wehub-resource-sync
249 lines
8.0 KiB
C++
249 lines
8.0 KiB
C++
// Copyright (c) 2024 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/funcs/math/beam_search.h"
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/operator.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/core/platform/device_context.h"
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void PrepareCPUTensors(phi::DenseTensor* ids,
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phi::DenseTensor* scores,
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phi::DenseTensor* pre_ids,
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phi::DenseTensor* pre_scores) {
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// lod
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phi::LegacyLoD lod;
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std::vector<size_t> level0({0, 2, 4});
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std::vector<size_t> level1({0, 1, 2, 3, 4});
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lod.push_back(level0);
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lod.push_back(level1);
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ids->set_lod(lod);
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scores->set_lod(lod);
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auto dims = common::make_ddim({4, 3});
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ids->Resize(dims);
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scores->Resize(dims);
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phi::CPUPlace place;
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auto* ids_data = ids->mutable_data<int64_t>(place);
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auto* scores_data = scores->mutable_data<float>(place);
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std::vector<int64_t> ids_vec_data({4, 2, 5, 2, 1, 3, 3, 5, 2, 8, 2, 1});
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std::vector<float> scores_vec_data(
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{0.6f, 0.3f, 0.5f, 0.2f, 0.3f, 0.1f, 0.9f, 0.5f, 0.1f, 0.7f, 0.5f, 0.1f});
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PADDLE_ENFORCE_EQ(
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static_cast<size_t>(ids->numel()),
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ids_vec_data.size(),
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common::errors::InvalidArgument(
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"Required ids->numel() should be equal to ids_vec_data.size(). "));
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PADDLE_ENFORCE_EQ(
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static_cast<size_t>(ids->numel()),
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scores_vec_data.size(),
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common::errors::InvalidArgument(
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"Required ids->numel() should be equal to scores_vec_data.size(). "));
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for (int i = 0; i < ids->numel(); i++) {
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ids_data[i] = ids_vec_data[i];
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scores_data[i] = scores_vec_data[i];
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}
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// pre_ids
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pre_ids->Resize(common::make_ddim({4, 1}));
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for (int i = 0; i < 4; i++) {
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pre_ids->mutable_data<int64_t>(place)[i] = i + 1;
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}
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// pre_scores
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pre_scores->Resize(common::make_ddim({4, 1}));
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for (int i = 0; i < 4; i++) {
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pre_scores->mutable_data<float>(place)[i] = 0.1 * (i + 1); // NOLINT
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}
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}
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template <typename DeviceContext, typename Place>
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void TestBeamSearch() {
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phi::DenseTensor ids;
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phi::DenseTensor scores;
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phi::DenseTensor pre_ids;
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phi::DenseTensor pre_scores;
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auto* place = new Place();
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DeviceContext* context = new DeviceContext(*place);
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context->SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
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.GetAllocator(phi::CPUPlace())
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.get());
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if (phi::is_cpu_place(*place)) {
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PrepareCPUTensors(&ids, &scores, &pre_ids, &pre_scores);
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} else {
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phi::DenseTensor cpu_ids;
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phi::DenseTensor cpu_scores;
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phi::DenseTensor cpu_pre_ids;
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phi::DenseTensor cpu_pre_scores;
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PrepareCPUTensors(&cpu_ids, &cpu_scores, &cpu_pre_ids, &cpu_pre_scores);
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paddle::framework::TensorCopySync(cpu_ids, *place, &ids);
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paddle::framework::TensorCopySync(cpu_scores, *place, &scores);
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paddle::framework::TensorCopySync(cpu_pre_ids, *place, &pre_ids);
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paddle::framework::TensorCopySync(cpu_pre_scores, *place, &pre_scores);
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ids.set_lod(cpu_ids.lod());
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scores.set_lod(cpu_scores.lod());
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pre_ids.set_lod(cpu_pre_ids.lod());
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pre_scores.set_lod(cpu_pre_scores.lod());
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}
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phi::DenseTensor selected_ids;
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phi::DenseTensor selected_scores;
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phi::DenseTensor parent_idx;
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size_t level = 0;
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size_t beam_size = 2;
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int end_id = 0;
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phi::math::BeamSearchFunctor<DeviceContext, float> beamsearch;
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beamsearch(*context,
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&pre_ids,
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&pre_scores,
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&ids,
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&scores,
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&selected_ids,
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&selected_scores,
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&parent_idx,
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level,
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beam_size,
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end_id,
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true);
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ASSERT_EQ(selected_ids.lod(), selected_scores.lod());
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phi::DenseTensor cpu_selected_ids;
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phi::DenseTensor cpu_selected_scores;
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if (phi::is_cpu_place(*place)) {
