Make elementwise operations with two inputs support all of the cases: elementwise, scalar, broadcast and const vector.

PiperOrigin-RevId: 302066884
Change-Id: I94a7497f006b466cc6d7a1b1fdba090b4ef30a00

tensorflow/lite/delegates/gpu/common/model_builder.cc

@@ -236,6 +236,12 @@ int GetNumberOfRuntimeInputsForNode(const TfLiteContext* context,
   return number_of_runtime_inputs;
 }
 
+int GetNumberOfConstInputsForNode(const TfLiteContext* context,
+                                  const TfLiteNode* tflite_node) {
+  return tflite_node->inputs->size -
+         GetNumberOfRuntimeInputsForNode(context, tflite_node);
+}
+
 int GetNumberOfRuntimeOutputsForNode(const TfLiteContext* context,
                                      const TfLiteNode* tflite_node) {
   int number_of_runtime_outputs = 0;
@@ -258,6 +264,42 @@ Status CheckTensorIsAvailable(const TfLiteContext* context,
   return OkStatus();
 }
 
+Status CheckInputsOutputs(const TfLiteContext* context,
+                          const TfLiteNode* tflite_node, int runtime_inputs,
+                          int outputs) {
+  int runtime_inputs_from_model =
+      GetNumberOfRuntimeInputsForNode(context, tflite_node);
+  if (runtime_inputs_from_model != runtime_inputs) {
+    return InternalError(absl::StrFormat(
+        "Expected %d runtime input tensor(s), but node has %d runtime "
+        "input(s).",
+        runtime_inputs, runtime_inputs_from_model));
+  }
+  int runtime_outputs = GetNumberOfRuntimeOutputsForNode(context, tflite_node);
+  if (runtime_outputs != outputs) {
+    return InternalError(
+        absl::StrFormat("Expected %d output tensor(s), but node has %d "
+                        "output(s).",
+                        outputs, runtime_outputs));
+  }
+  return OkStatus();
+}
+
+Status CheckInputsConstsOutputs(const TfLiteContext* context,
+                                const TfLiteNode* tflite_node,
+                                int runtime_inputs, int const_inputs,
+                                int outputs) {
+  int const_inputs_from_model =
+      GetNumberOfConstInputsForNode(context, tflite_node);
+  if (const_inputs_from_model != const_inputs) {
+    return InternalError(absl::StrFormat(
+        "Expected %d const input tensor(s), but node has %d const "
+        "input(s).",
+        const_inputs, const_inputs_from_model));
+  }
+  return CheckInputsOutputs(context, tflite_node, runtime_inputs, outputs);
+}
+
 class ObjectReader {
  public:
   ObjectReader(GraphFloat32* graph, TfLiteContext* context,
@@ -367,6 +409,13 @@ class ObjectReader {
                : nullptr;
   }
 
+  Status VerifyInputsConstsOutputs(const TfLiteNode* tflite_node,
+                                   int runtime_inputs, int const_inputs,
+                                   int outputs) {
+    return CheckInputsConstsOutputs(context_, tflite_node, runtime_inputs,
+                                    const_inputs, outputs);
+  }
+
  private:
   GraphFloat32* graph_ = nullptr;
   const TfLiteContext* context_ = nullptr;
@@ -374,59 +423,6 @@ class ObjectReader {
   std::vector<Value<TensorRef<BHWC>>*>* tensor_to_value_;
 };
 
