lstm_eval_test.cc

/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.

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 "tensorflow/lite/kernels/lstm_eval.h"

#include <stdint.h>
#include <stdlib.h>

#include <algorithm>
#include <memory>
#include <vector>

#include <gtest/gtest.h>
#include "tensorflow/lite/core/c/builtin_op_data.h"
#include "tensorflow/lite/core/c/common.h"
#include "tensorflow/lite/kernels/cpu_backend_context.h"

namespace tflite {
namespace {

// Validate result.
template <typename T>
bool ArrayEq(const T* result, const T* expected_result, int size) {
  for (int i = 0; i < size; ++i) {
    if (result[i] != expected_result[i]) {
      return false;
    }
  }
  return true;
}

template <typename T>
bool ArrayFloatNear(const T* result, const T* expected_result, int size,
                    double threshold) {
  for (int i = 0; i < size; ++i) {
    if (std::abs(result[i] - expected_result[i]) > threshold) {
      return false;
    }
  }
  return true;
}

// Base class that holds input parameters for quantized and hybrid lstm.
class BaseLstmParam {
 public:
  TfLiteTensor* Geti2i() {
    PackWeightToTensor(&i2i_tensor_, i2i_, i2i_size_);
    i2i_tensor_.data.int8 = i2i_.data();
    return &i2i_tensor_;
  }
  TfLiteTensor* Geti2f() {
    PackWeightToTensor(&i2f_tensor_, i2f_, i2f_size_);
    i2f_tensor_.data.int8 = i2f_.data();
    return &i2f_tensor_;
  }
  TfLiteTensor* Geti2c() {
    PackWeightToTensor(&i2c_tensor_, i2c_, i2c_size_);
    i2c_tensor_.data.int8 = i2c_.data();
    return &i2c_tensor_;
  }
  TfLiteTensor* Geti2o() {
    PackWeightToTensor(&i2o_tensor_, i2o_, i2o_size_);
    i2o_tensor_.data.int8 = i2o_.data();
    return &i2o_tensor_;
  }
  TfLiteTensor* Getr2i() {
    PackWeightToTensor(&r2i_tensor_, r2i_, r2i_size_);
    r2i_tensor_.data.int8 = r2i_.data();
    return &r2i_tensor_;
  }
  TfLiteTensor* Getr2f() {
    PackWeightToTensor(&r2f_tensor_, r2f_, r2f_size_);
    r2f_tensor_.data.int8 = r2f_.data();
    return &r2f_tensor_;
  }
  TfLiteTensor* Getr2c() {
    PackWeightToTensor(&r2c_tensor_, r2c_, r2c_size_);
    r2c_tensor_.data.int8 = r2c_.data();
    return &r2c_tensor_;
  }
  TfLiteTensor* Getr2o() {
    PackWeightToTensor(&r2o_tensor_, r2o_, r2o_size_);
    r2o_tensor_.data.int8 = r2o_.data();
    return &r2o_tensor_;
  }
  TfLiteTensor* GetProjection() {
    PackWeightToTensor(&projection_tensor_, projection_, projection_size_);
    projection_tensor_.data.int8 = projection_.data();
    return &projection_tensor_;
  }
  ~BaseLstmParam() {
    TfLiteIntArrayFree(input_tensor_.dims);
    TfLiteIntArrayFree(i2i_tensor_.dims);
    TfLiteIntArrayFree(i2f_tensor_.dims);
    TfLiteIntArrayFree(i2c_tensor_.dims);
    TfLiteIntArrayFree(i2o_tensor_.dims);
    TfLiteIntArrayFree(r2i_tensor_.dims);
    TfLiteIntArrayFree(r2f_tensor_.dims);
    TfLiteIntArrayFree(r2c_tensor_.dims);
    TfLiteIntArrayFree(r2o_tensor_.dims);
    TfLiteIntArrayFree(layer_norm_input_tensor_.dims);
    TfLiteIntArrayFree(layer_norm_forget_tensor_.dims);
    TfLiteIntArrayFree(layer_norm_cell_tensor_.dims);
    TfLiteIntArrayFree(layer_norm_output_tensor_.dims);
    TfLiteIntArrayFree(input_gate_bias_tensor_.dims);
    TfLiteIntArrayFree(forget_gate_bias_tensor_.dims);
    TfLiteIntArrayFree(cell_gate_bias_tensor_.dims);
    TfLiteIntArrayFree(output_gate_bias_tensor_.dims);
    TfLiteIntArrayFree(projection_tensor_.dims);
    TfLiteIntArrayFree(projection_bias_tensor_.dims);
    TfLiteIntArrayFree(activation_tensor_.dims);
    TfLiteIntArrayFree(cell_tensor_.dims);
    TfLiteIntArrayFree(output_tensor_.dims);
  }

 protected:
  template <typename T>
  void PackWeightToTensor(TfLiteTensor* tensor, std::vector<T>& data,
                          std::vector<int32_t> dims) {
    if (data.empty()) {
      int total = 1;
      for (int i = 0; i < dims.size(); ++i) {
        total *= dims[i];
      }
      for (int i = 0; i < total; ++i) {
        data.push_back(0);
      }
    }
    tensor->dims = TfLiteIntArrayCreate(dims.size());
    for (int i = 0; i < dims.size(); ++i) {
      tensor->dims->data[i] = dims[i];
    }
  }
  // Dimensions. Need proper size to trigger neon code.
  const int n_batch_ = 2;
  const int n_input_ = 18;
  const int n_cell_ = 10;
  const int n_output_ = 6;

  std::vector<int32_t> input_size_ = {n_batch_, n_input_};
  TfLiteTensor input_tensor_;

