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[onnx] Lowerings from `onnx.tan` (#2642) 

Started work on the `tan` lowerings for ONNX to Torch. Uses `sin` and
`cos` to represent a `tan`.
pull/2411/merge
Rob Suderman 2023-12-20 10:09:39 -08:00 committed by GitHub
parent a24aadbfab
commit 11cc92d4ab
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 147 additions and 11 deletions
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45
include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td
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@ -1066,6 +1066,51 @@ def Torch_AtenAcos_Op : Torch_Op<"aten.acos_", [
}];
}
def Torch_AtenTanOp : Torch_Op<"aten.tan", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::tan : (Tensor) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$self
);
let results = (outs
AnyTorchTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenTanOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 1, 1);
}
void AtenTanOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 1, 1);
}
}];
}
def Torch_AtenTan_Op : Torch_Op<"aten.tan_", [
IsTrailingUnderscoreInplaceVariant,
AllowsTypeRefinement
]> {
let summary = "Generated op for `aten::tan_ : (Tensor) -> (Tensor)`";
let arguments = (ins
Torch_NonValueTensorType:$self
);
let results = (outs
Torch_NonValueTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenTan_Op::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 1, 1);
}
void AtenTan_Op::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 1, 1);
}
}];
}
def Torch_AtenAtanOp : Torch_Op<"aten.atan", [
AllowsTypeRefinement,
HasValueSemantics,

14
lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
View File

@ -794,7 +794,19 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
binder.op, resultType, operand);
return success();
});
patterns.onOp("Tan", 7,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value operand;
if (binder.tensorOperand(operand) ||
binder.tensorResultType(resultType))
return failure();
rewriter.replaceOpWithNewOp<Torch::AtenTanOp>(
binder.op, resultType, operand);
return success();
});
patterns.onOp(
"Transpose", 13,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {

24
lib/Conversion/TorchToLinalg/Uncategorized.cpp
View File

@ -216,6 +216,10 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
return b.create<math::FloorOp>(loc, payloadArgs[0]);
if (isa<AtenCeilOp>(op))
return b.create<math::CeilOp>(loc, payloadArgs[0]);
if (isa<AtenTanOp>(op)) {
return createCalculationForMathOpWithDtypeConversion<math::TanOp>(
b, converter, payloadArgs[0], op);
}
if (isa<AtenTanhOp>(op)) {
return createCalculationForMathOpWithDtypeConversion<math::TanhOp>(
b, converter, payloadArgs[0], op);
@ -1319,15 +1323,15 @@ public:
LogicalResult
matchAndRewrite(Operation *op, ArrayRef<Value> operands,
ConversionPatternRewriter &rewriter) const override {
if (!isa<AtenTanhOp, AtenSinhOp, AtenCoshOp, AtenReluOp, AtenPreluOp,
AtenGeluOp, AtenGeluBackwardOp, AtenAddTensorOp, AtenMulTensorOp,
AtenDivTensorOp, AtenDivTensorModeOp, AtenSubTensorOp, AtenAtan2Op,
AtenLerpTensorOp, AtenSigmoidOp, AtenExpOp, AtenExpm1Op,
AtenMinimumOp, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
AtenClampTensorOp, AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp,
AtenErfOp, AtenSqrtOp, AtenFloorOp, AtenPowScalarOp,
AtenPowTensorScalarOp, AtenPowTensorTensorOp, AtenLog2Op,
AtenLog10Op, AtenLog1pOp, AtenRsqrtOp, AtenDivScalarOp,
if (!isa<AtenTanOp, AtenTanhOp, AtenSinhOp, AtenCoshOp, AtenReluOp,
AtenPreluOp, AtenGeluOp, AtenGeluBackwardOp, AtenAddTensorOp,
AtenMulTensorOp, AtenDivTensorOp, AtenDivTensorModeOp,
AtenSubTensorOp, AtenAtan2Op, AtenLerpTensorOp, AtenSigmoidOp,
AtenExpOp, AtenExpm1Op, AtenMinimumOp, AtenMaximumOp,
AtenToDtypeOp, AtenClampOp, AtenClampTensorOp, AtenRsubScalarOp,
AtenMulScalarOp, AtenLogOp, AtenErfOp, AtenSqrtOp, AtenFloorOp,
AtenPowScalarOp, AtenPowTensorScalarOp, AtenPowTensorTensorOp,
AtenLog2Op, AtenLog10Op, AtenLog1pOp, AtenRsqrtOp, AtenDivScalarOp,
AtenRemainderScalarOp, AtenAbsOp, AtenReciprocalOp,
AtenBitwiseAndTensorOp, AtenBitwiseAndScalarOp,
AtenBitwiseOrTensorOp, AtenBitwiseXorTensorOp,
@ -1972,7 +1976,7 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
ConversionTarget &target) {
MLIRContext *context = patterns.getContext();
target.addIllegalOp<
AtenTanhOp, AtenSinhOp, AtenCoshOp, AtenReluOp, AtenGeluOp,
AtenTanOp, AtenTanhOp, AtenSinhOp, AtenCoshOp, AtenReluOp, AtenGeluOp,
AtenGeluBackwardOp, AtenAddTensorOp, AtenMulTensorOp, AtenDivTensorOp,
AtenDivTensorModeOp, AtenSubTensorOp, AtenLerpTensorOp, AtenSigmoidOp,
AtenMinimumOp, AtenAtan2Op, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,

