wehub-resource-sync
67 lines
2.5 KiB
C++
67 lines
2.5 KiB
C++
// Copyright (c) 2025 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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#pragma once
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#include <algorithm>
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#include <mutex>
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#include <unordered_map>
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#include "paddle/phi/common/data_type.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/elementwise_base.h"
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#include "paddle/phi/kernels/funcs/reduce_function.h"
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#include "paddle/phi/kernels/impl/matmul_grad_kernel_impl.h"
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#include "paddle/phi/kernels/reduce_sum_kernel.h"
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#include "paddle/common/flags.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/core/dense_tensor.h"
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namespace phi {
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template <typename T, typename Context>
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void LinearV2GradKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const DenseTensor& weight,
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const DenseTensor& bias,
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const DenseTensor& out_grad,
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const bool transpose_weight,
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DenseTensor* input_grad,
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DenseTensor* weight_grad,
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DenseTensor* bias_grad) {
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phi::MatmulGradKernel<T, Context>(dev_ctx,
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input,
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weight,
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out_grad,
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false,
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transpose_weight,
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input_grad,
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weight_grad);
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if (bias_grad && bias.numel() != 0) {
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if (out_grad.numel() != bias_grad->numel()) {
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dev_ctx.template Alloc<T>(bias_grad);
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std::vector<int> reduce_dims =
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funcs::GetReduceDim(bias.dims(), out_grad.dims(), -1);
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phi::SumKernel<T, Context>(
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dev_ctx, out_grad, reduce_dims, out_grad.dtype(), false, bias_grad);
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bias_grad->Resize(bias.dims());
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} else {
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phi::Copy(dev_ctx, out_grad, dev_ctx.GetPlace(), false, bias_grad);
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bias_grad->Resize(bias.dims());
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}
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}
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}
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} // namespace phi
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