#include </home/runner/work/lima/lima/deeplima/libs/tasks/lemmatization/model/seq2seq_for_lemmatization.h>

Inheritance diagram for deeplima::lemmatization::train::Seq2SeqLemmatizerImpl:

Public Types
typedef torch::Tensor	tensor_t

Public Types inherited from deeplima::nets::BiRnnSeq2SeqImpl
typedef torch::Tensor	tensor_t

Public Member Functions
	Seq2SeqLemmatizerImpl ()

	Seq2SeqLemmatizerImpl (DictsHolder &&dicts, const morph_model::morph_model_t &lang_morph_model, const std::vector< nets::embd_descr_t > &encoder_embd_descr, const std::vector< nets::rnn_descr_t > &encoder_rnn_descr, const std::vector< nets::embd_descr_t > &decoder_embd_descr, const std::vector< nets::rnn_descr_t > &decoder_rnn_descr, const std::vector< nets::embd_descr_t > &cat_embd_descr, const std::string &fixed_upos)

void	train (const train_params_lemmatization_t &params, const std::vector< TorchMatrix< int64_t > > &train_input, const std::vector< std::vector< TorchMatrix< int64_t > > > &train_input_cat, const std::vector< TorchMatrix< int64_t > > &train_gold, const std::vector< TorchMatrix< int64_t > > &eval_input, const std::vector< std::vector< TorchMatrix< int64_t > > > &eval_input_cat, const std::vector< TorchMatrix< int64_t > > &eval_gold, torch::optim::Optimizer &opt, const torch::Device &device=torch::Device(torch::kCPU))

void	evaluate (const std::vector< TorchMatrix< int64_t > > &input, const std::vector< TorchMatrix< int64_t > > &gold, nets::epoch_stat_t &stat, const torch::Device &device=torch::Device(torch::kCPU))

nets::epoch_stat_t	train_on_subset (const train_params_lemmatization_t &params, const TorchMatrix< int64_t > &train_input, const std::vector< TorchMatrix< int64_t > > &train_input_cat, const TorchMatrix< int64_t > &train_gold, torch::optim::Optimizer &opt, const torch::Device &device=torch::Device(torch::kCPU))

virtual void	load (torch::serialize::InputArchive &archive)

virtual void	save (torch::serialize::OutputArchive &archive) const

void	load (const std::string &fn)

const morph_model::morph_model_t &	get_morph_model () const

const std::string &	get_fixed_upos () const

Public Member Functions inherited from deeplima::nets::BiRnnSeq2SeqImpl
	BiRnnSeq2SeqImpl ()

	BiRnnSeq2SeqImpl (DictsHolder &&dicts, const std::vector< embd_descr_t > &encoder_embd_descr, const std::vector< rnn_descr_t > &encoder_rnn_descr, const std::vector< embd_descr_t > &decoder_embd_descr, const std::vector< rnn_descr_t > &decoder_rnn_descr, const std::vector< embd_descr_t > &cat_embd_descr)

void	evaluate (const std::vector< std::string > &output_names, const TorchMatrix< int64_t > &input, const TorchMatrix< int64_t > &gold, epoch_stat_t &stat, const torch::Device &device=torch::Device(torch::kCPU))

Public Member Functions inherited from deeplima::nets::BiRnnClassifierImpl
	BiRnnClassifierImpl ()

	BiRnnClassifierImpl (DictsHolder &&dicts, const std::vector< embd_descr_t > &embd_descr, const std::vector< rnn_descr_t > &rnn_descr, const std::vector< std::string > &output_names, const std::vector< uint32_t > &classes, float input_dropout_prob)

	BiRnnClassifierImpl (DictsHolder &&dicts, const std::vector< embd_descr_t > &embd_descr, const std::string &script)

const std::vector< embd_descr_t > &	get_embd_descr () const

void	train (size_t epochs, size_t batch_size, size_t seq_len, const std::vector< std::string > &output_names, const TorchMatrix< int64_t > &train_input, const TorchMatrix< int64_t > &train_gold, const TorchMatrix< int64_t > &eval_input, const TorchMatrix< int64_t > &eval_gold, torch::optim::Optimizer &opt, const std::string &model_name="", const torch::Device &device=torch::Device(torch::kCPU))

void	evaluate (const std::vector< std::string > &output_name, const TorchMatrix< int64_t > &input, const TorchMatrix< int64_t > &gold, epoch_stat_t &stat, const torch::Device &device=torch::Device(torch::kCPU))

