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326 lines (295 loc) · 12.2 KB
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from dataclasses import dataclass, field
from functools import partial
import jax
import jax.numpy as jnp
import numpy as np
from braceexpand import braceexpand
from datasets import Dataset, load_dataset
from .text import TextNormalizer
@dataclass
class Dataset:
dataset_repo_or_path: str
train_file: str = None
validation_file: str = None
streaming: bool = True
use_auth_token: bool = False
text_column: str = "caption"
encoding_column: str = "encoding"
max_train_samples: int = None
max_eval_samples: int = None
preprocessing_num_workers: int = None
overwrite_cache: bool = False
do_train: bool = False
do_eval: bool = True
seed_dataset: int = None
shard_by_host: bool = False
blank_caption_prob: float = 0.0
train_dataset: Dataset = field(init=False)
eval_dataset: Dataset = field(init=False)
rng_dataset: jnp.ndarray = field(init=False)
multi_hosts: bool = field(init=False)
def __post_init__(self):
self.multi_hosts = jax.process_count() > 1
# feed blank captions only in streaming mode for now
if self.blank_caption_prob:
assert (
self.streaming is True
), "blank_caption_prob can only be used in streaming mode"
# define data_files
if self.train_file is not None or self.validation_file is not None:
# accept braceexpand notation
for k in ["train_file", "validation_file"]:
f = getattr(self, k)
if isinstance(f, str):
setattr(self, k, list(braceexpand(f)))
# for list of files, split training data shards by host
if (
isinstance(self.train_file, list)
and self.multi_hosts
and self.shard_by_host
):
self.train_file = self.train_file[
jax.process_index() :: jax.process_count()
]
data_files = {
"train": self.train_file,
"validation": self.validation_file,
}
else:
data_files = None
# load dataset
dataset = load_dataset(
self.dataset_repo_or_path,
data_files=data_files,
streaming=self.streaming,
use_auth_token=self.use_auth_token,
)
if self.do_train:
if "train" not in dataset:
raise ValueError("Training requires a training dataset")
self.train_dataset = dataset["train"]
if self.max_train_samples is not None:
self.train_dataset = (
self.train_dataset.take(self.max_train_samples)
if self.streaming
else self.train_dataset.select(range(self.max_train_samples))
)
if self.do_eval:
if "validation" not in dataset:
raise ValueError("Evaluating requires a validation dataset")
self.eval_dataset = dataset["validation"]
if self.max_eval_samples is not None:
self.eval_dataset = (
self.eval_dataset.take(self.max_eval_samples)
if self.streaming
else self.eval_dataset.select(range(self.max_eval_samples))
)
def preprocess(self, tokenizer, config):
# get required config variables
decoder_start_token_id = config.decoder_start_token_id
normalize_text = config.normalize_text
max_length = config.max_text_length
if self.streaming:
# we need to shuffle early in streaming mode
if hasattr(self, "train_dataset"):
self.train_dataset = self.train_dataset.shuffle(5000, self.seed_dataset)
else:
# prepare rng for later shuffling
if self.seed_dataset is None:
self.seed_dataset = np.random.get_state()[1][0]
self.rng_dataset = jax.random.PRNGKey(self.seed_dataset)
# blank captions
if self.blank_caption_prob:
partial_blank_caption_function = partial(
blank_caption_function,
text_column=self.text_column,
blank_caption_prob=self.blank_caption_prob,
)
if hasattr(self, "train_dataset"):
self.train_dataset = (
self.train_dataset.map(partial_blank_caption_function)
if self.streaming
else self.train_dataset.map(
partial_blank_caption_function,
num_proc=self.preprocessing_num_workers,
load_from_cache_file=False,
desc="Blanking some captions",
)
)
# normalize text
if normalize_text:
text_normalizer = TextNormalizer()
partial_normalize_function = partial(
normalize_function,
text_column=self.text_column,
text_normalizer=text_normalizer,
)
for ds in ["train_dataset", "eval_dataset"]:
if hasattr(self, ds):
setattr(
self,
ds,
(
getattr(self, ds).map(partial_normalize_function)
if self.streaming
else getattr(self, ds).map(
partial_normalize_function,
num_proc=self.preprocessing_num_workers,
load_from_cache_file=not self.overwrite_cache,
desc="Normalizing datasets",
)
),
)
# preprocess
partial_preprocess_function = partial(
preprocess_function,
tokenizer=tokenizer,
text_column=self.text_column,
encoding_column=self.encoding_column,
max_length=max_length,
decoder_start_token_id=decoder_start_token_id,
)
for ds in ["train_dataset", "eval_dataset"]:
if hasattr(self, ds):
setattr(
self,
ds,
(
getattr(self, ds).map(
partial_preprocess_function,
batched=True,
remove_columns=[
self.text_column,
self.encoding_column,
],
)
if self.streaming
else getattr(self, ds).map(
partial_preprocess_function,
batched=True,
remove_columns=getattr(ds, "column_names"),
num_proc=self.preprocessing_num_workers,
load_from_cache_file=not self.overwrite_cache,
desc="Preprocessing datasets",
)
),
)
def dataloader(self, split, batch_size, epoch=None):
def _dataloader_datasets_non_streaming(
dataset: Dataset,
rng: jax.random.PRNGKey = None,
):
"""
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
Shuffle batches if rng is set.
