# Copyright 2018 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.
# ==============================================================================
"""Executes Keras benchmarks and accuracy tests."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import time

from absl import flags
from absl import logging
from absl.testing import flagsaver
import tensorflow as tf
from official.benchmark import benchmark_wrappers
from official.benchmark import owner_utils
from official.benchmark.perfzero_benchmark import PerfZeroBenchmark
from official.recommendation import ncf_common
from official.recommendation import ncf_keras_main
from official.utils.flags import core

FLAGS = flags.FLAGS
NCF_DATA_DIR_NAME = 'movielens_data'
NCF_TF_REGRESSION_DATA_DIR_NAME = 'gs://tf-regression/ncf/data'


class NCFKerasBenchmarkBase(PerfZeroBenchmark):
  """Base class for NCF model benchmark."""

  def __init__(self, output_dir=None, default_flags=None, **kwargs):
    super(NCFKerasBenchmarkBase, self).__init__(output_dir, default_flags,
                                                **kwargs)

    # Run all benchmarks with ml_perf flag.
    self.default_flags['ml_perf'] = True

  def _setup(self):
    """Sets up and resets flags before each test."""
    logging.set_verbosity(logging.INFO)
    if NCFKerasBenchmarkBase.local_flags is None:
      ncf_common.define_ncf_flags()
      # Loads flags to get defaults to then override. List cannot be empty.
      flags.FLAGS(['foo'])
      core.set_defaults(**self.default_flags)
      saved_flag_values = flagsaver.save_flag_values()
      NCFKerasBenchmarkBase.local_flags = saved_flag_values
    else:
      flagsaver.restore_flag_values(NCFKerasBenchmarkBase.local_flags)

  @benchmark_wrappers.enable_runtime_flags
  def _run_and_report_benchmark(self, hr_at_10_min=0, hr_at_10_max=0):
    start_time_sec = time.time()
    stats = ncf_keras_main.run_ncf(FLAGS)
    wall_time_sec = time.time() - start_time_sec

    metrics = []
    metrics.append({'name': 'exp_per_second',
                    'value': stats['avg_exp_per_second']})

    if hr_at_10_min > 0:
      metrics.append({'name': 'hr_at_10',
                      'value': stats['eval_hit_rate'],
                      'min_value': hr_at_10_min,
                      'max_value': hr_at_10_max})

      metrics.append({'name': 'train_loss',
                      'value': stats['loss']})

    self.report_benchmark(iters=-1, wall_time=wall_time_sec, metrics=metrics)


class NCFKerasAccuracy(NCFKerasBenchmarkBase):
  """Benchmark NCF model using real data."""

  def __init__(self,
               output_dir=None,
               root_data_dir=None,
               default_flags=None,
               **kwargs):
    root_data_dir = root_data_dir if root_data_dir else ''
    default_flags = {}
    default_flags['dataset'] = 'ml-20m'
    default_flags['num_gpus'] = 1
    default_flags['train_epochs'] = 10
    default_flags['clean'] = True
    default_flags['batch_size'] = 99000
    default_flags['learning_rate'] = 0.00382059
    default_flags['beta1'] = 0.783529
    default_flags['beta2'] = 0.909003
    default_flags['epsilon'] = 1.45439e-07
    default_flags['layers'] = [256, 256, 128, 64]
    default_flags['num_factors'] = 64
    default_flags['hr_threshold'] = 0.635
    default_flags['ml_perf'] = True
    default_flags['use_synthetic_data'] = False
    default_flags['data_dir'] = os.path.join(root_data_dir, NCF_DATA_DIR_NAME)

    super(NCFKerasAccuracy, self).__init__(
        output_dir=output_dir,
        default_flags=default_flags,
        **kwargs)

  def _run_and_report_benchmark_mlperf_like(self):
    """Run test and report results.

    Note: MLPerf like tests are not tuned to hit a specific hr@10 value, but
    we want it recorded.
    """
    self._run_and_report_benchmark(hr_at_10_min=0.61)

  def _run_and_report_benchmark(self, hr_at_10_min=0.630, hr_at_10_max=0.645):
    """Run test and report results.

