Merge branch 'master' into matmul-shape
This commit is contained in:
Gheorghe-Teodor Bercea
GitHub
commit
3f5c543782
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@ -226,7 +226,7 @@ private:
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std::string _name;
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mlir::NamedAttribute operator()(int64_t const &r) {
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auto val = _builder.getI32IntegerAttr(r);
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auto val = _builder.getI64IntegerAttr(r);
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return _builder.getNamedAttr(_name, val);
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}
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@ -288,21 +288,12 @@ private:
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}
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std::vector<mlir::NamedAttribute> ImportNodeAttributes(
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const onnx::NodeProto &node,
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std::initializer_list<std::pair<std::string, AttrValueType>>
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defaultAttrList) {
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const onnx::NodeProto &node) {
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std::vector<mlir::NamedAttribute> attributes;
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std::set<std::string> definedAttributeSet;
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for (int i = 0; i < node.attribute_size(); ++i) {
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auto attr = node.attribute(i);
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auto nameValPair = convertAttributeProtoToNameValuePair(attr);
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attributes.push_back(convertNameValuePairToNamedAttribute(nameValPair));
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definedAttributeSet.insert(attr.name());
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}
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for (const auto &defaultAttr : defaultAttrList) {
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if (definedAttributeSet.find(defaultAttr.first) ==
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definedAttributeSet.end())
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attributes.push_back(convertNameValuePairToNamedAttribute(defaultAttr));
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}
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return attributes;
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}
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@ -340,9 +331,7 @@ private:
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*/
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template <typename T>
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void
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ImportNodeOneOut(const onnx::NodeProto &node, int nIn, int nOut,
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std::initializer_list<std::pair<std::string, AttrValueType>>
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defaultAttrList) {
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ImportNodeOneOut(const onnx::NodeProto &node, int nIn, int nOut) {
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std::vector<mlir::Value> inputs;
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for (const auto &item : node.input()) {
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if (frontend_symbols_.ContainKey(legalize_name(item))) {
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@ -356,7 +345,7 @@ private:
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mlir::UnrankedTensorType::get(builder_.getF32Type()));
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}
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auto attributes = ImportNodeAttributes(node, defaultAttrList);
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auto attributes = ImportNodeAttributes(node);
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llvm::StringRef OpName = node.op_type();
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@ -372,9 +361,7 @@ private:
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template <typename T>
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void ImportNodeMultipleOuts(
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const onnx::NodeProto &node, int nIn, int nOut,
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std::initializer_list<std::pair<std::string, AttrValueType>>
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defaultAttrList) {
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const onnx::NodeProto &node, int nIn, int nOut) {
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std::vector<mlir::Value> inputs;
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for (const auto &item : node.input()) {
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if (frontend_symbols_.ContainKey(legalize_name(item))) {
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@ -388,7 +375,7 @@ private:
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mlir::UnrankedTensorType::get(builder_.getF32Type()));
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}
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auto attributes = ImportNodeAttributes(node, defaultAttrList);
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auto attributes = ImportNodeAttributes(node);
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llvm::StringRef OpName = node.op_type();
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@ -410,9 +397,7 @@ private:
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* a specialized function is used
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*/
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void
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ImportNodeConv(onnx::NodeProto node, int nIn, int nOut,
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std::initializer_list<std::pair<std::string, AttrValueType>>
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defaultAttrList) {
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ImportNodeConv(onnx::NodeProto node, int nIn, int nOut) {
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// Conv has attribute dilations, kernel_shape, pads, the default value of
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// which is determined by the shape of first argument. However, since the
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// shape is unknown now, these attributes can be not generated auto
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@ -427,25 +412,23 @@ private:
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if (nOps == 2)
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ImportNodeOneOut<mlir::ONNXConvNoBiasOp>(
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node, nOps, nOut, defaultAttrList);
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node, nOps, nOut);
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else
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ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut, defaultAttrList);
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ImportNodeOneOut<mlir::ONNXConvOp>(node, nOps, nOut);
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}
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/*!
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* Special handle for MaxPool operations.
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*/
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void ImportNodeMaxPool(
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onnx::NodeProto node, int nIn, int nOut,
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std::initializer_list<std::pair<std::string, AttrValueType>>
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defaultAttrList) {
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onnx::NodeProto node, int nIn, int nOut) {
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int nOuts = node.output().size();
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if (nOuts == 1) {
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ImportNodeOneOut<mlir::ONNXMaxPoolSingleOutOp>(
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node, nIn, nOuts, defaultAttrList);
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node, nIn, nOuts);
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} else {
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ImportNodeMultipleOuts<mlir::ONNXMaxPoolOp>(
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node, nIn, nOuts, defaultAttrList);
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node, nIn, nOuts);
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}
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}
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@ -1,592 +1,314 @@
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if (OpName == "DUMMY") {
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}else if (OpName == "Abs") {
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ImportNodeOneOut<mlir::ONNXAbsOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAbsOp>(node, 1, 1);
