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[onnx] Simplify onnx.slice lowering (#2919) 

Onnx slice lowering used arange needlessly instead of directly
constructing the constant dimension values. This makes lowerings to
linalg struggle as multiple folders are required to get what is a
constant index value.
pull/2924/merge
Rob Suderman 2024-02-19 10:26:29 -08:00 committed by GitHub
parent fd08578bdb
commit cea51897a5
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
3 changed files with 24 additions and 47 deletions
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32
lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
View File

@ -1540,15 +1540,6 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
if (binder.tensorOperandAtIndex(axes, 3)) {
return failure();
}
} else {
// The default axes value is the range from 0 to the size of first
// dimension of `starts` and `ends`.
Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
Value arangeLength = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
rewriter.getIntegerAttr(rewriter.getIntegerType(64), startSize));
axes = rewriter.create<Torch::AtenArangeOp>(
loc, startsTorchTy, arangeLength, none, none, none, none);
}
// Binding `steps` from its arguments or through a default value
@ -1579,14 +1570,16 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
binder.op, "Expected the rank of starts and ends tensors to be 1 "
"and their dimensions to match");
auto axesTorchTy = axes.getType().cast<Torch::ValueTensorType>();
auto axesTy =
axesTorchTy.toBuiltinTensor().dyn_cast<RankedTensorType>();
int64_t numAxes = axesTy.getDimSize(0);
if (axes) {
auto axesTorchTy = axes.getType().cast<Torch::ValueTensorType>();
auto axesTy =
axesTorchTy.toBuiltinTensor().dyn_cast<RankedTensorType>();
int64_t numAxes = axesTy.getDimSize(0);
if (!(axesTy && numAxes == endSize))
return rewriter.notifyMatchFailure(
binder.op, "Axes should be the same size of starts and ends");
if (!(axesTy && numAxes == endSize))
return rewriter.notifyMatchFailure(
binder.op, "Axes should be the same size of starts and ends");
}
auto stepsTy = steps.getType()
.cast<Torch::ValueTensorType>()
@ -1622,7 +1615,7 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
}
auto intermediateType = Torch::ValueTensorType::get(
context, intermediateShape, resultTorchType.getOptionalDtype());
for (int i = 0; i < numAxes; ++i) {
for (int i = 0; i < endSize; ++i) {
Value k = rewriter.create<Torch::ConstantIntOp>(
loc, rewriter.getType<Torch::IntType>(),
@ -1636,12 +1629,11 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
Value start = select(starts, kTensor);
Value end = select(ends, kTensor);
Value axis = select(axes, kTensor);
Value axis = axes ? select(axes, kTensor) : k;
Value step = select(steps, kTensor);
auto sliceType = intermediateType;
if (i == numAxes - 1)
sliceType = resultTorchType;
sliceType = i == (endSize - 1) ? resultTorchType : sliceType;
operand = rewriter.create<Torch::AtenSliceTensorOp>(
loc, sliceType, operand, axis, start, end, step);
}

4
projects/pt1/e2e_testing/xfail_sets.py
View File

@ -2101,10 +2101,6 @@ ONNX_XFAIL_SET = {
"ReduceMaxSignedIntModule_basic",
"ReduceMaxUnsignedIntModule_basic",