-Status CheckInputsOutputs(const TfLiteContext* context,
-                          const TfLiteNode* tflite_node, int inputs,
-                          int outputs) {
-  int runtime_inputs = GetNumberOfRuntimeInputsForNode(context, tflite_node);
-  if (runtime_inputs != inputs) {
-    return InternalError(
-        absl::StrFormat("Expected %d input tensor(s), but node has %d runtime "
-                        "input(s).",
-                        inputs, runtime_inputs));
-  }
-  int runtime_outputs = GetNumberOfRuntimeOutputsForNode(context, tflite_node);
-  if (runtime_outputs != outputs) {
-    return InternalError(
-        absl::StrFormat("Expected %d output tensor(s), but node has %d runtime "
-                        "output(s).",
-                        outputs, runtime_outputs));
-  }
-  return OkStatus();
-}
-
-// The function checks input tensors including 1 constant tensor.
-Status CheckInputsOutputsAllowingOneConstInput(const TfLiteContext* context,
-                                               const TfLiteNode* tflite_node,
-                                               int inputs, int outputs) {
-  int number_of_const_inputs = 0;
-  int number_of_runtime_inputs = 0;
-  for (int i = 0; i < tflite_node->inputs->size; i++) {
-    if (IsConstantTensor(&context->tensors[tflite_node->inputs->data[i]])) {
-      number_of_const_inputs++;
-    } else {
-      number_of_runtime_inputs++;
-    }
-  }
-  if (tflite_node->inputs->size != inputs) {
-    return InternalError(absl::StrFormat(
-        "Expected %d input tensor(s), but node has %d input(s).", inputs,
-        tflite_node->inputs->size));
-  }
-  if (number_of_const_inputs > 1) {
-    return InternalError(absl::StrFormat(
-        "Expected 1 const input tensor, but node has %d const input(s).",
-        number_of_const_inputs));
-  }
-  int runtime_outputs = GetNumberOfRuntimeOutputsForNode(context, tflite_node);
-  if (runtime_outputs != outputs) {
-    return InternalError(
-        absl::StrFormat("Expected %d output tensor(s), but node has %d runtime "
-                        "output(s).",
-                        outputs, runtime_outputs));
-  }
-  return OkStatus();
-}
-
 // A parser responsible for parsing TFLite operation and adding it to a graph.
 class TFLiteOperationParser {
  public:
@@ -893,8 +889,8 @@ class Conv2DOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 2));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     RETURN_IF_ERROR(CheckTensorIsAvailable(context, tflite_node, 1));
     TfLiteConvParams* tf_options = nullptr;
     RETURN_IF_ERROR(RetrieveBuiltinData(tflite_node, &tf_options));
@@ -977,8 +973,8 @@ class DepthwiseConvolutionOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 2));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     RETURN_IF_ERROR(CheckTensorIsAvailable(context, tflite_node, 1));
     TfLiteDepthwiseConvParams* tf_options;
     RETURN_IF_ERROR(RetrieveBuiltinData(tflite_node, &tf_options));
@@ -1095,16 +1091,20 @@ class ElementwiseOperationParser : public TFLiteOperationParser {
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 1));
     if (IsOneArgumentOperation()) {
-      RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node, /*inputs=*/1,
-                                         /*outputs=*/1));
+      RETURN_IF_ERROR(CheckInputsConstsOutputs(context, tflite_node,
+                                               /*runtime_inputs=*/1,
+                                               /*const_inputs=*/0,
+                                               /*outputs=*/1));
     } else if (IsTwoArgumentOperation()) {
-      RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node, /*inputs=*/2,
-                                         /*outputs=*/1));
+      RETURN_IF_ERROR(CheckInputsConstsOutputs(context, tflite_node,
+                                               /*runtime_inputs=*/2,
+                                               /*const_inputs=*/0,
+                                               /*outputs=*/1));
     } else if (IsTwoArgumentOperationWithConst()) {
-      RETURN_IF_ERROR(CheckInputsOutputsAllowingOneConstInput(context,
-                                                              tflite_node,
-                                                              /*inputs=*/2,
-                                                              /*outputs=*/1));
+      RETURN_IF_ERROR(CheckInputsConstsOutputs(context, tflite_node,
+                                               /*runtime_inputs=*/1,
+                                               /*const_inputs=*/1,
+                                               /*outputs=*/1));
     } else {
       return InvalidArgumentError("Op can only handle 1 or 2 operand(s).");
     }
@@ -1120,8 +1120,17 @@ class ElementwiseOperationParser : public TFLiteOperationParser {
     node->operation.type = ToString(operation_type_);
 
     if (IsOneArgumentOperation()) {
+      RETURN_IF_ERROR(reader->VerifyInputsConstsOutputs(tflite_node,
+                                                        /*runtime_inputs=*/1,
+                                                        /*const_inputs=*/0,
+                                                        /*outputs=*/1));
+
       RETURN_IF_ERROR(reader->AddInput(node, 0));
     } else if (IsTwoArgumentOperation()) {
+      RETURN_IF_ERROR(reader->VerifyInputsConstsOutputs(tflite_node,
+                                                        /*runtime_inputs=*/2,
+                                                        /*const_inputs=*/0,
+                                                        /*outputs=*/1));
       if (tflite_node->inputs->size != 2) {
         return InvalidArgumentError("Applies only two input tensors");
       }
@@ -1156,14 +1165,12 @@ class ElementwiseOperationParser : public TFLiteOperationParser {
             MaybeFuseActivationToTheSingleOutput(activation, graph, node));
       }
     } else if (IsTwoArgumentOperationWithConst()) {
+      RETURN_IF_ERROR(reader->VerifyInputsConstsOutputs(tflite_node,
+                                                        /*runtime_inputs=*/1,
+                                                        /*const_inputs=*/1,
+                                                        /*outputs=*/1));
       ElementwiseAttributes attr;
       RETURN_IF_ERROR(ParseInputsWithConstTensor(node, reader, &attr.param));
-      auto const_vector =
-          absl::get_if<::tflite::gpu::Tensor<Linear, DataType::FLOAT32>>(
-              &attr.param);
-      if (const_vector) {
-        return InvalidArgumentError("Constant vector is not supported");
-      }
       node->operation.attributes = std::move(attr);
     } else {
       return InvalidArgumentError("Incorrect operation type passed");
@@ -1228,6 +1235,7 @@ class ElementwiseOperationParser : public TFLiteOperationParser {
     switch (operation_type_) {
       case OperationType::MINIMUM:
       case OperationType::MAXIMUM:
+      case OperationType::SUB:
         return true;
       default:
         return false;
@@ -1311,7 +1319,7 @@ class HardSwishOperationParser : public TFLiteOperationParser {
   Status IsSupported(const TfLiteContext* context,
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration*) final {
-    return CheckInputsOutputs(context, tflite_node, /*inputs=*/1,
+    return CheckInputsOutputs(context, tflite_node, /*runtime_inputs=*/1,
                               /*outputs=*/1);
   }
 
