mirror of https://github.com/hpcaitech/ColossalAI
1309 lines
58 KiB
Python
1309 lines
58 KiB
Python
import inspect
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import math
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import threading
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from abc import ABC, abstractmethod
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from enum import Enum
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from functools import partial
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from typing import Any, Callable, Dict, List, Tuple
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import torch
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import torch.distributed.rpc as rpc
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from torch import autograd, nn, optim
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from torch._C._distributed_rpc import PyRRef
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from torch.futures import Future
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from colossalai.pipeline.middleware import Partition, PartitionInputVal, PartitionOutputVal, Topo
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from colossalai.pipeline.pipeline_process_group import ppg
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from colossalai.pipeline.rpc.utils import (
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get_batch_lengths,
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pyobj_map,
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pytree_filter,
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pytree_map,
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split_batch,
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tensor_shape_list,
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type_detail,
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)
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class Phase(Enum):
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FORWARD = 0
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BACKWARD = 1
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UPDATE = 2
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INPUT = 3
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class UniqueKey:
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__slots__ = ('microbatch_id', 'phase')
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microbatch_id: int
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phase: Phase
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def __init__(self, microbatch_id, phase) -> None:
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self.microbatch_id = microbatch_id
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self.phase = phase
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def __eq__(self, __o: object) -> bool:
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return (self.microbatch_id == __o.microbatch_id) and (self.phase == __o.phase)
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def __hash__(self) -> int:
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return tuple.__hash__((self.microbatch_id, self.phase))
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def __repr__(self) -> str:
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return f'Key(microbatch_id={self.microbatch_id}, phase={self.phase})'
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class WorkItem:
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__slots__ = ('stage_id', 'phase', 'args', 'kwargs', 'output', 'refcount', 'microbatch_id', 'batch_id',
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'num_microbatches', 'forward_only')
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stage_id: int
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phase: Phase
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args: Tuple[Any]
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kwargs: Dict[str, Any]
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output: Future
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microbatch_id: int
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refcount: int
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batch_id: int
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num_microbatches: int
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forward_only: bool
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def __init__(self,
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stage_id,
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phase,
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args,
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kwargs,
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output,
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microbatch_id,
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batch_id,
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num_microbatches,
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forward_only,
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refcount=0) -> None:
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for attr_name in self.__slots__:
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setattr(self, attr_name, locals()[attr_name])
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class BackwardCache:
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__slots__ = ('checkpoint', 'stage_input_args', 'stage_input_kwargs', 'stage_outputs')
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checkpoint: bool
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stage_input_args: Tuple[Any]
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stage_input_kwargs: Dict[Any, Any]
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stage_outputs: Tuple[Any]
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def __init__(self,
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stage_input_args: Tuple[Any],
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stage_input_kwargs: Dict[Any, Any] = None,
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stage_outputs: Tuple[Any] = None,
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checkpoint: bool = False) -> None:
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for arg_name in self.__slots__:
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setattr(self, arg_name, locals()[arg_name])
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class WorkerBase(ABC):
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def __init__(self,
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partition_fn: Callable,
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partition_args: tuple,
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pp_rank: int,
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actual_stage_num: int,
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num_microbatches: int,
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device: str,
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criterion: Callable = None,
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metric: Callable = None,
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checkpoint: bool = False,
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data_process_func: Callable = None) -> None:
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super().__init__()
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self.pp_rank = pp_rank
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self.actual_stage_num = actual_stage_num
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self.num_microbatches = num_microbatches
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self.checkpoint = checkpoint
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if data_process_func is not None:
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self.data_process_func = partial(data_process_func, pp_rank)
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self.device = device
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self._initialize_outstanding_range()
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# variable and const for context managment
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self.outstanding = 0
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self.forward_times = 0
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self.backward_times = 0
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self.reset_key = UniqueKey(0, Phase.FORWARD)
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# rref of other workers
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self.pp_rank_to_worker_rref: Dict[int, PyRRef] = None
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# lock for the list
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self._initialize_lock()
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# topology info
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self.producer_stage_ids: List[int] = None
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self.consumer_stage_ids: List[int] = None
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# module partitions
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self.partition_fn = partition_fn
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self.partition_args = partition_args
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self.criterion = criterion
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self.metric = metric
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self.reset = False
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# context to maintain loop
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self._initialize_context_container()
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# main loop
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self.main_loop_thread = threading.Thread(target=self._work_loop, name=f'rank_{pp_rank}', daemon=True)
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self.main_loop_thread.start()
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def _get_future_by_device(self):
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return torch.futures.Future(devices=None if self.device in (None, 'cpu') else [self.device])
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def _initialize_outstanding_range(self):
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outstanding_range = None
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if self.pp_rank == self.actual_stage_num - 1:
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outstanding_range = (0, 1)
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else:
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outstanding_range = (self.actual_stage_num, self.actual_stage_num)
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self.outstanding_range = outstanding_range
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def _initialize_context_container(self):
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self.microbatch_id_to_backward_cache: Dict[int, BackwardCache] = dict()
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self.microbatch_id_to_labels: Dict[int, Any] = dict()
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self.work_list: Dict[UniqueKey, WorkItem] = dict()
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self.output_list: Dict[UniqueKey, WorkItem] = dict()
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def _initialize_lock(self):
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self.partition_condition_lock = threading.Condition(threading.Lock())
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self.work_list_condition_lock = threading.Condition(threading.Lock())
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self.output_list_condition_lock = threading.Condition(threading.Lock())
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self.label_lock = threading.Condition(threading.Lock())
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self.reset_condition = threading.Condition(threading.Lock())
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def _initialize_partition(self):
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partition_fn = self.partition_fn
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partition_args = self.partition_args
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device = self.device
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with self.partition_condition_lock:
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self.module_partition: nn.Module = partition_fn(*partition_args).to(device)
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self.partition_condition_lock.notify_all()
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def _get_output_all(self, key: UniqueKey, ref_use=False, rank=None):
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with self.output_list_condition_lock:
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self.output_list_condition_lock.wait_for(lambda: key in self.output_list)
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output_work_item = self.output_list[key]
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output = output_work_item.output
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if not ref_use and output_work_item.phase != Phase.INPUT:
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self.output_list.pop(key)
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if not ref_use and output_work_item.phase != Phase.INPUT:
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output_work_item.refcount += 1
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refcount = output_work_item.refcount
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# lifecycle management for DAG scheduler
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if output_work_item.phase == Phase.FORWARD:
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lifecycle = len(self.get_consumer_stage_ids())
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if self.is_model_output(): # an extra reference for scheduler collecting results
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lifecycle += 1
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elif output_work_item.phase == Phase.BACKWARD:
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lifecycle = len(self.get_producer_stage_ids())
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if self.is_model_input() and self._is_last_step(
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output_work_item): # an extra reference for ensure_backward
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lifecycle += 1
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else:
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lifecycle = 0
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refcount = 0
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with self.output_list_condition_lock:
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if refcount <= lifecycle:
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self.output_list[key] = output_work_item
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self.output_list_condition_lock.notify_all()
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if isinstance(output, Future):
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output = output.wait()
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return output
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def sync_global_worker_rrefs(self, pp_rank_to_worker_rref: Dict[int, PyRRef]) -> None:
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assert self.pp_rank_to_worker_rref is None, f"in rank {self.pp_rank}, worker has sync global workers rrefs"
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assert pp_rank_to_worker_rref is not None, "stage_to_workers must be a dict instead of None"
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self.pp_rank_to_worker_rref = pp_rank_to_worker_rref
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# for some schedule need the other worker's info to initialise partition (like Chimera)
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# construction of partition is executed after the registion of pp_rank_to_worker_rref
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self._initialize_partition()
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# res_use works for lifecycle counter,
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# if ref_use is True, lifecycle won't add.
