Preliminary implementation for the vocos model.

* Custom tokenizer for rwkv.

* Custom tokenizer.

* Getting the tokenizer to work.

.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

@@ -5,47 +5,15 @@ on:
   pull_request:
 
 jobs:
-  start-runner:
-    name: Start self-hosted EC2 runner
-    runs-on: ubuntu-latest
-    env:
-      AWS_REGION: us-east-1
-      EC2_AMI_ID: ami-03cfed9ea28f4b002
-      EC2_INSTANCE_TYPE: g5.xlarge
-      EC2_SUBNET_ID: subnet-931b34f5,subnet-ecb993cd,subnet-943dc2d8,subnet-45371f1a,subnet-ee93e0df,subnet-fddc3dfc
-      EC2_SECURITY_GROUP: sg-030175c435ac141d6
-    outputs:
-      label: ${{ steps.start-ec2-runner.outputs.label }}
-      ec2-instance-id: ${{ steps.start-ec2-runner.outputs.ec2-instance-id }}
-    steps:
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v1
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ env.AWS_REGION }}
-      - name: Start EC2 runner
-        id: start-ec2-runner
-        uses: philschmid/philschmid-ec2-github-runner@main
-        with:
-          mode: start
-          github-token: ${{ secrets.GH_PERSONAL_ACCESS_TOKEN }}
-          ec2-image-id: ${{ env.EC2_AMI_ID }}
-          ec2-instance-type: ${{ env.EC2_INSTANCE_TYPE }}
-          subnet-id: ${{ env.EC2_SUBNET_ID }}
-          security-group-id: ${{ env.EC2_SECURITY_GROUP }}
-          aws-resource-tags: > # optional, requires additional permissions
-            [
-              {"Key": "Name", "Value": "ec2-tgi-github-runner"},
-              {"Key": "GitHubRepository", "Value": "${{ github.repository }}"}
-            ]
-
   test-cuda:
     concurrency:
       group: ${{ github.workflow }}-${{ github.job }}-${{ github.head_ref || github.run_id }}
       cancel-in-progress: true
-    needs: start-runner # required to start the main job when the runner is ready
-    runs-on: ${{ needs.start-runner.outputs.label }} # run the job on the newly created runner
+    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    container:
+      image: nvidia/cuda:12.3.1-devel-ubuntu22.04
+      options: --gpus 0 
+    if: ${{ github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }}
     permissions:
       contents: write
       packages: write
@@ -56,32 +24,10 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
+      - name: Install dependencies
+        run: apt-get update && apt install curl build-essential libssl-dev protobuf-compiler pkg-config -y
       - name: Install Rust Stable
-        run: curl https://sh.rustup.rs -sSf | sh -s -- -y
+        uses: actions-rust-lang/setup-rust-toolchain@v1
       - uses: Swatinem/rust-cache@v2
-      - run: apt-get update -y && apt-get install libssl-dev -y
       - name: Test (cuda)
-        run: PATH=$PATH:/usr/local/cuda-11.8/bin/ /root/.cargo/bin/cargo test --features cuda
-  stop-runner:
-    name: Stop self-hosted EC2 runner
-    needs:
-      - start-runner
-      - test-cuda
-    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
-    steps:
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v1
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ env.AWS_REGION }}
-      - name: Stop EC2 runner
-        uses: philschmid/philschmid-ec2-github-runner@main
-        with:
-          mode: stop
-          github-token: ${{ secrets.GH_PERSONAL_ACCESS_TOKEN }}
-          label: ${{ needs.start-runner.outputs.label }}
-          ec2-instance-id: ${{ needs.start-runner.outputs.ec2-instance-id }}
+        run: cargo test --features cuda

.github/workflows/python.yml

@@ -39,6 +39,12 @@ jobs:
           path: ~/.cargo/registry
           key: ${{ runner.os }}-cargo-registry-${{ hashFiles('**/Cargo.lock') }}
 
+      - name: Install Protoc
+        uses: arduino/setup-protoc@v2
+        with:
+            version: "25.0"
+            repo-token: ${{ secrets.GITHUB_TOKEN }}
+
       - name: Install
         working-directory: ./candle-pyo3
         run: |
@@ -46,7 +52,7 @@ jobs:
           source .env/bin/activate
           pip install -U pip
           pip install pytest maturin black
-          python -m maturin develop -r
+          python -m maturin develop -r --features onnx
 
       - name: Check style
         working-directory: ./candle-pyo3

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

@@ -10,11 +10,16 @@ members = [
     "candle-wasm-examples/*",
     "candle-wasm-tests",
 ]
-exclude = ["candle-flash-attn", "candle-kernels"]
+exclude = [
+   "candle-flash-attn",
+   "candle-kernels",
+   "candle-metal-kernels",
+   "candle-onnx",
+]
 resolver = "2"
 
 [workspace.package]
-version = "0.3.0"
+version = "0.4.0"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -26,9 +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", version = "0.4.0" }
+candle-datasets = { path = "./candle-datasets", version = "0.4.0" }
+candle-flash-attn = { path = "./candle-flash-attn", version = "0.4.0" }
+candle-kernels = { path = "./candle-kernels", version = "0.4.0" }
+candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.4.0" }
+candle-nn = { path = "./candle-nn", version = "0.4.0" }
+candle-onnx = { path = "./candle-onnx", version = "0.4.0" }
+candle-transformers = { path = "./candle-transformers", version = "0.4.0" }
 clap = { version = "4.2.4", features = ["derive"] }
-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"] }
@@ -36,26 +50,27 @@ 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" }
+parquet = { version = "50.0.0" }
 rand = "0.8.5"
 rand_distr = "0.4.3"
 rayon = "1.7.0"
 rusttype = { version = "0.9", default-features = false }
-safetensors = "0.3.1"
+safetensors = "0.4.1"
 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 = { version = "0.27.0", features = ["mps"]}
 
 [profile.release-with-debug]
 inherits = "release"

README.md

@@ -51,24 +51,36 @@ For more advanced examples, please have a look at the following section.
 These online demos run entirely in your browser:
 - [yolo](https://huggingface.co/spaces/lmz/candle-yolo): pose estimation and
   object recognition.
-- [whisper](https://huggingface.co/spaces/lmz/candle-whisper): text to speech.
+- [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.
+  pre-trained on 1T tokens of English and code datasets. Also supports
+  StableLM-2, a 1.6b LLM trained on 2T tokens, as well as the code variants.
+- [Mamba](./candle-examples/examples/mamba/): an inference only
+  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.
+- [Qwen1.5](./candle-examples/examples/qwen/): Bilingual (English/Chinese) LLMs.
+- [RWKV v5](./candle-examples/examples/rwkv/): An RNN with transformer level LLM
+  performance.
 - [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
+- [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
+  (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 +88,7 @@ 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">
 
@@ -101,8 +113,12 @@ 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
   generate captions for an image.
+- [TrOCR](./candle-examples/examples/trocr/): a transformer OCR model, with
+  dedicated submodels for hand-writing and printed recognition.
 - [Marian-MT](./candle-examples/examples/marian-mt/): neural machine translation
   model, generates the translated text from the input text.
 
@@ -120,7 +136,7 @@ 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
@@ -137,12 +153,20 @@ 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/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.
 
@@ -161,25 +185,38 @@ 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.
+        - Mamba, Minimal Mamba
         - Mistral 7b v0.1.
-        - StableLM-3B-4E1T.
+        - Mixtral 8x7b v0.1.
+        - StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
         - Replit-code-v1.5-3B.
-        - T5.
         - Bert.
+        - Yi-6B and Yi-34B.
+        - Qwen1.5.
+        - RWKV.
+    - 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, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
+        - Marian MT (Machine Translation).
     - Whisper (multi-lingual support).
     - Text to image.
         - Stable Diffusion v1.5, v2.1, XL v1.0.
         - Wurstchen v2.
     - Image to text.
         - BLIP.
-    - Text to text.
-        - Marian MT (Machine Translation).
+        - TrOCR.
     - Computer Vision Models.
-        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT.
+        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
+          ConvNeXTv2.
         - yolo-v3, yolo-v8.
         - Segment-Anything Model (SAM).
 - File formats: load models from safetensors, npz, ggml, or PyTorch files.
@@ -218,6 +255,7 @@ Cheatsheet:
 - [candle-datasets](./candle-datasets/): Datasets and data loaders.
 - [candle-transformers](./candle-transformers): transformers-related utilities.
 - [candle-flash-attn](./candle-flash-attn): Flash attention v2 layer.
+- [candle-onnx](./candle-onnx/): ONNX model evaluation.
 
 ## FAQ
 

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

@@ -12,9 +12,8 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-tracing = { 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 }
@@ -35,6 +34,8 @@ zip = { workspace = true }
 [dev-dependencies]
 anyhow = { workspace = true }
 clap = { workspace = true }
+criterion = { workspace = true }
+
 
 [features]
 default = []
@@ -42,4 +43,8 @@ cuda = ["cudarc", "dep:candle-kernels"]
 cudnn = ["cuda", "cudarc/cudnn"]
 mkl = ["dep:libc", "dep:intel-mkl-src"]
 accelerate = ["dep:libc", "dep:accelerate-src"]
-metal = ["dep:candle-metal-kernels", "dep:metal"]
+metal = ["dep:metal", "dep:candle-metal-kernels"]
+
+[[bench]]
+name = "bench_main"
+harness = false

candle-core/benches/bench_main.rs

@@ -0,0 +1,9 @@
+mod benchmarks;
+
+use criterion::criterion_main;
+criterion_main!(
+    benchmarks::affine::benches,
+    benchmarks::matmul::benches,
+    benchmarks::random::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 +1,66 @@
+pub(crate) mod affine;
+pub(crate) mod matmul;
+pub(crate) mod random;
+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/random.rs

@@ -0,0 +1,63 @@
+use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
+use candle_core::{DType, Device, Tensor};
+use criterion::{black_box, criterion_group, Criterion, Throughput};
+use std::time::Instant;
+
+fn rand_uniform(a: &Tensor) {
+    a.rand_like(-1.0, 123.0).unwrap();
+}
+
+fn rand_normal(a: &Tensor) {
+    a.randn_like(100.0, 15.0).unwrap();
+}
+
+fn run_random_bench(c: &mut Criterion, device: &Device) {
+    let b = 1;
+
+    let rows = 2048;
+    let cols = 2048;
+
+    let dtype = DType::F32;
+    let tensor = Tensor::zeros((b, rows, cols), dtype, device).unwrap();
+
+    let flops = b * rows * cols * dtype.size_in_bytes();
+
+    let mut group = c.benchmark_group(device.bench_name("random_uniform"));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |benches| {
+        benches.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                rand_uniform(black_box(&tensor));
+            }
+            device.sync().unwrap();
+            start.elapsed()
+        })
+    });
+    group.finish();
+
+    let tensor = Tensor::zeros((b, rows, cols), dtype, device).unwrap();
+
+    let mut group = c.benchmark_group(device.bench_name("random_normal"));
+    group.throughput(Throughput::Bytes(flops as u64));
+    group.bench_function("iter", move |benches| {
+        benches.iter_custom(|iters| {
+            let start = Instant::now();
+            for _i in 0..iters {
+                rand_normal(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_random_bench(c, &device);
+    }
+}
+
+criterion_group!(benches, criterion_benchmark);

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) {
@@ -166,7 +196,7 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
             }
         }
         Format::Pth => {
-            let mut tensors = candle_core::pickle::read_pth_tensor_info(file, verbose)?;
+            let mut tensors = candle_core::pickle::read_pth_tensor_info(file, verbose, None)?;
             tensors.sort_by(|a, b| a.name.cmp(&b.name));
             for tensor_info in tensors.iter() {
                 println!(
@@ -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/accelerate.rs

@@ -380,6 +380,16 @@ pub fn vd_tanh_inplace(y: &mut [f64]) {
     unsafe { ffi::vvtanh(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
 }
 
+#[inline]
+pub fn vs_exp_inplace(y: &mut [f32]) {
+    unsafe { ffi::vvexpf(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
+}
+
+#[inline]
+pub fn vd_exp_inplace(y: &mut [f64]) {
+    unsafe { ffi::vvexp(y.as_mut_ptr(), y.as_ptr(), &(y.len() as i32)) }
+}
+
 #[inline]
 pub fn vs_gelu(vs: &[f32], ys: &mut [f32]) {
     for (&v, y) in vs.iter().zip(ys.iter_mut()) {
@@ -402,6 +412,28 @@ pub fn vd_gelu(vs: &[f64], ys: &mut [f64]) {
     }
 }
 
+#[inline]
+pub fn vs_silu(vs: &[f32], ys: &mut [f32]) {
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = -v
+    }
+    vs_exp_inplace(ys);
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = v / (1.0 + *y)
+    }
+}
+
+#[inline]
+pub fn vd_silu(vs: &[f64], ys: &mut [f64]) {
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = -v
+    }
+    vd_exp_inplace(ys);
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = v / (1.0 + *y)
+    }
+}
+
 macro_rules! binary_op {
     ($fn_name:ident, $ty:ty, $accelerate_name:ident) => {
         #[inline]

candle-core/src/backend.rs

@@ -39,6 +39,14 @@ pub trait BackendStorage: Sized {
         _params: &crate::conv::ParamsConv1D,
     ) -> Result<Self>;
 
+    fn conv_transpose1d(
+        &self,
+        _l: &Layout,
+        _kernel: &Self,
+        _kernel_l: &Layout,
+        _params: &crate::conv::ParamsConvTranspose1D,
+    ) -> Result<Self>;
+
     fn conv2d(
         &self,
         _l: &Layout,

candle-core/src/backprop.rs

@@ -15,6 +15,17 @@ fn broadcast_back(arg: &Tensor, node: &Tensor, reduced_dims: &[usize]) -> Result
     }
 }
 
