More happy tests.

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

Fix Python.

Fixing examples.

Fix: fmt + clippy + stub.

Moving everything around.

Only missing the actual implems.

Fixing everything + adding dequantized kernels.

More work.

Fixing matmul.

Fmt + Clippy

Some clippy fixes.

Working state.

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

Fixing Q2K bug (present in ggml).

.github/workflows/ci_cuda.yaml

@@ -5,15 +5,49 @@ on:
   pull_request:
 
 jobs:
+  start-runner:
+    name: Start self-hosted EC2 runner
+    runs-on: ubuntu-latest
+    # Don't run on forks, they won't have access to secrets anyway.
+    if: ${{ github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }}
+    env:
+      AWS_REGION: us-east-1
+      EC2_AMI_ID: ami-03cfed9ea28f4b002
+      EC2_INSTANCE_TYPE: g5.xlarge
+      EC2_SUBNET_ID: subnet-931b34f5,subnet-ecb993cd,subnet-943dc2d8,subnet-45371f1a,subnet-ee93e0df,subnet-fddc3dfc
+      EC2_SECURITY_GROUP: sg-030175c435ac141d6
+    outputs:
+      label: ${{ steps.start-ec2-runner.outputs.label }}
+      ec2-instance-id: ${{ steps.start-ec2-runner.outputs.ec2-instance-id }}
+    steps:
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ env.AWS_REGION }}
+      - name: Start EC2 runner
+        id: start-ec2-runner
+        uses: philschmid/philschmid-ec2-github-runner@main
+        with:
+          mode: start
+          github-token: ${{ secrets.GH_PERSONAL_ACCESS_TOKEN }}
+          ec2-image-id: ${{ env.EC2_AMI_ID }}
+          ec2-instance-type: ${{ env.EC2_INSTANCE_TYPE }}
+          subnet-id: ${{ env.EC2_SUBNET_ID }}
+          security-group-id: ${{ env.EC2_SECURITY_GROUP }}
+          aws-resource-tags: > # optional, requires additional permissions
+            [
+              {"Key": "Name", "Value": "ec2-tgi-github-runner"},
+              {"Key": "GitHubRepository", "Value": "${{ github.repository }}"}
+            ]
+
   test-cuda:
     concurrency:
       group: ${{ github.workflow }}-${{ github.job }}-${{ github.head_ref || github.run_id }}
       cancel-in-progress: true
-    runs-on: [single-gpu, nvidia-gpu, t4, ci]
+    needs: start-runner # required to start the main job when the runner is ready
-    container:
+    runs-on: ${{ needs.start-runner.outputs.label }} # run the job on the newly created runner
-      image: nvidia/cuda:12.3.1-devel-ubuntu22.04
-      options: --gpus 0 
-    if: ${{ github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }}
     permissions:
       contents: write
       packages: write
@@ -24,10 +58,32 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
-      - name: Install dependencies
-        run: apt-get update && apt install curl build-essential libssl-dev protobuf-compiler pkg-config -y
       - name: Install Rust Stable
-        uses: actions-rust-lang/setup-rust-toolchain@v1
+        run: curl https://sh.rustup.rs -sSf | sh -s -- -y
       - uses: Swatinem/rust-cache@v2
+      - run: apt-get update -y && apt-get install libssl-dev protobuf-compiler -y
       - name: Test (cuda)
-        run: cargo test --features cuda
+        run: PATH=$PATH:/usr/local/cuda-11.8/bin/ /root/.cargo/bin/cargo test --features cuda
+  stop-runner:
+    name: Stop self-hosted EC2 runner
+    needs:
+      - start-runner
+      - test-cuda
+    runs-on: ubuntu-latest
+    env:
+      AWS_REGION: us-east-1
+    if: ${{ (success() || failure()) && github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }} # required to stop the runner even if the error happened in the previous jobs
+    steps:
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ env.AWS_REGION }}
+      - name: Stop EC2 runner
+        uses: philschmid/philschmid-ec2-github-runner@main
+        with:
+          mode: stop
+          github-token: ${{ secrets.GH_PERSONAL_ACCESS_TOKEN }}
+          label: ${{ needs.start-runner.outputs.label }}
+          ec2-instance-id: ${{ needs.start-runner.outputs.ec2-instance-id }}

Cargo.toml

@@ -9,7 +9,6 @@ members = [
     "candle-transformers",
     "candle-wasm-examples/*",
     "candle-wasm-tests",
-    "tensor-tools",
 ]
 exclude = [
    "candle-flash-attn",
@@ -20,7 +19,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.5.0"
+version = "0.3.3"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -29,38 +28,37 @@ categories = ["science"]
 license = "MIT OR Apache-2.0"
 
 [workspace.dependencies]
-ab_glyph = "0.2.23"
 accelerate-src = { version = "0.3.2" }
 anyhow = { version = "1", features = ["backtrace"] }
 byteorder = "1.4.3"
-candle = { path = "./candle-core", package = "candle-core", version = "0.5.0" }
+candle = { path = "./candle-core", package = "candle-core" }
-candle-datasets = { path = "./candle-datasets", version = "0.5.0" }
+candle-datasets = { path = "./candle-datasets" }
-candle-flash-attn = { path = "./candle-flash-attn", version = "0.5.0" }
+candle-flash-attn = { path = "./candle-flash-attn" }
-candle-kernels = { path = "./candle-kernels", version = "0.5.0" }
+candle-kernels = { path = "./candle-kernels" }
-candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.5.0" }
+candle-metal-kernels = { path = "./candle-metal-kernels" }
-candle-nn = { path = "./candle-nn", version = "0.5.0" }
+candle-nn = { path = "./candle-nn" }
-candle-onnx = { path = "./candle-onnx", version = "0.5.0" }
+candle-onnx = { path = "./candle-onnx" }
-candle-transformers = { path = "./candle-transformers", version = "0.5.0" }
+candle-transformers = { path = "./candle-transformers" }
 clap = { version = "4.2.4", features = ["derive"] }
 criterion = { version = "0.5.1", default-features=false }
 cudarc = { version = "0.10.0", features = ["f16"] }
-fancy-regex = "0.13.0"
 gemm = { version = "0.17.0", features = ["wasm-simd128-enable"] }
 hf-hub = "0.3.0"
 half = { version = "2.3.1", features = ["num-traits", "use-intrinsics", "rand_distr"] }
-image = { version = "0.25.0", default-features = false, features = ["jpeg", "png"] }
+image = { version = "0.24.7", default-features = false, features = ["jpeg", "png"] }
-imageproc = { version = "0.24.0", default-features = false }
+imageproc = { version = "0.23.0", default-features = false }
 intel-mkl-src = { version = "0.8.1", features = ["mkl-static-lp64-iomp"] }
 libc = { version = "0.2.147" }
 log = "0.4"
 memmap2 = { version = "0.9.3", features = ["stable_deref_trait"] }
 num_cpus = "1.15.0"
 num-traits = "0.2.15"
-parquet = { version = "51.0.0" }
+parquet = { version = "45.0.0" }
 rand = "0.8.5"
 rand_distr = "0.4.3"
 rayon = "1.7.0"
-safetensors = "0.4.1"
+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"

README.md

@@ -63,25 +63,17 @@ We also provide a some command line based examples using state of the art models
 - [LLaMA and LLaMA-v2](./candle-examples/examples/llama/): general LLM, includes
   the SOLAR-10.7B variant.
 - [Falcon](./candle-examples/examples/falcon/): general LLM.
-- [Gemma](./candle-examples/examples/gemma/): 2b and 7b general LLMs from Google Deepmind.
-- [RecurrentGemma](./candle-examples/examples/recurrent-gemma/): 2b and 7b
-  Griffin based models from Google that mix attention with a RNN like state.
 - [Phi-1, Phi-1.5, and Phi-2](./candle-examples/examples/phi/): 1.3b and 2.7b general LLMs with performance on par with LLaMA-v2 7b.
 - [StableLM-3B-4E1T](./candle-examples/examples/stable-lm/): a 3b general LLM
-  pre-trained on 1T tokens of English and code datasets. Also supports
+  pre-trained on 1T tokens of English and code datasets.
-  StableLM-2, a 1.6b LLM trained on 2T tokens, as well as the code variants.
+- [Minimal Mamba](./candle-examples/examples/mamba-minimal/): a minimal
-- [Mamba](./candle-examples/examples/mamba/): an inference only
   implementation of the Mamba state space model.
 - [Mistral7b-v0.1](./candle-examples/examples/mistral/): a 7b general LLM with
   better performance than all publicly available 13b models as of 2023-09-28.
 - [Mixtral8x7b-v0.1](./candle-examples/examples/mixtral/): a sparse mixture of
   experts 8x7b general LLM with better performance than a Llama 2 70B model with
   much faster inference.
-- [StarCoder](./candle-examples/examples/bigcode/) and
+- [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code generation.
-  [StarCoder2](./candle-examples/examples/starcoder2/): LLM specialized to code generation.
-- [Qwen1.5](./candle-examples/examples/qwen/): Bilingual (English/Chinese) LLMs.
-- [RWKV v5 and v6](./candle-examples/examples/rwkv/): An RNN with transformer level LLM
-  performance.
 - [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
 - [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
   (English/Chinese) general LLMs with 6b and 34b parameters.
@@ -111,12 +103,7 @@ We also provide a some command line based examples using state of the art models
 
 <img src="https://github.com/huggingface/candle/raw/main/candle-examples/examples/segment-anything/assets/sam_merged.jpg" width="200">
 
-- [SegFormer](./candle-examples/examples/segformer/): transformer based semantic segmantation model.
 - [Whisper](./candle-examples/examples/whisper/): speech recognition model.
-- [EnCodec](./candle-examples/examples/encodec/): high-quality audio compression
-  model using residual vector quantization.
-- [MetaVoice](./candle-examples/examples/metavoice/): foundational model for
-  text-to-speech.
 - [T5](./candle-examples/examples/t5), [Bert](./candle-examples/examples/bert/),
   [JinaBert](./candle-examples/examples/jina-bert/) : useful for sentence embeddings.
 - [DINOv2](./candle-examples/examples/dinov2/): computer vision model trained
@@ -124,16 +111,11 @@ We also provide a some command line based examples using state of the art models
   evaluation, segmentation).
 - [VGG](./candle-examples/examples/vgg/),
   [RepVGG](./candle-examples/examples/repvgg): computer vision models.
+- [BLIP](./candle-examples/examples/blip/): image to text model, can be used to
 - [BLIP](./candle-examples/examples/blip/): image to text model, can be used to
   generate captions for an image.
-- [CLIP](./candle-examples/examples/clip/): multi-model vision and language
-  model.
-- [TrOCR](./candle-examples/examples/trocr/): a transformer OCR model, with
-  dedicated submodels for hand-writing and printed recognition.
 - [Marian-MT](./candle-examples/examples/marian-mt/): neural machine translation
   model, generates the translated text from the input text.
-- [Moondream](./candle-examples/examples/moondream/): tiny computer-vision model 
-  that can answer real-world questions about images.
 
 Run them using commands like:
 ```
@@ -177,11 +159,9 @@ And then head over to
 - [`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.
-- [`candle-coursera-ml`](https://github.com/vishpat/candle-coursera-ml): Implementation of ML algorithms from Coursera's [Machine Learning Specialization](https://www.coursera.org/specializations/machine-learning-introduction) course.
 - [`kalosm`](https://github.com/floneum/floneum/tree/master/interfaces/kalosm): A multi-modal meta-framework in Rust for interfacing with local pre-trained models with support for controlled generation, custom samplers, in-memory vector databases, audio transcription, and more.
 - [`candle-sampling`](https://github.com/EricLBuehler/candle-sampling): Sampling techniques for Candle.
 - [`gpt-from-scratch-rs`](https://github.com/jeroenvlek/gpt-from-scratch-rs): A port of Andrej Karpathy's _Let's build GPT_ tutorial on YouTube showcasing the Candle API on a toy problem.
-- [`candle-einops`](https://github.com/tomsanbear/candle-einops): A pure rust implementation of the python [einops](https://github.com/arogozhnikov/einops) library.
 
 If you have an addition to this list, please submit a pull request.
 
@@ -202,18 +182,15 @@ If you have an addition to this list, please submit a pull request.
     - Language Models.
         - LLaMA v1 and v2 with variants such as SOLAR-10.7B.
         - Falcon.
-        - StarCoder, StarCoder2.
+        - StarCoder.
         - Phi 1, 1.5, and 2.
-        - Mamba, Minimal Mamba
+        - Minimal Mamba
-        - Gemma 2b and 7b.
         - Mistral 7b v0.1.
         - Mixtral 8x7b v0.1.
-        - StableLM-3B-4E1T, StableLM-2-1.6B, Stable-Code-3B.
+        - StableLM-3B-4E1T.
         - Replit-code-v1.5-3B.
         - Bert.
         - Yi-6B and Yi-34B.
-        - Qwen1.5, Qwen1.5 MoE.
-        - RWKV v5 and v6.
     - Quantized LLMs.
         - Llama 7b, 13b, 70b, as well as the chat and code variants.
         - Mistral 7b, and 7b instruct.
@@ -223,22 +200,16 @@ If you have an addition to this list, please submit a pull request.
     - Text to text.
         - T5 and its variants: FlanT5, UL2, MADLAD400 (translation), CoEdit (Grammar correction).
         - Marian MT (Machine Translation).
+    - Whisper (multi-lingual support).
     - Text to image.
         - Stable Diffusion v1.5, v2.1, XL v1.0.
         - Wurstchen v2.
     - Image to text.
         - BLIP.
-        - TrOCR.
-    - Audio.
-        - Whisper, multi-lingual speech-to-text.
-        - EnCodec, audio compression model.
-        - MetaVoice-1B, text-to-speech model.
     - Computer Vision Models.
-        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
+        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG.
-          ConvNeXTv2, MobileOne, EfficientVit (MSRA).
         - yolo-v3, yolo-v8.
         - Segment-Anything Model (SAM).
-        - SegFormer.
 - File formats: load models from safetensors, npz, ggml, or PyTorch files.
 - Serverless (on CPU), small and fast deployments.
 - Quantization support using the llama.cpp quantized types.
@@ -375,9 +346,9 @@ git submodule update --init
 /usr/include/c++/11/bits/std_function.h:530:146: error: parameter packs not expanded with ‘...’:
 ```
 
-This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a different, supported gcc version - for example gcc-10, and specify the path to the compiler in the NVCC_CCBIN environment variable.
+This is a bug in gcc-11 triggered by the Cuda compiler. To fix this, install a different, supported gcc version - for example gcc-10, and specify the path to the compiler in the CANDLE_NVCC_CCBIN environment variable.
 ```
-env NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
+env CANDLE_NVCC_CCBIN=/usr/lib/gcc/x86_64-linux-gnu/10 cargo ...
 ```
 
 #### Linking error on windows when running rustdoc or mdbook tests

candle-core/benches/bench_main.rs

@@ -2,10 +2,7 @@ mod benchmarks;
 
 use criterion::criterion_main;
 criterion_main!(
-    benchmarks::affine::benches,
     benchmarks::matmul::benches,
-    benchmarks::random::benches,
+    benchmarks::affine::benches,
-    benchmarks::where_cond::benches,
+    benchmarks::where_cond::benches
-    benchmarks::conv_transpose2d::benches,
-    benchmarks::qmatmul::benches,
 );

candle-core/benches/benchmarks/conv_transpose2d.rs

@@ -1,59 +0,0 @@
-use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
-use candle_core::{DType, Device, Tensor};
-use criterion::{black_box, criterion_group, Criterion, Throughput};
-use std::time::Instant;
-
-fn run(
-    x: &Tensor,
-    k: &Tensor,
-    padding: usize,
-    output_padding: usize,
-    stride: usize,
-    dilation: usize,
-) {
-    x.conv_transpose2d(k, padding, output_padding, stride, dilation)
-        .unwrap();
-}
-
-fn run_benchmark(c: &mut Criterion, device: &Device, dtype: DType, name: &str) {
-    let t = Tensor::arange(0.0f32, 10000.0, device)
-        .unwrap()
-        .reshape((1, 4, 50, 50))
-        .unwrap()
-        .to_dtype(dtype)
-        .unwrap();
-
-    let kernel = Tensor::arange(0.0f32, 100.0, device)
-        .unwrap()
-        .reshape((4, 1, 5, 5))
-        .unwrap()
-        .to_dtype(dtype)
-        .unwrap();
-
-    let flops = t.dims().iter().product::<usize>() * dtype.size_in_bytes();
-
-    let mut group = c.benchmark_group(device.bench_name(name));
-    group.throughput(Throughput::Bytes(flops as u64));
-    group.bench_function("iter", move |b| {
-        b.iter_custom(|iters| {
-            let start = Instant::now();
-            for _i in 0..iters {
-                run(black_box(&t), black_box(&kernel), 1, 0, 1, 2);
-            }
-            device.sync().unwrap();
-            start.elapsed()
-        })
-    });
-    group.finish();
-}
-
-fn criterion_benchmark(c: &mut Criterion) {
-    let handler = BenchDeviceHandler::new().unwrap();
-    for device in handler.devices {
-        run_benchmark(c, &device, DType::F32, "conv_transpose2d_f32");
-        run_benchmark(c, &device, DType::F16, "conv_transpose2d_f16");
-        run_benchmark(c, &device, DType::BF16, "conv_transpose2d_bf16");
-    }
-}
-
-criterion_group!(benches, criterion_benchmark);

candle-core/benches/benchmarks/mod.rs

@@ -1,8 +1,5 @@
 pub(crate) mod affine;
-pub(crate) mod conv_transpose2d;
 pub(crate) mod matmul;
-pub(crate) mod qmatmul;
-pub(crate) mod random;
 pub(crate) mod where_cond;
 
 use candle_core::{Device, Result};

candle-core/benches/benchmarks/qmatmul.rs

@@ -1,72 +0,0 @@
-use crate::benchmarks::{BenchDevice, BenchDeviceHandler};
-use candle_core::{
-    quantized::{self, GgmlDType, QMatMul},
-    Device, Module, Tensor,
-};
-use criterion::{black_box, criterion_group, Criterion, Throughput};
-use std::time::Instant;
-
-fn run(matmul: &QMatMul, x: &Tensor) {
-    matmul.forward(&x).unwrap();
-}
-
-fn run_bench(c: &mut Criterion, device: &Device, dtype: GgmlDType) {
-    let b = 1;
-    let m = 1;
-    let n = 1024;
-    let k = 1024;
-
-    let lhs = (0..(m * k))
-        .map(|v| v as f32 / (m * k) as f32)
-        .collect::<Vec<_>>();
-    let rhs = (0..(k * n))
-        .map(|v| v as f32 / (n * k) as f32)
-        .collect::<Vec<_>>();
-
-    let lhs = Tensor::from_slice(&lhs, (m, k), device).unwrap();
-    let rhs = Tensor::from_slice(&rhs, (k, n), device).unwrap();
-
-    let qtensor = quantized::QTensor::quantize(&rhs.t().unwrap(), dtype).unwrap();
-    let matmul = quantized::QMatMul::from_qtensor(qtensor).unwrap();
-
-    let flops = b * m * n * k;
-
-    let mut group = c.benchmark_group(device.bench_name(format!("qmatmul_{:?}", dtype)));
-    group.sample_size(200);
-    group.throughput(Throughput::Bytes(flops as u64));
-    group.bench_function("iter", move |b| {
-        b.iter_custom(|iters| {
-            let start = Instant::now();
-            for _i in 0..iters {
-                run(black_box(&matmul), black_box(&lhs));
-            }
-            device.sync().unwrap();
-            start.elapsed()
-        })
-    });
-    group.finish();
-}
-
-fn criterion_benchmark(c: &mut Criterion) {
-    let handler = BenchDeviceHandler::new().unwrap();
-    for device in handler.devices {
-        for dtype in vec![
-            GgmlDType::F32,
-            GgmlDType::F16,
-            GgmlDType::Q4_0,
-            GgmlDType::Q4_1,
-            GgmlDType::Q5_0,
-            GgmlDType::Q5_1,
-            GgmlDType::Q8_0,
-            GgmlDType::Q2K,
-            GgmlDType::Q3K,
-            GgmlDType::Q4K,
-            GgmlDType::Q5K,
-            GgmlDType::Q6K,
-        ] {
-            run_bench(c, &device, dtype);
-        }
-    }
-}
-
-criterion_group!(benches, criterion_benchmark);

candle-core/benches/benchmarks/random.rs

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

candle-core/examples/cuda_basics.rs

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

candle-core/examples/tensor-tools.rs

@@ -1,5 +1,5 @@
-use candle::quantized::{gguf_file, GgmlDType, QTensor};
+use candle_core::quantized::{gguf_file, GgmlDType, QTensor};
-use candle::{Device, Result};
+use candle_core::{Device, Result};
 use clap::{Parser, Subcommand, ValueEnum};
 use rayon::prelude::*;
 
@@ -117,24 +117,6 @@ enum Command {
         verbose: bool,
     },
 
-    Print {
-        file: std::path::PathBuf,
-
-        names: Vec<String>,
-
-        /// The file format to use, if unspecified infer from the file extension.
-        #[arg(long, value_enum)]
-        format: Option<Format>,
-
-        /// Print the whole content of each tensor.
-        #[arg(long)]
-        full: bool,
-
-        /// Line width for printing the tensors.
-        #[arg(long)]
-        line_width: Option<usize>,
-    },
-
     Quantize {
         /// The input file(s), in safetensors format.
         in_file: Vec<std::path::PathBuf>,
@@ -168,105 +150,6 @@ struct Args {
     command: Command,
 }
 
-fn run_print(
-    file: &std::path::PathBuf,
-    names: Vec<String>,
-    format: Option<Format>,
-    full: bool,
-    line_width: Option<usize>,
-    device: &Device,
-) -> Result<()> {
-    if full {
-        candle::display::set_print_options_full();
-    }
-    if let Some(line_width) = line_width {
-        candle::display::set_line_width(line_width)
-    }
-    let format = match format {
-        Some(format) => format,
-        None => match Format::infer(file) {
-            Some(format) => format,
-            None => {
-                println!(
-                    "{file:?}: cannot infer format from file extension, use the --format flag"
-                );
-                return Ok(());
-            }
-        },
-    };
-    match format {
-        Format::Npz => {
-            let tensors = candle::npy::NpzTensors::new(file)?;
-            for name in names.iter() {
-                println!("==== {name} ====");
-                match tensors.get(name)? {
-                    Some(tensor) => println!("{tensor}"),
-                    None => println!("not found"),
-                }
-            }
-        }
-        Format::Safetensors => {
-            use candle::safetensors::Load;
-            let tensors = unsafe { candle::safetensors::MmapedSafetensors::new(file)? };
-            let tensors: std::collections::HashMap<_, _> = tensors.tensors().into_iter().collect();
-            for name in names.iter() {
-                println!("==== {name} ====");
-                match tensors.get(name) {
-                    Some(tensor_view) => {
-                        let tensor = tensor_view.load(device)?;
-                        println!("{tensor}")
-                    }
-                    None => println!("not found"),
-                }
-            }
-        }
-        Format::Pth => {
-            let pth_file = candle::pickle::PthTensors::new(file, None)?;
-            for name in names.iter() {
-                println!("==== {name} ====");
-                match pth_file.get(name)? {
-                    Some(tensor) => {
-                        println!("{tensor}")
-                    }
-                    None => println!("not found"),
-                }
-            }
-        }
-        Format::Pickle => {
-            candle::bail!("pickle format is not supported for print")
-        }
-        Format::Ggml => {
-            let mut file = std::fs::File::open(file)?;
-            let content = candle::quantized::ggml_file::Content::read(&mut file, device)?;
-            for name in names.iter() {
-                println!("==== {name} ====");
-                match content.tensors.get(name) {
-                    Some(tensor) => {
-                        let tensor = tensor.dequantize(device)?;
-                        println!("{tensor}")
-                    }
-                    None => println!("not found"),
-                }
-            }
-        }
-        Format::Gguf => {
-            let mut file = std::fs::File::open(file)?;
-            let content = gguf_file::Content::read(&mut file)?;
-            for name in names.iter() {
-                println!("==== {name} ====");
-                match content.tensor(&mut file, name, device) {
-                    Ok(tensor) => {
-                        let tensor = tensor.dequantize(device)?;
-                        println!("{tensor}")
-                    }
-                    Err(_) => println!("not found"),
-                }
-            }
-        }
-    }
-    Ok(())
-}
-
 fn run_ls(
     file: &std::path::PathBuf,
     format: Option<Format>,
@@ -287,7 +170,7 @@ fn run_ls(
     };
     match format {
         Format::Npz => {
-            let tensors = candle::npy::NpzTensors::new(file)?;
+            let tensors = candle_core::npy::NpzTensors::new(file)?;
             let mut names = tensors.names();
             names.sort();
             for name in names {
@@ -299,12 +182,12 @@ fn run_ls(
             }
         }
         Format::Safetensors => {
-            let tensors = unsafe { candle::safetensors::MmapedSafetensors::new(file)? };
+            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() {
                 let dtype = view.dtype();
-                let dtype = match candle::DType::try_from(dtype) {
+                let dtype = match candle_core::DType::try_from(dtype) {
                     Ok(dtype) => format!("{dtype:?}"),
                     Err(_) => format!("{dtype:?}"),
                 };
@@ -313,7 +196,7 @@ fn run_ls(
             }
         }
         Format::Pth => {
-            let mut tensors = candle::pickle::read_pth_tensor_info(file, verbose, None)?;
+            let mut tensors = candle_core::pickle::read_pth_tensor_info(file, verbose)?;
             tensors.sort_by(|a, b| a.name.cmp(&b.name));
             for tensor_info in tensors.iter() {
                 println!(
@@ -330,7 +213,7 @@ fn run_ls(
         Format::Pickle => {
             let file = std::fs::File::open(file)?;
             let mut reader = std::io::BufReader::new(file);
-            let mut stack = candle::pickle::Stack::empty();
+            let mut stack = candle_core::pickle::Stack::empty();
             stack.read_loop(&mut reader)?;
             for (i, obj) in stack.stack().iter().enumerate() {
                 println!("{i} {obj:?}");
@@ -338,7 +221,7 @@ fn run_ls(
         }
         Format::Ggml => {
             let mut file = std::fs::File::open(file)?;
-            let content = candle::quantized::ggml_file::Content::read(&mut file, device)?;
+            let content = candle_core::quantized::ggml_file::Content::read(&mut file, device)?;
             let mut tensors = content.tensors.into_iter().collect::<Vec<_>>();
             tensors.sort_by(|a, b| a.0.cmp(&b.0));
             for (name, qtensor) in tensors.iter() {
@@ -374,7 +257,7 @@ fn run_quantize_safetensors(
     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::safetensors::load(in_file, &Device::Cpu)?;
+        let in_tensors = candle_core::safetensors::load(in_file, &Device::Cpu)?;
         tensors.extend(in_tensors)
     }
     println!("tensors: {}", tensors.len());
@@ -416,7 +299,7 @@ fn run_dequantize(
         let tensor = tensor.dequantize(device)?;
         tensors.insert(tensor_name.to_string(), tensor);
     }
-    candle::safetensors::save(&tensors, out_file)?;
+    candle_core::safetensors::save(&tensors, out_file)?;
     Ok(())
 }
 
@@ -428,11 +311,11 @@ fn run_quantize(
     device: &Device,
 ) -> Result<()> {
     if in_files.is_empty() {
-        candle::bail!("no specified input files")
+        candle_core::bail!("no specified input files")
     }
     if let Some(extension) = out_file.extension() {
         if extension == "safetensors" {
-            candle::bail!("the generated file cannot use the safetensors extension")
+            candle_core::bail!("the generated file cannot use the safetensors extension")
         }
     }
     if let Some(extension) = in_files[0].extension() {
@@ -442,7 +325,7 @@ fn run_quantize(
     }
 
     if in_files.len() != 1 {
-        candle::bail!("only a single in-file can be used when quantizing gguf files")
+        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.
@@ -494,13 +377,6 @@ fn main() -> anyhow::Result<()> {
                 run_ls(file, format.clone(), verbose, &device)?
             }
         }
-        Command::Print {
-            file,
-            names,
-            format,
-            full,
-            line_width,
-        } => run_print(&file, names, format, full, line_width, &device)?,
         Command::Quantize {
             in_file,
             out_file,

candle-core/src/accelerate.rs

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

candle-core/src/backend.rs

@@ -98,19 +98,6 @@ pub trait BackendStorage: Sized {
     ) -> Result<Self>;
 
     fn copy_strided_src(&self, _: &mut Self, _: usize, _: &Layout) -> Result<()>;
-
-    #[allow(clippy::too_many_arguments)]
-    // Similar to cudaMemcpy2D, though values are in elements and not in bytes.
-    fn copy2d(
-        &self,
-        _: &mut Self,
-        _d1: usize,
-        _d2: usize,
-        _src_stride1: usize,
-        _dst_stride1: usize,
-        _src_offset: usize,
-        _dst_offset: usize,
-    ) -> Result<()>;
 }
 
 pub trait BackendDevice: Sized + std::fmt::Debug + Clone {
@@ -127,22 +114,11 @@ pub trait BackendDevice: Sized + std::fmt::Debug + Clone {
 
     fn ones_impl(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage>;
 
-    /// # Safety
-    /// This function is unsafe as it doesn't initialize the underlying data store.
-    /// The caller should ensure that the data is properly initialized as early as possible
-    /// after this call.
-    unsafe fn alloc_uninit(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage>;
-
     fn storage_from_cpu_storage(&self, _: &CpuStorage) -> Result<Self::Storage>;
 
-    fn storage_from_cpu_storage_owned(&self, _: CpuStorage) -> Result<Self::Storage>;
-
     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<()>;
-
-    /// Synchronize should block until all the operations on the device are completed.
-    fn synchronize(&self) -> Result<()>;
 }

candle-core/src/backprop.rs

@@ -1,4 +1,3 @@
-/// Methods for backpropagation of gradients.
 use crate::op::{BinaryOp, Op, ReduceOp, UnaryOp};
 use crate::{Error, Result, Tensor, TensorId};
 use std::collections::HashMap;
@@ -112,10 +111,9 @@ impl Tensor {
                     }
                     Op::Unary(_node, UnaryOp::Ceil)
                     | Op::Unary(_node, UnaryOp::Floor)
-                    | Op::Unary(_node, UnaryOp::Round)
+                    | Op::Unary(_node, UnaryOp::Round) => nodes,
-                    | Op::Unary(_node, UnaryOp::Sign) => nodes,
                     Op::Reshape(node)
-                    | Op::UpsampleNearest1D { arg: node, .. }
+                    | Op::UpsampleNearest1D(node)
                     | Op::UpsampleNearest2D { arg: node, .. }
                     | Op::AvgPool2D { arg: node, .. }
                     | Op::MaxPool2D { arg: node, .. }
@@ -177,7 +175,7 @@ impl Tensor {
             // 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() };
+            let grad = if do_not_detach { grad } else { grad.detach()? };
             if let Some(op) = node.op() {
                 match op {
                     Op::Binary(lhs, rhs, BinaryOp::Add) => {
@@ -252,7 +250,6 @@ impl Tensor {
                             out_padding,
                             *stride,
                             *dilation,
-                            /* groups */ 1,
                         )?;
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
@@ -312,32 +309,9 @@ impl Tensor {
                     Op::ConvTranspose1D { .. } => Err(Error::BackwardNotSupported {
                         op: "conv-transpose1d",
                     })?,
-                    Op::ConvTranspose2D {
+                    Op::ConvTranspose2D { .. } => Err(Error::BackwardNotSupported {
-                        arg,
+                        op: "conv-transpose2d",
-                        kernel,
+                    })?,
-                        padding,
-                        stride,
-                        dilation,
-                        output_padding: _output_padding,
-                    } => {
-                        let grad_arg = grad.conv2d(kernel, *padding, *dilation, *stride, 1)?;
-                        let sum_grad = grads.or_insert(arg)?;
-                        *sum_grad = sum_grad.add(&grad_arg)?;
-
-                        let grad_kernel = grad
-                            .transpose(0, 1)?
-                            .conv2d(&arg.transpose(0, 1)?, *padding, *stride, *dilation, 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::AvgPool2D {
                         arg,
                         kernel_size,
@@ -373,18 +347,9 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad_arg)?;
                     }
-                    Op::UpsampleNearest1D { arg, target_size } => {
+                    Op::UpsampleNearest1D { .. } => Err(Error::BackwardNotSupported {
-                        let (_n, c, size) = arg.dims3()?;
+                        op: "upsample-nearest1d",
-                        if target_size % size != 0 {
+                    })?,
-                            crate::bail!("backward not supported for non integer upscaling factors")
-                        }
-                        let scale = target_size / size;
-
-                        let kernel = Tensor::ones((c, 1, scale), arg.dtype(), arg.device())?;
-                        let conv_sum = grad.conv1d(&kernel, 0, scale, 1, c)?;
-                        let sum_grad = grads.or_insert(arg)?;
-                        *sum_grad = conv_sum;
-                    }
                     Op::UpsampleNearest2D {
                         arg,
                         target_h,
@@ -489,6 +454,7 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&grad)?;
                     }
+                    Op::Cmp(_args, _) => {}
                     Op::Reduce(arg, ReduceOp::Max, reduced_dims) => {
                         let node = broadcast_back(arg, node, reduced_dims)?;
                         let grad = broadcast_back(arg, &grad, reduced_dims)?;
@@ -578,18 +544,20 @@ impl Tensor {
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&arg_grad)?
                     }
-                    Op::Unary(_, UnaryOp::Floor)
+                    Op::Reduce(_, ReduceOp::ArgMin, _) => {}
-                    | Op::Unary(_, UnaryOp::Round)
+                    Op::Reduce(_, ReduceOp::ArgMax, _) => {}
-                    | Op::Reduce(_, ReduceOp::ArgMin, _)
-                    | Op::Reduce(_, ReduceOp::ArgMax, _)
-                    | Op::Unary(_, UnaryOp::Sign)
-                    | Op::Cmp(_, _) => {}
                     Op::Reshape(arg) => {
                         let arg_grad = grad.reshape(arg.dims())?;
                         let sum_grad = grads.or_insert(arg)?;
                         *sum_grad = sum_grad.add(&arg_grad)?
                     }
                     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.)?;
@@ -621,13 +589,6 @@ impl Tensor {
                         let relu_grad = arg.ge(&arg.zeros_like()?)?.to_dtype(arg.dtype())?;
                         *sum_grad = sum_grad.add(&(&grad * relu_grad)?)?
                     }
-                    Op::Unary(arg, UnaryOp::Silu) => {
-                        let sum_grad = grads.or_insert(arg)?;
-                        // d/dx silu = sigmoid(x) * (1 + x * (1 - sigmoid(x)))
-                        let sigmoid_arg = (arg.neg()?.exp()? + 1.)?.recip()?;
-                        let silu_grad = (&sigmoid_arg * (1. + (arg * (1. - &sigmoid_arg)?)?)?)?;
-                        *sum_grad = sum_grad.add(&(&grad * silu_grad)?)?
-                    }
                     Op::Elu(arg, alpha) => {
                         // d/dx elu(x) = 1 for x > 0, alpha * e^x for x <= 0
                         let sum_grad = grads.or_insert(arg)?;
@@ -712,38 +673,30 @@ impl Tensor {
     }
 }
 
-/// A store for gradients, associating a tensor id to the corresponding gradient tensor, used for back propagation.
 #[derive(Debug)]
 pub struct GradStore(HashMap<TensorId, Tensor>);
 
 impl GradStore {
-    /// Create a new gradient store
     fn new() -> Self {
         GradStore(HashMap::new())
     }
 
-    /// Get the gradient tensor corresponding to the given tensor id
     pub fn get_id(&self, id: TensorId) -> Option<&Tensor> {
         self.0.get(&id)
     }
 
-    /// Get the gradient tensor associated with the given tensor
     pub fn get(&self, tensor: &Tensor) -> Option<&Tensor> {
         self.0.get(&tensor.id())
     }
 
-    /// Remove the gradient tensor associated with the given tensor, returning it if it exists
     pub fn remove(&mut self, tensor: &Tensor) -> Option<Tensor> {
         self.0.remove(&tensor.id())
     }
 
-    /// Insert a gradient tensor associated with the given tensor, returning the previous gradient tensor if it existed
     pub fn insert(&mut self, tensor: &Tensor, grad: Tensor) -> Option<Tensor> {
         self.0.insert(tensor.id(), grad)
     }
 
-    /// Get the gradient tensor associated with the given tensor, or, if it does not exist,
-    /// insert a tensor of zeroes, with the same shape and type as the given tensors and return it
     fn or_insert(&mut self, tensor: &Tensor) -> Result<&mut Tensor> {
         use std::collections::hash_map::Entry;
         let grad = match self.0.entry(tensor.id()) {

candle-core/src/conv.rs

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

candle-core/src/cpu_backend.rs

@@ -4,13 +4,7 @@ use crate::{DType, Error, IntDType, Layout, Result, Shape, WithDType};
 use half::{bf16, f16};
 use rayon::prelude::*;
 
-mod utils;
-pub use utils::{
-    binary_map, binary_map_vec, unary_map, unary_map_vec, Map1, Map1Any, Map2, Map2U8,
-};
-
 const USE_IM2COL_CONV1D: bool = true;
-const USE_IM2COL_CONV1D_TR: bool = true;
 const USE_IM2COL_CONV2D: bool = true;
 
 // TODO: Maybe we should not implement [Clone] here and instead have an explicit allocator +
@@ -29,6 +23,102 @@ pub enum CpuStorage {
 #[derive(Debug, Clone)]
 pub struct CpuDevice;
 
+pub trait Map1 {
+    fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>>;
+
+    fn map(&self, vs: &CpuStorage, layout: &Layout) -> Result<CpuStorage> {
+        match vs {
+            CpuStorage::U8(vs) => Ok(CpuStorage::U8(self.f(vs, layout)?)),
+            CpuStorage::U32(vs) => Ok(CpuStorage::U32(self.f(vs, layout)?)),
+            CpuStorage::I64(vs) => Ok(CpuStorage::I64(self.f(vs, layout)?)),
+            CpuStorage::BF16(vs) => Ok(CpuStorage::BF16(self.f(vs, layout)?)),
+            CpuStorage::F16(vs) => Ok(CpuStorage::F16(self.f(vs, layout)?)),
+            CpuStorage::F32(vs) => Ok(CpuStorage::F32(self.f(vs, layout)?)),
+            CpuStorage::F64(vs) => Ok(CpuStorage::F64(self.f(vs, layout)?)),
+        }
+    }
+}
+
+pub trait Map1Any {
+    fn f<T: WithDType, W: Fn(Vec<T>) -> CpuStorage>(
+        &self,
+        vs: &[T],
+        layout: &Layout,
+        wrap: W,
+    ) -> Result<CpuStorage>;
+
+    fn map(&self, vs: &CpuStorage, layout: &Layout) -> Result<CpuStorage> {
+        match vs {
+            CpuStorage::U8(vs) => Ok(self.f(vs, layout, CpuStorage::U8)?),
+            CpuStorage::U32(vs) => Ok(self.f(vs, layout, CpuStorage::U32)?),
+            CpuStorage::I64(vs) => Ok(self.f(vs, layout, CpuStorage::I64)?),
+            CpuStorage::BF16(vs) => Ok(self.f(vs, layout, CpuStorage::BF16)?),
+            CpuStorage::F16(vs) => Ok(self.f(vs, layout, CpuStorage::F16)?),
+            CpuStorage::F32(vs) => Ok(self.f(vs, layout, CpuStorage::F32)?),
+            CpuStorage::F64(vs) => Ok(self.f(vs, layout, CpuStorage::F64)?),
+        }
+    }
+}
+
+type C = CpuStorage;
+pub trait Map2 {
+    const OP: &'static str;
+    fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<Vec<T>>;
+
+    fn map(
+        &self,
+        v1: &CpuStorage,
+        l1: &Layout,
+        v2: &CpuStorage,
+        l2: &Layout,
+    ) -> Result<CpuStorage> {
+        match (v1, v2) {
+            (C::U8(v1), C::U8(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            (C::U32(v1), C::U32(v2)) => Ok(C::U32(self.f(v1, l1, v2, l2)?)),
+            (C::I64(v1), C::I64(v2)) => Ok(C::I64(self.f(v1, l1, v2, l2)?)),
+            (C::BF16(v1), C::BF16(v2)) => Ok(C::BF16(self.f(v1, l1, v2, l2)?)),
+            (C::F16(v1), C::F16(v2)) => Ok(C::F16(self.f(v1, l1, v2, l2)?)),
+            (C::F32(v1), C::F32(v2)) => Ok(C::F32(self.f(v1, l1, v2, l2)?)),
+            (C::F64(v1), C::F64(v2)) => Ok(C::F64(self.f(v1, l1, v2, l2)?)),
+            _ => Err(Error::DTypeMismatchBinaryOp {
+                lhs: v1.dtype(),
+                rhs: v2.dtype(),
+                op: Self::OP,
+            }
+            .bt()),
+        }
+    }
+}
+
+pub trait Map2U8 {
+    const OP: &'static str;
+    fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<Vec<u8>>;
+
+    fn map(
+        &self,
+        v1: &CpuStorage,
+        l1: &Layout,
+        v2: &CpuStorage,
+        l2: &Layout,
+    ) -> Result<CpuStorage> {
+        match (v1, v2) {
+            (C::U8(v1), C::U8(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            (C::U32(v1), C::U32(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            (C::I64(v1), C::I64(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            (C::BF16(v1), C::BF16(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            (C::F16(v1), C::F16(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            (C::F32(v1), C::F32(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            (C::F64(v1), C::F64(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
+            _ => Err(Error::DTypeMismatchBinaryOp {
+                lhs: v1.dtype(),
+                rhs: v2.dtype(),
+                op: Self::OP,
+            }
+            .bt()),
+        }
+    }
+}
+
 struct Cmp(CmpOp);
 impl Map2U8 for Cmp {
     const OP: &'static str = "cmp";
@@ -275,6 +365,275 @@ impl<'a> Map1 for ReduceSum<'a> {
     }
 }
 
+pub fn unary_map<T: Copy, U: Copy, F: FnMut(T) -> U>(
+    vs: &[T],
+    layout: &Layout,
+    mut f: F,
+) -> Vec<U> {
+    match layout.strided_blocks() {
+        crate::StridedBlocks::SingleBlock { start_offset, len } => vs
+            [start_offset..start_offset + len]
+            .iter()
+            .map(|&v| f(v))
+            .collect(),
+        crate::StridedBlocks::MultipleBlocks {
+            block_start_index,
+            block_len,
+        } => {
+            let mut result = Vec::with_capacity(layout.shape().elem_count());
+            // Specialize the case where block_len is one to avoid the second loop.
+            if block_len == 1 {
+                for index in block_start_index {
+                    let v = unsafe { vs.get_unchecked(index) };
+                    result.push(f(*v))
+                }
+            } else {
+                for index in block_start_index {
+                    for offset in 0..block_len {
+                        let v = unsafe { vs.get_unchecked(index + offset) };
+                        result.push(f(*v))
+                    }
+                }
+            }
+            result
+        }
+    }
+}
+
+pub fn unary_map_vec<T: Copy, U: Copy, F: FnMut(T) -> U, FV: FnMut(&[T], &mut [U])>(
+    vs: &[T],
+    layout: &Layout,
+    mut f: F,
+    mut f_vec: FV,
+) -> Vec<U> {
+    match layout.strided_blocks() {
+        crate::StridedBlocks::SingleBlock { start_offset, len } => {
+            let mut ys: Vec<U> = Vec::with_capacity(len);
+            let ys_to_set = ys.spare_capacity_mut();
+            let ys_to_set = unsafe { std::mem::transmute::<_, &mut [U]>(ys_to_set) };
+            f_vec(&vs[start_offset..start_offset + len], ys_to_set);
+            // SAFETY: values are all set by f_vec.
+            unsafe { ys.set_len(len) };
+            ys
+        }
+        crate::StridedBlocks::MultipleBlocks {
+            block_start_index,
+            block_len,
+        } => {
+            let el_count = layout.shape().elem_count();
+            // Specialize the case where block_len is one to avoid the second loop.
+            if block_len == 1 {
+                let mut result = Vec::with_capacity(el_count);
+                for index in block_start_index {
+                    let v = unsafe { vs.get_unchecked(index) };
+                    result.push(f(*v))
+                }
+                result
+            } else {
+                let mut ys: Vec<U> = Vec::with_capacity(el_count);
+                let ys_to_set = ys.spare_capacity_mut();
+                let ys_to_set = unsafe { std::mem::transmute::<_, &mut [U]>(ys_to_set) };
+                let mut dst_index = 0;
+                for src_index in block_start_index {
+                    let vs = &vs[src_index..src_index + block_len];
+                    let ys = &mut ys_to_set[dst_index..dst_index + block_len];
+                    f_vec(vs, ys);
+                    dst_index += block_len;
+                }
+                // SAFETY: values are all set by f_vec.
+                unsafe { ys.set_len(el_count) };
+                ys
+            }
+        }
+    }
+}
+
+// This function maps over two strided index sequences.
+pub fn binary_map<T: Copy, U: Copy, F: FnMut(T, T) -> U>(
+    lhs_l: &Layout,
+    rhs_l: &Layout,
+    lhs: &[T],
+    rhs: &[T],
+    mut f: F,
+) -> Vec<U> {
+    match (lhs_l.contiguous_offsets(), rhs_l.contiguous_offsets()) {
+        (Some((o_l1, o_l2)), Some((o_r1, o_r2))) => lhs[o_l1..o_l2]
+            .iter()
+            .zip(rhs[o_r1..o_r2].iter())
+            .map(|(&l, &r)| f(l, r))
+            .collect(),
+        (Some((o_l1, o_l2)), None) => {
+            // TODO: Maybe we want to avoid going through the layout twice.
+            match rhs_l.offsets_b() {
+                Some(ob) => {
+                    let mut i_in_block = 0;
+                    let mut i_right_broadcast = 0;
+                    lhs[o_l1..o_l2]
+                        .iter()
+                        .map(|&l| {
+                            let r = unsafe { rhs.get_unchecked(i_in_block + ob.start) };
+                            i_right_broadcast += 1;
+                            if i_right_broadcast >= ob.right_broadcast {
+                                i_in_block += 1;
+                                i_right_broadcast = 0;
+                            }
+                            if i_in_block >= ob.len {
+                                i_in_block = 0
+                            }
+                            f(l, *r)
+                        })
+                        .collect()
+                }
+                None => lhs_l
+                    .strided_index()
+                    .zip(rhs_l.strided_index())
+                    .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
+                    .collect(),
+            }
+        }
+        (None, Some((o_r1, o_r2))) => {
+            // TODO: Maybe we want to avoid going through the layout twice.
+            match lhs_l.offsets_b() {
+                Some(ob) => {
+                    let mut i_in_block = 0;
+                    let mut i_right_broadcast = 0;
+                    rhs[o_r1..o_r2]
+                        .iter()
+                        .map(|&r| {
+                            let l = unsafe { lhs.get_unchecked(i_in_block + ob.start) };
+                            i_right_broadcast += 1;
+                            if i_right_broadcast >= ob.right_broadcast {
+                                i_in_block += 1;
+                                i_right_broadcast = 0;
+                            }
+                            if i_in_block >= ob.len {
+                                i_in_block = 0
+                            }
+                            f(*l, r)
+                        })
+                        .collect()
+                }
+                None => lhs_l
+                    .strided_index()
+                    .zip(rhs_l.strided_index())
+                    .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
+                    .collect(),
+            }
+        }
+        _ => lhs_l
+            .strided_index()
+            .zip(rhs_l.strided_index())
+            .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
+            .collect(),
+    }
+}
+
+// Similar to binary_map but with vectorized variants.
+pub fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>(
+    lhs_l: &Layout,
+    rhs_l: &Layout,
+    lhs: &[T],
+    rhs: &[T],
+    mut f: F,
+    mut f_vec: FV,
+) -> Vec<T> {
+    let el_count = lhs_l.shape().elem_count();
+    match (lhs_l.contiguous_offsets(), rhs_l.contiguous_offsets()) {
+        (Some((o_l1, o_l2)), Some((o_r1, o_r2))) => {
+            let mut ys: Vec<T> = Vec::with_capacity(el_count);
+            let ys_to_set = ys.spare_capacity_mut();
+            let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
+            f_vec(&lhs[o_l1..o_l2], &rhs[o_r1..o_r2], ys_to_set);
+            // SAFETY: values are all set by f_vec.
+            unsafe { ys.set_len(el_count) };
+            ys
+        }
+        (Some((o_l1, o_l2)), None) => match rhs_l.offsets_b() {
+            Some(ob) if ob.right_broadcast == 1 => {
+                let rhs = &rhs[ob.start..ob.start + ob.len];
+                let mut ys: Vec<T> = Vec::with_capacity(el_count);
+                let ys_to_set = ys.spare_capacity_mut();
+                let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
+                let mut dst_i = 0;
+                for src_i in (o_l1..o_l2).step_by(ob.len) {
+                    f_vec(
+                        &lhs[src_i..src_i + ob.len],
+                        rhs,
+                        &mut ys_to_set[dst_i..dst_i + ob.len],
+                    );
+                    dst_i += ob.len;
+                }
+                // SAFETY: values are all set by f_vec.
+                unsafe { ys.set_len(el_count) };
+                ys
+            }
+            Some(ob) => {
+                let rhs = &rhs[ob.start..ob.start + ob.len];
+                let mut ys = lhs[o_l1..o_l2].to_vec();
+                for idx_l in 0..ob.left_broadcast {
+                    let start = idx_l * ob.len * ob.right_broadcast;
+                    for (i, &r) in rhs.iter().enumerate() {
+                        let start = start + i * ob.right_broadcast;
+                        for v in ys[start..start + ob.right_broadcast].iter_mut() {
+                            *v = f(*v, r)
+                        }
+                    }
+                }
+                ys
+            }
+            None => lhs_l
+                .strided_index()
+                .zip(rhs_l.strided_index())
+                .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
+                .collect(),
+        },
+        (None, Some((o_r1, o_r2))) => match lhs_l.offsets_b() {
+            Some(ob) if ob.right_broadcast == 1 => {
+                let lhs = &lhs[ob.start..ob.start + ob.len];
+                let mut ys: Vec<T> = Vec::with_capacity(el_count);
+                let ys_to_set = ys.spare_capacity_mut();
+                let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
+                let mut dst_i = 0;
+                for src_i in (o_r1..o_r2).step_by(ob.len) {
+                    f_vec(
+                        lhs,
+                        &rhs[src_i..src_i + ob.len],
+                        &mut ys_to_set[dst_i..dst_i + ob.len],
+                    );
+                    dst_i += ob.len;
+                }
+                // SAFETY: values are all set by f_vec.
+                unsafe { ys.set_len(el_count) };
+                ys
+            }
+            Some(ob) => {
+                let lhs = &lhs[ob.start..ob.start + ob.len];
+                let mut ys = rhs[o_r1..o_r2].to_vec();
+                for idx_l in 0..ob.left_broadcast {
+                    let start = idx_l * ob.len * ob.right_broadcast;
+                    for (i, &l) in lhs.iter().enumerate() {
+                        let start = start + i * ob.right_broadcast;
+                        for v in ys[start..start + ob.right_broadcast].iter_mut() {
+                            *v = f(l, *v)
+                        }
+                    }
+                }
+                ys
+            }
+            None => lhs_l
+                .strided_index()
+                .zip(rhs_l.strided_index())
+                .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
+                .collect(),
+        },
+        _ => lhs_l
+            .strided_index()
+            .zip(rhs_l.strided_index())
+            .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
+            .collect(),
+    }
+}
+
 struct Affine(f64, f64);
 
 impl Map1 for Affine {
@@ -663,26 +1022,6 @@ impl<'a, I: IntDType> Map2 for IndexAdd<'a, I> {
     }
 }
 
-#[allow(clippy::too_many_arguments)]
-fn copy2d_<T: Copy>(
-    src: &[T],
-    dst: &mut [T],
-    d1: usize,
-    d2: usize,
-    src_stride1: usize,
-    dst_stride1: usize,
-    src_offset: usize,
-    dst_offset: usize,
-) {
-    for i1 in 0..d1 {
-        let dst_idx = i1 * dst_stride1 + dst_offset;
-        let src_idx = i1 * src_stride1 + src_offset;
-        let dst = &mut dst[dst_idx..dst_idx + d2];
-        let src = &src[src_idx..src_idx + d2];
-        dst.copy_from_slice(src)
-    }
-}
-
 fn copy_strided_src_<T: Copy>(src: &[T], dst: &mut [T], dst_offset: usize, src_l: &Layout) {
     match src_l.strided_blocks() {
         crate::StridedBlocks::SingleBlock { start_offset, len } => {
@@ -917,34 +1256,6 @@ impl Map1 for Im2Col {
     }
 }
 
