Update yew requirement from 0.20.0 to 0.21.0

Updates the requirements on [yew](https://github.com/yewstack/yew) to permit the latest version.
- [Release notes](https://github.com/yewstack/yew/releases)
- [Changelog](https://github.com/yewstack/yew/blob/master/CHANGELOG.md)
- [Commits](https://github.com/yewstack/yew/compare/yew-v0.20.0...yew-v0.21.0)

---
updated-dependencies:
- dependency-name: yew
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>

.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]
-    container:
-      image: nvidia/cuda:12.3.1-devel-ubuntu22.04
-      options: --gpus 0 
-    if: ${{ github.event.pull_request.head.repo.full_name == github.event.pull_request.base.repo.full_name }}
+    needs: start-runner # required to start the main job when the runner is ready
+    runs-on: ${{ needs.start-runner.outputs.label }} # run the job on the newly created runner
     permissions:
       contents: write
       packages: write
@@ -24,10 +58,32 @@ jobs:
     steps:
       - name: Checkout repository
         uses: actions/checkout@v3
-      - name: Install dependencies
-        run: apt-get update && apt install curl build-essential libssl-dev protobuf-compiler pkg-config -y
       - name: Install Rust Stable
-        uses: actions-rust-lang/setup-rust-toolchain@v1
+        run: curl https://sh.rustup.rs -sSf | sh -s -- -y
       - uses: Swatinem/rust-cache@v2
+      - run: apt-get update -y && apt-get install libssl-dev protobuf-compiler -y
       - name: Test (cuda)
-        run: cargo test --features cuda
+        run: PATH=$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

@@ -19,7 +19,7 @@ exclude = [
 resolver = "2"
 
 [workspace.package]
-version = "0.4.0"
+version = "0.3.3"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"
@@ -31,14 +31,14 @@ license = "MIT OR Apache-2.0"
 accelerate-src = { version = "0.3.2" }
 anyhow = { version = "1", features = ["backtrace"] }
 byteorder = "1.4.3"
-candle = { path = "./candle-core", package = "candle-core", version = "0.4.0" }
-candle-datasets = { path = "./candle-datasets", version = "0.4.0" }
-candle-flash-attn = { path = "./candle-flash-attn", version = "0.4.0" }
-candle-kernels = { path = "./candle-kernels", version = "0.4.0" }
-candle-metal-kernels = { path = "./candle-metal-kernels", version = "0.4.0" }
-candle-nn = { path = "./candle-nn", version = "0.4.0" }
-candle-onnx = { path = "./candle-onnx", version = "0.4.0" }
-candle-transformers = { path = "./candle-transformers", version = "0.4.0" }
+candle = { path = "./candle-core", package = "candle-core" }
+candle-datasets = { path = "./candle-datasets" }
+candle-flash-attn = { path = "./candle-flash-attn" }
+candle-kernels = { path = "./candle-kernels" }
+candle-metal-kernels = { path = "./candle-metal-kernels" }
+candle-nn = { path = "./candle-nn" }
+candle-onnx = { path = "./candle-onnx" }
+candle-transformers = { path = "./candle-transformers" }
 clap = { version = "4.2.4", features = ["derive"] }
 criterion = { version = "0.5.1", default-features=false }
 cudarc = { version = "0.10.0", features = ["f16"] }
@@ -53,12 +53,12 @@ log = "0.4"
 memmap2 = { version = "0.9.3", features = ["stable_deref_trait"] }
 num_cpus = "1.15.0"
 num-traits = "0.2.15"
-parquet = { version = "50.0.0" }
+parquet = { version = "45.0.0" }
 rand = "0.8.5"
 rand_distr = "0.4.3"
 rayon = "1.7.0"
 rusttype = { version = "0.9", default-features = false }
-safetensors = "0.4.1"
+safetensors = "0.3.1"
 serde = { version = "1.0.171", features = ["derive"] }
 serde_plain = "1.0.2"
 serde_json = "1.0.99"

README.md

@@ -65,9 +65,8 @@ We also provide a some command line based examples using state of the art models
 - [Falcon](./candle-examples/examples/falcon/): general LLM.
 - [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
-  StableLM-2, a 1.6b LLM trained on 2T tokens, as well as the code variants.
-- [Mamba](./candle-examples/examples/mamba/): an inference only
+  pre-trained on 1T tokens of English and code datasets.
+- [Minimal Mamba](./candle-examples/examples/mamba-minimal/): a minimal
   implementation of the Mamba state space model.
 - [Mistral7b-v0.1](./candle-examples/examples/mistral/): a 7b general LLM with
   better performance than all publicly available 13b models as of 2023-09-28.
@@ -75,9 +74,6 @@ We also provide a some command line based examples using state of the art models
   experts 8x7b general LLM with better performance than a Llama 2 70B model with
   much faster inference.
 - [StarCoder](./candle-examples/examples/bigcode/): LLM specialized to code generation.
-- [Qwen1.5](./candle-examples/examples/qwen/): Bilingual (English/Chinese) LLMs.
-- [RWKV v5](./candle-examples/examples/rwkv/): An RNN with transformer level LLM
-  performance.
 - [Replit-code-v1.5](./candle-examples/examples/replit-code/): a 3.3b LLM specialized for code completion.
 - [Yi-6B / Yi-34B](./candle-examples/examples/yi/): two bilingual
   (English/Chinese) general LLMs with 6b and 34b parameters.
@@ -115,10 +111,9 @@ 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.
-- [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.
 
@@ -189,15 +184,13 @@ If you have an addition to this list, please submit a pull request.
         - Falcon.
         - StarCoder.
         - Phi 1, 1.5, and 2.
-        - Mamba, Minimal Mamba
+        - Minimal Mamba
         - 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.
-        - RWKV.
     - Quantized LLMs.
         - Llama 7b, 13b, 70b, as well as the chat and code variants.
         - Mistral 7b, and 7b instruct.
@@ -213,10 +206,8 @@ If you have an addition to this list, please submit a pull request.
         - Wurstchen v2.
     - Image to text.
         - BLIP.
-        - TrOCR.
     - Computer Vision Models.
-        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG, ConvNeXT,
-          ConvNeXTv2.
+        - DINOv2, ConvMixer, EfficientNet, ResNet, ViT, VGG, RepVGG.
         - yolo-v3, yolo-v8.
         - Segment-Anything Model (SAM).
 - File formats: load models from safetensors, npz, ggml, or PyTorch files.

candle-core/benches/bench_main.rs

@@ -2,8 +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
 );

candle-core/benches/benchmarks/mod.rs

@@ -1,6 +1,5 @@
 pub(crate) mod affine;
 pub(crate) mod matmul;
-pub(crate) mod random;
 pub(crate) mod where_cond;
 
 use candle_core::{Device, Result};

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/tensor-tools.rs

@@ -1,5 +1,5 @@
-use candle_core::quantized::{gguf_file, GgmlDType, QTensor};
-use candle_core::{Device, Result};
+use candle_core::quantized::{gguf_file, k_quants, QTensor};
+use candle_core::{Device, Result, Tensor};
 use clap::{Parser, Subcommand, ValueEnum};
 use rayon::prelude::*;
 
@@ -11,7 +11,12 @@ enum QuantizationMode {
 }
 
 impl QuantizationMode {
-    fn quantize(&self, name: &str, tensor: QTensor, dtype: GgmlDType) -> Result<QTensor> {
+    fn quantize(
+        &self,
+        name: &str,
+        tensor: QTensor,
+        default: fn(&Tensor) -> Result<QTensor>,
+    ) -> Result<QTensor> {
         match self {
             Self::Llama => {
                 // Same behavior as the llama.cpp quantization.
@@ -19,9 +24,9 @@ impl QuantizationMode {
                 if should_quantize {
                     let tensor = tensor.dequantize(&Device::Cpu)?;
                     if name == "output.weight" {
-                        QTensor::quantize(&tensor, GgmlDType::Q6K)
+                        QTensor::quantize::<k_quants::BlockQ6K>(&tensor)
                     } else {
-                        QTensor::quantize(&tensor, dtype)
+                        default(&tensor)
                     }
                 } else {
                     Ok(tensor)
@@ -55,27 +60,6 @@ enum Quantization {
     F32,
 }
 
-impl Quantization {
-    fn dtype(&self) -> GgmlDType {
-        match self {
-            Quantization::Q4_0 => GgmlDType::Q4_0,
-            Quantization::Q4_1 => GgmlDType::Q4_1,
-            Quantization::Q5_0 => GgmlDType::Q5_0,
-            Quantization::Q5_1 => GgmlDType::Q5_1,
-            Quantization::Q8_0 => GgmlDType::Q8_0,
-            Quantization::Q8_1 => GgmlDType::Q8_1,
-            Quantization::Q2k => GgmlDType::Q2K,
-            Quantization::Q3k => GgmlDType::Q3K,
-            Quantization::Q4k => GgmlDType::Q4K,
-            Quantization::Q5k => GgmlDType::Q5K,
-            Quantization::Q6k => GgmlDType::Q6K,
-            Quantization::Q8k => GgmlDType::Q8K,
-            Quantization::F16 => GgmlDType::F16,
-            Quantization::F32 => GgmlDType::F32,
-        }
-    }
-}
-
 #[derive(ValueEnum, Debug, Clone)]
 enum Format {
     Safetensors,
@@ -150,12 +134,7 @@ struct Args {
     command: Command,
 }
 
-fn run_ls(
-    file: &std::path::PathBuf,
-    format: Option<Format>,
-    verbose: bool,
-    device: &Device,
-) -> Result<()> {
+fn run_ls(file: &std::path::PathBuf, format: Option<Format>, verbose: bool) -> Result<()> {
     let format = match format {
         Some(format) => format,
         None => match Format::infer(file) {
@@ -196,7 +175,7 @@ fn run_ls(
             }
         }
         Format::Pth => {
-            let mut tensors = candle_core::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!(
@@ -221,7 +200,7 @@ fn run_ls(
         }
         Format::Ggml => {
             let mut file = std::fs::File::open(file)?;
-            let content = candle_core::quantized::ggml_file::Content::read(&mut file, device)?;
+            let content = candle_core::quantized::ggml_file::Content::read(&mut file)?;
             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() {
@@ -262,8 +241,37 @@ fn run_quantize_safetensors(
     }
     println!("tensors: {}", tensors.len());
 
-    let dtype = q.dtype();
-    let block_size = dtype.block_size();
+    let quantize_fn = match q {
+        Quantization::Q4_0 => QTensor::quantize::<k_quants::BlockQ4_0>,
+        Quantization::Q4_1 => QTensor::quantize::<k_quants::BlockQ4_1>,
+        Quantization::Q5_0 => QTensor::quantize::<k_quants::BlockQ5_0>,
+        Quantization::Q5_1 => QTensor::quantize::<k_quants::BlockQ5_1>,
+        Quantization::Q8_0 => QTensor::quantize::<k_quants::BlockQ8_0>,
+        Quantization::Q8_1 => QTensor::quantize::<k_quants::BlockQ8_1>,
+        Quantization::Q2k => QTensor::quantize::<k_quants::BlockQ2K>,
+        Quantization::Q3k => QTensor::quantize::<k_quants::BlockQ3K>,
+        Quantization::Q4k => QTensor::quantize::<k_quants::BlockQ4K>,
+        Quantization::Q5k => QTensor::quantize::<k_quants::BlockQ5K>,
+        Quantization::Q6k => QTensor::quantize::<k_quants::BlockQ6K>,
+        Quantization::Q8k => QTensor::quantize::<k_quants::BlockQ8K>,
+        Quantization::F16 => QTensor::quantize::<half::f16>,
+        Quantization::F32 => QTensor::quantize::<f32>,
+    };
+    let block_size = match q {
+        Quantization::Q4_0 => k_quants::QK4_0,
+        Quantization::Q4_1 => k_quants::QK4_1,
+        Quantization::Q5_0 => k_quants::QK5_0,
+        Quantization::Q5_1 => k_quants::QK5_1,
+        Quantization::Q8_0 => k_quants::QK8_0,
+        Quantization::Q8_1 => k_quants::QK8_1,
+        Quantization::Q2k
+        | Quantization::Q3k
+        | Quantization::Q4k
+        | Quantization::Q5k
+        | Quantization::Q6k
+        | Quantization::Q8k => k_quants::QK_K,
+        Quantization::F16 | Quantization::F32 => 1,
+    };
 
     let qtensors = tensors
         .into_par_iter()
@@ -271,9 +279,9 @@ fn run_quantize_safetensors(
             let should_quantize = tensor.rank() == 2 && tensor.dim(1)? % block_size == 0;
             println!("  quantizing {name} {tensor:?} {should_quantize}");
             let tensor = if should_quantize {
-                QTensor::quantize(&tensor, dtype)?
+                quantize_fn(&tensor)?
             } else {
-                QTensor::quantize(&tensor, GgmlDType::F32)?
+                QTensor::quantize::<f32>(&tensor)?
             };
             Ok((name, tensor))
         })
@@ -286,17 +294,13 @@ fn run_quantize_safetensors(
     Ok(())
 }
 
-fn run_dequantize(
-    in_file: std::path::PathBuf,
-    out_file: std::path::PathBuf,
-    device: &Device,
-) -> Result<()> {
+fn run_dequantize(in_file: std::path::PathBuf, out_file: std::path::PathBuf) -> Result<()> {
     let mut in_file = std::fs::File::open(in_file)?;
     let content = gguf_file::Content::read(&mut in_file)?;
     let mut tensors = std::collections::HashMap::new();
     for (tensor_name, _) in content.tensor_infos.iter() {
-        let tensor = content.tensor(&mut in_file, tensor_name, device)?;
-        let tensor = tensor.dequantize(device)?;
+        let tensor = content.tensor(&mut in_file, tensor_name)?;
+        let tensor = tensor.dequantize(&Device::Cpu)?;
         tensors.insert(tensor_name.to_string(), tensor);
     }
     candle_core::safetensors::save(&tensors, out_file)?;
@@ -308,7 +312,6 @@ fn run_quantize(
     out_file: std::path::PathBuf,
     q: Quantization,
     qmode: QuantizationMode,
-    device: &Device,
 ) -> Result<()> {
     if in_files.is_empty() {
         candle_core::bail!("no specified input files")
@@ -334,15 +337,31 @@ fn run_quantize(
     let content = gguf_file::Content::read(&mut in_)?;
     println!("tensors: {}", content.tensor_infos.len());
 
-    let dtype = q.dtype();
+    let quantize_fn = match q {
+        Quantization::Q4_0 => QTensor::quantize::<k_quants::BlockQ4_0>,
+        Quantization::Q4_1 => QTensor::quantize::<k_quants::BlockQ4_1>,
+        Quantization::Q5_0 => QTensor::quantize::<k_quants::BlockQ5_0>,
+        Quantization::Q5_1 => QTensor::quantize::<k_quants::BlockQ5_1>,
+        Quantization::Q8_0 => QTensor::quantize::<k_quants::BlockQ8_0>,
+        Quantization::Q8_1 => QTensor::quantize::<k_quants::BlockQ8_1>,
+        Quantization::Q2k => QTensor::quantize::<k_quants::BlockQ2K>,
+        Quantization::Q3k => QTensor::quantize::<k_quants::BlockQ3K>,
+        Quantization::Q4k => QTensor::quantize::<k_quants::BlockQ4K>,
+        Quantization::Q5k => QTensor::quantize::<k_quants::BlockQ5K>,
+        Quantization::Q6k => QTensor::quantize::<k_quants::BlockQ6K>,
+        Quantization::Q8k => QTensor::quantize::<k_quants::BlockQ8K>,
+        Quantization::F16 => QTensor::quantize::<half::f16>,
+        Quantization::F32 => QTensor::quantize::<f32>,
+    };
+
     let qtensors = content
         .tensor_infos
         .par_iter()
         .map(|(name, _)| {
             println!("  quantizing {name}");
             let mut in_file = std::fs::File::open(&in_files[0])?;
-            let tensor = content.tensor(&mut in_file, name, device)?;
-            let tensor = qmode.quantize(name, tensor, dtype)?;
+            let tensor = content.tensor(&mut in_file, name)?;
+            let tensor = qmode.quantize(name, tensor, quantize_fn)?;
             Ok((name, tensor))
         })
         .collect::<Result<Vec<_>>>()?;
@@ -362,7 +381,6 @@ fn run_quantize(
 
 fn main() -> anyhow::Result<()> {
     let args = Args::parse();
-    let device = Device::Cpu;
     match args.command {
         Command::Ls {
             files,
@@ -374,7 +392,7 @@ fn main() -> anyhow::Result<()> {
                 if multiple_files {
                     println!("--- {file:?} ---");
                 }
-                run_ls(file, format.clone(), verbose, &device)?
+                run_ls(file, format.clone(), verbose)?
             }
         }
         Command::Quantize {
@@ -382,8 +400,8 @@ fn main() -> anyhow::Result<()> {
             out_file,
             quantization,
             mode,
-        } => run_quantize(&in_file, out_file, quantization, mode, &device)?,
-        Command::Dequantize { in_file, out_file } => run_dequantize(in_file, out_file, &device)?,
+        } => run_quantize(&in_file, out_file, quantization, mode)?,
+        Command::Dequantize { in_file, out_file } => run_dequantize(in_file, out_file)?,
     }
     Ok(())
 }

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/backprop.rs

@@ -175,7 +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) => {
@@ -589,13 +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 = (*node / arg)?;
-                        let silu_grad = (&sigmoid_arg * (1. + (arg * (1. - &sigmoid_arg)?)?)?)?;
-                        *sum_grad = sum_grad.add(&(&grad * silu_grad)?)?
-                    }
                     Op::Elu(arg, alpha) => {
                         // d/dx elu(x) = 1 for x > 0, alpha * e^x for x <= 0
                         let sum_grad = grads.or_insert(arg)?;

candle-core/src/cuda_backend.rs

@@ -1149,55 +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>(
@@ -1859,15 +1810,12 @@ impl BackendStorage for CudaStorage {
 
     fn conv_transpose1d(
         &self,
-        l: &Layout,
-        kernel: &Self,
-        kernel_l: &Layout,
-        params: &crate::conv::ParamsConvTranspose1D,
+        _: &Layout,
+        _: &Self,
+        _: &Layout,
+        _: &crate::conv::ParamsConvTranspose1D,
     ) -> Result<Self> {
-        let device = self.device().clone();
-        let slice =
-            ConvTranspose1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
-        Ok(Self { slice, device })
+        todo!()
     }
 
     #[cfg(not(feature = "cudnn"))]

candle-core/src/metal_backend.rs

@@ -7,9 +7,8 @@ use candle_metal_kernels::Kernels;
 use metal;
 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, TryLockError};
+use std::sync::{Arc, RwLock, TryLockError};
 
 /// Simple way to catch lock error without
 /// depending on T
@@ -85,8 +84,13 @@ pub struct MetalDevice {
     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)
     compute_per_buffer: usize,
+    /// Every compute command encoder (and blit encoders) are defended with this Fence, forcing the
+    /// execution order to be linear.
+    /// It could be relaxed in some circumstances, by managing ourselves the dependencies in the
+    /// compute graph.
+    fence: metal::Fence,
     /// Simple keeper struct to keep track of the already compiled kernels so we can reuse them.
-    /// Heavily used by [`candle_metal_kernels`]
+    /// Heavily used by [`candle_metal_kernels`], both fences need to match
     kernels: Arc<candle_metal_kernels::Kernels>,
     /// Simple allocator struct.
     /// The buffers are stored in size buckets since ML tends to use similar shapes over and over.
@@ -102,8 +106,6 @@ pub struct MetalDevice {
     /// Whenever we actually allocate a new buffer, we make a full sweep to cleanup unused buffers
     /// (strong_count = 1).
     buffers: AllocatedBuffers,
-    /// Seed for random number generation.
-    seed: Arc<Mutex<Buffer>>,
 }
 
 impl std::fmt::Debug for MetalDevice {
@@ -219,8 +221,10 @@ impl MetalDevice {
         let command_buffer = self.command_buffer()?;
         command_buffer.set_label("with_data");
         let blit = command_buffer.new_blit_command_encoder();
+        blit.wait_for_fence(&self.fence);
         blit.set_label("with_data_blit");
         blit.copy_from_buffer(&tmp, 0, &real, 0, tmp.length());
+        blit.update_fence(&self.fence);
         blit.end_encoding();
 
         // This is necessary, for mmaped safetensors
@@ -228,33 +232,12 @@ impl MetalDevice {
         // The slice might not live long enough for metal
         // To actually fill the GPU buffer.
         // Putting this wait forces the GPU buffer to be filled
-        // with the actual data allowing the CPU storage to do
+        // with the actual data allowing the CPU storage todo
         // deallocate properly.
         self.wait_until_completed()?;
         Ok(real)
     }
 
-    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)
-    }
-
     /// The critical allocator algorithm
     fn allocate_buffer(
         &self,
@@ -325,14 +308,35 @@ impl BackendStorage for MetalStorage {
     }
 
     fn to_cpu_storage(&self) -> Result<CpuStorage> {
+        let length = self.buffer.length() as usize;
+        let size = self.dtype.size_in_bytes();
+        if length % size != 0 {
+            crate::bail!(
+                "The Metal buffer length is not aligned with dtype {:?}",
+                self.dtype
+            );
+        }
+        let buffer = self.device.new_buffer_managed(self.buffer.length())?;
+        {
+            let command_buffer = self.device.command_buffer()?;
+            command_buffer.set_label("to_cpu");
+            let blit = command_buffer.new_blit_command_encoder();
+            blit.set_label("blit_to_cpu");
+            blit.wait_for_fence(&self.device.fence);
+            blit.copy_from_buffer(&self.buffer, 0, &buffer, 0, self.buffer.length());
+            blit.update_fence(&self.device.fence);
+            blit.end_encoding();
+        }
+        self.device.wait_until_completed()?;
+
         match self.dtype {
-            DType::U8 => Ok(CpuStorage::U8(self.to_cpu()?)),
-            DType::U32 => Ok(CpuStorage::U32(self.to_cpu()?)),
-            DType::I64 => Ok(CpuStorage::I64(self.to_cpu()?)),
-            DType::F16 => Ok(CpuStorage::F16(self.to_cpu()?)),
-            DType::BF16 => Ok(CpuStorage::BF16(self.to_cpu()?)),
-            DType::F32 => Ok(CpuStorage::F32(self.to_cpu()?)),
-            DType::F64 => Ok(CpuStorage::F64(self.to_cpu()?)),
+            DType::U8 => Ok(CpuStorage::U8(read_to_vec(&buffer, length / size))),
+            DType::U32 => Ok(CpuStorage::U32(read_to_vec(&buffer, length / size))),
+            DType::I64 => Ok(CpuStorage::I64(read_to_vec(&buffer, length / size))),
+            DType::F16 => Ok(CpuStorage::F16(read_to_vec(&buffer, length / size))),
+            DType::BF16 => Ok(CpuStorage::BF16(read_to_vec(&buffer, length / size))),
+            DType::F32 => Ok(CpuStorage::F32(read_to_vec(&buffer, length / size))),
+            DType::F64 => Ok(CpuStorage::F64(read_to_vec(&buffer, length / size))),
         }
     }
 
@@ -588,7 +592,6 @@ impl BackendStorage for MetalStorage {
                 (DType::U32, DType::F32) => "cast_u32_f32",
                 (DType::U32, DType::U8) => "cast_u32_u8",
                 (DType::U32, DType::I64) => "cast_u32_i64",
-                (DType::U32, DType::F16) => "cast_u32_f16",
                 (DType::U32, DType::BF16) => "cast_u32_bf16",
 
                 (DType::U8, DType::U32) => "cast_u8_u32",
@@ -680,7 +683,6 @@ impl BackendStorage for MetalStorage {
                 ("ugelu", DType::F32) => contiguous::gelu::FLOAT,
                 ("ugelu_erf", DType::F32) => contiguous::gelu_erf::FLOAT,
                 ("uerf", DType::F32) => contiguous::erf::FLOAT,
-                ("usilu", DType::F32) => contiguous::silu::FLOAT,
                 ("uabs", DType::F32) => contiguous::abs::FLOAT,
                 ("uceil", DType::F32) => contiguous::ceil::FLOAT,
                 ("ufloor", DType::F32) => contiguous::floor::FLOAT,
@@ -698,7 +700,6 @@ impl BackendStorage for MetalStorage {
                 ("ugelu", DType::F16) => contiguous::gelu::HALF,
                 ("ugelu_erf", DType::F16) => contiguous::gelu_erf::HALF,
                 ("uerf", DType::F16) => contiguous::erf::HALF,
-                ("usilu", DType::F16) => contiguous::silu::HALF,
                 ("uabs", DType::F16) => contiguous::abs::HALF,
                 ("uceil", DType::F16) => contiguous::ceil::HALF,
                 ("ufloor", DType::F16) => contiguous::floor::HALF,
@@ -733,7 +734,6 @@ impl BackendStorage for MetalStorage {
                 ("ugelu", DType::F32) => strided::gelu::FLOAT,
                 ("ugelu_erf", DType::F32) => strided::gelu_erf::FLOAT,
                 ("uerf", DType::F32) => strided::erf::FLOAT,
-                ("usilu", DType::F32) => strided::silu::FLOAT,
                 ("uabs", DType::F32) => strided::abs::FLOAT,
                 ("uceil", DType::F32) => strided::ceil::FLOAT,
                 ("ufloor", DType::F32) => strided::floor::FLOAT,
@@ -749,7 +749,6 @@ impl BackendStorage for MetalStorage {
                 ("ugelu", DType::F16) => strided::gelu::HALF,
                 ("ugelu_erf", DType::F16) => strided::gelu_erf::HALF,
                 ("uerf", DType::F16) => strided::erf::HALF,
-                ("usilu", DType::F16) => strided::silu::HALF,
                 ("uabs", DType::F16) => strided::abs::HALF,
                 ("uceil", DType::F16) => strided::ceil::HALF,
                 ("ufloor", DType::F16) => strided::floor::HALF,
@@ -1265,7 +1264,7 @@ impl BackendStorage for MetalStorage {
             let src_offset = (src_l.start_offset() * self.dtype.size_in_bytes()) as NSUInteger;
             let length = (src_l.shape().elem_count() * self.dtype.size_in_bytes()) as NSUInteger;
             let dst_offset = (dst_offset * dst.dtype().size_in_bytes()) as NSUInteger;
-            blit.copy_from_buffer(&self.buffer, src_offset, &dst.buffer(), dst_offset, length);
+            blit.copy_from_buffer(&self.buffer, src_offset, dst.buffer(), dst_offset, length);
             blit.end_encoding();
         } else {
             let src_shape = src_l.shape();
@@ -1522,28 +1521,6 @@ impl MetalStorage {
         command_buffer.set_label("binary");
         Ok(Self::new(buffer, device.clone(), dtype))
     }
-
-    pub(crate) fn to_cpu<T: Clone>(&self) -> Result<Vec<T>> {
-        let length = self.buffer.length() as usize;
-        let size = self.dtype.size_in_bytes();
-        if length % size != 0 {
-            crate::bail!(
-                "The Metal buffer length is not aligned with dtype {:?}",
-                self.dtype
-            );
-        }
-        let buffer = self.device.new_buffer_managed(self.buffer.length())?;
-        {
-            let command_buffer = self.device.command_buffer()?;
-            command_buffer.set_label("to_cpu");
-            let blit = command_buffer.new_blit_command_encoder();
-            blit.set_label("blit_to_cpu");
-            blit.copy_from_buffer(&self.buffer, 0, &buffer, 0, self.buffer.length());
-            blit.end_encoding();
-        }
-        self.device.wait_until_completed()?;
-        Ok(read_to_vec(&buffer, length / size))
-    }
 }
 
 impl BackendDevice for MetalDevice {
@@ -1556,29 +1533,29 @@ impl BackendDevice for MetalDevice {
         command_buffer.enqueue();
         let command_buffer = Arc::new(RwLock::new(command_buffer));
         let command_buffer_index = Arc::new(RwLock::new(0));
-        let kernels = Arc::new(Kernels::new());
+        let fence = device.new_fence();
+        let kernels = Arc::new(Kernels::new(fence.clone()));
         let buffers = Arc::new(RwLock::new(HashMap::new()));
         let compute_per_buffer = match std::env::var("CANDLE_METAL_COMPUTE_PER_BUFFER") {
             Ok(val) => val.parse()?,
-            _ => 10,
+            _ => 20,
         };
-        let seed = Arc::new(Mutex::new(device.new_buffer_with_data(
-            [299792458].as_ptr() as *const c_void,
-            4,
-            MTLResourceOptions::StorageModeManaged,
-        )));
         Ok(Self {
             device,
+            fence,
             command_queue,
             command_buffer,
             command_buffer_index,
             compute_per_buffer,
             buffers,
             kernels,
-            seed,
         })
     }
 
+    fn set_seed(&self, _seed: u64) -> Result<()> {
+        crate::bail!("Metal set_seed not implemented")
+    }
+
     fn location(&self) -> crate::DeviceLocation {
         crate::DeviceLocation::Metal {
             gpu_id: self.registry_id() as usize,
@@ -1590,8 +1567,21 @@ impl BackendDevice for MetalDevice {
     }
 
     fn zeros_impl(&self, shape: &Shape, dtype: DType) -> Result<MetalStorage> {
-        let size = shape.elem_count() * dtype.size_in_bytes();
-        let buffer = self.allocate_zeros(size)?;
+        let buffer = self.new_buffer(shape.elem_count(), dtype, "zeros")?;
+        let command_buffer = self.command_buffer()?;
+        command_buffer.set_label("zeros");
+        let blit = command_buffer.new_blit_command_encoder();
+        blit.wait_for_fence(&self.fence);
+        blit.fill_buffer(
+            &buffer,
+            metal::NSRange {
+                location: 0,
+                length: buffer.length(),
+            },
+            0,
+        );
+        blit.update_fence(&self.fence);
+        blit.end_encoding();
         Ok(MetalStorage::new(buffer, self.clone(), dtype))
     }
 
