1158 lines
43 KiB
Python
1158 lines
43 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 BERT on SQuAD 1.1 and SQuAD 2.0."""
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from __future__ import absolute_import, division, print_function
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import collections
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import json
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import math
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import os
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import random
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import shutil
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import time
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import horovod.tensorflow as hvd
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import numpy as np
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import six
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import tensorflow as tf
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from tensorflow.python.client import device_lib
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import modeling
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import optimization
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import tokenization
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from utils.create_squad_data import *
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from utils.utils import LogEvalRunHook, LogTrainRunHook
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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("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("train_file", None,
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"SQuAD json for training. E.g., train-v1.1.json")
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flags.DEFINE_string(
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"predict_file", None,
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"SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
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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", 384,
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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_integer(
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"doc_stride", 128,
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"When splitting up a long document into chunks, how much stride to "
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"take between chunks.")
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flags.DEFINE_integer(
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"max_query_length", 64,
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"The maximum number of tokens for the question. Questions longer than "
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"this will be truncated to this length.")
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flags.DEFINE_bool("do_train", False, "Whether to run training.")
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flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")
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flags.DEFINE_integer("train_batch_size", 8, "Total batch size for training.")
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flags.DEFINE_integer("predict_batch_size", 8,
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"Total batch size for predictions.")
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flags.DEFINE_float("learning_rate", 5e-6, "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_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")
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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_integer(
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"n_best_size", 20,
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"The total number of n-best predictions to generate in the "
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"nbest_predictions.json output file.")
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flags.DEFINE_integer(
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"max_answer_length", 30,
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"The maximum length of an answer that can be generated. This is needed "
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"because the start and end predictions are not conditioned on one another.")
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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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flags.DEFINE_bool(
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"version_2_with_negative", False,
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"If true, the SQuAD examples contain some that do not have an answer.")
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flags.DEFINE_float(
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"null_score_diff_threshold", 0.0,
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"If null_score - best_non_null is greater than the threshold predict null.")
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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_integer("num_eval_iterations", None,
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"How many eval iterations to run - performs inference on subset")
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# TRTIS Specific flags
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flags.DEFINE_bool("export_trtis", False, "Whether to export saved model or run inference with TRTIS")
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flags.DEFINE_string("trtis_model_name", "bert", "exports to appropriate directory for TRTIS")
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flags.DEFINE_integer("trtis_model_version", 1, "exports to appropriate directory for TRTIS")
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flags.DEFINE_string("trtis_server_url", "localhost:8001", "exports to appropriate directory for TRTIS")
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flags.DEFINE_bool("trtis_model_overwrite", False, "If True, will overwrite an existing directory with the specified 'model_name' and 'version_name'")
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flags.DEFINE_integer("trtis_max_batch_size", 8, "Specifies the 'max_batch_size' in the TRTIS model config. See the TRTIS documentation for more info.")
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flags.DEFINE_float("trtis_dyn_batching_delay", 0, "Determines the dynamic_batching queue delay in milliseconds(ms) for the TRTIS model config. Use '0' or '-1' to specify static batching. See the TRTIS documentation for more info.")
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flags.DEFINE_integer("trtis_engine_count", 1, "Specifies the 'instance_group' count value in the TRTIS model config. See the TRTIS documentation for more info.")
