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# 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
#
# 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.
"""Checkpointing with preceding recomputation.
PyTorch already provides the official checkpointing utilities in
:mod:`torch.utils.checkpoint`. The official checkpointing combines
recomputation and recursive backpropagation into one autograd function named
``CheckpointFunction``. Hence, the recomputation can be started only when the
gradients arrive to the function. In Pipe, the recomputation needs to precede
the gradient arrival to minimize the GPU idle time.
We solve this problem by introducing separate autograd functions named
:class:`Recompute` and :class:`Checkpoint`. Each function represents
recomputation and recursive backpropagation, respectively. We can manipulate
the control flow in aspect of both the autograd engine and CUDA with a pair of
the functions.
Specifically, we place CUDA stream synchronization between :class:`Recompute`
and :class:`Checkpoint` to delay only :class:`Checkpoint` until the gradient is
copied entirely.
"""
from collections import deque
from contextlib import contextmanager
import threading
from typing import TYPE_CHECKING, Deque, Generator, List, Optional, Tuple, Union
import torch
from torch import ByteTensor, Tensor
import torch.autograd
from .dependency import fork, join
from .microbatch import Batch
from .phony import get_phony
__all__ = ["is_checkpointing", "is_recomputing"]
Tensors = Tuple[Tensor, ...]
TensorOrTensors = Union[Tensor, Tensors]
# Types for shared memory between Checkpoint and Recompute.
Recomputed = Tuple[TensorOrTensors, Tensors] # (output, input_leaf)
RNGStates = Tuple[ByteTensor, Optional[ByteTensor]] # (cpu_rng_state, gpu_rng_state)
if TYPE_CHECKING:
from typing_extensions import Protocol
else:
Protocol = object
# Protocol with __call__ instead of Callable can be used as an attribute type.
# See: https://github.com/python/mypy/issues/708#issuecomment-561735949
class Function(Protocol):
def __call__(self, input: TensorOrTensors) -> TensorOrTensors:
...
class Checkpointing:
"""Generates a pair of :class:`Checkpoint` and :class:`Recompute`."""
def __init__(self, function: Function, batch: Batch) -> None:
self.function = function
self.batch = batch
# Shared memory between Checkpoint and Recompute. 1-length deque is
# used for mutability and length limitation.
self.recomputed: Deque[Recomputed] = deque(maxlen=1)
self.rng_states: Deque[RNGStates] = deque(maxlen=1)
def checkpoint(self) -> Batch:
"""Returns a batch applied by :class:`Checkpoint`."""
input_atomic = self.batch.atomic
input = tuple(self.batch)
# Use a phony which requires grad to ensure that Checkpoint can be
# tracked by the autograd engine even when none of the input tensors
# require grad.
phony = get_phony(self.batch[0].device, requires_grad=True)
output = Checkpoint.apply(phony, self.recomputed, self.rng_states, self.function, input_atomic, *input)
# Gradients are only supported for float Tensors.
if isinstance(output, tuple):
output = tuple([x if x.is_floating_point() else x.detach() for x in output])
return Batch(output, self.batch.index)
def recompute(self, batch: Batch) -> None:
"""Applies :class:`Recompute` to the batch in place."""
input_atomic = self.batch.atomic
input = tuple(self.batch)
# batch[0] is always requiring grad, because it has been passed
# checkpoint with a phony requiring grad.
batch[0], phony = fork(batch[0])
phony = Recompute.apply(phony, self.recomputed, self.rng_states, self.function, input_atomic, *input)
batch[0] = join(batch[0], phony)
class ThreadLocal(threading.local):
def __init__(self) -> None:
self.is_checkpointing = False
self.is_recomputing = False
thread_local = ThreadLocal()
@contextmanager
def enable_checkpointing() -> Generator[None, None, None]:
"""Makes :func:`is_checkpointing` return :data:`True` within a context."""
orig = thread_local.is_checkpointing
thread_local.is_checkpointing = True
try:
yield
finally:
thread_local.is_checkpointing = orig
@contextmanager
def enable_recomputing() -> Generator[None, None, None]:
"""Makes :func:`is_recomputing` return :data:`True` within a context."""
orig = thread_local.is_recomputing
thread_local.is_recomputing = True
try:
yield
finally:
thread_local.is_recomputing = orig
def is_checkpointing() -> bool:
"""Whether the current forward propagation is under checkpointing.
Returns:
bool: :data:`True` if it's under checkpointing.
