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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.
from collections import OrderedDict
from dataclasses import dataclass
from enum import Enum, auto
from threading import Event
from typing import Dict, Iterable, List, Optional, Tuple
import torch
from torch import Tensor, nn
from torch.autograd.profiler import record_function
from torch.distributed import ProcessGroup
from fairscale.nn.model_parallel import get_pipeline_parallel_ranks
from .checkpoint import Checkpointing
from .messages import Transport
from .microbatch import Batch
from .skip.tracker import SkipTrackerThroughPotals, use_skip_tracker
from .types import EVENT_LOOP_QUEUE, PipeMessage, TensorOrTensors, Tensors
from .worker import Task
def create_task(
checkpoint_stop: int,
chunk_id: int,
part_id: int,
batch: Batch,
partition: nn.Sequential,
skip_trackers: List[SkipTrackerThroughPotals],
) -> Task:
# Determine whether checkpointing or not.
if chunk_id < checkpoint_stop:
def function(
input: TensorOrTensors,
partition: nn.Sequential = partition,
skip_tracker: SkipTrackerThroughPotals = skip_trackers[chunk_id],
chunk_id: int = chunk_id,
part_id: int = part_id,
) -> TensorOrTensors:
with use_skip_tracker(skip_tracker), record_function("chunk%d-part%d" % (chunk_id, part_id)):
ret = partition(input)
# We do a check here because the backtrace from the checkpoint backward code path
# is very hard to make sense. It would be much easier to check earlier at this point.
assert type(ret) is not list, "Only Tensor or Tuple of Tensor output is supported"
return ret
chk = Checkpointing(function, batch)
task = Task(None, compute=chk.checkpoint, finalize=chk.recompute)
del function, chk # TODO(tom) maybe remove
else:
def compute(
batch: Batch = batch,
partition: nn.Sequential = partition,
skip_tracker: SkipTrackerThroughPotals = skip_trackers[chunk_id],
chunk_id: int = chunk_id,
part_id: int = part_id,
) -> Batch:
with use_skip_tracker(skip_tracker), record_function("chunk%d-part%d" % (chunk_id, part_id)):
return batch.call(partition)
task = Task(None, compute=compute, finalize=None)
del compute # TODO(tom) maybe remove
return task
@dataclass(frozen=True)
class Location:
stage: int
index: int
def __repr__(self) -> str:
return f"{self.stage}@{self.index}"
@dataclass(frozen=True)
class Invocation:
order: int
this: Location
source: Optional[Location]
dest: Optional[Location]
Activations = Dict[int, Dict[int, Dict[int, Batch]]]
Invocations = Dict[int, Invocation]
@dataclass(frozen=True)
class TailBackwardContext:
activations: Activations
invocations: Invocations
count_per_order: Dict[int, int]
expected_gradients: int
class ModuleWrapper:
def __init__(self, module: nn.Sequential, location: Location, invocations: Optional[List[Invocation]] = None):
self.module: nn.Sequential = module
self.location: Location = location
self.invocations: List[Invocation] = invocations or []
def __repr__(self) -> str:
return f"{self.location}:\n" + "\n".join(map(str, self.invocations)) + "\n\t" + str(self.module)
def __len__(self) -> int:
return len(self.module)
def __iter__(self) -> Iterable:
yield from self.module
class AsyncMessageType(Enum):
Activations = auto()
Gradients = auto()
@dataclass(frozen=True)
class AsyncMessageBody:
message_type: AsyncMessageType
microbatch_index: int
source: Location
dest: Location
order: int
class AutogradWithoutActivations(torch.autograd.Function):
"""A helper class to add another edge in the autograd graph which allows us
to delete the potentially large activations and still perform a backward
pass. Returns return a phony tensor which is connected to the graph."""
