def test_trainable_variables(self):
r"""Tests the functionality of automatically collecting trainable
variables.
"""
inputs = torch.zeros(32, 16, dtype=torch.int64)
# case 1: bert base
encoder = BertEncoder()
_, _ = encoder(inputs)
self.assertEqual(len(encoder.trainable_variables), 3 + 2 + 12 * 16 + 2)
# case 2: bert large
hparams = {"pretrained_model_name": "bert-large-uncased"}
encoder = BertEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(len(encoder.trainable_variables), 3 + 2 + 24 * 16 + 2)
# case 3: self-designed bert
hparams = {
"encoder": {
"num_blocks": 6,
},
"pretrained_model_name": None
}
encoder = BertEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(len(encoder.trainable_variables), 3 + 2 + 6 * 16 + 2)
def __init__(self,
pretrained_model_name: Optional[str] = None,
cache_dir: Optional[str] = None,
hparams=None):
super().__init__(hparams)
# Create the underlying encoder
encoder_hparams = utils.dict_fetch(hparams,
BertEncoder.default_hparams())
if encoder_hparams is not None:
encoder_hparams['name'] = None
self._encoder = BertEncoder(
pretrained_model_name=pretrained_model_name,
cache_dir=cache_dir,
hparams=encoder_hparams)
# Create a dropout layer
self._dropout_layer = nn.Dropout(self._hparams.dropout)
# Create an additional classification layer if needed
self.num_classes = self._hparams.num_classes
if self.num_classes <= 0:
self._logits_layer = None
else:
logit_kwargs = self._hparams.logit_layer_kwargs
if logit_kwargs is None:
logit_kwargs = {}
elif not isinstance(logit_kwargs, HParams):
raise ValueError("hparams['logit_layer_kwargs'] "
"must be a dict.")
else:
logit_kwargs = logit_kwargs.todict()
if self._hparams.clas_strategy == 'all_time':
self._logits_layer = nn.Linear(
self._hparams.hidden_size * self._hparams.max_seq_length,
self.num_classes, **logit_kwargs)
else:
self._logits_layer = nn.Linear(self._hparams.hidden_size,
self.num_classes,
**logit_kwargs)
self.is_binary = (self.num_classes == 1) or \
(self.num_classes <= 0 and
self._hparams.encoder.dim == 1)
def test_encode(self):
"""Tests encoding.
"""
# case 1: bert base
encoder = BertEncoder()
max_time = 8
batch_size = 16
inputs = tf.random_uniform([batch_size, max_time],
maxval=30521,
dtype=tf.int32)
outputs, pooled_output = encoder(inputs)
outputs_dim = encoder.hparams.encoder.dim
pooled_output_dim = encoder.hparams.hidden_size
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_, pooled_output_ = sess.run([outputs, pooled_output])
self.assertEqual(outputs_.shape,
(batch_size, max_time, outputs_dim))
self.assertEqual(pooled_output_.shape,
(batch_size, pooled_output_dim))
# case 2: self-designed bert
hparams = {"hidden_size": 100, "pretrained_model_name": None}
encoder = BertEncoder(hparams=hparams)
max_time = 8
batch_size = 16
inputs = tf.random_uniform([batch_size, max_time],
maxval=30521,
dtype=tf.int32)
outputs, pooled_output = encoder(inputs)
outputs_dim = encoder.hparams.encoder.dim
pooled_output_dim = encoder.hparams.hidden_size
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
outputs_, pooled_output_ = sess.run([outputs, pooled_output])
self.assertEqual(outputs_.shape,
(batch_size, max_time, outputs_dim))
self.assertEqual(pooled_output_.shape,
(batch_size, pooled_output_dim))
def test_hparams(self):
"""Tests the priority of the encoder arch parameter.
