The backward code of Sparse Conv3d (#40054)

Sparse Conv3d backward code

paddle/phi/kernels/sparse/convolution_grad_kernel.h

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename T, typename Context>
+void Conv3dGradKernel(const Context& dev_ctx,
+                      const SparseCooTensor& x,
+                      const DenseTensor& rulebook,
+                      const DenseTensor& kernel,
+                      const SparseCooTensor& out_grad,
+                      const std::vector<int>& paddings,
+                      const std::vector<int>& dilations,
+                      const std::vector<int>& strides,
+                      const int groups,
+                      DenseTensor* x_grad,
+                      DenseTensor* kernel_grad);
+
+template <typename T, typename Context>
+std::vector<DenseTensor> Conv3dGrad(const Context& dev_ctx,
+                                    const SparseCooTensor& x,
+                                    const DenseTensor& rulebook,
+                                    const DenseTensor& kernel,
+                                    const SparseCooTensor& out_grad,
+                                    const std::vector<int>& paddings,
+                                    const std::vector<int>& dilations,
+                                    const std::vector<int>& strides,
+                                    const int groups) {
+  DenseTensor x_grad = phi::Empty<T, Context>(dev_ctx);
+  DenseTensor kernel_grad = phi::Empty<T, Context>(dev_ctx);
+  Conv3dGradKernel<T, Context>(dev_ctx,
+                               x,
+                               rulebook,
+                               kernel,
+                               out_grad,
+                               paddings,
+                               dilations,
+                               strides,
+                               groups,
+                               &x_grad,
+                               &kernel_grad);
+  std::vector<DenseTensor> out(2);
+  out[0] = x_grad;
+  out[1] = kernel_grad;
+  return out;
+}
+
+}  // namespace sparse
+}  // namespace phi

paddle/phi/kernels/sparse/cpu/convolution.h

@@ -23,6 +23,7 @@ limitations under the License. */
 #include "paddle/phi/core/sparse_coo_tensor.h"
 #include "paddle/phi/core/tensor_meta.h"
 #include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/sparse/convolution_kernel.h"
 