-struct Col2Im1D {
-    stride: usize,
-}
-
-impl Map1 for Col2Im1D {
-    fn f<T: WithDType>(&self, col: &[T], l: &Layout) -> Result<Vec<T>> {
-        let (b_size, l_in, c_out, k_size) = l.shape().dims4()?;
-        let stride = self.stride;
-        let l_out = (l_in - 1) * stride + k_size;
-        let mut im = vec![T::zero(); b_size * c_out * l_out];
-        let (dst_s0, dst_s1) = (c_out * l_out, l_out);
-        let (src_s0, src_s1, src_s2) = (c_out * k_size * l_in, c_out * k_size, k_size);
-        for l_in_i in 0..l_in {
-            for k_i in 0..k_size {
-                let l_out_i = l_in_i * stride + k_i;
-                for b_i in 0..b_size {
-                    for c_i in 0..c_out {
-                        let dst_idx = b_i * dst_s0 + c_i * dst_s1 + l_out_i;
-                        let src_idx = b_i * src_s0 + l_in_i * src_s1 + c_i * src_s2 + k_i;
-                        im[dst_idx] += col[src_idx]
-                    }
-                }
-            }
-        }
-        Ok(im)
-    }
-}
-
 struct ConvTranspose1D<'a>(&'a crate::conv::ParamsConvTranspose1D);
 
 impl<'a> Map2 for ConvTranspose1D<'a> {
@@ -952,7 +1263,6 @@ impl<'a> Map2 for ConvTranspose1D<'a> {
     fn f<T: WithDType>(&self, inp: &[T], inp_l: &Layout, k: &[T], k_l: &Layout) -> Result<Vec<T>> {
         let p = self.0;
         let inp = &inp[inp_l.start_offset()..];
-        let k = &k[k_l.start_offset()..];
         let (inp_s0, inp_s1, inp_s2) = crate::shape::dims3(inp_l.stride())?;
         let (k_s0, k_s1, k_s2) = crate::shape::dims3(k_l.stride())?;
         let l_out = p.l_out();
@@ -1204,30 +1514,6 @@ impl MatMul {
         }))
         .bt()
     }
-
-    fn ab_skip(&self, lhs_l: &Layout, rhs_l: &Layout) -> Result<(usize, usize)> {
-        let lhs_stride = lhs_l.stride();
-        let rhs_stride = rhs_l.stride();
-        let rank = lhs_stride.len();
-        let (_b, m, n, k) = self.0;
-        let a_skip: usize = match lhs_stride[..rank - 2] {
-            [s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
-            [_, stride] if lhs_l.dims()[0] == 1 => stride,
-            [stride, _] if lhs_l.dims()[1] == 1 => stride,
-            [stride] => stride,
-            [] => m * k,
-            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?,
-        };
-        let b_skip: usize = match rhs_stride[..rank - 2] {
-            [s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
-            [_, stride] if rhs_l.dims()[0] == 1 => stride,
-            [stride, _] if rhs_l.dims()[1] == 1 => stride,
-            [stride] => stride,
-            [] => n * k,
-            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?,
-        };
-        Ok((a_skip, b_skip))
-    }
 }
 
 impl Map2 for MatMul {
@@ -1261,7 +1547,18 @@ impl Map2 for MatMul {
         let rhs_cs = rhs_stride[rank - 1];
         let rhs_rs = rhs_stride[rank - 2];
 
-        let (a_skip, b_skip) = self.ab_skip(lhs_l, rhs_l)?;
+        let a_skip: usize = match lhs_stride[..rank - 2] {
+            [s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
+            [stride] => stride,
+            [] => m * k,
+            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?,
+        };
+        let b_skip: usize = match rhs_stride[..rank - 2] {
+            [s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
+            [stride] => stride,
+            [] => n * k,
+            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?,
+        };
         let c_skip: usize = m * n;
 
         let dst_shape: Shape = (m, n).into();
@@ -1321,8 +1618,20 @@ impl Map2 for MatMul {
 
         let lhs_stride = lhs_l.stride();
         let rhs_stride = rhs_l.stride();
+        let rank = lhs_stride.len();
 
-        let (a_skip, b_skip) = self.ab_skip(lhs_l, rhs_l)?;
+        let a_skip: usize = match lhs_stride[..rank - 2] {
+            [s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
+            [stride] => stride,
+            [] => m * k,
+            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?,
+        };
+        let b_skip: usize = match rhs_stride[..rank - 2] {
+            [s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
+            [stride] => stride,
+            [] => n * k,
+            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?,
+        };
         let c_skip: usize = m * n;
 
         let rhs_m1 = rhs_stride[rhs_stride.len() - 1];
@@ -1330,7 +1639,7 @@ impl Map2 for MatMul {
         let lhs_m1 = lhs_stride[lhs_stride.len() - 1];
         let lhs_m2 = lhs_stride[lhs_stride.len() - 2];
 
-        let (lda, transa) = if (rhs_m1 == 1 || n == 1) && (rhs_m2 == n || k == 1) {
+        let (lda, transa) = if rhs_m1 == 1 && rhs_m2 == n {
             (n as i32, b'N')
         } else if rhs_m1 == k && rhs_m2 == 1 {
             (k as i32, b'T')
@@ -1338,7 +1647,7 @@ impl Map2 for MatMul {
             Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?
         };
         // The b tensor has dims batching, m, k (lhs)
-        let (ldb, transb) = if (lhs_m1 == 1 || k == 1) && (lhs_m2 == k || m == 1) {
+        let (ldb, transb) = if lhs_m1 == 1 && lhs_m2 == k {
             (k as i32, b'N')
         } else if lhs_m1 == m && lhs_m2 == 1 {
             (m as i32, b'T')
@@ -1412,8 +1721,20 @@ impl Map2 for MatMul {
 
         let lhs_stride = lhs_l.stride();
         let rhs_stride = rhs_l.stride();
+        let rank = lhs_stride.len();
 
-        let (a_skip, b_skip) = self.ab_skip(lhs_l, rhs_l)?;
+        let a_skip: usize = match lhs_stride[..rank - 2] {
+            [s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
+            [stride] => stride,
+            [] => m * k,
+            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous lhs"))?,
+        };
+        let b_skip: usize = match rhs_stride[..rank - 2] {
+            [s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
+            [stride] => stride,
+            [] => n * k,
+            _ => Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?,
+        };
         let c_skip: usize = m * n;
 
         let rhs_m1 = rhs_stride[rhs_stride.len() - 1];
@@ -1421,7 +1742,7 @@ impl Map2 for MatMul {
         let lhs_m1 = lhs_stride[lhs_stride.len() - 1];
         let lhs_m2 = lhs_stride[lhs_stride.len() - 2];
 
-        let (lda, transa) = if (rhs_m1 == 1 || n == 1) && (rhs_m2 == n || k == 1) {
+        let (lda, transa) = if rhs_m1 == 1 && rhs_m2 == n {
             (n as i32, b'N')
         } else if rhs_m1 == k && rhs_m2 == 1 {
             (k as i32, b'T')
@@ -1429,7 +1750,7 @@ impl Map2 for MatMul {
             Err(self.striding_error(lhs_l, rhs_l, "non-contiguous rhs"))?
         };
         // The b tensor has dims batching, m, k (lhs)
-        let (ldb, transb) = if (lhs_m1 == 1 || k == 1) && (lhs_m2 == k || m == 1) {
+        let (ldb, transb) = if lhs_m1 == 1 && lhs_m2 == k {
             (k as i32, b'N')
         } else if lhs_m1 == m && lhs_m2 == 1 {
             (m as i32, b'T')
@@ -2101,48 +2422,6 @@ impl BackendStorage for CpuStorage {
         }
     }
 
-    fn copy2d(
-        &self,
-        dst: &mut Self,
-        d1: usize,
-        d2: usize,
-        src_s: usize,
-        dst_s: usize,
-        src_o: usize,
-        dst_o: usize,
-    ) -> Result<()> {
-        match (self, dst) {
-            (Self::U8(src), Self::U8(dst)) => copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o),
-            (Self::U32(src), Self::U32(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::I64(src), Self::I64(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::BF16(src), Self::BF16(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::F16(src), Self::F16(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::F32(src), Self::F32(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (Self::F64(src), Self::F64(dst)) => {
-                copy2d_(src, dst, d1, d2, src_s, dst_s, src_o, dst_o)
-            }
-            (_, dst) => {
-                return Err(Error::DTypeMismatchBinaryOp {
-                    lhs: self.dtype(),
-                    rhs: dst.dtype(),
-                    op: "copy2d",
-                }
-                .bt());
-            }
-        }
-        Ok(())
-    }
-
     fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
         match (self, dst) {
             (Self::U8(src), Self::U8(dst)) => copy_strided_src_(src, dst, dst_offset, src_l),
@@ -2211,10 +2490,7 @@ impl BackendStorage for CpuStorage {
             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 = unsafe {
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
-                self.device()
-                    .alloc_uninit(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)?
@@ -2222,7 +2498,7 @@ impl BackendStorage for CpuStorage {
             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 = unsafe { self.device().alloc_uninit(res_l.shape(), res.dtype())? };
+        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)
     }
@@ -2234,52 +2510,7 @@ impl BackendStorage for CpuStorage {
         kernel_l: &Layout,
         params: &crate::conv::ParamsConvTranspose1D,
     ) -> Result<Self> {
-        let can_use_col2im = kernel_l.is_contiguous()
+        ConvTranspose1D(params).map(self, l, kernel, kernel_l)
-            && params.dilation == 1
-            && params.padding == 0
-            && params.output_padding == 0;
-        if USE_IM2COL_CONV1D_TR && can_use_col2im {
-            let (b_size, c_in, l_in) = l.shape().dims3()?;
-            let (c_in2, c_out, k_size) = kernel_l.shape().dims3()?;
-            if !kernel_l.is_contiguous() {
-                crate::bail!(
-                    "convtr1d: the second argument (kernel) has to be contiguous {kernel_l:?}"
-                )
-            }
-            if c_in != c_in2 {
-                crate::bail!(
-                    "convtr1d: shape mismatch on c_in {:?} {:?}",
-                    l.shape(),
-                    kernel_l.shape()
-                )
-            }
-            let col = {
-                // This merges the last two dimensions of the kernel together.
-                let kernel_l_mm = Layout::new(
-                    (b_size, c_in, k_size * c_out).into(),
-                    vec![0, k_size * c_out, 1],
-                    kernel_l.start_offset(),
-                );
-                self.matmul(
-                    kernel,
-                    (
-                        b_size,
-                        /* m */ l_in,
-                        /* n */ c_out * k_size,
-                        /* k */ c_in,
-                    ),
-                    &l.transpose(1, 2)?,
-                    &kernel_l_mm,
-                )?
-            };
-            let col_l = Layout::contiguous((b_size, l_in, c_out, k_size));
-            Col2Im1D {
-                stride: params.stride,
-            }
-            .map(&col, &col_l)
-        } else {
-            ConvTranspose1D(params).map(self, l, kernel, kernel_l)
-        }
     }
 
     fn conv2d(
@@ -2313,10 +2544,7 @@ impl BackendStorage for CpuStorage {
             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 = unsafe {
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
-                self.device()
-                    .alloc_uninit(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)?
@@ -2326,7 +2554,7 @@ impl BackendStorage for CpuStorage {
         let res_l = Layout::contiguous((b, h_out, w_out, params.c_out))
             .transpose(1, 2)?
             .transpose(1, 3)?;
-        let mut res_t = unsafe { self.device().alloc_uninit(res_l.shape(), res.dtype())? };
+        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)
     }
@@ -2346,7 +2574,7 @@ impl BackendStorage for CpuStorage {
             Self::U8(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
             Self::U32(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
             Self::I64(ids) => IndexSelect { ids, ids_l, dim }.map(self, l),
-            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-select").bt()),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "index-select")),
         }
     }
 
@@ -2355,7 +2583,7 @@ impl BackendStorage for CpuStorage {
             Self::U8(ids) => Gather { ids, ids_l, dim }.map(self, l),
             Self::U32(ids) => Gather { ids, ids_l, dim }.map(self, l),
             Self::I64(ids) => Gather { ids, ids_l, dim }.map(self, l),
-            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "gather").bt()),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "gather")),
         }
     }
 
@@ -2372,7 +2600,7 @@ impl BackendStorage for CpuStorage {
             Self::U8(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
             Self::U32(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
             Self::I64(ids) => ScatterAdd { ids, ids_l, dim }.map(self, l, src, src_l),
-            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "scatter-add").bt()),
+            _ => Err(Error::UnsupportedDTypeForOp(self.dtype(), "scatter-add")),
         }
     }
 
@@ -2449,10 +2677,6 @@ impl BackendDevice for CpuDevice {
         Ok(s.clone())
     }
 
-    fn storage_from_cpu_storage_owned(&self, s: CpuStorage) -> Result<Self::Storage> {
-        Ok(s)
-    }
-
     fn new(_: usize) -> Result<Self> {
         Ok(Self)
     }
@@ -2554,53 +2778,6 @@ impl BackendDevice for CpuDevice {
         }
     }
 
-    #[allow(clippy::uninit_vec)]
-    unsafe fn alloc_uninit(&self, shape: &Shape, dtype: DType) -> Result<CpuStorage> {
-        let elem_count = shape.elem_count();
-        // The code below is highly unsafe but hopefully not directly unsound as we only consider
-        // types that are Copy, not Drop, and for which all bit patterns are proper values.
-        // It's still pretty risky, see the following for more details:
-        // https://github.com/rust-lang/rust-clippy/issues/4483
-        let storage = match dtype {
-            DType::U8 => {
-                let mut v = Vec::with_capacity(elem_count);
-                v.set_len(elem_count);
-                CpuStorage::U8(v)
-            }
-            DType::U32 => {
-                let mut v = Vec::with_capacity(elem_count);
-                v.set_len(elem_count);
-                CpuStorage::U32(v)
-            }
-            DType::I64 => {
-                let mut v = Vec::with_capacity(elem_count);
-                v.set_len(elem_count);
-                CpuStorage::I64(v)
-            }
-            DType::BF16 => {
-                let mut v = Vec::with_capacity(elem_count);
-                v.set_len(elem_count);
-                CpuStorage::BF16(v)
-            }
-            DType::F16 => {
-                let mut v = Vec::with_capacity(elem_count);
-                v.set_len(elem_count);
-                CpuStorage::F16(v)
-            }
-            DType::F32 => {
-                let mut v = Vec::with_capacity(elem_count);
-                v.set_len(elem_count);
-                CpuStorage::F32(v)
-            }
-            DType::F64 => {
-                let mut v = Vec::with_capacity(elem_count);
-                v.set_len(elem_count);
-                CpuStorage::F64(v)
-            }
-        };
-        Ok(storage)
-    }
-
     fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<CpuStorage> {
         let elem_count = shape.elem_count();
         let storage = match dtype {
@@ -2628,10 +2805,6 @@ impl BackendDevice for CpuDevice {
         };
         Ok(storage)
     }
-
-    fn synchronize(&self) -> Result<()> {
-        Ok(())
-    }
 }
 
 #[macro_export]

candle-core/src/cpu_backend/utils.rs

@@ -1,350 +0,0 @@
-/// Helper functions to write CPU kernels.
-use crate::backend::BackendStorage;
-use crate::{Error, Layout, Result, WithDType};
-
-type C = super::CpuStorage;
-pub trait Map1 {
-    fn f<T: WithDType>(&self, vs: &[T], layout: &Layout) -> Result<Vec<T>>;
-
-    fn map(&self, vs: &C, layout: &Layout) -> Result<C> {
-        match vs {
-            C::U8(vs) => Ok(C::U8(self.f(vs, layout)?)),
-            C::U32(vs) => Ok(C::U32(self.f(vs, layout)?)),
-            C::I64(vs) => Ok(C::I64(self.f(vs, layout)?)),
-            C::BF16(vs) => Ok(C::BF16(self.f(vs, layout)?)),
-            C::F16(vs) => Ok(C::F16(self.f(vs, layout)?)),
-            C::F32(vs) => Ok(C::F32(self.f(vs, layout)?)),
-            C::F64(vs) => Ok(C::F64(self.f(vs, layout)?)),
-        }
-    }
-}
-
-pub trait Map1Any {
-    fn f<T: WithDType, W: Fn(Vec<T>) -> C>(&self, vs: &[T], layout: &Layout, wrap: W) -> Result<C>;
-
-    fn map(&self, vs: &C, layout: &Layout) -> Result<C> {
-        match vs {
-            C::U8(vs) => Ok(self.f(vs, layout, C::U8)?),
-            C::U32(vs) => Ok(self.f(vs, layout, C::U32)?),
-            C::I64(vs) => Ok(self.f(vs, layout, C::I64)?),
-            C::BF16(vs) => Ok(self.f(vs, layout, C::BF16)?),
-            C::F16(vs) => Ok(self.f(vs, layout, C::F16)?),
-            C::F32(vs) => Ok(self.f(vs, layout, C::F32)?),
-            C::F64(vs) => Ok(self.f(vs, layout, C::F64)?),
-        }
-    }
-}
-
-pub trait Map2 {
-    const OP: &'static str;
-    fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<Vec<T>>;
-
-    fn map(&self, v1: &C, l1: &Layout, v2: &C, l2: &Layout) -> Result<C> {
-        match (v1, v2) {
-            (C::U8(v1), C::U8(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            (C::U32(v1), C::U32(v2)) => Ok(C::U32(self.f(v1, l1, v2, l2)?)),
-            (C::I64(v1), C::I64(v2)) => Ok(C::I64(self.f(v1, l1, v2, l2)?)),
-            (C::BF16(v1), C::BF16(v2)) => Ok(C::BF16(self.f(v1, l1, v2, l2)?)),
-            (C::F16(v1), C::F16(v2)) => Ok(C::F16(self.f(v1, l1, v2, l2)?)),
-            (C::F32(v1), C::F32(v2)) => Ok(C::F32(self.f(v1, l1, v2, l2)?)),
-            (C::F64(v1), C::F64(v2)) => Ok(C::F64(self.f(v1, l1, v2, l2)?)),
-            _ => Err(Error::DTypeMismatchBinaryOp {
-                lhs: v1.dtype(),
-                rhs: v2.dtype(),
-                op: Self::OP,
-            }
-            .bt()),
-        }
-    }
-}
-
-pub trait Map2U8 {
-    const OP: &'static str;
-    fn f<T: WithDType>(&self, v1: &[T], l1: &Layout, v2: &[T], l2: &Layout) -> Result<Vec<u8>>;
-
-    fn map(&self, v1: &C, l1: &Layout, v2: &C, l2: &Layout) -> Result<C> {
-        match (v1, v2) {
-            (C::U8(v1), C::U8(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            (C::U32(v1), C::U32(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            (C::I64(v1), C::I64(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            (C::BF16(v1), C::BF16(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            (C::F16(v1), C::F16(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            (C::F32(v1), C::F32(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            (C::F64(v1), C::F64(v2)) => Ok(C::U8(self.f(v1, l1, v2, l2)?)),
-            _ => Err(Error::DTypeMismatchBinaryOp {
-                lhs: v1.dtype(),
-                rhs: v2.dtype(),
-                op: Self::OP,
-            }
-            .bt()),
-        }
-    }
-}
-
-pub fn binary_map<T: Copy, U: Copy, F: FnMut(T, T) -> U>(
-    lhs_l: &Layout,
-    rhs_l: &Layout,
-    lhs: &[T],
-    rhs: &[T],
-    mut f: F,
-) -> Vec<U> {
-    match (lhs_l.contiguous_offsets(), rhs_l.contiguous_offsets()) {
-        (Some((o_l1, o_l2)), Some((o_r1, o_r2))) => lhs[o_l1..o_l2]
-            .iter()
-            .zip(rhs[o_r1..o_r2].iter())
-            .map(|(&l, &r)| f(l, r))
-            .collect(),
-        (Some((o_l1, o_l2)), None) => {
-            // TODO: Maybe we want to avoid going through the layout twice.
-            match rhs_l.offsets_b() {
-                Some(ob) => {
-                    let mut i_in_block = 0;
-                    let mut i_right_broadcast = 0;
-                    lhs[o_l1..o_l2]
-                        .iter()
-                        .map(|&l| {
-                            let r = unsafe { rhs.get_unchecked(i_in_block + ob.start) };
-                            i_right_broadcast += 1;
-                            if i_right_broadcast >= ob.right_broadcast {
-                                i_in_block += 1;
-                                i_right_broadcast = 0;
-                            }
-                            if i_in_block >= ob.len {
-                                i_in_block = 0
-                            }
-                            f(l, *r)
-                        })
-                        .collect()
-                }
-                None => lhs_l
-                    .strided_index()
-                    .zip(rhs_l.strided_index())
-                    .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
-                    .collect(),
-            }
-        }
-        (None, Some((o_r1, o_r2))) => {
-            // TODO: Maybe we want to avoid going through the layout twice.
-            match lhs_l.offsets_b() {
-                Some(ob) => {
-                    let mut i_in_block = 0;
-                    let mut i_right_broadcast = 0;
-                    rhs[o_r1..o_r2]
-                        .iter()
-                        .map(|&r| {
-                            let l = unsafe { lhs.get_unchecked(i_in_block + ob.start) };
-                            i_right_broadcast += 1;
-                            if i_right_broadcast >= ob.right_broadcast {
-                                i_in_block += 1;
-                                i_right_broadcast = 0;
-                            }
-                            if i_in_block >= ob.len {
-                                i_in_block = 0
-                            }
-                            f(*l, r)
-                        })
-                        .collect()
-                }
-                None => lhs_l
-                    .strided_index()
-                    .zip(rhs_l.strided_index())
-                    .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
-                    .collect(),
-            }
-        }
-        _ => lhs_l
-            .strided_index()
-            .zip(rhs_l.strided_index())
-            .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
-            .collect(),
-    }
-}
-
-// Similar to binary_map but with vectorized variants.
-pub fn binary_map_vec<T: Copy, F: FnMut(T, T) -> T, FV: FnMut(&[T], &[T], &mut [T])>(
-    lhs_l: &Layout,
-    rhs_l: &Layout,
-    lhs: &[T],
-    rhs: &[T],
-    mut f: F,
-    mut f_vec: FV,
-) -> Vec<T> {
-    let el_count = lhs_l.shape().elem_count();
-    match (lhs_l.contiguous_offsets(), rhs_l.contiguous_offsets()) {
-        (Some((o_l1, o_l2)), Some((o_r1, o_r2))) => {
-            let mut ys: Vec<T> = Vec::with_capacity(el_count);
-            let ys_to_set = ys.spare_capacity_mut();
-            let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
-            f_vec(&lhs[o_l1..o_l2], &rhs[o_r1..o_r2], ys_to_set);
-            // SAFETY: values are all set by f_vec.
-            unsafe { ys.set_len(el_count) };
-            ys
-        }
-        (Some((o_l1, o_l2)), None) => match rhs_l.offsets_b() {
-            Some(ob) if ob.right_broadcast == 1 => {
-                let rhs = &rhs[ob.start..ob.start + ob.len];
-                let mut ys: Vec<T> = Vec::with_capacity(el_count);
-                let ys_to_set = ys.spare_capacity_mut();
-                let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
-                let mut dst_i = 0;
-                for src_i in (o_l1..o_l2).step_by(ob.len) {
-                    f_vec(
-                        &lhs[src_i..src_i + ob.len],
-                        rhs,
-                        &mut ys_to_set[dst_i..dst_i + ob.len],
-                    );
-                    dst_i += ob.len;
-                }
-                // SAFETY: values are all set by f_vec.
-                unsafe { ys.set_len(el_count) };
-                ys
-            }
-            Some(ob) => {
-                let rhs = &rhs[ob.start..ob.start + ob.len];
-                let mut ys = lhs[o_l1..o_l2].to_vec();
-                for idx_l in 0..ob.left_broadcast {
-                    let start = idx_l * ob.len * ob.right_broadcast;
-                    for (i, &r) in rhs.iter().enumerate() {
-                        let start = start + i * ob.right_broadcast;
-                        for v in ys[start..start + ob.right_broadcast].iter_mut() {
-                            *v = f(*v, r)
-                        }
-                    }
-                }
-                ys
-            }
-            None => lhs_l
-                .strided_index()
-                .zip(rhs_l.strided_index())
-                .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
-                .collect(),
-        },
-        (None, Some((o_r1, o_r2))) => match lhs_l.offsets_b() {
-            Some(ob) if ob.right_broadcast == 1 => {
-                let lhs = &lhs[ob.start..ob.start + ob.len];
-                let mut ys: Vec<T> = Vec::with_capacity(el_count);
-                let ys_to_set = ys.spare_capacity_mut();
-                let ys_to_set = unsafe { std::mem::transmute::<_, &mut [T]>(ys_to_set) };
-                let mut dst_i = 0;
-                for src_i in (o_r1..o_r2).step_by(ob.len) {
-                    f_vec(
-                        lhs,
-                        &rhs[src_i..src_i + ob.len],
-                        &mut ys_to_set[dst_i..dst_i + ob.len],
-                    );
-                    dst_i += ob.len;
-                }
-                // SAFETY: values are all set by f_vec.
-                unsafe { ys.set_len(el_count) };
-                ys
-            }
-            Some(ob) => {
-                let lhs = &lhs[ob.start..ob.start + ob.len];
-                let mut ys = rhs[o_r1..o_r2].to_vec();
-                for idx_l in 0..ob.left_broadcast {
-                    let start = idx_l * ob.len * ob.right_broadcast;
-                    for (i, &l) in lhs.iter().enumerate() {
-                        let start = start + i * ob.right_broadcast;
-                        for v in ys[start..start + ob.right_broadcast].iter_mut() {
-                            *v = f(l, *v)
-                        }
-                    }
-                }
-                ys
-            }
-            None => lhs_l
-                .strided_index()
-                .zip(rhs_l.strided_index())
-                .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
-                .collect(),
-        },
-        _ => lhs_l
-            .strided_index()
-            .zip(rhs_l.strided_index())
-            .map(|(lhs_i, rhs_i)| f(lhs[lhs_i], rhs[rhs_i]))
-            .collect(),
-    }
-}
-
-pub fn unary_map<T: Copy, U: Copy, F: FnMut(T) -> U>(
-    vs: &[T],
-    layout: &Layout,
-    mut f: F,
-) -> Vec<U> {
-    match layout.strided_blocks() {
-        crate::StridedBlocks::SingleBlock { start_offset, len } => vs
-            [start_offset..start_offset + len]
-            .iter()
-            .map(|&v| f(v))
-            .collect(),
-        crate::StridedBlocks::MultipleBlocks {
-            block_start_index,
-            block_len,
-        } => {
-            let mut result = Vec::with_capacity(layout.shape().elem_count());
-            // Specialize the case where block_len is one to avoid the second loop.
-            if block_len == 1 {
-                for index in block_start_index {
-                    let v = unsafe { vs.get_unchecked(index) };
-                    result.push(f(*v))
-                }
-            } else {
-                for index in block_start_index {
-                    for offset in 0..block_len {
-                        let v = unsafe { vs.get_unchecked(index + offset) };
-                        result.push(f(*v))
-                    }
-                }
-            }
-            result
-        }
-    }
-}
-
-pub fn unary_map_vec<T: Copy, U: Copy, F: FnMut(T) -> U, FV: FnMut(&[T], &mut [U])>(
-    vs: &[T],
-    layout: &Layout,
-    mut f: F,
-    mut f_vec: FV,
-) -> Vec<U> {
-    match layout.strided_blocks() {
-        crate::StridedBlocks::SingleBlock { start_offset, len } => {
-            let mut ys: Vec<U> = Vec::with_capacity(len);
-            let ys_to_set = ys.spare_capacity_mut();
-            let ys_to_set = unsafe { std::mem::transmute::<_, &mut [U]>(ys_to_set) };
-            f_vec(&vs[start_offset..start_offset + len], ys_to_set);
-            // SAFETY: values are all set by f_vec.
-            unsafe { ys.set_len(len) };
-            ys
-        }
-        crate::StridedBlocks::MultipleBlocks {
-            block_start_index,
-            block_len,
-        } => {
-            let el_count = layout.shape().elem_count();
-            // Specialize the case where block_len is one to avoid the second loop.
-            if block_len == 1 {
-                let mut result = Vec::with_capacity(el_count);
-                for index in block_start_index {
-                    let v = unsafe { vs.get_unchecked(index) };
-                    result.push(f(*v))
-                }
-                result
-            } else {
-                let mut ys: Vec<U> = Vec::with_capacity(el_count);
-                let ys_to_set = ys.spare_capacity_mut();
-                let ys_to_set = unsafe { std::mem::transmute::<_, &mut [U]>(ys_to_set) };
-                let mut dst_index = 0;
-                for src_index in block_start_index {
-                    let vs = &vs[src_index..src_index + block_len];
-                    let ys = &mut ys_to_set[dst_index..dst_index + block_len];
-                    f_vec(vs, ys);
-                    dst_index += block_len;
-                }
-                // SAFETY: values are all set by f_vec.
-                unsafe { ys.set_len(el_count) };
-                ys
-            }
-        }
-    }
-}

candle-core/src/cuda_backend.rs

@@ -5,41 +5,395 @@ pub use candle_kernels as kernels;
 pub use cudarc;
 use cudarc::cublas::{Gemm, GemmConfig, StridedBatchedConfig};
 use cudarc::driver::{
-    CudaSlice, DevicePtr, DeviceRepr, DeviceSlice, LaunchAsync, LaunchConfig, ValidAsZeroBits,
+    CudaFunction, CudaSlice, DevicePtr, DeviceRepr, DeviceSlice, LaunchAsync, LaunchConfig,
+    ValidAsZeroBits,
 };
 use half::{bf16, f16};
+use std::sync::{Arc, Mutex};
 
-#[cfg(feature = "cudnn")]
+/// cudarc related errors
-pub mod cudnn;
+#[derive(thiserror::Error, Debug)]
-mod device;
+pub enum CudaError {
-mod error;
+    #[error(transparent)]
-mod utils;
+    Cuda(#[from] cudarc::driver::DriverError),
-pub use device::{CudaDevice, DeviceId};
-pub use error::{CudaError, WrapErr};
-pub use utils::{Map1, Map1Any, Map2, Map2Any, Map2InPlace, S};
 
-enum SlicePtrOrNull<T> {
+    #[error(transparent)]
-    Ptr(CudaSlice<T>),
+    Compiler(#[from] cudarc::nvrtc::CompileError),
-    Null,
+
+    #[error(transparent)]
+    Cublas(#[from] cudarc::cublas::result::CublasError),
+
+    #[error(transparent)]
+    Curand(#[from] cudarc::curand::result::CurandError),
+
+    #[error("missing kernel '{module_name}'")]
+    MissingKernel { module_name: String },
+
+    #[error("unsupported dtype {dtype:?} for {op}")]
+    UnsupportedDtype { dtype: DType, op: &'static str },
+
+    #[error("internal error '{0}'")]
+    InternalError(&'static str),
+
+    #[error("matmul is only supported for contiguous tensors lstride: {lhs_stride:?} rstride: {rhs_stride:?} mnk: {mnk:?}")]
+    MatMulNonContiguous {
+        lhs_stride: Vec<usize>,
+        rhs_stride: Vec<usize>,
+        mnk: (usize, usize, usize),
+    },
+
+    #[error("{msg}, expected: {expected:?}, got: {got:?}")]
+    UnexpectedDType {
+        msg: &'static str,
+        expected: DType,
+        got: DType,
+    },
+
+    #[error("{cuda} when loading {module_name}")]
+    Load {
+        cuda: cudarc::driver::DriverError,
+        module_name: String,
+    },
 }
 
-unsafe impl<T: DeviceRepr> DeviceRepr for &SlicePtrOrNull<T> {
+impl From<CudaError> for crate::Error {
-    fn as_kernel_param(&self) -> *mut std::ffi::c_void {
+    fn from(val: CudaError) -> Self {
-        match self {
+        crate::Error::Cuda(Box::new(val)).bt()
-            SlicePtrOrNull::Ptr(slice) => slice.as_kernel_param(),
-            SlicePtrOrNull::Null => 0usize.as_kernel_param(),
-        }
     }
 }
 
-impl SlicePtrOrNull<usize> {
+/// Unique identifier for cuda devices.
-    fn params_from_layout(dev: &CudaDevice, l: &Layout) -> Result<Self> {
+#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
-        let ds = if l.is_contiguous() {
+pub struct DeviceId(usize);
-            SlicePtrOrNull::Null
+
-        } else {
+impl DeviceId {
-            SlicePtrOrNull::Ptr(dev.htod_copy([l.dims(), l.stride()].concat()).w()?)
+    fn new() -> Self {
+        // https://users.rust-lang.org/t/idiomatic-rust-way-to-generate-unique-id/33805
+        use std::sync::atomic;
+        static COUNTER: atomic::AtomicUsize = atomic::AtomicUsize::new(1);
+        Self(COUNTER.fetch_add(1, atomic::Ordering::Relaxed))
+    }
+}
+
+struct CudaRng(cudarc::curand::CudaRng);
+unsafe impl Send for CudaRng {}
+
+#[derive(Clone)]
+pub struct CudaDevice {
+    id: DeviceId,
+    device: Arc<cudarc::driver::CudaDevice>,
+    blas: Arc<cudarc::cublas::CudaBlas>,
+    curand: Arc<Mutex<CudaRng>>,
+}
+
+impl std::fmt::Debug for CudaDevice {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "CudaDevice({:?})", self.id)
+    }
+}
+
+impl std::ops::Deref for CudaDevice {
+    type Target = Arc<cudarc::driver::CudaDevice>;
+
+    fn deref(&self) -> &Self::Target {
+        &self.device
+    }
+}
+
+pub trait WrapErr<O> {
+    fn w(self) -> std::result::Result<O, crate::Error>;
+}
+
+impl<O, E: Into<CudaError>> WrapErr<O> for std::result::Result<O, E> {
+    fn w(self) -> std::result::Result<O, crate::Error> {
+        self.map_err(|e| crate::Error::Cuda(Box::new(e.into())))
+    }
+}
+
+impl CudaDevice {
+    pub fn cuda_device(&self) -> Arc<cudarc::driver::CudaDevice> {
+        self.device.clone()
+    }
+
+    pub fn id(&self) -> DeviceId {
+        self.id
+    }
+
+    fn const_impl(&self, v: f64, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
+        let elem_count = shape.elem_count();
+        let cfg = LaunchConfig::for_num_elems(elem_count as u32);
+        let slice = match dtype {
+            DType::U8 => {
+                // SAFETY: Set later by running the fill kernel.
+                let data = unsafe { self.alloc::<u8>(elem_count) }.w()?;
+                let func = self.get_or_load_func("fill_u8", kernels::FILL)?;
+                let params = (&data, v as u8, elem_count);
+                unsafe { func.launch(cfg, params) }.w()?;
+                CudaStorageSlice::U8(data)
+            }
+            DType::U32 => {
+                // SAFETY: Set later by running the fill kernel.
+                let data = unsafe { self.alloc::<u32>(elem_count) }.w()?;
+                let func = self.get_or_load_func("fill_u32", kernels::FILL)?;
+                let params = (&data, v as u32, elem_count);
+                unsafe { func.launch(cfg, params) }.w()?;
+                CudaStorageSlice::U32(data)
+            }
+            DType::I64 => {
+                // SAFETY: Set later by running the fill kernel.
+                let data = unsafe { self.alloc::<i64>(elem_count) }.w()?;
+                let func = self.get_or_load_func("fill_i64", kernels::FILL)?;
+                let params = (&data, v as i64, elem_count);
+                unsafe { func.launch(cfg, params) }.w()?;
+                CudaStorageSlice::I64(data)
+            }
+            DType::BF16 => {
+                // SAFETY: Set later by running the fill kernel.
+                let data = unsafe { self.alloc::<bf16>(elem_count) }.w()?;
+                let func = self.get_or_load_func("fill_bf16", kernels::FILL)?;
+                let params = (&data, bf16::from_f64(v), elem_count);
+                unsafe { func.launch(cfg, params) }.w()?;
+                CudaStorageSlice::BF16(data)
+            }
+            DType::F16 => {
+                // SAFETY: Set later by running the fill kernel.
+                let data = unsafe { self.alloc::<f16>(elem_count) }.w()?;
+                let func = self.get_or_load_func("fill_f16", kernels::FILL)?;
+                let params = (&data, f16::from_f64(v), elem_count);
+                unsafe { func.launch(cfg, params) }.w()?;
+                CudaStorageSlice::F16(data)
+            }
+            DType::F32 => {
+                // SAFETY: Set later by running the fill kernel.
+                let data = unsafe { self.alloc::<f32>(elem_count) }.w()?;
+                let func = self.get_or_load_func("fill_f32", kernels::FILL)?;
+                let params = (&data, v as f32, elem_count);
+                unsafe { func.launch(cfg, params) }.w()?;
+                CudaStorageSlice::F32(data)
+            }
+            DType::F64 => {
+                // SAFETY: Set later by running the fill kernel.
+                let data = unsafe { self.alloc::<f64>(elem_count) }.w()?;
+                let func = self.get_or_load_func("fill_f64", kernels::FILL)?;
+                let params = (&data, v, elem_count);
+                unsafe { func.launch(cfg, params) }.w()?;
+                CudaStorageSlice::F64(data)
+            }
         };
-        Ok(ds)
+        Ok(CudaStorage {
+            slice,
+            device: self.clone(),
+        })
+    }
+
+    pub fn get_or_load_func(&self, module_name: &str, ptx: &'static str) -> Result<CudaFunction> {
+        if !self.has_func(module_name, module_name) {
+            // Leaking the string here is a bit sad but we need a &'static str and this is only
+            // done once per kernel name.
+            let static_module_name = Box::leak(module_name.to_string().into_boxed_str());
+            self.load_ptx(ptx.into(), module_name, &[static_module_name])
+                .map_err(|cuda| CudaError::Load {
+                    cuda,
+                    module_name: module_name.to_string(),
+                })
+                .w()?;
+        }
+        self.get_func(module_name, module_name)
+            // Clippy recommends this `ok_or` rather than `ok_or_else` so hopefully the compiler is
+            // able to only build the error value if needed.
+            .ok_or(CudaError::MissingKernel {
+                module_name: module_name.to_string(),
+            })
+            .w()
+    }
+}
+
+impl BackendDevice for CudaDevice {
+    type Storage = CudaStorage;
+
+    fn new(ordinal: usize) -> Result<Self> {
+        let device = cudarc::driver::CudaDevice::new(ordinal).w()?;
+        let blas = cudarc::cublas::CudaBlas::new(device.clone()).w()?;
+        let curand = cudarc::curand::CudaRng::new(299792458, device.clone()).w()?;
+        Ok(Self {
+            id: DeviceId::new(),
+            device,
+            blas: Arc::new(blas),
+            curand: Arc::new(Mutex::new(CudaRng(curand))),
+        })
+    }
+
+    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(),
+        }
+    }
+
+    fn same_device(&self, rhs: &Self) -> bool {
+        self.id == rhs.id
+    }
+
+    fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
+        let elem_count = shape.elem_count();
+        let slice = match dtype {
+            DType::U8 => {
+                let data = self.alloc_zeros::<u8>(elem_count).w()?;
+                CudaStorageSlice::U8(data)
+            }
+            DType::U32 => {
+                let data = self.alloc_zeros::<u32>(elem_count).w()?;
+                CudaStorageSlice::U32(data)
+            }
+            DType::I64 => {
+                let data = self.alloc_zeros::<i64>(elem_count).w()?;
+                CudaStorageSlice::I64(data)
+            }
+            DType::BF16 => {
+                let data = self.alloc_zeros::<bf16>(elem_count).w()?;
+                CudaStorageSlice::BF16(data)
+            }
+            DType::F16 => {
+                let data = self.alloc_zeros::<f16>(elem_count).w()?;
+                CudaStorageSlice::F16(data)
+            }
+            DType::F32 => {
+                let data = self.alloc_zeros::<f32>(elem_count).w()?;
+                CudaStorageSlice::F32(data)
+            }
+            DType::F64 => {
+                let data = self.alloc_zeros::<f64>(elem_count).w()?;
+                CudaStorageSlice::F64(data)
+            }
+        };
+        Ok(CudaStorage {
+            slice,
+            device: self.clone(),
+        })
+    }
+
+    fn rand_uniform(&self, shape: &Shape, dtype: DType, lo: f64, up: f64) -> Result<CudaStorage> {
+        let elem_count = shape.elem_count();
+        let curand = self.curand.lock().unwrap();
+        let slice = match dtype {
+            // TODO: Add support for F16 and BF16 though this is likely to require some upstream
+            // cudarc changes.
+            DType::U8 | DType::U32 | DType::I64 | DType::F16 | DType::BF16 => {
+                Err(CudaError::UnsupportedDtype {
+                    dtype,
+                    op: "rand_uniform",
+                })
+                .w()?
+            }
+            DType::F32 => {
+                let mut data = unsafe { self.alloc::<f32>(elem_count) }.w()?;
+                curand.0.fill_with_uniform(&mut data).w()?;
+                CudaStorageSlice::F32(data)
+            }
+            DType::F64 => {
+                let mut data = unsafe { self.alloc::<f64>(elem_count) }.w()?;
+                curand.0.fill_with_uniform(&mut data).w()?;
+                CudaStorageSlice::F64(data)
+            }
+        };
+        let slice = if lo == 0. && up == 1.0 {
+            slice
+        } else {
+            let layout = Layout::contiguous(shape);
+            Affine(up - lo, lo).map(&slice, self, &layout)?
+        };
+        Ok(CudaStorage {
+            slice,
+            device: self.clone(),
+        })
+    }
+
+    fn rand_normal(&self, shape: &Shape, dtype: DType, mean: f64, std: f64) -> Result<CudaStorage> {
+        // TODO: Add support for F16 and BF16 though this is likely to require some upstream
+        // cudarc changes.
+        let elem_count = shape.elem_count();
+        let curand = self.curand.lock().unwrap();
+        // curand can only generate an odd number of values.
+        // https://github.com/huggingface/candle/issues/734
+        let elem_count_round = if elem_count % 2 == 1 {
+            elem_count + 1
+        } else {
+            elem_count
+        };
+        let slice = match dtype {
+            DType::U8 | DType::U32 | DType::I64 | DType::F16 | DType::BF16 => {
+                Err(CudaError::UnsupportedDtype {
+                    dtype,
+                    op: "rand_normal",
+                })
+                .w()?
+            }
+            DType::F32 => {
+                let mut data = unsafe { self.alloc::<f32>(elem_count_round) }.w()?;
+                curand
+                    .0
+                    .fill_with_normal(&mut data, mean as f32, std as f32)
+                    .w()?;
+                CudaStorageSlice::F32(data)
+            }
+            DType::F64 => {
+                let mut data = unsafe { self.alloc::<f64>(elem_count_round) }.w()?;
+                curand.0.fill_with_normal(&mut data, mean, std).w()?;
+                CudaStorageSlice::F64(data)
+            }
+        };
+        Ok(CudaStorage {
+            slice,
+            device: self.clone(),
+        })
+    }
+
+    fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
+        self.const_impl(1., shape, dtype)
+    }
+
+    fn storage_from_cpu_storage(&self, storage: &CpuStorage) -> Result<CudaStorage> {
+        let slice = match storage {
+            CpuStorage::U8(storage) => {
+                let data = self.htod_sync_copy(storage).w()?;
+                CudaStorageSlice::U8(data)
+            }
+            CpuStorage::U32(storage) => {
+                let data = self.htod_sync_copy(storage).w()?;
+                CudaStorageSlice::U32(data)
+            }
+            CpuStorage::I64(storage) => {
+                let data = self.htod_sync_copy(storage).w()?;
+                CudaStorageSlice::I64(data)
+            }
+            CpuStorage::BF16(storage) => {
+                let data = self.htod_sync_copy(storage).w()?;
+                CudaStorageSlice::BF16(data)
+            }
+            CpuStorage::F16(storage) => {
+                let data = self.htod_sync_copy(storage).w()?;
+                CudaStorageSlice::F16(data)
+            }
+            CpuStorage::F32(storage) => {
+                let data = self.htod_sync_copy(storage).w()?;
+                CudaStorageSlice::F32(data)
+            }
+            CpuStorage::F64(storage) => {
+                let data = self.htod_sync_copy(storage).w()?;
+                CudaStorageSlice::F64(data)
+            }
+        };
+        Ok(CudaStorage {
+            slice,
+            device: self.clone(),
+        })
     }
 }
 
@@ -53,6 +407,133 @@ pub enum CudaStorageSlice {
     F32(CudaSlice<f32>),
     F64(CudaSlice<f64>),
 }
+type S = CudaStorageSlice;
+
+pub trait Map1 {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
+        &self,
+        src: &CudaSlice<T>,
+        dev: &CudaDevice,
+        layout: &Layout,
+    ) -> Result<CudaSlice<T>>;
+
+    fn map(&self, s: &S, d: &CudaDevice, l: &Layout) -> Result<S> {
+        let out = match s {
+            S::U8(s) => S::U8(self.f(s, d, l)?),
+            S::U32(s) => S::U32(self.f(s, d, l)?),
+            S::I64(s) => S::I64(self.f(s, d, l)?),
+            S::BF16(s) => S::BF16(self.f(s, d, l)?),
+            S::F16(s) => S::F16(self.f(s, d, l)?),
+            S::F32(s) => S::F32(self.f(s, d, l)?),
+            S::F64(s) => S::F64(self.f(s, d, l)?),
+        };
+        Ok(out)
+    }
+}
+
+pub trait Map2 {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
+        &self,
+        src1: &CudaSlice<T>,
+        layout1: &Layout,
+        src2: &CudaSlice<T>,
+        layout2: &Layout,
+        dev: &CudaDevice,
+    ) -> Result<CudaSlice<T>>;
+
+    fn map(&self, s1: &S, l1: &Layout, s2: &S, l2: &Layout, d: &CudaDevice) -> Result<S> {
+        let out = match (s1, s2) {
+            (S::U8(s1), S::U8(s2)) => S::U8(self.f(s1, l1, s2, l2, d)?),
+            (S::U32(s1), S::U32(s2)) => S::U32(self.f(s1, l1, s2, l2, d)?),
+            (S::I64(s1), S::I64(s2)) => S::I64(self.f(s1, l1, s2, l2, d)?),
+            (S::BF16(s1), S::BF16(s2)) => S::BF16(self.f(s1, l1, s2, l2, d)?),
+            (S::F16(s1), S::F16(s2)) => S::F16(self.f(s1, l1, s2, l2, d)?),
+            (S::F32(s1), S::F32(s2)) => S::F32(self.f(s1, l1, s2, l2, d)?),
+            (S::F64(s1), S::F64(s2)) => S::F64(self.f(s1, l1, s2, l2, d)?),
+            _ => Err(CudaError::InternalError("dtype mismatch in binary op"))?,
+        };
+        Ok(out)
+    }
+}
+
+pub trait Map2InPlace {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
+        &self,
+        dst: &mut CudaSlice<T>,
+        dst_shape: &Shape,
+        src: &CudaSlice<T>,
+        src_l: &Layout,
+        dev: &CudaDevice,
+    ) -> Result<()>;
+
+    fn map(
+        &self,
+        dst: &mut S,
+        dst_s: &Shape,
+        src: &S,
+        src_l: &Layout,
+        d: &CudaDevice,
+    ) -> Result<()> {
+        match (dst, src) {
+            (S::U8(dst), S::U8(src)) => self.f(dst, dst_s, src, src_l, d),
+            (S::U32(dst), S::U32(src)) => self.f(dst, dst_s, src, src_l, d),
+            (S::I64(dst), S::I64(src)) => self.f(dst, dst_s, src, src_l, d),
+            (S::BF16(dst), S::BF16(src)) => self.f(dst, dst_s, src, src_l, d),
+            (S::F16(dst), S::F16(src)) => self.f(dst, dst_s, src, src_l, d),
+            (S::F32(dst), S::F32(src)) => self.f(dst, dst_s, src, src_l, d),
+            (S::F64(dst), S::F64(src)) => self.f(dst, dst_s, src, src_l, d),
+            _ => Err(CudaError::InternalError("dtype mismatch in binary op"))?,
+        }
+    }
+}
+
+pub trait Map1Any {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits, W: Fn(CudaSlice<T>) -> S>(
+        &self,
+        src: &CudaSlice<T>,
+        dev: &CudaDevice,
+        layout: &Layout,
+        wrap: W,
+    ) -> Result<S>;
+
+    fn map(&self, s: &S, d: &CudaDevice, l: &Layout) -> Result<S> {
+        let out = match s {
+            S::U8(s) => self.f(s, d, l, S::U8)?,
+            S::U32(s) => self.f(s, d, l, S::U32)?,
+            S::I64(s) => self.f(s, d, l, S::I64)?,
+            S::BF16(s) => self.f(s, d, l, S::BF16)?,
+            S::F16(s) => self.f(s, d, l, S::F16)?,
+            S::F32(s) => self.f(s, d, l, S::F32)?,
+            S::F64(s) => self.f(s, d, l, S::F64)?,
+        };
+        Ok(out)
+    }
+}
+
+pub trait Map2Any {
+    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
+        &self,
+        src1: &CudaSlice<T>,
+        layout1: &Layout,
+        src2: &CudaSlice<T>,
+        layout2: &Layout,
+        dev: &CudaDevice,
+    ) -> Result<S>;
+
+    fn map(&self, s1: &S, l1: &Layout, s2: &S, l2: &Layout, d: &CudaDevice) -> Result<S> {
+        let out = match (s1, s2) {
+            (S::U8(s1), S::U8(s2)) => self.f(s1, l1, s2, l2, d)?,
+            (S::U32(s1), S::U32(s2)) => self.f(s1, l1, s2, l2, d)?,
+            (S::I64(s1), S::I64(s2)) => self.f(s1, l1, s2, l2, d)?,
+            (S::BF16(s1), S::BF16(s2)) => self.f(s1, l1, s2, l2, d)?,
+            (S::F16(s1), S::F16(s2)) => self.f(s1, l1, s2, l2, d)?,
+            (S::F32(s1), S::F32(s2)) => self.f(s1, l1, s2, l2, d)?,
+            (S::F64(s1), S::F64(s2)) => self.f(s1, l1, s2, l2, d)?,
+            _ => Err(CudaError::InternalError("dtype mismatch in binary op")).w()?,
+        };
+        Ok(out)
+    }
+}
 
 struct Clone;
 impl Map1 for Clone {
@@ -83,7 +564,7 @@ impl Map1 for Affine {
         let dims = shape.dims();
         let el = shape.elem_count();
         let cfg = LaunchConfig::for_num_elems(el as u32);
-        let ds = SlicePtrOrNull::params_from_layout(dev, layout)?;
+        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>("affine"), kernels::AFFINE)?;
         // SAFETY: Set later by running the kernel.
@@ -115,7 +596,7 @@ impl Map1 for Elu {
         let dims = shape.dims();
         let el = shape.elem_count();
         let cfg = LaunchConfig::for_num_elems(el as u32);
-        let ds = SlicePtrOrNull::params_from_layout(dev, layout)?;
+        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>("uelu"), kernels::UNARY)?;
         // SAFETY: Set later by running the kernel.
@@ -238,7 +719,7 @@ impl Map1 for Powf {
         let dims = shape.dims();
         let el = shape.elem_count();
         let cfg = LaunchConfig::for_num_elems(el as u32);
-        let ds = SlicePtrOrNull::params_from_layout(dev, layout)?;
+        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>("upowf"), kernels::UNARY)?;
         // SAFETY: Set later by running the kernel.
@@ -371,7 +852,7 @@ impl<U: UnaryOpT> Map1 for U {
         let dims = shape.dims();
         let el_count = shape.elem_count();
         let cfg = LaunchConfig::for_num_elems(el_count as u32);
-        let ds = SlicePtrOrNull::params_from_layout(dev, layout)?;
+        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>(U::KERNEL), kernels::UNARY)?;
         // SAFETY: Set later by running the kernel.
@@ -668,55 +1149,6 @@ impl<'a> Map2 for Conv2D<'a> {
     }
 }
 
