mirror of
https://github.com/huggingface/transformers.git
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New TF embeddings (cleaner and faster) (#9418)
* Create new embeddings + add to BERT * Add Albert * Add DistilBert * Add Albert + Electra + Funnel * Add Longformer + Lxmert * Add last models * Apply style * Update the template * Remove unused imports * Rename attribute * Import embeddings in their own model file * Replace word_embeddings per weight * fix naming * Fix Albert * Fix Albert * Fix Longformer * Fix Lxmert Mobilebert and MPNet * Fix copy * Fix template * Update the get weights function * Update src/transformers/modeling_tf_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/models/electra/modeling_tf_electra.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * address Sylvain's comments Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Julien Plu
GitHub
parent
12f0d7e8e0
commit
14042d560f
@ -809,25 +809,29 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
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return model_embeds
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def _get_word_embedding_weight(self, embedding_layer):
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if hasattr(embedding_layer, "word_embeddings"):
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return embedding_layer.word_embeddings
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elif hasattr(embedding_layer, "weight"):
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return embedding_layer.weight
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elif hasattr(embedding_layer, "decoder"):
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return embedding_layer.decoder
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else:
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# Here we build the word embeddings weights if not exists.
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# And then we retry to get the attribute once built.
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self(self.dummy_inputs)
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if hasattr(embedding_layer, "word_embeddings"):
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return embedding_layer.word_embeddings
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elif hasattr(embedding_layer, "weight"):
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return embedding_layer.weight
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elif hasattr(embedding_layer, "decoder"):
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return embedding_layer.decoder
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else:
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return None
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def _get_word_embedding_weight(model, embedding_layer):
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embeds = getattr(embedding_layer, "weight", None)
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if embeds is not None:
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return embeds
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embeds = getattr(embedding_layer, "decoder", None)
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if embeds is not None:
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return embeds
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# The reason why the attributes don't exist might be
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# because the model is not built, so retry getting
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# the argument after building the model
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model(model.dummy_inputs)
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embeds = getattr(embedding_layer, "weight", None)
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if embeds is not None:
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return embeds
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embeds = getattr(embedding_layer, "decoder", None)
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if embeds is not None:
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return embeds
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return None
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def _resize_token_embeddings(self, new_num_tokens):
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old_embeddings = self._get_word_embedding_weight(self.get_input_embeddings())
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@ -1319,6 +1323,119 @@ class TFConv1D(tf.keras.layers.Layer):
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return x
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class WordEmbeddings(tf.keras.layers.Layer):
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def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.word_embeddings = self.add_weight(
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name="weight",
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shape=[self.vocab_size, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape=input_shape)
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def get_config(self):
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config = {
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"vocab_size": self.vocab_size,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, input_ids):
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flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
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embeddings = tf.gather(params=self.word_embeddings, indices=flat_input_ids)
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embeddings = tf.reshape(
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tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
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)
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embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
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return embeddings
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class TokenTypeEmbeddings(tf.keras.layers.Layer):
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def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.type_vocab_size = type_vocab_size
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.token_type_embeddings = self.add_weight(
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name="embeddings",
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shape=[self.type_vocab_size, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape=input_shape)
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def get_config(self):
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config = {
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"type_vocab_size": self.type_vocab_size,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, token_type_ids):
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flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
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one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
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embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
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embeddings = tf.reshape(
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tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
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)
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embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
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return embeddings
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class PositionEmbeddings(tf.keras.layers.Layer):
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def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.position_embeddings = self.add_weight(
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name="embeddings",
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shape=[self.max_position_embeddings, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape)
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def get_config(self):
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config = {
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"max_position_embeddings": self.max_position_embeddings,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, position_ids):
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input_shape = shape_list(tensor=position_ids)
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position_embeddings = self.position_embeddings[: input_shape[1], :]
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return tf.broadcast_to(input=position_embeddings, shape=input_shape)
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class TFSharedEmbeddings(tf.keras.layers.Layer):
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r"""
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Construct shared token embeddings.
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@ -73,124 +73,178 @@ TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
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]
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
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class TFAlbertWordEmbeddings(tf.keras.layers.Layer):
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def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.weight = self.add_weight(
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name="weight",
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shape=[self.vocab_size, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape=input_shape)
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def get_config(self):
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config = {
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"vocab_size": self.vocab_size,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, input_ids):
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flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
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embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
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embeddings = tf.reshape(
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tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
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)
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embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
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return embeddings
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
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class TFAlbertTokenTypeEmbeddings(tf.keras.layers.Layer):
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def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.type_vocab_size = type_vocab_size
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.token_type_embeddings = self.add_weight(
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name="embeddings",
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shape=[self.type_vocab_size, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape=input_shape)
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def get_config(self):
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config = {
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"type_vocab_size": self.type_vocab_size,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, token_type_ids):
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flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
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one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
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embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
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embeddings = tf.reshape(
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tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
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)
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embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
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return embeddings
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
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class TFAlbertPositionEmbeddings(tf.keras.layers.Layer):
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def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
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super().__init__(**kwargs)
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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def build(self, input_shape):
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self.position_embeddings = self.add_weight(
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name="embeddings",
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shape=[self.max_position_embeddings, self.hidden_size],
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initializer=get_initializer(initializer_range=self.initializer_range),
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)
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super().build(input_shape)
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def get_config(self):
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config = {
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"max_position_embeddings": self.max_position_embeddings,
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"hidden_size": self.hidden_size,
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"initializer_range": self.initializer_range,
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}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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def call(self, position_ids):
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input_shape = shape_list(tensor=position_ids)
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position_embeddings = self.position_embeddings[: input_shape[1], :]
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return tf.broadcast_to(input=position_embeddings, shape=input_shape)
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class TFAlbertEmbeddings(tf.keras.layers.Layer):
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"""Construct the embeddings from word, position and token_type embeddings."""
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def __init__(self, config, **kwargs):
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super().__init__(**kwargs)
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self.vocab_size = config.vocab_size
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self.embedding_size = config.embedding_size
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self.initializer_range = config.initializer_range
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self.max_position_embeddings = config.max_position_embeddings
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self.type_vocab_size = config.type_vocab_size
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self.layer_norm_eps = config.layer_norm_eps
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self.hidden_dropout_prob = config.hidden_dropout_prob
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self.position_embeddings = tf.keras.layers.Embedding(
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self.max_position_embeddings,
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self.embedding_size,
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embeddings_initializer=get_initializer(self.initializer_range),
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self.word_embeddings = TFAlbertWordEmbeddings(
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vocab_size=config.vocab_size,
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hidden_size=config.embedding_size,
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initializer_range=config.initializer_range,
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name="word_embeddings",
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)
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self.position_embeddings = TFAlbertPositionEmbeddings(
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max_position_embeddings=config.max_position_embeddings,
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hidden_size=config.embedding_size,
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initializer_range=config.initializer_range,
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name="position_embeddings",
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)
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self.token_type_embeddings = tf.keras.layers.Embedding(
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self.type_vocab_size,
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self.embedding_size,
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embeddings_initializer=get_initializer(self.initializer_range),
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self.token_type_embeddings = TFAlbertTokenTypeEmbeddings(
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type_vocab_size=config.type_vocab_size,
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hidden_size=config.embedding_size,
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initializer_range=config.initializer_range,
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name="token_type_embeddings",
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)
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self.embeddings_sum = tf.keras.layers.Add()
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self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
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self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
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# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
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# any TensorFlow checkpoint file
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self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=self.layer_norm_eps, name="LayerNorm")
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self.dropout = tf.keras.layers.Dropout(self.hidden_dropout_prob)
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def build(self, input_shape):
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"""Build shared word embedding layer """
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with tf.name_scope("word_embeddings"):
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# Create and initialize weights. The random normal initializer was chosen
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# arbitrarily, and works well.
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self.word_embeddings = self.add_weight(
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"weight",
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shape=[self.vocab_size, self.embedding_size],
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initializer=get_initializer(self.initializer_range),
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)
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super().build(input_shape)
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def call(
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self,
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input_ids=None,
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position_ids=None,
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token_type_ids=None,
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inputs_embeds=None,
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mode="embedding",
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training=False,
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):
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# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
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def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
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"""
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Get token embeddings of inputs
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Args:
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inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
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mode: string, a valid value is one of "embedding" and "linear"
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Applies embedding based on inputs tensor.
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Returns:
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outputs: (1) If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
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embedding_size]; (2) mode == "linear", output linear tensor, float32 with shape [batch_size, length,
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vocab_size]
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Raises:
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ValueError: if mode is not valid.