    Note: Target is 0.635, but some runs are below that level. Until we have
    multi-run tests, we have to accept a lower target.

    Args:
      hr_at_10_min: Minimum acceptable hr@10 value.
      hr_at_10_max: Maximum acceptable hr@10 value.
    """
    super(NCFKerasAccuracy, self)._run_and_report_benchmark(
        hr_at_10_min=hr_at_10_min,
        hr_at_10_max=hr_at_10_max)

  def _set_8_gpu_defaults(self):
    FLAGS.num_gpus = 8
    FLAGS.learning_rate = 0.0045
    FLAGS.beta1 = 0.25
    FLAGS.beta2 = 0.5
    FLAGS.epsilon = 1e-8
    FLAGS.train_epochs = 14
    FLAGS.batch_size = 99000
    FLAGS.eval_batch_size = 160000
    FLAGS.train_dataset_path = os.path.join(NCF_TF_REGRESSION_DATA_DIR_NAME,
                                            'training_cycle_*/*')
    FLAGS.eval_dataset_path = os.path.join(NCF_TF_REGRESSION_DATA_DIR_NAME,
                                           'eval_data/*')
    FLAGS.input_meta_data_path = os.path.join(NCF_TF_REGRESSION_DATA_DIR_NAME,
                                              'metadata')
    FLAGS.data_dir = NCF_TF_REGRESSION_DATA_DIR_NAME

  def benchmark_1_gpu_early_stop(self):
    self._setup()
    FLAGS.early_stopping = True
    self._run_and_report_benchmark()

  def benchmark_1_gpu_no_dist_strat_early_stop(self):
    self._setup()
    FLAGS.distribution_strategy = 'off'
    FLAGS.early_stopping = True
    self._run_and_report_benchmark()

  def benchmark_1_gpu_no_dist_strat_run_eagerly_early_stop(self):
    self._setup()
    FLAGS.distribution_strategy = 'off'
    FLAGS.early_stopping = True
    FLAGS.run_eagerly = True
    self._run_and_report_benchmark()

  def benchmark_xla_1_gpu_early_stop(self):
    self._setup()
    FLAGS.early_stopping = True
    FLAGS.enable_xla = True
    self._run_and_report_benchmark()

  def benchmark_1_gpu_ctl_early_stop(self):
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.early_stopping = True
    self._run_and_report_benchmark()

  def benchmark_1_gpu_ctl_run_eagerly_early_stop(self):
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.early_stopping = True
    FLAGS.run_eagerly = True
    self._run_and_report_benchmark()

  def benchmark_xla_1_gpu_ctl_early_stop(self):
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.early_stopping = True
    FLAGS.enable_xla = True
    self._run_and_report_benchmark()

  def benchmark_2_gpus_early_stop(self):
    self._setup()
    FLAGS.early_stopping = True
    FLAGS.num_gpus = 2
    FLAGS.eval_batch_size = 160000
    self._run_and_report_benchmark()

  def benchmark_2_gpus_ctl_early_stop(self):
    """NCF with custom training loop. Works only in TF 2.0."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.early_stopping = True
    FLAGS.num_gpus = 2
    FLAGS.eval_batch_size = 160000
    self._run_and_report_benchmark()

#############################################
# Tests below with mlperf in the test name are of two types:
#  1) 1 GPU tests are based on MLPerf 0.5 and the TensorFlow pulled submission.
#  2) 8 GPU tests are based on MLPerf 0.5 and use NVIDIA's hyper parameters.
#
# The purpose of both is to get a number to compare to existing results. To do
# this the number of epochs is held constant rather than a race to a given
# accuracy. The accuracy validation is done by the "early_stop" tests.
#############################################

