From a281e1016ec7bbd5e019a85a4b55bf7cb6107a19 Mon Sep 17 00:00:00 2001 From: guosheng Date: Thu, 7 Jun 2018 19:20:08 +0800 Subject: [PATCH] Make cc_test of beam_search_op and beam_searc_decode_op run correctly --- paddle/fluid/operators/CMakeLists.txt | 4 +- .../fluid/operators/beam_search_decode_op.cc | 4 - .../fluid/operators/beam_search_decode_op.h | 184 +----------------- .../operators/beam_search_decode_op_test.cc | 148 +++----------- paddle/fluid/operators/beam_search_op.cc | 21 +- paddle/fluid/operators/beam_search_op.h | 10 +- paddle/fluid/operators/beam_search_op_test.cc | 15 +- 7 files changed, 50 insertions(+), 336 deletions(-) diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index 4c7691b776f..7fce138e3f4 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -288,8 +288,8 @@ set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library") cc_test(gather_test SRCS gather_test.cc DEPS tensor) cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) -# cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor) -# cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op) +cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor) +cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op) cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory) cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op) cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op) diff --git a/paddle/fluid/operators/beam_search_decode_op.cc b/paddle/fluid/operators/beam_search_decode_op.cc index 39877cfdc10..b518c11e8cb 100644 --- a/paddle/fluid/operators/beam_search_decode_op.cc +++ b/paddle/fluid/operators/beam_search_decode_op.cc @@ -87,13 +87,9 @@ void BeamSearchDecodeFunctor::operator()() const { BeamSearchDecoder beam_search_decoder(beam_size_, end_id_); // Check if the tensor is on GPU. If so, use the CPU copy instead if (tensor_on_gpu_) { - // beam_search_decoder.PackAllSteps(step_ids_, step_scores_, id_tensor_, - // score_tensor_); beam_search_decoder.Backtrace(step_ids_, step_scores_, id_tensor_, score_tensor_); } else { - // beam_search_decoder.PackAllSteps(step_ids_origin_, step_scores_origin_, - // id_tensor_, score_tensor_); beam_search_decoder.Backtrace(step_ids_origin_, step_scores_origin_, id_tensor_, score_tensor_); } diff --git a/paddle/fluid/operators/beam_search_decode_op.h b/paddle/fluid/operators/beam_search_decode_op.h index 322838951bc..1da4fe26af5 100644 --- a/paddle/fluid/operators/beam_search_decode_op.h +++ b/paddle/fluid/operators/beam_search_decode_op.h @@ -28,41 +28,11 @@ using LoDTensorArray = framework::LoDTensorArray; // all the lod have 2 levels. // The First is source level, the second is sentence level. -// source level describe how many candidate words for this source. -// sentence level describe these candidates belong to which prefix +// source level describe how many prefixes (branchs) for each source sentece +// (beam). sentence level describe how these candidates belong to the prefixes. const size_t kSourceLevel = 0; const size_t kSentenceLevel = 1; -template -struct BeamNode { - BeamNode(int64_t word_id, T score) : word_id_(word_id), score_(score) {} - - ~BeamNode() { - if (parent_) { - parent_->DropKid(this); - if (parent_->kids_.size() == 0UL) { - delete parent_; - } - } - VLOG(3) << "Delete BeamNode root with