Source code for torch.distributed.pipeline.sync.pipe

# 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.
"""The Pipe interface."""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Iterable, Iterator, List, Optional, Union, Sequence, Tuple, cast

import torch
from torch import Tensor, nn
from torch.distributed.rpc import RRef
import torch.autograd
import torch.cuda

from . import microbatch
from .batchnorm import DeferredBatchNorm
from .pipeline import Pipeline
from .skip.layout import inspect_skip_layout
from .skip.skippable import verify_skippables
from .stream import AbstractStream, new_stream

__all__ = ["Pipe", "BalanceError", "PipeSequential", "WithDevice"]

Device = Union[torch.device, int, str]
Devices = Union[Iterable[Device], List[Device]]

Tensors = Sequence[Tensor]
TensorOrTensors = Union[Tensor, Tensors]

    # Typechecking: nn.Module is not a Generic
    Module = nn.Module[TensorOrTensors]  # type: ignore[type-arg]
    NamedModules = OrderedDict[str, Module]
    Module = nn.Module
    NamedModules = OrderedDict

def _recommend_auto_balance(message: str) -> str:
    """Expands a message with recommendation to :mod:`torchpipe.balance`."""
    return f"""{message}

If your model is still under development, its optimal balance would change
frequently. In this case, we highly recommend 'torch.distributed.pipeline.sync.balance' for
naive automatic balancing:

  from torch.distributed.pipeline.sync import Pipe
  from torch.distributed.pipeline.sync.balance import balance_by_time

  partitions = torch.cuda.device_count()
  sample = torch.empty(...)
  balance = balance_by_time(partitions, model, sample)

  model = Pipe(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")

def _verify_splitting(
    module: nn.Sequential, partitions: List[nn.Sequential], devices: List[torch.device]
) -> None:
    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:

    for i in range(len(partitions)):
        for j in range(i + 1, len(partitions)):
            parti = partitions[i]
            partj = partitions[j]
            if devices[i] == devices[j]:
            for p in parti.parameters():
                for q in partj.parameters():
                    if p is q:
                        raise ValueError("module with duplicate parameters on distinct devices is not supported")

class BalanceError(ValueError):

def _retrieve_device(module: nn.Module) -> torch.device:
    """Validates all parameters in the Module have the same device and returns
    the appropriate device.

        An ``nn.Module`` to process.

        ``torch.Device`` for the entire module.

            If devices for ``nn.Module`` parameters are not all same.

    device = None
    for parameter in module.parameters():
        if device is None:
            device = parameter.device
        elif device != parameter.device:
            raise ValueError(
                f'nn.Module: {module}, should have all parameters on a single device,'
                ' please use .to() to place the module on a single device')

    return device if device is not None else torch.device("cpu")

class PipeSequential(nn.Sequential):
    Pipe variant of ``nn.Sequential`` which supports multiple inputs.

    def forward(self, *inputs):
        for module in self:
            if isinstance(inputs, Tuple):  # type: ignore[arg-type]
                inputs = module(*inputs)
                # Don't expand single variables (ex: lists/Tensor)
                inputs = module(inputs)
        return inputs

class WithDevice(nn.Module):
    Wraps an ``nn.Module`` which is part of ``nn.Sequential`` passed into :class:`Pipe`
    that overrides the device for that module. In cases where :class:`Pipe`
    can't implicitly determine the device for the module and places it on CPU,
    this wrapper can be used to override the implicit behavior and explicitly
    specify which device a module should run on.

    The provided module is also moved to the given device via ``.to(device)``
    by :class:`Pipe`

        module(:class:`torch.nn.Module`): The module to be wrapped.
        device(:class:`torch.device`): The device to run the module on.

