text stringlengths 1 1.02k | class_index int64 0 271 | source stringclasses 76
values |
|---|---|---|
self._state_dict["shard_example_idx"] = 0 | 16 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "ArrowExamplesIterable":
"""Keep only the requested shard."""
rng = deepcopy(self.generator)
kwargs_with_shuffled_shards = _shuffle_gen_kwargs(rng, self.kwargs)
return ArrowExamplesIterable(self.generate_tables... | 16 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class RebatchedArrowExamplesIterable(_BaseExamplesIterable):
def __init__(self, ex_iterable: _BaseExamplesIterable, batch_size: Optional[int], drop_last_batch: bool = False):
super().__init__()
self.ex_iterable = ex_iterable
self.batch_size = batch_size
self.drop_last_batch = drop_la... | 17 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter_arrow(self) -> Iterator[Tuple[Key, pa.Table]]:
"""Iterate over sub-tables of size `batch_size`."""
if self._state_dict and self._state_dict["previous_state"]:
self.ex_iterable.load_state_dict(self._state_dict["previous_state"])
if self.ex_iterable.iter_arrow:
it... | 17 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
chunk_length_to_crop = self._state_dict["cropped_chunk_length"] if self._state_dict else 0
if self._state_dict:
previous_state = self.ex_iterable.state_dict()
self._state_dict["previous_state"] = previous_state
for key, pa_table in iterator:
for num_chunks_since_previ... | 17 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if chunks_buffer_size + len(chunk) < self.batch_size:
keys_buffer.append(key)
chunks_buffer.append(chunk)
chunks_buffer_size += len(chunk)
continue
elif chunks_buffer_size + len(chunk) == self.batch_size:
... | 17 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
self._state_dict["previous_state"] = previous_state
self._state_dict["num_chunks_since_previous_state"] = num_chunks_since_previous_state + 1
else:
cropped_chunk_length = self.batch_size - chunks_buffer_size
keys_buffer.append(f"{key}[:{cro... | 17 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
chunks_buffer = [chunk.slice(cropped_chunk_length, len(chunk) - cropped_chunk_length)]
chunks_buffer_size = len(chunk) - cropped_chunk_length
if self._state_dict:
self._state_dict["previous_state"] = previous_state
self._state_dict[... | 17 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, generator: np.random.Generator) -> "RebatchedArrowExamplesIterable":
return RebatchedArrowExamplesIterable(
self.ex_iterable.shuffle_data_sources(generator), self.batch_size, self.drop_last_batch
)
def shard_data_sources(self, num_shards: int, index: int, ... | 17 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class SelectColumnsIterable(_BaseExamplesIterable):
def __init__(self, ex_iterable: _BaseExamplesIterable, column_names: List[str]):
super().__init__()
self.ex_iterable = ex_iterable
self.column_names = column_names
@property
def iter_arrow(self):
if self.ex_iterable.iter_ar... | 18 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter_arrow(self) -> Iterator[Tuple[Key, pa.Table]]:
for idx, pa_table in self.ex_iterable.iter_arrow():
if len(pa_table) > 0: # empty tables have no schema
yield idx, pa_table.select(self.column_names)
def shuffle_data_sources(self, generator: np.random.Generator) -> "Sele... | 18 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class StepExamplesIterable(_BaseExamplesIterable):
def __init__(self, ex_iterable: _BaseExamplesIterable, step: int, offset: int):
super().__init__()
self.ex_iterable = ex_iterable
self.step = step
self.offset = offset
# TODO(QL): implement iter_arrow
@property
def i... | 19 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, generator: np.random.Generator) -> "StepExamplesIterable":
return StepExamplesIterable(
self.ex_iterable.shuffle_data_sources(generator), step=self.step, offset=self.offset
)
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "St... | 19 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class CyclingMultiSourcesExamplesIterable(_BaseExamplesIterable):
def __init__(
self,
ex_iterables: List[_BaseExamplesIterable],
stopping_strategy: Literal["first_exhausted", "all_exhausted"] = "first_exhausted",
):
super().__init__()
self.ex_iterables = ex_iterables
