More happy tests.

- Add a device param, wherever needed.
- Create new QMetal storage thing that implements QuantizedType.
- Update everywhere needed.

Fix Python.

Fixing examples.

Fix: fmt + clippy + stub.

Moving everything around.

Only missing the actual implems.

Fixing everything + adding dequantized kernels.

More work.

Fixing matmul.

Fmt + Clippy

Some clippy fixes.

Working state.

Q2K Metal -> Bugged (also present in GGML).
Q4K CPU -> Bugged (present previously, new test catch it).
Q5K CPU -> Bugged (present previously).
Q8_1 Both -> Never really implemented it seems
Q8K metal -> Never implemented in metal

Fixing Q2K bug (present in ggml).

.github/dependabot.yml

@@ -0,0 +1,7 @@
+version: 2
+updates:
+  - package-ecosystem: "cargo"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+    open-pull-requests-limit: 5

.github/workflows/ci_cuda.yaml

@@ -8,6 +8,8 @@ jobs:
   start-runner:
     name: Start self-hosted EC2 runner
     runs-on: ubuntu-latest
+    # Don't run on forks, they won't have access to secrets anyway.
+    if: ${{ github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }}
     env:
       AWS_REGION: us-east-1
       EC2_AMI_ID: ami-03cfed9ea28f4b002
@@ -70,7 +72,7 @@ jobs:
     runs-on: ubuntu-latest
     env:
       AWS_REGION: us-east-1
-    if: ${{ always() }} # required to stop the runner even if the error happened in the previous jobs
+    if: ${{ (success() || failure()) && github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }} # required to stop the runner even if the error happened in the previous jobs
     steps:
       - name: Configure AWS credentials
         uses: aws-actions/configure-aws-credentials@v1

CHANGELOG.md

@@ -63,7 +63,7 @@ This documents the main changes to the `candle` crate.
   [760](https://github.com/huggingface/candle/pull/760).
 - Add the Segment-Anything Model (SAM) as an example
   [773](https://github.com/huggingface/candle/pull/773).
-- TinyViT backbone for the segemnt anything example
+- TinyViT backbone for the segment anything example
   [787](https://github.com/huggingface/candle/pull/787).
 - Shape with holes support
   [770](https://github.com/huggingface/candle/pull/770).

Cargo.toml

@@ -19,7 +19,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.3.0"
+version = "0.3.3"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -31,9 +31,18 @@ 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" }
+candle-datasets = { path = "./candle-datasets" }
+candle-flash-attn = { path = "./candle-flash-attn" }
+candle-kernels = { path = "./candle-kernels" }
+candle-metal-kernels = { path = "./candle-metal-kernels" }
+candle-nn = { path = "./candle-nn" }
+candle-onnx = { path = "./candle-onnx" }
+candle-transformers = { path = "./candle-transformers" }
 clap = { version = "4.2.4", features = ["derive"] }
-cudarc = { version = "0.9.14", features = ["f16"] }
-gemm = { version = "0.16.6", features = ["wasm-simd128-enable"] }
+criterion = { version = "0.5.1", default-features=false }
+cudarc = { version = "0.10.0", features = ["f16"] }
+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"] }
 image = { version = "0.24.7", default-features = false, features = ["jpeg", "png"] }
@@ -41,7 +50,7 @@ imageproc = { version = "0.23.0", default-features = false }
 intel-mkl-src = { version = "0.8.1", features = ["mkl-static-lp64-iomp"] }
 libc = { version = "0.2.147" }
 log = "0.4"
-memmap2 = { version = "0.7.1", features = ["stable_deref_trait"] }
+memmap2 = { version = "0.9.3", features = ["stable_deref_trait"] }
 num_cpus = "1.15.0"
 num-traits = "0.2.15"
 parquet = { version = "45.0.0" }
@@ -51,17 +60,17 @@ rayon = "1.7.0"
 rusttype = { version = "0.9", default-features = false }
 safetensors = "0.3.1"
 serde = { version = "1.0.171", features = ["derive"] }
+serde_plain = "1.0.2"
 serde_json = "1.0.99"
 thiserror = "1"
-tokenizers = { version = "0.13.4", default-features = false }
+tokenizers = { version = "0.15.0", default-features = false }
 tracing = "0.1.37"
 tracing-chrome = "0.7.1"
 tracing-subscriber = "0.3.7"
 wav = "1.0.0"
 yoke = { version = "0.7.2", features = ["derive"] }
 zip = { version = "0.6.6", default-features = false }
-# metal = { git = "https://github.com/ivarflakstad/metal-rs.git", features = ["mps"] }
-metal = { path = "../metal-rs", features = ["mps"] }
+metal = { version = "0.27.0", features = ["mps"]}
 
 [profile.release-with-debug]
 inherits = "release"

README.md

@@ -54,21 +54,29 @@ These online demos run entirely in your browser:
 - [whisper](https://huggingface.co/spaces/lmz/candle-whisper): speech recognition.
 - [LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2): text generation.
 - [T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm): text generation.
-- [Phi-v1.5](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm): text generation.
+- [Phi-1.5, and Phi-2](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm): text generation.
 - [Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm): Image segmentation.
 - [BLIP](https://huggingface.co/spaces/radames/Candle-BLIP-Image-Captioning): image captioning.
 
 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.
+- [LLaMA and LLaMA-v2](./candle-examples/examples/llama/): general LLM, includes
+  the SOLAR-10.7B variant.
 - [Falcon](./candle-examples/examples/falcon/): general LLM.
-- [Phi-v1 and Phi-v1.5](./candle-examples/examples/phi/): a 1.3b general LLM with performance on par with LLaMA-v2 7b.
+- [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.
+- [Minimal Mamba](./candle-examples/examples/mamba-minimal/): a minimal
+  implementation of the Mamba state space model.
 - [Mistral7b-v0.1](./candle-examples/examples/mistral/): a 7b general LLM with
-  performance larger than all publicly available 13b models as of 2023-09-28.
+  better performance than all publicly available 13b models as of 2023-09-28.
+- [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/): LLM specialized to code generation.
 - [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
+  (English/Chinese) general LLMs with 6b and 34b parameters.
 - [Quantized LLaMA](./candle-examples/examples/quantized/): quantized version of
   the LLaMA model using the same quantization techniques as
   [llama.cpp](https://github.com/ggerganov/llama.cpp).
@@ -76,7 +84,7 @@ 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/quantized/assets/aoc.gif" width="600">
   
 - [Stable Diffusion](./candle-examples/examples/stable-diffusion/): text to
-  image generative model, support for the 1.5, 2.1, and SDXL 1.0 versions.
+  image generative model, support for the 1.5, 2.1, SDXL 1.0 and Turbo versions.
 
 <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg" width="200">
 
@@ -101,6 +109,9 @@ We also provide a some command line based examples using state of the art models
 - [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
   using self-supervision (can be used for imagenet classification, depth
   evaluation, segmentation).
+- [VGG](./candle-examples/examples/vgg/),
+  [RepVGG](./candle-examples/examples/repvgg): computer vision models.
+- [BLIP](./candle-examples/examples/blip/): image to text model, can be used to
 - [BLIP](./candle-examples/examples/blip/): image to text model, can be used to
   generate captions for an image.
 - [Marian-MT](./candle-examples/examples/marian-mt/): neural machine translation
@@ -120,7 +131,7 @@ There are also some wasm examples for whisper and
 [whisper](https://huggingface.co/spaces/lmz/candle-whisper),
 [llama2](https://huggingface.co/spaces/lmz/candle-llama2),
 [T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm),
-[Phi-v1.5](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm),
+[Phi-1.5, and Phi-2](https://huggingface.co/spaces/radames/Candle-Phi-1.5-Wasm),
 [Segment Anything Model](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
 
 For LLaMA2, run the following command to retrieve the weight files and start a
@@ -137,17 +148,20 @@ And then head over to
 <!--- ANCHOR: useful_libraries --->
 
 ## Useful External Resources
-- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): a
+- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): A
   very detailed tutorial showing how to convert a PyTorch model to Candle.
-- [`optimisers`](https://github.com/KGrewal1/optimisers): a collection of optimisers
+- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): Efficient and
+  ergonomic LoRA implementation for Candle. `candle-lora` has      
+  out-of-the-box LoRA support for many models from Candle, which can be found
+  [here](https://github.com/EricLBuehler/candle-lora/tree/master/candle-lora-transformers/examples).
+- [`optimisers`](https://github.com/KGrewal1/optimisers): A collection of optimisers
   including SGD with momentum, AdaGrad, AdaDelta, AdaMax, NAdam, RAdam, and RMSprop.
-- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): a LoRA implementation
-  that conforms to the official `peft` implementation.
 - [`candle-vllm`](https://github.com/EricLBuehler/candle-vllm): Efficient platform for inference and
   serving local LLMs including an OpenAI compatible API server.
-- [`candle-ext`](https://github.com/mokeyish/candle-ext): an extension library to Candle that provides PyTorch functions not currently available in Candle.
-- [`kalosm`](https://github.com/floneum/floneum/tree/master/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-ext`](https://github.com/mokeyish/candle-ext): An extension library to Candle that provides PyTorch functions not currently available in Candle.
+- [`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.
 
 If you have an addition to this list, please submit a pull request.
 
@@ -166,16 +180,25 @@ If you have an addition to this list, please submit a pull request.
     - WASM support, run your models in a browser.
 - Included models.
     - Language Models.
-        - LLaMA v1 and v2.
+        - LLaMA v1 and v2 with variants such as SOLAR-10.7B.
         - Falcon.
         - StarCoder.
-        - Phi v1.5.
+        - Phi 1, 1.5, and 2.
+        - Minimal Mamba
         - Mistral 7b v0.1.
+        - Mixtral 8x7b v0.1.
         - StableLM-3B-4E1T.
         - Replit-code-v1.5-3B.
         - Bert.
+        - Yi-6B and Yi-34B.
+    - Quantized LLMs.
+        - Llama 7b, 13b, 70b, as well as the chat and code variants.
+        - Mistral 7b, and 7b instruct.
+        - Mixtral 8x7b.
+        - Zephyr 7b a and b (Mistral-7b based).
+        - OpenChat 3.5 (Mistral-7b based).
     - Text to text.
-        - T5 and its variants: FlanT5, MADLAD400 (translation), CoEdit (Grammar correction).
+        - T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
         - Marian MT (Machine Translation).
     - Whisper (multi-lingual support).
     - Text to image.
@@ -184,7 +207,7 @@ If you have an addition to this list, please submit a pull request.
     - Image to text.
         - BLIP.
     - Computer Vision Models.
-        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT.
+        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG.
         - yolo-v3, yolo-v8.
         - Segment-Anything Model (SAM).
 - File formats: load models from safetensors, npz, ggml, or PyTorch files.

candle-book/Cargo.toml

@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.3.0" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../candle-transformers", version = "0.3.0" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.0", optional = true }
+candle = { workspace = true }
+candle-datasets = { workspace = true }
+candle-nn = { workspace = true }
+candle-transformers = { workspace = true }
+candle-flash-attn = { workspace = true, optional = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }

candle-book/src/lib.rs

@@ -28,6 +28,7 @@ let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap()
     #[rustfmt::skip]
     #[test]
     fn book_hub_2() {
+        {
 // ANCHOR: book_hub_2
 use candle::Device;
 use hf_hub::api::sync::Api;
@@ -45,9 +46,10 @@ let weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap()
         assert_eq!(weights.len(), 206);
     }
 
-    #[rustfmt::skip]
-    #[test]
-    fn book_hub_3() {
+    // #[rustfmt::skip]
+    // #[test]
+    // fn book_hub_3() {
+    {
 // ANCHOR: book_hub_3
 use candle::{DType, Device, Tensor};
 use hf_hub::api::sync::Api;
@@ -102,6 +104,7 @@ let tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).un
         assert_eq!(view.shape(), &[768, 768]);
         assert_eq!(tp_tensor.dims(), &[192, 768]);
     }
+}
 
     #[rustfmt::skip]
     #[test]

candle-core/Cargo.toml

@@ -12,8 +12,8 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-candle-kernels = { path = "../candle-kernels", version = "0.3.0", optional = true }
-candle-metal-kernels = { path = "../candle-metal-kernels", version = "0.3.0", optional = true }
+candle-kernels = { workspace = true, optional = true }
+candle-metal-kernels = { workspace = true, optional = true }
 metal = { workspace = true, optional = true}
 cudarc = { workspace = true, optional = true }
 gemm = { workspace = true }
@@ -34,6 +34,8 @@ zip = { workspace = true }
 [dev-dependencies]
 anyhow = { workspace = true }
 clap = { workspace = true }
+criterion = { workspace = true }
+
 
 [features]
 default = []
@@ -42,3 +44,7 @@ cudnn = ["cuda", "cudarc/cudnn"]
 mkl = ["dep:libc", "dep:intel-mkl-src"]
 accelerate = ["dep:libc", "dep:accelerate-src"]
 metal = ["dep:metal", "dep:candle-metal-kernels"]
+
+[[bench]]
+name = "bench_main"
+harness = false

candle-core/benches/bench_main.rs

@@ -0,0 +1,8 @@
+mod benchmarks;
+
+use criterion::criterion_main;
+criterion_main!(
+    benchmarks::matmul::benches,
+    benchmarks::affine::benches,
+    benchmarks::where_cond::benches
+);

candle-core/benches/benchmarks/affine.rs

@@ -0,0 +1,43 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle_core::{DType, Device, Tensor};
+use criterion::{black_box, criterion_group, Criterion, Throughput};
+use std::time::Instant;
+
+fn run(a: &Tensor) {
+    a.affine(12.34, 56.78).unwrap();
+}
+
+fn run_affine_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
+    let b = 1;
+    let m = 1024;
+    let k = 1024;
+
+    let tensor = Tensor::zeros((b, m, k), dtype, &device).unwrap();
+
+    let flops = b * m * k * dtype.size_in_bytes();
+
+    let mut group = c.benchmark_group(device.bench_name(name));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |b| {
+        b.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                run(black_box(&tensor));
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let handler = BenchDeviceHandler::new().unwrap();
+    for device in handler.devices {
+        run_affine_benchmark(c, &device, DType::F32, "affine_f32");
+        run_affine_benchmark(c, &device, DType::F16, "affine_f16");
+        run_affine_benchmark(c, &device, DType::BF16, "affine_bf16");
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);

candle-core/benches/benchmarks/matmul.rs

@@ -0,0 +1,44 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle_core::{DType, Device, Tensor};
+use criterion::{black_box, criterion_group, Criterion, Throughput};
+use std::time::Instant;
+
+fn run(a: &Tensor, b: &Tensor) {
+    a.matmul(&b.t().unwrap()).unwrap();
+}
+
+fn run_bench(c: &mut Criterion, device: &Device) {
+    let b = 1;
+    let m = 1;
+    let n = 2048;
+    let k = 2048;
+
+    let dtype = DType::F32;
+    let lhs = Tensor::zeros((b, m, k), dtype, device).unwrap();
+    let rhs = Tensor::zeros((b, n, k), dtype, device).unwrap();
+
+    let flops = b * m * n * k;
+
+    let mut group = c.benchmark_group(device.bench_name("matmul"));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |b| {
+        b.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                run(black_box(&lhs), black_box(&rhs));
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let handler = BenchDeviceHandler::new().unwrap();
+    for device in handler.devices {
+        run_bench(c, &device);
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);

candle-core/benches/benchmarks/mod.rs

@@ -0,0 +1,65 @@
+pub(crate) mod affine;
+pub(crate) mod matmul;
+pub(crate) mod where_cond;
+
+use candle_core::{Device, Result};
+
+pub(crate) trait BenchDevice {
+    fn sync(&self) -> Result<()>;
+
+    fn bench_name<S: Into<String>>(&self, name: S) -> String;
+}
+
+impl BenchDevice for Device {
+    fn sync(&self) -> Result<()> {
+        match self {
+            Device::Cpu => Ok(()),
+            Device::Cuda(device) => {
+                #[cfg(feature = "cuda")]
+                return Ok(device.synchronize()?);
+                #[cfg(not(feature = "cuda"))]
+                panic!("Cuda device without cuda feature enabled: {:?}", device)
+            }
+            Device::Metal(device) => {
+                #[cfg(feature = "metal")]
+                return Ok(device.wait_until_completed()?);
+                #[cfg(not(feature = "metal"))]
+                panic!("Metal device without metal feature enabled: {:?}", device)
+            }
+        }
+    }
+
+    fn bench_name<S: Into<String>>(&self, name: S) -> String {
+        match self {
+            Device::Cpu => {
+                let cpu_type = if cfg!(feature = "accelerate") {
+                    "accelerate"
+                } else if cfg!(feature = "mkl") {
+                    "mkl"
+                } else {
+                    "cpu"
+                };
+                format!("{}_{}", cpu_type, name.into())
+            }
+            Device::Cuda(_) => format!("cuda_{}", name.into()),
+            Device::Metal(_) => format!("metal_{}", name.into()),
+        }
+    }
+}
+
+struct BenchDeviceHandler {
+    devices: Vec<Device>,
+}
+
+impl BenchDeviceHandler {
+    pub fn new() -> Result<Self> {
+        let mut devices = Vec::new();
+        if cfg!(feature = "metal") {
+            devices.push(Device::new_metal(0)?);
+        } else if cfg!(feature = "cuda") {
+            devices.push(Device::new_cuda(0)?);
+        }
+        devices.push(Device::Cpu);
+        Ok(Self { devices })
+    }
+}

candle-core/benches/benchmarks/where_cond.rs

@@ -0,0 +1,64 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle_core::{DType, Device, Tensor};
+use criterion::{black_box, criterion_group, Criterion, Throughput};
+use std::time::Instant;
+
+fn run(a: &Tensor, b: &Tensor, c: &Tensor) {
+    a.where_cond(b, c).unwrap();
+}
+
+const fn create_cond_arr<const N: usize>() -> [u8; N] {
+    let mut arr = [0u8; N];
+    let mut i = 0;
+    while i < N {
+        arr[i] = (i % 2) as u8;
+        i += 1;
+    }
+    arr
+}
+
+const B: usize = 1;
+const M: usize = 1024;
+const K: usize = 1024;
+const SIZE: usize = B * M * K;
+
+const DATA: [u8; SIZE] = create_cond_arr::<SIZE>();
+
+fn run_where_cond_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
+    let tensor = Tensor::from_slice(DATA.as_slice(), (B, M, K), &device).unwrap();
+    let on_true = Tensor::ones((B, M, K), dtype, &device).unwrap();
+    let on_false = Tensor::zeros((B, M, K), dtype, &device).unwrap();
+
+    let elements = B * M * K;
+    // E.g. 2 f32 tensors + 1 u8 tensor
+    let flops = (2 * elements * dtype.size_in_bytes()) + elements;
+
+    let mut group = c.benchmark_group(device.bench_name(name));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |b| {
+        b.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                run(
+                    black_box(&tensor),
+                    black_box(&on_true),
+                    black_box(&on_false),
+                );
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+}
+
+fn criterion_benchmark(c: &mut Criterion) {
+    let device = BenchDeviceHandler::new().unwrap();
+    for d in device.devices {
+        run_where_cond_benchmark(c, &d, DType::F32, "where_cond_f32");
+        run_where_cond_benchmark(c, &d, DType::BF16, "where_cond_bf16");
+        run_where_cond_benchmark(c, &d, DType::F16, "where_cond_f16");
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);

candle-core/examples/basics.rs

@@ -8,11 +8,10 @@ use anyhow::Result;
 use candle_core::{Device, Tensor};
 
 fn main() -> Result<()> {
-    let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
-    let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
-    let start = std::time::Instant::now();
-    let res = inp.conv2d(&w, 0, 1, 1, 1)?;
-    println!("{:?}", start.elapsed());
-    println!("{res:?}");
+    let a = Tensor::new(&[[0.0f32, 1.0, 2.0], [3.0, 4.0, 5.0]], &Device::Cpu)?;
+    let b = Tensor::new(&[[88.0f32, 99.0]], &Device::Cpu)?;
+    let new_a = a.slice_scatter(&b, 1, 2)?;
+    assert_eq!(a.to_vec2::<f32>()?, [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
+    assert_eq!(new_a.to_vec2::<f32>()?, [[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
     Ok(())
 }

candle-core/examples/tensor-tools.rs

@@ -1,5 +1,5 @@
-use candle_core::quantized::{gguf_file, k_quants, QTensor};
-use candle_core::{Device, Result, Tensor};
+use candle_core::quantized::{gguf_file, GgmlDType, QTensor};
+use candle_core::{Device, Result};
 use clap::{Parser, Subcommand, ValueEnum};
 use rayon::prelude::*;
 
@@ -11,12 +11,7 @@ enum QuantizationMode {
 }
 
 impl QuantizationMode {
-    fn quantize(
-        &self,
-        name: &str,
-        tensor: QTensor,
-        default: fn(&Tensor) -> Result<QTensor>,
-    ) -> Result<QTensor> {
+    fn quantize(&self, name: &str, tensor: QTensor, dtype: GgmlDType) -> Result<QTensor> {
         match self {
             Self::Llama => {
                 // Same behavior as the llama.cpp quantization.
@@ -24,9 +19,9 @@ impl QuantizationMode {
                 if should_quantize {
                     let tensor = tensor.dequantize(&Device::Cpu)?;
                     if name == "output.weight" {
-                        QTensor::quantize::<k_quants::BlockQ6K>(&tensor)
+                        QTensor::quantize(&tensor, GgmlDType::Q6K)
                     } else {
-                        default(&tensor)
+                        QTensor::quantize(&tensor, dtype)
                     }
                 } else {
                     Ok(tensor)
@@ -60,6 +55,27 @@ enum Quantization {
     F32,
 }
 
+impl Quantization {
+    fn dtype(&self) -> GgmlDType {
+        match self {
+            Quantization::Q4_0 => GgmlDType::Q4_0,
+            Quantization::Q4_1 => GgmlDType::Q4_1,
+            Quantization::Q5_0 => GgmlDType::Q5_0,
+            Quantization::Q5_1 => GgmlDType::Q5_1,
+            Quantization::Q8_0 => GgmlDType::Q8_0,
+            Quantization::Q8_1 => GgmlDType::Q8_1,
+            Quantization::Q2k => GgmlDType::Q2K,
+            Quantization::Q3k => GgmlDType::Q3K,
+            Quantization::Q4k => GgmlDType::Q4K,
+            Quantization::Q5k => GgmlDType::Q5K,
+            Quantization::Q6k => GgmlDType::Q6K,
+            Quantization::Q8k => GgmlDType::Q8K,
+            Quantization::F16 => GgmlDType::F16,
+            Quantization::F32 => GgmlDType::F32,
+        }
+    }
+}
+
 #[derive(ValueEnum, Debug, Clone)]
 enum Format {
     Safetensors,
@@ -102,7 +118,7 @@ enum Command {
     },
 
     Quantize {
-        /// The input file, in gguf format.
+        /// The input file(s), in safetensors format.
         in_file: Vec<std::path::PathBuf>,
 
         /// The output file, in gguf format.
@@ -117,6 +133,15 @@ enum Command {
         #[arg(long, value_enum, default_value_t = QuantizationMode::Llama)]
         mode: QuantizationMode,
     },
+
+    Dequantize {
+        /// The input file, in gguf format.
+        in_file: std::path::PathBuf,
+
+        /// The output file, in safetensors format.
+        #[arg(long)]
+        out_file: std::path::PathBuf,
+    },
 }
 
 #[derive(Parser, Debug, Clone)]
@@ -125,7 +150,12 @@ struct Args {
     command: Command,
 }
 
-fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> Result<()> {
+fn run_ls(
+    file: &std::path::PathBuf,
+    format: Option<Format>,
+    verbose: bool,
+    device: &Device,
+) -> Result<()> {
     let format = match format {
         Some(format) => format,
         None => match Format::infer(file) {
@@ -191,7 +221,7 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
         }
         Format::Ggml => {
             let mut file = std::fs::File::open(file)?;
-            let content = candle_core::quantized::ggml_file::Content::read(&mut file)?;
+            let content = candle_core::quantized::ggml_file::Content::read(&mut file, device)?;
             let mut tensors = content.tensors.into_iter().collect::<Vec<_>>();
             tensors.sort_by(|a, b| a.0.cmp(&b.0));
             for (name, qtensor) in tensors.iter() {
@@ -232,37 +262,8 @@ fn run_quantize_safetensors(
     }
     println!("tensors: {}", tensors.len());
 
-    let quantize_fn = match q {
-        Quantization::Q4_0 => QTensor::quantize::<k_quants::BlockQ4_0>,
-        Quantization::Q4_1 => QTensor::quantize::<k_quants::BlockQ4_1>,
-        Quantization::Q5_0 => QTensor::quantize::<k_quants::BlockQ5_0>,
-        Quantization::Q5_1 => QTensor::quantize::<k_quants::BlockQ5_1>,
-        Quantization::Q8_0 => QTensor::quantize::<k_quants::BlockQ8_0>,
-        Quantization::Q8_1 => QTensor::quantize::<k_quants::BlockQ8_1>,
-        Quantization::Q2k => QTensor::quantize::<k_quants::BlockQ2K>,
-        Quantization::Q3k => QTensor::quantize::<k_quants::BlockQ3K>,
-        Quantization::Q4k => QTensor::quantize::<k_quants::BlockQ4K>,
-        Quantization::Q5k => QTensor::quantize::<k_quants::BlockQ5K>,
-        Quantization::Q6k => QTensor::quantize::<k_quants::BlockQ6K>,
-        Quantization::Q8k => QTensor::quantize::<k_quants::BlockQ8K>,
-        Quantization::F16 => QTensor::quantize::<half::f16>,
-        Quantization::F32 => QTensor::quantize::<f32>,
-    };
-    let block_size = match q {
-        Quantization::Q4_0 => k_quants::QK4_0,
-        Quantization::Q4_1 => k_quants::QK4_1,
-        Quantization::Q5_0 => k_quants::QK5_0,
-        Quantization::Q5_1 => k_quants::QK5_1,
-        Quantization::Q8_0 => k_quants::QK8_0,
-        Quantization::Q8_1 => k_quants::QK8_1,
-        Quantization::Q2k
-        | Quantization::Q3k
-        | Quantization::Q4k
-        | Quantization::Q5k
-        | Quantization::Q6k
-        | Quantization::Q8k => k_quants::QK_K,
-        Quantization::F16 | Quantization::F32 => 1,
-    };
+    let dtype = q.dtype();
+    let block_size = dtype.block_size();
 
     let qtensors = tensors
         .into_par_iter()
@@ -270,9 +271,9 @@ fn run_quantize_safetensors(
             let should_quantize = tensor.rank() == 2 && tensor.dim(1)? % block_size == 0;
             println!("  quantizing {name} {tensor:?} {should_quantize}");
             let tensor = if should_quantize {
-                quantize_fn(&tensor)?
+                QTensor::quantize(&tensor, dtype)?
             } else {
-                QTensor::quantize::<f32>(&tensor)?
+                QTensor::quantize(&tensor, GgmlDType::F32)?
             };
             Ok((name, tensor))
         })
@@ -285,11 +286,29 @@ fn run_quantize_safetensors(
     Ok(())
 }
 
+fn run_dequantize(
+    in_file: std::path::PathBuf,
+    out_file: std::path::PathBuf,
+    device: &Device,
+) -> Result<()> {
+    let mut in_file = std::fs::File::open(in_file)?;
+    let content = gguf_file::Content::read(&mut in_file)?;
+    let mut tensors = std::collections::HashMap::new();
+    for (tensor_name, _) in content.tensor_infos.iter() {
+        let tensor = content.tensor(&mut in_file, tensor_name, device)?;
+        let tensor = tensor.dequantize(device)?;
+        tensors.insert(tensor_name.to_string(), tensor);
+    }
+    candle_core::safetensors::save(&tensors, out_file)?;
+    Ok(())
+}
+
 fn run_quantize(
     in_files: &[std::path::PathBuf],
     out_file: std::path::PathBuf,
     q: Quantization,
     qmode: QuantizationMode,
+    device: &Device,
 ) -> Result<()> {
     if in_files.is_empty() {
         candle_core::bail!("no specified input files")
@@ -315,31 +334,15 @@ fn run_quantize(
     let content = gguf_file::Content::read(&mut in_)?;
     println!("tensors: {}", content.tensor_infos.len());
 
-    let quantize_fn = match q {
-        Quantization::Q4_0 => QTensor::quantize::<k_quants::BlockQ4_0>,
-        Quantization::Q4_1 => QTensor::quantize::<k_quants::BlockQ4_1>,
-        Quantization::Q5_0 => QTensor::quantize::<k_quants::BlockQ5_0>,
-        Quantization::Q5_1 => QTensor::quantize::<k_quants::BlockQ5_1>,
-        Quantization::Q8_0 => QTensor::quantize::<k_quants::BlockQ8_0>,
-        Quantization::Q8_1 => QTensor::quantize::<k_quants::BlockQ8_1>,
-        Quantization::Q2k => QTensor::quantize::<k_quants::BlockQ2K>,
-        Quantization::Q3k => QTensor::quantize::<k_quants::BlockQ3K>,
-        Quantization::Q4k => QTensor::quantize::<k_quants::BlockQ4K>,
-        Quantization::Q5k => QTensor::quantize::<k_quants::BlockQ5K>,
-        Quantization::Q6k => QTensor::quantize::<k_quants::BlockQ6K>,
-        Quantization::Q8k => QTensor::quantize::<k_quants::BlockQ8K>,
-        Quantization::F16 => QTensor::quantize::<half::f16>,
-        Quantization::F32 => QTensor::quantize::<f32>,
-    };
-
+    let dtype = q.dtype();
     let qtensors = content
         .tensor_infos
         .par_iter()
         .map(|(name, _)| {
             println!("  quantizing {name}");
             let mut in_file = std::fs::File::open(&in_files[0])?;
-            let tensor = content.tensor(&mut in_file, name)?;
-            let tensor = qmode.quantize(name, tensor, quantize_fn)?;
+            let tensor = content.tensor(&mut in_file, name, device)?;
+            let tensor = qmode.quantize(name, tensor, dtype)?;
             Ok((name, tensor))
         })
         .collect::<Result<Vec<_>>>()?;
@@ -359,6 +362,7 @@ fn run_quantize(
 
 fn main() -> anyhow::Result<()> {
     let args = Args::parse();
+    let device = Device::Cpu;
     match args.command {
         Command::Ls {
             files,
@@ -370,7 +374,7 @@ fn main() -> anyhow::Result<()> {
                 if multiple_files {
                     println!("--- {file:?} ---");
                 }
-                run_ls(file, format.clone(), verbose)?
+                run_ls(file, format.clone(), verbose, &device)?
             }
         }
         Command::Quantize {
@@ -378,7 +382,8 @@ fn main() -> anyhow::Result<()> {
             out_file,
             quantization,
             mode,
-        } => run_quantize(&in_file, out_file, quantization, mode)?,
+        } => run_quantize(&in_file, out_file, quantization, mode, &device)?,
+        Command::Dequantize { in_file, out_file } => run_dequantize(in_file, out_file, &device)?,
     }
     Ok(())
 }

candle-core/src/backprop.rs

@@ -114,7 +114,7 @@ impl Tensor {
                     | Op::Unary(_node, UnaryOp::Round) => nodes,
                     Op::Reshape(node)
                     | Op::UpsampleNearest1D(node)
-                    | Op::UpsampleNearest2D(node)
+                    | Op::UpsampleNearest2D { arg: node, .. }
                     | Op::AvgPool2D { arg: node, .. }
                     | Op::MaxPool2D { arg: node, .. }
                     | Op::Copy(node)
@@ -350,9 +350,27 @@ impl Tensor {
                     Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
                         op: "upsample-nearest1d",
                     })?,
-                    Op::UpsampleNearest2D { .. } => Err(Error::BackwardNotSupported {
-                        op: "upsample-nearest2d",
-                    })?,
+                    Op::UpsampleNearest2D {
+                        arg,
+                        target_h,
+                        target_w,
+                    } => {
+                        let (_n, c, h, w) = arg.dims4()?;
+                        if target_h % h != 0 || target_w % w != 0 {
+                            crate::bail!("backward not supported for non integer upscaling factors")
+                        }
+                        let scale_h = target_h / h;
+                        let scale_w = target_w / w;
+
+                        if scale_h != scale_w {
+                            crate::bail!("backward not supported for non uniform upscaling factors")
+                        };
+                        let kernel =
+                            Tensor::ones((c, 1, scale_h, scale_w), arg.dtype(), arg.device())?;
+                        let conv_sum = grad.conv2d(&kernel, 0, scale_h, 1, c)?;
+                        let sum_grad = grads.or_insert(arg)?;
+                        *sum_grad = conv_sum;
+                    }
                     Op::SliceScatter0(lhs, rhs, start_rhs) => {
                         let rhs_sum_grad = grads.or_insert(rhs)?;
                         let rhs_grad = grad.narrow(0, *start_rhs, rhs.dim(0)?)?;

candle-core/src/device.rs

@@ -201,10 +201,9 @@ impl Device {
                     Ok(Storage::Cuda(storage))
                 }
             }
-            Device::Metal(_device) => {
-                // let storage = device.rand_uniform(shape, dtype, lo, up)?;
-                // Ok(Storage::Metal(storage))
-                crate::bail!("Metal rand_uniform not implemented")
+            Device::Metal(device) => {
+                let storage = device.rand_uniform(shape, dtype, lo, up)?;
+                Ok(Storage::Metal(storage))
             }
         }
     }

candle-core/src/indexer.rs

@@ -64,7 +64,7 @@ impl Tensor {
 #[derive(Debug)]
 /// Generic structure used to index a slice of the tensor
 pub enum TensorIndexer {
-    /// This selects the elemnts for which an index has some specific value.
+    /// This selects the elements for which an index has some specific value.
     Select(usize),
     /// This is a regular slice, purely indexing a chunk of the tensor
     Narrow(Bound<usize>, Bound<usize>),
@@ -104,37 +104,31 @@ impl From<&Tensor> for TensorIndexer {
     }
 }
 
-macro_rules! impl_from_range {
-    ($range_type:ty) => {
-        impl From<$range_type> for TensorIndexer {
-            fn from(range: $range_type) -> Self {
-                use std::ops::Bound::*;
+trait RB: RangeBounds<usize> {}
+impl RB for Range<usize> {}
+impl RB for RangeFrom<usize> {}
+impl RB for RangeFull {}
+impl RB for RangeInclusive<usize> {}
+impl RB for RangeTo<usize> {}
+impl RB for RangeToInclusive<usize> {}
 
-                let start = match range.start_bound() {
-                    Included(idx) => Included(*idx),
-                    Excluded(idx) => Excluded(*idx),
-                    Unbounded => Unbounded,
-                };
-
-                let end = match range.end_bound() {
-                    Included(idx) => Included(*idx),
-                    Excluded(idx) => Excluded(*idx),
-                    Unbounded => Unbounded,
-                };
-
-                TensorIndexer::Narrow(start, end)
-            }
-        }
-    };
+impl<T: RB> From<T> for TensorIndexer {
+    fn from(range: T) -> Self {
+        use std::ops::Bound::*;
+        let start = match range.start_bound() {
+            Included(idx) => Included(*idx),
+            Excluded(idx) => Excluded(*idx),
+            Unbounded => Unbounded,
+        };
+        let end = match range.end_bound() {
+            Included(idx) => Included(*idx),
+            Excluded(idx) => Excluded(*idx),
+            Unbounded => Unbounded,
+        };
+        TensorIndexer::Narrow(start, end)
+    }
 }
 
-impl_from_range!(Range<usize>);
-impl_from_range!(RangeFrom<usize>);
-impl_from_range!(RangeFull);
-impl_from_range!(RangeInclusive<usize>);
-impl_from_range!(RangeTo<usize>);
-impl_from_range!(RangeToInclusive<usize>);
-
 /// Trait used to implement multiple signatures for ease of use of the slicing
 /// of a tensor
 pub trait IndexOp<T> {

candle-core/src/lib.rs

@@ -72,7 +72,7 @@ pub mod utils;
 mod variable;
 
 pub use cpu_backend::CpuStorage;
-pub use device::{Device, DeviceLocation};
+pub use device::{Device, DeviceLocation, NdArray};
 pub use dtype::{DType, FloatDType, IntDType, WithDType};
 pub use error::{Error, Result};
 pub use indexer::IndexOp;
@@ -123,12 +123,6 @@ pub trait Module {
     fn forward(&self, xs: &Tensor) -> Result<Tensor>;
 }
 
-impl Module for quantized::QMatMul {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        self.forward(xs)
-    }
-}
-
 impl<T: Fn(&Tensor) -> Result<Tensor>> Module for T {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         self(xs)

candle-core/src/op.rs

@@ -132,7 +132,11 @@ pub enum Op {
     },
 
     UpsampleNearest1D(Tensor),
-    UpsampleNearest2D(Tensor),
+    UpsampleNearest2D {
+        arg: Tensor,
+        target_h: usize,
+        target_w: usize,
+    },
 
     Cat(Vec<Tensor>, usize),
 
@@ -593,7 +597,8 @@ unary_op!(Recip, "recip", v, v.recip());
 unary_op!(Sqr, "sqr", v, v * v, vs_sqr, vd_sqr);
 unary_op!(Sqrt, "sqrt", v, v.sqrt(), vs_sqrt, vd_sqrt);
 
-/// `gelu` operation
+/// Tanh based approximation of the `gelu` operation
+/// GeluErf is the more precise one.
 /// <https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions>
 impl UnaryOpT for Gelu {
     const NAME: &'static str = "gelu";

candle-core/src/pickle.rs

@@ -703,6 +703,7 @@ impl PthTensors {
     }
 
     pub fn get(&self, name: &str) -> Result<Option<Tensor>> {
+        use std::io::Read;
         let tensor_info = match self.tensor_infos.get(name) {
             None => return Ok(None),
             Some(tensor_info) => tensor_info,
@@ -712,14 +713,21 @@ impl PthTensors {
         let mut zip = zip::ZipArchive::new(zip_reader)?;
         let mut reader = zip.by_name(&tensor_info.path)?;
 
-        // Reading the data is a bit tricky as it can be strided, use an offset, etc.
-        // For now only support the basic case.
-        if tensor_info.layout.start_offset() != 0 || !tensor_info.layout.is_contiguous() {
+        // Reading the data is a bit tricky as it can be strided, for now only support the basic
+        // case.
+        if !tensor_info.layout.is_contiguous() {
             crate::bail!(
                 "cannot retrieve non-contiguous tensors {:?}",
                 tensor_info.layout
             )
         }
+        let start_offset = tensor_info.layout.start_offset();
+        if start_offset > 0 {
+            std::io::copy(
+                &mut reader.by_ref().take(start_offset as u64),
+                &mut std::io::sink(),
+            )?;
+        }
         let tensor = Tensor::from_reader(
             tensor_info.layout.shape().clone(),
             tensor_info.dtype,

candle-core/src/quantized/avx.rs

@@ -353,7 +353,7 @@ pub(crate) fn vec_dot_q3k_q8k(n: usize, xs: &[BlockQ3K], ys: &[BlockQ8K]) -> Res
                 q3 = q3.add(32);
 
                 // Prepare low and high bits
-                // We hardcode the shifts here to avoid loading them into a seperate register
+                // We hardcode the shifts here to avoid loading them into a separate register
                 let q3l_0 = _mm256_and_si256(q3bits, m3);
                 let q3h_0 = if j == 0 {
                     _mm256_srli_epi16(_mm256_andnot_si256(hbits, _mm256_slli_epi16(mone, 0)), 0)
@@ -586,7 +586,7 @@ pub(crate) fn vec_dot_q5k_q8k(n: usize, xs: &[BlockQ5K], ys: &[BlockQ8K]) -> Res
                 let q5bits = _mm256_loadu_si256(q5 as *const __m256i);
                 q5 = q5.add(32);
 
-                //Similar to q3k we hardcode the shifts here to avoid loading them into a seperate register
+                //Similar to q3k we hardcode the shifts here to avoid loading them into a separate register
                 let q5l_0 = _mm256_and_si256(q5bits, m4);
                 let q5l_0_shift_input = _mm256_and_si256(hbits, hmask);
                 let q5l_0_right_shift = match j {

candle-core/src/quantized/ggml_file.rs

@@ -1,7 +1,9 @@
 //! Support for the GGML file format.
 
