mirror of https://github.com/llvm/torch-mlir
[onnx] Fix ReduceMean lowering to torch (#2956)
Torch lowering only supported the most recent version. Refactored the lowering so more easily handle default values and optional operands / attributes.pull/2960/head
Rob Suderman
GitHub
parent
d541779f37
commit
4a7a7d76f8
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@ -1104,129 +1104,145 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
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Value axes;
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int64_t keepDims;
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int64_t noop_with_empty_axes;
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// Deal with case when no axes arg is passed
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if (binder.op->getNumOperands() == 1) {
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if (binder.tensorOperand(data) ||
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binder.tensorResultType(resultType) ||
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binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
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binder.s64IntegerAttr(noop_with_empty_axes,
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"noop_with_empty_axes", 0))
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return failure();
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if (noop_with_empty_axes == 0) {
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Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
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Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
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binder.getLoc(), keepDimsConstInt);
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int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
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.getSizes()
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.size();
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SmallVector<Value> axesList;
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for (int i = 0; i < numDims; i++) {
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Value curr = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
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axesList.push_back(curr);
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}
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Value axesValueList = rewriter.create<Torch::PrimListConstructOp>(
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binder.getLoc(),
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Torch::ListType::get(
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Torch::IntType::get(binder.op->getContext())),
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axesList);
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rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
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binder.op, resultType, data, axesValueList, keepDimsBool);
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} else {
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rewriter.replaceOp(binder.op, data);
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}
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return success();
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}
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if (binder.tensorOperands(data, axes) ||
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if (binder.tensorOperandAtIndex(data, 0) ||
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binder.tensorResultType(resultType) ||
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binder.s64IntegerAttr(keepDims, "keepdims", 1) ||
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binder.s64IntegerAttr(noop_with_empty_axes, "noop_with_empty_axes",
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0))
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return failure();
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Torch::BaseTensorType axesType =
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axes.getType().cast<Torch::BaseTensorType>();
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SmallVector<Value> dimList;
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SmallVector<int64_t> selectSizes;
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selectSizes.push_back(1);
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Type selectResultType = axesType.getWithSizesAndDtype(
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llvm::ArrayRef(selectSizes), axesType.getOptionalDtype());
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auto sizes =
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dyn_cast<Torch::ValueTensorType>(axes.getType()).getSizes();
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// deal with case when axes is empty
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if (sizes.size() == 1 && sizes[0] == 0) {
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if (noop_with_empty_axes == 0) {
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// create dims list with all dims [0, data.getSizes().size())
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Value keepDimsConstInt = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), keepDims));
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Value keepDimsBool = rewriter.create<Torch::AtenBoolIntOp>(
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binder.getLoc(), keepDimsConstInt);
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int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
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.getSizes()
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.size();
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for (int i = 0; i < numDims; i++) {
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Value curr = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
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dimList.push_back(curr);
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}
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Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
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binder.getLoc(),
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Torch::ListType::get(
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Torch::IntType::get(binder.op->getContext())),
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dimList);
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rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
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binder.op, resultType, data, dimValueList, keepDimsBool);
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} else {
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rewriter.replaceOp(binder.op, data);
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auto dataTy = cast<Torch::BaseTensorType>(data.getType());
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Torch::IntType torchIntTy = rewriter.getType<Torch::IntType>();
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// If any of the input dims are 0 we set to the upper limit:
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if (llvm::any_of(dataTy.getSizes(), [](int64_t d) { return d == 0; }) &&
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(llvm::any_of(dataTy.getSizes(),
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[](int64_t d) { return d == Torch::kUnknownSize; }) ||
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keepDims)) {
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auto dty = dataTy.getDtype();
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Value scalar;
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if (FloatType fpTy = dyn_cast<FloatType>(dty)) {
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auto inf = APFloat::getInf(fpTy.getFloatSemantics());
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scalar = rewriter.create<Torch::ConstantFloatOp>(
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binder.getLoc(), rewriter.getType<Torch::FloatType>(),
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rewriter.getFloatAttr(rewriter.getF64Type(),
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inf.convertToDouble()));
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}
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if (IntegerType intTy = dyn_cast<IntegerType>(dty)) {
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auto mx =
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intTy.isSigned()
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? APInt::getSignedMaxValue(intTy.getIntOrFloatBitWidth())
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: APInt::getMaxValue(intTy.getIntOrFloatBitWidth());
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scalar = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), torchIntTy,
