Snap for 4393674 from 59c183a7ff87fffd47284a4a3f41479116d71d36 to oc-mr1-release

Change-Id: I4108aef1f08312bfaa33ed06c3a6a8394096396e

61 files changed, 5077 insertions, 324 deletions

diff --git a/nn/common/Android.bp b/nn/common/Android.bp
index 15ab87588..a499104bd 100644
--- a/nn/common/Android.bp
+++ b/nn/common/Android.bp
@@ -46,6 +46,9 @@ cc_library_static {
         "operations/RNN.cpp",
         "operations/SimpleMath.cpp",
         "operations/SVDF.cpp",
+        "operations/internal/optimized/neon_tensor_utils.cc",
+        "operations/internal/reference/portable_tensor_utils.cc",
+        "operations/internal/tensor_utils.cc",
     ],
     shared_libs: [
         "libbase",
@@ -79,6 +82,23 @@ cc_library_static {
 }
 
 cc_test {
+    name: "tensor_utils_test",
+    srcs: [
+        "operations/internal/tensor_utils_test.cc",
+    ],
+    shared_libs: [
+        "libneuralnetworks",
+    ],
+    local_include_dirs: [ "include" ],
+    header_libs: [
+        "libneuralnetworks_headers",
+    ],
+    static_libs: [
+        "libgmock",
+    ],
+}
+
+cc_test {
     name: "embedding_lookup_test",
     srcs: [
         "operations/EmbeddingLookupTest.cpp",
diff --git a/nn/common/CpuExecutor.cpp b/nn/common/CpuExecutor.cpp
index 480d6cb15..79f9255bd 100644
--- a/nn/common/CpuExecutor.cpp
+++ b/nn/common/CpuExecutor.cpp
@@ -1206,7 +1206,7 @@ int CpuExecutor::executeOperation(const Operation& operation) {
                 rnn_cell.Eval();
         } break;
         case OperationType::SVDF: {
-            RunTimeOperandInfo &state =
+            RunTimeOperandInfo &stateOut =
                 mOperands[outs[SVDF::kStateOutTensor]];
             RunTimeOperandInfo &output =
                 mOperands[outs[SVDF::kOutputTensor]];
@@ -1216,7 +1216,7 @@ int CpuExecutor::executeOperation(const Operation& operation) {
 
             success = SVDF::Prepare(operation, mOperands,
                                     &stateShape, &outputShape) &&
-                setInfoAndAllocateIfNeeded(&state, stateShape) &&
+                setInfoAndAllocateIfNeeded(&stateOut, stateShape) &&
                 setInfoAndAllocateIfNeeded(&output, outputShape) &&
                 svdf.Eval();
         } break;
diff --git a/nn/common/operations/LSTM.cpp b/nn/common/operations/LSTM.cpp
index e7b0dec75..b61463551 100644
--- a/nn/common/operations/LSTM.cpp
+++ b/nn/common/operations/LSTM.cpp
@@ -18,120 +18,13 @@
 
 #include "CpuExecutor.h"
 #include "HalInterfaces.h"
+#include "internal/tensor_utils.h"
 
 namespace android {
 namespace nn {
 
-// TODO: move the kernels to a separate file as soon as we have the
-// optimized version ready.
 namespace {
 
-// Limit a float input f between +abs_limit and -abs_limit.
-inline float Clip(float f, float abs_limit) {
-  float result = (abs_limit < f) ? abs_limit : f;
-  result = (-abs_limit > result) ? -abs_limit : result;
-  return result;
-}
-
-// Multiply a matrix by a batch vector, and store results in a batch-size
-// vector.
-void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows,
-                                         int m_cols, const float* vector,
-                                         int n_batch, float* result) {
-  for (int b = 0; b < n_batch; b++) {
-    float* result_in_batch = result + b * m_rows;
-    const float* matrix_ptr = matrix;
-    for (int r = 0; r < m_rows; r++) {
-      const float* vector_in_batch = vector + b * m_cols;
-      for (int c = 0; c < m_cols; c++) {
-        *result_in_batch += *matrix_ptr++ * *vector_in_batch++;
-      }
-      result_in_batch++;
-    }
-  }
-}
-
-// Cwise product of two vectors.
-void VectorVectorCwiseProduct(const float* vector1, const float* vector2,
-                              int v_size, float* result) {
-  for (int v = 0; v < v_size; v++) {
-    *result++ = *vector1++ * *vector2++;
-  }
-}
-
-// Cwise product and accumulation of two vectors. Since it's a MAC operation, the
-// assumption here is that result array is initialized to valid values.
-void VectorVectorCwiseProductAccumulate(const float* vector1,
-                                        const float* vector2, int v_size,
-                                        float* result) {
-  for (int v = 0; v < v_size; v++) {
-    *result++ += *vector1++ * *vector2++;
-  }
-}
-
-// Cwise product and accumulation of a vector and a batch-vector. Since it's a MAC
-// operation, the assumption here is that result array is initialized to valid
-// values.
-void VectorBatchVectorCwiseProductAccumulate(const float* vector, int v_size,
-                                             const float* batch_vector,
-                                             int n_batch, float* result) {
-  for (int b = 0; b < n_batch; b++) {
-    for (int v = 0; v < v_size; v++) {
-      *result++ += vector[v] * *batch_vector++;
-    }
-  }
-}
-
-// Batch vector initialization with another vector.
-void VectorBatchVectorAssign(const float* vector, int v_size, int n_batch,
-                             float* batch_vector) {
-  for (int b = 0; b < n_batch; b++) {
-    memcpy(batch_vector + b * v_size, vector, v_size * sizeof(float));
-  }
-}
-
-// Apply sigmoid to elements of a vector.
-void ApplySigmoidToVector(const float* vector, int v_size, float* result) {
-  auto sigmoid_func = ActivationFunctor(kActivationSigmoid);
-  for (int v = 0; v < v_size; v++) {
-    *result++ = (sigmoid_func)(*vector++);
-  }
-}
-
-// Apply activation function to elements of a vector.
-void ApplyActivationToVector(const float* vector, int v_size,
-                             ActivationFn activation, float* result) {
-  auto activation_func = ActivationFunctor(activation);
-  for (int v = 0; v < v_size; v++) {
-    *result++ = (activation_func)(*vector++);
-  }
-}
-
-// Copy vector to another vector.
-inline void CopyVector(const float* vector, int v_size, float* result) {
-  memcpy(result, vector, v_size * sizeof(float));
-}
-
-// Compute "1.0f - elements of vector" (used in CIFG).
-void Sub1Vector(const float* vector, int v_size, float* result) {
-  for (int v = 0; v < v_size; v++) {
-    *result++ = 1.0f - *vector++;
-  }
-}
-
-// Fill vector with 0.f.
-void ZeroVector(float* vector, int v_size) {
-  memset(vector, 0, v_size * sizeof(float));
-}
-
-// Clip elements of a vector using a abs_limit value.
-void ClipVector(const float* vector, int v_size, float abs_limit,
-                float* result) {
-  for (int v = 0; v < v_size; v++) {
-    *result++ = Clip(*vector++, abs_limit);
-  }
-}
-
 template <typename T>
 inline T *GetBuffer(RunTimeOperandInfo* operand) {
   return reinterpret_cast<T*>(operand->buffer);
@@ -436,100 +329,102 @@ bool LSTMCell::Eval() {
 
   // Initialize scratch buffers with bias.
   if (!use_cifg) {
-    VectorBatchVectorAssign(GetBuffer<float>(input_gate_bias_), n_cell, n_batch,
-                            input_gate_scratch);
+    tensor_utils::VectorBatchVectorAssign(GetBuffer<float>(input_gate_bias_), n_cell, n_batch,
+                                          input_gate_scratch);
   }
-  VectorBatchVectorAssign(GetBuffer<float>(forget_gate_bias_), n_cell, n_batch,
-                          forget_gate_scratch);
-  VectorBatchVectorAssign(GetBuffer<float>(cell_bias_), n_cell, n_batch,
-                          cell_scratch);
-  VectorBatchVectorAssign(GetBuffer<float>(output_gate_bias_), n_cell, n_batch,
-                          output_gate_scratch);
+  tensor_utils::VectorBatchVectorAssign(GetBuffer<float>(forget_gate_bias_), n_cell, n_batch,
+                                        forget_gate_scratch);
+  tensor_utils::VectorBatchVectorAssign(GetBuffer<float>(cell_bias_), n_cell, n_batch,
+                                        cell_scratch);
+  tensor_utils::VectorBatchVectorAssign(GetBuffer<float>(output_gate_bias_), n_cell, n_batch,
+                                        output_gate_scratch);
 
   // For each batch and cell: compute input_weight * input.
   if (!use_cifg) {
-    MatrixBatchVectorMultiplyAccumulate(
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
         GetBuffer<float>(input_to_input_weights_), n_cell, n_input,
-        GetBuffer<float>(input_), n_batch, input_gate_scratch);
+        GetBuffer<float>(input_), n_batch, input_gate_scratch, /*result_stride*/1);
   }
-  MatrixBatchVectorMultiplyAccumulate(
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
       GetBuffer<float>(input_to_forget_weights_), n_cell, n_input,
-      GetBuffer<float>(input_), n_batch, forget_gate_scratch);
-  MatrixBatchVectorMultiplyAccumulate(
+      GetBuffer<float>(input_), n_batch, forget_gate_scratch, /*result_stride*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
       GetBuffer<float>(input_to_cell_weights_), n_cell, n_input,
-      GetBuffer<float>(input_), n_batch, cell_scratch);
-  MatrixBatchVectorMultiplyAccumulate(
+      GetBuffer<float>(input_), n_batch, cell_scratch, /*result_stride*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
       GetBuffer<float>(input_to_output_weights_), n_cell, n_input,
-      GetBuffer<float>(input_), n_batch, output_gate_scratch);
+      GetBuffer<float>(input_), n_batch, output_gate_scratch, /*result_stride*/1);
 
   // For each batch and cell: compute recurrent_weight * output_state.
   if (!use_cifg) {
-    MatrixBatchVectorMultiplyAccumulate(
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
         GetBuffer<float>(recurrent_to_input_weights_), n_cell, n_output,
-        GetBuffer<float>(output_state_in_), n_batch, input_gate_scratch);
+        GetBuffer<float>(output_state_in_), n_batch, input_gate_scratch, /*result_stride*/1);
   }
-  MatrixBatchVectorMultiplyAccumulate(
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
       GetBuffer<float>(recurrent_to_forget_weights_), n_cell, n_output,
-      GetBuffer<float>(output_state_in_), n_batch, forget_gate_scratch);
-  MatrixBatchVectorMultiplyAccumulate(
+      GetBuffer<float>(output_state_in_), n_batch, forget_gate_scratch, /*result_stride*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
       GetBuffer<float>(recurrent_to_cell_weights_), n_cell, n_output,
-      GetBuffer<float>(output_state_in_), n_batch, cell_scratch);
-  MatrixBatchVectorMultiplyAccumulate(
+      GetBuffer<float>(output_state_in_), n_batch, cell_scratch, /*result_stride*/1);
+  tensor_utils::MatrixBatchVectorMultiplyAccumulate(
       GetBuffer<float>(recurrent_to_output_weights_), n_cell, n_output,
-      GetBuffer<float>(output_state_in_), n_batch, output_gate_scratch);
+      GetBuffer<float>(output_state_in_), n_batch, output_gate_scratch, /*result_stride*/1);
 
   // For each batch and cell: update input gate.
   if (!use_cifg) {
     if (use_peephole) {
-      VectorBatchVectorCwiseProductAccumulate(
+      tensor_utils::VectorBatchVectorCwiseProductAccumulate(
           GetBuffer<float>(cell_to_input_weights_), n_cell,
           GetBuffer<float>(cell_state_in_), n_batch, input_gate_scratch);
     }
-    ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
-                         input_gate_scratch);
+    tensor_utils::ApplySigmoidToVector(input_gate_scratch, n_cell * n_batch,
+                                       input_gate_scratch);
   }
 
   // For each batch and cell: update forget gate.
   if (use_peephole) {
-    VectorBatchVectorCwiseProductAccumulate(
+    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
         GetBuffer<float>(cell_to_forget_weights_), n_cell,
         GetBuffer<float>(cell_state_in_), n_batch, forget_gate_scratch);
   }
-  ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
-                       forget_gate_scratch);
+  tensor_utils::ApplySigmoidToVector(forget_gate_scratch, n_cell * n_batch,
+                                     forget_gate_scratch);
 
   // For each batch and cell: update the cell.
-  VectorVectorCwiseProduct(forget_gate_scratch, GetBuffer<float>(cell_state_in_),
-                           n_batch * n_cell, GetBuffer<float>(cell_state_out_));
-  ApplyActivationToVector(cell_scratch, n_batch * n_cell, params_.activation_,
-                          cell_scratch);
+  tensor_utils::VectorVectorCwiseProduct(
+      forget_gate_scratch, GetBuffer<float>(cell_state_in_), n_batch * n_cell,
+      GetBuffer<float>(cell_state_out_));
+  tensor_utils::ApplyActivationToVector(
+      cell_scratch, n_batch * n_cell, params_.activation_, cell_scratch);
   if (use_cifg) {
-    Sub1Vector(forget_gate_scratch, n_batch * n_cell, forget_gate_scratch);
-    VectorVectorCwiseProductAccumulate(cell_scratch, forget_gate_scratch,
-                                       n_batch * n_cell,
-                                       GetBuffer<float>(cell_state_out_));
+    tensor_utils::Sub1Vector(forget_gate_scratch, n_batch * n_cell,
+                             forget_gate_scratch);
+    tensor_utils::VectorVectorCwiseProductAccumulate(
+        cell_scratch, forget_gate_scratch, n_batch * n_cell,
+        GetBuffer<float>(cell_state_out_));
   } else {
-    VectorVectorCwiseProductAccumulate(cell_scratch, input_gate_scratch,
-                                       n_batch * n_cell,
-                                       GetBuffer<float>(cell_state_out_));
+    tensor_utils::VectorVectorCwiseProductAccumulate(
+        cell_scratch, input_gate_scratch, n_batch * n_cell,
+        GetBuffer<float>(cell_state_out_));
   }
   if (params_.cell_clip_ > 0.0) {
-    ClipVector(GetBuffer<float>(cell_state_out_), n_batch * n_cell,
-               params_.cell_clip_, GetBuffer<float>(cell_state_out_));
+    tensor_utils::ClipVector(GetBuffer<float>(cell_state_out_), n_batch * n_cell,
+                             params_.cell_clip_, GetBuffer<float>(cell_state_out_));
   }
 
   // For each batch and cell: update the output gate.
   if (use_peephole) {
-    VectorBatchVectorCwiseProductAccumulate(
+    tensor_utils::VectorBatchVectorCwiseProductAccumulate(
         GetBuffer<float>(cell_to_output_weights_), n_cell,
         GetBuffer<float>(cell_state_out_), n_batch, output_gate_scratch);
   }
-  ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
-                       output_gate_scratch);
-  ApplyActivationToVector(GetBuffer<float>(cell_state_out_), n_batch * n_cell,
-                          params_.activation_, cell_scratch);
-  VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, n_batch * n_cell,
-                           output_gate_scratch);
+  tensor_utils::ApplySigmoidToVector(output_gate_scratch, n_batch * n_cell,
+                                     output_gate_scratch);
+  tensor_utils::ApplyActivationToVector(GetBuffer<float>(cell_state_out_), n_batch * n_cell,
+                                        params_.activation_, cell_scratch);
+  tensor_utils::VectorVectorCwiseProduct(output_gate_scratch, cell_scratch, n_batch * n_cell,
+                                         output_gate_scratch);
 
   // For each batch: update the projection and output_state.
   const bool use_projection_weight =
@@ -537,24 +432,25 @@ bool LSTMCell::Eval() {
   const bool use_projection_bias = (projection_bias_->lifetime != OperandLifeTime::NO_VALUE);
   if (use_projection_weight) {
     if (use_projection_bias) {
-      VectorBatchVectorAssign(GetBuffer<float>(projection_bias_), n_output,
-                              n_batch, GetBuffer<float>(output_));
+      tensor_utils::VectorBatchVectorAssign(GetBuffer<float>(projection_bias_), n_output,
+                                            n_batch, GetBuffer<float>(output_));
     } else {
-      ZeroVector(GetBuffer<float>(output_), n_batch * n_output);
+      tensor_utils::ZeroVector(GetBuffer<float>(output_), n_batch * n_output);
     }
-    MatrixBatchVectorMultiplyAccumulate(GetBuffer<float>(projection_weights_),
-                                        n_output, n_cell, output_gate_scratch,
-                                        n_batch, GetBuffer<float>(output_));
+    tensor_utils::MatrixBatchVectorMultiplyAccumulate(
+        GetBuffer<float>(projection_weights_), n_output, n_cell,
+        output_gate_scratch, n_batch, GetBuffer<float>(output_),
+        /*result_stride*/1);
     if (params_.proj_clip_ > 0.0) {
-      ClipVector(GetBuffer<float>(output_), n_batch * n_output,
-                 params_.proj_clip_, GetBuffer<float>(output_));
+      tensor_utils::ClipVector(GetBuffer<float>(output_), n_batch * n_output,
+                               params_.proj_clip_, GetBuffer<float>(output_));
     }
   } else {
-    CopyVector(output_gate_scratch, n_batch * n_output,
-               GetBuffer<float>(output_));
+    tensor_utils::CopyVector(output_gate_scratch, n_batch * n_output,
+                             GetBuffer<float>(output_));
   }
-  CopyVector(GetBuffer<float>(output_), n_batch * n_output,
-             GetBuffer<float>(output_state_out_));
+  tensor_utils::CopyVector(GetBuffer<float>(output_), n_batch * n_output,
+                           GetBuffer<float>(output_state_out_));
 
   return true;
 }
diff --git a/nn/common/operations/LSTMTest.cpp b/nn/common/operations/LSTMTest.cpp
index 6a05e04da..ce2d5d72a 100644
--- a/nn/common/operations/LSTMTest.cpp
+++ b/nn/common/operations/LSTMTest.cpp
@@ -66,10 +66,10 @@ std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
 
 // For all output and intermediate states
 #define FOR_ALL_OUTPUT_TENSORS(ACTION)          \
-    ACTION(Output)                              \
+    ACTION(ScratchBuffer)                       \
     ACTION(OutputStateOut)                      \
     ACTION(CellStateOut)                        \
-    ACTION(ScratchBuffer)
+    ACTION(Output)                              \
 
 class LSTMOpModel {
 public:
@@ -86,10 +86,11 @@ public:
           activation_(ActivationFn::kActivationTanh),
           cell_clip_(cell_clip), proj_clip_(proj_clip) {
         std::vector<uint32_t> inputs;
-        std::vector<std::vector<uint32_t>> input_shapes(input_shapes0.begin(), input_shapes0.end());
-        auto it = input_shapes.begin();
+        std::vector<std::vector<uint32_t>> input_shapes(input_shapes0);
+
         input_shapes.push_back({n_batch, n_output});
         input_shapes.push_back({n_batch, n_cell});
+        auto it = input_shapes.begin();
 
         // Input and weights
 #define AddInput(X)                                                     \
@@ -110,10 +111,10 @@ public:
 
         // Output and other intermediate state
         std::vector<std::vector<uint32_t>> output_shapes{
-            {n_batch, n_output},
+            {n_batch, n_cell * (use_cifg ? 3 : 4)},
             {n_batch, n_output},
             {n_batch, n_cell},
-            {n_batch, n_cell, 4}
+            {n_batch, n_output},
         };
         std::vector<uint32_t> outputs;
 
@@ -131,6 +132,8 @@ public:
         model_.identifyInputsAndOutputs(inputs, outputs);
 
         Input_.insert(Input_.end(), n_batch * n_input, 0.f);
+        OutputStateIn_.insert(OutputStateIn_.end(), n_batch * n_output, 0.f);
+        CellStateIn_.insert(CellStateIn_.end(), n_batch * n_cell, 0.f);
 
         auto multiAll = [](const std::vector<uint32_t> &dims) -> uint32_t {
             uint32_t sz = 1;
@@ -160,10 +163,12 @@ public:
 
     void ResetOutputState() {
         std::fill(OutputStateIn_.begin(), OutputStateIn_.end(), 0.f);
+        std::fill(OutputStateOut_.begin(), OutputStateOut_.end(), 0.f);
     }
 
     void ResetCellState() {
         std::fill(CellStateIn_.begin(), CellStateIn_.end(), 0.f);
+        std::fill(CellStateOut_.begin(), CellStateOut_.end(), 0.f);
     }
 
     void SetInput(int offset, float *begin, float *end) {
@@ -180,12 +185,15 @@ public:
     void Invoke() {
         ASSERT_TRUE(model_.isValid());
 
+        OutputStateIn_.swap(OutputStateOut_);
+        CellStateIn_.swap(CellStateOut_);
+
         Compilation compilation(&model_);
         compilation.finish();
         Execution execution(&compilation);
 #define SetInputOrWeight(X)                                               \
         ASSERT_EQ(execution.setInput(LSTMCell::k##X##Tensor, X##_.data(), \
-                                     sizeof(X##_)),                       \
+                                     sizeof(float)*X##_.size()),          \
                   Result::NO_ERROR);
 
         FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);
@@ -194,7 +202,7 @@ public:
 
 #define SetOutput(X)                                                       \
         ASSERT_EQ(execution.setOutput(LSTMCell::k##X##Tensor, X##_.data(), \
-                                      sizeof(X##_)),                       \
+                                      sizeof(float)*X##_.size()),          \
                   Result::NO_ERROR);
 
         FOR_ALL_OUTPUT_TENSORS(SetOutput);
diff --git a/nn/common/operations/RNNTest.cpp b/nn/common/operations/RNNTest.cpp
index 323484bbd..58243d92b 100644
--- a/nn/common/operations/RNNTest.cpp
+++ b/nn/common/operations/RNNTest.cpp
@@ -203,6 +203,7 @@ class BasicRNNOpModel {
 
   void ResetHiddenState() {
     std::fill(HiddenStateIn_.begin(), HiddenStateIn_.end(), 0.f);
+    std::fill(HiddenStateOut_.begin(), HiddenStateOut_.end(), 0.f);
   }
 
   const std::vector<float>& GetOutput() const { return Output_; }
@@ -214,11 +215,14 @@ class BasicRNNOpModel {
   void Invoke() {
     ASSERT_TRUE(model_.isValid());
 
+    HiddenStateIn_.swap(HiddenStateOut_);
+
     Compilation compilation(&model_);
     compilation.finish();
     Execution execution(&compilation);
 #define SetInputOrWeight(X)                                                   \
-  ASSERT_EQ(execution.setInput(RNN::k##X##Tensor, X##_.data(), sizeof(X##_)), \
+  ASSERT_EQ(execution.setInput(RNN::k##X##Tensor, X##_.data(),                \
+                               sizeof(float) * X##_.size()),                  \
             Result::NO_ERROR);
 
     FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);
@@ -226,7 +230,8 @@ class BasicRNNOpModel {
 #undef SetInputOrWeight
 
 #define SetOutput(X)                                                           \
-  ASSERT_EQ(execution.setOutput(RNN::k##X##Tensor, X##_.data(), sizeof(X##_)), \
+  ASSERT_EQ(execution.setOutput(RNN::k##X##Tensor, X##_.data(),                \
+                                sizeof(float) * X##_.size()),                  \
             Result::NO_ERROR);
 
     FOR_ALL_OUTPUT_TENSORS(SetOutput);
diff --git a/nn/common/operations/SVDFTest.cpp b/nn/common/operations/SVDFTest.cpp
index b5940e045..0bd59bab6 100644
--- a/nn/common/operations/SVDFTest.cpp
+++ b/nn/common/operations/SVDFTest.cpp
@@ -120,17 +120,18 @@ static float svdf_golden_output[] = {
 class SVDFOpModel {
  public:
   SVDFOpModel(uint32_t batches, uint32_t units, uint32_t input_size,
-              uint32_t memory_size)
+              uint32_t memory_size, uint32_t rank)
       : batches_(batches),
         units_(units),
         input_size_(input_size),
-        memory_size_(memory_size) {
+        memory_size_(memory_size),
+        rank_(rank) {
     std::vector<std::vector<uint32_t>> input_shapes{
         {batches_, input_size_},  // Input tensor
-        {units_, input_size_},    // weights_feature tensor
-        {units_, memory_size_},   // weights_time tensor
+        {units_ * rank_, input_size_},    // weights_feature tensor
+        {units_ * rank_, memory_size_},   // weights_time tensor
         {units_},                  // bias tensor
-        {batches_, (memory_size_ - 1) * units_},  // state in
+        {batches_,  memory_size * units_ * rank_},   // state in tensor
     };
     std::vector<uint32_t> inputs;
     auto it = input_shapes.begin();
@@ -151,7 +152,7 @@ class SVDFOpModel {
     inputs.push_back(model_.addOperand(&ActivationParamTy));
 
     // Output and other intermediate state
-    std::vector<std::vector<uint32_t>> output_shapes{{batches_, (memory_size_ - 1) * units_},
+    std::vector<std::vector<uint32_t>> output_shapes{{batches_, memory_size_ * units_ * rank_},
                                                      {batches_, units_}};
     std::vector<uint32_t> outputs;
 
@@ -166,6 +167,7 @@ class SVDFOpModel {
 #undef AddOutput
 
     Input_.insert(Input_.end(), batches_ * input_size_, 0.f);
+    StateIn_.insert(StateIn_.end(), batches_ * units_ * rank_ * memory_size_, 0.f);
 
     auto multiAll = [](const std::vector<uint32_t> &dims) -> uint32_t {
         uint32_t sz = 1;
@@ -191,8 +193,12 @@ class SVDFOpModel {
     Compilation compilation(&model_);
     compilation.finish();
     Execution execution(&compilation);
+
+    StateIn_.swap(StateOut_);
+
 #define SetInputOrWeight(X)                                                    \
-  ASSERT_EQ(execution.setInput(SVDF::k##X##Tensor, X##_.data(), sizeof(X##_)), \
+  ASSERT_EQ(execution.setInput(SVDF::k##X##Tensor, X##_.data(),                \
+                               sizeof(float) * X##_.size()),                   \
             Result::NO_ERROR);
 
     FOR_ALL_INPUT_AND_WEIGHT_TENSORS(SetInputOrWeight);
@@ -200,15 +206,16 @@ class SVDFOpModel {
 #undef SetInputOrWeight
 
 #define SetOutput(X)                                                            \
-  ASSERT_EQ(execution.setOutput(SVDF::k##X##Tensor, X##_.data(), sizeof(X##_)), \
+  EXPECT_TRUE(X##_.data() != nullptr);                                          \
+  ASSERT_EQ(execution.setOutput(SVDF::k##X##Tensor, X##_.data(),                \
+                                sizeof(float) * X##_.size()),                   \
             Result::NO_ERROR);
 
     FOR_ALL_OUTPUT_TENSORS(SetOutput);
 
 #undef SetOutput
 
-    int rank = 1;
-    ASSERT_EQ(execution.setInput(SVDF::kRankParam, &rank, sizeof(rank)),
+    ASSERT_EQ(execution.setInput(SVDF::kRankParam, &rank_, sizeof(rank_)),
               Result::NO_ERROR);
 
     int activation = ActivationFn::kActivationNone;
@@ -235,7 +242,10 @@ class SVDFOpModel {
   }
 
   // Resets the state of SVDF op by filling it with 0's.
-  void ResetState() { std::fill(StateIn_.begin(), StateIn_.end(), 0.f); }
+  void ResetState() {
+      std::fill(StateIn_.begin(), StateIn_.end(), 0.f);
+      std::fill(StateOut_.begin(), StateOut_.end(), 0.f);
+  }
 
   // Extracts the output tensor from the SVDF op.
   const std::vector<float>& GetOutput() const { return Output_; }
@@ -251,6 +261,7 @@ class SVDFOpModel {
   const uint32_t units_;
   const uint32_t input_size_;
   const uint32_t memory_size_;
+  const uint32_t rank_;
 
 #define DefineTensor(X) std::vector<float> X##_;
 
@@ -262,7 +273,7 @@ class SVDFOpModel {
 
 TEST(SVDFOpTest, BlackBoxTest) {
   SVDFOpModel svdf(/*batches=*/2, /*units=*/4, /*input_size=*/3,
-                   /*memory_size=*/10);
+                   /*memory_size=*/10, /*rank=*/1);
   svdf.SetWeightsFeature({-0.31930989, -0.36118156, 0.0079667, 0.37613347,
                           0.22197971, 0.12416199, 0.27901134, 0.27557442,
                           0.3905206, -0.36137494, -0.06634006, -0.10640851});
@@ -280,6 +291,8 @@ TEST(SVDFOpTest, BlackBoxTest) {
        -0.10781813, 0.27201805,  0.14324132,  -0.23681851, -0.27115166,
        -0.01580888, -0.14943552, 0.15465137,  0.09784451,  -0.0337657});
 
