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Add Winogrande evaluation (#5015)

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* winogrande: simple implementation

It doesn't look like it is working - why?
For Mistral-7B it is barely better than
random chance (score ~60% for 1267 tasks), while I see
Mistral-7B scoring 78.4% on the HF leader board.
1-sigma statistical uncertainty for 1267 tasks is ~1.4,
so no way the difference is due to statistics.

* winogrande: somewhat better

Score for Mistrali7-B is now 68.9 on the validation set of
winogrande_debiased. Still far from the reported 78.4, but
better than what I had before.

* winogrande: improving

Mistral-7B score is now 73.56.
Still not quite 78.4 but getting there.
We are also getting a lower score on HellaSwag
compared to HF leader board, so I'm not expecting
we will get up to 78.4 anyway.

It looks like it is better to skip the choice word(s)
when evaluating the average log-likelihood. This kind of
makes sense because a more common word (in Winogrande this is
often a name) will have a higher probability without knowing
about the follow up context, and this will skew the log-likelihood
towards the more common word. We can only do this if the
choice words are not last in the sentence.

It also looks like it is better to skip the punctuation at the
end of the sentence, provided the choice words are not last.

* winogrande: add dataset instructions

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2024-01-18 13:46:27 +02:00 committed by GitHub
parent dcad445d0c
commit 682986a08e
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3 changed files with 251 additions and 5 deletions
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10
common/common.cpp
View File

@ -681,6 +681,14 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.hellaswag_tasks = std::stoi(argv[i]);
} else if (arg == "--winogrande") {
params.winogrande = true;
} else if (arg == "--winogrande-tasks") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.winogrande_tasks = std::stoi(argv[i]);
} else if (arg == "--ignore-eos") {
params.ignore_eos = true;
} else if (arg == "--no-penalize-nl") {
@ -926,6 +934,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
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(" --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

@ -105,6 +105,9 @@ struct gpt_params {
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
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 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

