params.prompt_cache_all = true;
} else if (arg == "--prompt-cache-ro") {
params.prompt_cache_ro = true;
+ } else if (arg == "-bf" || arg == "--binary-file") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ std::ifstream file(argv[i], std::ios::binary);
+ if (!file) {
+ fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
+ invalid_param = true;
+ break;
+ }
+ // store the external file name in params
+ params.prompt_file = argv[i];
+ file.seekg(0, std::ios::end);
+ size_t size = file.tellg();
+ file.seekg(0, std::ios::beg);
+ params.prompt.resize(size);
+ file.read((char *)params.prompt.data(), size);
+ fprintf(stderr, "Read %zu bytes from binary file %s\n", size, argv[i]);
} else if (arg == "-f" || arg == "--file") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.winogrande_tasks = std::stoi(argv[i]);
+ } else if (arg == "--multiple-choice") {
+ params.multiple_choice = true;
+ } else if (arg == "--multiple-choice-tasks") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ params.multiple_choice_tasks = std::stoi(argv[i]);
} else if (arg == "--ignore-eos") {
params.ignore_eos = true;
} else if (arg == "--no-penalize-nl") {
printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n");
printf(" -f FNAME, --file FNAME\n");
printf(" prompt file to start generation.\n");
+ printf(" -bf FNAME, --binary-file FNAME\n");
+ printf(" binary file containing multiple choice tasks.\n");
printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n");
printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks);
+ printf(" --multiple-choice compute multiple choice score over random tasks from datafile supplied with -f\n");
+ printf(" --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks);
printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft);
printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
// This is needed as usual for LLaMA models
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+ // The tasks should be randomized so the score stabilizes quickly.
+ bool randomize_tasks = true;
+
// Number of tasks to use when computing the score
if (params.hellaswag_tasks < hs_task_count) {
hs_task_count = params.hellaswag_tasks;
}
- // The tasks should be randomized so the score stabilizes quickly.
- bool randomize_tasks = true;
-
// The random seed should not impact the final result if the computation is done over enough tasks, so kept hardcoded for now
std::mt19937 rng(1);
printf("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma);
}
+static bool deserialize_string(std::istream& in, std::string& str) {
+ uint32_t size;
+ if (!in.read((char *)&size, sizeof(size)).fail()) {
+ str.resize(size);
+ if (!in.read((char *)str.data(), size).fail()) return true;
+ }
+ return false;
+}
+
+struct multiple_choice_answers {
+ std::vector<std::string> answers;
+ std::vector<int> labels;
+ bool deserialize(std::istream& in) {
+ uint32_t n;
+ in.read((char *)&n, sizeof(n));
+ if (in.fail() || n > 100) return false; // 100 as max. number of answers should be good enough for any practical purpose
+ answers.resize(n);
+ labels.resize(n);
+ for (auto& a : answers) {
+ if (!deserialize_string(in, a)) return false;
+ }
+ in.read((char *)labels.data(), n*sizeof(int));
+ return !in.fail();
+ }
+};
+
+struct multiple_choice_task {
+ std::string question; // the question (or context that needs to be continued)
+ multiple_choice_answers mc1; // possible answers (continuations) with a single correct answer
+ multiple_choice_answers mc2; // possible answers (continuations) with multiple correct answers - not handled yet
+ bool deserialize(std::istream& in) {
+ if (!deserialize_string(in, question)) return false;
+ return mc1.deserialize(in) && mc2.deserialize(in);
+ }
+
+ // For evaluation
+ size_t i_batch; // starting index in the llama_batch
+ size_t common_prefix; // max number of initial tokens that are the same in all sentences
+ size_t required_tokens; // needed number of tokens to evaluate all answers
+ std::vector<std::vector<llama_token>> seq_tokens;
+ std::vector<float> log_probs;
+};
+
+static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, multiple_choice_task& task, bool log_error) {
+ if (task.question.empty() || task.mc1.answers.empty()) {
+ if (log_error) {
+ printf("%s: found bad task with empty question and/or answers\n", __func__);
+ }
+ return false;
+ }
+ task.seq_tokens.reserve(task.mc1.answers.size());
+ for (auto& answer : task.mc1.answers) {
+ if (answer.empty()) {
+ if (log_error) {
+ printf("%s: found empty answer\n", __func__);
+ }
+ return false;
+ }
+ task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos));
+ }
+ auto min_len = task.seq_tokens.front().size();
+ for (auto& seq : task.seq_tokens) {
+ min_len = std::min(min_len, seq.size());
+ }
+ task.common_prefix = 0;
+ for (size_t k = 0; k < min_len; ++k) {
+ auto token = task.seq_tokens[0][k];
+ bool all_same = true;
+ for (size_t i = 1; i < task.seq_tokens.size(); ++i) {
+ if (task.seq_tokens[i][k] != token) {
+ all_same = false;
+ break;
+ }
+ }
+ if (!all_same) {
+ break;
+ }
+ ++task.common_prefix;
+ }
+ task.required_tokens = task.common_prefix;
+ for (auto& seq : task.seq_tokens) {
+ task.required_tokens += seq.size() - task.common_prefix;
+ }
+ return true;
+}
+
+//
+// Calculates score for multiple choice tasks with single correct answer from prompt.