  // input_to_input_weights.
  std::vector<int8_t> i2i_ = {
      18, 2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  6, 1, 2, 3, 4,  5,  6,   //
      1,  2,  3, 4,  5, 6, 5, 2,  3, 4, 5,  6, 1, 2, 3, 4,  5,  0,   //
      8,  2,  3, 4,  3, 6, 1, -2, 3, 4, 5,  6, 1, 2, 3, -4, 5,  6,   //
      1,  2,  3, 4,  5, 6, 1, 2,  3, 4, -5, 6, 1, 7, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 3, 2,  3, 4, 5,  6, 1, 2, 3, 4,  5,  6,   //
      1,  -2, 2, 4,  5, 6, 1, 2,  3, 4, 5,  6, 1, 2, 3, 8,  5,  -6,  //
      8,  2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  6, 1, 2, 3, 4,  5,  6,   //
      1,  2,  3, 4,  3, 6, 1, 2,  6, 4, 5,  6, 1, 2, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 7, 2,  3, 4, 5,  6, 1, 2, 3, 14, 5,  6,   //
      1,  2,  3, -4, 5, 6, 1, 2,  3, 4, 5,  6, 1, 2, 3, 4,  5,  6,   //
  };
  std::vector<int32_t> i2i_size_ = {n_cell_, n_input_};
  TfLiteTensor i2i_tensor_;

  // input_to_forget_weights.
  std::vector<int8_t> i2f_ = {
      1,  2,  3, 4,  5, 6, 5, 2,  3, 4, 5,  6,  1,  2, 3, 4,  5,  0,   //
      8,  2,  3, 4,  3, 6, 1, -2, 3, 4, 5,  6,  1,  2, 3, -4, 5,  6,   //
      1,  2,  3, 4,  5, 6, 1, 2,  3, 4, -5, 6,  1,  7, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  6,  1,  2, 3, 4,  5,  6,   //
      1,  2,  3, 4,  3, 6, 1, 2,  6, 4, 5,  6,  11, 2, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 7, 2,  3, 4, 5,  -6, 1,  2, 3, 14, 5,  6,   //
      1,  2,  3, -4, 5, 6, 1, 2,  3, 4, 5,  6,  1,  2, 3, 4,  5,  6,   //
      18, 2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  6,  1,  2, 3, 4,  5,  6,   //
      8,  2,  3, 4,  5, 6, 3, 2,  3, 4, 5,  6,  13, 2, 3, 4,  5,  6,   //
      1,  -2, 2, 4,  5, 6, 1, 2,  3, 4, 5,  6,  1,  2, 3, 8,  5,  -6,  //
  };
  std::vector<int32_t> i2f_size_ = {n_cell_, n_input_};
  TfLiteTensor i2f_tensor_;

  // input_to_cell_weights.
  std::vector<int8_t> i2c_ = {
      1,  2,  3, 4,  5, 6, 5, 2,  3, 4, 5,  6,  1, 2, 3, 4,  5,  0,   //
      1,  2,  3, 4,  3, 6, 1, 2,  6, 4, 5,  6,  1, 2, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 7, 2,  3, 4, 5,  16, 1, 2, 3, 14, 5,  6,   //
      1,  2,  3, -4, 5, 6, 1, 2,  3, 4, 5,  6,  7, 2, 3, 4,  5,  6,   //
      18, 2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  6,  1, 2, 3, 4,  5,  6,   //
      8,  2,  3, 4,  5, 6, 3, 2,  3, 4, 5,  6,  1, 2, 3, 4,  5,  6,   //
      1,  -2, 2, 4,  5, 6, 1, 2,  3, 4, 5,  6,  1, 2, 3, 8,  5,  -6,  //
      8,  2,  3, 4,  3, 6, 1, -2, 3, 4, 5,  6,  1, 2, 3, -4, 5,  6,   //
      1,  2,  3, 4,  5, 6, 1, 2,  3, 4, -5, 6,  1, 7, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  6,  1, 2, 3, 4,  5,  6,   //
  };
  std::vector<int32_t> i2c_size_ = {n_cell_, n_input_};
  TfLiteTensor i2c_tensor_;

  // input_to_output_weights.
  std::vector<int8_t> i2o_ = {
      1,  2,  3, 4,  5, 6, 1, 2,  3, 4, -5, 6,  1,  7, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  6,  -1, 2, 3, 4,  5,  6,   //
      1,  2,  3, 4,  3, 6, 1, 2,  6, 4, 5,  6,  1,  2, 3, 4,  -5, 6,   //
      8,  2,  3, 4,  5, 6, 7, 2,  3, 4, 5,  6,  1,  2, 3, 14, 5,  6,   //
      18, 2,  3, 4,  5, 6, 1, 2,  3, 4, 5,  -6, 1,  2, 3, 4,  5,  6,   //
      8,  2,  3, 4,  5, 6, 3, 2,  3, 4, 5,  6,  1,  2, 3, 4,  5,  6,   //
      1,  2,  3, 4,  5, 6, 5, 2,  3, 4, 5,  6,  1,  2, 3, 4,  5,  0,   //
      8,  2,  3, 4,  3, 6, 1, -2, 3, 4, 5,  6,  1,  2, 3, -4, 5,  6,   //
      1,  2,  3, -4, 5, 6, 1, 2,  3, 4, 5,  6,  -1, 2, 3, 4,  5,  6,   //
      1,  -2, 2, 4,  5, 6, 1, 2,  3, 4, 5,  6,  1,  2, 3, 8,  5,  -6,  //
  };
  std::vector<int32_t> i2o_size_ = {n_cell_, n_input_};
  TfLiteTensor i2o_tensor_;

  // recurrent_to_input_weights.
  std::vector<int8_t> r2i_ = {
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
  };
  std::vector<int32_t> r2i_size_ = {n_cell_, n_output_};
  TfLiteTensor r2i_tensor_;

  // recurrent_to_forget_weights.
  std::vector<int8_t> r2f_ = {
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
  };
  std::vector<int32_t> r2f_size_ = {n_cell_, n_output_};
  TfLiteTensor r2f_tensor_;

  // recurrent_to_cell_weights.
  std::vector<int8_t> r2c_ = {
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
  };
  std::vector<int32_t> r2c_size_ = {n_cell_, n_output_};
  TfLiteTensor r2c_tensor_;

