from __future__ import annotations
from typing import Callable, Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf
from .llama import LlamaModel
@ModelBase.register("ChameleonForConditionalGeneration")
@ModelBase.register("ChameleonForCausalLM") # obsolete
# [TAG_HF_EXAMPLE_GATED] facebook/chameleon-7b is gated
# [TAG_HF_EXAMPLE_MISSING]
class ChameleonModel(TextModel):
model_arch = gguf.MODEL_ARCH.CHAMELEON
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_swin_norm(self.hparams.get("swin_norm", False))
def set_vocab(self):
self._set_vocab_gpt2()
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
# ignore image tokenizer for now
# TODO: image support for Chameleon
if name.startswith("model.vqmodel"):
return None
return super().filter_tensors(item)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
n_kv_head = self.hparams.get("num_key_value_heads")
hidden_dim = self.hparams.get("hidden_size")
if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
if name.endswith(("q_norm.weight", "q_norm.bias")):
data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_head, hidden_dim)
if name.endswith(("k_norm.weight", "k_norm.bias")):
data_torch = ChameleonModel._reverse_hf_permute(data_torch, n_kv_head, hidden_dim)
yield from super().modify_tensors(data_torch, name, bid)
# see: https://github.com/huggingface/transformers/blob/72fb02c47dbbe1999ae105319f24631cad6e2e00/src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py#L176-L203
@staticmethod
def _reverse_hf_permute(data_torch, n_heads, hidden_dim):
head_dim = hidden_dim // n_heads
data_torch = data_torch[0].view(2, head_dim // 2).t().reshape(1, -1)
data_torch = data_torch.repeat_interleave(n_heads, 0)
return data_torch