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transformer.py
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transformer.py
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# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""RT1 decoder transformer.
Copied from:
https://www.tensorflow.org/text/tutorials/transformer#decoder
"""
from typing import Tuple, Union
import tensorflow as tf
class _TransformerLayer(tf.keras.layers.Layer):
"""A single transformer block."""
def __init__(self,
layer_size: int = 4096,
num_heads: int = 8,
feed_forward_size: int = 512,
dropout_rate: float = 0.1,
return_attention_scores: bool = False):
"""Creates a Transformer layer.
Args:
layer_size: Size of the multiple head attention layer.
num_heads: Number of heads for the multiple head attention layer.
feed_forward_size: Dimensionality of the feed_forward layer.
dropout_rate: Dropout rate.
return_attention_scores: Return attention scores.
"""
super(_TransformerLayer, self).__init__()
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.mha1 = tf.keras.layers.MultiHeadAttention(
key_dim=layer_size, num_heads=num_heads, dropout=dropout_rate)
self.ff = tf.keras.layers.Dense(feed_forward_size)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout_ff = tf.keras.layers.Dropout(dropout_rate)
self._return_attention_scores = return_attention_scores
def call(self, x: tf.Tensor, attention_mask: tf.Tensor,
training: bool) -> Tuple[tf.Tensor, Union[tf.Tensor, None]]:
"""Calls the layer.
Args:
x: Input Tensor of shape `(B, T, dim)`.
attention_mask: a boolean mask of shape `(B, T, T)`, that prevents
attention to certain positions. The boolean mask specifies which query
elements can attend to which key elements, 1 indicates attention and 0
indicates no attention. Broadcasting can happen for the missing batch
dimensions and the head dimension.
training: Python boolean indicating whether the layer should behave in
training mode (adding dropout) or in inference mode (no dropout).
Returns:
y: Output Tensor of shape `(B, T, dim)`. Also return the attention scores
of shape `(B, T, dim)` or None.
"""
x1 = self.layernorm1(x)
mha_results = self.mha1(
query=x1,
key=x1,
value=x1,
attention_mask=attention_mask,
return_attention_scores=self._return_attention_scores,
training=training)
if self._return_attention_scores:
x1, score = mha_results
else:
x1, score = mha_results, None
x = x + x1
y = self.layernorm2(x)
ff_y = self.ff(y)
ff_y = self.dropout_ff(ff_y, training=training)
x = x + ff_y
return x, score
class Transformer(tf.keras.layers.Layer):
"""A decoder only transformer."""
def __init__(self,
num_layers: int = 1,
layer_size: int = 4096,
num_heads: int = 8,
feed_forward_size: int = 512,
dropout_rate: float = 0.1,
vocab_size: int = 256,
return_attention_scores: bool = False):
"""Creates a transformer.
Args:
num_layers: Number of transformer layers.
layer_size: Size of the multiple head attention layer.
num_heads: Number of heads for the multiple head attention layer.
feed_forward_size: Dimensionality of the feed_forward layer.
dropout_rate: Dropout rate.
vocab_size: Dimensionality of tokens from the output layer.
return_attention_scores: Return attention scores.
"""
super(Transformer, self).__init__()
self._layers = [
_TransformerLayer( # pylint: disable=g-complex-comprehension
layer_size=layer_size,
num_heads=num_heads,
feed_forward_size=feed_forward_size,
dropout_rate=dropout_rate,
return_attention_scores=return_attention_scores)
for _ in range(num_layers)
]
self._token_emb = tf.keras.layers.Dense(feed_forward_size)
self._position_emb = tf.keras.layers.Dense(feed_forward_size)
self._output_tokens = tf.keras.layers.Dense(vocab_size)
def call(
self,
x: tf.Tensor,
training: bool,
attention_mask: tf.Tensor,
) -> Union[tf.Tensor, Tuple[tf.Tensor, list[tf.Tensor]]]:
"""Calls the layer.
Args:
x: Input Tensor of shape `(B, T, dim)`.
training: Python boolean indicating whether the layer should behave in
training mode (adding dropout) or in inference mode (no dropout).
attention_mask: a boolean mask of shape `(B, T, T)`, that prevents
attention to certain positions. The boolean mask specifies which query
elements can attend to which key elements, 1 indicates attention and 0
indicates no attention. Broadcasting can happen for the missing batch
dimensions and the head dimension.
Returns:
x: Output Tensor of shape `(B, T, vocab_size)`. If
`return_attention_scores`, also return attention scores of
a list of `layer` of elements with shape `(B, T, dim)`.
"""
seq_len = tf.shape(x)[1]
batch_size = tf.shape(x)[0]
positions = tf.one_hot(
tf.tile(tf.expand_dims(tf.range(0, seq_len, 1), 0), [batch_size, 1]),
seq_len)
x = self._token_emb(x)
x += self._position_emb(positions)
scores = []
for layer in self._layers:
x, score = layer(x, attention_mask=attention_mask, training=training)
if score is not None:
scores.append(score)
x = self._output_tokens(x)
return x, scores