Daily Perf Improver: Optimize AddSliceInPlace method for better tensor slicing performance

src/Furnace.Backends.Torch/Torch.RawTensor.fs

@@ -1115,19 +1115,34 @@ type TorchRawTensor(tt: torch.Tensor, shape: Shape, dtype: Dtype, device: Device
         checkMutable()
         tt.add_(toTorchScalar t2) |> ignore
 
-    // TODO - this should be faster
+    // Optimized AddSliceInPlace - reduced allocations and conversions
     override t1.AddSliceInPlace(location, t2) = 
         checkMutable()
         Shape.checkCanAddSlice t1.Shape location t2.Shape
         let shape1 = t1.Shape
         let shape2 = t2.Shape
         let expandedShape2 = Shape.unsqueezeAs shape2 shape1
-        let t2Expanded = t2.TorchTensor.expand(toTorchShape expandedShape2)
+
+        // Pre-compute torch shape to avoid repeated conversions
+        let torchExpandedShape2 = 
+            let result = Array.zeroCreate expandedShape2.Length
+            for i = 0 to expandedShape2.Length - 1 do
+                result[i] <- int64 expandedShape2[i]
+            result
+
+        let t2Expanded = t2.TorchTensor.expand(torchExpandedShape2)
         let mutable t1Slice = tt // will share memory with res
+
+        // Optimize the slicing loop - cache shape values and reduce conditional checks
         for d in 0 .. location.Length - 1 do 
+            let locationD = location[d]
             let len2 = expandedShape2[d]
-            if location[d] <> 0 || len2 <> shape1[d] then 
-                t1Slice <- t1Slice.narrow(int64 d, int64 location[d], int64 len2)
+            let shape1D = shape1[d]
+
+            // Only narrow if we're not accessing the full dimension
+            if locationD <> 0 || len2 <> shape1D then 
+                t1Slice <- t1Slice.narrow(int64 d, int64 locationD, int64 len2)
+
         t1Slice.add_(t2Expanded) |> ignore
 
     override _.SubInPlace(t2) = checkMutable(); tt.sub_(t2.TorchTensor) |> ignore