attention.py 21.2 KB
Newer Older
Hongkun Yu's avatar
Hongkun Yu committed
1
# Lint as: python3
Hongkun Yu's avatar
Hongkun Yu committed
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Keras-based attention layer."""
17
# pylint: disable=g-classes-have-attributes
Hongkun Yu's avatar
Hongkun Yu committed
18
19
20
21
22
from __future__ import absolute_import
from __future__ import division
# from __future__ import google_type_annotations
from __future__ import print_function

Hongkun Yu's avatar
Hongkun Yu committed
23
import collections
Hongkun Yu's avatar
Hongkun Yu committed
24
import math
Hongkun Yu's avatar
Hongkun Yu committed
25
26
27
import string

import numpy as np
Hongkun Yu's avatar
Hongkun Yu committed
28
29
30
31
import tensorflow as tf

from official.nlp.modeling.layers import masked_softmax

Hongkun Yu's avatar
Hongkun Yu committed
32
33
34
35
EinsumDense = tf.keras.layers.experimental.EinsumDense
_CHR_IDX = string.ascii_lowercase


36
def _build_attention_equation(rank, attn_axes):
Hongkun Yu's avatar
Hongkun Yu committed
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
  """Builds einsum equations for the attention computation.

  Query, key, value inputs after projection are expected to have the shape as:
  (bs, <non-attention dims>, <attention dims>, num_heads, channels).
  bs and <non-attention dims> are treated as <batch dims>.
  The attention operations can be generalized:
  (1) Query-key dot product:
  (<batch dims>, <query attention dims>, num_heads, channels), (<batch dims>,
  <key attention dims>, num_heads, channels) -> (<batch dims>,
  num_heads, <query attention dims>, <key attention dims>)
  (2) Combination:
  (<batch dims>, num_heads, <query attention dims>, <key attention dims>),
  (<batch dims>, <value attention dims>, num_heads, channels) -> (<batch dims>,
  <query attention dims>, num_heads, channels)

  Args:
53
    rank: the rank of query, key, value tensors.
Hongkun Yu's avatar
Hongkun Yu committed
54
    attn_axes: a list/tuple of axes, [1, rank), that will do attention.
55

Hongkun Yu's avatar
Hongkun Yu committed
56
57
58
  Returns:
    Einsum equations.
  """
59
  target_notation = _CHR_IDX[:rank]
Hongkun Yu's avatar
Hongkun Yu committed
60
  # `batch_dims` includes the head dim.
61
62
  batch_dims = tuple(np.delete(range(rank), attn_axes + (rank - 1,)))
  letter_offset = rank
Hongkun Yu's avatar
Hongkun Yu committed
63
  source_notation = ""
64
65
  for i in range(rank):
    if i in batch_dims or i == rank - 1:
Hongkun Yu's avatar
Hongkun Yu committed
66
67
68
69
70
71
72
73
74
75
      source_notation += target_notation[i]
    else:
      source_notation += _CHR_IDX[letter_offset]
      letter_offset += 1

  product_notation = "".join([target_notation[i] for i in batch_dims] +
                             [target_notation[i] for i in attn_axes] +
                             [source_notation[i] for i in attn_axes])
  dot_product_equation = "%s,%s->%s" % (source_notation, target_notation,
                                        product_notation)
76
  attn_scores_rank = len(product_notation)
Hongkun Yu's avatar
Hongkun Yu committed
77
78
  combine_equation = "%s,%s->%s" % (product_notation, source_notation,
                                    target_notation)
79
  return dot_product_equation, combine_equation, attn_scores_rank
Hongkun Yu's avatar
Hongkun Yu committed
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107


def _build_proj_equation(free_dims, bound_dims, output_dims):
  """Builds an einsum equation for projections inside multi-head attention."""
  input_str = ""
  kernel_str = ""
  output_str = ""
  bias_axes = ""
  letter_offset = 0
  for i in range(free_dims):
    char = _CHR_IDX[i + letter_offset]
    input_str += char
    output_str += char

  letter_offset += free_dims
  for i in range(bound_dims):
    char = _CHR_IDX[i + letter_offset]
    input_str += char
    kernel_str += char

  letter_offset += bound_dims
  for i in range(output_dims):
    char = _CHR_IDX[i + letter_offset]
    kernel_str += char
    output_str += char
    bias_axes += char
  equation = "%s,%s->%s" % (input_str, kernel_str, output_str)

