a somewhat compatible
+[tokenizer.json](https://huggingface.co/Xenova/gpt-4/resolve/main/tokenizer.json?download=true)
+and pass it via the --tokenizer-file argument.
+
 ## Running some example
 
 ```bash

candle-examples/examples/stable-lm/main.rs

@@ -239,7 +239,14 @@ fn main() -> Result<()> {
     ));
     let tokenizer_filename = match args.tokenizer_file {
         Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("tokenizer.json")?,
+        None => match args.which {
+            Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::Code => {
+                repo.get("tokenizer.json")?
+            }
+            Which::V2 | Which::V2Zephyr => api
+                .model("lmz/candle-stablelm".to_string())
+                .get("tokenizer-gpt4.json")?,
+        },
     };
     let filenames = match args.weight_files {
         Some(files) => files

candle-examples/examples/starcoder2/main.rs

@@ -1,253 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::{Error as E, Result};
-use clap::Parser;
-
-use candle_transformers::models::starcoder2::Model;
-
-use candle::{DType, Device, Tensor};
-use candle_examples::token_output_stream::TokenOutputStream;
-use candle_nn::VarBuilder;
-use candle_transformers::generation::LogitsProcessor;
-use hf_hub::{api::sync::Api, Repo, RepoType};
-use tokenizers::Tokenizer;
-
-struct TextGeneration {
-    model: Model,
-    device: Device,
-    tokenizer: TokenOutputStream,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            tokenizer: TokenOutputStream::new(tokenizer),
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        self.tokenizer.clear();
-        let mut tokens = self
-            .tokenizer
-            .tokenizer()
-            .encode(prompt, true)
-            .map_err(E::msg)?
-            .get_ids()
-            .to_vec();
-        for &t in tokens.iter() {
-            if let Some(t) = self.tokenizer.next_token(t)? {
-                print!("{t}")
-            }
-        }
-        std::io::stdout().flush()?;
-
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
-            Some(token) => token,
-            None => anyhow::bail!("cannot find the <|endoftext|> token"),
-        };
-        let start_gen = std::time::Instant::now();
-        for index in 0..sample_len {
-            let context_size = if index > 0 { 1 } else { tokens.len() };
-            let start_pos = tokens.len().saturating_sub(context_size);
-            let ctxt = &tokens[start_pos..];
-            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = self.model.forward(&input, start_pos)?;
-            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            if let Some(t) = self.tokenizer.next_token(next_token)? {
-                print!("{t}");
-                std::io::stdout().flush()?;
-            }
-        }
-        let dt = start_gen.elapsed();
-        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
-            print!("{rest}");
-        }
-        std::io::stdout().flush()?;
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    use_flash_attn: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// The temperature used to generate samples.
-    #[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,
-
-    /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 10000)]
-    sample_len: usize,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long, default_value = "main")]
-    revision: String,
-
-    #[arg(long)]
-    config_file: Option<String>,
-
-    #[arg(long)]
-    tokenizer_file: Option<String>,
-
-    #[arg(long)]
-    weight_files: Option<String>,
-
-    /// 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,
-}
-
-fn main() -> 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
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let model_id = match args.model_id {
-        Some(model_id) => model_id,
-        None => "bigcode/starcoder2-3b".to_string(),
-    };
-    let repo = api.repo(Repo::with_revision(
-        model_id,
-        RepoType::Model,
-        args.revision,
-    ));
-    let config_file = match args.config_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("config.json")?,
-    };
-    let tokenizer_file = match args.tokenizer_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("tokenizer.json")?,
-    };
-    let filenames = match args.weight_files {
-        Some(files) => files
-            .split(',')
-            .map(std::path::PathBuf::from)
-            .collect::<Vec<_>>(),
-        None => vec![repo.get("model.safetensors")?],
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer_file).map_err(E::msg)?;
-
-    let start = std::time::Instant::now();
-    let config = serde_json::from_reader(std::fs::File::open(config_file)?)?;
-    let device = candle_examples::device(args.cpu)?;
-    let dtype = if device.is_cuda() {
-        DType::BF16
-    } else {
-        DType::F32
-    };
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-    let model = Model::new(&config, vb)?;
-
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

candle-examples/examples/vgg/main.rs

@@ -33,7 +33,7 @@ struct Args {
 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)?.to_device(&device)?;
+    let image = candle_examples::imagenet::load_image224(args.image)?;
 
     println!("loaded image {image:?}");
 

candle-examples/examples/vit/main.rs

@@ -28,7 +28,7 @@ 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)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/src/audio.rs

@@ -1,29 +0,0 @@
-use candle::{Result, Tensor};
-
-// https://github.com/facebookresearch/audiocraft/blob/69fea8b290ad1b4b40d28f92d1dfc0ab01dbab85/audiocraft/data/audio_utils.py#L57
-pub fn normalize_loudness(
-    wav: &Tensor,
-    sample_rate: u32,
-    loudness_compressor: bool,
-) -> Result<Tensor> {
-    let energy = wav.sqr()?.mean_all()?.sqrt()?.to_vec0::<f32>()?;
-    if energy < 2e-3 {
-        return Ok(wav.clone());
-    }
-    let wav_array = wav.to_vec1::<f32>()?;
-    let mut meter = crate::bs1770::ChannelLoudnessMeter::new(sample_rate);
-    meter.push(wav_array.into_iter());
-    let power = meter.as_100ms_windows();
-    let loudness = match crate::bs1770::gated_mean(power) {
-        None => return Ok(wav.clone()),
-        Some(gp) => gp.loudness_lkfs() as f64,
-    };
-    let delta_loudness = -14. - loudness;
-    let gain = 10f64.powf(delta_loudness / 20.);
-    let wav = (wav * gain)?;
-    if loudness_compressor {
-        wav.tanh()
-    } else {
-        Ok(wav)
-    }
-}

candle-examples/src/bs1770.rs

@@ -1,506 +0,0 @@
-// Copied from https://github.com/ruuda/bs1770/blob/master/src/lib.rs
-// BS1770 -- Loudness analysis library conforming to ITU-R BS.1770
-// Copyright 2020 Ruud van Asseldonk
-
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// A copy of the License has been included in the root of the repository.
-
-//! Loudness analysis conforming to [ITU-R BS.1770-4][bs17704].
-//!
-//! This library offers the building blocks to perform BS.1770 loudness
-//! measurements, but you need to put the pieces together yourself.
-//!
-//! [bs17704]: https://www.itu.int/rec/R-REC-BS.1770-4-201510-I/en
-//!
-//! # Stereo integrated loudness example
-//!
-//! ```ignore
-//! # fn load_stereo_audio() -> [Vec<i16>; 2] {
-//! #     [vec![0; 48_000], vec![0; 48_000]]
-//! # }
-//! #
-//! let sample_rate_hz = 44_100;
-//! let bits_per_sample = 16;
-//! let channel_samples: [Vec<i16>; 2] = load_stereo_audio();
-//!
-//! // When converting integer samples to float, note that the maximum amplitude
-//! // is `1 << (bits_per_sample - 1)`, one bit is the sign bit.
-//! let normalizer = 1.0 / (1_u64 << (bits_per_sample - 1)) as f32;
-//!
-//! let channel_power: Vec<_> = channel_samples.iter().map(|samples| {
-//!     let mut meter = bs1770::ChannelLoudnessMeter::new(sample_rate_hz);
-//!     meter.push(samples.iter().map(|&s| s as f32 * normalizer));
-//!     meter.into_100ms_windows()
-//! }).collect();
-//!
-//! let stereo_power = bs1770::reduce_stereo(
-//!     channel_power[0].as_ref(),
-//!     channel_power[1].as_ref(),
-//! );
-//!
-//! let gated_power = bs1770::gated_mean(
-//!     stereo_power.as_ref()
-//! ).unwrap_or(bs1770::Power(0.0));
-//! println!("Integrated loudness: {:.1} LUFS", gated_power.loudness_lkfs());
-//! ```
-
-use std::f32;
-
-/// Coefficients for a 2nd-degree infinite impulse response filter.
-///
-/// Coefficient a0 is implicitly 1.0.
-#[derive(Clone)]
-struct Filter {
-    a1: f32,
-    a2: f32,
-    b0: f32,
-    b1: f32,
-    b2: f32,
-
-    // The past two input and output samples.
-    x1: f32,
-    x2: f32,
-    y1: f32,
-    y2: f32,
-}
-
-impl Filter {
-    /// Stage 1 of th BS.1770-4 pre-filter.
-    pub fn high_shelf(sample_rate_hz: f32) -> Filter {
-        // Coefficients taken from https://github.com/csteinmetz1/pyloudnorm/blob/
-        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/meter.py#L135-L136.
-        let gain_db = 3.999_843_8;
-        let q = 0.707_175_25;
-        let center_hz = 1_681.974_5;
-
-        // Formula taken from https://github.com/csteinmetz1/pyloudnorm/blob/
-        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/iirfilter.py#L134-L143.
-        let k = (f32::consts::PI * center_hz / sample_rate_hz).tan();
-        let vh = 10.0_f32.powf(gain_db / 20.0);
-        let vb = vh.powf(0.499_666_78);
-        let a0 = 1.0 + k / q + k * k;
-        Filter {
-            b0: (vh + vb * k / q + k * k) / a0,
-            b1: 2.0 * (k * k - vh) / a0,
-            b2: (vh - vb * k / q + k * k) / a0,
-            a1: 2.0 * (k * k - 1.0) / a0,
-            a2: (1.0 - k / q + k * k) / a0,
-
-            x1: 0.0,
-            x2: 0.0,
-            y1: 0.0,
-            y2: 0.0,
-        }
-    }
-
-    /// Stage 2 of th BS.1770-4 pre-filter.
-    pub fn high_pass(sample_rate_hz: f32) -> Filter {
-        // Coefficients taken from https://github.com/csteinmetz1/pyloudnorm/blob/
-        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/meter.py#L135-L136.
-        let q = 0.500_327_05;
-        let center_hz = 38.135_47;
-
-        // Formula taken from https://github.com/csteinmetz1/pyloudnorm/blob/
-        // 6baa64d59b7794bc812e124438692e7fd2e65c0c/pyloudnorm/iirfilter.py#L145-L151
-        let k = (f32::consts::PI * center_hz / sample_rate_hz).tan();
-        Filter {
-            a1: 2.0 * (k * k - 1.0) / (1.0 + k / q + k * k),
-            a2: (1.0 - k / q + k * k) / (1.0 + k / q + k * k),
-            b0: 1.0,
-            b1: -2.0,
-            b2: 1.0,
-
-            x1: 0.0,
-            x2: 0.0,
-            y1: 0.0,
-            y2: 0.0,
-        }
-    }
-
-    /// Feed the next input sample, get the next output sample.
-    #[inline(always)]
-    pub fn apply(&mut self, x0: f32) -> f32 {
-        let y0 = 0.0 + self.b0 * x0 + self.b1 * self.x1 + self.b2 * self.x2
-            - self.a1 * self.y1
-            - self.a2 * self.y2;
-
-        self.x2 = self.x1;
-        self.x1 = x0;
-        self.y2 = self.y1;
-        self.y1 = y0;
-
-        y0
-    }
-}
-
-/// Compensated sum, for summing many values of different orders of magnitude
-/// accurately.