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cpu_selected_ids = selected_ids;
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cpu_selected_scores = selected_scores;
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} else {
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paddle::framework::TensorCopySync(
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selected_ids, phi::CPUPlace(), &cpu_selected_ids);
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paddle::framework::TensorCopySync(
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selected_scores, phi::CPUPlace(), &cpu_selected_scores);
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cpu_selected_ids.set_lod(selected_ids.lod());
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cpu_selected_scores.set_lod(selected_scores.lod());
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}
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std::vector<int64_t> expected_ids({4, 5, 3, 8});
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std::vector<float> expected_scores({0.6f, 0.5f, 0.9f, 0.7f});
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for (int i = 0; i < 4; i++) {
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ASSERT_EQ(expected_ids[i], cpu_selected_ids.data<int64_t>()[i]);
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ASSERT_EQ(expected_scores[i], cpu_selected_scores.data<float>()[i]);
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}
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delete place;
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delete context;
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}
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#if defined(PADDLE_WITH_XPU)
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template <>
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void TestBeamSearch<phi::XPUContext, phi::XPUPlace>() {
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phi::DenseTensor ids;
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phi::DenseTensor scores;
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phi::DenseTensor pre_ids;
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phi::DenseTensor pre_scores;
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auto* place = new phi::XPUPlace();
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auto* context = new phi::XPUContext(*place);
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context->SetAllocator(paddle::memory::allocation::AllocatorFacade::Instance()
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.GetAllocator(*place, context->stream())
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.get());
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context->SetHostAllocator(
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paddle::memory::allocation::AllocatorFacade::Instance()
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.GetAllocator(phi::CPUPlace())
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.get());
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if (phi::is_cpu_place(*place)) {
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PrepareCPUTensors(&ids, &scores, &pre_ids, &pre_scores);
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} else {
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phi::DenseTensor cpu_ids;
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phi::DenseTensor cpu_scores;
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phi::DenseTensor cpu_pre_ids;
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phi::DenseTensor cpu_pre_scores;
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PrepareCPUTensors(&cpu_ids, &cpu_scores, &cpu_pre_ids, &cpu_pre_scores);
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paddle::framework::TensorCopySync(cpu_ids, *place, &ids);
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paddle::framework::TensorCopySync(cpu_scores, *place, &scores);
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paddle::framework::TensorCopySync(cpu_pre_ids, *place, &pre_ids);
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paddle::framework::TensorCopySync(cpu_pre_scores, *place, &pre_scores);
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ids.set_lod(cpu_ids.lod());
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scores.set_lod(cpu_scores.lod());
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pre_ids.set_lod(cpu_pre_ids.lod());
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pre_scores.set_lod(cpu_pre_scores.lod());
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}
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phi::DenseTensor selected_ids;
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phi::DenseTensor selected_scores;
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phi::DenseTensor parent_idx;
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size_t level = 0;
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size_t beam_size = 2;
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int end_id = 0;
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phi::math::BeamSearchFunctor<phi::XPUContext, float> beamsearch;
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beamsearch(*context,
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&pre_ids,
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&pre_scores,
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&ids,
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&scores,
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&selected_ids,
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&selected_scores,
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&parent_idx,
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level,
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beam_size,
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end_id,
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true);
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ASSERT_EQ(selected_ids.lod(), selected_scores.lod());
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phi::DenseTensor cpu_selected_ids;
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phi::DenseTensor cpu_selected_scores;
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if (phi::is_cpu_place(*place)) {
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cpu_selected_ids = selected_ids;
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cpu_selected_scores = selected_scores;
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} else {
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paddle::framework::TensorCopySync(
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selected_ids, phi::CPUPlace(), &cpu_selected_ids);
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paddle::framework::TensorCopySync(
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selected_scores, phi::CPUPlace(), &cpu_selected_scores);
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cpu_selected_ids.set_lod(selected_ids.lod());
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cpu_selected_scores.set_lod(selected_scores.lod());
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}
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std::vector<int64_t> expected_ids({4, 5, 3, 8});
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std::vector<float> expected_scores({0.6f, 0.5f, 0.9f, 0.7f});
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for (int i = 0; i < 4; i++) {
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ASSERT_EQ(expected_ids[i], cpu_selected_ids.data<int64_t>()[i]);
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ASSERT_EQ(expected_scores[i], cpu_selected_scores.data<float>()[i]);
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}
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delete place;
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delete context;
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}
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#endif
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TEST(BeamSearch, CPU) { TestBeamSearch<phi::CPUContext, phi::CPUPlace>(); }
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#if defined(PADDLE_WITH_XPU)
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TEST(BeamSearch, XPU) { TestBeamSearch<phi::XPUContext, phi::XPUPlace>(); }
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#endif
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