@@ -1618,31 +1608,12 @@ impl BackendDevice for MetalDevice {
         &self,
         shape: &Shape,
         dtype: DType,
-        min: f64,
-        max: f64,
+        mean: f64,
+        stddev: f64,
     ) -> Result<Self::Storage> {
-        let name = match dtype {
-            DType::F32 => "rand_uniform_f32",
-            DType::F16 => "rand_uniform_f16",
-            DType::BF16 => "rand_uniform_bf16",
-            dtype => crate::bail!("rand_uniform not implemented for {dtype:?}"),
-        };
-        let buffer = self.new_buffer(shape.elem_count(), dtype, "rand_uniform")?;
-        let command_buffer = self.command_buffer()?;
-        candle_metal_kernels::call_random_uniform(
-            &self.device,
-            &command_buffer,
-            &self.kernels,
-            name,
-            min as f32,
-            max as f32,
-            shape.elem_count(),
-            &*self.seed.lock().unwrap(),
-            &buffer,
-        )
-        .map_err(MetalError::from)?;
-
-        Ok(Self::Storage::new(buffer, self.clone(), dtype))
+        // TODO is there a better way ?
+        let cpu_storage = crate::cpu_backend::CpuDevice.rand_uniform(shape, dtype, mean, stddev)?;
+        self.storage_from_cpu_storage(&cpu_storage)
     }
 
     fn rand_normal(
@@ -1652,43 +1623,9 @@ impl BackendDevice for MetalDevice {
         mean: f64,
         stddev: f64,
     ) -> Result<Self::Storage> {
-        let name = match dtype {
-            DType::F32 => "rand_normal_f32",
-            DType::F16 => "rand_normal_f16",
-            DType::BF16 => "rand_normal_bf16",
-            dtype => crate::bail!("rand_uniform not implemented for {dtype:?}"),
-        };
-        let buffer = self.new_buffer(shape.elem_count(), dtype, "rand_normal")?;
-        let command_buffer = self.command_buffer()?;
-        candle_metal_kernels::call_random_normal(
-            &self.device,
-            &command_buffer,
-            &self.kernels,
-            name,
-            mean as f32,
-            stddev as f32,
-            shape.elem_count(),
-            &*self.seed.lock().unwrap(),
-            &buffer,
-        )
-        .map_err(MetalError::from)?;
-
-        Ok(Self::Storage::new(buffer, self.clone(), dtype))
-    }
-
-    fn set_seed(&self, seed: u64) -> Result<()> {
-        let seed: u32 = seed.try_into().map_err(|_| {
-            MetalError::Message("Metal seed must be less than or equal to u32::MAX".to_string())
-        })?;
-
-        let seed_buffer = self.seed.try_lock().map_err(MetalError::from)?;
-        let contents = seed_buffer.contents();
-        unsafe {
-            std::ptr::copy([seed].as_ptr(), contents as *mut u32, 4);
-        }
-        seed_buffer.did_modify_range(metal::NSRange::new(0, 4));
-
-        Ok(())
+        // TODO is there a better way ?
+        let cpu_storage = crate::cpu_backend::CpuDevice.rand_normal(shape, dtype, mean, stddev)?;
+        self.storage_from_cpu_storage(&cpu_storage)
     }
 }
 

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

@@ -61,7 +61,6 @@ pub enum UnaryOp {
     GeluErf,
     Erf,
     Relu,
-    Silu,
     Tanh,
     Floor,
     Ceil,
@@ -391,7 +390,6 @@ 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;
@@ -726,77 +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";

candle-core/src/pickle.rs

@@ -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"
-                    && (class_name == "OrderedDict" || class_name == "defaultdict")
-                {
-                    // TODO: have a separate ordered dict and a separate default dict.
+                if module_name == "collections" && class_name == "OrderedDict" {
+                    // TODO: have a separate ordered dict.
                     Some(Object::Dict(vec![]))
                 } else {
                     None
@@ -634,16 +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);
@@ -665,9 +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 {
@@ -681,24 +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) {
@@ -721,8 +688,8 @@ pub struct PthTensors {
 }
 
 impl PthTensors {
-    pub fn new<P: AsRef<std::path::Path>>(path: P, key: Option<&str>) -> Result<Self> {
-        let tensor_infos = read_pth_tensor_info(path.as_ref(), false, key)?;
+    pub fn new<P: AsRef<std::path::Path>>(path: P) -> Result<Self> {
+        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))
@@ -745,12 +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.
-        if !tensor_info.layout.is_contiguous() && !is_fortran_contiguous {
+        // case.
+        if !tensor_info.layout.is_contiguous() {
             crate::bail!(
                 "cannot retrieve non-contiguous tensors {:?}",
                 tensor_info.layout
@@ -768,33 +733,13 @@ impl PthTensors {
             tensor_info.dtype,
             &mut reader,
         )?;
-
-        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))
-        }
+        Ok(Some(tensor))
     }
 }
 
-/// Read all the tensors from a PyTorch pth file with a given key.
-///
-/// # Arguments
-/// * `path` - Path to the pth file.
-/// * `key` - Optional key to retrieve `state_dict` from the pth file. Sometimes the pth file
-///           contains multiple objects and the state_dict is the one we are interested in.
-pub fn read_all_with_key<P: AsRef<std::path::Path>>(
-    path: P,
-    key: Option<&str>,
-) -> Result<Vec<(String, Tensor)>> {
-    let pth = PthTensors::new(path, key)?;
+/// Read all the tensors from a PyTorch pth file.
+pub fn read_all<P: AsRef<std::path::Path>>(path: P) -> Result<Vec<(String, Tensor)>> {
+    let pth = PthTensors::new(path)?;
     let tensor_names = pth.tensor_infos.keys();
     let mut tensors = Vec::with_capacity(tensor_names.len());
     for name in tensor_names {
@@ -804,11 +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/dummy_metal.rs

@@ -1,43 +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)
-    }
-}

candle-core/src/quantized/ggml_file.rs

@@ -1,9 +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 super::{k_quants, GgmlDType};
+use crate::Result;
 use byteorder::{LittleEndian, ReadBytesExt};
 use std::collections::HashMap;
 
@@ -123,22 +121,11 @@ fn from_raw_data<T: super::GgmlType + Send + Sync + 'static>(
     raw_data: &[u8],
     size_in_bytes: usize,
     dims: Vec<usize>,
-    device: &Device,
 ) -> Result<super::QTensor> {
     let raw_data_ptr = raw_data.as_ptr();
     let n_blocks = size_in_bytes / std::mem::size_of::<T>();
     let data = unsafe { std::slice::from_raw_parts(raw_data_ptr as *const T, n_blocks) };
-    let data: QStorage = match device {
-        Device::Cpu => QStorage::Cpu(Box::new(data.to_vec())),
-        #[cfg(feature = "metal")]
-        Device::Metal(metal) => load_quantized_metal(metal, data)?,
-        #[cfg(not(feature = "metal"))]
-        Device::Metal(_metal) => {
-            crate::bail!("Metal backend requires `metal` feature")
-        }
-        device => unimplemented!("Implement quantized tensor for device {device:?}"),
-    };
-    super::QTensor::new(data, dims)
+    super::QTensor::new(data.to_vec(), dims)
 }
 
 /// Creates a [Tensor] from a raw GGML tensor.
@@ -146,50 +133,29 @@ pub fn qtensor_from_ggml(
     ggml_dtype: GgmlDType,
     raw_data: &[u8],
     dims: Vec<usize>,
-    device: &Device,
 ) -> Result<super::QTensor> {
     let tensor_elems = dims.iter().product::<usize>();
-    let block_size = ggml_dtype.block_size();
-    if tensor_elems % block_size != 0 {
+    let blck_size = ggml_dtype.blck_size();
+    if tensor_elems % blck_size != 0 {
         crate::bail!(
-            "the number of elements {tensor_elems} is not divisible by the block size {block_size}"
+            "the number of elements {tensor_elems} is not divisible by the block size {blck_size}"
         )
     }
-    let size_in_bytes = tensor_elems / block_size * ggml_dtype.type_size();
+    let size_in_bytes = tensor_elems / blck_size * ggml_dtype.type_size();
 
     match ggml_dtype {
-        GgmlDType::F32 => from_raw_data::<f32>(raw_data, size_in_bytes, dims, device),
-        GgmlDType::F16 => from_raw_data::<half::f16>(raw_data, size_in_bytes, dims, device),
-        GgmlDType::Q4_0 => {
-            from_raw_data::<k_quants::BlockQ4_0>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q4_1 => {
-            from_raw_data::<k_quants::BlockQ4_1>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q5_0 => {
-            from_raw_data::<k_quants::BlockQ5_0>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q5_1 => {
-            from_raw_data::<k_quants::BlockQ5_1>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q8_0 => {
-            from_raw_data::<k_quants::BlockQ8_0>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q2K => {
-            from_raw_data::<k_quants::BlockQ2K>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q3K => {
-            from_raw_data::<k_quants::BlockQ3K>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q4K => {
-            from_raw_data::<k_quants::BlockQ4K>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q5K => {
-            from_raw_data::<k_quants::BlockQ5K>(raw_data, size_in_bytes, dims, device)
-        }
-        GgmlDType::Q6K => {
-            from_raw_data::<k_quants::BlockQ6K>(raw_data, size_in_bytes, dims, device)
-        }
+        GgmlDType::F32 => from_raw_data::<f32>(raw_data, size_in_bytes, dims),
+        GgmlDType::F16 => from_raw_data::<half::f16>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q4_0 => from_raw_data::<k_quants::BlockQ4_0>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q4_1 => from_raw_data::<k_quants::BlockQ4_1>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q5_0 => from_raw_data::<k_quants::BlockQ5_0>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q5_1 => from_raw_data::<k_quants::BlockQ5_1>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q8_0 => from_raw_data::<k_quants::BlockQ8_0>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q2K => from_raw_data::<k_quants::BlockQ2K>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q3K => from_raw_data::<k_quants::BlockQ3K>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q4K => from_raw_data::<k_quants::BlockQ4K>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q5K => from_raw_data::<k_quants::BlockQ5K>(raw_data, size_in_bytes, dims),
+        GgmlDType::Q6K => from_raw_data::<k_quants::BlockQ6K>(raw_data, size_in_bytes, dims),
         _ => crate::bail!("quantized type {ggml_dtype:?} is not supported yet"),
     }
 }
@@ -197,7 +163,6 @@ pub fn qtensor_from_ggml(
 fn read_one_tensor<R: std::io::Seek + std::io::Read>(
     reader: &mut R,
     magic: VersionedMagic,
-    device: &Device,
 ) -> Result<(String, super::QTensor)> {
     let n_dims = reader.read_u32::<LittleEndian>()?;
     let name_len = reader.read_u32::<LittleEndian>()?;
@@ -218,11 +183,11 @@ fn read_one_tensor<R: std::io::Seek + std::io::Read>(
     }
     let dims = dims.iter().map(|&u| u as usize).collect::<Vec<_>>();
     let tensor_elems = dims.iter().product::<usize>();
-    let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.block_size();
+    let size_in_bytes = tensor_elems * ggml_dtype.type_size() / ggml_dtype.blck_size();
     // TODO: Mmap version to avoid copying the data around?
     let mut raw_data = vec![0u8; size_in_bytes];
     reader.read_exact(&mut raw_data)?;
-    match qtensor_from_ggml(ggml_dtype, &raw_data, dims, device) {
+    match qtensor_from_ggml(ggml_dtype, &raw_data, dims) {
         Ok(tensor) => Ok((name, tensor)),
         Err(e) => crate::bail!("Error creating tensor {name}: {e}"),
     }
@@ -233,14 +198,10 @@ pub struct Content {
     pub hparams: HParams,
     pub vocab: Vocab,
     pub tensors: HashMap<String, super::QTensor>,
-    pub device: Device,
 }
 
 impl Content {
-    pub fn read<R: std::io::Seek + std::io::Read>(
-        reader: &mut R,
-        device: &Device,
-    ) -> Result<Content> {
+    pub fn read<R: std::io::Seek + std::io::Read>(reader: &mut R) -> Result<Content> {
         // https://github.com/ggerganov/llama.cpp/blob/468ea24fb4633a0d681f7ac84089566c1c6190cb/llama.cpp#L505
         let last_position = reader.seek(std::io::SeekFrom::End(0))?;
         reader.seek(std::io::SeekFrom::Start(0))?;
@@ -250,16 +211,14 @@ impl Content {
         let mut tensors = HashMap::new();
 
         while reader.stream_position()? != last_position {
-            let (name, tensor) = read_one_tensor(reader, magic, device)?;
+            let (name, tensor) = read_one_tensor(reader, magic)?;
             tensors.insert(name, tensor);
         }
-        let device = device.clone();
         Ok(Self {
             magic,
             hparams,
             vocab,
             tensors,
-            device,
         })
     }
 

candle-core/src/quantized/gguf_file.rs

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

candle-core/src/quantized/metal.rs

@@ -1,234 +0,0 @@
-use super::{GgmlDType, QStorage};
-use crate::backend::BackendStorage;
-use crate::{DType, MetalDevice, MetalStorage, Result, Shape};
-use metal::Buffer;
-use std::sync::Arc;
-
-pub struct QMetalStorage {
-    dtype: GgmlDType,
-    device: MetalDevice,
-    buffer: Arc<Buffer>,
-}
-
-impl QMetalStorage {
-    pub fn zeros(device: &MetalDevice, elem_count: usize, dtype: GgmlDType) -> Result<Self> {
-        let size = elem_count * dtype.type_size() / dtype.block_size();
-        let buffer = device.allocate_zeros(size)?;
-        Ok(Self {
-            buffer,
-            device: device.clone(),
-            dtype,
-        })
-    }
-
-    pub fn dtype(&self) -> GgmlDType {
-        self.dtype
-    }
-
-    pub fn device(&self) -> &MetalDevice {
-        &self.device
-    }
-
-    pub fn buffer(&self) -> &Buffer {
-        &self.buffer
-    }
-
-    pub fn dequantize(&self, elem_count: usize) -> Result<MetalStorage> {
-        let buffer = self.device.new_buffer_managed(self.buffer.length())?;
-        let command_buffer = self.device.command_buffer()?;
-        command_buffer.set_label("to_cpu");
-        let blit = command_buffer.new_blit_command_encoder();
-        blit.set_label("blit_to_cpu");
-        blit.copy_from_buffer(&self.buffer, 0, &buffer, 0, self.buffer.length());
-        blit.end_encoding();
-        self.device.wait_until_completed()?;
-        let mut out = vec![0.0; elem_count];
-        match self.dtype {
-            GgmlDType::F32 => {
-                let vec: Vec<f32> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                f32::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::F16 => {
-                let vec: Vec<half::f16> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                half::f16::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q4_0 => {
-                let vec: Vec<crate::quantized::BlockQ4_0> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ4_0::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q4_1 => {
-                let vec: Vec<crate::quantized::BlockQ4_1> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ4_1::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q5_0 => {
-                let vec: Vec<crate::quantized::BlockQ5_0> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ5_0::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q5_1 => {
-                let vec: Vec<crate::quantized::BlockQ5_1> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ5_1::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q8_0 => {
-                let vec: Vec<crate::quantized::BlockQ8_0> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ8_0::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q8_1 => {
-                let vec: Vec<crate::quantized::BlockQ8_1> = read_to_vec(&buffer, elem_count);
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ8_1::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q2K => {
-                let vec: Vec<crate::quantized::BlockQ2K> =
-                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ2K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q3K => {
-                let vec: Vec<crate::quantized::BlockQ3K> =
-                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ3K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q4K => {
-                let vec: Vec<crate::quantized::BlockQ4K> =
-                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ4K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q5K => {
-                let vec: Vec<crate::quantized::BlockQ5K> =
-                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ5K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q6K => {
-                let vec: Vec<crate::quantized::BlockQ6K> =
-                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ6K::to_float(&vec, &mut out)?;
-            }
-            GgmlDType::Q8K => {
-                let vec: Vec<crate::quantized::BlockQ8K> =
-                    read_to_vec(&buffer, elem_count / self.dtype.block_size());
-                use crate::quantized::k_quants::GgmlType;
-                crate::quantized::BlockQ8K::to_float(&vec, &mut out)?;
-            }
-        }
-
-        let buffer = self.device.new_buffer_with_data(&out)?;
-        Ok(MetalStorage::new(buffer, self.device.clone(), DType::F32))
-    }
-
-    pub fn quantize(&mut self, src: &MetalStorage) -> Result<()> {
-        // Quantization only happens on CPU for now.
-        let src = src.to_cpu::<f32>()?;
-        let elem_count = src.len();
-        let src = crate::Storage::Cpu(crate::CpuStorage::F32(src));
-        let mut qcpu_storage = crate::Device::Cpu.qzeros(elem_count, self.dtype)?;
-        qcpu_storage.quantize(&src)?;
-        let buffer = self.device.new_buffer_with_data(&qcpu_storage.data()?)?;
-        self.buffer = buffer;
-        Ok(())
-    }
-
-    pub fn storage_size_in_bytes(&self) -> usize {
-        self.buffer.length() as usize
-    }
-
-    pub fn fwd(
-        &self,
-        self_shape: &Shape,
-        storage: &MetalStorage,
-        layout: &crate::Layout,
-    ) -> Result<(MetalStorage, Shape)> {
-        use crate::MetalError;
-
-        if !layout.is_contiguous() {
-            crate::bail!("input tensor is not contiguous {layout:?}")
-        }
-        let src_shape = layout.shape();
-        // self is transposed so n is first then k.
-        if src_shape.rank() < 2 {
-            crate::bail!("input tensor has only one dimension {layout:?}")
-        }
-        let (n, k) = self_shape.dims2()?;
-        let mut dst_shape = src_shape.dims().to_vec();
-
-        let (b, m) = match dst_shape.len() {
-            3 => (dst_shape[0], dst_shape[1]),
-            2 => (1, dst_shape[0]),
-            n => crate::bail!("Invalid rank {n} for quantized matmul metal"),
-        };
-        let last_k = dst_shape.pop().unwrap();
-        if last_k != k {
-            crate::bail!("input tensor {layout:?} incompatible with {:?}", self_shape)
-        }
-        dst_shape.push(n);
-        let dst_shape = Shape::from(dst_shape);
-        let device = storage.device().clone();
-        let dst = device.new_buffer(dst_shape.elem_count(), DType::F32, "qmatmul")?;
-        let command_buffer = device.command_buffer()?;
-        candle_metal_kernels::call_quantized_matmul_t(
-            device.device(),
-            &command_buffer,
-            device.kernels(),
-            self.dtype.into(),
-            (b, m, n, k),
-            storage.buffer(),
-            layout.start_offset() * storage.dtype().size_in_bytes(),
-            &self.buffer,
-            &dst,
-        )
-        .map_err(MetalError::from)?;
-        let dst_storage = crate::MetalStorage::new(dst, device, DType::F32);
-        Ok((dst_storage, dst_shape))
-    }
-}
-
-pub fn load_quantized_metal<T: super::GgmlType + Send + Sync + 'static>(
-    device: &MetalDevice,
-    data: &[T],
-) -> Result<QStorage> {
-    let buffer = device.new_buffer_with_data(data)?;
-    let device = device.clone();
-    Ok(QStorage::Metal(QMetalStorage {
-        dtype: T::DTYPE,
-        device,
-        buffer,
-    }))
-}
-
-fn read_to_vec<T: Clone>(buffer: &Buffer, n: usize) -> Vec<T> {
-    let ptr = buffer.contents() as *const T;
-    assert!(!ptr.is_null());
-    let slice = unsafe { std::slice::from_raw_parts(ptr, n) };
-    slice.to_vec()
-}
-
-impl From<GgmlDType> for candle_metal_kernels::GgmlDType {
-    fn from(value: GgmlDType) -> Self {
-        match value {
-            GgmlDType::Q4_0 => candle_metal_kernels::GgmlDType::Q4_0,
-            GgmlDType::Q4_1 => candle_metal_kernels::GgmlDType::Q4_1,
-            GgmlDType::Q5_0 => candle_metal_kernels::GgmlDType::Q5_0,
-            GgmlDType::Q5_1 => candle_metal_kernels::GgmlDType::Q5_1,
-            GgmlDType::Q8_0 => candle_metal_kernels::GgmlDType::Q8_0,
-            GgmlDType::Q8_1 => candle_metal_kernels::GgmlDType::Q8_1,
-            GgmlDType::Q2K => candle_metal_kernels::GgmlDType::Q2K,
-            GgmlDType::Q3K => candle_metal_kernels::GgmlDType::Q3K,
-            GgmlDType::Q4K => candle_metal_kernels::GgmlDType::Q4K,
-            GgmlDType::Q5K => candle_metal_kernels::GgmlDType::Q5K,
-            GgmlDType::Q6K => candle_metal_kernels::GgmlDType::Q6K,
-            GgmlDType::Q8K => candle_metal_kernels::GgmlDType::Q8K,
-            GgmlDType::F16 => candle_metal_kernels::GgmlDType::F16,
-            GgmlDType::F32 => candle_metal_kernels::GgmlDType::F32,
-        }
-    }
-}

candle-core/src/quantized/mod.rs

@@ -1,118 +1,23 @@
-use crate::{CpuStorage, Device, Result, Shape, Storage, Tensor};
-use k_quants::*;
-use std::borrow::Cow;
+use crate::{Device, Result, Shape, Tensor};
 
 #[cfg(target_feature = "avx")]
 pub mod avx;
-mod dummy_metal;
 pub mod ggml_file;
 pub mod gguf_file;
 pub mod k_quants;
-#[cfg(feature = "metal")]
-pub mod metal;
-#[cfg(not(feature = "metal"))]
-mod metal {
-    pub use super::dummy_metal::*;
-}
 #[cfg(target_feature = "neon")]
 pub mod neon;
 #[cfg(target_feature = "simd128")]
 pub mod simd128;
 pub mod utils;
-use half::f16;
 
 pub use k_quants::GgmlType;
 
 pub struct QTensor {
-    storage: QStorage,
+    data: Box<dyn QuantizedType>,
     shape: Shape,
 }
 
-impl Device {
-    fn qzeros(&self, elem_count: usize, dtype: GgmlDType) -> Result<QStorage> {
-        match self {
-            Device::Cpu => {
-                let storage = dtype.cpu_zeros(elem_count);
-                Ok(QStorage::Cpu(storage))
-            }
-            Device::Metal(metal) => {
-                let storage = metal::QMetalStorage::zeros(metal, elem_count, dtype)?;
-                Ok(QStorage::Metal(storage))
-            }
-            Device::Cuda(_cuda) => {
-                crate::bail!("Cuda ggml quantization not supported");
-            }
-        }
-    }
-}
-
-pub enum QStorage {
-    Cpu(Box<dyn QuantizedType>),
-    Metal(metal::QMetalStorage),
-}
-
-impl QStorage {
-    fn block_size(&self) -> usize {
-        match self {
-            QStorage::Cpu(storage) => storage.block_size(),
-            QStorage::Metal(storage) => storage.dtype().block_size(),
-        }
-    }
-
-    fn dtype(&self) -> GgmlDType {
-        match self {
-            QStorage::Cpu(storage) => storage.dtype(),
-            QStorage::Metal(storage) => storage.dtype(),
-        }
-    }
-
-    fn device(&self) -> Device {
-        match self {
-            QStorage::Cpu(_storage) => Device::Cpu,
-            QStorage::Metal(storage) => Device::Metal(storage.device().clone()),
-        }
-    }
-
-    fn size_in_bytes(&self) -> usize {
-        match self {
-            QStorage::Cpu(storage) => storage.storage_size_in_bytes(),
-            QStorage::Metal(storage) => storage.storage_size_in_bytes(),
-        }
-    }
-
-    fn quantize(&mut self, src: &Storage) -> Result<()> {
-        match (self, src) {
-            (QStorage::Cpu(storage), Storage::Cpu(src)) => {
-                storage.from_float(src.as_slice::<f32>()?)?;
-            }
-            (QStorage::Metal(storage), Storage::Metal(src)) => storage.quantize(src)?,
-            _ => crate::bail!("Invalid dequantize storage locations do not match"),
-        }
-        Ok(())
-    }
-
-    fn dequantize(&self, elem_count: usize) -> Result<Storage> {
-        match self {
-            QStorage::Cpu(storage) => Ok(Storage::Cpu(storage.dequantize(elem_count)?)),
-            QStorage::Metal(storage) => Ok(Storage::Metal(storage.dequantize(elem_count)?)),
-        }
-    }
-
-    fn data(&self) -> Result<Cow<[u8]>> {
-        match self {
-            QStorage::Cpu(storage) => {
-                let data_ptr = storage.as_ptr();
-                let size_in_bytes = storage.storage_size_in_bytes();
-                let data = unsafe { std::slice::from_raw_parts(data_ptr, size_in_bytes) };
-                Ok(Cow::from(data))
-            }
-            QStorage::Metal(_storage) => {
-                crate::bail!("not implemented");
-            }
-        }
-    }
-}
-
 #[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
 pub enum GgmlDType {
     F32,
@@ -172,25 +77,6 @@ impl GgmlDType {
         }
     }
 
-    /// The block dtype
-    pub fn cpu_zeros(&self, elem_count: usize) -> Box<dyn QuantizedType> {
-        match self {
-            Self::F32 => Box::new(vec![f32::zeros(); elem_count]),
-            Self::F16 => Box::new(vec![f16::zeros(); elem_count]),
-            Self::Q4_0 => Box::new(vec![BlockQ4_0::zeros(); elem_count / BlockQ4_0::BLCK_SIZE]),
-            Self::Q4_1 => Box::new(vec![BlockQ4_1::zeros(); elem_count / BlockQ4_1::BLCK_SIZE]),
-            Self::Q5_0 => Box::new(vec![BlockQ5_0::zeros(); elem_count / BlockQ5_0::BLCK_SIZE]),
-            Self::Q5_1 => Box::new(vec![BlockQ5_1::zeros(); elem_count / BlockQ5_1::BLCK_SIZE]),
-            Self::Q8_0 => Box::new(vec![BlockQ8_0::zeros(); elem_count / BlockQ8_0::BLCK_SIZE]),
-            Self::Q8_1 => Box::new(vec![BlockQ8_1::zeros(); elem_count / BlockQ8_1::BLCK_SIZE]),
-            Self::Q2K => Box::new(vec![BlockQ2K::zeros(); elem_count / BlockQ2K::BLCK_SIZE]),
-            Self::Q3K => Box::new(vec![BlockQ3K::zeros(); elem_count / BlockQ3K::BLCK_SIZE]),
-            Self::Q4K => Box::new(vec![BlockQ4K::zeros(); elem_count / BlockQ4K::BLCK_SIZE]),
-            Self::Q5K => Box::new(vec![BlockQ5K::zeros(); elem_count / BlockQ5K::BLCK_SIZE]),
-            Self::Q6K => Box::new(vec![BlockQ6K::zeros(); elem_count / BlockQ6K::BLCK_SIZE]),
-            Self::Q8K => Box::new(vec![BlockQ8K::zeros(); elem_count / BlockQ8K::BLCK_SIZE]),
-        }
-    }
     /// The type size for blocks in bytes.
     pub fn type_size(&self) -> usize {
         use k_quants::*;
@@ -214,7 +100,7 @@ impl GgmlDType {
     }
 
     /// The block size, i.e. the number of elements stored in each block.
-    pub fn block_size(&self) -> usize {
+    pub fn blck_size(&self) -> usize {
         match self {
             Self::F32 => 1,
             Self::F16 => 1,
@@ -233,13 +119,9 @@ impl GgmlDType {
 pub trait QuantizedType: Send + Sync {
     fn dtype(&self) -> GgmlDType;
     fn matmul_t(&self, mkn: (usize, usize, usize), lhs: &[f32], dst: &mut [f32]) -> Result<()>;
-    fn dequantize(&self, elem_count: usize) -> Result<CpuStorage>;
+    fn to_float(&self, ys: &mut [f32]) -> Result<()>;
     fn storage_size_in_bytes(&self) -> usize;
     fn as_ptr(&self) -> *const u8;
-    fn block_size(&self) -> usize;
-    #[allow(clippy::wrong_self_convention)]
-    fn from_float(&mut self, xs: &[f32]) -> Result<()>;
-    fn size(&self) -> usize;
 }
 
 impl<T: k_quants::GgmlType + Send + Sync> QuantizedType for Vec<T> {
@@ -247,26 +129,12 @@ impl<T: k_quants::GgmlType + Send + Sync> QuantizedType for Vec<T> {
         k_quants::matmul(mkn, lhs, self.as_slice(), dst)
     }
 
-    fn size(&self) -> usize {
-        self.len() * core::mem::size_of::<T>()
-    }
-
-    fn from_float(&mut self, xs: &[f32]) -> Result<()> {
-        T::from_float(xs, self)
-    }
-
     fn dtype(&self) -> GgmlDType {
         T::DTYPE
     }
 
-    fn block_size(&self) -> usize {
-        T::BLCK_SIZE
-    }
-
-    fn dequantize(&self, elem_count: usize) -> Result<CpuStorage> {
-        let mut ys = vec![0.0f32; elem_count];
-        T::to_float(self.as_slice(), &mut ys)?;
-        Ok(CpuStorage::F32(ys))
+    fn to_float(&self, ys: &mut [f32]) -> Result<()> {
+        T::to_float(self.as_slice(), ys)
     }
 
     fn storage_size_in_bytes(&self) -> usize {
@@ -284,53 +152,56 @@ impl std::fmt::Debug for QTensor {
     }
 }
 
-fn check_shape(shape: &Shape, block_size: usize) -> Result<()> {
+fn check_shape<T: k_quants::GgmlType>(shape: &Shape) -> Result<()> {
     let dims = shape.dims();
     if dims.is_empty() {
         crate::bail!("scalar tensor cannot be quantized {shape:?}")
     }
-    if dims[dims.len() - 1] % block_size != 0 {
+    if dims[dims.len() - 1] % T::BLCK_SIZE != 0 {
         crate::bail!(
             "quantized tensor must have their last dim divisible by block size {shape:?} {}",
-            block_size
+            T::BLCK_SIZE
         )
     }
     Ok(())
 }
 
 impl QTensor {
-    pub fn new<S: Into<Shape>>(storage: QStorage, shape: S) -> Result<Self> {
+    pub fn new<S: Into<Shape>, T: k_quants::GgmlType + Send + Sync + 'static>(
+        data: Vec<T>,
+        shape: S,
+    ) -> Result<Self> {
         let shape = shape.into();
-        check_shape(&shape, storage.block_size())?;
-        Ok(Self { storage, shape })
+        check_shape::<T>(&shape)?;
+        Ok(Self {
+            data: Box::new(data),
+            shape,
+        })
     }
 
-    pub fn quantize(src: &Tensor, dtype: GgmlDType) -> Result<Self> {
+    pub fn quantize<T: k_quants::GgmlType + Send + Sync + 'static>(src: &Tensor) -> Result<Self> {
         let shape = src.shape();
-        let block_size = dtype.block_size();
-        check_shape(shape, block_size)?;
-        let src = src.to_dtype(crate::DType::F32)?.flatten_all()?;
-        let elem_count = shape.elem_count();
-        if elem_count % block_size != 0 {
+        check_shape::<T>(shape)?;
+        let src = src
+            .to_dtype(crate::DType::F32)?
+            .flatten_all()?
+            .to_vec1::<f32>()?;
+        if src.len() % T::BLCK_SIZE != 0 {
             crate::bail!(
                 "tensor size ({shape:?}) is not divisible by block size {}",
-                block_size
+                T::BLCK_SIZE
             )
         }
-        let mut storage = src.device().qzeros(elem_count, dtype)?;
-        storage.quantize(&src.storage())?;
+        let mut data = vec![T::zeros(); src.len() / T::BLCK_SIZE];
+        T::from_float(&src, &mut data)?;
         Ok(Self {
-            storage,
+            data: Box::new(data),
             shape: shape.clone(),
         })
     }
 
     pub fn dtype(&self) -> GgmlDType {
-        self.storage.dtype()
-    }
-
-    pub fn device(&self) -> Device {
-        self.storage.device()
+        self.data.dtype()
     }
 
     pub fn rank(&self) -> usize {
@@ -342,19 +213,21 @@ impl QTensor {
     }
 
     pub fn dequantize(&self, device: &Device) -> Result<Tensor> {
-        let storage = self.storage.dequantize(self.shape.elem_count())?;
-        let none = crate::op::BackpropOp::none();
-        let is_variable = false;
-        crate::tensor::from_storage(storage, self.shape.clone(), none, is_variable)
-            .to_device(device)
+        let mut f32_data = vec![0f32; self.shape.elem_count()];
+        self.data.to_float(&mut f32_data)?;
+        Tensor::from_vec(f32_data, &self.shape, device)
+    }
+
+    pub fn matmul_t(&self, mkn: (usize, usize, usize), lhs: &[f32], dst: &mut [f32]) -> Result<()> {
+        self.data.matmul_t(mkn, lhs, dst)
     }
 
     pub fn storage_size_in_bytes(&self) -> usize {
-        self.storage.size_in_bytes()
+        self.data.storage_size_in_bytes()
     }
 
