Add some group parameter to convolutions. (#566)

* Add some group parameter to convolutions.

* Avoid some unnecessary groups checks.

* Move the tensor convolution bits.

* Properh handling of groups.

* Bump the crate version.

* And add a changelog.

CHANGELOG.md

@@ -0,0 +1,13 @@
+# Changelog
+This documents the main changes to the `candle` crate.
+
+## Unreleased
+### Added
+- Add a group parameter to convolutions
+  [566](https://github.com/huggingface/candle/pull/566).
+- New dtype: int64
+  [563](https://github.com/huggingface/candle/pull/563).
+- Handling of the GGUF file format.
+  [559](https://github.com/huggingface/candle/pull/559).
+
+## v0.1.2 - 2023-08-21

Cargo.toml

@@ -16,7 +16,7 @@ exclude = [
 ]
 
 [workspace.package]
-version = "0.1.2"
+version = "0.1.3"
 edition = "2021"
 description = "Minimalist ML framework."
 repository = "https://github.com/huggingface/candle"

candle-core/Cargo.toml

@@ -12,7 +12,7 @@ readme = "README.md"
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
 byteorder = { workspace = true }
-candle-kernels = { path = "../candle-kernels", version = "0.1.2", optional = true }
+candle-kernels = { path = "../candle-kernels", version = "0.1.3", optional = true }
 cudarc = { workspace = true, optional = true }
 gemm = { workspace = true }
 half = { workspace = true }

candle-core/examples/basics.rs

@@ -11,7 +11,7 @@ fn main() -> Result<()> {
     let inp = Tensor::randn(0f32, 1., (2, 320, 96, 96), &Device::Cpu)?;
     let w = Tensor::randn(0f32, 1., (320, 320, 3, 3), &Device::Cpu)?;
     let start = std::time::Instant::now();
-    let res = inp.conv2d(&w, 0, 1);
+    let res = inp.conv2d(&w, 0, 1, 1)?;
     println!("{:?}", start.elapsed());
     println!("{res:?}");
     Ok(())

candle-core/examples/cpu_benchmarks.rs

@@ -40,7 +40,7 @@ impl Benchmark for Conv1d {
     }
 
     fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
-        d.0.conv1d(&d.1, 0, 1)
+        d.0.conv1d(&d.1, 0, 1, 1)
     }
 
     const ITERS: usize = 5;
@@ -59,7 +59,7 @@ impl Benchmark for Conv2d {
     }
 
     fn run_one(d: &Self::PreProcessData) -> Result<Self::RunResult> {
-        d.0.conv2d(&d.1, 0, 1)
+        d.0.conv2d(&d.1, 0, 1, 1)
     }
 
     const ITERS: usize = 1;

candle-core/examples/cuda_basics.rs

@@ -11,7 +11,7 @@ fn main() -> Result<()> {
     let device = Device::new_cuda(0)?;
     let t = Tensor::randn(0f32, 1f32, (2, 4, 96, 96), &device)?;
     let w = Tensor::randn(0f32, 1f32, (320, 4, 3, 3), &device)?;
-    let res = t.conv2d(&w, 1, 1)?;
+    let res = t.conv2d(&w, 1, 1, 1)?;
     println!("{res:?}");
     Ok(())
 }

