112 lines
4.8 KiB
Python
112 lines
4.8 KiB
Python
import tempfile
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import json
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import logging
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from termcolor import colored
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import modeling
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import args
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import tensorflow as tf
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def set_logger(context, verbose=False):
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logger = logging.getLogger(context)
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logger.setLevel(logging.DEBUG if verbose else logging.INFO)
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formatter = logging.Formatter(
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'%(levelname)-.1s:' + context + ':[%(filename).5s:%(funcName).3s:%(lineno)3d]:%(message)s', datefmt=
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'%m-%d %H:%M:%S')
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console_handler = logging.StreamHandler()
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console_handler.setLevel(logging.DEBUG if verbose else logging.INFO)
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console_handler.setFormatter(formatter)
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logger.handlers = []
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logger.addHandler(console_handler)
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return logger
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def optimize_graph(logger=None, verbose=False):
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if not logger:
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logger = set_logger(colored('BERT_VEC', 'yellow'), verbose)
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try:
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# we don't need GPU for optimizing the graph
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from tensorflow.python.tools.optimize_for_inference_lib import optimize_for_inference
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# allow_soft_placement:自动选择运行设备
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config = tf.ConfigProto(allow_soft_placement=True)
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config_fp = args.config_name
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init_checkpoint = args.ckpt_name
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logger.info('model config: %s' % config_fp)
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# 加载bert配置文件
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with tf.gfile.GFile(config_fp, 'r') as f:
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bert_config = modeling.BertConfig.from_dict(json.load(f))
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logger.info('build graph...')
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# input placeholders, not sure if they are friendly to XLA
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input_ids = tf.placeholder(tf.int32, (None, args.max_seq_len), 'input_ids')
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input_mask = tf.placeholder(tf.int32, (None, args.max_seq_len), 'input_mask')
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input_type_ids = tf.placeholder(tf.int32, (None, args.max_seq_len), 'input_type_ids')
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jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
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with jit_scope():
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input_tensors = [input_ids, input_mask, input_type_ids]
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model = modeling.BertModel(
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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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token_type_ids=input_type_ids,
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use_one_hot_embeddings=False)
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# 获取所有要训练的变量
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tvars = tf.trainable_variables()
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(assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,
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init_checkpoint)
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tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
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mul_mask = lambda x, m: x * tf.expand_dims(m, axis=-1)
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masked_reduce_mean = lambda x, m: tf.reduce_sum(mul_mask(x, m), axis=1) / (
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tf.reduce_sum(m, axis=1, keepdims=True) + 1e-10)
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# 共享卷积核
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with tf.variable_scope("pooling"):
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# 如果只有一层，就只取对应那一层的weight
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if len(args.layer_indexes) == 1:
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encoder_layer = model.all_encoder_layers[args.layer_indexes[0]]
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else:
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# 否则遍历需要取的层，把所有层的weight取出来并拼接起来shape:768*层数
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all_layers = [model.all_encoder_layers[l] for l in args.layer_indexes]
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encoder_layer = tf.concat(all_layers, -1)
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input_mask = tf.cast(input_mask, tf.float32)
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# 以下代码是句向量的生成方法，可以理解为做了一个卷积的操作，但是没有把结果相加, 卷积核是input_mask
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pooled = masked_reduce_mean(encoder_layer, input_mask)
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pooled = tf.identity(pooled, 'final_encodes')
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output_tensors = [pooled]
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tmp_g = tf.get_default_graph().as_graph_def()
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with tf.Session(config=config) as sess:
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logger.info('load parameters from checkpoint...')
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sess.run(tf.global_variables_initializer())
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logger.info('freeze...')
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tmp_g = tf.graph_util.convert_variables_to_constants(sess, tmp_g, [n.name[:-2] for n in output_tensors])
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dtypes = [n.dtype for n in input_tensors]
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logger.info('optimize...')
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tmp_g = optimize_for_inference(
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tmp_g,
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[n.name[:-2] for n in input_tensors],
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[n.name[:-2] for n in output_tensors],
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[dtype.as_datatype_enum for dtype in dtypes],
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False)
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tmp_file = tempfile.NamedTemporaryFile('w', delete=False, dir=args.output_dir).name
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logger.info('write graph to a tmp file: %s' % tmp_file)
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with tf.gfile.GFile(tmp_file, 'wb') as f:
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f.write(tmp_g.SerializeToString())
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return tmp_file
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except Exception as e:
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logger.error('fail to optimize the graph!')
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logger.error(e)
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