Implement CustomOp Output Type Inference function

include/onnxruntime/core/session/onnxruntime_c_api.h

@@ -1837,14 +1837,28 @@ struct OrtApi {
 
   /** \brief Used for custom operators, get an input of a kernel
    *
-   * \see ::OrtCustomOp
+   * The function attempts fetches the input of the kernel. If the input is optional
+   * and not present, the function returns success and out is set to nullptr.
+   *
+   * \param[in] context ::OrtKernelContext instance
+   * \param[in] input index. See KernelContext_GetInputCount for boundaries check.
+   * \param[in, out] returns a ptr to OrtValue if the input is present
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
    */
   ORT_API2_STATUS(KernelContext_GetInput, _In_ const OrtKernelContext* context, _In_ size_t index,
                   _Out_ const OrtValue** out);
 
   /** \brief Used for custom operators, get an output of a kernel
    *
-   * \see ::OrtCustomOp
+   * The function attempts fetches the output of the kernel. If the output is optional
+   * and not present, the function returns success and out is set to nullptr.
+   *
+   * \param[in] context ::OrtKernelContext instance
+   * \param[in] output index. See KernelContext_GetOutputCount for boundaries check.
+   * \param[in, out] returns a ptr to OrtValue if the output is present
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
    */
   ORT_API2_STATUS(KernelContext_GetOutput, _Inout_ OrtKernelContext* context, _In_ size_t index,
                   _In_ const int64_t* dim_values, size_t dim_count, _Outptr_ OrtValue** out);

include/onnxruntime/core/session/onnxruntime_cxx_api.h

@@ -2055,7 +2055,11 @@ struct KernelContext {
   explicit KernelContext(OrtKernelContext* context);
   size_t GetInputCount() const;
   size_t GetOutputCount() const;
+  // If input is optional and is not present, the method returns en empty ConstValue
+  // which can be compared to nullptr.
   ConstValue GetInput(size_t index) const;
+  // If outout is optional and is not present, the method returns en empty UnownedValue
+  // which can be compared to nullptr.
   UnownedValue GetOutput(size_t index, const int64_t* dim_values, size_t dim_count) const;
   UnownedValue GetOutput(size_t index, const std::vector<int64_t>& dims) const;
   void* GetGPUComputeStream() const;

onnxruntime/core/session/custom_ops.cc

@@ -1040,59 +1040,120 @@ Status IsCompatible(const ONNX_NAMESPACE::OpSchema& schema, const OrtCustomOp* o
   return Status::OK();
 }
 
