[QNN EP] Add Einsum support for some equations

onnxruntime/core/providers/qnn/builder/op_builder_factory.cc

@@ -173,6 +173,10 @@ OpBuilderRegistrations::OpBuilderRegistrations() {
     CreateExpandOpBuilder("Expand", *this);
   }
 
+  {
+    CreateEinsumOpBuilder("Einsum", *this);
+  }
+
   {
     CreateMatMulOpBuilder("MatMul", *this);
   }

onnxruntime/core/providers/qnn/builder/op_builder_factory.h

@@ -100,5 +100,7 @@ void CreateExpandOpBuilder(const std::string& op_type, OpBuilderRegistrations& o
 void CreateHardSigmoidOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 
 void CreateMatMulOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
+
+void CreateEinsumOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations);
 }  // namespace qnn
 }  // namespace onnxruntime

onnxruntime/core/providers/qnn/builder/opbuilder/clip_op_builder.cc

@@ -94,13 +94,13 @@ Status ClipOpBuilder::ExplictOpCheck(QnnModelWrapper& qnn_model_wrapper, const N
   if (node_unit.Inputs().size() > 1) {
     const auto& min_input_name = node_unit.Inputs()[1].node_arg.Name();
     if (!min_input_name.empty() && !qnn_model_wrapper.IsConstantInput(min_input_name)) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN desn't support dynamic min/max.");
+      return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN doesn't support dynamic min/max.");
     }
   }
   if (node_unit.Inputs().size() > 2) {
     const auto& max_input_name = node_unit.Inputs()[2].node_arg.Name();
     if (!max_input_name.empty() && !qnn_model_wrapper.IsConstantInput(max_input_name)) {
-      return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN desn't support dynamic min/max.");
+      return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN doesn't support dynamic min/max.");
     }
   }
   return Status::OK();

onnxruntime/core/providers/qnn/builder/opbuilder/slice_op_builder.cc

@@ -46,7 +46,7 @@ Status SliceOpBuilder::ExplictOpCheck(QnnModelWrapper& qnn_model_wrapper, const
     for (size_t i = 1; i < input_count; i++) {
       const auto& next_input = node_unit.Inputs()[i].node_arg.Name();
       if (!qnn_model_wrapper.IsConstantInput(next_input)) {
-        return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN desn't support dynamic slice.");
+        return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "QNN doesn't support dynamic slice.");
       }
     }
   }

onnxruntime/core/providers/qnn/builder/opbuilder/softmax_op_builder.cc

@@ -44,7 +44,7 @@ std::vector<uint32_t> FlattenShapeFromAxis(std::vector<uint32_t>& input_shape, i
   Return the shape with all dimensions multiplied onward from the specified axis. If axis is 0, the returned shape
   will include an additional batch of size 1 as the first dimension.
   */
-  assert(axis >= 0 && axis < input_shape.size());
+  assert(axis >= 0 && static_cast<size_t>(axis) < input_shape.size());
   std::vector<uint32_t> output_shape(input_shape.begin(), input_shape.begin() + axis);
 
   if (axis == 0) {

onnxruntime/core/providers/qnn/builder/opbuilder/tile_op_builder.cc

@@ -42,7 +42,7 @@ Status TileOpBuilder::ProcessInputs(QnnModelWrapper& qnn_model_wrapper,
                                     std::vector<std::string>& input_names,
                                     bool do_op_validation) const {
   const auto& inputs = node_unit.Inputs();
-  // QNN Tile only support 1 input, the 2nd input need to be initialier and set as Qnn node parameter
+  // QNN Tile only support 1 input, the 2nd input need to be initializer and set as Qnn node parameter
   if (do_op_validation) {
     auto& repeats_input_name = inputs[1].node_arg.Name();
     ORT_RETURN_IF_NOT(qnn_model_wrapper.IsConstantInput(repeats_input_name),
@@ -60,7 +60,7 @@ Status TileOpBuilder::ProcessAttributesAndOutputs(QnnModelWrapper& qnn_model_wra
                                                   const logging::Logger& logger,
                                                   bool do_op_validation) const {
   std::vector<std::string> param_tensor_names;
-  // Already confirmed repeats input is initailizer in ProcessInputs()
+  // Already confirmed repeats input is initializer in ProcessInputs()
   const auto& repeats_input_name = node_unit.Inputs()[1].node_arg.Name();
 
   std::vector<uint8_t> unpacked_tensor;

onnxruntime/core/providers/qnn/builder/qnn_model.cc

@@ -180,14 +180,16 @@ Status QnnModel::SetupQnnInputOutput(const logging::Logger& logger) {
   auto result = SetupTensors(qnn_input_infos_, graph_info_->InputTensors());
 
   if (Status::OK() != result) {
-    LOGS(logger, ERROR) << "Failed to setup QNN input output tensors for graph: " << graph_info_->Name();
-    return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Failed to setup QNN input tensors!");
+    const std::string message = "Failed to setup QNN input tensors for graph: " + graph_info_->Name();
+    LOGS(logger, ERROR) << message;
+    return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, message);
   }
 
   result = SetupTensors(qnn_output_infos_, graph_info_->OutputTensors(), false);
   if (Status::OK() != result) {
-    LOGS(logger, ERROR) << "Failed to setup QNN input output tensors for graph: " << graph_info_->Name();
-    return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, "Failed to setup QNN output tensors!");
+    const std::string message = "Failed to setup QNN output tensors for graph: " + graph_info_->Name();
+    LOGS(logger, ERROR) << message;
+    return ORT_MAKE_STATUS(ONNXRUNTIME, FAIL, message);
   }
 
   return Status::OK();