Add xpu transpose2 op.test=kunlun (#28086)
TeslaZhao
4 years ago
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GitHub
parent
a5f65d516f
commit
a5c95cd588
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/* 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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namespace paddle {
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namespace operators {
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using framework::Tensor;
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bool XPUSupported(int ndims, const std::vector<int>& axis) {
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/*
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* XPU currently support:
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* permute = {0, 2, 1}, permute = {1, 0},
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* permute = {0, 2, 1, 3}, permute = {1, 0, 2},
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* permute = {0, 2, 3, 1}
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*/
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bool is_supported = false;
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std::vector<int> permute_10(2, 0);
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std::vector<int> permute_102(3, 0);
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std::vector<int> permute_021(3, 0);
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std::vector<int> permute_210(3, 0);
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std::vector<int> permute_0213(4, 0);
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std::vector<int> permute_0231(4, 0);
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std::vector<int> permute_0312(4, 0);
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std::vector<int> permute_3201(4, 0);
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permute_10[0] = 1;
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permute_102[0] = 1;
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permute_102[2] = 2;
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permute_021[1] = 2;
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permute_021[2] = 1;
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permute_210[0] = 2;
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permute_210[1] = 1;
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permute_0213[1] = 2;
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permute_0213[2] = 1;
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permute_0213[3] = 3;
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permute_0231[1] = 2;
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permute_0231[2] = 3;
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permute_0231[3] = 1;
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permute_0312[1] = 3;
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permute_0312[2] = 1;
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permute_0312[3] = 2;
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permute_3201[0] = 3;
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permute_3201[1] = 2;
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permute_3201[3] = 1;
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switch (ndims) {
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case 2:
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if (axis == permute_10) {
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is_supported = true;
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}
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break;
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case 3:
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if ((axis == permute_021) || (axis == permute_102) ||
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(axis == permute_210)) {
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is_supported = true;
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}
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break;
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case 4:
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if ((axis == permute_0213) || (axis == permute_0231) ||
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(axis == permute_0312) || (axis == permute_3201)) {
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is_supported = true;
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}
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break;
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default:
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PADDLE_THROW(platform::errors::Unimplemented(
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"Tensors with rank only 2, 3 and 4 are supported on XPU"));
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}
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return is_supported;
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}
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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 (!XPUSupported(ndims, axis)) {
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VLOG(0) << "XPU does not support the permute, try to do on cpu";
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framework::Tensor x_cpu;
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framework::Tensor out_cpu;
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auto x_cpu_data = x_cpu.mutable_data<T>(x->dims(), platform::CPUPlace());
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auto out_cpu_data =
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out_cpu.mutable_data<T>(out->dims(), platform::CPUPlace());
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memory::Copy(platform::CPUPlace(), reinterpret_cast<void*>(x_cpu_data),
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BOOST_GET_CONST(platform::XPUPlace, context.GetPlace()),
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(const void*)x_data, x->numel() * sizeof(T));
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const platform::CPUDeviceContext* cpu_dev_ctx =
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static_cast<const platform::CPUDeviceContext*>(
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platform::DeviceContextPool::Instance().Get(
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platform::CPUPlace()));
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TransCompute<platform::CPUDeviceContext, T>(ndims, *cpu_dev_ctx, x_cpu,
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&out_cpu, axis);
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memory::Copy(BOOST_GET_CONST(platform::XPUPlace, context.GetPlace()),
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reinterpret_cast<void*>(y_data), platform::CPUPlace(),
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(const void*)out_cpu_data, out->numel() * sizeof(T));
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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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int* permute_host = axis.data();
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = xpu::transpose(dev_ctx.x_context(), x_data, y_data,
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x_shape_host.data(), permute_host, ndims);
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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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if (!XPUSupported(ndims, reversed_axis)) {
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PADDLE_THROW(
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platform::errors::Unimplemented("XPU does not support the permute"));
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}
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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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int* permute_host = reversed_axis.data();
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auto& dev_ctx = context.template device_context<DeviceContext>();
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int r = xpu::transpose(dev_ctx.x_context(), out_grad->data<T>(),
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x_grad->data<T>(), out_shape_host.data(),
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permute_host, ndims);
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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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@ -0,0 +1,230 @@
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# Copyright (c) 2018 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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from __future__ import print_function
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import unittest
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import numpy as np
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import sys
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sys.path.append("..")
