mirror of https://github.com/llvm/torch-mlir
[onnx] Fix `onnx.split` by directly handling slicing (#3194)
Previous implementation erroneously mixed up num_outputs with slice_size. New version correctly computs the slice size and directly performs slicing rather than leveraging `aten.split.tensor`. This is due to `onnx` supporting a fixed number of splits making the size computation more easily computeable when lowering to `aten` rather than deferring to `aten.split.tensor`. --------- Co-authored-by: Robert Suderman <rsuderman@Roberts-MacBook-Pro.local>pull/3199/head
Rob Suderman
GitHub
parent
b6b01602d3
commit
733cace1df
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@ -1379,7 +1379,7 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
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"Split", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Value self;
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int64_t axis;
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int64_t num_outputs;
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int64_t numOutputs;
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if (binder.tensorOperand(self))
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return rewriter.notifyMatchFailure(
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binder.op, "Not converting to AtenSplitTensorOp due to input "
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@ -1387,49 +1387,65 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
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if (binder.s64IntegerAttr(axis, "axis", 0))
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return rewriter.notifyMatchFailure(binder.op,
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"Failed to get axis attribute");
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if (binder.s64IntegerAttr(num_outputs, "num_outputs", 0))
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if (binder.s64IntegerAttr(numOutputs, "num_outputs", 2))
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return rewriter.notifyMatchFailure(
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binder.op, "Failed to get num_outputs attribute");
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auto loc = binder.getLoc();
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auto result0Ty =
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binder.op->getResult(0).getType().cast<Torch::ValueTensorType>();
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auto resultNTy = binder.op->getResults()
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.back()
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.getType()
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.cast<Torch::ValueTensorType>();
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auto selfTy = self.getType().cast<Torch::ValueTensorType>();
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int64_t dim = axis;
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if (dim < 0)
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dim += selfTy.getSizes().size();
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// set intermediate shape to the shape of the first result
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// if the results are of different shapes
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// set the splitted axis to variable shape
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llvm::SmallVector<int64_t> intermediateShape(result0Ty.getSizes());
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for (auto result : binder.op->getResultTypes()) {
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int64_t d = cast<Torch::ValueTensorType>(result).getSizes()[dim];
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intermediateShape[dim] = d == intermediateShape[dim] ? d : -1;
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Value dimValue = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getType<Torch::IntType>(),
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rewriter.getI64IntegerAttr(dim));
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Value vNumOutputs = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getType<Torch::IntType>(),
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rewriter.getI64IntegerAttr(numOutputs));
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Value one = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(1));
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Value zero = rewriter.create<Torch::ConstantIntOp>(
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loc, rewriter.getI64IntegerAttr(0));
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Value vDimSize = rewriter.create<Torch::AtenSizeIntOp>(
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loc, rewriter.getType<Torch::IntType>(), self, dimValue);
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Value addNumOutputs =
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rewriter.create<Torch::AtenAddIntOp>(loc, vDimSize, vNumOutputs);
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Value subOne =
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rewriter.create<Torch::AtenSubIntOp>(loc, addNumOutputs, one);
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Value splitSize =
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rewriter.create<Torch::AtenFloordivIntOp>(loc, subOne, vNumOutputs);
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llvm::SmallVector<Value> outputs;
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Value step = one;
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Value start = zero;
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for (int i = 0; i < numOutputs - 1; ++i) {
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Value end =
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rewriter.create<Torch::AtenAddIntOp>(loc, start, splitSize);
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Value slice = rewriter.create<Torch::AtenSliceTensorOp>(
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loc, result0Ty, self, dimValue, start, end, step);
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start = end;
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outputs.push_back(slice);
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}
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Value dimValue = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), dim));
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Value end = vDimSize;
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Value lastSlice = rewriter.create<Torch::AtenSliceTensorOp>(
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loc, resultNTy, self, dimValue, start, end, step);
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outputs.push_back(lastSlice);
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Value splitSize = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), num_outputs));
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// TODO: Attempting to use the shape expected by the ONNX mlir as ground
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// truth. For now just use dynamic shapes.