"""
steps_per_epoch = len(dataset) // batch_size
if rng is not None:
batch_idx = jax.random.permutation(rng, len(dataset))
else:
batch_idx = jnp.arange(len(dataset))
batch_idx = batch_idx[
: steps_per_epoch * batch_size
] # Skip incomplete batch.
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
for idx in batch_idx:
batch = dataset[idx]
batch = {k: jnp.array(v) for k, v in batch.items()}
yield batch
def _dataloader_datasets_streaming(
dataset: Dataset,
epoch: int,
):
keys = ["input_ids", "attention_mask", "labels", "decoder_input_ids"]
batch = {k: [] for k in keys}
first_loop = True # stop after one loop in some cases
while (self.multi_hosts and split == "train") or first_loop:
# in multi-host, we run forever (no epoch) as hosts need to stop
# at the same time and training data may not be split equally
# For validation data we put the entire batch on each host and then
# keep only the one specific to each host (could be improved but not necessary)
if epoch is not None:
assert split == "train"
# reshuffle training data at each epoch
dataset.set_epoch(epoch)
epoch += 1
for item in dataset:
for k, v in item.items():
batch[k].append(v)
if len(batch[keys[0]]) == batch_size:
batch = {k: jnp.array(v) for k, v in batch.items()}
yield batch
batch = {k: [] for k in keys}
first_loop = False
if split == "train":
ds = self.train_dataset
elif split == "eval":
ds = self.eval_dataset
else:
raise ValueError(f'split must be "train" or "eval", got {split}')
if self.streaming:
return _dataloader_datasets_streaming(ds, epoch)
else:
if split == "train":
self.rng_dataset, input_rng = jax.random.split(self.rng_dataset)
return _dataloader_datasets_non_streaming(ds, input_rng)
@property
def length(self):
len_train_dataset, len_eval_dataset = None, None
if self.streaming:
# we don't know the length, let's just assume max_samples if defined
if self.max_train_samples is not None:
len_train_dataset = self.max_train_samples
if self.max_eval_samples is not None:
len_eval_dataset = self.max_eval_samples
else:
len_train_dataset = (
len(self.train_dataset) if hasattr(self, "train_dataset") else None
)
len_eval_dataset = (
len(self.eval_dataset) if hasattr(self, "eval_dataset") else None
)
return len_train_dataset, len_eval_dataset
def shift_tokens_right(input_ids: np.array, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = np.zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1]
shifted_input_ids[:, 0] = decoder_start_token_id
return shifted_input_ids
def blank_caption_function(example, text_column, blank_caption_prob):
if blank_caption_prob and np.random.rand() < blank_caption_prob:
example[text_column] = ""
return example
def normalize_function(example, text_column, text_normalizer):
example[text_column] = text_normalizer(example[text_column])
return example
def preprocess_function(
examples,
tokenizer,
text_column,
encoding_column,
max_length,
decoder_start_token_id,
):
inputs = examples[text_column]
# Setting padding="max_length" as we need fixed length inputs for jitted functions
model_inputs = tokenizer(
inputs,
max_length=max_length,
padding="max_length",
truncation=True,
return_tensors="np",
)
# set up targets
# Note: labels correspond to our target indices
# decoder input ids are the same but shifted to the right with bos at the beginning (and without last token)
labels = examples[encoding_column]
labels = np.asarray(labels)
# We need the labels, in addition to the decoder_input_ids, for the compute_loss function
model_inputs["labels"] = labels
# In our case, this prepends the bos token and removes the last one
decoder_input_ids = shift_tokens_right(labels, decoder_start_token_id)
model_inputs["decoder_input_ids"] = decoder_input_ids
return model_inputs