 namespace phi {
 namespace sparse {

paddle/phi/kernels/sparse/cpu/convolution_grad_kernel.cc

+/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/kernels/sparse/convolution_grad_kernel.h"
+#include "paddle/phi/kernels/funcs/blas/blas.h"
+#include "paddle/phi/kernels/sparse/cpu/convolution.h"
+
+namespace phi {
+namespace sparse {
+
+// rulebook:
+//[
+//  [kernel_index],
+//  [in_i],
+//  [out_i],
+//]
+// x_grad = out_grad * transpose(kenrel)
+// kernel_grad = transpose(x) * out_grad
+template <typename T, typename Context>
+void Conv3dGradKernel(const Context& dev_ctx,
+                      const SparseCooTensor& x,
+                      const DenseTensor& rulebook,
+                      const DenseTensor& kernel,
+                      const SparseCooTensor& out_grad,
+                      const std::vector<int>& paddings,
+                      const std::vector<int>& dilations,
+                      const std::vector<int>& strides,
+                      const int groups,
+                      DenseTensor* x_grad,
+                      DenseTensor* kernel_grad) {
+  const auto& kernel_dims = kernel.dims();
+  const int kernel_size = kernel_dims[0] * kernel_dims[1] * kernel_dims[2];
+  const int in_channels = kernel_dims[3];
+  const int out_channels = kernel_dims[4];
+  const int* rulebook_ptr = rulebook.data<int>();
+
+  const int rulebook_len = rulebook.dims()[1];
+
+  DenseTensorMeta in_features_meta(
+      x.dtype(), {rulebook_len, in_channels}, DataLayout::NCHW);
+  DenseTensorMeta d_x_features_meta(
+      x.dtype(), {rulebook_len, in_channels}, DataLayout::NCHW);
+  DenseTensorMeta out_grad_features_meta(
+      x.dtype(), {rulebook_len, out_channels}, DataLayout::NCHW);
+  phi::DenseTensor in_features =
+      phi::Empty(dev_ctx, std::move(in_features_meta));
+  phi::DenseTensor d_x_features =
+      phi::Empty(dev_ctx, std::move(d_x_features_meta));
+  phi::DenseTensor out_grad_features =
+      phi::Empty(dev_ctx, std::move(out_grad_features_meta));
+
+  dev_ctx.Alloc(
+      &in_features, in_features.dtype(), sizeof(T) * in_features.numel());
+  T* in_features_ptr = in_features.data<T>();
+  dev_ctx.Alloc(
+      &d_x_features, d_x_features.dtype(), sizeof(T) * d_x_features.numel());
+  T* d_x_features_ptr = d_x_features.data<T>();
+  dev_ctx.Alloc(&out_grad_features,
+                out_grad_features.dtype(),
+                sizeof(T) * out_grad_features.numel());
+  T* out_grad_features_ptr = out_grad_features.data<T>();
+  kernel_grad->Resize(kernel_dims);
+  dev_ctx.Alloc(
+      kernel_grad, kernel_grad->dtype(), kernel_grad->numel() * sizeof(T));
+  T* d_kernel_ptr = kernel_grad->data<T>();
+
+  Gather<T>(x.non_zero_elements().data<T>(),
+            rulebook_ptr + rulebook_len,
+            rulebook_len,
+            in_channels,
+            in_features_ptr);
+  Gather<T>(out_grad.non_zero_elements().data<T>(),
+            rulebook_ptr + rulebook_len * 2,
+            rulebook_len,
+            out_channels,
+            out_grad_features_ptr);
+
+  auto blas = phi::funcs::GetBlas<Context, T>(dev_ctx);
+  std::vector<int> offsets(kernel_size + 1), counter(kernel_size, 0);
+  for (int i = 0; i < rulebook_len; i++) {
+    counter[rulebook_ptr[i]] += 1;
+  }
+  int offset = 0;
+  for (int i = 0; i < kernel_size; i++) {
+    offsets[i] = offset;
+    offset += counter[i];
+  }
+  offsets[kernel_size] = offset;
+
+  const T* kernel_ptr = kernel.data<T>();
+  for (int i = 0; i < kernel_size; i++) {
+    if (counter[i] <= 0) {
+      continue;
+    }
+
+    const int M = counter[i];
+    const int K = in_channels;
+    const int N = out_channels;
+    T* tmp_in_ptr = in_features_ptr + offsets[i] * in_channels;
+    T* tmp_out_grad_ptr = out_grad_features_ptr + offsets[i] * out_channels;
+    const T* tmp_kernel_ptr = kernel_ptr + i * in_channels * out_channels;
+    T* tmp_d_x_ptr = d_x_features_ptr + offsets[i] * out_channels;
+    T* tmp_d_kernel_ptr = d_kernel_ptr + i * in_channels * out_channels;
+
+    // call gemm: d_kernel = transpose(x) * out_grad
+    // (in_channels, n) * (n, out_channels)
+    blas.GEMM(CblasTrans,
+              CblasNoTrans,
+              M,
+              N,
+              K,
+              static_cast<T>(1),
+              tmp_in_ptr,
+              tmp_out_grad_ptr,
+              static_cast<T>(0),
+              tmp_d_kernel_ptr);
+
+    // call gemm: d_x = out_grad * transpose(kernel)
+    // (n, out_channels) * (out_channels, in_channels)
+    blas.GEMM(CblasNoTrans,
+              CblasTrans,
+              M,
+              K,
+              N,
+              static_cast<T>(1),
+              tmp_out_grad_ptr,
+              tmp_kernel_ptr,
+              static_cast<T>(0),
+              tmp_d_x_ptr);
+  }
+
+  // 4. scatter
+  x_grad->Resize(x.non_zero_elements().dims());
+  dev_ctx.Alloc(x_grad, x_grad->dtype(), sizeof(T) * x_grad->numel());
+  T* x_grad_values_ptr = x_grad->data<T>();
+  memset(x_grad_values_ptr, 0, sizeof(T) * x_grad->numel());
+  Scatter<T>(d_x_features_ptr,
+             rulebook.data<int>() + rulebook_len,
+             rulebook_len,
+             in_channels,
+             x_grad_values_ptr);
+}
+
+}  // namespace sparse
+}  // namespace phi
+
+PD_REGISTER_KERNEL(sparse_conv_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::sparse::Conv3dGradKernel,
+                   float,
+                   double) {
+  kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
+  kernel->InputAt(3).SetDataLayout(phi::DataLayout::SPARSE_COO);
+}

paddle/phi/tests/kernels/test_sparse_conv3d_dev_api.cc

@@ -17,6 +17,7 @@ limitations under the License. */
 
 #include "paddle/phi/common/place.h"
 #include "paddle/phi/kernels/copy_kernel.h"
+#include "paddle/phi/kernels/sparse/convolution_grad_kernel.h"
 #include "paddle/phi/kernels/sparse/convolution_kernel.h"
 
 #include "paddle/fluid/memory/allocation/allocator_facade.h"
@@ -59,7 +60,10 @@ void TestConv3dBase(const std::vector<int>& indices,
                     const std::vector<int>& paddings,
                     const std::vector<int>& strides,
                     const std::vector<int>& dilations,
-                    const float diff = 1e-3) {
+                    const float diff = 1e-3,
+                    const bool backward = false,
+                    const std::vector<T> features_grad = {},
+                    const std::vector<T> kernel_grad = {}) {
   phi::CPUContext dev_ctx_cpu;
   dev_ctx_cpu.SetAllocator(
       paddle::memory::allocation::AllocatorFacade::Instance()
@@ -122,10 +126,29 @@ void TestConv3dBase(const std::vector<int>& indices,
                              correct_out_indices.size() * sizeof(int));
     ASSERT_EQ(cmp_indices, 0);
 