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# offset supports get partial output to reduce comm costs.
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def get_output_by_key(self, key: UniqueKey, ref_use=False, rank=None, offsets=None) -> Any:
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output = self._get_output_all(key, ref_use, rank)
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if offsets is None: # get all for non iterable output
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return output
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else: # get part for iterable output
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output = [output[i] for i in offsets]
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return output
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def get_numels(self) -> int:
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numel = sum(param.numel() for param in self.module_partition.parameters())
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return numel
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def get_parameters(self) -> List[torch.Tensor]:
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return [p for p in self.module_partition.parameters()]
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def get_parameter_gradients(self) -> List[torch.Tensor]:
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return [p.grad for p in self.module_partition.parameters()]
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def get_partition(self):
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with self.partition_condition_lock:
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self.partition_condition_lock.wait_for(lambda: hasattr(self, 'module_partition'))
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return self.module_partition
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def get_partition_state_dict(self):
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with self.partition_condition_lock:
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self.partition_condition_lock.wait_for(lambda: hasattr(self, 'module_partition'))
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return self.module_partition.state_dict()
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def _make_args_kwargs(self, microbatch, merge=False):
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if isinstance(microbatch, dict):
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if merge:
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return list(microbatch.values()), {}
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return [], microbatch
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elif isinstance(microbatch, torch.Tensor):
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return [microbatch], {}
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elif isinstance(microbatch, (tuple, list)):
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args = []
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kwargs = {}
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for arg in microbatch:
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if isinstance(arg, dict):
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kwargs.update(arg)
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else:
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args.append(arg)
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if merge:
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arg_lst = args
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for arg in kwargs.values():
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arg_lst.append(arg)
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return arg_lst, {}
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return args, kwargs
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else:
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raise TypeError(f"Input batch can be only dict, list, tuple or tensor, but receive {type(microbatch)}")
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# just for first pp_rank
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def set_input(self, microbatch_id: int, microbatch: Tuple[Any], forward_only: bool):
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key = UniqueKey(microbatch_id, Phase.FORWARD)
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output = self._get_future_by_device()
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if not self.use_middleware():
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# make args and kwargs
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args, kwargs = self._make_args_kwargs(microbatch)
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work_item = WorkItem(self.pp_rank, Phase.FORWARD, args, kwargs, output, microbatch_id, None,
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self.num_microbatches, forward_only)
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with self.work_list_condition_lock:
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self.work_list[key] = work_item
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self.work_list_condition_lock.notify_all()
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else:
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# make args and kwargs
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arg_lst, _ = self._make_args_kwargs(microbatch, merge=True)
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# first stage assign correct input into other stages
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topo: Topo = self.get_topo()
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self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
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input_partition = topo.get_input_partition()
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self_input_offsets = input_partition.get_output_offsets(self_partition_id)
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recv_input_key = UniqueKey(microbatch_id, Phase.INPUT)
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# set input for self rank
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self_arg_lst = []
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for off in self_input_offsets:
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self_arg_lst.append(arg_lst[off])
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work_item = WorkItem(self.pp_rank, Phase.FORWARD, self_arg_lst, {}, output, microbatch_id, None,
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self.num_microbatches, forward_only)
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with self.work_list_condition_lock:
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self.work_list[key] = work_item
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self.work_list_condition_lock.notify_all()
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# put input tensor which other nodes need into output_list as Phase.INPUT
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work_item_remote = WorkItem(self.pp_rank, Phase.INPUT, [], {}, arg_lst, microbatch_id, None,
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self.num_microbatches, forward_only)
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with self.output_list_condition_lock:
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self.output_list[recv_input_key] = work_item_remote
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self.output_list_condition_lock.notify_all()
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# just for last pp_rank
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def set_labels(self, microbatch_id: int, microlabels: Any):
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with self.label_lock:
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self.microbatch_id_to_labels[microbatch_id] = microlabels
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self.label_lock.notify_all()
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# just for last pp_rank
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def _begin_backward(self, microbatch_id: int):
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with self.work_list_condition_lock:
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assert self.producer_stage_ids is not None
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key = UniqueKey(microbatch_id, Phase.BACKWARD)
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output = self._get_future_by_device()
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grad_wrt_loss = None
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work_item = WorkItem(self.pp_rank, Phase.BACKWARD, grad_wrt_loss, {}, output, microbatch_id, None,
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self.num_microbatches, False)
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self.work_list[key] = work_item
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self.work_list_condition_lock.notify_all()
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def _subscribe_producer(self, microbatch_id: int, forward_only: bool):
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"""
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You should call this function asynchronously
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"""
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stage_id = self.pp_rank
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output = self._get_future_by_device()
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if not self.use_middleware():
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producer_num = len(self.producer_stage_ids)
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subscribe_forward_futures: List[Future] = [None] * producer_num
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for i in range(producer_num):
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producer_stage_id = self.producer_stage_ids[i]
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producer_output_key = UniqueKey(microbatch_id, Phase.FORWARD)
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producer_worker_rref = self.pp_rank_to_worker_rref[producer_stage_id]
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subscribe_forward_futures[i] = producer_worker_rref.rpc_async().get_output_by_key(producer_output_key)
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else:
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producer_stage_ids = self.get_producer_stage_ids()
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producer_num = len(producer_stage_ids)
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if self.need_model_input():
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producer_num += 1 # for input partition
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subscribe_forward_futures: List[Future] = [None] * producer_num
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# TODO(jiangziyue) get single value instead of the whole output
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if self.need_model_input():
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producer_stage_id = 0
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producer_output_key = UniqueKey(microbatch_id, Phase.INPUT)
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producer_worker_rref = self.pp_rank_to_worker_rref[producer_stage_id]
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offsets = self._get_input_offsets_by_index(target_index=0)
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subscribe_forward_futures[0] = producer_worker_rref.rpc_async().get_output_by_key(producer_output_key,
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rank=self.pp_rank,
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offsets=offsets)
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for i in range(0, producer_num - 1):
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producer_stage_id = producer_stage_ids[i]
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producer_output_key = UniqueKey(microbatch_id, Phase.FORWARD)
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producer_worker_rref = self.pp_rank_to_worker_rref[producer_stage_id]
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target_index = i + 1
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offsets = self._get_input_offsets_by_index(target_index=target_index)
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if offsets is not None and len(offsets) == 0: # no need to do rpc
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subscribe_forward_futures[target_index] = []
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else:
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subscribe_forward_futures[target_index] = producer_worker_rref.rpc_async().get_output_by_key(
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producer_output_key, rank=self.pp_rank, offsets=offsets)
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else:
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for i in range(producer_num):
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producer_stage_id = producer_stage_ids[i]
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producer_output_key = UniqueKey(microbatch_id, Phase.FORWARD)
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producer_worker_rref = self.pp_rank_to_worker_rref[producer_stage_id]
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target_index = i
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offsets = self._get_input_offsets_by_index(target_index=target_index)
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if offsets is not None and len(offsets) == 0: # no need to do rpc
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subscribe_forward_futures[target_index] = []
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else:
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subscribe_forward_futures[target_index] = producer_worker_rref.rpc_async().get_output_by_key(
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producer_output_key, rank=self.pp_rank, offsets=offsets)
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work_item_from_producer = WorkItem(stage_id, Phase.FORWARD, subscribe_forward_futures, {}, output,
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microbatch_id, None, self.num_microbatches, forward_only)
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return work_item_from_producer
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# TODO(jiangziyue) Profile the side effect of the lock for lifecycle protection and consider a better one.
