libreasr.lib.layers.haste.layer_norm_lstm.LayerNormLSTM¶
-
class
libreasr.lib.layers.haste.layer_norm_lstm.
LayerNormLSTM
(input_size, hidden_size, batch_first=False, forget_bias=1.0, dropout=0.0, zoneout=0.0, return_state_sequence=False)[source]¶ Bases:
libreasr.lib.layers.haste.base_rnn.BaseRNN
Layer Normalized Long Short-Term Memory layer.
This LSTM layer applies layer normalization to the input, recurrent, and output activations of a standard LSTM. The implementation is fused and GPU-accelerated. DropConnect and Zoneout regularization are built-in, and this layer allows setting a non-zero initial forget gate bias.
Details about the exact function this layer implements can be found at https://github.com/lmnt-com/haste/issues/1.
See [__init__](#__init__) and [forward](#forward) for usage.
Initialize the parameters of the LSTM layer.
- Parameters
input_size – int, the feature dimension of the input.
hidden_size – int, the feature dimension of the output.
batch_first – (optional) bool, if True, then the input and output tensors are provided as (batch, seq, feature).
forget_bias – (optional) float, sets the initial bias of the forget gate for this LSTM cell.
dropout – (optional) float, sets the dropout rate for DropConnect regularization on the recurrent matrix.
zoneout – (optional) float, sets the zoneout rate for Zoneout regularization.
return_state_sequence – (optional) bool, if True, the forward pass will return the entire state sequence instead of just the final state. Note that if the input is a padded sequence, the returned state will also be a padded sequence.
- Variables:
- kernel: the input projection weight matrix. Dimensions
(input_size, hidden_size * 4) with i,g,f,o gate layout. Initialized with Xavier uniform initialization.
- recurrent_kernel: the recurrent projection weight matrix. Dimensions
(hidden_size, hidden_size * 4) with i,g,f,o gate layout. Initialized with orthogonal initialization.
- bias: the projection bias vector. Dimensions (hidden_size * 4) with
i,g,f,o gate layout. The forget gate biases are initialized to forget_bias and the rest are zeros.
- gamma: the input and recurrent normalization gain. Dimensions
(2, hidden_size * 4) with gamma[0] specifying the input gain and gamma[1] specifying the recurrent gain. Initialized to ones.
- gamma_h: the output normalization gain. Dimensions (hidden_size).
Initialized to ones.
- beta_h: the output normalization bias. Dimensions (hidden_size).
Initialized to zeros.
Methods
Adds a child module to the current module.
Applies
fn
recursively to every submodule (as returned by.children()
) as well as self.Casts all floating point parameters and buffers to
bfloat16
datatype.Returns an iterator over module buffers.
Returns an iterator over immediate children modules.
Moves all model parameters and buffers to the CPU.
Moves all model parameters and buffers to the GPU.
Casts all floating point parameters and buffers to
double
datatype.Sets the module in evaluation mode.
Set the extra representation of the module
Casts all floating point parameters and buffers to float datatype.
Runs a forward pass of the LSTM layer.
Casts all floating point parameters and buffers to
half
datatype.Copies parameters and buffers from
state_dict
into this module and its descendants.Returns an iterator over all modules in the network.
Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
Returns an iterator over module parameters.
Registers a backward hook on the module.
Adds a buffer to the module.
Registers a forward hook on the module.
Registers a forward pre-hook on the module.
Adds a parameter to the module.
Change if autograd should record operations on parameters in this module.
Resets this layer’s parameters to their initial values.
share_memory
Returns a dictionary containing a whole state of the module.
Moves and/or casts the parameters and buffers.
Sets the module in training mode.
Casts all parameters and buffers to
dst_type
.Sets gradients of all model parameters to zero.
Attributes
T_destination
dump_patches
has_children
-
add_module
(name: str, module: torch.nn.modules.module.Module) → None¶ Adds a child module to the current module.
The module can be accessed as an attribute using the given name.
- Parameters
name (string) – name of the child module. The child module can be accessed from this module using the given name
module (Module) – child module to be added to the module.
-
apply
(fn: Callable[[Module], None]) → T¶ Applies
fn
recursively to every submodule (as returned by.children()
) as well as self. Typical use includes initializing the parameters of a model (see also nn-init-doc).- Parameters
fn (
Module
-> None) – function to be applied to each submodule- Returns
self
- Return type
Module
Example:
>>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )
-
bfloat16
() → T¶ Casts all floating point parameters and buffers to
bfloat16
datatype.- Returns
self
- Return type
Module
-
buffers
(recurse: bool = True) → Iterator[torch.Tensor]¶ Returns an iterator over module buffers.
