speechbrain.lobes.models.transformer.Transformer module¶

Transformer implementation in the SpeechBrain sytle.

Authors * Jianyuan Zhong 2020

Summary¶

Classes:

`NormalizedEmbedding`	This class implements the normalized embedding layer for the transformer.
`PositionalEncoding`	This class implements the positional encoding function.
`TransformerDecoder`	This class implements the Transformer decoder.
`TransformerDecoderLayer`	This class implements the self-attention decoder layer.
`TransformerEncoder`	This class implements the transformer encoder.
`TransformerEncoderLayer`	This is an implementation of self-attention encoder layer.
`TransformerInterface`	This is an interface for transformer model.

Functions:

`get_key_padding_mask`	Creates a binary mask to prevent attention to padded locations.
`get_lookahead_mask`	Creates a binary mask for each sequence.

Reference¶

class speechbrain.lobes.models.transformer.Transformer.TransformerInterface(d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, d_ffn=2048, dropout=0.1, activation=<class 'torch.nn.modules.activation.ReLU'>, custom_src_module=None, custom_tgt_module=None, positional_encoding=True, normalize_before=False, kernel_size: Optional[int] = 31, bias: Optional[bool] = True, encoder_module: Optional[str] = 'transformer', conformer_activation: Optional[torch.nn.modules.module.Module] = <class 'speechbrain.nnet.activations.Swish'>)[source]¶

Bases: torch.nn.modules.module.Module

This is an interface for transformer model.

Users can modify the attributes and define the forward function as needed according to their own tasks.

The architecture is based on the paper “Attention Is All You Need”: https://arxiv.org/pdf/1706.03762.pdf

Parameters

d_model (int) – The number of expected features in the encoder/decoder inputs (default=512).
nhead (int) – The number of heads in the multi-head attention models (default=8).
num_encoder_layers (int) – The number of sub-encoder-layers in the encoder (default=6).
num_decoder_layers (int) – The number of sub-decoder-layers in the decoder (default=6).
dim_ffn (int) – The dimension of the feedforward network model (default=2048).
dropout (int) – The dropout value (default=0.1).
activation (torch class) – The activation function of encoder/decoder intermediate layer, e.g., relu or gelu (default=relu)
custom_src_module (torch class) – Module that processes the src features to expected feature dim.
custom_tgt_module (torch class) – Module that processes the src features to expected feature dim.

forward(**kwags)[source]¶: Users should modify this function according to their own tasks.

training: bool¶

class speechbrain.lobes.models.transformer.Transformer.PositionalEncoding(input_size, max_len=2500)[source]¶

Bases: torch.nn.modules.module.Module

This class implements the positional encoding function.

PE(pos, 2i) = sin(pos/(10000^(2i/dmodel))) PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))

Parameters: max_len (int) – Max length of the input sequences (default 2500).

Example

>>> a = torch.rand((8, 120, 512))
>>> enc = PositionalEncoding(input_size=a.shape[-1])
>>> b = enc(a)
>>> b.shape
torch.Size([1, 120, 512])

forward(x)[source]¶

Parameters: x (tensor) – Input feature shape (batch, time, fea)

training: bool¶

class speechbrain.lobes.models.transformer.Transformer.TransformerEncoderLayer(d_ffn, nhead, d_model=None, kdim=None, vdim=None, dropout=0.1, activation=<class 'torch.nn.modules.activation.ReLU'>, normalize_before=False)[source]¶

Bases: torch.nn.modules.module.Module

This is an implementation of self-attention encoder layer.

Parameters

d_ffn (int) – Hidden size of self-attention Feed Forward layer.
nhead (int) – Number of attention heads.
d_model (int) – The expected size of the input embedding.
reshape (bool) – Whether to automatically shape 4-d input to 3-d.
kdim (int) – Dimension of the key (Optional).
vdim (int) – Dimension of the value (Optional).
dropout (float) – Dropout for the encoder (Optional).

Example

>>> import torch
>>> x = torch.rand((8, 60, 512))
>>> net = TransformerEncoderLayer(512, 8, d_model=512)
>>> output = net(x)
>>> output[0].shape
torch.Size([8, 60, 512])

forward(src, src_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None)[source]¶

Parameters

src (tensor) – The sequence to the encoder layer (required).
src_mask (tensor) – The mask for the src sequence (optional).
src_key_padding_mask (tensor) – The mask for the src keys per batch (optional).

training: bool¶

class speechbrain.lobes.models.transformer.Transformer.TransformerEncoder(num_layers, nhead, d_ffn, input_shape=None, d_model=None, kdim=None, vdim=None, dropout=0.1, activation=<class 'torch.nn.modules.activation.ReLU'>, normalize_before=False)[source]¶

Bases: torch.nn.modules.module.Module

This class implements the transformer encoder.

Parameters

num_layers (int) – Number of transformer layers to include.
nhead (int) – Number of attention heads.
d_ffn (int) – Hidden size of self-attention Feed Forward layer.
input_shape (tuple) – Expected shape of an example input.
d_model (int) – The dimension of the input embedding.
kdim (int) – Dimension for key (Optional).
vdim (int) – Dimension for value (Optional).
dropout (float) – Dropout for the encoder (Optional).
input_module (torch class) – The module to process the source input feature to expected feature dimension (Optional).