  def benchmark_1_gpu_mlperf_like(self):
    """1 GPU using keras fit/compile."""
    self._setup()
    FLAGS.train_epochs = 7
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_no_dist_strat_mlperf_like(self):
    """1 GPU using compile/fit without dist_strat."""
    self._setup()
    FLAGS.train_epochs = 7
    FLAGS.distribution_strategy = 'off'
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_no_dist_strat_run_eagerly_mlperf_like(self):
    self._setup()
    FLAGS.train_epochs = 7
    FLAGS.distribution_strategy = 'off'
    FLAGS.run_eagerly = True
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_xla_1_gpu_mlperf_like(self):
    """1 GPU using compile/fit with XLA."""
    self._setup()
    FLAGS.train_epochs = 7
    FLAGS.enable_xla = True
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_ctl_mlperf_like(self):
    """1 GPU using CTL."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.train_epochs = 7
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_ctl_fp16_mlperf_like(self):
    """1 GPU using CTL and FP16."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.train_epochs = 7
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_fp16_mlperf_like(self):
    """1 GPU using FP16."""
    self._setup()
    FLAGS.train_epochs = 7
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_ctl_fp16_graph_rewrite_mlperf_like(self):
    """1 GPU using CTL and FP16 graph rewrite."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.train_epochs = 7
    FLAGS.dtype = 'fp16'
    FLAGS.fp16_implementation = 'graph_rewrite'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_fp16_graph_rewrite_mlperf_like(self):
    """1 GPU using FP16 graph rewrite."""
    self._setup()
    FLAGS.train_epochs = 7
    FLAGS.dtype = 'fp16'
    FLAGS.fp16_implementation = 'graph_rewrite'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_1_gpu_ctl_run_eagerly_mlperf_like(self):
    """1 GPU using CTL with eager and distribution strategy."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.run_eagerly = True
    FLAGS.train_epochs = 7
    self._run_and_report_benchmark()

  def benchmark_xla_1_gpu_ctl_mlperf_like(self):
    """1 GPU using CTL with XLA."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.enable_xla = True
    FLAGS.train_epochs = 7
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_xla_1_gpu_fp16_mlperf_like(self):
    """1 GPU using with XLA and FP16."""
    self._setup()
    FLAGS.enable_xla = True
    FLAGS.train_epochs = 7
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_xla_1_gpu_ctl_fp16_mlperf_like(self):
    """1 GPU using CTL with XLA and FP16."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.enable_xla = True
    FLAGS.train_epochs = 7
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_8_gpu_mlperf_like(self):
    """8 GPU using keras fit/compile."""
    self._setup()
    FLAGS.num_gpus = 8
    FLAGS.train_epochs = 17
    FLAGS.batch_size = 1048576
    FLAGS.eval_batch_size = 160000
    FLAGS.learning_rate = 0.0045
    FLAGS.beta1 = 0.25
    FLAGS.beta2 = 0.5
    FLAGS.epsilon = 1e-8
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_8_gpu_ctl_mlperf_like(self):
    """8 GPU using CTL."""
    self._setup()
    FLAGS.keras_use_ctl = True
    FLAGS.num_gpus = 8
    FLAGS.train_epochs = 17
    FLAGS.batch_size = 1048576
    FLAGS.eval_batch_size = 160000
    FLAGS.learning_rate = 0.0045
    FLAGS.beta1 = 0.25
    FLAGS.beta2 = 0.5
    FLAGS.epsilon = 1e-8
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_8_gpu_tf_data_ctl_mlperf_like(self):
    """8 GPU using CTL."""
    self._setup()
    self._set_8_gpu_defaults()
    FLAGS.keras_use_ctl = True
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_8_gpu_tf_data_fp16_mlperf_like(self):
    """8 GPU FP16."""
    self._setup()
    self._set_8_gpu_defaults()
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_8_gpu_tf_data_ctl_fp16_mlperf_like(self):
    """8 GPU FP16 using CTL."""
    self._setup()
    self._set_8_gpu_defaults()
    FLAGS.keras_use_ctl = True
    FLAGS.dtype = 'fp16'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()

  def benchmark_8_gpu_tf_data_ctl_fp16_graph_rewrite_mlperf_like(self):
    """8 GPU FP16 graph rewrite using CTL."""
    self._setup()
    self._set_8_gpu_defaults()
    FLAGS.keras_use_ctl = True
    FLAGS.dtype = 'fp16'
    FLAGS.fp16_implementation = 'graph_rewrite'
    FLAGS.loss_scale = 8192
    self._run_and_report_benchmark_mlperf_like()


class NCFKerasBenchmarkReal(NCFKerasBenchmarkBase):
  """NCF Keras throughput benchmarks."""