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Shared weights logic adapted from
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https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
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final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
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"""
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if mode == "embedding":
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return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
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elif mode == "linear":
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return self._linear(input_ids)
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else:
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raise ValueError("mode {} is not valid.".format(mode))
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def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
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"""Applies embedding based on inputs tensor."""
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assert not (input_ids is None and inputs_embeds is None)
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if input_ids is not None:
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input_shape = shape_list(input_ids)
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else:
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input_shape = shape_list(inputs_embeds)[:-1]
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inputs_embeds = self.word_embeddings(input_ids=input_ids)
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
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if token_type_ids is None:
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token_type_ids = tf.fill(input_shape, 0)
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input_shape = shape_list(tensor=inputs_embeds)[:-1]
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token_type_ids = tf.fill(dims=input_shape, value=0)
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if inputs_embeds is None:
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inputs_embeds = tf.gather(self.word_embeddings, input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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if position_ids is None:
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position_embeds = self.position_embeddings(position_ids=inputs_embeds)
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else:
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position_embeds = self.position_embeddings(position_ids=position_ids)
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embeddings = inputs_embeds + position_embeddings + token_type_embeddings
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embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
return embeddings
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, embedding_size
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.embedding_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
class TFAlbertSelfOutput(tf.keras.layers.Layer):
|
||||
@ -446,8 +500,9 @@ class TFAlbertPreTrainedModel(TFPreTrainedModel):
|
||||
class TFAlbertMLMHead(tf.keras.layers.Layer):
|
||||
def __init__(self, config, input_embeddings, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.embedding_size = config.embedding_size
|
||||
self.dense = tf.keras.layers.Dense(
|
||||
config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
|
||||
)
|
||||
@ -474,7 +529,7 @@ class TFAlbertMLMHead(tf.keras.layers.Layer):
|
||||
return self.decoder
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.decoder.word_embeddings = value
|
||||
self.decoder.weight = value
|
||||
self.decoder.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -486,10 +541,15 @@ class TFAlbertMLMHead(tf.keras.layers.Layer):
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, hidden_states):
|
||||
hidden_states = self.dense(hidden_states)
|
||||
hidden_states = self.activation(hidden_states)
|
||||
hidden_states = self.LayerNorm(hidden_states)
|
||||
hidden_states = self.decoder(hidden_states, mode="linear") + self.decoder_bias
|
||||
hidden_states = self.dense(inputs=hidden_states)
|
||||
hidden_states = self.activation(inputs=hidden_states)
|
||||
hidden_states = self.LayerNorm(inputs=hidden_states)
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.decoder_bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
@ -516,11 +576,11 @@ class TFAlbertMainLayer(tf.keras.layers.Layer):
|
||||
)
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
"""
|
||||
@ -844,7 +904,7 @@ class TFAlbertForPreTraining(TFAlbertPreTrainedModel):
|
||||
self.num_labels = config.num_labels
|
||||
|
||||
self.albert = TFAlbertMainLayer(config, name="albert")
|
||||
self.predictions = TFAlbertMLMHead(config, self.albert.embeddings, name="predictions")
|
||||
self.predictions = TFAlbertMLMHead(config, self.albert.embeddings.word_embeddings, name="predictions")
|
||||
self.sop_classifier = TFAlbertSOPHead(config, name="sop_classifier")
|
||||
|
||||
def get_lm_head(self):
|
||||
@ -964,7 +1024,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
|
||||
super().__init__(config, *inputs, **kwargs)
|
||||
|
||||
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert")
|
||||
self.predictions = TFAlbertMLMHead(config, self.albert.embeddings, name="predictions")
|
||||
self.predictions = TFAlbertMLMHead(config, self.albert.embeddings.word_embeddings, name="predictions")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.predictions
|
||||
|
||||
@ -121,124 +121,174 @@ class TFBertPreTrainingLoss:
|
||||
return masked_lm_loss + next_sentence_loss
|
||||
|
||||
|
||||
class TFBertWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
class TFBertTokenTypeEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
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),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"type_vocab_size": self.type_vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, token_type_ids):
|
||||
flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
|
||||
one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
|
||||
embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
class TFBertPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
input_shape = shape_list(tensor=position_ids)
|
||||
position_embeddings = self.position_embeddings[: input_shape[1], :]
|
||||
|
||||
return tf.broadcast_to(input=position_embeddings, shape=input_shape)
|
||||
|
||||
|
||||
class TFBertEmbeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word, position and token_type embeddings."""
|
||||
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TFBertWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFBertPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
self.token_type_embeddings = tf.keras.layers.Embedding(
|
||||
config.type_vocab_size,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.token_type_embeddings = TFBertTokenTypeEmbeddings(
|
||||
type_vocab_size=config.type_vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="token_type_embeddings",
|
||||
)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
position_ids=None,
|
||||
token_type_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
|
||||
seq_length = input_shape[1]
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
input_shape = shape_list(tensor=inputs_embeds)[:-1]
|
||||
token_type_ids = tf.fill(dims=input_shape, value=0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
if position_ids is None:
|
||||
position_embeds = self.position_embeddings(position_ids=inputs_embeds)
|
||||
else:
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
|
||||
position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
|
||||
token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
|
||||
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer.
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size].
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
class TFBertSelfAttention(tf.keras.layers.Layer):
|
||||
@ -251,8 +301,8 @@ class TFBertSelfAttention(tf.keras.layers.Layer):
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
|
||||
self.query = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abc,cde->abde",
|
||||
output_shape=(None, config.num_attention_heads, self.attention_head_size),
|
||||
@ -318,9 +368,9 @@ class TFBertSelfOutput(tf.keras.layers.Layer):
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
||||
self.all_head_size = config.num_attention_heads * self.attention_head_size
|
||||
|
||||
self.dense = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abcd,cde->abe",
|
||||
output_shape=(None, self.all_head_size),
|
||||
@ -516,6 +566,8 @@ class TFBertLMPredictionHead(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.transform = TFBertPredictionHeadTransform(config, name="transform")
|
||||
|
||||
# The output weights are the same as the input embeddings, but there is
|
||||
@ -531,7 +583,7 @@ class TFBertLMPredictionHead(tf.keras.layers.Layer):
|
||||
return self.input_embeddings
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.input_embeddings.word_embeddings = value
|
||||
self.input_embeddings.weight = value
|
||||
self.input_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -542,9 +594,12 @@ class TFBertLMPredictionHead(tf.keras.layers.Layer):
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, hidden_states):
|
||||
hidden_states = self.transform(hidden_states)
|
||||
hidden_states = self.input_embeddings(hidden_states, mode="linear")
|
||||
hidden_states = hidden_states + self.bias
|
||||
hidden_states = self.transform(hidden_states=hidden_states)
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
@ -583,21 +638,17 @@ class TFBertMainLayer(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.config = config
|
||||
self.num_hidden_layers = config.num_hidden_layers
|
||||
self.initializer_range = config.initializer_range
|
||||
self.output_attentions = config.output_attentions
|
||||
self.output_hidden_states = config.output_hidden_states
|
||||
self.return_dict = config.use_return_dict
|
||||
|
||||
self.embeddings = TFBertEmbeddings(config, name="embeddings")
|
||||
self.encoder = TFBertEncoder(config, name="encoder")
|
||||
self.pooler = TFBertPooler(config, name="pooler") if add_pooling_layer else None
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
"""
|
||||
@ -682,7 +733,7 @@ class TFBertMainLayer(tf.keras.layers.Layer):
|
||||
if inputs["head_mask"] is not None:
|
||||
raise NotImplementedError
|
||||
else:
|
||||
inputs["head_mask"] = [None] * self.num_hidden_layers
|
||||
inputs["head_mask"] = [None] * self.config.num_hidden_layers
|
||||
|
||||
encoder_outputs = self.encoder(
|
||||
embedding_output,
|
||||
@ -931,7 +982,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
|
||||
|
||||
self.bert = TFBertMainLayer(config, name="bert")
|
||||
self.nsp = TFBertNSPHead(config, name="nsp___cls")
|
||||
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
|
||||
self.mlm = TFBertMLMHead(config, self.bert.embeddings.word_embeddings, name="mlm___cls")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.mlm.predictions
|
||||
@ -1055,7 +1106,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
|
||||
)
|
||||
|
||||
self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
|
||||
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
|
||||
self.mlm = TFBertMLMHead(config, self.bert.embeddings.word_embeddings, name="mlm___cls")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.mlm.predictions
|
||||
@ -1158,7 +1209,7 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
|
||||
logger.warning("If you want to use `TFBertLMHeadModel` as a standalone, add `is_decoder=True.`")
|
||||
|
||||
self.bert = TFBertMainLayer(config, add_pooling_layer=False, name="bert")
|
||||
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")
|
||||
self.mlm = TFBertMLMHead(config, self.bert.embeddings.word_embeddings, name="mlm___cls")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.mlm.predictions
|
||||
|
||||
@ -67,104 +67,128 @@ TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
||||
]
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFDistilBertWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
|
||||
class TFDistilBertPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
input_shape = shape_list(tensor=position_ids)
|
||||
position_embeddings = self.position_embeddings[: input_shape[1], :]
|
||||
|
||||
return tf.broadcast_to(input=position_embeddings, shape=input_shape)
|
||||
|
||||
|
||||
class TFEmbeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word, position and token_type embeddings."""
