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https://github.com/ggml-org/llama.cpp.git
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7dcbe39d36
* TruthfulQA: 1st attempt, does not look like it is working The same implementation can be used for HellaSwag as well, so I converted a HellaSwag validation dataset to the binary format used here and tested with that. The score is only around 50, so something is not quite right. * TruthfulQA: works but the result is bad I know it works because if I convert the HellaSwag validation data to the binary format used in the truthful_qa_score() function I get the exact same result as from the hellaswag_score() function. But I guess, the questions are tricky and the way I have done the combination of question + answer is very likely not the best. The TruthfulQA validation dataset contains 817 questions, with random chance result around 19%. With this version I get 29.1% for Mistral-7B and 55.2% for Mistral-7B-Instruct-v0.2. The HF leader board results for these two models are 42.2% and 68.3%, respectively. * TruthfulQA: fix random sample * TruthfulQA: prepare tasks in parallel for large test datasets * Rename truthful_qa to multiple_choice * Make MSVC happy I had forgotten that MSVC does not make constexpr's available inside a lambda. --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
256 lines
12 KiB
C++
256 lines
12 KiB
C++
// Various helper functions and utilities
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#pragma once
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#include "llama.h"
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#include "sampling.h"
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#define LOG_NO_FILE_LINE_FUNCTION
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#include "log.h"
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#include <cmath>
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#include <string>
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#include <vector>
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#include <random>
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#include <thread>
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#include <unordered_map>
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#include <tuple>
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#ifdef _WIN32
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#define DIRECTORY_SEPARATOR '\\'
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#else
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#define DIRECTORY_SEPARATOR '/'
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#endif // _WIN32
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#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0)
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#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
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#define print_build_info() do { \
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fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \
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fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \
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} while(0)
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// build info
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extern int LLAMA_BUILD_NUMBER;
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extern char const *LLAMA_COMMIT;
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extern char const *LLAMA_COMPILER;
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extern char const *LLAMA_BUILD_TARGET;
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//
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// CLI argument parsing
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//
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int32_t get_num_physical_cores();
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struct gpt_params {
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uint32_t seed = -1; // RNG seed
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int32_t n_threads = get_num_physical_cores();
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int32_t n_threads_draft = -1;
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int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
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int32_t n_threads_batch_draft = -1;
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int32_t n_predict = -1; // new tokens to predict
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int32_t n_ctx = 512; // context size
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int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
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int32_t n_keep = 0; // number of tokens to keep from initial prompt
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int32_t n_draft = 8; // number of tokens to draft during speculative decoding
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int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
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int32_t n_parallel = 1; // number of parallel sequences to decode
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int32_t n_sequences = 1; // number of sequences to decode
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float p_accept = 0.5f; // speculative decoding accept probability
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float p_split = 0.1f; // speculative decoding split probability
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int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
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int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
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llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs
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int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
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float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
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int32_t n_beams = 0; // if non-zero then use beam search of given width.