-    pub fn data(&self) -> Result<Cow<'_, [u8]>> {
-        self.storage.data()
+    pub fn as_ptr(&self) -> *const u8 {
+        self.data.as_ptr()
     }
 }
 
@@ -421,29 +294,17 @@ impl crate::CustomOp1 for QTensor {
         }
         dst_shape.push(n);
         let dst_shape = Shape::from(dst_shape);
-        #[allow(clippy::infallible_destructuring_match)]
-        let self_storage = match &self.storage {
-            QStorage::Cpu(storage) => storage,
-            QStorage::Metal(_) => crate::bail!("Invalid storage"),
-        };
-        let slice = storage.as_slice::<f32>()?;
-        let slice = &slice[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
+        let storage = storage.as_slice::<f32>()?;
+        let storage =
+            &storage[layout.start_offset()..layout.start_offset() + src_shape.elem_count()];
         let mut dst_storage = vec![0f32; dst_shape.elem_count()];
-        self_storage.matmul_t((dst_shape.elem_count() / n, k, n), slice, &mut dst_storage)?;
+        self.matmul_t(
+            (dst_shape.elem_count() / n, k, n),
+            storage,
+            &mut dst_storage,
+        )?;
         Ok((crate::CpuStorage::F32(dst_storage), dst_shape))
     }
-
-    fn metal_fwd(
-        &self,
-        storage: &crate::MetalStorage,
-        layout: &crate::Layout,
-    ) -> Result<(crate::MetalStorage, Shape)> {
-        let self_storage = match &self.storage {
-            QStorage::Metal(metal) => metal,
-            _ => unreachable!("Cannot call metal matmul on non metal QTensor"),
-        };
-        self_storage.fwd(&self.shape, storage, layout)
-    }
 }
 
 impl crate::Module for QMatMul {

candle-core/src/tensor.rs

@@ -508,7 +508,6 @@ 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);
@@ -805,35 +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.
     ///
@@ -1883,9 +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(),
@@ -1896,7 +1866,7 @@ impl Tensor {
                 dtype: self.dtype,
                 device: self.device.clone(),
             };
-            Tensor(Arc::new(tensor_))
+            Ok(Tensor(Arc::new(tensor_)))
         }
     }
 

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

@@ -50,15 +50,17 @@ fn conv1d(dev: &Device) -> Result<()> {
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [2.4509, 2.6357, -1.3336, 4.1393, 0.5657, 1.8091, -1.1784, 3.5675, 0.5069, 3.3352]
     );
-    let res = t.conv_transpose1d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
-    assert_eq!(res.dims(), [1, 2, 7]);
-    assert_eq!(
-        test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
-        [
-            0.0699, -1.2899, 8.3018, 5.5873, 2.4572, -2.6143, -0.0706, 1.8765, 4.8318, 1.1538,
-            4.7076, -5.9745, -0.8276, 1.621
-        ],
-    );
+    if dev.is_cpu() {
+        let res = t.conv_transpose1d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
+        assert_eq!(res.dims(), [1, 2, 7]);
+        assert_eq!(
+            test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
+            [
+                0.0699, -1.2899, 8.3018, 5.5873, 2.4572, -2.6143, -0.0706, 1.8765, 4.8318, 1.1538,
+                4.7076, -5.9745, -0.8276, 1.621
+            ],
+        );
+    }
     Ok(())
 }
 

candle-core/tests/grad_tests.rs

@@ -270,19 +270,6 @@ 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],
-    );
-
     // 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)?;

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

@@ -1,7 +1,6 @@
 use candle_core::{
     bail,
     quantized::{self, GgmlDType},
-    test_device,
     test_utils::to_vec2_round,
     Device, Module, Result, Tensor,
 };
@@ -15,48 +14,16 @@ const GGML_MAX_QUANTIZATION_TOTAL_ERROR_2BITS: f32 = 0.0075;
 const GGML_MAX_QUANTIZATION_TOTAL_ERROR_3BITS: f32 = 0.0040;
 const GGML_MAX_DOT_PRODUCT_ERROR: f32 = 0.02;
 
-fn test_matmul(
-    device: &Device,
-    (b, m, n, k): (usize, usize, usize, usize),
-    dtype: GgmlDType,
-) -> Result<()> {
-    let lhs = (0..(m * k))
-        .map(|v| v as f32 / (m * k) as f32)
-        .collect::<Vec<_>>();
-    let rhs = (0..(k * n))
-        .map(|v| v as f32 / (n * k) as f32)
-        .collect::<Vec<_>>();
-
-    let lhs = Tensor::from_slice(&lhs, (m, k), device)?;
-    let rhs = Tensor::from_slice(&rhs, (k, n), device)?;
-    let mm = lhs.matmul(&rhs)?;
-    let qtensor = quantized::QTensor::quantize(&rhs.t()?, dtype)?;
-    let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
-    let res = matmul.forward(&lhs)?;
-
-    let error: f32 = ((&mm - &res)?.abs()? / &mm.abs()?)?
-        .sum_all()?
-        .to_scalar()?;
-    let error = error / (b * m * n) as f32;
-    assert!(
-        error <= 0.02,
-        "Error {error} is too big. \nExpected:\n {mm} \nFound:\n {res}\n for {dtype:?}"
-    );
-
-    Ok(())
-}
-
-fn quantized_matmul(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
+#[test]
+fn quantized_matmul() -> Result<()> {
+    let cpu = &Device::Cpu;
     let (m, k, n) = (3, 64, 4);
     let lhs = (0..(m * k)).map(|v| v as f32).collect::<Vec<_>>();
-    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), device)?;
+    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), cpu)?;
     let mut dst = vec![42.; 3 * 4];
     let mut rhs_t = vec![k_quants::BlockQ4_0::zeros(); 8];
     let rhs = (0..(k * n)).map(|v| v as f32).collect::<Vec<_>>();
+    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), cpu)?.t()?;
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
     k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
@@ -66,7 +33,6 @@ fn quantized_matmul(device: &Device) -> Result<()> {
             341876.0, 994283.0, 1655709.0, 2301518.0
         ]
     );
-    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), device)?.t()?;
     let mm = tensor_lhs.matmul(&tensor_rhs)?;
     assert_eq!(
         mm.to_vec2::<f32>()?,
@@ -77,49 +43,35 @@ fn quantized_matmul(device: &Device) -> Result<()> {
         ]
     );
 
-    let qtensor = quantized::QTensor::quantize(&tensor_rhs.t()?, GgmlDType::Q4_0)?;
+    let qtensor = quantized::QTensor::new(rhs_t, (4, 64))?;
     let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
     let res = matmul.forward(&tensor_lhs)?;
-    match device {
-        Device::Metal(_) => assert_eq!(
-            to_vec2_round(&res, 0)?,
-            &[
-                [84946.0, 214126.0, 344757.0, 473798.0],
-                [213458.0, 604350.0, 1000469.0, 1387990.0],
-                [341970.0, 994574.0, 1656181.0, 2302182.0]
-            ]
-        ),
-        _ => assert_eq!(
-            to_vec2_round(&res, 0)?,
-            &[
-                [85120.0, 214562.0, 345455.0, 474748.0],
-                [213475.0, 604465.0, 1000686.0, 1388317.0],
-                [341876.0, 994283.0, 1655709.0, 2301518.0]
-            ]
-        ),
-    }
-
-    test_matmul(device, (1, 3, 4, 256), GgmlDType::Q4_0)?;
+    assert_eq!(
+        to_vec2_round(&res, 0)?,
+        &[
+            [85120.0, 214562.0, 345455.0, 474748.0],
+            [213475.0, 604465.0, 1000686.0, 1388317.0],
+            [341876.0, 994283.0, 1655709.0, 2301518.0]
+        ]
+    );
 
     Ok(())
 }
 
-fn quantized_matmul_neg(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
+#[test]
+fn quantized_matmul_neg() -> Result<()> {
+    let cpu = &Device::Cpu;
     let (m, k, n) = (3, 64, 4);
     let lhs = (0..(m * k))
         .map(|v| v as f32 - (m * k) as f32 / 2.0)
         .collect::<Vec<_>>();
-    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), device)?;
+    let tensor_lhs = Tensor::from_slice(&lhs, (m, k), cpu)?;
     let mut dst = vec![42.; 3 * 4];
     let mut rhs_t = vec![k_quants::BlockQ4_0::zeros(); 8];
     let rhs = (0..k * n)
         .map(|v| v as f32 - (k * n) as f32 / 3.0)
         .collect::<Vec<_>>();
-    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), device)?.t()?;
+    let tensor_rhs = Tensor::from_slice(&rhs, (n, k), cpu)?.t()?;
     k_quants::BlockQ4_0::from_float(&rhs, &mut rhs_t)?;
     k_quants::matmul((m, k, n), &lhs, &rhs_t, &mut dst)?;
     assert_eq!(
@@ -139,56 +91,32 @@ fn quantized_matmul_neg(device: &Device) -> Result<()> {
         ]
     );
 
-    let qtensor = quantized::QTensor::quantize(&tensor_rhs.t()?, GgmlDType::Q4_0)?;
+    let qtensor = quantized::QTensor::new(rhs_t, (4, 64))?;
     let matmul = quantized::QMatMul::from_qtensor(qtensor)?;
     let res = matmul.forward(&tensor_lhs)?;
-    match device {
-        Device::Metal(_) => assert_eq!(
-            to_vec2_round(&res, 0)?,
-            &[
-                [243666.0, -19714.0, -285433.0, -550453.0],
-                [23782.0, 21654.0, 19400.0, 18369.0],
-                [-196102.0, 63022.0, 324233.0, 587191.0]
-            ]
-        ),
-        _ => assert_eq!(
-            to_vec2_round(&res, 0)?,
-            &[
-                [243524.0, -19596.0, -285051.0, -549815.0],
-                [23777.0, 21651.0, 19398.0, 18367.0],
-                [-196472.0, 63012.0, 324585.0, 587902.0]
-            ]
-        ),
-    }
+    assert_eq!(
+        to_vec2_round(&res, 0)?,
+        &[
+            [243524.0, -19596.0, -285051.0, -549815.0],
+            [23777.0, 21651.0, 19398.0, 18367.0],
+            [-196472.0, 63012.0, 324585.0, 587902.0]
+        ]
+    );
 
     Ok(())
 }
 
-test_device!(
-    quantized_matmul,
-    quantized_matmul_cpu,
-    quantized_matmul_cuda,
-    quantized_matmul_metal
-);
-test_device!(
-    quantized_matmul_neg,
-    quantized_matmul_neg_cpu,
-    quantized_matmul_neg_cuda,
-    quantized_matmul_neg_metal
-);
+#[test]
+fn quantize_q4_0() -> Result<()> {
+    use k_quants::BlockQ4_0;
 
-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)?;
-    let quant = quantized::QTensor::quantize(&src, GgmlDType::Q4_0)?;
-    let dst = quant.dequantize(device)?;
+    let mut dst = vec![0f32; 32 * 4];
+    let mut quant = vec![BlockQ4_0::zeros(); 4];
+    BlockQ4_0::from_float(&src, &mut quant)?;
+    BlockQ4_0::to_float(&quant, dst.as_mut_slice())?;
     assert_eq!(
-        dst.to_vec1::<f32>()?,
+        dst,
         &[
             -0.0, -0.0, 3.875, 3.875, 3.875, 3.875, 7.75, 7.75, 7.75, 7.75, 11.625, 11.625, 11.625,
             11.625, 15.5, 15.5, 15.5, 15.5, 19.375, 19.375, 19.375, 19.375, 23.25, 23.25, 23.25,
@@ -204,21 +132,21 @@ fn quantize_q4_0(device: &Device) -> Result<()> {
             127.0, 127.0
         ]
     );
-    ggml_quantization_error_test(GgmlDType::Q4_0, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test::<BlockQ4_0>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-fn quantize_q4_1(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
+#[test]
+fn quantize_q4_1() -> Result<()> {
+    use k_quants::BlockQ4_1;
+
     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)?;
-    let dst = quant.dequantize(device)?;
+    let mut dst = vec![0f32; 32 * 4];
+    let mut quant = vec![BlockQ4_1::zeros(); 4];
+    BlockQ4_1::from_float(&src, &mut quant)?;
+    BlockQ4_1::to_float(&quant, dst.as_mut_slice())?;
     assert_eq!(
-        round_vector(&dst.to_vec1::<f32>()?),
+        round_vector(&dst),
         &[
             0.0, 0.0, 2.066, 2.066, 4.133, 4.133, 6.199, 6.199, 8.266, 8.266, 10.332, 10.332,
             12.398, 12.398, 14.465, 14.465, 16.531, 16.531, 18.598, 18.598, 20.664, 20.664, 22.73,
@@ -234,21 +162,21 @@ fn quantize_q4_1(device: &Device) -> Result<()> {
             118.73, 118.73, 120.797, 120.797, 122.863, 122.863, 124.93, 124.93, 126.996, 126.996
         ]
     );
-    ggml_quantization_error_test(GgmlDType::Q4_1, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test::<BlockQ4_1>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-fn quantize_q5_0(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
+#[test]
+fn quantize_q5_0() -> Result<()> {
+    use k_quants::BlockQ5_0;
+
     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)?;
-    let dst = quant.dequantize(device)?;
+    let mut dst = vec![0f32; 32 * 4];
+    let mut quant = vec![BlockQ5_0::zeros(); 4];
+    BlockQ5_0::from_float(&src, &mut quant)?;
+    BlockQ5_0::to_float(&quant, dst.as_mut_slice())?;
     assert_eq!(
-        round_vector(&dst.to_vec1::<f32>()?),
+        round_vector(&dst),
         &[
             -0.0, 1.938, 1.938, 3.875, 3.875, 5.813, 5.813, 7.75, 7.75, 9.688, 9.688, 11.625,
             11.625, 13.563, 13.563, 15.5, 15.5, 17.438, 17.438, 19.375, 19.375, 21.313, 21.313,
@@ -264,21 +192,21 @@ fn quantize_q5_0(device: &Device) -> Result<()> {
             119.063, 119.063, 119.063, 119.063, 127.0, 127.0, 127.0, 127.0
         ]
     );
-    ggml_quantization_error_test(GgmlDType::Q5_0, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test::<BlockQ5_0>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-fn quantize_q5_1(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
+#[test]
+fn quantize_q5_1() -> Result<()> {
+    use k_quants::BlockQ5_1;
+
     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)?;
-    let dst = quant.dequantize(device)?;
+    let mut dst = vec![0f32; 32 * 4];
+    let mut quant = vec![BlockQ5_1::zeros(); 4];
+    BlockQ5_1::from_float(&src, &mut quant)?;
+    BlockQ5_1::to_float(&quant, dst.as_mut_slice())?;
     assert_eq!(
-        round_vector(&dst.to_vec1::<f32>()?),
+        dst,
         &[
             0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0,
             16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0,
@@ -292,11 +220,13 @@ fn quantize_q5_1(device: &Device) -> Result<()> {
             124.0, 125.0, 126.0, 127.0
         ]
     );
-    ggml_quantization_error_test(GgmlDType::Q5_1, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+
+    ggml_quantization_error_test::<BlockQ5_1>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-fn get_test_vector2(bound: f32, size: usize, device: &Device) -> Result<Tensor> {
+/// Generates a small test vector ranging from -`bound` to `bound` with `size` steps
+fn get_test_vector(bound: f32, size: usize) -> (Vec<f32>, Vec<f32>) {
     assert!(
         size % crate::quantized::k_quants::QK_K == 0,
         "size must be a multiple of {}",
@@ -306,8 +236,10 @@ fn get_test_vector2(bound: f32, size: usize, device: &Device) -> Result<Tensor>
     let src = (0..size)
         .map(|v| (v as f32 - size as f32 / 2.) * bound / (size as f32 / 2.))
         .collect::<Vec<_>>();
+
+    let dst = vec![0f32; size];
     assert_eq!([src[0], src[size / 2]], [-bound, 0.0]);
-    Tensor::from_vec(src, (size,), device)
+    (src, dst)
 }
 
 /// Round a vector
@@ -356,12 +288,11 @@ fn calculate_rmse(a: &[f32], b: &[f32]) -> f32 {
 
 /// Similar to the GGML quantization unit test:
 /// https://github.com/ggerganov/llama.cpp/blob/master/tests/test-quantize-fns.cpp#L43-L50
-fn ggml_quantization_error_test(dtype: GgmlDType, device: &Device, max_error: f32) -> Result<()> {
+fn ggml_quantization_error_test<T: GgmlType>(max_error: f32) -> Result<()> {
     let src = create_ggml_like_vector(0.0);
-    let src = Tensor::from_slice(&src, (GGML_TEST_SIZE,), device)?;
-    let quant = quantized::QTensor::quantize(&src, dtype)?;
-    let dst = quant.dequantize(device)?;
-    let error = calculate_rmse(&src.to_vec1::<f32>()?, &dst.to_vec1::<f32>()?);
+    let mut dst = vec![0.0; GGML_TEST_SIZE];
+    let _quant = quantize_roundtrip::<T>(src.as_slice(), dst.as_mut_slice())?;
+    let error = calculate_rmse(src.as_slice(), dst.as_slice());
     if error > max_error {
         bail!(
             "Quantization error {} exceeds max error {}",
@@ -372,19 +303,19 @@ fn ggml_quantization_error_test(dtype: GgmlDType, device: &Device, max_error: f3
     Ok(())
 }
 
-fn quantize_q2k(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
-    let dtype = GgmlDType::Q2K;
+fn quantize_roundtrip<T: GgmlType>(src: &[f32], dst: &mut [f32]) -> Result<Vec<T>> {
+    let mut quant = vec![T::zeros(); src.len() / T::BLCK_SIZE];
+    T::from_float(src, &mut quant)?;
+    T::to_float(&quant, dst)?;
+    Ok(quant)
+}
 
-    let src = get_test_vector2(0.5, 1024, device)?;
-    let quant = quantized::QTensor::quantize(&src, dtype)?;
-    let dst = quant.dequantize(device)?;
+#[test]
+fn quantize_q2k() -> Result<()> {
+    use k_quants::BlockQ2K;
 
-    let src = src.to_vec1::<f32>()?;
-    let dst = dst.to_vec1::<f32>()?;
+    let (src, mut dst) = get_test_vector(0.5, 1024);
+    let _quant = quantize_roundtrip::<BlockQ2K>(src.as_slice(), dst.as_mut_slice())?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.1);
 
     // Test some specific values
@@ -398,30 +329,20 @@ fn quantize_q2k(device: &Device) -> Result<()> {
         [-0.499, -0.366, -0.249, 0.0, 0.295, 0.492]
     );
 
-    let src_big = get_test_vector2(128.0, 1024, device)?;
-    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
-    let dst_big = quant_big.dequantize(device)?;
-
-    let src_big = src_big.to_vec1::<f32>()?;
-    let dst_big = dst_big.to_vec1::<f32>()?;
+    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
+    let _quant_big = quantize_roundtrip::<BlockQ2K>(src_big.as_slice(), dst_big.as_mut_slice())?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 6.0);
 
-    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR_2BITS)?;
+    ggml_quantization_error_test::<BlockQ2K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR_2BITS)?;
     Ok(())
 }
 
-fn quantize_q3k(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
-    let dtype = GgmlDType::Q3K;
-    let src = get_test_vector2(0.5, 1024, device)?;
-    let quant = quantized::QTensor::quantize(&src, dtype)?;
-    let dst = quant.dequantize(device)?;
+#[test]
+fn quantize_q3k() -> Result<()> {
+    use k_quants::BlockQ3K;
 
-    let src = src.to_vec1::<f32>()?;
-    let dst = dst.to_vec1::<f32>()?;
+    let (src, mut dst) = get_test_vector(0.5, 1024);
+    let _quant = quantize_roundtrip::<BlockQ3K>(src.as_slice(), dst.as_mut_slice())?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.03);
 
     // Test some specific values
@@ -435,30 +356,20 @@ fn quantize_q3k(device: &Device) -> Result<()> {
         [-0.493, -0.37, -0.243, -0.0, 0.292, 0.492]
     );
 
-    let src_big = get_test_vector2(128.0, 1024, device)?;
-    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
-    let dst_big = quant_big.dequantize(device)?;
-
-    let src_big = src_big.to_vec1::<f32>()?;
-    let dst_big = dst_big.to_vec1::<f32>()?;
+    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
+    let _quant_big = quantize_roundtrip::<BlockQ3K>(src_big.as_slice(), dst_big.as_mut_slice())?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 3.5);
 
-    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR_3BITS)?;
+    ggml_quantization_error_test::<BlockQ3K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR_3BITS)?;
     Ok(())
 }
 
-fn quantize_q4k(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
-    let dtype = GgmlDType::Q4K;
-    let src = get_test_vector2(0.5, 1024, device)?;
-    let quant = quantized::QTensor::quantize(&src, dtype)?;
-    let dst = quant.dequantize(device)?;
+#[test]
+fn quantize_q4k() -> Result<()> {
+    use k_quants::BlockQ4K;
 
-    let src = src.to_vec1::<f32>()?;
-    let dst = dst.to_vec1::<f32>()?;
+    let (src, mut dst) = get_test_vector(0.5, 1024);
+    let _quant = quantize_roundtrip::<BlockQ4K>(src.as_slice(), dst.as_mut_slice())?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.017);
 
     // Test some specific values
@@ -472,31 +383,21 @@ fn quantize_q4k(device: &Device) -> Result<()> {
         [-0.5, -0.373, -0.25, 0.0, 0.288, 0.498]
     );
 
-    let src_big = get_test_vector2(128.0, 1024, device)?;
-    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
-    let dst_big = quant_big.dequantize(device)?;
-
-    let src_big = src_big.to_vec1::<f32>()?;
-    let dst_big = dst_big.to_vec1::<f32>()?;
+    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
+    let _quant_big = quantize_roundtrip::<BlockQ4K>(src_big.as_slice(), dst_big.as_mut_slice())?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 4.5);
 
-    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test::<BlockQ4K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
     Ok(())
 }
 
-fn quantize_q5k(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
-    let dtype = GgmlDType::Q5K;
-    let src = get_test_vector2(0.5, 1024, device)?;
-    let quant = quantized::QTensor::quantize(&src, dtype)?;
-    let dst = quant.dequantize(device)?;
+#[test]
+fn quantize_q5k() -> Result<()> {
+    use k_quants::BlockQ5K;
 
-    let src = src.to_vec1::<f32>()?;
-    let dst = dst.to_vec1::<f32>()?;
-    compare_with_error(dst.as_slice(), src.as_slice(), 0.009);
+    let (src, mut dst) = get_test_vector(0.5, 1024);
+    let _quant = quantize_roundtrip::<BlockQ5K>(src.as_slice(), dst.as_mut_slice())?;
+    compare_with_error(dst.as_slice(), src.as_slice(), 0.008);
 
     // Test some specific values
     assert_eq!(
@@ -509,30 +410,21 @@ fn quantize_q5k(device: &Device) -> Result<()> {
         [-0.5, -0.373, -0.25, 0.0, 0.279, 0.499]
     );
 
-    let src_big = get_test_vector2(128.0, 1024, device)?;
-    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
-    let dst_big = quant_big.dequantize(device)?;
-
-    let src_big = src_big.to_vec1::<f32>()?;
-    let dst_big = dst_big.to_vec1::<f32>()?;
+    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
+    let _quant_big = quantize_roundtrip::<BlockQ5K>(src_big.as_slice(), dst_big.as_mut_slice())?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 2.5);
 
-    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test::<BlockQ5K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+
     Ok(())
 }
 
-fn quantize_q6k(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
-    let dtype = GgmlDType::Q6K;
-    let src = get_test_vector2(0.5, 1024, device)?;
-    let quant = quantized::QTensor::quantize(&src, dtype)?;
-    let dst = quant.dequantize(device)?;
+#[test]
+fn quantize_q6k() -> Result<()> {
+    use k_quants::BlockQ6K;
 
-    let src = src.to_vec1::<f32>()?;
-    let dst = dst.to_vec1::<f32>()?;
+    let (src, mut dst) = get_test_vector(0.5, 1024);
+    let _quant = quantize_roundtrip::<BlockQ6K>(src.as_slice(), dst.as_mut_slice())?;
     compare_with_error(dst.as_slice(), src.as_slice(), 0.008);
 
     // Test some specific values
@@ -546,31 +438,22 @@ fn quantize_q6k(device: &Device) -> Result<()> {
         [-0.497, -0.372, -0.25, -0.0, 0.284, 0.5]
     );
 
-    let src_big = get_test_vector2(128.0, 1024, device)?;
-    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
-    let dst_big = quant_big.dequantize(device)?;
-
-    let src_big = src_big.to_vec1::<f32>()?;
-    let dst_big = dst_big.to_vec1::<f32>()?;
+    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
+    let _quant_big = quantize_roundtrip::<BlockQ6K>(src_big.as_slice(), dst_big.as_mut_slice())?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 2.0);
 
-    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test::<BlockQ6K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+
     Ok(())
 }
 
-fn quantize_q8k(device: &Device) -> Result<()> {
-    // TODO Enable this later when we enable cuda.
-    if device.is_cuda() {
-        return Ok(());
-    }
-    let dtype = GgmlDType::Q8K;
-    let src = get_test_vector2(0.5, 1024, device)?;
-    let quant = quantized::QTensor::quantize(&src, dtype)?;
-    let dst = quant.dequantize(device)?;
+#[test]
+fn quantize_q8k() -> Result<()> {
+    use k_quants::BlockQ8K;
 
-    let src = src.to_vec1::<f32>()?;
-    let dst = dst.to_vec1::<f32>()?;
-    compare_with_error(dst.as_slice(), src.as_slice(), 0.008);
+    let (src, mut dst) = get_test_vector(0.5, 1024);
+    let _quant = quantize_roundtrip::<BlockQ8K>(src.as_slice(), dst.as_mut_slice())?;
+    compare_with_error(dst.as_slice(), src.as_slice(), 0.003);
 
     // Test some specific values
     assert_eq!(
@@ -583,79 +466,15 @@ fn quantize_q8k(device: &Device) -> Result<()> {
         [-0.5, -0.375, -0.25, -0.0, 0.281, 0.499]
     );
 
-    let src_big = get_test_vector2(128.0, 1024, device)?;
-    let quant_big = quantized::QTensor::quantize(&src_big, dtype)?;
-    let dst_big = quant_big.dequantize(device)?;
-
-    let src_big = src_big.to_vec1::<f32>()?;
-    let dst_big = dst_big.to_vec1::<f32>()?;
+    let (src_big, mut dst_big) = get_test_vector(128.0, 1024);
+    let _quant_big = quantize_roundtrip::<BlockQ8K>(src_big.as_slice(), dst_big.as_mut_slice())?;
     compare_with_error(dst_big.as_slice(), src_big.as_slice(), 0.6);
 
-    ggml_quantization_error_test(dtype, device, GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+    ggml_quantization_error_test::<BlockQ8K>(GGML_MAX_QUANTIZATION_TOTAL_ERROR)?;
+
     Ok(())
 }
 
-test_device!(
-    quantize_q4_0,
-    quantize_q4_0_cpu,
-    quantize_q4_0_cuda,
-    quantize_q4_0_metal
-);
-test_device!(
-    quantize_q4_1,
-    quantize_q4_1_cpu,
-    quantize_q4_1_cuda,
-    quantize_q4_1_metal
-);
-test_device!(
-    quantize_q5_0,
-    quantize_q5_0_cpu,
-    quantize_q5_0_cuda,
-    quantize_q5_0_metal
-);
-test_device!(
-    quantize_q5_1,
-    quantize_q5_1_cpu,
-    quantize_q5_1_cuda,
-    quantize_q5_1_metal
-);
-test_device!(
-    quantize_q2k,
-    quantize_q2k_cpu,
-    quantize_q2k_cuda,
-    quantize_q2k_metal
-);
-test_device!(
-    quantize_q3k,
-    quantize_q3k_cpu,
-    quantize_q3k_cuda,
-    quantize_q3k_metal
-);
-test_device!(
-    quantize_q4k,
-    quantize_q4k_cpu,
-    quantize_q4k_cuda,
-    quantize_q4k_metal
-);
-test_device!(
-    quantize_q5k,
-    quantize_q5k_cpu,
-    quantize_q5k_cuda,
-    quantize_q5k_metal
-);
-test_device!(
-    quantize_q6k,
-    quantize_q6k_cpu,
-    quantize_q6k_cuda,
-    quantize_q6k_metal
-);
-test_device!(
-    quantize_q8k,
-    quantize_q8k_cpu,
-    quantize_q8k_cuda,
-    quantize_q8k_metal
-);
-
 /// Very simple dot product implementation
 fn vec_dot_reference(a: &[f32], b: &[f32]) -> f32 {
     a.iter().zip(b).map(|(a, b)| a * b).sum()
@@ -772,112 +591,6 @@ fn get_random_tensors(
     Ok((lhs, rhs, mm))
 }
 
