BaseFloat default_beam;

BaseFloat lattice_beam;

int32 ntokens_pre_allocated;

int32 main_q_capacity, aux_q_capacity;

int32 nlanes;

int32 nchannels;

int32 max_active;

CudaDecoderConfig()

: default_beam(15.0),

lattice_beam(10.0),

ntokens_pre_allocated(2000000),

main_q_capacity(50000),

aux_q_capacity(500000),

max_active(10000) {}

void Register(OptionsItf *opts) {

opts->Register("beam", &default_beam,

"Decoding beam. Larger->slower, more accurate. If "

"aux-q-capacity is too small, we may decrease the beam "

"dynamically to avoid overflow (adaptive beam, see "

"aux-q-capacity parameter)");

opts->Register("lattice-beam", &lattice_beam,

"The width of the lattice beam");

opts->Register("max-active", &max_active,

"At the end of each frame computation, we keep only its "

"best max-active tokens. One token is the instantiation of "

"a single arc. Typical values are within the 5k-10k range.");

opts->Register("ntokens-pre-allocated", &ntokens_pre_allocated,

"Advanced - Number of tokens pre-allocated in host buffers. "

"If this size is exceeded the buffer will reallocate, "

"reducing performance.");

opts->Register("aux-q-capacity", &aux_q_capacity,

"Advanced - Capacity of the auxiliary queue : Maximum "

"number of raw tokens that can be stored *before* pruning "

"for each frame. Lower -> less memory usage, Higher -> More "

"accurate. During the tokens generation, if we detect that "

"we are getting close to saturating that capacity, we will "

"reduce the beam dynamically (adaptive beam) to keep only "

"the best tokens in the remaining space. If the aux queue "

"is still too small, we will print an overflow warning, but "

"prevent the overflow. The computation can safely continue, "

"but the quality of the output may decrease. We strongly "

"recommend keeping aux-q-capacity large (>400k), to avoid "

"triggering the adaptive beam and/or the overflow.");

opts->Register("main-q-capacity", &main_q_capacity,

"Advanced - Capacity of the main queue : Maximum number of "

"tokens that can be stored *after* pruning for each frame. "

"Lower -> less memory usage, Higher -> More accurate. "

"Tokens stored in the main queue were already selected "

"through a max-active pre-selection. It means that for each "

"emitting/non-emitting iteration, we can add at most "

"~max-active tokens to the main queue. Typically only the "

"emitting iteration creates a large number of tokens. Using "

"main-q-capacity=k*max-active with k=4..10 should be safe. "

"If main-q-capacity is too small, we will print a warning "

"but prevent the overflow. The computation can safely "

"continue, but the quality of the output may decrease.");

}

void Check() const {

KALDI_ASSERT(default_beam > 0.0 && ntokens_pre_allocated >= 0 &&

main_q_capacity > 0 && aux_q_capacity >= main_q_capacity &&

lattice_beam >= 0.0f && max_active > 1);

}

public:

// Creating a new CudaDecoder, associated to the FST fst

// nlanes and nchannels are defined as follow

// A decoder channel is linked to one utterance.

// When we need to perform decoding on an utterance,

// we pick an available channel, call InitDecoding on that channel

// (with that ChannelId in the channels vector in the arguments)

// then call AdvanceDecoding whenever frames are ready for the decoder

// for that utterance (also passing the same ChannelId to AdvanceDecoding)

//

// A decoder lane is where the computation actually happens

// a decoder lane is channel, and perform the actual decoding

// of that channel.

// If we have 200 lanes, we can compute 200 utterances (channels)

// at the same time. We need many lanes in parallel to saturate the big GPUs

//

// An analogy would be lane -> a CPU core, channel -> a software thread

// A channel saves the current state of the decoding for a given utterance.

// It can be kept idle until more frames are ready to be processed

//

// We will use as many lanes as necessary to saturate the GPU, but not more.

// A lane has an higher memory usage than a channel. If you just want to be

// able to

// keep more audio channels open at the same time (when I/O is the bottleneck

// for instance,

// typically in the context of online decoding), you should instead use more

// channels.