        >>> # xdoctest: +SKIP("distributed")
        >>> fc1 = nn.Linear(16, 8).cuda(0)
        >>> fc2 = nn.Linear(8, 4).cuda(1)
        >>> dropout = nn.Dropout()
        >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)
        >>> # Dropout does not have any parameters/buffers, but we want to
        >>> # run it on cuda:1 to avoid any GPU to CPU transfers.
        >>> model = nn.Sequential(fc1, fc2, WithDevice(dropout, 'cuda:1'))
        >>> # xdoctest: +SKIP("Needs RPC framework init")
        >>> model = Pipe(model, chunks=8)
    def __init__(self, module: nn.Module, device: torch.device):
        self._module = module
        self._device = torch.device(device)

    def forward(self, *args, **kwargs):
        return self._module(*args, **kwargs)

    def module(self):
        return self._module

    def device(self):
        return self._device

def _assemble_partition(modules: List[nn.Module]):
    modules_list: List[nn.Module] = []
    for module in modules:
        if isinstance(module, nn.Sequential):
    return PipeSequential(*modules_list)

def _split_module(modules: nn.Sequential) -> Tuple[List[nn.Sequential], List[torch.device]]:
    partitions = []
    devices = []

    current_partition = []
    current_device = None
    for name, module in modules.named_children():
        if isinstance(module, WithDevice):
            # Process device override and move module to appropriate device.
            device = module.device
            module = module.module
            device = _retrieve_device(module)
        if current_device is not None and (current_device != device or device.type == 'cpu'):
            current_partition = []
        current_device = device

    if current_device is not None:

    partitions = cast(List[nn.Sequential], nn.ModuleList(partitions))

    return partitions, devices

MOVING_DENIED = TypeError("denied to move parameters and buffers, because Pipe should manage device placement")