@@ -1350,7 +1358,8 @@ class LSTMOperationParser : public TFLiteOperationParser {
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckExactSupportedOpVersion(registration, 2));
     // TODO(eignasheva): Fix bad check.
-    // RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node, /*inputs=*/5,
+    // RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+    // /*runtime_inputs=*/5,
     //                                    /*outputs=*/4));
     TfLiteLSTMParams* tf_options = nullptr;
     RETURN_IF_ERROR(RetrieveBuiltinData(tflite_node, &tf_options));
@@ -1599,8 +1608,8 @@ class PadOperationParser : public TFLiteOperationParser {
       }
     }
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 1));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     RETURN_IF_ERROR(CheckTensorIsAvailable(context, tflite_node, 1));
     return OkStatus();
   }
@@ -1648,11 +1657,13 @@ class Pooling2DOperationParser : public TFLiteOperationParser {
     TfLitePoolParams* tf_options = nullptr;
     auto status = RetrieveCustomInitialData(tflite_node, &tf_options);
     if (status.ok()) {  // custom case with indices as a second output
-      RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node, /*inputs=*/1,
+      RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                         /*runtime_inputs=*/1,
                                          /*outputs=*/2));
     } else {  // common pooling with 1 output
       RETURN_IF_ERROR(RetrieveBuiltinData(tflite_node, &tf_options));
-      RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node, /*inputs=*/1,
+      RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                         /*runtime_inputs=*/1,
                                          /*outputs=*/1));
     }
     RETURN_IF_ERROR(CheckKernelsAndStrides(
@@ -1752,8 +1763,8 @@ class ReshapeOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 1));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     // TODO(eignasheva): add shape checking
     return OkStatus();
   }
@@ -1786,8 +1797,8 @@ class Resize2DOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 3));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
 
     RETURN_IF_ERROR(CheckOnlyUpsamplingIsSupported(context, tflite_node));
     bool align_corners;
@@ -1974,8 +1985,8 @@ class SoftmaxOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 1));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     TfLiteSoftmaxParams* tf_options = nullptr;
     RETURN_IF_ERROR(RetrieveBuiltinData(tflite_node, &tf_options));
     if (tf_options->beta != 1) {
@@ -2018,8 +2029,8 @@ class SpaceToDepthOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 1));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     // TODO(impjdi): Dims check.
     TfLiteSpaceToDepthParams* s2d_params = nullptr;
     RETURN_IF_ERROR(RetrieveBuiltinData(tflite_node, &s2d_params));
@@ -2280,8 +2291,8 @@ class TransposeOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     RETURN_IF_ERROR(CheckMaxSupportedOpVersion(registration, 1));
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     return OkStatus();
   }
 
@@ -2317,8 +2328,8 @@ class Unpooling2DOperationParser : public TFLiteOperationParser {
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
     TfLitePoolParams* tf_options = nullptr;
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/2, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/2, /*outputs=*/1));
     RETURN_IF_ERROR(RetrieveCustomInitialData(tflite_node, &tf_options));
     RETURN_IF_ERROR(CheckKernelsAndStrides(
         tf_options->filter_height, tf_options->filter_width,
@@ -2445,8 +2456,8 @@ class RoIToTransformMatrixOperationParser : public TFLiteOperationParser {
   Status IsSupported(const TfLiteContext* context,
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/1, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/1, /*outputs=*/1));
     return OkStatus();
   }
 
@@ -2478,8 +2489,8 @@ class TransformTensorOperationParser : public TFLiteOperationParser {
   Status IsSupported(const TfLiteContext* context,
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/2, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/2, /*outputs=*/1));
     return OkStatus();
   }
 
@@ -2515,8 +2526,8 @@ class TransformLandmarksOperationParser : public TFLiteOperationParser {
   Status IsSupported(const TfLiteContext* context,
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
-    RETURN_IF_ERROR(
-        CheckInputsOutputs(context, tflite_node, /*inputs=*/2, /*outputs=*/1));
+    RETURN_IF_ERROR(CheckInputsOutputs(context, tflite_node,
+                                       /*runtime_inputs=*/2, /*outputs=*/1));
     return OkStatus();
   }
 
@@ -2549,7 +2560,7 @@ class Landmarks2TransformMatrixOperationParser : public TFLiteOperationParser {
   Status IsSupported(const TfLiteContext* context,
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
-    return CheckInputsOutputs(context, tflite_node, /*inputs=*/1,
+    return CheckInputsOutputs(context, tflite_node, /*runtime_inputs=*/1,
                               /*outputs=*/1);
   }
 
@@ -2581,7 +2592,7 @@ class MeanOperationParser : public TFLiteOperationParser {
   Status IsSupported(const TfLiteContext* context,
                      const TfLiteNode* tflite_node,
                      const TfLiteRegistration* registration) final {
-    return CheckInputsOutputs(context, tflite_node, /*inputs=*/1,
+    return CheckInputsOutputs(context, tflite_node, /*runtime_inputs=*/1,
                               /*outputs=*/1);
   }
 
@@ -2970,7 +2981,6 @@ bool IsAllFloatTensors(const TfLiteContext* context,
   }
   return true;
 }
-
 }  // namespace
 
 Status ConvertTfLiteTensorToTensorRef(const TfLiteTensor& tflite_tensor,

tensorflow/lite/delegates/gpu/gl/kernels/BUILD

@@ -198,6 +198,7 @@ cc_library(
         "//tensorflow/lite/delegates/gpu/common:types",
         "//tensorflow/lite/delegates/gpu/gl:node_shader",
         "@com_google_absl//absl/memory",
+        "@com_google_absl//absl/strings",
     ],
 )
 

tensorflow/lite/delegates/gpu/gl/kernels/elementwise.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <string>
 