//

// A channel is typically way smaller in term of memory usage, and can be used

// to oversubsribe lanes in the context of online decoding

// For instance, we could choose nlanes=200 because it gives us good

// performance

// on a given GPU. It gives us an end-to-end performance of 3000 XRTF. We are

// doing online,

// so we only get audio at realtime speed for a given utterance/channel.

// We then decide to receive audio from 2500 audio channels at the same time

// (each at realtime speed),

// and as soon as we have frames ready for nlanes=200 channels, we call

// AdvanceDecoding on those channels

// In that configuration, we have nlanes=200 (for performance), and

// nchannels=2500 (to have enough audio

// available at a given time).

// Using nlanes=2500 in that configuration would first not be possible (out of

// memory), but also not necessary.

// Increasing the number of lanes is only useful if it increases performance.

// If the GPU is saturated at nlanes=200,

// you should not increase that number

CudaDecoder(const CudaFst &fst, const CudaDecoderConfig &config, int32 nlanes,

int32 nchannels);

// Special constructor for nlanes = nchannels. Here for the non-advanced user

// Here we can consider nchannels = batch size. If we want to decode 10

// utterances at a time,

// we can use nchannels = 10

CudaDecoder(const CudaFst &fst, const CudaDecoderConfig &config,

int32 nchannels)

: CudaDecoder(fst, config, nchannels, nchannels) {}

~CudaDecoder();

// InitDecoding initializes the decoding, and should only be used if you

// intend to call AdvanceDecoding() on the channels listed in channels

void InitDecoding(const std::vector<ChannelId> &channels);

// AdvanceDecoding on a given batch

// a batch is defined by the channels vector

// We can compute N channels at the same time (in the same batch)

// where N = number of lanes, as defined in the constructor

// AdvanceDecoding will compute as many frames as possible while running the

// full batch

// when at least one channel has no more frames ready to be computed,

// AdvanceDecoding returns

// The user then decides what to do, i.e.:

//

// 1) either remove the empty channel from the channels list

// and call again AdvanceDecoding

// 2) or swap the empty channel with another one that has frames ready

// and call again AdvanceDecoding

//

// Solution 2) should be preferred because we need to run full, big batches to

// saturate the GPU

//

// If max_num_frames is >= 0 it will decode no more than

// that many frames.

void AdvanceDecoding(const std::vector<ChannelId> &channels,

std::vector<CudaDecodableInterface *> &decodables,

int32 max_num_frames = -1);

// Returns the number of frames already decoded in a given channel

int32 NumFramesDecoded(ChannelId ichannel) const;

// GetBestPath gets the one-best decoding traceback. If "use_final_probs" is

// true

// AND we reached a final state, it limits itself to final states;

// otherwise it gets the most likely token not taking into account

// final-probs.

void GetBestPath(const std::vector<ChannelId> &channels,

std::vector<Lattice *> &fst_out_vec,

bool use_final_probs = true);

// GetRawLattice gets the lattice decoding traceback (using the lattice-beam

// in the CudaConfig parameters).

// If "use_final_probs" is true

// AND we reached a final state, it limits itself to final states;

// otherwise it gets the most likely token not taking into account

// final-probs.

void GetRawLattice(const std::vector<ChannelId> &channels,

std::vector<Lattice *> &fst_out_vec, bool use_final_probs);

// GetBestCost finds the best cost in the last tokens queue

// for each channel in channels. If isfinal is true,

// we also add the final cost to the token costs before

// finding the minimum cost

// We list all tokens that have a cost within [best; best+lattice_beam]

// in list_lattice_tokens.