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def subscribe_producer(self, microbatch_id: int, forward_only: bool):
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key = UniqueKey(microbatch_id, Phase.FORWARD)
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with self.work_list_condition_lock:
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if key not in self.work_list:
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# On current PP middleware design for DAG, get_output_by_key used by _subscribe_producer
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# can only be executed once for every producer-consumer stage pair, which is necessary
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# to count the lifecycle of work_item. So, keeping the _subscribe_producer in the same
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# lock of work_item queue operation gurantees the consistency of lifecycle counter.
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work_item_from_producer = self._subscribe_producer(microbatch_id, forward_only)
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self.work_list[key] = work_item_from_producer
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self.work_list_condition_lock.notify_all()
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def _subscribe_consumer(self, microbatch_id: int):
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"""
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You should call this function asynchronously
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"""
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stage_id = self.pp_rank
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output = self._get_future_by_device()
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if not self.use_middleware():
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consumer_stage_ids = self.consumer_stage_ids
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else:
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consumer_stage_ids = self.get_consumer_stage_ids()
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consumer_num = len(consumer_stage_ids)
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subscribe_backward_futures: List[Future] = [None] * consumer_num
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for i in range(consumer_num):
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consumer_stage_id = consumer_stage_ids[i]
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consumer_output_key = UniqueKey(microbatch_id, Phase.BACKWARD)
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consumer_worker_rref = self.pp_rank_to_worker_rref[consumer_stage_id]
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target_index = i
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offsets = self._get_output_offsets_by_index(target_index=target_index)
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if offsets is not None and len(offsets) == 0: # no need to do rpc
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subscribe_backward_futures[target_index] = []
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else:
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subscribe_backward_futures[target_index] = consumer_worker_rref.rpc_async().get_output_by_key(
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consumer_output_key, rank=self.pp_rank, offsets=offsets)
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# flatten args
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work_item_from_consumer = WorkItem(stage_id, Phase.BACKWARD, subscribe_backward_futures, {}, output,
|
|
microbatch_id, None, self.num_microbatches, False)
|
|
|
|
return work_item_from_consumer
|
|
|
|
def subscribe_consumer(self, microbatch_id: int):
|
|
key = UniqueKey(microbatch_id, Phase.BACKWARD)
|
|
with self.work_list_condition_lock:
|
|
if key not in self.work_list:
|
|
# On current PP middleware design for DAG, get_output_by_key used by subscribe_consumer
|
|
# can only be executed once for every producer-consumer stage pair, which is necessary
|
|
# to count the lifecycle of work_item. So, keeping the subscribe_consumer in the same
|
|
# lock of work_item queue operation gurantees the consistency of lifecycle counter.
|
|
work_item_from_consumer = self._subscribe_consumer(microbatch_id)
|
|
self.work_list[key] = work_item_from_consumer
|
|
self.work_list_condition_lock.notify_all()
|
|
|
|
def get_producer_stage_ids(self):
|
|
producer_stage_ids = []
|
|
rank = self.pp_rank
|
|
if not self.use_middleware():
|
|
prev_rank = rank - 1
|
|
if prev_rank >= 0:
|
|
producer_stage_ids.append(prev_rank)
|
|
else:
|
|
topo: Topo = self.get_topo()
|
|
self_partition_id = self.pp_rank_to_partition_id(rank, topo)
|
|
self_partition: Partition = topo.get_partition_by_id(self_partition_id)
|
|
input_partition_ids = self_partition.get_input_partition_ids()
|
|
model_input_partition_id = topo.get_input_partition_id()
|
|
for partition_id in input_partition_ids:
|
|
# ignore input partition in current implementation.
|
|
# it will be specially tackled.
|
|
if partition_id != model_input_partition_id:
|
|
producer_stage_ids.append(self.partition_id_to_pp_rank(partition_id, topo))
|
|
return producer_stage_ids
|
|
|
|
def get_consumer_stage_ids(self):
|
|
consumer_stage_ids = []
|
|
rank = self.pp_rank
|
|
if not self.use_middleware():
|
|
next_rank = rank + 1
|
|
if next_rank <= self.actual_stage_num - 1:
|
|
consumer_stage_ids.append(next_rank)
|
|
else:
|
|
topo: Topo = self.get_topo()
|
|
self_partition_id = self.pp_rank_to_partition_id(rank, topo)
|
|
self_partition: Partition = topo.get_partition_by_id(self_partition_id)
|
|
output_partition_ids = self_partition.get_output_partition_ids()
|
|
model_output_partition_id = topo.get_output_partition_id()
|
|
for partition_id in output_partition_ids:
|
|
if model_output_partition_id != partition_id:
|
|
consumer_stage_ids.append(self.partition_id_to_pp_rank(partition_id, topo))
|
|
return consumer_stage_ids
|
|
|
|
def _get_producer_consumer(self) -> None:
|
|
rank = self.pp_rank
|
|
assert self.producer_stage_ids is None, f"all the producers of rank {rank} has been subscribed"
|
|
assert self.consumer_stage_ids is None, f"all the consumers of rank {rank} has been subscribed"
|
|
|
|
# should be aranged in order, the order of the input of current forward
|
|
self.producer_stage_ids = self.get_producer_stage_ids()
|
|
