import threading
from typing import Callable, Dict, List
import torch
import torch.distributed as dist
from torch._C._distributed_rpc import PyRRef
from torch.futures import Future
from colossalai.pipeline.pipeline_process_group import ppg
from colossalai.pipeline.rpc._pipeline_base import Phase, PipelineEngineBase, UniqueKey, WorkerBase, WorkItem
# Implementation of different Pipeline schedule
# <strategy>Worker defines the worker for each stage
# <strategy>PipelineEngine is the class for use
class FillDrainWorker(WorkerBase):
def _get_work_item_key(self) -> UniqueKey:
# execute backward first (if backward phase in work_list)
num_microbatches = self.num_microbatches
if self.forward_times < num_microbatches:
target_phase = Phase.FORWARD
target_microbatch_id = self.forward_times
else:
target_phase = Phase.BACKWARD
target_microbatch_id = self.backward_times
target_key = UniqueKey(target_microbatch_id, target_phase)
return target_key
class FillDrainPipelineEngine(PipelineEngineBase):
def __init__(self,
partition_fn: Callable,
stage_num: int,
num_microbatches: int,
device: str,
chunk: int = 1,
criterion: Callable = None,
metric: Callable = None,
checkpoint: bool = False,
data_process_func: Callable = None) -> None:
if chunk > 1:
assert num_microbatches % stage_num == 0, \
"if you use interleaving strategy, make sure 'num_microbatches' is a multiple of stage_num!"
use_1F1B = False
super().__init__(FillDrainWorker, partition_fn, stage_num, num_microbatches, device, use_1F1B, chunk, criterion,
metric, checkpoint, data_process_func)
class OneFOneBWorker(WorkerBase):
def _get_work_item_key(self) -> UniqueKey:
# execute backward first (if backward phase in work_list)
pp_rank = self.pp_rank
actual_stage_num = self.actual_stage_num
num_microbatches = self.num_microbatches
is_last_stage = pp_rank == actual_stage_num - 1
if self.outstanding <= self.outstanding_range[0]:
target_phase = Phase.FORWARD
target_microbatch_id = self.forward_times
elif self.outstanding >= self.outstanding_range[1]:
target_phase = Phase.BACKWARD
target_microbatch_id = self.backward_times
else:
raise ValueError("outstanding_range[1] - outstanding_range[0] must be in [0, 1]")
target_key = UniqueKey(target_microbatch_id, target_phase)
# change outstanding_range at:
# 1. forward times reach actual_stage_num, this is the end of continuous forward
# 2. forward times reach num_microbatches, this is the end of 1F1B mode
if not is_last_stage and \
target_key.phase == Phase.FORWARD:
if target_key.microbatch_id == actual_stage_num - 1 and num_microbatches > 2:
# Why need num_microbatches > 2 ? Because there is no steady stage when num_microbatches <= 2
outstanding_min = actual_stage_num - pp_rank - 1
outstanding_max = actual_stage_num - pp_rank
self.outstanding_range = (outstanding_min, outstanding_max)
if target_key.microbatch_id == num_microbatches - 1:
self.outstanding_range = (0, 0)
return target_key
class OneFOneBPipelineEngine(PipelineEngineBase):
def __init__(self,
partition_fn: Callable,
stage_num: int,
num_microbatches: int,
device: str,
chunk: int = 1,
criterion: Callable = None,
metric: Callable = None,
checkpoint: bool = False,
data_process_func: Callable = None) -> None:
if chunk > 1:
assert num_microbatches % stage_num == 0, \
"if you use interleaving strategy, make sure 'num_microbatches' is a multiple of stage_num!"