word_id:" << this->word_id_; - } - - void AppendTo(BeamNode* parent) { - parent_ = parent; - parent->kids_.insert(this); - } - - void DropKid(BeamNode* kid) { kids_.erase(kid); } - - BeamNode* parent_ = nullptr; - std::unordered_set kids_; - int64_t word_id_; - T score_; -}; - -template -using BeamNodeVector = std::vector>>; - template struct Sentence { std::vector word_ids; @@ -77,25 +47,6 @@ struct BeamSearchDecoder { BeamSearchDecoder(size_t beam_size, int end_id) : beam_size_(beam_size), end_id_(end_id) {} - /** - * make a BeamNode and all it's related prefix BeanNode into a Sentence. - */ - Sentence MakeSentence(const BeamNode* node) const; - - /** - * Param: - * cur_ids: LoDTensor of One step for word ID - * cur_scores: LoDTensor of One Step for word score - * prefixes_list: prefixes for each source sentence. - * sentence_vector_list: result sentence_vector for each source sentence. - * Return: - * a new prefixes list for each source of current step - */ - std::vector> PackTwoSteps( - const LoDTensor& cur_ids, const LoDTensor& cur_scores, - std::vector>* prefixes_list, - std::vector>* sentence_vector_list) const; - /** * convert the result sentence_vector for each source sentence into two * LodTensor. @@ -105,29 +56,18 @@ struct BeamSearchDecoder { * sentence_vector_list: sentence_vector for each source sentence. * id_tensor: result LoDTensor for sentences of id. * score_tensor: result LoDTensor for sentences of score. + * reverse: whether ids of sentence in sentence_vector_list is reversed + * sort_by_score: whether to sort hypotheses of each sentence by scores. */ void ConvertSentenceVectorToLodTensor( std::vector> sentence_vector_list, LoDTensor* id_tensor, - LoDTensor* score_tensor, bool reverse = false, + LoDTensor* score_tensor, bool reverse = true, bool sort_by_score = true) const; /** - * Pack all steps of id/score LodTensor into sentence LoDTensor - * it's main logic is: - * ```python - * prefix - * result_sentence - * result_lod_tensor - * - * for (step in steps): - * prefix = PackTwoSteps(prefix, step, &result_sentence) - * ConvertSentenceVectorToLodTensor(result_sentence, &result_lod_tensor) - * ``` + * Gather the hypotheses for each source sentence by backtrace though the + * LoDTensorArray step_ids whose lods reserve the path in the tree. */ - void PackAllSteps(const LoDTensorArray& step_ids, - const LoDTensorArray& step_scores, LoDTensor* id_tensor, - LoDTensor* score_tensor) const; - void Backtrace(const LoDTensorArray& step_ids, const LoDTensorArray& step_scores, LoDTensor* id_tensor, LoDTensor* score_tensor) const; @@ -136,80 +76,6 @@ struct BeamSearchDecoder { int end_id_; }; -template -Sentence BeamSearchDecoder::MakeSentence(const BeamNode* node) const { - Sentence sentence; - while (node != nullptr) { - sentence.word_ids.emplace_back(node->word_id_); - sentence.scores.emplace_back(node->score_); - node = node->parent_; - } - - std::reverse(std::begin(sentence.word_ids), std::end(sentence.word_ids)); - std::reverse(std::begin(sentence.scores), std::end(sentence.scores)); - - return sentence; -} - -template -std::vector> BeamSearchDecoder::PackTwoSteps( - const LoDTensor& cur_ids, const LoDTensor& cur_scores, - std::vector>* prefixes_list, - std::vector>* sentence_vector_list) const { - std::vector> result; - - for (size_t src_idx = 0; src_idx < cur_ids.lod()[kSourceLevel].size() - 1; - ++src_idx) { - size_t src_start = cur_ids.lod().at(kSourceLevel)[src_idx]; - size_t src_end = cur_ids.lod().at(kSourceLevel)[src_idx + 1]; - - BeamNodeVector beam_nodes; - - // if prefixes size is 0, it means this is the first step. In this step, - // all candidate id is the start of candidate sentences. - if (prefixes_list->empty()) { - PADDLE_ENFORCE_EQ(cur_ids.lod().at(kSourceLevel).back(), - cur_ids.lod().at(kSentenceLevel).back(), - "in the first step"); - for (size_t id_idx = src_start; id_idx < src_end; ++id_idx) { - beam_nodes.push_back(std::unique_ptr>(new BeamNode( - cur_ids.data()[id_idx], cur_scores.data()[id_idx]))); - } - } else { - BeamNodeVector& prefixes = prefixes_list->at(src_idx); - SentenceVector& sentence_vector = (*sentence_vector_list)[src_idx]; - - PADDLE_ENFORCE_EQ(src_end - src_start, prefixes.size(), - "prefix and candidate set number should be the same"); - - auto candidate_offset = cur_ids.lod()[kSentenceLevel]; - for (size_t prefix_idx = 0; prefix_idx < prefixes.size(); ++prefix_idx) { - std::unique_ptr>& prefix = prefixes[prefix_idx]; - size_t candidate_start = candidate_offset[src_start + prefix_idx]; - size_t candidate_end = candidate_offset[src_start + prefix_idx + 1]; - if (candidate_start == candidate_end) { - VLOG(3) << "this sentence has no more candidate, " - "add to result sentence and rm it from beam tree"; - sentence_vector.push_back(MakeSentence(prefix.get())); - prefix.reset(); - } else { - for (size_t candidate_idx = candidate_start; - candidate_idx < candidate_end; ++candidate_idx) { - auto* candidate = - new BeamNode(cur_ids.data()[candidate_idx], - cur_scores.data()[candidate_idx]); - candidate->AppendTo(prefix.get()); - beam_nodes.push_back(std::unique_ptr>(candidate)); - } - prefix.release(); - } - } - } - result.push_back(std::move(beam_nodes)); - } - return result; -} - template void BeamSearchDecoder::ConvertSentenceVectorToLodTensor( std::vector> sentence_vector_list, LoDTensor* id_tensor, @@ -273,42 +139,6 @@ void BeamSearchDecoder::ConvertSentenceVectorToLodTensor( framework::TensorFromVector(score_data, cpu_ctx, score_tensor); } -template -void BeamSearchDecoder::PackAllSteps(const LoDTensorArray& step_ids, - const LoDTensorArray& step_scores, - LoDTensor* id_tensor, - LoDTensor* score_tensor) const { - PADDLE_ENFORCE(!step_ids.empty(), "step num should be larger than 0"); - PADDLE_ENFORCE_EQ(step_ids.size(), step_scores.size(), - "step_ids and step_scores should be the same"); - const size_t step_num = step_ids.size(); - const size_t src_num = step_ids.at(0).lod().at(kSourceLevel).size() - 1; - - PADDLE_ENFORCE_GT(src_num, 0UL, "source num should be larger than 0"); - - // previous prefixes for each step, - // the init length is 0, means this is the first step. - std::vector> beamnode_vector_list(0); - std::vector> sentence_vector_list(src_num); - - // pack all steps for one batch first, then another batch - for (size_t step_id = 0; step_id < step_num; ++step_id) { - beamnode_vector_list = - PackTwoSteps(step_ids.at(step_id), step_scores.at(step_id), - &beamnode_vector_list, &sentence_vector_list); - } - // append last beam_node to result - for (size_t src_idx = 0; src_idx < src_num; ++src_idx) { - for (auto& beam_node : beamnode_vector_list.at(src_idx)) { - sentence_vector_list[src_idx].push_back(MakeSentence(beam_node.get())); - beam_node.reset(); - } - } - - ConvertSentenceVectorToLodTensor(sentence_vector_list, id_tensor, - score_tensor); -} - template void BeamSearchDecoder::Backtrace(const LoDTensorArray& step_ids, const LoDTensorArray& step_scores, diff --git a/paddle/fluid/operators/beam_search_decode_op_test.cc b/paddle/fluid/operators/beam_search_decode_op_test.cc index 36f9594969c..c6cccafcf46 100644 --- a/paddle/fluid/operators/beam_search_decode_op_test.cc +++ b/paddle/fluid/operators/beam_search_decode_op_test.cc @@ -20,15 +20,11 @@ using LoD = paddle::framework::LoD; using LoDTensor = paddle::framework::LoDTensor; using LoDTensorArray = paddle::framework::LoDTensorArray; -template -using BeamNode = paddle::operators::BeamNode; template using BeamSearchDecoder = paddle::operators::BeamSearchDecoder; template using Sentence = paddle::operators::Sentence; template -using BeamNodeVector = paddle::operators::BeamNodeVector; -template using SentenceVector = paddle::operators::SentenceVector; namespace paddle { @@ -77,138 +73,50 @@ void GenerateExample(const std::vector& level_0, } // namespace test } // namespace paddle -TEST(BeamSearchDecodeOp, DeleteBeamNode) { - auto* root = new BeamNode(0, 0); - auto* b1 = new BeamNode(1, 1); - auto* b2 = new BeamNode(2, 2); - auto* b3 = new BeamNode(3, 3); - - b1->AppendTo(root); - b2->AppendTo(root); - b3->AppendTo(b1); - - delete b3; - delete b2; -} - -TEST(BeamSearchDecodeOp, MakeSentence) { - auto* root = new BeamNode(0, 0); - auto* b1 = new BeamNode(1, 1); - auto* end = new BeamNode(2, 2); - b1->AppendTo(root); - end->AppendTo(b1); - - BeamSearchDecoder helper; - Sentence sentence = helper.MakeSentence(end); - delete end; - - std::vector expect_ids = {0, 1, 2}; - ASSERT_EQ(sentence.word_ids, expect_ids); - - std::vector expect_scores = {0, 1, 2}; - ASSERT_EQ(sentence.scores, expect_scores); -} - -TEST(BeamSearchDecodeOp, PackTwoStepsFistStep) { - CPUPlace place; - - LoDTensorArray ids; - LoDTensorArray scores; - - paddle::test::GenerateExample( - std::vector{0, 2, 6}, std::vector{0, 1, 2, 3, 4, 5, 6}, - std::vector{1, 2, 3, 4, 5, 6}, &ids, &scores); - - std::vector> beamnode_vector_list; - std::vector> sentence_vector_list( - 2, SentenceVector()); - - BeamSearchDecoder helper; - beamnode_vector_list = helper.PackTwoSteps( - ids[0], scores[0], &beamnode_vector_list, &sentence_vector_list); - ASSERT_EQ(beamnode_vector_list.size(), 2UL); - ASSERT_EQ(beamnode_vector_list[0].size(), 2UL); - ASSERT_EQ(beamnode_vector_list[1].size(), 4UL); -} - -TEST(BeamSearchDecodeOp, PackTwoSteps) { - CPUPlace place; - - // first source has three prefix - BeamNodeVector source0_prefixes; - source0_prefixes.push_back( - std::unique_ptr>(new BeamNode(1, 1))); - source0_prefixes.push_back( - std::unique_ptr>(new BeamNode(0, 0))); - source0_prefixes.push_back( - std::unique_ptr>(new BeamNode(3, 3))); - - // second source has two prefix - BeamNodeVector source1_prefixes; - source1_prefixes.push_back( - std::unique_ptr>(new BeamNode(4, 4))); - source1_prefixes.push_back( - std::unique_ptr>(new BeamNode(5, 5))); - - std::vector> beamnode_vector_list; - std::vector> sentence_vector_list( - 2, SentenceVector()); - - beamnode_vector_list.push_back(std::move(source0_prefixes)); - beamnode_vector_list.push_back(std::move(source1_prefixes)); - - // generate