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def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
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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.float32)
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final_hidden = model.get_sequence_output()
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final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
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batch_size = final_hidden_shape[0]
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seq_length = final_hidden_shape[1]
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hidden_size = final_hidden_shape[2]
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output_weights = tf.get_variable(
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"cls/squad/output_weights", [2, 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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"cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
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final_hidden_matrix = tf.reshape(final_hidden,
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[batch_size * seq_length, hidden_size])
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logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
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logits = tf.nn.bias_add(logits, output_bias)
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logits = tf.reshape(logits, [batch_size, seq_length, 2])
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logits = tf.transpose(logits, [2, 0, 1])
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unstacked_logits = tf.unstack(logits, axis=0, name='unstack')
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(start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
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return (start_logits, end_logits)
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def get_frozen_tftrt_model(bert_config, shape, use_one_hot_embeddings, init_checkpoint):
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tf_config = tf.ConfigProto()
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output_node_names = ['unstack']
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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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(start_logits, end_logits) = create_model(bert_config=bert_config,
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is_training=False,
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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use_one_hot_embeddings=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(bert_config, init_checkpoint, learning_rate,
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num_train_steps, num_warmup_steps,
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hvd=None, use_fp16=False, use_one_hot_embeddings=False):
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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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if FLAGS.verbose_logging:
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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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unique_ids = features["unique_ids"]
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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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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, use_one_hot_embeddings, init_checkpoint)
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(start_logits, end_logits) = tf.import_graph_def(trt_graph,
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input_map={'input_ids':input_ids, 'input_mask':input_mask, 'segment_ids':segment_ids},
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return_elements=['unstack:0', 'unstack:1'],
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name='')
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predictions = {
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"unique_ids": unique_ids,
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"start_logits": start_logits,
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"end_logits": end_logits,
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}
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output_spec = tf.estimator.TPUEstimatorSpec(
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mode=mode, predictions=predictions)
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return output_spec
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(start_logits, end_logits) = create_model(
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bert_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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segment_ids=segment_ids,
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use_one_hot_embeddings=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(" %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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seq_length = modeling.get_shape_list(input_ids)[1]
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def compute_loss(logits, positions):
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one_hot_positions = tf.one_hot(
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positions, depth=seq_length, dtype=tf.float32)
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log_probs = tf.nn.log_softmax(logits, axis=-1)
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loss = -tf.reduce_mean(
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tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
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return loss
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start_positions = features["start_positions"]
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end_positions = features["end_positions"]
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start_loss = compute_loss(start_logits, start_positions)
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end_loss = compute_loss(end_logits, end_positions)
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total_loss = (start_loss + end_loss) / 2.0
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train_op = optimization.create_optimizer(
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total_loss, learning_rate, num_train_steps, num_warmup_steps, hvd, False, 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.PREDICT:
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predictions = {
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"unique_ids": unique_ids,
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"start_logits": start_logits,
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"end_logits": end_logits,
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}
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output_spec = tf.estimator.EstimatorSpec(
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mode=mode, predictions=predictions)
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else:
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raise ValueError(
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"Only TRAIN and PREDICT modes are supported: %s" % (mode))
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return output_spec
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return model_fn
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def input_fn_builder(input_file, 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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name_to_features = {
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"unique_ids": tf.FixedLenFeature([], tf.int64),
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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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}
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if is_training:
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name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
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name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
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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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if is_training:
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d = tf.data.TFRecordDataset(input_file, num_parallel_reads=4)
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if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
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d = d.apply(tf.data.experimental.ignore_errors())
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d = d.shuffle(buffer_size=100)
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d = d.repeat()
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else:
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d = tf.data.TFRecordDataset(input_file)
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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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RawResult = collections.namedtuple("RawResult",
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["unique_id", "start_logits", "end_logits"])
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def write_predictions(all_examples, all_features, all_results, n_best_size,
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max_answer_length, do_lower_case, output_prediction_file,
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output_nbest_file, output_null_log_odds_file):
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"""Write final predictions to the json file and log-odds of null if needed."""
