Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into accelerate_embedding_grad
minqiyang
6 years ago
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/* Copyright (c) 2018 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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#include "paddle/fluid/operators/math/sequence_pooling.h"
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#include <gtest/gtest.h>
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#include <vector>
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template <typename DeviceContext, typename Place, typename T>
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void TestSequencePoolingSum(const paddle::framework::LoD& lod) {
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paddle::framework::LoDTensor cpu_out_grad;
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paddle::framework::LoDTensor cpu_in_grad;
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paddle::framework::LoDTensor out_grad;
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paddle::framework::LoDTensor in_grad;
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const size_t second_dim = 128u;
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// construct out_grad's tensor in cpu
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const size_t out_first_dim = lod[0].size() - 1;
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auto out_dims = paddle::framework::make_ddim(
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{static_cast<int64_t>(out_first_dim), static_cast<int64_t>(second_dim)});
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cpu_out_grad.mutable_data<T>(out_dims, paddle::platform::CPUPlace());
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for (int64_t i = 0; i < cpu_out_grad.numel(); ++i) {
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cpu_out_grad.data<T>()[i] = static_cast<T>(i);
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}
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// copy to dst out_grad
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auto* place = new Place();
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DeviceContext* context = new DeviceContext(*place);
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if (paddle::platform::is_cpu_place(*place)) {
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out_grad = cpu_out_grad;
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} else {
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TensorCopySync(cpu_out_grad, *place, &out_grad);
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}
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// construct in_grad
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in_grad.set_lod(lod);
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auto in_dims = paddle::framework::make_ddim(
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{static_cast<int64_t>(lod[0].back()), static_cast<int64_t>(second_dim)});
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in_grad.mutable_data<T>(in_dims, context->GetPlace());
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// check tensor contruction result
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PADDLE_ENFORCE_EQ(in_grad.dims().size(), out_grad.dims().size());
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for (int64_t i = 1; i < out_grad.dims().size(); ++i) {
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PADDLE_ENFORCE_EQ(in_grad.dims()[i], out_grad.dims()[i]);
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}
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// call functor
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paddle::operators::math::SequencePoolGradFunctor<DeviceContext, T>()(
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*context, "SUM", out_grad, &in_grad);
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if (paddle::platform::is_cpu_place(*place)) {
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cpu_in_grad = in_grad;
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} else {
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TensorCopySync(in_grad, paddle::platform::CPUPlace(), &cpu_in_grad);
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cpu_in_grad.set_lod(in_grad.lod());
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}
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EXPECT_EQ(in_grad.numel(), lod[0].back() * second_dim);
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EXPECT_EQ(in_grad.lod(), lod);
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if (paddle::platform::is_cpu_place(*place)) {
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for (int64_t i = 0; i < in_grad.lod()[0].size() - 1; ++i) {
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int64_t begin = in_grad.lod()[0][i];
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int64_t end = in_grad.lod()[0][i + 1];
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paddle::framework::Tensor tmp = in_grad.Slice(begin, end);
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for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
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for (int64_t m = 0; m != second_dim; ++m) {
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EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
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out_grad.data<T>()[m + i * second_dim]);
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}
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}
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}
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} else {
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for (int64_t i = 0; i < cpu_in_grad.lod()[0].size() - 1; ++i) {
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int64_t begin = cpu_in_grad.lod()[0][i];
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int64_t end = cpu_in_grad.lod()[0][i + 1];
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paddle::framework::Tensor tmp = cpu_in_grad.Slice(begin, end);
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for (int64_t j = 0; j != tmp.numel() / second_dim; ++j) {
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for (int64_t m = 0; m != second_dim; ++m) {
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EXPECT_EQ(tmp.data<T>()[m + j * second_dim],
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cpu_out_grad.data<T>()[m + i * second_dim]);
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}
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}
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}
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}
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delete place;
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delete context;
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}
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TEST(SequencePoolingGrad, CPU_SUM) {
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paddle::framework::LoD lod1;
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lod1.push_back(std::vector<size_t>{0, 10});
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TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
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paddle::platform::CPUPlace, float>(lod1);
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paddle::framework::LoD lod2;
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lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
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TestSequencePoolingSum<paddle::platform::CPUDeviceContext,
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paddle::platform::CPUPlace, float>(lod2);
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}
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#ifdef PADDLE_WITH_CUDA
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TEST(SequencePoolingGrad, CUDA_SUM) {
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paddle::framework::LoD lod1;
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lod1.push_back(std::vector<size_t>{0, 10});
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TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
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paddle::platform::CUDAPlace, float>(lod1);
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paddle::framework::LoD lod2;
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lod2.push_back(std::vector<size_t>{0, 2, 7, 10});
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TestSequencePoolingSum<paddle::platform::CUDADeviceContext,
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paddle::platform::CUDAPlace, float>(lod2);
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}
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#endif
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