ColossalAI/colossalai/zero/utils/gemini_hook.py

66 lines
2.3 KiB
Python

from contextlib import contextmanager
from enum import Enum
from functools import partial
from typing import List
import torch
from colossalai.gemini import TensorState
from colossalai.gemini.gemini_mgr import GeminiManager
from colossalai.tensor.param_op_hook import ParamOpHook
class TrainingPhase(Enum):
FORWARD = 0
BACKWARD = 1
class GeminiZeROHook(ParamOpHook):
def __init__(self, gemini_manager: GeminiManager) -> None:
super().__init__()
self._gemini_manager = gemini_manager
self._chunk_manager = gemini_manager.chunk_manager
self._training_phase = TrainingPhase.FORWARD
def pre_op(self, params):
params = [p for p in params if not getattr(p, '_ddp_to_ignore', False)]
chunks = self._chunk_manager.get_chunks(params)
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)
for chunk in chunks:
self._chunk_manager.access_chunk(chunk)
self._gemini_manager.sample_model_data()
def post_op(self, params):
params = [p for p in params if not getattr(p, '_ddp_to_ignore', False)]
for p in params:
tensor_state = TensorState.HOLD if self._training_phase == TrainingPhase.FORWARD or not p.requires_grad else TensorState.HOLD_AFTER_BWD
self._chunk_manager.trans_tensor_state(p, tensor_state)
def pre_forward(self, params: List[torch.Tensor]) -> None:
self.pre_op(params)
def post_forward(self, params: List[torch.Tensor]) -> None:
self.post_op(params)
def pre_backward(self, params: List[torch.Tensor]) -> None:
self.pre_op(params)
def post_backward(self, params: List[torch.Tensor]) -> None:
self.post_op(params)
@contextmanager
def switch_training_phase(self, training_phase: TrainingPhase = TrainingPhase.BACKWARD):
old_training_phase = self._training_phase
try:
self._training_phase = training_phase
yield
finally:
self._training_phase = old_training_phase
switch_to_backward = switch_training_phase
switch_to_forward = partial(switch_to_backward, training_phase=TrainingPhase.FORWARD)