wehub-resource-sync
69 lines
2.2 KiB
C++
69 lines
2.2 KiB
C++
/* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/softmax_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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#include "paddle/phi/kernels/funcs/axis_utils.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename Context>
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void SoftmaxGradKernel(const Context& dev_ctx,
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const DenseTensor& out,
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const DenseTensor& out_grad,
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int axis,
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DenseTensor* x_grad) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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const int rank = x_grad->dims().size();
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const int calc_axis = funcs::CanonicalAxis(axis, rank);
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// allocate memory on device.
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dev_ctx.template Alloc<T>(x_grad);
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if (x_grad->numel() == 0) {
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return;
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}
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// For 0D Tensor
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if (rank == 0) {
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funcs::set_constant(dev_ctx, x_grad, static_cast<T>(0.0));
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return;
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}
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std::vector<int64_t> x_dims;
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for (int i = 0; i < rank; i++) {
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x_dims.push_back(x_grad->dims()[i]);
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}
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int r = xpu::softmax_grad<XPUType>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(out.data<T>()),
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reinterpret_cast<const XPUType*>(out_grad.data<T>()),
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reinterpret_cast<XPUType*>(x_grad->data<T>()),
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x_dims,
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static_cast<int64_t>(calc_axis));
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "softmax_grad");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(softmax_grad,
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XPU,
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ALL_LAYOUT,
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phi::SoftmaxGradKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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