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vall_e.cpp cleanup (having to keep a map of something that can work without touching llama.cpp AND something minimally invasive, AND adhere to a C++ style that isn't mine, is making me bipolar)

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mrq 2024-12-23 14:16:16 -06:00
parent 497bdfc67b
commit a6945f981d
2 changed files with 299 additions and 245 deletions
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415
vall_e.cpp/vall_e.cpp
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@ -1,18 +1,7 @@
#include "llama-vocab.h"
#include "llama.h"
#include "encodec.h"
#define DR_WAV_IMPLEMENTATION #define DR_WAV_IMPLEMENTATION
#include "dr_wav.h" #include "vall_e.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <string>
#include <vector>
#include <array>
#include <unordered_map>
#include <iostream>
#define LLAMA_CPP_EXTENDED 1 // whether the underlying llama.cpp has some extra functions #define LLAMA_CPP_EXTENDED 1 // whether the underlying llama.cpp has some extra functions
#define LLAMA_CPP_USE_VALL_E_ARCH 1 // whether the underlying llama.cpp is to use the VALL_E arch (or using LLAMA arch) #define LLAMA_CPP_USE_VALL_E_ARCH 1 // whether the underlying llama.cpp is to use the VALL_E arch (or using LLAMA arch)
@ -21,69 +10,6 @@
#include "_llama.h" // cringe hotfix but I have to do this until llama.cpp's API exposes the tok_embd #include "_llama.h" // cringe hotfix but I have to do this until llama.cpp's API exposes the tok_embd
#endif #endif
std::vector<float> read_2d_tensor( struct ggml_tensor* tensor ) {
size_t size = tensor->ne[0] * tensor->ne[1];
std::vector<float> res( size );
auto* qtype = ggml_get_type_traits(tensor->type);
// dequantize if needed
if ( ggml_is_quantized(tensor->type) ) {
qtype->to_float(tensor->data, res.data(), res.size());
} else {
memcpy( res.data(), tensor->data, res.size() * sizeof(float) );
}
return res;
}
// stores the raw inputs to be fed
struct input_t {
std::string task = "tts";
std::string phonemes = "";
std::vector<llama_token> phn = {};
llama_token lang = 0;
llama_token rvq_l = 0;
std::vector<std::vector<llama_token>> prom = {};
std::vector<std::vector<llama_token>> resp = {};
};
/*
[(0, 256), 'text_emb.weight', 'classifiers.proj.9.weight', None],
[(256, 264), 'rvq_l_emb.weight', None, '<|RVQ:{l}|>'],
[(264, 270), 'langs_emb.weight', None, '<|lang:{lang}|>'],
[(270, 279), 'tasks_emb.weight', None, '<|task:{task}|>'],
[(279, 290), 'len_emb.weight', 'classifiers.proj.10.weight', '<|len:{id}|>'],
[(290, 291), 'tones_emb.weight', None, '<|tone:{tone}|>'],
[(291, 292), 'sep', None, '<|sep|>'],
[(292, 1316), 'proms_emb.embeddings.0.weight', None, '<|P|0|{id}|>'],
[(1316, 2340), 'proms_emb.embeddings.1.weight', None, '<|P|1|{id}|>'],
[(2340, 3364), 'proms_emb.embeddings.2.weight', None, '<|P|2|{id}|>'],
[(3364, 4388), 'proms_emb.embeddings.3.weight', None, '<|P|3|{id}|>'],
[(4388, 5412), 'proms_emb.embeddings.4.weight', None, '<|P|4|{id}|>'],
[(5412, 6436), 'proms_emb.embeddings.5.weight', None, '<|P|5|{id}|>'],
[(6436, 7460), 'proms_emb.embeddings.6.weight', None, '<|P|6|{id}|>'],
[(7460, 8484), 'proms_emb.embeddings.7.weight', None, '<|P|7|{id}|>'],
[(8484, 9509), 'resps_emb.embeddings.0.weight', 'classifiers.proj.0.weight', '<|R|AR|0:0|{id}|>'],
[(9509, 10533), 'resps_emb.embeddings.1.weight', 'classifiers.proj.1.weight', '<|R|NAR|0:1|{id}|>'],
[(10533, 11557), 'resps_emb.embeddings.2.weight', 'classifiers.proj.2.weight', '<|R|NAR|1:2|{id}|>'],
[(11557, 12581), 'resps_emb.embeddings.3.weight', 'classifiers.proj.3.weight', '<|R|NAR|2:3|{id}|>'],
[(12581, 13605), 'resps_emb.embeddings.4.weight', 'classifiers.proj.4.weight', '<|R|NAR|3:4|{id}|>'],
[(13605, 14629), 'resps_emb.embeddings.5.weight', 'classifiers.proj.5.weight', '<|R|NAR|4:5|{id}|>'],
[(14629, 15653), 'resps_emb.embeddings.6.weight', 'classifiers.proj.6.weight', '<|R|NAR|5:6|{id}|>'],
[(15653, 16677), 'resps_emb.embeddings.7.weight', 'classifiers.proj.7.weight', '<|R|NAR|6:7|{id}|>'],
[(16677, 17702), 'resps_emb.embeddings.8.weight', 'classifiers.proj.8.weight', '<|R|NAR|0:0|{id}|>']
*/
// handles all the cringe logic of slicing embeddings
struct ranges_t {
std::string name;
uint32_t start;
uint32_t end;
int32_t classifier_idx = -1;
};
ranges_t io_ranges[] = { ranges_t io_ranges[] = {
{ "text", 0, 256, 9, }, { "text", 0, 256, 9, },
{ "rvq_l", 256, 264, -1, }, { "rvq_l", 256, 264, -1, },
@ -113,94 +39,111 @@ ranges_t io_ranges[] = {
{ "resps|NAR:0: 16677, 17702, 8,0", }, { "resps|NAR:0: 16677, 17702, 8,0", },
}; };
struct embeddings_t { std::vector<float> read_2d_tensor( struct ggml_tensor* tensor ) {
int n_embd; size_t size = tensor->ne[0] * tensor->ne[1];
int n_vocab; std::vector<float> res( size );
ranges_t range;
std::vector<float> embds;
};
struct embeddings_map_t {
int n_embd = 0;
int n_vocab = 0;
// mapping auto* qtype = ggml_get_type_traits(tensor->type);
std::unordered_map<std::string, embeddings_t> mapped_embeddings; // dequantize if needed
if ( ggml_is_quantized(tensor->type) ) {
const embeddings_t& get_embeddings( const std::string& name ) { qtype->to_float(tensor->data, res.data(), res.size());
return mapped_embeddings[name]; } else {
} memcpy( res.data(), tensor->data, res.size() * sizeof(float) );
const float* get_embeddings_p( const std::string& name ) {
return mapped_embeddings[name].embds.data();
} }
int32_t get_classifier_idx( const std::string& name ) { return res;
return mapped_embeddings[name].range.classifier_idx; }
struct ggml_tensor * vall_e_get_prom_embds( llama_vall_e_userdata& userdata, int32_t idx ) {
return userdata.prom_embds[idx];
}
struct ggml_tensor * vall_e_get_resp_embds( llama_vall_e_userdata& userdata, int32_t idx ) {
return userdata.resp_embds[idx];
}
struct ggml_tensor * vall_e_get_aux_embds( llama_vall_e_userdata& userdata, int32_t idx ) {
return userdata.aux_embds[idx];
}
const embeddings_t& vall_e_inputs_map_get_embeddings( inputs_map_t& inputs_map, const std::string& name ) {
return inputs_map.embds[name];
}
const float* vall_e_inputs_map_get_embeddings_p( inputs_map_t& inputs_map, const std::string& name ) {
return inputs_map.embds[name].embds.data();
