text: # 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.
  # ==============================================================================
  """NCF framework to train and evaluate the NeuMF model.
  
  The NeuMF model assembles both MF and MLP models under the NCF framework. Check
  `neumf_model.py` for more details about the models.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import contextlib
  import heapq
  import math
  import multiprocessing
  import os
  import signal
  import typing
  
  # pylint: disable=g-bad-import-order
  import numpy as np
  from absl import app as absl_app
  from absl import flags
  import tensorflow as tf
  # pylint: enable=g-bad-import-order
  
  from tensorflow.contrib.compiler import xla
  from official.datasets import movielens
  from official.recommendation import constants as rconst
  from official.recommendation import data_preprocessing
  from official.recommendation import neumf_model
  from official.utils.flags import core as flags_core
  from official.utils.logs import hooks_helper
  from official.utils.logs import logger
  from official.utils.logs import mlperf_helper
  from official.utils.misc import distribution_utils
  from official.utils.misc import model_helpers
  
  
  def construct_estimator(num_gpus, model_dir, params, batch_size,
                          eval_batch_size):
    """Construct either an Estimator or TPUEstimator for NCF.
  
    Args:
      num_gpus: The number of gpus (Used to select distribution strategy)
      model_dir: The model directory for the estimator
      params: The params dict for the estimator
      batch_size: The mini-batch size for training.
      eval_batch_size: The batch size used during evaluation.
  
    Returns:
      An Estimator or TPUEstimator.
    """
  
    if params["use_tpu"]:
      tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
          tpu=params["tpu"],
          zone=params["tpu_zone"],
          project=params["tpu_gcp_project"],
      )
      tf.logging.info("Issuing reset command to TPU to ensure a clean state.")
      tf.Session.reset(tpu_cluster_resolver.get_master())
  
      tpu_config = tf.contrib.tpu.TPUConfig(
          iterations_per_loop=100,
          num_shards=8)
  
      run_config = tf.contrib.tpu.RunConfig(
          cluster=tpu_cluster_resolver,
          model_dir=model_dir,
          session_config=tf.ConfigProto(
              allow_soft_placement=True, log_device_placement=False),
          tpu_config=tpu_config)
  
      tpu_params = {k: v for k, v in params.items() if k != "batch_size"}
  
      train_estimator = tf.contrib.tpu.TPUEstimator(
          model_fn=neumf_model.neumf_model_fn,
          use_tpu=True,
          train_batch_size=batch_size,
          params=tpu_params,
          config=run_config)
  
      eval_estimator = tf.contrib.tpu.TPUEstimator(
          model_fn=neumf_model.neumf_model_fn,
          use_tpu=False,
          train_batch_size=1,
          eval_batch_size=eval_batch_size,
          params=tpu_params,
          config=run_config)
  
      return train_estimator, eval_estimator
  
    distribution = distribution_utils.get_distribution_strategy(num_gpus=num_gpus)
    run_config = tf.estimator.RunConfig(train_distribute=distribution,
                                        eval_distribute=distribution)
    params["eval_batch_size"] = eval_batch_size
    model_fn = neumf_model.neumf_model_fn
    if params["use_xla_for_gpu"]:
      tf.logging.info("Using XLA for GPU for training and evaluation.")
      model_fn = xla.estimator_model_fn(model_fn)
    estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir,
                                       config=run_config, params=params)
    return estimator, estimator
  
  
  def main(_):
    with logger.benchmark_context(FLAGS), mlperf_helper.LOGGER(FLAGS.ml_perf):
      mlperf_helper.set_ncf_root(os.path.split(os.path.abspath(__file__))[0])
      run_ncf(FLAGS)
      mlperf_helper.stitch_ncf()
  
  
  def run_ncf(_):
    """Run NCF training and eval loop."""
    if FLAGS.download_if_missing and not FLAGS.use_synthetic_data:
      movielens.download(FLAGS.dataset, FLAGS.data_dir)
  
    if FLAGS.seed is not None:
      np.random.seed(FLAGS.seed)
  
    num_gpus = flags_core.get_num_gpus(FLAGS)
    batch_size = distribution_utils.per_device_batch_size(
        int(FLAGS.batch_size), num_gpus)
  
    eval_per_user = rconst.NUM_EVAL_NEGATIVES + 1
    eval_batch_size = int(FLAGS.eval_batch_size or
                          max([FLAGS.batch_size, eval_per_user]))
    if eval_batch_size % eval_per_user:
      eval_batch_size = eval_batch_size // eval_per_user * eval_per_user
      tf.logging.warning(
          "eval examples per user does not evenly divide eval_batch_size. "
          "Overriding to {}".format(eval_batch_size))
  