-struct ConvTranspose1D<'a>(&'a crate::conv::ParamsConvTranspose1D);
-impl<'a> Map2 for ConvTranspose1D<'a> {
-    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
-        &self,
-        inp: &CudaSlice<T>,
-        inp_l: &Layout,
-        k: &CudaSlice<T>,
-        k_l: &Layout,
-        dev: &CudaDevice,
-    ) -> Result<CudaSlice<T>> {
-        // Kernel shape: (c_in_k, c_out, l_k)
-        // Input shape: (b_size, c_in, l_in)
-        let p = &self.0;
-        let l_out = p.l_out();
-        let dst_el = p.c_out * l_out * p.b_size;
-        let inp = &inp.slice(inp_l.start_offset()..);
-        let k = &k.slice(k_l.start_offset()..);
-        let shape = inp_l.shape();
-        let dims = shape.dims();
-        let el = shape.elem_count();
-
-        // SAFETY: Set later by running the kernel.
-        let out = unsafe { dev.alloc::<T>(dst_el) }.w()?;
-        let cfg = LaunchConfig::for_num_elems(dst_el as u32);
-        let func = dev.get_or_load_func(&kernel_name::<T>("conv_transpose1d"), kernels::CONV)?;
-        let ds = if dims.len() == 3 {
-            [dims, inp_l.stride(), k_l.dims(), k_l.stride()].concat()
-        } else {
-            crate::bail!("unexpected input shape for conv_transpose1d {dims:?}")
-        };
-        let ds = dev.htod_copy(ds).w()?;
-        let params = (
-            el,
-            l_out,
-            p.stride,
-            p.padding,
-            p.output_padding,
-            p.dilation,
-            &ds,
-            inp,
-            k,
-            &out,
-        );
-        // SAFETY: ffi.
-        unsafe { func.launch(cfg, params) }.w()?;
-        Ok(out)
-    }
-}
-
 struct ConvTranspose2D<'a>(&'a crate::conv::ParamsConvTranspose2D);
 impl<'a> Map2 for ConvTranspose2D<'a> {
     fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
@@ -921,14 +1353,9 @@ impl<U: crate::op::BinaryOpT> Map2 for U {
         let dims = shape.dims();
         let elem_count = shape.elem_count();
         let cfg = LaunchConfig::for_num_elems(elem_count as u32);
-        let dims_and_strides = if lhs_l.is_contiguous() && rhs_l.is_contiguous() {
+        let dims_and_strides = dev
-            SlicePtrOrNull::Null
+            .htod_copy([dims, lhs_l.stride(), rhs_l.stride()].concat())
-        } else {
+            .w()?;
-            SlicePtrOrNull::Ptr(
-                dev.htod_copy([dims, lhs_l.stride(), rhs_l.stride()].concat())
-                    .w()?,
-            )
-        };
         let lhs = &lhs.slice(lhs_l.start_offset()..);
         let rhs = &rhs.slice(rhs_l.start_offset()..);
         let func = dev.get_or_load_func(&kernel_name::<T>(U::KERNEL), kernels::BINARY)?;
@@ -955,14 +1382,9 @@ impl Map2Any for Cmp {
         let dims = shape.dims();
         let elem_count = shape.elem_count();
         let cfg = LaunchConfig::for_num_elems(elem_count as u32);
-        let dims_and_strides = if lhs_l.is_contiguous() && rhs_l.is_contiguous() {
+        let dims_and_strides = dev
-            SlicePtrOrNull::Null
+            .htod_copy([dims, lhs_l.stride(), rhs_l.stride()].concat())
-        } else {
+            .w()?;
-            SlicePtrOrNull::Ptr(
-                dev.htod_copy([dims, lhs_l.stride(), rhs_l.stride()].concat())
-                    .w()?,
-            )
-        };
         let lhs = &lhs.slice(lhs_l.start_offset()..);
         let rhs = &rhs.slice(rhs_l.start_offset()..);
         let name = match self.0 {
@@ -1070,30 +1492,26 @@ fn gemm_config<T>(
     let lhs_m1 = lhs_stride[lhs_stride.len() - 1];
     let lhs_m2 = lhs_stride[lhs_stride.len() - 2];
     // The a tensor has dims batching, k, n (rhs)
-    // We also allow for the case where the stride on the minor dimension is not as expected but
+    let (lda, transa) = if rhs_m1 == 1 && rhs_m2 == n {
-    // there is a single element.
-    let (lda, transa) = if (rhs_m1 == 1 || n == 1) && (rhs_m2 == n || k == 1) {
         (n as i32, cublasOperation_t::CUBLAS_OP_N)
-    } else if (rhs_m1 == k || n == 1) && (rhs_m2 == 1 || k == 1) {
+    } else if rhs_m1 == k && rhs_m2 == 1 {
         (k as i32, cublasOperation_t::CUBLAS_OP_T)
     } else {
         Err(CudaError::MatMulNonContiguous {
-            lhs_stride: lhs_l.clone(),
+            lhs_stride: lhs_stride.to_vec(),
-            rhs_stride: rhs_l.clone(),
+            rhs_stride: rhs_stride.to_vec(),
             mnk: (m, n, k),
         })?
     };
     // The b tensor has dims batching, m, k (lhs)
-    // We also allow for the case where the stride on the minor dimension is not as expected but
+    let (ldb, transb) = if lhs_m1 == 1 && lhs_m2 == k {
-    // there is a single element.
-    let (ldb, transb) = if (lhs_m1 == 1 || k == 1) && (lhs_m2 == k || m == 1) {
         (k as i32, cublasOperation_t::CUBLAS_OP_N)
-    } else if (lhs_m1 == m || k == 1) && (lhs_m2 == 1 || m == 1) {
+    } else if lhs_m1 == m && lhs_m2 == 1 {
         (m as i32, cublasOperation_t::CUBLAS_OP_T)
     } else {
         Err(CudaError::MatMulNonContiguous {
-            lhs_stride: lhs_l.clone(),
+            lhs_stride: lhs_stride.to_vec(),
-            rhs_stride: rhs_l.clone(),
+            rhs_stride: rhs_stride.to_vec(),
             mnk: (m, n, k),
         })?
     };
@@ -1114,25 +1532,21 @@ fn gemm_config<T>(
 
     let stride_b: usize = match lhs_stride[..lhs_stride.len() - 2] {
         [s1, stride] if s1 == stride * lhs_l.dims()[1] => stride,
-        [_, stride] if lhs_l.dims()[0] == 1 => stride,
-        [stride, _] if lhs_l.dims()[1] == 1 => stride,
         [stride] => stride,
         [] => m * k,
         _ => Err(CudaError::MatMulNonContiguous {
-            lhs_stride: lhs_l.clone(),
+            lhs_stride: lhs_stride.to_vec(),
-            rhs_stride: rhs_l.clone(),
+            rhs_stride: rhs_stride.to_vec(),
             mnk: (m, n, k),
         })?,
     };
     let stride_a: usize = match rhs_stride[..rhs_stride.len() - 2] {
         [s1, stride] if s1 == stride * rhs_l.dims()[1] => stride,
-        [_, stride] if rhs_l.dims()[0] == 1 => stride,
-        [stride, _] if rhs_l.dims()[1] == 1 => stride,
         [stride] => stride,
         [] => n * k,
         _ => Err(CudaError::MatMulNonContiguous {
-            lhs_stride: lhs_l.clone(),
+            lhs_stride: lhs_stride.to_vec(),
-            rhs_stride: rhs_l.clone(),
+            rhs_stride: rhs_stride.to_vec(),
             mnk: (m, n, k),
         })?,
     };
@@ -1177,7 +1591,7 @@ impl BackendStorage for CudaStorage {
         let el = shape.elem_count();
         let cfg = LaunchConfig::for_num_elems(el as u32);
         let dev = self.device();
-        let ds = SlicePtrOrNull::params_from_layout(dev, layout)?;
+        let ds = dev.htod_copy([dims, layout.stride()].concat()).w()?;
         let start_o = layout.start_offset();
         // This returns an i64 rather than a &i64, this is useful to get around some temporary
         // lifetime issue and is safe as long as self.slice does not go out of scope before inp
@@ -1381,10 +1795,7 @@ impl BackendStorage for CudaStorage {
             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 = unsafe {
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
-                self.device()
-                    .alloc_uninit(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)?
@@ -1392,22 +1803,19 @@ impl BackendStorage for CudaStorage {
             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 = unsafe { self.device().alloc_uninit(res_l.shape(), res.dtype())? };
+        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,
+        _: &Layout,
-        kernel: &Self,
+        _: &Self,
-        kernel_l: &Layout,
+        _: &Layout,
-        params: &crate::conv::ParamsConvTranspose1D,
+        _: &crate::conv::ParamsConvTranspose1D,
     ) -> Result<Self> {
-        let device = self.device().clone();
+        todo!()
-        let slice =
-            ConvTranspose1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
-        Ok(Self { slice, device })
     }
 
     #[cfg(not(feature = "cudnn"))]
@@ -1449,10 +1857,7 @@ impl BackendStorage for CudaStorage {
             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 = unsafe {
+            let mut kernel_c = self.device().zeros_impl(kernel_l.shape(), kernel.dtype())?;
-                self.device()
-                    .alloc_uninit(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)?
@@ -1462,7 +1867,7 @@ impl BackendStorage for CudaStorage {
         let res_l = Layout::contiguous((b, h_out, w_out, n))
             .transpose(1, 2)?
             .transpose(1, 3)?;
-        let mut res_t = unsafe { self.device().alloc_uninit(res_l.shape(), res.dtype())? };
+        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)
     }
@@ -1599,7 +2004,7 @@ impl BackendStorage for CudaStorage {
         dim: usize,
     ) -> Result<Self> {
         let device = self.device().clone();
-        let mut acc = unsafe { device.alloc_uninit(l.shape(), self.dtype())? };
+        let mut acc = device.zeros_impl(l.shape(), self.dtype())?;
         self.copy_strided_src(&mut acc, 0, l)?;
         ScatterAdd(ids, ids_l, dim).map(&mut acc.slice, l.shape(), &src.slice, src_l, &device)?;
         Ok(acc)
@@ -1614,7 +2019,7 @@ impl BackendStorage for CudaStorage {
         dim: usize,
     ) -> Result<Self> {
         let device = self.device().clone();
-        let mut acc = unsafe { device.alloc_uninit(l.shape(), self.dtype())? };
+        let mut acc = device.zeros_impl(l.shape(), self.dtype())?;
         self.copy_strided_src(&mut acc, 0, l)?;
         IndexAdd(ids, ids_l, dim).map(&mut acc.slice, l.shape(), &src.slice, src_l, &device)?;
         Ok(acc)
@@ -1688,72 +2093,6 @@ impl BackendStorage for CudaStorage {
         Ok(Self { slice, device })
     }
 
-    fn copy2d(
-        &self,
-        dst: &mut Self,
-        d1: usize,
-        d2: usize,
-        src_s: usize,
-        dst_s: usize,
-        src_o: usize,
-        dst_o: usize,
-    ) -> Result<()> {
-        let dev = &self.device;
-        let d1 = d1 as u32;
-        let d2 = d2 as u32;
-        // Nothing to copy so we exit early to avoid launching a kernel and some potential invalid
-        // argument with a null pointer.
-        if d1 == 0 || d2 == 0 {
-            return Ok(());
-        }
-        let dst_s = dst_s as u32;
-        let src_s = src_s as u32;
-        let (src, dst, kname) = match (&self.slice, &mut dst.slice) {
-            (S::U8(s), S::U8(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_u8",
-            ),
-            (S::U32(s), S::U32(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_u32",
-            ),
-            (S::I64(s), S::I64(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_i64",
-            ),
-            (S::BF16(s), S::BF16(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_bf16",
-            ),
-            (S::F16(s), S::F16(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_f16",
-            ),
-            (S::F32(s), S::F32(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_f32",
-            ),
-            (S::F64(s), S::F64(d)) => (
-                *s.slice(src_o..).device_ptr(),
-                *d.slice(dst_o..).device_ptr(),
-                "copy2d_f64",
-            ),
-            _ => Err(CudaError::InternalError("dtype mismatch in copy2d"))?,
-        };
-        let func = dev.get_or_load_func(kname, kernels::FILL)?;
-        let cfg = LaunchConfig::for_num_elems(d1 * d2);
-        let params = (src, dst, d1, d2, src_s, dst_s);
-        // SAFETY: ffi.
-        unsafe { func.launch(cfg, params) }.w()?;
-        Ok(())
-    }
-
     fn copy_strided_src(&self, dst: &mut Self, dst_offset: usize, src_l: &Layout) -> Result<()> {
         let src_shape = src_l.shape();
         let dims = src_shape.dims();
@@ -1763,7 +2102,7 @@ impl BackendStorage for CudaStorage {
         }
         let cfg = LaunchConfig::for_num_elems(el_count as u32);
         let dev = &self.device;
-        let ds = SlicePtrOrNull::params_from_layout(dev, src_l)?;
+        let ds = dev.htod_copy([dims, src_l.stride()].concat()).w()?;
         match (&self.slice, &mut dst.slice) {
             (CudaStorageSlice::BF16(src), CudaStorageSlice::BF16(dst)) => {
                 let (src, mut dst) = slice_src_and_dst(src, src_l, dst, dst_offset);

candle-core/src/cuda_backend/device.rs

@@ -1,415 +0,0 @@
-use crate::backend::BackendDevice;
-use crate::{CpuStorage, DType, Layout, Result, Shape};
-pub use candle_kernels as kernels;
-pub use cudarc;
-use cudarc::driver::{CudaFunction, LaunchAsync, LaunchConfig};
-use half::{bf16, f16};
-use std::sync::{Arc, Mutex};
-
-use super::{CudaError, CudaStorage, CudaStorageSlice, WrapErr};
-
-/// Unique identifier for cuda devices.
-#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
-pub struct DeviceId(usize);
-
-impl DeviceId {
-    fn new() -> Self {
-        // https://users.rust-lang.org/t/idiomatic-rust-way-to-generate-unique-id/33805
-        use std::sync::atomic;
-        static COUNTER: atomic::AtomicUsize = atomic::AtomicUsize::new(1);
-        Self(COUNTER.fetch_add(1, atomic::Ordering::Relaxed))
-    }
-}
-
-struct CudaRng(cudarc::curand::CudaRng);
-unsafe impl Send for CudaRng {}
-
-#[derive(Clone)]
-pub struct CudaDevice {
-    id: DeviceId,
-    device: Arc<cudarc::driver::CudaDevice>,
-    pub(crate) blas: Arc<cudarc::cublas::CudaBlas>,
-    curand: Arc<Mutex<CudaRng>>,
-}
-
-impl std::fmt::Debug for CudaDevice {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "CudaDevice({:?})", self.id)
-    }
-}
-
-impl std::ops::Deref for CudaDevice {
-    type Target = Arc<cudarc::driver::CudaDevice>;
-
-    fn deref(&self) -> &Self::Target {
-        &self.device
-    }
-}
-
-impl CudaDevice {
-    pub fn cuda_device(&self) -> Arc<cudarc::driver::CudaDevice> {
-        self.device.clone()
-    }
-
-    pub fn id(&self) -> DeviceId {
-        self.id
-    }
-
-    fn const_impl(&self, v: f64, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
-        let elem_count = shape.elem_count();
-        let cfg = LaunchConfig::for_num_elems(elem_count as u32);
-        let slice = match dtype {
-            DType::U8 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<u8>(elem_count) }.w()?;
-                let func = self.get_or_load_func("fill_u8", kernels::FILL)?;
-                let params = (&data, v as u8, elem_count);
-                unsafe { func.launch(cfg, params) }.w()?;
-                CudaStorageSlice::U8(data)
-            }
-            DType::U32 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<u32>(elem_count) }.w()?;
-                let func = self.get_or_load_func("fill_u32", kernels::FILL)?;
-                let params = (&data, v as u32, elem_count);
-                unsafe { func.launch(cfg, params) }.w()?;
-                CudaStorageSlice::U32(data)
-            }
-            DType::I64 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<i64>(elem_count) }.w()?;
-                let func = self.get_or_load_func("fill_i64", kernels::FILL)?;
-                let params = (&data, v as i64, elem_count);
-                unsafe { func.launch(cfg, params) }.w()?;
-                CudaStorageSlice::I64(data)
-            }
-            DType::BF16 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<bf16>(elem_count) }.w()?;
-                let func = self.get_or_load_func("fill_bf16", kernels::FILL)?;
-                let params = (&data, bf16::from_f64(v), elem_count);
-                unsafe { func.launch(cfg, params) }.w()?;
-                CudaStorageSlice::BF16(data)
-            }
-            DType::F16 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<f16>(elem_count) }.w()?;
-                let func = self.get_or_load_func("fill_f16", kernels::FILL)?;
-                let params = (&data, f16::from_f64(v), elem_count);
-                unsafe { func.launch(cfg, params) }.w()?;
-                CudaStorageSlice::F16(data)
-            }
-            DType::F32 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<f32>(elem_count) }.w()?;
-                let func = self.get_or_load_func("fill_f32", kernels::FILL)?;
-                let params = (&data, v as f32, elem_count);
-                unsafe { func.launch(cfg, params) }.w()?;
-                CudaStorageSlice::F32(data)
-            }
-            DType::F64 => {
-                // SAFETY: Set later by running the fill kernel.
-                let data = unsafe { self.alloc::<f64>(elem_count) }.w()?;
-                let func = self.get_or_load_func("fill_f64", kernels::FILL)?;
-                let params = (&data, v, elem_count);
-                unsafe { func.launch(cfg, params) }.w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
-    pub fn get_or_load_func(&self, module_name: &str, ptx: &'static str) -> Result<CudaFunction> {
-        if !self.has_func(module_name, module_name) {
-            // Leaking the string here is a bit sad but we need a &'static str and this is only
-            // done once per kernel name.
-            let static_module_name = Box::leak(module_name.to_string().into_boxed_str());
-            self.load_ptx(ptx.into(), module_name, &[static_module_name])
-                .map_err(|cuda| CudaError::Load {
-                    cuda,
-                    module_name: module_name.to_string(),
-                })
-                .w()?;
-        }
-        self.get_func(module_name, module_name)
-            // Clippy recommends this `ok_or` rather than `ok_or_else` so hopefully the compiler is
-            // able to only build the error value if needed.
-            .ok_or(CudaError::MissingKernel {
-                module_name: module_name.to_string(),
-            })
-            .w()
-    }
-}
-
-impl BackendDevice for CudaDevice {
-    type Storage = CudaStorage;
-
-    fn new(ordinal: usize) -> Result<Self> {
-        let device = cudarc::driver::CudaDevice::new(ordinal).w()?;
-        let blas = cudarc::cublas::CudaBlas::new(device.clone()).w()?;
-        let curand = cudarc::curand::CudaRng::new(299792458, device.clone()).w()?;
-        Ok(Self {
-            id: DeviceId::new(),
-            device,
-            blas: Arc::new(blas),
-            curand: Arc::new(Mutex::new(CudaRng(curand))),
-        })
-    }
-
-    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(),
-        }
-    }
-
-    fn same_device(&self, rhs: &Self) -> bool {
-        self.id == rhs.id
-    }
-
-    fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
-        let elem_count = shape.elem_count();
-        let slice = match dtype {
-            DType::U8 => {
-                let data = self.alloc_zeros::<u8>(elem_count).w()?;
-                CudaStorageSlice::U8(data)
-            }
-            DType::U32 => {
-                let data = self.alloc_zeros::<u32>(elem_count).w()?;
-                CudaStorageSlice::U32(data)
-            }
-            DType::I64 => {
-                let data = self.alloc_zeros::<i64>(elem_count).w()?;
-                CudaStorageSlice::I64(data)
-            }
-            DType::BF16 => {
-                let data = self.alloc_zeros::<bf16>(elem_count).w()?;
-                CudaStorageSlice::BF16(data)
-            }
-            DType::F16 => {
-                let data = self.alloc_zeros::<f16>(elem_count).w()?;
-                CudaStorageSlice::F16(data)
-            }
-            DType::F32 => {
-                let data = self.alloc_zeros::<f32>(elem_count).w()?;
-                CudaStorageSlice::F32(data)
-            }
-            DType::F64 => {
-                let data = self.alloc_zeros::<f64>(elem_count).w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
-    fn rand_uniform(&self, shape: &Shape, dtype: DType, lo: f64, up: f64) -> Result<CudaStorage> {
-        let elem_count = shape.elem_count();
-        let curand = self.curand.lock().unwrap();
-        let slice = match dtype {
-            // TODO: Add support for F16 and BF16 though this is likely to require some upstream
-            // cudarc changes.
-            DType::U8 | DType::U32 | DType::I64 | DType::F16 | DType::BF16 => {
-                Err(CudaError::UnsupportedDtype {
-                    dtype,
-                    op: "rand_uniform",
-                })
-                .w()?
-            }
-            DType::F32 => {
-                let mut data = unsafe { self.alloc::<f32>(elem_count) }.w()?;
-                curand.0.fill_with_uniform(&mut data).w()?;
-                CudaStorageSlice::F32(data)
-            }
-            DType::F64 => {
-                let mut data = unsafe { self.alloc::<f64>(elem_count) }.w()?;
-                curand.0.fill_with_uniform(&mut data).w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        let slice = if lo == 0. && up == 1.0 {
-            slice
-        } else {
-            use super::utils::Map1;
-            let layout = Layout::contiguous(shape);
-            super::Affine(up - lo, lo).map(&slice, self, &layout)?
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
-    fn rand_normal(&self, shape: &Shape, dtype: DType, mean: f64, std: f64) -> Result<CudaStorage> {
-        // TODO: Add support for F16 and BF16 though this is likely to require some upstream
-        // cudarc changes.
-        let elem_count = shape.elem_count();
-        let curand = self.curand.lock().unwrap();
-        // curand can only generate an odd number of values.
-        // https://github.com/huggingface/candle/issues/734
-        let elem_count_round = if elem_count % 2 == 1 {
-            elem_count + 1
-        } else {
-            elem_count
-        };
-        let slice = match dtype {
-            DType::U8 | DType::U32 | DType::I64 | DType::F16 | DType::BF16 => {
-                Err(CudaError::UnsupportedDtype {
-                    dtype,
-                    op: "rand_normal",
-                })
-                .w()?
-            }
-            DType::F32 => {
-                let mut data = unsafe { self.alloc::<f32>(elem_count_round) }.w()?;
-                curand
-                    .0
-                    .fill_with_normal(&mut data, mean as f32, std as f32)
-                    .w()?;
-                CudaStorageSlice::F32(data)
-            }
-            DType::F64 => {
-                let mut data = unsafe { self.alloc::<f64>(elem_count_round) }.w()?;
-                curand.0.fill_with_normal(&mut data, mean, std).w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
-    fn ones_impl(&self, shape: &Shape, dtype: DType) -> Result<CudaStorage> {
-        self.const_impl(1., shape, dtype)
-    }
-
-    unsafe fn alloc_uninit(&self, shape: &Shape, dtype: DType) -> Result<Self::Storage> {
-        let elem_count = shape.elem_count();
-        let slice = match dtype {
-            DType::U8 => {
-                let data = self.alloc::<u8>(elem_count).w()?;
-                CudaStorageSlice::U8(data)
-            }
-            DType::U32 => {
-                let data = self.alloc::<u32>(elem_count).w()?;
-                CudaStorageSlice::U32(data)
-            }
-            DType::I64 => {
-                let data = self.alloc::<i64>(elem_count).w()?;
-                CudaStorageSlice::I64(data)
-            }
-            DType::BF16 => {
-                let data = self.alloc::<bf16>(elem_count).w()?;
-                CudaStorageSlice::BF16(data)
-            }
-            DType::F16 => {
-                let data = self.alloc::<f16>(elem_count).w()?;
-                CudaStorageSlice::F16(data)
-            }
-            DType::F32 => {
-                let data = self.alloc::<f32>(elem_count).w()?;
-                CudaStorageSlice::F32(data)
-            }
-            DType::F64 => {
-                let data = self.alloc::<f64>(elem_count).w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
-    fn storage_from_cpu_storage(&self, storage: &CpuStorage) -> Result<CudaStorage> {
-        let slice = match storage {
-            CpuStorage::U8(storage) => {
-                let data = self.htod_sync_copy(storage).w()?;
-                CudaStorageSlice::U8(data)
-            }
-            CpuStorage::U32(storage) => {
-                let data = self.htod_sync_copy(storage).w()?;
-                CudaStorageSlice::U32(data)
-            }
-            CpuStorage::I64(storage) => {
-                let data = self.htod_sync_copy(storage).w()?;
-                CudaStorageSlice::I64(data)
-            }
-            CpuStorage::BF16(storage) => {
-                let data = self.htod_sync_copy(storage).w()?;
-                CudaStorageSlice::BF16(data)
-            }
-            CpuStorage::F16(storage) => {
-                let data = self.htod_sync_copy(storage).w()?;
-                CudaStorageSlice::F16(data)
-            }
-            CpuStorage::F32(storage) => {
-                let data = self.htod_sync_copy(storage).w()?;
-                CudaStorageSlice::F32(data)
-            }
-            CpuStorage::F64(storage) => {
-                let data = self.htod_sync_copy(storage).w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
-    fn storage_from_cpu_storage_owned(&self, storage: CpuStorage) -> Result<CudaStorage> {
-        let slice = match storage {
-            CpuStorage::U8(storage) => {
-                let data = self.htod_copy(storage).w()?;
-                CudaStorageSlice::U8(data)
-            }
-            CpuStorage::U32(storage) => {
-                let data = self.htod_copy(storage).w()?;
-                CudaStorageSlice::U32(data)
-            }
-            CpuStorage::I64(storage) => {
-                let data = self.htod_copy(storage).w()?;
-                CudaStorageSlice::I64(data)
-            }
-            CpuStorage::BF16(storage) => {
-                let data = self.htod_copy(storage).w()?;
-                CudaStorageSlice::BF16(data)
-            }
-            CpuStorage::F16(storage) => {
-                let data = self.htod_copy(storage).w()?;
-                CudaStorageSlice::F16(data)
-            }
-            CpuStorage::F32(storage) => {
-                let data = self.htod_copy(storage).w()?;
-                CudaStorageSlice::F32(data)
-            }
-            CpuStorage::F64(storage) => {
-                let data = self.htod_copy(storage).w()?;
-                CudaStorageSlice::F64(data)
-            }
-        };
-        Ok(CudaStorage {
-            slice,
-            device: self.clone(),
-        })
-    }
-
-    fn synchronize(&self) -> Result<()> {
-        self.device.synchronize().map_err(crate::Error::wrap)?;
-        Ok(())
-    }
-}

candle-core/src/cuda_backend/error.rs

@@ -1,62 +0,0 @@
-use crate::{DType, Layout};
-
-/// cudarc related errors
-#[derive(thiserror::Error, Debug)]
-pub enum CudaError {
-    #[error(transparent)]
-    Cuda(#[from] cudarc::driver::DriverError),
-
-    #[error(transparent)]
-    Compiler(#[from] cudarc::nvrtc::CompileError),
-
-    #[error(transparent)]
-    Cublas(#[from] cudarc::cublas::result::CublasError),
-
-    #[error(transparent)]
-    Curand(#[from] cudarc::curand::result::CurandError),
-
-    #[error("missing kernel '{module_name}'")]
-    MissingKernel { module_name: String },
-
-    #[error("unsupported dtype {dtype:?} for {op}")]
-    UnsupportedDtype { dtype: DType, op: &'static str },
-
-    #[error("internal error '{0}'")]
-    InternalError(&'static str),
-
-    #[error("matmul is only supported for contiguous tensors lstride: {lhs_stride:?} rstride: {rhs_stride:?} mnk: {mnk:?}")]
-    MatMulNonContiguous {
-        lhs_stride: Layout,
-        rhs_stride: Layout,
-        mnk: (usize, usize, usize),
-    },
-
-    #[error("{msg}, expected: {expected:?}, got: {got:?}")]
-    UnexpectedDType {
-        msg: &'static str,
-        expected: DType,
-        got: DType,
-    },
-
-    #[error("{cuda} when loading {module_name}")]
-    Load {
-        cuda: cudarc::driver::DriverError,
-        module_name: String,
-    },
-}
-
-impl From<CudaError> for crate::Error {
-    fn from(val: CudaError) -> Self {
-        crate::Error::Cuda(Box::new(val)).bt()
-    }
-}
-
-pub trait WrapErr<O> {
-    fn w(self) -> std::result::Result<O, crate::Error>;
-}
-
-impl<O, E: Into<CudaError>> WrapErr<O> for std::result::Result<O, E> {
-    fn w(self) -> std::result::Result<O, crate::Error> {
-        self.map_err(|e| crate::Error::Cuda(Box::new(e.into())).bt())
-    }
-}

candle-core/src/cuda_backend/utils.rs

@@ -1,134 +0,0 @@
-/// Helper functions to plug cuda kernels in candle.
-use crate::{Layout, Result, Shape, WithDType};
-pub use cudarc;
-use cudarc::driver::{CudaSlice, DeviceRepr, ValidAsZeroBits};
-
-use super::{CudaDevice, CudaError, WrapErr};
-
-pub type S = super::CudaStorageSlice;
-
-pub trait Map1 {
-    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
-        &self,
-        src: &CudaSlice<T>,
-        dev: &CudaDevice,
-        layout: &Layout,
-    ) -> Result<CudaSlice<T>>;
-
-    fn map(&self, s: &S, d: &CudaDevice, l: &Layout) -> Result<S> {
-        let out = match s {
-            S::U8(s) => S::U8(self.f(s, d, l)?),
-            S::U32(s) => S::U32(self.f(s, d, l)?),
-            S::I64(s) => S::I64(self.f(s, d, l)?),
-            S::BF16(s) => S::BF16(self.f(s, d, l)?),
-            S::F16(s) => S::F16(self.f(s, d, l)?),
-            S::F32(s) => S::F32(self.f(s, d, l)?),
-            S::F64(s) => S::F64(self.f(s, d, l)?),
-        };
-        Ok(out)
-    }
-}
-
-pub trait Map2 {
-    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
-        &self,
-        src1: &CudaSlice<T>,
-        layout1: &Layout,
-        src2: &CudaSlice<T>,
-        layout2: &Layout,
-        dev: &CudaDevice,
-    ) -> Result<CudaSlice<T>>;
-
-    fn map(&self, s1: &S, l1: &Layout, s2: &S, l2: &Layout, d: &CudaDevice) -> Result<S> {
-        let out = match (s1, s2) {
-            (S::U8(s1), S::U8(s2)) => S::U8(self.f(s1, l1, s2, l2, d)?),
-            (S::U32(s1), S::U32(s2)) => S::U32(self.f(s1, l1, s2, l2, d)?),
-            (S::I64(s1), S::I64(s2)) => S::I64(self.f(s1, l1, s2, l2, d)?),
-            (S::BF16(s1), S::BF16(s2)) => S::BF16(self.f(s1, l1, s2, l2, d)?),
-            (S::F16(s1), S::F16(s2)) => S::F16(self.f(s1, l1, s2, l2, d)?),
-            (S::F32(s1), S::F32(s2)) => S::F32(self.f(s1, l1, s2, l2, d)?),
-            (S::F64(s1), S::F64(s2)) => S::F64(self.f(s1, l1, s2, l2, d)?),
-            _ => Err(CudaError::InternalError("dtype mismatch in binary op"))?,
-        };
-        Ok(out)
-    }
-}
-
-pub trait Map2InPlace {
-    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
-        &self,
-        dst: &mut CudaSlice<T>,
-        dst_shape: &Shape,
-        src: &CudaSlice<T>,
-        src_l: &Layout,
-        dev: &CudaDevice,
-    ) -> Result<()>;
-
-    fn map(
-        &self,
-        dst: &mut S,
-        dst_s: &Shape,
-        src: &S,
-        src_l: &Layout,
-        d: &CudaDevice,
-    ) -> Result<()> {
-        match (dst, src) {
-            (S::U8(dst), S::U8(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::U32(dst), S::U32(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::I64(dst), S::I64(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::BF16(dst), S::BF16(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::F16(dst), S::F16(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::F32(dst), S::F32(src)) => self.f(dst, dst_s, src, src_l, d),
-            (S::F64(dst), S::F64(src)) => self.f(dst, dst_s, src, src_l, d),
-            _ => Err(CudaError::InternalError("dtype mismatch in binary op"))?,
-        }
-    }
-}
-
-pub trait Map1Any {
-    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits, W: Fn(CudaSlice<T>) -> S>(
-        &self,
-        src: &CudaSlice<T>,
-        dev: &CudaDevice,
-        layout: &Layout,
-        wrap: W,
-    ) -> Result<S>;
-
-    fn map(&self, s: &S, d: &CudaDevice, l: &Layout) -> Result<S> {
-        let out = match s {
-            S::U8(s) => self.f(s, d, l, S::U8)?,
-            S::U32(s) => self.f(s, d, l, S::U32)?,
-            S::I64(s) => self.f(s, d, l, S::I64)?,
-            S::BF16(s) => self.f(s, d, l, S::BF16)?,
-            S::F16(s) => self.f(s, d, l, S::F16)?,
-            S::F32(s) => self.f(s, d, l, S::F32)?,
-            S::F64(s) => self.f(s, d, l, S::F64)?,
-        };
-        Ok(out)
-    }
-}
-
-pub trait Map2Any {
-    fn f<T: DeviceRepr + WithDType + ValidAsZeroBits>(
-        &self,
-        src1: &CudaSlice<T>,
-        layout1: &Layout,
-        src2: &CudaSlice<T>,
-        layout2: &Layout,
-        dev: &CudaDevice,
-    ) -> Result<S>;
-
-    fn map(&self, s1: &S, l1: &Layout, s2: &S, l2: &Layout, d: &CudaDevice) -> Result<S> {
-        let out = match (s1, s2) {
-            (S::U8(s1), S::U8(s2)) => self.f(s1, l1, s2, l2, d)?,
-            (S::U32(s1), S::U32(s2)) => self.f(s1, l1, s2, l2, d)?,
-            (S::I64(s1), S::I64(s2)) => self.f(s1, l1, s2, l2, d)?,
-            (S::BF16(s1), S::BF16(s2)) => self.f(s1, l1, s2, l2, d)?,
-            (S::F16(s1), S::F16(s2)) => self.f(s1, l1, s2, l2, d)?,
-            (S::F32(s1), S::F32(s2)) => self.f(s1, l1, s2, l2, d)?,
-            (S::F64(s1), S::F64(s2)) => self.f(s1, l1, s2, l2, d)?,
-            _ => Err(CudaError::InternalError("dtype mismatch in binary op")).w()?,
-        };
-        Ok(out)
-    }
-}

candle-core/src/custom_op.rs

@@ -1,377 +0,0 @@
-use crate::op::{BackpropOp, Op};
-use crate::tensor::from_storage;
-use crate::{CpuStorage, CudaStorage, Layout, MetalStorage, Result, Shape, Tensor};
-use std::sync::Arc;
-
-/// Unary ops that can be defined in user-land.
-pub trait CustomOp1 {
-    // Box<dyn> does not support const yet, so use a function to get the name.
-    fn name(&self) -> &'static str;
-
-    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)>;
-
-    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cuda_fwd(&self, _storage: &CudaStorage, _layout: &Layout) -> Result<(CudaStorage, Shape)> {
-        Err(crate::Error::Cuda(
-            format!("no cuda implementation for {}", self.name()).into(),
-        ))
-    }
-
-    /// 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.
-    fn bwd(&self, _arg: &Tensor, _res: &Tensor, _grad_res: &Tensor) -> Result<Option<Tensor>> {
-        Err(crate::Error::BackwardNotSupported { op: self.name() })
-    }
-}
-
-pub trait CustomOp2 {
-    fn name(&self) -> &'static str;
-
-    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cpu_fwd(
-        &self,
-        s1: &CpuStorage,
-        l1: &Layout,
-        s2: &CpuStorage,
-        l2: &Layout,
-    ) -> Result<(CpuStorage, Shape)>;
-
-    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cuda_fwd(
-        &self,
-        _: &CudaStorage,
-        _: &Layout,
-        _: &CudaStorage,
-        _: &Layout,
-    ) -> Result<(CudaStorage, Shape)> {
-        Err(crate::Error::Cuda(
-            format!("no cuda implementation for {}", self.name()).into(),
-        ))
-    }
-
-    /// 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,
-        _arg2: &Tensor,
-        _res: &Tensor,
-        _grad_res: &Tensor,
-    ) -> Result<(Option<Tensor>, Option<Tensor>)> {
-        Err(crate::Error::BackwardNotSupported { op: self.name() })
-    }
-}
-
-pub trait CustomOp3 {
-    fn name(&self) -> &'static str;
-
-    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cpu_fwd(
-        &self,
-        s1: &CpuStorage,
-        l1: &Layout,
-        s2: &CpuStorage,
-        l2: &Layout,
-        s3: &CpuStorage,
-        l3: &Layout,
-    ) -> Result<(CpuStorage, Shape)>;
-
-    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cuda_fwd(
-        &self,
-        _: &CudaStorage,
-        _: &Layout,
-        _: &CudaStorage,
-        _: &Layout,
-        _: &CudaStorage,
-        _: &Layout,
-    ) -> Result<(CudaStorage, Shape)> {
-        Err(crate::Error::Cuda(
-            format!("no cuda implementation for {}", self.name()).into(),
-        ))
-    }
-
-    /// 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,
-        _arg2: &Tensor,
-        _arg3: &Tensor,
-        _res: &Tensor,
-        _grad_res: &Tensor,
-    ) -> Result<(Option<Tensor>, Option<Tensor>, Option<Tensor>)> {
-        Err(crate::Error::BackwardNotSupported { op: self.name() })
-    }
-}
-
-impl Tensor {
-    /// Applies a unary custom op without backward support
-    pub fn apply_op1_no_bwd<C: CustomOp1>(&self, c: &C) -> Result<Self> {
-        let (storage, shape) = self.storage().apply_op1(self.layout(), c)?;
-        Ok(from_storage(storage, shape, BackpropOp::none(), false))
-    }
-
-    /// Applies a binary custom op without backward support
-    pub fn apply_op2_no_bwd<C: CustomOp2>(&self, rhs: &Self, c: &C) -> Result<Self> {
-        let (storage, shape) =
-            self.storage()
-                .apply_op2(self.layout(), &rhs.storage(), rhs.layout(), c)?;
-        Ok(from_storage(storage, shape, BackpropOp::none(), false))
-    }
-
-    /// Applies a ternary custom op without backward support
-    pub fn apply_op3_no_bwd<C: CustomOp3>(&self, t2: &Self, t3: &Self, c: &C) -> Result<Self> {
-        let (storage, shape) = self.storage().apply_op3(
-            self.layout(),
-            &t2.storage(),
-            t2.layout(),
-            &t3.storage(),
-            t3.layout(),
-            c,
-        )?;
-        Ok(from_storage(storage, shape, BackpropOp::none(), false))
-    }
-
-    /// Applies a unary custom op.
-    pub fn apply_op1_arc(&self, c: Arc<Box<dyn CustomOp1 + Send + Sync>>) -> Result<Self> {
-        let (storage, shape) = self
-            .storage()
-            .apply_op1(self.layout(), c.as_ref().as_ref())?;
-        let op = BackpropOp::new1(self, |s| Op::CustomOp1(s, c.clone()));
-        Ok(from_storage(storage, shape, op, false))
-    }
-
-    pub fn apply_op1<C: 'static + CustomOp1 + Send + Sync>(&self, c: C) -> Result<Self> {
-        self.apply_op1_arc(Arc::new(Box::new(c)))
-    }
-
-    /// Applies a binary custom op.
-    pub fn apply_op2_arc(
-        &self,
-        rhs: &Self,
-        c: Arc<Box<dyn CustomOp2 + Send + Sync>>,
-    ) -> Result<Self> {
-        let (storage, shape) = self.storage().apply_op2(
-            self.layout(),
-            &rhs.storage(),
-            rhs.layout(),
-            c.as_ref().as_ref(),
-        )?;
-        let op = BackpropOp::new2(self, rhs, |t1, t2| Op::CustomOp2(t1, t2, c.clone()));
-        Ok(from_storage(storage, shape, op, false))
-    }
-
-    pub fn apply_op2<C: 'static + CustomOp2 + Send + Sync>(&self, r: &Self, c: C) -> Result<Self> {
-        self.apply_op2_arc(r, Arc::new(Box::new(c)))
-    }
-
-    /// Applies a ternary custom op.
-    pub fn apply_op3_arc(
-        &self,
-        t2: &Self,
-        t3: &Self,
-        c: Arc<Box<dyn CustomOp3 + Send + Sync>>,
-    ) -> Result<Self> {
-        let (storage, shape) = self.storage().apply_op3(
-            self.layout(),
-            &t2.storage(),
-            t2.layout(),
-            &t3.storage(),
-            t3.layout(),
-            c.as_ref().as_ref(),
-        )?;
-        let op = BackpropOp::new3(self, t2, t3, |t1, t2, t3| {
-            Op::CustomOp3(t1, t2, t3, c.clone())
-        });
-        Ok(from_storage(storage, shape, op, false))
-    }
-
-    pub fn apply_op3<C: 'static + CustomOp3 + Send + Sync>(
-        &self,
-        t2: &Self,
-        t3: &Self,
-        c: C,
-    ) -> Result<Self> {
-        self.apply_op3_arc(t2, t3, Arc::new(Box::new(c)))
-    }
-}
-
-// In place ops.
-
-/// Unary ops that can be defined in user-land.
-/// These ops work in place and as such back-prop is unsupported.
-pub trait InplaceOp1 {
-    // Box<dyn> does not support const yet, so use a function to get the name.
-    fn name(&self) -> &'static str;
-
-    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cpu_fwd(&self, storage: &mut CpuStorage, layout: &Layout) -> Result<()>;
-
-    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cuda_fwd(&self, _storage: &mut CudaStorage, _layout: &Layout) -> Result<()> {
-        Err(crate::Error::Cuda(
-            format!("no cuda implementation for {}", self.name()).into(),
-        ))
-    }
-
-    /// 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: &mut MetalStorage, _layout: &Layout) -> Result<()> {
-        Err(crate::Error::Metal(
-            format!("no metal implementation for {}", self.name()).into(),
-        ))
-    }
-}
-
-pub trait InplaceOp2 {
-    fn name(&self) -> &'static str;
-
-    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cpu_fwd(&self, s1: &mut CpuStorage, l1: &Layout, s2: &CpuStorage, l2: &Layout)
-        -> Result<()>;
-
-    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cuda_fwd(&self, _: &mut CudaStorage, _: &Layout, _: &CudaStorage, _: &Layout) -> Result<()> {
-        Err(crate::Error::Cuda(
-            format!("no cuda implementation for {}", self.name()).into(),
-        ))
-    }
-
-    /// 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,
-        _: &mut MetalStorage,
-        _: &Layout,
-        _: &MetalStorage,
-        _: &Layout,
-    ) -> Result<()> {
-        Err(crate::Error::Metal(
-            format!("no metal implementation for {}", self.name()).into(),
-        ))
-    }
-}
-
-pub trait InplaceOp3 {
-    fn name(&self) -> &'static str;
-
-    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cpu_fwd(
-        &self,
-        s1: &mut CpuStorage,
-        l1: &Layout,
-        s2: &CpuStorage,
-        l2: &Layout,
-        s3: &CpuStorage,
-        l3: &Layout,
-    ) -> Result<()>;
-
-    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
-    /// offsets etc so the associated layout should be used to access it.
-    fn cuda_fwd(
-        &self,
-        _: &mut CudaStorage,
-        _: &Layout,
-        _: &CudaStorage,
-        _: &Layout,
-        _: &CudaStorage,
-        _: &Layout,
-    ) -> Result<()> {
-        Err(crate::Error::Cuda(
-            format!("no cuda implementation for {}", self.name()).into(),
-        ))
-    }
-
-    /// 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,
-        _: &mut MetalStorage,
-        _: &Layout,
-        _: &MetalStorage,
-        _: &Layout,
-        _: &MetalStorage,
-        _: &Layout,
-    ) -> Result<()> {
-        Err(crate::Error::Metal(
-            format!("no metal implementation for {}", self.name()).into(),
-        ))
-    }
-}
-
-impl Tensor {
-    /// Applies a unary custom op in place.
-    pub fn inplace_op1<C: InplaceOp1>(&self, c: &C) -> Result<()> {
-        self.storage_mut().inplace_op1(self.layout(), c)
-    }
-
-    /// Applies a unary custom op in place (for the first tensor).
-    pub fn inplace_op2<C: InplaceOp2>(&self, rhs: &Self, c: &C) -> Result<()> {
-        self.storage_mut()
-            .inplace_op2(self.layout(), &rhs.storage(), rhs.layout(), c)
-    }
-
-    /// Applies a ternary custom op in place (for the first tensor).
-    pub fn inplace_op3<C: InplaceOp3>(&self, t2: &Self, t3: &Self, c: &C) -> Result<()> {
-        self.storage_mut().inplace_op3(
-            self.layout(),
-            &t2.storage(),
-            t2.layout(),
-            &t3.storage(),
-            t3.layout(),
-            c,
-        )
-    }
-}

candle-core/src/device.rs

@@ -289,34 +289,17 @@ impl Device {
         }
     }
 
-    pub(crate) unsafe fn alloc_uninit(&self, shape: &Shape, dtype: DType) -> Result<Storage> {
-        match self {
-            Device::Cpu => {
-                let storage = CpuDevice.alloc_uninit(shape, dtype)?;
-                Ok(Storage::Cpu(storage))
-            }
-            Device::Cuda(device) => {
-                let storage = device.alloc_uninit(shape, dtype)?;
-                Ok(Storage::Cuda(storage))
-            }
-            Device::Metal(device) => {
-                let storage = device.alloc_uninit(shape, dtype)?;
-                Ok(Storage::Metal(storage))
-            }
-        }
-    }
-
     pub(crate) fn storage<A: NdArray>(&self, array: A) -> Result<Storage> {
         match self {
             Device::Cpu => Ok(Storage::Cpu(array.to_cpu_storage())),
             Device::Cuda(device) => {
                 let storage = array.to_cpu_storage();
-                let storage = device.storage_from_cpu_storage_owned(storage)?;
+                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_owned(storage)?;
+                let storage = device.storage_from_cpu_storage(&storage)?;
                 Ok(Storage::Metal(storage))
             }
         }
@@ -327,22 +310,14 @@ impl Device {
             Device::Cpu => Ok(Storage::Cpu(S::to_cpu_storage_owned(data))),
             Device::Cuda(device) => {
                 let storage = S::to_cpu_storage_owned(data);
-                let storage = device.storage_from_cpu_storage_owned(storage)?;
+                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_owned(storage)?;
+                let storage = device.storage_from_cpu_storage(&storage)?;
                 Ok(Storage::Metal(storage))
             }
         }
     }
-
-    pub fn synchronize(&self) -> Result<()> {
-        match self {
-            Self::Cpu => Ok(()),
-            Self::Cuda(d) => d.synchronize(),
-            Self::Metal(d) => d.synchronize(),
-        }
-    }
 }

candle-core/src/display.rs

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

candle-core/src/dtype.rs

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

candle-core/src/dummy_cuda_backend.rs

@@ -154,19 +154,6 @@ impl crate::backend::BackendStorage for CudaStorage {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    fn copy2d(
-        &self,
-        _: &mut Self,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-    ) -> Result<()> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
     fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
         Err(Error::NotCompiledWithCudaSupport)
     }
@@ -210,18 +197,10 @@ impl crate::backend::BackendDevice for CudaDevice {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    unsafe fn alloc_uninit(&self, _shape: &Shape, _dtype: DType) -> Result<Self::Storage> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
     fn storage_from_cpu_storage(&self, _: &CpuStorage) -> Result<Self::Storage> {
         Err(Error::NotCompiledWithCudaSupport)
     }
 
-    fn storage_from_cpu_storage_owned(&self, _: CpuStorage) -> Result<Self::Storage> {
-        Err(Error::NotCompiledWithCudaSupport)
-    }
-
     fn rand_uniform(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
         Err(Error::NotCompiledWithCudaSupport)
     }
@@ -229,8 +208,4 @@ impl crate::backend::BackendDevice for CudaDevice {
     fn rand_normal(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
         Err(Error::NotCompiledWithCudaSupport)
     }
-
-    fn synchronize(&self) -> Result<()> {
-        Ok(())
-    }
 }

candle-core/src/dummy_metal_backend.rs

@@ -166,19 +166,6 @@ impl crate::backend::BackendStorage for MetalStorage {
         Err(Error::NotCompiledWithMetalSupport)
     }
 
-    fn copy2d(
-        &self,
-        _: &mut Self,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-        _: usize,
-    ) -> Result<()> {
-        Err(Error::NotCompiledWithMetalSupport)
-    }
-
     fn avg_pool2d(&self, _: &Layout, _: (usize, usize), _: (usize, usize)) -> Result<Self> {
         Err(Error::NotCompiledWithMetalSupport)
     }
@@ -222,18 +209,10 @@ impl crate::backend::BackendDevice for MetalDevice {
         Err(Error::NotCompiledWithMetalSupport)
     }
 
-    unsafe fn alloc_uninit(&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 storage_from_cpu_storage_owned(&self, _: CpuStorage) -> Result<Self::Storage> {
-        Err(Error::NotCompiledWithMetalSupport)
-    }
-
     fn rand_uniform(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
         Err(Error::NotCompiledWithMetalSupport)
     }
@@ -241,8 +220,4 @@ impl crate::backend::BackendDevice for MetalDevice {
     fn rand_normal(&self, _: &Shape, _: DType, _: f64, _: f64) -> Result<Self::Storage> {
         Err(Error::NotCompiledWithMetalSupport)
     }
-
-    fn synchronize(&self) -> Result<()> {
-        Ok(())
-    }
 }

candle-core/src/layout.rs

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

candle-core/src/lib.rs

@@ -14,7 +14,7 @@
 //!
 //! ## Features
 //!
-//! - Simple syntax (looks and feels like PyTorch)
+//! - Simple syntax (looks and like PyTorch)
 //! - CPU and Cuda backends (and M1 support)
 //! - Enable serverless (CPU) small and fast deployments
 //! - Model training
@@ -37,13 +37,14 @@
 mod accelerate;
 pub mod backend;
 pub mod backprop;
-pub mod conv;
+mod conv;
 mod convert;
 pub mod cpu;
 pub mod cpu_backend;
 #[cfg(feature = "cuda")]
 pub mod cuda_backend;
-mod custom_op;
+#[cfg(feature = "cudnn")]
+pub mod cudnn;
 mod device;
 pub mod display;
 mod dtype;
@@ -57,7 +58,7 @@ pub mod metal_backend;
 #[cfg(feature = "mkl")]
 mod mkl;
 pub mod npy;
-pub mod op;
+mod op;
 pub mod pickle;
 pub mod quantized;
 pub mod safetensors;
@@ -66,21 +67,17 @@ pub mod shape;
 mod storage;
 mod strided_index;
 mod tensor;
-mod tensor_cat;
 pub mod test_utils;
 pub mod utils;
 mod variable;
 
-#[cfg(feature = "cudnn")]
-pub use cuda_backend::cudnn;
-
 pub use cpu_backend::CpuStorage;
-pub use custom_op::{CustomOp1, CustomOp2, CustomOp3, InplaceOp1, InplaceOp2, InplaceOp3};
 pub use device::{Device, DeviceLocation, NdArray};
-pub use dtype::{DType, DTypeParseError, FloatDType, IntDType, WithDType};
+pub use dtype::{DType, FloatDType, IntDType, WithDType};
 pub use error::{Error, Result};
 pub use indexer::IndexOp;
 pub use layout::Layout;
+pub use op::{CustomOp1, CustomOp2, CustomOp3};
 pub use shape::{Shape, D};
 pub use storage::Storage;
 pub use strided_index::{StridedBlocks, StridedIndex};
@@ -132,15 +129,6 @@ impl<T: Fn(&Tensor) -> Result<Tensor>> Module for T {
     }
 }
 
