fix: use canonical torch.backends.mps.is_available() for reliable MPS detection

Replaces torch.mps.is_available() with torch.backends.mps.is_available()
across all files. The torch.backends API is the official PyTorch method
for MPS detection (since PyTorch 1.12) and works reliably on:
- macOS with Apple Silicon (returns True when MPS available)
- macOS without MPS support (returns False)
- Windows/Linux (hasattr guard prevents AttributeError)

src/utils/debug.py

@@ -340,7 +340,7 @@ class Debug:
         }
         
         # VRAM metrics
-        if torch.cuda.is_available() or (hasattr(torch, 'mps') and callable(getattr(torch.mps, 'is_available', None)) and torch.mps.is_available()):
+        if torch.cuda.is_available() or (hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()):
             metrics['vram_allocated'], metrics['vram_reserved'], current_global_peak = get_vram_usage(device=None, debug=self)
 
             # Calculate peak since last log_memory_state
@@ -353,7 +353,7 @@ class Debug:
                 metrics['vram_free'] = vram_info["free_gb"]
                 metrics['vram_total'] = vram_info["total_gb"]
                 
-                backend = "MPS" if (hasattr(torch, 'mps') and callable(getattr(torch.mps, 'is_available', None)) and torch.mps.is_available()) else "VRAM"
+                backend = "MPS" if (hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()) else "VRAM"
                 metrics['summary_vram'] = (f"  [{backend}] {metrics['vram_allocated']:.2f}GB allocated / "
                         f"{metrics['vram_reserved']:.2f}GB reserved / "
                         f"Peak: {metrics['vram_peak_since_last']:.2f}GB / "
@@ -376,7 +376,7 @@ class Debug:
             metrics['summary_ram'] = ""
         
         # Update VRAM history for tracking
-        if torch.cuda.is_available() or (hasattr(torch, 'mps') and callable(getattr(torch.mps, 'is_available', None)) and torch.mps.is_available()):
+        if torch.cuda.is_available() or (hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()):
             self.vram_history.append(metrics['vram_allocated'])
         
         return metrics