data for one step - LoDTensorArray ids; - LoDTensorArray scores; - - paddle::test::GenerateExample(std::vector{0, 3, 5}, - std::vector{0, 1, 1, 3, 4, 5}, - std::vector{0, 1, 2, 3, 4}, &ids, &scores); - - BeamSearchDecoder helper1; - beamnode_vector_list = helper1.PackTwoSteps( - ids[0], scores[0], &beamnode_vector_list, &sentence_vector_list); - - ASSERT_EQ(sentence_vector_list[0].size(), 1UL); - ASSERT_EQ(sentence_vector_list[1].size(), 0UL); - ASSERT_EQ(beamnode_vector_list[0].size(), 3UL); - ASSERT_EQ(beamnode_vector_list[1].size(), 2UL); -} - -TEST(BeamSearchDecodeOp, PackAllSteps) { +TEST(BeamSearchDecodeOp, Backtrace) { CPUPlace place; - // we will constuct a sample data with 3 steps and 2 source sentences + // we will constuct a sample data with 4 steps and 2 source sentences + // beam_size = 2, start_id = 0, end_id = 1 LoDTensorArray ids; LoDTensorArray scores; paddle::test::GenerateExample( - std::vector{0, 3, 6}, std::vector{0, 1, 2, 3, 4, 5, 6}, - std::vector{1, 2, 3, 4, 5, 6}, &ids, &scores); + std::vector{0, 1, 2}, std::vector{0, 1, 2}, + std::vector{0, 0}, &ids, &scores); // start with start_id + paddle::test::GenerateExample(std::vector{0, 1, 2}, + std::vector{0, 2, 4}, + std::vector{2, 3, 4, 5}, &ids, &scores); + paddle::test::GenerateExample(std::vector{0, 2, 4}, + std::vector{0, 2, 2, 4, 4}, + std::vector{3, 1, 5, 4}, &ids, &scores); + paddle::test::GenerateExample(std::vector{0, 2, 4}, + std::vector{0, 1, 2, 3, 4}, + std::vector{1, 1, 3, 5}, &ids, &scores); paddle::test::GenerateExample( - std::vector{0, 3, 6}, std::vector{0, 1, 1, 3, 5, 5, 6}, - std::vector{0, 1, 2, 3, 4, 5}, &ids, &scores); - paddle::test::GenerateExample(std::vector{0, 3, 6}, - std::vector{0, 0, 1, 2, 3, 4, 5}, - std::vector{0, 1, 2, 3, 4}, &ids, &scores); + std::vector{0, 2, 4}, + std::vector{0, 0, 0, 2, + 2}, // the branchs of the first source sentence + // are pruned since finished + std::vector{5, 1}, + &ids, &scores); - ASSERT_EQ(ids.size(), 3UL); - ASSERT_EQ(scores.size(), 3UL); + ASSERT_EQ(ids.size(), 5UL); + ASSERT_EQ(scores.size(), 5UL); - BeamSearchDecoder helper; + BeamSearchDecoder helper(2, 1); // beam_size = 2, end_id = 1 LoDTensor id_tensor; LoDTensor score_tensor; - helper.PackAllSteps(ids, scores, &id_tensor, &score_tensor); + helper.Backtrace(ids, scores, &id_tensor, &score_tensor); LoD lod = id_tensor.lod(); - std::vector expect_source_lod = {0, 4, 8}; + std::vector expect_source_lod = {0, 2, 4}; EXPECT_EQ(lod[0], expect_source_lod); - std::vector expect_sentence_lod = {0, 1, 3, 6, 9, 10, 13, 16, 19}; + std::vector expect_sentence_lod = {0, 4, 7, 12, 17}; EXPECT_EQ(lod[1], expect_sentence_lod); - // 2| 1, 0| 3, 1, 0| 3, 2, 1| 5| 4, 3, 2| 4, 4, 3| 6, 5, 4 - std::vector expect_data = {2, 1, 0, 3, 1, 0, 3, 2, 1, 5, - 4, 3, 2, 4, 4, 3, 6, 5, 4}; + std::vector expect_data = {0, 2, 3, 1, 0, 2, 1, 0, 4, + 5, 3, 5, 0, 4, 5, 3, 1}; ASSERT_EQ(id_tensor.dims()[0], static_cast(expect_data.size())); for (size_t i = 0; i < expect_data.size(); ++i) { ASSERT_EQ(id_tensor.data()[i], diff --git a/paddle/fluid/operators/beam_search_op.cc b/paddle/fluid/operators/beam_search_op.cc index 9b462ef8d0c..6d936a7142c 100644 --- a/paddle/fluid/operators/beam_search_op.cc +++ b/paddle/fluid/operators/beam_search_op.