"""The GPipe interface."""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union, cast
import torch
from torch import Tensor, nn
import torch.autograd
import torch.cuda
from torchgpipe import microbatch
from torchgpipe.batchnorm import DeferredBatchNorm
from torchgpipe.pipeline import Pipeline
from torchgpipe.skip.layout import inspect_skip_layout
from torchgpipe.skip.skippable import verify_skippables
from torchgpipe.stream import AbstractStream, new_stream
__all__ = ['GPipe']
Device = Union[torch.device, int, str]
Devices = Union[Iterable[Device], List[Device]]
Tensors = Tuple[Tensor, ...]
TensorOrTensors = Union[Tensor, Tensors]
if TYPE_CHECKING:
Module = nn.Module[TensorOrTensors]
NamedModules = OrderedDict[str, Module]
else:
Module = nn.Module
NamedModules = OrderedDict
def recommend_auto_balance(message: str) -> str:
"""Expands a message with recommendation to :mod:`torchgpipe.balance`."""
return f'''{message}
If your model is still under development, its optimal balance would change
frequently. In this case, we highly recommend 'torchgpipe.balance' for naive
automatic balancing:
from torchgpipe import GPipe
from torchgpipe.balance import balance_by_time
partitions = torch.cuda.device_count()
sample = torch.empty(...)
balance = balance_by_time(partitions, model, sample)
model = GPipe(model, balance, ...)
'''
def verify_module(module: nn.Sequential) -> None:
if not isinstance(module, nn.Sequential):
raise TypeError('module must be nn.Sequential to be partitioned')
named_children = list(module.named_children())
if len(named_children) != len(module):
raise ValueError('module with duplicate children is not supported')
num_parameters = len(list(module.parameters()))
num_child_parameters = sum(len(list(child.parameters())) for child in module.children())
if num_parameters != num_child_parameters:
raise ValueError('module with duplicate parameters in distinct children is not supported')
class BalanceError(ValueError):
pass
def split_module(module: nn.Sequential,
balance: Iterable[int],
devices: List[torch.device],
) -> Tuple[List[nn.Sequential], List[int], List[torch.device]]:
"""Splits a module into multiple partitions.
Returns:
A tuple of (partitions, balance, devices).
Partitions are represented as a :class:`~torch.nn.ModuleList` whose
item is a partition. All layers in a partition are placed in the
same device.
Raises:
BalanceError:
wrong balance
IndexError:
the number of devices is fewer than the number of partitions.
"""
balance = list(balance)
if len(module) != sum(balance):
raise BalanceError('module and sum of balance have different length '
f'(module: {len(module)}, sum of balance: {sum(balance)})')
if any(x <= 0 for x in balance):
raise BalanceError(f'all balance numbers must be positive integer (balance: {balance})')
if len(balance) > len(devices):
raise IndexError('too few devices to hold given partitions '
f'(devices: {len(devices)}, partitions: {len(balance)})')
j = 0
partitions = []
layers: NamedModules = OrderedDict()
for name, layer in module.named_children():
layers[name] = layer
if len(layers) == balance[j]:
# Group buffered layers as a partition.
partition = nn.Sequential(layers)
device = devices[j]
partition.to(device)
partitions.append(partition)
# Prepare for the next partition.
layers.clear()
j += 1
partitions = cast(List[nn.Sequential], nn.ModuleList(partitions))
del devices[j:]
return partitions, balance, devices
MOVING_DENIED = TypeError('denied to move parameters and buffers, '
'because GPipe should manage device placement')
class GPipe(Module):
"""Wraps an arbitrary :class:`nn.Sequential <torch.nn.Sequential>` module
to train on GPipe_. If the module requires lots of memory, GPipe will be
very efficient.
::
model = nn.Sequential(a, b, c, d)
model = GPipe(model, balance=[1, 1, 1, 1], chunks=8)
output = model(input)
.. _GPipe: https://arxiv.org/abs/1811.06965
GPipe combines pipeline parallelism with checkpointing to reduce peak
memory required to train while minimizing device under-utilization.
You should determine the balance when defining a :class:`GPipe` module, as
balancing will not be done automatically. The module will be partitioned
into multiple devices according to the given balance. You may rely on
heuristics to find your own optimal configuration.
Args:
module (torch.nn.Sequential):
sequential module to be parallelized
balance (ints):
list of number of layers in each partition
Keyword Args:
devices (iterable of devices):
devices to use (default: all CUDA devices)
chunks (int):
number of micro-batches (default: ``1``)
checkpoint (str):
when to enable checkpointing, one of ``'always'``,
``'except_last'``, or ``'never'`` (default: ``'except_last'``)
deferred_batch_norm (bool):
whether to use deferred BatchNorm moving statistics (default:
:data:`False`, see :ref:`Deferred Batch Normalization` for more
details)
Raises:
TypeError:
the module is not a :class:`nn.Sequential <torch.nn.Sequential>`.
ValueError:
invalid arguments, or wrong balance
IndexError:
the number of devices is fewer than the number of partitions.
"""
#: The number of layers in each partition.
balance: List[int] = []
# ^^
# The default value [] required for Sphinx's autoattribute.
#: The devices mapped to each partition.