// We alsos set has_reached_final[ichannel] to true if token associated to a

// final state

// exists in the last token queue of that channel

void GetBestCost(

const std::vector<ChannelId> &channels, bool isfinal,

std::vector<std::pair<int32, CostType>> *argmins,

std::vector<std::vector<std::pair<int, float>>> *list_lattice_tokens,

std::vector<bool> *has_reached_final);

private:

// Data allocation. Called in constructor

void AllocateDeviceData();

void AllocateHostData();

void AllocateDeviceKernelParams();

// Data initialization. Called in constructor

void InitDeviceData();

void InitHostData();

void InitDeviceParams();

// Computes the initial channel

// The initial channel is used to initialize a channel

// when a new utterance starts (we clone it into the given channel)

void ComputeInitialChannel();

// Updates *h_kernel_params using channels

void SetChannelsInKernelParams(const std::vector<ChannelId> &channels);

void ResetChannelsInKernelParams();

// Context-switch functions

// Used to perform the context-switch of load/saving the state of a channels

// into a lane. When a channel will be executed on a lane, we load that

// channel into that lane (same idea than when we load a software threads into

// the registers of a CPU)

void LoadChannelsStateToLanes(const std::vector<ChannelId> &channels);

void SaveChannelsStateFromLanes();

// We compute the decodes by batch. Each decodable in the batch has a

// different number of frames ready

// We compute the min number of frames ready (so that the full batch is

// executing). If max_num_frames

// is > 0, we apply that ceiling to the NumFramesToDecode.

int32 NumFramesToDecode(const std::vector<ChannelId> &channels,

std::vector<CudaDecodableInterface *> &decodables,

int32 max_num_frames);

// Expand the arcs, emitting stage. Must be called after

// a preprocess_in_place, which happens in PostProcessingMainQueue.

// ExpandArcsEmitting is called first when decoding a frame,

// using the preprocessing that happened at the end of the previous frame,

// in PostProcessingMainQueue

void ExpandArcsEmitting();

// ExpandArcs, non-emitting stage. Must be called after PruneAndPreprocess.

// if *should_iterate is true, we should do another iteration of the

// PruneAndPreprocess/ExpandArcsNonEmitting pair

void ExpandArcsNonEmitting(bool *should_iterate);

// If we have more than max_active_ tokens in the queue (either after an

// expand, or at the end of the frame)

// we will compute a new beam that will only keep a number of tokens as close

// as possible to max_active_ tokens

// (that number is >= max_active_) (soft topk)

// All ApplyMaxActiveAndReduceBeam is find the right beam for that topk and

// set it.

// We need to then call PruneAndPreprocess (explicitly pruning tokens with

// cost > beam)

// Or PostProcessingMainQueue (ignoring tokens with cost > beam in the next

// frame)

void ApplyMaxActiveAndReduceBeam(enum QUEUE_ID queue_id);

// Called after an ExpandArcs. Prune the aux_q (output of the ExpandArcs),

// move the survival tokens to the main_q, do the preprocessing at the same

// time

// We don't need it after the last ExpandArcsNonEmitting.

void PruneAndPreprocess();

// Moving the acoustic_costs of emitting tokens to host in two stage.

// StartCopyAcousticCostsToHostAsync concatenate the data on device and start

// the device2host copy

// FinalizeCopyAcousticCostsToHost move the data that arrived on host into the

// right vectors for storage

// a sync on compute_st_ has to happen between those two stage

void StartCopyAcousticCostsToHostAsync(); // stage 1

void FinalizeCopyAcousticCostsToHost(); // stage 2. Need a sync on

// compute_st_ between 1 on 2

// Once the non-emitting is done, the main_q is final for that frame.

// We now generate all the data associated with that main_q, such as listing

// the different tokens sharing the same token.next_state

// we also preprocess for the ExpandArcsEmitting of the next frame

// Once PostProcessingMainQueue, all working data is back to its original

// state, to make sure we're ready for the next context switch

void PostProcessingMainQueue();

// Moving the relevant data to host, ie the data that will be needed in

// GetBestPath/GetRawLattice.

// Happens when PostProcessingMainQueue is done generating that data

void CopyMainQueueDataToHost();

// CheckOverflow

// If a kernel sets the flag h_q_overflow, we send a warning to stderr

// Overflows are detected and prevented on the device. It only means

// that we've discarded the tokens that were created after the queue was full

// That's why we only send a warning. It is not a fatal error

void CheckOverflow();

// Evaluates the function func for each lane, returning the max of all return

// values

// (func returns int32)

// Used for instance to ge the max number of arcs for all lanes

// func is called with h_lanes_counters_[ilane] for each lane.

