wehub-resource-sync
53 lines
2.0 KiB
C++
53 lines
2.0 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/adagrad_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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namespace phi {
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template <typename T, typename Context>
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void AdagradDenseKernel(const Context& dev_ctx,
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const DenseTensor& param,
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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>& master_param,
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float epsilon_t,
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bool multi_precision,
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DenseTensor* param_out_tensor,
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DenseTensor* moment_out_tensor,
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DenseTensor* master_param_outs) {
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dev_ctx.template Alloc<T>(param_out_tensor);
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dev_ctx.template Alloc<T>(moment_out_tensor);
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T epsilon = static_cast<T>(epsilon_t);
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int r = xpu::adagrad(dev_ctx.x_context(),
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param.data<T>(),
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grad.data<T>(),
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moment.data<T>(),
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learning_rate.data<T>(),
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param_out_tensor->data<T>(),
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moment_out_tensor->data<T>(),
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param.numel(),
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epsilon);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "adagrad");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(adagrad, XPU, ALL_LAYOUT, phi::AdagradDenseKernel, float) {}
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