[docs]class Pipe(Module): """Wraps an arbitrary :class:`nn.Sequential <torch.nn.Sequential>` module to train on using synchronous pipeline parallelism. If the module requires lots of memory and doesn't fit on a single GPU, pipeline parallelism is a useful technique to employ for training. The implementation is based on the torchgpipe_ paper. .. _torchgpipe: Pipe combines pipeline parallelism with checkpointing to reduce peak memory required to train while minimizing device under-utilization. You should place all the modules on the appropriate devices and wrap them into an :class:`nn.Sequential <torch.nn.Sequential>` module defining the desired order of execution. If a module does not contain any parameters/buffers, it is assumed this module should be executed on CPU and appropriate input tensors to the module are moved to CPU before execution. This behavior can be overridden by the :class:`WithDevice` wrapper which can be used to explicitly specify which device a module should run on. Args: module (:class:`nn.Sequential <torch.nn.Sequential>`): sequential module to be parallelized using pipelining. Each module in the sequence has to have all of its parameters on a single device. Each module in the sequence has to either be an nn.Module or :class:`nn.Sequential <torch.nn.Sequential>` (to combine multiple sequential modules on a single device) chunks (int): number of micro-batches (default: ``1``) checkpoint (str): when to enable checkpointing, one of ``'always'``, ``'except_last'``, or ``'never'`` (default: ``'except_last'``). ``'never'`` disables checkpointing completely, ``'except_last'`` enables checkpointing for all micro-batches except the last one and ``'always'`` enables checkpointing for all micro-batches. deferred_batch_norm (bool): whether to use deferred ``BatchNorm`` moving statistics (default: :data:`False`). If set to :data:`True`, we track statistics across multiple micro-batches to update the running statistics per mini-batch. Raises: TypeError: the module is not a :class:`nn.Sequential <torch.nn.Sequential>`. ValueError: invalid arguments Example:: Pipeline of two FC layers across GPUs 0 and 1. >>> # Need to initialize RPC framework first. >>> # xdoctest: +SKIP >>> os.environ['MASTER_ADDR'] = 'localhost' >>> os.environ['MASTER_PORT'] = '29500' >>> torch.distributed.rpc.init_rpc('worker', rank=0, world_size=1) >>> >>> # Build pipe. >>> fc1 = nn.Linear(16, 8).cuda(0) >>> fc2 = nn.Linear(8, 4).cuda(1) >>> model = nn.Sequential(fc1, fc2) >>> model = Pipe(model, chunks=8) >>> input = torch.rand(16, 16).cuda(0) >>> output_rref = model(input) .. note:: You can wrap a :class:`Pipe` model with :class:`torch.nn.parallel.DistributedDataParallel` only when the checkpoint parameter of :class:`Pipe` is ``'never'``. .. note:: :class:`Pipe` only supports intra-node pipelining currently, but will be expanded to support inter-node pipelining in the future. The forward function returns an :class:`~torch.distributed.rpc.RRef` to allow for inter-node pipelining in the future, where the output might be on a remote host. For intra-node pipelining you can use :meth:`~torch.distributed.rpc.RRef.local_value` to retrieve the output locally. .. warning:: :class:`Pipe` is experimental and subject to change. """ def __init__( self, module: nn.Sequential, chunks: int = 1, checkpoint: str = "except_last", deferred_batch_norm: bool = False, ) -> None: super().__init__() # Check if RPC framework is initialized. if not torch.distributed.rpc._is_current_rpc_agent_set(): raise RuntimeError( 'Please initialize RPC framework for Pipe using ' 'torch.distributed.rpc.init_rpc') chunks = int(chunks) checkpoint = str(checkpoint) 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) self.partitions, self.devices = _split_module(module) _verify_splitting(module, self.partitions, self.devices) self._copy_streams: List[List[AbstractStream]] = [] self._skip_layout = inspect_skip_layout(self.partitions) # Separate CUDA streams for copy. copy_streams = self._ensure_copy_streams() # The micro-batch index where the checkpointing stops. checkpoint_stop = {"always": self.chunks, "except_last": self.chunks - 1, "never": 0}[self.checkpoint] self.pipeline = Pipeline(self.partitions, self.devices, copy_streams, self._skip_layout, checkpoint_stop) 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) -> Iterator[nn.Module]: """Iterates over children of the underlying sequential module.""" for partition in self.partitions: yield from partition # Pipe should manage the device of each partition. # Deny cuda(), cpu(), and to() with device, by TypeError. def cuda(self, device: Optional[Device] = None) -> "Pipe": raise MOVING_DENIED def cpu(self) -> "Pipe": raise MOVING_DENIED def to(self, *args: Any, **kwargs: Any) -> "Pipe": # 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:`Pipe` 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 fragmentation 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
[docs] def forward(self, *inputs) -> RRef: """ Processes a single input mini-batch through the pipe and returns an :class:`~torch.distributed.rpc.RRef` pointing to the output. :class:`Pipe` 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 contain at least one tensor. This restriction is applied at partition boundaries too. The sequence of inputs are fed into the first stage of the pipeline as ``*inputs``. As a result the positional args for this function should match the positional args for the first stage of the pipeline. The same condition applies for output of one stage of the pipeline which is the input for the next stage. The input tensor is split into multiple micro-batches based on the ``chunks`` parameter used to initialize :class:`Pipe`. The batch size is assumed to be the first dimension of the tensor and if the batch size is less than ``chunks``, the number of micro-batches is equal to the batch size. Only tensors are split into multiple micro-batches, non-Tensor inputs are just replicated as-is in each micro-batch. For non-Tensor outputs in the last stage of the pipeline, they are aggregated as a ``List`` and returned the user. For example, if you have 2 micro-batches returning the integer 5, the user would receive the consolidated output of `[5, 5]` All the input tensors need to be on the same device as the first partition of the pipeline. If a tensor is wrapped with the :class:`NoChunk` wrapper, the tensor is not split across micro-batches and is replicated as-is similar to non-tensors. Args: inputs: input mini-batch Returns: :class:`~torch.distributed.rpc.RRef` to the output of the mini-batch Raises: TypeError: input doesn't contain at least one tensor """ first_partition_device = self.devices[0] if len(self.devices) != 0 else torch.device("cpu") microbatch.check(first_partition_device, *inputs) if not self.devices: # Empty sequential module is not illegal. return RRef(*inputs) # Divide a mini-batch into micro-batches. batches = microbatch.scatter(*inputs, chunks=self.chunks) # Run pipeline parallelism. # Merge the micro-batches into one mini-batch. output = microbatch.gather(batches) return RRef(output)


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