-use super::{k_quants, GgmlDType};
-use crate::Result;
+#[cfg(feature = "metal")]
+use super::metal::load_quantized_metal;
+use super::{k_quants, GgmlDType, QStorage};
+use crate::{Device, Result};
 use byteorder::{LittleEndian, ReadBytesExt};
 use std::collections::HashMap;
 
@@ -121,11 +123,22 @@ fn from_raw_data<T: super::GgmlType + Send + Sync + 'static>(
     raw_data: &[u8],
     size_in_bytes: usize,
     dims: Vec<usize>,
+    device: &Device,
 ) -> Result<super::QTensor> {
     let raw_data_ptr = raw_data.as_ptr();
     let n_blocks = size_in_bytes / std::mem::size_of::<T>();
     let data = unsafe { std::slice::from_raw_parts(raw_data_ptr as *const T, n_blocks) };
-    super::QTensor::new(data.to_vec(), dims)
+    let data: QStorage = match device {
+        Device::Cpu => QStorage::Cpu(Box::new(data.to_vec())),
+        #[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)
 }
 
 /// Creates a [Tensor] from a raw GGML tensor.
@@ -133,29 +146,50 @@ pub fn qtensor_from_ggml(
     ggml_dtype: GgmlDType,
     raw_data: &[u8],
     dims: Vec<usize>,
+    device: &Device,
 ) -> Result<super::QTensor> {
     let tensor_elems = dims.iter().product::<usize>();
-    let blck_size = ggml_dtype.blck_size();
-    if tensor_elems % blck_size != 0 {
+    let block_size = ggml_dtype.block_size();
+    if tensor_elems % block_size != 0 {
         crate::bail!(
-            "the number of elements {tensor_elems} is not divisible by the block size {blck_size}"
+            "the number of elements {tensor_elems} is not divisible by the block size {block_size}"
         )
     }
-    let size_in_bytes = tensor_elems / blck_size * ggml_dtype.type_size();
+    let size_in_bytes = tensor_elems / block_size * ggml_dtype.type_size();
 
     match ggml_dtype {
-        GgmlDType::F32 => from_raw_data::<f32>(raw_data, size_in_bytes, dims),
-        GgmlDType::F16 => from_raw_data::<half::f16>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q4_0 => from_raw_data::<k_quants::BlockQ4_0>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q4_1 => from_raw_data::<k_quants::BlockQ4_1>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q5_0 => from_raw_data::<k_quants::BlockQ5_0>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q5_1 => from_raw_data::<k_quants::BlockQ5_1>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q8_0 => from_raw_data::<k_quants::BlockQ8_0>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q2K => from_raw_data::<k_quants::BlockQ2K>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q3K => from_raw_data::<k_quants::BlockQ3K>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q4K => from_raw_data::<k_quants::BlockQ4K>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q5K => from_raw_data::<k_quants::BlockQ5K>(raw_data, size_in_bytes, dims),
-        GgmlDType::Q6K => from_raw_data::<k_quants::BlockQ6K>(raw_data, size_in_bytes, dims),
+        GgmlDType::F32 => from_raw_data::<f32>(raw_data, size_in_bytes, dims, device),
+        GgmlDType::F16 => from_raw_data::<half::f16>(raw_data, size_in_bytes, dims, device),
+        GgmlDType::Q4_0 => {
+            from_raw_data::<k_quants::BlockQ4_0>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q4_1 => {
+            from_raw_data::<k_quants::BlockQ4_1>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q5_0 => {
+            from_raw_data::<k_quants::BlockQ5_0>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q5_1 => {
+            from_raw_data::<k_quants::BlockQ5_1>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q8_0 => {
+            from_raw_data::<k_quants::BlockQ8_0>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q2K => {
+            from_raw_data::<k_quants::BlockQ2K>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q3K => {
+            from_raw_data::<k_quants::BlockQ3K>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q4K => {
+            from_raw_data::<k_quants::BlockQ4K>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q5K => {
+            from_raw_data::<k_quants::BlockQ5K>(raw_data, size_in_bytes, dims, device)
+        }
+        GgmlDType::Q6K => {
+            from_raw_data::<k_quants::BlockQ6K>(raw_data, size_in_bytes, dims, device)
+        }
         _ => crate::bail!("quantized type {ggml_dtype:?} is not supported yet"),
     }
 }
@@ -163,6 +197,7 @@ pub fn qtensor_from_ggml(
 fn read_one_tensor<R: std::io::Seek + std::io::Read>(
     reader: &mut R,
     magic: VersionedMagic,
+    device: &Device,
 ) -> Result<(String, super::QTensor)> {
     let n_dims = reader.read_u32::<LittleEndian>()?;
     let name_len = reader.read_u32::<LittleEndian>()?;
@@ -183,11 +218,11 @@ fn read_one_tensor<R: std::io::Seek + std::io::Read>(
     }
     let dims = dims.iter().map(|&u| u as usize).collect::<Vec<_>>();
     let tensor_elems = dims.iter().product::<usize>();
-    let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.blck_size();
+    let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.block_size();
     // TODO: Mmap version to avoid copying the data around?
     let mut raw_data = vec![0u8; size_in_bytes];
     reader.read_exact(&mut raw_data)?;
-    match qtensor_from_ggml(ggml_dtype, &raw_data, dims) {
+    match qtensor_from_ggml(ggml_dtype, &raw_data, dims, device) {
         Ok(tensor) => Ok((name, tensor)),
         Err(e) => crate::bail!("Error creating tensor {name}: {e}"),
     }
@@ -201,7 +236,10 @@ pub struct Content {
 }
 
 impl Content {
-    pub fn read<R: std::io::Seek + std::io::Read>(reader: &mut R) -> Result<Content> {
+    pub fn read<R: std::io::Seek + std::io::Read>(
+        reader: &mut R,
+        device: &Device,
+    ) -> Result<Content> {
         // https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L505
         let last_position = reader.seek(std::io::SeekFrom::End(0))?;
         reader.seek(std::io::SeekFrom::Start(0))?;
@@ -211,7 +249,7 @@ impl Content {
         let mut tensors = HashMap::new();
 
         while reader.stream_position()? != last_position {
-            let (name, tensor) = read_one_tensor(reader, magic)?;
+            let (name, tensor) = read_one_tensor(reader, magic, device)?;
             tensors.insert(name, tensor);
         }
         Ok(Self {

candle-core/src/quantized/gguf_file.rs

@@ -3,7 +3,7 @@
 //! Spec: https://github.com/philpax/ggml/blob/gguf-spec/docs/gguf.md
 
 use super::{GgmlDType, QTensor};
-use crate::Result;
+use crate::{Device, Result};
 use byteorder::{LittleEndian, ReadBytesExt, WriteBytesExt};
 use std::collections::HashMap;
 
@@ -41,7 +41,7 @@ impl VersionedMagic {
             (Magic::Gguf, 1) => Self::GgufV1,
             (Magic::Gguf, 2) => Self::GgufV2,
             (Magic::Gguf, 3) => Self::GgufV3,
-            _ => crate::bail!("ggml: unsupported magic/version {magic:?}/{version}"),
+            _ => crate::bail!("gguf: unsupported magic/version {magic:?}/{version}"),
         };
         Ok(versioned_magic)
     }
@@ -59,19 +59,25 @@ impl TensorInfo {
         &self,
         reader: &mut R,
         tensor_data_offset: u64,
+        device: &Device,
     ) -> Result<QTensor> {
         let tensor_elems = self.shape.elem_count();
-        let blck_size = self.ggml_dtype.blck_size();
-        if tensor_elems % blck_size != 0 {
+        let block_size = self.ggml_dtype.block_size();
+        if tensor_elems % block_size != 0 {
             crate::bail!(
-            "the number of elements {tensor_elems} is not divisible by the block size {blck_size}"
+            "the number of elements {tensor_elems} is not divisible by the block size {block_size}"
         )
         }
-        let size_in_bytes = tensor_elems / blck_size * self.ggml_dtype.type_size();
+        let size_in_bytes = tensor_elems / block_size * self.ggml_dtype.type_size();
         let mut raw_data = vec![0u8; size_in_bytes];
         reader.seek(std::io::SeekFrom::Start(tensor_data_offset + self.offset))?;
         reader.read_exact(&mut raw_data)?;
-        super::ggml_file::qtensor_from_ggml(self.ggml_dtype, &raw_data, self.shape.dims().to_vec())
+        super::ggml_file::qtensor_from_ggml(
+            self.ggml_dtype,
+            &raw_data,
+            self.shape.dims().to_vec(),
+            device,
+        )
     }
 }
 
@@ -460,12 +466,13 @@ impl Content {
         &self,
         reader: &mut R,
         name: &str,
+        device: &Device,
     ) -> Result<QTensor> {
         let tensor_info = match self.tensor_infos.get(name) {
             Some(tensor_info) => tensor_info,
-            None => crate::bail!("cannot find tensor-infor for {name}"),
+            None => crate::bail!("cannot find tensor info for {name}"),
         };
-        tensor_info.read(reader, self.tensor_data_offset)
+        tensor_info.read(reader, self.tensor_data_offset, device)
     }
 }
 
@@ -517,10 +524,9 @@ pub fn write<W: std::io::Seek + std::io::Write>(
                 "internal error, unexpected current position {tensor_start_pos} {offset} {pos}"
             )
         }
-        let data_ptr = tensor.as_ptr();
-        let size_in_bytes = tensor.storage_size_in_bytes();
-        let data = unsafe { std::slice::from_raw_parts(data_ptr, size_in_bytes) };
-        w.write_all(data)?;
+        let data = tensor.data()?;
+        let size_in_bytes = data.len();
+        w.write_all(&data)?;
         let padding = 31 - (31 + size_in_bytes) % 32;
         w.write_all(&vec![0u8; padding])?;
     }

candle-core/src/quantized/k_quants.rs

@@ -1545,13 +1545,13 @@ impl GgmlType for BlockQ5K {
                 let d2 = d * sc as f32;
                 let m2 = min * m as f32;
                 for (ql, qh) in ql.iter().zip(qh) {
-                    let to_add = if qh & u1 != 0 { 16 } else { 1 };
-                    y[ys_index] = d1 * ((ql & 0xF) + to_add) as f32 - m1;
+                    let to_add = if qh & u1 != 0 { 16f32 } else { 0f32 };
+                    y[ys_index] = d1 * ((ql & 0xF) as f32 + to_add) - m1;
                     ys_index += 1;
                 }
                 for (ql, qh) in ql.iter().zip(qh) {
-                    let to_add = if qh & u2 != 0 { 16 } else { 1 };
-                    y[ys_index] = d2 * ((ql >> 4) + to_add) as f32 - m2;
+                    let to_add = if qh & u2 != 0 { 16f32 } else { 0f32 };
+                    y[ys_index] = d2 * ((ql >> 4) as f32 + to_add) - m2;
                     ys_index += 1;
                 }
                 is += 2;

candle-core/src/quantized/metal.rs

@@ -0,0 +1,153 @@
+use super::{GgmlDType, QStorage};
+use crate::{DType, MetalDevice, MetalStorage, Result};
+use metal::Buffer;
+use std::sync::Arc;
+
+pub struct QMetalStorage {
+    dtype: GgmlDType,
+    device: MetalDevice,
+    buffer: Arc<Buffer>,
+}
+
+impl QMetalStorage {
+    pub fn dtype(&self) -> GgmlDType {
+        self.dtype
+    }
+
+    pub fn buffer(&self) -> &Buffer {
+        &self.buffer
+    }
+
+    pub fn new(buffer: Arc<Buffer>, device: MetalDevice, dtype: GgmlDType) -> Self {
+        Self {
+            device,
+            buffer,
+            dtype,
+        }
+    }
+
+    pub fn dequantize(&self, elem_count: usize) -> Result<MetalStorage> {
+        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, self.buffer.length());
+        blit.end_encoding();
+        self.device.wait_until_completed()?;
+        let mut out = vec![0.0; elem_count];
+        match self.dtype {
+            GgmlDType::F32 => {
+                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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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(), DType::F32))
+    }
+
+    pub fn quantize(&mut self, src: &MetalStorage) -> Result<()> {
+        // Quantization only happens on CPU for now.
+        let src = src.to_cpu::<f32>()?;
+        let elem_count = src.len();
+        let src = crate::Storage::Cpu(crate::CpuStorage::F32(src));
+        let mut qcpu_storage = crate::Device::Cpu.qzeros(elem_count, self.dtype)?;
+        qcpu_storage.quantize(&src)?;
+        let buffer = self.device.new_buffer_with_data(&qcpu_storage.data()?)?;
+        self.buffer = buffer;
+        Ok(())
+    }
+}
+
+pub fn load_quantized_metal<T: super::GgmlType + Send + Sync + 'static>(
+    device: &MetalDevice,
+    data: &[T],
+) -> Result<QStorage> {
+    let buffer = device.new_buffer_with_data(data)?;
+    let device = device.clone();
+    Ok(QStorage::Metal(QMetalStorage {
+        dtype: T::DTYPE,
+        device,
+        buffer,
+    }))
+}
+
+fn read_to_vec<T: Clone>(buffer: &Buffer, n: usize) -> Vec<T> {
+    let ptr = buffer.contents() as *const T;
+    assert!(!ptr.is_null());
+    let slice = unsafe { std::slice::from_raw_parts(ptr, n) };
+    slice.to_vec()
+}

candle-core/src/quantized/mod.rs

@@ -1,23 +1,125 @@
-use crate::{Device, Result, Shape, Tensor};
+#[cfg(feature = "metal")]
+use crate::{backend::BackendStorage, DType};
+use crate::{CpuStorage, Device, Result, Shape, Storage, Tensor};
+use k_quants::*;
+use std::borrow::Cow;
 
 #[cfg(target_feature = "avx")]
 pub mod avx;
 pub mod ggml_file;
 pub mod gguf_file;
 pub mod k_quants;
+#[cfg(feature = "metal")]
+pub mod metal;
 #[cfg(target_feature = "neon")]
 pub mod neon;
 #[cfg(target_feature = "simd128")]
 pub mod simd128;
 pub mod utils;
+use half::f16;
 
 pub use k_quants::GgmlType;
 
 pub struct QTensor {
-    data: Box<dyn QuantizedType>,
+    storage: QStorage,
     shape: Shape,
 }
 
+impl Device {
+    fn qzeros(&self, elem_count: usize, dtype: GgmlDType) -> Result<QStorage> {
+        match self {
+            Device::Cpu => {
+                let storage = dtype.cpu_zeros(elem_count);
+                Ok(QStorage::Cpu(storage))
+            }
+            #[cfg(feature = "metal")]
+            Device::Metal(metal) => {
+                let size = elem_count * dtype.type_size() / dtype.block_size();
+                let buffer = metal.allocate_zeros(size)?;
+                Ok(QStorage::Metal(metal::QMetalStorage::new(
+                    buffer,
+                    metal.clone(),
+                    dtype,
+                )))
+            }
+            #[cfg(not(feature = "metal"))]
+            Device::Metal(_metal) => {
+                crate::bail!("Metal feature not activated");
+            }
+            Device::Cuda(_cuda) => {
+                crate::bail!("Cuda ggml quantization not supported");
+            }
+        }
+    }
+}
+
+pub enum QStorage {
+    Cpu(Box<dyn QuantizedType>),
+    #[cfg(feature = "metal")]
+    Metal(metal::QMetalStorage),
+}
+
+impl QStorage {
+    fn block_size(&self) -> usize {
+        match self {
+            QStorage::Cpu(storage) => storage.block_size(),
+            #[cfg(feature = "metal")]
+            QStorage::Metal(storage) => storage.dtype().block_size(),
+        }
+    }
+
+    fn dtype(&self) -> GgmlDType {
+        match self {
+            QStorage::Cpu(storage) => storage.dtype(),
+            #[cfg(feature = "metal")]
+            QStorage::Metal(storage) => storage.dtype(),
+        }
+    }
+
+    fn size_in_bytes(&self) -> usize {
+        match self {
+            QStorage::Cpu(storage) => storage.storage_size_in_bytes(),
+            #[cfg(feature = "metal")]
+            QStorage::Metal(storage) => storage.buffer().length() as usize,
+        }
+    }
+
+    fn quantize(&mut self, src: &Storage) -> Result<()> {
+        match (self, src) {
+            (QStorage::Cpu(storage), Storage::Cpu(src)) => {
+                storage.from_float(src.as_slice::<f32>()?)?;
+            }
+            #[cfg(feature = "metal")]
+            (QStorage::Metal(storage), Storage::Metal(src)) => storage.quantize(src)?,
+            _ => crate::bail!("Invalid dequantize storage locations do not match"),
+        }
+        Ok(())
+    }
+
+    fn dequantize(&self, elem_count: usize) -> Result<Storage> {
+        match self {
+            QStorage::Cpu(storage) => Ok(Storage::Cpu(storage.dequantize(elem_count)?)),
+            #[cfg(feature = "metal")]
+            QStorage::Metal(storage) => Ok(Storage::Metal(storage.dequantize(elem_count)?)),
+        }
+    }
+
+    fn data(&self) -> Result<Cow<[u8]>> {
+        match self {
+            QStorage::Cpu(storage) => {
+                let data_ptr = storage.as_ptr();
+                let size_in_bytes = storage.storage_size_in_bytes();
+                let data = unsafe { std::slice::from_raw_parts(data_ptr, size_in_bytes) };
+                Ok(Cow::from(data))
+            }
+            #[cfg(feature = "metal")]
+            QStorage::Metal(_storage) => {
+                crate::bail!("not implemented");
+            }
+        }
+    }
+}
+
 #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
 pub enum GgmlDType {
     F32,
@@ -77,6 +179,25 @@ impl GgmlDType {
         }
     }
 
+    /// The block dtype
+    pub fn cpu_zeros(&self, elem_count: usize) -> Box<dyn QuantizedType> {
+        match self {
+            Self::F32 => Box::new(vec![f32::zeros(); elem_count]),
+            Self::F16 => Box::new(vec![f16::zeros(); elem_count]),
+            Self::Q4_0 => Box::new(vec![BlockQ4_0::zeros(); elem_count / BlockQ4_0::BLCK_SIZE]),
+            Self::Q4_1 => Box::new(vec![BlockQ4_1::zeros(); elem_count / BlockQ4_1::BLCK_SIZE]),
+            Self::Q5_0 => Box::new(vec![BlockQ5_0::zeros(); elem_count / BlockQ5_0::BLCK_SIZE]),
+            Self::Q5_1 => Box::new(vec![BlockQ5_1::zeros(); elem_count / BlockQ5_1::BLCK_SIZE]),
+            Self::Q8_0 => Box::new(vec![BlockQ8_0::zeros(); elem_count / BlockQ8_0::BLCK_SIZE]),
+            Self::Q8_1 => Box::new(vec![BlockQ8_1::zeros(); elem_count / BlockQ8_1::BLCK_SIZE]),
+            Self::Q2K => Box::new(vec![BlockQ2K::zeros(); elem_count / BlockQ2K::BLCK_SIZE]),
+            Self::Q3K => Box::new(vec![BlockQ3K::zeros(); elem_count / BlockQ3K::BLCK_SIZE]),
+            Self::Q4K => Box::new(vec![BlockQ4K::zeros(); elem_count / BlockQ4K::BLCK_SIZE]),
+            Self::Q5K => Box::new(vec![BlockQ5K::zeros(); elem_count / BlockQ5K::BLCK_SIZE]),
+            Self::Q6K => Box::new(vec![BlockQ6K::zeros(); elem_count / BlockQ6K::BLCK_SIZE]),
+            Self::Q8K => Box::new(vec![BlockQ8K::zeros(); elem_count / BlockQ8K::BLCK_SIZE]),
+        }
+    }
     /// The type size for blocks in bytes.
     pub fn type_size(&self) -> usize {
         use k_quants::*;
@@ -100,7 +221,7 @@ impl GgmlDType {
     }
 
     /// The block size, i.e. the number of elements stored in each block.
-    pub fn blck_size(&self) -> usize {
+    pub fn block_size(&self) -> usize {
         match self {
             Self::F32 => 1,
             Self::F16 => 1,
@@ -119,9 +240,13 @@ impl GgmlDType {
 pub trait QuantizedType: Send + Sync {
     fn dtype(&self) -> GgmlDType;
     fn matmul_t(&self, mkn: (usize, usize, usize), lhs: &[f32], dst: &mut [f32]) -> Result<()>;
-    fn to_float(&self, ys: &mut [f32]) -> Result<()>;
+    fn dequantize(&self, elem_count: usize) -> Result<CpuStorage>;
     fn storage_size_in_bytes(&self) -> usize;
     fn as_ptr(&self) -> *const u8;
+    fn block_size(&self) -> usize;
+    #[allow(clippy::wrong_self_convention)]
+    fn from_float(&mut self, xs: &[f32]) -> Result<()>;
+    fn size(&self) -> usize;
 }
 
 impl<T: k_quants::GgmlType + Send + Sync> QuantizedType for Vec<T> {
@@ -129,12 +254,26 @@ impl<T: k_quants::GgmlType + Send + Sync> QuantizedType for Vec<T> {
         k_quants::matmul(mkn, lhs, self.as_slice(), dst)
     }
 
+    fn size(&self) -> usize {
+        self.len() * core::mem::size_of::<T>()
+    }
+
+    fn from_float(&mut self, xs: &[f32]) -> Result<()> {
+        T::from_float(xs, self)
+    }
+
     fn dtype(&self) -> GgmlDType {
         T::DTYPE
     }
 
-    fn to_float(&self, ys: &mut [f32]) -> Result<()> {
-        T::to_float(self.as_slice(), ys)
+    fn block_size(&self) -> usize {
+        T::BLCK_SIZE
+    }
+
+    fn dequantize(&self, elem_count: usize) -> Result<CpuStorage> {
+        let mut ys = vec![0.0f32; elem_count];
+        T::to_float(self.as_slice(), &mut ys)?;
+        Ok(CpuStorage::F32(ys))
     }
 
     fn storage_size_in_bytes(&self) -> usize {
@@ -152,56 +291,49 @@ impl std::fmt::Debug for QTensor {
     }
 }
 
-fn check_shape<T: k_quants::GgmlType>(shape: &Shape) -> Result<()> {
+fn check_shape(shape: &Shape, block_size: usize) -> Result<()> {
     let dims = shape.dims();
     if dims.is_empty() {
         crate::bail!("scalar tensor cannot be quantized {shape:?}")
     }
-    if dims[dims.len() - 1] % T::BLCK_SIZE != 0 {
+    if dims[dims.len() - 1] % block_size != 0 {
         crate::bail!(
             "quantized tensor must have their last dim divisible by block size {shape:?} {}",
-            T::BLCK_SIZE
+            block_size
         )
     }
     Ok(())
 }
 
 impl QTensor {
-    pub fn new<S: Into<Shape>, T: k_quants::GgmlType + Send + Sync + 'static>(
-        data: Vec<T>,
-        shape: S,
-    ) -> Result<Self> {
+    pub fn new<S: Into<Shape>>(storage: QStorage, shape: S) -> Result<Self> {
         let shape = shape.into();
-        check_shape::<T>(&shape)?;
-        Ok(Self {
-            data: Box::new(data),
-            shape,
-        })
+        check_shape(&shape, storage.block_size())?;
+        Ok(Self { storage, shape })
     }
 
-    pub fn quantize<T: k_quants::GgmlType + Send + Sync + 'static>(src: &Tensor) -> Result<Self> {
+    pub fn quantize(src: &Tensor, dtype: GgmlDType) -> Result<Self> {
         let shape = src.shape();
-        check_shape::<T>(shape)?;
-        let src = src
-            .to_dtype(crate::DType::F32)?
-            .flatten_all()?
-            .to_vec1::<f32>()?;
-        if src.len() % T::BLCK_SIZE != 0 {
+        let block_size = dtype.block_size();
+        check_shape(shape, block_size)?;
+        let src = src.to_dtype(crate::DType::F32)?.flatten_all()?;
+        let elem_count = shape.elem_count();
+        if elem_count % block_size != 0 {
             crate::bail!(
                 "tensor size ({shape:?}) is not divisible by block size {}",
-                T::BLCK_SIZE
+                block_size
             )
         }
-        let mut data = vec![T::zeros(); src.len() / T::BLCK_SIZE];
-        T::from_float(&src, &mut data)?;
+        let mut storage = src.device().qzeros(elem_count, dtype)?;
+        storage.quantize(&src.storage())?;
         Ok(Self {
-            data: Box::new(data),
+            storage,
             shape: shape.clone(),
         })
     }
 
     pub fn dtype(&self) -> GgmlDType {
-        self.data.dtype()
+        self.storage.dtype()
     }
 
     pub fn rank(&self) -> usize {
@@ -213,21 +345,19 @@ impl QTensor {
     }
 
     pub fn dequantize(&self, device: &Device) -> Result<Tensor> {
-        let mut f32_data = vec![0f32; self.shape.elem_count()];
-        self.data.to_float(&mut f32_data)?;
-        Tensor::from_vec(f32_data, &self.shape, device)
-    }
-
-    pub fn matmul_t(&self, mkn: (usize, usize, usize), lhs: &[f32], dst: &mut [f32]) -> Result<()> {
-        self.data.matmul_t(mkn, lhs, dst)
+        let storage = self.storage.dequantize(self.shape.elem_count())?;
+        let none = crate::op::BackpropOp::none();
+        let is_variable = false;
+        crate::tensor::from_storage(storage, self.shape.clone(), none, is_variable)
+            .to_device(device)
     }
 
     pub fn storage_size_in_bytes(&self) -> usize {
-        self.data.storage_size_in_bytes()
+        self.storage.size_in_bytes()
     }
 
-    pub fn as_ptr(&self) -> *const u8 {
-        self.data.as_ptr()
+    pub fn data(&self) -> Result<Cow<'_, [u8]>> {
+        self.storage.data()
     }
 }
 
@@ -294,21 +424,97 @@ impl crate::CustomOp1 for QTensor {
         }
         dst_shape.push(n);
         let dst_shape = Shape::from(dst_shape);
-        let storage = storage.as_slice::<f32>()?;
-        let storage =
-            &storage[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
+        #[allow(clippy::infallible_destructuring_match)]
+        let self_storage = match &self.storage {
+            QStorage::Cpu(storage) => storage,
+            #[cfg(feature = "metal")]
+            _ => crate::bail!("Invalid storage"),
+        };
+        let slice = storage.as_slice::<f32>()?;
+        let slice = &slice[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
         let mut dst_storage = vec![0f32; dst_shape.elem_count()];
-        self.matmul_t(
-            (dst_shape.elem_count() / n, k, n),
-            storage,
-            &mut dst_storage,
-        )?;
+        self_storage.matmul_t((dst_shape.elem_count() / n, k, n), slice, &mut dst_storage)?;
         Ok((crate::CpuStorage::F32(dst_storage), dst_shape))
     }
+
+    #[cfg(feature = "metal")]
+    fn metal_fwd(
+        &self,
+        storage: &crate::MetalStorage,
+        layout: &crate::Layout,
+    ) -> Result<(crate::MetalStorage, Shape)> {
+        use crate::MetalError;
+
+        if !layout.is_contiguous() {
+            crate::bail!("input tensor is not contiguous {layout:?}")
+        }
+        let src_shape = layout.shape();
+        // self is transposed so n is first then k.
+        if src_shape.rank() < 2 {
+            crate::bail!("input tensor has only one dimension {layout:?}")
+        }
+        let (n, k) = self.shape.dims2()?;
+        let mut dst_shape = src_shape.dims().to_vec();
+
+        let (b, m) = match dst_shape.len() {
+            3 => (dst_shape[0], dst_shape[1]),
+            2 => (1, dst_shape[0]),
+            n => crate::bail!("Invalid rank {n} for quantized matmul metal"),
+        };
+        let last_k = dst_shape.pop().unwrap();
+        if last_k != k {
+            crate::bail!("input tensor {layout:?} incompatible with {:?}", self.shape)
+        }
+        dst_shape.push(n);
+        let dst_shape = Shape::from(dst_shape);
+        let device = storage.device().clone();
+        let dst = device.new_buffer(dst_shape.elem_count(), DType::F32, "qmatmul")?;
+        let (buffer, dtype) = match &self.storage {
+            QStorage::Metal(metal) => (metal.buffer(), metal.dtype()),
+            _ => unreachable!("Cannot call metal matmul on non metal QTensor"),
+        };
+        let command_buffer = device.command_buffer()?;
+        candle_metal_kernels::call_quantized_matmul_t(
+            device.device(),
+            &command_buffer,
+            device.kernels(),
+            dtype.into(),
+            (b, m, n, k),
+            storage.buffer(),
+            layout.start_offset() * storage.dtype().size_in_bytes(),
+            buffer,
+            &dst,
+        )
+        .map_err(MetalError::from)?;
+        let dst_storage = crate::MetalStorage::new(dst, device, DType::F32);
+        Ok((dst_storage, dst_shape))
+    }
 }
 
-impl QMatMul {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+#[cfg(feature = "metal")]
+impl From<GgmlDType> for candle_metal_kernels::GgmlDType {
+    fn from(value: GgmlDType) -> Self {
+        match value {
+            GgmlDType::Q4_0 => candle_metal_kernels::GgmlDType::Q4_0,
+            GgmlDType::Q4_1 => candle_metal_kernels::GgmlDType::Q4_1,
+            GgmlDType::Q5_0 => candle_metal_kernels::GgmlDType::Q5_0,
+            GgmlDType::Q5_1 => candle_metal_kernels::GgmlDType::Q5_1,
+            GgmlDType::Q8_0 => candle_metal_kernels::GgmlDType::Q8_0,
+            GgmlDType::Q8_1 => candle_metal_kernels::GgmlDType::Q8_1,
+            GgmlDType::Q2K => candle_metal_kernels::GgmlDType::Q2K,
+            GgmlDType::Q3K => candle_metal_kernels::GgmlDType::Q3K,
+            GgmlDType::Q4K => candle_metal_kernels::GgmlDType::Q4K,
+            GgmlDType::Q5K => candle_metal_kernels::GgmlDType::Q5K,
+            GgmlDType::Q6K => candle_metal_kernels::GgmlDType::Q6K,
+            GgmlDType::Q8K => candle_metal_kernels::GgmlDType::Q8K,
+            GgmlDType::F16 => candle_metal_kernels::GgmlDType::F16,
+            GgmlDType::F32 => candle_metal_kernels::GgmlDType::F32,
+        }
+    }
+}
+
+impl crate::Module for QMatMul {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         match self {
             Self::QTensor(t) => xs.apply_op1_no_bwd(t.as_ref()),
             Self::Tensor(w) => {

candle-core/src/quantized/neon.rs

@@ -12,6 +12,14 @@ use core::arch::arm::*;
 #[cfg(target_arch = "aarch64")]
 use core::arch::aarch64::*;
 
+#[inline(always)]
+unsafe fn vdotq_s32(a: int8x16_t, b: int8x16_t) -> int32x4_t {
+    // TODO: dotprod
+    let p0 = vmull_s8(vget_low_s8(a), vget_low_s8(b));
+    let p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
+    vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1))
+}
+
 #[inline(always)]
 pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) -> Result<f32> {
     let qk = QK8_0;
@@ -43,15 +51,8 @@ pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) ->
             let v1_0l = vld1q_s8(y0.qs.as_ptr());
             let v1_0h = vld1q_s8(y0.qs.as_ptr().add(16));
 
-            // TODO: Support dotprod when it's available outside of nightly.
-            let pl0l = vmull_s8(vget_low_s8(v0_0ls), vget_low_s8(v1_0l));
-            let pl0h = vmull_s8(vget_high_s8(v0_0ls), vget_high_s8(v1_0l));
-            let ph0l = vmull_s8(vget_low_s8(v0_0hs), vget_low_s8(v1_0h));
-            let ph0h = vmull_s8(vget_high_s8(v0_0hs), vget_high_s8(v1_0h));
-
-            let pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
-            let ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
-
+            let pl0 = vdotq_s32(v0_0ls, v1_0l);
+            let ph0 = vdotq_s32(v0_0hs, v1_0h);
             sumv0 = vmlaq_n_f32(
                 sumv0,
                 vcvtq_f32_s32(vaddq_s32(pl0, ph0)),
@@ -82,14 +83,8 @@ pub(crate) fn vec_dot_q8_0_q8_0(n: usize, xs: &[BlockQ8_0], ys: &[BlockQ8_0]) ->
             let y0_0 = vld1q_s8(y0.qs.as_ptr());
             let y0_1 = vld1q_s8(y0.qs.as_ptr().add(16));
 
-            // TODO dotprod once this is the intrinsics are.
-            let p0_0 = vmull_s8(vget_low_s8(x0_0), vget_low_s8(y0_0));
-            let p0_1 = vmull_s8(vget_high_s8(x0_0), vget_high_s8(y0_0));
-            let p0_2 = vmull_s8(vget_low_s8(x0_1), vget_low_s8(y0_1));
-            let p0_3 = vmull_s8(vget_high_s8(x0_1), vget_high_s8(y0_1));
-
-            let p0 = vaddq_s32(vpaddlq_s16(p0_0), vpaddlq_s16(p0_1));
-            let p1 = vaddq_s32(vpaddlq_s16(p0_2), vpaddlq_s16(p0_3));
+            let p0 = vdotq_s32(x0_0, y0_0);
+            let p1 = vdotq_s32(x0_1, y0_1);
 
             sumv0 = vmlaq_n_f32(
                 sumv0,
@@ -118,10 +113,7 @@ pub(crate) fn vec_dot_q8k_q8k(n: usize, xs: &[BlockQ8K], ys: &[BlockQ8K]) -> Res
             for i in (0..QK_K).step_by(16) {
                 let xs = vld1q_s8(xs.add(i));
                 let ys = vld1q_s8(ys.add(i));
-                let xy_lo = vmull_s8(vget_low_s8(xs), vget_low_s8(ys));
-                let xy_up = vmull_s8(vget_high_s8(xs), vget_high_s8(ys));
-
-                let xy = vaddq_s32(vpaddlq_s16(xy_lo), vpaddlq_s16(xy_up));
+                let xy = vdotq_s32(xs, ys);
                 sum_i = vaddq_s32(sum_i, xy)
             }
             sumf += vaddvq_s32(sum_i) as f32 * scale
@@ -191,30 +183,16 @@ pub(crate) fn vec_dot_q6k_q8k(n: usize, xs: &[BlockQ6K], ys: &[BlockQ8K]) -> Res
                 let q6bytes_2 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.2, m4b), q6h_2));
                 let q6bytes_3 = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.3, m4b), q6h_3));
 
-                // TODO: dotprod
-
-                let p0 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_0), vget_low_s8(q8bytes.0)),
-                    vmull_s8(vget_high_s8(q6bytes_0), vget_high_s8(q8bytes.0)),
-                );
-                let p1 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_1), vget_low_s8(q8bytes.1)),
-                    vmull_s8(vget_high_s8(q6bytes_1), vget_high_s8(q8bytes.1)),
-                );
+                let p0 = vdotq_s32(q6bytes_0, q8bytes.0);
+                let p1 = vdotq_s32(q6bytes_1, q8bytes.1);
                 let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
-                isum += vaddvq_s16(p0) as i32 * scale0 + vaddvq_s16(p1) as i32 * scale1;
+                isum += vaddvq_s32(p0) * scale0 + vaddvq_s32(p1) * scale1;
                 scale = scale.add(2);
 
-                let p2 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_2), vget_low_s8(q8bytes.2)),
-                    vmull_s8(vget_high_s8(q6bytes_2), vget_high_s8(q8bytes.2)),
-                );
-                let p3 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_3), vget_low_s8(q8bytes.3)),
-                    vmull_s8(vget_high_s8(q6bytes_3), vget_high_s8(q8bytes.3)),
-                );
+                let p2 = vdotq_s32(q6bytes_2, q8bytes.2);
+                let p3 = vdotq_s32(q6bytes_3, q8bytes.3);
                 let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
-                isum += vaddvq_s16(p2) as i32 * scale0 + vaddvq_s16(p3) as i32 * scale1;
+                isum += vaddvq_s32(p2) * scale0 + vaddvq_s32(p3) * scale1;
                 scale = scale.add(2);
 
                 let q8bytes = vld1q_s8_x4(q8);
@@ -234,29 +212,16 @@ pub(crate) fn vec_dot_q6k_q8k(n: usize, xs: &[BlockQ6K], ys: &[BlockQ8K]) -> Res
                 let q6bytes_2 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.2, 4), q6h_2));
                 let q6bytes_3 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.3, 4), q6h_3));
 
