File size: 5,949 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
# 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.
# Copyright 2019 Kakao Brain
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Manipulation of micro-batches."""
import typing
from typing import Callable, Iterable, Iterator, List, Tuple, Union, cast
import torch
from torch import Tensor
import torch.cuda.comm
__all__: List[str] = []
Tensors = Tuple[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, index: int) -> None:
self.value = value
self.atomic = torch.is_tensor(value)
self.__index = index
@property
def index(self) -> int:
return self.__index
@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), self.index)
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 :]
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, tuple):
for x in input:
check(x)
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, i) for i, x in enumerate(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
|