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tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
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tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
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example_index_to_features = collections.defaultdict(list)
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for feature in all_features:
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example_index_to_features[feature.example_index].append(feature)
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unique_id_to_result = {}
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for result in all_results:
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unique_id_to_result[result.unique_id] = result
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_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
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"PrelimPrediction",
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["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
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all_predictions = collections.OrderedDict()
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all_nbest_json = collections.OrderedDict()
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scores_diff_json = collections.OrderedDict()
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for (example_index, example) in enumerate(all_examples):
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features = example_index_to_features[example_index]
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prelim_predictions = []
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# keep track of the minimum score of null start+end of position 0
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score_null = 1000000 # large and positive
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min_null_feature_index = 0 # the paragraph slice with min mull score
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null_start_logit = 0 # the start logit at the slice with min null score
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null_end_logit = 0 # the end logit at the slice with min null score
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for (feature_index, feature) in enumerate(features):
|
|
result = unique_id_to_result[feature.unique_id]
|
|
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
|
|
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
|
|
# if we could have irrelevant answers, get the min score of irrelevant
|
|
if FLAGS.version_2_with_negative:
|
|
feature_null_score = result.start_logits[0] + result.end_logits[0]
|
|
if feature_null_score < score_null:
|
|
score_null = feature_null_score
|
|
min_null_feature_index = feature_index
|
|
null_start_logit = result.start_logits[0]
|
|
null_end_logit = result.end_logits[0]
|
|
for start_index in start_indexes:
|
|
for end_index in end_indexes:
|
|
# We could hypothetically create invalid predictions, e.g., predict
|
|
# that the start of the span is in the question. We throw out all
|
|
# invalid predictions.
|
|
if start_index >= len(feature.tokens):
|
|
continue
|
|
if end_index >= len(feature.tokens):
|
|
continue
|
|
if start_index not in feature.token_to_orig_map:
|
|
continue
|
|
if end_index not in feature.token_to_orig_map:
|
|
continue
|
|
if not feature.token_is_max_context.get(start_index, False):
|
|
continue
|
|
if end_index < start_index:
|
|
continue
|
|
length = end_index - start_index + 1
|
|
if length > max_answer_length:
|
|
continue
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=feature_index,
|
|
start_index=start_index,
|
|
end_index=end_index,
|
|
start_logit=result.start_logits[start_index],
|
|
end_logit=result.end_logits[end_index]))
|
|
|
|
if FLAGS.version_2_with_negative:
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=min_null_feature_index,
|
|
start_index=0,
|
|
end_index=0,
|
|
start_logit=null_start_logit,
|
|
end_logit=null_end_logit))
|
|
prelim_predictions = sorted(
|
|
prelim_predictions,
|
|
key=lambda x: (x.start_logit + x.end_logit),
|
|
reverse=True)
|
|
|
|
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"NbestPrediction", ["text", "start_logit", "end_logit"])
|
|
|
|
seen_predictions = {}
|
|
nbest = []
|
|
for pred in prelim_predictions:
|
|
if len(nbest) >= n_best_size:
|
|
break
|
|
feature = features[pred.feature_index]
|
|
if pred.start_index > 0: # this is a non-null prediction
|
|
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
|
|
orig_doc_start = feature.token_to_orig_map[pred.start_index]
|
|
orig_doc_end = feature.token_to_orig_map[pred.end_index]
|
|
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
|
|
tok_text = " ".join(tok_tokens)
|
|
|
|
# De-tokenize WordPieces that have been split off.
|
|
tok_text = tok_text.replace(" ##", "")
|
|
tok_text = tok_text.replace("##", "")
|
|
|
|
# Clean whitespace
|
|
tok_text = tok_text.strip()
|
|
tok_text = " ".join(tok_text.split())
|
|
orig_text = " ".join(orig_tokens)
|
|
|
|
final_text = get_final_text(tok_text, orig_text, do_lower_case)
|
|
if final_text in seen_predictions:
|
|
continue
|
|
|
|
seen_predictions[final_text] = True
|
|
else:
|
|
final_text = ""
|
|
seen_predictions[final_text] = True
|
|
|
|
nbest.append(
|
|
_NbestPrediction(
|
|
text=final_text,
|
|
start_logit=pred.start_logit,
|
|
end_logit=pred.end_logit))
|
|
|
|
# if we didn't inlude the empty option in the n-best, inlcude it
|
|
if FLAGS.version_2_with_negative:
|
|
if "" not in seen_predictions:
|
|
nbest.append(
|
|
_NbestPrediction(
|
|
text="", start_logit=null_start_logit,
|
|
end_logit=null_end_logit))
