Revert bfloat16 detection - was causing quality regression / keep ensure_triton_compat()

src/optimization/compatibility.py

@@ -175,41 +175,6 @@ def _check_conv3d_memory_bug():
 NVIDIA_CONV3D_MEMORY_BUG_WORKAROUND = _check_conv3d_memory_bug()
 
 
-def get_supported_compute_dtype(debug=None) -> torch.dtype:
-    """
-    Detect the best supported compute dtype for the current hardware.
-    
-    bfloat16 requires CUDA compute capability 8.0+ (Ampere and newer).
-    Older CUDA GPUs fall back to float16 to avoid CUBLAS_STATUS_NOT_SUPPORTED errors.
-    
-    Args:
-        debug: Optional debug instance for logging warnings
-    
-    Returns:
-        torch.bfloat16 if supported, torch.float16 for older CUDA GPUs
-    """
-    try:
-        # CUDA: Check compute capability
-        if torch.cuda.is_available():
-            major, minor = torch.cuda.get_device_capability()
-            # bfloat16 requires compute capability 8.0+ (Ampere: A100, RTX 30xx)
-            if major < 8:
-                if debug:
-                    gpu_name = torch.cuda.get_device_name()
-                    debug.log(
-                        f"GPU '{gpu_name}' (compute capability {major}.{minor}) does not support bfloat16. "
-                        f"Using float16 instead - 7B models are not supported and will render black, 3B models may have artifacts.",
-                        level="WARNING", category="precision", force=True
-                    )
-                return torch.float16
-        
-        # Default: bfloat16 (MPS, CPU, or CUDA 8.0+)
-        return torch.bfloat16
-        
-    except Exception:
-        return torch.bfloat16
-
-
 # Log all optimization status once globally (cross-process) using environment variable
 if not os.environ.get("SEEDVR2_OPTIMIZATIONS_LOGGED"):
     os.environ["SEEDVR2_OPTIMIZATIONS_LOGGED"] = "1"