 #include "absl/memory/memory.h"
+#include "absl/strings/substitute.h"
 #include "tensorflow/lite/delegates/gpu/common/status.h"
 #include "tensorflow/lite/delegates/gpu/common/types.h"
 
@@ -130,89 +131,9 @@ class ElementwiseTwoArguments : public NodeShader {
     return true;
   }
 
-  Status ImplementElementwise(const GenerationContext& ctx,
-                              GeneratedCode* generated_code) const {
-    std::string source;
-    switch (operation_type_) {
-      case OperationType::SUB: {
-        source = "value_0 -= value_1;";
-        break;
-      }
-      case OperationType::DIV: {
-        source = "value_0 /= value_1;";
-        break;
-      }
-      case OperationType::MAXIMUM: {
-        source = "value_0 = max(value_0, value_1);";
-        break;
-      }
-      case OperationType::MINIMUM: {
-        source = "value_0 = min(value_0, value_1);";
-        break;
-      }
-      case OperationType::POW: {
-        // From documentation :
-        // The result is undefined if x<0 or if x=0 and y≤0.
-        source = "value_0 = pow(value_0, value_1);";
-        break;
-      }
-      case OperationType::SQUARED_DIFF: {
-        source = "value_0 = (value_0 - value_1) * (value_0 - value_1);";
-        break;
-      }
-
-      default:
-        return InvalidArgumentError(
-            "Incorrect elementwise with two arguments operation type.");
-    }
-    *generated_code = {
-        /*parameters=*/{},
-        /*objects=*/{},
-        /*shared_variables=*/{},
-        /*workload=*/uint3(),
-        /*workgroup=*/uint3(),
-        /*source_code=*/source,
-        /*input=*/IOStructure::AUTO,
-        /*output=*/IOStructure::AUTO,
-    };
-    return OkStatus();
-  }
-
-  Status ImplementElementwiseWithScalar(const GenerationContext& ctx,
-                                        const float scalar,
-                                        GeneratedCode* generated_code) const {
-    std::string source;
-    switch (operation_type_) {
-      case OperationType::MAXIMUM: {
-        source = "value_0 = max(value_0, $scalar$);";
-        break;
-      }
-      case OperationType::MINIMUM: {
-        source = "value_0 = min(value_0, $scalar$);";
-        break;
-      }
-
-      default:
-        return InvalidArgumentError(
-            "Incorrect elementwise with scalar operation type.");
-    }
-    *generated_code = {
-        /*parameters=*/{{"scalar", scalar}},
-        /*objects=*/{},
-        /*shared_variables=*/{},
-        /*workload=*/uint3(),
-        /*workgroup=*/uint3(),
-        /*source_code=*/source,
-        /*input=*/IOStructure::AUTO,
-        /*output=*/IOStructure::AUTO,
-    };
-    return OkStatus();
-  }
-
   bool IsSupportedBroadcast(const GenerationContext& ctx) const {
     auto inputs = ctx.graph->FindInputs(ctx.node->id);
     auto outputs = ctx.graph->FindOutputs(ctx.node->id);
-
     if (inputs.size() != 2) {
       return false;
     }
@@ -223,57 +144,87 @@ class ElementwiseTwoArguments : public NodeShader {
     return true;
   }
 
-  Status ImplementElementwiseBroadcast(const GenerationContext& ctx,
-                                       GeneratedCode* generated_code) const {
-    std::string source;
-    switch (operation_type_) {
-      case OperationType::SQUARED_DIFF: {
-        source = R"(
-        vec4 diff = $input_data_0[gid.x, gid.y, gid.z]$ -
-                    $input_data_1[0, 0, gid.z]$;
-        value_0 = diff * diff;
-        )";
-        break;
+  Status GenerateCode(const GenerationContext& ctx,
+                      GeneratedCode* generated_code) const final {
+    std::vector<Variable> parameters;
+    std::vector<std::pair<std::string, Object>> objects;
+    std::string argument0, argument1;
+    if (IsSupportedElemwise(ctx)) {
+      argument0 = "value_0";
+      argument1 = "value_1";
+    } else if (IsSupportedBroadcast(ctx)) {
+      argument0 = "$input_data_0[gid.x, gid.y, gid.z]$";
+      argument1 = "$input_data_1[0, 0, gid.z]$";
+    } else {  // Scalar of const vector case
+      const ElementwiseAttributes* attr = absl::any_cast<ElementwiseAttributes>(
+          &ctx.node->operation.attributes);
+      if (!attr) {
+        return InvalidArgumentError(
+            "Couldn't read attributes for the scalar of const vector case.");
+      }
+      auto* tensor =
+          absl::get_if<::tflite::gpu::Tensor<Linear, DataType::FLOAT32>>(
+              &attr->param);
+      auto* scalar = absl::get_if<float>(&attr->param);
+      if (!tensor && !scalar) {
+        return InvalidArgumentError(
+            "Couldn't read scalar of const vector data from the attributes.");
       }
 
