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ernie_encoder.py
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ernie_encoder.py
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# 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, 1]],
dtypes=['int64', 'int64', 'int64', 'float', 'int64'],
lod_levels=[0, 0, 0, 0, 0],
name=pyreader_name,
use_double_buffer=True)
(src_ids, sent_ids, pos_ids, input_mask,
seq_lens) = fluid.layers.read_file(pyreader)
ernie = ErnieModel(
src_ids=src_ids,
position_ids=pos_ids,
sentence_ids=sent_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)
fluid.memory_optimize(input_program=infer_program)
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)