wehub-resource-sync
97 lines
3.4 KiB
C++
97 lines
3.4 KiB
C++
// Copyright (c) 2024 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/logsumexp_grad_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void LogsumexpGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& dy,
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const std::vector<int>& axis_in,
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bool keepdim,
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bool reduce_all,
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DenseTensor* dx) {
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if (dx && dx->numel() == 0) {
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dev_ctx.template Alloc<T>(dx);
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return;
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}
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using XPUType = typename XPUTypeTrait<T>::Type;
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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reduce_all = recompute_reduce_all(x, axis_in, reduce_all);
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auto x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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auto y_data = reinterpret_cast<const XPUType*>(y.data<T>());
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auto dy_data = reinterpret_cast<const XPUType*>(dy.data<T>());
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auto dx_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(dx));
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std::vector<int64_t> xdims = vectorize<int64_t>(x.dims());
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std::vector<int64_t> ydims = xdims;
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if (reduce_all) {
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ydims = {1};
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xdims = {x.numel()};
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} else {
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std::vector<int64_t> axis;
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axis.reserve(axis_in.size());
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std::for_each(
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axis_in.begin(), axis_in.end(), [&axis, &xdims](const int& t) {
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if (t < 0) {
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axis.push_back(static_cast<int64_t>(t + xdims.size()));
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} else {
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axis.push_back(static_cast<int64_t>(t));
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}
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});
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for (size_t i = 0; i < axis.size(); ++i) {
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PADDLE_ENFORCE_LT(
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axis[i],
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ydims.size(),
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errors::InvalidArgument(
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"The axis should be less than the rank of Input(X)."));
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ydims[axis[i]] = 1;
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}
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}
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int64_t xlen = 1;
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for (size_t i = 0; i < xdims.size(); ++i) {
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PADDLE_ENFORCE_LT(0,
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xdims[i],
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errors::InvalidArgument(
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"The dims of Input(X) should be greater than 0."));
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xlen *= xdims[i];
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}
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XPUType* tmp_data = RAII_GUARD.alloc<XPUType>(xlen);
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int ret = xpu::broadcast_sub(
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dev_ctx.x_context(), x_data, y_data, tmp_data, xdims, ydims);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "broadcast_sub");
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ret = xpu::exp(dev_ctx.x_context(), tmp_data, tmp_data, xlen);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "exp");
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ret = xpu::broadcast_mul(
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dev_ctx.x_context(), dy_data, tmp_data, dx_data, ydims, xdims);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "broadcast_mul");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(logsumexp_grad,
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XPU,
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ALL_LAYOUT,
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phi::LogsumexpGradKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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