pyright-based changes for tools/mtmd/legacy-models/convert_image_encoder_to_gguf.py

robbiemu · robbiemu · commit c278affb1887 · 2025-05-16T09:43:59.000-04:00
Fix torch 2.5.1 / numpy 2.x compatibility in convert_image_encoder_to_gguf.py

- Updated Tensor-to-array conversions to use `np.asarray(..., dtype=...)` per NumPy 2.x migration rules (avoids copy error on float16).
- Used explicit typing and `cast(...)` to guide Pyright/Pylance under torch 2.5.1:
  - Annotated `model` as PreTrainedModel.
  - Re-cast `model.vision_model` to `CLIPVisionTransformer` to safely access `.encoder.layers`.
  - Replaced slice assignment with `__init__` to reset ModuleList contents.
- Verified compatibility by converting `openai/clip-vit-base-patch32` using `--clip-model-is-openclip`.
diff --git a/tools/mtmd/legacy-models/convert_image_encoder_to_gguf.py b/tools/mtmd/legacy-models/convert_image_encoder_to_gguf.py
@@ -6,6 +6,10 @@
 import torch
 import numpy as np
 from gguf import *
+from typing import cast
+from torch.nn import ModuleList
+from transformers.models.clip.modeling_clip import CLIPVisionTransformer
+from transformers import PreTrainedModel
 from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel, SiglipVisionModel
 
 TEXT = "clip.text"
@@ -162,13 +166,13 @@ def bytes_to_unicode():
     ftype = 0
 
 if args.clip_model_is_siglip:
-    model = SiglipVisionModel.from_pretrained(dir_model)
+    model: PreTrainedModel = SiglipVisionModel.from_pretrained(dir_model)
     processor = None
 elif args.clip_model_is_vision or args.clip_model_is_openclip:
-    model = CLIPVisionModel.from_pretrained(dir_model)
+    model: PreTrainedModel = CLIPVisionModel.from_pretrained(dir_model)
     processor = None
 else:
-    model = CLIPModel.from_pretrained(dir_model)
+    model: PreTrainedModel = CLIPModel.from_pretrained(dir_model)
     processor = CLIPProcessor.from_pretrained(dir_model)
 
 fname_middle = None
@@ -350,9 +354,14 @@ def get_non_negative_vision_feature_layers(v_hparams):
     # By default, we drop the last layer for llava projector
     # models unless we have explicitly set vision feature layers
     if feature_layers is None:
-        model.vision_model.encoder.layers.pop(-1)
+        vision_model = cast(CLIPVisionTransformer, model.vision_model)
+        encoder_layers = vision_model.encoder.layers
+        encoder_layers.pop(-1)
     else:
-        model.vision_model.encoder.layers = model.vision_model.encoder.layers[:max(feature_layers)]
+        vision_model = cast(CLIPVisionTransformer, model.vision_model)
+        encoder_layers = vision_model.encoder.layers
+        encoder_layers = cast(ModuleList, encoder_layers)
+        encoder_layers.__init__(encoder_layers[:max(feature_layers)])
 
     projector = torch.load(args.llava_projector)
     for name, data in projector.items():
@@ -375,24 +384,24 @@ def get_non_negative_vision_feature_layers(v_hparams):
         continue
 
     name = get_tensor_name(name)
-    data = data.squeeze().numpy()
+    data = np.ascontiguousarray(data.detach().cpu().squeeze().numpy())
 
     n_dims = len(data.shape)
 
     # ftype == 0 -> float32, ftype == 1 -> float16
     ftype_cur = 0
     if n_dims == 4:
         print(f"tensor {name} is always saved in f16")
-        data = data.astype(np.float16)
+        data = np.asarray(data, dtype=np.float16)
         ftype_cur = 1
     elif ftype == 1:
         if name[-7:] == ".weight" and n_dims == 2:
             print("  Converting to float16")
-            data = data.astype(np.float16)
+            data = np.asarray(data, dtype=np.float16)
             ftype_cur = 1
         else:
             print("  Converting to float32")
-            data = data.astype(np.float32)
+            data = np.asarray(data, dtype=np.float32)
             ftype_cur = 0
     else:
         if data.dtype != np.float32:
