wehub-resource-sync
64 lines
2.2 KiB
C++
64 lines
2.2 KiB
C++
// Copyright (c) 2023 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/cum_grad_kernel.h"
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#include "paddle/phi/kernels/cum_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/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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namespace phi {
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template <typename T, typename Context>
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void CumsumGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& out_grad,
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const Scalar& axis,
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bool flatten,
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bool exclusive,
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bool reverse,
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DenseTensor* x_grad) {
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const auto& x_dims = x.dims();
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// If the attribute of flatten is `True`, the cumsum kernel is compose of the
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// operation of flatten and cumsum, need to flatten the tensor of input
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// gradient, and last step need to unflatten the tensor
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if (flatten) {
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x_grad->Resize(out_grad.dims());
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} else {
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x_grad->Resize(x_dims);
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}
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CumsumKernel<T, Context>(
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dev_ctx, out_grad, axis, flatten, exclusive, !reverse, x_grad);
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if (flatten) {
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x_grad->Resize(x_dims);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(cumsum_grad,
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CPU,
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ALL_LAYOUT,
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phi::CumsumGradKernel,
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float,
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double,
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uint8_t,
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int8_t,
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int16_t,
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int,
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int64_t,
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phi::complex64,
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phi::complex128) {}
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