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rewriter.getIntegerAttr(rewriter.getIntegerType(64),
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mx.getSExtValue()));
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}
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llvm::SmallVector<Value> fillDims;
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for (int i = 0, s = resultType.getSizes().size(); i < s; ++i) {
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auto staticDim = resultType.getSizes()[i];
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if (staticDim != Torch::kUnknownSize) {
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fillDims.push_back(rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), torchIntTy,
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rewriter.getI64IntegerAttr(staticDim)));
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continue;
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}
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Value iv = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), torchIntTy, rewriter.getI64IntegerAttr(i));
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fillDims.push_back(rewriter.create<Torch::AtenSizeIntOp>(
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binder.getLoc(), torchIntTy, data, iv));
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}
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Value none = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
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Value fillDimsList = rewriter.create<Torch::PrimListConstructOp>(
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binder.getLoc(), Torch::ListType::get(torchIntTy), fillDims);
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rewriter.replaceOpWithNewOp<Torch::AtenFullOp>(
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binder.op, resultType, fillDimsList, scalar, none, none, none,
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none);
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return success();
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}
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// Previous version of the operation had the axes as an attribute:
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SmallVector<Value> axesList;
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llvm::SmallVector<int64_t> axesAttr;
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if (!binder.s64IntegerArrayAttr(axesAttr, "axes", {})) {
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for (int i = 0, s = axesAttr.size(); i < s; ++i) {
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axesList.push_back(rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), torchIntTy,
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rewriter.getI64IntegerAttr(axesAttr[i])));
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}
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}
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// Extract the axes values from the axes operand:
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if (!binder.tensorOperandAtIndex(axes, 1)) {
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Torch::BaseTensorType axesType =
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axes.getType().cast<Torch::BaseTensorType>();
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SmallVector<int64_t> selectSizes{1};
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Type selectResultType = axesType.getWithSizesAndDtype(
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selectSizes, axesType.getOptionalDtype());
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auto sizes = axesType.getSizes();
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Value zero = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
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// Extract the value of each axes:
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for (int i = 0; i < sizes[0]; i++) {
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// Go through the axes list and get each dim in the list
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Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
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Value extract = rewriter.create<Torch::AtenSelectIntOp>(
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binder.getLoc(), selectResultType, axes, zero, selectIndex);
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Value dim = rewriter.create<Torch::AtenItemOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
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axesList.push_back(dim);
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}
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}
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// Handle the noop case:
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if (axesList.empty() && noop_with_empty_axes) {
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rewriter.replaceOp(binder.op, data);
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return success();
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}
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// Deal with case when no axes arg is passed but not a noop:
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if (axesList.empty()) {
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int64_t numDims = dyn_cast<Torch::ValueTensorType>(data.getType())
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.getSizes()
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.size();
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for (int i = 0; i < numDims; i++) {
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Value curr = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
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axesList.push_back(curr);
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}
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}
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// Handle negative axis:
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Value rankVal = rewriter.create<Torch::AtenDimOp>(binder.getLoc(),
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torchIntTy, data);
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Value zero = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), 0));
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int64_t adjustmentInt =
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cast<Torch::ValueTensorType>(data.getType()).getSizes().size();
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Value adjustment = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64),
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adjustmentInt));
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// convert axes (tensor) into torch int list while dealing with neg axis
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for (int i = 0; i < sizes[0]; i++) {
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// Go through the axes list and get each dim in the list
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Value selectIndex = rewriter.create<Torch::ConstantIntOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(),
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rewriter.getIntegerAttr(rewriter.getIntegerType(64), i));
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Value extract = rewriter.create<Torch::AtenSelectIntOp>(
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binder.getLoc(), selectResultType, axes, zero, selectIndex);
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Value dim = rewriter.create<Torch::AtenItemOp>(
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binder.getLoc(), rewriter.getType<Torch::IntType>(), extract);
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// deal with neg axis: if (axis < 0) axis += rank
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rewriter.getI64IntegerAttr(0));
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for (Value &axes : axesList) {
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Value isNegative =
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rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), dim, zero);
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rewriter.create<Torch::AtenLtIntOp>(binder.getLoc(), axes, zero);
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isNegative = rewriter.create<Torch::AtenIntBoolOp>(binder.getLoc(),
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isNegative);
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Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
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binder.getLoc(), isNegative, adjustment);
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Value finalDim = rewriter.create<Torch::AtenAddIntOp>(
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binder.getLoc(), dim, finalOffset);