-#[macro_export]
-macro_rules! quantized_matmul {
-    // TODO: Switch to generating the two last arguments automatically once concat_idents is
-    // stable. https://github.com/rust-lang/rust/issues/29599
-    ($fn_name: ident, $fn_name_cpu: ident, $fn_name_cuda: ident, $fn_name_metal: ident, $dtype: expr) => {
-        fn $fn_name(device: &Device) -> Result<()> {
-            if device.is_cuda() {
-                // TODO Enable Cuda GGML sometime maybe.
-                return Ok(());
-            }
-            test_matmul(device, (1, 3, 4, 256), $dtype)?;
-            Ok(())
-        }
-
-        test_device!($fn_name, $fn_name_cpu, $fn_name_cuda, $fn_name_metal);
-    };
-}
-
-quantized_matmul!(
-    quantized_matmul_q4_0_bis,
-    quantized_matmul_q4_0_cpu,
-    quantized_matmul_q4_0_cuda,
-    quantized_matmul_q4_0_metal,
-    GgmlDType::Q4_0
-);
-quantized_matmul!(
-    quantized_matmul_q4_1_bis,
-    quantized_matmul_q4_1_cpu,
-    quantized_matmul_q4_1_cuda,
-    quantized_matmul_q4_1_metal,
-    GgmlDType::Q4_1
-);
-quantized_matmul!(
-    quantized_matmul_q5_0_bis,
-    quantized_matmul_q5_0_cpu,
-    quantized_matmul_q5_0_cuda,
-    quantized_matmul_q5_0_metal,
-    GgmlDType::Q5_0
-);
-quantized_matmul!(
-    quantized_matmul_q5_1_bis,
-    quantized_matmul_q5_1_cpu,
-    quantized_matmul_q5_1_cuda,
-    quantized_matmul_q5_1_metal,
-    GgmlDType::Q5_1
-);
-quantized_matmul!(
-    quantized_matmul_q8_0_bis,
-    quantized_matmul_q8_0_cpu,
-    quantized_matmul_q8_0_cuda,
-    quantized_matmul_q8_0_metal,
-    GgmlDType::Q8_0
-);
-// Not implemented in Ggml
-// quantized_matmul!(
-//     quantized_matmul_q8_1_bis,
-//     quantized_matmul_q8_1_cpu,
-//     quantized_matmul_q8_1_cuda,
-//     quantized_matmul_q8_1_metal,
-//     GgmlDType::Q8_1
-// );
-// TODO This is bugged (also bugged in GGML
-quantized_matmul!(
-    quantized_matmul_q2k_bis,
-    quantized_matmul_q2k_cpu,
-    quantized_matmul_q2k_cuda,
-    quantized_matmul_q2k_metal,
-    GgmlDType::Q2K
-);
-quantized_matmul!(
-    quantized_matmul_q3k_bis,
-    quantized_matmul_q3k_cpu,
-    quantized_matmul_q3k_cuda,
-    quantized_matmul_q3k_metal,
-    GgmlDType::Q3K
-);
-quantized_matmul!(
-    quantized_matmul_q4k_bis,
-    quantized_matmul_q4k_cpu,
-    quantized_matmul_q4k_cuda,
-    quantized_matmul_q4k_metal,
-    GgmlDType::Q4K
-);
-quantized_matmul!(
-    quantized_matmul_q5k_bis,
-    quantized_matmul_q5k_cpu,
-    quantized_matmul_q5k_cuda,
-    quantized_matmul_q5k_metal,
-    GgmlDType::Q5K
-);
-quantized_matmul!(
-    quantized_matmul_q6k_bis,
-    quantized_matmul_q6k_cpu,
-    quantized_matmul_q6k_cuda,
-    quantized_matmul_q6k_metal,
-    GgmlDType::Q6K
-);
-// Not implemented on metal
-// quantized_matmul!(
-//     quantized_matmul_q8k_bis,
-//     quantized_matmul_q8k_cpu,
-//     quantized_matmul_q8k_cuda,
-//     quantized_matmul_q8k_metal,
-//     GgmlDType::Q8K
-// );
-
 #[test]
 fn quantized_matmul_q2k() -> Result<()> {
     use k_quants::BlockQ2K;
@@ -890,7 +603,7 @@ fn quantized_matmul_q2k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q2K)?;
+    let rhs = quantized::QTensor::quantize::<BlockQ2K>(&rhs)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -916,7 +629,7 @@ fn quantized_matmul_q3k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q3K)?;
+    let rhs = quantized::QTensor::quantize::<BlockQ3K>(&rhs)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -942,7 +655,7 @@ fn quantized_matmul_q4k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q4K)?;
+    let rhs = quantized::QTensor::quantize::<BlockQ4K>(&rhs)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -968,7 +681,7 @@ fn quantized_matmul_q5k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q5K)?;
+    let rhs = quantized::QTensor::quantize::<BlockQ5K>(&rhs)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -995,7 +708,7 @@ fn quantized_matmul_q6k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q6K)?;
+    let rhs = quantized::QTensor::quantize::<BlockQ6K>(&rhs)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 
@@ -1020,7 +733,7 @@ fn quantized_matmul_q8k() -> Result<()> {
     let dst = round_vector(&[dst[0], dst[m * n / 3], dst[m * n * 2 / 3], dst[m * n - 1]]);
     assert_eq!(dst, [1.262, 1.513, -0.208, 1.702]);
 
-    let rhs = quantized::QTensor::quantize(&rhs, GgmlDType::Q8K)?;
+    let rhs = quantized::QTensor::quantize::<BlockQ8K>(&rhs)?;
     let rhs = quantized::QMatMul::from_qtensor(rhs)?;
     let mm = rhs.forward(&lhs)?;
 

candle-core/tests/tensor_tests.rs

@@ -120,13 +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]]

candle-examples/Cargo.toml

@@ -21,7 +21,7 @@ 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 }
@@ -30,9 +30,7 @@ rayon = { workspace = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }
-symphonia = { version = "0.5.3", features = ["all"] }
 tokenizers = { workspace = true, features = ["onig"] }
-cpal= { version = "0.15.2", optional = true }
 
 [dev-dependencies]
 anyhow = { workspace = true }
@@ -45,6 +43,7 @@ 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"
 
@@ -62,7 +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"]
 
 [[example]]
 name = "llama_multiprocess"
@@ -79,7 +77,3 @@ required-features = ["onnx"]
 [[example]]
 name = "onnx_basics"
 required-features = ["onnx"]
-
-[[example]]
-name = "whisper-microphone"
-required-features = ["microphone"]

candle-examples/examples/blip/main.rs

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

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/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)?;
-    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/llama/main.rs

@@ -57,7 +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.
@@ -143,6 +143,7 @@ fn main() -> Result<()> {
             }
             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)? };
@@ -156,7 +157,6 @@ fn main() -> Result<()> {
         .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}");
@@ -190,16 +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

@@ -262,7 +262,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
         .extension()
         .map_or(false, |v| v == "safetensors");
     let (model, config) = if is_gguf {
-        let vb = qmodel::VarBuilder::from_gguf(config_path, &device)?;
+        let vb = qmodel::VarBuilder::from_gguf(config_path)?;
         let (_vocab_size, dim) = vb
             .get_no_shape("model.embed_tokens.weight")?
             .shape()
@@ -279,13 +279,13 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
                 (config.seq_len, config.head_size() / 2),
                 "rot.freq_cis_real",
             )?
-            .dequantize(&device)?;
+            .dequantize(&candle::Device::Cpu)?;
         let freq_cis_imag = vb
             .get(
                 (config.seq_len, config.head_size() / 2),
                 "rot.freq_cis_imag",
             )?
-            .dequantize(&device)?;
+            .dequantize(&candle::Device::Cpu)?;
 
         let fake_vb = candle_nn::VarBuilder::from_tensors(
             [
@@ -295,7 +295,7 @@ fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
             .into_iter()
             .collect(),
             candle::DType::F32,
-            &device,
+            &candle::Device::Cpu,
         );
         let cache = model::Cache::new(true, &config, fake_vb)?;
         let model = Model::QLlama(QLlama::load(vb, &cache, config.clone())?);
@@ -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.. {
@@ -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)? {
-            print!("{t}");
+        // 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 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/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

@@ -1,299 +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 mut tokens = self
-            .tokenizer
-            .tokenizer()
-            .encode(prompt, true)
-            .map_err(E::msg)?
-            .get_ids()
-            .to_vec();
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
-            Some(token) => token,
-            None => anyhow::bail!("cannot find the </s> token"),
-        };
-        let mut state = State::new(1, &self.config, &self.device)?;
-        let mut next_logits = None;
-        for &t in tokens.iter() {
-            let input = Tensor::new(&[t], &self.device)?;
-            let logits = self.model.forward(&input, &mut state)?;
-            next_logits = Some(logits);
-            if let Some(t) = self.tokenizer.next_token(t)? {
-                print!("{t}")
-            }
-        }
-        std::io::stdout().flush()?;
-
-        let start_gen = std::time::Instant::now();
-        for _ in 0..sample_len {
-            let logits = match next_logits.as_ref() {
-                Some(logits) => logits,
-                None => anyhow::bail!("cannot work on an empty prompt"),
-            };
-            let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            if let Some(t) = self.tokenizer.next_token(next_token)? {
-                print!("{t}");
-                std::io::stdout().flush()?;
-            }
-
-            let input = Tensor::new(&[next_token], &self.device)?;
-            next_logits = Some(self.model.forward(&input, &mut state)?)
-        }
-        let dt = start_gen.elapsed();
-        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
-            print!("{rest}");
-        }
-        std::io::stdout().flush()?;
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(Parser, ValueEnum, Clone, Copy, PartialEq, Eq, Debug)]
-enum Which {
-    Mamba130m,
-    Mamba370m,
-    Mamba790m,
-    Mamba1_4b,
-    Mamba2_8b,
-    Mamba2_8bSlimPj,
-}
-
-impl std::fmt::Display for Which {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "{:?}", self)
-    }
-}
-
-impl Which {
-    fn model_id(&self) -> &'static str {
-        match self {
-            Self::Mamba130m => "state-spaces/mamba-130m",
-            Self::Mamba370m => "state-spaces/mamba-370m",
-            Self::Mamba790m => "state-spaces/mamba-790m",
-            Self::Mamba1_4b => "state-spaces/mamba-1.4b",
-            Self::Mamba2_8b => "state-spaces/mamba-2.8b",
-            Self::Mamba2_8bSlimPj => "state-spaces/mamba-2.8b-slimpj'",
-        }
-    }
-
-    fn revision(&self) -> &'static str {
-        match self {
-            Self::Mamba130m
-            | Self::Mamba370m
-            | Self::Mamba790m
-            | Self::Mamba1_4b
-            | Self::Mamba2_8bSlimPj => "refs/pr/1",
-            Self::Mamba2_8b => "refs/pr/4",
-        }
-    }
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    prompt: String,
-
-    /// The temperature used to generate samples.
-    #[arg(long)]
-    temperature: Option<f64>,
-
-    /// Nucleus sampling probability cutoff.
-    #[arg(long)]
-    top_p: Option<f64>,
-
-    /// The seed to use when generating random samples.
-    #[arg(long, default_value_t = 299792458)]
-    seed: u64,
-
-    /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 5000)]
-    sample_len: usize,
-
-    #[arg(long, default_value = "mamba130m")]
-    which: Which,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long)]
-    revision: Option<String>,
-
-    #[arg(long)]
-    tokenizer_file: Option<String>,
-
-    #[arg(long)]
-    weight_files: Option<String>,
-
-    #[arg(long)]
-    config_file: Option<String>,
-
-    /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.1)]
-    repeat_penalty: f32,
-
-    /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 64)]
-    repeat_last_n: usize,
-}
-
-fn main() -> Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let repo = api.repo(Repo::with_revision(
-        args.model_id
-            .unwrap_or_else(|| args.which.model_id().to_string()),
-        RepoType::Model,
-        args.revision
-            .unwrap_or_else(|| args.which.revision().to_string()),
-    ));
-    let tokenizer_filename = match args.tokenizer_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => api
-            .model("EleutherAI/gpt-neox-20b".to_string())
-            .get("tokenizer.json")?,
-    };
-    let config_filename = match args.config_file {
-        Some(file) => std::path::PathBuf::from(file),
-        None => repo.get("config.json")?,
-    };
-    let filenames = match args.weight_files {
-        Some(files) => files
-            .split(',')
-            .map(std::path::PathBuf::from)
-            .collect::<Vec<_>>(),
-        None => {
-            vec![repo.get("model.safetensors")?]
-        }
-    };
-    println!("retrieved the files in {:?}", start.elapsed());
-    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-
-    let start = std::time::Instant::now();
-    let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
-    let device = candle_examples::device(args.cpu)?;
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
-    let model = Model::new(&config, vb.pp("backbone"))?;
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        config,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

candle-examples/examples/mistral/main.rs

@@ -152,7 +152,7 @@ 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)]
@@ -244,14 +244,13 @@ fn main() -> Result<()> {
 
     let start = std::time::Instant::now();
     let config = Config::config_7b_v0_1(args.use_flash_attn);
-    let device = candle_examples::device(args.cpu)?;
     let (model, device) = if args.quantized {
         let filename = &filenames[0];
-        let vb =
-            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
+        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename)?;
         let model = QMistral::new(&config, vb)?;
-        (Model::Quantized(model), device)
+        (Model::Quantized(model), Device::Cpu)
     } else {
+        let device = candle_examples::device(args.cpu)?;
         let dtype = if device.is_cuda() {
             DType::BF16
         } else {

candle-examples/examples/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)?;
-    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/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 examples with following commands:
+You can run the example with the following command:
 
 ```bash
-cargo run --example onnx --features=onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
-```
-
-Use the `--which` flag to specify explicitly which network to use, i.e.
-
-```bash
-$ cargo run --example onnx --features=onnx --release -- --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg
-
-    Finished release [optimized] target(s) in 0.21s
-     Running `target/release/examples/onnx --which squeeze-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
-loaded image Tensor[dims 3, 224, 224; f32]
-unicycle, monocycle                               : 83.23%
-ballplayer, baseball player                       : 3.68%
-bearskin, busby, shako                            : 1.54%
-military uniform                                  : 0.78%
-cowboy hat, ten-gallon hat                        : 0.76%
-```
-
-```bash
-$ cargo run --example onnx --features=onnx --release -- --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg
-
-    Finished release [optimized] target(s) in 0.20s
-     Running `target/release/examples/onnx --which efficient-net --image candle-examples/examples/yolo-v8/assets/bike.jpg`
-loaded image Tensor[dims 224, 224, 3; f32]
-bicycle-built-for-two, tandem bicycle, tandem     : 99.16%
-mountain bike, all-terrain bike, off-roader       : 0.60%
-unicycle, monocycle                               : 0.17%
-crash helmet                                      : 0.02%
-alp                                               : 0.02%
+cargo run --example squeezenet-onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
 ```

candle-examples/examples/phi/main.rs

@@ -307,21 +307,18 @@ fn main() -> Result<()> {
         WhichModel::PuffinPhiV2 => Config::puffin_phi_v2(),
         WhichModel::PhiHermes => Config::phi_hermes_1_3b(),
     };
-    let device = candle_examples::device(args.cpu)?;
-    let model = if args.quantized {
+    let (model, device) = if args.quantized {
+        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(&filenames[0])?;
         let config = config();
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
-            &filenames[0],
-            &device,
-        )?;
         let model = match args.model {
             WhichModel::V2 | WhichModel::V2Old => QMixFormer::new_v2(&config, vb)?,
             _ => QMixFormer::new(&config, vb)?,
         };
-        Model::Quantized(model)
+        (Model::Quantized(model), Device::Cpu)
     } else {
+        let device = candle_examples::device(args.cpu)?;
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
-        match args.model {
+        let model = match args.model {
             WhichModel::V1 | WhichModel::V1_5 | WhichModel::V2 => {
                 let config_filename = repo.get("config.json")?;
                 let config = std::fs::read_to_string(config_filename)?;
@@ -337,7 +334,8 @@ fn main() -> Result<()> {
                 let config = config();
                 Model::MixFormer(MixFormer::new(&config, vb)?)
             }
-        }
+        };
+        (model, device)
     };
     println!("loaded the model in {:?}", start.elapsed());
 

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

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

candle-examples/examples/quantized/main.rs

@@ -9,7 +9,7 @@ use std::io::Write;
 use tokenizers::Tokenizer;
 
 use candle::quantized::{ggml_file, gguf_file};
-use candle::Tensor;
+use candle::{Device, Tensor};
 use candle_transformers::generation::LogitsProcessor;
 
 use candle_examples::token_output_stream::TokenOutputStream;
@@ -361,7 +361,6 @@ fn main() -> anyhow::Result<()> {
     let model_path = args.model()?;
     let mut file = std::fs::File::open(&model_path)?;
     let start = std::time::Instant::now();
-    let device = candle_examples::device(false)?;
 
     let mut model = match model_path.extension().and_then(|v| v.to_str()) {
         Some("gguf") => {
@@ -370,7 +369,7 @@ fn main() -> anyhow::Result<()> {
             for (_, tensor) in model.tensor_infos.iter() {
                 let elem_count = tensor.shape.elem_count();
                 total_size_in_bytes +=
-                    elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.block_size();
+                    elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.blck_size();
             }
             println!(
                 "loaded {:?} tensors ({}) in {:.2}s",
@@ -378,16 +377,15 @@ fn main() -> anyhow::Result<()> {
                 &format_size(total_size_in_bytes),
                 start.elapsed().as_secs_f32(),
             );
-            ModelWeights::from_gguf(model, &mut file, &device)?
+            ModelWeights::from_gguf(model, &mut file)?
         }
         Some("ggml" | "bin") | Some(_) | None => {
-            let model = ggml_file::Content::read(&mut file, &device)
-                .map_err(|e| e.with_path(model_path))?;
+            let model = ggml_file::Content::read(&mut file).map_err(|e| e.with_path(model_path))?;
             let mut total_size_in_bytes = 0;
             for (_, tensor) in model.tensors.iter() {
                 let elem_count = tensor.shape().elem_count();
                 total_size_in_bytes +=
-                    elem_count * tensor.dtype().type_size() / tensor.dtype().block_size();
+                    elem_count * tensor.dtype().type_size() / tensor.dtype().blck_size();
             }
             println!(
                 "loaded {:?} tensors ({}) in {:.2}s",
@@ -488,7 +486,7 @@ fn main() -> anyhow::Result<()> {
 
         let start_prompt_processing = std::time::Instant::now();
         let mut next_token = {
-            let input = Tensor::new(prompt_tokens.as_slice(), &device)?.unsqueeze(0)?;
+            let input = Tensor::new(prompt_tokens.as_slice(), &Device::Cpu)?.unsqueeze(0)?;
             let logits = model.forward(&input, 0)?;
             let logits = logits.squeeze(0)?;
             logits_processor.sample(&logits)?
@@ -509,7 +507,7 @@ fn main() -> anyhow::Result<()> {
         let start_post_prompt = std::time::Instant::now();
         let mut sampled = 0;
         for index in 0..to_sample {
-            let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
+            let input = Tensor::new(&[next_token], &Device::Cpu)?.unsqueeze(0)?;
             let logits = model.forward(&input, prompt_tokens.len() + index)?;
             let logits = logits.squeeze(0)?;
             let logits = if args.repeat_penalty == 1. {

candle-examples/examples/qwen/main.rs

@@ -1,281 +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, Model};
-
-use candle::{DType, Device, Tensor};
-use candle_examples::token_output_stream::TokenOutputStream;
-use candle_nn::VarBuilder;
-use candle_transformers::generation::LogitsProcessor;
-use hf_hub::{api::sync::Api, Repo, RepoType};
-use tokenizers::Tokenizer;
-
-struct TextGeneration {
-    model: Model,
-    device: Device,
-    tokenizer: TokenOutputStream,
-    logits_processor: LogitsProcessor,
-    repeat_penalty: f32,
-    repeat_last_n: usize,
-}
-
-impl TextGeneration {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        temp: Option<f64>,
-        top_p: Option<f64>,
-        repeat_penalty: f32,
-        repeat_last_n: usize,
-        device: &Device,
-    ) -> Self {
-        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
-        Self {
-            model,
-            tokenizer: TokenOutputStream::new(tokenizer),
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        self.tokenizer.clear();
-        let mut tokens = self
-            .tokenizer
-            .tokenizer()
-            .encode(prompt, true)
-            .map_err(E::msg)?
-            .get_ids()
-            .to_vec();
-        for &t in tokens.iter() {
-            if let Some(t) = self.tokenizer.next_token(t)? {
-                print!("{t}")
-            }
-        }
-        std::io::stdout().flush()?;
-
-        let mut generated_tokens = 0usize;
-        let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
-            Some(token) => token,
-            None => anyhow::bail!("cannot find the <|endoftext|> token"),
-        };
-        let start_gen = std::time::Instant::now();
-        for index in 0..sample_len {
-            let context_size = if index > 0 { 1 } else { tokens.len() };
-            let start_pos = tokens.len().saturating_sub(context_size);
-            let ctxt = &tokens[start_pos..];
-            let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
-            let logits = self.model.forward(&input, start_pos)?;
-            let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
-            let logits = if self.repeat_penalty == 1. {
-                logits
-            } else {
-                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
-                candle_transformers::utils::apply_repeat_penalty(
-                    &logits,
-                    self.repeat_penalty,
-                    &tokens[start_at..],
-                )?
-            };
-
-            let next_token = self.logits_processor.sample(&logits)?;
-            tokens.push(next_token);
-            generated_tokens += 1;
-            if next_token == eos_token {
-                break;
-            }
-            if let Some(t) = self.tokenizer.next_token(next_token)? {
-                print!("{t}");
-                std::io::stdout().flush()?;
-            }
-        }
-        let dt = start_gen.elapsed();
-        if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
-            print!("{rest}");
-        }
-        std::io::stdout().flush()?;
-        println!(
-            "\n{generated_tokens} tokens generated ({:.2} token/s)",
-            generated_tokens as f64 / dt.as_secs_f64(),
-        );
-        Ok(())
-    }
-}
-
-#[derive(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,
-}
-
-#[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",
-            };
-            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 => {
-                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 config: Config = serde_json::from_slice(&std::fs::read(config_file)?)?;
-    let device = candle_examples::device(args.cpu)?;
-    let dtype = if device.is_cuda() {
-        DType::BF16
-    } else {
-        DType::F32
-    };
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
-    let model = Model::new(&config, vb)?;
-
-    println!("loaded the model in {:?}", start.elapsed());
-
-    let mut pipeline = TextGeneration::new(
-        model,
-        tokenizer,
-        args.seed,
-        args.temperature,
-        args.top_p,
-        args.repeat_penalty,
-        args.repeat_last_n,
-        &device,
-    );
-    pipeline.run(&args.prompt, args.sample_len)?;
-    Ok(())
-}

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

@@ -411,7 +411,7 @@ impl DDPG<'_> {
     pub fn actions(&mut self, state: &Tensor) -> Result<f32> {
         let actions = self
             .actor
-            .forward(&state.detach().unsqueeze(0)?)?
+            .forward(&state.detach()?.unsqueeze(0)?)?
             .squeeze(0)?;
         let actions = if self.train {
             (actions + self.ou_noise.sample()?)?

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

@@ -74,7 +74,7 @@ pub fn run() -> Result<()> {
         loop {
             let action = {
                 let action_probs: Vec<f32> =
-                    softmax(&model.forward(&state.detach().unsqueeze(0)?)?, 1)?
+                    softmax(&model.forward(&state.detach()?.unsqueeze(0)?)?, 1)?
                         .squeeze(0)?
                         .to_vec1()?;
                 weighted_sample(action_probs, &mut rng)? as i64
@@ -109,7 +109,7 @@ pub fn run() -> Result<()> {
 
         let rewards = Tensor::from_vec(accumulate_rewards(&steps), batch_size, &Device::Cpu)?
             .to_dtype(DType::F32)?
-            .detach();
+            .detach()?;
 
         let actions_mask = {
             let actions: Vec<i64> = steps.iter().map(|s| s.action).collect();
@@ -126,12 +126,12 @@ pub fn run() -> Result<()> {
                         .unwrap()
                 })
                 .collect();
-            Tensor::stack(&actions_mask, 0)?.detach()
+            Tensor::stack(&actions_mask, 0)?.detach()?
         };
 
         let states = {
             let states: Vec<Tensor> = steps.into_iter().map(|s| s.state).collect();
-            Tensor::stack(&states, 0)?.detach()
+            Tensor::stack(&states, 0)?.detach()?
         };
 
         let log_probs = actions_mask

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

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

candle-examples/examples/rwkv/README.md

@@ -1,17 +0,0 @@
-## candle-rwkv
-
-The [RWKV model](https://wiki.rwkv.com/) is a recurrent neural network model
-with performance on par with transformer architectures. Several variants are
-available, candle implements the v5 version and can be used with Eagle 7B([blog
-post](https://blog.rwkv.com/p/eagle-7b-soaring-past-transformers)).
-
-```bash
-$ cargo run --example rwkv --release -- --prompt "The smallest prime is "
-avx: true, neon: false, simd128: false, f16c: true
-temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
-The smallest prime is ϕ(2) = 2.
-The smallest composite is ϕ(3) = 3.
-The smallest perfect number is ϕ(5) = 5.
-The smallest perfect square is ϕ(4) = 4.
-The smallest perfect cube is ϕ(6) = 6.
-```

candle-examples/examples/rwkv/main.rs

@@ -1,265 +0,0 @@
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-use anyhow::Result;
-use clap::{Parser, ValueEnum};
-
-use candle_transformers::models::rwkv_v5::{Config, Model, State, Tokenizer};
-
-use candle::{DType, Device, Tensor};
-use candle_nn::VarBuilder;
-use candle_transformers::generation::LogitsProcessor;
-use hf_hub::{api::sync::Api, Repo, RepoType};
-
-struct TextGeneration {
-    model: Model,
-    config: Config,
-    device: Device,
-    tokenizer: Tokenizer,
-    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,
-            logits_processor,
-            repeat_penalty,
-            repeat_last_n,
-            device: device.clone(),
-        }
-    }
-
-    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
-        use std::io::Write;
-        let mut tokens = self.tokenizer.encode(prompt)?;
-        let mut generated_tokens = 0usize;
-        let mut state = State::new(1, &self.config, &self.device)?;
-        let mut next_logits = None;
-        for &t in tokens.iter() {
-            let input = Tensor::new(&[[t]], &self.device)?;
-            let logits = self.model.forward(&input, &mut state)?;
-            next_logits = Some(logits);
-            print!("{}", self.tokenizer.decode(&[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)?.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;
-            print!("{}", self.tokenizer.decode(&[next_token])?);
-            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();
-        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 {
-    Eagle7b,
-    World1b5,
-    World3b,
-}
-
-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::Eagle7b => "RWKV/HF_v5-Eagle-7B",
-            Self::World1b5 => "RWKV/rwkv-5-world-1b5",
-            Self::World3b => "RWKV/rwkv-5-world-3b",
-        }
-    }
-
-    fn revision(&self) -> &'static str {
-        match self {
-            Self::Eagle7b => "refs/pr/1",
-            Self::World1b5 | Self::World3b => "refs/pr/2",
-        }
-    }
-}
-
-#[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 = "world1b5")]
-    which: Which,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    #[arg(long)]
-    revision: Option<String>,
-
-    #[arg(long)]
-    tokenizer: Option<String>,
-
-    #[arg(long)]
-    weight_files: Option<String>,
-
-    #[arg(long)]
-    config_file: Option<String>,
-
-    /// Penalty to be applied for repeating tokens, 1. means no penalty.
-    #[arg(long, default_value_t = 1.1)]
-    repeat_penalty: f32,
-
-    /// The context size to consider for the repeat penalty.
-    #[arg(long, default_value_t = 64)]
-    repeat_last_n: usize,
-}
-
-fn main() -> Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    println!(
-        "avx: {}, neon: {}, simd128: {}, f16c: {}",
-        candle::utils::with_avx(),
-        candle::utils::with_neon(),
-        candle::utils::with_simd128(),
-        candle::utils::with_f16c()
-    );
-    println!(
-        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
-        args.temperature.unwrap_or(0.),
-        args.repeat_penalty,
-        args.repeat_last_n
-    );
-
-    let start = std::time::Instant::now();
-    let api = Api::new()?;
-    let repo = api.repo(Repo::with_revision(
-        args.model_id
-            .unwrap_or_else(|| args.which.model_id().to_string()),
-        RepoType::Model,
-        args.revision
-            .unwrap_or_else(|| args.which.revision().to_string()),
-    ));
-    let tokenizer = match args.tokenizer {
-        Some(file) => std::path::PathBuf::from(file),
-        None => api
-            .model("lmz/candle-rwkv".to_string())
-            .get("rwkv_vocab_v20230424.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::new(tokenizer)?;
-
-    let start = std::time::Instant::now();
-    let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
-    let device = candle_examples::device(args.cpu)?;
-    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
-    let model = Model::new(&config, vb)?;
-    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/stable-lm/README.md

@@ -8,13 +8,6 @@ Card](https://huggingface.co/stabilityai/stablelm-3b-4e1t).
 Note that this model is gated so you will have to request access on the Hub in
 order to be able to use it.
 
