640 lines
27 KiB
Python
640 lines
27 KiB
Python
# coding=utf-8
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# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Run masked LM/next sentence masked_lm pre-training for BERT."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import time
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import modeling
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import optimization
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import tensorflow as tf
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import glob
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from utils.utils import LogEvalRunHook
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from tensorflow.core.protobuf import rewriter_config_pb2
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flags = tf.flags
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FLAGS = flags.FLAGS
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## Required parameters
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flags.DEFINE_string(
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"bert_config_file", None,
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"The config json file corresponding to the pre-trained BERT model. "
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"This specifies the model architecture.")
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flags.DEFINE_string(
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"input_files_dir", None,
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"Directory with input files, comma separated or single directory.")
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flags.DEFINE_string(
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"eval_files_dir", None,
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"Directory with eval files, comma separated or single directory. ")
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flags.DEFINE_string(
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"output_dir", None,
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"The output directory where the model checkpoints will be written.")
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## Other parameters
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flags.DEFINE_string(
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"init_checkpoint", None,
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"Initial checkpoint (usually from a pre-trained BERT model).")
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flags.DEFINE_string(
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"optimizer_type", "lamb",
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"Optimizer used for training - LAMB or ADAM")
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flags.DEFINE_integer(
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"max_seq_length", 512,
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"The maximum total input sequence length after WordPiece tokenization. "
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"Sequences longer than this will be truncated, and sequences shorter "
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"than this will be padded. Must match data generation.")
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flags.DEFINE_integer(
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"max_predictions_per_seq", 80,
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"Maximum number of masked LM predictions per sequence. "
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"Must match data generation.")
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flags.DEFINE_bool("do_train", False, "Whether to run training.")
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flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
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flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
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flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
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flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
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flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
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flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")
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flags.DEFINE_integer("save_checkpoints_steps", 1000,
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"How often to save the model checkpoint.")
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flags.DEFINE_integer("display_loss_steps", 10,
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"How often to print loss")
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flags.DEFINE_integer("iterations_per_loop", 1000,
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"How many steps to make in each estimator call.")
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flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.")
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flags.DEFINE_integer("num_accumulation_steps", 1,
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"Number of accumulation steps before gradient update."
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"Global batch size = num_accumulation_steps * train_batch_size")
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flags.DEFINE_bool("allreduce_post_accumulation", False, "Whether to all reduce after accumulation of N steps or after each step")
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flags.DEFINE_bool(
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"verbose_logging", False,
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"If true, all of the trainable parameters are printed")
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flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")
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flags.DEFINE_bool("report_loss", True, "Whether to report total loss during training.")
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flags.DEFINE_bool("manual_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU. "
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"Manual casting is done instead of using AMP")
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flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.")
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flags.DEFINE_bool("use_fp16", False, "Whether to enable AMP ops.")
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# report samples/sec, total loss and learning rate during training
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class _LogSessionRunHook(tf.estimator.SessionRunHook):
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def __init__(self, global_batch_size, num_accumulation_steps, display_every=10, hvd_rank=-1):
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self.global_batch_size = global_batch_size
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self.display_every = display_every
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self.hvd_rank = hvd_rank
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self.num_accumulation_steps = num_accumulation_steps
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def after_create_session(self, session, coord):
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self.elapsed_secs = 0.
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self.count = 0
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self.all_count = 0
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self.avg_loss = 0.0
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def before_run(self, run_context):
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self.t0 = time.time()
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if self.num_accumulation_steps <= 1:
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if FLAGS.manual_fp16 or FLAGS.use_fp16:
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return tf.estimator.SessionRunArgs(
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fetches=['step_update:0', 'total_loss:0',
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'learning_rate:0', 'nsp_loss:0',
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'mlm_loss:0', 'loss_scale:0'])
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else:
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return tf.estimator.SessionRunArgs(
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fetches=['step_update:0', 'total_loss:0',
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'learning_rate:0', 'nsp_loss:0',
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'mlm_loss:0'])
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else:
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if FLAGS.manual_fp16 or FLAGS.use_fp16:
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return tf.estimator.SessionRunArgs(
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fetches=['step_update:0', 'update_step:0', 'total_loss:0',
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'learning_rate:0', 'nsp_loss:0',
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'mlm_loss:0', 'loss_scale:0'])
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else:
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return tf.estimator.SessionRunArgs(
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fetches=['step_update:0', 'update_step:0', 'total_loss:0',
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'learning_rate:0', 'nsp_loss:0',
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'mlm_loss:0'])
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def after_run(self, run_context, run_values):
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self.elapsed_secs += time.time() - self.t0
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if self.num_accumulation_steps <=1:
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if FLAGS.manual_fp16 or FLAGS.use_fp16:
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global_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results
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else:
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global_step, total_loss, lr, nsp_loss, mlm_loss = run_values. \
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results
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update_step = True
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else:
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if FLAGS.manual_fp16 or FLAGS.use_fp16:
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global_step, update_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results
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else:
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global_step, update_step, total_loss, lr, nsp_loss, mlm_loss = run_values.\
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results
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print_step = global_step + 1 # One-based index for printing.