  // recurrent_to_output_weights.
  std::vector<int8_t> r2o_ = {
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
  };
  std::vector<int32_t> r2o_size_ = {n_cell_, n_output_};
  TfLiteTensor r2o_tensor_;

  std::vector<int32_t> layer_norm_input_size_ = {n_cell_};
  TfLiteTensor layer_norm_input_tensor_;

  TfLiteTensor layer_norm_forget_tensor_;
  std::vector<int32_t> layer_norm_forget_size_ = {n_cell_};

  std::vector<int32_t> layer_norm_cell_size_ = {n_cell_};
  TfLiteTensor layer_norm_cell_tensor_;

  std::vector<int32_t> layer_norm_output_size_ = {n_cell_};
  TfLiteTensor layer_norm_output_tensor_;

  std::vector<int32_t> input_gate_bias_size_ = {n_cell_};
  TfLiteTensor input_gate_bias_tensor_;

  std::vector<int32_t> forget_gate_bias_size_ = {n_cell_};
  TfLiteTensor forget_gate_bias_tensor_;

  std::vector<int32_t> cell_gate_bias_size_ = {n_cell_};
  TfLiteTensor cell_gate_bias_tensor_;

  std::vector<int32_t> output_gate_bias_size_ = {n_cell_};
  TfLiteTensor output_gate_bias_tensor_;

  // projection_weights.
  std::vector<int8_t> projection_ = {
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
      8, 2, 3, 4, 5, 6, 1, 2,  3,  4,  //
      6, 4, 5, 6, 1, 2, 3, 4,  -5, 6,  //
      1, 2, 3, 4, 7, 3, 4, -5, 6,  3,  //
  };
  std::vector<int32_t> projection_size_ = {n_cell_, n_output_};
  TfLiteTensor projection_tensor_;

  // projection_bias.
  std::vector<int32_t> projection_bias_ = {
      16, 4, 5, 6, 1, 1  //
  };

  std::vector<int32_t> projection_bias_size_ = {n_output_};
  TfLiteTensor projection_bias_tensor_;

  std::vector<int32_t> activation_size_ = {n_batch_, n_output_};
  TfLiteTensor activation_tensor_;

  std::vector<int32_t> cell_size_ = {n_batch_, n_cell_};
  TfLiteTensor cell_tensor_;

  std::vector<int32_t> output_size_ = {n_batch_, n_output_};
  TfLiteTensor output_tensor_;
};

class QuantizedLstmParam : public BaseLstmParam {
 public:
  // Getter methods.
  TfLiteTensor* GetInput() {
    PackWeightToTensor(&input_tensor_, input_, input_size_);
    input_tensor_.data.int8 = input_.data();
    return &input_tensor_;
  }
  TfLiteTensor* GetInputLayerNorm() {
    PackWeightToTensor(&layer_norm_input_tensor_, layer_norm_input_,
                       layer_norm_input_size_);
    layer_norm_input_tensor_.data.i16 = layer_norm_input_.data();
    return &layer_norm_input_tensor_;
  }
  TfLiteTensor* GetForgetLayerNorm() {
    PackWeightToTensor(&layer_norm_forget_tensor_, layer_norm_forget_,
                       layer_norm_forget_size_);
    layer_norm_forget_tensor_.data.i16 = layer_norm_forget_.data();
    return &layer_norm_forget_tensor_;
  }
  TfLiteTensor* GetCellLayerNorm() {
    PackWeightToTensor(&layer_norm_cell_tensor_, layer_norm_cell_,
                       layer_norm_cell_size_);
    layer_norm_cell_tensor_.data.i16 = layer_norm_cell_.data();
    return &layer_norm_cell_tensor_;
  }
  TfLiteTensor* GetOutputLayerNorm() {
    PackWeightToTensor(&layer_norm_output_tensor_, layer_norm_output_,
                       layer_norm_output_size_);
    layer_norm_output_tensor_.data.i16 = layer_norm_output_.data();
    return &layer_norm_output_tensor_;
  }
  TfLiteTensor* GetInputBias() {
    PackWeightToTensor(&input_gate_bias_tensor_, input_gate_bias_,
                       input_gate_bias_size_);
    input_gate_bias_tensor_.data.i32 = input_gate_bias_.data();
    return &input_gate_bias_tensor_;
  }
  TfLiteTensor* GetForgetBias() {
    PackWeightToTensor(&forget_gate_bias_tensor_, forget_gate_bias_,
                       forget_gate_bias_size_);
    forget_gate_bias_tensor_.data.i32 = forget_gate_bias_.data();
    return &forget_gate_bias_tensor_;
  }
  TfLiteTensor* GetCellBias() {
    PackWeightToTensor(&cell_gate_bias_tensor_, cell_gate_bias_,
                       cell_gate_bias_size_);
    cell_gate_bias_tensor_.data.i32 = cell_gate_bias_.data();
    return &cell_gate_bias_tensor_;
  }
  TfLiteTensor* GetOutputBias() {
    PackWeightToTensor(&output_gate_bias_tensor_, output_gate_bias_,
                       output_gate_bias_size_);
    output_gate_bias_tensor_.data.i32 = output_gate_bias_.data();
    return &output_gate_bias_tensor_;
  }
  TfLiteTensor* GetProjectionBias() {
    PackWeightToTensor(&projection_bias_tensor_, projection_bias_,
                       projection_bias_size_);
    projection_bias_tensor_.data.i32 = projection_bias_.data();
    return &projection_bias_tensor_;
  }

