706 lines
28 KiB
Python
706 lines
28 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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"""BERT finetuning runner."""
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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 collections
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import csv
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import os
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import modeling
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import optimization
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import tokenization
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import tensorflow as tf
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import horovod.tensorflow as hvd
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import time
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from utils.utils import LogEvalRunHook, LogTrainRunHook
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from utils.create_glue_data import *
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import numpy as np
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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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"data_dir", None,
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"The input data dir. Should contain the .tsv files (or other data files) "
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"for the task.")
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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("task_name", None, "The name of the task to train.")
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flags.DEFINE_string("vocab_file", None,
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"The vocabulary file that the BERT model was trained on.")
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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_bool(
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"do_lower_case", True,
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"Whether to lower case the input text. Should be True for uncased "
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"models and False for cased models.")
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flags.DEFINE_integer(
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"max_seq_length", 128,
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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.")
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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_bool(
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"do_predict", False,
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"Whether to run the model in inference mode on the test 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_integer("predict_batch_size", 8, "Total batch size for predict.")
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flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
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flags.DEFINE_bool("use_trt", False, "Whether to use TF-TRT")
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flags.DEFINE_float("num_train_epochs", 3.0,
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"Total number of training epochs to perform.")
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flags.DEFINE_float(
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"warmup_proportion", 0.1,
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"Proportion of training to perform linear learning rate warmup for. "
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"E.g., 0.1 = 10% of training.")
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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("iterations_per_loop", 1000,
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"How many steps to make in each estimator call.")
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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("use_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU.")
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flags.DEFINE_bool("use_xla", False, "Whether to enable XLA JIT compilation.")
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flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")
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flags.DEFINE_bool(
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"verbose_logging", False,
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"If true, all of the warnings related to data processing will be printed. "
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"A number of warnings are expected for a normal SQuAD evaluation.")
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def file_based_input_fn_builder(input_file, batch_size, seq_length, is_training,
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drop_remainder, hvd=None):
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"""Creates an `input_fn` closure to be passed to Estimator."""
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name_to_features = {
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"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
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"input_mask": tf.FixedLenFeature([seq_length], tf.int64),
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"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
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"label_ids": tf.FixedLenFeature([], tf.int64),
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}
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def _decode_record(record, name_to_features):
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"""Decodes a record to a TensorFlow example."""
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example = tf.parse_single_example(record, name_to_features)
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# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
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# So cast all int64 to int32.
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for name in list(example.keys()):
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t = example[name]
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if t.dtype == tf.int64:
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t = tf.to_int32(t)
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example[name] = t
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return example
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def input_fn():
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"""The actual input function."""
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# For training, we want a lot of parallel reading and shuffling.
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# For eval, we want no shuffling and parallel reading doesn't matter.
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d = tf.data.TFRecordDataset(input_file)
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if is_training:
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if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
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d = d.repeat()
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d = d.shuffle(buffer_size=100)
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d = d.apply(
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tf.contrib.data.map_and_batch(
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lambda record: _decode_record(record, name_to_features),
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batch_size=batch_size,
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drop_remainder=drop_remainder))
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return d
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return input_fn
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def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
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labels, num_labels, use_one_hot_embeddings):
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"""Creates a classification model."""
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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.use_fp16 else tf.float32)
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# In the demo, we are doing a simple classification task on the entire
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# segment.
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#
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# If you want to use the token-level output, use model.get_sequence_output()
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# instead.
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output_layer = model.get_pooled_output()
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hidden_size = output_layer.shape[-1].value
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output_weights = tf.get_variable(
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"output_weights", [num_labels, hidden_size],
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initializer=tf.truncated_normal_initializer(stddev=0.02))
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output_bias = tf.get_variable(
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"output_bias", [num_labels], initializer=tf.zeros_initializer())
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with tf.variable_scope("loss"):
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if is_training:
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# I.e., 0.1 dropout
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output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
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logits = tf.matmul(output_layer, output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias, name='cls_logits')
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probabilities = tf.nn.softmax(logits, axis=-1, name='cls_probabilities')
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
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per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1, name='cls_per_example_loss')
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loss = tf.reduce_mean(per_example_loss, name='cls_loss')
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return (loss, per_example_loss, logits, probabilities)
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def get_frozen_tftrt_model(bert_config, shape, num_labels, use_one_hot_embeddings, init_checkpoint):
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tf_config = tf.ConfigProto()
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output_node_names = ['loss/cls_loss', 'loss/cls_per_example_loss', 'loss/cls_logits', 'loss/cls_probabilities']
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with tf.Session(config=tf_config) as tf_sess:
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input_ids = tf.placeholder(tf.int32, shape, 'input_ids')
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input_mask = tf.placeholder(tf.int32, shape, 'input_mask')
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segment_ids = tf.placeholder(tf.int32, shape, 'segment_ids')
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label_ids = tf.placeholder(tf.int32, (None), 'label_ids')
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create_model(bert_config, False, input_ids, input_mask, segment_ids, label_ids,
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num_labels, use_one_hot_embeddings)
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tvars = tf.trainable_variables()
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(assignment_map, initialized_variable_names) = 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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tf_sess.run(tf.global_variables_initializer())
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print("LOADED!")
