Merge "Create tests for SVDF with non-null bias"
diff --git a/nn/runtime/test/for-cts/TestGeneratedOneFile.cpp b/nn/runtime/test/for-cts/TestGeneratedOneFile.cpp
index 7104f6e..cd96b55 100644
--- a/nn/runtime/test/for-cts/TestGeneratedOneFile.cpp
+++ b/nn/runtime/test/for-cts/TestGeneratedOneFile.cpp
@@ -147,6 +147,7 @@
 #include "../generated/tests/space_to_depth_quant8_2.mod.py.cpp"
 #include "../generated/tests/svdf.mod.py.cpp"
 #include "../generated/tests/svdf2.mod.py.cpp"
+#include "../generated/tests/svdf_bias_present.mod.py.cpp"
 #include "../generated/tests/svdf_state.mod.py.cpp"
 #include "../generated/tests/tanh.mod.py.cpp"
 #include "../generated/tests/add_relaxed.mod.py.cpp"
@@ -317,6 +318,7 @@
 #include "../generated/tests/sub_broadcast_float_relaxed.mod.py.cpp"
 #include "../generated/tests/sub_relaxed.mod.py.cpp"
 #include "../generated/tests/svdf2_relaxed.mod.py.cpp"
+#include "../generated/tests/svdf_bias_present_relaxed.mod.py.cpp"
 #include "../generated/tests/svdf_relaxed.mod.py.cpp"
 #include "../generated/tests/svdf_state_relaxed.mod.py.cpp"
 #include "../generated/tests/tanh_relaxed.mod.py.cpp"
@@ -463,6 +465,7 @@
 #include "../generated/tests/sub_quantized_different_scales.mod.py.cpp"
 #include "../generated/tests/sub_v1_2.mod.py.cpp"
 #include "../generated/tests/sub_v1_2_broadcast.mod.py.cpp"
+#include "../generated/tests/svdf_bias_present_float16.mod.py.cpp"
 #include "../generated/tests/svdf_float16.mod.py.cpp"
 #include "../generated/tests/svdf_state_float16.mod.py.cpp"
 #include "../generated/tests/tanh_v1_2.mod.py.cpp"
diff --git a/nn/runtime/test/generated/all_generated_V1_0_vts_tests.cpp b/nn/runtime/test/generated/all_generated_V1_0_vts_tests.cpp
index b32867e..0b54b3e 100644
--- a/nn/runtime/test/generated/all_generated_V1_0_vts_tests.cpp
+++ b/nn/runtime/test/generated/all_generated_V1_0_vts_tests.cpp
@@ -6057,6 +6057,46 @@
 
 
 #endif
+// Generated from: svdf_bias_present.mod.py.
+namespace svdf_bias_present {
+// Generated svdf_bias_present test
+#include "examples/svdf_bias_present.example.cpp"
+// Generated model constructor
+#include "vts_models/svdf_bias_present.model.cpp"
+} // namespace svdf_bias_present
+
+TEST_F(NeuralnetworksHidlTest, svdf_bias_present) {
+  generated_tests::Execute(device,
+                           svdf_bias_present::createTestModel,
+                           svdf_bias_present::is_ignored,
+                           svdf_bias_present::get_examples());
+}
+
+TEST_F(ValidationTest, svdf_bias_present) {
+  const Model model = svdf_bias_present::createTestModel();
+  const std::vector<Request> requests = createRequests(svdf_bias_present::get_examples());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, svdf_bias_present_dynamic_output_shape) {
+  generated_tests::Execute(device,
+                           svdf_bias_present::createTestModel_dynamic_output_shape,
+                           svdf_bias_present::is_ignored_dynamic_output_shape,
+                           svdf_bias_present::get_examples_dynamic_output_shape(), true);
+}
+
+TEST_F(ValidationTest, svdf_bias_present_dynamic_output_shape) {
+  const Model model = svdf_bias_present::createTestModel_dynamic_output_shape();
+  const std::vector<Request> requests = createRequests(svdf_bias_present::get_examples_dynamic_output_shape());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
 // Generated from: svdf_state.mod.py.
 namespace svdf_state {
 // Generated svdf_state test
diff --git a/nn/runtime/test/generated/all_generated_V1_1_vts_tests.cpp b/nn/runtime/test/generated/all_generated_V1_1_vts_tests.cpp
index a04b4a6..203ded8 100644
--- a/nn/runtime/test/generated/all_generated_V1_1_vts_tests.cpp
+++ b/nn/runtime/test/generated/all_generated_V1_1_vts_tests.cpp
@@ -6881,6 +6881,46 @@
 
 
 #endif
+// Generated from: svdf_bias_present_relaxed.mod.py.
+namespace svdf_bias_present_relaxed {
+// Generated svdf_bias_present_relaxed test
+#include "examples/svdf_bias_present_relaxed.example.cpp"
+// Generated model constructor
+#include "vts_models/svdf_bias_present_relaxed.model.cpp"
+} // namespace svdf_bias_present_relaxed
+
+TEST_F(NeuralnetworksHidlTest, svdf_bias_present_relaxed) {
+  generated_tests::Execute(device,
+                           svdf_bias_present_relaxed::createTestModel,
+                           svdf_bias_present_relaxed::is_ignored,
+                           svdf_bias_present_relaxed::get_examples());
+}
+
+TEST_F(ValidationTest, svdf_bias_present_relaxed) {
+  const Model model = svdf_bias_present_relaxed::createTestModel();
+  const std::vector<Request> requests = createRequests(svdf_bias_present_relaxed::get_examples());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, svdf_bias_present_relaxed_dynamic_output_shape) {
+  generated_tests::Execute(device,
+                           svdf_bias_present_relaxed::createTestModel_dynamic_output_shape,
+                           svdf_bias_present_relaxed::is_ignored_dynamic_output_shape,
+                           svdf_bias_present_relaxed::get_examples_dynamic_output_shape(), true);
+}
+
+TEST_F(ValidationTest, svdf_bias_present_relaxed_dynamic_output_shape) {
+  const Model model = svdf_bias_present_relaxed::createTestModel_dynamic_output_shape();
+  const std::vector<Request> requests = createRequests(svdf_bias_present_relaxed::get_examples_dynamic_output_shape());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
 // Generated from: svdf_relaxed.mod.py.
 namespace svdf_relaxed {
 // Generated svdf_relaxed test
diff --git a/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp b/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp
index 98f495d..a9bb624 100644
--- a/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp
+++ b/nn/runtime/test/generated/all_generated_V1_2_vts_tests.cpp
@@ -76113,6 +76113,46 @@
 
 
 #endif
+// Generated from: svdf_bias_present_float16.mod.py.