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}else if (OpName == "Acos") {
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ImportNodeOneOut<mlir::ONNXAcosOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAcosOp>(node, 1, 1);
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}else if (OpName == "Acosh") {
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ImportNodeOneOut<mlir::ONNXAcoshOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAcoshOp>(node, 1, 1);
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}else if (OpName == "Add") {
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ImportNodeOneOut<mlir::ONNXAddOp>(node, 2, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAddOp>(node, 2, 1);
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}else if (OpName == "And") {
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ImportNodeOneOut<mlir::ONNXAndOp>(node, 2, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAndOp>(node, 2, 1);
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}else if (OpName == "ArgMax") {
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ImportNodeOneOut<mlir::ONNXArgMaxOp>(node, 1, 1, {
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{"axis", 0}
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,{"keepdims", 1}
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});
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ImportNodeOneOut<mlir::ONNXArgMaxOp>(node, 1, 1);
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}else if (OpName == "ArgMin") {
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ImportNodeOneOut<mlir::ONNXArgMinOp>(node, 1, 1, {
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{"axis", 0}
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,{"keepdims", 1}
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});
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ImportNodeOneOut<mlir::ONNXArgMinOp>(node, 1, 1);
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}else if (OpName == "Asin") {
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ImportNodeOneOut<mlir::ONNXAsinOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAsinOp>(node, 1, 1);
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}else if (OpName == "Asinh") {
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ImportNodeOneOut<mlir::ONNXAsinhOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAsinhOp>(node, 1, 1);
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}else if (OpName == "Atan") {
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ImportNodeOneOut<mlir::ONNXAtanOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAtanOp>(node, 1, 1);
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}else if (OpName == "Atanh") {
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ImportNodeOneOut<mlir::ONNXAtanhOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXAtanhOp>(node, 1, 1);
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}else if (OpName == "AveragePool") {
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ImportNodeOneOut<mlir::ONNXAveragePoolOp>(node, 1, 1, {
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{"auto_pad", "NOTSET"}
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,{"ceil_mode", 0}
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,{"count_include_pad", 0}
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,{"kernel_shape", std::vector<int64_t> {}}
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});
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ImportNodeOneOut<mlir::ONNXAveragePoolOp>(node, 1, 1);
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}else if (OpName == "BatchNormalization") {
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ImportNodeMultipleOuts<mlir::ONNXBatchNormalizationOp>(node, 5, 5, {
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{"epsilon", (float)1e-05}
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,{"momentum", (float)0.9}
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});
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ImportNodeMultipleOuts<mlir::ONNXBatchNormalizationOp>(node, 5, 5);
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}else if (OpName == "BitShift") {
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ImportNodeOneOut<mlir::ONNXBitShiftOp>(node, 2, 1, {
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});
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ImportNodeOneOut<mlir::ONNXBitShiftOp>(node, 2, 1);
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}else if (OpName == "Cast") {
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ImportNodeOneOut<mlir::ONNXCastOp>(node, 1, 1, {
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{"to", 0}
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});
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ImportNodeOneOut<mlir::ONNXCastOp>(node, 1, 1);
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}else if (OpName == "Ceil") {
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ImportNodeOneOut<mlir::ONNXCeilOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXCeilOp>(node, 1, 1);
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}else if (OpName == "Clip") {
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ImportNodeOneOut<mlir::ONNXClipOp>(node, 3, 1, {
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});
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ImportNodeOneOut<mlir::ONNXClipOp>(node, 3, 1);
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}else if (OpName == "Compress") {
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ImportNodeOneOut<mlir::ONNXCompressOp>(node, 2, 1, {
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});
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ImportNodeOneOut<mlir::ONNXCompressOp>(node, 2, 1);
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}else if (OpName == "Concat") {
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ImportNodeOneOut<mlir::ONNXConcatOp>(node, 1, 1, {
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{"axis", 0}
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});
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ImportNodeOneOut<mlir::ONNXConcatOp>(node, 1, 1);
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}else if (OpName == "ConcatFromSequence") {
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ImportNodeOneOut<mlir::ONNXConcatFromSequenceOp>(node, 1, 1, {
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{"new_axis", 0}
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});
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ImportNodeOneOut<mlir::ONNXConcatFromSequenceOp>(node, 1, 1);
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}else if (OpName == "Constant") {
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ImportNodeOneOut<mlir::ONNXConstantOp>(node, 0, 1, {
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});
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ImportNodeOneOut<mlir::ONNXConstantOp>(node, 0, 1);
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}else if (OpName == "ConstantOfShape") {
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ImportNodeOneOut<mlir::ONNXConstantOfShapeOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXConstantOfShapeOp>(node, 1, 1);
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}else if (OpName == "Conv") {
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ImportNodeConv(node, 3, 1, {
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{"auto_pad", "NOTSET"}
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,{"group", 1}
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});
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ImportNodeConv(node, 3, 1);
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}else if (OpName == "ConvInteger") {
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ImportNodeOneOut<mlir::ONNXConvIntegerOp>(node, 4, 1, {
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{"auto_pad", "NOTSET"}
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,{"group", 1}
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});
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ImportNodeOneOut<mlir::ONNXConvIntegerOp>(node, 4, 1);
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}else if (OpName == "ConvTranspose") {
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ImportNodeOneOut<mlir::ONNXConvTransposeOp>(node, 3, 1, {
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{"auto_pad", "NOTSET"}
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,{"group", 1}
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});
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ImportNodeOneOut<mlir::ONNXConvTransposeOp>(node, 3, 1);
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}else if (OpName == "Cos") {
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ImportNodeOneOut<mlir::ONNXCosOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXCosOp>(node, 1, 1);
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}else if (OpName == "Cosh") {
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ImportNodeOneOut<mlir::ONNXCoshOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXCoshOp>(node, 1, 1);
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}else if (OpName == "CumSum") {
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ImportNodeOneOut<mlir::ONNXCumSumOp>(node, 2, 1, {
