torch::nn::detail::RNNImplBase< Derived > Class Template Reference

Base class for all RNN implementations (intended for code sharing). More...

#include <rnn.h>

Inheritance diagram for torch::nn::detail::RNNImplBase< Derived >:

Public Types
enum	CuDNNMode { RNN_RELU = 0, RNN_TANH = 1, LSTM = 2, GRU = 3 }
	These must line up with the CUDNN mode codes: https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnRNNMode_t.
Public Types inherited from torch::nn::Module
using	ModuleApplyFunction = std::function< void(Module &)>
using	ConstModuleApplyFunction = std::function< void(const Module &)>
using	NamedModuleApplyFunction = std::function< void(const std::string &, Module &)>
using	ConstNamedModuleApplyFunction = std::function< void(const std::string &, const Module &)>
using	ModulePointerApplyFunction = std::function< void(const std::shared_ptr< Module > &)>
using	NamedModulePointerApplyFunction = std::function< void(const std::string &, const std::shared_ptr< Module > &)>

Public Member Functions
	RNNImplBase (const RNNOptionsBase &options_, optional< CuDNNMode > cudnn_mode=nullopt, int64_t number_of_gates=1)
void	reset () override
	Initializes the parameters of the RNN module.
void	to (torch::Device device, torch::Dtype dtype, bool non_blocking=false) override
	Overrides nn::Module::to() to call flatten_parameters() after the original operation. More...
void	to (torch::Dtype dtype, bool non_blocking=false) override
	Recursively casts all parameters to the given dtype. More...
void	to (torch::Device device, bool non_blocking=false) override
	Recursively moves all parameters to the given device. More...
void	pretty_print (std::ostream &stream) const override
	Pretty prints the RNN module into the given stream.
void	flatten_parameters ()
	Modifies the internal storage of weights for optimization purposes. More...
Public Member Functions inherited from torch::nn::Cloneable< Derived >
std::shared_ptr< Module >	clone (const optional< Device > &device=nullopt) const override
	Performs a recursive "deep copy" of the Module, such that all parameters and submodules in the cloned module are different from those in the original module. More...
Public Member Functions inherited from torch::nn::Module
	Module (std::string name)
	Tells the base Module about the name of the submodule.
	Module ()
	Constructs the module without immediate knowledge of the submodule's name. More...
const std::string &	name () const noexcept
	Returns the name of the Module. More...
void	apply (const ModuleApplyFunction &function)
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const ConstModuleApplyFunction &function) const
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const NamedModuleApplyFunction &function, const std::string &name_prefix=std::string())
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const ConstNamedModuleApplyFunction &function, const std::string &name_prefix=std::string()) const
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const ModulePointerApplyFunction &function) const
	Applies the function to the Module and recursively to every submodule. More...
void	apply (const NamedModulePointerApplyFunction &function, const std::string &name_prefix=std::string()) const
	Applies the function to the Module and recursively to every submodule. More...
std::vector< Tensor >	parameters (bool recurse=true) const
	Returns the parameters of this Module and if recurse is true, also recursively of every submodule. More...
OrderedDict< std::string, Tensor >	named_parameters (bool recurse=true) const
	Returns an OrderedDict with the parameters of this Module along with their keys, and if recurse is true also recursively of every submodule. More...
std::vector< Tensor >	buffers (bool recurse=true) const
	Returns the buffers of this Module and if recurse is true, also recursively of every submodule. More...
OrderedDict< std::string, Tensor >	named_buffers (bool recurse=true) const
	Returns an OrderedDict with the buffers of this Module along with their keys, and if recurse is true also recursively of every submodule. More...
std::vector< std::shared_ptr< Module > >	modules (bool include_self=true) const
	Returns the submodules of this Module (the entire submodule hierarchy) and if include_self is true, also inserts a shared_ptr to this module in the first position. More...
OrderedDict< std::string, std::shared_ptr< Module > >	named_modules (const std::string &name_prefix=std::string(), bool include_self=true) const
	Returns an OrderedDict of he submodules of this Module (the entire submodule hierarchy) and thei keys, and if include_self is true, also inserts a shared_ptr to this module in the first position. More...
std::vector< std::shared_ptr< Module > >	children () const
	Returns the direct submodules of this Module.
OrderedDict< std::string, std::shared_ptr< Module > >	named_children () const
	Returns an OrderedDict of the direct submodules of this Module and their keys. More...
virtual void	train (bool on=true)
	Enables "training" mode.
void	eval ()
	Calls train(false) to enable "eval" mode. More...
virtual bool	is_training () const noexcept
	True if the module is in training mode. More...
virtual void	zero_grad ()
	Recursively zeros out the grad value of each registered parameter.
template<typename ModuleType >
ModuleType::ContainedType *	as () noexcept
	Attempts to cast this Module to the given ModuleType. More...
template<typename ModuleType >
const ModuleType::ContainedType *	as () const noexcept
	Attempts to cast this Module to the given ModuleType. More...
template<typename ModuleType , typename = torch::detail::disable_if_module_holder_t<ModuleType>>
ModuleType *	as () noexcept
	Attempts to cast this Module to the given ModuleType. More...
template<typename ModuleType , typename = torch::detail::disable_if_module_holder_t<ModuleType>>
const ModuleType *	as () const noexcept
	Attempts to cast this Module to the given ModuleType. More...
virtual void	save (serialize::OutputArchive &archive) const
	Serializes the Module into the given OutputArchive.
virtual void	load (serialize::InputArchive &archive)
	Deserializes the Module from the given InputArchive.