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dimList.push_back(finalDim);
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binder.getLoc(), isNegative, rankVal);
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axes = rewriter.create<Torch::AtenAddIntOp>(binder.getLoc(), axes,
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finalOffset);
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}
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Value dimValueList = rewriter.create<Torch::PrimListConstructOp>(
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binder.getLoc(),
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Torch::ListType::get(Torch::IntType::get(binder.op->getContext())),
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dimList);
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Value keepDimBool;
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if (keepDims == 1) {
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keepDimBool =
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rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), true);
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} else {
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keepDimBool =
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rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), false);
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}
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binder.getLoc(), Torch::ListType::get(torchIntTy), axesList);
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Value keepDimBool =
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rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), keepDims);
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rewriter.replaceOpWithNewOp<Torch::AtenAminOp>(
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binder.op, resultType, data, dimValueList, keepDimBool);
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return success();
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@ -60,18 +60,15 @@ public:
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Location loc = op.getLoc();
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Value input = adaptor.getSelf();
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RankedTensorType valResultType =
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getTypeConverter()
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->convertType(op.getResult(0).getType())
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.template cast<RankedTensorType>();
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RankedTensorType idxResultType =
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this->getTypeConverter()
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->convertType(op.getResult(1).getType())
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.template cast<RankedTensorType>();
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auto typec = this->getTypeConverter();
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auto valResultType =
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cast<RankedTensorType>(typec->convertType(op.getResult(0).getType()));
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auto idxResultType =
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cast<RankedTensorType>(typec->convertType(op.getResult(1).getType()));
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RankedTensorType inputType =
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input.getType().template cast<RankedTensorType>();
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Type idxElementType = idxResultType.getElementType();
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Type idxElementType =
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getElementTypeOrSelf(typec->convertType(idxResultType));
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if (!idxElementType.isa<IntegerType>())
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return rewriter.notifyMatchFailure(
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op, opName + " to linalg.* requires integer-like result type");
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@ -109,14 +106,12 @@ public:
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}
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// Constant op to account for the reduction along dim.
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auto c1 = rewriter.create<arith::ConstantIndexOp>(loc, /*value=*/1);
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SmallVector<Value> resultShape;
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for (int64_t i = 0; i < inputType.getRank(); i++) {
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if (dim != i) {
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auto currentDimSize = rewriter.create<tensor::DimOp>(loc, input, i);
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resultShape.push_back(currentDimSize);
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} else if (keepDim)
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resultShape.push_back(c1);
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}
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}
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// First fill the output buffer for the index.
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Value filledTensorIdx =
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@ -146,27 +141,23 @@ public:
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Value filledTensorVal =
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rewriter.create<linalg::FillOp>(loc, fillValue, initTensorVal).result();
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SmallVector<utils::IteratorType> iteratorTypes(
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inputType.getRank(), utils::IteratorType::parallel);
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iteratorTypes[dim] = utils::IteratorType::reduction;
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// Create the affine expressions that will be used to
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// iterate over the input and output tensors.
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// Here we also set the type of iterator: parallel or reduction.
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SmallVector<AffineExpr> exprs;
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SmallVector<utils::IteratorType> iteratorTypes;
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SmallVector<AffineExpr> resultExprs;
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for (auto size :
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llvm::enumerate(makeShapeTorchCompatible(inputType.getShape()))) {
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exprs.push_back(rewriter.getAffineDimExpr(size.index()));
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if (unsigned(dim) == size.index()) {
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iteratorTypes.push_back(utils::IteratorType::reduction);
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// If `keepDim`, create affine map to the first element
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// in the current dimension.
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if (keepDim)
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resultExprs.push_back(rewriter.getAffineConstantExpr(0));
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} else {
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iteratorTypes.push_back(utils::IteratorType::parallel);
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if (unsigned(dim) != size.index())
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resultExprs.push_back(rewriter.getAffineDimExpr(size.index()));
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}
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}
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auto maps = AffineMap::inferFromExprList({exprs, resultExprs, resultExprs},
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rewriter.getContext());
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auto linalgOp = rewriter.create<linalg::GenericOp>(
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@ -219,12 +210,58 @@ public:
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nestedLoc, ValueRange({resultVal, resultIndex}));
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});
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// This cast is required to fix the shape in the case of keepDim=True
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Value valuesCast = rewriter.create<tensor::CastOp>(loc, valResultType,
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linalgOp.getResult(0));
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Value idxCast = rewriter.create<tensor::CastOp>(loc, idxResultType,
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linalgOp.getResult(1));