|
|
# In very rare edge cases we could have no valid predictions. So we
|
|
# just create a nonce prediction in this case to avoid failure.
|
|
if not nbest:
|
|
nbest.append(
|
|
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
|
|
|
|
assert len(nbest) >= 1
|
|
|
|
total_scores = []
|
|
best_non_null_entry = None
|
|
for entry in nbest:
|
|
total_scores.append(entry.start_logit + entry.end_logit)
|
|
if not best_non_null_entry:
|
|
if entry.text:
|
|
best_non_null_entry = entry
|
|
|
|
probs = _compute_softmax(total_scores)
|
|
|
|
nbest_json = []
|
|
for (i, entry) in enumerate(nbest):
|
|
output = collections.OrderedDict()
|
|
output["text"] = entry.text
|
|
output["probability"] = probs[i]
|
|
output["start_logit"] = entry.start_logit
|
|
output["end_logit"] = entry.end_logit
|
|
nbest_json.append(output)
|
|
|
|
assert len(nbest_json) >= 1
|
|
|
|
if not FLAGS.version_2_with_negative:
|
|
all_predictions[example.qas_id] = nbest_json[0]["text"]
|
|
else:
|
|
# predict "" iff the null score - the score of best non-null > threshold
|
|
score_diff = score_null - best_non_null_entry.start_logit - (
|
|
best_non_null_entry.end_logit)
|
|
scores_diff_json[example.qas_id] = score_diff
|
|
if score_diff > FLAGS.null_score_diff_threshold:
|
|
all_predictions[example.qas_id] = ""
|
|
else:
|
|
all_predictions[example.qas_id] = best_non_null_entry.text
|
|
|
|
all_nbest_json[example.qas_id] = nbest_json
|
|
|
|
with tf.gfile.GFile(output_prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
|
|
with tf.gfile.GFile(output_nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
|
|
if FLAGS.version_2_with_negative:
|
|
with tf.gfile.GFile(output_null_log_odds_file, "w") as writer:
|
|
writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
|
|
|
|
|
|
def get_final_text(pred_text, orig_text, do_lower_case):
|
|
"""Project the tokenized prediction back to the original text."""
|
|
|
|
# When we created the data, we kept track of the alignment between original
|
|
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
|
|
# now `orig_text` contains the span of our original text corresponding to the
|
|
# span that we predicted.
|
|
#
|
|
# However, `orig_text` may contain extra characters that we don't want in
|
|
# our prediction.
|
|
#
|
|
# For example, let's say:
|
|
# pred_text = steve smith
|
|
# orig_text = Steve Smith's
|
|
#
|
|
# We don't want to return `orig_text` because it contains the extra "'s".
|
|
#
|
|
# We don't want to return `pred_text` because it's already been normalized
|
|
# (the SQuAD eval script also does punctuation stripping/lower casing but
|
|
# our tokenizer does additional normalization like stripping accent
|
|
# characters).
|
|
#
|
|
# What we really want to return is "Steve Smith".
|
|
#
|
|
# Therefore, we have to apply a semi-complicated alignment heruistic between
|
|
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
|
|
# can fail in certain cases in which case we just return `orig_text`.
|
|
|
|
def _strip_spaces(text):
|
|
ns_chars = []
|
|
ns_to_s_map = collections.OrderedDict()
|
|
for (i, c) in enumerate(text):
|
|
if c == " ":
|
|
continue
|
|
ns_to_s_map[len(ns_chars)] = i
|
|
ns_chars.append(c)
|
|
ns_text = "".join(ns_chars)
|
|
return (ns_text, ns_to_s_map)
