paddlespeech.t2s.modules.transformer.encoder_layer module

Encoder self-attention layer definition.

class paddlespeech.t2s.modules.transformer.encoder_layer.EncoderLayer(size, self_attn, feed_forward, dropout_rate, normalize_before=True, concat_after=False)

Bases: Layer

Encoder layer module.

Args:

size (int):

Input dimension.

self_attn (nn.Layer):

Self-attention module instance. MultiHeadedAttention instance can be used as the argument.

feed_forward (nn.Layer):

Feed-forward module instance. PositionwiseFeedForward, MultiLayeredConv1d, or Conv1dLinear instance can be used as the argument.

dropout_rate (float):

Dropout rate.

normalize_before (bool):

Whether to use layer_norm before the first block.

concat_after (bool):

Whether to concat attention layer's input and output. if True, additional linear will be applied. i.e. x -> x + linear(concat(x, att(x))) if False, no additional linear will be applied. i.e. x -> x + att(x)

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(x, mask[, cache])	Compute encoded features.
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(x, mask, cache=None)

Compute encoded features.

Args:

x(Tensor):

Input tensor (#batch, time, size).

mask(Tensor):

Mask tensor for the input (#batch, time).

cache(Tensor, optional):

Cache tensor of the input (#batch, time - 1, size).

Returns:

Tensor:

Output tensor (#batch, time, size).

Tensor:

Mask tensor (#batch, time).