+// Commonly used LLM evaluation metrics of this type are
+// * ARC
+// * HellaSwag
+// * MMLU
+// * TruthfulQA
+//
+// Validation datasets for these 4 tests can be found at
+// https://huggingface.co/datasets/ikawrakow/validation-datasets-for-llama.cpp
+// The data for these datasets was extracted from
+// git@hf.co:datasets/allenai/ai2_arc
+// https://github.com/rowanz/hellaswag/blob/master/data/hellaswag_val.jsonl
+// git@hf.co:datasets/Stevross/mmlu
+// https://huggingface.co/datasets/truthful_qa
+//
+static void multiple_choice_score(llama_context * ctx, const gpt_params & params) {
+
+ std::istringstream strstream(params.prompt);
+ uint32_t n_task;
+ strstream.read((char *)&n_task, sizeof(n_task));
+ if (strstream.fail() || n_task == 0) {
+ printf("%s: no tasks\n", __func__);
+ return;
+ }
+ printf("%s: there are %u tasks in prompt\n", __func__, n_task);
+ std::vector<uint32_t> task_pos(n_task);
+ strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
+ if (strstream.fail()) {
+ printf("%s: failed to raad task positions from prompt\n", __func__);
+ return;
+ }
+
+ std::vector<multiple_choice_task> tasks;
+ if (params.multiple_choice_tasks == 0 || params.multiple_choice_tasks >= (size_t)n_task) {
+ // Use all tasks
+ tasks.resize(n_task);
+ printf("%s: reading tasks", __func__);
+ int n_dot = n_task/100;
+ int i = 0;
+ for (auto& task : tasks) {
+ ++i;
+ if (!task.deserialize(strstream)) {
+ printf("%s: failed to read task %d of %u\n", __func__, i, n_task);
+ return;
+ }
+ if (i%n_dot == 0) printf(".");
+ }
+ printf("done\n");
+ }
+ else {
+ printf("%s: selecting %zu random tasks from %u tasks available\n", __func__, params.multiple_choice_tasks, n_task);
+ std::mt19937 rng(1);
+ std::vector<int> aux(n_task);
+ for (uint32_t i = 0; i < n_task; ++i) aux[i] = i;
+ float scale = 1.f/(1.f + (float)std::mt19937::max());
+ tasks.resize(params.multiple_choice_tasks);
+ for (auto& task : tasks) {
+ int j = (int)(scale * rng() * aux.size());
+ int idx = aux[j];
+ aux[j] = aux.back();
+ aux.pop_back();
+ strstream.seekg(task_pos[idx], std::ios::beg);
+ if (!task.deserialize(strstream)) {
+ printf("%s: failed to read task %d at position %u\n", __func__, idx, task_pos[idx]);
+ return;
+ }
+ }
+ n_task = params.multiple_choice_tasks;
+ }
+
+ // This is needed as usual for LLaMA models
+ const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+
+ printf("%s: preparing task data", __func__);
+ fflush(stdout);
+ if (n_task > 500) {
+ printf("...");
+ fflush(stdout);
+ std::atomic<int> counter(0);
+ std::atomic<int> n_bad(0);
+ auto prepare = [&counter, &n_bad, &tasks, ctx, add_bos] () {
+ int num_tasks = tasks.size();
+ int n_bad_local = 0;
+ while (true) {
+ int first = counter.fetch_add(K_TOKEN_CHUNK);
+ if (first >= num_tasks) {
+ if (n_bad_local > 0) n_bad += n_bad_local;
+ break;
+ }
+ int last = std::min(first + K_TOKEN_CHUNK, num_tasks);
+ for (int i = first; i < last; ++i) {
+ if (!multiple_choice_prepare_one_task(ctx, add_bos, tasks[i], false)) ++n_bad_local;
+ }
+ }
+ };
+ size_t max_thread = std::thread::hardware_concurrency();
