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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>
64 lines
2.8 KiB
Python
64 lines
2.8 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 MmprojModel, ModelBase, gguf, logger
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@ModelBase.register("YoutuVLForConditionalGeneration")
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class YoutuVLVisionModel(MmprojModel):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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assert self.hparams_vision is not None
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self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
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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_clip_projector_type(gguf.VisionProjectorType.YOUTUVL)
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self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
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# Handle activation function
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hidden_act = str(self.hparams.get("hidden_act", "gelu_pytorch_tanh")).lower()
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if hidden_act in ("gelu", "gelu_pytorch_tanh", "gelu_fast", "gelu_new", "gelu_accurate"):
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self.gguf_writer.add_vision_use_gelu(True)
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elif hidden_act == "silu":
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self.gguf_writer.add_vision_use_silu(True)
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else:
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raise ValueError(f"Unsupported activation function for YOUTUVL: {hidden_act}")
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self.gguf_writer.add_vision_spatial_merge_size(self.hparams.get("spatial_merge_size", 2))
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window_size = self.hparams.get("window_size")
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if window_size is not None:
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self.gguf_writer.add_vision_window_size(window_size)
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# fullatt_block_indexes contains explicit layer indices that use full attention
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# e.g., [2, 5, 8, 11] means layers 2, 5, 8, 11 use full attention
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# All other layers use window attention
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fullatt_block_indexes = self.hparams.get("fullatt_block_indexes")
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assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for youtuvl"
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# Store the explicit layer indices for YoutuVL (irregular pattern approach)
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self.gguf_writer.add_vision_wa_layer_indexes(layers=fullatt_block_indexes)
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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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# Skip language model tensors
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skip_prefixes = ('lm_head.', 'model.layers.', 'model.embed_tokens.', 'model.norm.')
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if name.startswith(skip_prefixes):
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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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# Try to map the tensor using TensorNameMap (handles vision encoder and projector)
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try:
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yield from super().modify_tensors(data_torch, name, bid)
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except ValueError:
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# If mapping fails, log warning and skip
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logger.warning(f"Cannot map tensor: {name}")
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return
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