}
int32_t vall_e_inputs_map_get_classifier_idx( inputs_map_t& inputs_map, const std::string& name ) {
return inputs_map.embds[name].range.classifier_idx;
}
void vall_e_inputs_map_init( inputs_map_t& inputs_map, llama_model* model ) {
auto n_embd = llama_n_embd( model );
auto n_vocab = llama_n_vocab( model );
inputs_map.n_embd = n_embd;
inputs_map.n_vocab = n_vocab;
auto& userdata = *llama_get_vall_e_userdata( model );
// to-do: figure a nicer way to do this
#if LLAMA_CPP_USE_VALL_E_ARCH
inputs_map.embds["text"] = { n_embd, 0, { "text", 0, 0, 9, }, read_2d_tensor(vall_e_get_aux_embds(userdata, 0)) };
inputs_map.embds["rvq_l"] = { n_embd, 0, { "rvq_l", 0, 0, -1, }, read_2d_tensor(vall_e_get_aux_embds(userdata, 1)) };
inputs_map.embds["lang"] = { n_embd, 0, { "lang", 0, 0, -1, }, read_2d_tensor(vall_e_get_aux_embds(userdata, 2)) };
inputs_map.embds["task"] = { n_embd, 0, { "task", 0, 0, -1, }, read_2d_tensor(vall_e_get_aux_embds(userdata, 3)) };
inputs_map.embds["len"] = { n_embd, 0, { "len", 0, 0, 10, }, read_2d_tensor(vall_e_get_aux_embds(userdata, 4)) };
inputs_map.embds["tone"] = { n_embd, 0, { "tone", 0, 0, -1, }, read_2d_tensor(vall_e_get_aux_embds(userdata, 5)) };
inputs_map.embds["sep"] = { n_embd, 0, { "sep", 0, 0, -1, }, read_2d_tensor(vall_e_get_aux_embds(userdata, 6)) };
inputs_map.embds["prom|0"] = { n_embd, 0, { "prom|0", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 0)) };
inputs_map.embds["prom|1"] = { n_embd, 0, { "prom|1", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 1)) };
inputs_map.embds["prom|2"] = { n_embd, 0, { "prom|2", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 2)) };
inputs_map.embds["prom|3"] = { n_embd, 0, { "prom|3", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 3)) };
inputs_map.embds["prom|4"] = { n_embd, 0, { "prom|4", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 4)) };
inputs_map.embds["prom|5"] = { n_embd, 0, { "prom|5", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 5)) };
inputs_map.embds["prom|6"] = { n_embd, 0, { "prom|6", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 6)) };
inputs_map.embds["prom|7"] = { n_embd, 0, { "prom|7", 0, 0, -1, }, read_2d_tensor(vall_e_get_prom_embds(userdata, 7)) };
inputs_map.embds["resps|AR:0:0"] = { n_embd, 0, { "resps|AR:0:0", 0, 0, 0, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 0)) };
inputs_map.embds["resps|NAR:0:1"] = { n_embd, 0, { "resps|NAR:0:1", 0, 0, 1, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 1)) };
inputs_map.embds["resps|NAR:1:2"] = { n_embd, 0, { "resps|NAR:1:2", 0, 0, 2, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 2)) };
inputs_map.embds["resps|NAR:2:3"] = { n_embd, 0, { "resps|NAR:2:3", 0, 0, 3, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 3)) };
inputs_map.embds["resps|NAR:3:4"] = { n_embd, 0, { "resps|NAR:3:4", 0, 0, 4, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 4)) };
inputs_map.embds["resps|NAR:4:5"] = { n_embd, 0, { "resps|NAR:4:5", 0, 0, 5, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 5)) };
inputs_map.embds["resps|NAR:5:6"] = { n_embd, 0, { "resps|NAR:5:6", 0, 0, 6, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 6)) };
inputs_map.embds["resps|NAR:6:7"] = { n_embd, 0, { "resps|NAR:6:7", 0, 0, 7, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 7)) };
inputs_map.embds["resps|NAR:0:0"] = { n_embd, 0, { "resps|NAR:0:0", 0, 0, 8, }, read_2d_tensor(vall_e_get_resp_embds(userdata, 8)) };
// update values
for ( auto& pair : inputs_map.embds ) {
auto& k = pair.first;
auto& v = pair.second;
auto& embds = v.embds;
v.n_vocab = embds.size() / n_embd;
v.range.end = v.n_vocab;
} }
#else
void init( llama_model* model ) { #if LLAMA_CPP_EXTENDED
this->n_embd = llama_n_embd( model ); auto* tensor = llama_get_embedding_weights( model );
this->n_vocab = llama_n_vocab( model ); #else
auto* tensor = model->tok_embd;
#endif
// to-do: figure a nicer way to do this // prepare slices
#if LLAMA_CPP_USE_VALL_E_ARCH std::vector<float> raw_embeddings = read_2d_tensor( tensor );
mapped_embeddings["text"] = { n_embd, 0, { "text", 0, 0, 9, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 0)) }; for ( auto& range : io_ranges ) {
mapped_embeddings["rvq_l"] = { n_embd, 0, { "rvq_l", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 1)) }; inputs_map.embds[range.name] = {
mapped_embeddings["lang"] = { n_embd, 0, { "lang", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 2)) }; n_embd,
mapped_embeddings["task"] = { n_embd, 0, { "task", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 3)) }; range.end - range.start,
mapped_embeddings["len"] = { n_embd, 0, { "len", 0, 0, 10, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 4)) }; range,
mapped_embeddings["tone"] = { n_embd, 0, { "tone", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 5)) }; std::vector<float>( raw_embeddings.data() + range.start, raw_embeddings.data() + range.end )
mapped_embeddings["sep"] = { n_embd, 0, { "sep", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 6)) }; };
mapped_embeddings["prom|0"] = { n_embd, 0, { "prom|0", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 0)) };
mapped_embeddings["prom|1"] = { n_embd, 0, { "prom|1", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 1)) };
mapped_embeddings["prom|2"] = { n_embd, 0, { "prom|2", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 2)) };
mapped_embeddings["prom|3"] = { n_embd, 0, { "prom|3", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 3)) };
mapped_embeddings["prom|4"] = { n_embd, 0, { "prom|4", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 4)) };
mapped_embeddings["prom|5"] = { n_embd, 0, { "prom|5", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 5)) };
mapped_embeddings["prom|6"] = { n_embd, 0, { "prom|6", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 6)) };
mapped_embeddings["prom|7"] = { n_embd, 0, { "prom|7", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_prom_embds(model, 7)) };
mapped_embeddings["resps|AR:0:0"] = { n_embd, 0, { "resps|AR:0:0", 0, 0, 0, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 0)) };
