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61 lines
1.9 KiB
61 lines
1.9 KiB
import os
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from typing import Dict, List, Tuple
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import torch.distributed as dist
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import torch.nn as nn
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from torch import Tensor
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from colossalai.cluster import DistCoordinator
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from ..policies.base_policy import Policy
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from .shard_config import ShardConfig
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from .sharder import ModelSharder
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# set CUDA_DEVICE_MAX_CONNECTIONS=1 to ensure that when communication and computation overlap, the order of core scheduling is correct
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os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "1"
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class ShardFormer:
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"""
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Parallelize model based on the given config and policy
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Example:
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```python
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from colossalai.shardformer import ShardFormer, ShardConfig
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from transformers import BertForMaskedLM
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import colossalai
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import torch
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colossalai.launch_from_torch()
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org_model = BertForMaskedLM.from_pretrained('bert-base-uncased')
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shard_config = ShardConfig()
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shard_former = ShardFormer(shard_config=shard_config)
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model, shared_params = shard_former.optimize(org_model)
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```
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"""
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def __init__(self, shard_config: ShardConfig):
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self.is_distributed = dist.is_initialized()
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if self.is_distributed:
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self.coordinator = DistCoordinator()
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else:
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self.coordinator = None
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self.shard_config = shard_config
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def optimize(self, model: nn.Module, policy: Policy = None) -> Tuple[nn.Module, List[Dict[int, Tensor]]]:
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r"""
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This method will optimize the model based on the given policy.
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Args:
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model (`torch.nn.Model`): the origin huggingface model
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shard_config (`ShardConfig`): the config for distribute information
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policy (`Policy`): the custom policy for sharding
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Returns: the sharded model and the shared parameters
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"""
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sharder = ModelSharder(model=model, shard_config=self.shard_config, policy=policy)
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shared_params = sharder.shard()
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return model, shared_params
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