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{"exclusive", 0}
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,{"reverse", 0}
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});
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ImportNodeOneOut<mlir::ONNXCumSumOp>(node, 2, 1);
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}else if (OpName == "DepthToSpace") {
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ImportNodeOneOut<mlir::ONNXDepthToSpaceOp>(node, 1, 1, {
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{"mode", "DCR"}
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});
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ImportNodeOneOut<mlir::ONNXDepthToSpaceOp>(node, 1, 1);
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}else if (OpName == "DequantizeLinear") {
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ImportNodeOneOut<mlir::ONNXDequantizeLinearOp>(node, 3, 1, {
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});
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ImportNodeOneOut<mlir::ONNXDequantizeLinearOp>(node, 3, 1);
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}else if (OpName == "Det") {
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ImportNodeOneOut<mlir::ONNXDetOp>(node, 1, 1, {
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});
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ImportNodeOneOut<mlir::ONNXDetOp>(node, 1, 1);
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}else if (OpName == "Div") {
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ImportNodeOneOut<mlir::ONNXDivOp>(node, 2, 1, {
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});
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ImportNodeOneOut<mlir::ONNXDivOp>(node, 2, 1);
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}else if (OpName == "Dropout") {
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ImportNodeMultipleOuts<mlir::ONNXDropoutOp>(node, 1, 2, {
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{"ratio", (float)0.5}
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||||
});
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||||
ImportNodeMultipleOuts<mlir::ONNXDropoutOp>(node, 1, 2);
|
||||
}else if (OpName == "DynamicQuantizeLinear") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXDynamicQuantizeLinearOp>(node, 1, 3, {
|
||||
});
|
||||
ImportNodeMultipleOuts<mlir::ONNXDynamicQuantizeLinearOp>(node, 1, 3);
|
||||
}else if (OpName == "Elu") {
|
||||
ImportNodeOneOut<mlir::ONNXEluOp>(node, 1, 1, {
|
||||
{"alpha", (float)1.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXEluOp>(node, 1, 1);
|
||||
}else if (OpName == "Equal") {
|
||||
ImportNodeOneOut<mlir::ONNXEqualOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXEqualOp>(node, 2, 1);
|
||||
}else if (OpName == "Erf") {
|
||||
ImportNodeOneOut<mlir::ONNXErfOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXErfOp>(node, 1, 1);
|
||||
}else if (OpName == "Exp") {
|
||||
ImportNodeOneOut<mlir::ONNXExpOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXExpOp>(node, 1, 1);
|
||||
}else if (OpName == "Expand") {
|
||||
ImportNodeOneOut<mlir::ONNXExpandOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXExpandOp>(node, 2, 1);
|
||||
}else if (OpName == "EyeLike") {
|
||||
ImportNodeOneOut<mlir::ONNXEyeLikeOp>(node, 1, 1, {
|
||||
{"k", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXEyeLikeOp>(node, 1, 1);
|
||||
}else if (OpName == "Flatten") {
|
||||
ImportNodeOneOut<mlir::ONNXFlattenOp>(node, 1, 1, {
|
||||
{"axis", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXFlattenOp>(node, 1, 1);
|
||||
}else if (OpName == "Floor") {
|
||||
ImportNodeOneOut<mlir::ONNXFloorOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXFloorOp>(node, 1, 1);
|
||||
}else if (OpName == "GRU") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXGRUOp>(node, 6, 2, {
|
||||
{"direction", "forward"}
|
||||
,{"linear_before_reset", 0}
|
||||
});
|
||||
ImportNodeMultipleOuts<mlir::ONNXGRUOp>(node, 6, 2);
|
||||
}else if (OpName == "Gather") {
|
||||
ImportNodeOneOut<mlir::ONNXGatherOp>(node, 2, 1, {
|
||||
{"axis", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGatherOp>(node, 2, 1);
|
||||
}else if (OpName == "GatherElements") {
|
||||
ImportNodeOneOut<mlir::ONNXGatherElementsOp>(node, 2, 1, {
|
||||
{"axis", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGatherElementsOp>(node, 2, 1);
|
||||
}else if (OpName == "GatherND") {
|
||||
ImportNodeOneOut<mlir::ONNXGatherNDOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGatherNDOp>(node, 2, 1);
|
||||
}else if (OpName == "Gemm") {
|
||||
ImportNodeOneOut<mlir::ONNXGemmOp>(node, 3, 1, {
|
||||
{"alpha", (float)1.0}
|
||||
,{"beta", (float)1.0}
|
||||
,{"transA", 0}
|
||||
,{"transB", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGemmOp>(node, 3, 1);
|
||||
}else if (OpName == "GlobalAveragePool") {
|
||||
ImportNodeOneOut<mlir::ONNXGlobalAveragePoolOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGlobalAveragePoolOp>(node, 1, 1);
|
||||
}else if (OpName == "GlobalLpPool") {
|
||||
ImportNodeOneOut<mlir::ONNXGlobalLpPoolOp>(node, 1, 1, {
|
||||
{"p", 2}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGlobalLpPoolOp>(node, 1, 1);
|
||||
}else if (OpName == "GlobalMaxPool") {
|
||||
ImportNodeOneOut<mlir::ONNXGlobalMaxPoolOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGlobalMaxPoolOp>(node, 1, 1);
|
||||
}else if (OpName == "Greater") {
|
||||
ImportNodeOneOut<mlir::ONNXGreaterOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXGreaterOp>(node, 2, 1);
|
||||
}else if (OpName == "HardSigmoid") {
|
||||
ImportNodeOneOut<mlir::ONNXHardSigmoidOp>(node, 1, 1, {
|
||||
{"alpha", (float)0.2}
|
||||
,{"beta", (float)0.5}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXHardSigmoidOp>(node, 1, 1);
|
||||
}else if (OpName == "Hardmax") {
|
||||
ImportNodeOneOut<mlir::ONNXHardmaxOp>(node, 1, 1, {
|
||||
{"axis", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXHardmaxOp>(node, 1, 1);
|
||||
}else if (OpName == "Identity") {
|
||||
ImportNodeOneOut<mlir::ONNXIdentityOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXIdentityOp>(node, 1, 1);
|
||||
}else if (OpName == "If") {
|
||||
ImportNodeOneOut<mlir::ONNXIfOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXIfOp>(node, 1, 1);
|
||||
}else if (OpName == "InstanceNormalization") {
|
||||
ImportNodeOneOut<mlir::ONNXInstanceNormalizationOp>(node, 3, 1, {
|
||||
{"epsilon", (float)1e-05}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXInstanceNormalizationOp>(node, 3, 1);
|
||||
}else if (OpName == "IsInf") {
|
||||
ImportNodeOneOut<mlir::ONNXIsInfOp>(node, 1, 1, {
|
||||
{"detect_negative", 1}
|
||||
,{"detect_positive", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXIsInfOp>(node, 1, 1);
|
||||
}else if (OpName == "IsNaN") {
|
||||
ImportNodeOneOut<mlir::ONNXIsNaNOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXIsNaNOp>(node, 1, 1);
|
||||
}else if (OpName == "LRN") {
|
||||
ImportNodeOneOut<mlir::ONNXLRNOp>(node, 1, 1, {
|
||||
{"alpha", (float)0.0001}
|
||||
,{"beta", (float)0.75}
|
||||
,{"bias", (float)1.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLRNOp>(node, 1, 1);
|
||||
}else if (OpName == "LSTM") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXLSTMOp>(node, 8, 3, {
|
||||
{"direction", "forward"}
|
||||
,{"input_forget", 0}
|
||||
});
|
||||
ImportNodeMultipleOuts<mlir::ONNXLSTMOp>(node, 8, 3);
|
||||
}else if (OpName == "LeakyRelu") {
|
||||
ImportNodeOneOut<mlir::ONNXLeakyReluOp>(node, 1, 1, {
|
||||
{"alpha", (float)0.01}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLeakyReluOp>(node, 1, 1);
|
||||
}else if (OpName == "Less") {
|
||||
ImportNodeOneOut<mlir::ONNXLessOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLessOp>(node, 2, 1);
|
||||
}else if (OpName == "Log") {
|
||||
ImportNodeOneOut<mlir::ONNXLogOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLogOp>(node, 1, 1);
|
||||
}else if (OpName == "LogSoftmax") {
|
||||
ImportNodeOneOut<mlir::ONNXLogSoftmaxOp>(node, 1, 1, {
|
||||
{"axis", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLogSoftmaxOp>(node, 1, 1);
|
||||
}else if (OpName == "Loop") {
|
||||
ImportNodeOneOut<mlir::ONNXLoopOp>(node, 3, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLoopOp>(node, 3, 1);
|
||||
}else if (OpName == "LpNormalization") {
|
||||
ImportNodeOneOut<mlir::ONNXLpNormalizationOp>(node, 1, 1, {
|
||||
{"axis", -1}
|
||||
,{"p", 2}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLpNormalizationOp>(node, 1, 1);
|
||||
}else if (OpName == "LpPool") {
|
||||
ImportNodeOneOut<mlir::ONNXLpPoolOp>(node, 1, 1, {
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"p", 2}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXLpPoolOp>(node, 1, 1);
|
||||
}else if (OpName == "MatMul") {
|
||||
ImportNodeOneOut<mlir::ONNXMatMulOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMatMulOp>(node, 2, 1);
|
||||
}else if (OpName == "MatMulInteger") {
|
||||
ImportNodeOneOut<mlir::ONNXMatMulIntegerOp>(node, 4, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMatMulIntegerOp>(node, 4, 1);
|
||||
}else if (OpName == "Max") {
|
||||
ImportNodeOneOut<mlir::ONNXMaxOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMaxOp>(node, 1, 1);
|
||||
}else if (OpName == "MaxPool") {
|
||||
ImportNodeMaxPool(node, 1, 2, {