+namespace svdf_bias_present_float16 {
+// Generated svdf_bias_present_float16 test
+#include "examples/svdf_bias_present_float16.example.cpp"
+// Generated model constructor
+#include "vts_models/svdf_bias_present_float16.model.cpp"
+} // namespace svdf_bias_present_float16
+
+TEST_F(NeuralnetworksHidlTest, svdf_bias_present_float16) {
+  generated_tests::Execute(device,
+                           svdf_bias_present_float16::createTestModel,
+                           svdf_bias_present_float16::is_ignored,
+                           svdf_bias_present_float16::get_examples());
+}
+
+TEST_F(ValidationTest, svdf_bias_present_float16) {
+  const Model model = svdf_bias_present_float16::createTestModel();
+  const std::vector<Request> requests = createRequests(svdf_bias_present_float16::get_examples());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#ifdef NN_TEST_DYNAMIC_OUTPUT_SHAPE
+TEST_F(DynamicOutputShapeTest, svdf_bias_present_float16_dynamic_output_shape) {
+  generated_tests::Execute(device,
+                           svdf_bias_present_float16::createTestModel_dynamic_output_shape,
+                           svdf_bias_present_float16::is_ignored_dynamic_output_shape,
+                           svdf_bias_present_float16::get_examples_dynamic_output_shape(), true);
+}
+
+TEST_F(ValidationTest, svdf_bias_present_float16_dynamic_output_shape) {
+  const Model model = svdf_bias_present_float16::createTestModel_dynamic_output_shape();
+  const std::vector<Request> requests = createRequests(svdf_bias_present_float16::get_examples_dynamic_output_shape());
+  validateModel(model);
+  validateRequests(model, requests);
+}
+
+
+#endif
 // Generated from: svdf_float16.mod.py.
 namespace svdf_float16 {
 // Generated svdf_float16 test
diff --git a/nn/runtime/test/generated/examples/svdf_bias_present.example.cpp b/nn/runtime/test/generated/examples/svdf_bias_present.example.cpp
new file mode 100644
index 0000000..1d119ee
--- /dev/null
+++ b/nn/runtime/test/generated/examples/svdf_bias_present.example.cpp
@@ -0,0 +1,116 @@
+// clang-format off
+// Generated file (from: svdf_bias_present.mod.py). Do not edit
+std::vector<MixedTypedExample>& get_examples() {
+static std::vector<MixedTypedExample> examples = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 3}}, {1, {4, 3}}, {2, {4, 10}}, {3, {4}}, {4, {2, 40}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {1.0f, 2.0f, 3.0f, 4.0f}}, {4, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 40}}, {1, {2, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {1, {1.014899f, 1.9482339f, 2.856275f, 3.99728117f, 1.014899f, 1.9482339f, 2.856275f, 3.99728117f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 3}}, {1, {4, 3}}, {2, {4, 10}}, {3, {4}}, {4, {2, 40}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {1.0f, 2.0f, 3.0f, 4.0f}}, {4, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 40}}, {1, {2, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {1, {1.014899f, 1.9482339f, 2.856275f, 3.99728117f, 1.014899f, 1.9482339f, 2.856275f, 3.99728117f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape;
+};
+
diff --git a/nn/runtime/test/generated/examples/svdf_bias_present_float16.example.cpp b/nn/runtime/test/generated/examples/svdf_bias_present_float16.example.cpp
new file mode 100644
index 0000000..7934e2d
--- /dev/null
+++ b/nn/runtime/test/generated/examples/svdf_bias_present_float16.example.cpp
@@ -0,0 +1,116 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_float16.mod.py). Do not edit
+std::vector<MixedTypedExample>& get_examples() {
+static std::vector<MixedTypedExample> examples = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 3}}, {1, {4, 3}}, {2, {4, 10}}, {3, {4}}, {4, {2, 40}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {1.0f, 2.0f, 3.0f, 4.0f}}, {4, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 40}}, {1, {2, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {1, {1.014899f, 1.9482339f, 2.856275f, 3.99728117f, 1.014899f, 1.9482339f, 2.856275f, 3.99728117f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 3}}, {1, {4, 3}}, {2, {4, 10}}, {3, {4}}, {4, {2, 40}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {1.0f, 2.0f, 3.0f, 4.0f}}, {4, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 40}}, {1, {2, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {1, {1.014899f, 1.9482339f, 2.856275f, 3.99728117f, 1.014899f, 1.9482339f, 2.856275f, 3.99728117f}}},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape;
+};
+
diff --git a/nn/runtime/test/generated/examples/svdf_bias_present_relaxed.example.cpp b/nn/runtime/test/generated/examples/svdf_bias_present_relaxed.example.cpp
new file mode 100644
index 0000000..ca1ca84
--- /dev/null
+++ b/nn/runtime/test/generated/examples/svdf_bias_present_relaxed.example.cpp
@@ -0,0 +1,116 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_relaxed.mod.py). Do not edit
+std::vector<MixedTypedExample>& get_examples() {
+static std::vector<MixedTypedExample> examples = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 3}}, {1, {4, 3}}, {2, {4, 10}}, {3, {4}}, {4, {2, 40}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {1.0f, 2.0f, 3.0f, 4.0f}}, {4, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 40}}, {1, {2, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {1, {1.014899f, 1.9482339f, 2.856275f, 3.99728117f, 1.014899f, 1.9482339f, 2.856275f, 3.99728117f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples;
+};
+
+std::vector<MixedTypedExample>& get_examples_dynamic_output_shape() {
+static std::vector<MixedTypedExample> examples_dynamic_output_shape = {
