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# Copyright (c) OpenMMLab. All rights reserved.
import logging
import os
from datetime import timedelta
from functools import partial
import numpy as np
from datasets import DatasetDict, concatenate_datasets
from mmengine import print_log
from mmengine.config import Config, ConfigDict
from mmengine.utils.misc import get_object_from_string
from torch import distributed as dist
from xtuner.registry import BUILDER, MAP_FUNC
from .utils import Packer, encode_fn
def get_lengths(example):
return {'length': len(example['input_ids'])}
def build_origin_dataset(dataset, split):
if isinstance(dataset, DatasetDict):
if split is None:
dataset = concatenate_datasets(dataset.values())
else:
dataset = dataset[split]
elif isinstance(dataset, dict) or isinstance(
dataset, Config) or isinstance(dataset, ConfigDict):
dataset = BUILDER.build(dataset)
if isinstance(dataset, DatasetDict):
if split is None:
dataset = concatenate_datasets(dataset.values())
else:
dataset = dataset[split]
return dataset
def map_dataset(dataset, dataset_map_fn, map_num_proc):
if isinstance(dataset_map_fn, str):
map_fn_obj = MAP_FUNC.get(dataset_map_fn) or get_object_from_string(
dataset_map_fn)
if map_fn_obj is not None:
dataset_map_fn = map_fn_obj
else:
raise TypeError('dataset_map_fn must be a function or a '
"registered function's string in MAP_FUNC, "
f"but got a string of '{dataset_map_fn}'")
dataset = dataset.map(dataset_map_fn, num_proc=map_num_proc)
return dataset
def add_template_to_dataset(dataset, template_map_fn, map_num_proc):
if isinstance(template_map_fn,
dict) or isinstance(template_map_fn, Config) or isinstance(
template_map_fn, ConfigDict):
template_map_fn = BUILDER.build(template_map_fn)
dataset = dataset.map(template_map_fn, num_proc=map_num_proc)
# remove invalid data
dataset = dataset.filter(
lambda example: len(example['conversation']) > 0,
num_proc=map_num_proc)
return dataset
def tokenize_dataset(dataset, tokenizer, max_length, with_image_token,
input_ids_with_output, remove_unused_columns,
map_num_proc):
assert (tokenizer is not None) and (max_length is not None), \
f'({tokenizer}, {max_length})'
if isinstance(tokenizer, dict) or isinstance(
tokenizer, Config) or isinstance(tokenizer, ConfigDict):
tokenizer = BUILDER.build(tokenizer)
dataset = dataset.map(
partial(
encode_fn,
tokenizer=tokenizer,
max_length=max_length,
with_image_token=with_image_token,
input_ids_with_output=input_ids_with_output),
remove_columns=list(dataset.column_names)
if remove_unused_columns else None,
num_proc=map_num_proc)
return dataset
def pack_dataset(dataset, max_length, use_varlen_attn, shuffle_before_pack,
map_num_proc):
if shuffle_before_pack:
dataset = dataset.shuffle()
dataset = dataset.flatten_indices(num_proc=map_num_proc)
dataset = dataset.map(
Packer(max_length, use_varlen_attn=use_varlen_attn),
batched=True,
num_proc=map_num_proc)
return dataset
def process(dataset,
do_dataset_tokenization=True,
tokenizer=None,
max_length=None,
dataset_map_fn=None,
template_map_fn=None,
max_dataset_length=None,
split='train',
remove_unused_columns=False,
rename_maps=[],
shuffle_before_pack=True,
pack_to_max_length=True,
use_varlen_attn=False,
input_ids_with_output=True,
with_image_token=False,
map_num_proc=32):
"""Post-process the dataset loaded from the Hugging Face Hub, or a local
dataset.
Args:
dataset: The dataset to be post-processed.
do_dataset_tokenization: Whether the dataset need to be tokenized
in this function. Default to True.
tokenizer: The tokenizer processes some raw text as input and outputs
an Encoding. If `do_dataset_tokenization` is True, this argument
should not be None. Default to None.
max_length: Max length of the sequence. If `do_dataset_tokenization`
or `pack_to_max_length` is True, this argument should not be None.
Default to None.
dataset_map_fn: Map the original dataset format to the one defined
by xTuner.
template_map_fn: Add the prompt template to the dataset
max_dataset_length: If the length of the dataset is too long, we can
randomly extract `max_dataset_length` from it.
split: Which split of the data to load.
If `None`, will return a single concatenated dataset with all
splits (typically `datasets.Split.TRAIN` and
`datasets.Split.TEST`).
If given, will return a single Dataset.
remove_unused_columns: Whether to remove columns from the dataset
that are not used during training.
rename_maps: Rename the column name of the dataset.
shuffle_before_pack: Whether to shuffle the dataset before
packing them.
pack_to_max_length: Whether to pack the dataset to the `max_length `.
This usually improves gpu utilization and therefore reduces
training time.
use_varlen_attn: If use_varlen_attn is True, we calculate attention
the actual length of the sequence rather than the actual length
of the sequence
input_ids_with_output: Whether to put the groundtruth output
corresponding to the question into the dataset. Typically set
it to True during training and False during testing.
with_image_token: Whether to convert DEFAULT_IMAGE_TOKEN to
IMAGE_TOKEN_INDEX. Typically set it to True during the training
of VLM.
map_num_proc: Max number of processes when mapping the dataset.
