# 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. """BERT finetuning runner.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import csv import logging import os, sys import numpy as np import tensorflow as tf sys.path.append("/workspace/bert") import modeling import optimization import tokenization import time import horovod.tensorflow as hvd from utils.utils import LogEvalRunHook, LogTrainRunHook import utils.dllogger_class from dllogger import Verbosity flags = tf.flags FLAGS = flags.FLAGS ## Required parameters flags.DEFINE_string( "data_dir", None, "The input data dir. Should contain the .tsv files (or other data files) " "for the task.") 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("task_name", None, "The name of the task to train.") flags.DEFINE_string("vocab_file", None, "The vocabulary file that the BERT model was trained on.") flags.DEFINE_string( "output_dir", None, "The output directory where the model checkpoints will be written.") ## Other parameters flags.DEFINE_string( "dllog_path", "/results/bert_dllog.json", "filename where dllogger writes to") flags.DEFINE_string( "init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).") flags.DEFINE_bool( "do_lower_case", True, "Whether to lower case the input text. Should be True for uncased " "models and False for cased models.") flags.DEFINE_integer( "max_seq_length", 128, "The maximum total input sequence length after WordPiece tokenization. " "Sequences longer than this will be truncated, and sequences shorter " "than this will be padded.") 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_bool( "do_predict", False, "Whether to run the model in inference mode on the test set.") flags.DEFINE_integer("train_batch_size", 16, "Total batch size for training.") flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.") flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.") flags.DEFINE_float("learning_rate", 5e-6, "The initial learning rate for Adam.") flags.DEFINE_float("num_train_epochs", 3.0, "Total number of training epochs to perform.") flags.DEFINE_float( "warmup_proportion", 0.1, "Proportion of training to perform linear learning rate warmup for. " "E.g., 0.1 = 10% of training.") flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.") flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.") tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.") flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs") flags.DEFINE_bool("amp", True, "Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.") flags.DEFINE_bool("use_xla", True, "Whether to enable XLA JIT compilation.") class InputExample(object): """A single training/test example for simple sequence classification.""" def __init__(self, guid, text_a, text_b=None, label=None): """Constructs a InputExample. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label class PaddingInputExample(object): """Fake example so the num input examples is a multiple of the batch size. When running eval/predict on the TPU, we need to pad the number of examples to be a multiple of the batch size, because the TPU requires a fixed batch size. The alternative is to drop the last batch, which is bad because it means the entire output data won't be generated. We use this class instead of `None` because treating `None` as padding battches could cause silent errors. """ class InputFeatures(object): """A single set of features of data.""" def __init__(self, input_ids, input_mask, segment_ids, label_id, is_real_example=True): self.input_ids = input_ids self.input_mask = input_mask self.segment_ids = segment_ids self.label_id = label_id self.is_real_example = is_real_example class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_test_examples(self, data_dir): """Gets a collection of `InputExample`s for prediction.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() @classmethod def _read_tsv(cls, input_file, quotechar=None): """Reads a tab separated value file.""" with tf.io.gfile.Open(input_file, "r") as f: reader = csv.reader(f, delimiter="\t", quotechar=quotechar) lines = [] for line in reader: lines.append(line) return lines class BioBERTChemprotProcessor(DataProcessor): """Processor for the BioBERT data set obtained from (https://github.com/arwhirang/recursive_chemprot/tree/master/Demo/tree_LSTM/data). """ def get_train_examples(self, data_dir, file_name="trainingPosit_chem"): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, file_name)), "train") def get_dev_examples(self, data_dir, file_name="developPosit_chem"): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, file_name)), "dev") def