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cc7200bf12
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
58 lines
2.3 KiB
Python
58 lines
2.3 KiB
Python
from __future__ import annotations
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from typing import Callable, Iterable, TYPE_CHECKING
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if TYPE_CHECKING:
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from torch import Tensor
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from .base import ModelBase, TextModel, gguf
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from .llama import LlamaModel
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@ModelBase.register("ChameleonForConditionalGeneration")
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@ModelBase.register("ChameleonForCausalLM") # obsolete
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class ChameleonModel(TextModel):
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model_arch = gguf.MODEL_ARCH.CHAMELEON
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False))
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def set_vocab(self):
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self._set_vocab_gpt2()
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@classmethod
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def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
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name, gen = item
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# ignore image tokenizer for now
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# TODO: image support for Chameleon
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if name.startswith("model.vqmodel"):
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return None
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return super().filter_tensors(item)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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n_head = self.hparams["num_attention_heads"]
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n_kv_head = self.hparams.get("num_key_value_heads")
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hidden_dim = self.hparams.get("hidden_size")
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if name.endswith(("q_proj.weight", "q_proj.bias")):
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data_torch = LlamaModel.permute(data_torch, n_head, n_head)
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if name.endswith(("k_proj.weight", "k_proj.bias")):
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data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
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if name.endswith(("q_norm.weight", "q_norm.bias")):
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data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim)
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if name.endswith(("k_norm.weight", "k_norm.bias")):
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data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim)
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yield from super().modify_tensors(data_torch, name, bid)
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# see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203
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@staticmethod
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def _reverse_hf_permute(data_torch, n_heads, hidden_dim):
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head_dim = hidden_dim // n_heads
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data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1)
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data_torch = data_torch.repeat_interleave(n_heads, 0)
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return data_torch
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