paddlespeech.t2s.modules.transformer.attention module

Multi-Head Attention layer definition.

class paddlespeech.t2s.modules.transformer.attention.LegacyRelPositionMultiHeadedAttention(n_head, n_feat, dropout_rate, zero_triu=False)

Bases: MultiHeadedAttention

Multi-Head Attention layer with relative position encoding (old version). Details can be found in https://github.com/espnet/espnet/pull/2816. Paper: https://arxiv.org/abs/1901.02860

Args:

n_head (int):

The number of heads.

n_feat (int):

The number of features.

dropout_rate (float):

Dropout rate.

zero_triu (bool):

Whether to zero the upper triangular part of attention matrix.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(query, key, value, pos_emb, mask)	Compute 'Scaled Dot Product Attention' with rel.
forward_attention(value, scores[, mask])	Compute attention context vector.
forward_qkv(query, key, value)	Transform query, key and value.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
rel_shift(x)	Compute relative positional encoding. Args: x(Tensor): Input tensor (batch, head, time1, time2). Returns: Tensor:Output tensor.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(query, key, value, pos_emb, mask)

Compute 'Scaled Dot Product Attention' with rel. positional encoding.

Args:

query(Tensor): Query tensor (#batch, time1, size). key(Tensor): Key tensor (#batch, time2, size). value(Tensor): Value tensor (#batch, time2, size). pos_emb(Tensor): Positional embedding tensor (#batch, time1, size). mask(Tensor): Mask tensor (#batch, 1, time2) or (#batch, time1, time2).

Returns:

Tensor: Output tensor (#batch, time1, d_model).

rel_shift(x)

Compute relative positional encoding. Args:

x(Tensor):

Input tensor (batch, head, time1, time2).

Returns:

Tensor:Output tensor.

class paddlespeech.t2s.modules.transformer.attention.MultiHeadedAttention(n_head, n_feat, dropout_rate)

Bases: Layer

Multi-Head Attention layer. Args:

n_head (int):

The number of heads.

n_feat (int):

The number of features.

dropout_rate (float):

Dropout rate.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(query, key, value[, mask])	Compute scaled dot product attention.
forward_attention(value, scores[, mask])	Compute attention context vector.
forward_qkv(query, key, value)	Transform query, key and value.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(query, key, value, mask=None)

Compute scaled dot product attention.

Args:

query(Tensor):

Query tensor (#batch, time1, size).

key(Tensor):

Key tensor (#batch, time2, size).

value(Tensor):

Value tensor (#batch, time2, size).

mask(Tensor, optional):

Mask tensor (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)

Returns:

Tensor: Output tensor (#batch, time1, d_model).

forward_attention(value, scores, mask=None)

Compute attention context vector.

Args:

value(Tensor):

Transformed value (#batch, n_head, time2, d_k).

scores(Tensor):

Attention score (#batch, n_head, time1, time2).

mask(Tensor, optional):

Mask (#batch, 1, time2) or (#batch, time1, time2). (Default value = None)

Returns:

Tensor: Transformed value (#batch, time1, d_model) weighted by the attention score (#batch, time1, time2).

forward_qkv(query, key, value)

Transform query, key and value.

Args:

query(Tensor):

query tensor (#batch, time1, size).

key(Tensor):

Key tensor (#batch, time2, size).

value(Tensor):

Value tensor (#batch, time2, size).

Returns:

Tensor:

Transformed query tensor (#batch, n_head, time1, d_k).

Tensor:

Transformed key tensor (#batch, n_head, time2, d_k).

Tensor:

Transformed value tensor (#batch, n_head, time2, d_k).

class paddlespeech.t2s.modules.transformer.attention.RelPositionMultiHeadedAttention(n_head, n_feat, dropout_rate, zero_triu=False)

Bases: MultiHeadedAttention

Multi-Head Attention layer with relative position encoding (new implementation). Details can be found in https://github.com/espnet/espnet/pull/2816. Paper: https://arxiv.org/abs/1901.02860

Args:

n_head (int):

The number of heads.

n_feat (int):

The number of features.

dropout_rate (float):

Dropout rate.

zero_triu (bool):

Whether to zero the upper triangular part of attention matrix.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(query, key, value, pos_emb, mask)	Compute 'Scaled Dot Product Attention' with rel.
forward_attention(value, scores[, mask])	Compute attention context vector.
forward_qkv(query, key, value)	Transform query, key and value.
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
rel_shift(x)	Compute relative positional encoding. Args: x(Tensor): Input tensor (batch, head, time1, 2*time1-1).
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(query, key, value, pos_emb, mask)

Compute 'Scaled Dot Product Attention' with rel. positional encoding.

Args:

query(Tensor):

Query tensor (#batch, time1, size).

key(Tensor):

Key tensor (#batch, time2, size).

value(Tensor):

Value tensor (#batch, time2, size).

pos_emb(Tensor):

Positional embedding tensor (#batch, 2*time1-1, size).

mask(Tensor):

Mask tensor (#batch, 1, time2) or (#batch, time1, time2).

Returns:

Tensor: Output tensor (#batch, time1, d_model).

rel_shift(x)

Compute relative positional encoding. Args:

x(Tensor):

Input tensor (batch, head, time1, 2*time1-1).

Returns:

Tensor: Output tensor.