"""
return thread_local.is_checkpointing
def is_recomputing() -> bool:
"""Whether the current forward propagation is under checkpoint
recomputation. Use this to prevent duplicated side-effects at forward
propagation::
class Counter(nn.Module):
def __init__(self):
super().__init__()
self.counter = 0
def forward(self, input):
if not is_recomputing():
self.counter += 1
return input
Returns:
bool: :data:`True` if it's under checkpoint recomputation.
.. seealso:: :ref:`Detecting Recomputation`
"""
return thread_local.is_recomputing
class Context:
"""The common interface between the :class:`Checkpoint` and
:class:`Recompute` context.
"""
recomputed: Deque[Recomputed]
rng_states: Deque[RNGStates]
function: Function
input_atomic: bool
saved_tensors: Tuple[Tensor, ...]
def save_for_backward(self, *tensors: Tensor) -> None: # pragma: no cover
pass
def save_rng_states(
device: torch.device,
rng_states: Deque[RNGStates],
) -> None:
""":meth:`Checkpoint.forward` captures the current PyTorch's random number
generator states at CPU and GPU to reuse in :meth:`Recompute.backward`.
.. seealso:: :ref:`Referential Transparency`
"""
cpu_rng_state = torch.get_rng_state()
gpu_rng_state: Optional[ByteTensor]
if device.type == "cuda":
gpu_rng_state = torch.cuda.get_rng_state(device)
else:
gpu_rng_state = None
rng_states.clear()
rng_states.append((cpu_rng_state, gpu_rng_state))
@contextmanager
def restore_rng_states(
device: torch.device,
rng_states: Deque[RNGStates],
) -> Generator[None, None, None]:
""":meth:`Recompute.backward` restores the random number generator states
captured by :func:`save_rng_states` within its context.
.. seealso:: :ref:`Referential Transparency`
"""
cpu_rng_state, gpu_rng_state = rng_states[0]
gpu_devices: List[torch.device] = []
if device.type == "cuda":
gpu_devices.append(device)
with torch.random.fork_rng(gpu_devices):
torch.set_rng_state(cpu_rng_state)
if gpu_rng_state is not None:
torch.cuda.set_rng_state(gpu_rng_state, device)
yield
class Checkpoint(torch.autograd.Function):
@staticmethod
# type: ignore
def forward(
ctx: Context,
phony: Tensor,
recomputed: Deque[Recomputed],
rng_states: Deque[RNGStates],
function: Function,
input_atomic: bool,
*input: Tensor,
) -> TensorOrTensors:
ctx.recomputed = recomputed
ctx.rng_states = rng_states
save_rng_states(input[0].device, ctx.rng_states)
ctx.function = function
ctx.input_atomic = input_atomic
ctx.save_for_backward(*input)
with torch.no_grad(), enable_checkpointing():
output = function(input[0] if input_atomic else input)
return output
@staticmethod
def backward(
ctx: Context,
*grad_output: Tensor,
) -> Tuple[Optional[Tensor], ...]: # pragma: no cover
output, input_leaf = ctx.recomputed.pop()
if isinstance(output, tuple):
tensors = output
else:
tensors = (output,)
if any(y.requires_grad for y in tensors):
tensors = tuple([x for x in tensors if x.requires_grad])
torch.autograd.backward(tensors, grad_output)
grad_input: List[Optional[Tensor]] = [None, None, None, None, None]
grad_input.extend(x.grad for x in input_leaf)
return tuple(grad_input)
class Recompute(torch.autograd.Function):
@staticmethod
# type: ignore
def forward(
ctx: Context,
phony: Tensor,
recomputed: Deque[Recomputed],
rng_states: Deque[RNGStates],
function: Function,
input_atomic: bool,
*input: Tensor,
) -> Tensor:
ctx.recomputed = recomputed
ctx.rng_states = rng_states
ctx.function = function
ctx.input_atomic = input_atomic
ctx.save_for_backward(*input)
return phony
@staticmethod
def backward(ctx: Context, *grad_output: Tensor) -> Tuple[None, ...]: # pragma: no cover
input = ctx.saved_tensors
input_leaf = tuple(x.detach().requires_grad_(x.requires_grad) for x in input)
with restore_rng_states(input[0].device, ctx.rng_states):
with torch.enable_grad(), enable_recomputing():
output = ctx.function(input_leaf[0] if ctx.input_atomic else input_leaf)
ctx.recomputed.append((output, input_leaf))
grad_input: List[None] = [None, None, None, None, None]
grad_input.extend(None for _ in ctx.saved_tensors)
return tuple(grad_input)
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