[GitHub] szha commented on a change in pull request #13680: [MXNET-1121] Example to demonstrate the inference workflow using RNN

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szha commented on a change in pull request #13680: [MXNET-1121] Example to demonstrate the inference workflow using RNN
URL: https://github.com/apache/incubator-mxnet/pull/13680#discussion_r254402392
 
 

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 File path: cpp-package/example/inference/sentiment_analysis_rnn.cpp
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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*
+ * This example demonstrates sentiment prediction workflow with pre-trained RNN model using MXNet C++ API.
+ * The example performs following tasks.
+ * 1. Load the pre-trained RNN model,
+ * 2. Load the dictionary file that contains word to index mapping.
+ * 3. Create executors for pre-determined input lengths.
+ * 4. Convert each line in the input to the vector of indices.
+ * 5. Predictor finds the right executor for each line.
+ * 4. Run the forward pass for each line and predicts the sentiment scores.
+ * The example uses a pre-trained RNN model that is trained with the IMDB dataset.
+ */
+
+#include <sys/stat.h>
+#include <iostream>
+#include <fstream>
+#include <cstdlib>
+#include <map>
+#include <string>
+#include <algorithm>
+#include <vector>
+#include <sstream>
+#include "mxnet-cpp/MxNetCpp.h"
+
+using namespace mxnet::cpp;
+
+static const int DEFAULT_BUCKET_KEYS[] = {30, 25, 20, 15, 10, 5};
+static const char DEFAULT_S3_URL[] = "https://s3.amazonaws.com/mxnet-cpp/RNN_model/";
+
+
+/*
+ * class Predictor
+ *
+ * This class encapsulates the functionality to load the model, process input image and run the forward pass.
+ */
+
+class Predictor {
+ public:
+    Predictor() {}
+    Predictor(const std::string& model_json,
+              const std::string& model_params,
+              const std::string& input_dictionary,
+              const std::vector<int>& bucket_keys,
+              bool use_gpu = false);
+    float PredictSentiment(const std::string &input_review);
+    ~Predictor();
+
+ private:
+    void LoadModel(const std::string& model_json_file);
+    void LoadParameters(const std::string& model_parameters_file);
+    void LoadDictionary(const std::string &input_dictionary);
+    inline bool FileExists(const std::string& name) {
+        struct stat buffer;
+        return (stat(name.c_str(), &buffer) == 0);
+    }
+    float PredictSentimentForOneLine(const std::string &input_line);
+    int ConvertToIndexVector(const std::string& input,
+                      std::vector<float> *input_vector);
+    int GetIndexForOutputSymbolName(const std::string& output_symbol_name);
+    float GetIndexForWord(const std::string& word);
+    int GetClosestBucketKey(int num_words);
+
+    std::map<std::string, NDArray> args_map;
+    std::map<std::string, NDArray> aux_map;
+    std::map<std::string, int>  wordToIndex;
+    Symbol net;
+    std::map<int, Executor*> executor_buckets;
+    Context global_ctx = Context::cpu();
+};
+
+
+/*
+ * The constructor takes the following parameters as input:
+ * 1. model_json:  The RNN model in json formatted file.
+ * 2. model_params: File containing model parameters
+ * 3. input_dictionary: File containing the word and associated index.
+ * 4. bucket_keys: A vector of bucket keys for creating executors.
+ *
+ * The constructor:
+ *  1. Loads the model and parameter files.
+ *  2. Loads the dictionary file to create index to word and word to index maps.
+ *  3. For each bucket key in the input vector of bucket keys, it creates an executor.
+ *     The executors share the memory. The bucket key determines the length of input data
+ *     required for that executor.
+ *  4. Creates a map of bucket key to corresponding executor.
+ *  5. The model is loaded only once. The executors share the memory for the parameters.
+ */
+Predictor::Predictor(const std::string& model_json,
+                     const std::string& model_params,
+                     const std::string& input_dictionary,
+                     const std::vector<int>& bucket_keys,
+                     bool use_gpu) {
+  if (use_gpu) {
+    global_ctx = Context::gpu();
+  }
+
+  /*
+   * Load the dictionary file that contains the word and its index.
+   * The function creates word to index and index to word map. The maps are used to create index
+   * vector for the input sentence.
+   */
+  LoadDictionary(input_dictionary);
+
+  // Load the model
+  LoadModel(model_json);
+
+  // Load the model parameters.
+  LoadParameters(model_params);
+
+  /*
+   * Create the executors for each bucket key. The bucket key represents the shape of input data.
+   * The executors will share the memory by using following technique:
+   * 1. Infer the executor arrays and bind the first executor with the first bucket key.
+   * 2. Then for creating the next bucket key, adjust the shape of input argument to match that key.
+   * 3. Create the executor for the next bucket key by passing the inferred executor arrays and
+   *    pointer to the executor created for the first key.
+   */
+  std::vector<NDArray> arg_arrays;
+  std::vector<NDArray> grad_arrays;
+  std::vector<OpReqType> grad_reqs;
+  std::vector<NDArray> aux_arrays;
+
+  /*
+   * Create master executor with highest bucket key for optimizing the shared memory between the
+   * executors for the remaining bucket keys.
+   */
+  int highest_bucket_key = *(std::max_element(bucket_keys.begin(), bucket_keys.end()));
+  args_map["data0"] = NDArray(Shape(highest_bucket_key, 1), global_ctx, false);
