Source code for torch.distributed.pipelining.schedules
# mypy: allow-untyped-defs
# Copyright (c) Meta Platforms, Inc. and affiliates
import csv
import itertools
import logging
import re
from abc import ABC, abstractmethod
from collections import defaultdict
from enum import Enum
from typing import (
Any,
Callable,
Dict,
List,
NamedTuple,
Optional,
Set,
Tuple,
TYPE_CHECKING,
Union,
)
import torch
import torch.distributed as dist
from torch.distributed._composable.fsdp.fully_shard import FSDPModule, UnshardHandle
from torch.profiler import record_function
from .microbatch import merge_chunks, split_args_kwargs_into_chunks, TensorChunkSpec
from .stage import _PipelineStageBase
if TYPE_CHECKING:
from torch.distributed import Work
__all__ = [
"get_schedule_class",
"PipelineScheduleSingle",
"PipelineScheduleMulti",
"Schedule1F1B",
"ScheduleFlexibleInterleaved1F1B",
"ScheduleGPipe",
"ScheduleInterleaved1F1B",
"ScheduleLoopedBFS",
"ScheduleInterleavedZeroBubble",
]
logger = logging.getLogger(__name__)
class _ComputationType(Enum):
# TODO(whc) rename to _ActType?
FORWARD = 1
BACKWARD = 2
WEIGHT = 3
UNSHARD = 4
RESHARD = 5
SEND_F = 6
RECV_F = 7
SEND_B = 8
RECV_B = 9
def __str__(self):
str_map = {
_ComputationType.FORWARD: "F",
_ComputationType.BACKWARD: "B",
_ComputationType.WEIGHT: "W",
_ComputationType.UNSHARD: "UNSHARD",
_ComputationType.RESHARD: "RESHARD",
_ComputationType.SEND_F: "SEND_F",
_ComputationType.RECV_F: "RECV_F",
_ComputationType.SEND_B: "SEND_B",
_ComputationType.RECV_B: "RECV_B",
}
return str_map[self]
@staticmethod
def from_str(action):
if action == "F":
return _ComputationType.FORWARD
elif action == "B":
return _ComputationType.BACKWARD
elif action == "W":
return _ComputationType.WEIGHT
elif action == "UNSHARD":
return _ComputationType.UNSHARD
elif action == "RESHARD":
return _ComputationType.RESHARD
elif action == "SEND_F":
return _ComputationType.SEND_F
elif action == "RECV_F":
return _ComputationType.RECV_F
elif action == "SEND_B":
return _ComputationType.SEND_B
elif action == "RECV_B":
return _ComputationType.RECV_B
else:
raise RuntimeError(f"Invalid computation type {action}")
FORWARD = _ComputationType.FORWARD
BACKWARD = _ComputationType.BACKWARD
WEIGHT = _ComputationType.WEIGHT
UNSHARD = _ComputationType.UNSHARD
RESHARD = _ComputationType.RESHARD
SEND_F = _ComputationType.SEND_F
RECV_F = _ComputationType.RECV_F
SEND_B = _ComputationType.SEND_B
RECV_B = _ComputationType.RECV_B
# Convenience shorthand for compute actions only since they are used in 'simple schedule format'
F = FORWARD
B = BACKWARD
W = WEIGHT
# Helper to parse an action string like 1F0 into a tuple of (stage_index, computation_type, microbatch_index)
_action_regex = re.compile(
r"(\d+)([F,B,W]|UNSHARD|RESHARD|SEND_F|RECV_F|SEND_B|RECV_B{0,1})(\d*)"
)
class _Action(NamedTuple):
stage_index: int
computation_type: _ComputationType
microbatch_index: Optional[int] = None
def __repr__(self):
repr = str(self.stage_index)
repr += str(self.computation_type)
if self.microbatch_index is not None:
repr += str(self.microbatch_index)
return repr
@staticmethod
def from_str(str):
"""
Reverse of __repr__
String should be formatted as [stage][action type][(microbatch)]
e.g. `2F0`, `1UNSHARD`, `3SEND_F1`
"""
if match := _action_regex.match(str):
stage_index, computation_type, microbatch_index = match.groups()
return _Action(
int(stage_index),
_ComputationType.from_str(computation_type),
int(microbatch_index) if len(microbatch_index) else None,
)
elif str == "" or str.isspace():
return None
raise RuntimeError(
f"Invalid action string: {str}, should be formatted as [stage][action type][(microbatch)] e.g. 2F0"
)
def _format_pipeline_order(pipeline_order: Dict[int, List[Optional[_Action]]]) -> str:
"""
Formats the pipeline order in a timestep (row) x rank (column) grid of actions
and returns the formatted string
"""
# Calculate the maximum number of steps across all ranks
num_steps = max(len(actions) for actions in pipeline_order.values())
step_labels = [
"Step " + str(i).zfill(len(str(num_steps - 1))) for i in range(num_steps)
]
# Sorting the dictionary by keys and retrieving values in that order
rank_actions = [
pipeline_order.get(key, [""] * num_steps) for key in sorted(pipeline_order)
]
# Transpose the list of lists (rows to columns)
transposed_actions = list(itertools.zip_longest(*rank_actions, fillvalue=""))
# Generate column labels for ranks
num_ranks = len(pipeline_order)
rank_labels = ["Rank " + str(i) for i in range(num_ranks)]
# Calculate the maximum length of each column, considering labels
max_lengths = [
max(len(str(item)) if item is not None else 0 for item in col)
for col in zip(step_labels, *transposed_actions)
]
# Format the header row with rank labels
header_row = " " * (len(step_labels[0]) + 2) + " ".join(
f"{label:<{max_lengths[i]}}" for i, label in enumerate(rank_labels)
)
# Format each row with its corresponding label
formatted_rows = [
f"{label}: "
+ " ".join(f"{str(item):<{max_lengths[i]}}" for i, item in enumerate(row))
for label, row in zip(step_labels, transposed_actions)
]
# Join the rows into a single string
formatted_table = header_row + "\n" + "\n".join(formatted_rows) + "\n"
return formatted_table
def _validate_pipeline_order(
pipeline_order: Dict[int, List[Optional[_Action]]],
num_microbatches: int,
num_stages: int,
enable_zero_bubble: bool = False,
):
"""
pipeline_order[rank] = [(computation_type, microbatch_index, stage_index), ...]
Validating that the pipeline order follows the rules:
1. Forward action for a microbatch must be before the Backward action for that microbatch
2. Recv for a microbatch must be before the send for that microbatch
3. Microbatch index is handled in sequential order for each stage
4. A later stage cannot operate on a microbatch before any of the previous stages have operated on it
5. Same microbatch cannot be handled in the same time step across ranks
"""
# microbatch_index: (current computation type, current stage)
microbatch_process_info: Dict[int, Tuple[_ComputationType, int]] = {}
max_timestep = max(len(rank_list) for rank_list in pipeline_order.values())
for timestep in range(max_timestep):
error_msg: List[str] = []
current_timestep_actions = []
for rank in range(len(pipeline_order)):
action = (
pipeline_order[rank][timestep]
if timestep < len(pipeline_order[rank])
else None
)
if action is not None:
computation_type = action.computation_type
if computation_type != _ComputationType.WEIGHT:
current_timestep_actions.append(action)
# TODO: enable this
# if len(current_timestep_actions) == 0:
# error_msg.append(
# "All actions were None, there is an unnecessary gap in the schedule"
# )
# Ensure that no microbatch is operated on twice in current_timestep_actions
unique_microbatch_indices = {
action.microbatch_index for action in current_timestep_actions
}
if len(unique_microbatch_indices) != len(current_timestep_actions):
error_msg.append(
"Duplicate microbatch index found in current_timestep_actions"
)
for action in current_timestep_actions:
stage_index = action.stage_index
computation_type = action.computation_type
mb_index = action.microbatch_index
assert (
mb_index is not None
), "All currently supported action types require valid microbatch_index"
if mb_index >= num_microbatches:
error_msg.append(f"Microbatch index {mb_index} out of range")
# first microbatch
if mb_index not in microbatch_process_info:
if computation_type != _ComputationType.FORWARD or stage_index != 0:
error_msg.append(f"Incorrect start for microbatch {mb_index}")
microbatch_process_info[mb_index] = (computation_type, stage_index)
else:
# if the microbatch is included, check that the current stage is right after prev
prev_computation, prev_stage = microbatch_process_info[mb_index]
if prev_computation == _ComputationType.FORWARD:
if prev_stage == num_stages - 1:
expected_stage = num_stages - 1
expected_computation = _ComputationType.BACKWARD
else:
expected_stage = prev_stage + 1
expected_computation = _ComputationType.FORWARD
elif prev_computation == _ComputationType.BACKWARD:
if prev_stage == 0:
error_msg.append(
f"[{mb_index=}] already finished backward computation"
)
break
else:
expected_stage = prev_stage - 1
expected_computation = _ComputationType.BACKWARD
else:
raise ValueError(
f"Computation type {prev_computation} not supported"
)
if expected_computation is not None:
if expected_computation != computation_type:
error_msg.append(
f"[{mb_index=}] {expected_computation=} VS. actual {computation_type=}"
)
if expected_stage != stage_index:
error_msg.append(
f"[{mb_index=}] {expected_stage=} VS. actual {stage_index=}"
)
microbatch_process_info[mb_index] = (
expected_computation,
expected_stage,
)
if not enable_zero_bubble:
if len(error_msg) != 0:
raise RuntimeError(
f"Error at timestep {timestep}: " + ",".join(error_msg)
)
return
for rank in range(len(pipeline_order)):
backward_steps: Set[Tuple[int, int]] = set()
weight_steps: Set[Tuple[int, int]] = set()
for action in pipeline_order[rank]:
if action is None:
continue
stage_index = action.stage_index
computation_type = action.computation_type
mb_index = action.microbatch_index
if computation_type == _ComputationType.BACKWARD:
if mb_index is not None:
backward_steps.add((mb_index, stage_index))
elif computation_type == _ComputationType.WEIGHT:
if (mb_index, stage_index) not in backward_steps:
error_msg.append(
f"{mb_index=}, {stage_index=} Weight happened before bwd"
)
if (mb_index, stage_index) in weight_steps:
error_msg.append(
f"{mb_index=}, {stage_index=} Duplicated weight step"
)
if mb_index is not None:
weight_steps.add((mb_index, stage_index))
if len(backward_steps) != len(weight_steps):
error_msg.append("Length weight steps != Length bwd steps")
if len(error_msg) != 0:
raise RuntimeError(f"Error at timestep {timestep}: " + ",".join(error_msg))
class _PipelineSchedule(ABC):
def __init__(
self,
n_microbatches: int,
loss_fn: Optional[Callable[..., torch.Tensor]] = None,
args_chunk_spec: Optional[Tuple[TensorChunkSpec, ...]] = None,
kwargs_chunk_spec: Optional[Dict[str, TensorChunkSpec]] = None,
output_merge_spec: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
):
# From arguments
self._n_microbatches = n_microbatches
self._loss_fn = loss_fn
# Chunking specification for positional inputs. (default: `None`)
self._args_chunk_spec = args_chunk_spec
# Chunking specification for keyword inputs. (default: `None`)
self._kwargs_chunk_spec = kwargs_chunk_spec
self._output_merge_spec = output_merge_spec
"""