+  svdf.SetBias({});
+
   svdf.ResetState();
   const int svdf_num_batches = svdf.num_batches();
   const int svdf_input_size = svdf.input_size();
diff --git a/nn/common/operations/internal/optimized/cpu_check.h b/nn/common/operations/internal/optimized/cpu_check.h
new file mode 100644
index 000000000..02f42fd42
--- /dev/null
+++ b/nn/common/operations/internal/optimized/cpu_check.h
@@ -0,0 +1,28 @@
+/*
+ * Copyright (C) 2017 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.
+ */
+
+#ifndef FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_CPU_CHECK_
+#define FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_CPU_CHECK_
+
+// NEON_OR_PORTABLE(SomeFunc, arcs) calls NeonSomeFunc(args) if NEON is
+// enabled at build time, or PortableSomeFunc(args) otherwise.
+#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+#define NEON_OR_PORTABLE(funcname, ...) Neon##funcname(__VA_ARGS__)
+#else
+#define NEON_OR_PORTABLE(funcname, ...) Portable##funcname(__VA_ARGS__)
+#endif
+
+#endif  // FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_CPU_CHECK_
diff --git a/nn/common/operations/internal/optimized/neon_tensor_utils.cc b/nn/common/operations/internal/optimized/neon_tensor_utils.cc
new file mode 100644
index 000000000..f17730fc5
--- /dev/null
+++ b/nn/common/operations/internal/optimized/neon_tensor_utils.cc
@@ -0,0 +1,216 @@
+/*
+ * Copyright 2017 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.
+ */
+
+#include <string.h>
+
+#include "ActivationFunctor.h"
+#include "tensor_utils_impl.h"
+
+#ifdef USE_NEON
+
+#include <arm_neon.h>
+#define kFloatWeightsPerNeonLane 4
+
+namespace android {
+namespace nn {
+namespace tensor_utils {
+
+void NeonMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows,
+                                             int m_cols, const float* vector,
+                                             int n_batch, float* result,
+                                             int result_stride) {
+  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
+  // vectorized loop, and we need to process sequentially. postamble_start shows
+  // the start index where this should happen.
+  const int postamble_start =
+      m_cols - (m_cols & (kFloatWeightsPerNeonLane - 1));
+
+  // The arrays used to cache the vector.
+  float32x4_t* vector_cache_float32x4 =
+      new float32x4_t[(m_cols / kFloatWeightsPerNeonLane) *
+                      sizeof(float32x4_t)];
+
+  for (int b = 0; b < n_batch; b++) {
+    float* result_in_batch = result + b * m_rows;
+    const float* vector_in_batch = vector + b * m_cols;
+    const float* matrix_ptr = matrix;
+    for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) {
+      vector_cache_float32x4[c >> 2] = vld1q_f32(vector_in_batch + c);
+    }
+    for (int r = 0; r < m_rows; r++) {
+      float32x4_t acc_32x4 = vmovq_n_f32(0.0);
+      for (int c = 0; c < postamble_start; c += kFloatWeightsPerNeonLane) {
+        float32x4_t temp = vector_cache_float32x4[c >> 2];
+        // Load 4 float values from vector1 and vector2 and accumulator.
+        float32x4_t v1_f32x4 = vld1q_f32(matrix_ptr + c);
+        // Vector multiply-accumulate 4 float
+        acc_32x4 = vmlaq_f32(acc_32x4, v1_f32x4, temp);
+      }
+      // Add the 4 intermediate sum values to get the final dot-prod value for
+      // this column.
+      *result_in_batch +=
+          (vgetq_lane_f32(acc_32x4, 0) + vgetq_lane_f32(acc_32x4, 1) +
+           vgetq_lane_f32(acc_32x4, 2) + vgetq_lane_f32(acc_32x4, 3));
+      for (int c = postamble_start; c < m_cols; c++) {
+        *result_in_batch += matrix_ptr[c] * vector_in_batch[c];
+      }
+      matrix_ptr += m_cols;
+      result_in_batch += result_stride;
+    }
+  }
+  delete[] vector_cache_float32x4;
+}
+
+void NeonVectorVectorCwiseProduct(const float* vector1, const float* vector2,
+                                  int v_size, float* result) {
+  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
+  // vectorized loop, and we need to process sequentially. postamble_start shows
+  // the start index where this should happen.
+  const int postamble_start =
+      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
+  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
+    // Load 4 float values from vector1 and vector2.
+    float32x4_t v1_f32x4 = vld1q_f32(vector1 + v);
+    float32x4_t v2_f32x4 = vld1q_f32(vector2 + v);
+    // Vector multiply 4 float
+    float32x4_t mul_32x4 = vmulq_f32(v1_f32x4, v2_f32x4);
+    // Save to result array.
+    vst1q_f32(&result[v], mul_32x4);
+  }
+  for (int v = postamble_start; v < v_size; v++) {
+    result[v] = vector1[v] * vector2[v];
+  }
+}
+
+void NeonVectorVectorCwiseProductAccumulate(const float* vector1,
+                                            const float* vector2, int v_size,
+                                            float* result) {
+  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
+  // vectorized loop, and we need to process sequentially. postamble_start shows
+  // the start index where this should happen.
+  const int postamble_start =
+      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
+  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
+    // Load 4 float values from vector1 and vector2 and accumulator.
+    float32x4_t v1_f32x4 = vld1q_f32(vector1 + v);
+    float32x4_t v2_f32x4 = vld1q_f32(vector2 + v);
+    float32x4_t acc_32x4 = vld1q_f32(result + v);
+    // Vector multiply-accumulate 4 float
+    acc_32x4 = vmlaq_f32(acc_32x4, v1_f32x4, v2_f32x4);
+    // Save to result array.
+    vst1q_f32(&result[v], acc_32x4);
+  }
+  for (int v = postamble_start; v < v_size; v++) {
+    result[v] += vector1[v] * vector2[v];
+  }
+}
+
+void NeonVectorBatchVectorCwiseProductAccumulate(const float* vector,
+                                                 int v_size,
+                                                 const float* batch_vector,
+                                                 int n_batch, float* result) {
+  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
+  // vectorized loop, and we need to process sequentially. postamble_start shows
+  // the start index where this should happen.
+  const int postamble_start =
+      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
+
+  // The arrays used to cache the vector.
+  float32x4_t* vector_cache_float32x4 =
+      new float32x4_t[(v_size / kFloatWeightsPerNeonLane) *
+                      sizeof(float32x4_t)];
+  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
+    vector_cache_float32x4[v >> 2] = vld1q_f32(vector + v);
+  }
+
+  float* result_ptr = result;
+  const float* batch_vector_ptr = batch_vector;
+  for (int b = 0; b < n_batch; b++) {
+    for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
+      // Load from memory to vectors.
+      float32x4_t result_f32x4 = vld1q_f32(result_ptr + v);
+      float32x4_t batch_vector_f32x4 = vld1q_f32(batch_vector_ptr + v);
+      // Multiply-accumulate.
+      result_f32x4 = vmlaq_f32(result_f32x4, batch_vector_f32x4,
+                               vector_cache_float32x4[v >> 2]);
+      // Store.
+      vst1q_f32(result_ptr + v, result_f32x4);
+    }
+    // Postamble loop
+    for (int v = postamble_start; v < v_size; v++) {
+      result_ptr[v] += vector[v] * batch_vector_ptr[v];
+    }
+    // Update the pointers.
+    result_ptr += v_size;
+    batch_vector_ptr += v_size;
+  }
+  delete[] vector_cache_float32x4;
+}
+
+void NeonSub1Vector(const float* vector, int v_size, float* result) {
+  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
+  // vectorized loop, and we need to process sequentially. postamble_start shows
+  // the start index where this should happen.
+  const int postamble_start =
+      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
+
+  float32x4_t one_f32x4 = vmovq_n_f32(1.0);
+  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
+    // Load 4 float values from the current pointers of the input column and
+    // subtract from 1.
+    float32x4_t v_f32x4 = vld1q_f32(vector + v);
+    float32x4_t result_f32x4 = vsubq_f32(one_f32x4, v_f32x4);
+    // Save to output.
+    vst1q_f32(result + v, result_f32x4);
+  }
+  for (int v = postamble_start; v < v_size; v++) {
+    result[v] = 1.0f - vector[v];
+  }
+}
+
+void NeonClipVector(const float* vector, int v_size, float abs_limit,
+                    float* result) {
+  // If v_size is not divisible by kWeightsPerNeonLane, we cannot use the main
+  // vectorized loop, and we need to process sequentially. postamble_start shows
+  // the start index where this should happen.
+  const int postamble_start =
+      v_size - (v_size & (kFloatWeightsPerNeonLane - 1));
+
+  // Replicate abs_limit and -abs_limit in two vectors.
+  const float32x4_t abs_limit_f32x4 = vmovq_n_f32(abs_limit);
+  const float32x4_t neg_abs_limit_f32x4 = vmovq_n_f32(-abs_limit);
+
+  for (int v = 0; v < postamble_start; v += kFloatWeightsPerNeonLane) {
+    // Load from memory to vector.
+    float32x4_t v_f32x4 = vld1q_f32(vector + v);
+    // Clip between abs_limit and -abs_limit.
+    float32x4_t result_f32x4 = vminq_f32(abs_limit_f32x4, v_f32x4);
+    result_f32x4 = vmaxq_f32(neg_abs_limit_f32x4, result_f32x4);
+    // Save to output.
+    vst1q_f32(result + v, result_f32x4);
+  }
+  // Postamble loop.
+  for (int v = postamble_start; v < v_size; v++) {
+    result[v] = (abs_limit < vector[v]) ? abs_limit : vector[v];
+    result[v] = (-abs_limit > result[v]) ? -abs_limit : result[v];
+  }
+}
+
+}  // namespace tensor_utils
+}  // namespace nn
+}  // namespace android
+
+#endif  // USE_NEON
diff --git a/nn/common/operations/internal/optimized/neon_tensor_utils.h b/nn/common/operations/internal/optimized/neon_tensor_utils.h
new file mode 100644
index 000000000..92a151cc1
--- /dev/null
+++ b/nn/common/operations/internal/optimized/neon_tensor_utils.h
@@ -0,0 +1,118 @@
+/*
+ * Copyright 2017 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.
+ */
+
+#ifndef FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_NEON_TENSOR_UTILS_H_
+#define FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_NEON_TENSOR_UTILS_H_
+
+#include "ActivationFunctor.h"
+#include "cpu_check.h"
+#include "tensor_utils_impl.h"
+
+namespace android {
+namespace nn {
+namespace tensor_utils {
+
+void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows,
+                                         int m_cols, const float* vector,
+                                         int n_batch, float* result,
+                                         int result_stride) {
+  NEON_OR_PORTABLE(MatrixBatchVectorMultiplyAccumulate, matrix, m_rows, m_cols,
+                   vector, n_batch, result, result_stride);
+}
+
+void VectorVectorCwiseProduct(const float* vector1, const float* vector2,
+                              int v_size, float* result) {
+  NEON_OR_PORTABLE(VectorVectorCwiseProduct, vector1, vector2, v_size, result);
+}
+
+void VectorVectorCwiseProductAccumulate(const float* vector1,
+                                        const float* vector2, int v_size,
+                                        float* result) {
+  NEON_OR_PORTABLE(VectorVectorCwiseProductAccumulate, vector1, vector2, v_size,
+                   result);
+}
+
+void VectorBatchVectorCwiseProductAccumulate(const float* vector, int v_size,
+                                             const float* batch_vector,
+                                             int n_batch, float* result) {
+  NEON_OR_PORTABLE(VectorBatchVectorCwiseProductAccumulate, vector, v_size,
+                   batch_vector, n_batch, result);
+}
+
+float VectorVectorDotProduct(const float* vector1, const float* vector2,
+                             int v_size) {
+  return PortableVectorVectorDotProduct(vector1, vector2, v_size);
+}
+
+void BatchVectorBatchVectorDotProduct(const float* vector1,
+                                      const float* vector2, int v_size,
+                                      int n_batch, float* result,
+                                      int result_stride) {
+  PortableBatchVectorBatchVectorDotProduct(vector1, vector2, v_size, n_batch,
+                                           result, result_stride);
+}
+
+void VectorBatchVectorAssign(const float* vector, int v_size, int n_batch,
+                             float* batch_vector) {
+  PortableVectorBatchVectorAssign(vector, v_size, n_batch, batch_vector);
+}
+
+void ApplySigmoidToVector(const float* vector, int v_size, float* result) {
+  PortableApplySigmoidToVector(vector, v_size, result);
+}
+
+void ApplyActivationToVector(const float* vector, int v_size,
+                             ActivationFn activation, float* result) {
+  PortableApplyActivationToVector(vector, v_size, activation, result);
+}
+
+void CopyVector(const float* vector, int v_size, float* result) {
+  PortableCopyVector(vector, v_size, result);
+}
+
+void Sub1Vector(const float* vector, int v_size, float* result) {
+  NEON_OR_PORTABLE(Sub1Vector, vector, v_size, result);
+}
+
+void ZeroVector(float* vector, int v_size) {
+  PortableZeroVector(vector, v_size);
+}
+
+float Clip(float f, float abs_limit) { return PortableClip(f, abs_limit); }
+
+void ClipVector(const float* vector, int v_size, float abs_limit,
+                float* result) {
+  NEON_OR_PORTABLE(ClipVector, vector, v_size, abs_limit, result);
+}
+
+// TODO(ghodrat): Implement Neon version.
+void VectorShiftLeft(float* vector, int v_size, float shift_value) {
+  PortableVectorShiftLeft(vector, v_size, shift_value);
+}
+
+// TODO(ghodrat): Implement Neon version.
+void ReductionSumVector(const float* input_vector, int input_stride,
+                        float* output_vector, int output_size,
+                        int reduction_size) {
+  PortableReductionSumVector(input_vector, input_stride, output_vector,
+                             output_size, reduction_size);
+}
+
+}  // namespace tensor_utils
+}  // namespace nn
+}  // namespace android
+
+#endif  // FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_NEON_TENSOR_UTILS_H_
diff --git a/nn/common/operations/internal/optimized/tensor_utils_impl.h b/nn/common/operations/internal/optimized/tensor_utils_impl.h
new file mode 100644
index 000000000..249a67255
--- /dev/null
+++ b/nn/common/operations/internal/optimized/tensor_utils_impl.h
@@ -0,0 +1,132 @@
+/*
+ * Copyright (C) 2017 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.
+ */
+
+#ifndef FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_TENSOR_UTILS_IMPL_H_
+#define FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_TENSOR_UTILS_IMPL_H_
+
+#include "ActivationFunctor.h"
+
+#ifndef USE_NEON
+#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+#define USE_NEON
+#endif  //  defined(__ARM_NEON__) || defined(__ARM_NEON)
+#endif  //  USE_NEON
+
+namespace android {
+namespace nn {
+namespace tensor_utils {
+
+// Multiply a matrix by a batch vector, and store results in a batch-size
+// vector.
+void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix,
+                                                 int m_rows, int m_cols,
+                                                 const float* vector,
+                                                 int n_batch, float* result,
+                                                 int result_stride);
+void NeonMatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows,
+                                             int m_cols, const float* vector,
+                                             int n_batch, float* result,
+                                             int result_stride);
+
+// Cwise product of two vectors.
+void PortableVectorVectorCwiseProduct(const float* vector1,
+                                      const float* vector2, int v_size,
+                                      float* result);
+void NeonVectorVectorCwiseProduct(const float* vector1, const float* vector2,
+                                  int v_size, float* result);
+
+// Cwise product and accumulate of two vectors. Since it's a MAC operation, the
+// assumption here is that result array is initialized to valid values.
+void PortableVectorVectorCwiseProductAccumulate(const float* vector1,
+                                                const float* vector2,
+                                                int v_size, float* result);
+void NeonVectorVectorCwiseProductAccumulate(const float* vector1,
+                                            const float* vector2, int v_size,
+                                            float* result);
+
+// Dot product of two vectors.
+float PortableVectorVectorDotProduct(const float* vector1, const float* vector2,
+                                     int v_size);
+
+// Dot product of two batch vectors.
+void PortableBatchVectorBatchVectorDotProduct(const float* vector1,
+                                              const float* vector2, int v_size,
+                                              int n_batch, float* result,
+                                              int result_stride);
+
+// Cwise product and accumulate of a vector and a batch-vector. Since it's a MAC
+// operation, the assumption here is that result array is initialized to valid
+// values.
+void PortableVectorBatchVectorCwiseProductAccumulate(const float* vector,
+                                                     int v_size,
+                                                     const float* batch_vector,
+                                                     int n_batch,
+                                                     float* result);
+void NeonVectorBatchVectorCwiseProductAccumulate(const float* vector,
+                                                 int v_size,
+                                                 const float* batch_vector,
+                                                 int n_batch, float* result);
+
+// Compute "1.0f - elements of vector" (used in CIFG).
+void PortableSub1Vector(const float* vector, int v_size, float* result);
+void NeonSub1Vector(const float* vector, int v_size, float* result);
+
+// Clip elements of a vector using a abs_limit value.
+void PortableClipVector(const float* vector, int v_size, float abs_limit,
+                        float* result);
+void NeonClipVector(const float* vector, int v_size, float abs_limit,
+                    float* result);
+
+// Batch vector initialization with another vector.
+void PortableVectorBatchVectorAssign(const float* vector, int v_size,
+                                     int n_batch, float* batch_vector);
+
+// Apply sigmoid to elements of a vector.
+void PortableApplySigmoidToVector(const float* vector, int v_size,
+                                  float* result);
+
+// Apply activation function to elements of a vector.
+void PortableApplyActivationToVector(const float* vector, int v_size,
+                                     ActivationFn activation,
+                                     float* result);
+
+// Copy vector to another vector.
+void PortableCopyVector(const float* vector, int v_size, float* result);
+
+// Fill vector with 0.f.
+void PortableZeroVector(float* vector, int v_size);
+
+// Limit a float input f between +abs_limit and -abs_limit.
+float PortableClip(float f, float abs_limit);
+
+// Shift left a vector in place with v_size size.
+void PortableVectorShiftLeft(float* vector, int v_size, float shift_value);
+
+// Reduce-sum on a float input vector:
+// input_vector: float pointer to input vector.
+// input_stride: input vector stride.
+// output_vector: float pointer to vector.
+// output_size: output vector size.
+// reduction_size: number of consecutive elements from input vector which are
+// added to get one element of output.
+void PortableReductionSumVector(const float* input_vector, int input_stride,
+                                float* output_vector, int output_size,
+                                int reduction_size);
+}  // namespace tensor_utils
+}  // namespace nn
+}  // namespace android
+
+#endif  //  FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_OPTIMIZED_TENSOR_UTILS_IMPL_H_
diff --git a/nn/common/operations/internal/reference/portable_tensor_utils.cc b/nn/common/operations/internal/reference/portable_tensor_utils.cc
new file mode 100644
index 000000000..007113ec3
--- /dev/null
+++ b/nn/common/operations/internal/reference/portable_tensor_utils.cc
@@ -0,0 +1,168 @@
+/*
+ * Copyright 2017 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.
+ */
+
+#include "ActivationFunctor.h"
+#include "Utils.h"
+
+namespace android {
+namespace nn {
+namespace tensor_utils {
+
+float PortableClip(float f, float abs_limit) {
+  float result = (abs_limit < f) ? abs_limit : f;
+  result = (-abs_limit > result) ? -abs_limit : result;
+  return result;
+}
+
+void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix,
+                                                 int m_rows, int m_cols,
+                                                 const float* vector,
+                                                 int n_batch, float* result,
+                                                 int result_stride) {
+  float* result_in_batch = result;
+  for (int b = 0; b < n_batch; b++) {
+    const float* matrix_ptr = matrix;
+    for (int r = 0; r < m_rows; r++) {
+      const float* vector_in_batch = vector + b * m_cols;
+      for (int c = 0; c < m_cols; c++) {
+        *result_in_batch += *matrix_ptr++ * *vector_in_batch++;
+      }
+      result_in_batch += result_stride;
+    }
+  }
+}
+
+void PortableVectorVectorCwiseProduct(const float* vector1,
+                                      const float* vector2, int v_size,
+                                      float* result) {
+  for (int v = 0; v < v_size; v++) {
+    *result++ = *vector1++ * *vector2++;
+  }
+}
+
+float PortableVectorVectorDotProduct(const float* vector1, const float* vector2,
+                                     int v_size) {
+  float result = 0.0;
+  for (int v = 0; v < v_size; v++) {
+    result += *vector1++ * *vector2++;
+  }
+  return result;
+}
+
+void PortableBatchVectorBatchVectorDotProduct(const float* vector1,
+                                              const float* vector2, int v_size,
+                                              int n_batch, float* result,
+                                              int result_stride) {
+  float* result_ptr = result;
+  const float* vector1_ptr = vector1;
+  const float* vector2_ptr = vector2;
+  for (int b = 0; b < n_batch; b++) {
+    *result_ptr =
+        PortableVectorVectorDotProduct(vector1_ptr, vector2_ptr, v_size);
+    vector1_ptr += v_size;
+    vector2_ptr += v_size;
+    result_ptr += result_stride;
+  }
+}
+
+void PortableVectorVectorCwiseProductAccumulate(const float* vector1,
+                                                const float* vector2,
+                                                int v_size, float* result) {
+  for (int v = 0; v < v_size; v++) {
+    *result++ += *vector1++ * *vector2++;
+  }
+}
+
+void PortableVectorBatchVectorCwiseProductAccumulate(const float* vector,
+                                                     int v_size,
+                                                     const float* batch_vector,
+                                                     int n_batch,
+                                                     float* result) {
+  for (int b = 0; b < n_batch; b++) {
+    for (int v = 0; v < v_size; v++) {
+      *result++ += vector[v] * *batch_vector++;
+    }
+  }
+}
+
+void PortableVectorBatchVectorAssign(const float* vector, int v_size,
+                                     int n_batch, float* batch_vector) {
+  for (int b = 0; b < n_batch; b++) {
+    memcpy(batch_vector + b * v_size, vector, v_size * sizeof(float));
+  }
+}
+
+void PortableApplySigmoidToVector(const float* vector, int v_size,
+                                  float* result) {
+  auto sigmoid_func = ActivationFunctor(kActivationSigmoid);
+  for (int v = 0; v < v_size; v++) {
+    *result++ = (sigmoid_func)(*vector++);
+  }
+}
+
+void PortableApplyActivationToVector(const float* vector, int v_size,
+                                     ActivationFn activation,
+                                     float* result) {
+  auto activation_func = ActivationFunctor(activation);
+  for (int v = 0; v < v_size; v++) {
+    *result++ = (activation_func)(*vector++);
+  }
+}
+
+void PortableCopyVector(const float* vector, int v_size, float* result) {
+  memcpy(result, vector, v_size * sizeof(float));
+}
+
+void PortableSub1Vector(const float* vector, int v_size, float* result) {
+  for (int v = 0; v < v_size; v++) {
+    *result++ = 1.0f - *vector++;
+  }
+}
+
+void PortableZeroVector(float* vector, int v_size) {
+  memset(vector, 0, v_size * sizeof(float));
+}
+
+void PortableClipVector(const float* vector, int v_size, float abs_limit,
+                        float* result) {
+  for (int v = 0; v < v_size; v++) {
+    *result++ = PortableClip(*vector++, abs_limit);
+  }
+}
+
+void PortableVectorShiftLeft(float* vector, int v_size, float shift_value) {
+  nnAssert(v_size > 0);
+  for (int i = 0; i < v_size - 1; i++) {
+    vector[i] = vector[i + 1];
+  }
+  vector[v_size - 1] = shift_value;
+}
+
+void PortableReductionSumVector(const float* input_vector, int input_stride,
+                                float* output_vector, int output_size,
+                                int reduction_size) {
+  const float* input_vector_ptr = input_vector;
+  for (int o = 0; o < output_size; o++) {
+    for (int r = 0; r < reduction_size; r++) {
+      output_vector[o] += *input_vector_ptr;
+      input_vector_ptr += input_stride;
+    }
+  }
+}
+
+}  // namespace tensor_utils
+}  // namespace nn
+}  // namespace android
diff --git a/nn/common/operations/internal/reference/portable_tensor_utils.h b/nn/common/operations/internal/reference/portable_tensor_utils.h
new file mode 100644
index 000000000..138845e78
--- /dev/null
+++ b/nn/common/operations/internal/reference/portable_tensor_utils.h
@@ -0,0 +1,194 @@
+/*
+ * Copyright 2017 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.
+ */
+
+#ifndef FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_
+#define FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_
+
+#include "ActivationFunctor.h"
+
+namespace android {
+namespace nn {
+namespace tensor_utils {
+
+// Limit a float input f betweeen +abs_limit and -abs_limit.
+float PortableClip(float f, float abs_limit);
+
+// Multiply a matrix by a batch vector, and store results in a batch-size
+// vector.
+void PortableMatrixBatchVectorMultiplyAccumulate(const float* matrix,
+                                                 int m_rows, int m_cols,
+                                                 const float* vector,
+                                                 int n_batch, float* result,
+                                                 int result_stride);
+
+// Cwise product of two vectors.
+void PortableVectorVectorCwiseProduct(const float* vector1,
+                                      const float* vector2, int v_size,
+                                      float* result);
+
+// Cwise product and accumulate of two vectors. Since it's a MAC opertation, the
+// assumption here is that result array is initialized to valid values.
+void PortableVectorVectorCwiseProductAccumulate(const float* vector1,
+                                                const float* vector2,
+                                                int v_size, float* result);
+
+// Dot product of two vectors.
+float PortableVectorVectorDotProduct(const float* vector1, const float* vector2,
+                                     int v_size);
+
+// Dot product of two batch vectors.
+void PortableBatchVectorBatchVectorDotProduct(const float* vector1,
+                                              const float* vector2, int v_size,
+                                              int n_batch, float* result,
+                                              int result_stride);
+
+// Cwise product and accumulate of a vector and a batch-vector. Since it's a MAC
+// operation, the assumption here is that result array is initialized to valid
+// values.
+void PortableVectorBatchVectorCwiseProductAccumulate(const float* vector,
+                                                     int v_size,
+                                                     const float* batch_vector,
+                                                     int n_batch,
+                                                     float* result);
+
+// Batch vector initialization with another vector.
+void PortableVectorBatchVectorAssign(const float* vector, int v_size,
+                                     int n_batch, float* batch_vector);
+
+// Apply sigmoid to elements of a vector.
+void PortableApplySigmoidToVector(const float* vector, int v_size,
+                                  float* result);
+
+// Apply activation function to elements of a vector.
+void PortableApplyActivationToVector(const float* vector, int v_size,
+                                     ActivationFn activation,
+                                     float* result);
+
+// Copy vector to another vector.
+void PortableCopyVector(const float* vector, int v_size, float* result);
+
+// Compute "1.0f - elements of vector" (used in CIFG).
+void PortableSub1Vector(const float* vector, int v_size, float* result);
+
+// Fill vector with 0.f.
+void PortableZeroVector(float* vector, int v_size);
+
+// Clip elements of a vector using a abs_limit value.
+void PortableClipVector(const float* vector, int v_size, float abs_limit,
+                        float* result);
+
+// Shift left a vector in place with v_size size.
+void PortableVectorShiftLeft(float* vector, int v_size, float shift_value);
+
+// Reduce-sum on a float input vector:
+// input_vector: float pointer to input vector.
+// input_stride: input vector stride.
+// output_vector: float pointer to vector.
+// output_size: output vector size.
+// reduction_size: number of consecutive elements from input vector which are
+// added to get one element of output.
+void PortableReductionSumVector(const float* input_vector, int input_stride,
+                                float* output_vector, int output_size,
+                                int reduction_size);
+
+float Clip(float f, float abs_limit) { return PortableClip(f, abs_limit); }
+
+void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows,
+                                         int m_cols, const float* vector,
+                                         int n_batch, float* result,
+                                         int result_stride) {
+  PortableMatrixBatchVectorMultiplyAccumulate(matrix, m_rows, m_cols, vector,
+                                              n_batch, result, result_stride);
+}
+
+void VectorVectorCwiseProduct(const float* vector1, const float* vector2,
+                              int v_size, float* result) {
+  PortableVectorVectorCwiseProduct(vector1, vector2, v_size, result);
+}
+
+void VectorVectorCwiseProductAccumulate(const float* vector1,
+                                        const float* vector2, int v_size,
+                                        float* result) {
+  PortableVectorVectorCwiseProductAccumulate(vector1, vector2, v_size, result);
+}
+
+void VectorBatchVectorCwiseProductAccumulate(const float* vector, int v_size,
+                                             const float* batch_vector,
+                                             int n_batch, float* result) {
+  PortableVectorBatchVectorCwiseProductAccumulate(vector, v_size, batch_vector,
+                                                  n_batch, result);
+}
+
+float VectorVectorDotProduct(const float* vector1, const float* vector2,
+                             int v_size) {
+  return PortableVectorVectorDotProduct(vector1, vector2, v_size);
+}
+
+void BatchVectorBatchVectorDotProduct(const float* vector1,
+                                      const float* vector2, int v_size,
+                                      int n_batch, float* result,
+                                      int result_stride) {
+  PortableBatchVectorBatchVectorDotProduct(vector1, vector2, v_size, n_batch,
+                                           result, result_stride);
+}
+
+void VectorBatchVectorAssign(const float* vector, int v_size, int n_batch,
+                             float* batch_vector) {
+  PortableVectorBatchVectorAssign(vector, v_size, n_batch, batch_vector);
+}
+
+void ApplySigmoidToVector(const float* vector, int v_size, float* result) {
+  PortableApplySigmoidToVector(vector, v_size, result);
+}
+
+void ApplyActivationToVector(const float* vector, int v_size,
+                             ActivationFn activation, float* result) {
+  PortableApplyActivationToVector(vector, v_size, activation, result);
+}
+
+void CopyVector(const float* vector, int v_size, float* result) {
+  PortableCopyVector(vector, v_size, result);
+}
+
+void Sub1Vector(const float* vector, int v_size, float* result) {
+  PortableSub1Vector(vector, v_size, result);
+}
+
+void ZeroVector(float* vector, int v_size) {
+  PortableZeroVector(vector, v_size);
+}
+
+void ClipVector(const float* vector, int v_size, float abs_limit,
+                float* result) {
+  PortableClipVector(vector, v_size, abs_limit, result);
+}
+
+void VectorShiftLeft(float* vector, int v_size, float shift_value) {
+  PortableVectorShiftLeft(vector, v_size, shift_value);
+}
+
+void ReductionSumVector(const float* input_vector, int input_stride,
+                        float* output_vector, int output_size,
+                        int reduction_size) {
+  PortableReductionSumVector(input_vector, input_stride, output_vector,
+                             output_size, reduction_size);
+}
+
+}  // namespace tensor_utils
+}  // namespace nn
+}  // namespace android
+
+#endif  // FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_REFERENCE_PORTABLE_TENSOR_UTILS_H_
diff --git a/nn/common/operations/internal/tensor_utils.cc b/nn/common/operations/internal/tensor_utils.cc
new file mode 100644
index 000000000..78275bb29
--- /dev/null
+++ b/nn/common/operations/internal/tensor_utils.cc
@@ -0,0 +1,29 @@
+/*
+ * Copyright 2017 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.
+ */
+
+#include "tensor_utils.h"
+
+#ifndef USE_NEON
+#if defined(__ARM_NEON__) || defined(__ARM_NEON)
+#define USE_NEON
+#endif  //  defined(__ARM_NEON__) || defined(__ARM_NEON)
+#endif  //  USE_NEON
+
+#ifdef USE_NEON
+#include "optimized/neon_tensor_utils.h"
+#else
+#include "reference/portable_tensor_utils.h"
+#endif  // USE_NEON
diff --git a/nn/common/operations/internal/tensor_utils.h b/nn/common/operations/internal/tensor_utils.h
new file mode 100644
index 000000000..56167d2b1
--- /dev/null
+++ b/nn/common/operations/internal/tensor_utils.h
@@ -0,0 +1,122 @@
+/*
+ * Copyright 2017 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.
+ */
+
+#ifndef FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_TENSOR_UTILS_H_
+#define FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_TENSOR_UTILS_H_
+
+#include "ActivationFunctor.h"
+
+namespace android {
+namespace nn {
+namespace tensor_utils {
+
+// Limit a float input f betweeen +abs_limit and -abs_limit.
+float Clip(float f, float abs_limit);
+
+// Multiply a matrix by a batch vector, and store results in a batch-size
+// vector using a stride value provided in result_stride. 'result_stride' shows
+// how the number of elements between consecutive result values. For example
+// result_stride = 1, will cause the output to look like this:
+// [O_1, 0_2, ... O_rows] in memory, but result_stride = 3, will cause it to be
+// arranged like this in memory: [O_1, x, x, 0_2, x, x, ..., O_rows]
+void MatrixBatchVectorMultiplyAccumulate(const float* matrix, int m_rows,
+                                         int m_cols, const float* vector,
+                                         int n_batch, float* result,
+                                         int result_stride);
+
+// Cwise product of two vectors.
+void VectorVectorCwiseProduct(const float* vector1, const float* vector2,
+                              int v_size, float* result);
+
+// Cwise product and accumulate of two vectors. Since it's a MAC opertation, the
+// assumption here is that result array is initialized to valid values.
+void VectorVectorCwiseProductAccumulate(const float* vector1,
+                                        const float* vector2, int v_size,
+                                        float* result);
+
+// Dot product of two vectors.
+float VectorVectorDotProduct(const float* vector1, const float* vector2,
+                             int v_size);
+
+// Dot product of two batch vectors of size n_batch * v_size:
+// vector1 = [x_1_1, x_1_2, ..., x_1_vsize,
+//            x_2_1, x_2_2, ..., x_2_vsize,
+//            ...
+//            x_nbatch_1,..., x_nbatch_vsize]
+// vector2 = [y_1_1, y_1_2, ..., y_1_vsize,
+//            y_2_1, y_2_2, ..., y_2_vsize,
+//            ...
+//            y_nbatch_1,..., y_nbatch_vsize]
+// Then result will be a vector of n_batch size which will be saved with a
+// stride of result_stride in memory starting from 'result':
+// [x_1_1 * y_1_1 + x_1_2 * y_1_2 + ... + x_1_vsize * y_1_vsize,
+//  x_2_1 * y_2_1 + x_2_2 * y_2_2 + ... + x_2_vsize * y_2_vsize,
+//  ...
+//  x_nbatch_1 * y_nbatch_1 + ... + x_nbatch_vsize * y_nbatch_vsize]
+void BatchVectorBatchVectorDotProduct(const float* vector1,
+                                      const float* vector2, int v_size,
+                                      int n_batch, float* result,
+                                      int result_stride);
+
+// Cwise product and accumulate of a vector and a batch-vector. Since it's a MAC
+// operation, the assumption here is that result array is initialized to valid
+// values.
+void VectorBatchVectorCwiseProductAccumulate(const float* vector, int v_size,
+                                             const float* batch_vector,
+                                             int n_batch, float* result);
+
+// Batch vector initialization with another vector.
+void VectorBatchVectorAssign(const float* vector, int v_size, int n_batch,
+                             float* batch_vector);
+
+// Apply sigmoid to elements of a vector.
+void ApplySigmoidToVector(const float* vector, int v_size, float* result);
+
+// Apply activation function to elements of a vector.
+void ApplyActivationToVector(const float* vector, int v_size,
+                             ActivationFn activation, float* result);
+
+// Copy vector to another vector.
+void CopyVector(const float* vector, int v_size, float* result);
+
+// Compute "1.0f - elements of vector" (used in CIFG).
+void Sub1Vector(const float* vector, int v_size, float* result);
+
+// Fill vector with 0.f.
+void ZeroVector(float* vector, int v_size);
+
+// Clip elements of a vector using a abs_limit value.
+void ClipVector(const float* vector, int v_size, float abs_limit,
+                float* result);
+
+// Shift left a vector in place with v_size size.
+void VectorShiftLeft(float* vector, int v_size, float shift_value);
+
+// Reduce-sum on a float input vector:
+// input_vector: float pointer to input vector.
+// input_stride: input vector stride.
+// output_vector: float pointer to vector.
+// output_size: output vector size.
+// reduction_size: number of consecutive elements from input vector which are
+// added to get one element of output.
+void ReductionSumVector(const float* input_vector, int input_stride,
+                        float* output_vector, int output_size,
+                        int reduction_size);
+}  // namespace tensor_utils
+}  // namespace nn
+}  // namespace android
+
+#endif  // FRAMEWORKS_ML_NN_COMMON_OPERATIONS_INTERNAL_TENSOR_UTILS_H_
diff --git a/nn/common/operations/internal/tensor_utils_test.cc b/nn/common/operations/internal/tensor_utils_test.cc
new file mode 100644
index 000000000..ad5204d0c
--- /dev/null
+++ b/nn/common/operations/internal/tensor_utils_test.cc
@@ -0,0 +1,197 @@
+/*
+ * Copyright 2017 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.
+ */
+
+#include "gmock/gmock-matchers.h"
+#include "gtest/gtest.h"
+#include "tensor_utils.h"
+
+namespace android {
+namespace nn {
+namespace tensor_utils {
+
+namespace {
+
+using ::testing::FloatNear;
+using ::testing::Matcher;
+
+std::vector<Matcher<float>> ArrayFloatNear(const std::vector<float>& values,
+                                           float max_abs_error=1.e-6) {
+  std::vector<Matcher<float>> matchers;
+  matchers.reserve(values.size());
+  for (const float& v : values) {
+    matchers.emplace_back(FloatNear(v, max_abs_error));
+  }
+  return matchers;
+}
+
+}  // anonymous namespace
+
+TEST(uKernels, ClipTest) {
+  constexpr int kVectorSize = 10;
+  constexpr float kAbsLimit = 2.0;
+  static float input[kVectorSize] = {0.0,  -0.5, 1.0,  -1.5, 2.0,
+                                     -2.5, 3.0,  -3.5, 4.0,  -4.5};
+  std::vector<float> output(kVectorSize);
+  ClipVector(input, kVectorSize, kAbsLimit, output.data());
+  EXPECT_THAT(output,
+              ElementsAreArray(ArrayFloatNear(
+                  {0.0, -0.5, 1.0, -1.5, 2.0, -2.0, 2.0, -2.0, 2.0, -2.0})));
+}
+
+TEST(uKernels, MatrixBatchVectorMultiplyAccumulateTest) {
+  constexpr int kRow = 3;
+  constexpr int kCol = 4;
+  constexpr int kBatch = 2;
+  static float matrix[kRow * kCol] = {1.0,  2.0,  3.0,  4.0,   //
+                                      -1.0, -2.0, -3.0, -4.0,  //
+                                      1.0,  -2.0, 3.0,  -4.0};
+  static float vector[kCol * kBatch] = {1.0, -1.0, 1.0, -1.0,  //
+                                        2.0, -2.0, 2.0, -2.0};
+  std::vector<float> output(kRow * kBatch);
+  std::fill(output.begin(), output.end(), 3.0);
+  MatrixBatchVectorMultiplyAccumulate(matrix, kRow, kCol, vector, kBatch,
+                                      output.data(), /*result_stride=*/1);
+  EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear({1., 5., 13.,  //
+                                                       -1., 7., 23.})));
+}
+
+TEST(uKernels, VectorVectorCwiseProductTest) {
+  constexpr int kVectorSize = 10;
+  static float input1[kVectorSize] = {0.0,  -0.5, 1.0,  -1.5, 2.0,
+                                      -2.5, 3.0,  -3.5, 4.0,  -4.5};
+  static float input2[kVectorSize] = {0.1,  -0.1, 0.1,  -0.1, 0.1,
+                                      -0.1, 0.1,  -0.1, 0.1,  -0.1};
+  std::vector<float> output(kVectorSize);
+  VectorVectorCwiseProduct(input1, input2, kVectorSize, output.data());
+  EXPECT_THAT(output,
+              ElementsAreArray(ArrayFloatNear(
+                  {0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45})));
+}
+
+TEST(uKernels, VectorVectorCwiseProductAccumulateTest) {
+  constexpr int kVectorSize = 10;
+  static float input1[kVectorSize] = {0.0,  -0.5, 1.0,  -1.5, 2.0,
+                                      -2.5, 3.0,  -3.5, 4.0,  -4.5};
+  static float input2[kVectorSize] = {0.1,  -0.1, 0.1,  -0.1, 0.1,
+                                      -0.1, 0.1,  -0.1, 0.1,  -0.1};
+  std::vector<float> output(kVectorSize);
+  std::fill(output.begin(), output.end(), 1.0);
+  VectorVectorCwiseProductAccumulate(input1, input2, kVectorSize,
+                                     output.data());
+  EXPECT_THAT(output,
+              ElementsAreArray(ArrayFloatNear(
+                  {1.0, 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45})));
+}
+
+TEST(uKernels, VectorBatchVectorAssignTest) {
+  constexpr int kVectorSize = 5;
+  constexpr int kBatchSize = 3;
+  static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
+  std::vector<float> output(kVectorSize * kBatchSize);
+  VectorBatchVectorAssign(input, kVectorSize, kBatchSize, output.data());
+  EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
+                          {0.0, -0.5, 1.0, -1.5, 2.0, 0.0, -0.5, 1.0, -1.5, 2.0,
+                           0.0, -0.5, 1.0, -1.5, 2.0})));
+}
+
+TEST(uKernels, ApplySigmoidToVectorTest) {
+  constexpr int kVectorSize = 5;
+  static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
+  std::vector<float> output(kVectorSize);
+  ApplySigmoidToVector(input, kVectorSize, output.data());
+  EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
+                          {0.5, 0.377541, 0.731059, 0.182426, 0.880797})));
+}
+
+TEST(uKernels, ApplyActivationToVectorTest) {
+  constexpr int kVectorSize = 5;
+  static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
+  std::vector<float> output(kVectorSize);
+  ApplyActivationToVector(input, kVectorSize, kActivationRelu, output.data());
+  EXPECT_THAT(output,
+              ElementsAreArray(ArrayFloatNear({0.0, 0.0, 1.0, 0.0, 2.0})));
+
+  ApplyActivationToVector(input, kVectorSize, kActivationTanh, output.data());
+  EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear(
+                          {0.0, -0.462117, 0.761594, -0.905148, 0.964028})));
+}
+
+TEST(uKernels, CopyVectorTest) {
+  constexpr int kVectorSize = 5;
+  static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
+  std::vector<float> output(kVectorSize);
+  CopyVector(input, kVectorSize, output.data());
+  EXPECT_THAT(output,
+              ElementsAreArray(ArrayFloatNear({0.0, -0.5, 1.0, -1.5, 2.0})));
+}
+
+TEST(uKernels, Sub1VectorTest) {
+  constexpr int kVectorSize = 5;
+  static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
+  std::vector<float> output(kVectorSize);
+  Sub1Vector(input, kVectorSize, output.data());
+  EXPECT_THAT(output,
+              ElementsAreArray(ArrayFloatNear({1.0, 1.5, 0.0, 2.5, -1.0})));
+}
+
+TEST(uKernels, ZeroVectorTest) {
+  constexpr int kVectorSize = 5;
+  std::vector<float> output(kVectorSize);
+  ZeroVector(output.data(), kVectorSize);
+  EXPECT_THAT(output,
+              ElementsAreArray(ArrayFloatNear({0.0, 0.0, 0.0, 0.0, 0.0})));
+}
+
+TEST(uKernels, BatchVectorBatchVectorDotProductTest) {
+  constexpr int kVectorSize = 5;
+  constexpr int kBatch = 2;
+  static float input1[kVectorSize * kBatch] = {0.0,  -0.5, 1.0,  -1.5, 2.0,
+                                               -2.5, 3.0,  -3.5, 4.0,  -4.5};
+  static float input2[kVectorSize * kBatch] = {0.1,  -0.1, 0.1,  -0.1, 0.1,
+                                               -0.1, 0.1,  -0.1, 0.1,  -0.1};
+  std::vector<float> output(kBatch);
+  BatchVectorBatchVectorDotProduct(input1, input2, kVectorSize, kBatch,
+                                   output.data(), /*result_stride=*/1);
+  EXPECT_THAT(output, ElementsAreArray(ArrayFloatNear({0.5, 1.75})));
+}
+
+TEST(uKernels, VectorShiftLeftTest) {
+  constexpr int kVectorSize = 5;
+  static float input[kVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0};
+  std::vector<float> result(kVectorSize);
+  VectorShiftLeft(input, kVectorSize, 3.0);
+  result.assign(input, input + kVectorSize);
+  EXPECT_THAT(result,
+              ElementsAreArray(ArrayFloatNear({-0.5, 1.0, -1.5, 2.0, 3.0})));
+}
+
+TEST(uKernels, ReductionSumVectorTest) {
+  constexpr int kInputVectorSize = 10;
+  constexpr int kOutputVectorSize = 5;
+  constexpr int kReductionSize = 2;
+  static float input[kInputVectorSize] = {0.0, -0.5, 1.0, -1.5, 2.0,
+                                          0.0, -0.5, 1.0, 1.0,  2.0};
+  std::vector<float> result(kOutputVectorSize);
+  ReductionSumVector(input,
+                     /*input_stride=*/1, result.data(), kOutputVectorSize,
+                     kReductionSize);
+  EXPECT_THAT(result,
+              ElementsAreArray(ArrayFloatNear({-0.5, -0.5, 2.0, 0.5, 3.0})));
+}
+
+}  // namespace tensor_utils
+}  // namespace nn
+}  // namespace android
diff --git a/nn/driver/sample/SampleDriverAll.cpp b/nn/driver/sample/SampleDriverAll.cpp
index 3e29481f3..5ddaaec52 100644
--- a/nn/driver/sample/SampleDriverAll.cpp
+++ b/nn/driver/sample/SampleDriverAll.cpp
@@ -37,6 +37,7 @@ public:
 };
 