-Other available models are Stable-Code-3B, StableLM-2 and Zephyr variants.
-
-StableLM-2 uses a Tiktoken based GPT-3.5/GPT-4 tokenizer not supported by
-Candle, so to run it you can download a somewhat compatible
-[tokenizer.json](https://huggingface.co/Xenova/gpt-4/resolve/main/tokenizer.json?download=true)
-and pass it via the --tokenizer-file argument.
-
 ## Running some example
 
 ```bash

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

@@ -5,7 +5,7 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use anyhow::{Error as E, Result};
-use clap::{Parser, ValueEnum};
+use clap::Parser;
 
 use candle_transformers::models::quantized_stable_lm::Model as QStableLM;
 use candle_transformers::models::stable_lm::{Config, Model as StableLM};
@@ -122,16 +122,6 @@ impl TextGeneration {
     }
 }
 
-#[derive(Clone, Copy, Debug, ValueEnum, PartialEq, Eq)]
-enum Which {
-    V1Orig,
-    V1,
-    V1Zephyr,
-    V2,
-    V2Zephyr,
-    Code,
-}
-
 #[derive(Parser, Debug)]
 #[command(author, version, about, long_about = None)]
 struct Args {
@@ -162,18 +152,15 @@ struct Args {
     seed: u64,
 
     /// The length of the sample to generate (in tokens).
-    #[arg(long, short = 'n', default_value_t = 1000)]
+    #[arg(long, short = 'n', default_value_t = 100)]
     sample_len: usize,
 
-    #[arg(long)]
-    model_id: Option<String>,
+    #[arg(long, default_value = "lmz/candle-stablelm-3b-4e1t")]
+    model_id: String,
 
     #[arg(long, default_value = "main")]
     revision: String,
 
-    #[arg(long, default_value = "v2")]
-    which: Which,
-
     #[arg(long)]
     tokenizer_file: Option<String>,
 
@@ -220,88 +207,40 @@ fn main() -> Result<()> {
 
     let start = std::time::Instant::now();
     let api = Api::new()?;
-    let model_id = match args.model_id {
-        Some(model_id) => model_id,
-        None => match args.which {
-            Which::V1Orig => "lmz/candle-stablelm-3b-4e1t".to_string(),
-            Which::V1 => "stabilityai/stablelm-3b-4e1t".to_string(),
-            Which::V1Zephyr => "stabilityai/stablelm-zephyr-3b".to_string(),
-            Which::Code => "stabilityai/stable-code-3b".to_string(),
-            Which::V2 => "stabilityai/stablelm-2-1_6b".to_string(),
-            Which::V2Zephyr => "stabilityai/stablelm-2-zephyr-1_6b".to_string(),
-        },
-    };
-
     let repo = api.repo(Repo::with_revision(
-        model_id,
+        args.model_id,
         RepoType::Model,
         args.revision,
     ));
     let tokenizer_filename = match args.tokenizer_file {
         Some(file) => std::path::PathBuf::from(file),
-        None => match args.which {
-            Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::Code => {
-                repo.get("tokenizer.json")?
-            }
-            Which::V2 | Which::V2Zephyr => api
-                .model("lmz/candle-stablelm".to_string())
-                .get("tokenizer-gpt4.json")?,
-        },
+        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.which, args.quantized) {
-            (Which::V1Orig | Which::V1, true) => vec![repo.get("model-q4k.gguf")?],
-            (Which::V2, true) => {
-                let gguf = api
-                    .model("lmz/candle-stablelm".to_string())
-                    .get("stablelm-2-1_6b-q4k.gguf")?;
-                vec![gguf]
-            }
-            (Which::V2Zephyr, true) => {
-                let gguf = api
-                    .model("lmz/candle-stablelm".to_string())
-                    .get("stablelm-2-zephyr-1_6b-q4k.gguf")?;
-                vec![gguf]
-            }
-            (Which::V1Zephyr | Which::Code, true) => {
-                anyhow::bail!("Quantized {:?} variant not supported.", args.which)
-            }
-            (Which::V1Orig | Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr, false) => {
+        None => {
+            if args.quantized {
+                vec![repo.get("model-q4k.gguf")?]
+            } else {
                 vec![repo.get("model.safetensors")?]
             }
-            (Which::Code, false) => {
-                candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
-            }
-        },
+        }
     };
-
     println!("retrieved the files in {:?}", start.elapsed());
     let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
 
     let start = std::time::Instant::now();
-    let config = match args.which {
-        Which::V1Orig => Config::stablelm_3b_4e1t(args.use_flash_attn),
-        Which::V1 | Which::V1Zephyr | Which::V2 | Which::V2Zephyr | Which::Code => {
-            let config_filename = repo.get("config.json")?;
-            let config = std::fs::read_to_string(config_filename)?;
-            let mut config: Config = serde_json::from_str(&config)?;
-            config.set_use_flash_attn(args.use_flash_attn);
-            config
-        }
-    };
-
-    let device = candle_examples::device(args.cpu)?;
+    let config = Config::stablelm_3b_4e1t(args.use_flash_attn);
     let (model, device) = if args.quantized {
         let filename = &filenames[0];
-        let vb =
-            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
+        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename)?;
         let model = QStableLM::new(&config, vb)?;
         (Model::Quantized(model), Device::Cpu)
     } else {
+        let device = candle_examples::device(args.cpu)?;
         let dtype = if device.is_cuda() {
             DType::BF16
         } else {

candle-examples/examples/trocr/main.rs

@@ -10,36 +10,15 @@ use clap::{Parser, ValueEnum};
 use candle::{DType, Tensor};
 use candle_examples::token_output_stream::TokenOutputStream;
 use candle_nn::VarBuilder;
-use candle_transformers::models::{trocr, vit};
+use candle_transformers::models::trocr;
 
 use tokenizers::Tokenizer;
 mod image_processor;
 
 #[derive(Clone, Debug, Copy, ValueEnum)]
 enum Which {
-    #[value(name = "base")]
-    BaseHandwritten,
-    #[value(name = "large")]
-    LargeHandwritten,
-    BasePrinted,
-    LargePrinted,
-}
-
-impl Which {
-    fn repo_and_branch_name(&self) -> (&str, &str) {
-        match self {
-            Self::BaseHandwritten => ("microsoft/trocr-base-handwritten", "refs/pr/3"),
-            Self::LargeHandwritten => ("microsoft/trocr-large-handwritten", "refs/pr/6"),
-            Self::BasePrinted => ("microsoft/trocr-base-printed", "refs/pr/7"),
-            Self::LargePrinted => ("microsoft/trocr-large-printed", "main"),
-        }
-    }
-}
-
-#[derive(Debug, Clone, serde::Deserialize)]
-struct Config {
-    encoder: vit::Config,
-    decoder: trocr::TrOCRConfig,
+    Base,
+    Large,
 }
 
 #[derive(Parser, Debug)]
@@ -55,64 +34,63 @@ struct Args {
     #[arg(long)]
     cpu: bool,
 
-    /// The image file to be processed.
+    /// Text to be translated
     #[arg(long)]
     image: String,
-
-    /// Tokenization config.
-    #[arg(long)]
-    tokenizer: Option<String>,
 }
 
 pub fn main() -> anyhow::Result<()> {
+    use hf_hub::api::sync::Api;
     let args = Args::parse();
-    let api = hf_hub::api::sync::Api::new()?;
 
-    let mut tokenizer_dec = {
-        let tokenizer_file = match args.tokenizer {
-            None => api
-                .model(String::from("ToluClassics/candle-trocr-tokenizer"))
-                .get("tokenizer.json")?,
-            Some(tokenizer) => std::path::PathBuf::from(tokenizer),
-        };
-        let tokenizer = Tokenizer::from_file(&tokenizer_file).map_err(E::msg)?;
-        TokenOutputStream::new(tokenizer)
+    let tokenizer_dec = {
+        let tokenizer = Api::new()?
+            .model(String::from("ToluClassics/candle-trocr-tokenizer"))
+            .get("tokenizer.json")?;
+
+        Tokenizer::from_file(&tokenizer).map_err(E::msg)?
     };
+
+    let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
+
     let device = candle_examples::device(args.cpu)?;
 
     let vb = {
         let model = match args.model {
             Some(model) => std::path::PathBuf::from(model),
-            None => {
-                let (repo, branch) = args.which.repo_and_branch_name();
-                api.repo(hf_hub::Repo::with_revision(
-                    repo.to_string(),
-                    hf_hub::RepoType::Model,
-                    branch.to_string(),
-                ))
-                .get("model.safetensors")?
-            }
+            None => match args.which {
+                Which::Base => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-base-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/3".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+                Which::Large => Api::new()?
+                    .repo(hf_hub::Repo::with_revision(
+                        "microsoft/trocr-large-handwritten".to_string(),
+                        hf_hub::RepoType::Model,
+                        "refs/pr/6".to_string(),
+                    ))
+                    .get("model.safetensors")?,
+            },
         };
         println!("model: {:?}", model);
         unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? }
     };
 
-    let (encoder_config, decoder_config) = {
-        let (repo, branch) = args.which.repo_and_branch_name();
-        let config_filename = api
-            .repo(hf_hub::Repo::with_revision(
-                repo.to_string(),
-                hf_hub::RepoType::Model,
-                branch.to_string(),
-            ))
-            .get("config.json")?;
-        let config: Config = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
-        (config.encoder, config.decoder)
+    let encoder_config = match args.which {
+        Which::Base => candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten(),
+        Which::Large => {
+            candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten()
+        }
     };
+
+    let decoder_config = trocr::TrOCRConfig::default();
     let mut model = trocr::TrOCRModel::new(&encoder_config, &decoder_config, vb)?;
 
-    let processor_config = image_processor::ProcessorConfig::default();
-    let processor = image_processor::ViTImageProcessor::new(&processor_config);
+    let config = image_processor::ProcessorConfig::default();
+    let processor = image_processor::ViTImageProcessor::new(&config);
 
     let image = vec![args.image.as_str()];
     let image = processor.preprocess(image)?;

candle-examples/examples/trocr/readme.md

@@ -5,27 +5,12 @@ transcribe image text. See the associated [model
 card](https://huggingface.co/microsoft/trocr-base-printed) for details on
 the model itself.
 
-Supported models include:
-
-- `--which base`: small handwritten OCR model.
-- `--which large`: large handwritten OCR model.
-- `--which base-printed`: small printed OCR model.
-- `--which large-printed`: large printed OCR model.
-
 ## Running an example
 