-void InferOutputTypes(const InlinedVector<const KernelDef*>& kernel_defs,
-                      ONNX_NAMESPACE::InferenceContext& infer_ctx) {
-  for (const auto& kernel_def : kernel_defs) {
+// This function attempts to do its best for older custom ops (most of them) who do not have
+// they own type and shape inference function. However, it falls short in some cases, and we leave
+// those for the user to handle in their own inference function.
+static void InferOutputTypes(const ONNX_NAMESPACE::OpSchema& schema, gsl::span<const KernelDef* const> kernel_defs,
+                             ONNX_NAMESPACE::InferenceContext& infer_ctx) {
+  const auto& inputs = schema.inputs();
+  const auto node_input_num = infer_ctx.getNumInputs();
+
+  const KernelDef* def_selected = nullptr;
+  bool is_variadic_input = false;
+  bool is_homogeneous_input = false;
+  int32_t output_propagate{0};
+
+  for (size_t kernel_index = 0;
+       kernel_index < kernel_defs.size() && def_selected == nullptr;
+       ++kernel_index) {
+    const auto* kernel_def = kernel_defs[kernel_index];
     const auto& type_constraints = kernel_def->TypeConstraints();
-    auto num_inputs = infer_ctx.getNumInputs();
-    bool matched = true;
-    ONNXTensorElementDataType undef = ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_UNDEFINED;
-    // first, make sure there is a constraint for every input
-    for (size_t i = 0; i < num_inputs && matched; ++i) {
-      auto input_name = "Input" + std::to_string(i);
-      auto input_type = infer_ctx.getInputType(i);
-      if (input_type) {
-        auto elem_type = static_cast<ONNXTensorElementDataType>(input_type->tensor_type().elem_type());
-        auto tc_iter = type_constraints.find(input_name);
-        if (tc_iter != type_constraints.end()) {
-          if (tc_iter->second.size() > 1) {
-            undef = elem_type;
-          } else if (tc_iter->second.size() != 1 ||
-                     tc_iter->second[0] != DataTypeImpl::TensorTypeFromONNXEnum(elem_type)) {
-            matched = false;
+    def_selected = kernel_def;
+
+    for (size_t i = 0; i < node_input_num; ++i) {
+      const auto input_type = infer_ctx.getInputType(i);
+
+      // Guard against variadic parameter index
+      const size_t schema_input_index = (i < inputs.size()) ? i : inputs.size() - 1;
+      const auto& param = inputs[schema_input_index];
+      const auto& input_name = param.GetName();
+      if (input_type == nullptr) {
+        if (param.GetOption() == ONNX_NAMESPACE::OpSchema::FormalParameterOption::Optional)
+          continue;
+
+        ORT_THROW("[CustomOP type inferencing error]: kernel Input: ", input_name,
+                  " is absent, but not optional. Op : ", schema.Name());
+      }
+
+      is_variadic_input = (param.GetOption() == ONNX_NAMESPACE::OpSchema::FormalParameterOption::Variadic);
+      is_homogeneous_input = param.GetIsHomogeneous();
+
+      if (!is_variadic_input || is_homogeneous_input) {
+        auto hit = type_constraints.find(input_name);
+        if (hit != type_constraints.end()) {
+          const auto& types = hit->second;
+          // For custom ops kernel constraints are never empty
+          assert(!types.empty());
+          if (!std::any_of(types.cbegin(), types.cend(),
+                           [input_type](const DataTypeImpl* type) {
+                             return type->IsCompatible(*input_type);
+                           })) {
+            def_selected = nullptr;
+            output_propagate = 0;
+            break;
+          }
+
+          // If we have multiple types possible from the constraints,
+          // record the last type and use it to guess the output type if
+          // output may have different types. Works well for symmetric single input/outputs
+          // otherwise give up and let the user supply their own function
+          if (types.size() > 1) {
+            output_propagate = input_type->tensor_type().elem_type();
           }
         } else {
-          matched = false;
+          ORT_THROW("[CustomOP type inferencing error]: no type constraint found for input: ",
+                    input_name, " Op: ", schema.Name());
         }
-      } else {
-        matched = false;
-      }
-    }  // for
-    // next, ensure that there is a constraint for every output
-    auto num_outputs = infer_ctx.getNumOutputs();
-    for (size_t i = 0; i < num_outputs && matched; i++) {
-      auto output_name = "Output" + std::to_string(i);
-      auto tc_iter = type_constraints.find(output_name);
-      if (tc_iter == type_constraints.end() || tc_iter->second.size() < 1) {
-        matched = false;
       }
     }
-    if (matched) {
-      for (size_t i = 0; i < num_outputs; i++) {
-        auto output_name = "Output" + std::to_string(i);
-        auto output_type = infer_ctx.getOutputType(i);
-        auto tc_iter = type_constraints.find(output_name);
-        if (tc_iter->second.size() > 1) {
-          output_type->mutable_tensor_type()->set_elem_type(undef);
-        } else {
-          output_type->mutable_tensor_type()->set_elem_type(
-              tc_iter->second[0]->GetTypeProto()->tensor_type().elem_type());
-        }
-      }
+  }
+
+  if (def_selected == nullptr) {
+    ORT_THROW("[CustomOP type inferencing error]: no kernel def matches node inputs for Op: ", schema.Name());
+  }
+
+  const auto& outputs = schema.outputs();
+  const auto node_output_num = infer_ctx.getNumOutputs();
+  const auto& selected_type_constraints = def_selected->TypeConstraints();
+
+  for (size_t i = 0; i < node_output_num; ++i) {
+    auto output_type = infer_ctx.getOutputType(i);
+    // Account for variadic outputs
+    const size_t schema_output_index = (i < outputs.size()) ? i : outputs.size() - 1;
+    const auto& param = outputs[schema_output_index];
+    const auto& output_name = param.GetName();
+
+    const bool is_variadic_output = (param.GetOption() == ONNX_NAMESPACE::OpSchema::FormalParameterOption::Variadic);
+    const bool is_homogeneous = param.GetIsHomogeneous();
+
+    // We give up on variadic non-homogeneous outputs
+    // Let the user handle it in their inference function
+    if (is_variadic_output && !is_homogeneous) {
       break;
     }
+
+    auto hit = selected_type_constraints.find(output_name);
+    if (hit != selected_type_constraints.end()) {
+      const auto& types = hit->second;
+      assert(!types.empty());
+
+      if (types.size() == 1) {
+        // Use the constraint type
+        output_type->mutable_tensor_type()->set_elem_type(
+            types[0]->GetTypeProto()->tensor_type().elem_type());
+      } else if (!is_variadic_input || is_homogeneous_input) {
+        // If not variadic or homogeneous, and there are multiple types possible, guess from the last input type
+        // as this works for symmetric varied single input/outputs
+        // otherwise give up and let the user supply their own function
+        output_type->mutable_tensor_type()->set_elem_type(output_propagate);
+      }
+    } else {
+      ORT_THROW("[CustomOP type inferencing error]: no type constraint found for output: ",
+                output_name, " Op: ", schema.Name());
+    }
   }
 }
+
 #endif
 
