# 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. """extract embeddings from ERNIE encoder.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import argparse import numpy as np import multiprocessing import paddle.fluid as fluid import reader.task_reader as task_reader from model.ernie import ErnieConfig, ErnieModel from utils.args import ArgumentGroup, print_arguments from utils.init import init_pretraining_params # yapf: disable parser = argparse.ArgumentParser(__doc__) model_g = 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_pretraining_params", str, None, "Init pre-training params which preforms fine-tuning from. If the " "arg 'init_checkpoint' has been set, this argument wouldn't be valid.") model_g.add_arg("output_dir", str, "embeddings", "path to save embeddings extracted by ernie_encoder.") data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options") data_g.add_arg("data_set", str, None, "Path to data for calculating ernie_embeddings.") data_g.add_arg("vocab_path", str, None, "Vocabulary path.") data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.") data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training.") 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 = ArgumentGroup(parser, "run_type", "running type options.") run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.") # yapf: enable def create_model(args, pyreader_name, ernie_config): pyreader = fluid.layers.py_reader( capacity=50, shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, 1]], dtypes=['int64', 'int64', 'int64', 'int64', 'float', 'int64'], lod_levels=[0, 0, 0, 0, 0, 0], name=pyreader_name, use_double_buffer=True) (src_ids, sent_ids, pos_ids, task_ids, input_mask, seq_lens) = fluid.layers.read_file(pyreader) ernie = ErnieModel( src_ids=src_ids, position_ids=pos_ids, sentence_ids=sent_ids, task_ids=task_ids, input_mask=input_mask, config=ernie_config) enc_out = ernie.get_sequence_output() unpad_enc_out = fluid.layers.sequence_unpad(enc_out, length=seq_lens) cls_feats = ernie.get_pooled_output() # set persistable = True to avoid memory opimizing enc_out.persistable = True unpad_enc_out.persistable = True cls_feats.persistable = True graph_vars = { "cls_embeddings": cls_feats, "top_layer_embeddings": unpad_enc_out, } return pyreader, graph_vars def main(args): args = parser.parse_args() ernie_config = 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.ExtractEmbeddingReader( vocab_path=args.vocab_path, max_seq_len=args.max_seq_len, do_lower_case=args.do_lower_case) startup_prog = fluid.Program() data_generator = reader.data_generator( input_file=args.data_set, batch_size=args.batch_size, epoch=1, shuffle=False) total_examples = reader.get_num_examples(args.data_set) print("Device count: %d" % dev_count) print("Total num examples: %d" % total_examples) infer_program = fluid.Program() with fluid.program_guard(infer_program, startup_prog): with fluid.unique_name.guard(): pyreader, graph_vars = create_model( args, pyreader_name='reader', ernie_config=ernie_config) infer_program = infer_program.clone(for_test=True) exe.run(startup_prog) if args.init_pretraining_params: init_pretraining_params( exe, args.init_pretraining_params, main_program=startup_prog) else: raise ValueError( "WARNING: args 'init_pretraining_params' must be specified") exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_threads = dev_count pyreader.decorate_tensor_provider(data_generator) pyreader.start() total_cls_emb = [] total_top_layer_emb = [] total_labels = [] while True: try: cls_emb, unpad_top_layer_emb = exe.run( program=infer_program, fetch_list=[ graph_vars["cls_embeddings"].name, graph_vars["top_layer_embeddings"].name ], return_numpy=False) # batch_size * embedding_size total_cls_emb.append(np.array(cls_emb)) total_top_layer_emb.append(np.array(unpad_top_layer_emb)) except fluid.core.EOFException: break total_cls_emb = np.concatenate(total_cls_emb) total_top_layer_emb = np.concatenate(total_top_layer_emb) with open(os.path.join(args.output_dir, "cls_emb.npy"), "w") as cls_emb_file: np.save(cls_emb_file, total_cls_emb) with open(os.path.join(args.output_dir, "top_layer_emb.npy"), "w") as top_layer_emb_file: np.save(top_layer_emb_file, total_top_layer_emb) if __name__ == '__main__': args = parser.parse_args() print_arguments(args) main(args)