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self.avg_loss += total_loss
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self.all_count += 1
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if update_step:
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self.count += 1
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if (print_step == 1 or print_step % self.display_every == 0):
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dt = self.elapsed_secs / self.count
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sent_per_sec = self.global_batch_size / dt
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avg_loss_step = self.avg_loss / self.all_count
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if self.hvd_rank >= 0:
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if FLAGS.manual_fp16 or FLAGS.use_fp16:
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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' %
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(self.hvd_rank, print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr, loss_scaler))
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else:
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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' %
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(self.hvd_rank, print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr))
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else:
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if FLAGS.manual_fp16 or FLAGS.use_fp16:
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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' %
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(print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr, loss_scaler))
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else:
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print('Step = %6i Throughput = %11.1f MLM Loss = %10.4e NSP Loss = %10.4e Loss = %6.3f Average Loss = %6.3f LR = %6.4e' %
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(print_step, sent_per_sec, mlm_loss, nsp_loss, total_loss, avg_loss_step, lr))
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self.elapsed_secs = 0.
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self.count = 0
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self.avg_loss = 0.0
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self.all_count = 0
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def model_fn_builder(bert_config, init_checkpoint, learning_rate,
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num_train_steps, num_warmup_steps,
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use_one_hot_embeddings, hvd=None):
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"""Returns `model_fn` closure for TPUEstimator."""
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def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
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"""The `model_fn` for TPUEstimator."""
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tf.compat.v1.logging.info("*** Features ***")
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for name in sorted(features.keys()):
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tf.compat.v1.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
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input_ids = features["input_ids"]
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input_mask = features["input_mask"]
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segment_ids = features["segment_ids"]
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masked_lm_positions = features["masked_lm_positions"]
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masked_lm_ids = features["masked_lm_ids"]
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masked_lm_weights = features["masked_lm_weights"]
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next_sentence_labels = features["next_sentence_labels"]
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is_training = (mode == tf.estimator.ModeKeys.TRAIN)
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model = modeling.BertModel(
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config=bert_config,
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is_training=is_training,
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input_ids=input_ids,
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input_mask=input_mask,
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token_type_ids=segment_ids,
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use_one_hot_embeddings=use_one_hot_embeddings,
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compute_type=tf.float16 if FLAGS.manual_fp16 else tf.float32)
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(masked_lm_loss,
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masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
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bert_config, model.get_sequence_output(), model.get_embedding_table(),
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masked_lm_positions, masked_lm_ids,
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masked_lm_weights)
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(next_sentence_loss, next_sentence_example_loss,
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next_sentence_log_probs) = get_next_sentence_output(
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bert_config, model.get_pooled_output(), next_sentence_labels)
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masked_lm_loss = tf.identity(masked_lm_loss, name="mlm_loss")
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next_sentence_loss = tf.identity(next_sentence_loss, name="nsp_loss")
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total_loss = masked_lm_loss + next_sentence_loss
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total_loss = tf.identity(total_loss, name='total_loss')
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tvars = tf.trainable_variables()
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initialized_variable_names = {}
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if init_checkpoint and (hvd is None or hvd.rank() == 0):
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print("Loading checkpoint", init_checkpoint)
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(assignment_map, initialized_variable_names
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) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
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tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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if FLAGS.verbose_logging:
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tf.compat.v1.logging.info("**** Trainable Variables ****")
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for var in tvars:
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init_string = ""
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if var.name in initialized_variable_names:
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init_string = ", *INIT_FROM_CKPT*"
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tf.compat.v1.logging.info(" %d :: name = %s, shape = %s%s", 0 if hvd is None else hvd.rank(), var.name, var.shape,
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init_string)
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output_spec = None
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if mode == tf.estimator.ModeKeys.TRAIN:
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train_op = optimization.create_optimizer(
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total_loss, learning_rate, num_train_steps, num_warmup_steps,
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hvd, FLAGS.manual_fp16, FLAGS.use_fp16, FLAGS.num_accumulation_steps, FLAGS.optimizer_type, FLAGS.allreduce_post_accumulation)
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output_spec = tf.estimator.EstimatorSpec(
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mode=mode,
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loss=total_loss,
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train_op=train_op)
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elif mode == tf.estimator.ModeKeys.EVAL:
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def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
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masked_lm_weights, next_sentence_example_loss,
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next_sentence_log_probs, next_sentence_labels):
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"""Computes the loss and accuracy of the model."""