# args_chunk_spec and kwargs_chunk_spec specify how to chunk inputs.
# They are used to convert batch to microbatches in `step(x)`. See
# `TensorChunkSpec` for helper methods for creating them.
"""
# Derived
self._has_backward = self._loss_fn is not None
# Holds the losses for each microbatch.
self._internal_losses: List[torch.Tensor] = []
logger.info("Using %s", self.__class__.__name__)
def _maybe_compute_loss(self, stage, output, target_mbs, mb_index):
if stage.is_last and self._has_backward:
loss = self._compute_loss(output, target_mbs[mb_index]) # type: ignore[index]
self._internal_losses.append(loss)
def _maybe_get_loss(self, stage, mb_index):
valid_index = 0 <= mb_index < len(self._internal_losses)
if stage.is_last and self._has_backward and valid_index:
return self._internal_losses[mb_index]
elif len(self._internal_losses) != 0 and not valid_index:
raise RuntimeError(
f"Loss for microbatch {mb_index} is not available. "
f"Available losses for microbatches: {self._internal_losses}"
)
else:
return None
def _update_losses(self, stages, losses):
"""
Update the losses to those in the internal state
"""
# if stages not a list turn into a list
if not isinstance(stages, list):
stages = [stages]
contains_last_stage = any(stage.is_last for stage in stages)
# Return losses if there is a container passed in
if contains_last_stage and losses is not None:
if len(self._internal_losses) != self._n_microbatches:
raise RuntimeError(
f"Expecting {self._n_microbatches} losses but got {len(self._internal_losses)}"
)
# Clean external container first
losses.clear()
# Copy internal losses to external container
losses.extend(self._internal_losses)
self._internal_losses.clear()
@abstractmethod
def _step_microbatches(
self,
arg_mbs: Optional[List] = None,
kwarg_mbs: Optional[List] = None,
target_mbs: Optional[List] = None,
losses: Optional[List] = None,
):
"""
Run one iteration of the pipeline schedule with list of microbatches.
Will go through all the microbatches according to the schedule
implementation.
Args:
microbatches: list of microbatch args.
"""
raise NotImplementedError
@abstractmethod
def step(self, *args, target=None, losses: Optional[List] = None, **kwargs):
"""
Run one iteration of the pipeline schedule with *whole-batch* input.
Will chunk the input into microbatches automatically, and go through the
microbatches according to the schedule implementation.
args: positional arguments to the model (as in non-pipeline case).
kwargs: keyword arguments to the model (as in non-pipeline case).
target: target for the loss function.
losses: a list to store the losses for each microbatch.
"""
raise NotImplementedError
def _check_inputs(
self,
arg_mbs: Optional[List] = None,
kwarg_mbs: Optional[List] = None,
target_mbs: Optional[List] = None,
losses: Optional[List] = None,
):
"""
Pre-process/check inputs
"""
def check_type_and_len(mbs, name: str):
if not isinstance(mbs, list):
raise TypeError(f"{name} must be a list but got a {type(mbs)}")
if len(mbs) != self._n_microbatches:
raise ValueError(
f"Expecting {self._n_microbatches} {name} but got {len(mbs)}"
)
if arg_mbs is not None:
check_type_and_len(arg_mbs, "arg_mbs")
else:
arg_mbs = [()] * self._n_microbatches
if kwarg_mbs is not None:
check_type_and_len(kwarg_mbs, "kwarg_mbs")
else:
kwarg_mbs = [{}] * self._n_microbatches
if target_mbs is not None:
check_type_and_len(target_mbs, "target_mbs")
if losses is not None:
if not isinstance(losses, list):
raise TypeError(f"losses must be a list but got a {type(losses)}")
return arg_mbs, kwarg_mbs
def _compute_loss(self, output, target):
return self._loss_fn(output, target) # type: ignore[misc]
def _split_inputs(
self,
args: Tuple[Any, ...],
kwargs: Optional[Dict[str, Any]] = None,
):
"""
Splits a full-batch input into chunks (i.e. microbatches) and returns
the chunks
"""
if args or kwargs:
args_split, kwargs_split = split_args_kwargs_into_chunks(
args,
kwargs,
self._n_microbatches,
self._args_chunk_spec,
self._kwargs_chunk_spec,
)
return args_split, kwargs_split
else:
# Empty inputs (e.g. when called on middle stages)
# Return a list of empty tuples/dicts with matching length as chunks
return [()] * self._n_microbatches, [{}] * self._n_microbatches
def _merge_outputs(self, output_chunks: List[Any]) -> Any:
"""
Merge output chunks back to a batch state.
If output_merge_spec is None, the utility will merge output chunks by dimension 0 (batch dim).
"""
return merge_chunks(
output_chunks,
self._output_merge_spec,
)
def _batch_p2p(p2p_ops: List[dist.P2POp], desc: Optional[str] = None):
"""
Simple wrapper over batch_isend_irecv from torch.distributed, which just adds a descriptive logger on top.
"""
if len(p2p_ops) == 0:
return None
desc_str = f"{desc}, " if desc else ""
logger.debug("batch_p2p %s%s", desc_str, p2p_ops)
return dist.batch_isend_irecv(p2p_ops).pop()
def _sorted_batch_p2p(
p2p_ops: List[dist.P2POp], desc: Optional[str] = None
) -> Dict[int, dist.Work]:
"""
Sorts the list of P2P ops by the peer rank, and then calls
batch_isend_irecv. Return a dictionary of works by peer rank. This function
helps us avoid hangs in case of skip connections.
"""
# Arrange p2p_ops by peer rank:
# int is the peer rank;
# List is the list of ops towards the peer
ops_by_peer: Dict[int, List[dist.P2POp]] = defaultdict(list)
work_by_peer: Dict[int, dist.Work] = {}
if len(p2p_ops) == 0:
return work_by_peer
# Classify the ops by peer rank
for op in p2p_ops:
ops_by_peer[op.peer].append(op)
# Call batch_isend_irecv per peer, in sorted order of the peers (to avoid hangs)
for peer, ops in sorted(ops_by_peer.items()):
work_by_peer[peer] = _batch_p2p(ops, desc=desc)
return work_by_peer
[docs]class PipelineScheduleSingle(_PipelineSchedule):
"""
Base class for single-stage schedules.
Implements the `step` method.
Derived classes should implement `_step_microbatches`.
"""
def __init__(
self,
stage: _PipelineStageBase,
n_microbatches: int,
loss_fn: Optional[Callable] = None,
args_chunk_spec: Optional[Tuple[TensorChunkSpec, ...]] = None,
kwargs_chunk_spec: Optional[Dict[str, TensorChunkSpec]] = None,
output_merge_spec: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
):
# Init parent
super().__init__(
n_microbatches=n_microbatches,
loss_fn=loss_fn,
args_chunk_spec=args_chunk_spec,
kwargs_chunk_spec=kwargs_chunk_spec,
output_merge_spec=output_merge_spec,
)
# Self attributes
self._stage = stage
self._num_stages = stage.num_stages
# Set the same has_backward flag for stage object
self._stage.has_backward = self._has_backward
# TODO: later replace this with lazy shape inference during forward
# Prepare forward send/recv infrastructure for stage
stage._prepare_forward_infra(n_microbatches)
if self._has_backward:
stage._prepare_backward_infra(n_microbatches)
[docs] def step(self, *args, target=None, losses: Optional[List] = None, **kwargs):
"""
Run one iteration of the pipeline schedule with *whole-batch* input.
