Add ernie ie task (PaddlePaddle#100)

FastDeploy.cmake.in

@@ -95,6 +95,10 @@ endif()
 
 if (ENABLE_TEXT)
 # Add dependency libs later
+  find_library(FASTER_TOKENIZER_LIB core_tokenizers ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/faster_tokenizer/lib NO_DEFAULT_PATH)
+  list(APPEND FASTDEPLOY_LIBS ${FASTER_TOKENIZER_LIB})
+  list(APPEND FASTDEPLOY_INCS ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/faster_tokenizer/include)
+  list(APPEND FASTDEPLOY_INCS ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/faster_tokenizer/third_party/include)
 endif()
 
 if(ENABLE_PADDLE_FRONTEND)

csrc/fastdeploy/core/config.h.in

@@ -45,10 +45,18 @@
 #cmakedefine ENABLE_VISION
 #endif
 
+#ifndef ENABLE_TEXT
+#cmakedefine ENABLE_TEXT
+#endif
+
 #ifndef ENABLE_OPENCV_CUDA
 #cmakedefine ENABLE_OPENCV_CUDA
 #endif
 
 #ifndef ENABLE_VISION_VISUALIZE
 #cmakedefine ENABLE_VISION_VISUALIZE
 #endif
+
+#ifndef ENABLE_FDTENSOR_FUNC
+#cmakedefine ENABLE_FDTENSOR_FUNC
+#endif

examples/text/information_extraction/ernie/cpp/CMakeLists.txt

@@ -0,0 +1,25 @@
+# Copyright (c) 2022 PaddlePaddle 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.
+
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
+
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_ernie_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+target_link_libraries(infer_ernie_demo ${FASTDEPLOY_LIBS})