108
  return equation, bias_axes, len(output_str)
Hongkun Yu's avatar
Hongkun Yu committed
109
110
111
112
113


def _get_output_shape(output_rank, known_last_dims):
  return [None] * (output_rank - len(known_last_dims)) + list(known_last_dims)

Hongkun Yu's avatar
Hongkun Yu committed
114
115

@tf.keras.utils.register_keras_serializable(package="Text")
116
117
class MultiHeadAttention(tf.keras.layers.Layer):
  """MultiHeadAttention layer.
Hongkun Yu's avatar
Hongkun Yu committed
118
119

  This is an implementation of multi-headed attention based on "Attention
Hongkun Yu's avatar
Hongkun Yu committed
120
121
122
  is all you Need". If `query`, `key,` `value` are the same, then
  this is self-attention. Each timestep in `query` attends to the
  corresponding sequence in `key`, and returns a fixed-width vector.
Hongkun Yu's avatar
Hongkun Yu committed
123

Hongkun Yu's avatar
Hongkun Yu committed
124
125
  This layer first projects `query`, `key` and `value`. These are
  (effectively) a list of tensors of length `num_attention_heads`, where the
126
127
128
  corresponding shapes are [batch_size, <query dimensions>, key_size],
  [batch_size, <key/value dimensions>, key_size],
  [batch_size, <key/value dimensions>, value_size].
Hongkun Yu's avatar
Hongkun Yu committed
129
130
131
132

  Then, the query and key tensors are dot-producted and scaled. These are
  softmaxed to obtain attention probabilities. The value tensors are then
  interpolated by these probabilities, then concatenated back to a single
Hongkun Yu's avatar
Hongkun Yu committed
133
134
135
136
  tensor.

  Finally, the result tensor with the last dimension as value_size can take an
  linear projection and return.
Hongkun Yu's avatar
Hongkun Yu committed
137

138
139
140
141
142
143
144
145
146
147
  Examples:

  Performs 1D cross-attention over two sequence inputs with an attention mask.
  Returns the additional attention weights over heads.

  >>> layer = MultiHeadAttention(num_heads=2, key_size=2,
  ...                            return_attention_scores=True)
  >>> target = tf.keras.Input(shape=[8, 16])
  >>> source = tf.keras.Input(shape=[4, 16])
  >>> mask_tensor = tf.keras.Input(shape=[8, 4])
Hongkun Yu's avatar
Hongkun Yu committed
148
  >>> output_tensor, weights = layer([target, source])
149
150
151
152
153
154
155
156
157
158
159
  >>> print(output_tensor.shape), print(weights.shape)
  (None, 8, 16)  (None, 2, 8, 4)

  Performs 2D self-attention over a 5D input tensor on axes 2 and 3.

  >>> layer = MultiHeadAttention(num_heads=2, key_size=2, attention_axes=(2, 3))
  >>> input_tensor = tf.keras.Input(shape=[5, 3, 4, 16])
  >>> output_tensor = layer([input_tensor, input_tensor])
  >>> print(output_tensor.shape)
  (None, 5, 3, 4, 16)

160
  Arguments:
Hongkun Yu's avatar
Hongkun Yu committed
161
    num_heads: Number of attention heads.
Hongkun Yu's avatar
Hongkun Yu committed
162
163
    key_size: Size of each attention head for query and key.
    value_size:  Size of each attention head for value.
Hongkun Yu's avatar
Hongkun Yu committed
164
    dropout: Dropout probability.
Hongkun Yu's avatar
Hongkun Yu committed
165
    use_bias: Boolean, whether the dense layers use bias vectors/matrices.
Hongkun Yu's avatar
Hongkun Yu committed
166
167
    output_shape: The expected shape of an output tensor, besides the batch and
      sequence dims. If not specified, projects back to the key feature dim.
168
169
    attention_axes: axes over which the attention is applied. `None` means
      attention over all axes, but batch, heads, and features.
170
171
    return_attention_scores: bool, if `True`, returns the multi-head attention
      scores as an additional output argument.
Hongkun Yu's avatar
Hongkun Yu committed
172
173
174
175
176
177
178
    kernel_initializer: Initializer for dense layer kernels.
    bias_initializer: Initializer for dense layer biases.
    kernel_regularizer: Regularizer for dense layer kernels.
    bias_regularizer: Regularizer for dense layer biases.
    activity_regularizer: Regularizer for dense layer activity.
    kernel_constraint: Constraint for dense layer kernels.
    bias_constraint: Constraint for dense layer kernels.
179
180
181
182
183
184
185
  Call args:
    query: Query `Tensor` of shape `[B, T, dim]`.
    value: Value `Tensor` of shape `[B, S, dim]`.
    key: Optional key `Tensor` of shape `[B, S, dim]`. If not given, will use
      `value` for both `key` and `value`, which is the most common case.
    attention_mask: a boolean mask of shape `[B, T, S]`, that prevents attention
      to certain positions.
Hongkun Yu's avatar
Hongkun Yu committed
186
187
188
189
  """

