Module fast_transformers.transformers
Implement transformer encoders and decoders that are going to be used with different attention mechanisms.
In all cases the batch dimension is first and the sequence dimension is second.
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 transformer encoders and decoders that are going to be used with
different attention mechanisms.
In all cases the batch dimension is first and the sequence dimension is second.
"""
import torch
from torch.nn import Dropout, LayerNorm, Linear, Module, ModuleList
import torch.nn.functional as F
from .events import EventDispatcher
from .masking import FullMask, LengthMask
class TransformerEncoderLayer(Module):
"""Self attention and feed forward network with skip connections.
This transformer encoder layer implements the same encoder layer as
PyTorch but is a bit more open for extension by receiving the attention
implementation as a constructor argument.
Arguments
---------
attention: The attention implementation to use given as a nn.Module
d_model: The input feature dimensionality
d_ff: The dimensionality of the intermediate features after the
attention (default: d_model*4)
dropout: The dropout rate to apply to the intermediate features
(default: 0.1)
activation: {'relu', 'gelu'} Which activation to use for the feed
forward part of the layer (default: relu)
event_dispatcher: str or EventDispatcher instance to be used by this
module for dispatching events (default: the default
global dispatcher)
"""
def __init__(self, attention, d_model, d_ff=None, dropout=0.1,
activation="relu", event_dispatcher=""):
super(TransformerEncoderLayer, self).__init__()
d_ff = d_ff or 4*d_model
self.attention = attention
self.linear1 = Linear(d_model, d_ff)
self.linear2 = Linear(d_ff, d_model)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.dropout = Dropout(dropout)
self.activation = F.relu if activation == "relu" else F.gelu
self.event_dispatcher = EventDispatcher.get(event_dispatcher)
def forward(self, x, attn_mask=None, length_mask=None):
"""Apply the transformer encoder to the input x.
Arguments
---------
x: The input features of shape (N, L, E) where N is the batch size,
L is the sequence length (padded) and E is d_model passed in the
constructor.
attn_mask: An implementation of fast_transformers.masking.BaseMask
that encodes where each element of x can attend to.
length_mask: An implementation of
fast_transformers.masking.BaseMask that encodes how
many elements each sequence in the batch consists of.
"""
# Normalize the masks
N = x.shape[0]
L = x.shape[1]
attn_mask = attn_mask or FullMask(L, device=x.device)
length_mask = length_mask or \
LengthMask(x.new_full((N,), L, dtype=torch.int64))
# Run self attention and add it to the input
x = x + self.dropout(self.attention(
x, x, x,
attn_mask=attn_mask,
query_lengths=length_mask,
key_lengths=length_mask
))
# Run the fully connected part of the layer
y = x = self.norm1(x)
y = self.dropout(self.activation(self.linear1(y)))
y = self.dropout(self.linear2(y))
return self.norm2(x+y)
class TransformerEncoder(Module):
"""TransformerEncoder is little more than a sequence of transformer encoder
layers.
It contains an optional final normalization layer as well as the ability to
create the masks once and save some computation.
Arguments
---------
layers: list, TransformerEncoderLayer instances or instances that
implement the same interface.
norm_layer: A normalization layer to be applied to the final output
(default: None which means no normalization)
event_dispatcher: str or EventDispatcher instance to be used by this
module for dispatching events (default: the default
global dispatcher)
"""
def __init__(self, layers, norm_layer=None, event_dispatcher=""):
super(TransformerEncoder, self).__init__()
self.layers = ModuleList(layers)
self.norm = norm_layer
self.event_dispatcher = EventDispatcher.get(event_dispatcher)
def forward(self, x, attn_mask=None, length_mask=None):
"""Apply all transformer encoder layers to the input x.
Arguments
---------
x: The input features of shape (N, L, E) where N is the batch size,
L is the sequence length (padded) and E is d_model passed in the
constructor of each transformer encoder layer.
attn_mask: An implementation of fast_transformers.masking.BaseMask
that encodes where each element of x can attend to.
length_mask: An implementation of
fast_transformers.masking.BaseMask that encodes how
many elements each sequence in the batch consists of.