Will chunk the input into microbatches automatically, and go through the
microbatches according to the schedule implementation.
args: positional arguments to the model (as in non-pipeline case).
kwargs: keyword arguments to the model (as in non-pipeline case).
target: target for the loss function.
losses: a list to store the losses for each microbatch.
"""
# Clean per iteration
self._stage.clear_runtime_states()
# Split inputs into microbatches
args_split, kwargs_split = self._split_inputs(args, kwargs)
# Split target into microbatches
if target is not None:
targets_split = list(torch.tensor_split(target, self._n_microbatches))
else:
targets_split = None
# Run microbatches
self._step_microbatches(args_split, kwargs_split, targets_split, losses)
# Return merged results per original format
if self._stage.is_last:
return self._merge_outputs(self._stage.output_chunks)
else:
return None
class _ScheduleForwardOnly(PipelineScheduleSingle):
"""
The forward-only schedule.
Will go through all the microbatches and perform only the forward pass
"""
def _step_microbatches(
self,
arg_mbs: Optional[List] = None,
kwarg_mbs: Optional[List] = None,
target_mbs: Optional[List] = None,
losses: Optional[List] = None,
):
"""
Run one iteration of the pipeline schedule
"""
if target_mbs is not None or losses is not None:
raise RuntimeError(
"Forward-only schedule does not support loss computation"
)
arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
# Delay send waits
fwd_sends_to_wait: List[dist.Work] = []
# Run microbatches
for i in range(self._n_microbatches):
with record_function(f"Forward {i}"):
ops = self._stage.get_fwd_recv_ops(i)
works = _sorted_batch_p2p(ops, desc="fwd_recv")
for work in works.values():
work.wait()
self._stage.forward_one_chunk(i, arg_mbs[i], kwarg_mbs[i]) # type: ignore[index]
ops = self._stage.get_fwd_send_ops(i)
works = _sorted_batch_p2p(ops, desc="fwd_send")
fwd_sends_to_wait.extend(works.values())
logger.debug("[%s] Forwarded microbatch %s", self._stage.stage_index, i)
# Wait for all forward sends to finish
# This should not have performance impact because by the time the first
# backward arrives all the forward sends should have been finished.
for work in fwd_sends_to_wait:
work.wait()
[docs]class ScheduleGPipe(PipelineScheduleSingle):
"""
The GPipe schedule.
Will go through all the microbatches in a fill-drain manner.
"""
def _step_microbatches(
self,
arg_mbs: Optional[List] = None,
kwarg_mbs: Optional[List] = None,
target_mbs: Optional[List] = None,
losses: Optional[List] = None,
):
"""
Run one iteration of the pipeline schedule with list of microbatches.
Will go through all the microbatches according to the GPipe schedule.
Args:
microbatches: list of microbatch args.
"""
arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
# Delay send waits
fwd_sends_to_wait: List[dist.Work] = []
# Run microbatches
for i in range(self._n_microbatches):
with record_function(f"Forward {i}"):
ops = self._stage.get_fwd_recv_ops(i)
works = _sorted_batch_p2p(ops, desc="fwd_recv")
for work in works.values():
work.wait()
output = self._stage.forward_one_chunk(i, arg_mbs[i], kwarg_mbs[i]) # type: ignore[index]
ops = self._stage.get_fwd_send_ops(i)
works = _sorted_batch_p2p(ops, desc="fwd_send")
fwd_sends_to_wait.extend(works.values())
logger.debug("[%s] Forwarded microbatch %s", self._stage.stage_index, i)
self._maybe_compute_loss(self._stage, output, target_mbs, i)
# Wait for all forward sends to finish
# This should not have performance impact because by the time the first
# backward arrives all the forward sends should have been finished.
for work in fwd_sends_to_wait:
work.wait()
# No loss function, no need to run backward
if not self._has_backward:
return
# Run backward
# Delay send waits
bwd_sends_to_wait: List[dist.Work] = []
for i in range(self._n_microbatches):
with record_function(f"Backward {i}"):
ops = self._stage.get_bwd_recv_ops(i)
works = _sorted_batch_p2p(ops, desc="bwd_recv")
for work in works.values():
work.wait()
loss = self._maybe_get_loss(self._stage, i)
self._stage.backward_one_chunk(i, loss=loss)
ops = self._stage.get_bwd_send_ops(i)
works = _sorted_batch_p2p(ops, desc="bwd_send")
bwd_sends_to_wait.extend(works.values())
logger.debug("[%s] Backwarded microbatch %s", self._stage.stage_index, i)
# Return losses if there is a container passed in
self._update_losses(self._stage, losses)
# Wait for all backward sends to finish
for work in bwd_sends_to_wait:
work.wait()
[docs]class Schedule1F1B(PipelineScheduleSingle):
"""
The 1F1B schedule.
Will perform one forward and one backward on the microbatches in steady state.
"""
def _step_microbatches(
self,
arg_mbs: Optional[List] = None,
kwarg_mbs: Optional[List] = None,
target_mbs: Optional[List] = None,
losses: Optional[List] = None,
):
"""
Run one iteration of the pipeline schedule with list of microbatches.
Will go through all the microbatches according to the 1F1B schedule.
Args:
microbatches: list of microbatch args.
"""
arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
# Last stage has 1 warmup, second-to-last 2 warmups, ...
# first stage `num_stages` warmups
warmup_chunks = min(
self._n_microbatches,
self._num_stages - self._stage.stage_index,
)
# Chunk counters
fwd_mb_index = 0
bwd_mb_index = 0
weight_stage_mb_index = 0
# Warmup phase
send_work = None
fwd_sends = []
for _ in range(warmup_chunks):
# Receive activations
fwd_recvs = self._stage.get_fwd_recv_ops(fwd_mb_index)
if recv_work := _batch_p2p(fwd_recvs, desc="fwd_recv"):
recv_work.wait()
# Compute
output = self._stage.forward_one_chunk(fwd_mb_index, arg_mbs[fwd_mb_index], kwarg_mbs[fwd_mb_index]) # type: ignore[index]
# Clear previous chunk's forward sends (hopefully they have well
# finished, otherwise, we are heavily communication bound, in which
# case it doesn't create a lot of benefit to compute next chunk
# eagerly either)
if send_work:
send_work.wait()
# Send activations
fwd_sends = self._stage.get_fwd_send_ops(fwd_mb_index)
if fwd_mb_index != warmup_chunks - 1:
# Safe to fire
send_work = _batch_p2p(fwd_sends, desc="fwd_send")
# otherwise:
# The last foward send is left for fuse with first 1B in 1B1F below
# Compute loss
self._maybe_compute_loss(self._stage, output, target_mbs, fwd_mb_index)
fwd_mb_index += 1
# Now we should have send ops left over, to be fused with first 1B of 1B1F phase below.
# 1B1F phase
while True: # Don't worry, we have a break inside
# We actually do 1B first as the `1B1F` name indicates, so prepare its recv ops
bwd_recvs = self._stage.get_bwd_recv_ops(bwd_mb_index)
# Now, we need to fire the fwd_sends and bwd_recvs together
if fuse_work := _batch_p2p(fwd_sends + bwd_recvs, desc="fwd_send_bwd_recv"):
fuse_work.wait()
# Backward one chunk
loss = self._maybe_get_loss(self._stage, bwd_mb_index)
self._stage.backward_one_chunk(bwd_mb_index, loss=loss)
# Get the bwd send ops, but don't fire, to be fused with the 1F below
bwd_sends = self._stage.get_bwd_send_ops(bwd_mb_index)
bwd_mb_index += 1
if fwd_mb_index == self._n_microbatches:
# We are done with 1B1F, so break with some left-over bwd_sends
break
# We prepare 1F of the `1B1F`
fwd_recvs = self._stage.get_fwd_recv_ops(fwd_mb_index)
# Fuse it with bwd_sends above
if fuse_work := _batch_p2p(bwd_sends + fwd_recvs, desc="bwd_send_fwd_recv"):
fuse_work.wait()
# Now do the fwd
output = self._stage.forward_one_chunk(fwd_mb_index, arg_mbs[fwd_mb_index], kwarg_mbs[fwd_mb_index]) # type: ignore[index]
# Compute loss
self._maybe_compute_loss(self._stage, output, target_mbs, fwd_mb_index)
# Get the fwd send ops, but don't fire, leave it for the next iter (wrap-around)
fwd_sends = self._stage.get_fwd_send_ops(fwd_mb_index)
fwd_mb_index += 1
# Remember we still have some bwd_sends left over after the break? Now it is time to fire it
send_work = _batch_p2p(bwd_sends, desc="bwd_send")
# Cooldown
while bwd_mb_index < self._n_microbatches:
# prepare bwd recv ops
bwd_recvs = self._stage.get_bwd_recv_ops(bwd_mb_index)
if recv_work := _batch_p2p(bwd_recvs, desc="bwd_recv"):
recv_work.wait()
# Backward one chunk
loss = self._maybe_get_loss(self._stage, bwd_mb_index)
self._stage.backward_one_chunk(bwd_mb_index, loss=loss)
# Clear previous chunk's backward sends (hopefully they have well finished)
if send_work:
send_work.wait()
# Get the bwd send ops, fire it
bwd_sends = self._stage.get_bwd_send_ops(bwd_mb_index)
send_work = _batch_p2p(bwd_sends, desc="bwd_send")
bwd_mb_index += 1
# Wait for the last backward send to finish
if send_work:
send_work.wait()
# Return losses if there is a container passed in
self._update_losses(self._stage, losses)
def _add_unshard_reshard(
compute_actions: List[Optional[_Action]],
max_active_stages: int = 3,
) -> List[_Action]:
"""Given a basic schedule involving only compute actions (F,B,W), add UNSHARD/RESHARD actions for FSDP.
