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36 lines
1.4 KiB
Python
36 lines
1.4 KiB
Python
import os
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from typing import Optional, Union
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from hivemind import get_logger
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from transformers.models.llama import LlamaConfig
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from transformers.models.llama.modeling_llama import LlamaAttention
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from petals.client.lm_head import LMHeadConfig
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from petals.client.ptune import PTuneConfig
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from petals.client.routing.sequence_manager import SequenceManagerConfig
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from petals.models.llama.block import WrappedLlamaBlock
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from petals.utils.auto_config import AutoDistributedConfig
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logger = get_logger(__name__)
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class DistributedLlamaConfig(LlamaConfig, SequenceManagerConfig, PTuneConfig, LMHeadConfig):
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block_class = WrappedLlamaBlock
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attn_class = LlamaAttention
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block_prefix = "model.layers"
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@classmethod
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def from_pretrained(
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cls, model_name_or_path: Union[str, os.PathLike, None], *args, dht_prefix: Optional[str] = None, **kwargs
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):
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loading_from_repo = model_name_or_path is not None and not os.path.isdir(model_name_or_path)
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if loading_from_repo and dht_prefix is None:
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dht_prefix = str(model_name_or_path)
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if "/" in dht_prefix: # If present, strip repository name to merge blocks hosted by different accounts
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dht_prefix = dht_prefix[dht_prefix.rfind("/") + 1 :]
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logger.info(f"Using DHT prefix: {dht_prefix}")
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return super().from_pretrained(model_name_or_path, *args, dht_prefix=dht_prefix, **kwargs)
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AutoDistributedConfig.register(DistributedLlamaConfig)
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