 common::Status CreateCustomRegistry(gsl::span<OrtCustomOpDomain* const> op_domains,
@@ -1152,13 +1213,13 @@ common::Status CreateCustomRegistry(gsl::span<OrtCustomOpDomain* const> op_domai
     }
 
     std::vector<ONNX_NAMESPACE::OpSchema> schemas;
-    for (auto schema_iter : schema_map) {
-      schemas.push_back(schema_iter.second);
-      InlinedVector<const KernelDef*> kernel_defs = std::move(kernel_def_map[schema_iter.first]);
+    for (auto& [name, schema] : schema_map) {
+      schemas.push_back(schema);
       auto infer_fn = schemas.back().GetTypeAndShapeInferenceFunction();
       ONNX_NAMESPACE::InferenceFunction extended_infer_fn =
-          [infer_fn, kernel_defs](ONNX_NAMESPACE::InferenceContext& infer_ctx) {
-            InferOutputTypes(kernel_defs, infer_ctx);
+          [sch = schema, infer_fn = std::move(infer_fn),
+           kernel_defs = std::move(kernel_def_map[name])](ONNX_NAMESPACE::InferenceContext& infer_ctx) {
+            InferOutputTypes(sch, kernel_defs, infer_ctx);
             if (infer_fn) {
               infer_fn(infer_ctx);
             }

onnxruntime/test/framework/shape_inference_test.cc

@@ -5,13 +5,16 @@
 #include <unordered_map>
 
 #include "gtest/gtest.h"
+#include "core/common/span_utils.h"
 #include "core/graph/model.h"
+#include "core/session/onnxruntime_cxx_api.h"
 #include "test/framework/model_builder_utils.h"
+#include "test/util/include/asserts.h"
 #include "test/util/include/test_utils.h"
+#include "test/util/include/inference_session_wrapper.h"
 #include "test/test_environment.h"
 
 using namespace ONNX_NAMESPACE;
-using namespace std;
 
 namespace onnxruntime {
 namespace test {
@@ -22,7 +25,7 @@
  protected:
   onnxruntime::Model model_;
   int node_count_;
-  std::unordered_map<string, std::unique_ptr<onnxruntime::NodeArg>> name_to_arg_;
+  std::unordered_map<std::string, std::unique_ptr<onnxruntime::NodeArg>> name_to_arg_;
 
  public:
   ShapeInferenceTest() : model_("Test", false, DefaultLoggingManager().DefaultLogger()), node_count_(0) {}
@@ -73,5 +76,91 @@
   CheckShapeEquality(InputShape(node), OutputShape(node));
 }
 