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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class TestXPUTransposeOp(OpTest):
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def setUp(self):
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self.init_op_type()
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self.initTestCase()
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self.inputs = {'X': np.random.random(self.shape).astype("float64")}
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self.attrs = {
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'axis': list(self.axis),
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'use_mkldnn': False,
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'use_xpu': True
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}
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self.outputs = {
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'XShape': np.random.random(self.shape).astype("float64"),
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'Out': self.inputs['X'].transpose(self.axis)
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}
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def init_op_type(self):
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self.op_type = "transpose2"
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self.use_mkldnn = False
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def test_check_output(self):
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if paddle.is_compiled_with_xpu():
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paddle.enable_static()
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place = paddle.XPUPlace(0)
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self.check_output_with_place(place=place, no_check_set=['XShape'])
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def test_check_grad(self):
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if paddle.is_compiled_with_xpu():
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paddle.enable_static()
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place = paddle.XPUPlace(0)
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self.check_grad_with_place(place, ['X'], 'Out')
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def initTestCase(self):
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self.shape = (3, 40)
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self.axis = (1, 0)
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class TestCase0(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (100, )
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self.axis = (0, )
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class TestCase1(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (3, 4, 10)
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self.axis = (0, 2, 1)
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class TestCase2(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5)
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self.axis = (0, 2, 3, 1)
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class TestCase3(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6)
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self.axis = (4, 2, 3, 1, 0)
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class TestCase4(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 4, 5, 6, 1)
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self.axis = (4, 2, 3, 1, 0, 5)
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class TestCase5(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 16, 96)
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self.axis = (0, 2, 1)
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class TestCase6(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 10, 12, 16)
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self.axis = (3, 1, 2, 0)
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class TestCase7(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 10, 2, 16)
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self.axis = (0, 1, 3, 2)
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class TestCase8(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
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self.axis = (0, 1, 3, 2, 4, 5, 6, 7)
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class TestCase9(TestXPUTransposeOp):
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def initTestCase(self):
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self.shape = (2, 3, 2, 3, 2, 4, 3, 3)
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self.axis = (6, 1, 3, 5, 0, 2, 4, 7)
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class TestTransposeOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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x = fluid.layers.data(name='x', shape=[10, 5, 3], dtype='float64')
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def test_x_Variable_check():
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# the Input(x)'s type must be Variable
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fluid.layers.transpose("not_variable", perm=[1, 0, 2])
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self.assertRaises(TypeError, test_x_Variable_check)
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def test_x_dtype_check():
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# the Input(x)'s dtype must be one of [float16, float32, float64, int32, int64]
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x1 = fluid.layers.data(
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name='x1', shape=[10, 5, 3], dtype='bool')
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fluid.layers.transpose(x1, perm=[1, 0, 2])
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self.assertRaises(TypeError, test_x_dtype_check)
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def test_perm_list_check():
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# Input(perm)'s type must be list
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fluid.layers.transpose(x, perm="[1, 0, 2]")
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self.assertRaises(TypeError, test_perm_list_check)
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def test_perm_length_and_x_dim_check():
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# Input(perm) is the permutation of dimensions of Input(input)
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# its length should be equal to dimensions of Input(input)
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fluid.layers.transpose(x, perm=[1, 0, 2, 3, 4])
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self.assertRaises(ValueError, test_perm_length_and_x_dim_check)
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def test_each_elem_value_check():
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# Each element in Input(perm) should be less than Input(x)'s dimension
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fluid.layers.transpose(x, perm=[3, 5, 7])
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self.assertRaises(ValueError, test_each_elem_value_check)
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class TestTAPI(unittest.TestCase):
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def test_out(self):
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with fluid.program_guard(fluid.Program()):
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data = fluid.data(shape=[10], dtype="float64", name="data")
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data_t = paddle.t(data)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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data_np = np.random.random([10]).astype("float64")
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result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
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expected_result = np.transpose(data_np)
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self.assertEqual((result == expected_result).all(), True)
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with fluid.program_guard(fluid.Program()):
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data = fluid.data(shape=[10, 5], dtype="float64", name="data")
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data_t = paddle.t(data)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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data_np = np.random.random([10, 5]).astype("float64")
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result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
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expected_result = np.transpose(data_np)
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self.assertEqual((result == expected_result).all(), True)
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with fluid.program_guard(fluid.Program()):
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data = fluid.data(shape=[1, 5], dtype="float64", name="data")
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data_t = paddle.t(data)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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data_np = np.random.random([1, 5]).astype("float64")
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result, = exe.run(feed={"data": data_np}, fetch_list=[data_t])
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expected_result = np.transpose(data_np)
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self.assertEqual((result == expected_result).all(), True)
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with fluid.dygraph.guard():
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np_x = np.random.random([10]).astype("float64")
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data = fluid.dygraph.to_variable(np_x)
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z = paddle.t(data)
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np_z = z.numpy()
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z_expected = np.array(np.transpose(np_x))
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self.assertEqual((np_z == z_expected).all(), True)
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with fluid.dygraph.guard():
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np_x = np.random.random([10, 5]).astype("float64")
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data = fluid.dygraph.to_variable(np_x)
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z = paddle.t(data)
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np_z = z.numpy()
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z_expected = np.array(np.transpose(np_x))
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self.assertEqual((np_z == z_expected).all(), True)
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with fluid.dygraph.guard():
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np_x = np.random.random([1, 5]).astype("float64")
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data = fluid.dygraph.to_variable(np_x)
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z = paddle.t(data)
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np_z = z.numpy()
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z_expected = np.array(np.transpose(np_x))
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self.assertEqual((np_z == z_expected).all(), True)
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def test_errors(self):
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with fluid.program_guard(fluid.Program()):
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x = fluid.data(name='x', shape=[10, 5, 3], dtype='float64')
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def test_x_dimension_check():
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paddle.t(x)
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self.assertRaises(ValueError, test_x_dimension_check)
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if __name__ == "__main__":
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unittest.main()
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