// h_lanes_counters_

// must be ready to be used when calling GetMaxForAllLanes (you might want to

// call

// CopyLaneCountersToHost[A|]sync to make sure everything is ready first)

int32 GetMaxForAllLanes(std::function<int32(const LaneCounters &)> func);

// Copy the lane counters back to host, async or sync

// The lanes counters contain all the information such as main_q_end (number

// of tokens in the main_q)

// main_q_narcs (number of arcs) during the computation. That's why we

// frequently copy it back to host

// to know what to do next

void CopyLaneCountersToHostAsync();

void CopyLaneCountersToHostSync();

// The selected tokens for each frame will be copied back to host. We will

// store them on host memory, and we wil use them to create the final lattice

// once we've reached the last frame

// We will also copy information on those tokens that we've generated on the

// device, such as which tokens are associated to the same FST state in the

// same frame, or their extra cost.

// We cannot call individuals Device2Host copies for each channel, because it

// would lead to a lot of small copies, reducing performance. Instead we

// concatenate all channels data into a single

// continuous array, copy that array to host, then unpack it to the individual

// channel vectors

// The first step (pack then copy to host, async) is done in

// PerformConcatenatedCopy

// The second step is done in MoveConcatenatedCopyToVector

// A sync on cudaStream st has to happen between the two functions to make

// sure that the copy is done

//

// Each lane contains X elements to be copied, where X = func(ilane)

// That data is contained in the array (pointer, X), with pointer = src[ilane]

// It will be concatenated in d_concat on device, then copied async into

// h_concat

// That copy is launched on stream st

// The offset of the data of each lane in the concatenate array is saved in

// *lanes_offsets_ptr

// it will be used for unpacking in MoveConcatenatedCopyToVector

//

// func is called with h_lanes_counters_[ilane] for each lane.

// h_lanes_counters_

// must be ready to be used when calling GetMaxForAllLanes (you might want to

// call

// CopyLaneCountersToHost[A|]sync to make sure everything is ready first)

template <typename T>

void PerformConcatenatedCopy(std::function<int32(const LaneCounters &)> func,

LaneMatrixView<T> src, T *d_concat, T *h_concat,

cudaStream_t st,

std::vector<int32> *lanes_offsets_ptr);

template <typename T>

void MoveConcatenatedCopyToVector(const std::vector<int32> &lanes_offsets,

T *h_concat,

std::vector<std::vector<T>> *vecvec);

// Computes a set of static asserts on the static values

// such as the defines : KALDI_CUDA_DECODER_MAX_N_LANES for example

// In theory we should do them at compile time

void CheckStaticAsserts();

// Can be called in GetRawLattice to do a bunch of deep asserts on the data

// Slow, so disabled by default

void DebugValidateLattice();

//

// Data members

//

// The CudaFst data structure contains the FST graph

// in the CSR format, on both the GPU and CPU memory

const CudaFst fst_;

// Counters used by a decoder lane

// Contains all the single values generated during computation,

// such as the current size of the main_q, the number of arcs currently in

// that queue

// We load data from the channel state during context-switch (for instance the

// size of the last token queue for that channel)

LaneCounters *h_lanes_counters_;

// Counters of channels

// Contains all the single values saved to remember the state of a channel

// not used during computation. Those values are loaded/saved into/from a lane

// during context switching

ChannelCounters *h_channels_counters_;

// Contain the various counters used by lanes/channels, such as main_q_end,

// main_q_narcs. On device memory (equivalent of h_channels_counters on

// device)

DeviceChannelMatrix<ChannelCounters> d_channels_counters_;

DeviceLaneMatrix<LaneCounters> d_lanes_counters_;

// Number of lanes and channels, as defined in the constructor arguments

int32 nlanes_, nchannels_;

// We will now define the data used on the GPU

// The data is mainly linked to two token queues

// - the main queue

// - the auxiliary queue

//

// The auxiliary queue is used to store the raw output of ExpandArcs.

