/* Copyright (c) 2018 PaddlePaddle 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 <glog/logging.h>
#include <gtest/gtest.h>
#include "gflags/gflags.h"

#include "paddle/fluid/inference/tensorrt/helper.h"
#include "paddle/fluid/inference/tests/api/trt_test_helper.h"

namespace paddle {
namespace inference {

void run(const AnalysisConfig& config, std::vector<float>* out_data, int bs) {
#if !defined(_WIN32)
  setenv("NVIDIA_TF32_OVERRIDE", "0", 1);
#endif
  auto predictor = CreatePaddlePredictor(config);
  auto input_names = predictor->GetInputNames();

  int run_batch = bs;
  const int run_seq_len = 128;
  size_t len = run_batch * run_seq_len;

  int32_t i0_bs1[run_seq_len] = {
      1,    3558, 4,   75,  491, 89, 340, 313, 93,   4,   255,   10, 75,    321,
      4095, 1902, 4,   134, 49,  75, 311, 14,  44,   178, 543,   15, 12043, 2,
      75,   201,  340, 9,   14,  44, 486, 218, 1140, 279, 12043, 2};
  int32_t i1_bs1[run_seq_len] = {
      0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
      0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
  int32_t i2_bs1[run_seq_len] = {0,  1,  2,  3,  4,  5,  6,  7,  8,  9,
                                 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
                                 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,
                                 30, 31, 32, 33, 34, 35, 36, 37, 38, 39};
  float i3_bs1[run_seq_len] = {
      1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
      1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
      1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
  std::vector<int32_t> i0_data(len), i1_data(len), i2_data(len);
  std::vector<float> i3_data(len);

  for (size_t i = 0; i < len; i++) {
    i0_data[i] = i0_bs1[i % run_seq_len];
    i1_data[i] = i1_bs1[i % run_seq_len];
    i2_data[i] = i2_bs1[i % run_seq_len];
    i3_data[i] = i3_bs1[i % run_seq_len];
  }
  // first input
  auto input_t = predictor->GetInputTensor(input_names[0]);
  input_t->Reshape({run_batch, run_seq_len, 1});
  input_t->copy_from_cpu(i0_data.data());

  // second input
  auto input_t2 = predictor->GetInputTensor(input_names[1]);
  input_t2->Reshape({run_batch, run_seq_len, 1});
  input_t2->copy_from_cpu(i1_data.data());

  // third input.
  auto input_t3 = predictor->GetInputTensor(input_names[2]);
  input_t3->Reshape({run_batch, run_seq_len, 1});
  input_t3->copy_from_cpu(i2_data.data());

  auto input_t4 = predictor->GetInputTensor(input_names[3]);
  input_t4->Reshape({run_batch, run_seq_len, 1});
  input_t4->copy_from_cpu(i3_data.data());

  ASSERT_TRUE(predictor->ZeroCopyRun());

  auto output_names = predictor->GetOutputNames();
  auto output_t = predictor->GetOutputTensor(output_names[0]);
  std::vector<int> output_shape = output_t->shape();
  int out_num = std::accumulate(
      output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
  out_data->resize(out_num);
  output_t->copy_to_cpu(out_data->data());
}

void trt_ernie(bool with_fp16,
               std::vector<float> result,
               float near_tolerance,
               int batch_size = 1) {
  AnalysisConfig config;
  std::string model_dir = FLAGS_infer_model;
  SetConfig(&config, model_dir, true);

  config.SwitchUseFeedFetchOps(false);

  int batch = 32;
  int min_seq_len = 1;
  int max_seq_len = 128;
  int opt_seq_len = 128;

