mirror of https://github.com/llvm/torch-mlir
Add OnnxToTorch support for Compress op (#3025)
jinchen
GitHub
parent
90e3d69c25
commit
9cf6c45a39
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@ -706,6 +706,72 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
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binder.op, resultType, source, min, max);
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return success();
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});
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patterns.onOp(
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"Compress", 9, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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Value operand, conditionTensor;
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int64_t axis;
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if (binder.tensorOperands(operand, conditionTensor) ||
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binder.s64IntegerAttr(axis, "axis", INT64_MAX) ||
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binder.tensorResultType(resultType))
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return failure();
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// get indexs from the condition tensor
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auto dtype = dyn_cast<Torch::ValueTensorType>(conditionTensor.getType())
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.getDtype();
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auto constOp = dyn_cast<Torch::ValueTensorLiteralOp>(
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conditionTensor.getDefiningOp());
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auto elementsAttr =
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dyn_cast<DenseIntElementsAttr>(constOp.getValueAttr());
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SmallVector<APInt> apValues;
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int64_t index = 0;
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for (auto intAttr : elementsAttr.getValues<Attribute>()) {
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int64_t i = dyn_cast<IntegerAttr>(intAttr).getSInt();
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if (i)
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apValues.push_back(APInt(dtype.getIntOrFloatBitWidth(), index));
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index++;
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}
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SmallVector<int64_t> indexShape = {static_cast<long>(apValues.size())};
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auto indexType = Torch::ValueTensorType::get(binder.op->getContext(),
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indexShape, dtype);
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auto attr = DenseElementsAttr::get(
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cast<ShapedType>(RankedTensorType::get(indexShape, dtype)),
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apValues);
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Value indexVal =
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rewriter.replaceOpWithNewOp<Torch::ValueTensorLiteralOp>(
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constOp, indexType, attr);
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auto shapeSizes =
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dyn_cast<Torch::ValueTensorType>(operand.getType()).getSizes();
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if (axis == INT64_MAX) {
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// flatten input tensor if using default axis
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Value cstZero = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(0));
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Value cstNegOne = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(-1));
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int64_t numElements = 1;
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for (auto i : shapeSizes) {
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numElements *= i;
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}
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SmallVector<int64_t> flattenShape = {numElements};
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auto flattenType = Torch::ValueTensorType::get(
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binder.op->getContext(), flattenShape, resultType.getDtype());
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Value flattenTensor = rewriter.create<Torch::AtenFlattenUsingIntsOp>(
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binder.getLoc(), flattenType, operand, cstZero, cstNegOne);
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rewriter.replaceOpWithNewOp<Torch::AtenIndexSelectOp>(
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binder.op, resultType, flattenTensor, cstZero, indexVal);
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return success();
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} else {
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if (axis < 0)
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// Negative axis value means counting dimensions from the back
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axis += shapeSizes.size();
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Value dim = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getI64IntegerAttr(axis));
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rewriter.replaceOpWithNewOp<Torch::AtenIndexSelectOp>(
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binder.op, resultType, operand, dim, indexVal);
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}
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return success();
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});
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patterns.onOp(
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"Concat", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
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Torch::ValueTensorType resultType;
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@ -1646,22 +1646,62 @@ func.func @test_constant_of_shape_dense_int_cst() -> !torch.vtensor<[2,3,4], si6
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return %0 : !torch.vtensor<[2,3,4], si64>
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}
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// -----
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// CHECK-LABEL: func.func @test_celu
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func.func @test_celu(%arg0: !torch.vtensor<[3,3,3,1],f32>) -> !torch.vtensor<[3,3,3,1],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[ALPHA:.*]] = torch.constant.float 2.000000e+00
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// CHECK: %0 = torch.aten.div.Scalar %arg0, %[[ALPHA]] : !torch.vtensor<[3,3,3,1],f32>, !torch.float -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %1 = torch.aten.exp %0 : !torch.vtensor<[3,3,3,1],f32> -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %int1 = torch.constant.int 1