-impl<M: Module> Module for Option<&M> {
-    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
-        match self {
-            None => Ok(xs.clone()),
-            Some(m) => m.forward(xs),
-        }
-    }
-}
-
 // A trait defining a module with forward method using a single tensor argument and a flag to
 // separate the training and evaluation behaviors.
 pub trait ModuleT {

candle-core/src/metal_backend/device.rs

@@ -1,287 +0,0 @@
-use crate::{DType, Result};
-use candle_metal_kernels::Kernels;
-use metal::{Buffer, CommandBuffer, CommandQueue, MTLResourceOptions, NSUInteger};
-use std::collections::HashMap;
-use std::ffi::c_void;
-use std::path::Path;
-use std::sync::{Arc, Mutex, RwLock, RwLockWriteGuard};
-
-use super::MetalError;
-
-/// Unique identifier for cuda devices.
-#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
-pub struct DeviceId(usize);
-
-impl DeviceId {
-    pub(crate) fn new() -> Self {
-        // https://users.rust-lang.org/t/idiomatic-rust-way-to-generate-unique-id/33805
-        use std::sync::atomic;
-        static COUNTER: atomic::AtomicUsize = atomic::AtomicUsize::new(1);
-        Self(COUNTER.fetch_add(1, atomic::Ordering::Relaxed))
-    }
-}
-
-type BufferMap = HashMap<(NSUInteger, MTLResourceOptions), Vec<Arc<Buffer>>>;
-type AllocatedBuffers = Arc<RwLock<BufferMap>>;
-
-#[derive(Clone)]
-pub struct MetalDevice {
-    /// Unique identifier, the registryID is not sufficient as it identifies the GPU rather than
-    /// the device itself.
-    pub(crate) id: DeviceId,
-
-    /// Raw metal device: <https://developer.apple.com/documentation/metal/mtldevice?language=objc>
-    pub(crate) device: metal::Device,
-
-    /// Single command queue for the entire device.
-    pub(crate) command_queue: CommandQueue,
-    /// One command buffer at a time.
-    /// The scheduler works by allowing multiple
-    /// [ComputeCommandEncoder](https://developer.apple.com/documentation/metal/mtlcomputecommandencoder?language=objc)
-    /// on a single command buffer. Using a single command buffer would be fastest on the GPU but
-    /// prevents overlapping of CPU and GPU commands (because command buffer needs to be committed
-    /// to start to work).
-    /// Despite what the documentation says, command buffers are NOT ordered. They are ordered
-    /// for their START time, but there's no guarantee that command buffer1 will finish before
-    /// command buffer2 starts (or there are metal bugs there)
-    pub(crate) command_buffer: Arc<RwLock<CommandBuffer>>,
-    /// Keeps track of the current amount of compute command encoders on the current
-    /// command buffer
-    /// Arc, RwLock because of the interior mutability.
-    pub(crate) command_buffer_index: Arc<RwLock<usize>>,
-    /// The maximum amount of [compute command encoder](https://developer.apple.com/documentation/metal/mtlcomputecommandencoder?language=objc) per [command buffer](https://developer.apple.com/documentation/metal/mtlcommandbuffer?language=objc)
-    pub(crate) compute_per_buffer: usize,
-    /// Simple keeper struct to keep track of the already compiled kernels so we can reuse them.
-    /// Heavily used by [`candle_metal_kernels`]
-    pub(crate) kernels: Arc<Kernels>,
-    /// Simple allocator struct.
-    /// The buffers are stored in size buckets since ML tends to use similar shapes over and over.
-    /// We store the buffers in [`Arc`] because it's much faster than Obj-c internal ref counting
-    /// (could be linked to FFI communication overhead).
-    ///
-    /// Whenever a buffer has a strong_count==1, we can reuse it, it means it was dropped in the
-    /// graph calculation, and only we the allocator kept a reference to it, therefore it's free
-    /// to be reused. However, in order for this to work, we need to guarantee the order of
-    /// operation, so that this buffer is not being used by another kernel at the same time.
-    /// Arc is the CPU reference count, it doesn't mean anything on the GPU side of things.
-    ///
-    /// Whenever we actually allocate a new buffer, we make a full sweep to clean up unused buffers
-    /// (strong_count = 1).
-    pub(crate) buffers: AllocatedBuffers,
-    /// Seed for random number generation.
-    pub(crate) seed: Arc<Mutex<Buffer>>,
-}
-
-impl std::fmt::Debug for MetalDevice {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "MetalDevice({:?})", self.id)
-    }
-}
-
-impl std::ops::Deref for MetalDevice {
-    type Target = metal::DeviceRef;
-
-    fn deref(&self) -> &Self::Target {
-        &self.device
-    }
-}
-
-impl MetalDevice {
-    pub fn id(&self) -> DeviceId {
-        self.id
-    }
-
-    pub fn metal_device(&self) -> &metal::Device {
-        &self.device
-    }
-
-    pub fn command_queue(&self) -> &CommandQueue {
-        &self.command_queue
-    }
-
-    pub fn command_buffer(&self) -> Result<CommandBuffer> {
-        let mut command_buffer_lock = self.command_buffer.try_write().map_err(MetalError::from)?;
-        let mut command_buffer = command_buffer_lock.to_owned();
-        let mut index = self
-            .command_buffer_index
-            .try_write()
-            .map_err(MetalError::from)?;
-        if *index > self.compute_per_buffer {
-            command_buffer.commit();
-            command_buffer = self.command_queue.new_command_buffer().to_owned();
-            *command_buffer_lock = command_buffer.clone();
-            *index = 0;
-
-            self.drop_unused_buffers()?;
-        }
-        *index += 1;
-        Ok(command_buffer)
-    }
-
-    pub fn wait_until_completed(&self) -> Result<()> {
-        let mut command_buffer = self.command_buffer.try_write().map_err(MetalError::from)?;
-        match command_buffer.status() {
-            metal::MTLCommandBufferStatus::Committed
-            | metal::MTLCommandBufferStatus::Scheduled
-            | metal::MTLCommandBufferStatus::Completed => {
-                panic!("Already committed");
-            }
-            _ => {}
-        }
-        command_buffer.commit();
-        command_buffer.wait_until_completed();
-        *command_buffer = self.command_queue.new_command_buffer().to_owned();
-
-        Ok(())
-    }
-
-    pub fn kernels(&self) -> &Kernels {
-        &self.kernels
-    }
-
-    pub fn device(&self) -> &metal::Device {
-        &self.device
-    }
-
-    /// Creates a new buffer (not necessarily zeroed).
-    /// The buffer is [MTLPrivate](https://developer.apple.com/documentation/metal/mtlstoragemode)
-    /// This means the buffer data cannot be read on the CPU directly.
-    ///
-    /// [`name`] is only used to keep track of the resource origin in case of bugs
-    pub fn new_buffer(
-        &self,
-        element_count: usize,
-        dtype: DType,
-        name: &str,
-    ) -> Result<Arc<Buffer>> {
-        let size = (element_count * dtype.size_in_bytes()) as NSUInteger;
-        self.allocate_buffer(size, MTLResourceOptions::StorageModePrivate, name)
-    }
-
-    /// Creates a new buffer (not necessarily zeroed).
-    /// The buffer is [MTLManaged](https://developer.apple.com/documentation/metal/mtlstoragemode)
-    /// This means the buffer can be read on the CPU but will require manual
-    /// synchronization when the CPU memory is modified
-    /// Used as a bridge to gather data back from the GPU
-    pub fn new_buffer_managed(&self, size: NSUInteger) -> Result<Arc<Buffer>> {
-        self.allocate_buffer(size, MTLResourceOptions::StorageModeManaged, "managed")
-    }
-
-    /// Creates a new buffer from data.
-    /// The buffer is [MTLManaged](https://developer.apple.com/documentation/metal/mtlstoragemode)
-    ///
-    /// Does not require synchronization, as [newBufferWithBytes](https://developer.apple.com/documentation/metal/mtldevice/1433429-newbufferwithbytes)
-    /// allocates the buffer and copies over the existing data before returning the MTLBuffer.
-    pub fn new_buffer_with_data<T>(&self, data: &[T]) -> Result<Arc<Buffer>> {
-        let size = core::mem::size_of_val(data) as NSUInteger;
-        let new_buffer = self.device.new_buffer_with_data(
-            data.as_ptr() as *const c_void,
-            size,
-            MTLResourceOptions::StorageModeManaged,
-        );
-        let mut buffers = self.buffers.try_write().map_err(MetalError::from)?;
-        let subbuffers = buffers
-            .entry((size, MTLResourceOptions::StorageModeManaged))
-            .or_insert(vec![]);
-
-        let new_buffer = Arc::new(new_buffer);
-        subbuffers.push(new_buffer.clone());
-        Ok(new_buffer)
-    }
-
-    pub fn allocate_zeros(&self, size_in_bytes: usize) -> Result<Arc<Buffer>> {
-        let buffer = self.allocate_buffer(
-            size_in_bytes as NSUInteger,
-            MTLResourceOptions::StorageModePrivate,
-            "allocate_zeros",
-        )?;
-        let command_buffer = self.command_buffer()?;
-        command_buffer.set_label("zeros");
-        let blit = command_buffer.new_blit_command_encoder();
-        blit.fill_buffer(
-            &buffer,
-            metal::NSRange {
-                location: 0,
-                length: buffer.length(),
-            },
-            0,
-        );
-        blit.end_encoding();
-        Ok(buffer)
-    }
-
-    fn find_available_buffer(
-        &self,
-        size: NSUInteger,
-        option: MTLResourceOptions,
-        buffers: &RwLockWriteGuard<BufferMap>,
-    ) -> Option<Arc<Buffer>> {
-        let mut best_buffer: Option<&Arc<Buffer>> = None;
-        let mut best_buffer_size: NSUInteger = NSUInteger::MAX;
-        for ((buffer_size, buffer_option), subbuffers) in buffers.iter() {
-            if buffer_size >= &size && buffer_size < &best_buffer_size && buffer_option == &option {
-                for sub in subbuffers {
-                    if Arc::strong_count(sub) == 1 {
-                        best_buffer = Some(sub);
-                        best_buffer_size = *buffer_size;
-                    }
-                }
-            }
-        }
-        best_buffer.cloned()
-    }
-
-    fn drop_unused_buffers(&self) -> Result<()> {
-        let mut buffers = self.buffers.try_write().map_err(MetalError::from)?;
-        for subbuffers in buffers.values_mut() {
-            let newbuffers = subbuffers
-                .iter()
-                .filter(|s| Arc::strong_count(*s) > 1)
-                .map(Arc::clone)
-                .collect();
-            *subbuffers = newbuffers;
-        }
-        Ok(())
-    }
-
-    /// The critical allocator algorithm
-    fn allocate_buffer(
-        &self,
-        size: NSUInteger,
-        option: MTLResourceOptions,
-        _name: &str,
-    ) -> Result<Arc<Buffer>> {
-        let mut buffers = self.buffers.try_write().map_err(MetalError::from)?;
-        if let Some(b) = self.find_available_buffer(size, option, &buffers) {
-            // Cloning also ensures we increment the strong count
-            return Ok(b.clone());
-        }
-
-        let size = buf_size(size);
-        let subbuffers = buffers.entry((size, option)).or_insert(vec![]);
-
-        let new_buffer = self.device.new_buffer(size as NSUInteger, option);
-        let new_buffer = Arc::new(new_buffer);
-        subbuffers.push(new_buffer.clone());
-
-        Ok(new_buffer)
-    }
-
-    /// Create a metal GPU capture trace on [`path`].
-    pub fn capture<P: AsRef<Path>>(&self, path: P) -> Result<()> {
-        let capture = metal::CaptureManager::shared();
-        let descriptor = metal::CaptureDescriptor::new();
-        descriptor.set_destination(metal::MTLCaptureDestination::GpuTraceDocument);
-        descriptor.set_capture_device(self);
-        descriptor.set_output_url(path);
-
-        capture
-            .start_capture(&descriptor)
-            .map_err(MetalError::from)?;
-        Ok(())
-    }
-}
-
-fn buf_size(size: NSUInteger) -> NSUInteger {
-    size.saturating_sub(1).next_power_of_two() as NSUInteger
-}

candle-core/src/mkl.rs

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

candle-core/src/op.rs

@@ -1,5 +1,5 @@
 #![allow(clippy::redundant_closure_call)]
-use crate::Tensor;
+use crate::{CpuStorage, CudaStorage, Layout, MetalStorage, Result, Shape, Tensor};
 use half::{bf16, f16};
 use num_traits::float::Float;
 
@@ -61,12 +61,10 @@ pub enum UnaryOp {
     GeluErf,
     Erf,
     Relu,
-    Silu,
     Tanh,
     Floor,
     Ceil,
     Round,
-    Sign,
 }
 
 #[derive(Clone)]
@@ -133,10 +131,7 @@ pub enum Op {
         stride: (usize, usize),
     },
 
-    UpsampleNearest1D {
+    UpsampleNearest1D(Tensor),
-        arg: Tensor,
-        target_size: usize,
-    },
     UpsampleNearest2D {
         arg: Tensor,
         target_h: usize,
@@ -162,23 +157,168 @@ pub enum Op {
     Permute(Tensor, Vec<usize>),
     Elu(Tensor, f64),
     Powf(Tensor, f64),
-    CustomOp1(
+    CustomOp1(Tensor, std::sync::Arc<Box<dyn CustomOp1 + Send + Sync>>),
-        Tensor,
-        std::sync::Arc<Box<dyn crate::CustomOp1 + Send + Sync>>,
-    ),
     CustomOp2(
         Tensor,
         Tensor,
-        std::sync::Arc<Box<dyn crate::CustomOp2 + Send + Sync>>,
+        std::sync::Arc<Box<dyn CustomOp2 + Send + Sync>>,
     ),
     CustomOp3(
         Tensor,
         Tensor,
         Tensor,
-        std::sync::Arc<Box<dyn crate::CustomOp3 + Send + Sync>>,
+        std::sync::Arc<Box<dyn CustomOp3 + Send + Sync>>,
     ),
 }
 
+/// Unary ops that can be defined in user-land.
+pub trait CustomOp1 {
+    // Box<dyn> does not support const yet, so use a function to get the name.
+    fn name(&self) -> &'static str;
+
+    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)>;
+
+    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn cuda_fwd(&self, _storage: &CudaStorage, _layout: &Layout) -> Result<(CudaStorage, Shape)> {
+        Err(crate::Error::Cuda(
+            format!("no cuda implementation for {}", self.name()).into(),
+        ))
+    }
+
+    /// 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.
+    fn bwd(&self, _arg: &Tensor, _res: &Tensor, _grad_res: &Tensor) -> Result<Option<Tensor>> {
+        Err(crate::Error::BackwardNotSupported { op: self.name() })
+    }
+}
+
+pub trait CustomOp2 {
+    fn name(&self) -> &'static str;
+
+    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn cpu_fwd(
+        &self,
+        s1: &CpuStorage,
+        l1: &Layout,
+        s2: &CpuStorage,
+        l2: &Layout,
+    ) -> Result<(CpuStorage, Shape)>;
+
+    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn cuda_fwd(
+        &self,
+        _: &CudaStorage,
+        _: &Layout,
+        _: &CudaStorage,
+        _: &Layout,
+    ) -> Result<(CudaStorage, Shape)> {
+        Err(crate::Error::Cuda(
+            format!("no cuda implementation for {}", self.name()).into(),
+        ))
+    }
+
+    /// 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,
+        _arg2: &Tensor,
+        _res: &Tensor,
+        _grad_res: &Tensor,
+    ) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        Err(crate::Error::BackwardNotSupported { op: self.name() })
+    }
+}
+
+pub trait CustomOp3 {
+    fn name(&self) -> &'static str;
+
+    /// The forward pass, as run on a cpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn cpu_fwd(
+        &self,
+        s1: &CpuStorage,
+        l1: &Layout,
+        s2: &CpuStorage,
+        l2: &Layout,
+        s3: &CpuStorage,
+        l3: &Layout,
+    ) -> Result<(CpuStorage, Shape)>;
+
+    /// The forward pass, as run on a gpu device. Note that the storage can use arbitrary strides,
+    /// offsets etc so the associated layout should be used to access it.
+    fn cuda_fwd(
+        &self,
+        _: &CudaStorage,
+        _: &Layout,
+        _: &CudaStorage,
+        _: &Layout,
+        _: &CudaStorage,
+        _: &Layout,
+    ) -> Result<(CudaStorage, Shape)> {
+        Err(crate::Error::Cuda(
+            format!("no cuda implementation for {}", self.name()).into(),
+        ))
+    }
+
+    /// 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,
+        _arg2: &Tensor,
+        _arg3: &Tensor,
+        _res: &Tensor,
+        _grad_res: &Tensor,
+    ) -> Result<(Option<Tensor>, Option<Tensor>, Option<Tensor>)> {
+        Err(crate::Error::BackwardNotSupported { op: self.name() })
+    }
+}
+
 pub trait UnaryOpT {
     const NAME: &'static str;
     const KERNEL: &'static str;
@@ -250,12 +390,10 @@ pub(crate) struct Gelu;
 pub(crate) struct GeluErf;
 pub(crate) struct Erf;
 pub(crate) struct Relu;
-pub(crate) struct Silu;
 pub(crate) struct Tanh;
 pub(crate) struct Floor;
 pub(crate) struct Ceil;
 pub(crate) struct Round;
-pub(crate) struct Sign;
 
 macro_rules! bin_op {
     ($op:ident, $name: literal, $e: expr, $f32_vec: ident, $f64_vec: ident) => {
@@ -459,13 +597,6 @@ 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);
 
-// Hardcode the value for sqrt(2/pi)
-// https://github.com/huggingface/candle/issues/1982
-#[allow(clippy::excessive_precision)]
-const SQRT_TWO_OVER_PI_F32: f32 = 0.79788456080286535587989211986876373;
-#[allow(clippy::excessive_precision)]
-const SQRT_TWO_OVER_PI_F64: f64 = 0.79788456080286535587989211986876373;
-
 /// Tanh based approximation of the `gelu` operation
 /// GeluErf is the more precise one.
 /// <https://en.wikipedia.org/wiki/Activation_function#Comparison_of_activation_functions>
@@ -478,7 +609,7 @@ impl UnaryOpT for Gelu {
             * v
             * (bf16::ONE
                 + bf16::tanh(
-                    bf16::from_f32_const(SQRT_TWO_OVER_PI_F32)
+                    (bf16::from_f32_const(2.0) / bf16::PI).sqrt()
                         * v
                         * (bf16::ONE + bf16::from_f32_const(0.044715) * v * v),
                 ))
@@ -489,18 +620,22 @@ impl UnaryOpT for Gelu {
             * v
             * (f16::ONE
                 + f16::tanh(
-                    f16::from_f32_const(SQRT_TWO_OVER_PI_F32)
+                    (f16::from_f32_const(2.0) / f16::PI).sqrt()
                         * v
                         * (f16::ONE + f16::from_f32_const(0.044715) * v * v),
                 ))
     }
     #[inline(always)]
     fn f32(v: f32) -> f32 {
-        0.5 * v * (1.0 + f32::tanh(SQRT_TWO_OVER_PI_F32 * v * (1.0 + 0.044715 * v * v)))
+        0.5 * v
+            * (1.0
+                + f32::tanh((2.0f32 / std::f32::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)))
     }
     #[inline(always)]
     fn f64(v: f64) -> f64 {
-        0.5 * v * (1.0 + f64::tanh(SQRT_TWO_OVER_PI_F64 * v * (1.0 + 0.044715 * v * v)))
+        0.5 * v
+            * (1.0
+                + f64::tanh((2.0f64 / std::f64::consts::PI).sqrt() * v * (1.0 + 0.044715 * v * v)))
     }
     #[inline(always)]
     fn u8(_: u8) -> u8 {
@@ -589,77 +724,6 @@ impl UnaryOpT for Erf {
     }
 }
 
-/// Silu operation
-impl UnaryOpT for Silu {
-    const NAME: &'static str = "silu";
-    const V: Self = Silu;
-    #[inline(always)]
-    fn bf16(v: bf16) -> bf16 {
-        v / (bf16::ONE + (-v).exp())
-    }
-    #[inline(always)]
-    fn f16(v: f16) -> f16 {
-        v / (f16::ONE + (-v).exp())
-    }
-    #[inline(always)]
-    fn f32(v: f32) -> f32 {
-        v / (1.0 + (-v).exp())
-    }
-    #[inline(always)]
-    fn f64(v: f64) -> f64 {
-        v / (1.0 + (-v).exp())
-    }
-    #[inline(always)]
-    fn u8(_: u8) -> u8 {
-        0
-    }
-    #[inline(always)]
-    fn u32(_: u32) -> u32 {
-        0
-    }
-    #[inline(always)]
-    fn i64(_: i64) -> i64 {
-        0
-    }
-    const KERNEL: &'static str = "usilu";
-
-    #[cfg(feature = "mkl")]
-    const F32_VEC: bool = true;
-
-    #[cfg(feature = "mkl")]
-    #[inline(always)]
-    fn f32_vec(xs: &[f32], ys: &mut [f32]) {
-        crate::mkl::vs_silu(xs, ys)
-    }
-
-    #[cfg(feature = "mkl")]
-    const F64_VEC: bool = true;
-
-    #[cfg(feature = "mkl")]
-    #[inline(always)]
-    fn f64_vec(xs: &[f64], ys: &mut [f64]) {
-        crate::mkl::vd_silu(xs, ys)
-    }
-
-    #[cfg(feature = "accelerate")]
-    const F32_VEC: bool = true;
-
-    #[cfg(feature = "accelerate")]
-    #[inline(always)]
-    fn f32_vec(xs: &[f32], ys: &mut [f32]) {
-        crate::accelerate::vs_silu(xs, ys)
-    }
-
-    #[cfg(feature = "accelerate")]
-    const F64_VEC: bool = true;
-
-    #[cfg(feature = "accelerate")]
-    #[inline(always)]
-    fn f64_vec(xs: &[f64], ys: &mut [f64]) {
-        crate::accelerate::vd_silu(xs, ys)
-    }
-}
-
 impl UnaryOpT for Abs {
     const NAME: &'static str = "abs";
     const KERNEL: &'static str = "uabs";
@@ -927,37 +991,3 @@ impl std::ops::Deref for BackpropOp {
         &self.0
     }
 }
-
-impl UnaryOpT for Sign {
-    const NAME: &'static str = "sign";
-    const KERNEL: &'static str = "usign";
-    const V: Self = Sign;
-    #[inline(always)]
-    fn bf16(v: bf16) -> bf16 {
-        bf16::from((v > bf16::ZERO) as i8) - bf16::from((v < bf16::ZERO) as i8)
-    }
-    #[inline(always)]
-    fn f16(v: f16) -> f16 {
-        f16::from((v > f16::ZERO) as i8) - f16::from((v < f16::ZERO) as i8)
-    }
-    #[inline(always)]
-    fn f32(v: f32) -> f32 {
-        f32::from(v > 0.) - f32::from(v < 0.)
-    }
-    #[inline(always)]
-    fn f64(v: f64) -> f64 {
-        f64::from(v > 0.) - f64::from(v < 0.)
-    }
-    #[inline(always)]
-    fn u8(v: u8) -> u8 {
-        u8::min(1, v)
-    }
-    #[inline(always)]
-    fn u32(v: u32) -> u32 {
-        u32::min(1, v)
-    }
-    #[inline(always)]
-    fn i64(v: i64) -> i64 {
-        (v > 0) as i64 - (v < 0) as i64
-    }
-}

candle-core/src/pickle.rs

@@ -42,7 +42,7 @@ pub enum OpCode {
     Stop = b'.',
     NewObj = 0x81,
     EmptyList = b']',
-    BinFloat = b'G',
+    BinFloat = b'g',
     Append = b'a',
     Appends = b'e',
 }
@@ -217,13 +217,6 @@ impl Object {
                 let args = args.remove(1);
                 (callable, args)
             }
-            Object::Class {
-                module_name,
-                class_name,
-            } if module_name == "torch._utils" && class_name == "_rebuild_parameter" => {
-                let mut args = args.tuple()?;
-                args.remove(0).reduce()?
-            }
             _ => (callable, args),
         };
         match callable {
@@ -234,11 +227,13 @@ impl Object {
             _ => return Ok(None),
         };
         let (layout, dtype, file_path, storage_size) = rebuild_args(args)?;
+        let mut path = dir_name.to_path_buf();
+        path.push(file_path);
         Ok(Some(TensorInfo {
             name,
             dtype,
             layout,
-            path: format!("{}/{}", dir_name.to_string_lossy(), file_path),
+            path: path.to_string_lossy().into_owned(),
             storage_size,
         }))
     }
@@ -350,10 +345,8 @@ impl Stack {
                 module_name,
                 class_name,
             } => {
-                if module_name == "collections"
+                if module_name == "collections" && class_name == "OrderedDict" {
-                    && (class_name == "OrderedDict" || class_name == "defaultdict")
+                    // TODO: have a separate ordered dict.
-                {
-                    // TODO: have a separate ordered dict and a separate default dict.
                     Some(Object::Dict(vec![]))
                 } else {
                     None
@@ -462,10 +455,7 @@ impl Stack {
                 self.push(Object::Int(arg))
             }
             OpCode::BinFloat => {
-                // Somehow floats are encoded using BigEndian whereas int types use LittleEndian.
+                let arg = r.read_f64::<LittleEndian>()?;
-                // https://github.com/python/cpython/blob/0c80da4c14d904a367968955544dd6ae58c8101c/Lib/pickletools.py#L855
-                // https://github.com/pytorch/pytorch/blob/372d078f361e726bb4ac0884ac334b04c58179ef/torch/_weights_only_unpickler.py#L243
-                let arg = r.read_f64::<byteorder::BigEndian>()?;
                 self.push(Object::Float(arg))
             }
             OpCode::BinUnicode => {
@@ -637,16 +627,9 @@ pub struct TensorInfo {
     pub storage_size: usize,
 }
 
-/// Read the tensor info from a .pth file.
-///
-/// # Arguments
-/// * `file` - The path to the .pth file.
-/// * `verbose` - Whether to print debug information.
-/// * `key` - Optional key to retrieve `state_dict` from the pth file.
 pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
     file: P,
     verbose: bool,
-    key: Option<&str>,
 ) -> Result<Vec<TensorInfo>> {
     let file = std::fs::File::open(file)?;
     let zip_reader = std::io::BufReader::new(file);
@@ -668,9 +651,8 @@ pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
         stack.read_loop(&mut reader)?;
         let obj = stack.finalize()?;
         if VERBOSE || verbose {
-            println!("{obj:#?}");
+            println!("{obj:?}");
         }
-
         let obj = match obj {
             Object::Build { callable, args } => match *callable {
                 Object::Reduce { callable, args: _ } => match *callable {
@@ -684,24 +666,6 @@ pub fn read_pth_tensor_info<P: AsRef<std::path::Path>>(
             },
             obj => obj,
         };
-
-        // If key is provided, then we need to extract the state_dict from the object.
-        let obj = if let Some(key) = key {
-            if let Object::Dict(key_values) = obj {
-                key_values
-                    .into_iter()
-                    .find(|(k, _)| *k == Object::Unicode(key.to_owned()))
-                    .map(|(_, v)| v)
-                    .ok_or_else(|| E::Msg(format!("key {key} not found")))?
-            } else {
-                obj
-            }
-        } else {
-            obj
-        };
-
-        // If the object is a dict, then we can extract the tensor info from it.
-        // NOTE: We are assuming that the `obj` is state_dict by this stage.
         if let Object::Dict(key_values) = obj {
             for (name, value) in key_values.into_iter() {
                 match value.into_tensor_info(name, &dir_name) {
@@ -724,8 +688,8 @@ pub struct PthTensors {
 }
 
 impl PthTensors {
-    pub fn new<P: AsRef<std::path::Path>>(path: P, key: Option<&str>) -> Result<Self> {
+    pub fn new<P: AsRef<std::path::Path>>(path: P) -> Result<Self> {
-        let tensor_infos = read_pth_tensor_info(path.as_ref(), false, key)?;
+        let tensor_infos = read_pth_tensor_info(path.as_ref(), false)?;
         let tensor_infos = tensor_infos
             .into_iter()
             .map(|ti| (ti.name.to_string(), ti))
@@ -748,12 +712,10 @@ impl PthTensors {
         let zip_reader = std::io::BufReader::new(std::fs::File::open(&self.path)?);
         let mut zip = zip::ZipArchive::new(zip_reader)?;
         let mut reader = zip.by_name(&tensor_info.path)?;
-        let is_fortran_contiguous = tensor_info.layout.is_fortran_contiguous();
-        let rank = tensor_info.layout.shape().rank();
 
         // Reading the data is a bit tricky as it can be strided, for now only support the basic
-        // case and when the tensor is fortran contiguous.
+        // case.
-        if !tensor_info.layout.is_contiguous() && !is_fortran_contiguous {
+        if !tensor_info.layout.is_contiguous() {
             crate::bail!(
                 "cannot retrieve non-contiguous tensors {:?}",
                 tensor_info.layout
@@ -771,33 +733,13 @@ impl PthTensors {
             tensor_info.dtype,
             &mut reader,
         )?;
-
+        Ok(Some(tensor))
-        if rank > 1 && is_fortran_contiguous {
-            // Reverse the shape, e.g. Shape(2, 3, 4) -> Shape(4, 3, 2)
-            let shape_reversed: Vec<_> = tensor_info.layout.dims().iter().rev().cloned().collect();
-            let tensor = tensor.reshape(shape_reversed)?;
-
-            // Permute (transpose) the dimensions, e.g. Shape(4, 3, 2) -> Shape(2, 3, 4)
-            let dim_indeces_reversed: Vec<_> = (0..rank).rev().collect();
-            let tensor = tensor.permute(dim_indeces_reversed)?;
-            Ok(Some(tensor))
-        } else {
-            Ok(Some(tensor))
-        }
     }
 }
 
-/// Read all the tensors from a PyTorch pth file with a given key.
+/// Read all the tensors from a PyTorch pth file.
-///
+pub fn read_all<P: AsRef<std::path::Path>>(path: P) -> Result<Vec<(String, Tensor)>> {
-/// # Arguments
+    let pth = PthTensors::new(path)?;
-/// * `path` - Path to the pth file.
-/// * `key` - Optional key to retrieve `state_dict` from the pth file. Sometimes the pth file
-///           contains multiple objects and the state_dict is the one we are interested in.
-pub fn read_all_with_key<P: AsRef<std::path::Path>>(
-    path: P,
-    key: Option<&str>,
-) -> Result<Vec<(String, Tensor)>> {
-    let pth = PthTensors::new(path, key)?;
     let tensor_names = pth.tensor_infos.keys();
     let mut tensors = Vec::with_capacity(tensor_names.len());
     for name in tensor_names {
@@ -807,11 +749,3 @@ pub fn read_all_with_key<P: AsRef<std::path::Path>>(
     }
     Ok(tensors)
 }
-
-/// Read all the tensors from a PyTorch pth file.
-///
-/// # Arguments
-/// * `path` - Path to the pth file.
-pub fn read_all<P: AsRef<std::path::Path>>(path: P) -> Result<Vec<(String, Tensor)>> {
-    read_all_with_key(path, None)
-}

candle-core/src/quantized/cuda.rs

@@ -1,618 +0,0 @@
-use super::{GgmlDType, QStorage};
-use crate::quantized::k_quants::GgmlType;
-use crate::{backend::BackendDevice, cuda_backend::WrapErr};
-use crate::{CudaDevice, CudaStorage, Result};
-
-use cudarc::driver::{CudaSlice, CudaView, DeviceSlice};
-
-#[derive(Clone, Debug)]
-pub struct QCudaStorage {
-    data: CudaSlice<u8>,
-    dtype: GgmlDType,
-    device: CudaDevice,
-}
-
-static FORCE_DMMV: std::sync::atomic::AtomicBool = std::sync::atomic::AtomicBool::new(false);
-
-pub fn set_force_dmmv(f: bool) {
-    FORCE_DMMV.store(f, std::sync::atomic::Ordering::Relaxed)
-}
-
-pub const WARP_SIZE: usize = 32;
-pub const MMQ_X_Q4_0_AMPERE: usize = 4;
-pub const MMQ_Y_Q4_0_AMPERE: usize = 32;
-pub const NWARPS_Q4_0_AMPERE: usize = 4;
-pub const GGML_CUDA_MMV_X: usize = 32;
-pub const GGML_CUDA_MMV_Y: usize = 1;
-pub const CUDA_QUANTIZE_BLOCK_SIZE: usize = 256;
-pub const CUDA_DEQUANTIZE_BLOCK_SIZE: usize = 256;
-pub const MATRIX_ROW_PADDING: usize = 512;
-
-fn ceil_div(p: usize, q: usize) -> usize {
-    (p + q - 1) / q
-}
-
-fn pad(p: usize, q: usize) -> usize {
-    ceil_div(p, q) * q
-}
-
-fn quantize_q8_1(
-    src: &CudaView<f32>,
-    dst: &mut CudaSlice<u8>,
-    elem_count: usize,
-    ky: usize,
-    dev: &CudaDevice,
-) -> Result<()> {
-    use cudarc::driver::LaunchAsync;
-
-    let kx = elem_count;
-    let kx_padded = pad(kx, MATRIX_ROW_PADDING);
-    let num_blocks = ceil_div(kx_padded, CUDA_QUANTIZE_BLOCK_SIZE);
-    let func = dev.get_or_load_func("quantize_q8_1", candle_kernels::QUANTIZED)?;
-    let cfg = cudarc::driver::LaunchConfig {
-        grid_dim: (num_blocks as u32, ky as u32, 1),
-        block_dim: (CUDA_QUANTIZE_BLOCK_SIZE as u32, 1, 1),
-        shared_mem_bytes: 0,
-    };
-    let params = (src, dst, kx as i32, kx_padded as i32);
-    unsafe { func.launch(cfg, params) }.w()?;
-    Ok(())
-}
-
-fn dequantize(
-    data: &CudaSlice<u8>,
-    dtype: GgmlDType,
-    elem_count: usize,
-    dev: &CudaDevice,
-) -> Result<CudaStorage> {
-    use cudarc::driver::LaunchAsync;
-
-    let nb = (elem_count + 255) / 256;
-    let (kernel_name, is_k, block_dim, num_blocks) = match dtype {
-        GgmlDType::Q4_0 => ("dequantize_block_q4_0", false, 32, nb),
-        GgmlDType::Q4_1 => ("dequantize_block_q4_1", false, 32, nb),
-        GgmlDType::Q5_0 => (
-            "dequantize_block_q5_0",
-            false,
-            CUDA_DEQUANTIZE_BLOCK_SIZE,
-            ceil_div(elem_count, 2 * CUDA_DEQUANTIZE_BLOCK_SIZE),
-        ),
-        GgmlDType::Q5_1 => (
-            "dequantize_block_q5_1",
-            false,
-            CUDA_DEQUANTIZE_BLOCK_SIZE,
-            ceil_div(elem_count, 2 * CUDA_DEQUANTIZE_BLOCK_SIZE),
-        ),
-        GgmlDType::Q8_0 => ("dequantize_block_q8_0", false, 32, nb),
-        GgmlDType::Q2K => ("dequantize_block_q2_K", true, 64, nb),
-        GgmlDType::Q3K => ("dequantize_block_q3_K", true, 64, nb),
-        GgmlDType::Q4K => ("dequantize_block_q4_K", true, 32, nb),
-        GgmlDType::Q5K => ("dequantize_block_q5_K", true, 64, nb),
-        GgmlDType::Q6K => ("dequantize_block_q6_K", true, 64, nb),
-        GgmlDType::Q8K => ("dequantize_block_q8_K", true, 32, nb),
-        _ => crate::bail!("unsupported dtype for dequantize {dtype:?}"),
-    };
-    let func = dev.get_or_load_func(kernel_name, candle_kernels::QUANTIZED)?;
-    let dst = unsafe { dev.alloc::<f32>(elem_count).w()? };
-    // See e.g.
-    // https://github.com/ggerganov/llama.cpp/blob/cbbd1efa06f8c09f9dff58ff9d9af509cc4c152b/ggml-cuda.cu#L7270
-    let cfg = cudarc::driver::LaunchConfig {
-        grid_dim: (num_blocks as u32, 1, 1),
-        block_dim: (block_dim as u32, 1, 1),
-        shared_mem_bytes: 0,
-    };
-
-    if is_k {
-        let params = (data, &dst);
-        unsafe { func.launch(cfg, params) }.w()?;
-    } else {
-        let nb32 = match dtype {
-            GgmlDType::Q5_0 | GgmlDType::Q5_1 => elem_count,
-            _ => elem_count / 32,
-        };
-        let params = (data, &dst, nb32 as i32);
-        unsafe { func.launch(cfg, params) }.w()?;
-    }
-    Ok(CudaStorage::wrap_cuda_slice(dst, dev.clone()))
-}
-
-fn dequantize_mul_mat_vec(
-    data: &CudaSlice<u8>,
-    y: &CudaView<f32>,
-    dtype: GgmlDType,
-    ncols: usize,
-    nrows: usize,
-    dev: &CudaDevice,
-) -> Result<CudaStorage> {
-    use cudarc::driver::LaunchAsync;
-
-    let data_elems = data.len() / dtype.type_size() * dtype.block_size();
-    if data_elems < ncols * nrows {
-        crate::bail!("unexpected data size {}, ncols {ncols} {nrows}", data_elems)
-    }
-    if y.len() != ncols {
-        crate::bail!("unexpected y size {}, ncols {ncols} {nrows}", y.len())
-    }
-    let kernel_name = match dtype {
-        GgmlDType::Q4_0 => "dequantize_mul_mat_vec_q4_0_cuda",
-        GgmlDType::Q4_1 => "dequantize_mul_mat_vec_q4_1_cuda",
-        GgmlDType::Q5_0 => "dequantize_mul_mat_vec_q5_0_cuda",
-        GgmlDType::Q5_1 => "dequantize_mul_mat_vec_q5_1_cuda",
-        GgmlDType::Q8_0 => "dequantize_mul_mat_vec_q8_0_cuda",
-        GgmlDType::Q2K => "dequantize_mul_mat_vec_q2_k",
-        GgmlDType::Q3K => "dequantize_mul_mat_vec_q3_k",
-        GgmlDType::Q4K => "dequantize_mul_mat_vec_q4_k",
-        GgmlDType::Q5K => "dequantize_mul_mat_vec_q5_k",
-        GgmlDType::Q6K => "dequantize_mul_mat_vec_q6_k",
-        _ => crate::bail!("unsupported dtype for quantized matmul {dtype:?}"),
-    };
-    let func = dev.get_or_load_func(kernel_name, candle_kernels::QUANTIZED)?;
-    let dst = unsafe { dev.alloc::<f32>(nrows).w()? };
-    let block_num_y = ceil_div(nrows, GGML_CUDA_MMV_Y);
-    let cfg = cudarc::driver::LaunchConfig {
-        grid_dim: (block_num_y as u32, 1, 1),
-        block_dim: (WARP_SIZE as u32, GGML_CUDA_MMV_Y as u32, 1),
-        shared_mem_bytes: 0,
-    };
-
-    let params = (data, y, &dst, ncols as i32, nrows as i32);
-    unsafe { func.launch(cfg, params) }.w()?;
-    Ok(CudaStorage::wrap_cuda_slice(dst, dev.clone()))
-}
-
-fn mul_mat_vec_via_q8_1(
-    data: &CudaSlice<u8>,
-    y: &CudaView<f32>,
-    dtype: GgmlDType,
-    ncols: usize,
-    nrows: usize,
-    b_size: usize,
-    dev: &CudaDevice,
-) -> Result<CudaStorage> {
-    use cudarc::driver::LaunchAsync;
-
-    let data_elems = data.len() / dtype.type_size() * dtype.block_size();
-    if data_elems < ncols * nrows {
-        crate::bail!("unexpected data size {}, ncols {ncols} {nrows}", data_elems)
-    }
-    if y.len() != ncols * b_size {
-        crate::bail!("unexpected y size {}, ncols {ncols} {nrows}", y.len())
-    }
-    if b_size == 0 || b_size > 8 {
-        crate::bail!("only bsize between 1 and 8 are supported, got {b_size}")
-    }
-    // Start by quantizing y
-    let ncols_padded = pad(ncols, MATRIX_ROW_PADDING);
-    let y_size_in_bytes =
-        b_size * ncols_padded * GgmlDType::Q8_1.type_size() / GgmlDType::Q8_1.block_size();
-    let mut y_q8_1 = unsafe { dev.alloc::<u8>(y_size_in_bytes).w()? };
-    quantize_q8_1(y, &mut y_q8_1, ncols, b_size, dev)?;
-
-    let kernel_name = match dtype {
-        GgmlDType::Q4_0 => "mul_mat_vec_q4_0_q8_1_cuda",
-        GgmlDType::Q4_1 => "mul_mat_vec_q4_1_q8_1_cuda",
-        GgmlDType::Q5_0 => "mul_mat_vec_q5_0_q8_1_cuda",
-        GgmlDType::Q5_1 => "mul_mat_vec_q5_1_q8_1_cuda",
-        GgmlDType::Q8_0 => "mul_mat_vec_q8_0_q8_1_cuda",
-        GgmlDType::Q2K => "mul_mat_vec_q2_K_q8_1_cuda",
-        GgmlDType::Q3K => "mul_mat_vec_q3_K_q8_1_cuda",
-        GgmlDType::Q4K => "mul_mat_vec_q4_K_q8_1_cuda",
-        GgmlDType::Q5K => "mul_mat_vec_q5_K_q8_1_cuda",
-        GgmlDType::Q6K => "mul_mat_vec_q6_K_q8_1_cuda",
-        _ => crate::bail!("unsupported dtype for quantized matmul {dtype:?}"),
-    };
-    let kernel_name = format!("{kernel_name}{b_size}");
-    let func = dev.get_or_load_func(&kernel_name, candle_kernels::QUANTIZED)?;
-    let dst = unsafe { dev.alloc::<f32>(nrows * b_size).w()? };
-    // https://github.com/ggerganov/llama.cpp/blob/facb8b56f8fd3bb10a693bf0943ae9d69d0828ef/ggml-cuda/mmvq.cu#L98
-    let (nblocks, nwarps) = match b_size {
-        1 => (nrows as u32, 4),
-        2..=4 => ((nrows as u32 + 1) / 2, 4),
-        5..=8 => ((nrows as u32 + 1) / 2, 2),
-        _ => crate::bail!("unexpected bsize {b_size}"),
-    };
-    let cfg = cudarc::driver::LaunchConfig {
-        grid_dim: (nblocks, 1, 1),
-        block_dim: (WARP_SIZE as u32, nwarps, 1),
-        shared_mem_bytes: 0,
-    };
-
-    let params = (
-        data,
-        &y_q8_1,
-        &dst,
-        /* ncols_x */ ncols as i32,
-        /* nrows_x */ nrows as i32,
-        /* nrows_y */ ncols_padded as i32,
-        /* nrows_dst */ nrows as i32,
-    );
-    unsafe { func.launch(cfg, params) }.w()?;
-    Ok(CudaStorage::wrap_cuda_slice(dst, dev.clone()))
-}
-
-#[allow(clippy::too_many_arguments)]
-fn mul_mat_via_q8_1(
-    data: &CudaSlice<u8>,
-    y: &CudaView<f32>,
-    dtype: GgmlDType,
-    x_rows: usize,
-    x_cols: usize,
-    y_rows: usize,
-    y_cols: usize,
-    dev: &CudaDevice,
-) -> Result<CudaStorage> {
-    use cudarc::driver::LaunchAsync;
-
-    let data_elems = data.len() / dtype.type_size() * dtype.block_size();
-    if data_elems < x_rows * x_cols {
-        crate::bail!("unexpected lhs size {}, {x_rows} {x_cols}", data_elems)
-    }
-    if y.len() != y_rows * y_cols {
-        crate::bail!("unexpected y size {}, {y_rows} {y_cols}", y.len())
-    }
-    if x_cols != y_rows {
-        crate::bail!("unexpected x/y size {x_rows} {x_cols} {y_rows} {y_cols}")
-    }
-    let k = x_cols;
-    // Start by quantizing y
-    let k_padded = pad(k, MATRIX_ROW_PADDING);
-    let y_size_in_bytes =
-        k_padded * y_rows * GgmlDType::Q8_1.type_size() / GgmlDType::Q8_1.block_size();
-    let mut y_q8_1 = unsafe { dev.alloc::<u8>(y_size_in_bytes).w()? };
-    quantize_q8_1(y, &mut y_q8_1, k, y_cols, dev)?;
-
-    let (kernel_name, mmq_x, mmq_y) = match dtype {
-        GgmlDType::Q4_0 => ("mul_mat_q4_0", 64, 128),
-        GgmlDType::Q4_1 => ("mul_mat_q4_1", 64, 128),
-        GgmlDType::Q5_0 => ("mul_mat_q5_0", 128, 64),
-        GgmlDType::Q5_1 => ("mul_mat_q5_1", 128, 64),
-        GgmlDType::Q8_0 => ("mul_mat_q8_0", 128, 64),
-        GgmlDType::Q2K => ("mul_mat_q2_K", 64, 128),
-        GgmlDType::Q3K => ("mul_mat_q3_K", 128, 128),
-        GgmlDType::Q4K => ("mul_mat_q4_K", 64, 128),
-        GgmlDType::Q5K => ("mul_mat_q5_K", 64, 128),
-        GgmlDType::Q6K => ("mul_mat_q6_K", 64, 64),
-        _ => crate::bail!("unsupported dtype for quantized matmul {dtype:?}"),
-    };
-    let func = dev.get_or_load_func(kernel_name, candle_kernels::QUANTIZED)?;
-    let dst = unsafe { dev.alloc::<f32>(x_rows * y_cols).w()? };
-    let cfg = cudarc::driver::LaunchConfig {
-        grid_dim: (
-            ceil_div(x_rows, mmq_y) as u32,
-            ceil_div(y_cols, mmq_x) as u32,
-            1,
-        ),
-        block_dim: (WARP_SIZE as u32, 4, 1),
-        shared_mem_bytes: 0,
-    };
-
-    let params = (
-        /* vx */ data,
-        /* vy */ &y_q8_1,
-        /* dst */ &dst,
-        /* ncols_x */ x_cols as i32,
-        /* nrows_x */ x_rows as i32,
-        /* ncols_y */ y_cols as i32,
-        /* nrows_y */ k_padded as i32,
-        /* nrows_dst */ x_rows as i32,
-    );
-    unsafe { func.launch(cfg, params) }.w()?;
-    Ok(CudaStorage::wrap_cuda_slice(dst, dev.clone()))
-}
-
-impl QCudaStorage {
-    pub fn zeros(device: &CudaDevice, el_count: usize, dtype: GgmlDType) -> Result<Self> {
-        let size_in_bytes = ceil_div(el_count, dtype.block_size()) * dtype.type_size();
-        let data = device.alloc_zeros::<u8>(size_in_bytes).w()?;
-        Ok(QCudaStorage {
-            data,
-            device: device.clone(),
-            dtype,
-        })
-    }
-
-    pub fn dtype(&self) -> GgmlDType {
-        self.dtype
-    }
-
-    pub fn device(&self) -> &CudaDevice {
-        &self.device
-    }
-
-    pub fn dequantize(&self, elem_count: usize) -> Result<CudaStorage> {
-        fn deq<T: GgmlType>(buffer: &[u8], n: usize, dst: &mut [f32]) -> Result<()> {
-            let slice = unsafe { std::slice::from_raw_parts(buffer.as_ptr() as *const T, n) };
-            let vec = slice.to_vec();
-            T::to_float(&vec, dst)
-        }
-
-        let fast_kernel = matches!(
-            self.dtype,
-            GgmlDType::Q4_0
-                | GgmlDType::Q4_1
-                | GgmlDType::Q5_0
-                | GgmlDType::Q5_1
-                | GgmlDType::Q8_0
-                | GgmlDType::Q2K
-                | GgmlDType::Q3K
-                | GgmlDType::Q4K
-                | GgmlDType::Q5K
-                | GgmlDType::Q6K
-                | GgmlDType::Q8K
-        );
-        if fast_kernel {
-            return dequantize(&self.data, self.dtype, elem_count, self.device());
-        }
-        // Run the dequantization on cpu.
-
-        let buffer = self.device.dtoh_sync_copy(&self.data).w()?;
-        let mut out = vec![0.0; elem_count];
-        let block_len = elem_count / self.dtype.block_size();
-        match self.dtype {
-            GgmlDType::F32 => deq::<f32>(&buffer, block_len, &mut out)?,
-            GgmlDType::F16 => deq::<half::f16>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q4_0 => deq::<crate::quantized::BlockQ4_0>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q4_1 => deq::<crate::quantized::BlockQ4_1>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q5_0 => deq::<crate::quantized::BlockQ5_0>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q5_1 => deq::<crate::quantized::BlockQ5_1>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q8_0 => deq::<crate::quantized::BlockQ8_0>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q8_1 => deq::<crate::quantized::BlockQ8_1>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q2K => deq::<crate::quantized::BlockQ2K>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q3K => deq::<crate::quantized::BlockQ3K>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q4K => deq::<crate::quantized::BlockQ4K>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q5K => deq::<crate::quantized::BlockQ5K>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q6K => deq::<crate::quantized::BlockQ6K>(&buffer, block_len, &mut out)?,
-            GgmlDType::Q8K => deq::<crate::quantized::BlockQ8K>(&buffer, block_len, &mut out)?,
-        }
-
-        self.device
-            .storage_from_cpu_storage(&crate::CpuStorage::F32(out))
-    }
-
-    pub fn quantize(&mut self, src: &CudaStorage) -> Result<()> {
-        // Run the quantization on cpu.
-        let src = match &src.slice {
-            crate::cuda_backend::CudaStorageSlice::F32(data) => {
-                self.device.dtoh_sync_copy(data).w()?
-            }
-            _ => crate::bail!("only f32 can be quantized"),
-        };
-        let src_len = src.len();
-        let src = crate::Storage::Cpu(crate::CpuStorage::F32(src));
-        let mut qcpu_storage = crate::Device::Cpu.qzeros(src_len, self.dtype)?;
-        qcpu_storage.quantize(&src)?;
-        let data = qcpu_storage.data()?;
-        let data = self.device.htod_sync_copy(data.as_ref()).w()?;
-        self.data = data;
-        Ok(())
-    }
-
-    pub fn storage_size_in_bytes(&self) -> usize {
-        self.data.len()
-    }
-
-    pub fn fwd(
-        &self,
-        self_shape: &crate::Shape,
-        storage: &CudaStorage,
-        layout: &crate::Layout,
-    ) -> Result<(CudaStorage, crate::Shape)> {
-        let max_bm = if FORCE_DMMV.load(std::sync::atomic::Ordering::Relaxed) {
-            1
-        } else {
-            8
-        };
-        let use_vec_kernel = match layout.shape().dims() {
-            [b, m, _k] => b * m <= max_bm,
-            [b, _k] => *b <= max_bm,
-            _ => false,
-        };
-        if use_vec_kernel {
-            self.dequantize_matmul_vec(self_shape, storage, layout)
-        } else {
-            self.dequantize_matmul(self_shape, storage, layout)
-        }
-    }
-}
-
-impl QCudaStorage {
-    fn dequantize_matmul_vec(
-        &self,
-        self_shape: &crate::Shape,
-        rhs: &CudaStorage,
-        rhs_l: &crate::Layout,
-    ) -> Result<(CudaStorage, crate::Shape)> {
-        let (nrows, ncols) = self_shape.dims2()?;
-        let rhs = rhs.as_cuda_slice::<f32>()?;
-        let rhs = match rhs_l.contiguous_offsets() {
-            Some((o1, o2)) => rhs.slice(o1..o2),
-            None => Err(crate::Error::RequiresContiguous { op: "dmmv" }.bt())?,
-        };
-        let (b_size, k) = match rhs_l.shape().dims() {
-            [b, m, k] => (b * m, *k),
-            [b, k] => (*b, *k),
-            _ => crate::bail!("unexpected rhs shape in dmmv {:?}", rhs_l.shape()),
-        };
-        if ncols != k {
-            crate::bail!("mismatch on matmul dim {self_shape:?} {:?}", rhs_l.shape())
-        }
-
-        let out = if FORCE_DMMV.load(std::sync::atomic::Ordering::Relaxed) {
-            dequantize_mul_mat_vec(&self.data, &rhs, self.dtype, ncols, nrows, self.device())?
-        } else {
-            mul_mat_vec_via_q8_1(
-                &self.data,
-                &rhs,
-                self.dtype,
-                ncols,
-                nrows,
-                b_size,
-                self.device(),
-            )?
-        };
-        let mut out_shape = rhs_l.shape().dims().to_vec();
-        out_shape.pop();
-        out_shape.push(nrows);
-        Ok((out, out_shape.into()))
-    }
-
-    fn dequantize_matmul(
-        &self,
-        self_shape: &crate::Shape,
-        storage: &CudaStorage,
-        layout: &crate::Layout,
-    ) -> Result<(CudaStorage, crate::Shape)> {
-        use crate::backend::BackendStorage;
-        let (n, k) = self_shape.dims2()?;
-        let (b, m, k2) = match layout.shape().dims() {
-            &[b, m, k2] => (b, m, k2),
-            &[m, k2] => (1, m, k2),
-            s => crate::bail!("unexpected shape for input {s:?}"),
-        };
-        if k2 != k {
-            crate::bail!("mismatch on matmul dim {self_shape:?} {:?}", layout.shape())
-        }
-
-        let out = if FORCE_DMMV.load(std::sync::atomic::Ordering::Relaxed) {
-            let data_f32 = self.dequantize(n * k)?;
-            let rhs_l = crate::Layout::new((k, n).into(), vec![1, k], 0).broadcast_as((b, k, n))?;
-            storage.matmul(&data_f32, (b, m, n, k), layout, &rhs_l)?
-        } else {
-            let storage = storage.as_cuda_slice::<f32>()?;
-            let storage = match layout.contiguous_offsets() {
-                Some((o1, o2)) => storage.slice(o1..o2),
-                None => Err(crate::Error::RequiresContiguous {
-                    op: "quantized-matmul",
-                }
-                .bt())?,
-            };
-            mul_mat_via_q8_1(
-                &self.data,
-                &storage,
-                self.dtype,
-                /* x_rows */ n,
-                /* x_cols */ k,
-                /* y_rows */ k,
-                /* y_cols */ b * m,
-                self.device(),
-            )?
-        };
-        let mut out_shape = layout.shape().dims().to_vec();
-        out_shape.pop();
-        out_shape.push(n);
-        Ok((out, out_shape.into()))
-    }
-}
-
-pub fn load_quantized<T: super::GgmlType + Send + Sync + 'static>(
-    device: &CudaDevice,
-    data: &[T],
-) -> Result<super::QStorage> {
-    let data = unsafe {
-        std::slice::from_raw_parts(data.as_ptr() as *const u8, core::mem::size_of_val(data))
-    };
-    let data = device.htod_sync_copy(data).w()?;
-    Ok(QStorage::Cuda(QCudaStorage {
-        data,
-        device: device.clone(),
-        dtype: T::DTYPE,
-    }))
-}
-
-#[cfg(test)]
-mod test {
-    use super::*;
-
-    #[test]
-    fn cuda_quantize_q8_1() -> Result<()> {
-        let dev = CudaDevice::new(0)?;
-        let el = 256;
-        let el_padded = pad(el, MATRIX_ROW_PADDING);
-        let y_size_in_bytes =
-            el_padded * GgmlDType::Q8_1.type_size() / GgmlDType::Q8_1.block_size();
-        let mut y_q8_1 = unsafe { dev.alloc::<u8>(y_size_in_bytes).w()? };
-        let vs: Vec<f32> = (0..el).map(|v| v as f32).collect();
-        let y = dev.htod_sync_copy(&vs).w()?;
-        quantize_q8_1(&y.slice(..), &mut y_q8_1, el, 1, &dev)?;
-        Ok(())
-    }
-
-    #[test]
-    fn cuda_mmv_q8_1() -> Result<()> {
-        let dev = CudaDevice::new(0)?;
-        let ncols = 256;
-        let vs: Vec<f32> = (0..ncols).map(|v| v as f32).collect();
-        let y = dev.htod_sync_copy(&vs).w()?;
-        let mut xs = QCudaStorage::zeros(&dev, ncols, GgmlDType::Q4_0)?;
-        xs.quantize(&CudaStorage::wrap_cuda_slice(y.clone(), dev.clone()))?;
-        let cuda_storage = mul_mat_vec_via_q8_1(
-            &xs.data,
-            &y.slice(..),
-            /* dtype */ GgmlDType::Q4_0,
-            /* ncols */ ncols,
-            /* nrows */ 1,
-            /* b_size */ 1,
-            &dev,
-        )?;
-        let vs = cuda_storage.as_cuda_slice::<f32>()?;
-        let vs = dev.dtoh_sync_copy(&vs.slice(..)).unwrap();
-        assert_eq!(vs.len(), 1);
-        // for n = 255, n.(n+1).(2n+1) / 6 = 5559680
-        // Q8 means 1/256 precision.
-        assert_eq!(vs[0], 5561664.5);
-
-        let cuda_storage = dequantize_mul_mat_vec(
-            &xs.data,
-            &y.slice(..),
-            /* dtype */ GgmlDType::Q4_0,
-            /* ncols */ ncols,
-            /* nrows */ 1,
-            &dev,
-        )?;
-        let vs = cuda_storage.as_cuda_slice::<f32>()?;
-        let vs = dev.dtoh_sync_copy(&vs.slice(..)).unwrap();
-        assert_eq!(vs.len(), 1);
-        assert_eq!(vs[0], 5561851.0);
-        Ok(())
-    }
-
-    #[test]
-    fn cuda_mm_q8_1() -> Result<()> {
-        let dev = CudaDevice::new(0)?;
-        let ncols = 256;
-        let vs: Vec<f32> = (0..ncols * 4).map(|v| v as f32 / 4.).collect();
-        let y = dev.htod_sync_copy(&vs).w()?;
-        let mut xs = QCudaStorage::zeros(&dev, ncols * 4, GgmlDType::Q4_0)?;
-        xs.quantize(&CudaStorage::wrap_cuda_slice(y.clone(), dev.clone()))?;
-        let cuda_storage = mul_mat_via_q8_1(
-            &xs.data,
-            &y.slice(..),
-            /* dtype */ GgmlDType::Q4_0,
-            /* x_rows */ 4,
-            /* x_cols */ ncols,
-            /* y_rows */ ncols,
-            /* y_cols */ 4,
-            &dev,
-        )?;
-        let vs = cuda_storage.as_cuda_slice::<f32>()?;
-        let vs = dev.dtoh_sync_copy(&vs.slice(..)).unwrap();
-
-        /*
-           x = torch.tensor([float(v) for v in range(1024)]).reshape(4, 256)
-           x @ x.t() / 16
-        tensor([[  347480.0000,   869720.0000,  1391960.0000,  1914200.0000],
-                [  869720.0000,  2440536.0000,  4011352.0000,  5582166.5000],
-                [ 1391960.0000,  4011352.0000,  6630742.0000,  9250132.0000],
-                [ 1914200.0000,  5582166.5000,  9250132.0000, 12918099.0000]])
-                */
-        assert_eq!(vs.len(), 16);
-        assert_eq!(vs[0], 347604.0);
-        assert_eq!(vs[1], 888153.06);
-        assert_eq!(vs[4], 869780.7);
-        assert_eq!(vs[5], 2483145.0);
-        assert_eq!(vs[11], 9407368.0);
-        assert_eq!(vs[14], 9470856.0);
-        assert_eq!(vs[15], 13138824.0);
-        Ok(())
-    }
-}

candle-core/src/quantized/dummy_cuda.rs

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

candle-core/src/quantized/dummy_metal.rs

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

candle-core/src/quantized/ggml_file.rs

@@ -1,5 +1,7 @@
 //! Support for the GGML file format.
 