|
||||
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.dim = config.dim
|
||||
self.initializer_range = config.initializer_range
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.dim,
|
||||
embeddings_initializer=get_initializer(config.initializer_range),
|
||||
|
||||
self.word_embeddings = TFDistilBertWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.dim,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFDistilBertPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.dim,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.dropout)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.dropout)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight", shape=[self.vocab_size, self.dim], initializer=get_initializer(self.initializer_range)
|
||||
)
|
||||
super().build(input_shape)
|
||||
|
||||
def call(self, input_ids=None, position_ids=None, inputs_embeds=None, mode="embedding", training=False):
|
||||
def call(self, input_ids=None, position_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of two int64 tensors with shape [batch_size, length]: (input_ids, position_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, position_ids, inputs_embeds, training=False):
|
||||
"""
|
||||
Parameters:
|
||||
input_ids: tf.Tensor(bs, max_seq_length) The token ids to embed.
|
||||
|
||||
Returns:
|
||||
tf.Tensor(bs, max_seq_length, dim) The embedded tokens (plus position embeddings, no token_type embeddings)
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
seq_length = shape_list(input_ids)[1]
|
||||
else:
|
||||
seq_length = shape_list(inputs_embeds)[1]
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
position_embeds = self.position_embeddings(position_ids=inputs_embeds)
|
||||
else:
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
position_embeddings = tf.cast(
|
||||
self.position_embeddings(position_ids), inputs_embeds.dtype
|
||||
) # (bs, max_seq_length, dim)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
embeddings = inputs_embeds + position_embeddings # (bs, max_seq_length, dim)
|
||||
embeddings = self.LayerNorm(embeddings) # (bs, max_seq_length, dim)
|
||||
embeddings = self.dropout(embeddings, training=training) # (bs, max_seq_length, dim)
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
|
||||
x = tf.reshape(inputs, [-1, self.dim])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
|
||||
@ -397,11 +421,11 @@ class TFDistilBertMainLayer(tf.keras.layers.Layer):
|
||||
self.transformer = TFTransformer(config, name="transformer") # Encoder
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = value.shape[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = value.shape[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
raise NotImplementedError
|
||||
@ -636,7 +660,9 @@ class TFDistilBertModel(TFDistilBertPreTrainedModel):
|
||||
class TFDistilBertLMHead(tf.keras.layers.Layer):
|
||||
def __init__(self, config, input_embeddings, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.dim = config.dim
|
||||
|
||||
# The output weights are the same as the input embeddings, but there is
|
||||
# an output-only bias for each token.
|
||||
@ -644,13 +670,14 @@ class TFDistilBertLMHead(tf.keras.layers.Layer):
|
||||
|
||||
def build(self, input_shape):
|
||||
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.input_embeddings
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.input_embeddings.word_embeddings = value
|
||||
self.input_embeddings.weight = value
|
||||
self.input_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -661,8 +688,12 @@ class TFDistilBertLMHead(tf.keras.layers.Layer):
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, hidden_states):
|
||||
hidden_states = self.input_embeddings(hidden_states, mode="linear")
|
||||
hidden_states = hidden_states + self.bias
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.dim])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
@ -681,7 +712,9 @@ class TFDistilBertForMaskedLM(TFDistilBertPreTrainedModel, TFMaskedLanguageModel
|
||||
)
|
||||
self.act = get_tf_activation("gelu")
|
||||
self.vocab_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="vocab_layer_norm")
|
||||
self.vocab_projector = TFDistilBertLMHead(config, self.distilbert.embeddings, name="vocab_projector")
|
||||
self.vocab_projector = TFDistilBertLMHead(
|
||||
config, self.distilbert.embeddings.word_embeddings, name="vocab_projector"
|
||||
)
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.vocab_projector
|
||||
|
||||
@ -70,6 +70,122 @@ TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
||||
]
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFElectraWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
|
||||
class TFElectraTokenTypeEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
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),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"type_vocab_size": self.type_vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, token_type_ids):
|
||||
flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
|
||||
one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
|
||||
embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
|
||||
class TFElectraPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
input_shape = shape_list(tensor=position_ids)
|
||||
position_embeddings = self.position_embeddings[: input_shape[1], :]
|
||||
|
||||
return tf.broadcast_to(input=position_embeddings, shape=input_shape)
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Electra
|
||||
class TFElectraSelfAttention(tf.keras.layers.Layer):
|
||||
def __init__(self, config, **kwargs):
|
||||
@ -81,8 +197,8 @@ class TFElectraSelfAttention(tf.keras.layers.Layer):
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
|
||||
self.query = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abc,cde->abde",
|
||||
output_shape=(None, config.num_attention_heads, self.attention_head_size),
|
||||
@ -138,7 +254,7 @@ class TFElectraSelfAttention(tf.keras.layers.Layer):
|
||||
return outputs
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Electra
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput
|
||||
class TFElectraSelfOutput(tf.keras.layers.Layer):
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
@ -149,9 +265,9 @@ class TFElectraSelfOutput(tf.keras.layers.Layer):
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
||||
self.all_head_size = config.num_attention_heads * self.attention_head_size
|
||||
|
||||
self.dense = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abcd,cde->abe",
|
||||
output_shape=(None, self.all_head_size),
|
||||
@ -331,120 +447,56 @@ class TFElectraEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.embedding_size = config.embedding_size
|
||||
self.initializer_range = config.initializer_range
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.embedding_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TFElectraWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.embedding_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFElectraPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.embedding_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
self.token_type_embeddings = tf.keras.layers.Embedding(
|
||||
config.type_vocab_size,
|
||||
config.embedding_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.token_type_embeddings = TFElectraTokenTypeEmbeddings(
|
||||
type_vocab_size=config.type_vocab_size,
|
||||
hidden_size=config.embedding_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="token_type_embeddings",
|
||||
)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.embedding_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
position_ids=None,
|
||||
token_type_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
# Copied from transformers.models.albert.modeling_tf_albert.TFAlbertEmbeddings.call with Albert->Electra
|
||||
def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings._embedding
|
||||
def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
|
||||
seq_length = input_shape[1]
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
input_shape = shape_list(tensor=inputs_embeds)[:-1]
|
||||
token_type_ids = tf.fill(dims=input_shape, value=0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
if position_ids is None:
|
||||
position_embeds = self.position_embeddings(position_ids=inputs_embeds)
|
||||
else:
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
|
||||
position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
|
||||
token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
|
||||
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer.
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.embedding_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
class TFElectraDiscriminatorPredictions(tf.keras.layers.Layer):
|
||||
@ -508,11 +560,11 @@ class TFElectraMainLayer(tf.keras.layers.Layer):
|
||||
self.config = config
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
"""
|
||||
@ -903,6 +955,7 @@ class TFElectraMaskedLMHead(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.embedding_size = config.embedding_size
|
||||
self.input_embeddings = input_embeddings
|
||||
|
||||
def build(self, input_shape):
|
||||
@ -914,7 +967,7 @@ class TFElectraMaskedLMHead(tf.keras.layers.Layer):
|
||||
return self.input_embeddings
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.input_embeddings.word_embeddings = value
|
||||
self.input_embeddings.weight = value
|
||||
self.input_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -924,9 +977,12 @@ class TFElectraMaskedLMHead(tf.keras.layers.Layer):
|
||||
self.bias = value["bias"]
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, hidden_states, training=False):
|
||||
hidden_states = self.input_embeddings(hidden_states, mode="linear")
|
||||
hidden_states = hidden_states + self.bias
|
||||
def call(self, hidden_states):
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
@ -953,7 +1009,9 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
|
||||
else:
|
||||
self.activation = config.hidden_act
|
||||
|
||||
self.generator_lm_head = TFElectraMaskedLMHead(config, self.electra.embeddings, name="generator_lm_head")
|
||||
self.generator_lm_head = TFElectraMaskedLMHead(
|
||||
config, self.electra.embeddings.word_embeddings, name="generator_lm_head"
|
||||
)
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.generator_lm_head
|
||||
|
||||
@ -74,89 +74,78 @@ TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
||||
INF = 1e6
|
||||
|
||||
|
||||
class TFFunnelEmbeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word embeddings."""