mapped_embeddings["resps|NAR:0:1"] = { n_embd, 0, { "resps|NAR:0:1", 0, 0, 1, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 1)) };
mapped_embeddings["resps|NAR:1:2"] = { n_embd, 0, { "resps|NAR:1:2", 0, 0, 2, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 2)) };
mapped_embeddings["resps|NAR:2:3"] = { n_embd, 0, { "resps|NAR:2:3", 0, 0, 3, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 3)) };
mapped_embeddings["resps|NAR:3:4"] = { n_embd, 0, { "resps|NAR:3:4", 0, 0, 4, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 4)) };
mapped_embeddings["resps|NAR:4:5"] = { n_embd, 0, { "resps|NAR:4:5", 0, 0, 5, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 5)) };
mapped_embeddings["resps|NAR:5:6"] = { n_embd, 0, { "resps|NAR:5:6", 0, 0, 6, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 6)) };
mapped_embeddings["resps|NAR:6:7"] = { n_embd, 0, { "resps|NAR:6:7", 0, 0, 7, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 7)) };
mapped_embeddings["resps|NAR:0:0"] = { n_embd, 0, { "resps|NAR:0:0", 0, 0, 8, }, read_2d_tensor(llama_get_vall_e_resp_embds(model, 8)) };
// update values
for ( auto& pair : mapped_embeddings ) {
auto& k = pair.first;
auto& v = pair.second;
auto& embds = v.embds;
v.n_vocab = embds.size() / n_embd;
v.range.end = v.n_vocab;
}
#else
#if LLAMA_CPP_EXTENDED
auto* tensor = llama_get_embedding_weights( model );
#else
auto* tensor = model->tok_embd;
#endif
// prepare slices
std::vector<float> raw_embeddings = read_2d_tensor( tensor );
for ( auto& range : io_ranges ) {
mapped_embeddings[range.name] = {
n_embd,
range.end - range.start,
range,
std::vector<float>( raw_embeddings.data() + range.start, raw_embeddings.data() + range.end )
};
}
#endif
} }
}; #endif
}
// maps embeddings easily // maps embeddings easily
std::vector<std::vector<float>> map_embeddings( const std::vector<llama_token>& tokens, int n_embd, const float* embds ) { std::vector<std::vector<float>> map_embeddings( const std::vector<llama_token>& tokens, int n_embd, const float* embds ) {
@ -213,7 +156,7 @@ std::vector<std::vector<float>> map_embeddings( const std::vector<llama_token>&
// handles adding either a token OR the embedding of that token into the batch // handles adding either a token OR the embedding of that token into the batch
// this really, really helps avoid needing to abuse the tokenizer // this really, really helps avoid needing to abuse the tokenizer
void batch_add( llama_batch& batch, llama_token id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids = {0} ) { void batch_add( llama_batch& batch, llama_token id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids ) {
GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded"); GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded");
// insert raw embedding instead // insert raw embedding instead
@ -339,23 +282,8 @@ std::vector<float> decode_audio( struct encodec_context* ectx, const std::vector
return std::vector<float>(audio_data, audio_data + audio_size); return std::vector<float>(audio_data, audio_data + audio_size);
} }
const int EMBEDDING_MODE_PROM = 0;
const int EMBEDDING_MODE_RESP_AR_NAR = 1;
const int EMBEDDING_MODE_RESP_NAR_LEN = 2;
const int INFERENCE_MODE_LEN = 0;
const int INFERENCE_MODE_AR = 1;
const int INFERENCE_MODE_NAR_DEMASK = 2;
const int INFERENCE_MODE_NAR = 3;
const int MODALITY_AR_NAR = 0;
const int MODALITY_NAR_LEN = 1;
const int MAX_DURATION = 75 * 12;
const int CTX_SIZE = 2048;
// sums embeddings over a 2D "tensor" // sums embeddings over a 2D "tensor"
std::vector<std::vector<float>> sum_embeddings( const std::vector<std::vector<llama_token>>& input, int n_embd, int rvq_l, const float** embds, int mode = EMBEDDING_MODE_PROM ) { std::vector<std::vector<float>> sum_embeddings( const std::vector<std::vector<llama_token>>& input, int n_embd, int rvq_l, const float** embds, int mode ) {
std::vector<std::vector<float>> res( input.size() ); std::vector<std::vector<float>> res( input.size() );
res.resize( input[0].size() ); res.resize( input[0].size() );
for ( auto& e : res ) e.resize( n_embd ); for ( auto& e : res ) e.resize( n_embd );
@ -383,38 +311,38 @@ std::vector<std::vector<float>> sum_embeddings( const std::vector<std::vector<ll
return res; return res;
} }
void fill_batch( llama_batch& batch, input_t& input, embeddings_map_t& embeddings_map, int mode ) { void fill_batch( llama_batch& batch, input_t& input, inputs_map_t& inputs_map, int mode ) {
// keeps track of the position for each sequence // keeps track of the position for each sequence
size_t pos = 0; size_t pos = 0;
auto n_embd = embeddings_map.n_embd; auto n_embd = inputs_map.n_embd;
const float* text_embds = embeddings_map.get_embeddings_p("text"); const float* text_embds = vall_e_inputs_map_get_embeddings_p(inputs_map, "text");
const float* rvq_l_embds = embeddings_map.get_embeddings_p("rvq_l"); const float* rvq_l_embds = vall_e_inputs_map_get_embeddings_p(inputs_map, "rvq_l");
const float* lang_embds = embeddings_map.get_embeddings_p("lang"); const float* lang_embds = vall_e_inputs_map_get_embeddings_p(inputs_map, "lang");
const float* task_embds = embeddings_map.get_embeddings_p("task"); const float* task_embds = vall_e_inputs_map_get_embeddings_p(inputs_map, "task");
const float* len_embds = embeddings_map.get_embeddings_p("len"); const float* len_embds = vall_e_inputs_map_get_embeddings_p(inputs_map, "len");
const float* tone_embds = embeddings_map.get_embeddings_p("tone"); const float* tone_embds = vall_e_inputs_map_get_embeddings_p(inputs_map, "tone");
const float* sep_embds = embeddings_map.get_embeddings_p("sep"); const float* sep_embds = vall_e_inputs_map_get_embeddings_p(inputs_map, "sep");
const float* prom_embds[] = { const float* prom_embds[] = {
embeddings_map.get_embeddings_p("prom|0"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|0"),
embeddings_map.get_embeddings_p("prom|1"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|1"),
embeddings_map.get_embeddings_p("prom|2"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|2"),
embeddings_map.get_embeddings_p("prom|3"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|3"),
embeddings_map.get_embeddings_p("prom|4"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|4"),