torch::Tensor	predict (const std::string &output_name, const torch::Tensor &input, const torch::Device &device=torch::Device(torch::kCPU))

torch::Tensor	predict (const std::vector< std::string > &output_names, const torch::Tensor &input, const torch::Device &device=torch::Device(torch::kCPU))

void	predict (size_t worker_id, const torch::Tensor &inputs, int64_t input_begin, int64_t input_end, int64_t output_begin, int64_t output_end, std::shared_ptr< StdMatrix< uint8_t > > &output, const std::vector< std::string > &outputs_names, const torch::Device &device=torch::Device(torch::kCPU))

Public Member Functions inherited from deeplima::nets::StaticGraphImpl
	StaticGraphImpl (const DictsHolder &dicts, const std::string &script)

	StaticGraphImpl (DictsHolder &&dicts, const std::string &script)

	StaticGraphImpl ()

virtual void	set_tags (const std::map< std::string, std::string > &tags)

virtual const std::map< std::string, std::string > &	get_tags () const

virtual bool	has_tag (const std::string &key) const

virtual void	to (torch::Device device, bool non_blocking=false) override

std::map< std::string, torch::Tensor >	forward (const std::map< std::string, torch::Tensor > &inputs, const std::string &output_name)

std::map< std::string, torch::Tensor >	forward (const std::map< std::string, torch::Tensor > &inputs, const std::vector< std::string > &output_names)

template<class InputIt >
std::map< std::string, torch::Tensor >	forward (const std::map< std::string, torch::Tensor > &inputs, InputIt outputs_begin, InputIt outputs_end)

virtual void	pretty_dump (std::ostream &stream) const

const DictsHolder &	get_dicts () const

const std::string &	get_script () const

const std::vector< torch::nn::LSTM > &	get_layers_lstm () const

const std::vector< torch::nn::Linear > &	get_layers_linear () const

const std::vector< torch::nn::Embedding > &	get_layers_embedding () const

const std::vector< torch::nn::Dropout > &	get_layers_dropout () const

const std::vector< deeplima::nets::torch_modules::DeepBiaffineAttentionDecoder > &	get_layers_deep_biaffine_attn_decoder () const

const std::vector< deeplima::nets::torch_modules::DeepBiaffineAttentionLabelDecoder > &	get_layers_deep_biaffine_attn_label_decoder () const

torch::nn::Embedding	get_module_by_name (const std::string &name) const

const std::string	get_module_name (size_t idx, const std::string &type) const

Protected Attributes
morph_model::morph_model_t	m_morph_model

std::string	m_fixed_upos

Protected Attributes inherited from deeplima::nets::BiRnnSeq2SeqImpl
std::vector< embd_descr_t >	m_cat_embd_descr

Protected Attributes inherited from deeplima::nets::BiRnnClassifierImpl
std::vector< embd_descr_t >	m_embd_descr

Protected Attributes inherited from deeplima::nets::StaticGraphImpl
std::map< std::string, std::vector< size_t > >	m_exec_plans

DictsHolder	m_dicts

std::string	m_script

std::map< std::string, std::string >	m_tags

std::vector< torch::nn::Embedding >	m_embedding

std::vector< torch::nn::LSTM >	m_lstm

std::vector< torch::nn::Linear >	m_linear

std::vector< torch::nn::Dropout >	m_dropout

std::vector< deeplima::nets::torch_modules::DeepBiaffineAttentionDecoder >	m_deep_biaffine_attention_decoder

std::vector< deeplima::nets::torch_modules::DeepBiaffineAttentionLabelDecoder >	m_deep_biaffine_attention_label_decoder

std::map< std::string, module_ref_t >	m_modules

std::set< std::string >	m_args

std::map< std::string, size_t >	m_tensor_name_to_idx

std::vector< op_t >	m_ops

std::map< size_t, size_t >	m_outidx_to_opidx

Additional Inherited Members
Protected Member Functions inherited from deeplima::nets::BiRnnSeq2SeqImpl
std::string	generate_script (const std::vector< embd_descr_t > &encoder_embd_descr, const std::vector< rnn_descr_t > &encoder_rnn_descr, const std::vector< embd_descr_t > &decoder_embd_descr, const std::vector< rnn_descr_t > &decoder_rnn_descr, const std::vector< embd_descr_t > &cat_embd_descr, size_t n_output_classes)

void	train_batch (const std::vector< std::string > &output_names, const torch::Tensor &input, const std::vector< torch::Tensor > &input_cat, const torch::Tensor &target, torch::optim::Optimizer &opt, epoch_stat_t &stat, const torch::Device &device)

