Module fast_transformers.attention.improved_clustered_attention
Implement improved 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 improved 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
from ..sparse_product import sparse_dot_product, sparse_weighted_average
from ..sparse_product import clustered_sparse_dot_product, \
clustered_sparse_weighted_average
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 ImprovedClusteredAttention(Module):
"""
Immproved clustered attention approximation by recompution attention
for each query with the top-k keys for the corresponding cluster.
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 identify the top-k keys with highest
dot products.
Subsequently, for each query we compute the sparse dot product with
the corresponding top-k keys to improve the attention approximation.
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)
topk: Number of top-k keys to for improved approximation (default: 32)
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, topk=32, softmax_temp=None,
attention_dropout=0.1, event_dispatcher=""):
super(ImprovedClusteredAttention, self).__init__()
self.clusters = clusters
self.iterations = iterations
self.bits = bits
self.hash_bias = hash_bias
self.topk = topk
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
clusters, counts = cluster(
hashes,
query_lengths._lengths.int(),
clusters=self.clusters,
iterations=self.iterations,
bits=self.bits
)
return clusters, counts
def _topk_attention(self, Q, K, V,
clusters, counts,
topk, topk_values,
A_bottomk, softmax_temp,
query_lengths):
"""Return the attention with just the topk heads."""
# Extract some indices
N, H, L, E = Q.shape
_, _, S, _ = K.shape
_, _, C, k = topk.shape
# We need to pass the output tensor to initialize to 0
QK = clustered_sparse_dot_product(
Q, K, topk,
clusters, counts,
query_lengths._lengths.int()
)
# We need to mask the topk dot products if topk > input_length
QK = QK.masked_fill(
torch.isinf(topk_values[:,0,0,:]).view(N, 1, 1, k),
float("-inf")
)
A = torch.softmax(softmax_temp * QK, dim=-1)
assert A_bottomk.is_contiguous()
A_bottomk = broadcast(
A_bottomk.unsqueeze(3),
clusters,
torch.ones_like(counts, dtype=torch.float32)
)
A = A * (1.0 - A_bottomk)
A = self.dropout(A)
assert A.is_contiguous()
V_new = clustered_sparse_weighted_average(A, V, topk, clusters)
return V_new
def _broadcast_values(self, V, clusters, counts):
"""Broadcast the values back to the correct positions but make sure
that the gradient flows properly."""
V_new = _BroadcastValues.apply(V.contiguous(), clusters, counts)
return V_new
def _bottomk_attention(self, QK, V, clusters, counts, topk, softmax_temp):
"""Return the attention with just the bottomk keys."""
N, H, C, S = QK.shape
A = torch.softmax(softmax_temp * QK, dim=-1)
mask = QK.new_ones(QK.shape)
mask[
torch.arange(N, device=QK.device).view(N, 1, 1, 1),
torch.arange(H, device=QK.device).view(1, H, 1, 1),
torch.arange(C, device=QK.device).view(1, 1, C, 1),
topk,
] = 0
A = A * mask
A_bottomk = A.sum(-1)
A = self.dropout(A)
# Compute the values
V_new = torch.einsum("nhls,nhse->nhle", A, V)
# Broadcast the values back depending on the groups
V_new = self._broadcast_values(V_new, clusters, counts)
return V_new, A_bottomk
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, ("Improved-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
clusters, counts = self._create_query_groups(queries, query_lengths)
Q_grouped = _GroupQueries.apply(queries, clusters, counts)
# Compute the attention
QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys)
QK = QK + key_lengths.additive_matrix[:, None, None, :]
topk_values, topk = torch.topk(QK, self.topk, sorted=False, dim=-1)
assert topk.is_contiguous()
# Now compute the attention with only the bottom keys
V_bottomk, A_bottomk = self._bottomk_attention(
QK, values,
clusters, counts,
topk,
softmax_temp
)
# Now compute the attention with only the top keys
V_topk = self._topk_attention(
queries, keys, values,
clusters, counts,
topk, topk_values,
A_bottomk,
softmax_temp,
query_lengths
)
V_new = V_topk + V_bottomk
return V_new.permute(0, 2, 1, 3).contiguous()
# Register the attention implementation so that it becomes available in our
# builders
AttentionRegistry.register(
"improved-clustered", ImprovedClusteredAttention,
[
("clusters", Int),
("iterations", Optional(Int, 10)),
("bits", Optional(Int, 32)),
("hash_bias", Optional(Bool, True)),
("topk", Optional(Int, 32)),
("softmax_temp", Optional(Float)),
("attention_dropout", Optional(Float, 0.1)),
("event_dispatcher", Optional(EventDispatcherInstance, ""))
]
)Functions
def clustered_sparse_dot_product(...)def clustered_sparse_weighted_average(...)def sparse_dot_product(...)def sparse_weighted_average(...)