# assert num_microbatches > stage_num * chunk, "num_microbatches must be greater than stage_num * chunk"
use_1F1B = True
super().__init__(OneFOneBWorker, partition_fn, stage_num, num_microbatches, device, use_1F1B, chunk, criterion,
metric, checkpoint, data_process_func)
class ChimeraWorker(WorkerBase):
def _get_producer_consumer(self) -> None:
rank = self.pp_rank
min_pp_rank = (rank // self.actual_stage_num) * self.actual_stage_num
max_pp_rank = min_pp_rank + self.actual_stage_num - 1
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 arranged in order, the order of the input of current forward
self.producer_stage_ids = []
self.consumer_stage_ids = []
# Just for demo
prev_rank = rank - 1
next_rank = rank + 1
if prev_rank >= min_pp_rank:
self.producer_stage_ids.append(prev_rank)
if next_rank <= max_pp_rank:
self.consumer_stage_ids.append(next_rank)
def _get_work_item_key(self) -> UniqueKey:
pp_rank = self.pp_rank
stage_num = self.actual_stage_num
real_microbatch_num = self.num_microbatches // 2
forward_block_size = 1 if self.num_microbatches < stage_num else self.num_microbatches // stage_num
forward_block_num = self.forward_times // forward_block_size
if self.forward_times >= real_microbatch_num or \
((pp_rank + 1) % stage_num == 0 and forward_block_num > self.backward_times):
target_phase = Phase.BACKWARD
target_microbatch_id = self.backward_times
else: # others
target_phase = Phase.FORWARD
target_microbatch_id = self.forward_times
# In up pipeline, microbatch_id to consume is 0, 2, 4 (2n)
# In down pipeline, microbatch_id to consume is 1, 3, 5 (2n + 1)
real_target_microbatch_id = target_microbatch_id * 2
if pp_rank >= stage_num:
real_target_microbatch_id += 1
target_key = UniqueKey(real_target_microbatch_id, target_phase)
with self.work_list_condition_lock:
self.work_list_condition_lock.wait_for(lambda: target_key in self.work_list)
return target_key
def _initialize_partition(self):
# In order to ensure the down pipeline share the same parameter
# with the up pipeline, partition of down partition will be copied
# from corresponding up stage
pp_rank = self.pp_rank
stage_num = self.actual_stage_num
device = self.device
if pp_rank < stage_num:
super()._initialize_partition()
else:
# if it is down pipeline, create partition by origin method
co_up_pp_worker_rref = self.pp_rank_to_worker_rref[pp_rank - stage_num]
# get the corresponding model state dict and wait for its init
state_dict = co_up_pp_worker_rref.rpc_sync().get_partition_state_dict()
super()._initialize_partition()
self.module_partition.load_state_dict(state_dict)
# init group for chimera in ppg
ppg.get_chimera_all_reduce_group(pp_rank)
# lock for step sync
self.step_sync_lock = threading.Lock()
self.step_sync_lock.acquire()
self.have_grad_lock = threading.Lock()
self.have_grad_lock.acquire()
def _get_lock_gradient(self):
self.have_grad_lock.acquire()
grads = self.get_parameter_gradients()
self.step_sync_lock.release()
return grads
def is_first_stage(self):
return (self.pp_rank % self.actual_stage_num) == 0
def is_last_stage(self):
return (self.pp_rank % self.actual_stage_num) == self.actual_stage_num - 1
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
last_microbatch_id = self.num_microbatches - 1
if self.pp_rank < self.actual_stage_num:
last_microbatch_id -= 1
is_last_microbatch = work_item.microbatch_id == last_microbatch_id
return is_last_phase and is_last_microbatch
def _get_step_order(self) -> List[int]:
# TODO : If you want to extend it to multi head chimera, overwrite here
stage_num = self.actual_stage_num
pp_rank = self.pp_rank
# pp_rank in the same device
local_device_pp_ranks = [pp_rank, stage_num * 2 - pp_rank - 1]
local_device_pp_ranks.sort(reverse=min(local_device_pp_ranks) < stage_num // 2)
return local_device_pp_ranks
def _hook_before_step(self):
self.have_grad_lock.release()
pp_rank = self.pp_rank
stage_num = self.actual_stage_num
co_pp_rank = (pp_rank + stage_num) % (2 * stage_num)
# if current pp_rank is not the first to do step
# wait its previous pp_rank finish step
grads = self.get_parameter_gradients()
# send
co_worker = self.pp_rank_to_worker_rref[co_pp_rank]
co_grads = co_worker.rpc_sync()._get_lock_gradient()
# sync
self.step_sync_lock.acquire()
for i in range(len(grads)):
grads[i] += co_grads[i]
class ChimeraPipelineEngine(PipelineEngineBase):
def __init__(self,
partition_fn: Callable,
stage_num: int,
num_microbatches: int,
device: str,
criterion: Callable = None,
metric: Callable = None,
checkpoint: bool = False,
data_process_func: Callable = None) -> None:
assert num_microbatches % stage_num == 0, \
"In Chimera, num_microbatches must be the multiply of stage_num!"