+ max_thread = std::min(max_thread, (tasks.size() + K_TOKEN_CHUNK - 1)/K_TOKEN_CHUNK);
+ std::vector<std::thread> workers(max_thread-1);
+ for (auto& w : workers) w = std::thread(prepare);
+ prepare();
+ for (auto& w : workers) w.join();
+ printf("done\n");
+ fflush(stdout);
+ int nbad = n_bad;
+ if (nbad > 0) {
+ printf("%s: found %d malformed tasks\n", __func__, nbad);
+ return;
+ }
+ } else {
+ int n_dot = n_task/100;
+ int i_task = 0;
+ for (auto& task : tasks) {
+ ++i_task;
+ if (!multiple_choice_prepare_one_task(ctx, add_bos, task, true)) {
+ return;
+ }
+ if (i_task%n_dot == 0) {
+ printf(".");
+ fflush(stdout);
+ }
+ }
+ printf("done\n");
+ }
+
+ printf("%s : calculating TruthfulQA score over %zu tasks.\n", __func__, tasks.size());
+
+ printf("\ntask\tacc_norm\n");
+
+ const int n_vocab = llama_n_vocab(llama_get_model(ctx));
+ const int n_ctx = llama_n_ctx(ctx);
+ const int n_batch = params.n_batch;
+
+ const int max_tasks_per_batch = 32;
+ const int max_seq = 4*max_tasks_per_batch;
+
+ llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
+
+ std::vector<float> tok_logits(n_vocab);
+ std::vector<float> batch_logits(n_vocab*n_ctx);
+
+ std::vector<std::pair<size_t, llama_token>> eval_pairs;
+ std::vector<float> eval_results;
+ std::vector<std::thread> workers(std::thread::hardware_concurrency());
+ std::vector<int> batch_indeces;
+
+ int n_done = 0;
+ int n_correct = 0;
+ int n_tot_answers = 0;
+
+ for (size_t i0 = 0; i0 < tasks.size(); i0++) {
+ int n_cur = 0;
+
+ size_t i1 = i0;
+ size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
+
+ llama_batch_clear(batch);
+
+ // batch as much tasks as possible into the available context
+ // each task has 4 unique seuqnce ids - one for each ending
+ // the common prefix is shared among the 4 sequences to save tokens
+ // we extract logits only from the last common token and from all ending tokens of each sequence
+ int s0 = 0;
+ while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
+ auto& cur_task = tasks[i1];
+
+ int num_answers = cur_task.seq_tokens.size();
+ if (s0 + num_answers > max_seq) {
+ break;
+ }
+
+ if (int(batch_indeces.size()) != num_answers) {
+ batch_indeces.resize(num_answers);
+ }
+ for (int s = 0; s < num_answers; ++s) batch_indeces[s] = s0 + s;
+
+ for (size_t i = 0; i < cur_task.common_prefix; ++i) {
+ //llama_batch_add(batch, cur_task.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
+ llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
+ }
+ batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
+
+ for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
+ for (size_t i = cur_task.common_prefix; i < cur_task.seq_tokens[s].size(); ++i) {
+ llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, true);
+ }
+ }
+
+ s0 += num_answers;
+
+ cur_task.i_batch = i_batch;
+ i_batch += cur_task.required_tokens;
+
+ n_cur += cur_task.required_tokens;
+ if (++i1 == tasks.size()) {
+ break;
+ }
+ }
+
+ if (i0 == i1) {
+ fprintf(stderr, "%s : task %zu does not fit in the context window\n", __func__, i0);
+ return;
+ }
+
+ llama_kv_cache_clear(ctx);
+
+ // decode all tasks [i0, i1)
