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config_xlnet.py
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"""Config
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=invalid-name
import copy
import texar as tx
import tensorflow as tf
initial_lr = 0.000025
max_nepochs = 50
pretrain_nepochs = 10 # Number of pre-train epochs (training as autoencoder)
display = 500 # Display the training results every N training steps.
display_eval = 1e10 # Display the dev results every N training steps (set to a
# very large value to disable it).
restore = ''
model_name = 'EvolveGTAE'
lambda_adj_final = 1 # Weight of the adj_final loss
lambda_rephraser = 1
lambda_adj_cft = 1
max_sequence_length_y = 128 # Maximum number of tokens in a sentence ###check
max_sequence_length_ctx = 128
max_decode_yy = 128 ###maybe can't be so big
batch_size=8
train_num = 28167
dev_num = 1856
test_num = 1854
vocab_size=32000
bos_token_id = 3
eos_token_id = 4
## Data configs
feature_original_types = {
# Reading features from TFRecord data file.
# E.g., Reading feature "text_ids" as dtype `tf.int64`;
# "FixedLenFeature" indicates its length is fixed for all data instances;
# and the sequence length is limited by `max_seq_length`.
"input_ids_x1x2ysx1xx2": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_mask_x1x2ysx1xx2": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_ids_x1x2ysx1xx2yy": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_mask_x1x2ysx1xx2yy": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_ids_x1x2": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_mask_x1x2": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_ids_x1xx2": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_mask_x1xx2": ["tf.int64", "FixedLenFeature", max_sequence_length_ctx],
"input_ids_y1": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_mask_y1": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_ids_y2": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_mask_y2": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_ids_y3": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_mask_y3": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_ids_yy1": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_mask_yy1": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_ids_yy2": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_mask_yy2": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_ids_yy3": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
"input_mask_yy3": ["tf.int64", "FixedLenFeature", max_sequence_length_y],
}
feature_convert_types = {
# Converting feature dtype after reading. E.g.,
# Converting the dtype of feature "text_ids" from `tf.int64` (as above)
# to `tf.int32`
"input_ids_x1x2ysx1xx2": "tf.int32",
"input_mask_x1x2ysx1xx2": "tf.int32",
"input_ids_x1x2ysx1xx2yy": "tf.int32",
"input_mask_x1x2ysx1xx2yy": "tf.int32",
"input_ids_x1x2": "tf.int32",
"input_mask_x1x2": "tf.int32",
"input_ids_x1xx2": "tf.int32",
"input_mask_x1xx2": "tf.int32",
"input_ids_y1": "tf.int32",
"input_mask_y1": "tf.int32",
"input_ids_y2": "tf.int32",
"input_mask_y2": "tf.int32",
"input_ids_y3": "tf.int32",
"input_mask_y3": "tf.int32",
"input_ids_yy1": "tf.int32",
"input_mask_yy1": "tf.int32",
"input_ids_yy2": "tf.int32",
"input_mask_yy2": "tf.int32",
"input_ids_yy3": "tf.int32",
"input_mask_yy3": "tf.int32",
}
mini=""
text_data_dir = "./data/TimeTravel/xlnet{}".format(mini)
adj_data_dir = "./data/TimeTravel{}".format(mini)
train_data = {
'batch_size': batch_size,
#'seed': 123,
'datasets': [
{
"files": "{}/train_supervised_large.tf_record".format(text_data_dir),
"data_name": "text",
'data_type': 'tf_record',
"feature_original_types": feature_original_types,
"feature_convert_types": feature_convert_types,
},
{
'files': '{}/TimeTravel.train_supervised_large_y1_adjs_dirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'y1_d',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_y2_adjs_dirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'y2_d',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_y3_adjs_dirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'y3_d',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_yy1_adjs_dirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'yy1_d',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_yy2_adjs_dirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'yy2_d',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_yy3_adjs_dirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'yy3_d',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_y1_adjs_undirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'y1_und',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_y2_adjs_undirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'y2_und',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_y3_adjs_undirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'y3_und',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_yy1_adjs_undirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'yy1_und',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_yy2_adjs_undirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'yy2_und',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
},
{
'files': '{}/TimeTravel.train_supervised_large_yy3_adjs_undirt.tfrecords'.format(adj_data_dir),
'data_type': 'tf_record',
'data_name': 'yy3_und',
'numpy_options': {
'numpy_ndarray_name': 'adjs',
'shape': [max_sequence_length_y + 2, max_sequence_length_y + 2],
'dtype': 'tf.int32'
},
'feature_original_types':{
'adjs':['tf.string', 'FixedLenFeature']
}
}
],
'name': 'train',
'shuffle':True,
"shuffle_buffer_size": 1000
}
val_data = copy.deepcopy(train_data)
val_data['datasets'][0]['files'] = '{}/dev_data.tf_record'.format(text_data_dir)
val_data['datasets'][1]['files'] = '{}/TimeTravel.dev_data_y1_adjs_dirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][2]['files'] = '{}/TimeTravel.dev_data_y2_adjs_dirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][3]['files'] = '{}/TimeTravel.dev_data_y3_adjs_dirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][4]['files'] = '{}/TimeTravel.dev_data_yy1_adjs_dirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][5]['files'] = '{}/TimeTravel.dev_data_yy2_adjs_dirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][6]['files'] = '{}/TimeTravel.dev_data_yy3_adjs_dirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][7]['files'] = '{}/TimeTravel.dev_data_y1_adjs_undirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][8]['files'] = '{}/TimeTravel.dev_data_y2_adjs_undirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][9]['files'] = '{}/TimeTravel.dev_data_y3_adjs_undirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][10]['files'] = '{}/TimeTravel.dev_data_yy1_adjs_undirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][11]['files'] = '{}/TimeTravel.dev_data_yy2_adjs_undirt.tfrecords'.format(adj_data_dir)
val_data['datasets'][12]['files'] = '{}/TimeTravel.dev_data_yy3_adjs_undirt.tfrecords'.format(adj_data_dir)
val_data['shuffle'] = False
test_data = copy.deepcopy(train_data)
test_data['datasets'][0]['files'] = '{}/test_data.tf_record'.format(text_data_dir)
test_data['datasets'][1]['files'] = '{}/TimeTravel.test_data_y1_adjs_dirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][2]['files'] = '{}/TimeTravel.test_data_y2_adjs_dirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][3]['files'] = '{}/TimeTravel.test_data_y3_adjs_dirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][4]['files'] = '{}/TimeTravel.test_data_yy1_adjs_dirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][5]['files'] = '{}/TimeTravel.test_data_yy2_adjs_dirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][6]['files'] = '{}/TimeTravel.test_data_yy3_adjs_dirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][7]['files'] = '{}/TimeTravel.test_data_y1_adjs_undirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][8]['files'] = '{}/TimeTravel.test_data_y2_adjs_undirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][9]['files'] = '{}/TimeTravel.test_data_y3_adjs_undirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][10]['files'] = '{}/TimeTravel.test_data_yy1_adjs_undirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][11]['files'] = '{}/TimeTravel.test_data_yy2_adjs_undirt.tfrecords'.format(adj_data_dir)
test_data['datasets'][12]['files'] = '{}/TimeTravel.test_data_yy3_adjs_undirt.tfrecords'.format(adj_data_dir)