"""
inputs = tf.placeholder(dtype=tf.int32, shape=[None, None])
# case 1: set "pretrained_mode_name" by constructor argument
hparams = {
"pretrained_model_name": "bert-large-uncased",
}
encoder = BertEncoder(pretrained_model_name="bert-base-uncased",
hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 12)
# case 2: set "pretrained_mode_name" by hparams
hparams = {
"pretrained_model_name": "bert-large-uncased",
"encoder": {
"num_blocks": 6
}
}
encoder = BertEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 24)
# case 3: set to None in both hparams and constructor argument
hparams = {
"pretrained_model_name": None,
"encoder": {
"num_blocks": 6
},
}
encoder = BertEncoder(hparams=hparams)
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 6)
# case 4: using default hparams
encoder = BertEncoder()
_, _ = encoder(inputs)
self.assertEqual(encoder.hparams.encoder.num_blocks, 12)
def test_encode(self):
r"""Tests encoding.
"""
# case 1: bert base
encoder = BertEncoder()
max_time = 8
batch_size = 16
inputs = torch.randint(30521, (batch_size, max_time),
dtype=torch.int64)
outputs, pooled_output = encoder(inputs)
outputs_dim = encoder.hparams.encoder.dim
pooled_output_dim = encoder.hparams.hidden_size
self.assertEqual(outputs.shape,
torch.Size([batch_size, max_time, outputs_dim]))
self.assertEqual(pooled_output.shape,
torch.Size([batch_size, pooled_output_dim]))
# case 2: self-designed bert
hparams = {
'pretrained_model_name': None,
'embed': {
'dim': 96
},
'segment_embed': {
'dim': 96
},
'position_embed': {
'dim': 96
},
'encoder': {
'dim': 96,
'multihead_attention': {
'num_units': 96,
'output_dim': 96,
},
'poswise_feedforward': {
'layers': [{
'kwargs': {
'in_features': 96,
'out_features': 96 * 4,
'bias': True
},
'type': 'Linear'
}, {
"type": "BertGELU"
}, {
'kwargs': {
'in_features': 96 * 4,
'out_features': 96,
'bias': True
},
'type': 'Linear'
}]
},
},
'hidden_size': 96
}
encoder = BertEncoder(hparams=hparams)
max_time = 8
batch_size = 16
inputs = torch.randint(30521, (batch_size, max_time),
dtype=torch.int64)
outputs, pooled_output = encoder(inputs)
outputs_dim = encoder.hparams.encoder.dim
pooled_output_dim = encoder.hparams.hidden_size
self.assertEqual(outputs.shape,
torch.Size([batch_size, max_time, outputs_dim]))
self.assertEqual(pooled_output.shape,
torch.Size([batch_size, pooled_output_dim]))
def default_hparams():
r"""Returns a dictionary of hyperparameters with default values.
.. code-block:: python
{
# (1) Same hyperparameters as in BertEncoder
...
# (2) Additional hyperparameters
"num_classes": 2,
"logit_layer_kwargs": None,
"clas_strategy": "cls_time",
"max_seq_length": None,
"dropout": 0.1,
"name": "bert_classifier"
}
Here:
1. Same hyperparameters as in
:class:`~texar.modules.BertEncoder`.
See the :meth:`~texar.modules.BertEncoder.default_hparams`.
An instance of BertEncoder is created for feature extraction.
2. Additional hyperparameters:
`num_classes`: int
Number of classes:
- If **> 0**, an additional `Linear`
layer is appended to the encoder to compute the logits over
classes.
- If **<= 0**, no dense layer is appended. The number of
classes is assumed to be the final dense layer size of the
encoder.
`logit_layer_kwargs`: dict
Keyword arguments for the logit Dense layer constructor,
except for argument "units" which is set to `num_classes`.
Ignored if no extra logit layer is appended.
`clas_strategy`: str
The classification strategy, one of:
- **cls_time**: Sequence-level classification based on the
output of the first time step (which is the `CLS` token).
Each sequence has a class.
- **all_time**: Sequence-level classification based on
the output of all time steps. Each sequence has a class.
- **time_wise**: Step-wise classification, i.e., make
classification for each time step based on its output.
`max_seq_length`: int, optional
Maximum possible length of input sequences. Required if
`clas_strategy` is `all_time`.
`dropout`: float
The dropout rate of the BERT encoder output.
`name`: str
Name of the classifier.
"""
hparams = BertEncoder.default_hparams()
hparams.update({
"num_classes": 2,
"logit_layer_kwargs": None,
"clas_strategy": "cls_time",
"max_seq_length": None,
"dropout": 0.1,
"name": "bert_classifier"
})
return hparams