candle-core/src/conv.rs

@@ -1,3 +1,5 @@
+use crate::{op::BackpropOp, op::Op, Error, Result, Tensor};
+
 #[derive(Debug, Clone, PartialEq, Eq)]
 pub struct ParamsConv1D {
     pub(crate) b_size: usize,
@@ -51,3 +53,113 @@ impl ParamsConv2D {
         vec![self.b_size, self.c_out, self.out_h(), self.out_w()]
     }
 }
+
+impl Tensor {
+    fn conv1d_single_group(&self, kernel: &Self, params: &ParamsConv1D) -> Result<Self> {
+        let storage =
+            self.storage()
+                .conv1d(self.layout(), &kernel.storage(), kernel.layout(), params)?;
+        let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::Conv1D {
+            arg,
+            kernel,
+            padding: params.padding,
+            stride: params.stride,
+        });
+        let out_dims = params.out_dims();
+        Ok(crate::tensor::from_storage(storage, out_dims, op, false))
+    }
+
+    /// Applies a 1D convolution over the input tensor.
+    pub fn conv1d(
+        &self,
+        kernel: &Self,
+        padding: usize,
+        stride: usize,
+        groups: usize,
+    ) -> Result<Self> {
+        let (c_out, c_in_k, k_size) = kernel.dims3()?;
+        let (b_size, c_in, l_in) = self.dims3()?;
+        if c_in != c_in_k * groups {
+            Err(Error::Conv1dInvalidArgs {
+                inp_shape: self.shape().clone(),
+                k_shape: kernel.shape().clone(),
+                padding,
+                stride,
+                msg: "the number of in-channels on the input doesn't match the kernel size",
+            }
+            .bt())?
+        }
+
+        let params = ParamsConv1D {
+            b_size,
+            l_in,
+            c_out,
+            c_in,
+            k_size,
+            padding,
+            stride,
+        };
+        if groups == 1 {
+            self.conv1d_single_group(kernel, &params)
+        } else {
+            let blocks = self.chunk(groups, 1)?;
+            let blocks = blocks
+                .iter()
+                .map(|block| block.conv1d_single_group(kernel, &params))
+                .collect::<Result<Vec<_>>>()?;
+            Tensor::cat(&blocks, 1)
+        }
+    }
+
+    fn conv2d_single_group(&self, kernel: &Self, params: &ParamsConv2D) -> Result<Self> {
+        let storage =
+            self.storage()
+                .conv2d(self.layout(), &kernel.storage(), kernel.layout(), params)?;
+        let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::Conv2D {
+            arg,
+            kernel,
+            padding: params.padding,
+            stride: params.stride,
+        });
+        let out_dims = params.out_dims();
+        Ok(crate::tensor::from_storage(storage, out_dims, op, false))
+    }
+
+    /// Applies a 2D convolution over the input tensor.
+    pub fn conv2d(
+        &self,
+        kernel: &Self,
+        padding: usize,
+        stride: usize,
+        groups: usize,
+    ) -> Result<Self> {
+        let (b_size, c_in, i_h, i_w) = self.dims4()?;
+        let (c_out, c_in_k, k_h, k_w) = kernel.dims4()?;
+        if c_in != c_in_k * groups {
+            crate::bail!(
+                "in_channel mismatch between input ({c_in}, groups {groups}) and kernel ({c_in_k})"
+            )
+        }
+        let params = ParamsConv2D {
+            b_size,
+            i_h,
+            i_w,
+            k_h,
+            k_w,
+            c_out,
+            c_in,
+            padding,
+            stride,
+        };
+        if groups == 1 {
+            self.conv2d_single_group(kernel, &params)
+        } else {
+            let blocks = self.chunk(groups, 1)?;
+            let blocks = blocks
+                .iter()
+                .map(|block| block.conv2d_single_group(kernel, &params))
+                .collect::<Result<Vec<_>>>()?;
+            Tensor::cat(&blocks, 1)
+        }
+    }
+}

candle-core/src/tensor.rs

@@ -124,7 +124,7 @@ macro_rules! broadcast_binary_op {
 }
 
 /// Creates a fresh tensor structure based on a storage and a shape, this uses contiguous strides.
-fn from_storage<S: Into<Shape>>(
+pub(crate) fn from_storage<S: Into<Shape>>(
     storage: Storage,
     shape: S,
     op: BackpropOp,
@@ -787,72 +787,6 @@ impl Tensor {
         self.cmp(rhs, CmpOp::Le)
     }
 