  // Set up quantization parameters.
  ops::builtin::lstm_eval::IntegerLstmParameter* GetQuantParam() {
    integer_lstm_param_.effective_input_to_input_scale_a = 1808677632;
    integer_lstm_param_.effective_input_to_input_scale_b = -1;
    integer_lstm_param_.effective_recurrent_to_input_scale_a = 1078887680;
    integer_lstm_param_.effective_recurrent_to_input_scale_b = -1;
    integer_lstm_param_.effective_cell_to_input_scale_a = 1073741824;
    integer_lstm_param_.effective_cell_to_input_scale_b = 1;
    integer_lstm_param_.effective_input_to_forget_scale_a = 1845996800;
    integer_lstm_param_.effective_input_to_forget_scale_b = -3;
    integer_lstm_param_.effective_recurrent_to_forget_scale_a = 1477412736;
    integer_lstm_param_.effective_recurrent_to_forget_scale_b = -2;
    integer_lstm_param_.effective_cell_to_forget_scale_a = 1073741824;
    integer_lstm_param_.effective_cell_to_forget_scale_b = 1;
    integer_lstm_param_.effective_input_to_cell_scale_a = 1648385408;
    integer_lstm_param_.effective_input_to_cell_scale_b = -2;
    integer_lstm_param_.effective_recurrent_to_cell_scale_a = 1185544192,
    integer_lstm_param_.effective_recurrent_to_cell_scale_b = -1;
    integer_lstm_param_.effective_input_to_output_scale_a = 1328153600;
    integer_lstm_param_.effective_input_to_output_scale_b = -1;
    integer_lstm_param_.effective_recurrent_to_output_scale_a = 1479582592;
    integer_lstm_param_.effective_recurrent_to_output_scale_b = -1;
    integer_lstm_param_.effective_cell_to_output_scale_a = 1073741824,
    integer_lstm_param_.effective_cell_to_output_scale_b = 1;
    integer_lstm_param_.effective_proj_scale_a = 1105682560;
    integer_lstm_param_.effective_proj_scale_b = -8;
    integer_lstm_param_.effective_hidden_scale_a = 0;
    integer_lstm_param_.effective_hidden_scale_b = 0;
    integer_lstm_param_.layer_norm_input_scale_a = 2011617664;
    integer_lstm_param_.layer_norm_input_scale_b = -11;
    integer_lstm_param_.layer_norm_forget_scale_a = 1968024960;
    integer_lstm_param_.layer_norm_forget_scale_b = -13;
    integer_lstm_param_.layer_norm_cell_scale_a = 1097334528,
    integer_lstm_param_.layer_norm_cell_scale_b = -12;
    integer_lstm_param_.layer_norm_output_scale_a = 1837163008;
    integer_lstm_param_.layer_norm_output_scale_b = -12;
    integer_lstm_param_.quantized_cell_clip = 20480;
    integer_lstm_param_.quantized_proj_clip = 0;
    integer_lstm_param_.cell_scale = -11;
    integer_lstm_param_.input_variance_guard = 1;
    integer_lstm_param_.forget_variance_guard = 2;
    integer_lstm_param_.cell_variance_guard = 2;
    integer_lstm_param_.output_variance_guard = 1;
    integer_lstm_param_.hidden_zp = 0;
    integer_lstm_param_.input_to_forget_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.recurrent_to_forget_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.input_to_cell_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.recurrent_to_cell_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.input_to_output_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.recurrent_to_output_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.input_to_input_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.recurrent_to_input_effective_bias.reset(
        new int32_t[n_cell_]);
    integer_lstm_param_.projection_effective_bias.reset(new int32_t[n_output_]);
    std::fill_n(integer_lstm_param_.input_to_forget_effective_bias.get(),
                n_cell_, 152);
    std::fill_n(integer_lstm_param_.recurrent_to_forget_effective_bias.get(),
                n_cell_, 315);
    std::fill_n(integer_lstm_param_.input_to_cell_effective_bias.get(), n_cell_,
                165);
    std::fill_n(integer_lstm_param_.recurrent_to_cell_effective_bias.get(),
                n_cell_, 1165);
    std::fill_n(integer_lstm_param_.input_to_output_effective_bias.get(),
                n_cell_, 159);
    std::fill_n(integer_lstm_param_.recurrent_to_output_effective_bias.get(),
                n_cell_, 915);
    std::fill_n(integer_lstm_param_.input_to_input_effective_bias.get(),
                n_cell_, -15);
    std::fill_n(integer_lstm_param_.recurrent_to_input_effective_bias.get(),
                n_cell_, 315);
    std::fill_n(integer_lstm_param_.projection_effective_bias.get(), n_output_,
                115);
    return &integer_lstm_param_;
  }

  // Create scratch buffers.
  TfLiteTensor* GetScratch0() {
    PackWeightToTensor(&scratch0_tensor_, scratch0_, scratch0_size_);
    scratch0_tensor_.data.i16 = scratch0_.data();
    return &scratch0_tensor_;
  }
  TfLiteTensor* GetScratch1() {
    PackWeightToTensor(&scratch1_tensor_, scratch1_, scratch1_size_);
    scratch1_tensor_.data.i16 = scratch1_.data();
    return &scratch1_tensor_;
  }
  TfLiteTensor* GetScratch2() {
    PackWeightToTensor(&scratch2_tensor_, scratch2_, scratch2_size_);
    scratch2_tensor_.data.i16 = scratch2_.data();
    return &scratch2_tensor_;
  }
  TfLiteTensor* GetScratch3() {
    PackWeightToTensor(&scratch3_tensor_, scratch3_, scratch3_size_);
    scratch3_tensor_.data.i16 = scratch3_.data();
    return &scratch3_tensor_;
  }
  TfLiteTensor* GetScratch4() {
    PackWeightToTensor(&scratch4_tensor_, scratch4_, scratch4_size_);
    scratch4_tensor_.data.int8 = scratch4_.data();
    return &scratch4_tensor_;
  }
  TfLiteTensor* GetScratch5() {
    PackWeightToTensor(&scratch5_tensor_, scratch5_, scratch5_size_);
    scratch5_tensor_.data.i32 = scratch5_.data();
    return &scratch5_tensor_;
  }
  TfLiteTensor* GetActivation() {
    PackWeightToTensor(&activation_tensor_, activation_, activation_size_);
    activation_tensor_.data.int8 = activation_.data();
    activation_tensor_.params.zero_point = 50;
    return &activation_tensor_;
  }
  TfLiteTensor* GetOutput() {
    PackWeightToTensor(&output_tensor_, output_, output_size_);
    output_tensor_.data.int8 = output_.data();
    return &output_tensor_;
  }
  TfLiteTensor* GetCell() {
    PackWeightToTensor(&cell_tensor_, cell_, cell_size_);
    cell_tensor_.data.i16 = cell_.data();
    return &cell_tensor_;
  }
  ~QuantizedLstmParam() {
    TfLiteIntArrayFree(scratch0_tensor_.dims);
    TfLiteIntArrayFree(scratch1_tensor_.dims);
    TfLiteIntArrayFree(scratch2_tensor_.dims);
    TfLiteIntArrayFree(scratch3_tensor_.dims);
    TfLiteIntArrayFree(scratch4_tensor_.dims);
    TfLiteIntArrayFree(scratch5_tensor_.dims);
  }