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tf.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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else:
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init_string = ", *NOTTTTTTTTTTTTTTTTTTTTT"
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tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string)
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frozen_graph = tf.graph_util.convert_variables_to_constants(tf_sess,
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tf_sess.graph.as_graph_def(), output_node_names)
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num_nodes = len(frozen_graph.node)
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print('Converting graph using TensorFlow-TensorRT...')
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from tensorflow.python.compiler.tensorrt import trt_convert as trt
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converter = trt.TrtGraphConverter(
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input_graph_def=frozen_graph,
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nodes_blacklist=output_node_names,
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max_workspace_size_bytes=(4096 << 20) - 1000,
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precision_mode = "FP16" if FLAGS.use_fp16 else "FP32",
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minimum_segment_size=4,
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is_dynamic_op=True,
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maximum_cached_engines=1000
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)
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frozen_graph = converter.convert()
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print('Total node count before and after TF-TRT conversion:',
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num_nodes, '->', len(frozen_graph.node))
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print('TRT node count:',
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len([1 for n in frozen_graph.node if str(n.op) == 'TRTEngineOp']))
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with tf.gfile.GFile("frozen_modelTRT.pb", "wb") as f:
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f.write(frozen_graph.SerializeToString())
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return frozen_graph
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def model_fn_builder(task_name, bert_config, num_labels, 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 Estimator."""
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def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
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"""The `model_fn` for Estimator."""
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def metric_fn(per_example_loss, label_ids, logits):
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predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
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if task_name == "cola":
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FN, FN_op = tf.metrics.false_negatives(labels=label_ids, predictions=predictions)
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FP, FP_op = tf.metrics.false_positives(labels=label_ids, predictions=predictions)
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TP, TP_op = tf.metrics.true_positives(labels=label_ids, predictions=predictions)
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TN, TN_op = tf.metrics.true_negatives(labels=label_ids, predictions=predictions)
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MCC = (TP * TN - FP * FN) / ((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN)) ** 0.5
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MCC_op = tf.group(FN_op, TN_op, TP_op, FP_op, tf.identity(MCC, name="MCC"))
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return {"MCC": (MCC, MCC_op)}
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else:
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accuracy = tf.metrics.accuracy(
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labels=label_ids, predictions=predictions)
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loss = tf.metrics.mean(values=per_example_loss)
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return {
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"eval_accuracy": accuracy,
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"eval_loss": loss,
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}
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tf.logging.info("*** Features ***")
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for name in sorted(features.keys()):
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tf.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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label_ids = features["label_ids"]
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is_training = (mode == tf.estimator.ModeKeys.TRAIN)
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if not is_training and FLAGS.use_trt:
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trt_graph = get_frozen_tftrt_model(bert_config, input_ids.shape, num_labels, use_one_hot_embeddings, init_checkpoint)
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(total_loss, per_example_loss, logits, probabilities) = tf.import_graph_def(trt_graph,
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input_map={'input_ids':input_ids, 'input_mask':input_mask, 'segment_ids':segment_ids, 'label_ids':label_ids},
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return_elements=['loss/cls_loss:0', 'loss/cls_per_example_loss:0', 'loss/cls_logits:0', 'loss/cls_probabilities:0'],
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name='')
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if mode == tf.estimator.ModeKeys.PREDICT:
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predictions = {"probabilities": probabilities}
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output_spec = tf.estimator.EstimatorSpec(
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mode=mode, predictions=predictions)
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elif mode == tf.estimator.ModeKeys.EVAL:
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eval_metric_ops = metric_fn(per_example_loss, label_ids, logits)
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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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return output_spec
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(total_loss, per_example_loss, logits, probabilities) = create_model(
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bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
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num_labels, use_one_hot_embeddings)
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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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(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.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.logging.info(" name = %s, shape = %s%s", 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, False, FLAGS.use_fp16, FLAGS.num_accumulation_steps)
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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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eval_metric_ops = metric_fn(per_example_loss, label_ids, logits)
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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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output_spec = tf.estimator.EstimatorSpec(
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mode=mode, predictions=probabilities)
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return output_spec
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return model_fn
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# This function is not used by this file but is still used by the Colab and
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# people who depend on it.