-#[derive(Copy, Clone, PartialEq)]
-struct Sum {
-    sum: f32,
-    residue: f32,
-}
-
-impl Sum {
-    #[inline(always)]
-    fn zero() -> Sum {
-        Sum {
-            sum: 0.0,
-            residue: 0.0,
-        }
-    }
-
-    #[inline(always)]
-    fn add(&mut self, x: f32) {
-        let sum = self.sum + (self.residue + x);
-        self.residue = (self.residue + x) - (sum - self.sum);
-        self.sum = sum;
-    }
-}
-
-/// The mean of the squares of the K-weighted samples in a window of time.
-///
-/// K-weighted power is equivalent to K-weighted loudness, the only difference
-/// is one of scale: power is quadratic in sample amplitudes, whereas loudness
-/// units are logarithmic. `loudness_lkfs` and `from_lkfs` convert between power,
-/// and K-weighted Loudness Units relative to nominal Full Scale (LKFS).
-///
-/// The term “LKFS” (Loudness Units, K-Weighted, relative to nominal Full Scale)
-/// is used in BS.1770-4 to emphasize K-weighting, but the term is otherwise
-/// interchangeable with the more widespread term “LUFS” (Loudness Units,
-/// relative to Full Scale). Loudness units are related to decibels in the
-/// following sense: boosting a signal that has a loudness of
-/// -<var>L<sub>K</sub></var> LUFS by <var>L<sub>K</sub></var> dB (by
-/// multiplying the amplitude by 10<sup><var>L<sub>K</sub></var>/20</sup>) will
-/// bring the loudness to 0 LUFS.
-///
-/// K-weighting refers to a high-shelf and high-pass filter that model the
-/// effect that humans perceive a certain amount of power in low frequencies to
-/// be less loud than the same amount of power in higher frequencies. In this
-/// library the `Power` type is used exclusively to refer to power after applying K-weighting.
-///
-/// The nominal “full scale” is the range [-1.0, 1.0]. Because the power is the
-/// mean square of the samples, if no input samples exceeded the full scale, the
-/// power will be in the range [0.0, 1.0]. However, the power delivered by
-/// multiple channels, which is a weighted sum over individual channel powers,
-/// can exceed this range, because the weighted sum is not normalized.
-#[derive(Copy, Clone, PartialEq, PartialOrd)]
-pub struct Power(pub f32);
-
-impl Power {
-    /// Convert Loudness Units relative to Full Scale into a squared sample amplitude.
-    ///
-    /// This is the inverse of `loudness_lkfs`.
-    pub fn from_lkfs(lkfs: f32) -> Power {
-        // The inverse of the formula below.
-        Power(10.0_f32.powf((lkfs + 0.691) * 0.1))
-    }
-
-    /// Return the loudness of this window in Loudness Units, K-weighted, relative to Full Scale.
-    ///
-    /// This is the inverse of `from_lkfs`.
-    pub fn loudness_lkfs(&self) -> f32 {
-        // Equation 2 (p.5) of BS.1770-4.
-        -0.691 + 10.0 * self.0.log10()
-    }
-}
-
-/// A `T` value for non-overlapping windows of audio, 100ms in length.
-///
-/// The `ChannelLoudnessMeter` applies K-weighting and then produces the power
-/// for non-overlapping windows of 100ms duration.
-///
-/// These non-overlapping 100ms windows can later be combined into overlapping
-/// windows of 400ms, spaced 100ms apart, to compute instantaneous loudness or
-/// to perform a gated measurement, or they can be combined into even larger
-/// windows for a momentary loudness measurement.
-#[derive(Copy, Clone, Debug)]
-pub struct Windows100ms<T> {
-    pub inner: T,
-}
-
-impl<T> Windows100ms<T> {
-    /// Wrap a new empty vector.
-    pub fn new() -> Windows100ms<Vec<T>> {
-        Windows100ms { inner: Vec::new() }
-    }
-
-    /// Apply `as_ref` to the inner value.
-    pub fn as_ref(&self) -> Windows100ms<&[Power]>
-    where
-        T: AsRef<[Power]>,
-    {
-        Windows100ms {
-            inner: self.inner.as_ref(),
-        }
-    }
-
-    /// Apply `as_mut` to the inner value.
-    pub fn as_mut(&mut self) -> Windows100ms<&mut [Power]>
-    where
-        T: AsMut<[Power]>,
-    {
-        Windows100ms {
-            inner: self.inner.as_mut(),
-        }
-    }
-
-    #[allow(clippy::len_without_is_empty)]
-    /// Apply `len` to the inner value.
-    pub fn len(&self) -> usize
-    where
-        T: AsRef<[Power]>,
-    {
-        self.inner.as_ref().len()
-    }
-}
-
-/// Measures K-weighted power of non-overlapping 100ms windows of a single channel of audio.
-///
-/// # Output
-///
-/// The output of the meter is an intermediate result in the form of power for
-/// 100ms non-overlapping windows. The windows need to be processed further to
-/// get one of the instantaneous, momentary, and integrated loudness
-/// measurements defined in BS.1770.
-///
-/// The windows can also be inspected directly; the data is meaningful
-/// on its own (the K-weighted power delivered in that window of time), but it
-/// is not something that BS.1770 defines a term for.
-///
-/// # Multichannel audio
-///
-/// To perform a loudness measurement of multichannel audio, construct a
-/// `ChannelLoudnessMeter` per channel, and later combine the measured power
-/// with e.g. `reduce_stereo`.
-///
-/// # Instantaneous loudness
-///
-/// The instantaneous loudness is the power over a 400ms window, so you can
-/// average four 100ms windows. No special functionality is implemented to help
-/// with that at this time. ([Pull requests would be accepted.][contribute])
-///
-/// # Momentary loudness
-///
-/// The momentary loudness is the power over a 3-second window, so you can
-/// average thirty 100ms windows. No special functionality is implemented to
-/// help with that at this time. ([Pull requests would be accepted.][contribute])
-///
-/// # Integrated loudness
-///
-/// Use `gated_mean` to perform an integrated loudness measurement:
-///
-/// ```ignore
-/// # use std::iter;
-/// # use bs1770::{ChannelLoudnessMeter, gated_mean};
-/// # let sample_rate_hz = 44_100;
-/// # let samples_per_100ms = sample_rate_hz / 10;
-/// # let mut meter = ChannelLoudnessMeter::new(sample_rate_hz);
-/// # meter.push((0..44_100).map(|i| (i as f32 * 0.01).sin()));
-/// let integrated_loudness_lkfs = gated_mean(meter.as_100ms_windows())
-///     .unwrap_or(bs1770::Power(0.0))
-///     .loudness_lkfs();
-/// ```
-///
-/// [contribute]: https://github.com/ruuda/bs1770/blob/master/CONTRIBUTING.md
-#[derive(Clone)]
-pub struct ChannelLoudnessMeter {
-    /// The number of samples that fit in 100ms of audio.
-    samples_per_100ms: u32,
-
-    /// Stage 1 filter (head effects, high shelf).
-    filter_stage1: Filter,
-
-    /// Stage 2 filter (high-pass).
-    filter_stage2: Filter,
-
-    /// Sum of the squares over non-overlapping windows of 100ms.
-    windows: Windows100ms<Vec<Power>>,
-
-    /// The number of samples in the current unfinished window.
-    count: u32,
-
-    /// The sum of the squares of the samples in the current unfinished window.
-    square_sum: Sum,
-}
-
-impl ChannelLoudnessMeter {
-    /// Construct a new loudness meter for the given sample rate.
-    pub fn new(sample_rate_hz: u32) -> ChannelLoudnessMeter {
-        ChannelLoudnessMeter {
-            samples_per_100ms: sample_rate_hz / 10,
-            filter_stage1: Filter::high_shelf(sample_rate_hz as f32),
-            filter_stage2: Filter::high_pass(sample_rate_hz as f32),
-            windows: Windows100ms::new(),
-            count: 0,
-            square_sum: Sum::zero(),
-        }
-    }
-
-    /// Feed input samples for loudness analysis.
-    ///
-    /// # Full scale
-    ///
-    /// Full scale for the input samples is the interval [-1.0, 1.0]. If your
-    /// input consists of signed integer samples, you can convert as follows:
-    ///
-    /// ```ignore
-    /// # let mut meter = bs1770::ChannelLoudnessMeter::new(44_100);
-    /// # let bits_per_sample = 16_usize;
-    /// # let samples = &[0_i16];
-    /// // Note that the maximum amplitude is `1 << (bits_per_sample - 1)`,
-    /// // one bit is the sign bit.
-    /// let normalizer = 1.0 / (1_u64 << (bits_per_sample - 1)) as f32;
-    /// meter.push(samples.iter().map(|&s| s as f32 * normalizer));
-    /// ```
-    ///
-    /// # Repeated calls
-    ///
-    /// You can call `push` multiple times to feed multiple batches of samples.
-    /// This is equivalent to feeding a single chained iterator. The leftover of
-    /// samples that did not fill a full 100ms window is not discarded:
-    ///
-    /// ```ignore
-    /// # use std::iter;
-    /// # use bs1770::ChannelLoudnessMeter;
-    /// let sample_rate_hz = 44_100;
-    /// let samples_per_100ms = sample_rate_hz / 10;
-    /// let mut meter = ChannelLoudnessMeter::new(sample_rate_hz);
-    ///
-    /// meter.push(iter::repeat(0.0).take(samples_per_100ms as usize - 1));
-    /// assert_eq!(meter.as_100ms_windows().len(), 0);
-    ///
-    /// meter.push(iter::once(0.0));
-    /// assert_eq!(meter.as_100ms_windows().len(), 1);
-    /// ```
-    pub fn push<I: Iterator<Item = f32>>(&mut self, samples: I) {
-        let normalizer = 1.0 / self.samples_per_100ms as f32;
-
-        // LLVM, if you could go ahead and inline those apply calls, and then
-        // unroll and vectorize the loop, that'd be terrific.
-        for x in samples {
-            let y = self.filter_stage1.apply(x);
-            let z = self.filter_stage2.apply(y);
-
-            self.square_sum.add(z * z);
-            self.count += 1;
-
-            // TODO: Should this branch be marked cold?
-            if self.count == self.samples_per_100ms {
-                let mean_squares = Power(self.square_sum.sum * normalizer);
-                self.windows.inner.push(mean_squares);
-                // We intentionally do not reset the residue. That way, leftover
-                // energy from this window is not lost, so for the file overall,
-                // the sum remains more accurate.
-                self.square_sum.sum = 0.0;
-                self.count = 0;
-            }
-        }
-    }
-
-    /// Return a reference to the 100ms windows analyzed so far.
-    pub fn as_100ms_windows(&self) -> Windows100ms<&[Power]> {
-        self.windows.as_ref()
-    }
-
-    /// Return all 100ms windows analyzed so far.
