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BertNLClassifier - a NLClassifier extension for bert models
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tensorflow_lite_support/cc/task/text/nlclassifier/bert_nl_classifier.cc
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| /* Copyright 2020 The TensorFlow Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
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| #include "tensorflow_lite_support/cc/task/text/nlclassifier/bert_nl_classifier.h" | ||
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| #include <stddef.h> | ||
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| #include <memory> | ||
| #include <string> | ||
| #include <utility> | ||
| #include <vector> | ||
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| #include "absl/status/status.h" | ||
| #include "absl/status/statusor.h" | ||
| #include "absl/strings/ascii.h" | ||
| #include "absl/strings/str_format.h" | ||
| #include "tensorflow/lite/c/common.h" | ||
| #include "tensorflow/lite/core/api/op_resolver.h" | ||
| #include "tensorflow/lite/string_type.h" | ||
| #include "tensorflow_lite_support/cc/common.h" | ||
| #include "tensorflow_lite_support/cc/port/status_macros.h" | ||
| #include "tensorflow_lite_support/cc/task/core/category.h" | ||
| #include "tensorflow_lite_support/cc/task/core/task_api_factory.h" | ||
| #include "tensorflow_lite_support/cc/task/core/task_utils.h" | ||
| #include "tensorflow_lite_support/cc/task/text/nlclassifier/nl_classifier.h" | ||
| #include "tensorflow_lite_support/cc/text/tokenizers/tokenizer.h" | ||
| #include "tensorflow_lite_support/cc/text/tokenizers/tokenizer_utils.h" | ||
| #include "tensorflow_lite_support/metadata/cc/metadata_extractor.h" | ||
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| namespace tflite { | ||
| namespace support { | ||
| namespace task { | ||
| namespace text { | ||
| namespace nlclassifier { | ||
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| using ::tflite::support::task::core::FindTensorByName; | ||
| using ::tflite::support::task::core::PopulateTensor; | ||
| using ::tflite::support::text::tokenizer::CreateTokenizerFromMetadata; | ||
| using ::tflite::support::text::tokenizer::TokenizerResult; | ||
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| namespace { | ||
| constexpr char kIdsTensorName[] = "ids"; | ||
| constexpr char kMaskTensorName[] = "mask"; | ||
| constexpr char kSegmentIdsTensorName[] = "segment_ids"; | ||
| constexpr char kScoreTensorName[] = "probability"; | ||
| constexpr char kClassificationToken[] = "[CLS]"; | ||
| constexpr char kSeparator[] = "[SEP]"; | ||
| } // namespace | ||
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| absl::Status BertNLClassifier::Preprocess( | ||
| const std::vector<TfLiteTensor*>& input_tensors, const std::string& input) { | ||
| auto* input_tensor_metadatas = | ||
| GetMetadataExtractor()->GetInputTensorMetadata(); | ||
| auto* ids_tensor = | ||
| FindTensorByName(input_tensors, input_tensor_metadatas, kIdsTensorName); | ||
| auto* mask_tensor = | ||
| FindTensorByName(input_tensors, input_tensor_metadatas, kMaskTensorName); | ||
| auto* segment_ids_tensor = FindTensorByName( | ||
| input_tensors, input_tensor_metadatas, kSegmentIdsTensorName); | ||
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| std::string processed_input = input; | ||
| absl::AsciiStrToLower(&processed_input); | ||
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| TokenizerResult input_tokenize_results; | ||
| input_tokenize_results = tokenizer_->Tokenize(processed_input); | ||
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| // 2 accounts for [CLS], [SEP] | ||
| absl::Span<const std::string> query_tokens = | ||
| absl::MakeSpan(input_tokenize_results.subwords.data(), | ||
| input_tokenize_results.subwords.data() + | ||
| std::min(static_cast<size_t>(kMaxSeqLen - 2), | ||
| input_tokenize_results.subwords.size())); | ||
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| std::vector<std::string> tokens; | ||
| tokens.reserve(2 + query_tokens.size()); | ||
| // Start of generating the features. | ||
| tokens.push_back(kClassificationToken); | ||
| // For query input. | ||
| for (const auto& query_token : query_tokens) { | ||
| tokens.push_back(query_token); | ||
| } | ||
| // For Separation. | ||
| tokens.push_back(kSeparator); | ||
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| std::vector<int> input_ids(kMaxSeqLen, 0); | ||
