Module fast_transformers.attention.clustered_attention
Implement clustered self attention.
Expand source code
#
# Copyright (c) 2020 Idiap Research Institute, http://www.idiap.ch/
# Written by Angelos Katharopoulos <angelos.katharopoulos@idiap.ch>,
# Apoorv Vyas <avyas@idiap.ch>
#
"""Implement clustered self attention."""
from math import sqrt
import torch
import torch.autograd
from torch.nn import Dropout, Module
from torch.nn.init import normal_
from ..attention_registry import AttentionRegistry, Optional, Float, Int, \
Bool, EventDispatcherInstance
from ..events import EventDispatcher
from ..masking import FullMask
from ..aggregate import aggregate, broadcast
from ..clustering.hamming import cluster
from ..hashing import compute_hashes
class _GroupQueries(torch.autograd.Function):
@staticmethod
def forward(ctx, Q, clusters, counts):
factors = 1/counts.float()
q_grouped = aggregate(Q, clusters, factors)
ctx.save_for_backward(clusters, factors)
return q_grouped
@staticmethod
def backward(ctx, grad_q_grouped):
clusters, factors = ctx.saved_tensors
grad_q = broadcast(grad_q_grouped, clusters, factors)
return grad_q, None, None
class _BroadcastValues(torch.autograd.Function):
@staticmethod
def forward(ctx, v_grouped, clusters, counts):
factors = torch.ones_like(counts, dtype=v_grouped.dtype)
V = broadcast(v_grouped, clusters, factors)
ctx.save_for_backward(clusters, factors)
return V
@staticmethod
def backward(ctx, grad_v):
clusters, factors = ctx.saved_tensors
grad_v_grouped = aggregate(grad_v, clusters, factors)
return grad_v_grouped, None, None
class ClusteredAttention(Module):
"""Use LSH and clustering in the resulting Hamming space to group queries
that will have minimal L2 distance from each other.
Given the queries, keys, and values as Q, K, and V respectively, we
first cluster the queries in "C" groups and compute the "C" query centroids
Q_c.
We now use to the centroids Q_c to compute the attention using:
V'_c = softmax(Q_c.mm(K.t()), dim=-1).mm(V).
Now the computed values V'_c are "broadcasted" back to the query members
of the corresponding cluster.
Arguments
---------
clusters: How many clusters to group the queries into
iterations: The number of lloyd iterations to perform (default: 10)
bits: How many bits to use for the hash (default: 32)
hash_bias: If true, hamming distance proportional to L2 distance
If false, hamming distance proportional to cosine distance
(default: True)
softmax_temp: The temperature to use for the softmax attention.
(default: 1/sqrt(d_keys) where d_keys is computed at
runtime)
attention_dropout: The dropout rate to apply to the attention
(default: 0.1)
event_dispatcher: str or EventDispatcher instance to be used by this
module for dispatching events (default: the default
global dispatcher)
"""
def __init__(self, clusters, iterations=10, bits=32,
hash_bias=True, softmax_temp=None, attention_dropout=0.1,
event_dispatcher=""):
super(ClusteredAttention, self).__init__()
self.clusters = clusters
self.iterations = iterations
self.bits = bits
self.hash_bias = hash_bias
self.softmax_temp = softmax_temp
self.dropout = Dropout(attention_dropout)
self.event_dispatcher = EventDispatcher.get(event_dispatcher)
def _create_query_groups(self, Q, query_lengths):
N, H, L, E = Q.shape
# Compute the hashes for all the queries
planes = Q.new_empty((self.bits, E+1))
normal_(planes)
if not self.hash_bias:
planes[:, -1] = 0
hashes = compute_hashes(Q.view(N*H*L, E), planes).view(N, H, L)
# Cluster the hashes and return the cluster index per query
groups = cluster(
hashes,
query_lengths._lengths.int(),
clusters=self.clusters,
iterations=self.iterations,
bits=self.bits
)
return groups
def _group_queries(self, Q, groups):
"""Aggregate the Qs based on the index of cluster they belong to. Make
sure to allow for gradient propagation backwards from the grouped
queries to each query."""
q_grouped = _GroupQueries.apply(Q, *groups)
return q_grouped
def _broadcast_values(self, V, groups):
"""Broadcast the values back to the correct positions but make sure
that the gradient flows properly."""