+#[cfg(feature = "metal")]
+use super::metal::load_quantized_metal;
 use super::{k_quants, GgmlDType, QStorage};
 use crate::{Device, Result};
 use byteorder::{LittleEndian, ReadBytesExt};
@@ -128,8 +130,13 @@ fn from_raw_data<T: super::GgmlType + Send + Sync + 'static>(
     let data = unsafe { std::slice::from_raw_parts(raw_data_ptr as *const T, n_blocks) };
     let data: QStorage = match device {
         Device::Cpu => QStorage::Cpu(Box::new(data.to_vec())),
-        Device::Metal(metal) => super::metal::load_quantized(metal, data)?,
+        #[cfg(feature = "metal")]
-        Device::Cuda(cuda) => super::cuda::load_quantized(cuda, data)?,
+        Device::Metal(metal) => load_quantized_metal(metal, data)?,
+        #[cfg(not(feature = "metal"))]
+        Device::Metal(_metal) => {
+            crate::bail!("Metal backend requires `metal` feature")
+        }
+        device => unimplemented!("Implement quantized tensor for device {device:?}"),
     };
     super::QTensor::new(data, dims)
 }
@@ -226,7 +233,6 @@ pub struct Content {
     pub hparams: HParams,
     pub vocab: Vocab,
     pub tensors: HashMap<String, super::QTensor>,
-    pub device: Device,
 }
 
 impl Content {
@@ -246,13 +252,11 @@ impl Content {
             let (name, tensor) = read_one_tensor(reader, magic, device)?;
             tensors.insert(name, tensor);
         }
-        let device = device.clone();
         Ok(Self {
             magic,
             hparams,
             vocab,
             tensors,
-            device,
         })
     }
 

candle-core/src/quantized/metal.rs

@@ -1,6 +1,5 @@
 use super::{GgmlDType, QStorage};
-use crate::backend::BackendStorage;
+use crate::{DType, MetalDevice, MetalStorage, Result};
-use crate::{DType, MetalDevice, MetalStorage, Result, Shape};
 use metal::Buffer;
 use std::sync::Arc;
 
@@ -11,31 +10,23 @@ pub struct QMetalStorage {
 }
 
 impl QMetalStorage {
-    pub fn zeros(device: &MetalDevice, elem_count: usize, dtype: GgmlDType) -> Result<Self> {
-        let size = elem_count * dtype.type_size() / dtype.block_size();
-        let buffer = device.allocate_zeros(size)?;
-        Ok(Self {
-            buffer,
-            device: device.clone(),
-            dtype,
-        })
-    }
-
     pub fn dtype(&self) -> GgmlDType {
         self.dtype
     }
 
-    pub fn device(&self) -> &MetalDevice {
-        &self.device
-    }
-
     pub fn buffer(&self) -> &Buffer {
         &self.buffer
     }
 
-    pub fn dequantize(&self, elem_count: usize) -> Result<MetalStorage> {
+    pub fn new(buffer: Arc<Buffer>, device: MetalDevice, dtype: GgmlDType) -> Self {
-        use crate::quantized::k_quants::GgmlType;
+        Self {
+            device,
+            buffer,
+            dtype,
+        }
+    }
 
+    pub fn dequantize(&self, elem_count: usize) -> Result<MetalStorage> {
         let buffer = self.device.new_buffer_managed(self.buffer.length())?;
         let command_buffer = self.device.command_buffer()?;
         command_buffer.set_label("to_cpu");
@@ -45,73 +36,87 @@ impl QMetalStorage {
         blit.end_encoding();
         self.device.wait_until_completed()?;
         let mut out = vec![0.0; elem_count];
-        let block_len = elem_count / self.dtype.block_size();
         match self.dtype {
             GgmlDType::F32 => {
-                let vec: Vec<f32> = read_to_vec(&buffer, block_len);
+                let vec: Vec<f32> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 f32::to_float(&vec, &mut out)?;
             }
             GgmlDType::F16 => {
-                let vec: Vec<half::f16> = read_to_vec(&buffer, block_len);
+                let vec: Vec<half::f16> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 half::f16::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q4_0 => {
-                let vec: Vec<crate::quantized::BlockQ4_0> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ4_0> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ4_0::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q4_1 => {
-                let vec: Vec<crate::quantized::BlockQ4_1> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ4_1> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ4_1::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q5_0 => {
-                let vec: Vec<crate::quantized::BlockQ5_0> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ5_0> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ5_0::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q5_1 => {
-                let vec: Vec<crate::quantized::BlockQ5_1> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ5_1> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ5_1::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q8_0 => {
-                let vec: Vec<crate::quantized::BlockQ8_0> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ8_0> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ8_0::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q8_1 => {
-                let vec: Vec<crate::quantized::BlockQ8_1> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ8_1> = read_to_vec(&buffer, elem_count);
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ8_1::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q2K => {
-                let vec: Vec<crate::quantized::BlockQ2K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ2K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ2K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q3K => {
-                let vec: Vec<crate::quantized::BlockQ3K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ3K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ3K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q4K => {
-                let vec: Vec<crate::quantized::BlockQ4K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ4K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ4K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q5K => {
-                let vec: Vec<crate::quantized::BlockQ5K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ5K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ5K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q6K => {
-                let vec: Vec<crate::quantized::BlockQ6K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ6K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ6K::to_float(&vec, &mut out)?;
             }
             GgmlDType::Q8K => {
-                let vec: Vec<crate::quantized::BlockQ8K> = read_to_vec(&buffer, block_len);
+                let vec: Vec<crate::quantized::BlockQ8K> =
+                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
+                use crate::quantized::k_quants::GgmlType;
                 crate::quantized::BlockQ8K::to_float(&vec, &mut out)?;
             }
         }
 
         let buffer = self.device.new_buffer_with_data(&out)?;
-        Ok(MetalStorage::new(
+        Ok(MetalStorage::new(buffer, self.device.clone(), DType::F32))
-            buffer,
-            self.device.clone(),
-            elem_count,
-            DType::F32,
-        ))
     }
 
     pub fn quantize(&mut self, src: &MetalStorage) -> Result<()> {
@@ -125,65 +130,9 @@ impl QMetalStorage {
         self.buffer = buffer;
         Ok(())
     }
-
-    pub fn storage_size_in_bytes(&self) -> usize {
-        self.buffer.length() as usize
-    }
-
-    pub fn fwd(
-        &self,
-        self_shape: &Shape,
-        storage: &MetalStorage,
-        layout: &crate::Layout,
-    ) -> Result<(MetalStorage, Shape)> {
-        use crate::MetalError;
-
-        if !layout.is_contiguous() {
-            crate::bail!("input tensor is not contiguous {layout:?}")
-        }
-        let src_shape = layout.shape();
-        // self is transposed so n is first then k.
-        if src_shape.rank() < 2 {
-            crate::bail!("input tensor has only one dimension {layout:?}")
-        }
-        let (n, k) = self_shape.dims2()?;
-        let mut dst_shape = src_shape.dims().to_vec();
-
-        // We always use a single batch dimension and stack all the tensors in the batch on the
-        // second dimension as the implementation in candle-metal-kernels doesn't handle batch
-        // properly.
-        let (b, m) = match dst_shape.len() {
-            3 => (1, dst_shape[0] * dst_shape[1]),
-            2 => (1, dst_shape[0]),
-            n => crate::bail!("Invalid rank {n} for quantized matmul metal"),
-        };
-        let last_k = dst_shape.pop().unwrap();
-        if last_k != k {
-            crate::bail!("input tensor {layout:?} incompatible with {:?}", self_shape)
-        }
-        dst_shape.push(n);
-        let dst_shape = Shape::from(dst_shape);
-        let device = storage.device().clone();
-        let dst = device.new_buffer(dst_shape.elem_count(), DType::F32, "qmatmul")?;
-        let command_buffer = device.command_buffer()?;
-        candle_metal_kernels::call_quantized_matmul_t(
-            device.device(),
-            &command_buffer,
-            device.kernels(),
-            self.dtype.into(),
-            (b, m, n, k),
-            storage.buffer(),
-            layout.start_offset() * storage.dtype().size_in_bytes(),
-            &self.buffer,
-            &dst,
-        )
-        .map_err(MetalError::from)?;
-        let dst_storage = crate::MetalStorage::new(dst, device, dst_shape.elem_count(), DType::F32);
-        Ok((dst_storage, dst_shape))
-    }
 }
 
-pub fn load_quantized<T: super::GgmlType + Send + Sync + 'static>(
+pub fn load_quantized_metal<T: super::GgmlType + Send + Sync + 'static>(
     device: &MetalDevice,
     data: &[T],
 ) -> Result<QStorage> {
@@ -202,24 +151,3 @@ fn read_to_vec<T: Clone>(buffer: &Buffer, n: usize) -> Vec<T> {
     let slice = unsafe { std::slice::from_raw_parts(ptr, n) };
     slice.to_vec()
 }
-
-impl From<GgmlDType> for candle_metal_kernels::GgmlDType {
-    fn from(value: GgmlDType) -> Self {
-        match value {
-            GgmlDType::Q4_0 => candle_metal_kernels::GgmlDType::Q4_0,
-            GgmlDType::Q4_1 => candle_metal_kernels::GgmlDType::Q4_1,
-            GgmlDType::Q5_0 => candle_metal_kernels::GgmlDType::Q5_0,
-            GgmlDType::Q5_1 => candle_metal_kernels::GgmlDType::Q5_1,
-            GgmlDType::Q8_0 => candle_metal_kernels::GgmlDType::Q8_0,
-            GgmlDType::Q8_1 => candle_metal_kernels::GgmlDType::Q8_1,
-            GgmlDType::Q2K => candle_metal_kernels::GgmlDType::Q2K,
-            GgmlDType::Q3K => candle_metal_kernels::GgmlDType::Q3K,
-            GgmlDType::Q4K => candle_metal_kernels::GgmlDType::Q4K,
-            GgmlDType::Q5K => candle_metal_kernels::GgmlDType::Q5K,
-            GgmlDType::Q6K => candle_metal_kernels::GgmlDType::Q6K,
-            GgmlDType::Q8K => candle_metal_kernels::GgmlDType::Q8K,
-            GgmlDType::F16 => candle_metal_kernels::GgmlDType::F16,
-            GgmlDType::F32 => candle_metal_kernels::GgmlDType::F32,
-        }
-    }
-}

candle-core/src/quantized/mod.rs

@@ -1,27 +1,16 @@
+#[cfg(feature = "metal")]
+use crate::{backend::BackendStorage, DType};
 use crate::{CpuStorage, Device, Result, Shape, Storage, Tensor};
 use k_quants::*;
 use std::borrow::Cow;
 
 #[cfg(target_feature = "avx")]
 pub mod avx;
-mod dummy_cuda;
-mod dummy_metal;
 pub mod ggml_file;
 pub mod gguf_file;
 pub mod k_quants;
 #[cfg(feature = "metal")]
 pub mod metal;
-#[cfg(not(feature = "metal"))]
-mod metal {
-    pub use super::dummy_metal::*;
-}
-#[cfg(feature = "cuda")]
-pub mod cuda;
-#[cfg(not(feature = "cuda"))]
-mod cuda {
-    pub use super::dummy_cuda::*;
-}
-
 #[cfg(target_feature = "neon")]
 pub mod neon;
 #[cfg(target_feature = "simd128")]
@@ -43,13 +32,22 @@ impl Device {
                 let storage = dtype.cpu_zeros(elem_count);
                 Ok(QStorage::Cpu(storage))
             }
+            #[cfg(feature = "metal")]
             Device::Metal(metal) => {
-                let storage = metal::QMetalStorage::zeros(metal, elem_count, dtype)?;
+                let size = elem_count * dtype.type_size() / dtype.block_size();
-                Ok(QStorage::Metal(storage))
+                let buffer = metal.allocate_zeros(size)?;
+                Ok(QStorage::Metal(metal::QMetalStorage::new(
+                    buffer,
+                    metal.clone(),
+                    dtype,
+                )))
             }
-            Device::Cuda(cuda) => {
+            #[cfg(not(feature = "metal"))]
-                let storage = cuda::QCudaStorage::zeros(cuda, elem_count, dtype)?;
+            Device::Metal(_metal) => {
-                Ok(QStorage::Cuda(storage))
+                crate::bail!("Metal feature not activated");
+            }
+            Device::Cuda(_cuda) => {
+                crate::bail!("Cuda ggml quantization not supported");
             }
         }
     }
@@ -57,40 +55,32 @@ impl Device {
 
 pub enum QStorage {
     Cpu(Box<dyn QuantizedType>),
+    #[cfg(feature = "metal")]
     Metal(metal::QMetalStorage),
-    Cuda(cuda::QCudaStorage),
 }
 
 impl QStorage {
     fn block_size(&self) -> usize {
         match self {
             QStorage::Cpu(storage) => storage.block_size(),
+            #[cfg(feature = "metal")]
             QStorage::Metal(storage) => storage.dtype().block_size(),
-            QStorage::Cuda(storage) => storage.dtype().block_size(),
         }
     }
 
     fn dtype(&self) -> GgmlDType {
         match self {
             QStorage::Cpu(storage) => storage.dtype(),
+            #[cfg(feature = "metal")]
             QStorage::Metal(storage) => storage.dtype(),
-            QStorage::Cuda(storage) => storage.dtype(),
-        }
-    }
-
-    fn device(&self) -> Device {
-        match self {
-            QStorage::Cpu(_storage) => Device::Cpu,
-            QStorage::Metal(storage) => Device::Metal(storage.device().clone()),
-            QStorage::Cuda(storage) => Device::Cuda(storage.device().clone()),
         }
     }
 
     fn size_in_bytes(&self) -> usize {
         match self {
             QStorage::Cpu(storage) => storage.storage_size_in_bytes(),
-            QStorage::Metal(storage) => storage.storage_size_in_bytes(),
+            #[cfg(feature = "metal")]
-            QStorage::Cuda(storage) => storage.storage_size_in_bytes(),
+            QStorage::Metal(storage) => storage.buffer().length() as usize,
         }
     }
 
@@ -99,8 +89,8 @@ impl QStorage {
             (QStorage::Cpu(storage), Storage::Cpu(src)) => {
                 storage.from_float(src.as_slice::<f32>()?)?;
             }
+            #[cfg(feature = "metal")]
             (QStorage::Metal(storage), Storage::Metal(src)) => storage.quantize(src)?,
-            (QStorage::Cuda(storage), Storage::Cuda(src)) => storage.quantize(src)?,
             _ => crate::bail!("Invalid dequantize storage locations do not match"),
         }
         Ok(())
@@ -109,8 +99,8 @@ impl QStorage {
     fn dequantize(&self, elem_count: usize) -> Result<Storage> {
         match self {
             QStorage::Cpu(storage) => Ok(Storage::Cpu(storage.dequantize(elem_count)?)),
+            #[cfg(feature = "metal")]
             QStorage::Metal(storage) => Ok(Storage::Metal(storage.dequantize(elem_count)?)),
-            QStorage::Cuda(storage) => Ok(Storage::Cuda(storage.dequantize(elem_count)?)),
         }
     }
 
@@ -122,7 +112,8 @@ impl QStorage {
                 let data = unsafe { std::slice::from_raw_parts(data_ptr, size_in_bytes) };
                 Ok(Cow::from(data))
             }
-            QStorage::Metal(_) | QStorage::Cuda(_) => {
+            #[cfg(feature = "metal")]
+            QStorage::Metal(_storage) => {
                 crate::bail!("not implemented");
             }
         }
@@ -345,10 +336,6 @@ impl QTensor {
         self.storage.dtype()
     }
 
-    pub fn device(&self) -> Device {
-        self.storage.device()
-    }
-
     pub fn rank(&self) -> usize {
         self.shape.rank()
     }
@@ -398,7 +385,7 @@ impl QMatMul {
             _ => DEQUANTIZE_ALL.with(|b| *b),
         };
         let t = if dequantize {
-            let tensor = qtensor.dequantize(&qtensor.device())?;
+            let tensor = qtensor.dequantize(&Device::Cpu)?;
             Self::Tensor(tensor)
         } else {
             Self::QTensor(qtensor)
@@ -440,7 +427,8 @@ impl crate::CustomOp1 for QTensor {
         #[allow(clippy::infallible_destructuring_match)]
         let self_storage = match &self.storage {
             QStorage::Cpu(storage) => storage,
-            QStorage::Metal(_) | QStorage::Cuda(_) => crate::bail!("Invalid storage"),
+            #[cfg(feature = "metal")]
+            _ => crate::bail!("Invalid storage"),
         };
         let slice = storage.as_slice::<f32>()?;
         let slice = &slice[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
@@ -449,28 +437,79 @@ impl crate::CustomOp1 for QTensor {
         Ok((crate::CpuStorage::F32(dst_storage), dst_shape))
     }
 
+    #[cfg(feature = "metal")]
     fn metal_fwd(
         &self,
         storage: &crate::MetalStorage,
         layout: &crate::Layout,
     ) -> Result<(crate::MetalStorage, Shape)> {
-        let self_storage = match &self.storage {
+        use crate::MetalError;
-            QStorage::Metal(metal) => metal,
+
+        if !layout.is_contiguous() {
+            crate::bail!("input tensor is not contiguous {layout:?}")
+        }
+        let src_shape = layout.shape();
+        // self is transposed so n is first then k.
+        if src_shape.rank() < 2 {
+            crate::bail!("input tensor has only one dimension {layout:?}")
+        }
+        let (n, k) = self.shape.dims2()?;
+        let mut dst_shape = src_shape.dims().to_vec();
+
+        let (b, m) = match dst_shape.len() {
+            3 => (dst_shape[0], dst_shape[1]),
+            2 => (1, dst_shape[0]),
+            n => crate::bail!("Invalid rank {n} for quantized matmul metal"),
+        };
+        let last_k = dst_shape.pop().unwrap();
+        if last_k != k {
+            crate::bail!("input tensor {layout:?} incompatible with {:?}", self.shape)
+        }
+        dst_shape.push(n);
+        let dst_shape = Shape::from(dst_shape);
+        let device = storage.device().clone();
+        let dst = device.new_buffer(dst_shape.elem_count(), DType::F32, "qmatmul")?;
+        let (buffer, dtype) = match &self.storage {
+            QStorage::Metal(metal) => (metal.buffer(), metal.dtype()),
             _ => unreachable!("Cannot call metal matmul on non metal QTensor"),
         };
-        self_storage.fwd(&self.shape, storage, layout)
+        let command_buffer = device.command_buffer()?;
+        candle_metal_kernels::call_quantized_matmul_t(
+            device.device(),
+            &command_buffer,
+            device.kernels(),
+            dtype.into(),
+            (b, m, n, k),
+            storage.buffer(),
+            layout.start_offset() * storage.dtype().size_in_bytes(),
+            buffer,
+            &dst,
+        )
+        .map_err(MetalError::from)?;
+        let dst_storage = crate::MetalStorage::new(dst, device, DType::F32);
+        Ok((dst_storage, dst_shape))
     }
+}
 
-    fn cuda_fwd(
+#[cfg(feature = "metal")]
-        &self,
+impl From<GgmlDType> for candle_metal_kernels::GgmlDType {
-        storage: &crate::CudaStorage,
+    fn from(value: GgmlDType) -> Self {
-        layout: &crate::Layout,
+        match value {
-    ) -> Result<(crate::CudaStorage, Shape)> {
+            GgmlDType::Q4_0 => candle_metal_kernels::GgmlDType::Q4_0,
-        let self_storage = match &self.storage {
+            GgmlDType::Q4_1 => candle_metal_kernels::GgmlDType::Q4_1,
-            QStorage::Cuda(cuda) => cuda,
+            GgmlDType::Q5_0 => candle_metal_kernels::GgmlDType::Q5_0,
-            _ => unreachable!("Cannot call cuda matmul on non cuda QTensor"),
+            GgmlDType::Q5_1 => candle_metal_kernels::GgmlDType::Q5_1,
-        };
+            GgmlDType::Q8_0 => candle_metal_kernels::GgmlDType::Q8_0,
-        self_storage.fwd(&self.shape, storage, layout)
+            GgmlDType::Q8_1 => candle_metal_kernels::GgmlDType::Q8_1,
+            GgmlDType::Q2K => candle_metal_kernels::GgmlDType::Q2K,
+            GgmlDType::Q3K => candle_metal_kernels::GgmlDType::Q3K,
+            GgmlDType::Q4K => candle_metal_kernels::GgmlDType::Q4K,
+            GgmlDType::Q5K => candle_metal_kernels::GgmlDType::Q5K,
+            GgmlDType::Q6K => candle_metal_kernels::GgmlDType::Q6K,
+            GgmlDType::Q8K => candle_metal_kernels::GgmlDType::Q8K,
+            GgmlDType::F16 => candle_metal_kernels::GgmlDType::F16,
+            GgmlDType::F32 => candle_metal_kernels::GgmlDType::F32,
+        }
     }
 }
 

candle-core/src/shape.rs

@@ -171,7 +171,7 @@ impl Shape {
         }
         let mut acc = 1;
         for (&stride, &dim) in stride.iter().zip(self.0.iter()).rev() {
-            if dim > 1 && stride != acc {
+            if stride != acc {
                 return false;
             }
             acc *= dim;
@@ -186,7 +186,7 @@ impl Shape {
         }
         let mut acc = 1;
         for (&stride, &dim) in stride.iter().zip(self.0.iter()) {
-            if dim > 1 && stride != acc {
+            if stride != acc {
                 return false;
             }
             acc *= dim;

candle-core/src/storage.rs

@@ -1,7 +1,6 @@
 use crate::backend::BackendStorage;
-use crate::op::{self, CmpOp, ReduceOp};
+use crate::op::{self, CmpOp, CustomOp1, CustomOp2, CustomOp3, ReduceOp};
 use crate::{CpuStorage, CudaStorage, DType, Device, Error, Layout, MetalStorage, Result, Shape};
-use crate::{CustomOp1, CustomOp2, CustomOp3, InplaceOp1, InplaceOp2, InplaceOp3};
 
 // We do not want to implement Clone on Storage as cloning may fail because of
 // out of memory. Instead try_clone should be used.
@@ -44,19 +43,9 @@ impl Storage {
     }
 
     pub(crate) fn same_device(&self, rhs: &Self, op: &'static str) -> Result<()> {
-        let lhs_device = self.device();
+        let lhs = self.device().location();
-        let rhs_device = rhs.device();
+        let rhs = rhs.device().location();
-        let lhs = lhs_device.location();
+        if lhs != rhs {
-        let rhs = rhs_device.location();
-        let same_device = if self.device().is_metal() {
-            // On metal, we require the device to be exactly the same rather than
-            // having the same location. In cuda this is not necessary as all CudaDevice on the
-            // same GPU will use the same cuda stream.
-            lhs_device.same_device(&rhs_device)
-        } else {
-            lhs == rhs
-        };
-        if !same_device {
             Err(Error::DeviceMismatchBinaryOp { lhs, rhs, op }.bt())
         } else {
             Ok(())
@@ -263,51 +252,6 @@ impl Storage {
         }
     }
 
-    pub(crate) fn inplace_op1(&mut self, l: &Layout, c: &dyn InplaceOp1) -> Result<()> {
-        match self {
-            Self::Cpu(storage) => c.cpu_fwd(storage, l),
-            Self::Cuda(storage) => c.cuda_fwd(storage, l),
-            Self::Metal(storage) => c.metal_fwd(storage, l),
-        }
-    }
-
-    pub(crate) fn inplace_op2(
-        &mut self,
-        l1: &Layout,
-        t2: &Self,
-        l2: &Layout,
-        c: &dyn InplaceOp2,
-    ) -> Result<()> {
-        self.same_device(t2, c.name())?;
-        match (self, t2) {
-            (Self::Cpu(s1), Self::Cpu(s2)) => c.cpu_fwd(s1, l1, s2, l2),
-            (Self::Cuda(s1), Self::Cuda(s2)) => c.cuda_fwd(s1, l1, s2, l2),
-            (Self::Metal(s1), Self::Metal(s2)) => c.metal_fwd(s1, l1, s2, l2),
-            _ => unreachable!(),
-        }
-    }
-
-    pub(crate) fn inplace_op3(
-        &mut self,
-        l1: &Layout,
-        t2: &Self,
-        l2: &Layout,
-        t3: &Self,
-        l3: &Layout,
-        c: &dyn InplaceOp3,
-    ) -> Result<()> {
-        self.same_device(t2, c.name())?;
-        self.same_device(t3, c.name())?;
-        match (self, t2, t3) {
-            (Self::Cpu(s1), Self::Cpu(s2), Self::Cpu(s3)) => c.cpu_fwd(s1, l1, s2, l2, s3, l3),
-            (Self::Cuda(s1), Self::Cuda(s2), Self::Cuda(s3)) => c.cuda_fwd(s1, l1, s2, l2, s3, l3),
-            (Self::Metal(s1), Self::Metal(s2), Self::Metal(s3)) => {
-                c.metal_fwd(s1, l1, s2, l2, s3, l3)
-            }
-            _ => unreachable!(),
-        }
-    }
-
     pub(crate) fn unary_impl<B: op::UnaryOpT>(&self, layout: &Layout) -> Result<Self> {
         match self {
             Storage::Cpu(storage) => {
@@ -408,10 +352,6 @@ impl Storage {
                 let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
                 Ok(Self::Cuda(s))
             }
-            (Storage::Metal(inp), Storage::Metal(kernel)) => {
-                let s = inp.conv_transpose1d(l, kernel, kernel_l, params)?;
-                Ok(Self::Metal(s))
-            }
             (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
                 lhs: lhs.device().location(),
                 rhs: rhs.device().location(),
@@ -757,32 +697,4 @@ impl Storage {
             .bt()),
         }
     }
-
-    #[allow(clippy::too_many_arguments)]
-    pub(crate) fn copy2d(
-        &self,
-        dst: &mut Self,
-        d1: usize,
-        d2: usize,
-        src_s: usize,
-        dst_s: usize,
-        src_o: usize,
-        dst_o: usize,
-    ) -> Result<()> {
-        match (self, dst) {
-            (Self::Cpu(src), Self::Cpu(dst)) => src.copy2d(dst, d1, d2, src_s, dst_s, src_o, dst_o),
-            (Self::Cuda(src), Self::Cuda(dst)) => {
-                Ok(src.copy2d(dst, d1, d2, src_s, dst_s, src_o, dst_o)?)
-            }
-            (Self::Metal(src), Self::Metal(dst)) => {
-                Ok(src.copy2d(dst, d1, d2, src_s, dst_s, src_o, dst_o)?)
-            }
-            (lhs, rhs) => Err(Error::DeviceMismatchBinaryOp {
-                lhs: lhs.device().location(),
-                rhs: rhs.device().location(),
-                op: "copy2d",
-            }
-            .bt()),
-        }
-    }
 }

candle-core/src/tensor.rs

@@ -1,7 +1,9 @@
 //! Tensors are N-dimensional matrixes of elements using a single data type.
 #![allow(clippy::redundant_closure_call)]
 use crate::backend::{BackendDevice, BackendStorage};
-use crate::op::{BackpropOp, BinaryOp, CmpOp, Op, ReduceOp, UnaryOp};
+use crate::op::{
+    BackpropOp, BinaryOp, CmpOp, CustomOp1, CustomOp2, CustomOp3, Op, ReduceOp, UnaryOp,
+};
 use crate::scalar::TensorOrScalar;
 use crate::shape::{Dim, Dims};
 use crate::{bail, storage::Storage, DType, Device, Error, Layout, Result, Shape};
@@ -79,9 +81,6 @@ macro_rules! unary_op {
     ($fn_name:ident, $op_name:ident) => {
         pub fn $fn_name(&self) -> Result<Self> {
             let shape = self.shape();
-            if shape.elem_count() == 0 {
-                return Ok(self.clone());
-            }
             let storage = self
                 .storage()
                 .unary_impl::<crate::op::$op_name>(self.layout())?;
@@ -95,9 +94,6 @@ macro_rules! binary_op {
     ($fn_name:ident, $op_name:ident) => {
         pub fn $fn_name(&self, rhs: &Self) -> Result<Self> {
             let shape = self.same_shape_binary_op(rhs, stringify!($fn_name))?;
-            if shape.elem_count() == 0 {
-                return Ok(self.clone());
-            }
             let storage = self.storage().binary_impl::<crate::op::$op_name>(
                 &*rhs.storage(),
                 self.layout(),
@@ -120,9 +116,6 @@ macro_rules! binary_op_scalar {
                     .broadcast_as(self.shape())?,
             };
             let shape = self.same_shape_binary_op(&rhs, stringify!($fn_name))?;
-            if self.elem_count() == 0 {
-                return Ok(self.clone());
-            }
             let storage = self.storage().binary_impl::<crate::op::$op_name>(
                 &*rhs.storage(),
                 self.layout(),
@@ -515,11 +508,9 @@ impl Tensor {
     unary_op!(gelu_erf, GeluErf);
     unary_op!(erf, Erf);
     unary_op!(relu, Relu);
-    unary_op!(silu, Silu);
     unary_op!(ceil, Ceil);
     unary_op!(floor, Floor);
     unary_op!(round, Round);
-    unary_op!(sign, Sign);
 
     /// Round element of the input tensor to the nearest integer.
     ///
@@ -655,9 +646,6 @@ impl Tensor {
     /// # Ok::<(), candle_core::Error>(())
     /// ```
     pub fn affine(&self, mul: f64, add: f64) -> Result<Self> {
-        if self.elem_count() == 0 {
-            return Ok(self.clone());
-        }
         let storage = self.storage().affine(self.layout(), mul, add)?;
         let op = BackpropOp::new1(self, |arg| Op::Affine { arg, mul, add });
         Ok(from_storage(storage, self.shape(), op, false))
@@ -665,9 +653,6 @@ impl Tensor {
 
     /// Applies the Exponential Linear Unit (ELU) function on each element of the input tensor.
     pub fn elu(&self, alpha: f64) -> Result<Self> {
-        if self.elem_count() == 0 {
-            return Ok(self.clone());
-        }
         let storage = self.storage().elu(self.layout(), alpha)?;
         let op = BackpropOp::new1(self, |t| Op::Elu(t, alpha));
         Ok(from_storage(storage, self.shape(), op, false))
@@ -675,15 +660,12 @@ impl Tensor {
 
     /// Raise the tensor to some float exponent `e`.
     pub fn powf(&self, e: f64) -> Result<Self> {
-        if self.elem_count() == 0 {
-            return Ok(self.clone());
-        }
         let storage = self.storage().powf(self.layout(), e)?;
         let op = BackpropOp::new1(self, |t| Op::Powf(t, e));
         Ok(from_storage(storage, self.shape(), op, false))
     }
 
-    pub(crate) fn check_dim(&self, dim: usize, op: &'static str) -> Result<()> {
+    fn check_dim(&self, dim: usize, op: &'static str) -> Result<()> {
         if dim >= self.dims().len() {
             Err(Error::DimOutOfRange {
                 shape: self.shape().clone(),
@@ -822,35 +804,6 @@ impl Tensor {
         }
     }
 
-    /// Roll the tensor input along the given dimension.
-    /// Elements that are shifted beyond the last position are re-introduced at the first position.
-    ///
-    /// ```rust
-    /// # use candle_core::{Tensor, Device};
-    /// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
-    /// let tensor = tensor.roll(1, 0)?;
-    /// assert_eq!(tensor.to_vec2::<f32>()?, &[[4., 5.], [0., 1.], [2., 3.]]);
-    /// let tensor = Tensor::new(&[[0f32, 1.], [2., 3.], [4., 5.]], &Device::Cpu)?;
-    /// let tensor = tensor.roll(-1, 0)?;
-    /// assert_eq!(tensor.to_vec2::<f32>()?, &[[2., 3.], [4., 5.], [0., 1.]]);
-    /// # Ok::<(), candle_core::Error>(())
-    /// ```
-    pub fn roll<D>(&self, shift: i32, dim: D) -> Result<Self>
-    where
-        D: Dim + Clone,
-    {
-        let dim = dim.to_index(self.shape(), "roll")?;
-        let dim_size = self.dim(dim)?;
-        let shift = shift.rem_euclid(dim_size as i32) as usize;
-        if shift == 0 {
-            Ok(self.clone())
-        } else {
-            let a = self.narrow(dim, 0, dim_size - shift)?;
-            let b = self.narrow(dim, dim_size - shift, shift)?;
-            Tensor::cat(&[&b, &a], dim)
-        }
-    }
-
     /// Returns the sum of all elements in the input tensor. The sum is performed over all the
     /// input dimensions.
     ///
@@ -1032,7 +985,7 @@ impl Tensor {
     /// tensor also has three dimensions, `(batch, channels, target_size)`.
     pub fn interpolate1d(&self, target_size: usize) -> Result<Self> {
         let (n, c, _l) = self.dims3()?;
-        let op = BackpropOp::new1(self, |arg| Op::UpsampleNearest1D { arg, target_size });
+        let op = BackpropOp::new1(self, Op::UpsampleNearest1D);
         let storage = self
             .storage()
             .upsample_nearest1d(self.layout(), target_size)?;
@@ -1172,9 +1125,6 @@ impl Tensor {
         let n = b_dims[dim - 1];
 
         let c_shape = Shape::from(&a_dims[..dim - 2]).extend(&[m, n]);
-        if c_shape.elem_count() == 0 || k == 0 {
-            return Tensor::zeros(c_shape, self.dtype(), self.device());
-        }
         let batching: usize = a_dims[..dim - 2].iter().product();
         let batching_b: usize = b_dims[..dim - 2].iter().product();
         if k != k2 || batching != batching_b {
@@ -1371,7 +1321,7 @@ impl Tensor {
             }
             .bt())?
         }
-        let mut storage = unsafe { self.device().alloc_uninit(self.shape(), self.dtype())? };
+        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>();
@@ -1903,9 +1853,9 @@ impl Tensor {
     /// 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) -> Tensor {
+    pub fn detach(&self) -> Result<Tensor> {
         if self.op.is_none() && !self.is_variable {
-            self.clone()
+            Ok(self.clone())
         } else {
             let tensor_ = Tensor_ {
                 id: TensorId::new(),
@@ -1916,7 +1866,7 @@ impl Tensor {
                 dtype: self.dtype,
                 device: self.device.clone(),
             };
-            Tensor(Arc::new(tensor_))
+            Ok(Tensor(Arc::new(tensor_)))
         }
     }
 
@@ -2021,7 +1971,7 @@ impl Tensor {
             Ok(self.clone())
         } else {
             let shape = self.shape();
-            let mut storage = unsafe { self.device().alloc_uninit(shape, self.dtype())? };
+            let mut storage = self.device().zeros(shape, self.dtype())?;
             self.storage()
                 .copy_strided_src(&mut storage, 0, self.layout())?;
             let op = BackpropOp::new1(self, Op::Copy);
@@ -2029,21 +1979,11 @@ impl Tensor {
         }
     }
 
-    /// Returns a tensor that is in row major order. This always makes a copy.
-    pub fn force_contiguous(&self) -> Result<Tensor> {
-        let shape = self.shape();
-        let mut storage = unsafe { self.device().alloc_uninit(shape, self.dtype())? };
-        self.storage()
-            .copy_strided_src(&mut storage, 0, self.layout())?;
-        let op = BackpropOp::new1(self, Op::Copy);
-        Ok(from_storage(storage, shape.clone(), op, false))
-    }
-
     /// Create a variable based on the values currently stored in a tensor. The storage is always
     /// copied.
     pub(crate) fn make_var(&self) -> Result<Tensor> {
         let shape = self.shape().clone();
-        let mut storage = unsafe { self.device().alloc_uninit(&shape, self.dtype())? };
+        let mut storage = self.device().zeros(&shape, self.dtype())?;
         self.storage()
             .copy_strided_src(&mut storage, 0, self.layout())?;
         Ok(from_storage(storage, shape, BackpropOp::none(), true))
@@ -2096,7 +2036,7 @@ impl Tensor {
             };
             Ok(Tensor(Arc::new(tensor_)))
         } else {
-            let mut storage = unsafe { self.device().alloc_uninit(&shape, self.dtype())? };
+            let mut storage = self.device().zeros(&shape, self.dtype())?;
             self.storage()
                 .copy_strided_src(&mut storage, 0, self.layout())?;
             Ok(from_storage(storage, shape, op, false))
@@ -2123,19 +2063,8 @@ impl Tensor {
         let dim = dim.to_index(self.shape(), "squeeze")?;
         if dims[dim] == 1 {
             let mut dims = dims.to_vec();
-            let mut strides = self.stride().to_vec();
             dims.remove(dim);
-            strides.remove(dim);
+            self.reshape(dims)
-            let tensor_ = Tensor_ {
-                id: TensorId::new(),
-                storage: self.storage.clone(),
-                layout: Layout::new(dims.into(), strides, self.layout.start_offset()),
-                op: BackpropOp::new1(self, Op::Reshape),
-                is_variable: false,
-                dtype: self.dtype,
-                device: self.device.clone(),
-            };
-            Ok(Tensor(Arc::new(tensor_)))
         } else {
             Ok(self.clone())
         }
@@ -2156,24 +2085,10 @@ impl Tensor {
     /// ```
     pub fn unsqueeze<D: Dim>(&self, dim: D) -> Result<Self> {
         let mut dims = self.dims().to_vec();
-        let mut strides = self.stride().to_vec();
         let dim = dim.to_index_plus_one(self.shape(), "unsqueeze")?;
         // Cannot panic because to_index_plus_one already checks dimensions
         dims.insert(dim, 1);
-        // Any stride would work here, but we pick one so as to maximize the probability to remain
+        self.reshape(dims)
-        // C contiguous.
-        let stride = if dim < strides.len() { strides[dim] } else { 1 };
-        strides.insert(dim, stride);
-        let tensor_ = Tensor_ {
-            id: TensorId::new(),
-            storage: self.storage.clone(),
-            layout: Layout::new(dims.into(), strides, self.layout.start_offset()),
-            op: BackpropOp::new1(self, Op::Reshape),
-            is_variable: false,
-            dtype: self.dtype,
-            device: self.device.clone(),
-        };
-        Ok(Tensor(Arc::new(tensor_)))
     }
 
     /// Stacks two or more tensors along a particular dimension.
@@ -2204,6 +2119,152 @@ impl Tensor {
         Self::cat(&args, dim)
     }
 
+    /// Concatenates two or more tensors along a particular dimension.
+    ///
+    /// All tensors must of the same rank, and the output will have
+    /// the same rank
+    ///
+    /// ```rust
+    /// # use candle_core::{Tensor, DType, Device};
+    /// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
+    /// let b = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
+    ///
+    /// let c = Tensor::cat(&[&a, &b], 0)?;
+    /// assert_eq!(c.shape().dims(), &[4, 3]);
+    ///
+    /// let c = Tensor::cat(&[&a, &b], 1)?;
+    /// assert_eq!(c.shape().dims(), &[2, 6]);
+    /// # Ok::<(), candle_core::Error>(())
+    /// ```
+    pub fn cat<A: AsRef<Tensor>, D: Dim>(args: &[A], dim: D) -> Result<Self> {
+        if args.is_empty() {
+            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
+        }
+        let arg0 = args[0].as_ref();
+        if args.len() == 1 {
+            return Ok(arg0.clone());
+        }
+        let dim = dim.to_index(arg0.shape(), "cat")?;
+        for arg in args {
+            arg.as_ref().check_dim(dim, "cat")?;
+        }
+        for (arg_idx, arg) in args.iter().enumerate() {
+            let arg = arg.as_ref();
+            if arg0.rank() != arg.rank() {
+                Err(Error::UnexpectedNumberOfDims {
+                    expected: arg0.rank(),
+                    got: arg.rank(),
+                    shape: arg.shape().clone(),
+                }
+                .bt())?
+            }
+            for (dim_idx, (v1, v2)) in arg0
+                .shape()
+                .dims()
+                .iter()
+                .zip(arg.shape().dims().iter())
+                .enumerate()
+            {
+                if dim_idx != dim && v1 != v2 {
+                    Err(Error::ShapeMismatchCat {
+                        dim: dim_idx,
+                        first_shape: arg0.shape().clone(),
+                        n: arg_idx + 1,
+                        nth_shape: arg.shape().clone(),
+                    }
+                    .bt())?
+                }
+            }
+        }
+        if dim == 0 {
+            Self::cat0(args)
+        } else {
+            // TODO: Avoid these transpositions and have an implementation that works
+            // for dim != 0...
+            let args: Vec<Tensor> = args
+                .iter()
+                .map(|a| a.as_ref().transpose(0, dim))
+                .collect::<Result<Vec<_>>>()?;
+            let cat = Self::cat0(&args)?;
+            cat.transpose(0, dim)
+        }
+    }
+
+    fn cat0<A: AsRef<Tensor>>(args: &[A]) -> Result<Self> {
+        if args.is_empty() {
+            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
+        }
+        let arg0 = args[0].as_ref();
+        if args.len() == 1 {
+            return Ok(arg0.clone());
+        }
+        let rank = arg0.rank();
+        let device = arg0.device();
+        let dtype = arg0.dtype();
+        let first_dims = arg0.shape().dims();
+        let mut cat_dims = first_dims.to_vec();
+        cat_dims[0] = 0;
+        let mut offsets = vec![0usize];
+        for (arg_idx, arg) in args.iter().enumerate() {
+            let arg = arg.as_ref();
+            if arg.dtype() != dtype {
+                Err(Error::DTypeMismatchBinaryOp {
+                    lhs: dtype,
+                    rhs: arg.dtype(),
+                    op: "cat",
+                }
+                .bt())?
+            }
+            if arg.device().location() != device.location() {
+                Err(Error::DeviceMismatchBinaryOp {
+                    lhs: device.location(),
+                    rhs: arg.device().location(),
+                    op: "cat",
+                }
+                .bt())?
+            }
+            if rank != arg.rank() {
+                Err(Error::UnexpectedNumberOfDims {
+                    expected: rank,
+                    got: arg.rank(),
+                    shape: arg.shape().clone(),
+                }
+                .bt())?
+            }
+            for (dim_idx, (v1, v2)) in arg0
+                .shape()
+                .dims()
+                .iter()
+                .zip(arg.shape().dims().iter())
+                .enumerate()
+            {
+                if dim_idx == 0 {
+                    cat_dims[0] += v2;
+                }
+                if dim_idx != 0 && v1 != v2 {
+                    Err(Error::ShapeMismatchCat {
+                        dim: dim_idx,
+                        first_shape: arg0.shape().clone(),
+                        n: arg_idx + 1,
+                        nth_shape: arg.shape().clone(),
+                    }
+                    .bt())?
+                }
+            }
+            let next_offset = offsets.last().unwrap() + arg.elem_count();
+            offsets.push(next_offset);
+        }
+        let shape = Shape::from(cat_dims);
+        let op = BackpropOp::new(args, |args| Op::Cat(args, 0));
+        let mut storage = device.zeros(&shape, dtype)?;
+        for (arg, &offset) in args.iter().zip(offsets.iter()) {
+            let arg = arg.as_ref();
+            arg.storage()
+                .copy_strided_src(&mut storage, offset, arg.layout())?;
+        }
+        Ok(from_storage(storage, shape, op, false))
+    }
+
     /// Pad the input tensor using 0s along dimension `dim`. This adds `left` elements before the
     /// input tensor values and `right` elements after.
     pub fn pad_with_zeros<D: Dim>(&self, dim: D, left: usize, right: usize) -> Result<Self> {
@@ -2286,10 +2347,6 @@ impl Tensor {
         self.storage.read().unwrap()
     }
 
-    pub(crate) fn storage_mut(&self) -> std::sync::RwLockWriteGuard<'_, Storage> {
-        self.storage.write().unwrap()
-    }
-
     // If we extend the visibility of this function to be usable outside of this crate, we should
     // make it unsafe.
     pub(crate) fn storage_mut_and_layout(
@@ -2311,6 +2368,96 @@ impl Tensor {
         std::ptr::eq(lhs, rhs)
     }
 
