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.
This commit is contained in:
Iwan Kawrakow
parent
97c1549808
commit
6ce06623fd
3 changed files with 336 additions and 0 deletions
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@ -203,6 +203,25 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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params.prompt_cache_all = true;
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} else if (arg == "--prompt-cache-ro") {
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params.prompt_cache_ro = true;
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} else if (arg == "-bf" || arg == "--binary-file") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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std::ifstream file(argv[i], std::ios::binary);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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invalid_param = true;
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break;
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}
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// store the external file name in params
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params.prompt_file = argv[i];
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file.seekg(0, std::ios::end);
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size_t size = file.tellg();
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file.seekg(0, std::ios::beg);
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params.prompt.resize(size);
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file.read((char *)params.prompt.data(), size);
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fprintf(stderr, "Read %zu bytes from binary file %s\n", size, argv[i]);
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} else if (arg == "-f" || arg == "--file") {
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if (++i >= argc) {
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invalid_param = true;
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@ -689,6 +708,14 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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break;
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}
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params.winogrande_tasks = std::stoi(argv[i]);
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} else if (arg == "--truthful-qa") {
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params.truthful_qa = true;
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} else if (arg == "--truthful-qa-tasks") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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params.thruthful_qa_tasks = std::stoi(argv[i]);
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} else if (arg == "--ignore-eos") {
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params.ignore_eos = true;
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} else if (arg == "--no-penalize-nl") {
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@ -936,6 +963,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
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printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n");
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printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks);
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printf(" --truthful-qa compute TruthFullQA multiple choice score over random tasks from datafile supplied with -f\n");
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printf(" --truthful-qa-tasks N number of tasks to use when computing the TruthFullQA score (default: %zu)\n", params.winogrande_tasks);
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printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
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printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
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printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
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@ -108,6 +108,9 @@ struct gpt_params {
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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 truthful_qa = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
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size_t thruthful_qa_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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@ -1031,6 +1031,308 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
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printf("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma);
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}
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static bool deserialize_string(std::istream& in, std::string& str) {
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uint32_t size;
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if (!in.read((char *)&size, sizeof(size)).fail()) {
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str.resize(size);
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if (!in.read((char *)str.data(), size).fail()) return true;
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}
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return false;
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}
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struct truthful_qa_answer {
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std::vector<std::string> answers;
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std::vector<int> labels;
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bool deserialize(std::istream& in) {
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uint32_t n;
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in.read((char *)&n, sizeof(n));
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if (in.fail() || n > 100) return false; // 100 as max. number of answers should be good enough for any practical purpose
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answers.resize(n);
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labels.resize(n);
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for (auto& a : answers) {
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if (!deserialize_string(in, a)) return false;
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}
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in.read((char *)labels.data(), n*sizeof(int));
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return !in.fail();
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}
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};
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struct truthful_qa_task {
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std::string question;
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truthful_qa_answer mc1;
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truthful_qa_answer mc2;
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bool deserialize(std::istream& in) {
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if (!deserialize_string(in, question)) return false;
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return mc1.deserialize(in) && mc2.deserialize(in);
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}
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// For evaluation
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size_t i_batch; // starting index in the llama_batch
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size_t common_prefix; // max number of initial tokens that are the same in all sentences
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size_t required_tokens; // needed number of tokens to evaluate all answers
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std::vector<std::vector<llama_token>> seq_tokens;
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std::vector<float> log_probs;
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};
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static void truthful_qa_score(llama_context * ctx, const gpt_params & params) {
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// Calculates TruthFulQA score (multiple choice with single correct answer) from prompt
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//
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// Data extracted from https://huggingface.co/datasets/truthful_qa
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//
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std::istringstream strstream(params.prompt);
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uint32_t n_task;
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strstream.read((char *)&n_task, sizeof(n_task));
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if (strstream.fail() || n_task == 0) {
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printf("%s: no tasks\n", __func__);
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return;
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}