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rewriter.replaceOp(op, {valuesCast, idxCast});
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if (!keepDim) {
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Value rVal = rewriter.create<tensor::CastOp>(loc, valResultType,
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linalgOp.getResult(0));
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Value rIdx = rewriter.create<tensor::CastOp>(loc, idxResultType,
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linalgOp.getResult(1));
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llvm::SmallVector<Value> res{rVal, rIdx};
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rewriter.replaceOp(op, res);
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return success();
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}
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llvm::SmallVector<int64_t> valShape(valResultType.getShape());
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llvm::SmallVector<int64_t> idxShape(idxResultType.getShape());
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for (int i = dim, s = valShape.size() - 1; i < s; ++i) {
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valShape[i] = valShape[i + 1];
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idxShape[i] = idxShape[i + 1];
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}
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valShape.resize(valShape.size() - 1);
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idxShape.resize(idxShape.size() - 1);
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Value rVal = rewriter.create<tensor::CastOp>(
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loc, valResultType.clone(valShape), linalgOp.getResult(0));
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Value rIdx = rewriter.create<tensor::CastOp>(
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loc, idxResultType.clone(idxShape), linalgOp.getResult(1));
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SmallVector<ReassociationIndices> reassociation(valShape.size());
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if (reassociation.size() > 0) {
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for (int i = 0; i < dim; ++i)
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reassociation[i].push_back(i);
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reassociation[std::max<int64_t>(0, dim - 1)].push_back(dim);
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for (int i = dim, s = reassociation.size(); i < s; ++i)
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reassociation[i].push_back(i + 1);
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}
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valShape.push_back(0);
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idxShape.push_back(0);
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for (int i = dim, s = valShape.size() - 1; i < s; ++i) {
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valShape[i + 1] = valShape[i];
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idxShape[i + 1] = idxShape[i];
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}
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valShape[dim] = 1;
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idxShape[dim] = 1;
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Value unsqueezeVal = rewriter.create<tensor::ExpandShapeOp>(
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loc, valResultType, rVal, reassociation);
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Value unsqueezeIdx = rewriter.create<tensor::ExpandShapeOp>(
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loc, idxResultType, rIdx, reassociation);
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llvm::SmallVector<Value> unsqueezes = {unsqueezeVal, unsqueezeIdx};
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rewriter.replaceOp(op, unsqueezes);
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return success();
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}
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};
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|
|
|
|||
|
|
@ -1316,6 +1316,57 @@ public:
|
|||
};
|
||||
} // namespace
|
||||
|
||||
namespace {
|
||||
class DecomposeAtenAMinMaxOp : public OpRewritePattern<Torch::AtenAminOp> {
|
||||
public:
|
||||
using OpRewritePattern<Torch::AtenAminOp>::OpRewritePattern;
|
||||
LogicalResult matchAndRewrite(Torch::AtenAminOp op,
|
||||
PatternRewriter &rewriter) const override {
|
||||
llvm::SmallVector<int64_t> dimList;
|
||||
if (!matchPattern(op.getDim(), m_TorchListOfConstantInts(dimList))) {
|
||||
return rewriter.notifyMatchFailure(op, "dims not foldable constants");
|
||||
}
|
||||
|
||||
bool keepdim;
|
||||
if (!matchPattern(op.getKeepdim(), m_TorchConstantBool(&keepdim))) {
|
||||
return rewriter.notifyMatchFailure(op, "keepdims not foldable constants");
|
||||
}
|
||||
|
||||
auto loc = op.getLoc();
|
||||
std::sort(dimList.begin(), dimList.end(), std::greater<int64_t>());
|
||||
|
||||
Value reduction = op.getSelf();
|
||||
auto resultTy = cast<Torch::ValueTensorType>(op.getType());
|
||||
auto reductionTy = cast<Torch::ValueTensorType>(reduction.getType());
|
||||
llvm::SmallVector<int64_t> reductionShape(reductionTy.getSizes());
|
||||
|
||||
for (auto dim : dimList) {
|
||||
auto dimValue = rewriter.create<Torch::ConstantIntOp>(
|
||||
loc, rewriter.getI64IntegerAttr(dim));
|
||||
reductionShape[dim] = 1;
|
||||
if (!keepdim) {
|
||||
for (int i = dim, s = reductionShape.size() - 1; i < s; ++i)
|
||||
reductionShape[i] = reductionShape[i + 1];
|
||||
reductionShape.resize(reductionShape.size() - 1);
|
||||
}
|
||||
|
||||
reductionTy = rewriter.getType<Torch::ValueTensorType>(
|
||||
reductionShape, resultTy.getOptionalDtype());
|
||||
auto idxTy = rewriter.getType<Torch::ValueTensorType>(
|
||||
reductionShape, rewriter.getIntegerType(32, /*is_signed*/ true));
|
||||
llvm::SmallVector<Type, 2> types{reductionTy, idxTy};
|
||||
reduction = rewriter
|
||||
.create<Torch::AtenMinDimOp>(loc, types, reduction,
|
||||
dimValue, op.getKeepdim())
|
||||
.getResult(0);
|
||||
}
|
||||
|
||||
rewriter.replaceOp(op, reduction);
|
||||
return success();
|
||||
}
|
||||
};
|
||||
} // namespace
|
||||
|
||||
// Decompose `AtenArgMaxOp` into `AtenMaxDimOp` as well as `AtenArgMinOp` into
|
||||
// `AtenMinDimOp`
|
||||
namespace {
|
||||
|
|
@ -6867,6 +6918,7 @@ public:
|
|||
addPatternIfTargetOpIsIllegal<DecomposeAtenAddmmOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenMeanOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenMeanDimOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenAMinMaxOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenSelectIntOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenMatmulOp>(patterns);
|
||||
addPatternIfTargetOpIsIllegal<DecomposeAtenMvOp>(patterns);
|
||||
|
|
|
|||
|
|
@ -77,6 +77,7 @@ void TorchConversion::createTorchBackendToLinalgOnTensorsBackendPipeline(
|
|||
pm.addNestedPass<func::FuncOp>(createConvertTorchToTMTensorPass());
|
||||
pm.addNestedPass<func::FuncOp>(createCanonicalizerPass());
|
||||
pm.addNestedPass<func::FuncOp>(createConvertTorchToLinalgPass());
|
||||
pm.addNestedPass<func::FuncOp>(createCanonicalizerPass());
|
||||
pm.addNestedPass<func::FuncOp>(createConvertTorchToSCFPass());
|
||||
pm.addNestedPass<func::FuncOp>(createConvertTorchToArithPass());
|
||||
pm.addNestedPass<func::FuncOp>(createConvertTorchToTensorPass());
|
||||
|
|
|
|||
|
|
@ -1472,6 +1472,62 @@ LTC_XFAIL_SET = {
|
|||
}
|
||||
|
||||
ONNX_XFAIL_SET = {
|
||||
# Failure - cast error
|
||||
"MeanDimNoneDimModule_basic",
|
||||
"MeanDtypeModule_basic",
|
||||
"MeanDynamicSizesModule_basic",
|
||||
"MeanModule_basic",
|
||||
"MseLossMeanReductionModule_basic",
|
||||
"PermuteNegativeIndexModule_basic",
|
||||
"StdBiasedModule_basic",
|
||||
"VarBiasedModule_basic",
|
||||
"VarMeanBiasedModule_basic",
|
||||
|
||||
# Failure - constant int lowering
|
||||
"SplitTensorGetItem_Module_basic",
|
||||
"SplitTensorLastSmallerModule_basic",
|
||||
"SplitTensorListUnpackModule_basic",
|
||||
"SplitTensorNegativeDimModule_basic",
|
||||
"SplitWithSizesListUnpackModule_basic",
|
||||
"UnbindIntGetItem_Module_basic",
|
||||
"UnbindIntListUnpack_Module_basic",
|
||||
|
||||
# Failure - incorrect numerics
|
||||
"AdaptiveAvgPool1dUnitOutputSizeDynamicModule_basic",
|
||||
"AdaptiveAvgPool2dUnitOutputSizeDynamicModule_basic",
|
||||
"ElementwiseAtan2TensorIntModule_basic",
|
||||
"ElementwiseLog10IntModule_basic",
|
||||
"ElementwiseLog2IntModule_basic",
|
||||
"ElementwiseSeluModule_basic",
|
||||
"FlipModuleStaticShape_basic",
|
||||
"FlipNegativeIndexModule_basic",
|
||||
"HardsigmoidModule_basic",
|
||||
"HardsigmoidRandomModule_basic",
|
||||
"IndexSelectDynamicInputSizeModule_basic",
|
||||
"IndexSelectWholeDimensionModule_basic",
|
||||
"IndexSelectWholeTensorModule_basic",
|
||||
"IndexTensorStaticModule_basic",
|
||||
