Converting a TensorFlow XLNet Model¶
Pretrained models for XLNet (Bidirectional Encoder Representations from Transformers) are publicly available.
Supported Models¶
The following models from the pretrained XLNet model list are currently supported:
Downloading the Pretrained Base XLNet Model¶
Download and unzip an archive with the XLNet-Base, Cased.
After the archive is unzipped, the directory cased_L-12_H-768_A-12
is created and contains the following files:
TensorFlow checkpoint (
xlnet_model.ckpt
), containing the pretrained weights (which is actually 3 files)sentence piece model (
spiece.model
) used for (de)tokenizationconfig file (
xlnet_config.json
), which specifies the hyperparameters of the model
To get pb-file from the archive contents, you need to do the following.
Run commands
cd ~ mkdir XLNet-Base cd XLNet-Base git clone https://github.com/zihangdai/xlnet wget https://storage.googleapis.com/xlnet/released_models/cased_L-12_H-768_A-12.zip unzip cased_L-12_H-768_A-12.zip mkdir try_save
Save and run the following Python script in
~/XLNet-Base/xlnet
:
Note
The original model repository has been tested with TensorFlow 1.13.1 under Python2.
from collections import namedtuple
import tensorflow as tf
from tensorflow.python.framework import graph_io
import model_utils
import xlnet
LENGTHS = 50
BATCH = 1
OUTPUT_DIR = '~/XLNet-Base/try_save/'
INIT_CKPT_PATH = '~/XLNet-Base/xlnet_cased_L-12_H-768_A-12/xlnet_model.ckpt'
XLNET_CONFIG_PATH = '~/XLNet-Base/xlnet_cased_L-12_H-768_A-12/xlnet_config.json'
FLags = namedtuple('FLags', 'use_tpu init_checkpoint')
FLAGS = FLags(use_tpu=False, init_checkpoint=INIT_CKPT_PATH)
xlnet_config = xlnet.XLNetConfig(json_path=XLNET_CONFIG_PATH)
run_config = xlnet.RunConfig(is_training=False, use_tpu=False, use_bfloat16=False, dropout=0.1, dropatt=0.1,)
sentence_features_input_idx = tf.compat.v1.placeholder(tf.int32, shape=[LENGTHS, BATCH], name='input_ids')
sentence_features_segment_ids = tf.compat.v1.placeholder(tf.int32, shape=[LENGTHS, BATCH], name='seg_ids')
sentence_features_input_mask = tf.compat.v1.placeholder(tf.float32, shape=[LENGTHS, BATCH], name='input_mask')
with tf.compat.v1.Session() as sess:
xlnet_model = xlnet.XLNetModel(xlnet_config=xlnet_config, run_config=run_config,
input_ids=sentence_features_input_idx,
seg_ids=sentence_features_segment_ids,
input_mask=sentence_features_input_mask)
sess.run(tf.compat.v1.global_variables_initializer())
model_utils.init_from_checkpoint(FLAGS, True)
# Save the variables to disk.
saver = tf.compat.v1.train.Saver()
# Saving checkpoint
save_path = saver.save(sess, OUTPUT_DIR + "model.ckpt")
# Freezing model
outputs = ['model/transformer/dropout_2/Identity']
graph_def_freezed = tf.compat.v1.graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), outputs)
# Saving non-frozen and frozen model to pb
graph_io.write_graph(sess.graph.as_graph_def(), OUTPUT_DIR, 'model.pb', as_text=False)
graph_io.write_graph(graph_def_freezed,OUTPUT_DIR, 'model_frozen.pb',
as_text=False)
# Write to tensorboard
with tf.compat.v1.summary.FileWriter(logdir=OUTPUT_DIR, graph_def=graph_def_freezed) as writer:
writer.flush()
Downloading the Pretrained Large XLNet Model¶
Download and unzip an archive with the XLNet-Large, Cased.
After unzipping the archive, the directory cased_L-12_H-1024_A-16
is created and contains the following files:
TensorFlow checkpoint (
xlnet_model.ckpt
) containing the pretrained weights (which is actually 3 files)sentence piece model (
spiece.model
) used for (de)tokenizationconfig file (
xlnet_config.json
) which specifies the hyperparameters of the model
To get pb-file
from the archive contents, follow the instructions below:
Run commands
cd ~ mkdir XLNet-Large cd XLNet-Large git clone https://github.com/zihangdai/xlnet wget https://storage.googleapis.com/xlnet/released_models/cased_L-24_H-1024_A-16.zip unzip cased_L-24_H-1024_A-16.zip mkdir try_save
Save and run the following Python script in
~/XLNet-Large/xlnet
:from collections import namedtuple import tensorflow as tf from tensorflow.python.framework import graph_io import model_utils import xlnet LENGTHS = 50 BATCH = 1 OUTPUT_DIR = '~/XLNet-Large/try_save' INIT_CKPT_PATH = '~/XLNet-Large/cased_L-24_H-1024_A-16/xlnet_model.ckpt' XLNET_CONFIG_PATH = '~/XLNet-Large/cased_L-24_H-1024_A-16/xlnet_config.json' FLags = namedtuple('FLags', 'use_tpu init_checkpoint') FLAGS = FLags(use_tpu=False, init_checkpoint=INIT_CKPT_PATH) xlnet_config = xlnet.XLNetConfig(json_path=XLNET_CONFIG_PATH) run_config = xlnet.RunConfig(is_training=False, use_tpu=False, use_bfloat16=False, dropout=0.1, dropatt=0.1,) sentence_features_input_idx = tf.compat.v1.placeholder(tf.int32, shape=[LENGTHS, BATCH], name='input_ids') sentence_features_segment_ids = tf.compat.v1.placeholder(tf.int32, shape=[LENGTHS, BATCH], name='seg_ids') sentence_features_input_mask = tf.compat.v1.placeholder(tf.float32, shape=[LENGTHS, BATCH], name='input_mask') with tf.compat.v1.Session() as sess: xlnet_model = xlnet.XLNetModel(xlnet_config=xlnet_config, run_config=run_config, input_ids=sentence_features_input_idx, seg_ids=sentence_features_segment_ids, input_mask=sentence_features_input_mask) sess.run(tf.compat.v1.global_variables_initializer()) model_utils.init_from_checkpoint(FLAGS, True) # Save the variables to disk. saver = tf.compat.v1.train.Saver() # Saving checkpoint save_path = saver.save(sess, OUTPUT_DIR + "model.ckpt") # Freezing model outputs = ['model/transformer/dropout_2/Identity'] graph_def_freezed = tf.compat.v1.graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), outputs) # Saving non-frozen and frozen model to pb graph_io.write_graph(sess.graph.as_graph_def(), OUTPUT_DIR, 'model.pb', as_text=False) graph_io.write_graph(graph_def_freezed,OUTPUT_DIR, 'model_frozen.pb', as_text=False) # Write to tensorboard with tf.compat.v1.summary.FileWriter(logdir=OUTPUT_DIR, graph_def=graph_def_freezed) as writer: writer.flush()
The script should save into ~/XLNet-Large/xlnet
.
Converting a frozen TensorFlow XLNet Model to IR¶
To generate the XLNet Intermediate Representation (IR) of the model, run Model Optimizer with the following parameters:
mo --input_model path-to-model/model_frozen.pb \
--input "input_mask[50,1],input_ids[50,1],seg_ids[50,1]"