# Failure - slice_lowering
"ScaledDotProductAttentionDifferentModule_basic",
"ScaledDotProductAttentionSameModule_basic",
# Failure - view_lowering
"AddSizeIntModule_basic",
"ElementwiseFlattenBroadcastModule_basic",

35
test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
View File

@ -1157,9 +1157,6 @@ func.func @test_slice(%arg0: !torch.vtensor<[20,10,5],f32>, %arg1: !torch.vtenso
func.func @test_slice_default_axes_and_slices(%arg0: !torch.vtensor<[20,10,5],f32>, %arg1: !torch.vtensor<[3],si64>, %arg2: !torch.vtensor<[3],si64>) -> !torch.vtensor<[20,10,1],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
//CHECK: %[[NONE_1:.*]] = torch.constant.none
//CHECK: %[[AXES_DEFAULT_SIZE:.*]] = torch.constant.int 3
//CHECK: %[[DEFAULT_AXES:.*]] = torch.aten.arange %[[AXES_DEFAULT_SIZE:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]], %[[NONE_1:.*]] : !torch.int, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[3],si64>
//CHECK: %[[NONE_2:.*]] = torch.constant.none
//CHECK: %[[DEFAULT_SIZE_AMOUNT:.*]] = torch.constant.int 3
//CHECK: %[[DEFAULT_SIZE_INPUT:.*]] = torch.prim.ListConstruct %[[DEFAULT_SIZE_AMOUNT:.*]] : (!torch.int) -> !torch.list<int>
//CHECK: %[[DEFAULT_SIZES:.*]] = torch.aten.ones %[[DEFAULT_SIZE_INPUT:.*]], %[[NONE_2:.*]], %[[NONE_2:.*]], %[[NONE_2:.*]], %[[NONE_2:.*]] : !torch.list<int>, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[3],si64>
//CHECK: %[[INDEX_TO_GRAB:.*]] = torch.constant.int 0
@ -1170,11 +1167,9 @@ func.func @test_slice_default_axes_and_slices(%arg0: !torch.vtensor<[20,10,5],f3
//CHECK: %[[STARTS_ELEMENT_0:.*]] = torch.aten.item %[[STARTS_INDEX_VEC_0:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[ENDS_INDEX_VEC_0:.*]] = torch.aten.index_select %arg2, %[[INDEX_TO_GRAB:.*]], %[[ZERO_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[ENDS_ELEMENT_0:.*]] = torch.aten.item %[[ENDS_INDEX_VEC_0:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[AXES_INDEX_VEC_0:.*]] = torch.aten.index_select %[[DEFAULT_AXES:.*]], %[[INDEX_TO_GRAB:.*]], %[[ZERO_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[AXES_ELEMENT_0:.*]] = torch.aten.item %[[AXES_INDEX_VEC_0:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[STEPS_INDEX_VEC_0:.*]] = torch.aten.index_select %[[DEFAULT_SIZES:.*]], %[[INDEX_TO_GRAB:.*]], %[[ZERO_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[STEPS_ELEMENT_0:.*]] = torch.aten.item %[[STEPS_INDEX_VEC_0:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[SLICE_0:.*]] = torch.aten.slice.Tensor %arg0, %[[AXES_ELEMENT_0:.*]], %[[STARTS_ELEMENT_0:.*]], %[[ENDS_ELEMENT_0:.*]], %[[STEPS_ELEMENT_0:.*]] : !torch.vtensor<[20,10,5],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,?],f32>
//CHECK: %[[SLICE_0:.*]] = torch.aten.slice.Tensor %arg0, %[[CONST_0:.*]], %[[STARTS_ELEMENT_0:.*]], %[[ENDS_ELEMENT_0:.*]], %[[STEPS_ELEMENT_0:.*]] : !torch.vtensor<[20,10,5],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,?],f32>
//CHECK: %[[CONST_1:.*]] = torch.constant.int 1
//CHECK: %[[ONE_INDEX_VEC:.*]] = torch.prim.NumToTensor.Scalar %[[CONST_1:.*]] : !torch.int -> !torch.vtensor<[1],si64>
@ -1182,11 +1177,9 @@ func.func @test_slice_default_axes_and_slices(%arg0: !torch.vtensor<[20,10,5],f3
