[Torch Dialect] add canonicalize pattern for aten.floor with integer … (#2534)

…type

Author: qingyunqu

e2e_testing/xfail_sets.py

@@ -957,6 +957,7 @@
     "ElementwiseEluModule_basic",
     "ElementwiseEluNonDefaultModule_basic",
     "ElementwiseFloorModule_basic",
+    "ElementwiseFloorIntModule_basic",
     "ElementwiseLogModule_basic",
     "ElementwiseBinaryStaticShapeModule_basic",
     "ElementwiseMinimumModule_basic",

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

@@ -1023,51 +1023,6 @@ def Torch_AtenNeg_Op : Torch_Op<"aten.neg_", [
   }];
 }
 
-def Torch_AtenFloorOp : Torch_Op<"aten.floor", [
-    AllowsTypeRefinement,
-    HasValueSemantics,
-    ReadOnly
-  ]> {
-  let summary = "Generated op for `aten::floor : (Tensor) -> (Tensor)`";
-  let arguments = (ins
-    AnyTorchTensorType:$self
-  );
-  let results = (outs
-    AnyTorchTensorType:$result
-  );
-  let hasCustomAssemblyFormat = 1;
-  let extraClassDefinition = [{
-    ParseResult AtenFloorOp::parse(OpAsmParser &parser, OperationState &result) {
-      return parseDefaultTorchOp(parser, result, 1, 1);
-    }
-    void AtenFloorOp::print(OpAsmPrinter &printer) {
-      printDefaultTorchOp(printer, *this, 1, 1);
-    }
-  }];
-}
-
-def Torch_AtenFloor_Op : Torch_Op<"aten.floor_", [
-    IsTrailingUnderscoreInplaceVariant,
-    AllowsTypeRefinement
-  ]> {
-  let summary = "Generated op for `aten::floor_ : (Tensor) -> (Tensor)`";
-  let arguments = (ins
-    Torch_NonValueTensorType:$self
-  );
-  let results = (outs
-    Torch_NonValueTensorType:$result
-  );
-  let hasCustomAssemblyFormat = 1;
-  let extraClassDefinition = [{
-    ParseResult AtenFloor_Op::parse(OpAsmParser &parser, OperationState &result) {
-      return parseDefaultTorchOp(parser, result, 1, 1);
-    }
-    void AtenFloor_Op::print(OpAsmPrinter &printer) {
-      printDefaultTorchOp(printer, *this, 1, 1);
-    }
-  }];
-}
-
 def Torch_AtenCeilOp : Torch_Op<"aten.ceil", [
     AllowsTypeRefinement,
     HasValueSemantics,
@@ -3657,6 +3612,52 @@ def Torch_AtenMul_ScalarOp : Torch_Op<"aten.mul_.Scalar", [
   }];
 }
 
+def Torch_AtenFloorOp : Torch_Op<"aten.floor", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::floor : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenFloorOp::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenFloorOp::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+  let hasCanonicalizer = 1;
+}
+
+def Torch_AtenFloor_Op : Torch_Op<"aten.floor_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::floor_ : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    Torch_NonValueTensorType:$self
+  );
+  let results = (outs
+    Torch_NonValueTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenFloor_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenFloor_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenAddcmulOp : Torch_Op<"aten.addcmul", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -1117,6 +1117,22 @@ void AtenMulTensorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
   });
 }
 
+//===----------------------------------------------------------------------===//
+// AtenFloorOp
+//===----------------------------------------------------------------------===//
+void AtenFloorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
+                                              MLIRContext *context) {
+  patterns.add(+[](AtenFloorOp op, PatternRewriter &rewriter) {
+    auto outputTy = op.getType().dyn_cast<ValueTensorType>();
+    if (outputTy && outputTy.hasDtype() &&
+        outputTy.getDtype().isa<mlir::IntegerType>()) {
+      rewriter.replaceOp(op, op.getSelf());
+      return success();
+    }
+    return failure();
+  });
+}
+
 //===----------------------------------------------------------------------===//
 // AtenMulScalarOp
 //===----------------------------------------------------------------------===//

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

@@ -273,7 +273,6 @@ def emit_with_mutating_variants(key, **kwargs):
             "aten::atan : (Tensor) -> (Tensor)",
             "aten::atan2 : (Tensor, Tensor) -> (Tensor)",
             "aten::neg : (Tensor) -> (Tensor)",
-            "aten::floor : (Tensor) -> (Tensor)",
             "aten::ceil : (Tensor) -> (Tensor)",
             "aten::bitwise_not : (Tensor) -> (Tensor)",
             "aten::div.Tensor : (Tensor, Tensor) -> (Tensor)",
@@ -333,6 +332,7 @@ def emit_with_mutating_variants(key, **kwargs):
     emit_with_mutating_variants("aten::add.Scalar : (Tensor, Scalar, Scalar) -> (Tensor)", has_canonicalizer=True)
     emit_with_mutating_variants("aten::sub.Scalar : (Tensor, Scalar, Scalar) -> (Tensor)", has_canonicalizer=True)
     emit_with_mutating_variants("aten::mul.Scalar : (Tensor, Scalar) -> (Tensor)", has_canonicalizer=True)
+    emit_with_mutating_variants("aten::floor : (Tensor) -> (Tensor)", has_canonicalizer=True)
 
     emit_with_mutating_variants("aten::addcmul : (Tensor, Tensor, Tensor, Scalar) -> (Tensor)")
     emit_with_mutating_variants("aten::addcdiv : (Tensor, Tensor, Tensor, Scalar) -> (Tensor)")

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -1420,6 +1420,24 @@ def forward(self, a):
 def ElementwiseFloorModule_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 4))
 
+class ElementwiseFloorIntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int32, True),
+    ])
+    def forward(self, a):
+        return torch.floor(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseFloorIntModule())
+def ElementwiseFloorIntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=-10, high=10).to(torch.int32))
+
 
 # ==============================================================================