+thread_local! {
+    static CANDLE_GRAD_DO_NOT_DETACH: bool = {
+        match std::env::var("CANDLE_GRAD_DO_NOT_DETACH") {
+            Ok(s) => {
+                !s.is_empty() && s != "0"
+            },
+            Err(_) => false,
+        }
+    }
+}
+
 impl Tensor {
     /// Return all the nodes that lead to this value in a topologically sorted vec, the first
     /// elements having dependencies on the latter ones, e.g. the first element if any is the
@@ -57,6 +68,11 @@ impl Tensor {
                         kernel: rhs,
                         ..
                     }
+                    | Op::ConvTranspose1D {
+                        arg: lhs,
+                        kernel: rhs,
+                        ..
+                    }
                     | Op::Conv2D {
                         arg: lhs,
                         kernel: rhs,
@@ -98,7 +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)
@@ -150,10 +166,16 @@ impl Tensor {
             if node.is_variable() {
                 continue;
             }
-            let grad = grads.remove(node).unwrap();
-            // TODO: We should perform all these operations in place (or at least not track the
-            // whole graph). The only drawback would be if we wanted to support grad of grad but
-            // this is out of scope.
+            let grad = grads
+                .remove(node)
+                .expect("candle internal error - grad not populated");
+            // https://github.com/huggingface/candle/issues/1241
+            // Ideally, we would make these operations in place where possible to ensure that we
+            // do not have to allocate too often. Here we just call `.detach` to avoid computing
+            // the backprop graph of the backprop itself. This would be an issue for second order
+            // derivatives but these are out of scope at the moment.
+            let do_not_detach = CANDLE_GRAD_DO_NOT_DETACH.with(|b| *b);
+            let grad = if do_not_detach { grad } else { grad.detach() };
             if let Some(op) = node.op() {
                 match op {
                     Op::Binary(lhs, rhs, BinaryOp::Add) => {
@@ -208,7 +230,44 @@ impl Tensor {
                         let f_grad = pred.where_cond(&zeros, &grad)?;
                         *f_sum_grad = f_sum_grad.add(&f_grad)?;
                     }
-                    Op::Conv1D { .. } => Err(Error::BackwardNotSupported { op: "conv1d" })?,
+                    Op::Conv1D {
+                        arg,
+                        kernel,
+                        padding,
+                        stride,
+                        dilation,
+                    } => {
+                        // The output height for conv_transpose1d is:
+                        // (l_in - 1) * stride - 2 * padding + dilation * (k_size - 1) + out_padding + 1
+                        let grad_l_in = grad.dim(2)?;
+                        let k_size = kernel.dim(2)?;
+                        let out_size =
+                            (grad_l_in - 1) * stride + dilation * (k_size - 1) + 1 - 2 * padding;
+                        let out_padding = arg.dim(2)? - out_size;
+                        let grad_arg = grad.conv_transpose1d(
+                            kernel,
+                            *padding,
+                            out_padding,
+                            *stride,
+                            *dilation,
+                        )?;
+                        let sum_grad = grads.or_insert(arg)?;
+                        *sum_grad = sum_grad.add(&grad_arg)?;
+
+                        let grad_kernel = arg
+                            .transpose(0, 1)?
+                            .conv1d(&grad.transpose(0, 1)?, *padding, *dilation, *stride, 1)?
+                            .transpose(0, 1)?;
+                        let sum_grad = grads.or_insert(kernel)?;
+                        let (_, _, k0) = kernel.dims3()?;
+                        let (_, _, g_k0) = grad_kernel.dims3()?;
+                        let grad_kernel = if g_k0 != k0 {
+                            grad_kernel.narrow(2, 0, k0)?
+                        } else {
+                            grad_kernel
+                        };
+                        *sum_grad = sum_grad.add(&grad_kernel)?;
+                    }
                     Op::Conv2D {
                         arg,
                         kernel,
@@ -247,6 +306,9 @@ impl Tensor {
                         };
                         *sum_grad = sum_grad.add(&grad_kernel)?;
                     }
+                    Op::ConvTranspose1D { .. } => Err(Error::BackwardNotSupported {
+                        op: "conv-transpose1d",
+                    })?,
                     Op::ConvTranspose2D { .. } => Err(Error::BackwardNotSupported {
                         op: "conv-transpose2d",
                     })?,
@@ -288,9 +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)?)?;
@@ -487,16 +567,45 @@ impl Tensor {
                             + 0.5)?;
                         *sum_grad = sum_grad.add(&(&grad * gelu_grad)?)?
                     }
-                    Op::Unary(_, UnaryOp::Erf) => Err(Error::BackwardNotSupported { op: "erf" })?,
-                    Op::Unary(_, UnaryOp::GeluErf) => {
-                        Err(Error::BackwardNotSupported { op: "gelu-erf" })?
+                    Op::Unary(arg, UnaryOp::Erf) => {
+                        let sum_grad = grads.or_insert(arg)?;
+                        // d/dx erf(x) = 2/sqrt(pi) * e^(-x^2)
+                        let erf_grad =
+                            (2. / std::f64::consts::PI.sqrt()) * (arg.sqr()?.neg()?).exp()?;
+                        *sum_grad = sum_grad.add(&(&grad * erf_grad)?)?
+                    }
+                    Op::Unary(arg, UnaryOp::GeluErf) => {
+                        let sum_grad = grads.or_insert(arg)?;
+                        // d/dx gelu_erf(x) = 0.5 + 0.398942 e^(-x^2/2) x + 0.5 erf(x/sqrt(2))
+                        let neg_half_square = (arg.sqr()?.neg()? / 2.)?;
+                        let scaled_exp_arg = (0.398942 * neg_half_square.exp()? * arg)?;
+                        let arg_scaled_sqrt = (arg / 2f64.sqrt())?;
+                        let erf_scaled_sqrt = (0.5 * arg_scaled_sqrt.erf()?)?;
+                        let gelu_erf_grad = (0.5 + scaled_exp_arg + erf_scaled_sqrt)?;
+                        *sum_grad = sum_grad.add(&(&grad * gelu_erf_grad)?)?;
                     }
                     Op::Unary(arg, UnaryOp::Relu) => {
                         let sum_grad = grads.or_insert(arg)?;
                         let relu_grad = arg.ge(&arg.zeros_like()?)?.to_dtype(arg.dtype())?;
                         *sum_grad = sum_grad.add(&(&grad * relu_grad)?)?
                     }
-                    Op::Elu(..) => Err(Error::BackwardNotSupported { op: "elu" })?,
+                    Op::Unary(arg, UnaryOp::Silu) => {
+                        let sum_grad = grads.or_insert(arg)?;
+                        // d/dx silu = sigmoid(x) * (1 + x * (1 - sigmoid(x)))
+                        let sigmoid_arg = (*node / arg)?;
+                        let silu_grad = (&sigmoid_arg * (1. + (arg * (1. - &sigmoid_arg)?)?)?)?;
+                        *sum_grad = sum_grad.add(&(&grad * silu_grad)?)?
+                    }
+                    Op::Elu(arg, alpha) => {
+                        // d/dx elu(x) = 1 for x > 0, alpha * e^x for x <= 0
+                        let sum_grad = grads.or_insert(arg)?;
+                        let zeros = arg.zeros_like()?;
+                        let positive_mask = arg.gt(&zeros)?.to_dtype(arg.dtype())?;
+                        let negative_mask = arg.le(&zeros)?.to_dtype(arg.dtype())?;
+                        let negative_exp_mask = ((negative_mask * arg.exp())? * *alpha)?;
+                        let combined_mask = (positive_mask + negative_exp_mask)?;
+                        *sum_grad = sum_grad.add(&(grad * combined_mask)?)?
+                    }
                     Op::Powf(arg, e) => {
                         let arg_grad = (&(grad * arg.powf(e - 1.)?)? * *e)?;
                         let sum_grad = grads.or_insert(arg)?;

candle-core/src/conv.rs

@@ -25,6 +25,33 @@ impl ParamsConv1D {
     }
 }
 
+#[derive(Debug, Clone, PartialEq, Eq)]
+pub struct ParamsConvTranspose1D {
+    pub(crate) b_size: usize,
+    pub(crate) l_in: usize,
+    pub(crate) c_out: usize,
+    pub(crate) c_in: usize,
+    pub(crate) k_size: usize,
+    pub(crate) padding: usize,
+    pub(crate) output_padding: usize,
+    pub(crate) stride: usize,
+    pub(crate) dilation: usize,
+}
+
+impl ParamsConvTranspose1D {
+    pub(crate) fn l_out(&self) -> usize {
+        (self.l_in - 1) * self.stride - 2 * self.padding
+            + self.dilation * (self.k_size - 1)
+            + self.output_padding
+            + 1
+    }
+
+    pub(crate) fn out_dims(&self) -> Vec<usize> {
+        let l_out = self.l_out();
+        vec![self.b_size, self.c_out, l_out]
+    }
+}
+
 #[derive(Debug, Clone, PartialEq, Eq, Hash)]
 pub enum CudnnFwdAlgo {
     ImplicitGemm,
@@ -160,6 +187,49 @@ impl Tensor {
         }
     }
 
+    /// Applies a 1D transposed convolution over the input tensor.
+    pub fn conv_transpose1d(
+        &self,
+        kernel: &Self,
+        padding: usize,
+        output_padding: usize,
+        stride: usize,
+        dilation: usize,
+    ) -> Result<Self> {
+        let (b_size, c_in, l_in) = self.dims3()?;
+        let (c_in_k, c_out, k_size) = kernel.dims3()?;
+        if c_in != c_in_k {
+            crate::bail!("in_channel mismatch between input ({c_in}) and kernel ({c_in_k})")
+        }
+        let params = ParamsConvTranspose1D {
+            b_size,
+            l_in,
+            k_size,
+            c_out,
+            c_in,
+            padding,
+            output_padding,
+            stride,
+            dilation,
+        };
+        let storage = self.storage().conv_transpose1d(
+            self.layout(),
+            &kernel.storage(),
+            kernel.layout(),
+            &params,
+        )?;
+        let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::ConvTranspose1D {
+            arg,
+            kernel,
+            padding: params.padding,
+            output_padding: params.output_padding,
+            stride: params.stride,
+            dilation: params.dilation,
+        });
+        let out_dims = params.out_dims();
+        Ok(crate::tensor::from_storage(storage, out_dims, op, false))
+    }
+
     fn conv2d_single_group(&self, kernel: &Self, params: &ParamsConv2D) -> Result<Self> {
         let storage =
             self.storage()

candle-core/src/cpu_backend.rs

@@ -1256,6 +1256,74 @@ impl Map1 for Im2Col {
     }
 }
 
+struct ConvTranspose1D<'a>(&'a crate::conv::ParamsConvTranspose1D);
+
+impl<'a> Map2 for ConvTranspose1D<'a> {
+    const OP: &'static str = "conv_transpose1d";
+    fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
+        let p = self.0;
+        let inp = &inp[inp_l.start_offset()..];
+        let (inp_s0, inp_s1, inp_s2) = crate::shape::dims3(inp_l.stride())?;
+        let (k_s0, k_s1, k_s2) = crate::shape::dims3(k_l.stride())?;
+        let l_out = p.l_out();
+
+        // Output shape: [b_size, c_out, l_out].
+        let dst_elems = p.c_out * l_out * p.b_size;
+        let dst = vec![T::zero(); dst_elems];
+        let dst_s0 = p.c_out * l_out;
+        let dst_s1 = l_out;
+        let dst_s2 = 1;
+
+        // TODO: Avoid making this copy if `inp` already has the appropriate layout.
+        let mut inp_cont = vec![T::zero(); p.b_size * p.c_in * p.l_in];
+        let cont_s0 = p.l_in * p.c_in;
+        let cont_s1 = p.c_in;
+        for b_idx in 0..p.b_size {
+            for l_idx in 0..p.l_in {
+                for c_idx in 0..p.c_in {
+                    let src_idx = b_idx * inp_s0 + c_idx * inp_s1 + l_idx * inp_s2;
+                    let dst_idx = b_idx * cont_s0 + l_idx * cont_s1 + c_idx;
+                    inp_cont[dst_idx] = inp[src_idx]
+                }
+            }
+        }
+
+        for k_idx in 0..p.k_size {
+            (0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
+                let k_cont = (0..p.c_in)
+                    .map(|c_in_idx| k[c_in_idx * k_s0 + dst_c_idx * k_s1 + k_idx * k_s2])
+                    .collect::<Vec<_>>();
+                for b_idx in 0..p.b_size {
+                    for l_idx in 0..p.l_in {
+                        let out_idx = l_idx * p.stride + k_idx * p.dilation;
+                        if out_idx < p.padding {
+                            continue;
+                        }
+                        let out_idx = out_idx - p.padding;
+                        if out_idx < l_out {
+                            let inp_cont = &inp_cont[b_idx * cont_s0 + l_idx * cont_s1..];
+                            let dst_idx = b_idx * dst_s0 + out_idx * dst_s2 + dst_c_idx * dst_s1;
+                            let mut d = T::zero();
+                            unsafe {
+                                T::vec_dot(inp_cont.as_ptr(), k_cont.as_ptr(), &mut d, p.c_in)
+                            }
+                            let dst_p = dst.as_ptr();
+                            // Safety: dst_idx are uniques per dst_c_idx which is used to
+                            // parallelise the different tasks so no two threads can try to
+                            // write at the same location.
+                            unsafe {
+                                let ptr = dst_p.add(dst_idx) as *mut T;
+                                *ptr += d
+                            }
+                        }
+                    }
+                }
+            })
+        }
+        Ok(dst)
+    }
+}
+
 struct Conv2D<'a>(&'a crate::conv::ParamsConv2D);
 
 impl<'a> Map2 for Conv2D<'a> {
@@ -2435,6 +2503,16 @@ impl BackendStorage for CpuStorage {
         Ok(res_t)
     }
 