+namespace {
+struct MyCustomKernelWithOptionalInput {
+  MyCustomKernelWithOptionalInput(const OrtKernelInfo* /*info*/) {
+  }
+
+  OrtStatusPtr ComputeV2(OrtKernelContext* /* context */) const {
+    return nullptr;
+  }
+};
+
+struct MyCustomOpWithOptionalInput : Ort::CustomOpBase<MyCustomOpWithOptionalInput,
+                                                       MyCustomKernelWithOptionalInput,
+                                                       true> {
+  explicit MyCustomOpWithOptionalInput(const char* provider) : provider_(provider) {}
+
+  OrtStatusPtr CreateKernelV2(const OrtApi& /* api */, const OrtKernelInfo* info, void** kernel) const {
+    *kernel = new MyCustomKernelWithOptionalInput(info);
+    return nullptr;
+  };
+
+  const char* GetName() const { return "FooBar"; };
+  const char* GetExecutionProviderType() const { return provider_; };
+
+  size_t GetInputTypeCount() const { return 3; };
+  ONNXTensorElementDataType GetInputType(size_t /*index*/) const { return ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT; };
+  OrtCustomOpInputOutputCharacteristic GetInputCharacteristic(size_t index) const {
+    // The second input (index == 1) is optional
+    if (index == 1)
+      return OrtCustomOpInputOutputCharacteristic::INPUT_OUTPUT_OPTIONAL;
+
+    return OrtCustomOpInputOutputCharacteristic::INPUT_OUTPUT_REQUIRED;
+  }
+
+  size_t GetOutputTypeCount() const { return 1; };
+  ONNXTensorElementDataType GetOutputType(size_t /*index*/) const { return ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT; };
+  OrtCustomOpInputOutputCharacteristic GetOutputCharacteristic(size_t /*index*/) const {
+    return OrtCustomOpInputOutputCharacteristic::INPUT_OUTPUT_REQUIRED;
+  }
+
+ private:
+  const char* provider_;
+};
+
+const ORTCHAR_T* const OPTIONAL_INPUT_CUSTOM_OP_MODEL_URI_2 = ORT_TSTR("testdata/foo_bar_2.onnx");
+
+}  // namespace
+
+// CustomOps Output type inference function quits if it
+// encounters the an output that is optional and absent.
+// It quits without any errors or logging. We want to make sure
+// that inference proceeds for all of the outputs when absent optional inputs are present
+TEST(ShapeInferenceCustomOpTest, custom_op_optional_input_inference_test) {
+  MyCustomOpWithOptionalInput custom_op{onnxruntime::kCpuExecutionProvider};
+
+  const auto& env = GetEnvironment();
+
+  Ort::CustomOpDomain op_domain("test");
+  op_domain.Add(&custom_op);
+
+  std::initializer_list<OrtCustomOpDomain*> op_domains = {static_cast<OrtCustomOpDomain*>(op_domain)};
+
+  SessionOptions sess_opts;
+  sess_opts.inter_op_param.thread_pool_size = 1;
+  sess_opts.intra_op_param.thread_pool_size = 1;
+
+  InferenceSessionWrapper session{sess_opts, env, OPTIONAL_INPUT_CUSTOM_OP_MODEL_URI_2};
+  ASSERT_STATUS_OK(session.AddCustomOpDomains(AsSpan(op_domains)));
+
+  ASSERT_STATUS_OK(session.Load());
+  ASSERT_STATUS_OK(session.Initialize());
+
+  const onnxruntime::Model& model = session.GetModel();
+  const auto& graph = model.MainGraph();
+  const auto& nodes = graph.Nodes();
+  for (const auto& node : nodes) {
+    if (node.OpType() == "FooBar") {
+      // check inferred shapes
+      const auto* node_arg = node.OutputDefs()[0];
+      const auto* type_proto = node_arg->TypeAsProto();
+      ASSERT_NE(nullptr, type_proto);
+      ASSERT_EQ(ONNX_NAMESPACE::TypeProto::ValueCase::kTensorType, type_proto->value_case());
+      ASSERT_EQ(ONNX_NAMESPACE::TensorProto_DataType_FLOAT, type_proto->tensor_type().elem_type());
+    }
+  }
+}
+
 }  // namespace test
 }  // namespace onnxruntime