  def __init__(self,
               num_heads,
Hongkun Yu's avatar
Hongkun Yu committed
190
191
               key_size,
               value_size=None,
192
               dropout=0.0,
Hongkun Yu's avatar
Hongkun Yu committed
193
194
               use_bias=True,
               output_shape=None,
195
196
               attention_axes=None,
               return_attention_scores=False,
Hongkun Yu's avatar
Hongkun Yu committed
197
198
199
200
201
202
203
204
               kernel_initializer="glorot_uniform",
               bias_initializer="zeros",
               kernel_regularizer=None,
               bias_regularizer=None,
               activity_regularizer=None,
               kernel_constraint=None,
               bias_constraint=None,
               **kwargs):
205
    super(MultiHeadAttention, self).__init__(**kwargs)
Hongkun Yu's avatar
Hongkun Yu committed
206
    self._num_heads = num_heads
Hongkun Yu's avatar
Hongkun Yu committed
207
208
    self._key_size = key_size
    self._value_size = value_size if value_size else key_size
209
    self._dropout = dropout
Hongkun Yu's avatar
Hongkun Yu committed
210
211
    self._use_bias = use_bias
    self._output_shape = output_shape
212
    self._return_attention_scores = return_attention_scores
Hongkun Yu's avatar
Hongkun Yu committed
213
214
215
216
217
218
    self._kernel_initializer = tf.keras.initializers.get(kernel_initializer)
    self._bias_initializer = tf.keras.initializers.get(bias_initializer)
    self._kernel_regularizer = tf.keras.regularizers.get(kernel_regularizer)
    self._bias_regularizer = tf.keras.regularizers.get(bias_regularizer)
    self._kernel_constraint = tf.keras.constraints.get(kernel_constraint)
    self._bias_constraint = tf.keras.constraints.get(bias_constraint)
219
220
221
222
223
    if attention_axes is not None and not isinstance(attention_axes,
                                                     collections.abc.Sized):
      self._attention_axes = (attention_axes,)
    else:
      self._attention_axes = attention_axes
224
    self._built_from_signature = False
Hongkun Yu's avatar
Hongkun Yu committed
225
226
227
228
229

  def get_config(self):
    config = {
        "num_heads":
            self._num_heads,
Hongkun Yu's avatar
Hongkun Yu committed
230
231
232
233
        "key_size":
            self._key_size,
        "value_size":
            self._value_size,
234
235
        "dropout":
            self._dropout,
Hongkun Yu's avatar
Hongkun Yu committed
236
237
238
239
        "use_bias":
            self._use_bias,
        "output_shape":
            self._output_shape,
240
241
242
243
        "attention_axes":
            self._attention_axes,
        "return_attention_scores":
            self._return_attention_scores,
Hongkun Yu's avatar
Hongkun Yu committed
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
        "kernel_initializer":
            tf.keras.initializers.serialize(self._kernel_initializer),
        "bias_initializer":
            tf.keras.initializers.serialize(self._bias_initializer),
        "kernel_regularizer":
            tf.keras.regularizers.serialize(self._kernel_regularizer),
        "bias_regularizer":
            tf.keras.regularizers.serialize(self._bias_regularizer),
        "activity_regularizer":
            tf.keras.regularizers.serialize(self._activity_regularizer),
        "kernel_constraint":
            tf.keras.constraints.serialize(self._kernel_constraint),
        "bias_constraint":
            tf.keras.constraints.serialize(self._bias_constraint)
    }
259
    base_config = super(MultiHeadAttention, self).get_config()
Hongkun Yu's avatar
Hongkun Yu committed
260
261
    return dict(list(base_config.items()) + list(config.items()))

262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
  def _build_from_signature(self, query, value, key=None):
    """Builds layers and variables.