+    fn conv_transpose1d(
+        &self,
+        l: &Layout,
+        kernel: &Self,
+        kernel_l: &Layout,
+        params: &crate::conv::ParamsConvTranspose1D,
+    ) -> Result<Self> {
+        ConvTranspose1D(params).map(self, l, kernel, kernel_l)
+    }
+
     fn conv2d(
         &self,
         l: &Layout,

candle-core/src/cuda_backend.rs

@@ -1149,6 +1149,55 @@ impl<'a> Map2 for Conv2D<'a> {
     }
 }
 
+struct ConvTranspose1D<'a>(&'a crate::conv::ParamsConvTranspose1D);
+impl<'a> Map2 for ConvTranspose1D<'a> {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
+        &self,
+        inp: &CudaSlice<T>,
+        inp_l: &Layout,
+        k: &CudaSlice<T>,
+        k_l: &Layout,
+        dev: &CudaDevice,
+    ) -> Result<CudaSlice<T>> {
+        // Kernel shape: (c_in_k, c_out, l_k)
+        // Input shape: (b_size, c_in, l_in)
+        let p = &self.0;
+        let l_out = p.l_out();
+        let dst_el = p.c_out * l_out * p.b_size;
+        let inp = &inp.slice(inp_l.start_offset()..);
+        let k = &k.slice(k_l.start_offset()..);
+        let shape = inp_l.shape();
+        let dims = shape.dims();
+        let el = shape.elem_count();
+
+        // SAFETY: Set later by running the kernel.
+        let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
+        let func = dev.get_or_load_func(&kernel_name::<T>("conv_transpose1d"), kernels::CONV)?;
+        let ds = if dims.len() == 3 {
+            [dims, inp_l.stride(), k_l.dims(), k_l.stride()].concat()
+        } else {
+            crate::bail!("unexpected input shape for conv_transpose1d {dims:?}")
+        };
+        let ds = dev.htod_copy(ds).w()?;
+        let params = (
+            el,
+            l_out,
+            p.stride,
+            p.padding,
+            p.output_padding,
+            p.dilation,
+            &ds,
+            inp,
+            k,
+            &out,
+        );
+        // SAFETY: ffi.
+        unsafe { func.launch(cfg, params) }.w()?;
+        Ok(out)
+    }
+}
+
 struct ConvTranspose2D<'a>(&'a crate::conv::ParamsConvTranspose2D);
 impl<'a> Map2 for ConvTranspose2D<'a> {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
@@ -1808,6 +1857,19 @@ impl BackendStorage for CudaStorage {
         Ok(res_t)
     }
 
+    fn conv_transpose1d(
+        &self,
+        l: &Layout,
+        kernel: &Self,
+        kernel_l: &Layout,
+        params: &crate::conv::ParamsConvTranspose1D,
+    ) -> Result<Self> {
+        let device = self.device().clone();
+        let slice =
+            ConvTranspose1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
+        Ok(Self { slice, device })
+    }
+
     #[cfg(not(feature = "cudnn"))]
     fn conv2d(
         &self,

candle-core/src/device.rs

@@ -1,6 +1,6 @@
 use crate::backend::BackendDevice;
 use crate::cpu_backend::CpuDevice;
-use crate::{bail, CpuStorage, DType, Result, Shape, Storage, WithDType};
+use crate::{CpuStorage, DType, Result, Shape, Storage, WithDType};
 
 /// A `DeviceLocation` represents a physical device whereas multiple `Device`
 /// can live on the same location (typically for cuda devices).
@@ -8,7 +8,7 @@ use crate::{bail, CpuStorage, DType, Result, Shape, Storage, WithDType};
 pub enum DeviceLocation {
     Cpu,
     Cuda { gpu_id: usize },
-    Metal,
+    Metal { gpu_id: usize },
 }
 
 #[derive(Debug, Clone)]
@@ -105,14 +105,14 @@ impl<S: WithDType, const N1: usize, const N2: usize, const N3: usize, const N4:
 impl<S: NdArray> NdArray for Vec<S> {
     fn shape(&self) -> Result<Shape> {
         if self.is_empty() {
-            bail!("empty array")
+            crate::bail!("empty array")
         }
         let shape0 = self[0].shape()?;
         let n = self.len();
         for v in self.iter() {
             let shape = v.shape()?;
             if shape != shape0 {
-                bail!("two elements have different shapes {shape:?} {shape0:?}")
+                crate::bail!("two elements have different shapes {shape:?} {shape0:?}")
             }
         }
         Ok(Shape::from([[n].as_slice(), shape0.dims()].concat()))
@@ -146,6 +146,7 @@ impl Device {
         match (self, rhs) {
             (Self::Cpu, Self::Cpu) => true,
             (Self::Cuda(lhs), Self::Cuda(rhs)) => lhs.same_device(rhs),
+            (Self::Metal(lhs), Self::Metal(rhs)) => lhs.same_device(rhs),
             _ => false,
         }
     }
@@ -166,6 +167,10 @@ impl Device {
         matches!(self, Self::Cuda(_))
     }
 
+    pub fn is_metal(&self) -> bool {
+        matches!(self, Self::Metal(_))
+    }
+
     pub fn cuda_if_available(ordinal: usize) -> Result<Self> {
         if crate::utils::cuda_is_available() {
             Self::new_cuda(ordinal)
@@ -187,13 +192,18 @@ impl Device {
                 Ok(Storage::Cpu(storage))
             }
             Device::Cuda(device) => {
-                let storage = device.rand_uniform(shape, dtype, lo, up)?;
-                Ok(Storage::Cuda(storage))
+                // TODO: Remove the special case if we start supporting generating f16/bf16 directly.
+                if dtype == DType::F16 || dtype == DType::BF16 {
+                    let storage = device.rand_uniform(shape, DType::F32, lo, up)?;
+                    Storage::Cuda(storage).to_dtype(&crate::Layout::contiguous(shape), dtype)
+                } else {
+                    let storage = device.rand_uniform(shape, dtype, lo, up)?;
+                    Ok(Storage::Cuda(storage))
+                }
             }
-            Device::Metal(_device) => {
-                // let storage = device.rand_uniform(shape, dtype, lo, up)?;
-                // Ok(Storage::Metal(storage))
-                bail!("Metal rand_uniform not implemented")
+            Device::Metal(device) => {
+                let storage = device.rand_uniform(shape, dtype, lo, up)?;
+                Ok(Storage::Metal(storage))
             }
         }
     }
@@ -220,8 +230,14 @@ impl Device {
                 Ok(Storage::Cpu(storage))
             }
             Device::Cuda(device) => {
-                let storage = device.rand_normal(shape, dtype, mean, std)?;
-                Ok(Storage::Cuda(storage))
+                // TODO: Remove the special case if we start supporting generating f16/bf16 directly.
+                if dtype == DType::F16 || dtype == DType::BF16 {
+                    let storage = device.rand_normal(shape, DType::F32, mean, std)?;
+                    Storage::Cuda(storage).to_dtype(&crate::Layout::contiguous(shape), dtype)
+                } else {
+                    let storage = device.rand_normal(shape, dtype, mean, std)?;
+                    Ok(Storage::Cuda(storage))
+                }
             }
             Device::Metal(device) => {
                 let storage = device.rand_normal(shape, dtype, mean, std)?;

candle-core/src/display.rs

@@ -14,7 +14,9 @@ impl Tensor {
             crate::DeviceLocation::Cuda { gpu_id } => {
                 format!(", cuda:{}", gpu_id)
             }
-            _ => todo!(),
+            crate::DeviceLocation::Metal { gpu_id } => {
+                format!(", metal:{}", gpu_id)
+            }
         };
 
         write!(f, "Tensor[")?;
@@ -477,7 +479,9 @@ impl std::fmt::Display for Tensor {
             crate::DeviceLocation::Cuda { gpu_id } => {
                 format!(", cuda:{}", gpu_id)
             }
-            crate::DeviceLocation::Metal => todo!(),
+            crate::DeviceLocation::Metal { gpu_id } => {
+                format!(", metal:{}", gpu_id)
+            }
         };
 
         write!(

candle-core/src/dummy_cuda_backend.rs

@@ -79,6 +79,16 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
+    fn conv_transpose1d(
+        &self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &crate::conv::ParamsConvTranspose1D,
+    ) -> Result<Self> {
+        Err(Error::NotCompiledWithCudaSupport)
+    }
+
     fn conv2d(
         &self,
         _: &Layout,

candle-core/src/dummy_metal_backend.rs

@@ -8,6 +8,18 @@ pub struct MetalDevice;
 #[derive(Debug)]
 pub struct MetalStorage;
 
+#[derive(thiserror::Error, Debug)]
+pub enum MetalError {
+    #[error("{0}")]
+    Message(String),
+}
+
+impl From<String> for MetalError {
+    fn from(e: String) -> Self {
+        MetalError::Message(e)
+    }
+}
+
 macro_rules! fail {
     () => {
         unimplemented!("metal support has not been enabled, add `metal` feature to enable.")
@@ -79,6 +91,16 @@ impl crate::backend::BackendStorage for MetalStorage {
         Err(Error::NotCompiledWithMetalSupport)
     }
 
+    fn conv_transpose1d(
+        &self,
+        _l: &Layout,
+        _kernel: &Self,
+        _kernel_l: &Layout,
+        _params: &crate::conv::ParamsConvTranspose1D,
+    ) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
     fn conv2d(
         &self,
         _: &Layout,

candle-core/src/error.rs

@@ -1,4 +1,4 @@
-use crate::{metal_backend, DType, DeviceLocation, Layout, Shape};
+use crate::{DType, DeviceLocation, Layout, MetalError, Shape};
 
 #[derive(Debug, Clone)]
 pub struct MatMulUnexpectedStriding {
@@ -163,7 +163,7 @@ pub enum Error {
     Cuda(Box<dyn std::error::Error + Send + Sync>),
 
     #[error("Metal error {0}")]
-    Metal(#[from] metal_backend::MetalError),
+    Metal(#[from] MetalError),
 
     #[error(transparent)]
     TryFromIntError(#[from] core::num::TryFromIntError),

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

@@ -49,13 +49,12 @@ mod device;
 pub mod display;
 mod dtype;
 mod dummy_cuda_backend;
+mod dummy_metal_backend;
 pub mod error;
 mod indexer;
 pub mod layout;
 #[cfg(feature = "metal")]
 pub mod metal_backend;
-#[cfg(feature = "accelerate")]
-mod metal_backend;
 #[cfg(feature = "mkl")]
 mod mkl;
 pub mod npy;
@@ -73,7 +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;
@@ -92,10 +91,10 @@ pub use cuda_backend::{CudaDevice, CudaStorage};
 pub use dummy_cuda_backend::{CudaDevice, CudaStorage};
 
 #[cfg(feature = "metal")]
-pub use metal_backend::{MetalDevice, MetalStorage};
+pub use metal_backend::{MetalDevice, MetalError, MetalStorage};
 
 #[cfg(not(feature = "metal"))]
-pub use dummy_metal_backend::{MetalDevice, MetalStorage};
+pub use dummy_metal_backend::{MetalDevice, MetalError, MetalStorage};
 
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
@@ -124,12 +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/mkl.rs

@@ -333,6 +333,16 @@ pub fn vd_tanh_inplace(y: &mut [f64]) {
     unsafe { ffi::vdTanh(y.len() as i32, y.as_ptr(), y.as_mut_ptr()) }
 }
 
+#[inline]
+pub fn vs_exp_inplace(y: &mut [f32]) {
+    unsafe { ffi::vsExp(y.len() as i32, y.as_ptr(), y.as_mut_ptr()) }
+}
+
+#[inline]
+pub fn vd_exp_inplace(y: &mut [f64]) {
+    unsafe { ffi::vdExp(y.len() as i32, y.as_ptr(), y.as_mut_ptr()) }
+}
+
 #[inline]
 pub fn vs_gelu(vs: &[f32], ys: &mut [f32]) {
     for (&v, y) in vs.iter().zip(ys.iter_mut()) {
@@ -355,6 +365,28 @@ pub fn vd_gelu(vs: &[f64], ys: &mut [f64]) {
     }
 }
 
+#[inline]
+pub fn vs_silu(vs: &[f32], ys: &mut [f32]) {
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = -v
+    }
+    vs_exp_inplace(ys);
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = v / (1.0 + *y)
+    }
+}
+
+#[inline]
+pub fn vd_silu(vs: &[f64], ys: &mut [f64]) {
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = -v
+    }
+    vd_exp_inplace(ys);
+    for (&v, y) in vs.iter().zip(ys.iter_mut()) {
+        *y = v / (1.0 + *y)
+    }
+}
+
 macro_rules! binary_op {
     ($fn_name:ident, $ty:ty, $mkl_name:ident) => {
         #[inline]

candle-core/src/op.rs

@@ -61,6 +61,7 @@ pub enum UnaryOp {
     GeluErf,
     Erf,
     Relu,
+    Silu,
     Tanh,
     Floor,
     Ceil,
@@ -90,6 +91,16 @@ pub enum Op {
         dilation: usize,
     },
 
+    #[allow(dead_code)]
+    ConvTranspose1D {
+        arg: Tensor,
+        kernel: Tensor,
+        padding: usize,
+        output_padding: usize,
+        stride: usize,
+        dilation: usize,
+    },
+
     #[allow(dead_code)]
     Conv2D {
         arg: Tensor,
@@ -122,7 +133,11 @@ pub enum Op {
     },
 
     UpsampleNearest1D(Tensor),
-    UpsampleNearest2D(Tensor),
+    UpsampleNearest2D {
+        arg: Tensor,
+        target_h: usize,
+        target_w: usize,
+    },
 
     Cat(Vec<Tensor>, usize),
 
@@ -376,6 +391,7 @@ pub(crate) struct Gelu;
 pub(crate) struct GeluErf;
 pub(crate) struct Erf;
 pub(crate) struct Relu;
+pub(crate) struct Silu;
 pub(crate) struct Tanh;
 pub(crate) struct Floor;
 pub(crate) struct Ceil;
@@ -583,7 +599,8 @@ 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";
@@ -673,6 +690,8 @@ impl UnaryOpT for Gelu {
     }
 }
 