-    pub fn into_100ms_windows(self) -> Windows100ms<Vec<Power>> {
-        self.windows
-    }
-}
-
-/// Combine power for multiple channels by taking a weighted sum.
-///
-/// Note that BS.1770-4 defines power for a multi-channel signal as a weighted
-/// sum over channels which is not normalized. This means that a stereo signal
-/// is inherently louder than a mono signal. For a mono signal played back on
-/// stereo speakers, you should therefore still apply `reduce_stereo`, passing
-/// in the same signal for both channels.
-pub fn reduce_stereo(
-    left: Windows100ms<&[Power]>,
-    right: Windows100ms<&[Power]>,
-) -> Windows100ms<Vec<Power>> {
-    assert_eq!(
-        left.len(),
-        right.len(),
-        "Channels must have the same length."
-    );
-    let mut result = Vec::with_capacity(left.len());
-    for (l, r) in left.inner.iter().zip(right.inner) {
-        result.push(Power(l.0 + r.0));
-    }
-    Windows100ms { inner: result }
-}
-
-/// In-place version of `reduce_stereo` that stores the result in the former left channel.
-pub fn reduce_stereo_in_place(left: Windows100ms<&mut [Power]>, right: Windows100ms<&[Power]>) {
-    assert_eq!(
-        left.len(),
-        right.len(),
-        "Channels must have the same length."
-    );
-    for (l, r) in left.inner.iter_mut().zip(right.inner) {
-        l.0 += r.0;
-    }
-}
-
-/// Perform gating and averaging for a BS.1770-4 integrated loudness measurement.
-///
-/// The integrated loudness measurement is not just the average power over the
-/// entire signal. BS.1770-4 defines defines two stages of gating that exclude
-/// parts of the signal, to ensure that silent parts do not contribute to the
-/// loudness measurment. This function performs that gating, and returns the
-/// average power over the windows that were not excluded.
-///
-/// The result of this function is the integrated loudness measurement.
-///
-/// When no signal remains after applying the gate, this function returns
-/// `None`. In particular, this happens when all of the signal is softer than
-/// -70 LKFS, including a signal that consists of pure silence.
-pub fn gated_mean(windows_100ms: Windows100ms<&[Power]>) -> Option<Power> {
-    let mut gating_blocks = Vec::with_capacity(windows_100ms.len());
-
-    // Stage 1: an absolute threshold of -70 LKFS. (Equation 6, p.6.)
-    let absolute_threshold = Power::from_lkfs(-70.0);
-
-    // Iterate over all 400ms windows.
-    for window in windows_100ms.inner.windows(4) {
-        // Note that the sum over channels has already been performed at this point.
-        let gating_block_power = Power(0.25 * window.iter().map(|mean| mean.0).sum::<f32>());
-
-        if gating_block_power > absolute_threshold {
-            gating_blocks.push(gating_block_power);
-        }
-    }
-
-    if gating_blocks.is_empty() {
-        return None;
-    }
-
-    // Compute the loudness after applying the absolute gate, in order to
-    // determine the threshold for the relative gate.
-    let mut sum_power = Sum::zero();
-    for &gating_block_power in &gating_blocks {
-        sum_power.add(gating_block_power.0);
-    }
-    let absolute_gated_power = Power(sum_power.sum / (gating_blocks.len() as f32));
-
-    // Stage 2: Apply the relative gate.
-    let relative_threshold = Power::from_lkfs(absolute_gated_power.loudness_lkfs() - 10.0);
-    let mut sum_power = Sum::zero();
-    let mut n_blocks = 0_usize;
-    for &gating_block_power in &gating_blocks {
-        if gating_block_power > relative_threshold {
-            sum_power.add(gating_block_power.0);
-            n_blocks += 1;
-        }
-    }
-
-    if n_blocks == 0 {
-        return None;
-    }
-
-    let relative_gated_power = Power(sum_power.sum / n_blocks as f32);
-    Some(relative_gated_power)
-}

candle-examples/src/lib.rs

@@ -1,9 +1,6 @@
-pub mod audio;
-pub mod bs1770;
 pub mod coco_classes;
 pub mod imagenet;
 pub mod token_output_stream;
-pub mod wav;
 
 use candle::utils::{cuda_is_available, metal_is_available};
 use candle::{Device, Result, Tensor};

candle-examples/src/token_output_stream.rs

@@ -40,7 +40,7 @@ impl TokenOutputStream {
         };
         self.tokens.push(token);
         let text = self.decode(&self.tokens[self.prev_index..])?;
-        if text.len() > prev_text.len() && text.chars().last().unwrap().is_alphanumeric() {
+        if text.len() > prev_text.len() && text.chars().last().unwrap().is_alphabetic() {
             let text = text.split_at(prev_text.len());
             self.prev_index = self.current_index;
             self.current_index = self.tokens.len();

candle-examples/src/wav.rs

@@ -1,56 +0,0 @@
-use std::io::prelude::*;
-
-pub trait Sample {
-    fn to_i16(&self) -> i16;
-}
-
-impl Sample for f32 {
-    fn to_i16(&self) -> i16 {
-        (self.clamp(-1.0, 1.0) * 32767.0) as i16
-    }
-}
-
-impl Sample for f64 {
-    fn to_i16(&self) -> i16 {
-        (self.clamp(-1.0, 1.0) * 32767.0) as i16
-    }
-}
-
-impl Sample for i16 {
-    fn to_i16(&self) -> i16 {
-        *self
-    }
-}
-
-pub fn write_pcm_as_wav<W: Write, S: Sample>(
-    w: &mut W,
-    samples: &[S],
-    sample_rate: u32,
-) -> std::io::Result<()> {
-    let len = 12u32; // header
-    let len = len + 24u32; // fmt
-    let len = len + samples.len() as u32 * 2 + 8; // data
-    let n_channels = 1u16;
-    let bytes_per_second = sample_rate * 2 * n_channels as u32;
-    w.write_all(b"RIFF")?;
-    w.write_all(&(len - 8).to_le_bytes())?; // total length minus 8 bytes
-    w.write_all(b"WAVE")?;
-
-    // Format block
-    w.write_all(b"fmt ")?;
-    w.write_all(&16u32.to_le_bytes())?; // block len minus 8 bytes
-    w.write_all(&1u16.to_le_bytes())?; // PCM
-    w.write_all(&n_channels.to_le_bytes())?; // one channel
-    w.write_all(&sample_rate.to_le_bytes())?;
-    w.write_all(&bytes_per_second.to_le_bytes())?;
-    w.write_all(&2u16.to_le_bytes())?; // 2 bytes of data per sample
-    w.write_all(&16u16.to_le_bytes())?; // bits per sample
-
-    // Data block
-    w.write_all(b"data")?;
-    w.write_all(&(samples.len() as u32 * 2).to_le_bytes())?;
-    for sample in samples.iter() {
-        w.write_all(&sample.to_i16().to_le_bytes())?
-    }
-    Ok(())
-}

candle-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.4.2"
+version = "0.4.0"
 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"], package = "candle-core", version = "0.4.2" }
+candle = { path = "../candle-core", features = ["cuda"], package = "candle-core", version = "0.4.0" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]

candle-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.4.2"
+version = "0.4.0"
 edition = "2021"
 
 description = "CUDA kernels for Candle"

candle-kernels/src/conv.cu

@@ -51,48 +51,6 @@ __device__ void conv1d(
   dst[dst_i] = static_cast<T>(d);
 }
 
-template <typename T>
-__device__ void col2im1d(
-    const size_t l_in,
-    const size_t l_out,
-    const size_t c_out,
-    const size_t k_size,
-    const size_t b_size,
-    const size_t stride,
-    const T *src,
-    T *dst
-) {
-  const size_t dst_i = blockIdx.x * blockDim.x + threadIdx.x;
-  // src: (b_size, l_in, c_out, k_size)
-  // dst: (b_size, c_out, l_out)
-  if (dst_i >= b_size * c_out * l_out) {
-    return;
-  }
-  const size_t dst_s0 = c_out * l_out;
-  const size_t dst_s1 = l_out;
-
-  // dst_idx = b_i * dst_s0 + c_i * dst_s1 + l_in_i * stride + k_i
-  const size_t b_i = dst_i / dst_s0;
-  const size_t dst_i2 = dst_i - b_i * dst_s0;
-  const size_t c_i = dst_i2 / dst_s1;
-  const size_t dst_i3 = dst_i2 - c_i * dst_s1; // l_in_i * stride + k_i
-
-  const size_t src_s0 = c_out * k_size * l_in;
-  const size_t src_s1 = c_out * k_size;
-  const size_t src_s2 = k_size;
-
-  T d = 0;
-  for (size_t k_i = 0; k_i < min(dst_i3 + 1, k_size); ++k_i) {
-    const size_t l_in_i_times_stride = dst_i3 - k_i;
-    const size_t l_in_i = l_in_i_times_stride / stride;
-    const size_t src_i = b_i * src_s0 + l_in_i * src_s1 + c_i * src_s2 + k_i;
-    if (l_in_i * stride == l_in_i_times_stride && l_in_i < l_in) {
-      d += src[src_i];
-    }
-  }
-  dst[dst_i] = d;
-}
-
 template <typename T>
 __device__ void im2col1d(
     const size_t dst_numel,
@@ -569,7 +527,7 @@ 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, FN_NAME2) \
+#define IM2COL1D_OP(TYPENAME, FN_NAME) \
 extern "C" __global__ void FN_NAME(  \
     const size_t dst_numel, \
     const size_t l_out, \
@@ -583,18 +541,6 @@ extern "C" __global__ void FN_NAME(  \
 ) {  \
   im2col1d<TYPENAME>(dst_numel, l_out, l_k, stride, padding, dilation, info, src, dst); \
 } \
-extern "C" __global__ void FN_NAME2(  \
-    const size_t l_in, \
-    const size_t l_out, \
-    const size_t c_out, \
-    const size_t k_size, \
-    const size_t b_size, \
-    const size_t stride, \
-    const TYPENAME *src, \
-    TYPENAME *dst \
-) {  \
-  col2im1d<TYPENAME>(l_in, l_out, c_out, k_size, b_size, stride, src, dst); \
-} \
 
 #define IM2COL_OP(TYPENAME, FN_NAME) \
