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'''
Dataset factory to load data from huggingface and others.
'''
import dataclasses
import logging
from typing import List, Optional
import numpy as np
import tensorflow as tf
from .data_utils import add_segment_ids
from .dataset_sizes import get_dataset_size
from .tasks import get_task
from .multimodal_preprocessor import MultiModalPreprocessor
import seqio
from .torch_util import get_global_rank
log = logging.getLogger(__name__)
@dataclasses.dataclass
class SeqioDataset:
mixture_or_task_name: str
seq_len: int
global_batch_size: int
max_crops: int = None
is_training: bool = False
for_inference: bool = False
split: str = 'train'
shuffle: bool = True
num_epochs: int = None
drop_remainder: bool = True
seed: int = None
pack: bool = False
use_custom_packing_ops: bool = False
use_memory_cache: bool = False
shuffle_buffer_size: Optional[int] = None
different_host_mixture_seeds: bool = True
disable_autotune: bool = True
trim_output_features: bool = True
@classmethod
def from_dict(cls, data):
return cls(**data)
def get_task_feature_lengths_dict(self, max_crops):
if self.max_crops is not None:
assert self.max_crops >= max_crops
max_crops = self.max_crops
return dict(
target_tokens=self.seq_len,
loss_masks=self.seq_len,
images=max_crops,
image_positions=max_crops,
image_input_idx=max_crops,
is_training=self.is_training
)
def build(self, preprocessor: MultiModalPreprocessor, shard_id, num_shards):
shard_info = seqio.ShardInfo(index=shard_id, num_shards=num_shards)
task_feature_lengths_dict = self.get_task_feature_lengths_dict(
preprocessor.get_max_total_crops())
seed = self.seed
assert seed is not None
batch_size = self.global_batch_size // num_shards
if isinstance(self.mixture_or_task_name, (dict, list, tuple)):
if isinstance(self.mixture_or_task_name, dict):
items = self.mixture_or_task_name.items()
else:
items = self.mixture_or_task_name
task_list = []
for task, weight in items:
task = get_task(preprocessor, task, self.is_training, self.for_inference)
task_list.append((task, weight))
mixture_or_task = task_list
else:
mixture_or_task = get_task(
preprocessor, self.mixture_or_task_name, self.is_training, self.for_inference)
in_memory_shuffle = self.shuffle
if not self.drop_remainder:
# Used if we want to evaluate on an eval dataset without dropping any examples.
# To do this, we pad the dataset with dummy examples marked as invalid in their
# metadata so we can still get fixed-sized batches.
assert self.num_epochs is not None
assert not self.pack
assert not isinstance(mixture_or_task, list), "Inference datasets cannot be mixtures"
logging.info(
f"Initializing inf. dataset {mixture_or_task.name}: replica_batch_size={batch_size}"
f' seed={seed}, sharding={shard_info.index}/{shard_info.num_shards}'
)
ds = mixture_or_task.get_dataset(
sequence_length=task_feature_lengths_dict,
split=self.split,
shuffle=in_memory_shuffle,
num_epochs=self.num_epochs,
seed=seed,
try_in_mem_cache=self.use_memory_cache,
trim_output_features=self.trim_output_features
)
try:
n = len(ds)
except TypeError:
dataset_len = get_dataset_size(self.mixture_or_task_name, self.split)
logging.info(f"Setting dataset len to {dataset_len} based on DATASET_SIZES")
n = dataset_len
ds = tf.data.experimental.assert_cardinality(n)(ds)
remainder = n % self.global_batch_size
if remainder > 0:
n_to_pad = self.global_batch_size - remainder
else:
n_to_pad = 0
assert "metadata/valid" not in ds.element_spec
def add_valid(x):
x["metadata/valid"] = True
return x
def add_invalid(x):
x["metadata/valid"] = False
return x
ds = ds.map(add_valid)
if n_to_pad > 0:
to_pad = ds.take(1).map(add_invalid).cache().repeat(n_to_pad)
ds = ds.concatenate(to_pad)
