Fix Spaces meta tensor model loading

src/evaluator.py

@@ -1,4 +1,4 @@
-"""Evaluation: per-aspect metrics, aggregated overall comparison.
+"""Evaluation: per-aspect metrics, aggregated overall comparison.
 
 Two label encodings live in this project (keep them straight!):
   Per-aspect (ACSA):   0=Not_Mentioned, 1=Positive, 2=Negative
@@ -53,7 +53,12 @@ def _move_model_to_device(model, device):
     try:
         return model.to(device)
     except NotImplementedError as exc:
-        if "meta tensor" not in str(exc):
+        if "meta" not in str(exc).lower():
+            raise
+        logger.warning("Model contains meta tensors; using to_empty() before loading checkpoint weights.")
+        return model.to_empty(device=device)
+    except RuntimeError as exc:
+        if "meta" not in str(exc).lower():
             raise
         logger.warning("Model contains meta tensors; using to_empty() before loading checkpoint weights.")
         return model.to_empty(device=device)
@@ -66,6 +71,32 @@ def _load_model_state(model, state_dict, strict=True):
         return model.load_state_dict(state_dict, strict=strict)
 
 
+
+
+def _refresh_runtime_buffers(model, device):
+    """Rebuild non-persistent buffers after a Spaces meta-tensor fallback."""
+    for module in model.modules():
+        if hasattr(module, "source_prior_bias") and hasattr(module, "num_aspects"):
+            prior_rows = []
+            prior_cfg = getattr(cfg, "CROSS_ATTN_SOURCE_PRIOR", {})
+            for aspect in cfg.ASPECTS[: module.num_aspects]:
+                row = list(prior_cfg.get(aspect, [1.0 / 3.0] * 3))
+                if len(row) < 3:
+                    row = row + [1.0 / 3.0] * (3 - len(row))
+                prior_rows.append(row[:3])
+            prior = torch.tensor(prior_rows, dtype=torch.float, device=device)
+            prior = prior - prior.mean(dim=-1, keepdim=True)
+            module.source_prior_bias = prior
+    meta_params = [name for name, param in model.named_parameters() if getattr(param, "is_meta", False)]
+    meta_buffers = [name for name, buf in model.named_buffers() if getattr(buf, "is_meta", False)]
+    if meta_params or meta_buffers:
+        raise RuntimeError(
+            "Checkpoint did not materialize model tensors. First meta tensors: "
+            + ", ".join((meta_params + meta_buffers)[:20])
+        )
+    return model
+
+
 def load_meta_acsa(checkpoint_dir: Path = None, meta_encoder: Optional[MetaEncoder] = None,
                    device=None):
     if checkpoint_dir is None:
@@ -94,6 +125,7 @@ def load_meta_acsa(checkpoint_dir: Path = None, meta_encoder: Optional[MetaEncod
         model = BertMetaFusionACSAModel(bert_name=bert_name, meta_in_dim=meta_in_dim)
     model = _move_model_to_device(model, device)
     _load_model_state(model, ckpt["model_state_dict"], strict=False)
+    model = _refresh_runtime_buffers(model, device)
     model.eval()
     tokenizer = AutoTokenizer.from_pretrained(checkpoint_dir / "tokenizer")
     return model, tokenizer, meta_encoder, device