# coding=utf-8 # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors. # # 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. """Run masked LM/next sentence masked_lm pre-training for BERT.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import time import modeling import optimization import tensorflow as tf import glob from utils.utils import LogEvalRunHook from tensorflow.core.protobuf import rewriter_config_pb2 flags = tf.flags FLAGS = flags.FLAGS ## Required parameters flags.DEFINE_string( "bert_config_file", None, "The config json file corresponding to the pre-trained BERT model. " "This specifies the model architecture.") flags.DEFINE_string( "input_files_dir", None, "Directory with input files, comma separated or single directory.") flags.DEFINE_string( "eval_files_dir", None, "Directory with eval files, comma separated or single directory. ") flags.DEFINE_string( "output_dir", None, "The output directory where the model checkpoints will be written.") ## Other parameters flags.DEFINE_string( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).") flags.DEFINE_string( "optimizer_type", "lamb", "Optimizer used for training - LAMB or ADAM") flags.DEFINE_integer( "max_seq_length", 512, "The maximum total input sequence length after WordPiece tokenization. " "Sequences longer than this will be truncated, and sequences shorter " "than this will be padded. Must match data generation.") flags.DEFINE_integer( "max_predictions_per_seq", 80, "Maximum number of masked LM predictions per sequence. " "Must match data generation.") flags.DEFINE_bool("do_train", False, "Whether to run training.") flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.") flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.") flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.") flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.") flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.") flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.") flags.DEFINE_integer("display_loss_steps", 10, "How often to print loss") flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.") flags.DEFINE_integer("num_accumulation_steps", 1, "Number of accumulation steps before gradient update." "Global batch size = num_accumulation_steps * train_batch_size") flags.DEFINE_bool("allreduce_post_accumulation", False, "Whether to all reduce after accumulation of N steps or after each step") flags.DEFINE_bool( "verbose_logging", False, "If true, all of the trainable parameters are printed") flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs") flags.DEFINE_bool("report_loss", True, "Whether to report total loss during training.") flags.DEFINE_bool("manual_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU. " "Manual casting is done instead of using AMP") flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.") flags.DEFINE_bool("use_fp16", False, "Whether to enable AMP ops.") # report samples/sec, total loss and learning rate during training class _LogSessionRunHook(tf.estimator.SessionRunHook): def __init__(self, global_batch_size, num_accumulation_steps, display_every=10, hvd_rank=-1): self.global_batch_size = global_batch_size self.display_every = display_every self.hvd_rank = hvd_rank self.num_accumulation_steps = num_accumulation_steps def after_create_session(self, session, coord): self.elapsed_secs = 0. self.count = 0 self.all_count = 0 self.avg_loss = 0.0 def before_run(self, run_context): self.t0 = time.time() if self.num_accumulation_steps <= 1: if FLAGS.manual_fp16 or FLAGS.use_fp16: return tf.estimator.SessionRunArgs( fetches=['step_update:0', 'total_loss:0', 'learning_rate:0', 'nsp_loss:0', 'mlm_loss:0', 'loss_scale:0']) else: return tf.estimator.SessionRunArgs( fetches=['step_update:0', 'total_loss:0', 'learning_rate:0', 'nsp_loss:0', 'mlm_loss:0']) else: if FLAGS.manual_fp16 or FLAGS.use_fp16: return tf.estimator.SessionRunArgs( fetches=['step_update:0', 'update_step:0', 'total_loss:0', 'learning_rate:0', 'nsp_loss:0', 'mlm_loss:0', 'loss_scale:0']) else: return tf.estimator.SessionRunArgs( fetches=['step_update:0', 'update_step:0', 'total_loss:0', 'learning_rate:0', 'nsp_loss:0', 'mlm_loss:0']) def after_run(self, run_context, run_values): self.elapsed_secs += time.time() - self.t0 if self.num_accumulation_steps <=1: if FLAGS.manual_fp16 or FLAGS.use_fp16: global_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results else: global_step, total_loss, lr, nsp_loss, mlm_loss = run_values. \ results update_step = True else: if FLAGS.manual_fp16 or FLAGS.use_fp16: global_step, update_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results else: global_step, update_step, total_loss, lr, nsp_loss, mlm_loss = run_values.