use_1F1B = False
chunk = 1
super().__init__(ChimeraWorker, partition_fn, stage_num, num_microbatches, device, use_1F1B, chunk, criterion,
metric, checkpoint, data_process_func)
def _consume_constraint(self, microbatch_id: int, forward_only: bool, input_pp_ranks: List[int],
output_pp_ranks: List[int], ret_future):
pass
def _create_pp_rank_to_rpc_worker_id(self) -> None:
stage_num = self.stage_num
self.pp_rank_to_rpc_worker_id = [0] * (stage_num * 2)
for pp_rank in range(stage_num):
self.pp_rank_to_rpc_worker_id[pp_rank] = pp_rank
self.pp_rank_to_rpc_worker_id[pp_rank + stage_num] = stage_num - pp_rank - 1
def _create_pp_rank_to_module_partition_id(self) -> None:
stage_num = self.stage_num
self.pp_rank_to_module_partition_id = [0] * (stage_num * 2)
for pp_rank in range(stage_num):
self.pp_rank_to_module_partition_id[pp_rank] = pp_rank
self.pp_rank_to_module_partition_id[pp_rank + stage_num] = pp_rank
def _create_ret_future(self, output_pp_ranks: List[int]) -> Dict[int, List[Future]]:
num_microbatches = self.num_microbatches
stage_num = self.stage_num
up_ret_future = {pp_rank: [None] * num_microbatches for pp_rank in output_pp_ranks}
down_ret_future = {pp_rank + stage_num: [None] * num_microbatches for pp_rank in output_pp_ranks}
# merge up and down
return {**up_ret_future, **down_ret_future}
def _set_input(self, input_pp_ranks: List[int], microbatch_id: int, microbatch, forward_only: bool):
# offset is 0 for all the ranks in up pipeline
# offset is stage_num for all the ranks in down pipeline
offset = (microbatch_id % 2) * self.stage_num
for pp_rank in input_pp_ranks:
worker_rref = self.pp_rank_to_worker_rref[pp_rank + offset]
worker_rref.remote().set_input(microbatch_id, microbatch, forward_only)
def _set_labels(self, output_pp_ranks: List[int], microbatch_id: int, microlabels):
# offset is 0 for all the ranks in up pipeline
# offset is stage_num for all the ranks in down pipeline
offset = (microbatch_id % 2) * self.stage_num
for pp_rank in output_pp_ranks:
worker_rref = self.pp_rank_to_worker_rref[pp_rank + offset]
worker_rref.remote().set_labels(microbatch_id, microlabels)
def _subscribe_forward(self, microbatch_id: int, output_pp_ranks: List[int], ret_future: Dict[int, List[Future]]):
key = UniqueKey(microbatch_id, Phase.FORWARD)
offset = (microbatch_id % 2) * self.stage_num
for pp_rank in output_pp_ranks:
worker_rref = self.pp_rank_to_worker_rref[pp_rank + offset]
ret_future[pp_rank + offset][microbatch_id] = worker_rref.rpc_async().get_output_by_key(key)
def _ensure_backward(self, forward_only: bool, input_pp_ranks: List[int]):
stage_num = self.stage_num
num_microbatches = self.num_microbatches
if not forward_only:
for pp_rank in input_pp_ranks:
up_last_microbatch_id = num_microbatches - 2
down_last_microbatch_id = num_microbatches - 1
up_worker_rref = self.pp_rank_to_worker_rref[pp_rank]
down_worker_rref = self.pp_rank_to_worker_rref[pp_rank + stage_num]
up_key = UniqueKey(up_last_microbatch_id, Phase.BACKWARD)
down_key = UniqueKey(down_last_microbatch_id, Phase.BACKWARD)
up_worker_rref.rpc_sync().get_output_by_key(up_key)
down_worker_rref.rpc_sync().get_output_by_key(down_key)
def _collect_forward_result(self, output_pp_ranks: List[int], ret_future: Dict[PyRRef, List[Future]]):
"""Logic of collection of forward in Chimera.
Currently, only one input one output model is supported
"""
stage_num = self.stage_num
forward_result = []
for pp_rank in output_pp_ranks:
worker_forward_result = [None] * self.num_microbatches
for microbatch_id in range(self.num_microbatches):
offset = (microbatch_id % 2) * stage_num
ret = ret_future[pp_rank + offset][microbatch_id].wait()
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