+    /// Applies a unary custom op without backward support
+    pub fn apply_op1_no_bwd<C: CustomOp1>(&self, c: &C) -> Result<Self> {
+        let (storage, shape) = self.storage().apply_op1(self.layout(), c)?;
+        Ok(from_storage(storage, shape, BackpropOp::none(), false))
+    }
+
+    /// Applies a binary custom op without backward support
+    pub fn apply_op2_no_bwd<C: CustomOp2>(&self, rhs: &Self, c: &C) -> Result<Self> {
+        let (storage, shape) =
+            self.storage()
+                .apply_op2(self.layout(), &rhs.storage(), rhs.layout(), c)?;
+        Ok(from_storage(storage, shape, BackpropOp::none(), false))
+    }
+
+    /// Applies a ternary custom op without backward support
+    pub fn apply_op3_no_bwd<C: CustomOp3>(&self, t2: &Self, t3: &Self, c: &C) -> Result<Self> {
+        let (storage, shape) = self.storage().apply_op3(
+            self.layout(),
+            &t2.storage(),
+            t2.layout(),
+            &t3.storage(),
+            t3.layout(),
+            c,
+        )?;
+        Ok(from_storage(storage, shape, BackpropOp::none(), false))
+    }
+
+    /// Applies a unary custom op.
+    pub fn apply_op1_arc(&self, c: Arc<Box<dyn CustomOp1 + Send + Sync>>) -> Result<Self> {
+        let (storage, shape) = self
+            .storage()
+            .apply_op1(self.layout(), c.as_ref().as_ref())?;
+        let op = BackpropOp::new1(self, |s| Op::CustomOp1(s, c.clone()));
+        Ok(from_storage(storage, shape, op, false))
+    }
+
+    pub fn apply_op1<C: 'static + CustomOp1 + Send + Sync>(&self, c: C) -> Result<Self> {
+        self.apply_op1_arc(Arc::new(Box::new(c)))
+    }
+
+    /// Applies a binary custom op.
+    pub fn apply_op2_arc(
+        &self,
+        rhs: &Self,
+        c: Arc<Box<dyn CustomOp2 + Send + Sync>>,
+    ) -> Result<Self> {
+        let (storage, shape) = self.storage().apply_op2(
+            self.layout(),
+            &rhs.storage(),
+            rhs.layout(),
+            c.as_ref().as_ref(),
+        )?;
+        let op = BackpropOp::new2(self, rhs, |t1, t2| Op::CustomOp2(t1, t2, c.clone()));
+        Ok(from_storage(storage, shape, op, false))
+    }
+
+    pub fn apply_op2<C: 'static + CustomOp2 + Send + Sync>(&self, r: &Self, c: C) -> Result<Self> {
+        self.apply_op2_arc(r, Arc::new(Box::new(c)))
+    }
+
+    /// Applies a ternary custom op.
+    pub fn apply_op3_arc(
+        &self,
+        t2: &Self,
+        t3: &Self,
+        c: Arc<Box<dyn CustomOp3 + Send + Sync>>,
+    ) -> Result<Self> {
+        let (storage, shape) = self.storage().apply_op3(
+            self.layout(),
+            &t2.storage(),
+            t2.layout(),
+            &t3.storage(),
+            t3.layout(),
+            c.as_ref().as_ref(),
+        )?;
+        let op = BackpropOp::new3(self, t2, t3, |t1, t2, t3| {
+            Op::CustomOp3(t1, t2, t3, c.clone())
+        });
+        Ok(from_storage(storage, shape, op, false))
+    }
+
+    pub fn apply_op3<C: 'static + CustomOp3 + Send + Sync>(
+        &self,
+        t2: &Self,
+        t3: &Self,
+        c: C,
+    ) -> 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> {

candle-core/src/tensor_cat.rs

@@ -1,238 +0,0 @@
-use crate::{shape::Dim, Error, Result, Shape, Tensor};
-
-impl Tensor {
-    /// Concatenates two or more tensors along a particular dimension.
-    ///
-    /// All tensors must of the same rank, and the output will have
-    /// the same rank
-    ///
-    /// ```rust
-    /// # use candle_core::{Tensor, DType, Device};
-    /// let a = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
-    /// let b = Tensor::zeros((2, 3), DType::F32, &Device::Cpu)?;
-    ///
-    /// let c = Tensor::cat(&[&a, &b], 0)?;
-    /// assert_eq!(c.shape().dims(), &[4, 3]);
-    ///
-    /// let c = Tensor::cat(&[&a, &b], 1)?;
-    /// assert_eq!(c.shape().dims(), &[2, 6]);
-    /// # Ok::<(), candle_core::Error>(())
-    /// ```
-    pub fn cat<A: AsRef<Tensor>, D: Dim>(args: &[A], dim: D) -> Result<Self> {
-        if args.is_empty() {
-            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
-        }
-        let arg0 = args[0].as_ref();
-        if args.len() == 1 {
-            return Ok(arg0.clone());
-        }
-        let dim = dim.to_index(arg0.shape(), "cat")?;
-        for arg in args {
-            arg.as_ref().check_dim(dim, "cat")?;
-        }
-        for (arg_idx, arg) in args.iter().enumerate() {
-            let arg = arg.as_ref();
-            if arg0.rank() != arg.rank() {
-                Err(Error::UnexpectedNumberOfDims {
-                    expected: arg0.rank(),
-                    got: arg.rank(),
-                    shape: arg.shape().clone(),
-                }
-                .bt())?
-            }
-            for (dim_idx, (v1, v2)) in arg0
-                .shape()
-                .dims()
-                .iter()
-                .zip(arg.shape().dims().iter())
-                .enumerate()
-            {
-                if dim_idx != dim && v1 != v2 {
-                    Err(Error::ShapeMismatchCat {
-                        dim: dim_idx,
-                        first_shape: arg0.shape().clone(),
-                        n: arg_idx + 1,
-                        nth_shape: arg.shape().clone(),
-                    }
-                    .bt())?
-                }
-            }
-        }
-        let all_contiguous = args.iter().all(|v| v.as_ref().is_contiguous());
-        if all_contiguous {
-            Self::cat_contiguous(args, dim)
-        } else if dim == 0 {
-            Self::cat0(args)
-        } else {
-            let args: Vec<Tensor> = args
-                .iter()
-                .map(|a| a.as_ref().transpose(0, dim))
-                .collect::<Result<Vec<_>>>()?;
-            let cat = Self::cat0(&args)?;
-            cat.transpose(0, dim)
-        }
-    }
-
-    fn cat0<A: AsRef<Tensor>>(args: &[A]) -> Result<Self> {
-        if args.is_empty() {
-            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
-        }
-        let arg0 = args[0].as_ref();
-        if args.len() == 1 {
-            return Ok(arg0.clone());
-        }
-        let rank = arg0.rank();
-        let device = arg0.device();
-        let dtype = arg0.dtype();
-        let first_dims = arg0.shape().dims();
-        let mut cat_dims = first_dims.to_vec();
-        cat_dims[0] = 0;
-        let mut offsets = vec![0usize];
-        for (arg_idx, arg) in args.iter().enumerate() {
-            let arg = arg.as_ref();
-            if arg.dtype() != dtype {
-                Err(Error::DTypeMismatchBinaryOp {
-                    lhs: dtype,
-                    rhs: arg.dtype(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if arg.device().location() != device.location() {
-                Err(Error::DeviceMismatchBinaryOp {
-                    lhs: device.location(),
-                    rhs: arg.device().location(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if rank != arg.rank() {
-                Err(Error::UnexpectedNumberOfDims {
-                    expected: rank,
-                    got: arg.rank(),
-                    shape: arg.shape().clone(),
-                }
-                .bt())?
-            }
-            for (dim_idx, (v1, v2)) in arg0
-                .shape()
-                .dims()
-                .iter()
-                .zip(arg.shape().dims().iter())
-                .enumerate()
-            {
-                if dim_idx == 0 {
-                    cat_dims[0] += v2;
-                }
-                if dim_idx != 0 && v1 != v2 {
-                    Err(Error::ShapeMismatchCat {
-                        dim: dim_idx,
-                        first_shape: arg0.shape().clone(),
-                        n: arg_idx + 1,
-                        nth_shape: arg.shape().clone(),
-                    }
-                    .bt())?
-                }
-            }
-            let next_offset = offsets.last().unwrap() + arg.elem_count();
-            offsets.push(next_offset);
-        }
-        let shape = Shape::from(cat_dims);
-        let op = crate::op::BackpropOp::new(args, |args| crate::op::Op::Cat(args, 0));
-        let mut storage = unsafe { device.alloc_uninit(&shape, dtype)? };
-        for (arg, &offset) in args.iter().zip(offsets.iter()) {
-            let arg = arg.as_ref();
-            arg.storage()
-                .copy_strided_src(&mut storage, offset, arg.layout())?;
-        }
-        Ok(crate::tensor::from_storage(storage, shape, op, false))
-    }
-
-    fn cat_contiguous<A: AsRef<Tensor>>(args: &[A], dim: usize) -> Result<Self> {
-        if args.is_empty() {
-            Err(Error::OpRequiresAtLeastOneTensor { op: "cat" }.bt())?
-        }
-        let arg0 = args[0].as_ref();
-        if args.len() == 1 {
-            return Ok(arg0.clone());
-        }
-        let rank = arg0.rank();
-        let device = arg0.device();
-        let dtype = arg0.dtype();
-        let first_dims = arg0.shape().dims();
-        let mut cat_dims = first_dims.to_vec();
-        cat_dims[dim] = 0;
-        for (arg_idx, arg) in args.iter().enumerate() {
-            let arg = arg.as_ref();
-            if arg.dtype() != dtype {
-                Err(Error::DTypeMismatchBinaryOp {
-                    lhs: dtype,
-                    rhs: arg.dtype(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if arg.device().location() != device.location() {
-                Err(Error::DeviceMismatchBinaryOp {
-                    lhs: device.location(),
-                    rhs: arg.device().location(),
-                    op: "cat",
-                }
-                .bt())?
-            }
-            if rank != arg.rank() {
-                Err(Error::UnexpectedNumberOfDims {
-                    expected: rank,
-                    got: arg.rank(),
-                    shape: arg.shape().clone(),
-                }
-                .bt())?
-            }
-            for (dim_idx, (v1, v2)) in arg0
-                .shape()
-                .dims()
-                .iter()
-                .zip(arg.shape().dims().iter())
-                .enumerate()
-            {
-                if dim_idx == dim {
-                    cat_dims[dim] += v2;
-                }
-                if dim_idx != dim && v1 != v2 {
-                    Err(Error::ShapeMismatchCat {
-                        dim: dim_idx,
-                        first_shape: arg0.shape().clone(),
-                        n: arg_idx + 1,
-                        nth_shape: arg.shape().clone(),
-                    }
-                    .bt())?
-                }
-            }
-        }
-        let cat_target_dim_len = cat_dims[dim];
-        let block_size: usize = cat_dims.iter().skip(1 + dim).product();
-        let shape = Shape::from(cat_dims);
-        let op = crate::op::BackpropOp::new(args, |args| crate::op::Op::Cat(args, dim));
-        let mut storage = unsafe { device.alloc_uninit(&shape, dtype)? };
-        let mut dst_o = 0;
-        for arg in args.iter() {
-            let arg = arg.as_ref();
-            let arg_dims = arg.shape().dims();
-            let d1: usize = arg_dims.iter().take(dim).product();
-            let d2 = block_size * arg_dims[dim];
-            let dst_s = block_size * cat_target_dim_len;
-            let src_o = arg.layout().start_offset();
-            arg.storage().copy2d(
-                &mut storage,
-                d1,
-                d2,
-                /* src_s */ d2,
-                dst_s,
-                src_o,
-                dst_o,
-            )?;
-            dst_o += d2;
-        }
-        Ok(crate::tensor::from_storage(storage, shape, op, false))
-    }
-}

candle-core/src/variable.rs

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

candle-core/tests/conv_tests.rs

@@ -18,9 +18,6 @@ w_t = w.transpose(0, 1)
 res = torch.nn.functional.conv_transpose1d(t, w_t)
 print(res.shape)
 print(res)
-res = torch.nn.functional.conv_transpose1d(t, w_t, groups=2)
-print(res.shape)
-print(res)
 */
 fn conv1d(dev: &Device) -> Result<()> {
     let t = Tensor::new(
@@ -53,11 +50,8 @@ 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 w = w.transpose(0, 1)?;
+        let res = t.conv_transpose1d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
-    // The CPU kernels applied in the contiguous and non contiguous cases are different.
-    for w in [w.clone(), w.contiguous()?] {
-        let res = t.conv_transpose1d(&w, 0, 0, 1, 1, 1)?;
         assert_eq!(res.dims(), [1, 2, 7]);
         assert_eq!(
             test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
@@ -66,17 +60,6 @@ fn conv1d(dev: &Device) -> Result<()> {
                 4.7076, -5.9745, -0.8276, 1.621
             ],
         );
-        let res = t.conv_transpose1d(&w, 0, 0, 1, 1, 2)?;
-        assert_eq!(res.dims(), [1, 4, 7]);
-        assert_eq!(
-            test_utils::to_vec2_round(&res.squeeze(0)?, 4)?,
-            [
-                [-1.5596, -1.8099, 2.0407, 4.8764, -0.1743, -0.735, -0.7819],
-                [0.7816, 3.8152, -0.5926, 2.2515, -5.1844, -0.3157, 1.4721],
-                [1.6295, 0.52, 6.2611, 0.7109, 2.6315, -1.8793, 0.7113],
-                [1.0949, 1.0166, 1.7464, 2.4561, -0.79, -0.5119, 0.1488]
-            ]
-        );
     }
     Ok(())
 }
@@ -135,7 +118,7 @@ fn conv2d(dev: &Device) -> Result<()> {
             0.6466, -0.5042, -0.0603, -1.6538, -1.2429, 1.8357, 1.6052, -1.3844, 0.3323, -1.3712,
             0.9634, -0.4799, -0.6451, -0.0840, -1.4247, 0.5512, -0.1747, -0.5509, -0.3742, 0.3790,
             -0.4431, -0.4720, -0.7890, 0.2620, 0.7875, 0.5377, -0.6779, -0.8088, 1.9098, 1.2006,
-            -0.8, -0.4983, 1.5480, 0.8265, -0.1025, 0.5138, 0.5748, 0.3821, -0.4607, 0.0085,
+            -0.8000, -0.4983, 1.5480, 0.8265, -0.1025, 0.5138, 0.5748, 0.3821, -0.4607, 0.0085,
         ],
         dev,
     )?;
@@ -163,9 +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)?;
-
     assert_eq!(res.dims(), [1, 2, 7, 7]);
     assert_eq!(
         test_utils::to_vec3_round(&res.i(0)?, 4)?,
@@ -190,7 +171,6 @@ fn conv2d(dev: &Device) -> Result<()> {
             ]
         ]
     );
-
     // Dilations.
     let res = t.conv2d(&w, 0, 1, 2, 1)?;
     assert_eq!(res.dims(), [1, 2, 1, 1]);
@@ -229,7 +209,6 @@ fn conv2d(dev: &Device) -> Result<()> {
             ]
         ]
     );
-
     Ok(())
 }
 
@@ -276,13 +255,13 @@ fn conv2d_small(dev: &Device) -> Result<()> {
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [
-            0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1640,
+            0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
-            -0.0111, -0.1742, 0.0, 0.0, 0.0, 0.0, 2.6437, -2.0268, 1.1823, 0.0, 0.0, 0.0, 0.0,
+            0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1640, -0.0111, -0.1742, 0.0000, 0.0000,
-            3.2855, -1.0324, 0.2539, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+            0.0000, 0.0000, 2.6437, -2.0268, 1.1823, 0.0000, 0.0000, 0.0000, 0.0000, 3.2855,
-            0.0, 0.0, 0.0, 0.0
+            -1.0324, 0.2539, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
+            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)?;
     assert_eq!(res.dims(), [1, 1, 3, 3]);
     assert_eq!(
@@ -384,7 +363,6 @@ print(w.grad.shape)
 print(w.grad[0])
 */
 fn conv2d_grad(dev: &Device) -> Result<()> {
-    // conv-transposes are not implemented for metal
     use candle_core::Var;
     let t = Var::from_slice(
         &[
@@ -397,7 +375,7 @@ fn conv2d_grad(dev: &Device) -> Result<()> {
             0.6466, -0.5042, -0.0603, -1.6538, -1.2429, 1.8357, 1.6052, -1.3844, 0.3323, -1.3712,
             0.9634, -0.4799, -0.6451, -0.0840, -1.4247, 0.5512, -0.1747, -0.5509, -0.3742, 0.3790,
             -0.4431, -0.4720, -0.7890, 0.2620, 0.7875, 0.5377, -0.6779, -0.8088, 1.9098, 1.2006,
-            -0.8, -0.4983, 1.5480, 0.8265, -0.1025, 0.5138, 0.5748, 0.3821, -0.4607, 0.0085,
+            -0.8000, -0.4983, 1.5480, 0.8265, -0.1025, 0.5138, 0.5748, 0.3821, -0.4607, 0.0085,
         ],
         (1, 4, 5, 5),
         dev,
@@ -582,154 +560,6 @@ fn conv2d_grad(dev: &Device) -> Result<()> {
         ]
     );
 
-    // Conv Transpose 2d Test
-    //tested against following python
-
-    // import torch
-    // torch.manual_seed(4242)
-    // padding = 4
-    // outpadding = 2
-    // dilation = 3
-    // stride = 3
-    // input = torch.randn((1, 4, 7, 5), requires_grad=True)
-    // kernel = torch.randn((4, 2, 3, 5), requires_grad=True)
-    // print("input", input.flatten())
-    // print("kernel", kernel.flatten())
-    // res = torch.nn.functional.conv_transpose2d(
-    //     input,
-    //     kernel,
-    //     stride=stride,
-    //     padding=padding,
-    //     dilation=dilation,
-    //     output_padding=outpadding,
-    // )
-    // res.retain_grad()
-    // print(res.shape)
-    // loss = (res**2).sum()
-    // print(loss)
-    // loss.backward()
-    // print(input.grad.shape)
-    // print("input grad", torch.round(input.grad, decimals=1))
-    // print(kernel.grad.shape)
-    // print("kernel grad", torch.round(kernel.grad.flatten(), decimals=1))
-
-    let padding = 4;
-    let outpadding = 2;
-    let dilation = 3;
-    let stride = 3;
-
-    let t = Var::from_slice(
-        &[
-            0.4056_f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997,
-            3.0616, 1.6541, 0.0964, -0.8338, -1.6523, -0.8323, -0.1699, 0.0823, 0.3526, 0.6843,
-            0.2395, 1.2279, -0.9287, -1.7030, 0.1370, 0.6047, 0.3770, -0.6266, 0.3529, 2.2013,
-            -0.6836, 0.2477, 1.3127, -0.2260, 0.2622, -1.2974, -0.8140, -0.8404, -0.3490, 0.0130,
-            1.3123, 1.7569, -0.3956, -1.8255, 0.1727, -0.3538, 2.6941, 1.0529, 0.4219, -0.2071,
-            1.1586, 0.4717, 0.3865, -0.5690, -0.5010, -0.1310, 0.7796, 0.6630, -0.2021, 2.6090,
-            0.2049, 0.6466, -0.5042, -0.0603, -1.6538, -1.2429, 1.8357, 1.6052, -1.3844, 0.3323,
-            -1.3712, 0.9634, -0.4799, -0.6451, -0.0840, -1.4247, 0.5512, -0.1747, -0.5509, -0.3742,
-            0.3790, -0.4431, -0.4720, -0.7890, 0.2620, 0.5411, -1.1715, -2.4997, 2.3249, -0.8912,
-            -0.4733, -0.5701, -2.8888, -1.4112, -0.5471, -0.9234, -1.1660, 0.4189, -0.7465,
-            -0.6473, 0.1402, 0.7875, 0.5377, -0.6779, -0.8088, -0.4864, -0.2312, 0.9279, 0.1264,
-            1.5480, 0.8265, -0.1025, 0.5138, -0.2512, 0.1576, 1.2705, 0.3641, -0.9325, 0.6451,
-            -0.8537, 0.2378, 0.1794, 0.2752, -0.3687, -1.1149, -0.1410, -0.5829, -0.0892, 1.4258,
-            -2.2789, 0.5270, 0.1825, 1.7007, -0.5263, -0.2954, 0.4440, 0.5537, 0.3492, 0.6186,
-            1.6475, 0.2219,
-        ],
-        (1, 4, 7, 5),
-        dev,
-    )?;
-
-    #[rustfmt::skip]
-    let w = Var::from_slice(
-        &[
-            -1.1744_f32, 0.3266, 2.5893, 1.0142, 0.1763, 0.7752, 0.6604, 0.2029, -0.2145, 0.7234,
-            -0.3441, -1.5400, -0.6333, 0.6613, 0.2083, 0.6230, -1.7002, 0.3393, 0.4049, 1.0762,
-            0.2723, 1.4181, 0.0029, -0.2122, 1.7668, 1.4168, 0.3320, -0.2719, 0.7932, -0.7204,
-            0.4447, 0.1211, 0.5908, 1.0089, -0.1646, 1.8033, -0.6286, 0.2016, -0.3370, 1.2555,
-            0.8009, -0.6488, -0.4652, -1.5685, 1.5860, 0.5583, 0.4623, 0.6026, 0.8828, 2.4990,
-            0.6811, -0.3369, 1.3320, 1.7669, -1.1067, 1.2958, -0.9415, -0.9655, -0.4462, 0.7181,
-            0.5181, -1.1658, -1.8467, -0.7763, 1.2769, 0.8651, 0.9890, 1.5092, 0.7207, -0.8481,
-            0.7417, 0.3375, -1.2685, 1.4572, 1.0915, 0.1093, -0.8550, -0.5831, -0.6309, -0.2509,
-            0.5220, -0.0914, 0.7900, 0.1096, 0.3258, 0.2723, -1.0942, -0.3393, -0.1653, 0.5732,
-            -0.8014, 1.8194, -1.9023, 0.2127, 1.8636, -0.8979, 0.1927, -0.2778, 0.3105, 0.0071,
-            -1.1823, 0.2476, -0.7178, -1.3821, 1.0769, -0.4376, -0.9967, -0.1227, 1.6197, -1.0604,
-            0.1372, 0.8141, -0.6163, 0.7304, -0.8285, 2.0636, -0.7176, 0.2495, -0.2581, -0.4478,
-        ],
-        (4, 2, 3, 5),
-        dev,
-    )?;
-    let res = t.conv_transpose2d(&w, padding, outpadding, stride, dilation)?;
-    let loss = res.sqr()?.sum_all()?;
-    assert_eq!(test_utils::to_vec0_round(&loss, 0)?, 2904.0);
-    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, 7, 5]);
-    assert_eq!(grad_w.dims(), [4, 2, 3, 5]);
-
-    assert_eq!(
-        test_utils::to_vec1_round(&grad_w.flatten_all()?, 1)?,
-        [
-            // torch gets 89.1
-            -89.0, -135.3, 136.7, 102.0, -53.4, 117.9, 118.6, -43.9, -218.0, -58.5, -114.3, -150.0,
-            -15.6, 172.1, 66.3, -64.3, -27.9, -19.8, 31.7, 62.1, 5.5, 92.6, 28.2, -29.6, 55.9,
-            52.7, -72.7, -119.8, 53.8, -25.5, 128.8, 19.3, 68.0, 190.9, -64.1, -86.2, -111.2,
-            106.6, -67.7, 37.8, 115.9, 50.4, -77.7, -54.9, 22.3, -4.6, 89.8, 61.7, 122.4, 192.6,
-            -27.8, -104.6, 57.0, 166.4, 27.1, 6.1, 18.7, -93.2, 31.5, 168.2, -3.7, -99.5, -55.5,
-            -10.8, 17.5, 20.8, 16.9, 43.8, 42.0, -89.2, 18.8, -9.6, -84.1, 212.6, 19.7, -50.0,
-            -52.0, -40.0, -166.6, -73.2, -10.8, -73.3, 31.5, -23.4, -79.3, -27.0, -84.4, -42.9,
-            -20.3, 51.8, -16.7, 76.3, -120.5, -65.8, 96.5, -10.7, -45.9, -88.1, 65.4, -7.0, -1.5,
-            92.8, -25.1, -114.2, -5.8, -14.8, -51.2, -20.7, 54.2, -79.8, 47.7, -29.2, -8.8, 53.5,
-            -28.4, 85.0, -18.3, 107.0, 28.3, -71.8
-        ]
-    );
-
-    assert_eq!(
-        test_utils::to_vec3_round(&grad_t.i(0)?, 1)?,
-        [
-            [
-                [32.3, -41.6, -24.0, 14.1, 17.6],
-                [-11.8, 72.5, 87.6, 46.4, 61.5],
-                [115.0, 108.5, -48.6, -63.4, -50.0],
-                [51.3, 5.4, 31.3, 91.1, -30.9],
-                [52.7, 92.8, -68.0, -47.0, 83.0],
-                // pytorch gets -107.1
-                [-10.2, -107.0, -5.4, 213.1, -31.4],
-                [-2.4, 65.1, 9.2, -146.2, -24.2]
-            ],
-            [
-                [-72.6, -63.9, -61.9, 45.3, 33.0],
-                [79.3, -0.5, -26.2, 78.2, 42.7],
-                [90.9, 141.6, 40.1, -62.7, 37.0],
-                [32.8, 198.2, -0.8, -31.1, 27.3],
-                // torch gets 48.0
-                [34.5, 34.9, -47.9, 127.6, -12.3],
-                [-61.4, -3.2, -2.9, -10.9, -16.6],
-                [74.6, 60.1, -68.9, 34.5, -50.4]
-            ],
-            [
-                [37.5, -56.9, -43.6, -13.5, -9.9],
-                [40.0, 97.3, 28.6, 14.2, -30.1],
-                [-22.3, -126.3, -68.8, -8.2, 26.1],
-                [-32.9, 37.3, 108.5, -54.8, 29.6],
-                [34.9, -176.9, -125.0, -28.3, -13.9],
-                [-54.9, 142.6, 62.1, -80.4, -65.6],
-                [7.4, -91.1, -67.6, 35.0, 39.7]
-            ],
-            [
-                [-57.2, -40.9, -10.1, 32.6, 29.4],
-                [18.7, -18.0, 29.5, -1.2, 59.2],
-                [-14.0, -74.4, 19.8, -117.0, 58.2],
-                [-21.8, 163.5, -71.1, -99.0, 80.9],
-                [-58.9, -10.9, 93.8, -139.6, 98.0],
-                // torch gets 54.5
-                [-54.4, 135.3, 6.0, -79.1, 134.6],
-                [27.5, -76.0, 43.4, -2.8, -7.8]
-            ]
-        ]
-    );
     Ok(())
 }
 

candle-core/tests/custom_op_tests.rs

@@ -112,34 +112,3 @@ fn custom_op1_with_backward() -> Result<()> {
 
     Ok(())
 }
-
-impl candle_core::InplaceOp1 for Elu {
-    fn name(&self) -> &'static str {
-        "elu"
-    }
-
-    fn cpu_fwd(&self, s: &mut CpuStorage, _l: &Layout) -> Result<()> {
-        let alpha = self.alpha;
-        match s {
-            CpuStorage::BF16(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
-            CpuStorage::F16(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
-            CpuStorage::F32(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
-            CpuStorage::F64(s) => s.iter_mut().for_each(|v| *v = fwd(*v, alpha)),
-            _ => candle_core::bail!("unsupported dtype for inplace elu"),
-        }
-        Ok(())
-    }
-}
-
-#[test]
-fn inplace_op1() -> Result<()> {
-    let cpu = &Device::Cpu;
-    let t = Tensor::arange(0u32, 12u32, cpu)?.to_dtype(DType::F32)?;
-    let t = (t - 5.)?;
-    t.inplace_op1(&Elu { alpha: 1. })?;
-    assert_eq!(
-        to_vec1_round(&t, 4)?,
-        &[-0.9933, -0.9817, -0.9502, -0.8647, -0.6321, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
-    );
-    Ok(())
-}

candle-core/tests/grad_tests.rs

@@ -1,4 +1,3 @@
-#![allow(clippy::approx_constant)]
 use anyhow::{Context, Result};
 use candle_core::{test_device, test_utils, Device, Shape, Tensor, Var};
 
@@ -97,24 +96,24 @@ fn unary_grad(device: &Device) -> Result<()> {
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(
-        test_utils::to_vec1_round(&y, 4)?,
+        y.to_vec1::<f32>()?,
-        [20.0855, 2.7183, 54.5982, 1.1618]
+        [20.085537, 2.7182817, 54.59815, 1.1618342]
     );
     assert_eq!(
-        test_utils::to_vec1_round(grad_x, 4)?,
+        grad_x.to_vec1::<f32>()?,
-        [20.0855, 2.7183, 54.5982, 1.1618]
+        [20.085537, 2.7182817, 54.59815, 1.1618342]
     );
     let y = x.exp()?.sqr()?;
     let grads = y.backward()?;
     let grad_x = grads.get(x).context("no grad for x")?;
     assert_eq!(
-        test_utils::to_vec1_round(&y, 3)?,
+        y.to_vec1::<f32>()?,
-        [403.429, 7.389, 2980.958, 1.35]
+        [403.4288, 7.3890557, 2980.9578, 1.3498588]
     );
     // exp(x)^2 = exp(2*x)
     assert_eq!(
-        test_utils::to_vec1_round(grad_x, 2)?,
+        grad_x.to_vec1::<f32>()?,
-        [806.86, 14.78, 5961.92, 2.7]
+        [806.8576, 14.778111, 5961.9155, 2.6997175]
     );
     let y = x.sin()?;
     let grads = y.backward()?;
@@ -262,7 +261,6 @@ fn unary_grad(device: &Device) -> Result<()> {
     let y = elu_x.elu(2.)?;
     let grads = y.backward()?;
     let grad_x = grads.get(&elu_x).context("no grad for x")?;
-
     assert_eq!(
         test_utils::to_vec1_round(&y, 4)?,
         [-1.2642, 0.0000, -1.7293, 3.0000]
@@ -272,51 +270,19 @@ fn unary_grad(device: &Device) -> Result<()> {
         [0.7358, 2.0000, 0.2707, 1.0000]
     );
 
-    // testing compared to pytorch nn.Silu()
-    let y = x.silu()?;
-    let grads = y.backward()?;
-    let grad_x = grads.get(&x).context("no grad for x")?;
-    assert_eq!(
-        test_utils::to_vec1_round(&y, 4)?,
-        [2.8577, 0.7311, 3.9281, 0.0806]
-    );
-    assert_eq!(
-        test_utils::to_vec1_round(grad_x, 4)?,
-        [1.0881, 0.9277, 1.0527, 0.5747],
-    );
-
-    if device.is_cpu() {
-        let x = Var::new(&[[[1f32, 2., 3.], [4., 5., 6.], [7., 8., 9.]]], device)?;
-        let y = x.interpolate1d(12)?.reshape(36)?;
-
-        let z = Tensor::new(
-            &[
-                1_f32, 02., 03., 04., 05., 06., 07., 08., 09., 10., 11., 12., 13., 14., 15., 16.,
-                17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32.,
-                33., 34., 35., 36.,
-            ],
-            device,
-        )?;
-
-        let loss = y.unsqueeze(1)?.transpose(0, 1)?.matmul(&z.unsqueeze(1)?)?;
-        let grads = loss.backward()?;
-        let grad_x = grads.get(&x).context("no grad for x")?;
-
-        assert_eq!(
-            test_utils::to_vec3_round(grad_x, 4)?,
-            [[[10_f32, 26., 42.], [58., 74., 90.], [106., 122., 138.]]]
-        );
-    }
-
     // manually checked: see comments
     let x = Var::new(&[[[[1f32, 2., 3.], [4., 5., 6.], [7., 8., 9.]]]], device)?;
     let y = x.interpolate2d(6, 6)?.reshape(36)?;
 
+    #[rustfmt::skip]
     let z = Tensor::new(
         &[
-            1_f32, 02., 03., 04., 05., 06., 07., 08., 09., 10., 11., 12., 13., 14., 15., 16., 17.,
+            1_f32, 02., 03., 04., 05., 06.,
-            18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34.,
+            07.,   08., 09., 10., 11., 12.,
-            35., 36.,
+            13.,   14., 15., 16., 17., 18.,
+            19.,   20., 21., 22., 23., 24.,
+            25.,   26., 27., 28., 29., 30.,
+            31.,   32., 33., 34., 35., 36.,
         ],
         device,
     )?;
@@ -347,11 +313,15 @@ fn unary_grad(device: &Device) -> Result<()> {
     let x = Var::new(&[[[[1f32, 2.], [4., 5.]]]], device)?;
     let y = x.interpolate2d(6, 6)?.reshape(36)?;
 
+    #[rustfmt::skip]
     let z = Tensor::new(
         &[
-            1_f32, 02., 03., 04., 05., 06., 07., 08., 09., 10., 11., 12., 13., 14., 15., 16., 17.,
+            1_f32, 02., 03., 04., 05., 06.,
-            18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32., 33., 34.,
+            07.,   08., 09., 10., 11., 12.,
-            35., 36.,
+            13.,   14., 15., 16., 17., 18.,
+            19.,   20., 21., 22., 23., 24.,
+            25.,   26., 27., 28., 29., 30.,
+            31.,   32., 33., 34., 35., 36.,
         ],
         device,
     )?;

candle-core/tests/layout_tests.rs

@@ -88,7 +88,7 @@ fn strided_blocks() -> Result<()> {
         }
     };
     let tensor = Tensor::arange(0u32, 24u32, &Cpu)?.reshape((2, 3, 4))?;
-    let tensor = tensor.i((.., 1))?.contiguous()?;
+    let tensor = tensor.i((.., 1))?;
     match tensor.strided_blocks() {
         candle::StridedBlocks::SingleBlock { start_offset, len } => {
             assert_eq!(start_offset, 0);
@@ -100,20 +100,6 @@ fn strided_blocks() -> Result<()> {
         }
     };
     let tensor = Tensor::arange(0u32, 24u32, &Cpu)?.reshape((2, 3, 4))?;
-    let tensor = tensor.i((.., 1))?;
-    match tensor.strided_blocks() {
-        candle::StridedBlocks::SingleBlock { .. } => {
-            panic!("unexpected block structure")
-        }
-        candle::StridedBlocks::MultipleBlocks {
-            block_len,
-            block_start_index,
-        } => {
-            assert_eq!(block_len, 4);
-            assert_eq!(block_start_index.collect::<Vec<_>>(), &[4, 16])
-        }
-    };
-    let tensor = Tensor::arange(0u32, 24u32, &Cpu)?.reshape((2, 3, 4))?;
     match tensor.t()?.strided_blocks() {
         candle::StridedBlocks::SingleBlock { .. } => {
             panic!("unexpected block structure")

candle-core/tests/matmul_tests.rs

@@ -1,106 +0,0 @@
-use candle_core::{test_device, DType, Device, IndexOp, Result, Tensor};
-
-fn matmul(device: &Device) -> Result<()> {
-    let data = vec![1.0f32, 2.0, 3.0, 4.0];
-    let a = Tensor::from_slice(&data, (2, 2), device)?;
-    let data = vec![1.0f32, 2.0, 3.0, 4.0];
-    let b = Tensor::from_slice(&data, (2, 2), device)?;
-
-    let c = a.matmul(&b)?;
-    assert_eq!(c.to_vec2::<f32>()?, &[[7.0f32, 10.0], [15.0, 22.0]]);
-
-    let data = vec![1.0f32, 2.0];
-    let a = Tensor::from_slice(&data, (2, 1), device)?;
-    let data = vec![3.0f32, 4.0];
-    let b = Tensor::from_slice(&data, (1, 2), device)?;
-    let c = a.matmul(&b)?;
-    assert_eq!(c.to_vec2::<f32>()?, &[&[3.0, 4.0], &[6.0, 8.0]]);
-
-    let data: Vec<_> = (0..6).map(|i| i as f32).collect();
-    let a = Tensor::from_slice(&data, (2, 3), device)?;
-    let data: Vec<_> = (0..6).map(|i| (i + 2) as f32).collect();
-    let b = Tensor::from_slice(&data, (3, 2), device)?;
-    let c = a.matmul(&b)?;
-    assert_eq!(c.to_vec2::<f32>()?, &[&[16., 19.], &[52., 64.]]);
-
-    let data: Vec<_> = (0..12).map(|i| i as f32).collect();
-    let a = Tensor::from_slice(&data, (2, 2, 3), device)?;
-    let data: Vec<_> = (0..12).map(|i| (i + 2) as f32).collect();
-    let b = Tensor::from_slice(&data, (2, 3, 2), device)?;
-    let expected = [[[16., 19.], [52., 64.]], [[214., 235.], [304., 334.]]];
-
-    let c = a.matmul(&b)?;
-    assert_eq!(c.to_vec3::<f32>()?, &expected);
-
-    // Also perform the matmul on contiguous transposed versions.
-    let a_tt = a.t()?.contiguous()?.t()?;
-    assert!(!a_tt.is_contiguous());
-    assert_eq!(a.dims(), a_tt.dims());
-    assert_eq!(a_tt.stride(), &[6, 1, 2]);
-
-    let b_tt = b.t()?.contiguous()?.t()?;
-    assert!(!b_tt.is_contiguous());
-    assert_eq!(b.dims(), b_tt.dims());
-    assert_eq!(b_tt.stride(), &[6, 1, 3]);
-
-    assert_eq!(a_tt.matmul(&b)?.to_vec3::<f32>()?, &expected);
-    assert_eq!(a.matmul(&b_tt)?.to_vec3::<f32>()?, &expected);
-    assert_eq!(a_tt.matmul(&b_tt)?.to_vec3::<f32>()?, &expected);
-    Ok(())
-}
-
-fn broadcast_matmul(device: &Device) -> Result<()> {
-    let lhs = Tensor::randn(0f32, 1f32, (3, 1, 4, 5), device)?;
-    let rhs = Tensor::randn(0f32, 1f32, (6, 5, 2), device)?;
-    let out = lhs.broadcast_matmul(&rhs)?;
-    assert_eq!(out.dims(), &[3, 6, 4, 2]);
-    for idx1 in 0..3 {
-        for idx2 in 0..6 {
-            let out = out.i((idx1, idx2))?;
-            let lhs = lhs.i((idx1, 0))?;
-            let rhs = rhs.i(idx2)?;
-            let out2 = lhs.matmul(&rhs);
-            let sum_diff2 = (out - out2)?.sqr()?.sum_all()?;
-            // With cuda, we see errors of up to ~1e-12.
-            assert!(sum_diff2.to_vec0::<f32>()? < 1e-6)
-        }
-    }
-    Ok(())
-}
-
-// https://github.com/huggingface/candle/issues/1948
-fn squeeze_mm(device: &Device) -> Result<()> {
-    let seq_len = 8_usize;
-    let a = Tensor::zeros((1, seq_len, 16), DType::F32, device)?;
-    let x = a.i((.., seq_len - 1, ..))?;
-    let w = Tensor::zeros((32, 16), DType::F32, device)?.t()?;
-    let x = x.matmul(&w)?;
-    assert_eq!(x.dims(), &[1, 32]);
-    Ok(())
-}
-
-// https://github.com/huggingface/candle/issues/1992
-fn mm_layout(device: &Device) -> Result<()> {
-    let a = Tensor::arange(0f32, 16f32, device)?.reshape((1, 1, 4, 4))?;
-    let b = Tensor::arange(0f32, 8f32, device)?.reshape((1, 1, 4, 2))?;
-    let mm1 = a.matmul(&b)?;
-    // Forces the layout to be:
-    // shape: [1, 1, 4, 2], stride: [8, 2, 2, 1], start_offset: 0
-    // This is still a contiguous matrix but matmul checks are only the two last dimensions have
-    // non 1 sizes but matmul check may be reluctant to handle it.
-    let b = b.transpose(1, 2)?.force_contiguous()?.transpose(1, 2)?;
-    let mm2 = a.matmul(&b)?;
-    let diff = (mm1 - mm2)?.abs()?.sum_all()?.to_vec0::<f32>()?;
-    assert_eq!(diff, 0.);
-    Ok(())
-}
-
-test_device!(matmul, matmul_cpu, matmul_gpu, matmul_metal);
-test_device!(
-    broadcast_matmul,
-    broadcast_matmul_cpu,
-    broadcast_matmul_gpu,
-    broadcast_matmul_metal
-);
-test_device!(squeeze_mm, squeeze_mm_cpu, squeeze_mm_gpu, squeeze_mm_metal);
-test_device!(mm_layout, mm_layout_cpu, mm_layout_gpu, mm_layout_metal);

candle-core/tests/pool_tests.rs

@@ -43,9 +43,6 @@ res = torch.nn.functional.avg_pool2d(t, 2)
 print(res)
 */
 fn avg_pool2d_pytorch(dev: &Device) -> Result<()> {
-    if dev.is_metal() {
-        return Ok(());
-    }
     let t = Tensor::new(
         &[
             0.4056f32, -0.8689, -0.0773, -1.5630, -2.8012, -1.5059, 0.3972, 1.0852, 0.4997, 3.0616,

candle-core/tests/pth.py

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

candle-core/tests/pth_tests.rs

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

candle-core/tests/quantized_tests.rs

@@ -3,7 +3,7 @@ use candle_core::{
     quantized::{self, GgmlDType},
     test_device,
     test_utils::to_vec2_round,
-    Device, IndexOp, Module, Result, Tensor,
+    Device, Module, Result, Tensor,
 };
 use quantized::{k_quants, GgmlType};
 use rand::prelude::*;
@@ -47,14 +47,18 @@ fn test_matmul(
 }
 
 fn quantized_matmul(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let (m, k, n) = (3, 64, 4);
-    let lhs_s = (0..(m * k)).map(|v| v as f32).collect::<Vec<_>>();
+    let lhs = (0..(m * k)).map(|v| v as f32).collect::<Vec<_>>();
-    let lhs = Tensor::from_slice(&lhs_s, (m, k), device)?;
+    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), device)?;
     let mut dst = vec![42.; 3 * 4];
     let mut rhs_t = vec![k_quants::BlockQ4_0::zeros(); 8];
     let rhs = (0..(k * n)).map(|v| v as f32).collect::<Vec<_>>();
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
-    k_quants::matmul((m, k, n), &lhs_s, &rhs_t, &mut dst)?;
+    k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
         dst.iter().map(|x| x.round()).collect::<Vec<_>>(),
         &[
@@ -63,7 +67,7 @@ fn quantized_matmul(device: &Device) -> Result<()> {
         ]
     );
     let tensor_rhs = Tensor::from_slice(&rhs, (n, k), device)?.t()?;
-    let mm = lhs.matmul(&tensor_rhs)?;
+    let mm = tensor_lhs.matmul(&tensor_rhs)?;
     assert_eq!(
         mm.to_vec2::<f32>()?,
         &[
@@ -75,7 +79,7 @@ fn quantized_matmul(device: &Device) -> Result<()> {
 
     let qtensor = quantized::QTensor::quantize(&tensor_rhs.t()?, GgmlDType::Q4_0)?;
     let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
-    let res = matmul.forward(&lhs)?;
+    let res = matmul.forward(&tensor_lhs)?;
     match device {
         Device::Metal(_) => assert_eq!(
             to_vec2_round(&res, 0)?,
@@ -85,15 +89,7 @@ fn quantized_matmul(device: &Device) -> Result<()> {
                 [341970.0, 994574.0, 1656181.0, 2302182.0]
             ]
         ),
-        Device::Cuda(_) => assert_eq!(
+        _ => assert_eq!(
-            to_vec2_round(&res, 0)?,
-            &[
-                [84866.0, 214045.0, 344676.0, 473707.0],
-                [213425.0, 604313.0, 1000431.0, 1387960.0],
-                [342030.0, 994630.0, 1656248.0, 2302250.0]
-            ]
-        ),
-        Device::Cpu => assert_eq!(
             to_vec2_round(&res, 0)?,
             &[
                 [85120.0, 214562.0, 345455.0, 474748.0],
@@ -102,16 +98,22 @@ fn quantized_matmul(device: &Device) -> Result<()> {
             ]
         ),
     }
+
     test_matmul(device, (1, 3, 4, 256), GgmlDType::Q4_0)?;
+
     Ok(())
 }
 
 fn quantized_matmul_neg(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let (m, k, n) = (3, 64, 4);
-    let lhs_s = (0..(m * k))
+    let lhs = (0..(m * k))
         .map(|v| v as f32 - (m * k) as f32 / 2.0)
         .collect::<Vec<_>>();
-    let lhs = Tensor::from_slice(&lhs_s, (m, k), device)?;
+    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), device)?;
     let mut dst = vec![42.; 3 * 4];
     let mut rhs_t = vec![k_quants::BlockQ4_0::zeros(); 8];
     let rhs = (0..k * n)
@@ -119,7 +121,7 @@ fn quantized_matmul_neg(device: &Device) -> Result<()> {
         .collect::<Vec<_>>();
     let tensor_rhs = Tensor::from_slice(&rhs, (n, k), device)?.t()?;
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
-    k_quants::matmul((m, k, n), &lhs_s, &rhs_t, &mut dst)?;
+    k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
         dst.iter().map(|x| x.round()).collect::<Vec<_>>(),
         &[
@@ -127,7 +129,7 @@ fn quantized_matmul_neg(device: &Device) -> Result<()> {
             -196472.0, 63012.0, 324585.0, 587902.0
         ]
     );
-    let mm = lhs.matmul(&tensor_rhs)?;
+    let mm = tensor_lhs.matmul(&tensor_rhs)?;
     assert_eq!(
         to_vec2_round(&mm, 0)?,
         &[
@@ -139,7 +141,7 @@ fn quantized_matmul_neg(device: &Device) -> Result<()> {
 
     let qtensor = quantized::QTensor::quantize(&tensor_rhs.t()?, GgmlDType::Q4_0)?;
     let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
-    let res = matmul.forward(&lhs)?;
+    let res = matmul.forward(&tensor_lhs)?;
     match device {
         Device::Metal(_) => assert_eq!(
             to_vec2_round(&res, 0)?,
@@ -149,15 +151,7 @@ fn quantized_matmul_neg(device: &Device) -> Result<()> {
                 [-196102.0, 63022.0, 324233.0, 587191.0]
             ]
         ),
-        Device::Cuda(_) => assert_eq!(
+        _ => assert_eq!(
-            to_vec2_round(&res, 0)?,
-            &[
-                [243740.0, -19762.0, -285476.0, -550498.0],
-                [23774.0, 21645.0, 19395.0, 18364.0],
-                [-196045.0, 63030.0, 324120.0, 587079.0]
-            ]
-        ),
-        Device::Cpu => assert_eq!(
             to_vec2_round(&res, 0)?,
             &[
                 [243524.0, -19596.0, -285051.0, -549815.0],
@@ -166,60 +160,28 @@ fn quantized_matmul_neg(device: &Device) -> Result<()> {
             ]
         ),
     }
-    let lhs2 = Tensor::stack(&[&lhs, &lhs], 0)?;
+
-    let res2 = matmul.forward(&lhs2)?;
-    let res2 = res2.i(1)?;
-    let diff = (res - res2)?.abs()?.sum_all()?.to_vec0::<f32>()?;
-    if device.is_cuda() {
-        assert!(diff < 0.1);
-    } else {
-        assert_eq!(diff, 0.);
-    }
     Ok(())
 }
 
-fn qmm_batch(dev: &Device) -> Result<()> {
+test_device!(
-    let (lhs, rhs, _mm) = get_random_tensors(2, 256, 6, dev)?;
+    quantized_matmul,
-    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q2K)?;
+    quantized_matmul_cpu,
-    let rhs = quantized::QMatMul::from_qtensor(rhs)?;
+    quantized_matmul_cuda,
-    let mm = rhs.forward(&lhs)?;
+    quantized_matmul_metal
-    assert_eq!(mm.shape().dims(), [2, 6]);
+);
-    let lhs2 = Tensor::cat(&[&lhs, &lhs], 0)?;
+test_device!(
-    let mm2 = rhs.forward(&lhs2)?;
+    quantized_matmul_neg,
-    assert_eq!(mm2.shape().dims(), [4, 6]);
+    quantized_matmul_neg_cpu,
-    let diff2 = (mm2.i(2..)? - &mm)?.abs()?.sum_all()?.to_vec0::<f32>()?;
+    quantized_matmul_neg_cuda,
-    assert_eq!(diff2, 0.0);
+    quantized_matmul_neg_metal
-    let lhs3 = Tensor::cat(&[&lhs2, &lhs], 0)?;
+);
-    let mm3 = rhs.forward(&lhs3)?;
-    assert_eq!(mm3.shape().dims(), [6, 6]);
-    let diff3 = (mm3.i(2..4)? - &mm)?.abs()?.sum_all()?.to_vec0::<f32>()?;
-    assert_eq!(diff3, 0.0);
-    let diff3 = (mm3.i(4..)? - &mm)?.abs()?.sum_all()?.to_vec0::<f32>()?;
-    assert_eq!(diff3, 0.0);
-    let lhs4 = Tensor::cat(&[&lhs3, &lhs3], 0)?;
-    let mm4 = rhs.forward(&lhs4)?;
-    assert_eq!(mm4.shape().dims(), [12, 6]);
-    let diff4 = (mm4.i(..6)? - &mm3)?.abs()?.sum_all()?.to_vec0::<f32>()?;
-    if dev.is_cuda() {
-        // We use a different kernel for sizes from 1 to 8 on cuda which explains
-        // the difference here.
-        assert!(0. < diff4 && diff4 < 1e-4)
-    } else {
-        assert_eq!(diff4, 0.0)
-    };
-    let diff4 = (mm4.i(6..)? - &mm4.i(..6)?)?
-        .abs()?
-        .sum_all()?
-        .to_vec0::<f32>()?;
-    assert_eq!(diff4, 0.0);
-    Ok(())
-}
-
-test_device!(quantized_matmul, qmm_cpu, qmm_cuda, qmm_metal);
-test_device!(quantized_matmul_neg, qmm_n_cpu, qmm_n_cuda, qmm_n_metal);
-test_device!(qmm_batch, qmm_b_cpu, qmm_b_cuda, qmm_b_metal);
 
 fn quantize_q4_0(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
 
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
@@ -247,6 +209,10 @@ fn quantize_q4_0(device: &Device) -> Result<()> {
 }
 
 fn quantize_q4_1(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
     let quant = quantized::QTensor::quantize(&src, GgmlDType::Q4_1)?;
@@ -273,6 +239,10 @@ fn quantize_q4_1(device: &Device) -> Result<()> {
 }
 
 fn quantize_q5_0(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
     let quant = quantized::QTensor::quantize(&src, GgmlDType::Q5_0)?;
@@ -299,6 +269,10 @@ fn quantize_q5_0(device: &Device) -> Result<()> {
 }
 
 fn quantize_q5_1(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let src = (0..32 * 4).map(|v| v as f32).collect::<Vec<_>>();
     let src = Tensor::from_slice(&src, (32 * 4,), device)?;
     let quant = quantized::QTensor::quantize(&src, GgmlDType::Q5_1)?;
@@ -399,6 +373,10 @@ fn ggml_quantization_error_test(dtype: GgmlDType, device: &Device, max_error: f3
 }
 
 fn quantize_q2k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q2K;
 
     let src = get_test_vector2(0.5, 1024, device)?;
@@ -433,6 +411,10 @@ fn quantize_q2k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q3k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q3K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -466,6 +448,10 @@ fn quantize_q3k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q4k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q4K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -499,6 +485,10 @@ fn quantize_q4k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q5k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q5K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -532,6 +522,10 @@ fn quantize_q5k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q6k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q6K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -565,6 +559,10 @@ fn quantize_q6k(device: &Device) -> Result<()> {
 }
 
 fn quantize_q8k(device: &Device) -> Result<()> {
+    // TODO Enable this later when we enable cuda.
+    if device.is_cuda() {
+        return Ok(());
+    }
     let dtype = GgmlDType::Q8K;
     let src = get_test_vector2(0.5, 1024, device)?;
     let quant = quantized::QTensor::quantize(&src, dtype)?;
@@ -780,6 +778,10 @@ macro_rules! quantized_matmul {
     // stable. https://github.com/rust-lang/rust/issues/29599
     ($fn_name: ident, $fn_name_cpu: ident, $fn_name_cuda: ident, $fn_name_metal: ident, $dtype: expr) => {
         fn $fn_name(device: &Device) -> Result<()> {
+            if device.is_cuda() {
+                // TODO Enable Cuda GGML sometime maybe.
+                return Ok(());
+            }
             test_matmul(device, (1, 3, 4, 256), $dtype)?;
             Ok(())
         }

candle-core/tests/tensor_tests.rs

@@ -106,9 +106,6 @@ fn unary_op(device: &Device) -> Result<()> {
             [2.6911, -0.0647, -0.1091, 1.7353, 2.7933]
         ]
     );
-    let t_f16 = tensor.to_dtype(DType::F16)?.gelu()?.to_dtype(DType::F32)?;
-    let max_diff = (tensor.gelu()? - t_f16)?.flatten_all()?.max(0)?;
-    assert!(max_diff.to_vec0::<f32>()? < 5e-3);
     assert_eq!(
         test_utils::to_vec2_round(&tensor.gelu_erf()?, 4)?,
         [
@@ -123,13 +120,6 @@ fn unary_op(device: &Device) -> Result<()> {
             [0.9999, -0.9891, -0.3079, 0.9891, 0.9999]
         ]
     );
-    assert_eq!(
-        test_utils::to_vec2_round(&tensor.silu()?, 4)?,
-        [
-            [-0.1423, 0.7311, 3.9281, -0.0475, 0.3112],
-            [2.53, -0.2553, -0.1205, 1.5447, 2.6395]
-        ]
-    );
     assert_eq!(
         test_utils::to_vec2_round(&tensor.ceil()?, 4)?,
         [[-3.0, 1.0, 4.0, -0.0, 1.0], [3.0, -1.0, -0.0, 2.0, 3.0]]
@@ -151,14 +141,6 @@ fn unary_op(device: &Device) -> Result<()> {
         test_utils::to_vec1_round(&tensor.round_to(-2)?, 4)?,
         [3000.0, 300.]
     );
-    let tensor = Tensor::new(
-        &[-1.01f32, -0.9, -0.1, 0.0, -0.0, 0.1, 0.9, 1.0, 1.1],
-        device,
-    )?;
-    assert_eq!(
-        tensor.sign()?.to_vec1::<f32>()?,
-        [-1., -1., -1., 0., 0., 1., 1., 1., 1.]
-    );
     Ok(())
 }
 
@@ -683,31 +665,6 @@ fn cat(device: &Device) -> Result<()> {
             [2.0, 7.0, 1.0, 8.0, 2.0, 2.0, 7.0, 1.0, 8.0, 2.0]
         ]
     );
-
-    // 3D
-    let t1 = Tensor::arange(0, 48i64, device)?.reshape((2, 6, 4))?;
-    let t2 = Tensor::arange(100, 124i64, device)?.reshape((2, 3, 4))?;
-    let t3 = Tensor::arange(10000, 10032i64, device)?.reshape((2, 4, 4))?;
-
-    let t_cat = Tensor::cat(&[&t1, &t2, &t3], 1)?;
-
-    let t1 = t1.t()?.contiguous()?.t()?;
-    let t2 = t2.t()?.contiguous()?.t()?;
-    let t3 = t3.t()?.contiguous()?.t()?;
-    let t_cat2 = Tensor::cat(&[&t1, &t2, &t3], 1)?;
-
-    let diff = t_cat.eq(&t_cat2)?.to_dtype(DType::F32)?.sum_all()?;
-    assert_eq!(diff.to_vec0::<f32>()?, 104.0);
-    assert_eq!(t_cat.i((0, 0, 0))?.to_vec0::<i64>()?, 0);
-    assert_eq!(t_cat.i((0, 4, 0))?.to_vec0::<i64>()?, 16);
-    assert_eq!(t_cat.i((0, 5, 0))?.to_vec0::<i64>()?, 20);
-    assert_eq!(t_cat.i((1, 5, 0))?.to_vec0::<i64>()?, 44);
-    assert_eq!(t_cat.i((0, 6, 0))?.to_vec0::<i64>()?, 100);
-    assert_eq!(t_cat.i((1, 6, 0))?.to_vec0::<i64>()?, 112);
-    assert_eq!(t_cat.i((0, 6, 1))?.to_vec0::<i64>()?, 101);
-    assert_eq!(t_cat.i((0, 7, 1))?.to_vec0::<i64>()?, 105);
-    assert_eq!(t_cat.i((0, 12, 1))?.to_vec0::<i64>()?, 10013);
-    assert_eq!(t_cat.i((1, 12, 3))?.to_vec0::<i64>()?, 10031);
     Ok(())
 }
 