onnxruntime/test/shared_lib/test_inference.cc

@@ -208,7 +208,7 @@ static constexpr PATH_TYPE MODEL_WITH_CUSTOM_MODEL_METADATA = TSTR("testdata/mod
 static constexpr PATH_TYPE VARIED_INPUT_CUSTOM_OP_MODEL_URI = TSTR("testdata/VariedInputCustomOp.onnx");
 static constexpr PATH_TYPE VARIED_INPUT_CUSTOM_OP_MODEL_URI_2 = TSTR("testdata/foo_3.onnx");
 static constexpr PATH_TYPE OPTIONAL_INPUT_OUTPUT_CUSTOM_OP_MODEL_URI = TSTR("testdata/foo_bar_1.onnx");
-static constexpr PATH_TYPE OPTIONAL_INPUT_OUTPUT_CUSTOM_OP_MODEL_URI_2 = TSTR("testdata/foo_bar_2.onnx");
+static constexpr PATH_TYPE OPTIONAL_INPUT_CUSTOM_OP_MODEL_URI_2 = TSTR("testdata/foo_bar_2.onnx");
 static constexpr PATH_TYPE VARIADIC_INPUT_OUTPUT_CUSTOM_OP_MODEL_URI = TSTR("testdata/custom_op_variadic_io.onnx");
 static constexpr PATH_TYPE VARIADIC_UNDEF_INPUT_OUTPUT_CUSTOM_OP_MODEL_URI = TSTR(
     "testdata/custom_op_variadic_undef_io.onnx");
@@ -1082,7 +1082,7 @@ TEST(CApiTest, invalid_variadic_input_homogeneity_custom_op) {
   }
 }
 
-TEST(CApiTest, optional_input_output_custom_op_handler) {
+TEST(CApiTest, optional_input_custom_op_handler) {
   MyCustomOpWithOptionalInput custom_op{onnxruntime::kCpuExecutionProvider};
 
   // `MyCustomOpFooBar` defines a custom op with atmost 3 inputs and the second input is optional.
@@ -1147,7 +1147,7 @@ TEST(CApiTest, optional_input_output_custom_op_handler) {
   {
     std::vector<const char*> input_names = {"X1", "X2"};
     ort_inputs.erase(ort_inputs.begin() + 2);  // remove the last input in the container
-    Ort::Session session(*ort_env, OPTIONAL_INPUT_OUTPUT_CUSTOM_OP_MODEL_URI_2, session_options);
+    Ort::Session session(*ort_env, OPTIONAL_INPUT_CUSTOM_OP_MODEL_URI_2, session_options);
     auto ort_outputs = session.Run(Ort::RunOptions{}, input_names.data(), ort_inputs.data(), ort_inputs.size(),
                                    &output_name, 1);
     ASSERT_EQ(ort_outputs.size(), 1u);
@@ -1166,6 +1166,7 @@ TEST(CApiTest, optional_input_output_custom_op_handler) {
     }
   }
 }
+
 TEST(CApiTest, custom_op_with_attributes_handler) {
   MyCustomOpWithAttributes custom_op{onnxruntime::kCpuExecutionProvider};