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"ceil_mode", 0}
|
||||
,{"kernel_shape", std::vector<int64_t> {}}
|
||||
,{"storage_order", 0}
|
||||
});
|
||||
ImportNodeMaxPool(node, 1, 2);
|
||||
}else if (OpName == "MaxRoiPool") {
|
||||
ImportNodeOneOut<mlir::ONNXMaxRoiPoolOp>(node, 2, 1, {
|
||||
{"spatial_scale", (float)1.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMaxRoiPoolOp>(node, 2, 1);
|
||||
}else if (OpName == "MaxUnpool") {
|
||||
ImportNodeOneOut<mlir::ONNXMaxUnpoolOp>(node, 3, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMaxUnpoolOp>(node, 3, 1);
|
||||
}else if (OpName == "Mean") {
|
||||
ImportNodeOneOut<mlir::ONNXMeanOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMeanOp>(node, 1, 1);
|
||||
}else if (OpName == "MeanVarianceNormalization") {
|
||||
ImportNodeOneOut<mlir::ONNXMeanVarianceNormalizationOp>(node, 1, 1, {
|
||||
{"axes", std::vector<int64_t>{0, 2, 3}}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMeanVarianceNormalizationOp>(node, 1, 1);
|
||||
}else if (OpName == "Min") {
|
||||
ImportNodeOneOut<mlir::ONNXMinOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMinOp>(node, 1, 1);
|
||||
}else if (OpName == "Mod") {
|
||||
ImportNodeOneOut<mlir::ONNXModOp>(node, 2, 1, {
|
||||
{"fmod", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXModOp>(node, 2, 1);
|
||||
}else if (OpName == "Mul") {
|
||||
ImportNodeOneOut<mlir::ONNXMulOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMulOp>(node, 2, 1);
|
||||
}else if (OpName == "Multinomial") {
|
||||
ImportNodeOneOut<mlir::ONNXMultinomialOp>(node, 1, 1, {
|
||||
{"dtype", 6}
|
||||
,{"sample_size", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXMultinomialOp>(node, 1, 1);
|
||||
}else if (OpName == "Neg") {
|
||||
ImportNodeOneOut<mlir::ONNXNegOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXNegOp>(node, 1, 1);
|
||||
}else if (OpName == "NonMaxSuppression") {
|
||||
ImportNodeOneOut<mlir::ONNXNonMaxSuppressionOp>(node, 5, 1, {
|
||||
{"center_point_box", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXNonMaxSuppressionOp>(node, 5, 1);
|
||||
}else if (OpName == "NonZero") {
|
||||
ImportNodeOneOut<mlir::ONNXNonZeroOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXNonZeroOp>(node, 1, 1);
|
||||
}else if (OpName == "Not") {
|
||||
ImportNodeOneOut<mlir::ONNXNotOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXNotOp>(node, 1, 1);
|
||||
}else if (OpName == "OneHot") {
|
||||
ImportNodeOneOut<mlir::ONNXOneHotOp>(node, 3, 1, {
|
||||
{"axis", -1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXOneHotOp>(node, 3, 1);
|
||||
}else if (OpName == "Or") {
|
||||
ImportNodeOneOut<mlir::ONNXOrOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXOrOp>(node, 2, 1);
|
||||
}else if (OpName == "PRelu") {
|
||||
ImportNodeOneOut<mlir::ONNXPReluOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXPReluOp>(node, 2, 1);
|
||||
}else if (OpName == "Pad") {
|
||||
ImportNodeOneOut<mlir::ONNXPadOp>(node, 3, 1, {
|
||||
{"mode", "constant"}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXPadOp>(node, 3, 1);
|
||||
}else if (OpName == "Pow") {
|
||||
ImportNodeOneOut<mlir::ONNXPowOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXPowOp>(node, 2, 1);
|
||||
}else if (OpName == "QLinearConv") {
|
||||
ImportNodeOneOut<mlir::ONNXQLinearConvOp>(node, 9, 1, {
|
||||
{"auto_pad", "NOTSET"}
|
||||
,{"group", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXQLinearConvOp>(node, 9, 1);
|
||||
}else if (OpName == "QLinearMatMul") {
|
||||
ImportNodeOneOut<mlir::ONNXQLinearMatMulOp>(node, 8, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXQLinearMatMulOp>(node, 8, 1);
|
||||
}else if (OpName == "QuantizeLinear") {
|
||||
ImportNodeOneOut<mlir::ONNXQuantizeLinearOp>(node, 3, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXQuantizeLinearOp>(node, 3, 1);
|
||||
}else if (OpName == "RNN") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXRNNOp>(node, 6, 2, {
|
||||
{"activation_alpha", std::vector<float> {}}
|
||||
,{"activation_beta", std::vector<float> {}}
|
||||
,{"activations", std::vector<std::string>{"Tanh", "Tanh"}}
|
||||
,{"direction", "forward"}
|
||||
});
|
||||
ImportNodeMultipleOuts<mlir::ONNXRNNOp>(node, 6, 2);
|
||||
}else if (OpName == "RandomNormal") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomNormalOp>(node, 0, 1, {
|
||||
{"dtype", 1}
|
||||
,{"mean", (float)0.0}
|
||||
,{"scale", (float)1.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXRandomNormalOp>(node, 0, 1);
|
||||
}else if (OpName == "RandomNormalLike") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomNormalLikeOp>(node, 1, 1, {
|
||||
{"mean", (float)0.0}
|
||||
,{"scale", (float)1.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXRandomNormalLikeOp>(node, 1, 1);
|
||||
}else if (OpName == "RandomUniform") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomUniformOp>(node, 0, 1, {
|
||||
{"dtype", 1}
|
||||
,{"high", (float)1.0}
|
||||
,{"low", (float)0.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXRandomUniformOp>(node, 0, 1);
|
||||
}else if (OpName == "RandomUniformLike") {
|
||||
ImportNodeOneOut<mlir::ONNXRandomUniformLikeOp>(node, 1, 1, {
|
||||
{"high", (float)1.0}
|
||||
,{"low", (float)0.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXRandomUniformLikeOp>(node, 1, 1);
|
||||
}else if (OpName == "Range") {
|
||||
ImportNodeOneOut<mlir::ONNXRangeOp>(node, 3, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXRangeOp>(node, 3, 1);
|
||||
}else if (OpName == "Reciprocal") {
|
||||
ImportNodeOneOut<mlir::ONNXReciprocalOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReciprocalOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceL1") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceL1Op>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceL1Op>(node, 1, 1);
|
||||
}else if (OpName == "ReduceL2") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceL2Op>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceL2Op>(node, 1, 1);
|
||||
}else if (OpName == "ReduceLogSum") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceLogSumOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceLogSumOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceLogSumExp") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceLogSumExpOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceLogSumExpOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceMax") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceMaxOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceMaxOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceMean") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceMeanOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceMeanOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceMin") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceMinOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceMinOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceProd") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceProdOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceProdOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceSum") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceSumOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceSumOp>(node, 1, 1);
|
||||
}else if (OpName == "ReduceSumSquare") {
|
||||
ImportNodeOneOut<mlir::ONNXReduceSumSquareOp>(node, 1, 1, {
|
||||
{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReduceSumSquareOp>(node, 1, 1);
|
||||
}else if (OpName == "Relu") {
|
||||
ImportNodeOneOut<mlir::ONNXReluOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReluOp>(node, 1, 1);
|
||||
}else if (OpName == "Reshape") {
|
||||
ImportNodeOneOut<mlir::ONNXReshapeOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReshapeOp>(node, 2, 1);
|
||||
}else if (OpName == "Resize") {
|
||||
ImportNodeOneOut<mlir::ONNXResizeOp>(node, 4, 1, {
|
||||
{"coordinate_transformation_mode", "half_pixel"}
|
||||
,{"cubic_coeff_a", (float)-0.75}
|
||||
,{"exclude_outside", 0}
|
||||
,{"extrapolation_value", (float)0.0}
|
||||
,{"mode", "nearest"}
|
||||
,{"nearest_mode", "round_prefer_floor"}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXResizeOp>(node, 4, 1);
|
||||
}else if (OpName == "ReverseSequence") {
|
||||
ImportNodeOneOut<mlir::ONNXReverseSequenceOp>(node, 2, 1, {
|
||||
{"batch_axis", 1}
|
||||
,{"time_axis", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXReverseSequenceOp>(node, 2, 1);
|
||||
}else if (OpName == "RoiAlign") {
|
||||
ImportNodeOneOut<mlir::ONNXRoiAlignOp>(node, 3, 1, {
|
||||
{"mode", "avg"}
|
||||
,{"output_height", 1}
|
||||
,{"output_width", 1}
|
||||
,{"sampling_ratio", 0}
|
||||
,{"spatial_scale", (float)1.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXRoiAlignOp>(node, 3, 1);
|
||||
}else if (OpName == "Round") {
|
||||
ImportNodeOneOut<mlir::ONNXRoundOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXRoundOp>(node, 1, 1);
|
||||
}else if (OpName == "Scan") {
|
||||
ImportNodeOneOut<mlir::ONNXScanOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXScanOp>(node, 1, 1);
|
||||
}else if (OpName == "Scatter") {
|
||||
ImportNodeOneOut<mlir::ONNXScatterOp>(node, 3, 1, {
|
||||
{"axis", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXScatterOp>(node, 3, 1);
|
||||
}else if (OpName == "ScatterElements") {
|
||||
ImportNodeOneOut<mlir::ONNXScatterElementsOp>(node, 3, 1, {
|
||||