+/// `erf` operation
+/// <https://en.wikipedia.org/wiki/Error_function>
 impl UnaryOpT for Erf {
     const NAME: &'static str = "erf";
     const KERNEL: &'static str = "uerf";
@@ -707,6 +726,77 @@ impl UnaryOpT for Erf {
     }
 }
 
+/// Silu operation
+impl UnaryOpT for Silu {
+    const NAME: &'static str = "silu";
+    const V: Self = Silu;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        v / (bf16::ONE + (-v).exp())
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        v / (f16::ONE + (-v).exp())
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        v / (1.0 + (-v).exp())
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        v / (1.0 + (-v).exp())
+    }
+    #[inline(always)]
+    fn u8(_: u8) -> u8 {
+        0
+    }
+    #[inline(always)]
+    fn u32(_: u32) -> u32 {
+        0
+    }
+    #[inline(always)]
+    fn i64(_: i64) -> i64 {
+        0
+    }
+    const KERNEL: &'static str = "usilu";
+
+    #[cfg(feature = "mkl")]
+    const F32_VEC: bool = true;
+
+    #[cfg(feature = "mkl")]
+    #[inline(always)]
+    fn f32_vec(xs: &[f32], ys: &mut [f32]) {
+        crate::mkl::vs_silu(xs, ys)
+    }
+
+    #[cfg(feature = "mkl")]
+    const F64_VEC: bool = true;
+
+    #[cfg(feature = "mkl")]
+    #[inline(always)]
+    fn f64_vec(xs: &[f64], ys: &mut [f64]) {
+        crate::mkl::vd_silu(xs, ys)
+    }
+
+    #[cfg(feature = "accelerate")]
+    const F32_VEC: bool = true;
+
+    #[cfg(feature = "accelerate")]
+    #[inline(always)]
+    fn f32_vec(xs: &[f32], ys: &mut [f32]) {
+        crate::accelerate::vs_silu(xs, ys)
+    }
+
+    #[cfg(feature = "accelerate")]
+    const F64_VEC: bool = true;
+
+    #[cfg(feature = "accelerate")]
+    #[inline(always)]
+    fn f64_vec(xs: &[f64], ys: &mut [f64]) {
+        crate::accelerate::vd_silu(xs, ys)
+    }
+}
+
 impl UnaryOpT for Abs {
     const NAME: &'static str = "abs";
     const KERNEL: &'static str = "uabs";
@@ -962,6 +1052,10 @@ impl BackpropOp {
         };
         Self(op)
     }
+
+    pub(crate) fn is_none(&self) -> bool {
+        self.0.is_none()
+    }
 }
 
 impl std::ops::Deref for BackpropOp {

candle-core/src/pickle.rs

@@ -217,6 +217,13 @@ impl Object {
                 let args = args.remove(1);
                 (callable, args)
             }
+            Object::Class {
+                module_name,
+                class_name,
+            } if module_name == "torch._utils" && class_name == "_rebuild_parameter" => {
+                let mut args = args.tuple()?;
+                args.remove(0).reduce()?
+            }
             _ => (callable, args),
         };
         match callable {
@@ -227,13 +234,11 @@ impl Object {
             _ => return Ok(None),
         };
         let (layout, dtype, file_path, storage_size) = rebuild_args(args)?;
-        let mut path = dir_name.to_path_buf();
-        path.push(file_path);
         Ok(Some(TensorInfo {
             name,
             dtype,
             layout,
-            path: path.to_string_lossy().into_owned(),
+            path: format!("{}/{}", dir_name.to_string_lossy(), file_path),
             storage_size,
         }))
     }
@@ -345,8 +350,10 @@ impl Stack {
                 module_name,
                 class_name,
             } => {
-                if module_name == "collections" && class_name == "OrderedDict" {
-                    // TODO: have a separate ordered dict.
+                if module_name == "collections"
+                    && (class_name == "OrderedDict" || class_name == "defaultdict")
+                {
+                    // TODO: have a separate ordered dict and a separate default dict.
                     Some(Object::Dict(vec![]))
                 } else {
                     None
@@ -627,9 +634,16 @@ pub struct TensorInfo {
     pub storage_size: usize,
 }
 
+/// Read the tensor info from a .pth file.
+///
+/// # Arguments
+/// * `file` - The path to the .pth file.
+/// * `verbose` - Whether to print debug information.
+/// * `key` - Optional key to retrieve `state_dict` from the pth file.
 pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
     file: P,
     verbose: bool,
+    key: Option<&str>,
 ) -> Result<Vec<TensorInfo>> {
     let file = std::fs::File::open(file)?;
     let zip_reader = std::io::BufReader::new(file);
@@ -651,8 +665,9 @@ pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
         stack.read_loop(&mut reader)?;
         let obj = stack.finalize()?;
         if VERBOSE || verbose {
-            println!("{obj:?}");
+            println!("{obj:#?}");
         }
+
         let obj = match obj {
             Object::Build { callable, args } => match *callable {
                 Object::Reduce { callable, args: _ } => match *callable {
@@ -666,6 +681,24 @@ pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
             },
             obj => obj,
         };
+
+        // If key is provided, then we need to extract the state_dict from the object.
+        let obj = if let Some(key) = key {
+            if let Object::Dict(key_values) = obj {
+                key_values
+                    .into_iter()
+                    .find(|(k, _)| *k == Object::Unicode(key.to_owned()))
+                    .map(|(_, v)| v)
+                    .ok_or_else(|| E::Msg(format!("key {key} not found")))?
+            } else {
+                obj
+            }
+        } else {
+            obj
+        };
+
+        // If the object is a dict, then we can extract the tensor info from it.
+        // NOTE: We are assuming that the `obj` is state_dict by this stage.
         if let Object::Dict(key_values) = obj {
             for (name, value) in key_values.into_iter() {
                 match value.into_tensor_info(name, &dir_name) {
@@ -688,8 +721,8 @@ pub struct PthTensors {
 }
 
 impl PthTensors {
-    pub fn new<P: AsRef<std::path::Path>>(path: P) -> Result<Self> {
-        let tensor_infos = read_pth_tensor_info(path.as_ref(), false)?;
+    pub fn new<P: AsRef<std::path::Path>>(path: P, key: Option<&str>) -> Result<Self> {
+        let tensor_infos = read_pth_tensor_info(path.as_ref(), false, key)?;
         let tensor_infos = tensor_infos
             .into_iter()
             .map(|ti| (ti.name.to_string(), ti))
@@ -703,6 +736,7 @@ 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,
@@ -711,27 +745,56 @@ impl PthTensors {
         let zip_reader = std::io::BufReader::new(std::fs::File::open(&self.path)?);
         let mut zip = zip::ZipArchive::new(zip_reader)?;
         let mut reader = zip.by_name(&tensor_info.path)?;
+        let is_fortran_contiguous = tensor_info.layout.is_fortran_contiguous();
+        let rank = tensor_info.layout.shape().rank();
 
-        // 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 and when the tensor is fortran contiguous.
+        if !tensor_info.layout.is_contiguous() && !is_fortran_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,
             &mut reader,
         )?;
-        Ok(Some(tensor))
+
+        if rank > 1 && is_fortran_contiguous {
+            // Reverse the shape, e.g. Shape(2, 3, 4) -> Shape(4, 3, 2)
+            let shape_reversed: Vec<_> = tensor_info.layout.dims().iter().rev().cloned().collect();
+            let tensor = tensor.reshape(shape_reversed)?;
+
+            // Permute (transpose) the dimensions, e.g. Shape(4, 3, 2) -> Shape(2, 3, 4)
+            let dim_indeces_reversed: Vec<_> = (0..rank).rev().collect();
+            let tensor = tensor.permute(dim_indeces_reversed)?;
+            Ok(Some(tensor))
+        } else {
+            Ok(Some(tensor))
+        }
     }
 }
 
-/// Read all the tensors from a PyTorch pth file.
-pub fn read_all<P: AsRef<std::path::Path>>(path: P) -> Result<Vec<(String, Tensor)>> {
-    let pth = PthTensors::new(path)?;
+/// Read all the tensors from a PyTorch pth file with a given key.
+///
+/// # Arguments
+/// * `path` - Path to the pth file.
+/// * `key` - Optional key to retrieve `state_dict` from the pth file. Sometimes the pth file
+///           contains multiple objects and the state_dict is the one we are interested in.
+pub fn read_all_with_key<P: AsRef<std::path::Path>>(
+    path: P,
+    key: Option<&str>,
+) -> Result<Vec<(String, Tensor)>> {
+    let pth = PthTensors::new(path, key)?;
     let tensor_names = pth.tensor_infos.keys();
     let mut tensors = Vec::with_capacity(tensor_names.len());
     for name in tensor_names {
@@ -741,3 +804,11 @@ pub fn read_all<P: AsRef<std::path::Path>>(path: P) -> Result<Vec<(String, Tenso
     }
     Ok(tensors)
 }
+
+/// Read all the tensors from a PyTorch pth file.
+///
+/// # Arguments
+/// * `path` - Path to the pth file.
+pub fn read_all<P: AsRef<std::path::Path>>(path: P) -> Result<Vec<(String, Tensor)>> {
+    read_all_with_key(path, None)
+}

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/dummy_metal.rs

@@ -0,0 +1,43 @@
+#![allow(unused)]
+use super::GgmlDType;
+use crate::{Error, MetalDevice, MetalStorage, Result};
+
+pub struct QMetalStorage {
+    dtype: GgmlDType,
+    device: MetalDevice,
+}
+
+impl QMetalStorage {
+    pub fn zeros(_: &MetalDevice, _: usize, _: GgmlDType) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    pub fn dtype(&self) -> GgmlDType {
+        self.dtype
+    }
+
+    pub fn device(&self) -> &MetalDevice {
+        &self.device
+    }
+
+    pub fn dequantize(&self, _elem_count: usize) -> Result<MetalStorage> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    pub fn quantize(&mut self, _src: &MetalStorage) -> Result<()> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    pub fn storage_size_in_bytes(&self) -> usize {
+        0
+    }
+
+    pub fn fwd(
+        &self,
+        _self_shape: &crate::Shape,
+        _storage: &MetalStorage,
+        _layout: &crate::Layout,
+    ) -> Result<(MetalStorage, crate::Shape)> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+}

candle-core/src/quantized/ggml_file.rs

@@ -1,6 +1,8 @@
 //! Support for the GGML file format.
 
-use super::{k_quants, GgmlDType};
+#[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;
@@ -126,7 +128,17 @@ fn from_raw_data<T: super::GgmlType + Send + Sync + 'static>(
     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, device)
+    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.
@@ -137,13 +149,13 @@ pub fn qtensor_from_ggml(
     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, device),
@@ -206,7 +218,7 @@ 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)?;
@@ -221,6 +233,7 @@ pub struct Content {
     pub hparams: HParams,
     pub vocab: Vocab,
     pub tensors: HashMap<String, super::QTensor>,
+    pub device: Device,
 }
 
 impl Content {
@@ -240,11 +253,13 @@ impl Content {
             let (name, tensor) = read_one_tensor(reader, magic, device)?;
             tensors.insert(name, tensor);
         }
+        let device = device.clone();
         Ok(Self {
             magic,
             hparams,
             vocab,
             tensors,
+            device,
         })
     }
 

candle-core/src/quantized/gguf_file.rs

@@ -29,6 +29,7 @@ impl TryFrom<u32> for Magic {
 pub enum VersionedMagic {
     GgufV1,
     GgufV2,
+    GgufV3,
 }
 
 impl VersionedMagic {
@@ -39,7 +40,8 @@ impl VersionedMagic {
         let versioned_magic = match (magic, version) {
             (Magic::Gguf, 1) => Self::GgufV1,
             (Magic::Gguf, 2) => Self::GgufV2,
-            _ => crate::bail!("ggml: unsupported magic/version {magic:?}/{version}"),
+            (Magic::Gguf, 3) => Self::GgufV3,
+            _ => crate::bail!("gguf: unsupported magic/version {magic:?}/{version}"),
         };
         Ok(versioned_magic)
     }
@@ -60,13 +62,13 @@ impl TensorInfo {
         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)?;
@@ -90,7 +92,9 @@ pub struct Content {
 fn read_string<R: std::io::Read>(reader: &mut R, magic: &VersionedMagic) -> Result<String> {
     let len = match magic {
         VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
-        VersionedMagic::GgufV2 => reader.read_u64::<LittleEndian>()? as usize,
+        VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
+            reader.read_u64::<LittleEndian>()? as usize
+        }
     };
     let mut v = vec![0u8; len];
     reader.read_exact(&mut v)?;
@@ -290,7 +294,9 @@ impl Value {
                 let value_type = ValueType::from_u32(value_type)?;
                 let len = match magic {
                     VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
-                    VersionedMagic::GgufV2 => reader.read_u64::<LittleEndian>()? as usize,
+                    VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
+                        reader.read_u64::<LittleEndian>()? as usize
+                    }
                 };
                 let mut vs = Vec::with_capacity(len);
                 for _ in 0..len {
@@ -387,11 +393,15 @@ impl Content {
 
         let tensor_count = match magic {
             VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
-            VersionedMagic::GgufV2 => reader.read_u64::<LittleEndian>()? as usize,
+            VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
+                reader.read_u64::<LittleEndian>()? as usize
+            }
         };
         let metadata_kv_count = match magic {
             VersionedMagic::GgufV1 => reader.read_u32::<LittleEndian>()? as usize,
-            VersionedMagic::GgufV2 => reader.read_u64::<LittleEndian>()? as usize,
+            VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
+                reader.read_u64::<LittleEndian>()? as usize
+            }
         };
 