 Return<void> SampleDriverAll::getCapabilities(getCapabilities_cb cb) {
+    android::nn::initVLogMask();
     VLOG(DRIVER) << "getCapabilities()";
     Capabilities capabilities = {.float32Performance = {.execTime = 1.1f, .powerUsage = 1.1f},
                                  .quantized8Performance = {.execTime = 1.1f, .powerUsage = 1.1f}};
@@ -66,7 +67,6 @@ using android::nn::sample_driver::SampleDriverAll;
 using android::sp;
 
 int main() {
-    android::nn::initVLogMask();
     sp<SampleDriverAll> driver(new SampleDriverAll());
     return driver->run();
 }
diff --git a/nn/driver/sample/SampleDriverFloatFast.cpp b/nn/driver/sample/SampleDriverFloatFast.cpp
index 4a2b74963..cf416297d 100644
--- a/nn/driver/sample/SampleDriverFloatFast.cpp
+++ b/nn/driver/sample/SampleDriverFloatFast.cpp
@@ -37,6 +37,7 @@ public:
 };
 
 Return<void> SampleDriverFloatFast::getCapabilities(getCapabilities_cb cb) {
+    android::nn::initVLogMask();
     VLOG(DRIVER) << "getCapabilities()";
     Capabilities capabilities = {.float32Performance = {.execTime = 0.8f, .powerUsage = 1.2f},
                                  .quantized8Performance = {.execTime = 1.0f, .powerUsage = 1.0f}};
@@ -73,7 +74,6 @@ using android::nn::sample_driver::SampleDriverFloatFast;
 using android::sp;
 
 int main() {
-    android::nn::initVLogMask();
     sp<SampleDriverFloatFast> driver(new SampleDriverFloatFast());
     return driver->run();
 }
diff --git a/nn/driver/sample/SampleDriverFloatSlow.cpp b/nn/driver/sample/SampleDriverFloatSlow.cpp
index f472b9b11..87ed39944 100644
--- a/nn/driver/sample/SampleDriverFloatSlow.cpp
+++ b/nn/driver/sample/SampleDriverFloatSlow.cpp
@@ -37,6 +37,7 @@ public:
 };
 
 Return<void> SampleDriverFloatSlow::getCapabilities(getCapabilities_cb cb) {
+    android::nn::initVLogMask();
     VLOG(DRIVER) << "getCapabilities()";
     Capabilities capabilities = {.float32Performance = {.execTime = 1.3f, .powerUsage = 0.7f},
                                  .quantized8Performance = {.execTime = 1.0f, .powerUsage = 1.0f}};
@@ -73,7 +74,6 @@ using android::nn::sample_driver::SampleDriverFloatSlow;
 using android::sp;
 
 int main() {
-    android::nn::initVLogMask();
     sp<SampleDriverFloatSlow> driver(new SampleDriverFloatSlow());
     return driver->run();
 }
diff --git a/nn/driver/sample/SampleDriverMinimal.cpp b/nn/driver/sample/SampleDriverMinimal.cpp
index 919ac1cd4..0b6500078 100644
--- a/nn/driver/sample/SampleDriverMinimal.cpp
+++ b/nn/driver/sample/SampleDriverMinimal.cpp
@@ -38,6 +38,7 @@ public:
 };
 
 Return<void> SampleDriverMinimal::getCapabilities(getCapabilities_cb cb) {
+    android::nn::initVLogMask();
     VLOG(DRIVER) << "getCapabilities()";
     Capabilities capabilities = {.float32Performance = {.execTime = 0.4f, .powerUsage = 0.5f},
                                  .quantized8Performance = {.execTime = 1.0f, .powerUsage = 1.0f}};
@@ -85,7 +86,6 @@ using android::nn::sample_driver::SampleDriverMinimal;
 using android::sp;
 
 int main() {
-    android::nn::initVLogMask();
     sp<SampleDriverMinimal> driver(new SampleDriverMinimal());
     return driver->run();
 }
diff --git a/nn/driver/sample/SampleDriverQuant.cpp b/nn/driver/sample/SampleDriverQuant.cpp
index 269541691..25b07d2a5 100644
--- a/nn/driver/sample/SampleDriverQuant.cpp
+++ b/nn/driver/sample/SampleDriverQuant.cpp
@@ -37,6 +37,7 @@ public:
 };
 
 Return<void> SampleDriverQuant::getCapabilities(getCapabilities_cb cb) {
+    android::nn::initVLogMask();
     VLOG(DRIVER) << "getCapabilities()";
     Capabilities capabilities = {.float32Performance = {.execTime = 50.0f, .powerUsage = 1.0f},
                                  .quantized8Performance = {.execTime = 50.0f, .powerUsage = 1.0f}};
@@ -73,7 +74,6 @@ using android::nn::sample_driver::SampleDriverQuant;
 using android::sp;
 
 int main() {
-    android::nn::initVLogMask();
     sp<SampleDriverQuant> driver(new SampleDriverQuant());
     return driver->run();
 }
diff --git a/nn/runtime/test/generated/all_generated_tests.cpp b/nn/runtime/test/generated/all_generated_tests.cpp
index 58d6cd6d5..7b59f0535 100644
--- a/nn/runtime/test/generated/all_generated_tests.cpp
+++ b/nn/runtime/test/generated/all_generated_tests.cpp
@@ -1051,6 +1051,34 @@ TEST_F(GeneratedTests, lstm2) {
             lstm2::examples);
 }
 
+namespace lstm2_state2 {
+std::vector<MixedTypedExample> examples = {
+// Generated lstm2_state2 test
+#include "generated/examples/lstm2_state2.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/lstm2_state2.model.cpp"
+} // namespace lstm2_state2
+TEST_F(GeneratedTests, lstm2_state2) {
+    Execute(lstm2_state2::CreateModel,
+            lstm2_state2::is_ignored,
+            lstm2_state2::examples);
+}
+
+namespace lstm2_state {
+std::vector<MixedTypedExample> examples = {
+// Generated lstm2_state test
+#include "generated/examples/lstm2_state.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/lstm2_state.model.cpp"
+} // namespace lstm2_state
+TEST_F(GeneratedTests, lstm2_state) {
+    Execute(lstm2_state::CreateModel,
+            lstm2_state::is_ignored,
+            lstm2_state::examples);
+}
+
 namespace lstm3 {
 std::vector<MixedTypedExample> examples = {
 // Generated lstm3 test
@@ -1065,6 +1093,48 @@ TEST_F(GeneratedTests, lstm3) {
             lstm3::examples);
 }
 
+namespace lstm3_state2 {
+std::vector<MixedTypedExample> examples = {
+// Generated lstm3_state2 test
+#include "generated/examples/lstm3_state2.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/lstm3_state2.model.cpp"
+} // namespace lstm3_state2
+TEST_F(GeneratedTests, lstm3_state2) {
+    Execute(lstm3_state2::CreateModel,
+            lstm3_state2::is_ignored,
+            lstm3_state2::examples);
+}
+
+namespace lstm3_state3 {
+std::vector<MixedTypedExample> examples = {
+// Generated lstm3_state3 test
+#include "generated/examples/lstm3_state3.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/lstm3_state3.model.cpp"
+} // namespace lstm3_state3
+TEST_F(GeneratedTests, lstm3_state3) {
+    Execute(lstm3_state3::CreateModel,
+            lstm3_state3::is_ignored,
+            lstm3_state3::examples);
+}
+
+namespace lstm3_state {
+std::vector<MixedTypedExample> examples = {
+// Generated lstm3_state test
+#include "generated/examples/lstm3_state.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/lstm3_state.model.cpp"
+} // namespace lstm3_state
+TEST_F(GeneratedTests, lstm3_state) {
+    Execute(lstm3_state::CreateModel,
+            lstm3_state::is_ignored,
+            lstm3_state::examples);
+}
+
 namespace lstm {
 std::vector<MixedTypedExample> examples = {
 // Generated lstm test
@@ -1079,6 +1149,34 @@ TEST_F(GeneratedTests, lstm) {
             lstm::examples);
 }
 
+namespace lstm_state2 {
+std::vector<MixedTypedExample> examples = {
+// Generated lstm_state2 test
+#include "generated/examples/lstm_state2.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/lstm_state2.model.cpp"
+} // namespace lstm_state2
+TEST_F(GeneratedTests, lstm_state2) {
+    Execute(lstm_state2::CreateModel,
+            lstm_state2::is_ignored,
+            lstm_state2::examples);
+}
+
+namespace lstm_state {
+std::vector<MixedTypedExample> examples = {
+// Generated lstm_state test
+#include "generated/examples/lstm_state.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/lstm_state.model.cpp"
+} // namespace lstm_state
+TEST_F(GeneratedTests, lstm_state) {
+    Execute(lstm_state::CreateModel,
+            lstm_state::is_ignored,
+            lstm_state::examples);
+}
+
 namespace max_pool_float_1 {
 std::vector<MixedTypedExample> examples = {
 // Generated max_pool_float_1 test
@@ -1485,6 +1583,20 @@ TEST_F(GeneratedTests, rnn) {
             rnn::examples);
 }
 
+namespace rnn_state {
+std::vector<MixedTypedExample> examples = {
+// Generated rnn_state test
+#include "generated/examples/rnn_state.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/rnn_state.model.cpp"
+} // namespace rnn_state
+TEST_F(GeneratedTests, rnn_state) {
+    Execute(rnn_state::CreateModel,
+            rnn_state::is_ignored,
+            rnn_state::examples);
+}
+
 namespace softmax_float_1 {
 std::vector<MixedTypedExample> examples = {
 // Generated softmax_float_1 test
@@ -1625,6 +1737,20 @@ TEST_F(GeneratedTests, svdf) {
             svdf::examples);
 }
 