+ if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
+ fprintf(stderr, "%s: llama_decode() failed\n", __func__);
+ return;
+ }
+
+ // Compute log-probs in parallel
+ // First we collect all tasks
+ eval_pairs.clear();
+ for (size_t i = i0; i < i1; ++i) {
+ auto& cur_task = tasks[i];
+ size_t li = cur_task.common_prefix;
+ for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
+ for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
+ eval_pairs.push_back(std::make_pair(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]));
+ }
+ ++li;
+ }
+ }
+ // Then we do the actual calculation
+ compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
+
+ size_t ir = 0;
+
+ // compute the logprobs for each ending of the decoded tasks
+ for (size_t i = i0; i < i1; ++i) {
+ auto & cur_task = tasks[i];
+ //printf("==== Evaluating <%s> with correct answer ", cur_task.question.c_str());
+ //for (int j = 0; j < int(cur_task.mc1.labels.size()); ++j) {
+ // if (cur_task.mc1.labels[j] == 1) {
+ // printf("%d", j+1);
+ // }
+ //}
+ //printf("\n common_prefix: %zu\n", cur_task.common_prefix);
+
+ std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(cur_task.i_batch + cur_task.common_prefix - 1), n_vocab*sizeof(float));
+
+ const auto first_probs = softmax(tok_logits);
+
+ cur_task.log_probs.resize(cur_task.seq_tokens.size());
+ for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
+ size_t count = 1;
+ float log_prob = std::log(first_probs[cur_task.seq_tokens[s][cur_task.common_prefix]]);
+ for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
+ //printf(" %zu %g\n", ir, eval_results[ir]);
+ ++count;
+ log_prob += eval_results[ir++];
+ }
+ cur_task.log_probs[s] = log_prob / count;
+ //printf(" Final: %g\n", log_prob / count);
+ //printf(" <%s> : %g\n", cur_task.mc1.answers[s].c_str(), log_prob/count);
+ }
+
+ // Find the ending with maximum logprob
+ size_t logprob_max_idx = 0;
+ float logprob_max_val = cur_task.log_probs[0];
+ for (size_t s = 1; s < cur_task.log_probs.size(); s++) {
+ if (cur_task.log_probs[s] > logprob_max_val) {
+ logprob_max_val = cur_task.log_probs[s];
+ logprob_max_idx = s;
+ }
+ }
+
+ n_tot_answers += cur_task.log_probs.size();
+ if (cur_task.mc1.labels[logprob_max_idx] == 1) {
+ ++n_correct;
+ }
+ ++n_done;
+
+ // Print the accumulated accuracy mean x 100
+ printf("%d\t%.8lf\n", n_done, 100.*n_correct/n_done);
+ fflush(stdout);
+ }
+
+ i0 = i1 - 1;
+ }
+
+ llama_batch_free(batch);
+
+ if (n_done < 100) return;
+
+ float p = 1.f*n_correct/n_done;
+ float sigma = sqrt(p*(1-p)/(n_done-1));
+ printf("\n Final result: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma);
+ p = 1.f*n_done/n_tot_answers;
+ sigma = sqrt(p*(1-p)/(n_done-1));
+ printf("Random chance: %.4f +/- %.4f\n", 100.f*p, 100.f*sigma);
+
+ printf("\n");
+}
+
int main(int argc, char ** argv) {
gpt_params params;
hellaswag_score(ctx, params);
} else if (params.winogrande) {
winogrande_score(ctx, params);
+ } else if (params.multiple_choice) {
+ multiple_choice_score(ctx, params);
} else {
results = perplexity(ctx, params);
}
overview / pkg / ggml / sources / llama.cpp / commitdiff