mirror of https://github.com/hpcaitech/ColossalAI
77 lines
3.9 KiB
Python
77 lines
3.9 KiB
Python
from dataclasses import dataclass
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from typing import Optional
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import torch.distributed as dist
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from torch.distributed import ProcessGroup
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from colossalai.pipeline.stage_manager import PipelineStageManager
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__all__ = ["ShardConfig"]
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@dataclass
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class ShardConfig:
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r"""
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The config for sharding the huggingface model
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Args:
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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.
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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.
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enable_tensor_parallelism (bool): Whether to use tensor parallelism. Defaults to True.
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enable_fused_normalization (bool): Whether to use fused layernorm. Defaults to False.
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enable_flash_attention (bool, optional): Whether to switch on flash attention. Defaults to False.
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enable_jit_fused (bool, optional): Whether to switch on JIT fused operators. Defaults to False.
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enable_sequence_parallelism (bool): Whether to turn on sequence parallelism, which partitions non-tensor-parallel regions along the sequence dimension. Defaults to False.
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enable_sequence_overlap (bool): Whether to turn on sequence overlap, wheich overlap the computation and communication in sequence parallelism. It can only be used when enable_sequence_parallelism is True. Defaults to False.
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enable_all_optimization (bool): Whether to turn on all optimization tools including 'fused normalizaion', 'flash attention', 'JIT fused operators', 'sequence parallelism' and 'sequence overlap'. Defaults to False.
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inference_only (bool): Whether only doing forward passing. Defaults to False.
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"""
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tensor_parallel_process_group: Optional[ProcessGroup] = None
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pipeline_stage_manager: Optional[PipelineStageManager] = None
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enable_tensor_parallelism: bool = True
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enable_fused_normalization: bool = False
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enable_flash_attention: bool = False
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enable_jit_fused: bool = False
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enable_sequence_parallelism: bool = False
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enable_sequence_overlap: bool = False
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enable_all_optimization: bool = False
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inference_only: bool = False
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# tensor_parallel_mode: Literal['1d', '2d', '2.5d', '3d']
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@property
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def tensor_parallel_size(self):
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return self._tensor_parallel_size
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def __post_init__(self):
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if not self.enable_tensor_parallelism and self.enable_sequence_parallelism:
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raise ValueError(
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"enable_sequence_parallelism can only be set to True when enable_tensor_parallelism is True"
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)
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if not self.enable_sequence_parallelism and self.enable_sequence_overlap:
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raise ValueError("enable_sequence_overlap can only be set to True when enable_sequence_parallelism is True")
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if not self.enable_tensor_parallelism:
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self._tensor_parallel_size = 1
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else:
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# get the parallel size
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self._tensor_parallel_size = dist.get_world_size(self.tensor_parallel_process_group)
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# turn on all optimization if all_optimization is set to True
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if self.enable_all_optimization:
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self._turn_on_all_optimization()
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def _turn_on_all_optimization(self):
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"""
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Turn on all optimization.
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"""
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# you can add all the optimization flag here
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self.enable_fused_normalization = True
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self.enable_flash_attention = True
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self.enable_jit_fused = True
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self.enable_sequence_parallelism = True
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self.enable_sequence_overlap = True
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def _infer(self):
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"""
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Set default params for inference.
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"""
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assert self.pipeline_stage_manager is None, "pipeline parallelism is not supported in inference for now"
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