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""" | |
Linear Transformer proposed in "Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention" | |
Modified from: https://github.com/idiap/fast-transformers/blob/master/fast_transformers/attention/linear_attention.py | |
""" | |
import torch | |
from torch.nn import Module, Dropout | |
def elu_feature_map(x): | |
return torch.nn.functional.elu(x) + 1 | |
class LinearAttention(Module): | |
def __init__(self, eps=1e-6): | |
super().__init__() | |
self.feature_map = elu_feature_map | |
self.eps = eps | |
def forward(self, queries, keys, values, q_mask=None, kv_mask=None): | |
"""Multi-Head linear attention proposed in "Transformers are RNNs" | |
Args: | |
queries: [N, L, H, D] | |
keys: [N, S, H, D] | |
values: [N, S, H, D] | |
q_mask: [N, L] | |
kv_mask: [N, S] | |
Returns: | |
queried_values: (N, L, H, D) | |
""" | |
Q = self.feature_map(queries) | |
K = self.feature_map(keys) | |
# set padded position to zero | |
if q_mask is not None: | |
Q = Q * q_mask[:, :, None, None] | |
if kv_mask is not None: | |
K = K * kv_mask[:, :, None, None] | |
values = values * kv_mask[:, :, None, None] | |
v_length = values.size(1) | |
values = values / v_length # prevent fp16 overflow | |
KV = torch.einsum("nshd,nshv->nhdv", K, values) # (S,D)' @ S,V | |
Z = 1 / (torch.einsum("nlhd,nhd->nlh", Q, K.sum(dim=1)) + self.eps) | |
queried_values = torch.einsum("nlhd,nhdv,nlh->nlhv", Q, KV, Z) * v_length | |
return queried_values.contiguous() | |
class FullAttention(Module): | |
def __init__(self, use_dropout=False, attention_dropout=0.1): | |
super().__init__() | |
self.use_dropout = use_dropout | |
self.dropout = Dropout(attention_dropout) | |
def forward(self, queries, keys, values, q_mask=None, kv_mask=None): | |
"""Multi-head scaled dot-product attention, a.k.a full attention. | |
Args: | |
queries: [N, L, H, D] | |
keys: [N, S, H, D] | |
values: [N, S, H, D] | |
q_mask: [N, L] | |
kv_mask: [N, S] | |
Returns: | |
queried_values: (N, L, H, D) | |
""" | |
# Compute the unnormalized attention and apply the masks | |
QK = torch.einsum("nlhd,nshd->nlsh", queries, keys) | |
if kv_mask is not None: | |
QK.masked_fill_( | |
~(q_mask[:, :, None, None] * kv_mask[:, None, :, None]).bool(), -1e9 | |
) | |
# Compute the attention and the weighted average | |
softmax_temp = 1.0 / queries.size(3) ** 0.5 # sqrt(D) | |
A = torch.softmax(softmax_temp * QK, dim=2) | |
if self.use_dropout: | |
A = self.dropout(A) | |
queried_values = torch.einsum("nlsh,nshd->nlhd", A, values) | |
return queried_values.contiguous() | |