Module fast_transformers.attention.exact_topk_attention
Implement the oracle top-k attention. The top-k keys are exact ones. MultiHeadAttention module. Note that this module is to be used in conjuction with the AttentionLayer in order to work.
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 the oracle top-k attention. The top-k keys are exact ones.
MultiHeadAttention module. Note that this module is to be used in conjuction
with the AttentionLayer in order to work."""
from math import sqrt
import torch
from torch.nn import Dropout, Module
from ..attention_registry import AttentionRegistry, Optional, Int, Float, \
EventDispatcherInstance
from ..events import EventDispatcher
class ExactTopKAttention(Module):
"""Implement the oracle top-k softmax attention.
Arguments
---------
top-k: The top k keys to attend to (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, topk=32, softmax_temp=None, attention_dropout=0.1,
event_dispatcher=""):
super(ExactTopKAttention, self).__init__()
self.topk = topk
self.softmax_temp = softmax_temp
self.dropout = Dropout(attention_dropout)
self.event_dispatcher = EventDispatcher.get(event_dispatcher)
def forward(self, queries, keys, values, attn_mask, query_lengths,
key_lengths):
# Extract some shapes and compute the temperature
N, L, H, E = queries.shape
_, S, _, D = values.shape
softmax_temp = self.softmax_temp or 1./sqrt(E)
# Compute the unnormalized attention and apply the masks
QK = torch.einsum("nlhe,nshe->nhls", queries, keys)
topk = min(self.topk, L)
if not attn_mask.all_ones:
QK = QK + attn_mask.additive_matrix
QK = QK + key_lengths.additive_matrix[:, None, None]
topk_values, topk_idx = torch.topk(QK, topk, sorted=False, dim=-1)
mask = QK.new_ones(QK.shape) * float("-inf")
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(L, device=QK.device).view(1, 1, L, 1),
topk_idx,
] = 0.
QK = QK + mask
# Compute the attention and the weighted average
A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1))
V = torch.einsum("nhls,nshd->nlhd", A, values)
# Make sure that what we return is contiguous
return V.contiguous()
# Register the attention implementation so that it becomes available in our
# builders
AttentionRegistry.register(
"exact-topk", ExactTopKAttention,
[
("topk", Optional(Int, 32)),
("softmax_temp", Optional(Float)),
("attention_dropout", Optional(Float, 0.1)),
("event_dispatcher", Optional(EventDispatcherInstance, ""))
]
)Classes
class ExactTopKAttention (topk=32, softmax_temp=None, attention_dropout=0.1, event_dispatcher='')-
Implement the oracle top-k softmax attention.
Arguments
top-k: The top k keys to attend to (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 ExactTopKAttention(Module): """Implement the oracle top-k softmax attention. Arguments --------- top-k: The top k keys to attend to (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, topk=32, softmax_temp=None, attention_dropout=0.1, event_dispatcher=""): super(ExactTopKAttention, self).__init__() self.topk = topk self.softmax_temp = softmax_temp self.dropout = Dropout(attention_dropout) self.event_dispatcher = EventDispatcher.get(event_dispatcher) def forward(self, queries, keys, values, attn_mask, query_lengths, key_lengths): # Extract some shapes and compute the temperature N, L, H, E = queries.shape _, S, _, D = values.shape softmax_temp = self.softmax_temp or 1./sqrt(E) # Compute the unnormalized attention and apply the masks QK = torch.einsum("nlhe,nshe->nhls", queries, keys) topk = min(self.topk, L) if not attn_mask.all_ones: QK = QK + attn_mask.additive_matrix QK = QK + key_lengths.additive_matrix[:, None, None] topk_values, topk_idx = torch.topk(QK, topk, sorted=False, dim=-1) mask = QK.new_ones(QK.shape) * float("-inf") 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(L, device=QK.device).view(1, 1, L, 1), topk_idx, ] = 0. QK = QK + mask # Compute the attention and the weighted average A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1)) V = torch.einsum("nhls,nshd->nlhd", A, values) # Make sure that what we return is contiguous return V.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): # Extract some shapes and compute the temperature N, L, H, E = queries.shape _, S, _, D = values.shape softmax_temp = self.softmax_temp or 1./sqrt(E) # Compute the unnormalized attention and apply the masks QK = torch.einsum("nlhe,nshe->nhls", queries, keys) topk = min(self.topk, L) if not attn_mask.all_ones: QK = QK + attn_mask.additive_matrix QK = QK + key_lengths.additive_matrix[:, None, None] topk_values, topk_idx = torch.topk(QK, topk, sorted=False, dim=-1) mask = QK.new_ones(QK.shape) * float("-inf") 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(L, device=QK.device).view(1, 1, L, 1), topk_idx, ] = 0. QK = QK + mask # Compute the attention and the weighted average A = self.dropout(torch.softmax(softmax_temp * QK, dim=-1)) V = torch.einsum("nhls,nshd->nlhd", A, values) # Make sure that what we return is contiguous return V.contiguous()