243
examples/perplexity/perplexity.cpp
View File

@ -9,6 +9,9 @@
#include <thread>
#include <mutex>
#include <vector>
#include <array>
#include <fstream>
#include <sstream>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
@ -419,9 +422,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
return {tokens, ppl, logit_history, prob_history};
}
static std::vector<float> hellaswag_evaluate_tokens(
llama_context * ctx, std::vector<int> & tokens, int n_past, int n_batch, int n_vocab
) {
static std::vector<float> evaluate_tokens(llama_context * ctx, std::vector<int> & tokens,
int n_past, int n_batch, int n_vocab) {
std::vector<float> result;
result.reserve(tokens.size() * n_vocab);
size_t n_chunk = (tokens.size() + n_batch - 1)/n_batch;
@ -573,7 +575,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
// clear the KV cache
llama_kv_cache_clear(ctx);
auto logits = hellaswag_evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab);
auto logits = evaluate_tokens(ctx, query_embd, 0, params.n_batch, n_vocab);
if (logits.empty()) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
@ -622,7 +624,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
//}
// Evaluate the query
logits = hellaswag_evaluate_tokens(ctx, query_embd, context_size, params.n_batch, n_vocab);
logits = evaluate_tokens(ctx, query_embd, context_size, params.n_batch, n_vocab);
if (logits.empty()) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
@ -676,6 +678,235 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
printf("\n");
}
struct winogrande_entry {
std::string first;
std::string second;
std::array<std::string, 2> choices;
int answer;
};
static std::vector<winogrande_entry> load_winogrande_from_csv(const std::string& prompt) {
std::vector<winogrande_entry> result;
std::istringstream in(prompt);
std::string line;
std::array<int, 4> comma_pos;
while (true) {
std::getline(in, line);
if (in.fail() || in.eof()) break;
int ipos = 0;
bool quote_open = false;
for (int i = 0; i < int(line.size()); ++i) {
if (!quote_open) {
if (line[i] == ',') {
comma_pos[ipos++] = i;
if (ipos == 4) break;
}
else if (line[i] == '"') {
quote_open = true;
}
}
else {
if (line[i] == '"') {
quote_open = false;
}
}
}
if (ipos != 4) {
printf("%s: failed to find comma separators in <%s>\n", __func__, line.c_str());
continue;
}
auto sentence = line[comma_pos[0]+1] == '"' ? line.substr(comma_pos[0]+2, comma_pos[1] - comma_pos[0] - 3)
: line.substr(comma_pos[0]+1, comma_pos[1] - comma_pos[0] - 1);
auto choice1 = line.substr(comma_pos[1]+1, comma_pos[2] - comma_pos[1] - 1);
auto choice2 = line.substr(comma_pos[2]+1, comma_pos[3] - comma_pos[2] - 1);
auto answer = line.substr(comma_pos[3]+1, line.size() - comma_pos[3] - 1);
auto index = line.substr(0, comma_pos[0]);
int where = 0;
for ( ; where < int(sentence.size()); ++where) {
if (sentence[where] == '_') break;
}
if (where == int(sentence.size())) {
printf("%s: no _ in <%s>\n", __func__, sentence.c_str());
continue;
}
std::istringstream stream(answer.c_str());
int i_answer; stream >> i_answer;
if (stream.fail() || i_answer < 1 || i_answer > 2) {
printf("%s: failed to parse answer <%s>\n", __func__, answer.c_str());
continue;
}
result.emplace_back();
auto& wg = result.back();
wg.first = sentence.substr(0, where);
wg.second = sentence.substr(where + 1, sentence.size() - where - 1);
wg.choices[0] = std::move(choice1);
wg.choices[1] = std::move(choice2);
wg.answer = i_answer;
}
return result;
}
/*
* Evaluates the Winogrande score.
* Uses a CSV containing task index, dentence, choice 1, choice 2, answer (1 or 2)
* You can get one such dataset from e.g. https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp
* As an example, the 1st row in the above dataset is
*
* 0,Sarah was a much better surgeon than Maria so _ always got the easier cases.,Sarah,Maria,2
*
*/
static void winogrande_score(llama_context * ctx, const gpt_params & params) {
constexpr int k_min_trailing_ctx = 3;
auto data = load_winogrande_from_csv(params.prompt);
if (data.empty()) {
fprintf(stderr, "%s: no tasks\n", __func__);
return;
}
fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, data.size());
if (params.winogrande_tasks > 0 && params.winogrande_tasks < data.size()) {
fprintf(stderr, "%s : selecting %zu random tasks\n", __func__, params.winogrande_tasks);
std::mt19937 rng(1);
std::vector<int> aux(data.size());
for (int i = 0; i < int(data.size()); ++i) {
aux[i] = i;
}
float scale = 1/(1.f + (float)rng.max());
std::vector<winogrande_entry> selected;
selected.reserve(params.winogrande_tasks);
