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[MLIR][ONNX] Add OnnxToTorch support for ReduceSumSquare Op #3188

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merged 21 commits into from
May 2, 2024
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[MLIR][ONNX] Add OnnxToTorch support for ReduceSumSquare Op #3188

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f6e5b01
Add support for reducesumsquare op
archana-ramalingam Apr 18, 2024
782c59e
Adding reducesumsquare op
archana-ramalingam Apr 18, 2024
99de153
Replace operand with data
archana-ramalingam Apr 19, 2024
f779994
Fixing mlir tests
archana-ramalingam Apr 19, 2024
6eb80a8
Fix clang-format issue
archana-ramalingam Apr 20, 2024
ea286ba
Fixing reviewed issues
Apr 23, 2024
4ff9f0d
Remove INT0
archana-ramalingam Apr 23, 2024
c03d9b7
Fixing noop test example
archana-ramalingam Apr 24, 2024
36559db
Merge branch 'llvm:main' into reducesumsquare
archana-ramalingam Apr 24, 2024
8ebd3d9
Remove failing noop example
archana-ramalingam Apr 24, 2024
7ae4610
Undo unintended change
archana-ramalingam Apr 26, 2024
dfe6420
Rebasing with commit Add support for reducesumsquare op
archana-ramalingam Apr 18, 2024
3dc34e3
Rebasing to commit Adding reducesumsquare op
archana-ramalingam Apr 18, 2024
dd90ca8
Rebasing to commit Replace operand with data
archana-ramalingam Apr 19, 2024
d43b165
Rebase to commit Fixing reviewed issues
Apr 23, 2024
8e491c0
Rebase to commit Fixing noop test example
archana-ramalingam Apr 24, 2024
35acbb0
Undo unintended change
archana-ramalingam Apr 26, 2024
11536b0
rebase to origin
archana-ramalingam Apr 26, 2024
8105c36
Merge branch 'llvm:main' into reducesumsquare
archana-ramalingam Apr 29, 2024
baf4d85
Merge branch 'llvm:main' into reducesumsquare
archana-ramalingam May 1, 2024
134b052
Merge branch 'llvm:main' into reducesumsquare
archana-ramalingam May 1, 2024
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20 changes: 20 additions & 0 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -914,6 +914,26 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
/*storeValue=*/data, keepDims,
noop_with_empty_axes, false);
});
patterns.onOp("ReduceSumSquare", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value data;
int64_t keepDims, noop_with_empty_axes;
if (binder.tensorOperandAtIndex(data, 0) ||
binder.tensorResultType(resultType) ||
binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
binder.s64IntegerAttr(noop_with_empty_axes,
"noop_with_empty_axes", 0))
return failure();

Value dataSquare = rewriter.create<Torch::AtenMulTensorOp>(
binder.getLoc(), data.getType(), data, data);

return reducedSumImpl(binder, rewriter, dataSquare,
resultType,
/*storeValue=*/dataSquare, keepDims,
noop_with_empty_axes, false);
});
patterns.onOp(
"ReduceMean", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Expand Down
94 changes: 93 additions & 1 deletion test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
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Expand Up @@ -866,7 +866,7 @@ func.func @test_reduce_max_attr(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtens
// -----

// CHECK-LABEL: func.func @test_reduce_l1_default_axes_keepdims_example
func.func @test_reduce_l1_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],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 = ""} {
func.func @test_reduce_l1_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],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_torch.aten.mul.Tensormeta.producer_version = ""} {
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// CHECK: %[[ABS:.+]] = torch.aten.abs %arg0 : !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct : () -> !torch.list<int>
Expand Down Expand Up @@ -998,6 +998,98 @@ func.func @test_reduce_sum_negative_axes_keepdims_example(%arg0: !torch.vtensor<

// -----

// CHECK-LABEL: func.func @test_reduce_sum_square_default_axes_keepdims_example
func.func @test_reduce_sum_square_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],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: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],f32>, !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
// CHECK: %[[NONE:.+]] = torch.constant.none
// CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
// CHECK: return %[[SUM]] : !torch.vtensor<[1,1,1],f32>
%0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[1,1,1],f32>
return %0 : !torch.vtensor<[1,1,1],f32>
}