embeddings_map.get_embeddings_p("prom|5"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|5"),
embeddings_map.get_embeddings_p("prom|6"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|6"),
embeddings_map.get_embeddings_p("prom|7"), vall_e_inputs_map_get_embeddings_p(inputs_map, "prom|7"),
}; };
const float* resp_embds[] = { const float* resp_embds[] = {
embeddings_map.get_embeddings_p("resps|AR:0:0"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|AR:0:0"),
embeddings_map.get_embeddings_p("resps|NAR:0:1"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:0:1"),
embeddings_map.get_embeddings_p("resps|NAR:1:2"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:1:2"),
embeddings_map.get_embeddings_p("resps|NAR:2:3"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:2:3"),
embeddings_map.get_embeddings_p("resps|NAR:3:4"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:3:4"),
embeddings_map.get_embeddings_p("resps|NAR:4:5"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:4:5"),
embeddings_map.get_embeddings_p("resps|NAR:5:6"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:5:6"),
embeddings_map.get_embeddings_p("resps|NAR:6:7"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:6:7"),
embeddings_map.get_embeddings_p("resps|NAR:0:0"), vall_e_inputs_map_get_embeddings_p(inputs_map, "resps|NAR:0:0"),
}; };
// insert text tokens // insert text tokens
@ -454,8 +382,8 @@ void fill_batch( llama_batch& batch, input_t& input, embeddings_map_t& embedding
} }
// generation code, should handle all modalities easily // generation code, should handle all modalities easily
std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama_sampler* smpl, input_t& input, embeddings_map_t& embeddings_map, int max_tokens, int mode, bool verbose = true ) { std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama_sampler* smpl, input_t& input, inputs_map_t& inputs_map, int max_tokens, int mode, bool verbose ) {
llama_batch batch = llama_batch_init( 22500, embeddings_map.n_embd, 22500 ); llama_batch batch = llama_batch_init( 22500, inputs_map.n_embd, 22500 );
// Decoding loop // Decoding loop
const auto t_main_start = ggml_time_us(); const auto t_main_start = ggml_time_us();
@ -463,7 +391,7 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
int rvq_l = input.rvq_l; int rvq_l = input.rvq_l;
llama_token stop_token = -1; llama_token stop_token = -1;
fill_batch( batch, input, embeddings_map, mode ); fill_batch( batch, input, inputs_map, mode );
// determine how many logits we need // determine how many logits we need
int n_logits = 0; int n_logits = 0;
@ -487,7 +415,7 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
ranges_t range; ranges_t range;
if ( mode == INFERENCE_MODE_AR ) { if ( mode == INFERENCE_MODE_AR ) {
auto& embeddings = embeddings_map.get_embeddings("resps|AR:0:0"); auto& embeddings = vall_e_inputs_map_get_embeddings(inputs_map, "resps|AR:0:0");
range = embeddings.range; range = embeddings.range;
embds = embeddings.embds.data(); embds = embeddings.embds.data();
stop_token = range.end - range.start - 1; stop_token = range.end - range.start - 1;
@ -504,20 +432,20 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
"resps|NAR:5:6", "resps|NAR:5:6",
"resps|NAR:6:7", "resps|NAR:6:7",
}; };
auto& embeddings = embeddings_map.get_embeddings(k_embds[rvq_l]); auto& embeddings = vall_e_inputs_map_get_embeddings(inputs_map, k_embds[rvq_l]);
range = embeddings.range; range = embeddings.range;
embds = embeddings.embds.data(); embds = embeddings.embds.data();
printf("Generating in %s (%i) mode (%i:%i)\n", "NAR", range.classifier_idx, range.start, range.end); printf("Generating in %s (%i) mode (%i:%i)\n", "NAR", range.classifier_idx, range.start, range.end);
} else if ( mode == INFERENCE_MODE_LEN ) { } else if ( mode == INFERENCE_MODE_LEN ) {
auto& embeddings = embeddings_map.get_embeddings("len"); auto& embeddings = vall_e_inputs_map_get_embeddings(inputs_map, "len");
range = embeddings.range; range = embeddings.range;
embds = embeddings.embds.data(); embds = embeddings.embds.data();
stop_token = range.end - range.start - 1; stop_token = range.end - range.start - 1;
printf("Generating in %s (%i) mode (%i:%i) (%i)\n", "len", range.classifier_idx, range.start, range.end, stop_token); printf("Generating in %s (%i) mode (%i:%i) (%i)\n", "len", range.classifier_idx, range.start, range.end, stop_token);
} else if ( mode == INFERENCE_MODE_NAR_DEMASK ) { } else if ( mode == INFERENCE_MODE_NAR_DEMASK ) {
auto& embeddings = embeddings_map.get_embeddings("resps|NAR:0:0"); auto& embeddings = vall_e_inputs_map_get_embeddings(inputs_map, "resps|NAR:0:0");
range = embeddings.range; range = embeddings.range;
embds = embeddings.embds.data(); embds = embeddings.embds.data();
@ -525,9 +453,10 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
} }
#if LLAMA_CPP_USE_VALL_E_ARCH #if LLAMA_CPP_USE_VALL_E_ARCH
llama_set_output_index( model, range.classifier_idx ); auto& userdata = *llama_get_vall_e_userdata( model );
llama_set_output_head( model, userdata.heads[range.classifier_idx] );
#endif #endif
llama_set_causal_attn( ctx, true ) ; // n_logits == 1 ); llama_set_causal_attn( ctx, n_logits == 1 );
// to-do: fix GGML_ASSERT(mask->ne[0] == a->ne[0]) // to-do: fix GGML_ASSERT(mask->ne[0] == a->ne[0])
std::vector<llama_token> output_tokens; std::vector<llama_token> output_tokens;
@ -536,7 +465,7 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
return output_tokens; return output_tokens;
} }
std::vector<llama_token> current_tokens;
// backwards iterate to start from beginning of sequence // backwards iterate to start from beginning of sequence
for ( auto i = n_logits; i > 0; --i ) { for ( auto i = n_logits; i > 0; --i ) {
// filter logits // filter logits
@ -544,20 +473,25 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
// ensures only tokens within our designated range are used // ensures only tokens within our designated range are used
#if !LLAMA_CPP_USE_VALL_E_ARCH #if !LLAMA_CPP_USE_VALL_E_ARCH
for ( auto i = 0; i < embeddings_map.n_vocab; ++i ) { for ( auto i = 0; i < inputs_map.n_vocab; ++i ) {