+      argument0 = "value_0";
+      if (tensor) {
+        argument1 = "$const_data[gid.z]$";
+        objects.push_back({"const_data", MakeReadonlyObject(tensor->data)});
+      } else {
+        argument1 = "vec4($const_data$)";
+        parameters.push_back({"const_data", *scalar});
+      }
+    }
+
+    std::string source;
+    switch (operation_type_) {
+      case OperationType::DIV: {
+        source = "value_0 = $0/$1;";
+        break;
+      }
+      case OperationType::MAXIMUM: {
+        source = "value_0 = max($0, $1);";
+        break;
+      }
+      case OperationType::MINIMUM: {
+        source = "value_0 = min($0, $1);";
+        break;
+      }
+      case OperationType::SQUARED_DIFF: {
+        source = "value_0 = ($0 - $1) * ($0 - $1);";
+        break;
+      }
+      case OperationType::SUB: {
+        source = "value_0 = $0 - $1;";
+        break;
+      }
+      case OperationType::POW: {
+        source = "value_0 = pow($0, $1);";
+        break;
+      }
       default:
         return InvalidArgumentError(
-            "Incorrect elementwise with two arguments operation type.");
+            "Incorrect elementwise with scalar operation type.");
     }
+    source = absl::Substitute(source, argument0, argument1);
     *generated_code = {
-        /*parameters=*/{},
-        /*objects=*/{},
+        /*parameters=*/std::move(parameters),
+        /*objects=*/std::move(objects),
         /*shared_variables=*/{},
         /*workload=*/uint3(),
         /*workgroup=*/uint3(),
         /*source_code=*/source,
-        /*input=*/IOStructure::ONLY_DEFINITIONS,
+        /*input=*/IOStructure::AUTO,
         /*output=*/IOStructure::AUTO,
     };
     return OkStatus();
   }
 
-  Status GenerateCode(const GenerationContext& ctx,
-                      GeneratedCode* generated_code) const final {
-    if (IsSupportedElemwise(ctx)) {
-      return ImplementElementwise(ctx, generated_code);
-    }
-    if (IsSupportedBroadcast(ctx)) {
-      return ImplementElementwiseBroadcast(ctx, generated_code);
-    }
-    const ElementwiseAttributes* attr =
-        absl::any_cast<ElementwiseAttributes>(&ctx.node->operation.attributes);
-    if (attr) {
-      auto scalar = absl::get_if<float>(&attr->param);
-      if (scalar) {
-        return ImplementElementwiseWithScalar(ctx, *scalar, generated_code);
-      }
-    }
-    return InvalidArgumentError(
-        "This case is not supported by elementwise with two arguments "
-        "operation");
-  }
-
  private:
   OperationType operation_type_;
 };

tensorflow/lite/delegates/gpu/gl/kernels/elementwise_test.cc

@@ -36,7 +36,7 @@ TensorRef<BHWC> GetTensorRef(int ref, const BHWC& shape) {
   return tensor_ref;
 }
 
-TEST(ElementwiseTest, Abs) {
+TEST(ElementwiseOneArgumentTest, Abs) {
   OperationType op_type = OperationType::ABS;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
@@ -48,7 +48,7 @@ TEST(ElementwiseTest, Abs) {
               Pointwise(FloatNear(1e-6), {0.0, 6.2, 2.0, 4.0}));
 }
 
-TEST(ElementwiseTest, Cos) {
+TEST(ElementwiseOneArgumentTest, Cos) {
   OperationType op_type = OperationType::COS;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
@@ -60,21 +60,7 @@ TEST(ElementwiseTest, Cos) {
               Pointwise(FloatNear(1e-6), {1.0, -1.0, -1.0, 0.540302}));
 }
 
-TEST(ElementwiseTest, Div) {
-  OperationType op_type = OperationType::DIV;
-  const BHWC shape(1, 2, 2, 1);
-  SingleOpModel model(
-      {/*type=*/ToString(op_type), /*attributes=*/{}},
-      /*inputs=*/{GetTensorRef(0, shape), GetTensorRef(1, shape)},
-      /*outputs=*/{GetTensorRef(2, shape)});
-  ASSERT_TRUE(model.PopulateTensor(0, {0.0, -6.2, 2.0, 4.0}));
-  ASSERT_TRUE(model.PopulateTensor(1, {1.0, 2.0, -0.5, 4.0}));
-  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
-  EXPECT_THAT(model.GetOutput(0),
-              Pointwise(FloatNear(1e-6), {0.0, -3.1, -4.0, 1.0}));
-}
-
-TEST(ElementwiseTest, Exp) {
+TEST(ElementwiseOneArgumentTest, Exp) {
   OperationType op_type = OperationType::EXP;
   const BHWC shape(1, 1, 1, 7);
   SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
@@ -90,7 +76,7 @@ TEST(ElementwiseTest, Exp) {
                          std::exp(-0.01f)}));
 }
 
-TEST(ElementwiseTest, HardSwish) {
+TEST(ElementwiseOneArgumentTest, HardSwish) {
   OperationType op_type = OperationType::HARD_SWISH;
   const BHWC shape(1, 1, 1, 7);
   SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
@@ -104,7 +90,7 @@ TEST(ElementwiseTest, HardSwish) {
                         {0.0f, 0.0f, -0.375f, 0.0f, 1.125f, 3.f, 4.5f}));
 }
 
-TEST(ElementwiseTest, Log) {
+TEST(ElementwiseOneArgumentTest, Log) {
   OperationType op_type = OperationType::LOG;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
@@ -116,7 +102,142 @@ TEST(ElementwiseTest, Log) {
               Pointwise(FloatNear(1e-6), {0.0, 1.14473, 0.0, 0.0}));
 }
 