// -----

// CHECK-LABEL: func.func @test_reduce_sum_square_do_not_keepdims_example
func.func @test_reduce_sum_square_do_not_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],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: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],f32>, !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[FALSE:.+]] = torch.constant.bool false
// CHECK: %[[NONE:.+]] = torch.constant.none
// CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2],f32>
// CHECK: return %[[SUM]] : !torch.vtensor<[3,2],f32>
%0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2],f32>
return %0 : !torch.vtensor<[3,2],f32>
}

// -----

// CHECK-LABEL: func.func @test_reduce_sum_square_empty_axes_input_noop_example
func.func @test_reduce_sum_square_empty_axes_input_noop_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[0],si64>) -> !torch.vtensor<[3,2,2],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 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64, torch.onnx.noop_with_empty_axes = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[0],si64>) -> !torch.vtensor<[3,2,2],f32>
return %0 : !torch.vtensor<[3,2,2],f32>
}
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// -----

// CHECK-LABEL: func.func @test_reduce_sum_square_empty_set_non_reduced_axis_zero
func.func @test_reduce_sum_square_empty_set_non_reduced_axis_zero(%arg0: !torch.vtensor<[2,0,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,0,1],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: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[2,0,4],f32>, !torch.vtensor<[2,0,4],f32> -> !torch.vtensor<[2,0,4],f32>
// CHECK: %[[INT2:.+]] = torch.constant.int 2
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[INT2]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
// CHECK: %[[NONE:.+]] = torch.constant.none
// CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[2,0,4],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[2,0,1],f32>
// CHECK: return %[[SUM]] : !torch.vtensor<[2,0,1],f32>
%0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[2,0,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,0,1],f32>
return %0 : !torch.vtensor<[2,0,1],f32>
}

// -----

// CHECK-LABEL: func.func @test_reduce_sum_square_keepdims_example
func.func @test_reduce_sum_square_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],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: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],f32>, !torch.vtensor<[3,2,2],f32> -> !torch.vtensor<[3,2,2],f32>
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
// CHECK: %[[NONE:.+]] = torch.constant.none
// CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
// CHECK: return %[[SUM]] : !torch.vtensor<[3,1,2],f32>
%0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32>
return %0 : !torch.vtensor<[3,1,2],f32>
}

// -----

// CHECK-LABEL: func.func @test_reduce_sum_square_keepdims_int_example
func.func @test_reduce_sum_square_keepdims_int_example(%arg0: !torch.vtensor<[3,2,2],si64>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],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: %[[MULT:.+]] = torch.aten.mul.Tensor %arg0, %arg0 : !torch.vtensor<[3,2,2],si64>, !torch.vtensor<[3,2,2],si64> -> !torch.vtensor<[3,2,2],si64>
// CHECK: %[[INT0:.+]] = torch.constant.int 0
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
// CHECK: %[[SELECT:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT0_0]] : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
// CHECK: %[[ITEM:.+]] = torch.aten.item %[[SELECT]] : !torch.vtensor<[1],si64> -> !torch.int
// CHECK: %[[DIMS:.+]] = torch.prim.ListConstruct %[[ITEM]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
// CHECK: %[[NONE:.+]] = torch.constant.none
// CHECK: %[[SUM:.+]] = torch.aten.sum.dim_IntList %[[MULT]], %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],si64>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,1,2],f32>
// CHECK: return %[[SUM]] : !torch.vtensor<[3,1,2],f32>
%0 = torch.operator "onnx.ReduceSumSquare"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],si64>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,1,2],f32>
return %0 : !torch.vtensor<[3,1,2],f32>
}
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// -----

// CHECK-LABEL: @test_reduce_mean_negative_axes_keepdims_example
func.func @test_reduce_mean_negative_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>) -> !torch.vtensor<[3,1,2],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 13 : si64} {
// CHECK: %[[TENSOR:.+]] = torch.vtensor.literal(dense<-2> : tensor<1xsi64>) : !torch.vtensor<[1],si64>
Expand Down
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