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# ------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ------------------------------------------------------------------------------------------
import os, sys
import glob
import random
from collections import Counter, OrderedDict
import numpy as np
import torch
import json
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
class LMOrderedIterator(object):
def __init__(self, data, bsz, bptt, eval_len=None, device='cpu', world_size=1, rank=0):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.data = data
self.bsz = bsz
self.world_size = world_size
self.rank = rank
self.bptt = bptt # tgt_len
# existing len.
self.eval_len = bptt if eval_len is None else eval_len
self.device = device
self.global_bsz = bsz * world_size
# Work out how cleanly we can divide the dataset into bsz parts.
self.n_step = len(data) // self.global_bsz # bsz
self.split_data = torch.tensor(
data[rank * self.n_step * bsz : (rank + 1) * self.n_step * bsz],
dtype=torch.long, device=self.device
) # data.view(-1)
self.split_data = self.split_data.view(bsz, -1)
def __iter__(self):
return self.get_fixlen_iter()
def get_batch(self, i, bptt, eval_len):
beg_idx = i
end_idx = i + bptt # seq_len
# batch_size, lengh;
_input = self.split_data[:, beg_idx : end_idx].contiguous()
_target = self.split_data[:, beg_idx+1 : end_idx+1].contiguous()
_msk = torch.cat(
[
torch.zeros(bptt-eval_len, dtype=torch.float, device=self.device),
torch.ones(eval_len, dtype=torch.float, device=self.device)
]
)
_msk = _msk.unsqueeze(0).expand_as(_input) # .unsqueeze(-1) # length, 1;
return _input, _target, _msk
def get_fixlen_iter(self, start=0):
self.data_len = self.split_data.size(1)
_eval_cursor = 0
for i in range(start, self.data_len - 1, self.eval_len):
bptt = min(self.bptt, self.data_len - i - 1)
_end_idx = i + bptt
yield self.get_batch(i, bptt, _end_idx - _eval_cursor)
_eval_cursor = _end_idx
class Corpus(object):
def __init__(self, path):
self.path = path
self.num_words = 0
self.tokens = []
with open(self.path, "r") as reader:
for line in reader:
items = json.loads(line.strip())
book = items['book']
tokens = items['tokens']
num_words = items['num_words']
self.num_words += num_words
self.tokens.extend(tokens)
class BinLMOrderedIterator(object):
def __init__(self, corpus, bsz, bptt, eval_len=None, device='cpu', world_size=1, rank=0):
"""
data -- LongTensor -- the LongTensor is strictly ordered
"""
self.corpus = corpus
self.bsz = bsz
self.world_size = world_size
self.rank = rank
self.bptt = bptt # tgt_len
# existing len.
self.eval_len = bptt if eval_len is None else eval_len
self.device = device
self.global_bsz = bsz * world_size
# Work out how cleanly we can divide the dataset into bsz parts.
self.n_step = corpus.length // self.global_bsz # bsz
self.offset = [(rank * bsz + _b) * self.n_step for _b in range(bsz)]
def __iter__(self):
return self.get_fixlen_iter()
def get_batch(self, i, bptt, eval_len):
# batch_size, lengh;
_inputs = []
_targets = []
for _b in range(0, self.bsz):
_input = self.corpus.get_tokens(self.offset[_b] + i, bptt)
_target = self.corpus.get_tokens(self.offset[_b] + i + 1, bptt)
_inputs.append(_input)
_targets.append(_target)
_input = torch.tensor(_inputs, dtype=torch.int64, device=self.device).contiguous()
_target = torch.tensor(_targets, dtype=torch.int64, device=self.device).contiguous()
_msk = torch.cat(
[
torch.zeros(bptt-eval_len, dtype=torch.float, device=self.device),
torch.ones(eval_len, dtype=torch.float, device=self.device)
]
)
_msk = _msk.unsqueeze(0).expand_as(_input) # .unsqueeze(-1) # length, 1;
return _input, _target, _msk
def get_fixlen_iter(self, start=0):
#self.data_len = self.split_data.size(1)
_eval_cursor = 0
for i in range(start, self.n_step - 1, self.eval_len):
bptt = min(self.bptt, self.n_step - i - 1)
_end_idx = i + bptt
yield self.get_batch(i, bptt, _end_idx - _eval_cursor)
_eval_cursor = _end_idx
class BinCorpus(object):
