CHECK-fail in SparseCross due to type confusion

@@ -27,6 +27,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/framework/types.pb.h"
 #include "tensorflow/core/lib/core/stringpiece.h"
 #include "tensorflow/core/lib/strings/str_util.h"
 #include "tensorflow/core/platform/fingerprint.h"
@@ -460,10 +461,19 @@ int64 CalculateBatchSize(const OpInputList& shapes_list_in,
 Status ValidateInput(const OpInputList& indices_list_in,
                      const OpInputList& values_list_in,
                      const OpInputList& shapes_list_in,
-                     const OpInputList& dense_list_in) {
+                     const OpInputList& dense_list_in,
+                     const DataType& internal_type) {
   const auto size = indices_list_in.size();
+  // Only perform internal_type check for SparseCrossOp.
+  // Check if the internal_type is not invalid before doing so.
+  bool check_type = internal_type != DT_INVALID;
   // Validates indices_list_in OpInputList.
   for (int i = 0; i < size; i++) {
+    if (check_type && indices_list_in[i].dtype() != DT_INT64) {
+      return errors::InvalidArgument("Input indices should be of type ",
+                                     DT_INT64, " but received ",
+                                     indices_list_in[i].dtype());
+    }
     if (!TensorShapeUtils::IsMatrix(indices_list_in[i].shape())) {
       return errors::InvalidArgument(
           "Input indices should be a matrix but received shape ",
@@ -482,6 +492,14 @@ Status ValidateInput(const OpInputList& indices_list_in,
                                    values_list_in.size());
   }
   for (int i = 0; i < size; i++) {
+    // Make sure to avoid the expected type to be string, but input values to be
+    // int64.
+    if (check_type && internal_type == DT_STRING &&
+        values_list_in[i].dtype() == DT_INT64) {
+      return errors::InvalidArgument("Input values should be of internal type ",
+                                     internal_type, " but received ",
+                                     values_list_in[i].dtype());
+    }
     if (!TensorShapeUtils::IsVector(values_list_in[i].shape())) {
       return errors::InvalidArgument(
           "Input values should be a vector but received shape ",
@@ -502,6 +520,11 @@ Status ValidateInput(const OpInputList& indices_list_in,
                                    shapes_list_in.size());
   }
   for (int i = 0; i < size; i++) {
+    if (check_type && shapes_list_in[i].dtype() != DT_INT64) {
+      return errors::InvalidArgument("Input shape should be of type ", DT_INT64,
+                                     " but received ",
+                                     shapes_list_in[i].dtype());
+    }
     if (!TensorShapeUtils::IsVector(shapes_list_in[i].shape())) {
       return errors::InvalidArgument(
           "Input shapes should be a vector but received shape ",
@@ -517,6 +540,14 @@ Status ValidateInput(const OpInputList& indices_list_in,
 
   // Validates dense_list_in OpInputList
   for (int i = 0; i < dense_list_in.size(); ++i) {
+    // Make sure to avoid the expected type to be string, but input values to be
+    // int64.
+    if (check_type && internal_type == DT_STRING &&
+        dense_list_in[i].dtype() == DT_INT64) {
+      return errors::InvalidArgument("Dense inputs should be of internal type ",
+                                     internal_type, " but received ",
+                                     dense_list_in[i].dtype());
+    }
     if (!TensorShapeUtils::IsMatrix(dense_list_in[i].shape())) {
       return errors::InvalidArgument(
           "Dense inputs should be a matrix but received shape ",
@@ -698,6 +729,7 @@ class SparseCrossOp : public OpKernel {
     int64 signed_hash_key_;
     OP_REQUIRES_OK(context, context->GetAttr("hash_key", &signed_hash_key_));
     hash_key_ = static_cast<uint64>(signed_hash_key_);
+    OP_REQUIRES_OK(context, context->GetAttr("internal_type", &internal_type_));
   }
 
   void Compute(OpKernelContext* context) override {
@@ -711,8 +743,10 @@ class SparseCrossOp : public OpKernel {
     OP_REQUIRES_OK(context,
                    context->input_list("dense_inputs", &dense_list_in));
 
-    OP_REQUIRES_OK(context, ValidateInput(indices_list_in, values_list_in,
-                                          shapes_list_in, dense_list_in));
+    DataType internal_type = internal_type_;
+    OP_REQUIRES_OK(
+        context, ValidateInput(indices_list_in, values_list_in, shapes_list_in,
+                               dense_list_in, internal_type));
 
     std::vector<std::unique_ptr<ColumnInterface<InternalType>>> columns =
         GenerateColumnsFromInput<InternalType>(indices_list_in, values_list_in,
@@ -756,6 +790,7 @@ class SparseCrossOp : public OpKernel {
  private:
   int64 num_buckets_;
   uint64 hash_key_;
+  DataType internal_type_;
 };
 
 class SparseCrossV2Op : public OpKernel {
@@ -773,8 +808,11 @@ class SparseCrossV2Op : public OpKernel {
     OP_REQUIRES_OK(context,
                    context->input_list("dense_inputs", &dense_list_in));
 
-    OP_REQUIRES_OK(context, ValidateInput(indices_list_in, values_list_in,
-                                          shapes_list_in, dense_list_in));
+    // Set internal_type to invalid_type so that the check will be ignored.
+    DataType internal_type = DT_INVALID;
+    OP_REQUIRES_OK(
+        context, ValidateInput(indices_list_in, values_list_in, shapes_list_in,
+                               dense_list_in, internal_type));
 
     const Tensor* sep_t;
     OP_REQUIRES_OK(context, context->input("sep", &sep_t));
@@ -832,8 +870,11 @@ class SparseCrossHashedOp : public OpKernel {
     OP_REQUIRES_OK(context,
                    context->input_list("dense_inputs", &dense_list_in));
 
-    OP_REQUIRES_OK(context, ValidateInput(indices_list_in, values_list_in,
-                                          shapes_list_in, dense_list_in));
+    // Set internal_type to invalid_type so that the check will be ignored.
+    DataType internal_type = DT_INVALID;
+    OP_REQUIRES_OK(
+        context, ValidateInput(indices_list_in, values_list_in, shapes_list_in,
+                               dense_list_in, internal_type));
 
     const Tensor* num_buckets_t;
     OP_REQUIRES_OK(context, context->input("num_buckets", &num_buckets_t));