[MPS] Fwd-fix for clamp regression

Forward fix for regresion introdcued by
#121381

- Do not call `minimumWithNaNPropagationWithPrimaryTensor` for integer
  typed tensors as it will crash with
  ```
    /AppleInternal/Library/BuildRoots/ce725a5f-c761-11ee-a4ec-b6ef2fd8d87b/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShaders/MPSCore/Utility/MPSKernelDAG.mm:805: failed assertion `Error getting visible function:
 (null) Function isNaN_i16_i8 was not found in the library'
   ```
- Change the order of max and min call as it's apparently important for
  consistency, as `min(max(a, b), c)` might not equal to `max(min(a, c), b)` if `c` is not always less or equal than `b`

aten/src/ATen/native/mps/operations/TensorCompare.mm

@@ -30,41 +30,52 @@ static void clamp_mps_graph(CachedGraph* cachedGraph,
                             const Tensor& min_tensor,
                             const Tensor& max_tensor) {
   auto input_dtype = input_tensor.scalar_type();
-  auto min_dtype = input_dtype;
-  auto max_dtype = input_dtype;
-  if (cachedGraph->minTensor) {
-    min_dtype = min_tensor.scalar_type();
-  }
-  if (cachedGraph->maxTensor) {
-    max_dtype = max_tensor.scalar_type();
-  }
+  auto min_dtype = cachedGraph->minTensor ? min_tensor.scalar_type() : input_dtype;
+  auto max_dtype = cachedGraph->maxTensor ? max_tensor.scalar_type() : input_dtype;
 
   MPSGraph* mpsGraph = cachedGraph->graph();
 
   cachedGraph->inputTensor = mpsGraphRankedPlaceHolder(mpsGraph, input_tensor);
 
   auto minTensor = cachedGraph->minTensor;
   auto maxTensor = cachedGraph->maxTensor;
-  auto outputTensor = cachedGraph->inputTensor;
 
   if (input_dtype != min_dtype) {
     minTensor = castMPSTensor(mpsGraph, cachedGraph->minTensor, input_dtype);
   }
   if (input_dtype != max_dtype) {
     maxTensor = castMPSTensor(mpsGraph, cachedGraph->maxTensor, input_dtype);
   }
-  // clampWithTensor doesn't propagate NaN through so simulate it as composition of
-  // minimumWithNaNPropagationWithPrimaryTensor and maximumWithNaNPropagationWithPrimaryTensor
-  if (maxTensor) {
-    outputTensor = [mpsGraph minimumWithNaNPropagationWithPrimaryTensor:outputTensor
-                                                        secondaryTensor:maxTensor
-                                                                   name:nil];
+  if (c10::isIntegralType(input_dtype, /*includeBool=*/true)) {
+    if (minTensor && maxTensor) {
+      cachedGraph->outputTensor = [mpsGraph clampWithTensor:cachedGraph->inputTensor
+                                             minValueTensor:minTensor
+                                             maxValueTensor:maxTensor
+                                                       name:nil];
+    } else if (maxTensor) {
+      cachedGraph->outputTensor = [mpsGraph minimumWithPrimaryTensor:cachedGraph->inputTensor
+                                                     secondaryTensor:maxTensor
+                                                                name:nil];
+    } else if (minTensor) {
+      cachedGraph->outputTensor = [mpsGraph maximumWithPrimaryTensor:cachedGraph->inputTensor
+                                                     secondaryTensor:minTensor
+                                                                name:nil];
+    }
+    return;
   }
+  // clampWithTensor doesn't propagate NaN through so simulate it as composition of
+  // maximumWithNaNPropagationWithPrimaryTensor and minimumWithNaNPropagationWithPrimaryTensor
+  auto outputTensor = cachedGraph->inputTensor;
   if (minTensor) {
     outputTensor = [mpsGraph maximumWithNaNPropagationWithPrimaryTensor:outputTensor
                                                         secondaryTensor:minTensor
                                                                    name:nil];
   }
+  if (maxTensor) {
+    outputTensor = [mpsGraph minimumWithNaNPropagationWithPrimaryTensor:outputTensor
+                                                        secondaryTensor:maxTensor
+                                                                   name:nil];
+  }
   cachedGraph->outputTensor = outputTensor;
 }