Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Add back top_k #56

Merged
merged 3 commits into from
Mar 12, 2023
Merged

Add back top_k #56

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
7412189
Add back top_k
BillHamShop Mar 8, 2023
2913d75
Update utils.cpp
ggerganov Mar 12, 2023
90b9b5b
Update utils.h
ggerganov Mar 12, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion main.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -823,6 +823,7 @@ int main(int argc, char ** argv) {

if (i >= embd_inp.size()) {
// sample next token
const float top_k = params.top_k;
const float top_p = params.top_p;
const float temp = params.temp;
const float repeat_penalty = params.repeat_penalty;
Expand All @@ -834,7 +835,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_sample_us = ggml_time_us();

id = llama_sample_top_p(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_p, temp, rng);
id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);

last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
Expand Down
79 changes: 4 additions & 75 deletions utils.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -295,25 +295,8 @@ bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
return true;
}

gpt_vocab::id gpt_sample_top_k_top_p(
const gpt_vocab & vocab,
const float * logits,
int top_k,
double top_p,
double temp,
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();

std::vector<std::pair<double, gpt_vocab::id>> logits_id;
logits_id.reserve(n_logits);

{
const double scale = 1.0/temp;
for (int i = 0; i < n_logits; ++i) {
logits_id.push_back(std::make_pair(logits[i]*scale, i));
}
}

void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) {
// find the top K tokens
std::partial_sort(
logits_id.begin(),
Expand All @@ -323,63 +306,14 @@ gpt_vocab::id gpt_sample_top_k_top_p(
});

logits_id.resize(top_k);

double maxl = -INFINITY;
for (const auto & kv : logits_id) {
maxl = std::max(maxl, kv.first);
}

// compute probs for the top K tokens
std::vector<double> probs;
probs.reserve(logits_id.size());

double sum = 0.0;
for (const auto & kv : logits_id) {
double p = exp(kv.first - maxl);
probs.push_back(p);
sum += p;
}

// normalize the probs
for (auto & p : probs) {
p /= sum;
}

if (top_p < 1.0f) {
double cumsum = 0.0f;
for (int i = 0; i < top_k; i++) {
cumsum += probs[i];
if (cumsum >= top_p) {
top_k = i + 1;
probs.resize(top_k);
logits_id.resize(top_k);
break;
}
}

cumsum = 1.0/cumsum;
for (int i = 0; i < (int) probs.size(); i++) {
probs[i] *= cumsum;
}
}

//printf("\n");
//for (int i = 0; i < (int) probs.size(); i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
//}
//exit(0);

std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);

return logits_id[idx].second;
}

gpt_vocab::id llama_sample_top_p(
gpt_vocab::id llama_sample_top_p_top_k(
const gpt_vocab & vocab,
const float * logits,
std::vector<gpt_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
double temp,
std::mt19937 & rng) {
Expand All @@ -406,12 +340,7 @@ gpt_vocab::id llama_sample_top_p(
}
}

std::sort(
logits_id.begin(),
logits_id.end(),
[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
return a.first > b.first;
});
sample_top_k(logits_id, top_k);

double maxl = -INFINITY;
for (const auto & kv : logits_id) {
Expand Down
19 changes: 6 additions & 13 deletions utils.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,7 @@ struct gpt_params {
int32_t repeat_last_n = 64; // last n tokens to penalize

// sampling parameters
int32_t top_k = 40; // unused
int32_t top_k = 40;
float top_p = 0.95f;
float temp = 0.80f;
float repeat_penalty = 1.30f;
Expand Down Expand Up @@ -77,26 +77,19 @@ bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
// - consider only the top K tokens
// - from them, consider only the top tokens with cumulative probability > P
//
// TODO: not sure if this implementation is correct
// TODO: temperature is not implemented
//
gpt_vocab::id gpt_sample_top_k_top_p(
const gpt_vocab & vocab,
const float * logits,
int top_k,
double top_p,
double temp,
std::mt19937 & rng);

gpt_vocab::id llama_sample_top_p(
gpt_vocab::id llama_sample_top_p_top_k(
const gpt_vocab & vocab,
const float * logits,
std::vector<gpt_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
double temp,
std::mt19937 & rng);

// filer to top K tokens from list of logits
void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k);

//
// Quantization
//
Expand Down