//CHECK: %[[STARTS_ELEMENT_1:.*]] = torch.aten.item %[[STARTS_INDEX_VEC_1:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[ENDS_INDEX_VEC_1:.*]] = torch.aten.index_select %arg2, %[[INDEX_TO_GRAB:.*]], %[[ONE_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[ENDS_ELEMENT_1:.*]] = torch.aten.item %[[ENDS_INDEX_VEC_1:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[AXES_INDEX_VEC_1:.*]] = torch.aten.index_select %[[DEFAULT_AXES:.*]], %[[INDEX_TO_GRAB:.*]], %[[ONE_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[AXES_ELEMENT_1:.*]] = torch.aten.item %[[AXES_INDEX_VEC_1:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[STEPS_INDEX_VEC_1:.*]] = torch.aten.index_select %[[DEFAULT_SIZES:.*]], %[[INDEX_TO_GRAB:.*]], %[[ONE_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[STEPS_ELEMENT_1:.*]] = torch.aten.item %[[STEPS_INDEX_VEC_1:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[TWO_INDEX_VEC:.*]] = torch.aten.slice.Tensor %[[SLICE_0:.*]], %[[AXES_ELEMENT_1:.*]], %[[STARTS_ELEMENT_1:.*]], %[[ENDS_ELEMENT_1:.*]], %[[STEPS_ELEMENT_1:.*]] : !torch.vtensor<[20,10,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,?],f32>
//CHECK: %[[TWO_INDEX_VEC:.*]] = torch.aten.slice.Tensor %[[SLICE_0:.*]], %[[CONST_1:.*]], %[[STARTS_ELEMENT_1:.*]], %[[ENDS_ELEMENT_1:.*]], %[[STEPS_ELEMENT_1:.*]] : !torch.vtensor<[20,10,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,?],f32>
//CHECK: %[[CONST_2:.*]] = torch.constant.int 2
//CHECK: %[[TWO_INDEX_VEC:.*]] = torch.prim.NumToTensor.Scalar %[[CONST_2:.*]] : !torch.int -> !torch.vtensor<[1],si64>
@ -1194,11 +1187,9 @@ func.func @test_slice_default_axes_and_slices(%arg0: !torch.vtensor<[20,10,5],f3
//CHECK: %[[STARTS_ELEMENT_2:.*]] = torch.aten.item %[[STARTS_INDEX_VEC_2:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[ENDS_INDEX_VEC_2:.*]] = torch.aten.index_select %arg2, %[[INDEX_TO_GRAB:.*]], %[[TWO_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[ENDS_ELEMENT_2:.*]] = torch.aten.item %[[ENDS_INDEX_VEC_2:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[AXES_INDEX_VEC_2:.*]] = torch.aten.index_select %[[DEFAULT_AXES:.*]], %[[INDEX_TO_GRAB:.*]], %[[TWO_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[AXES_ELEMENT_2:.*]] = torch.aten.item %[[AXES_INDEX_VEC_2:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: %[[STEPS_INDEX_VEC_2:.*]] = torch.aten.index_select %[[DEFAULT_SIZES:.*]], %[[INDEX_TO_GRAB:.*]], %[[TWO_INDEX_VEC:.*]] : !torch.vtensor<[3],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
//CHECK: %[[STEPS_ELEMENT_2:.*]] = torch.aten.item %[[STEPS_INDEX_VEC_2:.*]] : !torch.vtensor<[1],si64> -> !torch.int
//CHECK: torch.aten.slice.Tensor %[[TWO_INDEX_VEC:.*]], %[[AXES_ELEMENT_2:.*]], %[[STARTS_ELEMENT_2:.*]], %[[ENDS_ELEMENT_2:.*]], %[[STEPS_ELEMENT_2:.*]] : !torch.vtensor<[20,10,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,1],f32>
//CHECK: torch.aten.slice.Tensor %[[TWO_INDEX_VEC:.*]], %[[CONST_2:.*]], %[[STARTS_ELEMENT_2:.*]], %[[ENDS_ELEMENT_2:.*]], %[[STEPS_ELEMENT_2:.*]] : !torch.vtensor<[20,10,?],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,1],f32>
%0 = torch.operator "onnx.Slice"(%arg0, %arg1, %arg2) : (!torch.vtensor<[20,10,5],f32>, !torch.vtensor<[3],si64>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[20,10,1],f32>