if ( i < range.start || i >= range.end ) logits[i] = -INFINITY; if ( i < range.start || i >= range.end ) logits[i] = -INFINITY;
} }
#endif #endif
// sample the next token // sample the next token
printf("%i: %p\n [", -i, logits );
for ( auto i = 0; i < 1025; ++i ) {
printf("%f, ", logits[i]);
}
printf("]\n");
auto t = llama_sampler_sample(smpl, ctx, -i); auto t = llama_sampler_sample(smpl, ctx, -i);
//printf("%i: [%i]: %f | %p\n", -i, t, logits[t], logits );
// offset back into range // offset back into range
#if !LLAMA_CPP_USE_VALL_E_ARCH #if !LLAMA_CPP_USE_VALL_E_ARCH
t -= range.start; t -= range.start;
#endif #endif
printf("%s: %i: %i: %i\n", __func__, i, n_decode, t);
n_decode += 1; n_decode += 1;
// is stop token // is stop token
@ -568,10 +502,17 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
} }
// store token // store token
output_tokens.emplace_back(t); current_tokens.emplace_back(t);
// update batch with token // update batch with token
batch_add( batch, t, embeddings_map.n_embd, embds, output_tokens.size(), true ); batch_add( batch, t, inputs_map.n_embd, embds, output_tokens.size(), true );
} }
printf("%s: Tokens: [", __func__);
for ( auto& token : current_tokens ) {
printf("%i, ", token);
}
printf("]\n");
output_tokens.insert(output_tokens.end(), current_tokens.begin(), current_tokens.end());
} }
const auto t_main_end = ggml_time_us(); const auto t_main_end = ggml_time_us();
@ -591,37 +532,17 @@ std::vector<llama_token> generate( llama_context* ctx, llama_model* model, llama
return output_tokens; return output_tokens;
} }
int main(int argc, char ** argv) { int main( int argc, char** argv ) {
// to-do: replace all of this with proper loading code // to-do: replace all of this with proper loading code
int32_t ngl = 0; int32_t ngl = 0;
int modality = MODALITY_NAR_LEN; int modality = MODALITY_AR_NAR;
input_t input{}; input_t input{};
embeddings_map_t embeddings_map{}; inputs_map_t inputs_map{};
// input.phonemes = "hˈɛloː ʋˈɔrlt"; // input.phonemes = "hˈɛloː ʋˈɔrlt";
input.phn = {1,22,111,100,4,37,115,169,11,2}; // <bos>hˈɛloː ʋˈɔrlt</eos> input.phn = {1,22,111,100,4,37,115,169,11,2}; // <bos>hˈɛloː ʋˈɔrlt</eos>
input.prom = { input.prom = {};
{62,835,835,835,339,395,798,537,537,537,537,222,76,989,548,65,705,375,261,375,297,503,529,571,707,346,266,862,148,496,574,115,115,438,934,339,865,876,63,40,779,461,602,794,10,220,507,869,639,705,869,917,705,893,917,705,869,938,439,175,139,506,375,529,297,705,651,238,962,461,195,441,377,581,473,795,644,626,459,981,767,670,696,73,779,257,738,1017,1019,133,133,1017,835,604,699,626,67,92,707,92,179,179,772,869,441,799,630,238,745,904,904,904,106,133,133,1017,1017,395,883,87,519,594,1002,682,996,540,186,855,430,202,347,889,61,92,542,297,67,669,571,707,346,67,359,571,707,669,604,395,1008,810,35,621,67,600,333,123,284,568,817,243,778,464,638,610,359,538,464,975,321,700,377,484,179,284,284,621,538,464,745,171,171,159,744,744,287,461,69,15,529,67,92,669,464,515,605,24,822,865,293,865,172,638,359,562,138,839,846,775,556,775,1006,917,346,312,148,331,496,646,67,314,15,705,131,855,662,287,172,85,107,519,374,450,391,609,643,778,80,287,794,794,115,785,794,461,699,519,932,522,652,262,508,902,932,932,391,769,18,507,90,442,762,610,610,669,605,35,855,56,989,863,195,464,604,257,904,632,786,951,461,239,195,878,771,146,481,146,481,434,643,917,280,67,464,115,744,744,115,115,115,819,709,63,907,359,519,996,616,682,996,616,519,762,917,841,772,568,954,600,422,893,592,464,626,86,143,615,171,744,744,196,115,821,415,521,799,654,839,644,473,592,953,523,855,738,855,855,876,1017,63,329}, input.resp = {};
{913,859,740,740,937,601,961,961,877,747,747,559,474,618,20,316,58,316,180,112,290,869,610,869,869,943,127,153,236,794,282,857,984,196,875,648,993,913,860,616,38,833,620,133,123,992,247,367,252,50,298,27,27,631,163,784,271,20,843,514,869,258,180,66,803,281,123,493,831,102,556,992,385,122,31,251,990,827,26,347,460,43,43,460,228,43,841,913,302,544,544,277,859,404,646,775,315,848,726,185,203,314,203,174,252,174,378,954,214,993,924,809,277,765,363,544,363,518,791,185,454,193,193,193,193,193,573,977,924,76,434,56,193,962,610,24,954,459,396,112,903,137,398,474,506,791,839,399,102,25,205,792,459,474,526,817,869,192,792,593,878,506,24,410,539,788,522,667,566,584,588,992,444,24,869,925,635,393,903,742,320,1023,833,136,216,924,220,24,563,630,968,96,708,24,708,127,399,364,67,740,381,981,203,248,607,744,252,996,474,582,248,527,423,25,387,94,229,775,122,474,792,367,650,371,413,448,448,784,506,795,848,298,27,526,96,905,70,693,956,1002,1002,37,747,857,993,124,193,193,193,193,732,732,732,992,447,792,929,291,289,524,451,27,27,524,202,693,374,1002,125,732,585,367,317,679,395,413,189,493,386,650,110,912,505,384,399,851,367,367,27,230,988,810,975,842,956,1002,4,551,729,956,1002,750,648,231,950,193,96,912,410,732,539,103,193,904,491,213,792,792,998,193,399,151,410,96,673,497,1002,241,833,956,630,43,399,775,732,792,792,792,792,917,750,185,812,812,700,859,841,363,833,630},
{786,36,821,937,1000,705,1016,345,345,470,165,581,95,404,95,95,1006,477,95,95,691,254,997,657,176,124,95,673,489,326,218,437,907,590,752,541,1016,821,445,563,181,555,181,345,576,190,987,0,265,997,488,12,598,687,152,108,52,95,95,71,87,945,95,997,754,488,955,694,925,82,18,1020,1006,542,788,441,325,532,246,132,560,532,947,655,653,842,732,36,36,829,36,937,989,989,752,651,87,489,677,260,789,462,95,227,986,955,95,810,624,435,280,868,832,879,863,821,829,937,168,270,489,544,909,562,957,0,593,714,675,690,626,227,794,489,489,563,489,298,269,741,249,516,360,240,516,336,93,808,1022,682,555,737,147,405,476,895,323,694,412,689,963,72,193,298,181,521,741,193,93,153,773,677,689,495,30,564,719,1020,559,940,53,53,53,929,360,971,403,1012,997,919,957,433,919,787,401,401,355,276,370,414,690,697,330,629,552,930,720,259,579,221,62,945,135,1020,626,663,401,153,997,381,830,185,587,853,207,126,66,529,410,113,997,488,431,563,488,488,719,746,790,296,843,752,790,23,984,292,41,27,120,249,124,900,358,801,227,978,95,997,997,997,371,561,86,388,52,667,601,894,545,997,498,900,494,365,852,986,95,841,664,256,18,1020,963,901,447,498,262,388,691,997,646,651,757,468,114,601,437,940,212,655,541,970,870,521,237,957,563,794,563,564,620,489,351,489,489,257,733,629,489,227,622,962,7,598,374,470,114,159,211,298,363,843,818,153,59,452,529,258,419,605,689,526,39,982,829,982,752,678,1005,312},