    Once the method is called, self._built_from_signature will be set to True.

    Args:
      query: query tensor or TensorShape.
      value: value tensor or TensorShape.
      key: key tensor or TensorShape.
    """
    self._built_from_signature = True
    if hasattr(query, "shape"):
      query_shape = tf.TensorShape(query.shape)
    else:
      query_shape = query
    if hasattr(value, "shape"):
      value_shape = tf.TensorShape(value.shape)
    else:
      value_shape = value
    if key is None:
      key_shape = value_shape
    elif hasattr(key, "shape"):
      key_shape = tf.TensorShape(key.shape)
    else:
      key_shape = key
Hongkun Yu's avatar
Hongkun Yu committed
287
288

    common_kwargs = dict(
Hongkun Yu's avatar
Hongkun Yu committed
289
290
291
292
293
294
        kernel_initializer=self._kernel_initializer,
        bias_initializer=self._bias_initializer,
        kernel_regularizer=self._kernel_regularizer,
        bias_regularizer=self._bias_regularizer,
        activity_regularizer=self._activity_regularizer,
        kernel_constraint=self._kernel_constraint,
Hongkun Yu's avatar
Hongkun Yu committed
295
        bias_constraint=self._bias_constraint)
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
    with tf.init_scope():
      free_dims = query_shape.rank - 1
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          free_dims, bound_dims=1, output_dims=2)
      self._query_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1,
                                         [self._num_heads, self._key_size]),
          bias_axes=bias_axes if self._use_bias else None,
          name="query",
          **common_kwargs)
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          key_shape.rank - 1, bound_dims=1, output_dims=2)
      self._key_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1,
                                         [self._num_heads, self._key_size]),
          bias_axes=bias_axes if self._use_bias else None,
          name="key",
          **common_kwargs)
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          value_shape.rank - 1, bound_dims=1, output_dims=2)
      self._value_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1,
                                         [self._num_heads, self._value_size]),
          bias_axes=bias_axes if self._use_bias else None,
          name="value",
          **common_kwargs)

      # Builds the attention computations for multi-head dot product attention.
      # These computations could be wrapped into the keras attention layer once
      # it support mult-head einsum computations.
      self.build_attention(output_rank)
      if self._output_shape:
        if not isinstance(self._output_shape, collections.abc.Sized):
          output_shape = [self._output_shape]
        else:
          output_shape = self._output_shape
Hongkun Yu's avatar
Hongkun Yu committed
335
      else:
336
337
338
339
340
341
342
343
344
345
346
        output_shape = [query_shape[-1]]
      einsum_equation, bias_axes, output_rank = _build_proj_equation(
          free_dims, bound_dims=2, output_dims=len(output_shape))
      self._output_dense = EinsumDense(
          einsum_equation,
          output_shape=_get_output_shape(output_rank - 1, output_shape),
          bias_axes=bias_axes if self._use_bias else None,
          name="attention_output",
          **common_kwargs)

  def build_attention(self, rank):
347
348
    """Builds multi-head dot-product attention computations.

349
    This function builds attributes necessary for `compute_attention` to
350
351
352
353
    costomize attention computation to replace the default dot-product
    attention.

    Args:
354
      rank: the rank of query, key, value tensors.
355
356
    """
    if self._attention_axes is None:
357
      self._attention_axes = tuple(range(1, rank - 2))
358
359
360
    else:
      self._attention_axes = tuple(self._attention_axes)
    self._dot_product_equation, self._combine_equation, attn_scores_rank = (
361
        _build_attention_equation(rank, attn_axes=self._attention_axes))
362
363
364
    norm_axes = tuple(
        range(attn_scores_rank - len(self._attention_axes), attn_scores_rank))
    self._masked_softmax = masked_softmax.MaskedSoftmax(
365
366
        mask_expansion_axes=[-len(self._attention_axes) * 2 - 1],
        normalization_axes=norm_axes)
367
368
    self._dropout_layer = tf.keras.layers.Dropout(rate=self._dropout)

369
  def compute_attention(self, query, key, value, attention_mask=None):
370
371
372
373
374
375
376
    """Applies Dot-product attention with query, key, value tensors.

