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gpt : fix sampling to use the temperature (close #16)

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Georgi Gerganov 2 years ago
parent c40a5b51a0
commit fb64edddb7
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GPG Key ID: 449E073F9DC10735
3 changed files with 49 additions and 54 deletions
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14
examples/gpt-2/main.cpp
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@ -347,7 +347,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
// - n_threads: number of threads to use
// - n_past: the context size so far
// - embd_inp: the embeddings of the tokens in the context
// - embd_w: the predicted probabilities of the next token
// - embd_w: the predicted logits for the next token
//
bool gpt2_eval(
const gpt2_model & model,
@ -627,7 +627,7 @@ bool gpt2_eval(
inpL = ggml_mul_mat(ctx0, model.wte, inpL);
// logits -> probs
inpL = ggml_soft_max(ctx0, inpL);
//inpL = ggml_soft_max(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
@ -641,7 +641,7 @@ bool gpt2_eval(
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
// return result for just the last token
// return result just for the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
@ -698,7 +698,7 @@ int main(int argc, char ** argv) {
int64_t t_sample_us = 0;
int64_t t_predict_us = 0;
std::vector<float> embd_w;
std::vector<float> logits;
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
@ -714,14 +714,14 @@ int main(int argc, char ** argv) {
// determine the required inference memory per token:
size_t mem_per_token = 0;
gpt2_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, embd_w, mem_per_token);
gpt2_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
// predict
if (embd.size() > 0) {
const int64_t t_start_us = ggml_time_us();
if (!gpt2_eval(model, params.n_threads, n_past, embd, embd_w, mem_per_token)) {
if (!gpt2_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
printf("Failed to predict\n");
return 1;
}
@ -745,7 +745,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_sample_us = ggml_time_us();
id = gpt_sample_top_k_top_p(vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, rng);
id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
t_sample_us += ggml_time_us() - t_start_sample_us;
}

12
examples/gpt-j/main.cpp
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@ -355,7 +355,7 @@ bool gptj_model_load(const std::string & fname, gptj_model & model, gpt_vocab &
// - n_threads: number of threads to use
// - n_past: the context size so far
// - embd_inp: the embeddings of the tokens in the context
// - embd_w: the predicted probabilities of the next token
// - embd_w: the predicted logits for the next token
//
// The GPT-J model requires about 16MB of memory per input token.
//
@ -559,7 +559,7 @@ bool gptj_eval(
}
// logits -> probs
inpL = ggml_soft_max(ctx0, inpL);
//inpL = ggml_soft_max(ctx0, inpL);
// run the computation
ggml_build_forward_expand(&gf, inpL);
@ -630,7 +630,7 @@ int main(int argc, char ** argv) {
int64_t t_sample_us = 0;
int64_t t_predict_us = 0;
std::vector<float> embd_w;
std::vector<float> logits;
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
@ -644,14 +644,14 @@ int main(int argc, char ** argv) {
// determine the required inference memory per token:
size_t mem_per_token = 0;
gptj_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, embd_w, mem_per_token);
gptj_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
// predict
if (embd.size() > 0) {
const int64_t t_start_us = ggml_time_us();
if (!gptj_eval(model, params.n_threads, n_past, embd, embd_w, mem_per_token)) {
if (!gptj_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
printf("Failed to predict\n");
return 1;
}
@ -675,7 +675,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_sample_us = ggml_time_us();
id = gpt_sample_top_k_top_p(vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, rng);
id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
t_sample_us += ggml_time_us() - t_start_sample_us;
}

77
examples/utils.cpp
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@ -261,8 +261,11 @@ gpt_vocab::id gpt_sample_top_k_top_p(
std::vector<std::pair<double, gpt_vocab::id>> logits_id;
logits_id.reserve(n_logits);
for (int i = 0; i < n_logits; i++) {
logits_id.push_back(std::make_pair(logits[i], i));
{
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));
}
}
// find the top K tokens
@ -275,59 +278,51 @@ gpt_vocab::id gpt_sample_top_k_top_p(
logits_id.resize(top_k);
// normalize
{
double sum = 0.0f;
for (int i = 0; i < (int)logits_id.size(); i++) {
sum += logits_id[i].first;
}
double maxl = -INFINITY;
for (const auto & kv : logits_id) {
maxl = std::max(maxl, kv.first);
}
sum = 1.0/sum;
for (int i = 0; i < (int)logits_id.size(); i++) {
logits_id[i].first *= sum;
}
// 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 += logits_id[i].first;
if (cumsum >= top_p) {
logits_id.resize(i+1);
break;
}
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;
}
}
// normalize again
{
double sum = 0.0f;
for (int i = 0; i < (int)logits_id.size(); i++) {
sum += logits_id[i].first;
}
sum = 1.0/sum;
for (int i = 0; i < (int)logits_id.size(); i++) {
logits_id[i].first *= sum;
}
cumsum = 1.0/cumsum;
for (int i = 0; i < (int) probs.size(); i++) {
probs[i] *= cumsum;
}
}
//printf("\n");
//for (int i = 0; i < (int)logits_id.size(); i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), logits_id[i].first);
//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);
// sample from the obtained distribution
std::vector<double> probs;
probs.reserve(logits_id.size());
for (int i = 0; i < (int) logits_id.size(); i++) {
probs.push_back(logits_id[i].first);
}
std::discrete_distribution<> dist(probs.begin(), probs.end());
int idx = dist(rng);

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