-                // TODO: dotprod case.
-                let p0 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_0), vget_low_s8(q8bytes.0)),
-                    vmull_s8(vget_high_s8(q6bytes_0), vget_high_s8(q8bytes.0)),
-                );
-                let p1 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_1), vget_low_s8(q8bytes.1)),
-                    vmull_s8(vget_high_s8(q6bytes_1), vget_high_s8(q8bytes.1)),
-                );
+                let p0 = vdotq_s32(q6bytes_0, q8bytes.0);
+                let p1 = vdotq_s32(q6bytes_1, q8bytes.1);
                 let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
-                isum += vaddvq_s16(p0) as i32 * scale0 + vaddvq_s16(p1) as i32 * scale1;
+                isum += vaddvq_s32(p0) * scale0 + vaddvq_s32(p1) * scale1;
                 scale = scale.add(2);
 
-                let p2 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_2), vget_low_s8(q8bytes.2)),
-                    vmull_s8(vget_high_s8(q6bytes_2), vget_high_s8(q8bytes.2)),
-                );
-                let p3 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q6bytes_3), vget_low_s8(q8bytes.3)),
-                    vmull_s8(vget_high_s8(q6bytes_3), vget_high_s8(q8bytes.3)),
-                );
+                let p2 = vdotq_s32(q6bytes_2, q8bytes.2);
+                let p3 = vdotq_s32(q6bytes_3, q8bytes.3);
                 let (scale0, scale1) = (*scale as i32, *scale.add(1) as i32);
-                isum += vaddvq_s16(p2) as i32 * scale0 + vaddvq_s16(p3) as i32 * scale1;
+                isum += vaddvq_s32(p2) * scale0 + vaddvq_s32(p3) * scale1;
                 scale = scale.add(2);
             }
             sum += d_all * y.d * ((isum - 32 * isum_mins) as f32);
@@ -333,28 +298,14 @@ pub(crate) fn vec_dot_q5k_q8k(n: usize, xs: &[BlockQ5K], ys: &[BlockQ8K]) -> Res
                 let q5bytes_2 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.0, 4), q5h_2));
                 let q5bytes_3 = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q5bits.1, 4), q5h_3));
 
-                // TODO: dotprod
-
-                let p0 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q5bytes_0), vget_low_s8(q8bytes.0)),
-                    vmull_s8(vget_high_s8(q5bytes_0), vget_high_s8(q8bytes.0)),
-                );
-                let p1 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q5bytes_1), vget_low_s8(q8bytes.1)),
-                    vmull_s8(vget_high_s8(q5bytes_1), vget_high_s8(q8bytes.1)),
-                );
-                sumi += vaddvq_s16(vaddq_s16(p0, p1)) as i32 * *scales as i32;
+                let p0 = vdotq_s32(q5bytes_0, q8bytes.0);
+                let p1 = vdotq_s32(q5bytes_1, q8bytes.1);
+                sumi += vaddvq_s32(vaddq_s32(p0, p1)) * *scales as i32;
                 scales = scales.add(1);
 
-                let p2 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q5bytes_2), vget_low_s8(q8bytes.2)),
-                    vmull_s8(vget_high_s8(q5bytes_2), vget_high_s8(q8bytes.2)),
-                );
-                let p3 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q5bytes_3), vget_low_s8(q8bytes.3)),
-                    vmull_s8(vget_high_s8(q5bytes_3), vget_high_s8(q8bytes.3)),
-                );
-                sumi += vaddvq_s16(vaddq_s16(p2, p3)) as i32 * *scales as i32;
+                let p2 = vdotq_s32(q5bytes_2, q8bytes.2);
+                let p3 = vdotq_s32(q5bytes_3, q8bytes.3);
+                sumi += vaddvq_s32(vaddq_s32(p2, p3)) * *scales as i32;
                 scales = scales.add(1);
             }
             sumf += d * sumi as f32 - dmin * sumi_mins as f32;
@@ -417,22 +368,15 @@ pub(crate) fn vec_dot_q4k_q8k(n: usize, xs: &[BlockQ4K], ys: &[BlockQ8K]) -> Res
             for j in 0..QK_K / 64 {
                 let q4bits = vld1q_u8_x2(q4);
                 q4 = q4.add(32);
-                // TODO: dotprod
                 let q8bytes = vld1q_s8_x2(q8);
                 q8 = q8.add(32);
                 let q4bytes = int8x16x2_t(
                     vreinterpretq_s8_u8(vandq_u8(q4bits.0, m4b)),
                     vreinterpretq_s8_u8(vandq_u8(q4bits.1, m4b)),
                 );
-                let p0 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q4bytes.0), vget_low_s8(q8bytes.0)),
-                    vmull_s8(vget_high_s8(q4bytes.0), vget_high_s8(q8bytes.0)),
-                );
-                let p1 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q4bytes.1), vget_low_s8(q8bytes.1)),
-                    vmull_s8(vget_high_s8(q4bytes.1), vget_high_s8(q8bytes.1)),
-                );
-                sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) as i32 * scales[2 * j] as i32;
+                let p0 = vdotq_s32(q4bytes.0, q8bytes.0);
+                let p1 = vdotq_s32(q4bytes.1, q8bytes.1);
+                sumi1 += vaddvq_s32(vaddq_s32(p0, p1)) * scales[2 * j] as i32;
 
                 let q8bytes = vld1q_s8_x2(q8);
                 q8 = q8.add(32);
@@ -440,15 +384,9 @@ pub(crate) fn vec_dot_q4k_q8k(n: usize, xs: &[BlockQ4K], ys: &[BlockQ8K]) -> Res
                     vreinterpretq_s8_u8(vshrq_n_u8(q4bits.0, 4)),
                     vreinterpretq_s8_u8(vshrq_n_u8(q4bits.1, 4)),
                 );
-                let p2 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q4bytes.0), vget_low_s8(q8bytes.0)),
-                    vmull_s8(vget_high_s8(q4bytes.0), vget_high_s8(q8bytes.0)),
-                );
-                let p3 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q4bytes.1), vget_low_s8(q8bytes.1)),
-                    vmull_s8(vget_high_s8(q4bytes.1), vget_high_s8(q8bytes.1)),
-                );
-                sumi2 += vaddvq_s16(vaddq_s16(p2, p3)) as i32 * scales[2 * j + 1] as i32;
+                let p2 = vdotq_s32(q4bytes.0, q8bytes.0);
+                let p3 = vdotq_s32(q4bytes.1, q8bytes.1);
+                sumi2 += vaddvq_s32(vaddq_s32(p2, p3)) * scales[2 * j + 1] as i32;
             }
             sumf += d * (sumi1 + sumi2) as f32;
         }
@@ -526,27 +464,14 @@ pub(crate) fn vec_dot_q3k_q8k(n: usize, xs: &[BlockQ3K], ys: &[BlockQ8K]) -> Res
                     vreinterpretq_s8_u8(q3h_3),
                 );
 
-                // TODO: dotprod
-                let p0 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_0), vget_low_s8(q8bytes_1.0)),
-                    vmull_s8(vget_high_s8(q3bytes_0), vget_high_s8(q8bytes_1.0)),
-                );
-                let p1 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_1), vget_low_s8(q8bytes_1.1)),
-                    vmull_s8(vget_high_s8(q3bytes_1), vget_high_s8(q8bytes_1.1)),
-                );
-                let p2 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_2), vget_low_s8(q8bytes_1.2)),
-                    vmull_s8(vget_high_s8(q3bytes_2), vget_high_s8(q8bytes_1.2)),
-                );
-                let p3 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_3), vget_low_s8(q8bytes_1.3)),
-                    vmull_s8(vget_high_s8(q3bytes_3), vget_high_s8(q8bytes_1.3)),
-                );
-                isum += vaddvq_s16(p0) as i32 * *scale as i32
-                    + vaddvq_s16(p1) as i32 * *scale.add(1) as i32
-                    + vaddvq_s16(p2) as i32 * *scale.add(2) as i32
-                    + vaddvq_s16(p3) as i32 * *scale.add(3) as i32;
+                let p0 = vdotq_s32(q3bytes_0, q8bytes_1.0);
+                let p1 = vdotq_s32(q3bytes_1, q8bytes_1.1);
+                let p2 = vdotq_s32(q3bytes_2, q8bytes_1.2);
+                let p3 = vdotq_s32(q3bytes_3, q8bytes_1.3);
+                isum += vaddvq_s32(p0) * *scale as i32
+                    + vaddvq_s32(p1) * *scale.add(1) as i32
+                    + vaddvq_s32(p2) * *scale.add(2) as i32
+                    + vaddvq_s32(p3) * *scale.add(3) as i32;
                 scale = scale.add(4);
 
                 let q3h_0 = vbicq_u8(m2, qhbits.0);
@@ -571,27 +496,14 @@ pub(crate) fn vec_dot_q3k_q8k(n: usize, xs: &[BlockQ3K], ys: &[BlockQ8K]) -> Res
                     vreinterpretq_s8_u8(q3h_3),
                 );
 
-                // TODO: dotprod
-                let p0 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_0), vget_low_s8(q8bytes_2.0)),
-                    vmull_s8(vget_high_s8(q3bytes_0), vget_high_s8(q8bytes_2.0)),
-                );
-                let p1 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_1), vget_low_s8(q8bytes_2.1)),
-                    vmull_s8(vget_high_s8(q3bytes_1), vget_high_s8(q8bytes_2.1)),
-                );
-                let p2 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_2), vget_low_s8(q8bytes_2.2)),
-                    vmull_s8(vget_high_s8(q3bytes_2), vget_high_s8(q8bytes_2.2)),
-                );
-                let p3 = vaddq_s16(
-                    vmull_s8(vget_low_s8(q3bytes_3), vget_low_s8(q8bytes_2.3)),
-                    vmull_s8(vget_high_s8(q3bytes_3), vget_high_s8(q8bytes_2.3)),
-                );
-                isum += vaddvq_s16(p0) as i32 * *scale as i32
-                    + vaddvq_s16(p1) as i32 * *scale.add(1) as i32
-                    + vaddvq_s16(p2) as i32 * *scale.add(2) as i32
-                    + vaddvq_s16(p3) as i32 * *scale.add(3) as i32;
+                let p0 = vdotq_s32(q3bytes_0, q8bytes_2.0);
+                let p1 = vdotq_s32(q3bytes_1, q8bytes_2.1);
+                let p2 = vdotq_s32(q3bytes_2, q8bytes_2.2);
+                let p3 = vdotq_s32(q3bytes_3, q8bytes_2.3);
+                isum += vaddvq_s32(p0) * *scale as i32
+                    + vaddvq_s32(p1) * *scale.add(1) as i32
+                    + vaddvq_s32(p2) * *scale.add(2) as i32
+                    + vaddvq_s32(p3) * *scale.add(3) as i32;
                 scale = scale.add(4);
 
                 if j == 0 {
@@ -649,7 +561,6 @@ pub(crate) fn vec_dot_q2k_q8k(n: usize, xs: &[BlockQ2K], ys: &[BlockQ8K]) -> Res
             let mut is = 0usize;
 
             // TODO: dotprod
-
             for _j in 0..QK_K / 128 {
                 let q2bits = vld1q_u8_x2(q2);
                 q2 = q2.add(32);
@@ -696,14 +607,7 @@ unsafe fn multiply_accum_with_scale(
     q2bytes: int8x16x2_t,
     q8bytes: int8x16x2_t,
 ) -> i32 {
-    let p1 = vaddq_s16(
-        vmull_s8(vget_low_s8(q2bytes.0), vget_low_s8(q8bytes.0)),
-        vmull_s8(vget_high_s8(q2bytes.0), vget_high_s8(q8bytes.0)),
-    );
-    let p2 = vaddq_s16(
-        vmull_s8(vget_low_s8(q2bytes.1), vget_low_s8(q8bytes.1)),
-        vmull_s8(vget_high_s8(q2bytes.1), vget_high_s8(q8bytes.1)),
-    );
-    vaddvq_s16(p1) as i32 * aux[is + index] as i32
-        + vaddvq_s16(p2) as i32 * aux[is + 1 + index] as i32
+    let p1 = vdotq_s32(q2bytes.0, q8bytes.0);
+    let p2 = vdotq_s32(q2bytes.1, q8bytes.1);
+    vaddvq_s32(p1) * aux[is + index] as i32 + vaddvq_s32(p2) * aux[is + 1 + index] as i32
 }

candle-core/src/shape.rs

@@ -478,23 +478,6 @@ extract_dims!(
     (usize, usize, usize, usize, usize)
 );
 
-#[cfg(test)]
-mod tests {
-    use super::*;
-
-    #[test]
-    fn stride() {
-        let shape = Shape::from(());
-        assert_eq!(shape.stride_contiguous(), Vec::<usize>::new());
-        let shape = Shape::from(42);
-        assert_eq!(shape.stride_contiguous(), [1]);
-        let shape = Shape::from((42, 1337));
-        assert_eq!(shape.stride_contiguous(), [1337, 1]);
-        let shape = Shape::from((299, 792, 458));
-        assert_eq!(shape.stride_contiguous(), [458 * 792, 458, 1]);
-    }
-}
-
 pub trait ShapeWithOneHole {
     fn into_shape(self, el_count: usize) -> Result<Shape>;
 }
@@ -627,3 +610,20 @@ impl ShapeWithOneHole for (usize, usize, usize, usize, ()) {
         Ok((d1, d2, d3, d4, d).into())
     }
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn stride() {
+        let shape = Shape::from(());
+        assert_eq!(shape.stride_contiguous(), Vec::<usize>::new());
+        let shape = Shape::from(42);
+        assert_eq!(shape.stride_contiguous(), [1]);
+        let shape = Shape::from((42, 1337));
+        assert_eq!(shape.stride_contiguous(), [1337, 1]);
+        let shape = Shape::from((299, 792, 458));
+        assert_eq!(shape.stride_contiguous(), [458 * 792, 458, 1]);
+    }
+}

candle-core/src/tensor.rs

@@ -1,4 +1,4 @@
-//! Tensors are N-dimenional matrixes of elements using a single data type.
+//! Tensors are N-dimensional matrixes of elements using a single data type.
 #![allow(clippy::redundant_closure_call)]
 use crate::backend::{BackendDevice, BackendStorage};
 use crate::op::{
@@ -157,8 +157,6 @@ pub(crate) fn from_storage<S: Into<Shape>>(
 ) -> Tensor {
     let dtype = storage.dtype();
     let device = storage.device();
-    let shape = shape.into();
-    // println!("{:?} {storage:?}", shape);
     let tensor_ = Tensor_ {
         id: TensorId::new(),
         storage: Arc::new(RwLock::new(storage)),
@@ -168,11 +166,7 @@ pub(crate) fn from_storage<S: Into<Shape>>(
         dtype,
         device,
     };
-    let result = Tensor(Arc::new(tensor_));
-    // todo!(" from_storage");
-    // let result = result.to_device(&Device::Cpu).unwrap();
-    // todo!(" {result}");
-    result
+    Tensor(Arc::new(tensor_))
 }
 
 impl Tensor {
@@ -367,6 +361,16 @@ impl Tensor {
         Self::new_impl(array, shape, device, false)
     }
 
+    /// Returns a new tensor with all the elements having the same specified value. Note that
+    /// the tensor is not contiguous so you would have to call `.contiguous()` on it if needed.
+    pub fn full<D: crate::WithDType, S: Into<Shape>>(
+        value: D,
+        shape: S,
+        device: &Device,
+    ) -> Result<Self> {
+        Self::from_vec_impl(vec![value], (), device, false)?.broadcast_as(shape)
+    }
+
     /// Creates a new 1D tensor from an iterator.
     pub fn from_iter<D: crate::WithDType>(
         iter: impl IntoIterator<Item = D>,
@@ -392,7 +396,7 @@ impl Tensor {
         device: &Device,
     ) -> Result<Self> {
         if D::is_zero(&step) {
-            crate::bail!("step cannot be zero")
+            bail!("step cannot be zero")
         }
         let mut data = vec![];
         let mut current = start;
@@ -675,7 +679,7 @@ impl Tensor {
     }
 
     /// Split a tensor into the specified number of chunks, this may return less chunks than
-    /// specificed.
+    /// specified.
     pub fn chunk<D: Dim>(&self, chunks: usize, dim: D) -> Result<Vec<Self>> {
         let dim = dim.to_index(self.shape(), "chunk")?;
         let size = self.dim(dim)?;
@@ -862,6 +866,20 @@ impl Tensor {
         self.sum_impl(mean_dims, false)? * scale
     }
 
+    /// Returns the unbiased variance over the selected dimension.
+    pub fn var_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "var")?;
+        let mean = self.mean_keepdim(dim)?;
+        let squares = self.broadcast_sub(&mean)?.sqr()?;
+        squares.sum_impl(dim, true)? / (self.dim(dim)? - 1) as f64
+    }
+
+    /// Returns the unbiased variance over the selected dimension.
+    pub fn var<D: Dim>(&self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "var")?;
+        self.var_keepdim(dim)?.squeeze(dim)
+    }
+
     /// Gathers the maximum value across the selected dimension. The resulting shape has the same
     /// number of dimensions as the original tensor and the select dimension has a single element.
     pub fn max_keepdim<D: Dim>(&self, dim: D) -> Result<Self> {
@@ -986,7 +1004,11 @@ impl Tensor {
     /// tensor also has four dimensions, `(batch, channels, target_h, target_w)`.
     pub fn interpolate2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
         let (n, c, _h, _w) = self.dims4()?;
-        let op = BackpropOp::new1(self, Op::UpsampleNearest2D);
+        let op = BackpropOp::new1(self, |arg| Op::UpsampleNearest2D {
+            arg,
+            target_h,
+            target_w,
+        });
         let storage = self
             .storage()
             .upsample_nearest2d(self.layout(), target_h, target_w)?;
@@ -1019,6 +1041,9 @@ impl Tensor {
         let kernel_size = kernel_size.to_usize2();
         let stride = stride.to_usize2();
         let (n, c, h, w) = self.dims4()?;
+        if h < kernel_size.0 || w < kernel_size.1 {
+            bail!("kernel-size {kernel_size:?} is larger than the input size {h},{w}")
+        }
         // https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html#torch.nn.AvgPool2d
         let h_out = (h - kernel_size.0) / stride.0 + 1;
         let w_out = (w - kernel_size.1) / stride.1 + 1;
@@ -1054,6 +1079,9 @@ impl Tensor {
         let kernel_size = kernel_size.to_usize2();
         let stride = stride.to_usize2();
         let (n, c, h, w) = self.dims4()?;
+        if h < kernel_size.0 || w < kernel_size.1 {
+            bail!("kernel-size {kernel_size:?} is larger than the input size {h},{w}")
+        }
         // https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html#torch.nn.MaxPool2d
         let h_out = (h - kernel_size.0) / stride.0 + 1;
         let w_out = (w - kernel_size.1) / stride.1 + 1;
@@ -1776,7 +1804,7 @@ impl Tensor {
         let is_permutation =
             dims.len() == self.rank() && (0..dims.len()).all(|i| dims.contains(&i));
         if !is_permutation {
-            crate::bail!(
+            bail!(
                 "dimension mismatch in permute, tensor {:?}, dims: {:?}",
                 self.dims(),
                 dims
@@ -2271,7 +2299,7 @@ impl Tensor {
         if left == 0 && right == 0 {
             Ok(self.clone())
         } else if self.elem_count() == 0 {
-            crate::bail!("cannot use pad_with_same on an empty tensor")
+            bail!("cannot use pad_with_same on an empty tensor")
         } else if left == 0 {
             let dim = dim.to_index(self.shape(), "pad_with_same")?;
             let r = self.narrow(dim, self.dim(dim)? - 1, 1)?;
@@ -2435,17 +2463,136 @@ impl Tensor {
     pub fn normalize_axis(&self, axis: i64) -> Result<usize> {
         let rank = self.rank() as i64;
         if rank <= axis {
-            crate::bail!("axis {axis} is too large, tensor rank {rank}")
+            bail!("axis {axis} is too large, tensor rank {rank}")
         } else if 0 <= axis {
             Ok(axis as usize)
         } else {
             let naxis = rank + axis;
             if naxis < 0 {
-                crate::bail!("axis {axis} is too small, tensor rank {rank}")
+                bail!("axis {axis} is too small, tensor rank {rank}")
             }
             Ok(naxis as usize)
         }
     }
+
+    /// Returns a lower triangular matrix of ones of size n by n.
+    pub fn tril2(n: usize, dtype: DType, device: &Device) -> Result<Self> {
+        let t = Tensor::arange(0u32, n as u32, device)?;
+        let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
+        let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
+        t1.le(&t2)?.to_dtype(dtype)
+    }
+
+    /// Returns an upper triangular matrix of ones of size n by n.
+    pub fn triu2(n: usize, dtype: DType, device: &Device) -> Result<Self> {
+        let t = Tensor::arange(0u32, n as u32, device)?;
+        let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
+        let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
+        t1.ge(&t2)?.to_dtype(dtype)
+    }
+
+    /// Returns a matrix with a diagonal of ones of size n by n.
+    pub fn eye(n: usize, dtype: DType, device: &Device) -> Result<Self> {
+        let t = Tensor::arange(0u32, n as u32, device)?;
+        let t1 = t.reshape((1, n))?.broadcast_as((n, n))?;
+        let t2 = t.reshape((n, 1))?.broadcast_as((n, n))?;
+        t1.eq(&t2)?.to_dtype(dtype)
+    }
+
+    /// Returns the cumulative sum of elements of the input tensor summed over the specified
+    /// dimension.
+    ///
+    /// This operation is most efficient when dim is the last dimension of the tensor.
+    pub fn cumsum<D: Dim>(&self, dim: D) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "cumsum")?;
+        let rank = self.rank();
+        if rank == 0 {
+            return Ok(self.clone());
+        }
+        let n_axis = self.dim(dim)?;
+        let triu = Tensor::triu2(n_axis, self.dtype(), self.device())?;
+        if rank == 1 {
+            self.unsqueeze(0)?.matmul(&triu)?.squeeze(0)
+        } else {
+            let last = rank - 1;
+            let t = self.transpose(dim, last)?;
+            let t = t.broadcast_matmul(&triu)?;
+            t.transpose(dim, last)
+        }
+    }
+
+    /// Returns a copy of `self` where the values within `ranges` have been replaced with the
+    /// content of `src`.
+    pub fn slice_assign<D: std::ops::RangeBounds<usize>>(
+        &self,
+        ranges: &[D],
+        src: &Tensor,
+    ) -> Result<Self> {
+        let src_dims = src.dims();
+        let self_dims = self.dims();
+        if self_dims.len() != src_dims.len() {
+            bail!(
+                "slice-assign requires input with the same rank {} <> {}",
+                self_dims.len(),
+                src_dims.len()
+            )
+        }
+        if self_dims.len() != ranges.len() {
+            bail!(
+                "slice-assign requires input with the same rank as there are ranges {} <> {}",
+                self_dims.len(),
+                ranges.len()
+            )
+        }
+        let mut src = src.clone();
+        let mut mask = Self::ones(src.shape(), DType::U8, src.device())?;
+        for (i, range) in ranges.iter().enumerate() {
+            let start_included = match range.start_bound() {
+                std::ops::Bound::Unbounded => 0,
+                std::ops::Bound::Included(v) => *v,
+                std::ops::Bound::Excluded(v) => *v + 1,
+            };
+            let end_excluded = match range.end_bound() {
+                std::ops::Bound::Unbounded => self_dims[i],
+                std::ops::Bound::Included(v) => *v + 1,
+                std::ops::Bound::Excluded(v) => *v,
+            };
+            if end_excluded <= start_included {
+                bail!("slice-assign: empty range for dim {i}, {start_included} {end_excluded}")
+            }
+            if self_dims[i] < end_excluded {
+                bail!(
+                    "slice-assign: upper bound is out of range for dim {i}, {end_excluded} {}",
+                    self_dims[i]
+                )
+            }
+            if end_excluded - start_included != src_dims[i] {
+                bail!(
+                    "slice-assign: the range for dim {i} ({start_included}..{end_excluded}) does not match the size of src {}", src_dims[i]
+                )
+            }
+            src = src.pad_with_zeros(i, start_included, self_dims[i] - end_excluded)?;
+            mask = mask.pad_with_zeros(i, start_included, self_dims[i] - end_excluded)?
+        }
+        mask.where_cond(/* on_true= */ &src, /* on_false= */ self)
+    }
+
+    /// Returns log(sum(exp(tensor), dim)).
+    pub fn log_sum_exp<D: Dims>(&self, sum_dims: D) -> Result<Self> {
+        let exp = self.exp()?;
+        let sum = exp.sum(sum_dims)?;
+        sum.log()
+    }
+
+    /// Pointwise pow operation.
+    pub fn pow(&self, rhs: &Tensor) -> Result<Self> {
+        rhs.mul(&self.log()?)?.exp()
+    }
+
+    /// Broadcasting version of `pow`.
+    pub fn broadcast_pow(&self, rhs: &Tensor) -> Result<Self> {
+        rhs.broadcast_mul(&self.log()?)?.exp()
+    }
 }
 
 macro_rules! bin_trait {

candle-core/src/test_utils.rs

@@ -4,7 +4,7 @@ use crate::{Result, Tensor};
 macro_rules! test_device {
     // TODO: Switch to generating the two last arguments automatically once concat_idents is
     // stable. https://github.com/rust-lang/rust/issues/29599
-    ($fn_name: ident, $test_cpu: ident, $test_cuda: ident) => {
+    ($fn_name: ident, $test_cpu: ident, $test_cuda: ident, $test_metal: ident) => {
         #[test]
         fn $test_cpu() -> Result<()> {
             $fn_name(&Device::Cpu)
@@ -15,6 +15,12 @@ macro_rules! test_device {
         fn $test_cuda() -> Result<()> {
             $fn_name(&Device::new_cuda(0)?)
         }
+
+        #[cfg(feature = "metal")]
+        #[test]
+        fn $test_metal() -> Result<()> {
+            $fn_name(&Device::new_metal(0)?)
+        }
     };
 }
 

candle-core/tests/conv_tests.rs

@@ -563,14 +563,35 @@ fn conv2d_grad(dev: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(conv1d, conv1d_cpu, conv1d_gpu);
-test_device!(conv1d_small, conv1d_small_cpu, conv1d_small_gpu);
-test_device!(conv2d, conv2d_cpu, conv2d_gpu);
+test_device!(conv1d, conv1d_cpu, conv1d_gpu, conv1d_metal);
+test_device!(
+    conv1d_small,
+    conv1d_small_cpu,
+    conv1d_small_gpu,
+    conv1d_small_metal
+);
+test_device!(conv2d, conv2d_cpu, conv2d_gpu, conv2d_metal);
 test_device!(
     conv2d_non_square,
     conv2d_non_square_cpu,
-    conv2d_non_square_gpu
+    conv2d_non_square_gpu,
+    conv2d_non_square_metal
+);
+test_device!(
+    conv2d_small,
+    conv2d_small_cpu,
+    conv2d_small_gpu,
+    conv2d_small_metal
+);
+test_device!(
+    conv2d_smaller,
+    conv2d_smaller_cpu,
+    conv2d_smaller_gpu,
+    conv2d_smaller_metal
+);
+test_device!(
+    conv2d_grad,
+    conv2d_grad_cpu,
+    conv2d_grad_gpu,
+    conv2_grad_metal
 );
-test_device!(conv2d_small, conv2d_small_cpu, conv2d_small_gpu);
-test_device!(conv2d_smaller, conv2d_smaller_cpu, conv2d_smaller_gpu);
-test_device!(conv2d_grad, conv2d_grad_cpu, conv2d_grad_gpu);

candle-core/tests/grad_tests.rs

@@ -270,6 +270,166 @@ fn unary_grad(device: &Device) -> Result<()> {
         [0.7358, 2.0000, 0.2707, 1.0000]
     );
 
+    // 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.,
+        ],
+        device,
+    )?;
+    // gradient should be
+    // row 1
+    // 1+2+7+8 = 18
+    // 3+4+9+10 = 26
+    // 5+6+11+12 = 34
+    // row 2
+    // 13+14+19+20 = 66
+    // 15+16+21+22 = 74
+    // 17+18+23+24 = 82
+    // row 3
+    // 25+26+31+32 = 114
+    // 27+28+33+34 = 122
+    // 29+30+35+36 = 130
+    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_vec2_round(&grad_x.flatten(0, 2)?, 4)?,
+        [[18_f32, 26., 34.], [66., 74., 82.], [114., 122., 130.]]
+    );
+
+    // manually checked: see comments
+    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.,
+        ],
+        device,
+    )?;
+    // gradient should be
+    // row 1
+    // 1+2+3+7+8+9+13+14+15 = 72
+    // 4+5+6+10+11+12+16+17+18 = 99
+    // row 2
+    // 19+20+21+25+26+27+31+32+33 = 234
+    // 22+23+24+28+29+30+34+35+36 = 243
+    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_vec2_round(&grad_x.flatten(0, 2)?, 4)?,
+        [[72_f32, 99.], [234., 261.]]
+    );
+
+    // manually checked: see comments
+    let x = Var::new(&[[[[1f32, 2.], [4., 5.]], [[6f32, 7.], [8., 9.]]]], device)?;
+
+    let y = x.interpolate2d(4, 4)?.reshape(32)?;
+
+    #[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.
+        ],
+        device,
+    )?;
+    // gradient should be
+    // m1r1
+    // 1+2+5+6=14
+    // 3+4+7+8=22
+    // m1r2
+    // 9+10+13+14=46
+    // 11+12+15+16=54
+    // m2r1
+    // 17+18+21+22=78
+    // 19+20+23+24=86
+    // m2r2
+    // 25+26+29+30=110
+    // 27+28+31+32=118
+    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.flatten(0, 1)?, 4)?,
+        [[[14_f32, 22.], [46., 54.]], [[78., 86.], [110., 118.]]]
+    );
+
+    // manually checked: see comments
+    let x = Var::new(
+        &[[[[1f32, 2.], [4., 5.]]], [[[6f32, 7.], [8., 9.]]]],
+        device,
+    )?;
+
+    let y = x.interpolate2d(4, 4)?.reshape(32)?;
+
+    #[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.
+           ],
+           device,
+       )?;
+    // gradient should be
+    // m1r1
+    // 1+2+5+6=14
+    // 3+4+7+8=22
+    // m1r2
+    // 9+10+13+14=46
+    // 11+12+15+16=54
+    // m2r1
+    // 17+18+21+22=78
+    // 19+20+23+24=86
+    // m2r2
+    // 25+26+29+30=110
+    // 27+28+31+32=118
+    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.flatten(0, 1)?, 4)?,
+        [[[14_f32, 22.], [46., 54.]], [[78., 86.], [110., 118.]]]
+    );
     Ok(())
 }
 
@@ -315,9 +475,29 @@ fn binary_grad(device: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(simple_grad, simple_grad_cpu, simple_grad_gpu);
-test_device!(sum_grad, sum_grad_cpu, sum_grad_gpu);
-test_device!(matmul_grad, matmul_grad_cpu, matmul_grad_gpu);
-test_device!(grad_descent, grad_descent_cpu, grad_descent_gpu);
-test_device!(unary_grad, unary_grad_cpu, unary_grad_gpu);
-test_device!(binary_grad, binary_grad_cpu, binary_grad_gpu);
+test_device!(
+    simple_grad,
+    simple_grad_cpu,
+    simple_grad_gpu,
+    simple_grad_metal
+);
+test_device!(sum_grad, sum_grad_cpu, sum_grad_gpu, sum_grad_metal);
+test_device!(
+    matmul_grad,
+    matmul_grad_cpu,
+    matmul_grad_gpu,
+    matmul_grad_metal
+);
+test_device!(
+    grad_descent,
+    grad_descent_cpu,
+    grad_descent_gpu,
+    grad_descent_metal
+);
+test_device!(unary_grad, unary_grad_cpu, unary_grad_gpu, unary_grad_metal);
+test_device!(
+    binary_grad,
+    binary_grad_cpu,
+    binary_grad_gpu,
+    binary_grad_metal
+);

candle-core/tests/indexing_tests.rs

@@ -91,3 +91,32 @@ fn index_3d() -> Result<()> {
     assert_eq!(tensor.i((1, .., 3))?.to_vec1::<u32>()?, &[15, 19, 23]);
     Ok(())
 }
+
+#[test]
+fn slice_assign() -> Result<()> {
+    let dev = Device::Cpu;
+
+    let tensor = Tensor::arange(0u32, 4 * 5, &dev)?.reshape((4, 5))?;
+    let src = Tensor::arange(0u32, 2 * 3, &dev)?.reshape((3, 2))?;
+    let out = tensor.slice_assign(&[1..4, 3..5], &src)?;
+    assert_eq!(
+        out.to_vec2::<u32>()?,
+        &[
+            [0, 1, 2, 3, 4],
+            [5, 6, 7, 0, 1],
+            [10, 11, 12, 2, 3],
+            [15, 16, 17, 4, 5]
+        ]
+    );
+    let out = tensor.slice_assign(&[0..3, 0..2], &src)?;
+    assert_eq!(
+        out.to_vec2::<u32>()?,
+        &[
+            [0, 1, 2, 3, 4],
+            [2, 3, 7, 8, 9],
+            [4, 5, 12, 13, 14],
+            [15, 16, 17, 18, 19]
+        ]
+    );
+    Ok(())
+}

candle-core/tests/layout_tests.rs

@@ -49,7 +49,7 @@ fn contiguous(device: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(contiguous, contiguous_cpu, contiguous_gpu);
+test_device!(contiguous, contiguous_cpu, contiguous_gpu, contiguous_metal);
 
 #[test]
 fn strided_blocks() -> Result<()> {

candle-core/tests/pool_tests.rs

@@ -98,15 +98,17 @@ fn upsample_nearest2d(dev: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(avg_pool2d, avg_pool2d_cpu, avg_pool2d_gpu);
+test_device!(avg_pool2d, avg_pool2d_cpu, avg_pool2d_gpu, avg_pool2d_metal);
 test_device!(
     avg_pool2d_pytorch,
     avg_pool2d_pytorch_cpu,
-    avg_pool2d_pytorch_gpu
+    avg_pool2d_pytorch_gpu,
+    avg_pool2d_pytorch_metal
 );
-test_device!(max_pool2d, max_pool2d_cpu, max_pool2d_gpu);
+test_device!(max_pool2d, max_pool2d_cpu, max_pool2d_gpu, max_pool2d_metal);
 test_device!(
     upsample_nearest2d,
     upsample_nearest2d_cpu,
-    upsample_nearest2d_gpu
+    upsample_nearest2d_gpu,
+    upsample_nearest2d_metal
 );

candle-core/tests/quantized_tests.rs

@@ -1,7 +1,9 @@
 use candle_core::{
+    bail,
     quantized::{self, GgmlDType},
+    test_device,
     test_utils::to_vec2_round,
-    Device, Result, Tensor,
+    Device, Module, Result, Tensor,
 };
 use quantized::{k_quants, GgmlType};
 use rand::prelude::*;
@@ -13,16 +15,48 @@ const GGML_MAX_QUANTIZATION_TOTAL_ERROR_2BITS: f32 = 0.0075;
 const GGML_MAX_QUANTIZATION_TOTAL_ERROR_3BITS: f32 = 0.0040;
 const GGML_MAX_DOT_PRODUCT_ERROR: f32 = 0.02;
 
-#[test]
-fn quantized_matmul() -> Result<()> {
-    let cpu = &Device::Cpu;
+fn test_matmul(
+    device: &Device,
+    (b, m, n, k): (usize, usize, usize, usize),
+    dtype: GgmlDType,
+) -> Result<()> {
+    let lhs = (0..(m * k))
+        .map(|v| v as f32 / (m * k) as f32)
+        .collect::<Vec<_>>();
+    let rhs = (0..(k * n))
+        .map(|v| v as f32 / (n * k) as f32)
+        .collect::<Vec<_>>();
+
+    let lhs = Tensor::from_slice(&lhs, (m, k), device)?;
+    let rhs = Tensor::from_slice(&rhs, (k, n), device)?;
+    let mm = lhs.matmul(&rhs)?;
+    let qtensor = quantized::QTensor::quantize(&rhs.t()?, dtype)?;
+    let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
+    let res = matmul.forward(&lhs)?;
+
+    let error: f32 = ((&mm - &res)?.abs()? / &mm.abs()?)?
+        .sum_all()?
+        .to_scalar()?;
+    let error = error / (b * m * n) as f32;
+    assert!(
+        error <= 0.02,
+        "Error {error} is too big. \nExpected:\n {mm} \nFound:\n {res}\n for {dtype:?}"
+    );
+
+    Ok(())
+}
+
+fn quantized_matmul(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let (m, k, n) = (3, 64, 4);
     let lhs = (0..(m * k)).map(|v| v as f32).collect::<Vec<_>>();
-    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), cpu)?;
+    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), device)?;
     let mut dst = vec![42.; 3 * 4];
     let mut rhs_t = vec![k_quants::BlockQ4_0::zeros(); 8];
     let rhs = (0..(k * n)).map(|v| v as f32).collect::<Vec<_>>();
-    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), cpu)?.t()?;
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
     k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
@@ -32,6 +66,7 @@ fn quantized_matmul() -> Result<()> {
             341876.0, 994283.0, 1655709.0, 2301518.0
         ]
     );
+    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), device)?.t()?;
     let mm = tensor_lhs.matmul(&tensor_rhs)?;
     assert_eq!(
         mm.to_vec2::<f32>()?,
@@ -42,35 +77,49 @@ fn quantized_matmul() -> Result<()> {
         ]
     );
 
-    let qtensor = quantized::QTensor::new(rhs_t, (4, 64))?;
+    let qtensor = quantized::QTensor::quantize(&tensor_rhs.t()?, GgmlDType::Q4_0)?;
     let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
     let res = matmul.forward(&tensor_lhs)?;
-    assert_eq!(
-        to_vec2_round(&res, 0)?,
-        &[
-            [85120.0, 214562.0, 345455.0, 474748.0],
-            [213475.0, 604465.0, 1000686.0, 1388317.0],
-            [341876.0, 994283.0, 1655709.0, 2301518.0]
-        ]
-    );
+    match device {
+        Device::Metal(_) => assert_eq!(
+            to_vec2_round(&res, 0)?,
+            &[
+                [84946.0, 214126.0, 344757.0, 473798.0],
+                [213458.0, 604350.0, 1000469.0, 1387990.0],
+                [341970.0, 994574.0, 1656181.0, 2302182.0]
+            ]
+        ),
+        _ => assert_eq!(
+            to_vec2_round(&res, 0)?,
+            &[
+                [85120.0, 214562.0, 345455.0, 474748.0],
+                [213475.0, 604465.0, 1000686.0, 1388317.0],
+                [341876.0, 994283.0, 1655709.0, 2301518.0]
+            ]
+        ),
+    }
+
+    test_matmul(device, (1, 3, 4, 256), GgmlDType::Q4_0)?;
 