    This function defines the computation inside `call` with projected
    multi-head Q, K, V inputs. Users can override this function for customized
    attention implementation.

    Args:
377
378
379
      query: Projected query `Tensor` of shape `[B, T, N, key_size]`.
      key: Projected key `Tensor` of shape `[B, T, N, key_size]`.
      value: Projected value `Tensor` of shape `[B, T, N, value_size]`.
380
381
382
383
384
385
386
      attention_mask: a boolean mask of shape `[B, T, S]`, that prevents
        attention to certain positions.

    Returns:
      attention_output: Multi-headed outputs of attention computation.
      attention_scores: Multi-headed attention weights.
    """
Allen Wang's avatar
Allen Wang committed
387
388
389
    # Note: Applying scalar multiply at the smaller end of einsum improves
    # XLA performance, but may introduce slight numeric differences in
    # the Transformer attention head.
390
    query = tf.multiply(query, 1.0 / math.sqrt(float(self._key_size)))
Allen Wang's avatar
Allen Wang committed
391

392
393
    # Take the dot product between "query" and "key" to get the raw
    # attention scores.
394
    attention_scores = tf.einsum(self._dot_product_equation, key, query)
395
396
397

    # Normalize the attention scores to probabilities.
    # `attention_scores` = [B, N, T, S]
398
    attention_scores = self._masked_softmax(attention_scores, attention_mask)
399
400
401
402
403
404
405

    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
    attention_scores_dropout = self._dropout_layer(attention_scores)

    # `context_layer` = [B, T, N, H]
    attention_output = tf.einsum(self._combine_equation,
406
                                 attention_scores_dropout, value)
407
408
    return attention_output, attention_scores

409
  def call(self, query, value, key=None, attention_mask=None):
Hongkun Yu's avatar
Hongkun Yu committed
410
411
412
413
414
415
416
    """Implements the forward pass.

    Size glossary:
      * Number of heads (H): the number of attention heads.
      * Value size (V): the size of each value embedding per head.
      * Key size (K): the size of each key embedding per head. Equally, the size
          of each query embedding per head. Typically K <= V.
417
418
419
      * Batch dimensions (B).
      * Query (target) attention axes shape (T).
      * Value (source) attention axes shape (S), the rank must match the target.
Hongkun Yu's avatar
Hongkun Yu committed
420
421

    Args:
422
423
424
425
      query: Query `Tensor` of shape `[B, T, dim]`.
      value: Value `Tensor` of shape `[B, S, dim]`.
      key: Optional key `Tensor` of shape `[B, S, dim]`. If not given, will use
        `value` for both `key` and `value`, which is the most common case.
Hongkun Yu's avatar
Hongkun Yu committed
426
427
428
429
      attention_mask: a boolean mask of shape `[B, T, S]`, that prevents
        attention to certain positions.

    Returns:
430
431
432
433
434
435
436
      attention_output: The result of the computation, of shape [B, T, E],
        where `T` is for target sequence shapes and `E` is the query input last
        dimension if `output_shape` is `None`. Otherwise, the multi-head outputs
        are project to the shape specified by `output_shape`.
      attention_scores: [Optional] multi-head attention coeffients over
      attention
        axes.
Hongkun Yu's avatar
Hongkun Yu committed
437
    """
438
439
440
441
    if not self._built_from_signature:
      self._build_from_signature(query=query, value=value, key=key)
    if key is None:
      key = value
Hongkun Yu's avatar
Hongkun Yu committed
442
443
444

    #   N = `num_attention_heads`
    #   H = `size_per_head`
445
446
    # `query` = [B, T, N ,H]
    query = self._query_dense(query)
Hongkun Yu's avatar
Hongkun Yu committed
447

448
449
    # `key` = [B, S, N, H]
    key = self._key_dense(key)
Hongkun Yu's avatar
Hongkun Yu committed
450

