Clippy pass.

Co-authored-by: Ivar Flakstad <69173633+ivarflakstad@users.noreply.github.com>

.github/workflows/ci_cuda.yaml

@@ -59,7 +59,7 @@ jobs:
       - name: Install Rust Stable
         run: curl https://sh.rustup.rs -sSf | sh -s -- -y
       - uses: Swatinem/rust-cache@v2
-      - run: apt-get update -y && apt-get install libssl-dev -y
+      - run: apt-get update -y && apt-get install libssl-dev protobuf-compiler -y
       - name: Test (cuda)
         run: PATH=$PATH:/usr/local/cuda-11.8/bin/ /root/.cargo/bin/cargo test --features cuda
   stop-runner:

.github/workflows/python.yml

@@ -0,0 +1,68 @@
+name: PyO3-CI
+
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - candle-pyo3/**
+  pull_request:
+    paths:
+      - candle-pyo3/**
+
+jobs:
+  build_and_test:
+    name: Check everything builds & tests
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest] # For now, only test on Linux
+    steps: 
+      - name: Checkout repository
+        uses: actions/checkout@v2
+
+      - name: Install Rust
+        uses: actions-rs/toolchain@v1
+        with:
+          toolchain: stable
+
+      - name: Install Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+          architecture: "x64"
+
+      - name: Cache Cargo Registry
+        uses: actions/cache@v1
+        with:
+          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: |
+          python -m venv .env
+          source .env/bin/activate
+          pip install -U pip
+          pip install pytest maturin black
+          python -m maturin develop -r --features onnx
+
+      - name: Check style
+        working-directory: ./candle-pyo3
+        run: |
+          source .env/bin/activate
+          python stub.py --check
+          black --check .
+
+      - name: Run tests
+        working-directory: ./candle-pyo3
+        run: |
+          source .env/bin/activate
+          python -m pytest -s -v tests

.gitignore

@@ -23,14 +23,16 @@ flamegraph.svg
 *.dylib
 *.so
 *.swp
+*.swo
 trace-*.json
 
 candle-wasm-examples/*/build
 candle-wasm-examples/*/*.bin
 candle-wasm-examples/*/*.jpeg
-candle-wasm-examples/*/*.wav
-candle-wasm-examples/*/*.safetensors
+candle-wasm-examples/*/audios/*.wav
+candle-wasm-examples/**/*.safetensors
+candle-wasm-examples/**/*.gguf
 candle-wasm-examples/*/package-lock.json
-
+candle-wasm-examples/**/config*.json
 .DS_Store
 .idea/*

.vscode/settings.json

@@ -0,0 +1,11 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.black-formatter"
+    },
+    "python.formatting.provider": "none",
+    "python.testing.pytestArgs": [
+        "candle-pyo3"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

CHANGELOG.md

@@ -1,13 +1,84 @@
 # Changelog
 This documents the main changes to the `candle` crate.
 
-## v0.2.1 - Unreleased
+## v0.3.1 - Unreleased
 
 ### Added
 
+### Modified
+
+## v0.3.0 - 2023-10-01
+
+### Added
+
+- Added the Mistral 7b v0.1 model
+  [983](https://github.com/huggingface/candle/pull/983).
+- Quantized version of the Mistral model
+  [1009](https://github.com/huggingface/candle/pull/1009).
+- Add the gelu-erf op and activation function
+  [969](https://github.com/huggingface/candle/pull/969).
+- Add the mixformer/phi-v1.5 model
+  [930](https://github.com/huggingface/candle/pull/930).
+- Add the sclice-scatter op
+  [927](https://github.com/huggingface/candle/pull/927).
+- Add the Wuerstchen diffusion model
+  [911](https://github.com/huggingface/candle/pull/911).
+
+### Modified
+
+- Support for simd128 intrinsics in some quantized vecdots
+  [982](https://github.com/huggingface/candle/pull/982).
+- Optimize the index-select cuda kernel
+  [976](https://github.com/huggingface/candle/pull/976).
+- Self-contained safetensor wrappers
+  [946](https://github.com/huggingface/candle/pull/946).
+
+## v0.2.2 - 2023-09-18
+
+### Added
+- Support for `top_p` sampling
+  [819](https://github.com/huggingface/candle/pull/819).
+- T5 model including decoding
+  [864](https://github.com/huggingface/candle/pull/864).
+- 1-d upsampling
+  [839](https://github.com/huggingface/candle/pull/839).
+
+### Modified
+- Bugfix for conv2d
+  [820](https://github.com/huggingface/candle/pull/820).
+- Support tensor based indexing using `.i`
+  [842](https://github.com/huggingface/candle/pull/842).
+
+## v0.2.1 - 2023-09-11
+
+### Added
+- Add some RNNs (GRU and LSTM) in `candle-nn`
+  [674](https://github.com/huggingface/candle/pull/674),
+  [688](https://github.com/huggingface/candle/pull/688).
+- gguf v2 support
+  [725](https://github.com/huggingface/candle/pull/725).
+- Quantized llama example in Python using the pyo3 api
+  [716](https://github.com/huggingface/candle/pull/716).
+- `candle-nn` layer for conv2d-transposed
+  [760](https://github.com/huggingface/candle/pull/760).
+- Add the Segment-Anything Model (SAM) as an example
+  [773](https://github.com/huggingface/candle/pull/773).
+- TinyViT backbone for the segemnt anything example
+  [787](https://github.com/huggingface/candle/pull/787).
+- Shape with holes support
+  [770](https://github.com/huggingface/candle/pull/770).
+
 ### Modified
 - Dilations are now supported in conv-transpose2d.
   [671](https://github.com/huggingface/candle/pull/671).
+- Interactive mode for the quantized model
+  [690](https://github.com/huggingface/candle/pull/690).
+- Faster softmax operation
+  [747](https://github.com/huggingface/candle/pull/747).
+- Faster convolution operations on CPU and CUDA via im2col
+  [802](https://github.com/huggingface/candle/pull/802).
+- Moving some models to a more central location
+  [796](https://github.com/huggingface/candle/pull/796).
 
 ## v0.2.0 - 2023-08-30
 

Cargo.toml

@@ -7,18 +7,19 @@ members = [
     "candle-nn",
     "candle-pyo3",
     "candle-transformers",
-    "candle-wasm-examples/llama2-c",
-    "candle-wasm-examples/whisper",
-    "candle-wasm-examples/yolo",
+    "candle-wasm-examples/*",
+    "candle-wasm-tests",
 ]
 exclude = [
-    "candle-flash-attn",
-    "candle-kernels",
+   "candle-flash-attn",
+   "candle-kernels",
+   "candle-metal-kernels",
+   "candle-onnx",
 ]
 resolver = "2"
 
 [workspace.package]
-version = "0.2.1"
+version = "0.3.1"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -32,8 +33,7 @@ anyhow = { version = "1", features = ["backtrace"] }
 byteorder = "1.4.3"
 clap = { version = "4.2.4", features = ["derive"] }
 cudarc = { version = "0.9.14", features = ["f16"] }
-# TODO: Switch back to the official gemm implementation once it has caught up.
-gemm = { version = "0.15.6", package = "candle-gemm" }
+gemm = { version = "0.16.6", features = ["wasm-simd128-enable"] }
 hf-hub = "0.3.0"
 half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
 image = { version = "0.24.7", default-features = false, features = ["jpeg", "png"] }
@@ -41,15 +41,17 @@ 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 = "0.7.1"
+memmap2 = { version = "0.7.1", features = ["stable_deref_trait"] }
 num_cpus = "1.15.0"
 num-traits = "0.2.15"
+parquet = { version = "45.0.0" }
 rand = "0.8.5"
 rand_distr = "0.4.3"
 rayon = "1.7.0"
 rusttype = { version = "0.9", default-features = false }
 safetensors = "0.3.1"
 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 }
@@ -57,8 +59,9 @@ 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 }
-parquet = { version = "45.0.0" }
+metal = { version = "0.27.0", features = ["mps"]}
 
 [profile.release-with-debug]
 inherits = "release"

README.md

@@ -8,7 +8,10 @@ Candle is a minimalist ML framework for Rust with a focus on performance (includ
 and ease of use. Try our online demos: 
 [whisper](https://huggingface.co/spaces/lmz/candle-whisper),
 [LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2),
-[yolo](https://huggingface.co/spaces/lmz/candle-yolo).
+[T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm),
+[yolo](https://huggingface.co/spaces/lmz/candle-yolo),
+[Segment
+Anything](https://huggingface.co/spaces/radames/candle-segment-anything-wasm).
 
 ## Get started
 
@@ -45,40 +48,69 @@ For more advanced examples, please have a look at the following section.
 
 ## Check out our examples
 
-Check out our [examples](./candle-examples/examples/):
+These online demos run entirely in your browser:
+- [yolo](https://huggingface.co/spaces/lmz/candle-yolo): pose estimation and
+  object recognition.
+- [whisper](https://huggingface.co/spaces/lmz/candle-whisper): 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.
+- [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:
 
-- [Whisper](./candle-examples/examples/whisper/): speech recognition model.
 - [LLaMA and LLaMA-v2](./candle-examples/examples/llama/): general LLM.
 - [Falcon](./candle-examples/examples/falcon/): general LLM.
-- [Bert](./candle-examples/examples/bert/): useful for sentence embeddings.
-- [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code
-  generation.
-- [Stable Diffusion](./candle-examples/examples/stable-diffusion/): text to
-  image generative model, support for the 1.5, 2.1, and SDXL 1.0 versions.
-- [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
-  using self-supervision (can be used for imagenet classification, depth
-  evaluation, segmentation).
+- [Phi-v1 and Phi-v1.5](./candle-examples/examples/phi/): a 1.3b general LLM 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.
+- [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.
+- [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code generation.
+- [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
+- [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
+  (English/Chinese) general LLMs with 6b and 34b parameters.
 - [Quantized LLaMA](./candle-examples/examples/quantized/): quantized version of
   the LLaMA model using the same quantization techniques as
   [llama.cpp](https://github.com/ggerganov/llama.cpp).
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/quantized/assets/aoc.gif" width="600">
+  
+- [Stable Diffusion](./candle-examples/examples/stable-diffusion/): text to
+  image generative model, support for the 1.5, 2.1, and SDXL 1.0 versions.
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg" width="200">
+
+- [Wuerstchen](./candle-examples/examples/wuerstchen/): another text to
+  image generative model.
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/wuerstchen/assets/cat.jpg" width="200">
+
 - [yolo-v3](./candle-examples/examples/yolo-v3/) and
   [yolo-v8](./candle-examples/examples/yolo-v8/): object detection and pose
   estimation models.
-  [segment-anything](./candle-examples/examples/segment-anything/): image
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.od.jpg" width="200"><img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/yolo-v8/assets/bike.pose.jpg" width="200">
+- [segment-anything](./candle-examples/examples/segment-anything/): image
   segmentation model with prompt.
-Run them using the following commands:
+
+<img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/sam_merged.jpg" width="200">
+
+- [Whisper](./candle-examples/examples/whisper/): speech recognition model.
+- [T5](./candle-examples/examples/t5), [Bert](./candle-examples/examples/bert/),
+  [JinaBert](./candle-examples/examples/jina-bert/) : useful for sentence embeddings.
+- [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
+  using self-supervision (can be used for imagenet classification, depth
+  evaluation, segmentation).
+- [BLIP](./candle-examples/examples/blip/): image to text model, can be used to
+  generate captions for an image.
+- [Marian-MT](./candle-examples/examples/marian-mt/): neural machine translation
+  model, generates the translated text from the input text.
+
+Run them using commands like:
 ```
-cargo run --example whisper --release
-cargo run --example llama --release
-cargo run --example falcon --release
-cargo run --example bert --release
-cargo run --example bigcode --release
-cargo run --example stable-diffusion --release -- --prompt "a rusty robot holding a fire torch"
-cargo run --example dinov2 --release -- --image path/to/myinput.jpg
 cargo run --example quantized --release
-cargo run --example yolo-v3 --release -- myimage.jpg
-cargo run --example yolo-v8 --release -- myimage.jpg # for pose estimation, add --task pose 
-cargo run --example segment-anything --release -- --image myimage.jpg
 ```
 
 In order to use **CUDA** add `--features cuda` to the example command line. If
@@ -88,7 +120,10 @@ There are also some wasm examples for whisper and
 [llama2.c](https://github.com/karpathy/llama2.c). You can either build them with
 `trunk` or try them online:
 [whisper](https://huggingface.co/spaces/lmz/candle-whisper),
-[llama2](https://huggingface.co/spaces/lmz/candle-llama2).
+[llama2](https://huggingface.co/spaces/lmz/candle-llama2),
+[T5](https://huggingface.co/spaces/radames/Candle-T5-Generation-Wasm),
+[Phi-v1.5](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
 test server:
@@ -101,6 +136,25 @@ trunk serve --release --port 8081
 And then head over to
 [http://localhost:8081/](http://localhost:8081/).
 
+<!--- ANCHOR: useful_libraries --->
+
+## Useful External Resources
+- [`candle-tutorial`](https://github.com/ToluClassics/candle-tutorial): A
+  very detailed tutorial showing how to convert a PyTorch model to Candle.
+- [`candle-lora`](https://github.com/EricLBuehler/candle-lora): Efficient and ergonomic LoRA implemenation 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-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.
+
+If you have an addition to this list, please submit a pull request.
+
+<!--- ANCHOR_END: useful_libraries --->
+
 <!--- ANCHOR: features --->
 
 ## Features
@@ -113,10 +167,34 @@ And then head over to
     - CUDA backend for efficiently running on GPUs, multiple GPU distribution via NCCL.
     - WASM support, run your models in a browser.
 - Included models.
-    - LLMs: LLaMA v1 and v2, Falcon, StarCoder.
+    - Language Models.
+        - LLaMA v1 and v2.
+        - Falcon.
+        - StarCoder.
+        - Phi v1.5.
+        - Mistral 7b v0.1.
+        - StableLM-3B-4E1T.
+        - Replit-code-v1.5-3B.
+        - Bert.
+        - Yi-6B and Yi-34B.
+    - Quantized LLMs.
+        - Llama 7b, 13b, 70b, as well as the chat and code variants.
+        - Mistral 7b, and 7b instruct.
+        - Zephyr 7b a and b (Mistral based).
+        - OpenChat 3.5 (Mistral based).
+    - Text to text.
+        - T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
+        - Marian MT (Machine Translation).
     - Whisper (multi-lingual support).
-    - Stable Diffusion.
-    - Computer Vision: DINOv2, EfficientNet, yolo-v3, yolo-v8.
+    - Text to image.
+        - Stable Diffusion v1.5, v2.1, XL v1.0.
+        - Wurstchen v2.
+    - Image to text.
+        - BLIP.
+    - Computer Vision Models.
+        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT.
+        - yolo-v3, yolo-v8.
+        - Segment-Anything Model (SAM).
 - File formats: load models from safetensors, npz, ggml, or PyTorch files.
 - Serverless (on CPU), small and fast deployments.
 - Quantization support using the llama.cpp quantized types.
@@ -153,6 +231,7 @@ 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
 
@@ -257,6 +336,29 @@ This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a d
 env CANDLE_NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
 ```
 
+#### Linking error on windows when running rustdoc or mdbook tests
+
+```
+Couldn't compile the test.
+---- .\candle-book\src\inference\hub.md - Using_the_hub::Using_in_a_real_model_ (line 50) stdout ----
+error: linking with `link.exe` failed: exit code: 1181
+//very long chain of linking
+ = note: LINK : fatal error LNK1181: cannot open input file 'windows.0.48.5.lib'
+```
+
+Make sure you link all native libraries that might be located outside a project target, e.g., to run mdbook tests, you should run:
+
+```
+mdbook test candle-book -L .\target\debug\deps\ `
+-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.42.2\lib `
+-L native=$env:USERPROFILE\.cargo\registry\src\index.crates.io-6f17d22bba15001f\windows_x86_64_msvc-0.48.5\lib
+```
+
+#### Extremely slow model load time with WSL
+
+This may be caused by the models being loaded from `/mnt/c`, more details on
+[stackoverflow](https://stackoverflow.com/questions/68972448/why-is-wsl-extremely-slow-when-compared-with-native-windows-npm-yarn-processing).
+
 #### Tracking down errors
 
 You can set `RUST_BACKTRACE=1` to be provided with backtraces when a candle

candle-book/Cargo.toml

@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.2.1" }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
-candle-transformers = { path = "../candle-transformers", version = "0.2.1" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.1", optional = true }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.3.1" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../candle-transformers", version = "0.3.1" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.1", optional = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
@@ -24,9 +24,10 @@ intel-mkl-src = { workspace = true, optional = true }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true, optional = true }
+anyhow = { workspace = true }
+tokio = "1.29.1"
 
 [dev-dependencies]
-anyhow = { workspace = true }
 byteorder = { workspace = true }
 hf-hub = { workspace = true, features=["tokio"]}
 clap = { workspace = true }
@@ -38,7 +39,6 @@ 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"
 parquet = { workspace = true }
 image = { workspace = true }
 

candle-book/src/SUMMARY.md

@@ -10,10 +10,11 @@
 
 # Reference Guide
 
-- [Running a model](inference/README.md)
+- [Running a model](inference/inference.md)
     - [Using the hub](inference/hub.md)
 - [Error management](error_manage.md)
-- [Training](training/README.md)
+- [Training](training/training.md)
+    - [Simplified](training/simplified.md)
     - [MNIST](training/mnist.md)
     - [Fine-tuning]()
     - [Serialization]()

candle-book/src/error_manage.md

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

candle-book/src/guide/hello_world.md

@@ -6,7 +6,7 @@ Open `src/main.rs` and fill in this content:
 
 ```rust
 # extern crate candle_core;
-use candle_core::{DType, Device, Result, Tensor};
+use candle_core::{Device, Result, Tensor};
 
 struct Model {
     first: Tensor,
@@ -25,11 +25,11 @@ fn main() -> Result<()> {
     // Use Device::new_cuda(0)?; to use the GPU.
     let device = Device::Cpu;
 
-    let first = Tensor::zeros((784, 100), DType::F32, &device)?;
-    let second = Tensor::zeros((100, 10), DType::F32, &device)?;
+    let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
+    let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
     let model = Model { first, second };
 
-    let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
 
     let digit = model.forward(&dummy_image)?;
     println!("Digit {digit:?} digit");
@@ -50,7 +50,7 @@ the classical `Linear` layer. We can do as such
 
 ```rust
 # extern crate candle_core;
-# use candle_core::{DType, Device, Result, Tensor};
+# use candle_core::{Device, Result, Tensor};
 struct Linear{
     weight: Tensor,
     bias: Tensor,
@@ -80,7 +80,7 @@ This will change the model running code into a new function
 
 ```rust
 # extern crate candle_core;
-# use candle_core::{DType, Device, Result, Tensor};
+# use candle_core::{Device, Result, Tensor};
 # struct Linear{
 #     weight: Tensor,
 #     bias: Tensor,
@@ -110,15 +110,15 @@ fn main() -> Result<()> {
     let device = Device::cuda_if_available(0)?;
 
     // Creating a dummy model
-    let weight = Tensor::zeros((784, 100), DType::F32, &device)?;
-    let bias = Tensor::zeros((100, ), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
     let first = Linear{weight, bias};
-    let weight = Tensor::zeros((100, 10), DType::F32, &device)?;
-    let bias = Tensor::zeros((10, ), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
     let second = Linear{weight, bias};
     let model = Model { first, second };
 
-    let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
 
     // Inference on the model
     let digit = model.forward(&dummy_image)?;
@@ -146,7 +146,7 @@ And rewrite our examples using it
 ```rust
 # extern crate candle_core;
 # extern crate candle_nn;
-use candle_core::{DType, Device, Result, Tensor};
+use candle_core::{Device, Result, Tensor};
 use candle_nn::{Linear, Module};
 
 struct Model {
@@ -167,15 +167,15 @@ fn main() -> Result<()> {
     let device = Device::Cpu;
 
     // This has changed (784, 100) -> (100, 784) !
-    let weight = Tensor::zeros((100, 784), DType::F32, &device)?;
-    let bias = Tensor::zeros((100, ), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
     let first = Linear::new(weight, Some(bias));
-    let weight = Tensor::zeros((10, 100), DType::F32, &device)?;
-    let bias = Tensor::zeros((10, ), DType::F32, &device)?;
+    let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?;
+    let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
     let second = Linear::new(weight, Some(bias));
     let model = Model { first, second };
 
-    let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
+    let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
 
     let digit = model.forward(&dummy_image)?;
     println!("Digit {digit:?} digit");
@@ -188,8 +188,8 @@ Feel free to modify this example to use `Conv2d` to create a classical convnet i
 
 Now that we have the running dummy code we can get to more advanced topics:
 
-- [For PyTorch users](./guide/cheatsheet.md)
-- [Running existing models](./inference/README.md)
-- [Training models](./training/README.md)
+- [For PyTorch users](../guide/cheatsheet.md)
+- [Running existing models](../inference/inference.md)
+- [Training models](../training/training.md)
 
 

candle-book/src/guide/installation.md

@@ -12,6 +12,9 @@ compute_cap
 8.9
 ```
 
+You can also compile the Cuda kernels for a specific compute cap using the 
+`CUDA_COMPUTE_CAP=<compute cap>` environment variable.
+
 If any of the above commands errors out, please make sure to update your Cuda version.
 
 2. Create a new app and add [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) with Cuda support.

candle-book/src/lib.rs

@@ -1,3 +1,6 @@
+#[cfg(test)]
+pub mod simplified;
+
 #[cfg(test)]
 mod tests {
     use anyhow::Result;

candle-book/src/simplified.rs

@@ -0,0 +1,196 @@
+//! #A simplified example in Rust of training a neural network and then using it based on the Candle Framework by Hugging Face.
+//! Author: Evgeny Igumnov 2023 igumnovnsk@gmail.com
+//! This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
+//!
+//! ##Basic moments:
+//!
+//! A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
+//! The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.
+//! The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose.
+//! For training, samples with real data on the results of the first and second stages of different elections are used.
+//! The model is trained by backpropagation using gradient descent and the cross-entropy loss function.
+//! Model parameters (weights of neurons) are initialized randomly, then optimized during training.
+//! After training, the model is tested on a deferred sample to evaluate the accuracy.
+//! If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.
+//! Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data.
+
+#[rustfmt::skip]
+mod tests {
+
+use candle::{DType, Result, Tensor, D, Device};
+use candle_nn::{loss, ops, Linear, Module, VarBuilder, VarMap, Optimizer};
+
+// ANCHOR: book_training_simplified1
+const VOTE_DIM: usize = 2;
+const RESULTS: usize = 1;
+const EPOCHS: usize = 10;
+const LAYER1_OUT_SIZE: usize = 4;
+const LAYER2_OUT_SIZE: usize = 2;
+const LEARNING_RATE: f64 = 0.05;
+
+#[derive(Clone)]
+pub struct Dataset {
+    pub train_votes: Tensor,
+    pub train_results: Tensor,
+    pub test_votes: Tensor,
+    pub test_results: Tensor,
+}
+
+struct MultiLevelPerceptron {
+    ln1: Linear,
+    ln2: Linear,
+    ln3: Linear,
+}
+
+impl MultiLevelPerceptron {
+    fn new(vs: VarBuilder) -> Result<Self> {
+        let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp("ln1"))?;
+        let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp("ln2"))?;
+        let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp("ln3"))?;
+        Ok(Self { ln1, ln2, ln3 })
+    }
+
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.ln1.forward(xs)?;
+        let xs = xs.relu()?;
+        let xs = self.ln2.forward(&xs)?;
+        let xs = xs.relu()?;
+        self.ln3.forward(&xs)
+    }
+}
+
+// ANCHOR_END: book_training_simplified1
+
+
+
+// ANCHOR: book_training_simplified3
+#[tokio::test]
+async fn simplified() -> anyhow::Result<()> {
+
+    let dev = Device::cuda_if_available(0)?;
+
+    let train_votes_vec: Vec<u32> = vec![
+        15, 10,
+        10, 15,
+        5, 12,
+        30, 20,
+        16, 12,
+        13, 25,
+        6, 14,
+        31, 21,
+    ];
+    let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let train_results_vec: Vec<u32> = vec![
+        1,
+        0,
+        0,
+        1,
+        1,
+        0,
+        0,
+        1,
+    ];
+    let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?;
+
+    let test_votes_vec: Vec<u32> = vec![
+        13, 9,
+        8, 14,
+        3, 10,
+    ];
+    let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let test_results_vec: Vec<u32> = vec![
+        1,
+        0,
+        0,
+    ];
+    let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?;
+
+    let m = Dataset {
+        train_votes: train_votes_tensor,
+        train_results: train_results_tensor,
+        test_votes: test_votes_tensor,
+        test_results: test_results_tensor,
+    };
+
+    let trained_model: MultiLevelPerceptron;
+    loop {
+        println!("Trying to train neural network.");
+        match train(m.clone(), &dev) {
+            Ok(model) => {
+                trained_model = model;
+                break;
+            },
+            Err(e) => {
+                println!("Error: {}", e);
+                continue;
+            }
+        }
+
+    }
+
+    let real_world_votes: Vec<u32> = vec![
+        13, 22,
+    ];
+
+    let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
+
+    let final_result = trained_model.forward(&tensor_test_votes)?;
+
+    let result = final_result
+        .argmax(D::Minus1)?
+        .to_dtype(DType::F32)?
+        .get(0).map(|x| x.to_scalar::<f32>())??;
+    println!("real_life_votes: {:?}", real_world_votes);
+    println!("neural_network_prediction_result: {:?}", result);
+
+    Ok(())
+
+}
+// ANCHOR_END: book_training_simplified3
+
+// ANCHOR: book_training_simplified2
+fn train(m: Dataset, dev: &Device) -> anyhow::Result<MultiLevelPerceptron> {
+    let train_results = m.train_results.to_device(dev)?;
+    let train_votes = m.train_votes.to_device(dev)?;
+    let varmap = VarMap::new();
+    let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev);
+    let model = MultiLevelPerceptron::new(vs.clone())?;
+    let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?;
+    let test_votes = m.test_votes.to_device(dev)?;
+    let test_results = m.test_results.to_device(dev)?;
+    let mut final_accuracy: f32 = 0.0;
+    for epoch in 1..EPOCHS + 1 {
+        let logits = model.forward(&train_votes)?;
+        let log_sm = ops::log_softmax(&logits, D::Minus1)?;
+        let loss = loss::nll(&log_sm, &train_results)?;
+        sgd.backward_step(&loss)?;
+
+        let test_logits = model.forward(&test_votes)?;
+        let sum_ok = test_logits
+            .argmax(D::Minus1)?
+            .eq(&test_results)?
+            .to_dtype(DType::F32)?
+            .sum_all()?
+            .to_scalar::<f32>()?;
+        let test_accuracy = sum_ok / test_results.dims1()? as f32;
+        final_accuracy = 100. * test_accuracy;
+        println!("Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%",
+                 loss.to_scalar::<f32>()?,
+                 final_accuracy
+        );
+        if final_accuracy == 100.0 {
+            break;
+        }
+    }
+    if final_accuracy < 100.0 {
+        Err(anyhow::Error::msg("The model is not trained well enough."))
+    } else {
+        Ok(model)
+    }
+}
+// ANCHOR_END: book_training_simplified2
+
+
+}

candle-book/src/training/simplified.md

@@ -0,0 +1,45 @@
+# Simplified
+
+## How its works
+
+This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
+
+Basic moments:
+
+1. A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
+2. The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.
+3. The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose.
+4. For training, samples with real data on the results of the first and second stages of different elections are used.
+5. The model is trained by backpropagation using gradient descent and the cross-entropy loss function.
+6. Model parameters (weights of neurons) are initialized randomly, then optimized during training.
+7. After training, the model is tested on a deferred sample to evaluate the accuracy.
+8. If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.
+
+Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data.
+
+
+```rust,ignore
+{{#include ../simplified.rs:book_training_simplified1}}
+```
+
+```rust,ignore
+{{#include ../simplified.rs:book_training_simplified2}}
+```
+
+```rust,ignore
+{{#include ../simplified.rs:book_training_simplified3}}
+```
+
+
+## Example output
+
+```bash
+Trying to train neural network.
+Epoch:   1 Train loss:  4.42555 Test accuracy:  0.00%
+Epoch:   2 Train loss:  0.84677 Test accuracy: 33.33%
+Epoch:   3 Train loss:  2.54335 Test accuracy: 33.33%
+Epoch:   4 Train loss:  0.37806 Test accuracy: 33.33%
+Epoch:   5 Train loss:  0.36647 Test accuracy: 100.00%
+real_life_votes: [13, 22]
+neural_network_prediction_result: 0.0
+```

candle-core/Cargo.toml

@@ -12,7 +12,9 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-candle-kernels = { path = "../candle-kernels", version = "0.2.1", optional = true }
+candle-kernels = { path = "../candle-kernels", version = "0.3.1", optional = true }
+candle-metal-kernels = { path = "../candle-metal-kernels", version = "0.3.1", optional = true }
+metal = { workspace = true, optional = true}
 cudarc = { workspace = true, optional = true }
 gemm = { workspace = true }
 half = { workspace = true }
@@ -26,6 +28,7 @@ rand_distr = { workspace = true }
 rayon = { workspace = true }
 safetensors = { workspace = true }
 thiserror = { workspace = true }
+yoke = { workspace = true }
 zip = { workspace = true }
 
 [dev-dependencies]
@@ -38,3 +41,4 @@ cuda = ["cudarc", "dep:candle-kernels"]
 cudnn = ["cuda", "cudarc/cudnn"]
 mkl = ["dep:libc", "dep:intel-mkl-src"]
 accelerate = ["dep:libc", "dep:accelerate-src"]
+metal = ["dep:metal", "dep:candle-metal-kernels"]

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

@@ -103,8 +103,10 @@ enum Command {
 
     Quantize {
         /// The input file, in gguf format.
-        in_file: std::path::PathBuf,
+        in_file: Vec<std::path::PathBuf>,
+
         /// The output file, in gguf format.
+        #[arg(long)]
         out_file: std::path::PathBuf,
 
         /// The quantization schema to apply.
@@ -150,8 +152,7 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
             }
         }
         Format::Safetensors => {
-            let tensors = unsafe { candle_core::safetensors::MmapedFile::new(file)? };
-            let tensors = tensors.deserialize()?;
+            let tensors = unsafe { candle_core::safetensors::MmapedSafetensors::new(file)? };
             let mut tensors = tensors.tensors();
             tensors.sort_by(|a, b| a.0.cmp(&b.0));
             for (name, view) in tensors.iter() {
@@ -218,15 +219,99 @@ fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> R
     Ok(())
 }
 
+fn run_quantize_safetensors(
+    in_files: &[std::path::PathBuf],
+    out_file: std::path::PathBuf,
+    q: Quantization,
+) -> Result<()> {
+    let mut out_file = std::fs::File::create(out_file)?;
+    let mut tensors = std::collections::HashMap::new();
+    for in_file in in_files.iter() {
+        let in_tensors = candle_core::safetensors::load(in_file, &Device::Cpu)?;
+        tensors.extend(in_tensors)
+    }
+    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 qtensors = tensors
+        .into_par_iter()
+        .map(|(name, tensor)| {
+            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)?
+            } else {
+                QTensor::quantize::<f32>(&tensor)?
+            };
+            Ok((name, tensor))
+        })
+        .collect::<Result<Vec<_>>>()?;
+    let qtensors = qtensors
+        .iter()
+        .map(|(k, v)| (k.as_str(), v))
+        .collect::<Vec<_>>();
+    gguf_file::write(&mut out_file, &[], &qtensors)?;
+    Ok(())
+}
+
 fn run_quantize(
-    in_file: std::path::PathBuf,
+    in_files: &[std::path::PathBuf],
     out_file: std::path::PathBuf,
     q: Quantization,
     qmode: QuantizationMode,
 ) -> Result<()> {
+    if in_files.is_empty() {
+        candle_core::bail!("no specified input files")
+    }
+    if let Some(extension) = out_file.extension() {
+        if extension == "safetensors" {
+            candle_core::bail!("the generated file cannot use the safetensors extension")
+        }
+    }
+    if let Some(extension) = in_files[0].extension() {
+        if extension == "safetensors" {
+            return run_quantize_safetensors(in_files, out_file, q);
+        }
+    }
+
+    if in_files.len() != 1 {
+        candle_core::bail!("only a single in-file can be used when quantizing gguf files")
+    }
+
     // Open the out file early so as to fail directly on missing directories etc.
     let mut out_file = std::fs::File::create(out_file)?;
-    let mut in_ = std::fs::File::open(&in_file)?;
+    let mut in_ = std::fs::File::open(&in_files[0])?;
     let content = gguf_file::Content::read(&mut in_)?;
     println!("tensors: {}", content.tensor_infos.len());
 
@@ -252,7 +337,7 @@ fn run_quantize(
         .par_iter()
         .map(|(name, _)| {
             println!("  quantizing {name}");
-            let mut in_file = std::fs::File::open(&in_file)?;
+            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)?;
             Ok((name, tensor))
@@ -293,7 +378,7 @@ fn main() -> anyhow::Result<()> {
             out_file,
             quantization,
             mode,
-        } => run_quantize(in_file, out_file, quantization, mode)?,
+        } => run_quantize(&in_file, out_file, quantization, mode)?,
     }
     Ok(())
 }

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,
@@ -57,6 +65,7 @@ pub trait BackendStorage: Sized {
 
     fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
     fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self>;
+    fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self>;
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self>;
 
     fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self>;
@@ -110,4 +119,6 @@ pub trait BackendDevice: Sized + std::fmt::Debug + Clone {
     fn rand_uniform(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage>;
 
     fn rand_normal(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage>;
+
+    fn set_seed(&self, _: u64) -> Result<()>;
 }

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
@@ -36,6 +47,8 @@ impl Tensor {
                 // Do not call recursively on the "leaf" nodes.
                 track_grad = true;
                 nodes
+            } else if node.dtype().is_int() {
+                nodes
             } else if let Some(op) = node.op() {
                 match op {
                     Op::IndexAdd(t1, t2, t3, _)
@@ -55,6 +68,11 @@ impl Tensor {
                         kernel: rhs,
                         ..
                     }
+                    | Op::ConvTranspose1D {
+                        arg: lhs,
+                        kernel: rhs,
+                        ..
+                    }
                     | Op::Conv2D {
                         arg: lhs,
                         kernel: rhs,
@@ -69,7 +87,8 @@ impl Tensor {
                     | Op::Binary(lhs, rhs, _)
                     | Op::Gather(lhs, rhs, _)
                     | Op::IndexSelect(lhs, rhs, _)
-                    | Op::Matmul(lhs, rhs) => {
+                    | Op::Matmul(lhs, rhs)
+                    | Op::SliceScatter0(lhs, rhs, _) => {
                         let (tg, nodes) = walk(lhs, nodes, already_seen);
                         track_grad |= tg;
                         let (tg, nodes) = walk(rhs, nodes, already_seen);
@@ -90,15 +109,18 @@ impl Tensor {
                             nodes
                         }
                     }
+                    Op::Unary(_node, UnaryOp::Ceil)
+                    | Op::Unary(_node, UnaryOp::Floor)
+                    | Op::Unary(_node, UnaryOp::Round) => nodes,
                     Op::Reshape(node)
+                    | Op::UpsampleNearest1D(node)
                     | Op::UpsampleNearest2D(node)
                     | Op::AvgPool2D { arg: node, .. }
                     | Op::MaxPool2D { arg: node, .. }
                     | Op::Copy(node)
                     | Op::Broadcast(node)
                     | Op::Cmp(node, _)
-                    | Op::Reduce(node, _, _)
-                    | Op::ToDType(node)
+                    | Op::Reduce(node, ReduceOp::Min | ReduceOp::Sum | ReduceOp::Max, _)
                     | Op::ToDevice(node)
                     | Op::Transpose(node, _, _)
                     | Op::Permute(node, _)
@@ -111,6 +133,16 @@ impl Tensor {
                         track_grad |= tg;
                         nodes
                     }
+                    Op::ToDType(node) => {
+                        if node.dtype().is_float() {
+                            let (tg, nodes) = walk(node, nodes, already_seen);
+                            track_grad |= tg;
+                            nodes
+                        } else {
+                            nodes
+                        }
+                    }
+                    Op::Reduce(_, ReduceOp::ArgMin | ReduceOp::ArgMax, _) => nodes,
                 }
             } else {
                 nodes
@@ -134,10 +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) => {
@@ -192,7 +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,
@@ -213,7 +288,6 @@ impl Tensor {
                             out_padding,
                             *stride,
                             *dilation,
-                            1,
                         )?;
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
@@ -223,8 +297,18 @@ impl Tensor {
                             .conv2d(&grad.transpose(0, 1)?, *padding, *dilation, *stride, 1)?
                             .transpose(0, 1)?;
                         let sum_grad = grads.or_insert(kernel)?;
+                        let (_, _, k0, k1) = kernel.dims4()?;
+                        let (_, _, g_k0, g_k1) = grad_kernel.dims4()?;
+                        let grad_kernel = if g_k0 != k0 || g_k1 != k1 {
+                            grad_kernel.narrow(2, 0, k0)?.narrow(3, 0, k1)?
+                        } else {
+                            grad_kernel
+                        };
                         *sum_grad = sum_grad.add(&grad_kernel)?;
                     }
+                    Op::ConvTranspose1D { .. } => Err(Error::BackwardNotSupported {
+                        op: "conv-transpose1d",
+                    })?,
                     Op::ConvTranspose2D { .. } => Err(Error::BackwardNotSupported {
                         op: "conv-transpose2d",
                     })?,
@@ -263,9 +347,21 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
                     }
+                    Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
+                        op: "upsample-nearest1d",
+                    })?,
                     Op::UpsampleNearest2D { .. } => Err(Error::BackwardNotSupported {
                         op: "upsample-nearest2d",
                     })?,
+                    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)?)?;
+                        *rhs_sum_grad = rhs_sum_grad.add(&rhs_grad)?;
+
+                        let lhs_sum_grad = grads.or_insert(lhs)?;
+                        let lhs_grad = grad.slice_scatter0(&rhs.zeros_like()?, *start_rhs)?;
+                        *lhs_sum_grad = lhs_sum_grad.add(&lhs_grad)?
+                    }
                     Op::Gather(arg, indexes, dim) => {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.scatter_add(indexes, &grad, *dim)?;
@@ -357,7 +453,7 @@ impl Tensor {
                     }
                     Op::ToDType(arg) => {
                         let sum_grad = grads.or_insert(arg)?;
-                        *sum_grad = sum_grad.add(&grad.to_dtype(node.dtype())?)?
+                        *sum_grad = sum_grad.add(&grad.to_dtype(arg.dtype())?)?
                     }
                     Op::Copy(arg) => {
                         let sum_grad = grads.or_insert(arg)?;
@@ -437,13 +533,54 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&arg_grad)?
                     }
-                    Op::Unary(_, UnaryOp::Gelu) => Err(Error::BackwardNotSupported { op: "gelu" })?,
+                    Op::Unary(_, UnaryOp::Ceil) => Err(Error::BackwardNotSupported { op: "ceil" })?,
+                    Op::Unary(_, UnaryOp::Floor) => {
+                        Err(Error::BackwardNotSupported { op: "floor" })?
+                    }
+                    Op::Unary(_, UnaryOp::Round) => {
+                        Err(Error::BackwardNotSupported { op: "round" })?
+                    }
+                    Op::Unary(arg, UnaryOp::Gelu) => {
+                        let sum_grad = grads.or_insert(arg)?;
+                        let cube = arg.powf(3.)?;
+                        let tanh = (0.0356774 * &cube + (0.797885 * arg)?)?.tanh()?;
+                        let gelu_grad = (((0.5 * &tanh)?
+                            + (0.0535161 * cube + (0.398942 * arg)?)? * (1. - tanh.powf(2.)?))?
+                            + 0.5)?;
+                        *sum_grad = sum_grad.add(&(&grad * gelu_grad)?)?
+                    }
+                    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::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)?;
@@ -518,6 +655,7 @@ impl Tensor {
     }
 }
 
