mirror of
https://github.com/hpcaitech/ColossalAI.git
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554aa9592e
* [legacy] move communication to legacy (#4640) * [legacy] refactor logger and clean up legacy codes (#4654) * [legacy] make logger independent to gpc * [legacy] make optim independent to registry * [legacy] move test engine to legacy * [legacy] move nn to legacy (#4656) * [legacy] move nn to legacy * [checkpointio] fix save hf config * [test] remove useledd rpc pp test * [legacy] fix nn init * [example] skip tutorial hybriad parallel example * [devops] test doc check * [devops] test doc check
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from contextlib import contextmanager
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import torch.nn as nn
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from colossalai.context import ParallelMode
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from colossalai.core import global_context as gpc
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class ParallelLayer(nn.Module):
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global_state_dict: bool = True
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def __init__(self):
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super().__init__()
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self.data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(
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ParallelMode.DATA)
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self.data_parallel_size = 1 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_world_size(
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ParallelMode.DATA)
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self.tensor_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_local_rank(
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ParallelMode.TENSOR)
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self.tensor_parallel_size = 1 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_world_size(
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ParallelMode.TENSOR)
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self.pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
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ParallelMode.PIPELINE)
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self.pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
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ParallelMode.PIPELINE)
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def _load_from_global_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
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error_msgs):
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return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
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error_msgs)
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def _save_to_global_state_dict(self, destination, prefix, keep_vars):
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return super()._save_to_state_dict(destination, prefix, keep_vars)
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def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
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error_msgs):
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if self.global_state_dict:
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if gpc.get_local_rank(ParallelMode.TENSOR) != 0:
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missing_keys.clear()
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unexpected_keys.clear()
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return self._load_from_global_state_dict(state_dict, prefix, local_metadata, strict, missing_keys,
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unexpected_keys, error_msgs)
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return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
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error_msgs)
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def _save_to_state_dict(self, destination, prefix, keep_vars):
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if self.global_state_dict:
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return self._save_to_global_state_dict(destination, prefix, keep_vars)
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return super()._save_to_state_dict(destination, prefix, keep_vars)
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@classmethod
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@contextmanager
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def use_local_state_dict(cls):
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try:
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cls.global_state_dict = False
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yield
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finally:
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cls.global_state_dict = True
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