examples/text/information_extraction/ernie/cpp/infer.cc

@@ -0,0 +1,182 @@
+// Copyright (c) 2022 PaddlePaddle 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.
+#include <iostream>
+#include <sstream>
+
+#include "fastdeploy/function/reduce.h"
+#include "fastdeploy/function/softmax.h"
+#include "fastdeploy/text.h"
+#include "tokenizers/ernie_faster_tokenizer.h"
+
+using namespace paddlenlp;
+
+void LoadTransitionFromFile(const std::string& file,
+                            std::vector<float>* transitions, int* num_tags) {
+  std::ifstream fin(file);
+  std::string curr_transition;
+  float transition;
+  int i = 0;
+  while (fin) {
+    std::getline(fin, curr_transition);
+    std::istringstream iss(curr_transition);
+    while (iss) {
+      iss >> transition;
+      transitions->push_back(transition);
+    }
+    if (curr_transition != "") {
+      ++i;
+    }
+  }
+  *num_tags = i;
+}
+
+template <typename T>
+void ViterbiDecode(const fastdeploy::FDTensor& slot_logits,
+                   const fastdeploy::FDTensor& trans,
+                   fastdeploy::FDTensor* best_path) {
+  int batch_size = slot_logits.shape[0];
+  int seq_len = slot_logits.shape[1];
+  int num_tags = slot_logits.shape[2];
+  best_path->Allocate({batch_size, seq_len}, fastdeploy::FDDataType::INT64);
+
+  const T* slot_logits_ptr = reinterpret_cast<const T*>(slot_logits.Data());
+  const T* trans_ptr = reinterpret_cast<const T*>(trans.Data());
+  int64_t* best_path_ptr = reinterpret_cast<int64_t*>(best_path->Data());
+  std::vector<T> scores(num_tags);
+  std::copy(slot_logits_ptr, slot_logits_ptr + num_tags, scores.begin());
+  std::vector<std::vector<T>> M(num_tags, std::vector<T>(num_tags));
+  for (int b = 0; b < batch_size; ++b) {
+    std::vector<std::vector<int>> paths;
+    const T* curr_slot_logits_ptr = slot_logits_ptr + b * seq_len * num_tags;
+    int64_t* curr_best_path_ptr = best_path_ptr + b * seq_len;
+    for (int t = 1; t < seq_len; t++) {
+      for (size_t i = 0; i < num_tags; i++) {
+        for (size_t j = 0; j < num_tags; j++) {
+          auto trans_idx = i * num_tags * num_tags + j * num_tags;
+          auto slot_logit_idx = t * num_tags + j;
+          M[i][j] = scores[i] + trans_ptr[trans_idx] +
+                    curr_slot_logits_ptr[slot_logit_idx];
+        }
+      }
+      std::vector<int> idxs;
+      for (size_t i = 0; i < num_tags; i++) {
+        T max = 0.0f;
+        int idx = 0;
+        for (size_t j = 0; j < num_tags; j++) {
+          if (M[j][i] > max) {
+            max = M[j][i];
+            idx = j;
+          }
+        }
+        scores[i] = max;
+        idxs.push_back(idx);
+      }
+      paths.push_back(idxs);
+    }
+    int scores_max_index = 0;
+    float scores_max = 0.0f;
+    for (size_t i = 0; i < scores.size(); i++) {
+      if (scores[i] > scores_max) {
+        scores_max = scores[i];
+        scores_max_index = i;
+      }
+    }
+    curr_best_path_ptr[seq_len - 1] = scores_max_index;
+    for (int i = seq_len - 2; i >= 0; i--) {
+      int index = curr_best_path_ptr[i + 1];
+      curr_best_path_ptr[i] = paths[i][index];
+    }
+  }
+}
+
+int main() {
+  // 1. Define a ernie faster tokenizer
+  faster_tokenizer::tokenizers_impl::ErnieFasterTokenizer tokenizer(
+      "ernie_vocab.txt");
+  std::vector<faster_tokenizer::core::EncodeInput> strings_list = {
+      "导航去科技园二号楼", "屏幕亮度为我减小一点吧"};
+  std::vector<faster_tokenizer::core::Encoding> encodings;
+  tokenizer.EncodeBatchStrings(strings_list, &encodings);
+  size_t batch_size = strings_list.size();
+  size_t seq_len = encodings[0].GetLen();
+  for (auto&& encoding : encodings) {
+    std::cout << encoding.DebugString() << std::endl;
+  }
+  // 2. Initialize runtime
+  fastdeploy::RuntimeOption runtime_option;
+  runtime_option.SetModelPath("nano_static/model.pdmodel",
+                              "nano_static/model.pdiparams");
+  fastdeploy::Runtime runtime;
+  runtime.Init(runtime_option);
+
+  // 3. Construct input vector
+  // 3.1 Convert encodings to input_ids, token_type_ids
+  std::vector<int64_t> input_ids, token_type_ids;
+  for (int i = 0; i < encodings.size(); ++i) {
+    auto&& curr_input_ids = encodings[i].GetIds();
+    auto&& curr_type_ids = encodings[i].GetTypeIds();
+    input_ids.insert(input_ids.end(), curr_input_ids.begin(),
+                     curr_input_ids.end());
+    token_type_ids.insert(token_type_ids.end(), curr_type_ids.begin(),
+                          curr_type_ids.end());
+  }
+  // 3.2 Set data to input vector
+  std::vector<fastdeploy::FDTensor> inputs(runtime.NumInputs());
+  void* inputs_ptrs[] = {input_ids.data(), token_type_ids.data()};
+  for (int i = 0; i < runtime.NumInputs(); ++i) {
+    inputs[i].SetExternalData({batch_size, seq_len},
+                              fastdeploy::FDDataType::INT64, inputs_ptrs[i]);
+    inputs[i].name = runtime.GetInputInfo(i).name;
+  }
+
+  // 4. Infer
+  std::vector<fastdeploy::FDTensor> outputs(runtime.NumOutputs());
+  runtime.Infer(inputs, &outputs);
+
+  // 5. Postprocess
+  fastdeploy::FDTensor domain_probs, intent_probs;
+  fastdeploy::Softmax(outputs[0], &domain_probs);
+  fastdeploy::Softmax(outputs[1], &intent_probs);
+
+  fastdeploy::FDTensor domain_max_probs, intent_max_probs;
+  fastdeploy::Max(domain_probs, &domain_max_probs, {-1}, true);
+  fastdeploy::Max(intent_probs, &intent_max_probs, {-1}, true);
+
+  std::vector<float> transition;
+  int num_tags;
+  LoadTransitionFromFile("joint_transition.txt", &transition, &num_tags);
+  fastdeploy::FDTensor trans;
+  trans.SetExternalData({num_tags, num_tags}, fastdeploy::FDDataType::FP32,
+                        transition.data());
+
+  fastdeploy::FDTensor best_path;
+  ViterbiDecode<float>(outputs[2], trans, &best_path);
+  // 6. Print result
+  domain_max_probs.PrintInfo();
+  intent_max_probs.PrintInfo();
+
+  batch_size = best_path.shape[0];
+  seq_len = best_path.shape[1];
+  const int64_t* best_path_ptr =
+      reinterpret_cast<const int64_t*>(best_path.Data());
+  for (int i = 0; i < batch_size; ++i) {
+    std::cout << "best_path[" << i << "] = ";
+    for (int j = 0; j < seq_len; ++j) {
+      std::cout << best_path_ptr[i * seq_len + j] << ", ";
+    }
+    std::cout << std::endl;
+  }
+  best_path.PrintInfo();
+  return 0;
+}