wehub-resource-sync
64 lines
2.1 KiB
C++
64 lines
2.1 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/elementwise_divide_grad_kernel.h"
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#include <memory>
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#include <string>
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#include "paddle/phi/backends/xpu/xpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/elementwise_base.h"
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#include "paddle/phi/kernels/xpu/elementwise.h"
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namespace phi {
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template <typename T, typename Context>
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void DivideGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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const DenseTensor& out,
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const DenseTensor& dout,
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int axis,
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DenseTensor* dx,
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DenseTensor* dy) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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funcs::ElementwiseGradPreProcess(dout, dx);
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auto f = [](xpu::Context* xpu_ctx,
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const XPUType* x,
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const XPUType* y,
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const XPUType* z,
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const XPUType* dz,
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XPUType* dy,
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XPUType* dx,
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const std::vector<int64_t>& xshape,
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const std::vector<int64_t>& yshape) {
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return xpu::broadcast_div_grad<XPUType>(
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xpu_ctx, x, y, z, dz, dy, dx, xshape, yshape);
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};
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XPUElementwiseGrad<T, XPUType>(dev_ctx, x, y, dout, axis, dx, dy, f, true);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(divide_grad,
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XPU,
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ALL_LAYOUT,
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phi::DivideGradKernel,
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phi::float16,
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phi::bfloat16,
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float) {}
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