/* Copyright 2020 Google LLC. 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 <gtest/gtest.h>

#include <cstddef>
#include <vector>

#include "tfjs-backend-wasm/src/cc/backend.h"
#include "tfjs-backend-wasm/src/cc/kernels/BatchMatMul.h"

TEST(BATCH_MATMUL, xnn_operator_lifetime) {
  tfjs::wasm::init();

  ASSERT_EQ(0, tfjs::backend::num_tensors());

  size_t a0_id = 1;
  size_t a1_id = 2;
  size_t size = 2;
  float a_values[2] = {1, 2};
  std::vector<size_t> a_shape = {1, 2, 1};
  size_t* a_shape_ptr = a_shape.data();

  size_t b0_id = 3;
  size_t b1_id = 4;
  float b_values[2] = {1, 2};
  std::vector<size_t> b_shape = {1, 1, 2};
  size_t* b_shape_ptr = b_shape.data();

  size_t out_id = 5;
  float out_values[4] = {};

  tfjs::wasm::register_tensor(a0_id, size, a_values);
  tfjs::wasm::register_tensor(a1_id, size, a_values);
  tfjs::wasm::register_tensor(b0_id, size, b_values);
  tfjs::wasm::register_tensor(b1_id, size, b_values);
  tfjs::wasm::register_tensor(out_id, size, out_values);

  ASSERT_EQ(5, tfjs::backend::num_tensors());
  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);

  // One new xnn_operator should be created for the first call to BatchMatMul.
  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b0_id,
                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
                          false /* transpose_b */, out_id);
  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);

  // No new xnn_operators should be created for the second call to BatchMatMul
  // with the same b's.
  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b0_id,
                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
                          false /* transpose_b */, out_id);
  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);

  // One new xnn_operator should be created for another call to BatchMatMul with
  // new b's.
  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
                          false /* transpose_b */, out_id);
  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);

  // No new xnn_operators should be created for the next call to BatchMatMul
  // with the same b's.
  tfjs::wasm::BatchMatMul(a0_id, a_shape_ptr, a_shape.size(), b1_id,
                          b_shape_ptr, b_shape.size(), false /* transpose_a */,
                          false /* transpose_b */, out_id);
  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);

  // Disposing a's should not remove xnn operators.
  tfjs::wasm::dispose_data(a0_id);
  tfjs::wasm::dispose_data(a1_id);
  ASSERT_EQ(2, tfjs::backend::xnn_operator_count);

  // Disposing b's should remove xnn operators.
  tfjs::wasm::dispose_data(b0_id);
  ASSERT_EQ(1, tfjs::backend::xnn_operator_count);

  tfjs::wasm::dispose_data(b1_id);
  ASSERT_EQ(0, tfjs::backend::xnn_operator_count);

  tfjs::wasm::dispose();
}