|
|
|
|
# We first tokenize `orig_text`, strip whitespace from the result
|
|
# and `pred_text`, and check if they are the same length. If they are
|
|
# NOT the same length, the heuristic has failed. If they are the same
|
|
# length, we assume the characters are one-to-one aligned.
|
|
tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
|
|
|
|
tok_text = " ".join(tokenizer.tokenize(orig_text))
|
|
|
|
start_position = tok_text.find(pred_text)
|
|
if start_position == -1:
|
|
if FLAGS.verbose_logging:
|
|
tf.logging.info(
|
|
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
|
|
return orig_text
|
|
end_position = start_position + len(pred_text) - 1
|
|
|
|
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
|
|
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
|
|
|
|
if len(orig_ns_text) != len(tok_ns_text):
|
|
if FLAGS.verbose_logging:
|
|
tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
|
|
orig_ns_text, tok_ns_text)
|
|
return orig_text
|
|
|
|
# We then project the characters in `pred_text` back to `orig_text` using
|
|
# the character-to-character alignment.
|
|
tok_s_to_ns_map = {}
|
|
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
|
|
tok_s_to_ns_map[tok_index] = i
|
|
|
|
orig_start_position = None
|
|
if start_position in tok_s_to_ns_map:
|
|
ns_start_position = tok_s_to_ns_map[start_position]
|
|
if ns_start_position in orig_ns_to_s_map:
|
|
orig_start_position = orig_ns_to_s_map[ns_start_position]
|
|
|
|
if orig_start_position is None:
|
|
if FLAGS.verbose_logging:
|
|
tf.logging.info("Couldn't map start position")
|
|
return orig_text
|
|
|
|
orig_end_position = None
|
|
if end_position in tok_s_to_ns_map:
|
|
ns_end_position = tok_s_to_ns_map[end_position]
|
|
if ns_end_position in orig_ns_to_s_map:
|
|
orig_end_position = orig_ns_to_s_map[ns_end_position]
|
|
|
|
if orig_end_position is None:
|
|
if FLAGS.verbose_logging:
|
|
tf.logging.info("Couldn't map end position")
|
|
return orig_text
|
|
|
|
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
|
|
return output_text
|
|
|
|
|
|
def _get_best_indexes(logits, n_best_size):
|
|
"""Get the n-best logits from a list."""
|
|
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
|
|
|
|
best_indexes = []
|
|
for i in range(len(index_and_score)):
|
|
if i >= n_best_size:
|
|
break
|
|
best_indexes.append(index_and_score[i][0])
|
|
return best_indexes
|
|
|
|
|
|
def _compute_softmax(scores):
|
|
"""Compute softmax probability over raw logits."""
|
|
if not scores:
|
|
return []
|
|
|
|
max_score = None
|
|
for score in scores:
|
|
if max_score is None or score > max_score:
|
|
max_score = score
|
|
|
|
exp_scores = []
|
|
total_sum = 0.0
|
|
for score in scores:
|
|
x = math.exp(score - max_score)
|
|
exp_scores.append(x)
|
|
total_sum += x
|
|
|
|
probs = []
|
|
for score in exp_scores:
|
|
probs.append(score / total_sum)
|
|
return probs
|
|
|
|
|
|
|
|
def validate_flags_or_throw(bert_config):
|
|
"""Validate the input FLAGS or throw an exception."""
|
|
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
|
|
FLAGS.init_checkpoint)
|
|
|
|
if not FLAGS.do_train and not FLAGS.do_predict and not FLAGS.export_trtis:
|
|
raise ValueError("At least one of `do_train` or `do_predict` or `export_SavedModel` must be True.")
|
|
|
|
if FLAGS.do_train:
|
|
if not FLAGS.train_file:
|
|
raise ValueError(
|
|
"If `do_train` is True, then `train_file` must be specified.")
|
|
if FLAGS.do_predict:
|
|
if not FLAGS.predict_file:
|
|
raise ValueError(
|
|
"If `do_predict` is True, then `predict_file` must be specified.")
|
|
|
|
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))
|
|
|
|
if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:
|
|
raise ValueError(
|
|
"The max_seq_length (%d) must be greater than max_query_length "
|
|
"(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length))
|
|
|
|
|
|
def export_model(estimator, export_dir, init_checkpoint):
|
|
"""Exports a checkpoint in SavedModel format in a directory structure compatible with TRTIS."""