@@ -718,8 +675,6 @@ fn embeddings(device: &Device) -> Result<()> {
     assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 1.0], [4.0, 5.0], [2.0, 3.0]]);
     let hs = t.index_select(&ids, 0)?;
     assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 1.0], [4.0, 5.0], [2.0, 3.0]]);
-    let hs = t.index_select(&ids.to_dtype(DType::I64)?, 0)?;
-    assert_eq!(hs.to_vec2::<f32>()?, &[[0.0, 1.0], [4.0, 5.0], [2.0, 3.0]]);
     Ok(())
 }
 
@@ -747,47 +702,44 @@ fn index_select(device: &Device) -> Result<()> {
             [9.0, 10.0, 11.0]
         ]
     );
-    for dtype in [DType::U8, DType::U32, DType::I64] {
+    let hs = t.index_select(&ids, 1)?;
-        let ids = ids.to_dtype(dtype)?;
+    assert_eq!(
-        let hs = t.index_select(&ids, 1)?;
+        hs.to_vec2::<f32>()?,
-        assert_eq!(
+        &[
-            hs.to_vec2::<f32>()?,
+            [0.0, 2.0, 1.0],
-            &[
+            [3.0, 5.0, 4.0],
-                [0.0, 2.0, 1.0],
+            [6.0, 8.0, 7.0],
-                [3.0, 5.0, 4.0],
+            [9.0, 11.0, 10.0]
-                [6.0, 8.0, 7.0],
+        ]
-                [9.0, 11.0, 10.0]
+    );
-            ]
+    let hs = t.index_select(&ids, 0)?;
-        );
+    assert_eq!(
-        let hs = t.index_select(&ids, 0)?;
+        hs.to_vec2::<f32>()?,
-        assert_eq!(
+        &[[0.0, 1.0, 2.0], [6.0, 7.0, 8.0], [3.0, 4.0, 5.0]]
-            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.
-        // Prior to https://github.com/huggingface/candle/pull/1022
+    let ids = Tensor::new(&[0u32, 2u32, 1u32, 0u32, 2u32, 1u32], device)?;
-        // There would be a bug where the last values in the result tensor would be set to 0.
+    let hs = t.index_select(&ids, 0)?;
-        let ids = Tensor::new(&[0u32, 2u32, 1u32, 0u32, 2u32, 1u32], device)?;
+    assert_eq!(
-        let hs = t.index_select(&ids, 0)?;
+        hs.to_vec2::<f32>()?,
-        assert_eq!(
+        &[
-            hs.to_vec2::<f32>()?,
+            [0.0, 1.0, 2.0],
-            &[
+            [6.0, 7.0, 8.0],
-                [0.0, 1.0, 2.0],
+            [3.0, 4.0, 5.0],
-                [6.0, 7.0, 8.0],
+            [0.0, 1.0, 2.0],
-                [3.0, 4.0, 5.0],
+            [6.0, 7.0, 8.0],
-                [0.0, 1.0, 2.0],
+            [3.0, 4.0, 5.0],
-                [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
+    // Test when selecting dim > 0 with ids size different from elem count of
-        // target dim in source/input.
+    // target dim in source/input.
-        let ids = Tensor::new(&[1u32, 0u32, 1u32], device)?;
+    let ids = Tensor::new(&[1u32, 0u32, 1u32], device)?;
-        let t = Tensor::arange(1f32, 5f32, device)?.reshape((2, 2))?;
+    let t = Tensor::arange(1f32, 5f32, device)?.reshape((2, 2))?;
-        assert_eq!(t.to_vec2::<f32>()?, &[[1.0, 2.0], [3.0, 4.0]]);
+    assert_eq!(t.to_vec2::<f32>()?, &[[1.0, 2.0], [3.0, 4.0]]);
-        let hs = t.index_select(&ids, 1)?;
+    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]]);
+    assert_eq!(hs.to_vec2::<f32>()?, &[[2.0, 1.0, 2.0], [4.0, 3.0, 4.0]]);
-    }
 
     Ok(())
 }
@@ -949,6 +901,74 @@ fn gather(device: &Device) -> Result<()> {
     Ok(())
 }
 
+fn matmul(device: &Device) -> Result<()> {
+    let data = vec![1.0f32, 2.0, 3.0, 4.0];
+    let a = Tensor::from_slice(&data, (2, 2), device)?;
+    let data = vec![1.0f32, 2.0, 3.0, 4.0];
+    let b = Tensor::from_slice(&data, (2, 2), device)?;
+
+    let c = a.matmul(&b)?;
+    assert_eq!(c.to_vec2::<f32>()?, &[[7.0f32, 10.0], [15.0, 22.0]]);
+
+    let data = vec![1.0f32, 2.0];
+    let a = Tensor::from_slice(&data, (2, 1), device)?;
+    let data = vec![3.0f32, 4.0];
+    let b = Tensor::from_slice(&data, (1, 2), device)?;
+    let c = a.matmul(&b)?;
+    assert_eq!(c.to_vec2::<f32>()?, &[&[3.0, 4.0], &[6.0, 8.0]]);
+
+    let data: Vec<_> = (0..6).map(|i| i as f32).collect();
+    let a = Tensor::from_slice(&data, (2, 3), device)?;
+    let data: Vec<_> = (0..6).map(|i| (i + 2) as f32).collect();
+    let b = Tensor::from_slice(&data, (3, 2), device)?;
+    let c = a.matmul(&b)?;
+    assert_eq!(c.to_vec2::<f32>()?, &[&[16., 19.], &[52., 64.]]);
+
+    let data: Vec<_> = (0..12).map(|i| i as f32).collect();
+    let a = Tensor::from_slice(&data, (2, 2, 3), device)?;
+    let data: Vec<_> = (0..12).map(|i| (i + 2) as f32).collect();
+    let b = Tensor::from_slice(&data, (2, 3, 2), device)?;
+    let expected = [[[16., 19.], [52., 64.]], [[214., 235.], [304., 334.]]];
+
+    let c = a.matmul(&b)?;
+    assert_eq!(c.to_vec3::<f32>()?, &expected);
+
+    // Also perform the matmul on contiguous transposed versions.
+    let a_tt = a.t()?.contiguous()?.t()?;
+    assert!(!a_tt.is_contiguous());
+    assert_eq!(a.dims(), a_tt.dims());
+    assert_eq!(a_tt.stride(), &[6, 1, 2]);
+
+    let b_tt = b.t()?.contiguous()?.t()?;
+    assert!(!b_tt.is_contiguous());
+    assert_eq!(b.dims(), b_tt.dims());
+    assert_eq!(b_tt.stride(), &[6, 1, 3]);
+
+    assert_eq!(a_tt.matmul(&b)?.to_vec3::<f32>()?, &expected);
+    assert_eq!(a.matmul(&b_tt)?.to_vec3::<f32>()?, &expected);
+    assert_eq!(a_tt.matmul(&b_tt)?.to_vec3::<f32>()?, &expected);
+    Ok(())
+}
+
+fn broadcast_matmul(device: &Device) -> Result<()> {
+    let lhs = Tensor::randn(0f32, 1f32, (3, 1, 4, 5), device)?;
+    let rhs = Tensor::randn(0f32, 1f32, (6, 5, 2), device)?;
+    let out = lhs.broadcast_matmul(&rhs)?;
+    assert_eq!(out.dims(), &[3, 6, 4, 2]);
+    for idx1 in 0..3 {
+        for idx2 in 0..6 {
+            let out = out.i((idx1, idx2))?;
+            let lhs = lhs.i((idx1, 0))?;
+            let rhs = rhs.i(idx2)?;
+            let out2 = lhs.matmul(&rhs);
+            let sum_diff2 = (out - out2)?.sqr()?.sum_all()?;
+            // With cuda, we see errors of up to ~1e-12.
+            assert!(sum_diff2.to_vec0::<f32>()? < 1e-6)
+        }
+    }
+    Ok(())
+}
+
 fn broadcasting(device: &Device) -> Result<()> {
     let t1 = Tensor::arange(0f32, 24f32, device)?.reshape((4, 2, 3))?;
     let t2 = Tensor::new(&[100f32, 200f32], device)?;
@@ -1053,54 +1073,8 @@ fn broadcasting(device: &Device) -> Result<()> {
 fn randn(device: &Device) -> Result<()> {
     let tensor = Tensor::randn(0f32, 1f32, (5, 3), device)?;
     assert_eq!(tensor.dims(), [5, 3]);
-    // Check that the seed gets updated by checking that
-    // a new series of numbers is generated each time
-    let tensor2 = Tensor::randn(0f32, 1f32, (5, 3), device)?;
-    assert_ne!(tensor.to_vec2::<f32>()?, tensor2.to_vec2::<f32>()?);
     let tensor = Tensor::rand(0f32, 1f32, (5, 3), device)?;
     assert_eq!(tensor.dims(), [5, 3]);
-    // Check that the seed gets updated by checking that
-    // a new series of numbers is generated each time
-    let tensor2 = Tensor::rand(0f32, 1f32, (5, 3), device)?;
-    assert_ne!(tensor.to_vec2::<f32>()?, tensor2.to_vec2::<f32>()?);
-    // We do not expect deterministic elements at any index.
-    // There once was a bug that had a deterministic zero element in evenly sized tensors.
-    const N: usize = 2;
-    let v = (0..100)
-        .map(|_| Tensor::randn(0f32, 1f32, N, device).and_then(|t| t.to_vec1::<f32>()))
-        .collect::<Result<Vec<_>>>()?;
-    assert!(
-        (0..N).all(|i| v.windows(2).any(|pair| pair[0][i] != pair[1][i])),
-        "There are deterministic values in the randn tensors"
-    );
-    let v = (0..100)
-        .map(|_| Tensor::rand(0f32, 1f32, N, device).and_then(|t| t.to_vec1::<f32>()))
-        .collect::<Result<Vec<_>>>()?;
-    assert!(
-        (0..N).all(|i| v.windows(2).any(|pair| pair[0][i] != pair[1][i])),
-        "There are deterministic values in the rand tensors"
-    );
-    Ok(())
-}
-
-fn zero_dim(device: &Device) -> Result<()> {
-    let t = Tensor::zeros((4, 0, 1), DType::F32, device)?;
-    assert_eq!(t.dims3()?, (4, 0, 1));
-    let t2 = Tensor::zeros((4, 3, 1), DType::F32, device)?;
-    let t_cat = Tensor::cat(&[&t, &t2], 1)?;
-    assert_eq!(t_cat.dims3()?, (4, 3, 1));
-    let t_cat = Tensor::cat(&[&t, &t], 1)?;
-    assert_eq!(t_cat.dims3()?, (4, 0, 1));
-    let t_unary = t.sqrt()?;
-    assert_eq!(t_unary.dims3()?, (4, 0, 1));
-    let t_plus = (&t + 1.)?;
-    assert_eq!(t_plus.dims3()?, (4, 0, 1));
-    let t_mm = t2.matmul(&t.t()?)?;
-    assert_eq!(t_mm.dims3()?, (4, 3, 0));
-    let t_mm = t.matmul(&t2.t()?)?;
-    assert_eq!(t_mm.dims3()?, (4, 0, 3));
-    let t_mm = t.t()?.matmul(&t)?;
-    assert_eq!(t_mm.dims3()?, (4, 1, 1));
     Ok(())
 }
 
@@ -1123,6 +1097,13 @@ 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,
@@ -1152,7 +1133,6 @@ test_device!(
 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);
-test_device!(zero_dim, zero_dim_cpu, zero_dim_gpu, zero_dim_metal);
 
 // There was originally a bug on the CPU implementation for randn
 // https://github.com/huggingface/candle/issues/381
@@ -1280,8 +1260,8 @@ fn pow() -> Result<()> {
     let rhs = (&lhs - 2.)?;
     let res = lhs.pow(&rhs)?;
     assert_eq!(
-        test_utils::to_vec2_round(&res, 3)?,
+        test_utils::to_vec2_round(&res, 4)?,
-        [[1.0, 1.0, 3.0], [16.0, 125.0, 1296.0]]
+        [[1.0, 1.0, 3.0], [16.0, 125.0, 1296.0001]]
     );
     Ok(())
 }

candle-examples/Cargo.toml

@@ -12,7 +12,7 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 candle = { workspace = true }
-candle-datasets = { workspace = true, optional = true }
+candle-datasets = { workspace = true }
 candle-nn = { workspace = true }
 candle-transformers = { workspace = true }
 candle-flash-attn = { workspace = true, optional = true }
@@ -21,19 +21,16 @@ candle-onnx = { workspace = true, optional = true }
 csv = "1.3.0"
 cudarc = { workspace = true, optional = true }
 half = { workspace = true, optional = true }
-hf-hub = { workspace = true, features = ["tokio"] }
+hf-hub = { workspace = true, features=["tokio"]}
 image = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
 num-traits = { workspace = true }
-pyo3 = { version = "0.21.0", features = ["auto-initialize"], optional = true }
+pyo3 = { version = "0.20.0", features = ["auto-initialize"], optional = true }
 rayon = { workspace = true }
-rubato = { version = "0.15.0", optional = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
-symphonia = { version = "0.5.3", features = ["all"], optional = true }
 tokenizers = { workspace = true, features = ["onig"] }
-cpal= { version = "0.15.2", optional = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
@@ -42,10 +39,11 @@ clap = { workspace = true }
 imageproc = { workspace = true }
 memmap2 = { workspace = true }
 rand = { workspace = true }
-ab_glyph = { workspace = true }
+rusttype = { workspace = true }
 tracing = { workspace = true }
 tracing-chrome = { workspace = true }
 tracing-subscriber = { workspace = true }
+wav = { workspace = true }
 # Necessary to disambiguate with tokio in wasm examples which are 1.28.1
 tokio = "1.29.1"
 
@@ -63,8 +61,6 @@ mkl = ["dep:intel-mkl-src", "candle/mkl", "candle-nn/mkl", "candle-transformers/
 nccl = ["cuda", "cudarc/nccl", "dep:half"]
 onnx = ["candle-onnx"]
 metal = ["candle/metal", "candle-nn/metal"]
-microphone = ["cpal"]
-encodec = ["cpal", "symphonia", "rubato"]
 
 [[example]]
 name = "llama_multiprocess"
@@ -81,23 +77,3 @@ required-features = ["onnx"]
 [[example]]
 name = "onnx_basics"
 required-features = ["onnx"]
-
-[[example]]
-name = "whisper"
-required-features = ["symphonia"]
-
-[[example]]
-name = "whisper-microphone"
-required-features = ["microphone"]
-
-[[example]]
-name = "mnist-training"
-required-features = ["candle-datasets"]
-
-[[example]]
-name = "llama2-c"
-required-features = ["candle-datasets"]
-
-[[example]]
-name = "encodec"
-required-features = ["encodec"]

candle-examples/examples/chatglm/main.rs

@@ -1,237 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::{Error as E, Result};
-use clap::Parser;
-
-use candle_transformers::models::chatglm::{Config, Model};
-
-use candle::{DType, Device, Tensor};
-use candle_nn::VarBuilder;
-use candle_transformers::generation::LogitsProcessor;
-use hf_hub::{api::sync::Api, Repo, RepoType};
-use tokenizers::Tokenizer;
-
-struct TextGeneration {
-    model: Model,
-    device: Device,
-    tokenizer: Tokenizer,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-    verbose_prompt: bool,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        verbose_prompt: bool,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            tokenizer,
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            verbose_prompt,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        println!("starting the inference loop");
-        let tokens = self.tokenizer.encode(prompt, true).map_err(E::msg)?;
-        if tokens.is_empty() {
-            anyhow::bail!("Empty prompts are not supported in the chatglm model.")
-        }
-        if self.verbose_prompt {
-            for (token, id) in tokens.get_tokens().iter().zip(tokens.get_ids().iter()) {
-                let token = token.replace('▁', " ").replace("<0x0A>", "\n");
-                println!("{id:7} -> '{token}'");
-            }
-        }
-        let mut tokens = tokens.get_ids().to_vec();
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_vocab(true).get("</s>") {
-            Some(token) => *token,
-            None => anyhow::bail!("cannot find the endoftext token"),
-        };
-        print!("{prompt}");
-        std::io::stdout().flush()?;
-        let start_gen = std::time::Instant::now();
-        for index in 0..sample_len {
-            let context_size = if index > 0 { 1 } else { tokens.len() };
-            let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
-            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = self.model.forward(&input)?;
-            let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            let token = self.tokenizer.decode(&[next_token], true).map_err(E::msg)?;
-            print!("{token}");
-            std::io::stdout().flush()?;
-        }
-        let dt = start_gen.elapsed();
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    /// Display the token for the specified prompt.
-    #[arg(long)]
-    verbose_prompt: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// The temperature used to generate samples.
-    #[arg(long)]
-    temperature: Option<f64>,
-
-    /// Nucleus sampling probability cutoff.
-    #[arg(long)]
-    top_p: Option<f64>,
-
-    /// The seed to use when generating random samples.
-    #[arg(long, default_value_t = 299792458)]
-    seed: u64,
-
-    /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 5000)]
-    sample_len: usize,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long)]
-    revision: Option<String>,
-
-    #[arg(long)]
-    weight_file: Option<String>,
-
-    #[arg(long)]
-    tokenizer: Option<String>,
-
-    /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.1)]
-    repeat_penalty: f32,
-
-    /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 64)]
-    repeat_last_n: usize,
-}
-
-fn main() -> Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let model_id = match args.model_id {
-        Some(model_id) => model_id.to_string(),
-        None => "THUDM/chatglm3-6b".to_string(),
-    };
-    let revision = match args.revision {
-        Some(rev) => rev.to_string(),
-        None => "main".to_string(),
-    };
-    let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
-    let tokenizer_filename = match args.tokenizer {
-        Some(file) => std::path::PathBuf::from(file),
-        None => api
-            .model("lmz/candle-chatglm".to_string())
-            .get("chatglm-tokenizer.json")?,
-    };
-    let filenames = match args.weight_file {
-        Some(weight_file) => vec![std::path::PathBuf::from(weight_file)],
-        None => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-
-    let start = std::time::Instant::now();
-    let config = Config::glm3_6b();
-    let device = candle_examples::device(args.cpu)?;
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
-    let model = Model::new(&config, vb)?;
-
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        args.verbose_prompt,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

candle-examples/examples/clip/README.md

@@ -1,46 +0,0 @@
-Contrastive Language-Image Pre-Training
-
-Contrastive Language-Image Pre-Training (CLIP) is an architecture trained on
-pairs of images with related texts.
-
-https://github.com/openai/CLIP
-
-https://github.com/huggingface/transformers/tree/f6fa0f0bf0796ac66f201f23bdb8585de1609add/src/transformers/models/clip
-
-## Running on an example on cpu
-
-```
-$ cargo run --example clip --release -- --images "candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg","candle-examples/examples/yolo-v8/assets/bike.jpg" --cpu --sequences  "a cycling race","a photo of two cats","a robot holding a candle"
-
-
-Results for image: candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg
-
-INFO clip: Probability: 0.0000% Text: a cycling race
-INFO clip: Probability: 0.0000% Text: a photo of two cats
-INFO clip: Probability: 100.0000% Text: a robot holding a candle
-
-Results for image: candle-examples/examples/yolo-v8/assets/bike.jpg
-
-INFO clip: Probability: 99.9999% Text: a cycling race
-INFO clip: Probability: 0.0001% Text: a photo of two cats
-INFO clip: Probability: 0.0000% Text: a robot holding a candle
-```
-
-## Running on an example with metal feature (mac)
-
-```
-$ cargo run --features metal --example clip --release -- --images "candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg","candle-examples/examples/yolo-v8/assets/bike.jpg" --cpu --sequences "a cycling race","a photo of two cats","a robot holding a candle"
-
-
-Results for image: candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg
-
-INFO clip: Probability: 0.0000% Text: a cycling race
-INFO clip: Probability: 0.0000% Text: a photo of two cats
-INFO clip: Probability: 100.0000% Text: a robot holding a candle
-
-Results for image: candle-examples/examples/yolo-v8/assets/bike.jpg
-
-INFO clip: Probability: 99.9999% Text: a cycling race
-INFO clip: Probability: 0.0001% Text: a photo of two cats
-INFO clip: Probability: 0.0000% Text: a robot holding a candle
-```

candle-examples/examples/clip/main.rs

@@ -1,202 +0,0 @@
-#[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, Tensor};
-use candle_nn::{ops::softmax, VarBuilder};
-use candle_transformers::models::clip;
-
-use tokenizers::Tokenizer;
-use tracing::info;
-
-#[derive(Parser)]
-struct Args {
-    #[arg(long)]
-    model: Option<String>,
-
-    #[arg(long)]
-    tokenizer: Option<String>,
-
-    #[arg(long, use_value_delimiter = true)]
-    images: Option<Vec<String>>,
-
-    #[arg(long)]
-    cpu: bool,
-
-    #[arg(long, use_value_delimiter = true)]
-    sequences: Option<Vec<String>>,
-}
-
-fn load_image<T: AsRef<std::path::Path>>(path: T, image_size: usize) -> anyhow::Result<Tensor> {
-    let img = image::io::Reader::open(path)?.decode()?;
-    let (height, width) = (image_size, image_size);
-    let img = img.resize_to_fill(
-        width as u32,
-        height as u32,
-        image::imageops::FilterType::Triangle,
-    );
-
-    let img = img.to_rgb8();
-
-    let img = img.into_raw();
-    let img = Tensor::from_vec(img, (height, width, 3), &Device::Cpu)?
-        .permute((2, 0, 1))?
-        .to_dtype(DType::F32)?
-        .affine(2. / 255., -1.)?;
-    // .unsqueeze(0)?;
-    Ok(img)
-}
-
-fn load_images<T: AsRef<std::path::Path>>(
-    paths: &Vec<T>,
-    image_size: usize,
-) -> anyhow::Result<Tensor> {
-    let mut images = vec![];
-
-    for path in paths {
-        let tensor = load_image(path, image_size)?;
-        images.push(tensor);
-    }
-
-    let images = Tensor::stack(&images, 0)?;
-
-    Ok(images)
-}
-
-pub fn main() -> anyhow::Result<()> {
-    // std::env::set_var("RUST_BACKTRACE", "full");
-
-    let args = Args::parse();
-
-    tracing_subscriber::fmt::init();
-
-    let model_file = match args.model {
-        None => {
-            let api = hf_hub::api::sync::Api::new()?;
-
-            let api = api.repo(hf_hub::Repo::with_revision(
-                "openai/clip-vit-base-patch32".to_string(),
-                hf_hub::RepoType::Model,
-                "refs/pr/15".to_string(),
-            ));
-
-            api.get("model.safetensors")?
-        }
-        Some(model) => model.into(),
-    };
-
-    let tokenizer = get_tokenizer(args.tokenizer)?;
-
-    let config = clip::ClipConfig::vit_base_patch32();
-
-    let device = candle_examples::device(args.cpu)?;
-
-    let vec_imgs = match args.images {
-        Some(imgs) => imgs,
-        None => vec![
-            "candle-examples/examples/stable-diffusion/assets/stable-diffusion-xl.jpg".to_string(),
-            "candle-examples/examples/yolo-v8/assets/bike.jpg".to_string(),
-        ],
-    };
-
-    // let image = load_image(args.image, config.image_size)?.to_device(&device)?;
-    let images = load_images(&vec_imgs, config.image_size)?.to_device(&device)?;
-
-    let vb =
-        unsafe { VarBuilder::from_mmaped_safetensors(&[model_file.clone()], DType::F32, &device)? };
-
-    let model = clip::ClipModel::new(vb, &config)?;
-
-    let (input_ids, vec_seq) = tokenize_sequences(args.sequences, &tokenizer, &device)?;
-
-    let (_logits_per_text, logits_per_image) = model.forward(&images, &input_ids)?;
-
-    let softmax_image = softmax(&logits_per_image, 1)?;
-
-    let softmax_image_vec = softmax_image.flatten_all()?.to_vec1::<f32>()?;
-
-    info!("softmax_image_vec: {:?}", softmax_image_vec);
-
-    let probability_vec = softmax_image_vec
-        .iter()
-        .map(|v| v * 100.0)
-        .collect::<Vec<f32>>();
-
-    let probability_per_image = probability_vec.len() / vec_imgs.len();
-
-    for (i, img) in vec_imgs.iter().enumerate() {
-        let start = i * probability_per_image;
-        let end = start + probability_per_image;
-        let prob = &probability_vec[start..end];
-        info!("\n\nResults for image: {}\n", img);
-
-        for (i, p) in prob.iter().enumerate() {
-            info!("Probability: {:.4}% Text: {} ", p, vec_seq[i]);
-        }
-    }
-
-    Ok(())
-}
-
-pub fn get_tokenizer(tokenizer: Option<String>) -> anyhow::Result<Tokenizer> {
-    let tokenizer = match tokenizer {
-        None => {
-            let api = hf_hub::api::sync::Api::new()?;
-            let api = api.repo(hf_hub::Repo::with_revision(
-                "openai/clip-vit-base-patch32".to_string(),
-                hf_hub::RepoType::Model,
-                "refs/pr/15".to_string(),
-            ));
-            api.get("tokenizer.json")?
-        }
-        Some(file) => file.into(),
-    };
-
-    Tokenizer::from_file(tokenizer).map_err(E::msg)
-}
-
-pub fn tokenize_sequences(
-    sequences: Option<Vec<String>>,
-    tokenizer: &Tokenizer,
-    device: &Device,
-) -> anyhow::Result<(Tensor, Vec<String>)> {
-    let pad_id = *tokenizer
-        .get_vocab(true)
-        .get("<|endoftext|>")
-        .ok_or(E::msg("No pad token"))?;
-
-    let vec_seq = match sequences {
-        Some(seq) => seq,
-        None => vec![
-            "a cycling race".to_string(),
-            "a photo of two cats".to_string(),
-            "a robot holding a candle".to_string(),
-        ],
-    };
-
-    let mut tokens = vec![];
-
-    for seq in vec_seq.clone() {
-        let encoding = tokenizer.encode(seq, true).map_err(E::msg)?;
-        tokens.push(encoding.get_ids().to_vec());
-    }
-
-    let max_len = tokens.iter().map(|v| v.len()).max().unwrap_or(0);
-
-    // Pad the sequences to have the same length
-    for token_vec in tokens.iter_mut() {
-        let len_diff = max_len - token_vec.len();
-        if len_diff > 0 {
-            token_vec.extend(vec![pad_id; len_diff]);
-        }
-    }
-
-    let input_ids = Tensor::new(tokens, device)?;
-
-    Ok((input_ids, vec_seq))
-}

candle-examples/examples/convmixer/main.rs

@@ -28,7 +28,7 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
+    let image = candle_examples::imagenet::load_image224(args.image)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/convnext/README.md

@@ -1,23 +0,0 @@
-# candle-convnext
-
-[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) and
-[ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808).
-
-This candle implementation uses a pre-trained ConvNeXt network for inference. The
-classification head has been trained on the ImageNet dataset and returns the
-probabilities for the top-5 classes.
-
-## Running an example
-
-```
-$ cargo run --example convnext --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which tiny
-
-loaded image Tensor[dims 3, 224, 224; f32]
-model built
-mountain bike, all-terrain bike, off-roader: 84.09%
-bicycle-built-for-two, tandem bicycle, tandem: 4.15%
-maillot                 : 0.74%
-crash helmet            : 0.54%
-unicycle, monocycle     : 0.44%
-
-```

candle-examples/examples/convnext/main.rs

@@ -1,126 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use clap::{Parser, ValueEnum};
-
-use candle::{DType, IndexOp, D};
-use candle_nn::{Module, VarBuilder};
-use candle_transformers::models::convnext;
-
-#[derive(Clone, Copy, Debug, ValueEnum)]
-enum Which {
-    Atto,
-    Femto,
-    Pico,
-    Nano,
-    Tiny,
-    Small,
-    Base,
-    Large,
-    AttoV2,
-    FemtoV2,
-    PicoV2,
-    NanoV2,
-    TinyV2,
-    BaseV2,
-    LargeV2,
-    XLarge,
-    Huge,
-}
-
-impl Which {
-    fn model_filename(&self) -> String {
-        let name = match self {
-            Self::Atto => "convnext_atto.d2_in1k",
-            Self::Femto => "convnext_femto.d1_in1k",
-            Self::Pico => "convnext_pico.d1_in1k",
-            Self::Nano => "convnext_nano.d1h_in1k",
-            Self::Tiny => "convnext_tiny.fb_in1k",
-            Self::Small => "convnext_small.fb_in1k",
-            Self::Base => "convnext_base.fb_in1k",
-            Self::Large => "convnext_large.fb_in1k",
-            Self::AttoV2 => "convnextv2_atto.fcmae_ft_in1k",
-            Self::FemtoV2 => "convnextv2_femto.fcmae_ft_in1k",
-            Self::PicoV2 => "convnextv2_pico.fcmae_ft_in1k",
-            Self::NanoV2 => "convnextv2_nano.fcmae_ft_in1k",
-            Self::TinyV2 => "convnextv2_tiny.fcmae_ft_in1k",
-            Self::BaseV2 => "convnextv2_base.fcmae_ft_in1k",
-            Self::LargeV2 => "convnextv2_large.fcmae_ft_in1k",
-            Self::XLarge => "convnext_xlarge.fb_in22k_ft_in1k",
-            Self::Huge => "convnextv2_huge.fcmae_ft_in1k",
-        };
-
-        format!("timm/{name}")
-    }
-
-    fn config(&self) -> convnext::Config {
-        match self {
-            Self::Atto | Self::AttoV2 => convnext::Config::atto(),
-            Self::Femto | Self::FemtoV2 => convnext::Config::femto(),
-            Self::Pico | Self::PicoV2 => convnext::Config::pico(),
-            Self::Nano | Self::NanoV2 => convnext::Config::nano(),
-            Self::Tiny | Self::TinyV2 => convnext::Config::tiny(),
-            Self::Small => convnext::Config::small(),
-            Self::Base | Self::BaseV2 => convnext::Config::base(),
-            Self::Large | Self::LargeV2 => convnext::Config::large(),
-            Self::XLarge => convnext::Config::xlarge(),
-            Self::Huge => convnext::Config::huge(),
-        }
-    }
-}
-
-#[derive(Parser)]
-struct Args {
-    #[arg(long)]
-    model: Option<String>,
-
-    #[arg(long)]
-    image: String,
-
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    #[arg(value_enum, long, default_value_t=Which::Tiny)]
-    which: Which,
-}
-
-pub fn main() -> anyhow::Result<()> {
-    let args = Args::parse();
-
-    let device = candle_examples::device(args.cpu)?;
-
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
-    println!("loaded image {image:?}");
-
-    let model_file = match args.model {
-        None => {
-            let model_name = args.which.model_filename();
-            let api = hf_hub::api::sync::Api::new()?;
-            let api = api.model(model_name);
-            api.get("model.safetensors")?
-        }
-        Some(model) => model.into(),
-    };
-
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
-    let model = convnext::convnext(&args.which.config(), 1000, vb)?;
-    println!("model built");
-    let logits = model.forward(&image.unsqueeze(0)?)?;
-    let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
-        .i(0)?
-        .to_vec1::<f32>()?;
-    let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
-    prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
-    for &(category_idx, pr) in prs.iter().take(5) {
-        println!(
-            "{:24}: {:.2}%",
-            candle_examples::imagenet::CLASSES[category_idx],
-            100. * pr
-        );
-    }
-    Ok(())
-}

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

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

candle-examples/examples/dinov2/main.rs

@@ -31,7 +31,7 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
+    let image = candle_examples::imagenet::load_image224(args.image)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/efficientnet/main.rs

@@ -47,7 +47,7 @@ pub fn main() -> anyhow::Result<()> {
 
     let device = candle_examples::device(args.cpu)?;
 
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
+    let image = candle_examples::imagenet::load_image224(args.image)?;
     println!("loaded image {image:?}");
 
     let model_file = match args.model {

candle-examples/examples/efficientvit/README.md

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

candle-examples/examples/efficientvit/main.rs

@@ -1,99 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use clap::{Parser, ValueEnum};
-
-use candle::{DType, IndexOp, D};
-use candle_nn::{Module, VarBuilder};
-use candle_transformers::models::efficientvit;
-
-#[derive(Clone, Copy, Debug, ValueEnum)]
-enum Which {
-    M0,
-    M1,
-    M2,
-    M3,
-    M4,
-    M5,
-}
-
-impl Which {
-    fn model_filename(&self) -> String {
-        let name = match self {
-            Self::M0 => "m0",
-            Self::M1 => "m1",
-            Self::M2 => "m2",
-            Self::M3 => "m3",
-            Self::M4 => "m4",
-            Self::M5 => "m5",
-        };
-        format!("timm/efficientvit_{}.r224_in1k", name)
-    }
-
-    fn config(&self) -> efficientvit::Config {
-        match self {
-            Self::M0 => efficientvit::Config::m0(),
-            Self::M1 => efficientvit::Config::m1(),
-            Self::M2 => efficientvit::Config::m2(),
-            Self::M3 => efficientvit::Config::m3(),
-            Self::M4 => efficientvit::Config::m4(),
-            Self::M5 => efficientvit::Config::m5(),
-        }
-    }
-}
-
-#[derive(Parser)]
-struct Args {
-    #[arg(long)]
-    model: Option<String>,
-
-    #[arg(long)]
-    image: String,
-
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    #[arg(value_enum, long, default_value_t=Which::M0)]
-    which: Which,
-}
-
-pub fn main() -> anyhow::Result<()> {
-    let args = Args::parse();
-
-    let device = candle_examples::device(args.cpu)?;
-
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
-    println!("loaded image {image:?}");
-
-    let model_file = match args.model {
-        None => {
-            let model_name = args.which.model_filename();
-            let api = hf_hub::api::sync::Api::new()?;
-            let api = api.model(model_name);
-            api.get("model.safetensors")?
-        }
-        Some(model) => model.into(),
-    };
-
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
-    let model = efficientvit::efficientvit(&args.which.config(), 1000, vb)?;
-    println!("model built");
-    let logits = model.forward(&image.unsqueeze(0)?)?;
-    let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
-        .i(0)?
-        .to_vec1::<f32>()?;
-    let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
-    prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
-    for &(category_idx, pr) in prs.iter().take(5) {
-        println!(
-            "{:24}: {:.2}%",
-            candle_examples::imagenet::CLASSES[category_idx],
-            100. * pr
-        );
-    }
-    Ok(())
-}

candle-examples/examples/encodec/README.md

@@ -1,25 +0,0 @@
-# candle-endocec
-
-[EnCodec](https://huggingface.co/facebook/encodec_24khz) is a high-quality audio
-compression model using an encoder/decoder architecture with residual vector
-quantization.
-
-## Running one example
-
-```bash
-cargo run --example encodec --features symphonia --release -- code-to-audio \
-    candle-examples/examples/encodec/jfk-codes.safetensors \
-    jfk.wav
-```
-
-This decodes the EnCodec tokens stored in `jfk-codes.safetensors` and generates
-an output wav file containing the audio data.
-
-Instead of `code-to-audio` one can use:
-- `audio-to-audio in.mp3 out.wav`: encodes the input audio file then decodes it to a wav file.
-- `audio-to-code in.mp3 out.safetensors`: generates a safetensors file
-  containing EnCodec tokens for the input audio file.
-
-If the audio output file name is set to `-`, the audio content directly gets
-played on default audio output device. If the audio input file is set to `-`, the audio
-gets recorded from the default audio input.

candle-examples/examples/encodec/audio_io.rs

@@ -1,275 +0,0 @@
-#![allow(unused)]
-use anyhow::{Context, Result};
-use std::sync::{Arc, Mutex};
-
-pub const SAMPLE_RATE: usize = 24_000;
-
-pub(crate) struct AudioOutputData_ {
-    resampled_data: std::collections::VecDeque<f32>,
-    resampler: rubato::FastFixedIn<f32>,
-    output_buffer: Vec<f32>,
-    input_buffer: Vec<f32>,
-    input_len: usize,
-}
-
-impl AudioOutputData_ {
-    pub(crate) fn new(input_sample_rate: usize, output_sample_rate: usize) -> Result<Self> {
-        use rubato::Resampler;
-
-        let resampled_data = std::collections::VecDeque::with_capacity(output_sample_rate * 10);
-        let resample_ratio = output_sample_rate as f64 / input_sample_rate as f64;
-        let resampler = rubato::FastFixedIn::new(
-            resample_ratio,
-            f64::max(resample_ratio, 1.0),
-            rubato::PolynomialDegree::Septic,
-            1024,
-            1,
-        )?;
-        let input_buffer = resampler.input_buffer_allocate(true).remove(0);
-        let output_buffer = resampler.output_buffer_allocate(true).remove(0);
-        Ok(Self {
-            resampled_data,
-            resampler,
-            input_buffer,
-            output_buffer,
-            input_len: 0,
-        })
-    }
-
-    pub fn reset(&mut self) {
-        use rubato::Resampler;
-        self.output_buffer.fill(0.);
-        self.input_buffer.fill(0.);
-        self.resampler.reset();
-        self.resampled_data.clear();
-    }
-
-    pub(crate) fn take_all(&mut self) -> Vec<f32> {
-        let mut data = Vec::with_capacity(self.resampled_data.len());
-        while let Some(elem) = self.resampled_data.pop_back() {
-            data.push(elem);
-        }
-        data
-    }
-
-    pub(crate) fn is_empty(&self) -> bool {
-        self.resampled_data.is_empty()
-    }
-
-    // Assumes that the input buffer is large enough.
-    fn push_input_buffer(&mut self, samples: &[f32]) {
-        self.input_buffer[self.input_len..self.input_len + samples.len()].copy_from_slice(samples);
-        self.input_len += samples.len()
-    }
-
-    pub(crate) fn push_samples(&mut self, samples: &[f32]) -> Result<()> {
-        use rubato::Resampler;
-
-        let mut pos_in = 0;
-        loop {
-            let rem = self.input_buffer.len() - self.input_len;
-            let pos_end = usize::min(pos_in + rem, samples.len());
-            self.push_input_buffer(&samples[pos_in..pos_end]);
-            pos_in = pos_end;
-            if self.input_len < self.input_buffer.len() {
-                break;
-            }
-            let (_, out_len) = self.resampler.process_into_buffer(
-                &[&self.input_buffer],
-                &mut [&mut self.output_buffer],
-                None,
-            )?;
-            for &elem in self.output_buffer[..out_len].iter() {
-                self.resampled_data.push_front(elem)
-            }
-            self.input_len = 0;
-        }
-        Ok(())
-    }
-}
-
-type AudioOutputData = Arc<Mutex<AudioOutputData_>>;
-
-pub(crate) fn setup_output_stream() -> Result<(cpal::Stream, AudioOutputData)> {
-    use cpal::traits::{DeviceTrait, HostTrait, StreamTrait};
-
-    println!("Setup audio output stream!");
-    let host = cpal::default_host();
-    let device = host
-        .default_output_device()
-        .context("no output device available")?;
-    let mut supported_configs_range = device.supported_output_configs()?;
-    let config_range = match supported_configs_range.find(|c| c.channels() == 1) {
-        // On macOS, it's commonly the case that there are only stereo outputs.
-        None => device
-            .supported_output_configs()?
-            .next()
-            .context("no audio output available")?,
-        Some(config_range) => config_range,
-    };
-    let sample_rate = cpal::SampleRate(SAMPLE_RATE as u32).clamp(
-        config_range.min_sample_rate(),
-        config_range.max_sample_rate(),
-    );
-    let config: cpal::StreamConfig = config_range.with_sample_rate(sample_rate).into();
-    let channels = config.channels as usize;
-    println!(
-        "cpal device: {} {} {config:?}",
-        device.name().unwrap_or_else(|_| "unk".to_string()),
-        config.sample_rate.0
-    );
-    let audio_data = Arc::new(Mutex::new(AudioOutputData_::new(
-        SAMPLE_RATE,
-        config.sample_rate.0 as usize,
-    )?));
-    let ad = audio_data.clone();
-    let stream = device.build_output_stream(
-        &config,
-        move |data: &mut [f32], _: &cpal::OutputCallbackInfo| {
-            data.fill(0.);
-            let mut ad = ad.lock().unwrap();
-            let mut last_elem = 0f32;
-            for (idx, elem) in data.iter_mut().enumerate() {
-                if idx % channels == 0 {
-                    match ad.resampled_data.pop_back() {
-                        None => break,
-                        Some(v) => {
-                            last_elem = v;
-                            *elem = v
-                        }
-                    }
-                } else {
-                    *elem = last_elem
-                }
-            }
-        },
-        move |err| eprintln!("cpal error: {err}"),
-        None, // None=blocking, Some(Duration)=timeout
-    )?;
-    stream.play()?;
-    Ok((stream, audio_data))
-}
-
-pub(crate) fn setup_input_stream() -> Result<(cpal::Stream, AudioOutputData)> {
-    use cpal::traits::{DeviceTrait, HostTrait, StreamTrait};
-
-    println!("Setup audio input stream!");
-    let host = cpal::default_host();
-    let device = host
-        .default_input_device()
-        .context("no input device available")?;
-    let mut supported_configs_range = device.supported_input_configs()?;
-    let config_range = supported_configs_range
-        .find(|c| c.channels() == 1)
-        .context("no audio input available")?;
-    let sample_rate = cpal::SampleRate(SAMPLE_RATE as u32).clamp(
-        config_range.min_sample_rate(),
-        config_range.max_sample_rate(),
-    );
-    let config: cpal::StreamConfig = config_range.with_sample_rate(sample_rate).into();
-    println!(
-        "cpal device: {} {} {config:?}",
-        device.name().unwrap_or_else(|_| "unk".to_string()),
-        config.sample_rate.0
-    );
-    let audio_data = Arc::new(Mutex::new(AudioOutputData_::new(
-        config.sample_rate.0 as usize,
-        SAMPLE_RATE,
-    )?));
-    let ad = audio_data.clone();
-    let stream = device.build_input_stream(
-        &config,
-        move |data: &[f32], _: &cpal::InputCallbackInfo| {
-            let mut ad = ad.lock().unwrap();
-            if let Err(err) = ad.push_samples(data) {
-                eprintln!("error processing audio input {err:?}")
-            }
-        },
-        move |err| eprintln!("cpal error: {err}"),
-        None, // None=blocking, Some(Duration)=timeout
-    )?;
-    stream.play()?;
-    Ok((stream, audio_data))
-}
-
-fn conv<T>(samples: &mut Vec<f32>, data: std::borrow::Cow<symphonia::core::audio::AudioBuffer<T>>)
-where
-    T: symphonia::core::sample::Sample,
-    f32: symphonia::core::conv::FromSample<T>,
-{
-    use symphonia::core::audio::Signal;
-    use symphonia::core::conv::FromSample;
-    samples.extend(data.chan(0).iter().map(|v| f32::from_sample(*v)))
-}
-
-pub(crate) fn pcm_decode<P: AsRef<std::path::Path>>(path: P) -> Result<(Vec<f32>, u32)> {
-    use symphonia::core::audio::{AudioBufferRef, Signal};
-
-    let src = std::fs::File::open(path)?;
-    let mss = symphonia::core::io::MediaSourceStream::new(Box::new(src), Default::default());
-    let hint = symphonia::core::probe::Hint::new();
-    let meta_opts: symphonia::core::meta::MetadataOptions = Default::default();
-    let fmt_opts: symphonia::core::formats::FormatOptions = Default::default();
-    let probed = symphonia::default::get_probe().format(&hint, mss, &fmt_opts, &meta_opts)?;
-    let mut format = probed.format;
-    let track = format
-        .tracks()
-        .iter()
-        .find(|t| t.codec_params.codec != symphonia::core::codecs::CODEC_TYPE_NULL)
-        .expect("no supported audio tracks");
-    let mut decoder = symphonia::default::get_codecs()
-        .make(&track.codec_params, &Default::default())
-        .expect("unsupported codec");
-    let track_id = track.id;
-    let sample_rate = track.codec_params.sample_rate.unwrap_or(0);
-    let mut pcm_data = Vec::new();
-    while let Ok(packet) = format.next_packet() {
-        while !format.metadata().is_latest() {
-            format.metadata().pop();
-        }
-        if packet.track_id() != track_id {
-            continue;
-        }
-        match decoder.decode(&packet)? {
-            AudioBufferRef::F32(buf) => pcm_data.extend(buf.chan(0)),
-            AudioBufferRef::U8(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::U16(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::U24(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::U32(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S8(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S16(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S24(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::S32(data) => conv(&mut pcm_data, data),
-            AudioBufferRef::F64(data) => conv(&mut pcm_data, data),
-        }
-    }
-    Ok((pcm_data, sample_rate))
-}
-
-pub(crate) fn resample(pcm_in: &[f32], sr_in: usize, sr_out: usize) -> Result<Vec<f32>> {
-    use rubato::Resampler;
-
-    let mut pcm_out =
-        Vec::with_capacity((pcm_in.len() as f64 * sr_out as f64 / sr_in as f64) as usize + 1024);
-
-    let mut resampler = rubato::FftFixedInOut::<f32>::new(sr_in, sr_out, 1024, 1)?;
-    let mut output_buffer = resampler.output_buffer_allocate(true);
-    let mut pos_in = 0;
-    while pos_in + resampler.input_frames_next() < pcm_in.len() {
-        let (in_len, out_len) =
-            resampler.process_into_buffer(&[&pcm_in[pos_in..]], &mut output_buffer, None)?;
-        pos_in += in_len;
-        pcm_out.extend_from_slice(&output_buffer[0][..out_len]);
-    }
-
-    if pos_in < pcm_in.len() {
-        let (_in_len, out_len) = resampler.process_partial_into_buffer(
-            Some(&[&pcm_in[pos_in..]]),
-            &mut output_buffer,
-            None,
-        )?;
-        pcm_out.extend_from_slice(&output_buffer[0][..out_len]);
-    }
-
-    Ok(pcm_out)
-}

candle-examples/examples/encodec/main.rs

@@ -1,131 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::Result;
-use candle::{DType, IndexOp, Tensor};
-use candle_nn::VarBuilder;
-use candle_transformers::models::encodec::{Config, Model};
-use clap::{Parser, ValueEnum};
-use hf_hub::api::sync::Api;
-
-mod audio_io;
-
-#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
-enum Action {
-    AudioToAudio,
-    AudioToCode,
-    CodeToAudio,
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// The action to be performed, specifies the format for the input and output data.
-    action: Action,
-
-    /// The input file, either an audio file or some encodec tokens stored as safetensors.
-    in_file: String,
-
-    /// The output file, either a wave audio file or some encodec tokens stored as safetensors.
-    out_file: String,
-
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// The model weight file, in safetensor format.
-    #[arg(long)]
-    model: Option<String>,
-}
-
-fn main() -> Result<()> {
-    let args = Args::parse();
-    let device = candle_examples::device(args.cpu)?;
-    let model = match args.model {
-        Some(model) => std::path::PathBuf::from(model),
-        None => Api::new()?
-            .model("facebook/encodec_24khz".to_string())
-            .get("model.safetensors")?,
-    };
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? };
-    let config = Config::default();
-    let model = Model::new(&config, vb)?;
-
-    let codes = match args.action {
-        Action::CodeToAudio => {
-            let codes = candle::safetensors::load(args.in_file, &device)?;
-            codes.get("codes").expect("no codes in input file").clone()
-        }
-        Action::AudioToCode | Action::AudioToAudio => {
-            let pcm = if args.in_file == "-" {
-                println!(">>>> RECORDING AUDIO, PRESS ENTER ONCE DONE <<<<");
-                let (stream, input_audio) = audio_io::setup_input_stream()?;
-                let mut pcms = vec![];
-                let stdin = std::thread::spawn(|| {
-                    let mut s = String::new();
-                    std::io::stdin().read_line(&mut s)
-                });
-                while !stdin.is_finished() {
-                    let input = input_audio.lock().unwrap().take_all();
-                    if input.is_empty() {
-                        std::thread::sleep(std::time::Duration::from_millis(100));
-                        continue;
-                    }
-                    pcms.push(input)
-                }
-                drop(stream);
-                pcms.concat()
-            } else {
-                let (pcm, sample_rate) = audio_io::pcm_decode(args.in_file)?;
-                if sample_rate != 24_000 {
-                    println!("WARNING: encodec uses a 24khz sample rate, input uses {sample_rate}, resampling...");
-                    audio_io::resample(&pcm, sample_rate as usize, 24_000)?
-                } else {
-                    pcm
-                }
-            };
-            let pcm_len = pcm.len();
-            let pcm = Tensor::from_vec(pcm, (1, 1, pcm_len), &device)?;
-            println!("input pcm shape: {:?}", pcm.shape());
-            model.encode(&pcm)?
-        }
-    };
-    println!("codes shape: {:?}", codes.shape());
-
-    match args.action {
-        Action::AudioToCode => {
-            codes.save_safetensors("codes", &args.out_file)?;
-        }
-        Action::AudioToAudio | Action::CodeToAudio => {
-            let pcm = model.decode(&codes)?;
-            println!("output pcm shape: {:?}", pcm.shape());
-            let pcm = pcm.i(0)?.i(0)?;
-            let pcm = candle_examples::audio::normalize_loudness(&pcm, 24_000, true)?;
-            let pcm = pcm.to_vec1::<f32>()?;
-            if args.out_file == "-" {
-                let (stream, ad) = audio_io::setup_output_stream()?;
-                {
-                    let mut ad = ad.lock().unwrap();
-                    ad.push_samples(&pcm)?;
-                }
-                loop {
-                    let ad = ad.lock().unwrap();
-                    if ad.is_empty() {
-                        break;
-                    }
-                    // That's very weird, calling thread::sleep here triggers the stream to stop
-                    // playing (the callback doesn't seem to be called anymore).
-                    // std::thread::sleep(std::time::Duration::from_millis(100));
-                }
-                drop(stream)
-            } else {
-                let mut output = std::fs::File::create(&args.out_file)?;
-                candle_examples::wav::write_pcm_as_wav(&mut output, &pcm, 24_000)?;
-            }
-        }
-    }
-    Ok(())
-}

candle-examples/examples/gemma/README.md

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

candle-examples/examples/gemma/main.rs

@@ -1,289 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::{Error as E, Result};
-use clap::Parser;
-
-use candle_transformers::models::gemma::{Config, Model};
-
-use candle::{DType, Device, Tensor};
-use candle_examples::token_output_stream::TokenOutputStream;
-use candle_nn::VarBuilder;
-use candle_transformers::generation::LogitsProcessor;
-use hf_hub::{api::sync::Api, Repo, RepoType};
-use tokenizers::Tokenizer;
-
-#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
-enum Which {
-    #[value(name = "2b")]
-    Base2B,
-    #[value(name = "7b")]
-    Base7B,
-    #[value(name = "2b-it")]
-    Instruct2B,
-    #[value(name = "7b-it")]
-    Instruct7B,
-    #[value(name = "1.1-2b-it")]
-    InstructV1_1_2B,
-    #[value(name = "1.1-7b-it")]
-    InstructV1_1_7B,
-    #[value(name = "code-2b")]
-    CodeBase2B,
-    #[value(name = "code-7b")]
-    CodeBase7B,
-    #[value(name = "code-2b-it")]
-    CodeInstruct2B,
-    #[value(name = "code-7b-it")]
-    CodeInstruct7B,
-}
-
-struct TextGeneration {
-    model: Model,
-    device: Device,
-    tokenizer: TokenOutputStream,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            tokenizer: TokenOutputStream::new(tokenizer),
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        self.tokenizer.clear();
-        let mut tokens = self
-            .tokenizer
-            .tokenizer()
-            .encode(prompt, true)
-            .map_err(E::msg)?
-            .get_ids()
-            .to_vec();
-        for &t in tokens.iter() {
-            if let Some(t) = self.tokenizer.next_token(t)? {
-                print!("{t}")
-            }
-        }
-        std::io::stdout().flush()?;
-
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_token("<eos>") {
-            Some(token) => token,
-            None => anyhow::bail!("cannot find the <eos> token"),
-        };
-        let start_gen = std::time::Instant::now();
-        for index in 0..sample_len {
-            let context_size = if index > 0 { 1 } else { tokens.len() };
-            let start_pos = tokens.len().saturating_sub(context_size);
-            let ctxt = &tokens[start_pos..];
-            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = self.model.forward(&input, start_pos)?;
-            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            if let Some(t) = self.tokenizer.next_token(next_token)? {
-                print!("{t}");
-                std::io::stdout().flush()?;
-            }
-        }
-        let dt = start_gen.elapsed();
-        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
-            print!("{rest}");
-        }
-        std::io::stdout().flush()?;
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// The temperature used to generate samples.
-    #[arg(long)]
-    temperature: Option<f64>,
-
-    /// Nucleus sampling probability cutoff.
-    #[arg(long)]
-    top_p: Option<f64>,
-
-    /// The seed to use when generating random samples.
-    #[arg(long, default_value_t = 299792458)]
-    seed: u64,
-
-    /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 10000)]
-    sample_len: usize,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long, default_value = "main")]
-    revision: String,
-
-    #[arg(long)]
-    tokenizer_file: Option<String>,
-
-    #[arg(long)]
-    config_file: Option<String>,
-
-    #[arg(long)]
-    weight_files: Option<String>,
-
-    /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.1)]
-    repeat_penalty: f32,
-
-    /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 64)]
-    repeat_last_n: usize,
-
-    /// The model to use.
-    #[arg(long, default_value = "2b")]
-    which: Which,
-}
-
-fn main() -> Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let model_id = match &args.model_id {
-        Some(model_id) => model_id.to_string(),
-        None => match args.which {
-            Which::InstructV1_1_2B => "google/gemma-1.1-2b-it".to_string(),
-            Which::InstructV1_1_7B => "google/gemma-1.1-7b-it".to_string(),
-            Which::Base2B => "google/gemma-2b".to_string(),
-            Which::Base7B => "google/gemma-7b".to_string(),
-            Which::Instruct2B => "google/gemma-2b-it".to_string(),
-            Which::Instruct7B => "google/gemma-7b-it".to_string(),
-            Which::CodeBase2B => "google/codegemma-2b".to_string(),
-            Which::CodeBase7B => "google/codegemma-7b".to_string(),
-            Which::CodeInstruct2B => "google/codegemma-2b-it".to_string(),
-            Which::CodeInstruct7B => "google/codegemma-7b-it".to_string(),
-        },
-    };
-    let repo = api.repo(Repo::with_revision(
-        model_id,
-        RepoType::Model,
-        args.revision,
-    ));
-    let tokenizer_filename = match args.tokenizer_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("tokenizer.json")?,
-    };
-    let config_filename = match args.config_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("config.json")?,
-    };
-    let filenames = match args.weight_files {
-        Some(files) => files
-            .split(',')
-            .map(std::path::PathBuf::from)
-            .collect::<Vec<_>>(),
-        None => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-    let config: Config = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
-
-    let start = std::time::Instant::now();
-    let device = candle_examples::device(args.cpu)?;
-    let dtype = if device.is_cuda() {
-        DType::BF16
-    } else {
-        DType::F32
-    };
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-    let model = Model::new(&config, vb)?;
-
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

candle-examples/examples/llama/main.rs

@@ -31,8 +31,6 @@ const DEFAULT_PROMPT: &str = "My favorite theorem is ";
 enum Which {
     V1,
     V2,
-    V3,
-    V3Instruct,
     #[value(name = "solar-10.7b")]
     Solar10_7B,
     #[value(name = "tiny-llama-1.1b-chat")]
@@ -47,8 +45,8 @@ struct Args {
     cpu: bool,
 