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printf("%s: there are %u tasks in prompt\n", __func__, n_task);
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std::vector<uint32_t> task_pos(n_task);
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strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
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if (strstream.fail()) {
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printf("%s: failed to raad task positions from prompt\n", __func__);
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return;
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}
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std::vector<truthful_qa_task> tasks;
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if (params.thruthful_qa_tasks == 0 || params.thruthful_qa_tasks >= (size_t)n_task) {
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// Use all tasks
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tasks.resize(n_task);
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printf("%s: reading tasks", __func__);
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int n_dot = n_task/100;
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int i = 0;
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for (auto& task : tasks) {
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++i;
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if (!task.deserialize(strstream)) {
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printf("%s: failed to read task %d of %u\n", __func__, i, n_task);
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return;
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}
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if (i%n_dot == 0) printf(".");
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}
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printf("done\n");
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}
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else {
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printf("%s: selecting %zu random tasks from %u tasks available\n", __func__, params.thruthful_qa_tasks, n_task);
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std::mt19937 rng(1);
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std::vector<int> aux(n_task);
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float scale = 1.f/(1.f + (float)std::mt19937::max());
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tasks.resize(params.thruthful_qa_tasks);
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for (auto& task : tasks) {
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int j = (int)(scale * rng() * aux.size());
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int idx = aux[j];
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aux[j] = aux.back();
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aux.pop_back();
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strstream.seekg(task_pos[idx], std::ios::beg);
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if (!task.deserialize(strstream)) {
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printf("%s: failed to read task %d at position %u\n", __func__, idx, task_pos[idx]);
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return;
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}
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}
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n_task = params.thruthful_qa_tasks;
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}
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// This is needed as usual for LLaMA models
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const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
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printf("%s: preparing task data", __func__);
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fflush(stdout);
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int n_dot = n_task/100;
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int i_task = 0;
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for (auto& task : tasks) {
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++i_task;
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if (task.question.empty() || task.mc1.answers.empty()) {
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printf("%s: found bad task with empty question and/or answers\n", __func__);
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return;
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}
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task.seq_tokens.reserve(task.mc1.answers.size());
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for (auto& answer : task.mc1.answers) {
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if (answer.empty()) {
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printf("%s: found empty answer\n", __func__);
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return;
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}
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task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos));
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}
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auto min_len = task.seq_tokens.front().size();
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for (auto& seq : task.seq_tokens) {
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min_len = std::min(min_len, seq.size());
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}
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task.common_prefix = 0;
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for (size_t k = 0; k < min_len; ++k) {
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auto token = task.seq_tokens[0][k];
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bool all_same = true;
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for (size_t i = 1; i < task.seq_tokens.size(); ++i) {
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if (task.seq_tokens[i][k] != token) {
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all_same = false;
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break;
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}
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}
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if (!all_same) {
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break;
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}
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++task.common_prefix;
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}
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task.required_tokens = task.common_prefix;
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for (auto& seq : task.seq_tokens) {
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task.required_tokens += seq.size() - task.common_prefix;
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}
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if (i_task%n_dot == 0) {
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printf(".");
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fflush(stdout);
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}
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}
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printf("done\n");
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printf("%s : calculating TruthfulQA score over %zu tasks.\n", __func__, tasks.size());
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printf("\ntask\tacc_norm\n");
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const int n_vocab = llama_n_vocab(llama_get_model(ctx));
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const int n_ctx = llama_n_ctx(ctx);
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const int n_batch = params.n_batch;
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const int max_tasks_per_batch = 32;
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const int max_seq = 4*max_tasks_per_batch;
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llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
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std::vector<float> tok_logits(n_vocab);
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std::vector<float> batch_logits(n_vocab*n_ctx);
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std::vector<std::pair<size_t, llama_token>> eval_pairs;
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std::vector<float> eval_results;
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std::vector<std::thread> workers(std::thread::hardware_concurrency());
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std::vector<int> batch_indeces;
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int n_done = 0;
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int n_correct = 0;
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for (size_t i0 = 0; i0 < tasks.size(); i0++) {
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int n_cur = 0;
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size_t i1 = i0;