+namespace svdf_state {
+std::vector<MixedTypedExample> examples = {
+// Generated svdf_state test
+#include "generated/examples/svdf_state.example.cpp"
+};
+// Generated model constructor
+#include "generated/models/svdf_state.model.cpp"
+} // namespace svdf_state
+TEST_F(GeneratedTests, svdf_state) {
+    Execute(svdf_state::CreateModel,
+            svdf_state::is_ignored,
+            svdf_state::examples);
+}
+
 namespace tanh {
 std::vector<MixedTypedExample> examples = {
 // Generated tanh test
@@ -1638,3 +1764,4 @@ TEST_F(GeneratedTests, tanh) {
             tanh::is_ignored,
             tanh::examples);
 }
+
diff --git a/nn/runtime/test/generated/examples/lstm.example.cpp b/nn/runtime/test/generated/examples/lstm.example.cpp
index b5313fad1..33a2b19c3 100644
--- a/nn/runtime/test/generated/examples/lstm.example.cpp
+++ b/nn/runtime/test/generated/examples/lstm.example.cpp
@@ -13,7 +13,7 @@
 //Output(s)
 { // See tools/test_generator/include/TestHarness.h:MixedTyped
   // int -> FLOAT32 map
-  {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  {{1, {-0.0297319f, 0.122947f, 0.208851f, -0.153588f}}, {2, {-0.145439f, 0.157475f, 0.293663f, -0.277353f}}, {3, {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
   // int -> INT32 map
   {},
   // int -> QUANT8_ASYMM map
diff --git a/nn/runtime/test/generated/examples/lstm2.example.cpp b/nn/runtime/test/generated/examples/lstm2.example.cpp
index 619cad0cd..7f1009482 100644
--- a/nn/runtime/test/generated/examples/lstm2.example.cpp
+++ b/nn/runtime/test/generated/examples/lstm2.example.cpp
@@ -13,7 +13,7 @@
 //Output(s)
 { // See tools/test_generator/include/TestHarness.h:MixedTyped
   // int -> FLOAT32 map
-  {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  {{1, {-0.364445f, -0.00352185f, 0.128866f, -0.0516365f}}, {2, {-0.760444f, -0.0180416f, 0.182264f, -0.0649371f}}, {3, {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
   // int -> INT32 map
   {},
   // int -> QUANT8_ASYMM map
diff --git a/nn/runtime/test/generated/examples/lstm2_state.example.cpp b/nn/runtime/test/generated/examples/lstm2_state.example.cpp
new file mode 100644
index 000000000..ff9f90948
--- /dev/null
+++ b/nn/runtime/test/generated/examples/lstm2_state.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: lstm2_state.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {3.0f, 4.0f}}, {1, {}}, {2, {-0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f}}, {3, {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f}}, {4, {0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f}}, {5, {}}, {6, {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}}, {7, {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}}, {8, {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}}, {9, {}}, {10, {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}}, {11, {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}}, {12, {}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.364445f, -0.00352185f, 0.128866f, -0.0516365f}}, {19, {-0.760444f, -0.0180416f, 0.182264f, -0.0649371f}}, {21, {0.0f}}, {22, {0.0f}}},
+  // int -> INT32 map
+  {{20, {4}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {-0.423122f, -0.0121822f, 0.24201f, -0.0812458f}}, {2, {-0.978419f, -0.139203f, 0.338163f, -0.0983904f}}, {3, {-0.42312205f, -0.01218222f, 0.24201041f, -0.08124574f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/lstm2_state2.example.cpp b/nn/runtime/test/generated/examples/lstm2_state2.example.cpp
new file mode 100644
index 000000000..b49e6f627
--- /dev/null
+++ b/nn/runtime/test/generated/examples/lstm2_state2.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: lstm2_state2.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {1.0f, 1.0f}}, {1, {}}, {2, {-0.55291498f, -0.42866567f, 0.13056988f, -0.3633365f, -0.22755712f, 0.28253698f, 0.24407166f, 0.33826375f}}, {3, {-0.49770179f, -0.27711356f, -0.09624726f, 0.05100781f, 0.04717243f, 0.48944736f, -0.38535351f, -0.17212132f}}, {4, {0.10725588f, -0.02335852f, -0.55932593f, -0.09426838f, -0.44257352f, 0.54939759f, 0.01533556f, 0.42751634f}}, {5, {}}, {6, {-0.13832897f, -0.0515101f, -0.2359007f, -0.16661474f, -0.14340827f, 0.36986142f, 0.23414481f, 0.55899f, 0.10798943f, -0.41174671f, 0.17751795f, -0.34484994f, -0.35874045f, -0.11352962f, 0.27268326f, 0.54058349f}}, {7, {0.54066205f, -0.32668582f, -0.43562764f, -0.56094903f, 0.42957711f, 0.01841056f, -0.32764608f, -0.33027974f, -0.10826075f, 0.20675004f, 0.19069612f, -0.03026325f, -0.54532051f, 0.33003211f, 0.44901288f, 0.21193194f}}, {8, {0.41613156f, 0.42610586f, -0.16495961f, -0.5663873f, 0.30579174f, -0.05115908f, -0.33941799f, 0.23364776f, 0.11178309f, 0.09481031f, -0.26424935f, 0.46261835f, 0.50248802f, 0.26114327f, -0.43736315f, 0.33149987f}}, {9, {}}, {10, {0.47485286f, -0.51955009f, -0.24458408f, 0.31544167f}}, {11, {-0.17135078f, 0.82760304f, 0.85573703f, -0.77109635f}}, {12, {}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.423122f, -0.0121822f, 0.24201f, -0.0812458f}}, {19, {-0.978419f, -0.139203f, 0.338163f, -0.0983904f}}, {21, {0.0f}}, {22, {0.0f}}},
+  // int -> INT32 map
+  {{20, {4}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.358325f, -0.04621704f, 0.21641694f, -0.06471302f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/lstm3.example.cpp b/nn/runtime/test/generated/examples/lstm3.example.cpp
index 89d381479..c78dee690 100644
--- a/nn/runtime/test/generated/examples/lstm3.example.cpp
+++ b/nn/runtime/test/generated/examples/lstm3.example.cpp
@@ -13,7 +13,7 @@
 //Output(s)
 { // See tools/test_generator/include/TestHarness.h:MixedTyped
   // int -> FLOAT32 map
-  {{1, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {2, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {3, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  {{1, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835577f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415119f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334694f, 0.00733397f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.0216801f}}, {2, {-0.0531632f, -0.0118138f, 0.0870833f, 0.0347929f, -0.076144f, -0.0659219f, -0.0463811f, 0.0141307f, -0.0127706f, -0.03782f, -0.00402401f, -0.00571876f, -0.187957f, -0.0247127f, 0.0711425f, 0.008244f, 0.0492649f, 0.126972f, 0.0933097f, 0.29848f, -0.0966178f, -0.114417f, 0.0387229f, 0.0453255f, -0.181286f, -0.0651251f, -0.0996879f, -0.00276995f, 0.0617558f, -0.0100728f, 0.056304f, -0.077416f, -0.162858f, -0.0541251f, 0.0571202f, -0.0525331f, 0.0724297f, 0.171029f, 0.141738f, 0.295483f}}, {3, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.02168f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
   // int -> INT32 map
   {},
   // int -> QUANT8_ASYMM map
diff --git a/nn/runtime/test/generated/examples/lstm3_state.example.cpp b/nn/runtime/test/generated/examples/lstm3_state.example.cpp
new file mode 100644
index 000000000..7f916759c
--- /dev/null
+++ b/nn/runtime/test/generated/examples/lstm3_state.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: lstm3_state.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {0.596268f, 0.998386f, 0.568695f, 0.864524f, 0.571277f, 0.642421f, 0.52426f, 0.134799f, 0.003639f, 0.162482f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, -0.028545704f, 0.024939531f, 0.050929718f, 0.0076203286f, -0.0029723682f, -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f, -0.16327988f, -0.2273378f, -0.0993647f, -0.017155107f, 0.0023917493f, 0.049272764f, 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, -0.04544234f, -0.0497073f, -0.07135631f, -0.048929106f, -0.004042012f, -0.009284026f, 0.018042054f, 0.0036860977f, -0.07427302f, -0.11434604f, -0.018995456f, 0.031487543f, 0.012834908f, 0.019977754f, 0.044256654f, -0.39292613f, -0.18519334f, -0.11651281f, -0.06809892f, 0.011373677f}}, {2, {-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f, 0.0814421f, -0.12257899f, -0.033945758f, -0.031303465f, 0.045630626f, 0.06843887f, -0.13492945f, -0.012480007f, -0.0811829f, -0.07224499f, -0.09628791f, 0.045100946f, 0.0012300825f, 0.013964662f, 0.099372394f, 0.02543059f, 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f, 0.052625068f, 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f, 0.07241905f, 0.018668644f, -0.037377294f, -0.06277783f, -0.08833636f, -0.040120605f, -0.011405586f, -0.007808335f, -0.010301386f, -0.005102167f, 0.027717464f, 0.05483423f, 0.11449111f, 0.11289652f, 0.10939839f, 0.13396506f, -0.08402166f, -0.01901462f, -0.044678304f, -0.07720565f, 0.014350063f, -0.11757958f, -0.0652038f, -0.08185733f, -0.076754324f, -0.092614375f, 0.10405491f, 0.052960336f, 0.035755895f, 0.035839386f, -0.012540553f, 0.036881298f, 0.02913376f, 0.03420159f, 0.05448447f, -0.054523353f, 0.02582715f, 0.02327355f, -0.011857179f, -0.0011980024f, -0.034641717f, -0.026125094f, -0.17582615f, -0.15923657f, -0.27486774f, -0.0006143371f, 0.0001771948f, -8.470171e-05f, 0.02651807f, 0.045790765f, 0.06956496f}}, {3, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f, -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f, 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f, -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f, -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f, -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f, 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f, 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f, 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f, -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f, -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f, -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f, -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f, -0.009768936f, -0.04223324f, 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-0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}}, {17, {}}, {18, {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835577f, -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, -0.0152191f, -0.0259063f, 0.00914318f, 0.00415119f, 0.017147f, 0.0134203f, -0.013869f, 0.0287268f, -0.00334694f, 0.00733397f, -0.0287926f, -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, 0.0216801f}}, {19, {-0.0531632f, -0.0118138f, 0.0870833f, 0.0347929f, -0.076144f, -0.0659219f, -0.0463811f, 0.0141307f, -0.0127706f, -0.03782f, -0.00402401f, -0.00571876f, -0.187957f, -0.0247127f, 0.0711425f, 0.008244f, 0.0492649f, 0.126972f, 0.0933097f, 0.29848f, -0.0966178f, -0.114417f, 0.0387229f, 0.0453255f, -0.181286f, -0.0651251f, -0.0996879f, -0.00276995f, 0.0617558f, -0.0100728f, 0.056304f, -0.077416f, -0.162858f, -0.0541251f, 0.0571202f, -0.0525331f, 0.0724297f, 0.171029f, 0.141738f, 0.295483f}}, {21, {0.0f}}, {22, {0.0f}}},
+  // int -> INT32 map
+  {{20, {4}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {-0.0166936f, 0.0381209f, 0.000889684f, 0.0143363f, -0.0328911f, -0.0234288f, 0.0333051f, -0.012229f, 0.0110322f, -0.0457725f, -0.000832209f, -0.0202817f, 0.0327257f, 0.0121309f, 0.0155969f, 0.0312091f, -0.0141913f, 0.0322082f, 0.00227024f, 0.0260507f, -0.0188721f, -0.0296489f, 0.0399134f, -0.0160509f, 0.011604f, -0.0447318f, -0.0150515f, -0.0277406f, 0.0316596f, 0.0118233f, 0.0214762f, 0.0293641f}}, {2, {-0.154022f, -0.124934f, 0.0478463f, 0.0607819f, -0.218727f, -0.111053f, -0.103885f, -0.00447221f, 0.0554757f, -0.0207068f, 0.0595767f, -0.116297f, -0.249466f, -0.0723206f, 0.0794942f, -0.0377107f, 0.124532f, 0.249952f, 0.188641f, 0.411865f, -0.11012f, -0.0694494f, 0.103501f, 0.0428427f, -0.167345f, -0.106061f, -0.0775679f, 0.00936161f, 0.0105526f, -0.0314523f, 0.0243475f, -0.132179f, -0.258763f, -0.0307266f, 0.107047f, -0.0115197f, 0.0995485f, 0.220027f, 0.158355f, 0.436369f}}, {3, {-0.0166936f, 0.0381209f, 0.000889694f, 0.0143363f, -0.0328911f, -0.0234288f, 0.0333051f, -0.012229f, 0.0110322f, -0.0457725f, -0.000832209f, -0.0202817f, 0.0327257f, 0.0121308f, 0.0155969f, 0.0312091f, -0.0141913f, 0.0322082f, 0.00227024f, 0.0260507f, -0.0188721f, -0.0296489f, 0.0399134f, -0.0160509f, 0.0116039f, -0.0447318f, -0.0150515f, -0.0277406f, 0.0316596f, 0.0118233f, 0.0214762f, 0.0293641f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/lstm3_state2.example.cpp b/nn/runtime/test/generated/examples/lstm3_state2.example.cpp
new file mode 100644
index 000000000..770143bd1
--- /dev/null
+++ b/nn/runtime/test/generated/examples/lstm3_state2.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: lstm3_state2.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {0.073204f, 0.296072f, 0.743333f, 0.069199f, 0.045348f, 0.640394f, 0.930399f, 0.050782f, 0.432485f, 0.988078f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, -0.028545704f, 0.024939531f, 0.050929718f, 0.0076203286f, -0.0029723682f, -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f, -0.16327988f, -0.2273378f, -0.0993647f, -0.017155107f, 0.0023917493f, 0.049272764f, 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, -0.04544234f, -0.0497073f, -0.07135631f, -0.048929106f, -0.004042012f, -0.009284026f, 0.018042054f, 0.0036860977f, -0.07427302f, -0.11434604f, -0.018995456f, 0.031487543f, 0.012834908f, 0.019977754f, 0.044256654f, -0.39292613f, -0.18519334f, -0.11651281f, -0.06809892f, 0.011373677f}}, {2, {-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f, 0.0814421f, -0.12257899f, -0.033945758f, -0.031303465f, 0.045630626f, 0.06843887f, -0.13492945f, -0.012480007f, -0.0811829f, -0.07224499f, -0.09628791f, 0.045100946f, 0.0012300825f, 0.013964662f, 0.099372394f, 0.02543059f, 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f, 0.052625068f, 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f, 0.07241905f, 0.018668644f, -0.037377294f, -0.06277783f, -0.08833636f, -0.040120605f, -0.011405586f, -0.007808335f, -0.010301386f, -0.005102167f, 0.027717464f, 0.05483423f, 0.11449111f, 0.11289652f, 0.10939839f, 0.13396506f, -0.08402166f, -0.01901462f, -0.044678304f, -0.07720565f, 0.014350063f, -0.11757958f, -0.0652038f, -0.08185733f, -0.076754324f, -0.092614375f, 0.10405491f, 0.052960336f, 0.035755895f, 0.035839386f, -0.012540553f, 0.036881298f, 0.02913376f, 0.03420159f, 0.05448447f, -0.054523353f, 0.02582715f, 0.02327355f, -0.011857179f, -0.0011980024f, -0.034641717f, -0.026125094f, -0.17582615f, -0.15923657f, -0.27486774f, -0.0006143371f, 0.0001771948f, -8.470171e-05f, 0.02651807f, 0.045790765f, 0.06956496f}}, {3, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f, -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f, 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f, -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f, -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f, -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f, 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f, 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f, 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f, -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f, -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f, -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f, -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f, -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f, -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f, 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f, 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f, 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f, 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f, 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f}}, {4, {-0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f, -0.001285394f, 0.10124236f, 0.083122835f, 0.053313006f, -0.062235646f, -0.075637154f, -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f, -0.086665764f, -0.037162706f, -0.038880914f, -0.035832845f, -0.014481564f, -0.09825003f, -0.12048569f, -0.097665586f, -0.05287633f, -0.0964047f, -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f, 0.050649304f, 0.05798951f, -0.021852335f, -0.099848844f, 0.014740475f, -0.078897946f, 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f, 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f, 0.06945813f, -0.078907564f, -0.06707616f, -0.11844508f, -0.09986688f, -0.07509403f, 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f, 0.14639415f, 0.0015017595f, -0.014267382f, -0.03417257f, 0.012711468f, 0.0028300495f, -0.024758482f, -0.05098548f, -0.0821182f, 0.014225672f, 0.021544158f, 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f, 0.06476295f, -0.022907063f, 0.027562456f, 0.040185735f, 0.019567577f, -0.015598739f, -0.049097303f, -0.017121866f, -0.083368234f, -0.02332002f, -0.0840956f}}, {5, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, -0.055839065f, -0.046598054f, 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+  // int -> INT32 map
+  {{20, {4}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {-0.0213783f, 0.0350169f, 0.000324787f, 0.0276012f, -0.0263374f, -0.0371449f, 0.0446149f, -0.0205474f, 0.0103729f, -0.0576349f, -0.0150052f, -0.0292043f, 0.0376827f, 0.0136115f, 0.0243435f, 0.0354492f, -0.0204549f, 0.0450315f, -0.00117379f, 0.0167673f, -0.0375007f, -0.0238314f, 0.038784f, -0.0174034f, 0.0131743f, -0.0506589f, -0.00484469f, -0.0240239f, 0.0325789f, 0.00790064f, 0.0220157f, 0.0333314f}}, {2, {-0.126572f, -0.121882f, 0.121569f, 0.0489971f, -0.240177f, -0.124685f, -0.122565f, 0.0162748f, 0.0317536f, -0.0270355f, 0.0418199f, -0.179755f, -0.327279f, -0.0342741f, 0.133831f, -0.0238279f, 0.122148f, 0.269115f, 0.185989f, 0.525976f, -0.167208f, -0.109612f, 0.0531226f, 0.0695387f, -0.248335f, -0.134123f, -0.108246f, 0.00628498f, 0.0492984f, -0.0264919f, 0.0698144f, -0.0635602f, -0.295363f, -0.0760078f, 0.102725f, -0.0351708f, 0.149804f, 0.259131f, 0.202573f, 0.500664f}}, {3, {-0.0213783f, 0.0350169f, 0.000324794f, 0.0276012f, -0.0263374f, -0.0371449f, 0.0446149f, -0.0205474f, 0.0103729f, -0.0576349f, -0.0150052f, -0.0292043f, 0.0376827f, 0.0136115f, 0.0243435f, 0.0354492f, -0.0204549f, 0.0450315f, -0.00117378f, 0.0167673f, -0.0375007f, -0.0238314f, 0.038784f, -0.0174034f, 0.0131743f, -0.0506589f, -0.0048447f, -0.0240239f, 0.0325789f, 0.00790065f, 0.0220157f, 0.0333314f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/lstm3_state3.example.cpp b/nn/runtime/test/generated/examples/lstm3_state3.example.cpp
new file mode 100644
index 000000000..0c269a6db
--- /dev/null
+++ b/nn/runtime/test/generated/examples/lstm3_state3.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: lstm3_state3.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {0.867394f, 0.291279f, 0.013714f, 0.482521f, 0.626339f, 0.082922f, 0.563329f, 0.865614f, 0.333232f, 0.259916f}}, {1, {0.021393683f, 0.06124551f, 0.046905167f, -0.014657677f, -0.03149463f, 0.09171803f, 0.14647801f, 0.10797193f, -0.0057968358f, 0.0019193048f, -0.2726754f, 0.10154029f, -0.018539885f, 0.080349885f, -0.10262385f, -0.022599787f, -0.09121155f, -0.008675967f, -0.045206103f, -0.0821282f, -0.008045952f, 0.015478081f, 0.055217247f, 0.038719587f, 0.044153627f, -0.06453243f, 0.05031825f, -0.046935108f, -0.008164439f, 0.014574226f, -0.1671009f, -0.15519552f, -0.16819797f, -0.13971269f, -0.11953059f, 0.25005487f, -0.22790983f, 0.009855087f, -0.028140958f, -0.11200698f, 0.11295408f, -0.0035217577f, 0.054485075f, 0.05184695f, 0.064711206f, 0.10989193f, 0.11674786f, 0.03490607f, 0.07727357f, 0.11390585f, -0.1863375f, -0.1034451f, -0.13945189f, -0.049401227f, -0.18767063f, 0.042483903f, 0.14233552f, 0.13832581f, 0.18350165f, 0.14545603f, -0.028545704f, 0.024939531f, 0.050929718f, 0.0076203286f, -0.0029723682f, -0.042484224f, -0.11827596f, -0.09171104f, -0.10808628f, -0.16327988f, -0.2273378f, -0.0993647f, -0.017155107f, 0.0023917493f, 0.049272764f, 0.0038534778f, 0.054764505f, 0.089753784f, 0.06947234f, 0.08014476f, -0.04544234f, -0.0497073f, -0.07135631f, -0.048929106f, -0.004042012f, -0.009284026f, 0.018042054f, 0.0036860977f, -0.07427302f, -0.11434604f, -0.018995456f, 0.031487543f, 0.012834908f, 0.019977754f, 0.044256654f, -0.39292613f, -0.18519334f, -0.11651281f, -0.06809892f, 0.011373677f}}, {2, {-0.0018401089f, -0.004852237f, 0.03698424f, 0.014181704f, 0.028273236f, -0.016726194f, -0.05249759f, -0.10204261f, 0.00861066f, -0.040979505f, -0.009899187f, 0.01923892f, -0.028177269f, -0.08535103f, -0.14585495f, 0.10662567f, -0.01909731f, -0.017883534f, -0.0047269356f, -0.045103323f, 0.0030784295f, 0.076784775f, 0.07463696f, 0.094531395f, 0.0814421f, -0.12257899f, -0.033945758f, -0.031303465f, 0.045630626f, 0.06843887f, -0.13492945f, -0.012480007f, -0.0811829f, -0.07224499f, -0.09628791f, 0.045100946f, 0.0012300825f, 0.013964662f, 0.099372394f, 0.02543059f, 0.06958324f, 0.034257296f, 0.0482646f, 0.06267997f, 0.052625068f, 0.12784666f, 0.07077897f, 0.025725935f, 0.04165009f, 0.07241905f, 0.018668644f, -0.037377294f, -0.06277783f, -0.08833636f, -0.040120605f, -0.011405586f, -0.007808335f, -0.010301386f, -0.005102167f, 0.027717464f, 0.05483423f, 0.11449111f, 0.11289652f, 0.10939839f, 0.13396506f, -0.08402166f, -0.01901462f, -0.044678304f, -0.07720565f, 0.014350063f, -0.11757958f, -0.0652038f, -0.08185733f, -0.076754324f, -0.092614375f, 0.10405491f, 0.052960336f, 0.035755895f, 0.035839386f, -0.012540553f, 0.036881298f, 0.02913376f, 0.03420159f, 0.05448447f, -0.054523353f, 0.02582715f, 0.02327355f, -0.011857179f, -0.0011980024f, -0.034641717f, -0.026125094f, -0.17582615f, -0.15923657f, -0.27486774f, -0.0006143371f, 0.0001771948f, -8.470171e-05f, 0.02651807f, 0.045790765f, 0.06956496f}}, {3, {-0.04580283f, -0.09549462f, -0.032418985f, -0.06454633f, -0.043528453f, 0.043018587f, -0.049152344f, -0.12418144f, -0.078985475f, -0.07596889f, 0.019484362f, -0.11434962f, -0.0074034138f, -0.06314844f, -0.092981495f, 0.0062155537f, -0.025034338f, -0.0028890965f, 0.048929527f, 0.06235075f, 0.10665918f, -0.032036792f, -0.08505916f, -0.10843358f, -0.13002433f, -0.036816437f, -0.02130134f, -0.016518239f, 0.0047691227f, -0.0025825808f, 0.066017866f, 0.029991534f, -0.10652836f, -0.1037554f, -0.13056071f, -0.03266643f, -0.033702414f, -0.006473424f, -0.04611692f, 0.014419339f, -0.025174323f, 0.0396852f, 0.081777506f, 0.06157468f, 0.10210095f, -0.009658194f, 0.046511717f, 0.03603906f, 0.0069369148f, 0.015960095f, -0.06507666f, 0.09551598f, 0.053568836f, 0.06408714f, 0.12835667f, -0.008714329f, -0.20211966f, -0.12093674f, 0.029450472f, 0.2849013f, -0.029227901f, 0.1164364f, -0.08560263f, 0.09941786f, -0.036999565f, -0.028842626f, -0.0033637602f, -0.017012902f, -0.09720865f, -0.11193351f, -0.029155117f, -0.017936034f, -0.009768936f, -0.04223324f, -0.036159635f, 0.06505112f, -0.021742892f, -0.023377212f, -0.07221364f, -0.06430552f, 0.05453865f, 0.091149814f, 0.06387331f, 0.007518393f, 0.055960953f, 0.069779344f, 0.046411168f, 0.10509911f, 0.07463894f, 0.0075130584f, 0.012850982f, 0.04555431f, 0.056955688f, 0.06555285f, 0.050801456f, -0.009862683f, 0.00826772f, -0.026555609f, -0.0073611983f, -0.0014897042f}}, {4, {-0.0998932f, -0.07201956f, -0.052803773f, -0.15629593f, -0.15001918f, -0.07650751f, 0.02359855f, -0.075155355f, -0.08037709f, -0.15093534f, 0.029517552f, -0.04751393f, 0.010350531f, -0.02664851f, -0.016839722f, -0.023121163f, 0.0077019283f, 0.012851257f, -0.05040649f, -0.0129761f, -0.021737747f, -0.038305793f, -0.06870586f, -0.01481247f, -0.001285394f, 0.10124236f, 0.083122835f, 0.053313006f, -0.062235646f, -0.075637154f, -0.027833903f, 0.029774971f, 0.1130802f, 0.09218906f, 0.09506135f, -0.086665764f, -0.037162706f, -0.038880914f, -0.035832845f, -0.014481564f, -0.09825003f, -0.12048569f, -0.097665586f, -0.05287633f, -0.0964047f, -0.11366429f, 0.035777505f, 0.13568819f, 0.052451383f, 0.050649304f, 0.05798951f, -0.021852335f, -0.099848844f, 0.014740475f, -0.078897946f, 0.04974699f, 0.014160473f, 0.06973932f, 0.04964942f, 0.033364646f, 0.08190124f, 0.025535367f, 0.050893165f, 0.048514254f, 0.06945813f, -0.078907564f, -0.06707616f, -0.11844508f, -0.09986688f, -0.07509403f, 0.06263226f, 0.14925587f, 0.20188436f, 0.12098451f, 0.14639415f, 0.0015017595f, -0.014267382f, -0.03417257f, 0.012711468f, 0.0028300495f, -0.024758482f, -0.05098548f, -0.0821182f, 0.014225672f, 0.021544158f, 0.08949725f, 0.07505268f, -0.0020780868f, 0.04908258f, 0.06476295f, -0.022907063f, 0.027562456f, 0.040185735f, 0.019567577f, -0.015598739f, -0.049097303f, -0.017121866f, -0.083368234f, -0.02332002f, -0.0840956f}}, {5, {-0.001374326f, -0.078856036f, 0.10672688f, 0.029162422f, -0.11585556f, 0.02557986f, -0.13446963f, -0.035785314f, -0.01244275f, 0.025961924f, -0.02337298f, -0.044228926f, -0.055839065f, -0.046598054f, -0.010546039f, -0.06900766f, 0.027239809f, 0.022582639f, -0.013296484f, -0.05459212f, 0.08981f, -0.045407712f, 0.08682226f, -0.06867011f, -0.14390695f, -0.02916037f, 0.000996957f, 0.091420636f, 0.14283475f, -0.07390571f, -0.06402044f, 0.062524505f, -0.093129106f, 0.04860203f, -0.08364217f, -0.08119002f, 0.009352075f, 0.22920375f, 0.0016303885f, 0.11583097f, -0.13732095f, 0.012405723f, -0.07551853f, 0.06343048f, 0.12162708f, -0.031923793f, -0.014335606f, 0.01790974f, -0.10650317f, -0.0724401f, 0.08554849f, -0.05727212f, 0.06556731f, -0.042729504f, -0.043227166f, 0.011683251f, -0.013082158f, -0.029302018f, -0.010899579f, -0.062036745f, -0.022509435f, -0.00964907f, -0.01567329f, 0.04260106f, -0.07787477f, -0.11576462f, 0.017356863f, 0.048673786f, -0.017577527f, -0.05527947f, -0.082487635f, -0.040137455f, -0.10820036f, -0.04666372f, 0.022746278f, -0.07851417f, 0.01068115f, 0.032956902f, 0.022433773f, 0.0026891115f, 0.08944216f, -0.0685835f, 0.010513544f, 0.07228705f, 0.02032331f, -0.059686817f, -0.0005566496f, -0.086984694f, 0.040414046f, -0.1380399f, 0.094208956f, -0.05722982f, 0.012092817f, -0.04989123f, -0.086576f, -0.003399834f, -0.04696032f, -0.045747425f, 0.10091314f, 0.048676282f, -0.029037097f, 0.031399418f, -0.0040285117f, 0.047237843f, 0.09504992f, 0.041799378f, -0.049185462f, -0.031518843f, -0.10516937f, 0.026374253f, 0.10058866f, -0.0033195973f, -0.041975245f, 0.0073591834f, 0.0033782164f, -0.004325073f, -0.10167381f, 0.042500053f, -0.01447153f, 0.06464186f, -0.017142897f, 0.03312627f, 0.009205989f, 0.024138335f, -0.011337001f, 0.035530265f, -0.010912711f, 0.0706555f, -0.005894094f, 0.051841937f, -0.1401738f, -0.02351249f, 0.0365468f, 0.07590991f, 0.08838724f, 0.021681072f, -0.10086113f, 0.019608743f, -0.06195883f, 0.077335775f, 0.023646897f, -0.095322326f, 0.02233014f, 0.09756986f, -0.048691444f, -0.009579111f, 0.07595467f, 0.11480546f, -0.09801813f, 0.019894179f, 0.08502348f, 0.004032281f, 0.037211012f, 0.068537936f, -0.048005626f, -0.091520436f, -0.028379958f, -0.01556313f, 0.06554592f, -0.045599163f, -0.01672207f, -0.020169014f, -0.011877351f, -0.20212261f, 0.010889619f, 0.0047078193f, 0.038385306f, 0.08540671f, -0.017140968f, -0.0035865551f, 0.016678626f, 0.005633034f, 0.015963363f, 0.00871737f, 0.060130805f, 0.028611384f, 0.10109069f, -0.015060172f, -0.07894427f, 0.06401885f, 0.011584063f, -0.024466386f, 0.0047652307f, -0.09041358f, 0.030737216f, -0.0046374933f, 0.14215417f, -0.11823516f, 0.019899689f, 0.006106124f, -0.027092824f, 0.0786356f, 0.05052217f, -0.058925f, -0.011402121f, -0.024987547f, -0.0013661642f, -0.06832946f, -0.015667673f, -0.1083353f, -0.00096863037f, -0.06988685f, -0.053350925f, -0.027275559f, -0.033664223f, -0.07978348f, -0.025200296f, -0.017207067f, -0.058403496f, -0.055697463f, 0.005798788f, 0.12965427f, -0.062582195f, 0.0013350133f, -0.10482091f, 0.0379771f, 0.072521195f, -0.0029455067f, -0.13797039f, -0.03628521f, 0.013806405f, -0.017858358f, -0.01008298f, -0.07700066f, -0.017081132f, 0.019358726f, 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-0.10549954f, 0.090567775f, 0.042013682f, -0.03176985f, 0.12569028f, -0.02421228f, -0.029526481f, 0.023851605f, 0.031539805f, 0.05292009f, -0.02344001f, -0.07811758f, -0.08834428f, 0.10094801f, 0.16594367f, -0.06861939f, -0.021256343f, -0.041093912f, -0.06669611f, 0.035498552f, 0.021757556f, -0.09302526f, -0.015403468f, -0.06614931f, -0.051798206f, -0.013874718f, 0.03630673f, 0.010412845f, -0.08077351f, 0.046185967f, 0.0035662893f, 0.03541868f, -0.094149634f, -0.034814864f, 0.003128424f, -0.020674974f, -0.03944324f, -0.008110165f, -0.11113267f, 0.08484226f, 0.043586485f, 0.040582247f, 0.0968012f, -0.065249965f, -0.028036479f, 0.0050708856f, 0.0017462453f, 0.0326779f, 0.041296225f, 0.09164146f, -0.047743853f, -0.015952192f, -0.034451712f, 0.084197424f, -0.05347844f, -0.11768019f, 0.085926116f, -0.08251791f, -0.045081906f, 0.0948852f, 0.068401024f, 0.024856757f, 0.06978981f, -0.057309967f, -0.012775832f, -0.0032452994f, 0.01977615f, -0.041040014f, -0.024264973f, 0.063464895f, 0.05431621f}}, {9, {0.040369894f, 0.030746894f, 0.24704495f, 0.018586371f, -0.037586458f, -0.15312155f, -0.11812848f, -0.11465643f, 0.20259799f, 0.11418174f, -0.10116027f, -0.011334949f, 0.12411352f, -0.076769054f, -0.052169047f, 0.21198851f, -0.38871562f, -0.09061183f, -0.09683246f, -0.21929175f}}, {10, {-0.01998659f, -0.15568835f, -0.24248174f, -0.012770197f, 0.041331276f, -0.072311886f, -0.052123554f, -0.0066330447f, -0.043891653f, 0.036225766f, -0.047248036f, 0.021479502f, 0.033189066f, 0.11952997f, -0.020432774f, 0.64658105f, -0.06650122f, -0.03467612f, 0.095340036f, 0.23647355f}}, {11, {0.08286371f, -0.08261836f, -0.51210177f, 0.002913762f, 0.17764764f, -0.5495371f, -0.08460716f, -0.24552552f, 0.030037103f, 0.04123544f, -0.11940523f, 0.007358328f, 0.1890978f, 0.4833202f, -0.34441817f, 0.36312827f, -0.26375428f, 0.1457655f, -0.19724406f, 0.15548733f}}, {12, {0.02234832f, 0.14757581f, 0.18176508f, 0.10380666f, 0.053110216f, -0.06928846f, -0.13942584f, -0.11816189f, 0.19483899f, 0.03652339f, -0.10250295f, 0.036714908f, -0.18426876f, 0.036065217f, 0.21810818f, 0.02383196f, -0.043370757f, 0.08690144f, -0.04444982f, 0.00030581196f}}, {13, {0.035185695f, -0.042891346f, -0.03032477f, 0.23027696f, 0.11098921f, 0.15378423f, 0.09263801f, 0.09790885f, 0.09508917f, 0.061199076f, 0.07665568f, -0.015443159f, -0.03499149f, 0.046190713f, 0.08895977f, 0.10899629f, 0.40694186f, 0.06030037f, 0.012413437f, -0.06108739f}}, {14, {-0.024379363f, 0.0055531194f, 0.23377132f, 0.033463873f, -0.1483596f, -0.10639995f, -0.091433935f, 0.058573797f, -0.06809782f, -0.07889636f, -0.043246906f, -0.09829136f, -0.4279842f, 0.034901652f, 0.18797937f, 0.0075234566f, 0.016178843f, 0.1749513f, 0.13975595f, 0.92058027f}}, {15, {0.046159424f, -0.0012809046f, 0.03563469f, 0.12648113f, 0.027195795f, 0.35373217f, -0.018957434f, 0.008907322f, -0.0762701f, 0.12018895f, 0.04216877f, 0.0022856654f, 0.040952638f, 0.3147856f, 0.08225149f, -0.057416286f, -0.14995944f, -0.008040261f, 0.13208859f, 0.029760877f}}, {16, {-0.009802181f, 0.09401916f, 0.0717386f, -0.13895074f, 0.09641832f, 0.060420845f, 0.08539281f, 0.054285463f, 0.061395317f, 0.034448683f, -0.042991187f, 0.019801661f, -0.16840284f, -0.015726732f, -0.23041931f, -0.024478018f, -0.10959692f, -0.013875541f, 0.18600968f, -0.061274476f, 0.0138165f, -0.08160894f, -0.07661644f, 0.032372914f, 0.16169067f, 0.22465782f, -0.03993472f, -0.004017731f, 0.08633481f, -0.28869787f, 0.08682067f, 0.17240396f, 0.014975425f, 0.056431185f, 0.031037588f, 0.16702051f, 0.0077946745f, 0.15140012f, 0.29405436f, 0.120285f, -0.188994f, -0.027265169f, 0.043389652f, -0.022061434f, 0.014777949f, -0.20203483f, 0.094781205f, 0.19100232f, 0.13987629f, -0.036132768f, -0.06426278f, -0.05108664f, 0.13221376f, 0.009441198f, -0.16715929f, 0.15859416f, -0.040437475f, 0.050779544f, -0.022187516f, 0.012166504f, 0.027685808f, -0.07675938f, -0.0055694645f, -0.09444123f, 0.0046453946f, 0.050794356f, 0.10770313f, -0.20790008f, -0.07149004f, -0.11425117f, 0.008225835f, -0.035802525f, 0.14374903f, 0.15262283f, 0.048710253f, 0.1847461f, -0.007487823f, 0.11000021f, -0.09542012f, 0.22619456f, -0.029149994f, 0.08527916f, 0.009043713f, 0.0042746216f, 0.016261552f, 0.022461696f, 0.12689082f, -0.043589946f, -0.12035478f, -0.08361797f, -0.050666027f, -0.1248618f, -0.1275799f, -0.071875185f, 0.07377272f, 0.09944291f, -0.18897448f, -0.1593054f, -0.06526116f, -0.040107165f, -0.004618631f, -0.067624845f, -0.007576253f, 0.10727444f, 0.041546922f, -0.20424393f, 0.06907816f, 0.050412357f, 0.00724631f, 0.039827548f, 0.12449835f, 0.10747581f, 0.13708383f, 0.09134148f, -0.12617786f, -0.06428341f, 0.09956831f, 0.1208086f, -0.14676677f, -0.0727722f, 0.1126304f, 0.010139365f, 0.015571211f, -0.038128063f, 0.022913318f, -0.042050496f, 0.16842307f, -0.060597885f, 0.10531834f, -0.06411776f, -0.07451711f, -0.03410368f, -0.13393489f, 0.06534304f, 0.003620307f, 0.04490757f, 0.05970546f, 0.05197996f, 0.02839995f, 0.10434969f, -0.013699693f, -0.028353551f, -0.07260381f, 0.047201227f, -0.024575593f, -0.036445823f, 0.07155557f, 0.009672501f, -0.02328883f, 0.009533515f, -0.03606021f, -0.07421458f, -0.028082801f, -0.2678904f, -0.13221288f, 0.18419984f, -0.13012612f, -0.014588381f, -0.035059117f, -0.04824723f, 0.07830115f, -0.056184657f, 0.03277091f, 0.025466874f, 0.14494097f, -0.12522776f, -0.098633975f, -0.10766018f, -0.08317623f, 0.08594209f, 0.07749552f, 0.039474737f, 0.1776665f, -0.07409566f, -0.0477268f, 0.29323658f, 0.10801441f, 0.1154011f, 0.013952499f, 0.10739139f, 0.10708251f, -0.051456142f, 0.0074137426f, -0.10430189f, 0.10034707f, 0.045594677f, 0.0635285f, -0.0715442f, -0.089667566f, -0.10811871f, 0.00026344223f, 0.08298446f, -0.009525053f, 0.006585689f, -0.24567553f, -0.09450807f, 0.09648481f, 0.026996298f, -0.06419476f, -0.04752702f, -0.11063944f, -0.23441927f, -0.17608605f, -0.052156363f, 0.067035615f, 0.19271925f, -0.0032889997f, -0.043264326f, 0.09663576f, -0.057112187f, -0.10100678f, 0.0628376f, 0.04447668f, 0.017961001f, -0.10094388f, -0.10190601f, 0.18335468f, 0.10494553f, -0.052095775f, -0.0026118709f, 0.10539724f, -0.04383912f, -0.042349473f, 0.08438151f, -0.1947263f, 0.02251204f, 0.11216432f, -0.10307853f, 0.17351969f, -0.039091777f, 0.08066188f, -0.00561982f, 0.12633002f, 0.11335965f, -0.0088127935f, -0.019777594f, 0.06864014f, -0.059751723f, 0.016233567f, -0.06894641f, -0.28651384f, -0.004228674f, 0.019708522f, -0.16305895f, -0.07468996f, -0.0855457f, 0.099339016f, -0.07580735f, -0.13775392f, 0.08434318f, 0.08330512f, -0.12131499f, 0.031935584f, 0.09180414f, -0.08876437f, -0.08049874f, 0.008753825f, 0.03498998f, 0.030215185f, 0.03907079f, 0.089751154f, 0.029194152f, -0.03337423f, -0.019092513f, 0.04331237f, 0.04299654f, -0.036394123f, -0.12915532f, 0.09793732f, 0.07512415f, -0.11319543f, -0.032502122f, 0.15661901f, 0.07671967f, -0.005491124f, -0.19379048f, -0.218606f, 0.21448623f, 0.017840758f, 0.1416943f, -0.07051762f, 0.19488361f, 0.02664691f, -0.18104725f, -0.09334311f, 0.15026465f, -0.15493552f, -0.057762887f, -0.11604192f, -0.262013f, -0.01391798f, 0.012185008f, 0.11156489f, -0.07483202f, 0.06693364f, -0.26151478f, 0.046425626f, 0.036540434f, -0.16435726f, 0.17338543f, -0.21401681f, -0.11385144f, -0.08283257f, -0.069031075f, 0.030635102f, 0.010969227f, 0.11109743f, 0.010919218f, 0.027526086f, 0.13519906f, 0.01891392f, -0.046839405f, -0.040167913f, 0.017953383f, -0.09700955f, 0.0061885654f, -0.07000971f, 0.026893595f, -0.038844477f, 0.14543656f}}, {17, {}}, {18, {-0.0213783f, 0.0350169f, 0.000324787f, 0.0276012f, -0.0263374f, -0.0371449f, 0.0446149f, -0.0205474f, 0.0103729f, -0.0576349f, -0.0150052f, -0.0292043f, 0.0376827f, 0.0136115f, 0.0243435f, 0.0354492f, -0.0204549f, 0.0450315f, -0.00117379f, 0.0167673f, -0.0375007f, -0.0238314f, 0.038784f, -0.0174034f, 0.0131743f, -0.0506589f, -0.00484469f, -0.0240239f, 0.0325789f, 0.00790064f, 0.0220157f, 0.0333314f}}, {19, {-0.126572f, -0.121882f, 0.121569f, 0.0489971f, -0.240177f, -0.124685f, -0.122565f, 0.0162748f, 0.0317536f, -0.0270355f, 0.0418199f, -0.179755f, -0.327279f, -0.0342741f, 0.133831f, -0.0238279f, 0.122148f, 0.269115f, 0.185989f, 0.525976f, -0.167208f, -0.109612f, 0.0531226f, 0.0695387f, -0.248335f, -0.134123f, -0.108246f, 0.00628498f, 0.0492984f, -0.0264919f, 0.0698144f, -0.0635602f, -0.295363f, -0.0760078f, 0.102725f, -0.0351708f, 0.149804f, 0.259131f, 0.202573f, 0.500664f}}, {21, {0.0f}}, {22, {0.0f}}},
+  // int -> INT32 map
+  {{20, {4}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {2, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}, {3, {-0.0189322f, 0.0464512f, -0.00251373f, 0.0225745f, -0.0308346f, -0.0317124f, 0.0460407f, -0.0189395f, 0.0149363f, -0.0530162f, -0.0150767f, -0.0340193f, 0.0286833f, 0.00824207f, 0.0264887f, 0.0305169f, -0.0264787f, 0.0387855f, -0.000764675f, 0.0217599f, -0.037537f, -0.0335206f, 0.0431679f, -0.0211424f, 0.010203f, -0.062785f, -0.00832363f, -0.025181f, 0.0412031f, 0.0118723f, 0.0239643f, 0.0394009f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/lstm_state.example.cpp b/nn/runtime/test/generated/examples/lstm_state.example.cpp
new file mode 100644
index 000000000..2a8c9c449
--- /dev/null
+++ b/nn/runtime/test/generated/examples/lstm_state.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: lstm_state.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {3.0f, 4.0f}}, {1, {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}}, {2, {0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}}, {3, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}}, {4, {-0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}}, {5, {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}}, {6, {-0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}}, {7, {-0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}}, {8, {0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}}, {9, {}}, {10, {}}, {11, {}}, {12, {0.0f, 0.0f, 0.0f, 0.0f}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.0297319f, 0.122947f, 0.208851f, -0.153588f}}, {19, {-0.145439f, 0.157475f, 0.293663f, -0.277353f}}, {21, {0.0f}}, {22, {0.0f}}},
+  // int -> INT32 map
+  {{20, {4}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {-0.0371611f, 0.125073f, 0.411934f, -0.208605f}}, {2, {-0.287121f, 0.148115f, 0.556837f, -0.388276f}}, {3, {-0.03716109f, 0.12507336f, 0.41193449f, -0.20860538f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/lstm_state2.example.cpp b/nn/runtime/test/generated/examples/lstm_state2.example.cpp
new file mode 100644
index 000000000..c67c7d537
--- /dev/null
+++ b/nn/runtime/test/generated/examples/lstm_state2.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: lstm_state2.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {1.0f, 1.0f}}, {1, {-0.45018822f, -0.02338299f, -0.0870589f, -0.34550029f, 0.04266912f, -0.15680569f, -0.34856534f, 0.43890524f}}, {2, {0.09701663f, 0.20334584f, -0.50592935f, -0.31343272f, -0.40032279f, 0.44781327f, 0.01387155f, -0.35593212f}}, {3, {-0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, -0.20583314f, 0.44344562f, 0.22077113f, -0.29909778f}}, {4, {-0.25065863f, -0.28290087f, 0.04613829f, 0.40525138f, 0.44272184f, 0.03897077f, -0.1556896f, 0.19487578f}}, {5, {-0.0063535f, -0.2042388f, 0.31454784f, -0.35746509f, 0.28902304f, 0.08183324f, -0.16555229f, 0.02286911f, -0.13566875f, 0.03034258f, 0.48091322f, -0.12528998f, 0.24077177f, -0.51332325f, -0.33502164f, 0.10629296f}}, {6, {-0.48684245f, -0.06655136f, 0.42224967f, 0.2112639f, 0.27654213f, 0.20864892f, -0.07646349f, 0.45877004f, 0.00141793f, -0.14609534f, 0.36447752f, 0.09196436f, 0.28053468f, 0.01560611f, -0.20127171f, -0.01140004f}}, {7, {-0.3407414f, 0.24443203f, -0.2078532f, 0.26320225f, 0.05695659f, -0.00123841f, -0.4744786f, -0.35869038f, -0.06418842f, -0.13502428f, -0.501764f, 0.22830659f, -0.46367589f, 0.26016325f, -0.03894562f, -0.16368064f}}, {8, {0.43385774f, -0.17194885f, 0.2718237f, 0.09215671f, 0.24107647f, -0.39835793f, 0.18212086f, 0.01301402f, 0.48572797f, -0.50656658f, 0.20047462f, -0.20607421f, -0.51818722f, -0.15390486f, 0.0468148f, 0.39922136f}}, {9, {}}, {10, {}}, {11, {}}, {12, {0.0f, 0.0f, 0.0f, 0.0f}}, {13, {1.0f, 1.0f, 1.0f, 1.0f}}, {14, {0.0f, 0.0f, 0.0f, 0.0f}}, {15, {0.0f, 0.0f, 0.0f, 0.0f}}, {16, {}}, {17, {}}, {18, {-0.0371611f, 0.125073f, 0.411934f, -0.208605f}}, {19, {-0.287121f, 0.148115f, 0.556837f, -0.388276f}}, {21, {0.0f}}, {22, {0.0f}}},
+  // int -> INT32 map
+  {{20, {4}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {0, 0, 0, 0}}, {2, {0, 0, 0, 0}}, {3, {-0.15053082f, 0.09120187f, 0.24278517f, -0.12222792f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/rnn_state.example.cpp b/nn/runtime/test/generated/examples/rnn_state.example.cpp
new file mode 100644
index 000000000..d62b30e98
--- /dev/null
+++ b/nn/runtime/test/generated/examples/rnn_state.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: rnn_state.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {-0.69424844f, -0.93421471f, -0.87287879f, 0.37144363f, -0.62476718f, 0.23791671f, 0.40060222f, 0.1356622f, -0.69424844f, -0.93421471f, -0.87287879f, 0.37144363f, -0.62476718f, 0.23791671f, 0.40060222f, 0.1356622f}}, {1, {0.461459f, 0.153381f, 0.529743f, -0.00371218f, 0.676267f, -0.211346f, 0.317493f, 0.969689f, -0.343251f, 0.186423f, 0.398151f, 0.152399f, 0.448504f, 0.317662f, 0.523556f, -0.323514f, 0.480877f, 0.333113f, -0.757714f, -0.674487f, -0.643585f, 0.217766f, -0.0251462f, 0.79512f, -0.595574f, -0.422444f, 0.371572f, -0.452178f, -0.556069f, -0.482188f, -0.685456f, -0.727851f, 0.841829f, 0.551535f, -0.232336f, 0.729158f, -0.00294906f, -0.69754f, 0.766073f, -0.178424f, 0.369513f, -0.423241f, 0.548547f, -0.0152023f, -0.757482f, -0.85491f, 0.251331f, -0.989183f, 0.306261f, -0.340716f, 0.886103f, -0.0726757f, -0.723523f, -0.784303f, 0.0354295f, 0.566564f, -0.485469f, -0.620498f, 0.832546f, 0.697884f, -0.279115f, 0.294415f, -0.584313f, 0.548772f, 0.0648819f, 0.968726f, 0.723834f, -0.0080452f, -0.350386f, -0.272803f, 0.115121f, -0.412644f, -0.824713f, -0.992843f, -0.592904f, -0.417893f, 0.863791f, -0.423461f, -0.147601f, -0.770664f, -0.479006f, 0.654782f, 0.587314f, -0.639158f, 0.816969f, -0.337228f, 0.659878f, 0.73107f, 0.754768f, -0.337042f, 0.0960841f, 0.368357f, 0.244191f, -0.817703f, -0.211223f, 0.442012f, 0.37225f, -0.623598f, -0.405423f, 0.455101f, 0.673656f, -0.145345f, -0.511346f, -0.901675f, -0.81252f, -0.127006f, 0.809865f, -0.721884f, 0.636255f, 0.868989f, -0.347973f, -0.10179f, -0.777449f, 0.917274f, 0.819286f, 0.206218f, -0.00785118f, 0.167141f, 0.45872f, 0.972934f, -0.276798f, 0.837861f, 0.747958f, -0.0151566f, -0.330057f, -0.469077f, 0.277308f, 0.415818f}}, {2, {0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1f}}, {3, {0.065691948f, -0.69055247f, 0.1107955f, -0.97084129f, -0.23957068f, -0.23566568f, -0.389184f, 0.47481549f, -0.4791103f, 0.29931796f, 0.10463274f, 0.83918178f, 0.37197268f, 0.61957061f, 0.3956964f, -0.37609905f}}, {4, {0.496726f, 0, 0.965996f, 0, 0.0584256f, 0, 0, 0.12315f, 0, 0, 0.612267f, 0.456601f, 0, 0.52286f, 1.16099f, 0.0291233f, 0.496726f, 0, 0.965996f, 0, 0.0584256f, 0, 0, 0.12315f, 0, 0, 0.612267f, 0.456601f, 0, 0.52286f, 1.16099f, 0.0291233f}}},
+  // int -> INT32 map
+  {{5, {1}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {0, 0, 0.524902f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355731f, 0, 0, 0, 0, 0.524902f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355731f, 0, 0}}, {1, {0, 0, 0.524901f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355727f, 0, 0, 0, 0, 0.524901f, 0, 0, 0, 0, 1.02116f, 0, 1.35762f, 0, 0.356909f, 0.436415f, 0.0355727f, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/examples/svdf_state.example.cpp b/nn/runtime/test/generated/examples/svdf_state.example.cpp
new file mode 100644
index 000000000..ffe02125c
--- /dev/null
+++ b/nn/runtime/test/generated/examples/svdf_state.example.cpp
@@ -0,0 +1,22 @@
+// Generated file (from: svdf_state.mod.py). Do not edit
+// Begin of an example
+{
+//Input(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{0, {0.14278367f, -1.64410412f, -0.75222826f, 0.14278367f, -1.64410412f, -0.75222826f}}, {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, {}}, {4, {0, 0, 0, 0, 0, 0, 0, 0, 0.119996f, 0, 0, 0, 0, 0, 0, 0, 0, -0.166701f, 0, 0, 0, 0, 0, 0, 0, 0, -0.44244f, 0, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0, 0, 0, 0, 0, 0, 0, 0, 0.119996f, 0, 0, 0, 0, 0, 0, 0, 0, -0.166701f, 0, 0, 0, 0, 0, 0, 0, 0, -0.44244f, 0, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {{5, {1}}, {6, {0}}},
+  // int -> QUANT8_ASYMM map
+  {}
+},
+//Output(s)
+{ // See tools/test_generator/include/TestHarness.h:MixedTyped
+  // int -> FLOAT32 map
+  {{1, {0.068281f, -0.162217f, -0.152268f, 0.00323521f, 0.068281f, -0.162217f, -0.152268f, 0.00323521f}}, {0, {0, 0, 0, 0, 0, 0, 0, 0.119996f, 0.542235f, 0, 0, 0, 0, 0, 0, 0, -0.166701f, -0.40465f, 0, 0, 0, 0, 0, 0, 0, -0.44244f, -0.706995f, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0.137515f, 0, 0, 0, 0, 0, 0, 0, 0.119996f, 0.542235f, 0, 0, 0, 0, 0, 0, 0, -0.166701f, -0.40465f, 0, 0, 0, 0, 0, 0, 0, -0.44244f, -0.706995f, 0, 0, 0, 0, 0, 0, 0, 0.0805206f, 0.137515f, 0, 0, 0, 0, 0, 0, 0, 0}}},
+  // int -> INT32 map
+  {},
+  // int -> QUANT8_ASYMM map
+  {}
+}
+}, // End of an example
diff --git a/nn/runtime/test/generated/models/lstm.model.cpp b/nn/runtime/test/generated/models/lstm.model.cpp
index 528bc5fbb..2308ba84d 100644
--- a/nn/runtime/test/generated/models/lstm.model.cpp
+++ b/nn/runtime/test/generated/models/lstm.model.cpp
@@ -48,6 +48,6 @@ void CreateModel(Model *model) {
 }
 