  std::vector<int> min_shape = {1, min_seq_len, 1};
  std::vector<int> max_shape = {batch, max_seq_len, 1};
  std::vector<int> opt_shape = {batch, opt_seq_len, 1};
  // Set the input's min, max, opt shape
  std::map<std::string, std::vector<int>> min_input_shape = {
      {"read_file_0.tmp_0", min_shape},
      {"read_file_0.tmp_1", min_shape},
      {"read_file_0.tmp_2", min_shape},
      {"read_file_0.tmp_4", min_shape}};
  std::map<std::string, std::vector<int>> max_input_shape = {
      {"read_file_0.tmp_0", max_shape},
      {"read_file_0.tmp_1", max_shape},
      {"read_file_0.tmp_2", max_shape},
      {"read_file_0.tmp_4", max_shape}};
  std::map<std::string, std::vector<int>> opt_input_shape = {
      {"read_file_0.tmp_0", opt_shape},
      {"read_file_0.tmp_1", opt_shape},
      {"read_file_0.tmp_2", opt_shape},
      {"read_file_0.tmp_4", opt_shape}};

  auto precision = AnalysisConfig::Precision::kFloat32;
  if (with_fp16) {
    precision = AnalysisConfig::Precision::kHalf;
  }
  config.EnableTensorRtEngine(1 << 30, 1, 5, precision, false, false);
  config.SetTRTDynamicShapeInfo(
      min_input_shape, max_input_shape, opt_input_shape);
  paddle_infer::experimental::InternalUtils::SetTransformerMaskid(
      &config, "read_file_0.tmp_4");
  std::vector<float> out_data;
  run(config, &out_data, batch_size);

  for (size_t i = 0; i < out_data.size(); i++) {
    EXPECT_NEAR(result[i], out_data[i], near_tolerance);
  }
}

TEST(AnalysisPredictor, no_fp16) {
  std::vector<float> result = {0.597841, 0.219972, 0.182187};
  trt_ernie(false, result, 1e-4);
}

TEST(AnalysisPredictor, fp16) {
#ifdef TRT_PLUGIN_FP16_AVALIABLE
  std::vector<float> result = {0.598, 0.219, 0.182};
  trt_ernie(true, result, 4e-3);
#endif
}

TEST(AnalysisPredictor, no_fp16_bs2) {
  std::vector<float> result = {
      0.597841, 0.219972, 0.182187, 0.597841, 0.219972, 0.182187};
  trt_ernie(false, result, 1e-4, 2);
}

TEST(AnalysisPredictor, fp16_bs2) {
#ifdef TRT_PLUGIN_FP16_AVALIABLE
  std::vector<float> result = {0.598, 0.219, 0.182, 0.598, 0.219, 0.182};
  trt_ernie(true, result, 4e-3, 2);
#endif
}

// ernie_varlen
std::shared_ptr<paddle_infer::Predictor> InitPredictor() {
  paddle_infer::Config config;
  config.SetModel(FLAGS_infer_model);

  config.EnableUseGpu(100, 0);

  // Open the memory optim.
  config.EnableMemoryOptim();

  int max_batch = 32;
  int max_single_seq_len = 128;
  int opt_single_seq_len = 64;
  int min_batch_seq_len = 1;
  int max_batch_seq_len = 512;
  int opt_batch_seq_len = 256;

  std::string input_name0 = "read_file_0.tmp_0";
  std::string input_name1 = "read_file_0.tmp_1";
  std::string input_name2 = "read_file_0.tmp_2";
  std::string input_name3 = "read_file_0.tmp_4";

  std::vector<int> min_shape = {min_batch_seq_len};
  std::vector<int> max_shape = {max_batch_seq_len};
  std::vector<int> opt_shape = {opt_batch_seq_len};
  // Set the input's min, max, opt shape
  std::map<std::string, std::vector<int>> min_input_shape = {
      {input_name0, min_shape},
      {input_name1, min_shape},
      {input_name2, {1}},
      {input_name3, {1, 1, 1}}};
  std::map<std::string, std::vector<int>> max_input_shape = {
      {input_name0, max_shape},
      {input_name1, max_shape},
      {input_name2, {max_batch + 1}},
      {input_name3, {1, max_single_seq_len, 1}}};
  std::map<std::string, std::vector<int>> opt_input_shape = {
      {input_name0, opt_shape},
      {input_name1, opt_shape},
      {input_name2, {max_batch + 1}},
      {input_name3, {1, opt_single_seq_len, 1}}};