test_data['shuffle'] = False
dim_hidden = 768#768
dim_hidden_mini = 512
model = {
'gpt2_hidden_dim':dim_hidden,
'dim_c_big': dim_hidden,
'dim_c': dim_hidden,
'wordEmbedder':{
'dim':dim_hidden
},
'gpt2_posEmbedder':{
'dim':dim_hidden
},
'xlnet_encoder':{
"name": "xlnet_encoder",
"pretrained_model_name": "xlnet-base-cased",
"untie_r": True,
"num_layers": 12,
"mem_len": 0,
"reuse_len": 0,
"initializer": None,
"num_heads": 12,
"hidden_dim": 768,
"head_dim": 64,
"dropout": 0.1,
"attention_dropout": 0.1,
"use_segments": True,
"ffn_inner_dim": 3072,
"activation": 'gelu',
"vocab_size": 32000,
"max_seq_len": 512,
},
'bert_encoder':{
"pretrained_model_name":"bert-large-cased",
# default params are enough
},
'bidirectionalRNNEncoder':{
"rnn_cell_fw": {
'type': 'GRUCell',
'kwargs': {
'num_units': dim_hidden/2, ###check Q
},
},
"rnn_cell_share_config": True,
"output_layer_fw": {
"num_layers": 0,
"layer_size": 128,
"activation": "identity",
"final_layer_activation": None,
"other_dense_kwargs": None,
"dropout_layer_ids": [],
"dropout_rate": 0.5,
"variational_dropout": False
},
"output_layer_bw": {
# Same hyperparams and default values as "output_layer_fw"
# ...
},
"output_layer_share_config": True,
"name": "bidirectional_rnn_encoder"
},
'unidirectionalRNNEncoder':{
"rnn_cell": {
'type': 'GRUCell',
'kwargs': {
'num_units': dim_hidden, ###check Q ###debug
},
},
"output_layer": {
"num_layers": 0,
"layer_size": 128,
"activation": "identity",
"final_layer_activation": None,
"other_dense_kwargs": None,
"dropout_layer_ids": [],
"dropout_rate": 0.5,
"variational_dropout": False
},
"name": "unidirectional_rnn_encoder"
},
'EmbeddingNormalize':{
"name": "EmbeddingNormalize"
},
'EmbeddingNormalizeNN1':{
"name": "EmbeddingNormalizeNN",
"size":dim_hidden,
"epsilon": 1e-3,
"decay":0.99,
"name_scope":'nnBN1'
},
'EmbeddingNormalizeNN2':{
"name": "EmbeddingNormalizeNN",
"size":dim_hidden,
"epsilon": 1e-3,
"decay":0.99,
"name_scope":'nnBN2'
},
'encoder': {
'num_blocks': 2,
'dim': dim_hidden,
'use_bert_config': False,
'embedding_dropout': 0.1,
'residual_dropout': 0.1,
'graph_multihead_attention': {
'name': 'multihead_attention',
'num_units': dim_hidden,
'num_heads': 8,
'dropout_rate': 0.1,
'output_dim': dim_hidden,
'use_bias': False,
},
'initializer': None,
'poswise_feedforward': {
"layers": [
{
"type": "Dense",
"kwargs": {
"name": "conv1",
"units": dim_hidden*4, ###debug
"activation": "relu",
"use_bias": True,
}
},
{
"type": "Dropout",
"kwargs": {
"rate": 0.1,
}
},
{
"type": "Dense",
"kwargs": {
"name": "conv2",
"units": dim_hidden,
"use_bias": True,
}
}
],
"name": "ffn"
},
'name': 'graph_transformer_encoder',
},
'cross_graph_encoder': {
'num_blocks': 2,
'dim': dim_hidden,
'use_bert_config': False,
'use_adj':False,
'embedding_dropout': 0.1,
'residual_dropout': 0.1,
'graph_multihead_attention': {
'name': 'multihead_attention',
'num_units': dim_hidden,
