chore: import upstream snapshot with attribution

conversion/internvl.py

@@ -0,0 +1,98 @@
+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")
+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)