| # 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, Union |
| |
| import torch |
| import torch.distributed as dist |
| from torch.profiler import record_function |
| |
| from .microbatch import merge_chunks, split_args_kwargs_into_chunks, TensorChunkSpec |
| from .stage import _PipelineStageBase |
| |
| |
| __all__ = [ |
| "PipelineScheduleSingle", |
| "PipelineScheduleMulti", |
| "Schedule1F1B", |
| "ScheduleFlexibleInterleaved1F1B", |
| "ScheduleGPipe", |
| "ScheduleInterleaved1F1B", |
| "ScheduleLoopedBFS", |
| ] |
| |
| logger = logging.getLogger(__name__) |
| |
| |
| class _ComputationType(Enum): |
| # TODO(whc) rename to _ActType? |
| FORWARD = 1 |
| BACKWARD = 2 |
| WEIGHT = 3 |
| |
| def __str__(self): |
| str_map = { |
| _ComputationType.FORWARD: "F", |
| _ComputationType.BACKWARD: "B", |
| _ComputationType.WEIGHT: "W", |
| } |
| 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 |
| else: |
| raise RuntimeError(f"Invalid computation type {action}") |
| |
| |
| F = _ComputationType.FORWARD |
| B = _ComputationType.BACKWARD |
| W = _ComputationType.WEIGHT |
| |
| _action_regex = re.compile(r"(\d+)([F,B,W])(\d*)") |
| |
| |
| class _Action(NamedTuple): |
| stage_index: int |
| computation_type: _ComputationType |
| microbatch_index: Optional[int] = None |
| |
| def __repr__(self): |
| if self.microbatch_index is not None: |
| return f"{self.stage_index}{self.computation_type}{self.microbatch_index}" |
| return f"{self.stage_index}{self.computation_type}" |
| |
| @staticmethod |
| def from_str(str): |
| """ |
| Reverse of __repr__ |
| |
| String should be formatted as [stage][action type][microbatch] e.g. `2F0` |
| """ |
| 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 == "": |
| 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(f"Using {self.__class__.__name__}") # noqa: G004 |
| |
| 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(f"batch_p2p {desc_str}{p2p_ops}") # noqa: G004 |
| 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 |
| |
| |
| 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) |
| |
| 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 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( |
| f"[{self._stage.stage_index}] Forwarded microbatch {i}" # noqa: G004 |
| ) |
| |
| 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( |
| f"[{self._stage.stage_index}] Backwarded microbatch {i}" # noqa: G004 |
| ) |
| |
| # 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() |
| |
| |
| 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) |
| |
| |
| 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. |
| This API will most likely get renamed/refactored so is marked as internal for now. |
| """ |
| 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): |
| """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. |
| """ |
| 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() |
| |
| 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 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 |
| |
| |
| 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, |
| ) |
| |
| |
| 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( |
| f"Non zero bubbles added: {total_bubbles_added=} {bubbles_added=}" # noqa: G004 |
| ) |
| return result |