 bool is_ignored(int i) {
-  static std::set<int> ignore = {1, 2, 0};
+  static std::set<int> ignore = {0};
   return ignore.find(i) != ignore.end();
 }
diff --git a/nn/runtime/test/generated/models/lstm2.model.cpp b/nn/runtime/test/generated/models/lstm2.model.cpp
index 4286acd71..cf7a8f401 100644
--- a/nn/runtime/test/generated/models/lstm2.model.cpp
+++ b/nn/runtime/test/generated/models/lstm2.model.cpp
@@ -48,6 +48,6 @@ void CreateModel(Model *model) {
 }
 
 bool is_ignored(int i) {
-  static std::set<int> ignore = {1, 2, 0};
+  static std::set<int> ignore = {0};
   return ignore.find(i) != ignore.end();
 }
diff --git a/nn/runtime/test/generated/models/lstm2_state.model.cpp b/nn/runtime/test/generated/models/lstm2_state.model.cpp
new file mode 100644
index 000000000..ec59fe7b0
--- /dev/null
+++ b/nn/runtime/test/generated/models/lstm2_state.model.cpp
@@ -0,0 +1,53 @@
+// Generated file (from: lstm2_state.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::TENSOR_FLOAT32, {0,0});
+  OperandType type3(Type::TENSOR_FLOAT32, {0});
+  OperandType type9(Type::TENSOR_FLOAT32, {1, 12});
+  OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
+  OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type8(Type::TENSOR_FLOAT32, {1});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
+  OperandType type4(Type::TENSOR_FLOAT32, {4});
+  OperandType type7(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto input_to_input_weights = model->addOperand(&type1);
+  auto input_to_forget_weights = model->addOperand(&type1);
+  auto input_to_cell_weights = model->addOperand(&type1);
+  auto input_to_output_weights = model->addOperand(&type1);
+  auto recurrent_to_intput_weights = model->addOperand(&type2);
+  auto recurrent_to_forget_weights = model->addOperand(&type2);
+  auto recurrent_to_cell_weights = model->addOperand(&type2);
+  auto recurrent_to_output_weights = model->addOperand(&type2);
+  auto cell_to_input_weights = model->addOperand(&type3);
+  auto cell_to_forget_weights = model->addOperand(&type4);
+  auto cell_to_output_weights = model->addOperand(&type4);
+  auto input_gate_bias = model->addOperand(&type4);
+  auto forget_gate_bias = model->addOperand(&type4);
+  auto cell_gate_bias = model->addOperand(&type4);
+  auto output_gate_bias = model->addOperand(&type4);
+  auto projection_weights = model->addOperand(&type5);
+  auto projection_bias = model->addOperand(&type3);
+  auto output_state_in = model->addOperand(&type6);
+  auto cell_state_in = model->addOperand(&type6);
+  auto activation_param = model->addOperand(&type7);
+  auto cell_clip_param = model->addOperand(&type8);
+  auto proj_clip_param = model->addOperand(&type8);
+  auto scratch_buffer = model->addOperand(&type9);
+  auto output_state_out = model->addOperand(&type6);
+  auto cell_state_out = model->addOperand(&type6);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+    {scratch_buffer, output_state_out, cell_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/lstm2_state2.model.cpp b/nn/runtime/test/generated/models/lstm2_state2.model.cpp
new file mode 100644
index 000000000..60d0e147d
--- /dev/null
+++ b/nn/runtime/test/generated/models/lstm2_state2.model.cpp
@@ -0,0 +1,53 @@
+// Generated file (from: lstm2_state2.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::TENSOR_FLOAT32, {0,0});
+  OperandType type3(Type::TENSOR_FLOAT32, {0});
+  OperandType type9(Type::TENSOR_FLOAT32, {1, 12});
+  OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
+  OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type8(Type::TENSOR_FLOAT32, {1});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
+  OperandType type4(Type::TENSOR_FLOAT32, {4});
+  OperandType type7(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto input_to_input_weights = model->addOperand(&type1);
+  auto input_to_forget_weights = model->addOperand(&type1);
+  auto input_to_cell_weights = model->addOperand(&type1);
+  auto input_to_output_weights = model->addOperand(&type1);
+  auto recurrent_to_intput_weights = model->addOperand(&type2);
+  auto recurrent_to_forget_weights = model->addOperand(&type2);
+  auto recurrent_to_cell_weights = model->addOperand(&type2);
+  auto recurrent_to_output_weights = model->addOperand(&type2);
+  auto cell_to_input_weights = model->addOperand(&type3);
+  auto cell_to_forget_weights = model->addOperand(&type4);
+  auto cell_to_output_weights = model->addOperand(&type4);
+  auto input_gate_bias = model->addOperand(&type4);
+  auto forget_gate_bias = model->addOperand(&type4);
+  auto cell_gate_bias = model->addOperand(&type4);
+  auto output_gate_bias = model->addOperand(&type4);
+  auto projection_weights = model->addOperand(&type5);
+  auto projection_bias = model->addOperand(&type3);
+  auto output_state_in = model->addOperand(&type6);
+  auto cell_state_in = model->addOperand(&type6);
+  auto activation_param = model->addOperand(&type7);
+  auto cell_clip_param = model->addOperand(&type8);
+  auto proj_clip_param = model->addOperand(&type8);
+  auto scratch_buffer = model->addOperand(&type9);
+  auto output_state_out = model->addOperand(&type6);
+  auto cell_state_out = model->addOperand(&type6);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+    {scratch_buffer, output_state_out, cell_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {1, 2, 0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/lstm3.model.cpp b/nn/runtime/test/generated/models/lstm3.model.cpp
index 64b00560b..2100fc949 100644
--- a/nn/runtime/test/generated/models/lstm3.model.cpp
+++ b/nn/runtime/test/generated/models/lstm3.model.cpp
@@ -49,6 +49,6 @@ void CreateModel(Model *model) {
 }
 
 bool is_ignored(int i) {
-  static std::set<int> ignore = {1, 2, 0};
+  static std::set<int> ignore = {0};
   return ignore.find(i) != ignore.end();
 }
diff --git a/nn/runtime/test/generated/models/lstm3_state.model.cpp b/nn/runtime/test/generated/models/lstm3_state.model.cpp
new file mode 100644
index 000000000..bee11fa10
--- /dev/null
+++ b/nn/runtime/test/generated/models/lstm3_state.model.cpp
@@ -0,0 +1,54 @@
+// Generated file (from: lstm3_state.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::TENSOR_FLOAT32, {0});
+  OperandType type4(Type::TENSOR_FLOAT32, {16,20});
+  OperandType type9(Type::TENSOR_FLOAT32, {1});
+  OperandType type6(Type::TENSOR_FLOAT32, {2, 16});
+  OperandType type7(Type::TENSOR_FLOAT32, {2, 20});
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 5});
+  OperandType type10(Type::TENSOR_FLOAT32, {2, 80});
+  OperandType type2(Type::TENSOR_FLOAT32, {20, 16});
+  OperandType type1(Type::TENSOR_FLOAT32, {20, 5});
+  OperandType type3(Type::TENSOR_FLOAT32, {20});
+  OperandType type8(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto input_to_input_weights = model->addOperand(&type1);
+  auto input_to_forget_weights = model->addOperand(&type1);
+  auto input_to_cell_weights = model->addOperand(&type1);
+  auto input_to_output_weights = model->addOperand(&type1);
+  auto recurrent_to_intput_weights = model->addOperand(&type2);
+  auto recurrent_to_forget_weights = model->addOperand(&type2);
+  auto recurrent_to_cell_weights = model->addOperand(&type2);
+  auto recurrent_to_output_weights = model->addOperand(&type2);
+  auto cell_to_input_weights = model->addOperand(&type3);
+  auto cell_to_forget_weights = model->addOperand(&type3);
+  auto cell_to_output_weights = model->addOperand(&type3);
+  auto input_gate_bias = model->addOperand(&type3);
+  auto forget_gate_bias = model->addOperand(&type3);
+  auto cell_gate_bias = model->addOperand(&type3);
+  auto output_gate_bias = model->addOperand(&type3);
+  auto projection_weights = model->addOperand(&type4);
+  auto projection_bias = model->addOperand(&type5);
+  auto output_state_in = model->addOperand(&type6);
+  auto cell_state_in = model->addOperand(&type7);
+  auto activation_param = model->addOperand(&type8);
+  auto cell_clip_param = model->addOperand(&type9);
+  auto proj_clip_param = model->addOperand(&type9);
+  auto scratch_buffer = model->addOperand(&type10);
+  auto output_state_out = model->addOperand(&type6);
+  auto cell_state_out = model->addOperand(&type7);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+    {scratch_buffer, output_state_out, cell_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/lstm3_state2.model.cpp b/nn/runtime/test/generated/models/lstm3_state2.model.cpp
new file mode 100644
index 000000000..ae6ba8db3
--- /dev/null
+++ b/nn/runtime/test/generated/models/lstm3_state2.model.cpp
@@ -0,0 +1,54 @@
+// Generated file (from: lstm3_state2.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::TENSOR_FLOAT32, {0});
+  OperandType type4(Type::TENSOR_FLOAT32, {16,20});
+  OperandType type9(Type::TENSOR_FLOAT32, {1});
+  OperandType type6(Type::TENSOR_FLOAT32, {2, 16});
+  OperandType type7(Type::TENSOR_FLOAT32, {2, 20});
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 5});
+  OperandType type10(Type::TENSOR_FLOAT32, {2, 80});
+  OperandType type2(Type::TENSOR_FLOAT32, {20, 16});
+  OperandType type1(Type::TENSOR_FLOAT32, {20, 5});
+  OperandType type3(Type::TENSOR_FLOAT32, {20});
+  OperandType type8(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto input_to_input_weights = model->addOperand(&type1);
+  auto input_to_forget_weights = model->addOperand(&type1);
+  auto input_to_cell_weights = model->addOperand(&type1);
+  auto input_to_output_weights = model->addOperand(&type1);
+  auto recurrent_to_intput_weights = model->addOperand(&type2);
+  auto recurrent_to_forget_weights = model->addOperand(&type2);
+  auto recurrent_to_cell_weights = model->addOperand(&type2);
+  auto recurrent_to_output_weights = model->addOperand(&type2);
+  auto cell_to_input_weights = model->addOperand(&type3);
+  auto cell_to_forget_weights = model->addOperand(&type3);
+  auto cell_to_output_weights = model->addOperand(&type3);
+  auto input_gate_bias = model->addOperand(&type3);
+  auto forget_gate_bias = model->addOperand(&type3);
+  auto cell_gate_bias = model->addOperand(&type3);
+  auto output_gate_bias = model->addOperand(&type3);
+  auto projection_weights = model->addOperand(&type4);
+  auto projection_bias = model->addOperand(&type5);
+  auto output_state_in = model->addOperand(&type6);
+  auto cell_state_in = model->addOperand(&type7);
+  auto activation_param = model->addOperand(&type8);
+  auto cell_clip_param = model->addOperand(&type9);
+  auto proj_clip_param = model->addOperand(&type9);
+  auto scratch_buffer = model->addOperand(&type10);
+  auto output_state_out = model->addOperand(&type6);
+  auto cell_state_out = model->addOperand(&type7);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+    {scratch_buffer, output_state_out, cell_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/lstm3_state3.model.cpp b/nn/runtime/test/generated/models/lstm3_state3.model.cpp
new file mode 100644
index 000000000..65ab3c18f
--- /dev/null
+++ b/nn/runtime/test/generated/models/lstm3_state3.model.cpp
@@ -0,0 +1,54 @@
+// Generated file (from: lstm3_state3.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::TENSOR_FLOAT32, {0});
+  OperandType type4(Type::TENSOR_FLOAT32, {16,20});
+  OperandType type9(Type::TENSOR_FLOAT32, {1});
+  OperandType type6(Type::TENSOR_FLOAT32, {2, 16});
+  OperandType type7(Type::TENSOR_FLOAT32, {2, 20});
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 5});
+  OperandType type10(Type::TENSOR_FLOAT32, {2, 80});
+  OperandType type2(Type::TENSOR_FLOAT32, {20, 16});
+  OperandType type1(Type::TENSOR_FLOAT32, {20, 5});
+  OperandType type3(Type::TENSOR_FLOAT32, {20});
+  OperandType type8(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto input_to_input_weights = model->addOperand(&type1);
+  auto input_to_forget_weights = model->addOperand(&type1);
+  auto input_to_cell_weights = model->addOperand(&type1);
+  auto input_to_output_weights = model->addOperand(&type1);
+  auto recurrent_to_intput_weights = model->addOperand(&type2);
+  auto recurrent_to_forget_weights = model->addOperand(&type2);
+  auto recurrent_to_cell_weights = model->addOperand(&type2);
+  auto recurrent_to_output_weights = model->addOperand(&type2);
+  auto cell_to_input_weights = model->addOperand(&type3);
+  auto cell_to_forget_weights = model->addOperand(&type3);
+  auto cell_to_output_weights = model->addOperand(&type3);
+  auto input_gate_bias = model->addOperand(&type3);
+  auto forget_gate_bias = model->addOperand(&type3);
+  auto cell_gate_bias = model->addOperand(&type3);
+  auto output_gate_bias = model->addOperand(&type3);
+  auto projection_weights = model->addOperand(&type4);
+  auto projection_bias = model->addOperand(&type5);
+  auto output_state_in = model->addOperand(&type6);
+  auto cell_state_in = model->addOperand(&type7);
+  auto activation_param = model->addOperand(&type8);
+  auto cell_clip_param = model->addOperand(&type9);
+  auto proj_clip_param = model->addOperand(&type9);
+  auto scratch_buffer = model->addOperand(&type10);
+  auto output_state_out = model->addOperand(&type6);
+  auto cell_state_out = model->addOperand(&type7);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+    {scratch_buffer, output_state_out, cell_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {1, 2, 0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/lstm_state.model.cpp b/nn/runtime/test/generated/models/lstm_state.model.cpp
new file mode 100644
index 000000000..cf7ce867b
--- /dev/null
+++ b/nn/runtime/test/generated/models/lstm_state.model.cpp
@@ -0,0 +1,53 @@
+// Generated file (from: lstm_state.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::TENSOR_FLOAT32, {0,0});
+  OperandType type3(Type::TENSOR_FLOAT32, {0});
+  OperandType type9(Type::TENSOR_FLOAT32, {1, 16});
+  OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
+  OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type8(Type::TENSOR_FLOAT32, {1});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
+  OperandType type4(Type::TENSOR_FLOAT32, {4});
+  OperandType type7(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto input_to_input_weights = model->addOperand(&type1);
+  auto input_to_forget_weights = model->addOperand(&type1);
+  auto input_to_cell_weights = model->addOperand(&type1);
+  auto input_to_output_weights = model->addOperand(&type1);
+  auto recurrent_to_intput_weights = model->addOperand(&type2);
+  auto recurrent_to_forget_weights = model->addOperand(&type2);
+  auto recurrent_to_cell_weights = model->addOperand(&type2);
+  auto recurrent_to_output_weights = model->addOperand(&type2);
+  auto cell_to_input_weights = model->addOperand(&type3);
+  auto cell_to_forget_weights = model->addOperand(&type3);
+  auto cell_to_output_weights = model->addOperand(&type3);
+  auto input_gate_bias = model->addOperand(&type4);
+  auto forget_gate_bias = model->addOperand(&type4);
+  auto cell_gate_bias = model->addOperand(&type4);
+  auto output_gate_bias = model->addOperand(&type4);
+  auto projection_weights = model->addOperand(&type5);
+  auto projection_bias = model->addOperand(&type3);
+  auto output_state_in = model->addOperand(&type6);
+  auto cell_state_in = model->addOperand(&type6);
+  auto activation_param = model->addOperand(&type7);
+  auto cell_clip_param = model->addOperand(&type8);
+  auto proj_clip_param = model->addOperand(&type8);
+  auto scratch_buffer = model->addOperand(&type9);
+  auto output_state_out = model->addOperand(&type6);
+  auto cell_state_out = model->addOperand(&type6);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+    {scratch_buffer, output_state_out, cell_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/lstm_state2.model.cpp b/nn/runtime/test/generated/models/lstm_state2.model.cpp
new file mode 100644
index 000000000..1cdf66622
--- /dev/null
+++ b/nn/runtime/test/generated/models/lstm_state2.model.cpp
@@ -0,0 +1,53 @@
+// Generated file (from: lstm_state2.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type5(Type::TENSOR_FLOAT32, {0,0});
+  OperandType type3(Type::TENSOR_FLOAT32, {0});
+  OperandType type9(Type::TENSOR_FLOAT32, {1, 16});
+  OperandType type0(Type::TENSOR_FLOAT32, {1, 2});
+  OperandType type6(Type::TENSOR_FLOAT32, {1, 4});
+  OperandType type8(Type::TENSOR_FLOAT32, {1});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 2});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 4});
+  OperandType type4(Type::TENSOR_FLOAT32, {4});
+  OperandType type7(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto input_to_input_weights = model->addOperand(&type1);
+  auto input_to_forget_weights = model->addOperand(&type1);
+  auto input_to_cell_weights = model->addOperand(&type1);
+  auto input_to_output_weights = model->addOperand(&type1);
+  auto recurrent_to_intput_weights = model->addOperand(&type2);
+  auto recurrent_to_forget_weights = model->addOperand(&type2);
+  auto recurrent_to_cell_weights = model->addOperand(&type2);
+  auto recurrent_to_output_weights = model->addOperand(&type2);
+  auto cell_to_input_weights = model->addOperand(&type3);
+  auto cell_to_forget_weights = model->addOperand(&type3);
+  auto cell_to_output_weights = model->addOperand(&type3);
+  auto input_gate_bias = model->addOperand(&type4);
+  auto forget_gate_bias = model->addOperand(&type4);
+  auto cell_gate_bias = model->addOperand(&type4);
+  auto output_gate_bias = model->addOperand(&type4);
+  auto projection_weights = model->addOperand(&type5);
+  auto projection_bias = model->addOperand(&type3);
+  auto output_state_in = model->addOperand(&type6);
+  auto cell_state_in = model->addOperand(&type6);
+  auto activation_param = model->addOperand(&type7);
+  auto cell_clip_param = model->addOperand(&type8);
+  auto proj_clip_param = model->addOperand(&type8);
+  auto scratch_buffer = model->addOperand(&type9);
+  auto output_state_out = model->addOperand(&type6);
+  auto cell_state_out = model->addOperand(&type6);
+  auto output = model->addOperand(&type6);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_LSTM, {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param}, {scratch_buffer, output_state_out, cell_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, input_to_input_weights, input_to_forget_weights, input_to_cell_weights, input_to_output_weights, recurrent_to_intput_weights, recurrent_to_forget_weights, recurrent_to_cell_weights, recurrent_to_output_weights, cell_to_input_weights, cell_to_forget_weights, cell_to_output_weights, input_gate_bias, forget_gate_bias, cell_gate_bias, output_gate_bias, projection_weights, projection_bias, output_state_in, cell_state_in, activation_param, cell_clip_param, proj_clip_param},
+    {scratch_buffer, output_state_out, cell_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {1, 2, 0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/rnn_state.model.cpp b/nn/runtime/test/generated/models/rnn_state.model.cpp
new file mode 100644
index 000000000..489575937
--- /dev/null
+++ b/nn/runtime/test/generated/models/rnn_state.model.cpp
@@ -0,0 +1,30 @@
+// Generated file (from: rnn_state.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type2(Type::TENSOR_FLOAT32, {16, 16});
+  OperandType type1(Type::TENSOR_FLOAT32, {16, 8});
+  OperandType type3(Type::TENSOR_FLOAT32, {16});
+  OperandType type4(Type::TENSOR_FLOAT32, {2, 16});
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 8});
+  OperandType type5(Type::TENSOR_INT32, {1});
+  // Phase 1, operands
+  auto input = model->addOperand(&type0);
+  auto weights = model->addOperand(&type1);
+  auto recurrent_weights = model->addOperand(&type2);
+  auto bias = model->addOperand(&type3);
+  auto hidden_state_in = model->addOperand(&type4);
+  auto activation_param = model->addOperand(&type5);
+  auto hidden_state_out = model->addOperand(&type4);
+  auto output = model->addOperand(&type4);
+  // Phase 2, operations
+  model->addOperation(ANEURALNETWORKS_RNN, {input, weights, recurrent_weights, bias, hidden_state_in, activation_param}, {hidden_state_out, output});
+  // Phase 3, inputs and outputs
+  model->identifyInputsAndOutputs(
+    {input, weights, recurrent_weights, bias, hidden_state_in, activation_param},
+    {hidden_state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {0};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/generated/models/svdf_state.model.cpp b/nn/runtime/test/generated/models/svdf_state.model.cpp
new file mode 100644
index 000000000..f63662d3b
--- /dev/null
+++ b/nn/runtime/test/generated/models/svdf_state.model.cpp
@@ -0,0 +1,32 @@
+// Generated file (from: svdf_state.mod.py). Do not edit
+void CreateModel(Model *model) {
+  OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
+  OperandType type4(Type::TENSOR_FLOAT32, {2, 40});
+  OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
+  OperandType type2(Type::TENSOR_FLOAT32, {4, 10});
+  OperandType type1(Type::TENSOR_FLOAT32, {4, 3});
+  OperandType type3(Type::TENSOR_FLOAT32, {4});
+  OperandType type5(Type::TENSOR_INT32, {1});
+  // 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
+  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, rank_param, activation_param},
+    {state_out, output});
+  assert(model->isValid());
+}
+
+bool is_ignored(int i) {
+  static std::set<int> ignore = {};
+  return ignore.find(i) != ignore.end();
+}
diff --git a/nn/runtime/test/specs/generate_test.sh b/nn/runtime/test/specs/generate_test.sh
index 0eccfeb0b..1f3407b9c 100755
--- a/nn/runtime/test/specs/generate_test.sh
+++ b/nn/runtime/test/specs/generate_test.sh
@@ -16,9 +16,16 @@
 function generate_one_testcase {
   # Generate one testcase
   BASENAME=`basename -s .mod.py $1`
+  EXAMPLE="-e ../generated/examples/$BASENAME.example.cpp"
+  # Mobilenet quantized has its example file generated elsewhere, so we
+  # need make sure it does not get overwritten.
+  if [ $1 = "mobilenet_quantized.mod.py" ]; then
+    (>&2 echo "Skipping mobilenet quantized example generation")
+    EXAMPLE=
+  fi
+
   ../../../tools/test_generator/test_generator.py ./`basename $1`\
-    -m ../generated/models/$BASENAME.model.cpp \
-    -e ../generated/examples/$BASENAME.example.cpp
+    -m ../generated/models/$BASENAME.model.cpp $EXAMPLE
   # Paste these lines into TestGenerated.cpp
   echo
   echo namespace $BASENAME {
diff --git a/nn/runtime/test/specs/generate_vts_test.sh b/nn/runtime/test/specs/generate_vts_test.sh
index 76aa32bc2..ccbc3eb25 100755
--- a/nn/runtime/test/specs/generate_vts_test.sh
+++ b/nn/runtime/test/specs/generate_vts_test.sh
@@ -47,6 +47,10 @@ echo "// DO NOT EDIT;" > $OUTFILE
 echo "// Generated by ml/nn/runtime/test/specs/generate_vts_test.sh" >> $OUTFILE
 for f in *.mod.py;
 do
+  if [ $f = "mobilenet_quantized.mod.py" ]; then
+    echo "Skipping mobilenet quantized"
+    continue
+  fi
   echo "Processing $f"
   generate_one_testcase $f >> $OUTFILE
 done
diff --git a/nn/runtime/test/specs/lstm.mod.py b/nn/runtime/test/specs/lstm.mod.py
index cb1bf6010..e670d2758 100644
--- a/nn/runtime/test/specs/lstm.mod.py
+++ b/nn/runtime/test/specs/lstm.mod.py
@@ -56,8 +56,8 @@ cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
 proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
 
 scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
-output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
 
 model = model.Operation("LSTM",
@@ -136,26 +136,17 @@ input0 = {input_to_input_weights:  [-0.45018822, -0.02338299, -0.0870589, -0.345
           proj_clip_param: [0.],
 }
 
-# Instantiate examples
-# TODO: Add more examples after fixing the reference issue
-test_inputs = [
-    [2., 3.],
-#    [3., 4.],[1., 1.]
-]
-golden_outputs = [
-    [-0.02973187, 0.1229473, 0.20885126, -0.15358765,],
-#    [-0.03716109, 0.12507336, 0.41193449,  -0.20860538],
-#    [-0.15053082, 0.09120187,  0.24278517,  -0.12222792]
-]
-
-for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs):
-  output0 = {
-      scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
-      cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
-      output_state_out: [ 0 for x in range(n_batch * n_output) ],
-      output: output_tensor
-  }
-  input0[input] = input_tensor
-  input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
-  input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
-  Example((input0, output0))
+test_input = [2., 3.]
+output_state = [0, 0, 0, 0]
+cell_state = [0, 0, 0, 0]
+golden_output = [-0.02973187, 0.1229473, 0.20885126, -0.15358765,]
+output0 = {
+  scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+  cell_state_out: [ -0.145439, 0.157475, 0.293663, -0.277353 ],
+  output_state_out: [ -0.0297319, 0.122947, 0.208851, -0.153588 ],
+  output: golden_output
+}
+input0[input] = test_input
+input0[output_state_in] = output_state
+input0[cell_state_in] = cell_state
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm2.mod.py b/nn/runtime/test/specs/lstm2.mod.py
index d5afb0cd9..580464f4f 100644
--- a/nn/runtime/test/specs/lstm2.mod.py
+++ b/nn/runtime/test/specs/lstm2.mod.py
@@ -56,8 +56,8 @@ cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
 proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
 
 scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell * 3))
-output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
 
 model = model.Operation("LSTM",
@@ -134,25 +134,13 @@ input0 = {input_to_input_weights:[],
 
 output0 = {
     scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
-    cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
-    output_state_out: [ 0 for x in range(n_batch * n_output) ],
+    cell_state_out: [ -0.760444, -0.0180416, 0.182264, -0.0649371 ],
+    output_state_out: [ -0.364445, -0.00352185, 0.128866, -0.0516365 ],
 }
 