 ```bash
-cargo run --example trocr --release -- --image candle-examples/examples/trocr/assets/trocr.png
-cargo run --example trocr --release -- --which large --image candle-examples/examples/trocr/assets/trocr.png
-cargo run --example trocr --release -- --which base-printed --image candle-examples/examples/trocr/assets/noto.png
-cargo run --example trocr --release -- --which large-printed --image candle-examples/examples/trocr/assets/noto.png
+cargo run --example trocr --release --  --which base --cpu --image candle-examples/examples/trocr/assets/trocr.png
 ```
 
-### Outputs
-
 ```
-industry , Mr. Brown commented icily . " Let us have a
-industry , " Mr. Brown commented icily . " Let us have a
-THE QUICK BROWN FOR JUMPS OVER THE LAY DOG
-THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG
+<s> industry , Mr. Brown commented icily . " Let us have a</s>
 ```

candle-examples/examples/whisper-microphone/main.rs

@@ -1,673 +0,0 @@
-#[cfg(feature = "accelerate")]
-extern crate accelerate_src;
-
-#[cfg(feature = "mkl")]
-extern crate intel_mkl_src;
-
-use anyhow::{Error as E, Result};
-use candle::{Device, IndexOp, Tensor};
-use candle_nn::{ops::softmax, VarBuilder};
-use clap::{Parser, ValueEnum};
-use hf_hub::{api::sync::Api, Repo, RepoType};
-use rand::{distributions::Distribution, SeedableRng};
-use std::iter;
-use tokenizers::Tokenizer;
-
-mod multilingual;
-
-use candle_transformers::models::whisper::{self as m, audio, Config};
-
-use cpal::traits::{DeviceTrait, HostTrait, StreamTrait};
-use std::sync::{Arc, Mutex};
-
-pub enum Model {
-    Normal(m::model::Whisper),
-    Quantized(m::quantized_model::Whisper),
-}
-
-// Maybe we should use some traits rather than doing the dispatch for all these.
-impl Model {
-    pub fn config(&self) -> &Config {
-        match self {
-            Self::Normal(m) => &m.config,
-            Self::Quantized(m) => &m.config,
-        }
-    }
-
-    pub fn encoder_forward(&mut self, x: &Tensor, flush: bool) -> candle::Result<Tensor> {
-        match self {
-            Self::Normal(m) => m.encoder.forward(x, flush),
-            Self::Quantized(m) => m.encoder.forward(x, flush),
-        }
-    }
-
-    pub fn decoder_forward(
-        &mut self,
-        x: &Tensor,
-        xa: &Tensor,
-        flush: bool,
-    ) -> candle::Result<Tensor> {
-        match self {
-            Self::Normal(m) => m.decoder.forward(x, xa, flush),
-            Self::Quantized(m) => m.decoder.forward(x, xa, flush),
-        }
-    }
-
-    pub fn decoder_final_linear(&self, x: &Tensor) -> candle::Result<Tensor> {
-        match self {
-            Self::Normal(m) => m.decoder.final_linear(x),
-            Self::Quantized(m) => m.decoder.final_linear(x),
-        }
-    }
-}
-
-#[allow(dead_code)]
-#[derive(Debug, Clone)]
-struct DecodingResult {
-    tokens: Vec<u32>,
-    text: String,
-    avg_logprob: f64,
-    no_speech_prob: f64,
-    temperature: f64,
-    compression_ratio: f64,
-}
-
-#[allow(dead_code)]
-#[derive(Debug, Clone)]
-struct Segment {
-    start: f64,
-    duration: f64,
-    dr: DecodingResult,
-}
-
-struct Decoder {
-    model: Model,
-    rng: rand::rngs::StdRng,
-    task: Option<Task>,
-    timestamps: bool,
-    verbose: bool,
-    tokenizer: Tokenizer,
-    suppress_tokens: Tensor,
-    sot_token: u32,
-    transcribe_token: u32,
-    translate_token: u32,
-    eot_token: u32,
-    no_speech_token: u32,
-    no_timestamps_token: u32,
-    language_token: Option<u32>,
-}
-
-impl Decoder {
-    #[allow(clippy::too_many_arguments)]
-    fn new(
-        model: Model,
-        tokenizer: Tokenizer,
-        seed: u64,
-        device: &Device,
-        language_token: Option<u32>,
-        task: Option<Task>,
-        timestamps: bool,
-        verbose: bool,
-    ) -> Result<Self> {
-        let no_timestamps_token = token_id(&tokenizer, m::NO_TIMESTAMPS_TOKEN)?;
-        // Suppress the notimestamps token when in timestamps mode.
-        // https://github.com/openai/whisper/blob/e8622f9afc4eba139bf796c210f5c01081000472/whisper/decoding.py#L452
-        let suppress_tokens: Vec<f32> = (0..model.config().vocab_size as u32)
-            .map(|i| {
-                if model.config().suppress_tokens.contains(&i)
-                    || timestamps && i == no_timestamps_token
-                {
-                    f32::NEG_INFINITY
-                } else {
-                    0f32
-                }
-            })
-            .collect();
-        let suppress_tokens = Tensor::new(suppress_tokens.as_slice(), device)?;
-        let sot_token = token_id(&tokenizer, m::SOT_TOKEN)?;
-        let transcribe_token = token_id(&tokenizer, m::TRANSCRIBE_TOKEN)?;
-        let translate_token = token_id(&tokenizer, m::TRANSLATE_TOKEN)?;
-        let eot_token = token_id(&tokenizer, m::EOT_TOKEN)?;
-        let no_speech_token = m::NO_SPEECH_TOKENS
-            .iter()
-            .find_map(|token| token_id(&tokenizer, token).ok());
-        let no_speech_token = match no_speech_token {
-            None => anyhow::bail!("unable to find any non-speech token"),
-            Some(n) => n,
-        };
-        Ok(Self {
-            model,
-            rng: rand::rngs::StdRng::seed_from_u64(seed),
-            tokenizer,
-            task,
-            timestamps,
-            verbose,
-            suppress_tokens,
-            sot_token,
-            transcribe_token,
-            translate_token,
-            eot_token,
-            no_speech_token,
-            language_token,
-            no_timestamps_token,
-        })
-    }
-
-    fn decode(&mut self, mel: &Tensor, t: f64) -> Result<DecodingResult> {
-        let model = &mut self.model;
-        let audio_features = model.encoder_forward(mel, true)?;
-        if self.verbose {
-            println!("audio features: {:?}", audio_features.dims());
-        }
-        let sample_len = model.config().max_target_positions / 2;
-        let mut sum_logprob = 0f64;
-        let mut no_speech_prob = f64::NAN;
-        let mut tokens = vec![self.sot_token];
-        if let Some(language_token) = self.language_token {
-            tokens.push(language_token);
-        }
-        match self.task {
-            None | Some(Task::Transcribe) => tokens.push(self.transcribe_token),
-            Some(Task::Translate) => tokens.push(self.translate_token),
-        }
-        if !self.timestamps {
-            tokens.push(self.no_timestamps_token);
-        }
-        for i in 0..sample_len {
-            let tokens_t = Tensor::new(tokens.as_slice(), mel.device())?;
-
-            // The model expects a batch dim but this inference loop does not handle
-            // it so we add it at this point.
-            let tokens_t = tokens_t.unsqueeze(0)?;
-            let ys = model.decoder_forward(&tokens_t, &audio_features, i == 0)?;
-
-            // Extract the no speech probability on the first iteration by looking at the first
-            // token logits and the probability for the according token.
-            if i == 0 {
-                let logits = model.decoder_final_linear(&ys.i(..1)?)?.i(0)?.i(0)?;
-                no_speech_prob = softmax(&logits, 0)?
-                    .i(self.no_speech_token as usize)?
-                    .to_scalar::<f32>()? as f64;
-            }
-
-            let (_, seq_len, _) = ys.dims3()?;
-            let logits = model
-                .decoder_final_linear(&ys.i((..1, seq_len - 1..))?)?
-                .i(0)?
-                .i(0)?;
-            // TODO: Besides suppress tokens, we should apply the heuristics from
-            // ApplyTimestampRules, i.e.:
-            // - Timestamps come in pairs, except before EOT.
-            // - Timestamps should be non-decreasing.
-            // - If the sum of the probabilities of timestamps is higher than any other tokens,
-            //   only consider timestamps when sampling.
-            // https://github.com/openai/whisper/blob/e8622f9afc4eba139bf796c210f5c01081000472/whisper/decoding.py#L439
-            let logits = logits.broadcast_add(&self.suppress_tokens)?;
-            let next_token = if t > 0f64 {
-                let prs = softmax(&(&logits / t)?, 0)?;
-                let logits_v: Vec<f32> = prs.to_vec1()?;
-                let distr = rand::distributions::WeightedIndex::new(&logits_v)?;
-                distr.sample(&mut self.rng) as u32
-            } else {
-                let logits_v: Vec<f32> = logits.to_vec1()?;
-                logits_v
-                    .iter()
-                    .enumerate()
-                    .max_by(|(_, u), (_, v)| u.total_cmp(v))
-                    .map(|(i, _)| i as u32)
-                    .unwrap()
-            };
-            tokens.push(next_token);
-            let prob = softmax(&logits, candle::D::Minus1)?
-                .i(next_token as usize)?
-                .to_scalar::<f32>()? as f64;
-            if next_token == self.eot_token || tokens.len() > model.config().max_target_positions {
-                break;
-            }
-            sum_logprob += prob.ln();
-        }
-        let text = self.tokenizer.decode(&tokens, true).map_err(E::msg)?;
-        let avg_logprob = sum_logprob / tokens.len() as f64;
-
-        Ok(DecodingResult {
-            tokens,
-            text,
-            avg_logprob,
-            no_speech_prob,
-            temperature: t,
-            compression_ratio: f64::NAN,
-        })
-    }
-
-    fn decode_with_fallback(&mut self, segment: &Tensor) -> Result<DecodingResult> {
-        for (i, &t) in m::TEMPERATURES.iter().enumerate() {
-            let dr: Result<DecodingResult> = self.decode(segment, t);
-            if i == m::TEMPERATURES.len() - 1 {
-                return dr;
-            }
-            // On errors, we try again with a different temperature.
-            match dr {
-                Ok(dr) => {
-                    let needs_fallback = dr.compression_ratio > m::COMPRESSION_RATIO_THRESHOLD
-                        || dr.avg_logprob < m::LOGPROB_THRESHOLD;
-                    if !needs_fallback || dr.no_speech_prob > m::NO_SPEECH_THRESHOLD {
-                        return Ok(dr);
-                    }
-                }
-                Err(err) => {
-                    println!("Error running at {t}: {err}")
-                }
-            }
-        }
-        unreachable!()
-    }
-
-    fn run(&mut self, mel: &Tensor, times: Option<(f64, f64)>) -> Result<Vec<Segment>> {
-        let (_, _, content_frames) = mel.dims3()?;
-        let mut seek = 0;
-        let mut segments = vec![];
-        while seek < content_frames {
-            let start = std::time::Instant::now();
-            let time_offset = (seek * m::HOP_LENGTH) as f64 / m::SAMPLE_RATE as f64;
-            let segment_size = usize::min(content_frames - seek, m::N_FRAMES);
-            let mel_segment = mel.narrow(2, seek, segment_size)?;
-            let segment_duration = (segment_size * m::HOP_LENGTH) as f64 / m::SAMPLE_RATE as f64;
-            let dr = self.decode_with_fallback(&mel_segment)?;
-            seek += segment_size;
-            if dr.no_speech_prob > m::NO_SPEECH_THRESHOLD && dr.avg_logprob < m::LOGPROB_THRESHOLD {
-                println!("no speech detected, skipping {seek} {dr:?}");
-                continue;
-            }
-            let segment = Segment {
-                start: time_offset,
-                duration: segment_duration,
-                dr,
-            };
-            if self.timestamps {
-                println!(
-                    "{:.1}s -- {:.1}s",
-                    segment.start,
-                    segment.start + segment.duration,
-                );
-                let mut tokens_to_decode = vec![];
-                let mut prev_timestamp_s = 0f32;
-                for &token in segment.dr.tokens.iter() {
-                    if token == self.sot_token || token == self.eot_token {
-                        continue;
-                    }
-                    // The no_timestamp_token is the last before the timestamp ones.
-                    if token > self.no_timestamps_token {
-                        let timestamp_s = (token - self.no_timestamps_token + 1) as f32 / 50.;
-                        if !tokens_to_decode.is_empty() {
-                            let text = self
-                                .tokenizer
-                                .decode(&tokens_to_decode, true)
-                                .map_err(E::msg)?;
-                            println!("  {:.1}s-{:.1}s: {}", prev_timestamp_s, timestamp_s, text);
-                            tokens_to_decode.clear()
-                        }
-                        prev_timestamp_s = timestamp_s;
-                    } else {
-                        tokens_to_decode.push(token)
-                    }
-                }
-                if !tokens_to_decode.is_empty() {
-                    let text = self
-                        .tokenizer
-                        .decode(&tokens_to_decode, true)
-                        .map_err(E::msg)?;
-                    if !text.is_empty() {
-                        println!("  {:.1}s-...: {}", prev_timestamp_s, text);
-                    }
-                    tokens_to_decode.clear()
-                }
-            } else {
-                match times {
-                    Some((start, end)) => {
-                        println!("{:.1}s -- {:.1}s: {}", start, end, segment.dr.text)
-                    }
-                    None => {
-                        println!(
-                            "{:.1}s -- {:.1}s: {}",
-                            segment.start,
-                            segment.start + segment.duration,
-                            segment.dr.text,
-                        )
-                    }
-                }
-            }
-            if self.verbose {
-                println!("{seek}: {segment:?}, in {:?}", start.elapsed());
-            }
-            segments.push(segment)
-        }
-        Ok(segments)
-    }
-
-    fn set_language_token(&mut self, language_token: Option<u32>) {
-        self.language_token = language_token;
-    }
-
-    #[allow(dead_code)]
-    fn reset_kv_cache(&mut self) {
-        match &mut self.model {
-            Model::Normal(m) => m.reset_kv_cache(),
-            Model::Quantized(m) => m.reset_kv_cache(),
-        }
-    }
-
-    fn model(&mut self) -> &mut Model {
-        &mut self.model
-    }
-}
-
-pub fn token_id(tokenizer: &Tokenizer, token: &str) -> candle::Result<u32> {
-    match tokenizer.token_to_id(token) {
-        None => candle::bail!("no token-id for {token}"),
-        Some(id) => Ok(id),
-    }
-}
-
-#[derive(Clone, Copy, Debug, ValueEnum)]
-enum Task {
-    Transcribe,
-    Translate,
-}
-
-#[derive(Clone, Copy, Debug, PartialEq, Eq, ValueEnum)]
-enum WhichModel {
-    Tiny,
-    #[value(name = "tiny.en")]
-    TinyEn,
-    Base,
-    #[value(name = "base.en")]
-    BaseEn,
-    Small,
-    #[value(name = "small.en")]
-    SmallEn,
-    Medium,
-    #[value(name = "medium.en")]
-    MediumEn,
-    Large,
-    LargeV2,
-    LargeV3,
-    #[value(name = "distil-medium.en")]
-    DistilMediumEn,
-    #[value(name = "distil-large-v2")]
-    DistilLargeV2,
-}
-
-impl WhichModel {
-    fn is_multilingual(&self) -> bool {
-        match self {
-            Self::Tiny
-            | Self::Base
-            | Self::Small
-            | Self::Medium
-            | Self::Large
-            | Self::LargeV2
-            | Self::LargeV3
-            | Self::DistilLargeV2 => true,
-            Self::TinyEn | Self::BaseEn | Self::SmallEn | Self::MediumEn | Self::DistilMediumEn => {
-                false
-            }
-        }
-    }
-
-    fn model_and_revision(&self) -> (&'static str, &'static str) {
-        match self {
-            Self::Tiny => ("openai/whisper-tiny", "main"),
-            Self::TinyEn => ("openai/whisper-tiny.en", "refs/pr/15"),
-            Self::Base => ("openai/whisper-base", "refs/pr/22"),
-            Self::BaseEn => ("openai/whisper-base.en", "refs/pr/13"),
-            Self::Small => ("openai/whisper-small", "main"),
-            Self::SmallEn => ("openai/whisper-small.en", "refs/pr/10"),
-            Self::Medium => ("openai/whisper-medium", "main"),
-            Self::MediumEn => ("openai/whisper-medium.en", "main"),
-            Self::Large => ("openai/whisper-large", "refs/pr/36"),
-            Self::LargeV2 => ("openai/whisper-large-v2", "refs/pr/57"),
-            Self::LargeV3 => ("openai/whisper-large-v3", "main"),
-            Self::DistilMediumEn => ("distil-whisper/distil-medium.en", "main"),
-            Self::DistilLargeV2 => ("distil-whisper/distil-large-v2", "main"),
-        }
-    }
-}
-
-#[derive(Parser, Debug)]
-#[command(author, version, about, long_about = None)]
-struct Args {
-    /// Run on CPU rather than on GPU.
-    #[arg(long)]
-    cpu: bool,
-
-    #[arg(long)]
-    model_id: Option<String>,
-
-    /// The model to use, check out available models:
-    /// https://huggingface.co/models?search=whisper
-    #[arg(long)]
-    revision: Option<String>,
-
-    /// The model to be used, can be tiny, small, medium.
-    #[arg(long, default_value = "tiny.en")]
-    model: WhichModel,
-
-    /// The seed to use when generating random samples.
-    #[arg(long, default_value_t = 299792458)]
-    seed: u64,
-
-    /// Enable tracing (generates a trace-timestamp.json file).
-    #[arg(long)]
-    tracing: bool,
-
-    #[arg(long)]
-    quantized: bool,
-
-    /// Language.
-    #[arg(long)]
-    language: Option<String>,
-
-    /// Task, when no task is specified, the input tokens contain only the sot token which can
-    /// improve things when in no-timestamp mode.
-    #[arg(long)]
-    task: Option<Task>,
-
-    /// Timestamps mode, this is not fully implemented yet.
-    #[arg(long)]
-    timestamps: bool,
-
-    /// Print the full DecodingResult structure rather than just the text.
-    #[arg(long)]
-    verbose: bool,
-}
-
-pub fn main() -> Result<()> {
-    use tracing_chrome::ChromeLayerBuilder;
-    use tracing_subscriber::prelude::*;
-
-    let args = Args::parse();
-    let _guard = if args.tracing {
-        let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
-        tracing_subscriber::registry().with(chrome_layer).init();
-        Some(guard)
-    } else {
-        None
-    };
-    let device = candle_examples::device(args.cpu)?;
-    let (default_model, default_revision) = if args.quantized {
-        ("lmz/candle-whisper", "main")
-    } else {
-        args.model.model_and_revision()
-    };
-    let default_model = default_model.to_string();
-    let default_revision = default_revision.to_string();
-    let (model_id, revision) = match (args.model_id, args.revision) {
-        (Some(model_id), Some(revision)) => (model_id, revision),
-        (Some(model_id), None) => (model_id, "main".to_string()),
-        (None, Some(revision)) => (default_model, revision),
-        (None, None) => (default_model, default_revision),
-    };
-
-    let (config_filename, tokenizer_filename, weights_filename) = {
-        let api = Api::new()?;
-        let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
-        let (config, tokenizer, model) = if args.quantized {
-            let ext = match args.model {
-                WhichModel::TinyEn => "tiny-en",
-                WhichModel::Tiny => "tiny",
-                _ => unimplemented!("no quantized support for {:?}", args.model),
-            };
-            (
-                repo.get(&format!("config-{ext}.json"))?,
-                repo.get(&format!("tokenizer-{ext}.json"))?,
-                repo.get(&format!("model-{ext}-q80.gguf"))?,
-            )
-        } else {
-            let config = repo.get("config.json")?;
-            let tokenizer = repo.get("tokenizer.json")?;
-            let model = repo.get("model.safetensors")?;
-            (config, tokenizer, model)
-        };
-        (config, tokenizer, model)
-    };
-    let config: Config = serde_json::from_str(&std::fs::read_to_string(config_filename)?)?;
-    let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
-    let model = if args.quantized {
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
-            &weights_filename,
-            &device,
-        )?;
-        Model::Quantized(m::quantized_model::Whisper::load(&vb, config.clone())?)
-    } else {
-        let vb =
-            unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], m::DTYPE, &device)? };
-        Model::Normal(m::model::Whisper::load(&vb, config.clone())?)
-    };
-    let language_token = None;
-    let mut dc = Decoder::new(
-        model,
-        tokenizer.clone(),
-        args.seed,
-        &device,
-        language_token,
-        args.task,
-        args.timestamps,
-        args.verbose,
-    )?;
-
-    let mel_bytes = match config.num_mel_bins {
-        80 => include_bytes!("../whisper/melfilters.bytes").as_slice(),
-        128 => include_bytes!("../whisper/melfilters128.bytes").as_slice(),
-        nmel => anyhow::bail!("unexpected num_mel_bins {nmel}"),
-    };
-    let mut mel_filters = vec![0f32; mel_bytes.len() / 4];
-    <byteorder::LittleEndian as byteorder::ByteOrder>::read_f32_into(mel_bytes, &mut mel_filters);
-
-    // Set up the input device and stream with the default input config.
-    let host = cpal::default_host();
-    let _device = "default";
-    let _device = if _device == "default" {
-        host.default_input_device()
-    } else {
-        host.input_devices()?
-            .find(|x| x.name().map(|y| y == _device).unwrap_or(false))
-    }
-    .expect("failed to find input device");
-
-    let _config = _device
-        .default_input_config()
-        .expect("Failed to get default input config");
-
-    let channel_count = _config.channels() as usize;
-
-    let audio_ring_buffer = Arc::new(Mutex::new(Vec::new()));
-    let audio_ring_buffer_2 = audio_ring_buffer.clone();
-
-    std::thread::spawn(move || loop {
-        let data = record_audio(&_device, &_config, 300).unwrap();
-        audio_ring_buffer.lock().unwrap().extend_from_slice(&data);
-        let max_len = data.len() * 16;
-        let data_len = data.len();
-        let len = audio_ring_buffer.lock().unwrap().len();
-        if len > max_len {
-            let mut data = audio_ring_buffer.lock().unwrap();
-            let new_data = data[data_len..].to_vec();
-            *data = new_data;
-        }
-    });
-
-    // loop to process the audio data forever (until the user stops the program)
-    println!("Transcribing audio...");
-    for (i, _) in iter::repeat(()).enumerate() {
-        std::thread::sleep(std::time::Duration::from_millis(1000));
-        let data = audio_ring_buffer_2.lock().unwrap().clone();
-        let pcm_data: Vec<_> = data[..data.len() / channel_count as usize]
-            .iter()
-            .map(|v| *v as f32 / 32768.)
-            .collect();
-        let mel = audio::pcm_to_mel(&config, &pcm_data, &mel_filters);
-        let mel_len = mel.len();
-        let mel = Tensor::from_vec(
-            mel,
-            (1, config.num_mel_bins, mel_len / config.num_mel_bins),
-            &device,
-        )?;
-
-        // on the first iteration, we detect the language and set the language token.
-        if i == 0 {
-            let language_token = match (args.model.is_multilingual(), args.language.clone()) {
-                (true, None) => Some(multilingual::detect_language(dc.model(), &tokenizer, &mel)?),
-                (false, None) => None,
-                (true, Some(language)) => match token_id(&tokenizer, &format!("<|{language}|>")) {
-                    Ok(token_id) => Some(token_id),
-                    Err(_) => anyhow::bail!("language {language} is not supported"),
-                },
-                (false, Some(_)) => {
-                    anyhow::bail!("a language cannot be set for non-multilingual models")
-                }
-            };
-            println!("language_token: {:?}", language_token);
-            dc.set_language_token(language_token);
-        }
-        dc.run(
-            &mel,
-            Some((
-                i as f64,
-                i as f64 + data.len() as f64 / m::SAMPLE_RATE as f64,
-            )),
-        )?;
-        dc.reset_kv_cache();
-    }
-
-    Ok(())
-}
-
-fn record_audio(
-    device: &cpal::Device,
-    config: &cpal::SupportedStreamConfig,
-    milliseconds: u64,
-) -> Result<Vec<i16>> {
-    let writer = Arc::new(Mutex::new(Vec::new()));
-    let writer_2 = writer.clone();
-    let stream = device.build_input_stream(
-        &config.config(),
-        move |data: &[f32], _: &cpal::InputCallbackInfo| {
-            let processed = data
-                .iter()
-                .map(|v| (v * 32768.0) as i16)
-                .collect::<Vec<i16>>();
-            writer_2.lock().unwrap().extend_from_slice(&processed);
-        },
-        move |err| {
-            eprintln!("an error occurred on stream: {}", err);
-        },
-        None,
-    )?;
-    stream.play()?;
-    std::thread::sleep(std::time::Duration::from_millis(milliseconds));
-    drop(stream);
-    let data = writer.lock().unwrap().clone();
-    let step = 3;
-    let data: Vec<i16> = data.iter().step_by(step).copied().collect();
-    Ok(data)
-}

candle-examples/examples/whisper-microphone/multilingual.rs

@@ -1,137 +0,0 @@
-use crate::{token_id, Model};
-use candle::{IndexOp, Result, Tensor, D};
-use candle_transformers::models::whisper::{self as m};
-use tokenizers::Tokenizer;
-
-const LANGUAGES: [(&str, &str); 99] = [
-    ("en", "english"),
-    ("zh", "chinese"),
-    ("de", "german"),
-    ("es", "spanish"),
-    ("ru", "russian"),
-    ("ko", "korean"),
-    ("fr", "french"),
-    ("ja", "japanese"),
-    ("pt", "portuguese"),
-    ("tr", "turkish"),
-    ("pl", "polish"),
-    ("ca", "catalan"),
-    ("nl", "dutch"),
-    ("ar", "arabic"),
-    ("sv", "swedish"),
-    ("it", "italian"),
-    ("id", "indonesian"),
-    ("hi", "hindi"),
-    ("fi", "finnish"),
-    ("vi", "vietnamese"),
-    ("he", "hebrew"),
-    ("uk", "ukrainian"),
-    ("el", "greek"),
-    ("ms", "malay"),
-    ("cs", "czech"),
-    ("ro", "romanian"),
-    ("da", "danish"),
-    ("hu", "hungarian"),
-    ("ta", "tamil"),
-    ("no", "norwegian"),
-    ("th", "thai"),
-    ("ur", "urdu"),
-    ("hr", "croatian"),
-    ("bg", "bulgarian"),
-    ("lt", "lithuanian"),
-    ("la", "latin"),
-    ("mi", "maori"),
-    ("ml", "malayalam"),
-    ("cy", "welsh"),
-    ("sk", "slovak"),
-    ("te", "telugu"),
-    ("fa", "persian"),
-    ("lv", "latvian"),
-    ("bn", "bengali"),
-    ("sr", "serbian"),
-    ("az", "azerbaijani"),
-    ("sl", "slovenian"),
-    ("kn", "kannada"),
-    ("et", "estonian"),
-    ("mk", "macedonian"),
-    ("br", "breton"),
-    ("eu", "basque"),
-    ("is", "icelandic"),
-    ("hy", "armenian"),
-    ("ne", "nepali"),
-    ("mn", "mongolian"),
-    ("bs", "bosnian"),
-    ("kk", "kazakh"),
-    ("sq", "albanian"),
-    ("sw", "swahili"),
-    ("gl", "galician"),
-    ("mr", "marathi"),
-    ("pa", "punjabi"),
-    ("si", "sinhala"),
-    ("km", "khmer"),
-    ("sn", "shona"),
-    ("yo", "yoruba"),
-    ("so", "somali"),
-    ("af", "afrikaans"),
-    ("oc", "occitan"),
-    ("ka", "georgian"),
-    ("be", "belarusian"),
-    ("tg", "tajik"),
-    ("sd", "sindhi"),
-    ("gu", "gujarati"),
-    ("am", "amharic"),
-    ("yi", "yiddish"),
-    ("lo", "lao"),
-    ("uz", "uzbek"),
-    ("fo", "faroese"),
-    ("ht", "haitian creole"),
-    ("ps", "pashto"),
-    ("tk", "turkmen"),
-    ("nn", "nynorsk"),
-    ("mt", "maltese"),
-    ("sa", "sanskrit"),
-    ("lb", "luxembourgish"),
-    ("my", "myanmar"),
-    ("bo", "tibetan"),
-    ("tl", "tagalog"),
-    ("mg", "malagasy"),
-    ("as", "assamese"),
-    ("tt", "tatar"),
-    ("haw", "hawaiian"),
-    ("ln", "lingala"),
-    ("ha", "hausa"),
-    ("ba", "bashkir"),
-    ("jw", "javanese"),
-    ("su", "sundanese"),
-];
-
-/// Returns the token id for the selected language.
-pub fn detect_language(model: &mut Model, tokenizer: &Tokenizer, mel: &Tensor) -> Result<u32> {
-    let (_bsize, _, seq_len) = mel.dims3()?;
-    let mel = mel.narrow(
-        2,
-        0,
-        usize::min(seq_len, model.config().max_source_positions),
-    )?;
-    let device = mel.device();
-    let language_token_ids = LANGUAGES
-        .iter()
-        .map(|(t, _)| token_id(tokenizer, &format!("<|{t}|>")))
-        .collect::<Result<Vec<_>>>()?;
-    let sot_token = token_id(tokenizer, m::SOT_TOKEN)?;
-    let audio_features = model.encoder_forward(&mel, true)?;
-    let tokens = Tensor::new(&[[sot_token]], device)?;
-    let language_token_ids = Tensor::new(language_token_ids.as_slice(), device)?;
-    let ys = model.decoder_forward(&tokens, &audio_features, true)?;
-    let logits = model.decoder_final_linear(&ys.i(..1)?)?.i(0)?.i(0)?;
-    let logits = logits.index_select(&language_token_ids, 0)?;
-    let probs = candle_nn::ops::softmax(&logits, D::Minus1)?;
-    let probs = probs.to_vec1::<f32>()?;
-    let mut probs = LANGUAGES.iter().zip(probs.iter()).collect::<Vec<_>>();
-    probs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
-    for ((_, language), p) in probs.iter().take(5) {
-        println!("{language}: {p}")
-    }
-    let language = token_id(tokenizer, &format!("<|{}|>", probs[0].0 .0))?;
-    Ok(language)
-}

candle-examples/examples/whisper/main.rs

@@ -18,8 +18,6 @@ use rand::{distributions::Distribution, SeedableRng};
 use tokenizers::Tokenizer;
 
 mod multilingual;
-mod pcm_decode;
-
 use candle_transformers::models::whisper::{self as m, audio, Config};
 
 pub enum Model {
@@ -537,10 +535,17 @@ fn main() -> Result<()> {
     let mut mel_filters = vec![0f32; mel_bytes.len() / 4];
     <byteorder::LittleEndian as byteorder::ByteOrder>::read_f32_into(mel_bytes, &mut mel_filters);
 
-    let (pcm_data, sample_rate) = pcm_decode::pcm_decode(input)?;
-    if sample_rate != m::SAMPLE_RATE as u32 {
-        anyhow::bail!("input file must have a {} sampling rate", m::SAMPLE_RATE)
+    let mut input = std::fs::File::open(input)?;
+    let (header, data) = wav::read(&mut input)?;
+    println!("loaded wav data: {header:?}");
+    if header.sampling_rate != m::SAMPLE_RATE as u32 {
+        anyhow::bail!("wav file must have a {} sampling rate", m::SAMPLE_RATE)
     }
+    let data = data.as_sixteen().expect("expected 16 bit wav file");
+    let pcm_data: Vec<_> = data[..data.len() / header.channel_count as usize]
+        .iter()
+        .map(|v| *v as f32 / 32768.)
+        .collect();
     println!("pcm data loaded {}", pcm_data.len());
     let mel = audio::pcm_to_mel(&config, &pcm_data, &mel_filters);
     let mel_len = mel.len();
@@ -552,10 +557,8 @@ fn main() -> Result<()> {
     println!("loaded mel: {:?}", mel.dims());
 
     let mut model = if args.quantized {
-        let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
-            &weights_filename,
-            &device,
-        )?;
+        let vb =
+            candle_transformers::quantized_var_builder::VarBuilder::from_gguf(&weights_filename)?;
         Model::Quantized(m::quantized_model::Whisper::load(&vb, config)?)
     } else {
         let vb =

candle-examples/examples/whisper/pcm_decode.rs

@@ -1,74 +0,0 @@
-use symphonia::core::audio::{AudioBufferRef, Signal};
-use symphonia::core::codecs::{DecoderOptions, CODEC_TYPE_NULL};
-use symphonia::core::conv::FromSample;
-
-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>,
-{
-    samples.extend(data.chan(0).iter().map(|v| f32::from_sample(*v)))
-}
-
-pub(crate) fn pcm_decode<P: AsRef<std::path::Path>>(path: P) -> anyhow::Result<(Vec<f32>, u32)> {
-    // Open the media source.
-    let src = std::fs::File::open(path)?;
-
-    // Create the media source stream.
-    let mss = symphonia::core::io::MediaSourceStream::new(Box::new(src), Default::default());
-
-    // Create a probe hint using the file's extension. [Optional]
-    let hint = symphonia::core::probe::Hint::new();
-
-    // Use the default options for metadata and format readers.
-    let meta_opts: symphonia::core::meta::MetadataOptions = Default::default();
-    let fmt_opts: symphonia::core::formats::FormatOptions = Default::default();
-
-    // Probe the media source.
-    let probed = symphonia::default::get_probe().format(&hint, mss, &fmt_opts, &meta_opts)?;
-    // Get the instantiated format reader.
-    let mut format = probed.format;
-
-    // Find the first audio track with a known (decodeable) codec.
-    let track = format
-        .tracks()
-        .iter()
-        .find(|t| t.codec_params.codec != CODEC_TYPE_NULL)
-        .expect("no supported audio tracks");
-
-    // Use the default options for the decoder.
-    let dec_opts: DecoderOptions = Default::default();
-
-    // Create a decoder for the track.
-    let mut decoder = symphonia::default::get_codecs()
-        .make(&track.codec_params, &dec_opts)
-        .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();
-    // The decode loop.
-    while let Ok(packet) = format.next_packet() {
-        // Consume any new metadata that has been read since the last packet.
-        while !format.metadata().is_latest() {
-            format.metadata().pop();
-        }
-
-        // If the packet does not belong to the selected track, skip over it.
-        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))
-}

candle-examples/examples/yi/main.rs

@@ -104,7 +104,6 @@ impl TextGeneration {
                 break;
             }
             if let Some(t) = self.tokenizer.next_token(next_token)? {
-                let t = t.replace("<|im_end|>", "\n");
                 print!("{t}");
                 std::io::stdout().flush()?;
             }

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

@@ -216,7 +216,7 @@ fn detect(
     xs: &Tensor,
     image_height: usize,
     classes: usize,
-    anchors: &[(usize, usize)],
+    anchors: &Vec<(usize, usize)>,
 ) -> Result<Tensor> {
     let (bsize, _channels, height, _width) = xs.dims4()?;
     let stride = image_height / height;

candle-examples/src/token_output_stream.rs

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

candle-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.4.0"
+version = "0.3.3"
 edition = "2021"
 
 description = "Flash attention layer for the candle ML framework."
@@ -11,7 +11,7 @@ license = "MIT OR Apache-2.0"
 readme = "README.md"
 
 [dependencies]
-candle = { path = "../candle-core", features = ["cuda"], package = "candle-core", version = "0.4.0" }
+candle = { path = "../candle-core", features = ["cuda"], package = "candle-core" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]

candle-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.4.0"
+version = "0.3.3"
 edition = "2021"
 
 description = "CUDA kernels for Candle"

candle-kernels/src/conv.cu

@@ -71,6 +71,7 @@ __device__ void im2col1d(
   }
   const size_t *src_dims = info;
   const size_t *src_s = info + 3;
+  const size_t b_in = src_dims[0];
   const size_t c_in = src_dims[1];
   const size_t l_in = src_dims[2];
 
@@ -119,6 +120,7 @@ __device__ void im2col(
   }
   const size_t *src_dims = info;
   const size_t *src_s = info + 4;
+  const size_t b_in = src_dims[0];
   const size_t c_in = src_dims[1];
   const size_t h_in = src_dims[2];
   const size_t w_in = src_dims[3];
@@ -223,60 +225,6 @@ __device__ void conv2d(
   dst[dst_i] = static_cast<T>(d);
 }
 
-// Naive implementation of conv_transpose1d.
-template <typename T, typename A>
-__device__ void conv_transpose1d(
-    const size_t src_numel,
-    const size_t l_out,
-    const size_t stride,
-    const size_t padding,
-    const size_t out_padding,
-    const size_t dilation,
-    const size_t *info,
-    const T *src,
-    const T *kernel,
-    T *dst
-) {
-  const size_t dst_i = blockIdx.x * blockDim.x + threadIdx.x;
-  // src: (b_size, c_in, l_in)
-  // k: (c_in, c_out, l_k)
-  const size_t *src_dims = info;
-  const size_t *src_s = info + 3;
-  const size_t *k_dims = info + 6;
-  const size_t *k_s = info + 9;
-  const size_t l_k = k_dims[2];
-  const size_t c_out = k_dims[1];
-  const size_t c_in = src_dims[1];
-  const size_t l_in = src_dims[2];
-  if (dst_i >= src_dims[0] * c_out * l_out) {
-    return;
-  }
-
-  // TODO
-  const size_t b_idx = dst_i / (l_out * c_out);
-  const size_t dst_c_idx = (dst_i / l_out) % c_out;
-  // NCL layout.
-  const size_t out_x = dst_i % l_out;
-
-  const size_t src_idx0 = b_idx * src_s[0];
-  A d = 0;
-  for (int k_x = 0; k_x < (int)l_k; ++k_x) {
-      // let out_x = inp_x * p.stride + k_x * p.dilation - p.padding;
-      int inp_x_stride = (int)(out_x + padding) - k_x * dilation;
-      if (inp_x_stride < 0 || inp_x_stride % stride) {
-          continue;
-      }
-      int inp_x = inp_x_stride / stride;
-      if (inp_x >= l_in) continue;
-      for (size_t src_c_idx = 0; src_c_idx < c_in; ++src_c_idx) {
-          const size_t src_idx = src_idx0 + src_c_idx * src_s[1] + inp_x * src_s[2];
-          const size_t k_idx = src_c_idx * k_s[0] + dst_c_idx * k_s[1] + k_x * k_s[2];
-          d += static_cast<A>(src[src_idx]) * static_cast<A>(kernel[k_idx]);
-      }
-  }
-  dst[dst_i] = static_cast<T>(d);
-}
-
 // Naive implementation of conv_transpose2d.
 template <typename T, typename A>
 __device__ void conv_transpose2d(
@@ -559,22 +507,6 @@ extern "C" __global__ void FN_NAME(  \
   im2col<TYPENAME>(dst_numel, h_out, w_out, h_k, w_k, stride, padding, dilation, info, src, dst); \
 } \
 
-#define CONVT1D_OP(TYPENAME, TYPEACC, FN_NAME) \
