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96 lines
4.2 KiB
96 lines
4.2 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/optimizers/sgd_op.h"
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#include <string>
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class SGDOpXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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const auto *learning_rate = ctx.Input<framework::Tensor>("LearningRate");
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const auto *param_var = ctx.InputVar("Param");
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const auto *grad_var = ctx.InputVar("Grad");
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if (param_var->IsType<framework::LoDTensor>() &&
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grad_var->IsType<framework::LoDTensor>()) {
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const auto *param = ctx.Input<framework::Tensor>("Param");
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auto *param_out = ctx.Output<framework::Tensor>("ParamOut");
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// Actually, all tensors are LoDTensor except SelectedRows.
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const auto *grad = ctx.Input<framework::Tensor>("Grad");
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auto sz = param_out->numel();
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PADDLE_ENFORCE_EQ(param->numel(), sz,
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platform::errors::InvalidArgument(
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"The input tensor Param's numel of SgdOp "
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"should be equal with ParamOut's numel. "
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"But received Param's "
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"numel = [%s], ParamOut's numel = [%s]",
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param->numel(), sz));
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PADDLE_ENFORCE_EQ(grad->numel(), sz,
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platform::errors::InvalidArgument(
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"The input tensor Grad's numel of SgdOp "
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"should be equal with ParamOut's numel. "
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"But received Grad's "
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"numel = [%s], ParamOut's numel = [%s]",
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grad->numel(), sz));
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const T *lr = learning_rate->data<T>();
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const T *param_data = param->data<T>();
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const T *grad_data = grad->data<T>();
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T *out_data = param_out->mutable_data<T>(ctx.GetPlace());
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auto &dev_ctx = ctx.template device_context<DeviceContext>();
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int r = xpu::sgd(dev_ctx.x_context(), sz, grad_data, param_data, lr,
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out_data);
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if (r == xpu::Error_t::INVALID_PARAM) {
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::InvalidArgument(
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"XPU kernel error of SgdOp, error message: INVALID_PARAM, "
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"please check your input & output."));
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} else if (r == xpu::Error_t::RUNTIME_ERROR) {
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::Unavailable(
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"XPU kernel error of SgdOp, error message: "
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"RUNTIME_ERROR, please check whether Baidu "
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"Kunlun Card is properly installed."));
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} else if (r == xpu::Error_t::NO_ENOUGH_WORKSPACE) {
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PADDLE_ENFORCE_EQ(r, xpu::Error_t::SUCCESS,
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platform::errors::ResourceExhausted(
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"XPU kernel error of SgdOp, error "
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"message: NO_ENOUGH_WORKSPACE, XPU "
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"has no enough memory."));
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}
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} else {
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PADDLE_ENFORCE_EQ(false, true,
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platform::errors::PermissionDenied(
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"Unsupported Variable Type of Param & Grad in "
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"SgdOp-XPU. Excepted "
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"LodTensor, But received [%s] and [%s]",
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paddle::framework::ToTypeName(param_var->Type())));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_XPU_KERNEL(
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sgd, ops::SGDOpXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif
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