-# Instantiate examples
-# TODO: Add more examples after fixing the reference issue
-test_inputs = [
-    [2., 3.],
-#    [3., 4.],[1., 1.]
-]
-golden_outputs = [
-    [-0.36444446, -0.00352185, 0.12886585, -0.05163646],
-#    [-0.42312205, -0.01218222, 0.24201041, -0.08124574],
-#    [-0.358325,   -0.04621704, 0.21641694, -0.06471302]
-]
-
-for (input_tensor, output_tensor) in zip(test_inputs, golden_outputs):
-  input0[input] = input_tensor
-  input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
-  input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
-  output0[output] = output_tensor
-  Example((input0, output0))
+input0[input] = [2., 3.]
+input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
+input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
+output0[output] = [-0.36444446, -0.00352185, 0.12886585, -0.05163646]
+
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm2_state.mod.py b/nn/runtime/test/specs/lstm2_state.mod.py
new file mode 100644
index 000000000..a89f265b8
--- /dev/null
+++ b/nn/runtime/test/specs/lstm2_state.mod.py
@@ -0,0 +1,145 @@
+#
+# Copyright (C) 2017 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.
+#
+
+# LSTM Test, With Cifg, With Peephole, No Projection, No Clipping.
+
+model = Model()
+
+n_batch = 1
+n_input = 2
+# n_cell and n_output have the same size when there is no projection.
+n_cell = 4
+n_output = 4
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+
+cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}")
+cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+
+input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+
+projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
+projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}");
+cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
+proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell * 3))
+output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+
+model = model.Operation("LSTM",
+                        input,
+
+                        input_to_input_weights,
+                        input_to_forget_weights,
+                        input_to_cell_weights,
+                        input_to_output_weights,
+
+                        recurrent_to_input_weights,
+                        recurrent_to_forget_weights,
+                        recurrent_to_cell_weights,
+                        recurrent_to_output_weights,
+
+                        cell_to_input_weights,
+                        cell_to_forget_weights,
+                        cell_to_output_weights,
+
+                        input_gate_bias,
+                        forget_gate_bias,
+                        cell_gate_bias,
+                        output_gate_bias,
+
+                        projection_weights,
+                        projection_bias,
+
+                        output_state_in,
+                        cell_state_in,
+
+                        activation_param,
+                        cell_clip_param,
+                        proj_clip_param
+).To([scratch_buffer, output_state_out, cell_state_out, output])
+
+input0 = {input_to_input_weights:[],
+          input_to_cell_weights: [-0.49770179, -0.27711356, -0.09624726, 0.05100781, 0.04717243, 0.48944736, -0.38535351, -0.17212132],
+          input_to_forget_weights: [-0.55291498, -0.42866567, 0.13056988, -0.3633365, -0.22755712, 0.28253698, 0.24407166, 0.33826375],
+          input_to_output_weights: [0.10725588, -0.02335852, -0.55932593, -0.09426838, -0.44257352, 0.54939759, 0.01533556, 0.42751634],
+
+          input_gate_bias:  [],
+          forget_gate_bias: [1.,1.,1.,1.],
+          cell_gate_bias:   [0.,0.,0.,0.],
+          output_gate_bias: [0.,0.,0.,0.],
+
+          recurrent_to_input_weights: [],
+          recurrent_to_cell_weights: [
+              0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711,
+              0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004,
+              0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288,
+              0.21193194],
+
+          recurrent_to_forget_weights: [
+              -0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827,
+            0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795,
+            -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349],
+
+          recurrent_to_output_weights: [
+              0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908,
+              -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835,
+              0.50248802, 0.26114327, -0.43736315, 0.33149987],
+
+          cell_to_input_weights: [],
+          cell_to_forget_weights: [0.47485286, -0.51955009, -0.24458408, 0.31544167],
+          cell_to_output_weights: [-0.17135078, 0.82760304, 0.85573703, -0.77109635],
+
+          projection_weights: [],
+          projection_bias: [],
+
+          activation_param: [4],  # Tanh
+          cell_clip_param: [0.],
+          proj_clip_param: [0.],
+}
+
+output0 = {
+    scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+    cell_state_out: [ -0.978419, -0.139203, 0.338163, -0.0983904 ],
+    output_state_out: [ -0.423122, -0.0121822, 0.24201, -0.0812458 ],
+}
+
+input0[input] = [3., 4.]
+input0[output_state_in] = [-0.364445, -0.00352185, 0.128866, -0.0516365]
+input0[cell_state_in] = [-0.760444, -0.0180416, 0.182264, -0.0649371]
+output0[output] = [-0.42312205, -0.01218222, 0.24201041, -0.08124574]
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm2_state2.mod.py b/nn/runtime/test/specs/lstm2_state2.mod.py
new file mode 100644
index 000000000..3e84440df
--- /dev/null
+++ b/nn/runtime/test/specs/lstm2_state2.mod.py
@@ -0,0 +1,146 @@
+#
+# Copyright (C) 2017 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.
+#
+
+# LSTM Test, With Cifg, With Peephole, No Projection, No Clipping.
+
+model = Model()
+
+n_batch = 1
+n_input = 2
+# n_cell and n_output have the same size when there is no projection.
+n_cell = 4
+n_output = 4
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+
+cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}")
+cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+
+input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+
+projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
+projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}");
+cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
+proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell * 3))
+output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+
+model = model.Operation("LSTM",
+                        input,
+
+                        input_to_input_weights,
+                        input_to_forget_weights,
+                        input_to_cell_weights,
+                        input_to_output_weights,
+
+                        recurrent_to_input_weights,
+                        recurrent_to_forget_weights,
+                        recurrent_to_cell_weights,
+                        recurrent_to_output_weights,
+
+                        cell_to_input_weights,
+                        cell_to_forget_weights,
+                        cell_to_output_weights,
+
+                        input_gate_bias,
+                        forget_gate_bias,
+                        cell_gate_bias,
+                        output_gate_bias,
+
+                        projection_weights,
+                        projection_bias,
+
+                        output_state_in,
+                        cell_state_in,
+
+                        activation_param,
+                        cell_clip_param,
+                        proj_clip_param
+).To([scratch_buffer, output_state_out, cell_state_out, output])
+
+input0 = {input_to_input_weights:[],
+          input_to_cell_weights: [-0.49770179, -0.27711356, -0.09624726, 0.05100781, 0.04717243, 0.48944736, -0.38535351, -0.17212132],
+          input_to_forget_weights: [-0.55291498, -0.42866567, 0.13056988, -0.3633365, -0.22755712, 0.28253698, 0.24407166, 0.33826375],
+          input_to_output_weights: [0.10725588, -0.02335852, -0.55932593, -0.09426838, -0.44257352, 0.54939759, 0.01533556, 0.42751634],
+
+          input_gate_bias:  [],
+          forget_gate_bias: [1.,1.,1.,1.],
+          cell_gate_bias:   [0.,0.,0.,0.],
+          output_gate_bias: [0.,0.,0.,0.],
+
+          recurrent_to_input_weights: [],
+          recurrent_to_cell_weights: [
+              0.54066205, -0.32668582, -0.43562764, -0.56094903, 0.42957711,
+              0.01841056, -0.32764608, -0.33027974, -0.10826075, 0.20675004,
+              0.19069612, -0.03026325, -0.54532051, 0.33003211, 0.44901288,
+              0.21193194],
+
+          recurrent_to_forget_weights: [
+              -0.13832897, -0.0515101, -0.2359007, -0.16661474, -0.14340827,
+            0.36986142, 0.23414481, 0.55899, 0.10798943, -0.41174671, 0.17751795,
+            -0.34484994, -0.35874045, -0.11352962, 0.27268326, 0.54058349],
+
+          recurrent_to_output_weights: [
+              0.41613156, 0.42610586, -0.16495961, -0.5663873, 0.30579174, -0.05115908,
+              -0.33941799, 0.23364776, 0.11178309, 0.09481031, -0.26424935, 0.46261835,
+              0.50248802, 0.26114327, -0.43736315, 0.33149987],
+
+          cell_to_input_weights: [],
+          cell_to_forget_weights: [0.47485286, -0.51955009, -0.24458408, 0.31544167],
+          cell_to_output_weights: [-0.17135078, 0.82760304, 0.85573703, -0.77109635],
+
+          projection_weights: [],
+          projection_bias: [],
+
+          activation_param: [4],  # Tanh
+          cell_clip_param: [0.],
+          proj_clip_param: [0.],
+}
+
+output0 = {
+    scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+    cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
+    output_state_out: [ 0 for x in range(n_batch * n_output) ],
+}
+
+input0[input] = [1., 1.]
+input0[output_state_in] = [-0.423122, -0.0121822, 0.24201, -0.0812458]
+input0[cell_state_in] = [-0.978419, -0.139203, 0.338163, -0.0983904]
+output0[output] = [-0.358325, -0.04621704, 0.21641694, -0.06471302]
+
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm3.mod.py b/nn/runtime/test/specs/lstm3.mod.py
index 23e2b2e28..acfaa4cc5 100644
--- a/nn/runtime/test/specs/lstm3.mod.py
+++ b/nn/runtime/test/specs/lstm3.mod.py
@@ -56,8 +56,8 @@ cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
 proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
 
 scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
-output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
-cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
 output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
 
 # TODO: need support for more than one output
@@ -616,74 +616,51 @@ input0 = {input_to_input_weights: [
           proj_clip_param: [0.],
 }
 
