mirror of https://github.com/hpcaitech/ColossalAI
260 lines
9.6 KiB
Python
260 lines
9.6 KiB
Python
import argparse
|
||
import multiprocessing
|
||
import os
|
||
import time
|
||
from random import shuffle
|
||
|
||
import h5py
|
||
import numpy as np
|
||
import psutil
|
||
from get_mask import PreTrainingDataset
|
||
from tqdm import tqdm
|
||
from transformers import AutoTokenizer
|
||
|
||
|
||
def get_raw_instance(document, max_sequence_length=512):
|
||
"""
|
||
Get the initial training instances, split the whole segment into multiple parts according to the max_sequence_length, and return as multiple processed instances.
|
||
:param document: document
|
||
:param max_sequence_length:
|
||
:return: a list. each element is a sequence of text
|
||
"""
|
||
# document = self.documents[index]
|
||
max_sequence_length_allowed = max_sequence_length - 2
|
||
# document = [seq for seq in document if len(seq)<max_sequence_length_allowed]
|
||
sizes = [len(seq) for seq in document]
|
||
|
||
result_list = []
|
||
curr_seq = []
|
||
sz_idx = 0
|
||
while sz_idx < len(sizes):
|
||
if len(curr_seq) + sizes[sz_idx] <= max_sequence_length_allowed: # or len(curr_seq)==0:
|
||
curr_seq += document[sz_idx]
|
||
sz_idx += 1
|
||
elif sizes[sz_idx] >= max_sequence_length_allowed:
|
||
if len(curr_seq) > 0:
|
||
result_list.append(curr_seq)
|
||
curr_seq = []
|
||
result_list.append(document[sz_idx][:max_sequence_length_allowed])
|
||
sz_idx += 1
|
||
else:
|
||
result_list.append(curr_seq)
|
||
curr_seq = []
|
||
|
||
if len(curr_seq) > max_sequence_length_allowed / 2: # /2
|
||
result_list.append(curr_seq)
|
||
|
||
# num_instance=int(len(big_list)/max_sequence_length_allowed)+1
|
||
# print("num_instance:",num_instance)
|
||
|
||
# result_list=[]
|
||
# for j in range(num_instance):
|
||
# index=j*max_sequence_length_allowed
|
||
# end_index=index+max_sequence_length_allowed if j!=num_instance-1 else -1
|
||
# result_list.append(big_list[index:end_index])
|
||
return result_list
|
||
|
||
|
||
def split_numpy_chunk(path, tokenizer, pretrain_data, host):
|
||
documents = []
|
||
instances = []
|
||
|
||
s = time.time()
|
||
with open(path, encoding="utf-8") as fd:
|
||
document = []
|
||
for i, line in enumerate(tqdm(fd)):
|
||
line = line.strip()
|
||
# document = line
|
||
# if len(document.split("<sep>")) <= 3:
|
||
# continue
|
||
if len(line) > 0 and line[:2] == "]]": # This is end of document
|
||
documents.append(document)
|
||
document = []
|
||
elif len(line) >= 2:
|
||
document.append(line)
|
||
if len(document) > 0:
|
||
documents.append(document)
|
||
print("read_file ", time.time() - s)
|
||
|
||
# documents = [x for x in documents if x]
|
||
# print(len(documents))
|
||
# print(len(documents[0]))
|
||
# print(documents[0][0:10])
|
||
|
||
ans = []
|
||
for docs in tqdm(documents):
|
||
ans.append(pretrain_data.tokenize(docs))
|
||
print(time.time() - s)
|
||
del documents
|
||
|
||
instances = []
|
||
for a in tqdm(ans):
|
||
raw_ins = get_raw_instance(a)
|
||
instances.extend(raw_ins)
|
||
del ans
|
||
|
||
print("len instance", len(instances))
|
||
|
||
sen_num = len(instances)
|
||
seq_len = 512
|
||
input_ids = np.zeros([sen_num, seq_len], dtype=np.int32)
|
||
input_mask = np.zeros([sen_num, seq_len], dtype=np.int32)
|
||
segment_ids = np.zeros([sen_num, seq_len], dtype=np.int32)
|
||
masked_lm_output = np.zeros([sen_num, seq_len], dtype=np.int32)
|
||
|
||
for index, ins in tqdm(enumerate(instances)):
|
||
mask_dict = pretrain_data.create_training_instance(ins)
|
||
input_ids[index] = mask_dict[0]
|
||
input_mask[index] = mask_dict[1]
|
||
segment_ids[index] = mask_dict[2]
|
||
masked_lm_output[index] = mask_dict[3]
|
||
|
||
with h5py.File(f"/output/{host}.h5", "w") as hf:
|
||
hf.create_dataset("input_ids", data=input_ids)
|
||
hf.create_dataset("input_mask", data=input_ids)
|
||
hf.create_dataset("segment_ids", data=segment_ids)
|
||
hf.create_dataset("masked_lm_positions", data=masked_lm_output)
|
||
|
||
del instances
|
||
|
||
|
||
def split_numpy_chunk_pool(input_path, output_path, pretrain_data, worker, dupe_factor, seq_len, file_name):
|
||
if os.path.exists(os.path.join(output_path, f"{file_name}.h5")):
|
||
print(f"{file_name}.h5 exists")
|
||
return
|
||
|
||
documents = []
|
||
instances = []
|
||
|
||
s = time.time()
|
||
with open(input_path, "r", encoding="utf-8") as fd:
|
||
document = []
|
||
for i, line in enumerate(tqdm(fd)):
|
||
line = line.strip()
|
||
if len(line) > 0 and line[:2] == "]]": # This is end of document
|
||
documents.append(document)
|
||
document = []
|
||
elif len(line) >= 2:
|
||
document.append(line)
|
||
if len(document) > 0:
|
||
documents.append(document)
|
||