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size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
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llama_batch_clear(batch);
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// batch as much tasks as possible into the available context
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// each task has 4 unique seuqnce ids - one for each ending
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// the common prefix is shared among the 4 sequences to save tokens
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// we extract logits only from the last common token and from all ending tokens of each sequence
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int s0 = 0;
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while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
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auto& cur_task = tasks[i1];
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int num_answers = cur_task.seq_tokens.size();
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if (s0 + num_answers > max_seq) {
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break;
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}
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if (int(batch_indeces.size()) != num_answers) {
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batch_indeces.resize(num_answers);
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}
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for (int s = 0; s < num_answers; ++s) batch_indeces[s] = s0 + s;
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for (size_t i = 0; i < cur_task.common_prefix; ++i) {
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//llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
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llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
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}
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batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
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for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
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for (size_t i = cur_task.common_prefix; i < cur_task.seq_tokens[s].size(); ++i) {
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llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, true);
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}
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}
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cur_task.i_batch = i_batch;
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i_batch += cur_task.required_tokens;
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n_cur += cur_task.required_tokens;
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if (++i1 == tasks.size()) {
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break;
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}
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}
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if (i0 == i1) {
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fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0);
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return;
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}
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llama_kv_cache_clear(ctx);
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// decode all tasks [i0, i1)
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if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
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fprintf(stderr, "%s: llama_decode() failed\n", __func__);
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return;
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}
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// Compute log-probs in parallel
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// First we collect all tasks
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eval_pairs.clear();
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for (size_t i = i0; i < i1; ++i) {
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auto& cur_task = tasks[i];
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size_t li = cur_task.common_prefix;
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for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
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for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
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eval_pairs.push_back(std::make_pair(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]));
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}
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++li;
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}
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}
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// Then we do the actual calculation
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compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
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size_t ir = 0;
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// compute the logprobs for each ending of the decoded tasks
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for (size_t i = i0; i < i1; ++i) {
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auto & cur_task = tasks[i];
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std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(cur_task.i_batch + cur_task.common_prefix - 1), n_vocab*sizeof(float));
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const auto first_probs = softmax(tok_logits);
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cur_task.log_probs.resize(cur_task.seq_tokens.size());
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for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
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size_t count = 1;
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float log_prob = std::log(first_probs[cur_task.seq_tokens[s][cur_task.common_prefix]]);
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for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
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++count;
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log_prob += eval_results[ir++];
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}
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cur_task.log_probs[s] = log_prob / count;
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}
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// Find the ending with maximum logprob
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size_t logprob_max_idx = 0;
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float logprob_max_val = cur_task.log_probs[0];
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for (size_t s = 1; s < cur_task.log_probs.size(); s++) {
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if (cur_task.log_probs[s] > logprob_max_val) {
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logprob_max_val = cur_task.log_probs[s];
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logprob_max_idx = s;
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}
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}
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if (cur_task.mc1.labels[logprob_max_idx] == 1) {
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++n_correct;
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}
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++n_done;
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// Print the accumulated accuracy mean x 100
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printf("%zu\t%.8lf\n", i + 1, 100.*n_correct/n_done);
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fflush(stdout);
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}
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i0 = i1 - 1;
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}
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llama_batch_free(batch);
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printf("\n");
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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@ -1091,6 +1393,8 @@ int main(int argc, char ** argv) {
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hellaswag_score(ctx, params);
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} else if (params.winogrande) {
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winogrande_score(ctx, params);
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} else if (params.truthful_qa) {
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truthful_qa_score(ctx, params);
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} else {
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results = perplexity(ctx, params);
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}
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