from __future__ import annotations
from typing import Callable, Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import MmprojModel, ModelBase, gguf
@ModelBase.register("InternVisionModel")
@ModelBase.example("OpenGVLab/InternVL3-2B", "OpenGVLab/InternVL2_5-1B")
class InternVisionModel(MmprojModel):
min_dynamic_tiles: int = 0
max_dynamic_tiles: int = 0
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
self.min_dynamic_tiles = self.global_config.get("min_dynamic_patch", 0)
self.max_dynamic_tiles = self.global_config.get("max_dynamic_patch", 0)
def set_gguf_parameters(self):
assert self.hparams_vision is not None
if isinstance(self.hparams_vision['image_size'], list):
self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
if isinstance(self.hparams_vision['patch_size'], list):
self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
# hidden_act
if hparams["hidden_act"] == "silu":
self.gguf_writer.add_vision_use_silu(True)
elif hparams["hidden_act"] == "gelu":
self.gguf_writer.add_vision_use_gelu(True)
else:
raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
# downsample_ratio
downsample_ratio = self.global_config.get("downsample_ratio")
assert downsample_ratio is not None
self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
# older models may not have min/max_dynamic_patch in config
if self.min_dynamic_tiles > 0:
self.gguf_writer.add_vision_preproc_min_tiles(self.min_dynamic_tiles)
if self.max_dynamic_tiles > 0:
self.gguf_writer.add_vision_preproc_max_tiles(self.max_dynamic_tiles)
def tensor_force_quant(self, name, new_name, bid, n_dims):
if ".position_embd." in new_name:
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)
@classmethod
def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
name, gen = item
vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
if not any([name.startswith(prefix) for prefix in vision_prefix]):
return None
# deal with intern-s1 special case
names_map = {
"model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
"model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
"model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
"model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
"model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
"model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
}
if name in names_map:
name = names_map[name]
# correct name
if name.startswith("vision_model"):
name = "vision_tower." + name
if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
name += ".weight"
return super().filter_tensors((name, gen))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# split QKV tensors if needed
if ".qkv." in name:
if data_torch.ndim == 2: # weight
c3, _ = data_torch.shape
else: # bias
c3 = data_torch.shape[0]
assert c3 % 3 == 0
c = c3 // 3
wq = data_torch[:c]
wk = data_torch[c: c * 2]
wv = data_torch[c * 2:]
yield from super().modify_tensors(wq, name.replace("attn.qkv", "self_attn.q_proj"), bid)
yield from super().modify_tensors(wk, name.replace("attn.qkv", "self_attn.k_proj"), bid)
yield from super().modify_tensors(wv, name.replace("attn.qkv", "self_attn.v_proj"), bid)
else:
yield from super().modify_tensors(data_torch, name, bid)