+// Begin of an example
+{
+.operands = {
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 3}}, {1, {4, 3}}, {2, {4, 10}}, {3, {4}}, {4, {2, 40}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.12609188f, -0.46347019f, -0.89598465f, 0.12609188f, -0.46347019f, -0.89598465f}}, {1, {-0.31930989f, -0.36118156f, 0.0079667f, 0.37613347f, 0.22197971f, 0.12416199f, 0.27901134f, 0.27557442f, 0.3905206f, -0.36137494f, -0.06634006f, -0.10640851f}}, {2, {-0.31930989f, 0.37613347f, 0.27901134f, -0.36137494f, -0.36118156f, 0.22197971f, 0.27557442f, -0.06634006f, 0.0079667f, 0.12416199f, 0.3905206f, -0.10640851f, -0.0976817f, 0.15294972f, 0.39635518f, -0.02702999f, 0.39296314f, 0.15785322f, 0.21931258f, 0.31053296f, -0.36916667f, 0.38031587f, -0.21580373f, 0.27072677f, 0.23622236f, 0.34936687f, 0.18174365f, 0.35907319f, -0.17493086f, 0.324846f, -0.10781813f, 0.27201805f, 0.14324132f, -0.23681851f, -0.27115166f, -0.01580888f, -0.14943552f, 0.15465137f, 0.09784451f, -0.0337657f}}, {3, {1.0f, 2.0f, 3.0f, 4.0f}}, {4, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> Dimensions map
+  .operandDimensions = {{0, {2, 40}}, {1, {2, 4}}},
+  // int -> FLOAT32 map
+  .float32Operands = {{0, {0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}}, {1, {1.014899f, 1.9482339f, 2.856275f, 3.99728117f, 1.014899f, 1.9482339f, 2.856275f, 3.99728117f}}},
+  // int -> INT32 map
+  .int32Operands = {},
+  // int -> QUANT8_ASYMM map
+  .quant8AsymmOperands = {},
+  // int -> QUANT16_SYMM map
+  .quant16SymmOperands = {},
+  // int -> FLOAT16 map
+  .float16Operands = {},
+  // int -> BOOL8 map
+  .bool8Operands = {},
+  // int -> QUANT8_SYMM_PER_CHANNEL map
+  .quant8ChannelOperands = {},
+  // int -> QUANT16_ASYMM map
+  .quant16AsymmOperands = {},
+  // int -> QUANT8_SYMM map
+  .quant8SymmOperands = {},
+}
+},
+}, // End of an example
+};
+return examples_dynamic_output_shape;
+};
+
diff --git a/nn/runtime/test/generated/models/svdf_bias_present.model.cpp b/nn/runtime/test/generated/models/svdf_bias_present.model.cpp
new file mode 100644
index 0000000..9392833
--- /dev/null
+++ b/nn/runtime/test/generated/models/svdf_bias_present.model.cpp
@@ -0,0 +1,74 @@
+// clang-format off
+// Generated file (from: svdf_bias_present.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
+  OperandType type3(Type::TENSOR_FLOAT32, {4});
+  OperandType type4(Type::TENSOR_FLOAT32, {2, 40});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights_feature = model->addOperand(&type1);
+  auto weights_time = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto state_in = model->addOperand(&type4);
+  auto rank_param = model->addOperand(&type5);
+  auto activation_param = model->addOperand(&type5);
+  auto state_out = model->addOperand(&type4);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  static int32_t rank_param_init[] = {1};
+  model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
+  static int32_t activation_param_init[] = {0};
+  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
+  model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights_feature, weights_time, bias, state_in},
+    {state_out, output});
+  assert(model->isValid());
+}
+
+inline bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape(Model *model) {
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
+  OperandType type3(Type::TENSOR_FLOAT32, {4});
+  OperandType type4(Type::TENSOR_FLOAT32, {2, 40});
+  OperandType type5(Type::INT32, {});
+  OperandType type7(Type::TENSOR_FLOAT32, {0, 0});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights_feature = model->addOperand(&type1);
+  auto weights_time = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto state_in = model->addOperand(&type4);
+  auto rank_param = model->addOperand(&type5);
+  auto activation_param = model->addOperand(&type5);
+  auto state_out = model->addOperand(&type7);
+  auto output = model->addOperand(&type7);
+  // Phase 2, operations
+  static int32_t rank_param_init[] = {1};
+  model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
+  static int32_t activation_param_init[] = {0};
+  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
+  model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights_feature, weights_time, bias, state_in},
+    {state_out, output});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
diff --git a/nn/runtime/test/generated/models/svdf_bias_present_float16.model.cpp b/nn/runtime/test/generated/models/svdf_bias_present_float16.model.cpp
new file mode 100644
index 0000000..30a2c9d
--- /dev/null
+++ b/nn/runtime/test/generated/models/svdf_bias_present_float16.model.cpp
@@ -0,0 +1,74 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_float16.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type0(Type::TENSOR_FLOAT16, {2, 3});
+  OperandType type1(Type::TENSOR_FLOAT16, {4, 3});
+  OperandType type2(Type::TENSOR_FLOAT16, {4, 10});
+  OperandType type3(Type::TENSOR_FLOAT16, {4});
+  OperandType type4(Type::TENSOR_FLOAT16, {2, 40});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::TENSOR_FLOAT16, {2, 4});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights_feature = model->addOperand(&type1);
+  auto weights_time = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto state_in = model->addOperand(&type4);
+  auto rank_param = model->addOperand(&type5);
+  auto activation_param = model->addOperand(&type5);
+  auto state_out = model->addOperand(&type4);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  static int32_t rank_param_init[] = {1};