+  args_map["data1"] = NDArray(Shape(1), global_ctx, false);
+
+  net.InferExecutorArrays(global_ctx, &arg_arrays, &grad_arrays, &grad_reqs,
+                          &aux_arrays, args_map, std::map<std::string, NDArray>(),
+                              std::map<std::string, OpReqType>(), aux_map);
+  Executor *master_executor = net.Bind(global_ctx, arg_arrays, grad_arrays, grad_reqs, aux_arrays,
+                                 std::map<std::string, Context>(), nullptr);
+  executor_buckets[highest_bucket_key] = master_executor;
+
+  for (int bucket : bucket_keys) {
+    if (executor_buckets.find(bucket) == executor_buckets.end()) {
+      arg_arrays[0]  = NDArray(Shape(bucket, 1), global_ctx, false);
+      Executor *executor = net.Bind(global_ctx, arg_arrays, grad_arrays, grad_reqs, aux_arrays,
+                                    std::map<std::string, Context>(), master_executor);
+      executor_buckets[bucket] = executor;
+    }
+  }
+}
+
+
+/*
+ * The following function loads the model from json file.
+ */
+void Predictor::LoadModel(const std::string& model_json_file) {
+  if (!FileExists(model_json_file)) {
+    LG << "Model file " << model_json_file << " does not exist";
+    throw std::runtime_error("Model file does not exist");
+  }
+  LG << "Loading the model from " << model_json_file << std::endl;
+  net = Symbol::Load(model_json_file);
+}
+
+
+/*
+ * The following function loads the model parameters.
+ */
+void Predictor::LoadParameters(const std::string& model_parameters_file) {
+  if (!FileExists(model_parameters_file)) {
+    LG << "Parameter file " << model_parameters_file << " does not exist";
+    throw std::runtime_error("Model parameters does not exist");
+  }
+  LG << "Loading the model parameters from " << model_parameters_file << std::endl;
+  std::map<std::string, NDArray> parameters;
+  NDArray::Load(model_parameters_file, 0, &parameters);
+  for (const auto &k : parameters) {
+    if (k.first.substr(0, 4) == "aux:") {
+      auto name = k.first.substr(4, k.first.size() - 4);
+      aux_map[name] = k.second.Copy(global_ctx);
+    }
+    if (k.first.substr(0, 4) == "arg:") {
+      auto name = k.first.substr(4, k.first.size() - 4);
+      args_map[name] = k.second.Copy(global_ctx);
+    }
+  }
+  /*WaitAll is need when we copy data between GPU and the main memory*/
+  NDArray::WaitAll();
+}
+
+
+/*
+ * The following function loads the dictionary file.
+ * The function constructs the word to index and index to word maps.
+ * These maps will be used to represent words in the input sentence to their indices.
+ * Ensure to use the same dictionary file that was used for training the network.
+ */
+void Predictor::LoadDictionary(const std::string& input_dictionary) {
+  if (!FileExists(input_dictionary)) {
+    LG << "Dictionary file " << input_dictionary << " does not exist";
+    throw std::runtime_error("Dictionary file does not exist");
+  }
+  LG << "Loading the dictionary file.";
+  std::ifstream fi(input_dictionary.c_str());
+  if (!fi.is_open()) {
+    std::cerr << "Error opening dictionary file " << input_dictionary << std::endl;
+    assert(false);
+  }
+
+  std::string line;
+  std::string word;
+  int index;
+  while (std::getline(fi, line)) {
+    std::istringstream stringline(line);
+    stringline >> word >> index;
+    wordToIndex[word] = index;
+  }
+  fi.close();
+}
+
+
+/*
+ * The function returns the index associated with the word in the dictionary.
+ * If the word is not present, the index representing "<unk>" is returned.
+ * If the "<unk>" is not present then 0 is returned.
+ */
+float Predictor::GetIndexForWord(const std::string& word) {
+  if (wordToIndex.find(word) == wordToIndex.end()) {
+    if (wordToIndex.find("<unk>") == wordToIndex.end())
+      return 0;
+    else
+      return static_cast<float>(wordToIndex["<unk>"]);
+  }
+  return static_cast<float>(wordToIndex[word]);
+}
+
+/*
+ * The function populates the input vector with indices from the dictionary that
+ * correspond to the words in the input string.
+ * The function returns the number of words in the input line.
+ */
+int Predictor::ConvertToIndexVector(const std::string& input, std::vector<float> *input_vector) {
+  std::istringstream input_string(input);
+  input_vector->clear();
+  const char delimiter = ' ';
+  std::string token;
+  size_t words = 0;
+  while (std::getline(input_string, token, delimiter) && (words <= input_vector->size())) {
+    input_vector->push_back(GetIndexForWord(token));
+    words++;
+  }
+  return words;
+}
+
+
+/*
+ * The function returns the index at which the given symbol name will appear
+ * in the output vector of NDArrays obtained after running the forward pass on the executor.
+ */
+int Predictor::GetIndexForOutputSymbolName(const std::string& output_symbol_name) {
+  int index = 0;
+  for (const std::string op : net.ListOutputs()) {
+    if (op == output_symbol_name) {
+      return index;
+    } else {
+      index++;
+    }
+  }
+  throw std::runtime_error("The output symbol name can not be found");
+}
+
+
+/*
+ * The function finds the closest bucket for the given num_words in the input line.
+ * If the exact bucket key exists, function returns that bucket key.
+ * If the matching bucket key does not exist, function looks for the next bucket key
+ * that is greater than given num_words.
+ * If the next larger bucket does not exist, function looks for the previous bucket key
+ * that is lesser than given num_words.
+ */
+int Predictor::GetClosestBucketKey(int num_words) {
 
 Review comment:
   it seems that you can simply use lower_bound and return the largest key if not found.

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