diff --git a/vall_e.cpp/vall_e.cpp b/vall_e.cpp/vall_e.cpp index c266011..356e06f 100644 --- a/vall_e.cpp/vall_e.cpp +++ b/vall_e.cpp/vall_e.cpp @@ -1,18 +1,7 @@ -#include "llama-vocab.h" -#include "llama.h" -#include "encodec.h" - #define DR_WAV_IMPLEMENTATION -#include "dr_wav.h" +#include "vall_e.h" + -#include -#include -#include -#include -#include -#include -#include -#include #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) @@ -21,69 +10,6 @@ #include "_llama.h" // cringe hotfix but I have to do this until llama.cpp's API exposes the tok_embd #endif -std::vector read_2d_tensor( struct ggml_tensor* tensor ) { - size_t size = tensor->ne[0] * tensor->ne[1]; - std::vector 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 phn = {}; - llama_token lang = 0; - llama_token rvq_l = 0; - std::vector> prom = {}; - std::vector> 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[] = { { "text", 0, 256, 9, }, { "rvq_l", 256, 264, -1, }, @@ -113,94 +39,111 @@ ranges_t io_ranges[] = { { "resps|NAR:0: 16677, 17702, 8,0", }, }; -struct embeddings_t { - int n_embd; - int n_vocab; - - ranges_t range; - std::vector embds; -}; -struct embeddings_map_t { - int n_embd = 0; - int n_vocab = 0; +std::vector read_2d_tensor( struct ggml_tensor* tensor ) { + size_t size = tensor->ne[0] * tensor->ne[1]; + std::vector res( size ); - // mapping - std::unordered_map mapped_embeddings; - - const embeddings_t& get_embeddings( const std::string& name ) { - return mapped_embeddings[name]; - } - const float* get_embeddings_p( const std::string& name ) { - return mapped_embeddings[name].embds.data(); + 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) ); } - int32_t get_classifier_idx( const std::string& name ) { - return mapped_embeddings[name].range.classifier_idx; + return res; +} + + +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 ) { - this->n_embd = llama_n_embd( model ); - this->n_vocab = llama_n_vocab( model ); +#if LLAMA_CPP_EXTENDED + auto* tensor = llama_get_embedding_weights( model ); +#else + auto* tensor = model->tok_embd; +#endif - // to-do: figure a nicer way to do this - #if LLAMA_CPP_USE_VALL_E_ARCH - mapped_embeddings["text"] = { n_embd, 0, { "text", 0, 0, 9, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 0)) }; - mapped_embeddings["rvq_l"] = { n_embd, 0, { "rvq_l", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 1)) }; - mapped_embeddings["lang"] = { n_embd, 0, { "lang", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 2)) }; - mapped_embeddings["task"] = { n_embd, 0, { "task", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 3)) }; - mapped_embeddings["len"] = { n_embd, 0, { "len", 0, 0, 10, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 4)) }; - mapped_embeddings["tone"] = { n_embd, 0, { "tone", 0, 0, -1, }, read_2d_tensor(llama_get_vall_e_aux_embds(model, 5)) }; - 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 raw_embeddings = read_2d_tensor( tensor ); - for ( auto& range : io_ranges ) { - mapped_embeddings[range.name] = { - n_embd, - range.end - range.start, - range, - std::vector( raw_embeddings.data() + range.start, raw_embeddings.data() + range.end ) - }; - } - #endif + // prepare slices + std::vector raw_embeddings = read_2d_tensor( tensor ); + for ( auto& range : io_ranges ) { + inputs_map.embds[range.name] = { + n_embd, + range.end - range.start, + range, + std::vector( raw_embeddings.data() + range.start, raw_embeddings.data() + range.end ) + }; } -}; +#endif +} // maps embeddings easily std::vector> map_embeddings( const std::vector& tokens, int n_embd, const float* embds ) { @@ -213,7 +156,7 @@ std::vector> map_embeddings( const std::vector& // handles adding either a token OR the embedding of that token into the batch // 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 & 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 & seq_ids ) { GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded"); // insert raw embedding instead @@ -339,23 +282,8 @@ std::vector decode_audio( struct encodec_context* ectx, const std::vector return std::vector(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" -std::vector> sum_embeddings( const std::vector>& input, int n_embd, int rvq_l, const float** embds, int mode = EMBEDDING_MODE_PROM ) { +std::vector> sum_embeddings( const std::vector>& input, int n_embd, int rvq_l, const float** embds, int mode ) { std::vector> res( input.size() ); res.resize( input[0].size() ); for ( auto& e : res ) e.resize( n_embd ); @@ -383,38 +311,38 @@ std::vector> sum_embeddings( const std::vector 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 ) { - llama_batch batch = llama_batch_init( 22500, embeddings_map.n_embd, 22500 ); +std::vector 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, inputs_map.n_embd, 22500 ); // Decoding loop const auto t_main_start = ggml_time_us(); @@ -463,7 +391,7 @@ std::vector generate( llama_context* ctx, llama_model* model, llama int rvq_l = input.rvq_l; 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 int n_logits = 0; @@ -487,7 +415,7 @@ std::vector generate( llama_context* ctx, llama_model* model, llama ranges_t