-    /// Applies a 1D convolution over the input tensor.
-    pub fn conv1d(&self, kernel: &Self, padding: usize, stride: usize) -> Result<Self> {
-        let (c_out, c_in_k, k_size) = kernel.dims3()?;
-        let (b_size, c_in, l_in) = self.dims3()?;
-        if c_in != c_in_k {
-            Err(Error::Conv1dInvalidArgs {
-                inp_shape: self.shape().clone(),
-                k_shape: kernel.shape().clone(),
-                padding,
-                stride,
-                msg: "the number of in-channels on the input doesn't match the kernel size",
-            }
-            .bt())?
-        }
-        let params = crate::conv::ParamsConv1D {
-            b_size,
-            l_in,
-            c_out,
-            c_in,
-            k_size,
-            padding,
-            stride,
-        };
-        let storage =
-            self.storage()
-                .conv1d(self.layout(), &kernel.storage(), kernel.layout(), &params)?;
-        let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::Conv1D {
-            arg,
-            kernel,
-            padding,
-            stride,
-        });
-        let out_dims = params.out_dims();
-        Ok(from_storage(storage, out_dims, op, false))
-    }
-
-    pub fn conv2d(&self, kernel: &Self, padding: usize, stride: usize) -> Result<Self> {
-        let (b_size, c_in, i_h, i_w) = self.dims4()?;
-        let (c_out, c_in_k, k_h, k_w) = kernel.dims4()?;
-        if c_in != c_in_k {
-            crate::bail!("in_channel mismatch between input ({c_in}) and kernel ({c_in_k})")
-        }
-        let params = crate::conv::ParamsConv2D {
-            b_size,
-            i_h,
-            i_w,
-            k_h,
-            k_w,
-            c_out,
-            c_in,
-            padding,
-            stride,
-        };
-        let storage =
-            self.storage()
-                .conv2d(self.layout(), &kernel.storage(), kernel.layout(), &params)?;
-        let op = BackpropOp::new2(self, kernel, |arg, kernel| Op::Conv2D {
-            arg,
-            kernel,
-            padding,
-            stride,
-        });
-        let out_dims = params.out_dims();
-        Ok(from_storage(storage, out_dims, op, false))
-    }
-
     pub fn upsample_nearest2d(&self, target_h: usize, target_w: usize) -> Result<Self> {
         let (n, c, _h, _w) = self.dims4()?;
         let op = BackpropOp::new1(self, Op::UpsampleNearest2D);
@@ -1920,7 +1854,7 @@ impl Tensor {
         }
     }
 
-    fn storage(&self) -> std::sync::RwLockReadGuard<'_, Storage> {
+    pub(crate) fn storage(&self) -> std::sync::RwLockReadGuard<'_, Storage> {
         self.storage.read().unwrap()
     }
 

candle-core/tests/conv_tests.rs

@@ -33,13 +33,13 @@ fn conv1d(dev: &Device) -> Result<()> {
         dev,
     )?
     .reshape((2, 4, 3))?;
-    let res = t.conv1d(&w, 0, 1)?;
+    let res = t.conv1d(&w, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 2, 3]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [2.6357, -1.3336, 4.1393, -1.1784, 3.5675, 0.5069]
     );
-    let res = t.conv1d(&w, /*padding*/ 1, 1)?;
+    let res = t.conv1d(&w, /*padding*/ 1, 1, 1)?;
     assert_eq!(res.dims(), [1, 2, 5]);
     // Same as pytorch default padding: use zeros.
     assert_eq!(
@@ -52,13 +52,13 @@ fn conv1d(dev: &Device) -> Result<()> {
 fn conv1d_small(dev: &Device) -> Result<()> {
     let t = Tensor::new(&[0.4056f32, -0.8689, -0.0773, -1.5630], dev)?.reshape((1, 1, 4))?;
     let w = Tensor::new(&[1f32, 0., 0.], dev)?.reshape((1, 1, 3))?;
-    let res = t.conv1d(&w, 0, 1)?;
+    let res = t.conv1d(&w, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 1, 2]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [0.4056, -0.8689]
     );
-    let res = t.conv1d(&w, /*padding*/ 1, 1)?;
+    let res = t.conv1d(&w, /*padding*/ 1, 1, 1)?;
     assert_eq!(res.dims(), [1, 1, 4]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
@@ -109,7 +109,7 @@ fn conv2d(dev: &Device) -> Result<()> {
     )?;
     let t = t.reshape((1, 4, 5, 5))?;
     let w = w.reshape((2, 4, 3, 3))?;
-    let res = t.conv2d(&w, 0, 1)?;
+    let res = t.conv2d(&w, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 2, 3, 3]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
@@ -143,7 +143,7 @@ fn conv2d_small(dev: &Device) -> Result<()> {
     let w = Tensor::new(&[-0.9259f32, 1.3017], dev)?;
     let t = t.reshape((1, 2, 3, 3))?;
     let w = w.reshape((1, 2, 1, 1))?;
-    let res = t.conv2d(&w, 0, 1)?;
+    let res = t.conv2d(&w, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 1, 3, 3]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
@@ -162,7 +162,7 @@ fn conv2d_smaller(dev: &Device) -> Result<()> {
     let w = Tensor::new(&[1f32, 1., 1., 1., 1., 1., 1., 1., 1.], dev)?;
     let t = t.reshape((1, 1, 3, 3))?;
     let w = w.reshape((1, 1, 3, 3))?;
-    let res = t.conv2d(&w, 0, 1)?;
+    let res = t.conv2d(&w, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 1, 1, 1]);
     assert_eq!(
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,