+#[derive(Debug)]
 pub struct GradStore(HashMap<TensorId, Tensor>);
 
 impl GradStore {

candle-core/src/conv.rs

@@ -25,6 +25,46 @@ 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,
+    ImplicitPrecompGemm,
+    Gemm,
+    Direct,
+    Fft,
+    FftTiling,
+    Winograd,
+    WinogradNonFused,
+    Count,
+}
+
 #[derive(Debug, Clone, PartialEq, Eq)]
 pub struct ParamsConv2D {
     pub(crate) b_size: usize,
@@ -37,6 +77,7 @@ pub struct ParamsConv2D {
     pub(crate) padding: usize,
     pub(crate) stride: usize,
     pub(crate) dilation: usize,
+    pub cudnn_fwd_algo: Option<CudnnFwdAlgo>,
 }
 
 impl ParamsConv2D {
@@ -60,13 +101,12 @@ pub struct ParamsConvTranspose2D {
     pub(crate) i_w: usize,
     pub(crate) k_h: usize,
     pub(crate) k_w: usize,
-    pub(crate) c_out_per_group: usize,
+    pub(crate) c_out: usize,
     pub(crate) c_in: usize,
     pub(crate) padding: usize,
     pub(crate) output_padding: usize,
     pub(crate) stride: usize,
     pub(crate) dilation: usize,
-    pub(crate) groups: usize,
 }
 
 impl ParamsConvTranspose2D {
@@ -81,8 +121,7 @@ impl ParamsConvTranspose2D {
     }
 
     pub(crate) fn out_dims(&self) -> Vec<usize> {
-        let c_out = self.c_out_per_group * self.groups;
-        vec![self.b_size, c_out, self.out_h(), self.out_w()]
+        vec![self.b_size, self.c_out, self.out_h(), self.out_w()]
     }
 }
 
@@ -148,6 +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()
@@ -190,6 +272,7 @@ impl Tensor {
             padding,
             stride,
             dilation,
+            cudnn_fwd_algo: None,
         };
         if groups == 1 {
             self.conv2d_single_group(kernel, &params)
@@ -213,29 +296,24 @@ impl Tensor {
         output_padding: usize,
         stride: usize,
         dilation: usize,
-        groups: usize,
     ) -> Result<Self> {
         let (b_size, c_in, i_h, i_w) = self.dims4()?;
-        let (c_in_k, c_out_per_group, k_h, k_w) = kernel.dims4()?;
+        let (c_in_k, c_out, k_h, k_w) = kernel.dims4()?;
         if c_in != c_in_k {
             crate::bail!("in_channel mismatch between input ({c_in}) and kernel ({c_in_k})")
         }
-        if c_in % groups != 0 {
-            crate::bail!("in_channel {c_in} must be divisible by groups {groups}")
-        }
         let params = ParamsConvTranspose2D {
             b_size,
             i_h,
             i_w,
             k_h,
             k_w,
-            c_out_per_group,
+            c_out,
             c_in,
             padding,
             output_padding,
             stride,
             dilation,
-            groups,
         };
         let storage = self.storage().conv_transpose2d(
             self.layout(),
@@ -250,7 +328,6 @@ impl Tensor {
             output_padding: params.output_padding,
             stride: params.stride,
             dilation: params.dilation,
-            groups: params.groups,
         });
         let out_dims = params.out_dims();
         Ok(crate::tensor::from_storage(storage, out_dims, op, false))

candle-core/src/cpu/erf.rs

@@ -0,0 +1,763 @@
+#![allow(clippy::excessive_precision)]
+// Code taken from https://github.com/statrs-dev/statrs
+//! Provides the [error](https://en.wikipedia.org/wiki/Error_function) and
+//! related functions
+
+mod evaluate {
+    //! Provides functions that don't have a numerical solution and must
+    //! be solved computationally (e.g. evaluation of a polynomial)
+
+    /// evaluates a polynomial at `z` where `coeff` are the coeffecients
+    /// to a polynomial of order `k` where `k` is the length of `coeff` and the
+    /// coeffecient
+    /// to the `k`th power is the `k`th element in coeff. E.g. [3,-1,2] equates to
+    /// `2z^2 - z + 3`
+    ///
+    /// # Remarks
+    ///
+    /// Returns 0 for a 0 length coefficient slice
+    pub fn polynomial(z: f64, coeff: &[f64]) -> f64 {
+        let n = coeff.len();
+        if n == 0 {
+            return 0.0;
+        }
+
+        let mut sum = *coeff.last().unwrap();
+        for c in coeff[0..n - 1].iter().rev() {
+            sum = *c + z * sum;
+        }
+        sum
+    }
+}
+use std::f64;
+
+/// `erf` calculates the error function at `x`.
+pub fn erf(x: f64) -> f64 {
+    if x.is_nan() {
+        f64::NAN
+    } else if x >= 0.0 && x.is_infinite() {
+        1.0
+    } else if x <= 0.0 && x.is_infinite() {
+        -1.0
+    } else if x == 0. {
+        0.0
+    } else {
+        erf_impl(x, false)
+    }
+}
+
+/// `erf_inv` calculates the inverse error function
+/// at `x`.
+pub fn erf_inv(x: f64) -> f64 {
+    if x == 0.0 {
+        0.0
+    } else if x >= 1.0 {
+        f64::INFINITY
+    } else if x <= -1.0 {
+        f64::NEG_INFINITY
+    } else if x < 0.0 {
+        erf_inv_impl(-x, 1.0 + x, -1.0)
+    } else {
+        erf_inv_impl(x, 1.0 - x, 1.0)
+    }
+}
+
+/// `erfc` calculates the complementary error function
+/// at `x`.
+pub fn erfc(x: f64) -> f64 {
+    if x.is_nan() {
+        f64::NAN
+    } else if x == f64::INFINITY {
+        0.0
+    } else if x == f64::NEG_INFINITY {
+        2.0
+    } else {
+        erf_impl(x, true)
+    }
+}
+
+/// `erfc_inv` calculates the complementary inverse
+/// error function at `x`.
+pub fn erfc_inv(x: f64) -> f64 {
+    if x <= 0.0 {
+        f64::INFINITY
+    } else if x >= 2.0 {
+        f64::NEG_INFINITY
+    } else if x > 1.0 {
+        erf_inv_impl(-1.0 + x, 2.0 - x, -1.0)
+    } else {
+        erf_inv_impl(1.0 - x, x, 1.0)
+    }
+}
+
+// **********************************************************
+// ********** Coefficients for erf_impl polynomial **********
+// **********************************************************
+
+/// Polynomial coefficients for a numerator of `erf_impl`
+/// in the interval [1e-10, 0.5].
+const ERF_IMPL_AN: &[f64] = &[
+    0.00337916709551257388990745,
+    -0.00073695653048167948530905,
+    -0.374732337392919607868241,
+    0.0817442448733587196071743,
+    -0.0421089319936548595203468,
+    0.0070165709512095756344528,
+    -0.00495091255982435110337458,
+    0.000871646599037922480317225,
+];
+
+/// Polynomial coefficients for a denominator of `erf_impl`
+/// in the interval [1e-10, 0.5]
+const ERF_IMPL_AD: &[f64] = &[
+    1.0,
+    -0.218088218087924645390535,
+    0.412542972725442099083918,
+    -0.0841891147873106755410271,
+    0.0655338856400241519690695,
+    -0.0120019604454941768171266,
+    0.00408165558926174048329689,
+    -0.000615900721557769691924509,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [0.5, 0.75].
+const ERF_IMPL_BN: &[f64] = &[
+    -0.0361790390718262471360258,
+    0.292251883444882683221149,
+    0.281447041797604512774415,
+    0.125610208862766947294894,
+    0.0274135028268930549240776,
+    0.00250839672168065762786937,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [0.5, 0.75].
+const ERF_IMPL_BD: &[f64] = &[
+    1.0,
+    1.8545005897903486499845,
+    1.43575803037831418074962,
+    0.582827658753036572454135,
+    0.124810476932949746447682,
+    0.0113724176546353285778481,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [0.75, 1.25].
+const ERF_IMPL_CN: &[f64] = &[
+    -0.0397876892611136856954425,
+    0.153165212467878293257683,
+    0.191260295600936245503129,
+    0.10276327061989304213645,
+    0.029637090615738836726027,
+    0.0046093486780275489468812,
+    0.000307607820348680180548455,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [0.75, 1.25].
+const ERF_IMPL_CD: &[f64] = &[
+    1.0,
+    1.95520072987627704987886,
+    1.64762317199384860109595,
+    0.768238607022126250082483,
+    0.209793185936509782784315,
+    0.0319569316899913392596356,
+    0.00213363160895785378615014,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [1.25, 2.25].
+const ERF_IMPL_DN: &[f64] = &[
+    -0.0300838560557949717328341,
+    0.0538578829844454508530552,
+    0.0726211541651914182692959,
+    0.0367628469888049348429018,
+    0.00964629015572527529605267,
+    0.00133453480075291076745275,
+    0.778087599782504251917881e-4,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [1.25, 2.25].
+const ERF_IMPL_DD: &[f64] = &[
+    1.0,
+    1.75967098147167528287343,
+    1.32883571437961120556307,
+    0.552528596508757581287907,
+    0.133793056941332861912279,
+    0.0179509645176280768640766,
+    0.00104712440019937356634038,
+    -0.106640381820357337177643e-7,
+];
+
+///  Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [2.25, 3.5].
+const ERF_IMPL_EN: &[f64] = &[
+    -0.0117907570137227847827732,
+    0.014262132090538809896674,
+    0.0202234435902960820020765,
+    0.00930668299990432009042239,
+    0.00213357802422065994322516,
+    0.00025022987386460102395382,
+    0.120534912219588189822126e-4,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [2.25, 3.5].
+const ERF_IMPL_ED: &[f64] = &[
+    1.0,
+    1.50376225203620482047419,
+    0.965397786204462896346934,
+    0.339265230476796681555511,
+    0.0689740649541569716897427,
+    0.00771060262491768307365526,
+    0.000371421101531069302990367,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [3.5, 5.25].
+const ERF_IMPL_FN: &[f64] = &[
+    -0.00546954795538729307482955,
+    0.00404190278731707110245394,
+    0.0054963369553161170521356,
+    0.00212616472603945399437862,
+    0.000394984014495083900689956,
+    0.365565477064442377259271e-4,
+    0.135485897109932323253786e-5,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [3.5, 5.25].
+const ERF_IMPL_FD: &[f64] = &[
+    1.0,
+    1.21019697773630784832251,
+    0.620914668221143886601045,
+    0.173038430661142762569515,
+    0.0276550813773432047594539,
+    0.00240625974424309709745382,
+    0.891811817251336577241006e-4,
+    -0.465528836283382684461025e-11,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [5.25, 8].
+const ERF_IMPL_GN: &[f64] = &[
+    -0.00270722535905778347999196,
+    0.0013187563425029400461378,
+    0.00119925933261002333923989,
+    0.00027849619811344664248235,
+    0.267822988218331849989363e-4,
+    0.923043672315028197865066e-6,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [5.25, 8].
+const ERF_IMPL_GD: &[f64] = &[
+    1.0,
+    0.814632808543141591118279,
+    0.268901665856299542168425,
+    0.0449877216103041118694989,
+    0.00381759663320248459168994,
+    0.000131571897888596914350697,
+    0.404815359675764138445257e-11,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [8, 11.5].
+const ERF_IMPL_HN: &[f64] = &[
+    -0.00109946720691742196814323,
+    0.000406425442750422675169153,
+    0.000274499489416900707787024,
+    0.465293770646659383436343e-4,
+    0.320955425395767463401993e-5,
+    0.778286018145020892261936e-7,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [8, 11.5].
+const ERF_IMPL_HD: &[f64] = &[
+    1.0,
+    0.588173710611846046373373,
+    0.139363331289409746077541,
+    0.0166329340417083678763028,
+    0.00100023921310234908642639,
+    0.24254837521587225125068e-4,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [11.5, 17].
+const ERF_IMPL_IN: &[f64] = &[
+    -0.00056907993601094962855594,
+    0.000169498540373762264416984,
+    0.518472354581100890120501e-4,
+    0.382819312231928859704678e-5,
+    0.824989931281894431781794e-7,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [11.5, 17].
+const ERF_IMPL_ID: &[f64] = &[
+    1.0,
+    0.339637250051139347430323,
+    0.043472647870310663055044,
+    0.00248549335224637114641629,
+    0.535633305337152900549536e-4,
+    -0.117490944405459578783846e-12,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [17, 24].
+const ERF_IMPL_JN: &[f64] = &[
+    -0.000241313599483991337479091,
+    0.574224975202501512365975e-4,
+    0.115998962927383778460557e-4,
+    0.581762134402593739370875e-6,
+    0.853971555085673614607418e-8,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [17, 24].
+const ERF_IMPL_JD: &[f64] = &[
+    1.0,
+    0.233044138299687841018015,
+    0.0204186940546440312625597,
+    0.000797185647564398289151125,
+    0.117019281670172327758019e-4,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [24, 38].
+const ERF_IMPL_KN: &[f64] = &[
+    -0.000146674699277760365803642,
+    0.162666552112280519955647e-4,
+    0.269116248509165239294897e-5,
+    0.979584479468091935086972e-7,
+    0.101994647625723465722285e-8,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [24, 38].
+const ERF_IMPL_KD: &[f64] = &[
+    1.0,
+    0.165907812944847226546036,
+    0.0103361716191505884359634,
+    0.000286593026373868366935721,
+    0.298401570840900340874568e-5,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [38, 60].
+const ERF_IMPL_LN: &[f64] = &[
+    -0.583905797629771786720406e-4,
+    0.412510325105496173512992e-5,
+    0.431790922420250949096906e-6,
+    0.993365155590013193345569e-8,
+    0.653480510020104699270084e-10,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [38, 60].
+const ERF_IMPL_LD: &[f64] = &[
+    1.0,
+    0.105077086072039915406159,
+    0.00414278428675475620830226,
+    0.726338754644523769144108e-4,
+    0.477818471047398785369849e-6,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [60, 85].
+const ERF_IMPL_MN: &[f64] = &[
+    -0.196457797609229579459841e-4,
+    0.157243887666800692441195e-5,
+    0.543902511192700878690335e-7,
+    0.317472492369117710852685e-9,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [60, 85].
+const ERF_IMPL_MD: &[f64] = &[
+    1.0,
+    0.052803989240957632204885,
+    0.000926876069151753290378112,
+    0.541011723226630257077328e-5,
+    0.535093845803642394908747e-15,
+];
+
+/// Polynomial coefficients for a numerator in `erf_impl`
+/// in the interval [85, 110].
+const ERF_IMPL_NN: &[f64] = &[
+    -0.789224703978722689089794e-5,
+    0.622088451660986955124162e-6,
+    0.145728445676882396797184e-7,
+    0.603715505542715364529243e-10,
+];
+
+/// Polynomial coefficients for a denominator in `erf_impl`
+/// in the interval [85, 110].
+const ERF_IMPL_ND: &[f64] = &[
+    1.0,
+    0.0375328846356293715248719,
+    0.000467919535974625308126054,
+    0.193847039275845656900547e-5,
+];
+
+// **********************************************************
+// ********** Coefficients for erf_inv_impl polynomial ******
+// **********************************************************
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0, 0.5].
+const ERF_INV_IMPL_AN: &[f64] = &[
+    -0.000508781949658280665617,
+    -0.00836874819741736770379,
+    0.0334806625409744615033,
+    -0.0126926147662974029034,
+    -0.0365637971411762664006,
+    0.0219878681111168899165,
+    0.00822687874676915743155,
+    -0.00538772965071242932965,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0, 0.5].
+const ERF_INV_IMPL_AD: &[f64] = &[
+    1.0,
+    -0.970005043303290640362,
+    -1.56574558234175846809,
+    1.56221558398423026363,
+    0.662328840472002992063,
+    -0.71228902341542847553,
+    -0.0527396382340099713954,
+    0.0795283687341571680018,
+    -0.00233393759374190016776,
+    0.000886216390456424707504,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.5, 0.75].
+const ERF_INV_IMPL_BN: &[f64] = &[
+    -0.202433508355938759655,
+    0.105264680699391713268,
+    8.37050328343119927838,
+    17.6447298408374015486,
+    -18.8510648058714251895,
+    -44.6382324441786960818,
+    17.445385985570866523,
+    21.1294655448340526258,
+    -3.67192254707729348546,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.5, 0.75].
+const ERF_INV_IMPL_BD: &[f64] = &[
+    1.0,
+    6.24264124854247537712,
+    3.9713437953343869095,
+    -28.6608180499800029974,
+    -20.1432634680485188801,
+    48.5609213108739935468,
+    10.8268667355460159008,
+    -22.6436933413139721736,
+    1.72114765761200282724,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x less than 3.
+const ERF_INV_IMPL_CN: &[f64] = &[
+    -0.131102781679951906451,
+    -0.163794047193317060787,
+    0.117030156341995252019,
+    0.387079738972604337464,
+    0.337785538912035898924,
+    0.142869534408157156766,
+    0.0290157910005329060432,
+    0.00214558995388805277169,
+    -0.679465575181126350155e-6,
+    0.285225331782217055858e-7,
+    -0.681149956853776992068e-9,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x less than 3.
+const ERF_INV_IMPL_CD: &[f64] = &[
+    1.0,
+    3.46625407242567245975,
+    5.38168345707006855425,
+    4.77846592945843778382,
+    2.59301921623620271374,
+    0.848854343457902036425,
+    0.152264338295331783612,
+    0.01105924229346489121,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 3 and 6.
+const ERF_INV_IMPL_DN: &[f64] = &[
+    -0.0350353787183177984712,
+    -0.00222426529213447927281,
+    0.0185573306514231072324,
+    0.00950804701325919603619,
+    0.00187123492819559223345,
+    0.000157544617424960554631,
+    0.460469890584317994083e-5,
+    -0.230404776911882601748e-9,
+    0.266339227425782031962e-11,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 3 and 6.
+const ERF_INV_IMPL_DD: &[f64] = &[
+    1.0,
+    1.3653349817554063097,
+    0.762059164553623404043,
+    0.220091105764131249824,
+    0.0341589143670947727934,
+    0.00263861676657015992959,
+    0.764675292302794483503e-4,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 6 and 18.
+const ERF_INV_IMPL_EN: &[f64] = &[
+    -0.0167431005076633737133,
+    -0.00112951438745580278863,
+    0.00105628862152492910091,
+    0.000209386317487588078668,
+    0.149624783758342370182e-4,
+    0.449696789927706453732e-6,
+    0.462596163522878599135e-8,
+    -0.281128735628831791805e-13,
+    0.99055709973310326855e-16,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 6 and 18.
+const ERF_INV_IMPL_ED: &[f64] = &[
+    1.0,
+    0.591429344886417493481,
+    0.138151865749083321638,
+    0.0160746087093676504695,
+    0.000964011807005165528527,
+    0.275335474764726041141e-4,
+    0.282243172016108031869e-6,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 18 and 44.
+const ERF_INV_IMPL_FN: &[f64] = &[
+    -0.0024978212791898131227,
+    -0.779190719229053954292e-5,
+    0.254723037413027451751e-4,
+    0.162397777342510920873e-5,
+    0.396341011304801168516e-7,
+    0.411632831190944208473e-9,
+    0.145596286718675035587e-11,
+    -0.116765012397184275695e-17,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x between 18 and 44.
+const ERF_INV_IMPL_FD: &[f64] = &[
+    1.0,
+    0.207123112214422517181,
+    0.0169410838120975906478,
+    0.000690538265622684595676,
+    0.145007359818232637924e-4,
+    0.144437756628144157666e-6,
+    0.509761276599778486139e-9,
+];
+
+/// Polynomial coefficients for a numerator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x greater than 44.
+const ERF_INV_IMPL_GN: &[f64] = &[
+    -0.000539042911019078575891,
+    -0.28398759004727721098e-6,
+    0.899465114892291446442e-6,
+    0.229345859265920864296e-7,
+    0.225561444863500149219e-9,
+    0.947846627503022684216e-12,
+    0.135880130108924861008e-14,
+    -0.348890393399948882918e-21,
+];
+
+/// Polynomial coefficients for a denominator of `erf_inv_impl`
+/// in the interval [0.75, 1] with x greater than 44.
+const ERF_INV_IMPL_GD: &[f64] = &[
+    1.0,
+    0.0845746234001899436914,
+    0.00282092984726264681981,
+    0.468292921940894236786e-4,
+    0.399968812193862100054e-6,
+    0.161809290887904476097e-8,
+    0.231558608310259605225e-11,
+];
+
+/// `erf_impl` computes the error function at `z`.
+/// If `inv` is true, `1 - erf` is calculated as opposed to `erf`
+fn erf_impl(z: f64, inv: bool) -> f64 {
+    if z < 0.0 {
+        if !inv {
+            return -erf_impl(-z, false);
+        }
+        if z < -0.5 {
+            return 2.0 - erf_impl(-z, true);
+        }
+        return 1.0 + erf_impl(-z, false);
+    }
+
+    let result = if z < 0.5 {
+        if z < 1e-10 {
+            z * 1.125 + z * 0.003379167095512573896158903121545171688
+        } else {
+            z * 1.125
+                + z * evaluate::polynomial(z, ERF_IMPL_AN) / evaluate::polynomial(z, ERF_IMPL_AD)
+        }
+    } else if z < 110.0 {
+        let (r, b) = if z < 0.75 {
+            (
+                evaluate::polynomial(z - 0.5, ERF_IMPL_BN)
+                    / evaluate::polynomial(z - 0.5, ERF_IMPL_BD),
+                0.3440242112,
+            )
+        } else if z < 1.25 {
+            (
+                evaluate::polynomial(z - 0.75, ERF_IMPL_CN)
+                    / evaluate::polynomial(z - 0.75, ERF_IMPL_CD),
+                0.419990927,
+            )
+        } else if z < 2.25 {
+            (
+                evaluate::polynomial(z - 1.25, ERF_IMPL_DN)
+                    / evaluate::polynomial(z - 1.25, ERF_IMPL_DD),
+                0.4898625016,
+            )
+        } else if z < 3.5 {
+            (
+                evaluate::polynomial(z - 2.25, ERF_IMPL_EN)
+                    / evaluate::polynomial(z - 2.25, ERF_IMPL_ED),
+                0.5317370892,
+            )
+        } else if z < 5.25 {
+            (
+                evaluate::polynomial(z - 3.5, ERF_IMPL_FN)
+                    / evaluate::polynomial(z - 3.5, ERF_IMPL_FD),
+                0.5489973426,
+            )
+        } else if z < 8.0 {
+            (
+                evaluate::polynomial(z - 5.25, ERF_IMPL_GN)
+                    / evaluate::polynomial(z - 5.25, ERF_IMPL_GD),
+                0.5571740866,
+            )
+        } else if z < 11.5 {
+            (
+                evaluate::polynomial(z - 8.0, ERF_IMPL_HN)
+                    / evaluate::polynomial(z - 8.0, ERF_IMPL_HD),
+                0.5609807968,
+            )
+        } else if z < 17.0 {
+            (
+                evaluate::polynomial(z - 11.5, ERF_IMPL_IN)
+                    / evaluate::polynomial(z - 11.5, ERF_IMPL_ID),
+                0.5626493692,
+            )
+        } else if z < 24.0 {
+            (
+                evaluate::polynomial(z - 17.0, ERF_IMPL_JN)
+                    / evaluate::polynomial(z - 17.0, ERF_IMPL_JD),
+                0.5634598136,
+            )
+        } else if z < 38.0 {
+            (
+                evaluate::polynomial(z - 24.0, ERF_IMPL_KN)
+                    / evaluate::polynomial(z - 24.0, ERF_IMPL_KD),
+                0.5638477802,
+            )
+        } else if z < 60.0 {
+            (
+                evaluate::polynomial(z - 38.0, ERF_IMPL_LN)
+                    / evaluate::polynomial(z - 38.0, ERF_IMPL_LD),
+                0.5640528202,
+            )
+        } else if z < 85.0 {
+            (
+                evaluate::polynomial(z - 60.0, ERF_IMPL_MN)
+                    / evaluate::polynomial(z - 60.0, ERF_IMPL_MD),
+                0.5641309023,
+            )
+        } else {
+            (
+                evaluate::polynomial(z - 85.0, ERF_IMPL_NN)
+                    / evaluate::polynomial(z - 85.0, ERF_IMPL_ND),
+                0.5641584396,
+            )
+        };
+        let g = (-z * z).exp() / z;
+        g * b + g * r
+    } else {
+        0.0
+    };
+
+    if inv && z >= 0.5 {
+        result
+    } else if z >= 0.5 || inv {
+        1.0 - result
+    } else {
+        result
+    }
+}
+
+// `erf_inv_impl` computes the inverse error function where
+// `p`,`q`, and `s` are the first, second, and third intermediate
+// parameters respectively
+fn erf_inv_impl(p: f64, q: f64, s: f64) -> f64 {
+    let result = if p <= 0.5 {
+        let y = 0.0891314744949340820313;
+        let g = p * (p + 10.0);
+        let r = evaluate::polynomial(p, ERF_INV_IMPL_AN) / evaluate::polynomial(p, ERF_INV_IMPL_AD);
+        g * y + g * r
+    } else if q >= 0.25 {
+        let y = 2.249481201171875;
+        let g = (-2.0 * q.ln()).sqrt();
+        let xs = q - 0.25;
+        let r =
+            evaluate::polynomial(xs, ERF_INV_IMPL_BN) / evaluate::polynomial(xs, ERF_INV_IMPL_BD);
+        g / (y + r)
+    } else {
+        let x = (-q.ln()).sqrt();
+        if x < 3.0 {
+            let y = 0.807220458984375;
+            let xs = x - 1.125;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_CN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_CD);
+            y * x + r * x
+        } else if x < 6.0 {
+            let y = 0.93995571136474609375;
+            let xs = x - 3.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_DN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_DD);
+            y * x + r * x
+        } else if x < 18.0 {
+            let y = 0.98362827301025390625;
+            let xs = x - 6.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_EN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_ED);
+            y * x + r * x
+        } else if x < 44.0 {
+            let y = 0.99714565277099609375;
+            let xs = x - 18.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_FN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_FD);
+            y * x + r * x
+        } else {
+            let y = 0.99941349029541015625;
+            let xs = x - 44.0;
+            let r = evaluate::polynomial(xs, ERF_INV_IMPL_GN)
+                / evaluate::polynomial(xs, ERF_INV_IMPL_GD);
+            y * x + r * x
+        }
+    };
+    s * result
+}

candle-core/src/cpu/mod.rs

@@ -1,3 +1,4 @@
+pub mod erf;
 pub mod kernels;
 
 trait Cpu<const ARR: usize> {

candle-core/src/cpu_backend.rs

@@ -4,6 +4,9 @@ use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType};
 use half::{bf16, f16};
 use rayon::prelude::*;
 
+const USE_IM2COL_CONV1D: bool = true;
+const USE_IM2COL_CONV2D: bool = true;
+
 // TODO: Maybe we should not implement [Clone] here and instead have an explicit allocator +
 // intercept the oom errors to avoid panicking and provide a proper error.
 #[derive(Debug, Clone)]
@@ -724,6 +727,36 @@ impl Map1 for MaxPool2D {
     }
 }
 
+struct UpsampleNearest1D(usize);
+
+impl Map1 for UpsampleNearest1D {
+    fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
+        // TODO: Specialized implementation for the case 2*sz?
+        let dst_sz = self.0;
+        let (b_sz, c, src_sz) = layout.shape().dims3()?;
+        let stride = layout.stride();
+        let stride_sz = stride[2];
+        let src_index = layout.start_offset();
+        let scale_sz = src_sz as f64 / dst_sz as f64;
+        let mut dst = vec![T::zero(); b_sz * c * dst_sz];
+        let src_idxs = (0..dst_sz)
+            .map(|idx| usize::min(src_sz - 1, (idx as f64 * scale_sz) as usize))
+            .collect::<Vec<_>>();
+        for b_idx in 0..b_sz {
+            let dst = &mut dst[b_idx * c * dst_sz..];
+            let src_index = src_index + b_idx * stride[0];
+            for c_idx in 0..c {
+                let dst = &mut dst[c_idx * dst_sz..];
+                let src_index = src_index + c_idx * stride[1];
+                for (idx, src_idx) in src_idxs.iter().enumerate() {
+                    dst[idx] = src[src_index + src_idx * stride_sz]
+                }
+            }
+        }
+        Ok(dst)
+    }
+}
+
 struct UpsampleNearest2D(usize, usize);
 
 impl Map1 for UpsampleNearest2D {
@@ -771,11 +804,11 @@ impl<'a, I: IntDType> Map1 for Gather<'a, I> {
     fn f<T: WithDType>(&self, src: &[T], src_l: &Layout) -> Result<Vec<T>> {
         let ids = match self.ids_l.contiguous_offsets() {
             Some((a, b)) => &self.ids[a..b],
-            None => Err(Error::RequiresContiguous { op: "gather" })?,
+            None => Err(Error::RequiresContiguous { op: "gather" }.bt())?,
         };
         let src = match src_l.contiguous_offsets() {
             Some((a, b)) => &src[a..b],
-            None => Err(Error::RequiresContiguous { op: "gather" })?,
+            None => Err(Error::RequiresContiguous { op: "gather" }.bt())?,
         };
         let dim = self.dim;
         let ids_dims = self.ids_l.dims();
@@ -824,7 +857,7 @@ impl<'a, I: IntDType> Map1 for IndexSelect<'a, I> {
     fn f<T: WithDType>(&self, src: &[T], layout: &Layout) -> Result<Vec<T>> {
         let src = match layout.contiguous_offsets() {
             Some((a, b)) => &src[a..b],
-            None => Err(Error::RequiresContiguous { op: "index-select" })?,
+            None => Err(Error::RequiresContiguous { op: "index-select" }.bt())?,
         };
         let dim = self.dim;
         let n_ids = match self.ids_l.dims() {
@@ -880,7 +913,7 @@ impl<'a, I: IntDType> Map2 for ScatterAdd<'a, I> {
         let mut dst = vec![T::zero(); dst_len];
         copy_strided_src_(v1, &mut dst, 0, l1);
         let src = match src_l.contiguous_offsets() {
-            None => Err(Error::RequiresContiguous { op: "scatter-add" })?,
+            None => Err(Error::RequiresContiguous { op: "scatter-add" }.bt())?,
             Some((o1, o2)) => &src[o1..o2],
         };
 
@@ -896,7 +929,7 @@ impl<'a, I: IntDType> Map2 for ScatterAdd<'a, I> {
 
         let ids = match self.ids_l.contiguous_offsets() {
             Some((a, b)) => &self.ids[a..b],
-            None => Err(Error::RequiresContiguous { op: "gather" })?,
+            None => Err(Error::RequiresContiguous { op: "gather" }.bt())?,
         };
         for left_i in 0..ids_left_len {
             let start_ids_idx = left_i * ids_right_len * ids_dim_len;
@@ -938,7 +971,7 @@ impl<'a, I: IntDType> Map2 for IndexAdd<'a, I> {
         let mut dst = vec![T::zero(); dst_len];
         copy_strided_src_(v1, &mut dst, 0, l1);
         let src = match src_l.contiguous_offsets() {
-            None => Err(Error::RequiresContiguous { op: "index-add" })?,
+            None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
             Some((o1, o2)) => &src[o1..o2],
         };
         let dim = self.dim;
@@ -1089,6 +1122,208 @@ impl<'a> Map2 for Conv1D<'a> {
     }
 }
 
+struct Im2Col1D {
+    l_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col1D {
+    fn l_out(&self, l: usize) -> usize {
+        (l + 2 * self.padding - self.dilation * (self.l_k - 1) - 1) / self.stride + 1
+    }
+}
+
+impl Map1 for Im2Col1D {
+    fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>> {
+        let &Self {
+            l_k,
+            stride,
+            dilation,
+            padding,
+        } = self;
+        let (b, c, l) = layout.shape().dims3()?;
+        let l_out = self.l_out(l);
+        let src = &vs[layout.start_offset()..];
+        let mut dst = vec![T::zero(); b * l_out * c * l_k];
+        let (src_s0, src_s1, src_s2) = {
+            let s = layout.stride();
+            (s[0], s[1], s[2])
+        };
+        // TODO: provide specialized kernels for the common use cases.
+        // - l_k = 1
+        // - padding = 0
+        // - stride = 1
+        // - dilation = 1
+        for b_idx in 0..b {
+            let src_idx = b_idx * src_s0;
+            let dst_idx = b_idx * l_out * c * l_k;
+            for l_idx in 0..l_out {
+                let dst_idx = dst_idx + l_idx * c * l_k;
+                for c_idx in 0..c {
+                    let dst_idx = dst_idx + c_idx * l_k;
+                    let src_idx = c_idx * src_s1 + src_idx;
+                    for l_k_idx in 0..l_k {
+                        let src_l = l_idx * stride + l_k_idx * dilation;
+                        if padding != 0 && (src_l < padding || src_l >= l + padding) {
+                            continue;
+                        }
+                        let src_l = src_l - padding;
+                        let src_idx = src_idx + src_l * src_s2;
+                        let dst_idx = dst_idx + l_k_idx;
+                        dst[dst_idx] = src[src_idx]
+                    }
+                }
+            }
+        }
+        Ok(dst)
+    }
+}
+
+struct Im2Col {
+    h_k: usize,
+    w_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col {
+    fn hw_out(&self, h: usize, w: usize) -> (usize, usize) {
+        let h_out = (h + 2 * self.padding - self.dilation * (self.h_k - 1) - 1) / self.stride + 1;
+        let w_out = (w + 2 * self.padding - self.dilation * (self.w_k - 1) - 1) / self.stride + 1;
+        (h_out, w_out)
+    }
+}
+
+impl Map1 for Im2Col {
+    fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>> {
+        let &Self {
+            h_k,
+            w_k,
+            stride,
+            dilation,
+            padding,
+        } = self;
+        let (b, c, h, w) = layout.shape().dims4()?;
+        let (h_out, w_out) = self.hw_out(h, w);
+        let src = &vs[layout.start_offset()..];
+        let mut dst = vec![T::zero(); b * h_out * w_out * c * h_k * w_k];
+        let (src_s0, src_s1, src_s2, src_s3) = {
+            let s = layout.stride();
+            (s[0], s[1], s[2], s[3])
+        };
+        // TODO: provide specialized kernels for the common use cases.
+        // - h_k = w_k = 1
+        // - padding = 0
+        // - stride = 1
+        // - dilation = 1
+        for b_idx in 0..b {
+            let src_idx = b_idx * src_s0;
+            let dst_idx = b_idx * h_out * w_out * c * h_k * w_k;
+            for h_idx in 0..h_out {
+                let dst_idx = dst_idx + h_idx * w_out * c * h_k * w_k;
+                for w_idx in 0..w_out {
+                    let dst_idx = dst_idx + w_idx * c * h_k * w_k;
+                    for c_idx in 0..c {
+                        let dst_idx = dst_idx + c_idx * h_k * w_k;
+                        let src_idx = c_idx * src_s1 + src_idx;
+                        for h_k_idx in 0..h_k {
+                            let src_h = h_idx * stride + h_k_idx * dilation;
+                            if padding != 0 && (src_h < padding || src_h >= h + padding) {
+                                continue;
+                            }
+                            let src_h = src_h - padding;
+                            let src_idx = src_idx + src_h * src_s2;
+                            let dst_idx = dst_idx + h_k_idx * w_k;
+                            for w_k_idx in 0..w_k {
+                                let src_w = w_idx * stride + w_k_idx * dilation;
+                                if padding != 0 && (src_w < padding || src_w >= w + padding) {
+                                    continue;
+                                }
+                                let src_w = src_w - padding;
+                                let src_idx = src_idx + src_w * src_s3;
+                                let dst_idx = dst_idx + w_k_idx;
+                                dst[dst_idx] = src[src_idx]
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        Ok(dst)
+    }
+}
+
+struct 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> {
@@ -1190,9 +1425,8 @@ impl<'a> Map2 for ConvTranspose2D<'a> {
         let (out_h, out_w) = (p.out_h(), p.out_w());
 
         // Output shape: [b_size, c_out, out_h, out_w].
-        let c_out = p.groups * p.c_out_per_group;
-        let dst = vec![T::zero(); p.b_size * c_out * out_h * out_w];
-        let dst_s0 = c_out * out_h * out_w;
+        let dst = vec![T::zero(); p.b_size * p.c_out * out_h * out_w];
+        let dst_s0 = p.c_out * out_h * out_w;
         let dst_s1 = out_h * out_w;
         let dst_s2 = out_w;
         let dst_s3 = 1;
@@ -1215,16 +1449,12 @@ impl<'a> Map2 for ConvTranspose2D<'a> {
             }
         }
 
-        let c_in_per_group = p.c_in / p.groups;
         for k_y in 0..p.k_h {
             for k_x in 0..p.k_w {
-                (0..c_out).into_par_iter().for_each(|dst_c_idx| {
-                    let (group_idx, dst_c_idx_in_group) =
-                        (dst_c_idx / p.c_out_per_group, dst_c_idx % p.c_out_per_group);
-                    let k_cont = (0..c_in_per_group)
+                (0..p.c_out).into_par_iter().for_each(|dst_c_idx| {
+                    let k_cont = (0..p.c_in)
                         .map(|c_in_idx| {
-                            let c_in_idx = group_idx * c_in_per_group + c_in_idx;
-                            k[c_in_idx * k_s0 + dst_c_idx_in_group * k_s1 + k_y * k_s2 + k_x * k_s3]
+                            k[c_in_idx * k_s0 + dst_c_idx * k_s1 + k_y * k_s2 + k_x * k_s3]
                         })
                         .collect::<Vec<_>>();
                     for b_idx in 0..p.b_size {
@@ -1250,7 +1480,7 @@ impl<'a> Map2 for ConvTranspose2D<'a> {
                                             inp_cont.as_ptr(),
                                             k_cont.as_ptr(),
                                             &mut d,
-                                            c_in_per_group,
+                                            p.c_in,
                                         )
                                     }
                                     let dst_p = dst.as_ptr();
@@ -1299,8 +1529,9 @@ impl Map2 for MatMul {
     ) -> Result<Vec<T>> {
         use gemm::{gemm, Parallelism};
 
-        if T::DTYPE == DType::BF16 {
-            return Err(Error::UnsupportedDTypeForOp(T::DTYPE, "matmul").bt())?;
+        match T::DTYPE {
+            DType::F16 | DType::F32 | DType::F64 => {}
+            _ => Err(Error::UnsupportedDTypeForOp(T::DTYPE, "matmul").bt())?,
         }
 
         let (b, m, n, k) = self.0;
@@ -2004,6 +2235,10 @@ impl BackendStorage for CpuStorage {
         MaxPool2D(kernel_size, stride).map(self, layout)
     }
 
+    fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
+        UpsampleNearest1D(sz).map(self, layout)
+    }
+
     fn upsample_nearest2d(&self, layout: &Layout, h: usize, w: usize) -> Result<Self> {
         UpsampleNearest2D(h, w).map(self, layout)
     }
@@ -2232,7 +2467,50 @@ impl BackendStorage for CpuStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv1D,
     ) -> Result<Self> {
-        Conv1D(params).map(self, l, kernel, kernel_l)
+        if !USE_IM2COL_CONV1D {
+            return Conv1D(params).map(self, l, kernel, kernel_l);
+        }
+        let op = Im2Col1D {
+            l_k: params.k_size,
+            padding: params.padding,
+            stride: params.stride,
+            dilation: params.dilation,
+        };
+        let col = op.map(self, l)?;
+        let b = params.b_size;
+        let n = params.c_out;
+        let l_out = params.l_out();
+        let k = op.l_k * params.c_in;
+        let m = l_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
+        let res_l = Layout::contiguous((b, l_out, params.c_out)).transpose(1, 2)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
+    }
+
+    fn 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(
@@ -2242,7 +2520,43 @@ impl BackendStorage for CpuStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv2D,
     ) -> Result<Self> {
-        Conv2D(params).map(self, l, kernel, kernel_l)
+        if !USE_IM2COL_CONV2D {
+            return Conv2D(params).map(self, l, kernel, kernel_l);
+        }
+        let op = Im2Col {
+            h_k: params.k_h,
+            w_k: params.k_w,
+            padding: params.padding,
+            stride: params.stride,
+            dilation: params.dilation,
+        };
+        let col = op.map(self, l)?;
+        let b = params.b_size;
+        let n = params.c_out;
+        let (h_out, w_out) = (params.out_h(), params.out_w());
+        let k = op.h_k * op.w_k * params.c_in;
+        let m = h_out * w_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
+        let res_l = Layout::contiguous((b, h_out, w_out, params.c_out))
+            .transpose(1, 2)?
+            .transpose(1, 3)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
     }
 
     fn conv_transpose2d(
@@ -2303,25 +2617,25 @@ impl BackendStorage for CpuStorage {
             Self::U8(ids) => {
                 let ids = match ids_l.contiguous_offsets() {
                     Some((a, b)) => &ids[a..b],
-                    None => Err(Error::RequiresContiguous { op: "index-add" })?,
+                    None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
                 };
                 IndexAdd { ids, dim }.map(self, l, src, src_l)
             }
             Self::U32(ids) => {
                 let ids = match ids_l.contiguous_offsets() {
                     Some((a, b)) => &ids[a..b],
-                    None => Err(Error::RequiresContiguous { op: "index-add" })?,
+                    None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
                 };
                 IndexAdd { ids, dim }.map(self, l, src, src_l)
             }
             Self::I64(ids) => {
                 let ids = match ids_l.contiguous_offsets() {
                     Some((a, b)) => &ids[a..b],
-                    None => Err(Error::RequiresContiguous { op: "index-add" })?,
+                    None => Err(Error::RequiresContiguous { op: "index-add" }.bt())?,
                 };
                 IndexAdd { ids, dim }.map(self, l, src, src_l)
             }
-            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-add")),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-add").bt()),
         }
     }
 