#:
#: ``devices[-1]`` refers to the device of the last partition, which means
#: it is the output device. Probably, you need to use it to transfer the
#: target to calculate the loss without a device mismatch
#: :exc:`RuntimeError`. For example::
#:
#: out_device = gpipe.devices[-1]
#:
#: for input, target in loader:
#: target = target.to(out_device, non_blocking=True)
#: output = gpipe(input)
#: loss = F.cross_entropy(output, target)
#:
devices: List[torch.device] = []
#: The number of micro-batches.
chunks: int = 1
#: The checkpoint mode to determine when to enable checkpointing. It is one
#: of ``'always'``, ``'except_last'``, or ``'never'``.
checkpoint: str = 'except_last'
def __init__(self,
module: nn.Sequential,
balance: Optional[Iterable[int]] = None,
*,
devices: Optional[Devices] = None,
chunks: int = chunks,
checkpoint: str = checkpoint,
deferred_batch_norm: bool = False,
) -> None:
super().__init__()
chunks = int(chunks)
checkpoint = str(checkpoint)
if balance is None:
raise ValueError(recommend_auto_balance('balance is required'))
if chunks <= 0:
raise ValueError('number of chunks must be positive integer')
if checkpoint not in ['always', 'except_last', 'never']:
raise ValueError("checkpoint is not one of 'always', 'except_last', or 'never'")
verify_module(module)
# Verify if the underlying skippable modules satisfy integrity. The
# integrity can be verified before forward() because it is static.
verify_skippables(module)
self.chunks = chunks
self.checkpoint = checkpoint
if deferred_batch_norm:
module = DeferredBatchNorm.convert_deferred_batch_norm(module, chunks)
if devices is None:
devices = range(torch.cuda.device_count())
devices = [torch.device(d) for d in devices]
devices = cast(List[torch.device], devices)
try:
self.partitions, self.balance, self.devices = split_module(module, balance, devices)
except BalanceError as exc:
raise ValueError(recommend_auto_balance(str(exc)))
self._copy_streams: List[List[AbstractStream]] = []
self._skip_layout = inspect_skip_layout(self.partitions)
def __len__(self) -> int:
"""Counts the length of the underlying sequential module."""
return sum(len(p) for p in self.partitions)
def __getitem__(self, index: int) -> nn.Module:
"""Gets a layer in the underlying sequential module."""
partitions = self.partitions
if index < 0:
partitions = partitions[::-1]
for partition in partitions:
try:
return partition[index]
except IndexError:
pass
shift = len(partition)
if index < 0:
index += shift
else:
index -= shift
raise IndexError
def __iter__(self) -> Iterable[nn.Module]:
"""Iterates over children of the underlying sequential module."""
for partition in self.partitions:
yield from partition
# GPipe should manage the device of each partition.
# Deny cuda(), cpu(), and to() with device, by TypeError.
def cuda(self, device: Optional[Device] = None) -> 'GPipe':
raise MOVING_DENIED
def cpu(self) -> 'GPipe':
raise MOVING_DENIED
def to(self, *args: Any, **kwargs: Any) -> 'GPipe':
# Deny these usages:
#
# - to(device[, dtype, non_blocking])
# - to(tensor[, non_blocking])
#
# But allow this:
#
# - to(dtype[, non_blocking])
#
if 'device' in kwargs or 'tensor' in kwargs:
raise MOVING_DENIED
if args:
if isinstance(args[0], (torch.device, int, str)):
raise MOVING_DENIED
if torch.is_tensor(args[0]):
raise MOVING_DENIED
return super().to(*args, **kwargs)
def _ensure_copy_streams(self) -> List[List[AbstractStream]]:
"""Ensures that :class:`GPipe` caches CUDA streams for copy.
It's worth to cache CUDA streams although PyTorch already manages a
pool of pre-allocated CUDA streams, because it may reduce GPU memory
fragementation when the number of micro-batches is small.
"""
if not self._copy_streams:
for device in self.devices:
self._copy_streams.append([new_stream(device) for _ in range(self.chunks)])
return self._copy_streams
def forward(self, input: TensorOrTensors) -> TensorOrTensors: # type: ignore
""":class:`GPipe` is a fairly transparent module wrapper. It doesn't
modify the input and output signature of the underlying module. But
there's type restriction. Input and output have to be a
:class:`~torch.Tensor` or a tuple of tensors. This restriction is
applied at partition boundaries too.
Args:
input (torch.Tensor or tensors): input mini-batch
Returns:
tensor or tensors: output mini-batch
Raises:
TypeError: input is not a tensor or tensors.
"""
microbatch.check(input)
if not self.devices:
# Empty sequential module is not illegal.
return input
# Divide a mini-batch into micro-batches.
batches = microbatch.scatter(input, self.chunks)
# Separate CUDA streams for copy.
copy_streams = self._ensure_copy_streams()
# The micro-batch index where the checkpointing stops.
if self.training:
checkpoint_stop = {
'always': self.chunks,
'except_last': self.chunks-1,
'never': 0,
}[self.checkpoint]
else:
checkpoint_stop = 0
# Run pipeline parallelism.
pipeline = Pipeline(batches,
self.partitions,
self.devices,
copy_streams,
self._skip_layout,
checkpoint_stop)
pipeline.run()
# Merge the micro-batches into one mini-batch.
output = microbatch.gather(batches)
return output