print(f"read_file cost {time.time() - s}, length is {len(documents)}")
|
||
|
||
ans = []
|
||
s = time.time()
|
||
pool = multiprocessing.Pool(worker)
|
||
encoded_doc = pool.imap_unordered(pretrain_data.tokenize, documents, 100)
|
||
for index, res in tqdm(enumerate(encoded_doc, start=1), total=len(documents), colour="cyan"):
|
||
ans.append(res)
|
||
pool.close()
|
||
print((time.time() - s) / 60)
|
||
del documents
|
||
|
||
instances = []
|
||
for a in tqdm(ans, colour="MAGENTA"):
|
||
raw_ins = get_raw_instance(a, max_sequence_length=seq_len)
|
||
instances.extend(raw_ins)
|
||
del ans
|
||
|
||
print("len instance", len(instances))
|
||
|
||
new_instances = []
|
||
for _ in range(dupe_factor):
|
||
for ins in instances:
|
||
new_instances.append(ins)
|
||
|
||
shuffle(new_instances)
|
||
instances = new_instances
|
||
print("after dupe_factor, len instance", len(instances))
|
||
|
||
sentence_num = len(instances)
|
||
input_ids = np.zeros([sentence_num, seq_len], dtype=np.int32)
|
||
input_mask = np.zeros([sentence_num, seq_len], dtype=np.int32)
|
||
segment_ids = np.zeros([sentence_num, seq_len], dtype=np.int32)
|
||
masked_lm_output = np.zeros([sentence_num, seq_len], dtype=np.int32)
|
||
|
||
s = time.time()
|
||
pool = multiprocessing.Pool(worker)
|
||
encoded_docs = pool.imap_unordered(pretrain_data.create_training_instance, instances, 32)
|
||
for index, mask_dict in tqdm(enumerate(encoded_docs), total=len(instances), colour="blue"):
|
||
input_ids[index] = mask_dict[0]
|
||
input_mask[index] = mask_dict[1]
|
||
segment_ids[index] = mask_dict[2]
|
||
masked_lm_output[index] = mask_dict[3]
|
||
pool.close()
|
||
print((time.time() - s) / 60)
|
||
|
||
with h5py.File(os.path.join(output_path, f"{file_name}.h5"), "w") as hf:
|
||
hf.create_dataset("input_ids", data=input_ids)
|
||
hf.create_dataset("input_mask", data=input_mask)
|
||
hf.create_dataset("segment_ids", data=segment_ids)
|
||
hf.create_dataset("masked_lm_positions", data=masked_lm_output)
|
||
|
||
del instances
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument("--tokenizer_path", type=str, required=True, default=10, help="path of tokenizer")
|
||
parser.add_argument("--seq_len", type=int, default=512, help="sequence length")
|
||
parser.add_argument(
|
||
"--max_predictions_per_seq", type=int, default=80, help="number of shards, e.g., 10, 50, or 100"
|
||
)
|
||
parser.add_argument("--input_path", type=str, required=True, help="input path of shard which has split sentence")
|
||
parser.add_argument("--output_path", type=str, required=True, help="output path of h5 contains token id")
|
||
parser.add_argument(
|
||
"--backend", type=str, default="python", help="backend of mask token, python, c++, numpy respectively"
|
||
)
|
||
parser.add_argument(
|
||
"--dupe_factor",
|
||
type=int,
|
||
default=1,
|
||
help="specifies how many times the preprocessor repeats to create the input from the same article/document",
|
||
)
|
||
parser.add_argument("--worker", type=int, default=32, help="number of process")
|
||
parser.add_argument("--server_num", type=int, default=10, help="number of servers")
|
||
args = parser.parse_args()
|
||
|
||
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
|
||
pretrain_data = PreTrainingDataset(
|
||
tokenizer, args.seq_len, args.backend, max_predictions_per_seq=args.max_predictions_per_seq
|
||
)
|
||
|
||
data_len = len(os.listdir(args.input_path))
|
||
|
||
for i in range(data_len):
|
||
input_path = os.path.join(args.input_path, f"{i}.txt")
|
||
if os.path.exists(input_path):
|
||
start = time.time()
|
||
print(f"process {input_path}")
|
||
split_numpy_chunk_pool(
|
||
input_path, args.output_path, pretrain_data, args.worker, args.dupe_factor, args.seq_len, i
|
||
)
|
||
end_ = time.time()
|
||
print("memory：%.4f GB" % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024))
|
||
print(f"has cost {(end_ - start) / 60}")
|
||
print("-" * 100)
|
||
print("")
|
||
|
||
# if you have multiple server, you can use code below or modify code to openmpi
|
||
|
||
# host = int(socket.gethostname().split('GPU')[-1])
|
||
# for i in range(data_len // args.server_num + 1):
|
||
# h = args.server_num * i + host - 1
|
||
# input_path = os.path.join(args.input_path, f'{h}.txt')
|
||
# if os.path.exists(input_path):
|
||
# start = time.time()
|
||
# print(f'I am server {host}, process {input_path}')
|
||
# split_numpy_chunk_pool(input_path,
|
||
# args.output_path,
|
||
# pretrain_data,
|
||
# args.worker,
|
||
# args.dupe_factor,
|
||
# args.seq_len,
|
||
# h)
|
||
# end_ = time.time()
|
||
# print(u'memory：%.4f GB' % (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) )
|
||
# print(f'has cost {(end_ - start) / 60}')
|
||
# print('-' * 100)
|
||
# print('')
|