"IndexTensorStaticNonContiguousWithNoneModule_basic",
|
||||
"PixelShuffleModuleStaticRank4Float32_basic",
|
||||
"ResNet18Module_basic",
|
||||
"SliceCopyEndGreaterThanDimSize_Module_basic",
|
||||
"SliceCopyNegative_Module_basic",
|
||||
"SliceCopyNonZeroDim_Module_basic",
|
||||
"SliceCopy_Module_basic",
|
||||
"TupleModule_basic",
|
||||
|
||||
# Failure - incorrect shape
|
||||
"ArangeStartOutDtypeModule_basic",
|
||||
"ArangeStartOutViewModule_basic",
|
||||
"BroadcastDynamicDimModule_basic",
|
||||
"BroadcastToModule_basic",
|
||||
"ExpandModule_basic",
|
||||
"MoveDimIntNegativeIndexModule_basic",
|
||||
"ReduceAmaxKeepDim_basic",
|
||||
"ReduceMaxKeepDimReturnBoth_basic",
|
||||
"ReduceMaxNegativeDim_basic",
|
||||
"ViewSizeFromOtherTensor_basic",
|
||||
|
||||
# Failure - onnx_export
|
||||
"AdaptiveAvgPool1dGeneralDynamic_basic",
|
||||
"AdaptiveAvgPool1dNonUnitOutputSizeDynamicModule_basic",
|
||||
|
|
@ -1594,6 +1650,7 @@ ONNX_XFAIL_SET = {
|
|||
"EmptyStridedSizeIntStrideModule_basic",
|
||||
"EqIntModule_basic",
|
||||
"ExponentialModule_basic",
|
||||
"FloatImplicitModule_basic",
|
||||
"GeFloatIntModule_basic",
|
||||
"GeFloatModule_basic",
|
||||
"GeIntModule_basic",
|
||||
|
|
@ -1613,6 +1670,7 @@ ONNX_XFAIL_SET = {
|
|||
"IndexPutImpl3DFloatNonAccumulateModule_basic",
|
||||
"IndexPutImplIndexWithNoneModule_basic",
|
||||
"IntFloatModule_basic",
|
||||
"IntImplicitModule_basic",
|
||||
"IouOfModule_basic",
|
||||
"IsFloatingPointFloat_True",
|
||||
"IsFloatingPointInt_False",
|
||||
|
|
@ -1818,13 +1876,8 @@ ONNX_XFAIL_SET = {
|
|||
"_ConvolutionDeprecated2DCudnnModule_basic",
|
||||
"_ConvolutionDeprecated2DDeterministicModule_basic",
|
||||
"_SoftmaxModule_basic",
|
||||
|
||||
|
||||
# Failure - onnx_import
|
||||
"BucketizeTensorFloatModule_basic",
|
||||
"BucketizeTensorModule_basic",
|
||||
"BucketizeTensorOutInt32RightModule_basic",
|
||||
"BucketizeTensorStaticFloatModule_basic",
|
||||
"BucketizeTensorStaticModule_basic",
|
||||
"DiagonalModule_basic",
|
||||
"DiagonalModule_nonsquare",
|
||||
"DiagonalModule_transposed",
|
||||
|
|
@ -1832,31 +1885,6 @@ ONNX_XFAIL_SET = {
|
|||
"DiagonalModule_with_dims_and_offset",
|
||||
"DiagonalModule_with_negative_dims",
|
||||
"DiagonalModule_with_offset",
|
||||
"ElementwiseClampMaxModule_basic",
|
||||
"ElementwiseClampMinModule_basic",
|
||||
"ElementwiseClampMinTensorFloatModule_basic",
|
||||
"ElementwiseClampMinTensorIntModule_basic",
|
||||
"ElementwiseClampModule_basic",
|
||||
"ElementwiseClampTensorFloatModule_basic",
|
||||
"ElementwiseClampTensorInt8Module_basic",
|
||||
"ElementwiseClampTensorIntModule_basic",
|
||||
"HBC_basic",
|
||||
"IndexPut1DFloatAccumulateModule_basic",
|
||||
"IndexPut1DIntAccumulateModule_basic",
|
||||
"IndexPut2DFloatAccumulateModule_basic",
|
||||
"IndexPut2DIntAccumulateModule_basic",
|
||||
"IndexPut3DFloatAccumulateModule_basic",
|
||||
"IndexPut3DIntAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DFloatAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DIntAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DFloatAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DIntAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DFloatAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DIntAccumulateModule_basic",
|
||||
"NormalizeModule_basic",
|
||||
"PadWithNoneValModule_basic",
|
||||
"QuantizedMLP_basic",
|
||||
"RandModule_basic",
|
||||
"ScatterReduceFloatMaxModuleIncludeSelf",
|
||||
"ScatterReduceFloatMinModuleIncludeSelf",
|
||||
"ScatterReduceFloatProdModuleIncludeSelf",
|
||||
|
|
@ -1867,21 +1895,11 @@ ONNX_XFAIL_SET = {
|
|||
"ScatterReduceIntSumModuleIncludeSelf",
|
||||
"TileBigDimsSizeModule_basic",
|
||||
"TileSmallDimsSizeModule_basic",
|
||||
"UpSampleNearest2dDynamicSize_basic",
|
||||
"UpSampleNearest2dStaticSize_basic",
|
||||
|
||||
# Failure - onnx_lowering
|
||||
|
||||
# Failure - onnx_lowering: onnx.AveragePool
|
||||
"AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
|
||||
"AdaptiveAvgPool1dStaticEvenMultiple_basic",
|
||||
"AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic",
|
||||
"AtenMmFloatTypes_basic",
|
||||
"AtenMmIntTypes_basic",
|
||||
"AtenTrilModule_basic",
|
||||
"AtenTrilWithNegDiagonalModule_basic",
|
||||
"AtenTrilWithPosDiagonalModule_basic",
|
||||
"AtenTriuModule_basic",
|
||||
"AtenTriuWithNegDiagonalModule_basic",
|
||||
"AtenTriuWithPosDiagonalModule_basic",
|
||||
"AvgPool1dFloatModule_basic",
|
||||
"AvgPool1dIntModule_basic",
|
||||
"AvgPool1dStaticModule_basic",
|
||||
|
|
@ -1890,78 +1908,73 @@ ONNX_XFAIL_SET = {
|
|||
"AvgPool2dFloatModule_basic",
|
||||
"AvgPool2dIntModule_basic",
|
||||
"AvgPool2dStaticModule_basic",
|
||||
"BernoulliFloatModule_basic",
|
||||
"BernoulliModule_basic",
|
||||
"BernoulliPModule_basic",
|
||||
"BernoulliTensorModule_basic",
|
||||
"ConstantPad2dStaticModule_basic",
|
||||
"ConstantPadNdModule_basic",
|
||||
"ConstantPadNdPartialStaticModule_basic",
|
||||
"ConstantPadNdStaticModule_basic",
|
||||
"CrossEntropyLossModule_basic",
|
||||
"CrossEntropyLossNoReductionModule_basic",
|
||||
"DropoutTrainModule_basic",
|
||||
"DropoutTrainStaticShapeModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Cast
|
||||
"BucketizeTensorOutInt32RightModule_basic",
|
||||
"ElementwiseToDtypeI64ToI8Module_basic",
|
||||
"ElementwiseToDtypeI64ToUI8Module_basic",
|
||||
"HBC_basic",
|
||||
"QuantizedMLP_basic",
|
||||
"TypeConversionI1ToI32Module_basic",
|
||||
"TypeConversionI64ToI32Module_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Clip
|
||||
"ElementwiseClampMaxModule_basic",
|
||||
"ElementwiseClampMinModule_basic",
|
||||
"ElementwiseClampMinTensorFloatModule_basic",
|
||||
"ElementwiseClampMinTensorIntModule_basic",
|
||||
"ElementwiseClampModule_basic",
|
||||
"ElementwiseClampTensorFloatModule_basic",
|
||||
"ElementwiseClampTensorInt8Module_basic",
|
||||
"ElementwiseClampTensorIntModule_basic",
|
||||
"NormalizeModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Einsum
|
||||
"EinsumStaticContractRhsModule_basic",
|
||||
"EinsumStaticFourDimensionModule_basic",
|
||||
"EinsumStaticModule_basic",
|
||||
"ElementwiseMishModule_basic",
|
||||
"ElementwiseRemainderScalarModule_Bool_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_basic",
|
||||
"ElementwiseToDtypeI64ToI8Module_basic",
|
||||
"ElementwiseToDtypeI64ToUI8Module_basic",
|
||||
"GroupNormModule_basic",
|
||||
"GroupNormNoWeightAndBiasModule_basic",
|
||||
"HardswishModule_basic",
|
||||
"HardswishRandomModule_basic",
|
||||
"IndexPut1DFloatNonAccumulateModule_basic",
|
||||
"IndexPut1DIntNonAccumulateModule_basic",
|
||||
"IndexPut2DFloatNonAccumulateModule_basic",
|
||||
"IndexPut2DIntNonAccumulateModule_basic",
|
||||
"IndexPut3DFloatNonAccumulateModule_basic",
|
||||
"IndexPut3DIntNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DFloatNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DIntNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DFloatNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DIntNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DFloatNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DIntNonAccumulateModule_basic",
|
||||
"LogSoftmaxIntModule_basic",
|
||||
"MaxPool2dWithIndicesAllNegativeValuesModule_basic",
|
||||
"MaxPool2dWithIndicesNonDefaultPaddingModule_basic",
|
||||
"MaxPool2dWithIndicesStaticModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Gemm
|
||||
"AtenMmFloatTypes_basic",
|
||||
"AtenMmIntTypes_basic",
|
||||
"MmDagModule_basic",
|
||||
"MmModule_basic",
|
||||
"MmModule_chained",
|
||||
"MmTanhModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.HardSwish
|
||||
"HardswishModule_basic",
|
||||
"HardswishRandomModule_basic",
|
||||
"MobilenetV3Module_basic",
|
||||
"MseLossSumReductionWithDifferentElemTypeModule_basic",
|
||||
"NativeDropoutTrainModule_basic",
|
||||
"NativeDropoutTrainStaticShapeModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.LogSoftmax
|
||||
"LogSoftmaxIntModule_basic",
|
||||
"_LogSoftmaxModuleStable_basic",
|
||||
"_LogSoftmaxModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.MaxPool
|
||||
"MaxPool2dWithIndicesAllNegativeValuesModule_basic",
|
||||
"MaxPool2dWithIndicesNonDefaultPaddingModule_basic",
|
||||
"MaxPool2dWithIndicesStaticModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Mod
|
||||
"ElementwiseRemainderScalarModule_Bool_basic",
|
||||
"ElementwiseRemainderScalarModule_Int_basic",
|
||||
"UnflattenIntNegativeOneDimStaticModule_basic",
|
||||
"UnflattenIntNegativeOneSizeStaticModule_basic",
|
||||
"UnflattenIntStaticModule_basic",
|
||||
"UnflattenStaticModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.OneHot
|
||||
"OneHotModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Pad
|
||||
"ConstantPad2dStaticModule_basic",
|
||||
"ConstantPadNdModule_basic",
|
||||
"ConstantPadNdPartialStaticModule_basic",
|
||||
"ConstantPadNdStaticModule_basic",
|
||||
"PadModule_basic",
|
||||
"RandIntLowDtypeModule_basic",
|
||||
"RandIntLowModule_basic",
|
||||
"RandLikeDtypeModule_basic",
|
||||
"RandLikeModule_basic",
|
||||
"RandnDtypeDeviceModule_basic",