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def input_fn_builder(features, batch_size, seq_length, is_training, drop_remainder, hvd=None):
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"""Creates an `input_fn` closure to be passed to Estimator."""
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all_input_ids = []
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all_input_mask = []
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all_segment_ids = []
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all_label_ids = []
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for feature in features:
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all_input_ids.append(feature.input_ids)
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all_input_mask.append(feature.input_mask)
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all_segment_ids.append(feature.segment_ids)
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all_label_ids.append(feature.label_id)
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def input_fn():
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"""The actual input function."""
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num_examples = len(features)
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# This is for demo purposes and does NOT scale to large data sets. We do
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# not use Dataset.from_generator() because that uses tf.py_func which is
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# not TPU compatible. The right way to load data is with TFRecordReader.
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d = tf.data.Dataset.from_tensor_slices({
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"input_ids":
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tf.constant(
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all_input_ids, shape=[num_examples, seq_length],
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dtype=tf.int32),
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"input_mask":
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tf.constant(
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all_input_mask,
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shape=[num_examples, seq_length],
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dtype=tf.int32),
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"segment_ids":
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tf.constant(
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all_segment_ids,
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shape=[num_examples, seq_length],
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dtype=tf.int32),
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"label_ids":
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tf.constant(all_label_ids, shape=[num_examples], dtype=tf.int32),
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})
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if is_training:
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if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
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d = d.repeat()
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d = d.shuffle(buffer_size=100)
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d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
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return d
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return input_fn
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def main(_):
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tf.logging.set_verbosity(tf.logging.INFO)
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if FLAGS.horovod:
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hvd.init()
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if FLAGS.use_fp16:
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os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
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processors = {
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"cola": ColaProcessor,
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"mnli": MnliProcessor,
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"mrpc": MrpcProcessor,
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"xnli": XnliProcessor,
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}
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if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
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raise ValueError(
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"At least one of `do_train`, `do_eval` or `do_predict' must be True.")
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bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
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if FLAGS.max_seq_length > bert_config.max_position_embeddings:
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raise ValueError(
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"Cannot use sequence length %d because the BERT model "
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"was only trained up to sequence length %d" %
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(FLAGS.max_seq_length, bert_config.max_position_embeddings))
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tf.gfile.MakeDirs(FLAGS.output_dir)
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task_name = FLAGS.task_name.lower()
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if task_name not in processors:
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raise ValueError("Task not found: %s" % (task_name))
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processor = processors[task_name]()
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label_list = processor.get_labels()
|
|
|
|
tokenizer = tokenization.FullTokenizer(
|
|
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
|
|
|
|
master_process = True
