[Torch Dialect] enhance aten.int.tensor's canonicalize (#3058)

support fold with literal vtensor.  
change it to canonicalize because this pattern will create new op.

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -11444,7 +11444,7 @@ def Torch_AtenIntTensorOp : Torch_Op<"aten.Int.Tensor", [
       printDefaultTorchOp(printer, *this, 1, 1);
     }
   }];
-  let hasFolder = 1;
+  let hasCanonicalizer = 1;
 }
 
 def Torch_AtenFloatTensorOp : Torch_Op<"aten.Float.Tensor", [

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -150,7 +150,7 @@ static Value getScalarIntValue(Value input, Location loc,
 
   if (auto valueTensorLiteralOp = input.getDefiningOp<ValueTensorLiteralOp>()) {
     auto val = valueTensorLiteralOp.getValue()
-                   .cast<DenseElementsAttr>()
+                   .cast<DenseIntElementsAttr>()
                    .getSplatValue<int64_t>();
     return rewriter.create<Torch::ConstantIntOp>(
         loc, rewriter.getI64IntegerAttr(val));
@@ -3646,14 +3646,15 @@ OpFoldResult Aten_ShapeAsTensorOp::fold(FoldAdaptor adaptor) {
 // AtenIntTensorOp
 //===----------------------------------------------------------------------===//
 
-OpFoldResult AtenIntTensorOp::fold(FoldAdaptor adaptor) {
-  // If a scalar number is converted to a 0-d tensor and passed on to
-  // aten.Int.Tensor, fold to the scalar number.
-  if (auto numToTensorScalar = getA().getDefiningOp<PrimNumToTensorScalarOp>())
-    return numToTensorScalar.getA();
-  if (auto tensorIntOp = getA().getDefiningOp<AtenTensorIntOp>())
-    return tensorIntOp.getT();
-  return nullptr;
+void AtenIntTensorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
+                                                  MLIRContext *context) {
+  patterns.add(+[](AtenIntTensorOp op, PatternRewriter &rewriter) {
+    Value scalarInt = getScalarIntValue(op.getA(), op.getLoc(), rewriter);
+    if (!scalarInt)
+      return failure();
+    rewriter.replaceOp(op, scalarInt);
+    return success();
+  });
 }
 
 //===----------------------------------------------------------------------===//
@@ -4099,6 +4100,9 @@ OpFoldResult PrimNumToTensorScalarOp::fold(FoldAdaptor adaptor) {
     a = IntegerAttr::get(dty, iattr.getInt());
   } else if (auto fattr = dyn_cast<FloatAttr>(a)) {
     a = FloatAttr::get(dty, fattr.getValueAsDouble());
+  } else {
+    // doesn't handle other types, like complex type
+    return {};
   }
 
   auto mlirTensorType =

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py

@@ -679,7 +679,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::IntImplicit : (Tensor) -> (int)", has_canonicalizer=True)
     emit("aten::FloatImplicit : (Tensor) -> (float)", has_canonicalizer=True)
     emit("aten::tensor.float : (float, int?, Device?, bool) -> (Tensor)")
-    emit("aten::Int.Tensor : (Tensor) -> (int)", has_folder=True)
+    emit("aten::Int.Tensor : (Tensor) -> (int)", has_canonicalizer=True)
     emit("aten::Float.Tensor : (Tensor) -> (float)", has_folder=True)
     emit_with_mutating_variants("aten::dropout : (Tensor, float, bool) -> (Tensor)")
     emit("aten::native_dropout : (Tensor, float, bool?) -> (Tensor, Tensor)")

test/Dialect/Torch/canonicalize.mlir

@@ -1425,6 +1425,15 @@ func.func @torch.aten.Int.Tensor(%arg0: !torch.int) -> !torch.int {
   return %scalar : !torch.int
 }
 
+// CHECK-LABEL:  @torch.aten.Int.Tensor$canonicalize_0d_const() -> !torch.int {
+// CHECK:           %[[NUM:.*]] = torch.constant.int 1
+// CHECK:           return %[[NUM]] : !torch.int
+func.func @torch.aten.Int.Tensor$canonicalize_0d_const() -> !torch.int {
+  %cst = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
+  %scalar = torch.aten.Int.Tensor %cst : !torch.vtensor<[],si64> -> !torch.int
+  return %scalar : !torch.int
+}
+
 // CHECK-LABEL:   func.func @torch.aten.Int.float() -> !torch.int {
 // CHECK:             %[[NUM:.*]] = torch.constant.int 1
 // CHECK:             return %[[NUM]] : !torch.int