wehub-resource-sync
74 lines
2.8 KiB
C++
74 lines
2.8 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/asgd_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/jit/kernels.h"
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namespace phi {
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template <typename T, typename Context>
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void ASGDKernelCPUImpl(const Context& dev_ctx,
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const DenseTensor& param,
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const DenseTensor& grad,
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const DenseTensor& learning_rate,
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const DenseTensor& d,
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const DenseTensor& y,
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const DenseTensor& n,
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DenseTensor* param_out,
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DenseTensor* d_out,
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DenseTensor* y_out) {
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auto param_eigen = EigenVector<T>::Flatten(param);
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auto grad_eigen = EigenVector<T>::Flatten(grad);
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auto d_eigen = EigenVector<T>::Flatten(d);
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auto y_eigen = EigenVector<T>::Flatten(y);
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auto param_out_eigen = EigenVector<T>::Flatten(*param_out);
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auto d_out_eigen = EigenVector<T>::Flatten(*d_out);
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auto y_out_eigen = EigenVector<T>::Flatten(*y_out);
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T learning_rate_T = learning_rate.data<T>()[0];
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T n_T = n.data<T>()[0];
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d_out_eigen = d_eigen - y_eigen + grad_eigen;
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y_out_eigen = grad_eigen;
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param_out_eigen = param_eigen - (learning_rate_T / n_T) * d_out_eigen;
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}
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template <typename T, typename Context>
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void ASGDKernel(const Context& dev_ctx,
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const DenseTensor& param,
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const DenseTensor& grad,
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const DenseTensor& learning_rate,
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const DenseTensor& d,
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const DenseTensor& y,
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const DenseTensor& n,
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const optional<DenseTensor>& master_param UNUSED,
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bool multi_precision UNUSED,
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DenseTensor* param_out,
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DenseTensor* d_out,
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DenseTensor* y_out,
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DenseTensor* master_param_out UNUSED) {
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dev_ctx.template Alloc<T>(param_out);
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dev_ctx.template Alloc<T>(d_out);
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dev_ctx.template Alloc<T>(y_out);
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ASGDKernelCPUImpl<T, Context>(
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dev_ctx, param, grad, learning_rate, d, y, n, param_out, d_out, y_out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(asgd, CPU, ALL_LAYOUT, phi::ASGDKernel, float, double) {}
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