candle-datasets/Cargo.toml

@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 byteorder = { workspace = true }
-candle = { path = "../candle-core", version = "0.1.2", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.1.2" }
+candle = { path = "../candle-core", version = "0.1.3", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.1.3" }
 hf-hub = { workspace = true}
 intel-mkl-src = { workspace = true, optional = true }
 memmap2 = { workspace = true }

candle-examples/Cargo.toml

@@ -11,11 +11,11 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.1.2", package = "candle-core" }
-candle-datasets = { path = "../candle-datasets", version = "0.1.2" }
-candle-nn = { path = "../candle-nn", version = "0.1.2" }
-candle-transformers = { path = "../candle-transformers", version = "0.1.2" }
-candle-flash-attn = { path = "../candle-flash-attn", version = "0.1.2", optional = true }
+candle = { path = "../candle-core", version = "0.1.3", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.1.3" }
+candle-nn = { path = "../candle-nn", version = "0.1.3" }
+candle-transformers = { path = "../candle-transformers", version = "0.1.3" }
+candle-flash-attn = { path = "../candle-flash-attn", version = "0.1.3", optional = true }
 safetensors = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }

candle-examples/examples/musicgen/encodec_model.rs

@@ -274,14 +274,22 @@ impl EncodecConv1d {
                 in_c,
                 out_c,
                 kernel_size,
-                Conv1dConfig { padding: 0, stride },
+                Conv1dConfig {
+                    padding: 0,
+                    stride,
+                    groups: 1,
+                },
                 vb.pp("conv"),
             )?,
             NormType::None => conv1d(
                 in_c,
                 out_c,
                 kernel_size,
-                Conv1dConfig { padding: 0, stride },
+                Conv1dConfig {
+                    padding: 0,
+                    stride,
+                    groups: 1,
+                },
                 vb.pp("conv"),
             )?,
         };

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

@@ -66,6 +66,7 @@ impl ResnetBlock2D {
         let conv_cfg = nn::Conv2dConfig {
             stride: 1,
             padding: 1,
+            groups: 1,
         };
         let norm1 = nn::group_norm(config.groups, in_channels, config.eps, vs.pp("norm1"))?;
         let conv1 = conv2d(in_channels, out_channels, 3, conv_cfg, vs.pp("conv1"))?;
@@ -79,6 +80,7 @@ impl ResnetBlock2D {
             let conv_cfg = nn::Conv2dConfig {
                 stride: 1,
                 padding: 0,
+                groups: 1,
             };
             Some(conv2d(
                 in_channels,

candle-examples/examples/stable-diffusion/unet_2d.rs

@@ -112,8 +112,8 @@ impl UNet2DConditionModel {
         let bl_attention_head_dim = config.blocks.last().unwrap().attention_head_dim;
         let time_embed_dim = b_channels * 4;
         let conv_cfg = nn::Conv2dConfig {
-            stride: 1,
             padding: 1,
+            ..Default::default()
         };
         let conv_in = conv2d(in_channels, b_channels, 3, conv_cfg, vs.pp("conv_in"))?;
 

candle-examples/examples/stable-diffusion/unet_2d_blocks.rs

@@ -24,7 +24,11 @@ impl Downsample2D {
         padding: usize,
     ) -> Result<Self> {
         let conv = if use_conv {
-            let config = nn::Conv2dConfig { stride: 2, padding };
+            let config = nn::Conv2dConfig {
+                stride: 2,
+                padding,
+                ..Default::default()
+            };
             let conv = conv2d(in_channels, out_channels, 3, config, vs.pp("conv"))?;
             Some(conv)
         } else {