Protected Member Functions inherited from deeplima::nets::BiRnnClassifierImpl
void	split_input (const torch::Tensor &src, std::map< std::string, torch::Tensor > &dst, const torch::Device &device)

void	evaluate (const std::vector< std::string > &output_names, const std::map< std::string, torch::Tensor > &input, const torch::Tensor &target, epoch_stat_t &stat, const torch::Device &device)

void	train_epoch (size_t batch_size, size_t seq_len, const std::vector< std::string > &output_names, const torch::Tensor &input_batches, const torch::Tensor &gold_batches, torch::optim::Optimizer &opt, epoch_stat_t &stat, const torch::Device &device)

void	train_batch (size_t batch_size, size_t seq_len, const std::vector< std::string > &output_names, const torch::Tensor &input, const torch::Tensor &gold, torch::optim::Optimizer &opt, epoch_stat_t &stat, const torch::Device &device)

void	train_batch (size_t batch_size, size_t seq_len, const std::vector< std::string > &output_names, const std::map< std::string, torch::Tensor > &input, const torch::Tensor &target, torch::optim::Optimizer &opt, epoch_stat_t &stat, const torch::Device &device)

Protected Member Functions inherited from deeplima::nets::StaticGraphImpl
virtual void	parse_script (const std::string &script)

virtual step_descr_t	parse_script_line (const std::string &line)

virtual std::map< std::string, std::string >	parse_options (std::istringstream &ss)

virtual std::vector< std::string >	parse_list (std::string &str, char sep)

virtual std::vector< int64_t >	parse_iargs (const std::string &str)

template<class InputIt >
void	prepare_exec_plan (const std::map< std::string, torch::Tensor > &inputs, InputIt outputs_begin, InputIt outputs_end)

template<class InputIt >
std::string	get_exec_plan_key (const std::map< std::string, torch::Tensor > &inputs, InputIt outputs_begin, InputIt outputs_end)

template<class T >
T	get_option (const std::map< std::string, std::string > &opts, const std::string &name)

bool	get_bool_option (const std::map< std::string, std::string > &opts, const std::string &name)

virtual void	create_arg (const std::vector< std::string > &names, const std::map< std::string, std::string > &opts)

virtual void	create_submodule_Embedding (const std::string &name, const std::map< std::string, std::string > &opts)

virtual void	create_submodule_LSTM (const std::string &name, const std::map< std::string, std::string > &opts)

virtual void	create_submodule_Linear (const std::string &name, const std::map< std::string, std::string > &opts)

virtual void	create_submodule_Dropout (const std::string &name, const std::map< std::string, std::string > &opts)

virtual void	create_submodule_DeepBiaffineAttentionDecoder (const std::string &name, const std::map< std::string, std::string > &opts)

virtual void	create_submodule_DeepBiaffineAttentionLabelDecoder (const std::string &name, const std::map< std::string, std::string > &opts)

virtual void	init_rnns ()

Static Protected Member Functions inherited from deeplima::nets::BiRnnSeq2SeqImpl
template<class T >
static std::vector< T >	concatenate (const std::vector< T > &a, const std::vector< T > &b, const std::vector< T > &c={})

Static Protected Member Functions inherited from deeplima::nets::BiRnnClassifierImpl
static std::string	generate_script (const std::vector< embd_descr_t > &embd_descr, const std::vector< rnn_descr_t > &rnn_descr, const std::vector< std::string > &output_names, const std::vector< uint32_t > &classes, float input_dropout_prob)

Detailed Description

Definition at line 22 of file seq2seq_for_lemmatization.h.

Member Typedef Documentation

◆ tensor_t

typedef torch::Tensor deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::tensor_t

Definition at line 25 of file seq2seq_for_lemmatization.h.

Constructor & Destructor Documentation

◆ Seq2SeqLemmatizerImpl() [1/2]

deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::Seq2SeqLemmatizerImpl ( )

inline

Definition at line 27 of file seq2seq_for_lemmatization.h.