Data Fields
RNNOptionsBase	options
	The RNN's options.
std::vector< Tensor >	w_ih
	The weights for input x hidden gates.
std::vector< Tensor >	w_hh
	The weights for hidden x hidden gates.
std::vector< Tensor >	b_ih
	The biases for input x hidden gates.
std::vector< Tensor >	b_hh
	The biases for hidden x hidden gates.

Protected Types
using	RNNFunctionSignature = std::tuple< Tensor, Tensor >(const Tensor &, const Tensor &, TensorList, bool, int64_t, double, bool, bool, bool)
	The function signature of rnn_relu, rnn_tanh and gru.

Protected Member Functions
RNNOutput	generic_forward (std::function< RNNFunctionSignature > function, const Tensor &input, Tensor state)
	A generic forward() used for RNN and GRU (but not LSTM!). More...
std::vector< Tensor >	flat_weights () const
	Returns a flat vector of all weights, with layer weights following each other sequentially in (w_ih, w_hh, b_ih, b_hh) order. More...
bool	any_parameters_alias () const
	Very simple check if any of the parameters (weights, biases) are the same.
Protected Member Functions inherited from torch::nn::Module
Tensor &	register_parameter (std::string name, Tensor tensor, bool requires_grad=true)
	Registers a parameter with this Module. More...
Tensor &	register_buffer (std::string name, Tensor tensor)
	Registers a buffer with this Module. More...
template<typename ModuleType >
std::shared_ptr< ModuleType >	register_module (std::string name, std::shared_ptr< ModuleType > module)
	Registers a submodule with this Module. More...
template<typename ModuleType >
std::shared_ptr< ModuleType >	register_module (std::string name, ModuleHolder< ModuleType > module_holder)
	Registers a submodule with this Module. More...

Protected Attributes
int64_t	number_of_gates_
	The number of gate weights/biases required by the RNN subclass.
optional< CuDNNMode >	cudnn_mode_
	The cuDNN RNN mode, if this RNN subclass has any.
std::vector< Tensor >	flat_weights_
	The cached result of the latest flat_weights() call.

Detailed Description

template<typename Derived>
class torch::nn::detail::RNNImplBase< Derived >

Base class for all RNN implementations (intended for code sharing).

Definition at line 56 of file rnn.h.

Member Function Documentation

template<typename Derived >

std::vector< Tensor > torch::nn::detail::RNNImplBase< Derived >::flat_weights ( ) const

protected

Returns a flat vector of all weights, with layer weights following each other sequentially in (w_ih, w_hh, b_ih, b_hh) order.

Definition at line 157 of file rnn.cpp.

template<typename Derived >

void torch::nn::detail::RNNImplBase< Derived >::flatten_parameters

(

)

Modifies the internal storage of weights for optimization purposes.

On CPU, this method should be called if any of the weight or bias vectors are changed (i.e. weights are added or removed). On GPU, it should be called any time the storage of any parameter is modified, e.g. any time a parameter is assigned a new value. This allows using the fast path in cuDNN implementations of respective RNN forward() methods. It is called once upon construction, inside reset().

Definition at line 111 of file rnn.cpp.

template<typename Derived >

RNNOutput torch::nn::detail::RNNImplBase< Derived >::generic_forward ( std::function< RNNFunctionSignature >  function, const Tensor &  input, Tensor  state  )

protected

A generic forward() used for RNN and GRU (but not LSTM!).

Takes the ATen RNN function as first argument.

Definition at line 132 of file rnn.cpp.

template<typename Derived >

void torch::nn::detail::RNNImplBase< Derived >::to ( torch::Device  device, torch::Dtype  dtype, bool  non_blocking = false  )

overridevirtual

Overrides nn::Module::to() to call flatten_parameters() after the original operation.

Reimplemented from torch::nn::Module.

Definition at line 80 of file rnn.cpp.

template<typename Derived >

void torch::nn::detail::RNNImplBase< Derived >::to ( torch::Dtype  dtype, bool  non_blocking = false  )

overridevirtual

Recursively casts all parameters to the given dtype.

If non_blocking is true and the source is in pinned memory and destination is on the GPU or vice versa, the copy is performed asynchronously with respect to the host. Otherwise, the argument has no effect.

Reimplemented from torch::nn::Module.

Definition at line 89 of file rnn.cpp.

template<typename Derived >

void torch::nn::detail::RNNImplBase< Derived >::to ( torch::Device  device, bool  non_blocking = false  )

overridevirtual

Recursively moves all parameters to the given device.

If non_blocking is true and the source is in pinned memory and destination is on the GPU or vice versa, the copy is performed asynchronously with respect to the host. Otherwise, the argument has no effect.

Reimplemented from torch::nn::Module.

Definition at line 95 of file rnn.cpp.

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The documentation for this class was generated from the following files:

• torch/csrc/api/include/torch/nn/modules/rnn.h
• torch/csrc/api/src/nn/modules/rnn.cpp