for (int i = 0; i < int(params.winogrande_tasks); ++i) {
int j = int(scale*rng()*aux.size());
selected[i] = std::move(data[aux[j]]);
aux[j] = aux.back();
aux.pop_back();
}
data = std::move(selected);
}
// This is needed as usual for LLaMA models
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
std::vector<float> tok_logits(n_vocab);
int n_correct = 0;
int n_done = 0;
for (size_t task_idx = 0; task_idx < data.size(); task_idx++) {
const auto& task = data[task_idx];
auto base_context = ::llama_tokenize(ctx, task.first, add_bos);
auto base_ctx_1st = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos);
auto base_ctx_2nd = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos);
auto sentence_1st = task.first + task.choices[0] + task.second;
auto sentence_2nd = task.first + task.choices[1] + task.second;
auto query_1st = ::llama_tokenize(ctx, sentence_1st, add_bos);
auto query_2nd = ::llama_tokenize(ctx, sentence_2nd, add_bos);
if (query_1st.size() > (size_t)n_ctx || query_2nd.size() > (size_t)n_ctx) {
fprintf(stderr, "%s : number of tokens in queries %zu, %zu > n_ctxl\n", __func__, query_1st.size(), query_2nd.size());
return;
}
auto query_1st_size = query_1st.size();
auto query_2nd_size = query_2nd.size();
// Speedup small evaluations by evaluating atleast 32 tokens
// For Winogrande this seems to slow it down rather than speed it up.
//if (query_1st.size() < 32) query_1st.resize(32);
//if (query_2nd.size() < 32) query_2nd.resize(32);
llama_kv_cache_clear(ctx);
auto logits_1st = evaluate_tokens(ctx, query_1st, 0, params.n_batch, n_vocab);
llama_kv_cache_clear(ctx);
auto logits_2nd = evaluate_tokens(ctx, query_2nd, 0, params.n_batch, n_vocab);
if (logits_1st.empty() || logits_2nd.empty()) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}
bool skip_choice = query_1st_size - base_ctx_1st.size() > k_min_trailing_ctx &&
query_2nd_size - base_ctx_2nd.size() > k_min_trailing_ctx;
float score_1st = 0;
bool is_nan_1st = false;
const auto& base_1 = skip_choice ? base_ctx_1st : base_context;
const int last_1st = query_1st_size - base_1.size() > 1 ? 1 : 0;
for (size_t j = base_1.size()-1; j < query_1st_size-1-last_1st; ++j) {
std::memcpy(tok_logits.data(), logits_1st.data() + j*n_vocab, n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[query_1st[j+1]];
if (std::isnan(prob) || !prob) {
fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
prob, j, sentence_1st.c_str(), base_context.size());
is_nan_1st = true;
break;
}
score_1st += std::log(prob);
}
score_1st /= (query_1st_size - base_1.size() - last_1st);
float score_2nd = 0;
bool is_nan_2nd = false;
const auto& base_2 = skip_choice ? base_ctx_2nd : base_context;
const int last_2nd = query_2nd_size - base_2.size() > 1 ? 1 : 0;
for (size_t j = base_2.size()-1; j < query_2nd_size-1-last_2nd; ++j) {
std::memcpy(tok_logits.data(), logits_2nd.data() + j*n_vocab, n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[query_2nd[j+1]];
if (std::isnan(prob) || !prob) {
fprintf(stderr, "%s: %g probability for token %zu when evaluating <%s>. Base context has %zu tokens\n", __func__,
prob, j, sentence_2nd.c_str(), base_context.size());
is_nan_2nd = true;
break;
}
score_2nd += std::log(prob);
}
score_2nd /= (query_2nd_size - base_2.size() - last_2nd);
if (is_nan_1st || is_nan_2nd) {
continue;
}
if (std::isnan(score_1st) || std::isnan(score_2nd)) {
printf("================== NaN score %g, %g) for:\n", score_1st, score_2nd);
printf("Q1: <%s> - %zu tokens\n", sentence_1st.c_str(), query_1st_size);
printf("Q2: <%s> - %zu tokens\n", sentence_2nd.c_str(), query_2nd_size);
printf("B : <%s> - %zu tokens\n", task.first.c_str(), base_context.size());
printf("base_1 has %zu tokens, base_2 has %zu tokens, skip_choice = %d\n", base_1.size(), base_2.size(), skip_choice);
continue;
}
int result = score_1st > score_2nd ? 1 : 2;
if (result == task.answer) {
++n_correct;
}
++n_done;
// Print the accumulated accuracy mean x 100
printf("%zu\t%.4lf\t%10.6f %10.6f %d %d\n",task_idx+1, 100.0 * n_correct/n_done,score_1st,score_2nd,result,task.answer);
fflush(stdout);
}
printf("\n");
if (n_done < 100) return;
const float p = 1.f*n_correct/n_done;
const float sigma = 100.f*sqrt(p*(1-p)/(n_done-1));
printf("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma);
}
int main(int argc, char ** argv) {
gpt_params params;
@ -733,6 +964,8 @@ int main(int argc, char ** argv) {
struct results_perplexity results;
if (params.hellaswag) {
hellaswag_score(ctx, params);
} else if (params.winogrande) {
winogrande_score(ctx, params);
} else {
results = perplexity(ctx, params);
}