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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.
"""The pipeline parallelism of Pipe."""
from queue import Queue
from types import TracebackType
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Type, Union, cast, Sequence
import torch
from torch import Tensor, nn
from torch.autograd.profiler import record_function
from .checkpoint import Checkpointing
from .copy import Copy, Wait
from .dependency import fork, join
from .microbatch import Batch
from .skip.layout import SkipLayout
from .skip.tracker import SkipTrackerThroughPotals, use_skip_tracker
from .stream import AbstractStream, current_stream, use_device
from .worker import Task, create_workers
__all__: List[str] = ["Pipeline"]
Tensors = Sequence[Tensor]
TensorOrTensors = Union[Tensor, Tensors]
ExcInfo = Tuple[Type[BaseException], BaseException, TracebackType]
# Queue is generic only in stubs.
# https://mypy.readthedocs.io/en/latest/common_issues.html#using-classes-that-are-generic-in-stubs-but-not-at-runtime
if TYPE_CHECKING:
InQueue = Queue[Optional["Task"]]
OutQueue = Queue[Tuple[bool, Union[Tuple["Task", Batch], ExcInfo, None]]]
else:
InQueue = Queue
OutQueue = Queue
def _depend(fork_from: Batch, join_to: Batch) -> None:
fork_from_idx = fork_from.find_tensor_idx()
join_to_idx = join_to.find_tensor_idx()
fork_from[fork_from_idx], phony = fork(fork_from[fork_from_idx])
join_to[join_to_idx] = join(join_to[join_to_idx], phony)
def _copy(batch: Batch, prev_stream: AbstractStream, next_stream: AbstractStream) -> None:
batch[:] = Copy.apply(prev_stream, next_stream, *batch)
# Gradients are only supported for float Tensors.
batch[:] = tuple([x.detach() if torch.is_tensor(x) and not x.is_floating_point() else x for x in batch])
def _wait(batch: Batch, prev_stream: AbstractStream, next_stream: AbstractStream) -> None:
batch[:] = Wait.apply(prev_stream, next_stream, *batch)
# Gradients are only supported for float Tensors.
batch[:] = tuple([x.detach() if torch.is_tensor(x) and not x.is_floating_point() else x for x in batch])
def _clock_cycles(m: int, n: int) -> Iterable[List[Tuple[int, int]]]:
"""Generate schedules for each clock cycle."""
# m: number of micro-batches
# n: number of partitions
# i: index of micro-batch
# j: index of partition
# k: clock number
#
# k (i,j) (i,j) (i,j)
# - ----- ----- -----
# 0 (0,0)
# 1 (1,0) (0,1)
# 2 (2,0) (1,1) (0,2)
# 3 (2,1) (1,2)
# 4 (2,2)
for k in range(m + n - 1):
yield [(k - j, j) for j in range(max(1 + k - m, 0), min(1 + k, n))]
class Pipeline:
"""The pipeline parallelism for Pipe."""
def __init__(
self,
partitions: List[nn.Sequential],
devices: List[torch.device],
copy_streams: List[List[AbstractStream]],
skip_layout: SkipLayout,
checkpoint_stop: int,
) -> None:
self.partitions = partitions
self.devices = devices
self.copy_streams = copy_streams
self.skip_layout = skip_layout
self.checkpoint_stop = checkpoint_stop
(self.in_queues, self.out_queues) = create_workers(devices)
def run(self, batches: List[Batch]) -> None:
"""Runs pipeline parallelism.
It modifies the given batches in place.
"""
partitions = self.partitions
devices = self.devices
skip_layout = self.skip_layout
m = len(batches)
n = len(partitions)
skip_trackers = [SkipTrackerThroughPotals(skip_layout) for _ in batches]
for schedule in _clock_cycles(m, n):
self.fence(batches, schedule, skip_trackers)
self.compute(batches, schedule, skip_trackers)
def fence(
self, batches: List[Batch], schedule: List[Tuple[int, int]], skip_trackers: List[SkipTrackerThroughPotals],
) -> None:
"""Copy micro-batches after computation for the previous micro-batches."""