UNSHARD refers to fetching the full contents of an FSDP-sharded layer, requiring an all-gather operation.
RESHARD does the opposite, releasing memory (but doing no commmunication)
We abandon the "timestep lock" during lowering
max_active_stages controls how many prefetches we allow. It should be measured in mb and tuneable but in practice
3 stages is probably the thing we want?
(to account for having one f and one b active, and something else prefetching?)
"""
def next_stage_indices(
count: int, next_actions: List[Optional[_Action]]
) -> List[int]:
"""Remove duplicates (same stage, different microbatch), find next 'count' stages that will do compute."""
seen: Set[int] = set()
ret: List[int] = []
for a in next_actions:
if a is not None and a.stage_index not in seen:
seen.add(a.stage_index)
ret.append(a.stage_index)
if len(ret) == count:
break
return ret
active_stages: Set[int] = set()
fsdp_aware_actions: List[_Action] = []
def _unshard(stage_index: int):
active_stages.add(stage_index)
fsdp_aware_actions.append(_Action(stage_index, UNSHARD, None))
def _reshard(stage_index: int):
active_stages.remove(stage_index)
fsdp_aware_actions.append(_Action(stage_index, RESHARD, None))
for i, action in enumerate(compute_actions):
if action is None:
continue
# We prefetch the next N stages we'll see, dropping existing stages to make room
next_n = next_stage_indices(max_active_stages, compute_actions[i:])
# Fetch needs to be ordered correctly, so don't use a set
fetch = list(filter(lambda s: s not in active_stages, next_n))
# Unclear what the best policy is for eviction, but we can maintain order so we do
evict = list(filter(lambda s: s not in next_n, active_stages))
# logger.debug(
# "_add_unshard_reshard Step %d active: %s fetch %s, evict %s",
# i,
# active_stages,
# fetch,
# evict,
# )
for stage in evict:
_reshard(stage)
for stage in fetch:
_unshard(stage)
fsdp_aware_actions.append(action)
return fsdp_aware_actions
def _add_send_recv(
compute_actions: Dict[int, List[_Action]],
stage_to_rank: Callable[[int], int],
num_stages: int,
) -> Dict[int, List[_Action]]:
comm_actions: Dict[int, List[_Action]] = {rank: [] for rank in compute_actions}
def _has_comms(action: _Action) -> bool:
if action.computation_type == F:
return action.stage_index != num_stages - 1
elif action.computation_type == B:
return action.stage_index != 0
return False
def _get_comms(action: _Action) -> Tuple[_Action, _Action]:
assert _has_comms(action), f"{action} is not a valid comm action"
stage_idx = action.stage_index
ctype = action.computation_type
mb_idx = action.microbatch_index
send = _Action(stage_idx, SEND_F if ctype == F else SEND_B, mb_idx)
recv_stage_idx = stage_idx + 1 if ctype == F else stage_idx - 1
recv = _Action(recv_stage_idx, RECV_F if ctype == F else RECV_B, mb_idx)
return send, recv
def _ready_to_schedule(
action: Optional[_Action], prev_actions: List[_Action]
) -> bool:
"""We don't put our own recv ops in the schedule, we let a sender on another rank put our recv ops in place.
This helps ensure a sane (non-hanging) ordering of sends and recvs.
But it also means we might not be able to schedule our next compute action yet.
"""
if action is None:
return True
elif action.computation_type == F and not action.stage_index == 0:
expected_recv = _Action(
action.stage_index,
RECV_F if action.computation_type == F else RECV_B,
action.microbatch_index,
)
return expected_recv in prev_actions
elif action.computation_type == B and not action.stage_index == num_stages - 1:
expected_recv = _Action(
action.stage_index,
RECV_F if action.computation_type == F else RECV_B,
action.microbatch_index,
)
return expected_recv in prev_actions
else:
return True
while compute_actions:
progress = False
# go in order of ranks even if dict keys aren't ordered
for rank in range(len(compute_actions)):
assert len(compute_actions[rank]) > 0
action = compute_actions[rank][0]
if not _ready_to_schedule(action, comm_actions[rank]):
continue
if action is not None:
comm_actions[rank].append(action)
if _has_comms(action):
send, recv = _get_comms(action)
# TODO we can avoid send/recv if the 2 stages are on the same rank.
# should we avoid that in the runtime or here?
comm_actions[rank].append(send)
comm_actions[stage_to_rank(recv.stage_index)].append(recv)
compute_actions[rank].pop(0)
if len(compute_actions[rank]) == 0:
del compute_actions[rank]
progress = True
assert progress, "Malformed compute schedule, can't schedule sends/recvs"
return comm_actions
[docs]class PipelineScheduleMulti(_PipelineSchedule):
"""
Base class for multi-stage schedules.
Implements the `step` method.
"""
def __init__(
self,
stages: List[_PipelineStageBase],
n_microbatches: int,
loss_fn: Optional[Callable] = None,
args_chunk_spec: Optional[Tuple[TensorChunkSpec, ...]] = None,
kwargs_chunk_spec: Optional[Dict[str, TensorChunkSpec]] = None,
output_merge_spec: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
stage_index_to_group_rank: Optional[Dict[int, int]] = None,
use_full_backward: bool = True,
):
if len(stages) <= 1:
raise ValueError(
f"Multi-stage schedule expects at least two stages but got {len(stages)}"
)
# Init parent
super().__init__(
n_microbatches=n_microbatches,
loss_fn=loss_fn,
args_chunk_spec=args_chunk_spec,
kwargs_chunk_spec=kwargs_chunk_spec,
output_merge_spec=output_merge_spec,
)
# Self attributes
self._stages = stages
self._num_stages = stages[0].num_stages
self.pp_group_size = stages[0].group_size
self.rank = stages[0].group_rank
# Set the pipeline stage states
if stage_index_to_group_rank is not None:
for stage in self._stages:
stage.stage_index_to_group_rank = stage_index_to_group_rank
self.stage_index_to_group_rank = stages[0].stage_index_to_group_rank
# Set the same has_backward flag for stage object
for stage in self._stages:
stage.has_backward = self._has_backward
self._should_compute_loss = (
lambda stage: stage.is_last and self._loss_fn is not None
)
# This will be set during init of derived schedules
self.pipeline_order: Dict[int, List[Optional[_Action]]] = {}
self.use_full_backward = use_full_backward
# TODO: later replace this with lazy shape inference during forward
# Prepare forward send/recv infrastructure for stage
for stage in self._stages:
stage._prepare_forward_infra(n_microbatches)
if self._has_backward:
stage._prepare_backward_infra(n_microbatches)
def _dump_csv(self, filename):
"""Dump a CSV representation of the schedule into a file with the provided filename."""
with open(filename, "w", newline="") as csvfile:
writer = csv.writer(csvfile)
for rank in self.pipeline_order:
writer.writerow(self.pipeline_order[rank])
def _validate_schedule(self):
# TODO(whc) this should be merged with the logic in test_schedule.py#L453-L554
def _validate_rank_actions(
actions: Dict[int, List[_Action | None]],
num_stages: int,
num_microbatches: int,
):
# We will count all the actions per stage and ensure they happen in a valid order
# (e.g. F before B before W for a given microbatch)
stage_actions: Dict[int, Dict[_ComputationType, Set]] = {
stage_id: {
F: set(),
B: set(),
W: set(),
}
for stage_id in range(num_stages)
}
for rank in actions:
for action in actions[rank]:
if action is None:
continue
assert isinstance(
action, _Action
), f"Got an invalid action: {action}, expected instance of _Action"
s_id = action.stage_index
ctype = action.computation_type
mb_id = action.microbatch_index
if ctype == F:
stage_actions[s_id][F].add(mb_id)
elif ctype == B:
assert (
mb_id in stage_actions[s_id][F]
), f"Running Backward for stage {s_id}, microbatch {mb_id} without first running Forward"
stage_actions[s_id][B].add(mb_id)
elif ctype == W:
assert (
not self.use_full_backward
), "Schedule contains 'W' actions, but is configured to use full backward"
assert (
mb_id in stage_actions[s_id][B]
), f"Running Weight for stage {s_id}, microbatch {mb_id} without first running Backward"
stage_actions[s_id][W].add(mb_id)
for s_id in stage_actions:
for ctype in (F, B, W):
stage_mb = len(stage_actions[s_id][ctype])
assert (
stage_mb == num_microbatches
), f"Got {stage_mb} {ctype} microbatches for stage {s_id}, expected {num_microbatches}"
assert (
len(self.pipeline_order) == self.pp_group_size
), f"Schedule has incorrect number of ranks - expected {self.pp_group_size}, actual {len(self.pipeline_order)}"
for rank in range(self.pp_group_size):
assert (
rank in self.pipeline_order
), f"Schedule is missing actions for rank {rank}"
_validate_rank_actions(
self.pipeline_order,
self._num_stages,
self._n_microbatches,
)
def _load_csv(self, filename, format="compute_only"):
"""Load a CSV representation of the schedule from a file with the provided filename.