|
||||
|
||||
def __init__(self, config, **kwargs):
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFFunnelWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
|
||||
self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
super().build(input_shape)
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
"""
|
||||
Get token embeddings of inputs
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear"
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
Returns:
|
||||
outputs: (1) If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; (2) mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size]
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
|
||||
embeddings = self.layer_norm(inputs_embeds)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size
|
||||
class TFFunnelEmbeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word, position and token_type embeddings."""
|
||||
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.word_embeddings = TFFunnelWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout)
|
||||
|
||||
def call(self, input_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
assert not (input_ids is not None and inputs_embeds is not None)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
if input_ids is not None:
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
final_embeddings = self.LayerNorm(inputs=inputs_embeds)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
return final_embeddings
|
||||
|
||||
|
||||
class TFFunnelAttentionStructure:
|
||||
@ -784,11 +773,11 @@ class TFFunnelBaseLayer(tf.keras.layers.Layer):
|
||||
self.encoder = TFFunnelEncoder(config, name="encoder")
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
|
||||
@ -870,11 +859,11 @@ class TFFunnelMainLayer(tf.keras.layers.Layer):
|
||||
self.decoder = TFFunnelDecoder(config, name="decoder")
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
raise NotImplementedError # Not implemented yet in the library fr TF 2.0 models
|
||||
@ -987,17 +976,19 @@ class TFFunnelMaskedLMHead(tf.keras.layers.Layer):
|
||||
def __init__(self, config, input_embeddings, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.input_embeddings = input_embeddings
|
||||
|
||||
def build(self, input_shape):
|
||||
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.input_embeddings
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.input_embeddings.word_embeddings = value
|
||||
self.input_embeddings.weight = value
|
||||
self.input_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -1008,8 +999,12 @@ class TFFunnelMaskedLMHead(tf.keras.layers.Layer):
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, hidden_states, training=False):
|
||||
hidden_states = self.input_embeddings(hidden_states, mode="linear")
|
||||
hidden_states = hidden_states + self.bias
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
@ -1362,7 +1357,7 @@ class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss)
|
||||
super().__init__(config, *inputs, **kwargs)
|
||||
|
||||
self.funnel = TFFunnelMainLayer(config, name="funnel")
|
||||
self.lm_head = TFFunnelMaskedLMHead(config, self.funnel.embeddings, name="lm_head")
|
||||
self.lm_head = TFFunnelMaskedLMHead(config, self.funnel.embeddings.word_embeddings, name="lm_head")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.lm_head
|
||||
|
||||
@ -415,14 +415,135 @@ def _compute_global_attention_mask(input_ids_shape, sep_token_indices, before_se
|
||||
return attention_mask
|
||||
|
||||
|
||||
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFLongformerWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
|
||||
class TFLongformerTokenTypeEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
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),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"type_vocab_size": self.type_vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, token_type_ids):
|
||||
flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
|
||||
one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
|
||||
embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
class TFLongformerPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
flat_position_ids = tf.reshape(tensor=position_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.position_embeddings, indices=flat_position_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=position_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=position_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->Longformer
|
||||
class TFLongformerLMHead(tf.keras.layers.Layer):
|
||||
"""Roberta Head for masked language modeling."""
|
||||
"""Longformer Head for masked language modeling."""
|
||||
|
||||
def __init__(self, config, input_embeddings, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.dense = tf.keras.layers.Dense(
|
||||
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
|
||||
)
|
||||
@ -442,7 +563,7 @@ class TFLongformerLMHead(tf.keras.layers.Layer):
|
||||
return self.decoder
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.decoder.word_embeddings = value
|
||||
self.decoder.weight = value
|
||||
self.decoder.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -458,11 +579,16 @@ class TFLongformerLMHead(tf.keras.layers.Layer):
|
||||
hidden_states = self.layer_norm(hidden_states)
|
||||
|
||||
# project back to size of vocabulary with bias
|
||||
hidden_states = self.decoder(hidden_states, mode="linear") + self.bias
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaEmbeddings with Roberta->Longformer
|
||||
class TFLongformerEmbeddings(tf.keras.layers.Layer):
|
||||
"""
|
||||
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
|
||||
@ -472,39 +598,27 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.padding_idx = 1
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TFLongformerWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFLongformerPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
self.token_type_embeddings = tf.keras.layers.Embedding(
|
||||
config.type_vocab_size,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.token_type_embeddings = TFLongformerTokenTypeEmbeddings(
|
||||
type_vocab_size=config.type_vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="token_type_embeddings",
|
||||
)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def create_position_ids_from_input_ids(self, input_ids):
|
||||
"""
|
||||
@ -516,14 +630,16 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
|
||||
|
||||
Returns: tf.Tensor
|
||||
"""
|
||||
input_ids_shape = shape_list(input_ids)
|
||||
input_ids_shape = shape_list(tensor=input_ids)
|
||||
|
||||
# multiple choice has 3 dimensions
|
||||
if len(input_ids_shape) == 3:
|
||||
input_ids = tf.reshape(input_ids, (input_ids_shape[0] * input_ids_shape[1], input_ids_shape[2]))
|
||||
input_ids = tf.reshape(
|
||||
tensor=input_ids, shape=(input_ids_shape[0] * input_ids_shape[1], input_ids_shape[2])
|
||||
)
|
||||
|
||||
mask = tf.cast(tf.math.not_equal(input_ids, self.padding_idx), dtype=tf.int32)
|
||||
incremental_indices = tf.math.cumsum(mask, axis=1) * mask
|
||||
mask = tf.cast(x=tf.math.not_equal(x=input_ids, y=self.padding_idx), dtype=input_ids.dtype)
|
||||
incremental_indices = tf.math.cumsum(x=mask, axis=1) * mask
|
||||
|
||||
return incremental_indices + self.padding_idx
|
||||
|
||||
@ -536,96 +652,41 @@ class TFLongformerEmbeddings(tf.keras.layers.Layer):
|
||||
|
||||
Returns: tf.Tensor
|
||||
"""
|
||||
seq_length = shape_list(inputs_embeds)[1]
|
||||
position_ids = tf.range(self.padding_idx + 1, seq_length + self.padding_idx + 1, dtype=tf.int32)[tf.newaxis, :]
|
||||
batch_size, seq_length = shape_list(tensor=inputs_embeds)[:2]
|
||||
position_ids = tf.range(start=self.padding_idx + 1, limit=seq_length + self.padding_idx + 1)[tf.newaxis, :]
|
||||
|
||||
return position_ids
|
||||
return tf.tile(input=position_ids, multiples=(batch_size, 1))
|
||||
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
position_ids=None,
|
||||
token_type_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
if token_type_ids is None:
|
||||
input_shape = shape_list(tensor=inputs_embeds)[:-1]
|
||||
token_type_ids = tf.fill(dims=input_shape, value=0)
|
||||
|
||||
if position_ids is None:
|
||||
if input_ids is not None:
|
||||
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
||||
position_ids = self.create_position_ids_from_input_ids(input_ids)
|
||||
position_ids = self.create_position_ids_from_input_ids(input_ids=input_ids)
|
||||
else:
|
||||
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
||||
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds=inputs_embeds)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
seq_length = input_shape[1]
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
|
||||
position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
|
||||
token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
|
||||
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer.
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate
|
||||
@ -1613,11 +1674,11 @@ class TFLongformerMainLayer(tf.keras.layers.Layer):
|
||||
self.pooler = TFLongformerPooler(config, name="pooler") if add_pooling_layer else None
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
"""
|
||||
@ -2053,7 +2114,7 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
|
||||
super().__init__(config, *inputs, **kwargs)
|
||||
|
||||
self.longformer = TFLongformerMainLayer(config, add_pooling_layer=False, name="longformer")
|
||||
self.lm_head = TFLongformerLMHead(config, self.longformer.embeddings, name="lm_head")
|
||||
self.lm_head = TFLongformerLMHead(config, self.longformer.embeddings.word_embeddings, name="lm_head")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.lm_head
|
||||
|
||||
@ -177,112 +177,173 @@ class TFLxmertVisualFeatureEncoder(tf.keras.layers.Layer):
|
||||
return output
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFLxmertWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
|
||||
class TFLxmertTokenTypeEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
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),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"type_vocab_size": self.type_vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, token_type_ids):
|
||||
flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
|
||||
one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
|
||||
embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
|
||||
class TFLxmertPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
input_shape = shape_list(tensor=position_ids)
|
||||
position_embeddings = self.position_embeddings[: input_shape[1], :]
|
||||
|
||||
return tf.broadcast_to(input=position_embeddings, shape=input_shape)
|
||||
|
||||
|
||||
class TFLxmertEmbeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word, position and token_type embeddings."""
|
||||
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TFLxmertWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFLxmertPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
self.token_type_embeddings = tf.keras.layers.Embedding(
|
||||
config.type_vocab_size,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.token_type_embeddings = TFLxmertTokenTypeEmbeddings(
|
||||
type_vocab_size=config.type_vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="token_type_embeddings",
|
||||
)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
super().build(input_shape)
|
||||
|
||||
def call(self, inputs, mode="embedding", training=False):
|
||||
def call(self, input_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(inputs, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(inputs)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, inputs, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
input_ids, token_type_ids, inputs_embeds = inputs
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
seq_length = input_shape[1]
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
input_shape = shape_list(tensor=inputs_embeds)[:-1]
|
||||
token_type_ids = tf.fill(dims=input_shape, value=0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
position_embeddings = self.position_embeddings(position_ids)
|
||||
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
||||
position_embeds = self.position_embeddings(position_ids=inputs_embeds)
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer.
