chore: import upstream snapshot with attribution
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wehub-resource-sync
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// 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/mode_grad_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/mode.h"
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namespace phi {
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template <typename T, typename Context>
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void ModeGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& indices,
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const DenseTensor& out_grad,
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int axis,
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bool keepdim,
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DenseTensor* x_grad) {
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auto in_dims = x.dims();
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auto out_dims = indices.dims();
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T* x_grad_data = dev_ctx.template Alloc<T>(x_grad);
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if (x_grad && x_grad->numel() == 0) {
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return;
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}
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// axis < 0, get the real axis
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axis = (axis < 0) ? (in_dims.size() + axis) : axis;
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// For 0D Tensor
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if (in_dims.size() == 0) {
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funcs::set_constant(dev_ctx, x_grad, static_cast<T>(1.0));
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return;
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}
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if (!keepdim) {
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std::vector<int> tmp_out_shape;
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for (int i = 0; i < axis; i++) {
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tmp_out_shape.emplace_back(out_dims[i]);
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}
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tmp_out_shape.emplace_back(1);
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for (int i = axis + 1; i < in_dims.size(); i++) {
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tmp_out_shape.emplace_back(out_dims[i - 1]);
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}
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out_dims = make_ddim(tmp_out_shape);
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}
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if (axis == in_dims.size() - 1) {
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// allocate the memory for the input_grad
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// assign the out_grad to input_grad directly
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const int64_t input_height =
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common::product(slice_ddim(in_dims, 0, in_dims.size() - 1));
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const int64_t input_width = in_dims[in_dims.size() - 1];
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// init the output grad with 0, because some input elements has no grad
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memset(x_grad_data, 0, x_grad->numel() * sizeof(T));
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// Assign the output_grad to input_grad
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if (keepdim) {
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funcs::ModeAssign(input_height,
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input_width,
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in_dims.size(),
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&out_grad,
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&indices,
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x_grad_data);
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} else {
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DenseTensor out_grad_tmp;
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dev_ctx.template Alloc<T>(&out_grad_tmp);
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DenseTensor indices_tmp;
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dev_ctx.template Alloc<int64_t>(&indices_tmp);
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Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, &out_grad_tmp);
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Copy(dev_ctx, indices, dev_ctx.GetPlace(), false, &indices_tmp);
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out_grad_tmp.Resize(out_dims);
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indices_tmp.Resize(out_dims);
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funcs::ModeAssign(input_height,
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input_width,
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in_dims.size(),
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&out_grad_tmp,
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&indices_tmp,
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x_grad_data);
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}
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} else {
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// can not assign grad to input_grad, must do the transpose
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std::vector<int> trans_axis;
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for (int i = 0; i < axis; i++) {
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trans_axis.emplace_back(i);
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}
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trans_axis.emplace_back(out_dims.size() - 1);
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for (int i = axis + 1; i < out_dims.size() - 1; i++) {
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trans_axis.emplace_back(i);
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}
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trans_axis.emplace_back(axis);
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DDim trans_shape(out_dims);
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DDim trans_in_shape(in_dims);
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for (int i = 0; i < static_cast<int>(trans_axis.size()); i++) {
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trans_shape[i] = out_dims[trans_axis[i]];
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trans_in_shape[i] = in_dims[trans_axis[i]];
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}
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// transpose the out_grad, indices
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DenseTensor trans_dO;
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trans_dO.Resize(trans_shape);
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dev_ctx.template Alloc<T>(&trans_dO);
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DenseTensor trans_ind;
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trans_ind.Resize(trans_shape);
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dev_ctx.template Alloc<int64_t>(&trans_ind);
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int ndims = static_cast<int>(trans_axis.size());
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if (keepdim) {
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// Do transpose
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funcs::TransCompute<CPUContext, T>(
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ndims, dev_ctx, out_grad, &trans_dO, trans_axis);
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funcs::TransCompute<CPUContext, int64_t>(
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ndims, dev_ctx, indices, &trans_ind, trans_axis);
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} else {
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DenseTensor out_grad_tmp;
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dev_ctx.template Alloc<T>(&out_grad_tmp);
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DenseTensor indices_tmp;
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dev_ctx.template Alloc<int64_t>(&indices_tmp);
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Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, &out_grad_tmp);
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Copy(dev_ctx, indices, dev_ctx.GetPlace(), false, &indices_tmp);
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out_grad_tmp.Resize(out_dims);
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indices_tmp.Resize(out_dims);
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// Do transpose
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funcs::TransCompute<CPUContext, T>(
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ndims, dev_ctx, out_grad_tmp, &trans_dO, trans_axis);
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funcs::TransCompute<CPUContext, int64_t>(
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ndims, dev_ctx, indices_tmp, &trans_ind, trans_axis);
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}
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const int64_t input_height = common::product(
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slice_ddim(trans_in_shape, 0, trans_in_shape.size() - 1));
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const int64_t input_width = trans_in_shape[trans_in_shape.size() - 1];
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// Assign the out_grad to transpose input_grad
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DenseTensor tmp_out;
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tmp_out.Resize(trans_in_shape);
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T* t_out = dev_ctx.template Alloc<T>(&tmp_out);
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memset(t_out, 0, x_grad->numel() * sizeof(T));
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funcs::ModeAssign<T, int64_t>(input_height,
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input_width,
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in_dims.size(),
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&trans_dO,
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&trans_ind,
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t_out);
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// Transpose back
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funcs::TransCompute<CPUContext, T>(
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ndims, dev_ctx, tmp_out, x_grad, trans_axis);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(mode_grad,
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CPU,
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ALL_LAYOUT,
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phi::ModeGradKernel,
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float,
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double,
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int32_t,
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int64_t) {}
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