  def __init__(self,
               output_dir=None,
               root_data_dir=None,
               default_flags=None,
               **kwargs):

    root_data_dir = root_data_dir if root_data_dir else ''
    default_flags = {}
    default_flags['dataset'] = 'ml-20m'
    default_flags['num_gpus'] = 1
    default_flags['train_epochs'] = 14
    default_flags['clean'] = True
    default_flags['batch_size'] = 99000
    default_flags['eval_batch_size'] = 160000
    default_flags['learning_rate'] = 0.00382059
    default_flags['beta1'] = 0.783529
    default_flags['beta2'] = 0.909003
    default_flags['epsilon'] = 1.45439e-07
    default_flags['layers'] = [256, 256, 128, 64]
    default_flags['num_factors'] = 64
    default_flags['hr_threshold'] = 0.635
    default_flags['ml_perf'] = True
    default_flags['use_synthetic_data'] = False
    default_flags['train_dataset_path'] = os.path.join(
        NCF_TF_REGRESSION_DATA_DIR_NAME, 'training_cycle_*/*')
    default_flags['eval_dataset_path'] = os.path.join(
        NCF_TF_REGRESSION_DATA_DIR_NAME, 'eval_data/*')
    default_flags['input_meta_data_path'] = os.path.join(
        NCF_TF_REGRESSION_DATA_DIR_NAME, 'metadata')
    default_flags['data_dir'] = NCF_TF_REGRESSION_DATA_DIR_NAME

    super(NCFKerasBenchmarkReal, self).__init__(
        output_dir=output_dir, default_flags=default_flags, **kwargs)

  def benchmark_2x2_tpu(self):
    """2x2 TPU using CTL with distribution strategy."""
    self._setup()
    FLAGS.distribution_strategy = 'tpu'
    FLAGS.keras_use_ctl = True
    FLAGS.num_gpus = 0
    FLAGS.train_epochs = 1
    self._run_and_report_benchmark()

  @owner_utils.Owner('tf-graph-compiler')
  def benchmark_2x2_tpu_mlir(self):
    """2x2 TPU using CTL with distribution strategy using the MLIR bridge."""
    self._setup()
    FLAGS.distribution_strategy = 'tpu'
    FLAGS.keras_use_ctl = True
    FLAGS.num_gpus = 0
    FLAGS.train_epochs = 1
    tf.config.experimental.enable_mlir_bridge()
    self._run_and_report_benchmark()


class NCFKerasSynth(NCFKerasBenchmarkBase):
  """Benchmark NCF model using synthetic data."""

  def __init__(self,
               output_dir=None,
               default_flags=None,
               **kwargs):

    default_flags = {}
    default_flags['dataset'] = 'ml-20m'
    default_flags['num_gpus'] = 1
    default_flags['train_epochs'] = 8
    default_flags['batch_size'] = 99000
    default_flags['eval_batch_size'] = 160000
    default_flags['learning_rate'] = 0.00382059
    default_flags['beta1'] = 0.783529
    default_flags['beta2'] = 0.909003
    default_flags['epsilon'] = 1.45439e-07
    default_flags['layers'] = [256, 256, 128, 64]
    default_flags['num_factors'] = 64
    default_flags['hr_threshold'] = 0.635
    default_flags['use_synthetic_data'] = True

    super(NCFKerasSynth, self).__init__(
        output_dir=output_dir,
        default_flags=default_flags,
        **kwargs)

  def benchmark_1_gpu(self):
    self._setup()
    self._run_and_report_benchmark()

  def benchmark_2_gpus(self):
    self._setup()
    FLAGS.num_gpus = 2
    self._run_and_report_benchmark()


if __name__ == '__main__':
  tf.test.main()