\ results print_step = global_step + 1 # One-based index for printing. self.avg_loss += total_loss self.all_count += 1 if update_step: self.count += 1 if (print_step == 1 or print_step % self.display_every == 0): dt = self.elapsed_secs / self.count sent_per_sec = self.global_batch_size / dt avg_loss_step = self.avg_loss / self.all_count if self.hvd_rank >= 0: if FLAGS.manual_fp16 or FLAGS.use_fp16: print('Rank = %2d :: Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e Loss scale = %6.4e' % (self.hvd_rank, print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr, loss_scaler)) else: print('Rank = %2d :: Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e' % (self.hvd_rank, print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr)) else: if FLAGS.manual_fp16 or FLAGS.use_fp16: print('Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e Loss scale = %6.4e' % (print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr, loss_scaler)) else: print('Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e' % (print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr)) self.elapsed_secs = 0. self.count = 0 self.avg_loss = 0.0 self.all_count = 0 def model_fn_builder(bert_config, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_one_hot_embeddings, hvd=None): """Returns `model_fn` closure for TPUEstimator.""" def model_fn(features, labels, mode, params): # pylint: disable=unused-argument """The `model_fn` for TPUEstimator.""" tf.compat.v1.logging.info("*** Features ***") for name in sorted(features.keys()): tf.compat.v1.logging.info(" name = %s, shape = %s" % (name, features[name].shape)) input_ids = features["input_ids"] input_mask = features["input_mask"] segment_ids = features["segment_ids"] masked_lm_positions = features["masked_lm_positions"] masked_lm_ids = features["masked_lm_ids"] masked_lm_weights = features["masked_lm_weights"] next_sentence_labels = features["next_sentence_labels"] is_training = (mode == tf.estimator.ModeKeys.TRAIN) model = modeling.BertModel( config=bert_config, is_training=is_training, input_ids=input_ids, input_mask=input_mask, token_type_ids=segment_ids, use_one_hot_embeddings=use_one_hot_embeddings, compute_type=tf.float16 if FLAGS.manual_fp16 else tf.float32) (masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output( bert_config, model.get_sequence_output(), model.get_embedding_table(), masked_lm_positions, masked_lm_ids, masked_lm_weights) (next_sentence_loss, next_sentence_example_loss, next_sentence_log_probs) = get_next_sentence_output( bert_config, model.get_pooled_output(), next_sentence_labels) masked_lm_loss = tf.identity(masked_lm_loss, name="mlm_loss") next_sentence_loss = tf.identity(next_sentence_loss, name="nsp_loss") total_loss = masked_lm_loss + next_sentence_loss total_loss = tf.identity(total_loss, name='total_loss') tvars = tf.trainable_variables() initialized_variable_names = {} if init_checkpoint and (hvd is None or hvd.rank() == 0): print("Loading checkpoint", init_checkpoint) (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) tf.train.init_from_checkpoint(init_checkpoint, assignment_map) if FLAGS.verbose_logging: tf.compat.v1.logging.info("**** Trainable Variables ****") for var in tvars: init_string = "" if var.name in initialized_variable_names: init_string = ", *INIT_FROM_CKPT*" tf.compat.v1.logging.info(" %d :: name = %s, shape = %s%s", 0 if hvd is None else hvd.rank(), var.name, var.shape, init_string) output_spec = None if mode == tf.estimator.ModeKeys.TRAIN: train_op = optimization.create_optimizer( total_loss, learning_rate, num_train_steps, num_warmup_steps, hvd, FLAGS.manual_fp16, FLAGS.use_fp16, FLAGS.num_accumulation_steps, FLAGS.optimizer_type, FLAGS.allreduce_post_accumulation) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, train_op=train_op) elif mode == tf.estimator.ModeKeys.EVAL: def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels): """Computes