         let mut metadata = HashMap::new();
@@ -413,7 +423,7 @@ impl Content {
                     reader.read_u32_into::<LittleEndian>(&mut dimensions)?;
                     dimensions.into_iter().map(|c| c as usize).collect()
                 }
-                VersionedMagic::GgufV2 => {
+                VersionedMagic::GgufV2 | VersionedMagic::GgufV3 => {
                     let mut dimensions = vec![0; n_dimensions as usize];
                     reader.read_u64_into::<LittleEndian>(&mut dimensions)?;
                     dimensions.into_iter().map(|c| c as usize).collect()
@@ -460,7 +470,7 @@ impl Content {
     ) -> 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, device)
     }
@@ -514,10 +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 +1,234 @@
+use super::{GgmlDType, QStorage};
+use crate::backend::BackendStorage;
+use crate::{DType, MetalDevice, MetalStorage, Result, Shape};
+use metal::Buffer;
+use std::sync::Arc;
+
+pub struct QMetalStorage {
+    dtype: GgmlDType,
+    device: MetalDevice,
+    buffer: Arc<Buffer>,
+}
+
+impl QMetalStorage {
+    pub fn zeros(device: &MetalDevice, elem_count: usize, dtype: GgmlDType) -> Result<Self> {
+        let size = elem_count * dtype.type_size() / dtype.block_size();
+        let buffer = device.allocate_zeros(size)?;
+        Ok(Self {
+            buffer,
+            device: device.clone(),
+            dtype,
+        })
+    }
+
+    pub fn dtype(&self) -> GgmlDType {
+        self.dtype
+    }
+
+    pub fn device(&self) -> &MetalDevice {
+        &self.device
+    }
+
+    pub fn buffer(&self) -> &Buffer {
+        &self.buffer
+    }
+
+    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 storage_size_in_bytes(&self) -> usize {
+        self.buffer.length() as usize
+    }
+
+    pub fn fwd(
+        &self,
+        self_shape: &Shape,
+        storage: &MetalStorage,
+        layout: &crate::Layout,
+    ) -> Result<(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 command_buffer = device.command_buffer()?;
+        candle_metal_kernels::call_quantized_matmul_t(
+            device.device(),
+            &command_buffer,
+            device.kernels(),
+            self.dtype.into(),
+            (b, m, n, k),
+            storage.buffer(),
+            layout.start_offset() * storage.dtype().size_in_bytes(),
+            &self.buffer,
+            &dst,
+        )
+        .map_err(MetalError::from)?;
+        let dst_storage = crate::MetalStorage::new(dst, device, DType::F32);
+        Ok((dst_storage, dst_shape))
+    }
+}
+
+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()
+}
+
+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,
+        }
+    }
+}

candle-core/src/quantized/mod.rs

@@ -1,25 +1,118 @@
-use crate::{Device, Result, Shape, Tensor};
-use tracing::debug;
+use crate::{CpuStorage, Device, Result, Shape, Storage, Tensor};
+use k_quants::*;
+use std::borrow::Cow;
 
 #[cfg(target_feature = "avx")]
 pub mod avx;
+mod dummy_metal;
 pub mod ggml_file;
 pub mod gguf_file;
 pub mod k_quants;
+#[cfg(feature = "metal")]
+pub mod metal;
+#[cfg(not(feature = "metal"))]
+mod metal {
+    pub use super::dummy_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 {
-    device: Device,
-    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))
+            }
+            Device::Metal(metal) => {
+                let storage = metal::QMetalStorage::zeros(metal, elem_count, dtype)?;
+                Ok(QStorage::Metal(storage))
+            }
+            Device::Cuda(_cuda) => {
+                crate::bail!("Cuda ggml quantization not supported");
+            }
+        }
+    }
+}
+
+pub enum QStorage {
+    Cpu(Box<dyn QuantizedType>),
+    Metal(metal::QMetalStorage),
+}
+
+impl QStorage {
+    fn block_size(&self) -> usize {
+        match self {
+            QStorage::Cpu(storage) => storage.block_size(),
+            QStorage::Metal(storage) => storage.dtype().block_size(),
+        }
+    }
+
+    fn dtype(&self) -> GgmlDType {
+        match self {
+            QStorage::Cpu(storage) => storage.dtype(),
+            QStorage::Metal(storage) => storage.dtype(),
+        }
+    }
+
+    fn device(&self) -> Device {
+        match self {
+            QStorage::Cpu(_storage) => Device::Cpu,
+            QStorage::Metal(storage) => Device::Metal(storage.device().clone()),
+        }
+    }
+
+    fn size_in_bytes(&self) -> usize {
+        match self {
+            QStorage::Cpu(storage) => storage.storage_size_in_bytes(),
+            QStorage::Metal(storage) => storage.storage_size_in_bytes(),
+        }
+    }
+
+    fn quantize(&mut self, src: &Storage) -> Result<()> {
+        match (self, src) {
+            (QStorage::Cpu(storage), Storage::Cpu(src)) => {
+                storage.from_float(src.as_slice::<f32>()?)?;
+            }
+            (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)?)),
+            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))
+            }
+            QStorage::Metal(_storage) => {
+                crate::bail!("not implemented");
+            }
+        }
+    }
+}
+
 #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
 pub enum GgmlDType {
     F32,
@@ -79,6 +172,25 @@ 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::*;
@@ -102,7 +214,7 @@ 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,
@@ -121,9 +233,13 @@ 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> {
@@ -131,12 +247,26 @@ 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 {
@@ -154,60 +284,53 @@ 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,
-        device: &Device,
-    ) -> 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,
-            device: device.clone(),
-        })
+        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();
-        let device = src.device();
-        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(),
-            device: device.clone(),
         })
     }
 
     pub fn dtype(&self) -> GgmlDType {
-        self.data.dtype()
+        self.storage.dtype()
+    }
+
+    pub fn device(&self) -> Device {
+        self.storage.device()
     }
 
     pub fn rank(&self) -> usize {
@@ -218,27 +341,20 @@ impl QTensor {
         &self.shape
     }
 
-    pub fn device(&self) -> &Device {
-        &self.device
-    }
-
     pub fn dequantize(&self, device: &Device) -> Result<Tensor> {
-        // TODO Skip the CPU part on metal
-        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()
     }
 }
 
@@ -305,15 +421,15 @@ 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,
+            QStorage::Metal(_) => crate::bail!("Invalid storage"),
+        };
+        let slice = storage.as_slice::<f32>()?;
+        let slice = &slice[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
         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))
     }
 
@@ -322,44 +438,16 @@ impl crate::CustomOp1 for QTensor {
         storage: &crate::MetalStorage,
         layout: &crate::Layout,
     ) -> Result<(crate::MetalStorage, Shape)> {
-        debug!("TODO qmatmul");
-        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.
-        let (n, k) = self.shape.dims2()?;
-        if src_shape.rank() < 2 {
-            crate::bail!("input tensor has only one dimension {layout:?}")
-        }
-        let mut dst_shape = src_shape.dims().to_vec();
-        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 storage = storage.as_slice::<f32>()?;
-        // let storage =
-        //     &storage[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
-        let dst_storage = vec![0f32; dst_shape.elem_count()];
-        // self.matmul_t(
-        //     (dst_shape.elem_count() / n, k, n),
-        //     storage,
-        //     &mut dst_storage,
-        // )?;
-        let cpu_storage = crate::CpuStorage::F32(dst_storage);
-        use crate::backend::{BackendDevice, BackendStorage};
-        if let Device::Metal(device) = &self.device {
-            Ok((device.storage_from_cpu_storage(&cpu_storage)?, dst_shape))
-        } else {
-            crate::bail!("qtensor not on metal device")
-        }
+        let self_storage = match &self.storage {
+            QStorage::Metal(metal) => metal,
+            _ => unreachable!("Cannot call metal matmul on non metal QTensor"),
+        };
+        self_storage.fwd(&self.shape, storage, layout)
     }
 }
 
-impl QMatMul {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+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/storage.rs

@@ -334,6 +334,33 @@ impl Storage {
         }
     }
 
+    pub(crate) fn conv_transpose1d(
+        &self,
+        l: &Layout,
+        kernel: &Self,
+        kernel_l: &Layout,
+        params: &crate::conv::ParamsConvTranspose1D,
+    ) -> Result<Self> {
+        self.same_device(kernel, "conv-transpose1d")?;
+        self.same_dtype(kernel, "conv-transpose1d")?;
+        match (self, &kernel) {
+            (Storage::Cpu(inp), Storage::Cpu(kernel)) => {
+                let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
+                Ok(Self::Cpu(s))
+            }
+            (Storage::Cuda(inp), Storage::Cuda(kernel)) => {
+                let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
+                Ok(Self::Cuda(s))
+            }
+            (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
+                lhs: lhs.device().location(),
+                rhs: rhs.device().location(),
+                op: "conv-transpose1d",
+            }
+            .bt()),
+        }
+    }
+
     pub(crate) fn conv2d(
         &self,
         l: &Layout,

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::{
@@ -361,6 +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>,
@@ -386,7 +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;
@@ -477,6 +487,12 @@ impl Tensor {
     broadcast_binary_op!(broadcast_div, div);
     broadcast_binary_op!(broadcast_maximum, maximum);
     broadcast_binary_op!(broadcast_minimum, minimum);
+    broadcast_binary_op!(broadcast_eq, eq);
+    broadcast_binary_op!(broadcast_ne, ne);
+    broadcast_binary_op!(broadcast_lt, lt);
+    broadcast_binary_op!(broadcast_le, le);
+    broadcast_binary_op!(broadcast_gt, gt);
+    broadcast_binary_op!(broadcast_ge, ge);
 
     unary_op!(recip, Recip);
     unary_op!(neg, Neg);
@@ -492,6 +508,7 @@ impl Tensor {
     unary_op!(gelu_erf, GeluErf);
     unary_op!(erf, Erf);
     unary_op!(relu, Relu);
+    unary_op!(silu, Silu);
     unary_op!(ceil, Ceil);
     unary_op!(floor, Floor);
     unary_op!(round, Round);
@@ -663,7 +680,7 @@ 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)?;
@@ -788,6 +805,35 @@ impl Tensor {
         }
     }
 
+    /// Roll the tensor input along the given dimension.
+    /// Elements that are shifted beyond the last position are re-introduced at the first position.
+    ///
+    /// ```rust
+    /// # use candle_core::{Tensor, Device};
+    /// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
+    /// let tensor = tensor.roll(1, 0)?;
+    /// assert_eq!(tensor.to_vec2::<f32>()?, &[[4., 5.], [0., 1.], [2., 3.]]);
+    /// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
+    /// let tensor = tensor.roll(-1, 0)?;
+    /// assert_eq!(tensor.to_vec2::<f32>()?, &[[2., 3.], [4., 5.], [0., 1.]]);
+    /// # Ok::<(), candle_core::Error>(())
+    /// ```
+    pub fn roll<D>(&self, shift: i32, dim: D) -> Result<Self>
+    where
+        D: Dim + Clone,
+    {
+        let dim = dim.to_index(self.shape(), "roll")?;
+        let dim_size = self.dim(dim)?;
+        let shift = shift.rem_euclid(dim_size as i32) as usize;
+        if shift == 0 {
+            Ok(self.clone())
+        } else {
+            let a = self.narrow(dim, 0, dim_size - shift)?;
+            let b = self.narrow(dim, dim_size - shift, shift)?;
+            Tensor::cat(&[&b, &a], dim)
+        }
+    }
+
     /// Returns the sum of all elements in the input tensor. The sum is performed over all the
     /// input dimensions.
     ///
@@ -850,6 +896,20 @@ 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> {
@@ -974,7 +1034,11 @@ 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)?;
@@ -1007,6 +1071,9 @@ 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;
@@ -1042,6 +1109,9 @@ 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;
@@ -1764,7 +1834,7 @@ 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
@@ -1811,17 +1881,23 @@ impl Tensor {
 
     /// Returns a new tensor detached from the current graph, gradient are not propagated through
     /// this new node. The storage of this tensor is shared with the initial tensor.
-    pub fn detach(&self) -> Result<Tensor> {
-        let tensor_ = Tensor_ {
-            id: TensorId::new(),
-            storage: self.storage.clone(),
-            layout: self.layout.clone(),
-            op: BackpropOp::none(),
-            is_variable: false,
-            dtype: self.dtype,
-            device: self.device.clone(),
-        };
-        Ok(Tensor(Arc::new(tensor_)))
+    ///
+    /// If the tensor is already detached from the computation graph, the same tensor is returned.
+    pub fn detach(&self) -> Tensor {
+        if self.op.is_none() && !self.is_variable {
+            self.clone()
+        } else {
+            let tensor_ = Tensor_ {
+                id: TensorId::new(),
+                storage: self.storage.clone(),
+                layout: self.layout.clone(),
+                op: BackpropOp::none(),
+                is_variable: false,
+                dtype: self.dtype,
+                device: self.device.clone(),
+            };
+            Tensor(Arc::new(tensor_))
+        }
     }
 
     /// If the target device is the same as the tensor device, only a shallow copy is performed.
@@ -1833,7 +1909,11 @@ impl Tensor {
                 (Storage::Cpu(storage), Device::Cuda(cuda)) => {
                     Storage::Cuda(cuda.storage_from_cpu_storage(storage)?)
                 }
+                (Storage::Cpu(storage), Device::Metal(metal)) => {
+                    Storage::Metal(metal.storage_from_cpu_storage(storage)?)
+                }
                 (Storage::Cuda(storage), Device::Cpu) => Storage::Cpu(storage.to_cpu_storage()?),
+                (Storage::Metal(storage), Device::Cpu) => Storage::Cpu(storage.to_cpu_storage()?),
                 (Storage::Cuda(storage), Device::Cuda(cuda)) => {
                     // TODO: Avoid passing through the cpu storage here, especially if the gpu ids
                     // are the same.
@@ -2249,7 +2329,7 @@ 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)?;
@@ -2407,6 +2487,142 @@ impl Tensor {
     ) -> Result<Self> {
         self.apply_op3_arc(t2, t3, Arc::new(Box::new(c)))
     }
+
+    /// Normalize a 'relative' axis value: positive values are kept, negative
+    /// values means counting the dimensions from the back.
+    pub fn normalize_axis(&self, axis: i64) -> Result<usize> {
+        let rank = self.rank() as i64;
+        if rank <= axis {
+            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 {
+                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/src/variable.rs

