wehub-resource-sync
344 lines
14 KiB
Python
344 lines
14 KiB
Python
import glob
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import os
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import torch
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import numpy as np
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import time
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import json
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import random
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import itertools
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import hydra
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import copy
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from omegaconf import DictConfig, OmegaConf
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from infinibatch import iterators
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from unilm.data.basic_loader import BaseBatchGen
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from unilm.data.utils import NativeCheckpointableIterator, WeightIterator, EOD_SYMBOL
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from fairseq.utils import safe_getattr, safe_hasattr
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class LMLoader(BaseBatchGen):
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def __init__(
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self,
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args,
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dataset,
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dictionary,
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tokenizer,
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max_tokens=None,
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max_sentences=None,
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max_positions=None,
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ignore_invalid_inputs=False,
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required_batch_size_multiple=1,
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seed=1,
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epoch=1,
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num_shards=1,
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shard_id=0,
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):
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super().__init__()
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self.args = args
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self.data = dataset.data
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self.data_dir = dataset.data_dir
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self.shuffle = dataset.shuffle
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self.dictionary = dictionary
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self.tokenizer = tokenizer
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self.max_tokens = max_tokens
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self.max_sentences = max_sentences
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self.max_positions = max_positions
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self.tokens_per_sample = args.tokens_per_sample
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self.mlm_cut_length = safe_getattr(args, "mlm_cut_length", 0)
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self.mlm_tokens_proportion = safe_getattr(args, "mlm_tokens_proportion", 0)
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self.ignore_invalid_inputs = ignore_invalid_inputs
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self.required_batch_size_multiple = required_batch_size_multiple
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self.seed = str(seed)
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self.epoch = epoch
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self.num_shards = num_shards
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self.shard_id = shard_id
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self.batch_read_ahead = args.batch_read_ahead
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self._build_iter()
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def _build_iter(self):
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tokenized_lines = self._tokenize()
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self.padded_batches = self._batchify(tokenized_lines)
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prefetch_batches = iterators.PrefetchIterator(
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self.padded_batches,
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buffer_size=10000,
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buffer_in_main_process=True,
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log_empty_buffer_warning=True and self.shard_id == 0,
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)
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prefetch_batches = iterators.MapIterator(
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prefetch_batches, self._move_to_tensor
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)
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self._iter = prefetch_batches
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def _tokenize(self):
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'''
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data:
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{
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'source': list[Path],
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}
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'''
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dataset = list(zip(self.data['source']))
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if self.shuffle:
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chunk_files = \
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iterators.InfinitePermutationSourceIterator(
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dataset,
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seed=self.seed,
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shuffle=self.shuffle,
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num_instances=self.num_shards,
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instance_rank=self.shard_id,
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)
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else:
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chunk_files = \
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iterators.ChunkedSourceIterator(
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dataset,
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num_instances=self.num_shards,
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instance_rank=self.shard_id,
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)
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tokenized_lines = iterators.SelectManyIterator(chunk_files, lambda files: self._read_from_files(*files))
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tokenized_lines = iterators.SamplingRandomMapIterator(tokenized_lines, self._prepare, self.seed)
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return tokenized_lines
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def getstate(self):
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state = super().getstate()
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state["epoch"] = self.epoch
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state["iterations_in_epoch"] = None
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return state
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def _batchify(self, lines):
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if self.max_sentences is not None:
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if self.batch_read_ahead > 0:
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lines = iterators.BlockwiseShuffleIterator(lines, self.batch_read_ahead, self.seed)
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batches = iterators.FixedBatchIterator(lines, self.max_sentences)
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else:
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# -
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def dynamic_batch_size(sample):