the loss and accuracy of the model.""" masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]]) masked_lm_predictions = tf.argmax( masked_lm_log_probs, axis=-1, output_type=tf.int32) masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1]) masked_lm_ids = tf.reshape(masked_lm_ids, [-1]) masked_lm_weights = tf.reshape(masked_lm_weights, [-1]) masked_lm_accuracy = tf.metrics.accuracy( labels=masked_lm_ids, predictions=masked_lm_predictions, weights=masked_lm_weights) masked_lm_mean_loss = tf.metrics.mean( values=masked_lm_example_loss, weights=masked_lm_weights) next_sentence_log_probs = tf.reshape( next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]]) next_sentence_predictions = tf.argmax( next_sentence_log_probs, axis=-1, output_type=tf.int32) next_sentence_labels = tf.reshape(next_sentence_labels, [-1]) next_sentence_accuracy = tf.metrics.accuracy( labels=next_sentence_labels, predictions=next_sentence_predictions) next_sentence_mean_loss = tf.metrics.mean( values=next_sentence_example_loss) return { "masked_lm_accuracy": masked_lm_accuracy, "masked_lm_loss": masked_lm_mean_loss, "next_sentence_accuracy": next_sentence_accuracy, "next_sentence_loss": next_sentence_mean_loss, } eval_metric_ops = metric_fn( masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights, next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels ) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, eval_metric_ops=eval_metric_ops) else: raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode)) return output_spec return model_fn def get_masked_lm_output(bert_config, input_tensor, output_weights, positions, label_ids, label_weights): """Get loss and log probs for the masked LM.""" input_tensor = gather_indexes(input_tensor, positions) with tf.variable_scope("cls/predictions"): # We apply one more non-linear transformation before the output layer. # This matrix is not used after pre-training. with tf.variable_scope("transform"): input_tensor = tf.layers.dense( input_tensor, units=bert_config.hidden_size, activation=modeling.get_activation(bert_config.hidden_act), kernel_initializer=modeling.create_initializer( bert_config.initializer_range)) input_tensor = modeling.layer_norm(input_tensor) # The output weights are the same as the input embeddings, but there is # an output-only bias for each token. output_bias = tf.get_variable( "output_bias", shape=[bert_config.vocab_size], initializer=tf.zeros_initializer()) logits = tf.matmul(tf.cast(input_tensor, tf.float32), output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) label_ids = tf.reshape(label_ids, [-1]) label_weights = tf.reshape(label_weights, [-1]) one_hot_labels = tf.one_hot( label_ids, depth=bert_config.vocab_size, dtype=tf.float32) # The `positions` tensor might be zero-padded (if the sequence is too # short to have the maximum number of predictions). The `label_weights` # tensor has a value of 1.0 for every real prediction and 0.0 for the # padding predictions. per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1]) numerator = tf.reduce_sum(label_weights * per_example_loss) denominator = tf.reduce_sum(label_weights) + 1e-5 loss = numerator / denominator return (loss, per_example_loss, log_probs) def get_next_sentence_output(bert_config, input_tensor, labels): """Get loss and log probs for the next sentence prediction.""" # Simple binary classification. Note that 0 is "next sentence" and 1 is # "random sentence". This weight matrix is not used after pre-training. with tf.variable_scope("cls/seq_relationship"): output_weights = tf.get_variable( "output_weights", shape=[2, bert_config.hidden_size], initializer=modeling.create_initializer(bert_config.initializer_range)) output_bias = tf.get_variable( "output_bias", shape=[2], initializer=tf.zeros_initializer()) logits = tf.matmul(tf.cast(input_tensor, tf.float32), output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) log_probs = tf.nn.log_softmax(logits, axis=-1) labels = tf.reshape(labels, [-1]) one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32) per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1) loss = tf.reduce_mean(per_example_loss) return (loss, per_example_loss, log_probs) def gather_indexes(sequence_tensor, positions): """Gathers the vectors at the specific positions over a