{"axis", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXScatterElementsOp>(node, 3, 1);
|
||||
}else if (OpName == "ScatterND") {
|
||||
ImportNodeOneOut<mlir::ONNXScatterNDOp>(node, 3, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXScatterNDOp>(node, 3, 1);
|
||||
}else if (OpName == "Selu") {
|
||||
ImportNodeOneOut<mlir::ONNXSeluOp>(node, 1, 1, {
|
||||
{"alpha", (float)1.67326}
|
||||
,{"gamma", (float)1.0507}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSeluOp>(node, 1, 1);
|
||||
}else if (OpName == "SequenceAt") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceAtOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSequenceAtOp>(node, 2, 1);
|
||||
}else if (OpName == "SequenceConstruct") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceConstructOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSequenceConstructOp>(node, 1, 1);
|
||||
}else if (OpName == "SequenceEmpty") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceEmptyOp>(node, 0, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSequenceEmptyOp>(node, 0, 1);
|
||||
}else if (OpName == "SequenceErase") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceEraseOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSequenceEraseOp>(node, 2, 1);
|
||||
}else if (OpName == "SequenceInsert") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceInsertOp>(node, 3, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSequenceInsertOp>(node, 3, 1);
|
||||
}else if (OpName == "SequenceLength") {
|
||||
ImportNodeOneOut<mlir::ONNXSequenceLengthOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSequenceLengthOp>(node, 1, 1);
|
||||
}else if (OpName == "Shape") {
|
||||
ImportNodeOneOut<mlir::ONNXShapeOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXShapeOp>(node, 1, 1);
|
||||
}else if (OpName == "Shrink") {
|
||||
ImportNodeOneOut<mlir::ONNXShrinkOp>(node, 1, 1, {
|
||||
{"bias", (float)0.0}
|
||||
,{"lambd", (float)0.5}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXShrinkOp>(node, 1, 1);
|
||||
}else if (OpName == "Sigmoid") {
|
||||
ImportNodeOneOut<mlir::ONNXSigmoidOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSigmoidOp>(node, 1, 1);
|
||||
}else if (OpName == "Sign") {
|
||||
ImportNodeOneOut<mlir::ONNXSignOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSignOp>(node, 1, 1);
|
||||
}else if (OpName == "Sin") {
|
||||
ImportNodeOneOut<mlir::ONNXSinOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSinOp>(node, 1, 1);
|
||||
}else if (OpName == "Sinh") {
|
||||
ImportNodeOneOut<mlir::ONNXSinhOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSinhOp>(node, 1, 1);
|
||||
}else if (OpName == "Size") {
|
||||
ImportNodeOneOut<mlir::ONNXSizeOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSizeOp>(node, 1, 1);
|
||||
}else if (OpName == "Slice") {
|
||||
ImportNodeOneOut<mlir::ONNXSliceOp>(node, 5, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSliceOp>(node, 5, 1);
|
||||
}else if (OpName == "Softmax") {
|
||||
ImportNodeOneOut<mlir::ONNXSoftmaxOp>(node, 1, 1, {
|
||||
{"axis", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSoftmaxOp>(node, 1, 1);
|
||||
}else if (OpName == "Softplus") {
|
||||
ImportNodeOneOut<mlir::ONNXSoftplusOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSoftplusOp>(node, 1, 1);
|
||||
}else if (OpName == "Softsign") {
|
||||
ImportNodeOneOut<mlir::ONNXSoftsignOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSoftsignOp>(node, 1, 1);
|
||||
}else if (OpName == "SpaceToDepth") {
|
||||
ImportNodeOneOut<mlir::ONNXSpaceToDepthOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSpaceToDepthOp>(node, 1, 1);
|
||||
}else if (OpName == "Split") {
|
||||
ImportNodeOneOut<mlir::ONNXSplitOp>(node, 1, 1, {
|
||||
{"axis", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSplitOp>(node, 1, 1);
|
||||
}else if (OpName == "SplitToSequence") {
|
||||
ImportNodeOneOut<mlir::ONNXSplitToSequenceOp>(node, 2, 1, {
|
||||
{"axis", 0}
|
||||
,{"keepdims", 1}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSplitToSequenceOp>(node, 2, 1);
|
||||
}else if (OpName == "Sqrt") {
|
||||
ImportNodeOneOut<mlir::ONNXSqrtOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSqrtOp>(node, 1, 1);
|
||||
}else if (OpName == "Squeeze") {
|
||||
ImportNodeOneOut<mlir::ONNXSqueezeOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSqueezeOp>(node, 1, 1);
|
||||
}else if (OpName == "StringNormalizer") {
|
||||
ImportNodeOneOut<mlir::ONNXStringNormalizerOp>(node, 1, 1, {
|
||||
{"case_change_action", "NONE"}
|
||||
,{"is_case_sensitive", 0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXStringNormalizerOp>(node, 1, 1);
|
||||
}else if (OpName == "Sub") {
|
||||
ImportNodeOneOut<mlir::ONNXSubOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSubOp>(node, 2, 1);
|
||||
}else if (OpName == "Sum") {
|
||||
ImportNodeOneOut<mlir::ONNXSumOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXSumOp>(node, 1, 1);
|
||||
}else if (OpName == "Tan") {
|
||||
ImportNodeOneOut<mlir::ONNXTanOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXTanOp>(node, 1, 1);
|
||||
}else if (OpName == "Tanh") {
|
||||
ImportNodeOneOut<mlir::ONNXTanhOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXTanhOp>(node, 1, 1);
|
||||
}else if (OpName == "TfIdfVectorizer") {
|
||||
ImportNodeOneOut<mlir::ONNXTfIdfVectorizerOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXTfIdfVectorizerOp>(node, 1, 1);
|
||||
}else if (OpName == "ThresholdedRelu") {
|
||||
ImportNodeOneOut<mlir::ONNXThresholdedReluOp>(node, 1, 1, {
|
||||
{"alpha", (float)1.0}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXThresholdedReluOp>(node, 1, 1);
|
||||
}else if (OpName == "Tile") {
|
||||
ImportNodeOneOut<mlir::ONNXTileOp>(node, 2, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXTileOp>(node, 2, 1);
|
||||
}else if (OpName == "TopK") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXTopKOp>(node, 2, 2, {
|
||||
{"axis", -1}
|
||||
,{"largest", 1}
|
||||
,{"sorted", 1}
|
||||
});
|
||||
ImportNodeMultipleOuts<mlir::ONNXTopKOp>(node, 2, 2);
|
||||
}else if (OpName == "Transpose") {
|
||||
ImportNodeOneOut<mlir::ONNXTransposeOp>(node, 1, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXTransposeOp>(node, 1, 1);
|
||||
}else if (OpName == "Unique") {
|
||||
ImportNodeMultipleOuts<mlir::ONNXUniqueOp>(node, 1, 4, {
|
||||
{"sorted", 1}
|
||||
});
|
||||
ImportNodeMultipleOuts<mlir::ONNXUniqueOp>(node, 1, 4);
|
||||
}else if (OpName == "Unsqueeze") {
|
||||
ImportNodeOneOut<mlir::ONNXUnsqueezeOp>(node, 1, 1, {
|
||||
{"axes", std::vector<int64_t> {}}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXUnsqueezeOp>(node, 1, 1);
|
||||
}else if (OpName == "Upsample") {
|
||||
ImportNodeOneOut<mlir::ONNXUpsampleOp>(node, 2, 1, {
|
||||
{"mode", "nearest"}
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXUpsampleOp>(node, 2, 1);
|
||||
}else if (OpName == "Where") {
|
||||
ImportNodeOneOut<mlir::ONNXWhereOp>(node, 3, 1, {
|
||||
});
|
||||
ImportNodeOneOut<mlir::ONNXWhereOp>(node, 3, 1);
|
||||
}else if (OpName == "Xor") {
|
||||
ImportNodeOneOut<mlir::ONNXXorOp>(node, 2, 1, {
|
||||
});
|
||||
}
|
||||
ImportNodeOneOut<mlir::ONNXXorOp>(node, 2, 1);
|
||||
}
|
||||
|
|
@ -270,6 +270,12 @@ def gen_schema(schema) :
|
|||
'Identity', 'Cos', 'Log', 'Transpose', 'Softmax',
|
||||
'Softplus', 'Softsign']
|
||||
CanonicalList=['Add', 'Identity']
|
||||
manual_code = dict([
|
||||
('DummyExample', ' let extraClassDeclaration = [{ \n'+
|
||||
' static StringRef getPermAttrName() { return "perm"; }\n'+
|
||||
' }];\n')
|
||||
])
|
||||
skip_attr_gen = []
|
||||
line_indent = ' '
|
||||
|
||||
#s = 'def ONNX'+schema.name+str(schema.since_version)+'Op:ONNX_Op<"'+schema.name+'", \n'
|
||||
|
|
@ -303,21 +309,23 @@ def gen_schema(schema) :
|
|||
|
||||
#input
|
||||
s+= '\n'+line_indent+'let arguments = (ins '
|
||||
isfirst = True
|
||||
if schema.inputs:
|
||||
isfirst = False
|
||||
for input in schema.inputs:
|
||||
if input != schema.inputs[0] :
|
||||
s+= ', '
|
||||
s+= ',\n '
|
||||
etypes=collect_types(schema, input)
|
||||
|
||||
if OpSchema.FormalParameterOption.Optional == input.option:
|
||||
#TODO: handle optional
|
||||
print("optional ", input.name)
|
||||
print("warning: optional input for"+schema.name+' '+input.name)
|
||||
elif OpSchema.FormalParameterOption.Variadic == input.option:
|
||||
if input.isHomogeneous:
|
||||
s+= 'Variadic<'
|
||||
else:
|
||||
#TODO handle (variadic, heterogeneous)"
|
||||
print('variadic, heterogeneous', input.name)
|
||||
print("warning: (variadic, heterogeneous) for"+schema.name+' '+input.name)
|
||||
if etypes == '':
|
||||
s+= 'AnyTypeOf<[AnyMemRef, AnyTensor]>'
|
||||
else:
|
||||
|
|
@ -333,6 +341,8 @@ def gen_schema(schema) :
|
|||
#TODO handle (variadic, heterogeneous)"
|
||||
t=''
|
||||
s+=':$'+input.name
|
||||
if not schema.name in skip_attr_gen :
|
||||
s += gen_attr_ins(schema, isfirst)
|
||||
s+= ');'
|
||||
|
||||
#output
|
||||
|
|
@ -347,11 +357,15 @@ def gen_schema(schema) :
|
|||
s+= 'AnyTypeOf<[AnyMemRef, AnyTensor]>'
|
||||
else:
|
||||
s+= 'TensorOf<['+etypes+']>'
|
||||
s+= ');'
|
||||
s += ':$o_'+output.name
|
||||
s+= ');\n'
|
||||
|
||||
#s+= 'let hasCanonicalizer = 1;'
|
||||
#add special code
|
||||
if schema.name in manual_code :
|
||||
s += manual_code[schema.name]
|
||||
|
||||
s += '\n}\n\n'
|
||||
s += '}\n\n'
|
||||
|
||||
return s
|
||||
|
||||
|
|