"""
# Normalize the masks
N = x.shape[0]
L = x.shape[1]
attn_mask = attn_mask or FullMask(L, device=x.device)
length_mask = length_mask or \
LengthMask(x.new_full((N,), L, dtype=torch.int64))
# Apply all the transformers
for layer in self.layers:
x = layer(x, attn_mask=attn_mask, length_mask=length_mask)
# Apply the normalization if needed
if self.norm is not None:
x = self.norm(x)
return x
class TransformerDecoderLayer(Module):
"""The decoder layer from "Attention Is All You Need".
Similar to the encoder layer, this layer implements the decoder that
PyTorch implements but can be used with any attention implementation
because it receives the attention layers as constructor arguments.
Arguments
---------
self_attention: The attention implementation to use for self attention
given as a nn.Module
cross_attention: The attention implementation to use for cross
attention given as a nn.Module
d_model: The input feature dimensionality
d_ff: The dimensionality of the intermediate features after the
attention (default: d_model*4)
dropout: The dropout rate to apply to the intermediate features
(default: 0.1)
activation: {'relu', 'gelu'} Which activation to use for the feed
forward part of the layer (default: relu)
event_dispatcher: str or EventDispatcher instance to be used by this
module for dispatching events (default: the default
global dispatcher)
"""
def __init__(self, self_attention, cross_attention, d_model, d_ff=None,
dropout=0.1, activation="relu", event_dispatcher=""):
super(TransformerDecoderLayer, self).__init__()
d_ff = d_ff or 4*d_model
self.self_attention = self_attention
self.cross_attention = cross_attention
self.linear1 = Linear(d_model, d_ff)
self.linear2 = Linear(d_ff, d_model)
self.norm1 = LayerNorm(d_model)
self.norm2 = LayerNorm(d_model)
self.norm3 = LayerNorm(d_model)
self.dropout = Dropout(dropout)
self.activation = F.relu if activation == "relu" else F.gelu
self.event_dispatcher = EventDispatcher.get(event_dispatcher)
def forward(self, x, memory, x_mask=None, x_length_mask=None,
memory_mask=None, memory_length_mask=None):
"""Apply the transformer decoder to the input x using the memory
`memory`.
Arguments
---------
x: The input features of shape (N, L, E) where N is the batch size,
L is the sequence length (padded) and E should be the same as
the d_model passed in the constructor.
memory: The memory features of shape (N, L', E) where N is the
batch size, L' is the memory's sequence length (padded) and
E should be the same as the d_model.
x_mask: An implementation of fast_transformers.masking.BaseMask
that encodes where each element of x can attend to in x.
Namely the self attention mask.
x_length_mask: An implementation of a BaseMask that encodes how
many elements each sequence in the batch consists
of.
memory_mask: An implementation of BaseMask that encodes where each
element of x can attend to in the memory. Namely the
cross attention mask.
memory_length_mask: An implementation of a BaseMask that encodes how
many elements each memory sequence in the batch
consists of.
"""
# Normalize the masks
N = x.shape[0]
L = x.shape[1]
L_prime = memory.shape[1]
x_mask = x_mask or FullMask(L, device=x.device)
x_length_mask = x_length_mask or \
LengthMask(x.new_full((N,), L, dtype=torch.int64))
memory_mask = memory_mask or FullMask(L, L_prime, device=x.device)
memory_length_mask = memory_length_mask or \
LengthMask(x.new_full((N,), L_prime, dtype=torch.int64))
# First apply the self attention and add it to the input
x = x + self.dropout(self.self_attention(
x, x, x,
attn_mask=x_mask,
query_lengths=x_length_mask,
key_lengths=x_length_mask
))
x = self.norm1(x)
# Secondly apply the cross attention and add it to the previous output
x = x + self.dropout(self.cross_attention(
x, memory, memory,
attn_mask=memory_mask,
query_lengths=x_length_mask,
key_lengths=memory_length_mask
))
# Finally run the fully connected part of the layer
y = x = self.norm2(x)
y = self.dropout(self.activation(self.linear1(y)))
y = self.dropout(self.linear2(y))
return self.norm3(x+y)
class TransformerDecoder(Module):
"""TransformerDecoder is little more than a sequence of transformer decoder
layers.
It contains an optional final normalization layer as well as the ability to
create the masks once and save some computation.