... | 20 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _get_indices_iterator(self):
# this is an infinite iterator to keep track of which iterator we want to pick examples from
ex_iterable_idx = self._state_dict["ex_iterable_idx"] if self._state_dict else 0
for next_ex_iterable_idx in islice(cycle(range(len(self.ex_iterables))), ex_iterable_idx ... | 20 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __iter__(self):
# we use this to buffer one example of each iterator to know if an iterator is exhausted
nexts = [None] * len(self.ex_iterables)
# because of that, we need to rewind 1 example when reloading the state dict
if self._state_dict:
for i in range(len(self.ex_it... | 20 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
is_exhausted = (
np.array(self._state_dict["is_exhausted"]) if self._state_dict else np.full(len(self.ex_iterables), False)
)
for i in indices_iterator:
# if the stopping criteria is met, break the main for loop
if self.bool_strategy_func(is_exhausted):
... | 20 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
# the iterator is exhausted
if nexts[i] is False:
is_exhausted[i] = True
if self._state_dict:
self._state_dict["is_exhausted"][i] = True
# we reset it in case the stopping crtieria isn't met yet
nexts[i] = None
... | 20 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
@property
def num_shards(self) -> int:
return min(ex_iterable.num_shards for ex_iterable in self.ex_iterables)
def shard_data_sources(
self, num_shards: int, index: int, contiguous=True
) -> "CyclingMultiSourcesExamplesIterable":
"""Either keep only the requested shard, or propagate... | 20 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class VerticallyConcatenatedMultiSourcesExamplesIterable(_BaseExamplesIterable):
"""
VerticallyConcatenatedMultiSourcesExamplesIterable simply chains the input iterables.
It doesn't require the examples iterables to always yield the same columns.
Instead, this is handled by the `IterableDataset` class o... | 21 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
@property
def features(self):
return self.ex_iterables[0].features
@property
def iter_arrow(self):
if all(ex_iterable.iter_arrow is not None for ex_iterable in self.ex_iterables):
return self._iter_arrow
def _init_state_dict(self) -> dict:
self._state_dict = {
... | 21 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter_arrow(self):
ex_iterable_idx_start = self._state_dict["ex_iterable_idx"] if self._state_dict else 0
for ex_iterable in islice(self.ex_iterables, ex_iterable_idx_start, None):
yield from ex_iterable.iter_arrow()
if self._state_dict:
self._state_dict["ex_i... | 21 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shard_data_sources(
self, num_shards: int, index: int, contiguous=True
) -> "VerticallyConcatenatedMultiSourcesExamplesIterable":
"""Either keep only the requested shard, or propagate the request to the underlying iterable."""
return VerticallyConcatenatedMultiSourcesExamplesIterable(
... | 21 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class HorizontallyConcatenatedMultiSourcesExamplesIterable(_BaseExamplesIterable):
"""
HorizontallyConcatenatedMultiSourcesExamplesIterable merges examples together for the input list of iterables.
It also checks that there are no duplicate columns (otherwise we don't know which one to keep).
This check... | 22 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __init__(self, ex_iterables: List[_BaseExamplesIterable]):
super().__init__()
self.ex_iterables = ex_iterables
# TODO(QL): implement iter_arrow
@property
def is_typed(self):
return self.ex_iterables[0].is_typed
@property
def features(self):
return self.ex_it... | 22 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __iter__(self):
ex_iterators = [iter(ex_iterable) for ex_iterable in self.ex_iterables]
for i in itertools.count():
keys = []
examples = []
for ex_iterator in list(ex_iterators):
try:
key, example = next(ex_iterator)
... | 22 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(
self, generator: np.random.Generator
) -> "HorizontallyConcatenatedMultiSourcesExamplesIterable":
"""Doesn't shuffle the wrapped examples iterable since it would break the alignment between them."""