@@ -107,6 +107,10 @@ impl Var {
         Ok(Self(inner))
     }
 
+    pub fn as_detached_tensor(&self) -> Tensor {
+        self.0.detach()
+    }
+
     pub fn as_tensor(&self) -> &Tensor {
         &self.0
     }

candle-core/tests/conv_tests.rs

@@ -13,6 +13,11 @@ res = torch.nn.functional.conv1d(t, w)
 print(res.flatten())
 res = torch.nn.functional.conv1d(t, w, padding=1)
 print(res.flatten())
+
+w_t = w.transpose(0, 1)
+res = torch.nn.functional.conv_transpose1d(t, w_t)
+print(res.shape)
+print(res)
 */
 fn conv1d(dev: &Device) -> Result<()> {
     let t = Tensor::new(
@@ -45,6 +50,15 @@ fn conv1d(dev: &Device) -> Result<()> {
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [2.4509, 2.6357, -1.3336, 4.1393, 0.5657, 1.8091, -1.1784, 3.5675, 0.5069, 3.3352]
     );
+    let res = t.conv_transpose1d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
+    assert_eq!(res.dims(), [1, 2, 7]);
+    assert_eq!(
+        test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
+        [
+            0.0699, -1.2899, 8.3018, 5.5873, 2.4572, -2.6143, -0.0706, 1.8765, 4.8318, 1.1538,
+            4.7076, -5.9745, -0.8276, 1.621
+        ],
+    );
     Ok(())
 }
 
@@ -547,14 +561,35 @@ 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

@@ -205,6 +205,244 @@ fn unary_grad(device: &Device) -> Result<()> {
         test_utils::to_vec1_round(grad_x, 4)?,
         [1.0116, 1.0830, 1.0003, 0.6188],
     );
+
+    // Testing compared to pytorch torch.erf
+    //
+    // import torch
+    // x = torch.tensor([3.0, 1.0, 4.0, 0.15], requires_grad=True)
+    // y = x.erf()
+    // print(y)
+    // loss = y.sum()
+    // loss.backward()
+    // print(x.grad)
+    let y = x.erf()?;
+    let grads = y.backward()?;
+    let grad_x = grads.get(&x).context("no grad for x")?;
+    assert_eq!(test_utils::to_vec1_round(&y, 4)?, [1.0, 0.8427, 1.0, 0.168]);
+    assert_eq!(
+        test_utils::to_vec1_round(grad_x, 4)?,
+        [0.0001, 0.4151, 0.0, 1.1033],
+    );
+
+    // Testing compared to pytorch nn.GELU(approximate = 'none')
+    //
+    // import torch
+    // import torch.nn.functional as F
+    // x = torch.tensor([3.0, 1.0, 4.0, 0.15], requires_grad=True)
+    // y = F.gelu(x, approximate='none')
+    // print(y)
+    // loss = y.sum()
+    // loss.backward()
+    // print(x.grad)
+    let y = x.gelu_erf()?;
+    let grads = y.backward()?;
+    let grad_x = grads.get(&x).context("no grad for x")?;
+    assert_eq!(
+        test_utils::to_vec1_round(&y, 4)?,
+        [2.9960, 0.8413, 3.9999, 0.0839]
+    );
+    assert_eq!(
+        test_utils::to_vec1_round(grad_x, 4)?,
+        [1.0119, 1.0833, 1.0005, 0.6188],
+    );
+
+    // Testing compared to pytorch elu
+    //
+    // import torch
+    // import torch.nn.functional as F
+    // x = torch.tensor([-1.0, 0.0, -2.0, 3.0], requires_grad=True)
+    // y = F.elu(x, alpha=2.0)
+    // print(y)
+    // loss = y.min
+    // loss = y.sum()
+    // loss.backward()
+    // print(x.grad)
+    let elu_x = Var::new(&[-1.0f32, 0., -2., 3.], device)?;
+    let y = elu_x.elu(2.)?;
+    let grads = y.backward()?;
+    let grad_x = grads.get(&elu_x).context("no grad for x")?;
+    assert_eq!(
+        test_utils::to_vec1_round(&y, 4)?,
+        [-1.2642, 0.0000, -1.7293, 3.0000]
+    );
+    assert_eq!(
+        test_utils::to_vec1_round(grad_x, 4)?,
+        [0.7358, 2.0000, 0.2707, 1.0000]
+    );
+
+    // testing compared to pytorch nn.Silu()
+    let y = x.silu()?;
+    let grads = y.backward()?;
+    let grad_x = grads.get(&x).context("no grad for x")?;
+    assert_eq!(
+        test_utils::to_vec1_round(&y, 4)?,
+        [2.8577, 0.7311, 3.9281, 0.0806]
+    );
+    assert_eq!(
+        test_utils::to_vec1_round(grad_x, 4)?,
+        [1.0881, 0.9277, 1.0527, 0.5747],
+    );
+
+    // 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(())
 }
 
@@ -250,9 +488,29 @@ 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/pth.py

@@ -0,0 +1,37 @@
+import torch
+from collections import OrderedDict
+
+# Write a trivial tensor to a pt file
+a= torch.tensor([[1,2,3,4], [5,6,7,8]])
+o = OrderedDict()
+o["test"] = a
+
+# Write a trivial tensor to a pt file
+torch.save(o, "test.pt")
+
+############################################################################################################
+# Write a trivial tensor to a pt file with a key
+torch.save({"model_state_dict": o}, "test_with_key.pt")
+
+############################################################################################################
+# Create a tensor with fortran contiguous memory layout
+import numpy as np
+
+# Step 1: Create a 3D NumPy array with Fortran order using a range of numbers
+# For example, creating a 2x3x4 array
+array_fortran = np.asfortranarray(np.arange(1, 2*3*4 + 1).reshape(2, 3, 4))
+
+# Verify the memory order
+print("Is Fortran contiguous (F order):", array_fortran.flags['F_CONTIGUOUS'])  # Should be True
+print("Is C contiguous (C order):", array_fortran.flags['C_CONTIGUOUS'])  # Should be False
+
+# Step 2: Convert the NumPy array to a PyTorch tensor
+tensor_fortran = torch.from_numpy(array_fortran)
+
+# Verify the tensor layout
+print("Tensor stride:", tensor_fortran.stride())  # Stride will reflect the Fortran memory layout
+
+# Step 3: Save the PyTorch tensor to a .pth file
+torch.save({"tensor_fortran": tensor_fortran}, 'fortran_tensor_3d.pth')
+
+print("3D Tensor saved with Fortran layout.")

candle-core/tests/pth_tests.rs

@@ -0,0 +1,31 @@
+/// Regression test for pth files not loading on Windows.
+#[test]
+fn test_pth() {
+    let tensors = candle_core::pickle::PthTensors::new("tests/test.pt", None).unwrap();
+    tensors.get("test").unwrap().unwrap();
+}
+
+#[test]
+fn test_pth_with_key() {
+    let tensors =
+        candle_core::pickle::PthTensors::new("tests/test_with_key.pt", Some("model_state_dict"))
+            .unwrap();
+    tensors.get("test").unwrap().unwrap();
+}
+
+#[test]
+fn test_pth_fortran_congiguous() {
+    let tensors =
+        candle_core::pickle::PthTensors::new("tests/fortran_tensor_3d.pth", None).unwrap();
+    let tensor = tensors.get("tensor_fortran").unwrap().unwrap();
+
+    assert_eq!(tensor.dims3().unwrap(), (2, 3, 4));
+
+    assert_eq!(
+        tensor.to_vec3::<i64>().unwrap(),
+        [
+            [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]],
+            [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]
+        ]
+    );
+}

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>()?,
@@ -112,6 +120,13 @@ fn unary_op(device: &Device) -> Result<()> {
             [0.9999, -0.9891, -0.3079, 0.9891, 0.9999]
         ]
     );
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.silu()?, 4)?,
+        [
+            [-0.1423, 0.7311, 3.9281, -0.0475, 0.3112],
+            [2.53, -0.2553, -0.1205, 1.5447, 2.6395]
+        ]
+    );
     assert_eq!(
         test_utils::to_vec2_round(&tensor.ceil()?, 4)?,
         [[-3.0, 1.0, 4.0, -0.0, 1.0], [3.0, -1.0, -0.0, 2.0, 3.0]]
@@ -180,6 +195,22 @@ 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 +1085,61 @@ 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 +1175,100 @@ 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-datasets/src/vision/cifar.rs

@@ -4,7 +4,9 @@
 //! <https://www.cs.toronto.edu/~kriz/cifar.html>
 //! The binary version of the dataset is used.
 use crate::vision::Dataset;
-use candle::{DType, Device, Result, Tensor};
+use candle::{DType, Device, Error, Result, Tensor};
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use parquet::file::reader::{FileReader, SerializedFileReader};
 use std::fs::File;
 use std::io::{BufReader, Read};
 
@@ -60,3 +62,58 @@ pub fn load_dir<T: AsRef<std::path::Path>>(dir: T) -> Result<Dataset> {
         labels: 10,
     })
 }
+
+fn load_parquet(parquet: SerializedFileReader<std::fs::File>) -> Result<(Tensor, Tensor)> {
+    let samples = parquet.metadata().file_metadata().num_rows() as usize;
+    let mut buffer_images: Vec<u8> = Vec::with_capacity(samples * 1_024);
+    let mut buffer_labels: Vec<u8> = Vec::with_capacity(samples);
+    for row in parquet.into_iter().flatten() {
+        for (_name, field) in row.get_column_iter() {
+            if let parquet::record::Field::Group(subrow) = field {
+                for (_name, field) in subrow.get_column_iter() {
+                    if let parquet::record::Field::Bytes(value) = field {
+                        let image = image::load_from_memory(value.data()).unwrap();
+                        buffer_images.extend(image.to_rgb8().as_raw());
+                    }
+                }
+            } else if let parquet::record::Field::Long(label) = field {
+                buffer_labels.push(*label as u8);
+            }
+        }
+    }
+    let images = (Tensor::from_vec(buffer_images, (samples, 3, 32, 32), &Device::Cpu)?
+        .to_dtype(DType::U8)?
+        / 255.)?;
+    let labels = Tensor::from_vec(buffer_labels, (samples,), &Device::Cpu)?;
+    Ok((images, labels))
+}
+
+pub fn load() -> Result<Dataset> {
+    let api = Api::new().map_err(|e| Error::Msg(format!("Api error: {e}")))?;
+    let dataset_id = "cifar10".to_string();
+    let repo = Repo::with_revision(
+        dataset_id,
+        RepoType::Dataset,
+        "refs/convert/parquet".to_string(),
+    );
+    let repo = api.repo(repo);
+    let test_parquet_filename = repo
+        .get("plain_text/test/0000.parquet")
+        .map_err(|e| Error::Msg(format!("Api error: {e}")))?;
+    let train_parquet_filename = repo
+        .get("plain_text/train/0000.parquet")
+        .map_err(|e| Error::Msg(format!("Api error: {e}")))?;
+    let test_parquet = SerializedFileReader::new(std::fs::File::open(test_parquet_filename)?)
+        .map_err(|e| Error::Msg(format!("Parquet error: {e}")))?;
+    let train_parquet = SerializedFileReader::new(std::fs::File::open(train_parquet_filename)?)
+        .map_err(|e| Error::Msg(format!("Parquet error: {e}")))?;
+    let (test_images, test_labels) = load_parquet(test_parquet)?;
+    let (train_images, train_labels) = load_parquet(train_parquet)?;
+    Ok(crate::vision::Dataset {
+        train_images,
+        train_labels,
+        test_images,
+        test_labels,
+        labels: 10,
+    })
+}

candle-examples/Cargo.toml

@@ -11,13 +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 = { 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 }
@@ -26,13 +30,14 @@ rayon = { workspace = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
+symphonia = { version = "0.5.3", features = ["all"] }
 tokenizers = { workspace = true, features = ["onig"] }
+cpal= { version = "0.15.2", optional = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
 byteorder = { workspace = true }
 clap = { workspace = true }
-hf-hub = { workspace = true, features=["tokio"]}
 imageproc = { workspace = true }
 memmap2 = { workspace = true }
 rand = { workspace = true }
@@ -40,22 +45,24 @@ rusttype = { workspace = true }
 tracing = { workspace = true }
 tracing-chrome = { workspace = true }
 tracing-subscriber = { workspace = true }
-wav = { workspace = true }
 # Necessary to disambiguate with tokio in wasm examples which are 1.28.1
 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"]
-metal = ["candle/metal", "candle-nn/metal", "candle-transformers/metal"]
+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"]
+microphone = ["cpal"]
 
 [[example]]
 name = "llama_multiprocess"
@@ -64,3 +71,15 @@ required-features = ["cuda", "nccl", "flash-attn"]
 [[example]]
 name = "reinforcement-learning"
 required-features = ["pyo3"]
+
+[[example]]
+name = "onnx"
+required-features = ["onnx"]
+
+[[example]]
+name = "onnx_basics"
+required-features = ["onnx"]
+
+[[example]]
+name = "whisper-microphone"
+required-features = ["microphone"]