range; 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; embds = embeddings.embds.data(); stop_token = range.end - range.start - 1; @@ -504,20 +432,20 @@ std::vector generate( llama_context* ctx, llama_model* model, llama "resps|NAR:5:6", "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; embds = embeddings.embds.data(); printf("Generating in %s (%i) mode (%i:%i)\n", "NAR", range.classifier_idx, range.start, range.end); } 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; embds = embeddings.embds.data(); 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); } 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; embds = embeddings.embds.data(); @@ -525,9 +453,10 @@ std::vector generate( llama_context* ctx, llama_model* model, llama } #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 - 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]) std::vector output_tokens; @@ -536,7 +465,7 @@ std::vector generate( llama_context* ctx, llama_model* model, llama fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1); return output_tokens; } - + std::vector current_tokens; // backwards iterate to start from beginning of sequence for ( auto i = n_logits; i > 0; --i ) { // filter logits @@ -544,20 +473,25 @@ std::vector generate( llama_context* ctx, llama_model* model, llama // ensures only tokens within our designated range are used #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; } #endif // 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); + //printf("%i: [%i]: %f | %p\n", -i, t, logits[t], logits ); // offset back into range #if !LLAMA_CPP_USE_VALL_E_ARCH t -= range.start; #endif - printf("%s: %i: %i: %i\n", __func__, i, n_decode, t); n_decode += 1; // is stop token @@ -568,10 +502,17 @@ std::vector generate( llama_context* ctx, llama_model* model, llama } // store token - output_tokens.emplace_back(t); + current_tokens.emplace_back(t); // 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(); @@ -591,37 +532,17 @@ std::vector generate( llama_context* ctx, llama_model* model, llama 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 int32_t ngl = 0; - int modality = MODALITY_NAR_LEN; + int modality = MODALITY_AR_NAR; input_t input{}; - embeddings_map_t embeddings_map{}; + inputs_map_t inputs_map{}; // input.phonemes = "hˈɛloː ʋˈɔrlt"; input.phn = {1,22,111,100,4,37,115,169,11,2}; // hˈɛloː ʋˈɔrlt - 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}, - {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], - */ - }; + input.prom = {}; + input.resp = {}; std::string vall_e_model_path = "./data/vall_e.gguf"; 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_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()); 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 ); // grab input embeddings - embeddings_map.init( model ); + vall_e_inputs_map_init( inputs_map, model ); // tokenize phonemes // to-do: make this work, the vocab does not work @@ -707,10 +632,10 @@ int main(int argc, char ** argv) { // NAR-len demasking if ( modality == MODALITY_NAR_LEN ) { // inference len - int len = 290; // 0; + int len = 0; if ( !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; for (int i = output_tokens.size() - 1; i >= 0; i--) { @@ -732,7 +657,7 @@ int main(int argc, char ** argv) { input.task = "tts"; for ( auto l = 0; l < 8; ++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 ); } // AR+NAR @@ -740,7 +665,7 @@ int main(int argc, char ** argv) { input.task = "tts"; for ( auto l = 0; l < 8; ++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 ); } } diff --git a/vall_e.cpp/vall_e.h b/vall_e.cpp/vall_e.h new file mode 100644 index 0000000..f376596 --- /dev/null +++ b/vall_e.cpp/vall_e.h @@ -0,0 +1,129 @@ +#pragma once + +#include "llama-vocab.h" +#include "llama.h" +#include "encodec.h" + +#include "dr_wav.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +// to-do: copy over import/export stuff from engine project (because I don't remember how I set it up in ) +#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 phn = {}; + llama_token lang = 0; + llama_token rvq_l = 0; + std::vector> prom = {}; + std::vector> 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 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 embds = {}; +}; + +// helper tensor functions +std::vector VALL_E_API read_2d_tensor( struct ggml_tensor* tensor ); +std::vector> VALL_E_API map_embeddings( const std::vector& tokens, int n_embd, const float* embds ); +std::vector> VALL_E_API sum_embeddings( const std::vector>& 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 & seq_ids = {0} ); +void VALL_E_API fill_batch( llama_batch& batch, input_t& input, inputs_map_t& inputs_map, int mode ); +std::vector 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& audio_arr ); +void VALL_E_API write_wav_on_disk( std::vector& audio_arr, std::string dest_path ); +std::vector> VALL_E_API encode_audio_from_disk( struct encodec_context* ectx, const std::string& path ); +std::vector VALL_E_API decode_audio( struct encodec_context* ectx, const std::vector>& 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 ); \ No newline at end of file