[Torch Dialect] add canonicalize for aten.min.other (#2452)

Author: qingyunqu

e2e_testing/xfail_sets.py

@@ -934,6 +934,8 @@
     "ElementwiseBinaryStaticShapeModule_basic",
     "ElementwiseMinimumModule_basic",
     "ElementwiseMinimumIntModule_basic",
+    "ElementwiseMinOtherIntModule_basic",
+    "ElementwiseMinOtherModule_basic",
     "ElementwiseMaximumModule_basic",
     "ElementwiseMaximumIntModule_basic",
     "ElementwiseMaxOtherIntModule_basic",

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

@@ -8967,6 +8967,31 @@ def Torch_AtenMinOp : Torch_Op<"aten.min", [
   }];
 }
 
+def Torch_AtenMinOtherOp : Torch_Op<"aten.min.other", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::min.other : (Tensor, Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self,
+    AnyTorchTensorType:$other
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenMinOtherOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 2, 1);
+    }
+    void AtenMinOtherOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 2, 1);
+    }
+  }];
+  let hasCanonicalizer = 1;
+}
+
 def Torch_AtenMinDimOp : Torch_Op<"aten.min.dim", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -930,6 +930,20 @@ void AtenLenTOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
   });
 }
 
+//===----------------------------------------------------------------------===//
+// AtenMinOtherOp
+//===----------------------------------------------------------------------===//
+
+void AtenMinOtherOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
+                                                 MLIRContext *context) {
+  // `aten.min.other` -> `aten.minimum`
+  patterns.add(+[](AtenMinOtherOp op, PatternRewriter &rewriter) {
+    rewriter.replaceOpWithNewOp<AtenMinimumOp>(op, op.getType(), op.getSelf(),
+                                               op.getOther());
+    return success();
+  });
+}
+
 //===----------------------------------------------------------------------===//
 // AtenMaxOtherOp
 //===----------------------------------------------------------------------===//

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6570,6 +6570,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = torch.prim.ListConstruct  : () -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.min.other\"(%arg0: !torch.list<int>, %arg1: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.broadcast(%arg0, %arg1) : (!torch.list<int>, !torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.max\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
 "    %0 = torch.prim.ListConstruct  : () -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -10266,6 +10270,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.min.other\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %0 = call @\"__torch_mlir_dtype_fn.aten.minimum\"(%arg0, %arg1) : (!torch.tuple<int, int>, !torch.tuple<int, int>) -> !torch.int\n"
+"    return %0 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.max\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    return %0#1 : !torch.int\n"

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

@@ -312,6 +312,9 @@ def aten〇all〡shape(self: List[int]) -> List[int]:
 def aten〇min〡shape(self: List[int]) -> List[int]:
     return []
 
+def aten〇min〇other〡shape(self: List[int], other: List[int]) -> List[int]:
+    return upstream_shape_functions.broadcast(self, other)
+
 def aten〇max〡shape(self: List[int]) -> List[int]:
     return []
 
@@ -3059,6 +3062,10 @@ def aten〇min〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return self_dtype
 
+@check_dtype_function(_check_two_tensor_op())
+def aten〇min〇other〡dtype(self_rank_dtype: Tuple[int, int], other_rank_dtype: Tuple[int, int]) -> int:
+    return aten〇minimum〡dtype(self_rank_dtype, other_rank_dtype)
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇max〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype

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

@@ -567,6 +567,7 @@ def emit_with_mutating_variants(key, **kwargs):
     emit("aten::max.dim : (Tensor, int, bool) -> (Tensor, Tensor)")
     emit("aten::amax : (Tensor, int[], bool) -> (Tensor)")
     emit("aten::min : (Tensor) -> (Tensor)")
+    emit("aten::min.other : (Tensor, Tensor) -> (Tensor)", has_canonicalizer=True)
     emit("aten::min.dim : (Tensor, int, bool) -> (Tensor, Tensor)")
     emit("aten::amin : (Tensor, int[], bool) -> (Tensor)")
     emit("aten::to.dtype : (Tensor, int, bool, bool, int?) -> (Tensor)", has_folder=True)

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -653,6 +653,52 @@ def ElementwiseMinimumIntModule_basic(module, tu: TestUtils):
 # ==============================================================================
 
 
+class ElementwiseMinOtherModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, x, y):
+        return x.min(y)
+
+
+@register_test_case(module_factory=lambda: ElementwiseMinOtherModule())
+def ElementwiseMinOtherModule_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 5), tu.rand(3, 5))
+
+
+# ==============================================================================
+
+
+class ElementwiseMinOtherIntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int64, True),
+        ([-1, -1], torch.int64, True),
+    ])
+    def forward(self, x, y):
+        return x.min(y)
+
+
+@register_test_case(module_factory=lambda: ElementwiseMinOtherIntModule())
+def ElementwiseMinOtherIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 5, high=10), tu.randint(3, 5, high=10))
+
+
+# ==============================================================================
+
+
 class ElementwiseMaximumModule(torch.nn.Module):
 
     def __init__(self):