@staticmethod
# type: ignore
def forward(ctx, *x):
return torch.tensor(1.0)
@staticmethod
# type: ignore
def backward(ctx, grad):
assert ctx.grad_from_pipeline is not None
return ctx.grad_from_pipeline
class AsyncRecvOperator(torch.autograd.Function):
"""Receive activations to the previous pipeline stage"""
@staticmethod
# type: ignore
def forward(ctx, phony: Tensor, transport: Transport, message: PipeMessage, queue_name: int) -> Tensors:
ctx.transport = transport
ctx.index = message.args.microbatch_index
ctx.queue_name = queue_name
result = transport.recv_message_tensors(message)
ctx.args = result.args
def maybe_requires_grad(t: Tensor) -> Tensor:
if t.dtype.is_floating_point:
return t.requires_grad_()
return t
return tuple(maybe_requires_grad(r) for r in result.tensors)
@staticmethod
# type: ignore
def backward(
ctx,
*grad: Tensor,
) -> Tuple[Optional[Tensor], ...]:
ranks = get_pipeline_parallel_ranks()
this_rank = torch.distributed.get_rank()
body = AsyncMessageBody(
AsyncMessageType.Gradients, ctx.index, source=ctx.args.dest, dest=ctx.args.source, order=ctx.args.order - 1
)
ctx.transport.send_message(
PipeMessage(
this_rank,
ranks[ctx.args.source.stage],
queue_name=ctx.queue_name,
args=body,
tensors=tuple(grad),
),
sync=True,
)
tail_ctx = getattr(ctx, "tail_ctx", None)
if tail_ctx:
expected_gradients = tail_ctx.expected_gradients
while expected_gradients > 0:
message = ctx.transport.recv_message_header(ctx.queue_name)
args: AsyncMessageBody = message.args
assert args.message_type is AsyncMessageType.Gradients
invocation = tail_ctx.invocations[args.order]
expected_gradients -= tail_ctx.count_per_order[invocation.order]
AsyncEventLoop.perform_backward_for_invocation(ctx.transport, message, tail_ctx.activations, invocation)
return (None, None, None, None, None)
class AsyncEventLoop:
def __init__(
self,
partitions: List[ModuleWrapper],
group: ProcessGroup,
transport: Transport,
training: bool,
checkpoint_stop: int,
):
self.training = training
self.checkpoint_stop = checkpoint_stop
self.transport = transport
self.group = group
self.partitions: List[ModuleWrapper] = partitions
def send_async_message(self, dst_rank: int, result: Batch, invocation: Invocation) -> Batch:
"""Send batch to dst_rank, and use AutogradWithoutActivations to delete
the activations since we no longer need them"""
assert invocation.dest
src_rank = torch.distributed.get_rank()
body = AsyncMessageBody(
AsyncMessageType.Activations, result.index, invocation.this, invocation.dest, invocation.order + 1
)
self.transport.send_message(
PipeMessage(src_rank, dst_rank, queue_name=EVENT_LOOP_QUEUE, args=body, tensors=tuple([*result])),
sync=True,
)
phony = AutogradWithoutActivations.apply(*result)
return Batch(phony, result.index)
def run_invocation(
self,
batch: Batch,
partition: ModuleWrapper,
skip_trackers: List[SkipTrackerThroughPotals],
invocation: Invocation,
) -> Batch:
"""Actually run the forward pass for a given module, and send the result
to the next stage in the pipeline if needed."""