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/chatglm/main.rs

@@ -0,0 +1,237 @@
+#[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::chatglm::{Config, Model};
+
+use candle::{DType, Device, Tensor};
+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: Tokenizer,
+    logits_processor: LogitsProcessor,
+    repeat_penalty: f32,
+    repeat_last_n: usize,
+    verbose_prompt: bool,
+}
+
+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,
+        verbose_prompt: bool,
+        device: &Device,
+    ) -> Self {
+        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
+        Self {
+            model,
+            tokenizer,
+            logits_processor,
+            repeat_penalty,
+            repeat_last_n,
+            verbose_prompt,
+            device: device.clone(),
+        }
+    }
+
+    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
+        use std::io::Write;
+        println!("starting the inference loop");
+        let tokens = self.tokenizer.encode(prompt, true).map_err(E::msg)?;
+        if tokens.is_empty() {
+            anyhow::bail!("Empty prompts are not supported in the chatglm model.")
+        }
+        if self.verbose_prompt {
+            for (token, id) in tokens.get_tokens().iter().zip(tokens.get_ids().iter()) {
+                let token = token.replace('▁', " ").replace("<0x0A>", "\n");
+                println!("{id:7} -> '{token}'");
+            }
+        }
+        let mut tokens = tokens.get_ids().to_vec();
+        let mut generated_tokens = 0usize;
+        let eos_token = match self.tokenizer.get_vocab(true).get("</s>") {
+            Some(token) => *token,
+            None => anyhow::bail!("cannot find the endoftext token"),
+        };
+        print!("{prompt}");
+        std::io::stdout().flush()?;
+        let start_gen = std::time::Instant::now();
+        for index in 0..sample_len {
+            let context_size = if index > 0 { 1 } else { tokens.len() };
+            let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
+            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
+            let logits = self.model.forward(&input)?;
+            let logits = logits.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;
+            }
+            let token = self.tokenizer.decode(&[next_token], true).map_err(E::msg)?;
+            print!("{token}");
+            std::io::stdout().flush()?;
+        }
+        let dt = start_gen.elapsed();
+        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,
+
+    /// Display the token for the specified prompt.
+    #[arg(long)]
+    verbose_prompt: 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)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    #[arg(long)]
+    weight_file: Option<String>,
+
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// The context size to consider for the repeat penalty.
+    #[arg(long, default_value_t = 64)]
+    repeat_last_n: usize,
+}
+
+fn main() -> Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+    println!(
+        "avx: {}, neon: {}, simd128: {}, f16c: {}",
+        candle::utils::with_avx(),
+        candle::utils::with_neon(),
+        candle::utils::with_simd128(),
+        candle::utils::with_f16c()
+    );
+    println!(
+        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
+        args.temperature.unwrap_or(0.),
+        args.repeat_penalty,
+        args.repeat_last_n
+    );
+
+    let start = std::time::Instant::now();
+    let api = Api::new()?;
+    let model_id = match args.model_id {
+        Some(model_id) => model_id.to_string(),
+        None => "THUDM/chatglm3-6b".to_string(),
+    };
+    let revision = match args.revision {
+        Some(rev) => rev.to_string(),
+        None => "main".to_string(),
+    };
+    let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
+    let tokenizer_filename = match args.tokenizer {
+        Some(file) => std::path::PathBuf::from(file),
+        None => api
+            .model("lmz/candle-chatglm".to_string())
+            .get("chatglm-tokenizer.json")?,
+    };
+    let filenames = match args.weight_file {
+        Some(weight_file) => vec![std::path::PathBuf::from(weight_file)],
+        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::glm3_6b();
+    let device = candle_examples::device(args.cpu)?;
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
+    let model = Model::new(&config, vb)?;
+
+    println!("loaded the model in {:?}", start.elapsed());
+
+    let mut pipeline = TextGeneration::new(
+        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)?;
+    Ok(())
+}

candle-examples/examples/convnext/README.md

@@ -0,0 +1,23 @@
+# candle-convnext
+
+[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) and
+[ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808).
+
+This candle implementation uses a pre-trained ConvNeXt 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 convnext --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which tiny
+
+loaded image Tensor[dims 3, 224, 224; f32]
+model built
+mountain bike, all-terrain bike, off-roader: 84.09%
+bicycle-built-for-two, tandem bicycle, tandem: 4.15%
+maillot                 : 0.74%
+crash helmet            : 0.54%
+unicycle, monocycle     : 0.44%
+
+```

candle-examples/examples/convnext/main.rs

@@ -0,0 +1,126 @@
+#[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::convnext;
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    Atto,
+    Femto,
+    Pico,
+    Nano,
+    Tiny,
+    Small,
+    Base,
+    Large,
+    AttoV2,
+    FemtoV2,
+    PicoV2,
+    NanoV2,
+    TinyV2,
+    BaseV2,
+    LargeV2,
+    XLarge,
+    Huge,
+}
+
+impl Which {
+    fn model_filename(&self) -> String {
+        let name = match self {
+            Self::Atto => "convnext_atto.d2_in1k",
+            Self::Femto => "convnext_femto.d1_in1k",
+            Self::Pico => "convnext_pico.d1_in1k",
+            Self::Nano => "convnext_nano.d1h_in1k",
+            Self::Tiny => "convnext_tiny.fb_in1k",
+            Self::Small => "convnext_small.fb_in1k",
+            Self::Base => "convnext_base.fb_in1k",
+            Self::Large => "convnext_large.fb_in1k",
+            Self::AttoV2 => "convnextv2_atto.fcmae_ft_in1k",
+            Self::FemtoV2 => "convnextv2_femto.fcmae_ft_in1k",
+            Self::PicoV2 => "convnextv2_pico.fcmae_ft_in1k",
+            Self::NanoV2 => "convnextv2_nano.fcmae_ft_in1k",
+            Self::TinyV2 => "convnextv2_tiny.fcmae_ft_in1k",
+            Self::BaseV2 => "convnextv2_base.fcmae_ft_in1k",
+            Self::LargeV2 => "convnextv2_large.fcmae_ft_in1k",
+            Self::XLarge => "convnext_xlarge.fb_in22k_ft_in1k",
+            Self::Huge => "convnextv2_huge.fcmae_ft_in1k",
+        };
+
+        format!("timm/{name}")
+    }
+
+    fn config(&self) -> convnext::Config {
+        match self {
+            Self::Atto | Self::AttoV2 => convnext::Config::atto(),
+            Self::Femto | Self::FemtoV2 => convnext::Config::femto(),
+            Self::Pico | Self::PicoV2 => convnext::Config::pico(),
+            Self::Nano | Self::NanoV2 => convnext::Config::nano(),
+            Self::Tiny | Self::TinyV2 => convnext::Config::tiny(),
+            Self::Small => convnext::Config::small(),
+            Self::Base | Self::BaseV2 => convnext::Config::base(),
+            Self::Large | Self::LargeV2 => convnext::Config::large(),
+            Self::XLarge => convnext::Config::xlarge(),
+            Self::Huge => convnext::Config::huge(),
+        }
+    }
+}
+
+#[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::Tiny)]
+    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 = convnext::convnext(&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/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())?);

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 +1,15 @@
+# candle-mamba-minimal: minimal implementation of Mamba
+
+This is based on [mamba-minimal](https://github.com/johnma2006/mamba-minimal).
+
+Compared to the mamba example, this version can handle training but is much
+slower.
+
+## 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/mamba/README.md

@@ -0,0 +1,17 @@
+# candle-mamba: Mamba implementation
+
+Candle implementation of *Mamba* [1] inference only. Mamba is an alternative to
+the transformer architecture. It leverages State Space Models (SSMs) with the
+goal of being computationally efficient on long sequences. The implementation is
+based on [mamba.rs](https://github.com/LaurentMazare/mamba.rs).
+
+- [1]. [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://arxiv.org/abs/2312.00752).
+
+Compared to the mamba-minimal example, this version is far more efficient but
+would only work for inference.
+## Running the example
+
+```bash
+$ cargo run --example mamba-minimal --release -- --prompt "Mamba is the"
+```
+

candle-examples/examples/mamba/main.rs

@@ -0,0 +1,299 @@
+#[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::mamba::{Config, Model, State};
+
+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,
+    config: Config,
+    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,
+        config: Config,
+        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,
+            config,
+            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();
+        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 mut state = State::new(1, &self.config, &self.device)?;
+        let mut next_logits = None;
+        for &t in tokens.iter() {
+            let input = Tensor::new(&[t], &self.device)?;
+            let logits = self.model.forward(&input, &mut state)?;
+            next_logits = Some(logits);
+            if let Some(t) = self.tokenizer.next_token(t)? {
+                print!("{t}")
+            }
+        }
+        std::io::stdout().flush()?;
+
+        let start_gen = std::time::Instant::now();
+        for _ in 0..sample_len {
+            let logits = match next_logits.as_ref() {
+                Some(logits) => logits,
+                None => anyhow::bail!("cannot work on an empty prompt"),
+            };
+            let logits = logits.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 input = Tensor::new(&[next_token], &self.device)?;
+            next_logits = Some(self.model.forward(&input, &mut state)?)
+        }
+        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,
+        config,
+        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/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/mobileone/README.md

@@ -0,0 +1,22 @@
+# candle-mobileone
+
+[MobileOne: An Improved One millisecond Mobile Backbone](https://arxiv.org/abs/2206.04040).
+
+This candle implementation uses a pre-trained MobileOne 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 mobileone --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which s2
+
+loaded image Tensor[dims 3, 224, 224; f32]
+model built
+mountain bike, all-terrain bike, off-roader: 79.33%
+bicycle-built-for-two, tandem bicycle, tandem: 15.32%
+crash helmet            : 2.58%
+unicycle, monocycle     : 1.70%
+alp                     : 0.21%
+
+```

candle-examples/examples/mobileone/main.rs

@@ -0,0 +1,96 @@
+#[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::mobileone;
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    S0,
+    S1,
+    S2,
+    S3,
+    S4,
+}
+
+impl Which {
+    fn model_filename(&self) -> String {
+        let name = match self {
+            Self::S0 => "s0",
+            Self::S1 => "s1",
+            Self::S2 => "s2",
+            Self::S3 => "s3",
+            Self::S4 => "s4",
+        };
+        format!("timm/mobileone_{}.apple_in1k", name)
+    }
+
+    fn config(&self) -> mobileone::Config {
+        match self {
+            Self::S0 => mobileone::Config::s0(),
+            Self::S1 => mobileone::Config::s1(),
+            Self::S2 => mobileone::Config::s2(),
+            Self::S3 => mobileone::Config::s3(),
+            Self::S4 => mobileone::Config::s4(),
+        }
+    }
+}
+
+#[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::S0)]
+    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 = mobileone::mobileone(&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/musicgen/encodec_model.rs

@@ -8,6 +8,7 @@ use candle_nn::{conv1d, Conv1d, Conv1dConfig, VarBuilder};
 #[derive(Debug, Clone, PartialEq)]
 enum NormType {
     WeightNorm,
+    TimeGroupNorm,
     None,
 }
 
@@ -268,6 +269,7 @@ impl Module for EncodecConvTranspose1d {
 struct EncodecConv1d {
     causal: bool,
     conv: Conv1d,
+    norm: Option<candle_nn::GroupNorm>,
 }
 
 impl EncodecConv1d {
@@ -292,7 +294,7 @@ impl EncodecConv1d {
                 },
                 vb.pp("conv"),
             )?,
-            NormType::None => conv1d(
+            NormType::None | NormType::TimeGroupNorm => conv1d(
                 in_c,
                 out_c,
                 kernel_size,
@@ -305,9 +307,17 @@ impl EncodecConv1d {
                 vb.pp("conv"),
             )?,
         };
+        let norm = match cfg.norm_type {
+            NormType::None | NormType::WeightNorm => None,
+            NormType::TimeGroupNorm => {
+                let gn = candle_nn::group_norm(1, out_c, 1e-5, vb.pp("norm"))?;
+                Some(gn)
+            }
+        };
         Ok(Self {
             causal: cfg.use_causal_conv,
             conv,
+            norm,
         })
     }
 }
@@ -316,8 +326,10 @@ impl Module for EncodecConv1d {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         // TODO: padding, depending on causal.
         let xs = self.conv.forward(xs)?;
-        // If we add support for NormType "time_group_norm", we should add some normalization here.
-        Ok(xs)
+        match &self.norm {
+            None => Ok(xs),
+            Some(norm) => xs.apply(norm),
+        }
     }
 }
 

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/onnx/README.md

@@ -0,0 +1,39 @@
+## Using ONNX models in Candle
+
+This example demonstrates how to run [ONNX](https://github.com/onnx/onnx) based models in Candle.
+
+It contains small variants of two models, [SqueezeNet](https://arxiv.org/pdf/1602.07360.pdf) (default) and [EfficientNet](https://arxiv.org/pdf/1905.11946.pdf).
+
+You can run the examples with following commands:
+
+```bash
+cargo run --example onnx --features=onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+```
+
+Use the `--which` flag to specify explicitly which network to use, i.e.
+
+```bash
+$ cargo run --example onnx --features=onnx --release -- --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg
+
+    Finished release [optimized] target(s) in 0.21s
+     Running `target/release/examples/onnx --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
+loaded image Tensor[dims 3, 224, 224; f32]
+unicycle, monocycle                               : 83.23%
+ballplayer, baseball player                       : 3.68%
+bearskin, busby, shako                            : 1.54%
+military uniform                                  : 0.78%
+cowboy hat, ten-gallon hat                        : 0.76%
+```
+
+```bash
+$ cargo run --example onnx --features=onnx --release -- --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg
+
+    Finished release [optimized] target(s) in 0.20s
+     Running `target/release/examples/onnx --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
+loaded image Tensor[dims 224, 224, 3; f32]
+bicycle-built-for-two, tandem bicycle, tandem     : 99.16%
+mountain bike, all-terrain bike, off-roader       : 0.60%
+unicycle, monocycle                               : 0.17%
+crash helmet                                      : 0.02%
+alp                                               : 0.02%
+```