|
|
|
|
|
|
def serving_input_fn():
|
|
label_ids = tf.placeholder(tf.int32, [None,], name='unique_ids')
|
|
input_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_ids')
|
|
input_mask = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_mask')
|
|
segment_ids = tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='segment_ids')
|
|
input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({
|
|
'unique_ids': label_ids,
|
|
'input_ids': input_ids,
|
|
'input_mask': input_mask,
|
|
'segment_ids': segment_ids,
|
|
})()
|
|
return input_fn
|
|
|
|
saved_dir = estimator.export_savedmodel(
|
|
export_dir,
|
|
serving_input_fn,
|
|
assets_extra=None,
|
|
as_text=False,
|
|
checkpoint_path=init_checkpoint,
|
|
strip_default_attrs=False)
|
|
|
|
model_name = FLAGS.trtis_model_name
|
|
|
|
model_folder = export_dir + "/trtis_models/" + model_name
|
|
version_folder = model_folder + "/" + str(FLAGS.trtis_model_version)
|
|
final_model_folder = version_folder + "/model.savedmodel"
|
|
|
|
if not os.path.exists(version_folder):
|
|
os.makedirs(version_folder)
|
|
|
|
if (not os.path.exists(final_model_folder)):
|
|
os.rename(saved_dir, final_model_folder)
|
|
print("Model saved to dir", final_model_folder)
|
|
else:
|
|
if (FLAGS.trtis_model_overwrite):
|
|
shutil.rmtree(final_model_folder)
|
|
os.rename(saved_dir, final_model_folder)
|
|
print("WARNING: Existing model was overwritten. Model dir: {}".format(final_model_folder))
|
|
else:
|
|
print("ERROR: Could not save TRTIS model. Folder already exists. Use '--trtis_model_overwrite=True' if you would like to overwrite an existing model. Model dir: {}".format(final_model_folder))
|
|
return
|
|
|
|
# Now build the config for TRTIS. Check to make sure we can overwrite it, if it exists
|
|
config_filename = os.path.join(model_folder, "config.pbtxt")
|
|
|
|
if (os.path.exists(config_filename) and not FLAGS.trtis_model_overwrite):
|
|
print("ERROR: Could not save TRTIS model config. Config file already exists. Use '--trtis_model_overwrite=True' if you would like to overwrite an existing model config. Model config: {}".format(config_filename))
|
|
return
|
|
|
|
config_template = r"""
|
|
name: "{model_name}"
|
|
platform: "tensorflow_savedmodel"
|
|
max_batch_size: {max_batch_size}
|
|
input [
|
|
{{
|
|
name: "unique_ids"
|
|
data_type: TYPE_INT32
|
|
dims: [ 1 ]
|
|
reshape: {{ shape: [ ] }}
|
|
}},
|
|
{{
|
|
name: "segment_ids"
|
|
data_type: TYPE_INT32
|
|
dims: {seq_length}
|
|
}},
|
|
{{
|
|
name: "input_ids"
|
|
data_type: TYPE_INT32
|
|
dims: {seq_length}
|
|
}},
|
|
{{
|
|
name: "input_mask"
|
|
data_type: TYPE_INT32
|
|
dims: {seq_length}
|
|
}}
|
|
]
|
|
output [
|
|
{{
|
|
name: "end_logits"
|
|
data_type: TYPE_FP32
|
|
dims: {seq_length}
|
|
}},
|
|
{{
|
|
name: "start_logits"
|