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
class TFLxmertAttention(tf.keras.layers.Layer):
|
||||
@ -703,11 +764,11 @@ class TFLxmertMainLayer(tf.keras.layers.Layer):
|
||||
self.config = config
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
raise NotImplementedError
|
||||
@ -787,7 +848,7 @@ class TFLxmertMainLayer(tf.keras.layers.Layer):
|
||||
|
||||
# Positional Word Embeddings
|
||||
embedding_output = self.embeddings(
|
||||
[inputs["input_ids"], inputs["token_type_ids"], inputs["inputs_embeds"]], training=inputs["training"]
|
||||
inputs["input_ids"], inputs["token_type_ids"], inputs["inputs_embeds"], training=inputs["training"]
|
||||
)
|
||||
|
||||
# Run Lxmert encoder
|
||||
@ -1066,31 +1127,38 @@ class TFLxmertPooler(tf.keras.layers.Layer):
|
||||
return pooled_output
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Lxmert
|
||||
class TFLxmertPredictionHeadTransform(tf.keras.layers.Layer):
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.dense = tf.keras.layers.Dense(
|
||||
config.hidden_size,
|
||||
kernel_initializer=get_initializer(config.initializer_range),
|
||||
name="dense",
|
||||
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
|
||||
)
|
||||
|
||||
if isinstance(config.hidden_act, str):
|
||||
self.transform_act_fn = get_tf_activation(config.hidden_act)
|
||||
else:
|
||||
self.transform_act_fn = config.hidden_act
|
||||
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
|
||||
def call(self, hidden_states):
|
||||
hidden_states = self.dense(hidden_states)
|
||||
hidden_states = self.transform_act_fn(hidden_states)
|
||||
hidden_states = self.LayerNorm(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Lxmert
|
||||
class TFLxmertLMPredictionHead(tf.keras.layers.Layer):
|
||||
def __init__(self, config, input_embeddings, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.transform = TFLxmertPredictionHeadTransform(config, name="transform")
|
||||
|
||||
# The output weights are the same as the input embeddings, but there is
|
||||
@ -1099,13 +1167,14 @@ class TFLxmertLMPredictionHead(tf.keras.layers.Layer):
|
||||
|
||||
def build(self, input_shape):
|
||||
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.input_embeddings
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.input_embeddings.word_embeddings = value
|
||||
self.input_embeddings.weight = value
|
||||
self.input_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -1116,12 +1185,17 @@ class TFLxmertLMPredictionHead(tf.keras.layers.Layer):
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, hidden_states):
|
||||
hidden_states = self.transform(hidden_states)
|
||||
hidden_states = self.input_embeddings(hidden_states, mode="linear")
|
||||
hidden_states = hidden_states + self.bias
|
||||
hidden_states = self.transform(hidden_states=hidden_states)
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->Lxmert
|
||||
class TFLxmertMLMHead(tf.keras.layers.Layer):
|
||||
def __init__(self, config, input_embeddings, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
@ -1130,6 +1204,7 @@ class TFLxmertMLMHead(tf.keras.layers.Layer):
|
||||
|
||||
def call(self, sequence_output):
|
||||
prediction_scores = self.predictions(sequence_output)
|
||||
|
||||
return prediction_scores
|
||||
|
||||
|
||||
@ -1229,7 +1304,7 @@ class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
|
||||
self.lxmert = TFLxmertMainLayer(config, name="lxmert")
|
||||
|
||||
# Pre-training heads
|
||||
self.cls = TFLxmertPreTrainingHeads(config, self.lxmert.embeddings, name="cls")
|
||||
self.cls = TFLxmertPreTrainingHeads(config, self.lxmert.embeddings.word_embeddings, name="cls")
|
||||
if self.task_obj_predict:
|
||||
self.obj_predict_head = TFLxmertVisualObjHead(config, name="obj_predict_head")
|
||||
if self.task_qa:
|
||||
|
||||
@ -107,30 +107,150 @@ class TFNoNorm(tf.keras.layers.Layer):
|
||||
NORM2FN = {"layer_norm": TFLayerNorm, "no_norm": TFNoNorm}
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFMobileBertWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
|
||||
class TFMobileBertTokenTypeEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
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),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"type_vocab_size": self.type_vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, token_type_ids):
|
||||
flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
|
||||
one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
|
||||
embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
|
||||
class TFMobileBertPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
input_shape = shape_list(tensor=position_ids)
|
||||
position_embeddings = self.position_embeddings[: input_shape[1], :]
|
||||
|
||||
return tf.broadcast_to(input=position_embeddings, shape=input_shape)
|
||||
|
||||
|
||||
class TFMobileBertEmbeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word, position and token_type embeddings."""
|
||||
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.trigram_input = config.trigram_input
|
||||
self.embedding_size = config.embedding_size
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TFMobileBertWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.embedding_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFMobileBertPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
self.token_type_embeddings = tf.keras.layers.Embedding(
|
||||
config.type_vocab_size,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.token_type_embeddings = TFMobileBertTokenTypeEmbeddings(
|
||||
type_vocab_size=config.type_vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="token_type_embeddings",
|
||||
)
|
||||
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.embedding_transformation = tf.keras.layers.Dense(config.hidden_size, name="embedding_transformation")
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
@ -138,71 +258,23 @@ class TFMobileBertEmbeddings(tf.keras.layers.Layer):
|
||||
self.LayerNorm = NORM2FN[config.normalization_type](
|
||||
config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
|
||||
)
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.embedding_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
super().build(input_shape)
|
||||
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
position_ids=None,
|
||||
token_type_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
seq_length = input_shape[1]
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
input_shape = shape_list(tensor=inputs_embeds)[:-1]
|
||||
token_type_ids = tf.fill(dims=input_shape, value=0)
|
||||
|
||||
if self.trigram_input:
|
||||
# From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
|
||||
@ -224,32 +296,17 @@ class TFMobileBertEmbeddings(tf.keras.layers.Layer):
|
||||
if self.trigram_input or self.embedding_size != self.hidden_size:
|
||||
inputs_embeds = self.embedding_transformation(inputs_embeds)
|
||||
|
||||
position_embeddings = self.position_embeddings(position_ids)
|
||||
token_type_embeddings = self.token_type_embeddings(token_type_ids)
|
||||
if position_ids is None:
|
||||
position_embeds = self.position_embeddings(position_ids=inputs_embeds)
|
||||
else:
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
|
||||
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer.
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
class TFMobileBertSelfAttention(tf.keras.layers.Layer):
|
||||
@ -715,11 +772,11 @@ class TFMobileBertMainLayer(tf.keras.layers.Layer):
|
||||
self.pooler = TFMobileBertPooler(config, name="pooler") if add_pooling_layer else None
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
"""
|
||||
|
||||
@ -86,6 +86,86 @@ class TFMPNetPreTrainedModel(TFPreTrainedModel):
|
||||
return self.serving_output(output)
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFMPNetWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.longformer.modeling_tf_longformer.TFLongformerPositionEmbeddings
|
||||
class TFMPNetPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
flat_position_ids = tf.reshape(tensor=position_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.position_embeddings, indices=flat_position_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=position_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=position_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
class TFMPNetEmbeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word, position embeddings."""
|
||||
|
||||
@ -93,136 +173,84 @@ class TFMPNetEmbeddings(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.padding_idx = 1
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TFMPNetWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFMPNetPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer"""
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def create_position_ids_from_input_ids(self, x):
|
||||
def create_position_ids_from_input_ids(self, input_ids):
|
||||
"""
|
||||
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
|
||||
symbols are ignored. This is modified from fairseq's `utils.make_positions`. :param tf.Tensor x: :return
|
||||
tf.Tensor:
|
||||
"""
|
||||
mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
|
||||
incremental_indicies = tf.math.cumsum(mask, axis=1) * mask
|
||||
symbols are ignored. This is modified from fairseq's `utils.make_positions`.
