def prepare(self, query, key=None, value=None, mask=None):
r""" Preparing input for attention model
Returns:
query: Query (or target sequence) tensor of shape `[batch_size, Tq, dim]`.
key: Key (or source sequence) tensor of shape `[batch_size, Tv, dim]`.
value: Value (or source sequence) tensor of shape `[batch_size, Tv, dim]`.
mask: list of the following
* query_mask: A boolean mask `Tensor` of shape `[batch_size, Tq]`.
If given, the output will be zero at the positions where
`mask==False`.
* value_mask: A boolean mask `Tensor` of shape `[batch_size, Tv]`.
If given, will apply the mask such that values at positions where
`mask==False` do not contribute to the result.
"""
# by default, if key is not provide, using value
query = bk.array(query, ignore_none=True)
key = bk.array(key, ignore_none=True)
value = bk.array(value, ignore_none=True)
# ====== check if intra-attention ====== #
if self.is_self_attention:
if (key is not None or value is not None):
warnings.warn(
"Self-attention (intra-attention) need only query, "
"ignore provided key and value",
category=UserWarning)
if key is not None:
key = query
if value is not None:
value = query
### inter-attention
else:
if key is None:
key = value
if value is None: # value must always provided
raise RuntimeError(
"value must be given of inter-sequences attention.")
# ====== masks ====== #
if self.is_self_attention: # only 1 mask is need
if isinstance(mask, (tuple, list)):
q_mask = mask[0]
else:
q_mask = mask
v_mask = None
else:
q_mask = mask[0] if mask else None
v_mask = mask[1] if mask else None
if v_mask is not None:
if v_mask.shape[1] != value.shape[1]:
raise RuntimeError(
"Value mask has time dimension %d, but value has time dimension %d"
% (v_mask.shape[1], value.shape[1]))
# ====== return ====== #
return query, key, value, \
bk.array(q_mask, ignore_none=True), bk.array(v_mask, ignore_none=True)
def test_matmul(self):
for shape1, shape2, outshape in [
[(2, 3), (4, 3, 5), (4, 2, 5)],
[(2, 3, 4), (4, 5), (2, 3, 5)],
[(5, 3, 4), (5, 4, 6), (5, 3, 6)],
]:
x = np.random.rand(*shape1)
y = np.random.rand(*shape2)
for fw in FRAMEWORKS:
a = bk.array(x, fw)
b = bk.array(y, fw)
c = bk.matmul(a, b)
self.assertEqual(c.shape, outshape, msg=fw)
def test_countnonzero(self):
x = np.random.randint(0, 10, size=(25, 12, 8))
for axis in (None, 0, 1, 2, (1, 2)):
for keepdims in (True, False):
for dtype in ('int32', 'float32'):
y = [
bk.count_nonzero(bk.array(x, fw),
axis=axis,
keepdims=keepdims,
dtype=dtype) for fw in FRAMEWORKS
]
assert_equal(self, (axis, keepdims, dtype), *y)
def __init__(self,
output_dim,
max_len=10000,
trainable=False,
mask_zero=False):
super().__init__()
self.output_dim = output_dim
self.mask_zero = bool(mask_zero)
self.trainable = bool(trainable)
