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Merged
merged 5 commits into from
Dec 28, 2023
Merged

Lower onnx.split to torch.aten #2686

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dcd9e7e
add onnx variable split
renxida Dec 21, 2023
6cd173a
add fixed split and test
renxida Dec 21, 2023
7af4b40
remove extraneous line
renxida Dec 21, 2023
7312511
address rob's comments
renxida Dec 27, 2023
754327d
clang format
renxida Dec 27, 2023
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142 changes: 142 additions & 0 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -794,6 +794,148 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
return success();
});

// split with fixed-size parts
// Arguments:
// - input: the tensor to split
// Attributes:
// - axis: the axis along which to split the input
// - num_outputs: the number of outputs to produce
// Outputs:
// - outputs: the produced outputs. Variadic with num_outputs elements.
// Note: torch.aten gives a list of tensors, but ONNX gives a variadic list of
// tensors
// so we need to unpack the list
patterns.onOp(
"Split", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Value self;
int64_t axis;
int64_t num_outputs;
if (binder.tensorOperand(self))
return rewriter.notifyMatchFailure(
binder.op, "Not converting to AtenSplitTensorOp due to input "
"tensor mismatch");
if (binder.s64IntegerAttr(axis, "axis", 0))
return rewriter.notifyMatchFailure(binder.op,
"Failed to get axis attribute");
if (binder.s64IntegerAttr(num_outputs, "num_outputs", 0))
return rewriter.notifyMatchFailure(
binder.op, "Failed to get num_outputs attribute");

auto result0Ty =
binder.op->getResult(0).getType().cast<Torch::ValueTensorType>();
auto selfTy = self.getType().cast<Torch::ValueTensorType>();

int64_t dim = axis;
if (dim < 0)
dim += selfTy.getSizes().size();

// set intermediate shape to the shape of the first result
// if the results are of different shapes
// set the splitted axis to variable shape
llvm::SmallVector<int64_t> intermediateShape(result0Ty.getSizes());
for (auto result : binder.op->getResultTypes()) {
int64_t d = result.cast<Torch::ValueTensorType>().getSizes()[dim];
intermediateShape[dim] = d == intermediateShape[dim] ? d : -1;
}

Value dimValue = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), dim));

Value splitSize = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), num_outputs));

// TODO: Attempting to use the shape expected by the ONNX mlir as ground
// truth. For now just use dynamic shapes.
auto resultOuterType =
Torch::ListType::get(rewriter.getType<Torch::ValueTensorType>(
/*std::optional<llvm::ArrayRef<int64_t>>=*/intermediateShape,
result0Ty.getOptionalDtype()));
Torch::AtenSplitTensorOp new_op =
rewriter.create<Torch::AtenSplitTensorOp>(
binder.getLoc(), resultOuterType, self, splitSize, dimValue);

// the onnx op is variadic with multiple results, but AtenSplitWithSizes
// outputs a list so we need to unpack the list
rewriter.replaceOpWithNewOp<Torch::PrimListUnpackOp>(
binder.op, binder.op->getResults().getType(), new_op.getResult());

return success();
});

// split with variable parts
// Arguments:
// - input: the tensor to split
// - split: the sizes of the splits to be produced
// Attributes:
// - axis: the axis along which to split the input
// - num_outputs: the number of outputs to produce
// Outputs:
// - outputs: the produced outputs. Variadic with num_outputs elements.
// Note: torch.aten gives a list of tensors, but ONNX gives a variadic list of
// tensors
// so we need to unpack the list
patterns.onOp(
"Split", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Value self;
Value split;
int64_t axis;
int64_t num_outputs;
if (binder.tensorOperandAtIndex(self, 0) ||
binder.tensorOperandAtIndex(split, 1))
return rewriter.notifyMatchFailure(
binder.op, "Not converting to AtenSplitWithSizesOp due to input "
"tensor mismatch");
if (binder.s64IntegerAttr(axis, "axis", 0))
return rewriter.notifyMatchFailure(binder.op,
"Failed to get axis attribute");
if (binder.s64IntegerAttr(num_outputs, "num_outputs", 0))
return rewriter.notifyMatchFailure(
binder.op, "Failed to get num_outputs attribute");

auto result0Ty =
binder.op->getResult(0).getType().cast<Torch::ValueTensorType>();
auto selfTy =
cast<Torch::ValueTensorType>(binder.op->getOperand(0).getType());

int64_t dim = axis;
if (dim < 0)
dim += selfTy.getSizes().size();

llvm::SmallVector<int64_t> intermediateShape(result0Ty.getSizes());
for (auto result : binder.op->getResultTypes()) {
int64_t d = result.cast<Torch::ValueTensorType>().getSizes()[dim];
intermediateShape[dim] = d == intermediateShape[dim] ? d : -1;
}

Torch::PrimTolistOp splitToList = rewriter.create<Torch::PrimTolistOp>(
binder.getLoc(),
Torch::ListType::get(rewriter.getType<Torch::IntType>()), split);