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

@@ -51,8 +51,8 @@ impl Encoder {
         config: EncoderConfig,
     ) -> Result<Self> {
         let conv_cfg = nn::Conv2dConfig {
-            stride: 1,
             padding: 1,
+            ..Default::default()
         };
         let conv_in = nn::conv2d(
             in_channels,
@@ -182,8 +182,8 @@ impl Decoder {
         let n_block_out_channels = config.block_out_channels.len();
         let last_block_out_channels = *config.block_out_channels.last().unwrap();
         let conv_cfg = nn::Conv2dConfig {
-            stride: 1,
             padding: 1,
+            ..Default::default()
         };
         let conv_in = nn::conv2d(
             in_channels,

candle-examples/examples/whisper/model.rs

@@ -308,10 +308,12 @@ impl AudioEncoder {
         let cfg1 = Conv1dConfig {
             padding: 1,
             stride: 1,
+            groups: 1,
         };
         let cfg2 = Conv1dConfig {
             padding: 1,
             stride: 2,
+            groups: 1,
         };
         let conv1 = conv1d(cfg.num_mel_bins, n_state, 3, cfg1, vb.pp("conv1"))?;
         let conv2 = conv1d(n_state, n_state, 3, cfg2, vb.pp("conv2"))?;

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

@@ -128,7 +128,11 @@ fn conv(vb: VarBuilder, index: usize, p: usize, b: &Block) -> Result<(usize, Bl)
         }
         Some(_) | None => (None, true),
     };
-    let conv_cfg = candle_nn::Conv2dConfig { stride, padding };
+    let conv_cfg = candle_nn::Conv2dConfig {
+        stride,
+        padding,
+        groups: 1,
+    };
     let conv = if bias {
         conv2d(p, filters, size, conv_cfg, vb.pp(&format!("conv_{index}")))?
     } else {

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

@@ -101,7 +101,11 @@ impl ConvBlock {
         padding: Option<usize>,
     ) -> Result<Self> {
         let padding = padding.unwrap_or(k / 2);
-        let cfg = Conv2dConfig { padding, stride };
+        let cfg = Conv2dConfig {
+            padding,
+            stride,
+            groups: 1,
+        };
         let conv = conv2d_no_bias(c1, c2, k, cfg, vb.pp("conv"))?;
         let bn = batch_norm(c2, 1e-3, vb.pp("bn"))?;
         Ok(Self { conv, bn })

candle-flash-attn/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-flash-attn"
-version = "0.1.2"
+version = "0.1.3"
 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"], version = "0.1.2", package = "candle-core" }
+candle = { path = "../candle-core", features = ["cuda"], version = "0.1.3", package = "candle-core" }
 half = { version = "2.3.1", features = ["num-traits"] }
 
 [build-dependencies]
@@ -21,4 +21,4 @@ rayon = "1.7.0"
 
 [dev-dependencies]
 anyhow = { version = "1", features = ["backtrace"] }
-candle-nn = { path = "../candle-nn", version = "0.1.2", features = ["cuda"] }
+candle-nn = { path = "../candle-nn", version = "0.1.3", features = ["cuda"] }

candle-kernels/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "candle-kernels"
-version = "0.1.2"
+version = "0.1.3"
 edition = "2021"
 
 description = "CUDA kernels for Candle"

candle-nn/Cargo.toml

@@ -11,7 +11,7 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.1.2", package = "candle-core" }
+candle = { path = "../candle-core", version = "0.1.3", package = "candle-core" }
 thiserror = { workspace = true }
 intel-mkl-src = { workspace = true, optional = true }
 safetensors = { workspace = true }

candle-nn/src/conv.rs

@@ -5,6 +5,7 @@ use candle::{Result, Tensor};
 pub struct Conv1dConfig {
     pub padding: usize,
     pub stride: usize,
+    pub groups: usize,
 }
 
 impl Default for Conv1dConfig {
@@ -12,6 +13,7 @@ impl Default for Conv1dConfig {
         Self {
             padding: 0,
             stride: 1,
+            groups: 1,
         }
     }
 }
@@ -39,7 +41,12 @@ impl Conv1d {
 
