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107 lines
3.6 KiB
107 lines
3.6 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/transpose_op.h"
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#include <memory>
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#include <string>
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#include <vector>
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#include "paddle/fluid/platform/xpu_header.h"
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namespace paddle {
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namespace operators {
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using framework::Tensor;
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template <typename DeviceContext, typename T>
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class TransposeXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto x = context.Input<framework::Tensor>("X");
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auto out = context.Output<framework::Tensor>("Out");
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// axis is permute
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auto axis = context.Attr<std::vector<int>>("axis");
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int ndims = axis.size();
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const auto x_dims = x->dims();
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const T* x_data = x->data<T>();
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T* y_data = out->mutable_data<T>(context.GetPlace());
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if (out->numel() == 0) {
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return;
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}
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std::vector<int> x_shape_host(ndims, 0);
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for (int i = 0; i < ndims; ++i) {
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x_shape_host[i] = x_dims[i];
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}
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = xpu::transpose<T>(dev_ctx.x_context(), x_data, y_data, x_shape_host,
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axis);
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External("XPU kernel error! error code=%d", r));
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}
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};
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template <typename DeviceContext, typename T>
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class TransposeGradXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* out_grad =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* x_grad =
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context.Output<framework::Tensor>(framework::GradVarName("X"));
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if (!x_grad) return;
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x_grad->mutable_data<T>(context.GetPlace());
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std::vector<int> axis = context.Attr<std::vector<int>>("axis");
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std::vector<int> reversed_axis(axis);
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for (size_t i = 0; i < axis.size(); i++) {
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reversed_axis[axis[i]] = i;
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}
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int ndims = axis.size();
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std::vector<int> out_shape_host(ndims, 0);
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for (int i = 0; i < ndims; ++i) {
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out_shape_host[i] = out_grad->dims()[i];
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}
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = xpu::transpose<T>(dev_ctx.x_context(), out_grad->data<T>(),
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x_grad->data<T>(), out_shape_host, reversed_axis);
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PADDLE_ENFORCE_EQ(
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r, xpu::Error_t::SUCCESS,
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platform::errors::External("XPU kernel error! error code=%d", r));
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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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transpose,
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ops::TransposeXPUKernel<paddle::platform::XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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transpose_grad,
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ops::TransposeGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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transpose2,
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ops::TransposeXPUKernel<paddle::platform::XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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transpose2_grad,
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ops::TransposeGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif // PADDLE_WITH_XPU
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