 extern "C" __global__ void FN_NAME(  \
@@ -696,7 +642,7 @@ 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, col2im1d_bf16)
+IM2COL1D_OP(__nv_bfloat16, im2col1d_bf16)
 #endif
 
 #if __CUDA_ARCH__ >= 530
@@ -708,7 +654,7 @@ 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, col2im1d_f16)
+IM2COL1D_OP(__half, im2col1d_f16)
 #endif
 
 CONV1D_OP(float, float, conv1d_f32)
@@ -751,7 +697,7 @@ IM2COL_OP(double, im2col_f64)
 IM2COL_OP(uint8_t, im2col_u8)
 IM2COL_OP(uint32_t, im2col_u32)
 
-IM2COL1D_OP(float, im2col1d_f32, col2im1d_f32)
-IM2COL1D_OP(double, im2col1d_f64, col2im1d_f64)
-IM2COL1D_OP(uint8_t, im2col1d_u8, col2im1d_u8)
-IM2COL1D_OP(uint32_t, im2col1d_u32, col2im1d_u32)
+IM2COL1D_OP(float, im2col1d_f32)
+IM2COL1D_OP(double, im2col1d_f64)
+IM2COL1D_OP(uint8_t, im2col1d_u8)
+IM2COL1D_OP(uint32_t, im2col1d_u32)

candle-kernels/src/fill.cu

@@ -10,39 +10,11 @@ __device__ void fill_with(T *buf, T value, const size_t numel) {
 extern "C" __global__ void fill_u8(uint8_t *buf, uint8_t value, const size_t numel) { fill_with(buf, value, numel); }
 extern "C" __global__ void fill_u32(uint32_t *buf, uint32_t value, const size_t numel) { fill_with(buf, value, numel); }
 extern "C" __global__ void fill_i64(int64_t *buf, int64_t value, const size_t numel) { fill_with(buf, value, numel); }
+extern "C" __global__ void fill_f16(__half *buf, __half value, const size_t numel) { fill_with(buf, value, numel); }
 extern "C" __global__ void fill_f32(float *buf, float value, const size_t numel) { fill_with(buf, value, numel); }
 extern "C" __global__ void fill_f64(double *buf, double value, const size_t numel) { fill_with(buf, value, numel); }
 
-template<typename T>
-__device__ void copy2d(const T *src, T *dst, uint32_t d1, uint32_t d2, uint32_t src_s, uint32_t dst_s) {
-  uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x;
-  if (idx >= d1 * d2) {
-    return;
-  }
-  uint32_t idx1 = idx / d2;
-  uint32_t idx2 = idx - d2 * idx1;
-  dst[idx1 * dst_s + idx2] = src[idx1 * src_s + idx2];
-}
-
-#define COPY2D_OP(TYPENAME, FNNAME) \
-extern "C" __global__ \
-void FNNAME(const TYPENAME *src, TYPENAME *dst, uint32_t d1, uint32_t d2, uint32_t src_s, uint32_t dst_s) { \
-  copy2d(src, dst, d1, d2, src_s, dst_s); \
-} \
-
-COPY2D_OP(float, copy2d_f32)
-COPY2D_OP(double, copy2d_f64)
-COPY2D_OP(uint8_t, copy2d_u8)
-COPY2D_OP(uint32_t, copy2d_u32)
-COPY2D_OP(int64_t, copy2d_i64)
-
-#if __CUDA_ARCH__ >= 530
-extern "C" __global__ void fill_f16(__half *buf, __half value, const size_t numel) { fill_with(buf, value, numel); }
-COPY2D_OP(__half, copy2d_f16)
-#endif
-
 #if __CUDA_ARCH__ >= 800
 #include <cuda_bf16.h>
 extern "C" __global__ void fill_bf16(__nv_bfloat16 *buf, __nv_bfloat16 value, const size_t numel) { fill_with(buf, value, numel); }
-COPY2D_OP(__nv_bfloat16, copy2d_bf16)
 #endif

candle-kernels/src/lib.rs

@@ -4,7 +4,6 @@ pub const CAST: &str = include_str!(concat!(env!("OUT_DIR"), "/cast.ptx"));
 pub const CONV: &str = include_str!(concat!(env!("OUT_DIR"), "/conv.ptx"));
 pub const FILL: &str = include_str!(concat!(env!("OUT_DIR"), "/fill.ptx"));
 pub const INDEXING: &str = include_str!(concat!(env!("OUT_DIR"), "/indexing.ptx"));
-pub const QUANTIZED: &str = include_str!(concat!(env!("OUT_DIR"), "/quantized.ptx"));
 pub const REDUCE: &str = include_str!(concat!(env!("OUT_DIR"), "/reduce.ptx"));
 pub const TERNARY: &str = include_str!(concat!(env!("OUT_DIR"), "/ternary.ptx"));
 pub const UNARY: &str = include_str!(concat!(env!("OUT_DIR"), "/unary.ptx"));

candle-metal-kernels/.gitignore

@@ -0,0 +1,2 @@
+src/compiled/
+

candle-metal-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-metal-kernels"
-version = "0.4.2"
+version = "0.4.0"
 edition = "2021"
 
 description = "Metal kernels for Candle"

candle-metal-kernels/build.rs

@@ -0,0 +1,45 @@
+use std::path::Path;
+use std::process::Command;
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let files: std::fs::ReadDir = std::fs::read_dir("src/").unwrap();
+    for file in files {
+        let file = file?;
+        let path = file.path();
+        if let Some(extension) = path.extension() {
+            if extension == "metal" {
+                build_kernel(&path)?;
+            }
+            println!("cargo:warning=output {:?}", path.file_stem());
+        }
+    }
+    Ok(())
+}
+
+fn build_kernel(path: &Path) -> Result<(), Box<dyn std::error::Error>> {
+    let stem = path
+        .file_stem()
+        .expect("expect real filename")
+        .to_str()
+        .expect("expect real stem");
+    Command::new("xcrun")
+        .args([
+            "metal",
+            "-c",
+            path.as_os_str().to_str().expect("Expect a real filename"),
+            "-I",
+            "src/",
+            "-o",
+            &format!("src/compiled/{stem}.air"),
+        ])
+        .output()?;
+    Command::new("xcrun")
+        .args([
+            "metallib",
+            &format!("src/compiled/{stem}.air"),
+            "-o",
+            &format!("src/compiled/{stem}.metallib"),
+        ])
+        .output()?;
+    Ok(())
+}

candle-metal-kernels/src/affine.metal

@@ -89,7 +89,7 @@ kernel void FN_NAME( \
         return; \
     } \
     const TYPENAME x = input[id]; \
-    output[id] = TYPENAME((x > 0)?x: mul * (exp(x) - 1)); \
+    output[id] = TYPENAME((x > 0)?x: mul * exp(x - 1)); \
 } \
 kernel void FN_NAME##_strided( \
     constant size_t &dim, \

candle-metal-kernels/src/cast.metal

@@ -72,60 +72,28 @@ kernel void FN_NAME_STRIDED( \
     output[tid] = static_cast<RIGHT_TYPENAME>(static_cast<IR_TYPENAME>(input[get_strided_index(tid, num_dims, dims, strides)])); \
 } \
 
-// u32
 CAST(cast_u32_f32, cast_u32_f32_strided, uint32_t, float)
-CAST(cast_u32_u8, cast_u32_u8_strided, uint32_t, uint8_t)
 CAST(cast_u32_f16, cast_u32_f16_strided, uint32_t, half)
-#if __METAL_VERSION__ >= 220
-CAST(cast_u32_i64, cast_u32_i64_strided, uint32_t, int64_t)
-#endif
-#if defined(__HAVE_BFLOAT__)
-CAST(cast_u32_bf16, cast_u32_bf16_strided, uint32_t, bfloat)
-#endif
-
-// u8
+CAST(cast_u32_u8, cast_u32_u8_strided, uint32_t, uint8_t)
 CAST(cast_u8_u32, cast_u8_u32_strided, uint8_t, uint32_t)
 CAST(cast_u8_f32, cast_u8_f32_strided, uint8_t, float)
-CAST(cast_u8_f16, cast_u8_f16_strided, uint8_t, half)
+CAST(cast_f16_f32, cast_f16_f32_strided, half, float)
+CAST(cast_f32_f16, cast_f32_f16_strided, float, half)
+
 #if __METAL_VERSION__ >= 220
 CAST(cast_u8_i64, cast_u8_i64_strided, uint8_t, int64_t)
-#endif
-#if defined(__HAVE_BFLOAT__)
-CAST(cast_u8_bf16, cast_u8_bf16_strided, uint8_t, bfloat)
-#endif
-
-// f16
-CAST(cast_f16_f32, cast_f16_f32_strided, half, float)
-CAST(cast_f16_u8, cast_f16_u8_strided, half, uint8_t)
-CAST(cast_f16_u32, cast_f16_u32_strided, half, uint32_t)
-CAST(cast_f16_i64, cast_f16_i64_strided, half, int64_t)
-#if defined(__HAVE_BFLOAT__)
-CAST_THROUGH(cast_f16_bf16, cast_f16_bf16_strided, half, bfloat, float)
-#endif
-
-// i64
+CAST(cast_u32_i64, cast_u32_i64_strided, uint32_t, int64_t)
 CAST(cast_i64_f32, cast_i64_f32_strided, int64_t, float)
-CAST(cast_i64_u8, cast_i64_u8_strided, int64_t, uint8_t)
-CAST(cast_i64_u32, cast_i64_u32_strided, int64_t, uint32_t)
-CAST(cast_i64_f16, cast_i64_f16_strided, int64_t, half)
-#if defined(__HAVE_BFLOAT__)
-CAST_THROUGH(cast_i64_bf16, cast_i64_bf16_strided, int64_t, bfloat, float)
 #endif
 
-// f32
-CAST(cast_f32_f16, cast_f32_f16_strided, float, half)
-CAST(cast_f32_u32, cast_f32_u32_strided, float, uint32_t)
-CAST(cast_f32_u8, cast_f32_u8_strided, float, uint8_t)
-CAST(cast_f32_i64, cast_f32_i64_strided, float, int64_t)
-#if defined(__HAVE_BFLOAT__)
-CAST(cast_f32_bf16, cast_f32_bf16_strided, float, bfloat)
-#endif
-
-// bf16
 #if defined(__HAVE_BFLOAT__)
 CAST(cast_bf16_u32, cast_bf16_u32_strided, bfloat, uint32_t)
-CAST(cast_bf16_i64, cast_bf16_i64_strided, bfloat, int64_t)
 CAST(cast_bf16_f32, cast_bf16_f32_strided, bfloat, float)
+CAST(cast_u8_bf16, cast_u8_bf16_strided, uint8_t, bfloat)
+CAST(cast_u32_bf16, cast_u32_bf16_strided, uint32_t, bfloat)
+CAST(cast_f32_bf16, cast_f32_bf16_strided, float, bfloat)
+
 CAST_THROUGH(cast_bf16_u8, cast_bf16_u8_strided, bfloat, uint8_t, float)
 CAST_THROUGH(cast_bf16_f16, cast_bf16_f16_strided, bfloat, half, float)
-#endif
+CAST_THROUGH(cast_f16_bf16, cast_f16_bf16_strided, half, bfloat, float)
+#endif

candle-metal-kernels/src/indexing.metal

@@ -167,16 +167,11 @@ kernel void NAME( \
 
 INDEX_OP(is_u32_f32, uint, float)
 INDEX_OP(is_u32_f16, uint, half)
-
 GATHER_OP(gather_u32_f32, uint, float)
 GATHER_OP(gather_u32_f16, uint, half)
+SCATTER_ADD_OP(sa_u32_f32, uint, float)
+SCATTER_ADD_OP(sa_u32_f16, uint, half)
 
-SCATTER_ADD_OP(sa_u32_f32, uint32_t, float)
-SCATTER_ADD_OP(sa_u8_f32, uint8_t, float)
-SCATTER_ADD_OP(sa_i64_f32, int64_t, float)
-SCATTER_ADD_OP(sa_u32_f16, uint32_t, half)
-SCATTER_ADD_OP(sa_u8_f16, uint8_t, half)
-SCATTER_ADD_OP(sa_i64_f16, int64_t, half)
 
 #if defined(__HAVE_BFLOAT__)
 INDEX_OP(is_u32_bf16, uint32_t, bfloat)
@@ -185,10 +180,6 @@ INDEX_OP(is_u8_bf16, uint8_t, bfloat)
