_Arg node requires _handle_dtypes and _handle_shapes attr when T is DT_RESOURCE.

Otherwise, function node with DT_RESOURCE type input fails to infer output shapes.

PiperOrigin-RevId: 308927584
Change-Id: I653b293aaa36ffd4a6bed68e4692398eebd9d578

tensorflow/core/grappler/costs/graph_properties.cc

@@ -804,14 +804,47 @@ class SymbolicShapeRefiner {
       int output_port_num = input_tensor.index();
       AttrValue attr_output_shape;
       TensorShapeProto proto;
-      const auto& handle = input_ic->output(output_port_num);
+      const auto handle = input_ic->output(output_port_num);
       input_ic->ShapeHandleToProto(handle, &proto);
       // There may be dim.size < -1 in SymbolicShapeRefiner. Change those to -1.
       NormalizeShapeForOutput(&proto);
-
+      // _Arg op's output shape uses _output_shapes attr.
       AttrValue output_attr;
       output_attr.mutable_list()->add_shape()->Swap(&proto);
       (*fun_node->mutable_attr())["_output_shapes"] = output_attr;
+
+      // If dtype is DT_RESOURCE, ops that read _Arg op use _handle_dtypes and
+      // _handle_shapes attr for its shapes and dtypes.
+      if (fun_input.data_type == DT_RESOURCE) {
+        auto* shapes_and_types =
+            input_ic->output_handle_shapes_and_types(output_port_num);
+        if (shapes_and_types != nullptr && !shapes_and_types->empty()) {
+          AttrValue dtype_attr;
+          AttrValue shape_attr;
+          for (const auto& shape_and_type : *shapes_and_types) {
+            const auto& dtype = shape_and_type.dtype;
+            const auto& shape_handle = shape_and_type.shape;
+            dtype_attr.mutable_list()->add_type(dtype);
+            input_ic->ShapeHandleToProto(
+                shape_handle, shape_attr.mutable_list()->add_shape());
+          }
+          (*fun_node->mutable_attr())["_handle_dtypes"] = dtype_attr;
+          (*fun_node->mutable_attr())["_handle_shapes"] = shape_attr;
+        } else {
+          // Note that we do not return error here, even if the input node does
+          // not have shapes_and_types. Within the function, we cannot infer the
+          // output shape of the DT_RESOURCE input; hence, potentially unknown
+          // shapes/dims in the function output shapes.
+          VLOG(2)
+              << "A function node (" << function_node->name()
+              << ") has input with DT_RESOURCE, but the input node does not "
+              << "have shapes_and_types information: \n"
+              << "function_node: " << function_node->ShortDebugString() << "\n"
+              << "function input: " << i
+              << ", input node's output: " << output_port_num << "\n"
+              << "input node: " << input_node->ShortDebugString();
+        }
+      }
     }
 
     // Replace input nodes with Consts, if values are known. Note that
@@ -820,7 +853,7 @@ class SymbolicShapeRefiner {
     auto* ic = ctx->inference_context.get();
     for (int i = grappler_function_item.inputs().size() - 1; i >= 0; --i) {
       const string& input = function_node->input(i);
-      const string& node_name = NodeName(input);
+      const string node_name = NodeName(input);
       const NodeDef* input_node = graph_.GetNode(node_name);
       if (IsConstant(*input_node)) {
         TF_CHECK_OK(
@@ -993,9 +1026,11 @@ class SymbolicShapeRefiner {
     // Convert all kUnknownDimFromConst to -1 for shape inference.
     ic->set_input_tensors_as_shapes(ReplaceUnknownDimFromConstWithUnknownDim(
         ic, ctx->input_tensors_as_shapes_to_propagate));
-    // Notice: UpdateFunction only uses input_tensors_as_shapes, so for function
-    // nodes, we dont' perform the conversion from TensorProtos to Tensors for
-    // constant inputs here.
+    // Note: UpdateFunction uses input_tensors_as_shapes and
+    // input_tensor_protos (not the Tensor object) for input values.
+    // so for function nodes, we don't need to convert TensorProtos
+    // to Tensors here. If the current op is not a function op, we convert
+    // TensorProtos to Tensors before calling InferShapes.
 
     // Properly handle function nodes.
     if (ctx->op_data && ctx->op_data->is_function_op) {

tensorflow/core/grappler/costs/graph_properties_testdata/function_with_dt_resource_input.pbtxt

@@ -0,0 +1,130 @@
+node {
+  name: "x"
+  op: "Const"
+  attr {
+    key: "dtype"
+    value {
+      type: DT_FLOAT
+    }
+  }
+  attr {
+    key: "value"
+    value {
+      tensor {
+        dtype: DT_FLOAT
+        tensor_shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 3
+          }
+        }
+        float_val: 2.0
+      }
+    }
+  }
+}
+node {
+  name: "y"
+  op: "_Arg"
+  attr {
+    key: "T"
+    value {
+      type: DT_RESOURCE
+    }
+  }
+  attr {
+    key: "_handle_dtypes"
+    value {
+      list {
+        type: DT_FLOAT
+      }
+    }
+  }
+  attr {
+    key: "_handle_shapes"
+    value {
+      list {
+        shape {
+          dim {
+            size: 1
+          }
+          dim {
+            size: 3
+          }
+        }
+      }
+    }
+  }
+  attr {
+    key: "index"
+    value {
+      i: 0
+    }
+  }
+}
+node {
+  name: "FunctionWithDtResourceInput"
+  op: "FunctionWithDtResourceInput"
+  input: "x"
+  input: "y"
+}
+library {
+  function {
+    signature {
+      name: "FunctionWithDtResourceInput"
+      input_arg {
+        name: "x"
+        type: DT_FLOAT
+      }
+      input_arg {
+        name: "y"
+        type: DT_RESOURCE
+      }
+      output_arg {
+        name: "z1"
+        type: DT_FLOAT
+      }
+      output_arg {
+        name: "z2"
+        type: DT_FLOAT
+      }
+    }
+    node_def {
+      name: "y1"
+      op: "ReadVariableOp"
+      input: "y"
+      attr {
+        key: "dtype"
+        value {
+          type: DT_FLOAT
+        }
+      }
+    }
+    node_def {
+      name: "Add"
+      op: "Add"
+      input: "x"
+      input: "y1:value:0"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+    }
+    ret {
+      key: "z1"
+      value: "Add:z:0"
+    }
+    ret {
+      key: "z2"
+      value: "x"
+    }
+  }
+}
+versions {
+  producer: 26
+  min_consumer: 12
+}