- Parameters
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
- Yields
torch.Tensor – module buffer
Example:
>>> for buf in model.buffers(): >>> print(type(buf), buf.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L)
-
children
() → Iterator[torch.nn.modules.module.Module]¶ Returns an iterator over immediate children modules.
- Yields
Module – a child module
-
cpu
() → T¶ Moves all model parameters and buffers to the CPU.
- Returns
self
- Return type
Module
-
cuda
(device: Optional[Union[int, torch.device]] = None) → T¶ Moves all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.
- Parameters
device (int, optional) – if specified, all parameters will be copied to that device
- Returns
self
- Return type
Module
-
double
() → T¶ Casts all floating point parameters and buffers to
double
datatype.- Returns
self
- Return type
Module
-
eval
() → T¶ Sets the module in evaluation mode.
This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.
Dropout
,BatchNorm
, etc.This is equivalent with
self.train(False)
.- Returns
self
- Return type
Module
-
extra_repr
() → str¶ Set the extra representation of the module
To print customized extra information, you should reimplement this method in your own modules. Both single-line and multi-line strings are acceptable.
-
float
() → T¶ Casts all floating point parameters and buffers to float datatype.
- Returns
self
- Return type
Module
-
forward
(input, state=None, lengths=None)[source]¶ Runs a forward pass of the LSTM layer.
- Parameters
input – Tensor, a batch of input sequences to pass through the LSTM. Dimensions (seq_len, batch_size, input_size) if batch_first is False, otherwise (batch_size, seq_len, input_size).
lengths – (optional) Tensor, list of sequence lengths for each batch element. Dimension (batch_size). This argument may be omitted if all batch elements are unpadded and have the same sequence length.
- Returns
- Tensor, the output of the LSTM layer. Dimensions
(seq_len, batch_size, hidden_size) if batch_first is False (default) or (batch_size, seq_len, hidden_size) if batch_first is True. Note that if lengths was specified, the output tensor will not be masked. It’s the caller’s responsibility to either not use the invalid entries or to mask them out before using them.
- (h_n, c_n): the hidden and cell states, respectively, for the last
sequence item. Dimensions (1, batch_size, hidden_size).
- Return type
output
-
half
() → T¶ Casts all floating point parameters and buffers to
half
datatype.- Returns
self
- Return type
Module
-
load_state_dict
(state_dict: Dict[str, torch.Tensor], strict: bool = True)¶ Copies parameters and buffers from
state_dict
into this module and its descendants. Ifstrict
isTrue
, then the keys ofstate_dict
must exactly match the keys returned by this module’sstate_dict()
function.- Parameters
state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in
state_dict
match the keys returned by this module’sstate_dict()
function. Default:True
- Returns
missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys
- Return type
NamedTuple
withmissing_keys
andunexpected_keys
fields
-
modules
() → Iterator[torch.nn.modules.module.Module]¶ Returns an iterator over all modules in the network.
- Yields
Module – a module in the network
Note
Duplicate modules are returned only once. In the following example,
l
will be returned only once.Example:
>>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True)
-
named_buffers
(prefix: str = '', recurse: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
- Parameters
prefix (str) – prefix to prepend to all buffer names.
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
- Yields
(string, torch.Tensor) – Tuple containing the name and buffer
Example:
>>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size())
-
named_children
() → Iterator[Tuple[str, torch.nn.modules.module.Module]]¶ Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
- Yields
(string, Module) – Tuple containing a name and child module
Example:
>>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module)
-
named_modules
(memo: Optional[Set[Module]] = None, prefix: str = '')¶ Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
- Yields
(string, Module) – Tuple of name and module
Note
Duplicate modules are returned only once. In the following example,
l
will be returned only once.Example:
>>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
-
named_parameters
(prefix: str = '', recurse: bool = True) → Iterator[Tuple[str, torch.Tensor]]¶ Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
- Parameters
prefix (str) – prefix to prepend to all parameter names.
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
- Yields
(string, Parameter) – Tuple containing the name and parameter
Example:
>>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size())
-
parameters
(recurse: bool = True) → Iterator[torch.nn.parameter.Parameter]¶ Returns an iterator over module parameters.
This is typically passed to an optimizer.
- Parameters
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
- Yields
Parameter – module parameter
Example:
>>> for param in model.parameters(): >>> print(type(param), param.size()) <class 'torch.Tensor'> (20L,) <class 'torch.Tensor'> (20L, 1L, 5L, 5L)
-
register_backward_hook
(hook: Callable[[Module, Union[Tuple[torch.Tensor, …], torch.Tensor], Union[Tuple[torch.Tensor, …], torch.Tensor]], Union[None, torch.Tensor]]) → torch.utils.hooks.RemovableHandle¶ Registers a backward hook on the module.