return self
@property
def num_shards(self) -> int:
... | 22 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class RandomlyCyclingMultiSourcesExamplesIterable(CyclingMultiSourcesExamplesIterable):
def __init__(
self,
ex_iterables: List[_BaseExamplesIterable],
generator: np.random.Generator,
probabilities: Optional[List[float]] = None,
stopping_strategy: Literal["first_exhausted", "a... | 23 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _get_indices_iterator(self):
rng = deepcopy(self.generator)
num_sources = len(self.ex_iterables)
random_batch_size = 1000
# this is an infinite iterator that randomly samples the index of the source to pick examples from
index_offset = self._state_dict["bit_generator_index_of... | 23 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
while True:
for i in islice(
rng.choice(num_sources, size=random_batch_size, p=self.probabilities), index_offset, None
):
index_offset = (index_offset + 1) % random_batch_size
if self._state_dict:
self._s... | 23 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _init_state_dict(self) -> dict:
self._state_dict = {
"bit_generator_state": self.generator.bit_generator.state,
"bit_generator_index_offset": 0,
"ex_iterables": [ex_iterable._init_state_dict() for ex_iterable in self.ex_iterables],
"previous_states": [None] * ... | 23 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shard_data_sources(
self, num_shards: int, index: int, contiguous=True
) -> "RandomlyCyclingMultiSourcesExamplesIterable":
"""Either keep only the requested shard, or propagate the request to the underlying iterable."""
return RandomlyCyclingMultiSourcesExamplesIterable(
[ite... | 23 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class MappedExamplesIterable(_BaseExamplesIterable):
def __init__(
self,
ex_iterable: _BaseExamplesIterable,
function: Callable,
with_indices: bool = False,
input_columns: Optional[List[str]] = None,
batched: bool = False,
batch_size: Optional[int] = 1000,
... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
self._features = features
# sanity checks
if formatting and formatting.format_type == "arrow":
# batch_size should match for iter_arrow
if not isinstance(ex_iterable, RebatchedArrowExamplesIterable):
raise ValueError(
"The Arrow-formatted Mappe... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
@property
def iter_arrow(self):
if self.formatting and self.formatting.format_type == "arrow":
return self._iter_arrow
@property
def is_typed(self):
return self.features is not None # user has extracted features
@property
def features(self):
return self._featur... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter(self):
current_idx = self._state_dict["previous_state_example_idx"] if self._state_dict else 0
if self._state_dict and self._state_dict["previous_state"]:
self.ex_iterable.load_state_dict(self._state_dict["previous_state"])
num_examples_to_skip = self._state_dict["num_e... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if self.batched:
if self._state_dict:
self._state_dict["previous_state"] = self.ex_iterable.state_dict()
self._state_dict["num_examples_since_previous_state"] = 0
self._state_dict["previous_state_example_idx"] = current_idx
for key, example in iter... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
): # ignore last batch
return
batch = _examples_to_batch(examples)
batch = format_dict(batch) if format_dict else batch
# then apply the transform
inputs = batch
function_args = [inputs] if self.input_columns is None el... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
del processed_inputs[c]
transformed_batch = {**inputs_to_merge, **processed_inputs}
if transformed_batch:
first_col = next(iter(transformed_batch))
bad_cols = [
col
for col in transformed_batch
... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
transformed_batch[c] = [None] * len(transformed_batch[first_col])
transformed_batch = self.features.decode_batch(transformed_batch)
# the new key is the concatenation of the examples keys from the batch
new_key = "_".join(str(key) for key in keys)
# yi... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
self._state_dict["previous_state_example_idx"] = current_idx
else:
for key, example in iterator:
# If not batched, we can apply the transform and yield the example directly
# first copy the example, since we might drop some keys
example = dict(example)... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if c in inputs_to_merge:
del inputs_to_merge[c]
if processed_inputs is inputs and c in processed_inputs:
del processed_inputs[c]
transformed_example = {**inputs_to_merge, **processed_inputs}
if self.features:... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter_arrow(self, max_chunksize: Optional[int] = None) -> Iterator[Tuple[Key, pa.Table]]:
if self.ex_iterable.iter_arrow:
iterator = self.ex_iterable.iter_arrow()
else:
iterator = _convert_to_arrow(
self.ex_iterable,
batch_size=self.batch_size ... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
for key, pa_table in iterator:
if (
self.batched
and self.batch_size is not None
and len(pa_table) < self.batch_size
and self.drop_last_batch
):
return
# first build the batch
function_args = ... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
f"{type(output_table)}. Make sure provided `function` returns a a pyarrow table to update the dataset."