Arguments
----------
layers: list, TransformerDecoderLayer instances or instances that
implement the same interface
norm_layer: A normalization layer to be applied to the final output
(default: None which means no normalization)
event_dispatcher: str or EventDispatcher instance to be used by this
module for dispatching events (default: the default
global dispatcher)
"""
def __init__(self, layers, norm_layer=None, event_dispatcher=""):
super(TransformerDecoder, self).__init__()
self.layers = ModuleList(layers)
self.norm = norm_layer
self.event_dispatcher = EventDispatcher.get(event_dispatcher)
def forward(self, x, memory, x_mask=None, x_length_mask=None,
memory_mask=None, memory_length_mask=None):
# Normalize the masks
N = x.shape[0]
L = x.shape[1]
L_prime = memory.shape[1]
x_mask = x_mask or FullMask(L, device=x.device)
x_length_mask = x_length_mask or \
LengthMask(x.new_full((N,), L, dtype=torch.int64))
memory_mask = memory_mask or FullMask(L, L_prime, device=x.device)
memory_length_mask = memory_length_mask or \
LengthMask(x.new_full((N,), L_prime, dtype=torch.int64))
# Apply all the transformer decoders
for layer in self.layers:
x = layer(x, memory, x_mask=x_mask, x_length_mask=x_length_mask,
memory_mask=memory_mask,
memory_length_mask=memory_length_mask)
# Apply the normalization if needed
if self.norm is not None:
x = self.norm(x)
return xClasses
class TransformerDecoder (layers, norm_layer=None, event_dispatcher='')-
TransformerDecoder is little more than a sequence of transformer decoder layers.
It contains an optional final normalization layer as well as the ability to create the masks once and save some computation.
Arguments
layers: list, TransformerDecoderLayer instances or instances that implement the same interface norm_layer: A normalization layer to be applied to the final output (default: None which means no normalization) 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 TransformerDecoder(Module): """TransformerDecoder is little more than a sequence of transformer decoder layers. It contains an optional final normalization layer as well as the ability to create the masks once and save some computation. Arguments ---------- layers: list, TransformerDecoderLayer instances or instances that implement the same interface norm_layer: A normalization layer to be applied to the final output (default: None which means no normalization) event_dispatcher: str or EventDispatcher instance to be used by this module for dispatching events (default: the default global dispatcher) """ def __init__(self, layers, norm_layer=None, event_dispatcher=""): super(TransformerDecoder, self).__init__() self.layers = ModuleList(layers) self.norm = norm_layer self.event_dispatcher = EventDispatcher.get(event_dispatcher) def forward(self, x, memory, x_mask=None, x_length_mask=None, memory_mask=None, memory_length_mask=None): # Normalize the masks N = x.shape[0] L = x.shape[1] L_prime = memory.shape[1] x_mask = x_mask or FullMask(L, device=x.device) x_length_mask = x_length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) memory_mask = memory_mask or FullMask(L, L_prime, device=x.device) memory_length_mask = memory_length_mask or \ LengthMask(x.new_full((N,), L_prime, dtype=torch.int64)) # Apply all the transformer decoders for layer in self.layers: x = layer(x, memory, x_mask=x_mask, x_length_mask=x_length_mask, memory_mask=memory_mask, memory_length_mask=memory_length_mask) # Apply the normalization if needed if self.norm is not None: x = self.norm(x) return xAncestors
- torch.nn.modules.module.Module
Methods
def forward(self, x, memory, x_mask=None, x_length_mask=None, memory_mask=None, memory_length_mask=None)-
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, x, memory, x_mask=None, x_length_mask=None, memory_mask=None, memory_length_mask=None): # Normalize the masks N = x.shape[0] L = x.shape[1] L_prime = memory.shape[1] x_mask = x_mask or FullMask(L, device=x.device) x_length_mask = x_length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) memory_mask = memory_mask or FullMask(L, L_prime, device=x.device) memory_length_mask = memory_length_mask or \ LengthMask(x.new_full((N,), L_prime, dtype=torch.int64)) # Apply all the transformer decoders for layer in self.layers: x = layer(x, memory, x_mask=x_mask, x_length_mask=x_length_mask, memory_mask=memory_mask, memory_length_mask=memory_length_mask) # Apply the normalization if needed if self.norm is not None: x = self.norm(x) return x
class TransformerDecoderLayer (self_attention, cross_attention, d_model, d_ff=None, dropout=0.1, activation='relu', event_dispatcher='')-
The decoder layer from "Attention Is All You Need".
Similar to the encoder layer, this layer implements the decoder that PyTorch implements but can be used with any attention implementation because it receives the attention layers as constructor arguments.