Warning
The current implementation will not have the presented behavior for complex
Module
that perform many operations. In some failure cases,grad_input
andgrad_output
will only contain the gradients for a subset of the inputs and outputs. For suchModule
, you should usetorch.Tensor.register_hook()
directly on a specific input or output to get the required gradients.The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:
hook(module, grad_input, grad_output) -> Tensor or None
The
grad_input
andgrad_output
may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place ofgrad_input
in subsequent computations.grad_input
will only correspond to the inputs given as positional arguments.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_buffer
(name: str, tensor: torch.Tensor, persistent: bool = True) → None¶ Adds a buffer to the module.
This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s
running_mean
is not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by settingpersistent
toFalse
. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’sstate_dict
.Buffers can be accessed as attributes using given names.
- Parameters
name (string) – name of the buffer. The buffer can be accessed from this module using the given name
tensor (Tensor) – buffer to be registered.
persistent (bool) – whether the buffer is part of this module’s
state_dict
.
Example:
>>> self.register_buffer('running_mean', torch.zeros(num_features))
-
register_forward_hook
(hook: Callable[[…], None]) → torch.utils.hooks.RemovableHandle¶ Registers a forward hook on the module.
The hook will be called every time after
forward()
has computed an output. It should have the following signature:hook(module, input, output) -> None or modified output
The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the
forward
. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called afterforward()
is called.- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_forward_pre_hook
(hook: Callable[[…], None]) → torch.utils.hooks.RemovableHandle¶ Registers a forward pre-hook on the module.
The hook will be called every time before
forward()
is invoked. It should have the following signature:hook(module, input) -> None or modified input
The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the
forward
. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).- Returns
a handle that can be used to remove the added hook by calling
handle.remove()
- Return type
torch.utils.hooks.RemovableHandle
-
register_parameter
(name: str, param: torch.nn.parameter.Parameter) → None¶ Adds a parameter to the module.
The parameter can be accessed as an attribute using given name.
- Parameters
name (string) – name of the parameter. The parameter can be accessed from this module using the given name
param (Parameter) – parameter to be added to the module.
-
requires_grad_
(requires_grad: bool = True) → T¶ Change if autograd should record operations on parameters in this module.
This method sets the parameters’
requires_grad
attributes in-place.This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training).
- Parameters
requires_grad (bool) – whether autograd should record operations on parameters in this module. Default:
True
.- Returns
self
- Return type
Module
-
state_dict
(destination=None, prefix='', keep_vars=False)¶ Returns a dictionary containing a whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names.
- Returns
a dictionary containing a whole state of the module
- Return type
dict
Example:
>>> module.state_dict().keys() ['bias', 'weight']
-
to
(*args, **kwargs)¶ Moves and/or casts the parameters and buffers.
This can be called as
-
to
(device=None, dtype=None, non_blocking=False)¶
-
to
(dtype, non_blocking=False)¶
-
to
(tensor, non_blocking=False)¶
-
to
(memory_format=torch.channels_last)¶
Its signature is similar to
torch.Tensor.to()
, but only accepts floating point desireddtype
s. In addition, this method will only cast the floating point parameters and buffers todtype
(if given). The integral parameters and buffers will be moveddevice
, if that is given, but with dtypes unchanged. Whennon_blocking
is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices.See below for examples.
Note
This method modifies the module in-place.
- Parameters
device (
torch.device
) – the desired device of the parameters and buffers in this moduledtype (
torch.dtype
) – the desired floating point type of the floating point parameters and buffers in this moduletensor (torch.Tensor) – Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module
memory_format (
torch.memory_format
) – the desired memory format for 4D parameters and buffers in this module (keyword only argument)
- Returns
self
- Return type
Module
Example:
>>> linear = nn.Linear(2, 2) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]]) >>> linear.to(torch.double) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]], dtype=torch.float64) >>> gpu1 = torch.device("cuda:1") >>> linear.to(gpu1, dtype=torch.half, non_blocking=True) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1') >>> cpu = torch.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16)
-
-
train
(mode: bool = True) → T¶ Sets the module in training mode.
This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g.
Dropout
,BatchNorm
, etc.- Parameters
mode (bool) – whether to set training mode (
True
) or evaluation mode (False
). Default:True
.- Returns
self
- Return type
Module
-
type
(dst_type: Union[torch.dtype, str]) → T¶ Casts all parameters and buffers to
dst_type
.- Parameters
dst_type (type or string) – the desired type
- Returns
self
- Return type
Module
-
zero_grad
() → None¶ Sets gradients of all model parameters to zero.