diff --git a/mlir/lib/CAPI/IR/IR.cpp b/mlir/lib/CAPI/IR/IR.cpp
--- a/mlir/lib/CAPI/IR/IR.cpp
+++ b/mlir/lib/CAPI/IR/IR.cpp
@@ -15,10 +15,12 @@
 #include "mlir/CAPI/Support.h"
 #include "mlir/CAPI/Utils.h"
 #include "mlir/IR/Attributes.h"
+#include "mlir/IR/BuiltinAttributes.h"
 #include "mlir/IR/BuiltinOps.h"
 #include "mlir/IR/Dialect.h"
 #include "mlir/IR/Location.h"
 #include "mlir/IR/Operation.h"
+#include "mlir/IR/OperationSupport.h"
 #include "mlir/IR/Types.h"
 #include "mlir/IR/Value.h"
 #include "mlir/IR/Verifier.h"
@@ -26,6 +28,7 @@
 #include "mlir/Parser/Parser.h"
 
 #include <cstddef>
+#include <memory>
 #include <optional>
 
 using namespace mlir;
@@ -345,25 +348,43 @@
   if (!info) {
     emitError(state.location)
         << "type inference was requested for the operation " << state.name
-        << ", but the operation was not registered. Ensure that the dialect "
+        << ", but the operation was not registered; ensure that the dialect "
            "containing the operation is linked into MLIR and registered with "
            "the context";
     return failure();
   }
 
-  // Fallback to inference via an op interface.
   auto *inferInterface = info->getInterface<InferTypeOpInterface>();
   if (!inferInterface) {
     emitError(state.location)
         << "type inference was requested for the operation " << state.name
-        << ", but the operation does not support type inference. Result "
-           "types must be specified explicitly.";
+        << ", but the operation does not support type inference; result "
+           "types must be specified explicitly";
+    return failure();
+  }
+
+  DictionaryAttr attributes = state.attributes.getDictionary(context);
+  OpaqueProperties properties = state.getRawProperties();
+
+  if (!properties && info->getOpPropertyByteSize() > 0 && !attributes.empty()) {
+    auto prop = std::make_unique<char[]>(info->getOpPropertyByteSize());
+    properties = OpaqueProperties(prop.get());
+    if (failed(info->setOpPropertiesFromAttribute(state.name, properties,
+                                                  attributes, nullptr))) {
+      return failure();
+    }
+
+    if (succeeded(inferInterface->inferReturnTypes(
+            context, state.location, state.operands, attributes, properties,
+            state.regions, state.types))) {
+      return success();
+    }
+    // Diagnostic emitted by interface.
     return failure();
   }
 
   if (succeeded(inferInterface->inferReturnTypes(
-          context, state.location, state.operands,
-          state.attributes.getDictionary(context), state.getRawProperties(),
+          context, state.location, state.operands, attributes, properties,
           state.regions, state.types)))
     return success();
 
@@ -405,8 +426,7 @@
       return {nullptr};
   }
 
-  MlirOperation result = wrap(Operation::create(cppState));
-  return result;
+  return wrap(Operation::create(cppState));
 }
 
 MlirOperation mlirOperationCreateParse(MlirContext context,
diff --git a/mlir/lib/Dialect/Shape/IR/Shape.cpp b/mlir/lib/Dialect/Shape/IR/Shape.cpp
--- a/mlir/lib/Dialect/Shape/IR/Shape.cpp
+++ b/mlir/lib/Dialect/Shape/IR/Shape.cpp
@@ -919,15 +919,8 @@
     ConstShapeOp::Adaptor adaptor, SmallVectorImpl<Type> &inferredReturnTypes) {
   Builder b(context);
   const Properties prop = adaptor.getProperties();
-  DenseIntElementsAttr shape;
-  // TODO: this is only exercised by the Python bindings codepath which does not
-  // support properties
-  shape = prop.shape ? prop.shape : 
-            adaptor.getAttributes().getAs<DenseIntElementsAttr>("shape");
-  if (!shape)
-    return emitOptionalError(location, "missing shape attribute");
   inferredReturnTypes.assign({RankedTensorType::get(
-      {static_cast<int64_t>(shape.size())}, b.getIndexType())});
+      {static_cast<int64_t>(prop.shape.size())}, b.getIndexType())});
   return success();
 }