-TEST(ElementwiseTest, Maximum) {
+TEST(ElementwiseOneArgumentTest, Rsqrt) {
+  OperationType op_type = OperationType::RSQRT;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {1.0, 2.0, 4.0, 9.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {1.0, 0.707106, 0.5, 0.333333}));
+}
+
+TEST(ElementwiseOneArgumentTest, Sigmoid) {
+  OperationType op_type = OperationType::SIGMOID;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, -6.0, 2.0, 4.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.5, 0.002473, 0.880797, 0.982014}));
+}
+
+TEST(ElementwiseOneArgumentTest, Sin) {
+  OperationType op_type = OperationType::SIN;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 3.1415926, -3.1415926, 1.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, 0.0, 0.0, 0.841471}));
+}
+
+TEST(ElementwiseOneArgumentTest, Sqrt) {
+  OperationType op_type = OperationType::SQRT;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 4.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, 1.0, 1.414213, 2.0}));
+}
+
+TEST(ElementwiseOneArgumentTest, Square) {
+  OperationType op_type = OperationType::SQUARE;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {1.0, 2.0, 0.5, -3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {1.0, 4.0, 0.25, 9.0}));
+}
+
+TEST(ElementwiseOneArgumentTest, Tanh) {
+  OperationType op_type = OperationType::TANH;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
+                      /*inputs=*/{GetTensorRef(0, shape)},
+                      /*outputs=*/{GetTensorRef(1, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, -6.0, 2.0, 4.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, -0.999987, 0.964027, 0.999329}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, DivElementwise) {
+  OperationType op_type = OperationType::DIV;
+  const BHWC shape(1, 2, 2, 1);
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/{}},
+      /*inputs=*/{GetTensorRef(0, shape), GetTensorRef(1, shape)},
+      /*outputs=*/{GetTensorRef(2, shape)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, -6.2, 2.0, 4.0}));
+  ASSERT_TRUE(model.PopulateTensor(1, {1.0, 2.0, -0.5, 4.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, -3.1, -4.0, 1.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, DivBroadcast) {
+  OperationType op_type = OperationType::DIV;
+  const BHWC shape0(1, 2, 1, 2);
+  const BHWC shape1(1, 1, 1, 2);
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/{}},
+      /*inputs=*/{GetTensorRef(0, shape0), GetTensorRef(1, shape1)},
+      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_TRUE(model.PopulateTensor(1, {0.5, 0.2}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, 5.0, 4.0, 15.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, DivScalar) {
+  OperationType op_type = OperationType::DIV;
+  const BHWC shape0(1, 2, 1, 2);
+  ElementwiseAttributes attr;
+  attr.param = static_cast<float>(0.5);
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, 2.0, 4.0, 6.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, DivConstVector) {
+  OperationType op_type = OperationType::DIV;
+  const BHWC shape0(1, 2, 1, 2);
+
+  ElementwiseAttributes attr;
+  Tensor<Linear, DataType::FLOAT32> param;
+  param.shape = Linear(2);
+  param.id = 1;
+  param.data = {0.4, 0.5};
+  attr.param = std::move(param);
+
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, 2.0, 5.0, 6.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, MaximumElementwise) {
   OperationType op_type = OperationType::MAXIMUM;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model(
@@ -130,7 +251,22 @@ TEST(ElementwiseTest, Maximum) {
               Pointwise(FloatNear(1e-6), {1.0, 2.0, 3.0, -2.0}));
 }
 
-TEST(ElementwiseTest, MaximumWithScalar) {
+TEST(ElementwiseTwoArgumentsTest, MaximumBroadcast) {
+  OperationType op_type = OperationType::MAXIMUM;
+  const BHWC shape0(1, 2, 1, 2);
+  const BHWC shape1(1, 1, 1, 2);
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/{}},
+      /*inputs=*/{GetTensorRef(0, shape0), GetTensorRef(1, shape1)},
+      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_TRUE(model.PopulateTensor(1, {0.5, 0.2}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.5, 1.0, 2.0, 3.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, MaximumScalar) {
   OperationType op_type = OperationType::MAXIMUM;
   const BHWC shape(1, 2, 2, 1);
   ElementwiseAttributes attr;
@@ -145,7 +281,27 @@ TEST(ElementwiseTest, MaximumWithScalar) {
               Pointwise(FloatNear(1e-6), {0.0, -1.0, 2.0, -1.0}));
 }
 
-TEST(ElementwiseTest, Minimum) {
+TEST(ElementwiseTwoArgumentsTest, MaximumConstVector) {
+  OperationType op_type = OperationType::MAXIMUM;
+  const BHWC shape0(1, 2, 1, 2);
+
+  ElementwiseAttributes attr;
+  Tensor<Linear, DataType::FLOAT32> param;
+  param.shape = Linear(2);
+  param.id = 1;
+  param.data = {0.4, 0.5};
+  attr.param = std::move(param);
+
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.4, 1.0, 2.0, 3.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, MinimumElementwise) {
   OperationType op_type = OperationType::MINIMUM;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model(
@@ -159,7 +315,22 @@ TEST(ElementwiseTest, Minimum) {
               Pointwise(FloatNear(1e-6), {0.0, -6.2, 2.0, -3.0}));
 }
 