    if FLAGS.use_synthetic_data:
      ncf_dataset = None
      cleanup_fn = lambda: None
      num_users, num_items = data_preprocessing.DATASET_TO_NUM_USERS_AND_ITEMS[
          FLAGS.dataset]
      num_train_steps = data_preprocessing.SYNTHETIC_BATCHES_PER_EPOCH
      num_eval_steps = data_preprocessing.SYNTHETIC_BATCHES_PER_EPOCH
    else:
      ncf_dataset, cleanup_fn = data_preprocessing.instantiate_pipeline(
          dataset=FLAGS.dataset, data_dir=FLAGS.data_dir,
          batch_size=batch_size,
          eval_batch_size=eval_batch_size,
          num_neg=FLAGS.num_neg,
          epochs_per_cycle=FLAGS.epochs_between_evals,
          match_mlperf=FLAGS.ml_perf,
          deterministic=FLAGS.seed is not None,
          use_subprocess=FLAGS.use_subprocess,
          cache_id=FLAGS.cache_id)
      num_users = ncf_dataset.num_users
      num_items = ncf_dataset.num_items
      num_train_steps = int(np.ceil(
          FLAGS.epochs_between_evals * ncf_dataset.num_train_positives *
          (1 + FLAGS.num_neg) / FLAGS.batch_size))
      num_eval_steps = int(np.ceil((1 + rconst.NUM_EVAL_NEGATIVES) *
                                   ncf_dataset.num_users / eval_batch_size))
  
    model_helpers.apply_clean(flags.FLAGS)
  
    train_estimator, eval_estimator = construct_estimator(
        num_gpus=num_gpus, model_dir=FLAGS.model_dir, params={
            "use_seed": FLAGS.seed is not None,
            "hash_pipeline": FLAGS.hash_pipeline,
            "batch_size": batch_size,
            "eval_batch_size": eval_batch_size,
            "learning_rate": FLAGS.learning_rate,
            "num_users": num_users,
            "num_items": num_items,
            "mf_dim": FLAGS.num_factors,
            "model_layers": [int(layer) for layer in FLAGS.layers],
            "mf_regularization": FLAGS.mf_regularization,
            "mlp_reg_layers": [float(reg) for reg in FLAGS.mlp_regularization],
            "num_neg": FLAGS.num_neg,
            "use_tpu": FLAGS.tpu is not None,
            "tpu": FLAGS.tpu,
            "tpu_zone": FLAGS.tpu_zone,
            "tpu_gcp_project": FLAGS.tpu_gcp_project,
            "beta1": FLAGS.beta1,
            "beta2": FLAGS.beta2,
            "epsilon": FLAGS.epsilon,
            "match_mlperf": FLAGS.ml_perf,
            "use_xla_for_gpu": FLAGS.use_xla_for_gpu,
        }, batch_size=flags.FLAGS.batch_size, eval_batch_size=eval_batch_size)
  
    # Create hooks that log information about the training and metric values
    train_hooks = hooks_helper.get_train_hooks(
        FLAGS.hooks,
        model_dir=FLAGS.model_dir,
        batch_size=FLAGS.batch_size,  # for ExamplesPerSecondHook
        tensors_to_log={"cross_entropy": "cross_entropy"}
    )
    run_params = {
        "batch_size": FLAGS.batch_size,
        "eval_batch_size": eval_batch_size,
        "number_factors": FLAGS.num_factors,
        "hr_threshold": FLAGS.hr_threshold,
        "train_epochs": FLAGS.train_epochs,
    }
    benchmark_logger = logger.get_benchmark_logger()
    benchmark_logger.log_run_info(
        model_name="recommendation",
        dataset_name=FLAGS.dataset,
        run_params=run_params,
        test_id=FLAGS.benchmark_test_id)
  
  
    pred_input_fn = None
    total_training_cycle = FLAGS.train_epochs // FLAGS.epochs_between_evals
    target_reached = False
    mlperf_helper.ncf_print(key=mlperf_helper.TAGS.TRAIN_LOOP)
    for cycle_index in range(total_training_cycle):
      assert FLAGS.epochs_between_evals == 1 or not mlperf_helper.LOGGER.enabled
      tf.logging.info("Starting a training cycle: {}/{}".format(
          cycle_index + 1, total_training_cycle))
  
      mlperf_helper.ncf_print(key=mlperf_helper.TAGS.TRAIN_EPOCH,
                              value=cycle_index)
  