)
# we don't need to merge results for consistency with Dataset.map which merges iif both input and output are dicts
# then remove the unwanted columns
if self.remo... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if self._state_dict:
self._state_dict["num_examples_since_previous_state"] += 1
if num_examples_to_skip > 0:
num_examples_to_skip -= 1
continue
yield f"{key}_{i}", pa_subtable
if self._state_d... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, generator: np.random.Generator) -> "MappedExamplesIterable":
"""Shuffle the wrapped examples iterable."""
return MappedExamplesIterable(
self.ex_iterable.shuffle_data_sources(generator),
function=self.function,
with_indices=self.with_ind... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "MappedExamplesIterable":
"""Keep only the requested shard."""
return MappedExamplesIterable(
self.ex_iterable.shard_data_sources(num_shards, index, contiguous=contiguous),
function=self.function,
... | 24 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class FilteredExamplesIterable(_BaseExamplesIterable):
def __init__(
self,
ex_iterable: _BaseExamplesIterable,
function: Callable,
with_indices: bool = False,
input_columns: Optional[List[str]] = None,
batched: bool = False,
batch_size: Optional[int] = 1000,
... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
raise ValueError(
"The Arrow-formatted FilteredExamplesIterable has underlying iterable"
f"that is a {type(ex_iterable).__name__} instead of a RebatchedArrowExamplesIterable."
)
elif ex_iterable.batch_size != (batch_size if batched else 1):
... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
@property
def iter_arrow(self):
if self.formatting and self.formatting.format_type == "arrow":
return self._iter_arrow
@property
def is_typed(self):
return self.ex_iterable.is_typed
@property
def features(self):
return self.ex_iterable.features
def _init_st... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter(self):
current_idx = self._state_dict["previous_state_example_idx"] if self._state_dict else 0
if self._state_dict and self._state_dict["previous_state"]:
self.ex_iterable.load_state_dict(self._state_dict["previous_state"])
num_examples_to_skip = self._state_dict["num_e... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if self.batched:
if self._state_dict:
self._state_dict["previous_state"] = self.ex_iterable.state_dict()
self._state_dict["num_examples_since_previous_state"] = 0
self._state_dict["previous_state_example_idx"] = current_idx
for key, example in iter... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
function_args = [inputs] if self.input_columns is None else [inputs[col] for col in self.input_columns]
if self.with_indices:
function_args.append([current_idx + i for i in range(len(key_examples_list))])
mask = self.function(*function_args, **self.fn_kwargs)
... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
self._state_dict["num_examples_since_previous_state"] = 0
self._state_dict["previous_state_example_idx"] = current_idx
else:
for key, example in iterator:
# If not batched, we can apply the filtering function direcly
example = dict(example)
... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter_arrow(self, max_chunksize: Optional[int] = None):
if self.ex_iterable.iter_arrow:
iterator = self.ex_iterable.iter_arrow()
else:
iterator = _convert_to_arrow(self.ex_iterable, batch_size=self.batch_size if self.batched else 1) | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if self._state_dict and self._state_dict["previous_state"]:
self.ex_iterable.load_state_dict(self._state_dict["previous_state"])
num_examples_to_skip = self._state_dict["num_examples_since_previous_state"]
else:
num_examples_to_skip = 0
if self._state_dict and max_chu... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
function_args = [pa_table] if self.input_columns is None else [pa_table[col] for col in self.input_columns]
if self.with_indices:
if self.batched:
function_args.append([current_idx + i for i in range(len(pa_table))])
else:
function_args... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if max_chunksize is None:
current_idx += len(pa_table)
if self._state_dict:
self._state_dict["previous_state_example_idx"] += len(pa_table)
if len(output_table) > 0:
yield key, output_table
else:
for i, p... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
self._state_dict["previous_state_example_idx"] += len(pa_table) | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, seed: Optional[int]) -> "FilteredExamplesIterable":
"""Shuffle the wrapped examples iterable."""