self.consumer_stage_ids = self.get_consumer_stage_ids()
|
|
|
|
def pp_rank_to_partition_id(self, pp_rank: int, topo: Topo):
|
|
partition_ids = topo.get_mid_partition_ids()
|
|
return partition_ids[pp_rank]
|
|
|
|
def partition_id_to_pp_rank(self, partition_id: int, topo: Topo):
|
|
partition_ids = topo.get_mid_partition_ids()
|
|
for i, id in enumerate(partition_ids):
|
|
if id == partition_id:
|
|
return i
|
|
|
|
def get_topo(self):
|
|
with self.partition_condition_lock:
|
|
self.partition_condition_lock.wait_for(lambda: hasattr(self, 'module_partition'))
|
|
if hasattr(self.module_partition, '_topo'):
|
|
return self.module_partition._topo
|
|
else:
|
|
return None
|
|
|
|
def use_middleware(self):
|
|
topo = self.get_topo()
|
|
return topo is not None
|
|
|
|
def _get_input_offsets_by_index(self, target_index):
|
|
res = []
|
|
topo: Topo = self.get_topo()
|
|
self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
|
|
self_partition: Partition = topo.get_partition_by_id(self_partition_id)
|
|
model_input_partition_id = topo.get_input_partition_id()
|
|
input_vals = self_partition.get_input_vals()
|
|
producer_stage_ids = self.get_producer_stage_ids()
|
|
if self.need_model_input():
|
|
# 0 for data from input batch
|
|
# >= 1 for data from prev stages
|
|
base = 1
|
|
else:
|
|
# data from prev stages
|
|
base = 0
|
|
for val in input_vals:
|
|
val_pos = val.get()
|
|
src_partition_id = val_pos.partition_id
|
|
src_offset = val_pos.offset
|
|
src_index = base
|
|
src_partition = topo.get_partition_by_id(src_partition_id)
|
|
output_len = len(src_partition.get_output_vals())
|
|
# data from not-input partition
|
|
if src_partition_id != model_input_partition_id:
|
|
src_stage_id = self.partition_id_to_pp_rank(src_partition_id, topo)
|
|
src_index = base
|
|
for i, stage_id in enumerate(producer_stage_ids):
|
|
if stage_id == src_stage_id:
|
|
src_index += i
|
|
break
|
|
else: # data from input partition
|
|
src_index = 0
|
|
# when output_len = 1, not iterable
|
|
if target_index == src_index:
|
|
if output_len == 1:
|
|
res = None # offset = None to get all outputs
|
|
return res
|
|
else:
|
|
res.append(src_offset)
|
|
return res
|
|
|
|
def _get_output_offsets_by_index(self, target_index):
|
|
res = []
|
|
topo: Topo = self.get_topo()
|
|
self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
|
|
self_partition: Partition = topo.get_partition_by_id(self_partition_id)
|
|
output_vals = self_partition.get_output_vals()
|
|
consumer_stage_ids = self.get_consumer_stage_ids()
|
|
for val_list in output_vals:
|
|
# An output may be passed to many down stages.
|
|
target = None
|
|
for val_pos in val_list.get():
|
|
dst_partition_id = val_pos.partition_id
|
|
dst_offset = val_pos.offset
|
|
dst_partition = topo.get_partition_by_id(dst_partition_id)
|
|
input_len = len(dst_partition.get_input_vals())
|
|
dst_stage_id = self.partition_id_to_pp_rank(dst_partition_id, topo)
|
|
for i, stage_id in enumerate(consumer_stage_ids):
|
|
if stage_id == dst_stage_id:
|
|
dst_index = i
|
|
break
|
|
if target_index == dst_index:
|
|
if input_len == 1:
|
|
res = None # offset = None to get all outputs
|
|
return res
|
|
else:
|
|
res.append(dst_offset)
|
|
return res
|
|
|
|
# TODO(jiangziyue) get single value instead of the whole output
|
|
def _get_real_args_kwargs_fwd(self, args_or_kwargs):
|
|
if not self.use_middleware():
|
|
args_or_kwargs = pytree_map(args_or_kwargs, fn=lambda x: x.wait(), process_types=Future)
|
|
if args_or_kwargs is not None:
|
|
if isinstance(args_or_kwargs, dict):
|
|
pass
|
|
else:
|
|
flatten_args = []
|
|
pytree_map(args_or_kwargs, fn=lambda x: flatten_args.append(x), map_all=True)
|
|
args_or_kwargs = flatten_args
|
|
else:
|
|
args_or_kwargs = pytree_map(args_or_kwargs, fn=lambda x: x.wait(), process_types=Future)
|
|
if args_or_kwargs is not None:
|
|
if isinstance(args_or_kwargs, dict):
|
|
pass
|
|
else:
|
|
flatten_args = []
|
|
if self.is_first_stage():
|
|
pytree_map(args_or_kwargs, fn=lambda x: flatten_args.append(x), map_all=True)
|
|
else: # get by offset
|
|
topo: Topo = self.get_topo()
|
|
self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
|
|
self_partition: Partition = topo.get_partition_by_id(self_partition_id)
|
|
model_input_partition_id = topo.get_input_partition_id()
|
|
input_vals = self_partition.get_input_vals()
|
|
producer_stage_ids = self.get_producer_stage_ids()
|
|
if self.need_model_input():
|
|
# 0 for data from input batch
|
|
# >= 1 for data from prev stages
|
|
base = 1
|
|
else:
|
|
# data from prev stages
|
|
base = 0
|
|
for val in input_vals:
|
|
val_pos = val.get()
|
|
src_partition_id = val_pos.partition_id
|
|
src_offset = val_pos.offset
|
|
src_index = base
|
|
src_partition = topo.get_partition_by_id(src_partition_id)
|
|
output_len = len(src_partition.get_output_vals())
|
|
# data from not-input partition
|
|
if src_partition_id != model_input_partition_id:
|
|
src_stage_id = self.partition_id_to_pp_rank(src_partition_id, topo)
|
|
src_index = base
|
|
for i, stage_id in enumerate(producer_stage_ids):
|
|
if stage_id == src_stage_id:
|
|
src_index += i
|
|
break
|
|
else: # data from input partition
|
|
src_index = 0
|
|
# when output_len = 1, not iterable
|
|
if output_len == 1:
|
|
target = args_or_kwargs[src_index]
|
|
else:
|
|
offsets = self._get_input_offsets_by_index(src_index)
|
|
real_offset = offsets.index(src_offset)
|
|
target = args_or_kwargs[src_index][real_offset]
|
|
flatten_args.append(target)
|
|
args_or_kwargs = flatten_args
|
|
return args_or_kwargs
|
|
|
|
# TODO(jiangziyue) get single value instead of the whole output
|
|
def _get_real_args_kwargs_bwd(self, args_or_kwargs):
|
|
if not self.use_middleware():
|
|
args_or_kwargs = pytree_map(args_or_kwargs, fn=lambda x: x.wait(), process_types=Future)
|
|
if args_or_kwargs is not None:
|
|
if isinstance(args_or_kwargs, dict):
|
|
pass
|
|
else:
|
|
flatten_args = []
|
|
pytree_map(args_or_kwargs, fn=lambda x: flatten_args.append(x), map_all=True)
|
|
args_or_kwargs = flatten_args
|
|
else:
|
|
for i, arg in enumerate(args_or_kwargs):
|
|
args_or_kwargs[i] = arg.wait()
|
|
if args_or_kwargs is not None: # get by offset
|
|
flatten_args = []
|
|
topo: Topo = self.get_topo()
|
|
self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
|
|
self_partition: Partition = topo.get_partition_by_id(self_partition_id)
|
|
output_vals = self_partition.get_output_vals()
|
|
consumer_stage_ids = self.get_consumer_stage_ids()
|
|
for val_list in output_vals:
