wehub-resource-sync
110 lines
4.1 KiB
C++
110 lines
4.1 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/rmsprop_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/common/memory_utils.h"
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namespace phi {
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template <typename T, typename Context>
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void RmspropDenseKernel(const Context& dev_ctx,
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const DenseTensor& param,
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const DenseTensor& mean_square,
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const DenseTensor& grad,
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const DenseTensor& moment,
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const DenseTensor& learning_rate,
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const optional<DenseTensor>& mean_grad,
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const optional<DenseTensor>& master_param,
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float epsilon,
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float decay,
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float momentum,
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bool centered,
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bool multi_precision,
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DenseTensor* param_out,
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DenseTensor* moment_out,
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DenseTensor* mean_square_out,
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DenseTensor* mean_grad_out,
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DenseTensor* master_param_outs) {
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// copy learning_rate to cpu
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T learning_rate_cpu = 0.0f;
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memory_utils::Copy(CPUPlace(),
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static_cast<void*>(&learning_rate_cpu),
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dev_ctx.GetPlace(),
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static_cast<const void*>(learning_rate.data()),
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sizeof(T));
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// alloc output
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dev_ctx.template Alloc<T>(param_out);
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dev_ctx.template Alloc<T>(moment_out);
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dev_ctx.template Alloc<T>(mean_square_out);
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if (centered) {
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dev_ctx.template Alloc<T>(mean_grad_out);
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auto mg_tensor = mean_grad.get_ptr();
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if (mg_tensor) {
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PADDLE_ENFORCE_EQ(
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mg_tensor->Holder(),
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mean_grad_out->Holder(),
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common::errors::InvalidArgument(
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"MeanGrad and MeanGradOut must be the same Tensor"));
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} else {
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PADDLE_ENFORCE_EQ(
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mg_tensor,
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mean_grad_out,
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common::errors::InvalidArgument(
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"MeanGrad and MeanGradOut must be the same Tensor"));
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}
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int r = xpu::rmsprop(dev_ctx.x_context(),
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grad.data<T>(),
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param.data<T>(),
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mean_square.data<T>(),
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moment.data<T>(),
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param_out->data<T>(),
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mean_square_out->data<T>(),
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moment_out->data<T>(),
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epsilon,
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decay,
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momentum,
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learning_rate_cpu,
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param.numel(),
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centered,
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mg_tensor->data<T>(),
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mean_grad_out->data<T>());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "centered rmsprop");
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} else {
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int r = xpu::rmsprop(dev_ctx.x_context(),
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grad.data<T>(),
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param.data<T>(),
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mean_square.data<T>(),
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moment.data<T>(),
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param_out->data<T>(),
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mean_square_out->data<T>(),
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moment_out->data<T>(),
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epsilon,
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decay,
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momentum,
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learning_rate_cpu,
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param.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "uncentered rmsprop");
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(rmsprop, XPU, ALL_LAYOUT, phi::RmspropDenseKernel, float) {}
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