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#!/usr/bin/env python
# coding: utf-8
# auther = 'liuzhiyong'
# date = 20201204
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import datetime
import threading
import time
from flask import Flask, abort, request, jsonify
from concurrent.futures import ThreadPoolExecutor
import collections
import math
import os
import random
import modeling
import optimization
import tokenization
import six
import tensorflow as tf
import sys
from api_squad import *
from global_setting import *
from global_setting import FLAGS_bert_config_file, FLAGS_vocab_file, FLAGS_init_checkpoint_squad, questions
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = str(CUDA_VISIBLE_DEVICES)
app = Flask(__name__)
def serving_input_fn():
input_ids = tf.placeholder(tf.int32, [None, FLAGS_max_seq_length], name='input_ids')
unique_id = tf.placeholder(tf.int32,[None])
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({
'input_ids': input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids,
'unique_ids': unique_id,
})()
return input_fn
def main(FLAGS_output_dir, FLAGS_init_checkpoint_squad, FLAGS_export_dir, FLAGS_predict_file=None, FLAGS_train_file=None, FLAGS_do_predict=False,
FLAGS_do_train=False, FLAGS_train_batch_size=16, FLAGS_predict_batch_size=8, FLAGS_learning_rate=5e-5, FLAGS_num_train_epochs=3.0,
FLAGS_max_answer_length=100, FLAGS_max_query_length=64, FLAGS_version_2_with_negative=False):
tf.logging.set_verbosity(tf.logging.INFO)
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)
tpu_cluster_resolver = None
if FLAGS_use_tpu and FLAGS_tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS_tpu_name, zone=FLAGS_tpu_zone, project=FLAGS_gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS_master,
model_dir=FLAGS_output_dir,
save_checkpoints_steps=FLAGS_save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS_iterations_per_loop,
num_shards=FLAGS_num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS_do_train:
train_examples = read_squad_examples(
input_file=FLAGS_train_file, is_training=True,questions = questions,FLAGS_version_2_with_negative = FLAGS_version_2_with_negative)
num_train_steps = int(
len(train_examples) / FLAGS_train_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)
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS_init_checkpoint_squad,
learning_rate=FLAGS_learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS_use_tpu,
use_one_hot_embeddings=FLAGS_use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS_use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS_train_batch_size,
predict_batch_size=FLAGS_predict_batch_size)
if FLAGS_do_train:
# We write to a temporary file to avoid storing very large constant tensors
# in memory.
train_writer = FeatureWriter(
filename=os.path.join(FLAGS_output_dir, "train.tf_record"),
is_training=True)
convert_examples_to_features(
examples=train_examples,
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)
train_writer.close()
tf.logging.info("***** Running training *****")
tf.logging.info(" Num orig examples = %d", len(train_examples))
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)
del train_examples
train_input_fn = input_fn_builder(
input_file=train_writer.filename,
seq_length=FLAGS_max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
estimator._export_to_tpu = False
estimator.export_savedmodel(FLAGS_export_dir, serving_input_fn)
return 'success'
class AI2Flask:
def __init__(self, port=5000, workers=4):
self.app = app
self.port = port
p = ThreadPoolExecutor(max_workers=workers)
threads_mapping = {}
def check_threads():
flag = False
pop_keys = set()
if len(threads_mapping) >= workers:
for k, v in threads_mapping.items():
if v.running():
flag = True
else:
pop_keys.add(k)
for k in pop_keys:
threads_mapping.pop(k)
return flag
@app.route('/api/offline/train', methods=['POST'])
def text_analyse():
if not request.json or not 'task_id' in request.json:
abort(400)
if check_threads():
return jsonify({"Des": "Task list is full. Can not submit new task! ", "Result": "Failed to submit the training task ", "Status": "ERROR"})
else:
try:
FLAGS_train_batch_size = request.json['FLAGS_train_batch_size']
except:
FLAGS_train_batch_size = 16
try:
FLAGS_learning_rate = request.json['FLAGS_learning_rate']
except:
FLAGS_learning_rate = 5e-5
try:
FLAGS_num_train_epochs = request.json['FLAGS_num_train_epochs']
except:
FLAGS_num_train_epochs = 3.0
try:
FLAGS_max_answer_length = request.json['FLAGS_max_answer_length']
except:
FLAGS_max_answer_length = 100
try:
FLAGS_max_query_length = request.json['FLAGS_max_query_length']
except:
FLAGS_max_query_length = 64
try:
FLAGS_version_2_with_negative = request.json['FLAGS_version_2_with_negative']
except:
FLAGS_version_2_with_negative = True
try:
FLAGS_predict_file = None
FLAGS_predict_batch_size = 8
FLAGS_do_predict = False
FLAGS_do_train = True
FLAGS_output_dir = request.json['FLAGS_output_dir']
FLAGS_train_file = request.json['FLAGS_train_file']
FLAGS_export_dir = request.json['FLAGS_export_dir']
task_id = request.json['task_id']
task = p.submit(main, FLAGS_output_dir, FLAGS_init_checkpoint_squad, FLAGS_export_dir, FLAGS_predict_file, FLAGS_train_file, FLAGS_do_predict,
FLAGS_do_train, FLAGS_train_batch_size, FLAGS_predict_batch_size, FLAGS_learning_rate, FLAGS_num_train_epochs,
FLAGS_max_answer_length, FLAGS_max_query_length, FLAGS_version_2_with_negative)
threads_mapping[task_id] = task
return jsonify({"message": "Task submitted successfully", "status": "0"})
except KeyError as e:
return jsonify({"Des": 'KeyError: {}'.format(str(e)), "Result": 'None', "Status": "Error"})
except Exception as e:
return jsonify({"Des": str(e), "Result": 'None', "Status": "Error"})
@app.route('/api/offline/status', methods=['POST'])
def todo_status():
task_id = request.json['task_id']
task = threads_mapping.get(task_id, None)
try:
if task is None:
return jsonify({'Des': 'The task was not found', 'Status': 'ERROR'})
else:
if task.done():
print(task.result)
if task.result() == 'success':
return jsonify({'Des': 'DONE', 'Status': 'OK'})
else:
return jsonify({'Des': 'Program execution error. Please check the execution log ', 'Status': 'ERROR'})
else:
return jsonify({'Des': 'RUNNING', 'Status': 'OK'})
except Exception as e:
return jsonify({'Des': str(e), 'Status': 'ERROR'})
def start(self):
self.app.run(host="0.0.0.0", port=self.port, threaded=True)
if __name__ == '__main__':
port = sys.argv[1]
AI2Flask(port=port).start()
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