|
|
# An output may be passed to many down stages.
|
|
target = None
|
|
for val_pos in val_list.get():
|
|
dst_partition_id = val_pos.partition_id
|
|
dst_offset = val_pos.offset
|
|
dst_partition = topo.get_partition_by_id(dst_partition_id)
|
|
input_len = len(dst_partition.get_input_vals())
|
|
dst_stage_id = self.partition_id_to_pp_rank(dst_partition_id, topo)
|
|
for i, stage_id in enumerate(consumer_stage_ids):
|
|
if stage_id == dst_stage_id:
|
|
dst_index = i
|
|
break
|
|
if input_len == 1:
|
|
part_grad = args_or_kwargs[dst_index]
|
|
else:
|
|
offsets = self._get_output_offsets_by_index(dst_index)
|
|
real_offsets = offsets.index(dst_offset)
|
|
part_grad = args_or_kwargs[dst_index][real_offsets]
|
|
|
|
if target is None:
|
|
target = part_grad
|
|
elif part_grad is not None:
|
|
target += part_grad
|
|
else:
|
|
continue
|
|
flatten_args.append(target)
|
|
args_or_kwargs = flatten_args
|
|
return args_or_kwargs
|
|
|
|
@abstractmethod
|
|
def _get_work_item_key(self) -> UniqueKey:
|
|
"""
|
|
this method control the order of the microbatch to consume
|
|
"""
|
|
|
|
def is_first_stage(self):
|
|
return self.pp_rank == 0
|
|
|
|
def is_last_stage(self):
|
|
return self.pp_rank == self.actual_stage_num - 1
|
|
|
|
def need_model_input(self):
|
|
need_input = False
|
|
topo: Topo = self.get_topo()
|
|
self_partition_id = self.pp_rank_to_partition_id(self.pp_rank, topo)
|
|
self_partition = topo.get_partition_by_id(self_partition_id)
|
|
partition_inputs = self_partition.get_input_partition_ids()
|
|
model_input_partition_id = topo.get_input_partition_id()
|
|
if model_input_partition_id in partition_inputs:
|
|
need_input = True
|
|
return not self.is_first_stage() and need_input
|
|
|
|
def is_model_output(self):
|
|
return self.is_last_stage()
|
|
|
|
def is_model_input(self):
|
|
return self.is_first_stage()
|
|
|
|
def _default_data_process_func(self, args_kwargs):
|
|
if self.is_first_stage():
|
|
args = args_kwargs[0]
|
|
kwargs = args_kwargs[1]
|
|
else:
|
|
args = args_kwargs
|
|
kwargs = {}
|
|
|
|
return args, kwargs
|
|
|
|
def _consume_work_item_by_phase(self, work_item: WorkItem):
|
|
phase = work_item.phase
|
|
args = work_item.args
|
|
kwargs = work_item.kwargs
|
|
microbatch_id = work_item.microbatch_id
|
|
forward_only = work_item.forward_only
|
|
data_process_func = getattr(self, 'data_process_func', self._default_data_process_func)
|
|
consume_result = None
|
|
|
|
is_first_stage = self.is_first_stage()
|
|
is_last_stage = self.is_last_stage()
|
|
|
|
if phase == Phase.FORWARD:
|
|
# remind its consumer to get data before forward
|
|
if not is_last_stage:
|
|
for stage_id in self.consumer_stage_ids:
|
|
consumer_worker_rref = self.pp_rank_to_worker_rref[stage_id]
|
|
consumer_worker_rref.remote().subscribe_producer(microbatch_id, forward_only)
|
|
|
|
# sustain pipeline context
|
|
self.forward_times += 1
|
|
if not forward_only:
|
|
self.outstanding += 1
|
|
|
|
# parse and integrate args and kwargs
|
|
if is_first_stage:
|
|
args = self._get_real_args_kwargs_fwd(args)
|
|
kwargs = self._get_real_args_kwargs_fwd(kwargs)
|
|
args_kwargs = (args, kwargs)
|
|
else:
|
|
args_kwargs = self._get_real_args_kwargs_fwd(args)
|
|
|
|
args_kwargs = pyobj_map(args_kwargs, fn=lambda x: x.to(self.device).detach(),
|
|
process_types=torch.Tensor) # torch rpc doesn't support args or rets in GPU
|
|
args_kwargs = pyobj_map(args_kwargs, fn=lambda x: self.device,
|
|
process_types=torch.device) # change devices from last stage to current device
|
|
|
|
args, kwargs = data_process_func(args_kwargs)
|
|
|
|
stage_outputs = None
|
|
stage_input_args = args
|
|
stage_input_kwargs = kwargs
|
|
use_checkpoint = None
|
|
|
|
if forward_only:
|
|
with torch.no_grad():
|
|
consume_result = self.module_partition(*args, **kwargs)
|
|
|
|
if is_last_stage and self.criterion:
|
|
with self.label_lock:
|
|
self.label_lock.wait_for(lambda: microbatch_id in self.microbatch_id_to_labels)
|
|
labels = self.microbatch_id_to_labels.pop(microbatch_id)
|
|
loss: torch.Tensor = self.criterion(consume_result, labels)
|
|
if self.metric is not None:
|
|
metric_result = self.metric(consume_result, labels)
|
|
if isinstance(metric_result, torch.Tensor):
|
|
metric_result = metric_result.item()
|
|
else:
|
|
metric_result = None
|
|
consume_result = [loss.item(), metric_result]
|
|
|
|
# last stage doesn't need to do checkpoint, for it will do backward instantly
|
|
stage_input_args = None
|
|
stage_input_kwargs = None
|
|
stage_outputs = consume_result
|
|
|
|
elif self.checkpoint and not is_last_stage:
|
|
with torch.no_grad():
|
|
consume_result = self.module_partition(*args, **kwargs)
|
|
|
|
stage_outputs = consume_result
|
|
use_checkpoint = True
|
|
|
|
else:
|
|
consume_result = self.module_partition(*args, **kwargs)
|
|
|
|
if is_last_stage and self.criterion:
|
|
with self.label_lock:
|
|
self.label_lock.wait_for(lambda: microbatch_id in self.microbatch_id_to_labels)
|
|
labels = self.microbatch_id_to_labels.pop(microbatch_id)
|
|
loss: torch.Tensor = self.criterion(consume_result, labels)
|
|
if self.metric is not None:
|
|
metric_result = self.metric(consume_result, labels)
|
|
if isinstance(metric_result, torch.Tensor):
|
|
metric_result = metric_result.item()
|
|
else:
|
|
metric_result = None
|
|
|
|
consume_result = [loss.item(), metric_result]
|
|
else:
|
|
loss = consume_result
|
|
|
|
stage_outputs = loss
|
|
use_checkpoint = False
|
|
|
|
if not forward_only:
|
|
self.microbatch_id_to_backward_cache[microbatch_id] = BackwardCache(stage_input_args,
|
|
stage_input_kwargs,
|
|
stage_outputs,
|
|
checkpoint=use_checkpoint)
|
|
consume_result = pyobj_map(consume_result, fn=lambda x: x.to('cpu'),
|
|
process_types=torch.Tensor) # torch rpc doesn't support args or rets in
|
|
|
|
# if not forward_only, do the backward
|
|
if not forward_only:
|
|
if is_last_stage: # if it is the last stage, trigger backward automatic
|
|
self._begin_backward(microbatch_id)
|
|
|
|
elif phase == Phase.BACKWARD:
|
|
# remind its producer to get data before backward
|
|
if not is_first_stage:
|
|
for stage_id in self.producer_stage_ids:
|
|
producer_worker_rref = self.pp_rank_to_worker_rref[stage_id]
|
|
producer_worker_rref.remote().subscribe_consumer(microbatch_id)
|
|
self.backward_times += 1
|
|
self.outstanding -= 1
|
|
|
|
assert microbatch_id in self.microbatch_id_to_backward_cache, f"microbatch_id {microbatch_id} not in backward cache"
|
|
backward_cache = self.microbatch_id_to_backward_cache.pop(microbatch_id)
|
|
|
|
stage_outputs = backward_cache.stage_outputs
|
|
stage_input_args = backward_cache.stage_input_args
|
|
stage_input_kwargs = backward_cache.stage_input_kwargs
|
|
use_checkpoint = backward_cache.checkpoint
|
|
|
|
if use_checkpoint:
|
|
stage_outputs = [self.module_partition(*stage_input_args, **stage_input_kwargs)]
|
|
|
|
# overlap recompute and future.wait
|
|
if not is_last_stage:
|
|
grad_tensors = self._get_real_args_kwargs_bwd(args)
|
|
else:
