wehub-resource-sync
78 lines
2.9 KiB
C++
78 lines
2.9 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/lars_momentum_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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namespace phi {
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template <typename T, typename Context>
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void LarsMomentumKernel(
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const Context& dev_ctx,
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const std::vector<const DenseTensor*>& param,
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const std::vector<const DenseTensor*>& velocity,
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const std::vector<const DenseTensor*>& learning_rate,
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const std::vector<const DenseTensor*>& grad,
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const optional<std::vector<const DenseTensor*>>& master_param,
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const std::vector<float>& weight_decay_arr,
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float mu,
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float lars_coeff,
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float epsilon,
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bool multi_precision,
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float rescale_grad,
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std::vector<DenseTensor*> param_out,
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std::vector<DenseTensor*> velocity_out,
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std::vector<DenseTensor*> master_param_out) {
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int op_num = static_cast<int>(param.size());
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T mu_ = static_cast<T>(mu);
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for (int i = 0; i < op_num; ++i) {
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auto* lr = learning_rate[i]->data<T>();
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T lars_weight_decay = weight_decay_arr[i];
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dev_ctx.template Alloc<T>(param_out[i]);
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dev_ctx.template Alloc<T>(velocity_out[i]);
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auto p_out = EigenVector<T>::Flatten(*(param_out[i]));
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auto v_out = EigenVector<T>::Flatten(*(velocity_out[i]));
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auto p = EigenVector<T>::Flatten(*(param[i]));
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auto v = EigenVector<T>::Flatten(*(velocity[i]));
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auto g = EigenVector<T>::Flatten(*(grad[i]));
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auto rescale_g = static_cast<T>(rescale_grad) * g;
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DenseTensor p_norm_t, g_norm_t;
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p_norm_t.Resize({1});
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g_norm_t.Resize({1});
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dev_ctx.template Alloc<T>(&p_norm_t);
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dev_ctx.template Alloc<T>(&g_norm_t);
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auto ep_norm = EigenScalar<T>::From(p_norm_t);
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auto eg_norm = EigenScalar<T>::From(g_norm_t);
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ep_norm = p.square().sum().sqrt();
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eg_norm = rescale_g.square().sum().sqrt();
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T local_lr = lr[0];
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if (lars_weight_decay > 0 && ep_norm(0) > 0 && eg_norm(0) > 0) {
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local_lr = lr[0] * lars_coeff * ep_norm(0) /
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(eg_norm(0) + lars_weight_decay * ep_norm(0) + epsilon);
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}
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v_out = v * mu_ + local_lr * (rescale_g + lars_weight_decay * p);
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p_out = p - v_out;
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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lars_momentum, CPU, ALL_LAYOUT, phi::LarsMomentumKernel, float, double) {}
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