get_test_examples(self, data_dir, file_name="testPosit_chem"): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, file_name)), "test") def get_labels(self): """See base class.""" return ["CPR:3", "CPR:4", "CPR:5", "CPR:6", "CPR:9", "False"] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): guid = "%s-%s" % (set_type, i) if set_type == "test": text_a = tokenization.convert_to_unicode(line[1]) label = "False" else: text_a = tokenization.convert_to_unicode(line[1]) label = tokenization.convert_to_unicode(line[2]) if label == "True": label = tokenization.convert_to_unicode(line[3]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples class _ChemProtProcessor(DataProcessor): """Processor for the ChemProt data set.""" def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir, file_name="dev.tsv"): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, file_name)), "dev") def get_test_examples(self, data_dir, file_name="test.tsv"): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, file_name)), "test") def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): # skip header if i == 0: continue guid = line[0] text_a = tokenization.convert_to_unicode(line[1]) if set_type == "test": label = self.get_labels()[-1] else: try: label = tokenization.convert_to_unicode(line[2]) except IndexError: logging.exception(line) exit(1) examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label)) return examples class ChemProtProcessor(_ChemProtProcessor): def get_labels(self): """See base class.""" return ["CPR:3", "CPR:4", "CPR:5", "CPR:6", "CPR:9", "false"] class MedNLIProcessor(DataProcessor): def get_train_examples(self, data_dir): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, "train.tsv")), "train") def get_dev_examples(self, data_dir, file_name="dev.tsv"): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, file_name)), "dev") def get_test_examples(self, data_dir, file_name="test.tsv"): """See base class.""" return self._create_examples( self._read_tsv(os.path.join(data_dir, file_name)), "test") def get_labels(self): """See base class.""" return ['contradiction', 'entailment', 'neutral'] def _create_examples(self, lines, set_type): """Creates examples for the training and dev sets.""" examples = [] for (i, line) in enumerate(lines): if i == 0: continue guid = line[1] text_a = tokenization.convert_to_unicode(line[2]) text_b = tokenization.convert_to_unicode(line[3]) if set_type == "test": label = self.get_labels()[-1] else: label = tokenization.convert_to_unicode(line[0]) examples.append( InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)) return examples def convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer): """Converts a single `InputExample` into a single `InputFeatures`.""" if isinstance(example, PaddingInputExample): return InputFeatures( input_ids=[0] * max_seq_length, input_mask=[0] * max_seq_length, segment_ids=[0] * max_seq_length, label_id=0, is_real_example=False) label_map = {} for (i, label) in enumerate(label_list): label_map[label] = i tokens_a = tokenizer.tokenize(example.text_a) tokens_b = None if example.text_b: tokens_b = tokenizer.tokenize(example.text_b) if tokens_b: # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3) else: # Account for [CLS] and [SEP] with "- 2" if len(tokens_a) > max_seq_length - 2: tokens_a = tokens_a[0:(max_seq_length - 2)] # The convention in BERT is: # (a) For sequence pairs: # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 # (b) For single sequences: # tokens: [CLS] the dog is hairy . [SEP] # type_ids: 0 0 0 0 0 0 0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambiguously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. tokens = [] segment_ids = [] tokens.append("[CLS]") segment_ids.append(0) for token in tokens_a: tokens.append(token) segment_ids.append(0) tokens.append("[SEP]") segment_ids.append(0) if tokens_b: for token in tokens_b: tokens.append(token) segment_ids.append(1) tokens.append("[SEP]") segment_ids.append(1) input_ids = tokenizer.convert_tokens_to_ids(tokens) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1] * len(input_ids) # Zero-pad up to the sequence length. while len(input_ids) < max_seq_length: input_ids.append(0) input_mask.append(0) segment_ids.append(0) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length label_id = label_map[example.label] if ex_index < 5: tf.compat.v1.logging.info("*** Example ***") tf.compat.v1.logging.info("guid: %s" % (example.guid)) tf.compat.v1.logging.info("tokens: %s" % " ".join( [tokenization.printable_text(x) for x in tokens])) tf.compat.v1.