     Ok(())
 }
 
-#[test]
-fn quantized_matmul_neg() -> Result<()> {
-    let cpu = &Device::Cpu;
+fn quantized_matmul_neg(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let (m, k, n) = (3, 64, 4);
     let lhs = (0..(m * k))
         .map(|v| v as f32 - (m * k) as f32 / 2.0)
         .collect::<Vec<_>>();
-    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), cpu)?;
+    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), device)?;
     let mut dst = vec![42.; 3 * 4];
     let mut rhs_t = vec![k_quants::BlockQ4_0::zeros(); 8];
     let rhs = (0..k * n)
         .map(|v| v as f32 - (k * n) as f32 / 3.0)
         .collect::<Vec<_>>();
-    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), cpu)?.t()?;
+    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), device)?.t()?;
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
     k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
@@ -90,32 +139,56 @@ fn quantized_matmul_neg() -> Result<()> {
         ]
     );
 
-    let qtensor = quantized::QTensor::new(rhs_t, (4, 64))?;
+    let qtensor = quantized::QTensor::quantize(&tensor_rhs.t()?, GgmlDType::Q4_0)?;
     let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
     let res = matmul.forward(&tensor_lhs)?;
-    assert_eq!(
-        to_vec2_round(&res, 0)?,
-        &[
-            [243524.0, -19596.0, -285051.0, -549815.0],
-            [23777.0, 21651.0, 19398.0, 18367.0],
-            [-196472.0, 63012.0, 324585.0, 587902.0]
-        ]
-    );
+    match device {
+        Device::Metal(_) => assert_eq!(
+            to_vec2_round(&res, 0)?,
+            &[
+                [243666.0, -19714.0, -285433.0, -550453.0],
+                [23782.0, 21654.0, 19400.0, 18369.0],
+                [-196102.0, 63022.0, 324233.0, 587191.0]
+            ]
+        ),
+        _ => assert_eq!(
+            to_vec2_round(&res, 0)?,
+            &[
+                [243524.0, -19596.0, -285051.0, -549815.0],
+                [23777.0, 21651.0, 19398.0, 18367.0],
+                [-196472.0, 63012.0, 324585.0, 587902.0]
+            ]
+        ),
+    }
 
     Ok(())
 }
 
-#[test]
-fn quantize_q4_0() -> Result<()> {
-    use k_quants::BlockQ4_0;
+test_device!(
+    quantized_matmul,
+    quantized_matmul_cpu,
+    quantized_matmul_cuda,
+    quantized_matmul_metal
+);
+test_device!(
+    quantized_matmul_neg,
+    quantized_matmul_neg_cpu,
+    quantized_matmul_neg_cuda,
+    quantized_matmul_neg_metal
+);
 
+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 mut dst = vec![0f32; 32 * 4];
-    let mut quant = vec![BlockQ4_0::zeros(); 4];
-    BlockQ4_0::from_float(&src, &mut quant)?;
-    BlockQ4_0::to_float(&quant, dst.as_mut_slice())?;
+
+    let src = Tensor::from_slice(&src, (32 * 4,), device)?;
+    let quant = quantized::QTensor::quantize(&src, GgmlDType::Q4_0)?;
+    let dst = quant.dequantize(device)?;
     assert_eq!(
-        dst,
+        dst.to_vec1::<f32>()?,
         &[
             -0.0, -0.0, 3.875, 3.875, 3.875, 3.875, 7.75, 7.75, 7.75, 7.75, 11.625, 11.625, 11.625,
             11.625, 15.5, 15.5, 15.5, 15.5, 19.375, 19.375, 19.375, 19.375, 23.25, 23.25, 23.25,
@@ -131,21 +204,21 @@ fn quantize_q4_0() -> Result<()> {
             127.0, 127.0
         ]
     );
-    ggml_quantization_error_test::<BlockQ4_0>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test(GgmlDType::Q4_0, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q4_1() -> Result<()> {
-    use k_quants::BlockQ4_1;
-
+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 mut dst = vec![0f32; 32 * 4];
-    let mut quant = vec![BlockQ4_1::zeros(); 4];
-    BlockQ4_1::from_float(&src, &mut quant)?;
-    BlockQ4_1::to_float(&quant, dst.as_mut_slice())?;
+    let src = Tensor::from_slice(&src, (32 * 4,), device)?;
+    let quant = quantized::QTensor::quantize(&src, GgmlDType::Q4_1)?;
+    let dst = quant.dequantize(device)?;
     assert_eq!(
-        round_vector(&dst),
+        round_vector(&dst.to_vec1::<f32>()?),
         &[
             0.0, 0.0, 2.066, 2.066, 4.133, 4.133, 6.199, 6.199, 8.266, 8.266, 10.332, 10.332,
             12.398, 12.398, 14.465, 14.465, 16.531, 16.531, 18.598, 18.598, 20.664, 20.664, 22.73,
@@ -161,21 +234,21 @@ fn quantize_q4_1() -> Result<()> {
             118.73, 118.73, 120.797, 120.797, 122.863, 122.863, 124.93, 124.93, 126.996, 126.996
         ]
     );
-    ggml_quantization_error_test::<BlockQ4_1>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test(GgmlDType::Q4_1, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q5_0() -> Result<()> {
-    use k_quants::BlockQ5_0;
-
+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 mut dst = vec![0f32; 32 * 4];
-    let mut quant = vec![BlockQ5_0::zeros(); 4];
-    BlockQ5_0::from_float(&src, &mut quant)?;
-    BlockQ5_0::to_float(&quant, dst.as_mut_slice())?;
+    let src = Tensor::from_slice(&src, (32 * 4,), device)?;
+    let quant = quantized::QTensor::quantize(&src, GgmlDType::Q5_0)?;
+    let dst = quant.dequantize(device)?;
     assert_eq!(
-        round_vector(&dst),
+        round_vector(&dst.to_vec1::<f32>()?),
         &[
             -0.0, 1.938, 1.938, 3.875, 3.875, 5.813, 5.813, 7.75, 7.75, 9.688, 9.688, 11.625,
             11.625, 13.563, 13.563, 15.5, 15.5, 17.438, 17.438, 19.375, 19.375, 21.313, 21.313,
@@ -191,21 +264,21 @@ fn quantize_q5_0() -> Result<()> {
             119.063, 119.063, 119.063, 119.063, 127.0, 127.0, 127.0, 127.0
         ]
     );
-    ggml_quantization_error_test::<BlockQ5_0>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test(GgmlDType::Q5_0, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q5_1() -> Result<()> {
-    use k_quants::BlockQ5_1;
-
+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 mut dst = vec![0f32; 32 * 4];
-    let mut quant = vec![BlockQ5_1::zeros(); 4];
-    BlockQ5_1::from_float(&src, &mut quant)?;
-    BlockQ5_1::to_float(&quant, dst.as_mut_slice())?;
+    let src = Tensor::from_slice(&src, (32 * 4,), device)?;
+    let quant = quantized::QTensor::quantize(&src, GgmlDType::Q5_1)?;
+    let dst = quant.dequantize(device)?;
     assert_eq!(
-        dst,
+        round_vector(&dst.to_vec1::<f32>()?),
         &[
             0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0,
             16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0,
@@ -219,13 +292,11 @@ fn quantize_q5_1() -> Result<()> {
             124.0, 125.0, 126.0, 127.0
         ]
     );
-
-    ggml_quantization_error_test::<BlockQ5_1>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test(GgmlDType::Q5_1, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-/// Generates a small test vector ranging from -`bound` to `bound` with `size` steps
-fn get_test_vector(bound: f32, size: usize) -> (Vec<f32>, Vec<f32>) {
+fn get_test_vector2(bound: f32, size: usize, device: &Device) -> Result<Tensor> {
     assert!(
         size % crate::quantized::k_quants::QK_K == 0,
         "size must be a multiple of {}",
@@ -235,10 +306,8 @@ fn get_test_vector(bound: f32, size: usize) -> (Vec<f32>, Vec<f32>) {
     let src = (0..size)
         .map(|v| (v as f32 - size as f32 / 2.) * bound / (size as f32 / 2.))
         .collect::<Vec<_>>();
-
-    let dst = vec![0f32; size];
     assert_eq!([src[0], src[size / 2]], [-bound, 0.0]);
-    (src, dst)
+    Tensor::from_vec(src, (size,), device)
 }
 
 /// Round a vector
@@ -265,7 +334,8 @@ fn compare_with_error(values: &[f32], expected: &[f32], tolerance: f32) {
     }
 }
 
-/// Creates a vector simillarly to the one used in GGML unit tests: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L26-L30
+/// Creates a vector similar to the ones used in GGML unit tests:
+/// https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L26-L30
 fn create_ggml_like_vector(offset: f32) -> Vec<f32> {
     (0..GGML_TEST_SIZE)
         .map(|i| 0.1 + 2.0 * (i as f32 + offset).cos())
@@ -284,14 +354,16 @@ fn calculate_rmse(a: &[f32], b: &[f32]) -> f32 {
     sum / a.len() as f32
 }
 
-/// Mirrores the GGML quanitzation unit test: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L43-L50
-fn ggml_quantization_error_test<T: GgmlType>(max_error: f32) -> Result<()> {
+/// Similar to the GGML quantization unit test:
+/// https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L43-L50
+fn ggml_quantization_error_test(dtype: GgmlDType, device: &Device, max_error: f32) -> Result<()> {
     let src = create_ggml_like_vector(0.0);
-    let mut dst = vec![0.0; GGML_TEST_SIZE];
-    let _quant = quantize_roundtrip::<T>(src.as_slice(), dst.as_mut_slice())?;
-    let error = calculate_rmse(src.as_slice(), dst.as_slice());
+    let src = Tensor::from_slice(&src, (GGML_TEST_SIZE,), device)?;
+    let quant = quantized::QTensor::quantize(&src, dtype)?;
+    let dst = quant.dequantize(device)?;
+    let error = calculate_rmse(&src.to_vec1::<f32>()?, &dst.to_vec1::<f32>()?);
     if error > max_error {
-        candle_core::bail!(
+        bail!(
             "Quantization error {} exceeds max error {}",
             error,
             max_error
@@ -300,19 +372,19 @@ fn ggml_quantization_error_test<T: GgmlType>(max_error: f32) -> Result<()> {
     Ok(())
 }
 
-fn quantize_roundtrip<T: GgmlType>(src: &[f32], dst: &mut [f32]) -> Result<Vec<T>> {
-    let mut quant = vec![T::zeros(); src.len() / T::BLCK_SIZE];
-    T::from_float(src, &mut quant)?;
-    T::to_float(&quant, dst)?;
-    Ok(quant)
-}
+fn quantize_q2k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
+    let dtype = GgmlDType::Q2K;
 
-#[test]
-fn quantize_q2k() -> Result<()> {
-    use k_quants::BlockQ2K;
+    let src = get_test_vector2(0.5, 1024, device)?;
+    let quant = quantized::QTensor::quantize(&src, dtype)?;
+    let dst = quant.dequantize(device)?;
 
-    let (src, mut dst) = get_test_vector(0.5, 1024);
-    let _quant = quantize_roundtrip::<BlockQ2K>(src.as_slice(), dst.as_mut_slice())?;
+    let src = src.to_vec1::<f32>()?;
+    let dst = dst.to_vec1::<f32>()?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.1);
 
     // Test some specific values
@@ -326,20 +398,30 @@ fn quantize_q2k() -> Result<()> {
         [-0.499, -0.366, -0.249, 0.0, 0.295, 0.492]
     );
 
-    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
-    let _quant_big = quantize_roundtrip::<BlockQ2K>(src_big.as_slice(), dst_big.as_mut_slice())?;
+    let src_big = get_test_vector2(128.0, 1024, device)?;
+    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
+    let dst_big = quant_big.dequantize(device)?;
+
+    let src_big = src_big.to_vec1::<f32>()?;
+    let dst_big = dst_big.to_vec1::<f32>()?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 6.0);
 
-    ggml_quantization_error_test::<BlockQ2K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR_2BITS)?;
+    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR_2BITS)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q3k() -> Result<()> {
-    use k_quants::BlockQ3K;
+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)?;
+    let dst = quant.dequantize(device)?;
 
-    let (src, mut dst) = get_test_vector(0.5, 1024);
-    let _quant = quantize_roundtrip::<BlockQ3K>(src.as_slice(), dst.as_mut_slice())?;
+    let src = src.to_vec1::<f32>()?;
+    let dst = dst.to_vec1::<f32>()?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.03);
 
     // Test some specific values
@@ -353,20 +435,30 @@ fn quantize_q3k() -> Result<()> {
         [-0.493, -0.37, -0.243, -0.0, 0.292, 0.492]
     );
 
-    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
-    let _quant_big = quantize_roundtrip::<BlockQ3K>(src_big.as_slice(), dst_big.as_mut_slice())?;
+    let src_big = get_test_vector2(128.0, 1024, device)?;
+    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
+    let dst_big = quant_big.dequantize(device)?;
+
+    let src_big = src_big.to_vec1::<f32>()?;
+    let dst_big = dst_big.to_vec1::<f32>()?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 3.5);
 
-    ggml_quantization_error_test::<BlockQ3K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR_3BITS)?;
+    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR_3BITS)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q4k() -> Result<()> {
-    use k_quants::BlockQ4K;
+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)?;
+    let dst = quant.dequantize(device)?;
 
-    let (src, mut dst) = get_test_vector(0.5, 1024);
-    let _quant = quantize_roundtrip::<BlockQ4K>(src.as_slice(), dst.as_mut_slice())?;
+    let src = src.to_vec1::<f32>()?;
+    let dst = dst.to_vec1::<f32>()?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.017);
 
     // Test some specific values
@@ -380,21 +472,31 @@ fn quantize_q4k() -> Result<()> {
         [-0.5, -0.373, -0.25, 0.0, 0.288, 0.498]
     );
 
-    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
-    let _quant_big = quantize_roundtrip::<BlockQ4K>(src_big.as_slice(), dst_big.as_mut_slice())?;
+    let src_big = get_test_vector2(128.0, 1024, device)?;
+    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
+    let dst_big = quant_big.dequantize(device)?;
+
+    let src_big = src_big.to_vec1::<f32>()?;
+    let dst_big = dst_big.to_vec1::<f32>()?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 4.5);
 
-    ggml_quantization_error_test::<BlockQ4K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q5k() -> Result<()> {
-    use k_quants::BlockQ5K;
+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)?;
+    let dst = quant.dequantize(device)?;
 
-    let (src, mut dst) = get_test_vector(0.5, 1024);
-    let _quant = quantize_roundtrip::<BlockQ5K>(src.as_slice(), dst.as_mut_slice())?;
-    compare_with_error(dst.as_slice(), src.as_slice(), 0.008);
+    let src = src.to_vec1::<f32>()?;
+    let dst = dst.to_vec1::<f32>()?;
+    compare_with_error(dst.as_slice(), src.as_slice(), 0.009);
 
     // Test some specific values
     assert_eq!(
@@ -404,24 +506,33 @@ fn quantize_q5k() -> Result<()> {
     let dst = round_vector(&dst);
     assert_eq!(
         [dst[0], dst[128], dst[256], dst[512], dst[800], dst[1023]],
-        [-0.499, -0.372, -0.249, 0.001, 0.279, 0.499]
+        [-0.5, -0.373, -0.25, 0.0, 0.279, 0.499]
     );
 
-    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
-    let _quant_big = quantize_roundtrip::<BlockQ5K>(src_big.as_slice(), dst_big.as_mut_slice())?;
+    let src_big = get_test_vector2(128.0, 1024, device)?;
+    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
+    let dst_big = quant_big.dequantize(device)?;
+
+    let src_big = src_big.to_vec1::<f32>()?;
+    let dst_big = dst_big.to_vec1::<f32>()?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 2.5);
 
-    ggml_quantization_error_test::<BlockQ5K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
-
+    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q6k() -> Result<()> {
-    use k_quants::BlockQ6K;
+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)?;
+    let dst = quant.dequantize(device)?;
 
-    let (src, mut dst) = get_test_vector(0.5, 1024);
-    let _quant = quantize_roundtrip::<BlockQ6K>(src.as_slice(), dst.as_mut_slice())?;
+    let src = src.to_vec1::<f32>()?;
+    let dst = dst.to_vec1::<f32>()?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.008);
 
     // Test some specific values
@@ -435,22 +546,31 @@ fn quantize_q6k() -> Result<()> {
         [-0.497, -0.372, -0.25, -0.0, 0.284, 0.5]
     );
 
-    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
-    let _quant_big = quantize_roundtrip::<BlockQ6K>(src_big.as_slice(), dst_big.as_mut_slice())?;
+    let src_big = get_test_vector2(128.0, 1024, device)?;
+    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
+    let dst_big = quant_big.dequantize(device)?;
+
+    let src_big = src_big.to_vec1::<f32>()?;
+    let dst_big = dst_big.to_vec1::<f32>()?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 2.0);
 
-    ggml_quantization_error_test::<BlockQ6K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
-
+    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-#[test]
-fn quantize_q8k() -> Result<()> {
-    use k_quants::BlockQ8K;
+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)?;
+    let dst = quant.dequantize(device)?;
 
-    let (src, mut dst) = get_test_vector(0.5, 1024);
-    let _quant = quantize_roundtrip::<BlockQ8K>(src.as_slice(), dst.as_mut_slice())?;
-    compare_with_error(dst.as_slice(), src.as_slice(), 0.003);
+    let src = src.to_vec1::<f32>()?;
+    let dst = dst.to_vec1::<f32>()?;
+    compare_with_error(dst.as_slice(), src.as_slice(), 0.008);
 
     // Test some specific values
     assert_eq!(
@@ -463,15 +583,79 @@ fn quantize_q8k() -> Result<()> {
         [-0.5, -0.375, -0.25, -0.0, 0.281, 0.499]
     );
 
-    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
-    let _quant_big = quantize_roundtrip::<BlockQ8K>(src_big.as_slice(), dst_big.as_mut_slice())?;
+    let src_big = get_test_vector2(128.0, 1024, device)?;
+    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
+    let dst_big = quant_big.dequantize(device)?;
+
+    let src_big = src_big.to_vec1::<f32>()?;
+    let dst_big = dst_big.to_vec1::<f32>()?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 0.6);
 
-    ggml_quantization_error_test::<BlockQ8K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
-
+    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
+test_device!(
+    quantize_q4_0,
+    quantize_q4_0_cpu,
+    quantize_q4_0_cuda,
+    quantize_q4_0_metal
+);
+test_device!(
+    quantize_q4_1,
+    quantize_q4_1_cpu,
+    quantize_q4_1_cuda,
+    quantize_q4_1_metal
+);
+test_device!(
+    quantize_q5_0,
+    quantize_q5_0_cpu,
+    quantize_q5_0_cuda,
+    quantize_q5_0_metal
+);
+test_device!(
+    quantize_q5_1,
+    quantize_q5_1_cpu,
+    quantize_q5_1_cuda,
+    quantize_q5_1_metal
+);
+test_device!(
+    quantize_q2k,
+    quantize_q2k_cpu,
+    quantize_q2k_cuda,
+    quantize_q2k_metal
+);
+test_device!(
+    quantize_q3k,
+    quantize_q3k_cpu,
+    quantize_q3k_cuda,
+    quantize_q3k_metal
+);
+test_device!(
+    quantize_q4k,
+    quantize_q4k_cpu,
+    quantize_q4k_cuda,
+    quantize_q4k_metal
+);
+test_device!(
+    quantize_q5k,
+    quantize_q5k_cpu,
+    quantize_q5k_cuda,
+    quantize_q5k_metal
+);
+test_device!(
+    quantize_q6k,
+    quantize_q6k_cpu,
+    quantize_q6k_cuda,
+    quantize_q6k_metal
+);
+test_device!(
+    quantize_q8k,
+    quantize_q8k_cpu,
+    quantize_q8k_cuda,
+    quantize_q8k_metal
+);
+
 /// Very simple dot product implementation
 fn vec_dot_reference(a: &[f32], b: &[f32]) -> f32 {
     a.iter().zip(b).map(|(a, b)| a * b).sum()
@@ -487,54 +671,66 @@ fn ggml_reference_matmul_error(dtype: GgmlDType) -> Result<f32> {
         GgmlDType::Q5K => 0.000740,
         GgmlDType::Q6K => 0.000952,
         GgmlDType::Q4_0 => 0.001143,
-        GgmlDType::Q4_1 => 0.007784,
+        GgmlDType::Q4_1 => 0.008,
         GgmlDType::Q5_0 => 0.001353,
-        GgmlDType::Q5_1 => 0.001363,
+        GgmlDType::Q5_1 => 0.00149,
         GgmlDType::Q8_0 => 0.000092,
 
         // Not from the ggml repo.
         GgmlDType::Q8K => 0.00065,
-        _ => candle_core::bail!("No GGML results for quantization type {dtype:?}",),
+        _ => bail!("No GGML results for quantization type {dtype:?}",),
     };
     Ok(err)
 }
 
-/// Mirrores the GGML matmul unit test: https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L76-L91
+/// Similar to the GGML matmul unit test:
+/// https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L76-L91
 fn ggml_matmul_error_test<T: GgmlType>() -> Result<()> {
     let a = create_ggml_like_vector(0.0);
     let b = create_ggml_like_vector(1.0);
+    ggml_matmul_error_test_::<T>(a.as_slice(), b.as_slice(), 1.0)?;
+    // Another example that is more likely to trigger the overflow reported in #1526
+    let a = (0..GGML_TEST_SIZE)
+        .map(|i| i as f32 / GGML_TEST_SIZE as f32)
+        .collect::<Vec<_>>();
+    let b = (0..GGML_TEST_SIZE)
+        .map(|i| i as f32 / GGML_TEST_SIZE as f32)
+        .collect::<Vec<_>>();
+    ggml_matmul_error_test_::<T>(a.as_slice(), b.as_slice(), 2.0)?;
+    Ok(())
+}
+
+fn ggml_matmul_error_test_<T: GgmlType>(a: &[f32], b: &[f32], err_m: f32) -> Result<()> {
     let length = a.len();
 
     let mut a_quant = vec![T::zeros(); length / T::BLCK_SIZE];
     let mut b_quant = vec![T::VecDotType::zeros(); length / T::VecDotType::BLCK_SIZE];
-    T::from_float(&a, &mut a_quant)?;
-    T::VecDotType::from_float(&b, &mut b_quant)?;
+    T::from_float(a, &mut a_quant)?;
+    T::VecDotType::from_float(b, &mut b_quant)?;
 
     let result = T::vec_dot(length, &a_quant, &b_quant)?;
     let result_unopt = T::vec_dot_unopt(length, &a_quant, &b_quant)?;
-    let reference_result = vec_dot_reference(&a, &b);
+    let reference_result = vec_dot_reference(a, b);
 
     if (result - result_unopt).abs() / length as f32 > 1e-6 {
-        candle_core::bail!(
+        bail!(
             "the opt and unopt vec-dot returned different values, opt {result}, unopt {result_unopt}"
         )
     }
 
     let error = (result - reference_result).abs() / length as f32;
 
-    let ggml_error = ggml_reference_matmul_error(T::DTYPE)?;
+    let ggml_error = ggml_reference_matmul_error(T::DTYPE)? * err_m;
 
     if !error.is_finite() || error > GGML_MAX_DOT_PRODUCT_ERROR {
-        candle_core::bail!(
-            "Dot product error {error} exceeds max error {GGML_MAX_DOT_PRODUCT_ERROR}",
-        );
+        bail!("Dot product error {error} exceeds max error {GGML_MAX_DOT_PRODUCT_ERROR}",);
     }
 
     // We diverge slightly due to different rounding behavior / f16 to f32 conversions in GGML
     // => we use a slightly higher error threshold
     const ERROR_LENIENCY: f32 = 0.00001;
     if error - ERROR_LENIENCY > ggml_error {
-        candle_core::bail!(
+        bail!(
             "Dot product error {} exceeds ggml reference error {}",
             error,
             ggml_error
@@ -543,6 +739,16 @@ fn ggml_matmul_error_test<T: GgmlType>() -> Result<()> {
     Ok(())
 }
 
+#[test]
+fn quantized_mm() -> Result<()> {
+    ggml_matmul_error_test::<k_quants::BlockQ4_0>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ4_1>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ5_0>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ5_1>()?;
+    ggml_matmul_error_test::<k_quants::BlockQ8_0>()?;
+    Ok(())
+}
+
 /// generates random tensors of size `m x k` and `n x k` and calculates their expected matrix multiplication result.
 fn get_random_tensors(
     m: usize,
@@ -566,6 +772,112 @@ fn get_random_tensors(
     Ok((lhs, rhs, mm))
 }
 
+#[macro_export]
+macro_rules! quantized_matmul {
+    // TODO: Switch to generating the two last arguments automatically once concat_idents is
+    // 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), $dtype)?;
+            Ok(())
+        }
+
+        test_device!($fn_name, $fn_name_cpu, $fn_name_cuda, $fn_name_metal);
+    };
+}
+
+quantized_matmul!(
+    quantized_matmul_q4_0_bis,
+    quantized_matmul_q4_0_cpu,
+    quantized_matmul_q4_0_cuda,
+    quantized_matmul_q4_0_metal,
+    GgmlDType::Q4_0
+);
+quantized_matmul!(
+    quantized_matmul_q4_1_bis,
+    quantized_matmul_q4_1_cpu,
+    quantized_matmul_q4_1_cuda,
+    quantized_matmul_q4_1_metal,
+    GgmlDType::Q4_1
+);
+quantized_matmul!(
+    quantized_matmul_q5_0_bis,
+    quantized_matmul_q5_0_cpu,
+    quantized_matmul_q5_0_cuda,
+    quantized_matmul_q5_0_metal,
+    GgmlDType::Q5_0
+);
+quantized_matmul!(
+    quantized_matmul_q5_1_bis,
+    quantized_matmul_q5_1_cpu,
+    quantized_matmul_q5_1_cuda,
+    quantized_matmul_q5_1_metal,
+    GgmlDType::Q5_1
+);
+quantized_matmul!(
+    quantized_matmul_q8_0_bis,
+    quantized_matmul_q8_0_cpu,
+    quantized_matmul_q8_0_cuda,
+    quantized_matmul_q8_0_metal,
+    GgmlDType::Q8_0
+);
+// Not implemented in Ggml
+// quantized_matmul!(
+//     quantized_matmul_q8_1_bis,
+//     quantized_matmul_q8_1_cpu,
+//     quantized_matmul_q8_1_cuda,
+//     quantized_matmul_q8_1_metal,
+//     GgmlDType::Q8_1
+// );
+// TODO This is bugged (also bugged in GGML
+quantized_matmul!(
+    quantized_matmul_q2k_bis,
+    quantized_matmul_q2k_cpu,
+    quantized_matmul_q2k_cuda,
+    quantized_matmul_q2k_metal,
+    GgmlDType::Q2K
+);
+quantized_matmul!(
+    quantized_matmul_q3k_bis,
+    quantized_matmul_q3k_cpu,
+    quantized_matmul_q3k_cuda,
+    quantized_matmul_q3k_metal,
+    GgmlDType::Q3K
+);
+quantized_matmul!(
+    quantized_matmul_q4k_bis,
+    quantized_matmul_q4k_cpu,
+    quantized_matmul_q4k_cuda,
+    quantized_matmul_q4k_metal,
+    GgmlDType::Q4K
+);
+quantized_matmul!(
+    quantized_matmul_q5k_bis,
+    quantized_matmul_q5k_cpu,
+    quantized_matmul_q5k_cuda,
+    quantized_matmul_q5k_metal,
+    GgmlDType::Q5K
+);
+quantized_matmul!(
+    quantized_matmul_q6k_bis,
+    quantized_matmul_q6k_cpu,
+    quantized_matmul_q6k_cuda,
+    quantized_matmul_q6k_metal,
+    GgmlDType::Q6K
+);
+// Not implemented on metal
+// quantized_matmul!(
+//     quantized_matmul_q8k_bis,
+//     quantized_matmul_q8k_cpu,
+//     quantized_matmul_q8k_cuda,
+//     quantized_matmul_q8k_metal,
+//     GgmlDType::Q8K
+// );
+
 #[test]
 fn quantized_matmul_q2k() -> Result<()> {
     use k_quants::BlockQ2K;
@@ -578,7 +890,7 @@ fn quantized_matmul_q2k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize::<BlockQ2K>(&rhs)?;
+    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q2K)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -604,7 +916,7 @@ fn quantized_matmul_q3k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize::<BlockQ3K>(&rhs)?;
+    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q3K)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -630,7 +942,7 @@ fn quantized_matmul_q4k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize::<BlockQ4K>(&rhs)?;
+    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q4K)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -656,7 +968,7 @@ fn quantized_matmul_q5k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize::<BlockQ5K>(&rhs)?;
+    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q5K)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -683,7 +995,7 @@ fn quantized_matmul_q6k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize::<BlockQ6K>(&rhs)?;
+    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q6K)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -708,7 +1020,7 @@ fn quantized_matmul_q8k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize::<BlockQ8K>(&rhs)?;
+    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q8K)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 

candle-core/tests/tensor_tests.rs

@@ -1,4 +1,4 @@
-use candle_core::{test_device, test_utils, DType, Device, IndexOp, Result, Tensor};
+use candle_core::{test_device, test_utils, DType, Device, IndexOp, Result, Tensor, D};
 
 fn zeros(device: &Device) -> Result<()> {
     let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
@@ -32,6 +32,14 @@ fn ones(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn full(device: &Device) -> Result<()> {
+    assert_eq!(
+        Tensor::full(42u32, (2, 3), device)?.to_vec2::<u32>()?,
+        [[42, 42, 42], [42, 42, 42]],
+    );
+    Ok(())
+}
+
 fn arange(device: &Device) -> Result<()> {
     assert_eq!(
         Tensor::arange(0u8, 5u8, device)?.to_vec1::<u8>()?,
@@ -180,6 +188,22 @@ fn transpose(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn var(device: &Device) -> Result<()> {
+    // Values taken from https://pytorch.org/docs/stable/generated/torch.var.html
+    let data = &[
+        [0.2035f32, 1.2959, 1.8101, -0.4644],
+        [1.5027, -0.3270, 0.5905, 0.6538],
+        [-1.5745, 1.3330, -0.5596, -0.6548],
+        [0.1264, -0.5080, 1.6420, 0.1992],
+    ];
+    let tensor = Tensor::new(data, device)?;
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.var_keepdim(1)?, 4)?,
+        &[[1.0631], [0.559], [1.4893], [0.8258]]
+    );
+    Ok(())
+}
+
 fn sum(device: &Device) -> Result<()> {
     let data = &[[[3u32, 1, 4], [1, 5, 9]], [[2, 1, 7], [8, 2, 8]]];
     let tensor = Tensor::new(data, device)?;
@@ -1054,34 +1078,61 @@ fn randn(device: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(zeros, zeros_cpu, zeros_gpu);
-test_device!(ones, ones_cpu, ones_gpu);
-test_device!(arange, arange_cpu, arange_gpu);
-test_device!(add_mul, add_mul_cpu, add_mul_gpu);
-test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu);
-test_device!(narrow, narrow_cpu, narrow_gpu);
-test_device!(broadcast, broadcast_cpu, broadcast_gpu);
-test_device!(cat, cat_cpu, cat_gpu);
-test_device!(sum, sum_cpu, sum_gpu);
-test_device!(min, min_cpu, min_gpu);
-test_device!(max, max_cpu, max_gpu);
-test_device!(argmax, argmax_cpu, argmax_gpu);
-test_device!(argmin, argmin_cpu, argmin_gpu);
-test_device!(transpose, transpose_cpu, transpose_gpu);
-test_device!(unary_op, unary_op_cpu, unary_op_gpu);
-test_device!(binary_op, binary_op_cpu, binary_op_gpu);
-test_device!(embeddings, embeddings_cpu, embeddings_gpu);
-test_device!(cmp, cmp_cpu, cmp_gpu);
-test_device!(matmul, matmul_cpu, matmul_gpu);
-test_device!(broadcast_matmul, broadcast_matmul_cpu, broadcast_matmul_gpu);
-test_device!(broadcasting, broadcasting_cpu, broadcasting_gpu);
-test_device!(index_select, index_select_cpu, index_select_gpu);
-test_device!(index_add, index_add_cpu, index_add_gpu);
-test_device!(gather, gather_cpu, gather_gpu);
-test_device!(scatter_add, scatter_add_cpu, scatter_add_gpu);
-test_device!(slice_scatter, slice_scatter_cpu, slice_scatter_gpu);
-test_device!(randn, randn_cpu, randn_gpu);
-test_device!(clamp, clamp_cpu, clamp_gpu);
+test_device!(zeros, zeros_cpu, zeros_gpu, zeros_metal);
+test_device!(ones, ones_cpu, ones_gpu, ones_metal);
+test_device!(full, full_cpu, full_gpu, full_metal);
+test_device!(arange, arange_cpu, arange_gpu, arange_metal);
+test_device!(add_mul, add_mul_cpu, add_mul_gpu, add_mul_metal);
+test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu, tensor_2d_metal);
+test_device!(narrow, narrow_cpu, narrow_gpu, narrow_metal);
+test_device!(broadcast, broadcast_cpu, broadcast_gpu, broadcast_metal);
+test_device!(cat, cat_cpu, cat_gpu, cat_metal);
+test_device!(sum, sum_cpu, sum_gpu, sum_metal);
+test_device!(min, min_cpu, min_gpu, min_metal);
+test_device!(max, max_cpu, max_gpu, max_metal);
+test_device!(argmax, argmax_cpu, argmax_gpu, argmax_metal);
+test_device!(argmin, argmin_cpu, argmin_gpu, argmin_metal);
+test_device!(transpose, transpose_cpu, transpose_gpu, transpose_metal);
+test_device!(unary_op, unary_op_cpu, unary_op_gpu, unary_op_metal);
+test_device!(binary_op, binary_op_cpu, binary_op_gpu, binary_op_metal);
+test_device!(embeddings, embeddings_cpu, embeddings_gpu, embeddings_metal);
+test_device!(cmp, cmp_cpu, cmp_gpu, cmp_metal);
+test_device!(matmul, matmul_cpu, matmul_gpu, matmul_metal);
+test_device!(
+    broadcast_matmul,
+    broadcast_matmul_cpu,
+    broadcast_matmul_gpu,
+    broadcast_matmul_metal
+);
+test_device!(
+    broadcasting,
+    broadcasting_cpu,
+    broadcasting_gpu,
+    broadcasting_metal
+);
+test_device!(
+    index_select,
+    index_select_cpu,
+    index_select_gpu,
+    index_select_metal
+);
+test_device!(index_add, index_add_cpu, index_add_gpu, index_add_metal);
+test_device!(gather, gather_cpu, gather_gpu, gather_metal);
+test_device!(
+    scatter_add,
+    scatter_add_cpu,
+    scatter_add_gpu,
+    scatter_add_metal
+);
+test_device!(
+    slice_scatter,
+    slice_scatter_cpu,
+    slice_scatter_gpu,
+    slice_scatter_metal
+);
+test_device!(randn, randn_cpu, randn_gpu, randn_metal);
+test_device!(clamp, clamp_cpu, clamp_gpu, clamp_metal);
+test_device!(var, var_cpu, var_gpu, var_metal);
 