|
|
grad_tensors = None
|
|
|
|
# take tensor only (for only tensor can do backward)
|
|
# TODO(jiangziyue) : All values which should do bp are torch.Tensor?
|
|
stage_outputs = pytree_filter(lambda x: True, stage_outputs, process_types=torch.Tensor)
|
|
grad_tensors = pytree_filter(lambda x: True, grad_tensors, process_types=torch.Tensor)
|
|
|
|
# output all input's grad to producer, even it has no grad(output None)
|
|
# to make the offset aligned to the topo's record.
|
|
if grad_tensors is not None:
|
|
filtered_outputs = []
|
|
filtered_grads = []
|
|
for i, grad in enumerate(grad_tensors):
|
|
stage_output = stage_outputs[i]
|
|
if stage_output.requires_grad and grad is not None:
|
|
filtered_outputs.append(stage_output)
|
|
filtered_grads.append(grad)
|
|
|
|
stage_outputs = filtered_outputs
|
|
grad_tensors = pyobj_map(filtered_grads, fn=lambda x: x.to(self.device),
|
|
process_types=torch.Tensor) # torch rpc doesn't support args or rets in GPU
|
|
autograd.backward(stage_outputs, grad_tensors=grad_tensors)
|
|
|
|
# collect grad of input tensor
|
|
consume_result = []
|
|
if not is_first_stage:
|
|
# In current design, input mush be a flatten args.
|
|
for arg in stage_input_args:
|
|
if isinstance(arg, torch.Tensor):
|
|
consume_result.append(arg.grad)
|
|
else:
|
|
consume_result.append(None)
|
|
consume_result = pyobj_map(
|
|
consume_result, fn=lambda x: x.to('cpu'),
|
|
process_types=torch.Tensor) # torch rpc doesn't support args or rets in GPU
|
|
|
|
else:
|
|
raise TypeError(f"Unknown phase appears in _consume_work_item_by_phase {phase}")
|
|
|
|
return consume_result
|
|
|
|
def _get_store_len(self):
|
|
return f'work_list:{len(self.work_list)} output_list:{len(self.output_list)} backward_cache:{len(self.microbatch_id_to_backward_cache)} label_cache:{len(self.microbatch_id_to_labels)}'
|
|
|
|
def _get_parameter_grad_sum(self):
|
|
grad_sum = 0
|
|
for p in self.module_partition.parameters():
|
|
if p.grad is not None:
|
|
grad_sum += p.grad.sum()
|
|
return grad_sum
|
|
|
|
def _is_first_step(self, work_item: WorkItem) -> bool:
|
|
return work_item.phase == Phase.FORWARD and work_item.microbatch_id == 0
|
|
|
|
def _is_last_step(self, work_item: WorkItem) -> bool:
|
|
if work_item.forward_only:
|
|
last_phase = Phase.FORWARD
|
|
else:
|
|
last_phase = Phase.BACKWARD
|
|
is_last_phase = work_item.phase == last_phase
|
|
is_last_microbatch = work_item.microbatch_id == self.num_microbatches - 1
|
|
return is_last_phase and is_last_microbatch
|
|
|
|
def _hook_before_step(self):
|
|
pass
|
|
|
|
# install the main loop to wait for next batch input
|
|
def _wait_for_reset(self):
|
|
with self.reset_condition:
|
|
self.reset_condition.wait_for(lambda: self.reset)
|
|
self.reset = False
|
|
|
|
# do the main loop to consume ready_list
|
|
def _work_loop(self):
|
|
# for init
|
|
self._get_producer_consumer()
|
|
torch.cuda.set_device(ppg.get_local_pp_rank())
|
|
|
|
# main loop
|
|
while True:
|
|
work_item_key = self._get_work_item_key()
|
|
# move current work item to output_list to activate subscribe in advance
|
|
with self.work_list_condition_lock:
|
|
self.work_list_condition_lock.wait_for(lambda: work_item_key in self.work_list)
|
|
work_item = self.work_list[work_item_key]
|
|
|
|
with self.output_list_condition_lock:
|
|
# assert work_item_key not in self.output_list
|
|
self.output_list[work_item_key] = work_item
|
|
self.output_list_condition_lock.notify_all()
|
|
|
|
consume_result = self._consume_work_item_by_phase(work_item)
|
|
|
|
with self.work_list_condition_lock:
|
|
self.work_list.pop(work_item_key)
|
|
work_item.output.set_result(consume_result)
|
|
|
|
# if is last step in one batch reset context and do step
|
|
if self._is_last_step(work_item):
|
|
self._wait_for_reset()
|
|
|
|
# reset context and resume loop
|
|
def reset_context(self):
|
|
self.forward_times = 0
|
|
self.backward_times = 0
|
|
self.outstanding = 0
|
|
self._initialize_outstanding_range()
|
|
with self.work_list_condition_lock:
|
|
self.work_list.clear()
|
|
|
|
with self.output_list_condition_lock:
|
|
self.output_list.clear()
|
|
|
|
with self.reset_condition:
|
|
self.reset = True
|
|
self.reset_condition.notify_all()
|
|
|
|
def initialize_optimizer(self, optimizer_class: type, **kwargs):
|
|
self.optimizer: optim.Optimizer = optimizer_class(self.module_partition.parameters(), **kwargs)
|
|
|
|
def step(self):
|
|
self._hook_before_step()
|
|
self.optimizer.step()
|
|
self.optimizer.zero_grad()
|
|
|
|
|
|
class PipelineEngineBase(ABC, nn.Module):
|
|
|
|
def __init__(self,
|
|
worker_type,
|
|
partition_fn: Callable,
|
|
stage_num,
|
|
num_microbatches,
|
|
device: str,
|
|
use_1F1B=False,
|
|
chunk: int = 1,
|
|
criterion: Callable = None,
|
|
metric: Callable = None,
|
|
checkpoint: bool = False,
|
|
data_process_func: Callable = None) -> None:
|
|
super().__init__()
|
|
self.worker_type = worker_type
|
|
self.partition_fn: Callable = partition_fn
|
|
self.chunk = chunk
|
|
self.criterion = criterion
|
|
self.metric = metric
|
|
self.num_microbatches = num_microbatches
|
|
self.device = device
|
|
self.use_1F1B = use_1F1B
|
|
self.stage_num = stage_num
|
|
self.checkpoint = checkpoint
|
|
self.data_process_func = data_process_func
|
|
|
|
self.pp_rank_to_worker_rref: Dict[int, PyRRef] = dict()
|
|
|
|
self._check_argument()
|
|
self._create_pp_rank_to_rpc_worker_id()
|
|
self._create_pp_rank_to_module_partition_id()
|
|
self._init_worker()
|
|
|
|
def _check_argument(self) -> None:
|
|
# make virtual stage num
|
|
self.virtual_stage_num = self.stage_num * self.chunk
|
|
assert self.stage_num <= torch.cuda.device_count(), "stage_num must be smaller than device count!"