@@ -2367,6 +2681,10 @@ impl BackendDevice for CpuDevice {
         Ok(Self)
     }
 
+    fn set_seed(&self, _seed: u64) -> Result<()> {
+        crate::bail!("cannot seed the CPU rng with set_seed")
+    }
+
     fn rand_uniform(&self, shape: &Shape, dtype: DType, min: f64, max: f64) -> Result<CpuStorage> {
         use rand::prelude::*;
 

candle-core/src/cuda_backend.rs

@@ -223,6 +223,14 @@ impl BackendDevice for CudaDevice {
         })
     }
 
+    fn set_seed(&self, seed: u64) -> Result<()> {
+        // We do not call set_seed but instead create a new curand object. This ensures that the
+        // state will be identical and the same random numbers will be generated.
+        let mut curand = self.curand.lock().unwrap();
+        curand.0 = cudarc::curand::CudaRng::new(seed, self.device.clone()).w()?;
+        Ok(())
+    }
+
     fn location(&self) -> crate::DeviceLocation {
         crate::DeviceLocation::Cuda {
             gpu_id: self.device.ordinal(),
@@ -600,6 +608,105 @@ impl Map1 for Elu {
     }
 }
 
+struct Im2Col1D {
+    l_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col1D {
+    fn l_out(&self, l: usize) -> usize {
+        (l + 2 * self.padding - self.dilation * (self.l_k - 1) - 1) / self.stride + 1
+    }
+}
+
+impl Map1 for Im2Col1D {
+    fn f<T: DeviceRepr + WithDType>(
+        &self,
+        src: &CudaSlice<T>,
+        dev: &CudaDevice,
+        layout: &Layout,
+    ) -> Result<CudaSlice<T>> {
+        let shape = layout.shape();
+        let dims = shape.dims();
+        let l_out = self.l_out(dims[2]);
+        let dst_el = dims[0] * l_out * dims[1] * self.l_k;
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
+        let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
+        let src = &src.slice(layout.start_offset()..);
+        let func = dev.get_or_load_func(&kernel_name::<T>("im2col1d"), kernels::CONV)?;
+        // SAFETY: Set later by running the kernel.
+        let dst = unsafe { dev.alloc::<T>(dst_el) }.w()?;
+        let params = (
+            dst_el,
+            l_out,
+            self.l_k,
+            self.stride,
+            self.padding,
+            self.dilation,
+            &ds,
+            src,
+            &dst,
+        );
+        // SAFETY: ffi.
+        unsafe { func.launch(cfg, params) }.w()?;
+        Ok(dst)
+    }
+}
+
+struct Im2Col {
+    h_k: usize,
+    w_k: usize,
+    stride: usize,
+    dilation: usize,
+    padding: usize,
+}
+
+impl Im2Col {
+    fn hw_out(&self, h: usize, w: usize) -> (usize, usize) {
+        let h_out = (h + 2 * self.padding - self.dilation * (self.h_k - 1) - 1) / self.stride + 1;
+        let w_out = (w + 2 * self.padding - self.dilation * (self.w_k - 1) - 1) / self.stride + 1;
+        (h_out, w_out)
+    }
+}
+
+impl Map1 for Im2Col {
+    fn f<T: DeviceRepr + WithDType>(
+        &self,
+        src: &CudaSlice<T>,
+        dev: &CudaDevice,
+        layout: &Layout,
+    ) -> Result<CudaSlice<T>> {
+        let shape = layout.shape();
+        let dims = shape.dims();
+        let (h_out, w_out) = self.hw_out(dims[2], dims[3]);
+        let dst_el = dims[0] * h_out * w_out * dims[1] * self.h_k * self.w_k;
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
+        let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
+        let src = &src.slice(layout.start_offset()..);
+        let func = dev.get_or_load_func(&kernel_name::<T>("im2col"), kernels::CONV)?;
+        // SAFETY: Set later by running the kernel.
+        let dst = unsafe { dev.alloc::<T>(dst_el) }.w()?;
+        let params = (
+            dst_el,
+            h_out,
+            w_out,
+            self.h_k,
+            self.w_k,
+            self.stride,
+            self.padding,
+            self.dilation,
+            &ds,
+            src,
+            &dst,
+        );
+        // SAFETY: ffi.
+        unsafe { func.launch(cfg, params) }.w()?;
+        Ok(dst)
+    }
+}
+
 struct Powf(f64);
 impl Map1 for Powf {
     fn f<T: DeviceRepr + WithDType>(
@@ -785,8 +892,6 @@ impl<'a> Map1 for IndexSelect<'a> {
         };
         let ids_shape = ids_l.shape();
         let ids_dims = ids_shape.dims();
-        let ids_el = ids_shape.elem_count();
-        let cfg = LaunchConfig::for_num_elems(ids_el as u32);
         let ds = dev.htod_copy([ids_dims, ids_l.stride()].concat()).w()?;
         let src = match src_l.contiguous_offsets() {
             Some((o1, o2)) => src.slice(o1..o2),
@@ -794,19 +899,23 @@ impl<'a> Map1 for IndexSelect<'a> {
         };
         let left_size: usize = src_l.dims()[..self.2].iter().product();
         let right_size: usize = src_l.dims()[self.2 + 1..].iter().product();
-        let dim_size = src_l.dims()[self.2];
+        let src_dim_size = src_l.dims()[self.2];
+        let ids_dim_size = ids_shape.elem_count();
+        let dst_el = ids_shape.elem_count() * left_size * right_size;
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
         let func = dev.get_or_load_func(&kernel_name::<T>(name), kernels::INDEXING)?;
         // SAFETY: Set later by running the kernel.
-        let out = unsafe { dev.alloc::<T>(ids_el * left_size * right_size) }.w()?;
+        let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
         let params = (
-            ids_el,
+            dst_el,
             ids_dims.len(),
             &ds,
             ids,
             &src,
             &out,
             left_size,
-            dim_size,
+            src_dim_size,
+            ids_dim_size,
             right_size,
         );
         // SAFETY: ffi.
@@ -1050,11 +1159,11 @@ impl<'a> Map2 for ConvTranspose2D<'a> {
         k_l: &Layout,
         dev: &CudaDevice,
     ) -> Result<CudaSlice<T>> {
-        // Kernel shape: (c_in_k, c_out / groups, h_k, w_k)
+        // Kernel shape: (c_in_k, c_out, h_k, w_k)
         // Input shape: (b_size, c_in, h_in, w_in)
         let p = &self.0;
         let (out_w, out_h) = (p.out_w(), p.out_h());
-        let dst_el = p.c_out_per_group * p.groups * out_w * out_h * p.b_size;
+        let dst_el = p.c_out * out_w * out_h * p.b_size;
         let inp = &inp.slice(inp_l.start_offset()..);
         let k = &k.slice(k_l.start_offset()..);
         let shape = inp_l.shape();
@@ -1063,13 +1172,7 @@ impl<'a> Map2 for ConvTranspose2D<'a> {
 
         // SAFETY: Set later by running the kernel.
         let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
-        const NUM_THREADS: u32 = 512;
-        let num_blocks = (dst_el as u32 + NUM_THREADS - 1) / NUM_THREADS;
-        let mut cfg = LaunchConfig {
-            grid_dim: (num_blocks, 1, 1),
-            block_dim: (NUM_THREADS, 1, 1),
-            shared_mem_bytes: 0,
-        };
+        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
         let func = dev.get_or_load_func(&kernel_name::<T>("conv_transpose2d"), kernels::CONV)?;
         let ds = if dims.len() == 4 {
             [dims, inp_l.stride(), k_l.dims(), k_l.stride()].concat()
@@ -1078,13 +1181,13 @@ impl<'a> Map2 for ConvTranspose2D<'a> {
         };
         let ds = dev.htod_copy(ds).w()?;
         let params = (
+            el,
             out_w,
             out_h,
             p.stride,
             p.padding,
             p.output_padding,
             p.dilation,
-            p.groups,
             &ds,
             inp,
             k,
@@ -1663,9 +1766,56 @@ impl BackendStorage for CudaStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv1D,
     ) -> Result<Self> {
+        const USE_IM2COL_CONV1D: bool = true;
+
         let device = self.device().clone();
-        let slice = Conv1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
-        Ok(Self { slice, device })
+        if !USE_IM2COL_CONV1D {
+            let slice = Conv1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
+            return Ok(Self { slice, device });
+        }
+
+        let col = Im2Col1D {
+            l_k: params.k_size,
+            stride: params.stride,
+            dilation: params.dilation,
+            padding: params.padding,
+        }
+        .map(&self.slice, &device, l)?;
+        let col = Self { slice: col, device };
+        let l_out = params.l_out();
+        let b = params.b_size;
+        let n = params.c_out;
+        let k = params.k_size * params.c_in;
+        let m = l_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
+        let res_l = Layout::contiguous((b, l_out, n)).transpose(1, 2)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
+    }
+
+    fn conv_transpose1d(
+        &self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &crate::conv::ParamsConvTranspose1D,
+    ) -> Result<Self> {
+        todo!()
     }
 
     #[cfg(not(feature = "cudnn"))]
@@ -1676,9 +1826,50 @@ impl BackendStorage for CudaStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConv2D,
     ) -> Result<Self> {
+        const USE_IM2COL_CONV2D: bool = true;
+
         let device = self.device().clone();
-        let slice = Conv2D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
-        Ok(Self { slice, device })
+        if !USE_IM2COL_CONV2D {
+            let slice = Conv2D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
+            return Ok(Self { slice, device });
+        }
+
+        let col = Im2Col {
+            h_k: params.k_h,
+            w_k: params.k_w,
+            stride: params.stride,
+            dilation: params.dilation,
+            padding: params.padding,
+        }
+        .map(&self.slice, &device, l)?;
+        let col = Self { slice: col, device };
+        let h_out = params.out_h();
+        let w_out = params.out_w();
+        let b = params.b_size;
+        let n = params.c_out;
+        let k = params.k_h * params.k_w * params.c_in;
+        let m = h_out * w_out;
+        let col_l = Layout::contiguous((b, m, k));
+        let res = if kernel_l.is_contiguous() {
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        } else {
+            // Make the kernel contiguous if not already the case.
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
+            kernel.copy_strided_src(&mut kernel_c, 0, kernel_l)?;
+            let kernel_l = Layout::contiguous_with_offset((1, n, k), kernel_l.start_offset())
+                .transpose(1, 2)?
+                .broadcast_as((b, k, n))?;
+            col.matmul(kernel, (b, m, n, k), &col_l, &kernel_l)?
+        };
+        let res_l = Layout::contiguous((b, h_out, w_out, n))
+            .transpose(1, 2)?
+            .transpose(1, 3)?;
+        let mut res_t = self.device().zeros_impl(res_l.shape(), res.dtype())?;
+        res.copy_strided_src(&mut res_t, 0, &res_l)?;
+        Ok(res_t)
     }
 
     #[cfg(feature = "cudnn")]
@@ -1783,6 +1974,10 @@ impl BackendStorage for CudaStorage {
         Ok(Self { slice, device })
     }
 
+    fn upsample_nearest1d(&self, _: &Layout, _out_sz: usize) -> Result<Self> {
+        crate::bail!("upsample-nearest1d is not supported on cuda")
+    }
+
     fn upsample_nearest2d(&self, l: &Layout, out_w: usize, out_h: usize) -> Result<Self> {
         let device = self.device().clone();
         let slice = UpsampleNearest2D(out_w, out_h).map(&self.slice, &device, l)?;
@@ -1986,7 +2181,7 @@ impl BackendStorage for CudaStorage {
                 if src_l.is_contiguous() {
                     dev.dtod_copy(&src, &mut dst).w()?
                 } else {
-                    let func = dev.get_or_load_func("ucopy_64", kernels::UNARY)?;
+                    let func = dev.get_or_load_func("ucopy_f64", kernels::UNARY)?;
                     // SAFETY: Set later by running the kernel.
                     let params = (el_count, dims.len(), &ds, &src, &mut dst);
                     // SAFETY: ffi.

candle-core/src/cudnn.rs

@@ -34,6 +34,9 @@ pub(crate) fn launch_conv2d<
     params: &crate::conv::ParamsConv2D,
     dev: &crate::cuda_backend::CudaDevice,
 ) -> crate::Result<()> {
+    use crate::conv::CudnnFwdAlgo as CandleAlgo;
+    use cudarc::cudnn::sys::cudnnConvolutionFwdAlgo_t as A;
+
     let device_id = dev.id();
     let cudnn = CUDNN.with(|cudnn| {
         if let Some(cudnn) = cudnn.borrow().get(&device_id) {
@@ -90,7 +93,20 @@ pub(crate) fn launch_conv2d<
         w: &w,
         y: &y,
     };
-    let alg = conv2d.pick_algorithm()?;
+    let alg = match params.cudnn_fwd_algo {
+        None => conv2d.pick_algorithm()?,
+        Some(CandleAlgo::ImplicitGemm) => A::CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM,
+        Some(CandleAlgo::ImplicitPrecompGemm) => {
+            A::CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM
+        }
+        Some(CandleAlgo::Gemm) => A::CUDNN_CONVOLUTION_FWD_ALGO_GEMM,
+        Some(CandleAlgo::Direct) => A::CUDNN_CONVOLUTION_FWD_ALGO_DIRECT,
+        Some(CandleAlgo::Fft) => A::CUDNN_CONVOLUTION_FWD_ALGO_FFT,
+        Some(CandleAlgo::FftTiling) => A::CUDNN_CONVOLUTION_FWD_ALGO_FFT_TILING,
+        Some(CandleAlgo::Winograd) => A::CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD,
+        Some(CandleAlgo::WinogradNonFused) => A::CUDNN_CONVOLUTION_FWD_ALGO_WINOGRAD_NONFUSED,
+        Some(CandleAlgo::Count) => A::CUDNN_CONVOLUTION_FWD_ALGO_COUNT,
+    };
     let workspace_size = conv2d.get_workspace_size(alg)?;
     let mut workspace = dev.cuda_device().alloc_zeros::<u8>(workspace_size)?;
     unsafe {

candle-core/src/device.rs

@@ -8,12 +8,14 @@ use crate::{CpuStorage, DType, Result, Shape, Storage, WithDType};
 pub enum DeviceLocation {
     Cpu,
     Cuda { gpu_id: usize },
+    Metal { gpu_id: usize },
 }
 
 #[derive(Debug, Clone)]
 pub enum Device {
     Cpu,
     Cuda(crate::CudaDevice),
+    Metal(crate::MetalDevice),
 }
 
 pub trait NdArray {
@@ -128,10 +130,23 @@ impl Device {
         Ok(Self::Cuda(crate::CudaDevice::new(ordinal)?))
     }
 
+    pub fn new_metal(ordinal: usize) -> Result<Self> {
+        Ok(Self::Metal(crate::MetalDevice::new(ordinal)?))
+    }
+
+    pub fn set_seed(&self, seed: u64) -> Result<()> {
+        match self {
+            Self::Cpu => CpuDevice.set_seed(seed),
+            Self::Cuda(c) => c.set_seed(seed),
+            Self::Metal(m) => m.set_seed(seed),
+        }
+    }
+
     pub fn same_device(&self, rhs: &Self) -> bool {
         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,
         }
     }
@@ -140,21 +155,20 @@ impl Device {
         match self {
             Self::Cpu => DeviceLocation::Cpu,
             Self::Cuda(device) => device.location(),
+            Device::Metal(device) => device.location(),
         }
     }
 
     pub fn is_cpu(&self) -> bool {
-        match self {
-            Self::Cpu => true,
-            Self::Cuda(_) => false,
-        }
+        matches!(self, Self::Cpu)
     }
 
     pub fn is_cuda(&self) -> bool {
-        match self {
-            Self::Cpu => false,
-            Self::Cuda(_) => true,
-        }
+        matches!(self, Self::Cuda(_))
+    }
+
+    pub fn is_metal(&self) -> bool {
+        matches!(self, Self::Metal(_))
     }
 
     pub fn cuda_if_available(ordinal: usize) -> Result<Self> {
@@ -178,8 +192,18 @@ impl Device {
                 Ok(Storage::Cpu(storage))
             }
             Device::Cuda(device) => {
+                // 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::Cuda(storage))
+                Ok(Storage::Metal(storage))
             }
         }
     }
@@ -206,8 +230,18 @@ impl Device {
                 Ok(Storage::Cpu(storage))
             }
             Device::Cuda(device) => {
+                // 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)?;
-                Ok(Storage::Cuda(storage))
+                Ok(Storage::Metal(storage))
             }
         }
     }
@@ -231,6 +265,10 @@ impl Device {
                 let storage = device.ones_impl(shape, dtype)?;
                 Ok(Storage::Cuda(storage))
             }
+            Device::Metal(device) => {
+                let storage = device.ones_impl(shape, dtype)?;
+                Ok(Storage::Metal(storage))
+            }
         }
     }
 
@@ -244,6 +282,10 @@ impl Device {
                 let storage = device.zeros_impl(shape, dtype)?;
                 Ok(Storage::Cuda(storage))
             }
+            Device::Metal(device) => {
+                let storage = device.zeros_impl(shape, dtype)?;
+                Ok(Storage::Metal(storage))
+            }
         }
     }
 
@@ -255,6 +297,11 @@ impl Device {
                 let storage = device.storage_from_cpu_storage(&storage)?;
                 Ok(Storage::Cuda(storage))
             }
+            Device::Metal(device) => {
+                let storage = array.to_cpu_storage();
+                let storage = device.storage_from_cpu_storage(&storage)?;
+                Ok(Storage::Metal(storage))
+            }
         }
     }
 
@@ -266,6 +313,11 @@ impl Device {
                 let storage = device.storage_from_cpu_storage(&storage)?;
                 Ok(Storage::Cuda(storage))
             }
+            Device::Metal(device) => {
+                let storage = S::to_cpu_storage_owned(data);
+                let storage = device.storage_from_cpu_storage(&storage)?;
+                Ok(Storage::Metal(storage))
+            }
         }
     }
 }

candle-core/src/display.rs

@@ -14,6 +14,9 @@ impl Tensor {
             crate::DeviceLocation::Cuda { gpu_id } => {
                 format!(", cuda:{}", gpu_id)
             }
+            crate::DeviceLocation::Metal { gpu_id } => {
+                format!(", metal:{}", gpu_id)
+            }
         };
 
         write!(f, "Tensor[")?;
@@ -476,6 +479,9 @@ impl std::fmt::Display for Tensor {
             crate::DeviceLocation::Cuda { gpu_id } => {
                 format!(", cuda:{}", gpu_id)
             }
+            crate::DeviceLocation::Metal { gpu_id } => {
+                format!(", metal:{}", gpu_id)
+            }
         };
 
         write!(

candle-core/src/dtype.rs

@@ -67,6 +67,20 @@ impl DType {
             Self::F64 => 8,
         }
     }
+
+    pub fn is_int(&self) -> bool {
+        match self {
+            Self::U8 | Self::U32 | Self::I64 => true,
+            Self::BF16 | Self::F16 | Self::F32 | Self::F64 => false,
+        }
+    }
+
+    pub fn is_float(&self) -> bool {
+        match self {
+            Self::U8 | Self::U32 | Self::I64 => false,
+            Self::BF16 | Self::F16 | Self::F32 | Self::F64 => true,
+        }
+    }
 }
 
 pub trait WithDType:

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,
@@ -152,6 +162,10 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
+    fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithCudaSupport)
+    }
+
     fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {
         Err(Error::NotCompiledWithCudaSupport)
     }
@@ -163,6 +177,10 @@ impl crate::backend::BackendDevice for CudaDevice {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
+    fn set_seed(&self, _: u64) -> Result<()> {
+        Err(Error::NotCompiledWithCudaSupport)
+    }
+
     fn location(&self) -> crate::DeviceLocation {
         fail!()
     }

candle-core/src/dummy_metal_backend.rs

@@ -0,0 +1,223 @@
+#![allow(dead_code)]
+use crate::op::{BinaryOpT, CmpOp, ReduceOp, UnaryOpT};
+use crate::{CpuStorage, DType, Error, Layout, Result, Shape};
+
+#[derive(Debug, Clone)]
+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.")
+    };
+}
+
+impl crate::backend::BackendStorage for MetalStorage {
+    type Device = MetalDevice;
+
+    fn try_clone(&self, _: &Layout) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn dtype(&self) -> DType {
+        fail!()
+    }
+
+    fn device(&self) -> &Self::Device {
+        fail!()
+    }
+
+    fn to_cpu_storage(&self) -> Result<CpuStorage> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn affine(&self, _: &Layout, _: f64, _: f64) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn powf(&self, _: &Layout, _: f64) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn elu(&self, _: &Layout, _: f64) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn reduce_op(&self, _: ReduceOp, _: &Layout, _: &[usize]) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn cmp(&self, _: CmpOp, _: &Self, _: &Layout, _: &Layout) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn to_dtype(&self, _: &Layout, _: DType) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn unary_impl<B: UnaryOpT>(&self, _: &Layout) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn binary_impl<B: BinaryOpT>(&self, _: &Self, _: &Layout, _: &Layout) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn where_cond(&self, _: &Layout, _: &Self, _: &Layout, _: &Self, _: &Layout) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn conv1d(
+        &self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &crate::conv::ParamsConv1D,
+    ) -> Result<Self> {
+        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,
+        _: &Self,
+        _: &Layout,
+        _: &crate::conv::ParamsConv2D,
+    ) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn conv_transpose2d(
+        &self,
+        _l: &Layout,
+        _kernel: &Self,
+        _kernel_l: &Layout,
+        _params: &crate::conv::ParamsConvTranspose2D,
+    ) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn index_select(&self, _: &Self, _: &Layout, _: &Layout, _: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+    fn gather(&self, _: &Layout, _: &Self, _: &Layout, _: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn scatter_add(
+        &self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: usize,
+    ) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn index_add(
+        &self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: usize,
+    ) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn matmul(
+        &self,
+        _: &Self,
+        _: (usize, usize, usize, usize),
+        _: &Layout,
+        _: &Layout,
+    ) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn copy_strided_src(&self, _: &mut Self, _: usize, _: &Layout) -> Result<()> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn max_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn upsample_nearest1d(&self, _: &Layout, _: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn upsample_nearest2d(&self, _: &Layout, _: usize, _: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+}
+
+impl crate::backend::BackendDevice for MetalDevice {
+    type Storage = MetalStorage;
+    fn new(_: usize) -> Result<Self> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn set_seed(&self, _: u64) -> Result<()> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn location(&self) -> crate::DeviceLocation {
+        fail!()
+    }
+
+    fn same_device(&self, _: &Self) -> bool {
+        fail!()
+    }
+
+    fn zeros_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn storage_from_cpu_storage(&self, _: &CpuStorage) -> Result<Self::Storage> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn rand_uniform(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+
+    fn rand_normal(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
+        Err(Error::NotCompiledWithMetalSupport)
+    }
+}

candle-core/src/error.rs

@@ -1,4 +1,4 @@
-use crate::{DType, DeviceLocation, Layout, Shape};
+use crate::{DType, DeviceLocation, Layout, MetalError, Shape};
 
 #[derive(Debug, Clone)]
 pub struct MatMulUnexpectedStriding {
@@ -142,6 +142,9 @@ pub enum Error {
     #[error("{op} expects at least one tensor")]
     OpRequiresAtLeastOneTensor { op: &'static str },
 
+    #[error("{op} expects at least two tensors")]
+    OpRequiresAtLeastTwoTensors { op: &'static str },
+
     #[error("backward is not supported for {op}")]
     BackwardNotSupported { op: &'static str },
 
@@ -149,6 +152,9 @@ pub enum Error {
     #[error("the candle crate has not been built with cuda support")]
     NotCompiledWithCudaSupport,
 
+    #[error("the candle crate has not been built with metal support")]
+    NotCompiledWithMetalSupport,
+
     #[error("cannot find tensor {path}")]
     CannotFindTensor { path: String },
 
@@ -156,6 +162,9 @@ pub enum Error {
     #[error(transparent)]
     Cuda(Box<dyn std::error::Error + Send + Sync>),
 
+    #[error("Metal error {0}")]
+    Metal(#[from] MetalError),
+
     #[error(transparent)]
     TryFromIntError(#[from] core::num::TryFromIntError),
 

candle-core/src/indexer.rs

@@ -46,19 +46,31 @@ impl Tensor {
                     current_dim += 1;
                     out
                 }
+                TensorIndexer::IndexSelect(indexes) => {
+                    if indexes.rank() != 1 {
+                        crate::bail!("multi-dimensional tensor indexing is not supported")
+                    }
+                    let out = x.index_select(&indexes.to_device(x.device())?, current_dim)?;
+                    current_dim += 1;
+                    out
+                }
+                TensorIndexer::Err(e) => crate::bail!("indexing error {e:?}"),
             };
         }
         Ok(x)
     }
 }
 
-#[derive(Debug, Clone)]
+#[derive(Debug)]
 /// Generic structure used to index a slice of the tensor
 pub enum TensorIndexer {
     /// This selects the elemnts for which an index has some specific value.
     Select(usize),
     /// This is a regular slice, purely indexing a chunk of the tensor
     Narrow(Bound<usize>, Bound<usize>),
+    /// Indexing via a 1d tensor
+    IndexSelect(Tensor),
+    Err(Error),
 }
 
 impl From<usize> for TensorIndexer {
@@ -67,36 +79,55 @@ impl From<usize> 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::*;
-
-                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<&[u32]> for TensorIndexer {
+    fn from(index: &[u32]) -> Self {
+        match Tensor::new(index, &crate::Device::Cpu) {
+            Ok(tensor) => TensorIndexer::IndexSelect(tensor),
+            Err(e) => TensorIndexer::Err(e),
         }
-    };
+    }
 }
 
-impl_from_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>);
+impl From<Vec<u32>> for TensorIndexer {
+    fn from(index: Vec<u32>) -> Self {
+        let len = index.len();
+        match Tensor::from_vec(index, len, &crate::Device::Cpu) {
+            Ok(tensor) => TensorIndexer::IndexSelect(tensor),
+            Err(e) => TensorIndexer::Err(e),
+        }
+    }
+}
+
+impl From<&Tensor> for TensorIndexer {
+    fn from(tensor: &Tensor) -> Self {
+        TensorIndexer::IndexSelect(tensor.clone())
+    }
+}
+
+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> {}
+
+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)
+    }
+}
 
 /// Trait used to implement multiple signatures for ease of use of the slicing
 /// of a tensor

candle-core/src/lib.rs

@@ -49,9 +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 = "mkl")]
 mod mkl;
 pub mod npy;
@@ -87,6 +90,12 @@ pub use cuda_backend::{CudaDevice, CudaStorage};
 #[cfg(not(feature = "cuda"))]
 pub use dummy_cuda_backend::{CudaDevice, CudaStorage};
 
+#[cfg(feature = "metal")]
+pub use metal_backend::{MetalDevice, MetalError, MetalStorage};
+
+#[cfg(not(feature = "metal"))]
+pub use dummy_metal_backend::{MetalDevice, MetalError, MetalStorage};
+
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
@@ -110,12 +119,24 @@ impl ToUsize2 for (usize, usize) {
 }
 
 // A simple trait defining a module with forward method using a single argument.
-pub trait Module: std::fmt::Debug {
+pub trait Module {
     fn forward(&self, xs: &Tensor) -> Result<Tensor>;
 }
 
-impl Module for quantized::QMatMul {
+impl<T: Fn(&Tensor) -> Result<Tensor>> Module for T {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        self(xs)
+    }
+}
+
+// A trait defining a module with forward method using a single tensor argument and a flag to
+// separate the training and evaluation behaviors.
+pub trait ModuleT {
+    fn forward_t(&self, xs: &Tensor, train: bool) -> Result<Tensor>;
+}
+
+impl<M: Module> ModuleT for M {
+    fn forward_t(&self, xs: &Tensor, _train: bool) -> Result<Tensor> {
         self.forward(xs)
     }
 }

candle-core/src/npy.rs

@@ -250,8 +250,6 @@ impl Tensor {
         if header.fortran_order {
             return Err(Error::Npy("fortran order not supported".to_string()));
         }
-        let mut data: Vec<u8> = vec![];
-        reader.read_to_end(&mut data)?;
         Self::from_reader(header.shape(), header.descr, &mut reader)
     }
 

candle-core/src/op.rs

@@ -1,5 +1,5 @@
 #![allow(clippy::redundant_closure_call)]
-use crate::{CpuStorage, CudaStorage, Layout, Result, Shape, Tensor};
+use crate::{CpuStorage, CudaStorage, Layout, MetalStorage, Result, Shape, Tensor};
 use half::{bf16, f16};
 use num_traits::float::Float;
 
@@ -58,8 +58,13 @@ pub enum UnaryOp {
     Sqr,
     Sqrt,
     Gelu,
+    GeluErf,
+    Erf,
     Relu,
     Tanh,
+    Floor,
+    Ceil,
+    Round,
 }
 
 #[derive(Clone)]
@@ -85,6 +90,16 @@ 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,
@@ -102,7 +117,6 @@ pub enum Op {
         output_padding: usize,
         stride: usize,
         dilation: usize,
-        groups: usize,
     },
 
     AvgPool2D {
@@ -117,6 +131,7 @@ pub enum Op {
         stride: (usize, usize),
     },
 
+    UpsampleNearest1D(Tensor),
     UpsampleNearest2D(Tensor),
 
     Cat(Vec<Tensor>, usize),
@@ -131,6 +146,7 @@ pub enum Op {
     Copy(Tensor),
     Broadcast(Tensor),
     Narrow(Tensor, usize, usize, usize),
+    SliceScatter0(Tensor, Tensor, usize),
     Reshape(Tensor),
     ToDevice(Tensor),
     Transpose(Tensor, usize, usize),
@@ -168,6 +184,18 @@ pub trait CustomOp1 {
         ))
     }
 
+    /// The forward pass, as run on a metal gpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn metal_fwd(
+        &self,
+        _storage: &MetalStorage,
+        _layout: &Layout,
+    ) -> Result<(MetalStorage, Shape)> {
+        Err(crate::Error::Metal(
+            format!("no metal implementation for {}", self.name()).into(),
+        ))
+    }
+
     /// This function takes as argument the argument `arg` used in the forward pass, the result
     /// produced by the forward operation `res` and the gradient of the result `grad_res`.
     /// The function should return the gradient of the argument.
@@ -203,6 +231,20 @@ pub trait CustomOp2 {
         ))
     }
 
+    /// The forward pass, as run on a metal gpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn metal_fwd(
+        &self,
+        _: &MetalStorage,
+        _: &Layout,
+        _: &MetalStorage,
+        _: &Layout,
+    ) -> Result<(MetalStorage, Shape)> {
+        Err(crate::Error::Metal(
+            format!("no metal implementation for {}", self.name()).into(),
+        ))
+    }
+
     fn bwd(
         &self,
         _arg1: &Tensor,
@@ -245,6 +287,22 @@ pub trait CustomOp3 {
         ))
     }
 
+    /// The forward pass, as run on a metal gpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn metal_fwd(
+        &self,
+        _: &MetalStorage,
+        _: &Layout,
+        _: &MetalStorage,
+        _: &Layout,
+        _: &MetalStorage,
+        _: &Layout,
+    ) -> Result<(MetalStorage, Shape)> {
+        Err(crate::Error::Metal(
+            format!("no metal implementation for {}", self.name()).into(),
+        ))
+    }
+
     fn bwd(
         &self,
         _arg1: &Tensor,
@@ -325,8 +383,13 @@ pub(crate) struct Recip;
 pub(crate) struct Sqr;
 pub(crate) struct Sqrt;
 pub(crate) struct Gelu;
+pub(crate) struct GeluErf;
+pub(crate) struct Erf;
 pub(crate) struct Relu;
 pub(crate) struct Tanh;
+pub(crate) struct Floor;
+pub(crate) struct Ceil;
+pub(crate) struct Round;
 
 macro_rules! bin_op {
     ($op:ident, $name: literal, $e: expr, $f32_vec: ident, $f64_vec: ident) => {
@@ -525,13 +588,13 @@ unary_op!(Log, "log", v, v.ln(), vs_ln, vd_ln);
 unary_op!(Sin, "sin", v, v.sin(), vs_sin, vd_sin);
 unary_op!(Cos, "cos", v, v.cos(), vs_cos, vd_cos);
 unary_op!(Tanh, "tanh", v, v.tanh(), vs_tanh, vd_tanh);
-unary_op!(Abs, "abs", v, v.abs());
 unary_op!(Neg, "neg", v, -v);
 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";
@@ -621,6 +684,212 @@ 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";
+    const V: Self = Erf;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        bf16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        f16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        Self::f64(v as f64) as f32
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        crate::cpu::erf::erf(v)
+    }
+    #[inline(always)]
+    fn u8(_: u8) -> u8 {
+        0
+    }
+    #[inline(always)]
+    fn u32(_: u32) -> u32 {
+        0
+    }
+    #[inline(always)]
+    fn i64(_: i64) -> i64 {
+        0
+    }
+}
+
+impl UnaryOpT for Abs {
+    const NAME: &'static str = "abs";
+    const KERNEL: &'static str = "uabs";
+    const V: Self = Abs;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        v.abs()
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        v.abs()
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        v.abs()
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        v.abs()
+    }
+    #[inline(always)]
+    fn u8(v: u8) -> u8 {
+        v
+    }
+    #[inline(always)]
+    fn u32(v: u32) -> u32 {
+        v
+    }
+    #[inline(always)]
+    fn i64(v: i64) -> i64 {
+        v.abs()
+    }
+}
+
+impl UnaryOpT for Ceil {
+    const NAME: &'static str = "ceil";
+    const KERNEL: &'static str = "uceil";
+    const V: Self = Ceil;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        v.ceil()
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        v.ceil()
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        v.ceil()
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        v.ceil()
+    }
+    #[inline(always)]
+    fn u8(v: u8) -> u8 {
+        v
+    }
+    #[inline(always)]
+    fn u32(v: u32) -> u32 {
+        v
+    }
+    #[inline(always)]
+    fn i64(v: i64) -> i64 {
+        v
+    }
+}
+
+impl UnaryOpT for Floor {
+    const NAME: &'static str = "floor";
+    const KERNEL: &'static str = "ufloor";
+    const V: Self = Floor;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        v.floor()
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        v.floor()
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        v.floor()
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        v.floor()
+    }
+    #[inline(always)]
+    fn u8(v: u8) -> u8 {
+        v
+    }
+    #[inline(always)]
+    fn u32(v: u32) -> u32 {
+        v
+    }
+    #[inline(always)]
+    fn i64(v: i64) -> i64 {
+        v
+    }
+}
+
+impl UnaryOpT for Round {
+    const NAME: &'static str = "round";
+    const KERNEL: &'static str = "uround";
+    const V: Self = Round;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        v.round()
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        v.round()
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        v.round()
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        v.round()
+    }
+    #[inline(always)]
+    fn u8(v: u8) -> u8 {
+        v
+    }
+    #[inline(always)]
+    fn u32(v: u32) -> u32 {
+        v
+    }
+    #[inline(always)]
+    fn i64(v: i64) -> i64 {
+        v
+    }
+}
+
+impl UnaryOpT for GeluErf {
+    const NAME: &'static str = "gelu_erf";
+    const KERNEL: &'static str = "ugelu_erf";
+    const V: Self = GeluErf;
+    #[inline(always)]
+    fn bf16(v: bf16) -> bf16 {
+        bf16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f16(v: f16) -> f16 {
+        f16::from_f64(Self::f64(v.to_f64()))
+    }
+    #[inline(always)]
+    fn f32(v: f32) -> f32 {
+        Self::f64(v as f64) as f32
+    }
+    #[inline(always)]
+    fn f64(v: f64) -> f64 {
+        (crate::cpu::erf::erf(v / 2f64.sqrt()) + 1.) * 0.5 * v
+    }
+    #[inline(always)]
+    fn u8(_: u8) -> u8 {
+        0
+    }
+    #[inline(always)]
+    fn u32(_: u32) -> u32 {
+        0
+    }
+    #[inline(always)]
+    fn i64(_: i64) -> i64 {
+        0
+    }
+}
+
 impl UnaryOpT for Relu {
     const NAME: &'static str = "relu";
     const KERNEL: &'static str = "urelu";
@@ -706,6 +975,10 @@ 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