|
||||
"RandnGeneratorF64Module_basic",
|
||||
"RandnGeneratorModule_basic",
|
||||
"RandnLikeDtypeModule_basic",
|
||||
"RandnLikeModule_basic",
|
||||
"RandnModule_basic",
|
||||
"ReduceL1NormModule_basic",
|
||||
"ReduceL1NormWithDTypeModule_basic",
|
||||
"ReduceL2NormModule_basic",
|
||||
"ReduceL3NormAllDimsModule_basic",
|
||||
"ReduceL3NormKeepDimModule_basic",
|
||||
"ReduceProdDimIntFloatModule_basic",
|
||||
"ReduceSumDtypeFloatModule_basic",
|
||||
"ReduceSumDtypeIntModule_basic",
|
||||
"ReduceSumElementTypeBoolModule_basic",
|
||||
"ReduceSumFloatModule_basic",
|
||||
"ReduceSumSignedIntModule_basic",
|
||||
"ReduceSumUnsignedIntModule_basic",
|
||||
"PadWithNoneValModule_basic",
|
||||
"ReflectionPad1dModule2dInput_Right",
|
||||
"ReflectionPad1dModule2dInput_basic",
|
||||
"ReflectionPad1dModule3dInput_Left",
|
||||
|
|
@ -1976,19 +1989,43 @@ ONNX_XFAIL_SET = {
|
|||
"ReplicationPad2dModule_left0",
|
||||
"ReplicationPad2dModule_right0",
|
||||
"ReplicationPad2dModule_top0",
|
||||
"ScatterSrcModule_basic",
|
||||
"ScatterSrcStaticModule_basic",
|
||||
"ScatterValueFloatModule_basic",
|
||||
"ScatterValueIntModule_basic",
|
||||
"SoftplusModule_basic",
|
||||
"SortTensorDescending_basic",
|
||||
"SortTensorInteger_basic",
|
||||
"SortTensorNegativeDimension_basic",
|
||||
"SortTensorSpecificDimension_basic",
|
||||
"SortTensor_basic",
|
||||
"SqueezeModule_allUnitDim",
|
||||
"SqueezeModule_broadcast",
|
||||
"SqueezeModule_static",
|
||||
|
||||
# Failure - onnx_lowering: onnx.RandomNormal
|
||||
"RandnDtypeDeviceModule_basic",
|
||||
"RandnGeneratorF64Module_basic",
|
||||
"RandnGeneratorModule_basic",
|
||||
"RandnModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.RandomNormalLike
|
||||
"RandnLikeDtypeModule_basic",
|
||||
"RandnLikeModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.RandomUniform
|
||||
"RandIntLowDtypeModule_basic",
|
||||
"RandIntLowModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.RandomUniformLike
|
||||
"BernoulliFloatModule_basic",
|
||||
"BernoulliPModule_basic",
|
||||
"BernoulliTensorModule_basic",
|
||||
"RandLikeDtypeModule_basic",
|
||||
"RandLikeModule_basic",
|
||||
"RandModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.ReduceL1
|
||||
"ReduceL1NormModule_basic",
|
||||
"ReduceL1NormWithDTypeModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.ReduceL2
|
||||
"ReduceL2NormModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.ReduceProd
|
||||
"BernoulliModule_basic",
|
||||
"DropoutTrainModule_basic",
|
||||
"DropoutTrainStaticShapeModule_basic",
|
||||
"NativeDropoutTrainModule_basic",
|
||||
"NativeDropoutTrainStaticShapeModule_basic",
|
||||
"ReduceProdDimIntFloatModule_basic",
|
||||
"StdCorrectionAllDimReduceModule_basic",
|
||||
"StdCorrectionKeepDimModule_basic",
|
||||
"StdCorrectionLargeInputModule_basic",
|
||||
|
|
@ -1999,14 +2036,6 @@ ONNX_XFAIL_SET = {
|
|||
"StdDimKeepDimTrueModule_basic",
|
||||
"StdDimNoneDimModule_basic",
|
||||
"StdUnbiasedModule_basic",
|
||||
"TriuBroadcastModule_basic",
|
||||
"TriuModule_basic",
|
||||
"TypeConversionI1ToI32Module_basic",
|
||||
"TypeConversionI64ToI32Module_basic",
|
||||
"UnflattenIntNegativeOneDimStaticModule_basic",
|
||||
"UnflattenIntNegativeOneSizeStaticModule_basic",
|
||||
"UnflattenIntStaticModule_basic",
|
||||
"UnflattenStaticModule_basic",
|
||||
"VarCorrectionAllDimReduceModule_basic",
|
||||
"VarCorrectionKeepDimModule_basic",
|
||||
"VarCorrectionLargeInputModule_basic",
|
||||
|
|
@ -2025,58 +2054,85 @@ ONNX_XFAIL_SET = {
|
|||
"VarMeanDimModule_basic",
|
||||
"VarMeanUnbiasedModule_basic",
|
||||
"VarUnbiasedModule_basic",
|
||||
"_LogSoftmaxModuleStable_basic",
|
||||
"_LogSoftmaxModule_basic",
|
||||
|
||||
# Failure - cast_error
|
||||
"MeanDimNoneDimModule_basic",
|
||||
"MeanDtypeModule_basic",
|
||||
"MeanDynamicSizesModule_basic",
|
||||
"MeanModule_basic",
|
||||
"MseLossMeanReductionModule_basic",
|
||||
"StdBiasedModule_basic",
|
||||
"VarBiasedModule_basic",
|
||||
"VarMeanBiasedModule_basic",
|
||||
|
||||
# Failure - constant_int
|
||||
"ReduceMinAlongDimNegative_basic",
|
||||
"ReduceMinAlongDimSignedInt_basic",
|
||||
"ReduceMinAlongDim_basic",
|
||||
"ReduceMinFloatModule_basic",
|
||||
"ReduceMinKeepDimReturnBoth_basic",
|
||||
"ReduceMinSignedIntModule_basic",
|
||||
"ReduceMinUnsignedIntModule_basic",
|
||||
"SplitTensorGetItem_Module_basic",
|
||||
"SplitTensorLastSmallerModule_basic",
|
||||
"SplitTensorListUnpackModule_basic",
|
||||
"SplitTensorNegativeDimModule_basic",
|
||||
"SplitWithSizesListUnpackModule_basic",
|
||||
"UnbindIntGetItem_Module_basic",
|
||||
"UnbindIntListUnpack_Module_basic",
|
||||
|
||||
# Failure - operand_type
|
||||
"ElementwiseAcosIntModule_basic",
|
||||
"ElementwiseAsinIntModule_basic",
|
||||
"ElementwiseAtanTensorIntModule_basic",
|
||||
"ElementwiseCosIntModule_basic",
|
||||
"ElementwiseErfIntModule_basic",
|
||||
"ElementwiseExpIntModule_basic",
|
||||
"ElementwiseLog10IntModule_basic",
|
||||
"ElementwiseLog2IntModule_basic",
|
||||
"ElementwiseLogIntModule_basic",
|
||||
"ElementwiseSinIntModule_basic",
|
||||
"ElementwiseTanIntModule_basic",
|
||||
"ElementwiseUnaryIntModule_basic",
|
||||
|
||||
# Failure - expand_multidim
|
||||
"IndexTensorHackedTwinModule3dInput_basic",
|
||||
"IndexTensorHackedTwinModule_basic",
|
||||
"IndexTensorModule3dInput_basic",
|
||||
"IndexTensorModule_basic",
|
||||
"IndexTensorMultiInputContiguousOneDimDynamic_basic",
|
||||
"IndexTensorMultiInputNonContiguousOneDimDynamic_basic",
|
||||
|
||||
# Failure - rankless_return
|
||||
|
||||
# Failure - onnx_lowering: onnx.ReduceSum
|
||||
"MseLossSumReductionWithDifferentElemTypeModule_basic",
|
||||
"ReduceL3NormAllDimsModule_basic",
|
||||
"ReduceL3NormKeepDimModule_basic",
|
||||
"ReduceSumDtypeFloatModule_basic",
|
||||
"ReduceSumDtypeIntModule_basic",
|
||||
"ReduceSumElementTypeBoolModule_basic",
|
||||
"ReduceSumFloatModule_basic",
|
||||
"ReduceSumSignedIntModule_basic",
|
||||
"ReduceSumUnsignedIntModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Resize
|
||||
"UpSampleNearest2dDynamicSize_basic",
|
||||
"UpSampleNearest2dStaticSize_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.ScatterElements
|
||||
"ScatterSrcModule_basic",
|
||||
"ScatterSrcStaticModule_basic",
|
||||
"ScatterValueFloatModule_basic",
|
||||
"ScatterValueIntModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.ScatterND
|
||||
"IndexPut1DFloatAccumulateModule_basic",
|
||||
"IndexPut1DFloatNonAccumulateModule_basic",
|
||||
"IndexPut1DIntAccumulateModule_basic",
|
||||
"IndexPut1DIntNonAccumulateModule_basic",
|
||||
"IndexPut2DFloatAccumulateModule_basic",
|
||||
"IndexPut2DFloatNonAccumulateModule_basic",
|
||||
"IndexPut2DIntAccumulateModule_basic",
|
||||
"IndexPut2DIntNonAccumulateModule_basic",
|
||||
"IndexPut3DFloatAccumulateModule_basic",
|
||||
"IndexPut3DFloatNonAccumulateModule_basic",
|
||||
"IndexPut3DIntAccumulateModule_basic",
|
||||
"IndexPut3DIntNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DFloatAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DFloatNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DIntAccumulateModule_basic",
|
||||
"IndexPutHackedTwin1DIntNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DFloatAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DFloatNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DIntAccumulateModule_basic",
|
||||
"IndexPutHackedTwin2DIntNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DFloatAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DFloatNonAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DIntAccumulateModule_basic",
|
||||
"IndexPutHackedTwin3DIntNonAccumulateModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.SoftmaxCrossEntropyLoss
|
||||
"CrossEntropyLossModule_basic",
|
||||
"CrossEntropyLossNoReductionModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Softplus
|
||||
"ElementwiseMishModule_basic",
|
||||
"SoftplusModule_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Squeeze
|
||||
"SqueezeModule_allUnitDim",
|
||||
"SqueezeModule_broadcast",
|
||||
"SqueezeModule_static",
|
||||
|
||||
# Failure - onnx_lowering: onnx.TopK
|
||||
"SortTensorDescending_basic",
|
||||
"SortTensorInteger_basic",
|
||||
"SortTensorNegativeDimension_basic",
|
||||
"SortTensorSpecificDimension_basic",
|
||||
"SortTensor_basic",
|
||||
|
||||
# Failure - onnx_lowering: onnx.Trilu
|
||||
"AtenTrilModule_basic",
|
||||
"AtenTrilWithNegDiagonalModule_basic",
|
||||
"AtenTrilWithPosDiagonalModule_basic",
|
||||
"AtenTriuModule_basic",
|
||||
"AtenTriuWithNegDiagonalModule_basic",
|
||||
"AtenTriuWithPosDiagonalModule_basic",
|
||||
"TriuBroadcastModule_basic",
|
||||
"TriuModule_basic",
|
||||
|
||||
# Failure - rankless return
|
||||
"ReduceAmaxMultiDim_basic",
|
||||
"ReduceAmaxOutOfOrderDim_basic",
|
||||
"ReduceAmaxSingleDim_basic",
|
||||
|
|
@ -2088,8 +2144,8 @@ ONNX_XFAIL_SET = {
|
|||
"ReduceMaxFloatModule_basic",
|
||||
"ReduceMaxSignedIntModule_basic",
|
||||
"ReduceMaxUnsignedIntModule_basic",
|
||||
|
||||
# Failure - view_lowering
|