"""
if use_varlen_attn:
assert pack_to_max_length, \
'`pack_to_max_length` in `process_hf_dataset` should be set to ' \
'True if `use_varlen_attn` is True.'
if pack_to_max_length:
assert split == 'train' or split is None, \
('`split` should be `train` or `None` if `pack_to_max_length` is '
f'True, but got {split}.')
dataset = build_origin_dataset(dataset, split)
# sample `max_dataset_length` items from the original dataset to
# save time consumed by map function
if max_dataset_length is not None:
max_dataset_length = min(max_dataset_length, len(dataset))
indices = np.random.choice(
len(dataset), max_dataset_length, replace=False)
dataset = dataset.select(indices)
# Extract the useful data for training from the original dataset.
if dataset_map_fn is not None:
dataset = map_dataset(dataset, dataset_map_fn, map_num_proc)
# Add prompt template, such as <|System|>: xxx <|User|>: xxx <|Bot|>: xxx
if template_map_fn is not None:
dataset = add_template_to_dataset(dataset, template_map_fn,
map_num_proc)
for old, new in rename_maps:
dataset = dataset.rename_column(old, new)
# remove unused columns
if pack_to_max_length and (not remove_unused_columns):
print_log(
'We have to remove unused columns if '
'`pack_to_max_length` is set to True.',
logger='current',
level=logging.WARNING)
remove_unused_columns = True
if do_dataset_tokenization:
dataset = tokenize_dataset(dataset, tokenizer, max_length,
with_image_token, input_ids_with_output,
remove_unused_columns, map_num_proc)
else:
assert {'input_ids', 'labels'}.issubset(dataset.column_names)
if input_ids_with_output:
# remove data that does not have the valid labels.
dataset = dataset.filter(
lambda example: any(label >= 0 for label in example['labels']),
num_proc=map_num_proc)
# pack to max length
if pack_to_max_length:
dataset = pack_dataset(dataset, max_length, use_varlen_attn,
shuffle_before_pack, map_num_proc)
# add 'length'
dataset = dataset.map(get_lengths, num_proc=map_num_proc)
setattr(dataset, 'length', dataset['length'])
return dataset
def process_hf_dataset(dataset,
do_dataset_tokenization=True,
tokenizer=None,
max_length=None,
dataset_map_fn=None,
template_map_fn=None,
max_dataset_length=None,
split='train',
remove_unused_columns=False,
rename_maps=[],
shuffle_before_pack=True,
pack_to_max_length=True,
use_varlen_attn=False,
input_ids_with_output=True,
with_image_token=False,
map_num_proc=4):
"""Post-process the dataset loaded from the Hugging Face Hub, or a local
dataset.
Args:
dataset: The dataset to be post-processed.
do_dataset_tokenization: Whether the dataset need to be tokenized
in this function. Default to True.
tokenizer: The tokenizer processes some raw text as input and outputs
an Encoding. If `do_dataset_tokenization` is True, this argument
should not be None. Default to None.
max_length: Max length of the sequence. If `do_dataset_tokenization`
or `pack_to_max_length` is True, this argument should not be None.
Default to None.
dataset_map_fn: Map the original dataset format to the one defined
by xTuner.
template_map_fn: Add the prompt template to the dataset
max_dataset_length: If the length of the dataset is too long, we can
randomly extract `max_dataset_length` from it.
split: Which split of the data to load.
If `None`, will return a single concatenated dataset with all
splits (typically `datasets.Split.TRAIN` and
`datasets.Split.TEST`).
If given, will return a single Dataset.
remove_unused_columns: Whether to remove columns from the dataset
that are not used during training.
rename_maps: Rename the column name of the dataset.
shuffle_before_pack: Whether to shuffle the dataset before
packing them.
pack_to_max_length: Whether to pack the dataset to the `max_length `.
This usually improves gpu utilization and therefore reduces
training time.
use_varlen_attn: If use_varlen_attn is True, we calculate attention
the actual length of the sequence rather than the actual length
of the sequence
input_ids_with_output: Whether to put the groundtruth output
corresponding to the question into the dataset. Typically set
it to True during training and False during testing.
with_image_token: Whether to convert DEFAULT_IMAGE_TOKEN to
IMAGE_TOKEN_INDEX. Typically set it to True during the training
of VLM.
map_num_proc: Max number of processes when mapping the dataset.
"""
kwargs = dict(
dataset=dataset,
do_dataset_tokenization=do_dataset_tokenization,
tokenizer=tokenizer,
max_length=max_length,
dataset_map_fn=dataset_map_fn,
template_map_fn=template_map_fn,
max_dataset_length=max_dataset_length,
split=split,
remove_unused_columns=remove_unused_columns,
rename_maps=rename_maps,
shuffle_before_pack=shuffle_before_pack,
pack_to_max_length=pack_to_max_length,
use_varlen_attn=use_varlen_attn,
input_ids_with_output=input_ids_with_output,
with_image_token=with_image_token,
map_num_proc=map_num_proc)
if not (dist.is_available() and dist.is_initialized()):
return process(**kwargs)
xtuner_dataset_timeout = timedelta(
minutes=int(os.getenv('XTUNER_DATASET_TIMEOUT', default=30)))
print_log(
f'xtuner_dataset_timeout = {xtuner_dataset_timeout}', logger='current')
# monitored barrier requires gloo process group to perform host-side sync.
group_gloo = dist.new_group(backend='gloo', timeout=xtuner_dataset_timeout)
if dist.get_rank() == 0:
dataset = process(**kwargs)
objects = [dataset]
else:
objects = [None]
dist.monitored_barrier(group=group_gloo, timeout=xtuner_dataset_timeout)
dist.broadcast_object_list(objects, src=0)
return objects[0]
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