task = create_task(
self.checkpoint_stop,
batch.index,
self.group.rank(),
batch,
partition.module,
skip_trackers,
)
result = task.compute()
task.finalize(result)
if invocation.dest and invocation.dest.stage != invocation.this.stage:
ranks = get_pipeline_parallel_ranks()
dst_rank = ranks[invocation.dest.stage]
result = self.send_async_message(dst_rank, result, invocation)
return result
@staticmethod
def perform_backward_for_invocation(
transport: Transport, message: PipeMessage, activations: Activations, invocation: Invocation
) -> None:
"""Perform the backward pass by looking up the appropriate `Batch` and
then calling `backward` on the tensor"""
recvd_grads = transport.recv_message_tensors(message)
batch: Batch = activations[invocation.this.index][invocation.order][message.args.microbatch_index]
# All batches saved in `activations` are generated by AutogradWithoutActivations,
# so we store the gradients in `grad_from_pipeline` so it will be used
# during the backward pass
batch.tensor.grad_fn.grad_from_pipeline = tuple(recvd_grads.tensors)
batch.tensor.backward(retain_graph=True)
def run_invocations_on_batch(
self,
batch: Batch,
invocations: Invocations,
order: int,
skip_trackers: List[SkipTrackerThroughPotals],
activations: Activations,
) -> Tuple[int, int]:
"""Run invocations on the batch until we hit one that receives its input
from a different stage (i.e. another process)"""
invocations_handled = 0
last_order = 0
for invocation in invocations.values():
if invocation.order < order:
continue
pi = invocation.this.index
partition = self.partitions[pi]
if invocation.order == order:
invocations_handled += 1
last_order = invocation.order
activations[pi][invocation.order][batch.index] = self.run_invocation(
batch, partition, skip_trackers, invocation
)
elif invocation.source and invocation.source.stage == self.group.rank():
invocations_handled += 1
last_order = invocation.order
batch = activations[invocation.source.index][invocation.order - 1][batch.index]
activations[pi][invocation.order][batch.index] = self.run_invocation(
batch, partition, skip_trackers, invocation
)
del activations[invocation.source.index][invocation.order - 1][batch.index]
elif invocation.source and invocation.source.stage != self.group.rank():
break
return (invocations_handled, last_order)
def event_loop_head(
self, batches: List[Batch], skip_trackers: List[SkipTrackerThroughPotals], event: Optional[Event]
) -> None:
"""The event loop for the "head", which first performs the forward pass
on any applicable layers for this stage, and then enters the common
`event_loop_inner`"""
invocations, activations = self.get_invocations_and_activations()
expected_invocations = len(invocations) * len(batches)
actual_invocations = 0
count_per_order = dict()
for batch in batches:
inv_count, last_order = self.run_invocations_on_batch(batch, invocations, 0, skip_trackers, activations)
actual_invocations += inv_count
count_per_order[last_order] = inv_count
if actual_invocations < expected_invocations or self.training:
self.event_loop_inner(
expected_invocations,
skip_trackers,
activations,
invocations,
count_per_order,
already_received=actual_invocations,
event=event,
)
def get_batch_from_message(self, message: PipeMessage) -> Batch:
"""Get the tensor(s) wrapped in a `Batch` from a `PipeMessage`, applying
AsyncRecvOperator so we can intercept the backward pass"""
microbatch_index = message.args.microbatch_index
phony = torch.empty(0, device=self.transport.input_device, requires_grad=True)
result = AsyncRecvOperator.apply(phony, self.transport, message, EVENT_LOOP_QUEUE)
if len(result) == 1:
batch = Batch(result[0], microbatch_index)
else:
batch = Batch(result, microbatch_index)
return batch
def event_loop_tail(self, batches: List[Batch], skip_trackers: List[SkipTrackerThroughPotals]) -> None:
"""The event loop for the "tail", or final stage which only processes
activations and then returns to the caller so that the loss can be
calculated. This also handles the first/only stage for the special
case of a 1-stage pipeline."""
