[BugFix] Implement RoPE for GPT-J #941
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This was referenced Sep 4, 2023
zhuohan123
approved these changes
Sep 6, 2023
| @@ -253,8 +253,10 @@ def __init__( | |||
| max_position: int = 8192, | |||
| base: int = 10000, | |||
| num_kv_heads: Optional[int] = None, | |||
| is_neox_style: bool = True, | |||
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Nit: Do you think this will be more clear?
Suggested change
| is_neox_style: bool = True, | |
| style: str = "neox", # Options: ["neox", "gptj"] |
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To my knowledge, there are only two types of RoPE in terms of how they rotate the query and key vectors (To my understanding, the rope scaling stuffs use the GPT-NeoX RoPE). I think we can change the interface after we find more RoPEs to support.
hongxiayang
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Fixes #747 and fixes #590
This PR fixes a bug in the GPT-J model implementation. The GPT-J model uses a rotary embedding that is slightly different from the GPT-NeoX style rotary embedding (which is commonly used for LLaMA and recent models). The difference isn't considered in the vLLM's current implementation. The PR resolves this by adding a RoPE kernel for GPT-J. After this fix, I've checked that the outputs of GPT-J when using FP32 and argmax sampling match the HF's outputs.
NOTE: The PR should be merged after #938