 INDEX_ADD_OP(ia_i64_bf16, int64_t, bfloat)
 INDEX_ADD_OP(ia_u32_bf16, uint32_t, bfloat)
 INDEX_ADD_OP(ia_u8_bf16, uint8_t, bfloat)
-
-SCATTER_ADD_OP(sa_u32_bf16, uint32_t, bfloat)
-SCATTER_ADD_OP(sa_u8_bf16, uint8_t, bfloat)
-SCATTER_ADD_OP(sa_i64_bf16, int64_t, bfloat)
 #endif
 
 INDEX_ADD_OP(ia_u32_f16, uint32_t, half)

candle-metal-kernels/src/lib.rs

@@ -1,22 +1,22 @@
 use metal::{
-    Buffer, CommandBufferRef, CompileOptions, ComputeCommandEncoderRef, ComputePipelineState,
-    Device, Function, FunctionConstantValues, Library, MTLDataType, MTLSize, NSUInteger,
+    Buffer, CommandBufferRef, ComputeCommandEncoderRef, ComputePipelineState, Device, Function,
+    FunctionConstantValues, Library, MTLDataType, MTLSize, NSUInteger,
 };
 use std::collections::HashMap;
 use std::ffi::c_void;
 use std::sync::RwLock;
 
-const AFFINE: &str = include_str!("affine.metal");
-const INDEXING: &str = include_str!("indexing.metal");
-const UNARY: &str = include_str!("unary.metal");
-const BINARY: &str = include_str!("binary.metal");
-const TERNARY: &str = include_str!("ternary.metal");
-const CAST: &str = include_str!("cast.metal");
-const CONV: &str = include_str!("conv.metal");
-const REDUCE: &str = include_str!("reduce.metal");
-const RANDOM: &str = include_str!("random.metal");
+const AFFINE: &[u8] = include_bytes!("compiled/affine.metallib");
+const INDEXING: &[u8] = include_bytes!("compiled/indexing.metallib");
+const UNARY: &[u8] = include_bytes!("compiled/unary.metallib");
+const BINARY: &[u8] = include_bytes!("compiled/binary.metallib");
+const TERNARY: &[u8] = include_bytes!("compiled/ternary.metallib");
+const CAST: &[u8] = include_bytes!("compiled/cast.metallib");
+const CONV: &[u8] = include_bytes!("compiled/conv.metallib");
+const REDUCE: &[u8] = include_bytes!("compiled/reduce.metallib");
+const RANDOM: &[u8] = include_bytes!("compiled/random.metallib");
 const MFA: &[u8] = include_bytes!("libMetalFlashAttention.metallib");
-const QUANTIZED: &str = include_str!("quantized.metal");
+const QUANTIZED: &[u8] = include_bytes!("compiled/quantized.metallib");
 
 /// Most kernels apply similarly across the tensors
 /// This creates a strategy that uses the maximum amount of threads per threadgroup (capped at the
@@ -127,16 +127,6 @@ pub enum Source {
     Quantized,
 }
 
-pub mod copy2d {
-    pub struct Kernel(pub &'static str);
-    pub const FLOAT: Kernel = Kernel("copy2d_f32");
-    pub const HALF: Kernel = Kernel("copy2d_f16");
-    pub const BFLOAT: Kernel = Kernel("copy2d_bf16");
-    pub const I64: Kernel = Kernel("copy2d_i64");
-    pub const U32: Kernel = Kernel("copy2d_u32");
-    pub const U8: Kernel = Kernel("copy2d_u8");
-}
-
 macro_rules! ops{
     ($($name:ident),+) => {
 
@@ -245,7 +235,7 @@ impl Kernels {
         }
     }
 
-    fn get_library_source(&self, source: Source) -> &'static str {
+    fn get_library_source(&self, source: Source) -> &'static [u8] {
         match source {
             Source::Affine => AFFINE,
             Source::Unary => UNARY,
@@ -257,7 +247,7 @@ impl Kernels {
             Source::Conv => CONV,
             Source::Random => RANDOM,
             Source::Quantized => QUANTIZED,
-            Source::Mfa => panic!("Invalid lib"),
+            Source::Mfa => MFA,
         }
     }
 
@@ -272,22 +262,12 @@ impl Kernels {
         if let Some(lib) = libraries.get(&source) {
             Ok(lib.clone())
         } else {
-            let lib = match source {
-                Source::Mfa => {
-                    let source_data = MFA;
-                    device.new_library_with_data(source_data).map_err(|e| {
-                        MetalKernelError::LoadLibraryError(format!(
-                            "Candle metal requires macosx > 13.0 or higher, cannot load mfa: {e}"
-                        ))
-                    })?
-                }
-                source => {
-                    let source_content = self.get_library_source(source);
-                    device
-                        .new_library_with_source(source_content, &CompileOptions::new())
-                        .map_err(|e| MetalKernelError::LoadLibraryError(e.to_string()))?
-                }
-            };
+            let source_data = self.get_library_source(source);
+            let lib = device.new_library_with_data(source_data).map_err(|e| {
+                MetalKernelError::LoadLibraryError(format!(
+                    "Candle metal requires macosx > 13.0 or higher, cannot load mfa: {e}"
+                ))
+            })?;
             libraries.insert(source, lib.clone());
             Ok(lib)
         }
@@ -375,46 +355,6 @@ pub fn call_unary_contiguous(
     Ok(())
 }
 
-#[allow(clippy::too_many_arguments)]
-pub fn call_copy2d(
-    device: &Device,
-    command_buffer: &CommandBufferRef,
-    kernels: &Kernels,
-    name: copy2d::Kernel,
-    input: &Buffer,
-    output: &Buffer,
-    d1: usize,
-    d2: usize,
-    src_s: usize,
-    dst_s: usize,
-    src_o_in_bytes: usize,
-    dst_o_in_bytes: usize,
-) -> Result<(), MetalKernelError> {
-    let pipeline = kernels.load_pipeline(device, Source::Unary, name.0)?;
-    let encoder = command_buffer.new_compute_command_encoder();
-    encoder.set_compute_pipeline_state(&pipeline);
-    set_params!(
-        encoder,
-        (
-            d1,
-            d2,
-            src_s,
-            dst_s,
-            (input, src_o_in_bytes),
-            (output, dst_o_in_bytes)
-        )
-    );
-
-    let width: usize = d1 * d2;
-    let (thread_group_count, thread_group_size) = linear_split(&pipeline, width);
-
-    encoder.use_resource(input, metal::MTLResourceUsage::Read);
-    encoder.use_resource(output, metal::MTLResourceUsage::Write);
-    encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
-    encoder.end_encoding();
-    Ok(())
-}
-
 #[allow(clippy::too_many_arguments)]
 pub fn call_unary_strided(
     device: &Device,
@@ -1608,10 +1548,8 @@ pub fn call_random_uniform(
 
     set_params!(encoder, (length, min, max, seed, buffer));
 
-    encoder.use_resource(
-        seed,
-        metal::MTLResourceUsage::Read | metal::MTLResourceUsage::Write,
-    );
+    encoder.use_resource(seed, metal::MTLResourceUsage::Read);
+    encoder.use_resource(seed, metal::MTLResourceUsage::Write);
     encoder.use_resource(buffer, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
     encoder.end_encoding();
@@ -1641,10 +1579,8 @@ pub fn call_random_normal(
 
     set_params!(encoder, (length, mean, stddev, seed, buffer));
 
-    encoder.use_resource(
-        seed,
-        metal::MTLResourceUsage::Read | metal::MTLResourceUsage::Write,
-    );
+    encoder.use_resource(seed, metal::MTLResourceUsage::Read);
+    encoder.use_resource(seed, metal::MTLResourceUsage::Write);
     encoder.use_resource(buffer, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
     encoder.end_encoding();

candle-metal-kernels/src/random.metal

@@ -123,20 +123,16 @@ template<typename T> METAL_FUNC void rand_uniform(
         return;
     }
 
-    // Evenly sized vectors need an offset when writing the mirror element.
-    uint off = 1 - size % 2;
     float diff = abs(min - max);
-    uint s = atomic_load_explicit(seed, memory_order_relaxed);
-    HybridTaus rng = HybridTaus::init({ulong(s), tid, 1, 1});
+    HybridTaus rng = HybridTaus::init({ulong(seed), tid, 1, 1});
     out[tid] = static_cast<T>(rng.rand() * diff + min);
     if (tid == 0) {
         atomic_store_explicit(seed, uint(rng.rand() * UNIF01_NORM32), memory_order_relaxed);
-        // Return early if tid == 0 && off == 0, otherwise we will write to out[size].
-        if (off == 0)
-            return;
+        // Return early if tid == 0, otherwise we will write to out[size].
+        return;
     }
     // Use symmetry to fill the other half of the array.
-    out[size - off - tid] = static_cast<T>(rng.rand() * diff + min);
+    out[size - tid] = static_cast<T>(rng.rand() * diff + min);
 }
 
 // Create Gaussian normal distribution using Box-Muller transform:
@@ -152,10 +148,7 @@ template<typename T> METAL_FUNC void normal(
     if (tid >= size) {
         return;
     }
-    // Evenly sized vectors need an offset when writing the mirror element.
-    uint off = 1 - size % 2;
-    uint s = atomic_load_explicit(seed, memory_order_relaxed);
-    HybridTaus rng = HybridTaus::init({ulong(s), tid, 1, 1});
+    HybridTaus rng = HybridTaus::init({ulong(seed), tid, 1, 1});
     float u1 = rng.rand();
     float u2 = rng.rand();
 
@@ -169,12 +162,11 @@ template<typename T> METAL_FUNC void normal(
 
     if (tid == 0) {
         atomic_store_explicit(seed, uint(rng.rand() * UNIF01_NORM32), memory_order_relaxed);
-        // Return early if tid == 0 && off == 0, otherwise we will write to out[size].
-        if (off == 0)
-            return;
+        // Return early if tid == 0, otherwise we will write to out[size].
+        return;
     }
     // Use symmetry to fill the other half of the array.