@ -369,44 +383,91 @@ def gen_code(schema,fefile) :
|
|||
("MaxPool", "ImportNodeMaxPool"),
|
||||
#("Transpose", "ImportNodeTranspose")
|
||||
])
|
||||
list_str = 'std::vector'
|
||||
empty_ints = list_str+'<int> {}'
|
||||
empty_floats = list_str+'<float> {}'
|
||||
special_default = dict([
|
||||
("AveragePool "+"kernel_shape", empty_ints),
|
||||
("MaxPool "+"kernel_shape", empty_ints),
|
||||
("Cast "+"to", '0'),
|
||||
("Concat "+"axis", '0'),
|
||||
("Unsqueeze "+"axes", empty_ints),
|
||||
("RNN "+"activation_alpha", empty_floats),
|
||||
("RNN "+"activation_beta", empty_floats)
|
||||
])
|
||||
|
||||
line_indent = ' '
|
||||
fefile.write(' '+'}else if (OpName == "'+schema.name+'") {\n')
|
||||
op_type_str='mlir::ONNX'+schema.name+'Op'
|
||||
if schema.name in special_handler :
|
||||
fefile.write(' '+special_handler[schema.name]+'(node, '
|
||||
+str(len(schema.inputs))
|
||||
+', ' +str(len(schema.outputs))+', {\n')
|
||||
+', ' +str(len(schema.outputs)))
|
||||
elif len(schema.outputs) > 1 :
|
||||
fefile.write(' '+'ImportNodeMultipleOuts<'+op_type_str+'>(node, '
|
||||
+str(len(schema.inputs))
|
||||
+', ' +str(len(schema.outputs))+', {\n')
|
||||
+', ' +str(len(schema.outputs)))
|
||||
else :
|
||||
fefile.write(' '+'ImportNodeOneOut<'+op_type_str+'>(node, '
|
||||
+str(len(schema.inputs))
|
||||
+', ' +str(len(schema.outputs))+', {\n')
|
||||
+', ' +str(len(schema.outputs)))
|
||||
fefile.write(');\n')
|
||||
|
||||
def gen_attr_ins(schema, isfirst) :
|
||||
special_defaults = dict([
|
||||
("AveragePool "+"kernel_shape", ('ints', '{}')),
|
||||
("MaxPool "+"kernel_shape", ('ints', '{}')),
|
||||
("Cast "+"to", ('int', '0')),
|
||||
("Concat "+"axis", ('int', '0')),
|
||||
("Conv "+"group", ('int', '1')),
|
||||
("Unsqueeze "+"axes", ('ints', '{}')),
|
||||
("RNN "+"activation_alpha", ('floats', '{}')),
|
||||
("RNN "+"activation_beta", ('floats', '{}')),
|
||||
])
|
||||
|
||||
def get_attr_type_basic(attr_type) :
|
||||
if attr_type == 'int' :
|
||||
mytype = 'I64Attr'
|
||||
elif attr_type == 'float' :
|
||||
mytype = 'F32Attr'
|
||||
elif attr_type == 'ints' :
|
||||
mytype = 'I64ArrayAttr'
|
||||
elif attr_type == 'floats' :
|
||||
mytype = 'F32ArrayAttr'
|
||||
elif attr_type == "string" :
|
||||
mytype = 'StrAttr'
|
||||
elif attr_type == "strings" :
|
||||
mytype = 'StrArrayAttr'
|
||||
else :
|
||||
mytype ='AnyAttr'
|
||||
#TODO: tensor and sparse tensor
|
||||
return mytype
|
||||
|
||||
def get_attr_type_optional(attr_type) :
|
||||
mytype = 'OptionalAttr<'
|
||||
mytype += get_attr_type_basic(attr_type)
|
||||
mytype += '>'
|
||||
return mytype
|
||||
|
||||
def get_attr_type_with_default(attr_type, attr_default) :
|
||||
mytype = 'DefaultValuedAttr<'
|
||||
mytype += get_attr_type_basic(attr_type)
|
||||
mytype += ', "'+attr_default+'">'
|
||||
return mytype
|
||||
|
||||
attr_line = ''
|
||||
if schema.attributes:
|
||||
first_attr = True
|
||||
for _, attr in sorted(schema.attributes.items()):
|
||||
#only generate default attr list
|
||||
if schema.name+' '+attr.name in special_default:
|
||||
attr_value = special_default[schema.name+' '+attr.name]
|
||||
elif attr.default_value.name:
|
||||
default_value = helper.get_attribute_value(attr.default_value)
|
||||
#attr_line = line_indent+line_indent+line_indent+line_indent
|
||||
if not isfirst:
|
||||
attr_line += ',\n '
|
||||
else :
|
||||
isfirst = False
|
||||
|
||||
if schema.name+' '+attr.name in special_defaults:
|
||||
(attr_type_str, attr_default_str) = special_defaults[schema.name+' '+attr.name]
|
||||
attr_line += get_attr_type_with_default(attr_type_str, attr_default_str)
|
||||
attr_line += ':$'+attr.name
|
||||
elif attr.required:
|
||||
s = Text(attr.type)
|
||||
attr_type_str = s[s.rfind('.') + 1:].lower()
|
||||
attr_line += get_attr_type_basic(attr_type_str)
|
||||
attr_line += ':$'+attr.name
|
||||
|
||||
# option holds either required or default value
|
||||
elif attr.default_value.name:
|
||||
s = Text(attr.type)
|
||||
attr_type_str = s[s.rfind('.') + 1:].lower()
|
||||
|
||||
default_value = helper.get_attribute_value(attr.default_value)
|
||||
def format_value(value): # type: (Any) -> Text
|
||||
if isinstance(value, float):
|
||||
formatted = str(np.round(value, 5))
|
||||
|
|
@ -419,66 +480,25 @@ def gen_code(schema,fefile) :
|
|||
return str(value)
|
||||
|
||||
if isinstance(default_value, list):
|
||||
|
||||
value = default_value[0]
|
||||
default_value = [format_value(val) for val in default_value]
|
||||
attr_option_str = '{}'.format(default_value)
|
||||
attr_option_str = attr_option_str.replace('[', '{', 1)
|
||||
attr_option_str = attr_option_str.replace(']', '}', 1)
|
||||
# TODO the list type is homogenous or htergeneous?
|
||||
|
||||
if isinstance(value, float) :
|
||||
attr_type_str = list_str+'<float>'
|
||||
attr_option_str = attr_option_str.replace("'", '')
|
||||
elif isinstance(value, int) :
|
||||
attr_type_str = list_str+'<int>'
|
||||
attr_option_str = attr_option_str.replace("'", '')
|
||||
elif isinstance(value, str) :
|
||||
attr_type_str = list_str+'<std::string>'
|
||||
attr_option_str = attr_option_str.replace("'", '"')
|
||||
elif isinstance(value, (bytes, bytearray)) :
|
||||
attr_type_str = list_str+'<std::string>'
|
||||
attr_option_str = attr_option_str.replace("'", '"')
|
||||
if attr_type_str == 'strings' :
|
||||
attr_option_str = attr_option_str.replace("'", '\\"')
|
||||
else :
|
||||
attr_type_str = '"unknowns"'
|
||||
attr_option_str = attr_option_str.replace("'", '')
|
||||
else:
|
||||
if isinstance(default_value, float) :
|
||||
attr_type_str = '(float)'
|
||||
attr_option_str = default_value
|
||||
elif isinstance(default_value, int) :
|
||||
attr_option_str = default_value
|
||||
attr_type_str=''
|
||||
elif isinstance(default_value, str) :
|
||||
attr_type_str = '"str"'
|
||||
elif isinstance(default_value, (bytes, bytearray)) :
|
||||
attr_type_str = '"str"'
|
||||
else :
|
||||
attr_type_str = '"unknown"'
|
||||
default_value = format_value(default_value)
|
||||
if attr_type_str == '"str"' :
|
||||
attr_option_str = '"'+default_value+'"'
|
||||
attr_type_str=''
|
||||
else :
|
||||
attr_option_str = default_value
|
||||
attr_value = attr_type_str+attr_option_str
|
||||
attr_option_str = default_value
|
||||
attr_line += get_attr_type_with_default(attr_type_str, attr_option_str)
|
||||
attr_line += ':$'+attr.name
|
||||
else:
|
||||
#no default value
|
||||
continue
|
||||
|
||||
attr_line = line_indent+line_indent+line_indent+line_indent
|
||||
if not first_attr:
|
||||
attr_line += ',{'
|
||||
else :
|
||||
attr_line += ' {'
|
||||
first_attr = False
|
||||
|
||||
attr_line += '"'+attr.name+'", '
|
||||
attr_line += attr_value
|
||||
attr_line += '}\n'
|
||||
fefile.write(attr_line)
|
||||
fefile.write(line_indent+line_indent+line_indent+'});\n')
|
||||
|
||||
|
||||
s = Text(attr.type)
|
||||
attr_type_str = s[s.rfind('.') + 1:].lower()
|
||||
attr_line += get_attr_type_optional(attr_type_str)
|
||||
attr_line += ':$'+attr.name
|
||||
return attr_line
|
||||
|
||||
def main(args): # type: (Type[Args]) -> None
|
||||
with io.open(args.changelog, 'w', newline='') as fout:
|
||||
|
|
@ -496,7 +516,6 @@ def main(args): # type: (Type[Args]) -> None
|
|||
fout.write('\n')
|
||||
|
||||
for domain, versionmap in sorted(dv_index.items()):
|
||||
print("domain", domain)
|
||||
if not should_render_domain(domain):
|
||||
continue
|
||||
|
||||
|
|
@ -633,6 +652,6 @@ if __name__ == '__main__':
|
|||
class Args(object):
|
||||
output = os.path.join(docs_dir, 'Operators' + ext)
|
||||
changelog = os.path.join(docs_dir, 'Changelog' + ext)
|
||||
tdfile = os.path.join(docs_dir, 'onnxop.inc')
|
||||
tdfile = os.path.join(base_dir, 'onnxop.inc')
|
||||
print(Args)
|
||||
main(Args)
|
||||
|
|
|
|||
|
|
@ -99,8 +99,14 @@ def ONNXGemmNoBiasOp: ONNX_Op<"GemmNoBias",
|
|||
|
||||
}];
|
||||
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$lhs_in, AnyTypeOf<[AnyMemRef, AnyTensor]>:$rhs_in);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$A,
|
||||
AnyTypeOf<[AnyMemRef, AnyTensor]>:$B,
|
||||
DefaultValuedAttr<F32Attr, "1.0">:$alpha,
|
||||
DefaultValuedAttr<F32Attr, "1.0">:$beta,
|
||||
DefaultValuedAttr<I64Attr, "0">:$transA,
|
||||
DefaultValuedAttr<I64Attr, "0">:$transB);
|
||||
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_Y);
|
||||
}
|
||||
|
||||
def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
|
||||
|
|
@ -110,10 +116,15 @@ def ONNXConvNoBiasOp:ONNX_Op<"ConvNoBias",
|
|||
"The convolution operator consumes an input tensor and a filter, and"
|
||||
"computes the output."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X, AnyTypeOf<[AnyMemRef, AnyTensor]>:$W);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
|
||||
|
||||
let verifier = [{ return ::verify(*this); }];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
AnyTypeOf<[AnyMemRef, AnyTensor]>:$W,
|
||||
DefaultValuedAttr<StrAttr, "NOTSET">:$auto_pad,
|
||||
OptionalAttr<I64ArrayAttr>:$dilations,
|
||||
DefaultValuedAttr<I64Attr, "1">:$group,
|
||||
OptionalAttr<I64ArrayAttr>:$kernel_shape,
|
||||
OptionalAttr<I64ArrayAttr>:$pads,
|
||||
OptionalAttr<I64ArrayAttr>:$strides);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_Y);
|
||||
}
|
||||
|
||||
def ONNXMaxPoolSingleOutOp: ONNX_Op<"MaxPoolSingleOut",
|
||||
|
|
@ -123,8 +134,15 @@ def ONNXMaxPoolSingleOutOp: ONNX_Op<"MaxPoolSingleOut",
|
|||
"ONNX MaxPool operation with a single output."