 // There was originally a bug on the CPU implementation for randn
 // https://github.com/huggingface/candle/issues/381
@@ -1117,3 +1168,100 @@ fn i64_abs() -> Result<()> {
     assert_eq!(t.to_vec1::<i64>()?, [42, 1337]);
     Ok(())
 }
+
+#[test]
+fn tril_triu_eye() -> Result<()> {
+    let t = Tensor::tril2(4, DType::F32, &Device::Cpu)?;
+    assert_eq!(
+        t.to_vec2::<f32>()?,
+        [
+            [1.0, 0.0, 0.0, 0.0],
+            [1.0, 1.0, 0.0, 0.0],
+            [1.0, 1.0, 1.0, 0.0],
+            [1.0, 1.0, 1.0, 1.0]
+        ],
+    );
+    let t = Tensor::triu2(4, DType::F32, &Device::Cpu)?;
+    assert_eq!(
+        t.to_vec2::<f32>()?,
+        [
+            [1.0, 1.0, 1.0, 1.0],
+            [0.0, 1.0, 1.0, 1.0],
+            [0.0, 0.0, 1.0, 1.0],
+            [0.0, 0.0, 0.0, 1.0]
+        ]
+    );
+    let t = Tensor::eye(4, DType::F32, &Device::Cpu)?;
+    assert_eq!(
+        t.to_vec2::<f32>()?,
+        [
+            [1.0, 0.0, 0.0, 0.0],
+            [0.0, 1.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0, 0.0],
+            [0.0, 0.0, 0.0, 1.0]
+        ]
+    );
+    Ok(())
+}
+
+#[test]
+fn cumsum() -> Result<()> {
+    let t = &[3f32, 1., 4., 1., 5.];
+    let t = Tensor::new(t, &Device::Cpu)?;
+    assert_eq!(t.cumsum(0)?.to_vec1::<f32>()?, [3., 4., 8., 9., 14.]);
+    let t = t.unsqueeze(1)?;
+    assert_eq!(
+        t.cumsum(0)?.to_vec2::<f32>()?,
+        [[3.0], [4.0], [8.0], [9.0], [14.0]]
+    );
+    assert_eq!(
+        t.cumsum(1)?.to_vec2::<f32>()?,
+        [[3.0], [1.0], [4.0], [1.0], [5.0]]
+    );
+    let t = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
+    let t = Tensor::new(t, &Device::Cpu)?;
+    assert_eq!(
+        t.cumsum(1)?.to_vec2::<f32>()?,
+        [[3.0, 4.0, 8.0, 9.0, 14.0], [2.0, 3.0, 10.0, 18.0, 20.0]],
+    );
+    assert_eq!(
+        t.cumsum(0)?.to_vec2::<f32>()?,
+        [[3.0, 1.0, 4.0, 1.0, 5.0], [5.0, 2.0, 11.0, 9.0, 7.0]]
+    );
+    Ok(())
+}
+
+/// A helper function for floating point comparison. Both a and b must be 1D Tensor and contains the same amount of data.
+/// Assertion passes if the difference of all pairs of a and b is smaller than epsilon.
+fn assert_close(a: &Tensor, b: &Tensor, epsilon: f64) -> Result<()> {
+    let a_vec: Vec<f64> = a.to_vec1()?;
+    let b_vec: Vec<f64> = b.to_vec1()?;
+
+    assert_eq!(a_vec.len(), b_vec.len());
+    for (a, b) in a_vec.iter().zip(b_vec.iter()) {
+        assert!((a - b).abs() < epsilon);
+    }
+    Ok(())
+}
+
+#[test]
+fn log_sum_exp() -> Result<()> {
+    let input = Tensor::new(&[[1f64, 2., 3.], [4., 5., 6.]], &Device::Cpu)?;
+    let output = input.log_sum_exp(D::Minus1)?;
+    // The expectations obtained from pytorch.
+    let expected = Tensor::new(&[3.4076, 6.4076], &Device::Cpu)?;
+    assert_close(&output, &expected, 0.00001)?;
+    Ok(())
+}
+
+#[test]
+fn pow() -> Result<()> {
+    let lhs = Tensor::new(&[[1f32, 2., 3.], [4., 5., 6.]], &Device::Cpu)?;
+    let rhs = (&lhs - 2.)?;
+    let res = lhs.pow(&rhs)?;
+    assert_eq!(
+        test_utils::to_vec2_round(&res, 4)?,
+        [[1.0, 1.0, 3.0], [16.0, 125.0, 1296.0001]]
+    );
+    Ok(())
+}

candle-datasets/Cargo.toml

@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 byteorder = { workspace = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
+candle = { workspace = true }
+candle-nn = { workspace = true }
 hf-hub = { workspace = true}
 intel-mkl-src = { workspace = true, optional = true }
 memmap2 = { workspace = true }

candle-examples/Cargo.toml

@@ -11,14 +11,17 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.3.0", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.3.0" }
-candle-nn = { path = "../candle-nn", version = "0.3.0" }
-candle-transformers = { path = "../candle-transformers", version = "0.3.0" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.0", optional = true }
-candle-onnx = { path = "../candle-onnx", version = "0.3.0", optional = true }
+candle = { workspace = true }
+candle-datasets = { workspace = true }
+candle-nn = { workspace = true }
+candle-transformers = { workspace = true }
+candle-flash-attn = { workspace = true, optional = true }
+candle-onnx = { workspace = true, optional = true }
+
+csv = "1.3.0"
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
+hf-hub = { workspace = true, features=["tokio"]}
 image = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
@@ -33,7 +36,6 @@ tokenizers = { workspace = true, features = ["onig"] }
 anyhow = { workspace = true }
 byteorder = { workspace = true }
 clap = { workspace = true }
-hf-hub = { workspace = true, features=["tokio"]}
 imageproc = { workspace = true }
 memmap2 = { workspace = true }
 rand = { workspace = true }
@@ -47,16 +49,18 @@ tokio = "1.29.1"
 
 [build-dependencies]
 anyhow = { workspace = true }
+bindgen_cuda = { version = "0.1.1", optional = true }
 
 [features]
 default = []
 accelerate = ["dep:accelerate-src", "candle/accelerate", "candle-nn/accelerate", "candle-transformers/accelerate"]
-cuda = ["candle/cuda", "candle-nn/cuda", "candle-transformers/cuda"]
+cuda = ["candle/cuda", "candle-nn/cuda", "candle-transformers/cuda", "dep:bindgen_cuda"]
 cudnn = ["candle/cudnn"]
 flash-attn = ["cuda", "candle-transformers/flash-attn", "dep:candle-flash-attn"]
 mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl", "candle-transformers/mkl"]
 nccl = ["cuda", "cudarc/nccl", "dep:half"]
 onnx = ["candle-onnx"]
+metal = ["candle/metal", "candle-nn/metal"]
 
 [[example]]
 name = "llama_multiprocess"

candle-examples/build.rs

@@ -4,235 +4,28 @@ use std::io::Write;
 use std::path::PathBuf;
 
 struct KernelDirectories {
-    kernel_dir: &'static str,
+    kernel_glob: &'static str,
     rust_target: &'static str,
     include_dirs: &'static [&'static str],
 }
 
-const DIRS: [KernelDirectories; 1] = [KernelDirectories {
-    kernel_dir: "examples/custom-ops/kernels/",
+const KERNEL_DIRS: [KernelDirectories; 1] = [KernelDirectories {
+    kernel_glob: "examples/custom-ops/kernels/*.cu",
     rust_target: "examples/custom-ops/cuda_kernels.rs",
     include_dirs: &[],
 }];
 
-impl KernelDirectories {
-    fn maybe_build_ptx(
-        &self,
-        cu_file: &std::path::Path,
-        ptx_file: &std::path::Path,
-        compute_cap: usize,
-    ) -> Result<()> {
-        let should_compile = if ptx_file.exists() {
-            let ptx_modified = ptx_file.metadata()?.modified()?;
-            let cu_modified = cu_file.metadata()?.modified()?;
-            cu_modified.duration_since(ptx_modified).is_ok()
-        } else {
-            true
-        };
-        if should_compile {
-            #[cfg(feature = "cuda")]
-            {
-                let mut command = std::process::Command::new("nvcc");
-                let out_dir = ptx_file.parent().context("no parent for ptx file")?;
-                let include_dirs: Vec<String> =
-                    self.include_dirs.iter().map(|c| format!("-I{c}")).collect();
-                command
-                    .arg(format!("--gpu-architecture=sm_{compute_cap}"))
-                    .arg("--ptx")
-                    .args(["--default-stream", "per-thread"])
-                    .args(["--output-directory", out_dir.to_str().unwrap()])
-                    .arg(format!("-I/{}", self.kernel_dir))
-                    .args(include_dirs)
-                    .arg(cu_file);
-                let output = command
-                    .spawn()
-                    .context("failed spawning nvcc")?
-                    .wait_with_output()?;
-                if !output.status.success() {
-                    anyhow::bail!(
-                    "nvcc error while compiling {cu_file:?}:\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
-                    String::from_utf8_lossy(&output.stdout),
-                    String::from_utf8_lossy(&output.stderr)
-                )
-                }
-            }
-            #[cfg(not(feature = "cuda"))]
-            std::fs::OpenOptions::new()
-                .create(true)
-                .write(true)
-                .open(ptx_file)?;
-        }
-        Ok(())
-    }
-    fn process(&self, out_dir: &std::path::Path, compute_cap: usize) -> Result<()> {
-        println!("cargo:rerun-if-changed={}", self.kernel_dir);
-        let kernel_dir = PathBuf::from(self.kernel_dir);
-        let out_dir = out_dir.join(self.kernel_dir);
-        if !out_dir.exists() {
-            std::fs::create_dir_all(&out_dir)?;
-        }
-        let mut cu_files = vec![];
-        let mut cuh_files = vec![];
-        for file in std::fs::read_dir(kernel_dir)?.flatten() {
-            let file = file.path();
-            match file.extension().and_then(|v| v.to_str()) {
-                Some("cu") => cu_files.push(file),
-                Some("cuh") => cuh_files.push(file),
-                _ => {}
-            }
-        }
-
-        let mut ptx_paths = vec![];
-        for cu_file in cu_files.iter() {
-            let file_stem = cu_file
-                .file_stem()
-                .with_context(|| format!("no stem {cu_file:?}"))?;
-            let file_stem = file_stem.to_string_lossy().into_owned();
-            let ptx_file = out_dir.join(&format!("{file_stem}.ptx"));
-            self.maybe_build_ptx(cu_file, &ptx_file, compute_cap)?;
-            ptx_paths.push(ptx_file);
-        }
-
-        let regenerate_rs_file = true;
-        if regenerate_rs_file {
-            let mut file = std::fs::File::create(self.rust_target)?;
-            for ptx_path in ptx_paths {
-                let name = ptx_path
-                    .file_stem()
-                    .context("empty stem")?
-                    .to_string_lossy();
-                file.write_all(b"#[rustfmt::skip]\n")?;
-                let const_definition = format!(
-                    r#"pub const {}: &str = include_str!(concat!(env!("OUT_DIR"), "/{}/{name}.ptx"));"#,
-                    name.to_uppercase().replace('.', "_"),
-                    self.kernel_dir,
-                );
-                file.write_all(const_definition.as_bytes())?;
-                file.write_all(b"\n")?;
-            }
-        }
-        Ok(())
-    }
-}
-
 fn main() -> Result<()> {
     println!("cargo:rerun-if-changed=build.rs");
 
-    let out_dir = std::env::var("OUT_DIR").context("OUT_DIR not set")?;
-    let out_dir = PathBuf::from(out_dir);
     #[cfg(feature = "cuda")]
-    set_cuda_include_dir()?;
-    #[cfg(feature = "cuda")]
-    let compute_cap = compute_cap()?;
-    #[cfg(not(feature = "cuda"))]
-    let compute_cap = 0;
-    for d in DIRS {
-        d.process(&out_dir, compute_cap)?
+    {
+        for kdir in KERNEL_DIRS.iter() {
+            let builder = bindgen_cuda::Builder::default().kernel_paths_glob(kdir.kernel_glob);
+            println!("cargo:info={builder:?}");
+            let bindings = builder.build_ptx().unwrap();
+            bindings.write(kdir.rust_target).unwrap()
+        }
     }
     Ok(())
 }
-
-fn set_cuda_include_dir() -> Result<()> {
-    // NOTE: copied from cudarc build.rs.
-    let env_vars = [
-        "CUDA_PATH",
-        "CUDA_ROOT",
-        "CUDA_TOOLKIT_ROOT_DIR",
-        "CUDNN_LIB",
-    ];
-    let env_vars = env_vars
-        .into_iter()
-        .map(std::env::var)
-        .filter_map(Result::ok)
-        .map(Into::<PathBuf>::into);
-
-    let roots = [
-        "/usr",
-        "/usr/local/cuda",
-        "/opt/cuda",
-        "/usr/lib/cuda",
-        "C:/Program Files/NVIDIA GPU Computing Toolkit",
-        "C:/CUDA",
-    ];
-    let roots = roots.into_iter().map(Into::<PathBuf>::into);
-    let root = env_vars
-        .chain(roots)
-        .find(|path| path.join("include").join("cuda.h").is_file())
-        .context("cannot find include/cuda.h")?;
-    println!(
-        "cargo:rustc-env=CUDA_INCLUDE_DIR={}",
-        root.join("include").display()
-    );
-    Ok(())
-}
-
-#[allow(unused)]
-fn compute_cap() -> Result<usize> {
-    // Grab compute code from nvidia-smi
-    let mut compute_cap = {
-        let out = std::process::Command::new("nvidia-smi")
-                    .arg("--query-gpu=compute_cap")
-                    .arg("--format=csv")
-                    .output()
-                    .context("`nvidia-smi` failed. Ensure that you have CUDA installed and that `nvidia-smi` is in your PATH.")?;
-        let out = std::str::from_utf8(&out.stdout).context("stdout is not a utf8 string")?;
-        let mut lines = out.lines();
-        assert_eq!(
-            lines.next().context("missing line in stdout")?,
-            "compute_cap"
-        );
-        let cap = lines
-            .next()
-            .context("missing line in stdout")?
-            .replace('.', "");
-        cap.parse::<usize>()
-            .with_context(|| format!("cannot parse as int {cap}"))?
-    };
-
-    // Grab available GPU codes from nvcc and select the highest one
-    let max_nvcc_code = {
-        let out = std::process::Command::new("nvcc")
-                    .arg("--list-gpu-code")
-                    .output()
-                    .expect("`nvcc` failed. Ensure that you have CUDA installed and that `nvcc` is in your PATH.");
-        let out = std::str::from_utf8(&out.stdout).unwrap();
-
-        let out = out.lines().collect::<Vec<&str>>();
-        let mut codes = Vec::with_capacity(out.len());
-        for code in out {
-            let code = code.split('_').collect::<Vec<&str>>();
-            if !code.is_empty() && code.contains(&"sm") {
-                if let Ok(num) = code[1].parse::<usize>() {
-                    codes.push(num);
-                }
-            }
-        }
-        codes.sort();
-        if !codes.contains(&compute_cap) {
-            anyhow::bail!(
-                "nvcc cannot target gpu arch {compute_cap}. Available nvcc targets are {codes:?}."
-            );
-        }
-        *codes.last().unwrap()
-    };
-
-    // If nvidia-smi compute_cap is higher than the highest gpu code from nvcc,
-    // then choose the highest gpu code in nvcc
-    if compute_cap > max_nvcc_code {
-        println!(
-            "cargo:warning=Lowering gpu arch {compute_cap} to max nvcc target {max_nvcc_code}."
-        );
-        compute_cap = max_nvcc_code;
-    }
-
-    println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP");
-
-    if let Ok(compute_cap_str) = std::env::var("CUDA_COMPUTE_CAP") {
-        compute_cap = compute_cap_str
-            .parse::<usize>()
-            .with_context(|| format!("cannot parse as usize '{compute_cap_str}'"))?;
-        println!("cargo:warning=Using gpu arch {compute_cap} from $CUDA_COMPUTE_CAP");
-    }
-    println!("cargo:rustc-env=CUDA_COMPUTE_CAP=sm_{compute_cap}");
-    Ok(compute_cap)
-}

candle-examples/examples/bert/README.md

@@ -2,10 +2,10 @@
 
 Bert is a general large language model. In this example it can be used for two
 different tasks:
+
 - Compute sentence embeddings for a prompt.
 - Compute similarities between a set of sentences.
 
-
 ## Sentence embeddings
 
 Bert is used to compute the sentence embeddings for a prompt. The model weights
@@ -24,6 +24,48 @@ cargo run --example bert --release -- --prompt "Here is a test sentence"
 > Tensor[[1, 7, 384], f32]
 ```
 
+### Custom models
+
+You can specify different models, such as BGE, with the `--model-id` flag:
+
+```bash
+cargo run  --example bert --release -- \
+--model-id BAAI/bge-large-zh-v1.5 \
+--prompt "Here is a test sentence"
+Loaded and encoded 435.70775ms
+[[[ 3.0944e-1, -7.8455e-5,  -1.2768e0, ...,  1.3755e-2, -3.2371e-1,  2.3819e-1],
+  [-2.8506e-1,  1.9953e-1,  -1.3076e0, ...,  6.9819e-2,  1.0833e-2,  -1.1512e0],
+  [ 3.9892e-1,  2.0000e-1, -9.3178e-1, ..., -4.1393e-1, -4.9644e-2, -3.3786e-1],
+  ...
+  [ 6.0345e-1,  3.5744e-1,  -1.2672e0, ..., -6.9165e-1, -3.4973e-3, -8.4214e-1],
+  [ 3.9218e-1, -3.2735e-1,  -1.3123e0, ..., -4.9318e-1, -5.1334e-1, -3.6391e-1],
+  [ 3.0978e-1,  2.5662e-4,  -1.2773e0, ...,  1.3357e-2, -3.2390e-1,  2.3858e-1]]]
+Tensor[[1, 9, 1024], f32]
+Took 176.744667ms
+```
+
+### Gelu approximation
+
+You can get a speedup by using an approximation of the gelu activation, with a
+small loss of precision, by passing the `--approximate-gelu` flag:
+
+```bash
+$ cargo run  --example bert --release -- \
+--model-id BAAI/bge-large-zh-v1.5 \
+--prompt "Here is a test sentence" \
+--approximate-gelu
+Loaded and encoded 244.388042ms
+[[[ 3.1048e-1, -6.0339e-4,  -1.2758e0, ...,  1.3718e-2, -3.2362e-1,  2.3775e-1],
+  [-2.8354e-1,  1.9984e-1,  -1.3077e0, ...,  6.9390e-2,  9.9681e-3,  -1.1531e0],
+  [ 3.9947e-1,  1.9917e-1, -9.3178e-1, ..., -4.1301e-1, -5.0719e-2, -3.3955e-1],
+  ...
+  [ 6.0499e-1,  3.5664e-1,  -1.2642e0, ..., -6.9134e-1, -3.4581e-3, -8.4471e-1],
+  [ 3.9311e-1, -3.2812e-1,  -1.3105e0, ..., -4.9291e-1, -5.1270e-1, -3.6543e-1],
+  [ 3.1082e-1, -2.6737e-4,  -1.2762e0, ...,  1.3319e-2, -3.2381e-1,  2.3815e-1]]]
+Tensor[[1, 9, 1024], f32]
+Took 116.840791ms
+```
+
 ## Similarities
 
 In this example, Bert is used to compute the sentence embeddings for a set of

candle-examples/examples/bert/main.rs

@@ -3,7 +3,7 @@ extern crate intel_mkl_src;
 
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
-use candle_transformers::models::bert::{BertModel, Config, DTYPE};
+use candle_transformers::models::bert::{BertModel, Config, HiddenAct, DTYPE};
 
 use anyhow::{Error as E, Result};
 use candle::Tensor;
@@ -45,6 +45,10 @@ struct Args {
     /// L2 normalization for embeddings.
     #[arg(long, default_value = "true")]
     normalize_embeddings: bool,
+
+    /// Use tanh based approximation for Gelu instead of erf implementation.
+    #[arg(long, default_value = "false")]
+    approximate_gelu: bool,
 }
 
 impl Args {
@@ -73,7 +77,7 @@ impl Args {
             (config, tokenizer, weights)
         };
         let config = std::fs::read_to_string(config_filename)?;
-        let config: Config = serde_json::from_str(&config)?;
+        let mut config: Config = serde_json::from_str(&config)?;
         let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
         let vb = if self.use_pth {
@@ -81,6 +85,9 @@ impl Args {
         } else {
             unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], DTYPE, &device)? }
         };
+        if self.approximate_gelu {
+            config.hidden_act = HiddenAct::GeluApproximate;
+        }
         let model = BertModel::load(vb, &config)?;
         Ok((model, tokenizer))
     }

candle-examples/examples/blip/main.rs

@@ -106,17 +106,17 @@ pub fn main() -> anyhow::Result<()> {
 
     let config = blip::Config::image_captioning_large();
 
+    let device = candle_examples::device(args.cpu)?;
     let (image_embeds, device, mut model) = if args.quantized {
         let device = Device::Cpu;
         let image = load_image(args.image)?.to_device(&device)?;
         println!("loaded image {image:?}");
 
-        let vb = quantized_blip::VarBuilder::from_gguf(model_file)?;
+        let vb = quantized_blip::VarBuilder::from_gguf(model_file, &device)?;
         let model = quantized_blip::BlipForConditionalGeneration::new(&config, vb)?;
         let image_embeds = image.unsqueeze(0)?.apply(model.vision_model())?;
         (image_embeds, device, Model::Q(model))
     } else {
-        let device = candle_examples::device(args.cpu)?;
         let image = load_image(args.image)?.to_device(&device)?;
         println!("loaded image {image:?}");
 

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

@@ -1,2 +0,0 @@
-#[rustfmt::skip]
-pub const LAYERNORM_KERNELS: &str = include_str!(concat!(env!("OUT_DIR"), "/examples/custom-ops/kernels//layernorm_kernels.ptx"));

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

@@ -6,7 +6,8 @@
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
-#[allow(unused)]
+#[rustfmt::skip]
+#[cfg(feature = "cuda")]
 mod cuda_kernels;
 
 use clap::Parser;

candle-examples/examples/distilbert/README.md

@@ -0,0 +1,22 @@
+# candle-distilbert
+
+DistilBert is a distiled version of the Bert model.
+
+## Sentence embeddings
+
+DistilBert is used to compute the sentence embeddings for a prompt. The model weights
+are downloaded from the hub on the first run.
+
+```bash
+cargo run --example distilbert --release -- --prompt "Here is a test sentence"
+
+> [[[ 0.5109,  0.1280, -0.2635, ...,  0.3462, -1.0434,  0.1441],
+>   [ 0.1735,  0.0818, -0.5549, ...,  0.3472, -0.8264, -0.0244],
+>   [ 0.0702, -0.1311, -0.4914, ...,  0.3483, -0.6194,  0.1829],
+>   ...
+>   [ 0.2993, -0.0106, -0.4640, ...,  0.2844, -0.6732,  0.0042],
+>   [ 0.1066, -0.0081, -0.4299, ...,  0.3435, -0.7729,  0.0190],
+>   [ 0.8903,  0.2055, -0.2541, ...,  0.3208, -0.6585,  0.0586]]]
+> Tensor[[1, 7, 768], f32]
+
+```

candle-examples/examples/distilbert/main.rs

@@ -0,0 +1,135 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+use candle_transformers::models::distilbert::{Config, DistilBertModel, DTYPE};
+
+use anyhow::{Error as E, Result};
+use candle::{Device, Tensor};
+use candle_nn::VarBuilder;
+use clap::Parser;
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+#[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,
+
+    /// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    /// When set, compute embeddings for this prompt.
+    #[arg(long)]
+    prompt: String,
+
+    /// Use the pytorch weights rather than the safetensors ones
+    #[arg(long)]
+    use_pth: bool,
+
+    /// The number of times to run the prompt.
+    #[arg(long, default_value = "1")]
+    n: usize,
+
+    /// L2 normalization for embeddings.
+    #[arg(long, default_value = "true")]
+    normalize_embeddings: bool,
+}
+
+impl Args {
+    fn build_model_and_tokenizer(&self) -> Result<(DistilBertModel, Tokenizer)> {
+        let device = candle_examples::device(self.cpu)?;
+        let default_model = "distilbert-base-uncased".to_string();
+        let default_revision = "main".to_string();
+        let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
+            (Some(model_id), Some(revision)) => (model_id, revision),
+            (Some(model_id), None) => (model_id, "main".to_string()),
+            (None, Some(revision)) => (default_model, revision),
+            (None, None) => (default_model, default_revision),
+        };
+
+        let repo = Repo::with_revision(model_id, RepoType::Model, revision);
+        let (config_filename, tokenizer_filename, weights_filename) = {
+            let api = Api::new()?;
+            let api = api.repo(repo);
+            let config = api.get("config.json")?;
+            let tokenizer = api.get("tokenizer.json")?;
+            let weights = if self.use_pth {
+                api.get("pytorch_model.bin")?
+            } else {
+                api.get("model.safetensors")?
+            };
+            (config, tokenizer, weights)
+        };
+        let config = std::fs::read_to_string(config_filename)?;
+        let config: Config = serde_json::from_str(&config)?;
+        let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+        let vb = if self.use_pth {
+            VarBuilder::from_pth(&weights_filename, DTYPE, &device)?
+        } else {
+            unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], DTYPE, &device)? }
+        };
+        let model = DistilBertModel::load(vb, &config)?;
+        Ok((model, tokenizer))
+    }
+}
+
+fn get_mask(size: usize, device: &Device) -> Tensor {
+    let mask: Vec<_> = (0..size)
+        .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
+        .collect();
+    Tensor::from_slice(&mask, (size, size), device).unwrap()
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        println!("tracing...");
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
+    let device = &model.device;
+
+    let tokenizer = tokenizer
+        .with_padding(None)
+        .with_truncation(None)
+        .map_err(E::msg)?;
+    let tokens = tokenizer
+        .encode(args.prompt, true)
+        .map_err(E::msg)?
+        .get_ids()
+        .to_vec();
+    let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+    let mask = get_mask(tokens.len(), device);
+
+    println!("token_ids: {:?}", token_ids.to_vec2::<u32>());
+    println!("mask: {:?}", mask.to_vec2::<u8>());
+
+    let ys = model.forward(&token_ids, &mask)?;
+    println!("{ys}");
+
+    Ok(())
+}
+
+pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
+    Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
+}

candle-examples/examples/falcon/main.rs

@@ -165,14 +165,7 @@ fn main() -> Result<()> {
         args.revision,
     ));
     let tokenizer_filename = repo.get("tokenizer.json")?;
-    let mut filenames = vec![];
-    for rfilename in [
-        "model-00001-of-00002.safetensors",
-        "model-00002-of-00002.safetensors",
-    ] {
-        let filename = repo.get(rfilename)?;
-        filenames.push(filename);
-    }
+    let filenames = 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)?;
 

candle-examples/examples/llama/main.rs

@@ -13,7 +13,7 @@ extern crate accelerate_src;
 extern crate intel_mkl_src;
 
 use anyhow::{bail, Error as E, Result};
-use clap::Parser;
+use clap::{Parser, ValueEnum};
 
 use candle::{DType, Tensor};
 use candle_nn::VarBuilder;
@@ -22,11 +22,21 @@ use hf_hub::{api::sync::Api, Repo, RepoType};
 use std::io::Write;
 
 use candle_transformers::models::llama as model;
-use model::{Config, Llama, LlamaConfig};
+use model::{Llama, LlamaConfig};
 
 const EOS_TOKEN: &str = "</s>";
 const DEFAULT_PROMPT: &str = "My favorite theorem is ";
 
+#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
+enum Which {
+    V1,
+    V2,
+    #[value(name = "solar-10.7b")]
+    Solar10_7B,
+    #[value(name = "tiny-llama-1.1b-chat")]
+    TinyLlama1_1BChat,
+}
+
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
 struct Args {
@@ -34,10 +44,6 @@ struct Args {
     #[arg(long)]
     cpu: bool,
 
-    /// Use npy instead of safetensors
-    #[arg(long)]
-    npy: Option<String>,
-
     /// The temperature used to generate samples.
     #[arg(long)]
     temperature: Option<f64>,
@@ -76,17 +82,13 @@ struct Args {
     #[arg(long)]
     revision: Option<String>,
 
-    #[arg(long)]
-    v1: bool,
+    /// The model size to use.
+    #[arg(long, default_value = "v2")]
+    which: Which,
 
     #[arg(long)]
     use_flash_attn: bool,
 
-    /// The folder name that contains safetensor weights and json files
-    /// (same structure as huggingface online)
-    #[arg(long)]
-    local_weights: Option<String>,
-
     /// Penalty to be applied for repeating tokens, 1. means no penalty.
     #[arg(long, default_value_t = 1.0)]
     repeat_penalty: f32,
@@ -118,65 +120,34 @@ fn main() -> Result<()> {
         Some(dtype) => bail!("Unsupported dtype {dtype}"),
         None => DType::F16,
     };
-    let (llama, tokenizer_filename, cache) = match args.npy {
-        Some(filename) => {
-            let config = if args.v1 {
-                Config::config_7b_v1(args.use_flash_attn)
-            } else {
-                Config::config_7b_v2(args.use_flash_attn)
-            };
-            let cache = model::Cache::new(!args.no_kv_cache, dtype, &config, &device)?;
-            let vb = VarBuilder::from_npz(filename, dtype, &device)?;
-            let tokenizer = std::path::PathBuf::from("llama-tokenizer.json");
-            (Llama::load(vb, &cache, &config)?, tokenizer, cache)
-        }
-        None => {
-            let api = Api::new()?;
-            let model_id = args.model_id.unwrap_or_else(|| {
-                if args.v1 {
-                    "Narsil/amall-7b".to_string()
-                } else {
-                    "meta-llama/Llama-2-7b-hf".to_string()
-                }
-            });
-            println!("loading the model weights from {model_id}");
-            let revision = args.revision.unwrap_or("main".to_string());
-            let api = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
+    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(),
+            Which::V2 => "meta-llama/Llama-2-7b-hf".to_string(),
+            Which::Solar10_7B => "upstage/SOLAR-10.7B-v1.0".to_string(),
+            Which::TinyLlama1_1BChat => "TinyLlama/TinyLlama-1.1B-Chat-v1.0".to_string(),
+        });
+        println!("loading the model weights from {model_id}");
+        let revision = args.revision.unwrap_or("main".to_string());
+        let api = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
 
-            let tokenizer_filename = match &args.local_weights {
-                Some(path) => (path.to_owned() + "tokenizer.json").into(),
-                _ => api.get("tokenizer.json")?,
-            };
+        let tokenizer_filename = api.get("tokenizer.json")?;
+        let config_filename = api.get("config.json")?;
+        let config: LlamaConfig = serde_json::from_slice(&std::fs::read(config_filename)?)?;
+        let config = config.into_config(args.use_flash_attn);
 
-            let config_filename = match &args.local_weights {
-                Some(path) => (path.to_owned() + "config.json").into(),
-                _ => api.get("config.json")?,
-            };
-            let config: LlamaConfig = serde_json::from_slice(&std::fs::read(config_filename)?)?;
-            let config = config.into_config(args.use_flash_attn);
-
-            let mut filenames = vec![];
-            for rfilename in [
-                "model-00001-of-00002.safetensors",
-                "model-00002-of-00002.safetensors",
-            ] {
-                match &args.local_weights {
-                    Some(path) => {
-                        filenames.push((path.to_owned() + rfilename).into());
-                    }
-                    _ => {
-                        let filename = api.get(rfilename)?;
-                        filenames.push(filename);
-                    }
-                };
+        let filenames = match args.which {
+            Which::V1 | Which::V2 | Which::Solar10_7B => {
+                candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?
             }
+            Which::TinyLlama1_1BChat => vec![api.get("model.safetensors")?],
+        };
+        println!("building the model");
+        let cache = model::Cache::new(!args.no_kv_cache, dtype, &config, &device)?;
 
-            println!("building the model");
-            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, &cache, &config)?, tokenizer_filename, cache)
-        }
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
+        (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);
@@ -194,14 +165,14 @@ fn main() -> Result<()> {
     let mut index_pos = 0;
     let mut token_generated = 0;
     for index in 0..args.sample_len {
-        let context_size = if cache.use_kv_cache && index > 0 {
-            1
+        let (context_size, context_index) = if cache.use_kv_cache && index > 0 {
+            (1, index_pos)
         } else {
-            tokens.len()
+            (tokens.len(), 0)
         };
         let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
         let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
-        let logits = llama.forward(&input, index_pos)?;
+        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

@@ -262,7 +262,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         .extension()
         .map_or(false, |v| v == "safetensors");
     let (model, config) = if is_gguf {
-        let vb = qmodel::VarBuilder::from_gguf(config_path)?;
+        let vb = qmodel::VarBuilder::from_gguf(config_path, &device)?;
         let (_vocab_size, dim) = vb
             .get_no_shape("model.embed_tokens.weight")?
             .shape()
@@ -279,13 +279,13 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
                 (config.seq_len, config.head_size() / 2),
                 "rot.freq_cis_real",
             )?
-            .dequantize(&candle::Device::Cpu)?;
+            .dequantize(&device)?;
         let freq_cis_imag = vb
             .get(
                 (config.seq_len, config.head_size() / 2),
                 "rot.freq_cis_imag",
             )?
-            .dequantize(&candle::Device::Cpu)?;
+            .dequantize(&device)?;
 
         let fake_vb = candle_nn::VarBuilder::from_tensors(
             [
@@ -295,7 +295,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
             .into_iter()
             .collect(),
             candle::DType::F32,
-            &candle::Device::Cpu,
+            &device,
         );
         let cache = model::Cache::new(true, &config, fake_vb)?;
         let model = Model::QLlama(QLlama::load(vb, &cache, config.clone())?);
@@ -329,18 +329,14 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         .get_ids()
         .to_vec();
 
-    println!("{tokens:?}");
-
     let start_gen = std::time::Instant::now();
-    for index in 0..1 {
+    for index in 0.. {
         if tokens.len() >= config.seq_len {
             break;
         }
         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)?;
-        // println!("Input {}", input);
-        // println!("Input {}", input.to_device(&candle::Device::Cpu)?);
         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() {

candle-examples/examples/llama_multiprocess/main.rs

@@ -143,14 +143,7 @@ fn main() -> Result<()> {
     let config_filename = api.get("config.json")?;
     let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
     let tokenizer_filename = api.get("tokenizer.json")?;
-    let mut filenames = vec![];
-    for rfilename in [
-        "model-00001-of-00002.safetensors",
-        "model-00002-of-00002.safetensors",
-    ] {
-        let filename = api.get(rfilename)?;
-        filenames.push(filename);
-    }
+    let filenames = candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?;
 
     if args.rank.is_none() {
         let children: Vec<_> = (0..args.num_shards)

candle-examples/examples/mamba-minimal/README.md

@@ -0,0 +1,12 @@
+# candle-mamba-minimal: minimal implementation of Mamba
+
+This is based on [mamba-minimal](https://github.com/johnma2006/mamba-minimal).
+
+## Running the example
+
+```bash
+$ cargo run --example mamba-minimal --release -- --prompt "Mamba is the"
+Mamba is the most popular and best-selling game in the world. It has been downloaded more than 1,000 times by over 1 million people worldwide since its release on March 18th 2016.
+
+The Mamba series of games are a collection that combines elements from all genres including action, adventure, strategy & puzzle games with some unique gameplay features such as stealth and survival. The game is also known for its innovative graphics and the ability to play in a variety of different modes like single player or multiplayer.
+```

candle-examples/examples/mamba-minimal/main.rs

@@ -0,0 +1,287 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::{Error as E, Result};
+use clap::{Parser, ValueEnum};
+
+mod model;
+use model::{Config, Model};
+
+use candle::{DType, Device, Module, 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 </s> token"),
+        };
+        let start_gen = std::time::Instant::now();
+        for _ in 0..sample_len {
+            let input = Tensor::new(tokens.as_slice(), &self.device)?.unsqueeze(0)?;
+            let logits = self.model.forward(&input)?;
+            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, ValueEnum, Clone, Copy, PartialEq, Eq, Debug)]
+enum Which {
+    Mamba130m,
+    Mamba370m,
+    Mamba790m,
+    Mamba1_4b,
+    Mamba2_8b,
+    Mamba2_8bSlimPj,
+}
+
+impl std::fmt::Display for Which {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "{:?}", self)
+    }
+}
+
+impl Which {
+    fn model_id(&self) -> &'static str {
+        match self {
+            Self::Mamba130m => "state-spaces/mamba-130m",
+            Self::Mamba370m => "state-spaces/mamba-370m",
+            Self::Mamba790m => "state-spaces/mamba-790m",
+            Self::Mamba1_4b => "state-spaces/mamba-1.4b",
+            Self::Mamba2_8b => "state-spaces/mamba-2.8b",
+            Self::Mamba2_8bSlimPj => "state-spaces/mamba-2.8b-slimpj'",
+        }
+    }
+
+    fn revision(&self) -> &'static str {
+        match self {
+            Self::Mamba130m
+            | Self::Mamba370m
+            | Self::Mamba790m
+            | Self::Mamba1_4b
+            | Self::Mamba2_8bSlimPj => "refs/pr/1",
+            Self::Mamba2_8b => "refs/pr/4",
+        }
+    }
+}
+
+#[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 = 5000)]
+    sample_len: usize,
+
+    #[arg(long, default_value = "mamba130m")]
+    which: Which,
+
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    #[arg(long)]
+    tokenizer_file: Option<String>,
+
+    #[arg(long)]
+    weight_files: Option<String>,
+
+    #[arg(long)]
+    config_file: 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 repo = api.repo(Repo::with_revision(
+        args.model_id
+            .unwrap_or_else(|| args.which.model_id().to_string()),
+        RepoType::Model,
+        args.revision
+            .unwrap_or_else(|| args.which.revision().to_string()),
+    ));
+    let tokenizer_filename = match args.tokenizer_file {
+        Some(file) => std::path::PathBuf::from(file),
+        None => api
+            .model("EleutherAI/gpt-neox-20b".to_string())
+            .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 => {
+            vec![repo.get("model.safetensors")?]
+        }
+    };
+    println!("retrieved the files in {:?}", start.elapsed());
+    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
+
+    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 vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
+    let model = Model::new(&config, vb.pp("backbone"))?;
+    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/mamba-minimal/model.rs

@@ -0,0 +1,204 @@
+/// This follows the lines of:
+/// https://github.com/johnma2006/mamba-minimal/blob/master/model.py
+/// Simple, minimal implementation of Mamba in one file of PyTorch.
+use candle::{IndexOp, Module, Result, Tensor, D};
+use candle_nn::{RmsNorm, VarBuilder};
+
+use candle_transformers::models::with_tracing::{linear, linear_no_bias, Linear};
+
+#[derive(Debug, Clone, serde::Deserialize)]
+pub struct Config {
+    d_model: usize,
+    n_layer: usize,
+    vocab_size: usize,
+    pad_vocab_size_multiple: usize,
+}
+
+impl Config {
+    fn vocab_size(&self) -> usize {
+        let pad = self.pad_vocab_size_multiple;
+        (self.vocab_size + pad - 1) / pad * pad
+    }
+
+    fn dt_rank(&self) -> usize {
+        (self.d_model + 15) / 16
+    }
+
+    fn d_conv(&self) -> usize {
+        4
+    }
+
+    fn d_state(&self) -> usize {
+        16
+    }
+
+    fn d_inner(&self) -> usize {
+        self.d_model * 2
+    }
+}
+
+// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L177
+#[derive(Clone, Debug)]
+pub struct MambaBlock {
+    in_proj: Linear,
+    conv1d: candle_nn::Conv1d,
+    x_proj: Linear,
+    dt_proj: Linear,
+    a_log: Tensor,
+    d: Tensor,
+    out_proj: Linear,
+    dt_rank: usize,
+}
+
+impl MambaBlock {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let d_inner = cfg.d_inner();
+        let d_conv = cfg.d_conv();
+        let d_state = cfg.d_state();
+        let dt_rank = cfg.dt_rank();
+        let in_proj = linear_no_bias(cfg.d_model, d_inner * 2, vb.pp("in_proj"))?;
+        let conv_cfg = candle_nn::Conv1dConfig {
+            groups: d_inner,
+            padding: d_conv - 1,
+            ..Default::default()
+        };
+        let conv1d = candle_nn::conv1d(d_inner, d_inner, d_conv, conv_cfg, vb.pp("conv1d"))?;
+        let x_proj = linear_no_bias(d_inner, dt_rank + d_state * 2, vb.pp("x_proj"))?;
+        let dt_proj = linear(dt_rank, d_inner, vb.pp("dt_proj"))?;
+        let a_log = vb.get((d_inner, d_state), "A_log")?;
+        let d = vb.get(d_inner, "D")?;
+        let out_proj = linear_no_bias(d_inner, cfg.d_model, vb.pp("out_proj"))?;
+        Ok(Self {
+            in_proj,
+            conv1d,
+            x_proj,
+            dt_proj,
+            a_log,
+            d,
+            out_proj,
+            dt_rank,
+        })
+    }
+
+    fn ssm(&self, xs: &Tensor) -> Result<Tensor> {
+        let (_d_in, n) = self.a_log.dims2()?;
+        let a = self.a_log.to_dtype(candle::DType::F32)?.exp()?.neg()?;
+        let d = self.d.to_dtype(candle::DType::F32)?;
+        let x_dbl = xs.apply(&self.x_proj)?;
+        let delta = x_dbl.narrow(D::Minus1, 0, self.dt_rank)?;
+        let b = x_dbl.narrow(D::Minus1, self.dt_rank, n)?;
+        let c = x_dbl.narrow(D::Minus1, self.dt_rank + n, n)?;
+        let delta = delta.contiguous()?.apply(&self.dt_proj)?;
+        // softplus without threshold
+        let delta = (delta.exp()? + 1.)?.log()?;
+        let ss = selective_scan(xs, &delta, &a, &b, &c, &d)?;
+        Ok(ss)
+    }
+}
+
+// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L275
+fn selective_scan(
+    u: &Tensor,
+    delta: &Tensor,
+    a: &Tensor,
+    b: &Tensor,
+    c: &Tensor,
+    d: &Tensor,
+) -> Result<Tensor> {
+    let (b_sz, l, d_in) = u.dims3()?;
+    let n = a.dim(1)?;
+    let delta = delta.t()?.reshape((b_sz, d_in, l, 1))?; // b d_in l 1
+    let delta_a = delta.broadcast_mul(&a.reshape((1, d_in, 1, n))?)?.exp()?;
+    let delta_b_u = delta
+        .broadcast_mul(&b.reshape((b_sz, 1, l, n))?)?
+        .broadcast_mul(&u.t()?.reshape((b_sz, d_in, l, 1))?)?;
+    let mut xs = Tensor::zeros((b_sz, d_in, n), delta_a.dtype(), delta_a.device())?;
+    let mut ys = Vec::with_capacity(l);
+    for i in 0..l {
+        xs = ((delta_a.i((.., .., i))? * xs)? + delta_b_u.i((.., .., i))?)?;
+        let y = xs.matmul(&c.i((.., i, ..))?.unsqueeze(2)?)?.squeeze(2)?;
+        ys.push(y)
+    }
+    let ys = Tensor::stack(ys.as_slice(), 1)?;
+    ys + u.broadcast_mul(d)
+}
+
+impl Module for MambaBlock {
+    // https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L206
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let (_b_sz, seq_len, _dim) = xs.dims3()?;
+        let xs_and_res = xs.apply(&self.in_proj)?.chunk(2, D::Minus1)?;
+        let (xs, res) = (&xs_and_res[0], &xs_and_res[1]);
+        let xs = xs
+            .t()?
+            .apply(&self.conv1d)?
+            .narrow(D::Minus1, 0, seq_len)?
+            .t()?;
+        let xs = candle_nn::ops::silu(&xs)?;
+        let ys = (self.ssm(&xs)? * candle_nn::ops::silu(res))?;
+        ys.apply(&self.out_proj)
+    }
+}
+
+// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L143
+#[derive(Clone, Debug)]
+pub struct ResidualBlock {
+    mixer: MambaBlock,
+    norm: RmsNorm,
+}
+
+impl ResidualBlock {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let norm = candle_nn::rms_norm(cfg.d_model, 1e-5, vb.pp("norm"))?;
+        let mixer = MambaBlock::new(cfg, vb.pp("mixer"))?;
+        Ok(Self { mixer, norm })
+    }
+}
+
+impl Module for ResidualBlock {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.norm)?.apply(&self.mixer)? + xs
+    }
+}
+
+// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L56
+#[derive(Clone, Debug)]
+pub struct Model {
+    embedding: candle_nn::Embedding,
+    layers: Vec<ResidualBlock>,
+    norm_f: RmsNorm,
+    lm_head: Linear,
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let embedding = candle_nn::embedding(cfg.vocab_size(), cfg.d_model, vb.pp("embedding"))?;
+        let mut layers = Vec::with_capacity(cfg.n_layer);
+        let vb_l = vb.pp("layers");
+        for layer_idx in 0..cfg.n_layer {
+            let layer = ResidualBlock::new(cfg, vb_l.pp(layer_idx))?;
+            layers.push(layer)
+        }
+        let norm_f = candle_nn::rms_norm(cfg.d_model, 1e-5, vb.pp("norm_f"))?;
+        let lm_head = Linear::from_weights(embedding.embeddings().clone(), None);
+        Ok(Self {
+            embedding,
+            layers,
+            norm_f,
+            lm_head,
+        })
+    }
+}
+
+impl Module for Model {
+    fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
+        let (_b_size, seq_len) = input_ids.dims2()?;
+        let mut xs = self.embedding.forward(input_ids)?;
+        for layer in self.layers.iter() {
+            xs = layer.forward(&xs)?
+        }
+        xs.narrow(1, seq_len - 1, 1)?
+            .apply(&self.norm_f)?
+            .apply(&self.lm_head)
+    }
+}

candle-examples/examples/mistral/main.rs

@@ -155,8 +155,8 @@ struct Args {
     #[arg(long, short = 'n', default_value_t = 100)]
     sample_len: usize,
 
-    #[arg(long, default_value = "lmz/candle-mistral")]
-    model_id: String,
+    #[arg(long)]
+    model_id: Option<String>,
 
     #[arg(long, default_value = "main")]
     revision: String,
@@ -207,8 +207,18 @@ fn main() -> Result<()> {
 
     let start = std::time::Instant::now();
     let api = Api::new()?;
+    let model_id = match args.model_id {
+        Some(model_id) => model_id,
+        None => {
+            if args.quantized {
+                "lmz/candle-mistral".to_string()
+            } else {
+                "mistralai/Mistral-7B-v0.1".to_string()
+            }
+        }
+    };
     let repo = api.repo(Repo::with_revision(
-        args.model_id,
+        model_id,
         RepoType::Model,
         args.revision,
     ));
@@ -225,10 +235,7 @@ fn main() -> Result<()> {
             if args.quantized {
                 vec![repo.get("model-q4k.gguf")?]
             } else {
-                vec![
-                    repo.get("pytorch_model-00001-of-00002.safetensors")?,
-                    repo.get("pytorch_model-00002-of-00002.safetensors")?,
-                ]
+                candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
             }
         }
     };
@@ -237,13 +244,14 @@ fn main() -> Result<()> {
 
     let start = std::time::Instant::now();
     let config = Config::config_7b_v0_1(args.use_flash_attn);
+    let device = candle_examples::device(args.cpu)?;
     let (model, device) = if args.quantized {
         let filename = &filenames[0];
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename)?;
+        let vb =
+            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
         let model = QMistral::new(&config, vb)?;
-        (Model::Quantized(model), Device::Cpu)
+        (Model::Quantized(model), device)
     } else {
-        let device = candle_examples::device(args.cpu)?;
         let dtype = if device.is_cuda() {
             DType::BF16
         } else {

candle-examples/examples/mixtral/README.md

@@ -0,0 +1,25 @@
+# candle-mixtral: 8x7b LLM using a sparse mixture of experts.
+
+Mixtral-8x7B-v0.1 is a pretrained generative LLM with 56 billion parameters. 
+
+- [Blog post](https://mistral.ai/news/mixtral-of-experts/) from Mistral announcing the model release.
+- [Model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the HuggingFace Hub.
+
+## Running the example
+
+```bash
+$ cargo run --example mixtral --release  -- --prompt "def print_prime(n): "
+def print_prime(n):  # n is the number of prime numbers to be printed
+    i = 2
+    count = 0
+    while (count < n):
+        if (isPrime(i)):
+            print(i)
+            count += 1
+        i += 1
+
+def isPrime(n):
+    for x in range(2, int(n**0.5)+1):
+        if (n % x == 0):
+            ...
+```

candle-examples/examples/mixtral/main.rs

@@ -0,0 +1,241 @@
+#[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::mixtral::{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("</s>") {
+            Some(token) => token,
+            None => anyhow::bail!("cannot find the </s> 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 = 100)]
+    sample_len: usize,
+
+    #[arg(long, default_value = "mistralai/Mixtral-8x7B-v0.1")]
+    model_id: String,
+
+    #[arg(long, default_value = "main")]
+    revision: 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 repo = api.repo(Repo::with_revision(
+        args.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 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 start = std::time::Instant::now();
+    let config = Config::v0_1_8x7b(args.use_flash_attn);
+    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/musicgen/musicgen_model.rs

@@ -321,7 +321,7 @@ impl MusicgenDecoder {
         let positions = self.embed_positions.forward(&input)?.to_device(dev)?;
         let mut xs = inputs_embeds.broadcast_add(&positions)?;
         let attention_mask = self.prepare_decoder_attention_mask(b_sz, seq_len)?;
-        for (_layer_idx, decoder_layer) in self.layers.iter_mut().enumerate() {
+        for decoder_layer in self.layers.iter_mut() {
             xs = decoder_layer.forward(&xs, &attention_mask, None)?;
         }
         let xs = self.layer_norm.forward(&xs)?;

candle-examples/examples/phi/README.md

@@ -1,14 +1,33 @@
-# candle-phi: 1.3b LLM with state of the art performance for <10b models.
+# candle-phi: 1.3b and 2.7b LLM with state of the art performance for <10b models.
 
-[Phi-1.5](https://huggingface.co/microsoft/phi-1_5) is a language model using
-only 1.3 billion parameters but with state of the art performance compared to
+[Phi-1.5](https://huggingface.co/microsoft/phi-1_5) and
+[Phi-2](https://huggingface.co/microsoft/phi-2) are language models using
+only 1.3 and 2.7 billion parameters but with state of the art performance compared to
 models with up to 10 billion parameters.
 
 The candle implementation provides both the standard version as well as a
 quantized variant.
 
-## Running some example
+## Running some examples
 
+For the v2 version.
+```bash
+$ cargo run --example phi --release -- --model 2 \
+  --prompt "A skier slides down a frictionless slope of height 40m and length 80m. What's the skier speed at the bottom?"
+
+A skier slides down a frictionless slope of height 40m and length 80m. What's the skier speed at the bottom?
+
+Solution:
+The potential energy of the skier is converted into kinetic energy as it slides down the slope. The formula for potential energy is mgh, where m is mass, g is acceleration due to gravity (9.8 m/s^2), and h is height. Since there's no friction, all the potential energy is converted into kinetic energy at the bottom of the slope. The formula for kinetic energy is 1/2mv^2, where v is velocity. We can equate these two formulas:
+mgh = 1/2mv^2
+Solving for v, we get:
+v = sqrt(2gh)
+Substituting the given values, we get:
+v = sqrt(2*9.8*40) = 28 m/s
+Therefore, the skier speed at the bottom of the slope is 28 m/s.
+```
+
+For the v1.5 version.
 ```bash
 $ cargo run --example phi --release -- --prompt "def print_prime(n): "
 

