paddlespeech.t2s.modules.transformer.lightconv module
Lightweight Convolution Module.
- class paddlespeech.t2s.modules.transformer.lightconv.LightweightConvolution(wshare, n_feat, dropout_rate, kernel_size, use_kernel_mask=False, use_bias=False)[source]
Bases:
Layer
Lightweight Convolution layer.
This implementation is based on https://github.com/pytorch/fairseq/tree/master/fairseq
- Args:
- wshare (int):
the number of kernel of convolution
- n_feat (int):
the number of features
- dropout_rate (float):
dropout_rate
- kernel_size (int):
kernel size (length)
- use_kernel_mask (bool):
Use causal mask or not for convolution kernel
- use_bias (bool):
Use bias term or not.
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)Forward of 'Lightweight Convolution'.
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)[source]
Forward of 'Lightweight Convolution'.
This function takes query, key and value but uses only query. This is just for compatibility with self-attention layer (attention.py)
- Args:
- query (Tensor):
input tensor. (batch, time1, d_model)
- key (Tensor):
NOT USED. (batch, time2, d_model)
- value (Tensor):
NOT USED. (batch, time2, d_model)
- mask(Tensor):
(batch, time1, time2) mask
- Return:
Tensor: output. (batch, time1, d_model)