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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 5, 11:00 UTC

fix(test): recompute Jina ColBERT rotary inv_freq cleared by transformers v5 weight loader (#39176)

Signed-off-by: Ilya Boytsov <ilyaboytsov1805@gmail.com>

Ilya Boytsov committed Apr 7, 2026 at 16:40 UTC 6e1100889e6a675d17ad82815acf8f02f1cc419e
1 file changed +8
tests/models/language/pooling/test_colbert.py
+8
@@ -109,6 +109,14 @@ def _load_hf_model(model_name: str, hf_spec: dict, device: torch.device):
109 **extra,
110 ).to(device)
111 model.eval()
112 +
113 + # Transformers 5.0 weight materialization can clear non-persistent
114 + # buffers (e.g. rotary inv_freq) that were registered with
115 + # persistent=False. Re-compute them so the model produces valid output.
116 + for mod in model.modules():
117 + if hasattr(mod, "_compute_inv_freq") and hasattr(mod, "inv_freq"):
118 + mod.inv_freq = mod._compute_inv_freq(device=device)
119 +
120 return model
121
122