minibatch.""" sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3) batch_size = sequence_shape[0] seq_length = sequence_shape[1] width = sequence_shape[2] flat_offsets = tf.reshape( tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]) flat_positions = tf.reshape(positions + flat_offsets, [-1]) flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length, width]) output_tensor = tf.gather(flat_sequence_tensor, flat_positions) return output_tensor def input_fn_builder(input_files, batch_size, max_seq_length, max_predictions_per_seq, is_training, num_cpu_threads=4, hvd=None): """Creates an `input_fn` closure to be passed to Estimator.""" def input_fn(): """The actual input function.""" name_to_features = { "input_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64), "input_mask": tf.io.FixedLenFeature([max_seq_length], tf.int64), "segment_ids": tf.io.FixedLenFeature([max_seq_length], tf.int64), "masked_lm_positions": tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_ids": tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64), "masked_lm_weights": tf.io.FixedLenFeature([max_predictions_per_seq], tf.float32), "next_sentence_labels": tf.io.FixedLenFeature([1], tf.int64), } # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. if is_training: d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files)) if hvd is not None: d = d.shard(hvd.size(), hvd.rank()) d = d.repeat() d = d.shuffle(buffer_size=len(input_files)) # `cycle_length` is the number of parallel files that get read. cycle_length = min(num_cpu_threads, len(input_files)) # `sloppy` mode means that the interleaving is not exact. This adds # even more randomness to the training pipeline. d = d.apply( tf.contrib.data.parallel_interleave( tf.data.TFRecordDataset, sloppy=is_training, cycle_length=cycle_length)) d = d.shuffle(buffer_size=100) else: d = tf.data.TFRecordDataset(input_files) # Since we evaluate for a fixed number of steps we don't want to encounter # out-of-range exceptions. d = d.repeat() # We must `drop_remainder` on training because the TPU requires fixed # size dimensions. For eval, we assume we are evaluating on the CPU or GPU # and we *don't* want to drop the remainder, otherwise we wont cover # every sample. d = d.apply( tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, num_parallel_batches=num_cpu_threads, drop_remainder=True if is_training else False)) return d return input_fn def _decode_record(record, name_to_features): """Decodes a record to a TensorFlow example.""" example = tf.parse_single_example(record, name_to_features) # tf.Example only supports tf.int64, but the TPU only supports tf.int32. # So cast all int64 to int32. for name in list(example.keys()): t = example[name] if t.dtype == tf.int64: t = tf.to_int32(t) example[name] = t return example def main(_): tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO) if not FLAGS.do_train and not FLAGS.do_eval: raise ValueError("At least one of `do_train` or `do_eval` must be True.") if FLAGS.use_fp16: os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1" if FLAGS.horovod: import horovod.tensorflow as hvd hvd.init() bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) tf.io.gfile.makedirs(FLAGS.output_dir) input_files = [] for input_file_dir in FLAGS.input_files_dir.split(","): input_files.extend(tf.io.gfile.glob(os.path.join(input_file_dir, "*"))) if FLAGS.horovod and len(input_files) < hvd.size(): raise ValueError("Input Files must be sharded") if FLAGS.use_fp16 and FLAGS.manual_fp16: raise ValueError("AMP and Manual Mixed Precision Training are both activated! Error") is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 config = tf.compat.v1.ConfigProto() if FLAGS.horovod: config.gpu_options.visible_device_list = str(hvd.local_rank()) if hvd.rank() == 0: tf.compat.v1.logging.info("***** Configuaration *****") for key in FLAGS.