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// CHECK: %2 = torch.aten.sub.Scalar %1, %int1, %int1 : !torch.vtensor<[3,3,3,1],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %3 = torch.aten.mul.Scalar %2, %[[ALPHA]] : !torch.vtensor<[3,3,3,1],f32>, !torch.float -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %int0 = torch.constant.int 0
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// CHECK: %4 = torch.prim.ListConstruct : () -> !torch.list<int>
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// CHECK: %none = torch.constant.none
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// CHECK: %int6 = torch.constant.int 6
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// CHECK: %[[ZERO:.*]] = torch.aten.full %4, %int0, %int6, %none, %none, %none : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
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// CHECK: %[[MIN:.*]] = torch.aten.minimum %[[ZERO]], %3 : !torch.vtensor<[],f32>, !torch.vtensor<[3,3,3,1],f32> -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %[[MAX:.*]] = torch.aten.maximum %[[ZERO]], %arg0 : !torch.vtensor<[],f32>, !torch.vtensor<[3,3,3,1],f32> -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %8 = torch.aten.add.Tensor %[[MAX]], %[[MIN]], %int1 : !torch.vtensor<[3,3,3,1],f32>, !torch.vtensor<[3,3,3,1],f32>, !torch.int -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %[[ALPHA:.*]] = torch.constant.float 2.000000e+00
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// CHECK: %0 = torch.aten.div.Scalar %arg0, %[[ALPHA]] : !torch.vtensor<[3,3,3,1],f32>, !torch.float -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %1 = torch.aten.exp %0 : !torch.vtensor<[3,3,3,1],f32> -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %int1 = torch.constant.int 1
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// CHECK: %2 = torch.aten.sub.Scalar %1, %int1, %int1 : !torch.vtensor<[3,3,3,1],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %3 = torch.aten.mul.Scalar %2, %[[ALPHA]] : !torch.vtensor<[3,3,3,1],f32>, !torch.float -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %int0 = torch.constant.int 0
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// CHECK: %4 = torch.prim.ListConstruct : () -> !torch.list<int>
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// CHECK: %none = torch.constant.none
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// CHECK: %int6 = torch.constant.int 6
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// CHECK: %[[ZERO:.*]] = torch.aten.full %4, %int0, %int6, %none, %none, %none : !torch.list<int>, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
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// CHECK: %[[MIN:.*]] = torch.aten.minimum %[[ZERO]], %3 : !torch.vtensor<[],f32>, !torch.vtensor<[3,3,3,1],f32> -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %[[MAX:.*]] = torch.aten.maximum %[[ZERO]], %arg0 : !torch.vtensor<[],f32>, !torch.vtensor<[3,3,3,1],f32> -> !torch.vtensor<[3,3,3,1],f32>
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// CHECK: %8 = torch.aten.add.Tensor %[[MAX]], %[[MIN]], %int1 : !torch.vtensor<[3,3,3,1],f32>, !torch.vtensor<[3,3,3,1],f32>, !torch.int -> !torch.vtensor<[3,3,3,1],f32>
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%0 = torch.operator "onnx.Celu"(%arg0) {torch.onnx.alpha = 2.000000e+00 : f32} : (!torch.vtensor<[3,3,3,1],f32>) -> !torch.vtensor<[3,3,3,1],f32>
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return %0 : !torch.vtensor<[3,3,3,1],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_compress
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func.func @test_compress(%arg0: !torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,3,2], f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[INDEX:.*]] = torch.vtensor.literal(dense<[1, 2]> : tensor<2xsi64>) : !torch.vtensor<[2],si64>
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// CHECK: %[[DIM:.*]] = torch.constant.int 2
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// CHECK: %[[ATEN_INDEX_SELECT:.*]] = torch.aten.index_select %arg0, %[[DIM]], %[[INDEX]] : !torch.vtensor<[2,3,4],f32>, !torch.int, !torch.vtensor<[2],si64> -> !torch.vtensor<[2,3,2],f32>
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%cst = torch.vtensor.literal(dense<[0,1,1]> : tensor<3xsi64>) : !torch.vtensor<[3], si64>
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%0 = torch.operator "onnx.Compress"(%arg0, %cst) {torch.onnx.axis = 2 : si64} : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,3,2],f32>
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return %0 : !torch.vtensor<[2,3,2],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_compress_default_axis
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func.func @test_compress_default_axis(%arg0: !torch.vtensor<[2,3],f32>) -> !torch.vtensor<[3], f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[INDEX:.*]] = torch.vtensor.literal(dense<[1, 3, 5]> : tensor<3xsi64>) : !torch.vtensor<[3],si64>
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// CHECK: %[[INT0:.*]] = torch.constant.int 0
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// CHECK: %[[END_DIM:.*]] = torch.constant.int -1
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// CHECK: %[[ATEN_FLATTEN:.*]] = torch.aten.flatten.using_ints %arg0, %[[INT0]], %[[END_DIM]] : !torch.vtensor<[2,3],f32>, !torch.int, !torch.int -> !torch.vtensor<[6],f32>
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// CHECK: %[[ATEN_INDEX_SELECT:.*]] = torch.aten.index_select %[[ATEN_FLATTEN]], %[[INT0]], %[[INDEX]] : !torch.vtensor<[6],f32>, !torch.int, !torch.vtensor<[3],si64> -> !torch.vtensor<[3],f32>
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%cst = torch.vtensor.literal(dense<[0,1,0,1,0,1]> : tensor<6xsi64>) : !torch.vtensor<[6], si64>
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%0 = torch.operator "onnx.Compress"(%arg0, %cst) : (!torch.vtensor<[2,3],f32>, !torch.vtensor<[6],si64>) -> !torch.vtensor<[3],f32>
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return %0 : !torch.vtensor<[3],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_compress_neg_axis
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func.func @test_compress_neg_axis(%arg0: !torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,2,4], f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 9 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[INDEX:.*]] = torch.vtensor.literal(dense<[1, 2]> : tensor<2xsi64>) : !torch.vtensor<[2],si64>
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// CHECK: %[[DIM:.*]] = torch.constant.int 1
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// CHECK: %[[ATEN_INDEX_SELECT:.*]] = torch.aten.index_select %arg0, %[[DIM]], %[[INDEX]] : !torch.vtensor<[2,3,4],f32>, !torch.int, !torch.vtensor<[2],si64> -> !torch.vtensor<[2,2,4],f32>
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%cst = torch.vtensor.literal(dense<[0,1,1]> : tensor<3xsi64>) : !torch.vtensor<[3], si64>
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%0 = torch.operator "onnx.Compress"(%arg0, %cst) {torch.onnx.axis = -2 : si64} : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[3],si64>) -> !torch.vtensor<[2,2,4],f32>
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return %0 : !torch.vtensor<[2,2,4],f32>
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}
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