|
||||
|
||||
return incremental_indicies + self.padding_idx
|
||||
Args:
|
||||
input_ids: tf.Tensor
|
||||
|
||||
Returns: tf.Tensor
|
||||
"""
|
||||
input_ids_shape = shape_list(tensor=input_ids)
|
||||
|
||||
# multiple choice has 3 dimensions
|
||||
if len(input_ids_shape) == 3:
|
||||
input_ids = tf.reshape(
|
||||
tensor=input_ids, shape=(input_ids_shape[0] * input_ids_shape[1], input_ids_shape[2])
|
||||
)
|
||||
|
||||
mask = tf.cast(x=tf.math.not_equal(x=input_ids, y=self.padding_idx), dtype=input_ids.dtype)
|
||||
incremental_indices = tf.math.cumsum(x=mask, axis=1) * mask
|
||||
|
||||
return incremental_indices + self.padding_idx
|
||||
|
||||
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
|
||||
"""
|
||||
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
|
||||
:param tf.Tensor inputs_embeds: :return tf.Tensor:
|
||||
"""
|
||||
seq_length = shape_list(inputs_embeds)[1]
|
||||
position_ids = tf.range(self.padding_idx + 1, seq_length + self.padding_idx + 1, dtype=tf.int32)[tf.newaxis, :]
|
||||
|
||||
return position_ids
|
||||
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
position_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
"""
|
||||
Get token embeddings of inputs
|
||||
|
||||
Args:
|
||||
inputs: list of two int64 tensors with shape [batch_size, length]: (input_ids, position_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear"
|
||||
inputs_embeds: tf.Tensor
|
||||
|
||||
Returns: tf.Tensor
|
||||
"""
|
||||
batch_size, seq_length = shape_list(tensor=inputs_embeds)[:2]
|
||||
position_ids = tf.range(start=self.padding_idx + 1, limit=seq_length + self.padding_idx + 1)[tf.newaxis, :]
|
||||
|
||||
return tf.tile(input=position_ids, multiples=(batch_size, 1))
|
||||
|
||||
def call(self, input_ids=None, position_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: (1) If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; (2) mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size]
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid. Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, position_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
if position_ids is None:
|
||||
if input_ids is not None:
|
||||
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
||||
position_ids = self.create_position_ids_from_input_ids(input_ids)
|
||||
position_ids = self.create_position_ids_from_input_ids(input_ids=input_ids)
|
||||
else:
|
||||
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
||||
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds=inputs_embeds)
|
||||
|
||||
assert position_ids is None or len(position_ids.shape) <= 2
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
|
||||
seq_length = input_shape[1]
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
|
||||
position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
|
||||
embeddings = inputs_embeds + position_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler
|
||||
@ -536,12 +564,12 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
@ -808,6 +836,7 @@ class TFMPNetLMHead(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.dense = tf.keras.layers.Dense(
|
||||
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
|
||||
)
|
||||
@ -827,7 +856,7 @@ class TFMPNetLMHead(tf.keras.layers.Layer):
|
||||
return self.decoder
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.decoder.word_embeddings = value
|
||||
self.decoder.weight = value
|
||||
self.decoder.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -837,15 +866,19 @@ class TFMPNetLMHead(tf.keras.layers.Layer):
|
||||
self.bias = value["bias"]
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, features):
|
||||
x = self.dense(features)
|
||||
x = self.act(x)
|
||||
x = self.layer_norm(x)
|
||||
def call(self, hidden_states):
|
||||
hidden_states = self.dense(hidden_states)
|
||||
hidden_states = self.act(hidden_states)
|
||||
hidden_states = self.layer_norm(hidden_states)
|
||||
|
||||
# project back to size of vocabulary with bias
|
||||
x = self.decoder(x, mode="linear") + self.bias
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return x
|
||||
return hidden_states
|
||||
|
||||
|
||||
@add_start_docstrings("""MPNet Model with a `language modeling` head on top. """, MPNET_START_DOCSTRING)
|
||||
@ -857,7 +890,7 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
|
||||
super().__init__(config, *inputs, **kwargs)
|
||||
|
||||
self.mpnet = TFMPNetMainLayer(config, name="mpnet")
|
||||
self.lm_head = TFMPNetLMHead(config, self.mpnet.embeddings, name="lm_head")
|
||||
self.lm_head = TFMPNetLMHead(config, self.mpnet.embeddings.word_embeddings, name="lm_head")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.lm_head
|
||||
|
||||
@ -65,6 +65,127 @@ TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
||||
]
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TFRobertaWordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
|
||||
class TFRobertaTokenTypeEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
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),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"type_vocab_size": self.type_vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, token_type_ids):
|
||||
flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
|
||||
one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
|
||||
embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.longformer.modeling_tf_longformer.TFLongformerPositionEmbeddings
|
||||
class TFRobertaPositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
flat_position_ids = tf.reshape(tensor=position_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.position_embeddings, indices=flat_position_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=position_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=position_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
class TFRobertaEmbeddings(tf.keras.layers.Layer):
|
||||
"""
|
||||
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
|
||||
@ -74,52 +195,48 @@ class TFRobertaEmbeddings(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.padding_idx = 1
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TFRobertaWordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TFRobertaPositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
self.token_type_embeddings = tf.keras.layers.Embedding(
|
||||
config.type_vocab_size,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.token_type_embeddings = TFRobertaTokenTypeEmbeddings(
|
||||
type_vocab_size=config.type_vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="token_type_embeddings",
|
||||
)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def create_position_ids_from_input_ids(self, x):
|
||||
def create_position_ids_from_input_ids(self, input_ids):
|
||||
"""
|
||||
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
|
||||
symbols are ignored. This is modified from fairseq's `utils.make_positions`.
|
||||
|
||||
Args:
|
||||
x: tf.Tensor
|
||||
input_ids: tf.Tensor
|
||||
|
||||
Returns: tf.Tensor
|
||||
"""
|
||||
mask = tf.cast(tf.math.not_equal(x, self.padding_idx), dtype=tf.int32)
|
||||
incremental_indices = tf.math.cumsum(mask, axis=1) * mask
|
||||
input_ids_shape = shape_list(tensor=input_ids)
|
||||
|
||||
# multiple choice has 3 dimensions
|
||||
if len(input_ids_shape) == 3:
|
||||
input_ids = tf.reshape(
|
||||
tensor=input_ids, shape=(input_ids_shape[0] * input_ids_shape[1], input_ids_shape[2])
|
||||
)
|
||||
|
||||
mask = tf.cast(x=tf.math.not_equal(x=input_ids, y=self.padding_idx), dtype=input_ids.dtype)
|
||||
incremental_indices = tf.math.cumsum(x=mask, axis=1) * mask
|
||||
|
||||
return incremental_indices + self.padding_idx
|
||||
|
||||
@ -132,96 +249,41 @@ class TFRobertaEmbeddings(tf.keras.layers.Layer):
|
||||
|
||||
Returns: tf.Tensor
|
||||
"""
|
||||
seq_length = shape_list(inputs_embeds)[1]
|
||||
position_ids = tf.range(self.padding_idx + 1, seq_length + self.padding_idx + 1, dtype=tf.int32)[tf.newaxis, :]
|
||||
batch_size, seq_length = shape_list(tensor=inputs_embeds)[:2]
|
||||
position_ids = tf.range(start=self.padding_idx + 1, limit=seq_length + self.padding_idx + 1)[tf.newaxis, :]
|
||||
|
||||
return position_ids
|
||||
return tf.tile(input=position_ids, multiples=(batch_size, 1))
|
||||
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
position_ids=None,
|
||||
token_type_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
if token_type_ids is None:
|
||||
input_shape = shape_list(tensor=inputs_embeds)[:-1]
|
||||
token_type_ids = tf.fill(dims=input_shape, value=0)
|
||||
|
||||
if position_ids is None:
|
||||
if input_ids is not None:
|
||||
# Create the position ids from the input token ids. Any padded tokens remain padded.
|
||||
position_ids = self.create_position_ids_from_input_ids(input_ids)
|
||||
position_ids = self.create_position_ids_from_input_ids(input_ids=input_ids)
|
||||
else:
|
||||
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
|
||||
position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds=inputs_embeds)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
seq_length = input_shape[1]
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
|
||||
position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
|
||||
token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
|
||||
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer.