// We then prune that aux queue (and apply max-active) and move the survival

// tokens in the main queue.

// Tokens stored in the main q can then be used to generate new tokens (using

// ExpandArcs)

// We also generate more information about what's in the main_q at the end of

// a frame (in PostProcessingMainQueue)

//

// As a reminder, here's the data structure of a token :

//

// struct Token { state, cost, prev_token, arc_idx }

//

// Please keep in mind that this structure is also used in the context

// of lattice decoding. We are not storing a list of forward links like in the

// CPU decoder. A token stays an instanciation of an single arc.

//

// For performance reasons, we split the tokens in three parts :

// { state } , { cost }, { prev_token, arc_idx }

// Each part has its associated queue

// For instance, d_main_q_state[i], d_main_q_cost[i], d_main_q_info[i]

// all refer to the same token (at index i)

// The data structure InfoToken contains { prev_token, arc_idx }

// We also store the acoustic costs independently in d_main_q_acoustic_cost_

//

// The data is eiher linked to a channel, or to a lane.

//

// Channel data (DeviceChannelMatrix):

//

// The data linked with a channel contains the data of frame i we need to

// remember

// to compute frame i+1. It is the list of tokens from frame i, with some

// additional info

// (ie the prefix sum of the emitting arcs degrees from those tokens).

// We are only storing d_main_q_state_and_cost_ as channel data because that's

// all we need in a token to compute

// frame i+1. We don't need token.arc_idx or token.prev_token.

// The reason why we also store that prefix sum is because we do the emitting

// preprocessing

// at the end of frame i. The reason for that is that we need infos from the

// hashmap to do that preprocessing.

// The hashmap is always cleared at the end of a frame. So we need to do the

// preprocessing at the end of frame i,

// and then save d_main_q_degrees_prefix_sum_. d_main_q_arc_offsets is

// generated also during preprocessing.

//

// Lane data (DeviceLaneMatrix):

//

// The lane data is everything we use during computation, but which we reset

// at the end of each frame.

// For instance we use a hashmap at some point during the computation, but at

// the end of each frame we reset it. That

// way that hashmap is able to compute whichever channel the next time

// AdvanceDecoding is called. The reasons why we do that is :

//

// - We use context switching. Before and after every frames, we can do a

// context switching. Which means that a lane cannot save a channel's state

// in any way once AdvanceDecoding returns. e.g., during a call of

// AdvanceDecoding, ilane=2 may compute 5 frames from channel=57 (as defined

// in the std::vector<ChannelId> channels).

// In the next call, the same ilane=2 may compute 10 frames from channel=231.

// A lane data has to be reset to its original state at the end of each

// AdvanceDecoding call.

// If somehow some data has to be saved, it needs to be declared as channel

// data.

//

// - The reason why we make the distinction between lane and channel data (in

// theory everything could be consider channel data), is because

// a lane uses more memory than a channel. In the context of online decoding,

// we need to create a lot channels, and we need them to be as small as

// possible in memory.

// Everything that can be reused between channels is stored as lane data.

//

// Channel data members:

//

DeviceChannelMatrix<int2> d_main_q_state_and_cost_;

// Prefix sum of the arc's degrees in the main_q. Used by ExpandArcs,

// set in the preprocess stages (either PruneAndPreprocess or

// preprocess_in_place in PostProcessingMainQueue)

DeviceChannelMatrix<int32> d_main_q_degrees_prefix_sum_;

// d_main_q_arc_offsets[i] = fst_.arc_offsets[d_main_q_state[i]]

// we pay the price for the random memory accesses of fst_.arc_offsets in the

// preprocess kernel

// we cache the results in d_main_q_arc_offsets which will be read in a

// coalesced fashion in expand

DeviceChannelMatrix<int32> d_main_q_arc_offsets_;

//

// Lane data members:

//

// InfoToken

// Usually contains {prev_token, arc_idx}

// If more than one token is associated to a fst_state,

// it will contain where to find the list of those tokens in

// d_main_q_extra_prev_tokens

// ie {offset,size} in that list. We differentiate the two situations by

// calling InfoToken.IsUniqueTokenForStateAndFrame()