Arguments
self_attention: The attention implementation to use for self attention given as a nn.Module cross_attention: The attention implementation to use for cross attention given as a nn.Module d_model: The input feature dimensionality d_ff: The dimensionality of the intermediate features after the attention (default: d_model*4) dropout: The dropout rate to apply to the intermediate features (default: 0.1) activation: {'relu', 'gelu'} Which activation to use for the feed forward part of the layer (default: relu) 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 TransformerDecoderLayer(Module): """The decoder layer from "Attention Is All You Need". Similar to the encoder layer, this layer implements the decoder that PyTorch implements but can be used with any attention implementation because it receives the attention layers as constructor arguments. Arguments --------- self_attention: The attention implementation to use for self attention given as a nn.Module cross_attention: The attention implementation to use for cross attention given as a nn.Module d_model: The input feature dimensionality d_ff: The dimensionality of the intermediate features after the attention (default: d_model*4) dropout: The dropout rate to apply to the intermediate features (default: 0.1) activation: {'relu', 'gelu'} Which activation to use for the feed forward part of the layer (default: relu) event_dispatcher: str or EventDispatcher instance to be used by this module for dispatching events (default: the default global dispatcher) """ def __init__(self, self_attention, cross_attention, d_model, d_ff=None, dropout=0.1, activation="relu", event_dispatcher=""): super(TransformerDecoderLayer, self).__init__() d_ff = d_ff or 4*d_model self.self_attention = self_attention self.cross_attention = cross_attention self.linear1 = Linear(d_model, d_ff) self.linear2 = Linear(d_ff, d_model) self.norm1 = LayerNorm(d_model) self.norm2 = LayerNorm(d_model) self.norm3 = LayerNorm(d_model) self.dropout = Dropout(dropout) self.activation = F.relu if activation == "relu" else F.gelu self.event_dispatcher = EventDispatcher.get(event_dispatcher) def forward(self, x, memory, x_mask=None, x_length_mask=None, memory_mask=None, memory_length_mask=None): """Apply the transformer decoder to the input x using the memory `memory`. Arguments --------- x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E should be the same as the d_model passed in the constructor. memory: The memory features of shape (N, L', E) where N is the batch size, L' is the memory's sequence length (padded) and E should be the same as the d_model. x_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to in x. Namely the self attention mask. x_length_mask: An implementation of a BaseMask that encodes how many elements each sequence in the batch consists of. memory_mask: An implementation of BaseMask that encodes where each element of x can attend to in the memory. Namely the cross attention mask. memory_length_mask: An implementation of a BaseMask that encodes how many elements each memory sequence in the batch consists of. """ # Normalize the masks N = x.shape[0] L = x.shape[1] L_prime = memory.shape[1] x_mask = x_mask or FullMask(L, device=x.device) x_length_mask = x_length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) memory_mask = memory_mask or FullMask(L, L_prime, device=x.device) memory_length_mask = memory_length_mask or \ LengthMask(x.new_full((N,), L_prime, dtype=torch.int64)) # First apply the self attention and add it to the input x = x + self.dropout(self.self_attention( x, x, x, attn_mask=x_mask, query_lengths=x_length_mask, key_lengths=x_length_mask )) x = self.norm1(x) # Secondly apply the cross attention and add it to the previous output x = x + self.dropout(self.cross_attention( x, memory, memory, attn_mask=memory_mask, query_lengths=x_length_mask, key_lengths=memory_length_mask )) # Finally run the fully connected part of the layer y = x = self.norm2(x) y = self.dropout(self.activation(self.linear1(y))) y = self.dropout(self.linear2(y)) return self.norm3(x+y)Ancestors
- torch.nn.modules.module.Module
Methods
def forward(self, x, memory, x_mask=None, x_length_mask=None, memory_mask=None, memory_length_mask=None)-
Apply the transformer decoder to the input x using the memory
memory.Arguments
x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E should be the same as the d_model passed in the constructor. memory: The memory features of shape (N, L', E) where N is the batch size, L' is the memory's sequence length (padded) and E should be the same as the d_model. x_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to in x. Namely the self attention mask. x_length_mask: An implementation of a BaseMask that encodes how many elements each sequence in the batch consists of. memory_mask: An implementation of BaseMask that encodes where each element of x can attend to in the memory. Namely the cross attention mask. memory_length_mask: An implementation of a BaseMask that encodes how many elements each memory sequence in the batch consists of.Expand source code