-# Instantiate examples
-# TODO: Add more examples after fixing the reference issue
-test_inputs_batch0 = [
-    # Batch0: 4 (input_sequence_size) * 5 (n_input)
-    [0.787926, 0.151646, 0.071352, 0.118426, 0.458058],
-#    [0.596268, 0.998386, 0.568695, 0.864524, 0.571277],
-#    [0.073204, 0.296072, 0.743333, 0.069199, 0.045348],
-#    [0.867394, 0.291279, 0.013714, 0.482521, 0.626339]
-    ]
-
-test_inputs_batch1 = [
-    # Batch1: 4 (input_sequence_size) * 5 (n_input)
-    [0.295743, 0.544053, 0.690064, 0.858138, 0.497181],
-#    [0.642421, 0.524260, 0.134799, 0.003639, 0.162482],
-#    [0.640394, 0.930399, 0.050782, 0.432485, 0.988078],
-#    [0.082922, 0.563329, 0.865614, 0.333232, 0.259916]
-]
-
-golden_outputs_batch0 = [
-    # Batch0: 4 (input_sequence_size) * 16 (n_output)
-    [-0.00396806, 0.029352,     -0.00279226, 0.0159977,   -0.00835576,
-     -0.0211779,  0.0283512,    -0.0114597,  0.00907307,  -0.0244004,
-     -0.0152191,  -0.0259063,   0.00914318,  0.00415118,  0.017147,
-     0.0134203],
-#   [-0.0166936,   0.0381209,   0.000889694, 0.0143363,
-#    -0.0328911,  -0.0234288,   0.0333051,   -0.012229,   0.0110322,
-#    -0.0457725,  -0.000832209, -0.0202817,  0.0327257,   0.0121308,
-#    0.0155969,   0.0312091],
-#   [-0.0213783,  0.0350169,   0.000324794,
-#    0.0276012,   -0.0263374,   -0.0371449,  0.0446149,   -0.0205474,
-#    0.0103729,   -0.0576349,   -0.0150052,  -0.0292043,  0.0376827,
-#    0.0136115,   0.0243435,    0.0354492],
-#   [-0.0189322,  0.0464512,
-#    -0.00251373, 0.0225745,    -0.0308346,  -0.0317124,  0.0460407,
-#    -0.0189395,  0.0149363,    -0.0530162,  -0.0150767,  -0.0340193,
-#    0.0286833,   0.00824207,   0.0264887,   0.0305169]
-]
-
-golden_outputs_batch1 = [
-    # Batch1: 4 (input_sequence_size) * 16 (n_output)
+# Batch0: 4 (input_sequence_size) * 5 (n_input)
+input0[input] = [0.787926, 0.151646, 0.071352, 0.118426, 0.458058]
+# Batch1: 4 (input_sequence_size) * 5 (n_input)
+input0[input].extend(
+  [0.295743, 0.544053, 0.690064, 0.858138, 0.497181],
+)
+input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
+input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
+output0 = {
+  scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+  cell_state_out: [
+      -0.0531632, -0.0118138, 0.0870833, 0.0347929,
+      -0.076144, -0.0659219, -0.0463811, 0.0141307,
+      -0.0127706, -0.03782, -0.00402401, -0.00571876,
+      -0.187957, -0.0247127, 0.0711425, 0.008244,
+      0.0492649, 0.126972, 0.0933097, 0.29848,
+      -0.0966178, -0.114417, 0.0387229, 0.0453255,
+      -0.181286, -0.0651251, -0.0996879, -0.00276995,
+      0.0617558, -0.0100728, 0.056304, -0.077416,
+      -0.162858, -0.0541251, 0.0571202, -0.0525331,
+      0.0724297, 0.171029, 0.141738, 0.295483,
+  ],
+  output_state_out: [
+      -0.00396806, 0.029352, -0.00279226, 0.0159977,
+      -0.00835577, -0.0211779, 0.0283512, -0.0114597,
+      0.00907307, -0.0244004, -0.0152191, -0.0259063,
+      0.00914318, 0.00415119, 0.017147, 0.0134203,
+      -0.013869, 0.0287268, -0.00334694, 0.00733397,
+      -0.0287926, -0.0186926, 0.0193662, -0.0115437,
+      0.00422612, -0.0345232, 0.00223253, -0.00957321,
+      0.0210624, 0.013331, 0.0150954, 0.0216801
+  ],
+}
+# Batch0: 4 (input_sequence_size) * 16 (n_output)
+output0[output] = [
+  -0.00396806, 0.029352,     -0.00279226, 0.0159977,   -0.00835576,
+  -0.0211779,  0.0283512,    -0.0114597,  0.00907307,  -0.0244004,
+  -0.0152191,  -0.0259063,   0.00914318,  0.00415118,  0.017147,
+  0.0134203]
+# Batch1: 4 (input_sequence_size) * 16 (n_output)
+output0[output].extend(
     [-0.013869,    0.0287268,   -0.00334693, 0.00733398,  -0.0287926,
      -0.0186926,   0.0193662,   -0.0115437,  0.00422612,  -0.0345232,
      0.00223253,   -0.00957321, 0.0210624,   0.013331,    0.0150954,
      0.02168],
-#    [-0.0141913,  0.0322082,   0.00227024,  0.0260507,
-#     -0.0188721,   -0.0296489,  0.0399134,   -0.0160509,  0.0116039,
-#     -0.0447318,   -0.0150515,  -0.0277406,  0.0316596,   0.0118233,
-#     0.0214762,    0.0293641],
-#    [-0.0204549,  0.0450315,   -0.00117378,
-#     0.0167673,    -0.0375007,  -0.0238314,  0.038784,    -0.0174034,
-#     0.0131743,    -0.0506589,  -0.0048447,  -0.0240239,  0.0325789,
-#     0.00790065,   0.0220157,   0.0333314],
-#    [-0.0264787,  0.0387855,
-#     -0.000764675, 0.0217599,   -0.037537,   -0.0335206,  0.0431679,
-#     -0.0211424,   0.010203,    -0.062785,   -0.00832363, -0.025181,
-#     0.0412031,    0.0118723,   0.0239643,   0.0394009]
-]
-
-for (input_tensor0, input_tensor1, output_tensor0, output_tensor1) in zip(test_inputs_batch0, test_inputs_batch1, golden_outputs_batch0, golden_outputs_batch1):
-  input_tensor0.extend(input_tensor1)
-  input0[input] = input_tensor0
-  input0[cell_state_in] = [ 0 for _ in range(n_batch * n_cell) ]
-  input0[output_state_in] = [ 0 for _ in range(n_batch * n_output) ]
-  output_tensor0.extend(output_tensor1)
-  output0 = {
-      scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
-      cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
-      output_state_out: [ 0 for x in range(n_batch * n_output) ],
-      output: output_tensor0
-  }
-  Example((input0, output0))
+  )
+
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm3_state.mod.py b/nn/runtime/test/specs/lstm3_state.mod.py
new file mode 100644
index 000000000..46a1f8166
--- /dev/null
+++ b/nn/runtime/test/specs/lstm3_state.mod.py
@@ -0,0 +1,687 @@
+#
+# Copyright (C) 2017 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.
+#
+
+# LSTM Test, With Peephole, With Projection, No Clipping
+
+model = Model()
+
+n_batch = 2
+n_input = 5
+# n_cell and n_output have the same size when there is no projection.
+n_cell = 20
+n_output = 16
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+
+cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" %(n_cell))
+cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+
+input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+
+projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
+projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}");
+cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
+proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
+output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+
+# TODO: need support for more than one output
+model = model.Operation("LSTM",
+                        input,
+
+                        input_to_input_weights,
+                        input_to_forget_weights,
+                        input_to_cell_weights,
+                        input_to_output_weights,
+
+                        recurrent_to_input_weights,
+                        recurrent_to_forget_weights,
+                        recurrent_to_cell_weights,
+                        recurrent_to_output_weights,
+
+                        cell_to_input_weights,
+                        cell_to_forget_weights,
+                        cell_to_output_weights,
+
+                        input_gate_bias,
+                        forget_gate_bias,
+                        cell_gate_bias,
+                        output_gate_bias,
+
+                        projection_weights,
+                        projection_bias,
+
+                        output_state_in,
+                        cell_state_in,
+
+                        activation_param,
+                        cell_clip_param,
+                        proj_clip_param
+).To([scratch_buffer, output_state_out, cell_state_out, output])
+
+input0 = {input_to_input_weights: [
+    0.021393683,  0.06124551,    0.046905167,  -0.014657677,  -0.03149463,
+    0.09171803,   0.14647801,    0.10797193,   -0.0057968358, 0.0019193048,
+    -0.2726754,   0.10154029,    -0.018539885, 0.080349885,   -0.10262385,
+    -0.022599787, -0.09121155,   -0.008675967, -0.045206103,  -0.0821282,
+    -0.008045952, 0.015478081,   0.055217247,  0.038719587,   0.044153627,
+    -0.06453243,  0.05031825,    -0.046935108, -0.008164439,  0.014574226,
+    -0.1671009,   -0.15519552,   -0.16819797,  -0.13971269,   -0.11953059,
+    0.25005487,   -0.22790983,   0.009855087,  -0.028140958,  -0.11200698,
+    0.11295408,   -0.0035217577, 0.054485075,  0.05184695,    0.064711206,
+    0.10989193,   0.11674786,    0.03490607,   0.07727357,    0.11390585,
+    -0.1863375,   -0.1034451,    -0.13945189,  -0.049401227,  -0.18767063,
+    0.042483903,  0.14233552,    0.13832581,   0.18350165,    0.14545603,
+    -0.028545704, 0.024939531,   0.050929718,  0.0076203286,  -0.0029723682,
+    -0.042484224, -0.11827596,   -0.09171104,  -0.10808628,   -0.16327988,
+    -0.2273378,   -0.0993647,    -0.017155107, 0.0023917493,  0.049272764,
+    0.0038534778, 0.054764505,   0.089753784,  0.06947234,    0.08014476,
+    -0.04544234,  -0.0497073,    -0.07135631,  -0.048929106,  -0.004042012,
+    -0.009284026, 0.018042054,   0.0036860977, -0.07427302,   -0.11434604,
+    -0.018995456, 0.031487543,   0.012834908,  0.019977754,   0.044256654,
+    -0.39292613,  -0.18519334,   -0.11651281,  -0.06809892,   0.011373677],
+
+          input_to_forget_weights: [
+              -0.0018401089, -0.004852237,  0.03698424,   0.014181704,   0.028273236,
+            -0.016726194,  -0.05249759,   -0.10204261,  0.00861066,    -0.040979505,
+            -0.009899187,  0.01923892,    -0.028177269, -0.08535103,   -0.14585495,
+            0.10662567,    -0.01909731,   -0.017883534, -0.0047269356, -0.045103323,
+            0.0030784295,  0.076784775,   0.07463696,   0.094531395,   0.0814421,
+            -0.12257899,   -0.033945758,  -0.031303465, 0.045630626,   0.06843887,
+            -0.13492945,   -0.012480007,  -0.0811829,   -0.07224499,   -0.09628791,
+            0.045100946,   0.0012300825,  0.013964662,  0.099372394,   0.02543059,
+            0.06958324,    0.034257296,   0.0482646,    0.06267997,    0.052625068,
+            0.12784666,    0.07077897,    0.025725935,  0.04165009,    0.07241905,
+            0.018668644,   -0.037377294,  -0.06277783,  -0.08833636,   -0.040120605,
+            -0.011405586,  -0.007808335,  -0.010301386, -0.005102167,  0.027717464,
+            0.05483423,    0.11449111,    0.11289652,   0.10939839,    0.13396506,
+            -0.08402166,   -0.01901462,   -0.044678304, -0.07720565,   0.014350063,
+            -0.11757958,   -0.0652038,    -0.08185733,  -0.076754324,  -0.092614375,
+            0.10405491,    0.052960336,   0.035755895,  0.035839386,   -0.012540553,
+            0.036881298,   0.02913376,    0.03420159,   0.05448447,    -0.054523353,
+            0.02582715,    0.02327355,    -0.011857179, -0.0011980024, -0.034641717,
+            -0.026125094,  -0.17582615,   -0.15923657,  -0.27486774,   -0.0006143371,
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+            0.19271925,    -0.0032889997, -0.043264326,  0.09663576,   -0.057112187,
+            -0.10100678,   0.0628376,     0.04447668,    0.017961001,  -0.10094388,
+            -0.10190601,   0.18335468,    0.10494553,    -0.052095775, -0.0026118709,
+            0.10539724,    -0.04383912,   -0.042349473,  0.08438151,   -0.1947263,
+            0.02251204,    0.11216432,    -0.10307853,   0.17351969,   -0.039091777,
+            0.08066188,    -0.00561982,   0.12633002,    0.11335965,   -0.0088127935,
+            -0.019777594,  0.06864014,    -0.059751723,  0.016233567,  -0.06894641,
+            -0.28651384,   -0.004228674,  0.019708522,   -0.16305895,  -0.07468996,
+            -0.0855457,    0.099339016,   -0.07580735,   -0.13775392,  0.08434318,
+            0.08330512,    -0.12131499,   0.031935584,   0.09180414,   -0.08876437,
+            -0.08049874,   0.008753825,   0.03498998,    0.030215185,  0.03907079,
+            0.089751154,   0.029194152,   -0.03337423,   -0.019092513, 0.04331237,
+            0.04299654,    -0.036394123,  -0.12915532,   0.09793732,   0.07512415,
+            -0.11319543,   -0.032502122,  0.15661901,    0.07671967,   -0.005491124,
+            -0.19379048,   -0.218606,     0.21448623,    0.017840758,  0.1416943,
+            -0.07051762,   0.19488361,    0.02664691,    -0.18104725,  -0.09334311,
+            0.15026465,    -0.15493552,   -0.057762887,  -0.11604192,  -0.262013,
+            -0.01391798,   0.012185008,   0.11156489,    -0.07483202,  0.06693364,
+            -0.26151478,   0.046425626,   0.036540434,   -0.16435726,  0.17338543,
+            -0.21401681,   -0.11385144,   -0.08283257,   -0.069031075, 0.030635102,
+            0.010969227,   0.11109743,    0.010919218,   0.027526086,  0.13519906,
+            0.01891392,    -0.046839405,  -0.040167913,  0.017953383,  -0.09700955,
+            0.0061885654,  -0.07000971,   0.026893595,   -0.038844477, 0.14543656],
+
+          projection_bias: [],
+
+          activation_param: [4],  # Tanh
+          cell_clip_param: [0.],
+          proj_clip_param: [0.],
+}
+
+# Batch0: 4 (input_sequence_size) * 5 (n_input)
+input0[input] = [0.596268, 0.998386, 0.568695, 0.864524, 0.571277]
+# Batch1: 4 (input_sequence_size) * 5 (n_input)
+input0[input].extend(
+    [0.642421, 0.524260, 0.134799, 0.003639, 0.162482]
+)
+input0[output_state_in] = [
+  -0.00396806, 0.029352, -0.00279226, 0.0159977,
+  -0.00835577, -0.0211779, 0.0283512, -0.0114597,
+  0.00907307, -0.0244004, -0.0152191, -0.0259063,
+  0.00914318, 0.00415119, 0.017147, 0.0134203,
+  -0.013869, 0.0287268, -0.00334694, 0.00733397,
+  -0.0287926, -0.0186926, 0.0193662, -0.0115437,
+  0.00422612, -0.0345232, 0.00223253, -0.00957321,
+  0.0210624, 0.013331, 0.0150954, 0.0216801,
+]
+input0[cell_state_in] = [
+  -0.0531632, -0.0118138, 0.0870833, 0.0347929,
+  -0.076144, -0.0659219, -0.0463811, 0.0141307,
+  -0.0127706, -0.03782, -0.00402401, -0.00571876,
+  -0.187957, -0.0247127, 0.0711425, 0.008244,
+  0.0492649, 0.126972, 0.0933097, 0.29848,
+  -0.0966178, -0.114417, 0.0387229, 0.0453255,
+  -0.181286, -0.0651251, -0.0996879, -0.00276995,
+  0.0617558, -0.0100728, 0.056304, -0.077416,
+  -0.162858, -0.0541251, 0.0571202, -0.0525331,
+  0.0724297, 0.171029, 0.141738, 0.295483,
+]
+output0 = {
+  scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+  cell_state_out: [
+      -0.154022, -0.124934, 0.0478463, 0.0607819,
+      -0.218727, -0.111053, -0.103885, -0.00447221,
+      0.0554757, -0.0207068, 0.0595767, -0.116297,
+      -0.249466, -0.0723206, 0.0794942, -0.0377107,
+      0.124532, 0.249952, 0.188641, 0.411865,
+      -0.11012, -0.0694494, 0.103501, 0.0428427,
+      -0.167345, -0.106061, -0.0775679, 0.00936161,
+      0.0105526, -0.0314523, 0.0243475, -0.132179,
+      -0.258763, -0.0307266, 0.107047, -0.0115197,
+      0.0995485, 0.220027, 0.158355, 0.436369,
+  ],
+  output_state_out: [
+      -0.0166936, 0.0381209, 0.000889684, 0.0143363,
+      -0.0328911, -0.0234288, 0.0333051, -0.012229,
+      0.0110322, -0.0457725, -0.000832209, -0.0202817,
+      0.0327257, 0.0121309, 0.0155969, 0.0312091,
+      -0.0141913, 0.0322082, 0.00227024, 0.0260507,
+      -0.0188721, -0.0296489, 0.0399134, -0.0160509,
+      0.011604, -0.0447318, -0.0150515, -0.0277406,
+      0.0316596, 0.0118233, 0.0214762, 0.0293641
+  ],
+}
+
+# Batch0: 4 (input_sequence_size) * 16 (n_output)
+output0[output] = [
+    -0.0166936,   0.0381209,   0.000889694, 0.0143363,
+    -0.0328911,  -0.0234288,   0.0333051,   -0.012229,   0.0110322,
+    -0.0457725,  -0.000832209, -0.0202817,  0.0327257,   0.0121308,
+    0.0155969,   0.0312091]
+# Batch1: 4 (input_sequence_size) * 16 (n_output)
+output0[output].extend(
+    [-0.0141913,  0.0322082,   0.00227024,  0.0260507,
+     -0.0188721,   -0.0296489,  0.0399134,   -0.0160509,  0.0116039,
+     -0.0447318,   -0.0150515,  -0.0277406,  0.0316596,   0.0118233,
+     0.0214762,    0.0293641]
+    )
+
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm3_state2.mod.py b/nn/runtime/test/specs/lstm3_state2.mod.py
new file mode 100644
index 000000000..da6004f7c
--- /dev/null
+++ b/nn/runtime/test/specs/lstm3_state2.mod.py
@@ -0,0 +1,687 @@
+#
+# Copyright (C) 2017 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.
+#
+
+# LSTM Test, With Peephole, With Projection, No Clipping
+
+model = Model()
+
+n_batch = 2
+n_input = 5
+# n_cell and n_output have the same size when there is no projection.
+n_cell = 20
+n_output = 16
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+
+cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" %(n_cell))
+cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+
+input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+
+projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
+projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}");
+cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
+proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
+output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+
+# TODO: need support for more than one output
+model = model.Operation("LSTM",
+                        input,
+
+                        input_to_input_weights,
+                        input_to_forget_weights,
+                        input_to_cell_weights,
+                        input_to_output_weights,
+
+                        recurrent_to_input_weights,
+                        recurrent_to_forget_weights,
+                        recurrent_to_cell_weights,
+                        recurrent_to_output_weights,
+
+                        cell_to_input_weights,
+                        cell_to_forget_weights,
+                        cell_to_output_weights,
+
+                        input_gate_bias,
+                        forget_gate_bias,
+                        cell_gate_bias,
+                        output_gate_bias,
+
+                        projection_weights,
+                        projection_bias,
+
+                        output_state_in,
+                        cell_state_in,
+
+                        activation_param,
+                        cell_clip_param,
+                        proj_clip_param
+).To([scratch_buffer, output_state_out, cell_state_out, output])
+
+input0 = {input_to_input_weights: [
+    0.021393683,  0.06124551,    0.046905167,  -0.014657677,  -0.03149463,
+    0.09171803,   0.14647801,    0.10797193,   -0.0057968358, 0.0019193048,
+    -0.2726754,   0.10154029,    -0.018539885, 0.080349885,   -0.10262385,
+    -0.022599787, -0.09121155,   -0.008675967, -0.045206103,  -0.0821282,
+    -0.008045952, 0.015478081,   0.055217247,  0.038719587,   0.044153627,
+    -0.06453243,  0.05031825,    -0.046935108, -0.008164439,  0.014574226,
+    -0.1671009,   -0.15519552,   -0.16819797,  -0.13971269,   -0.11953059,
+    0.25005487,   -0.22790983,   0.009855087,  -0.028140958,  -0.11200698,
+    0.11295408,   -0.0035217577, 0.054485075,  0.05184695,    0.064711206,
+    0.10989193,   0.11674786,    0.03490607,   0.07727357,    0.11390585,
+    -0.1863375,   -0.1034451,    -0.13945189,  -0.049401227,  -0.18767063,
+    0.042483903,  0.14233552,    0.13832581,   0.18350165,    0.14545603,
+    -0.028545704, 0.024939531,   0.050929718,  0.0076203286,  -0.0029723682,
+    -0.042484224, -0.11827596,   -0.09171104,  -0.10808628,   -0.16327988,
+    -0.2273378,   -0.0993647,    -0.017155107, 0.0023917493,  0.049272764,
+    0.0038534778, 0.054764505,   0.089753784,  0.06947234,    0.08014476,
+    -0.04544234,  -0.0497073,    -0.07135631,  -0.048929106,  -0.004042012,
+    -0.009284026, 0.018042054,   0.0036860977, -0.07427302,   -0.11434604,
+    -0.018995456, 0.031487543,   0.012834908,  0.019977754,   0.044256654,
+    -0.39292613,  -0.18519334,   -0.11651281,  -0.06809892,   0.011373677],
+
+          input_to_forget_weights: [
+              -0.0018401089, -0.004852237,  0.03698424,   0.014181704,   0.028273236,
+            -0.016726194,  -0.05249759,   -0.10204261,  0.00861066,    -0.040979505,
+            -0.009899187,  0.01923892,    -0.028177269, -0.08535103,   -0.14585495,
+            0.10662567,    -0.01909731,   -0.017883534, -0.0047269356, -0.045103323,
+            0.0030784295,  0.076784775,   0.07463696,   0.094531395,   0.0814421,
+            -0.12257899,   -0.033945758,  -0.031303465, 0.045630626,   0.06843887,
+            -0.13492945,   -0.012480007,  -0.0811829,   -0.07224499,   -0.09628791,
+            0.045100946,   0.0012300825,  0.013964662,  0.099372394,   0.02543059,
+            0.06958324,    0.034257296,   0.0482646,    0.06267997,    0.052625068,
+            0.12784666,    0.07077897,    0.025725935,  0.04165009,    0.07241905,
+            0.018668644,   -0.037377294,  -0.06277783,  -0.08833636,   -0.040120605,
+            -0.011405586,  -0.007808335,  -0.010301386, -0.005102167,  0.027717464,
+            0.05483423,    0.11449111,    0.11289652,   0.10939839,    0.13396506,
+            -0.08402166,   -0.01901462,   -0.044678304, -0.07720565,   0.014350063,
+            -0.11757958,   -0.0652038,    -0.08185733,  -0.076754324,  -0.092614375,
+            0.10405491,    0.052960336,   0.035755895,  0.035839386,   -0.012540553,
+            0.036881298,   0.02913376,    0.03420159,   0.05448447,    -0.054523353,
+            0.02582715,    0.02327355,    -0.011857179, -0.0011980024, -0.034641717,
+            -0.026125094,  -0.17582615,   -0.15923657,  -0.27486774,   -0.0006143371,
+            0.0001771948,  -8.470171e-05, 0.02651807,   0.045790765,   0.06956496],
+
+          input_to_cell_weights: [
+              -0.04580283,   -0.09549462,   -0.032418985,  -0.06454633,
+            -0.043528453,  0.043018587,   -0.049152344,  -0.12418144,
+            -0.078985475,  -0.07596889,   0.019484362,   -0.11434962,
+            -0.0074034138, -0.06314844,   -0.092981495,  0.0062155537,
+            -0.025034338,  -0.0028890965, 0.048929527,   0.06235075,
+            0.10665918,    -0.032036792,  -0.08505916,   -0.10843358,
+            -0.13002433,   -0.036816437,  -0.02130134,   -0.016518239,
+            0.0047691227,  -0.0025825808, 0.066017866,   0.029991534,
+            -0.10652836,   -0.1037554,    -0.13056071,   -0.03266643,
+            -0.033702414,  -0.006473424,  -0.04611692,   0.014419339,
+            -0.025174323,  0.0396852,     0.081777506,   0.06157468,
+            0.10210095,    -0.009658194,  0.046511717,   0.03603906,
+            0.0069369148,  0.015960095,   -0.06507666,   0.09551598,
+            0.053568836,   0.06408714,    0.12835667,    -0.008714329,
+            -0.20211966,   -0.12093674,   0.029450472,   0.2849013,
+            -0.029227901,  0.1164364,     -0.08560263,   0.09941786,
+            -0.036999565,  -0.028842626,  -0.0033637602, -0.017012902,
+            -0.09720865,   -0.11193351,   -0.029155117,  -0.017936034,
+            -0.009768936,  -0.04223324,   -0.036159635,  0.06505112,
+            -0.021742892,  -0.023377212,  -0.07221364,   -0.06430552,
+            0.05453865,    0.091149814,   0.06387331,    0.007518393,
+            0.055960953,   0.069779344,   0.046411168,   0.10509911,
+            0.07463894,    0.0075130584,  0.012850982,   0.04555431,
+            0.056955688,   0.06555285,    0.050801456,   -0.009862683,
+            0.00826772,    -0.026555609,  -0.0073611983, -0.0014897042],
+
+          input_to_output_weights: [
+              -0.0998932,   -0.07201956,  -0.052803773,  -0.15629593,  -0.15001918,
+            -0.07650751,  0.02359855,   -0.075155355,  -0.08037709,  -0.15093534,
+            0.029517552,  -0.04751393,  0.010350531,   -0.02664851,  -0.016839722,
+            -0.023121163, 0.0077019283, 0.012851257,   -0.05040649,  -0.0129761,
+            -0.021737747, -0.038305793, -0.06870586,   -0.01481247,  -0.001285394,
+            0.10124236,   0.083122835,  0.053313006,   -0.062235646, -0.075637154,
+            -0.027833903, 0.029774971,  0.1130802,     0.09218906,   0.09506135,
+            -0.086665764, -0.037162706, -0.038880914,  -0.035832845, -0.014481564,
+            -0.09825003,  -0.12048569,  -0.097665586,  -0.05287633,  -0.0964047,
+            -0.11366429,  0.035777505,  0.13568819,    0.052451383,  0.050649304,
+            0.05798951,   -0.021852335, -0.099848844,  0.014740475,  -0.078897946,
+            0.04974699,   0.014160473,  0.06973932,    0.04964942,   0.033364646,
+            0.08190124,   0.025535367,  0.050893165,   0.048514254,  0.06945813,
+            -0.078907564, -0.06707616,  -0.11844508,   -0.09986688,  -0.07509403,
+            0.06263226,   0.14925587,   0.20188436,    0.12098451,   0.14639415,
+            0.0015017595, -0.014267382, -0.03417257,   0.012711468,  0.0028300495,
+            -0.024758482, -0.05098548,  -0.0821182,    0.014225672,  0.021544158,
+            0.08949725,   0.07505268,   -0.0020780868, 0.04908258,   0.06476295,
+            -0.022907063, 0.027562456,  0.040185735,   0.019567577,  -0.015598739,
+            -0.049097303, -0.017121866, -0.083368234,  -0.02332002,  -0.0840956],
+
+          input_gate_bias:  [
+              0.02234832,  0.14757581,   0.18176508,  0.10380666,  0.053110216,
+              -0.06928846, -0.13942584,  -0.11816189, 0.19483899,  0.03652339,
+              -0.10250295, 0.036714908,  -0.18426876, 0.036065217, 0.21810818,
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+            -0.017840602,   0.024307009,   -0.040079936,  -0.019946516,
+            0.045318738,    -0.12233574,   0.026170589,   0.0074471775,
+            0.15978073,     0.10185836,    0.10298046,    -0.015476589,
+            -0.039390966,   -0.072174534,  0.0739445,     -0.1211869,
+            -0.0347889,     -0.07943156,   0.014809798,   -0.12412325,
+            -0.0030663363,  0.039695457,   0.0647603,     -0.08291318,
+            -0.018529687,   -0.004423833,  0.0037507233,  0.084633216,
+            -0.01514876,    -0.056505352,  -0.012800942,  -0.06994386,
+            0.012962922,    -0.031234352,  0.07029052,    0.016418684,
+            0.03618972,     0.055686004,   -0.08663945,   -0.017404709,
+            -0.054761406,   0.029065743,   0.052404847,   0.020238016,
+            0.0048197987,   -0.0214882,    0.07078733,    0.013016777,
+            0.06262858,     0.009184685,   0.020785125,   -0.043904778,
+            -0.0270329,     -0.03299152,   -0.060088247,  -0.015162964,
+            -0.001828936,   0.12642565,    -0.056757294,  0.013586685,
+            0.09232601,     -0.035886683,  0.06000002,    0.05229691,
+            -0.052580316,   -0.082029596,  -0.010794592,  0.012947712,
+            -0.036429964,   -0.085508935,  -0.13127148,   -0.017744139,
+            0.031502828,    0.036232427,   -0.031581745,  0.023051167,
+            -0.05325106,    -0.03421577,   0.028793324,   -0.034633752,
+            -0.009881397,   -0.043551125,  -0.018609839,  0.0019097115,
+            -0.008799762,   0.056595087,   0.0022273948,  0.055752404],
+
+          recurrent_to_output_weights: [
+              0.025825322,   -0.05813119,  0.09495884,   -0.045984812,   -0.01255415,
+              -0.0026479573, -0.08196161,  -0.054914974, -0.0046604523,  -0.029587349,
+              -0.044576716,  -0.07480124,  -0.082868785, 0.023254942,    0.027502948,
+              -0.0039728214, -0.08683098,  -0.08116779,  -0.014675607,   -0.037924774,
+              -0.023314456,  -0.007401714, -0.09255757,  0.029460307,    -0.08829125,
+              -0.005139627,  -0.08989442,  -0.0555066,   0.13596267,     -0.025062224,
+              -0.048351806,  -0.03850004,  0.07266485,   -0.022414139,   0.05940088,
+              0.075114764,   0.09597592,   -0.010211725, -0.0049794707,  -0.011523867,
+              -0.025980417,  0.072999895,  0.11091378,   -0.081685916,   0.014416728,
+              0.043229222,   0.034178585,  -0.07530371,  0.035837382,    -0.085607,
+              -0.007721233,  -0.03287832,  -0.043848954, -0.06404588,    -0.06632928,
+              -0.073643476,  0.008214239,  -0.045984086, 0.039764922,    0.03474462,
+              0.060612556,   -0.080590084, 0.049127717,  0.04151091,     -0.030063879,
+              0.008801774,   -0.023021035, -0.019558564, 0.05158114,     -0.010947698,
+              -0.011825728,  0.0075720972, 0.0699727,    -0.0039981045,  0.069350146,
+              0.08799282,    0.016156472,  0.035502106,  0.11695009,     0.006217345,
+              0.13392477,    -0.037875112, 0.025745004,  0.08940699,     -0.00924166,
+              0.0046702605,  -0.036598757, -0.08811812,  0.10522024,     -0.032441203,
+              0.008176899,   -0.04454919,  0.07058152,   0.0067963637,   0.039206743,
+              0.03259838,    0.03725492,   -0.09515802,  0.013326398,    -0.052055415,
+              -0.025676316,  0.03198509,   -0.015951829, -0.058556724,   0.036879618,
+              0.043357447,   0.028362012,  -0.05908629,  0.0059240665,   -0.04995891,
+              -0.019187413,  0.0276265,    -0.01628143,  0.0025863599,   0.08800015,
+              0.035250366,   -0.022165963, -0.07328642,  -0.009415526,   -0.07455109,
+              0.11690406,    0.0363299,    0.07411125,   0.042103454,    -0.009660886,
+              0.019076364,   0.018299393,  -0.046004917, 0.08891175,     0.0431396,
+              -0.026327137,  -0.051502608, 0.08979574,   -0.051670972,   0.04940282,
+              -0.07491107,   -0.021240504, 0.022596184,  -0.034280192,   0.060163025,
+              -0.058211457,  -0.051837247, -0.01349775,  -0.04639988,    -0.035936575,
+              -0.011681591,  0.064818054,  0.0073146066, -0.021745546,   -0.043124277,
+              -0.06471268,   -0.07053354,  -0.029321948, -0.05330136,    0.016933719,
+              -0.053782392,  0.13747959,   -0.1361751,   -0.11569455,    0.0033329215,
+              0.05693899,    -0.053219706, 0.063698,     0.07977434,     -0.07924483,
+              0.06936997,    0.0034815092, -0.007305279, -0.037325785,   -0.07251102,
+              -0.033633437,  -0.08677009,  0.091591336,  -0.14165086,    0.021752775,
+              0.019683983,   0.0011612234, -0.058154266, 0.049996935,    0.0288841,
+              -0.0024567875, -0.14345716,  0.010955264,  -0.10234828,    0.1183656,
+              -0.0010731248, -0.023590032, -0.072285876, -0.0724771,     -0.026382286,
+              -0.0014920527, 0.042667855,  0.0018776858, 0.02986552,     0.009814309,
+              0.0733756,     0.12289186,   0.018043943,  -0.0458958,     0.049412545,
+              0.033632483,   0.05495232,   0.036686596,  -0.013781798,   -0.010036754,
+              0.02576849,    -0.08307328,  0.010112348,  0.042521734,    -0.05869831,
+              -0.071689695,  0.03876447,   -0.13275425,  -0.0352966,     -0.023077697,
+              0.10285965,    0.084736146,  0.15568255,   -0.00040734606, 0.027835453,
+              -0.10292561,   -0.032401145, 0.10053256,   -0.026142767,   -0.08271222,
+              -0.0030240538, -0.016368777, 0.1070414,    0.042672627,    0.013456989,
+              -0.0437609,    -0.022309763, 0.11576483,   0.04108048,     0.061026827,
+              -0.0190714,    -0.0869359,   0.037901703,  0.0610107,      0.07202949,
+              0.01675338,    0.086139716,  -0.08795751,  -0.014898893,   -0.023771819,
+              -0.01965048,   0.007955471,  -0.043740474, 0.03346837,     -0.10549954,
+              0.090567775,   0.042013682,  -0.03176985,  0.12569028,     -0.02421228,
+              -0.029526481,  0.023851605,  0.031539805,  0.05292009,     -0.02344001,
+              -0.07811758,   -0.08834428,  0.10094801,   0.16594367,     -0.06861939,
+              -0.021256343,  -0.041093912, -0.06669611,  0.035498552,    0.021757556,
+              -0.09302526,   -0.015403468, -0.06614931,  -0.051798206,   -0.013874718,
+              0.03630673,    0.010412845,  -0.08077351,  0.046185967,    0.0035662893,
+              0.03541868,    -0.094149634, -0.034814864, 0.003128424,    -0.020674974,
+              -0.03944324,   -0.008110165, -0.11113267,  0.08484226,     0.043586485,
+              0.040582247,   0.0968012,    -0.065249965, -0.028036479,   0.0050708856,
+              0.0017462453,  0.0326779,    0.041296225,  0.09164146,     -0.047743853,
+              -0.015952192,  -0.034451712, 0.084197424,  -0.05347844,    -0.11768019,
+              0.085926116,   -0.08251791,  -0.045081906, 0.0948852,      0.068401024,
+              0.024856757,   0.06978981,   -0.057309967, -0.012775832,   -0.0032452994,
+              0.01977615,    -0.041040014, -0.024264973, 0.063464895,    0.05431621],
+
+          cell_to_input_weights: [
+              0.040369894, 0.030746894,  0.24704495,  0.018586371,  -0.037586458,
+              -0.15312155, -0.11812848,  -0.11465643, 0.20259799,   0.11418174,
+              -0.10116027, -0.011334949, 0.12411352,  -0.076769054, -0.052169047,
+              0.21198851,  -0.38871562,  -0.09061183, -0.09683246,  -0.21929175],
+
+          cell_to_forget_weights: [
+              -0.01998659,  -0.15568835,  -0.24248174,   -0.012770197, 0.041331276,
+            -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766,
+            -0.047248036, 0.021479502,  0.033189066,   0.11952997,   -0.020432774,
+            0.64658105,   -0.06650122,  -0.03467612,   0.095340036,  0.23647355],
+
+          cell_to_output_weights: [
+              0.08286371,  -0.08261836, -0.51210177, 0.002913762, 0.17764764,
+              -0.5495371,  -0.08460716, -0.24552552, 0.030037103, 0.04123544,
+              -0.11940523, 0.007358328, 0.1890978,   0.4833202,   -0.34441817,
+              0.36312827,  -0.26375428, 0.1457655,   -0.19724406, 0.15548733],
+
+          projection_weights: [
+              -0.009802181,  0.09401916,    0.0717386,     -0.13895074,  0.09641832,
+            0.060420845,   0.08539281,    0.054285463,   0.061395317,  0.034448683,
+            -0.042991187,  0.019801661,   -0.16840284,   -0.015726732, -0.23041931,
+            -0.024478018,  -0.10959692,   -0.013875541,  0.18600968,   -0.061274476,
+            0.0138165,     -0.08160894,   -0.07661644,   0.032372914,  0.16169067,
+            0.22465782,    -0.03993472,   -0.004017731,  0.08633481,   -0.28869787,
+            0.08682067,    0.17240396,    0.014975425,   0.056431185,  0.031037588,
+            0.16702051,    0.0077946745,  0.15140012,    0.29405436,   0.120285,
+            -0.188994,     -0.027265169,  0.043389652,   -0.022061434, 0.014777949,
+            -0.20203483,   0.094781205,   0.19100232,    0.13987629,   -0.036132768,
+            -0.06426278,   -0.05108664,   0.13221376,    0.009441198,  -0.16715929,
+            0.15859416,    -0.040437475,  0.050779544,   -0.022187516, 0.012166504,
+            0.027685808,   -0.07675938,   -0.0055694645, -0.09444123,  0.0046453946,
+            0.050794356,   0.10770313,    -0.20790008,   -0.07149004,  -0.11425117,
+            0.008225835,   -0.035802525,  0.14374903,    0.15262283,   0.048710253,
+            0.1847461,     -0.007487823,  0.11000021,    -0.09542012,  0.22619456,
+            -0.029149994,  0.08527916,    0.009043713,   0.0042746216, 0.016261552,
+            0.022461696,   0.12689082,    -0.043589946,  -0.12035478,  -0.08361797,
+            -0.050666027,  -0.1248618,    -0.1275799,    -0.071875185, 0.07377272,
+            0.09944291,    -0.18897448,   -0.1593054,    -0.06526116,  -0.040107165,
+            -0.004618631,  -0.067624845,  -0.007576253,  0.10727444,   0.041546922,
+            -0.20424393,   0.06907816,    0.050412357,   0.00724631,   0.039827548,
+            0.12449835,    0.10747581,    0.13708383,    0.09134148,   -0.12617786,
+            -0.06428341,   0.09956831,    0.1208086,     -0.14676677,  -0.0727722,
+            0.1126304,     0.010139365,   0.015571211,   -0.038128063, 0.022913318,
+            -0.042050496,  0.16842307,    -0.060597885,  0.10531834,   -0.06411776,
+            -0.07451711,   -0.03410368,   -0.13393489,   0.06534304,   0.003620307,
+            0.04490757,    0.05970546,    0.05197996,    0.02839995,   0.10434969,
+            -0.013699693,  -0.028353551,  -0.07260381,   0.047201227,  -0.024575593,
+            -0.036445823,  0.07155557,    0.009672501,   -0.02328883,  0.009533515,
+            -0.03606021,   -0.07421458,   -0.028082801,  -0.2678904,   -0.13221288,
+            0.18419984,    -0.13012612,   -0.014588381,  -0.035059117, -0.04824723,
+            0.07830115,    -0.056184657,  0.03277091,    0.025466874,  0.14494097,
+            -0.12522776,   -0.098633975,  -0.10766018,   -0.08317623,  0.08594209,
+            0.07749552,    0.039474737,   0.1776665,     -0.07409566,  -0.0477268,
+            0.29323658,    0.10801441,    0.1154011,     0.013952499,  0.10739139,
+            0.10708251,    -0.051456142,  0.0074137426,  -0.10430189,  0.10034707,
+            0.045594677,   0.0635285,     -0.0715442,    -0.089667566, -0.10811871,
+            0.00026344223, 0.08298446,    -0.009525053,  0.006585689,  -0.24567553,
+            -0.09450807,   0.09648481,    0.026996298,   -0.06419476,  -0.04752702,
+            -0.11063944,   -0.23441927,   -0.17608605,   -0.052156363, 0.067035615,
+            0.19271925,    -0.0032889997, -0.043264326,  0.09663576,   -0.057112187,
+            -0.10100678,   0.0628376,     0.04447668,    0.017961001,  -0.10094388,
+            -0.10190601,   0.18335468,    0.10494553,    -0.052095775, -0.0026118709,
+            0.10539724,    -0.04383912,   -0.042349473,  0.08438151,   -0.1947263,
+            0.02251204,    0.11216432,    -0.10307853,   0.17351969,   -0.039091777,
+            0.08066188,    -0.00561982,   0.12633002,    0.11335965,   -0.0088127935,
+            -0.019777594,  0.06864014,    -0.059751723,  0.016233567,  -0.06894641,
+            -0.28651384,   -0.004228674,  0.019708522,   -0.16305895,  -0.07468996,
+            -0.0855457,    0.099339016,   -0.07580735,   -0.13775392,  0.08434318,
+            0.08330512,    -0.12131499,   0.031935584,   0.09180414,   -0.08876437,
+            -0.08049874,   0.008753825,   0.03498998,    0.030215185,  0.03907079,
+            0.089751154,   0.029194152,   -0.03337423,   -0.019092513, 0.04331237,
+            0.04299654,    -0.036394123,  -0.12915532,   0.09793732,   0.07512415,
+            -0.11319543,   -0.032502122,  0.15661901,    0.07671967,   -0.005491124,
+            -0.19379048,   -0.218606,     0.21448623,    0.017840758,  0.1416943,
+            -0.07051762,   0.19488361,    0.02664691,    -0.18104725,  -0.09334311,
+            0.15026465,    -0.15493552,   -0.057762887,  -0.11604192,  -0.262013,
+            -0.01391798,   0.012185008,   0.11156489,    -0.07483202,  0.06693364,
+            -0.26151478,   0.046425626,   0.036540434,   -0.16435726,  0.17338543,
+            -0.21401681,   -0.11385144,   -0.08283257,   -0.069031075, 0.030635102,
+            0.010969227,   0.11109743,    0.010919218,   0.027526086,  0.13519906,
+            0.01891392,    -0.046839405,  -0.040167913,  0.017953383,  -0.09700955,
+            0.0061885654,  -0.07000971,   0.026893595,   -0.038844477, 0.14543656],
+
+          projection_bias: [],
+
+          activation_param: [4],  # Tanh
+          cell_clip_param: [0.],
+          proj_clip_param: [0.],
+}
+
+# Batch0: 4 (input_sequence_size) * 5 (n_input)
+input0[input] = [0.073204, 0.296072, 0.743333, 0.069199, 0.045348]
+# Batch1: 4 (input_sequence_size) * 5 (n_input)
+input0[input].extend(
+    [0.640394, 0.930399, 0.050782, 0.432485, 0.988078]
+)
+input0[output_state_in] = [
+    -0.0166936, 0.0381209, 0.000889684, 0.0143363,
+    -0.0328911, -0.0234288, 0.0333051, -0.012229,
+    0.0110322, -0.0457725, -0.000832209, -0.0202817,
+    0.0327257, 0.0121309, 0.0155969, 0.0312091,
+    -0.0141913, 0.0322082, 0.00227024, 0.0260507,
+    -0.0188721, -0.0296489, 0.0399134, -0.0160509,
+    0.011604, -0.0447318, -0.0150515, -0.0277406,
+    0.0316596, 0.0118233, 0.0214762, 0.0293641,
+]
+input0[cell_state_in] = [
+    -0.154022, -0.124934, 0.0478463, 0.0607819,
+    -0.218727, -0.111053, -0.103885, -0.00447221,
+    0.0554757, -0.0207068, 0.0595767, -0.116297,
+    -0.249466, -0.0723206, 0.0794942, -0.0377107,
+    0.124532, 0.249952, 0.188641, 0.411865,
+    -0.11012, -0.0694494, 0.103501, 0.0428427,
+    -0.167345, -0.106061, -0.0775679, 0.00936161,
+    0.0105526, -0.0314523, 0.0243475, -0.132179,
+    -0.258763, -0.0307266, 0.107047, -0.0115197,
+    0.0995485, 0.220027, 0.158355, 0.436369,
+]
+output0 = {
+  scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+  cell_state_out: [
+      -0.126572, -0.121882, 0.121569, 0.0489971,
+      -0.240177, -0.124685, -0.122565, 0.0162748,
+      0.0317536, -0.0270355, 0.0418199, -0.179755,
+      -0.327279, -0.0342741, 0.133831, -0.0238279,
+      0.122148, 0.269115, 0.185989, 0.525976,
+      -0.167208, -0.109612, 0.0531226, 0.0695387,
+      -0.248335, -0.134123, -0.108246, 0.00628498,
+      0.0492984, -0.0264919, 0.0698144, -0.0635602,
+      -0.295363, -0.0760078, 0.102725, -0.0351708,
+      0.149804, 0.259131, 0.202573, 0.500664,
+  ],
+  output_state_out: [
+      -0.0213783, 0.0350169, 0.000324787, 0.0276012,
+      -0.0263374, -0.0371449, 0.0446149, -0.0205474,
+      0.0103729, -0.0576349, -0.0150052, -0.0292043,
+      0.0376827, 0.0136115, 0.0243435, 0.0354492,
+      -0.0204549, 0.0450315, -0.00117379, 0.0167673,
+      -0.0375007, -0.0238314, 0.038784, -0.0174034,
+      0.0131743, -0.0506589, -0.00484469, -0.0240239,
+      0.0325789, 0.00790064, 0.0220157, 0.0333314,
+  ],
+}
+
+# Batch0: 4 (input_sequence_size) * 16 (n_output)
+output0[output] = [
+    -0.0213783,  0.0350169,   0.000324794,
+    0.0276012,   -0.0263374,   -0.0371449,  0.0446149,   -0.0205474,
+    0.0103729,   -0.0576349,   -0.0150052,  -0.0292043,  0.0376827,
+    0.0136115,   0.0243435,    0.0354492]
+# Batch1: 4 (input_sequence_size) * 16 (n_output)
+output0[output].extend(
+    [-0.0204549,  0.0450315,   -0.00117378,
+     0.0167673,    -0.0375007,  -0.0238314,  0.038784,    -0.0174034,
+     0.0131743,    -0.0506589,  -0.0048447,  -0.0240239,  0.0325789,
+     0.00790065,   0.0220157,   0.0333314],
+    )
+
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm3_state3.mod.py b/nn/runtime/test/specs/lstm3_state3.mod.py
new file mode 100644
index 000000000..b55511471
--- /dev/null
+++ b/nn/runtime/test/specs/lstm3_state3.mod.py
@@ -0,0 +1,667 @@
+#
+# Copyright (C) 2017 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.
+#
+
+# LSTM Test, With Peephole, With Projection, No Clipping
+
+model = Model()
+
+n_batch = 2
+n_input = 5
+# n_cell and n_output have the same size when there is no projection.
+n_cell = 20
+n_output = 16
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+
+cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{%d}" %(n_cell))
+cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{%d}" % (n_cell))
+
+input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+
+projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{%d,%d}" % (n_output, n_cell))
+projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}");
+cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
+proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
+output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+
+# TODO: need support for more than one output
+model = model.Operation("LSTM",
+                        input,
+
+                        input_to_input_weights,
+                        input_to_forget_weights,
+                        input_to_cell_weights,
+                        input_to_output_weights,
+
+                        recurrent_to_input_weights,
+                        recurrent_to_forget_weights,