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auto resultOuterType =
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Torch::ListType::get(rewriter.getType<Torch::ValueTensorType>(
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/*std::optional<llvm::ArrayRef<int64_t>>=*/intermediateShape,
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result0Ty.getOptionalDtype()));
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Torch::AtenSplitTensorOp new_op =
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rewriter.create<Torch::AtenSplitTensorOp>(
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binder.getLoc(), resultOuterType, self, splitSize, dimValue);
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// the onnx op is variadic with multiple results, but AtenSplitWithSizes
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// outputs a list so we need to unpack the list
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rewriter.replaceOpWithNewOp<Torch::PrimListUnpackOp>(
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binder.op, binder.op->getResults().getType(), new_op.getResult());
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rewriter.replaceOp(binder.op, outputs);
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return success();
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});
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@ -2718,7 +2718,6 @@ ONNX_XFAIL_SET = {
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"ElementwiseToDtypeI64ToUI8Module_basic",
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"ElementwiseUnaryIntModule_basic",
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"ElementwiseUnsqueezeNegDimsModule_basic",
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"GluStaticModule_basic",
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"GroupNormModule_basic",
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"MaskedFillTensorFloatValueModule_basic",
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"NativeDropoutTrainModule_basic",
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@ -1288,12 +1288,20 @@ func.func @test_split_variable_parts_2d_opset18(%arg0: !torch.vtensor<[2,6],f32>
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// CHECK-LABEL: func.func @test_split_2d_uneven_split_opset18(
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// 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 = ""} {
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// CHECK: %[[AXIS:.*]] = torch.constant.int 1
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// CHECK: %[[SPLIT_SIZE:.*]] = torch.constant.int 3
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// 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>>
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// 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>
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// 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>
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// CHECK: }
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// CHECK-DAG: %[[DIM:.+]] = torch.constant.int 1
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// CHECK-DAG: %[[SPLITS:.+]] = torch.constant.int 3
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// CHECK-DAG: %[[ONE:.+]] = torch.constant.int 1
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// CHECK-DAG: %[[ZERO:.+]] = torch.constant.int 0
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// CHECK-DAG: %[[SZ1:.+]] = torch.aten.size.int %arg0, %[[DIM]]
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// CHECK-DAG: %[[ADD:.+]] = torch.aten.add.int %[[SZ1]], %[[SPLITS]]
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// CHECK-DAG: %[[SUB:.+]] = torch.aten.sub.int %[[ADD]], %[[ONE]]
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// CHECK-DAG: %[[SLICESZ:.+]] = torch.aten.floordiv.int %[[SUB]], %[[SPLITS]]
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// CHECK-DAG: %[[START1:.+]] = torch.aten.add.int %[[ZERO]], %[[SLICESZ]] : !torch.int, !torch.int -> !torch.int
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// CHECK-DAG: %[[SLICE0:.+]] = torch.aten.slice.Tensor %arg0, %[[DIM]], %[[ZERO]], %[[START1]], %[[ONE]] : !torch.vtensor<[2,8],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[2,3],f32>
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// CHECK-DAG: %[[START2:.+]] = torch.aten.add.int %[[START1]], %[[SLICESZ]] : !torch.int, !torch.int -> !torch.int
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// CHECK-DAG: %[[SLICE1:.+]] = torch.aten.slice.Tensor %arg0, %[[DIM]], %[[START1]], %[[START2]], %[[ONE]] : !torch.vtensor<[2,8],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[2,3],f32>
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// CHECK-DAG: %[[SLICE2:.+]] = torch.aten.slice.Tensor %arg0, %[[DIM]], %[[START2]], %[[SZ1]], %[[ONE]] : !torch.vtensor<[2,8],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[2,2],f32>
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// CHECK: return %[[SLICE0]], %[[SLICE1]], %[[SLICE2]]
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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 = ""} {
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%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>)
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return %0#0, %0#1, %0#2 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,2],f32>
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