93 lines
3.3 KiB
C++
93 lines
3.3 KiB
C++
#pragma once
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// stores all the backend stuff
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// external deps
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#include <llama.h>
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#include <encodec.h>
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#include <dr_wav.h>
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#include <espeak-ng/speak_lib.h>
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#define LLAMA_CPP_EXTENDED 0 // whether the underlying llama.cpp has some extra functions
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#define LLAMA_CPP_USE_VALL_E_ARCH 0 // whether the underlying llama.cpp is to use the VALL_E arch (or using LLAMA arch)
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#if !LLAMA_CPP_EXTENDED
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#include "llama_hack.h" // cringe hotfix but I have to do this until llama.cpp's API exposes the tok_embd
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#endif
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// to-do: clean up spaghetti enums
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const int EMBEDDING_MODE_PROM = 0;
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const int EMBEDDING_MODE_RESP_AR_NAR = 1;
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const int EMBEDDING_MODE_RESP_NAR_LEN = 2;
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const int INFERENCE_MODE_LEN = 0;
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const int INFERENCE_MODE_AR = 1;
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const int INFERENCE_MODE_NAR_DEMASK = 2;
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const int INFERENCE_MODE_NAR = 3;
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// stores metadata for inputs/outputs
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struct io_t {
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std::string name;
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uint32_t start;
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uint32_t end;
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int32_t head_idx = -1;
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int32_t n_embd = 0;
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int32_t n_vocab = 0;
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std::vector<float> embds = {};
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ggml_tensor* head = NULL;
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};
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// stores the mappings between tokens, input embeddings, and output heads
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struct io_map_t {
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// model's original params
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int32_t n_embd = 0;
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int32_t n_vocab = 0;
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// mapping
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std::unordered_map<std::string, io_t> io = {};
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// context to store slices
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ggml_context* ctx = NULL;
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};
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// used for top-k (mainly for demasking)
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struct score_t {
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int32_t idx;
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float value;
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bool operator<( const score_t& that ) const { return this->value < that.value; }
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};
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// handles storing metadata for token merges
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struct merge_entry_t {
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std::u32string pre;
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std::u32string post;
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std::u32string resolved;
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token_t pre_token;
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token_t post_token;
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token_t resolved_token;
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};
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// helper tensor functions
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std::vector<float> read_2d_tensor( struct ggml_tensor* tensor );
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//ggml_tensor* view_2d_tensor( ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim = 0 ); // cringe method to keep in my pocket
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ggml_tensor* view_2d_tensor( ggml_context* ctx, ggml_tensor* tensor, int32_t start, int32_t end, int32_t dim = 0 );
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void print_tokens( const std::vector<token_t>& tokens, const std::string& prefix = "Tokens: " );
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std::vector<std::vector<float>> map_embeddings( const std::vector<token_t>& tokens, int n_embd, const float* embds );
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std::vector<std::vector<float>> sum_embeddings( const vall_e_audio_codes_t& input, int n_embd, int rvq_l, const float** embds, int mode = EMBEDDING_MODE_PROM );
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std::vector<float> soft_max( int n_logits, const float* logits );
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// batch and inferencing
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void batch_add( llama_batch& batch, token_t id, int n_embd, const float* embds, llama_pos pos, bool output, const std::vector<llama_seq_id> & seq_ids = {0} );
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void fill_batch( llama_batch& batch, vall_e_inputs_t& input, io_map_t& inputs_map, int mode );
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std::vector<token_t> generate( vall_e_context_t* ctx, vall_e_inputs_t& input, int max_tokens, int mode, bool verbose = true );
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// (handles text)
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std::vector<token_t> phonemize( vall_e_context_t* ctx, const std::string& text, const std::string& language = "auto" );
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// model-accessing helpers
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const io_t& vall_e_inputs_map_get_embeddings( io_map_t& inputs_map, const std::string& name );
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const float* vall_e_inputs_map_get_embeddings_p( io_map_t& inputs_map, const std::string& name );
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int32_t vall_e_inputs_map_get_classifier_idx( io_map_t& inputs_map, const std::string& name );
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void vall_e_inputs_map_init( io_map_t&, llama_model* model ); |