Classes
class ImprovedClusteredAttention (clusters, iterations=10, bits=32, hash_bias=True, topk=32, softmax_temp=None, attention_dropout=0.1, event_dispatcher='')-
Immproved clustered attention approximation by recompution attention for each query with the top-k keys for the corresponding cluster.
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 identify the top-k keys with highest dot products.
Subsequently, for each query we compute the sparse dot product with the corresponding top-k keys to improve the attention approximation.
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) topk: Number of top-k keys to for improved approximation (default: 32) 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 ImprovedClusteredAttention(Module): """ Immproved clustered attention approximation by recompution attention for each query with the top-k keys for the corresponding cluster. 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 identify the top-k keys with highest dot products. Subsequently, for each query we compute the sparse dot product with the corresponding top-k keys to improve the attention approximation. 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) topk: Number of top-k keys to for improved approximation (default: 32) 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, topk=32, softmax_temp=None, attention_dropout=0.1, event_dispatcher=""): super(ImprovedClusteredAttention, self).__init__() self.clusters = clusters self.iterations = iterations self.bits = bits self.hash_bias = hash_bias self.topk = topk 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 clusters, counts = cluster( hashes, query_lengths._lengths.int(), clusters=self.clusters, iterations=self.iterations, bits=self.bits ) return clusters, counts def _topk_attention(self, Q, K, V, clusters, counts, topk, topk_values, A_bottomk, softmax_temp, query_lengths): """Return the attention with just the topk heads.""" # Extract some indices N, H, L, E = Q.shape _, _, S, _ = K.shape _, _, C, k = topk.shape # We need to pass the output tensor to initialize to 0 QK = clustered_sparse_dot_product( Q, K, topk, clusters, counts, query_lengths._lengths.int() ) # We need to mask the topk dot products if topk > input_length QK = QK.masked_fill( torch.isinf(topk_values[:,0,0,:]).view(N, 1, 1, k), float("-inf") ) A = torch.softmax(softmax_temp * QK, dim=-1) assert A_bottomk.is_contiguous() A_bottomk = broadcast( A_bottomk.unsqueeze(3), clusters, torch.ones_like(counts, dtype=torch.float32) ) A = A * (1.0 - A_bottomk) A = self.dropout(A) assert A.is_contiguous() V_new = clustered_sparse_weighted_average(A, V, topk, clusters) return V_new def _broadcast_values(self, V, clusters, counts): """Broadcast the values back to the correct positions but make sure that the gradient flows properly.""" V_new = _BroadcastValues.apply(V.contiguous(), clusters, counts) return V_new def _bottomk_attention(self, QK, V, clusters, counts, topk, softmax_temp): """Return the attention with just the bottomk keys.""" N, H, C, S = QK.shape A = torch.softmax(softmax_temp * QK, dim=-1) mask = QK.new_ones(QK.shape) mask[ torch.arange(N, device=QK.device).view(N, 1, 1, 1), torch.arange(H, device=QK.device).view(1, H, 1, 1), torch.arange(C, device=QK.device).view(1, 1, C, 1), topk, ] = 0 A = A * mask A_bottomk = A.sum(-1) A = self.dropout(A) # Compute the values V_new = torch.einsum("nhls,nhse->nhle", A, V) # Broadcast the values back depending on the groups V_new = self._broadcast_values(V_new, clusters, counts) return V_new, A_bottomk 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, ("Improved-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 clusters, counts = self._create_query_groups(queries, query_lengths) Q_grouped = _GroupQueries.apply(queries, clusters, counts) # Compute the attention QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys) QK = QK + key_lengths.additive_matrix[:, None, None, :] topk_values, topk = torch.topk(QK, self.topk, sorted=False, dim=-1) assert topk.is_contiguous() # Now compute the attention with only the bottom keys V_bottomk, A_bottomk = self._bottomk_attention( QK, values, clusters, counts, topk, softmax_temp ) # Now compute the attention with only the top keys V_topk = self._topk_attention( queries, keys, values, clusters, counts, topk, topk_values, A_bottomk, softmax_temp, query_lengths ) V_new = V_topk + V_bottomk return V_new.permute(0, 2, 1, 3).contiguous()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, ("Improved-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 clusters, counts = self._create_query_groups(queries, query_lengths) Q_grouped = _GroupQueries.apply(queries, clusters, counts) # Compute the attention QK = torch.einsum("nhle,nhse->nhls", Q_grouped, keys) QK = QK + key_lengths.additive_matrix[:, None, None, :] topk_values, topk = torch.topk(QK, self.topk, sorted=False, dim=-1) assert topk.is_contiguous() # Now compute the attention with only the bottom keys V_bottomk, A_bottomk = self._bottomk_attention( QK, values, clusters, counts, topk, softmax_temp ) # Now compute the attention with only the top keys V_topk = self._topk_attention( queries, keys, values, clusters, counts, topk, topk_values, A_bottomk, softmax_temp, query_lengths ) V_new = V_topk + V_bottomk return V_new.permute(0, 2, 1, 3).contiguous()