  // only kHalf supported
  config.EnableTensorRtEngine(
      1 << 30, 1, 5, paddle_infer::Config::Precision::kHalf, false, false);
  // erinie varlen must be used with dynamic shape
  config.SetTRTDynamicShapeInfo(
      min_input_shape, max_input_shape, opt_input_shape);
  // erinie varlen must be used with oss
  config.EnableVarseqlen();
  paddle_infer::experimental::InternalUtils::SetTransformerPosid(&config,
                                                                 input_name2);
  paddle_infer::experimental::InternalUtils::SetTransformerMaskid(&config,
                                                                  input_name3);

  return paddle_infer::CreatePredictor(config);
}

void run(paddle_infer::Predictor* predictor, std::vector<float>* out_data) {
#if !defined(_WIN32)
  setenv("NVIDIA_TF32_OVERRIDE", "0", 1);
#endif
  const int run_batch = 2;
  const int run_seq_len = 71;
  const int max_seq_len = 128;

  int32_t i1[run_seq_len] = {
      // sentence 1
      1,
      3558,
      4,
      75,
      491,
      89,
      340,
      313,
      93,
      4,
      255,
      10,
      75,
      321,
      4095,
      1902,
      4,
      134,
      49,
      75,
      311,
      14,
      44,
      178,
      543,
      15,
      12043,
      2,
      75,
      201,
      340,
      9,
      14,
      44,
      486,
      218,
      1140,
      279,
      12043,
      2,
      // sentence 2
      101,
      2054,
      2234,
      2046,
      2486,
      2044,
      1996,
      2047,
      4552,
      2001,
      9536,
      1029,
      102,
      2004,
      1997,
      2008,
      2154,
      1010,
      1996,
      2047,
      4552,
      9536,
      2075,
      1996,
      2117,
      3072,
      2234,
      2046,
      2486,
      1012,
      102,
  };
  int32_t i2[run_seq_len] = {// sentence 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,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             // sentence 2
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             0,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1,
                             1};
  // shape info of this batch
  int32_t i3[3] = {0, 40, 71};
  // max_seq_len represents the max sentence length of all the sentences, only
  // length of
  // input i4 is useful, data means nothing.
  float i4[max_seq_len] = {0};

  auto input_names = predictor->GetInputNames();
  // first input
  auto input_t1 = predictor->GetInputHandle(input_names[0]);
  input_t1->Reshape({run_seq_len});
  input_t1->CopyFromCpu(i1);

  // second input
  auto input_t2 = predictor->GetInputHandle(input_names[1]);
  input_t2->Reshape({run_seq_len});
  input_t2->CopyFromCpu(i2);

  // third input
  auto input_t3 = predictor->GetInputHandle(input_names[2]);
  input_t3->Reshape({run_batch + 1});
  input_t3->CopyFromCpu(i3);

  // fourth input
  auto input_t4 = predictor->GetInputHandle(input_names[3]);
  input_t4->Reshape({1, max_seq_len, 1});
  input_t4->CopyFromCpu(i4);

  CHECK(predictor->Run());

  auto output_names = predictor->GetOutputNames();
  auto output_t = predictor->GetOutputHandle(output_names[0]);
  std::vector<int> output_shape = output_t->shape();
  int out_num = std::accumulate(
      output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
  out_data->resize(out_num);
  output_t->CopyToCpu(out_data->data());

  return;
}

TEST(AnalysisPredictor, ernie_varlen) {
#if IS_TRT_VERSION_GE(7234)
  if (platform::GetGPUComputeCapability(platform::GetCurrentDeviceId()) >= 75) {
    auto predictor = InitPredictor();
    std::vector<float> out_data;
    run(predictor.get(), &out_data);
    std::vector<float> ref_data{
        0.59814, 0.219882, 0.181978, 0.359796, 0.577414, 0.0627908};
    float near_tolerance = 4e-3;
    for (size_t i = 0; i < out_data.size(); i++) {
      EXPECT_NEAR(ref_data[i], out_data[i], near_tolerance);
    }
  }
#endif
}

}  // namespace inference
}  // namespace paddle