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler
|
||||
@ -245,7 +307,7 @@ class TFRobertaPooler(tf.keras.layers.Layer):
|
||||
return pooled_output
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Roberta
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention
|
||||
class TFRobertaSelfAttention(tf.keras.layers.Layer):
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
@ -256,8 +318,8 @@ class TFRobertaSelfAttention(tf.keras.layers.Layer):
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
|
||||
self.query = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abc,cde->abde",
|
||||
output_shape=(None, config.num_attention_heads, self.attention_head_size),
|
||||
@ -293,7 +355,7 @@ class TFRobertaSelfAttention(tf.keras.layers.Layer):
|
||||
attention_scores = tf.einsum("aecd,abcd->acbe", key_layer, query_layer)
|
||||
|
||||
if attention_mask is not None:
|
||||
# Apply the attention mask is (precomputed for all layers in TFRobertaModel call() function)
|
||||
# Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
|
||||
attention_scores = attention_scores + attention_mask
|
||||
|
||||
# Normalize the attention scores to probabilities.
|
||||
@ -324,9 +386,9 @@ class TFRobertaSelfOutput(tf.keras.layers.Layer):
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
||||
self.all_head_size = config.num_attention_heads * self.attention_head_size
|
||||
|
||||
self.dense = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abcd,cde->abe",
|
||||
output_shape=(None, self.all_head_size),
|
||||
@ -499,12 +561,12 @@ class TFRobertaMainLayer(tf.keras.layers.Layer):
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
@ -814,6 +876,7 @@ class TFRobertaLMHead(tf.keras.layers.Layer):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.dense = tf.keras.layers.Dense(
|
||||
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
|
||||
)
|
||||
@ -833,7 +896,7 @@ class TFRobertaLMHead(tf.keras.layers.Layer):
|
||||
return self.decoder
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.decoder.word_embeddings = value
|
||||
self.decoder.weight = value
|
||||
self.decoder.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -849,7 +912,11 @@ class TFRobertaLMHead(tf.keras.layers.Layer):
|
||||
hidden_states = self.layer_norm(hidden_states)
|
||||
|
||||
# project back to size of vocabulary with bias
|
||||
hidden_states = self.decoder(hidden_states, mode="linear") + self.bias
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
@ -863,7 +930,7 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLos
|
||||
super().__init__(config, *inputs, **kwargs)
|
||||
|
||||
self.roberta = TFRobertaMainLayer(config, add_pooling_layer=False, name="roberta")
|
||||
self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings, name="lm_head")
|
||||
self.lm_head = TFRobertaLMHead(config, self.roberta.embeddings.word_embeddings, name="lm_head")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.lm_head
|
||||
|
||||
@ -66,6 +66,122 @@ TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
||||
]
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertWordEmbeddings
|
||||
class TF{{cookiecutter.camelcase_modelname}}WordEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.weight = self.add_weight(
|
||||
name="weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"vocab_size": self.vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, input_ids):
|
||||
flat_input_ids = tf.reshape(tensor=input_ids, shape=[-1])
|
||||
embeddings = tf.gather(params=self.weight, indices=flat_input_ids)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=input_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=input_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertTokenTypeEmbeddings
|
||||
class TF{{cookiecutter.camelcase_modelname}}TokenTypeEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, type_vocab_size: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.type_vocab_size = type_vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
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),
|
||||
)
|
||||
|
||||
super().build(input_shape=input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"type_vocab_size": self.type_vocab_size,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, token_type_ids):
|
||||
flat_token_type_ids = tf.reshape(tensor=token_type_ids, shape=[-1])
|
||||
one_hot_data = tf.one_hot(indices=flat_token_type_ids, depth=self.type_vocab_size, dtype=self._compute_dtype)
|
||||
embeddings = tf.matmul(a=one_hot_data, b=self.token_type_embeddings)
|
||||
embeddings = tf.reshape(
|
||||
tensor=embeddings, shape=tf.concat(values=[shape_list(tensor=token_type_ids), [self.hidden_size]], axis=0)
|
||||
)
|
||||
|
||||
embeddings.set_shape(shape=token_type_ids.shape.as_list() + [self.hidden_size])
|
||||
|
||||
return embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPositionEmbeddings
|
||||
class TF{{cookiecutter.camelcase_modelname}}PositionEmbeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, max_position_embeddings: int, hidden_size: int, initializer_range: float, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.initializer_range = initializer_range
|
||||
|
||||
def build(self, input_shape):
|
||||
self.position_embeddings = self.add_weight(
|
||||
name="embeddings",
|
||||
shape=[self.max_position_embeddings, self.hidden_size],
|
||||
initializer=get_initializer(initializer_range=self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
"max_position_embeddings": self.max_position_embeddings,
|
||||
"hidden_size": self.hidden_size,
|
||||
"initializer_range": self.initializer_range,
|
||||
}
|
||||
base_config = super().get_config()
|
||||
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
def call(self, position_ids):
|
||||
input_shape = shape_list(tensor=position_ids)
|
||||
position_embeddings = self.position_embeddings[: input_shape[1], :]
|
||||
|
||||
return tf.broadcast_to(input=position_embeddings, shape=input_shape)
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}}
|
||||
class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
|
||||
"""Construct the embeddings from word, position and token_type embeddings."""
|
||||
@ -73,121 +189,59 @@ class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer):
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
self.initializer_range = config.initializer_range
|
||||
self.position_embeddings = tf.keras.layers.Embedding(
|
||||
config.max_position_embeddings,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.word_embeddings = TF{{cookiecutter.camelcase_modelname}}WordEmbeddings(
|
||||
vocab_size=config.vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="word_embeddings",
|
||||
)
|
||||
self.position_embeddings = TF{{cookiecutter.camelcase_modelname}}PositionEmbeddings(
|
||||
max_position_embeddings=config.max_position_embeddings,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="position_embeddings",
|
||||
)
|
||||
self.token_type_embeddings = tf.keras.layers.Embedding(
|
||||
config.type_vocab_size,
|
||||
config.hidden_size,
|
||||
embeddings_initializer=get_initializer(self.initializer_range),
|
||||
self.token_type_embeddings = TF{{cookiecutter.camelcase_modelname}}TokenTypeEmbeddings(
|
||||
type_vocab_size=config.type_vocab_size,
|
||||
hidden_size=config.hidden_size,
|
||||
initializer_range=config.initializer_range,
|
||||
name="token_type_embeddings",
|
||||
)
|
||||
|
||||
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
|
||||
# any TensorFlow checkpoint file
|
||||
self.embeddings_sum = tf.keras.layers.Add()
|
||||
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
|
||||
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
|
||||
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
|
||||
|
||||
def build(self, input_shape):
|
||||
"""Build shared word embedding layer """
|
||||
with tf.name_scope("word_embeddings"):
|
||||
# Create and initialize weights. The random normal initializer was chosen
|
||||
# arbitrarily, and works well.
|
||||
self.word_embeddings = self.add_weight(
|
||||
"weight",
|
||||
shape=[self.vocab_size, self.hidden_size],
|
||||
initializer=get_initializer(self.initializer_range),
|
||||
)
|
||||
|
||||
super().build(input_shape)
|
||||
|
||||
def call(
|
||||
self,
|
||||
input_ids=None,
|
||||
position_ids=None,
|
||||
token_type_ids=None,
|
||||
inputs_embeds=None,
|
||||
mode="embedding",
|
||||
training=False,
|
||||
):
|
||||
def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
|
||||
"""
|
||||
Get token embeddings of inputs.
|
||||
|
||||
Args:
|
||||
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
|
||||
mode: string, a valid value is one of "embedding" and "linear".
|
||||
Applies embedding based on inputs tensor.
|
||||
|
||||
Returns:
|
||||
outputs: If mode == "embedding", output embedding tensor, float32 with shape [batch_size, length,
|
||||
embedding_size]; if mode == "linear", output linear tensor, float32 with shape [batch_size, length,
|
||||
vocab_size].
|
||||
|
||||
Raises:
|
||||
ValueError: if mode is not valid.
|
||||
|
||||
Shared weights logic adapted from
|
||||
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
|
||||
final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
|
||||
"""
|
||||
if mode == "embedding":
|
||||
return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
|
||||
elif mode == "linear":
|
||||
return self._linear(input_ids)
|
||||
else:
|
||||
raise ValueError("mode {} is not valid.".format(mode))
|
||||
|
||||
def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
|
||||
"""Applies embedding based on inputs tensor."""