This API will most likely get renamed/refactored so is marked as internal for now.
format must be "compute_only" for PipelineScheduleMulti
"""
assert format == "compute_only"
with open(filename, newline="") as csvfile:
reader = csv.reader(csvfile)
for rank, row in enumerate(reader):
self.pipeline_order[rank] = [_Action.from_str(s) for s in row]
self._validate_schedule()
[docs] def step(self, *args, target=None, losses: Optional[List] = None, **kwargs):
"""
Run one iteration of the pipeline schedule with *whole-batch* input.
Will chunk the input into microbatches automatically, and go through the
microbatches according to the schedule implementation.
args: positional arguments to the model (as in non-pipeline case).
kwargs: keyword arguments to the model (as in non-pipeline case).
target: target for the loss function.
losses: a list to store the losses for each microbatch.
"""
# Clean per iteration
for stage in self._stages:
stage.clear_runtime_states()
# Split inputs into microbatches
args_split, kwargs_split = self._split_inputs(args, kwargs)
# Split target into microbatches
if target is not None:
targets_split = list(torch.tensor_split(target, self._n_microbatches))
else:
targets_split = None
# Run microbatches
self._step_microbatches(args_split, kwargs_split, targets_split, losses)
# Return merged results per original format
for stage in self._stages:
if stage.is_last:
return self._merge_outputs(stage.output_chunks)
# Does not contain the last stage
return None
def _step_microbatches(
self,
arg_mbs: Optional[List] = None,
kwarg_mbs: Optional[List] = None,
target_mbs: Optional[List] = None,
losses: Optional[List] = None,
):
"""
Operate on the microbatches for looped schedules (multiple stages on each rank).
TODO: Does not use sorted_batch_isend_irecv(). As a result, this schedule does
not support models with skip connections.
"""
arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
# Based on the plan in Step 1 created in __init__:
# 2. Perform communication based on the pipeline_order
stage_index_to_stage: Dict[int, _PipelineStageBase] = {
stage.stage_index: stage for stage in self._stages
}
# determine prev_rank and next_rank based on which ranks are next to
# the stages in the pipeline_order
all_prev_ranks: Set[int] = set()
all_next_ranks: Set[int] = set()
for stage_index in stage_index_to_stage.keys():
# TODO: assumption that stages only communicate from distances of +1/-1 (no skip connections)
if stage_index > 0:
all_prev_ranks.add(self.stage_index_to_group_rank[stage_index - 1])
if stage_index < self._num_stages - 1:
all_next_ranks.add(self.stage_index_to_group_rank[stage_index + 1])
for time_step, action in enumerate(self.pipeline_order[self.rank]):
try:
ops: List[dist.P2POp] = []
if action is not None:
computation_type = action.computation_type
mb_index = action.microbatch_index
stage_index = action.stage_index
assert (
mb_index is not None
), "All currently supported action types require valid microbatch_index"
if computation_type == _ComputationType.FORWARD:
# perform forward computation
stage = stage_index_to_stage[stage_index]
output = stage.forward_one_chunk(
mb_index, arg_mbs[mb_index], kwarg_mbs[mb_index]
)
self._maybe_compute_loss(stage, output, target_mbs, mb_index)
ops.extend(stage.get_fwd_send_ops(mb_index))
elif computation_type == _ComputationType.BACKWARD:
# perform backward computation
stage = stage_index_to_stage[stage_index]
loss = self._maybe_get_loss(stage, mb_index)
stage.backward_one_chunk(
mb_index, loss=loss, full_backward=self.use_full_backward
)
ops.extend(stage.get_bwd_send_ops(mb_index))
elif computation_type == _ComputationType.WEIGHT:
# perform weight update
if self.use_full_backward:
raise ValueError(
f"We detected a weight update in the pipeline schedule, but \
{self.use_full_backward=}"
)
stage = stage_index_to_stage[stage_index]
stage.backward_weight_one_chunk(mb_index)
else:
raise ValueError(f"Unknown computation type {computation_type}")
# Look at the neighboring ranks for this current timestep and determine whether
# this current rank needs to do any recv communication
for prev_rank in all_prev_ranks:
prev_rank_ops = self.pipeline_order[prev_rank]
prev_rank_action = None
if time_step < len(prev_rank_ops):
prev_rank_action = prev_rank_ops[time_step]
if prev_rank_action is not None:
computation_type = prev_rank_action.computation_type
mb_index = prev_rank_action.microbatch_index
stage_index = prev_rank_action.stage_index
assert (
mb_index is not None
), "All currently supported action types require valid microbatch_index"
# Only handle sends for the forward from a previous rank
if computation_type == _ComputationType.FORWARD:
# If not the last stage, then receive fwd activations
if stage_index + 1 in stage_index_to_stage:
# TODO: We are assuming that stage will always receive from stage-1
# however that is not necessarily true of get_fwd_recv_ops
stage = stage_index_to_stage[stage_index + 1]
ops.extend(stage.get_fwd_recv_ops(mb_index))
elif (
computation_type == _ComputationType.BACKWARD
or computation_type == _ComputationType.WEIGHT
):
# Previous rank doing backward or weight update has no influence for the current rank forward recv
pass
else:
raise ValueError(
f"Unknown computation type {computation_type}"
)
for next_rank in all_next_ranks:
next_rank_ops = self.pipeline_order[next_rank]
next_rank_action = None
if time_step < len(next_rank_ops):
next_rank_action = next_rank_ops[time_step]
if next_rank_action is not None:
computation_type = next_rank_action.computation_type
mb_index = next_rank_action.microbatch_index
stage_index = next_rank_action.stage_index
assert (
mb_index is not None
), "All currently supported action types require valid microbatch_index"
# Only handle receives for the backwards from a next rank
if (
computation_type == _ComputationType.FORWARD
or computation_type == _ComputationType.WEIGHT
):
# Next rank doing forward or weight update has no influence for the current rank backward recv
pass
elif computation_type == _ComputationType.BACKWARD:
# If not the first stage, then receive bwd gradients
if stage_index - 1 in stage_index_to_stage:
# TODO: We are assuming that stage will always receive from stage+1
# however that is not necessarily true of get_bwd_recv_ops
stage = stage_index_to_stage[stage_index - 1]
ops.extend(stage.get_bwd_recv_ops(mb_index))
else:
raise ValueError(
f"Unknown computation type {computation_type}"
)
# do the communication
if ops:
_batch_p2p(ops).wait()
except Exception as e:
logger.error(
"[Rank %s] pipeline schedule %s caught the following exception \
at time_step %s when running action %s",
self.rank,
self.__class__.__name__,
time_step,
action,
)
logger.error("%s", _format_pipeline_order(self.pipeline_order))
raise e
# Return losses if there is a container passed in
self._update_losses(self._stages, losses)
class _PipelineScheduleRuntime(PipelineScheduleMulti):
"""
Provides a simple runtime that requires a 'schedule IR' including specified communication operations.
Can be instantiated directly by creating _PipelineScheduleRuntime and calling load_csv, or can be
subclassed and the subclass can be responsible for creating a schedule IR.
"""
def _load_actions(
self,
actions: Dict[int, List[Optional[_Action]]],
format: str = "compute_only",
):
"""
Given an in-memory representation for a simple compute-only schedule, lower it to a complex schedule including
communication actions. Stores the schedule in self, and must be called before running step_mo()
"""
assert (
self.stage_index_to_group_rank is not None
), "stage_index_to_group_rank is required for PipelineScheduleRuntime"
self.pipeline_order_with_comms: Dict[int, List[_Action]] = {}
if format == "compute_comms":
for rank in actions:
self.pipeline_order_with_comms[rank] = []
for action in actions[rank]:
assert action is not None
self.pipeline_order_with_comms[rank].append(action)
# TODO what level of validation should we offer for compute+comms schedule?
elif format == "compute_only":
# Perform schedule lowering
for rank in actions:
self.pipeline_order_with_comms[rank] = _add_unshard_reshard(
actions[rank]
)
self.pipeline_order_with_comms = _add_send_recv(
self.pipeline_order_with_comms,
stage_to_rank=lambda s: self.stage_index_to_group_rank[s],
num_stages=self._num_stages,
)
else:
raise NotImplementedError(f"{format=} is not implemented")
def _load_csv(self, filename: str, format: str = "compute_only"):
"""Loads a csv in simple format and then lowers it to include comunication actions
format must be either "compute_only" or "compute_comms". If compute_only, the lowering passes
will automatically be run to generate a compute_comms schedule.
