haraldwolff/llama.cpp
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Add ability to evauate multiple choice tasks (#5047)

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* 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>
This commit is contained in:
Kawrakow 2024-01-21 14:42:44 +02:00 committed by GitHub
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commit 7dcbe39d36
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3 changed files with 422 additions and 3 deletions
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31
common/common.cpp
View file

@ -203,6 +203,25 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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;
@ -689,6 +708,14 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
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") {
@ -888,6 +915,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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);
@ -936,6 +965,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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);

3
common/common.h
View file

@ -108,6 +108,9 @@ struct gpt_params {
bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt
size_t winogrande_tasks= 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs

391
examples/perplexity/perplexity.cpp
View file

@ -540,14 +540,14 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// 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);
@ -1031,6 +1031,389 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
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;
@ -1091,6 +1474,8 @@ int main(int argc, char ** argv) {
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);
}