# Shard after padding to ensure shards are the same length
ds = ds.shard(num_shards=num_shards, index=shard_id)
ds = preprocessor.get_post_mixing_preprocessor()(
ds, task_feature_lengths=task_feature_lengths_dict)
data_iter = ds.batch(batch_size, drop_remainder=True, num_parallel_calls=tf.data.experimental.AUTOTUNE)
# Make it possible for client to get the size of the batched/sharded dataset with `len()`
new_len = (n + n_to_pad) // self.global_batch_size
data_iter = tf.data.experimental.assert_cardinality(new_len)(data_iter)
else:
if isinstance(mixture_or_task, list):
total_rate = sum(x[1] for x in mixture_or_task)
mixture_or_task = [(task, r/total_rate) for task, r in mixture_or_task]
sorted_tasks: List[seqio.Task] = sorted(mixture_or_task, key=lambda x: -x[1])
if self.different_host_mixture_seeds and shard_info:
# If each process has the same seed they will draw from the datasets in the same
# order, which can make the global batches very non-random if there are
# many processes each with a small batch size. To fix this, we give each host
# a different seed based on its rank to use when mixing
mix_seed = seed + shard_info.index*4397
else:
mix_seed = seed
logging.info(
f"Initializing mixture: replica_batch_size={batch_size} seed={seed}, "
f"mix_seed={mix_seed}, sharding={shard_info.index}/{shard_info.num_shards} rates:"
)
for task, rate in sorted_tasks:
logging.info(f"\t{task.name}: {rate:0.4f}")
datasets = []
rates = []
for task, rate in sorted_tasks:
assert rate > 0
datasets.append(task.get_dataset(
task_feature_lengths_dict,
split=self.split,
shuffle=self.shuffle,
seed=seed,
shard_info=shard_info,
num_epochs=self.num_epochs,
try_in_mem_cache=self.use_memory_cache,
trim_output_features=self.trim_output_features
))
rates.append(rate)
# If any of the sub-tasks have subsegment_ids, we need to ensure all the tasks have
# a subsegment_ids field so they can be mixed
if any("subsegment_ids" in ds.element_spec for ds in datasets):
for ix, ds in enumerate(datasets):
if "subsegment_ids" not in ds.element_spec:
datasets[ix] = add_segment_ids(ds)
ds = tf.data.Dataset.sample_from_datasets(
datasets, rates, seed=mix_seed, stop_on_empty_dataset=False)
else:
logging.info(
f"Initializing dataset {mixture_or_task.name}: replica_batch_size={batch_size}"
f' seed={seed}, sharding={shard_info.index}/{shard_info.num_shards}'
)
ds = mixture_or_task.get_dataset(
task_feature_lengths_dict,
split=self.split,
shuffle=self.shuffle,
seed=seed,
shard_info=shard_info,
num_epochs=self.num_epochs,
try_in_mem_cache=self.use_memory_cache,
trim_output_features=self.trim_output_features
)
data_iter = preprocessor.get_post_mixing_preprocessor()(
ds, task_feature_lengths=task_feature_lengths_dict)
data_iter = data_iter.batch(batch_size, drop_remainder=True, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds = ds.prefetch(2)
# Following https://github.com/google-research/big_vision/blob/b8dab6e4de3436849415f37c591399c93b1eaf39/big_vision/input_pipeline.py#L228
# These options try to stop tf datasets from eating all our RAM if we are using a
# large mixture
# This options are used by default in some google codebases
# For example: (https://github.com/google-research/big_vision/blob/b8dab6e4de3436849415f37c591399c93b1eaf39/big_vision/input_pipeline.py#L228)
# They don't seem to harm throughput and can save RAM so we use them as well
options = tf.data.Options()
options.experimental_optimization.inject_prefetch = False
options.threading.max_intra_op_parallelism = 1
if self.disable_autotune:
# Following https://www.tensorflow.org/datasets/performances
# This reduces RAM and checkpoint size by a lot
options.autotune.enabled = False
data_iter = data_iter.with_options(options)
return data_iter
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