[Torch Dialect] add folder for prim.min.int (#1864)

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

@@ -10894,6 +10894,7 @@ def Torch_PrimMinIntOp : Torch_Op<"prim.min.int", [
       printDefaultTorchOp(printer, *this, 2, 1);
     }
   }];
+  let hasFolder = 1;
 }
 
 def Torch_PrimMaxSelfIntOp : Torch_Op<"prim.max.self_int", [

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -2348,6 +2348,25 @@ OpFoldResult PrimMinSelfIntOp::fold(FoldAdaptor adaptor) {
                            *std::min_element(values.begin(), values.end()));
 }
 
+//===----------------------------------------------------------------------===//
+// PrimMinIntOp
+//===----------------------------------------------------------------------===//
+
+OpFoldResult PrimMinIntOp::fold(FoldAdaptor adaptor) {
+  // If both operands are the same, then the operation is an identity.
+  if (getA() == getB())
+    return getA();
+
+  auto lhs = adaptor.getA().dyn_cast_or_null<IntegerAttr>();
+  auto rhs = adaptor.getB().dyn_cast_or_null<IntegerAttr>();
+  if (!lhs || !rhs)
+    return nullptr;
+  // Torch semantics are that !torch.int is 64-bit signed.
+  return IntegerAttr::get(
+      lhs.getType(),
+      std::min(lhs.getValue().getSExtValue(), rhs.getValue().getSExtValue()));
+}
+
 //===----------------------------------------------------------------------===//
 // ShapeCalculateOp
 //===----------------------------------------------------------------------===//

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -662,7 +662,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("prim::TupleUnpack : (Any) -> (...)", has_canonicalizer=True)
     emit("prim::NumToTensor.Scalar : (Scalar) -> (Tensor)")
     emit("prim::min.self_int : (int[]) -> (int)", has_folder=True)
-    emit("prim::min.int : (int, int) -> (int)")
+    emit("prim::min.int : (int, int) -> (int)", has_folder=True)
     emit("prim::max.self_int : (int[]) -> (int)")
     emit("prim::max.int : (int, int) -> (int)", has_folder=True)
     emit("prim::RaiseException : (str, str?) -> ()")

python/torch_mlir_e2e_test/test_suite/basic.py

@@ -1270,6 +1270,25 @@ def LogSoftmaxIntModule_basic(module, tu: TestUtils):
 
 # ==============================================================================
 
+class PrimMinIntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+    ])
+    def forward(self):
+        return torch.ops.prim.min(1, -1)
+
+
+@register_test_case(module_factory=lambda: PrimMinIntModule())
+def PrimMinIntModule_basic(module, tu: TestUtils):
+    module.forward()
+
+
+# ==============================================================================
 
 class NumToTensorIntModule(torch.nn.Module):
 

test/Dialect/Torch/canonicalize.mlir

@@ -502,6 +502,24 @@ func.func @torch.prim.max.int$constant() -> !torch.int {
   return %0 : !torch.int
 }
 
+// CHECK-LABEL:   func.func @torch.prim.min.int$identity(
+// CHECK-SAME:                                      %[[ARG:.*]]: !torch.int) -> !torch.int {
+// CHECK:           return %[[ARG]] : !torch.int
+func.func @torch.prim.min.int$identity(%arg0: !torch.int) -> !torch.int {
+  %0 = torch.prim.min.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.int
+  return %0 : !torch.int
+}
+
+// CHECK-LABEL:   func.func @torch.prim.min.int$constant() -> !torch.int {
+// CHECK:           %[[INT1:.*]] = torch.constant.int -1
+// CHECK:           return %[[INT1]] : !torch.int
+func.func @torch.prim.min.int$constant() -> !torch.int {
+  %int-1 = torch.constant.int -1
+  %int3 = torch.constant.int 3
+  %0 = torch.prim.min.int %int-1, %int3 : !torch.int, !torch.int -> !torch.int
+  return %0 : !torch.int
+}
+
 // CHECK-LABEL:   func.func @torch.prim.min.self_int$basic() -> !torch.int {
 // CHECK:           %[[M1:.*]] = torch.constant.int -1
 // CHECK:           return %[[M1]] : !torch.int