|
||||
"See ONNXMaxPoolOp for a full description of the MaxPool semantics."
|
||||
}];
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>);
|
||||
let arguments = (ins AnyTypeOf<[AnyMemRef, AnyTensor]>:$X,
|
||||
DefaultValuedAttr<StrAttr, "NOTSET">:$auto_pad,
|
||||
DefaultValuedAttr<I64Attr, "0">:$ceil_mode,
|
||||
OptionalAttr<I64ArrayAttr>:$dilations,
|
||||
DefaultValuedAttr<I64ArrayAttr, "{}">:$kernel_shape,
|
||||
OptionalAttr<I64ArrayAttr>:$pads,
|
||||
DefaultValuedAttr<I64Attr, "0">:$storage_order,
|
||||
OptionalAttr<I64ArrayAttr>:$strides);
|
||||
let results = (outs AnyTypeOf<[AnyMemRef, AnyTensor]>:$o_Y);
|
||||
}
|
||||
|
||||
#endif // ONNX_OPS
|
||||
|
|
|
|||
|
|
@ -512,8 +512,10 @@ void ONNXTransposeOp::inferShapes() {
|
|||
auto arrayTy = getOperand().getType().cast<RankedTensorType>();
|
||||
SmallVector<int64_t, 2> dims;
|
||||
|
||||
if (auto permutation = getAttrOfType<ArrayAttr>(
|
||||
ONNXTransposeOp::getPermAttrName())) {
|
||||
//if (auto permutation = getAttrOfType<ArrayAttr>(
|
||||
// ONNXTransposeOp::getPermAttrName())) {
|
||||
auto permutation = ONNXTransposeOp::permAttr();
|
||||
if (permutation) {
|
||||
// Perform transposition according to perm attribute.
|
||||
for (auto perm : permutation.getValue())
|
||||
dims.emplace_back(arrayTy.getShape()[perm.cast<IntegerAttr>().getInt()]);
|
||||
|
|
@ -526,20 +528,6 @@ void ONNXTransposeOp::inferShapes() {
|
|||
getResult().setType(RankedTensorType::get(dims, arrayTy.getElementType()));
|
||||
}
|
||||
|
||||
LogicalResult verify(ONNXTransposeOp op) {
|
||||
auto module = op.getParentOfType<ModuleOp>();
|
||||
if (!module)
|
||||
op.emitError("Expected to belong to a module.");
|
||||
|
||||
if (auto permutation = op.getAttrOfType<ArrayAttr>(
|
||||
ONNXTransposeOp::getPermAttrName())) {
|
||||
for (auto perm : permutation.getValue())
|
||||
if (perm.cast<IntegerAttr>().getInt() < 0)
|
||||
op.emitError("Cannot tranpose, permuation contains negative index.");
|
||||
}
|
||||
|
||||
return success();
|
||||
}
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
|
|
@ -568,11 +556,9 @@ void ONNXConvNoBiasOp::inferShapes() {
|
|||
emitError("Weight size not compatible with data size.");
|
||||
|
||||
// Required attribute auto_pad defaults to NOTSET.
|
||||
auto autoPad = getAttrOfType<StringAttr>(
|
||||
ONNXConvOp::getAutoPadAttrName()).getValue();
|
||||
auto autoPad = auto_pad();
|
||||
// Group is a required attribute and should have default value of 1.
|
||||
int64_t group = getAttrOfType<IntegerAttr>(
|
||||
ONNXConvOp::getGroupAttrName()).getInt();
|
||||
int64_t group = ONNXConvNoBiasOp::group().getSExtValue(); //.getLimitedValue();
|
||||
// Check that the X.shape[1] == (W.shape[1] * group) == C condition holds.
|
||||
if (dataShape[1] != (weightShape[1] * group))
|
||||
emitError("Channel dimension mismatch.");
|
||||
|
|
@ -604,8 +590,7 @@ void ONNXConvNoBiasOp::inferShapes() {
|
|||
// Use kernel_shape attribute if present otherwise use size from weight
|
||||
// argument.
|
||||
SmallVector<int64_t, 2> kernelDims;
|
||||
if (auto kernelShape = getAttrOfType<ArrayAttr>(
|
||||
ONNXConvOp::getKernelShapeAttrName())) {
|
||||
if (auto kernelShape = kernel_shapeAttr()) {
|
||||
if (kernelShape.getValue().size() != nDims)
|
||||
emitError("kernel_shape length incompatible with spatial dimensions.");
|
||||
for (int i = 0; i < nDims; ++i)
|
||||
|
|
@ -627,8 +612,7 @@ void ONNXConvNoBiasOp::inferShapes() {
|
|||
//
|
||||
// From a dimensionality perspective the kernel size becomes the dilated
|
||||
// kernel size.
|
||||
if (auto dilations = getAttrOfType<ArrayAttr>(
|
||||
ONNXConvOp::getDilationsAttrName())) {
|
||||
if (auto dilations = dilationsAttr()) {
|
||||
if (dilations.getValue().size() != nDims)
|
||||
emitError("dilations length incompatible with spatial dimensions.");
|
||||
for (int i = 0; i < nDims; ++i)
|
||||
|
|
@ -644,8 +628,7 @@ void ONNXConvNoBiasOp::inferShapes() {
|
|||
if (autoPad == "NOTSET") {
|
||||
// Use pads to to determine the padding. If attribute is not
|
||||
// present then pads is considered to be all zeros (no padding).
|
||||
if (auto pads = getAttrOfType<ArrayAttr>(
|
||||
ONNXConvOp::getPadsAttrName())) {
|
||||
if (auto pads = padsAttr()) {
|
||||
// pads consists of two entries for each spatial axis.
|
||||
if (pads.getValue().size() != 2 * nDims)
|
||||
emitError("pads size is not twice the spatial size.");
|
||||
|
|
@ -676,13 +659,12 @@ void ONNXConvNoBiasOp::inferShapes() {
|
|||
}
|
||||
|
||||
// Strides
|
||||
if (auto strides = getAttrOfType<ArrayAttr>(
|
||||
ONNXConvOp::getStridesAttrName())) {
|
||||
if (auto strides = ONNXConvNoBiasOp::stridesAttr()) {
|
||||
if (strides.getValue().size() != nDims)
|
||||
emitError("strides length incompatible with spatial dimensions.");
|
||||
for (int i = 0; i < nDims; ++i) {
|
||||
int64_t stride =
|
||||
(strides.getValue()[i]).cast<IntegerAttr>().getInt();
|
||||
strides.getValue()[i].cast<IntegerAttr>().getInt();
|
||||
outSpatialDims[i] = floor(outSpatialDims[i] / stride);
|
||||
}
|
||||
}
|
||||
|
|
@ -694,28 +676,6 @@ void ONNXConvNoBiasOp::inferShapes() {
|
|||
getResult().setType(RankedTensorType::get(dims, dataTy.getElementType()));
|
||||
}
|
||||
|
||||
LogicalResult verify(ONNXConvNoBiasOp op) {
|
||||
auto module = op.getParentOfType<ModuleOp>();
|
||||
if (!module)
|
||||
op.emitError("expected to belong to a module");
|
||||
|
||||
auto autoPadAttr = op.getAttrOfType<StringAttr>(
|
||||
ONNXConvOp::getAutoPadAttrName());
|
||||
if (!autoPadAttr)
|
||||
op.emitError("auto_pad attribute not specified.");
|
||||
if (autoPadAttr.getValue() != "NOTSET")
|
||||
if (auto pads = op.getAttrOfType<ArrayAttr>(
|
||||
ONNXConvOp::getPadsAttrName()))
|
||||
op.emitError("auto_pad and pads are both set.");
|
||||
|
||||
auto groupAttr =
|
||||
op.getAttrOfType<IntegerAttr>(ONNXConvOp::getGroupAttrName());
|
||||
if (!groupAttr)
|
||||
op.emitError("group attribute not specified.");
|
||||
|
||||
return success();
|
||||
}
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
// TableGen'd op method definitions
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -420,14 +420,16 @@ Value mapToLowerScalarOp<ONNXHardSigmoidOp>(
|
|||
// Constant 1)
|
||||
auto loc = op->getLoc();
|
||||
Value operand = operands[0];
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto betaAttr = op->getAttrOfType<FloatAttr>("beta");
|
||||
auto alphaAttribute = FloatAttr::get(rewriter.getF32Type(),
|
||||
llvm::dyn_cast<ONNXHardSigmoidOp>(op).alpha().convertToFloat());
|
||||
auto betaAttribute = FloatAttr::get(rewriter.getF32Type(),
|
||||
llvm::dyn_cast<ONNXHardSigmoidOp>(op).beta().convertToFloat());
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
auto one = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 1));
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
|
||||
auto beta = rewriter.create<ConstantOp>(loc, betaAttr);
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttribute);
|
||||
auto beta = rewriter.create<ConstantOp>(loc, betaAttribute);
|
||||
|
||||
auto add = rewriter.create<AddFOp>(
|
||||
loc, rewriter.create<MulFOp>(loc, alpha, operand), beta);
|
||||
|
|
@ -455,10 +457,11 @@ Value mapToLowerScalarOp<ONNXEluOp>(Operation *op, ArrayRef<Type> result_types,
|
|||
Value operand = operands[0];
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto alphaAttribute = FloatAttr::get(rewriter.getF32Type(),
|
||||
llvm::dyn_cast<ONNXEluOp>(op).alpha().convertToFloat());
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
auto one = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 1));
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttribute);
|
||||
auto exp = rewriter.create<ExpOp>(loc, operand);
|
||||
auto lessThanZero =
|
||||
rewriter.create<CmpFOp>(loc, CmpFPredicate::OLT, operand, zero);
|
||||
|
|
@ -508,9 +511,10 @@ Value mapToLowerScalarOp<ONNXLeakyReluOp>(Operation *op,
|
|||
Value operand = operands[0];
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto alphaAttribute = FloatAttr::get(rewriter.getF32Type(),
|
||||
llvm::dyn_cast<ONNXLeakyReluOp>(op).alpha().convertToFloat());