@@ -193,6 +193,50 @@ impl Object {
             _ => Err(self),
         }
     }
+
+    pub fn into_tensor_info(
+        self,
+        name: Self,
+        dir_name: &std::path::Path,
+    ) -> Result<Option<TensorInfo>> {
+        let name = match name.unicode() {
+            Ok(name) => name,
+            Err(_) => return Ok(None),
+        };
+        let (callable, args) = match self.reduce() {
+            Ok(callable_args) => callable_args,
+            _ => return Ok(None),
+        };
+        let (callable, args) = match callable {
+            Object::Class {
+                module_name,
+                class_name,
+            } if module_name == "torch._tensor" && class_name == "_rebuild_from_type_v2" => {
+                let mut args = args.tuple()?;
+                let callable = args.remove(0);
+                let args = args.remove(1);
+                (callable, args)
+            }
+            _ => (callable, args),
+        };
+        match callable {
+            Object::Class {
+                module_name,
+                class_name,
+            } if module_name == "torch._utils" && class_name == "_rebuild_tensor_v2" => {}
+            _ => 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(),
+            storage_size,
+        }))
+    }
 }
 
 impl TryFrom<Object> for String {
@@ -565,6 +609,7 @@ fn rebuild_args(args: Object) -> Result<(Layout, DType, String, usize)> {
         "HalfStorage" => DType::F16,
         "BFloat16Storage" => DType::BF16,
         "ByteStorage" => DType::U8,
+        "LongStorage" => DType::I64,
         other => {
             crate::bail!("unsupported storage type {other}")
         }
@@ -623,50 +668,10 @@ pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
         };
         if let Object::Dict(key_values) = obj {
             for (name, value) in key_values.into_iter() {
-                let name = match name.unicode() {
-                    Ok(name) => name,
-                    Err(_) => continue,
-                };
-                let (callable, args) = match value.reduce() {
-                    Ok(callable_args) => callable_args,
-                    _ => continue,
-                };
-                let (callable, args) = match callable {
-                    Object::Class {
-                        module_name,
-                        class_name,
-                    } if module_name == "torch._tensor"
-                        && class_name == "_rebuild_from_type_v2" =>
-                    {
-                        let mut args = args.tuple()?;
-                        let callable = args.remove(0);
-                        let args = args.remove(1);
-                        (callable, args)
-                    }
-                    _ => (callable, args),
-                };
-                match callable {
-                    Object::Class {
-                        module_name,
-                        class_name,
-                    } if module_name == "torch._utils" && class_name == "_rebuild_tensor_v2" => {}
-                    _ => continue,
-                };
-                match rebuild_args(args) {
-                    Ok((layout, dtype, file_path, storage_size)) => {
-                        let mut path = dir_name.clone();
-                        path.push(file_path);
-                        tensor_infos.push(TensorInfo {
-                            name,
-                            dtype,
-                            layout,
-                            path: path.to_string_lossy().into_owned(),
-                            storage_size,
-                        })
-                    }
-                    Err(err) => {
-                        eprintln!("skipping {name}: {err:?}")
-                    }
+                match value.into_tensor_info(name, &dir_name) {
+                    Ok(Some(tensor_info)) => tensor_infos.push(tensor_info),
+                    Ok(None) => {}
+                    Err(err) => eprintln!("skipping: {err:?}"),
                 }
             }
         }
@@ -723,3 +728,16 @@ impl PthTensors {
         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)?;
+    let tensor_names = pth.tensor_infos.keys();
+    let mut tensors = Vec::with_capacity(tensor_names.len());
+    for name in tensor_names {
+        if let Some(tensor) = pth.get(name)? {
+            tensors.push((name.to_string(), tensor))
+        }
+    }
+    Ok(tensors)
+}

candle-core/src/quantized/avx.rs

@@ -50,14 +50,9 @@ pub(crate) unsafe fn mul_sum_i8_pairs_float(x: __m256i, y: __m256i) -> __m256 {
 #[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;
-    let nb = n / qk;
     if n % QK8_0 != 0 {
         crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
     }
-    if nb % 2 != 0 {
-        crate::bail!("vec_dot_q4_0_q8_0: {nb} is not even")
-    }
-
     unsafe {
         let mut acc = _mm256_setzero_ps();
         for (x, y) in xs.iter().zip(ys.iter()) {
@@ -638,3 +633,35 @@ pub(crate) fn vec_dot_q5k_q8k(n: usize, xs: &[BlockQ5K], ys: &[BlockQ8K]) -> Res
         Ok(hsum_float_8(acc) + summs)
     }
 }
+
+#[inline(always)]
+pub(crate) fn vec_dot_q8k_q8k(n: usize, xs: &[BlockQ8K], ys: &[BlockQ8K]) -> Result<f32> {
+    let qk = QK_K;
+    if n % qk != 0 {
+        crate::bail!("vec_dot_q8k_8k: {n} is not divisible by {qk}")
+    }
+
+    unsafe {
+        let mut acc = _mm256_setzero_ps();
+        for (xs, ys) in xs.iter().zip(ys.iter()) {
+            let mut sumi = _mm256_setzero_si256();
+            let x_qs = xs.qs.as_ptr();
+            let y_qs = ys.qs.as_ptr();
+            for j in (0..QK_K).step_by(32) {
+                let xs = _mm256_loadu_si256(x_qs.add(j) as *const __m256i);
+                let ys = _mm256_loadu_si256(y_qs.add(j) as *const __m256i);
+
+                let xs0 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(xs, 0));
+                let ys0 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(ys, 0));
+                sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(xs0, ys0));
+
+                let xs1 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(xs, 1));
+                let ys1 = _mm256_cvtepi8_epi16(_mm256_extracti128_si256(ys, 1));
+                sumi = _mm256_add_epi32(sumi, _mm256_madd_epi16(xs1, ys1));
+            }
+            let d = _mm256_set1_ps(xs.d * ys.d);
+            acc = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi), acc);
+        }
+        Ok(hsum_float_8(acc))
+    }
+}

candle-core/src/quantized/ggml_file.rs

@@ -135,7 +135,13 @@ pub fn qtensor_from_ggml(
     dims: Vec<usize>,
 ) -> Result<super::QTensor> {
     let tensor_elems = dims.iter().product::<usize>();
-    let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.blck_size();
+    let blck_size = ggml_dtype.blck_size();
+    if tensor_elems % blck_size != 0 {
+        crate::bail!(
+            "the number of elements {tensor_elems} is not divisible by the block size {blck_size}"
+        )
+    }
+    let size_in_bytes = tensor_elems / blck_size * ggml_dtype.type_size();
 
     match ggml_dtype {
         GgmlDType::F32 => from_raw_data::<f32>(raw_data, size_in_bytes, dims),

candle-core/src/quantized/gguf_file.rs

@@ -29,6 +29,7 @@ impl TryFrom<u32> for Magic {
 pub enum VersionedMagic {
     GgufV1,
     GgufV2,
+    GgufV3,
 }
 
 impl VersionedMagic {
@@ -39,6 +40,7 @@ impl VersionedMagic {
         let versioned_magic = match (magic, version) {
             (Magic::Gguf, 1) => Self::GgufV1,
             (Magic::Gguf, 2) => Self::GgufV2,
+            (Magic::Gguf, 3) => Self::GgufV3,
             _ => crate::bail!("ggml: unsupported magic/version {magic:?}/{version}"),
         };
         Ok(versioned_magic)
@@ -59,8 +61,13 @@ impl TensorInfo {
         tensor_data_offset: u64,
     ) -> Result<QTensor> {
         let tensor_elems = self.shape.elem_count();
-        let size_in_bytes =
-            tensor_elems * self.ggml_dtype.type_size() / self.ggml_dtype.blck_size();
+        let blck_size = self.ggml_dtype.blck_size();
+        if tensor_elems % blck_size != 0 {
+            crate::bail!(
+            "the number of elements {tensor_elems} is not divisible by the block size {blck_size}"
+        )
+        }
+        let size_in_bytes = tensor_elems / blck_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)?;
@@ -79,7 +86,9 @@ 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)?;
@@ -279,7 +288,9 @@ 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 {
@@ -376,11 +387,15 @@ 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();
@@ -402,7 +417,7 @@ 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()

candle-core/src/quantized/k_quants.rs

@@ -34,6 +34,9 @@ pub trait GgmlType: Sized + Clone + Send + Sync {
     /// Dot product used as a building block for quantized mat-mul.
     /// n is the number of elements to be considered.
     fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32>;
+
+    /// Generic implementation of the dot product without simd optimizations.
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32>;
 }
 
 #[derive(Debug, Clone, PartialEq)]
@@ -225,15 +228,17 @@ impl GgmlType for BlockQ4_0 {
         #[cfg(target_feature = "neon")]
         return super::neon::vec_dot_q4_0_q8_0(n, xs, ys);
 
+        #[cfg(target_feature = "simd128")]
+        return super::simd128::vec_dot_q4_0_q8_0(n, xs, ys);
+
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         let qk = QK8_0;
-        let nb = n / qk;
         if n % QK8_0 != 0 {
             crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
         }
-        if nb % 2 != 0 {
-            crate::bail!("vec_dot_q4_0_q8_0: {nb} is not even")
-        }
-
         // Generic implementation.
         let mut sumf = 0f32;
         for (xs, ys) in xs.iter().zip(ys.iter()) {
@@ -255,6 +260,10 @@ impl GgmlType for BlockQ4_1 {
     type VecDotType = BlockQ8_1;
 
     fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         // ggml_vec_dot_q4_1_q8_1
         let qk = QK8_1;
         if n % qk != 0 {
@@ -354,7 +363,10 @@ impl GgmlType for BlockQ5_0 {
         if nb % 2 != 0 {
             crate::bail!("vec_dot_q5_0_q8_0: {n}, nb is not divisible by 2")
         }
+        Self::vec_dot_unopt(n, xs, ys)
+    }
 
+    fn vec_dot_unopt(_n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         // Generic implementation.
         let mut sumf = 0f32;
 
@@ -445,6 +457,10 @@ impl GgmlType for BlockQ5_1 {
     type VecDotType = BlockQ8_1;
 
     fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         let qk = Self::BLCK_SIZE;
         if n % Self::BLCK_SIZE != 0 {
             crate::bail!("vec_dot_q5_1_q8_1: {n} is not divisible by {qk}")
@@ -606,6 +622,13 @@ impl GgmlType for BlockQ8_0 {
         #[cfg(target_feature = "neon")]
         return super::neon::vec_dot_q8_0_q8_0(n, xs, ys);
 
+        #[cfg(target_feature = "simd128")]
+        return super::simd128::vec_dot_q8_0_q8_0(n, xs, ys);
+
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         let qk = QK8_0;
         if n % QK8_0 != 0 {
             crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
@@ -631,7 +654,11 @@ impl GgmlType for BlockQ8_1 {
     const BLCK_SIZE: usize = QK8_1;
     type VecDotType = BlockQ8_1;
 
-    fn vec_dot(_n: usize, _xs: &[Self], _ys: &[Self::VecDotType]) -> Result<f32> {
+    fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(_n: usize, _xs: &[Self], _ys: &[Self::VecDotType]) -> Result<f32> {
         unimplemented!("no support for vec-dot on Q8_1")
     }
 
@@ -681,6 +708,13 @@ impl GgmlType for BlockQ2K {
         #[cfg(target_feature = "neon")]
         return super::neon::vec_dot_q2k_q8k(n, xs, ys);
 
+        #[cfg(target_feature = "simd128")]
+        return super::simd128::vec_dot_q2k_q8k(n, xs, ys);
+
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         if n % QK_K != 0 {
             crate::bail!("vec_dot_q2k_q8k: {n} is not divisible by {QK_K}")
         }
@@ -701,18 +735,17 @@ impl GgmlType for BlockQ2K {
 
             let mut isum = 0;
             let mut is = 0;
-            let mut d;
             for _ in 0..(QK_K / 128) {
                 let mut shift = 0;
                 for _ in 0..4 {
-                    d = (sc[is] & 0xF) as i32;
+                    let d = (sc[is] & 0xF) as i32;
                     is += 1;
                     let mut isuml = 0;
                     for l in 0..16 {
                         isuml += q8[l] as i32 * (((q2[l] >> shift) & 3) as i32);
                     }
                     isum += d * isuml;
-                    d = (sc[is] & 0xF) as i32;
+                    let d = (sc[is] & 0xF) as i32;
                     is += 1;
                     isuml = 0;
                     for l in 16..32 {
@@ -851,6 +884,10 @@ impl GgmlType for BlockQ3K {
         #[cfg(target_feature = "neon")]
         return super::neon::vec_dot_q3k_q8k(n, xs, ys);
 
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         if n % QK_K != 0 {
             crate::bail!("vec_dot_q3k_q8k: {n} is not divisible by {QK_K}")
         }
@@ -1077,7 +1114,6 @@ impl GgmlType for BlockQ3K {
             let d_all = block.d.to_f32();
             let mut m = 1;
             let mut is = 0;
-            let mut dl;
 
             // Dequantize both 128 long blocks
             // 32 qs values per 128 long block
@@ -1088,7 +1124,7 @@ impl GgmlType for BlockQ3K {
                     for (scale_index, scale_scoped_y) in
                         shift_scoped_y.chunks_exact_mut(16).enumerate()
                     {
-                        dl = d_all * (scales[is] as f32 - 32.0);
+                        let dl = d_all * (scales[is] as f32 - 32.0);
                         for (i, inner_y) in scale_scoped_y.iter_mut().enumerate() {
                             let new_y = dl
                                 * (((qs[i + 16 * scale_index] >> shift) & 3) as i8
@@ -1126,6 +1162,13 @@ impl GgmlType for BlockQ4K {
         #[cfg(target_feature = "neon")]
         return super::neon::vec_dot_q4k_q8k(n, xs, ys);
 
+        #[cfg(target_feature = "simd128")]
+        return super::simd128::vec_dot_q4k_q8k(n, xs, ys);
+
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         if n % QK_K != 0 {
             crate::bail!("vec_dot_q4k_q8k: {n} is not divisible by {QK_K}")
         }
@@ -1312,6 +1355,10 @@ impl GgmlType for BlockQ5K {
         #[cfg(target_feature = "neon")]
         return super::neon::vec_dot_q5k_q8k(n, xs, ys);
 
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         if n % QK_K != 0 {
             crate::bail!("vec_dot_q5k_q8k: {n} is not divisible by {QK_K}")
         }
@@ -1529,6 +1576,13 @@ impl GgmlType for BlockQ6K {
         #[cfg(target_feature = "neon")]
         return super::neon::vec_dot_q6k_q8k(n, xs, ys);
 
+        #[cfg(target_feature = "simd128")]
+        return super::simd128::vec_dot_q6k_q8k(n, xs, ys);
+
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         if n % QK_K != 0 {
             crate::bail!("vec_dot_q6k_q8k: {n} is not divisible by {QK_K}")
         }
@@ -1697,8 +1751,38 @@ impl GgmlType for BlockQ8K {
     const BLCK_SIZE: usize = QK_K;
     type VecDotType = BlockQ8K;
 
-    fn vec_dot(_n: usize, _xs: &[Self], _ys: &[Self::VecDotType]) -> Result<f32> {
-        unreachable!()
+    #[allow(unreachable_code)]
+    fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        #[cfg(target_feature = "avx")]
+        return super::avx::vec_dot_q8k_q8k(n, xs, ys);
+
+        #[cfg(target_feature = "neon")]
+        return super::neon::vec_dot_q8k_q8k(n, xs, ys);
+
+        #[cfg(target_feature = "simd128")]
+        return super::simd128::vec_dot_q8k_q8k(n, xs, ys);
+
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        let qk = QK_K;
+        if n % QK_K != 0 {
+            crate::bail!("vec_dot_q8k_q8k: {n} is not divisible by {qk}")
+        }
+
+        // Generic implementation.
+        let mut sumf = 0f32;
+        for (xs, ys) in xs.iter().zip(ys.iter()) {
+            let sum_i = xs
+                .qs
+                .iter()
+                .zip(ys.qs.iter())
+                .map(|(&x, &y)| x as i32 * y as i32)
+                .sum::<i32>();
+            sumf += sum_i as f32 * xs.d * ys.d
+        }
+        Ok(sumf)
     }
 
     fn from_float(xs: &[f32], ys: &mut [Self]) -> Result<()> {
@@ -1804,6 +1888,10 @@ impl GgmlType for f32 {
     type VecDotType = f32;
 
     fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         if xs.len() < n {
             crate::bail!("size mismatch {} < {n}", xs.len())
         }
@@ -1838,6 +1926,10 @@ impl GgmlType for f16 {
     type VecDotType = f16;
 
     fn vec_dot(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
+        Self::vec_dot_unopt(n, xs, ys)
+    }
+
+    fn vec_dot_unopt(n: usize, xs: &[Self], ys: &[Self::VecDotType]) -> Result<f32> {
         if xs.len() < n {
             crate::bail!("size mismatch {} < {n}", xs.len())
         }

candle-core/src/quantized/mod.rs

@@ -7,6 +7,8 @@ pub mod gguf_file;
 pub mod k_quants;
 #[cfg(target_feature = "neon")]
 pub mod neon;
+#[cfg(target_feature = "simd128")]
+pub mod simd128;
 pub mod utils;
 
 pub use k_quants::GgmlType;
@@ -229,20 +231,40 @@ impl QTensor {
     }
 }
 
-#[derive(Debug)]
-pub struct QMatMul(std::sync::Arc<QTensor>);
+#[derive(Clone, Debug)]
+pub enum QMatMul {
+    QTensor(std::sync::Arc<QTensor>),
+    Tensor(Tensor),
+}
+
+thread_local! {
+    static DEQUANTIZE_ALL: bool = {
+        match std::env::var("CANDLE_DEQUANTIZE_ALL") {
+            Ok(s) => {
+                !s.is_empty() && s != "0"
+            },
+            Err(_) => false,
+        }
+    }
+}
 
 impl QMatMul {
-    pub fn from_arc(qtensor: std::sync::Arc<QTensor>) -> Self {
-        Self(qtensor)
+    pub fn from_arc(qtensor: std::sync::Arc<QTensor>) -> Result<Self> {
+        let dequantize = match qtensor.dtype() {
+            GgmlDType::F32 | GgmlDType::F16 => true,
+            _ => DEQUANTIZE_ALL.with(|b| *b),
+        };
+        let t = if dequantize {
+            let tensor = qtensor.dequantize(&Device::Cpu)?;
+            Self::Tensor(tensor)
+        } else {
+            Self::QTensor(qtensor)
+        };
+        Ok(t)
     }
 
-    pub fn from_qtensor(qtensor: QTensor) -> Self {
-        Self(std::sync::Arc::new(qtensor))
-    }
-
-    pub fn inner(&self) -> &std::sync::Arc<QTensor> {
-        &self.0
+    pub fn from_qtensor(qtensor: QTensor) -> Result<Self> {
+        Self::from_arc(std::sync::Arc::new(qtensor))
     }
 }
 
@@ -285,8 +307,18 @@ impl crate::CustomOp1 for QTensor {
     }
 }
 
-impl QMatMul {
-    pub fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        xs.apply_op1_no_bwd(self.0.as_ref())
+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) => {
+                let w = match *xs.dims() {
+                    [b1, b2, _, _] => w.broadcast_left((b1, b2))?.t()?,
+                    [bsize, _, _] => w.broadcast_left(bsize)?.t()?,
+                    _ => w.t()?,
+                };
+                xs.matmul(&w)
+            }
+        }
     }
 }

candle-core/src/quantized/neon.rs

@@ -19,42 +19,29 @@ pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) ->
     if n % QK8_0 != 0 {
         crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
     }
-    if nb % 2 != 0 {
-        crate::bail!("vec_dot_q4_0_q8_0: {nb} is not even")
-    }
 
     unsafe {
         let mut sumv0 = vdupq_n_f32(0.0f32);
-        let mut sumv1 = vdupq_n_f32(0.0f32);
-        for i in (0..nb).step_by(2) {
+        for i in 0..nb {
             let x0 = &xs[i];
-            let x1 = &xs[i + 1];
             let y0 = &ys[i];
-            let y1 = &ys[i + 1];
 
             let m4b = vdupq_n_u8(0x0F);
             let s8b = vdupq_n_s8(0x8);
 
             let v0_0 = vld1q_u8(x0.qs.as_ptr());
-            let v0_1 = vld1q_u8(x1.qs.as_ptr());
 
             // 4-bit -> 8-bit
             let v0_0l = vreinterpretq_s8_u8(vandq_u8(v0_0, m4b));
             let v0_0h = vreinterpretq_s8_u8(vshrq_n_u8(v0_0, 4));
-            let v0_1l = vreinterpretq_s8_u8(vandq_u8(v0_1, m4b));
-            let v0_1h = vreinterpretq_s8_u8(vshrq_n_u8(v0_1, 4));
 
             // sub 8
             let v0_0ls = vsubq_s8(v0_0l, s8b);
             let v0_0hs = vsubq_s8(v0_0h, s8b);
-            let v0_1ls = vsubq_s8(v0_1l, s8b);
-            let v0_1hs = vsubq_s8(v0_1h, s8b);
 
             // load y
             let v1_0l = vld1q_s8(y0.qs.as_ptr());
             let v1_0h = vld1q_s8(y0.qs.as_ptr().add(16));
-            let v1_1l = vld1q_s8(y1.qs.as_ptr());
-            let v1_1h = vld1q_s8(y1.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));
@@ -62,28 +49,16 @@ pub(crate) fn vec_dot_q4_0_q8_0(n: usize, xs: &[BlockQ4_0], ys: &[BlockQ8_0]) ->
             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 pl1l = vmull_s8(vget_low_s8(v0_1ls), vget_low_s8(v1_1l));
-            let pl1h = vmull_s8(vget_high_s8(v0_1ls), vget_high_s8(v1_1l));
-            let ph1l = vmull_s8(vget_low_s8(v0_1hs), vget_low_s8(v1_1h));
-            let ph1h = vmull_s8(vget_high_s8(v0_1hs), vget_high_s8(v1_1h));
-
             let pl0 = vaddq_s32(vpaddlq_s16(pl0l), vpaddlq_s16(pl0h));
             let ph0 = vaddq_s32(vpaddlq_s16(ph0l), vpaddlq_s16(ph0h));
-            let pl1 = vaddq_s32(vpaddlq_s16(pl1l), vpaddlq_s16(pl1h));
-            let ph1 = vaddq_s32(vpaddlq_s16(ph1l), vpaddlq_s16(ph1h));
 
             sumv0 = vmlaq_n_f32(
                 sumv0,
                 vcvtq_f32_s32(vaddq_s32(pl0, ph0)),
                 x0.d.to_f32() * y0.d.to_f32(),
             );
-            sumv1 = vmlaq_n_f32(
-                sumv1,
-                vcvtq_f32_s32(vaddq_s32(pl1, ph1)),
-                x1.d.to_f32() * y1.d.to_f32(),
-            );
         }
-        Ok(vaddvq_f32(sumv0) + vaddvq_f32(sumv1))
+        Ok(vaddvq_f32(sumv0))
     }
 }
 
@@ -94,28 +69,18 @@ pub(crate) fn vec_dot_q8_0_q8_0(n: usize, xs: &[BlockQ8_0], ys: &[BlockQ8_0]) ->
         crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
     }
     let nb = n / QK8_0;
-    if nb % 2 != 0 {
-        crate::bail!("vec_dot_q8_0_q8_0: {nb} is not even")
-    }
     unsafe {
         let mut sumv0 = vdupq_n_f32(0.0f32);
-        let mut sumv1 = vdupq_n_f32(0.0f32);
-        for i in (0..nb).step_by(2) {
+        for i in 0..nb {
             let x0 = &xs[i];
-            let x1 = &xs[i + 1];
             let y0 = &ys[i];
-            let y1 = &ys[i + 1];
 
             let x0_0 = vld1q_s8(x0.qs.as_ptr());
             let x0_1 = vld1q_s8(x0.qs.as_ptr().add(16));
-            let x1_0 = vld1q_s8(x1.qs.as_ptr());
-            let x1_1 = vld1q_s8(x1.qs.as_ptr().add(16));
 
             // load y
             let y0_0 = vld1q_s8(y0.qs.as_ptr());
             let y0_1 = vld1q_s8(y0.qs.as_ptr().add(16));
-            let y1_0 = vld1q_s8(y1.qs.as_ptr());
-            let y1_1 = vld1q_s8(y1.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));
@@ -123,31 +88,48 @@ pub(crate) fn vec_dot_q8_0_q8_0(n: usize, xs: &[BlockQ8_0], ys: &[BlockQ8_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 p1_0 = vmull_s8(vget_low_s8(x1_0), vget_low_s8(y1_0));
-            let p1_1 = vmull_s8(vget_high_s8(x1_0), vget_high_s8(y1_0));
-            let p1_2 = vmull_s8(vget_low_s8(x1_1), vget_low_s8(y1_1));
-            let p1_3 = vmull_s8(vget_high_s8(x1_1), vget_high_s8(y1_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 p2 = vaddq_s32(vpaddlq_s16(p1_0), vpaddlq_s16(p1_1));
-            let p3 = vaddq_s32(vpaddlq_s16(p1_2), vpaddlq_s16(p1_3));
 
             sumv0 = vmlaq_n_f32(
                 sumv0,
                 vcvtq_f32_s32(vaddq_s32(p0, p1)),
                 x0.d.to_f32() * y0.d.to_f32(),
             );
-            sumv1 = vmlaq_n_f32(
-                sumv1,
-                vcvtq_f32_s32(vaddq_s32(p2, p3)),
-                x1.d.to_f32() * y1.d.to_f32(),
-            );
         }
-        Ok(vaddvq_f32(sumv0) + vaddvq_f32(sumv1))
+        Ok(vaddvq_f32(sumv0))
     }
 }
 
+#[inline(always)]
+pub(crate) fn vec_dot_q8k_q8k(n: usize, xs: &[BlockQ8K], ys: &[BlockQ8K]) -> Result<f32> {
+    let qk = QK_K;
+    if n % QK_K != 0 {
+        crate::bail!("vec_dot_q8k_q8k: {n} is not divisible by {qk}")
+    }
+
+    let mut sumf = 0f32;
+    for (xs, ys) in xs.iter().zip(ys.iter()) {
+        unsafe {
+            let mut sum_i = vdupq_n_s32(0);
+            let scale = xs.d * ys.d;
+            let xs = xs.qs.as_ptr();
+            let ys = ys.qs.as_ptr();
+            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));
+                sum_i = vaddq_s32(sum_i, xy)
+            }
+            sumf += vaddvq_s32(sum_i) as f32 * scale
+        }
+    }
+    Ok(sumf)
+}
+
 #[inline(always)]
 pub(crate) fn vec_dot_q6k_q8k(n: usize, xs: &[BlockQ6K], ys: &[BlockQ8K]) -> Result<f32> {
     if n % QK_K != 0 {

candle-core/src/quantized/simd128.rs

@@ -0,0 +1,419 @@
+use super::k_quants::{BlockQ2K, BlockQ4K, BlockQ4_0, BlockQ6K, BlockQ8K, BlockQ8_0, QK8_0, QK_K};
+use crate::Result;
+use byteorder::{ByteOrder, LittleEndian};
+use half::f16;
+
+use core::arch::wasm32::*;
+
+#[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;
+    if n % QK8_0 != 0 {
+        crate::bail!("vec_dot_q4_0_q8_0: {n} is not divisible by {qk}")
+    }
+    unsafe {
+        let mut acc = f32x4_splat(0.0f32);
+        for (x, y) in xs.iter().zip(ys.iter()) {
+            let x1234 = v128_load(x.qs.as_ptr() as *const v128);
+            let x12 = v128_and(x1234, u8x16_splat(0x0F));
+            let x12 = i8x16_sub(x12, i8x16_splat(8));
+            let x34 = u8x16_shr(x1234, 4);
+            let x34 = i8x16_sub(x34, i8x16_splat(8));
+
+            let x1 = i16x8_extend_low_i8x16(x12);
+            let y1 = i16x8_load_extend_i8x8(y.qs.as_ptr());
+            let sum_xy = i32x4_dot_i16x8(x1, y1);
+
+            let x2 = i16x8_extend_high_i8x16(x12);
+            let y2 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(8));
+            let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x2, y2));
+
+            let x3 = i16x8_extend_low_i8x16(x34);
+            let y3 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(16));
+            let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x3, y3));
+
+            let x4 = i16x8_extend_high_i8x16(x34);
+            let y4 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(24));
+            let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x4, y4));
+
+            let sum_xy = f32x4_convert_i32x4(sum_xy);
+
+            // f32x4_relaxed_madd is nightly only.
+            let d = f32x4_splat(f16::to_f32(x.d) * f16::to_f32(y.d));
+            let scaled = f32x4_mul(sum_xy, d);
+            acc = f32x4_add(acc, scaled)
+        }
+        let res = f32x4_extract_lane::<0>(acc)
+            + f32x4_extract_lane::<1>(acc)
+            + f32x4_extract_lane::<2>(acc)
+            + f32x4_extract_lane::<3>(acc);
+        Ok(res)
+    }
+}
+
+#[inline(always)]
+pub(crate) fn vec_dot_q8_0_q8_0(n: usize, xs: &[BlockQ8_0], ys: &[BlockQ8_0]) -> Result<f32> {
+    let qk = QK8_0;
+    if n % QK8_0 != 0 {
+        crate::bail!("vec_dot_q8_0_q8_0: {n} is not divisible by {qk}")
+    }
+    unsafe {
+        let mut acc = f32x4_splat(0.0f32);
+        for (x, y) in xs.iter().zip(ys.iter()) {
+            let x1 = i16x8_load_extend_i8x8(x.qs.as_ptr());
+            let y1 = i16x8_load_extend_i8x8(y.qs.as_ptr());
+            let sum_xy = i32x4_dot_i16x8(x1, y1);
+
+            let x2 = i16x8_load_extend_i8x8(x.qs.as_ptr().add(8));
+            let y2 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(8));
+            let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x2, y2));
+
+            let x3 = i16x8_load_extend_i8x8(x.qs.as_ptr().add(16));
+            let y3 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(16));
+            let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x3, y3));
+
+            let x4 = i16x8_load_extend_i8x8(x.qs.as_ptr().add(24));
+            let y4 = i16x8_load_extend_i8x8(y.qs.as_ptr().add(24));
+            let sum_xy = i32x4_add(sum_xy, i32x4_dot_i16x8(x4, y4));
+
+            let sum_xy = f32x4_convert_i32x4(sum_xy);
+
+            // f32x4_relaxed_madd is nightly only.
+            let d = f32x4_splat(f16::to_f32(x.d) * f16::to_f32(y.d));
+            let scaled = f32x4_mul(sum_xy, d);
+            acc = f32x4_add(acc, scaled)
+        }
+        let res = f32x4_extract_lane::<0>(acc)
+            + f32x4_extract_lane::<1>(acc)
+            + f32x4_extract_lane::<2>(acc)
+            + f32x4_extract_lane::<3>(acc);
+        Ok(res)
+    }
+}
+
+#[inline(always)]
+pub(crate) fn vec_dot_q2k_q8k(n: usize, xs: &[BlockQ2K], ys: &[BlockQ8K]) -> Result<f32> {
+    if n % QK_K != 0 {
+        crate::bail!("vec_dot_q2k_q8k: {n} is not divisible by {QK_K}")
+    }
+    unsafe {
+        let mut sumf = f32x4_splat(0f32);
+        for (x, y) in xs.iter().zip(ys.iter()) {
+            let mut q2: &[_] = &x.qs;
+            let mut q8: &[_] = &y.qs;
+            let sc = &x.scales;
+
+            let mut summs = i32x4_splat(0);
+            for i in (0..(QK_K / 16)).step_by(4) {
+                let bsums = i32x4_load_extend_i16x4(y.bsums.as_ptr().add(i));
+                let scales = i32x4_shr(
+                    i32x4(
+                        sc[i] as i32,
+                        sc[i + 1] as i32,
+                        sc[i + 2] as i32,
+                        sc[i + 3] as i32,
+                    ),
+                    4,
+                );
+                summs = i32x4_add(summs, i32x4_mul(bsums, scales))
+            }
+            let summs = f32x4_convert_i32x4(summs);
+
+            let dall = y.d * x.d.to_f32();
+            let dmin = y.d * x.dmin.to_f32();
+
+            let mut isum = i32x4_splat(0);
+            let mut is = 0;
+            for _ in 0..(QK_K / 128) {
+                let mut shift = 0;
+                for _ in 0..4 {
+                    let d = (sc[is] & 0xF) as i32;
+                    is += 1;
+                    let mut isuml = i16x8_splat(0);
+                    for l in (0..16).step_by(8) {
+                        let q8 = i16x8_load_extend_i8x8(q8.as_ptr().add(l));
+                        let q2 = i16x8_load_extend_u8x8(q2.as_ptr().add(l));
+                        let q2 = v128_and(i16x8_shr(q2, shift), i16x8_splat(3));
+                        isuml = i16x8_add(isuml, i16x8_mul(q2, q8))
+                    }
+                    let dd = i32x4_splat(d);
+                    isum = i32x4_add(isum, i32x4_mul(i32x4_extend_low_i16x8(isuml), dd));
+                    isum = i32x4_add(isum, i32x4_mul(i32x4_extend_high_i16x8(isuml), dd));
+                    let d = (sc[is] & 0xF) as i32;
+                    is += 1;
+                    let mut isuml = i16x8_splat(0);
+                    for l in (16..32).step_by(8) {
+                        let q8 = i16x8_load_extend_i8x8(q8.as_ptr().add(l));
+                        let q2 = i16x8_load_extend_u8x8(q2.as_ptr().add(l));
+                        let q2 = v128_and(i16x8_shr(q2, shift), i16x8_splat(3));
+                        isuml = i16x8_add(isuml, i16x8_mul(q2, q8))
+                    }
+                    let dd = i32x4_splat(d);
+                    isum = i32x4_add(isum, i32x4_mul(i32x4_extend_low_i16x8(isuml), dd));
+                    isum = i32x4_add(isum, i32x4_mul(i32x4_extend_high_i16x8(isuml), dd));
+                    shift += 2;
+                    // adjust the indexing
+                    q8 = &q8[32..];
+                }
+                // adjust the indexing
+                q2 = &q2[32..];
+            }
+            let isum = f32x4_convert_i32x4(isum);
+            sumf = f32x4_add(
+                sumf,
+                f32x4_sub(
+                    f32x4_mul(isum, f32x4_splat(dall)),
+                    f32x4_mul(summs, f32x4_splat(dmin)),
+                ),
+            );
+        }
+        let sumf = f32x4_extract_lane::<0>(sumf)
+            + f32x4_extract_lane::<1>(sumf)
+            + f32x4_extract_lane::<2>(sumf)
+            + f32x4_extract_lane::<3>(sumf);
+        Ok(sumf)
+    }
+}
+
+#[inline(always)]
+pub(crate) fn vec_dot_q4k_q8k(n: usize, xs: &[BlockQ4K], ys: &[BlockQ8K]) -> Result<f32> {
+    if n % QK_K != 0 {
+        crate::bail!("vec_dot_q4k_q8k: {n} is not divisible by {QK_K}")
+    }
+
+    const KMASK1: u32 = 0x3f3f3f3f;
+    const KMASK2: u32 = 0x0f0f0f0f;
+    const KMASK3: u32 = 0x03030303;
+
+    let mut utmp: [u32; 4] = [0; 4];
+    let mut scales: [u8; 8] = [0; 8];
+    let mut mins: [u8; 8] = [0; 8];
+
+    let mut aux8: [u8; QK_K] = [0; QK_K];
+    let mut sums = f32x4_splat(0f32);
+    unsafe {
+        for (y, x) in ys.iter().zip(xs.iter()) {
+            let q4 = &x.qs;
+            let q8 = &y.qs;
+
+            for j in 0..QK_K / 64 {
+                let q4_1 = v128_load(q4.as_ptr().add(32 * j) as *const v128);
+                let q4_2 = v128_load(q4.as_ptr().add(32 * j + 16) as *const v128);
+                v128_store(
+                    aux8.as_mut_ptr().add(64 * j) as *mut v128,
+                    v128_and(q4_1, u8x16_splat(0x0F)),
+                );
+                v128_store(
+                    aux8.as_mut_ptr().add(64 * j + 16) as *mut v128,
+                    v128_and(q4_2, u8x16_splat(0x0F)),
+                );
+                v128_store(
+                    aux8.as_mut_ptr().add(64 * j + 32) as *mut v128,
+                    u8x16_shr(q4_1, 4),
+                );
+                v128_store(
+                    aux8.as_mut_ptr().add(64 * j + 48) as *mut v128,
+                    u8x16_shr(q4_2, 4),
+                );
+            }
+
+            LittleEndian::read_u32_into(&x.scales, &mut utmp[0..3]);
+
+            utmp[3] = ((utmp[2] >> 4) & KMASK2) | (((utmp[1] >> 6) & KMASK3) << 4);
+            let uaux = utmp[1] & KMASK1;
+            utmp[1] = (utmp[2] & KMASK2) | (((utmp[0] >> 6) & KMASK3) << 4);
+            utmp[2] = uaux;
+            utmp[0] &= KMASK1;
+
+            //extract scales and mins
+            LittleEndian::write_u32_into(&utmp[0..2], &mut scales);
+            LittleEndian::write_u32_into(&utmp[2..4], &mut mins);
+
+            let mut sumi = i32x4_splat(0);
+            for j in (0..QK_K / 16).step_by(4) {
+                let bsums = i32x4_load_extend_i16x4(y.bsums.as_ptr().add(j));
+                let (m1, m2) = (mins[j / 2] as i32, mins[j / 2 + 1] as i32);
+                let mins = i32x4(m1, m1, m2, m2);
+                sumi = i32x4_add(sumi, i32x4_mul(bsums, mins));
+            }
+
+            let mut aux32 = i32x4_splat(0i32);
+            for (scale_i, scale) in scales.iter().enumerate() {
+                let scale = i32x4_splat(*scale as i32);
+                for j in 0..4 {
+                    let i = 32 * scale_i + 8 * j;
+                    let q8 = i16x8_load_extend_i8x8(q8.as_ptr().add(i));
+                    let aux8 = i16x8_load_extend_u8x8(aux8.as_ptr().add(i));
+                    let aux16 = i16x8_mul(q8, aux8);
+                    aux32 = i32x4_add(aux32, i32x4_mul(scale, i32x4_extend_low_i16x8(aux16)));
+                    aux32 = i32x4_add(aux32, i32x4_mul(scale, i32x4_extend_high_i16x8(aux16)));
+                }
+            }
+            let aux32 = f32x4_convert_i32x4(aux32);
+            let d = f32x4_splat(x.d.to_f32() * y.d);
+            sums = f32x4_add(sums, f32x4_mul(aux32, d));
+            let dmin = x.dmin.to_f32() * y.d;
+            let dmin = f32x4_splat(dmin);
+            let sumi = f32x4_convert_i32x4(sumi);
+            sums = f32x4_sub(sums, f32x4_mul(sumi, dmin));
+        }
+        let sums = f32x4_extract_lane::<0>(sums)
+            + f32x4_extract_lane::<1>(sums)
+            + f32x4_extract_lane::<2>(sums)
+            + f32x4_extract_lane::<3>(sums);
+        Ok(sums)
+    }
+}
+
+#[inline(always)]
+pub(crate) fn vec_dot_q6k_q8k(n: usize, xs: &[BlockQ6K], ys: &[BlockQ8K]) -> Result<f32> {
+    if n % QK_K != 0 {
+        crate::bail!("vec_dot_q6k_q8k: {n} is not divisible by {QK_K}")
+    }
+
+    let mut aux8 = [0i8; QK_K];
+    unsafe {
+        let mut sums = f32x4_splat(0f32);
+
+        for (x, y) in xs.iter().zip(ys.iter()) {
+            let q4 = &x.ql;
+            let qh = &x.qh;
+            let q8 = &y.qs;
+            let mut aux32 = f32x4_splat(0f32);
+
+            for j in (0..QK_K).step_by(128) {
+                let aux8 = aux8.as_mut_ptr().add(j);
+                let q4 = &q4.as_ptr().add(j / 2);
+                let qh = &qh.as_ptr().add(j / 4);
+                for l in (0..32).step_by(16) {
+                    // aux8[l] = (((q4[l] & 0xF) | ((qh[l] & 3) << 4)) as i32 - 32) as i8;
+                    let a8 = v128_or(
+                        v128_and(v128_load(q4.add(l) as *const v128), u8x16_splat(0xF)),
+                        u8x16_shl(
+                            v128_and(v128_load(qh.add(l) as *const v128), u8x16_splat(3)),
+                            4,
+                        ),
+                    );
+                    let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
+                    let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
+                    v128_store(
+                        aux8.add(l) as *mut v128,
+                        i8x16_narrow_i16x8(a8_low, a8_high),
+                    );
+
+                    // aux8[l + 32] =
+                    //    (((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) as i32 - 32) as i8;
+                    let a8 = v128_or(
+                        v128_and(v128_load(q4.add(l + 32) as *const v128), u8x16_splat(0xF)),
+                        u8x16_shl(
+                            v128_and(
+                                u8x16_shr(v128_load(qh.add(l) as *const v128), 2),
+                                u8x16_splat(3),
+                            ),
+                            4,
+                        ),
+                    );
+                    let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
+                    let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
+                    v128_store(
+                        aux8.add(l + 32) as *mut v128,
+                        i8x16_narrow_i16x8(a8_low, a8_high),
+                    );
+
+                    // aux8[l + 64] = (((q4[l] >> 4) | (((qh[l] >> 4) & 3) << 4)) as i32 - 32) as i8;
+                    let a8 = v128_or(
+                        u8x16_shr(v128_load(q4.add(l) as *const v128), 4),
+                        u8x16_shl(
+                            v128_and(
+                                u8x16_shr(v128_load(qh.add(l) as *const v128), 4),
+                                u8x16_splat(3),
+                            ),
+                            4,
+                        ),
+                    );
+                    let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
+                    let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
+                    v128_store(
+                        aux8.add(l + 64) as *mut v128,
+                        i8x16_narrow_i16x8(a8_low, a8_high),
+                    );
+
+                    // aux8[l + 96] =
+                    //    (((q4[l + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) as i32 - 32) as i8;
+                    let a8 = v128_or(
+                        u8x16_shr(v128_load(q4.add(l + 32) as *const v128), 4),
+                        u8x16_shl(
+                            v128_and(
+                                u8x16_shr(v128_load(qh.add(l) as *const v128), 6),
+                                u8x16_splat(3),
+                            ),
+                            4,
+                        ),
+                    );
+                    let a8_low = i16x8_sub(i16x8_extend_low_u8x16(a8), i16x8_splat(32));
+                    let a8_high = i16x8_sub(i16x8_extend_high_u8x16(a8), i16x8_splat(32));
+                    v128_store(
+                        aux8.add(l + 96) as *mut v128,
+                        i8x16_narrow_i16x8(a8_low, a8_high),
+                    );
+                }
+            }
+
+            for (j, &scale) in x.scales.iter().enumerate() {
+                let scale = f32x4_splat(scale as f32);
+                for offset in [0, 8] {
+                    let aux16 = i16x8_mul(
+                        i16x8_load_extend_i8x8(q8.as_ptr().add(16 * j + offset)),
+                        i16x8_load_extend_i8x8(aux8.as_ptr().add(16 * j + offset)),
+                    );
+                    aux32 = f32x4_add(
+                        aux32,
+                        f32x4_mul(f32x4_convert_i32x4(i32x4_extend_low_i16x8(aux16)), scale),
+                    );
+                    aux32 = f32x4_add(
+                        aux32,
+                        f32x4_mul(f32x4_convert_i32x4(i32x4_extend_high_i16x8(aux16)), scale),
+                    );
+                }
+            }
+
+            let d = f32x4_splat(x.d.to_f32() * y.d);
+            sums = f32x4_add(sums, f32x4_mul(aux32, d));
+        }
+        let sums = f32x4_extract_lane::<0>(sums)
+            + f32x4_extract_lane::<1>(sums)
+            + f32x4_extract_lane::<2>(sums)
+            + f32x4_extract_lane::<3>(sums);
+        Ok(sums)
+    }
+}
+
+#[inline(always)]
+pub(crate) fn vec_dot_q8k_q8k(n: usize, xs: &[BlockQ8K], ys: &[BlockQ8K]) -> Result<f32> {
+    let qk = QK_K;
+    if n % QK_K != 0 {
+        crate::bail!("vec_dot_q8k_q8k: {n} is not divisible by {qk}")
+    }
+
+    unsafe {
+        let mut acc = f32x4_splat(0.0f32);
+        for (xs, ys) in xs.iter().zip(ys.iter()) {
+            let x_qs = xs.qs.as_ptr();
+            let y_qs = ys.qs.as_ptr();
+            let mut sumi = i32x4_splat(0);
+            for j in (0..QK_K).step_by(8) {
+                let xs = i16x8_load_extend_i8x8(x_qs.add(j));
+                let ys = i16x8_load_extend_i8x8(y_qs.add(j));
+                let sum_xy = i32x4_dot_i16x8(xs, ys);
+                sumi = i32x4_add(sumi, sum_xy)
+            }
+            let d = f32x4_splat(xs.d * ys.d);
+            acc = f32x4_add(acc, f32x4_mul(f32x4_convert_i32x4(sumi), d))
+        }
+        let res = f32x4_extract_lane::<0>(acc)
+            + f32x4_extract_lane::<1>(acc)
+            + f32x4_extract_lane::<2>(acc)
+            + f32x4_extract_lane::<3>(acc);
+        Ok(res)
+    }
+}

candle-core/src/quantized/utils.rs

@@ -17,7 +17,7 @@ pub(super) fn group_for_quantization<'a, 'b, T: super::k_quants::GgmlType>(
     let expected_blocks = xs.len() / block_size;
     let actual_blocks = ys.len();
 
