Phase 1

extensions_built_in/captioner/Qwen3VLCaptioner.py

@@ -12,6 +12,8 @@ from optimum.quanto import freeze
 from toolkit.basic import flush
 from toolkit.util.quantize import quantize, get_qtype
 
+from toolkit.models.v2.text_encoders.qwen3_vl import patch_qwen_vl_patch_embed
+
 from .BaseCaptioner import BaseCaptioner
 import transformers
 import logging
@@ -19,29 +21,6 @@ import traceback
 import warnings
 
 
-def patch_qwen_vl_patch_embed(model):
-    """Qwen-VL's vision patch_embed is a Conv3d whose kernel == stride, i.e. a plain
-    linear projection of each flattened patch. bf16 Conv3d has no fast cuDNN kernel and
-    falls back to a slow, GPU-underutilizing path. Swap it for the equivalent F.linear
-    (a GEMM). The weight is read lazily so this survives later .to(device)/dtype moves.
-    Returns the number of patch_embed modules patched."""
-    patched = 0
-    for module in model.modules():
-        proj = getattr(module, "proj", None)
-        if isinstance(proj, torch.nn.Conv3d) and tuple(proj.kernel_size) == tuple(
-            proj.stride
-        ):
-
-            def fast_forward(hidden_states, _proj=proj):
-                w = _proj.weight.reshape(_proj.weight.shape[0], -1)
-                x = hidden_states.view(-1, w.shape[1]).to(w.dtype)
-                return F.linear(x, w, _proj.bias)
-
-            module.forward = fast_forward
-            patched += 1
-    return patched
-
-
 # transformers.logging.set_verbosity_error()
 warnings.filterwarnings("ignore")
 logging.disable(logging.WARNING)