def optimize_graph(params: Params):
config = tf.ConfigProto(device_count={'GPU': 0}, allow_soft_placement=True)
init_checkpoint = params.ckpt_dir
tf.logging.info('build graph...')
# input placeholders, not sure if they are friendly to XLA
input_ids = tf.placeholder(tf.int32, (None, params.max_seq_len),
'input_ids')
input_mask = tf.placeholder(tf.int32, (None, params.max_seq_len),
'input_mask')
input_type_ids = tf.placeholder(tf.int32, (None, params.max_seq_len),
'segment_ids')
jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
with jit_scope():
features = {}
features['input_ids'] = input_ids
features['input_mask'] = input_mask
features['segment_ids'] = input_type_ids
model = BertMultiTask(params)
hidden_feature = model.body(features, tf.estimator.ModeKeys.PREDICT)
pred = model.top(features, hidden_feature,
tf.estimator.ModeKeys.PREDICT)
output_tensors = [pred[k] for k in pred]
tvars = tf.trainable_variables()
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tmp_g = tf.get_default_graph().as_graph_def()
with tf.Session(config=config) as sess:
tf.logging.info('load parameters from checkpoint...')
sess.run(tf.global_variables_initializer())
tf.logging.info('freeze...')
tmp_g = tf.graph_util.convert_variables_to_constants(
sess, tmp_g, [n.name[:-2] for n in output_tensors])
tmp_file = os.path.join(params.ckpt_dir, 'export_model')
tf.logging.info('write graph to a tmp file: %s' % tmp_file)
with tf.gfile.GFile(tmp_file, 'wb') as f:
f.write(tmp_g.SerializeToString())
return tmp_file
def optimize_graph(params):
config = tf.ConfigProto(device_count={'GPU': 0}, allow_soft_placement=True)
init_checkpoint = params.ckpt_dir
tf.logging.info('build graph...')
# input placeholders, not sure if they are friendly to XLA
input_ids = tf.placeholder(tf.int32, (None, params.max_seq_len),
'input_ids')
input_mask = tf.placeholder(tf.int32, (None, params.max_seq_len),
'input_mask')
input_type_ids = tf.placeholder(tf.int32, (None, params.max_seq_len),
'segment_ids')
jit_scope = tf.contrib.compiler.jit.experimental_jit_scope
with jit_scope():
features = {}
features['input_ids'] = input_ids
features['input_mask'] = input_mask
features['segment_ids'] = input_type_ids
model = BertMultiTask(params)
hidden_feature = model.body(features, tf.estimator.ModeKeys.PREDICT)
pred = model.top(features, hidden_feature,
tf.estimator.ModeKeys.PREDICT)
output_tensors = [pred[k] for k in pred]
tvars = tf.trainable_variables()
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(
tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tmp_g = tf.get_default_graph().as_graph_def()
input_node_names = ['input_ids', 'input_mask', 'segment_ids']
output_node_names = [
'%s_top/%s_predict' %
(params.share_top[problem], params.share_top[problem])
for problem in params.problem_list
]
transforms = [
'remove_nodes(op=Identity)',
'fold_constants(ignore_errors=true)',
'fold_batch_norms',
# 'quantize_weights',
# 'quantize_nodes',
'merge_duplicate_nodes',
'strip_unused_nodes',
'sort_by_execution_order'
]
with tf.Session(config=config) as sess:
tf.logging.info('load parameters from checkpoint...')
sess.run(tf.global_variables_initializer())
tf.logging.info('freeze...')
tmp_g = tf.graph_util.convert_variables_to_constants(
sess, tmp_g, [n.name[:-2] for n in output_tensors])
tmp_g = TransformGraph(tmp_g, input_node_names, output_node_names,
transforms)
tmp_file = os.path.join(params.ckpt_dir, 'export_model')
tf.logging.info('write graph to a tmp file: %s' % tmp_file)
with tf.gfile.GFile(tmp_file, 'wb') as f:
f.write(tmp_g.SerializeToString())
return tmp_file