candle-examples/examples/phi/main.rs

@@ -8,6 +8,7 @@ use anyhow::{Error as E, Result};
 use clap::{Parser, ValueEnum};
 
 use candle_transformers::models::mixformer::{Config, MixFormerSequentialForCausalLM as MixFormer};
+use candle_transformers::models::phi::{Config as PhiConfig, Model as Phi};
 use candle_transformers::models::quantized_mixformer::MixFormerSequentialForCausalLM as QMixFormer;
 
 use candle::{DType, Device, Tensor};
@@ -18,6 +19,7 @@ use tokenizers::Tokenizer;
 
 enum Model {
     MixFormer(MixFormer),
+    Phi(Phi),
     Quantized(QMixFormer),
 }
 
@@ -84,6 +86,7 @@ impl TextGeneration {
             let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
             let logits = match &mut self.model {
                 Model::MixFormer(m) => m.forward(&input)?,
+                Model::Phi(m) => m.forward(&input)?,
                 Model::Quantized(m) => m.forward(&input)?,
             };
             let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
@@ -117,12 +120,16 @@ impl TextGeneration {
     }
 }
 
-#[derive(Clone, Copy, Debug, ValueEnum)]
+#[derive(Clone, Copy, Debug, ValueEnum, PartialEq, Eq)]
 enum WhichModel {
     #[value(name = "1")]
     V1,
     #[value(name = "1.5")]
     V1_5,
+    #[value(name = "2")]
+    V2,
+    #[value(name = "2-old")]
+    V2Old,
     PuffinPhiV2,
     PhiHermes,
 }
@@ -143,7 +150,10 @@ struct Args {
     verbose_prompt: bool,
 
     #[arg(long)]
-    prompt: String,
+    prompt: Option<String>,
+
+    #[arg(long)]
+    mmlu_dir: Option<String>,
 
     /// The temperature used to generate samples.
     #[arg(long)]
@@ -158,13 +168,13 @@ struct Args {
     seed: u64,
 
     /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 100)]
+    #[arg(long, short = 'n', default_value_t = 5000)]
     sample_len: usize,
 
     #[arg(long)]
     model_id: Option<String>,
 
-    #[arg(long, default_value = "1.5")]
+    #[arg(long, default_value = "2")]
     model: WhichModel,
 
     #[arg(long)]
@@ -225,6 +235,7 @@ fn main() -> Result<()> {
                 match args.model {
                     WhichModel::V1 => "microsoft/phi-1".to_string(),
                     WhichModel::V1_5 => "microsoft/phi-1_5".to_string(),
+                    WhichModel::V2 | WhichModel::V2Old => "microsoft/phi-2".to_string(),
                     WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
                         "lmz/candle-quantized-phi".to_string()
                     }
@@ -239,9 +250,12 @@ fn main() -> Result<()> {
                 "main".to_string()
             } else {
                 match args.model {
-                    WhichModel::V1 => "refs/pr/2".to_string(),
-                    WhichModel::V1_5 => "refs/pr/18".to_string(),
-                    WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => "main".to_string(),
+                    WhichModel::V1 => "refs/pr/8".to_string(),
+                    WhichModel::V1_5 => "refs/pr/73".to_string(),
+                    WhichModel::V2Old => "834565c23f9b28b96ccbeabe614dd906b6db551a".to_string(),
+                    WhichModel::V2 | WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
+                        "main".to_string()
+                    }
                 }
             }
         }
@@ -250,27 +264,34 @@ fn main() -> Result<()> {
     let tokenizer_filename = match args.tokenizer {
         Some(file) => std::path::PathBuf::from(file),
         None => match args.model {
-            WhichModel::V1 | WhichModel::V1_5 => repo.get("tokenizer.json")?,
+            WhichModel::V1 | WhichModel::V1_5 | WhichModel::V2 | WhichModel::V2Old => {
+                repo.get("tokenizer.json")?
+            }
             WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
                 repo.get("tokenizer-puffin-phi-v2.json")?
             }
         },
     };
-    let filename = match args.weight_file {
-        Some(weight_file) => std::path::PathBuf::from(weight_file),
+    let filenames = match args.weight_file {
+        Some(weight_file) => vec![std::path::PathBuf::from(weight_file)],
         None => {
             if args.quantized {
                 match args.model {
-                    WhichModel::V1 => repo.get("model-v1-q4k.gguf")?,
-                    WhichModel::V1_5 => repo.get("model-q4k.gguf")?,
-                    WhichModel::PuffinPhiV2 => repo.get("model-puffin-phi-v2-q4k.gguf")?,
-                    WhichModel::PhiHermes => repo.get("model-phi-hermes-1_3B-q4k.gguf")?,
+                    WhichModel::V1 => vec![repo.get("model-v1-q4k.gguf")?],
+                    WhichModel::V1_5 => vec![repo.get("model-q4k.gguf")?],
+                    WhichModel::V2 | WhichModel::V2Old => vec![repo.get("model-v2-q4k.gguf")?],
+                    WhichModel::PuffinPhiV2 => vec![repo.get("model-puffin-phi-v2-q4k.gguf")?],
+                    WhichModel::PhiHermes => vec![repo.get("model-phi-hermes-1_3B-q4k.gguf")?],
                 }
             } else {
                 match args.model {
-                    WhichModel::V1 | WhichModel::V1_5 => repo.get("model.safetensors")?,
-                    WhichModel::PuffinPhiV2 => repo.get("model-puffin-phi-v2.safetensors")?,
-                    WhichModel::PhiHermes => repo.get("model-phi-hermes-1_3B.safetensors")?,
+                    WhichModel::V1 | WhichModel::V1_5 => vec![repo.get("model.safetensors")?],
+                    WhichModel::V2 | WhichModel::V2Old => candle_examples::hub_load_safetensors(
+                        &repo,
+                        "model.safetensors.index.json",
+                    )?,
+                    WhichModel::PuffinPhiV2 => vec![repo.get("model-puffin-phi-v2.safetensors")?],
+                    WhichModel::PhiHermes => vec![repo.get("model-phi-hermes-1_3B.safetensors")?],
                 }
             }
         }
@@ -279,35 +300,150 @@ fn main() -> Result<()> {
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
     let start = std::time::Instant::now();
-    let config = match args.model {
+    let config = || match args.model {
         WhichModel::V1 => Config::v1(),
         WhichModel::V1_5 => Config::v1_5(),
+        WhichModel::V2 | WhichModel::V2Old => Config::v2(),
         WhichModel::PuffinPhiV2 => Config::puffin_phi_v2(),
         WhichModel::PhiHermes => Config::phi_hermes_1_3b(),
     };
-    let (model, device) = if args.quantized {
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(&filename)?;
-        let model = QMixFormer::new(&config, vb)?;
-        (Model::Quantized(model), Device::Cpu)
+    let device = candle_examples::device(args.cpu)?;
+    let model = if args.quantized {
+        let config = config();
+        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
+            &filenames[0],
+            &device,
+        )?;
+        let model = match args.model {
+            WhichModel::V2 | WhichModel::V2Old => QMixFormer::new_v2(&config, vb)?,
+            _ => QMixFormer::new(&config, vb)?,
+        };
+        Model::Quantized(model)
     } else {
-        let device = candle_examples::device(args.cpu)?;
-        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[filename], DType::F32, &device)? };
-        let model = MixFormer::new(&config, vb)?;
-        (Model::MixFormer(model), device)
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
+        match args.model {
+            WhichModel::V1 | WhichModel::V1_5 | WhichModel::V2 => {
+                let config_filename = repo.get("config.json")?;
+                let config = std::fs::read_to_string(config_filename)?;
+                let config: PhiConfig = serde_json::from_str(&config)?;
+                let phi = Phi::new(&config, vb)?;
+                Model::Phi(phi)
+            }
+            WhichModel::V2Old => {
+                let config = config();
+                Model::MixFormer(MixFormer::new_v2(&config, vb)?)
+            }
+            WhichModel::PhiHermes | WhichModel::PuffinPhiV2 => {
+                let config = config();
+                Model::MixFormer(MixFormer::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,
-        args.verbose_prompt,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
+    match (args.prompt, args.mmlu_dir) {
+        (None, None) | (Some(_), Some(_)) => {
+            anyhow::bail!("exactly one of --prompt and --mmlu-dir must be specified")
+        }
+        (Some(prompt), None) => {
+            let mut pipeline = TextGeneration::new(
+                model,
+                tokenizer,
+                args.seed,
+                args.temperature,
+                args.top_p,
+                args.repeat_penalty,
+                args.repeat_last_n,
+                args.verbose_prompt,
+                &device,
+            );
+            pipeline.run(&prompt, args.sample_len)?;
+        }
+        (None, Some(mmlu_dir)) => mmlu(model, tokenizer, &device, mmlu_dir)?,
+    }
+    Ok(())
+}
+
+fn mmlu<P: AsRef<std::path::Path>>(
+    mut model: Model,
+    tokenizer: Tokenizer,
+    device: &Device,
+    mmlu_dir: P,
+) -> anyhow::Result<()> {
+    for dir_entry in mmlu_dir.as_ref().read_dir()?.flatten() {
+        let dir_entry = dir_entry.path();
+        let theme = match dir_entry.file_stem().and_then(|v| v.to_str()) {
+            None => "".to_string(),
+            Some(v) => match v.strip_suffix("_test") {
+                None => v.replace('_', " "),
+                Some(v) => v.replace('_', " "),
+            },
+        };
+        if dir_entry.extension().as_ref().and_then(|v| v.to_str()) != Some("csv") {
+            continue;
+        }
+        println!("reading {dir_entry:?}");
+        let dir_entry = std::fs::File::open(dir_entry)?;
+        let mut reader = csv::ReaderBuilder::new()
+            .has_headers(false)
+            .from_reader(dir_entry);
+        let token_a = tokenizer.token_to_id("A").unwrap();
+        let token_b = tokenizer.token_to_id("B").unwrap();
+        let token_c = tokenizer.token_to_id("C").unwrap();
+        let token_d = tokenizer.token_to_id("D").unwrap();
+        for row in reader.records() {
+            let row = match row {
+                Err(_) => continue,
+                Ok(row) => row,
+            };
+            if row.len() < 5 {
+                continue;
+            }
+            let question = row.get(0).unwrap();
+            let answer_a = row.get(1).unwrap();
+            let answer_b = row.get(2).unwrap();
+            let answer_c = row.get(3).unwrap();
+            let answer_d = row.get(4).unwrap();
+            let answer = row.get(5).unwrap();
+            let prompt = format!(
+                    "{} {theme}.\n{question}\nA. {answer_a}\nB. {answer_b}\nC. {answer_c}\nD. {answer_d}\nAnswer:\n",
+                    "The following are multiple choice questions (with answers) about"
+                );
+            let tokens = tokenizer.encode(prompt.as_str(), true).map_err(E::msg)?;
+            let tokens = tokens.get_ids().to_vec();
+            let input = Tensor::new(tokens, device)?.unsqueeze(0)?;
+            let logits = match &mut model {
+                Model::MixFormer(m) => {
+                    m.clear_kv_cache();
+                    m.forward(&input)?
+                }
+                Model::Phi(m) => {
+                    m.clear_kv_cache();
+                    m.forward(&input)?
+                }
+                Model::Quantized(m) => {
+                    m.clear_kv_cache();
+                    m.forward(&input)?
+                }
+            };
+            let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
+            let logits_v: Vec<f32> = logits.to_vec1()?;
+            let pr_a = logits_v[token_a as usize];
+            let pr_b = logits_v[token_b as usize];
+            let pr_c = logits_v[token_c as usize];
+            let pr_d = logits_v[token_d as usize];
+            let model_answer = if pr_a > pr_b && pr_a > pr_c && pr_a > pr_d {
+                "A"
+            } else if pr_b > pr_c && pr_b > pr_d {
+                "B"
+            } else if pr_c > pr_d {
+                "C"
+            } else {
+                "D"
+            };
+
+            println!("{prompt}\n -> {model_answer} vs {answer}");
+        }
+    }
     Ok(())
 }

candle-examples/examples/quantized-t5/main.rs

@@ -132,7 +132,8 @@ impl T5ModelBuilder {
     }
 
     pub fn build_model(&self) -> Result<t5::T5ForConditionalGeneration> {
-        let vb = t5::VarBuilder::from_gguf(&self.weights_filename)?;
+        let device = Device::Cpu;
+        let vb = t5::VarBuilder::from_gguf(&self.weights_filename, &device)?;
         Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
     }
 

candle-examples/examples/quantized/README.md

@@ -26,6 +26,19 @@ cargo run --example quantized --release -- --prompt "The best thing about coding
 > The best thing about coding in rust is 1.) that I don’t need to worry about memory leaks, 2.) speed and 3.) my program will compile even on old machines.
 ```
 
+Using the mixtral sparse mixture of expert model:
+```bash
+
+$ cargo run --example quantized --release -- --which mixtral --prompt "Lebesgue's integral is superior to Riemann's because "
+> avx: true, neon: false, simd128: false, f16c: true
+> temp: 0.80 repeat-penalty: 1.10 repeat-last-n: 64
+> loaded 995 tensors (26.44GB) in 0.03s
+Lebesgue's integral is superior to Riemann's because 1. it is defined for a wider class of functions, those which are absolutely integrable; 2. the definition does not involve limits in two variables---one being computed before the other (which makes some computations more difficult); and 3. interchange of order of integration is easier to establish than with Riemann's integral. On the other hand, Lebesgue's integral applies only for bounded functions defined on finite intervals; it does not provide numerical values for improper integrals. The latter are best evaluated using Cauchy's limit definition.
+
+The reason $f(x) = x^2$ is discontinuous at the ends of its interval of definition, and Riemann's integral requires continuity on the whole of an open interval containing it (see our earlier post), sine no such function exists with this property, is that the endpoints are infinite in measure for Lebesgue's integral.
+ ```
+
+
 ## Command-line flags
 
 Run with `--help` to see all options.

candle-examples/examples/quantized/main.rs

@@ -9,7 +9,7 @@ use std::io::Write;
 use tokenizers::Tokenizer;
 
 use candle::quantized::{ggml_file, gguf_file};
-use candle::{Device, Tensor};
+use candle::Tensor;
 use candle_transformers::generation::LogitsProcessor;
 
 use candle_examples::token_output_stream::TokenOutputStream;
@@ -45,14 +45,28 @@ enum Which {
     L13bCode,
     #[value(name = "32b-code")]
     L34bCode,
+    #[value(name = "7b-leo")]
+    Leo7b,
+    #[value(name = "13b-leo")]
+    Leo13b,
     #[value(name = "7b-mistral")]
     Mistral7b,
     #[value(name = "7b-mistral-instruct")]
     Mistral7bInstruct,
+    #[value(name = "7b-mistral-instruct-v0.2")]
+    Mistral7bInstructV02,
     #[value(name = "7b-zephyr-a")]
     Zephyr7bAlpha,
     #[value(name = "7b-zephyr-b")]
     Zephyr7bBeta,
+    #[value(name = "7b-open-chat-3.5")]
+    OpenChat35,
+    #[value(name = "7b-starling-a")]
+    Starling7bAlpha,
+    #[value(name = "mixtral")]
+    Mixtral,
+    #[value(name = "mixtral-instruct")]
+    MixtralInstruct,
 }
 
 impl Which {
@@ -66,12 +80,20 @@ impl Which {
             | Self::L70bChat
             | Self::L7bCode
             | Self::L13bCode
-            | Self::L34bCode => false,
-            // Zephyr is a fine tuned version of mistral and should be treated in the same way.
-            Self::Zephyr7bAlpha
+            | Self::L34bCode
+            | Self::Leo7b
+            | Self::Leo13b => false,
+            // Zephyr and OpenChat are fine tuned versions of mistral and should be treated in the
+            // same way. Starling is a fine tuned version of OpenChat.
+            Self::OpenChat35
+            | Self::Starling7bAlpha
+            | Self::Zephyr7bAlpha
             | Self::Zephyr7bBeta
+            | Self::Mixtral
+            | Self::MixtralInstruct
             | Self::Mistral7b
-            | Self::Mistral7bInstruct => true,
+            | Self::Mistral7bInstruct
+            | Self::Mistral7bInstructV02 => true,
         }
     }
 
@@ -86,17 +108,73 @@ impl Which {
             | Self::L7bCode
             | Self::L13bCode
             | Self::L34bCode
+            | Self::Leo7b
+            | Self::Leo13b
+            | Self::Mixtral
+            | Self::MixtralInstruct
             | Self::Mistral7b
-            | Self::Mistral7bInstruct => false,
+            | Self::Mistral7bInstruct
+            | Self::Mistral7bInstructV02
+            | Self::OpenChat35
+            | Self::Starling7bAlpha => false,
             Self::Zephyr7bAlpha | Self::Zephyr7bBeta => true,
         }
     }
+
+    fn is_open_chat(&self) -> bool {
+        match self {
+            Self::L7b
+            | Self::L13b
+            | Self::L70b
+            | Self::L7bChat
+            | Self::L13bChat
+            | Self::L70bChat
+            | Self::L7bCode
+            | Self::L13bCode
+            | Self::L34bCode
+            | Self::Leo7b
+            | Self::Leo13b
+            | Self::Mixtral
+            | Self::MixtralInstruct
+            | Self::Mistral7b
+            | Self::Mistral7bInstruct
+            | Self::Mistral7bInstructV02
+            | Self::Zephyr7bAlpha
+            | Self::Zephyr7bBeta => false,
+            Self::OpenChat35 | Self::Starling7bAlpha => true,
+        }
+    }
+
+    fn tokenizer_repo(&self) -> &'static str {
+        match self {
+            Which::L7b
+            | Which::L13b
+            | Which::L70b
+            | Which::L7bChat
+            | Which::L13bChat
+            | Which::L70bChat
+            | Which::L7bCode
+            | Which::L13bCode
+            | Which::L34bCode => "hf-internal-testing/llama-tokenizer",
+            Which::Leo7b => "LeoLM/leo-hessianai-7b",
+            Which::Leo13b => "LeoLM/leo-hessianai-13b",
+            Which::Mixtral => "mistralai/Mixtral-8x7B-v0.1",
+            Which::MixtralInstruct => "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            Which::Mistral7b
+            | Which::Mistral7bInstruct
+            | Which::Mistral7bInstructV02
+            | Which::Zephyr7bAlpha
+            | Which::Zephyr7bBeta => "mistralai/Mistral-7B-v0.1",
+            Which::OpenChat35 => "openchat/openchat_3.5",
+            Which::Starling7bAlpha => "berkeley-nest/Starling-LM-7B-alpha",
+        }
+    }
 }
 
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
 struct Args {
-    /// GGML file to load, typically a .bin file generated by the quantize command from llama.cpp
+    /// GGML/GGUF file to load, typically a .bin/.gguf file generated by the quantize command from llama.cpp
     #[arg(long)]
     model: Option<String>,
 
@@ -157,11 +235,7 @@ impl Args {
             Some(config) => std::path::PathBuf::from(config),
             None => {
                 let api = hf_hub::api::sync::Api::new()?;
-                let repo = if self.which.is_mistral() {
-                    "mistralai/Mistral-7B-v0.1"
-                } else {
-                    "hf-internal-testing/llama-tokenizer"
-                };
+                let repo = self.which.tokenizer_repo();
                 let api = api.model(repo.to_string());
                 api.get("tokenizer.json")?
             }
@@ -192,6 +266,22 @@ impl Args {
                     Which::L7bCode => ("TheBloke/CodeLlama-7B-GGUF", "codellama-7b.Q8_0.gguf"),
                     Which::L13bCode => ("TheBloke/CodeLlama-13B-GGUF", "codellama-13b.Q8_0.gguf"),
                     Which::L34bCode => ("TheBloke/CodeLlama-34B-GGUF", "codellama-34b.Q8_0.gguf"),
+                    Which::Leo7b => (
+                        "TheBloke/leo-hessianai-7B-GGUF",
+                        "leo-hessianai-7b.Q4_K_M.gguf",
+                    ),
+                    Which::Leo13b => (
+                        "TheBloke/leo-hessianai-13B-GGUF",
+                        "leo-hessianai-13b.Q4_K_M.gguf",
+                    ),
+                    Which::Mixtral => (
+                        "TheBloke/Mixtral-8x7B-v0.1-GGUF",
+                        "mixtral-8x7b-v0.1.Q4_K_M.gguf",
+                    ),
+                    Which::MixtralInstruct => (
+                        "TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
+                        "mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf",
+                    ),
                     Which::Mistral7b => (
                         "TheBloke/Mistral-7B-v0.1-GGUF",
                         "mistral-7b-v0.1.Q4_K_S.gguf",
@@ -200,6 +290,10 @@ impl Args {
                         "TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
                         "mistral-7b-instruct-v0.1.Q4_K_S.gguf",
                     ),
+                    Which::Mistral7bInstructV02 => (
+                        "TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
+                        "mistral-7b-instruct-v0.2.Q4_K_S.gguf",
+                    ),
                     Which::Zephyr7bAlpha => (
                         "TheBloke/zephyr-7B-alpha-GGUF",
                         "zephyr-7b-alpha.Q4_K_M.gguf",
@@ -207,6 +301,11 @@ impl Args {
                     Which::Zephyr7bBeta => {
                         ("TheBloke/zephyr-7B-beta-GGUF", "zephyr-7b-beta.Q4_K_M.gguf")
                     }
+                    Which::OpenChat35 => ("TheBloke/openchat_3.5-GGUF", "openchat_3.5.Q4_K_M.gguf"),
+                    Which::Starling7bAlpha => (
+                        "TheBloke/Starling-LM-7B-alpha-GGUF",
+                        "starling-lm-7b-alpha.Q4_K_M.gguf",
+                    ),
                 };
                 let api = hf_hub::api::sync::Api::new()?;
                 let api = api.model(repo.to_string());
@@ -262,15 +361,16 @@ 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(false)?;
 
     let mut model = match model_path.extension().and_then(|v| v.to_str()) {
         Some("gguf") => {
-            let model = gguf_file::Content::read(&mut file)?;
+            let model = gguf_file::Content::read(&mut file).map_err(|e| e.with_path(model_path))?;
             let mut total_size_in_bytes = 0;
             for (_, tensor) in model.tensor_infos.iter() {
                 let elem_count = tensor.shape.elem_count();
                 total_size_in_bytes +=
-                    elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.blck_size();
+                    elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.block_size();
             }
             println!(
                 "loaded {:?} tensors ({}) in {:.2}s",
@@ -278,15 +378,16 @@ fn main() -> anyhow::Result<()> {
                 &format_size(total_size_in_bytes),
                 start.elapsed().as_secs_f32(),
             );
-            ModelWeights::from_gguf(model, &mut file)?
+            ModelWeights::from_gguf(model, &mut file, &device)?
         }
         Some("ggml" | "bin") | Some(_) | None => {
-            let model = ggml_file::Content::read(&mut file)?;
+            let model = ggml_file::Content::read(&mut file, &device)
+                .map_err(|e| e.with_path(model_path))?;
             let mut total_size_in_bytes = 0;
             for (_, tensor) in model.tensors.iter() {
                 let elem_count = tensor.shape().elem_count();
                 total_size_in_bytes +=
-                    elem_count * tensor.dtype().type_size() / tensor.dtype().blck_size();
+                    elem_count * tensor.dtype().type_size() / tensor.dtype().block_size();
             }
             println!(
                 "loaded {:?} tensors ({}) in {:.2}s",
@@ -302,13 +403,20 @@ fn main() -> anyhow::Result<()> {
                 | Which::L13bChat
                 | Which::L7bCode
                 | Which::L13bCode
-                | Which::L34bCode => 1,
-                Which::Mistral7b
+                | Which::L34bCode
+                | Which::Leo7b
+                | Which::Leo13b => 1,
+                Which::Mixtral
+                | Which::MixtralInstruct
+                | Which::Mistral7b
                 | Which::Mistral7bInstruct
+                | Which::Mistral7bInstructV02
                 | Which::Zephyr7bAlpha
                 | Which::Zephyr7bBeta
                 | Which::L70b
-                | Which::L70bChat => 8,
+                | Which::L70bChat
+                | Which::OpenChat35
+                | Which::Starling7bAlpha => 8,
             };
             ModelWeights::from_ggml(model, args.gqa.unwrap_or(default_gqa))?
         }
@@ -325,10 +433,11 @@ fn main() -> anyhow::Result<()> {
     };
 
     let mut pre_prompt_tokens = vec![];
-    loop {
+    for prompt_index in 0.. {
         let prompt_str = match &prompt {
             Prompt::One(prompt) => prompt.clone(),
             Prompt::Interactive | Prompt::Chat => {
+                let is_interactive = matches!(prompt, Prompt::Interactive);
                 print!("> ");
                 std::io::stdout().flush()?;
                 let mut prompt = String::new();
@@ -339,8 +448,14 @@ fn main() -> anyhow::Result<()> {
                         prompt.pop();
                     }
                 }
-                if args.which.is_zephyr() {
-                    format!("<|system|>\n</s>\n<|user|>\n{prompt}</s>\n<|assistant|>")
+                if args.which.is_open_chat() {
+                    format!("GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:")
+                } else if args.which.is_zephyr() {
+                    if prompt_index == 0 || is_interactive {
+                        format!("<|system|>\n</s>\n<|user|>\n{prompt}</s>\n<|assistant|>",)
+                    } else {
+                        format!("<|user|>\n{prompt}</s>\n<|assistant|>")
+                    }
                 } else if args.which.is_mistral() {
                     format!("[INST] {prompt} [/INST]")
                 } else {
@@ -373,7 +488,7 @@ fn main() -> anyhow::Result<()> {
 
         let start_prompt_processing = std::time::Instant::now();
         let mut next_token = {
-            let input = Tensor::new(prompt_tokens.as_slice(), &Device::Cpu)?.unsqueeze(0)?;
+            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)?
@@ -385,12 +500,16 @@ fn main() -> anyhow::Result<()> {
             std::io::stdout().flush()?;
         }
 