return FilteredExamplesIterable(
self.ex_iterable.shuffle_data_sources(seed),
function=self.function,
with_indices=self.with_indices,
... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "FilteredExamplesIterable":
"""Keep only the requested shard."""
return FilteredExamplesIterable(
self.ex_iterable.shard_data_sources(num_shards, index, contiguous=contiguous),
function=self.function,
... | 25 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class BufferShuffledExamplesIterable(_BaseExamplesIterable):
def __init__(self, ex_iterable: _BaseExamplesIterable, buffer_size: int, generator: np.random.Generator):
super().__init__()
self.ex_iterable = ex_iterable
self.buffer_size = buffer_size
self.generator = generator
#... | 26 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def load_state_dict(self, state_dict: dict) -> dict:
if self._state_dict:
if state_dict != self._original_state_dict:
logger.warning(
"Loading a state dict of a shuffle buffer of a dataset without the buffer content."
"The shuffle buffer will b... | 26 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __iter__(self):
buffer_size = self.buffer_size
rng = deepcopy(self.generator)
indices_iterator = self._iter_random_indices(rng, buffer_size)
# this is the shuffle buffer that we keep in memory
mem_buffer = []
for x in self.ex_iterable:
if len(mem_buffer) =... | 26 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, generator: np.random.Generator) -> "BufferShuffledExamplesIterable":
"""Shuffle the wrapped examples iterable as well as the shuffling buffer."""
return BufferShuffledExamplesIterable(
self.ex_iterable.shuffle_data_sources(generator), buffer_size=self.buffer_si... | 26 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class SkipExamplesIterable(_BaseExamplesIterable):
def __init__(
self,
ex_iterable: _BaseExamplesIterable,
n: int,
block_sources_order_when_shuffling: bool = True,
split_when_sharding: bool = True,
):
super().__init__()
self.ex_iterable = ex_iterable
... | 27 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __iter__(self):
ex_iterable_idx_start = 0 if self._state_dict and self._state_dict["skipped"] else self.n
if self._state_dict:
self._state_dict["skipped"] = True
yield from islice(self.ex_iterable, ex_iterable_idx_start, None)
@staticmethod
def split_number(num, n):
... | 27 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, generator: np.random.Generator) -> "SkipExamplesIterable":
"""May not shuffle the wrapped examples iterable since it would skip examples from other shards instead."""
if self.block_sources_order_when_shuffling:
return self
else:
return SkipE... | 27 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "SkipExamplesIterable":
"""Keep only the requested shard."""
if self.split_when_sharding:
return SkipExamplesIterable(
self.ex_iterable.shard_data_sources(num_shards, index, contiguous=contiguous),
... | 27 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class TakeExamplesIterable(_BaseExamplesIterable):
def __init__(
self,
ex_iterable: _BaseExamplesIterable,
n: int,
block_sources_order_when_shuffling: bool = True,
split_when_sharding: bool = True,
):
super().__init__()
self.ex_iterable = ex_iterable
... | 28 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __iter__(self):
ex_iterable_num_taken = self._state_dict["num_taken"] if self._state_dict else 0
for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
if self._state_dict:
self._state_dict["num_taken"] += 1
yield key_example
@static... | 28 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, generator: np.random.Generator) -> "TakeExamplesIterable":
"""May not shuffle the wrapped examples iterable since it would take examples from other shards instead."""