{673,673,919,866,762,961,52,674,528,528,675,526,12,753,297,967,661,845,482,303,338,1021,506,445,247,214,206,94,434,799,210,885,552,695,853,1022,916,762,764,721,445,434,529,999,771,708,767,498,282,736,227,150,299,12,536,767,321,561,12,530,147,530,262,325,196,990,874,997,944,875,426,12,282,571,571,282,365,534,365,424,89,388,563,222,31,1019,624,74,215,651,1018,74,956,1022,74,18,633,350,72,448,454,769,267,938,12,534,929,723,829,614,505,364,1018,1014,838,673,919,74,223,761,266,78,177,736,20,718,425,1001,366,58,874,58,153,627,312,197,801,530,767,674,196,633,327,425,376,413,1019,209,594,383,744,458,468,711,282,885,640,435,655,571,556,1020,310,116,273,116,504,633,15,736,633,448,662,612,487,345,19,612,665,556,198,778,705,403,706,31,196,197,536,805,427,339,161,241,116,504,58,945,853,734,670,424,807,19,397,175,144,419,19,221,697,68,321,800,210,824,972,712,911,362,427,694,182,651,972,863,684,887,548,806,27,627,639,432,193,103,198,436,837,366,212,125,1001,493,874,808,17,17,127,204,530,300,345,425,246,240,640,906,340,310,633,246,774,114,633,522,777,874,494,577,353,939,571,693,857,722,530,521,354,492,735,214,806,483,736,530,118,234,536,177,132,522,349,259,436,973,528,414,224,762,212,854,744,271,568,127,323,736,304,499,499,78,536,736,805,232,126,468,566,611,52,339,450,258,157,602,594,854,602,599,82,124,472,563,666,174,936,818,66,758,627,52,350,999,734,215,919,1018,874,885},
{528,448,646,190,222,884,939,907,907,673,413,786,527,517,710,449,119,531,565,762,531,501,522,246,162,871,8,594,206,937,462,712,862,151,103,261,882,990,1007,314,683,864,693,812,319,786,107,531,31,342,632,460,269,429,531,531,717,417,321,671,1015,152,467,863,285,875,941,417,475,825,596,957,117,460,162,162,117,630,735,527,272,558,38,39,605,375,39,900,862,646,712,804,622,963,407,93,828,796,306,415,70,667,371,531,1000,411,710,162,812,381,673,498,691,884,928,712,528,48,630,24,593,901,973,579,722,75,139,909,919,328,764,393,777,753,512,577,175,577,512,922,834,863,30,69,94,68,616,691,835,335,486,345,306,374,732,938,580,311,715,495,527,1008,306,369,663,512,369,320,360,80,42,1021,1021,1021,175,568,526,362,320,317,488,613,937,548,966,545,596,177,306,480,522,577,512,512,638,1008,82,100,696,89,714,531,639,460,679,718,492,509,492,624,460,572,531,306,19,473,915,558,285,319,713,1018,381,877,667,425,905,43,437,632,634,324,306,207,324,303,48,69,467,39,902,599,3,617,465,78,918,459,1009,427,751,145,531,349,356,1021,157,507,780,624,165,507,144,270,94,414,899,379,947,994,853,107,586,652,877,92,19,91,188,544,624,470,503,513,13,192,563,145,531,618,743,470,62,701,499,436,679,505,198,959,3,766,839,437,491,395,1021,512,306,512,356,851,1021,1021,78,690,856,735,286,280,4,1008,369,359,309,651,864,561,170,692,952,877,520,959,306,37,1021,31,236,162,773,522,254,446,606,691,804,882,58,974},
{1011,939,881,881,140,937,724,724,937,1011,381,229,965,251,745,69,305,206,566,813,503,116,940,127,353,621,57,779,595,744,755,530,701,862,760,443,293,768,156,281,960,504,327,979,55,790,545,953,830,759,667,485,861,63,485,55,898,581,520,49,99,651,940,945,685,621,728,487,650,530,934,378,522,522,522,996,534,522,739,534,378,543,94,602,390,948,692,692,41,41,768,412,982,692,692,774,176,791,526,497,57,940,542,685,694,916,813,890,357,193,430,863,929,412,412,903,140,763,465,707,569,925,859,985,24,411,835,298,293,791,837,460,182,296,137,474,809,111,376,1021,111,490,111,938,542,578,477,506,57,385,300,873,240,104,667,204,515,834,24,125,113,980,111,997,859,997,376,193,490,824,511,799,719,575,451,575,251,222,630,429,920,788,300,993,641,154,816,940,618,130,940,462,823,955,1001,569,508,632,2,903,399,333,709,489,726,932,725,777,970,843,717,940,211,534,274,161,392,103,31,462,813,985,638,213,352,219,236,381,287,111,87,818,953,112,336,980,1016,72,960,426,238,60,9,487,665,129,24,24,162,312,411,111,157,473,466,222,940,341,55,457,712,179,451,111,831,918,826,814,940,30,468,240,207,389,923,186,95,300,876,679,576,543,582,111,227,312,112,545,747,378,165,158,610,601,425,238,704,630,124,644,949,982,297,868,569,24,57,465,24,859,111,24,752,775,24,647,465,495,57,24,57,227,907,296,581,843,1013,514,555,319,937,347,478,186,684,15,241,534,369,381,846,578,314,711,814,435,41,986,673,991},
{485,748,562,562,485,380,834,997,78,963,755,142,978,135,362,421,217,79,530,1012,972,946,127,587,838,818,456,548,424,479,944,650,694,447,391,616,938,908,206,259,998,292,818,128,353,273,566,796,333,146,110,986,571,451,166,229,421,300,911,689,329,145,287,273,542,808,301,491,0,278,825,442,0,100,818,826,66,904,642,566,135,305,999,993,905,485,755,782,365,977,485,1015,570,1002,755,169,967,36,721,1019,273,931,273,166,216,31,346,946,32,290,362,828,464,748,782,1002,1015,755,1014,100,315,777,549,177,882,110,603,975,531,608,67,1011,950,465,368,416,798,941,635,602,553,300,200,644,498,325,786,734,342,222,403,1,716,175,899,273,40,333,999,74,54,644,408,976,407,631,577,338,435,612,333,273,162,709,882,555,384,995,173,459,442,72,72,200,72,711,219,282,716,442,431,801,976,130,622,72,582,384,516,772,0,440,1001,249,1,953,65,945,438,249,511,561,205,507,821,998,427,746,290,544,426,693,999,190,214,167,219,534,166,325,975,414,326,326,268,679,991,418,868,445,632,160,380,890,346,315,806,258,806,486,326,797,471,18,790,33,66,63,66,224,38,599,599,110,801,761,18,936,230,253,171,393,774,887,887,403,466,495,524,261,666,256,687,759,263,713,185,454,242,988,185,161,911,430,86,550,439,327,527,671,782,383,916,590,315,806,583,465,785,321,315,421,856,66,352,0,634,540,362,948,185,16,224,372,694,259,648,87,733,659,603,67,269,901,66,566,173,705,746,566,911,10,743,860,78,782,1002,755,389,175},