self.supports_masking = mask_zero
self.max_len = max_len
# Applying the cosine to even columns and sin to odds.
# if zero-masked, dont use the 0 position
# (i - i % 2) create a sequence of (0,0,1,1,2,2,...) which is needed
# for two running sequence of sin and cos in odd and even position
position_encoding = np.array([[
pos / np.power(10000, (i - i % 2) / output_dim)
for i in range(output_dim)
] if pos != 0 or not mask_zero else [0.] * output_dim
for pos in range(max_len)])
# [max_len, output_dim]
position_encoding[:, 0::2] = np.sin(position_encoding[:,
0::2]) # dim 2i
position_encoding[:,
1::2] = np.cos(position_encoding[:,
1::2]) # dim 2i+1
if not trainable:
self.position_encoding = bk.array(position_encoding,
dtype='float32',
framework=self)
else:
self.position_encoding = bk.variable(
initial_value=position_encoding,
dtype='float32',
trainable=True,
framework=self)
def compute_mask(self, mask=None):
if mask:
q_mask = mask[0] if isinstance(mask, (tuple, list)) else mask
return bk.array(q_mask)
def align(self,
scores,
value,
query=None,
q_mask=None,
v_mask=None,
causal=False,
residual=False,
dropout=0,
temporal_dropout=False,
sample_shape=1,
temperature=0.5,
training=None):
r"""Applies attention scores to the given value tensor.
Arguments:
scores: Attention Scores float tensor of shape
`[num_heads, batch_size, Tq, Tv]`.
value: Value (or source sequence) tensor of shape
`[num_heads, batch_size, Tv, dim]`.
query: Query (or target sequence) tensor of shape
`[num_heads, batch_size, Tq, dim]`.
q_mask: A boolean query mask `Tensor` of shape `[batch_size, Tq]`.
If given, the output will be zero at the positions where
`mask==False`.
v_mask: A boolean value mask `Tensor` of shape `[batch_size, Tv]`.
If given, will apply the mask such that values at positions where
`mask==False` do not contribute to the result.
dropout : Float. Dropout probability of the attention scores.
temporal_dropout : Boolean. If `True`, using the same dropout mask along
temporal axis (i.e. the 1-st dimension)
sample_shape (`Integer`) : number of mcmc samples for estimating the gradient
of hard attention
temperature: An 0-D `Tensor`, representing the temperature
of a set of RelaxedOneHotCategorical distributions. The temperature
should be positive.
Returns:
attended sequence: Tensor of shape
* `[sample_shape, num_heads, batch_size, Tq, dim]` for (hard + multi-heads)
* `[sample_shape, batch_size, Tq, dim]` for (hard + no-head)
* `[num_heads, batch_size, Tq, dim]` for (soft + multi-heads)
* `[batch_size, Tq, dim]` for (soft + no-head)
attention distribution : for soft attention, return Tensor of shape
* `[num_heads, batch_size, Tq]` for self-attention
* `[num_heads, batch_size, Tq, Tv]` for inter-attention.
for hard attention, return one-hot categorical distribution of shape
* `[sample_shape, num_heads, batch_size, Tq]` for self-attention
* `[sample_shape, num_heads, batch_size, Tq, Tv]` for inter-attention.
if multi-heads attention wasn't used, omit the `[num_heads]`.
"""
num_heads = _get_num_heads(scores)
if num_heads == 0:
Tq = scores.shape[1]
Tv = scores.shape[2]
else:
Tq = scores.shape[2]
Tv = scores.shape[3]
if value is None:
if query is None:
raise ValueError("both query and value are None, "
"at least one of them must be given")
value = query
# ====== Causal mask ====== #
if causal:
# Creates a lower triangular mask, so position i cannot attend to
# positions j>i. This prevents the flow of information from the future
# into the past.
scores_shape = scores.shape
# causal_mask_shape = [1, Tq, Tv].
causal_mask_shape = bk.concatenate(
[bk.ones_like(scores_shape[:-2]), scores_shape[-2:]], axis=0)
causal_mask = bk.tril_mask(causal_mask_shape)
else:
causal_mask = None
if v_mask is not None:
# LocalM applied
if PosLocalM in self:
v_mask = v_mask[:, -Tv:]
# Mask of shape [batch_size, 1, Tv].
v_mask = bk.expand_dims(v_mask, axis=-2)
v_mask = bk.cast(v_mask, 'bool')
if num_heads > 0:
v_mask = bk.expand_dims(v_mask, axis=0)
scores_mask = bk.logical_and(v_mask, causal_mask)
### applying the scores mask
if scores_mask is not None:
padding_mask = bk.logical_not(scores_mask)
