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