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lengths = [len(x) for x in sample]
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batch_size = self.max_tokens // max(lengths) // self.required_batch_size_multiple * self.required_batch_size_multiple
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return max(1, batch_size)
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batches = iterators.BucketedReadaheadBatchIterator(
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lines,
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read_ahead=self.batch_read_ahead,
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key=(lambda x: max(len(x[0]), len(x[1]))) if self.shuffle else None,
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batch_size=dynamic_batch_size,
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shuffle=self.shuffle,
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seed=self.seed,
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)
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def collate(batch):
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batch_size = len(batch)
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mlm_batch_size = sum([len(x[2]) for x in batch])
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gpt_max_length = max([len(x[0]) for x in batch])
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mlm_max_length = 0
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mlm_ntokens = 0
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for x in batch:
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for y in x[2]:
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mlm_max_length = max(mlm_max_length, len(y))
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mlm_ntokens += len(y)
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gpt_source_ids = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32,
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fill_value=self.dictionary.pad())
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gpt_target_ids = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32,
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fill_value=self.dictionary.pad())
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mlm_source_ids = np.full(shape=(mlm_batch_size, mlm_max_length), dtype=np.int32,
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fill_value=self.dictionary.pad())
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gpt_input_mask_all = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32, fill_value=0)
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gpt_loss_mask_all = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32, fill_value=1)
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mlm_mask_all = np.full(shape=(mlm_batch_size, mlm_max_length), dtype=np.int32, fill_value=0)
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mlm_index = 0
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for i, (gpt_ids, gpt_input_mask, mlm_ids_list, mlm_mask_list, gpt_loss_mask) in enumerate(batch):
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gpt_source_ids[i, :len(gpt_ids)-1] = gpt_ids[:-1]
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gpt_target_ids[i, :len(gpt_ids)-1] = gpt_ids[1:]
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gpt_input_mask_all[i, :len(gpt_ids)-1] = gpt_input_mask[:-1]
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gpt_loss_mask_all[i, :len(gpt_ids)-1] = gpt_loss_mask[1:]
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for j, (mlm_ids, mlm_mask) in enumerate(zip(mlm_ids_list, mlm_mask_list)):
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mlm_source_ids[mlm_index, :len(mlm_ids)] = mlm_ids
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mlm_mask_all[mlm_index, :len(mlm_mask)] = mlm_mask
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mlm_index += 1
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ret_batch = {
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'text':{
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'net_input': {
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'src_tokens': gpt_source_ids.astype(np.int64),
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'mlm_src_tokens': mlm_source_ids.astype(np.int64) if mlm_batch_size !=0 else None,
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'gpt_input_mask': gpt_input_mask_all.astype(np.bool_),
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'gpt_loss_mask': gpt_loss_mask_all.astype(np.bool_),
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'mlm_mask': mlm_mask_all.astype(np.bool_) if mlm_batch_size !=0 else None
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},
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'target': gpt_target_ids.astype(np.int64),
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'nsentences': batch_size,
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'ntokens': sum([len(x[0]) for x in batch]),
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'mlm_ntokens': mlm_ntokens
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}
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}
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return ret_batch
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def collate_for_gpt(batch):
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batch_size = len(batch)
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gpt_max_length = max([len(x[0]) for x in batch])
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gpt_source_ids = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32,
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fill_value=self.dictionary.pad())
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gpt_target_ids = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32,
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fill_value=self.dictionary.pad())
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gpt_input_mask_all = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32, fill_value=0)
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gpt_loss_mask_all = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32, fill_value=1)
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chunk_tokens_all = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32, fill_value=0)
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segment_tokens_all = np.full(shape=(batch_size, gpt_max_length-1), dtype=np.int32, fill_value=0)
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for i, (gpt_ids, gpt_input_mask, mlm_ids_list, mlm_mask_list, gpt_loss_mask, chunk_tokens, segment_tokens) in enumerate(batch):
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gpt_source_ids[i, :len(gpt_ids)-1] = gpt_ids[:-1]
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gpt_target_ids[i, :len(gpt_ids)-1] = gpt_ids[1:]
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gpt_input_mask_all[i, :len(gpt_ids)-1] = gpt_input_mask[:-1]
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gpt_loss_mask_all[i, :len(gpt_ids)-1] = gpt_loss_mask[1:]
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chunk_tokens_all[i, :len(gpt_ids)-1] = chunk_tokens[:-1]
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segment_tokens_all[i, :len(gpt_ids)-1] = segment_tokens[:-1]
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ret_batch = {
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'gpt':{
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'net_input': {
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'src_tokens': gpt_source_ids.astype(np.int64),
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'mlm_src_tokens': None,
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'gpt_input_mask': gpt_input_mask_all.astype(np.bool_),
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'gpt_loss_mask': gpt_loss_mask_all.astype(np.bool_),
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'chunk_tokens': chunk_tokens_all.astype(np.int64),
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'segment_tokens': segment_tokens_all.astype(np.int64),
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'mlm_mask': None
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},
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'target': gpt_target_ids.astype(np.int64),
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'nsentences': batch_size,