 private:
  // input.
  std::vector<int8_t> input_ = {
      8, 2, 3,  4, 5, 6, 1, -2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6,  //
      1, 2, -3, 4, 5, 6, 1, 2,  3, 4, 5, 6, 1, 2, 3, 4, 5, 6,  //
  };

  std::vector<int16_t> layer_norm_input_ = {8, 2, 3, 4, 5, 6, 1, 2, 3, 4};

  // forget_layer_norm_coefficient.
  std::vector<int16_t> layer_norm_forget_ = {
      1, 2, 3, 4, 7, 3, 4, -5, 6, 3,  //
  };

  // cell_layer_norm_coefficients.
  std::vector<int16_t> layer_norm_cell_ = {
      6, 4, 5, 6, 1, 2, 3, 4, -5, 6,  //
  };

  // output_layer_norm_coefficients.
  std::vector<int16_t> layer_norm_output_ = {
      16, 4, 5, 6, 1, 1, 3, 4, -5, 6,  //
  };

  // input_gate_bias.
  std::vector<int32_t> input_gate_bias_ = {
      16, 4, 5, 6, 1, 1, 3, 4, -5, 6,  //
  };

  // forget_gate_bias.
  std::vector<int32_t> forget_gate_bias_ = {
      16, 4, 5, 6, 1, 1, 3, 4, -5, 6,  //
  };

  // cell_gate_bias.
  std::vector<int32_t> cell_gate_bias_ = {
      16, 4, 5, 6, 1, 1, 3, 4, -5, 6,  //
  };

  // output_gate_bias.
  std::vector<int32_t> output_gate_bias_ = {
      16, 4, 5, 6, 1, 1, 3, 4, -5, 6,  //
  };

  // activation.
  std::vector<int8_t> activation_;

  // cell.
  std::vector<int16_t> cell_ = {
      16, 4,  5, 6, 1, 1, 3, 4, -5, 6,  //
      1,  14, 5, 6, 1, 1, 3, 4, -5, 6,  //
  };

  // output.
  std::vector<int8_t> output_ = {
      1, 1, 3, 4, -5, 6,  //
      1, 4, 3, 4, -5, 6,  //
  };

  // quantized_lstm_param
  ops::builtin::lstm_eval::IntegerLstmParameter integer_lstm_param_;

  // 5 scratch buffers.
  std::vector<int16_t> scratch0_;
  std::vector<int32_t> scratch0_size_ = {n_batch_, n_cell_};
  TfLiteTensor scratch0_tensor_;
  std::vector<int16_t> scratch1_;
  std::vector<int32_t> scratch1_size_ = {n_batch_, n_cell_};
  TfLiteTensor scratch1_tensor_;
  std::vector<int16_t> scratch2_;
  std::vector<int32_t> scratch2_size_ = {n_batch_, n_cell_};
  TfLiteTensor scratch2_tensor_;
  std::vector<int16_t> scratch3_;
  std::vector<int32_t> scratch3_size_ = {n_batch_, n_cell_};
  TfLiteTensor scratch3_tensor_;
  std::vector<int8_t> scratch4_;
  std::vector<int32_t> scratch4_size_ = {n_batch_, n_cell_};
  TfLiteTensor scratch4_tensor_;
  std::vector<int32_t> scratch5_;
  std::vector<int32_t> scratch5_size_ = {n_batch_, n_cell_};
  TfLiteTensor scratch5_tensor_;
};

void TestOneFullyQuantizedLSTM() {
  CpuBackendContext context;
  QuantizedLstmParam one_parameter;
  auto activation = one_parameter.GetActivation();
  auto output = one_parameter.GetOutput();
  auto cell = one_parameter.GetCell();
  auto param = one_parameter.GetQuantParam();
  ops::builtin::lstm_eval::EvalInteger8x8_16(
      one_parameter.GetInput(), one_parameter.Geti2i(), one_parameter.Geti2f(),
      one_parameter.Geti2c(), one_parameter.Geti2o(), one_parameter.Getr2i(),
      one_parameter.Getr2f(), one_parameter.Getr2c(), one_parameter.Getr2o(),
      nullptr, nullptr, nullptr, one_parameter.GetInputLayerNorm(),
      one_parameter.GetForgetLayerNorm(), one_parameter.GetCellLayerNorm(),
      one_parameter.GetOutputLayerNorm(), one_parameter.GetInputBias(),
      one_parameter.GetForgetBias(), one_parameter.GetCellBias(),
      one_parameter.GetOutputBias(), one_parameter.GetProjection(),
      one_parameter.GetProjectionBias(), nullptr, /*forward_sequence=*/true,
      /*time_major=*/true, param, activation, cell, output,
      one_parameter.GetScratch0(), one_parameter.GetScratch1(),
      one_parameter.GetScratch2(), one_parameter.GetScratch3(),
      one_parameter.GetScratch4(), one_parameter.GetScratch5(), &context);

  // Verify results.
  const std::vector<int16_t> expected_cell = {
      7, 1, 3, 2, 0, 1, 0, 2, -2, 4, 1, 6, 4, 3, 0, 1, 0, 2, -2, 4,
  };
  const std::vector<int8_t> expected_activation = {
      50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
  };
  EXPECT_TRUE(ArrayEq(cell->data.i16, expected_cell.data(), 20));
  EXPECT_TRUE(ArrayEq(activation->data.int8, expected_activation.data(), 12));
  EXPECT_TRUE(ArrayEq(output->data.int8, expected_activation.data(), 12));
}