-    for (uint64_t i = 0; i < correct_out_features.size(); i++) {
-      float tmp = std::fabs(static_cast<float>(
-          correct_out_features[i] - out.non_zero_elements().data<T>()[i]));
-      ASSERT_LT(tmp, diff);
+    auto f_verify = [&](const T* real_data,
+                        const std::vector<T>& correct_data) {
+      for (uint64_t i = 0; i < correct_data.size(); i++) {
+        float tmp =
+            std::fabs(static_cast<float>(correct_data[i] - real_data[i]));
+        ASSERT_LT(tmp, diff);
+      }
+    };
+
+    f_verify(out.non_zero_elements().data<T>(), correct_out_features);
+
+    if (backward) {
+      std::vector<DenseTensor> grads = sparse::Conv3dGrad<T>(dev_ctx_cpu,
+                                                             x_tensor,
+                                                             rulebook,
+                                                             kernel_tensor,
+                                                             out,
+                                                             paddings,
+                                                             dilations,
+                                                             strides,
+                                                             1);
+      f_verify(grads[0].data<T>(), features_grad);
+      f_verify(grads[1].data<T>(), kernel_grad);
     }
   }
 }
@@ -141,7 +164,11 @@ void TestConv3d(const std::vector<int>& indices,
                 const int non_zero_num,
                 const std::vector<int>& paddings,
                 const std::vector<int>& strides,
-                const std::vector<int>& dilations) {
+                const std::vector<int>& dilations,
+                const float diff = 1e-3,
+                const bool backward = false,
+                const std::vector<float> features_grad = {},
+                const std::vector<float> kernel_grad = {}) {
   // test float
   TestConv3dBase<float>(indices,
                         features,
@@ -154,7 +181,11 @@ void TestConv3d(const std::vector<int>& indices,
                         non_zero_num,
                         paddings,
                         strides,
-                        dilations);
+                        dilations,
+                        diff,
+                        backward,
+                        features_grad,
+                        kernel_grad);
   // test double
   TestConv3dBase<double>(indices,
                          cast<float, double>(features),
@@ -167,7 +198,11 @@ void TestConv3d(const std::vector<int>& indices,
                          non_zero_num,
                          paddings,
                          strides,
-                         dilations);
+                         dilations,
+                         diff,
+                         backward,
+                         cast<float, double>(features_grad),
+                         cast<float, double>(kernel_grad));
 }
 
 TEST(DEV_API, sparse_conv3d) {
@@ -467,5 +502,66 @@ TEST(DEV_API, sparse_conv2d) {
              dilations);
 }
 
+TEST(DEV_API, sparse_conv3d_backward) {
+  const int in_channels = 1;
+  const int out_channels = 1;
+  DDim x_dims = {1, 4, 4, 4, in_channels};
+  DDim kernel_dims = {3, 3, 3, in_channels, out_channels};
+  DDim out_dims = {1, 2, 2, 2, out_channels};
+  std::vector<int> paddings = {0, 0, 0};
+  std::vector<int> strides = {1, 1, 1};
+  std::vector<int> dilations = {1, 1, 1};
+
+  const int non_zero_num = 2;
+  std::vector<int> indices_flatten = {0, 0, 0, 2, 3, 2, 3, 2};
+
+  std::vector<float> features = {-0.28833008, 0.0287323};
+  // 3*3*3=27
+  std::vector<float> kernel = {
+      0.64306641, 0.45043945, 0.47216797, 0.22924805, 0.97509766, 0.86181641,
+      0.57861328, 0.91796875, 0.87255859, 0.16589355, 0.44555664, 0.01889038,
+      0.46459961, 0.44726562, 0.19909668, 0.89697266, 0.37158203, 0.00513077,
+      0.69628906, 0.26904297, 0.74707031, 0.54003906, 0.5390625,  0.07958984,
+      0.47338867, 0.90966797, 0.17126465};
+
+  std::vector<int> out_indices_flatten = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                                          0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,
+                                          1, 1, 0, 1, 0, 1, 0, 1, 0, 1};
+
+  std::vector<float> out_features = {4.9200e-03,
+                                     2.6140e-02,
+                                     2.2900e-03,
+                                     -2.3596e-01,
+                                     1.5000e-04,
+                                     1.0670e-02,
+                                     5.7200e-03,
+                                     1.2850e-02};
+
+  std::vector<float> features_grad = {-0.20593, -0.09149};
+  std::vector<float> kernel_grad = {
+      0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,  0.000e+00,
+      0.000e+00, 0.000e+00, 6.805e-02, 0.000e+00, 0.000e+00,  0.000e+00,
+      0.000e+00, 3.700e-04, 1.600e-04, 0.000e+00, 3.100e-04,  0.000e+00,
+      0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, -6.780e-03, 7.000e-05,
+      0.000e+00, 7.500e-04, 1.400e-04};
+
+  TestConv3d(indices_flatten,
+             features,
+             x_dims,
+             kernel,
+             kernel_dims,
+             out_indices_flatten,
+             out_features,
+             out_dims,
+             non_zero_num,
+             paddings,
+             strides,
+             dilations,
+             1e-3,
+             true,
+             features_grad,
+             kernel_grad);
+}
+
 }  // namespace tests
 }  // namespace phi