|
|
training_hooks = []
|
|
global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps
|
|
hvd_rank = 0
|
|
|
|
config = tf.ConfigProto()
|
|
if FLAGS.horovod:
|
|
|
|
tf.logging.info("Multi-GPU training with TF Horovod")
|
|
tf.logging.info("hvd.size() = %d hvd.rank() = %d", hvd.size(), hvd.rank())
|
|
global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps * 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.OptimizerOptions.ON_1
|
|
|
|
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.logging.info("***** Configuaration *****")
|
|
for key in FLAGS.__flags.keys():
|
|
tf.logging.info(' {}: {}'.format(key, getattr(FLAGS, key)))
|
|
tf.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(
|
|
task_name=task_name,
|
|
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)
|
|
|
|
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.logging.info("***** Running training *****")
|
|
tf.logging.info(" Num examples = %d", len(train_examples))
|
|
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
|
|
tf.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.logging.info("-----------------------------")
|
|
tf.logging.info("Total Training Time = %0.2f for Sentences = %d", train_time_elapsed,
|
|
num_train_steps * global_batch_size)
|
|
tf.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.logging.info("Throughput Average (sentences/sec) with overhead = %0.2f", avg_sentences_per_second)
|
|
tf.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
|
|
tf.logging.info("-----------------------------")
|
|
|
|
if FLAGS.do_eval and master_process:
|
|
eval_examples = processor.get_dev_examples(FLAGS.data_dir)
|
|
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.logging.info("***** Running evaluation *****")
|
|
tf.logging.info(" Num examples = %d", len(eval_examples))
|
|
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
|
|
|
|
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)
|
|
|
|
eval_hooks = [LogEvalRunHook(FLAGS.eval_batch_size)]
|
|
eval_start_time = time.time()
|
|
result = estimator.evaluate(input_fn=eval_input_fn, hooks=eval_hooks)
|
|
|
|
eval_time_elapsed = time.time() - eval_start_time
|
|
eval_time_wo_overhead = eval_hooks[-1].total_time
|
|
|
|
time_list = eval_hooks[-1].time_list
|
|
time_list.sort()
|
|
num_sentences = (eval_hooks[-1].count - eval_hooks[-1].skipped) * FLAGS.eval_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.logging.info("-----------------------------")
|
|
tf.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed,
|
|
eval_hooks[-1].count * FLAGS.eval_batch_size)
|
|
tf.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.logging.info("Summary Inference Statistics on EVAL set")
|
|
tf.logging.info("Batch size = %d", FLAGS.eval_batch_size)
|
|
tf.logging.info("Sequence Length = %d", FLAGS.max_seq_length)
|
|
tf.logging.info("Precision = %s", "fp16" if FLAGS.use_fp16 else "fp32")
|
|
tf.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000)
|
|
tf.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000)
|
|
tf.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000)
|
|
tf.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000)
|
|
tf.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000)
|
|
tf.logging.info("Latency Average (ms) = %0.2f", avg * 1000)
|
|
tf.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
|
|
tf.logging.info("-----------------------------")
|
|
|
|
|
|
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
|
|
with tf.gfile.GFile(output_eval_file, "w") as writer:
|
|
tf.logging.info("***** Eval results *****")
|
|
for key in sorted(result.keys()):
|
|
tf.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)
|
|
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.logging.info("***** Running prediction*****")
|
|
tf.logging.info(" Num examples = %d", len(predict_examples))
|
|
tf.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)
|
|
|
|
predict_hooks = [LogEvalRunHook(FLAGS.predict_batch_size)]
|
|
predict_start_time = time.time()
|
|
|
|
output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
|
|
with tf.gfile.GFile(output_predict_file, "w") as writer:
|
|
tf.logging.info("***** Predict results *****")
|
|
for prediction in estimator.predict(input_fn=predict_input_fn, hooks=predict_hooks,
|
|
yield_single_examples=False):
|
|
output_line = "\t".join(
|
|
str(class_probability) for class_probability in prediction) + "\n"
|
|
writer.write(output_line)
|
|
|
|
|
|
predict_time_elapsed = time.time() - predict_start_time
|
|
predict_time_wo_overhead = predict_hooks[-1].total_time
|
|
|
|
time_list = predict_hooks[-1].time_list
|
|
time_list.sort()
|
|
num_sentences = (predict_hooks[-1].count - predict_hooks[-1].skipped) * 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 / predict_time_wo_overhead
|
|
|
|
tf.logging.info("-----------------------------")
|
|
tf.logging.info("Total Inference Time = %0.2f for Sentences = %d", predict_time_elapsed,
|
|
predict_hooks[-1].count * FLAGS.predict_batch_size)
|
|
tf.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", predict_time_wo_overhead,
|
|
(predict_hooks[-1].count - predict_hooks[-1].skipped) * FLAGS.predict_batch_size)
|
|
|
|
tf.logging.info("Summary Inference Statistics on TEST SET")
|
|
tf.logging.info("Batch size = %d", FLAGS.predict_batch_size)
|
|
tf.logging.info("Sequence Length = %d", FLAGS.max_seq_length)
|
|
tf.logging.info("Precision = %s", "fp16" if FLAGS.use_fp16 else "fp32")
|
|
tf.logging.info("Latency Confidence Level 50 (ms) = %0.2f", cf_50 * 1000)
|
|
tf.logging.info("Latency Confidence Level 90 (ms) = %0.2f", cf_90 * 1000)
|
|
tf.logging.info("Latency Confidence Level 95 (ms) = %0.2f", cf_95 * 1000)
|
|
tf.logging.info("Latency Confidence Level 99 (ms) = %0.2f", cf_99 * 1000)
|
|
tf.logging.info("Latency Confidence Level 100 (ms) = %0.2f", cf_100 * 1000)
|
|
tf.logging.info("Latency Average (ms) = %0.2f", avg * 1000)
|
|
tf.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
|
|
tf.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.app.run() |