TEST(TestOneFullyQuantizedLSTM, TestOneFullyQuantizedLSTM) {
  TestOneFullyQuantizedLSTM();
}

class HybridLstmParam : public BaseLstmParam {
 public:
  TfLiteTensor* GetFloatOutput() {
    PackWeightToTensor(&output_tensor_, output_float_, output_size_);
    output_tensor_.data.f = output_float_.data();
    return &output_tensor_;
  }
  const TfLiteLSTMParams GetLSTMParam() {
    return {kTfLiteActRelu, 0, 0, kTfLiteLSTMFullKernel, true};
  }
  TfLiteTensor* GetScratchBuffer() {
    PackWeightToTensor(&scratch_buffer_tensor_, scratch_buffer_,
                       scratch_buffer_size_);
    scratch_buffer_tensor_.data.f = scratch_buffer_.data();
    return &scratch_buffer_tensor_;
  }
  TfLiteTensor* GetInputScalingFactors() {
    PackWeightToTensor(&input_sf_tensor_, input_sf_,
                       quantization_extra_scratch_buffer_sizes_);
    input_sf_tensor_.data.f = input_sf_.data();
    return &input_sf_tensor_;
  }
  TfLiteTensor* GetAuxInputScalingFactors() {
    PackWeightToTensor(&aux_input_sf_tensor_, aux_input_sf_,
                       quantization_extra_scratch_buffer_sizes_);
    aux_input_sf_tensor_.data.f = aux_input_sf_.data();
    return &aux_input_sf_tensor_;
  }
  TfLiteTensor* GetOutputStateScalingFactors() {
    PackWeightToTensor(&output_state_sf_tensor_, output_state_sf_,
                       quantization_extra_scratch_buffer_sizes_);
    output_state_sf_tensor_.data.f = output_state_sf_.data();
    return &output_state_sf_tensor_;
  }
  TfLiteTensor* GetProdScalingFactors() {
    PackWeightToTensor(&prod_scaling_factors_tensor_, prod_scaling_factors_,
                       quantization_extra_scratch_buffer_sizes_);
    prod_scaling_factors_tensor_.data.f = prod_scaling_factors_.data();
    return &prod_scaling_factors_tensor_;
  }
  TfLiteTensor* GetInputQuantized() {
    PackWeightToTensor(&input_quantized_tensor_, input_quantized_, input_size_);
    input_quantized_tensor_.data.int8 = input_quantized_.data();
    return &input_quantized_tensor_;
  }
  TfLiteTensor* GetActivationStateQuantized() {
    PackWeightToTensor(&activation_quantized_tensor_, activation_quantized_,
                       activation_size_);
    activation_quantized_tensor_.data.int8 = activation_quantized_.data();
    return &activation_quantized_tensor_;
  }
  TfLiteTensor* GetCellStateQuantized() {
    PackWeightToTensor(&cell_quantized_tensor_, cell_quantized_, cell_size_);
    cell_quantized_tensor_.data.int8 = cell_quantized_.data();
    return &cell_quantized_tensor_;
  }
  TfLiteTensor* GetInputZeroPoints() {
    PackWeightToTensor(&input_zp_tensor_, input_zp_,
                       quantization_extra_scratch_buffer_sizes_);
    input_zp_tensor_.data.i32 = input_zp_.data();
    return &input_zp_tensor_;
  }
  TfLiteTensor* GetAuxInputZeroPoints() {
    PackWeightToTensor(&aux_input_zp_tensor_, aux_input_zp_,
                       quantization_extra_scratch_buffer_sizes_);
    aux_input_zp_tensor_.data.i32 = aux_input_zp_.data();
    return &aux_input_zp_tensor_;
  }
  TfLiteTensor* GetOutputStateZeroPoints() {
    PackWeightToTensor(&output_state_zp_tensor_, output_state_zp_,
                       quantization_extra_scratch_buffer_sizes_);
    output_state_zp_tensor_.data.i32 = output_state_zp_.data();
    return &output_state_zp_tensor_;
  }
  TfLiteTensor* GetRowSums() {
    PackWeightToTensor(&row_sums_tensor_, row_sums_, row_sums_size_);
    row_sums_tensor_.data.i32 = row_sums_.data();
    return &row_sums_tensor_;
  }
  TfLiteTensor* GetFloatInput() {
    PackWeightToTensor(&input_tensor_, input_float_, input_size_);
    input_tensor_.data.f = input_float_.data();
    return &input_tensor_;
  }
  TfLiteTensor* GetActivation() {
    PackWeightToTensor(&activation_tensor_, activation_state_,
                       activation_size_);
    activation_tensor_.data.f = activation_state_.data();
    return &activation_tensor_;
  }
  TfLiteTensor* GetCell() {
    PackWeightToTensor(&cell_tensor_, cell_state_, cell_size_);
    cell_tensor_.data.f = cell_state_.data();
    return &cell_tensor_;
  }
  TfLiteTensor* GetAccumScratchBuffer() {
    PackWeightToTensor(&accum_scratch_tensor_, accum_scratch_,
                       accum_scratch_size_);
    accum_scratch_tensor_.data.i32 = accum_scratch_.data();
    return &accum_scratch_tensor_;
  }
  TfLiteTensor* GetInputBias() {
    PackWeightToTensor(&input_gate_bias_tensor_, input_float_bias_,
                       input_gate_bias_size_);
    input_gate_bias_tensor_.data.f = input_float_bias_.data();
    return &input_gate_bias_tensor_;
  }
  TfLiteTensor* GetForgetBias() {
    PackWeightToTensor(&forget_gate_bias_tensor_, forget_float_bias_,
                       forget_gate_bias_size_);
    forget_gate_bias_tensor_.data.f = forget_float_bias_.data();
    return &forget_gate_bias_tensor_;
  }
  TfLiteTensor* GetCellBias() {
    PackWeightToTensor(&cell_gate_bias_tensor_, cell_float_bias_,
                       cell_gate_bias_size_);
    cell_gate_bias_tensor_.data.f = cell_float_bias_.data();
    return &cell_gate_bias_tensor_;
  }
  TfLiteTensor* GetOutputBias() {
    PackWeightToTensor(&output_gate_bias_tensor_, output_float_bias_,
                       output_gate_bias_size_);
    output_gate_bias_tensor_.data.f = output_float_bias_.data();
    return &output_gate_bias_tensor_;
  }
  TfLiteTensor* GetProjectionBias() {
    PackWeightToTensor(&projection_bias_tensor_, projection_float_bias_,
                       projection_bias_size_);
    projection_bias_tensor_.data.f = projection_float_bias_.data();
    return &projection_bias_tensor_;
  }
  int GetNumRowSums() { return n_row_sums_; }
  TfLiteTensor* GetInputLayerNorm() {
    PackWeightToTensor(&layer_norm_input_tensor_, layer_norm_float_input_,
                       layer_norm_input_size_);
    layer_norm_input_tensor_.data.f = layer_norm_float_input_.data();
    return &layer_norm_input_tensor_;
  }
  TfLiteTensor* GetForgetLayerNorm() {
    PackWeightToTensor(&layer_norm_forget_tensor_, layer_norm_float_forget_,
                       layer_norm_forget_size_);
    layer_norm_forget_tensor_.data.f = layer_norm_float_forget_.data();
    return &layer_norm_forget_tensor_;
  }
  TfLiteTensor* GetCellLayerNorm() {
    PackWeightToTensor(&layer_norm_cell_tensor_, layer_norm_float_cell_,
                       layer_norm_cell_size_);
    layer_norm_cell_tensor_.data.f = layer_norm_float_cell_.data();
    return &layer_norm_cell_tensor_;
  }
  TfLiteTensor* GetOutputLayerNorm() {
    PackWeightToTensor(&layer_norm_output_tensor_, layer_norm_float_output_,
                       layer_norm_output_size_);
    layer_norm_output_tensor_.data.f = layer_norm_float_output_.data();
    return &layer_norm_output_tensor_;
  }
  static TfLiteTensor* addScale(TfLiteTensor* t, float scale) {
    t->params.scale = scale;
    return t;
  }
  ~HybridLstmParam() {
    TfLiteIntArrayFree(scratch_buffer_tensor_.dims);
    TfLiteIntArrayFree(accum_scratch_tensor_.dims);
    TfLiteIntArrayFree(input_sf_tensor_.dims);
    TfLiteIntArrayFree(aux_input_sf_tensor_.dims);
    TfLiteIntArrayFree(output_state_sf_tensor_.dims);
    TfLiteIntArrayFree(prod_scaling_factors_tensor_.dims);
    TfLiteIntArrayFree(input_quantized_tensor_.dims);
    TfLiteIntArrayFree(activation_quantized_tensor_.dims);
    TfLiteIntArrayFree(cell_quantized_tensor_.dims);
    TfLiteIntArrayFree(input_zp_tensor_.dims);
    TfLiteIntArrayFree(aux_input_zp_tensor_.dims);
    TfLiteIntArrayFree(output_state_zp_tensor_.dims);
    TfLiteIntArrayFree(row_sums_tensor_.dims);
  }