# Bias so padding positions do not contribute to attention distribution.
scores -= 1.e9 * bk.cast(padding_mask, dtype=scores.dtype)
# ====== convert attention score to distribution ====== #
# if the last dimension is 1, no point for applying softmax, hence,
# softmax to the second last dimension
### soft attention
if AlignSoft in self:
attention_distribution = bk.softmax(
scores, axis=-2 if scores.shape[-1] == 1 else -1)
### relaxed hard attention
elif AlignRelax in self:
attention_distribution = bay.distributions.RelaxedOneHotCategorical(
temperature=temperature,
logits=bk.squeeze(scores, axis=-1)
if scores.shape[-1] == 1 else scores)
fsample = partial(bay.Distribution.sample,
sample_shape=sample_shape)
attention_distribution = bay.coercible_tensor(
attention_distribution, convert_to_tensor_fn=fsample)
### hard attention
elif AlignHard in self:
attention_distribution = bay.distributions.OneHotCategorical(
logits=bk.squeeze(scores, axis=-1)
if scores.shape[-1] == 1 else scores,
dtype=value.dtype)
fsample = partial(bay.Distribution.sample,
sample_shape=sample_shape)
attention_distribution = bay.coercible_tensor(
attention_distribution, convert_to_tensor_fn=fsample)
# ====== dropout the attention scores ====== #
attention = bk.dropout(attention_distribution,
p_drop=dropout,
axis=1 if temporal_dropout else None,
training=training and dropout > 0)
# ====== applying the attention ====== #
if self.is_self_attention and ScoreLocation in self:
result = bk.expand_dims(bk.array(attention), axis=-1) * value \
if attention.shape[-1] != 1 else \
attention * value
else:
if PosLocalM in self:
value = value[:, -Tv:] if num_heads == 0 else value[:, :, -Tv:]
result = bk.matmul(attention, value)
# ====== applying the Query mask ====== #
if q_mask is not None:
assert q_mask.shape[1] == Tq,\
"Query mask has time dimension %d, but query has time dimension %d" \
% (q_mask.shape[1], Tq)
# Mask of shape [batch_size, Tq, 1].
q_mask = bk.expand_dims(q_mask, axis=-1)
result *= bk.cast(q_mask, dtype=result.dtype)
# ====== residual connection ====== #
if residual:
if query is None:
raise ValueError("query must be given for residual connection")
result += query
# ====== return ====== #
return result, attention_distribution
def score(self,
query,
key=None,
scale=1,
window_width=None,
q_proj=None,
target_proj=None):
r"""
Arguments:
query: Query (or target sequence) tensor of shape
`[batch_size, Tq, dim]` or `[num_heads, batch_size, Tq, dim]` in case
of multi-heads attention.
key: Key (or source sequence) tensor of shape
`[batch_size, Tv, dim]` or `[num_heads, batch_size, Tv, dim]` in case
of multi-heads attention.
scale: single `Scalar` or `Tensor` of shape `[dim]` for scaling
the attention scores, suggested `1/sqrt(dim)` in (Vaswani et al. 2017).
window_width : `None`, `Integer` or `Float` ([0, 1]). The total number of
frames for a single window in local attention (i.e. `left + 1 + right`)
Can be given as a fixed number of frames (`int`), or percentage of
the sequence length (`float`). If `None`, use `Tq`
q_proj : `Dense`, instance of dense or fully connected layer
- for `ScoreLocation`, the number of hidden unit is `1`
- for `ScoreGeneral`, the number of hidden unit is `dim`
target_proj : `Dense`, for predictive local attention, applying
a fully connected network on target sequence (i.e. the query) to
predict the position on source sequence (i.e. the key).
The layer must has output dimension equal to 1 and return logit value.