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) tf.compat.v1.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask])) tf.compat.v1.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids])) tf.compat.v1.logging.info("label: %s (id = %d)" % (example.label, label_id)) feature = InputFeatures( input_ids=input_ids, input_mask=input_mask, segment_ids=segment_ids, label_id=label_id, is_real_example=True) return feature def file_based_convert_examples_to_features( examples, label_list, max_seq_length, tokenizer, output_file): """Convert a set of `InputExample`s to a TFRecord file.""" writer = tf.python_io.TFRecordWriter(output_file) for (ex_index, example) in enumerate(examples): if ex_index % 10000 == 0: tf.compat.v1.logging.info("Writing example %d of %d" % (ex_index, len(examples))) feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) def create_int_feature(values): f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) return f features = collections.OrderedDict() features["input_ids"] = create_int_feature(feature.input_ids) features["input_mask"] = create_int_feature(feature.input_mask) features["segment_ids"] = create_int_feature(feature.segment_ids) features["label_ids"] = create_int_feature([feature.label_id]) features["is_real_example"] = create_int_feature( [int(feature.is_real_example)]) tf_example = tf.train.Example(features=tf.train.Features(feature=features)) writer.write(tf_example.SerializeToString()) writer.close() def file_based_input_fn_builder(input_file, batch_size, seq_length, is_training, drop_remainder, hvd=None): """Creates an `input_fn` closure to be passed to TPUEstimator.""" name_to_features = { "input_ids": tf.io.FixedLenFeature([seq_length], tf.int64), "input_mask": tf.io.FixedLenFeature([seq_length], tf.int64), "segment_ids": tf.io.FixedLenFeature([seq_length], tf.int64), "label_ids": tf.io.FixedLenFeature([], tf.int64), "is_real_example": tf.io.FixedLenFeature([], tf.int64), } 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 input_fn(params): """The actual input function.""" #batch_size = params["batch_size"] # For training, we want a lot of parallel reading and shuffling. # For eval, we want no shuffling and parallel reading doesn't matter. d = tf.data.TFRecordDataset(input_file) if is_training: if hvd is not None: d = d.shard(hvd.size(), hvd.rank()) d = d.repeat() d = d.shuffle(buffer_size=100) d = d.apply( tf.contrib.data.map_and_batch( lambda record: _decode_record(record, name_to_features), batch_size=batch_size, drop_remainder=drop_remainder)) return d return input_fn def _truncate_seq_pair(tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. while True: total_length = len(tokens_a) + len(tokens_b) if total_length <= max_length: break if len(tokens_a) > len(tokens_b): tokens_a.pop() else: tokens_b.pop() def create_model(bert_config, is_training, input_ids, input_mask, segment_ids, labels, num_labels, use_one_hot_embeddings): """Creates a classification model.""" 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) # In the demo, we are doing a simple classification task on the entire # segment. # # If you want to use the token-level output, use model.get_sequence_output() # instead. output_layer = model.get_pooled_output() hidden_size = output_layer.shape[-1].value output_weights = tf.get_variable( "output_weights", [num_labels, hidden_size], initializer=tf.truncated_normal_initializer(stddev=0.02)) output_bias = tf.get_variable( "output_bias", [num_labels], initializer=tf.zeros_initializer()) with tf.variable_scope("loss"): if is_training: # I.e., 0.1 dropout output_layer = tf.nn.dropout(output_layer, keep_prob=0.9) logits = tf.matmul(output_layer, output_weights, transpose_b=True) logits = tf.nn.bias_add(logits, output_bias) probabilities = tf.nn.softmax(logits, axis=-1) log_probs = tf.nn.log_softmax(logits, axis=-1) one_hot_labels = tf.one_hot(labels, depth=num_labels, 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, logits, probabilities) def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate=None, num_train_steps=None, num_warmup_steps=None, use_one_hot_embeddings=False, hvd=None, amp=False): """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"] label_ids = features["label_ids"] is_real_example = None if "is_real_example" in features: is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32) else: is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32) is_training = (mode == tf.estimator.ModeKeys.TRAIN) (total_loss, per_example_loss, logits, probabilities) = create_model( bert_config, is_training, input_ids, input_mask, segment_ids, label_ids, num_labels, use_one_hot_embeddings) tvars = tf.trainable_variables() initialized_variable_names = {} scaffold_fn = None if init_checkpoint and (hvd is None or hvd.rank() == 0): (assignment_map, initialized_variable_names ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint) tf.train.init_from_checkpoint(init_checkpoint, assignment_map) 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(" name = %s, shape = %s%s", 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, False, amp) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, train_op=train_op) elif mode == tf.estimator.ModeKeys.EVAL: dummy_op = tf.no_op() # Need to call mixed precision graph rewrite if fp16 to enable graph rewrite if amp: loss_scaler = tf.train.experimental.FixedLossScale(1) dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite( optimization.LAMBOptimizer(learning_rate=0.0), loss_scaler) def metric_fn(per_example_loss, label_ids, logits, is_real_example): predictions = tf.argmax(logits, axis=-1, output_type=tf.int32) accuracy = tf.metrics.accuracy( labels=label_ids, predictions=predictions, weights=is_real_example) loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example) return { "eval_accuracy": accuracy, "eval_loss": loss, } eval_metric_ops = metric_fn(per_example_loss, label_ids, logits, is_real_example) output_spec = tf.estimator.EstimatorSpec( mode=mode, loss=total_loss, eval_metric_ops=eval_metric_ops) else: dummy_op = tf.no_op() # Need to call mixed precision graph rewrite if fp16 to enable graph rewrite if amp: dummy_op = tf.train.experimental.enable_mixed_precision_graph_rewrite( optimization.LAMBOptimizer(learning_rate=0.0)) output_spec = tf.estimator.EstimatorSpec( mode=mode, predictions={"probabilities": probabilities})#predicts)#probabilities) return output_spec return model_fn # This function is not used by this file but is still used by the Colab and # people who depend on it. def input_fn_builder(features, seq_length, is_training, drop_remainder): """Creates an `input_fn` closure to be passed to TPUEstimator.""" all_input_ids = [] all_input_mask = [] all_segment_ids = [] all_label_ids = [] for feature in features: all_input_ids.append(feature.input_ids) all_input_mask.append(feature.input_mask) all_segment_ids.append(feature.segment_ids) all_label_ids.append(feature.label_id) def input_fn(params): """The actual input function.""" batch_size = params["batch_size"] num_examples = len(features) # This is for demo purposes and does NOT scale to large data sets. We do # not use Dataset.from_generator() because that uses tf.py_func which is # not TPU compatible. The right way to load data is with TFRecordReader. d = tf.data.Dataset.from_tensor_slices({ "input_ids": tf.constant( all_input_ids, shape=[num_examples, seq_length], dtype=tf.int32), "input_mask": tf.constant( all_input_mask, shape=[num_examples, seq_length], dtype=tf.int32), "segment_ids": tf.constant( all_segment_ids, shape=[num_examples, seq_length], dtype=tf.int32), "label_ids": tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32), }) if is_training: d = d.repeat() d = d.shuffle(buffer_size=100) d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder) return d return input_fn # This function is not used by this file but is still used by the Colab and # people who depend on it. def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer): """Convert a set of `InputExample`s to a list of `InputFeatures`.""" features = [] for (ex_index, example) in enumerate(examples): if ex_index % 10000 == 0: tf.compat.v1.logging.info("Writing example %d of %d" % (ex_index, len(examples))) feature = convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer) features.append(feature) return features def main(_): # causes memory fragmentation for bert leading to OOM if os.environ.get("TF_XLA_FLAGS", None) is not None: os.environ["TF_XLA_FLAGS"] += " --tf_xla_enable_lazy_compilation false" else: os.environ["TF_XLA_FLAGS"] = " --tf_xla_enable_lazy_compilation false" tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO) dllogging = utils.dllogger_class.dllogger_class(FLAGS.dllog_path) if FLAGS.horovod: hvd.init() processors = { "chemprot": BioBERTChemprotProcessor, 'mednli': MedNLIProcessor, } tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, FLAGS.init_checkpoint) if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict: raise ValueError( "At least one of `do_train`, `do_eval` or `do_predict' must be True.") bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file) if