+  model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
+  static int32_t activation_param_init[] = {0};
+  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
+  model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights_feature, weights_time, bias, state_in},
+    {state_out, output});
+  assert(model->isValid());
+}
+
+inline bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape(Model *model) {
+  OperandType type0(Type::TENSOR_FLOAT16, {2, 3});
+  OperandType type1(Type::TENSOR_FLOAT16, {4, 3});
+  OperandType type2(Type::TENSOR_FLOAT16, {4, 10});
+  OperandType type3(Type::TENSOR_FLOAT16, {4});
+  OperandType type4(Type::TENSOR_FLOAT16, {2, 40});
+  OperandType type5(Type::INT32, {});
+  OperandType type7(Type::TENSOR_FLOAT16, {0, 0});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights_feature = model->addOperand(&type1);
+  auto weights_time = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto state_in = model->addOperand(&type4);
+  auto rank_param = model->addOperand(&type5);
+  auto activation_param = model->addOperand(&type5);
+  auto state_out = model->addOperand(&type7);
+  auto output = model->addOperand(&type7);
+  // Phase 2, operations
+  static int32_t rank_param_init[] = {1};
+  model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
+  static int32_t activation_param_init[] = {0};
+  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
+  model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights_feature, weights_time, bias, state_in},
+    {state_out, output});
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
diff --git a/nn/runtime/test/generated/models/svdf_bias_present_relaxed.model.cpp b/nn/runtime/test/generated/models/svdf_bias_present_relaxed.model.cpp
new file mode 100644
index 0000000..6ec9810
--- /dev/null
+++ b/nn/runtime/test/generated/models/svdf_bias_present_relaxed.model.cpp
@@ -0,0 +1,78 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_relaxed.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
+  OperandType type3(Type::TENSOR_FLOAT32, {4});
+  OperandType type4(Type::TENSOR_FLOAT32, {2, 40});
+  OperandType type5(Type::INT32, {});
+  OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights_feature = model->addOperand(&type1);
+  auto weights_time = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto state_in = model->addOperand(&type4);
+  auto rank_param = model->addOperand(&type5);
+  auto activation_param = model->addOperand(&type5);
+  auto state_out = model->addOperand(&type4);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  static int32_t rank_param_init[] = {1};
+  model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
+  static int32_t activation_param_init[] = {0};
+  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
+  model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights_feature, weights_time, bias, state_in},
+    {state_out, output});
+  // Phase 4: set relaxed execution
+  model->relaxComputationFloat32toFloat16(true);
+  assert(model->isValid());
+}
+
+inline bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
+void CreateModel_dynamic_output_shape(Model *model) {
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
+  OperandType type3(Type::TENSOR_FLOAT32, {4});
+  OperandType type4(Type::TENSOR_FLOAT32, {2, 40});
+  OperandType type5(Type::INT32, {});
+  OperandType type7(Type::TENSOR_FLOAT32, {0, 0});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights_feature = model->addOperand(&type1);
+  auto weights_time = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto state_in = model->addOperand(&type4);
+  auto rank_param = model->addOperand(&type5);
+  auto activation_param = model->addOperand(&type5);
+  auto state_out = model->addOperand(&type7);
+  auto output = model->addOperand(&type7);
+  // Phase 2, operations
+  static int32_t rank_param_init[] = {1};
+  model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
+  static int32_t activation_param_init[] = {0};
+  model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
+  model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights_feature, weights_time, bias, state_in},
+    {state_out, output});
+  // Phase 4: set relaxed execution
+  model->relaxComputationFloat32toFloat16(true);
+  assert(model->isValid());
+}
+
+inline bool is_ignored_dynamic_output_shape(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
diff --git a/nn/runtime/test/generated/tests/svdf_bias_present.mod.py.cpp b/nn/runtime/test/generated/tests/svdf_bias_present.mod.py.cpp
new file mode 100644
index 0000000..4236ee8
--- /dev/null
+++ b/nn/runtime/test/generated/tests/svdf_bias_present.mod.py.cpp
@@ -0,0 +1,23 @@
+// clang-format off
+// Generated file (from: svdf_bias_present.mod.py). Do not edit
+#include "../../TestGenerated.h"
+
+namespace svdf_bias_present {
+// Generated svdf_bias_present test
+#include "generated/examples/svdf_bias_present.example.cpp"
+// Generated model constructor
+#include "generated/models/svdf_bias_present.model.cpp"
+} // namespace svdf_bias_present
+
+TEST_F(GeneratedTests, svdf_bias_present) {
+    execute(svdf_bias_present::CreateModel,
+            svdf_bias_present::is_ignored,
+            svdf_bias_present::get_examples());
+}
+
+TEST_F(DynamicOutputShapeTest, svdf_bias_present_dynamic_output_shape) {
+    execute(svdf_bias_present::CreateModel_dynamic_output_shape,
+            svdf_bias_present::is_ignored_dynamic_output_shape,
+            svdf_bias_present::get_examples_dynamic_output_shape());
+}
+
diff --git a/nn/runtime/test/generated/tests/svdf_bias_present_float16.mod.py.cpp b/nn/runtime/test/generated/tests/svdf_bias_present_float16.mod.py.cpp
