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https://github.com/ggerganov/llama.cpp.git
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486ae645fd
* Compute perplexity over prompt * More accurate perplexity calculation - over all logits in the context window (so 512x more tokens!) * Output all perplexitiies * Add timing/ETA
109 lines
3.5 KiB
C++
109 lines
3.5 KiB
C++
// Various helper functions and utilities
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#pragma once
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#include <string>
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#include <map>
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#include <vector>
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#include <random>
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#include <thread>
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//
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// CLI argument parsing
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//
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t n_predict = 128; // new tokens to predict
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int32_t repeat_last_n = 64; // last n tokens to penalize
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int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
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int32_t n_ctx = 512; //context size
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// sampling parameters
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int32_t top_k = 40;
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float top_p = 0.95f;
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float temp = 0.80f;
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float repeat_penalty = 1.10f;
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int32_t n_batch = 8; // batch size for prompt processing
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std::string model = "models/lamma-7B/ggml-model.bin"; // model path
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std::string prompt = "";
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std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
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bool memory_f16 = false; // use f16 instead of f32 for memory kv
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bool random_prompt = false; // do not randomize prompt if none provided
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bool use_color = false; // use color to distinguish generations and inputs
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bool interactive = false; // interactive mode
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bool interactive_start = false; // reverse prompt immediately
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bool instruct = false; // instruction mode (used for Alpaca models)
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bool ignore_eos = false; // do not stop generating after eos
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bool perplexity = false; // compute perplexity over the prompt
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
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std::string gpt_random_prompt(std::mt19937 & rng);
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//
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// Model file parsing
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//
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#define FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
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#define FILE_MAGIC 0x67676d66 // 'ggmf' in hex
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#define FILE_VERSION 1
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//
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// Vocab utils
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//
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struct llama_vocab {
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using id = int32_t;
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using token = std::string;
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std::map<token, id> token_to_id;
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std::map<id, token> id_to_token;
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std::map<id, float> score;
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};
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void replace(std::string & str, const std::string & needle, const std::string & replacement);
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// poor-man's JSON parsing
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std::map<std::string, int32_t> json_parse(const std::string & fname);
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// TODO: temporary until #77 is merged, need this now for some tokenizer tests
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bool llama_vocab_load(const std::string & fname, llama_vocab & vocab);
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// TODO: this is probably wrong, but I cannot figure out how this tokenizer works ..
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// ref: https://github.com/google/sentencepiece
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std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos);
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// sample next token given probabilities for each embedding
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//
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// - consider only the top K tokens
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// - from them, consider only the top tokens with cumulative probability > P
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//
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llama_vocab::id llama_sample_top_p_top_k(
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const llama_vocab & vocab,
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const float * logits,
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std::vector<llama_vocab::id> & last_n_tokens,
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double repeat_penalty,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng);
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// filer to top K tokens from list of logits
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void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k);
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//
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// Quantization
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//
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size_t ggml_quantize_q4_0(float * src, void * dst, int n, int k, int qk, int64_t * hist);
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size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t * hist);
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