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microbatch.py
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microbatch.py
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# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Manipulation of micro-batches."""
import typing
from typing import Callable, Iterable, Iterator, List, Union, cast, Sequence
import torch
from torch import Tensor
import torch.cuda.comm
__all__: List[str] = []
Tensors = Sequence[Tensor]
TensorOrTensors = Union[Tensor, Tensors]
Function = Callable[[TensorOrTensors], TensorOrTensors]
class Batch:
"""An abstraction of an atomic tensor or a tuple of tensors. This
eliminates every boilerplate code to classify an atomic tensor or a tuple
of tensors.
::
x = generate_tensor_or_tensors()
x = Batch(x)
# in-place update
x[0] = F.apply(x[0])
x[:] = F.apply(*x)
# f(x) if x is a tensor.
# f(*x) if x is a tuple of tensors.
# y is also a batch.
y = x.call(f)
"""
def __init__(self, value: TensorOrTensors) -> None:
self.value = value
self.atomic = torch.is_tensor(value)
@property
def tensor(self) -> Tensor:
"""Retrieves the underlying tensor."""
if not self.atomic:
raise AttributeError("not atomic batch")
return cast(Tensor, self.value)
@property
def tensors(self) -> Tensors:
"""Retrieves the underlying tensors."""
if self.atomic:
raise AttributeError("batch is atomic")
return cast(Tensors, self.value)
@property
def tensor_or_tensors(self) -> TensorOrTensors:
"""Retrieves the underlying tensor or tensors regardless of type."""
return self.value
def call(self, function: Function) -> "Batch":
"""Calls a function by the underlying tensor or tensors. It also wraps
the output with :class:`Batch`.
"""
return Batch(function(self.value))
def __repr__(self) -> str:
return f"Batch[atomic={self.atomic!r}]({self.value!r})"
def __iter__(self) -> Iterator[Tensor]:
if self.atomic:
yield self.tensor
else:
yield from self.tensors
def __len__(self) -> int:
return 1 if self.atomic else len(self.tensors)
def __getitem__(self, index: int) -> Tensor:
if not self.atomic:
return self.tensors[index]
if index != 0:
raise IndexError("atomic batch allows index 0 only")
return self.tensor
# NOTE(sublee): pyflakes can't detect "overload" instead of "typing.overload".
@typing.overload
def __setitem__(self, index: int, value: Tensor) -> None:
...
@typing.overload
def __setitem__(self, index: slice, value: Tensors) -> None:
...
def __setitem__(self, index: Union[int, slice], value: TensorOrTensors) -> None:
if isinstance(index, int):
value = cast(Tensor, value)
self._setitem_by_index(index, value)
else:
value = cast(Tensors, value)
self._setitem_by_slice(index, value)
def _setitem_by_index(self, index: int, value: Tensor) -> None:
if not self.atomic:
i = index
self.value = self.value[:i] + (value,) + self.value[i + 1 :] # type: ignore
return
if index != 0:
raise IndexError("atomic batch allows index 0 only")
self.value = value
def _setitem_by_slice(self, index: slice, value: Tensors) -> None:
if not (index.start is index.stop is index.step is None):
raise NotImplementedError("only slice [:] supported")
if not self.atomic:
self.value = value
return
if len(value) != 1:
raise IndexError("atomic batch cannot be replaced with multiple tensors")
self.value = value[0]
def check(input: TensorOrTensors) -> None:
"""Checks whether the input is a tensor or tensors.
Raises:
TypeError: input is not a tensor or tensors.
"""
if isinstance(input, Sequence):
for x in input:
if not isinstance(x, Tensor):
raise TypeError(f"expected Tensor, but got {input.__class__.__name__}")
return
if not isinstance(input, Tensor):
raise TypeError(f"expected Tensor, but got {input.__class__.__name__}")
def scatter(input: TensorOrTensors, chunks: int) -> List[Batch]:
"""Splits an input mini-batch into multiple micro-batches."""
inputs: Iterable[TensorOrTensors]
if isinstance(input, Tensor):
inputs = input.chunk(chunks)
else:
rotated: List[Tensors] = []
for tensor in input:
tensors = tensor.chunk(chunks)
rotated.append(cast(Tensors, tensors))
inputs = zip(*rotated)
return [Batch(x) for x in inputs]
def gather(outputs: List[Batch]) -> TensorOrTensors:
"""Concatenates output micro-batches into a mini-batch."""
output: TensorOrTensors
if outputs[0].atomic:
tensors = tuple(b.tensor for b in outputs)
output = torch.cat(tensors)
else:
rotated = [b.tensors for b in outputs]
output_buf = []
for tensors in zip(*rotated):
output_buf.append(torch.cat(tensors))
output = tuple(output_buf)
return output