# Copyright (c) 2019 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. """ Emotion Detection Task, based on ERNIE """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import argparse import multiprocessing import sys sys.path.append("../") import paddle import paddle.fluid as fluid import numpy as np from preprocess.ernie import task_reader from models.representation import ernie from models.model_check import check_cuda import utils import config # yapf: disable parser = argparse.ArgumentParser(__doc__) model_g = config.ArgumentGroup(parser, "model", "model configuration and paths.") model_g.add_arg("ernie_config_path", str, None, "Path to the json file for ernie model config.") model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.") model_g.add_arg("save_checkpoint_dir", str, "checkpoints", "Path to save checkpoints") model_g.add_arg("use_paddle_hub", bool, False, "Whether to load ERNIE using PaddleHub") train_g = config.ArgumentGroup(parser, "training", "training options.") train_g.add_arg("epoch", int, 10, "Number of epoches for training.") train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.") train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.") train_g.add_arg("lr", float, 0.002, "The Learning rate value for training.") log_g = config.ArgumentGroup(parser, "logging", "logging related") log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.") log_g.add_arg("verbose", bool, False, "Whether to output verbose log") data_g = config.ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options") data_g.add_arg("data_dir", str, None, "Directory path to training data.") data_g.add_arg("vocab_path", str, None, "Vocabulary path.") data_g.add_arg("batch_size", int, 256, "Total examples' number in batch for training.") data_g.add_arg("random_seed", int, 0, "Random seed.") data_g.add_arg("num_labels", int, 3, "label number") data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.") data_g.add_arg("train_set", str, None, "Path to training data.") data_g.add_arg("test_set", str, None, "Path to test data.") data_g.add_arg("dev_set", str, None, "Path to validation data.") data_g.add_arg("infer_set", str, None, "Path to validation data.") data_g.add_arg("label_map_config", str, None, "label_map_path.") data_g.add_arg("do_lower_case", bool, True, "Whether to lower case the input text. Should be True for uncased models and False for cased models.") run_type_g = config.ArgumentGroup(parser, "run_type", "running type options.") run_type_g.add_arg("use_cuda", bool, False, "If set, use GPU for training.") run_type_g.add_arg("task_name", str, None, "The name of task to perform sentiment classification.") run_type_g.add_arg("do_train", bool, False, "Whether to perform training.") run_type_g.add_arg("do_val", bool, False, "Whether to perform evaluation.") run_type_g.add_arg("do_infer", bool, False, "Whether to perform inference.") args = parser.parse_args() # yapf: enable. def create_model(args, embeddings, labels, is_prediction=False): """ Create Model for sentiment classification based on ERNIE encoder """ sentence_embeddings = embeddings["sentence_embeddings"] token_embeddings = embeddings["token_embeddings"] cls_feats = fluid.layers.dropout( x=sentence_embeddings, dropout_prob=0.1, dropout_implementation="upscale_in_train") logits = fluid.layers.fc( input=cls_feats, size=args.num_labels, param_attr=fluid.ParamAttr( name="cls_out_w", initializer=fluid.initializer.TruncatedNormal(scale=0.02)), bias_attr=fluid.ParamAttr( name="cls_out_b", initializer=fluid.initializer.Constant(0.))) ce_loss, probs = fluid.layers.softmax_with_cross_entropy( logits=logits, label=labels, return_softmax=True) if is_prediction: return probs loss = fluid.layers.mean(x=ce_loss) num_seqs = fluid.layers.create_tensor(dtype='int64') accuracy = fluid.layers.accuracy(input=probs, label=labels, total=num_seqs) return loss, accuracy, num_seqs def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase): """ Evaluation Function """ test_pyreader.start() total_cost, total_acc, total_num_seqs = [], [], [] time_begin = time.time() while True: try: np_loss, np_acc, np_num_seqs = exe.run(program=test_program, fetch_list=fetch_list, return_numpy=False) np_loss = np.array(np_loss) np_acc = np.array(np_acc) np_num_seqs = np.array(np_num_seqs) total_cost.extend(np_loss * np_num_seqs) total_acc.extend(np_acc * np_num_seqs) total_num_seqs.extend(np_num_seqs) except fluid.core.EOFException: test_pyreader.reset() break time_end = time.time() print("[%s evaluation] avg loss: %f, ave acc: %f, elapsed time: %f s" % (eval_phase, np.sum(total_cost) / np.sum(total_num_seqs), np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin)) def infer(exe, infer_program, infer_pyreader, fetch_list, infer_phase): """Infer""" infer_pyreader.start() time_begin = time.time() while True: try: batch_probs = exe.run(program=infer_program, fetch_list=fetch_list, return_numpy=True) for probs in batch_probs[0]: print("%d\t%f\t%f\t%f" % (np.argmax(probs), probs[0], probs[1], probs[2])) except fluid.core.EOFException: infer_pyreader.reset() break time_end = time.time() print("[%s] elapsed time: %f s" % (infer_phase, time_end - time_begin)) def main(args): """ Main Function """ args = parser.parse_args() ernie_config = ernie.ErnieConfig(args.ernie_config_path) ernie_config.print_config() if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0'))) dev_count = fluid.core.get_cuda_device_count() else: place = fluid.CPUPlace() dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count())) exe = fluid.Executor(place) reader = task_reader.ClassifyReader( vocab_path=args.vocab_path, label_map_config=args.label_map_config, max_seq_len=args.max_seq_len, do_lower_case=args.do_lower_case, random_seed=args.random_seed) if not (args.do_train or args.do_val or args.do_infer): raise ValueError("For args `do_train`, `do_val` and `do_infer`, at " "least one of them must be True.") startup_prog = fluid.Program() if args.random_seed is not None: startup_prog.random_seed = args.random_seed if args.do_train: train_data_generator = reader.data_generator( input_file=args.train_set, batch_size=args.batch_size, epoch=args.epoch, shuffle=True, phase="train") num_train_examples = reader.get_num_examples(args.train_set) max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count + 1 print("Device count: %d" % dev_count) print("Num train examples: %d" % num_train_examples) print("Max train steps: %d" % max_train_steps) train_program = fluid.Program() with fluid.program_guard(train_program, startup_prog): with fluid.unique_name.guard(): # create ernie_pyreader train_pyreader, ernie_inputs, labels = ernie.ernie_pyreader( args, pyreader_name='train_reader') # get ernie_embeddings if args.use_paddle_hub: embeddings = ernie.ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len) else: embeddings = ernie.ernie_encoder(ernie_inputs, ernie_config=ernie_config) # user defined model based on ernie embeddings loss, accuracy, num_seqs = create_model( args, embeddings, labels=labels, is_prediction=False) """ sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.lr) sgd_optimizer.minimize(loss) """ optimizer = fluid.optimizer.Adam(learning_rate=args.lr) optimizer.minimize(loss) if args.verbose: lower_mem, upper_mem, unit = fluid.contrib.memory_usage( program=train_program, batch_size=args.batch_size) print("Theoretical memory usage in training: %.3f - %.3f %s" % (lower_mem, upper_mem, unit)) if args.do_val: test_prog = fluid.Program() with fluid.program_guard(test_prog, startup_prog): with fluid.unique_name.guard(): # create ernie_pyreader test_pyreader, ernie_inputs, labels = ernie.ernie_pyreader( args, pyreader_name='eval_reader') # get ernie_embeddings if args.use_paddle_hub: embeddings = ernie.ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len) else: embeddings = ernie.ernie_encoder(ernie_inputs, ernie_config=ernie_config) # user defined model based on ernie embeddings loss, accuracy, num_seqs = create_model( args, embeddings, labels=labels, is_prediction=False) test_prog = test_prog.clone(for_test=True) if args.do_infer: test_prog = fluid.Program() with fluid.program_guard(test_prog, startup_prog): with fluid.unique_name.guard(): infer_pyreader, ernie_inputs, labels = ernie.ernie_pyreader( args, pyreader_name='infer_reader') # get ernie_embeddings if args.use_paddle_hub: embeddings = ernie.ernie_encoder_with_paddle_hub(ernie_inputs, args.max_seq_len) else: embeddings = ernie.ernie_encoder(ernie_inputs, ernie_config=ernie_config) probs = create_model(args, embeddings, labels=labels, is_prediction=True) test_prog = test_prog.clone(for_test=True) exe.run(startup_prog) if args.do_train: if args.init_checkpoint: utils.init_checkpoint( exe, args.init_checkpoint, main_program=train_program) elif args.do_val or args.do_infer: if not args.init_checkpoint: raise ValueError("args 'init_checkpoint' should be set if" "only doing validation or infer!") utils.init_checkpoint( exe, args.init_checkpoint, main_program=test_prog) if args.do_train: train_exe = exe train_pyreader.set_batch_generator(train_data_generator) else: train_exe = None if args.do_val or args.do_infer: test_exe = exe if args.do_train: train_pyreader.start() steps = 0 total_cost, total_acc, total_num_seqs = [], [], [] time_begin = time.time() while True: try: steps += 1 if steps % args.skip_steps == 0: fetch_list = [loss.name, accuracy.name, num_seqs.name] else: fetch_list = [] outputs = train_exe.run(program=train_program, fetch_list=fetch_list, return_numpy=False) if steps % args.skip_steps == 0: np_loss, np_acc, np_num_seqs = outputs np_loss = np.array(np_loss) np_acc = np.array(np_acc) np_num_seqs = np.array(np_num_seqs) total_cost.extend(np_loss * np_num_seqs) total_acc.extend(np_acc * np_num_seqs) total_num_seqs.extend(np_num_seqs) if args.verbose: verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size() print(verbose) time_end = time.time() used_time = time_end - time_begin print("step: %d, avg loss: %f, " "avg acc: %f, speed: %f steps/s" % (steps, np.sum(total_cost) / np.sum(total_num_seqs), np.sum(total_acc) / np.sum(total_num_seqs), args.skip_steps / used_time)) total_cost, total_acc, total_num_seqs = [], [], [] time_begin = time.time() if steps % args.save_steps == 0: save_path = os.path.join(args.save_checkpoint_dir, "step_" + str(steps)) fluid.save(train_program, save_path) if steps % args.validation_steps == 0: # evaluate dev set if args.do_val: test_pyreader.set_batch_generator( reader.data_generator( input_file=args.dev_set, batch_size=args.batch_size, phase='dev', epoch=1, shuffle=False)) evaluate(exe, test_prog, test_pyreader, [loss.name, accuracy.name, num_seqs.name], "dev") except fluid.core.EOFException: save_path = os.path.join(args.save_checkpoint_dir, "step_" + str(steps)) fluid.save(train_program, save_path) train_pyreader.reset() break # eval on test set if not args.do_train and args.do_val: test_pyreader.set_batch_generator( reader.data_generator( input_file=args.test_set, batch_size=args.batch_size, phase='test', epoch=1, shuffle=False)) print("Final validation result:") evaluate(exe, test_prog, test_pyreader, [loss.name, accuracy.name, num_seqs.name], "test") # infer on infer set if args.do_infer: infer_pyreader.set_batch_generator( reader.data_generator( input_file=args.infer_set, batch_size=args.batch_size, phase='infer', epoch=1, shuffle=False)) print("Final test result:") infer(exe, test_prog, infer_pyreader, [probs.name], "infer") if __name__ == "__main__": utils.print_arguments(args) check_cuda(args.use_cuda) main(args)