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttribute);
|
||||
auto lessThanZero =
|
||||
rewriter.create<CmpFOp>(loc, CmpFPredicate::OLT, operand, zero);
|
||||
auto result = rewriter.create<SelectOp>(
|
||||
|
|
@ -533,13 +537,15 @@ Value mapToLowerScalarOp<ONNXSeluOp>(Operation *op, ArrayRef<Type> result_types,
|
|||
// alpha)))
|
||||
auto loc = op->getLoc();
|
||||
Value operand = operands[0];
|
||||
auto alphaAttr = op->getAttrOfType<FloatAttr>("alpha");
|
||||
auto gammaAttr = op->getAttrOfType<FloatAttr>("gamma");
|
||||
auto alphaAttribute = FloatAttr::get(rewriter.getF32Type(),
|
||||
llvm::dyn_cast<ONNXSeluOp>(op).alpha().convertToFloat());
|
||||
auto gammaAttribute = FloatAttr::get(rewriter.getF32Type(),
|
||||
llvm::dyn_cast<ONNXSeluOp>(op).gamma().convertToFloat());
|
||||
auto elementType = result_types[0];
|
||||
|
||||
auto zero = rewriter.create<ConstantOp>(loc, FloatAttr::get(elementType, 0));
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttr);
|
||||
auto gamma = rewriter.create<ConstantOp>(loc, gammaAttr);
|
||||
auto alpha = rewriter.create<ConstantOp>(loc, alphaAttribute);
|
||||
auto gamma = rewriter.create<ConstantOp>(loc, gammaAttribute);
|
||||
auto exp = rewriter.create<ExpOp>(loc, operand);
|
||||
auto greaterThanZero =
|
||||
rewriter.create<CmpFOp>(loc, CmpFPredicate::OGT, operand, zero);
|
||||
|
|
@ -876,7 +882,7 @@ struct ONNXSoftmaxOpLowering : public ConversionPattern {
|
|||
// exp_x / sum
|
||||
auto tensorType = (*op->result_type_begin()).cast<RankedTensorType>();
|
||||
int64_t rank = tensorType.getRank();
|
||||
int64_t axis = op->getAttrOfType<IntegerAttr>("axis").getInt();
|
||||
int64_t axis = llvm::dyn_cast<ONNXSoftmaxOp>(op).axis().getSExtValue();
|
||||
axis = axis >= 0 ? axis : rank + axis;
|
||||
assert(axis >= -rank && axis <= rank - 1);
|
||||
|
||||
|
|
|
|||
|
|
@ -30,9 +30,14 @@ def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;
|
|||
// Pattern-Match and Rewrite
|
||||
//===----------------------------------------------------------------------===//
|
||||
|
||||
def GemmAlpha : NativeCodeCall<"$_builder.getF32FloatAttr(1.0)">;
|
||||
def GemmBeta : NativeCodeCall<"$_builder.getF32FloatAttr(1.0)">;
|
||||
def GemmTransA : NativeCodeCall<"$_builder.getI64IntegerAttr(0)">;
|
||||
def GemmTransB : NativeCodeCall<"$_builder.getI64IntegerAttr(0)">;
|
||||
|
||||
// onnx.add(onnx.matmul(%X, %Y), %Z) = onnx.Gemm(%X, %Y, %Z)
|
||||
def MulAddToGemmOptPattern : Pat<(ONNXAddOp (ONNXMatMulOp:$res $m1, $m2), $m3),
|
||||
(ONNXGemmOp $m1, $m2, $m3),
|
||||
(ONNXGemmOp $m1, $m2, $m3, (GemmAlpha), (GemmBeta), (GemmTransA), (GemmTransB)),
|
||||
[(HasOneUse $res)]>;
|
||||
|
||||
// ONNX_Op (onnx.Identity (%X)) = ONNX_Op (%X)
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
func @test_matmul_add_simplification(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {
|
||||
// CHECK-LABEL: test_matmul_add_simplification
|
||||
// CHECK: %{{[0-9]+}} = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
|
||||
// CHECK: %{{[0-9]+}} = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) {alpha = 1.000000e+00 : f32, beta = 1.000000e+00 : f32, transA = 0 : i64, transB = 0 : i64} : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
|
||||
%0 = "onnx.MatMul"(%a0, %a1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
|
||||
%1 = "onnx.Add"(%0, %a2) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
|
||||
"std.return"(%1) : (tensor<10x10xf32>) -> ()
|
||||
|
|
|
|||
|
|
@ -579,7 +579,7 @@ func @test_add_with_broadcasting(%arg0 : tensor<?xf32>, %arg1 : tensor<?x10xf32>
|
|||
}
|
||||
|
||||
func @test_softmax(%arg0 : tensor<10x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.Softmax"(%arg0) {axis=1:i32} : (tensor<10x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.Softmax"(%arg0) {axis=1:i64} : (tensor<10x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_softmax
|
||||
|
|
|
|||
|
|
@ -124,120 +124,120 @@ func @test_matmul_8(%arg0 : tensor<32x64xf32>, %arg1 : tensor<64x128xf32>) -> te
|
|||
/// Default and required attributes.
|
||||
|
||||
func @test_conv_no_bias_1(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_1
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x27x58xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x58xf32>
|
||||
|
||||
/// kernel_shape attribute.
|
||||
|
||||
func @test_conv_no_bias_2(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_2
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, kernel_shape = [8, 9]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x25x56xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x25x56xf32>
|
||||
|
||||
/// pads attribute.
|
||||
/// Use pads to make output size equal to input size by adding K - 1 to the result.
|
||||
|
||||
func @test_conv_no_bias_3(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_3
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, pads = [2, 4, 3, 5]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
||||
|
||||
/// auto_pad set to SAME_UPPER and SAME_LOWER.
|
||||
|
||||
func @test_conv_no_bias_4(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_4
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
||||
|
||||
func @test_conv_no_bias_5(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_5
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_LOWER", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
||||
|
||||
/// auto_pad set to VALID.
|
||||
|
||||
func @test_conv_no_bias_6(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x10xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_6
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "VALID", group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x10xf32>) -> tensor<1x5x27x55xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x27x55xf32>
|
||||
|
||||
/// With strides attribute.
|
||||
|
||||
func @test_conv_no_bias_7(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_7
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x14x20xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x14x20xf32>
|
||||
|
||||
/// auto_pad set to SAME_UPPER with strides attribute.
|
||||
/// The auto_pad will pas as if stride is equal to 1.
|
||||
|
||||
func @test_conv_no_bias_8(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_8
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, strides = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x16x22xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x16x22xf32>
|
||||
|
||||
/// dilations attribute.
|
||||
|
||||
func @test_conv_no_bias_9(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_9
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x20x42xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x20x42xf32>
|
||||
|
||||
/// dilations attribute with stride.
|
||||
|
||||
func @test_conv_no_bias_10(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i32, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", group = 1 : i64, dilations = [2, 3], strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_10
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i32, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "NOTSET", dilations = [2, 3], group = 1 : i64, strides = [2, 2]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x10x21xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x10x21xf32>
|
||||
|
||||
/// dilations attribute with auto_pad set to SAME_UPPER.
|
||||
|
||||
func @test_conv_no_bias_11(%arg0 : tensor<1x2x32x64xf32>, %arg1 : tensor<5x2x6x7xf32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i32, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
%0 = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", group = 1 : i64, dilations = [2, 3]} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
}
|
||||
|
||||
// CHECK-LABEL: test_conv_no_bias_11
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i32} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.ConvNoBias"(%arg0, %arg1) {auto_pad = "SAME_UPPER", dilations = [2, 3], group = 1 : i64} : (tensor<1x2x32x64xf32>, tensor<5x2x6x7xf32>) -> tensor<1x5x32x64xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<1x5x32x64xf32>
|
||||
|
|
|
|||
Loading…
Reference in New Issue