|
|
|
|
# check data_process_func
|
|
data_process_func = self.data_process_func
|
|
if data_process_func is not None:
|
|
assert callable(data_process_func), "data_process_func must be a function"
|
|
assert '<locals>' not in data_process_func.__repr__(), "data_process_func must be a global function"
|
|
assert '<lambda>' not in data_process_func.__repr__(), "data_process_func cannot be a lambda expression"
|
|
sig = inspect.signature(data_process_func)
|
|
assert len(
|
|
sig.parameters
|
|
) == 2, f"length of data_process_func' arguments must be 2, receive {len(sig.parameters)} arguments instead"
|
|
|
|
def _get_actual_stage_num(self) -> int:
|
|
return self.stage_num if self.chunk == 1 else self.virtual_stage_num
|
|
|
|
def _create_pp_rank_to_rpc_worker_id(self) -> None:
|
|
"""create a map from model partition to stage_id, which is useful when use_interleave is True.
|
|
e.g. If a model is splited into 4 parts, which means stage_num is 2, chunk is 2, then
|
|
pp_rank_to_rpc_worker_id = [0, 1, 0, 1], that means first and third part
|
|
of partitions will be moved to device 0 and the others to device 1
|
|
"""
|
|
stage_num = self.stage_num
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
self.pp_rank_to_rpc_worker_id = [0] * actual_stage_num
|
|
for pp_rank in range(actual_stage_num):
|
|
self.pp_rank_to_rpc_worker_id[pp_rank] = pp_rank % stage_num
|
|
|
|
def _create_pp_rank_to_module_partition_id(self) -> None:
|
|
"""By default(both fill drain and 1F1B), length of model partitions equal to
|
|
actual_stage_num, so allocate model partition to corresponding stage
|
|
"""
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
self.pp_rank_to_module_partition_id = [0] * actual_stage_num
|
|
for pp_rank in range(actual_stage_num):
|
|
self.pp_rank_to_module_partition_id[pp_rank] = pp_rank
|
|
|
|
def _init_worker(self) -> None:
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
|
|
worker_type = self.worker_type
|
|
checkpoint = self.checkpoint
|
|
num_microbatches = self.num_microbatches
|
|
device = self.device
|
|
criterion = self.criterion
|
|
metric = self.metric
|
|
partition_fn = self.partition_fn
|
|
chunk = self.chunk
|
|
data_process_func = self.data_process_func
|
|
|
|
for pp_rank in range(len(self.pp_rank_to_rpc_worker_id)):
|
|
partition_id = self.pp_rank_to_module_partition_id[pp_rank]
|
|
partition_args = (partition_id, chunk, actual_stage_num)
|
|
rpc_worker_id = self.pp_rank_to_rpc_worker_id[pp_rank]
|
|
if device[:4] == 'cuda':
|
|
device = f'cuda:{rpc_worker_id}'
|
|
self.pp_rank_to_worker_rref[pp_rank] = rpc.remote(rpc_worker_id,
|
|
worker_type,
|
|
args=(partition_fn, partition_args, pp_rank,
|
|
actual_stage_num, num_microbatches, device,
|
|
criterion, metric, checkpoint, data_process_func))
|
|
|
|
# let each worker know global worker rref (include itself)
|
|
sync_futs = []
|
|
for pp_rank in self.pp_rank_to_worker_rref:
|
|
fut = self.pp_rank_to_worker_rref[pp_rank].rpc_async().sync_global_worker_rrefs(self.pp_rank_to_worker_rref)
|
|
sync_futs.append(fut)
|
|
|
|
for fut in sync_futs:
|
|
fut.wait()
|
|
|
|
def remote_numels(self) -> Dict[int, int]:
|
|
numels = {}
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
for stage_id in range(actual_stage_num):
|
|
worker_rref = self.pp_rank_to_worker_rref[stage_id]
|
|
numel = worker_rref.rpc_sync().get_numels()
|
|
numels[stage_id] = numel
|
|
return numels
|
|
|
|
def remote_parameters(self) -> Dict[int, List[torch.Tensor]]:
|
|
parameters = {}
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
for stage_id in range(actual_stage_num):
|
|
parameters[stage_id] = []
|
|
worker_rref = self.pp_rank_to_worker_rref[stage_id]
|
|
for p in worker_rref.rpc_sync().get_parameters():
|
|
parameters[stage_id].append(p)
|
|
return parameters
|
|
|
|
def remote_grad(self) -> Dict[int, List[torch.Tensor]]:
|
|
grads = {}
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
for stage_id in range(actual_stage_num):
|
|
grads[stage_id] = []
|
|
worker_rref = self.pp_rank_to_worker_rref[stage_id]
|
|
for grad in worker_rref.rpc_sync().get_parameter_gradients():
|
|
grads[stage_id].append(grad)
|
|
return grads
|
|
|
|
def get_input_pp_ranks(self) -> List[int]:
|
|
return [0]
|
|
|
|
def get_output_pp_ranks(self) -> List[int]:
|
|
return [self._get_actual_stage_num() - 1]
|
|
|
|
def _consume_constraint(self, microbatch_id: int, forward_only: bool, input_pp_ranks: List[int],
|
|
output_pp_ranks: List[int], ret_future):
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
use_1F1B = self.use_1F1B
|
|
if microbatch_id >= actual_stage_num:
|
|
if forward_only or not use_1F1B:
|
|
for pp_rank in output_pp_ranks:
|
|
ret_future[pp_rank][microbatch_id - actual_stage_num].wait()
|
|
else:
|
|
key = UniqueKey(microbatch_id - actual_stage_num, Phase.BACKWARD)
|
|
futs = []
|
|
for pp_rank in input_pp_ranks:
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
fut = worker_rref.rpc_async().get_output_by_key(key, ref_use=True, offsets=[])
|
|
futs.append(fut)
|
|
|
|
for fut in futs:
|
|
fut.wait()
|
|
|
|
def _create_ret_future(self, output_pp_ranks: List[int]) -> Dict[int, List[Future]]:
|
|
num_microbatches = self.num_microbatches
|
|
return {pp_rank: [None] * num_microbatches for pp_rank in output_pp_ranks}
|
|
|
|
def _set_input(self, input_pp_ranks: List[int], microbatch_id: int, microbatch, forward_only: bool):
|
|
for pp_rank in input_pp_ranks:
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
# TODO : add relationship between input_pp_ranks and parts of microbatch
|
|
worker_rref.remote().set_input(microbatch_id, microbatch, forward_only)
|
|
|
|
def _set_labels(self, output_pp_ranks: List[int], microbatch_id: int, microlabels):
|
|
for pp_rank in output_pp_ranks:
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
# TODO : add relationship between output_pp_ranks and parts of microlabels
|
|
worker_rref.remote().set_labels(microbatch_id, microlabels)