V_new = _BroadcastValues.apply(V.contiguous(), *groups)
V_new = V_new.permute(0, 2, 1, 3).contiguous()
return V_new
def forward(self, queries, keys, values, attn_mask, query_lengths,
key_lengths):
# Make sure that there is no attention mask
assert attn_mask.all_ones, ("Clustered attention cannot use an "
"arbitrary attention mask.")
queries = queries.permute(0,2,1,3).contiguous()
keys = keys.permute(0,2,1,3).contiguous()
values = values.permute(0,2,1,3).contiguous()
N, H, L, E = queries.shape
softmax_temp = self.softmax_temp or 1./sqrt(E)
# Cluster the queries into groups
groups = self._create_query_groups(queries, query_lengths)
Q_grouped = self._group_queries(queries, groups)
# Compute the attention
QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys)
QK = QK + key_lengths.additive_matrix[:, None, None, :]
A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1))
V = torch.einsum("nhls,nhsd->nhld", A, values)
# Broadcast grouped attention
return self._broadcast_values(V, groups)
# Register the attention implementation so that it becomes available in our
# builders
AttentionRegistry.register(
"clustered", ClusteredAttention,
[
("clusters", Int),
("iterations", Optional(Int, 10)),
("bits", Optional(Int, 32)),
("hash_bias", Optional(Bool, True)),
("softmax_temp", Optional(Float)),
("attention_dropout", Optional(Float, 0.1)),
("event_dispatcher", Optional(EventDispatcherInstance, ""))
]
)Classes
class ClusteredAttention (clusters, iterations=10, bits=32, hash_bias=True, softmax_temp=None, attention_dropout=0.1, event_dispatcher='')-
Use LSH and clustering in the resulting Hamming space to group queries that will have minimal L2 distance from each other.
Given the queries, keys, and values as Q, K, and V respectively, we first cluster the queries in "C" groups and compute the "C" query centroids Q_c.
We now use to the centroids Q_c to compute the attention using:
V'_c = softmax(Q_c.mm(K.t()), dim=-1).mm(V).Now the computed values V'_c are "broadcasted" back to the query members of the corresponding cluster.
Arguments
clusters: How many clusters to group the queries into iterations: The number of lloyd iterations to perform (default: 10) bits: How many bits to use for the hash (default: 32) hash_bias: If true, hamming distance proportional to L2 distance If false, hamming distance proportional to cosine distance (default: True) softmax_temp: The temperature to use for the softmax attention. (default: 1/sqrt(d_keys) where d_keys is computed at runtime) attention_dropout: The dropout rate to apply to the attention (default: 0.1) event_dispatcher: str or EventDispatcher instance to be used by this module for dispatching events (default: the default global dispatcher)Initializes internal Module state, shared by both nn.Module and ScriptModule.
