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)

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]

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.SqueezeformerEncoderLayer(size: int, self_attn: Layer, feed_forward1: Optional[Layer] = None, conv_module: Optional[Layer] = None, feed_forward2: Optional[Layer] = None, normalize_before: bool = False, dropout_rate: float = 0.1, concat_after: bool = False)

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]

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)

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): (#batch, time, size) mask (paddle.Tensor): Mask tensor for the input (#batch, time，time), (0, 0, 0) means fake mask. pos_emb (paddle.Tensor): just for interface compatibility to ConformerEncoderLayer mask_pad (paddle.Tensor): does not used in transformer layer, just for unified api with conformer. 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 (#batch=1, size, cache_t2), not used here, it's for interface compatibility to ConformerEncoderLayer. 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=1, 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]

Compute encoded features. Args:

x (paddle.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): just for interface compatibility

to ConformerEncoderLayer

mask_pad (paddle.Tensor): does not used in transformer layer,

just for unified api with conformer.

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

(#batch=1, size, cache_t2), not used here, it's for interface compatibility to ConformerEncoderLayer.

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=1, size, cache_t2).