"""
if format == "compute_only":
# this will populate self.pipeline_order
super()._load_csv(filename)
# this will populate self.pipeline_order_with_comms
self._load_actions(self.pipeline_order)
elif format == "compute_comms":
actions = {}
with open(filename, newline="") as csvfile:
reader = csv.reader(csvfile)
for rank, row in enumerate(reader):
actions[rank] = [_Action.from_str(s) for s in row]
self._load_actions(actions, format=format)
else:
raise NotImplementedError(f"{format=} is not implemented")
def _dump_csv(self, filename: str):
"""Dump a CSV representation of the compute + comms schedule into a file with the provided filename."""
# TODO should there be an option to dump the compute_only schedule from PipelineScheduleRuntime? It's possible
# that it does not exist if it was created from a compute_comms schedule.
assert (
self.pipeline_order_with_comms is not None
), "Must initialize compute_comms schedule before dump_csv"
with open(filename, "w", newline="") as csvfile:
writer = csv.writer(csvfile)
for rank in self.pipeline_order_with_comms:
writer.writerow(self.pipeline_order_with_comms[rank])
def _step_microbatches(
self,
arg_mbs: Optional[List] = None,
kwarg_mbs: Optional[List] = None,
target_mbs: Optional[List] = None,
losses: Optional[List] = None,
):
"""
Operate on the microbatches for looped schedules (multiple stages on each rank).
TODO: Does not use sorted_batch_isend_irecv(). As a result, this schedule does
not support models with skip connections.
"""
arg_mbs, kwarg_mbs = self._check_inputs(arg_mbs, kwarg_mbs, target_mbs, losses)
# Based on the plan in Step 1 created in __init__:
# 2. Perform communication based on the pipeline_order
stage_index_to_stage: Dict[int, _PipelineStageBase] = {
stage.stage_index: stage for stage in self._stages
}
assert (
self.pipeline_order_with_comms is not None
), "Must call _load_actions() before calling _step_microbatches()"
# recv ops indexed by (stage_idx, mb_idx) need to be waited on before use
bwd_recv_ops: Dict[Tuple[int, int], Work] = {}
fwd_recv_ops: Dict[Tuple[int, int], Work] = {}
# send ops should be waited on before step() exists, mainly for hygeine
send_ops: List[Work] = []
# we track which stages are 'active' when used with FSDP, and wait on unshard ops before computing on stages
unshard_ops: Dict[int, UnshardHandle] = {}
unsharded_stages = set()
def _assert_unsharded(stage_idx: int):
"""If an unshard is active for `stage_idx`, wait() it and mark `stage_idx` unshared."""
if stage_idx in unshard_ops:
unshard_ops[stage_idx].wait()
del unshard_ops[stage_idx]
unsharded_stages.add(stage_idx)
assert (
stage_idx in unsharded_stages
), f"Attempted to compute on sharded {stage_idx=}"
for time_step, action in enumerate(self.pipeline_order_with_comms[self.rank]):
try:
comp_type = action.computation_type
mb_index: int = (
action.microbatch_index
if action.microbatch_index is not None
else -1
)
assert mb_index >= 0 or comp_type in (
UNSHARD,
RESHARD,
), f"{action=} missing mb_index"
stage_idx = action.stage_index
stage = stage_index_to_stage[stage_idx]
stage_uses_fsdp = isinstance(stage.submod, FSDPModule)
logger.debug(
"_PipelineScheduleRuntime running time_step %d, action %s",
time_step,
action,
)
# TODO(whc) it's not actually safe to use _batch_p2p here in the uncommon case the model has skip-connections,
# since we do not want to batch up ops between more than a pair of ranks. _sorted_batch_p2p would be
# safe to use instead.
# However, I was wondering if I should avoid calling batched operators at all in the case that there is
# only one operator per batch. I could iterate through the 'fwd_send_ops' one by one and run them.
if comp_type == SEND_F:
send_ops.append(_batch_p2p(stage.get_fwd_send_ops(mb_index)))
elif comp_type == SEND_B:
send_ops.append(_batch_p2p(stage.get_bwd_send_ops(mb_index)))
elif comp_type == RECV_F:
assert (
stage_idx,
mb_index,
) not in fwd_recv_ops, "Recv twice for {stage_idx=} {mb_index=} without executing forward"
fwd_recv_ops[(stage_idx, mb_index)] = _batch_p2p(
stage.get_fwd_recv_ops(mb_index)
)
elif comp_type == RECV_B:
assert (
stage_idx,
mb_index,
) not in bwd_recv_ops, "Recv twice for {stage_idx=} {mb_index=} without executing backward"
bwd_recv_ops[(stage_idx, mb_index)] = _batch_p2p(
stage.get_bwd_recv_ops(mb_index)
)
elif comp_type == UNSHARD:
if stage_uses_fsdp:
assert (
stage_idx not in unsharded_stages
and stage_idx not in unshard_ops
), f"Unsharding the same {stage_idx=} twice"
unshard_ops[stage_idx] = stage.submod.unshard(async_op=True)
elif comp_type == RESHARD:
if stage_uses_fsdp:
assert (
stage_idx in unsharded_stages
), f"Resharding {stage_idx=} without unsharding"
assert (
stage_idx not in unshard_ops
), f"Resharding {stage_idx=} before finishing unshard"
stage.submod.reshard()
elif comp_type == FORWARD:
if stage_uses_fsdp:
_assert_unsharded(stage_idx)
if not stage.is_first:
assert (
stage_idx,
mb_index,
) in fwd_recv_ops, f"Computing {action=} before receiving input"
fwd_recv_ops.pop((stage_idx, mb_index)).wait()
output = stage.forward_one_chunk(
mb_index, arg_mbs[mb_index], kwarg_mbs[mb_index]
)
self._maybe_compute_loss(stage, output, target_mbs, mb_index)
elif comp_type == BACKWARD:
if stage_uses_fsdp:
_assert_unsharded(stage_idx)
if not stage.is_last:
assert (
stage_idx,
mb_index,
) in bwd_recv_ops, (
f"Attempted to run compute {action=} before receiving input"
)
bwd_recv_ops.pop((stage_idx, mb_index)).wait()
loss = self._maybe_get_loss(stage, mb_index)
stage.backward_one_chunk(
mb_index, loss=loss, full_backward=self.use_full_backward
)
elif comp_type == WEIGHT:
if stage_uses_fsdp:
_assert_unsharded(stage_idx)
if self.use_full_backward:
raise ValueError(
f"We detected a weight update in the pipeline schedule, but \
{self.use_full_backward=}"
)
stage.backward_weight_one_chunk(mb_index)
else:
raise ValueError(f"{action=} is unknown or unsupported")
except Exception as e:
logger.error(
"_PipelineScheduleRuntime caught exception at step %s when running action %s. Full Schedule:",
time_step,
action,
)
# TODO(whc) what is the best practice for printing a multiline log?
# logger will split it into multiple log lines, but this makes it hard to read (too wide)
print(_format_pipeline_order(self.pipeline_order_with_comms)) # type: ignore[arg-type]
raise e
# Mostly these operations should have finished long ago, but there isn't an obvious time when to wait for them
while len(send_ops):
send_ops.pop().wait()
assert len(unshard_ops) == 0, "Unused unshard operations"
# Return losses if there is a container passed in
self._update_losses(self._stages, losses)
[docs]class ScheduleLoopedBFS(PipelineScheduleMulti):
"""
Breadth-First Pipeline Parallelism.
See https://arxiv.org/abs/2211.05953 for details.
Simliar to Interleaved 1F1B, Looped BFS supports multiple stages per rank.
What is different is that when microbatches are ready for multiple local
stages, Loops BFS will prioritizes the earlier stage, running all available
microbatches at once.
