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[chore] sync

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pull/5722/head
hxwang 6 months ago
parent 6e38eafebe
commit b2e9745888
2 changed files with 39 additions and 20 deletions
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2
colossalai/booster/plugin/gemini_plugin.py
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@ -329,6 +329,7 @@ class GeminiPlugin(DPPluginBase):
chunk_init_device: Optional[torch.device] = None,
placement_policy: str = "static",
enable_gradient_accumulation: bool = False,
max_prefetch:int = 0,
shard_param_frac: float = 1.0, # only for static placement
offload_optim_frac: float = 0.0, # only for static placement
offload_param_frac: float = 0.0, # only for static placement
@ -386,6 +387,7 @@ class GeminiPlugin(DPPluginBase):
memstats=memstats,
mixed_precision=PRECISION_STR_TO_DTYPE[precision],
master_weights=master_weights,
max_prefetch=max_prefetch,
)
self.zero_optim_config = dict(
gpu_margin_mem_ratio=gpu_margin_mem_ratio,

57
colossalai/zero/gemini/gemini_hook.py
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@ -1,4 +1,3 @@
from chunk import Chunk
from contextlib import contextmanager
from enum import Enum
from functools import partial
@ -13,15 +12,16 @@ from colossalai.utils import is_ddp_ignored
from colossalai.zero.gemini import TensorState
from colossalai.zero.gemini.gemini_mgr import GeminiManager
from colossalai.zero.gemini.memory_tracer.param_runtime_order import OrderedParamGenerator
from colossalai.logging import DistributedLogger
from .chunk import Chunk
class TrainingPhase(Enum):
FORWARD = 0
BACKWARD = 1
DEBUG = True # TODO @botbw: remove
logger = DistributedLogger("gemini_hook")
class GeminiZeROHook(ColoParamOpHook):
def __init__(
@ -31,16 +31,14 @@ class GeminiZeROHook(ColoParamOpHook):
self._gemini_manager = gemini_manager
self._chunk_manager = gemini_manager.chunk_manager
self._training_phase = TrainingPhase.FORWARD
self._cur_param = None
# param_visited_order might be updated somewhere else
self._param_visited_order = param_order.param_visited_order
self._max_prefetch = max_prefetch
self._async_works: Dict[Chunk, dist.work] = {}
# used by get_prefetch_chunks to track current param
self._cur_param_idx = 0
def get_prefetch_chunks(self, all_params: List[ColoParameter]) -> List[Chunk]:
def get_prefetch_chunks(self, all_params: List[ColoParameter], cur_chunks: List[Chunk]) -> List[Chunk]:
chunks_to_prefetch = set()
if self._training_phase == TrainingPhase.FORWARD: # forward phrase: increase
self._cur_param_idx += len(all_params) # need to update first
@ -52,10 +50,10 @@ class GeminiZeROHook(ColoParamOpHook):
idx += 1
continue
chunk = self._chunk_manager.get_chunk(param)
chunks_to_prefetch.add(chunk)
if chunk not in cur_chunks:
chunks_to_prefetch.add(chunk)
idx += 1
else:
assert self._training_phase == TrainingPhase.BACKWARD
self._cur_param_idx -= len(all_params)
idx = self._cur_param_idx - 1
chunks_to_prefetch = set()
@ -65,14 +63,17 @@ class GeminiZeROHook(ColoParamOpHook):
idx -= 1
continue
chunk = self._chunk_manager.get_chunk(self._param_visited_order[idx])
chunks_to_prefetch.add(chunk)
if chunk not in cur_chunks:
chunks_to_prefetch.add(chunk)
idx -= 1
print(f"cur id {self._cur_param_idx}")
return list(chunks_to_prefetch)
def wait_chunks(self, chunks: List[Chunk]) -> List[Chunk]:
non_prefetched_chunks = []
for chunk in chunks:
if chunk in self._async_works:
print(f"prefetched {chunk.count_id}")
self._async_works[chunk].wait()
del self._async_works[chunk]
else:
@ -80,31 +81,42 @@ class GeminiZeROHook(ColoParamOpHook):
return non_prefetched_chunks
def pre_op(self, all_params):
if DEBUG: # TODO @botbw: remove
idxs = list(map(lambda x: self._linked_param_order.param_visited_order.index(x), all_params))
mx = max(idxs)
idxs = sorted(map(lambda x: x - mx, idxs))
assert list(range(len(idxs))) == idxs, f"{idxs=}"
# def find_idx(param):
# for i, p in enumerate(self._param_visited_order):
# if param is p:
# return i
# assert False
# idxs = [find_idx(p) for p in all_params]
# max_id = min(idxs)
# idxs = [i - max_id for i in idxs]
# assert list(range(len(idxs))) == sorted(idxs), f'{idxs}'
# deal with current needed chunks
params = [p for p in all_params if not is_ddp_ignored(p)]
all_chunks = self._chunk_manager.get_chunks(params)
chunks_wo_work = self.wait_chunks(all_chunks)
chunks_need_to_fetch_sync = tuple(self.wait_chunks(all_chunks))
for p in params:
self._chunk_manager.trans_tensor_state(p, TensorState.COMPUTE)
self._gemini_manager.sample_overall_data()
self._gemini_manager.adjust_layout(chunks_wo_work)
self._gemini_manager.adjust_layout(chunks_need_to_fetch_sync)
# deal with chunks that are to be async fetched
prefetch_chunks = self.get_prefetch_chunks(all_params)
chunks_can_be_fetch_async = self.get_prefetch_chunks(all_params=all_params, cur_chunks=chunks_need_to_fetch_sync)
print(f"cur_chunks {' '.join([str(x.count_id) for x in chunks_need_to_fetch_sync])}, prefetch {' '.join([str(x.count_id) for x in chunks_can_be_fetch_async])}")
# deal with chunks that are to be fetched now
for chunk in chunks_wo_work:
for chunk in chunks_need_to_fetch_sync:
self._chunk_manager.access_chunk(chunk)
# deal with chunks that are to be pre fetched TODO @botbw: the order here matters?
for chunk in prefetch_chunks:
self._async_works[chunk] = self._chunk_manager.access_chunk(chunk, async_access=True)
for chunk in chunks_can_be_fetch_async:
if chunk in self._async_works:
continue
maybe_work = self._chunk_manager.access_chunk(chunk, async_access=True)
if maybe_work is not None:
print(f"prefetch {chunk.count_id}")
self._async_works[chunk] = maybe_work
# record cuda model data of the current OP
self._gemini_manager.record_model_data_volume()
@ -133,6 +145,11 @@ class GeminiZeROHook(ColoParamOpHook):
@contextmanager
def switch_training_phase(self, training_phase: TrainingPhase = TrainingPhase.BACKWARD):
if training_phase == TrainingPhase.FORWARD:
self._cur_param_idx = 0
else:
self._cur_param_idx = len(self._param_visited_order) - 1
old_training_phase = self._training_phase
try:
self._training_phase = training_phase

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