-extern "C" __global__ void FN_NAME(  \
-    const size_t src_numel, \
-    const size_t l_out, \
-    const size_t stride, \
-    const size_t padding, \
-    const size_t out_padding, \
-    const size_t dilation, \
-    const size_t *info, \
-    const TYPENAME *src, \
-    const TYPENAME *kernel, \
-    TYPENAME *dst \
-) {  \
-  conv_transpose1d<TYPENAME, TYPEACC>(src_numel, l_out, stride, padding, out_padding, dilation, info, src, kernel, dst); \
-} \
-
 #define CONVT2D_OP(TYPENAME, TYPEACC, FN_NAME) \
 extern "C" __global__ void FN_NAME(  \
     const size_t src_numel, \
@@ -636,7 +568,6 @@ extern "C" __global__ void FN_NAME(  \
 #if __CUDA_ARCH__ >= 800
 CONV1D_OP(__nv_bfloat16, float, conv1d_bf16)
 CONV2D_OP(__nv_bfloat16, float, conv2d_bf16)
-CONVT1D_OP(__nv_bfloat16, float, conv_transpose1d_bf16)
 CONVT2D_OP(__nv_bfloat16, float, conv_transpose2d_bf16)
 AVG_POOL2D_OP(__nv_bfloat16, float, avg_pool2d_bf16)
 MAX_POOL2D_OP(__nv_bfloat16, max_pool2d_bf16)
@@ -648,7 +579,6 @@ IM2COL1D_OP(__nv_bfloat16, im2col1d_bf16)
 #if __CUDA_ARCH__ >= 530
 CONV1D_OP(__half, float, conv1d_f16)
 CONV2D_OP(__half, float, conv2d_f16)
-CONVT1D_OP(__half, float, conv_transpose1d_f16)
 CONVT2D_OP(__half, float, conv_transpose2d_f16)
 AVG_POOL2D_OP(__half, float, avg_pool2d_f16)
 MAX_POOL2D_OP(__half, max_pool2d_f16)
@@ -667,11 +597,6 @@ CONV2D_OP(double, double, conv2d_f64)
 CONV2D_OP(uint8_t, uint8_t, conv2d_u8)
 CONV2D_OP(uint32_t, uint32_t, conv2d_u32)
 
-CONVT1D_OP(float, float, conv_transpose1d_f32)
-CONVT1D_OP(double, double, conv_transpose1d_f64)
-CONVT1D_OP(uint8_t, uint8_t, conv_transpose1d_u8)
-CONVT1D_OP(uint32_t, uint32_t, conv_transpose1d_u32)
-
 CONVT2D_OP(float, float, conv_transpose2d_f32)
 CONVT2D_OP(double, double, conv_transpose2d_f64)
 CONVT2D_OP(uint8_t, uint8_t, conv_transpose2d_u8)

candle-kernels/src/unary.cu

@@ -55,11 +55,6 @@ __device__ __forceinline__ T relu_fwd(T x) {
     return maxg(x, zero);
 }
 
-template<typename T>
-__device__ __forceinline__ T silu_fwd(T x) {
-    return x / (static_cast<T>(1) + expg(-x));
-}
-
 #define UNARY_OP1(TYPENAME, FN_NAME, FUNC) \
 extern "C" __global__ void FN_NAME( \
     const size_t numel, \
@@ -108,7 +103,6 @@ UNARY_OP(__nv_bfloat16, ugelu_bf16, gelu_fwd(x))
 UNARY_OP(__nv_bfloat16, ugelu_erf_bf16, gelu_erf_fwd(x))
 UNARY_OP(__nv_bfloat16, urelu_bf16, relu_fwd(x))
 UNARY_OP1(__nv_bfloat16, uelu_bf16, elu_fwd(x, param))
-UNARY_OP(__nv_bfloat16, usilu_bf16, silu_fwd(x))
 UNARY_OP1(__nv_bfloat16, upowf_bf16, powg(x, param))
 #endif
 
@@ -133,7 +127,6 @@ UNARY_OP(__half, ugelu_f16, gelu_fwd(x))
 UNARY_OP(__half, ugelu_erf_f16, gelu_erf_fwd(x))
 UNARY_OP(__half, urelu_f16, relu_fwd(x))
 UNARY_OP1(__half, uelu_f16, elu_fwd(x, param))
-UNARY_OP(__half, usilu_f16, silu_fwd(x))
 UNARY_OP1(__half, upowf_f16, powg(x, param))
 #endif
 
@@ -180,7 +173,5 @@ UNARY_OP(float, urelu_f32, relu_fwd(x))
 UNARY_OP(double, urelu_f64, relu_fwd(x))
 UNARY_OP1(float, uelu_f32, elu_fwd(x, param))
 UNARY_OP1(double, uelu_f64, elu_fwd(x, param))
-UNARY_OP(float, usilu_f32, silu_fwd(x))
-UNARY_OP(double, usilu_f64, silu_fwd(x))
 UNARY_OP1(float, upowf_f32, powg(x, param))
 UNARY_OP1(double, upowf_f64, powg(x, param))

candle-metal-kernels/.gitignore

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

candle-metal-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-metal-kernels"
-version = "0.4.0"
+version = "0.3.3"
 edition = "2021"
 
 description = "Metal kernels for Candle"

candle-metal-kernels/build.rs

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

candle-metal-kernels/src/binary.metal

@@ -73,7 +73,7 @@ BINARY(FN, uint32_t, uint8_t, NAME##_u32, NAME##_u32_strided); \
 BINARY(FN, uint8_t, uint8_t, NAME##_u8, NAME##_u8_strided);
 
 #define INT64_BINARY_OP_OUT(NAME, FN) \
-BINARY(FN, int64_t, uint8_t, NAME##_i64, NAME##_i64_strided);
+BINARY(FN, int64_t, int8_t, NAME##_i64, NAME##_i64_strided);
 
 BINARY_OP(x + y, add)
 BINARY_OP(x - y, sub)

candle-metal-kernels/src/cast.metal

@@ -73,7 +73,6 @@ kernel void FN_NAME_STRIDED( \
 } \
 
 CAST(cast_u32_f32, cast_u32_f32_strided, uint32_t, float)
-CAST(cast_u32_f16, cast_u32_f16_strided, uint32_t, half)
 CAST(cast_u32_u8, cast_u32_u8_strided, uint32_t, uint8_t)
 CAST(cast_u8_u32, cast_u8_u32_strided, uint8_t, uint32_t)
 CAST(cast_u8_f32, cast_u8_f32_strided, uint8_t, float)
@@ -96,4 +95,4 @@ CAST(cast_f32_bf16, cast_f32_bf16_strided, float, bfloat)
 CAST_THROUGH(cast_bf16_u8, cast_bf16_u8_strided, bfloat, uint8_t, float)
 CAST_THROUGH(cast_bf16_f16, cast_bf16_f16_strided, bfloat, half, float)
 CAST_THROUGH(cast_f16_bf16, cast_f16_bf16_strided, half, bfloat, float)
-#endif
+#endif

candle-metal-kernels/src/lib.rs

@@ -1,22 +1,20 @@
 use metal::{
-    Buffer, CommandBufferRef, ComputeCommandEncoderRef, ComputePipelineState, Device, Function,
-    FunctionConstantValues, Library, MTLDataType, MTLSize, NSUInteger,
+    Buffer, CommandBufferRef, CompileOptions, ComputeCommandEncoderRef, ComputePipelineState,
+    Device, Function, FunctionConstantValues, Library, MTLDataType, MTLSize, NSUInteger,
 };
 use std::collections::HashMap;
 use std::ffi::c_void;
 use std::sync::RwLock;
 
-const AFFINE: &[u8] = include_bytes!("compiled/affine.metallib");
-const INDEXING: &[u8] = include_bytes!("compiled/indexing.metallib");
-const UNARY: &[u8] = include_bytes!("compiled/unary.metallib");
-const BINARY: &[u8] = include_bytes!("compiled/binary.metallib");
-const TERNARY: &[u8] = include_bytes!("compiled/ternary.metallib");
-const CAST: &[u8] = include_bytes!("compiled/cast.metallib");
-const CONV: &[u8] = include_bytes!("compiled/conv.metallib");
-const REDUCE: &[u8] = include_bytes!("compiled/reduce.metallib");
-const RANDOM: &[u8] = include_bytes!("compiled/random.metallib");
+const AFFINE: &str = include_str!("affine.metal");
+const INDEXING: &str = include_str!("indexing.metal");
+const UNARY: &str = include_str!("unary.metal");
+const BINARY: &str = include_str!("binary.metal");
+const TERNARY: &str = include_str!("ternary.metal");
+const CAST: &str = include_str!("cast.metal");
+const REDUCE: &str = include_str!("reduce.metal");
+const CONV: &str = include_str!("conv.metal");
 const MFA: &[u8] = include_bytes!("libMetalFlashAttention.metallib");
-const QUANTIZED: &[u8] = include_bytes!("compiled/quantized.metallib");
 
 /// Most kernels apply similarly across the tensors
 /// This creates a strategy that uses the maximum amount of threads per threadgroup (capped at the
@@ -63,12 +61,8 @@ macro_rules! primitive {
         }
     };
 }
-primitive!(bool);
 primitive!(usize);
-primitive!(i32);
-primitive!(i64);
 primitive!(u32);
-primitive!(u64);
 primitive!(f32);
 
 impl<T> EncoderParam for &[T] {
@@ -123,8 +117,6 @@ pub enum Source {
     Reduce,
     Mfa,
     Conv,
-    Random,
-    Quantized,
 }
 
 macro_rules! ops{
@@ -183,7 +175,7 @@ macro_rules! ops{
 pub mod unary {
     ops!(
         cos, sin, exp, sqr, sqrt, neg, log, gelu, abs, ceil, floor, relu, round, erf, gelu_erf,
-        tanh, recip, silu
+        tanh, recip
     );
 }
 pub mod binary {
@@ -223,19 +215,21 @@ type Pipelines = HashMap<(&'static str, Option<ConstantValues>), ComputePipeline
 pub struct Kernels {
     libraries: RwLock<Libraries>,
     pipelines: RwLock<Pipelines>,
+    fence: metal::Fence,
 }
 
 impl Kernels {
-    pub fn new() -> Self {
+    pub fn new(fence: metal::Fence) -> Self {
         let libraries = RwLock::new(Libraries::new());
         let pipelines = RwLock::new(Pipelines::new());
         Self {
             libraries,
             pipelines,
+            fence,
         }
     }
 
-    fn get_library_source(&self, source: Source) -> &'static [u8] {
+    fn get_library_source(&self, source: Source) -> &'static str {
         match source {
             Source::Affine => AFFINE,
             Source::Unary => UNARY,
@@ -245,9 +239,7 @@ impl Kernels {
             Source::Cast => CAST,
             Source::Reduce => REDUCE,
             Source::Conv => CONV,
-            Source::Random => RANDOM,
-            Source::Quantized => QUANTIZED,
-            Source::Mfa => MFA,
+            Source::Mfa => panic!("Invalid lib"),
         }
     }
 
@@ -262,12 +254,22 @@ impl Kernels {
         if let Some(lib) = libraries.get(&source) {
             Ok(lib.clone())
         } else {
-            let source_data = self.get_library_source(source);
-            let lib = device.new_library_with_data(source_data).map_err(|e| {
-                MetalKernelError::LoadLibraryError(format!(
-                    "Candle metal requires macosx > 13.0 or higher, cannot load mfa: {e}"
-                ))
-            })?;
+            let lib = match source {
+                Source::Mfa => {
+                    let source_data = MFA;
+                    device.new_library_with_data(source_data).map_err(|e| {
+                        MetalKernelError::LoadLibraryError(format!(
+                            "Candle metal requires macosx > 13.0 or higher, cannot load mfa: {e}"
+                        ))
+                    })?
+                }
+                source => {
+                    let source_content = self.get_library_source(source);
+                    device
+                        .new_library_with_source(source_content, &CompileOptions::new())
+                        .map_err(|e| MetalKernelError::LoadLibraryError(e.to_string()))?
+                }
+            };
             libraries.insert(source, lib.clone());
             Ok(lib)
         }
@@ -343,6 +345,7 @@ pub fn call_unary_contiguous(
 ) -> Result<(), MetalKernelError> {
     let pipeline = kernels.load_pipeline(device, Source::Unary, kernel_name.0)?;
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(encoder, (length, input, output));
@@ -351,6 +354,7 @@ pub fn call_unary_contiguous(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -372,6 +376,7 @@ pub fn call_unary_strided(
 
     let num_dims: usize = shape.len();
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     let length: usize = shape.iter().product();
@@ -393,6 +398,7 @@ pub fn call_unary_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -411,6 +417,7 @@ pub fn call_binary_contiguous(
     let pipeline = kernels.load_pipeline(device, Source::Binary, kernel_name.0)?;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(encoder, (length, left, right, output));
@@ -421,6 +428,7 @@ pub fn call_binary_contiguous(
     encoder.use_resource(right, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -445,6 +453,7 @@ pub fn call_binary_strided(
     let num_dims: usize = shape.len();
     let encoder = command_buffer.new_compute_command_encoder();
     let width: usize = shape.iter().product();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     let length: usize = shape.iter().product();
@@ -469,6 +478,7 @@ pub fn call_binary_strided(
     encoder.use_resource(right_input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -487,6 +497,7 @@ pub fn call_cast_contiguous(
     let pipeline = kernels.load_pipeline(device, Source::Cast, kernel_name)?;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(encoder, (length, (input, input_offset), output));
@@ -495,6 +506,7 @@ pub fn call_cast_contiguous(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -514,6 +526,7 @@ pub fn call_cast_strided(
     let pipeline = kernels.load_pipeline(device, Source::Cast, kernel_name)?;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     let length: usize = shape.iter().product();
@@ -535,6 +548,7 @@ pub fn call_cast_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -554,6 +568,7 @@ pub fn call_reduce_contiguous(
     let elements_to_sum = length / out_length;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -582,6 +597,7 @@ pub fn call_reduce_contiguous(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -603,6 +619,7 @@ pub fn call_reduce_strided(
     let elements_to_sum = length / out_length;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -638,6 +655,7 @@ pub fn call_reduce_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -656,6 +674,7 @@ pub fn call_last_softmax(
 ) -> Result<(), MetalKernelError> {
     let pipeline = kernels.load_pipeline(device, Source::Reduce, kernel_name)?;
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -686,6 +705,7 @@ pub fn call_last_softmax(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -705,6 +725,7 @@ pub fn call_affine(
     let pipeline = kernels.load_pipeline(device, Source::Affine, name)?;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(encoder, (size, mul, add, input, output));
@@ -713,6 +734,7 @@ pub fn call_affine(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -735,6 +757,7 @@ pub fn call_affine_strided(
     let size: usize = shape.iter().product();
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -755,6 +778,7 @@ pub fn call_affine_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -773,6 +797,7 @@ pub fn call_powf(
     let pipeline = kernels.load_pipeline(device, Source::Affine, name)?;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(encoder, (size, mul, input, output));
@@ -781,6 +806,7 @@ pub fn call_powf(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -802,6 +828,7 @@ pub fn call_powf_strided(
     let size: usize = shape.iter().product();
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -821,6 +848,7 @@ pub fn call_powf_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -839,6 +867,7 @@ pub fn call_elu(
     let pipeline = kernels.load_pipeline(device, Source::Affine, name)?;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(encoder, (size, mul, input, output));
@@ -847,6 +876,7 @@ pub fn call_elu(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -868,6 +898,7 @@ pub fn call_elu_strided(
     let size: usize = shape.iter().product();
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -887,6 +918,7 @@ pub fn call_elu_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -908,6 +940,7 @@ pub fn call_where_cond_strided(
     let pipeline = kernels.load_pipeline(device, Source::Ternary, name)?;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     let size: usize = shape.iter().product();
@@ -936,6 +969,7 @@ pub fn call_where_cond_strided(
     encoder.use_resource(right, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -962,6 +996,7 @@ pub fn call_index_select(
 
     let encoder = command_buffer.new_compute_command_encoder();
 
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -984,6 +1019,7 @@ pub fn call_index_select(
     encoder.use_resource(ids, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -1012,6 +1048,7 @@ pub fn call_gather(
 
     let encoder = command_buffer.new_compute_command_encoder();
 
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -1034,6 +1071,7 @@ pub fn call_gather(
     encoder.use_resource(ids, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -1062,6 +1100,7 @@ pub fn call_scatter_add(
 
     let encoder = command_buffer.new_compute_command_encoder();
 
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -1084,6 +1123,7 @@ pub fn call_scatter_add(
     encoder.use_resource(ids, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -1113,6 +1153,7 @@ pub fn call_index_add(
     let pipeline = kernels.load_pipeline(device, Source::Indexing, name)?;
     let encoder = command_buffer.new_compute_command_encoder();
 
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
 
     set_params!(
@@ -1136,6 +1177,7 @@ pub fn call_index_add(
     encoder.use_resource(ids, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
     Ok(())
 }
@@ -1339,6 +1381,7 @@ pub fn call_gemm(
     let block_bytes = block_elements * bytes;
 
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
     encoder.set_threadgroup_memory_length(0, block_bytes.into());
     encoder.set_buffer(0, Some(lhs_buffer), lhs_offset as NSUInteger);
@@ -1354,12 +1397,13 @@ pub fn call_gemm(
         // TODO byte_stride_d
         let byte_stride_d = 0;
 
-        let buffer: Vec<u64> = vec![
-            byte_stride_a as _,
-            byte_stride_b as _,
-            byte_stride_c as _,
-            byte_stride_d as _,
-        ];
+        let mut buffer: Vec<u64> = Vec::with_capacity(b * 4);
+        for i in 0..b {
+            buffer.push((i * byte_stride_a) as u64);
+            buffer.push((i * byte_stride_b) as u64);
+            buffer.push((i * byte_stride_c) as u64);
+            buffer.push((i * byte_stride_d) as u64);
+        }
         encoder.set_bytes(
             10,
             (buffer.len() * core::mem::size_of::<u64>()) as NSUInteger,
@@ -1377,10 +1421,12 @@ pub fn call_gemm(
         height: 1,
         depth: 1,
     };
+    // println!("grid size {grid_size:?} group size {group_size:?}");
     encoder.use_resource(lhs_buffer, metal::MTLResourceUsage::Read);
     encoder.use_resource(rhs_buffer, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(grid_size, group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
 
     Ok(())
@@ -1405,6 +1451,7 @@ pub fn call_im2col1d_strided(
 
     let encoder = command_buffer.new_compute_command_encoder();
     let (thread_group_count, thread_group_size) = linear_split(&pipeline, dst_el);
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
     set_params!(
         encoder,
@@ -1424,6 +1471,7 @@ pub fn call_im2col1d_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
 
     Ok(())
@@ -1453,6 +1501,7 @@ pub fn call_im2col_strided(
 
     let encoder = command_buffer.new_compute_command_encoder();
     let (thread_group_count, thread_group_size) = linear_split(&pipeline, dst_el);
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
     set_params!(
         encoder,
@@ -1474,6 +1523,7 @@ pub fn call_im2col_strided(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
 
     Ok(())
@@ -1499,6 +1549,7 @@ pub fn call_upsample_nearest_2d(
     let scale_h = shape[3] as f32 / out_h as f32;
     let (thread_group_count, thread_group_size) = linear_split(&pipeline, dst_el);
     let encoder = command_buffer.new_compute_command_encoder();
+    encoder.wait_for_fence(&kernels.fence);
     encoder.set_compute_pipeline_state(&pipeline);
     set_params!(
         encoder,
@@ -1516,243 +1567,7 @@ pub fn call_upsample_nearest_2d(
     encoder.use_resource(input, metal::MTLResourceUsage::Read);
     encoder.use_resource(output, metal::MTLResourceUsage::Write);
     encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
-    encoder.end_encoding();
-
-    Ok(())
-}
-
-#[allow(clippy::too_many_arguments)]
-pub fn call_random_uniform(
-    device: &Device,
-    command_buffer: &CommandBufferRef,
-    kernels: &Kernels,
-    name: &'static str,
-    min: f32,
-    max: f32,
-    length: usize,
-    seed: &Buffer,
-    buffer: &Buffer,
-) -> Result<(), MetalKernelError> {
-    if min >= max {
-        return Err(MetalKernelError::LoadLibraryError(
-            "min must be less than max".to_string(),
-        ));
-    }
-    let pipeline = kernels.load_pipeline(device, Source::Random, name)?;
-    let encoder = command_buffer.new_compute_command_encoder();
-
-    let odd = (length % 2 != 0) as usize;
-    let (thread_group_count, thread_group_size) = linear_split(&pipeline, length / 2 + odd);
-
-    encoder.set_compute_pipeline_state(&pipeline);
-
-    set_params!(encoder, (length, min, max, seed, buffer));
-
-    encoder.use_resource(seed, metal::MTLResourceUsage::Read);
-    encoder.use_resource(seed, metal::MTLResourceUsage::Write);
-    encoder.use_resource(buffer, metal::MTLResourceUsage::Write);
-    encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
-    encoder.end_encoding();
-
-    Ok(())
-}
-
-#[allow(clippy::too_many_arguments)]
-pub fn call_random_normal(
-    device: &Device,
-    command_buffer: &CommandBufferRef,
-    kernels: &Kernels,
-    name: &'static str,
-    mean: f32,
-    stddev: f32,
-    length: usize,
-    seed: &Buffer,
-    buffer: &Buffer,
-) -> Result<(), MetalKernelError> {
-    let pipeline = kernels.load_pipeline(device, Source::Random, name)?;
-    let encoder = command_buffer.new_compute_command_encoder();
-
-    let odd = (length % 2 != 0) as usize;
-    let (thread_group_count, thread_group_size) = linear_split(&pipeline, length / 2 + odd);
-
-    encoder.set_compute_pipeline_state(&pipeline);
-
-    set_params!(encoder, (length, mean, stddev, seed, buffer));
-
-    encoder.use_resource(seed, metal::MTLResourceUsage::Read);
-    encoder.use_resource(seed, metal::MTLResourceUsage::Write);
-    encoder.use_resource(buffer, metal::MTLResourceUsage::Write);
-    encoder.dispatch_thread_groups(thread_group_count, thread_group_size);
-    encoder.end_encoding();
-
-    Ok(())
-}
-
-#[derive(Debug, Clone, Copy)]
-pub enum GgmlDType {
-    Q4_0,
-    Q4_1,
-    Q5_0,
-    Q5_1,
-    Q8_0,
-    Q8_1,
-    Q2K,
-    Q3K,
-    Q4K,
-    Q5K,
-    Q6K,
-    Q8K,
-    F16,
-    F32,
-}
-
-pub fn call_quantized_matmul_t(
-    device: &Device,
-    command_buffer: &CommandBufferRef,
-    kernels: &Kernels,
-    dtype: GgmlDType,
-    (b, m, n, k): (usize, usize, usize, usize),
-    lhs: &Buffer,
-    lhs_offset: usize,
-    rhs: &Buffer,
-    output: &Buffer,
-) -> Result<(), MetalKernelError> {
-    // Everything is in reverse
-    let ne00 = k as i64;
-    let ne01 = n as i64;
-    let ne02 = b as i64;
-    let ne03 = 1 as i64;
-
-    let nb00 = 0i64;
-    let nb01 = 0 as i64;
-    let nb02 = 0 as i64;
-
-    let ne10 = k as i64;
-    let ne11 = m as i64;
-    let ne12 = b as i64;
-    let ne13 = 1 as i64;
-
-    let nb10 = 0i64;
-    let nb11 = 0i64;
-    let nb12 = 0i64;
-
-    let ne0 = n as i64;
-    let ne1 = m as i64;
-    let r2: u32 = (ne12 / ne02) as u32;
-    let r3: u32 = (ne13 / ne03) as u32;
-
-    let (nth0, nth1, align) = match dtype {
-        GgmlDType::Q4_0
-        | GgmlDType::Q4_1
-        | GgmlDType::Q5_0
-        | GgmlDType::Q5_1
-        | GgmlDType::Q8_0
-        | GgmlDType::Q8_1 => {
-            let nth0 = 8;
-            let nth1 = 8;
-            let align = 8;
-            (nth0, nth1, align)
-        }
-        GgmlDType::Q2K => {
-            // Fixing a bug in Metal for GGML
-            let nth0 = 4;
-            let nth1 = 8;
-            let align = 4;
-            (nth0, nth1, align)
-        }
-        GgmlDType::Q4K => {
-            let nth0 = 4;
-            let nth1 = 8;
-            let align = 4;
-            (nth0, nth1, align)
-        }
-        GgmlDType::Q3K | GgmlDType::Q5K => {
-            let nth0 = 2;
-            let nth1 = 32;
-            let align = 4;
-            (nth0, nth1, align)
-        }
-        GgmlDType::Q6K => {
-            let nth0 = 2;
-            let nth1 = 32;
-            let align = 2;
-            (nth0, nth1, align)
-        }
-        GgmlDType::F16 | GgmlDType::Q8K => {
-            // Original implem uses rows
-            let nth0 = 32;
-            let nth1 = 1;
-            let align = 8;
-            (nth0, nth1, align)
-        }
-        GgmlDType::F32 => {
-            let nth0 = 32;
-            let nth1 = 1;
-            let align = 8;
-            (nth0, nth1, align)
-        }
-    };
-    let thread_groups_count = MTLSize {
-        width: divide(ne01 as usize, align),
-        height: ne11 as u64,
-        depth: (ne12 * ne13) as u64,
-    };
-    let threads_per_threadgroup = MTLSize {
-        width: nth0,
-        height: nth1,
-        depth: 1,
-    };
-    let name = match dtype {
-        GgmlDType::Q4_0 => "kernel_mul_mv_q4_0_f32",
-        GgmlDType::Q4_1 => "kernel_mul_mv_q4_1_f32",
-        GgmlDType::Q5_0 => "kernel_mul_mv_q5_0_f32",
-        GgmlDType::Q5_1 => "kernel_mul_mv_q5_1_f32",
-        GgmlDType::Q8_0 => "kernel_mul_mv_q8_0_f32",
-        GgmlDType::Q8_1 => "kernel_mul_mv_q8_1_f32",
-        GgmlDType::Q2K => "kernel_mul_mv_q2_K_f32",
-        GgmlDType::Q3K => "kernel_mul_mv_q3_K_f32",
-        GgmlDType::Q4K => "kernel_mul_mv_q4_K_f32",
-        GgmlDType::Q5K => "kernel_mul_mv_q5_K_f32",
-        GgmlDType::Q6K => "kernel_mul_mv_q6_K_f32",
-        GgmlDType::Q8K => "kernel_mul_mv_q8_K_f32",
-        GgmlDType::F16 => "kernel_mul_mv_f16_f32",
-        GgmlDType::F32 => "kernel_mul_mv_f32_f32",
-    };
-
-    let pipeline = kernels.load_pipeline(device, Source::Quantized, name)?;
-    let encoder = command_buffer.new_compute_command_encoder();
-    encoder.set_compute_pipeline_state(&pipeline);
-
-    set_params!(
-        encoder,
-        (
-            rhs,
-            (lhs, lhs_offset),
-            output,
-            ne00,
-            ne01,
-            ne02,
-            nb00,
-            nb01,
-            nb02,
-            ne10,
-            ne11,
-            ne12,
-            nb10,
-            nb11,
-            nb12,
-            ne0,
-            ne1,
-            r2,
-            r3
-        )
-    );
-    encoder.set_threadgroup_memory_length(0, 8192);
-    encoder.use_resource(lhs, metal::MTLResourceUsage::Read);
-    encoder.use_resource(rhs, metal::MTLResourceUsage::Read);
-    encoder.use_resource(output, metal::MTLResourceUsage::Write);
-
-    encoder.dispatch_thread_groups(thread_groups_count, threads_per_threadgroup);
+    encoder.update_fence(&kernels.fence);
     encoder.end_encoding();
 
     Ok(())

candle-metal-kernels/src/random.metal

@@ -1,206 +0,0 @@
-#include <metal_stdlib>
-#include <metal_integer>
-#include <metal_atomic>
-
-using namespace metal;
-
-// Constants
-// 2^32 and 1/2^32. Useful for converting between float and uint.
-static constexpr constant ulong UNIF01_NORM32 = 4294967296;
-static constexpr constant float UNIF01_INV32 = 2.328306436538696289e-10;
-// 2 * pi
-static constexpr constant float TWO_PI = 2.0 * M_PI_F;
-static constexpr constant int3 S1 = {13, 19, 12};
-static constexpr constant int3 S2 = {2, 25, 4};
-static constexpr constant int3 S3 = {3, 11, 17};
-
-// Used to prevent bad seeds.
-static constexpr constant uint64_t PHI[16] = {
-    0x9E3779B97F4A7C15,
-    0xF39CC0605CEDC834,
-    0x1082276BF3A27251,
-    0xF86C6A11D0C18E95,
-    0x2767F0B153D27B7F,
-    0x0347045B5BF1827F,
-    0x01886F0928403002,
-    0xC1D64BA40F335E36,
-    0xF06AD7AE9717877E,
-    0x85839D6EFFBD7DC6,
-    0x64D325D1C5371682,
-    0xCADD0CCCFDFFBBE1,
-    0x626E33B8D04B4331,
-    0xBBF73C790D94F79D,
-    0x471C4AB3ED3D82A5,
-    0xFEC507705E4AE6E5,
-};
-
-// Combined Tausworthe and LCG Random Number Generator.
-// https://developer.nvidia.com/gpugems/gpugems3/part-vi-gpu-computing/chapter-37-efficient-random-number-generation-and-application
-// https://indico.cern.ch/event/93877/contributions/2118070/attachments/1104200/1575343/acat3_revised_final.pdf
-struct HybridTaus {
-
-    float state;
-
-    HybridTaus() thread = default;
-    HybridTaus() threadgroup = default;
-    HybridTaus() device = default;
-    HybridTaus() constant = default;
-
-    // Generate seeds for each thread.
-    METAL_FUNC static uint4 seed_per_thread(const ulong4 seeds) {
-        return uint4(ulong4(seeds) * ulong4(PHI[0], PHI[1], PHI[2], PHI[3]) * ulong4(1099087573UL));
-    }
-
-    // Tausworthe generator.
-    METAL_FUNC static uint taus(const uint z, const int3 s, const uint M) {
-        uint b = (((z << s.x) ^ z) >> s.y);
-        return (((z & M) << s.z) ^ b);
-    }
-
-    // LCG generator.
-    METAL_FUNC static uint lcg(const uint z) {
-        return (1664525 * z + 1013904223UL);
-    }
-
-    // Initialize the RNG state.
-    METAL_FUNC static HybridTaus init(const ulong4 seeds) {
-        uint4 seed = seed_per_thread(seeds);
-
-        // Seed #1
-        uint z1 = taus(seed.x, S1, 4294967294UL);
-        uint z2 = taus(seed.y, S2, 4294967288UL);
-        uint z3 = taus(seed.z, S3, 4294967280UL);
-        uint z4 = lcg(seed.x);
-
-        // Seed #2
-        uint r1 = (z1^z2^z3^z4^seed.y);
-        z1 = taus(r1, S1, 429496729UL);
-        z2 = taus(r1, S2, 4294967288UL);
-        z3 = taus(r1, S3, 429496280UL);
-        z4 = lcg(r1);
-
-        // Seed #3
-        r1 = (z1^z2^z3^z4^seed.z);
-        z1 = taus(r1, S1, 429496729UL);
-        z2 = taus(r1, S2, 4294967288UL);
-        z3 = taus(r1, S3, 429496280UL);
-        z4 = lcg(r1);
-
-        // Seed #4
-        r1 = (z1^z2^z3^z4^seed.w);
-        z1 = taus(r1, S1, 429496729UL);
-        z2 = taus(r1, S2, 4294967288UL);
-        z3 = taus(r1, S3, 429496280UL);
-        z4 = lcg(r1);
-
-        HybridTaus rng;
-        rng.state = (z1^z2^z3^z4) * UNIF01_INV32;
-        return rng;
-    }
-
-    METAL_FUNC float rand() {
-        uint seed = this->state * UNIF01_NORM32;
-        uint z1 = taus(seed, S1, 429496729UL);
-        uint z2 = taus(seed, S2, 4294967288UL);
-        uint z3 = taus(seed, S3, 429496280UL);
-        uint z4 = lcg(seed);
-
-        thread float result = this->state;
-        this->state = (z1^z2^z3^z4) * UNIF01_INV32;
-        return result;
-    }
-};
-
-template<typename T> METAL_FUNC void rand_uniform(
-    constant size_t &size,
-    constant float &min,
-    constant float &max,
-    device atomic_uint *seed,
-    device T *out,
-    uint tid [[thread_position_in_grid]]
-) {
-    if (tid >= size) {
-        return;
-    }
-
-    float diff = abs(min - max);
-    HybridTaus rng = HybridTaus::init({ulong(seed), tid, 1, 1});
-    out[tid] = static_cast<T>(rng.rand() * diff + min);
-    if (tid == 0) {
-        atomic_store_explicit(seed, uint(rng.rand() * UNIF01_NORM32), memory_order_relaxed);
-        // Return early if tid == 0, otherwise we will write to out[size].
-        return;
-    }
-    // Use symmetry to fill the other half of the array.
-    out[size - tid] = static_cast<T>(rng.rand() * diff + min);
-}
-
-// Create Gaussian normal distribution using Box-Muller transform:
-// https://en.wikipedia.org/wiki/Box–Muller_transform
-template<typename T> METAL_FUNC void normal(
-    constant size_t &size,
-    constant float &mean,
-    constant float &stddev,
-    device atomic_uint *seed,
-    device T *out,
-    uint tid [[thread_position_in_grid]]
-) {
-    if (tid >= size) {
-        return;
-    }
-    HybridTaus rng = HybridTaus::init({ulong(seed), tid, 1, 1});
-    float u1 = rng.rand();
-    float u2 = rng.rand();
-
-    float cosval;
-    float sinval = sincos(TWO_PI * u2, cosval);
-    float mag = stddev * sqrt(-2.0 * log(u1));
-    float z0  = mag * cosval + mean;
-    float z1  = mag * sinval + mean;
-
-    out[tid] = static_cast<T>(z0);
-
-    if (tid == 0) {
-        atomic_store_explicit(seed, uint(rng.rand() * UNIF01_NORM32), memory_order_relaxed);
-        // Return early if tid == 0, otherwise we will write to out[size].
-        return;
-    }
-    // Use symmetry to fill the other half of the array.
-    out[size - tid] = static_cast<T>(z1);
-}
-
-#define UNIFORM_OP(NAME, T)                             \
-kernel void rand_uniform_##NAME(                        \
-    constant size_t &size,                              \
-    constant float &min,                                \
-    constant float &max,                                \
-    device atomic_uint *seed,                           \
-    device T *out,                                      \
-    uint tid [[thread_position_in_grid]]                \
-) {                                                     \
-    rand_uniform<T>(size, min, max, seed, out, tid);    \
-}                                                       \
-
-#define NORMAL_OP(NAME, T)                              \
-kernel void rand_normal_##NAME(                         \
-    constant size_t &size,                              \
-    constant float &mean,                               \
-    constant float &stddev,                             \
-    device atomic_uint *seed,                           \
-    device T *out,                                      \
-    uint tid [[thread_position_in_grid]]                \
-) {                                                     \
-    normal<T>(size, mean, stddev, seed, out, tid);      \
-}                                                       \
-
-
-#define RANDOM_OPS(NAME, T) \
-UNIFORM_OP(NAME, T)         \
-NORMAL_OP(NAME, T)          \
-
-RANDOM_OPS(f32, float)
-RANDOM_OPS(f16, half)
-
-#if __METAL_VERSION__ >= 310
-RANDOM_OPS(bf16, bfloat)
-#endif

candle-metal-kernels/src/tests.rs

@@ -11,7 +11,7 @@ fn read_to_vec<T: Clone>(buffer: &Buffer, n: usize) -> Vec<T> {
 
 fn new_buffer<T>(device: &Device, data: &[T]) -> Buffer {
     let options = MTLResourceOptions::StorageModeManaged;
-    let ptr = data.as_ptr() as *const c_void;
+    let ptr = data.as_ptr() as *const core::ffi::c_void;
     let size = (data.len() * std::mem::size_of::<T>()) as u64;
     device.new_buffer_with_data(ptr, size, options)
 }
@@ -37,7 +37,8 @@ fn approx_bf16(v: Vec<bf16>, digits: i32) -> Vec<f32> {
 
 fn run<T: Clone>(v: &[T], name: unary::contiguous::Kernel) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
     let input = new_buffer(&device, v);
@@ -59,7 +60,8 @@ fn run<T: Clone>(v: &[T], name: unary::contiguous::Kernel) -> Vec<T> {
 
 fn run_binary<T: Clone>(x: &[T], y: &[T], name: binary::contiguous::Kernel) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
     let options = MTLResourceOptions::StorageModeManaged;
@@ -94,7 +96,8 @@ fn run_strided<T: Clone>(
     let command_buffer = command_queue.new_command_buffer();
     let input = new_buffer(&device, v);
     let output = new_buffer(&device, v);
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     call_unary_strided(
         &device,
         command_buffer,
@@ -231,25 +234,6 @@ fn gelu_f32() {
     assert_eq!(approx(results, 3), expected);
 }
 
-#[test]
-fn silu_f16() {
-    let v: Vec<f16> = [-10f32, -1.0, 0., 1., 2., 3., 10.0, 20.0]
-        .iter()
-        .map(|v| f16::from_f32(*v))
-        .collect();
-    let expected: Vec<f32> = vec![-0.0, -0.27, 0.0, 0.73, 1.76, 2.86, 10.0, 20.0];
-    let results = run(&v, unary::contiguous::silu::HALF);
-    assert_eq!(approx_f16(results, 2), expected);
-}
-
-#[test]
-fn silu_f32() {
-    let v: Vec<f32> = vec![-10f32, -1.0, 0., 1., 2., 3., 10.0, 20.0];
-    let expected: Vec<f32> = vec![-0.0, -0.269, 0.0, 0.731, 1.762, 2.858, 10.0, 20.0];
-    let results = run(&v, unary::contiguous::silu::FLOAT);
-    assert_eq!(approx(results, 3), expected);
-}
-
 #[test]
 fn binary_add_f32() {
     let left = vec![1.0f32, 2.0, 3.0];
@@ -294,7 +278,8 @@ fn binary_ops_bf16() {
 
 fn cast<T: Clone, U: Clone>(v: &[T], name: &'static str) -> Vec<U> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
     let input = new_buffer(&device, v);
@@ -424,7 +409,8 @@ fn it_cast_f16_bf16() {
 
 fn run_affine<T: Clone>(v: &[T], mul: f64, add: f64) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
 
@@ -459,7 +445,8 @@ fn run_affine_strided<T: Clone>(
     add: f64,
 ) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
 
@@ -608,7 +595,8 @@ fn run_index_select<T: Clone, I: Clone + std::fmt::Debug>(
     let dst_el = ids.len() * left_size * right_size;
     let dst_buffer = new_buffer(&device, &vec![0.0f32; dst_el]);
 
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     call_index_select(
         &device,
         &command_buffer,
@@ -643,7 +631,8 @@ fn cos_f16() {
 
 fn run_reduce<T: Clone>(v: &[T], out_length: usize, name: &'static str) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
     let input = new_buffer(&device, v);
@@ -673,7 +662,8 @@ fn run_reduce<T: Clone>(v: &[T], out_length: usize, name: &'static str) -> Vec<T
 
 fn run_softmax<T: Clone + std::fmt::Debug>(v: &[T], last_dim: usize, name: &'static str) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
     let input = new_buffer(&device, v);
@@ -732,6 +722,7 @@ fn softmax() {
     }
     let results = run_softmax(&v, last_dim, "softmax_f32");
     let results = approx(results, 4);
+    println!("{results:?}");
     assert_eq!(
         results.iter().map(|&s| s.round() as usize).sum::<usize>(),
         n
@@ -791,7 +782,8 @@ fn run_where_cond<I: Clone, T: Clone>(
     name: &'static str,
 ) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
     let options = MTLResourceOptions::StorageModeManaged;
@@ -867,7 +859,8 @@ fn run_gemm<T: Clone>(
     rhs_offset: usize,
 ) -> Vec<T> {
     let device = device();
-    let kernels = Kernels::new();
+    let fence = device.new_fence();
+    let kernels = Kernels::new(fence);
     let command_queue = device.new_command_queue();
     let command_buffer = command_queue.new_command_buffer();
     let options = MTLResourceOptions::StorageModeManaged;
@@ -945,124 +938,3 @@ fn gemm() {
         vec![56.0, 59.0, 62.0, 65.0, 200.0, 212.0, 224.0, 236.0]
     );
 }
-
-fn run_random<T: Clone>(name: &'static str, seed: u32, length: usize, a: f32, b: f32) -> Vec<T> {
-    let device = device();
-    let kernels = Kernels::new();
-    let command_queue = device.new_command_queue();
-    let command_buffer = command_queue.new_command_buffer();
-
-    let options = MTLResourceOptions::StorageModeManaged;
-    let output = device.new_buffer((length * core::mem::size_of::<T>()) as NSUInteger, options);
-
-    let seed = device.new_buffer_with_data(
-        &seed as *const u32 as *const core::ffi::c_void,
-        std::mem::size_of::<u32>() as NSUInteger,
-        options,
-    );
-
-    if name.starts_with("rand_uniform") {
-        call_random_uniform(
-            &device,
-            command_buffer,
-            &kernels,
-            name,
-            a,
-            b,
-            length,
-            &seed,
-            &output,
-        )
-        .unwrap();
-    } else {
-        call_random_normal(
-            &device,
-            command_buffer,
-            &kernels,
-            name,