new file mode 100644
index 0000000..3b94f84
--- /dev/null
+++ b/nn/runtime/test/generated/tests/svdf_bias_present_float16.mod.py.cpp
@@ -0,0 +1,23 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_float16.mod.py). Do not edit
+#include "../../TestGenerated.h"
+
+namespace svdf_bias_present_float16 {
+// Generated svdf_bias_present_float16 test
+#include "generated/examples/svdf_bias_present_float16.example.cpp"
+// Generated model constructor
+#include "generated/models/svdf_bias_present_float16.model.cpp"
+} // namespace svdf_bias_present_float16
+
+TEST_F(GeneratedTests, svdf_bias_present_float16) {
+    execute(svdf_bias_present_float16::CreateModel,
+            svdf_bias_present_float16::is_ignored,
+            svdf_bias_present_float16::get_examples());
+}
+
+TEST_F(DynamicOutputShapeTest, svdf_bias_present_float16_dynamic_output_shape) {
+    execute(svdf_bias_present_float16::CreateModel_dynamic_output_shape,
+            svdf_bias_present_float16::is_ignored_dynamic_output_shape,
+            svdf_bias_present_float16::get_examples_dynamic_output_shape());
+}
+
diff --git a/nn/runtime/test/generated/tests/svdf_bias_present_relaxed.mod.py.cpp b/nn/runtime/test/generated/tests/svdf_bias_present_relaxed.mod.py.cpp
new file mode 100644
index 0000000..5c566d8
--- /dev/null
+++ b/nn/runtime/test/generated/tests/svdf_bias_present_relaxed.mod.py.cpp
@@ -0,0 +1,23 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_relaxed.mod.py). Do not edit
+#include "../../TestGenerated.h"
+
+namespace svdf_bias_present_relaxed {
+// Generated svdf_bias_present_relaxed test
+#include "generated/examples/svdf_bias_present_relaxed.example.cpp"
+// Generated model constructor
+#include "generated/models/svdf_bias_present_relaxed.model.cpp"
+} // namespace svdf_bias_present_relaxed
+
+TEST_F(GeneratedTests, svdf_bias_present_relaxed) {
+    execute(svdf_bias_present_relaxed::CreateModel,
+            svdf_bias_present_relaxed::is_ignored,
+            svdf_bias_present_relaxed::get_examples());
+}
+
+TEST_F(DynamicOutputShapeTest, svdf_bias_present_relaxed_dynamic_output_shape) {
+    execute(svdf_bias_present_relaxed::CreateModel_dynamic_output_shape,
+            svdf_bias_present_relaxed::is_ignored_dynamic_output_shape,
+            svdf_bias_present_relaxed::get_examples_dynamic_output_shape());
+}
+
diff --git a/nn/runtime/test/generated/vts_models/svdf_bias_present.model.cpp b/nn/runtime/test/generated/vts_models/svdf_bias_present.model.cpp
new file mode 100644
index 0000000..5317d54
--- /dev/null
+++ b/nn/runtime/test/generated/vts_models/svdf_bias_present.model.cpp
@@ -0,0 +1,234 @@
+// clang-format off
+// Generated file (from: svdf_bias_present.mod.py). Do not edit
+// Create the model
+Model createTestModel() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 10},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 4},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::SVDF,
+            .inputs = {0, 1, 2, 3, 4, 5, 6},
+            .outputs = {7, 8},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1, 2, 3, 4};
+    const std::vector<uint32_t> outputIndexes = {7, 8};
+    std::vector<uint8_t> operandValues = {
+      1, 0, 0, 0, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 10},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::SVDF,
+            .inputs = {0, 1, 2, 3, 4, 5, 6},
+            .outputs = {7, 8},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1, 2, 3, 4};
+    const std::vector<uint32_t> outputIndexes = {7, 8};
+    std::vector<uint8_t> operandValues = {
+      1, 0, 0, 0, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
diff --git a/nn/runtime/test/generated/vts_models/svdf_bias_present_float16.model.cpp b/nn/runtime/test/generated/vts_models/svdf_bias_present_float16.model.cpp
new file mode 100644
index 0000000..bfda48c
--- /dev/null
+++ b/nn/runtime/test/generated/vts_models/svdf_bias_present_float16.model.cpp
@@ -0,0 +1,234 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_float16.mod.py). Do not edit
+// Create the model
+Model createTestModel() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {2, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {4, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {4, 10},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {2, 4},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::SVDF,
+            .inputs = {0, 1, 2, 3, 4, 5, 6},
+            .outputs = {7, 8},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1, 2, 3, 4};
+    const std::vector<uint32_t> outputIndexes = {7, 8};
+    std::vector<uint8_t> operandValues = {
+      1, 0, 0, 0, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {2, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {4, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {4, 10},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT16,
+            .dimensions = {0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::SVDF,
+            .inputs = {0, 1, 2, 3, 4, 5, 6},
+            .outputs = {7, 8},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1, 2, 3, 4};
+    const std::vector<uint32_t> outputIndexes = {7, 8};
+    std::vector<uint8_t> operandValues = {
+      1, 0, 0, 0, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
diff --git a/nn/runtime/test/generated/vts_models/svdf_bias_present_relaxed.model.cpp b/nn/runtime/test/generated/vts_models/svdf_bias_present_relaxed.model.cpp
new file mode 100644
index 0000000..9e7d398
--- /dev/null
+++ b/nn/runtime/test/generated/vts_models/svdf_bias_present_relaxed.model.cpp