     /// The temperature used to generate samples.
-    #[arg(long, default_value_t = 0.8)]
+    #[arg(long)]
-    temperature: f64,
+    temperature: Option<f64>,
 
     /// Nucleus sampling probability cutoff.
     #[arg(long)]
@@ -59,7 +57,7 @@ struct Args {
     seed: u64,
 
     /// The length of the sample to generate (in tokens).
-    #[arg(long, default_value_t = 10000)]
+    #[arg(long, default_value_t = 100)]
     sample_len: usize,
 
     /// Disable the key-value cache.
@@ -92,11 +90,11 @@ struct Args {
     use_flash_attn: bool,
 
     /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.1)]
+    #[arg(long, default_value_t = 1.0)]
     repeat_penalty: f32,
 
     /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 128)]
+    #[arg(long, default_value_t = 64)]
     repeat_last_n: usize,
 }
 
@@ -120,18 +118,13 @@ fn main() -> Result<()> {
         Some("bf16") => DType::BF16,
         Some("f32") => DType::F32,
         Some(dtype) => bail!("Unsupported dtype {dtype}"),
-        None => match args.which {
+        None => DType::F16,
-            Which::V3 | Which::V3Instruct => DType::BF16,
-            Which::V1 | Which::V2 | Which::Solar10_7B | Which::TinyLlama1_1BChat => DType::F16,
-        },
     };
-    let (llama, tokenizer_filename, mut cache, config) = {
+    let (llama, tokenizer_filename, cache) = {
         let api = Api::new()?;
         let model_id = args.model_id.unwrap_or_else(|| match args.which {
             Which::V1 => "Narsil/amall-7b".to_string(),
             Which::V2 => "meta-llama/Llama-2-7b-hf".to_string(),
-            Which::V3 => "meta-llama/Meta-Llama-3-8B".to_string(),
-            Which::V3Instruct => "meta-llama/Meta-Llama-3-8B-Instruct".to_string(),
             Which::Solar10_7B => "upstage/SOLAR-10.7B-v1.0".to_string(),
             Which::TinyLlama1_1BChat => "TinyLlama/TinyLlama-1.1B-Chat-v1.0".to_string(),
         });
@@ -145,31 +138,29 @@ fn main() -> Result<()> {
         let config = config.into_config(args.use_flash_attn);
 
         let filenames = match args.which {
-            Which::V1 | Which::V2 | Which::V3 | Which::V3Instruct | Which::Solar10_7B => {
+            Which::V1 | Which::V2 | Which::Solar10_7B => {
                 candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?
             }
             Which::TinyLlama1_1BChat => vec![api.get("model.safetensors")?],
         };
+        println!("building the model");
         let cache = model::Cache::new(!args.no_kv_cache, dtype, &config, &device)?;
 
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-        (Llama::load(vb, &config)?, tokenizer_filename, cache, config)
+        (Llama::load(vb, &cache, &config)?, tokenizer_filename, cache)
     };
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-    let eos_token_id = config
+    let eos_token_id = tokenizer.token_to_id(EOS_TOKEN);
-        .eos_token_id
-        .or_else(|| tokenizer.token_to_id(EOS_TOKEN));
     let prompt = args.prompt.as_ref().map_or(DEFAULT_PROMPT, |p| p.as_str());
     let mut tokens = tokenizer
         .encode(prompt, true)
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
-    let mut tokenizer = candle_examples::token_output_stream::TokenOutputStream::new(tokenizer);
 
     println!("starting the inference loop");
     print!("{prompt}");
-    let mut logits_processor = LogitsProcessor::new(args.seed, Some(args.temperature), args.top_p);
+    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;
@@ -181,7 +172,7 @@ fn main() -> Result<()> {
         };
         let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
         let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
-        let logits = llama.forward(&input, context_index, &mut cache)?;
+        let logits = llama.forward(&input, context_index)?;
         let logits = logits.squeeze(0)?;
         let logits = if args.repeat_penalty == 1. {
             logits
@@ -199,16 +190,18 @@ fn main() -> Result<()> {
         token_generated += 1;
         tokens.push(next_token);
 
+        // Extracting the last token as a string is complicated, here we just apply some simple
+        // heuristics as it seems to work well enough for this example. See the following for more
+        // details:
+        // https://github.com/huggingface/tokenizers/issues/1141#issuecomment-1562644141
+        if let Some(text) = tokenizer.id_to_token(next_token) {
+            let text = text.replace('▁', " ").replace("<0x0A>", "\n");
+            print!("{text}");
+            std::io::stdout().flush()?;
+        }
         if Some(next_token) == eos_token_id {
             break;
         }
-        if let Some(t) = tokenizer.next_token(next_token)? {
-            print!("{t}");
-            std::io::stdout().flush()?;
-        }
-    }
-    if let Some(rest) = tokenizer.decode_rest().map_err(E::msg)? {
-        print!("{rest}");
     }
     let dt = start_gen.elapsed();
     println!(

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

@@ -19,7 +19,7 @@ use candle_transformers::generation::LogitsProcessor;
 use std::io::Write;
 use tokenizers::Tokenizer;
 
-use model::{Cache, Config, Llama};
+use model::{Config, Llama};
 use qmodel::QLlama;
 use weights::TransformerWeights;
 
@@ -160,10 +160,10 @@ enum Model {
 }
 
 impl Model {
-    fn forward(&self, xs: &Tensor, pos: usize, cache: &mut Cache) -> anyhow::Result<Tensor> {
+    fn forward(&self, xs: &Tensor, pos: usize) -> anyhow::Result<Tensor> {
         match self {
-            Self::Llama(l) => Ok(l.forward(xs, pos, cache)?),
+            Self::Llama(l) => Ok(l.forward(xs, pos)?),
-            Self::QLlama(l) => Ok(l.forward(xs, pos, cache)?),
+            Self::QLlama(l) => Ok(l.forward(xs, pos)?),
         }
     }
 }
@@ -188,8 +188,8 @@ fn run_eval(args: &EvaluationCmd, common_args: &Args) -> Result<()> {
     let config = Config::from_reader(&mut file)?;
     let weights = TransformerWeights::from_reader(&mut file, &config, &device)?;
     let vb = weights.var_builder(&config, &device)?;
-    let mut cache = Cache::new(false, &config, vb.pp("rot"))?;
+    let cache = model::Cache::new(false, &config, vb.pp("rot"))?;
-    let model = Llama::load(vb, config)?;
+    let model = Llama::load(vb, &cache, config)?;
 
     let tokens = match &args.pretokenized_dir {
         None => {
@@ -235,7 +235,7 @@ fn run_eval(args: &EvaluationCmd, common_args: &Args) -> Result<()> {
     let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
     for inp_tgt in batch_iter {
         let (inp, tgt) = inp_tgt?;
-        let logits = model.forward(&inp, 0, &mut cache)?;
+        let logits = model.forward(&inp, 0)?;
         let loss = candle_nn::loss::cross_entropy(&logits.flatten_to(1)?, &tgt.flatten_to(1)?)?;
         println!("{}", loss.to_vec0::<f32>()?);
     }
@@ -261,7 +261,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
     let is_safetensors = config_path
         .extension()
         .map_or(false, |v| v == "safetensors");
-    let (model, config, mut cache) = if is_gguf {
+    let (model, config) = if is_gguf {
         let vb = qmodel::VarBuilder::from_gguf(config_path, &device)?;
         let (_vocab_size, dim) = vb
             .get_no_shape("model.embed_tokens.weight")?
@@ -298,15 +298,15 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
             &device,
         );
         let cache = model::Cache::new(true, &config, fake_vb)?;
-        let model = Model::QLlama(QLlama::load(vb, config.clone())?);
+        let model = Model::QLlama(QLlama::load(vb, &cache, config.clone())?);
-        (model, config, cache)
+        (model, config)
     } else if is_safetensors {
         let config = Config::tiny_15m();
         let tensors = candle::safetensors::load(config_path, &device)?;
         let vb = candle_nn::VarBuilder::from_tensors(tensors, candle::DType::F32, &device);
         let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
-        let model = Model::Llama(Llama::load(vb, config.clone())?);
+        let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
-        (model, config, cache)
+        (model, config)
     } else {
         let mut file = std::fs::File::open(config_path)?;
         let config = Config::from_reader(&mut file)?;
@@ -314,8 +314,8 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         let weights = TransformerWeights::from_reader(&mut file, &config, &device)?;
         let vb = weights.var_builder(&config, &device)?;
         let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
-        let model = Model::Llama(Llama::load(vb, config.clone())?);
+        let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
-        (model, config, cache)
+        (model, config)
     };
 
     println!("starting the inference loop");
@@ -328,7 +328,6 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         .map_err(E::msg)?
         .get_ids()
         .to_vec();
-    let mut tokenizer = candle_examples::token_output_stream::TokenOutputStream::new(tokenizer);
 
     let start_gen = std::time::Instant::now();
     for index in 0.. {
@@ -338,7 +337,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         let context_size = if index > 0 { 1 } else { tokens.len() };
         let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
         let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
-        let logits = model.forward(&input, index_pos, &mut cache)?;
+        let logits = model.forward(&input, index_pos)?;
         let logits = logits.i((0, logits.dim(1)? - 1))?;
         let logits = if common_args.repeat_penalty == 1. || tokens.is_empty() {
             logits
@@ -354,14 +353,16 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
 
         let next_token = logits_processor.sample(&logits)?;
         tokens.push(next_token);
-        if let Some(t) = tokenizer.next_token(next_token)? {
+        // Extracting the last token as a string is complicated, here we just apply some simple
-            print!("{t}");
+        // heuristics as it seems to work well enough for this example. See the following for more
+        // details:
+        // https://github.com/huggingface/tokenizers/issues/1141#issuecomment-1562644141
+        if let Some(text) = tokenizer.id_to_token(next_token) {
+            let text = text.replace('▁', " ").replace("<0x0A>", "\n");
+            print!("{text}");
             std::io::stdout().flush()?;
         }
     }
-    if let Some(rest) = tokenizer.decode_rest().map_err(E::msg)? {
-        print!("{rest}");
-    }
     let dt = start_gen.elapsed();
     println!(
         "\n{} tokens generated ({:.2} token/s)\n",

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

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

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

@@ -2,9 +2,6 @@
 
 This is based on [mamba-minimal](https://github.com/johnma2006/mamba-minimal).
 
-Compared to the mamba example, this version can handle training but is much
-slower.
-
 ## Running the example
 
 ```bash

candle-examples/examples/mamba/README.md

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

candle-examples/examples/mamba/main.rs

@@ -1,305 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::{Error as E, Result};
-use clap::{Parser, ValueEnum};
-
-use candle_transformers::models::mamba::{Config, Model, State};
-
-use candle::{DType, Device, Tensor};
-use candle_examples::token_output_stream::TokenOutputStream;
-use candle_nn::VarBuilder;
-use candle_transformers::generation::LogitsProcessor;
-use hf_hub::{api::sync::Api, Repo, RepoType};
-use tokenizers::Tokenizer;
-
-struct TextGeneration {
-    model: Model,
-    config: Config,
-    device: Device,
-    tokenizer: TokenOutputStream,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        config: Config,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            config,
-            tokenizer: TokenOutputStream::new(tokenizer),
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        self.tokenizer.clear();
-        let dtype = self.model.dtype();
-        let mut tokens = self
-            .tokenizer
-            .tokenizer()
-            .encode(prompt, true)
-            .map_err(E::msg)?
-            .get_ids()
-            .to_vec();
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
-            Some(token) => token,
-            None => anyhow::bail!("cannot find the </s> token"),
-        };
-        let mut state = State::new(1, &self.config, dtype, &self.device)?;
-        let mut next_logits = None;
-        for &t in tokens.iter() {
-            let input = Tensor::new(&[t], &self.device)?;
-            let logits = self.model.forward(&input, &mut state)?;
-            next_logits = Some(logits);
-            if let Some(t) = self.tokenizer.next_token(t)? {
-                print!("{t}")
-            }
-        }
-        std::io::stdout().flush()?;
-
-        let start_gen = std::time::Instant::now();
-        for _ in 0..sample_len {
-            let logits = match next_logits.as_ref() {
-                Some(logits) => logits,
-                None => anyhow::bail!("cannot work on an empty prompt"),
-            };
-            let logits = logits.squeeze(0)?.to_dtype(dtype)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            if let Some(t) = self.tokenizer.next_token(next_token)? {
-                print!("{t}");
-                std::io::stdout().flush()?;
-            }
-
-            let input = Tensor::new(&[next_token], &self.device)?;
-            next_logits = Some(self.model.forward(&input, &mut state)?)
-        }
-        let dt = start_gen.elapsed();
-        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
-            print!("{rest}");
-        }
-        std::io::stdout().flush()?;
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(Parser, ValueEnum, Clone, Copy, PartialEq, Eq, Debug)]
-enum Which {
-    Mamba130m,
-    Mamba370m,
-    Mamba790m,
-    Mamba1_4b,
-    Mamba2_8b,
-    Mamba2_8bSlimPj,
-}
-
-impl std::fmt::Display for Which {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "{:?}", self)
-    }
-}
-
-impl Which {
-    fn model_id(&self) -> &'static str {
-        match self {
-            Self::Mamba130m => "state-spaces/mamba-130m",
-            Self::Mamba370m => "state-spaces/mamba-370m",
-            Self::Mamba790m => "state-spaces/mamba-790m",
-            Self::Mamba1_4b => "state-spaces/mamba-1.4b",
-            Self::Mamba2_8b => "state-spaces/mamba-2.8b",
-            Self::Mamba2_8bSlimPj => "state-spaces/mamba-2.8b-slimpj'",
-        }
-    }
-
-    fn revision(&self) -> &'static str {
-        match self {
-            Self::Mamba130m
-            | Self::Mamba370m
-            | Self::Mamba790m
-            | Self::Mamba1_4b
-            | Self::Mamba2_8bSlimPj => "refs/pr/1",
-            Self::Mamba2_8b => "refs/pr/4",
-        }
-    }
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// The temperature used to generate samples.
-    #[arg(long)]
-    temperature: Option<f64>,
-
-    /// Nucleus sampling probability cutoff.
-    #[arg(long)]
-    top_p: Option<f64>,
-
-    /// The seed to use when generating random samples.
-    #[arg(long, default_value_t = 299792458)]
-    seed: u64,
-
-    /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 5000)]
-    sample_len: usize,
-
-    #[arg(long, default_value = "mamba130m")]
-    which: Which,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long)]
-    revision: Option<String>,
-
-    #[arg(long)]
-    tokenizer_file: Option<String>,
-
-    #[arg(long)]
-    weight_files: Option<String>,
-
-    #[arg(long)]
-    config_file: Option<String>,
-
-    #[arg(long, default_value = "f32")]
-    dtype: String,
-
-    /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.1)]
-    repeat_penalty: f32,
-
-    /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 64)]
-    repeat_last_n: usize,
-}
-
-fn main() -> Result<()> {
-    use std::str::FromStr;
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let repo = api.repo(Repo::with_revision(
-        args.model_id
-            .unwrap_or_else(|| args.which.model_id().to_string()),
-        RepoType::Model,
-        args.revision
-            .unwrap_or_else(|| args.which.revision().to_string()),
-    ));
-    let tokenizer_filename = match args.tokenizer_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => api
-            .model("EleutherAI/gpt-neox-20b".to_string())
-            .get("tokenizer.json")?,
-    };
-    let config_filename = match args.config_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("config.json")?,
-    };
-    let filenames = match args.weight_files {
-        Some(files) => files
-            .split(',')
-            .map(std::path::PathBuf::from)
-            .collect::<Vec<_>>(),
-        None => {
-            vec![repo.get("model.safetensors")?]
-        }
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-
-    let start = std::time::Instant::now();
-    let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
-    let device = candle_examples::device(args.cpu)?;
-    let dtype = DType::from_str(&args.dtype)?;
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-    let model = Model::new(&config, vb.pp("backbone"))?;
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        config,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

candle-examples/examples/metavoice/README.md

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

candle-examples/examples/metavoice/main.rs

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

candle-examples/examples/mistral/main.rs

@@ -13,7 +13,7 @@ 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, Sampling};
+use candle_transformers::generation::LogitsProcessor;
 use hf_hub::{api::sync::Api, Repo, RepoType};
 use tokenizers::Tokenizer;
 
@@ -39,26 +39,11 @@ impl TextGeneration {
         seed: u64,
         temp: Option<f64>,
         top_p: Option<f64>,
-        top_k: Option<usize>,
         repeat_penalty: f32,
         repeat_last_n: usize,
         device: &Device,
     ) -> Self {
-        let logits_processor = {
+        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-            let temperature = temp.unwrap_or(0.);
-            let sampling = if temperature <= 0. {
-                Sampling::ArgMax
-            } else {
-                match (top_k, top_p) {
-                    (None, None) => Sampling::All { temperature },
-                    (Some(k), None) => Sampling::TopK { k, temperature },
-                    (None, Some(p)) => Sampling::TopP { p, temperature },
-                    (Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },
-                }
-            };
-            LogitsProcessor::from_sampling(seed, sampling)
-        };
-
         Self {
             model,
             tokenizer: TokenOutputStream::new(tokenizer),
@@ -137,18 +122,6 @@ impl TextGeneration {
     }
 }
 
-#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
-enum Which {
-    #[value(name = "7b-v0.1")]
-    Mistral7bV01,
-    #[value(name = "7b-v0.2")]
-    Mistral7bV02,
-    #[value(name = "7b-instruct-v0.1")]
-    Mistral7bInstructV01,
-    #[value(name = "7b-instruct-v0.2")]
-    Mistral7bInstructV02,
-}
-
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
 struct Args {
@@ -174,22 +147,14 @@ struct Args {
     #[arg(long)]
     top_p: Option<f64>,
 
-    /// Only sample among the top K samples.
-    #[arg(long)]
-    top_k: Option<usize>,
-
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
 
     /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 10000)]
+    #[arg(long, short = 'n', default_value_t = 100)]
     sample_len: usize,
 
-    /// The model size to use.
-    #[arg(long, default_value = "7b-v0.1")]
-    which: Which,
-
     #[arg(long)]
     model_id: Option<String>,
 
@@ -199,9 +164,6 @@ struct Args {
     #[arg(long)]
     tokenizer_file: Option<String>,
 
-    #[arg(long)]
-    config_file: Option<String>,
-
     #[arg(long)]
     weight_files: Option<String>,
 
@@ -215,10 +177,6 @@ struct Args {
     /// The context size to consider for the repeat penalty.
     #[arg(long, default_value_t = 64)]
     repeat_last_n: usize,
-
-    /// Use the slower dmmv cuda kernel.
-    #[arg(long)]
-    force_dmmv: bool,
 }
 
 fn main() -> Result<()> {
@@ -226,9 +184,6 @@ fn main() -> Result<()> {
     use tracing_subscriber::prelude::*;
 
     let args = Args::parse();
-    #[cfg(feature = "cuda")]
-    candle::quantized::cuda::set_force_dmmv(args.force_dmmv);
-
     let _guard = if args.tracing {
         let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
         tracing_subscriber::registry().with(chrome_layer).init();
@@ -256,17 +211,9 @@ fn main() -> Result<()> {
         Some(model_id) => model_id,
         None => {
             if args.quantized {
-                if args.which != Which::Mistral7bV01 {
-                    anyhow::bail!("only 7b-v0.1 is available as a quantized model for now")
-                }
                 "lmz/candle-mistral".to_string()
             } else {
-                match args.which {
+                "mistralai/Mistral-7B-v0.1".to_string()
-                    Which::Mistral7bV01 => "mistralai/Mistral-7B-v0.1".to_string(),
-                    Which::Mistral7bV02 => "mistralai/Mistral-7B-v0.2".to_string(),
-                    Which::Mistral7bInstructV01 => "mistralai/Mistral-7B-Instruct-v0.1".to_string(),
-                    Which::Mistral7bInstructV02 => "mistralai/Mistral-7B-Instruct-v0.2".to_string(),
-                }
             }
         }
     };
@@ -296,17 +243,7 @@ fn main() -> Result<()> {
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
     let start = std::time::Instant::now();
-    let config = match args.config_file {
+    let config = Config::config_7b_v0_1(args.use_flash_attn);
-        Some(config_file) => serde_json::from_slice(&std::fs::read(config_file)?)?,
-        None => {
-            if args.quantized {
-                Config::config_7b_v0_1(args.use_flash_attn)
-            } else {
-                let config_file = repo.get("config.json")?;
-                serde_json::from_slice(&std::fs::read(config_file)?)?
-            }
-        }
-    };
     let device = candle_examples::device(args.cpu)?;
     let (model, device) = if args.quantized {
         let filename = &filenames[0];
@@ -333,7 +270,6 @@ fn main() -> Result<()> {
         args.seed,
         args.temperature,
         args.top_p,
-        args.top_k,
         args.repeat_penalty,
         args.repeat_last_n,
         &device,

candle-examples/examples/mixtral/main.rs

@@ -143,7 +143,7 @@ struct Args {
     seed: u64,
 
     /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 10000)]
+    #[arg(long, short = 'n', default_value_t = 100)]
     sample_len: usize,
 
     #[arg(long, default_value = "mistralai/Mixtral-8x7B-v0.1")]

candle-examples/examples/mobileone/README.md

@@ -1,22 +0,0 @@
-# candle-mobileone
-
-[MobileOne: An Improved One millisecond Mobile Backbone](https://arxiv.org/abs/2206.04040).
-
-This candle implementation uses a pre-trained MobileOne network for inference. The
-classification head has been trained on the ImageNet dataset and returns the
-probabilities for the top-5 classes.
-
-## Running an example
-
-```
-$ cargo run --example mobileone --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which s2
-
-loaded image Tensor[dims 3, 224, 224; f32]
-model built
-mountain bike, all-terrain bike, off-roader: 79.33%
-bicycle-built-for-two, tandem bicycle, tandem: 15.32%
-crash helmet            : 2.58%
-unicycle, monocycle     : 1.70%
-alp                     : 0.21%
-
-```

candle-examples/examples/mobileone/main.rs

@@ -1,96 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use clap::{Parser, ValueEnum};
-
-use candle::{DType, IndexOp, D};
-use candle_nn::{Module, VarBuilder};
-use candle_transformers::models::mobileone;
-
-#[derive(Clone, Copy, Debug, ValueEnum)]
-enum Which {
-    S0,
-    S1,
-    S2,
-    S3,
-    S4,
-}
-
-impl Which {
-    fn model_filename(&self) -> String {
-        let name = match self {
-            Self::S0 => "s0",
-            Self::S1 => "s1",
-            Self::S2 => "s2",
-            Self::S3 => "s3",
-            Self::S4 => "s4",
-        };
-        format!("timm/mobileone_{}.apple_in1k", name)
-    }
-
-    fn config(&self) -> mobileone::Config {
-        match self {
-            Self::S0 => mobileone::Config::s0(),
-            Self::S1 => mobileone::Config::s1(),
-            Self::S2 => mobileone::Config::s2(),
-            Self::S3 => mobileone::Config::s3(),
-            Self::S4 => mobileone::Config::s4(),
-        }
-    }
-}
-
-#[derive(Parser)]
-struct Args {
-    #[arg(long)]
-    model: Option<String>,
-
-    #[arg(long)]
-    image: String,
-
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    #[arg(value_enum, long, default_value_t=Which::S0)]
-    which: Which,
-}
-
-pub fn main() -> anyhow::Result<()> {
-    let args = Args::parse();
-
-    let device = candle_examples::device(args.cpu)?;
-
-    let image = candle_examples::imagenet::load_image224(args.image)?.to_device(&device)?;
-    println!("loaded image {image:?}");
-
-    let model_file = match args.model {
-        None => {
-            let model_name = args.which.model_filename();
-            let api = hf_hub::api::sync::Api::new()?;
-            let api = api.model(model_name);
-            api.get("model.safetensors")?
-        }
-        Some(model) => model.into(),
-    };
-
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
-    let model = mobileone::mobileone(&args.which.config(), 1000, vb)?;
-    println!("model built");
-    let logits = model.forward(&image.unsqueeze(0)?)?;
-    let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
-        .i(0)?
-        .to_vec1::<f32>()?;
-    let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
-    prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
-    for &(category_idx, pr) in prs.iter().take(5) {
-        println!(
-            "{:24}: {:.2}%",
-            candle_examples::imagenet::CLASSES[category_idx],
-            100. * pr
-        );
-    }
-    Ok(())
-}

candle-examples/examples/moondream/README.md

@@ -1,26 +0,0 @@
-# candle-moondream
-
-[Moondream](https://github.com/vikhyat/moondream) is a computer-vision model can answer real-world questions about images. It's tiny by today's models, with only 1.6B parameters. That enables it to run on a variety of devices, including mobile phones and edge devices.
-
-## Running some examples
-First download an example image
-```bash
-$ wget https://raw.githubusercontent.com/vikhyat/moondream/main/assets/demo-1.jpg
-```
-
-<img src="https://raw.githubusercontent.com/vikhyat/moondream/main/assets/demo-1.jpg" width="200">
-
-Now you can run Moondream from the `candle-examples` crate:
-```bash
-$ cargo run --example moondream --release -- --prompt "What is the girl eating?" --image "./demo-1.jpg"
-
-avavx: false, neon: true, simd128: false, f16c: false
-temp: 0.00 repeat-penalty: 1.00 repeat-last-n: 64
-retrieved the files in 3.395583ms
-Running on CPU, to run on GPU(metal), build this example with `--features metal`
-loaded the model in 5.485493792s
-loaded and encoded the image Tensor[dims 3, 378, 378; f32] in 4.801396417s
-starting the inference loop
- The girl is eating a hamburger.<
-9 tokens generated (0.68 token/s)
-```

candle-examples/examples/moondream/main.rs

@@ -1,343 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::{Error as E, Result};
-use clap::Parser;
-
-use candle::{DType, Device, Tensor};
-use candle_nn::VarBuilder;
-use candle_transformers::{
-    generation::LogitsProcessor,
-    models::{moondream, quantized_moondream},
-};
-use tokenizers::Tokenizer;
-
-enum Model {
-    Moondream(moondream::Model),
-    Quantized(quantized_moondream::Model),
-}
-
-struct TextGeneration {
-    model: Model,
-    device: Device,
-    tokenizer: Tokenizer,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-    verbose_prompt: bool,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        verbose_prompt: bool,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            tokenizer,
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            verbose_prompt,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, image_embeds: &Tensor, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        println!("starting the inference loop");
-        let tokens = self.tokenizer.encode(prompt, true).map_err(E::msg)?;
-        if tokens.is_empty() {
-            anyhow::bail!("Empty prompts are not supported in the Moondream model.")
-        }
-        if self.verbose_prompt {
-            for (token, id) in tokens.get_tokens().iter().zip(tokens.get_ids().iter()) {
-                let token = token.replace('▁', " ").replace("<0x0A>", "\n");
-                println!("{id:7} -> '{token}'");
-            }
-        }
-
-        let mut tokens = tokens.get_ids().to_vec();
-        let mut generated_tokens = 0usize;
-
-        // Moondream tokenizer bos_token and eos_token is "<|endoftext|>"
-        // https://huggingface.co/vikhyatk/moondream2/blob/main/special_tokens_map.json
-        let special_token = match self.tokenizer.get_vocab(true).get("<|endoftext|>") {
-            Some(token) => *token,
-            None => anyhow::bail!("cannot find the special token"),
-        };
-        let (bos_token, eos_token) = (special_token, special_token);
-
-        let start_gen = std::time::Instant::now();
-        let mut load_t = std::time::Duration::from_secs_f64(0f64);
-        for index in 0..sample_len {
-            let context_size = if index > 0 { 1 } else { tokens.len() };
-            let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
-            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = if index > 0 {
-                match self.model {
-                    Model::Moondream(ref mut model) => model.text_model.forward(&input)?,
-                    Model::Quantized(ref mut model) => model.text_model.forward(&input)?,
-                }
-            } else {
-                let bos_token = Tensor::new(&[bos_token], &self.device)?.unsqueeze(0)?;
-                let logits = match self.model {
-                    Model::Moondream(ref mut model) => {
-                        model
-                            .text_model
-                            .forward_with_img(&bos_token, &input, image_embeds)?
-                    }
-                    Model::Quantized(ref mut model) => {
-                        model
-                            .text_model
-                            .forward_with_img(&bos_token, &input, image_embeds)?
-                    }
-                };
-                load_t = start_gen.elapsed();
-                println!("load_t: {:?}", load_t);
-                logits
-            };
-            let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token || tokens.ends_with(&[27, 10619, 29] /* <END> */) {
-                break;
-            }
-            let token = self.tokenizer.decode(&[next_token], true).map_err(E::msg)?;
-            print!("{token}");
-            std::io::stdout().flush()?;
-        }
-
-        let dt = start_gen.elapsed() - load_t;
-        println!(
-            "\ngenerated in {} seconds\n{generated_tokens} tokens generated ({:.2} token/s)",
-            dt.as_secs_f64(),
-            (generated_tokens - 1) as f64 / dt.as_secs_f64()
-        );
-
-        Ok(())
-    }
-}
-
-#[derive(Parser)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    /// Display the token for the specified prompt.
-    #[arg(long)]
-    verbose_prompt: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    #[arg(long)]
-    image: 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 = 0)]
-    seed: u64,
-
-    #[arg(long, default_value_t = 5000)]
-    sample_len: usize,
-
-    /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.0)]
-    repeat_penalty: f32,
-
-    /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 64)]
-    repeat_last_n: usize,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long, default_value = "main")]
-    revision: String,
-
-    #[arg(long)]
-    quantized: bool,
-
-    /// Use f16 precision for all the computations rather than f32.
-    #[arg(long)]
-    f16: bool,
-
-    #[arg(long)]
-    model_file: Option<String>,
-
-    #[arg(long)]
-    tokenizer_file: Option<String>,
-}
-
-/// Loads an image from disk using the image crate, this returns a tensor with shape
-/// (3, 378, 378).
-pub fn load_image<P: AsRef<std::path::Path>>(p: P) -> candle::Result<Tensor> {
-    let img = image::io::Reader::open(p)?
-        .decode()
-        .map_err(candle::Error::wrap)?
-        .resize_to_fill(378, 378, image::imageops::FilterType::Triangle); // Adjusted to 378x378
-    let img = img.to_rgb8();
-    let data = img.into_raw();
-    let data = Tensor::from_vec(data, (378, 378, 3), &Device::Cpu)?.permute((2, 0, 1))?;
-    let mean = Tensor::new(&[0.5f32, 0.5, 0.5], &Device::Cpu)?.reshape((3, 1, 1))?;
-    let std = Tensor::new(&[0.5f32, 0.5, 0.5], &Device::Cpu)?.reshape((3, 1, 1))?;
-    (data.to_dtype(candle::DType::F32)? / 255.)?
-        .broadcast_sub(&mean)?
-        .broadcast_div(&std)
-}
-
-#[tokio::main]
-async fn main() -> anyhow::Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    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 = hf_hub::api::tokio::Api::new()?;
-    let model_id = match args.model_id {
-        Some(model_id) => model_id.to_string(),
-        None => {
-            if args.quantized {
-                "santiagomed/candle-moondream".to_string()
-            } else {
-                "vikhyatk/moondream2".to_string()
-            }
-        }
-    };
-    let repo = api.repo(hf_hub::Repo::with_revision(
-        model_id,
-        hf_hub::RepoType::Model,
-        args.revision,
-    ));
-    let model_file = match args.model_file {
-        Some(m) => m.into(),
-        None => {
-            if args.quantized {
-                repo.get("model-q4_0.gguf").await?
-            } else {
-                repo.get("model.safetensors").await?
-            }
-        }
-    };
-    let tokenizer = match args.tokenizer_file {
-        Some(m) => m.into(),
-        None => repo.get("tokenizer.json").await?,
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
-
-    let start = std::time::Instant::now();
-    let device = candle_examples::device(args.cpu)?;
-    let config = moondream::Config::v2();
-    let dtype = if args.quantized {
-        if args.f16 {
-            anyhow::bail!("Quantized model does not support f16");
-        }
-        DType::F32
-    } else if device.is_cuda() || args.f16 {
-        DType::F16
-    } else {
-        DType::F32
-    };
-    let model = if args.quantized {
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
-            &model_file,
-            &device,
-        )?;
-        let model = quantized_moondream::Model::new(&config, vb)?;
-        Model::Quantized(model)
-    } else {
-        let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], dtype, &device)? };
-        let model = moondream::Model::new(&config, vb)?;
-        Model::Moondream(model)
-    };
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let start = std::time::Instant::now();
-    let image = load_image(args.image)?
-        .to_device(&device)?
-        .to_dtype(dtype)?;
-    let image_embeds = image.unsqueeze(0)?;
-    let image_embeds = match model {
-        Model::Moondream(ref m) => image_embeds.apply(m.vision_encoder())?,
-        Model::Quantized(ref m) => image_embeds.apply(m.vision_encoder())?,
-    };
-    println!(
-        "loaded and encoded the image {image:?} in {:?}",
-        start.elapsed()
-    );
-
-    let prompt = format!("\n\nQuestion: {0}\n\nAnswer:", args.prompt);
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        args.verbose_prompt,
-        &device,
-    );
-    pipeline.run(&prompt, &image_embeds, args.sample_len)?;
-
-    Ok(())
-}

candle-examples/examples/musicgen/encodec_model.rs

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

candle-examples/examples/musicgen/main.rs

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

candle-examples/examples/musicgen/musicgen_model.rs

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

candle-examples/examples/musicgen/nn.rs

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

candle-examples/examples/onnx/README.md

@@ -1,39 +1,10 @@
 ## Using ONNX models in Candle
 
-This example demonstrates how to run [ONNX](https://github.com/onnx/onnx) based models in Candle.
+This example demonstrates how to run ONNX based models in Candle, the model
+being used here is a small sequeezenet variant.
 
-It contains small variants of two models, [SqueezeNet](https://arxiv.org/pdf/1602.07360.pdf) (default) and [EfficientNet](https://arxiv.org/pdf/1905.11946.pdf).
+You can run the example with the following command:
-
-You can run the examples with following commands:
 
 ```bash
-cargo run --example onnx --features=onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
+cargo run --example squeezenet-onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
-```
-
-Use the `--which` flag to specify explicitly which network to use, i.e.
-
-```bash
-$ cargo run --example onnx --features=onnx --release -- --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg
-
-    Finished release [optimized] target(s) in 0.21s
-     Running `target/release/examples/onnx --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
-loaded image Tensor[dims 3, 224, 224; f32]
-unicycle, monocycle                               : 83.23%
-ballplayer, baseball player                       : 3.68%
-bearskin, busby, shako                            : 1.54%
-military uniform                                  : 0.78%
-cowboy hat, ten-gallon hat                        : 0.76%
-```
-
-```bash
-$ cargo run --example onnx --features=onnx --release -- --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg
-
-    Finished release [optimized] target(s) in 0.20s
-     Running `target/release/examples/onnx --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
-loaded image Tensor[dims 224, 224, 3; f32]
-bicycle-built-for-two, tandem bicycle, tandem     : 99.16%
-mountain bike, all-terrain bike, off-roader       : 0.60%
-unicycle, monocycle                               : 0.17%
-crash helmet                                      : 0.02%
-alp                                               : 0.02%
 ```

candle-examples/examples/quantized-t5/README.md

@@ -17,7 +17,7 @@ generate quantized weight files from the original safetensors file by using the
 `tensor-tools` command line utility via:
 
 ```bash
-$ cargo run --bin tensor-tools --release -- quantize --quantization q6k PATH/TO/T5/model.safetensors /tmp/model.gguf
+$ cargo run --example tensor-tools --release -- quantize --quantization q6k PATH/TO/T5/model.safetensors /tmp/model.gguf
 ```
 
 ## Using custom models

candle-examples/examples/quantized/main.rs

@@ -10,7 +10,7 @@ use tokenizers::Tokenizer;
 
 use candle::quantized::{ggml_file, gguf_file};
 use candle::Tensor;
-use candle_transformers::generation::{LogitsProcessor, Sampling};
+use candle_transformers::generation::LogitsProcessor;
 
 use candle_examples::token_output_stream::TokenOutputStream;
 use candle_transformers::models::quantized_llama as model;
@@ -67,8 +67,6 @@ enum Which {
     Mixtral,
     #[value(name = "mixtral-instruct")]
     MixtralInstruct,
-    #[value(name = "llama3-8b")]
-    L8b,
 }
 
 impl Which {
@@ -84,8 +82,7 @@ impl Which {
             | Self::L13bCode
             | Self::L34bCode
             | Self::Leo7b
-            | Self::Leo13b
+            | Self::Leo13b => false,
-            | Self::L8b => false,
             // Zephyr and OpenChat are fine tuned versions of mistral and should be treated in the
             // same way. Starling is a fine tuned version of OpenChat.
             Self::OpenChat35
@@ -119,8 +116,7 @@ impl Which {
             | Self::Mistral7bInstruct
             | Self::Mistral7bInstructV02
             | Self::OpenChat35
-            | Self::Starling7bAlpha
+            | Self::Starling7bAlpha => false,
-            | Self::L8b => false,
             Self::Zephyr7bAlpha | Self::Zephyr7bBeta => true,
         }
     }
@@ -144,8 +140,7 @@ impl Which {
             | Self::Mistral7bInstruct
             | Self::Mistral7bInstructV02
             | Self::Zephyr7bAlpha
-            | Self::Zephyr7bBeta
+            | Self::Zephyr7bBeta => false,
-            | Self::L8b => false,
             Self::OpenChat35 | Self::Starling7bAlpha => true,
         }
     }
@@ -172,7 +167,6 @@ impl Which {
             | Which::Zephyr7bBeta => "mistralai/Mistral-7B-v0.1",
             Which::OpenChat35 => "openchat/openchat_3.5",
             Which::Starling7bAlpha => "berkeley-nest/Starling-LM-7B-alpha",
-            Self::L8b => "meta-llama/Meta-Llama-3-8B",
         }
     }
 }
@@ -206,10 +200,6 @@ struct Args {
     #[arg(long)]
     top_p: Option<f64>,
 
-    /// Only sample among the top K samples.
-    #[arg(long)]
-    top_k: Option<usize>,
-
     /// The seed to use when generating random samples.
     #[arg(long, default_value_t = 299792458)]
     seed: u64,
@@ -222,14 +212,6 @@ struct Args {
     #[arg(long)]
     verbose_prompt: bool,
 
-    /// Process prompt elements separately.
-    #[arg(long)]
-    split_prompt: bool,
-
-    /// Run on CPU rather than GPU even if a GPU is available.
-    #[arg(long)]
-    cpu: bool,
-
     /// Penalty to be applied for repeating tokens, 1. means no penalty.
     #[arg(long, default_value_t = 1.1)]
     repeat_penalty: f32,
@@ -245,10 +227,6 @@ struct Args {
     /// Group-Query Attention, use 8 for the 70B version of LLaMAv2.
     #[arg(long)]
     gqa: Option<usize>,
-
-    /// Use the slower dmmv cuda kernel.
-    #[arg(long)]
-    force_dmmv: bool,
 }
 
 impl Args {
@@ -328,11 +306,6 @@ impl Args {
                         "TheBloke/Starling-LM-7B-alpha-GGUF",
                         "starling-lm-7b-alpha.Q4_K_M.gguf",
                     ),
-                    // TODO: swap to TheBloke model when available
-                    Which::L8b => (
-                        "QuantFactory/Meta-Llama-3-8B-GGUF",
-                        "Meta-Llama-3-8B.Q4_K_S.gguf",
-                    ),
                 };
                 let api = hf_hub::api::sync::Api::new()?;
                 let api = api.model(repo.to_string());
@@ -360,10 +333,11 @@ fn main() -> anyhow::Result<()> {
     use tracing_subscriber::prelude::*;
 
     let args = Args::parse();
-
+    let temperature = if args.temperature == 0. {
-    #[cfg(feature = "cuda")]
+        None
-    candle::quantized::cuda::set_force_dmmv(args.force_dmmv);
+    } else {
-
+        Some(args.temperature)
+    };
     let _guard = if args.tracing {
         let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
         tracing_subscriber::registry().with(chrome_layer).init();
@@ -387,7 +361,7 @@ fn main() -> anyhow::Result<()> {
     let model_path = args.model()?;
     let mut file = std::fs::File::open(&model_path)?;
     let start = std::time::Instant::now();
-    let device = candle_examples::device(args.cpu)?;
+    let device = candle_examples::device(false)?;
 
     let mut model = match model_path.extension().and_then(|v| v.to_str()) {
         Some("gguf") => {
@@ -431,8 +405,7 @@ fn main() -> anyhow::Result<()> {
                 | Which::L13bCode
                 | Which::L34bCode
                 | Which::Leo7b
-                | Which::Leo13b
+                | Which::Leo13b => 1,
-                | Which::L8b => 1,
                 Which::Mixtral
                 | Which::MixtralInstruct
                 | Which::Mistral7b
@@ -511,36 +484,14 @@ fn main() -> anyhow::Result<()> {
             prompt_tokens
         };
         let mut all_tokens = vec![];
-        let mut logits_processor = {
+        let mut logits_processor = LogitsProcessor::new(args.seed, temperature, args.top_p);
-            let temperature = args.temperature;
-            let sampling = if temperature <= 0. {
-                Sampling::ArgMax
-            } else {
-                match (args.top_k, args.top_p) {
-                    (None, None) => Sampling::All { temperature },
-                    (Some(k), None) => Sampling::TopK { k, temperature },
-                    (None, Some(p)) => Sampling::TopP { p, temperature },
-                    (Some(k), Some(p)) => Sampling::TopKThenTopP { k, p, temperature },
-                }
-            };
-            LogitsProcessor::from_sampling(args.seed, sampling)
-        };
 
         let start_prompt_processing = std::time::Instant::now();
-        let mut next_token = if !args.split_prompt {
+        let mut next_token = {
             let input = Tensor::new(prompt_tokens.as_slice(), &device)?.unsqueeze(0)?;
             let logits = model.forward(&input, 0)?;
             let logits = logits.squeeze(0)?;
             logits_processor.sample(&logits)?
-        } else {
-            let mut next_token = 0;
-            for (pos, token) in prompt_tokens.iter().enumerate() {
-                let input = Tensor::new(&[*token], &device)?.unsqueeze(0)?;
-                let logits = model.forward(&input, pos)?;
-                let logits = logits.squeeze(0)?;
-                next_token = logits_processor.sample(&logits)?
-            }
-            next_token
         };
         let prompt_dt = start_prompt_processing.elapsed();
         all_tokens.push(next_token);
@@ -549,14 +500,11 @@ fn main() -> anyhow::Result<()> {
             std::io::stdout().flush()?;
         }
 
-        let eos_token = match args.which {
+        let eos_token = if args.which.is_open_chat() {
-            Which::L8b => "<|end_of_text|>",
+            "<|end_of_turn|>"
-            _ => match args.which.is_open_chat() {
+        } else {
-                true => "<|end_of_turn|>",
+            "</s>"
-                false => "</s>",
-            },
         };
-
         let eos_token = *tos.tokenizer().get_vocab(true).get(eos_token).unwrap();
         let start_post_prompt = std::time::Instant::now();
         let mut sampled = 0;

candle-examples/examples/qwen/README.md

@@ -1,27 +0,0 @@
-# candle-qwen: large language model series from Alibaba Cloud
-
-Qwen 1.5 is a series of large language models that provide strong performances
-on English and Chinese.
-
-- [Blog post](https://qwenlm.github.io/blog/qwen1.5/) introducing Qwen1.5.
-- [Model card](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the HuggingFace Hub.
-- [Blog post](https://qwenlm.github.io/blog/qwen-moe/) for the
-  mixture-of-experts (MoE) variant.
-
-## Running the example
-
-```bash
-$ cargo run --example qwen --release  -- --prompt "Hello there "
-```
-
-Various model sizes are available via the `--model` argument, including the MoE
-variant.
-
-```bash
-$ cargo run --example qwen --release  -- --model moe-a2.7b --prompt 'def print_prime(n: int): '
-def print_prime(n: int):  # n is the number of primes to be printed
-    for i in range(2, n + 1):
-        if all(i % j != 0 for j in range(2, i)):
-            print(i)
-```
-

candle-examples/examples/qwen/main.rs

@@ -1,311 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::{Error as E, Result};
-use clap::Parser;
-
-use candle_transformers::models::qwen2::{Config as ConfigBase, Model as ModelBase};
-use candle_transformers::models::qwen2_moe::{Config as ConfigMoe, Model as ModelMoe};
-
-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 {
-    Base(ModelBase),
-    Moe(ModelMoe),
-}
-
-impl Model {
-    fn forward(&mut self, xs: &Tensor, s: usize) -> candle::Result<Tensor> {
-        match self {
-            Self::Moe(ref mut m) => m.forward(xs, s),
-            Self::Base(ref mut m) => m.forward(xs, s),
-        }
-    }
-}
-
-struct TextGeneration {
-    model: Model,
-    device: Device,
-    tokenizer: TokenOutputStream,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            tokenizer: TokenOutputStream::new(tokenizer),
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        self.tokenizer.clear();
-        let mut tokens = self
-            .tokenizer
-            .tokenizer()
-            .encode(prompt, true)
-            .map_err(E::msg)?
-            .get_ids()
-            .to_vec();
-        for &t in tokens.iter() {
-            if let Some(t) = self.tokenizer.next_token(t)? {
-                print!("{t}")
-            }
-        }
-        std::io::stdout().flush()?;
-
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
-            Some(token) => token,
-            None => anyhow::bail!("cannot find the <|endoftext|> token"),
-        };
-        let start_gen = std::time::Instant::now();
-        for index in 0..sample_len {
-            let context_size = if index > 0 { 1 } else { tokens.len() };
-            let start_pos = tokens.len().saturating_sub(context_size);
-            let ctxt = &tokens[start_pos..];
-            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = self.model.forward(&input, start_pos)?;
-            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            if let Some(t) = self.tokenizer.next_token(next_token)? {
-                print!("{t}");
-                std::io::stdout().flush()?;
-            }
-        }
-        let dt = start_gen.elapsed();
-        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
-            print!("{rest}");
-        }
-        std::io::stdout().flush()?;
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(Clone, Copy, Debug, clap::ValueEnum, PartialEq, Eq)]
-enum WhichModel {
-    #[value(name = "0.5b")]
-    W0_5b,
-    #[value(name = "1.8b")]
-    W1_8b,
-    #[value(name = "4b")]
-    W4b,
-    #[value(name = "7b")]
-    W7b,
-    #[value(name = "14b")]
-    W14b,
-    #[value(name = "72b")]
-    W72b,
-    #[value(name = "moe-a2.7b")]
-    MoeA27b,
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    use_flash_attn: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// The temperature used to generate samples.
-    #[arg(long)]
-    temperature: Option<f64>,
-
-    /// Nucleus sampling probability cutoff.
-    #[arg(long)]
-    top_p: Option<f64>,
-
-    /// The seed to use when generating random samples.
-    #[arg(long, default_value_t = 299792458)]
-    seed: u64,
-
-    /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 10000)]
-    sample_len: usize,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long, default_value = "main")]
-    revision: String,
-
-    #[arg(long)]
-    tokenizer_file: Option<String>,
-
-    #[arg(long)]
-    weight_files: Option<String>,
-
-    /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.1)]
-    repeat_penalty: f32,
-
-    /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 64)]
-    repeat_last_n: usize,
-
-    #[arg(long, default_value = "0.5b")]
-    model: WhichModel,
-}
-
-fn main() -> Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let model_id = match args.model_id {
-        Some(model_id) => model_id,
-        None => {
-            let size = match args.model {
-                WhichModel::W0_5b => "0.5B",
-                WhichModel::W1_8b => "1.8B",
-                WhichModel::W4b => "4B",
-                WhichModel::W7b => "7B",
-                WhichModel::W14b => "14B",
-                WhichModel::W72b => "72B",
-                WhichModel::MoeA27b => "MoE-A2.7B",
-            };
-            format!("Qwen/Qwen1.5-{size}")
-        }
-    };
-    let repo = api.repo(Repo::with_revision(
-        model_id,
-        RepoType::Model,
-        args.revision,
-    ));
-    let tokenizer_filename = match args.tokenizer_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("tokenizer.json")?,
-    };
-    let filenames = match args.weight_files {
-        Some(files) => files
-            .split(',')
-            .map(std::path::PathBuf::from)
-            .collect::<Vec<_>>(),
-        None => match args.model {
-            WhichModel::W0_5b | WhichModel::W1_8b => vec![repo.get("model.safetensors")?],
-            WhichModel::W4b
-            | WhichModel::W7b
-            | WhichModel::W14b
-            | WhichModel::W72b
-            | WhichModel::MoeA27b => {
-                candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
-            }
-        },
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-
-    let start = std::time::Instant::now();
-    let config_file = repo.get("config.json")?;
-    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 = match args.model {
-        WhichModel::MoeA27b => {
-            let config: ConfigMoe = serde_json::from_slice(&std::fs::read(config_file)?)?;
-            Model::Moe(ModelMoe::new(&config, vb)?)
-        }
-        _ => {
-            let config: ConfigBase = serde_json::from_slice(&std::fs::read(config_file)?)?;
-            Model::Base(ModelBase::new(&config, vb)?)
-        }
-    };
-
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

candle-examples/examples/recurrent-gemma/README.md

@@ -1,9 +0,0 @@
-# candle-recurrent-gemma
-
-This model card corresponds to the 2B base version of the RecurrentGemma model
-[huggingface model card](https://huggingface.co/google/recurrentgemma-2b).
-
-```bash
-cargo run --features cuda -r --example recurrent-gemma -- \
-    --prompt "Write me a poem about Machine Learning."  
-```