"""
def __init__(
self,
stages: List[_PipelineStageBase],
n_microbatches: int,
loss_fn: Optional[Callable] = None,
output_merge_spec: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
):
super().__init__(
stages=stages,
n_microbatches=n_microbatches,
loss_fn=loss_fn,
output_merge_spec=output_merge_spec,
)
# 1. Create the pipeline_order (all ranks do this calculation)
# This will be used to keep track of the current state of the entire pipeline
# pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
self.pipeline_order: Dict[int, List[Optional[_Action]]] = {}
# ========================================================================
for rank in range(self.pp_group_size):
rank_ops = self._calculate_single_rank_operations(rank)
self.pipeline_order[rank] = rank_ops
def _calculate_single_rank_operations(self, rank):
n_local_stages = len(self._stages)
stage_indices = range(
rank, self.pp_group_size * n_local_stages, self.pp_group_size
)
# Store the list of operations used for that rank
rank_ops: List[Optional[_Action]] = []
# Pre-padding, rank starts with no-ops based on the warmup.
for _ in range(rank):
rank_ops.append(None)
for stage_index in stage_indices:
for mb_index in range(self._n_microbatches):
rank_ops.append(
_Action(stage_index, _ComputationType.FORWARD, mb_index)
)
# wait for the first backward to trickle up
# which is 2 for every hop away
post_warmup_ops = 2 * (self.pp_group_size - 1 - rank)
rank_ops.extend([None] * post_warmup_ops)
for stage_index in reversed(stage_indices):
for mb_index in reversed(range(self._n_microbatches)):
rank_ops.append(
_Action(stage_index, _ComputationType.BACKWARD, mb_index)
)
return rank_ops
def _get_1f1b_rank_ops(
n_local_stages,
pp_group_size,
warmup_ops,
fwd_bwd_ops,
cooldown_ops,
rank,
forward_stage_index,
backward_stage_index,
num_1f1b_microbatches=0,
enable_zero_bubble=False,
):
# All stages start with handling microbatch 0
fwd_stage_mb_index: Dict[int, int] = defaultdict(int)
bwd_stage_mb_index: Dict[int, int] = defaultdict(int)
weight_stage_mb_index: Dict[int, int] = defaultdict(int)
# Store the list of operations used for that rank
rank_ops: List[Optional[_Action]] = []
# Pre-padding, rank starts with no-ops based on the warmup.
for _ in range(rank):
rank_ops.append(None)
# These are used to calculate the number of slots to fill with no-ops, to account for the delay in warmup
# when we want to wait for the backward to trickle back up and start 1f1b to align all ranks.
# Formula:
# pre-padding + warmup_ops + post_warmup_ops = earliest time step of first backward
# post_warmup_ops = [earliest time step of first backward] - (warmup_ops + pre-padding)
# earliest time step of first backward = [local_stages * group_size + 2 * (group_size - 1 - rank)]
# warmup_ops = calculated above
post_warmup_ops = (
n_local_stages * pp_group_size + 2 * (pp_group_size - 1 - rank)
) - (warmup_ops + rank)
if enable_zero_bubble:
post_warmup_ops = pp_group_size - rank - 1
total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
backward_op_ids = []
weight_op_count = 0
for op in range(total_ops):
# Warmup phase
if op < warmup_ops:
fwd_stage_index = forward_stage_index(op)
# This will assign the current microbatch index and update it as well
fwd_stage_mb_index[fwd_stage_index] = (
mb_index := fwd_stage_mb_index[fwd_stage_index]
) + 1
rank_ops.append(
_Action(fwd_stage_index, _ComputationType.FORWARD, mb_index)
)
if op == warmup_ops - 1:
# This is the last step in the warmup phase, so we need to wait for the backward to trickle back up
rank_ops.extend([None] * post_warmup_ops)
# 1F1B Phase (forward and backward)
elif warmup_ops <= op < warmup_ops + fwd_bwd_ops:
fwd_stage_index = forward_stage_index(op)
fwd_stage_mb_index[fwd_stage_index] = (
fwd_mb_index := fwd_stage_mb_index[fwd_stage_index]
) + 1
rank_ops.append(
_Action(fwd_stage_index, _ComputationType.FORWARD, fwd_mb_index)
)
bwd_stage_index = backward_stage_index(op)
bwd_stage_mb_index[bwd_stage_index] = (
bwd_mb_index := bwd_stage_mb_index[bwd_stage_index]
) + 1
rank_ops.append(
_Action(bwd_stage_index, _ComputationType.BACKWARD, bwd_mb_index)
)
backward_op_ids.append(op)
if enable_zero_bubble and op - warmup_ops >= num_1f1b_microbatches:
weight_stage_index = backward_stage_index(
backward_op_ids[weight_op_count]
)
weight_stage_mb_index[weight_stage_index] = (
weight_mb_index := weight_stage_mb_index[weight_stage_index]
) + 1
rank_ops.append(
_Action(
weight_stage_index, _ComputationType.WEIGHT, weight_mb_index
)
)
weight_op_count += 1
# Cooldown phase
else:
# During cooldown phase, we need steps to align with 1f1b happening in other ranks
# TODO: we don't need to always append, after all 1f1b are finished we can stop appending None
if not enable_zero_bubble:
rank_ops.append(None)
bwd_stage_index = backward_stage_index(op)
bwd_stage_mb_index[bwd_stage_index] = (
bwd_mb_index := bwd_stage_mb_index[bwd_stage_index]
) + 1
rank_ops.append(
_Action(bwd_stage_index, _ComputationType.BACKWARD, bwd_mb_index)
)
backward_op_ids.append(op)
if enable_zero_bubble and op - warmup_ops >= num_1f1b_microbatches:
weight_stage_index = backward_stage_index(
backward_op_ids[weight_op_count]
)
weight_stage_mb_index[weight_stage_index] = (
weight_mb_index := weight_stage_mb_index[weight_stage_index]
) + 1
rank_ops.append(
_Action(
weight_stage_index, _ComputationType.WEIGHT, weight_mb_index
)
)
weight_op_count += 1
while enable_zero_bubble and weight_op_count < len(backward_op_ids):
weight_stage_index = backward_stage_index(backward_op_ids[weight_op_count])
weight_stage_mb_index[weight_stage_index] = (
weight_mb_index := weight_stage_mb_index[weight_stage_index]
) + 1
rank_ops.append(
_Action(weight_stage_index, _ComputationType.WEIGHT, weight_mb_index)
)
weight_op_count += 1
return rank_ops
[docs]class ScheduleInterleaved1F1B(PipelineScheduleMulti):
"""
The Interleaved 1F1B schedule.
See https://arxiv.org/pdf/2104.04473 for details.
Will perform one forward and one backward on the microbatches in steady
state and supports multiple stages per rank. When microbatches are ready for
multiple local stages, Interleaved 1F1B prioritizes the earlier microbatch
(also called "depth first").
"""
def __init__(
self,
stages: List[_PipelineStageBase],
n_microbatches: int,
loss_fn: Optional[Callable] = None,
args_chunk_spec: Optional[Tuple[TensorChunkSpec, ...]] = None,
kwargs_chunk_spec: Optional[Dict[str, TensorChunkSpec]] = None,
output_merge_spec: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
):
self.pp_group_size = stages[0].group_size
# TODO: is this limitation a must?
if n_microbatches % self.pp_group_size != 0:
raise ValueError(
f"Interleaved 1F1B schedule requires the number of microbatches ({n_microbatches}) \
to be a multiple of the number of pipeline ranks ({self.pp_group_size})."
)
super().__init__(
stages=stages,
n_microbatches=n_microbatches,
loss_fn=loss_fn,
args_chunk_spec=args_chunk_spec,
kwargs_chunk_spec=kwargs_chunk_spec,
output_merge_spec=output_merge_spec,
)
self.n_local_stages = len(stages)
self.rank = stages[0].group_rank
self.group = stages[0].group
# 1. Create the pipeline_order (all ranks do this calculation)
# This will be used to keep track of the current state of the entire pipeline
# pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
self.pipeline_order: Dict[int, List[Optional[_Action]]] = {}
for rank in range(self.pp_group_size):
rank_ops = self._calculate_single_rank_operations(rank)
self.pipeline_order[rank] = rank_ops
def _calculate_single_rank_operations(self, rank) -> List[Optional[_Action]]:
def get_rank_warmup_ops(rank):
# Warms up operations for last stage
warmups_ops_last_stage = (self.n_local_stages - 1) * self.pp_group_size
# Increment warmup operations by 2 for each hop away from the last stage
warmup_ops = warmups_ops_last_stage + 2 * ((self.pp_group_size - 1) - rank)
# We cannot have more warmup operations than there are number of microbatches, so cap it there
return min(warmup_ops, self._n_microbatches * self.n_local_stages)
warmup_ops = get_rank_warmup_ops(rank)
microbatch_ops = self.n_local_stages * self._n_microbatches
# fwd_bwd_ops should encompass the remaining forwards
fwd_bwd_ops = microbatch_ops - warmup_ops
# cooldown_ops should encompass the remaining backwards
cooldown_ops = microbatch_ops - fwd_bwd_ops
# total ops encompass both forward and backward ops
total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
# warmup_ops + fwd_bwd_ops * 2 + cooldown_ops == microbatch_ops * 2
logger.debug(
"rank %s, warmup_ops %s, 1f1b %s, cooldown_ops %s total_ops %s",
rank,
warmup_ops,
fwd_bwd_ops,
cooldown_ops,
total_ops,
)
# Calculates the stage index based on step and pp_group_size
def forward_stage_index(step):
# Get the local index from 0 to n_local_stages-1
local_index = (step // self.pp_group_size) % self.n_local_stages
return (local_index * self.pp_group_size) + rank
def backward_stage_index(step):
local_index = (
self.n_local_stages
- 1
- ((step - warmup_ops) // self.pp_group_size) % self.n_local_stages
)
return (local_index * self.pp_group_size) + rank
return _get_1f1b_rank_ops(
self.n_local_stages,
self.pp_group_size,
warmup_ops,
fwd_bwd_ops,
cooldown_ops,
rank,
forward_stage_index,
backward_stage_index,
)
[docs]class ScheduleFlexibleInterleaved1F1B(PipelineScheduleMulti):
"""
The Flexible Interleaved 1F1B schedule.