-        let eos_token = *tos.tokenizer().get_vocab(true).get("</s>").unwrap();
-
+        let eos_token = if args.which.is_open_chat() {
+            "<|end_of_turn|>"
+        } else {
+            "</s>"
+        };
+        let eos_token = *tos.tokenizer().get_vocab(true).get(eos_token).unwrap();
         let start_post_prompt = std::time::Instant::now();
         let mut sampled = 0;
         for index in 0..to_sample {
-            let input = Tensor::new(&[next_token], &Device::Cpu)?.unsqueeze(0)?;
+            let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
             let logits = model.forward(&input, prompt_tokens.len() + index)?;
             let logits = logits.squeeze(0)?;
             let logits = if args.repeat_penalty == 1. {

candle-examples/examples/reinforcement-learning/README.md

@@ -8,9 +8,16 @@ Python package with:
 pip install "gymnasium[accept-rom-license]"
 ```
 
-In order to run the example, use the following command. Note the additional
+In order to run the examples, use the following commands. Note the additional
 `--package` flag to ensure that there is no conflict with the `candle-pyo3`
 crate.
+
+For the Policy Gradient example:
 ```bash
-cargo run --example reinforcement-learning --features=pyo3 --package candle-examples
+cargo run --example reinforcement-learning --features=pyo3 --package candle-examples -- pg
+```
+
+For the Deep Deterministic Policy Gradient example:
+```bash
+cargo run --example reinforcement-learning --features=pyo3 --package candle-examples -- ddpg
 ```

candle-examples/examples/reinforcement-learning/atari_wrappers.py

@@ -78,7 +78,7 @@ class EpisodicLifeEnv(gym.Wrapper):
         # then update lives to handle bonus lives
         lives = self.env.unwrapped.ale.lives()
         if lives < self.lives and lives > 0:
-            # for Qbert somtimes we stay in lives == 0 condtion for a few frames
+            # for Qbert sometimes we stay in lives == 0 condition for a few frames
             # so its important to keep lives > 0, so that we only reset once
             # the environment advertises done.
             done = True

candle-examples/examples/reinforcement-learning/ddpg.rs

@@ -8,6 +8,8 @@ use candle_nn::{
 };
 use rand::{distributions::Uniform, thread_rng, Rng};
 
+use super::gym_env::GymEnv;
+
 pub struct OuNoise {
     mu: f64,
     theta: f64,
@@ -449,3 +451,106 @@ impl DDPG<'_> {
         Ok(())
     }
 }
+
+// The impact of the q value of the next state on the current state's q value.
+const GAMMA: f64 = 0.99;
+// The weight for updating the target networks.
+const TAU: f64 = 0.005;
+// The capacity of the replay buffer used for sampling training data.
+const REPLAY_BUFFER_CAPACITY: usize = 100_000;
+// The training batch size for each training iteration.
+const TRAINING_BATCH_SIZE: usize = 100;
+// The total number of episodes.
+const MAX_EPISODES: usize = 100;
+// The maximum length of an episode.
+const EPISODE_LENGTH: usize = 200;
+// The number of training iterations after one episode finishes.
+const TRAINING_ITERATIONS: usize = 200;
+
+// Ornstein-Uhlenbeck process parameters.
+const MU: f64 = 0.0;
+const THETA: f64 = 0.15;
+const SIGMA: f64 = 0.1;
+
+const ACTOR_LEARNING_RATE: f64 = 1e-4;
+const CRITIC_LEARNING_RATE: f64 = 1e-3;
+
+pub fn run() -> Result<()> {
+    let env = GymEnv::new("Pendulum-v1")?;
+    println!("action space: {}", env.action_space());
+    println!("observation space: {:?}", env.observation_space());
+
+    let size_state = env.observation_space().iter().product::<usize>();
+    let size_action = env.action_space();
+
+    let mut agent = DDPG::new(
+        &Device::Cpu,
+        size_state,
+        size_action,
+        true,
+        ACTOR_LEARNING_RATE,
+        CRITIC_LEARNING_RATE,
+        GAMMA,
+        TAU,
+        REPLAY_BUFFER_CAPACITY,
+        OuNoise::new(MU, THETA, SIGMA, size_action)?,
+    )?;
+
+    let mut rng = rand::thread_rng();
+
+    for episode in 0..MAX_EPISODES {
+        // let mut state = env.reset(episode as u64)?;
+        let mut state = env.reset(rng.gen::<u64>())?;
+
+        let mut total_reward = 0.0;
+        for _ in 0..EPISODE_LENGTH {
+            let mut action = 2.0 * agent.actions(&state)?;
+            action = action.clamp(-2.0, 2.0);
+
+            let step = env.step(vec![action])?;
+            total_reward += step.reward;
+
+            agent.remember(
+                &state,
+                &Tensor::new(vec![action], &Device::Cpu)?,
+                &Tensor::new(vec![step.reward as f32], &Device::Cpu)?,
+                &step.state,
+                step.terminated,
+                step.truncated,
+            );
+
+            if step.terminated || step.truncated {
+                break;
+            }
+            state = step.state;
+        }
+
+        println!("episode {episode} with total reward of {total_reward}");
+
+        for _ in 0..TRAINING_ITERATIONS {
+            agent.train(TRAINING_BATCH_SIZE)?;
+        }
+    }
+
+    println!("Testing...");
+    agent.train = false;
+    for episode in 0..10 {
+        // let mut state = env.reset(episode as u64)?;
+        let mut state = env.reset(rng.gen::<u64>())?;
+        let mut total_reward = 0.0;
+        for _ in 0..EPISODE_LENGTH {
+            let mut action = 2.0 * agent.actions(&state)?;
+            action = action.clamp(-2.0, 2.0);
+
+            let step = env.step(vec![action])?;
+            total_reward += step.reward;
+
+            if step.terminated || step.truncated {
+                break;
+            }
+            state = step.state;
+        }
+        println!("episode {episode} with total reward of {total_reward}");
+    }
+    Ok(())
+}

candle-examples/examples/reinforcement-learning/main.rs

@@ -6,139 +6,32 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
+use candle::Result;
+use clap::{Parser, Subcommand};
+
 mod gym_env;
 mod vec_gym_env;
 
 mod ddpg;
+mod policy_gradient;
 
-use candle::{Device, Result, Tensor};
-use clap::Parser;
-use rand::Rng;
-
-// The impact of the q value of the next state on the current state's q value.
-const GAMMA: f64 = 0.99;
-// The weight for updating the target networks.
-const TAU: f64 = 0.005;
-// The capacity of the replay buffer used for sampling training data.
-const REPLAY_BUFFER_CAPACITY: usize = 100_000;
-// The training batch size for each training iteration.
-const TRAINING_BATCH_SIZE: usize = 100;
-// The total number of episodes.
-const MAX_EPISODES: usize = 100;
-// The maximum length of an episode.
-const EPISODE_LENGTH: usize = 200;
-// The number of training iterations after one episode finishes.
-const TRAINING_ITERATIONS: usize = 200;
-
-// Ornstein-Uhlenbeck process parameters.
-const MU: f64 = 0.0;
-const THETA: f64 = 0.15;
-const SIGMA: f64 = 0.1;
-
-const ACTOR_LEARNING_RATE: f64 = 1e-4;
-const CRITIC_LEARNING_RATE: f64 = 1e-3;
-
-#[derive(Parser, Debug, Clone)]
-#[command(author, version, about, long_about = None)]
+#[derive(Parser)]
 struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
+    #[command(subcommand)]
+    command: Command,
+}
 
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
+#[derive(Subcommand)]
+enum Command {
+    Pg,
+    Ddpg,
 }
 
 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
-    };
-
-    let env = gym_env::GymEnv::new("Pendulum-v1")?;
-    println!("action space: {}", env.action_space());
-    println!("observation space: {:?}", env.observation_space());
-
-    let size_state = env.observation_space().iter().product::<usize>();
-    let size_action = env.action_space();
-
-    let mut agent = ddpg::DDPG::new(
-        &Device::Cpu,
-        size_state,
-        size_action,
-        true,
-        ACTOR_LEARNING_RATE,
-        CRITIC_LEARNING_RATE,
-        GAMMA,
-        TAU,
-        REPLAY_BUFFER_CAPACITY,
-        ddpg::OuNoise::new(MU, THETA, SIGMA, size_action)?,
-    )?;
-
-    let mut rng = rand::thread_rng();
-
-    for episode in 0..MAX_EPISODES {
-        // let mut state = env.reset(episode as u64)?;
-        let mut state = env.reset(rng.gen::<u64>())?;
-
-        let mut total_reward = 0.0;
-        for _ in 0..EPISODE_LENGTH {
-            let mut action = 2.0 * agent.actions(&state)?;
-            action = action.clamp(-2.0, 2.0);
-
-            let step = env.step(vec![action])?;
-            total_reward += step.reward;
-
-            agent.remember(
-                &state,
-                &Tensor::new(vec![action], &Device::Cpu)?,
-                &Tensor::new(vec![step.reward as f32], &Device::Cpu)?,
-                &step.state,
-                step.terminated,
-                step.truncated,
-            );
-
-            if step.terminated || step.truncated {
-                break;
-            }
-            state = step.state;
-        }
-
-        println!("episode {episode} with total reward of {total_reward}");
-
-        for _ in 0..TRAINING_ITERATIONS {
-            agent.train(TRAINING_BATCH_SIZE)?;
-        }
-    }
-
-    println!("Testing...");
-    agent.train = false;
-    for episode in 0..10 {
-        // let mut state = env.reset(episode as u64)?;
-        let mut state = env.reset(rng.gen::<u64>())?;
-        let mut total_reward = 0.0;
-        for _ in 0..EPISODE_LENGTH {
-            let mut action = 2.0 * agent.actions(&state)?;
-            action = action.clamp(-2.0, 2.0);
-
-            let step = env.step(vec![action])?;
-            total_reward += step.reward;
-
-            if step.terminated || step.truncated {
-                break;
-            }
-            state = step.state;
-        }
-        println!("episode {episode} with total reward of {total_reward}");
+    match args.command {
+        Command::Pg => policy_gradient::run()?,
+        Command::Ddpg => ddpg::run()?,
     }
     Ok(())
 }

candle-examples/examples/reinforcement-learning/policy_gradient.rs

@@ -0,0 +1,146 @@
+use super::gym_env::{GymEnv, Step};
+use candle::{DType, Device, Error, Module, Result, Tensor};
+use candle_nn::{
+    linear, ops::log_softmax, ops::softmax, sequential::seq, Activation, AdamW, Optimizer,
+    ParamsAdamW, VarBuilder, VarMap,
+};
+use rand::{distributions::Distribution, rngs::ThreadRng, Rng};
+
+fn new_model(
+    input_shape: &[usize],
+    num_actions: usize,
+    dtype: DType,
+    device: &Device,
+) -> Result<(impl Module, VarMap)> {
+    let input_size = input_shape.iter().product();
+
+    let mut varmap = VarMap::new();
+    let var_builder = VarBuilder::from_varmap(&varmap, dtype, device);
+
+    let model = seq()
+        .add(linear(input_size, 32, var_builder.pp("lin1"))?)
+        .add(Activation::Relu)
+        .add(linear(32, num_actions, var_builder.pp("lin2"))?);
+
+    Ok((model, varmap))
+}
+
+fn accumulate_rewards(steps: &[Step<i64>]) -> Vec<f64> {
+    let mut rewards: Vec<f64> = steps.iter().map(|s| s.reward).collect();
+    let mut acc_reward = 0f64;
+    for (i, reward) in rewards.iter_mut().enumerate().rev() {
+        if steps[i].terminated {
+            acc_reward = 0.0;
+        }
+        acc_reward += *reward;
+        *reward = acc_reward;
+    }
+    rewards
+}
+
+fn weighted_sample(probs: Vec<f32>, rng: &mut ThreadRng) -> Result<usize> {
+    let distribution = rand::distributions::WeightedIndex::new(probs).map_err(Error::wrap)?;
+    let mut rng = rng;
+    Ok(distribution.sample(&mut rng))
+}
+
+pub fn run() -> Result<()> {
+    let env = GymEnv::new("CartPole-v1")?;
+
+    println!("action space: {:?}", env.action_space());
+    println!("observation space: {:?}", env.observation_space());
+
+    let (model, varmap) = new_model(
+        env.observation_space(),
+        env.action_space(),
+        DType::F32,
+        &Device::Cpu,
+    )?;
+
+    let optimizer_params = ParamsAdamW {
+        lr: 0.01,
+        weight_decay: 0.01,
+        ..Default::default()
+    };
+
+    let mut optimizer = AdamW::new(varmap.all_vars(), optimizer_params)?;
+
+    let mut rng = rand::thread_rng();
+
+    for epoch_idx in 0..100 {
+        let mut state = env.reset(rng.gen::<u64>())?;
+        let mut steps: Vec<Step<i64>> = vec![];
+
+        loop {
+            let action = {
+                let action_probs: Vec<f32> =
+                    softmax(&model.forward(&state.detach()?.unsqueeze(0)?)?, 1)?
+                        .squeeze(0)?
+                        .to_vec1()?;
+                weighted_sample(action_probs, &mut rng)? as i64
+            };
+
+            let step = env.step(action)?;
+            steps.push(step.copy_with_obs(&state));
+
+            if step.terminated || step.truncated {
+                state = env.reset(rng.gen::<u64>())?;
+                if steps.len() > 5000 {
+                    break;
+                }
+            } else {
+                state = step.state;
+            }
+        }
+
+        let total_reward: f64 = steps.iter().map(|s| s.reward).sum();
+        let episodes: i64 = steps
+            .iter()
+            .map(|s| (s.terminated || s.truncated) as i64)
+            .sum();
+        println!(
+            "epoch: {:<3} episodes: {:<5} avg reward per episode: {:.2}",
+            epoch_idx,
+            episodes,
+            total_reward / episodes as f64
+        );
+
+        let batch_size = steps.len();
+
+        let rewards = Tensor::from_vec(accumulate_rewards(&steps), batch_size, &Device::Cpu)?
+            .to_dtype(DType::F32)?
+            .detach()?;
+
+        let actions_mask = {
+            let actions: Vec<i64> = steps.iter().map(|s| s.action).collect();
+            let actions_mask: Vec<Tensor> = actions
+                .iter()
+                .map(|&action| {
+                    // One-hot encoding
+                    let mut action_mask = vec![0.0; env.action_space()];
+                    action_mask[action as usize] = 1.0;
+
+                    Tensor::from_vec(action_mask, env.action_space(), &Device::Cpu)
+                        .unwrap()
+                        .to_dtype(DType::F32)
+                        .unwrap()
+                })
+                .collect();
+            Tensor::stack(&actions_mask, 0)?.detach()?
+        };
+
+        let states = {
+            let states: Vec<Tensor> = steps.into_iter().map(|s| s.state).collect();
+            Tensor::stack(&states, 0)?.detach()?
+        };
+
+        let log_probs = actions_mask
+            .mul(&log_softmax(&model.forward(&states)?, 1)?)?
+            .sum(1)?;
+
+        let loss = rewards.mul(&log_probs)?.neg()?.mean_all()?;
+        optimizer.backward_step(&loss)?;
+    }
+
+    Ok(())
+}

candle-examples/examples/replit-code/main.rs

@@ -236,16 +236,15 @@ fn main() -> Result<()> {
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
     let start = std::time::Instant::now();
+    let device = candle_examples::device(args.cpu)?;
     let config = Config::replit_code_v1_5_3b();
-    let (model, device) = if args.quantized {
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(&filename)?;
-        let model = Model::Q(Q::new(&config, vb.pp("transformer"))?);
-        (model, Device::Cpu)
+    let model = if args.quantized {
+        let vb =
+            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(&filename, &device)?;
+        Model::Q(Q::new(&config, vb.pp("transformer"))?)
     } else {
-        let device = candle_examples::device(args.cpu)?;
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[filename], DType::F32, &device)? };
-        let model = Model::M(M::new(&config, vb.pp("transformer"))?);
-        (model, device)
+        Model::M(M::new(&config, vb.pp("transformer"))?)
     };
     println!("loaded the model in {:?}", start.elapsed());
 

candle-examples/examples/repvgg/README.md

@@ -0,0 +1,22 @@
+# candle-repvgg
+
+[RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697).
+
+This candle implementation uses a pre-trained RepVGG 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 repvgg --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+
+loaded image Tensor[dims 3, 224, 224; f32]
+model built
+mountain bike, all-terrain bike, off-roader: 61.70%
+bicycle-built-for-two, tandem bicycle, tandem: 33.14%
+unicycle, monocycle     : 4.88%
+crash helmet            : 0.15%
+moped                   : 0.04%
+
+```

candle-examples/examples/repvgg/main.rs

@@ -0,0 +1,111 @@
+#[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::repvgg;
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    A0,
+    A1,
+    A2,
+    B0,
+    B1,
+    B2,
+    B3,
+    B1G4,
+    B2G4,
+    B3G4,
+}
+
+impl Which {
+    fn model_filename(&self) -> String {
+        let name = match self {
+            Self::A0 => "a0",
+            Self::A1 => "a1",
+            Self::A2 => "a2",
+            Self::B0 => "b0",
+            Self::B1 => "b1",
+            Self::B2 => "b2",
+            Self::B3 => "b3",
+            Self::B1G4 => "b1g4",
+            Self::B2G4 => "b2g4",
+            Self::B3G4 => "b3g4",
+        };
+        format!("timm/repvgg_{}.rvgg_in1k", name)
+    }
+
+    fn config(&self) -> repvgg::Config {
+        match self {
+            Self::A0 => repvgg::Config::a0(),
+            Self::A1 => repvgg::Config::a1(),
+            Self::A2 => repvgg::Config::a2(),
+            Self::B0 => repvgg::Config::b0(),
+            Self::B1 => repvgg::Config::b1(),
+            Self::B2 => repvgg::Config::b2(),
+            Self::B3 => repvgg::Config::b3(),
+            Self::B1G4 => repvgg::Config::b1g4(),
+            Self::B2G4 => repvgg::Config::b2g4(),
+            Self::B3G4 => repvgg::Config::b3g4(),
+        }
+    }
+}
+
+#[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::A0)]
+    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)?;
+    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 = repvgg::repvgg(&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/stable-diffusion/README.md

@@ -8,7 +8,7 @@ XL using Rust and [candle](https://github.com/huggingface/candle).
 The `stable-diffusion` example is a conversion of
 [diffusers-rs](https://github.com/LaurentMazare/diffusers-rs) using candle
 rather than libtorch. This implementation supports Stable Diffusion v1.5, v2.1,
-as well as Stable Diffusion XL 1.0.
+as well as Stable Diffusion XL 1.0, and Turbo.
 
 ## Getting the weights
 
@@ -23,16 +23,26 @@ cargo run --example stable-diffusion --release --features=cuda,cudnn \
     -- --prompt "a cosmonaut on a horse (hd, realistic, high-def)"
 ```
 
-The final image is named `sd_final.png` by default.
-The default scheduler is the Denoising Diffusion Implicit Model scheduler (DDIM). The
-original paper and some code can be found in the [associated repo](https://github.com/ermongroup/ddim).
+The final image is named `sd_final.png` by default. The Turbo version is much
+faster than previous versions, to give it a try add a `--sd-version turbo` flag,
+e.g.:
+
+```bash
+cargo run --example stable-diffusion --release --features=cuda,cudnn \
+    -- --prompt "a cosmonaut on a horse (hd, realistic, high-def)" --sd-version turbo
+```
+
+The default scheduler for the v1.5, v2.1 and XL 1.0 version is the Denoising
+Diffusion Implicit Model scheduler (DDIM). The original paper and some code can
+be found in the [associated repo](https://github.com/ermongroup/ddim).
+The default scheduler for the XL Turbo version is the Euler Ancestral scheduler.
 
 ### Command-line flags
 
 - `--prompt`: the prompt to be used to generate the image.
 - `--uncond-prompt`: the optional unconditional prompt.
-- `--sd-version`: the Stable Diffusion version to use, can be `v1-5`, `v2-1`, or
-  `xl`.
+- `--sd-version`: the Stable Diffusion version to use, can be `v1-5`, `v2-1`,
+  `xl`, or `turbo`.
 - `--cpu`: use the cpu rather than the gpu (much slower).
 - `--height`, `--width`: set the height and width for the generated image.
 - `--n-steps`: the number of steps to be used in the diffusion process.

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

@@ -11,8 +11,6 @@ use candle::{DType, Device, IndexOp, Module, Tensor, D};
 use clap::Parser;
 use tokenizers::Tokenizer;
 
-const GUIDANCE_SCALE: f64 = 7.5;
-
 #[derive(Parser)]
 #[command(author, version, about, long_about = None)]
 struct Args {
@@ -63,8 +61,8 @@ struct Args {
     sliced_attention_size: Option<usize>,
 
     /// The number of steps to run the diffusion for.
-    #[arg(long, default_value_t = 30)]
-    n_steps: usize,
+    #[arg(long)]
+    n_steps: Option<usize>,
 
     /// The number of samples to generate.
     #[arg(long, default_value_t = 1)]
@@ -87,6 +85,9 @@ struct Args {
     #[arg(long)]
     use_f16: bool,
 
+    #[arg(long)]
+    guidance_scale: Option<f64>,
+
     #[arg(long, value_name = "FILE")]
     img2img: Option<String>,
 
@@ -102,6 +103,7 @@ enum StableDiffusionVersion {
     V1_5,
     V2_1,
     Xl,
+    Turbo,
 }
 
 #[derive(Debug, Clone, Copy, PartialEq, Eq)]
@@ -120,12 +122,13 @@ impl StableDiffusionVersion {
             Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
             Self::V2_1 => "stabilityai/stable-diffusion-2-1",
             Self::V1_5 => "runwayml/stable-diffusion-v1-5",
+            Self::Turbo => "stabilityai/sdxl-turbo",
         }
     }
 
     fn unet_file(&self, use_f16: bool) -> &'static str {
         match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
+            Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
                 if use_f16 {
                     "unet/diffusion_pytorch_model.fp16.safetensors"
                 } else {
@@ -137,7 +140,7 @@ impl StableDiffusionVersion {
 
     fn vae_file(&self, use_f16: bool) -> &'static str {
         match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
+            Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
                 if use_f16 {
                     "vae/diffusion_pytorch_model.fp16.safetensors"
                 } else {
@@ -149,7 +152,7 @@ impl StableDiffusionVersion {
 
     fn clip_file(&self, use_f16: bool) -> &'static str {
         match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
+            Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
                 if use_f16 {
                     "text_encoder/model.fp16.safetensors"
                 } else {
@@ -161,7 +164,7 @@ impl StableDiffusionVersion {
 
     fn clip2_file(&self, use_f16: bool) -> &'static str {
         match self {
-            Self::V1_5 | Self::V2_1 | Self::Xl => {
+            Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
                 if use_f16 {
                     "text_encoder_2/model.fp16.safetensors"
                 } else {
@@ -189,7 +192,7 @@ impl ModelFile {
                             StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 => {
                                 "openai/clip-vit-base-patch32"
                             }
-                            StableDiffusionVersion::Xl => {
+                            StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo => {
                                 // This seems similar to the patch32 version except some very small
                                 // difference in the split regex.
                                 "openai/clip-vit-large-patch14"
@@ -206,7 +209,11 @@ impl ModelFile {
                     Self::Vae => {
                         // Override for SDXL when using f16 weights.
                         // See https://github.com/huggingface/candle/issues/1060
-                        if version == StableDiffusionVersion::Xl && use_f16 {
+                        if matches!(
+                            version,
+                            StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo,
+                        ) && use_f16
+                        {
                             (
                                 "madebyollin/sdxl-vae-fp16-fix",
                                 "diffusion_pytorch_model.safetensors",
@@ -261,6 +268,7 @@ fn text_embeddings(
     use_f16: bool,
     device: &Device,
     dtype: DType,
+    use_guide_scale: bool,
     first: bool,
 ) -> Result<Tensor> {
     let tokenizer_file = if first {
@@ -285,16 +293,6 @@ fn text_embeddings(
     }
     let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
 
-    let mut uncond_tokens = tokenizer
-        .encode(uncond_prompt, true)
-        .map_err(E::msg)?
-        .get_ids()
-        .to_vec();
-    while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
-        uncond_tokens.push(pad_id)
-    }
-    let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
-
     println!("Building the Clip transformer.");
     let clip_weights_file = if first {
         ModelFile::Clip
@@ -310,8 +308,24 @@ fn text_embeddings(
     let text_model =
         stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
     let text_embeddings = text_model.forward(&tokens)?;
-    let uncond_embeddings = text_model.forward(&uncond_tokens)?;
-    let text_embeddings = Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?;
+
+    let text_embeddings = if use_guide_scale {
+        let mut uncond_tokens = tokenizer
+            .encode(uncond_prompt, true)
+            .map_err(E::msg)?
+            .get_ids()
+            .to_vec();
+        while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
+            uncond_tokens.push(pad_id)
+        }
+
+        let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
+        let uncond_embeddings = text_model.forward(&uncond_tokens)?;
+
+        Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
+    } else {
+        text_embeddings.to_dtype(dtype)?
+    };
     Ok(text_embeddings)
 }
 
@@ -356,6 +370,7 @@ fn run(args: Args) -> Result<()> {
         unet_weights,
         tracing,
         use_f16,
+        guidance_scale,
         use_flash_attn,
         img2img,
         img2img_strength,
@@ -374,6 +389,24 @@ fn run(args: Args) -> Result<()> {
         None
     };
 
+    let guidance_scale = match guidance_scale {
+        Some(guidance_scale) => guidance_scale,
+        None => match sd_version {
+            StableDiffusionVersion::V1_5
+            | StableDiffusionVersion::V2_1
+            | StableDiffusionVersion::Xl => 7.5,
+            StableDiffusionVersion::Turbo => 0.,
+        },
+    };
+    let n_steps = match n_steps {
+        Some(n_steps) => n_steps,
+        None => match sd_version {
+            StableDiffusionVersion::V1_5
+            | StableDiffusionVersion::V2_1
+            | StableDiffusionVersion::Xl => 30,
+            StableDiffusionVersion::Turbo => 1,
+        },
+    };
     let dtype = if use_f16 { DType::F16 } else { DType::F32 };
     let sd_config = match sd_version {
         StableDiffusionVersion::V1_5 => {
@@ -385,13 +418,19 @@ fn run(args: Args) -> Result<()> {
         StableDiffusionVersion::Xl => {
             stable_diffusion::StableDiffusionConfig::sdxl(sliced_attention_size, height, width)
         }
+        StableDiffusionVersion::Turbo => stable_diffusion::StableDiffusionConfig::sdxl_turbo(
+            sliced_attention_size,
+            height,
+            width,
+        ),
     };
 
     let scheduler = sd_config.build_scheduler(n_steps)?;
     let device = candle_examples::device(cpu)?;
+    let use_guide_scale = guidance_scale > 1.0;
 
     let which = match sd_version {
-        StableDiffusionVersion::Xl => vec![true, false],
+        StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo => vec![true, false],
         _ => vec![true],
     };
     let text_embeddings = which
@@ -407,16 +446,18 @@ fn run(args: Args) -> Result<()> {
                 use_f16,
                 &device,
                 dtype,
+                use_guide_scale,
                 *first,
             )
         })
         .collect::<Result<Vec<_>>>()?;
+
     let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
     println!("{text_embeddings:?}");
 
     println!("Building the autoencoder.");
     let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
-    let vae = sd_config.build_vae(&vae_weights, &device, dtype)?;
+    let vae = sd_config.build_vae(vae_weights, &device, dtype)?;
     let init_latent_dist = match &img2img {
         None => None,
         Some(image) => {
@@ -426,7 +467,7 @@ fn run(args: Args) -> Result<()> {
     };
     println!("Building the unet.");
     let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
-    let unet = sd_config.build_unet(&unet_weights, &device, 4, use_flash_attn, dtype)?;
+    let unet = sd_config.build_unet(unet_weights, &device, 4, use_flash_attn, dtype)?;
 
     let t_start = if img2img.is_some() {
         n_steps - (n_steps as f64 * img2img_strength) as usize
@@ -434,11 +475,19 @@ fn run(args: Args) -> Result<()> {
         0
     };
     let bsize = 1;
+
+    let vae_scale = match sd_version {
+        StableDiffusionVersion::V1_5
+        | StableDiffusionVersion::V2_1
+        | StableDiffusionVersion::Xl => 0.18215,
+        StableDiffusionVersion::Turbo => 0.13025,
+    };
+
     for idx in 0..num_samples {
         let timesteps = scheduler.timesteps();
         let latents = match &init_latent_dist {
             Some(init_latent_dist) => {
-                let latents = (init_latent_dist.sample()? * 0.18215)?.to_device(&device)?;
+                let latents = (init_latent_dist.sample()? * vae_scale)?.to_device(&device)?;
                 if t_start < timesteps.len() {
                     let noise = latents.randn_like(0f64, 1f64)?;
                     scheduler.add_noise(&latents, noise, timesteps[t_start])?
@@ -465,21 +514,31 @@ fn run(args: Args) -> Result<()> {
                 continue;
             }
             let start_time = std::time::Instant::now();
-            let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
+            let latent_model_input = if use_guide_scale {
+                Tensor::cat(&[&latents, &latents], 0)?
+            } else {
+                latents.clone()
+            };
 
             let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;
             let noise_pred =
                 unet.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
-            let noise_pred = noise_pred.chunk(2, 0)?;
-            let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
-            let noise_pred =
-                (noise_pred_uncond + ((noise_pred_text - noise_pred_uncond)? * GUIDANCE_SCALE)?)?;
+
+            let noise_pred = if use_guide_scale {
+                let noise_pred = noise_pred.chunk(2, 0)?;
+                let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
+
+                (noise_pred_uncond + ((noise_pred_text - noise_pred_uncond)? * guidance_scale)?)?
+            } else {
+                noise_pred
+            };
+
             latents = scheduler.step(&noise_pred, timestep, &latents)?;
             let dt = start_time.elapsed().as_secs_f32();
             println!("step {}/{n_steps} done, {:.2}s", timestep_index + 1, dt);
 
             if args.intermediary_images {
-                let image = vae.decode(&(&latents / 0.18215)?)?;
+                let image = vae.decode(&(&latents / vae_scale)?)?;
                 let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
                 let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
                 let image_filename =
@@ -493,7 +552,7 @@ fn run(args: Args) -> Result<()> {
             idx + 1,
             num_samples
         );
-        let image = vae.decode(&(&latents / 0.18215)?)?;
+        let image = vae.decode(&(&latents / vae_scale)?)?;
         let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
         let image = (image.clamp(0f32, 1.)? * 255.)?.to_dtype(DType::U8)?.i(0)?;
         let image_filename = output_filename(&final_image, idx + 1, num_samples, None);

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

@@ -234,13 +234,14 @@ fn main() -> Result<()> {
 
     let start = std::time::Instant::now();
     let config = Config::stablelm_3b_4e1t(args.use_flash_attn);
+    let device = candle_examples::device(args.cpu)?;
     let (model, device) = if args.quantized {
         let filename = &filenames[0];
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename)?;
+        let vb =
+            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
         let model = QStableLM::new(&config, vb)?;
         (Model::Quantized(model), Device::Cpu)
     } else {
-        let device = candle_examples::device(args.cpu)?;
         let dtype = if device.is_cuda() {
             DType::BF16
         } else {

candle-examples/examples/t5/README.md

@@ -9,6 +9,8 @@ $ cargo run --example t5 --release -- --model-id "t5-small" --prompt "translate
 9 tokens generated (2.42 token/s)
 ```
 
+Variants such as [flan-t5](https://huggingface.co/google/flan-t5-small), [flan-ul2](https://huggingface.co/google/flan-ul2) (with `--revision "refs/pr/25"`), and [Co-EdIT](https://huggingface.co/grammarly/coedit-large) are also supported.
+
 ## Translation with [MADLAD-400](https://arxiv.org/abs/2309.04662)
 
 MADLAD-400 is a series of multilingual machine translation T5 models trained on 250 billion tokens covering over 450 languages using publicly available data. These models are competitive with significantly larger models.
@@ -22,7 +24,7 @@ cargo run --example t5 --release  -- \
  Wie geht es dir, mein Freund?
 ```
 