-            a,
-            b,
-            length,
-            &seed,
-            &output,
-        )
-        .unwrap();
-    }
-    command_buffer.commit();
-    command_buffer.wait_until_completed();
-
-    read_to_vec(&output, length)
-}
-
-#[test]
-fn random() {
-    fn calc_mean(data: &[f32]) -> f32 {
-        let sum = data.iter().sum::<f32>() as f32;
-        let count = data.len();
-        assert!(count > 0);
-        sum / count as f32
-    }
-
-    fn calc_stddev(data: &[f32]) -> f32 {
-        let mean = calc_mean(data);
-        let count = data.len();
-        assert!(count > 0);
-
-        let variance = data
-            .iter()
-            .map(|value| {
-                let diff = mean - (*value as f32);
-                diff * diff
-            })
-            .sum::<f32>()
-            / count as f32;
-
-        variance.sqrt()
-    }
-
-    let shape = vec![1024, 10];
-
-    let length = shape.iter().product::<usize>();
-    let seed = 299792458;
-
-    let min = -30.0;
-    let max = 30.0;
-    let mean = 100.0;
-    let stddev = 50.0;
-
-    macro_rules! validate_random {
-        ($type:ty) => {
-            let results: Vec<f32> = run_random::<$type>(
-                concat!("rand_uniform_", stringify!($type)),
-                seed,
-                length,
-                min,
-                max,
-            )
-            .into_iter()
-            .map(f32::from)
-            .collect();
-            results.iter().for_each(|v| {
-                assert!(*v >= min && *v <= max);
-            });
-            assert!(calc_mean(&results) > -1.0 && calc_mean(&results) < 1.0);
-
-            let results: Vec<f32> = run_random::<$type>(
-                concat!("rand_normal_", stringify!($type)),
-                seed,
-                length,
-                mean,
-                stddev,
-            )
-            .into_iter()
-            .map(f32::from)
-            .collect();
-            assert!((calc_mean(&results) - mean).abs() < mean / 10.0);
-            assert!((calc_stddev(&results) - stddev).abs() < stddev / 10.0);
-        };
-    }
-
-    validate_random!(f32);
-    validate_random!(f16);
-    validate_random!(bf16);
-}

candle-metal-kernels/src/unary.metal

@@ -64,9 +64,6 @@ template <typename T> METAL_FUNC T relu(T in){
     }
     return in;
 }
-template <typename T> METAL_FUNC T silu(T in){
-    return in / (static_cast<T>(1) + exp(-in));
-}
 
 #define UNARY(FN, TYPENAME, FN_NAME, FN_NAME_STRIDED) \
 kernel void FN_NAME( \
@@ -111,7 +108,6 @@ UNARY_OP(neg)
 UNARY_OP(exp)
 UNARY_OP(log)
 UNARY_OP(gelu)
-UNARY_OP(silu)
 UNARY_OP(abs)
 UNARY_OP(ceil)
 UNARY_OP(floor)
@@ -121,6 +117,7 @@ UNARY_OP(erf)
 UNARY_OP(tanh)
 UNARY_OP(recip)
 UNARY_OP(relu)
+
 UNARY(id, float, copy_f32, copy_f32_strided)
 UNARY(id, half, copy_f16, copy_f16_strided)
 UNARY(id, uint8_t, copy_u8, copy_u8_strided)
@@ -139,8 +136,6 @@ BFLOAT_UNARY_OP(neg)
 BFLOAT_UNARY_OP(exp)
 BFLOAT_UNARY_OP(log)
 BFLOAT_UNARY_OP(gelu)
-BFLOAT_UNARY_OP(silu)
-BFLOAT_UNARY_OP(abs)
 BFLOAT_UNARY_OP(ceil)
 BFLOAT_UNARY_OP(floor)
 BFLOAT_UNARY_OP(round)

candle-nn/examples/cpu_benchmarks.rs

@@ -222,10 +222,7 @@ impl Benchmark for QMatMul {
     type RunResult = Tensor;
     fn preprocess() -> Result<Self::PreProcessData> {
         let zeros = vec![candle::quantized::k_quants::BlockQ4_0::zeros(); 4096 * 11008 / 32];
-        let mm = candle::quantized::QTensor::new(
-            candle::quantized::QStorage::Cpu(Box::new(zeros)),
-            (4096, 11008),
-        )?;
+        let mm = candle::quantized::QTensor::new(zeros, (4096, 11008))?;
         let mm = candle::quantized::QMatMul::from_qtensor(mm)?;
         let arg = Tensor::randn(0f32, 1., (128, 11008), &Device::Cpu)?;
         Ok((mm, arg))

candle-nn/src/activation.rs

@@ -30,7 +30,7 @@ impl super::Module for Activation {
             Self::Relu => xs.relu(),
             Self::Relu2 => xs.relu()?.sqr(),
             Self::Relu6 => xs.clamp(0f32, 6f32),
-            Self::Silu => xs.silu(),
+            Self::Silu => crate::ops::silu(xs),
             Self::Sigmoid => crate::ops::sigmoid(xs),
             Self::HardSigmoid => crate::ops::hard_sigmoid(xs),
             Self::Swiglu => crate::ops::swiglu(xs),

candle-nn/src/batch_norm.rs

@@ -262,19 +262,9 @@ impl BatchNorm {
         let target_shape = target_shape.as_slice();
 
         let x = x
-            .broadcast_sub(
-                &self
-                    .running_mean
-                    .as_detached_tensor()
-                    .reshape(target_shape)?,
-            )?
+            .broadcast_sub(&self.running_mean.as_tensor().reshape(target_shape)?)?
             .broadcast_div(
-                &(self
-                    .running_var
-                    .as_detached_tensor()
-                    .reshape(target_shape)?
-                    + self.eps)?
-                    .sqrt()?,
+                &(self.running_var.as_tensor().reshape(target_shape)? + self.eps)?.sqrt()?,
             )?;
 
         match &self.weight_and_bias {

candle-nn/src/conv.rs

@@ -302,22 +302,6 @@ pub fn conv1d(
     Ok(Conv1d::new(ws, Some(bs), cfg))
 }
 
-pub fn conv1d_no_bias(
-    in_channels: usize,
-    out_channels: usize,
-    kernel_size: usize,
-    cfg: Conv1dConfig,
-    vb: crate::VarBuilder,
-) -> Result<Conv1d> {
-    let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
-    let ws = vb.get_with_hints(
-        (out_channels, in_channels / cfg.groups, kernel_size),
-        "weight",
-        init_ws,
-    )?;
-    Ok(Conv1d::new(ws, None, cfg))
-}
-
 pub fn conv_transpose1d(
     in_channels: usize,
     out_channels: usize,

candle-nn/src/encoding.rs

@@ -124,7 +124,7 @@ fn set_at_index<D: WithDType, I: Into<i64>>(
     value: I,
     offset: usize,
     depth: usize,
-    v: &mut [D],
+    v: &mut Vec<D>,
     on_value: D,
 ) -> Result<()> {
     let value = value.into();

candle-nn/src/lib.rs

@@ -19,9 +19,8 @@ pub mod var_map;
 pub use activation::{prelu, Activation, PReLU};
 pub use batch_norm::{batch_norm, BatchNorm, BatchNormConfig};
 pub use conv::{
-    conv1d, conv1d_no_bias, conv2d, conv2d_no_bias, conv_transpose1d, conv_transpose1d_no_bias,
-    conv_transpose2d, conv_transpose2d_no_bias, Conv1d, Conv1dConfig, Conv2d, Conv2dConfig,
-    ConvTranspose1d, ConvTranspose1dConfig, ConvTranspose2d, ConvTranspose2dConfig,
+    conv1d, conv2d, conv2d_no_bias, conv_transpose2d, conv_transpose2d_no_bias, Conv1d,
+    Conv1dConfig, Conv2d, Conv2dConfig, ConvTranspose2d, ConvTranspose2dConfig,
 };
 pub use embedding::{embedding, Embedding};
 pub use func::{func, func_t, Func, FuncT};

candle-nn/src/ops.rs

@@ -35,12 +35,13 @@ pub fn log_softmax<D: candle::shape::Dim>(xs: &Tensor, d: D) -> Result<Tensor> {
 }
 
 pub fn silu(xs: &Tensor) -> Result<Tensor> {
-    xs.silu()
+    // TODO: Should we have a specialized op for this?
+    xs / (xs.neg()?.exp()? + 1.0)?
 }
 
 pub fn swiglu(xs: &Tensor) -> Result<Tensor> {
     let xs = xs.chunk(2, candle::D::Minus1)?;
-    &xs[0].silu()? * &xs[1]
+    crate::ops::silu(&xs[0])? * &xs[1]
 }
 
 pub fn sigmoid(xs: &Tensor) -> Result<Tensor> {

candle-nn/src/var_builder.rs

@@ -412,16 +412,7 @@ impl SimpleBackend for candle::safetensors::BufferedSafetensors {
 }
 
 impl<'a> VarBuilder<'a> {
-    /// Initializes a `VarBuilder` using a custom backend.
-    ///
-    /// It is preferred to use one of the more specific constructors. This
-    /// constructor is provided to allow downstream users to define their own
-    /// backends.
-    pub fn from_backend(
-        backend: Box<dyn SimpleBackend + 'a>,
-        dtype: DType,
-        device: Device,
-    ) -> Self {
+    fn new(backend: Box<dyn SimpleBackend + 'a>, dtype: DType, device: Device) -> Self {
         let data = TensorData {
             backend,
             dtype,
@@ -436,13 +427,13 @@ impl<'a> VarBuilder<'a> {
 
     /// Initializes a `VarBuilder` that uses zeros for any tensor.
     pub fn zeros(dtype: DType, dev: &Device) -> Self {
-        Self::from_backend(Box::new(Zeros), dtype, dev.clone())
+        Self::new(Box::new(Zeros), dtype, dev.clone())
     }
 
     /// Initializes a `VarBuilder` that retrieves tensors stored in a hashtable. An error is
     /// returned if no tensor is available under the requested path or on shape mismatches.
     pub fn from_tensors(ts: HashMap<String, Tensor>, dtype: DType, dev: &Device) -> Self {
-        Self::from_backend(Box::new(ts), dtype, dev.clone())
+        Self::new(Box::new(ts), dtype, dev.clone())
     }
 
     /// Initializes a `VarBuilder` using a `VarMap`. The requested tensors are created and
@@ -452,7 +443,7 @@ impl<'a> VarBuilder<'a> {
     /// Note that it is possible to load the tensor values after model creation using the `load`
     /// method on `varmap`, this can be used to start model training from an existing checkpoint.
     pub fn from_varmap(varmap: &VarMap, dtype: DType, dev: &Device) -> Self {
-        Self::from_backend(Box::new(varmap.clone()), dtype, dev.clone())
+        Self::new(Box::new(varmap.clone()), dtype, dev.clone())
     }
 
     /// Initializes a `VarBuilder` that retrieves tensors stored in a collection of safetensors
@@ -467,25 +458,25 @@ impl<'a> VarBuilder<'a> {
         dev: &Device,
     ) -> Result<Self> {
         let tensors = candle::safetensors::MmapedSafetensors::multi(paths)?;
-        Ok(Self::from_backend(Box::new(tensors), dtype, dev.clone()))
+        Ok(Self::new(Box::new(tensors), dtype, dev.clone()))
     }
 
     /// Initializes a `VarBuilder` from a binary builder in the safetensor format.
     pub fn from_buffered_safetensors(data: Vec<u8>, dtype: DType, dev: &Device) -> Result<Self> {
         let tensors = candle::safetensors::BufferedSafetensors::new(data)?;
-        Ok(Self::from_backend(Box::new(tensors), dtype, dev.clone()))
+        Ok(Self::new(Box::new(tensors), dtype, dev.clone()))
     }
 
     /// Initializes a `VarBuilder` that retrieves tensors stored in a numpy npz file.
     pub fn from_npz<P: AsRef<std::path::Path>>(p: P, dtype: DType, dev: &Device) -> Result<Self> {
         let npz = candle::npy::NpzTensors::new(p)?;
-        Ok(Self::from_backend(Box::new(npz), dtype, dev.clone()))
+        Ok(Self::new(Box::new(npz), dtype, dev.clone()))
     }
 
     /// Initializes a `VarBuilder` that retrieves tensors stored in a pytorch pth file.
     pub fn from_pth<P: AsRef<std::path::Path>>(p: P, dtype: DType, dev: &Device) -> Result<Self> {
-        let pth = candle::pickle::PthTensors::new(p, None)?;
-        Ok(Self::from_backend(Box::new(pth), dtype, dev.clone()))
+        let pth = candle::pickle::PthTensors::new(p)?;
+        Ok(Self::new(Box::new(pth), dtype, dev.clone()))
     }
 }
 

candle-onnx/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-onnx"
-version = "0.4.0"
+version = "0.3.3"
 edition = "2021"
 
 description = "ONNX support for Candle"
@@ -10,8 +10,8 @@ categories = ["science"]
 license = "MIT OR Apache-2.0"
 
 [dependencies]
-candle = { path = "../candle-core", package = "candle-core", version = "0.4.0" }
-candle-nn = { path = "../candle-nn", version = "0.4.0" }
+candle = { path = "../candle-core", package = "candle-core" }
+candle-nn = { path = "../candle-nn" }
 prost = "0.12.1"
 
 [build-dependencies]

candle-onnx/src/eval.rs

@@ -766,16 +766,6 @@ pub fn simple_eval(
                 let output = input.cumsum(axis as usize)?;
                 values.insert(node.output[0].clone(), output);
             }
-            //  https://github.com/onnx/onnx/blob/main/docs/Operators.md#flatten
-            "Flatten" => {
-                let axis = get_attr_opt::<i64>(node, "axis")?.copied().unwrap_or(1) as usize;
-                let input = get(&node.input[0])?;
-                let first_part: usize = input.shape().dims().iter().take(axis).product();
-                let end_index = input.shape().dims().iter().product::<usize>();
-                let new_shape = (first_part, end_index / first_part);
-                let output = input.reshape(new_shape)?;
-                values.insert(node.output[0].clone(), output);
-            }
             op_type => bail!("unsupported op_type {op_type} for op {node:?}"),
         }
     }

candle-onnx/tests/ops.rs

@@ -5,7 +5,7 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use candle::{Device, Result, Tensor};
-use candle_onnx::onnx::{AttributeProto, GraphProto, ModelProto, NodeProto, ValueInfoProto};
+use candle_onnx::onnx::{GraphProto, ModelProto, NodeProto, ValueInfoProto};
 use std::collections::HashMap;
 
 const INPUT_X: &str = "x";
@@ -677,134 +677,6 @@ fn test_dropout_operation() -> Result<()> {
     Ok(())
 }
 
-// "Flatten"
-#[test]
-fn test_flatten_operation() -> Result<()> {
-    let mut att_axis = AttributeProto {
-        name: "axis".to_string(),
-        ref_attr_name: "axis".to_string(),
-        i: 0,
-        doc_string: "axis".to_string(),
-        r#type: 2,
-        f: 0.0,
-        s: vec![],
-        t: None,
-        g: None,
-        sparse_tensor: None,
-        tp: None,
-        floats: vec![],
-        ints: vec![],
-        strings: vec![],
-        tensors: vec![],
-        graphs: vec![],
-        sparse_tensors: vec![],
-        type_protos: vec![],
-    };
-    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
-        node: vec![NodeProto {
-            op_type: "Flatten".to_string(),
-            domain: "".to_string(),
-            attribute: vec![att_axis.clone()],
-            input: vec![INPUT_X.to_string()],
-            output: vec![OUTPUT_Z.to_string()],
-            name: "".to_string(),
-            doc_string: "".to_string(),
-        }],
-        name: "".to_string(),
-        initializer: vec![],
-        input: vec![
-            ValueInfoProto {
-                name: INPUT_X.to_string(),
-                doc_string: "".to_string(),
-                r#type: None,
-            },
-            ValueInfoProto {
-                name: INPUT_Y.to_string(),
-                doc_string: "".to_string(),
-                r#type: None,
-            },
-        ],
-        output: vec![ValueInfoProto {
-            name: OUTPUT_Z.to_string(),
-            doc_string: "".to_string(),
-            r#type: None,
-        }],
-        value_info: vec![],
-        doc_string: "".to_string(),
-        sparse_initializer: vec![],
-        quantization_annotation: vec![],
-    }));
-    let x = Tensor::from_vec(
-        vec![
-            1.0f32, 2.0f32, 3.0f32, 4.0f32, 5.0f32, 6.0f32, 7.0f32, 8.0f32,
-        ],
-        &[2, 2, 2],
-        &Device::Cpu,
-    )?;
-
-    let mut inputs: HashMap<String, Tensor> = HashMap::new();
-    inputs.insert(INPUT_X.to_string(), x);
-
-    let eval = candle_onnx::simple_eval(&manual_graph, inputs.clone())?;
-    assert_eq!(eval.len(), 1);
-
-    let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
-
-    let results = z.to_vec2::<f32>()?;
-
-    assert_eq!(results, vec![vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]]);
-
-    att_axis.i = 1;
-    let manual_graph = create_model_proto_with_graph(Some(GraphProto {
-        node: vec![NodeProto {
-            op_type: "Flatten".to_string(),
-            domain: "".to_string(),
-            attribute: vec![att_axis.clone()],
-            input: vec![INPUT_X.to_string()],
-            output: vec![OUTPUT_Z.to_string()],
-            name: "".to_string(),
-            doc_string: "".to_string(),
-        }],
-        name: "".to_string(),
-        initializer: vec![],
-        input: vec![
-            ValueInfoProto {
-                name: INPUT_X.to_string(),
-                doc_string: "".to_string(),
-                r#type: None,
-            },
-            ValueInfoProto {
-                name: INPUT_Y.to_string(),
-                doc_string: "".to_string(),
-                r#type: None,
-            },
-        ],
-        output: vec![ValueInfoProto {
-            name: OUTPUT_Z.to_string(),
-            doc_string: "".to_string(),
-            r#type: None,
-        }],
-        value_info: vec![],
-        doc_string: "".to_string(),
-        sparse_initializer: vec![],
-        quantization_annotation: vec![],
-    }));
-
-    let eval = candle_onnx::simple_eval(&manual_graph, inputs)?;
-    assert_eq!(eval.len(), 1);
-
-    let z = eval.get(OUTPUT_Z).expect("Output 'z' not found");
-
-    let results = z.to_vec2::<f32>()?;
-
-    assert_eq!(
-        results,
-        vec![vec![1.0, 2.0, 3.0, 4.0], vec![5.0, 6.0, 7.0, 8.0]]
-    );
-
-    Ok(())
-}
-
 // Below are ops that are implemented but not tested yet
 
 // "MaxPool"

candle-pyo3/py_src/candle/__init__.pyi

@@ -88,27 +88,23 @@ class QTensor:
         Dequantizes the tensor.
         """
         pass
-
     @property
     def ggml_dtype(self) -> str:
         """
         Gets the tensors quantized dtype.
         """
         pass
-
     def matmul_t(self, lhs: Tensor) -> Tensor:
         """
         Performs a quantized matrix multiplication, with the quantized tensor as the right hand side.
         """
         pass
-
     @property
     def rank(self) -> int:
         """
         Gets the rank of the tensor.
         """
         pass
-
     @property
     def shape(self) -> Tuple[int]:
         """
@@ -123,213 +119,178 @@ class Tensor:
 
     def __init__(self, data: _ArrayLike):
         pass
-
     def __add__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Add a scalar to a tensor or two tensors together.
         """
         pass
-
     def __eq__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Compare a tensor with a scalar or one tensor with another.
         """
         pass
-
     def __ge__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Compare a tensor with a scalar or one tensor with another.
         """
         pass
-
     def __getitem__(self, index: Union[Index, Tensor, Sequence[Index]]) -> "Tensor":
         """
         Return a slice of a tensor.
         """
         pass
-
     def __gt__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Compare a tensor with a scalar or one tensor with another.
         """
         pass
-
     def __le__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Compare a tensor with a scalar or one tensor with another.
         """
         pass
-
     def __lt__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Compare a tensor with a scalar or one tensor with another.
         """
         pass
-
     def __mul__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Multiply a tensor by a scalar or one tensor by another.
         """
         pass
-
     def __ne__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Compare a tensor with a scalar or one tensor with another.
         """
         pass
-
     def __radd__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Add a scalar to a tensor or two tensors together.
         """
         pass
-
     def __richcmp__(self, rhs: Union[Tensor, Scalar], op) -> "Tensor":
         """
         Compare a tensor with a scalar or one tensor with another.
         """
         pass
-
     def __rmul__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Multiply a tensor by a scalar or one tensor by another.
         """
         pass
-
     def __sub__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Subtract a scalar from a tensor or one tensor from another.
         """
         pass
-
     def __truediv__(self, rhs: Union[Tensor, Scalar]) -> "Tensor":
         """
         Divide a tensor by a scalar or one tensor by another.
         """
         pass
-
     def abs(self) -> Tensor:
         """
         Performs the `abs` operation on the tensor.
         """
         pass
-
     def argmax_keepdim(self, dim: int) -> Tensor:
         """
         Returns the indices of the maximum value(s) across the selected dimension.
         """
         pass
-
     def argmin_keepdim(self, dim: int) -> Tensor:
         """
         Returns the indices of the minimum value(s) across the selected dimension.
         """
         pass
-
     def broadcast_add(self, rhs: Tensor) -> Tensor:
         """
         Adds the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
         """
         pass
-
     def broadcast_as(self, *shape: Shape) -> Tensor:
         """
         Broadcasts the tensor to the given shape.
         """
         pass
-
     def broadcast_div(self, rhs: Tensor) -> Tensor:
         """
         Divides the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
         """
         pass
-
     def broadcast_left(self, *shape: Shape) -> Tensor:
         """
         Broadcasts the tensor to the given shape, adding new dimensions on the left.
         """
         pass
-
     def broadcast_mul(self, rhs: Tensor) -> Tensor:
         """
         Multiplies the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
         """
         pass
-
     def broadcast_sub(self, rhs: Tensor) -> Tensor:
         """
         Subtracts the two tensors, while broadcasting the right-hand-side tensor to match the shape of the left-hand-side tensor.
         """
         pass
-
     def contiguous(self) -> Tensor:
         """
         Makes the tensor contiguous in memory.
         """
         pass
-
     def copy(self) -> Tensor:
         """
         Returns a copy of the tensor.
         """
         pass
-
     def cos(self) -> Tensor:
         """
         Performs the `cos` operation on the tensor.
         """
         pass
-
     def detach(self) -> Tensor:
         """
         Detach the tensor from the computation graph.
         """
         pass
-
     @property
     def device(self) -> Device:
         """
         Gets the tensor's device.
         """
         pass
-
     @property
     def dtype(self) -> DType:
         """
         Gets the tensor's dtype.
         """
         pass
-
     def exp(self) -> Tensor:
         """
         Performs the `exp` operation on the tensor.
         """
         pass
-
     def flatten_all(self) -> Tensor:
         """
         Flattens the tensor into a 1D tensor.
         """
         pass
-
     def flatten_from(self, dim: int) -> Tensor:
         """
         Flattens the tensor on the dimension indexes from `dim` (inclusive) to the last dimension.
         """
         pass
-
     def flatten_to(self, dim: int) -> Tensor:
         """
         Flattens the tensor on the dimension indexes from `0` to `dim` (inclusive).
         """
         pass
-
     def get(self, index: int) -> Tensor:
         """
         Gets the value at the specified index.
         """
         pass
-
     def index_select(self, rhs: Tensor, dim: int) -> Tensor:
         """
         Select values for the input tensor at the target indexes across the specified dimension.
@@ -341,192 +302,161 @@ class Tensor:
         tensor.
         """
         pass
-
     def is_contiguous(self) -> bool:
         """
         Returns true if the tensor is contiguous in C order.
         """
         pass
-
     def is_fortran_contiguous(self) -> bool:
         """
         Returns true if the tensor is contiguous in Fortran order.
         """
         pass
-
     def log(self) -> Tensor:
         """
         Performs the `log` operation on the tensor.
         """
         pass
-
     def matmul(self, rhs: Tensor) -> Tensor:
         """
         Performs a matrix multiplication between the two tensors.
         """
         pass
-
     def max_keepdim(self, dim: int) -> Tensor:
         """
         Gathers the maximum value across the selected dimension.
         """
         pass
-
     def mean_all(self) -> Tensor:
         """
         Returns the mean of the tensor.
         """
         pass
-
     def min_keepdim(self, dim: int) -> Tensor:
         """
         Gathers the minimum value across the selected dimension.
         """
         pass
-
     def narrow(self, dim: int, start: int, len: int) -> Tensor:
         """
         Returns a new tensor that is a narrowed version of the input, the dimension `dim`
         ranges from `start` to `start + len`.
         """
         pass
-
     @property
     def nelement(self) -> int:
         """
         Gets the tensor's element count.
         """
         pass
-
     def powf(self, p: float) -> Tensor:
         """
         Performs the `pow` operation on the tensor with the given exponent.
         """
         pass
-
     def quantize(self, quantized_dtype: str) -> QTensor:
         """
         Quantize the tensor.
         """
         pass
-
     @property
     def rank(self) -> int:
         """
         Gets the tensor's rank.
         """
         pass
-
     def recip(self) -> Tensor:
         """
         Get the `recip` of the tensor.
         """
         pass
-
     def reshape(self, *shape: Shape) -> Tensor:
         """
         Reshapes the tensor to the given shape.
         """
         pass
-
     @property
     def shape(self) -> Tuple[int]:
         """
         Gets the tensor's shape.
         """
         pass
-
     def sin(self) -> Tensor:
         """
         Performs the `sin` operation on the tensor.
         """
         pass
-
     def sqr(self) -> Tensor:
         """
         Squares the tensor.
         """
         pass
-
     def sqrt(self) -> Tensor:
         """
         Calculates the square root of the tensor.
         """
         pass
-
     def squeeze(self, dim: int) -> Tensor:
         """
         Creates a new tensor with the specified dimension removed if its size was one.
         """
         pass
-
     @property
     def stride(self) -> Tuple[int]:
         """
         Gets the tensor's strides.
         """
         pass
-
     def sum_all(self) -> Tensor:
         """
         Returns the sum of the tensor.
         """
         pass
-
     def sum_keepdim(self, dim: Union[int, List[int]]) -> Tensor:
         """
         Returns the sum of all elements in the input tensor. The sum is performed over all the input dimensions.
         """
         pass
-
     def t(self) -> Tensor:
         """
         Transposes the tensor.
         """
         pass
-
     def to(self, *args, **kwargs) -> Tensor:
         """
         Performs Tensor dtype and/or device conversion.
         """
         pass
-
     def to_device(self, device: Union[str, Device]) -> Tensor:
         """
         Move the tensor to a new device.
         """
         pass
-
     def to_dtype(self, dtype: Union[str, DType]) -> Tensor:
         """
         Convert the tensor to a new dtype.
         """
         pass
-
     def to_torch(self) -> torch.Tensor:
         """
         Converts candle's tensor to pytorch's tensor
         """
         pass
-
     def transpose(self, dim1: int, dim2: int) -> Tensor:
         """
         Returns a tensor that is a transposed version of the input, the given dimensions are swapped.
         """
         pass
-
     def unsqueeze(self, dim: int) -> Tensor:
         """
         Creates a new tensor with a dimension of size one inserted at the specified position.
         """
         pass
-
     def values(self) -> _ArrayLike:
         """
         Gets the tensor's data as a Python scalar or array-like object.
         """
         pass
-
     def where_cond(self, on_true: Tensor, on_false: Tensor) -> Tensor:
         """
         Returns a tensor with the same shape as the input tensor, the values are taken from

candle-pyo3/py_src/candle/nn/container.py

@@ -57,10 +57,12 @@ class Sequential(Module):
     _modules: Dict[str, Module]  # type: ignore[assignment]
 
     @overload
-    def __init__(self, *args: Module) -> None: ...
+    def __init__(self, *args: Module) -> None:
+        ...
 
     @overload
-    def __init__(self, arg: "OrderedDict[str, Module]") -> None: ...
+    def __init__(self, arg: "OrderedDict[str, Module]") -> None:
+        ...
 
     def __init__(self, *args):
         super().__init__()

candle-pyo3/py_src/candle/nn/module.py

@@ -204,10 +204,12 @@ class Module:
     T_destination = TypeVar("T_destination", bound=Dict[str, Any])
 
     @overload
-    def state_dict(self, *, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination: ...
+    def state_dict(self, *, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination:
+        ...
 
     @overload
-    def state_dict(self, *, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Any]: ...
+    def state_dict(self, *, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Any]:
+        ...
 
     def state_dict(self, *args, destination=None, prefix="", keep_vars=False):
         r"""Returns a dictionary containing references to the whole state of the module.
@@ -584,10 +586,12 @@ class Module:
         self: T,
         device: str = ...,
         dtype: Optional[Union[DType, str]] = ...,
-    ) -> T: ...
+    ) -> T:
+        ...
 
     @overload
-    def to(self: T, dtype: Union[DType, str]) -> T: ...
+    def to(self: T, dtype: Union[DType, str]) -> T:
+        ...
 
     def to(self, *args, **kwargs):
         r"""Moves and/or casts the parameters and buffers.

candle-pyo3/py_src/candle/nn/normalization.py

@@ -14,7 +14,6 @@ class LayerNorm(Module):
     math::
         y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
     """
-
     __constants__ = ["normalized_shape", "eps"]
     normalized_shape: Tuple[int, ...]
     eps: float

candle-pyo3/py_src/candle/onnx/__init__.pyi

@@ -11,69 +11,59 @@ class ONNXModel:
 
     def __init__(self, path: str):
         pass
-
     @property
     def doc_string(self) -> str:
         """
         The doc string of the model.
         """
         pass
-
     @property
     def domain(self) -> str:
         """
         The domain of the operator set of the model.
         """
         pass
-
     def initializers(self) -> Dict[str, Tensor]:
         """
         Get the weights of the model.
         """
         pass
-
     @property
     def inputs(self) -> Optional[Dict[str, ONNXTensorDescription]]:
         """
         The inputs of the model.
         """
         pass
-
     @property
     def ir_version(self) -> int:
         """
         The version of the IR this model targets.
         """
         pass
-
     @property
     def model_version(self) -> int:
         """
         The version of the model.
         """
         pass
-
     @property
     def outputs(self) -> Optional[Dict[str, ONNXTensorDescription]]:
         """
         The outputs of the model.
         """
         pass
-
     @property
     def producer_name(self) -> str:
         """
         The producer of the model.
         """
         pass
-
     @property
     def producer_version(self) -> str:
         """
         The version of the producer of the model.
         """
         pass
-
     def run(self, inputs: Dict[str, Tensor]) -> Dict[str, Tensor]:
         """
         Run the model on the given inputs.
@@ -91,7 +81,6 @@ class ONNXTensorDescription:
         The data type of the tensor.
         """
         pass
-
     @property
     def shape(self) -> Tuple[Union[int, str, Any]]:
         """

candle-pyo3/py_src/candle/utils/__init__.pyi

@@ -33,9 +33,7 @@ def has_mkl() -> bool:
     pass
 
 @staticmethod
-def load_ggml(
-    path: Union[str, PathLike], device: Optional[Device] = None
-) -> Tuple[Dict[str, QTensor], Dict[str, Any], List[str]]:
+def load_ggml(path: Union[str, PathLike]) -> Tuple[Dict[str, QTensor], Dict[str, Any], List[str]]:
     """
     Load a GGML file. Returns a tuple of three objects: a dictionary mapping tensor names to tensors,
     a dictionary mapping hyperparameter names to hyperparameter values, and a vocabulary.
@@ -43,9 +41,7 @@ def load_ggml(
     pass
 
 @staticmethod
-def load_gguf(
-    path: Union[str, PathLike], device: Optional[Device] = None
-) -> Tuple[Dict[str, QTensor], Dict[str, Any]]:
+def load_gguf(path: Union[str, PathLike]) -> Tuple[Dict[str, QTensor], Dict[str, Any]]:
     """
     Loads a GGUF file. Returns a tuple of two dictionaries: the first maps tensor names to tensors,
     and the second maps metadata keys to metadata values.

candle-pyo3/src/lib.rs

@@ -938,8 +938,8 @@ impl PyTensor {
 
     /// Detach the tensor from the computation graph.
     /// &RETURNS&: Tensor
-    fn detach(&self) -> Self {
-        PyTensor(self.0.detach())
+    fn detach(&self) -> PyResult<Self> {
+        Ok(PyTensor(self.0.detach().map_err(wrap_err)?))
     }
 
     /// Returns a copy of the tensor.
@@ -1074,20 +1074,20 @@ impl PyTensor {
     fn quantize(&self, quantized_dtype: &str) -> PyResult<PyQTensor> {
         use ::candle::quantized;
         let res = match quantized_dtype.to_lowercase().as_str() {
-            "q2k" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q2K),
-            "q3k" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q3K),
-            "q4_0" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q4_0),
-            "q4_1" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q4_1),
-            "q4k" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q4K),
-            "q5_0" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q5_0),
-            "q5_1" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q5_1),
-            "q5k" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q5K),
-            "q6k" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q6K),
-            "q8_0" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q8_0),
-            "q8_1" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q8_1),
-            "q8k" => quantized::QTensor::quantize(self, quantized::GgmlDType::Q8K),
-            "f16" => quantized::QTensor::quantize(self, quantized::GgmlDType::F16),
-            "f32" => quantized::QTensor::quantize(self, quantized::GgmlDType::F32),
+            "q2k" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ2K>(self),
+            "q3k" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ3K>(self),
+            "q4_0" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ4_0>(self),
+            "q4_1" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ4_1>(self),
+            "q4k" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ4K>(self),
+            "q5_0" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ5_0>(self),
+            "q5_1" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ5_1>(self),
+            "q5k" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ5K>(self),
+            "q6k" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ6K>(self),
+            "q8_0" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ8_0>(self),
+            "q8_1" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ8_1>(self),
+            "q8k" => quantized::QTensor::quantize::<quantized::k_quants::BlockQ8K>(self),
+            "f16" => quantized::QTensor::quantize::<f16>(self),
+            "f32" => quantized::QTensor::quantize::<f32>(self),
             dt => {
                 return Err(PyErr::new::<PyValueError, _>(format!(
                     "unknown quantized-dtype {dt}"
@@ -1278,19 +1278,13 @@ fn save_safetensors(
 }
 
 #[pyfunction]
-#[pyo3(text_signature = "(path:Union[str,PathLike], device: Optional[Device] = None)")]
+#[pyo3(text_signature = "(path:Union[str,PathLike])")]
 /// Load a GGML file. Returns a tuple of three objects: a dictionary mapping tensor names to tensors,
 /// a dictionary mapping hyperparameter names to hyperparameter values, and a vocabulary.
 /// &RETURNS&: Tuple[Dict[str,QTensor], Dict[str,Any], List[str]]
-fn load_ggml(
-    path: &str,
-    device: Option<PyDevice>,
-    py: Python<'_>,
-) -> PyResult<(PyObject, PyObject, PyObject)> {
+fn load_ggml(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject, PyObject)> {
     let mut file = std::fs::File::open(path)?;
-    let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
-    let ggml =
-        ::candle::quantized::ggml_file::Content::read(&mut file, &device).map_err(wrap_err)?;
+    let ggml = ::candle::quantized::ggml_file::Content::read(&mut file).map_err(wrap_err)?;
     let tensors = ggml
         .tensors
         .into_iter()
@@ -1319,16 +1313,11 @@ fn load_ggml(
 }
 
 #[pyfunction]
-#[pyo3(text_signature = "(path:Union[str,PathLike], device: Optional[Device] = None)")]
+#[pyo3(text_signature = "(path:Union[str,PathLike])")]
 /// Loads a GGUF file. Returns a tuple of two dictionaries: the first maps tensor names to tensors,
 /// and the second maps metadata keys to metadata values.
 /// &RETURNS&: Tuple[Dict[str,QTensor], Dict[str,Any]]
-fn load_gguf(
-    path: &str,
-    device: Option<PyDevice>,
-    py: Python<'_>,
-) -> PyResult<(PyObject, PyObject)> {
-    let device = device.unwrap_or(PyDevice::Cpu).as_device()?;
+fn load_gguf(path: &str, py: Python<'_>) -> PyResult<(PyObject, PyObject)> {
     use ::candle::quantized::gguf_file;
     fn gguf_value_to_pyobject(v: &gguf_file::Value, py: Python<'_>) -> PyResult<PyObject> {
         let v: PyObject = match v {
@@ -1360,7 +1349,7 @@ fn load_gguf(
         .tensor_infos
         .keys()
         .map(|key| {
-            let qtensor = gguf.tensor(&mut file, key, &device)?;
+            let qtensor = gguf.tensor(&mut file, key)?;
             Ok((key, PyQTensor(Arc::new(qtensor)).into_py(py)))
         })
         .collect::<::candle::Result<Vec<_>>>()

candle-pyo3/stub.py

@@ -189,6 +189,7 @@ def do_black(content, is_pyi):
         line_length=119,
         is_pyi=is_pyi,
         string_normalization=True,
+        experimental_string_processing=False,
     )
     try:
         return black.format_file_contents(content, fast=True, mode=mode)