Expand source code
class ClusteredAttention(Module): """Use LSH and clustering in the resulting Hamming space to group queries that will have minimal L2 distance from each other. Given the queries, keys, and values as Q, K, and V respectively, we first cluster the queries in "C" groups and compute the "C" query centroids Q_c. We now use to the centroids Q_c to compute the attention using: V'_c = softmax(Q_c.mm(K.t()), dim=-1).mm(V). Now the computed values V'_c are "broadcasted" back to the query members of the corresponding cluster. Arguments --------- clusters: How many clusters to group the queries into iterations: The number of lloyd iterations to perform (default: 10) bits: How many bits to use for the hash (default: 32) hash_bias: If true, hamming distance proportional to L2 distance If false, hamming distance proportional to cosine distance (default: True) softmax_temp: The temperature to use for the softmax attention. (default: 1/sqrt(d_keys) where d_keys is computed at runtime) attention_dropout: The dropout rate to apply to the attention (default: 0.1) event_dispatcher: str or EventDispatcher instance to be used by this module for dispatching events (default: the default global dispatcher) """ def __init__(self, clusters, iterations=10, bits=32, hash_bias=True, softmax_temp=None, attention_dropout=0.1, event_dispatcher=""): super(ClusteredAttention, self).__init__() self.clusters = clusters self.iterations = iterations self.bits = bits self.hash_bias = hash_bias self.softmax_temp = softmax_temp self.dropout = Dropout(attention_dropout) self.event_dispatcher = EventDispatcher.get(event_dispatcher) def _create_query_groups(self, Q, query_lengths): N, H, L, E = Q.shape # Compute the hashes for all the queries planes = Q.new_empty((self.bits, E+1)) normal_(planes) if not self.hash_bias: planes[:, -1] = 0 hashes = compute_hashes(Q.view(N*H*L, E), planes).view(N, H, L) # Cluster the hashes and return the cluster index per query groups = cluster( hashes, query_lengths._lengths.int(), clusters=self.clusters, iterations=self.iterations, bits=self.bits ) return groups def _group_queries(self, Q, groups): """Aggregate the Qs based on the index of cluster they belong to. Make sure to allow for gradient propagation backwards from the grouped queries to each query.""" q_grouped = _GroupQueries.apply(Q, *groups) return q_grouped def _broadcast_values(self, V, groups): """Broadcast the values back to the correct positions but make sure that the gradient flows properly.""" V_new = _BroadcastValues.apply(V.contiguous(), *groups) V_new = V_new.permute(0, 2, 1, 3).contiguous() return V_new def forward(self, queries, keys, values, attn_mask, query_lengths, key_lengths): # Make sure that there is no attention mask assert attn_mask.all_ones, ("Clustered attention cannot use an " "arbitrary attention mask.") queries = queries.permute(0,2,1,3).contiguous() keys = keys.permute(0,2,1,3).contiguous() values = values.permute(0,2,1,3).contiguous() N, H, L, E = queries.shape softmax_temp = self.softmax_temp or 1./sqrt(E) # Cluster the queries into groups groups = self._create_query_groups(queries, query_lengths) Q_grouped = self._group_queries(queries, groups) # Compute the attention QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys) QK = QK + key_lengths.additive_matrix[:, None, None, :] A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1)) V = torch.einsum("nhls,nhsd->nhld", A, values) # Broadcast grouped attention return self._broadcast_values(V, groups)Ancestors
- torch.nn.modules.module.Module
Methods
def forward(self, queries, keys, values, attn_mask, query_lengths, key_lengths)-
Defines the computation performed at every call.
Should be overridden by all subclasses.
Note
Although the recipe for forward pass needs to be defined within this function, one should call the :class:
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.Expand source code
def forward(self, queries, keys, values, attn_mask, query_lengths, key_lengths): # Make sure that there is no attention mask assert attn_mask.all_ones, ("Clustered attention cannot use an " "arbitrary attention mask.") queries = queries.permute(0,2,1,3).contiguous() keys = keys.permute(0,2,1,3).contiguous() values = values.permute(0,2,1,3).contiguous() N, H, L, E = queries.shape softmax_temp = self.softmax_temp or 1./sqrt(E) # Cluster the queries into groups groups = self._create_query_groups(queries, query_lengths) Q_grouped = self._group_queries(queries, groups) # Compute the attention QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys) QK = QK + key_lengths.additive_matrix[:, None, None, :] A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1)) V = torch.einsum("nhls,nhsd->nhld", A, values) # Broadcast grouped attention return self._broadcast_values(V, groups)