|
data_type: TYPE_FP32
|
|
dims: {seq_length}
|
|
}}
|
|
]
|
|
{dynamic_batching}
|
|
instance_group [
|
|
{{
|
|
count: {engine_count}
|
|
kind: KIND_GPU
|
|
gpus: [{gpu_list}]
|
|
}}
|
|
]"""
|
|
|
|
batching_str = ""
|
|
max_batch_size = FLAGS.trtis_max_batch_size
|
|
|
|
if (FLAGS.trtis_dyn_batching_delay > 0):
|
|
|
|
# Use only full and half full batches
|
|
pref_batch_size = [int(max_batch_size / 2.0), max_batch_size]
|
|
|
|
batching_str = r"""
|
|
dynamic_batching {{
|
|
preferred_batch_size: [{0}]
|
|
max_queue_delay_microseconds: {1}
|
|
}}""".format(", ".join([str(x) for x in pref_batch_size]), int(FLAGS.trtis_dyn_batching_delay * 1000.0))
|
|
|
|
config_values = {
|
|
"model_name": model_name,
|
|
"max_batch_size": max_batch_size,
|
|
"seq_length": FLAGS.max_seq_length,
|
|
"dynamic_batching": batching_str,
|
|
"gpu_list": ", ".join([x.name.split(":")[-1] for x in device_lib.list_local_devices() if x.device_type == "GPU"]),
|
|
"engine_count": FLAGS.trtis_engine_count
|
|
}
|
|
|
|
with open(model_folder + "/config.pbtxt", "w") as file:
|
|
|
|
final_config_str = config_template.format_map(config_values)
|
|
file.write(final_config_str)
|
|
|
|
def main(_):
|
|
tf.logging.set_verbosity(tf.logging.INFO)
|
|
|
|
if FLAGS.horovod:
|
|
hvd.init()
|
|
if FLAGS.use_fp16:
|
|
os.environ["TF_ENABLE_AUTO_MIXED_PRECISION_GRAPH_REWRITE"] = "1"
|
|
|
|
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
|
|
|
|
validate_flags_or_throw(bert_config)
|
|
|
|
tf.gfile.MakeDirs(FLAGS.output_dir)
|
|
|
|
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()
|
|
learning_rate = FLAGS.learning_rate
|
|
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 * hvd.size() * FLAGS.num_accumulation_steps
|
|
learning_rate = learning_rate * 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, FLAGS.save_checkpoints_steps))
|
|
|
|
# Prepare Training Data
|
|
if FLAGS.do_train:
|
|
train_examples = read_squad_examples(
|
|
input_file=FLAGS.train_file, is_training=True,
|
|
version_2_with_negative=FLAGS.version_2_with_negative)
|
|
num_train_steps = int(
|
|
len(train_examples) / global_batch_size * FLAGS.num_train_epochs)
|
|
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
|
|
|
|
# Pre-shuffle the input to avoid having to make a very large shuffle
|
|
# buffer in in the `input_fn`.
|
|
rng = random.Random(12345)
|
|
rng.shuffle(train_examples)
|
|
|
|
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,
|
|
init_checkpoint=FLAGS.init_checkpoint,
|
|
learning_rate=learning_rate,
|
|
num_train_steps=num_train_steps,
|
|
num_warmup_steps=num_warmup_steps,
|
|
hvd=None if not FLAGS.horovod else hvd,
|
|
use_fp16=FLAGS.use_fp16)
|
|
|
|
estimator = tf.estimator.Estimator(
|
|
model_fn=model_fn,
|
|
config=run_config)
|
|
|
|
if FLAGS.do_train:
|
|
|
|
# We write to a temporary file to avoid storing very large constant tensors
|
|
# in memory.
|
|
train_writer = FeatureWriter(
|
|
filename=tmp_filenames[hvd_rank],
|
|
is_training=True)
|
|
convert_examples_to_features(
|
|
examples=train_examples[start_index:end_index],
|
|
tokenizer=tokenizer,
|
|
max_seq_length=FLAGS.max_seq_length,
|
|
doc_stride=FLAGS.doc_stride,
|
|
max_query_length=FLAGS.max_query_length,
|
|
is_training=True,
|
|
output_fn=train_writer.process_feature,
|
|
verbose_logging=FLAGS.verbose_logging)
|
|
train_writer.close()
|
|
|
|
tf.logging.info("***** Running training *****")
|
|
tf.logging.info(" Num orig examples = %d", end_index - start_index)
|
|
tf.logging.info(" Num split examples = %d", train_writer.num_features)
|
|
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
|
|
tf.logging.info(" Num steps = %d", num_train_steps)
|
|
tf.logging.info(" LR = %f", learning_rate)
|
|
del train_examples
|
|
|
|
train_input_fn = 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, hooks=training_hooks, max_steps=num_train_steps)
|
|
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.export_trtis and master_process:
|
|
export_model(estimator, FLAGS.output_dir, FLAGS.init_checkpoint)
|
|
|
|
if FLAGS.do_predict and master_process:
|
|
eval_examples = read_squad_examples(
|
|
input_file=FLAGS.predict_file, is_training=False,
|
|
version_2_with_negative=FLAGS.version_2_with_negative)
|
|
|
|
# Perform evaluation on subset, useful for profiling
|
|
if FLAGS.num_eval_iterations is not None:
|
|
eval_examples = eval_examples[:FLAGS.num_eval_iterations*FLAGS.predict_batch_size]
|
|
|
|
eval_writer = FeatureWriter(
|
|
filename=os.path.join(FLAGS.output_dir, "eval.tf_record"),
|
|
is_training=False)
|
|
eval_features = []
|
|
|
|
def append_feature(feature):
|
|
eval_features.append(feature)
|
|
eval_writer.process_feature(feature)
|
|
|
|
convert_examples_to_features(
|
|
examples=eval_examples,
|
|
tokenizer=tokenizer,
|
|
max_seq_length=FLAGS.max_seq_length,
|
|
doc_stride=FLAGS.doc_stride,
|
|
max_query_length=FLAGS.max_query_length,
|
|
is_training=False,
|
|
output_fn=append_feature,
|
|
verbose_logging=FLAGS.verbose_logging)
|
|
eval_writer.close()
|
|
|
|
tf.logging.info("***** Running predictions *****")
|
|
tf.logging.info(" Num orig examples = %d", len(eval_examples))
|
|
tf.logging.info(" Num split examples = %d", len(eval_features))
|
|
tf.logging.info(" Batch size = %d", FLAGS.predict_batch_size)
|
|
|
|
predict_input_fn = input_fn_builder(
|
|
input_file=eval_writer.filename,
|
|
batch_size=FLAGS.predict_batch_size,
|
|
seq_length=FLAGS.max_seq_length,
|
|
is_training=False,
|
|
drop_remainder=False)
|
|
|
|
all_results = []
|
|
eval_hooks = [LogEvalRunHook(FLAGS.predict_batch_size)]
|
|
eval_start_time = time.time()
|
|
for result in estimator.predict(
|
|
predict_input_fn, yield_single_examples=True, hooks=eval_hooks):
|
|
if len(all_results) % 1000 == 0:
|
|
tf.logging.info("Processing example: %d" % (len(all_results)))
|
|
unique_id = int(result["unique_ids"])
|
|
start_logits = [float(x) for x in result["start_logits"].flat]
|
|
end_logits = [float(x) for x in result["end_logits"].flat]
|
|
all_results.append(
|
|
RawResult(
|
|
unique_id=unique_id,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits))
|
|
|
|
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.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.logging.info("-----------------------------")
|
|
tf.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed,
|
|
eval_hooks[-1].count * FLAGS.predict_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.predict_batch_size)
|
|
tf.logging.info("Summary Inference Statistics")
|
|
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("-----------------------------")
|
|
|
|
output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
|
|
output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
|
|
output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")
|
|
|
|
write_predictions(eval_examples, eval_features, all_results,
|
|
FLAGS.n_best_size, FLAGS.max_answer_length,
|
|
FLAGS.do_lower_case, output_prediction_file,
|
|
output_nbest_file, output_null_log_odds_file)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
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()
|