||||
|
||||
# Failure - torch.aten.view lower
|
||||
"AddSizeIntModule_basic",
|
||||
"ElementwiseFlattenBroadcastModule_basic",
|
||||
"FlattenRank0Module_basic",
|
||||
|
|
@ -2097,13 +2153,11 @@ ONNX_XFAIL_SET = {
|
|||
"IndexTensorDyanmicInputNonContiguousWithNoneModule_basic",
|
||||
"IndexTensorHackedTwinMultiInputNonContiguousMultipleStaticDims_basic",
|
||||
"IndexTensorMultiInputContiguousCenter_basic",
|
||||
"IndexTensorMultiInputNonContiguousDynamic_basic",
|
||||
"IndexTensorMultiInputNonContiguousMultipleStaticDims_basic",
|
||||
"IndexTensorMultiInputNonContiguous_basic",
|
||||
"IndexTensorMultiInputOneDim_basic",
|
||||
"IndexTensorMultiInputThreeIndexers_basic",
|
||||
"IndexTensorMultiInput_basic",
|
||||
"IndexTensorSelectDimModule_basic",
|
||||
"IndexTensorStaticContiguousWithNoneModule_basic",
|
||||
"RepeatModule_basic",
|
||||
"SelectIntModule_basic",
|
||||
|
|
@ -2116,63 +2170,50 @@ ONNX_XFAIL_SET = {
|
|||
"ViewSizeDimLedAndFollowedByExpandedOnesModule_basic",
|
||||
"ViewSizeDimLedByCollapsedOnesModule_basic",
|
||||
"ViewSizeDimLedByExpandedOnesModule_basic",
|
||||
|
||||
# Failure - numerical
|
||||
"AdaptiveAvgPool1dUnitOutputSizeDynamicModule_basic",
|
||||
"AdaptiveAvgPool2dUnitOutputSizeDynamicModule_basic",
|
||||
"ElementwiseSeluModule_basic",
|
||||
"EmbeddingModule1DIndices_basic",
|
||||
"FlipNegativeIndexModule_basic",
|
||||
"HardsigmoidModule_basic",
|
||||
"HardsigmoidRandomModule_basic",
|
||||
"IndexSelectDynamicIndexSizeModule_basic",
|
||||
"IndexSelectDynamicInputSizeModule_basic",
|
||||
"IndexSelectDynamicModulebasic",
|
||||
"IndexSelectWholeDimensionModule_basic",
|
||||
"IndexSelectWholeTensorModule_basic",
|
||||
"IndexTensorStaticModule_basic",
|
||||
"IndexTensorStaticNonContiguousWithNoneModule_basic",
|
||||
"PixelShuffleModuleStaticRank4Float32_basic",
|
||||
"ResNet18Module_basic",
|
||||
"SliceCopyEndGreaterThanDimSize_Module_basic",
|
||||
"SliceCopyNegative_Module_basic",
|
||||
"SliceCopyNonZeroDim_Module_basic",
|
||||
"SliceCopy_Module_basic",
|
||||
"TupleModule_basic",
|
||||
|
||||
# Failure - shape
|
||||
"ArangeStartOutDtypeModule_basic",
|
||||
"ArangeStartOutViewModule_basic",
|
||||
"BroadcastDynamicDimModule_basic",
|
||||
"BroadcastToModule_basic",
|
||||
"EmbeddingModuleF16_basic",
|
||||
"EmbeddingModuleI32_basic",
|
||||
"EmbeddingModuleI64_basic",
|
||||
"ExpandModule_basic",
|
||||
"MoveDimIntNegativeIndexModule_basic",
|
||||
"PermuteNegativeIndexModule_basic",
|
||||
"ReduceAmaxKeepDim_basic",
|
||||
"ReduceMaxKeepDimReturnBoth_basic",
|
||||
"ReduceMaxNegativeDim_basic",
|
||||
"ViewSizeFromOtherTensor_basic",
|
||||
|
||||
# Failure - onnx traces differently
|
||||
"ElementwiseSigmoidIntModule_basic",
|
||||
|
||||
# Failure - unknown
|
||||
"BucketizeTensorFloatModule_basic",
|
||||
"BucketizeTensorModule_basic",
|
||||
"BucketizeTensorStaticFloatModule_basic",
|
||||
"BucketizeTensorStaticModule_basic",
|
||||
"Conv2dWithPaddingDilationStrideStaticModule_depthwise_multiplier",
|
||||
"CopyWithDifferentDTypesAndSizesModule_basic",
|
||||
"CopyWithDifferentDTypesModule_basic",
|
||||
"CosineSimilarityStaticBroadcastModule_basic",
|
||||
"CumsumInputDtypeInt32Module_basic",
|
||||
"ElementwiseAtan2TensorIntModule_basic",
|
||||
"ElementwiseAcosIntModule_basic",
|
||||
"ElementwiseAsinIntModule_basic",
|
||||
"ElementwiseAtanTensorIntModule_basic",
|
||||
"ElementwiseCosIntModule_basic",
|
||||
"ElementwiseDivRoundingModeTruncModule_basic",
|
||||
"ElementwiseErfIntModule_basic",
|
||||
"ElementwiseExpIntModule_basic",
|
||||
"ElementwiseLogIntModule_basic",
|
||||
"ElementwisePreluModule_basic",
|
||||
"ElementwiseSigmoidIntModule_basic",
|
||||
"ElementwiseSinIntModule_basic",
|
||||
"ElementwiseTanIntModule_basic",
|
||||
"ElementwiseUnaryIntModule_basic",
|
||||
"ElementwiseUnsqueezeNegDimsModule_basic",
|
||||
"ElementwiseWhereScalarModule_basic",
|
||||
"EmbeddingModule1DIndices_basic",
|
||||
"EmbeddingModuleF16_basic",
|
||||
"EmbeddingModuleI32_basic",
|
||||
"EmbeddingModuleI64_basic",
|
||||
"FlattenDynamicModule_basic",
|
||||
"FlipModuleStaticShape_basic",
|
||||
"GluStaticModule_basic",
|
||||
"GroupNormModule_basic",
|
||||
"GroupNormNoWeightAndBiasModule_basic",
|
||||
"IndexSelectDynamicIndexSizeModule_basic",
|
||||
"IndexSelectDynamicModulebasic",
|
||||
"IndexTensorHackedTwinModule3dInput_basic",
|
||||
"IndexTensorHackedTwinModule_basic",
|
||||
"IndexTensorModule3dInput_basic",
|
||||
"IndexTensorModule_basic",
|
||||
"IndexTensorMultiInputContiguousOneDimDynamic_basic",
|
||||
"IndexTensorMultiInputNonContiguousDynamic_basic",
|
||||
"IndexTensorMultiInputNonContiguousOneDimDynamic_basic",
|
||||
"IndexTensorSelectDimModule_basic",
|
||||
"MaskedFillTensorFloatValueModule_basic",
|
||||
"ReduceAllDimEmpty_basic",
|
||||
"ReduceAllDimFloat_basic",
|
||||
|
|
@ -2180,8 +2221,6 @@ ONNX_XFAIL_SET = {
|
|||
"ReduceMinAlongDimUnsignedInt_basic",
|
||||
"TensorsStackNegativeDimModule_basic",
|
||||
"TensorsStackPromoteDTypeModule_basic",
|
||||
"FloatImplicitModule_basic",
|
||||
"IntImplicitModule_basic",
|
||||
}
|
||||
|
||||
ONNX_CRASHING_SET = { }
|
||||
|
|
|
|||
|
|
@ -926,107 +926,121 @@ func.func @test_reduce_mean_negative_axes_keepdims_example(%arg0: !torch.vtensor
|
|||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_min_bool_inputs
|
||||
func.func @test_reduce_min_bool_inputs(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,1],i1> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[INT2:.+]] = torch.constant.int 2
|
||||
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
|
||||
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
|
||||
// CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
|
||||
// CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
|
||||
// CHECK: torch.aten.mul.int %3, %int2 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
|
||||
// CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4,1],i1>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[4,2],i1>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,1],i1>
|
||||
return %0 : !torch.vtensor<[4,1],i1>
|
||||
}
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_min_default_axes_keepdims_example
|
||||
func.func @test_reduce_min_default_axes_keepdims_example(%arg0: !torch.vtensor<[3,2,2],f32>) -> !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: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK: torch.aten.Bool.int %int1 : !torch.int -> !torch.bool
|
||||
// CHECK: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[INT1_0:.+]] = torch.constant.int 1
|
||||
// CHECK: %[[INT2:.+]] = torch.constant.int 2
|
||||
// CHECK: torch.prim.ListConstruct %int0, %int1_0, %int2 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
|
||||
// CHECK: torch.aten.amin %arg0, %1, %0 : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[1,1,1],f32>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[3,2,2],f32>) -> !torch.vtensor<[1,1,1],f32>
|
||||
return %0 : !torch.vtensor<[1,1,1],f32>
|
||||
}
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_min_do_not_keepdims_example
|
||||
func.func @test_reduce_min_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: %[[INT3:.+]] = torch.constant.int 3
|
||||
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
|
||||
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
|
||||
// CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
|
||||
// CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
|
||||
// CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[FALSE:.+]] = torch.constant.bool false
|
||||
// CHECK: torch.aten.amin %arg0, %6, %false : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[3,2],f32>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%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_min_empty_set
|
||||
func.func @test_reduce_min_empty_set(%arg0: !torch.vtensor<[2,0,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,1,4],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[INT3:.+]] = torch.constant.int 3
|
||||
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
|
||||
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
|
||||
// CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
|
||||
// CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
|
||||
// CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
|
||||
// CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[2,0,4],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[2,1,4],f32>
|
||||
// CHECK-LABEL: func.func @test_reduce_min_empty_set_fp
|
||||
func.func @test_reduce_min_empty_set_fp(%arg0: !torch.vtensor<[2,0,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,1,4],f32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK-DAG: %[[INF:.+]] = torch.constant.float 0x7FF0000000000000
|
||||
// CHECK-DAG: %[[INT2:.+]] = torch.constant.int 2
|
||||
// CHECK-DAG: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK-DAG: %[[INT4:.+]] = torch.constant.int 4
|
||||
// CHECK-DAG: %[[NONE:.+]] = torch.constant.none
|
||||