{948,948,975,975,948,322,672,639,902,55,916,439,498,389,407,682,451,401,386,440,499,348,736,891,603,762,783,407,886,76,543,699,137,458,639,253,63,475,55,436,502,888,542,131,524,167,738,131,907,29,378,545,227,382,478,399,218,872,917,202,330,2,371,264,667,355,1016,768,590,408,463,542,214,202,715,891,840,297,509,689,290,439,672,714,528,940,1019,534,975,475,1019,835,975,558,975,981,330,635,96,858,606,627,367,191,191,669,40,873,359,267,701,426,210,1012,899,975,475,1012,610,6,300,749,231,616,877,631,720,574,551,398,503,789,684,664,390,277,150,990,823,190,971,903,175,863,316,965,988,988,800,612,336,506,242,847,389,939,415,202,83,317,2,153,365,363,57,2,891,965,300,754,763,426,555,621,303,415,367,902,829,741,119,380,902,25,884,439,822,49,76,760,566,316,249,555,774,955,834,309,859,173,935,812,682,586,141,606,197,131,644,631,913,586,202,117,810,884,76,592,754,531,586,925,649,583,145,816,821,283,871,1017,316,377,646,339,201,76,780,76,976,217,38,598,977,617,825,833,49,231,749,749,633,205,231,271,50,249,684,555,982,526,895,288,22,57,722,996,260,1018,110,833,644,738,648,468,798,297,769,282,197,402,465,510,194,930,182,909,749,986,187,187,917,38,38,985,985,988,815,878,814,459,237,768,781,649,683,749,934,729,463,181,625,231,917,96,499,839,720,439,842,205,808,338,617,681,326,446,905,346,647,533,49,728,147,432,846,536,586,611,49,879,872,893,859,859,961,989,975,701,495,65},
};
input.resp = {
/*
[922,738,461,341,341,10,416,416,416,416,346,346,346,346,346,484,484,484,484,484,484,333,442,442,359,359,359,459,459,975,975,626,626,626,626,626,610,359,359,359,359,359,359,359,359,359,610,610,442,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,90,638,638,638,638,975,975,672,875,63,144],
[993,700,384,213,794,10,305,778,58,225,118,260,768,768,260,474,903,732,70,992,447,70,1000,665,848,379,485,934,181,795,438,298,688,324,934,756,395,795,110,328,343,172,768,871,593,355,396,783,24,24,911,20,27,562,697,616,668,27,27,755,20,505,248,79,822,461,197,156,27,492,151,1013,669,669,562],
[626,989,936,488,511,624,997,112,112,648,210,650,563,650,41,41,490,920,977,986,920,927,131,167,167,968,346,168,167,168,120,355,766,599,712,390,558,810,948,332,332,867,994,346,955,392,920,452,576,346,52,254,52,307,897,307,968,920,167,563,167,167,167,968,167,488,968,488,1001,938,563,741,432,566,758],
[916,874,798,212,496,751,620,616,982,745,975,890,890,141,141,321,321,214,899,42,151,722,310,971,774,35,627,995,27,43,248,248,595,774,942,352,810,35,384,340,654,639,89,214,737,197,657,45,622,321,337,19,483,679,938,938,682,938,938,141,938,310,114,724,116,327,372,607,607,310,204,713,762,853,853],
[528,222,992,727,536,191,202,483,306,568,533,577,398,533,202,24,753,753,739,739,643,513,4,324,369,66,447,201,66,802,66,957,665,526,602,749,483,447,193,853,531,201,201,71,888,202,66,66,650,228,533,102,639,513,533,531,533,471,344,566,201,639,471,639,732,594,464,308,116,533,116,174,959,621,539],
[692,632,478,375,910,857,775,503,503,193,717,548,344,717,55,808,162,112,112,112,543,582,847,712,691,679,427,940,369,475,153,526,729,269,323,721,526,211,191,192,685,844,731,813,914,545,582,712,925,916,375,111,340,162,844,940,844,162,844,990,111,491,232,582,491,582,618,121,1020,664,670,254,315,438,723],
[365,908,896,819,206,153,515,471,75,79,664,145,145,801,135,321,79,216,233,223,79,66,724,517,135,474,818,818,105,892,971,337,818,19,932,981,469,135,163,75,135,818,999,555,135,710,256,105,590,31,539,1003,517,130,445,40,549,130,859,385,1003,1003,549,33,286,932,329,774,321,664,686,16,834,703,290],
[899,237,832,748,425,121,460,872,391,586,857,215,306,76,306,554,187,57,482,406,802,555,710,895,448,517,506,316,18,772,779,697,855,1005,792,96,402,96,517,775,506,938,114,986,986,503,749,984,524,527,506,749,463,490,188,374,506,49,537,188,494,900,526,524,524,500,500,345,630,338,982,761,700,598,749],
*/
};
std::string vall_e_model_path = "./data/vall_e.gguf"; std::string vall_e_model_path = "./data/vall_e.gguf";
std::string encodec_model_path = "./data/encodec.bin"; std::string encodec_model_path = "./data/encodec.bin";
@ -662,7 +583,11 @@ int main(int argc, char ** argv) {
llama_sampler * smpl_ar = llama_sampler_chain_init(sparams); llama_sampler * smpl_ar = llama_sampler_chain_init(sparams);
llama_sampler * smpl_nar = llama_sampler_chain_init(sparams); llama_sampler * smpl_nar = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl_ar, llama_sampler_init_temp (1.0)); llama_sampler_chain_add(smpl_ar, llama_sampler_init_top_k(0));
llama_sampler_chain_add(smpl_ar, llama_sampler_init_top_p(1.0, 1));
llama_sampler_chain_add(smpl_ar, llama_sampler_init_temp (1.0));
llama_sampler_chain_add(smpl_ar, llama_sampler_init_dist (1130));
llama_sampler_chain_add(smpl_nar, llama_sampler_init_greedy()); llama_sampler_chain_add(smpl_nar, llama_sampler_init_greedy());
struct encodec_context* ectx = encodec_load_model(encodec_model_path.c_str(), 0, ngl); struct encodec_context* ectx = encodec_load_model(encodec_model_path.c_str(), 0, ngl);
@ -685,7 +610,7 @@ int main(int argc, char ** argv) {
auto n_vocab = llama_n_vocab( model ); auto n_vocab = llama_n_vocab( model );
// grab input embeddings // grab input embeddings
embeddings_map.init( model ); vall_e_inputs_map_init( inputs_map, model );
// tokenize phonemes // tokenize phonemes
// to-do: make this work, the vocab does not work // to-do: make this work, the vocab does not work
@ -707,10 +632,10 @@ int main(int argc, char ** argv) {
// NAR-len demasking // NAR-len demasking
if ( modality == MODALITY_NAR_LEN ) { if ( modality == MODALITY_NAR_LEN ) {
// inference len // inference len
int len = 290; // 0; int len = 0;
if ( !len ) { if ( !len ) {
input.task = "len"; input.task = "len";
output_tokens = generate( ctx, model, smpl_nar, input, embeddings_map, 5, INFERENCE_MODE_LEN ); output_tokens = generate( ctx, model, smpl_nar, input, inputs_map, 5, INFERENCE_MODE_LEN );
{ {
int digit = 1; int digit = 1;
for (int i = output_tokens.size() - 1; i >= 0; i--) { for (int i = output_tokens.size() - 1; i >= 0; i--) {
@ -732,7 +657,7 @@ int main(int argc, char ** argv) {
input.task = "tts"; input.task = "tts";
for ( auto l = 0; l < 8; ++l ) { for ( auto l = 0; l < 8; ++l ) {
input.rvq_l = l; input.rvq_l = l;
output_tokens = generate( ctx, model, smpl_nar, input, embeddings_map, 5, l == 0 ? INFERENCE_MODE_NAR_DEMASK : INFERENCE_MODE_NAR ); output_tokens = generate( ctx, model, smpl_nar, input, inputs_map, 5, l == 0 ? INFERENCE_MODE_NAR_DEMASK : INFERENCE_MODE_NAR );
input.resp.emplace_back( output_tokens ); input.resp.emplace_back( output_tokens );
} }
// AR+NAR // AR+NAR
@ -740,7 +665,7 @@ int main(int argc, char ** argv) {
input.task = "tts"; input.task = "tts";
for ( auto l = 0; l < 8; ++l ) { for ( auto l = 0; l < 8; ++l ) {
