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Add AMP for TF Albert #10141

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Feb 15, 2021
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Add AMP for TF Albert #10141

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684 changes: 375 additions & 309 deletions src/transformers/models/albert/modeling_tf_albert.py
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14 changes: 7 additions & 7 deletions src/transformers/models/bert/modeling_tf_bert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -148,21 +148,21 @@ def build(self, input_shape: tf.TensorShape):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

super().build(input_shape)
Expand Down Expand Up @@ -253,8 +253,7 @@ def call(
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

# Take the dot product between "query" and "key" to get the raw
# attention scores.
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
Expand Down Expand Up @@ -1009,7 +1008,8 @@ def call(
total_loss = self.compute_loss(labels=d_labels, logits=(prediction_scores, seq_relationship_score))

if not inputs["return_dict"]:
return (prediction_scores, seq_relationship_score) + outputs[2:]
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
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It was not returning the loss before that?

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This should have been caught by a test

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The test on the loss computation was working because the test is run only for return_dict=True, it would have failed if return_dict=False.


return TFBertForPreTrainingOutput(
loss=total_loss,
Expand Down Expand Up @@ -1598,7 +1598,7 @@ def call(
}
]
)
def serving(self, inputs: Dict[str, tf.Tensor]):
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFMultipleChoiceModelOutput:
output = self.call(input_ids=inputs)

return self.serving_output(output)
Expand Down
10 changes: 5 additions & 5 deletions src/transformers/models/convbert/modeling_tf_convbert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,11 +62,11 @@
]


# Copied from transformers.models.albert.modeling_tf_albert.TFAlbertEmbeddings
# Copied from transformers.models.albert.modeling_tf_albert.TFAlbertEmbeddings with Albert->ConvBert
class TFConvBertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""

def __init__(self, config, **kwargs):
def __init__(self, config: ConvBertConfig, **kwargs):
super().__init__(**kwargs)

self.vocab_size = config.vocab_size
Expand All @@ -83,21 +83,21 @@ def build(self, input_shape: tf.TensorShape):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.embedding_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.embedding_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.embedding_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

super().build(input_shape)
Expand Down
11 changes: 5 additions & 6 deletions src/transformers/models/electra/modeling_tf_electra.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,8 +121,7 @@ def call(
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

# Take the dot product between "query" and "key" to get the raw
# attention scores.
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
Expand Down Expand Up @@ -353,7 +352,7 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
class TFElectraEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""

def __init__(self, config, **kwargs):
def __init__(self, config: ElectraConfig, **kwargs):
super().__init__(**kwargs)

self.vocab_size = config.vocab_size
Expand All @@ -370,21 +369,21 @@ def build(self, input_shape: tf.TensorShape):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.embedding_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.embedding_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.embedding_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

super().build(input_shape)
Expand Down
6 changes: 3 additions & 3 deletions src/transformers/models/longformer/modeling_tf_longformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -491,21 +491,21 @@ def build(self, input_shape: tf.TensorShape):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

super().build(input_shape)
Expand Down
9 changes: 4 additions & 5 deletions src/transformers/models/roberta/modeling_tf_roberta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,21 +92,21 @@ def build(self, input_shape: tf.TensorShape):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

super().build(input_shape)
Expand Down Expand Up @@ -232,8 +232,7 @@ def call(
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

# Take the dot product between "query" and "key" to get the raw
# attention scores.
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
Expand Down
11 changes: 5 additions & 6 deletions ...r-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py
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Original file line number Diff line number Diff line change
Expand Up @@ -90,21 +90,21 @@ def build(self, input_shape: tf.TensorShape):
self.weight = self.add_weight(
name="weight",
shape=[self.vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("token_type_embeddings"):
self.token_type_embeddings = self.add_weight(
name="embeddings",
shape=[self.type_vocab_size, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

with tf.name_scope("position_embeddings"):
self.position_embeddings = self.add_weight(
name="embeddings",
shape=[self.max_position_embeddings, self.hidden_size],
initializer=get_initializer(initializer_range=self.initializer_range),
initializer=get_initializer(self.initializer_range),
)

super().build(input_shape)
Expand Down Expand Up @@ -197,8 +197,7 @@ def call(
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

# Take the dot product between "query" and "key" to get the raw
# attention scores.
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
Expand Down Expand Up @@ -1247,7 +1246,7 @@ def call(
"token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
}])
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving
def serving(self, inputs: Dict[str, tf.Tensor]):
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFMultipleChoiceModelOutput:
output = self.call(input_ids=inputs)

return self.serving_output(output)
Expand Down
15 changes: 11 additions & 4 deletions tests/test_modeling_tf_albert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
if is_tf_available():
import tensorflow as tf

from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
from transformers.models.albert.modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
Expand Down Expand Up @@ -242,6 +243,16 @@ class TFAlbertModelTest(TFModelTesterMixin, unittest.TestCase):
)
test_head_masking = False

# special case for ForPreTraining model
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)

if return_labels:
if model_class in TF_MODEL_FOR_PRETRAINING_MAPPING.values():
inputs_dict["sentence_order_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)

return inputs_dict

Comment on lines +246 to +255
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I see it will get caught by a test from now on :)

def setUp(self):
self.model_tester = TFAlbertModelTester(self)
self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37)
Expand Down Expand Up @@ -294,10 +305,6 @@ def test_model_common_attributes(self):
name = model.get_bias()
assert name is None

def test_mixed_precision(self):
# TODO JP: Make ALBERT float16 compliant
pass

@slow
def test_model_from_pretrained(self):
for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
Expand Down