candle-examples/examples/onnx/main.rs

@@ -0,0 +1,78 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{IndexOp, D};
+use clap::{Parser, ValueEnum};
+
+#[derive(Clone, Copy, Debug, ValueEnum)]
+enum Which {
+    SqueezeNet,
+    EfficientNet,
+}
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    image: String,
+
+    #[arg(long)]
+    model: Option<String>,
+
+    /// The model to be used.
+    #[arg(value_enum, long, default_value_t = Which::SqueezeNet)]
+    which: Which,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+    let image = candle_examples::imagenet::load_image224(args.image)?;
+    let image = match args.which {
+        Which::SqueezeNet => image,
+        Which::EfficientNet => image.permute((1, 2, 0))?,
+    };
+
+    println!("loaded image {image:?}");
+
+    let model = match args.model {
+        Some(model) => std::path::PathBuf::from(model),
+        None => match args.which {
+            Which::SqueezeNet => hf_hub::api::sync::Api::new()?
+                .model("lmz/candle-onnx".into())
+                .get("squeezenet1.1-7.onnx")?,
+            Which::EfficientNet => hf_hub::api::sync::Api::new()?
+                .model("onnx/EfficientNet-Lite4".into())
+                .get("efficientnet-lite4-11.onnx")?,
+        },
+    };
+
+    let model = candle_onnx::read_file(model)?;
+    let graph = model.graph.as_ref().unwrap();
+    let mut inputs = std::collections::HashMap::new();
+    inputs.insert(graph.input[0].name.to_string(), image.unsqueeze(0)?);
+    let mut outputs = candle_onnx::simple_eval(&model, inputs)?;
+    let output = outputs.remove(&graph.output[0].name).unwrap();
+    let prs = match args.which {
+        Which::SqueezeNet => candle_nn::ops::softmax(&output, D::Minus1)?,
+        Which::EfficientNet => output,
+    };
+    let prs = prs.i(0)?.to_vec1::<f32>()?;
+
+    // Sort the predictions and take the top 5
+    let mut top: Vec<_> = prs.iter().enumerate().collect();
+    top.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap());
+    let top = top.into_iter().take(5).collect::<Vec<_>>();
+
+    // Print the top predictions
+    for &(i, p) in &top {
+        println!(
+            "{:50}: {:.2}%",
+            candle_examples::imagenet::CLASSES[i],
+            p * 100.0
+        );
+    }
+
+    Ok(())
+}

candle-examples/examples/onnx_basics.rs

@@ -0,0 +1,87 @@
+use anyhow::Result;
+use candle::{Device, Tensor};
+
+use clap::{Parser, Subcommand};
+
+#[derive(Subcommand, Debug, Clone)]
+enum Command {
+    Print {
+        #[arg(long)]
+        file: String,
+    },
+    SimpleEval {
+        #[arg(long)]
+        file: String,
+    },
+}
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+pub struct Args {
+    #[command(subcommand)]
+    command: Command,
+}
+
+pub fn main() -> Result<()> {
+    let args = Args::parse();
+    match args.command {
+        Command::Print { file } => {
+            let model = candle_onnx::read_file(file)?;
+            println!("{model:?}");
+            let graph = model.graph.unwrap();
+            for node in graph.node.iter() {
+                println!("{node:?}");
+            }
+        }
+        Command::SimpleEval { file } => {
+            let model = candle_onnx::read_file(file)?;
+            let graph = model.graph.as_ref().unwrap();
+            let constants: std::collections::HashSet<_> =
+                graph.initializer.iter().map(|i| i.name.as_str()).collect();
+            let mut inputs = std::collections::HashMap::new();
+            for input in graph.input.iter() {
+                use candle_onnx::onnx::tensor_proto::DataType;
+                if constants.contains(input.name.as_str()) {
+                    continue;
+                }
+
+                let type_ = input.r#type.as_ref().expect("no type for input");
+                let type_ = type_.value.as_ref().expect("no type.value for input");
+                let value = match type_ {
+                    candle_onnx::onnx::type_proto::Value::TensorType(tt) => {
+                        let dt = match DataType::try_from(tt.elem_type) {
+                            Ok(dt) => match candle_onnx::dtype(dt) {
+                                Some(dt) => dt,
+                                None => {
+                                    anyhow::bail!(
+                                        "unsupported 'value' data-type {dt:?} for {}",
+                                        input.name
+                                    )
+                                }
+                            },
+                            type_ => anyhow::bail!("unsupported input type {type_:?}"),
+                        };
+                        let shape = tt.shape.as_ref().expect("no tensortype.shape for input");
+                        let dims = shape
+                                .dim
+                                .iter()
+                                .map(|dim| match dim.value.as_ref().expect("no dim value") {
+                                    candle_onnx::onnx::tensor_shape_proto::dimension::Value::DimValue(v) => Ok(*v as usize),
+                                    candle_onnx::onnx::tensor_shape_proto::dimension::Value::DimParam(_) => Ok(42),
+                                })
+                                .collect::<Result<Vec<usize>>>()?;
+                        Tensor::zeros(dims, dt, &Device::Cpu)?
+                    }
+                    type_ => anyhow::bail!("unsupported input type {type_:?}"),
+                };
+                println!("input {}: {value:?}", input.name);
+                inputs.insert(input.name.clone(), value);
+            }
+            let outputs = candle_onnx::simple_eval(&model, inputs)?;
+            for (name, value) in outputs.iter() {
+                println!("output {name}: {value:?}")
+            }
+        }
+    }
+    Ok(())
+}

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/README.md

@@ -1,5 +1,7 @@
 # candle-quantized-t5
 
+## Seq2Seq example
+
 This example uses a quantized version of the t5 model.
 
 ```bash
@@ -8,6 +10,8 @@ $ cargo run --example quantized-t5 --release -- --prompt "translate to German: A
  Eine schöne Kerze.
 ```
 
+## Generating Quantized weight files
+
 The weight file is automatically retrieved from the hub. It is also possible to
 generate quantized weight files from the original safetensors file by using the
 `tensor-tools` command line utility via:
@@ -16,8 +20,11 @@ generate quantized weight files from the original safetensors file by using the
 $ cargo run --example tensor-tools --release -- quantize --quantization q6k PATH/TO/T5/model.safetensors /tmp/model.gguf
 ```
 
-To use a different model, specify the `model-id`. For example, you can use
-quantized [CoEdit models](https://huggingface.co/jbochi/candle-coedit-quantized).
+## Using custom models
+
+To use a different model, specify the `model-id`.
+
+For example, for text editing, you can use quantized [CoEdit models](https://huggingface.co/jbochi/candle-coedit-quantized).
 
 ```bash
 $ cargo run --example quantized-t5 --release  -- \
@@ -26,6 +33,7 @@ $ cargo run --example quantized-t5 --release  -- \
   --temperature 0
 ...
  Although their flight is weak, they run quickly through the tree canopy.
+```
 
 By default, it will look for `model.gguf` and `config.json`, but you can specify
 custom local or remote `weight-file` and `config-file`s:
@@ -40,3 +48,16 @@ cargo run --example quantized-t5 --release  -- \
 ...
  Note that a storm surge is what forecasters consider a hurricane's most dangerous part.
 ```
+
+### [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.
+
+```bash
+cargo run --example quantized-t5 --release  -- \
+  --model-id "jbochi/madlad400-3b-mt" --weight-file "model-q4k.gguf" \
+  --prompt "<2de> How are you, my friend?" \
+  --temperature 0
+...
+ Wie geht es dir, mein Freund?
+```

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)?)
     }
 
@@ -173,7 +174,11 @@ fn main() -> Result<()> {
         .to_vec();
     let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
     let mut model = builder.build_model()?;
-    let mut output_token_ids = [builder.config.pad_token_id as u32].to_vec();
+    let mut output_token_ids = [builder
+        .config
+        .decoder_start_token_id
+        .unwrap_or(builder.config.pad_token_id) as u32]
+    .to_vec();
     let temperature = if args.temperature <= 0. {
         None
     } else {

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

@@ -12,6 +12,7 @@ use candle::quantized::{ggml_file, gguf_file};
 use candle::Tensor;
 use candle_transformers::generation::LogitsProcessor;
 
+use candle_examples::token_output_stream::TokenOutputStream;
 use candle_transformers::models::quantized_llama as model;
 use model::ModelWeights;
 
@@ -24,7 +25,7 @@ enum Prompt {
     One(String),
 }
 
-#[derive(Clone, Debug, Copy, ValueEnum)]
+#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
 enum Which {
     #[value(name = "7b")]
     L7b,
@@ -44,12 +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-zephyr")]
-    Zephyr7b,
+    #[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 {
@@ -63,8 +80,93 @@ impl Which {
             | Self::L70bChat
             | Self::L7bCode
             | Self::L13bCode
-            | Self::L34bCode => false,
-            Self::Mistral7b | Self::Mistral7bInstruct | Self::Zephyr7b => true,
+            | 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
+            | Self::Mistral7bInstructV02 => true,
+        }
+    }
+
+    fn is_zephyr(&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::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",
         }
     }
 }
@@ -72,7 +174,7 @@ impl Which {
 #[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>,
 
@@ -83,7 +185,7 @@ struct Args {
     prompt: Option<String>,
 
     /// The length of the sample to generate (in tokens).
-    #[arg(short = 'n', long, default_value_t = 100)]
+    #[arg(short = 'n', long, default_value_t = 1000)]
     sample_len: usize,
 
     /// The tokenizer config in json format.
@@ -133,11 +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")?
             }
@@ -168,6 +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",
@@ -176,10 +290,22 @@ impl Args {
                         "TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
                         "mistral-7b-instruct-v0.1.Q4_K_S.gguf",
                     ),
-                    Which::Zephyr7b => (
+                    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",
                     ),
+                    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());
@@ -190,31 +316,6 @@ impl Args {
     }
 }
 
-fn print_token(next_token: u32, tokenizer: &Tokenizer) {
-    // Extracting the last token as a string is complicated, here we just apply some simple
-    // heuristics as it seems to work well enough for this example. See the following for more
-    // details:
-    // https://github.com/huggingface/tokenizers/issues/1141#issuecomment-1562644141
-    if let Some(text) = tokenizer.id_to_token(next_token) {
-        let text = text.replace('▁', " ");
-        let ascii = text
-            .strip_prefix("<0x")
-            .and_then(|t| t.strip_suffix('>'))
-            .and_then(|t| u8::from_str_radix(t, 16).ok());
-        match ascii {
-            None => print!("{text}"),
-            Some(ascii) => {
-                if let Some(chr) = char::from_u32(ascii as u32) {
-                    if chr.is_ascii() {
-                        print!("{chr}")
-                    }
-                }
-            }
-        }
-        let _ = std::io::stdout().flush();
-    }
-}
-
 fn format_size(size_in_bytes: usize) -> String {
     if size_in_bytes < 1_000 {
         format!("{}B", size_in_bytes)
@@ -232,13 +333,11 @@ fn main() -> anyhow::Result<()> {
     use tracing_subscriber::prelude::*;
 
     let args = Args::parse();
-    let device = candle_examples::device(false)?;
     let temperature = if args.temperature == 0. {
         None
     } else {
         Some(args.temperature)
     };
-    tracing_subscriber::fmt::init();
     let _guard = if args.tracing {
         let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
         tracing_subscriber::registry().with(chrome_layer).init();
@@ -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",
@@ -281,12 +381,13 @@ fn main() -> anyhow::Result<()> {
             ModelWeights::from_gguf(model, &mut file, &device)?
         }
         Some("ggml" | "bin") | Some(_) | None => {
-            let model = ggml_file::Content::read(&mut file, &device)?;
+            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",
@@ -302,19 +403,28 @@ 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::Zephyr7b
+                | 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), &device)?
+            ModelWeights::from_ggml(model, args.gqa.unwrap_or(default_gqa))?
         }
     };
     println!("model built");
 
     let tokenizer = args.tokenizer()?;
+    let mut tos = TokenOutputStream::new(tokenizer);
     let prompt = match args.prompt.as_deref() {
         Some("chat") => Prompt::Chat,
         Some("interactive") => Prompt::Interactive,
@@ -323,10 +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();
@@ -337,7 +448,15 @@ fn main() -> anyhow::Result<()> {
                         prompt.pop();
                     }
                 }
-                if args.which.is_mistral() {
+                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 {
                     prompt
@@ -345,7 +464,8 @@ fn main() -> anyhow::Result<()> {
             }
         };
         print!("{}", &prompt_str);
-        let tokens = tokenizer
+        let tokens = tos
+            .tokenizer()
             .encode(prompt_str, true)
             .map_err(anyhow::Error::msg)?;
         if args.verbose_prompt {
@@ -371,18 +491,23 @@ fn main() -> anyhow::Result<()> {
             let input = Tensor::new(prompt_tokens.as_slice(), &device)?.unsqueeze(0)?;
             let logits = model.forward(&input, 0)?;
             let logits = logits.squeeze(0)?;
-            // TODO Remove this once implementation is finished.
-            let logits = logits.ones_like()?;
-            // logits_processor.sample(&logits)?
-            15043
+            logits_processor.sample(&logits)?
         };
         let prompt_dt = start_prompt_processing.elapsed();
         all_tokens.push(next_token);
-        print_token(next_token, &tokenizer);
-
-        let eos_token = *tokenizer.get_vocab(true).get("</s>").unwrap();
+        if let Some(t) = tos.next_token(next_token)? {
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
 
+        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)?.unsqueeze(0)?;
             let logits = model.forward(&input, prompt_tokens.len() + index)?;
@@ -397,16 +522,21 @@ fn main() -> anyhow::Result<()> {
                     &all_tokens[start_at..],
                 )?
             };
-            // TODO Remove this once implementation is finished.
-            // let logits = logits.ones_like()?;
-            // next_token = logits_processor.sample(&logits)?;
-            let next_token = 15043;
+            next_token = logits_processor.sample(&logits)?;
             all_tokens.push(next_token);
-            print_token(next_token, &tokenizer);
+            if let Some(t) = tos.next_token(next_token)? {
+                print!("{t}");
+                std::io::stdout().flush()?;
+            }
+            sampled += 1;
             if next_token == eos_token {
                 break;
             };
         }
+        if let Some(rest) = tos.decode_rest().map_err(candle::Error::msg)? {
+            print!("{rest}");
+        }
+        std::io::stdout().flush()?;
         let dt = start_post_prompt.elapsed();
         println!(
             "\n\n{:4} prompt tokens processed: {:.2} token/s",
@@ -414,9 +544,8 @@ fn main() -> anyhow::Result<()> {
             prompt_tokens.len() as f64 / prompt_dt.as_secs_f64(),
         );
         println!(
-            "{:4} tokens generated: {:.2} token/s",
-            to_sample,
-            to_sample as f64 / dt.as_secs_f64(),
+            "{sampled:4} tokens generated: {:.2} token/s",
+            sampled as f64 / dt.as_secs_f64(),
         );
 
         match prompt {