 private:
  const int n_row_sums_ = 9;  // Number of weights + 1 for projection weights.

  std::vector<float> scratch_buffer_;
  std::vector<int32_t> scratch_buffer_size_ = {n_batch_, n_cell_ * 4};
  TfLiteTensor scratch_buffer_tensor_;

  std::vector<int32_t> quantization_extra_scratch_buffer_sizes_ = {n_batch_};
  std::vector<float> input_sf_;
  TfLiteTensor input_sf_tensor_;
  std::vector<float> aux_input_sf_;
  TfLiteTensor aux_input_sf_tensor_;
  std::vector<float> output_state_sf_;
  TfLiteTensor output_state_sf_tensor_;

  std::vector<float> prod_scaling_factors_;
  TfLiteTensor prod_scaling_factors_tensor_;

  std::vector<int32_t> input_zp_;
  TfLiteTensor input_zp_tensor_;
  std::vector<int32_t> aux_input_zp_;
  TfLiteTensor aux_input_zp_tensor_;
  std::vector<int32_t> output_state_zp_;
  TfLiteTensor output_state_zp_tensor_;

  std::vector<int8_t> input_quantized_;
  TfLiteTensor input_quantized_tensor_;

  std::vector<int8_t> activation_quantized_;
  TfLiteTensor activation_quantized_tensor_;

  std::vector<int8_t> cell_quantized_;
  TfLiteTensor cell_quantized_tensor_;

  std::vector<float> cell_state_ = {
      16, 4, 5, 6, 1, 1, 3, 4, -5, 6, 1, 14, 5, 6, 1, 1, 3, 4, -5, 6,
  };

  std::vector<int32_t> row_sums_;
  std::vector<int32_t> row_sums_size_ = {n_row_sums_, n_cell_};
  TfLiteTensor row_sums_tensor_;

  std::vector<float> activation_state_;