if self.block_sources_order_when_shuffling:
return self
else:
return TakeE... | 28 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shard_data_sources(self, num_shards: int, index: int, contiguous=True) -> "TakeExamplesIterable":
"""Keep only the requested shard."""
if self.split_when_sharding:
return TakeExamplesIterable(
self.ex_iterable.shard_data_sources(num_shards, index, contiguous=contiguous),
... | 28 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class FormattingConfig:
format_type: Optional[str]
def __post_init__(self):
if self.format_type == "pandas":
raise NotImplementedError(
"The 'pandas' formatting is not implemented for iterable datasets. You can use 'numpy' or 'arrow' instead."
) | 29 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class FormattedExamplesIterable(_BaseExamplesIterable):
def __init__(
self,
ex_iterable: _BaseExamplesIterable,
formatting: Optional[FormattingConfig],
features: Optional[Features],
token_per_repo_id: Dict[str, Union[str, bool, None]],
):
super().__init__()
... | 30 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __iter__(self):
if not self.formatting or self.formatting.format_type == "arrow":
formatter = PythonFormatter()
else:
formatter = get_formatter(
self.formatting.format_type,
features=self._features if not self.ex_iterable.is_typed else None,
... | 30 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
# don't apply feature types if already applied by ex_iterable (e.g. in case of chained with_format)
if self.features and not self.ex_iterable.is_typed:
example = _apply_feature_types_on_example(
example, self.features, token_per_repo_id=self.token_per_repo_id
... | 30 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter_arrow(self) -> Iterator[Tuple[Key, pa.Table]]:
if not self.features:
yield from self.ex_iterable._iter_arrow()
for key, pa_table in self.ex_iterable._iter_arrow():
columns = set(pa_table.column_names)
schema = self.features.arrow_schema
# add mis... | 30 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def shuffle_data_sources(self, generator: np.random.Generator) -> "FormattedExamplesIterable":
"""Shuffle the wrapped examples iterable."""
return FormattedExamplesIterable(
self.ex_iterable.shuffle_data_sources(generator),
features=self.features,
token_per_repo_id=se... | 30 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class ShufflingConfig:
generator: np.random.Generator
_original_seed: Optional[int] = None | 31 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class DistributedConfig:
rank: int
world_size: int | 32 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
class IterableDataset(DatasetInfoMixin):
"""A Dataset backed by an iterable."""
def __init__(
self,
ex_iterable: _BaseExamplesIterable,
info: Optional[DatasetInfo] = None,
split: Optional[NamedSplit] = None,
formatting: Optional[FormattingConfig] = None,
shufflin... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
self._ex_iterable = copy.copy(ex_iterable)
self._formatting = formatting
self._shuffling = shuffling
self._distributed = distributed
self._token_per_repo_id: Dict[str, Union[str, bool, None]] = token_per_repo_id or {}
self._epoch: Union[int, "torch.Tensor"] = _maybe_share_with_to... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
1. examples from shuffle buffers are lost when resuming and the buffers are refilled with new data
2. combinations of `.with_format(arrow)` and batched `.map()` may skip one batch.
Returns:
`dict`
Example:
```py
>>> from datasets import Dataset, concatenate_dataset... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
```py
>>> from torchdata.stateful_dataloader import StatefulDataLoader
>>> ds = load_dataset("deepmind/code_contests", streaming=True, split="train")
>>> dataloader = StatefulDataLoader(ds, batch_size=32, num_workers=4)
>>> # checkpoint
>>> state_dict = dataloader.state_dict() #... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
1. examples from shuffle buffers are lost when resuming and the buffers are refilled with new data
2. combinations of `.with_format(arrow)` and batched `.map()` may skip one batch.