Value dimValue = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), dim));

// TODO: Attempting to use the shape expected by the ONNX mlir as ground
// truth. For now just use dynamic shapes.
auto resultOuterType =
Torch::ListType::get(rewriter.getType<Torch::ValueTensorType>(
/*std::optional<llvm::ArrayRef<int64_t>>=*/intermediateShape,
result0Ty.getOptionalDtype()));
Torch::AtenSplitWithSizesOp new_op =
rewriter.create<Torch::AtenSplitWithSizesOp>(
binder.getLoc(), resultOuterType, self,
splitToList.getResult(0), dimValue);

// the onnx op is variadic with multiple results, but AtenSplitWithSizes
// outputs a list so we need to unpack the list
rewriter.replaceOpWithNewOp<Torch::PrimListUnpackOp>(
binder.op, binder.op->getResults().getType(), new_op.getResult());

return success();
});

patterns.onOp("Tan", 7,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Expand Down
30 changes: 30 additions & 0 deletions test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -795,6 +795,36 @@ func.func @test_sinh_example(%arg0: !torch.vtensor<[3],f32>) -> !torch.vtensor<[

// -----

// CHECK-LABEL: func.func @test_split_variable_parts_2d_opset18(
// CHECK-SAME: %[[VAL_INPUT:.*]]: !torch.vtensor<[2,6],f32>,
// CHECK-SAME: %[[VAL_SPLIT:.*]]: !torch.vtensor<[2],si64>
// CHECK: %[[VAL_SPLIT_LIST:.*]] = torch.prim.tolist(%[[VAL_SPLIT]]) : !torch.vtensor<[2],si64> -> !torch.list<int>
// CHECK: %[[VAL_AXIS:.*]] = torch.constant.int 1
// CHECK: %[[VAL_RESULT_LIST:.*]] = torch.aten.split_with_sizes %[[VAL_INPUT]], %[[VAL_SPLIT_LIST]], %[[VAL_AXIS]] : !torch.vtensor<[2,6],f32>, !torch.list<int>, !torch.int -> !torch.list<vtensor<[2,?],f32>>
// CHECK: %[[VAL_VARIADIC_RETURN_VALUE:.*]]:2 = torch.prim.ListUnpack %[[VAL_RESULT_LIST]] : !torch.list<vtensor<[2,?],f32>> -> !torch.vtensor<[2,2],f32>, !torch.vtensor<[2,4],f32>
// CHECK: return %[[VAL_VARIADIC_RETURN_VALUE]]#0, %[[VAL_VARIADIC_RETURN_VALUE]]#1 : !torch.vtensor<[2,2],f32>, !torch.vtensor<[2,4],f32>
func.func @test_split_variable_parts_2d_opset18(%arg0: !torch.vtensor<[2,6],f32>, %arg1: !torch.vtensor<[2],si64>) -> (!torch.vtensor<[2,2],f32>, !torch.vtensor<[2,4],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
%0:2 = torch.operator "onnx.Split"(%arg0, %arg1) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[2,6],f32>, !torch.vtensor<[2],si64>) -> (!torch.vtensor<[2,2],f32>, !torch.vtensor<[2,4],f32>)
return %0#0, %0#1 : !torch.vtensor<[2,2],f32>, !torch.vtensor<[2,4],f32>
}

// -----

// CHECK-LABEL: func.func @test_split_2d_uneven_split_opset18(
// CHECK-SAME: %[[INPUT_TENSOR:.*]]: !torch.vtensor<[2,8],f32>) -> (!torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,2],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
// CHECK: %[[AXIS:.*]] = torch.constant.int 1
// CHECK: %[[SPLIT_SIZE:.*]] = torch.constant.int 3
// CHECK: %[[SPLIT_RESULT:.*]] = torch.aten.split.Tensor %[[INPUT_TENSOR]], %[[SPLIT_SIZE]], %[[AXIS]] : !torch.vtensor<[2,8],f32>, !torch.int, !torch.int -> !torch.list<vtensor<[2,?],f32>>
// CHECK: %[[UNPACKED_TENSORS:.*]]:3 = torch.prim.ListUnpack %[[SPLIT_RESULT]] : !torch.list<vtensor<[2,?],f32>> -> !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,2],f32>
// CHECK: return %[[UNPACKED_TENSORS]]#0, %[[UNPACKED_TENSORS]]#1, %[[UNPACKED_TENSORS]]#2 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,2],f32>
// CHECK: }
func.func @test_split_2d_uneven_split_opset18(%arg0: !torch.vtensor<[2,8],f32>) -> (!torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,2],f32>) attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
%0:3 = torch.operator "onnx.Split"(%arg0) {torch.onnx.axis = 1 : si64, torch.onnx.num_outputs = 3 : si64} : (!torch.vtensor<[2,8],f32>) -> (!torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,2],f32>)
return %0#0, %0#1, %0#2 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,2],f32>
}

// -----

// 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
Expand Down