def forward(self, x, memory, x_mask=None, x_length_mask=None, memory_mask=None, memory_length_mask=None): """Apply the transformer decoder to the input x using the memory `memory`. Arguments --------- x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E should be the same as the d_model passed in the constructor. memory: The memory features of shape (N, L', E) where N is the batch size, L' is the memory's sequence length (padded) and E should be the same as the d_model. x_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to in x. Namely the self attention mask. x_length_mask: An implementation of a BaseMask that encodes how many elements each sequence in the batch consists of. memory_mask: An implementation of BaseMask that encodes where each element of x can attend to in the memory. Namely the cross attention mask. memory_length_mask: An implementation of a BaseMask that encodes how many elements each memory sequence in the batch consists of. """ # Normalize the masks N = x.shape[0] L = x.shape[1] L_prime = memory.shape[1] x_mask = x_mask or FullMask(L, device=x.device) x_length_mask = x_length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) memory_mask = memory_mask or FullMask(L, L_prime, device=x.device) memory_length_mask = memory_length_mask or \ LengthMask(x.new_full((N,), L_prime, dtype=torch.int64)) # First apply the self attention and add it to the input x = x + self.dropout(self.self_attention( x, x, x, attn_mask=x_mask, query_lengths=x_length_mask, key_lengths=x_length_mask )) x = self.norm1(x) # Secondly apply the cross attention and add it to the previous output x = x + self.dropout(self.cross_attention( x, memory, memory, attn_mask=memory_mask, query_lengths=x_length_mask, key_lengths=memory_length_mask )) # Finally run the fully connected part of the layer y = x = self.norm2(x) y = self.dropout(self.activation(self.linear1(y))) y = self.dropout(self.linear2(y)) return self.norm3(x+y)
class TransformerEncoder (layers, norm_layer=None, event_dispatcher='')-
TransformerEncoder is little more than a sequence of transformer encoder layers.
It contains an optional final normalization layer as well as the ability to create the masks once and save some computation.
Arguments
layers: list, TransformerEncoderLayer instances or instances that implement the same interface. norm_layer: A normalization layer to be applied to the final output (default: None which means no normalization) 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 TransformerEncoder(Module): """TransformerEncoder is little more than a sequence of transformer encoder layers. It contains an optional final normalization layer as well as the ability to create the masks once and save some computation. Arguments --------- layers: list, TransformerEncoderLayer instances or instances that implement the same interface. norm_layer: A normalization layer to be applied to the final output (default: None which means no normalization) event_dispatcher: str or EventDispatcher instance to be used by this module for dispatching events (default: the default global dispatcher) """ def __init__(self, layers, norm_layer=None, event_dispatcher=""): super(TransformerEncoder, self).__init__() self.layers = ModuleList(layers) self.norm = norm_layer self.event_dispatcher = EventDispatcher.get(event_dispatcher) def forward(self, x, attn_mask=None, length_mask=None): """Apply all transformer encoder layers to the input x. Arguments --------- x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E is d_model passed in the constructor of each transformer encoder layer. attn_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to. length_mask: An implementation of fast_transformers.masking.BaseMask that encodes how many elements each sequence in the batch consists of. """ # Normalize the masks N = x.shape[0] L = x.shape[1] attn_mask = attn_mask or FullMask(L, device=x.device) length_mask = length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) # Apply all the transformers for layer in self.layers: x = layer(x, attn_mask=attn_mask, length_mask=length_mask) # Apply the normalization if needed if self.norm is not None: x = self.norm(x) return xAncestors
- torch.nn.modules.module.Module
Methods
def forward(self, x, attn_mask=None, length_mask=None)-
Apply all transformer encoder layers to the input x.
Arguments
x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E is d_model passed in the constructor of each transformer encoder layer. attn_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to. length_mask: An implementation of fast_transformers.masking.BaseMask that encodes how many elements each sequence in the batch consists of.Expand source code
def forward(self, x, attn_mask=None, length_mask=None): """Apply all transformer encoder layers to the input x. Arguments --------- x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E is d_model passed in the constructor of each transformer encoder layer. attn_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to. length_mask: An implementation of fast_transformers.masking.BaseMask that encodes how many elements each sequence in the batch consists of. """ # Normalize the masks N = x.shape[0] L = x.shape[1] attn_mask = attn_mask or FullMask(L, device=x.device) length_mask = length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) # Apply all the transformers for layer in self.layers: x = layer(x, attn_mask=attn_mask, length_mask=length_mask) # Apply the normalization if needed if self.norm is not None: x = self.norm(x) return x
class TransformerEncoderLayer (attention, d_model, d_ff=None, dropout=0.1, activation='relu', event_dispatcher='')-
Self attention and feed forward network with skip connections.