451
452
    # `value` = [B, S, N, H]
    value = self._value_dense(value)
Hongkun Yu's avatar
Hongkun Yu committed
453

454
455
    attention_output, attention_scores = self.compute_attention(
        query, key, value, attention_mask)
Hongkun Yu's avatar
Hongkun Yu committed
456
    attention_output = self._output_dense(attention_output)
457
458
459

    if self._return_attention_scores:
      return attention_output, attention_scores
Hongkun Yu's avatar
Hongkun Yu committed
460
    return attention_output
461
462
463


@tf.keras.utils.register_keras_serializable(package="Text")
464
class CachedAttention(MultiHeadAttention):
465
466
  """Attention layer with cache used for auto-agressive decoding.

467
  Arguments are the same as `MultiHeadAttention` layer.
468
469
  """

470
  def _update_cache(self, key, value, cache, decode_loop_step):
471
472
473
474
475
476
    """Updates cache states and gets full-length key/value tensors."""
    # Combines cached keys and values with new keys and values.
    if decode_loop_step is not None:
      # TPU special case.
      key_seq_dim = cache["key"].shape.as_list()[1]
      indices = tf.reshape(
477
          tf.one_hot(decode_loop_step, key_seq_dim, dtype=key.dtype),
478
          [1, key_seq_dim, 1, 1])
479
      key = cache["key"] + key * indices
480
481
      value_seq_dim = cache["value"].shape.as_list()[1]
      indices = tf.reshape(
482
          tf.one_hot(decode_loop_step, value_seq_dim, dtype=value.dtype),
483
          [1, value_seq_dim, 1, 1])
484
      value = cache["value"] + value * indices
485
    else:
486
487
      key = tf.concat([tf.cast(cache["key"], key.dtype), key], axis=1)
      value = tf.concat([tf.cast(cache["value"], value.dtype), value], axis=1)
488
489

    # Update cache
490
491
    cache["key"] = key
    cache["value"] = value
492

493
    return key, value
494

Hongkun Yu's avatar
Hongkun Yu committed
495
  def call(self,
496
497
498
           query,
           value,
           key=None,
Hongkun Yu's avatar
Hongkun Yu committed
499
500
501
           attention_mask=None,
           cache=None,
           decode_loop_step=None):
502
503
504
505
    if not self._built_from_signature:
      self._build_from_signature(query=query, value=value, key=key)
    if key is None:
      key = value
Hongkun Yu's avatar
Hongkun Yu committed
506

507
508
509
510
511
512
    # Scalar dimensions referenced here:
    #   B = batch size (number of sequences)
    #   F = `from_tensor` sequence length
    #   T = `to_tensor` sequence length
    #   N = `num_attention_heads`
    #   H = `size_per_head`
513
514
    # `query` = [B, F, N ,H]
    query = self._query_dense(query)
515

516
517
    # `key` = [B, T, N, H]
    key = self._key_dense(key)
518

519
520
    # `value` = [B, T, N, H]
    value = self._value_dense(value)
521
522

    if cache:
523
      key, value = self._update_cache(key, value, cache, decode_loop_step)
524

xinliupitt's avatar
xinliupitt committed
525
    query = tf.multiply(query, 1.0 / math.sqrt(float(self._key_size)))
xinliupitt's avatar
xinliupitt committed
526

527
528
    # Take the dot product between "query" and "key" to get the raw
    # attention scores.
529
    attention_scores = tf.einsum(self._dot_product_equation, key, query)
530
531

    # Normalize the attention scores to probabilities.
532
    # `attention_scores` = [B, N, F, T]
533
    attention_scores = self._masked_softmax(attention_scores, attention_mask)
534
535
536

    # This is actually dropping out entire tokens to attend to, which might
    # seem a bit unusual, but is taken from the original Transformer paper.
537
    attention_scores = self._dropout_layer(attention_scores)
538
    # `context_layer` = [B, F, N, H]
539
    attention_output = tf.einsum(self._combine_equation, attention_scores,
540
                                 value)
Hongkun Yu's avatar
Hongkun Yu committed
541
    attention_output = self._output_dense(attention_output)
542
543
    if self._return_attention_scores:
      return attention_output, attention_scores, cache
Hongkun Yu's avatar
Hongkun Yu committed
544
    return attention_output, cache