|
|
|
|
# TODO(jiangziyue) : get model output with single value, instead of merging into last stage.
|
|
def _subscribe_forward(self, microbatch_id: int, output_pp_ranks: List[int], ret_future: Dict[int, List[Future]]):
|
|
key = UniqueKey(microbatch_id, Phase.FORWARD)
|
|
for pp_rank in output_pp_ranks:
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
ret_future[pp_rank][microbatch_id] = worker_rref.rpc_async().get_output_by_key(key)
|
|
|
|
def _ensure_backward(self, forward_only: bool, input_pp_ranks: List[int]):
|
|
if not forward_only:
|
|
backward_result = []
|
|
for pp_rank in input_pp_ranks:
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
key = UniqueKey(self.num_microbatches - 1, Phase.BACKWARD)
|
|
fut = worker_rref.rpc_async().get_output_by_key(
|
|
key, offsets=[]) # only ensure the res exists, no need for real data.
|
|
backward_result.append(fut)
|
|
|
|
for fut in backward_result:
|
|
fut.wait()
|
|
|
|
def _collect_forward_result(self, output_pp_ranks: List[int], ret_future: Dict[int, List[Future]]):
|
|
forward_result = []
|
|
for pp_rank in output_pp_ranks:
|
|
worker_forward_result = [None] * self.num_microbatches
|
|
for microbatch_id in range(self.num_microbatches):
|
|
ret = ret_future[pp_rank][microbatch_id].wait()
|
|
# TODO : more stable format
|
|
ret = [ret] if isinstance(ret, torch.Tensor) else ret
|
|
worker_forward_result[microbatch_id] = ret
|
|
|
|
worker_forward_result = list(zip(*worker_forward_result))
|
|
forward_result.extend(worker_forward_result)
|
|
|
|
return forward_result
|
|
|
|
def _reset_worker(self):
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
reset_futs: List[Future] = []
|
|
for pp_rank in range(actual_stage_num):
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
fut = worker_rref.rpc_async().reset_context()
|
|
reset_futs.append(fut)
|
|
|
|
for fut in reset_futs:
|
|
fut.wait()
|
|
|
|
def forward_backward(self, batch: torch.Tensor, labels: torch.Tensor = None, forward_only: bool = False):
|
|
batch_lengths = get_batch_lengths(batch)
|
|
batch_length = batch_lengths[0]
|
|
|
|
if labels is not None and not forward_only:
|
|
assert hasattr(
|
|
self, 'optimizer_class'), "call `initialize_optimizer` to initialize optimizer before forward_backward"
|
|
|
|
num_microbatches = self.num_microbatches
|
|
|
|
assert batch_length >= num_microbatches, "num_microbatches is greater than the size of a batch, which is illegal"
|
|
microbatch_size = math.ceil(batch_length / num_microbatches)
|
|
device = self.device
|
|
|
|
# If Chimera mode is used, then rank of down pipeline is excluded from 'input_pp_ranks' or 'output_pp_ranks'
|
|
input_pp_ranks = self.get_input_pp_ranks()
|
|
output_pp_ranks = self.get_output_pp_ranks()
|
|
|
|
# a cache to collect data and control flow
|
|
ret_future = self._create_ret_future(output_pp_ranks)
|
|
|
|
for microbatch_id in range(num_microbatches):
|
|
# control data input speed
|
|
# to prevent exceed of wait limitations
|
|
# self._consume_constraint(microbatch_id, forward_only, input_pp_ranks, output_pp_ranks, ret_future)
|
|
batch_start = microbatch_size * microbatch_id
|
|
batch_end = min(batch_start + microbatch_size, batch_length)
|
|
|
|
# set input
|
|
microbatch = split_batch(batch, batch_start, batch_end, device)
|
|
self._set_input(input_pp_ranks, microbatch_id, microbatch, forward_only)
|
|
|
|
# set labels
|
|
if labels is not None:
|
|
# microlabels = labels[microbatch_size * microbatch_id:microbatch_size * (microbatch_id + 1)]
|
|
microlabels = split_batch(labels, batch_start, batch_end, device)
|
|
self._set_labels(output_pp_ranks, microbatch_id, microlabels)
|
|
|
|
# get data asynchronously
|
|
self._subscribe_forward(microbatch_id, output_pp_ranks, ret_future)
|
|
|
|
# wait for first rank to ensure all backwards are done
|
|
self._ensure_backward(forward_only, input_pp_ranks)
|
|
|
|
# collect forward result
|
|
forward_result = self._collect_forward_result(output_pp_ranks, ret_future)
|
|
|
|
if not forward_only and hasattr(self, 'optimizer_class'):
|
|
self.step()
|
|
|
|
self._reset_worker() # reset worker attributes for next batch
|
|
return forward_result
|
|
|
|
def initialize_optimizer(self, optimizer_class: type, **kwargs):
|
|
self.optimizer_class = optimizer_class
|
|
for pp_rank in self.pp_rank_to_worker_rref:
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
worker_rref.remote().initialize_optimizer(optimizer_class, **kwargs)
|
|
|
|
def step(self):
|
|
actual_stage_num = self._get_actual_stage_num()
|
|
step_futs: List[Future] = []
|
|
for pp_rank in range(actual_stage_num):
|
|
worker_rref = self.pp_rank_to_worker_rref[pp_rank]
|
|
fut = worker_rref.rpc_async().step()
|
|
step_futs.append(fut)
|
|
|
|
for fut in step_futs:
|
|
fut.wait()
|