|
||||
assert not (input_ids is None and inputs_embeds is None)
|
||||
|
||||
if input_ids is not None:
|
||||
input_shape = shape_list(input_ids)
|
||||
else:
|
||||
input_shape = shape_list(inputs_embeds)[:-1]
|
||||
|
||||
seq_length = input_shape[1]
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
|
||||
inputs_embeds = self.word_embeddings(input_ids=input_ids)
|
||||
|
||||
if token_type_ids is None:
|
||||
token_type_ids = tf.fill(input_shape, 0)
|
||||
input_shape = shape_list(tensor=inputs_embeds)[:-1]
|
||||
token_type_ids = tf.fill(dims=input_shape, value=0)
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
|
||||
if position_ids is None:
|
||||
position_embeds = self.position_embeddings(position_ids=inputs_embeds)
|
||||
else:
|
||||
position_embeds = self.position_embeddings(position_ids=position_ids)
|
||||
|
||||
position_embeddings = tf.cast(self.position_embeddings(position_ids), inputs_embeds.dtype)
|
||||
token_type_embeddings = tf.cast(self.token_type_embeddings(token_type_ids), inputs_embeds.dtype)
|
||||
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
|
||||
embeddings = self.LayerNorm(embeddings)
|
||||
embeddings = self.dropout(embeddings, training=training)
|
||||
token_type_embeds = self.token_type_embeddings(token_type_ids=token_type_ids)
|
||||
final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
|
||||
final_embeddings = self.LayerNorm(inputs=final_embeddings)
|
||||
final_embeddings = self.dropout(inputs=final_embeddings, training=training)
|
||||
|
||||
return embeddings
|
||||
|
||||
def _linear(self, inputs):
|
||||
"""
|
||||
Computes logits by running inputs through a linear layer.
|
||||
|
||||
Args:
|
||||
inputs: A float32 tensor with shape [batch_size, length, hidden_size].
|
||||
|
||||
Returns:
|
||||
float32 tensor with shape [batch_size, length, vocab_size].
|
||||
"""
|
||||
batch_size = shape_list(inputs)[0]
|
||||
length = shape_list(inputs)[1]
|
||||
x = tf.reshape(inputs, [-1, self.hidden_size])
|
||||
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
|
||||
|
||||
return tf.reshape(logits, [batch_size, length, self.vocab_size])
|
||||
return final_embeddings
|
||||
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}}
|
||||
|
||||
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention
|
||||
class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer):
|
||||
def __init__(self, config, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
@ -198,8 +252,8 @@ class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer)
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
|
||||
self.query = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abc,cde->abde",
|
||||
output_shape=(None, config.num_attention_heads, self.attention_head_size),
|
||||
@ -266,9 +320,9 @@ class TF{{cookiecutter.camelcase_modelname}}SelfOutput(tf.keras.layers.Layer):
|
||||
f"of attention heads ({config.num_attention_heads})"
|
||||
)
|
||||
|
||||
self.num_attention_heads = config.num_attention_heads
|
||||
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
||||
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
||||
self.all_head_size = config.num_attention_heads * self.attention_head_size
|
||||
|
||||
self.dense = tf.keras.layers.experimental.EinsumDense(
|
||||
equation="abcd,cde->abe",
|
||||
output_shape=(None, self.all_head_size),
|
||||
@ -450,6 +504,8 @@ class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Lay
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.vocab_size = config.vocab_size
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.transform = TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(config, name="transform")
|
||||
|
||||
# The output weights are the same as the input embeddings, but there is
|
||||
@ -465,7 +521,7 @@ class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Lay
|
||||
return self.input_embeddings
|
||||
|
||||
def set_output_embeddings(self, value):
|
||||
self.input_embeddings.word_embeddings = value
|
||||
self.input_embeddings.weight = value
|
||||
self.input_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def get_bias(self):
|
||||
@ -476,9 +532,12 @@ class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Lay
|
||||
self.vocab_size = shape_list(value["bias"])[0]
|
||||
|
||||
def call(self, hidden_states):
|
||||
hidden_states = self.transform(hidden_states)
|
||||
hidden_states = self.input_embeddings(hidden_states, mode="linear")
|
||||
hidden_states = hidden_states + self.bias
|
||||
hidden_states = self.transform(hidden_states=hidden_states)
|
||||
seq_length = shape_list(tensor=hidden_states)[1]
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size])
|
||||
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True)
|
||||
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size])
|
||||
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias)
|
||||
|
||||
return hidden_states
|
||||
|
||||
@ -514,11 +573,11 @@ class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer):
|
||||
self.config = config
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embeddings
|
||||
return self.embeddings.word_embeddings
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embeddings.word_embeddings = value
|
||||
self.embeddings.vocab_size = shape_list(value)[0]
|
||||
self.embeddings.word_embeddings.weight = value
|
||||
self.embeddings.word_embeddings.vocab_size = shape_list(value)[0]
|
||||
|
||||
def _prune_heads(self, heads_to_prune):
|
||||
"""Prunes heads of the model.
|
||||
@ -812,7 +871,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
|
||||
)
|
||||
|
||||
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
|
||||
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
|
||||
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.mlm.predictions
|
||||
@ -909,7 +968,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca
|
||||
logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`")
|
||||
|
||||
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}")
|
||||
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls")
|
||||
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, self.{{cookiecutter.lowercase_modelname}}.embeddings.word_embeddings, name="mlm___cls")
|
||||
|
||||
def get_lm_head(self):
|
||||
return self.mlm.predictions
|
||||
|
||||
@ -760,31 +760,6 @@ class TFModelTesterMixin:
|
||||
model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
|
||||
)
|
||||
|
||||
def _get_embeds(self, wte, input_ids):
|
||||
# ^^ In our TF models, the input_embeddings can take slightly different forms,
|
||||
# so we try a few of them.
|
||||
# We used to fall back to just synthetically creating a dummy tensor of ones:
|
||||
try:
|
||||
x = wte(input_ids, mode="embedding")
|
||||
except Exception:
|
||||
try:
|
||||
x = wte([input_ids], mode="embedding")
|
||||
except Exception:
|
||||
try:
|
||||
x = wte([input_ids, None, None, None], mode="embedding")
|
||||
except Exception:
|
||||
if hasattr(self.model_tester, "embedding_size"):
|
||||
x = tf.ones(
|
||||
input_ids.shape + [self.model_tester.embedding_size],
|
||||
dtype=tf.dtypes.float32,
|
||||
)
|
||||
else:
|
||||
x = tf.ones(
|
||||
input_ids.shape + [self.model_tester.hidden_size],
|
||||
dtype=tf.dtypes.float32,
|
||||
)
|
||||
return x
|
||||
|
||||
def test_inputs_embeds(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
@ -801,12 +776,11 @@ class TFModelTesterMixin:
|
||||
del inputs["input_ids"]
|
||||
inputs.pop("decoder_input_ids", None)
|
||||
|
||||
wte = model.get_input_embeddings()
|
||||
if not self.is_encoder_decoder:
|
||||
inputs["inputs_embeds"] = self._get_embeds(wte, input_ids)
|
||||
inputs["inputs_embeds"] = model.get_input_embeddings()(input_ids)
|
||||
else:
|
||||
inputs["inputs_embeds"] = self._get_embeds(wte, encoder_input_ids)
|
||||
inputs["decoder_inputs_embeds"] = self._get_embeds(wte, decoder_input_ids)
|
||||
inputs["inputs_embeds"] = model.get_input_embeddings()(encoder_input_ids)
|
||||
inputs["decoder_inputs_embeds"] = model.get_input_embeddings()(decoder_input_ids)
|
||||
|
||||
model(inputs)
|
||||
|
||||
@ -837,24 +811,25 @@ class TFModelTesterMixin:
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
def _get_word_embedding_weight(model, embedding_layer):
|
||||
if hasattr(embedding_layer, "word_embeddings"):
|
||||
return embedding_layer.word_embeddings
|
||||
elif hasattr(embedding_layer, "weight"):
|
||||
return embedding_layer.weight
|
||||
elif hasattr(embedding_layer, "decoder"):
|
||||
return embedding_layer.decoder
|
||||
else:
|
||||
# Here we build the word embeddings weights if not exists.
|
||||
# And then we retry to get the attribute once built.
|
||||
model(model.dummy_inputs)
|
||||
if hasattr(embedding_layer, "word_embeddings"):
|
||||
return embedding_layer.word_embeddings
|
||||
elif hasattr(embedding_layer, "weight"):
|
||||
return embedding_layer.weight
|
||||
elif hasattr(embedding_layer, "decoder"):
|
||||
return embedding_layer.decoder
|
||||
else:
|
||||
return None
|
||||
embeds = getattr(embedding_layer, "weight", None)
|
||||
if embeds is not None:
|
||||
return embeds
|
||||
|
||||
embeds = getattr(embedding_layer, "decoder", None)
|
||||
if embeds is not None:
|
||||
return embeds
|
||||
|
||||
model(model.dummy_inputs)
|
||||
|
||||
embeds = getattr(embedding_layer, "weight", None)
|
||||
if embeds is not None:
|
||||
return embeds
|
||||
|
||||
embeds = getattr(embedding_layer, "decoder", None)
|
||||
if embeds is not None:
|
||||
return embeds
|
||||
|
||||
return None
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user