-    out[size - off - tid] = static_cast<T>(z1);
+    out[size - tid] = static_cast<T>(z1);
 }
 
 #define UNIFORM_OP(NAME, T)                             \

candle-metal-kernels/src/tests.rs

@@ -292,7 +292,7 @@ fn binary_ops_bf16() {
     binary_op!(max, |x: bf16, y| x.max(y));
 }
 
-fn run_cast<T: Clone, U: Clone>(v: &[T], name: &'static str) -> Vec<U> {
+fn cast<T: Clone, U: Clone>(v: &[T], name: &'static str) -> Vec<U> {
     let device = device();
     let kernels = Kernels::new();
     let command_queue = device.new_command_queue();
@@ -319,189 +319,107 @@ fn run_cast<T: Clone, U: Clone>(v: &[T], name: &'static str) -> Vec<U> {
 }
 
 #[test]
-fn cast_f32() {
-    let v_f64 = vec![1.0f64, 2.0, 3.0];
-    let v_f32: Vec<f32> = v_f64.iter().map(|&v| v as f32).collect();
-    let v_f16: Vec<f16> = v_f64.iter().map(|&v| f16::from_f32(v as f32)).collect();
-    let v_bf16: Vec<bf16> = v_f64.iter().map(|&v| bf16::from_f32(v as f32)).collect();
-    let v_u32: Vec<u32> = v_f64.iter().map(|&v| v as u32).collect();
-    let v_u8: Vec<u8> = v_f64.iter().map(|&v| v as u8).collect();
-    let v_i64: Vec<i64> = v_f64.iter().map(|&v| v as i64).collect();
+fn cast_u32_f32() {
+    let v = vec![1u32, 2, 3];
+    let results = cast(&v, "cast_u32_f32");
+    let expected: Vec<_> = v.iter().map(|&v| v as f32).collect();
+    assert_eq!(approx(results, 4), vec![1.0f32, 2.0, 3.0]);
+    assert_eq!(approx(expected, 4), vec![1.0f32, 2.0, 3.0]);
 
-    // f32 -> f16
-    let results: Vec<half::f16> = run_cast(&v_f32, "cast_f32_f16");
-    assert_eq!(results, v_f16);
+    let v = vec![1.0f32, 2.0, 3.0];
+    let input: Vec<f16> = v.iter().map(|v| f16::from_f32(*v)).collect();
+    let results: Vec<f32> = cast(&input, "cast_f16_f32");
+    assert_eq!(results, vec![1.0f32, 2.0, 3.0]);
 
-    // f32 -> bf16
-    let results: Vec<bf16> = run_cast(&v_f32, "cast_f32_bf16");
-    assert_eq!(results, v_bf16);
-
-    // f32 -> u32
-    let results: Vec<u32> = run_cast(&v_f32, "cast_f32_u32");
-    assert_eq!(results, v_u32);
-
-    // f32 -> u8
-    let results: Vec<u8> = run_cast(&v_f32, "cast_f32_u8");
-    assert_eq!(results, v_u8);
-
-    // f32 -> i64
-    let results: Vec<i64> = run_cast(&v_f32, "cast_f32_i64");
-    assert_eq!(results, v_i64);
+    let v = vec![1.0f32; 10_000];
+    let input: Vec<f16> = v.iter().map(|v| f16::from_f32(*v)).collect();
+    let results: Vec<f32> = cast(&input, "cast_f16_f32");
+    assert_eq!(results.len(), 10_000);
+    assert_eq!(&results[..10], vec![1.0f32; 10]);
+    assert_eq!(results, vec![1.0f32; 10_000]);
 }
 
 #[test]
-fn cast_f16() {
-    let v_f64 = vec![1.0f64, 2.0, 3.0];
-    let v_f32: Vec<f32> = v_f64.iter().map(|&v| v as f32).collect();
-    let v_f16: Vec<f16> = v_f64.iter().map(|&v| f16::from_f32(v as f32)).collect();
-    let v_bf16: Vec<bf16> = v_f64.iter().map(|&v| bf16::from_f32(v as f32)).collect();
-    let v_u32: Vec<u32> = v_f64.iter().map(|&v| v as u32).collect();
-    let v_u8: Vec<u8> = v_f64.iter().map(|&v| v as u8).collect();
-    let v_i64: Vec<i64> = v_f64.iter().map(|&v| v as i64).collect();
+fn it_cast_bf16_u32() {
+    let input: Vec<bf16> = (1..=3).map(|v| bf16::from_f32(v as f32)).collect();
 
-    // f16 -> f32
-    let results: Vec<f32> = run_cast(&v_f16, "cast_f16_f32");
-    assert_eq!(results, v_f32);
+    let output: Vec<u32> = cast(&input, "cast_bf16_u32");
+    let expected: Vec<u32> = (1..=3).map(|v| v as u32).collect();
 
-    // f16 -> bf16
-    let results: Vec<bf16> = run_cast(&v_f16, "cast_f16_bf16");
-    assert_eq!(results, v_bf16);
-
-    // f16 -> u32
-    let results: Vec<u32> = run_cast(&v_f16, "cast_f16_u32");
-    assert_eq!(results, v_u32);
-
-    // f16 -> u8
-    let results: Vec<u8> = run_cast(&v_f16, "cast_f16_u8");
-    assert_eq!(results, v_u8);
-
-    // f16 -> i64
-    let results: Vec<i64> = run_cast(&v_f16, "cast_f16_i64");
-    assert_eq!(results, v_i64);
+    assert_eq!(output, expected);
 }
 
 #[test]
-fn cast_bf16() {
-    let v_f64 = vec![1.0f64, 2.0, 3.0];
-    let v_f32: Vec<f32> = v_f64.iter().map(|&v| v as f32).collect();
-    let v_f16: Vec<f16> = v_f64.iter().map(|&v| f16::from_f32(v as f32)).collect();
-    let v_bf16: Vec<bf16> = v_f64.iter().map(|&v| bf16::from_f32(v as f32)).collect();
-    let v_u32: Vec<u32> = v_f64.iter().map(|&v| v as u32).collect();
-    let v_u8: Vec<u8> = v_f64.iter().map(|&v| v as u8).collect();
-    let v_i64: Vec<i64> = v_f64.iter().map(|&v| v as i64).collect();
+fn it_cast_bf16_f32() {
+    let input: Vec<bf16> = (1..=3).map(|v| bf16::from_f32(v as f32)).collect();
 
-    // bf16 -> f32
-    let results: Vec<f32> = run_cast(&v_bf16, "cast_bf16_f32");
-    assert_eq!(results, v_f32);
+    let output: Vec<f32> = cast(&input, "cast_bf16_f32");
+    let expected: Vec<f32> = (1..=3).map(|v| v as f32).collect();
 
-    // bf16 -> f16
-    let results: Vec<f16> = run_cast(&v_bf16, "cast_bf16_f16");
-    assert_eq!(results, v_f16);
-
-    // bf16 -> u32
-    let results: Vec<u32> = run_cast(&v_bf16, "cast_bf16_u32");
-    assert_eq!(results, v_u32);
-
-    // bf16 -> u8
-    let results: Vec<u8> = run_cast(&v_bf16, "cast_bf16_u8");
-    assert_eq!(results, v_u8);
-
-    // bf16 -> i64
-    let results: Vec<i64> = run_cast(&v_bf16, "cast_bf16_i64");
-    assert_eq!(results, v_i64);
+    assert_eq!(output, expected);
 }
 
 #[test]
-fn cast_u32() {
-    let v_f64 = vec![1.0f64, 2.0, 3.0];
-    let v_f32: Vec<f32> = v_f64.iter().map(|&v| v as f32).collect();
-    let v_f16: Vec<f16> = v_f64.iter().map(|&v| f16::from_f32(v as f32)).collect();
-    let v_bf16: Vec<bf16> = v_f64.iter().map(|&v| bf16::from_f32(v as f32)).collect();
-    let v_u32: Vec<u32> = v_f64.iter().map(|&v| v as u32).collect();
-    let v_u8: Vec<u8> = v_f64.iter().map(|&v| v as u8).collect();
-    let v_i64: Vec<i64> = v_f64.iter().map(|&v| v as i64).collect();
+fn it_cast_u8_bf16() {
+    let input: Vec<u8> = (1..=3).map(|v| v as u8).collect();
 
-    // u32 -> f32
-    let results: Vec<f32> = run_cast(&v_u32, "cast_u32_f32");
-    assert_eq!(results, v_f32);
+    let output: Vec<bf16> = cast(&input, "cast_u8_bf16");
+    let expected: Vec<bf16> = input
+        .iter()
+        .map(|v| bf16::from_f32(*v as f32))
+        .collect::<Vec<_>>();
 
-    // u32 -> f16
-    let results: Vec<f16> = run_cast(&v_u32, "cast_u32_f16");
-    assert_eq!(results, v_f16);
-
-    // u32 -> bf16
-    let results: Vec<bf16> = run_cast(&v_u32, "cast_u32_bf16");
-    assert_eq!(results, v_bf16);
-
-    // u32 -> u8
-    let results: Vec<u8> = run_cast(&v_u32, "cast_u32_u8");
-    assert_eq!(results, v_u8);
-
-    // u32 -> i64
-    let results: Vec<i64> = run_cast(&v_u32, "cast_u32_i64");
-    assert_eq!(results, v_i64);
+    assert_eq!(output, expected);
 }
 
 #[test]
-fn cast_u8() {
-    let v_f64 = vec![1.0f64, 2.0, 3.0];
-    let v_f32: Vec<f32> = v_f64.iter().map(|&v| v as f32).collect();
-    let v_f16: Vec<f16> = v_f64.iter().map(|&v| f16::from_f32(v as f32)).collect();
-    let v_bf16: Vec<bf16> = v_f64.iter().map(|&v| bf16::from_f32(v as f32)).collect();
-    let v_u32: Vec<u32> = v_f64.iter().map(|&v| v as u32).collect();
-    let v_u8: Vec<u8> = v_f64.iter().map(|&v| v as u8).collect();
-    let v_i64: Vec<i64> = v_f64.iter().map(|&v| v as i64).collect();
+fn it_cast_u32_bf16() {
+    let input: Vec<u32> = (1..=3).map(|v| v as u32).collect();
 
-    // u8 -> f32
-    let results: Vec<f32> = run_cast(&v_u8, "cast_u8_f32");
-    assert_eq!(results, v_f32);
+    let output: Vec<bf16> = cast(&input, "cast_u32_bf16");
+    let expected: Vec<bf16> = input.iter().map(|v| bf16::from_f32(*v as f32)).collect();
 
-    // u8 -> f16
-    let results: Vec<f16> = run_cast(&v_u8, "cast_u8_f16");
-    assert_eq!(results, v_f16);
-
-    // u8 -> bf16
-    let results: Vec<bf16> = run_cast(&v_u8, "cast_u8_bf16");
-    assert_eq!(results, v_bf16);
-
-    // u8 -> u32
-    let results: Vec<u32> = run_cast(&v_u8, "cast_u8_u32");
-    assert_eq!(results, v_u32);
-
-    // u8 -> i64
-    let results: Vec<i64> = run_cast(&v_u8, "cast_u8_i64");
-    assert_eq!(results, v_i64);
+    assert_eq!(output, expected);
 }
 
 #[test]
-fn cast_i64() {
-    let v_f64 = vec![1.0f64, 2.0, 3.0];
-    let v_f32: Vec<f32> = v_f64.iter().map(|&v| v as f32).collect();
-    let v_f16: Vec<f16> = v_f64.iter().map(|&v| f16::from_f32(v as f32)).collect();
-    let v_bf16: Vec<bf16> = v_f64.iter().map(|&v| bf16::from_f32(v as f32)).collect();
-    let v_u32: Vec<u32> = v_f64.iter().map(|&v| v as u32).collect();
-    let v_u8: Vec<u8> = v_f64.iter().map(|&v| v as u8).collect();
-    let v_i64: Vec<i64> = v_f64.iter().map(|&v| v as i64).collect();
+fn it_cast_f32_bf16() {
