wehub-resource-sync
210 lines
8.0 KiB
C++
210 lines
8.0 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/broadcast_tensors_grad_kernel.h"
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#include <vector>
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#define SWITCH_RESHAPE_DIMS(n) \
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case n: { \
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Eigen::DSizes<int64_t, n> reshape_dims; \
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for (size_t i = 0; i < reshape_dims_vec.size(); ++i) { \
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reshape_dims[i] = reshape_dims_vec[i]; \
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} \
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dX.device(place) = \
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dOut.reshape(reshape_dims).sum(reduce_dims).reshape(dX.dimensions()); \
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break; \
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}
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#define UPPER_SWITCH_REDUCE_DIMS(m) \
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case m: { \
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Eigen::DSizes<int64_t, m> reduce_dims; \
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for (size_t i = 0; i < reduce_dims_vec.size(); ++i) { \
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reduce_dims[i] = reduce_dims_vec[i]; \
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} \
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switch (reshape_size) {
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#define LOWER_SWITCH_REDUCE_DIMS \
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default: { \
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PADDLE_THROW(errors::InvalidArgument( \
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"Detected reshape size: %d out of range. " \
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"Minimum value should be larger than reduce size %d. " \
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"While maximum supported is: 5", \
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reshape_size, \
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reduce_size)); \
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} \
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} \
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break; \
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}
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namespace phi {
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template <typename T, typename Context>
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void BroadcastTensorsGradKernel(const Context& dev_ctx,
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const std::vector<const DenseTensor*>& inputs,
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const std::vector<const DenseTensor*>& dout,
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std::vector<DenseTensor*> dx) {
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(void)inputs;
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// Find reduce dimensions
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const auto& in_tensors = dout;
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auto& out_tensors = dx;
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size_t num_ins = in_tensors.size();
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if (dout[0] && dout[0]->numel() == 0) {
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for (auto dx : out_tensors) {
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if (dx) Full<T, Context>(dev_ctx, dx->dims(), 0, dx);
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}
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return;
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}
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PADDLE_ENFORCE_GT(
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num_ins,
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1,
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errors::InvalidArgument(
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"Expected at least 2 input tensors, but only received %d.",
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in_tensors.size()));
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PADDLE_ENFORCE_EQ(num_ins,
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out_tensors.size(),
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errors::InvalidArgument(
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"BroadcastTensorsOp expects equal number of inputs and "
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"outputs, but received: %d inputs v.s %d outputs",
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num_ins,
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out_tensors.size()));
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// For each In-Out tensor pair,
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// Prepare and apply broadcast dims array
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for (size_t i = 0; i < num_ins; i++) {
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const auto* input_tensor = in_tensors[i];
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auto* output_tensor = out_tensors[i];
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const auto& input_dims = input_tensor->dims();
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const auto& output_dims = output_tensor->dims();
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int in_rank = input_dims.size();
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int out_rank = output_dims.size();
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// BroadcastTensorsGrad is simply a reduce_sum along broadcasted axes
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// Here we perform the following Eigen operations:
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// dOut(Flattened) -> reshape(reshape_dims) -> reduce(reduce_dims) ->
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// reshape(dX_shape) -> dX
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// Note the last "reshape(dX_shape)" will be performed implicitly,
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// and we only need to collect reduce_dims and reshape_dims
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std::vector<int> reduce_dims_vec;
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std::vector<int> reshape_dims_vec;
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for (int j = 0; j < in_rank; j++) {
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int out_axis = out_rank - j - 1;
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int in_axis = in_rank - j - 1;
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reshape_dims_vec.push_back(static_cast<int>(input_dims[j]));
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if (out_axis < 0 || output_dims[out_axis] != input_dims[in_axis]) {
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reduce_dims_vec.push_back(in_axis);
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}
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}
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size_t reduce_size = reduce_dims_vec.size();
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size_t reshape_size = reshape_dims_vec.size();
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bool just_copy = (reduce_dims_vec.empty());
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dev_ctx.template Alloc<T>(output_tensor);
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if (just_copy) {
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// If this turns out to be a No-Op, simply perform a tensor copy
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Copy(dev_ctx, *input_tensor, dev_ctx.GetPlace(), false, output_tensor);
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} else {
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PADDLE_ENFORCE_GE(
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reduce_dims_vec.size(),
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1,
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errors::InvalidArgument("The number of dimensions of the input "
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"'Out@GRAD' for Op(broadcast_tensors)"
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" must be greater than or equal to 1, but "
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"the value received is %d.",
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reduce_dims_vec.size()));
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PADDLE_ENFORCE_LE(
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reduce_dims_vec.size(),
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5,
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errors::InvalidArgument(
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"The number of dimensions of the input 'Out@GRAD' "
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"for Op(broadcast_tensors) must be less than or equal "
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"to 5, but the value received is %d.",
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reduce_dims_vec.size()));
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// Overall:
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// dOut(Flattened) -> reshape(reshape_dims) -> reduce(reduce_dims) ->
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// reshape(dX_shape) -> dX
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auto dX = EigenVector<T>::Flatten(*output_tensor);
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auto dOut = EigenVector<T>::Flatten(*input_tensor);
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auto& place = *dev_ctx.eigen_device();
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// Expand ReduceSize and ReshapeSize into static values
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switch (reduce_size) {
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UPPER_SWITCH_REDUCE_DIMS(1)
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SWITCH_RESHAPE_DIMS(1)
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SWITCH_RESHAPE_DIMS(2)
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SWITCH_RESHAPE_DIMS(3)
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SWITCH_RESHAPE_DIMS(4)
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SWITCH_RESHAPE_DIMS(5)
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LOWER_SWITCH_REDUCE_DIMS
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UPPER_SWITCH_REDUCE_DIMS(2)
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SWITCH_RESHAPE_DIMS(2)
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SWITCH_RESHAPE_DIMS(3)
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SWITCH_RESHAPE_DIMS(4)
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SWITCH_RESHAPE_DIMS(5)
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LOWER_SWITCH_REDUCE_DIMS
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UPPER_SWITCH_REDUCE_DIMS(3)
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SWITCH_RESHAPE_DIMS(3)
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SWITCH_RESHAPE_DIMS(4)
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SWITCH_RESHAPE_DIMS(5)
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LOWER_SWITCH_REDUCE_DIMS
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UPPER_SWITCH_REDUCE_DIMS(4)
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SWITCH_RESHAPE_DIMS(4)
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SWITCH_RESHAPE_DIMS(5)
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LOWER_SWITCH_REDUCE_DIMS
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UPPER_SWITCH_REDUCE_DIMS(5)
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SWITCH_RESHAPE_DIMS(5)
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LOWER_SWITCH_REDUCE_DIMS
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default: {
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PADDLE_THROW(
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errors::InvalidArgument("Detected reduce size: %d out of range. "
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"While maximum supported is: 5",
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reduce_size));
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}
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}
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(broadcast_tensors_grad,
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CPU,
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ALL_LAYOUT,
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phi::BroadcastTensorsGradKernel,
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int,
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int64_t,
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float,
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double,
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phi::float16,
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phi::complex64,
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phi::complex128) {}
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