FLAGS.max_seq_length > bert_config.max_position_embeddings: raise ValueError( "Cannot use sequence length %d because the BERT model " "was only trained up to sequence length %d" % (FLAGS.max_seq_length, bert_config.max_position_embeddings)) tf.io.gfile.makedirs(FLAGS.output_dir) task_name = FLAGS.task_name.lower() if task_name not in processors: raise ValueError("Task not found: %s" % (task_name)) processor = processors[task_name]() label_list = processor.get_labels() tokenizer = tokenization.FullTokenizer( vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case) is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2 master_process = True training_hooks = [] global_batch_size = FLAGS.train_batch_size hvd_rank = 0 config = tf.compat.v1.ConfigProto() if FLAGS.horovod: global_batch_size = FLAGS.train_batch_size * hvd.size() master_process = (hvd.rank() == 0) hvd_rank = hvd.rank() config.gpu_options.visible_device_list = str(hvd.local_rank()) if hvd.size() > 1: training_hooks.append(hvd.BroadcastGlobalVariablesHook(0)) if FLAGS.use_xla: config.graph_options.optimizer_options.global_jit_level = tf.compat.v1.OptimizerOptions.ON_1 tf.enable_resource_variables() run_config = tf.estimator.RunConfig( model_dir=FLAGS.output_dir if master_process else None, session_config=config, save_checkpoints_steps=FLAGS.save_checkpoints_steps if master_process else None, keep_checkpoint_max=1) if master_process: 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("**************************") train_examples = None num_train_steps = None num_warmup_steps = None training_hooks.append(LogTrainRunHook(global_batch_size, hvd_rank)) if FLAGS.do_train: train_examples = processor.get_train_examples(FLAGS.data_dir) num_train_steps = int( len(train_examples) / global_batch_size * FLAGS.num_train_epochs) num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion) start_index = 0 end_index = len(train_examples) tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record")] if FLAGS.horovod: tmp_filenames = [os.path.join(FLAGS.output_dir, "train.tf_record{}".format(i)) for i in range(hvd.size())] num_examples_per_rank = len(train_examples) // hvd.size() remainder = len(train_examples) % hvd.size() if hvd.rank() < remainder: start_index = hvd.rank() * (num_examples_per_rank+1) end_index = start_index + num_examples_per_rank + 1 else: start_index = hvd.rank() * num_examples_per_rank + remainder end_index = start_index + (num_examples_per_rank) model_fn = model_fn_builder( bert_config=bert_config, num_labels=len(label_list), init_checkpoint=FLAGS.init_checkpoint, learning_rate=FLAGS.learning_rate if not FLAGS.horovod else FLAGS.learning_rate * hvd.size(), num_train_steps=num_train_steps, num_warmup_steps=num_warmup_steps, use_one_hot_embeddings=False, hvd=None if not FLAGS.horovod else hvd, amp=FLAGS.amp) estimator = tf.estimator.Estimator( model_fn=model_fn, config=run_config) if FLAGS.do_train: file_based_convert_examples_to_features( train_examples[start_index:end_index], label_list, FLAGS.max_seq_length, tokenizer, tmp_filenames[hvd_rank]) tf.compat.v1.logging.info("***** Running training *****") tf.compat.v1.logging.info(" Num examples = %d", len(train_examples)) tf.compat.v1.logging.info(" Batch size = %d", FLAGS.train_batch_size) tf.compat.v1.logging.info(" Num steps = %d", num_train_steps) train_input_fn = file_based_input_fn_builder( input_file=tmp_filenames, batch_size=FLAGS.train_batch_size, seq_length=FLAGS.max_seq_length, is_training=True, drop_remainder=True, hvd=None if not FLAGS.horovod else hvd) train_start_time = time.time() estimator.train(input_fn=train_input_fn, max_steps=num_train_steps, hooks=training_hooks) train_time_elapsed = time.time() - train_start_time train_time_wo_overhead = training_hooks[-1].total_time avg_sentences_per_second = num_train_steps * global_batch_size * 1.0 / train_time_elapsed ss_sentences_per_second = (num_train_steps - training_hooks[-1].skipped) * global_batch_size * 1.0 / train_time_wo_overhead if master_process: tf.compat.v1.logging.info("-----------------------------") tf.compat.v1.logging.info("Total Training Time = %0.2f for Sentences = %d", train_time_elapsed, num_train_steps * global_batch_size) tf.compat.v1.logging.info("Total Training Time W/O Overhead = %0.2f for Sentences = %d", train_time_wo_overhead, (num_train_steps - training_hooks[-1].skipped) * global_batch_size) tf.compat.v1.logging.info("Throughput Average (sentences/sec) with overhead = %0.2f", avg_sentences_per_second) tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second) dllogging.logger.log(step=(), data={"throughput_train": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT) tf.compat.v1.logging.info("-----------------------------") if FLAGS.do_eval and master_process: eval_examples = processor.get_dev_examples(FLAGS.data_dir) num_actual_eval_examples = len(eval_examples) eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record") file_based_convert_examples_to_features( eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file) tf.compat.v1.logging.info("***** Running evaluation *****") tf.compat.v1.logging.info(" Num examples = %d (%d actual, %d padding)", len(eval_examples), num_actual_eval_examples, len(eval_examples) - num_actual_eval_examples) tf.compat.v1.logging.info(" Batch size = %d", FLAGS.eval_batch_size) # This tells the estimator to run through the entire set. eval_steps = None eval_drop_remainder = False eval_input_fn = file_based_input_fn_builder( input_file=eval_file, batch_size=FLAGS.eval_batch_size, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=eval_drop_remainder) result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps) 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 FLAGS.do_predict and master_process: predict_examples = processor.get_test_examples(FLAGS.data_dir) num_actual_predict_examples = len(predict_examples) predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record") file_based_convert_examples_to_features(predict_examples, label_list, FLAGS.max_seq_length, tokenizer, predict_file) tf.compat.v1.logging.info("***** Running prediction*****") tf.compat.v1.logging.info(" Num examples = %d (%d actual, %d padding)", len(predict_examples), num_actual_predict_examples, len(predict_examples) - num_actual_predict_examples) tf.compat.v1.logging.info(" Batch size = %d", FLAGS.predict_batch_size) predict_drop_remainder = False predict_input_fn = file_based_input_fn_builder( input_file=predict_file, batch_size=FLAGS.predict_batch_size, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=predict_drop_remainder) eval_hooks = [LogEvalRunHook(FLAGS.predict_batch_size)] eval_start_time = time.time() output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv") with tf.io.gfile.GFile(output_predict_file, "w") as writer: num_written_lines = 0 tf.compat.v1.logging.info("***** Predict results *****") for prediction in estimator.predict(input_fn=predict_input_fn, hooks=eval_hooks, yield_single_examples=True): probabilities = prediction["probabilities"] output_line = "\t".join( str(class_probability) for class_probability in probabilities) + "\n" writer.write(output_line) num_written_lines += 1 assert num_written_lines == num_actual_predict_examples eval_time_elapsed = time.time() - eval_start_time time_list = eval_hooks[-1].time_list time_list.sort() # Removing outliers (init/warmup) in throughput computation. eval_time_wo_overhead = sum(time_list[:int(len(time_list) * 0.99)]) num_sentences = (int(len(time_list) * 0.99)) * FLAGS.predict_batch_size avg = np.mean(time_list) cf_50 = max(time_list[:int(len(time_list) * 0.50)]) cf_90 = max(time_list[:int(len(time_list) * 0.90)]) cf_95 = max(time_list[:int(len(time_list) * 0.95)]) cf_99 = max(time_list[:int(len(time_list) * 0.99)]) cf_100 = max(time_list[:int(len(time_list) * 1)]) 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.predict_batch_size) tf.compat.v1.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead, num_sentences) tf.compat.v1.logging.info("Summary Inference Statistics") tf.compat.v1.logging.info("Batch size = %d", FLAGS.predict_batch_size) tf.compat.v1.logging.info("Sequence Length = %d", FLAGS.max_seq_length) tf.compat.v1.logging.info("Precision = %s", "fp16" if FLAGS.amp else "fp32") tf.compat.v1.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000) tf.compat.v1.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000) tf.compat.v1.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000) tf.compat.v1.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000) tf.compat.v1.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000) tf.compat.v1.logging.info("Latency Average (ms) = %0.2f", avg * 1000) tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second) dllogging.logger.log(step=(), data={"throughput_val": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT) tf.compat.v1.logging.info("-----------------------------") if __name__ == "__main__": flags.mark_flag_as_required("data_dir") flags.mark_flag_as_required("task_name") flags.mark_flag_as_required("vocab_file") flags.mark_flag_as_required("bert_config_file") flags.mark_flag_as_required("output_dir") tf.compat.v1.app.run()