__flags.keys(): tf.compat.v1.logging.info(' {}: {}'.format(key, getattr(FLAGS, key))) tf.compat.v1.logging.info("**************************") # config.gpu_options.per_process_gpu_memory_fraction = 0.7 if FLAGS.use_xla: config.graph_options.optimizer_options.global_jit_level = tf.compat.v1.OptimizerOptions.ON_1 config.graph_options.rewrite_options.memory_optimization = rewriter_config_pb2.RewriterConfig.NO_MEM_OPT run_config = tf.estimator.RunConfig( model_dir=FLAGS.output_dir, session_config=config, save_checkpoints_steps=FLAGS.save_checkpoints_steps if not FLAGS.horovod or hvd.rank() == 0 else None, # This variable controls how often estimator reports examples/sec. # Default value is every 100 steps. # When --report_loss is True, we set to very large value to prevent # default info reporting from estimator. # Ideally we should set it to None, but that does not work. log_step_count_steps=10000 if FLAGS.report_loss else 100) model_fn = model_fn_builder( bert_config=bert_config, init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate if not FLAGS.horovod else FLAGS.learning_rate*hvd.size(), num_train_steps=FLAGS.num_train_steps, num_warmup_steps=FLAGS.num_warmup_steps, use_one_hot_embeddings=False, hvd=None if not FLAGS.horovod else hvd) training_hooks = [] if FLAGS.report_loss and (not FLAGS.horovod or hvd.rank() == 0): global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps if not FLAGS.horovod else FLAGS.train_batch_size * FLAGS.num_accumulation_steps * hvd.size() training_hooks.append(_LogSessionRunHook(global_batch_size, FLAGS.num_accumulation_steps, FLAGS.display_loss_steps)) if FLAGS.horovod and hvd.size() > 1: training_hooks.append(hvd.BroadcastGlobalVariablesHook(0)) estimator = tf.estimator.Estimator( model_fn=model_fn, config=run_config) if FLAGS.do_train: tf.compat.v1.logging.info("***** Running training *****") tf.compat.v1.logging.info(" Batch size = %d", FLAGS.train_batch_size) train_input_fn = input_fn_builder( input_files=input_files, batch_size=FLAGS.train_batch_size, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=True, hvd=None if not FLAGS.horovod else hvd) estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=FLAGS.num_train_steps) if FLAGS.do_eval and (not FLAGS.horovod or hvd.rank() == 0): tf.compat.v1.logging.info("***** Running evaluation *****") tf.compat.v1.logging.info(" Batch size = %d", FLAGS.eval_batch_size) eval_files = [] for eval_file_dir in FLAGS.eval_files_dir.split(","): eval_files.extend(tf.io.gfile.glob(os.path.join(eval_file_dir, "*"))) eval_input_fn = input_fn_builder( input_files=eval_files, batch_size=FLAGS.eval_batch_size, max_seq_length=FLAGS.max_seq_length, max_predictions_per_seq=FLAGS.max_predictions_per_seq, is_training=False, hvd=None if not FLAGS.horovod else hvd) eval_hooks = [LogEvalRunHook(FLAGS.eval_batch_size)] eval_start_time = time.time() result = estimator.evaluate( input_fn=eval_input_fn, steps=FLAGS.max_eval_steps, hooks=eval_hooks) eval_time_elapsed = time.time() - eval_start_time eval_time_wo_overhead = eval_hooks[-1].total_time num_sentences = (eval_hooks[-1].count - eval_hooks[-1].skipped) * FLAGS.eval_batch_size ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead tf.compat.v1.logging.info("-----------------------------") tf.compat.v1.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed, eval_hooks[-1].count * FLAGS.eval_batch_size) tf.compat.v1.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead, (eval_hooks[-1].count - eval_hooks[-1].skipped) * FLAGS.eval_batch_size) tf.compat.v1.logging.info("Summary Inference Statistics on EVAL set") tf.compat.v1.logging.info("Batch size = %d", FLAGS.eval_batch_size) tf.compat.v1.logging.info("Sequence Length = %d", FLAGS.max_seq_length) tf.compat.v1.logging.info("Precision = %s", "fp16" if FLAGS.use_fp16 else "fp32") tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second) tf.compat.v1.logging.info("-----------------------------") output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt") with tf.io.gfile.GFile(output_eval_file, "w") as writer: tf.compat.v1.logging.info("***** Eval results *****") for key in sorted(result.keys()): tf.compat.v1.logging.info(" %s = %s", key, str(result[key])) writer.write("%s = %s\n" % (key, str(result[key]))) if __name__ == "__main__": flags.mark_flag_as_required("input_files_dir") if FLAGS.do_eval: flags.mark_flag_as_required("eval_files_dir") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") if FLAGS.use_xla and FLAGS.manual_fp16: print('WARNING! Combining --use_xla with --manual_fp16 may prevent convergence.') print(' This warning message will be removed when the underlying') print(' issues have been fixed and you are running a TF version') print(' that has that fix.') tf.compat.v1.app.run()