wehub-resource-sync
107 lines
3.8 KiB
Plaintext
107 lines
3.8 KiB
Plaintext
// 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/gpu/gpu_context.h"
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#include "paddle/phi/backends/gpu/gpu_helper.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/common/amp_type_traits.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/mixed_vector.h"
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namespace phi {
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template <typename T, typename MT>
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__global__ void ASGDKernelGPUImpl(const T* param,
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const T* grad,
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const T* learning_rate,
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const T* d,
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const T* y,
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const T* n,
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const MT* master_param,
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int num,
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T* param_out,
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T* d_out,
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T* y_out,
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MT* master_param_out) {
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MT learning_rate_MT = static_cast<MT>(learning_rate[0]);
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MT n_MT = static_cast<MT>(n[0]);
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CUDA_KERNEL_LOOP_TYPE(i, num, int64_t) {
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MT param_data = master_param ? master_param[i] : static_cast<MT>(param[i]);
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MT grad_data = static_cast<MT>(grad[i]);
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MT d_data = static_cast<MT>(d[i]);
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MT y_data = static_cast<MT>(y[i]);
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d_data = d_data - y_data + grad_data;
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y_data = grad_data;
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param_data = param_data - (learning_rate_MT / n_MT) * d_data;
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param_out[i] = static_cast<T>(param_data);
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d_out[i] = static_cast<T>(d_data);
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y_out[i] = static_cast<T>(y_data);
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if (master_param_out) {
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master_param_out[i] = param_data;
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}
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}
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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,
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bool multi_precision,
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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) {
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using MT = typename MPTypeTrait<T>::Type;
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const MT* master_in_data =
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multi_precision ? master_param->data<MT>() : nullptr;
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MT* master_out_data =
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multi_precision ? dev_ctx.template Alloc<MT>(master_param_out) : nullptr;
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int block = 512;
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int64_t grid_max = dev_ctx.GetCUDAMaxGridDimSize()[0];
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int grid = std::min((param.numel() + block - 1) / block, grid_max);
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ASGDKernelGPUImpl<T, MT><<<grid, block, 0, dev_ctx.stream()>>>(
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param.data<T>(),
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grad.data<T>(),
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learning_rate.data<T>(),
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d.data<T>(),
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y.data<T>(),
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n.data<T>(),
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master_in_data,
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param.numel(),
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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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master_out_data);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(asgd,
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GPU,
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ALL_LAYOUT,
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phi::ASGDKernel,
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phi::float16,
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phi::bfloat16,
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float,
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double) {}
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