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convert-llama-h5-to-gguf.py : clarify the reverse permute
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@ -18,7 +18,9 @@ from sentencepiece import SentencePieceProcessor
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# compatible with python < 3.9
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NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
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def permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
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# reverse HF permute back to original pth layout
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
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def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
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if n_kv_head is not None and n_head != n_kv_head: n_head //= n_kv_head
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return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
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.swapaxes(1, 2)
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@ -219,9 +221,9 @@ for part_name in part_names:
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data = data.squeeze().numpy()
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# permute these
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# reverse permute these
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if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
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data = permute(data, head_count, head_count_kv)
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data = reverse_hf_permute(data, head_count, head_count_kv)
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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@ -288,9 +290,9 @@ for part_name in part_names:
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data = data.squeeze().numpy()
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# permute these
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# reverse permute these
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if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
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data = permute(data, head_count, head_count_kv)
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data = reverse_hf_permute(data, head_count, head_count_kv)
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# map tensor names
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if name.endswith(".weight") and name[:-7] in tensor_map:
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