ColossalAI/colossalai/shardformer/shard/shard_config.py

125 lines
6.2 KiB
Python

import warnings
from dataclasses import dataclass, field
from typing import Any, Dict, Optional
import torch.distributed as dist
from torch.distributed import ProcessGroup
from colossalai.pipeline.stage_manager import PipelineStageManager
from .grad_ckpt_config import GradientCheckpointConfig
__all__ = ["ShardConfig"]
SUPPORT_SP_MODE = ["split_gather", "ring", "all_to_all"]
@dataclass
class ShardConfig:
r"""
The config for sharding the huggingface model
Args:
tensor_parallel_process_group (Optional[ProcessGroup]): The process group of tensor parallelism, it's necessary when using tensor parallel. Defaults to None, which is the global process group.
pipeline_stage_manager (Optional[PipelineStageManager]): If using pipeline parallelism, it's necessary to specify a pipeline stage manager for inter-process communication in pipeline parallelism. Defaults to None, which means not using pipeline parallelism.
enable_tensor_parallelism (bool): Whether to use tensor parallelism. Defaults to True.
enable_fused_normalization (bool): Whether to use fused layernorm. Defaults to False.
enable_flash_attention (bool, optional): Whether to switch on flash attention. Defaults to False.
enable_jit_fused (bool, optional): Whether to switch on JIT fused operators. Defaults to False.
enable_sequence_parallelism (bool): Whether to turn on sequence parallelism, which partitions non-tensor-parallel regions along the sequence dimension. Defaults to False.
enable_sequence_overlap (bool): Whether to turn on sequence overlap, which overlap the computation and communication in sequence parallelism. It can only be used when enable_sequence_parallelism is True. Defaults to False.
gradient_checkpoint_config (Optional[GradientCheckpointConfig]): The gradient checkpoint config. Defaults to None.
enable_all_optimization (bool): Whether to turn on all optimization tools including 'fused normalization', 'flash attention', 'JIT fused operators', 'sequence parallelism' and 'sequence overlap'. Defaults to False.
"""
tensor_parallel_process_group: Optional[ProcessGroup] = None
sequence_parallel_process_group: Optional[ProcessGroup] = None
pipeline_stage_manager: Optional[PipelineStageManager] = None
enable_tensor_parallelism: bool = True
enable_all_optimization: bool = False
enable_fused_normalization: bool = False
enable_flash_attention: bool = False
enable_jit_fused: bool = False
enable_sequence_parallelism: bool = False
sequence_parallelism_mode: str = None
enable_sequence_overlap: bool = False
parallel_output: bool = True
make_vocab_size_divisible_by: int = 64
gradient_checkpoint_config: Optional[GradientCheckpointConfig] = None
extra_kwargs: Dict[str, Any] = field(default_factory=dict)
# pipeline_parallel_size: int
# data_parallel_size: int
# tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d']
@property
def tensor_parallel_size(self):
return self._tensor_parallel_size
@property
def sequence_parallel_size(self):
return self._sequence_parallel_size
def __post_init__(self):
# turn on all optimization if all_optimization is set to True
if self.enable_all_optimization:
self._turn_on_all_optimization()
if self.enable_sequence_parallelism:
self.sequence_parallelism_mode = (
"split_gather" if self.sequence_parallelism_mode is None else self.sequence_parallelism_mode
)
assert (
self.sequence_parallelism_mode in SUPPORT_SP_MODE
), f"Sequence parallelism mode {self.sequence_parallelism_mode} is not in the supported list {SUPPORT_SP_MODE}"
if self.sequence_parallelism_mode in ["split_gather", "ring"]:
assert (
self.enable_tensor_parallelism
), f"sequence parallelism mode {self.sequence_parallelism_mode} can only be used when enable_tensor_parallelism is True"
elif self.sequence_parallelism_mode in ["all_to_all"]:
assert (
not self.enable_tensor_parallelism
), f"sequence parallelism mode {self.sequence_parallelism_mode} can only be used when enable_tensor_parallelism is False"
if self.enable_sequence_overlap:
self.enable_sequence_overlap = False
warnings.warn(
f"The enable_sequence_overlap flag will be ignored in sequence parallelism mode {self.sequence_parallelism_mode}"
)
else:
if self.sequence_parallelism_mode:
self.sequence_parallelism_mode = None
warnings.warn(
f"The sequence_parallelism_mode will be ignored when enable_sequence_parallelism is False"
)
assert (
not self.enable_sequence_overlap
), f"enable_sequence_overlap can only be set to True when enable_sequence_parallelism is True"
# get the tensor parallel size
if not self.enable_tensor_parallelism:
self._tensor_parallel_size = 1
else:
self._tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group)
# get the sequence parallel size
if not self.enable_sequence_parallelism:
self._sequence_parallel_size = 1
else:
self._sequence_parallel_size = dist.get_world_size(self.sequence_parallel_process_group)
def _turn_on_all_optimization(self):
"""
Turn on all optimization.
"""
# you can add all the optimization flag here
self.enable_fused_normalization = True
self.enable_flash_attention = True
self.enable_jit_fused = True
# This can cause non-in-place param sharding when used without ZeRO.
# It may also slow down training when seq len is small. Plz enable manually.
# self.enable_sequence_parallelism = True
# self.enable_sequence_overlap = True
def _infer(self):
"""
Set default params for inference.
"""
# assert self.pipeline_stage_manager is None, "pipeline parallelism is not supported in inference for now"