copy_streams = self.copy_streams
skip_layout = self.skip_layout
for i, j in schedule:
# Ensure that batches[i-1] is executed after batches[i] in
# backpropagation by an explicit dependency.
if i != 0 and j != 0:
_depend(batches[i - 1], batches[i])
next_stream = copy_streams[j][i]
for prev_j, ns, name in skip_layout.copy_policy(j):
prev_stream = copy_streams[prev_j][i]
skip_trackers[i].copy(batches[i], prev_stream, next_stream, ns, name)
if j != 0:
prev_stream = copy_streams[j - 1][i]
_copy(batches[i], prev_stream, next_stream)
def compute(
self, batches: List[Batch], schedule: List[Tuple[int, int]], skip_trackers: List[SkipTrackerThroughPotals],
) -> None:
"""Run tasks with synchronization to copy streams."""
partitions = self.partitions
devices = self.devices
copy_streams = self.copy_streams
checkpoint_stop = self.checkpoint_stop
# Disable checkpointing if in eval mode.
if not self.partitions[0].training:
checkpoint_stop = 0
n = len(partitions)
streams = [current_stream(d) for d in devices]
exc_info: Optional[ExcInfo] = None
# With checkpointing, the autograd graph looks like this diagram:
# ┌─────┸──────┐
# │ Copy │
# └─────┰──────┘ (fence)
# ─ ─ ─ ╂ ─ ─ ─ ─ ─ ─ ─ ─ ─
# ┃ (compute)
# ┌─────┸──────┐
# │ Wait │ [1] Synchronize the current stream with the copy stream.
# └─────┰──────┘
# ┌─────┸──────┐
# │ Checkpoint │ [2] Compute a partition within checkpointing.
# └─────┰──────┘
# ┌─────┸──────┐
# │ Wait │ [3] Synchronize the copy stream with the current stream.
# └─────┰──────┘
# ┠ ─ ─ ─ ┐
# ┃ ┌─────┴─────┐
# ┃ │ Recompute │ [4] Schedule the recomputation at backpropagation.
# ┃ └─────┬─────┘
# ┠ ─ ─ ─ ┘
# ┃
# ─ ─ ─ ╂ ─ ─ ─ ─ ─ ─ ─ ─ ─
# ┌─────┸──────┐ (fence)
# │ Copy │
# └─────┰──────┘
for i, j in schedule:
batch = batches[i]
partition = partitions[j]
# Synchronize with the copied input. ([1] in the diagram)
if j != 0:
_wait(batch, copy_streams[j][i], streams[j])
# Determine whether checkpointing or not.
checkpoint = i < checkpoint_stop
if checkpoint:
def function(
*inputs,
partition: nn.Module = partition,
skip_tracker: SkipTrackerThroughPotals = skip_trackers[i],
chunk_id: int = i,
part_id: int = j,
) -> TensorOrTensors:
with use_skip_tracker(skip_tracker), record_function("chunk%d-part%d" % (chunk_id, part_id)):
return partition(*inputs)
chk = Checkpointing(function, batch) # type: ignore[arg-type]
task = Task(streams[j], compute=chk.checkpoint, finalize=chk.recompute)
del function, chk
else:
def compute(
batch: Batch = batch,
partition: nn.Module = partition,
skip_tracker: SkipTrackerThroughPotals = skip_trackers[i],
chunk_id: int = i,
part_id: int = j,
) -> Batch:
with use_skip_tracker(skip_tracker), record_function("chunk%d-part%d" % (chunk_id, part_id)):
return batch.call(partition)
task = Task(streams[j], compute=compute, finalize=None)
del compute
# Compute tasks in parallel. ([2] in the diagram)
self.in_queues[j].put(task)
for i, j in schedule:
ok, payload = self.out_queues[j].get()
# Hold the first exception.
if exc_info is not None:
continue
elif not ok:
exc_info = cast(ExcInfo, payload)
continue
task, batch = cast(Tuple[Task, Batch], payload)
# The copy stream synchronizes to copy the output. ([3] in the
# diagram)
if j != n - 1:
_wait(batch, streams[j], copy_streams[j][i])
# Finalize tasks. If checkpointing is enabled, here the
# recomputation is scheduled at backpropagation. ([4] in the
# diagram)
with use_device(devices[j]):
task.finalize(batch)
batches[i] = batch
# Fail at the first exception.
if exc_info is not None:
raise exc_info[0].with_traceback(exc_info[1], exc_info[2])
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