This schedule is mostly similar to the interleaved 1F1B schedule.
It differs by being relaxing the requirement of num_microbatch % pp_size == 0.
Using the flex_pp schedule, we will have num_rounds = max(1, n_microbatches // pp_group_size) and
it works as long as n_microbatches % num_rounds is 0. As a few examples, support
1. pp_group_size = 4, n_microbatches = 10. We will have num_rounds = 2 and n_microbatches % 2 is 0.
2. pp_group_size = 4, n_microbatches = 3. We will have num_rounds = 1 and n_microbatches % 1 is 0.
When enable_zero_bubble is True, we will use the ZB1P schedule in https://openreview.net/pdf?id=tuzTN0eIO5
"""
def __init__(
self,
stages: List[_PipelineStageBase],
n_microbatches: int,
loss_fn: Optional[Callable] = None,
args_chunk_spec: Optional[Tuple[TensorChunkSpec, ...]] = None,
kwargs_chunk_spec: Optional[Dict[str, TensorChunkSpec]] = None,
output_merge_spec: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
enable_zero_bubble: bool = False,
):
self.pp_group_size = stages[0].group_size
super().__init__(
stages=stages,
n_microbatches=n_microbatches,
loss_fn=loss_fn,
args_chunk_spec=args_chunk_spec,
kwargs_chunk_spec=kwargs_chunk_spec,
output_merge_spec=output_merge_spec,
use_full_backward=not enable_zero_bubble,
)
self.n_local_stages = len(stages)
self.rank = stages[0].group_rank
self.number_of_rounds = max(1, n_microbatches // self.pp_group_size)
self.microbatches_per_round = n_microbatches // self.number_of_rounds
self.enable_zero_bubble = enable_zero_bubble
if n_microbatches % self.number_of_rounds != 0:
raise ValueError(
"Flexible Interleaved 1F1B requires the number of microbatches to be a "
f"multiple of the number of rounds ({self.number_of_rounds}), "
f"but got {n_microbatches}."
)
# 1. Create the pipeline_order (all ranks do this calculation)
# This will be used to keep track of the current state of the entire pipeline
# pipeline_order[rank] = [Action(computation_type, microbatch_index, stage_index), ...]
self.pipeline_order: Dict[int, List[Optional[_Action]]] = {}
for rank in range(self.pp_group_size):
rank_ops = self._calculate_single_rank_operations(rank)
self.pipeline_order[rank] = rank_ops
# This function add bubbles to the generated schedule based on dependencies of actions
# Note that the ZB1P schedule will not require bubbles to be manually added and it is
# only useful when n_microbatches <= microbatches_per_round
self.pipeline_order = self._add_bubbles_to_actions(
self.n_local_stages * self.pp_group_size,
)
def _calculate_single_rank_operations(self, rank) -> List[Optional[_Action]]:
def get_rank_warmup_ops(rank):
# Warms up operations for last stage
warmups_ops_last_stage = (
self.n_local_stages - 1
) * self.microbatches_per_round
# Increment warmup operations by 2 for each hop away from the last stage
multiply_factor = 1 if self.enable_zero_bubble else 2
warmup_ops = warmups_ops_last_stage + multiply_factor * (
(self.pp_group_size - 1) - rank
)
# We cannot have more warmup operations than there are number of microbatches, so cap it there
return min(warmup_ops, self._n_microbatches * self.n_local_stages)
warmup_ops = get_rank_warmup_ops(rank)
microbatch_ops = self.n_local_stages * self._n_microbatches
# fwd_bwd_ops should encompass the remaining forwards
fwd_bwd_ops = microbatch_ops - warmup_ops
# cooldown_ops should encompass the remaining backwards
cooldown_ops = microbatch_ops - fwd_bwd_ops
# total ops encompass both forward and backward ops
total_ops = warmup_ops + fwd_bwd_ops + cooldown_ops
# warmup_ops + fwd_bwd_ops * 2 + cooldown_ops == microbatch_ops * 2
logger.debug(
"rank %s, warmup_ops %s, 1f1b %s, cooldown_ops %s total_ops %s",
rank,
warmup_ops,
fwd_bwd_ops,
cooldown_ops,
total_ops,
)
# Calculates the stage index based on step and pp_group_size
def forward_stage_index(step):
# Get the local index from 0 to n_local_stages-1
local_index = (step // self.microbatches_per_round) % self.n_local_stages
return (local_index * self.pp_group_size) + rank
def backward_stage_index(step):
local_index = (
self.n_local_stages
- 1
- ((step - warmup_ops) // self.microbatches_per_round)
% self.n_local_stages
)
return (local_index * self.pp_group_size) + rank
if self.enable_zero_bubble:
num_1f1b_microbatches = rank
return _get_1f1b_rank_ops(
self.n_local_stages,
self.pp_group_size,
warmup_ops,
fwd_bwd_ops,
cooldown_ops,
rank,
forward_stage_index,
backward_stage_index,
num_1f1b_microbatches,
enable_zero_bubble=True,
)
return _get_1f1b_rank_ops(
self.n_local_stages,
self.pp_group_size,
warmup_ops,
fwd_bwd_ops,
cooldown_ops,
rank,
forward_stage_index,
backward_stage_index,
)
def _add_bubbles_to_actions(self, num_stages_global):
actions = self.pipeline_order
if not self.enable_zero_bubble:
return actions
def need_bubble(stage, op, microbatch, num_stages_global, seen_ops):
if op == _ComputationType.FORWARD:
if stage != 0 and (stage - 1, op, microbatch) not in seen_ops:
return True
elif op == _ComputationType.BACKWARD:
if stage == num_stages_global - 1:
return (stage, _ComputationType.FORWARD, microbatch) not in seen_ops
return (stage + 1, op, microbatch) not in seen_ops
return False
seen_ops: Set[Tuple[int, _ComputationType, int]] = set()
result: Dict[int, List[Optional[_Action]]] = {}
next_pointer: Dict[int, int] = {}
bubbles_added: Dict[int, int] = {}
total_bubbles_added = 0
for rank in range(self.pp_group_size):
result[rank] = []
next_pointer[rank] = 0
bubbles_added[rank] = 0
while True:
should_stop = True
temp_seen_ops: Set[Tuple[int, _ComputationType, int]] = set()
for rank in range(self.pp_group_size):
timestamp = next_pointer[rank]
if timestamp >= len(actions[rank]):
continue
should_stop = False
if actions[rank][timestamp] is not None:
temp_action = actions[rank][timestamp]
assert temp_action is not None
stage_index, op, microbatch = temp_action
if not need_bubble(
stage_index, op, microbatch, num_stages_global, seen_ops
):
result[rank].append(actions[rank][timestamp])
if microbatch is not None:
temp_seen_ops.add((stage_index, op, microbatch))
next_pointer[rank] += 1
else:
result[rank].append(None)
bubbles_added[rank] += 1
else:
next_pointer[rank] += 1
result[rank].append(None)
seen_ops.update(temp_seen_ops)
if should_stop:
break
if total_bubbles_added > 0:
logger.warning(
"Non zero bubbles added: total_bubbles_added=%s bubbles_added=%s",
total_bubbles_added,
bubbles_added,
)
return result
[docs]class ScheduleInterleavedZeroBubble(ScheduleFlexibleInterleaved1F1B):
"""
The Interleaved Zero Bubble schedule.
See https://arxiv.org/pdf/2401.10241 for details.
Will perform one forward and one backward on inputs for the microbatches in steady
state and supports multiple stages per rank. Uses the backward for weights to fill in
the pipeline bubble.
"""
def __init__(
self,
stages: List[_PipelineStageBase],
n_microbatches: int,
loss_fn: Optional[Callable] = None,
args_chunk_spec: Optional[Tuple[TensorChunkSpec, ...]] = None,
kwargs_chunk_spec: Optional[Dict[str, TensorChunkSpec]] = None,
output_merge_spec: Optional[Union[Dict[str, Any], Tuple[Any]]] = None,
):
super().__init__(
stages=stages,
n_microbatches=n_microbatches,
loss_fn=loss_fn,
args_chunk_spec=args_chunk_spec,
kwargs_chunk_spec=kwargs_chunk_spec,
output_merge_spec=output_merge_spec,
enable_zero_bubble=True,
)
def get_schedule_class(schedule_name: str):
"""
Maps a schedule name to its corresponding class object.
Args:
schedule_name (str): The name of the schedule.
"""
schedule_map = {
"1F1B": Schedule1F1B,
"Interleaved1F1B": ScheduleInterleaved1F1B,
"GPipe": ScheduleGPipe,
"FlexibleInterleaved1F1B": ScheduleFlexibleInterleaved1F1B,
"LoopedBFS": ScheduleLoopedBFS,
"InterleavedZeroBubble": ScheduleInterleavedZeroBubble,
"PipelineScheduleSingle": PipelineScheduleSingle,
"PipelineScheduleMulti": PipelineScheduleMulti,
}
if schedule_name not in schedule_map:
raise ValueError(f"Unknown schedule name: {schedule_name}")
return schedule_map[schedule_name]
