boris's picture
feat(train): improve pjit speed
f254058
raw
history blame
10.9 kB
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
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
# 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, decoder_start_token_id, normalize_text, max_length):
if self.streaming:
# we need to shuffle early in streaming mode
if hasattr(self, "train_dataset"):
self.train_dataset = self.train_dataset.shuffle(1000, 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)
# 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,
)
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 set on each host as we could lose
# too many samples on pods
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 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