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.

1 files changed, 2 insertions, 68 deletions

diff --git a/candle-core/src/tensor.rs b/candle-core/src/tensor.rs
index a4b9795b..46f9c53f 100644
--- a/candle-core/src/tensor.rs
+++ b/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()
     }