-    //validate that the input is the right size
+    // Validate that the input is the right size
     if expected_blocks != actual_blocks {
         crate::bail!("quantize {dtype:?}: expected {expected_blocks} blocks but only {actual_blocks} were provided!")
     }
@@ -37,12 +37,12 @@ pub(super) fn group_for_dequantization<'a, 'b, T: super::k_quants::GgmlType>(
 
     let actual_output_len = ys.len();
     let expected_output_len = xs.len() * block_size;
-    //validate that the output is the right size
+    // Validate that the output is the right size
     if expected_output_len != actual_output_len {
         crate::bail!("dequantize {dtype:?}: ys (len = {actual_output_len}) does not match the expected length of {expected_output_len}!")
     }
 
-    //zip the blocks and outputs together
+    // Zip the blocks and outputs together
     Ok(xs.iter().zip(ys.chunks_exact_mut(block_size)).collect())
 }
 

candle-core/src/safetensors.rs

@@ -78,11 +78,7 @@ impl st::View for &Tensor {
 }
 
 impl Tensor {
-    pub fn save_safetensors<P: AsRef<std::path::Path>>(
-        &self,
-        name: &str,
-        filename: P,
-    ) -> Result<()> {
+    pub fn save_safetensors<P: AsRef<Path>>(&self, name: &str, filename: P) -> Result<()> {
         let data = [(name, self.clone())];
         Ok(st::serialize_to_file(data, &None, filename.as_ref())?)
     }
@@ -255,6 +251,134 @@ pub fn save<K: AsRef<str> + Ord + std::fmt::Display, P: AsRef<Path>>(
     Ok(st::serialize_to_file(tensors, &None, filename.as_ref())?)
 }
 
+#[derive(yoke::Yokeable)]
+struct SafeTensors_<'a>(SafeTensors<'a>);
+
+pub struct MmapedSafetensors {
+    safetensors: Vec<yoke::Yoke<SafeTensors_<'static>, memmap2::Mmap>>,
+    routing: Option<HashMap<String, usize>>,
+}
+
+impl MmapedSafetensors {
+    /// Creates a wrapper around a memory mapped file and deserialize the safetensors header.
+    ///
+    /// # Safety
+    ///
+    /// The unsafe is inherited from [`memmap2::MmapOptions`].
+    pub unsafe fn new<P: AsRef<Path>>(p: P) -> Result<Self> {
+        let p = p.as_ref();
+        let file = std::fs::File::open(p).map_err(|e| Error::from(e).with_path(p))?;
+        let file = memmap2::MmapOptions::new()
+            .map(&file)
+            .map_err(|e| Error::from(e).with_path(p))?;
+        let safetensors = yoke::Yoke::<SafeTensors_<'static>, memmap2::Mmap>::try_attach_to_cart(
+            file,
+            |data: &[u8]| {
+                let st = safetensors::SafeTensors::deserialize(data)
+                    .map_err(|e| Error::from(e).with_path(p))?;
+                Ok::<_, Error>(SafeTensors_(st))
+            },
+        )?;
+        Ok(Self {
+            safetensors: vec![safetensors],
+            routing: None,
+        })
+    }
+
+    /// Creates a wrapper around multiple memory mapped file and deserialize the safetensors headers.
+    ///
+    /// If a tensor name appears in multiple files, the last entry is returned.
+    ///
+    /// # Safety
+    ///
+    /// The unsafe is inherited from [`memmap2::MmapOptions`].
+    pub unsafe fn multi<P: AsRef<Path>>(paths: &[P]) -> Result<Self> {
+        let mut routing = HashMap::new();
+        let mut safetensors = vec![];
+        for (index, p) in paths.iter().enumerate() {
+            let p = p.as_ref();
+            let file = std::fs::File::open(p).map_err(|e| Error::from(e).with_path(p))?;
+            let file = memmap2::MmapOptions::new()
+                .map(&file)
+                .map_err(|e| Error::from(e).with_path(p))?;
+            let data = yoke::Yoke::<SafeTensors_<'static>, memmap2::Mmap>::try_attach_to_cart(
+                file,
+                |data: &[u8]| {
+                    let st = safetensors::SafeTensors::deserialize(data)
+                        .map_err(|e| Error::from(e).with_path(p))?;
+                    Ok::<_, Error>(SafeTensors_(st))
+                },
+            )?;
+            for k in data.get().0.names() {
+                routing.insert(k.to_string(), index);
+            }
+            safetensors.push(data)
+        }
+        Ok(Self {
+            safetensors,
+            routing: Some(routing),
+        })
+    }
+
+    pub fn load(&self, name: &str, dev: &Device) -> Result<Tensor> {
+        self.get(name)?.load(dev)
+    }
+
+    pub fn tensors(&self) -> Vec<(String, st::TensorView<'_>)> {
+        let mut tensors = vec![];
+        for safetensors in self.safetensors.iter() {
+            tensors.push(safetensors.get().0.tensors())
+        }
+        tensors.into_iter().flatten().collect()
+    }
+
+    pub fn get(&self, name: &str) -> Result<st::TensorView<'_>> {
+        let index = match &self.routing {
+            None => 0,
+            Some(routing) => {
+                let index = routing.get(name).ok_or_else(|| {
+                    Error::CannotFindTensor {
+                        path: name.to_string(),
+                    }
+                    .bt()
+                })?;
+                *index
+            }
+        };
+        Ok(self.safetensors[index].get().0.tensor(name)?)
+    }
+}
+
+pub struct BufferedSafetensors {
+    safetensors: yoke::Yoke<SafeTensors_<'static>, Vec<u8>>,
+}
+
+impl BufferedSafetensors {
+    /// Creates a wrapper around a binary buffer and deserialize the safetensors header.
+    pub fn new(buffer: Vec<u8>) -> Result<Self> {
+        let safetensors = yoke::Yoke::<SafeTensors_<'static>, Vec<u8>>::try_attach_to_cart(
+            buffer,
+            |data: &[u8]| {
+                let st = safetensors::SafeTensors::deserialize(data)?;
+                Ok::<_, Error>(SafeTensors_(st))
+            },
+        )?;
+        Ok(Self { safetensors })
+    }
+
+    pub fn load(&self, name: &str, dev: &Device) -> Result<Tensor> {
+        self.get(name)?.load(dev)
+    }
+
+    pub fn tensors(&self) -> Vec<(String, st::TensorView<'_>)> {
+        self.safetensors.get().0.tensors()
+    }
+
+    pub fn get(&self, name: &str) -> Result<st::TensorView<'_>> {
+        Ok(self.safetensors.get().0.tensor(name)?)
+    }
+}
+
 pub struct MmapedFile {
     path: std::path::PathBuf,
     inner: memmap2::Mmap,
@@ -267,7 +391,7 @@ impl MmapedFile {
     /// # Safety
     ///
     /// The unsafe is inherited from [`memmap2::MmapOptions`].
-    pub unsafe fn new<P: AsRef<std::path::Path>>(p: P) -> Result<Self> {
+    pub unsafe fn new<P: AsRef<Path>>(p: P) -> Result<Self> {
         let p = p.as_ref();
         let file = std::fs::File::open(p).map_err(|e| Error::from(e).with_path(p))?;
         let inner = memmap2::MmapOptions::new()

candle-core/src/shape.rs

@@ -203,7 +203,7 @@ impl Shape {
 
     /// Check whether the two shapes are compatible for broadcast, and if it is the case return the
     /// broadcasted shape. This is to be used for binary pointwise ops.
-    pub(crate) fn broadcast_shape_binary_op(&self, rhs: &Self, op: &'static str) -> Result<Shape> {
+    pub fn broadcast_shape_binary_op(&self, rhs: &Self, op: &'static str) -> Result<Shape> {
         let lhs = self;
         let lhs_dims = lhs.dims();
         let rhs_dims = rhs.dims();
@@ -444,6 +444,18 @@ impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim, D5: Dim> Dims for (D1, D2, D3, D4, D5)
     }
 }
 
+impl<D1: Dim, D2: Dim, D3: Dim, D4: Dim, D5: Dim, D6: Dim> Dims for (D1, D2, D3, D4, D5, D6) {
+    fn to_indexes_internal(self, shape: &Shape, op: &'static str) -> Result<Vec<usize>> {
+        let d0 = self.0.to_index(shape, op)?;
+        let d1 = self.1.to_index(shape, op)?;
+        let d2 = self.2.to_index(shape, op)?;
+        let d3 = self.3.to_index(shape, op)?;
+        let d4 = self.4.to_index(shape, op)?;
+        let d5 = self.5.to_index(shape, op)?;
+        Ok(vec![d0, d1, d2, d3, d4, d5])
+    }
+}
+
 extract_dims!(dims0, 0, |_: &[usize]| (), ());
 extract_dims!(dims1, 1, |d: &[usize]| d[0], usize);
 extract_dims!(dims2, 2, |d: &[usize]| (d[0], d[1]), (usize, usize));
@@ -499,154 +511,119 @@ impl ShapeWithOneHole for ((),) {
     }
 }
 
+fn hole_size(el_count: usize, prod_d: usize, s: &dyn std::fmt::Debug) -> Result<usize> {
+    if prod_d == 0 {
+        crate::bail!("cannot reshape tensor of {el_count} elements to {s:?}")
+    }
+    if el_count % prod_d != 0 {
+        crate::bail!("cannot reshape tensor with {el_count} elements to {s:?}")
+    }
+    Ok(el_count / prod_d)
+}
+
 impl ShapeWithOneHole for ((), usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let ((), d1) = self;
-        if el_count % d1 != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d1}")
-        }
-        Ok((el_count / d1, d1).into())
+        Ok((hole_size(el_count, d1, &self)?, d1).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, ()) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, ()) = self;
-        if el_count % d1 != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d1}")
-        }
-        Ok((d1, el_count / d1).into())
+        Ok((d1, hole_size(el_count, d1, &self)?).into())
     }
 }
 
 impl ShapeWithOneHole for ((), usize, usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let ((), d1, d2) = self;
-        let d = d1 * d2;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((el_count / d, d1, d2).into())
+        Ok((hole_size(el_count, d1 * d2, &self)?, d1, d2).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, (), usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, (), d2) = self;
-        let d = d1 * d2;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, el_count / d, d2).into())
+        Ok((d1, hole_size(el_count, d1 * d2, &self)?, d2).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, usize, ()) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, d2, ()) = self;
-        let d = d1 * d2;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, d2, el_count / d).into())
+        Ok((d1, d2, hole_size(el_count, d1 * d2, &self)?).into())
     }
 }
 
 impl ShapeWithOneHole for ((), usize, usize, usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let ((), d1, d2, d3) = self;
-        let d = d1 * d2 * d3;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((el_count / d, d1, d2, d3).into())
+        let d = hole_size(el_count, d1 * d2 * d3, &self)?;
+        Ok((d, d1, d2, d3).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, (), usize, usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, (), d2, d3) = self;
-        let d = d1 * d2 * d3;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, el_count / d, d2, d3).into())
+        let d = hole_size(el_count, d1 * d2 * d3, &self)?;
+        Ok((d1, d, d2, d3).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, usize, (), usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, d2, (), d3) = self;
-        let d = d1 * d2 * d3;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, d2, el_count / d, d3).into())
+        let d = hole_size(el_count, d1 * d2 * d3, &self)?;
+        Ok((d1, d2, d, d3).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, usize, usize, ()) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, d2, d3, ()) = self;
-        let d = d1 * d2 * d3;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, d2, d3, el_count / d).into())
+        let d = hole_size(el_count, d1 * d2 * d3, &self)?;
+        Ok((d1, d2, d3, d).into())
     }
 }
 
 impl ShapeWithOneHole for ((), usize, usize, usize, usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let ((), d1, d2, d3, d4) = self;
-        let d = d1 * d2 * d3 * d4;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((el_count / d, d1, d2, d3, d4).into())
+        let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
+        Ok((d, d1, d2, d3, d4).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, (), usize, usize, usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, (), d2, d3, d4) = self;
-        let d = d1 * d2 * d3 * d4;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, el_count / d, d2, d3, d4).into())
+        let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
+        Ok((d1, d, d2, d3, d4).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, usize, (), usize, usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, d2, (), d3, d4) = self;
-        let d = d1 * d2 * d3 * d4;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, d2, el_count / d, d3, d4).into())
+        let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
+        Ok((d1, d2, d, d3, d4).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, usize, usize, (), usize) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, d2, d3, (), d4) = self;
-        let d = d1 * d2 * d3 * d4;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, d2, d3, el_count / d, d4).into())
+        let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
+        Ok((d1, d2, d3, d, d4).into())
     }
 }
 
 impl ShapeWithOneHole for (usize, usize, usize, usize, ()) {
     fn into_shape(self, el_count: usize) -> Result<Shape> {
         let (d1, d2, d3, d4, ()) = self;
-        let d = d1 * d2 * d3 * d4;
-        if el_count % d != 0 {
-            crate::bail!("tensor number of elements {el_count} is not divisible by {d}")
-        }
-        Ok((d1, d2, d3, d4, el_count / d).into())
+        let d = hole_size(el_count, d1 * d2 * d3 * d4, &self)?;
+        Ok((d1, d2, d3, d4, d).into())
     }
 }

candle-core/src/storage.rs

@@ -1,6 +1,6 @@
 use crate::backend::BackendStorage;
 use crate::op::{self, CmpOp, CustomOp1, CustomOp2, CustomOp3, ReduceOp};
-use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, Result, Shape};
+use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, MetalStorage, Result, Shape};
 
 // We do not want to implement Clone on Storage as cloning may fail because of
 // out of memory. Instead try_clone should be used.
@@ -8,6 +8,7 @@ use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, Result, Shape
 pub enum Storage {
     Cpu(CpuStorage),
     Cuda(CudaStorage),
+    Metal(MetalStorage),
 }
 
 impl Storage {
@@ -18,6 +19,10 @@ impl Storage {
                 let storage = storage.try_clone(layout)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.try_clone(layout)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -25,6 +30,7 @@ impl Storage {
         match self {
             Self::Cpu(_) => Device::Cpu,
             Self::Cuda(storage) => Device::Cuda(storage.device().clone()),
+            Self::Metal(storage) => Device::Metal(storage.device().clone()),
         }
     }
 
@@ -32,6 +38,7 @@ impl Storage {
         match self {
             Self::Cpu(storage) => storage.dtype(),
             Self::Cuda(storage) => storage.dtype(),
+            Self::Metal(storage) => storage.dtype(),
         }
     }
 
@@ -65,6 +72,10 @@ impl Storage {
                 let storage = storage.affine(layout, mul, add)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.affine(layout, mul, add)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -78,6 +89,10 @@ impl Storage {
                 let storage = storage.powf(layout, alpha)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.powf(layout, alpha)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -91,6 +106,10 @@ impl Storage {
                 let storage = storage.elu(layout, alpha)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.elu(layout, alpha)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -112,6 +131,10 @@ impl Storage {
                 let storage = lhs.cmp(op, rhs, lhs_layout, rhs_layout)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(lhs), Self::Metal(rhs)) => {
+                let storage = lhs.cmp(op, rhs, lhs_layout, rhs_layout)?;
+                Ok(Self::Metal(storage))
+            }
             (lhs, rhs) => {
                 // Should not happen because of the same device check above but we're defensive
                 // anyway.
@@ -135,6 +158,10 @@ impl Storage {
                 let storage = storage.reduce_op(op, layout, s)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.reduce_op(op, layout, s)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -148,6 +175,10 @@ impl Storage {
                 let storage = storage.to_dtype(layout, dtype)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.to_dtype(layout, dtype)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -161,6 +192,10 @@ impl Storage {
                 let (storage, shape) = c.cuda_fwd(storage, l)?;
                 Ok((Self::Cuda(storage), shape))
             }
+            Self::Metal(storage) => {
+                let (storage, shape) = c.metal_fwd(storage, l)?;
+                Ok((Self::Metal(storage), shape))
+            }
         }
     }
 
@@ -181,6 +216,10 @@ impl Storage {
                 let (s, shape) = c.cuda_fwd(s1, l1, s2, l2)?;
                 Ok((Self::Cuda(s), shape))
             }
+            (Self::Metal(s1), Self::Metal(s2)) => {
+                let (s, shape) = c.metal_fwd(s1, l1, s2, l2)?;
+                Ok((Self::Metal(s), shape))
+            }
             _ => unreachable!(),
         }
     }
@@ -205,6 +244,10 @@ impl Storage {
                 let (s, shape) = c.cuda_fwd(s1, l1, s2, l2, s3, l3)?;
                 Ok((Self::Cuda(s), shape))
             }
+            (Self::Metal(s1), Self::Metal(s2), Self::Metal(s3)) => {
+                let (s, shape) = c.metal_fwd(s1, l1, s2, l2, s3, l3)?;
+                Ok((Self::Metal(s), shape))
+            }
             _ => unreachable!(),
         }
     }
@@ -219,6 +262,10 @@ impl Storage {
                 let storage = storage.unary_impl::<B>(layout)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.unary_impl::<B>(layout)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -239,6 +286,10 @@ impl Storage {
                 let storage = lhs.binary_impl::<B>(rhs, lhs_layout, rhs_layout)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(lhs), Self::Metal(rhs)) => {
+                let storage = lhs.binary_impl::<B>(rhs, lhs_layout, rhs_layout)?;
+                Ok(Self::Metal(storage))
+            }
             (lhs, rhs) => {
                 // Should not happen because of the same device check above but we're defensive
                 // anyway.
@@ -270,6 +321,10 @@ impl Storage {
                 let s = inp.conv1d(l, kernel, kernel_l, params)?;
                 Ok(Self::Cuda(s))
             }
+            (Storage::Metal(inp), Storage::Metal(kernel)) => {
+                let s = inp.conv1d(l, kernel, kernel_l, params)?;
+                Ok(Self::Metal(s))
+            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -279,6 +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,
@@ -297,6 +379,10 @@ impl Storage {
                 let s = inp.conv2d(l, kernel, kernel_l, params)?;
                 Ok(Self::Cuda(s))
             }
+            (Storage::Metal(inp), Storage::Metal(kernel)) => {
+                let s = inp.conv2d(l, kernel, kernel_l, params)?;
+                Ok(Self::Metal(s))
+            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -324,6 +410,10 @@ impl Storage {
                 let s = inp.conv_transpose2d(l, kernel, kernel_l, params)?;
                 Ok(Self::Cuda(s))
             }
+            (Storage::Metal(inp), Storage::Metal(kernel)) => {
+                let s = inp.conv_transpose2d(l, kernel, kernel_l, params)?;
+                Ok(Self::Metal(s))
+            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -348,6 +438,10 @@ impl Storage {
                 let storage = storage.avg_pool2d(layout, kernel_size, stride)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.avg_pool2d(layout, kernel_size, stride)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -366,6 +460,27 @@ impl Storage {
                 let storage = storage.max_pool2d(layout, kernel_size, stride)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.max_pool2d(layout, kernel_size, stride)?;
+                Ok(Self::Metal(storage))
+            }
+        }
+    }
+
+    pub(crate) fn upsample_nearest1d(&self, layout: &Layout, sz: usize) -> Result<Self> {
+        match self {
+            Storage::Cpu(storage) => {
+                let storage = storage.upsample_nearest1d(layout, sz)?;
+                Ok(Self::Cpu(storage))
+            }
+            Self::Cuda(storage) => {
+                let storage = storage.upsample_nearest1d(layout, sz)?;
+                Ok(Self::Cuda(storage))
+            }
+            Self::Metal(storage) => {
+                let storage = storage.upsample_nearest1d(layout, sz)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -379,6 +494,10 @@ impl Storage {
                 let storage = storage.upsample_nearest2d(layout, h, w)?;
                 Ok(Self::Cuda(storage))
             }
+            Self::Metal(storage) => {
+                let storage = storage.upsample_nearest2d(layout, h, w)?;
+                Ok(Self::Metal(storage))
+            }
         }
     }
 
@@ -402,6 +521,10 @@ impl Storage {
                 let storage = cond.where_cond(layout, t, layout_t, f, layout_f)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(cond), Self::Metal(t), Self::Metal(f)) => {
+                let storage = cond.where_cond(layout, t, layout_t, f, layout_f)?;
+                Ok(Self::Metal(storage))
+            }
             (_, lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -428,6 +551,10 @@ impl Storage {
                 let storage = s.gather(l, indexes, indexes_l, d)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(s), Self::Metal(indexes)) => {
+                let storage = s.gather(l, indexes, indexes_l, d)?;
+                Ok(Self::Metal(storage))
+            }
             _ => unreachable!(),
         }
     }
@@ -452,6 +579,10 @@ impl Storage {
                 let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(s), Self::Metal(indexes), Self::Metal(source)) => {
+                let storage = s.scatter_add(l, indexes, indexes_l, source, source_l, d)?;
+                Ok(Self::Metal(storage))
+            }
             _ => unreachable!(),
         }
     }
@@ -476,6 +607,10 @@ impl Storage {
                 let storage = s.index_add(l, indexes, indexes_l, source, source_l, d)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(s), Self::Metal(indexes), Self::Metal(source)) => {
+                let storage = s.index_add(l, indexes, indexes_l, source, source_l, d)?;
+                Ok(Self::Metal(storage))
+            }
             _ => unreachable!(),
         }
     }
@@ -497,6 +632,10 @@ impl Storage {
                 let storage = lhs.index_select(rhs, lhs_l, rhs_l, d)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(lhs), Self::Metal(rhs)) => {
+                let storage = lhs.index_select(rhs, lhs_l, rhs_l, d)?;
+                Ok(Self::Metal(storage))
+            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -524,6 +663,10 @@ impl Storage {
                 let storage = lhs.matmul(rhs, bmnk, lhs_layout, rhs_layout)?;
                 Ok(Self::Cuda(storage))
             }
+            (Self::Metal(lhs), Self::Metal(rhs)) => {
+                let storage = lhs.matmul(rhs, bmnk, lhs_layout, rhs_layout)?;
+                Ok(Self::Metal(storage))
+            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -543,6 +686,9 @@ impl Storage {
         match (self, dst) {
             (Self::Cpu(src), Self::Cpu(dst)) => src.copy_strided_src(dst, dst_offset, src_l),
             (Self::Cuda(src), Self::Cuda(dst)) => Ok(src.copy_strided_src(dst, dst_offset, src_l)?),
+            (Self::Metal(src), Self::Metal(dst)) => {
+                Ok(src.copy_strided_src(dst, dst_offset, src_l)?)
+            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),

candle-core/src/tensor.rs

@@ -6,7 +6,7 @@ use crate::op::{
 };
 use crate::scalar::TensorOrScalar;
 use crate::shape::{Dim, Dims};
-use crate::{storage::Storage, DType, Device, Error, Layout, Result, Shape};
+use crate::{bail, storage::Storage, DType, Device, Error, Layout, Result, Shape};
 use std::sync::{Arc, RwLock};
 
 /// Unique identifier for tensors.
@@ -105,6 +105,28 @@ macro_rules! binary_op {
     };
 }
 
+macro_rules! binary_op_scalar {
+    ($fn_name:ident, $op_name:ident) => {
+        pub fn $fn_name<T: TensorOrScalar>(&self, rhs: T) -> Result<Self> {
+            let rhs = match rhs.to_tensor_scalar()? {
+                crate::scalar::TensorScalar::Tensor(rhs) => rhs,
+                crate::scalar::TensorScalar::Scalar(rhs) => rhs
+                    .to_dtype(self.dtype())?
+                    .to_device(self.device())?
+                    .broadcast_as(self.shape())?,
+            };
+            let shape = self.same_shape_binary_op(&rhs, stringify!($fn_name))?;
+            let storage = self.storage().binary_impl::<crate::op::$op_name>(
+                &*rhs.storage(),
+                self.layout(),
+                rhs.layout(),
+            )?;
+            let op = BackpropOp::new2(self, &rhs, |t1, t2| Op::Binary(t1, t2, BinaryOp::$op_name));
+            Ok(from_storage(storage, shape.clone(), op, false))
+        }
+    };
+}
+
 macro_rules! broadcast_binary_op {
     ($fn_name:ident, $inner_fn_name:ident) => {
         pub fn $fn_name(&self, rhs: &Self) -> Result<Self> {
@@ -155,14 +177,9 @@ impl Tensor {
         is_variable: bool,
     ) -> Result<Self> {
         let none = BackpropOp::none();
-        if is_variable {
-            let shape = shape.into();
-            let storage = device.ones(&shape, dtype)?;
-            Ok(from_storage(storage, shape, none, is_variable))
-        } else {
-            let storage = device.ones(&crate::shape::SCALAR, dtype)?;
-            from_storage(storage, crate::shape::SCALAR, none, is_variable).broadcast_as(shape)
-        }
+        let shape = shape.into();
+        let storage = device.ones(&shape, dtype)?;
+        Ok(from_storage(storage, shape, none, is_variable))
     }
 
     /// Creates a new tensor filled with ones.
@@ -200,14 +217,9 @@ impl Tensor {
         is_variable: bool,
     ) -> Result<Self> {
         let none = BackpropOp::none();
-        if is_variable {
-            let shape = shape.into();
-            let storage = device.zeros(&shape, dtype)?;
-            Ok(from_storage(storage, shape, none, is_variable))
-        } else {
-            let storage = device.zeros(&crate::shape::SCALAR, dtype)?;
-            from_storage(storage, crate::shape::SCALAR, none, is_variable).broadcast_as(shape)
-        }
+        let shape = shape.into();
+        let storage = device.zeros(&shape, dtype)?;
+        Ok(from_storage(storage, shape, none, is_variable))
     }
 
     /// Creates a new tensor filled with zeros.
@@ -373,11 +385,21 @@ impl Tensor {
         step: D,
         device: &Device,
     ) -> Result<Self> {
+        if D::is_zero(&step) {
+            crate::bail!("step cannot be zero")
+        }
         let mut data = vec![];
         let mut current = start;
-        while current < end {
-            data.push(current);
-            current += step;
+        if step >= D::zero() {
+            while current < end {
+                data.push(current);
+                current += step;
+            }
+        } else {
+            while current > end {
+                data.push(current);
+                current += step;
+            }
         }
         let len = data.len();
         Self::from_vec_impl(data, len, device, false)
@@ -437,7 +459,7 @@ impl Tensor {
 
     /// Returns true if the computation graph should track this op, that is if it is
     /// a variable or if it has some variable as dependencies.
-    pub(crate) fn track_op(&self) -> bool {
+    pub fn track_op(&self) -> bool {
         self.is_variable || self.op.is_some()
     }
 
@@ -447,14 +469,20 @@ impl Tensor {
     binary_op!(mul, Mul);
     binary_op!(sub, Sub);
     binary_op!(div, Div);
-    binary_op!(maximum, Maximum);
-    binary_op!(minimum, Minimum);
+    binary_op_scalar!(maximum, Maximum);
+    binary_op_scalar!(minimum, Minimum);
     broadcast_binary_op!(broadcast_add, add);
     broadcast_binary_op!(broadcast_mul, mul);
     broadcast_binary_op!(broadcast_sub, sub);
     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);
@@ -467,7 +495,21 @@ impl Tensor {
     unary_op!(sqr, Sqr);
     unary_op!(sqrt, Sqrt);
     unary_op!(gelu, Gelu);
+    unary_op!(gelu_erf, GeluErf);
+    unary_op!(erf, Erf);
     unary_op!(relu, Relu);
+    unary_op!(ceil, Ceil);
+    unary_op!(floor, Floor);
+    unary_op!(round, Round);
+
+    /// Round element of the input tensor to the nearest integer.
+    ///
+    /// If the number of decimals is negative, it specifies the number of positions to the left of
+    /// the decimal point.
+    pub fn round_to(&self, decimals: i32) -> Result<Self> {
+        let mult = 10f64.powi(decimals);
+        (self * mult)?.round()? * (1f64 / mult)
+    }
 
     /// Retrieves the single scalar value hold in the tensor. If the tensor contains multiple
     /// dimensions, an error is returned instead.
@@ -487,6 +529,7 @@ impl Tensor {
         match &*self.storage() {
             Storage::Cpu(cpu_storage) => from_cpu_storage(cpu_storage),
             Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
+            Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
         }
     }
 
@@ -514,6 +557,73 @@ impl Tensor {
         Ok(inp)
     }
 
+    /// Creates grids of coordinates specified by the 1D inputs.
+    ///
+    /// # Arguments
+    ///
+    /// * `args` - A slice of 1D tensors.
+    /// * `xy_indexing` - Whether to use xy indexing or ij indexing. If xy is selected, the
+    /// first dimension corresponds to the cardinality of the second input and the second
+    /// dimension corresponds to the cardinality of the first input. If ij is selected, the
+    /// dimensions are in the same order as the cardinality of the inputs.
+    ///
+    /// # Examples
+    ///
+    /// ```rust
+    /// use candle_core::{Tensor, Device, Shape};
+    /// let x = Tensor::new(&[1f32, 2., 3.], &Device::Cpu)?;
+    /// let y = Tensor::new(&[4f32, 5., 6.], &Device::Cpu)?;
+    ///
+    /// let grids_xy = Tensor::meshgrid(&[&x, &y], true)?;
+    ///
+    /// assert_eq!(grids_xy.len(), 2);
+    /// assert_eq!(grids_xy[0].dims(), &[3, 3]);
+    ///
+    /// assert_eq!(grids_xy[0].to_vec2::<f32>()?, &[[1., 2., 3.], [1., 2., 3.], [1., 2., 3.]]);
+    /// assert_eq!(grids_xy[1].to_vec2::<f32>()?, &[[4., 4., 4.], [5., 5., 5.], [6., 6., 6.]]);
+    ///
+    /// let grids_ij = Tensor::meshgrid(&[&x, &y], false)?;
+    ///
+    /// assert_eq!(grids_ij[0].to_vec2::<f32>()?, &[[1., 1., 1.], [2., 2., 2.], [3., 3., 3.]]);
+    /// assert_eq!(grids_ij[1].to_vec2::<f32>()?, &[[4., 5., 6.], [4., 5., 6.], [4., 5., 6.]]);
+    /// # Ok::<(), candle_core::Error>(())
+    /// ```
+    ///
+    /// # Errors
+    ///
+    /// * Will return `Err` if `args` contains less than 2 tensors.
+    ///
+    pub fn meshgrid<A: AsRef<Tensor>>(args: &[A], xy_indexing: bool) -> Result<Vec<Self>> {
+        if args.len() <= 1 {
+            Err(Error::OpRequiresAtLeastTwoTensors { op: "meshgrid" }.bt())?
+        }
+        let args: Vec<_> = if xy_indexing {
+            args.iter().rev().collect()
+        } else {
+            args.iter().collect()
+        };
+
+        let mut shape = Vec::with_capacity(args.len());
+        for arg in args.iter() {
+            shape.push(arg.as_ref().dims1()?)
+        }
+
+        let mut grids = Vec::with_capacity(args.len());
+        for idx in 0..args.len() {
+            let mut ones = vec![1usize; args.len()];
+            ones[idx] = shape[idx];
+            let arg = args[idx].as_ref().reshape(ones)?;
+            let mut repeats = shape.clone();
+            repeats[idx] = 1;
+            let repeated_tensor = arg.repeat(repeats)?;
+            grids.push(repeated_tensor);
+        }
+        if xy_indexing {
+            grids.reverse();
+        }
+        Ok(grids)
+    }
+
     /// This operation multiplies the input tensor by `mul` then adds `add` and return the result.
     /// The input values `mul` and `add` are casted to the appropriate type so some rounding might
     /// be performed.
@@ -589,15 +699,23 @@ impl Tensor {
     pub fn narrow<D: Dim>(&self, dim: D, start: usize, len: usize) -> Result<Self> {
         let dims = self.dims();
         let dim = dim.to_index(self.shape(), "narrow")?;
-        if start + len > dims[dim] {
-            Err(Error::NarrowInvalidArgs {
-                shape: self.shape().clone(),
-                dim,
-                start,
-                len,
-                msg: "start + len > dim_len",
-            }
-            .bt())?
+        let err = |msg| {
+            Err::<(), _>(
+                Error::NarrowInvalidArgs {
+                    shape: self.shape().clone(),
+                    dim,
+                    start,
+                    len,
+                    msg,
+                }
+                .bt(),
+            )
+        };
+        if start > dims[dim] {
+            err("start > dim_len")?
+        }
+        if start.saturating_add(len) > dims[dim] {
+            err("start + len > dim_len")?
         }
         if start == 0 && dims[dim] == len {
             Ok(self.clone())
@@ -644,7 +762,12 @@ impl Tensor {
         let storage = self.storage().reduce_op(op, self.layout(), &[dim])?;
         let mut dims = self.dims().to_vec();
         dims[dim] = 1;
-        let op = BackpropOp::new1(self, |arg| Op::Reduce(arg, op, dims.to_vec()));
+        let op = match op {
+            ReduceOp::Sum | ReduceOp::Min | ReduceOp::Max => {
+                BackpropOp::new1(self, |arg| Op::Reduce(arg, op, dims.to_vec()))
+            }
+            ReduceOp::ArgMin | ReduceOp::ArgMax => BackpropOp::none(),
+        };
         let res = from_storage(storage, dims, op, false);
         if keepdim {
             Ok(res)
@@ -733,6 +856,20 @@ 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> {
@@ -827,12 +964,35 @@ impl Tensor {
         self.cmp(rhs, CmpOp::Le)
     }
 
-    /// Upsample the input tensor to the `(target_h, target_w)` size, taking the value of the
+    /// Clamp the tensor values to be between `min` and `max`.
+    pub fn clamp<T1: TensorOrScalar, T2: TensorOrScalar>(&self, min: T1, max: T2) -> Result<Self> {
+        self.maximum(min)?.minimum(max)
+    }
+
+    /// Interpolate the input tensor to the `target_size` size, taking the value of the nearest element.
+    ///
+    /// The input tensor should have three dimensions, `(batch, channels, l)`, the returned
+    /// tensor also has three dimensions, `(batch, channels, target_size)`.
+    pub fn interpolate1d(&self, target_size: usize) -> Result<Self> {
+        let (n, c, _l) = self.dims3()?;
+        let op = BackpropOp::new1(self, Op::UpsampleNearest1D);
+        let storage = self
+            .storage()
+            .upsample_nearest1d(self.layout(), target_size)?;
+        Ok(from_storage(storage, (n, c, target_size), op, false))
+    }
+
+    /// Alias for `interpolate1d`.
+    pub fn upsample_nearest1d(&self, target_size: usize) -> Result<Self> {
+        self.interpolate1d(target_size)
+    }
+
+    /// Interpolate the input tensor to the `(target_h, target_w)` size, taking the value of the
     /// nearest element.
     ///
     /// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned
     /// tensor also has four dimensions, `(batch, channels, target_h, target_w)`.
-    pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
+    pub fn interpolate2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
         let (n, c, _h, _w) = self.dims4()?;
         let op = BackpropOp::new1(self, Op::UpsampleNearest2D);
         let storage = self
@@ -841,6 +1001,11 @@ impl Tensor {
         Ok(from_storage(storage, (n, c, target_h, target_w), op, false))
     }
 
+    /// Alias for `interpolate2d`.
+    pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
+        self.interpolate2d(target_h, target_w)
+    }
+
     /// 2D average pooling over an input tensor with multiple channels.
     ///
     /// The input tensor should have four dimensions, `(batch, channels, h, w)`, the returned
@@ -1052,14 +1217,16 @@ impl Tensor {
                 op: "scatter-add (self, src)",
                 lhs: self.shape().clone(),
                 rhs: source.shape().clone(),
-            })?
+            }
+            .bt())?
         }
         if indexes.dims() != source.dims() {
             Err(Error::ShapeMismatchBinaryOp {
                 op: "scatter-add (indexes, src)",
                 lhs: indexes.shape().clone(),
                 rhs: source.shape().clone(),
-            })?
+            }
+            .bt())?
         }
         let storage = self.storage().scatter_add(
             self.layout(),
@@ -1075,6 +1242,75 @@ impl Tensor {
         Ok(from_storage(storage, self.shape(), op, false))
     }
 