@@ -0,0 +1,236 @@
+// clang-format off
+// Generated file (from: svdf_bias_present_relaxed.mod.py). Do not edit
+// Create the model
+Model createTestModel() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 10},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 4},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::SVDF,
+            .inputs = {0, 1, 2, 3, 4, 5, 6},
+            .outputs = {7, 8},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1, 2, 3, 4};
+    const std::vector<uint32_t> outputIndexes = {7, 8};
+    std::vector<uint8_t> operandValues = {
+      1, 0, 0, 0, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+        .relaxComputationFloat32toFloat16 = true,
+    };
+}
+
+inline bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
+// Create the model
+Model createTestModel_dynamic_output_shape() {
+    const std::vector<Operand> operands = {
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 3},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4, 10},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {4},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {2, 40},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_INPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 0, .length = 4},
+        },
+        {
+            .type = OperandType::INT32,
+            .dimensions = {},
+            .numberOfConsumers = 1,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::CONSTANT_COPY,
+            .location = {.poolIndex = 0, .offset = 4, .length = 4},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        },
+        {
+            .type = OperandType::TENSOR_FLOAT32,
+            .dimensions = {0, 0},
+            .numberOfConsumers = 0,
+            .scale = 0.0f,
+            .zeroPoint = 0,
+            .lifetime = OperandLifeTime::MODEL_OUTPUT,
+            .location = {.poolIndex = 0, .offset = 0, .length = 0},
+        }
+    };
+
+    const std::vector<Operation> operations = {
+        {
+            .type = OperationType::SVDF,
+            .inputs = {0, 1, 2, 3, 4, 5, 6},
+            .outputs = {7, 8},
+        }
+    };
+
+    const std::vector<uint32_t> inputIndexes = {0, 1, 2, 3, 4};
+    const std::vector<uint32_t> outputIndexes = {7, 8};
+    std::vector<uint8_t> operandValues = {
+      1, 0, 0, 0, 0, 0, 0, 0
+    };
+    const std::vector<hidl_memory> pools = {};
+
+    return {
+        .operands = operands,
+        .operations = operations,
+        .inputIndexes = inputIndexes,
+        .outputIndexes = outputIndexes,
+        .operandValues = operandValues,
+        .pools = pools,
+        .relaxComputationFloat32toFloat16 = true,
+    };
+}
+
+inline bool is_ignored_dynamic_output_shape(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
+
diff --git a/nn/runtime/test/specs/V1_0/svdf_bias_present.mod.py b/nn/runtime/test/specs/V1_0/svdf_bias_present.mod.py
new file mode 100644
index 0000000..ae7d1e7
--- /dev/null
+++ b/nn/runtime/test/specs/V1_0/svdf_bias_present.mod.py
@@ -0,0 +1,138 @@
+#
+# Copyright (C) 2019 The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+batches = 2
+features = 4
+rank = 1
+units = int(features / rank)
+input_size = 3
+memory_size = 10
+
+model = Model()
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
+weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
+weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
+bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
+state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
+rank_param = Int32Scalar("rank_param", rank)
+activation_param = Int32Scalar("activation_param", 0)
+state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
+
+model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
+                        rank_param, activation_param).To([state_out, output])
+
+input0 = {
+    input: [],
+    weights_feature: [
+        -0.31930989, -0.36118156, 0.0079667, 0.37613347,
+      0.22197971, 0.12416199, 0.27901134, 0.27557442,
+      0.3905206, -0.36137494, -0.06634006, -0.10640851
+    ],
+    weights_time: [
+        -0.31930989, 0.37613347,  0.27901134,  -0.36137494, -0.36118156,
+      0.22197971,  0.27557442,  -0.06634006, 0.0079667,   0.12416199,
+
+       0.3905206,   -0.10640851, -0.0976817,  0.15294972,  0.39635518,
+      -0.02702999, 0.39296314,  0.15785322,  0.21931258,  0.31053296,
+
+       -0.36916667, 0.38031587,  -0.21580373, 0.27072677,  0.23622236,
+      0.34936687,  0.18174365,  0.35907319,  -0.17493086, 0.324846,
+
+       -0.10781813, 0.27201805,  0.14324132,  -0.23681851, -0.27115166,
+      -0.01580888, -0.14943552, 0.15465137,  0.09784451,  -0.0337657
+    ],
+    bias: [1.0, 2.0, 3.0, 4.0],
+    state_in: [0 for _ in range(batches * memory_size * features)],
+}
+
+test_inputs = [
+    0.12609188,  -0.46347019, -0.89598465,
+    0.12609188,  -0.46347019, -0.89598465,
+
+    0.14278367,  -1.64410412, -0.75222826,
+    0.14278367,  -1.64410412, -0.75222826,
+
+    0.49837467,  0.19278903,  0.26584083,
+    0.49837467,  0.19278903,  0.26584083,
+
+    -0.11186574, 0.13164264,  -0.05349274,
+    -0.11186574, 0.13164264,  -0.05349274,
+
+    -0.68892461, 0.37783599,  0.18263303,
+    -0.68892461, 0.37783599,  0.18263303,
+
+    -0.81299269, -0.86831826, 1.43940818,
+    -0.81299269, -0.86831826, 1.43940818,
+
+    -1.45006323, -0.82251364, -1.69082689,
+    -1.45006323, -0.82251364, -1.69082689,
+
+    0.03966608,  -0.24936394, -0.77526885,
+    0.03966608,  -0.24936394, -0.77526885,
+
+    0.11771342,  -0.23761693, -0.65898693,
+    0.11771342,  -0.23761693, -0.65898693,