input.rvq_l = l; input.rvq_l = l;
output_tokens = generate( ctx, model, l == 0 ? smpl_ar : smpl_nar, input, embeddings_map, l == 0 ? MAX_DURATION : 1, l == 0 ? INFERENCE_MODE_AR : INFERENCE_MODE_NAR ); output_tokens = generate( ctx, model, l == 0 ? smpl_ar : smpl_nar, input, inputs_map, l == 0 ? MAX_DURATION : 1, l == 0 ? INFERENCE_MODE_AR : INFERENCE_MODE_NAR );
input.resp.emplace_back( output_tokens ); input.resp.emplace_back( output_tokens );
} }
} }

129
vall_e.cpp/vall_e.h Normal file
View File

@ -0,0 +1,129 @@
#pragma once
#include "llama-vocab.h"
#include "llama.h"
#include "encodec.h"
#include "dr_wav.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <string>
#include <vector>
#include <array>
#include <unordered_map>
#include <iostream>
// to-do: copy over import/export stuff from engine project (because I don't remember how I set it up in <uf/config.h>)
#define VALL_E_API
// to-do: clean up spaghetti enums
const int EMBEDDING_MODE_PROM = 0;
const int EMBEDDING_MODE_RESP_AR_NAR = 1;
const int EMBEDDING_MODE_RESP_NAR_LEN = 2;
const int INFERENCE_MODE_LEN = 0;
const int INFERENCE_MODE_AR = 1;
const int INFERENCE_MODE_NAR_DEMASK = 2;
const int INFERENCE_MODE_NAR = 3;
const int MODALITY_AR_NAR = 0;
const int MODALITY_NAR_LEN = 1;
const int MAX_DURATION = 75 * 12;
const int CTX_SIZE = 2048;
// stores the raw inputs to be fed
struct input_t {
std::string task = "tts";
std::string phonemes = "";
std::vector<llama_token> phn = {};
llama_token lang = 0;
llama_token rvq_l = 0;
std::vector<std::vector<llama_token>> prom = {};
std::vector<std::vector<llama_token>> resp = {};
};
// reference mapping from vall_e.export.py
/*
[(0, 256), 'text_emb.weight', 'classifiers.proj.9.weight', None],
[(256, 264), 'rvq_l_emb.weight', None, '<|RVQ:{l}|>'],
[(264, 270), 'langs_emb.weight', None, '<|lang:{lang}|>'],
[(270, 279), 'tasks_emb.weight', None, '<|task:{task}|>'],
[(279, 290), 'len_emb.weight', 'classifiers.proj.10.weight', '<|len:{id}|>'],
[(290, 291), 'tones_emb.weight', None, '<|tone:{tone}|>'],
[(291, 292), 'sep', None, '<|sep|>'],
[(292, 1316), 'proms_emb.embeddings.0.weight', None, '<|P|0|{id}|>'],
[(1316, 2340), 'proms_emb.embeddings.1.weight', None, '<|P|1|{id}|>'],
[(2340, 3364), 'proms_emb.embeddings.2.weight', None, '<|P|2|{id}|>'],
[(3364, 4388), 'proms_emb.embeddings.3.weight', None, '<|P|3|{id}|>'],
[(4388, 5412), 'proms_emb.embeddings.4.weight', None, '<|P|4|{id}|>'],
[(5412, 6436), 'proms_emb.embeddings.5.weight', None, '<|P|5|{id}|>'],
[(6436, 7460), 'proms_emb.embeddings.6.weight', None, '<|P|6|{id}|>'],
[(7460, 8484), 'proms_emb.embeddings.7.weight', None, '<|P|7|{id}|>'],
[(8484, 9509), 'resps_emb.embeddings.0.weight', 'classifiers.proj.0.weight', '<|R|AR|0:0|{id}|>'],
[(9509, 10533), 'resps_emb.embeddings.1.weight', 'classifiers.proj.1.weight', '<|R|NAR|0:1|{id}|>'],
[(10533, 11557), 'resps_emb.embeddings.2.weight', 'classifiers.proj.2.weight', '<|R|NAR|1:2|{id}|>'],
[(11557, 12581), 'resps_emb.embeddings.3.weight', 'classifiers.proj.3.weight', '<|R|NAR|2:3|{id}|>'],
[(12581, 13605), 'resps_emb.embeddings.4.weight', 'classifiers.proj.4.weight', '<|R|NAR|3:4|{id}|>'],
[(13605, 14629), 'resps_emb.embeddings.5.weight', 'classifiers.proj.5.weight', '<|R|NAR|4:5|{id}|>'],
[(14629, 15653), 'resps_emb.embeddings.6.weight', 'classifiers.proj.6.weight', '<|R|NAR|5:6|{id}|>'],
[(15653, 16677), 'resps_emb.embeddings.7.weight', 'classifiers.proj.7.weight', '<|R|NAR|6:7|{id}|>'],
[(16677, 17702), 'resps_emb.embeddings.8.weight', 'classifiers.proj.8.weight', '<|R|NAR|0:0|{id}|>']
*/
// handles all the cringe logic of slicing embeddings
struct ranges_t {
std::string name;
uint32_t start;
uint32_t end;
int32_t classifier_idx = -1;
};
// stores embeddings + metadata for an embedding range
struct embeddings_t {
int32_t n_embd = 0;
int32_t n_vocab = 0;
ranges_t range = {};
std::vector<float> embds = {};
};
// stores the mappings between tokens, input embeddings, and output heads
struct inputs_map_t {
int32_t n_embd = 0;
int32_t n_vocab = 0;
// mapping
std::unordered_map<std::string, embeddings_t> embds = {};
};
// helper tensor functions
std::vector<float> VALL_E_API read_2d_tensor( struct ggml_tensor* tensor );
std::vector<std::vector<float>> VALL_E_API map_embeddings( const std::vector<llama_token>& tokens, int n_embd, const float* embds );
std::vector<std::vector<float>> VALL_E_API sum_embeddings( const std::vector<std::vector<llama_token>>& input, int n_embd, int rvq_l, const float** embds, int mode = EMBEDDING_MODE_PROM );
// batch and inferencing
void VALL_E_API batch_add( llama_batch& batch, llama_token id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids = {0} );
void VALL_E_API fill_batch( llama_batch& batch, input_t& input, inputs_map_t& inputs_map, int mode );
std::vector<llama_token> VALL_E_API generate( llama_context* ctx, llama_model* model, llama_sampler* smpl, input_t& input, inputs_map_t& inputs_map, int max_tokens, int mode, bool verbose = true );
// encodec helpers
bool VALL_E_API read_wav_from_disk( std::string in_path, std::vector<float>& audio_arr );
void VALL_E_API write_wav_on_disk( std::vector<float>& audio_arr, std::string dest_path );
std::vector<std::vector<int32_t>> VALL_E_API encode_audio_from_disk( struct encodec_context* ectx, const std::string& path );
std::vector<float> VALL_E_API decode_audio( struct encodec_context* ectx, const std::vector<std::vector<int32_t>>& codes_2d );
// model-accessing helpers
const embeddings_t& VALL_E_API vall_e_inputs_map_get_embeddings( inputs_map_t& inputs_map, const std::string& name );
const float* VALL_E_API vall_e_inputs_map_get_embeddings_p( inputs_map_t& inputs_map, const std::string& name );
int32_t VALL_E_API vall_e_inputs_map_get_classifier_idx( inputs_map_t& inputs_map, const std::string& name );
void VALL_E_API vall_e_inputs_map_init( inputs_map_t&, llama_model* model );
struct ggml_tensor * VALL_E_API vall_e_get_prom_embds( llama_vall_e_userdata& userdata, int32_t idx );
struct ggml_tensor * VALL_E_API vall_e_get_resp_embds( llama_vall_e_userdata& userdata, int32_t idx );
struct ggml_tensor * VALL_E_API vall_e_get_aux_embds( llama_vall_e_userdata& userdata, int32_t idx );