Example

>>> import torch
>>> x = torch.rand((8, 60, 512))
>>> net = TransformerEncoder(1, 8, 512, d_model=512)
>>> output, _ = net(x)
>>> output.shape
torch.Size([8, 60, 512])

forward(src, src_mask: Optional[torch.Tensor] = None, src_key_padding_mask: Optional[torch.Tensor] = None)[source]¶

Parameters

src (tensor) – The sequence to the encoder layer (required).
src_mask (tensor) – The mask for the src sequence (optional).
src_key_padding_mask (tensor) – The mask for the src keys per batch (optional).

training: bool¶

class speechbrain.lobes.models.transformer.Transformer.TransformerDecoderLayer(d_ffn, nhead, d_model, kdim=None, vdim=None, dropout=0.1, activation=<class 'torch.nn.modules.activation.ReLU'>, normalize_before=False)[source]¶

Bases: torch.nn.modules.module.Module

This class implements the self-attention decoder layer.

Parameters

d_ffn (int) – Hidden size of self-attention Feed Forward layer.
nhead (int) – Number of attention heads.
d_model (int) – Dimension of the model.
kdim (int) – Dimension for key (optional).
vdim (int) – Dimension for value (optional).
dropout (float) – Dropout for the decoder (optional).

Example

>>> src = torch.rand((8, 60, 512))
>>> tgt = torch.rand((8, 60, 512))
>>> net = TransformerDecoderLayer(1024, 8, d_model=512)
>>> output, self_attn, multihead_attn = net(src, tgt)
>>> output.shape
torch.Size([8, 60, 512])

forward(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)[source]¶

Parameters

tgt (tensor) – The sequence to the decoder layer (required).
memory (tensor) – The sequence from the last layer of the encoder (required).
tgt_mask (tensor) – The mask for the tgt sequence (optional).
memory_mask (tensor) – The mask for the memory sequence (optional).
tgt_key_padding_mask (tensor) – The mask for the tgt keys per batch (optional).
memory_key_padding_mask (tensor) – The mask for the memory keys per batch (optional).

training: bool¶

class speechbrain.lobes.models.transformer.Transformer.TransformerDecoder(num_layers, nhead, d_ffn, d_model, kdim=None, vdim=None, dropout=0.1, activation=<class 'torch.nn.modules.activation.ReLU'>, normalize_before=False)[source]¶

Bases: torch.nn.modules.module.Module

This class implements the Transformer decoder.

Parameters

d_ffn (int) – Hidden size of self-attention Feed Forward layer.
nhead (int) – Number of attention heads.
d_model (int) – Dimension of the model.
kdim (int) – Dimension for key (Optional).
vdim (int) – Dimension for value (Optional).
dropout (float) – Dropout for the decoder (Optional).

Example

>>> src = torch.rand((8, 60, 512))
>>> tgt = torch.rand((8, 60, 512))
>>> net = TransformerDecoder(1, 8, 1024, d_model=512)
>>> output, _, _ = net(src, tgt)
>>> output.shape
torch.Size([8, 60, 512])

forward(tgt, memory, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None)[source]¶

Parameters

tgt (tensor) – The sequence to the decoder layer (required).
memory (tensor) – The sequence from the last layer of the encoder (required).
tgt_mask (tensor) – The mask for the tgt sequence (optional).
memory_mask (tensor) – The mask for the memory sequence (optional).
tgt_key_padding_mask (tensor) – The mask for the tgt keys per batch (optional).
memory_key_padding_mask (tensor) – The mask for the memory keys per batch (optional).

training: bool¶

class speechbrain.lobes.models.transformer.Transformer.NormalizedEmbedding(d_model, vocab)[source]¶

Bases: torch.nn.modules.module.Module

This class implements the normalized embedding layer for the transformer.

Since the dot product of the self-attention is always normalized by sqrt(d_model) and the final linear projection for prediction shares weight with the embedding layer, we multiply the output of the embedding by sqrt(d_model).

Parameters

d_model (int) – The number of expected features in the encoder/decoder inputs (default=512).
vocab (int) – The vocab size.

Example

>>> emb = NormalizedEmbedding(512, 1000)
>>> trg = torch.randint(0, 999, (8, 50))
>>> emb_fea = emb(trg)

forward(x)[source]¶

training: bool¶

speechbrain.lobes.models.transformer.Transformer.get_key_padding_mask(padded_input, pad_idx)[source]¶

Creates a binary mask to prevent attention to padded locations.

Parameters

padded_input (int) – Padded input.
pad_idx – idx for padding element.

Example

>>> a = torch.LongTensor([[1,1,0], [2,3,0], [4,5,0]])
>>> get_key_padding_mask(a, pad_idx=0)
tensor([[False, False,  True],
        [False, False,  True],
        [False, False,  True]])

speechbrain.lobes.models.transformer.Transformer.get_lookahead_mask(padded_input)[source]¶

Creates a binary mask for each sequence.

Parameters: padded_input (tensor) – Padded input tensor.

Example

>>> a = torch.LongTensor([[1,1,0], [2,3,0], [4,5,0]])
>>> get_lookahead_mask(a)
tensor([[0., -inf, -inf],
        [0., 0., -inf],
        [0., 0., 0.]])