DeviceLaneMatrix<InfoToken> d_main_q_info_;

// Acoustic cost of a given token

DeviceLaneMatrix<CostType> d_main_q_acoustic_cost_;

// At the end of a frame, we use a hashmap to detect the tokens that are

// associated with the same FST state S

// We do it that the very end, to only use the hashmap on post-prune, post-max

// active tokens

DeviceLaneMatrix<HashmapValueT> d_hashmap_values_;

// Reminder: in the GPU lattice decoder, a token is always associated

// to a single arc. Which means that multiple tokens in the same frame

// can be associated with the same FST state.

//

// We are NOT listing those duplicates as ForwardLinks in an unique meta-token

// like in the CPU lattice decoder

//

// When more than one token is associated to a single FST state,

// we will list those tokens into another list : d_main_q_extra_prev_tokens

// we will also save data useful in such a case, such as the extra_cost of a

// token compared to the best for that state

DeviceLaneMatrix<InfoToken> d_main_q_extra_prev_tokens_;

DeviceLaneMatrix<float2> d_main_q_extra_and_acoustic_cost_;

// Histogram. Used to perform the histogram of the token costs

// in the main_q. Used to perform a soft topk of the main_q (max-active)

DeviceLaneMatrix<int32> d_histograms_;

// When filling the hashmap in PostProcessingMainQueue, we create a hashmap

// value for each FST state

// presents in the main_q (if at least one token is associated with that

// state)

// d_main_q_state_hash_idx_[token_idx] is the index of the state token.state

// in the hashmap

// Stored into a FSTStateHashIndex, which is actually a int32.

// FSTStateHashIndex should only

// be accessed through [Get|Set]FSTStateHashIndex, because it uses the bit

// sign to also remember if that token is the representative of that state.

// If only one token is associated with S, its representative will be itself

DeviceLaneMatrix<FSTStateHashIndex> d_main_q_state_hash_idx_;

// local_idx of the extra cost list for a state

// For a given state S, first token associated with S will have local_idx=0

// the second one local_idx=1, etc. The order of the local_idxs is random

DeviceLaneMatrix<int32> d_main_q_n_extra_prev_tokens_local_idx_;

// Where to write the extra_prev_tokens in the d_main_q_extra_prev_tokens_

// queue

DeviceLaneMatrix<int32> d_main_q_extra_prev_tokens_prefix_sum_;

// Used when computing the prefix_sums in preprocess_in_place. Stores

// the local_sums per CTA

DeviceLaneMatrix<int2> d_main_q_block_sums_prefix_sum_;

// Defining the aux_q. Filled by ExpandArcs.

// The tokens are moved to the main_q by PruneAndPreprocess

DeviceLaneMatrix<int2> d_aux_q_state_and_cost_;

DeviceLaneMatrix<CostType> d_aux_q_acoustic_cost_;

DeviceLaneMatrix<InfoToken> d_aux_q_info_;

// Dedicated space for the concat of extra_cost. We should reuse memory

DeviceLaneMatrix<float2> d_extra_and_acoustic_cost_concat_matrix;

// We will list in d_list_final_tokens_in_main_q all tokens within [min_cost;

// min_cost+lattice_beam]

// It is used when calling GetBestCost

// We only use an interface here because we will actually reuse data from

// d_aux_q_state_and_cost

// We are done using the aux_q when GetBestCost is called, so we can reuse

// that memory

LaneMatrixView<int2> d_list_final_tokens_in_main_q_;

// Parameters used by the kernels

// DeviceParams contains all the parameters that won't change

// i.e. memory address of the main_q for instance

// KernelParams contains information that can change.