-TEST(ElementwiseTest, MinimumWithScalar) {
+TEST(ElementwiseTwoArgumentsTest, MinimumBroadcast) {
+  OperationType op_type = OperationType::MINIMUM;
+  const BHWC shape0(1, 2, 1, 2);
+  const BHWC shape1(1, 1, 1, 2);
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/{}},
+      /*inputs=*/{GetTensorRef(0, shape0), GetTensorRef(1, shape1)},
+      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_TRUE(model.PopulateTensor(1, {0.5, 0.2}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, 0.2, 0.5, 0.2}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, MinimumScalar) {
   OperationType op_type = OperationType::MINIMUM;
   const BHWC shape(1, 2, 2, 1);
   ElementwiseAttributes attr;
@@ -174,7 +345,27 @@ TEST(ElementwiseTest, MinimumWithScalar) {
               Pointwise(FloatNear(1e-6), {-1.0, -6.2, -1.0, -3.0}));
 }
 
-TEST(ElementwiseTest, Pow) {
+TEST(ElementwiseTwoArgumentsTest, MinimumConstVector) {
+  OperationType op_type = OperationType::MINIMUM;
+  const BHWC shape0(1, 2, 1, 2);
+
+  ElementwiseAttributes attr;
+  Tensor<Linear, DataType::FLOAT32> param;
+  param.shape = Linear(2);
+  param.id = 1;
+  param.data = {0.5, 0.2};
+  attr.param = std::move(param);
+
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {0.0, 0.2, 0.5, 0.2}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, PowElementwise) {
   OperationType op_type = OperationType::POW;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model(
@@ -188,67 +379,57 @@ TEST(ElementwiseTest, Pow) {
               Pointwise(FloatNear(1e-6), {0.0, 1.0, 8.0, 256.0}));
 }
 
-TEST(ElementwiseTest, Rsqrt) {
-  OperationType op_type = OperationType::RSQRT;
-  const BHWC shape(1, 2, 2, 1);
-  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
-                      /*inputs=*/{GetTensorRef(0, shape)},
-                      /*outputs=*/{GetTensorRef(1, shape)});
-  ASSERT_TRUE(model.PopulateTensor(0, {1.0, 2.0, 4.0, 9.0}));
+TEST(ElementwiseTwoArgumentsTest, PowBroadcast) {
+  OperationType op_type = OperationType::POW;
+  const BHWC shape0(1, 2, 1, 2);
+  const BHWC shape1(1, 1, 1, 2);
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/{}},
+      /*inputs=*/{GetTensorRef(0, shape0), GetTensorRef(1, shape1)},
+      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 4.0}));
+  ASSERT_TRUE(model.PopulateTensor(1, {2.0, 0.5}));
   ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
   EXPECT_THAT(model.GetOutput(0),
-              Pointwise(FloatNear(1e-6), {1.0, 0.707106, 0.5, 0.333333}));
+              Pointwise(FloatNear(1e-6), {0.0, 1.0, 4.0, 2.0}));
 }
 
-TEST(ElementwiseTest, Sigmoid) {
-  OperationType op_type = OperationType::SIGMOID;
+TEST(ElementwiseTwoArgumentsTest, PowScalar) {
+  OperationType op_type = OperationType::POW;
   const BHWC shape(1, 2, 2, 1);
-  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
-                      /*inputs=*/{GetTensorRef(0, shape)},
-                      /*outputs=*/{GetTensorRef(1, shape)});
-  ASSERT_TRUE(model.PopulateTensor(0, {0.0, -6.0, 2.0, 4.0}));
-  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
-  EXPECT_THAT(model.GetOutput(0),
-              Pointwise(FloatNear(1e-6), {0.5, 0.002473, 0.880797, 0.982014}));
-}
-
-TEST(ElementwiseTest, Sin) {
-  OperationType op_type = OperationType::SIN;
-  const BHWC shape(1, 2, 2, 1);
-  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
-                      /*inputs=*/{GetTensorRef(0, shape)},
-                      /*outputs=*/{GetTensorRef(1, shape)});
-  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 3.1415926, -3.1415926, 1.0}));
-  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
-  EXPECT_THAT(model.GetOutput(0),
-              Pointwise(FloatNear(1e-6), {0.0, 0.0, 0.0, 0.841471}));
-}
-
-TEST(ElementwiseTest, Sqrt) {
-  OperationType op_type = OperationType::SQRT;
-  const BHWC shape(1, 2, 2, 1);
-  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
-                      /*inputs=*/{GetTensorRef(0, shape)},
-                      /*outputs=*/{GetTensorRef(1, shape)});
+  ElementwiseAttributes attr;
+  attr.param = 2.0f;
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/std::move(attr)},
+      /*inputs=*/{GetTensorRef(0, shape)},
+      /*outputs=*/{GetTensorRef(2, shape)});
   ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 4.0}));
   ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
   EXPECT_THAT(model.GetOutput(0),
-              Pointwise(FloatNear(1e-6), {0.0, 1.0, 1.414213, 2.0}));
+              Pointwise(FloatNear(1e-6), {0.0, 1.0, 4.0, 16.0}));
 }
 
