paddlespeech.s2t.modules.encoder_layer module

Encoder self-attention layer definition.

class paddlespeech.s2t.modules.encoder_layer.ConformerEncoderLayer(size: int, self_attn: Layer, feed_forward: Optional[Layer] = None, feed_forward_macaron: Optional[Layer] = None, conv_module: Optional[Layer] = None, dropout_rate: float = 0.1, normalize_before: bool = True, concat_after: bool = False)[source]

Bases: Layer

Encoder layer module.

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, pos_emb[, mask_pad, ...])	Compute encoded features. Args: x (paddle.Tensor): Input tensor (#batch, time, size). mask (paddle.Tensor): Mask tensor for the input (#batch, time, time). (0,0,0) means fake mask. pos_emb (paddle.Tensor): postional encoding, must not be None for ConformerEncoderLayer mask_pad (paddle.Tensor): batch padding mask used for conv module. (#batch, 1，time), (0, 0, 0) means fake mask. att_cache (paddle.Tensor): Cache tensor of the KEY & VALUE (#batch=1, head, cache_t1, d_k * 2), head * d_k == size. cnn_cache (paddle.Tensor): Convolution cache in conformer layer (1, #batch=1, size, cache_t2). First dim will not be used, just for dy2st. Returns: paddle.Tensor: Output tensor (#batch, time, size). paddle.Tensor: Mask tensor (#batch, time, time). paddle.Tensor: att_cache tensor, (#batch=1, head, cache_t1 + time, d_k * 2). paddle.Tensor: cnn_cahce tensor (#batch, size, cache_t2).
`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: ~paddle.Tensor, mask: ~paddle.Tensor, pos_emb: ~paddle.Tensor, mask_pad: ~paddle.Tensor = Tensor(shape=[0, 0, 0], dtype=bool, place=Place(cpu), stop_gradient=True, []), att_cache: ~paddle.Tensor = Tensor(shape=[0, 0, 0, 0], dtype=float32, place=Place(cpu), stop_gradient=True, []), cnn_cache: ~paddle.Tensor = Tensor(shape=[0, 0, 0, 0], dtype=float32, place=Place(cpu), stop_gradient=True, [])) → Tuple[Tensor, Tensor, Tensor, Tensor][source]

Compute encoded features. Args:

x (paddle.Tensor): Input tensor (#batch, time, size). mask (paddle.Tensor): Mask tensor for the input (#batch, time, time).

(0,0,0) means fake mask.

pos_emb (paddle.Tensor): postional encoding, must not be None
for ConformerEncoderLayer

mask_pad (paddle.Tensor): batch padding mask used for conv module.
(#batch, 1，time), (0, 0, 0) means fake mask.

att_cache (paddle.Tensor): Cache tensor of the KEY & VALUE
(#batch=1, head, cache_t1, d_k * 2), head * d_k == size.

cnn_cache (paddle.Tensor): Convolution cache in conformer layer
(1, #batch=1, size, cache_t2). First dim will not be used, just for dy2st.

Returns:

paddle.Tensor: Output tensor (#batch, time, size). paddle.Tensor: Mask tensor (#batch, time, time). paddle.Tensor: att_cache tensor,

(#batch=1, head, cache_t1 + time, d_k * 2).

paddle.Tensor: cnn_cahce tensor (#batch, size, cache_t2).

class paddlespeech.s2t.modules.encoder_layer.TransformerEncoderLayer(size: int, self_attn: Layer, feed_forward: Layer, dropout_rate: float, normalize_before: bool = True, concat_after: bool = False)[source]