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int32_t grp_attn_n = 1; // group-attention factor
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int32_t grp_attn_w = 512; // group-attention width
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int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
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float rope_freq_base = 0.0f; // RoPE base frequency
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float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
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float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor
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float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
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float yarn_beta_fast = 32.0f; // YaRN low correction dim
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float yarn_beta_slow = 1.0f; // YaRN high correction dim
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int32_t yarn_orig_ctx = 0; // YaRN original context length
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int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
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// pinging @cebtenzzre
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// // sampling parameters
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struct llama_sampling_params sparams;
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std::string model = "models/7B/ggml-model-f16.gguf"; // model path
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std::string model_draft = ""; // draft model for speculative decoding
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std::string model_alias = "unknown"; // model alias
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std::string prompt = "";
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std::string prompt_file = ""; // store the external prompt file name
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std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
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std::string input_prefix = ""; // string to prefix user inputs with
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std::string input_suffix = ""; // string to suffix user inputs with
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std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
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std::string logdir = ""; // directory in which to save YAML log files
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std::vector<llama_model_kv_override> kv_overrides;
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// TODO: avoid tuple, use struct
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std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
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std::string lora_base = ""; // base model path for the lora adapter
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int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
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int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
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// (which is more convenient to use for plotting)
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//
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bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
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size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
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bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt
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size_t winogrande_tasks= 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
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bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
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size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
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bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
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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 chatml = false; // chatml mode (used for models trained on chatml syntax)
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bool prompt_cache_all = false; // save user input and generations to prompt cache
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bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
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bool embedding = false; // get only sentence embedding
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bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
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bool interactive_first = false; // wait for user input immediately
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bool multiline_input = false; // reverse the usage of `\`
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bool simple_io = false; // improves compatibility with subprocesses and limited consoles
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bool cont_batching = false; // insert new sequences for decoding on-the-fly
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bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
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bool ignore_eos = false; // ignore generated EOS tokens
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bool instruct = false; // instruction mode (used for Alpaca models)
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bool logits_all = false; // return logits for all tokens in the batch
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bool use_mmap = true; // use mmap for faster loads
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bool use_mlock = false; // use mlock to keep model in memory
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bool numa = false; // attempt optimizations that help on some NUMA systems
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bool verbose_prompt = false; // print prompt tokens before generation
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bool display_prompt = true; // print prompt before generation
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bool infill = false; // use infill mode
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bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
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bool no_kv_offload = false; // disable KV offloading
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std::string cache_type_k = "f16"; // KV cache data type for the K
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std::string cache_type_v = "f16"; // KV cache data type for the V
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// multimodal models (see examples/llava)
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std::string mmproj = ""; // path to multimodal projector
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std::string image = ""; // path to an image file
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};
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bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
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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 get_system_info(const gpt_params & params);
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std::string gpt_random_prompt(std::mt19937 & rng);
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void process_escapes(std::string& input);
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//
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// String parsing
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//
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std::string parse_samplers_input(std::string input);
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//
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// Model utils
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//
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// TODO: avoid tuplue, use struct
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std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params);
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struct llama_model_params llama_model_params_from_gpt_params (const gpt_params & params);
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struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
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// Batch utils
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void llama_batch_clear(struct llama_batch & batch);
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void llama_batch_add(
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struct llama_batch & batch,
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llama_token id,
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llama_pos pos,
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const std::vector<llama_seq_id> & seq_ids,
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bool logits);
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//
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// Vocab utils
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//
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// tokenizes a string into a vector of tokens
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// should work similar to Python's `tokenizer.encode`
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std::vector<llama_token> llama_tokenize(
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const struct llama_context * ctx,
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const std::string & text,
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bool add_bos,
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bool special = false);
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std::vector<llama_token> llama_tokenize(
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const struct llama_model * model,
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const std::string & text,
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bool add_bos,
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bool special = false);
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// tokenizes a token into a piece
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// should work similar to Python's `tokenizer.id_to_piece`
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std::string llama_token_to_piece(
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const struct llama_context * ctx,
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llama_token token);
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// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
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// that takes into account the tokenizer type and decides how to handle the leading space
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//
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// detokenizes a vector of tokens into a string
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// should work similar to Python's `tokenizer.decode`
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// removes the leading space from the first non-BOS token
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std::string llama_detokenize_spm(
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llama_context * ctx,
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const std::vector<llama_token> & tokens);
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// detokenizes a vector of tokens into a string
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// should work similar to Python's `tokenizer.decode`
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std::string llama_detokenize_bpe(
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llama_context * ctx,
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const std::vector<llama_token> & tokens);
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// Uses the value from the model metadata if possible, otherwise
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// defaults to true when model type is SPM, otherwise false.
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bool llama_should_add_bos_token(const llama_model * model);
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//
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// YAML utils
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//
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bool create_directory_with_parents(const std::string & path);
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void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
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void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
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void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
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std::string get_sortable_timestamp();
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void dump_non_result_info_yaml(
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FILE * stream, const gpt_params & params, const llama_context * lctx,
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const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
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//
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// KV cache utils
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//
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// Dump the KV cache view with the number of sequences per cell.
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void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
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// Dump the KV cache view showing individual sequences in each cell (long output).
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void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
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