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46 lines
1.8 KiB
Python
46 lines
1.8 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.config import ClientConfig
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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.models.llama.block import WrappedLlamaBlock
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logger = get_logger(__name__)
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class DistributedLlamaConfig(LlamaConfig, ClientConfig, 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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@property
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def num_key_value_groups(self):
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return self.num_attention_heads // self.num_key_value_heads
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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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logger.info(
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"Make sure you follow the LLaMA's terms of use: "
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"https://bit.ly/llama2-license for LLaMA 2, https://bit.ly/llama-license for LLaMA 1"
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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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dht_prefix = dht_prefix.split("/")[-1] # Use only repo name to merge blocks hosted by different accounts
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if not dht_prefix.endswith("-hf"):
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dht_prefix += "-hf"
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logger.info(f"Using DHT prefix: {dht_prefix}")
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result = super().from_pretrained(model_name_or_path, *args, dht_prefix=dht_prefix, **kwargs)
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config = result[0] if isinstance(result, tuple) else result
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config.pretraining_tp = 1 # This may give less accurate results but it doesn't matter if we use quantization
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return result
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