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'ntokens': sum([len(x[0]) for x in batch]),
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'mlm_ntokens': 0
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}
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}
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return ret_batch
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if self.mlm_tokens_proportion == 0:
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padded_batches = iterators.MapIterator(
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batches, collate_for_gpt
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)
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else:
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padded_batches = iterators.MapIterator(
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batches, collate
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)
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return padded_batches
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def _prepare(self, _random, doc):
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mlm_tokens, mlm_mask, gpt_input_mask, gpt_loss_mask, chunk_tokens, segment_tokens = self._mlm_cut(_random, doc)
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full_tokens = self._gpt(doc)
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return full_tokens, gpt_input_mask, mlm_tokens, mlm_mask, gpt_loss_mask, chunk_tokens, segment_tokens
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def _mlm_cut(self, _random, doc):
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eod_index = self.dictionary.indices[EOD_SYMBOL]
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if self.mlm_tokens_proportion == 0:
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mlm_tokens = []
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mlm_mask = []
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gpt_input_mask = [0] * len(doc)
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gpt_loss_mask = [1] * len(doc)
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chunk_tokens = [0] * len(doc)
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segment_tokens = [0] * len(doc)
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return mlm_tokens, mlm_mask, gpt_input_mask, gpt_loss_mask, chunk_tokens, segment_tokens
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cut_start = np.arange(1, len(doc)-3/2*self.mlm_cut_length, self.mlm_cut_length, dtype=int)
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_random.shuffle(cut_start)
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mlm_tokens = []
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mlm_mask = []
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start_list = []
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gpt_input_mask = np.zeros(len(doc), dtype=int)
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gpt_loss_mask = np.ones(len(doc), dtype=int)
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mlm_tokens_total_num = (len(doc)-1) * self.mlm_tokens_proportion
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mlm_tokens_cur_num = 0
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for start in cut_start:
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eod_num = doc[start:start+self.mlm_cut_length].count(eod_index)
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if eod_num >= 2:
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continue
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elif eod_num == 1:
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eod_pos = doc[start:start+self.mlm_cut_length].index(eod_index)
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if self.mlm_cut_length - eod_pos < 20:
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continue
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start_ind, end_ind = start+eod_pos+1, start + self.mlm_cut_length
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else:
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cut_pos = _random.randint(0, self.mlm_cut_length-1)
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if cut_pos >= self.mlm_cut_length/2:
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start_ind, end_ind = start, start + cut_pos + 1
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else:
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start_ind, end_ind = start + cut_pos, start + self.mlm_cut_length
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assert eod_index not in doc[start_ind:end_ind]
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start_list.append(start)
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mlm_tokens.append([self.dictionary.bos()] + doc[start_ind:end_ind])
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mlm_tokens_cur_num += end_ind - start_ind
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mlm_mask.append([0] + [1]*(end_ind - start_ind))
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gpt_input_mask[start_ind:end_ind] = 1
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gpt_loss_mask[start_ind:end_ind-1] = 0
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if mlm_tokens_cur_num > mlm_tokens_total_num:
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break
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ind = np.array(start_list).argsort()
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start_list = np.array(start_list)[ind]
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mlm_tokens = np.array(mlm_tokens, dtype=object)[ind]
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mlm_mask = np.array(mlm_mask, dtype=object)[ind]
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return mlm_tokens, mlm_mask, gpt_input_mask, gpt_loss_mask
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def _gpt(self, doc):
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return doc
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def _read_from_files(self, source_file):
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data = []
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file_path = os.path.join(self.data_dir, source_file)
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if not os.path.exists(file_path):
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print('| file {} not exists'.format(file_path), flush=True)
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return iter([]) # skip bad file
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with open(file_path, 'r', encoding='utf8') as f:
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lines = f.read().strip().split('\n')
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gpt_format_text = []
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for line in lines:
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gpt_format_text.extend(list(filter(None, json.loads(line)["text"].split("\n"))))
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gpt_format_text.append('')
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tokenized_lines = [self.tokenizer.encode(line) for line in gpt_format_text]
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tokenized_ids = [self.dictionary.encode_line(line, add_if_not_exist=False) for line in tokenized_lines]
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doc = [self.dictionary.bos()]
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for ids in tokenized_ids:
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if len(ids) > self.tokens_per_sample: # drop too long sentence
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continue
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if len(doc) + len(ids) > self.tokens_per_sample:
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if len(doc) > 5/2*self.mlm_cut_length + 1:
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data.append(doc)
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doc = [self.dictionary.bos()]
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doc.extend(ids)
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if len(doc) > 1 and len(doc) <= self.tokens_per_sample:
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if len(doc) > 5/2*self.mlm_cut_length + 1:
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data.append(doc)
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return data |