Revert "embeddings: CPU fallback when the GPU is owned by another workload"

This reverts commit 7908670c.

codai/__init__.py

@@ -16,7 +16,7 @@
 
 # Canonical product version for CoderAI — single source of truth. Both the API
 # metadata and the admin web UI read from here.
-__version__ = "0.1.34"
+__version__ = "0.1.33"
 
 # Configure the CUDA caching allocator BEFORE torch is imported anywhere.
 # expandable_segments lets the allocator return freed pages to the driver even

codai/api/embeddings.py

@@ -83,30 +83,6 @@ class _EmbeddingModel:
         self.model = None
 
 
-def _is_cuda_error(e: Exception) -> bool:
-    s = str(e)
-    return 'CUDA' in s or 'cuda' in s or 'out of memory' in s.lower()
-
-
-def _move_to_cpu(model_obj: '_EmbeddingModel') -> '_EmbeddingModel':
-    """Move a loaded embedding model to CPU in place (VRAM-contention fallback).
-    Mutates model_obj so the manager's registry entry stays valid."""
-    backend, model = model_obj
-    if backend == 'sentence_transformers':
-        model.to('cpu')
-    else:
-        proc, hf_model, _dev = model
-        if hf_model is not None and hasattr(hf_model, 'to'):
-            hf_model.to('cpu')
-        model_obj.model = (proc, hf_model, 'cpu')
-    try:
-        import torch
-        torch.cuda.empty_cache()
-    except Exception:
-        pass
-    return model_obj
-
-
 def _trust_remote_code(model_config: dict = None) -> bool:
     cfg = model_config or {}
     raw = cfg.get('_raw_cfg') if isinstance(cfg.get('_raw_cfg'), dict) else {}
@@ -551,20 +527,7 @@ async def _run_embeddings(request: EmbeddingsRequest, http_request: Request = No
                 model_obj = await asyncio.get_event_loop().run_in_executor(
                     None, _load_embedding_model, model_name, device, _emb_cfg)
         except Exception as e:
-            # GPU full (e.g. a video job owns the card): serve degraded from CPU
-            # rather than 500 — embedding is small enough for host RAM.
-            if _is_cuda_error(e):
-                print(f"[embeddings] GPU load failed ({str(e)[:120]}) — "
-                      f"falling back to CPU for '{model_name}'")
-                try:
-                    with loading_task(model_name, model_type="embedding"):
-                        model_obj = await asyncio.get_event_loop().run_in_executor(
-                            None, _load_embedding_model, model_name, 'cpu', _emb_cfg)
-                except Exception as e2:
-                    raise HTTPException(status_code=500,
-                                        detail=f"Failed to load embedding model: {e2}")
-            else:
-                raise HTTPException(status_code=500, detail=f"Failed to load embedding model: {e}")
+            raise HTTPException(status_code=500, detail=f"Failed to load embedding model: {e}")
         # Register through add_model (pool + models_in_vram bookkeeping) rather than
         # a bare dict assignment, so eviction/unload treat it like every other model.
         multi_model_manager.add_model(model_key, model_obj)
@@ -600,26 +563,7 @@ async def _run_embeddings(request: EmbeddingsRequest, http_request: Request = No
     except ValueError as e:
         raise HTTPException(status_code=400, detail=str(e))
     except Exception as e:
-        # Encode-time CUDA failure (VRAM grabbed by another engine mid-flight):
-        # move the model to CPU and retry once before giving up.
-        if not _is_cuda_error(e):
-            raise HTTPException(status_code=500, detail=f"Embedding failed: {e}")
-        print(f"[embeddings] CUDA encode failed ({str(e)[:120]}) — "
-              f"retrying '{model_name}' on CPU")
-        try:
-            model_obj = await asyncio.get_event_loop().run_in_executor(
-                None, _move_to_cpu, model_obj)
-            vectors = []
-            if texts:
-                vectors += await asyncio.get_event_loop().run_in_executor(
-                    None, _embed_texts, model_obj, texts, request.dimensions)
-            if images:
-                vectors += await asyncio.get_event_loop().run_in_executor(
-                    None, _embed_images, model_obj, images, request.dimensions)
-        except ValueError as e2:
-            raise HTTPException(status_code=400, detail=str(e2))
-        except Exception as e2:
-            raise HTTPException(status_code=500, detail=f"Embedding failed: {e2}")
+        raise HTTPException(status_code=500, detail=f"Embedding failed: {e}")
 
     # Optional TurboQuant vector quantization (data-free, inner-product preserving).
     # The per-model config block (turboquant: {enabled, backend, bits}) is the