Example:
```py
>>> from datasets import Dataset, concatenate_datasets
>>> ds = Dataset.from_dict(... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
```py
>>> from torchdata.stateful_dataloader import StatefulDataLoader
>>> ds = load_dataset("deepmind/code_contests", streaming=True, split="train")
>>> dataloader = StatefulDataLoader(ds, batch_size=32, num_workers=4)
>>> # checkpoint
>>> state_dict = dataloader.state_dict() #... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def __setstate__(self, d):
self.__dict__ = d
# Re-add torch shared memory, since shared memory is not always kept when pickling
self._epoch = _maybe_share_with_torch_persistent_workers(self._epoch)
# Re-add torch iterable dataset as a parent class, since dynamically added parent classes ... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _effective_generator(self):
if self._shuffling and self.epoch == 0:
return self._shuffling.generator
elif self._shuffling:
# Create effective seed using self.epoch (we subtract in order to avoir overflow in long_scalars)
effective_seed = deepcopy(self._shuffling.g... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _iter_pytorch(self):
ex_iterable = self._prepare_ex_iterable_for_iteration()
# Fix for fsspec when using multiprocess to avoid hanging in the ML training loop. (only required for fsspec >= 0.9.0)
# See https://github.com/fsspec/gcsfs/issues/379
fsspec.asyn.reset_lock()
# chec... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
worker_info = torch.utils.data.get_worker_info()
if self._is_main_process() and ex_iterable.num_shards < worker_info.num_workers:
logger.warning(
f"Too many dataloader workers: {worker_info.num_workers} (max is dataset.num_shards={ex_iterable.num_shards}). "
f"Stoppin... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
num_shards=worker_info.num_workers, index=worker_info.id, contiguous=False
)
if shards_indices:
logger.debug(
f"{_log_prefix}dataloader worker#{worker_info.id}, ': Starting to iterate over {len(shards_indices)}/{ex_iterable.num_shards} shards."
)
ex_it... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if self._formatting:
formatter = get_formatter(self._formatting.format_type, features=self.features)
format_dict = (
formatter.recursive_tensorize if isinstance(formatter, TensorFormatter) else cast_to_python_objects
)
else:
... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if self._formatting and (ex_iterable.iter_arrow or self._formatting == "arrow"):
if ex_iterable.iter_arrow:
iterator = ex_iterable.iter_arrow()
else:
iterator = _convert_to_arrow(ex_iterable, batch_size=1)
for key, pa_table in itera... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
f"{_log_prefix}dataloader worker#{worker_info.id}, ': Finished iterating over {len(shards_indices)}/{ex_iterable.num_shards} shards."
)
else:
logger.debug(
f"{_log_prefix}dataloader worker#{worker_info.id}, ': Stopping... Number of dataset shards < num_workers ({ex_iterab... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
def _is_main_process(self):
if self._distributed and self._distributed.rank > 0:
return False
if "torch" in sys.modules:
import torch.utils.data
worker_info = torch.utils.data.get_worker_info()
if worker_info is not None and worker_info.id > 0:
... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
if self._distributed:
rank = self._distributed.rank
world_size = self._distributed.world_size
if ex_iterable.num_shards % world_size == 0:
if self._is_main_process():
num_shards_per_node = ex_iterable.num_shards // world_size
pl... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
f"It is more optimized to distribute the dataset shards (or data sources) across nodes. "
f"You can do that by using a dataset with number of shards that is a factor of world_size={world_size}. "
f"The current dataset has {ex_iterable.num_shards} which is not a factor of ... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
self._state_dict = ex_iterable._init_state_dict()
if self._starting_state_dict:
ex_iterable.load_state_dict(self._starting_state_dict)
return ex_iterable
def __iter__(self):
if "torch" in sys.modules:
import torch.utils.data
worker_info = torch.utils.dat... | 33 | /Users/nielsrogge/Documents/python_projecten/datasets/src/datasets/iterable_dataset.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.