// CHECK-DAG: %[[LIST:.+]] = torch.prim.ListConstruct %[[INT2]], %[[INT1]], %[[INT4]]
|
||||
// CHECK-DAG: %[[FULL:.+]] = torch.aten.full %[[LIST]], %[[INF]], %[[NONE]], %[[NONE]], %[[NONE]]
|
||||
// CHECK: return %[[FULL]]
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[2,0,4],f32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,1,4],f32>
|
||||
return %0 : !torch.vtensor<[2,1,4],f32>
|
||||
}
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_min_keepdims_example
|
||||
func.func @test_reduce_min_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: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[INT3:.+]] = torch.constant.int 3
|
||||
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
|
||||
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
|
||||
// CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
|
||||
// CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
|
||||
// CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
|
||||
// CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[3,1,2],f32>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%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_min_empty_set_int
|
||||
func.func @test_reduce_min_empty_set_int(%arg0: !torch.vtensor<[2,0,4],si32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,1,4],si32> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK-DAG: %[[INF:.+]] = torch.constant.int 2147483647
|
||||
// CHECK-DAG: %[[INT2:.+]] = torch.constant.int 2
|
||||
// CHECK-DAG: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK-DAG: %[[INT4:.+]] = torch.constant.int 4
|
||||
// CHECK-DAG: %[[NONE:.+]] = torch.constant.none
|
||||
// CHECK-DAG: %[[LIST:.+]] = torch.prim.ListConstruct %[[INT2]], %[[INT1]], %[[INT4]]
|
||||
// CHECK-DAG: %[[FULL:.+]] = torch.aten.full %[[LIST]], %[[INF]], %[[NONE]], %[[NONE]], %[[NONE]]
|
||||
// CHECK: return %[[FULL]]
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[2,0,4],si32>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[2,1,4],si32>
|
||||
return %0 : !torch.vtensor<[2,1,4],si32>
|
||||
}
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_min_negative_axes_keepdims_example
|
||||
func.func @test_reduce_min_negative_axes_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: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[INT3:.+]] = torch.constant.int 3
|
||||
// CHECK: %[[INT0_0:.+]] = torch.constant.int 0
|
||||
// CHECK: torch.aten.select.int %arg1, %int0, %int0_0 : !torch.vtensor<[1],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
|
||||
// CHECK: torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
|
||||
// CHECK: torch.aten.lt.int %1, %int0 : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: torch.aten.Int.bool %2 : !torch.bool -> !torch.int
|
||||
// CHECK: torch.aten.mul.int %3, %int3 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.aten.add.int %1, %4 : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: torch.prim.ListConstruct %5 : (!torch.int) -> !torch.list<int>
|
||||
// -----
|
||||
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_min_bool_inputs
|
||||
func.func @test_reduce_min_bool_inputs(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,1],i1> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[IDX:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[SZ:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[SEL:.+]] = torch.aten.select.int %arg1, %[[IDX]], %[[SZ]]
|
||||
// CHECK: %[[ITEM:.+]] = torch.aten.item %[[SEL]]
|
||||
// CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
|
||||
// CHECK: %[[C0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[LT:.+]] = torch.aten.lt.int %[[ITEM]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
|
||||
// CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[ADD:.+]] = torch.aten.add.int %[[ITEM]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[LST:.+]] = torch.prim.ListConstruct %6 : (!torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[TRUE:.+]] = torch.constant.bool true
|
||||
// CHECK: torch.aten.amin %arg0, %6, %true : !torch.vtensor<[3,2,2],f32>, !torch.list<int>, !torch.bool -> !torch.vtensor<[3,1,2],f32>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%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: %[[AMIN:.+]] = torch.aten.amin %arg0, %[[LST]], %[[TRUE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4,1],i1>
|
||||
// CHECK: return %[[AMIN]] : !torch.vtensor<[4,1],i1>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0, %arg1) {torch.onnx.keepdims = 1 : si64} : (!torch.vtensor<[4,2],i1>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4,1],i1>
|
||||
return %0 : !torch.vtensor<[4,1],i1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_min_bool_inputs_nokeepdims
|
||||
func.func @test_reduce_min_bool_inputs_nokeepdims(%arg0: !torch.vtensor<[4,2],i1>, %arg1: !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],i1> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[IDX:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[SZ:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[SEL:.+]] = torch.aten.select.int %arg1, %[[IDX]], %[[SZ]]
|
||||
// CHECK: %[[ITEM:.+]] = torch.aten.item %[[SEL]]
|
||||
// CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
|
||||
// CHECK: %[[C0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[LT:.+]] = torch.aten.lt.int %[[ITEM]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
|
||||
// CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[ADD:.+]] = torch.aten.add.int %[[ITEM]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[LST:.+]] = torch.prim.ListConstruct %6 : (!torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[FALSE:.+]] = torch.constant.bool false
|
||||
// CHECK: %[[AMIN:.+]] = torch.aten.amin %arg0, %[[LST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4],i1>
|
||||
// CHECK: return %[[AMIN]] : !torch.vtensor<[4],i1>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0, %arg1) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[4,2],i1>, !torch.vtensor<[1],si64>) -> !torch.vtensor<[4],i1>
|
||||
return %0 : !torch.vtensor<[4],i1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func.func @test_reduce_all_dims_default
|
||||
func.func @test_reduce_all_dims_default(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[I0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[I1:.+]] = torch.constant.int 1
|
||||
// CHECK: %[[RANK:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
|
||||
// CHECK: %[[C0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[LT:.+]] = torch.aten.lt.int %[[I0]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
|
||||
// CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[RANK]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[A0:.+]] = torch.aten.add.int %[[I0]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[LT:.+]] = torch.aten.lt.int %[[I1]], %[[C0]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
|
||||
// CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[RANK]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[A1:.+]] = torch.aten.add.int %[[I1]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[A0]], %[[A1]]
|
||||
// CHECK: %[[FALSE:.+]] = torch.constant.bool false
|
||||
// CHECK: %[[MIN:.+]] = torch.aten.amin %arg0, %[[LIST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[],i1>
|
||||
// CHECK: return %[[MIN]] : !torch.vtensor<[],i1>
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0) {torch.onnx.keepdims = 0 : si64} : (!torch.vtensor<[4,2],i1>) -> !torch.vtensor<[],i1>
|
||||
return %0 : !torch.vtensor<[],i1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
func.func @test_reduce_min_attr(%arg0: !torch.vtensor<[4,2],i1>) -> !torch.vtensor<[4],i1> attributes {torch.onnx_meta.ir_version = 9 : si64, torch.onnx_meta.opset_version = 20 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
|
||||
// CHECK: %[[INT1:.+]] = torch.constant.int 1
|
||||
// CHECK: %[[DIM:.+]] = torch.aten.dim %arg0 : !torch.vtensor<[4,2],i1> -> !torch.int
|
||||
// CHECK: %[[INT0:.+]] = torch.constant.int 0
|
||||
// CHECK: %[[LT:.+]] = torch.aten.lt.int %[[INT1]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
|
||||
// CHECK: %[[BOOL:.+]] = torch.aten.Int.bool %[[LT]] : !torch.bool -> !torch.int
|
||||
// CHECK: %[[MUL:.+]] = torch.aten.mul.int %[[BOOL]], %[[DIM]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[ADD:.+]] = torch.aten.add.int %[[INT1]], %[[MUL]] : !torch.int, !torch.int -> !torch.int
|
||||
// CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[ADD]] : (!torch.int) -> !torch.list<int>
|
||||
// CHECK: %[[FALSE:.+]] = torch.constant.bool false
|
||||
// CHECK: %[[AMIN:.+]] = torch.aten.amin %arg0, %[[LIST]], %[[FALSE]] : !torch.vtensor<[4,2],i1>, !torch.list<int>, !torch.bool -> !torch.vtensor<[4],i1>
|
||||
// CHECK: return %[[AMIN]]
|
||||
%0 = torch.operator "onnx.ReduceMin"(%arg0) {torch.onnx.keepdims = 0 : si64, torch.onnx.axes=[1 : si64]} : (!torch.vtensor<[4,2],i1>) -> !torch.vtensor<[4],i1>
|
||||
return %0 : !torch.vtensor<[4],i1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
|
|
|||
Loading…
Reference in New Issue