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[MLIR][ONNX] Add OnnxToTorch support for ReduceLogSum Op #3229

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merged 7 commits into from
Apr 25, 2024
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[MLIR][ONNX] Add OnnxToTorch support for ReduceLogSum Op #3229

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21d79c8
Add support for reducelogsum op
archana-ramalingam Apr 25, 2024
580241b
Merge branch 'llvm:main' into reducelogsum
archana-ramalingam Apr 25, 2024
5db1304
Revert setup.py changes Revert "Add support for reducelogsum op"
archana-ramalingam Apr 25, 2024
fecb34f
Add support for reducelogsum op
archana-ramalingam Apr 25, 2024
7e8252d
Merge latest commits Merge branch 'reducelogsum' of https://github.co…
archana-ramalingam Apr 25, 2024
b3c0859
Clang formatting
archana-ramalingam Apr 25, 2024
570e8bf
Fix lit test issues
archana-ramalingam Apr 25, 2024
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26 changes: 26 additions & 0 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -962,6 +962,32 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
/*storeValue=*/data, keepDims,
noop_with_empty_axes, false);
});
patterns.onOp("ReduceLogSum", 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();

auto reducedSumBool =
reducedSumImpl(binder, rewriter, data, resultType,
/*storeValue=*/data, keepDims,
noop_with_empty_axes, true);

if (failed(reducedSumBool))
return rewriter.notifyMatchFailure(
binder.op,
"Failed to perform sum operation on square of operand");

rewriter.replaceOpWithNewOp<Torch::AtenLogOp>(
binder.op, resultType, data);
return success();
});
patterns.onOp(
"ReduceMean", 1,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
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51 changes: 51 additions & 0 deletions test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
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Expand Up @@ -942,6 +942,57 @@ func.func @test_reduce_sum_negative_axes_keepdims_example(%arg0: !torch.vtensor<

// -----

// CHECK-LABEL: func.func @test_reduce_log_sum_default_axes_keepdims_example
func.func @test_reduce_log_sum_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: %[[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 %arg0, %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[1,1,1],f32>
// CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[1,1,1],f32> -> !torch.vtensor<[1,1,1],f32>
// CHECK: return %[[LOG]] : !torch.vtensor<[1,1,1],f32>
%0 = torch.operator "onnx.ReduceLogSum"(%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_log_sum_keep_dims_example
func.func @test_reduce_log_sum_keep_dims_example(%arg0: !torch.vtensor<[3,2,2],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2,1],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: %[[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 %arg0, %[[DIMS]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2,1],f32>
// CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[3,2,1],f32> -> !torch.vtensor<[3,2,1],f32>
// CHECK: return %[[LOG]] : !torch.vtensor<[3,2,1],f32>
%0 = torch.operator "onnx.ReduceLogSum"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[3,2,1],f32>
return %0 : !torch.vtensor<[3,2,1],f32>
}

// -----

// CHECK-LABEL: func.func @test_reduce_log_sum_do_not_keepdims_example
func.func @test_reduce_log_sum_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: %[[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 %arg0, %[[DIMS]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,2],f32>
// CHECK: %[[LOG:.+]] = torch.aten.log %[[SUM]] : !torch.vtensor<[3,2],f32> -> !torch.vtensor<[3,2],f32>
// CHECK: return %[[LOG]] : !torch.vtensor<[3,2],f32>
%0 = torch.operator "onnx.ReduceLogSum"(%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: @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>
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