+    /// Embeds the values of the `src` tensor into the `self` tensor on the specified dimension.
+    pub fn slice_scatter<D: Dim>(&self, src: &Self, dim: D, start: usize) -> Result<Self> {
+        let dim = dim.to_index(self.shape(), "slice-scatter")?;
+        if dim == 0 {
+            self.slice_scatter0(src, start)
+        } else {
+            // TODO: Maybe we want to add a more efficient implementation at some point.
+            self.transpose(0, dim)?
+                .slice_scatter0(&src.transpose(0, dim)?, start)?
+                .transpose(0, dim)
+        }
+    }
+
+    /// Embeds the values of the `src` tensor into the `self` tensor on the first dimension.
+    pub fn slice_scatter0(&self, src: &Self, start: usize) -> Result<Self> {
+        if self.dtype() != src.dtype() {
+            Err(Error::DTypeMismatchBinaryOp {
+                lhs: self.dtype(),
+                rhs: src.dtype(),
+                op: "slice-scatter",
+            }
+            .bt())?
+        }
+        if self.device().location() != src.device.location() {
+            Err(Error::DeviceMismatchBinaryOp {
+                lhs: self.device().location(),
+                rhs: src.device().location(),
+                op: "slice-scatter",
+            }
+            .bt())?
+        }
+        if self.rank() != src.rank() {
+            Err(Error::UnexpectedNumberOfDims {
+                expected: self.rank(),
+                got: src.rank(),
+                shape: src.shape().clone(),
+            }
+            .bt())?
+        }
+        let shape_ok =
+            self.dims()
+                .iter()
+                .zip(src.dims().iter())
+                .enumerate()
+                .all(|(dim_idx, (&d1, &d2))| {
+                    if 0 == dim_idx {
+                        d2 + start <= d1
+                    } else {
+                        d1 == d2
+                    }
+                });
+        if !shape_ok {
+            Err(Error::ShapeMismatchBinaryOp {
+                op: "slice-scatter (self, src)",
+                lhs: self.shape().clone(),
+                rhs: src.shape().clone(),
+            }
+            .bt())?
+        }
+        let mut storage = self.device().zeros(self.shape(), self.dtype())?;
+        self.storage()
+            .copy_strided_src(&mut storage, 0, self.layout())?;
+        let offset = start * src.dims()[1..].iter().product::<usize>();
+        src.storage()
+            .copy_strided_src(&mut storage, offset, src.layout())?;
+        let op = BackpropOp::new2(self, src, |t1, t2| Op::SliceScatter0(t1, t2, start));
+        Ok(from_storage(storage, self.shape(), op, false))
+    }
+
     /// Accumulate element from `source` at indexes `indexes` and add them to `self`.
     pub fn index_add<D: Dim>(&self, indexes: &Self, source: &Self, dim: D) -> Result<Self> {
         let dim = dim.to_index(self.shape(), "index-add")?;
@@ -1097,7 +1333,8 @@ impl Tensor {
                 op: "index-add (self, source)",
                 lhs: self.shape().clone(),
                 rhs: source.shape().clone(),
-            })?
+            }
+            .bt())?
         }
         // The number of element in indexes must match the dimension on which the add is
         // performed on the source tensor (and the index values from `indexes` are taken from
@@ -1108,7 +1345,8 @@ impl Tensor {
                 op: "index-add (ids, source))",
                 lhs: indexes.shape().clone(),
                 rhs: source.shape().clone(),
-            })?
+            }
+            .bt())?
         }
         let storage = self.storage().index_add(
             self.layout(),
@@ -1156,7 +1394,8 @@ impl Tensor {
                 op: "gather",
                 lhs: self.shape().clone(),
                 rhs: indexes.shape().clone(),
-            })?
+            }
+            .bt())?
         }
         let storage =
             self.storage()
@@ -1230,6 +1469,7 @@ impl Tensor {
         match &*self.storage() {
             Storage::Cpu(storage) => from_cpu_storage(storage),
             Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
+            Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
         }
     }
 
@@ -1260,6 +1500,7 @@ impl Tensor {
         match &*self.storage() {
             Storage::Cpu(storage) => from_cpu_storage(storage),
             Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
+            Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
         }
     }
 
@@ -1300,6 +1541,7 @@ impl Tensor {
         match &*self.storage() {
             Storage::Cpu(storage) => from_cpu_storage(storage),
             Storage::Cuda(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
+            Storage::Metal(storage) => from_cpu_storage(&storage.to_cpu_storage()?),
         }
     }
 
@@ -1463,6 +1705,24 @@ impl Tensor {
         }
     }
 
+    /// Returns the sub-tensor fixing the index at `index` on the dimension `dim`.
+    ///
+    /// ```rust
+    /// use candle_core::{Tensor, Device};
+    /// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
+    /// let t = tensor.get_on_dim(1, 0)?;
+    /// assert_eq!(t.to_vec1::<f32>()?, &[0., 2., 4.]);
+    /// let t = tensor.get_on_dim(1, 1)?;
+    /// assert_eq!(t.to_vec1::<f32>()?, &[1., 3., 5.]);
+    /// let t = tensor.get_on_dim(0, 1)?;
+    /// assert_eq!(t.to_vec1::<f32>()?, &[2., 3.]);
+    /// # Ok::<(), candle_core::Error>(())
+    /// ```
+    pub fn get_on_dim<D: Dim>(&self, dim: D, index: usize) -> Result<Tensor> {
+        let dim = dim.to_index(self.shape(), "get_on_dim")?;
+        self.narrow(dim, index, 1)?.squeeze(dim)
+    }
+
     /// Returns a tensor that is a transposed version of the input, the two last dimensions of the
     /// input are swapped.
     ///
@@ -1491,6 +1751,9 @@ impl Tensor {
     pub fn transpose<D1: Dim, D2: Dim>(&self, dim1: D1, dim2: D2) -> Result<Tensor> {
         let dim1 = dim1.to_index(self.shape(), "transpose")?;
         let dim2 = dim2.to_index(self.shape(), "transpose")?;
+        if dim1 == dim2 {
+            return Ok(self.clone());
+        }
         let op = BackpropOp::new1(self, |t| Op::Transpose(t, dim1, dim2));
         let tensor_ = Tensor_ {
             id: TensorId::new(),
@@ -1568,17 +1831,23 @@ 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.
+    ///
+    /// If the tensor is already detached from the computation graph, the same tensor is returned.
     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 self.op.is_none() && !self.is_variable {
+            Ok(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(),
+            };
+            Ok(Tensor(Arc::new(tensor_)))
+        }
     }
 
     /// If the target device is the same as the tensor device, only a shallow copy is performed.
@@ -1590,7 +1859,11 @@ 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.
@@ -1598,6 +1871,9 @@ impl Tensor {
                     Storage::Cuda(cuda.storage_from_cpu_storage(&cpu_storage)?)
                 }
                 (Storage::Cpu(storage), Device::Cpu) => Storage::Cpu(storage.clone()),
+                _ => {
+                    bail!("not implemented yet")
+                }
             };
             let op = BackpropOp::new1(self, Op::ToDevice);
             let tensor_ = Tensor_ {
@@ -1852,6 +2128,34 @@ impl Tensor {
         for arg in args {
             arg.as_ref().check_dim(dim, "cat")?;
         }
+        for (arg_idx, arg) in args.iter().enumerate() {
+            let arg = arg.as_ref();
+            if arg0.rank() != arg.rank() {
+                Err(Error::UnexpectedNumberOfDims {
+                    expected: arg0.rank(),
+                    got: arg.rank(),
+                    shape: arg.shape().clone(),
+                }
+                .bt())?
+            }
+            for (dim_idx, (v1, v2)) in arg0
+                .shape()
+                .dims()
+                .iter()
+                .zip(arg.shape().dims().iter())
+                .enumerate()
+            {
+                if dim_idx != dim && v1 != v2 {
+                    Err(Error::ShapeMismatchCat {
+                        dim: dim_idx,
+                        first_shape: arg0.shape().clone(),
+                        n: arg_idx + 1,
+                        nth_shape: arg.shape().clone(),
+                    }
+                    .bt())?
+                }
+            }
+        }
         if dim == 0 {
             Self::cat0(args)
         } else {
@@ -1969,11 +2273,56 @@ impl Tensor {
         }
     }
 
+    /// Pad the input tensor using same values along dimension `dim`. This adds `left` elements before the
+    /// input tensor values and `right` elements after.
+    pub fn pad_with_same<D: Dim>(&self, dim: D, left: usize, right: usize) -> Result<Self> {
+        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")
+        } else if left == 0 {
+            let dim = dim.to_index(self.shape(), "pad_with_same")?;
+            let r = self.narrow(dim, self.dim(dim)? - 1, 1)?;
+            let mut v = vec![self];
+            for _ in 0..right {
+                v.push(&r)
+            }
+            Tensor::cat(&v, dim)
+        } else if right == 0 {
+            let dim = dim.to_index(self.shape(), "pad_with_same")?;
+            let l = self.narrow(dim, 0, 1)?;
+            let mut v = vec![];
+            for _ in 0..left {
+                v.push(&l)
+            }
+            v.push(self);
+            Tensor::cat(&v, dim)
+        } else {
+            let dim = dim.to_index(self.shape(), "pad_with_same")?;
+            let l = self.narrow(dim, 0, 1)?;
+            let r = self.narrow(dim, self.dim(dim)? - 1, 1)?;
+            let mut v = vec![];
+            for _ in 0..left {
+                v.push(&l)
+            }
+            v.push(self);
+            for _ in 0..right {
+                v.push(&r)
+            }
+            Tensor::cat(&v, dim)
+        }
+    }
+
     /// Run the `forward` method of `m` on `self`.
     pub fn apply<M: crate::Module>(&self, m: &M) -> Result<Self> {
         m.forward(self)
     }
 
+    /// Run the `forward` method of `m` on `self`.
+    pub fn apply_t<M: crate::ModuleT>(&self, m: &M, train: bool) -> Result<Self> {
+        m.forward_t(self, train)
+    }
+
     pub(crate) fn storage(&self) -> std::sync::RwLockReadGuard<'_, Storage> {
         self.storage.read().unwrap()
     }
@@ -2088,6 +2437,127 @@ 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 {
+            crate::bail!("axis {axis} is too large, tensor rank {rank}")
+        } else if 0 <= axis {
+            Ok(axis as usize)
+        } else {
+            let naxis = rank + axis;
+            if naxis < 0 {
+                crate::bail!("axis {axis} is too small, tensor rank {rank}")
+            }
+            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() {
+            crate::bail!(
+                "slice-assign requires input with the same rank {} <> {}",
+                self_dims.len(),
+                src_dims.len()
+            )
+        }
+        if self_dims.len() != ranges.len() {
+            crate::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 {
+                crate::bail!(
+                    "slice-assign: empty range for dim {i}, {start_included} {end_excluded}"
+                )
+            }
+            if self_dims[i] < end_excluded {
+                crate::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] {
+                crate::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)
+    }
 }
 
 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/utils.rs

@@ -23,6 +23,10 @@ pub fn cuda_is_available() -> bool {
     cfg!(feature = "cuda")
 }
 
+pub fn metal_is_available() -> bool {
+    cfg!(feature = "metal")
+}
+
 pub fn with_avx() -> bool {
     cfg!(target_feature = "avx")
 }

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 +50,17 @@ 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]
     );
+    if dev.is_cpu() {
+        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(())
 }
 
@@ -130,7 +146,7 @@ fn conv2d(dev: &Device) -> Result<()> {
             10.389, 3.6023, -4.2808, 0.2672, 5.3646, -5.2023, -2.1955, -9.4075
         ]
     );
-    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1, 1)?;
+    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 2, 7, 7]);
     assert_eq!(
         test_utils::to_vec3_round(&res.i(0)?, 4)?,
@@ -164,7 +180,7 @@ fn conv2d(dev: &Device) -> Result<()> {
     );
 
     // Transpose and dilations.
-    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 2, 1)?;
+    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 2)?;
     assert_eq!(res.dims(), [1, 2, 9, 9]);
     assert_eq!(
         test_utils::to_vec3_round(&res.i(0)?, 4)?,
@@ -246,13 +262,13 @@ fn conv2d_small(dev: &Device) -> Result<()> {
             0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000
         ]
     );
-    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1, 1)?;
+    let res = t.conv_transpose2d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 1, 3, 3]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [0.164, -0.0111, -0.1742, 2.6437, -2.0268, 1.1823, 3.2855, -1.0324, 0.2539],
     );
-    let res = t.transpose(0, 1)?.conv_transpose2d(&w, 0, 0, 1, 1, 1)?;
+    let res = t.transpose(0, 1)?.conv_transpose2d(&w, 0, 0, 1, 1)?;
     assert_eq!(res.dims(), [2, 2, 3, 3]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
@@ -479,17 +495,103 @@ fn conv2d_grad(dev: &Device) -> Result<()> {
             ]
         ]
     );
+
+    // Replicate the issue from https://github.com/huggingface/candle/issues/1212
+    let res = t.i((.., .., 0..4, 0..4))?.conv2d(&w, 0, 2, 1, 1)?;
+    let loss = res.sqr()?.sum_all()?;
+    assert_eq!(test_utils::to_vec0_round(&loss, 2)?, 21.12f32);
+    let grads = loss.backward()?;
+    let grad_t = grads.get(&t).unwrap();
+    let grad_w = grads.get(&w).unwrap();
+    assert_eq!(grad_t.dims(), [1, 4, 5, 5]);
+    assert_eq!(grad_w.dims(), [2, 4, 3, 3]);
+    assert_eq!(
+        test_utils::to_vec3_round(&grad_t.i(0)?, 2)?,
+        [
+            [
+                [9.29, -7.03, 7.87, 0.0, 0.0],
+                [-1.8, -7.82, 5.9, 0.0, 0.0],
+                [-3.12, 4.49, 5.52, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0]
+            ],
+            [
+                [21.73, 3.39, 4.77, 0.0, 0.0],
+                [8.25, 3.73, 27.61, 0.0, 0.0],
+                [-20.55, -5.61, -2.77, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0]
+            ],
+            [
+                [-8.98, 9.91, -7.15, 0.0, 0.0],
+                [4.93, -0.33, 4.56, 0.0, 0.0],
+                [-6.7, -5.76, -8.05, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0]
+            ],
+            [
+                [23.54, 6.98, -10.0, 0.0, 0.0],
+                [9.65, 6.18, 18.72, 0.0, 0.0],
+                [3.29, -5.27, 0.79, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0]
+            ]
+        ]
+    );
+    assert_eq!(
+        test_utils::to_vec3_round(&grad_w.i(0)?, 2)?,
+        [
+            [
+                [-3.47, 7.44, 0.66],
+                [12.89, -3.4, -9.29],
+                [-14.16, -0.83, 7.14]
+            ],
+            [
+                [-3.23, 5.37, -3.02],
+                [-2.12, -11.24, 1.94],
+                [6.97, 7.2, 2.99]
+            ],
+            [
+                [-4.04, -3.31, 4.87],
+                [-6.68, -5.68, 1.73],
+                [-5.54, 4.32, 0.52]
+            ],
+            [[-4.72, 1.5, 4.72], [3.79, 4.04, 6.76], [-4.6, 5.8, 6.93]]
+        ]
+    );
+
     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

@@ -192,6 +192,84 @@ fn unary_grad(device: &Device) -> Result<()> {
         test_utils::to_vec1_round(grad_x, 2)?,
         [0.01, 0.42, 0.0, 0.98],
     );
+
+    // testing compared to pytorch nn.GELU(approximate = 'tanh')
+    let y = x.gelu()?;
+    let grads = y.backward()?;
+    let grad_x = grads.get(&x).context("no grad for x")?;
+    assert_eq!(
+        test_utils::to_vec1_round(&y, 4)?,
+        [2.9964, 0.8412, 3.9999, 0.0839]
+    );
+    assert_eq!(
+        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]
+    );
+
     Ok(())
 }
 
@@ -218,12 +296,48 @@ fn binary_grad(device: &Device) -> Result<()> {
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(y.to_vec1::<f32>()?, [3., 1., -4., -1.]);
     assert_eq!(grad_x.to_vec1::<f32>()?, [1., 1., 1., 1.]);
+
+    let x_var = Var::new(&[3f32, 1., -4., -1., 5., 9.], device)?;
+    let x = x_var.as_tensor();
+    let y_var = Var::new(&[2f32, 7., 1.], device)?;
+    let y = y_var.as_tensor();
+
+    let ss = x
+        .reshape((2, 3))?
+        .slice_scatter0(&y.reshape((1, 3))?, 1)?
+        .sqr()?;
+    let grads = ss.backward()?;
+    let grad_x = grads.get(x).context("no grad for x")?;
+    let grad_y = grads.get(y).context("no grad for y")?;
+    assert_eq!(ss.to_vec2::<f32>()?, [[9., 1., 16.], [4., 49., 1.]]);
+    assert_eq!(grad_x.to_vec1::<f32>()?, [6.0, 2.0, -8.0, 0.0, 0.0, 0.0]);
+    assert_eq!(grad_y.to_vec1::<f32>()?, [4.0, 14.0, 2.0]);
     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/npy.py

@@ -0,0 +1,9 @@
+import numpy as np
+x = np.arange(10)
+
+# Write a npy file.
+np.save("test.npy", x)
+
+# Write multiple values to a npz file.
+values = { "x": x, "x_plus_one": x + 1 }
+np.savez("test.npz", **values)

candle-core/tests/pool_tests.rs

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

candle-core/tests/quantized_tests.rs

@@ -1,7 +1,7 @@
 use candle_core::{
     quantized::{self, GgmlDType},
     test_utils::to_vec2_round,
-    Device, Result, Tensor,
+    Device, Module, Result, Tensor,
 };
 use quantized::{k_quants, GgmlType};
 use rand::prelude::*;
@@ -43,7 +43,7 @@ fn quantized_matmul() -> Result<()> {
     );
 
     let qtensor = quantized::QTensor::new(rhs_t, (4, 64))?;
-    let matmul = quantized::QMatMul::from_qtensor(qtensor);
+    let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
     let res = matmul.forward(&tensor_lhs)?;
     assert_eq!(
         to_vec2_round(&res, 0)?,
@@ -91,7 +91,7 @@ fn quantized_matmul_neg() -> Result<()> {
     );
 
     let qtensor = quantized::QTensor::new(rhs_t, (4, 64))?;
-    let matmul = quantized::QMatMul::from_qtensor(qtensor);
+    let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
     let res = matmul.forward(&tensor_lhs)?;
     assert_eq!(
         to_vec2_round(&res, 0)?,
@@ -491,6 +491,9 @@ fn ggml_reference_matmul_error(dtype: GgmlDType) -> Result<f32> {
         GgmlDType::Q5_0 => 0.001353,
         GgmlDType::Q5_1 => 0.001363,
         GgmlDType::Q8_0 => 0.000092,
+
+        // Not from the ggml repo.
+        GgmlDType::Q8K => 0.00065,
         _ => candle_core::bail!("No GGML results for quantization type {dtype:?}",),
     };
     Ok(err)
@@ -508,17 +511,22 @@ fn ggml_matmul_error_test<T: GgmlType>() -> Result<()> {
     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);
 
+    if (result - result_unopt).abs() / length as f32 > 1e-6 {
+        candle_core::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)?;
 
-    if error > GGML_MAX_DOT_PRODUCT_ERROR {
+    if !error.is_finite() || error > GGML_MAX_DOT_PRODUCT_ERROR {
         candle_core::bail!(
-            "Dot product error {} exceeds max error {}",
-            error,
-            GGML_MAX_DOT_PRODUCT_ERROR
+            "Dot product error {error} exceeds max error {GGML_MAX_DOT_PRODUCT_ERROR}",
         );
     }
 
@@ -571,7 +579,7 @@ fn quantized_matmul_q2k() -> Result<()> {
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
     let rhs = quantized::QTensor::quantize::<BlockQ2K>(&rhs)?;
-    let rhs = quantized::QMatMul::from_qtensor(rhs);
+    let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
     assert_eq!(mm.dims(), [m, n]);
@@ -597,7 +605,7 @@ fn quantized_matmul_q3k() -> Result<()> {
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
     let rhs = quantized::QTensor::quantize::<BlockQ3K>(&rhs)?;
-    let rhs = quantized::QMatMul::from_qtensor(rhs);
+    let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
     assert_eq!(mm.dims(), [m, n]);
@@ -623,7 +631,7 @@ fn quantized_matmul_q4k() -> Result<()> {
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
     let rhs = quantized::QTensor::quantize::<BlockQ4K>(&rhs)?;
-    let rhs = quantized::QMatMul::from_qtensor(rhs);
+    let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
     assert_eq!(mm.dims(), [m, n]);
@@ -649,7 +657,7 @@ fn quantized_matmul_q5k() -> Result<()> {
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
     let rhs = quantized::QTensor::quantize::<BlockQ5K>(&rhs)?;
-    let rhs = quantized::QMatMul::from_qtensor(rhs);
+    let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
     assert_eq!(mm.dims(), [m, n]);
@@ -676,7 +684,7 @@ fn quantized_matmul_q6k() -> Result<()> {
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
     let rhs = quantized::QTensor::quantize::<BlockQ6K>(&rhs)?;
-    let rhs = quantized::QMatMul::from_qtensor(rhs);
+    let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
     assert_eq!(mm.dims(), [m, n]);
@@ -687,3 +695,28 @@ fn quantized_matmul_q6k() -> Result<()> {
     ggml_matmul_error_test::<BlockQ6K>()?;
     Ok(())
 }
+
+#[test]
+fn quantized_matmul_q8k() -> Result<()> {
+    use k_quants::BlockQ8K;
+
+    let cpu = &Device::Cpu;
+    let (m, k, n) = (11, 512, 21);
+    let (lhs, rhs, mm) = get_random_tensors(m, k, n, cpu)?;
+    assert_eq!(mm.dims(), [m, n]);
+    let dst = mm.flatten_all()?.to_vec1::<f32>()?;
+    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::QMatMul::from_qtensor(rhs)?;
+    let mm = rhs.forward(&lhs)?;
+
+    assert_eq!(mm.dims(), [m, n]);
+    let dst = mm.flatten_all()?.to_vec1::<f32>()?;
+    let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
+    assert_eq!(dst, [1.266, 1.504, -0.204, 1.7]);
+
+    ggml_matmul_error_test::<BlockQ8K>()?;
+    Ok(())
+}

candle-core/tests/serialization_tests.rs

@@ -0,0 +1,24 @@
+use candle_core::{DType, Result, Tensor};
+
+#[test]
+fn npy() -> Result<()> {
+    let npy = Tensor::read_npy("tests/test.npy")?;
+    assert_eq!(
+        npy.to_dtype(DType::U8)?.to_vec1::<u8>()?,
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+    );
+    Ok(())
+}
+
+#[test]
+fn npz() -> Result<()> {
+    let npz = Tensor::read_npz("tests/test.npz")?;
+    assert_eq!(npz.len(), 2);
+    assert_eq!(npz[0].0, "x");
+    assert_eq!(npz[1].0, "x_plus_one");
+    assert_eq!(
+        npz[1].1.to_dtype(DType::U8)?.to_vec1::<u8>()?,
+        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+    );
+    Ok(())
+}

candle-core/tests/tensor_tests.rs

@@ -1,4 +1,4 @@
-use candle_core::{test_device, DType, Device, IndexOp, Result, Tensor};
+use candle_core::{test_device, test_utils, DType, Device, IndexOp, Result, Tensor};
 
 fn zeros(device: &Device) -> Result<()> {
     let tensor = Tensor::zeros((5, 2), DType::F32, device)?;
@@ -8,6 +8,50 @@ fn zeros(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn ones(device: &Device) -> Result<()> {
+    assert_eq!(
+        Tensor::ones((2, 3), DType::U8, device)?.to_vec2::<u8>()?,
+        [[1, 1, 1], [1, 1, 1]],
+    );
+    assert_eq!(
+        Tensor::ones((2, 3), DType::U32, device)?.to_vec2::<u32>()?,
+        [[1, 1, 1], [1, 1, 1]],
+    );
+    assert_eq!(
+        Tensor::ones((2, 3), DType::I64, device)?.to_vec2::<i64>()?,
+        [[1, 1, 1], [1, 1, 1]],
+    );
+    assert_eq!(
+        Tensor::ones((2, 3), DType::F32, device)?.to_vec2::<f32>()?,
+        [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+    );
+    assert_eq!(
+        Tensor::ones((2, 3), DType::F64, device)?.to_vec2::<f64>()?,
+        [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]],
+    );
+    Ok(())
+}
+
+fn arange(device: &Device) -> Result<()> {
+    assert_eq!(
+        Tensor::arange(0u8, 5u8, device)?.to_vec1::<u8>()?,
+        [0, 1, 2, 3, 4],
+    );
+    assert_eq!(
+        Tensor::arange_step(0u8, 5u8, 2, device)?.to_vec1::<u8>()?,
+        [0, 2, 4],
+    );
+    assert_eq!(
+        Tensor::arange_step(0u8, 5u8, 3, device)?.to_vec1::<u8>()?,
+        [0, 3],
+    );
+    assert_eq!(
+        Tensor::arange_step(5i64, 0i64, -1, device)?.to_vec1::<i64>()?,
+        [5, 4, 3, 2, 1],
+    );
+    Ok(())
+}
+
 fn add_mul(device: &Device) -> Result<()> {
     let tensor = Tensor::new(&[3f32, 1., 4.], device)?;
     let dim1 = tensor.dims1()?;
@@ -33,6 +77,65 @@ fn tensor_2d(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn clamp(device: &Device) -> Result<()> {
+    let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
+    let tensor = Tensor::new(data, device)?;
+    let tensor = tensor.clamp(1.5, 6.2)?;
+    assert_eq!(
+        tensor.to_vec2::<f32>()?,
+        [[3.0, 1.5, 4.0, 1.5, 5.0], [2.0, 1.5, 6.2, 6.2, 2.0]],
+    );
+    Ok(())
+}
+
+fn unary_op(device: &Device) -> Result<()> {
+    let data = &[[-3f32, 1., 4., -0.1, 0.5], [2.7, -1.8, -0.28, 1.8, 2.8]];
+    let tensor = Tensor::new(data, device)?;
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.gelu()?, 4)?,
+        [
+            [-0.0036, 0.8412, 3.9999, -0.046, 0.3457],
+            [2.6911, -0.0647, -0.1091, 1.7353, 2.7933]
+        ]
+    );
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.gelu_erf()?, 4)?,
+        [
+            [-0.004, 0.8413, 3.9999, -0.046, 0.3457],
+            [2.6906, -0.0647, -0.1091, 1.7353, 2.7928]
+        ]
+    );
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.erf()?, 4)?,
+        [
+            [-1.0, 0.8427, 1.0, -0.1125, 0.5205],
+            [0.9999, -0.9891, -0.3079, 0.9891, 0.9999]
+        ]
+    );
+    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]]
+    );
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.floor()?, 4)?,
+        [[-3.0, 1.0, 4.0, -1.0, 0.0], [2.0, -2.0, -1.0, 1.0, 2.0]]
+    );
+    assert_eq!(
+        test_utils::to_vec2_round(&tensor.round()?, 4)?,
+        [[-3.0, 1.0, 4.0, -0.0, 1.0], [3.0, -2.0, -0.0, 2.0, 3.0]]
+    );
+    let tensor = Tensor::new(&[2997.9246, 314.15926f32], device)?;
+    assert_eq!(
+        test_utils::to_vec1_round(&tensor.round_to(2)?, 4)?,
+        [2997.92, 314.16]
+    );
+    assert_eq!(
+        test_utils::to_vec1_round(&tensor.round_to(-2)?, 4)?,
+        [3000.0, 300.]
+    );
+    Ok(())
+}
+
 fn binary_op(device: &Device) -> Result<()> {
     let data = &[[3f32, 1., 4., 1., 5.], [2., 1., 7., 8., 2.]];
     let tensor1 = Tensor::new(data, device)?;
@@ -77,6 +180,22 @@ 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)?;
@@ -590,6 +709,30 @@ fn index_select(device: &Device) -> Result<()> {
         hs.to_vec2::<f32>()?,
         &[[0.0, 1.0, 2.0], [6.0, 7.0, 8.0], [3.0, 4.0, 5.0]]
     );
+    // Prior to https://github.com/huggingface/candle/pull/1022
+    // There would be a bug where the last values in the result tensor would be set to 0.
+    let ids = Tensor::new(&[0u32, 2u32, 1u32, 0u32, 2u32, 1u32], device)?;
+    let hs = t.index_select(&ids, 0)?;
+    assert_eq!(
+        hs.to_vec2::<f32>()?,
+        &[
+            [0.0, 1.0, 2.0],
+            [6.0, 7.0, 8.0],
+            [3.0, 4.0, 5.0],
+            [0.0, 1.0, 2.0],
+            [6.0, 7.0, 8.0],
+            [3.0, 4.0, 5.0],
+        ]
+    );
+
+    // Test when selecting dim > 0 with ids size different from elem count of
+    // target dim in source/input.
+    let ids = Tensor::new(&[1u32, 0u32, 1u32], device)?;
+    let t = Tensor::arange(1f32, 5f32, device)?.reshape((2, 2))?;
+    assert_eq!(t.to_vec2::<f32>()?, &[[1.0, 2.0], [3.0, 4.0]]);
+    let hs = t.index_select(&ids, 1)?;
+    assert_eq!(hs.to_vec2::<f32>()?, &[[2.0, 1.0, 2.0], [4.0, 3.0, 4.0]]);
+
     Ok(())
 }
 
@@ -636,6 +779,48 @@ fn index_add(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn slice_scatter(device: &Device) -> Result<()> {
+    let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
+    assert_eq!(
+        t.to_vec2::<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]
+        ]
+    );
+    let src = Tensor::arange(100f32, 106f32, device)?.reshape((2, 3))?;
+    assert_eq!(
+        t.slice_scatter0(&src, 0)?.to_vec2::<f32>()?,
+        &[
+            [100.0, 101.0, 102.0],
+            [103.0, 104.0, 105.0],
+            [6.0, 7.0, 8.0],
+            [9.0, 10.0, 11.0]
+        ]
+    );
+    assert_eq!(
+        t.slice_scatter0(&src, 1)?.to_vec2::<f32>()?,
+        &[
+            [0.0, 1.0, 2.0],
+            [100.0, 101.0, 102.0],
+            [103.0, 104.0, 105.0],
+            [9.0, 10.0, 11.0]
+        ]
+    );
+    assert_eq!(
+        t.slice_scatter0(&src, 2)?.to_vec2::<f32>()?,
+        &[
+            [0.0, 1.0, 2.0],
+            [3.0, 4.0, 5.0],
+            [100.0, 101.0, 102.0],
+            [103.0, 104.0, 105.0],
+        ]
+    );
+    Ok(())
+}
+
 fn scatter_add(device: &Device) -> Result<()> {
     let t = Tensor::arange(0f32, 12f32, device)?.reshape((4, 3))?;
     assert_eq!(
@@ -885,29 +1070,60 @@ fn randn(device: &Device) -> Result<()> {
     Ok(())
 }
 
-test_device!(zeros, zeros_cpu, zeros_gpu);
-test_device!(add_mul, add_mul_cpu, add_mul_gpu);
-test_device!(tensor_2d, tensor_2d_cpu, tensor_2d_gpu);
-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!(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!(randn, randn_cpu, randn_gpu);
+test_device!(zeros, zeros_cpu, zeros_gpu, zeros_metal);
+test_device!(ones, ones_cpu, ones_gpu, ones_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
@@ -919,3 +1135,89 @@ fn randn_hasneg() -> Result<()> {
     }
     Ok(())
 }
+
+#[test]
+fn pad_with_same() -> Result<()> {
+    let t = Tensor::arange(1f32, 5f32, &Device::Cpu)?.reshape((2, 2))?;
+    let t0 = t.pad_with_same(0, 1, 2)?;
+    assert_eq!(
+        t0.to_vec2::<f32>()?,
+        [[1.0, 2.0], [1.0, 2.0], [3.0, 4.0], [3.0, 4.0], [3.0, 4.0]]
+    );
+    let t1 = t.pad_with_same(1, 1, 2)?;
+    assert_eq!(
+        t1.to_vec2::<f32>()?,
+        [[1.0, 1.0, 2.0, 2.0, 2.0], [3.0, 3.0, 4.0, 4.0, 4.0]]
+    );
+    Ok(())
+}
+
+#[test]
+fn i64_abs() -> Result<()> {
+    let t = Tensor::new(&[-42i64, 1337], &Device::Cpu)?;
+    let t = t.abs()?;
+    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(())
+}

candle-datasets/Cargo.toml

@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 byteorder = { workspace = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
 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

@@ -11,20 +11,23 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.2.1", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.2.1" }
-candle-nn = { path = "../candle-nn", version = "0.2.1" }
-candle-transformers = { path = "../candle-transformers", version = "0.2.1" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.2.1", optional = true }
-safetensors = { workspace = true }
-serde = { workspace = true }
-serde_json = { workspace = true }
-num-traits = { workspace = true }
-intel-mkl-src = { workspace = true, optional = true }
+candle = { path = "../candle-core", version = "0.3.1", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.3.1" }
+candle-nn = { path = "../candle-nn", version = "0.3.1" }
+candle-transformers = { path = "../candle-transformers", version = "0.3.1" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.3.1", optional = true }
+candle-onnx = { path = "../candle-onnx", version = "0.3.1", optional = true }
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
 image = { workspace = true }
+intel-mkl-src = { workspace = true, optional = true }
+num-traits = { workspace = true }
+pyo3 = { version = "0.20.0", features = ["auto-initialize"], optional = true }
 rayon = { workspace = true }
+safetensors = { workspace = true }
+serde = { workspace = true }
+serde_json = { workspace = true }
+tokenizers = { workspace = true, features = ["onig"] }
 
 [dev-dependencies]
 anyhow = { workspace = true }
@@ -35,7 +38,6 @@ imageproc = { workspace = true }
 memmap2 = { workspace = true }
 rand = { workspace = true }
 rusttype = { workspace = true }
-tokenizers = { workspace = true, features = ["onig"] }
 tracing = { workspace = true }
 tracing-chrome = { workspace = true }
 tracing-subscriber = { workspace = true }
@@ -51,10 +53,24 @@ default = []
 accelerate = ["dep:accelerate-src", "candle/accelerate", "candle-nn/accelerate", "candle-transformers/accelerate"]
 cuda = ["candle/cuda", "candle-nn/cuda", "candle-transformers/cuda"]
 cudnn = ["candle/cudnn"]
-flash-attn = ["cuda", "dep:candle-flash-attn"]
+flash-attn = ["cuda", "candle-transformers/flash-attn", "dep:candle-flash-attn"]
 mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl", "candle-transformers/mkl"]
 nccl = ["cuda", "cudarc/nccl", "dep:half"]
+onnx = ["candle-onnx"]
+metal = ["candle/metal", "candle-nn/metal"]
 
 [[example]]
 name = "llama_multiprocess"
 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"]

candle-examples/examples/bert/README.md

@@ -0,0 +1,44 @@
+# candle-bert
+
+Bert is a general large language model. In this example it can be used for two
+different tasks:
+- Compute sentence embeddings for a prompt.
+- Compute similarities between a set of sentences.
+
+
+## Sentence embeddings
+
+Bert is used to compute the sentence embeddings for a prompt. The model weights
+are downloaded from the hub on the first run.
+
+```bash
+cargo run --example bert --release -- --prompt "Here is a test sentence"
+
+> [[[ 0.0798, -0.0665, -0.0247, ..., -0.1082, -0.1000, -0.2751],
+>   [ 0.4218,  0.2690,  0.2740, ...,  0.3889,  1.3503,  0.9908],
+>   [ 0.0466,  0.3041, -0.1143, ...,  0.4427,  0.6926, -0.1515],
+>   ...
+>   [ 0.3396,  0.4320, -0.4408, ...,  0.9212,  0.2331, -0.6777],
+>   [ 0.2789,  0.7539,  0.4306, ..., -0.0095,  0.3375, -1.7529],
+>   [ 0.6737,  0.7882,  0.0548, ...,  0.1836,  0.7299, -0.6617]]]
+> Tensor[[1, 7, 384], f32]
+```
+
+## Similarities
+
+In this example, Bert is used to compute the sentence embeddings for a set of
+sentences (hardcoded in the examples). Then cosine similarities are computed for
+each sentence pair and they are reported by decreasing values, hence the first
+reported pair contains the two sentences that have the highest similarity score.
+The sentence embeddings are computed using average pooling through all the
+sentence tokens, including some potential padding.
+
+```bash
+cargo run --example bert --release
+
+> score: 0.85 'The new movie is awesome' 'The new movie is so great'
+> score: 0.61 'The cat sits outside' 'The cat plays in the garden'
+> score: 0.52 'I love pasta' 'Do you like pizza?'
+> score: 0.23 'The new movie is awesome' 'Do you like pizza?'
+> score: 0.22 'I love pasta' 'The new movie is awesome'
+```

candle-examples/examples/bert/main.rs

@@ -3,14 +3,13 @@ extern crate intel_mkl_src;
 
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
-mod model;
+use candle_transformers::models::bert::{BertModel, Config, DTYPE};
 
-use anyhow::{anyhow, Error as E, Result};
+use anyhow::{Error as E, Result};
 use candle::Tensor;
 use candle_nn::VarBuilder;
 use clap::Parser;
-use hf_hub::{api::sync::Api, Cache, Repo, RepoType};
-use model::{BertModel, Config, DTYPE};
+use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::{PaddingParams, Tokenizer};
 
 #[derive(Parser, Debug)]
@@ -20,10 +19,6 @@ struct Args {
     #[arg(long)]
     cpu: bool,
 
-    /// Run offline (you must have the files already cached)
-    #[arg(long)]
-    offline: bool,
-
     /// Enable tracing (generates a trace-timestamp.json file).
     #[arg(long)]
     tracing: bool,
@@ -39,6 +34,10 @@ struct Args {
     #[arg(long)]
     prompt: Option<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,
@@ -61,35 +60,27 @@ impl Args {
         };
 
         let repo = Repo::with_revision(model_id, RepoType::Model, revision);
-        let (config_filename, tokenizer_filename, weights_filename) = if self.offline {
-            let cache = Cache::default().repo(repo);
-            (
-                cache
-                    .get("config.json")
-                    .ok_or(anyhow!("Missing config file in cache"))?,
-                cache
-                    .get("tokenizer.json")
-                    .ok_or(anyhow!("Missing tokenizer file in cache"))?,
-                cache
-                    .get("model.safetensors")
-                    .ok_or(anyhow!("Missing weights file in cache"))?,
-            )
-        } else {
+        let (config_filename, tokenizer_filename, weights_filename) = {
             let api = Api::new()?;
             let api = api.repo(repo);
-            (
-                api.get("config.json")?,
-                api.get("tokenizer.json")?,
-                api.get("model.safetensors")?,
-            )
+            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 weights = unsafe { candle::safetensors::MmapedFile::new(weights_filename)? };
-        let weights = weights.deserialize()?;
-        let vb = VarBuilder::from_safetensors(vec![weights], DTYPE, &device);
+        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 = BertModel::load(vb, &config)?;
         Ok((model, tokenizer))
     }

candle-examples/examples/bigcode/README.md

@@ -0,0 +1,19 @@
+# candle-starcoder: code generation model
+
+[StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
+model specialized to code generation. The initial model was trained on 80
+programming languages.
+
+## Running some example
+
+```bash
+cargo run --example bigcode --release -- --prompt "fn fact(n: u64) -> u64 "
+
+> fn fact(n: u64) -> u64  {
+>     if n == 0 {
+>         1
+>     } else {
+>         n * fact(n - 1)
+>     }
+> }
+```

candle-examples/examples/bigcode/main.rs

@@ -7,8 +7,7 @@ extern crate accelerate_src;
 use anyhow::{Error as E, Result};
 use clap::Parser;
 
-mod model;
-use model::{Config, GPTBigCode};
+use candle_transformers::models::bigcode::{Config, GPTBigCode};
 
 use candle::{DType, Device, Tensor};
 use candle_nn::VarBuilder;
@@ -29,9 +28,10 @@ impl TextGeneration {
         tokenizer: Tokenizer,
         seed: u64,
         temp: Option<f64>,
+        top_p: Option<f64>,
         device: &Device,
     ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp);
+        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
         Self {
             model,
             tokenizer,
@@ -95,6 +95,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -134,23 +138,21 @@ fn main() -> Result<()> {
     println!("retrieved the files in {:?}", start.elapsed());
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
-    let weights = filenames
-        .iter()
-        .map(|f| Ok(unsafe { candle::safetensors::MmapedFile::new(f)? }))
-        .collect::<Result<Vec<_>>>()?;
-    let weights = weights
-        .iter()
-        .map(|f| Ok(f.deserialize()?))
-        .collect::<Result<Vec<_>>>()?;
-
     let start = std::time::Instant::now();
     let device = candle_examples::device(args.cpu)?;
-    let vb = VarBuilder::from_safetensors(weights, DType::F32, &device);
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
     let config = Config::starcoder_1b();
     let model = GPTBigCode::load(vb, config)?;
     println!("loaded the model in {:?}", start.elapsed());
 
