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backend: dedupe tokenizing code in mpt/gptj

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This commit is contained in:
Aaron Miller 2023-05-15 17:42:20 -07:00 committed by AT
parent 6182026c70
commit d14936bfd6
4 changed files with 6 additions and 102 deletions
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2
gpt4all-backend/gptj.cpp
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@ -983,7 +983,7 @@ void GPTJ::prompt(const std::string &prompt,
gpt_vocab::id id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
id = gpt_sample_top_k_top_p(d_ptr->vocab,
id = gpt_sample_top_k_top_p(d_ptr->vocab, n_vocab,
promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.n_ctx,
promptCtx.n_ctx,
promptCtx.logits,

100
gpt4all-backend/mpt.cpp
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@ -691,104 +691,6 @@ size_t mpt_copy_state_data(const mpt_model &model, const std::mt19937 &rng, uint
return written;
}
gpt_vocab::id mpt_sample_top_k_top_p(
const gpt_vocab & vocab,
const size_t actualVocabSize,
const int32_t * last_n_tokens_data,
int last_n_tokens_size,
const std::vector<float> logits,
int top_k,
double top_p,
double temp,
float repeat_penalty,
std::mt19937 & rng) {
int n_logits = actualVocabSize;
const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
const auto * plogits = logits.data() + logits.size() - n_logits;
std::vector<std::pair<double, gpt_vocab::id>> logits_id;
logits_id.reserve(n_logits);
{
const float scale = 1.0f/temp;
for (int i = 0; i < n_logits; ++i) {
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
if (plogits[i] < 0.0f) {
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
} else {
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
}
} else {
logits_id.push_back(std::make_pair(plogits[i]*scale, i));
}
}
}
// find the top K tokens
std::partial_sort(
logits_id.begin(),
logits_id.begin() + top_k, logits_id.end(),
[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
return a.first > b.first;
});
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;
}
size_t mpt_set_state_data(mpt_model *model, std::mt19937 *rng, const uint8_t *src)
{
const uint8_t * in = src;
@ -1006,7 +908,7 @@ void MPT::prompt(const std::string &prompt,
int id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
id = mpt_sample_top_k_top_p(d_ptr->vocab, n_vocab,
id = gpt_sample_top_k_top_p(d_ptr->vocab, n_vocab,
promptCtx.tokens.data() + promptCtx.n_ctx - promptCtx.n_ctx,
promptCtx.n_ctx,
promptCtx.logits,

3
gpt4all-backend/utils.cpp
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@ -219,6 +219,7 @@ bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
gpt_vocab::id gpt_sample_top_k_top_p(
const gpt_vocab & vocab,
const size_t actualVocabSize,
const int32_t * last_n_tokens_data,
int last_n_tokens_size,
const std::vector<float> logits,
@ -227,7 +228,7 @@ gpt_vocab::id gpt_sample_top_k_top_p(
double temp,
float repeat_penalty,
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();
int n_logits = actualVocabSize;
const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
const auto * plogits = logits.data() + logits.size() - n_logits;

1
gpt4all-backend/utils.h
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@ -80,6 +80,7 @@ bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
//
gpt_vocab::id gpt_sample_top_k_top_p(
const gpt_vocab & vocab,
const size_t actualVocabSize,
const int32_t * last_n_tokens_data,
int last_n_tokens_size,
const std::vector<float> logits,