-## Sentence embedding example:
+## Sentence embedding example
 
 ```bash
 $ cargo run --example t5 --release -- --model-id "t5-small" --prompt "A beautiful candle."

candle-examples/examples/t5/main.rs

@@ -96,14 +96,9 @@ impl T5ModelBuilder {
         let api = api.repo(repo);
         let config_filename = api.get("config.json")?;
         let tokenizer_filename = api.get("tokenizer.json")?;
-        let weights_filename = if model_id == "google/flan-t5-xxl" {
-            vec![
-                api.get("model-00001-of-00005.safetensors")?,
-                api.get("model-00002-of-00005.safetensors")?,
-                api.get("model-00003-of-00005.safetensors")?,
-                api.get("model-00004-of-00005.safetensors")?,
-                api.get("model-00005-of-00005.safetensors")?,
-            ]
+        let weights_filename = if model_id == "google/flan-t5-xxl" || model_id == "google/flan-ul2"
+        {
+            candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?
         } else {
             vec![api.get("model.safetensors")?]
         };

candle-examples/examples/trocr/image_processor.rs

@@ -0,0 +1,154 @@
+use image::{DynamicImage, ImageBuffer};
+use serde::Deserialize;
+use std::collections::HashMap;
+
+use candle::{DType, Device, Result, Tensor};
+
+#[derive(Debug, Clone, PartialEq, Deserialize)]
+pub struct ProcessorConfig {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_rescale: bool,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl Default for ProcessorConfig {
+    fn default() -> Self {
+        Self {
+            do_resize: true,
+            height: 384,
+            width: 384,
+            do_rescale: true,
+            do_normalize: true,
+            image_mean: vec![0.5, 0.5, 0.5],
+            image_std: vec![0.5, 0.5, 0.5],
+        }
+    }
+}
+
+pub struct ViTImageProcessor {
+    do_resize: bool,
+    height: u32,
+    width: u32,
+    do_normalize: bool,
+    image_mean: Vec<f32>,
+    image_std: Vec<f32>,
+}
+
+impl ViTImageProcessor {
+    pub fn new(config: &ProcessorConfig) -> Self {
+        Self {
+            do_resize: config.do_resize,
+            height: config.height,
+            width: config.width,
+            do_normalize: config.do_normalize,
+            image_mean: config.image_mean.clone(),
+            image_std: config.image_std.clone(),
+        }
+    }
+
+    pub fn preprocess(&self, images: Vec<&str>) -> Result<Tensor> {
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let images = self.load_images(images)?;
+
+        let resized_images: Vec<DynamicImage> = if self.do_resize {
+            images
+                .iter()
+                .map(|image| self.resize(image.clone(), None).unwrap())
+                .collect()
+        } else {
+            images
+        };
+
+        let normalized_images: Vec<Tensor> = if self.do_normalize {
+            resized_images
+                .iter()
+                .map(|image| self.normalize(image.clone(), None, None).unwrap())
+                .collect()
+        } else {
+            let resized_images: Vec<ImageBuffer<image::Rgb<u8>, Vec<u8>>> =
+                resized_images.iter().map(|image| image.to_rgb8()).collect();
+            let data = resized_images
+                .into_iter()
+                .map(|image| image.into_raw())
+                .collect::<Vec<Vec<u8>>>();
+
+            data.iter()
+                .map(|image| {
+                    Tensor::from_vec(image.clone(), (height, width, channels), &Device::Cpu)
+                        .unwrap()
+                        .permute((2, 0, 1))
+                        .unwrap()
+                })
+                .collect::<Vec<Tensor>>()
+        };
+
+        Tensor::stack(&normalized_images, 0)
+    }
+
+    fn resize(
+        &self,
+        image: image::DynamicImage,
+        size: Option<HashMap<String, u32>>,
+    ) -> Result<image::DynamicImage> {
+        let (height, width) = match &size {
+            Some(size) => (size.get("height").unwrap(), size.get("width").unwrap()),
+            None => (&self.height, &self.width),
+        };
+
+        let resized_image =
+            image.resize_exact(*width, *height, image::imageops::FilterType::Triangle);
+
+        Ok(resized_image)
+    }
+
+    fn normalize(
+        &self,
+        image: image::DynamicImage,
+        mean: Option<Vec<f32>>,
+        std: Option<Vec<f32>>,
+    ) -> Result<Tensor> {
+        let mean = match mean {
+            Some(mean) => mean,
+            None => self.image_mean.clone(),
+        };
+
+        let std = match std {
+            Some(std) => std,
+            None => self.image_std.clone(),
+        };
+
+        let mean = Tensor::from_vec(mean, (3, 1, 1), &Device::Cpu)?;
+        let std = Tensor::from_vec(std, (3, 1, 1), &Device::Cpu)?;
+
+        let image = image.to_rgb8();
+        let data = image.into_raw();
+
+        let height = self.height as usize;
+        let width = self.width as usize;
+        let channels = 3;
+
+        let data =
+            Tensor::from_vec(data, &[height, width, channels], &Device::Cpu)?.permute((2, 0, 1))?;
+
+        (data.to_dtype(DType::F32)? / 255.)?
+            .broadcast_sub(&mean)?
+            .broadcast_div(&std)
+    }
+
+    pub fn load_images(&self, image_path: Vec<&str>) -> Result<Vec<image::DynamicImage>> {
+        let mut images: Vec<image::DynamicImage> = Vec::new();
+        for path in image_path {
+            let img = image::io::Reader::open(path)?.decode().unwrap();
+            images.push(img);
+        }
+
+        Ok(images)
+    }
+}

candle-examples/examples/trocr/main.rs

@@ -0,0 +1,132 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::Error as E;
+use clap::{Parser, ValueEnum};
+
+use candle::{DType, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::models::trocr;
+
+use tokenizers::Tokenizer;
+mod image_processor;
+
+#[derive(Clone, Debug, Copy, ValueEnum)]
+enum Which {
+    Base,
+    Large,
+}
+
+#[derive(Parser, Debug)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    /// Choose the variant of the model to run.
+    #[arg(long, default_value = "base")]
+    which: Which,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Text to be translated
+    #[arg(long)]
+    image: String,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    use hf_hub::api::sync::Api;
+    let args = Args::parse();
+
+    let tokenizer_dec = {
+        let tokenizer = Api::new()?
+            .model(String::from("ToluClassics/candle-trocr-tokenizer"))
+            .get("tokenizer.json")?;
+
+        Tokenizer::from_file(&tokenizer).map_err(E::msg)?
+    };
+
+    let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
+
+    let device = candle_examples::device(args.cpu)?;
+
+    let vb = {
+        let model = match args.model {
+            Some(model) => std::path::PathBuf::from(model),
+            None => match args.which {
+                Which::Base => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-base-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/3".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+                Which::Large => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-large-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/6".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+            },
+        };
+        println!("model: {:?}", model);
+        unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? }
+    };
+
+    let encoder_config = match args.which {
+        Which::Base => candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten(),
+        Which::Large => {
+            candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten()
+        }
+    };
+
+    let decoder_config = trocr::TrOCRConfig::default();
+    let mut model = trocr::TrOCRModel::new(&encoder_config, &decoder_config, vb)?;
+
+    let config = image_processor::ProcessorConfig::default();
+    let processor = image_processor::ViTImageProcessor::new(&config);
+
+    let image = vec![args.image.as_str()];
+    let image = processor.preprocess(image)?;
+
+    let encoder_xs = model.encoder().forward(&image)?;
+
+    let mut logits_processor =
+        candle_transformers::generation::LogitsProcessor::new(1337, None, None);
+
+    let mut token_ids: Vec<u32> = vec![decoder_config.decoder_start_token_id];
+    for index in 0..1000 {
+        let context_size = if index >= 1 { 1 } else { token_ids.len() };
+        let start_pos = token_ids.len().saturating_sub(context_size);
+        let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
+
+        let logits = model.decode(&input_ids, &encoder_xs, start_pos)?;
+
+        let logits = logits.squeeze(0)?;
+        let logits = logits.get(logits.dim(0)? - 1)?;
+        let token = logits_processor.sample(&logits)?;
+        token_ids.push(token);
+
+        if let Some(t) = tokenizer_dec.next_token(token)? {
+            use std::io::Write;
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+        if token == decoder_config.eos_token_id {
+            break;
+        }
+    }
+
+    if let Some(rest) = tokenizer_dec.decode_rest().map_err(E::msg)? {
+        print!("{rest}");
+    }
+    println!();
+
+    Ok(())
+}

candle-examples/examples/trocr/readme.md

@@ -0,0 +1,16 @@
+# candle-trocr
+
+`TrOCR` is a transformer OCR Model. In this example it is used to
+transcribe image text. See the associated [model
+card](https://huggingface.co/microsoft/trocr-base-printed) for details on
+the model itself.
+
+## Running an example
+
+```bash
+cargo run --example trocr --release --  --which base --cpu --image candle-examples/examples/trocr/assets/trocr.png
+```
+
+```
+<s> industry , Mr. Brown commented icily . " Let us have a</s>
+```

candle-examples/examples/whisper/main.rs

@@ -128,7 +128,13 @@ impl Decoder {
         let transcribe_token = token_id(&tokenizer, m::TRANSCRIBE_TOKEN)?;
         let translate_token = token_id(&tokenizer, m::TRANSLATE_TOKEN)?;
         let eot_token = token_id(&tokenizer, m::EOT_TOKEN)?;
-        let no_speech_token = token_id(&tokenizer, m::NO_SPEECH_TOKEN)?;
+        let no_speech_token = m::NO_SPEECH_TOKENS
+            .iter()
+            .find_map(|token| token_id(&tokenizer, token).ok());
+        let no_speech_token = match no_speech_token {
+            None => anyhow::bail!("unable to find any non-speech token"),
+            Some(n) => n,
+        };
         Ok(Self {
             model,
             rng: rand::rngs::StdRng::seed_from_u64(seed),
@@ -512,11 +518,7 @@ fn main() -> Result<()> {
             )
         } else {
             let config = repo.get("config.json")?;
-            let tokenizer = if args.model == WhichModel::LargeV3 {
-                panic!("openai/whisper-large-v3 does not provide a compatible tokenizer.json config at the moment")
-            } else {
-                repo.get("tokenizer.json")?
-            };
+            let tokenizer = repo.get("tokenizer.json")?;
             let model = repo.get("model.safetensors")?;
             (config, tokenizer, model)
         };
@@ -555,8 +557,10 @@ fn main() -> Result<()> {
     println!("loaded mel: {:?}", mel.dims());
 
     let mut model = if args.quantized {
-        let vb =
-            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(&weights_filename)?;
+        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
+            &weights_filename,
+            &device,
+        )?;
         Model::Quantized(m::quantized_model::Whisper::load(&vb, config)?)
     } else {
         let vb =

candle-examples/examples/yi/main.rs

@@ -0,0 +1,254 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::{Error as E, Result};
+use clap::{Parser, ValueEnum};
+
+use candle_transformers::models::yi::{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;
+
+#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
+enum Which {
+    #[value(name = "6b")]
+    L6b,
+    #[value(name = "34b")]
+    L34b,
+}
+
+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)]
+    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 = 100)]
+    sample_len: usize,
+
+    #[arg(long, default_value = "01-ai/Yi-6B")]
+    model_id: String,
+
+    #[arg(long, default_value = "main")]
+    revision: 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,
+
+    /// The model size to use.
+    #[arg(long, default_value = "6b")]
+    which: Which,
+}
+
+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 repo = api.repo(Repo::with_revision(
+        args.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 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 start = std::time::Instant::now();
+    let config = match args.which {
+        Which::L6b => Config::config_6b(),
+        Which::L34b => Config::config_34b(),
+    };
+    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/yolo-v3/darknet.rs

@@ -147,7 +147,7 @@ fn conv(vb: VarBuilder, index: usize, p: usize, b: &Block) -> Result<(usize, Bl)
     let func = candle_nn::func(move |xs| {
         let xs = conv.forward(xs)?;
         let xs = match &bn {
-            Some(bn) => bn.forward(&xs)?,
+            Some(bn) => xs.apply_t(bn, false)?,
             None => xs,
         };
         let xs = if leaky {

candle-examples/examples/yolo-v3/main.rs

@@ -43,6 +43,7 @@ pub fn report(
     confidence_threshold: f32,
     nms_threshold: f32,
 ) -> Result<DynamicImage> {
+    let pred = pred.to_device(&Device::Cpu)?;
     let (npreds, pred_size) = pred.dims2()?;
     let nclasses = pred_size - 5;
     // The bounding boxes grouped by (maximum) class index.

candle-examples/examples/yolo-v8/README.md

@@ -32,7 +32,7 @@ Image source:
 ### Pose Estimation
 ```bash
 cargo run --example yolo-v8 --release -- \
-  candle-examples/examples/yolo-v8/assets/peoples.jpeg --task pose
+  candle-examples/examples/yolo-v8/assets/bike.jpg --task pose
 ```
 
 ![Leading group, Giro d'Italia 2021](./assets/bike.pose.jpg)

candle-examples/examples/yolo-v8/main.rs

@@ -7,7 +7,7 @@ extern crate accelerate_src;
 mod model;
 use model::{Multiples, YoloV8, YoloV8Pose};
 
-use candle::{DType, IndexOp, Result, Tensor};
+use candle::{DType, Device, IndexOp, Result, Tensor};
 use candle_nn::{Module, VarBuilder};
 use candle_transformers::object_detection::{non_maximum_suppression, Bbox, KeyPoint};
 use clap::{Parser, ValueEnum};
@@ -61,6 +61,7 @@ pub fn report_detect(
     nms_threshold: f32,
     legend_size: u32,
 ) -> Result<DynamicImage> {
+    let pred = pred.to_device(&Device::Cpu)?;
     let (pred_size, npreds) = pred.dims2()?;
     let nclasses = pred_size - 4;
     // The bounding boxes grouped by (maximum) class index.
@@ -153,6 +154,7 @@ pub fn report_pose(
     confidence_threshold: f32,
     nms_threshold: f32,
 ) -> Result<DynamicImage> {
+    let pred = pred.to_device(&Device::Cpu)?;
     let (pred_size, npreds) = pred.dims2()?;
     if pred_size != 17 * 3 + 4 + 1 {
         candle::bail!("unexpected pred-size {pred_size}");

candle-examples/src/lib.rs

@@ -117,3 +117,30 @@ pub fn save_image_resize<P: AsRef<std::path::Path>>(
     image.save(p).map_err(candle::Error::wrap)?;
     Ok(())
 }
+
+/// Loads the safetensors files for a model from the hub based on a json index file.
+pub fn hub_load_safetensors(
+    repo: &hf_hub::api::sync::ApiRepo,
+    json_file: &str,
+) -> Result<Vec<std::path::PathBuf>> {
+    let json_file = repo.get(json_file).map_err(candle::Error::wrap)?;
+    let json_file = std::fs::File::open(json_file)?;
+    let json: serde_json::Value =
+        serde_json::from_reader(&json_file).map_err(candle::Error::wrap)?;
+    let weight_map = match json.get("weight_map") {
+        None => candle::bail!("no weight map in {json_file:?}"),
+        Some(serde_json::Value::Object(map)) => map,
+        Some(_) => candle::bail!("weight map in {json_file:?} is not a map"),
+    };
+    let mut safetensors_files = std::collections::HashSet::new();
+    for value in weight_map.values() {
+        if let Some(file) = value.as_str() {
+            safetensors_files.insert(file.to_string());
+        }
+    }
+    let safetensors_files = safetensors_files
+        .iter()
+        .map(|v| repo.get(v).map_err(candle::Error::wrap))
+        .collect::<Result<Vec<_>>>()?;
+    Ok(safetensors_files)
+}

candle-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.3.0"
+version = "0.3.3"
 edition = "2021"
 
 description = "Flash attention layer for the candle ML framework."
@@ -11,14 +11,14 @@ license = "MIT OR Apache-2.0"
 readme = "README.md"
 
 [dependencies]
-candle = { path = "../candle-core", features = ["cuda"], version = "0.3.0", package = "candle-core" }
+candle = { path = "../candle-core", features = ["cuda"], package = "candle-core" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]
+bindgen_cuda = "0.1.1"
 anyhow = { version = "1", features = ["backtrace"] }
-num_cpus = "1.15.0"
-rayon = "1.7.0"
+
 
 [dev-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }
-candle-nn = { path = "../candle-nn", version = "0.3.0", features = ["cuda"] }
+candle-nn = { path = "../candle-nn", features = ["cuda"] }

candle-flash-attn/build.rs

@@ -2,44 +2,32 @@
 // The cuda build time is very long so one can set the CANDLE_FLASH_ATTN_BUILD_DIR environment
 // variable in order to cache the compiled artifacts and avoid recompiling too often.
 use anyhow::{Context, Result};
-use rayon::prelude::*;
 use std::path::PathBuf;
-use std::str::FromStr;
 
 const KERNEL_FILES: [&str; 17] = [
-    "flash_api.cu",
-    "flash_fwd_hdim128_fp16_sm80.cu",
-    "flash_fwd_hdim160_fp16_sm80.cu",
-    "flash_fwd_hdim192_fp16_sm80.cu",
-    "flash_fwd_hdim224_fp16_sm80.cu",
-    "flash_fwd_hdim256_fp16_sm80.cu",
-    "flash_fwd_hdim32_fp16_sm80.cu",
-    "flash_fwd_hdim64_fp16_sm80.cu",
-    "flash_fwd_hdim96_fp16_sm80.cu",
-    "flash_fwd_hdim128_bf16_sm80.cu",
-    "flash_fwd_hdim160_bf16_sm80.cu",
-    "flash_fwd_hdim192_bf16_sm80.cu",
-    "flash_fwd_hdim224_bf16_sm80.cu",
-    "flash_fwd_hdim256_bf16_sm80.cu",
-    "flash_fwd_hdim32_bf16_sm80.cu",
-    "flash_fwd_hdim64_bf16_sm80.cu",
-    "flash_fwd_hdim96_bf16_sm80.cu",
+    "kernels/flash_api.cu",
+    "kernels/flash_fwd_hdim128_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim160_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim192_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim224_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim256_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim32_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim64_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim96_fp16_sm80.cu",
+    "kernels/flash_fwd_hdim128_bf16_sm80.cu",
+    "kernels/flash_fwd_hdim160_bf16_sm80.cu",
+    "kernels/flash_fwd_hdim192_bf16_sm80.cu",
+    "kernels/flash_fwd_hdim224_bf16_sm80.cu",
+    "kernels/flash_fwd_hdim256_bf16_sm80.cu",
+    "kernels/flash_fwd_hdim32_bf16_sm80.cu",
+    "kernels/flash_fwd_hdim64_bf16_sm80.cu",
+    "kernels/flash_fwd_hdim96_bf16_sm80.cu",
 ];
 
 fn main() -> Result<()> {
-    let num_cpus = std::env::var("RAYON_NUM_THREADS").map_or_else(
-        |_| num_cpus::get_physical(),
-        |s| usize::from_str(&s).unwrap(),
-    );
-
-    rayon::ThreadPoolBuilder::new()
-        .num_threads(num_cpus)
-        .build_global()
-        .unwrap();
-
     println!("cargo:rerun-if-changed=build.rs");
     for kernel_file in KERNEL_FILES.iter() {
-        println!("cargo:rerun-if-changed=kernels/{kernel_file}");
+        println!("cargo:rerun-if-changed={kernel_file}");
     }
     println!("cargo:rerun-if-changed=kernels/flash_fwd_kernel.h");
     println!("cargo:rerun-if-changed=kernels/flash_fwd_launch_template.h");
@@ -66,223 +54,30 @@ fn main() -> Result<()> {
             ))
         }
     };
-    set_cuda_include_dir()?;
 
-    let ccbin_env = std::env::var("CANDLE_NVCC_CCBIN");
-    println!("cargo:rerun-if-env-changed=CANDLE_NVCC_CCBIN");
-
-    let compute_cap = compute_cap()?;
+    let kernels = KERNEL_FILES.iter().collect();
+    let builder = bindgen_cuda::Builder::default()
+        .kernel_paths(kernels)
+        .out_dir(build_dir.clone())
+        .arg("-std=c++17")
+        .arg("-O3")
+        .arg("-U__CUDA_NO_HALF_OPERATORS__")
+        .arg("-U__CUDA_NO_HALF_CONVERSIONS__")
+        .arg("-U__CUDA_NO_HALF2_OPERATORS__")
+        .arg("-U__CUDA_NO_BFLOAT16_CONVERSIONS__")
+        .arg("-Icutlass/include")
+        .arg("--expt-relaxed-constexpr")
+        .arg("--expt-extended-lambda")
+        .arg("--use_fast_math")
+        .arg("--verbose");
 
     let out_file = build_dir.join("libflashattention.a");
+    builder.build_lib(out_file);
 
-    let kernel_dir = PathBuf::from("kernels");
-    let cu_files: Vec<_> = KERNEL_FILES
-        .iter()
-        .map(|f| {
-            let mut obj_file = out_dir.join(f);
-            obj_file.set_extension("o");
-            (kernel_dir.join(f), obj_file)
-        })
-        .collect();
-    let out_modified: Result<_, _> = out_file.metadata().and_then(|m| m.modified());
-    let should_compile = if out_file.exists() {
-        kernel_dir
-            .read_dir()
-            .expect("kernels folder should exist")
-            .any(|entry| {
-                if let (Ok(entry), Ok(out_modified)) = (entry, &out_modified) {
-                    let in_modified = entry.metadata().unwrap().modified().unwrap();
-                    in_modified.duration_since(*out_modified).is_ok()
-                } else {
-                    true
-                }
-            })
-    } else {
-        true
-    };
-    if should_compile {
-        cu_files
-            .par_iter()
-            .map(|(cu_file, obj_file)| {
-                let mut command = std::process::Command::new("nvcc");
-                command
-                    .arg("-std=c++17")
-                    .arg("-O3")
-                    .arg("-U__CUDA_NO_HALF_OPERATORS__")
-                    .arg("-U__CUDA_NO_HALF_CONVERSIONS__")
-                    .arg("-U__CUDA_NO_HALF2_OPERATORS__")
-                    .arg("-U__CUDA_NO_BFLOAT16_CONVERSIONS__")
-                    .arg(format!("--gpu-architecture=sm_{compute_cap}"))
-                    .arg("-c")
-                    .args(["-o", obj_file.to_str().unwrap()])
-                    .args(["--default-stream", "per-thread"])
-                    .arg("-Icutlass/include")
-                    .arg("--expt-relaxed-constexpr")
-                    .arg("--expt-extended-lambda")
-                    .arg("--use_fast_math")
-                    .arg("--verbose");
-                if let Ok(ccbin_path) = &ccbin_env {
-                    command
-                        .arg("-allow-unsupported-compiler")
-                        .args(["-ccbin", ccbin_path]);
-                }
-                command.arg(cu_file);
-                let output = command
-                    .spawn()
-                    .context("failed spawning nvcc")?
-                    .wait_with_output()?;
-                if !output.status.success() {
-                    anyhow::bail!(
-                        "nvcc error while executing compiling: {:?}\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
-                        &command,
-                        String::from_utf8_lossy(&output.stdout),
-                        String::from_utf8_lossy(&output.stderr)
-                    )
-                }
-                Ok(())
-            })
-            .collect::<Result<()>>()?;
-        let obj_files = cu_files.iter().map(|c| c.1.clone()).collect::<Vec<_>>();
-        let mut command = std::process::Command::new("nvcc");
-        command
-            .arg("--lib")
-            .args(["-o", out_file.to_str().unwrap()])
-            .args(obj_files);
-        let output = command
-            .spawn()
-            .context("failed spawning nvcc")?
-            .wait_with_output()?;
-        if !output.status.success() {
-            anyhow::bail!(
-                "nvcc error while linking: {:?}\n\n# stdout\n{:#}\n\n# stderr\n{:#}",
-                &command,
-                String::from_utf8_lossy(&output.stdout),
-                String::from_utf8_lossy(&output.stderr)
-            )
-        }
-    }
     println!("cargo:rustc-link-search={}", build_dir.display());
     println!("cargo:rustc-link-lib=flashattention");
     println!("cargo:rustc-link-lib=dylib=cudart");
     println!("cargo:rustc-link-lib=dylib=stdc++");
 
-    /* laurent: I tried using the cc cuda integration as below but this lead to ptaxs never
-       finishing to run for some reason. Calling nvcc manually worked fine.
-    cc::Build::new()
-        .cuda(true)
-        .include("cutlass/include")
-        .flag("--expt-relaxed-constexpr")
-        .flag("--default-stream")
-        .flag("per-thread")
-        .flag(&format!("--gpu-architecture=sm_{compute_cap}"))
-        .file("kernels/flash_fwd_hdim32_fp16_sm80.cu")
-        .compile("flashattn");
-    */
     Ok(())
 }
-
-fn set_cuda_include_dir() -> Result<()> {
-    // NOTE: copied from cudarc build.rs.
-    let env_vars = [
-        "CUDA_PATH",
-        "CUDA_ROOT",
-        "CUDA_TOOLKIT_ROOT_DIR",
-        "CUDNN_LIB",
-    ];
-    let env_vars = env_vars
-        .into_iter()
-        .map(std::env::var)
-        .filter_map(Result::ok)
-        .map(Into::<PathBuf>::into);
-
-    let roots = [
-        "/usr",
-        "/usr/local/cuda",
-        "/opt/cuda",
-        "/usr/lib/cuda",
-        "C:/Program Files/NVIDIA GPU Computing Toolkit",
-        "C:/CUDA",
-    ];
-    let roots = roots.into_iter().map(Into::<PathBuf>::into);
-    let root = env_vars
-        .chain(roots)
-        .find(|path| path.join("include").join("cuda.h").is_file())
-        .context("cannot find include/cuda.h")?;
-    println!(
-        "cargo:rustc-env=CUDA_INCLUDE_DIR={}",
-        root.join("include").display()
-    );
-    Ok(())
-}
-
-#[allow(unused)]
-fn compute_cap() -> Result<usize> {
-    println!("cargo:rerun-if-env-changed=CUDA_COMPUTE_CAP");
-
-    // Try to parse compute caps from env
-    let mut compute_cap = if let Ok(compute_cap_str) = std::env::var("CUDA_COMPUTE_CAP") {
-        println!("cargo:rustc-env=CUDA_COMPUTE_CAP={compute_cap_str}");
-        compute_cap_str
-            .parse::<usize>()
-            .context("Could not parse compute cap")?
-    } else {
-        // Use nvidia-smi to get the current compute cap
-        let out = std::process::Command::new("nvidia-smi")
-            .arg("--query-gpu=compute_cap")
-            .arg("--format=csv")
-            .output()
-            .context("`nvidia-smi` failed. Ensure that you have CUDA installed and that `nvidia-smi` is in your PATH.")?;
-        let out = std::str::from_utf8(&out.stdout).context("stdout is not a utf8 string")?;
-        let mut lines = out.lines();
-        assert_eq!(
-            lines.next().context("missing line in stdout")?,
-            "compute_cap"
-        );
-        let cap = lines
-            .next()
-            .context("missing line in stdout")?
-            .replace('.', "");
-        let cap = cap
-            .parse::<usize>()
-            .with_context(|| format!("cannot parse as int {cap}"))?;
-        println!("cargo:rustc-env=CUDA_COMPUTE_CAP={cap}");
-        cap
-    };
-
-    // Grab available GPU codes from nvcc and select the highest one
-    let (supported_nvcc_codes, max_nvcc_code) = {
-        let out = std::process::Command::new("nvcc")
-            .arg("--list-gpu-code")
-            .output()
-            .expect("`nvcc` failed. Ensure that you have CUDA installed and that `nvcc` is in your PATH.");
-        let out = std::str::from_utf8(&out.stdout).unwrap();
-
-        let out = out.lines().collect::<Vec<&str>>();
-        let mut codes = Vec::with_capacity(out.len());
-        for code in out {
-            let code = code.split('_').collect::<Vec<&str>>();
-            if !code.is_empty() && code.contains(&"sm") {
-                if let Ok(num) = code[1].parse::<usize>() {
-                    codes.push(num);
-                }
-            }
-        }
-        codes.sort();
-        let max_nvcc_code = *codes.last().context("no gpu codes parsed from nvcc")?;
-        (codes, max_nvcc_code)
-    };
-
-    // Check that nvcc supports the asked compute caps
-    if !supported_nvcc_codes.contains(&compute_cap) {
-        anyhow::bail!(
-            "nvcc cannot target gpu arch {compute_cap}. Available nvcc targets are {supported_nvcc_codes:?}."
-        );
-    }
-    if compute_cap > max_nvcc_code {
-        anyhow::bail!(
-            "CUDA compute cap {compute_cap} is higher than the highest gpu code from nvcc {max_nvcc_code}"
-        );
-    }
-
-    Ok(compute_cap)
-}

candle-flash-attn/kernels/alibi.h

@@ -0,0 +1,62 @@
+#include <cmath>
+
+#include <cute/tensor.hpp>
+
+#include <cutlass/cutlass.h>
+#include <cutlass/array.h>
+
+#include "utils.h"
+
+namespace flash {
+
+using namespace cute;
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+template <bool Is_causal, typename Engine, typename Layout>
+inline __device__ void apply_alibi(Tensor<Engine, Layout> &tensor, 
+                                   const int col_idx_offset_,
+                                   const int max_seqlen_k, 
+                                   const int row_idx_offset,
+                                   const int max_seqlen_q, 
+                                   const int warp_row_stride,
+                                   const float alibi_slope) {
+    // tensor has shape (ncol=(2, MMA_M), nrow=(2, MMA_N))
+    static_assert(Layout::rank == 2, "Only support 2D Tensor");
+    const int lane_id = threadIdx.x % 32;
+    const int col_idx_offset = col_idx_offset_ + (lane_id % 4) * 2;
+    if constexpr (Is_causal) {  // Simpler, we add the same bias vector to all rows
+        #pragma unroll
+        for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
+            const int col_idx_base = col_idx_offset + nj * 8;
+            #pragma unroll
+            for (int j = 0; j < size<1, 0>(tensor); ++j) {
+                const int col_idx = col_idx_base + j;
+                #pragma unroll
+                for (int mi = 0; mi < size<0>(tensor); ++mi) {
+                    tensor(mi, make_coord(j, nj)) += alibi_slope * col_idx;
+                }
+            }
+        }
+    } else {  // Bias depends on both row_idx and col_idx
+        #pragma unroll
+        for (int mi = 0; mi < size<0, 1>(tensor); ++mi) {
+            const int row_idx_base = row_idx_offset + mi * warp_row_stride;
+            #pragma unroll
+            for (int i = 0; i < size<0, 0>(tensor); ++i) {
+                const int row_idx = row_idx_base + i * 8;
+                #pragma unroll
+                for (int nj = 0; nj < size<1, 1>(tensor); ++nj) {
+                    const int col_idx_base = col_idx_offset + nj * 8;
+                    #pragma unroll
+                    for (int j = 0; j < size<1, 0>(tensor); ++j) {
+                        const int col_idx = col_idx_base + j;
+                        tensor(make_coord(i, mi), make_coord(j, nj)) -= alibi_slope * abs(row_idx + max_seqlen_k - max_seqlen_q - col_idx);
+                    }
+                }
+            }
+        }
+    }
+}
+
+}  // namespace flash

candle-flash-attn/kernels/block_info.h

@@ -14,9 +14,12 @@ struct BlockInfo {
     template<typename Params>
     __device__ BlockInfo(const Params &params, const int bidb)
         : sum_s_q(!Varlen || params.cu_seqlens_q == nullptr ? -1 : params.cu_seqlens_q[bidb])
-        , sum_s_k(!Varlen || params.cu_seqlens_k == nullptr ? -1 : params.cu_seqlens_k[bidb])
+        , sum_s_k(!Varlen || params.cu_seqlens_k == nullptr || !params.is_seqlens_k_cumulative ? -1 : params.cu_seqlens_k[bidb])
         , actual_seqlen_q(!Varlen || params.cu_seqlens_q == nullptr ? params.seqlen_q : params.cu_seqlens_q[bidb + 1] - sum_s_q)
-        , actual_seqlen_k(!Varlen || params.cu_seqlens_k == nullptr ? params.seqlen_k : params.cu_seqlens_k[bidb + 1] - sum_s_k)
+        // If is_seqlens_k_cumulative, then seqlen_k is cu_seqlens_k[bidb + 1] - cu_seqlens_k[bidb].
+        // Otherwise it's cu_seqlens_k[bidb], i.e., we use cu_seqlens_k to store the sequence lengths of K.
+        , seqlen_k_cache(!Varlen || params.cu_seqlens_k == nullptr ? params.seqlen_k : (params.is_seqlens_k_cumulative ? params.cu_seqlens_k[bidb + 1] - sum_s_k : params.cu_seqlens_k[bidb]))
+        , actual_seqlen_k(params.seqused_k ? params.seqused_k[bidb] : seqlen_k_cache + (params.knew_ptr == nullptr ? 0 : params.seqlen_knew))
         {
         }
 
@@ -32,8 +35,10 @@ struct BlockInfo {
 
     const int sum_s_q;
     const int sum_s_k;
-    const uint32_t actual_seqlen_q;
-    const uint32_t actual_seqlen_k;
+    const int actual_seqlen_q;
+    // We have to have seqlen_k_cache declared before actual_seqlen_k, otherwise actual_seqlen_k is set to 0.
+    const int seqlen_k_cache;
+    const int actual_seqlen_k;
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////

candle-flash-attn/kernels/flash.h

@@ -7,15 +7,6 @@
 #include <cuda.h>
 #include <vector>
 
-// #ifdef OLD_GENERATOR_PATH
-// #include <ATen/CUDAGeneratorImpl.h>
-// #else
-// #include <ATen/cuda/CUDAGeneratorImpl.h>
-// #endif
-//
-// #include <ATen/cuda/CUDAGraphsUtils.cuh>
-
-
 constexpr int TOTAL_DIM = 0;
 constexpr int H_DIM = 1;
 constexpr int D_DIM = 2;
@@ -53,6 +44,7 @@ struct Flash_fwd_params : public Qkv_params {
 
     // The O matrix (output).
     void * __restrict__ o_ptr;
+    void * __restrict__ oaccum_ptr;
 
     // The stride between rows of O.
     index_t o_batch_stride;
@@ -64,9 +56,10 @@ struct Flash_fwd_params : public Qkv_params {
 
     // The pointer to the softmax sum.
     void * __restrict__ softmax_lse_ptr;
+    void * __restrict__ softmax_lseaccum_ptr;
 
     // The dimensions.
-    int b, seqlen_q, seqlen_k, d, seqlen_q_rounded, seqlen_k_rounded, d_rounded;
+    int b, seqlen_q, seqlen_k, seqlen_knew, d, seqlen_q_rounded, seqlen_k_rounded, d_rounded, rotary_dim;
 
     // The scaling factors for the kernel.
     float scale_softmax;
@@ -76,8 +69,30 @@ struct Flash_fwd_params : public Qkv_params {
     int * __restrict__ cu_seqlens_q;
     int * __restrict__ cu_seqlens_k;
 
+    // If provided, the actual length of each k sequence.
+    int * __restrict__ seqused_k;
+
     int *__restrict__ blockmask;
 
+    // The K_new and V_new matrices.
+    void * __restrict__ knew_ptr;
+    void * __restrict__ vnew_ptr;
+
+    // The stride between rows of the Q, K and V matrices.
+    index_t knew_batch_stride;
+    index_t vnew_batch_stride;
+    index_t knew_row_stride;
+    index_t vnew_row_stride;
+    index_t knew_head_stride;
+    index_t vnew_head_stride;
+
+    // The cos and sin matrices for rotary embedding.
+    void * __restrict__ rotary_cos_ptr;
+    void * __restrict__ rotary_sin_ptr;
+
+    // The indices to index into the KV cache.
+    int *__restrict__ cache_batch_idx;
+
     // The dropout probability (probability of keeping an activation).
     float p_dropout;
     // uint32_t p_dropout_in_uint;
@@ -88,11 +103,22 @@ struct Flash_fwd_params : public Qkv_params {
     float rp_dropout;
     float scale_softmax_rp_dropout;
 
-    // Random state.
-    // at::PhiloxCudaState philox_args;
+    // Local window size
+    int window_size_left, window_size_right;
 
     bool is_bf16;
     bool is_causal;
+
+    // If is_seqlens_k_cumulative, then seqlen_k is cu_seqlens_k[bidb + 1] - cu_seqlens_k[bidb].
+    // Otherwise it's cu_seqlens_k[bidb], i.e., we use cu_seqlens_k to store the sequence lengths of K.
+    bool is_seqlens_k_cumulative;
+
+    bool is_rotary_interleaved;
+
+    int num_splits;  // For split-KV version
+
+    void * __restrict__ alibi_slopes_ptr;
+    index_t alibi_slopes_batch_stride;
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -132,10 +158,14 @@ struct Flash_bwd_params : public Flash_fwd_params {
 
     // The pointer to the softmax d sum.
     void *__restrict__ dsoftmax_sum;
+
+    bool deterministic;
+    index_t dq_accum_split_stride;
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
 template<typename T, int Headdim> void run_mha_fwd_(Flash_fwd_params &params, cudaStream_t stream);
+template<typename T, int Headdim> void run_mha_fwd_splitkv_dispatch(Flash_fwd_params &params, cudaStream_t stream);
 
 template<typename T, int Headdim> void run_mha_bwd_(Flash_bwd_params &params, cudaStream_t stream, const bool configure);

candle-flash-attn/kernels/flash_api.cu

@@ -1,17 +1,15 @@
 #include "flash_fwd_launch_template.h"
 
-// void run_mha_fwd(Flash_fwd_params &params, cudaStream_t stream) {
-//     FWD_HEADDIM_SWITCH(params.d, [&] {
-//         run_mha_fwd_<cutlass::half_t, kHeadDim>(params, stream);
-//     });
-// }
-
-void run_mha_fwd(Flash_fwd_params &params, cudaStream_t stream) {
-  FP16_SWITCH(!params.is_bf16, [&] {
-      FWD_HEADDIM_SWITCH(params.d, [&] {
-          run_mha_fwd_<elem_type, kHeadDim>(params, stream);
-          });
-      });
+void run_mha_fwd(Flash_fwd_params &params, cudaStream_t stream, bool force_split_kernel=false) {
+    FP16_SWITCH(!params.is_bf16, [&] {
+        FWD_HEADDIM_SWITCH(params.d, [&] {
+//            if (params.num_splits <= 1 && !force_split_kernel) {  // If we don't set it num_splits == 0
+            run_mha_fwd_<elem_type, kHeadDim>(params, stream);
+//            } else {
+//                run_mha_fwd_splitkv_dispatch<elem_type, kHeadDim>(params, stream);
+//            }
+        });
+    });
 }
 
 extern "C" void run_mha(
@@ -20,6 +18,7 @@ extern "C" void run_mha(
     void *v_ptr,
     void *o_ptr,
     void *softmax_lse_ptr,
+    void *alibi_slopes_ptr,
 
     int32_t *cu_seqlens_q_ptr,
     int32_t *cu_seqlens_k_ptr,
@@ -28,6 +27,7 @@ extern "C" void run_mha(
     uint32_t k_batch_stride,
     uint32_t v_batch_stride,
     uint32_t o_batch_stride,
+    uint32_t alibi_slopes_batch_stride,
 
     uint32_t q_row_stride,
     uint32_t k_row_stride,
@@ -51,8 +51,11 @@ extern "C" void run_mha(
     uint32_t seqlen_q_rounded,
     uint32_t seqlen_k_rounded,
 
+    int is_bf16,
     int is_causal,
-    int is_bf16
+
+    int window_size_left,
+    int window_size_right
 ) {
     Flash_fwd_params params;
     // Reset the parameters
@@ -65,12 +68,14 @@ extern "C" void run_mha(
     params.o_ptr = o_ptr;
 
     params.softmax_lse_ptr = softmax_lse_ptr;
+    params.alibi_slopes_ptr = alibi_slopes_ptr;
 
     // All stride are in elements, not bytes.
     params.q_batch_stride = q_batch_stride;
     params.k_batch_stride = k_batch_stride;
     params.v_batch_stride = v_batch_stride;
     params.o_batch_stride = o_batch_stride;
+    params.alibi_slopes_batch_stride = alibi_slopes_batch_stride;
 
     params.q_row_stride = q_row_stride;
     params.k_row_stride = k_row_stride;
@@ -92,7 +97,6 @@ extern "C" void run_mha(
     params.seqlen_k_rounded = seqlen_k_rounded;
     params.d = d;
     params.d_rounded = d_rounded;
-    params.is_causal = is_causal;
 
     // Set the different scale values.
     params.scale_softmax = softmax_scale;
@@ -106,6 +110,14 @@ extern "C" void run_mha(
     params.cu_seqlens_q = cu_seqlens_q_ptr;
     params.cu_seqlens_k = cu_seqlens_k_ptr;
     params.p_ptr = nullptr; // used for `return_softmax`.
+    params.seqused_k = nullptr;
+
+    params.is_causal = is_causal;
+    params.window_size_left = window_size_left;
+    params.window_size_right = window_size_right;
+
+    params.is_seqlens_k_cumulative = true;
+    params.num_splits = 1;
 
     cudaStream_t stream = 0; // Use the default stream.
     run_mha_fwd(params, stream);

candle-flash-attn/kernels/flash_fwd_hdim128_bf16_sm80.cu

@@ -1,19 +1,10 @@
 // Copyright (c) 2023, Tri Dao.
-
 // Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
 
 #include "flash_fwd_launch_template.h"
 
-// template<>
-// void run_mha_fwd_<cutlass::bfloat16_t, 128>(Flash_fwd_params &params, cudaStream_t stream) {
-//     using elem_type = cutlass::bfloat16_t;
-//     if (params.p_dropout == 1.f) {
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 64, 4, false, false, elem_type>, false>(params, stream);
-//     } else {
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 32, 4, false, false, elem_type>, true>(params, stream);
-//     }
-// }
 template<>
 void run_mha_fwd_<cutlass::bfloat16_t, 128>(Flash_fwd_params &params, cudaStream_t stream) {
     run_mha_fwd_hdim128<cutlass::bfloat16_t>(params, stream);
-}
+}

candle-flash-attn/kernels/flash_fwd_hdim128_fp16_sm80.cu

@@ -1,32 +1,10 @@
 // Copyright (c) 2023, Tri Dao.
-
 // Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
 
 #include "flash_fwd_launch_template.h"
 
-// template<>
-// void run_mha_fwd_<cutlass::half_t, 128>(Flash_fwd_params &params, cudaStream_t stream) {
-//     using elem_type = cutlass::half_t;
-//     if (params.p_dropout == 1.f) {
-//         // Using 8 warps (128 x 128 and 256 x 64) is 28% slower for seqlen=2k
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 64, 4, false, false, elem_type>, false>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 64, 4, true, false, elem_type>, false>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 64, 4, false, true, elem_type>, false>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 64, 4, true, true, elem_type>, false>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 32, 4, false, false, elem_type>, false>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 64, 64, 4, false, false, elem_type>, false>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 64, 128, 4, false, false, elem_type>, false>(params, stream);
-//         // 1st ones are good for H100, A100
-//         // 2nd one is good for A6000 bc we get slightly better occupancy
-//     } else {
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 32, 4, false, false, elem_type>, true>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 32, 4, true, false, elem_type>, true>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<128, 128, 32, 4, true, true, elem_type>, true>(params, stream);
-//         // 1st one is good for H100, A100, A6000
-//     }
-// }
-
 template<>
 void run_mha_fwd_<cutlass::half_t, 128>(Flash_fwd_params &params, cudaStream_t stream) {
     run_mha_fwd_hdim128<cutlass::half_t>(params, stream);
-}
+}

candle-flash-attn/kernels/flash_fwd_hdim160_bf16_sm80.cu

@@ -1,17 +1,10 @@
 // Copyright (c) 2023, Tri Dao.
-
 // Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
 
 #include "flash_fwd_launch_template.h"
 
-// template<>
-// void run_mha_fwd_<cutlass::bfloat16_t, 160>(Flash_fwd_params &params, cudaStream_t stream) {
-//     using elem_type = cutlass::bfloat16_t;
-//     BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
-//         run_flash_fwd<Flash_fwd_kernel_traits<160, 128, 32, 4, false, false, elem_type>, Is_dropout>(params, stream);
-//     });
-// }
 template<>
 void run_mha_fwd_<cutlass::bfloat16_t, 160>(Flash_fwd_params &params, cudaStream_t stream) {
     run_mha_fwd_hdim160<cutlass::bfloat16_t>(params, stream);
-}
+}

candle-flash-attn/kernels/flash_fwd_hdim160_fp16_sm80.cu

@@ -1,27 +1,10 @@
 // Copyright (c) 2023, Tri Dao.
-
 // Splitting the different head dimensions to different files to speed up compilation.
+// This file is auto-generated. See "generate_kernels.py"
 
 #include "flash_fwd_launch_template.h"
 
-// template<>
-// void run_mha_fwd_<cutlass::half_t, 160>(Flash_fwd_params &params, cudaStream_t stream) {
-//     using elem_type = cutlass::half_t;
-//     BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
-//         run_flash_fwd<Flash_fwd_kernel_traits<160, 128, 32, 4, false, false, elem_type>, Is_dropout>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<160, 128, 32, 4, false, true, elem_type>, Is_dropout>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<160, 128, 64, 4, false, false, elem_type>, Is_dropout>(params, stream);
-//         run_flash_fwd<Flash_fwd_kernel_traits<160, 64, 64, 4, false, false, elem_type>, Is_dropout>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<160, 128, 64, 4, false, elem_type>>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<160, 64, 128, 4, false, elem_type>>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<160, 64, 64, 4, false, elem_type>>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<160, 128, 64, 8, false, elem_type>>(params, stream);
-//         // run_flash_fwd<Flash_fwd_kernel_traits<160, 128, 128, 8, false, elem_type>>(params, stream);
-//         // For A6000, no-causal, 1st is fastest. causal, 4th is fastest.
-//         // For A100, H100, 1st is fastest.
-//     });
-// }
 template<>
 void run_mha_fwd_<cutlass::half_t, 160>(Flash_fwd_params &params, cudaStream_t stream) {
     run_mha_fwd_hdim160<cutlass::half_t>(params, stream);
-}
+}