chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/unpool_grad_kernel.cc

@@ -0,0 +1,188 @@
+// 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/unpool_grad_kernel.h"
+
+#include <algorithm>
+#include <string>
+#include <vector>
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename T, typename IndT, typename Context>
+void UnpoolGrad(const Context& dev_ctx,
+                const DenseTensor& x,
+                const DenseTensor& indices,
+                const DenseTensor& out,
+                const DenseTensor& out_grad,
+                DenseTensor* x_grad) {
+  T* input_grad_data = dev_ctx.template Alloc<T>(x_grad);
+  const T* output_grad_data = out_grad.data<T>();
+  funcs::SetConstant<Context, T> zero;
+  zero(dev_ctx, x_grad, static_cast<T>(0));
+  const int batch_size = static_cast<int>(x.dims()[0]);
+  const int input_height = static_cast<int>(x.dims()[2]);
+  const int input_width = static_cast<int>(x.dims()[3]);
+  const int output_channels = static_cast<int>(out.dims()[1]);
+  const int output_height = static_cast<int>(out.dims()[2]);
+  const int output_width = static_cast<int>(out.dims()[3]);
+  int64_t input_feasize = static_cast<int64_t>(input_height) * input_width;
+  int64_t output_feasize = static_cast<int64_t>(output_height) * output_width;
+  const IndT* indices_data = indices.data<IndT>();
+
+  for (int b = 0; b < batch_size; ++b) {
+    for (int c = 0; c < output_channels; ++c) {
+      for (int i = 0; i < input_feasize; ++i) {
+        IndT index = indices_data[i];
+        PADDLE_ENFORCE_LT(
+            index,
+            output_feasize,
+            common::errors::InvalidArgument(
+                "index should less than output tensor height * output tensor "
+                "width. Expected %ld < %ld, but got "
+                "%ld >= %ld. Please check input value.",
+                index,
+                output_feasize,
+                index,
+                output_feasize));
+        input_grad_data[i] = output_grad_data[index];
+      }
+      input_grad_data += input_feasize;
+      indices_data += input_feasize;
+      output_grad_data += output_feasize;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void UnpoolGradKernel(const Context& dev_ctx,
+                      const DenseTensor& x,
+                      const DenseTensor& indices,
+                      const DenseTensor& out,
+                      const DenseTensor& out_grad,
+                      const std::vector<int>& ksize UNUSED,
+                      const std::vector<int>& strides UNUSED,
+                      const std::vector<int>& paddings UNUSED,
+                      const IntArray& output_size UNUSED,
+                      const std::string& data_format UNUSED,
+                      DenseTensor* x_grad) {
+  if (x_grad && x_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(x_grad);
+    return;
+  }
+  const auto& indices_type = indices.dtype();
+  if (indices_type == DataType::INT32) {
+    UnpoolGrad<T, int, Context>(dev_ctx, x, indices, out, out_grad, x_grad);
+  } else {
+    UnpoolGrad<T, int64_t, Context>(dev_ctx, x, indices, out, out_grad, x_grad);
+  }
+}
+
+template <typename T, typename IndT, typename Context>
+void Unpool3dGrad(const Context& dev_ctx,
+                  const DenseTensor& x,
+                  const DenseTensor& indices,
+                  const DenseTensor& out,
+                  const DenseTensor& out_grad,
+                  DenseTensor* x_grad) {
+  T* input_grad_data = dev_ctx.template Alloc<T>(x_grad);
+  const T* output_grad_data = out_grad.data<T>();
+  funcs::SetConstant<Context, T> zero;
+  zero(dev_ctx, x_grad, static_cast<T>(0));
+
+  const int batch_size = static_cast<int>(x.dims()[0]);
+  const int input_depth = static_cast<int>(x.dims()[2]);
+  const int input_height = static_cast<int>(x.dims()[3]);
+  const int input_width = static_cast<int>(x.dims()[4]);
+  const int output_channels = static_cast<int>(out.dims()[1]);
+  const int output_depth = static_cast<int>(out.dims()[2]);
+  const int output_height = static_cast<int>(out.dims()[3]);
+  const int output_width = static_cast<int>(out.dims()[4]);
+  int64_t input_feasize =
+      static_cast<int64_t>(input_depth) * input_height * input_width;
+  int64_t output_feasize =
+      static_cast<int64_t>(output_depth) * output_height * output_width;
+  const IndT* indices_data = indices.data<IndT>();
+
+  for (int b = 0; b < batch_size; ++b) {
+    for (int c = 0; c < output_channels; ++c) {
+      for (int i = 0; i < input_feasize; ++i) {
+        IndT index = indices_data[i];
+        PADDLE_ENFORCE_LT(
+            index,
+            output_feasize,
+            common::errors::InvalidArgument(
+                "index should less than output tensor depth * output tensor "
+                "height "
+                "* output tensor width. Expected %ld < %ld, but got "
+                "%ld >= %ld. Please check input value.",
+                index,
+                output_feasize,
+                index,
+                output_feasize));
+        input_grad_data[i] = output_grad_data[index];
+      }
+      input_grad_data += input_feasize;
+      indices_data += input_feasize;
+      output_grad_data += output_feasize;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void Unpool3dGradKernel(const Context& dev_ctx,
+                        const DenseTensor& x,
+                        const DenseTensor& indices,
+                        const DenseTensor& out,
+                        const DenseTensor& out_grad,
+                        const std::vector<int>& ksize UNUSED,
+                        const std::vector<int>& strides,
+                        const std::vector<int>& paddings UNUSED,
+                        const std::vector<int>& output_size UNUSED,
+                        const std::string& data_format UNUSED,
+                        DenseTensor* x_grad) {
+  if (x_grad && x_grad->numel() == 0) {
+    dev_ctx.template Alloc<T>(x_grad);
+    return;
+  }
+  const auto& indices_type = indices.dtype();
+  if (indices_type == DataType::INT32) {
+    Unpool3dGrad<T, int, Context>(dev_ctx, x, indices, out, out_grad, x_grad);
+  } else {
+    Unpool3dGrad<T, int64_t, Context>(
+        dev_ctx, x, indices, out, out_grad, x_grad);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(unpool_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::UnpoolGradKernel,
+                   float,
+                   double,
+                   int64_t) {}
+
+PD_REGISTER_KERNEL(unpool3d_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::Unpool3dGradKernel,
+                   float,
+                   double,
+                   int64_t) {}