| std::vector<int> input_mask(kMaxSeqLen, 0); | ||
| // Convert tokens back into ids and set mask | ||
| for (int i = 0; i < tokens.size(); ++i) { | ||
| tokenizer_->LookupId(tokens[i], &input_ids[i]); | ||
| input_mask[i] = 1; | ||
| } | ||
| // |<-----------kMaxSeqLen---------->| | ||
| // input_ids [CLS] s1 s2... sn [SEP] 0 0... 0 | ||
| // input_masks 1 1 1... 1 1 0 0... 0 | ||
| // segment_ids 0 0 0... 0 0 0 0... 0 | ||
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| PopulateTensor(input_ids, ids_tensor); | ||
| PopulateTensor(input_mask, mask_tensor); | ||
| PopulateTensor(std::vector<int>(kMaxSeqLen, 0), segment_ids_tensor); | ||
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| return absl::OkStatus(); | ||
| } | ||
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| StatusOr<std::vector<core::Category>> BertNLClassifier::Postprocess( | ||
| const std::vector<const TfLiteTensor*>& output_tensors, | ||
| const std::string& /*input*/) { | ||
| if (output_tensors.size() != 1) { | ||
| return CreateStatusWithPayload( | ||
| absl::StatusCode::kInvalidArgument, | ||
| absl::StrFormat("BertNLClassifier models are expected to have only 1 " | ||
| "output, found %d", | ||
| output_tensors.size()), | ||
| TfLiteSupportStatus::kInvalidNumOutputTensorsError); | ||
| } | ||
| const TfLiteTensor* scores = FindTensorByName( | ||
| output_tensors, GetMetadataExtractor()->GetOutputTensorMetadata(), | ||
| kScoreTensorName); | ||
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| // optional labels extracted from metadata | ||
| return BuildResults(scores, /*labels=*/nullptr); | ||
| } | ||
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| StatusOr<std::unique_ptr<BertNLClassifier>> | ||
| BertNLClassifier::CreateBertNLClassifierWithMetadata( | ||
| const std::string& path_to_model_with_metadata, | ||
| std::unique_ptr<tflite::OpResolver> resolver) { | ||
| std::unique_ptr<BertNLClassifier> bert_nl_classifier; | ||
| ASSIGN_OR_RETURN(bert_nl_classifier, | ||
| core::TaskAPIFactory::CreateFromFile<BertNLClassifier>( | ||
| path_to_model_with_metadata, std::move(resolver))); | ||
| RETURN_IF_ERROR(bert_nl_classifier->InitializeFromMetadata()); | ||
| return std::move(bert_nl_classifier); | ||
| } | ||
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| StatusOr<std::unique_ptr<BertNLClassifier>> | ||
| BertNLClassifier::CreateBertNLClassifierWithMetadataFromBinary( | ||
| const char* model_with_metadata_buffer_data, | ||
| size_t model_with_metadata_buffer_size, | ||
| std::unique_ptr<tflite::OpResolver> resolver) { | ||
| std::unique_ptr<BertNLClassifier> bert_nl_classifier; | ||
| ASSIGN_OR_RETURN(bert_nl_classifier, | ||
| core::TaskAPIFactory::CreateFromBuffer<BertNLClassifier>( | ||
| model_with_metadata_buffer_data, | ||
| model_with_metadata_buffer_size, std::move(resolver))); | ||
| RETURN_IF_ERROR(bert_nl_classifier->InitializeFromMetadata()); | ||
| return std::move(bert_nl_classifier); | ||
| } | ||
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| absl::Status BertNLClassifier::InitializeFromMetadata() { | ||
| // Set up mandatory tokenizer. | ||
| ASSIGN_OR_RETURN(tokenizer_, | ||
| CreateTokenizerFromMetadata(*GetMetadataExtractor())); | ||
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| // Set up optional label vector. | ||
| TrySetLabelFromMetadata( | ||
| GetMetadataExtractor()->GetOutputTensorMetadata(kOutputTensorIndex)) | ||
| .IgnoreError(); | ||
| return absl::OkStatus(); | ||
| } | ||
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| } // namespace nlclassifier | ||
| } // namespace text | ||
| } // namespace task | ||
| } // namespace support | ||
| } // namespace tflite |
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tensorflow_lite_support/cc/task/text/nlclassifier/bert_nl_classifier.h
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,101 @@ | ||
| /* Copyright 2020 The TensorFlow Authors. All Rights Reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); | ||
| you may not use this file except in compliance with the License. | ||
| You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software | ||
| distributed under the License is distributed on an "AS IS" BASIS, | ||
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| See the License for the specific language governing permissions and | ||
| limitations under the License. | ||
| ==============================================================================*/ | ||