  std::vector<int32_t> accum_scratch_;
  std::vector<int32_t> accum_scratch_size_ = {n_cell_, n_batch_};
  TfLiteTensor accum_scratch_tensor_;
  std::vector<float> output_float_ = {
      1, 1, 3, 4, -5, 6,  //
      1, 4, 3, 4, -5, 6,  //
  };
  std::vector<float> input_float_ = {
      6.06, 7.66, 7.10, 9.32, 3.85, 0.33, 7.15, 1.56, 9.54,
      5.30, 4.53, 0.19, 1.83, 4.60, 0.84, 5.08, 4.37, 9.92,  //
      4.08, 3.79, 1.17, 8.99, 0.14, 9.22, 3.18, 2.97, 7.53,
      0.59, 9.89, 9.13, 7.68, 0.63, 2.15, 4.31, 7.20, 4.09,  //
  };
  std::vector<float> input_float_bias_ = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  };
  std::vector<float> forget_float_bias_ = {
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
  };
  std::vector<float> cell_float_bias_ = {
      -11, -7, -4, -5, -1, -1, -2, -3.5, -3, -4,
  };
  std::vector<float> output_float_bias_ = {0.16, 0.4, 0.5, 0.6,  0.1,
                                           0.1,  0.3, 0.4, -0.5, 0.6};
  std::vector<float> projection_float_bias_ = {0, 0, 0, 0, 0, 0};
  std::vector<float> layer_norm_float_input_ = {8, 2, 3, 4, 5, 6, 1, -2, 3, 4};
  std::vector<float> layer_norm_float_forget_ = {
      0.1, 0.2, 0.3, 0.4, 0.7, 0.3, 0.4, -0.5, 0.6, 0.3,  //
  };
  std::vector<float> layer_norm_float_cell_ = {
      0.6, 0.4, 0.5, 0.6, 0.1, 0.2, 0.3, 0.4, -0.5, 0.6,  //
  };
  std::vector<float> layer_norm_float_output_ = {
      0.6, 0.4, 0.5, 0.6, 0.1, 0.2, 0.3, 0.4, -0.5, 0.6,  //
  };
};

void TestOneHybridAsymmLSTM() {
  CpuBackendContext context;
  HybridLstmParam one_parameter;
  auto activation = one_parameter.GetActivation();
  auto output = one_parameter.GetFloatOutput();
  auto cell = one_parameter.GetCell();
  auto param = one_parameter.GetLSTMParam();
  bool compute_row_sums = true;
  constexpr float kDefaultScale = 18.0;
  ops::builtin::lstm_eval::EvalHybrid(
      one_parameter.GetFloatInput(),
      HybridLstmParam::addScale(one_parameter.Geti2i(), kDefaultScale), nullptr,
      HybridLstmParam::addScale(one_parameter.Geti2f(), kDefaultScale), nullptr,
      HybridLstmParam::addScale(one_parameter.Geti2c(), kDefaultScale), nullptr,
      HybridLstmParam::addScale(one_parameter.Geti2o(), kDefaultScale), nullptr,
      HybridLstmParam::addScale(one_parameter.Getr2i(), kDefaultScale), nullptr,
      HybridLstmParam::addScale(one_parameter.Getr2f(), kDefaultScale), nullptr,
      HybridLstmParam::addScale(one_parameter.Getr2c(), kDefaultScale), nullptr,
      HybridLstmParam::addScale(one_parameter.Getr2o(), kDefaultScale), nullptr,
      /*cell_to_input_weights=*/nullptr,
      /*cell_to_forget_weights=*/nullptr,
      /*cell_to_output_weights=*/nullptr, one_parameter.GetInputLayerNorm(),
      one_parameter.GetForgetLayerNorm(), one_parameter.GetCellLayerNorm(),
      one_parameter.GetOutputLayerNorm(),
      /*aux_input=*/nullptr,
      /*aux_input_to_input_weights=*/nullptr,
      /*aux_input_to_forget_weights=*/nullptr,
      /*aux_input_to_cell_weights=*/nullptr,
      /*aux_input_to_output_weights=*/nullptr, one_parameter.GetInputBias(),
      one_parameter.GetForgetBias(), one_parameter.GetCellBias(),
      one_parameter.GetOutputBias(),
      HybridLstmParam::addScale(one_parameter.GetProjection(), 1.0), nullptr,
      one_parameter.GetProjectionBias(), &param,
      /*forward_sequence=*/true,
      /*time_major=*/true,
      /*output_offset=*/0, one_parameter.GetScratchBuffer(),
      one_parameter.GetInputScalingFactors(),
      one_parameter.GetAuxInputScalingFactors(),
      one_parameter.GetOutputStateScalingFactors(),
      one_parameter.GetProdScalingFactors(),
      /*recovered_cell_weights=*/nullptr, one_parameter.GetInputQuantized(),
      /*aux_input_quantized=*/nullptr,
      one_parameter.GetActivationStateQuantized(),
      one_parameter.GetCellStateQuantized(), activation, cell,
      one_parameter.GetAccumScratchBuffer(), output,
      one_parameter.GetInputZeroPoints(), one_parameter.GetAuxInputZeroPoints(),
      one_parameter.GetOutputStateZeroPoints(), one_parameter.GetRowSums(),
      one_parameter.GetNumRowSums(), &compute_row_sums,
      /*recurrent_to_input_is_diag=*/false,
      /*recurrent_to_forget_is_diag=*/false,
      /*recurrent_to_cell_is_diag=*/false,
      /*recurrent_to_output_is_diag=*/false, &context);
  const std::vector<float> expected_cell = {
      7.83134,  1.96158, 2.18285, 3.28739,  0.483214,
      0.618206, 1.21539, 1.4052,  -3.17735, 2.24296,  //
      0.498944, 6.91104, 1.74126, 3.28993,  0.580477,
      0.489936, 1.2527,  1.50157, -3.71849, 2.76743,  //
  };
  const std::vector<float> expected_activation = {
      53.0403, 59.3623, 24.8493, 53.0403, 59.3623, 24.8493,  //
      36.7559, 57.5202, 29.7217, 36.7559, 57.5202, 29.7217,
  };
  EXPECT_TRUE(ArrayFloatNear(cell->data.f, expected_cell.data(), 20, 1e-2));
  EXPECT_TRUE(
      ArrayFloatNear(activation->data.f, expected_activation.data(), 12, 2e-4));
  EXPECT_TRUE(
      ArrayFloatNear(output->data.f, expected_activation.data(), 12, 2e-4));
}

TEST(TestOneHybridAsymmLSTM, TestOneHybridAsymmLSTM) {
  TestOneHybridAsymmLSTM();
}

}  // namespace
}  // namespace tflite