'num_heads': 8,
'dropout_rate': 0.1,
'output_dim': dim_hidden,
'use_bias': False,
},
'initializer': None,
'poswise_feedforward': {
"layers": [
{
"type": "Dense",
"kwargs": {
"name": "conv1",
"units": dim_hidden*4, ###debug
"activation": "relu",
"use_bias": True,
}
},
{
"type": "Dropout",
"kwargs": {
"rate": 0.1,
}
},
{
"type": "Dense",
"kwargs": {
"name": "conv2",
"units": dim_hidden,
"use_bias": True,
}
}
],
"name": "ffn"
},
'name': 'cross_graph_encoder',
},
'adjMultiheadAttention_encoder':{
'initializer': None,
'num_heads': 8,
'output_dim': 1,
'num_units': 512,
'dropout_rate': 0.1,
'use_bias': True,
"name": "adjMultiheadAttention_encoder",
},
'transformer_encoder':{
'num_blocks': 2,
'dim': dim_hidden, ###debug
'use_bert_config': False,
'embedding_dropout': 0.1,
'residual_dropout': 0.1,
'multihead_attention':{
'name': 'multihead_attention',
'num_units': dim_hidden,
'output_dim': dim_hidden,
'num_heads': 8,
'dropout_rate': 0.1,
'output_dim': dim_hidden,
'use_bias': False,
},
'initializer': None,
'poswise_feedforward': {
"layers": [
{
"type": "Dense",
"kwargs": {
"name": "conv1",
"units": dim_hidden*4,
"activation": "relu",
"use_bias": True,
}
},
{
"type": "Dropout",
"kwargs": {
"rate": 0.1,
}
},
{
"type": "Dense",
"kwargs": {
"name": "conv2",
"units": dim_hidden,
"use_bias": True,
}
}
],
"name": "ffn"
},
'name': 'transformer_encoder',
},
'transformer_decoderToEncoder':{
'dim': dim_hidden,
'num_blocks': 12,
'multihead_attention': {
'use_bias': True,
'num_units': dim_hidden,
'num_heads': 12,
'output_dim': dim_hidden,
},
'initializer': {
'type': 'variance_scaling_initializer',
'kwargs': {
'scale': 1.0,
'mode': 'fan_avg',
'distribution': 'uniform',
},
},
'poswise_feedforward': {
"layers": [
{
"type": "Dense",
"kwargs": {
"name": "conv1",
"units": dim_hidden*4,
"activation": "relu",
"use_bias": True,
}
},
{
"type": "Dropout",
"kwargs": {
"rate": 0.1,
}
},
{
"type": "Dense",
"kwargs": {
"name": "conv2",
"units": dim_hidden,
"use_bias": True,
}
}
],
"name": "ffn"
}
},
'pooling_aggregator':{
"output_dim": dim_hidden,
"input_dim":dim_hidden,
"concat": True,
"pooling":'meanpooling',
"dropout_rate":0.0,
"l2_reg":0.1,
"use_bias":True,
"activation":tf.nn.relu,
"seed":1024,
"update_weights":False,
"name": "pooling_aggregator",
},
'rephrase_encoder': {
'rnn_cell': {
'type': 'GRUCell',
'kwargs': {
'num_units': dim_hidden
},
'dropout': {
'input_keep_prob': 0.5
}
}
},
'rephrase_decoder': {
'rnn_cell': {
'type': 'GRUCell',
'kwargs': {
'num_units': dim_hidden,
},
'dropout': {
'input_keep_prob': 0.5,
'output_keep_prob': 0.5
},
},
'attention': {
'type': 'DynamicBahdanauAttention',
'kwargs': {
'num_units': dim_hidden,
},
'attention_layer_size': dim_hidden,
},
'max_decoding_length_train': max_decode_yy,
'max_decoding_length_infer': max_decode_yy,
},
'opt': {
'optimizer': {
'type': 'AdamOptimizer',
'kwargs': {
'learning_rate': 0.000025,
},
},
},
}