-    let mut pipeline = TextGeneration::new(model, tokenizer, args.seed, args.temperature, &device);
+    let mut pipeline = TextGeneration::new(
+        model,
+        tokenizer,
+        args.seed,
+        args.temperature,
+        args.top_p,
+        &device,
+    );
     pipeline.run(&args.prompt, args.sample_len)?;
     Ok(())
 }

candle-examples/examples/blip/README.md

@@ -0,0 +1,19 @@
+# candle-blip
+
+The
+[blip-image-captioning](https://huggingface.co/Salesforce/blip-image-captioning-base)
+model can generate captions for an input image.
+
+## Running on an example
+
+```bash
+cargo run --example blip --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+```
+
+```
+Running on CPU, to run on GPU, build this example with `--features cuda`
+loaded image Tensor[dims 3, 384, 384; f32]
+model built
+several cyclists are riding down a road with cars behind them%
+```
+![Leading group, Giro d'Italia 2021](../yolo-v8/assets/bike.jpg)

candle-examples/examples/blip/main.rs

@@ -0,0 +1,154 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::Error as E;
+use clap::Parser;
+
+use candle::{DType, Device, Result, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::models::blip;
+use candle_transformers::models::quantized_blip;
+
+use tokenizers::Tokenizer;
+
+enum Model {
+    M(blip::BlipForConditionalGeneration),
+    Q(quantized_blip::BlipForConditionalGeneration),
+}
+
+impl Model {
+    fn text_decoder_forward(&mut self, xs: &Tensor, img_xs: &Tensor) -> Result<Tensor> {
+        match self {
+            Self::M(m) => m.text_decoder().forward(xs, img_xs),
+            Self::Q(m) => m.text_decoder().forward(xs, img_xs),
+        }
+    }
+}
+
+// TODO: Maybe add support for the conditional prompt.
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    #[arg(long)]
+    image: String,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Use the quantized version of the model.
+    #[arg(long)]
+    quantized: bool,
+}
+
+const SEP_TOKEN_ID: u32 = 102;
+
+/// Loads an image from disk using the image crate, this returns a tensor with shape
+/// (3, 384, 384). OpenAI normalization is applied.
+pub fn load_image<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
+    let img = image::io::Reader::open(p)?
+        .decode()
+        .map_err(candle::Error::wrap)?
+        .resize_to_fill(384, 384, image::imageops::FilterType::Triangle);
+    let img = img.to_rgb8();
+    let data = img.into_raw();
+    let data = Tensor::from_vec(data, (384, 384, 3), &Device::Cpu)?.permute((2, 0, 1))?;
+    let mean =
+        Tensor::new(&[0.48145466f32, 0.4578275, 0.40821073], &Device::Cpu)?.reshape((3, 1, 1))?;
+    let std = Tensor::new(&[0.26862954f32, 0.261_302_6, 0.275_777_1], &Device::Cpu)?
+        .reshape((3, 1, 1))?;
+    (data.to_dtype(candle::DType::F32)? / 255.)?
+        .broadcast_sub(&mean)?
+        .broadcast_div(&std)
+}
+
+pub fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+
+    let model_file = match args.model {
+        None => {
+            let api = hf_hub::api::sync::Api::new()?;
+            if args.quantized {
+                let api = api.model("lmz/candle-blip".to_string());
+                api.get("blip-image-captioning-large-q4k.gguf")?
+            } else {
+                let api = api.repo(hf_hub::Repo::with_revision(
+                    "Salesforce/blip-image-captioning-large".to_string(),
+                    hf_hub::RepoType::Model,
+                    "refs/pr/18".to_string(),
+                ));
+                api.get("model.safetensors")?
+            }
+        }
+        Some(model) => model.into(),
+    };
+    let tokenizer = match args.tokenizer {
+        None => {
+            let api = hf_hub::api::sync::Api::new()?;
+            let api = api.model("Salesforce/blip-image-captioning-large".to_string());
+            api.get("tokenizer.json")?
+        }
+        Some(file) => file.into(),
+    };
+    let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
+    let mut tokenizer = TokenOutputStream::new(tokenizer);
+    let mut logits_processor =
+        candle_transformers::generation::LogitsProcessor::new(1337, None, None);
+
+    let config = blip::Config::image_captioning_large();
+
+    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 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:?}");
+
+        let vb =
+            unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
+        let model = blip::BlipForConditionalGeneration::new(&config, vb)?;
+        let image_embeds = image.unsqueeze(0)?.apply(model.vision_model())?;
+        (image_embeds, device, Model::M(model))
+    };
+
+    let mut token_ids = vec![30522u32];
+    for index in 0..1000 {
+        let context_size = if index > 0 { 1 } else { token_ids.len() };
+        let start_pos = token_ids.len().saturating_sub(context_size);
+        let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
+        let logits = model.text_decoder_forward(&input_ids, &image_embeds)?;
+        let logits = logits.squeeze(0)?;
+        let logits = logits.get(logits.dim(0)? - 1)?;
+        let token = logits_processor.sample(&logits)?;
+        if token == SEP_TOKEN_ID {
+            break;
+        }
+        token_ids.push(token);
+        if let Some(t) = tokenizer.next_token(token)? {
+            use std::io::Write;
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+    }
+    if let Some(rest) = tokenizer.decode_rest().map_err(E::msg)? {
+        print!("{rest}");
+    }
+    println!();
+    Ok(())
+}

candle-examples/examples/convmixer/main.rs

@@ -0,0 +1,59 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use clap::Parser;
+
+use candle::{DType, IndexOp, D};
+use candle_nn::{Module, VarBuilder};
+use candle_transformers::models::convmixer;
+
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    #[arg(long)]
+    image: String,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    let args = Args::parse();
+
+    let device = candle_examples::device(args.cpu)?;
+
+    let image = candle_examples::imagenet::load_image224(args.image)?;
+    println!("loaded image {image:?}");
+
+    let model_file = match args.model {
+        None => {
+            let api = hf_hub::api::sync::Api::new()?;
+            let api = api.model("lmz/candle-convmixer".into());
+            api.get("convmixer_1024_20_ks9_p14.safetensors")?
+        }
+        Some(model) => model.into(),
+    };
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
+    let model = convmixer::c1024_20(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/dinov2/README.md

@@ -0,0 +1,19 @@
+# candle-dinov2
+
+[DINOv2](https://github.com/facebookresearch/dinov2) is a computer vision model.
+In this example, it is used as an ImageNet classifier: the model returns the
+probability for the image to belong to each of the 1000 ImageNet categories.
+
+## Running some example
+
+```bash
+cargo run --example dinov2 --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+
+> mountain bike, all-terrain bike, off-roader: 43.67%
+> bicycle-built-for-two, tandem bicycle, tandem: 33.20%
+> crash helmet            : 13.23%
+> unicycle, monocycle     : 2.44%
+> maillot                 : 2.42%
+```
+
+![Leading group, Giro d'Italia 2021](../yolo-v8/assets/bike.jpg)

candle-examples/examples/dinov2/main.rs

@@ -9,285 +9,10 @@ extern crate accelerate_src;
 
 use clap::Parser;
 
-use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn::{layer_norm, LayerNorm, Linear, Module, VarBuilder};
+use candle::{DType, IndexOp, D};
+use candle_nn::{Module, VarBuilder};
+use candle_transformers::models::dinov2;
 
-const IMG_SIZE: usize = 518;
-const PATCH_SIZE: usize = 14;
-const NUM_CLASSES: usize = 1000;
-
-fn linear(vb: VarBuilder, in_dim: usize, out_dim: usize, bias: bool) -> Result<Linear> {
-    if bias {
-        candle_nn::linear(in_dim, out_dim, vb)
-    } else {
-        candle_nn::linear_no_bias(in_dim, out_dim, vb)
-    }
-}
-
-#[derive(Debug)]
-struct Attention {
-    qkv: Linear,
-    proj: Linear,
-    num_heads: usize,
-    scale: f64,
-}
-
-impl Attention {
-    fn new(
-        vb: VarBuilder,
-        dim: usize,
-        num_heads: usize,
-        qkv_bias: bool,
-        proj_bias: bool,
-    ) -> Result<Self> {
-        let qkv = linear(vb.pp("qkv"), dim, dim * 3, qkv_bias)?;
-        let proj = linear(vb.pp("proj"), dim, dim, proj_bias)?;
-        let scale = 1. / ((dim / num_heads) as f64).sqrt();
-        Ok(Self {
-            qkv,
-            proj,
-            num_heads,
-            scale,
-        })
-    }
-}
-
-impl Module for Attention {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let (b, n, c) = xs.dims3()?;
-        let qkv = self
-            .qkv
-            .forward(xs)?
-            .reshape((b, n, 3, self.num_heads, c / self.num_heads))?
-            .transpose(1, 2)? // 02134
-            .transpose(0, 1)? // 20134
-            .transpose(2, 3)?; // 20314
-        let q = (qkv.i(0)? * self.scale)?;
-        let k = qkv.i(1)?;
-        let v = qkv.i(2)?;
-        let attn = candle_nn::ops::softmax(&q.matmul(&k.t()?)?, D::Minus1)?;
-        let attn = attn.matmul(&v)?.transpose(1, 2)?.reshape((b, n, c))?;
-        self.proj.forward(&attn)
-    }
-}
-
-#[derive(Debug)]
-struct LayerScale {
-    gamma: Tensor,
-}
-
-impl LayerScale {
-    fn new(vb: VarBuilder, dim: usize) -> Result<Self> {
-        let gamma = vb.get(dim, "gamma")?;
-        Ok(Self { gamma })
-    }
-}
-
-impl Module for LayerScale {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        xs.broadcast_mul(&self.gamma)
-    }
-}
-
-#[derive(Debug)]
-struct Mlp {
-    fc1: Linear,
-    fc2: Linear,
-}
-
-impl Mlp {
-    fn new(vb: VarBuilder, in_features: usize, hidden_features: usize, bias: bool) -> Result<Self> {
-        let out_features = in_features;
-        let fc1 = linear(vb.pp("fc1"), in_features, hidden_features, bias)?;
-        let fc2 = linear(vb.pp("fc2"), hidden_features, out_features, bias)?;
-        Ok(Self { fc1, fc2 })
-    }
-}
-
-impl Module for Mlp {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let xs = self.fc1.forward(xs)?.gelu()?;
-        self.fc2.forward(&xs)
-    }
-}
-
-#[derive(Debug)]
-struct Block {
-    norm1: LayerNorm,
-    attn: Attention,
-    ls1: LayerScale,
-    norm2: LayerNorm,
-    mlp: Mlp,
-    ls2: LayerScale,
-}
-
-impl Block {
-    fn new(vb: VarBuilder, dim: usize, num_heads: usize) -> Result<Self> {
-        let norm1 = layer_norm(dim, 1e-5, vb.pp("norm1"))?;
-        let attn = Attention::new(vb.pp("attn"), dim, num_heads, true, true)?;
-        let ls1 = LayerScale::new(vb.pp("ls1"), dim)?;
-        let norm2 = layer_norm(dim, 1e-5, vb.pp("norm2"))?;
-        let mlp = Mlp::new(vb.pp("mlp"), dim, dim * 4, true)?;
-        let ls2 = LayerScale::new(vb.pp("ls2"), dim)?;
-        Ok(Self {
-            norm1,
-            attn,
-            ls1,
-            norm2,
-            mlp,
-            ls2,
-        })
-    }
-}
-
-impl Module for Block {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let residual = xs;
-        let xs = self
-            .ls1
-            .forward(&self.attn.forward(&self.norm1.forward(xs)?)?)?;
-        let xs = (xs + residual)?;
-        let residual = &xs;
-        let xs = self
-            .ls2
-            .forward(&self.mlp.forward(&self.norm2.forward(&xs)?)?)?;
-        xs + residual
-    }
-}
-
-#[derive(Debug)]
-struct PatchEmbed {
-    proj: candle_nn::Conv2d,
-    patch_size: (usize, usize),
-    num_patches: usize,
-}
-
-impl PatchEmbed {
-    fn new(
-        vb: VarBuilder,
-        img_size: usize,
-        patch_size: usize,
-        in_chans: usize,
-        embed_dim: usize,
-    ) -> Result<Self> {
-        let config = candle_nn::Conv2dConfig {
-            stride: patch_size,
-            ..Default::default()
-        };
-        let proj = candle_nn::conv2d(in_chans, embed_dim, patch_size, config, vb.pp("proj"))?;
-        let num_patches = (img_size / patch_size) * (img_size / patch_size);
-        Ok(Self {
-            proj,
-            patch_size: (patch_size, patch_size),
-            num_patches,
-        })
-    }
-}
-
-impl Module for PatchEmbed {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let (_b, _c, h, w) = xs.dims4()?;
-        let (patch_h, patch_w) = self.patch_size;
-        if (h % patch_h) != 0 {
-            candle::bail!("image height {h} is not a multiple of patch height {patch_h}")
-        }
-        if (w % patch_w) != 0 {
-            candle::bail!("image width {w} is not a multiple of patch width {patch_w}")
-        }
-        let xs = self.proj.forward(xs)?;
-        let (b, c, h, w) = xs.dims4()?;
-        // flatten embeddings.
-        xs.reshape((b, c, h * w))?.transpose(1, 2)
-    }
-}
-
-#[derive(Debug)]
-pub struct DinoVisionTransformer {
-    patch_embed: PatchEmbed,
-    cls_token: Tensor,
-    pos_embed: Tensor,
-    blocks: Vec<Block>,
-    norm: LayerNorm,
-    head: Linear,
-}
-
-impl DinoVisionTransformer {
-    pub fn new(vb: VarBuilder, depth: usize, embed_dim: usize, num_heads: usize) -> Result<Self> {
-        let patch_embed =
-            PatchEmbed::new(vb.pp("patch_embed"), IMG_SIZE, PATCH_SIZE, 3, embed_dim)?;
-        let cls_token = vb.get((1, 1, embed_dim), "cls_token")?;
-        let num_tokens = 1;
-        let pos_embed = vb.get(
-            (1, patch_embed.num_patches + num_tokens, embed_dim),
-            "pos_embed",
-        )?;
-        let head = linear(vb.pp("head"), 2 * embed_dim, NUM_CLASSES, true)?;
-        let norm = layer_norm(embed_dim, 1e-5, vb.pp("norm"))?;
-        let vb_b = vb.pp("blocks");
-        let blocks = (0..depth)
-            .map(|i| Block::new(vb_b.pp(&i.to_string()), embed_dim, num_heads))
-            .collect::<Result<Vec<_>>>()?;
-        Ok(Self {
-            patch_embed,
-            cls_token,
-            pos_embed,
-            blocks,
-            norm,
-            head,
-        })
-    }
-
-    fn interpolate_pos_encoding(&self, xs: &Tensor, w: usize, h: usize) -> Result<Tensor> {
-        let npatch = xs.dim(1)? - 1;
-        let n = self.pos_embed.dim(1)? - 1;
-        let sqrt_n = (n as f64).sqrt();
-        if npatch == n && w == h {
-            return Ok(xs.clone());
-        }
-        let class_pos_embed = self.pos_embed.i((.., ..1))?;
-        let patch_pos_embed = self.pos_embed.i((.., 1..))?;
-        let dim = xs.dim(D::Minus1)?;
-        let (w0, h0) = ((w / PATCH_SIZE) as f64 + 0.1, (h / PATCH_SIZE) as f64 + 0.1);
-        let patch_pos_embed = patch_pos_embed
-            .reshape((1, sqrt_n as usize, sqrt_n as usize, dim))?
-            .transpose(2, 3)?
-            .transpose(1, 2)?;
-        // This uses bicubic interpolation in the original implementation.
-        let patch_pos_embed = patch_pos_embed.upsample_nearest2d(h0 as usize, w0 as usize)?;
-        let el_count = patch_pos_embed.shape().elem_count();
-        let patch_pos_embed =
-            patch_pos_embed
-                .transpose(1, 2)?
-                .transpose(2, 3)?
-                .reshape((1, el_count / dim, dim))?;
-        Tensor::cat(&[&class_pos_embed, &patch_pos_embed], 1)
-    }
-
-    fn prepare_tokens_with_mask(&self, xs: &Tensor) -> Result<Tensor> {
-        let (_b, _nc, w, h) = xs.dims4()?;
-        let xs = self.patch_embed.forward(xs)?;
-        let xs = Tensor::cat(&[&self.cls_token, &xs], 1)?;
-        &xs + &self.interpolate_pos_encoding(&xs, w, h)?
-    }
-}
-
-impl Module for DinoVisionTransformer {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = self.prepare_tokens_with_mask(xs)?;
-        for blk in self.blocks.iter() {
-            xs = blk.forward(&xs)?
-        }
-        let xs = self.norm.forward(&xs)?;
-        let xs_norm_clstoken = xs.i((.., 0))?;
-        let xs_norm_patchtokens = xs.i((.., 1..))?.mean(1)?;
-        let xs = Tensor::cat(&[xs_norm_clstoken, xs_norm_patchtokens], D::Minus1)?;
-        self.head.forward(&xs)
-    }
-}
-
-pub fn vit_small(vb: VarBuilder) -> Result<DinoVisionTransformer> {
-    DinoVisionTransformer::new(vb, 12, 384, 6)
-}
 #[derive(Parser)]
 struct Args {
     #[arg(long)]
@@ -317,10 +42,8 @@ pub fn main() -> anyhow::Result<()> {
         }
         Some(model) => model.into(),
     };
-    let weights = unsafe { candle::safetensors::MmapedFile::new(model_file)? };
-    let weights = weights.deserialize()?;
-    let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
-    let model = vit_small(vb)?;
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
+    let model = dinov2::vit_small(vb)?;
     println!("model built");
     let logits = model.forward(&image.unsqueeze(0)?)?;
     let prs = candle_nn::ops::softmax(&logits, D::Minus1)?

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/efficientnet/main.rs

@@ -8,340 +8,11 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
+use candle::{DType, IndexOp, D};
+use candle_nn::{Module, VarBuilder};
+use candle_transformers::models::efficientnet::{EfficientNet, MBConvConfig};
 use clap::{Parser, ValueEnum};
 
-use candle::{DType, IndexOp, Result, Tensor, D};
-use candle_nn as nn;
-use nn::{Module, VarBuilder};
-
-// Based on the Python version from torchvision.
-// https://github.com/pytorch/vision/blob/0d75d9e5516f446c9c0ef93bd4ed9fea13992d06/torchvision/models/efficientnet.py#L47
-#[derive(Debug, Clone, Copy)]
-pub struct MBConvConfig {
-    expand_ratio: f64,
-    kernel: usize,
-    stride: usize,
-    input_channels: usize,
-    out_channels: usize,
-    num_layers: usize,
-}
-
-fn make_divisible(v: f64, divisor: usize) -> usize {
-    let min_value = divisor;
-    let new_v = usize::max(
-        min_value,
-        (v + divisor as f64 * 0.5) as usize / divisor * divisor,
-    );
-    if (new_v as f64) < 0.9 * v {
-        new_v + divisor
-    } else {
-        new_v
-    }
-}
-
-fn bneck_confs(width_mult: f64, depth_mult: f64) -> Vec<MBConvConfig> {
-    let bneck_conf = |e, k, s, i, o, n| {
-        let input_channels = make_divisible(i as f64 * width_mult, 8);
-        let out_channels = make_divisible(o as f64 * width_mult, 8);
-        let num_layers = (n as f64 * depth_mult).ceil() as usize;
-        MBConvConfig {
-            expand_ratio: e,
-            kernel: k,
-            stride: s,
-            input_channels,
-            out_channels,
-            num_layers,
-        }
-    };
-    vec![
-        bneck_conf(1., 3, 1, 32, 16, 1),
-        bneck_conf(6., 3, 2, 16, 24, 2),
-        bneck_conf(6., 5, 2, 24, 40, 2),
-        bneck_conf(6., 3, 2, 40, 80, 3),
-        bneck_conf(6., 5, 1, 80, 112, 3),
-        bneck_conf(6., 5, 2, 112, 192, 4),
-        bneck_conf(6., 3, 1, 192, 320, 1),
-    ]
-}
-
-impl MBConvConfig {
-    fn b0() -> Vec<Self> {
-        bneck_confs(1.0, 1.0)
-    }
-    fn b1() -> Vec<Self> {
-        bneck_confs(1.0, 1.1)
-    }
-    fn b2() -> Vec<Self> {
-        bneck_confs(1.1, 1.2)
-    }
-    fn b3() -> Vec<Self> {
-        bneck_confs(1.2, 1.4)
-    }
-    fn b4() -> Vec<Self> {
-        bneck_confs(1.4, 1.8)
-    }
-    fn b5() -> Vec<Self> {
-        bneck_confs(1.6, 2.2)
-    }
-    fn b6() -> Vec<Self> {
-        bneck_confs(1.8, 2.6)
-    }
-    fn b7() -> Vec<Self> {
-        bneck_confs(2.0, 3.1)
-    }
-}
-
-/// Conv2D with same padding.
-#[derive(Debug)]
-struct Conv2DSame {
-    conv2d: nn::Conv2d,
-    s: usize,
-    k: usize,
-}
-
-impl Conv2DSame {
-    fn new(
-        vb: VarBuilder,
-        i: usize,
-        o: usize,
-        k: usize,
-        stride: usize,
-        groups: usize,
-        bias: bool,
-    ) -> Result<Self> {
-        let conv_config = nn::Conv2dConfig {
-            stride,
-            groups,
-            ..Default::default()
-        };
-        let conv2d = if bias {
-            nn::conv2d(i, o, k, conv_config, vb)?
-        } else {
-            nn::conv2d_no_bias(i, o, k, conv_config, vb)?
-        };
-        Ok(Self {
-            conv2d,
-            s: stride,
-            k,
-        })
-    }
-}
-
-impl Module for Conv2DSame {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let s = self.s;
-        let k = self.k;
-        let (_, _, ih, iw) = xs.dims4()?;
-        let oh = (ih + s - 1) / s;
-        let ow = (iw + s - 1) / s;
-        let pad_h = usize::max((oh - 1) * s + k - ih, 0);
-        let pad_w = usize::max((ow - 1) * s + k - iw, 0);
-        if pad_h > 0 || pad_w > 0 {
-            let xs = xs.pad_with_zeros(2, pad_h / 2, pad_h - pad_h / 2)?;
-            let xs = xs.pad_with_zeros(3, pad_w / 2, pad_w - pad_w / 2)?;
-            self.conv2d.forward(&xs)
-        } else {
-            self.conv2d.forward(xs)
-        }
-    }
-}
-
-#[derive(Debug)]
-struct ConvNormActivation {
-    conv2d: Conv2DSame,
-    bn2d: nn::BatchNorm,
-    activation: bool,
-}
-
-impl ConvNormActivation {
-    fn new(
-        vb: VarBuilder,
-        i: usize,
-        o: usize,
-        k: usize,
-        stride: usize,
-        groups: usize,
-    ) -> Result<Self> {
-        let conv2d = Conv2DSame::new(vb.pp("0"), i, o, k, stride, groups, false)?;
-        let bn2d = nn::batch_norm(o, 1e-3, vb.pp("1"))?;
-        Ok(Self {
-            conv2d,
-            bn2d,
-            activation: true,
-        })
-    }
-
-    fn no_activation(self) -> Self {
-        Self {
-            activation: false,
-            ..self
-        }
-    }
-}
-
-impl Module for ConvNormActivation {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let xs = self.conv2d.forward(xs)?;
-        let xs = self.bn2d.forward(&xs)?;
-        if self.activation {
-            swish(&xs)
-        } else {
-            Ok(xs)
-        }
-    }
-}
-
-#[derive(Debug)]
-struct SqueezeExcitation {
-    fc1: Conv2DSame,
-    fc2: Conv2DSame,
-}
-
-impl SqueezeExcitation {
-    fn new(vb: VarBuilder, in_channels: usize, squeeze_channels: usize) -> Result<Self> {
-        let fc1 = Conv2DSame::new(vb.pp("fc1"), in_channels, squeeze_channels, 1, 1, 1, true)?;
-        let fc2 = Conv2DSame::new(vb.pp("fc2"), squeeze_channels, in_channels, 1, 1, 1, true)?;
-        Ok(Self { fc1, fc2 })
-    }
-}
-
-impl Module for SqueezeExcitation {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let residual = xs;
-        // equivalent to adaptive_avg_pool2d([1, 1])
-        let xs = xs.mean_keepdim(D::Minus2)?.mean_keepdim(D::Minus1)?;
-        let xs = self.fc1.forward(&xs)?;
-        let xs = swish(&xs)?;
-        let xs = self.fc2.forward(&xs)?;
-        let xs = nn::ops::sigmoid(&xs)?;
-        residual.broadcast_mul(&xs)
-    }
-}
-
-#[derive(Debug)]
-struct MBConv {
-    expand_cna: Option<ConvNormActivation>,
-    depthwise_cna: ConvNormActivation,
-    squeeze_excitation: SqueezeExcitation,
-    project_cna: ConvNormActivation,
-    config: MBConvConfig,
-}
-
-impl MBConv {
-    fn new(vb: VarBuilder, c: MBConvConfig) -> Result<Self> {
-        let vb = vb.pp("block");
-        let exp = make_divisible(c.input_channels as f64 * c.expand_ratio, 8);
-        let expand_cna = if exp != c.input_channels {
-            Some(ConvNormActivation::new(
-                vb.pp("0"),
-                c.input_channels,
-                exp,
-                1,
-                1,
-                1,
-            )?)
-        } else {
-            None
-        };
-        let start_index = if expand_cna.is_some() { 1 } else { 0 };
-        let depthwise_cna =
-            ConvNormActivation::new(vb.pp(start_index), exp, exp, c.kernel, c.stride, exp)?;
-        let squeeze_channels = usize::max(1, c.input_channels / 4);
-        let squeeze_excitation =
-            SqueezeExcitation::new(vb.pp(start_index + 1), exp, squeeze_channels)?;
-        let project_cna =
-            ConvNormActivation::new(vb.pp(start_index + 2), exp, c.out_channels, 1, 1, 1)?
-                .no_activation();
-        Ok(Self {
-            expand_cna,
-            depthwise_cna,
-            squeeze_excitation,
-            project_cna,
-            config: c,
-        })
-    }
-}
-
-impl Module for MBConv {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let use_res_connect =
-            self.config.stride == 1 && self.config.input_channels == self.config.out_channels;
-        let ys = match &self.expand_cna {
-            Some(expand_cna) => expand_cna.forward(xs)?,
-            None => xs.clone(),
-        };
-        let ys = self.depthwise_cna.forward(&ys)?;
-        let ys = self.squeeze_excitation.forward(&ys)?;
-        let ys = self.project_cna.forward(&ys)?;
-        if use_res_connect {
-            ys + xs
-        } else {
-            Ok(ys)
-        }
-    }
-}
-
-fn swish(s: &Tensor) -> Result<Tensor> {
-    s * nn::ops::sigmoid(s)?
-}
-
-#[derive(Debug)]
-struct EfficientNet {
-    init_cna: ConvNormActivation,
-    blocks: Vec<MBConv>,
-    final_cna: ConvNormActivation,
-    classifier: nn::Linear,
-}
-
-impl EfficientNet {
-    fn new(p: VarBuilder, configs: Vec<MBConvConfig>, nclasses: usize) -> Result<Self> {
-        let f_p = p.pp("features");
-        let first_in_c = configs[0].input_channels;
-        let last_out_c = configs.last().unwrap().out_channels;
-        let final_out_c = 4 * last_out_c;
-        let init_cna = ConvNormActivation::new(f_p.pp(0), 3, first_in_c, 3, 2, 1)?;
-        let nconfigs = configs.len();
-        let mut blocks = vec![];
-        for (index, cnf) in configs.into_iter().enumerate() {
-            let f_p = f_p.pp(index + 1);
-            for r_index in 0..cnf.num_layers {
-                let cnf = if r_index == 0 {
-                    cnf
-                } else {
-                    MBConvConfig {
-                        input_channels: cnf.out_channels,
-                        stride: 1,
-                        ..cnf
-                    }
-                };
-                blocks.push(MBConv::new(f_p.pp(r_index), cnf)?)
-            }
-        }
-        let final_cna =
-            ConvNormActivation::new(f_p.pp(nconfigs + 1), last_out_c, final_out_c, 1, 1, 1)?;
-        let classifier = nn::linear(final_out_c, nclasses, p.pp("classifier.1"))?;
-        Ok(Self {
-            init_cna,
-            blocks,
-            final_cna,
-            classifier,
-        })
-    }
-}
-
-impl Module for EfficientNet {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let mut xs = self.init_cna.forward(xs)?;
-        for block in self.blocks.iter() {
-            xs = block.forward(&xs)?
-        }
-        let xs = self.final_cna.forward(&xs)?;
-        // Equivalent to adaptive_avg_pool2d([1, 1]) -> squeeze(-1) -> squeeze(-1)
-        let xs = xs.mean(D::Minus1)?.mean(D::Minus1)?;
-        self.classifier.forward(&xs)
-    }
-}
-
 #[derive(Clone, Copy, Debug, ValueEnum)]
 enum Which {
     B0,
@@ -397,9 +68,7 @@ pub fn main() -> anyhow::Result<()> {
         }
         Some(model) => model.into(),
     };
-    let weights = unsafe { candle::safetensors::MmapedFile::new(model_file)? };
-    let weights = weights.deserialize()?;
-    let vb = VarBuilder::from_safetensors(vec![weights], DType::F32, &device);
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
     let cfg = match args.which {
         Which::B0 => MBConvConfig::b0(),
         Which::B1 => MBConvConfig::b1(),

candle-examples/examples/falcon/README.md

@@ -0,0 +1,3 @@
+# candle-falcon
+
+Falcon is a general large language model.

candle-examples/examples/falcon/main.rs

@@ -14,8 +14,7 @@ use clap::Parser;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::Tokenizer;
 
-mod model;
-use model::{Config, Falcon};
+use candle_transformers::models::falcon::{Config, Falcon};
 
 struct TextGeneration {
     model: Falcon,
@@ -26,17 +25,25 @@ struct TextGeneration {
     repeat_last_n: usize,
 }
 
+struct GenerationOptions {
+    temp: Option<f64>,
+    top_p: Option<f64>,
+    repeat_penalty: f32,
+    repeat_last_n: usize,
+}
+
 impl TextGeneration {
     fn new(
         model: Falcon,
         tokenizer: Tokenizer,
+        generation_options: GenerationOptions,
         seed: u64,
-        temp: Option<f64>,
         device: &Device,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
     ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp);
+        let logits_processor =
+            LogitsProcessor::new(seed, generation_options.temp, generation_options.top_p);
+        let repeat_penalty = generation_options.repeat_penalty;
+        let repeat_last_n = generation_options.repeat_last_n;
         Self {
             model,
             tokenizer,
@@ -119,6 +126,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -166,35 +177,25 @@ fn main() -> Result<()> {
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
     let start = std::time::Instant::now();
-    let weights = filenames
-        .iter()
-        .map(|f| Ok(unsafe { candle::safetensors::MmapedFile::new(f)? }))
-        .collect::<Result<Vec<_>>>()?;
-    let weights = weights
-        .iter()
-        .map(|f| Ok(f.deserialize()?))
-        .collect::<Result<Vec<_>>>()?;
-
     let dtype = if args.use_f32 {
         DType::F32
     } else {
         DType::BF16
     };
-    let vb = VarBuilder::from_safetensors(weights, dtype, &device);
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
     let config = Config::falcon7b();
     config.validate()?;
     let model = Falcon::load(vb, config)?;
     println!("loaded the model in {:?}", start.elapsed());
 
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        &device,
-        args.repeat_penalty,
-        args.repeat_last_n,
-    );
+    let generation_options = GenerationOptions {
+        temp: args.temperature,
+        top_p: args.top_p,
+        repeat_penalty: args.repeat_penalty,
+        repeat_last_n: args.repeat_last_n,
+    };
+    let mut pipeline =
+        TextGeneration::new(model, tokenizer, generation_options, args.seed, &device);
     pipeline.run(&args.prompt, args.sample_len)?;
     Ok(())
 }

candle-examples/examples/jina-bert/README.md

@@ -0,0 +1,45 @@
+# candle-jina-bert
+
+Jina-Bert is a general large language model with a context size of 8192, [model
+card](https://huggingface.co/jinaai/jina-embeddings-v2-base-en). 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
+
+Jina-Bert is used to compute the sentence embeddings for a prompt. The model weights
+are downloaded from the hub on the first run.
+
+```bash
+cargo run --example jina-bert --release -- --prompt "Here is a test sentence"
+
+> [[[ 0.1595, -0.9885,  0.6494, ...,  0.3003, -0.6901, -1.2355],
+>   [ 0.0374, -0.1798,  1.3359, ...,  0.6731,  0.2133, -1.6807],
+>   [ 0.1700, -0.8534,  0.8924, ..., -0.1785, -0.0727, -1.5087],
+>   ...
+>   [-0.3113, -1.3665,  0.2027, ..., -0.2519,  0.1711, -1.5811],
+>   [ 0.0907, -1.0492,  0.5382, ...,  0.0242, -0.7077, -1.0830],
+>   [ 0.0369, -0.6343,  0.6105, ...,  0.0671,  0.3778, -1.1505]]]
+> Tensor[[1, 7, 768], f32]
+```
+
+## Similarities
+
+In this example, Jina-Bert is used to compute the sentence embeddings for a set of
+sentences (hardcoded in the examples). Then cosine similarities are computed for
+each sentence pair and they are reported by decreasing values, hence the first
+reported pair contains the two sentences that have the highest similarity score.
+The sentence embeddings are computed using average pooling through all the
+sentence tokens, including some potential padding.
+
+```bash
+cargo run --example jina-bert --release
+
+> score: 0.94 'The new movie is awesome' 'The new movie is so great'
+> score: 0.81 'The cat sits outside' 'The cat plays in the garden'
+> score: 0.78 'I love pasta' 'Do you like pizza?'
+> score: 0.68 'I love pasta' 'The new movie is awesome'
+> score: 0.67 'A man is playing guitar' 'A woman watches TV'
+```

candle-examples/examples/jina-bert/main.rs

@@ -0,0 +1,180 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle_transformers::models::jina_bert::{BertModel, Config};
+
+use anyhow::Error as E;
+use candle::{DType, Module, Tensor};
+use candle_nn::VarBuilder;
+use clap::Parser;
+
+#[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,
+
+    /// When set, compute embeddings for this prompt.
+    #[arg(long)]
+    prompt: Option<String>,
+
+    /// The number of times to run the prompt.
+    #[arg(long, default_value = "1")]
+    n: usize,
+
+    /// L2 normalization for embeddings.
+    #[arg(long, default_value = "true")]
+    normalize_embeddings: bool,
+
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    #[arg(long)]
+    model: Option<String>,
+}
+
+impl Args {
+    fn build_model_and_tokenizer(&self) -> anyhow::Result<(BertModel, tokenizers::Tokenizer)> {
+        use hf_hub::{api::sync::Api, Repo, RepoType};
+        let model = match &self.model {
+            Some(model_file) => std::path::PathBuf::from(model_file),
+            None => Api::new()?
+                .repo(Repo::new(
+                    "jinaai/jina-embeddings-v2-base-en".to_string(),
+                    RepoType::Model,
+                ))
+                .get("model.safetensors")?,
+        };
+        let tokenizer = match &self.tokenizer {
+            Some(file) => std::path::PathBuf::from(file),
+            None => Api::new()?
+                .repo(Repo::new(
+                    "sentence-transformers/all-MiniLM-L6-v2".to_string(),
+                    RepoType::Model,
+                ))
+                .get("tokenizer.json")?,
+        };
+        let device = candle_examples::device(self.cpu)?;
+        let config = Config::v2_base();
+        let tokenizer = tokenizers::Tokenizer::from_file(tokenizer).map_err(E::msg)?;
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? };
+        let model = BertModel::new(vb, &config)?;
+        Ok((model, tokenizer))
+    }
+}
+
+fn main() -> anyhow::Result<()> {
+    use tracing_chrome::ChromeLayerBuilder;
+    use tracing_subscriber::prelude::*;
+
+    let args = Args::parse();
+    let _guard = if args.tracing {
+        println!("tracing...");
+        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
+        tracing_subscriber::registry().with(chrome_layer).init();
+        Some(guard)
+    } else {
+        None
+    };
+    let start = std::time::Instant::now();
+
+    let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
+    let device = &model.device;
+
+    if let Some(prompt) = args.prompt {
+        let tokenizer = tokenizer
+            .with_padding(None)
+            .with_truncation(None)
+            .map_err(E::msg)?;
+        let tokens = tokenizer
+            .encode(prompt, true)
+            .map_err(E::msg)?
+            .get_ids()
+            .to_vec();
+        let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
+        println!("Loaded and encoded {:?}", start.elapsed());
+        for idx in 0..args.n {
+            let start = std::time::Instant::now();
+            let ys = model.forward(&token_ids)?;
+            if idx == 0 {
+                println!("{ys}");
+            }
+            println!("Took {:?}", start.elapsed());
+        }
+    } else {
+        let sentences = [
+            "The cat sits outside",
+            "A man is playing guitar",
+            "I love pasta",
+            "The new movie is awesome",
+            "The cat plays in the garden",
+            "A woman watches TV",
+            "The new movie is so great",
+            "Do you like pizza?",
+        ];
+        let n_sentences = sentences.len();
+        if let Some(pp) = tokenizer.get_padding_mut() {
+            pp.strategy = tokenizers::PaddingStrategy::BatchLongest
+        } else {
+            let pp = tokenizers::PaddingParams {
+                strategy: tokenizers::PaddingStrategy::BatchLongest,
+                ..Default::default()
+            };
+            tokenizer.with_padding(Some(pp));
+        }
+        let tokens = tokenizer
+            .encode_batch(sentences.to_vec(), true)
+            .map_err(E::msg)?;
+        let token_ids = tokens
+            .iter()
+            .map(|tokens| {
+                let tokens = tokens.get_ids().to_vec();
+                Tensor::new(tokens.as_slice(), device)
+            })
+            .collect::<candle::Result<Vec<_>>>()?;
+
+        let token_ids = Tensor::stack(&token_ids, 0)?;
+        println!("running inference on batch {:?}", token_ids.shape());
+        let embeddings = model.forward(&token_ids)?;
+        println!("generated embeddings {:?}", embeddings.shape());
+        // Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
+        let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
+        let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
+        let embeddings = if args.normalize_embeddings {
+            normalize_l2(&embeddings)?
+        } else {
+            embeddings
+        };
+        println!("pooled embeddings {:?}", embeddings.shape());
+
+        let mut similarities = vec![];
+        for i in 0..n_sentences {
+            let e_i = embeddings.get(i)?;
+            for j in (i + 1)..n_sentences {
+                let e_j = embeddings.get(j)?;
+                let sum_ij = (&e_i * &e_j)?.sum_all()?.to_scalar::<f32>()?;
+                let sum_i2 = (&e_i * &e_i)?.sum_all()?.to_scalar::<f32>()?;
+                let sum_j2 = (&e_j * &e_j)?.sum_all()?.to_scalar::<f32>()?;
+                let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
+                similarities.push((cosine_similarity, i, j))
+            }
+        }
+        similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
+        for &(score, i, j) in similarities[..5].iter() {
+            println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
+        }
+    }
+    Ok(())
+}
+
+pub fn normalize_l2(v: &Tensor) -> candle::Result<Tensor> {
+    v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)
+}

candle-examples/examples/llama/main.rs

@@ -21,11 +21,10 @@ use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use std::io::Write;
 
-mod model;
+use candle_transformers::models::llama as model;
 use model::{Config, Llama, LlamaConfig};
 
 const EOS_TOKEN: &str = "</s>";
-const MAX_SEQ_LEN: usize = 4096;
 const DEFAULT_PROMPT: &str = "My favorite theorem is ";
 
 #[derive(Parser, Debug)]
@@ -43,6 +42,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -169,17 +172,9 @@ fn main() -> Result<()> {
             }
 
             println!("building the model");
-            let handles = filenames
-                .iter()
-                .map(|f| Ok(unsafe { candle::safetensors::MmapedFile::new(f.as_path())? }))
-                .collect::<Result<Vec<_>>>()?;
-            let tensors: Vec<_> = handles
-                .iter()
-                .map(|h| Ok(h.deserialize()?))
-                .collect::<Result<Vec<_>>>()?;
             let cache = model::Cache::new(!args.no_kv_cache, dtype, &config, &device)?;
 
-            let vb = VarBuilder::from_safetensors(tensors, dtype, &device);
+            let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
             (Llama::load(vb, &cache, &config)?, tokenizer_filename, cache)
         }
     };
@@ -194,7 +189,7 @@ fn main() -> Result<()> {
 
     println!("starting the inference loop");
     print!("{prompt}");
-    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature);
+    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature, args.top_p);
     let start_gen = std::time::Instant::now();
     let mut index_pos = 0;
     let mut token_generated = 0;

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

@@ -6,9 +6,10 @@ extern crate accelerate_src;
 #[cfg(feature = "mkl")]
 extern crate intel_mkl_src;
 