+    let input: Vec<f32> = (1..=3).map(|v| v as f32).collect();
 
-    // i64 -> f32
-    let results: Vec<f32> = run_cast(&v_i64, "cast_i64_f32");
-    assert_eq!(results, v_f32);
+    let output: Vec<bf16> = cast(&input, "cast_f32_bf16");
+    let expected: Vec<bf16> = input.iter().map(|v| bf16::from_f32(*v as f32)).collect();
 
-    // i64 -> f16
-    let results: Vec<f16> = run_cast(&v_i64, "cast_i64_f16");
-    assert_eq!(results, v_f16);
+    assert_eq!(output, expected);
+}
 
-    // i64 -> bf16
-    let results: Vec<bf16> = run_cast(&v_i64, "cast_i64_bf16");
-    assert_eq!(results, v_bf16);
+#[test]
+fn it_cast_bf16_u8() {
+    let input: Vec<bf16> = (1..=3).map(|v| bf16::from_f32(v as f32)).collect();
 
-    // i64 -> u32
-    let results: Vec<u32> = run_cast(&v_i64, "cast_i64_u32");
-    assert_eq!(results, v_u32);
+    let output: Vec<u8> = cast(&input, "cast_bf16_u8");
+    let expected: Vec<u8> = input.iter().map(|v| v.to_f32() as u8).collect();
 
-    // i64 -> u8
-    let results: Vec<u8> = run_cast(&v_i64, "cast_i64_u8");
-    assert_eq!(results, v_u8);
+    assert_eq!(output, expected);
+}
+
+#[test]
+fn it_cast_bf16_f16() {
+    let input: Vec<bf16> = (1..=3).map(|v| bf16::from_f32(v as f32)).collect();
+
+    let output: Vec<f16> = cast(&input, "cast_bf16_f16");
+    let expected: Vec<f16> = input.iter().map(|v| f16::from_f32(v.to_f32())).collect();
+
+    assert_eq!(output, expected);
+}
+
+#[test]
+fn it_cast_f16_bf16() {
+    let input: Vec<f16> = (1..=3).map(|v| f16::from_f32(v as f32)).collect();
+
+    let output: Vec<bf16> = cast(&input, "cast_f16_bf16");
+    let expected: Vec<bf16> = input.iter().map(|v| bf16::from_f32(v.to_f32())).collect();
+
+    assert_eq!(output, expected);
 }
 
 fn run_affine<T: Clone>(v: &[T], mul: f64, add: f64) -> Vec<T> {
@@ -1148,107 +1066,3 @@ fn random() {
     validate_random!(f16);
     validate_random!(bf16);
 }
-
-fn run_scatter_add<T: Clone, I: Clone + std::fmt::Debug>(
-    input: &[T],
-    ids: &[I],
-    shape: &[usize],
-    dim: usize,
-    name: &'static str,
-) -> Vec<T> {
-    let device = device();
-    let kernels = Kernels::new();
-    let command_queue = device.new_command_queue();
-    let command_buffer = command_queue.new_command_buffer();
-    let options = MTLResourceOptions::StorageModeManaged;
-    let input_buffer = new_buffer(&device, input);
-    let ids_buffer = new_buffer(&device, ids);
-    let output = device.new_buffer(std::mem::size_of_val(input) as u64, options);
-    call_scatter_add(
-        &device,
-        command_buffer,
-        &kernels,
-        name,
-        shape,
-        shape,
-        dim,
-        &input_buffer,
-        0,
-        &ids_buffer,
-        0,
-        &output,
-    )
-    .unwrap();
-    command_buffer.commit();
-    command_buffer.wait_until_completed();
-    read_to_vec(&output, input.len())
-}
-
-#[test]
-fn scatter_add() {
-    let ids_u8 = [0u8, 0, 1, 0, 2, 2, 3, 3];
-    let ids_u32 = [0u32, 0, 1, 0, 2, 2, 3, 3];
-    let ids_i64 = [0i64, 0, 1, 0, 2, 2, 3, 3];
-
-    let input_f32 = [5.0f32, 1.0, 7.0, 2.0, 3.0, 2.0, 1.0, 3.0];
-    let input_f16 = input_f32
-        .iter()
-        .map(|v| f16::from_f32(*v))
-        .collect::<Vec<_>>();
-    let input_bf16 = input_f32
-        .iter()
-        .map(|v| bf16::from_f32(*v))
-        .collect::<Vec<_>>();
-
-    let output_dim1_f32 = vec![8.0, 7.0, 5.0, 4.0, 0.0, 0.0, 0.0, 0.0];
-    let output_dim1_f16 = output_dim1_f32
-        .iter()
-        .map(|v| f16::from_f32(*v))
-        .collect::<Vec<_>>();
-    let output_dim1_bf16 = output_dim1_f32
-        .iter()
-        .map(|v| bf16::from_f32(*v))
-        .collect::<Vec<_>>();
-
-    let output_dim2_f32 = vec![5.0, 3.0, 7.0, 0.0, 3.0, 2.0, 1.0, 3.0];
-    let output_dim2_f16 = output_dim2_f32
-        .iter()
-        .map(|v| f16::from_f32(*v))
-        .collect::<Vec<_>>();
-    let output_dim2_bf16 = output_dim2_f32
-        .iter()
-        .map(|v| bf16::from_f32(*v))
-        .collect::<Vec<_>>();
-
-    for (shape, output_f32, output_f16, output_bf16) in [
-        (vec![8], output_dim1_f32, output_dim1_f16, output_dim1_bf16),
-        (
-            vec![4, 2],
-            output_dim2_f32,
-            output_dim2_f16,
-            output_dim2_bf16,
-        ),
-    ] {
-        for results in [
-            run_scatter_add(&input_f32, &ids_u8, &shape, 0, "sa_u8_f32"),
-            run_scatter_add(&input_f32, &ids_u32, &shape, 0, "sa_u32_f32"),
-            run_scatter_add(&input_f32, &ids_i64, &shape, 0, "sa_i64_f32"),
-        ] {
-            assert_eq!(results, output_f32);
-        }
-        for results in [
-            run_scatter_add(&input_f16, &ids_u8, &shape, 0, "sa_u8_f16"),
-            run_scatter_add(&input_f16, &ids_u32, &shape, 0, "sa_u32_f16"),
-            run_scatter_add(&input_f16, &ids_i64, &shape, 0, "sa_i64_f16"),
-        ] {
-            assert_eq!(results, output_f16);
-        }
-        for results in [
-            run_scatter_add(&input_bf16, &ids_u8, &shape, 0, "sa_u8_bf16"),
-            run_scatter_add(&input_bf16, &ids_u32, &shape, 0, "sa_u32_bf16"),
-            run_scatter_add(&input_bf16, &ids_i64, &shape, 0, "sa_i64_bf16"),
-        ] {
-            assert_eq!(results, output_bf16);
-        }
-    }
-}

candle-metal-kernels/src/unary.metal

@@ -102,30 +102,6 @@ UNARY(NAME, half, NAME##_f16, NAME##_f16_strided);
 #define BFLOAT_UNARY_OP(NAME) \
 UNARY(NAME, bfloat, NAME##_bf16, NAME##_bf16_strided);
 
-#define COPY2D(FN_NAME, TYPENAME) \
-kernel void FN_NAME( \
-    constant size_t &d1, \
-    constant size_t &d2, \
-    constant size_t &src_s, \
-    constant size_t &dst_s, \
-    device const TYPENAME *input,  \
-    device TYPENAME *output, \
-    uint tid [[ thread_position_in_grid ]] \
-) { \
-    if (tid >= d1 * d2) { \
-        return; \
-    } \
-    size_t idx1 = tid / d2; \
-    size_t idx2 = tid - idx1 * d2; \
-    size_t src_idx = idx1 * src_s + idx2; \
-    size_t dst_idx = idx1 * dst_s + idx2; \
-    output[dst_idx] = input[src_idx]; \
-}
-
-COPY2D(copy2d_f32, float)
-COPY2D(copy2d_f16, half)
-COPY2D(copy2d_u8, uint8_t)
-COPY2D(copy2d_u32, uint32_t)
 
 UNARY_OP(cos)
 UNARY_OP(sin)
@@ -152,7 +128,6 @@ UNARY(id, uint32_t, copy_u32, copy_u32_strided)
 
 #if __METAL_VERSION__ >= 220
 UNARY(id, int64_t, copy_i64, copy_i64_strided)
-COPY2D(copy2d_i64, int64_t)
 #endif
 
 #if defined(__HAVE_BFLOAT__)
@@ -176,6 +151,4 @@ BFLOAT_UNARY_OP(recip)
 BFLOAT_UNARY_OP(relu)
 
 UNARY(id, bfloat, copy_bf16, copy_bf16_strided)
-
-COPY2D(copy2d_bf64, bfloat)
 #endif

candle-nn/examples/cpu_benchmarks.rs

@@ -238,23 +238,6 @@ impl Benchmark for QMatMul {
     const ITERS: usize = 100;
 }
 
-struct Cat;
-impl Benchmark for Cat {
-    type PreProcessData = (Tensor, Tensor);
-    type RunResult = Tensor;
-    fn preprocess() -> Result<Self::PreProcessData> {
-        let lhs = Tensor::randn(0f32, 1., (1, 32, 2000, 128), &Device::Cpu)?;
-        let rhs = Tensor::randn(0f32, 1., (1, 32, 1, 128), &Device::Cpu)?;
-        Ok((lhs, rhs))
-    }
-
-    fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
-        Tensor::cat(&[&d.0, &d.1], 2)
-    }
-
-    const ITERS: usize = 1000;
-}
-
 struct Softmax;
 impl Benchmark for Softmax {
     type PreProcessData = Tensor;
@@ -312,7 +295,6 @@ enum Task {
     Qmatmul,
     Softmax,
     SoftmaxLastDim,
-    Cat,
 }
 
 #[derive(Parser, Debug)]
@@ -337,7 +319,6 @@ fn main() -> Result<()> {
         Task::Softmax => run::<Softmax>(args.iters)?,
         Task::SoftmaxLastDim => run::<SoftmaxLastDim>(args.iters)?,
         Task::Qmatmul => run::<QMatMul>(args.iters)?,
-        Task::Cat => run::<Cat>(args.iters)?,
     }
     Ok(())
 }

candle-nn/src/activation.rs

@@ -5,7 +5,6 @@ use serde::Deserialize;
 #[serde(rename_all = "lowercase")]
 pub enum Activation {
     #[default]
-    #[serde(alias = "gelu")]
     Gelu,
     #[serde(alias = "gelu_new")]
     NewGelu,
@@ -20,8 +19,6 @@ pub enum Activation {
     HardSwish,
     Elu(f64),
     LeakyRelu(f64),
-    #[serde(alias = "gelu_pytorch_tanh")]
-    GeluPytorchTanh,
 }
 
 impl super::Module for Activation {
@@ -41,7 +38,6 @@ impl super::Module for Activation {
             Self::HardSwish => xs * crate::ops::hard_sigmoid(xs)?,
             &Self::Elu(alpha) => xs.elu(alpha),
             &Self::LeakyRelu(negative_slope) => crate::ops::leaky_relu(xs, negative_slope),
-            Self::GeluPytorchTanh => xs.gelu(),
         }
     }
 }

candle-nn/src/conv.rs

@@ -76,7 +76,7 @@ pub struct ConvTranspose1dConfig {
     pub output_padding: usize,
     pub stride: usize,
     pub dilation: usize,
-    pub groups: usize,
+    // TODO: support groups.