+                        recurrent_to_cell_weights,
+                        recurrent_to_output_weights,
+
+                        cell_to_input_weights,
+                        cell_to_forget_weights,
+                        cell_to_output_weights,
+
+                        input_gate_bias,
+                        forget_gate_bias,
+                        cell_gate_bias,
+                        output_gate_bias,
+
+                        projection_weights,
+                        projection_bias,
+
+                        output_state_in,
+                        cell_state_in,
+
+                        activation_param,
+                        cell_clip_param,
+                        proj_clip_param
+).To([scratch_buffer, output_state_out, cell_state_out, output])
+
+input0 = {input_to_input_weights: [
+    0.021393683,  0.06124551,    0.046905167,  -0.014657677,  -0.03149463,
+    0.09171803,   0.14647801,    0.10797193,   -0.0057968358, 0.0019193048,
+    -0.2726754,   0.10154029,    -0.018539885, 0.080349885,   -0.10262385,
+    -0.022599787, -0.09121155,   -0.008675967, -0.045206103,  -0.0821282,
+    -0.008045952, 0.015478081,   0.055217247,  0.038719587,   0.044153627,
+    -0.06453243,  0.05031825,    -0.046935108, -0.008164439,  0.014574226,
+    -0.1671009,   -0.15519552,   -0.16819797,  -0.13971269,   -0.11953059,
+    0.25005487,   -0.22790983,   0.009855087,  -0.028140958,  -0.11200698,
+    0.11295408,   -0.0035217577, 0.054485075,  0.05184695,    0.064711206,
+    0.10989193,   0.11674786,    0.03490607,   0.07727357,    0.11390585,
+    -0.1863375,   -0.1034451,    -0.13945189,  -0.049401227,  -0.18767063,
+    0.042483903,  0.14233552,    0.13832581,   0.18350165,    0.14545603,
+    -0.028545704, 0.024939531,   0.050929718,  0.0076203286,  -0.0029723682,
+    -0.042484224, -0.11827596,   -0.09171104,  -0.10808628,   -0.16327988,
+    -0.2273378,   -0.0993647,    -0.017155107, 0.0023917493,  0.049272764,
+    0.0038534778, 0.054764505,   0.089753784,  0.06947234,    0.08014476,
+    -0.04544234,  -0.0497073,    -0.07135631,  -0.048929106,  -0.004042012,
+    -0.009284026, 0.018042054,   0.0036860977, -0.07427302,   -0.11434604,
+    -0.018995456, 0.031487543,   0.012834908,  0.019977754,   0.044256654,
+    -0.39292613,  -0.18519334,   -0.11651281,  -0.06809892,   0.011373677],
+
+          input_to_forget_weights: [
+              -0.0018401089, -0.004852237,  0.03698424,   0.014181704,   0.028273236,
+            -0.016726194,  -0.05249759,   -0.10204261,  0.00861066,    -0.040979505,
+            -0.009899187,  0.01923892,    -0.028177269, -0.08535103,   -0.14585495,
+            0.10662567,    -0.01909731,   -0.017883534, -0.0047269356, -0.045103323,
+            0.0030784295,  0.076784775,   0.07463696,   0.094531395,   0.0814421,
+            -0.12257899,   -0.033945758,  -0.031303465, 0.045630626,   0.06843887,
+            -0.13492945,   -0.012480007,  -0.0811829,   -0.07224499,   -0.09628791,
+            0.045100946,   0.0012300825,  0.013964662,  0.099372394,   0.02543059,
+            0.06958324,    0.034257296,   0.0482646,    0.06267997,    0.052625068,
+            0.12784666,    0.07077897,    0.025725935,  0.04165009,    0.07241905,
+            0.018668644,   -0.037377294,  -0.06277783,  -0.08833636,   -0.040120605,
+            -0.011405586,  -0.007808335,  -0.010301386, -0.005102167,  0.027717464,
+            0.05483423,    0.11449111,    0.11289652,   0.10939839,    0.13396506,
+            -0.08402166,   -0.01901462,   -0.044678304, -0.07720565,   0.014350063,
+            -0.11757958,   -0.0652038,    -0.08185733,  -0.076754324,  -0.092614375,
+            0.10405491,    0.052960336,   0.035755895,  0.035839386,   -0.012540553,
+            0.036881298,   0.02913376,    0.03420159,   0.05448447,    -0.054523353,
+            0.02582715,    0.02327355,    -0.011857179, -0.0011980024, -0.034641717,
+            -0.026125094,  -0.17582615,   -0.15923657,  -0.27486774,   -0.0006143371,
+            0.0001771948,  -8.470171e-05, 0.02651807,   0.045790765,   0.06956496],
+
+          input_to_cell_weights: [
+              -0.04580283,   -0.09549462,   -0.032418985,  -0.06454633,
+            -0.043528453,  0.043018587,   -0.049152344,  -0.12418144,
+            -0.078985475,  -0.07596889,   0.019484362,   -0.11434962,
+            -0.0074034138, -0.06314844,   -0.092981495,  0.0062155537,
+            -0.025034338,  -0.0028890965, 0.048929527,   0.06235075,
+            0.10665918,    -0.032036792,  -0.08505916,   -0.10843358,
+            -0.13002433,   -0.036816437,  -0.02130134,   -0.016518239,
+            0.0047691227,  -0.0025825808, 0.066017866,   0.029991534,
+            -0.10652836,   -0.1037554,    -0.13056071,   -0.03266643,
+            -0.033702414,  -0.006473424,  -0.04611692,   0.014419339,
+            -0.025174323,  0.0396852,     0.081777506,   0.06157468,
+            0.10210095,    -0.009658194,  0.046511717,   0.03603906,
+            0.0069369148,  0.015960095,   -0.06507666,   0.09551598,
+            0.053568836,   0.06408714,    0.12835667,    -0.008714329,
+            -0.20211966,   -0.12093674,   0.029450472,   0.2849013,
+            -0.029227901,  0.1164364,     -0.08560263,   0.09941786,
+            -0.036999565,  -0.028842626,  -0.0033637602, -0.017012902,
+            -0.09720865,   -0.11193351,   -0.029155117,  -0.017936034,
+            -0.009768936,  -0.04223324,   -0.036159635,  0.06505112,
+            -0.021742892,  -0.023377212,  -0.07221364,   -0.06430552,
+            0.05453865,    0.091149814,   0.06387331,    0.007518393,
+            0.055960953,   0.069779344,   0.046411168,   0.10509911,
+            0.07463894,    0.0075130584,  0.012850982,   0.04555431,
+            0.056955688,   0.06555285,    0.050801456,   -0.009862683,
+            0.00826772,    -0.026555609,  -0.0073611983, -0.0014897042],
+
+          input_to_output_weights: [
+              -0.0998932,   -0.07201956,  -0.052803773,  -0.15629593,  -0.15001918,
+            -0.07650751,  0.02359855,   -0.075155355,  -0.08037709,  -0.15093534,
+            0.029517552,  -0.04751393,  0.010350531,   -0.02664851,  -0.016839722,
+            -0.023121163, 0.0077019283, 0.012851257,   -0.05040649,  -0.0129761,
+            -0.021737747, -0.038305793, -0.06870586,   -0.01481247,  -0.001285394,
+            0.10124236,   0.083122835,  0.053313006,   -0.062235646, -0.075637154,
+            -0.027833903, 0.029774971,  0.1130802,     0.09218906,   0.09506135,
+            -0.086665764, -0.037162706, -0.038880914,  -0.035832845, -0.014481564,
+            -0.09825003,  -0.12048569,  -0.097665586,  -0.05287633,  -0.0964047,
+            -0.11366429,  0.035777505,  0.13568819,    0.052451383,  0.050649304,
+            0.05798951,   -0.021852335, -0.099848844,  0.014740475,  -0.078897946,
+            0.04974699,   0.014160473,  0.06973932,    0.04964942,   0.033364646,
+            0.08190124,   0.025535367,  0.050893165,   0.048514254,  0.06945813,
+            -0.078907564, -0.06707616,  -0.11844508,   -0.09986688,  -0.07509403,
+            0.06263226,   0.14925587,   0.20188436,    0.12098451,   0.14639415,
+            0.0015017595, -0.014267382, -0.03417257,   0.012711468,  0.0028300495,
+            -0.024758482, -0.05098548,  -0.0821182,    0.014225672,  0.021544158,
+            0.08949725,   0.07505268,   -0.0020780868, 0.04908258,   0.06476295,
+            -0.022907063, 0.027562456,  0.040185735,   0.019567577,  -0.015598739,
+            -0.049097303, -0.017121866, -0.083368234,  -0.02332002,  -0.0840956],
+
+          input_gate_bias:  [
+              0.02234832,  0.14757581,   0.18176508,  0.10380666,  0.053110216,
+              -0.06928846, -0.13942584,  -0.11816189, 0.19483899,  0.03652339,
+              -0.10250295, 0.036714908,  -0.18426876, 0.036065217, 0.21810818,
+              0.02383196,  -0.043370757, 0.08690144,  -0.04444982, 0.00030581196],
+
+          forget_gate_bias: [
+              0.035185695, -0.042891346, -0.03032477, 0.23027696,
+              0.11098921,  0.15378423,   0.09263801,  0.09790885,
+              0.09508917,  0.061199076,  0.07665568,  -0.015443159,
+              -0.03499149, 0.046190713,  0.08895977,  0.10899629,
+              0.40694186,  0.06030037,   0.012413437, -0.06108739],
+
+          cell_gate_bias:   [
+              -0.024379363, 0.0055531194, 0.23377132,   0.033463873,
+            -0.1483596,   -0.10639995,  -0.091433935, 0.058573797,
+            -0.06809782,  -0.07889636,  -0.043246906, -0.09829136,
+            -0.4279842,   0.034901652,  0.18797937,   0.0075234566,
+            0.016178843,  0.1749513,    0.13975595,   0.92058027],
+
+          output_gate_bias: [
+              0.046159424,  -0.0012809046, 0.03563469,   0.12648113, 0.027195795,
+              0.35373217,   -0.018957434,  0.008907322,  -0.0762701, 0.12018895,
+              0.04216877,   0.0022856654,  0.040952638,  0.3147856,  0.08225149,
+              -0.057416286, -0.14995944,   -0.008040261, 0.13208859, 0.029760877],
+
+          recurrent_to_input_weights: [
+              -0.001374326,   -0.078856036,   0.10672688,    0.029162422,
+            -0.11585556,    0.02557986,     -0.13446963,   -0.035785314,
+            -0.01244275,    0.025961924,    -0.02337298,   -0.044228926,
+            -0.055839065,   -0.046598054,   -0.010546039,  -0.06900766,
+            0.027239809,    0.022582639,    -0.013296484,  -0.05459212,
+            0.08981,        -0.045407712,   0.08682226,    -0.06867011,
+            -0.14390695,    -0.02916037,    0.000996957,   0.091420636,
+            0.14283475,     -0.07390571,    -0.06402044,   0.062524505,
+            -0.093129106,   0.04860203,     -0.08364217,   -0.08119002,
+            0.009352075,    0.22920375,     0.0016303885,  0.11583097,
+            -0.13732095,    0.012405723,    -0.07551853,   0.06343048,
+            0.12162708,     -0.031923793,   -0.014335606,  0.01790974,
+            -0.10650317,    -0.0724401,     0.08554849,    -0.05727212,
+            0.06556731,     -0.042729504,   -0.043227166,  0.011683251,
+            -0.013082158,   -0.029302018,   -0.010899579,  -0.062036745,
+            -0.022509435,   -0.00964907,    -0.01567329,   0.04260106,
+            -0.07787477,    -0.11576462,    0.017356863,   0.048673786,
+            -0.017577527,   -0.05527947,    -0.082487635,  -0.040137455,
+            -0.10820036,    -0.04666372,    0.022746278,   -0.07851417,
+            0.01068115,     0.032956902,    0.022433773,   0.0026891115,
+            0.08944216,     -0.0685835,     0.010513544,   0.07228705,
+            0.02032331,     -0.059686817,   -0.0005566496, -0.086984694,
+            0.040414046,    -0.1380399,     0.094208956,   -0.05722982,
+            0.012092817,    -0.04989123,    -0.086576,     -0.003399834,
+            -0.04696032,    -0.045747425,   0.10091314,    0.048676282,
+            -0.029037097,   0.031399418,    -0.0040285117, 0.047237843,
+            0.09504992,     0.041799378,    -0.049185462,  -0.031518843,
+            -0.10516937,    0.026374253,    0.10058866,    -0.0033195973,
+            -0.041975245,   0.0073591834,   0.0033782164,  -0.004325073,
+            -0.10167381,    0.042500053,    -0.01447153,   0.06464186,
+            -0.017142897,   0.03312627,     0.009205989,   0.024138335,
+            -0.011337001,   0.035530265,    -0.010912711,  0.0706555,
+            -0.005894094,   0.051841937,    -0.1401738,    -0.02351249,
+            0.0365468,      0.07590991,     0.08838724,    0.021681072,
+            -0.10086113,    0.019608743,    -0.06195883,   0.077335775,
+            0.023646897,    -0.095322326,   0.02233014,    0.09756986,
+            -0.048691444,   -0.009579111,   0.07595467,    0.11480546,
+            -0.09801813,    0.019894179,    0.08502348,    0.004032281,
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+              -0.019187413,  0.0276265,    -0.01628143,  0.0025863599,   0.08800015,
+              0.035250366,   -0.022165963, -0.07328642,  -0.009415526,   -0.07455109,
+              0.11690406,    0.0363299,    0.07411125,   0.042103454,    -0.009660886,
+              0.019076364,   0.018299393,  -0.046004917, 0.08891175,     0.0431396,
+              -0.026327137,  -0.051502608, 0.08979574,   -0.051670972,   0.04940282,
+              -0.07491107,   -0.021240504, 0.022596184,  -0.034280192,   0.060163025,
+              -0.058211457,  -0.051837247, -0.01349775,  -0.04639988,    -0.035936575,
+              -0.011681591,  0.064818054,  0.0073146066, -0.021745546,   -0.043124277,
+              -0.06471268,   -0.07053354,  -0.029321948, -0.05330136,    0.016933719,
+              -0.053782392,  0.13747959,   -0.1361751,   -0.11569455,    0.0033329215,
+              0.05693899,    -0.053219706, 0.063698,     0.07977434,     -0.07924483,
+              0.06936997,    0.0034815092, -0.007305279, -0.037325785,   -0.07251102,
+              -0.033633437,  -0.08677009,  0.091591336,  -0.14165086,    0.021752775,
+              0.019683983,   0.0011612234, -0.058154266, 0.049996935,    0.0288841,
+              -0.0024567875, -0.14345716,  0.010955264,  -0.10234828,    0.1183656,
+              -0.0010731248, -0.023590032, -0.072285876, -0.0724771,     -0.026382286,
+              -0.0014920527, 0.042667855,  0.0018776858, 0.02986552,     0.009814309,
+              0.0733756,     0.12289186,   0.018043943,  -0.0458958,     0.049412545,
+              0.033632483,   0.05495232,   0.036686596,  -0.013781798,   -0.010036754,
+              0.02576849,    -0.08307328,  0.010112348,  0.042521734,    -0.05869831,
+              -0.071689695,  0.03876447,   -0.13275425,  -0.0352966,     -0.023077697,
+              0.10285965,    0.084736146,  0.15568255,   -0.00040734606, 0.027835453,
+              -0.10292561,   -0.032401145, 0.10053256,   -0.026142767,   -0.08271222,
+              -0.0030240538, -0.016368777, 0.1070414,    0.042672627,    0.013456989,
+              -0.0437609,    -0.022309763, 0.11576483,   0.04108048,     0.061026827,
+              -0.0190714,    -0.0869359,   0.037901703,  0.0610107,      0.07202949,
+              0.01675338,    0.086139716,  -0.08795751,  -0.014898893,   -0.023771819,
+              -0.01965048,   0.007955471,  -0.043740474, 0.03346837,     -0.10549954,
+              0.090567775,   0.042013682,  -0.03176985,  0.12569028,     -0.02421228,
+              -0.029526481,  0.023851605,  0.031539805,  0.05292009,     -0.02344001,
+              -0.07811758,   -0.08834428,  0.10094801,   0.16594367,     -0.06861939,
+              -0.021256343,  -0.041093912, -0.06669611,  0.035498552,    0.021757556,
+              -0.09302526,   -0.015403468, -0.06614931,  -0.051798206,   -0.013874718,
+              0.03630673,    0.010412845,  -0.08077351,  0.046185967,    0.0035662893,
+              0.03541868,    -0.094149634, -0.034814864, 0.003128424,    -0.020674974,
+              -0.03944324,   -0.008110165, -0.11113267,  0.08484226,     0.043586485,
+              0.040582247,   0.0968012,    -0.065249965, -0.028036479,   0.0050708856,
+              0.0017462453,  0.0326779,    0.041296225,  0.09164146,     -0.047743853,
+              -0.015952192,  -0.034451712, 0.084197424,  -0.05347844,    -0.11768019,
+              0.085926116,   -0.08251791,  -0.045081906, 0.0948852,      0.068401024,
+              0.024856757,   0.06978981,   -0.057309967, -0.012775832,   -0.0032452994,
+              0.01977615,    -0.041040014, -0.024264973, 0.063464895,    0.05431621],
+
+          cell_to_input_weights: [
+              0.040369894, 0.030746894,  0.24704495,  0.018586371,  -0.037586458,
+              -0.15312155, -0.11812848,  -0.11465643, 0.20259799,   0.11418174,
+              -0.10116027, -0.011334949, 0.12411352,  -0.076769054, -0.052169047,
+              0.21198851,  -0.38871562,  -0.09061183, -0.09683246,  -0.21929175],
+
+          cell_to_forget_weights: [
+              -0.01998659,  -0.15568835,  -0.24248174,   -0.012770197, 0.041331276,
+            -0.072311886, -0.052123554, -0.0066330447, -0.043891653, 0.036225766,
+            -0.047248036, 0.021479502,  0.033189066,   0.11952997,   -0.020432774,
+            0.64658105,   -0.06650122,  -0.03467612,   0.095340036,  0.23647355],
+
+          cell_to_output_weights: [
+              0.08286371,  -0.08261836, -0.51210177, 0.002913762, 0.17764764,
+              -0.5495371,  -0.08460716, -0.24552552, 0.030037103, 0.04123544,
+              -0.11940523, 0.007358328, 0.1890978,   0.4833202,   -0.34441817,
+              0.36312827,  -0.26375428, 0.1457655,   -0.19724406, 0.15548733],
+
+          projection_weights: [
+              -0.009802181,  0.09401916,    0.0717386,     -0.13895074,  0.09641832,
+            0.060420845,   0.08539281,    0.054285463,   0.061395317,  0.034448683,
+            -0.042991187,  0.019801661,   -0.16840284,   -0.015726732, -0.23041931,
+            -0.024478018,  -0.10959692,   -0.013875541,  0.18600968,   -0.061274476,
+            0.0138165,     -0.08160894,   -0.07661644,   0.032372914,  0.16169067,
+            0.22465782,    -0.03993472,   -0.004017731,  0.08633481,   -0.28869787,
+            0.08682067,    0.17240396,    0.014975425,   0.056431185,  0.031037588,
+            0.16702051,    0.0077946745,  0.15140012,    0.29405436,   0.120285,
+            -0.188994,     -0.027265169,  0.043389652,   -0.022061434, 0.014777949,
+            -0.20203483,   0.094781205,   0.19100232,    0.13987629,   -0.036132768,
+            -0.06426278,   -0.05108664,   0.13221376,    0.009441198,  -0.16715929,
+            0.15859416,    -0.040437475,  0.050779544,   -0.022187516, 0.012166504,
+            0.027685808,   -0.07675938,   -0.0055694645, -0.09444123,  0.0046453946,
+            0.050794356,   0.10770313,    -0.20790008,   -0.07149004,  -0.11425117,
+            0.008225835,   -0.035802525,  0.14374903,    0.15262283,   0.048710253,
+            0.1847461,     -0.007487823,  0.11000021,    -0.09542012,  0.22619456,
+            -0.029149994,  0.08527916,    0.009043713,   0.0042746216, 0.016261552,
+            0.022461696,   0.12689082,    -0.043589946,  -0.12035478,  -0.08361797,
+            -0.050666027,  -0.1248618,    -0.1275799,    -0.071875185, 0.07377272,
+            0.09944291,    -0.18897448,   -0.1593054,    -0.06526116,  -0.040107165,
+            -0.004618631,  -0.067624845,  -0.007576253,  0.10727444,   0.041546922,
+            -0.20424393,   0.06907816,    0.050412357,   0.00724631,   0.039827548,
+            0.12449835,    0.10747581,    0.13708383,    0.09134148,   -0.12617786,
+            -0.06428341,   0.09956831,    0.1208086,     -0.14676677,  -0.0727722,
+            0.1126304,     0.010139365,   0.015571211,   -0.038128063, 0.022913318,
+            -0.042050496,  0.16842307,    -0.060597885,  0.10531834,   -0.06411776,
+            -0.07451711,   -0.03410368,   -0.13393489,   0.06534304,   0.003620307,
+            0.04490757,    0.05970546,    0.05197996,    0.02839995,   0.10434969,
+            -0.013699693,  -0.028353551,  -0.07260381,   0.047201227,  -0.024575593,
+            -0.036445823,  0.07155557,    0.009672501,   -0.02328883,  0.009533515,
+            -0.03606021,   -0.07421458,   -0.028082801,  -0.2678904,   -0.13221288,
+            0.18419984,    -0.13012612,   -0.014588381,  -0.035059117, -0.04824723,
+            0.07830115,    -0.056184657,  0.03277091,    0.025466874,  0.14494097,
+            -0.12522776,   -0.098633975,  -0.10766018,   -0.08317623,  0.08594209,
+            0.07749552,    0.039474737,   0.1776665,     -0.07409566,  -0.0477268,
+            0.29323658,    0.10801441,    0.1154011,     0.013952499,  0.10739139,
+            0.10708251,    -0.051456142,  0.0074137426,  -0.10430189,  0.10034707,
+            0.045594677,   0.0635285,     -0.0715442,    -0.089667566, -0.10811871,
+            0.00026344223, 0.08298446,    -0.009525053,  0.006585689,  -0.24567553,
+            -0.09450807,   0.09648481,    0.026996298,   -0.06419476,  -0.04752702,
+            -0.11063944,   -0.23441927,   -0.17608605,   -0.052156363, 0.067035615,
+            0.19271925,    -0.0032889997, -0.043264326,  0.09663576,   -0.057112187,
+            -0.10100678,   0.0628376,     0.04447668,    0.017961001,  -0.10094388,
+            -0.10190601,   0.18335468,    0.10494553,    -0.052095775, -0.0026118709,
+            0.10539724,    -0.04383912,   -0.042349473,  0.08438151,   -0.1947263,
+            0.02251204,    0.11216432,    -0.10307853,   0.17351969,   -0.039091777,
+            0.08066188,    -0.00561982,   0.12633002,    0.11335965,   -0.0088127935,
+            -0.019777594,  0.06864014,    -0.059751723,  0.016233567,  -0.06894641,
+            -0.28651384,   -0.004228674,  0.019708522,   -0.16305895,  -0.07468996,
+            -0.0855457,    0.099339016,   -0.07580735,   -0.13775392,  0.08434318,
+            0.08330512,    -0.12131499,   0.031935584,   0.09180414,   -0.08876437,
+            -0.08049874,   0.008753825,   0.03498998,    0.030215185,  0.03907079,
+            0.089751154,   0.029194152,   -0.03337423,   -0.019092513, 0.04331237,
+            0.04299654,    -0.036394123,  -0.12915532,   0.09793732,   0.07512415,
+            -0.11319543,   -0.032502122,  0.15661901,    0.07671967,   -0.005491124,
+            -0.19379048,   -0.218606,     0.21448623,    0.017840758,  0.1416943,
+            -0.07051762,   0.19488361,    0.02664691,    -0.18104725,  -0.09334311,
+            0.15026465,    -0.15493552,   -0.057762887,  -0.11604192,  -0.262013,
+            -0.01391798,   0.012185008,   0.11156489,    -0.07483202,  0.06693364,
+            -0.26151478,   0.046425626,   0.036540434,   -0.16435726,  0.17338543,
+            -0.21401681,   -0.11385144,   -0.08283257,   -0.069031075, 0.030635102,
+            0.010969227,   0.11109743,    0.010919218,   0.027526086,  0.13519906,
+            0.01891392,    -0.046839405,  -0.040167913,  0.017953383,  -0.09700955,
+            0.0061885654,  -0.07000971,   0.026893595,   -0.038844477, 0.14543656],
+
+          projection_bias: [],
+
+          activation_param: [4],  # Tanh
+          cell_clip_param: [0.],
+          proj_clip_param: [0.],
+}
+
+# Batch0: 4 (input_sequence_size) * 5 (n_input)
+input0[input] = [0.867394, 0.291279, 0.013714, 0.482521, 0.626339]
+# Batch1: 4 (input_sequence_size) * 5 (n_input)
+input0[input].extend(
+    [0.082922, 0.563329, 0.865614, 0.333232, 0.259916]
+)
+input0[output_state_in] = [
+    -0.0213783, 0.0350169, 0.000324787, 0.0276012,
+    -0.0263374, -0.0371449, 0.0446149, -0.0205474,
+    0.0103729, -0.0576349, -0.0150052, -0.0292043,
+    0.0376827, 0.0136115, 0.0243435, 0.0354492,
+    -0.0204549, 0.0450315, -0.00117379, 0.0167673,
+    -0.0375007, -0.0238314, 0.038784, -0.0174034,
+    0.0131743, -0.0506589, -0.00484469, -0.0240239,
+    0.0325789, 0.00790064, 0.0220157, 0.0333314,
+]
+input0[cell_state_in] = [
+    -0.126572, -0.121882, 0.121569, 0.0489971,
+    -0.240177, -0.124685, -0.122565, 0.0162748,
+    0.0317536, -0.0270355, 0.0418199, -0.179755,
+    -0.327279, -0.0342741, 0.133831, -0.0238279,
+    0.122148, 0.269115, 0.185989, 0.525976,
+    -0.167208, -0.109612, 0.0531226, 0.0695387,
+    -0.248335, -0.134123, -0.108246, 0.00628498,
+    0.0492984, -0.0264919, 0.0698144, -0.0635602,
+    -0.295363, -0.0760078, 0.102725, -0.0351708,
+    0.149804, 0.259131, 0.202573, 0.500664,
+]
+output0 = {
+  scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+  cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
+  output_state_out: [ 0 for x in range(n_batch * n_output) ],
+}
+
+# Batch0: 4 (input_sequence_size) * 16 (n_output)
+output0[output] = [
+    -0.0189322,  0.0464512, -0.00251373, 0.0225745,
+    -0.0308346,  -0.0317124,  0.0460407, -0.0189395,
+    0.0149363,    -0.0530162,  -0.0150767,  -0.0340193,
+    0.0286833,   0.00824207,   0.0264887,   0.0305169]
+# Batch1: 4 (input_sequence_size) * 16 (n_output)
+output0[output].extend(
+    [-0.0264787,  0.0387855, -0.000764675, 0.0217599,
+     -0.037537,   -0.0335206,  0.0431679, -0.0211424,
+     0.010203,    -0.062785,   -0.00832363, -0.025181,
+     0.0412031,    0.0118723,   0.0239643,   0.0394009]
+    )
+
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm_state.mod.py b/nn/runtime/test/specs/lstm_state.mod.py
new file mode 100644
index 000000000..8862f1063
--- /dev/null
+++ b/nn/runtime/test/specs/lstm_state.mod.py
@@ -0,0 +1,152 @@
+#
+# Copyright (C) 2017 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.
+#
+
+# LSTM Test: No Cifg, No Peephole, No Projection, and No Clipping.
+
+model = Model()
+
+n_batch = 1
+n_input = 2
+# n_cell and n_output have the same size when there is no projection.
+n_cell = 4
+n_output = 4
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+
+cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}")
+cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{0}")
+cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}")
+
+input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+
+projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
+projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}")
+cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
+proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
+output_state_out = Output("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = Output("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+
+model = model.Operation("LSTM",
+                        input,
+
+                        input_to_input_weights,
+                        input_to_forget_weights,
+                        input_to_cell_weights,
+                        input_to_output_weights,
+
+                        recurrent_to_input_weights,
+                        recurrent_to_forget_weights,
+                        recurrent_to_cell_weights,
+                        recurrent_to_output_weights,
+
+                        cell_to_input_weights,
+                        cell_to_forget_weights,
+                        cell_to_output_weights,
+
+                        input_gate_bias,
+                        forget_gate_bias,
+                        cell_gate_bias,
+                        output_gate_bias,
+
+                        projection_weights,
+                        projection_bias,
+
+                        output_state_in,
+                        cell_state_in,
+
+                        activation_param,
+                        cell_clip_param,
+                        proj_clip_param
+).To([scratch_buffer, output_state_out, cell_state_out, output])
+
+# Example 1. Input in operand 0,
+input0 = {input_to_input_weights:  [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524],
+          input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212],
+          input_to_cell_weights:   [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778],
+          input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578],
+
+          input_gate_bias:  [0.,0.,0.,0.],
+          forget_gate_bias: [1.,1.,1.,1.],
+          cell_gate_bias:   [0.,0.,0.,0.],
+          output_gate_bias: [0.,0.,0.,0.],
+
+          recurrent_to_input_weights: [
+              -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324,
+            -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322,
+            -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296],
+
+          recurrent_to_cell_weights: [
+              -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841,
+            -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659,
+            -0.46367589, 0.26016325, -0.03894562, -0.16368064],
+
+          recurrent_to_forget_weights: [
+              -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892,
+            -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436,
+            0.28053468, 0.01560611, -0.20127171, -0.01140004],
+
+          recurrent_to_output_weights: [
+              0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793,
+              0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421,
+              -0.51818722, -0.15390486, 0.0468148, 0.39922136],
+
+          cell_to_input_weights: [],
+          cell_to_forget_weights: [],
+          cell_to_output_weights: [],
+
+          projection_weights: [],
+          projection_bias: [],
+
+          activation_param: [4],  # Tanh
+          cell_clip_param: [0.],
+          proj_clip_param: [0.],
+}
+
+test_input = [3., 4.]
+output_state = [-0.0297319, 0.122947, 0.208851, -0.153588]
+cell_state = [-0.145439, 0.157475, 0.293663, -0.277353,]
+golden_output = [-0.03716109, 0.12507336, 0.41193449,  -0.20860538]
+output0 = {
+    scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+    cell_state_out: [ -0.287121, 0.148115, 0.556837, -0.388276 ],
+    output_state_out: [ -0.0371611, 0.125073, 0.411934, -0.208605 ],
+    output: golden_output
+}
+input0[input] = test_input
+input0[output_state_in] = output_state
+input0[cell_state_in] = cell_state
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/lstm_state2.mod.py b/nn/runtime/test/specs/lstm_state2.mod.py
new file mode 100644
index 000000000..945c631ef
--- /dev/null
+++ b/nn/runtime/test/specs/lstm_state2.mod.py
@@ -0,0 +1,152 @@
+#
+# Copyright (C) 2017 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.
+#
+
+# LSTM Test: No Cifg, No Peephole, No Projection, and No Clipping.
+
+model = Model()
+
+n_batch = 1
+n_input = 2
+# n_cell and n_output have the same size when there is no projection.
+n_cell = 4
+n_output = 4
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_input))
+
+input_to_input_weights = Input("input_to_input_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_forget_weights = Input("input_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_cell_weights = Input("input_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+input_to_output_weights = Input("input_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_input))
+
+recurrent_to_input_weights = Input("recurrent_to_intput_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_forget_weights = Input("recurrent_to_forget_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_cell_weights = Input("recurrent_to_cell_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+recurrent_to_output_weights = Input("recurrent_to_output_weights", "TENSOR_FLOAT32", "{%d, %d}" % (n_cell, n_output))
+
+cell_to_input_weights = Input("cell_to_input_weights", "TENSOR_FLOAT32", "{0}")
+cell_to_forget_weights = Input("cell_to_forget_weights", "TENSOR_FLOAT32", "{0}")
+cell_to_output_weights = Input("cell_to_output_weights", "TENSOR_FLOAT32", "{0}")
+
+input_gate_bias = Input("input_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+forget_gate_bias = Input("forget_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+cell_gate_bias = Input("cell_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+output_gate_bias = Input("output_gate_bias", "TENSOR_FLOAT32", "{%d}"%(n_cell))
+
+projection_weights = Input("projection_weights", "TENSOR_FLOAT32", "{0,0}")
+projection_bias = Input("projection_bias", "TENSOR_FLOAT32", "{0}")
+
+output_state_in = Input("output_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_in = Input("cell_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}")
+cell_clip_param = Input("cell_clip_param", "TENSOR_FLOAT32", "{1}")
+proj_clip_param = Input("proj_clip_param", "TENSOR_FLOAT32", "{1}")
+
+scratch_buffer = IgnoredOutput("scratch_buffer", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, (n_cell * 4)))
+output_state_out = IgnoredOutput("output_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+cell_state_out = IgnoredOutput("cell_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_cell))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (n_batch, n_output))
+
+model = model.Operation("LSTM",
+                        input,
+
+                        input_to_input_weights,
+                        input_to_forget_weights,
+                        input_to_cell_weights,
+                        input_to_output_weights,
+
+                        recurrent_to_input_weights,
+                        recurrent_to_forget_weights,
+                        recurrent_to_cell_weights,
+                        recurrent_to_output_weights,
+
+                        cell_to_input_weights,
+                        cell_to_forget_weights,
+                        cell_to_output_weights,
+
+                        input_gate_bias,
+                        forget_gate_bias,
+                        cell_gate_bias,
+                        output_gate_bias,
+
+                        projection_weights,
+                        projection_bias,
+
+                        output_state_in,
+                        cell_state_in,
+
+                        activation_param,
+                        cell_clip_param,
+                        proj_clip_param
+).To([scratch_buffer, output_state_out, cell_state_out, output])
+
+# Example 1. Input in operand 0,
+input0 = {input_to_input_weights:  [-0.45018822, -0.02338299, -0.0870589, -0.34550029, 0.04266912, -0.15680569, -0.34856534, 0.43890524],
+          input_to_forget_weights: [0.09701663, 0.20334584, -0.50592935, -0.31343272, -0.40032279, 0.44781327, 0.01387155, -0.35593212],
+          input_to_cell_weights:   [-0.50013041, 0.1370284, 0.11810488, 0.2013163, -0.20583314, 0.44344562, 0.22077113, -0.29909778],
+          input_to_output_weights: [-0.25065863, -0.28290087, 0.04613829, 0.40525138, 0.44272184, 0.03897077, -0.1556896, 0.19487578],
+
+          input_gate_bias:  [0.,0.,0.,0.],
+          forget_gate_bias: [1.,1.,1.,1.],
+          cell_gate_bias:   [0.,0.,0.,0.],
+          output_gate_bias: [0.,0.,0.,0.],
+
+          recurrent_to_input_weights: [
+              -0.0063535, -0.2042388, 0.31454784, -0.35746509, 0.28902304, 0.08183324,
+            -0.16555229, 0.02286911, -0.13566875, 0.03034258, 0.48091322,
+            -0.12528998, 0.24077177, -0.51332325, -0.33502164, 0.10629296],
+
+          recurrent_to_cell_weights: [
+              -0.3407414, 0.24443203, -0.2078532, 0.26320225, 0.05695659, -0.00123841,
+            -0.4744786, -0.35869038, -0.06418842, -0.13502428, -0.501764, 0.22830659,
+            -0.46367589, 0.26016325, -0.03894562, -0.16368064],
+
+          recurrent_to_forget_weights: [
+              -0.48684245, -0.06655136, 0.42224967, 0.2112639, 0.27654213, 0.20864892,
+            -0.07646349, 0.45877004, 0.00141793, -0.14609534, 0.36447752, 0.09196436,
+            0.28053468, 0.01560611, -0.20127171, -0.01140004],
+
+          recurrent_to_output_weights: [
+              0.43385774, -0.17194885, 0.2718237, 0.09215671, 0.24107647, -0.39835793,
+              0.18212086, 0.01301402, 0.48572797, -0.50656658, 0.20047462, -0.20607421,
+              -0.51818722, -0.15390486, 0.0468148, 0.39922136],
+
+          cell_to_input_weights: [],
+          cell_to_forget_weights: [],
+          cell_to_output_weights: [],
+
+          projection_weights: [],
+          projection_bias: [],
+
+          activation_param: [4],  # Tanh
+          cell_clip_param: [0.],
+          proj_clip_param: [0.],
+}
+
+test_input = [1., 1.]
+output_state = [-0.0371611, 0.125073, 0.411934, -0.208605]
+cell_state = [-0.287121, 0.148115, 0.556837, -0.388276]
+golden_output = [-0.15053082, 0.09120187,  0.24278517,  -0.12222792]
+output0 = {
+    scratch_buffer: [ 0 for x in range(n_batch * n_cell * 4) ],
+    cell_state_out: [ 0 for x in range(n_batch * n_cell) ],
+    output_state_out: [ 0 for x in range(n_batch * n_output) ],
+    output: golden_output
+}
+input0[input] = test_input
+input0[output_state_in] = output_state
+input0[cell_state_in] = cell_state
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/mobilenet_quantized.model.py b/nn/runtime/test/specs/mobilenet_quantized.mod.py
index 6612c8dba..6612c8dba 100644
--- a/nn/runtime/test/specs/mobilenet_quantized.model.py
+++ b/nn/runtime/test/specs/mobilenet_quantized.mod.py
diff --git a/nn/runtime/test/specs/rnn_state.mod.py b/nn/runtime/test/specs/rnn_state.mod.py
new file mode 100644
index 000000000..723a6e9e7
--- /dev/null
+++ b/nn/runtime/test/specs/rnn_state.mod.py
@@ -0,0 +1,127 @@
+#
+# Copyright (C) 2017 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
+units = 16
+input_size = 8
+
+model = Model()
+
+input = Input("input", "TENSOR_FLOAT32", "{%d, %d}" % (batches, input_size))
+weights = Input("weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, input_size))
+recurrent_weights = Input("recurrent_weights", "TENSOR_FLOAT32", "{%d, %d}" % (units, units))
+bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
+hidden_state_in = Input("hidden_state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
+
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}")
+
+hidden_state_out = IgnoredOutput("hidden_state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
+output = Output("output", "TENSOR_FLOAT32", "{%d, %d}" % (batches, units))
+
+model = model.Operation("RNN", input, weights, recurrent_weights, bias, hidden_state_in,
+                        activation_param).To([hidden_state_out, output])
+
+input0 = {
+    weights: [
+        0.461459,    0.153381,   0.529743,    -0.00371218, 0.676267,   -0.211346,
+       0.317493,    0.969689,   -0.343251,   0.186423,    0.398151,   0.152399,
+       0.448504,    0.317662,   0.523556,    -0.323514,   0.480877,   0.333113,
+       -0.757714,   -0.674487,  -0.643585,   0.217766,    -0.0251462, 0.79512,
+       -0.595574,   -0.422444,  0.371572,    -0.452178,   -0.556069,  -0.482188,
+       -0.685456,   -0.727851,  0.841829,    0.551535,    -0.232336,  0.729158,
+       -0.00294906, -0.69754,   0.766073,    -0.178424,   0.369513,   -0.423241,
+       0.548547,    -0.0152023, -0.757482,   -0.85491,    0.251331,   -0.989183,
+       0.306261,    -0.340716,  0.886103,    -0.0726757,  -0.723523,  -0.784303,
+       0.0354295,   0.566564,   -0.485469,   -0.620498,   0.832546,   0.697884,
+       -0.279115,   0.294415,   -0.584313,   0.548772,    0.0648819,  0.968726,
+       0.723834,    -0.0080452, -0.350386,   -0.272803,   0.115121,   -0.412644,
+       -0.824713,   -0.992843,  -0.592904,   -0.417893,   0.863791,   -0.423461,
+       -0.147601,   -0.770664,  -0.479006,   0.654782,    0.587314,   -0.639158,
+       0.816969,    -0.337228,  0.659878,    0.73107,     0.754768,   -0.337042,
+       0.0960841,   0.368357,   0.244191,    -0.817703,   -0.211223,  0.442012,
+       0.37225,     -0.623598,  -0.405423,   0.455101,    0.673656,   -0.145345,
+       -0.511346,   -0.901675,  -0.81252,    -0.127006,   0.809865,   -0.721884,
+       0.636255,    0.868989,   -0.347973,   -0.10179,    -0.777449,  0.917274,
+       0.819286,    0.206218,   -0.00785118, 0.167141,    0.45872,    0.972934,
+       -0.276798,   0.837861,   0.747958,    -0.0151566,  -0.330057,  -0.469077,
+       0.277308,    0.415818
+    ],
+    recurrent_weights: [
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0.1
+    ],
+    bias: [
+        0.065691948, -0.69055247, 0.1107955, -0.97084129, -0.23957068,
+        -0.23566568, -0.389184, 0.47481549, -0.4791103, 0.29931796,
+        0.10463274, 0.83918178, 0.37197268, 0.61957061, 0.3956964,
+        -0.37609905
+    ],
+    activation_param: [1],  # Relu
+}
+
+input0[input] = [
+  -0.69424844, -0.93421471, -0.87287879, 0.37144363,
+  -0.62476718, 0.23791671, 0.40060222, 0.1356622,
+  -0.69424844, -0.93421471, -0.87287879, 0.37144363,
+  -0.62476718, 0.23791671, 0.40060222, 0.1356622,
+]
+input0[hidden_state_in] = [
+  0.496726, 0, 0.965996, 0,
+  0.0584256, 0, 0, 0.12315,
+  0, 0, 0.612267, 0.456601,
+  0, 0.52286, 1.16099, 0.0291233,
+  0.496726, 0, 0.965996, 0,
+  0.0584256, 0, 0, 0.12315,
+  0, 0, 0.612267, 0.456601,
+  0, 0.52286, 1.16099, 0.0291233,
+]
+output0 = {
+  hidden_state_out : [
+  0, 0, 0.524902, 0,
+  0, 0, 0, 1.02116,
+  0, 1.35762, 0, 0.356909,
+  0.436415, 0.0355731, 0, 0,
+  0, 0, 0.524902, 0,
+  0, 0, 0, 1.02116,
+  0, 1.35762, 0, 0.356909,
+  0.436415, 0.0355731, 0, 0,
+  ]
+}
+output0[output] = [
+  0,          0,          0.524901,  0,         0,         0,
+  0,          1.02116,    0,         1.35762,   0,         0.356909,
+  0.436415,   0.0355727,  0,         0,
+
+  0,          0,          0.524901,  0,         0,         0,
+  0,          1.02116,    0,         1.35762,   0,         0.356909,
+  0.436415,   0.0355727,  0,         0,
+]
+
+Example((input0, output0))
diff --git a/nn/runtime/test/specs/svdf_state.mod.py b/nn/runtime/test/specs/svdf_state.mod.py
new file mode 100644
index 000000000..aad2114e7
--- /dev/null
+++ b/nn/runtime/test/specs/svdf_state.mod.py
@@ -0,0 +1,116 @@
+#
+# Copyright (C) 2017 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
+units = 4
+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}" % (units, input_size))
+weights_time = Input("weights_time", "TENSOR_FLOAT32", "{%d, %d}" % (units, memory_size))
+bias = Input("bias", "TENSOR_FLOAT32", "{%d}" % (units))
+state_in = Input("state_in", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
+rank_param = Input("rank_param", "TENSOR_INT32", "{1}")
+activation_param = Input("activation_param", "TENSOR_INT32", "{1}")
+state_out = Output("state_out", "TENSOR_FLOAT32", "{%d, %d}" % (batches, memory_size*units))
+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 = {
+    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: [],
+    rank_param: [1],
+    activation_param: [0],
+}
+
+input0[input] = [
+  0.14278367,  -1.64410412, -0.75222826,
+  0.14278367,  -1.64410412, -0.75222826,
+]
+input0[state_in]  = [
+  0, 0, 0, 0,
+  0, 0, 0, 0,
+  0.119996, 0, 0, 0,
+  0, 0, 0, 0,
+  0, -0.166701, 0, 0,
+  0, 0, 0, 0,
+  0, 0, -0.44244, 0,
+  0, 0, 0, 0,
+  0, 0, 0, 0.0805206,
+  0, 0, 0, 0,
+  0, 0, 0, 0,
+  0.119996, 0, 0, 0,
+  0, 0, 0, 0,
+  0, -0.166701, 0, 0,
+  0, 0, 0, 0,
+  0, 0, -0.44244, 0,
+  0, 0, 0, 0,
+  0, 0, 0, 0.0805206,
+  0, 0, 0, 0,
+  0, 0, 0, 0,
+]
+output0 = {
+    state_out : [
+  0, 0, 0, 0,
+  0, 0, 0, 0.119996,
+  0.542235, 0, 0, 0,
+  0, 0, 0, 0,
+  -0.166701, -0.40465, 0, 0,
+  0, 0, 0, 0,
+  0, -0.44244, -0.706995, 0,
+  0, 0, 0, 0,
+  0, 0, 0.0805206, 0.137515,
+  0, 0, 0, 0,
+  0, 0, 0, 0.119996,
+  0.542235, 0, 0, 0,
+  0, 0, 0, 0,
+  -0.166701, -0.40465, 0, 0,
+  0, 0, 0, 0,
+  0, -0.44244, -0.706995, 0,
+  0, 0, 0, 0,
+  0, 0, 0.0805206, 0.137515,
+  0, 0, 0, 0,
+  0, 0, 0, 0,
+    ],
+    output : [
+  0.068281,    -0.162217,  -0.152268, 0.00323521,
+  0.068281,    -0.162217,  -0.152268, 0.00323521,
+    ]
+}
+
+Example((input0, output0))
diff --git a/nn/tools/test_generator/include/TestHarness.h b/nn/tools/test_generator/include/TestHarness.h
index 256a206e9..3e3abf0ff 100644
--- a/nn/tools/test_generator/include/TestHarness.h
+++ b/nn/tools/test_generator/include/TestHarness.h
@@ -175,7 +175,7 @@ inline void compare(const MixedTyped& golden, const MixedTyped& test) {
     compare_<0>(golden, test,
                 [](float g, float t) { EXPECT_NEAR(g, t, 1.e-5f); });
     compare_<1>(golden, test, [](int32_t g, int32_t t) { EXPECT_EQ(g, t); });
-    compare_<2>(golden, test, [](uint8_t g, uint8_t t) { EXPECT_EQ(g, t); });
+    compare_<2>(golden, test, [](uint8_t g, uint8_t t) { EXPECT_NEAR(g, t, 1); });
 }
 
 };  // namespace generated_tests