15
lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp
View File

@ -6238,6 +6238,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.tan\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
" }\n"
" func.func @\"__torch_mlir_shape_fn.aten.atan\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
" %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
" return %0 : !torch.list<int>\n"
@ -11396,6 +11400,17 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
" }\n"
" return %4 : !torch.tuple<int, int>\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten.tan\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
" %int6 = torch.constant.int 6\n"
" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
" %1 = call @__torch__.torch_mlir.jit_ir_importer.build_tools.library_generator.is_integer_dtype(%0#1) : (!torch.int) -> !torch.bool\n"
" %2 = torch.prim.If %1 -> (!torch.int) {\n"
" torch.prim.If.yield %int6 : !torch.int\n"
" } else {\n"
" torch.prim.If.yield %0#1 : !torch.int\n"
" }\n"
" return %2 : !torch.int\n"
" }\n"
" func.func @\"__torch_mlir_dtype_fn.aten.atan2\"(%arg0: !torch.tuple<int, int>, %arg1: !torch.tuple<int, int>) -> !torch.int {\n"
" %int6 = torch.constant.int 6\n"
" %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"

10
projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/abstract_interp_lib_gen.py
View File

@ -59,6 +59,9 @@ def aten〇triu〡shape(self: List[int], diagonal: int = 0) -> List[int]:
def aten〇tril〡shape(self: List[int], diagonal: int = 0) -> List[int]:
return upstream_shape_functions.unary(self)
def aten〇tan〡shape(self: List[int]) -> List[int]:
return upstream_shape_functions.unary(self)
def aten〇atan〡shape(self: List[int]) -> List[int]:
return upstream_shape_functions.unary(self)
@ -3721,6 +3724,13 @@ def aten〇var_mean〡dtype(self_rank_dtype: Tuple[int, int], unbiased: bool = T
return torch.float64, self_dtype
return self_dtype, self_dtype
@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
def aten〇tan〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
self_rank, self_dtype = self_rank_dtype
if is_integer_dtype(self_dtype):
return torch.float32
return self_dtype
@check_dtype_function(_check_two_tensor_op())
def aten〇atan2〡dtype(self_rank_dtype: Tuple[int, int], other_rank_dtype: Tuple[int, int]) -> int:
self_rank, self_dtype = self_rank_dtype

1
projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py
View File

@ -278,6 +278,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
"aten::expm1 : (Tensor) -> (Tensor)",
"aten::cos : (Tensor) -> (Tensor)",
"aten::acos : (Tensor) -> (Tensor)",
"aten::tan : (Tensor) -> (Tensor)",
"aten::atan : (Tensor) -> (Tensor)",
"aten::atan2 : (Tensor, Tensor) -> (Tensor)",
"aten::neg : (Tensor) -> (Tensor)",

40
projects/pt1/python/torch_mlir_e2e_test/test_suite/elementwise.py
View File

@ -3009,6 +3009,46 @@ def ElementwiseAcosIntModule_basic(module, tu: TestUtils):
# ==============================================================================
class ElementwiseTanModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.float32, True),
])
def forward(self, a):
return torch.tan(a)
@register_test_case(module_factory=lambda: ElementwiseTanModule())
def ElementwiseTanModule_basic(module, tu: TestUtils):
module.forward(tu.rand(3, 4))
# ==============================================================================
class ElementwiseTanIntModule(torch.nn.Module):
def __init__(self):
super().__init__()
@export
@annotate_args([
None,
([-1, -1], torch.int32, True),
])
def forward(self, a):
return torch.tan(a)
@register_test_case(module_factory=lambda: ElementwiseTanIntModule())
def ElementwiseTanIntModule_basic(module, tu: TestUtils):
module.forward(tu.randint(3, 4, low=1, high=10).to(torch.int32))
# ==============================================================================
class ElementwiseNegModule(torch.nn.Module):
def __init__(self):

9
test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
View File

@ -795,6 +795,15 @@ func.func @test_sinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[
// -----
// CHECK-LABEL: func.func @test_tan
func.func @test_tan(%arg0: !torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 7 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[TAN:.+]] = torch.aten.tan %arg0
%0 = torch.operator "onnx.Tan"(%arg0) : (!torch.vtensor<[3,4,5],f32>) -> !torch.vtensor<[3,4,5],f32>
return %0 : !torch.vtensor<[3,4,5],f32>
}
// -----
// CHECK-LABEL: func.func @test_transpose_default
func.func @test_transpose_default(%arg0: !torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[4,3,2],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64} {
// CHECK-DAG: %[[I0:.+]] = torch.constant.int 0