Returns:
Tensor of shape `[num_heads, batch_size, Tq, Tv]`, or
`[num_heads, batch_size, Tq, 1]` if `ScoreLocation`
"""
### Check if multi-head attention is used
num_heads = _get_num_heads(query)
if num_heads > 0:
query = bk.reshape(query, [-1] + [i for i in query.shape[2:]])
if key is not None:
key = bk.reshape(key, [-1] + [i for i in key.shape[2:]])
Tq = query.shape[1]
Tv = Tq if key is None else key.shape[1]
# scale shape is `[]` or `[dim]`
scale = bk.array(scale, dtype=query.dtype)
### Check the window width
if window_width is None:
window_width = Tq
elif window_width < 1:
window_width = window_width * Tv
window_width = int(window_width)
### Locative attention
if AttentionMechanism.ScoreLocation in self:
if PosLocalM in self or PosLocalP in self:
raise NotImplementedError(
"ScoreLocation only support Global attention, but given: %s"
% str(self))
# [batch_size * num_heads, Tq, dim]
scores = bk.reduce_mean(scale) * q_proj(query)
assert scores.shape[-1] == 1, \
" q_proj must have only 1 hidden unit, but given %d" % scores.shape[-1]
### Other score mode need the key tensor
else:
if key is None:
raise ValueError(
"key must be provided for attention type: %s" % str(self))
### Attention position (local or global)
if PosLocalM in self:
key = key[:, -window_width:]
elif PosLocalP in self:
pt = bk.sigmoid(target_proj(bk.reshape(query, ([0], -1))))
assert pt.shape[-1] == 1, \
"target_proj must project the query [., Tq * dim] to [., 1], i.e. " + \
"predicting the attention position on source sequence using " + \
"knowledge from target sequence."
pt = Tv * pt # `[batch_size * num_heads, 1]`
# `[batch_size * num_heads, Tv]`
# Eq (10) (Luong et al. 2015)
gauss_est = bk.exp(
-bk.square(bk.arange(Tv, dtype=pt.dtype) - pt) /
(2 * bk.square(window_width / 2)))
# `[batch_size * num_heads, 1, Tv]`
gauss_est = bk.expand_dims(gauss_est, axis=1)
### Additive or concat method
if AttentionMechanism.ScoreAdditive in self:
# [batch_size * num_heads, Tq, 1, dim]
q = bk.expand_dims(query, axis=2)
# [batch_size * num_heads, 1, Tv, dim]
k = bk.expand_dims(key, axis=1)
# [batch_size * num_heads, Tq, Tv]
scores = bk.reduce_sum(scale * bk.tanh(q + k), axis=-1)
### Dot product or multiplicative scoring
elif AttentionMechanism.ScoreDotProd in self:
# this is a trick to make attention_scale broadcastable when
# scale_tied=False
scores = bk.matmul(scale * query, bk.swapaxes(key, 1, 2))
### cosine scoring
elif AttentionMechanism.ScoreCosine in self:
# [batch_size * num_heads, Tq, 1, dim]
q = bk.expand_dims(query, axis=2)
# [batch_size * num_heads, 1, Tv, dim]
k = bk.expand_dims(key, axis=1)
# [batch_size * num_heads, Tq, Tv, dim]
scores = (q * k) / (bk.norm(q, p=2) * bk.norm(k, p=2))
scores = bk.reduce_sum(scale * scores, axis=-1, keepdims=False)
### general method with only project on the query
elif AttentionMechanism.ScoreGeneral in self:
query = q_proj(query)
assert query.shape[-1] == key.shape[-1], \
" q_proj must have %d hidden units, but given %d units" % \
(key.shape[-1], query.shape[-1])
scores = bk.matmul(scale * query, bk.swapaxes(key, 1, 2))
else:
raise NotImplementedError(
"No support for attention_type='%s'" % str(self))
### applying the local-predictive attention
if PosLocalP in self:
scores = scores * gauss_est
### get back the multi-heads shape
if num_heads > 0:
scores = bk.reshape(scores,
shape=[num_heads, -1] +
[i for i in scores.shape[1:]])
return scores