◆ Seq2SeqLemmatizerImpl() [2/2]

deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::Seq2SeqLemmatizerImpl	(	DictsHolder &&	dicts,
		const morph_model::morph_model_t &	lang_morph_model,
		const std::vector< nets::embd_descr_t > &	encoder_embd_descr,
		const std::vector< nets::rnn_descr_t > &	encoder_rnn_descr,
		const std::vector< nets::embd_descr_t > &	decoder_embd_descr,
		const std::vector< nets::rnn_descr_t > &	decoder_rnn_descr,
		const std::vector< nets::embd_descr_t > &	cat_embd_descr,
		const std::string &	fixed_upos
	)

inline

Definition at line 31 of file seq2seq_for_lemmatization.h.

Member Function Documentation

◆ evaluate()

void deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::evaluate	(	const std::vector< TorchMatrix< int64_t > > &	input,
		const std::vector< TorchMatrix< int64_t > > &	gold,
		nets::epoch_stat_t &	stat,
		const torch::Device &	device = `torch::Device(torch::kCPU)`
	)

Definition at line 124 of file seq2seq_for_lemmatization.cpp.

◆ get_fixed_upos()

const std::string & deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::get_fixed_upos ( ) const

inline

Definition at line 84 of file seq2seq_for_lemmatization.h.

◆ get_morph_model()

const morph_model::morph_model_t & deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::get_morph_model ( ) const

inline

Definition at line 79 of file seq2seq_for_lemmatization.h.

◆ load() [1/2]

void deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::load ( const std::string & fn )

inline

Definition at line 74 of file seq2seq_for_lemmatization.h.

◆ load() [2/2]

void deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::load ( torch::serialize::InputArchive & archive )

virtual

Reimplemented from deeplima::nets::BiRnnSeq2SeqImpl.

Definition at line 139 of file seq2seq_for_lemmatization.cpp.

◆ save()

void deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::save ( torch::serialize::OutputArchive & archive ) const

virtual

Reimplemented from deeplima::nets::BiRnnSeq2SeqImpl.

Definition at line 151 of file seq2seq_for_lemmatization.cpp.

◆ train()

void deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::train	(	const train_params_lemmatization_t &	params,
		const std::vector< TorchMatrix< int64_t > > &	train_input,
		const std::vector< std::vector< TorchMatrix< int64_t > > > &	train_input_cat,
		const std::vector< TorchMatrix< int64_t > > &	train_gold,
		const std::vector< TorchMatrix< int64_t > > &	eval_input,
		const std::vector< std::vector< TorchMatrix< int64_t > > > &	eval_input_cat,
		const std::vector< TorchMatrix< int64_t > > &	eval_gold,
		torch::optim::Optimizer &	opt,
		const torch::Device &	device = `torch::Device(torch::kCPU)`
	)

Definition at line 25 of file seq2seq_for_lemmatization.cpp.

◆ train_on_subset()

nets::epoch_stat_t deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::train_on_subset	(	const train_params_lemmatization_t &	params,
		const TorchMatrix< int64_t > &	train_input,
		const std::vector< TorchMatrix< int64_t > > &	train_input_cat,
		const TorchMatrix< int64_t > &	train_gold,
		torch::optim::Optimizer &	opt,
		const torch::Device &	device = `torch::Device(torch::kCPU)`
	)

Definition at line 85 of file seq2seq_for_lemmatization.cpp.

Member Data Documentation

◆ m_fixed_upos

std::string deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::m_fixed_upos

protected

Definition at line 92 of file seq2seq_for_lemmatization.h.

◆ m_morph_model

morph_model::morph_model_t deeplima::lemmatization::train::Seq2SeqLemmatizerImpl::m_morph_model

protected

Definition at line 91 of file seq2seq_for_lemmatization.h.

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

deeplima/libs/tasks/lemmatization/model/seq2seq_for_lemmatization.h
deeplima/libs/tasks/lemmatization/model/seq2seq_for_lemmatization.cpp

Public Types

Public Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ tensor_t

Constructor & Destructor Documentation

◆ Seq2SeqLemmatizerImpl() [1/2]

◆ Seq2SeqLemmatizerImpl() [2/2]

Member Function Documentation

◆ evaluate()

◆ get_fixed_upos()

◆ get_morph_model()

◆ load() [1/2]

◆ load() [2/2]

◆ save()

◆ train()

◆ train_on_subset()

Member Data Documentation

◆ m_fixed_upos

◆ m_morph_model