 }
 
 impl Default for ConvTranspose1dConfig {
@@ -86,7 +86,6 @@ impl Default for ConvTranspose1dConfig {
             output_padding: 0,
             stride: 1,
             dilation: 1,
-            groups: 1,
         }
     }
 }
@@ -110,14 +109,6 @@ impl ConvTranspose1d {
     pub fn config(&self) -> &ConvTranspose1dConfig {
         &self.config
     }
-
-    pub fn weight(&self) -> &Tensor {
-        &self.weight
-    }
-
-    pub fn bias(&self) -> Option<&Tensor> {
-        self.bias.as_ref()
-    }
 }
 
 impl crate::Module for ConvTranspose1d {
@@ -128,13 +119,12 @@ impl crate::Module for ConvTranspose1d {
             self.config.output_padding,
             self.config.stride,
             self.config.dilation,
-            self.config.groups,
         )?;
         match &self.bias {
             None => Ok(x),
             Some(bias) => {
                 let b = bias.dims1()?;
-                let bias = bias.reshape((1, b, 1))?;
+                let bias = bias.reshape((1, b, 1, 1))?;
                 Ok(x.broadcast_add(&bias)?)
             }
         }
@@ -268,14 +258,6 @@ impl ConvTranspose2d {
     pub fn config(&self) -> &ConvTranspose2dConfig {
         &self.config
     }
-
-    pub fn weight(&self) -> &Tensor {
-        &self.weight
-    }
-
-    pub fn bias(&self) -> Option<&Tensor> {
-        self.bias.as_ref()
-    }
 }
 
 impl crate::Module for ConvTranspose2d {
@@ -348,11 +330,7 @@ pub fn conv_transpose1d(
         lo: -bound,
         up: bound,
     };
-    let ws = vb.get_with_hints(
-        (in_channels, out_channels / cfg.groups, kernel_size),
-        "weight",
-        init,
-    )?;
+    let ws = vb.get_with_hints((in_channels, out_channels, kernel_size), "weight", init)?;
     let bs = vb.get_with_hints(out_channels, "bias", init)?;
     Ok(ConvTranspose1d::new(ws, Some(bs), cfg))
 }
@@ -369,11 +347,7 @@ pub fn conv_transpose1d_no_bias(
         lo: -bound,
         up: bound,
     };
-    let ws = vb.get_with_hints(
-        (in_channels, out_channels / cfg.groups, kernel_size),
-        "weight",
-        init,
-    )?;
+    let ws = vb.get_with_hints((in_channels, out_channels, kernel_size), "weight", init)?;
     Ok(ConvTranspose1d::new(ws, None, cfg))
 }
 

candle-nn/src/lib.rs

@@ -28,7 +28,7 @@ pub use func::{func, func_t, Func, FuncT};
 pub use group_norm::{group_norm, GroupNorm};
 pub use init::Init;
 pub use layer_norm::{layer_norm, rms_norm, LayerNorm, LayerNormConfig, RmsNorm};
-pub use linear::{linear, linear_b, linear_no_bias, Linear};
+pub use linear::{linear, linear_no_bias, Linear};
 pub use ops::Dropout;
 pub use optim::{AdamW, Optimizer, ParamsAdamW, SGD};
 pub use rnn::{gru, lstm, GRUConfig, LSTMConfig, GRU, LSTM, RNN};

candle-nn/src/linear.rs

@@ -57,34 +57,21 @@ impl super::Module for Linear {
 /// Create or initialize a new linear layer.
 ///
 /// This uses some default names for weights and biases, namely `"weight"` and `"bias"`.
-pub fn linear(in_dim: usize, out_dim: usize, vb: crate::VarBuilder) -> Result<Linear> {
+pub fn linear(in_dim: usize, out_dim: usize, vs: crate::VarBuilder) -> Result<Linear> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
-    let ws = vb.get_with_hints((out_dim, in_dim), "weight", init_ws)?;
+    let ws = vs.get_with_hints((out_dim, in_dim), "weight", init_ws)?;
     let bound = 1. / (in_dim as f64).sqrt();
     let init_bs = crate::Init::Uniform {
         lo: -bound,
         up: bound,
     };
-    let bs = vb.get_with_hints(out_dim, "bias", init_bs)?;
+    let bs = vs.get_with_hints(out_dim, "bias", init_bs)?;
     Ok(Linear::new(ws, Some(bs)))
 }
 
 /// Create or initialize a new linear layer without biases.
-pub fn linear_no_bias(in_dim: usize, out_dim: usize, vb: crate::VarBuilder) -> Result<Linear> {
+pub fn linear_no_bias(in_dim: usize, out_dim: usize, vs: crate::VarBuilder) -> Result<Linear> {
     let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
-    let ws = vb.get_with_hints((out_dim, in_dim), "weight", init_ws)?;
+    let ws = vs.get_with_hints((out_dim, in_dim), "weight", init_ws)?;
     Ok(Linear::new(ws, None))
 }
-
-pub fn linear_b(
-    in_dim: usize,
-    out_dim: usize,
-    bias: bool,
-    vb: crate::VarBuilder,
-) -> Result<Linear> {
-    if bias {
-        linear(in_dim, out_dim, vb)
-    } else {
-        linear_no_bias(in_dim, out_dim, vb)
-    }
-}

candle-nn/src/ops.rs

@@ -74,7 +74,7 @@ pub fn dropout(xs: &Tensor, drop_p: f32) -> Result<Tensor> {
     xs * mask
 }
 
-#[derive(Clone, Debug)]
+#[derive(Debug)]
 pub struct Dropout {
     drop_p: f32,
 }
@@ -238,8 +238,7 @@ impl candle::CustomOp1 for SoftmaxLastDim {
             &output,
         )
         .unwrap();
-        let newstorage =
-            candle::MetalStorage::new(output, device.clone(), elem_count, storage.dtype());
+        let newstorage = candle::MetalStorage::new(output, device.clone(), storage.dtype());
         Ok((newstorage, layout.shape().clone()))
     }
 }

candle-nn/src/rnn.rs

@@ -197,7 +197,7 @@ impl RNN for LSTM {
 
     fn states_to_tensor(&self, states: &[Self::State]) -> Result<Tensor> {
         let states = states.iter().map(|s| s.h.clone()).collect::<Vec<_>>();
-        Tensor::stack(&states, 1)
+        Tensor::cat(&states, 1)
     }
 }
 

candle-nn/src/var_map.rs

@@ -70,7 +70,7 @@ impl VarMap {
     ///
     /// If an error is returned, some of the variables might have already been set to their new
     /// values.
-    pub fn set<I: Iterator<Item = (K, V)>, K: AsRef<str>, V: AsRef<Tensor>>(
+    pub fn set<I: Iterator<Item = (K, V)>, K: AsRef<String>, V: AsRef<Tensor>>(
         &mut self,
         iter: I,
     ) -> Result<()> {

candle-nn/tests/optim.rs

@@ -7,7 +7,7 @@ extern crate accelerate_src;
 use candle::test_utils::{to_vec0_round, to_vec2_round};
 
 use anyhow::Result;
-use candle::{DType, Device, Tensor, Var};
+use candle::{Device, Tensor, Var};
 use candle_nn::{AdamW, Linear, Module, Optimizer, ParamsAdamW, SGD};
 
 #[test]
@@ -121,40 +121,3 @@ fn adamw_linear_regression() -> Result<()> {
     assert_eq!(to_vec0_round(b.as_tensor(), 4)?, 0.7873);
     Ok(())
 }
-
-#[test]
-fn adamw_linear_regression_varmap() -> Result<()> {
-    use candle_nn::Init::Const;
-
-    // Similar as the previous test but using a VarMap.
-    let w_gen = Tensor::new(&[[3f32, 1.]], &Device::Cpu)?;
-    let b_gen = Tensor::new(-2f32, &Device::Cpu)?;
-    let gen = Linear::new(w_gen, Some(b_gen));
-    let sample_xs = Tensor::new(&[[2f32, 1.], [7., 4.], [-4., 12.], [5., 8.]], &Device::Cpu)?;
-    let sample_ys = gen.forward(&sample_xs)?;
-
-    let mut var_map = candle_nn::VarMap::new();
-
-    let w = var_map.get((1, 2), "w", Const(0.), DType::F32, &Device::Cpu)?;
-    let b = var_map.get((), "b", Const(0.), DType::F32, &Device::Cpu)?;
-    let params = ParamsAdamW {
-        lr: 0.1,
-        ..Default::default()
-    };
-    let mut opt = AdamW::new(var_map.all_vars(), params)?;
-    let lin = Linear::new(w, Some(b));
-    for _step in 0..100 {
-        let ys = lin.forward(&sample_xs)?;
-        let loss = ys.sub(&sample_ys)?.sqr()?.sum_all()?;
-        opt.backward_step(&loss)?;
-    }
-    assert_eq!(to_vec2_round(lin.weight(), 4)?, &[[2.7257, 0.7097]]);
-    assert_eq!(to_vec0_round(lin.bias().unwrap(), 4)?, 0.7873);
-
-    var_map.set([("w", Tensor::zeros((1, 2), DType::F32, &Device::Cpu)?)].into_iter())?;
-    var_map.set([("b", Tensor::ones((), DType::F32, &Device::Cpu)?)].into_iter())?;
-
-    assert_eq!(to_vec2_round(lin.weight(), 4)?, &[[0., 0.]]);
-    assert_eq!(to_vec0_round(lin.bias().unwrap(), 4)?, 1.);
-    Ok(())
-}

candle-onnx/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-onnx"
-version = "0.4.2"
+version = "0.4.0"
 edition = "2021"
 
 description = "ONNX support for Candle"
@@ -10,8 +10,8 @@ categories = ["science"]
 license = "MIT OR Apache-2.0"
 
 [dependencies]
-candle = { path = "../candle-core", package = "candle-core", version = "0.4.2" }
-candle-nn = { path = "../candle-nn", version = "0.4.2" }
+candle = { path = "../candle-core", package = "candle-core", version = "0.4.0" }
+candle-nn = { path = "../candle-nn", version = "0.4.0" }
 prost = "0.12.1"
 
 [build-dependencies]