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. */ #include -#include #include #include #include @@ -110,23 +109,6 @@ void BeamSearch::PruneEndBeams(const framework::LoDTensor &pre_ids, } } -int BeamSearch::PruneEndidCandidates(const framework::LoDTensor &pre_ids, - std::vector> *items) { - auto *pre_ids_data = pre_ids.data(); - - int res = 0; - for (size_t offset = 0; offset < items->size(); offset++) { - auto prefix_id = pre_ids_data[offset]; - if (prefix_id == end_id_) { - items->at(offset).clear(); - } else { - res++; - } - } - - return res; -} - std::vector> BeamSearch::ToMap( const std::vector> &items, size_t element_num) { std::vector> result; @@ -201,8 +183,7 @@ bool BeamSearch::NextItemSet(const framework::LoDTensor &pre_ids, auto pre_score = pre_scores_data[offset]; if (pre_id == end_id_) { // Allocate all probability mass to eos_id for finished branchs and the - // other - // candidate ids can be ignored. + // other candidate ids can be ignored. items->emplace_back(offset, end_id_, pre_score); } else { for (size_t d = 0; d < instance_dim; d++) { diff --git a/paddle/fluid/operators/beam_search_op.h b/paddle/fluid/operators/beam_search_op.h index a595726f12f..b5e2ed05924 100644 --- a/paddle/fluid/operators/beam_search_op.h +++ b/paddle/fluid/operators/beam_search_op.h @@ -155,17 +155,11 @@ class BeamSearch { protected: /* * Prune the source sentences all branchs finished, and it is optional. - * Pruning must one step later than finishing, since the end tokens - * must be writed out. Also the finished branchs with top 1 score can - * be pruned. + * Pruning must one step later than finishing (thus pre_ids is needed here), + * since the end tokens must be writed out. */ void PruneEndBeams(const framework::LoDTensor& pre_ids, std::vector>* items); - /* - * Delete all the records that follows the end token. - */ - int PruneEndidCandidates(const framework::LoDTensor& pre_ids, - std::vector>* items); /* * Transform the items into a map whose key is offset, value is the items. diff --git a/paddle/fluid/operators/beam_search_op_test.cc b/paddle/fluid/operators/beam_search_op_test.cc index ec666359aa2..df30c0a54c7 100644 --- a/paddle/fluid/operators/beam_search_op_test.cc +++ b/paddle/fluid/operators/beam_search_op_test.cc @@ -30,7 +30,7 @@ using std::endl; void CreateInput(LoDTensor* ids, LoDTensor* scores) { LoD lod; - vector level0({0, 1, 4}); + vector level0({0, 2, 4}); vector level1({0, 1, 2, 3, 4}); lod.push_back(level0); lod.push_back(level1); @@ -64,17 +64,22 @@ TEST(beam_search_op, run) { for (int i = 0; i < 4; i++) { pre_ids.mutable_data(place)[i] = i + 1; } + LoDTensor pre_scores; + pre_scores.Resize(framework::make_ddim(vector(4, 1))); + for (int i = 0; i < 4; i++) { + pre_scores.mutable_data(place)[i] = 0.1; + } - BeamSearch beamsearch(ids, scores, (int64_t)0, (int64_t)2, 0); + BeamSearch beamsearch(ids, scores, (size_t)0, (size_t)2, 0); LoDTensor sids, sscores; - beamsearch(pre_ids, &sids, &sscores); + beamsearch(pre_ids, pre_scores, &sids, &sscores); LOG(INFO) << "score: " << sscores << endl; ASSERT_EQ(sids.lod(), sscores.lod()); - vector tids({2, 4, 3, 8}); - vector tscores({0.3, 0.5, 0.9, 0.7}); + vector tids({4, 2, 3, 8}); + vector tscores({0.5, 0.6, 0.9, 0.7}); for (int i = 0; i < 4; i++) { ASSERT_EQ(tids[i], sids.data()[i]); -- GitLab