+
+    -0.89477462, 1.67204106,  -0.53235275,
+    -0.89477462, 1.67204106,  -0.53235275
+]
+
+golden_outputs = [
+    1.014899,    1.9482339,  2.856275,  3.99728117,
+    1.014899,    1.9482339,  2.856275,  3.99728117,
+
+    1.068281,    1.837783,   2.847732,  4.00323521,
+    1.068281,    1.837783,   2.847732,  4.00323521,
+
+    0.9682179,   1.9666911,  3.0609602, 4.0333759,
+    0.9682179,   1.9666911,  3.0609602, 4.0333759,
+
+    0.99376901,  1.922299,   2.608807,  3.9863309,
+    0.99376901,  1.922299,   2.608807,  3.9863309,
+
+    1.201551,    1.835393,   2.820538,  3.9407261,
+    1.201551,    1.835393,   2.820538,  3.9407261,
+
+    1.0886511,   1.9124599,  2.730717,  4.0281379,
+    1.0886511,   1.9124599,  2.730717,  4.0281379,
+
+    0.798826,    1.413855,   2.371376,  3.9669588,
+    0.798826,    1.413855,   2.371376,  3.9669588,
+
+    0.9160904,   1.700671,   3.108746,  4.109808,
+    0.9160904,   1.700671,   3.108746,  4.109808,
+
+    1.419114,    1.762176,   2.577373,  4.175115,
+    1.419114,    1.762176,   2.577373,  4.175115,
+
+    1.36726,     1.477697,   2.543498,  3.824525,
+    1.36726,     1.477697,   2.543498,  3.824525
+]
+
+output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
+           output: []}
+
+# TODO: enable more data points after fixing the reference issue
+for i in range(1):
+  batch_start = i * input_size * batches
+  batch_end = batch_start + input_size * batches
+  input0[input] = test_inputs[batch_start:batch_end]
+  golden_start = i * units * batches
+  golden_end = golden_start + units * batches
+  output0[output] = golden_outputs[golden_start:golden_end]
+  Example((input0, output0))
diff --git a/nn/runtime/test/specs/V1_1/svdf_bias_present_relaxed.mod.py b/nn/runtime/test/specs/V1_1/svdf_bias_present_relaxed.mod.py
new file mode 100644
index 0000000..7bff435
--- /dev/null
+++ b/nn/runtime/test/specs/V1_1/svdf_bias_present_relaxed.mod.py
@@ -0,0 +1,139 @@
+#
+# Copyright (C) 2019 The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+batches = 2
+features = 4
+rank = 1
+units = int(features / rank)
+input_size = 3
+memory_size = 10
+
+model = Model()
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
+weights_feature = Input("weights_feature", "TENSOR_FLOAT32", "{%d, %d}" % (features, input_size))
+weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (features, memory_size))
+bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
+state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
+rank_param = Int32Scalar("rank_param", rank)
+activation_param = Int32Scalar("activation_param", 0)
+state_out = IgnoredOutput("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*features))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
+
+model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
+                        rank_param, activation_param).To([state_out, output])
+model = model.RelaxedExecution(True)
+
+input0 = {
+    input: [],
+    weights_feature: [
+        -0.31930989, -0.36118156, 0.0079667, 0.37613347,
+      0.22197971, 0.12416199, 0.27901134, 0.27557442,
+      0.3905206, -0.36137494, -0.06634006, -0.10640851
+    ],
+    weights_time: [
+        -0.31930989, 0.37613347,  0.27901134,  -0.36137494, -0.36118156,
+      0.22197971,  0.27557442,  -0.06634006, 0.0079667,   0.12416199,
+
+       0.3905206,   -0.10640851, -0.0976817,  0.15294972,  0.39635518,
+      -0.02702999, 0.39296314,  0.15785322,  0.21931258,  0.31053296,
+
+       -0.36916667, 0.38031587,  -0.21580373, 0.27072677,  0.23622236,
+      0.34936687,  0.18174365,  0.35907319,  -0.17493086, 0.324846,
+
+       -0.10781813, 0.27201805,  0.14324132,  -0.23681851, -0.27115166,
+      -0.01580888, -0.14943552, 0.15465137,  0.09784451,  -0.0337657
+    ],
+    bias: [1.0, 2.0, 3.0, 4.0],
+    state_in: [0 for _ in range(batches * memory_size * features)],
+}
+
+test_inputs = [
+    0.12609188,  -0.46347019, -0.89598465,
+    0.12609188,  -0.46347019, -0.89598465,
+
+    0.14278367,  -1.64410412, -0.75222826,
+    0.14278367,  -1.64410412, -0.75222826,
+
+    0.49837467,  0.19278903,  0.26584083,
+    0.49837467,  0.19278903,  0.26584083,
+
+    -0.11186574, 0.13164264,  -0.05349274,
+    -0.11186574, 0.13164264,  -0.05349274,
+
+    -0.68892461, 0.37783599,  0.18263303,
+    -0.68892461, 0.37783599,  0.18263303,
+
+    -0.81299269, -0.86831826, 1.43940818,
+    -0.81299269, -0.86831826, 1.43940818,
+
+    -1.45006323, -0.82251364, -1.69082689,
+    -1.45006323, -0.82251364, -1.69082689,
+
+    0.03966608,  -0.24936394, -0.77526885,
+    0.03966608,  -0.24936394, -0.77526885,
+
+    0.11771342,  -0.23761693, -0.65898693,
+    0.11771342,  -0.23761693, -0.65898693,
+
+    -0.89477462, 1.67204106,  -0.53235275,
+    -0.89477462, 1.67204106,  -0.53235275
+]
+
+golden_outputs = [
+    1.014899,    1.9482339,  2.856275,  3.99728117,
+    1.014899,    1.9482339,  2.856275,  3.99728117,
+
+    1.068281,    1.837783,   2.847732,  4.00323521,
+    1.068281,    1.837783,   2.847732,  4.00323521,
+
+    0.9682179,   1.9666911,  3.0609602, 4.0333759,
+    0.9682179,   1.9666911,  3.0609602, 4.0333759,
+
+    0.99376901,  1.922299,   2.608807,  3.9863309,
+    0.99376901,  1.922299,   2.608807,  3.9863309,
+
+    1.201551,    1.835393,   2.820538,  3.9407261,
+    1.201551,    1.835393,   2.820538,  3.9407261,
+
+    1.0886511,   1.9124599,  2.730717,  4.0281379,
+    1.0886511,   1.9124599,  2.730717,  4.0281379,
+
+    0.798826,    1.413855,   2.371376,  3.9669588,
+    0.798826,    1.413855,   2.371376,  3.9669588,
+