      # Train the model
      train_input_fn, train_record_dir, batch_count = \
        data_preprocessing.make_input_fn(
            ncf_dataset=ncf_dataset, is_training=True)
  
      if batch_count != num_train_steps:
        raise ValueError(
            "Step counts do not match. ({} vs. {}) The async process is "
            "producing incorrect shards.".format(batch_count, num_train_steps))
  
      train_estimator.train(input_fn=train_input_fn, hooks=train_hooks,
                            steps=num_train_steps)
      if train_record_dir:
        tf.gfile.DeleteRecursively(train_record_dir)
  
      tf.logging.info("Beginning evaluation.")
      if pred_input_fn is None:
        pred_input_fn, _, eval_batch_count = data_preprocessing.make_input_fn(
            ncf_dataset=ncf_dataset, is_training=False)
  
        if eval_batch_count != num_eval_steps:
          raise ValueError(
              "Step counts do not match. ({} vs. {}) The async process is "
              "producing incorrect shards.".format(
                  eval_batch_count, num_eval_steps))
  
      mlperf_helper.ncf_print(key=mlperf_helper.TAGS.EVAL_START,
                              value=cycle_index)
      eval_results = eval_estimator.evaluate(pred_input_fn, steps=num_eval_steps)
      hr = float(eval_results[rconst.HR_KEY])
      ndcg = float(eval_results[rconst.NDCG_KEY])
      tf.logging.info("Evaluation complete.")
  
      mlperf_helper.ncf_print(
          key=mlperf_helper.TAGS.EVAL_TARGET,
          value={"epoch": cycle_index, "value": FLAGS.hr_threshold})
      mlperf_helper.ncf_print(key=mlperf_helper.TAGS.EVAL_ACCURACY,
                              value={"epoch": cycle_index, "value": hr})
      mlperf_helper.ncf_print(
          key=mlperf_helper.TAGS.EVAL_HP_NUM_NEG,
          value={"epoch": cycle_index, "value": rconst.NUM_EVAL_NEGATIVES})
  
      # Logged by the async process during record creation.
      mlperf_helper.ncf_print(key=mlperf_helper.TAGS.EVAL_HP_NUM_USERS,
                              deferred=True)
  
      mlperf_helper.ncf_print(key=mlperf_helper.TAGS.EVAL_STOP, value=cycle_index)
  
      # Benchmark the evaluation results
      benchmark_logger.log_evaluation_result(eval_results)
      # Log the HR and NDCG results.
      tf.logging.info(
          "Iteration {}: HR = {:.4f}, NDCG = {:.4f}".format(
              cycle_index + 1, hr, ndcg))
  
      # If some evaluation threshold is met
      if model_helpers.past_stop_threshold(FLAGS.hr_threshold, hr):
        target_reached = True
        break
  
    mlperf_helper.ncf_print(key=mlperf_helper.TAGS.RUN_STOP,
                            value={"success": target_reached})
    cleanup_fn()  # Cleanup data construction artifacts and subprocess.
  
    # Clear the session explicitly to avoid session delete error
    tf.keras.backend.clear_session()
  
    mlperf_helper.ncf_print(key=mlperf_helper.TAGS.RUN_FINAL)
  
  
  def define_ncf_flags():
    """Add flags for running ncf_main."""
    # Add common flags
    flags_core.define_base(export_dir=False)
    flags_core.define_performance(
        num_parallel_calls=False,
        inter_op=False,
        intra_op=False,
        synthetic_data=True,
        max_train_steps=False,
        dtype=False,
        all_reduce_alg=False
    )
    flags_core.define_device(tpu=True)
    flags_core.define_benchmark()
  
    flags.adopt_module_key_flags(flags_core)
  
    flags_core.set_defaults(
        model_dir="/tmp/ncf/",
        data_dir="/tmp/movielens-data/",
        train_epochs=2,
        batch_size=256,
        hooks="ProfilerHook",
        tpu=None
    )
  
    # Add ncf-specific flags
    flags.DEFINE_enum(
        name="dataset", default="ml-1m",
        enum_values=["ml-1m", "ml-20m"], case_sensitive=False,
        help=flags_core.help_wrap(
            "Dataset to be trained and evaluated."))
  
    flags.DEFINE_boolean(
        name="download_if_missing", default=True, help=flags_core.help_wrap(
            "Download data to data_dir if it is not already present."))
  
    flags.DEFINE_string(
        name="eval_batch_size", default=None, help=flags_core.help_wrap(
            "The batch size used for evaluation. This should generally be larger"
            "than the training batch size as the lack of back propagation during"
            "evaluation can allow for larger batch sizes to fit in memory. If not"
            "specified, the training batch size (--batch_size) will be used."))
  
    flags.DEFINE_integer(
        name="num_factors", default=8,
        help=flags_core.help_wrap("The Embedding size of MF model."))
  