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masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
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[-1, masked_lm_log_probs.shape[-1]])
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masked_lm_predictions = tf.argmax(
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masked_lm_log_probs, axis=-1, output_type=tf.int32)
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masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
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masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
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masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
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masked_lm_accuracy = tf.metrics.accuracy(
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labels=masked_lm_ids,
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predictions=masked_lm_predictions,
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weights=masked_lm_weights)
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masked_lm_mean_loss = tf.metrics.mean(
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values=masked_lm_example_loss, weights=masked_lm_weights)
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next_sentence_log_probs = tf.reshape(
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next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
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next_sentence_predictions = tf.argmax(
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next_sentence_log_probs, axis=-1, output_type=tf.int32)
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next_sentence_labels = tf.reshape(next_sentence_labels, [-1])
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next_sentence_accuracy = tf.metrics.accuracy(
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labels=next_sentence_labels, predictions=next_sentence_predictions)
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next_sentence_mean_loss = tf.metrics.mean(
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values=next_sentence_example_loss)
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return {
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"masked_lm_accuracy": masked_lm_accuracy,
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"masked_lm_loss": masked_lm_mean_loss,
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"next_sentence_accuracy": next_sentence_accuracy,
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"next_sentence_loss": next_sentence_mean_loss,
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}
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eval_metric_ops = metric_fn(
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masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
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masked_lm_weights, next_sentence_example_loss,
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next_sentence_log_probs, next_sentence_labels
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)
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output_spec = tf.estimator.EstimatorSpec(
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mode=mode,
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loss=total_loss,
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eval_metric_ops=eval_metric_ops)
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else:
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raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
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return output_spec
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return model_fn
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def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
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label_ids, label_weights):
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"""Get loss and log probs for the masked LM."""
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input_tensor = gather_indexes(input_tensor, positions)
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with tf.variable_scope("cls/predictions"):
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# We apply one more non-linear transformation before the output layer.
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# This matrix is not used after pre-training.
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with tf.variable_scope("transform"):
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input_tensor = tf.layers.dense(
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input_tensor,
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units=bert_config.hidden_size,
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activation=modeling.get_activation(bert_config.hidden_act),
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kernel_initializer=modeling.create_initializer(
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bert_config.initializer_range))
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input_tensor = modeling.layer_norm(input_tensor)
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# The output weights are the same as the input embeddings, but there is
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# an output-only bias for each token.
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output_bias = tf.get_variable(
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"output_bias",
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shape=[bert_config.vocab_size],
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initializer=tf.zeros_initializer())
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logits = tf.matmul(tf.cast(input_tensor, tf.float32), output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias)
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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label_ids = tf.reshape(label_ids, [-1])
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label_weights = tf.reshape(label_weights, [-1])
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one_hot_labels = tf.one_hot(
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label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
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# The `positions` tensor might be zero-padded (if the sequence is too
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# short to have the maximum number of predictions). The `label_weights`
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# tensor has a value of 1.0 for every real prediction and 0.0 for the
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# padding predictions.
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per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
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numerator = tf.reduce_sum(label_weights * per_example_loss)
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denominator = tf.reduce_sum(label_weights) + 1e-5
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loss = numerator / denominator
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return (loss, per_example_loss, log_probs)
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def get_next_sentence_output(bert_config, input_tensor, labels):
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"""Get loss and log probs for the next sentence prediction."""
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# Simple binary classification. Note that 0 is "next sentence" and 1 is
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# "random sentence". This weight matrix is not used after pre-training.
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with tf.variable_scope("cls/seq_relationship"):
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output_weights = tf.get_variable(
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"output_weights",
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shape=[2, bert_config.hidden_size],
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initializer=modeling.create_initializer(bert_config.initializer_range))
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output_bias = tf.get_variable(
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"output_bias", shape=[2], initializer=tf.zeros_initializer())
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logits = tf.matmul(tf.cast(input_tensor, tf.float32), output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias)
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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labels = tf.reshape(labels, [-1])
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one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
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per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
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loss = tf.reduce_mean(per_example_loss)
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return (loss, per_example_loss, log_probs)
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def gather_indexes(sequence_tensor, positions):
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"""Gathers the vectors at the specific positions over a minibatch."""
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sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
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batch_size = sequence_shape[0]
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seq_length = sequence_shape[1]
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width = sequence_shape[2]
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flat_offsets = tf.reshape(
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tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
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flat_positions = tf.reshape(positions + flat_offsets, [-1])
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flat_sequence_tensor = tf.reshape(sequence_tensor,
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[batch_size * seq_length, width])
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output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
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return output_tensor
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def input_fn_builder(input_files,
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batch_size,
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max_seq_length,
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max_predictions_per_seq,
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is_training,
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num_cpu_threads=4,
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hvd=None):
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"""Creates an `input_fn` closure to be passed to Estimator."""
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def input_fn():
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"""The actual input function."""
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|
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")
|
|
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()
|