// For instance which channel is executing on which lane

DeviceParams *h_device_params_;

KernelParams *h_kernel_params_;

int32 nlanes_used_; // number of lanes used in h_kernel_params_

// Initial lane

// When starting a new utterance,

// init_channel_id is used to initialize a channel

int32 init_channel_id_;

// CUDA streams used by the decoder

cudaStream_t compute_st_;

// Parameters extracted from CudaDecoderConfig

// Those are defined in CudaDecoderConfig

CostType default_beam_;

CostType lattice_beam_;

int32 ntokens_pre_allocated_;

int32 max_active_; // Target value from the parameters

int32 max_active_thresh_; // Target value + tolerance

int32 aux_q_capacity_;

int32 main_q_capacity_;

// Hashmap capacity. Multiple of max_tokens_per_frame

int32 hashmap_capacity_;

// The first index of all the following vectors (or vector<vector>)

// is the ChannelId. e.g., to get the number of frames decoded in channel 2,

// look into num_frames_decoded_[2].

// Keep track of the number of frames decoded in the current file.

std::vector<int32> num_frames_decoded_;

// Offsets of each frame in h_all_tokens_info_

std::vector<std::vector<int32>> frame_offsets_;

// Size of the main_q at the end of the emitting stage

std::vector<int32> main_q_emitting_end_;

// Data storage. We store on host what we will need in

// GetRawLattice/GetBestPath

std::vector<std::vector<InfoToken>> h_all_tokens_info_;

std::vector<std::vector<CostType>> h_all_tokens_acoustic_cost_;

std::vector<std::vector<InfoToken>> h_all_tokens_extra_prev_tokens_;

std::vector<std::vector<float2>>

h_all_tokens_extra_prev_tokens_extra_and_acoustic_cost_;

// Pinned memory arrays. Used for the DeviceToHost copies

float2 *h_extra_and_acoustic_cost_concat__,

*d_extra_and_acoustic_cost_concat__;

InfoToken *h_infotoken_concat_, *d_infotoken_concat_;

CostType *h_acoustic_cost_concat_, *d_acoustic_cost_concat_;

InfoToken *h_extra_prev_tokens_concat_;

int2 *h_list_final_tokens_in_main_q_;

// Offsets used in MoveConcatenatedCopyToVector

std::vector<int32> h_main_q_end_lane_offsets_,

h_emitting_main_q_end_lane_offsets_;

std::vector<int32> h_n_extra_prev_tokens_lane_offsets_;

// Used when calling GetBestCost

std::vector<std::pair<int32, CostType>> argmins_;

std::vector<bool> has_reached_final_;

std::vector<std::vector<std::pair<int32, CostType>>>

list_finals_token_idx_and_cost_;

// GetRawLattice helper

// Data used when building the lattice in GetRawLattice

// few typedef to make GetRawLattice easier to understand

// Returns a unique id for each (iframe, fst_state) pair

// We need to be able to quickly identity a (iframe, fst_state) ID

//

// A lattice state is defined by the pair (iframe, fst_state)

// A token is associated to a lattice state (iframe, token.next_state)

// Multiple token in the same frame can be associated to the same lattice

// state

// (they all go to the same token.next_state)

// We need to quickly identify what is the lattice state of a token.

// We are able to do that through GetLatticeStateInternalId(token),

// which returns the internal unique ID for each lattice state for a token

//

// When we build the output lattice, we a get new lattice state

// output_lattice_state = fst_out->AddState()

// We call this one OutputLatticeState

// The conversion between the two is done through maps

// [curr|prev]_f_raw_lattice_state_

typedef int32 LatticeStateInternalId;

typedef StateId OutputLatticeState;

typedef int32 TokenId;

LatticeStateInternalId GetLatticeStateInternalId(int32 total_ntokens,

TokenId token_idx,

InfoToken token);

// Keeping track of a variety of info about states in the lattice

// - token_extra_cost. A path going from the current lattice_state to the

// end has an extra cost

// compared to the best path (which has an extra cost of 0).

// token_extra_cost is the minimum of the extra_cost of all paths going from

// the current lattice_state

// to the final frame.

// - fst_lattice_state is the StateId of the lattice_state in fst_out (in

// the output lattice). lattice_state is an internal state used in

// GetRawLattice.

