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winogrande: evaluate log-probs in parallel (#5036)

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This is a relatively minor performance tweak resulting in
~10% speedup on my system.

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Kawrakow 2024-01-19 11:39:11 +02:00 committed by GitHub
parent 2b3b999cac
commit 7051aacfac
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1 changed files with 33 additions and 38 deletions
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71
examples/perplexity/perplexity.cpp
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@ -458,7 +458,7 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
return true;
}
static void hellaswag_compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers,
static void compute_logprobs(const float * batch_logits, int n_vocab, std::vector<std::thread>& workers,
const std::vector<std::pair<size_t, llama_token>>& eval_pairs, std::vector<float>& eval_results) {
constexpr int k_token_chunk = 4;
if (eval_results.size() != eval_pairs.size()) {
@ -700,7 +700,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
}
}
// Then we do the actual calculation
hellaswag_compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
size_t ir = 0;
@ -906,6 +906,10 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
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());
int n_correct = 0;
int n_done = 0;
@ -956,6 +960,30 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
return;
}
eval_pairs.clear();
for (size_t i = i0; i < i1; ++i) {
auto & task = data[i];
const bool skip_choice =
task.seq_tokens[0].size() - task.common_prefix > k_min_trailing_ctx &&
task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
size_t li = n_base1 - 1;
for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[0][j+1]));
}
const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[1][j+1]));
}
}
compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
size_t ir = 0;
for (size_t i = i0; i < i1; ++i) {
auto & task = data[i];
@ -964,54 +992,21 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.seq_tokens[1].size() - task.common_prefix > k_min_trailing_ctx;
float score_1st = 0;
bool is_nan_1st = false;
const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
size_t li = n_base1 - 1;
for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(task.i_batch + li++), n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[task.seq_tokens[0][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, (task.first + task.choices[0] + task.second).c_str(), n_base1);
is_nan_1st = true;
break;
}
score_1st += std::log(prob);
score_1st += eval_results[ir++];
}
score_1st /= (task.seq_tokens[0].size() - n_base1 - last_1st);
float score_2nd = 0;
bool is_nan_2nd = false;
const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(task.i_batch + li++), n_vocab*sizeof(float));
const float prob = softmax(tok_logits)[task.seq_tokens[1][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, (task.first + task.choices[1] + task.second).c_str(), n_base2);
is_nan_2nd = true;
break;
}
score_2nd += std::log(prob);
score_2nd += eval_results[ir++];
}
score_2nd /= (task.seq_tokens[1].size() - n_base2 - 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", (task.first + task.choices[0] + task.second).c_str(), task.seq_tokens[0].size());
printf("Q2: <%s> - %zu tokens\n", (task.first + task.choices[1] + task.second).c_str(), task.seq_tokens[1].size());
printf("B : <%s> - %zu tokens\n", task.first.c_str(), task.common_prefix);
printf("base_1 has %zu tokens, base_2 has %zu tokens, skip_choice = %d\n", n_base1, n_base2, skip_choice);
continue;
}
int result = score_1st > score_2nd ? 1 : 2;
if (result == task.answer) {
@ -1019,7 +1014,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
}
++n_done;
// Print the accumulated accuracy mean x 100
// print the accumulated accuracy mean x 100
printf("%zu\t%.4lf\t%10.6f %10.6f %d %d\n", i+1, 100.0 * n_correct/n_done, score_1st, score_2nd, result, task.answer);
fflush(stdout);
}