ConvTranspose1d cuda support. (#1697)

* ConvTranspose1d cuda support.

* Add the conv-transpose1d kernel.

* Remove some unused variables.

candle-core/src/cuda_backend.rs

@@ -1149,6 +1149,55 @@ 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>(
@@ -1810,12 +1859,15 @@ impl BackendStorage for CudaStorage {
 
     fn conv_transpose1d(
         &self,
-        _: &Layout,
-        _: &Self,
-        _: &Layout,
-        _: &crate::conv::ParamsConvTranspose1D,
+        l: &Layout,
+        kernel: &Self,
+        kernel_l: &Layout,
+        params: &crate::conv::ParamsConvTranspose1D,
     ) -> Result<Self> {
-        todo!()
+        let device = self.device().clone();
+        let slice =
+            ConvTranspose1D(params).map(&self.slice, l, &kernel.slice, kernel_l, &device)?;
+        Ok(Self { slice, device })
     }
 
     #[cfg(not(feature = "cudnn"))]

candle-core/tests/conv_tests.rs

@@ -50,7 +50,6 @@ fn conv1d(dev: &Device) -> Result<()> {
         test_utils::to_vec1_round(&res.flatten_all()?, 4)?,
         [2.4509, 2.6357, -1.3336, 4.1393, 0.5657, 1.8091, -1.1784, 3.5675, 0.5069, 3.3352]
     );
-    if dev.is_cpu() {
     let res = t.conv_transpose1d(&w.transpose(0, 1)?, 0, 0, 1, 1)?;
     assert_eq!(res.dims(), [1, 2, 7]);
     assert_eq!(
@@ -60,7 +59,6 @@ fn conv1d(dev: &Device) -> Result<()> {
             4.7076, -5.9745, -0.8276, 1.621
         ],
     );
-    }
     Ok(())
 }
 

candle-kernels/src/conv.cu

@@ -71,7 +71,6 @@ __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];
 
@@ -120,7 +119,6 @@ __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];
@@ -225,6 +223,60 @@ __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(
@@ -507,6 +559,22 @@ 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, \
@@ -568,6 +636,7 @@ 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)
@@ -579,6 +648,7 @@ 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)
@@ -597,6 +667,11 @@ 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)