return %0 : !torch.vtensor<[20,10,1],f32>
}
@ -1211,17 +1202,15 @@ func.func @test_slice_default_axes_and_slices(%arg0: !torch.vtensor<[20,10,5],f3
// CHECK-SAME: %[[ARG2:.*]]: !torch.vtensor<[1],si64>
// CHECK: %[[ZERO0:.*]] = torch.constant.int 0
// CHECK: %[[ZERO1:.*]] = torch.constant.int 0
// CHECK: %[[SCALAR:.*]] = torch.prim.NumToTensor.Scalar %[[ZERO1]] : !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[SELECT0:.*]] = torch.aten.index_select %[[ARG1]], %[[ZERO]], %[[SCALAR]] : !torch.vtensor<[1],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
// CHECK: %[[ITEM0:.*]] = torch.aten.item %[[SELECT0]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: %[[SELECT1:.*]] = torch.aten.index_select %[[ARG2]], %[[ZERO]], %[[SCALAR]] : !torch.vtensor<[1],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
// CHECK: %[[ITEM1:.*]] = torch.aten.item %[[SELECT1]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: %[[SELECT2:.*]] = torch.aten.index_select %{{.*}}, %[[ZERO]], %[[SCALAR]] : !torch.vtensor<[1],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
// CHECK: %[[ITEM2:.*]] = torch.aten.item %[[SELECT2]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: %[[SELECT3:.*]] = torch.aten.index_select %{{.*}}, %[[ZERO]], %[[SCALAR]] : !torch.vtensor<[1],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
// CHECK: %[[ITEM3:.*]] = torch.aten.item %[[SELECT3]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: torch.aten.slice.Tensor %[[ARG0]], %[[ITEM2]], %[[ITEM0]], %[[ITEM1]], %[[ITEM3]] : !torch.vtensor<[20,10,5],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,1],f32>
// CHECK-NEXT: %[[ZERO1:.*]] = torch.constant.int 0
// CHECK-NEXT: %[[SCALAR:.*]] = torch.prim.NumToTensor.Scalar %[[ZERO1]] : !torch.int -> !torch.vtensor<[1],si64>
// CHECK-NEXT: %[[SELECT0:.*]] = torch.aten.index_select %[[ARG1]], %[[ZERO]], %[[SCALAR]] : !torch.vtensor<[1],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
// CHECK-NEXT: %[[ITEM0:.*]] = torch.aten.item %[[SELECT0]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK-NEXT: %[[SELECT1:.*]] = torch.aten.index_select %[[ARG2]], %[[ZERO]], %[[SCALAR]] : !torch.vtensor<[1],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
// CHECK-NEXT: %[[ITEM1:.*]] = torch.aten.item %[[SELECT1]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK-NEXT: %[[SELECT3:.*]] = torch.aten.index_select %{{.*}}, %[[ZERO]], %[[SCALAR]] : !torch.vtensor<[1],si64>, !torch.int, !torch.vtensor<[1],si64> -> !torch.vtensor<[1],si64>
// CHECK-NEXT: %[[ITEM3:.*]] = torch.aten.item %[[SELECT3]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: torch.aten.slice.Tensor %[[ARG0]], %[[ZERO1]], %[[ITEM0]], %[[ITEM1]], %[[ITEM3]] : !torch.vtensor<[20,10,5],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[20,10,1],f32>
func.func @test_slice_default_axes_and_steps(%arg0: !torch.vtensor<[20,10,5],f32>, %arg1: !torch.vtensor<[1],si64>, %arg2: !torch.vtensor<[1],si64>) -> !torch.vtensor<[20,10,1],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64} {
%0 = torch.operator "onnx.Slice"(%arg0, %arg1, %arg2) : (!torch.vtensor<[20,10,5],f32>, !torch.vtensor<[1],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[20,10,1],f32>