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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
from flask import Flask, abort, request, jsonify
from concurrent.futures import ThreadPoolExecutor
import os
import random
import modeling
import tokenization
import tensorflow as tf
import sys
from api_squad import FLAGS_max_seq_length
from api_squad import FLAGS_do_lower_case
from api_squad import FLAGS_use_tpu
from api_squad import FLAGS_tpu_name
from api_squad import FLAGS_tpu_zone
from api_squad import FLAGS_gcp_project
from api_squad import FLAGS_master
from api_squad import FLAGS_save_checkpoints_steps
from api_squad import FLAGS_iterations_per_loop
from api_squad import FLAGS_num_tpu_cores
from api_squad import FLAGS_warmup_proportion
from api_squad import FLAGS_doc_stride
from api_squad import model_fn_builder
from api_squad import FeatureWriter
from api_squad import convert_examples_to_features
from api_squad import input_fn_builder
from api_squad import validate_flags_or_throw
from api_squad import read_squad_examples
from global_setting import CUDA_VISIBLE_DEVICES
from global_setting import FLAGS_bert_config_file, FLAGS_vocab_file, FLAGS_init_checkpoint_squad
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, questions=[]):
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 'task_id' not 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']
questions = request.json['questions']
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, questions)
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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