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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>
98 lines
4.3 KiB
Python
98 lines
4.3 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 MmprojModel, ModelBase, gguf
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@ModelBase.register("InternVisionModel")
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class InternVisionModel(MmprojModel):
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min_dynamic_tiles: int = 0
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max_dynamic_tiles: int = 0
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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.min_dynamic_tiles = self.global_config.get("min_dynamic_patch", 0)
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self.max_dynamic_tiles = self.global_config.get("max_dynamic_patch", 0)
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def set_gguf_parameters(self):
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assert self.hparams_vision is not None
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if isinstance(self.hparams_vision['image_size'], list):
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self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
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if isinstance(self.hparams_vision['patch_size'], list):
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self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
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super().set_gguf_parameters()
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hparams = self.hparams
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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
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self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
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# hidden_act
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if hparams["hidden_act"] == "silu":
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self.gguf_writer.add_vision_use_silu(True)
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elif hparams["hidden_act"] == "gelu":
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self.gguf_writer.add_vision_use_gelu(True)
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else:
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raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
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# downsample_ratio
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downsample_ratio = self.global_config.get("downsample_ratio")
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assert downsample_ratio is not None
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self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
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# older models may not have min/max_dynamic_patch in config
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if self.min_dynamic_tiles > 0:
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self.gguf_writer.add_vision_preproc_min_tiles(self.min_dynamic_tiles)
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if self.max_dynamic_tiles > 0:
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self.gguf_writer.add_vision_preproc_max_tiles(self.max_dynamic_tiles)
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def tensor_force_quant(self, name, new_name, bid, n_dims):
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if ".position_embd." in new_name:
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return gguf.GGMLQuantizationType.F32
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return super().tensor_force_quant(name, new_name, bid, n_dims)
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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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vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
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if not any([name.startswith(prefix) for prefix in vision_prefix]):
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return None
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# deal with intern-s1 special case
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names_map = {
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"model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
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"model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
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"model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
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"model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
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"model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
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"model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
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}
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if name in names_map:
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name = names_map[name]
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# correct name
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if name.startswith("vision_model"):
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name = "vision_tower." + name
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if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
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name += ".weight"
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return super().filter_tensors((name, gen))
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# split QKV tensors if needed
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if ".qkv." in name:
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if data_torch.ndim == 2: # weight
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c3, _ = data_torch.shape
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else: # bias
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c3 = data_torch.shape[0]
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assert c3 % 3 == 0
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c = c3 // 3
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wq = data_torch[:c]
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wk = data_torch[c: c * 2]
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wv = data_torch[c * 2:]
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yield from super().modify_tensors(wq, name.replace("attn.qkv", "self_attn.q_proj"), bid)
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yield from super().modify_tensors(wk, name.replace("attn.qkv", "self_attn.k_proj"), bid)
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yield from super().modify_tensors(wv, name.replace("attn.qkv", "self_attn.v_proj"), bid)
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else:
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yield from super().modify_tensors(data_torch, name, bid)
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