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| #ifndef TENSORFLOW_LITE_SUPPORT_CC_TASK_TEXT_NLCLASSIFIER_BERT_NL_CLASSIFIER_H_ | ||
| #define TENSORFLOW_LITE_SUPPORT_CC_TASK_TEXT_NLCLASSIFIER_BERT_NL_CLASSIFIER_H_ | ||
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| #include <stddef.h> | ||
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| #include <memory> | ||
| #include <string> | ||
| #include <vector> | ||
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| #include "absl/status/status.h" | ||
| #include "absl/status/statusor.h" | ||
| #include "tensorflow/lite/c/common.h" | ||
| #include "tensorflow/lite/core/api/op_resolver.h" | ||
| #include "tensorflow/lite/kernels/register.h" | ||
| #include "tensorflow/lite/string_type.h" | ||
| #include "tensorflow_lite_support/cc/task/core/category.h" | ||
| #include "tensorflow_lite_support/cc/task/text/nlclassifier/nl_classifier.h" | ||
| #include "tensorflow_lite_support/cc/text/tokenizers/tokenizer.h" | ||
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| namespace tflite { | ||
| namespace support { | ||
| namespace task { | ||
| namespace text { | ||
| namespace nlclassifier { | ||
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| // Classifier API for NLClassification tasks with Bert models, categorizes | ||
| // string into different classes. | ||
| // | ||
| // The API expects a Bert based TFLite model with metadata populated. | ||
| // The metadata should contain the following information: | ||
| // - input_process_units for Wordpiece/Sentencepiece Tokenizer | ||
| // - 3 input tensors with names "ids", "mask" and "segment_ids" | ||
| // - 1 output tensor of type float32[1, 2], with a optionally attached label | ||
| // file. If a label file is attached, the file should be a plain text file | ||
| // with one label per line, the number of labels should match the number of | ||
| // categories the model outputs. | ||
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| class BertNLClassifier : public NLClassifier { | ||
| public: | ||
| using NLClassifier::NLClassifier; | ||
| // Max number of tokens to pass to the model. | ||
| static constexpr int kMaxSeqLen = 128; | ||
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| // Factory function to create a BertNLClassifier from TFLite model with | ||
| // metadata. | ||
| static StatusOr<std::unique_ptr<BertNLClassifier>> | ||
| CreateBertNLClassifierWithMetadata( | ||
| const std::string& path_to_model_with_metadata, | ||
| std::unique_ptr<tflite::OpResolver> resolver = | ||
| absl::make_unique<tflite::ops::builtin::BuiltinOpResolver>()); | ||
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| // Factory function to create a BertNLClassifier from in memory buffer of a | ||
| // TFLite model with metadata. | ||
| static StatusOr<std::unique_ptr<BertNLClassifier>> | ||
| CreateBertNLClassifierWithMetadataFromBinary( | ||
| const char* model_with_metadata_buffer_data, | ||
| size_t model_with_metadata_buffer_size, | ||
| std::unique_ptr<tflite::OpResolver> resolver = | ||
| absl::make_unique<tflite::ops::builtin::BuiltinOpResolver>()); | ||
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| protected: | ||
| // Run tokenization on input text and construct three input tensors ids, mask | ||
| // and segment_ids for the model input. | ||
| absl::Status Preprocess(const std::vector<TfLiteTensor*>& input_tensors, | ||
| const std::string& input) override; | ||
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| // Extract model output and create results with label file attached in | ||
| // metadata. If no label file is attached, use output score index as labels. | ||
| StatusOr<std::vector<core::Category>> Postprocess( | ||
| const std::vector<const TfLiteTensor*>& output_tensors, | ||
| const std::string& input) override; | ||
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| private: | ||
| // Initialize the API with the tokenizer and label files set in the metadata. | ||
| absl::Status InitializeFromMetadata(); | ||
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| std::unique_ptr<tflite::support::text::tokenizer::Tokenizer> tokenizer_; | ||
| }; | ||
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| } // namespace nlclassifier | ||
| } // namespace text | ||
| } // namespace task | ||
| } // namespace support | ||
| } // namespace tflite | ||
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| #endif // TENSORFLOW_LITE_SUPPORT_CC_TASK_TEXT_NLCLASSIFIER_BERT_NL_CLASSIFIER_H_ |
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