-TEST(ElementwiseTest, Square) {
-  OperationType op_type = OperationType::SQUARE;
-  const BHWC shape(1, 2, 2, 1);
-  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
-                      /*inputs=*/{GetTensorRef(0, shape)},
-                      /*outputs=*/{GetTensorRef(1, shape)});
-  ASSERT_TRUE(model.PopulateTensor(0, {1.0, 2.0, 0.5, -3.0}));
+TEST(ElementwiseTwoArgumentsTest, PowConstVector) {
+  OperationType op_type = OperationType::POW;
+  const BHWC shape0(1, 2, 1, 2);
+
+  ElementwiseAttributes attr;
+  Tensor<Linear, DataType::FLOAT32> param;
+  param.shape = Linear(2);
+  param.id = 1;
+  param.data = {2.0, 0.5};
+  attr.param = std::move(param);
+
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 4.0}));
   ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
   EXPECT_THAT(model.GetOutput(0),
-              Pointwise(FloatNear(1e-6), {1.0, 4.0, 0.25, 9.0}));
+              Pointwise(FloatNear(1e-6), {0.0, 1.0, 4.0, 2.0}));
 }
 
-TEST(ElementwiseTest, SquaredDiff) {
+TEST(ElementwiseTwoArgumentsTest, SquaredDiffElementwise) {
   OperationType op_type = OperationType::SQUARED_DIFF;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model(
@@ -262,7 +443,56 @@ TEST(ElementwiseTest, SquaredDiff) {
               Pointwise(FloatNear(1e-6), {1.0, 1.0, 9.0, 0.0}));
 }
 
-TEST(ElementwiseTest, Sub) {
+TEST(ElementwiseTwoArgumentsTest, SquaredDiffBroadcast) {
+  OperationType op_type = OperationType::SQUARED_DIFF;
+  const BHWC shape0(1, 2, 1, 2);
+  const BHWC shape1(1, 1, 1, 2);
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/{}},
+      /*inputs=*/{GetTensorRef(0, shape0), GetTensorRef(1, shape1)},
+      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_TRUE(model.PopulateTensor(1, {-1.0, 5.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {1.0, 16.0, 9.0, 4.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, SquaredDiffScalar) {
+  OperationType op_type = OperationType::SQUARED_DIFF;
+  const BHWC shape0(1, 2, 1, 2);
+  ElementwiseAttributes attr;
+  attr.param = static_cast<float>(5.0);
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {25.0, 16.0, 9.0, 4.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, SquaredDiffConstVector) {
+  OperationType op_type = OperationType::SQUARED_DIFF;
+  const BHWC shape0(1, 2, 1, 2);
+
+  ElementwiseAttributes attr;
+  Tensor<Linear, DataType::FLOAT32> param;
+  param.shape = Linear(2);
+  param.id = 1;
+  param.data = {-1.0, 5.0};
+  attr.param = std::move(param);
+
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {1.0, 16.0, 9.0, 4.0}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, SubElementwise) {
   OperationType op_type = OperationType::SUB;
   const BHWC shape(1, 2, 2, 1);
   SingleOpModel model(
@@ -276,16 +506,53 @@ TEST(ElementwiseTest, Sub) {
               Pointwise(FloatNear(1e-6), {-1.0, -8.2, -1.0, 0.0}));
 }
 
-TEST(ElementwiseTest, Tanh) {
-  OperationType op_type = OperationType::TANH;
-  const BHWC shape(1, 2, 2, 1);
-  SingleOpModel model({/*type=*/ToString(op_type), /*attributes=*/{}},
-                      /*inputs=*/{GetTensorRef(0, shape)},
-                      /*outputs=*/{GetTensorRef(1, shape)});
-  ASSERT_TRUE(model.PopulateTensor(0, {0.0, -6.0, 2.0, 4.0}));
+TEST(ElementwiseTwoArgumentsTest, SubBroadcast) {
+  OperationType op_type = OperationType::SUB;
+  const BHWC shape0(1, 2, 1, 2);
+  const BHWC shape1(1, 1, 1, 2);
+  SingleOpModel model(
+      {/*type=*/ToString(op_type), /*attributes=*/{}},
+      /*inputs=*/{GetTensorRef(0, shape0), GetTensorRef(1, shape1)},
+      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_TRUE(model.PopulateTensor(1, {0.3, 0.2}));
   ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
   EXPECT_THAT(model.GetOutput(0),
-              Pointwise(FloatNear(1e-6), {0.0, -0.999987, 0.964027, 0.999329}));
+              Pointwise(FloatNear(1e-6), {-0.3, 0.8, 1.7, 2.8}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, SubScalar) {
+  OperationType op_type = OperationType::SUB;
+  const BHWC shape0(1, 2, 1, 2);
+  ElementwiseAttributes attr;
+  attr.param = static_cast<float>(0.5);
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {-0.5, 0.5, 1.5, 2.5}));
+}
+
+TEST(ElementwiseTwoArgumentsTest, SubConstVector) {
+  OperationType op_type = OperationType::SUB;
+  const BHWC shape0(1, 2, 1, 2);
+
+  ElementwiseAttributes attr;
+  Tensor<Linear, DataType::FLOAT32> param;
+  param.shape = Linear(2);
+  param.id = 1;
+  param.data = {0.3, 0.2};
+  attr.param = std::move(param);
+
+  SingleOpModel model({/*type=*/ToString(op_type), attr},
+                      /*inputs=*/{GetTensorRef(0, shape0)},
+                      /*outputs=*/{GetTensorRef(2, shape0)});
+  ASSERT_TRUE(model.PopulateTensor(0, {0.0, 1.0, 2.0, 3.0}));
+  ASSERT_OK(model.Invoke(*NewElementwiseNodeShader(op_type)));
+  EXPECT_THAT(model.GetOutput(0),
+              Pointwise(FloatNear(1e-6), {-0.3, 0.8, 1.7, 2.8}));
 }
 
 }  // namespace