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

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Merged
qingyunqu merged 1 commit into from
Nov 2, 2023
Merged

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

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1 change: 1 addition & 0 deletions e2e_testing/xfail_sets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -953,6 +953,7 @@
"ElementwiseEluModule_basic",
"ElementwiseEluNonDefaultModule_basic",
"ElementwiseFloorModule_basic",
"ElementwiseFloorIntModule_basic",
"ElementwiseLogModule_basic",
"ElementwiseBinaryStaticShapeModule_basic",
"ElementwiseMinimumModule_basic",
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91 changes: 46 additions & 45 deletions include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td
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Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand Down Expand Up @@ -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,
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16 changes: 16 additions & 0 deletions lib/Dialect/Torch/IR/TorchOps.cpp
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Expand Up @@ -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
//===----------------------------------------------------------------------===//
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2 changes: 1 addition & 1 deletion python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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)",
Expand Down Expand Up @@ -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)")
Expand Down
18 changes: 18 additions & 0 deletions python/torch_mlir_e2e_test/test_suite/elementwise.py
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Expand Up @@ -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))


# ==============================================================================

Expand Down