-mod model;
+use candle_transformers::models::llama2_c as model;
+use candle_transformers::models::llama2_c_weights as weights;
+use candle_transformers::models::quantized_llama2_c as qmodel;
 mod training;
-mod weights;
 use clap::{Parser, Subcommand};
 
 use anyhow::{Error as E, Result};
@@ -19,6 +20,7 @@ use std::io::Write;
 use tokenizers::Tokenizer;
 
 use model::{Config, Llama};
+use qmodel::QLlama;
 use weights::TransformerWeights;
 
 #[derive(Parser, Debug, Clone)]
@@ -27,6 +29,10 @@ struct InferenceCmd {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     #[arg(long, default_value = "")]
     prompt: String,
 
@@ -133,6 +139,7 @@ fn main() -> anyhow::Result<()> {
         None => {
             let cmd = InferenceCmd {
                 temperature: None,
+                top_p: None,
                 prompt: "".to_string(),
                 config: None,
                 model_id: "karpathy/tinyllamas".to_string(),
@@ -147,6 +154,20 @@ fn main() -> anyhow::Result<()> {
     Ok(())
 }
 
+enum Model {
+    Llama(Llama),
+    QLlama(QLlama),
+}
+
+impl Model {
+    fn forward(&self, xs: &Tensor, pos: usize) -> anyhow::Result<Tensor> {
+        match self {
+            Self::Llama(l) => Ok(l.forward(xs, pos)?),
+            Self::QLlama(l) => Ok(l.forward(xs, pos)?),
+        }
+    }
+}
+
 fn run_eval(args: &EvaluationCmd, common_args: &Args) -> Result<()> {
     use std::io::BufRead;
 
@@ -236,27 +257,69 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
 
     let device = candle_examples::device(common_args.cpu)?;
 
+    let is_gguf = config_path.extension().map_or(false, |v| v == "gguf");
     let is_safetensors = config_path
         .extension()
         .map_or(false, |v| v == "safetensors");
-    let (vb, config) = if is_safetensors {
-        let config = Config::tiny();
+    let (model, config) = if is_gguf {
+        let vb = qmodel::VarBuilder::from_gguf(config_path)?;
+        let (_vocab_size, dim) = vb
+            .get_no_shape("model.embed_tokens.weight")?
+            .shape()
+            .dims2()?;
+        let config = match dim {
+            64 => Config::tiny_260k(),
+            288 => Config::tiny_15m(),
+            512 => Config::tiny_42m(),
+            768 => Config::tiny_110m(),
+            _ => anyhow::bail!("no config for dim {dim}"),
+        };
+        let freq_cis_real = vb
+            .get(
+                (config.seq_len, config.head_size() / 2),
+                "rot.freq_cis_real",
+            )?
+            .dequantize(&candle::Device::Cpu)?;
+        let freq_cis_imag = vb
+            .get(
+                (config.seq_len, config.head_size() / 2),
+                "rot.freq_cis_imag",
+            )?
+            .dequantize(&candle::Device::Cpu)?;
+
+        let fake_vb = candle_nn::VarBuilder::from_tensors(
+            [
+                ("freq_cis_real".to_string(), freq_cis_real),
+                ("freq_cis_imag".to_string(), freq_cis_imag),
+            ]
+            .into_iter()
+            .collect(),
+            candle::DType::F32,
+            &candle::Device::Cpu,
+        );
+        let cache = model::Cache::new(true, &config, fake_vb)?;
+        let model = Model::QLlama(QLlama::load(vb, &cache, config.clone())?);
+        (model, config)
+    } else if is_safetensors {
+        let config = Config::tiny_15m();
         let tensors = candle::safetensors::load(config_path, &device)?;
         let vb = candle_nn::VarBuilder::from_tensors(tensors, candle::DType::F32, &device);
-        (vb, config)
+        let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
+        let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
+        (model, config)
     } else {
         let mut file = std::fs::File::open(config_path)?;
         let config = Config::from_reader(&mut file)?;
         println!("{config:?}");
         let weights = TransformerWeights::from_reader(&mut file, &config, &device)?;
         let vb = weights.var_builder(&config, &device)?;
-        (vb, config)
+        let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
+        let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
+        (model, config)
     };
-    let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
-    let model = Llama::load(vb, &cache, config)?;
 
     println!("starting the inference loop");
-    let mut logits_processor = LogitsProcessor::new(299792458, args.temperature);
+    let mut logits_processor = LogitsProcessor::new(299792458, args.temperature, args.top_p);
     let mut index_pos = 0;
 
     print!("{}", args.prompt);
@@ -268,7 +331,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
 
     let start_gen = std::time::Instant::now();
     for index in 0.. {
-        if tokens.len() >= model.config.seq_len {
+        if tokens.len() >= config.seq_len {
             break;
         }
         let context_size = if index > 0 { 1 } else { tokens.len() };

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

@@ -33,7 +33,7 @@ pub fn run(args: &crate::TrainingCmd, common_args: &crate::Args) -> Result<()> {
     );
     let varmap = candle_nn::VarMap::new();
     let vb = candle_nn::VarBuilder::from_varmap(&varmap, DType::F32, &device);
-    let config = Config::tiny();
+    let config = Config::tiny_15m();
     let iter = DatasetRandomIter::new(&dataset, false, config.seq_len, device.clone());
     let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
 

candle-examples/examples/llama_multiprocess/main.rs

@@ -89,6 +89,10 @@ struct Args {
     #[arg(long)]
     temperature: Option<f64>,
 
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -201,16 +205,9 @@ fn main() -> Result<()> {
     let cache = model::Cache::new(dtype, &config, &device)?;
 
     println!("building the model");
-    let handles = filenames
-        .iter()
-        .map(|f| Ok(unsafe { candle::safetensors::MmapedFile::new(f.as_path())? }))
-        .collect::<Result<Vec<_>>>()?;
-    let tensors: Vec<_> = handles
-        .iter()
-        .map(|h| Ok(h.deserialize()?))
-        .collect::<Result<Vec<_>>>()?;
-
-    let vb = candle_nn::var_builder::ShardedSafeTensors::var_builder(tensors, dtype, &device);
+    let vb = unsafe {
+        candle_nn::var_builder::ShardedSafeTensors::var_builder(&filenames, dtype, &device)?
+    };
     let llama = Llama::load(vb, &cache, &config, comm)?;
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
@@ -222,7 +219,7 @@ fn main() -> Result<()> {
         .to_vec();
 
     println!("starting the inference loop");
-    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature);
+    let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature, args.top_p);
     let mut new_tokens = vec![];
     let start_gen = std::time::Instant::now();
     let mut index_pos = 0;

candle-examples/examples/marian-mt/README.md

@@ -0,0 +1,38 @@
+# candle-marian-mt
+
+`marian-mt` is a neural machine translation model. In this example it is used to
+translate text from French to English. See the associated [model
+card](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-fr-en) for details on
+the model itself.
+
+## Running an example
+
+```bash
+cargo run --example marian-mt --release -- \
+    --text "Demain, dès l'aube, à l'heure où blanchit la campagne, Je partirai. Vois-tu, je sais que tu m'attends. J'irai par la forêt, j'irai par la montagne. Je ne puis demeurer loin de toi plus longtemps."
+```
+
+```
+<NIL> Tomorrow, at dawn, at the time when the country is whitening, I will go. See,
+I know you are waiting for me. I will go through the forest, I will go through the
+mountain. I cannot stay far from you any longer.</s>
+```
+
+## Generating the tokenizer.json files
+
+You can use the following script to generate the `tokenizer.json` config files
+from the hf-hub repos. This requires the `tokenizers` and `sentencepiece`
+packages to be install and use the `convert_slow_tokenizer.py` script from this
+directory.
+
+```python
+from convert_slow_tokenizer import MarianConverter
+from transformers import AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en", use_fast=False)
+fast_tokenizer = MarianConverter(tokenizer, index=0).converted()
+fast_tokenizer.save(f"tokenizer-marian-base-fr.json")
+fast_tokenizer = MarianConverter(tokenizer, index=1).converted()
+fast_tokenizer.save(f"tokenizer-marian-base-en.json")
+```

candle-examples/examples/marian-mt/main.rs

@@ -0,0 +1,152 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use anyhow::Error as E;
+use clap::{Parser, ValueEnum};
+
+use candle::{DType, Tensor};
+use candle_examples::token_output_stream::TokenOutputStream;
+use candle_nn::VarBuilder;
+use candle_transformers::models::marian;
+
+use tokenizers::Tokenizer;
+
+#[derive(Clone, Debug, Copy, ValueEnum)]
+enum Which {
+    Base,
+    Big,
+}
+
+// TODO: Maybe add support for the conditional prompt.
+#[derive(Parser)]
+struct Args {
+    #[arg(long)]
+    model: Option<String>,
+
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    #[arg(long)]
+    tokenizer_dec: Option<String>,
+
+    /// Choose the variant of the model to run.
+    #[arg(long, default_value = "big")]
+    which: Which,
+
+    /// Run on CPU rather than on GPU.
+    #[arg(long)]
+    cpu: bool,
+
+    /// Use the quantized version of the model.
+    #[arg(long)]
+    quantized: bool,
+
+    /// Text to be translated
+    #[arg(long)]
+    text: String,
+}
+
+pub fn main() -> anyhow::Result<()> {
+    use hf_hub::api::sync::Api;
+    let args = Args::parse();
+
+    let config = match args.which {
+        Which::Base => marian::Config::opus_mt_fr_en(),
+        Which::Big => marian::Config::opus_mt_tc_big_fr_en(),
+    };
+    let tokenizer = {
+        let tokenizer = match args.tokenizer {
+            Some(tokenizer) => std::path::PathBuf::from(tokenizer),
+            None => {
+                let name = match args.which {
+                    Which::Base => "tokenizer-marian-base-fr.json",
+                    Which::Big => "tokenizer-marian-fr.json",
+                };
+                Api::new()?
+                    .model("lmz/candle-marian".to_string())
+                    .get(name)?
+            }
+        };
+        Tokenizer::from_file(&tokenizer).map_err(E::msg)?
+    };
+
+    let tokenizer_dec = {
+        let tokenizer = match args.tokenizer_dec {
+            Some(tokenizer) => std::path::PathBuf::from(tokenizer),
+            None => {
+                let name = match args.which {
+                    Which::Base => "tokenizer-marian-base-en.json",
+                    Which::Big => "tokenizer-marian-en.json",
+                };
+                Api::new()?
+                    .model("lmz/candle-marian".to_string())
+                    .get(name)?
+            }
+        };
+        Tokenizer::from_file(&tokenizer).map_err(E::msg)?
+    };
+    let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
+
+    let device = candle_examples::device(args.cpu)?;
+    let vb = {
+        let model = match args.model {
+            Some(model) => std::path::PathBuf::from(model),
+            None => match args.which {
+                Which::Base => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "Helsinki-NLP/opus-mt-fr-en".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/4".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+                Which::Big => Api::new()?
+                    .model("Helsinki-NLP/opus-mt-tc-big-fr-en".to_string())
+                    .get("model.safetensors")?,
+            },
+        };
+        unsafe { VarBuilder::from_mmaped_safetensors(&[&model], DType::F32, &device)? }
+    };
+    let mut model = marian::MTModel::new(&config, vb)?;
+
+    let mut logits_processor =
+        candle_transformers::generation::LogitsProcessor::new(1337, None, None);
+
+    let encoder_xs = {
+        let mut tokens = tokenizer
+            .encode(args.text, true)
+            .map_err(E::msg)?
+            .get_ids()
+            .to_vec();
+        tokens.push(config.eos_token_id);
+        let tokens = Tensor::new(tokens.as_slice(), &device)?.unsqueeze(0)?;
+        model.encoder().forward(&tokens, 0)?
+    };
+
+    let mut token_ids = vec![config.decoder_start_token_id];
+    for index in 0..1000 {
+        let context_size = if index >= 1 { 1 } else { token_ids.len() };
+        let start_pos = token_ids.len().saturating_sub(context_size);
+        let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
+        let logits = model.decode(&input_ids, &encoder_xs, start_pos)?;
+        let logits = logits.squeeze(0)?;
+        let logits = logits.get(logits.dim(0)? - 1)?;
+        let token = logits_processor.sample(&logits)?;
+        token_ids.push(token);
+        if let Some(t) = tokenizer_dec.next_token(token)? {
+            use std::io::Write;
+            print!("{t}");
+            std::io::stdout().flush()?;
+        }
+        if token == config.eos_token_id || token == config.forced_eos_token_id {
+            break;
+        }
+    }
+    if let Some(rest) = tokenizer_dec.decode_rest().map_err(E::msg)? {
+        print!("{rest}");
+    }
+    println!();
+    Ok(())
+}

candle-examples/examples/mistral/README.md

@@ -0,0 +1,90 @@
+# candle-mistral: 7b LLM with Apache 2.0 licensed weights
+
+Mistral-7B-v0.1 is a pretrained generative LLM with 7 billion parameters. It outperforms all the publicly available 13b models
+as of 2023-09-28. Weights (and the original Python model code) are released under the permissive Apache 2.0 license.
+
+- [Blog post](https://mistral.ai/news/announcing-mistral-7b/) from Mistral announcing the model release.
+- [Model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the
+  HuggingFace Hub.
+This example supports the initial model as well as a quantized variant.
+
+## Running the example
+
+```bash
+$ cargo run --example mistral --release --features cuda -- --prompt 'Write helloworld code in Rust' --sample-len 150
+
+Generated text:
+Write helloworld code in Rust
+=============================
+
+This is a simple example of how to write "Hello, world!" program in Rust.
+
+## Compile and run
+
+``bash
+$ cargo build --release
+   Compiling hello-world v0.1.0 (/home/user/rust/hello-world)
+    Finished release [optimized] target(s) in 0.26s
+$ ./target/release/hello-world
+Hello, world!
+``
+
+## Source code
+
+``rust
+fn main() {
+    println!("Hello, world!");
+}
+``
+
+## License
+
+This example is released under the terms
+```
+
+## Running the quantized version of the model
+
+```bash
+$ cargo run --example mistral --features accelerate --release -- \
+$   --prompt "Here is a sample quick sort implementation in rust " --quantized -n 400
+avx: false, neon: true, simd128: false, f16c: false
+temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
+retrieved the files in 562.292µs
+loaded the model in 1.100323667s
+Here is a sample quick sort implementation in rust
+
+``rust
+fn quick_sort(arr: &mut [i32]) {
+    if arr.len() <= 1 {
+        return;
+    }
+
+    let pivot = arr[0];
+    let mut left = vec![];
+    let mut right = vec![];
+
+    for i in 1..arr.len() {
+        if arr[i] < pivot {
+            left.push(arr[i]);
+        } else {
+            right.push(arr[i]);
+        }
+    }
+
+    quick_sort(&mut left);
+    quick_sort(&mut right);
+
+    let mut i = 0;
+    for _ in &left {
+        arr[i] = left.pop().unwrap();
+        i += 1;
+    }
+
+    for _ in &right {
+        arr[i] = right.pop().unwrap();
+        i += 1;
+    }
+}
+``
+226 tokens generated (10.91 token/s)
+```

candle-examples/examples/mistral/main.rs

@@ -0,0 +1,271 @@
+#[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::mistral::{Config, Model as Mistral};
+use candle_transformers::models::quantized_mistral::Model as QMistral;
+
+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;
+
+enum Model {
+    Mistral(Mistral),
+    Quantized(QMistral),
+}
+
+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 = match &mut self.model {
+                Model::Mistral(m) => m.forward(&input, start_pos)?,
+                Model::Quantized(m) => m.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 = "lmz/candle-mistral")]
+    model_id: String,
+
+    #[arg(long, default_value = "main")]
+    revision: String,
+
+    #[arg(long)]
+    tokenizer_file: Option<String>,
+
+    #[arg(long)]
+    weight_files: Option<String>,
+
+    #[arg(long)]
+    quantized: bool,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// 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 => {
+            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")?,
+                ]
+            }
+        }
+    };
+    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::config_7b_v0_1(args.use_flash_attn);
+    let (model, device) = if args.quantized {
+        let filename = &filenames[0];
+        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename)?;
+        let model = QMistral::new(&config, vb)?;
+        (Model::Quantized(model), Device::Cpu)
+    } else {
+        let device = candle_examples::device(args.cpu)?;
+        let dtype = if device.is_cuda() {
+            DType::BF16
+        } else {
+            DType::F32
+        };
+        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
+        let model = Mistral::new(&config, vb)?;
+        (Model::Mistral(model), device)
+    };
+
+    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/mnist-training/main.rs

@@ -9,7 +9,7 @@ use clap::{Parser, ValueEnum};
 use rand::prelude::*;
 
 use candle::{DType, Result, Tensor, D};
-use candle_nn::{loss, ops, Conv2d, Linear, Module, Optimizer, VarBuilder, VarMap};
+use candle_nn::{loss, ops, Conv2d, Linear, Module, ModuleT, Optimizer, VarBuilder, VarMap};
 
 const IMAGE_DIM: usize = 784;
 const LABELS: usize = 10;
@@ -95,7 +95,7 @@ impl ConvNet {
             .flatten_from(1)?
             .apply(&self.fc1)?
             .relu()?;
-        self.dropout.forward(&xs, train)?.apply(&self.fc2)
+        self.dropout.forward_t(&xs, train)?.apply(&self.fc2)
     }
 }
 

candle-examples/examples/musicgen/encodec_model.rs

@@ -1,6 +1,6 @@
 use crate::nn::conv1d_weight_norm;
-use candle::{DType, IndexOp, Result, Tensor};
-use candle_nn::{conv1d, Conv1d, Conv1dConfig, Module, VarBuilder};
+use candle::{DType, IndexOp, Module, Result, Tensor};
+use candle_nn::{conv1d, Conv1d, Conv1dConfig, VarBuilder};
 
 // Encodec Model
 // https://github.com/huggingface/transformers/blob/main/src/transformers/models/encodec/modeling_encodec.py
@@ -8,6 +8,7 @@ use candle_nn::{conv1d, Conv1d, Conv1dConfig, Module, VarBuilder};
 #[derive(Debug, Clone, PartialEq)]
 enum NormType {
     WeightNorm,
+    TimeGroupNorm,
     None,
 }
 
@@ -199,25 +200,34 @@ impl EncodecResidualVectorQuantizer {
 // https://github.com/huggingface/transformers/blob/abaca9f9432a84cfaa95531de4c72334f38a42f2/src/transformers/models/encodec/modeling_encodec.py#L226
 #[derive(Debug)]
 struct EncodecLSTM {
-    layers: Vec<(Tensor, Tensor, Tensor, Tensor)>,
+    layers: Vec<candle_nn::LSTM>,
 }
 
 impl EncodecLSTM {
     fn load(dim: usize, vb: VarBuilder, cfg: &Config) -> Result<Self> {
         let vb = &vb.pp("lstm");
         let mut layers = vec![];
-        for i in 0..cfg.num_lstm_layers {
-            let w_hh = vb.get((4 * dim, dim), &format!("weight_hh_l{i}"))?;
-            let w_ih = vb.get((4 * dim, dim), &format!("weight_ih_l{i}"))?;
-            let b_hh = vb.get(4 * dim, &format!("bias_hh_l{i}"))?;
-            let b_ih = vb.get(4 * dim, &format!("bias_ih_l{i}"))?;
-            layers.push((w_hh, w_ih, b_hh, b_ih))
+        for layer_idx in 0..cfg.num_lstm_layers {
+            let config = candle_nn::LSTMConfig {
+                layer_idx,
+                ..Default::default()
+            };
+            let lstm = candle_nn::lstm(dim, dim, config, vb.clone())?;
+            layers.push(lstm)
         }
         Ok(Self { layers })
     }
+}
 
-    fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
-        todo!()
+impl Module for EncodecLSTM {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        use candle_nn::RNN;
+        let mut xs = xs.clone();
+        for layer in self.layers.iter() {
+            let states = layer.seq(&xs)?;
+            xs = layer.states_to_tensor(&states)?;
+        }
+        Ok(xs)
     }
 }
 
@@ -247,7 +257,9 @@ impl EncodecConvTranspose1d {
             bias,
         })
     }
+}
 
+impl Module for EncodecConvTranspose1d {
     fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
         todo!()
     }
@@ -257,6 +269,7 @@ impl EncodecConvTranspose1d {
 struct EncodecConv1d {
     causal: bool,
     conv: Conv1d,
+    norm: Option<candle_nn::GroupNorm>,
 }
 
 impl EncodecConv1d {
@@ -281,7 +294,7 @@ impl EncodecConv1d {
                 },
                 vb.pp("conv"),
             )?,
-            NormType::None => conv1d(
+            NormType::None | NormType::TimeGroupNorm => conv1d(
                 in_c,
                 out_c,
                 kernel_size,
@@ -294,17 +307,29 @@ 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,
         })
     }
+}
 
+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),
+        }
     }
 }
 
@@ -340,7 +365,9 @@ impl EncodecResnetBlock {
             shortcut,
         })
     }
+}
 
+impl Module for EncodecResnetBlock {
     fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         let residual = xs.clone();
         let xs = xs.elu(1.)?;
@@ -439,8 +466,17 @@ impl EncodecEncoder {
         })
     }
 
-    fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
-        todo!()
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.apply(&self.init_conv)?;
+        for (resnets, conv) in self.sampling_layers.iter() {
+            for resnet in resnets.iter() {
+                xs = xs.apply(resnet)?;
+            }
+            xs = xs.elu(1.0)?.apply(conv)?;
+        }
+        xs.apply(&self.final_lstm)?
+            .elu(1.0)?
+            .apply(&self.final_conv)
     }
 }
 
@@ -507,8 +543,15 @@ impl EncodecDecoder {
         })
     }
 
-    fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
-        todo!()
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let mut xs = xs.apply(&self.init_conv)?.apply(&self.init_lstm)?;
+        for (conv, resnets) in self.sampling_layers.iter() {
+            xs = xs.elu(1.)?.apply(conv)?;
+            for resnet in resnets.iter() {
+                xs = xs.apply(resnet)?
+            }
+        }
+        xs.elu(1.)?.apply(&self.final_conv)
     }
 }
 

candle-examples/examples/musicgen/main.rs

@@ -13,7 +13,6 @@ extern crate accelerate_src;
 mod encodec_model;
 mod musicgen_model;
 mod nn;
-mod t5_model;
 
 use musicgen_model::{GenConfig, MusicgenForConditionalGeneration};
 
@@ -74,11 +73,9 @@ fn main() -> Result<()> {
             ))
             .get("model.safetensors")?,
     };
-    let model = unsafe { candle::safetensors::MmapedFile::new(model)? };
-    let model = model.deserialize()?;
-    let vb = VarBuilder::from_safetensors(vec![model], DTYPE, &device);
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DTYPE, &device)? };
     let config = GenConfig::small();
-    let model = MusicgenForConditionalGeneration::load(vb, config)?;
+    let mut model = MusicgenForConditionalGeneration::load(vb, config)?;
 
     let tokens = tokenizer
         .encode(args.prompt.as_str(), true)

candle-examples/examples/musicgen/musicgen_model.rs

@@ -1,9 +1,10 @@
-use crate::{encodec_model, t5_model};
+use crate::encodec_model;
 use candle::{DType, Device, Result, Tensor, D};
 use candle_nn::{
     embedding, layer_norm, linear_no_bias, Activation, Embedding, LayerNorm, Linear, Module,
     VarBuilder,
 };
+use candle_transformers::models::t5;
 
 // https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/models/musicgen/configuration_musicgen.py#L83
 #[derive(Debug, Clone, PartialEq)]
@@ -39,7 +40,7 @@ impl Default for Config {
             num_attention_heads: 16,
             layerdrop: 0.0,
             use_cache: true,
-            activation_function: Activation::Gelu, // TODO: Handle old style gelu.
+            activation_function: Activation::Gelu,
             hidden_size: 1024,
             dropout: 0.1,
             attention_dropout: 0.0,
@@ -65,7 +66,7 @@ impl Config {
             num_attention_heads: 16,
             layerdrop: 0.0,
             use_cache: true,
-            activation_function: Activation::Gelu, // TODO: Handle old style gelu.
+            activation_function: Activation::Gelu,
             hidden_size: 1024,
             dropout: 0.1,
             attention_dropout: 0.0,
@@ -370,7 +371,7 @@ impl MusicgenForCausalLM {
 
 #[derive(Debug)]
 pub struct MusicgenForConditionalGeneration {
-    pub text_encoder: crate::t5_model::T5EncoderModel,
+    pub text_encoder: t5::T5EncoderModel,
     pub audio_encoder: crate::encodec_model::EncodecModel,
     pub decoder: MusicgenForCausalLM,
     cfg: GenConfig,
@@ -379,7 +380,7 @@ pub struct MusicgenForConditionalGeneration {
 #[derive(Debug, Clone, PartialEq)]
 pub struct GenConfig {
     musicgen: Config,
-    t5: crate::t5_model::Config,
+    t5: t5::Config,
     encodec: crate::encodec_model::Config,
 }
 
@@ -387,7 +388,7 @@ impl GenConfig {
     pub fn small() -> Self {
         Self {
             musicgen: Config::musicgen_small(),
-            t5: t5_model::Config::musicgen_small(),
+            t5: t5::Config::musicgen_small(),
             encodec: encodec_model::Config::musicgen_small(),
         }
     }
@@ -399,7 +400,7 @@ impl MusicgenForConditionalGeneration {
     }
 
     pub fn load(vb: VarBuilder, cfg: GenConfig) -> Result<Self> {
-        let text_encoder = t5_model::T5EncoderModel::load(vb.pp("text_encoder"), &cfg.t5)?;
+        let text_encoder = t5::T5EncoderModel::load(vb.pp("text_encoder"), &cfg.t5)?;
         let audio_encoder =
             encodec_model::EncodecModel::load(vb.pp("audio_encoder"), &cfg.encodec)?;
         let decoder = MusicgenForCausalLM::load(vb.pp("decoder"), &cfg.musicgen)?;

candle-examples/examples/musicgen/t5_model.rs

@@ -1,434 +0,0 @@
-// T5 Text Encoder
-// https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py
-
-use candle::{DType, Result, Tensor, D};
-use candle_nn::{embedding, linear_no_bias, Activation, Embedding, Linear, Module, VarBuilder};
-use std::sync::Arc;
-
-#[derive(Debug, Clone, PartialEq)]
-pub struct Config {
-    vocab_size: usize,
-    d_model: usize,
-    d_kv: usize,
-    d_ff: usize,
-    num_layers: usize,
-    num_decoder_layers: Option<usize>,
-    num_heads: usize,
-    relative_attention_num_buckets: usize,
-    relative_attention_max_distance: usize,
-    dropout_rate: f64,
-    layer_norm_epsilon: f64,
-    initializer_factor: f64,
-    feed_forward_proj: Activation,
-    is_decoder: bool,
-    is_encoder_decoder: bool,
-    use_cache: bool,
-    pad_token_id: usize,
-    eos_token_id: usize,
-}
-
-impl Default for Config {
-    fn default() -> Self {
-        Self {
-            vocab_size: 32128,
-            d_model: 512,
-            d_kv: 64,
-            d_ff: 2048,
-            num_layers: 6,
-            num_decoder_layers: None,
-            num_heads: 8,
-            relative_attention_num_buckets: 32,
-            relative_attention_max_distance: 128,
-            dropout_rate: 0.1,
-            layer_norm_epsilon: 1e-6,
-            initializer_factor: 1.0,
-            feed_forward_proj: Activation::Relu,
-            is_decoder: false,
-            is_encoder_decoder: true,
-            use_cache: true,
-            pad_token_id: 0,
-            eos_token_id: 1,
-        }
-    }
-}
-
-impl Config {
-    // https://huggingface.co/facebook/musicgen-small/blob/495da4ad086b3416a27c6187f9239f9fd96f3962/config.json#L184
-    pub fn musicgen_small() -> Self {
-        Self {
-            d_ff: 3072,
-            d_kv: 64,
-            d_model: 768,
-            dropout_rate: 0.1,
-            eos_token_id: 1,
-            feed_forward_proj: Activation::Relu,
-            initializer_factor: 1.0,
-            is_decoder: false,
-            is_encoder_decoder: true,
-            layer_norm_epsilon: 1e-6,
-            num_decoder_layers: Some(12),
-            num_heads: 12,
-            num_layers: 12,
-            pad_token_id: 0,
-            relative_attention_max_distance: 128,
-            relative_attention_num_buckets: 32,
-            use_cache: true,
-            vocab_size: 32128,
-        }
-    }
-}
-
-#[derive(Debug)]
-struct T5LayerNorm {
-    weight: Tensor,
-    variance_epsilon: f64,
-}
-
-impl T5LayerNorm {
-    fn load(h: usize, eps: f64, vb: VarBuilder) -> Result<Self> {
-        let weight = vb.get(h, "weight")?;
-        Ok(Self {
-            weight,
-            variance_epsilon: eps,
-        })
-    }
-
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let dtype = xs.dtype();
-        let xs_f32 = xs.to_dtype(DType::F32)?;
-        // variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
-        let variance = xs_f32.sqr()?.mean_keepdim(D::Minus1)?;
-        let xs = xs.broadcast_div(&(variance + self.variance_epsilon)?.sqrt()?)?;
-        let xs = xs.to_dtype(dtype)?;
-        let xs = xs.broadcast_mul(&self.weight)?;
-        Ok(xs)
-    }
-}
-
-#[derive(Debug)]
-struct T5DenseActDense {
-    wi: Linear,
-    wo: Linear,
-    act: Activation,
-}
-
-impl T5DenseActDense {
-    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let wi = linear_no_bias(cfg.d_model, cfg.d_ff, vb.pp("wi"))?;
-        let wo = linear_no_bias(cfg.d_ff, cfg.d_model, vb.pp("wo"))?;
-        Ok(Self {
-            wi,
-            wo,
-            act: Activation::Relu,
-        })
-    }
-
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let xs = self.wi.forward(xs)?;
-        let xs = self.act.forward(&xs)?;
-        let xs = self.wo.forward(&xs)?;
-        Ok(xs)
-    }
-}
-
-#[derive(Debug)]
-struct T5LayerFF {
-    dense_relu_dense: T5DenseActDense,
-    layer_norm: T5LayerNorm,
-}
-
-impl T5LayerFF {
-    fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        // is_gated_act is not supported.
-        let dense_relu_dense = T5DenseActDense::load(vb.pp("DenseReluDense"), cfg)?;
-        let layer_norm =
-            T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
-        Ok(Self {
-            dense_relu_dense,
-            layer_norm,
-        })
-    }
-
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        let ys = self.layer_norm.forward(xs)?;
-        let ys = self.dense_relu_dense.forward(&ys)?;
-        let xs = (xs + ys)?;
-        Ok(xs)
-    }
-}
-
-#[derive(Debug)]
-struct T5Attention {
-    q: Linear,
-    k: Linear,
-    v: Linear,
-    o: Linear,
-    n_heads: usize,
-    d_kv: usize,
-    relative_attention_bias: Option<Embedding>,
-    relative_attention_num_buckets: usize,
-    relative_attention_max_distance: usize,
-    inner_dim: usize,
-}
-
-impl T5Attention {
-    fn load(h: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let inner_dim = cfg.num_heads * cfg.d_kv;
-        let q = linear_no_bias(cfg.d_model, inner_dim, vb.pp("q"))?;
-        let k = linear_no_bias(cfg.d_model, inner_dim, vb.pp("k"))?;
-        let v = linear_no_bias(cfg.d_model, inner_dim, vb.pp("v"))?;
-        let o = linear_no_bias(inner_dim, cfg.d_model, vb.pp("o"))?;
-        let relative_attention_bias = if h {
-            let emb = embedding(
-                cfg.relative_attention_num_buckets,
-                cfg.num_heads,
-                vb.pp("relative_attention_bias"),
-            )?;
-            Some(emb)
-        } else {
-            None
-        };
-        Ok(Self {
-            q,
-            k,
-            v,
-            o,
-            n_heads: cfg.num_heads,
-            d_kv: cfg.d_kv,
-            relative_attention_bias,
-            relative_attention_num_buckets: cfg.relative_attention_num_buckets,
-            relative_attention_max_distance: cfg.relative_attention_max_distance,
-            inner_dim,
-        })
-    }
-
-    fn forward(
-        &self,
-        xs: &Tensor,
-        position_bias: Option<&Tensor>,
-    ) -> Result<(Tensor, Option<Tensor>)> {
-        // TODO: Apply the mask(s)?
-        // TODO: kv caching.
-        let (b_sz, seq_len) = (xs.dim(0)?, xs.dim(1)?);
-        let q = self.q.forward(xs)?;
-        let k = self.k.forward(xs)?;
-        let v = self.v.forward(xs)?;
-        let q = q
-            .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
-            .transpose(1, 2)?
-            .contiguous()?;
-        let k = k
-            .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
-            .transpose(1, 2)?
-            .contiguous()?;
-        let v = v
-            .reshape((b_sz, seq_len, self.n_heads, self.d_kv))?
-            .transpose(1, 2)?
-            .contiguous()?;
-        let scores = q.matmul(&k.t()?)?;
-
-        let (scores, position_bias) = match position_bias {
-            Some(position_bias) => ((scores + position_bias)?, Some(position_bias.clone())),
-            None => match &self.relative_attention_bias {
-                None => (scores, None),
-                Some(relative_attention_bias) => {
-                    let query_length = seq_len;
-                    let key_length = seq_len;
-                    // This only handles the bidirectional case.
-                    let num_buckets = self.relative_attention_num_buckets as u32 / 2;
-                    let max_exact = num_buckets / 2;
-                    let relative_position = (0..query_length as u32)
-                        .map(|i| {
-                            (0..key_length as u32)
-                                .map(|j| {
-                                    if i < j {
-                                        if j - i < max_exact {
-                                            j - i + num_buckets
-                                        } else {
-                                            let b = f32::log(
-                                                (j - i) as f32 / max_exact as f32,
-                                                self.relative_attention_max_distance as f32
-                                                    / max_exact as f32,
-                                            ) * (num_buckets - max_exact) as f32;
-                                            u32::min(
-                                                max_exact + num_buckets + b as u32,
-                                                self.relative_attention_num_buckets as u32 - 1,
-                                            )
-                                        }
-                                    } else if i - j < max_exact {
-                                        i - j
-                                    } else {
-                                        let b = f32::log(
-                                            (i - j) as f32 / max_exact as f32,
-                                            self.relative_attention_max_distance as f32
-                                                / max_exact as f32,
-                                        ) * (num_buckets - max_exact) as f32;
-                                        max_exact + b as u32
-                                    }
-                                })
-                                .collect::<Vec<u32>>()
-                        })
-                        .collect::<Vec<Vec<_>>>();
-                    let relative_buckets = Tensor::new(relative_position, q.device())?;
-                    let position_bias = relative_attention_bias
-                        .forward(&relative_buckets)?
-                        .permute((2, 0, 1))?
-                        .unsqueeze(0)?;
-                    ((scores + &position_bias)?, Some(position_bias))
-                    // TODO: position_bias_masked?
-                }
-            },
-        };
-
-        let attn_weights = candle_nn::ops::softmax(&scores, D::Minus1)?;
-        let attn_output = attn_weights.matmul(&v)?;
-        let attn_output = attn_output
-            .transpose(1, 2)?
-            .reshape((b_sz, seq_len, self.inner_dim))?;
-        let attn_output = self.o.forward(&attn_output)?;
-        Ok((attn_output, position_bias))
-    }
-}
-
-#[derive(Debug)]
-struct T5LayerSelfAttention {
-    self_attention: T5Attention,
-    layer_norm: T5LayerNorm,
-}
-
-impl T5LayerSelfAttention {
-    fn load(h: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let self_attention = T5Attention::load(h, vb.pp("SelfAttention"), cfg)?;
-        let layer_norm =
-            T5LayerNorm::load(cfg.d_model, cfg.layer_norm_epsilon, vb.pp("layer_norm"))?;
-        Ok(Self {
-            self_attention,
-            layer_norm,
-        })
-    }
-
-    fn forward(
-        &self,
-        xs: &Tensor,
-        position_bias: Option<&Tensor>,
-    ) -> Result<(Tensor, Option<Tensor>)> {
-        let normed_xs = self.layer_norm.forward(xs)?;
-        let (ys, position_bias) = self.self_attention.forward(&normed_xs, position_bias)?;
-        let ys = (xs + ys)?;
-        Ok((ys, position_bias))
-    }
-}
-
-#[derive(Debug)]
-struct T5LayerCrossAttention {}
-
-impl T5LayerCrossAttention {
-    fn load(_vb: VarBuilder, _cfg: &Config) -> Result<Self> {
-        todo!()
-    }
-
-    fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
-        todo!()
-    }
-}
-
-#[derive(Debug)]
-struct T5Block {
-    self_attn: T5LayerSelfAttention,
-    cross_attn: Option<T5LayerCrossAttention>,
-    ff: T5LayerFF,
-}
-
-impl T5Block {
-    fn load(has_relative_attention_bias: bool, vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let vb = vb.pp("layer");
-        let self_attn = T5LayerSelfAttention::load(has_relative_attention_bias, vb.pp("0"), cfg)?;
-        let cross_attn = if cfg.is_decoder {
-            Some(T5LayerCrossAttention::load(vb.pp("1"), cfg)?)
-        } else {
-            None
-        };
-        let ff_i = if cross_attn.is_some() { 2 } else { 1 };
-        let ff = T5LayerFF::load(vb.pp(&ff_i.to_string()), cfg)?;
-        Ok(Self {
-            self_attn,
-            cross_attn,
-            ff,
-        })
-    }
-
-    fn forward(
-        &self,
-        xs: &Tensor,
-        position_bias: Option<&Tensor>,
-    ) -> Result<(Tensor, Option<Tensor>)> {
-        let (mut xs, position_bias) = self.self_attn.forward(xs, position_bias)?;
-        // TODO: clamp for f16?
-        if let Some(cross_attn) = &self.cross_attn {
-            xs = cross_attn.forward(&xs)?;
-            // TODO: clamp for f16?
-        }
-        let xs = self.ff.forward(&xs)?;
-        // TODO: clamp for f16?
-        Ok((xs, position_bias))
-    }
-}
-
-#[derive(Debug)]
-struct T5Stack {
-    block: Vec<T5Block>,
-    shared: Arc<Embedding>,
-    final_layer_norm: T5LayerNorm,
-}
-
-impl T5Stack {
-    fn load(vb: VarBuilder, shared: &Arc<Embedding>, cfg: &Config) -> Result<Self> {
-        let block = (0..cfg.num_layers)
-            .map(|i| T5Block::load(i == 0, vb.pp(&format!("block.{i}")), cfg))
-            .collect::<Result<Vec<_>>>()?;
-        let final_layer_norm = T5LayerNorm::load(
-            cfg.d_model,
-            cfg.layer_norm_epsilon,
-            vb.pp("final_layer_norm"),
-        )?;
-        Ok(Self {
-            block,
-            shared: shared.clone(),
-            final_layer_norm,
-        })
-    }
-
-    fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
-        let input_embeds = self.shared.as_ref().forward(input_ids)?;
-        let (_b_sz, _seq_len) = (input_embeds.dim(0)?, input_embeds.dim(1)?);
-
-        let mut hidden_states = input_embeds;
-        let mut position_bias = None;
-        for block in self.block.iter() {
-            (hidden_states, position_bias) =
-                block.forward(&hidden_states, position_bias.as_ref())?
-        }
-        let hidden_states = self.final_layer_norm.forward(&hidden_states)?;
-        Ok(hidden_states)
-    }
-}
-
-#[derive(Debug)]
-pub struct T5EncoderModel {
-    shared: Arc<Embedding>,
-    encoder: T5Stack,
-}
-
-impl T5EncoderModel {
-    pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
-        let shared = embedding(cfg.vocab_size, cfg.d_model, vb.pp("shared"))?;
-        let shared = Arc::new(shared);
-        let encoder = T5Stack::load(vb.pp("encoder"), &shared, cfg)?;
-        Ok(Self { shared, encoder })
-    }
-
-    pub fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
-        let encoder_outputs = self.encoder.forward(input_ids)?;
-        Ok(encoder_outputs)
-    }
-}

candle-examples/examples/onnx/README.md

@@ -0,0 +1,10 @@
+## Using ONNX models in Candle
+
+This example demonstrates how to run ONNX based models in Candle, the model
+being used here is a small sequeezenet variant.
+
+You can run the example with the following command:
+
+```bash
+cargo run --example squeezenet-onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+```