invocations, activations = self.get_invocations_and_activations()
expected_invocations = len(invocations) * len(batches)
actual_invocations = 0
rank = self.group.rank()
count_per_order = dict()
for batch in batches:
if rank == 0:
order = 0
else:
message = self.transport.recv_message_header(EVENT_LOOP_QUEUE)
args: AsyncMessageBody = message.args
batch = self.get_batch_from_message(message)
order = args.order
inv_count, last_order = self.run_invocations_on_batch(batch, invocations, order, skip_trackers, activations)
actual_invocations += inv_count
count_per_order[last_order] = inv_count
if invocations[last_order].dest is None:
self.prepare_tail_backward(
batch, activations, invocations, count_per_order, len(invocations) - inv_count
)
if actual_invocations < expected_invocations:
expected_gradients = 0 # (len(invocations) - 1) * len(batches)
self.event_loop_inner(
expected_invocations,
skip_trackers,
activations,
invocations,
count_per_order,
already_received=actual_invocations,
ignore_gradients=True,
tail=True,
)
_, last_invocation = invocations.popitem()
for index, batch in activations[len(self.partitions) - 1][last_invocation.order].items():
batches[index] = batch
def get_invocations_and_activations(self) -> Tuple[Invocations, Activations]:
activations: Activations = dict()
invocations: Invocations = OrderedDict()
for pi, partition in enumerate(self.partitions):
activations[pi] = dict()
for invocation in partition.invocations:
activations[pi][invocation.order] = dict()
invocations[invocation.order] = invocation
invocations = OrderedDict(sorted(invocations.items(), key=lambda entry: entry[0]))
return (invocations, activations)
def event_loop(self, num_microbatch: int, skip_trackers: List[SkipTrackerThroughPotals]) -> None:
"""The event loop for the "middle", i.e. neither the head nor the tail"""
invocations, activations = self.get_invocations_and_activations()
expected_invocations = len(invocations) * num_microbatch
self.event_loop_inner(expected_invocations, skip_trackers, activations, invocations, dict())
def event_loop_inner(
self,
expected_invocations: int,
skip_trackers: List[SkipTrackerThroughPotals],
activations: Activations,
invocations: Invocations,
count_per_order: Dict[int, int],
*,
already_received: int = 0,
ignore_gradients: bool = False,
event: Optional[Event] = None,
tail: bool = False,
) -> None:
"""The common event loop shared by all stages. This processses
activations for the forward pass, and if `self.training` is true,
processes gradients for the backward pass."""
num_activations = already_received
if self.training and not ignore_gradients:
num_gradients = 0
else:
num_gradients = expected_invocations
while num_activations < expected_invocations or num_gradients < expected_invocations:
if num_activations == expected_invocations and num_gradients == 0 and event is not None:
# We are ready to do the backward pass, but must wait for
# PipeRPCWrapper to signal that it is safe to proceed, otherwise
# deadlock
event.wait()
message = self.transport.recv_message_header(EVENT_LOOP_QUEUE)
args: AsyncMessageBody = message.args
invocation = invocations[args.order]
# FIXME(tom) for combining pipeline with megatron, I currently don't
# control the order of received activations or gradients, so it is
# possible for a reused ColumnParallelLinear for example to receive
# a different order of activations w.r.t. the sending stage, which
# would result in incorrect values being used for the all_gather
if args.message_type is AsyncMessageType.Activations:
batch = self.get_batch_from_message(message)
inv_count, last_order = self.run_invocations_on_batch(
batch, invocations, args.order, skip_trackers, activations
)
count_per_order[last_order] = inv_count
num_activations += inv_count
if tail and invocations[last_order].dest is None:
self.prepare_tail_backward(
batch, activations, invocations, count_per_order, len(invocations) - inv_count
)
assert num_activations <= expected_invocations
elif args.message_type is AsyncMessageType.Gradients:
num_gradients += count_per_order[invocation.order]
self.perform_backward_for_invocation(self.transport, message, activations, invocation)
@staticmethod
def prepare_tail_backward(
batch: Batch,
activations: Activations,
invocations: Invocations,
count_per_order: Dict[int, int],
expected_gradients: int,
) -> None:
if expected_gradients > 0:
grad_fn = next(b.grad_fn for b in batch if b.requires_grad)
assert grad_fn
grad_fn.tail_ctx = TailBackwardContext(activations, invocations, count_per_order, expected_gradients)
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