+    0.9160904,   1.700671,   3.108746,  4.109808,
+    0.9160904,   1.700671,   3.108746,  4.109808,
+
+    1.419114,    1.762176,   2.577373,  4.175115,
+    1.419114,    1.762176,   2.577373,  4.175115,
+
+    1.36726,     1.477697,   2.543498,  3.824525,
+    1.36726,     1.477697,   2.543498,  3.824525
+]
+
+output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
+           output: []}
+
+# TODO: enable more data points after fixing the reference issue
+for i in range(1):
+  batch_start = i * input_size * batches
+  batch_end = batch_start + input_size * batches
+  input0[input] = test_inputs[batch_start:batch_end]
+  golden_start = i * units * batches
+  golden_end = golden_start + units * batches
+  output0[output] = golden_outputs[golden_start:golden_end]
+  Example((input0, output0))
diff --git a/nn/runtime/test/specs/V1_2/svdf_bias_present_float16.mod.py b/nn/runtime/test/specs/V1_2/svdf_bias_present_float16.mod.py
new file mode 100644
index 0000000..4dc6914
--- /dev/null
+++ b/nn/runtime/test/specs/V1_2/svdf_bias_present_float16.mod.py
@@ -0,0 +1,138 @@
+#
+# Copyright (C) 2019 The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+batches = 2
+features = 4
+rank = 1
+units = int(features / rank)
+input_size = 3
+memory_size = 10
+
+model = Model()
+
+input = Input("input", "TENSOR_FLOAT16", "{%d, %d}" % (batches, input_size))
+weights_feature = Input("weights_feature", "TENSOR_FLOAT16", "{%d, %d}" % (features, input_size))
+weights_time = Input("weights_time", "TENSOR_FLOAT16", "{%d, %d}" % (features, memory_size))
+bias = Input("bias", "TENSOR_FLOAT16", "{%d}" % (units))
+state_in = Input("state_in", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features))
+rank_param = Int32Scalar("rank_param", rank)
+activation_param = Int32Scalar("activation_param", 0)
+state_out = IgnoredOutput("state_out", "TENSOR_FLOAT16", "{%d, %d}" % (batches, memory_size*features))
+output = Output("output", "TENSOR_FLOAT16", "{%d, %d}" % (batches, units))
+
+model = model.Operation("SVDF", input, weights_feature, weights_time, bias, state_in,
+                        rank_param, activation_param).To([state_out, output])
+
+input0 = {
+    input: [],
+    weights_feature: [
+        -0.31930989, -0.36118156, 0.0079667, 0.37613347,
+      0.22197971, 0.12416199, 0.27901134, 0.27557442,
+      0.3905206, -0.36137494, -0.06634006, -0.10640851
+    ],
+    weights_time: [
+        -0.31930989, 0.37613347,  0.27901134,  -0.36137494, -0.36118156,
+      0.22197971,  0.27557442,  -0.06634006, 0.0079667,   0.12416199,
+
+       0.3905206,   -0.10640851, -0.0976817,  0.15294972,  0.39635518,
+      -0.02702999, 0.39296314,  0.15785322,  0.21931258,  0.31053296,
+
+       -0.36916667, 0.38031587,  -0.21580373, 0.27072677,  0.23622236,
+      0.34936687,  0.18174365,  0.35907319,  -0.17493086, 0.324846,
+
+       -0.10781813, 0.27201805,  0.14324132,  -0.23681851, -0.27115166,
+      -0.01580888, -0.14943552, 0.15465137,  0.09784451,  -0.0337657
+    ],
+    bias: [1.0, 2.0, 3.0, 4.0],
+    state_in: [0 for _ in range(batches * memory_size * features)],
+}
+
+test_inputs = [
+    0.12609188,  -0.46347019, -0.89598465,
+    0.12609188,  -0.46347019, -0.89598465,
+
+    0.14278367,  -1.64410412, -0.75222826,
+    0.14278367,  -1.64410412, -0.75222826,
+
+    0.49837467,  0.19278903,  0.26584083,
+    0.49837467,  0.19278903,  0.26584083,
+
+    -0.11186574, 0.13164264,  -0.05349274,
+    -0.11186574, 0.13164264,  -0.05349274,
+
+    -0.68892461, 0.37783599,  0.18263303,
+    -0.68892461, 0.37783599,  0.18263303,
+
+    -0.81299269, -0.86831826, 1.43940818,
+    -0.81299269, -0.86831826, 1.43940818,
+
+    -1.45006323, -0.82251364, -1.69082689,
+    -1.45006323, -0.82251364, -1.69082689,
+
+    0.03966608,  -0.24936394, -0.77526885,
+    0.03966608,  -0.24936394, -0.77526885,
+
+    0.11771342,  -0.23761693, -0.65898693,
+    0.11771342,  -0.23761693, -0.65898693,
+
+    -0.89477462, 1.67204106,  -0.53235275,
+    -0.89477462, 1.67204106,  -0.53235275
+]
+
+golden_outputs = [
+    1.014899,    1.9482339,  2.856275,  3.99728117,
+    1.014899,    1.9482339,  2.856275,  3.99728117,
+
+    1.068281,    1.837783,   2.847732,  4.00323521,
+    1.068281,    1.837783,   2.847732,  4.00323521,
+
+    0.9682179,   1.9666911,  3.0609602, 4.0333759,
+    0.9682179,   1.9666911,  3.0609602, 4.0333759,
+
+    0.99376901,  1.922299,   2.608807,  3.9863309,
+    0.99376901,  1.922299,   2.608807,  3.9863309,
+
+    1.201551,    1.835393,   2.820538,  3.9407261,
+    1.201551,    1.835393,   2.820538,  3.9407261,
+
+    1.0886511,   1.9124599,  2.730717,  4.0281379,
+    1.0886511,   1.9124599,  2.730717,  4.0281379,
+
+    0.798826,    1.413855,   2.371376,  3.9669588,
+    0.798826,    1.413855,   2.371376,  3.9669588,
+
+    0.9160904,   1.700671,   3.108746,  4.109808,
+    0.9160904,   1.700671,   3.108746,  4.109808,
+
+    1.419114,    1.762176,   2.577373,  4.175115,
+    1.419114,    1.762176,   2.577373,  4.175115,
+
+    1.36726,     1.477697,   2.543498,  3.824525,
+    1.36726,     1.477697,   2.543498,  3.824525
+]
+
+output0 = {state_out: [0 for _ in range(batches * memory_size * features)],
+           output: []}
+
+# TODO: enable more data points after fixing the reference issue
+for i in range(1):
+  batch_start = i * input_size * batches
+  batch_end = batch_start + input_size * batches
+  input0[input] = test_inputs[batch_start:batch_end]
+  golden_start = i * units * batches
+  golden_end = golden_start + units * batches
+  output0[output] = golden_outputs[golden_start:golden_end]
+  Example((input0, output0))