This transformer encoder layer implements the same encoder layer as PyTorch but is a bit more open for extension by receiving the attention implementation as a constructor argument.
Arguments
attention: The attention implementation to use given as a nn.Module d_model: The input feature dimensionality d_ff: The dimensionality of the intermediate features after the attention (default: d_model*4) dropout: The dropout rate to apply to the intermediate features (default: 0.1) activation: {'relu', 'gelu'} Which activation to use for the feed forward part of the layer (default: relu) 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 TransformerEncoderLayer(Module): """Self attention and feed forward network with skip connections. This transformer encoder layer implements the same encoder layer as PyTorch but is a bit more open for extension by receiving the attention implementation as a constructor argument. Arguments --------- attention: The attention implementation to use given as a nn.Module d_model: The input feature dimensionality d_ff: The dimensionality of the intermediate features after the attention (default: d_model*4) dropout: The dropout rate to apply to the intermediate features (default: 0.1) activation: {'relu', 'gelu'} Which activation to use for the feed forward part of the layer (default: relu) event_dispatcher: str or EventDispatcher instance to be used by this module for dispatching events (default: the default global dispatcher) """ def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation="relu", event_dispatcher=""): super(TransformerEncoderLayer, self).__init__() d_ff = d_ff or 4*d_model self.attention = attention self.linear1 = Linear(d_model, d_ff) self.linear2 = Linear(d_ff, d_model) self.norm1 = LayerNorm(d_model) self.norm2 = LayerNorm(d_model) self.dropout = Dropout(dropout) self.activation = F.relu if activation == "relu" else F.gelu self.event_dispatcher = EventDispatcher.get(event_dispatcher) def forward(self, x, attn_mask=None, length_mask=None): """Apply the transformer encoder to the input x. Arguments --------- x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E is d_model passed in the constructor. attn_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to. length_mask: An implementation of fast_transformers.masking.BaseMask that encodes how many elements each sequence in the batch consists of. """ # Normalize the masks N = x.shape[0] L = x.shape[1] attn_mask = attn_mask or FullMask(L, device=x.device) length_mask = length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) # Run self attention and add it to the input x = x + self.dropout(self.attention( x, x, x, attn_mask=attn_mask, query_lengths=length_mask, key_lengths=length_mask )) # Run the fully connected part of the layer y = x = self.norm1(x) y = self.dropout(self.activation(self.linear1(y))) y = self.dropout(self.linear2(y)) return self.norm2(x+y)Ancestors
- torch.nn.modules.module.Module
Methods
def forward(self, x, attn_mask=None, length_mask=None)-
Apply the transformer encoder to the input x.
Arguments
x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E is d_model passed in the constructor. attn_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to. length_mask: An implementation of fast_transformers.masking.BaseMask that encodes how many elements each sequence in the batch consists of.Expand source code
def forward(self, x, attn_mask=None, length_mask=None): """Apply the transformer encoder to the input x. Arguments --------- x: The input features of shape (N, L, E) where N is the batch size, L is the sequence length (padded) and E is d_model passed in the constructor. attn_mask: An implementation of fast_transformers.masking.BaseMask that encodes where each element of x can attend to. length_mask: An implementation of fast_transformers.masking.BaseMask that encodes how many elements each sequence in the batch consists of. """ # Normalize the masks N = x.shape[0] L = x.shape[1] attn_mask = attn_mask or FullMask(L, device=x.device) length_mask = length_mask or \ LengthMask(x.new_full((N,), L, dtype=torch.int64)) # Run self attention and add it to the input x = x + self.dropout(self.attention( x, x, x, attn_mask=attn_mask, query_lengths=length_mask, key_lengths=length_mask )) # Run the fully connected part of the layer y = x = self.norm1(x) y = self.dropout(self.activation(self.linear1(y))) y = self.dropout(self.linear2(y)) return self.norm2(x+y)