// - is_state_closed is true if the token_extra_cost has been read by

// another token. It means that the

// token_extra_cost value has been used, and if we modify token_extra_cost

// again, we may need to recompute the current frame (so that everyone uses

// the latest

// token_extra_cost value)

struct RawLatticeState {

CostType token_extra_cost;

OutputLatticeState fst_lattice_state;

bool is_state_closed;

};

// [prev|curr]_f_raw_lattice_state_

// Used to get information about a lattice state (i.e. a (iframe, fst_state)

// pair)

// using its LatticeStateInternalId (its ID inside of the decoder)

// It gives us the OutputLatticeState (its ID in the output lattice)

// alongside with the extra_cost of that state in the lattice

// Those maps are used to build the external lattice using what we know

// internally

// Using one map per frame. We always know to which frame a token belongs.

// Using one big map slows everything down

std::unordered_map<LatticeStateInternalId, RawLatticeState>

prev_f_raw_lattice_state_, curr_f_raw_lattice_state_;

// We want the unicity of each arc_idx for one frame. Important because we

// can replay a frame (and possibly add multiple time the same arc)

std::unordered_set<int32> f_arc_idx_added_;

// When backtracking, we read tokens in the current frame (in

// q_curr_frame_todo_),

// we backtrack the associated arc, and we add the predecessor either to

// q_curr_frame_todo_ (non-emitting arc, same frame)

// or q_prev_frame_todo_ (emitting arc, source in previous frame)

std::vector<std::pair<TokenId, InfoToken>> q_curr_frame_todo_;

std::vector<std::pair<TokenId, InfoToken>> q_prev_frame_todo_;

// extra_cost_min_delta_ used in the must_replay_frame situation. Please read

// comments

// associated with must_replay_frame in GetRawLattice to understand what it

// does

CostType extra_cost_min_delta_;

// Resets the GetRawLattice datastructures for a new lattice generation

void ResetDataForGetRawLattice();

// Using the output from GetBestPath, we add the best tokens (as selected in

// GetBestCost)

// from the final frame to the output lattice. We also fill the data

// structures

// (such as q_curr_frame_todo_, or curr_f_raw_lattice_state_) accordingly

void AddFinalTokensToLattice(LaneId ilane, ChannelId ichannel,

Lattice *fst_out);

// Check if a token should be added to the lattice. If it should, then

// keep_arc will be true

void ConsiderTokenForLattice(

ChannelId ichannel, int32 iprev, int32 total_ntokens, TokenId token_idx,

OutputLatticeState fst_lattice_start, InfoToken *tok_beg,

float2 *arc_extra_cost_beg, CostType token_extra_cost,

TokenId list_prev_token_idx, int32 list_arc_idx,

InfoToken *list_prev_token, CostType *this_arc_prev_token_extra_cost,

CostType *acoustic_cost, OutputLatticeState *lattice_src_state,

bool *keep_arc, bool *dbg_found_zero);

// Add the arc to the lattice. Also updates what needs to be updated in the

// GetRawLattice datastructures.

void AddArcToLattice(int32 list_arc_idx, TokenId list_prev_token_idx,

InfoToken list_prev_token, int32 curr_frame_offset,

CostType acoustic_cost,

CostType this_arc_prev_token_extra_cost,

LatticeStateInternalId src_state_internal_id,

OutputLatticeState fst_lattice_start,

OutputLatticeState to_fst_lattice_state,

Lattice *fst_out, bool *must_replay_frame);

// Read a token information

void GetTokenRawLatticeData(TokenId token_idx, InfoToken token,

int32 total_ntokens, CostType *token_extra_cost,

OutputLatticeState *to_fst_lattice_state);

// A token is an instance of an arc. It goes to a FST state (token.next_state)

// Multiple token in the same frame can go to the same FST state.

// GetSameFSTStateTokenList

// returns that list

void GetSameFSTStateTokenList(ChannelId ichannel, InfoToken token,

InfoToken **tok_beg,

float2 **arc_extra_cost_beg, int32 *nprevs);

// Swap datastructures at the end of a frame. prev becomes curr (we go

// backward)

void SwapPrevAndCurrLatticeMap(int32 iframe, bool dbg_found_best_path);

KALDI_DISALLOW_COPY_AND_ASSIGN(CudaDecoder);