 impl crate::Module for Conv1d {
     fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        let x = x.conv1d(&self.weight, self.config.padding, self.config.stride)?;
+        let x = x.conv1d(
+            &self.weight,
+            self.config.padding,
+            self.config.stride,
+            self.config.groups,
+        )?;
         match &self.bias {
             None => Ok(x),
             Some(bias) => {
@@ -55,6 +62,7 @@ impl crate::Module for Conv1d {
 pub struct Conv2dConfig {
     pub padding: usize,
     pub stride: usize,
+    pub groups: usize,
 }
 
 impl Default for Conv2dConfig {
@@ -62,6 +70,7 @@ impl Default for Conv2dConfig {
         Self {
             padding: 0,
             stride: 1,
+            groups: 1,
         }
     }
 }
@@ -90,7 +99,12 @@ impl Conv2d {
 
 impl crate::Module for Conv2d {
     fn forward(&self, x: &Tensor) -> Result<Tensor> {
-        let x = x.conv2d(&self.weight, self.config.padding, self.config.stride)?;
+        let x = x.conv2d(
+            &self.weight,
+            self.config.padding,
+            self.config.stride,
+            self.config.groups,
+        )?;
         match &self.bias {
             None => Ok(x),
             Some(bias) => {

candle-pyo3/Cargo.toml

@@ -15,7 +15,7 @@ crate-type = ["cdylib"]
 doc = false
 
 [dependencies]
-candle = { path = "../candle-core", version = "0.1.2", package = "candle-core" }
+candle = { path = "../candle-core", version = "0.1.3", package = "candle-core" }
 half = { workspace = true }
 pyo3 = { version = "0.19.0", features = ["extension-module"] }
 

candle-transformers/Cargo.toml

@@ -11,8 +11,8 @@ readme = "README.md"
 
 [dependencies]
 accelerate-src = { workspace = true, optional = true }
-candle = { path = "../candle-core", version = "0.1.2", package = "candle-core" }
-candle-nn = { path = "../candle-nn", version = "0.1.2" }
+candle = { path = "../candle-core", version = "0.1.3", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.1.3" }
 intel-mkl-src = { workspace = true, optional = true }
 rand = { workspace = true }
 wav = { workspace = true }

candle-wasm-examples/llama2-c/Cargo.toml

@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.1.2", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.1.2" }
+candle = { path = "../../candle-core", version = "0.1.3", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.1.3" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 

candle-wasm-examples/whisper/Cargo.toml

@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.1.2", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.1.2" }
+candle = { path = "../../candle-core", version = "0.1.3", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.1.3" }
 num-traits = { workspace = true }
 tokenizers = { workspace = true, features = ["unstable_wasm"] }
 

candle-wasm-examples/whisper/src/model.rs

@@ -295,10 +295,12 @@ impl AudioEncoder {
         let cfg1 = Conv1dConfig {
             padding: 1,
             stride: 1,
+            groups: 1,
         };
         let cfg2 = Conv1dConfig {
             padding: 1,
             stride: 2,
+            groups: 1,
         };
         let conv1 = conv1d(cfg.num_mel_bins, n_state, 3, cfg1, vb.pp("conv1"))?;
         let conv2 = conv1d(n_state, n_state, 3, cfg2, vb.pp("conv2"))?;

candle-wasm-examples/yolo/Cargo.toml

@@ -9,8 +9,8 @@ categories.workspace = true
 license.workspace = true
 
 [dependencies]
-candle = { path = "../../candle-core", version = "0.1.2", package = "candle-core" }
-candle-nn = { path = "../../candle-nn", version = "0.1.2" }
+candle = { path = "../../candle-core", version = "0.1.3", package = "candle-core" }
+candle-nn = { path = "../../candle-nn", version = "0.1.3" }
 num-traits = { workspace = true }
 serde = { workspace = true }
 serde_json = { workspace = true }

candle-wasm-examples/yolo/src/model.rs

@@ -97,7 +97,11 @@ impl ConvBlock {
         padding: Option<usize>,
     ) -> Result<Self> {
         let padding = padding.unwrap_or(k / 2);
-        let cfg = Conv2dConfig { padding, stride };
+        let cfg = Conv2dConfig {
+            padding,
+            stride,
+            groups: 1,
+        };
         let conv = conv2d_no_bias(c1, c2, k, cfg, vb.pp("conv"))?;
         let bn = batch_norm(c2, 1e-3, vb.pp("bn"))?;
         Ok(Self { conv, bn })