    # Set the default as a list of strings to be consistent with input arguments
    flags.DEFINE_list(
        name="layers", default=["64", "32", "16", "8"],
        help=flags_core.help_wrap(
            "The sizes of hidden layers for MLP. Example "
            "to specify different sizes of MLP layers: --layers=32,16,8,4"))
  
    flags.DEFINE_float(
        name="mf_regularization", default=0.,
        help=flags_core.help_wrap(
            "The regularization factor for MF embeddings. The factor is used by "
            "regularizer which allows to apply penalties on layer parameters or "
            "layer activity during optimization."))
  
    flags.DEFINE_list(
        name="mlp_regularization", default=["0.", "0.", "0.", "0."],
        help=flags_core.help_wrap(
            "The regularization factor for each MLP layer. See mf_regularization "
            "help for more info about regularization factor."))
  
    flags.DEFINE_integer(
        name="num_neg", default=4,
        help=flags_core.help_wrap(
            "The Number of negative instances to pair with a positive instance."))
  
    flags.DEFINE_float(
        name="learning_rate", default=0.001,
        help=flags_core.help_wrap("The learning rate."))
  
    flags.DEFINE_float(
        name="beta1", default=0.9,
        help=flags_core.help_wrap("beta1 hyperparameter for the Adam optimizer."))
  
    flags.DEFINE_float(
        name="beta2", default=0.999,
        help=flags_core.help_wrap("beta2 hyperparameter for the Adam optimizer."))
  
    flags.DEFINE_float(
        name="epsilon", default=1e-8,
        help=flags_core.help_wrap("epsilon hyperparameter for the Adam "
                                  "optimizer."))
  
    flags.DEFINE_float(
        name="hr_threshold", default=None,
        help=flags_core.help_wrap(
            "If passed, training will stop when the evaluation metric HR is "
            "greater than or equal to hr_threshold. For dataset ml-1m, the "
            "desired hr_threshold is 0.68 which is the result from the paper; "
            "For dataset ml-20m, the threshold can be set as 0.95 which is "
            "achieved by MLPerf implementation."))
  
    flags.DEFINE_bool(
        name="ml_perf", default=False,
        help=flags_core.help_wrap(
            "If set, changes the behavior of the model slightly to match the "
            "MLPerf reference implementations here: \n"
            "https://github.com/mlperf/reference/tree/master/recommendation/"
            "pytorch\n"
            "The two changes are:\n"
            "1. When computing the HR and NDCG during evaluation, remove "
            "duplicate user-item pairs before the computation. This results in "
            "better HRs and NDCGs.\n"
            "2. Use a different soring algorithm when sorting the input data, "
            "which performs better due to the fact the sorting algorithms are "
            "not stable."))
  
    flags.DEFINE_integer(
        name="seed", default=None, help=flags_core.help_wrap(
            "This value will be used to seed both NumPy and TensorFlow."))
  
    flags.DEFINE_bool(
        name="hash_pipeline", default=False, help=flags_core.help_wrap(
            "This flag will perform a separate run of the pipeline and hash "
            "batches as they are produced. \nNOTE: this will significantly slow "
            "training. However it is useful to confirm that a random seed is "
            "does indeed make the data pipeline deterministic."))
  
    @flags.validator("eval_batch_size", "eval_batch_size must be at least {}"
                     .format(rconst.NUM_EVAL_NEGATIVES + 1))
    def eval_size_check(eval_batch_size):
      return (eval_batch_size is None or
              int(eval_batch_size) > rconst.NUM_EVAL_NEGATIVES)
  
    flags.DEFINE_bool(
        name="use_subprocess", default=True, help=flags_core.help_wrap(
            "By default, ncf_main.py starts async data generation process as a "
            "subprocess. If set to False, ncf_main.py will assume the async data "
            "generation process has already been started by the user."))
  
    flags.DEFINE_integer(name="cache_id", default=None, help=flags_core.help_wrap(
        "Use a specified cache_id rather than using a timestamp. This is only "
        "needed to synchronize across multiple workers. Generally this flag will "
        "not need to be set."
    ))
  
    flags.DEFINE_bool(
        name="use_xla_for_gpu", default=False, help=flags_core.help_wrap(
            "If True, use XLA for the model function. Only works when using a "
            "GPU. On TPUs, XLA is always used"))
  
    xla_message = "--use_xla_for_gpu is incompatible with --tpu"
    @flags.multi_flags_validator(["use_xla_for_gpu", "tpu"], message=xla_message)
    def xla_validator(flag_dict):
      return not flag_dict["use_xla_for_gpu"] or not flag_dict["tpu"]
  
  
  if __name__ == "__main__":
    tf.logging.set_verbosity(tf.logging.INFO)
    define_ncf_flags()
    FLAGS = flags.FLAGS
    absl_app.run(main)
  