def __init__(self, path):
self.path = path
self.book_token_span = []
self.book_token_span.append(0)
tokens_sum = 0
self.num_words = 0
with open(path+'.info', 'r') as info_reader:
for line in info_reader:
items = json.loads(line.strip())
book = items['book']
num_tokens = items['num_subtokens']
num_words = items['num_words']
tokens_sum += num_tokens
self.book_token_span.append(tokens_sum)
self.num_words += num_words
self.length = self.book_token_span[-1]
self.bin_reader = open(path+'.bin', 'rb')
def get_tokens(self, offset, count):
INT64_SIZE = 8
self.bin_reader.seek(offset * INT64_SIZE)
x = np.fromfile(self.bin_reader, count=count, dtype=np.int)
return x
def get_lm_corpus(data):
print('Producing dataset {}...'.format(data))
corpus = Corpus(data)
return corpus
def padding_tokens(tokens, max_seq_length, pad_token, direct, max_context_length=0):
if max_context_length == 0:
max_context_length = max_seq_length
if len(tokens) > max_context_length:
if direct > 0:
pad_tokens = tokens[:max_context_length]
else:
pad_tokens = tokens[-max_context_length:]
else:
pad_tokens = tokens
token_len = len(pad_tokens)
pad_tokens = pad_tokens + [pad_token for _ in range(max_seq_length - token_len)]
return pad_tokens, token_len
class FT_Dataset(Dataset):
def __init__(self, ft_file, batch_size, max_seq_length,
max_eval_length=0, joint_lm=False, prefix_len=0, infix_len=0,
prefix_cursor=1000000, infix_cursor=2000000):
self.ft_file = ft_file
self.ft_samples = self.read_ft_file(ft_file)
self.batch_size = batch_size
self.num_examples = len(self.ft_samples)
self.max_seq_length = max_seq_length
self.max_eval_length = max_eval_length
self.rng = random.Random(911)
self.joint_lm = joint_lm
self.num_batches = int((self.num_examples + self.batch_size - 1) / self.batch_size)
self.prefix_len = prefix_len
self.infix_len = infix_len
self.prefix_cursor = prefix_cursor
self.infix_cursor = infix_cursor
def __len__(self):
return self.num_batches * self.batch_size
def __getitem__(self, item):
if(item >= self.num_examples):
item = self.rng.randint(0, self.num_examples - 1)
example = self.ft_samples[item]
context = example[0]
completion = example[1]
pretokens = [i + self.prefix_cursor for i in range(0, self.prefix_len)]
intokens = [i + self.infix_cursor for i in range(0, self.infix_len)]
conditions = pretokens + context + intokens
_input, _input_len = padding_tokens(conditions + completion, self.max_seq_length, 0, 1)
pad_targets = [0 for i in range(0, self.prefix_len)] + context + [0 for i in range(0, self.infix_len)] + completion
_target, _ = padding_tokens(pad_targets[1:], self.max_seq_length, 0, 1)
if not self.joint_lm:
_msk = [0.0] * (len(conditions) - 1) + [1.0] * (_input_len - len(conditions))
else:
_msk = [1.0] * (_input_len - 1)
_msk, _ = padding_tokens(_msk, self.max_seq_length, 0.0, 1)
output = {}
output["id"] = torch.tensor(item, dtype=torch.long)
_query, _query_len = padding_tokens(
conditions, self.max_seq_length, 0, -1,
max_context_length = self.max_seq_length - self.max_eval_length
)
output["query"] = torch.tensor(_query, dtype=torch.long)
output["query_len"] = torch.tensor(_query_len, dtype=torch.long)
output["input"] = torch.tensor(_input, dtype=torch.long)
output["target"] = torch.tensor(_target, dtype=torch.long)
output["mask"] = torch.tensor(_msk, dtype=torch.float)
return output
def read_ft_file(self, ft_file):
ft_samples = []
with open(ft_file, 'r') as reader:
for line in reader:
items = json.loads(line.strip())
context = items['context']
completion = items['completion']
ft_samples.append([context, completion])
return ft_samples
def get_item_list(self, start, interval):
start = min(start, self.num_examples-1)
start = max(0,start)
if(start + interval >= self.num_examples):
end = self.num_examples
else:
end = start + interval
samples = []
for index in range(start, end):
output = self.__getitem__(index)
samples.append(output)
return samples
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