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Backend prompt dedup (#822)

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* Deduplicated prompt() function code
This commit is contained in:
AT
2023-06-04 08:59:24 -04:00
committed by GitHub
parent 945297d837
commit bbe195ee02
10 changed files with 286 additions and 457 deletions
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171
gpt4all-backend/gptj.cpp
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@@ -890,159 +890,50 @@ size_t GPTJ::restoreState(const uint8_t *src)
return gptj_set_state_data(d_ptr->model, &d_ptr->rng, src);
}
void GPTJ::prompt(const std::string &prompt,
std::function<bool(int32_t)> promptCallback,
std::function<bool(int32_t, const std::string&)> responseCallback,
std::function<bool(bool)> recalculateCallback,
PromptContext &promptCtx) {
std::vector<LLModel::Token> GPTJ::tokenize(const std::string &str) const
{
return ::gpt_tokenize(d_ptr->vocab, str);
}
if (!isModelLoaded()) {
std::cerr << "GPT-J ERROR: prompt won't work with an unloaded model!\n";
return;
}
LLModel::Token GPTJ::sampleToken(PromptContext &promptCtx) const
{
const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
return gpt_sample_top_k_top_p(d_ptr->model->hparams.n_vocab,
promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
n_prev_toks,
promptCtx.logits,
promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
promptCtx.repeat_penalty,
d_ptr->rng);
}
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(d_ptr->vocab, prompt);
// save the context size
promptCtx.n_ctx = d_ptr->model->hparams.n_ctx;
if ((int) embd_inp.size() > promptCtx.n_ctx - 4) {
responseCallback(-1, "ERROR: The prompt size exceeds the context window size and cannot be processed.");
std::cerr << "GPT-J ERROR: The prompt is" << embd_inp.size() <<
"tokens and the context window is" << promptCtx.n_ctx << "!\n";
return;
}
promptCtx.n_predict = std::min(promptCtx.n_predict, promptCtx.n_ctx - (int) embd_inp.size());
promptCtx.n_past = std::min(promptCtx.n_past, promptCtx.n_ctx);
std::string_view GPTJ::tokenToString(Token id) const
{
return d_ptr->vocab.id_to_token[id];
}
bool GPTJ::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens) const
{
// determine the required inference memory per token:
static bool initialized = false;
static std::vector<gpt_vocab::id> p_instruct;
static std::vector<gpt_vocab::id> r_instruct;
if (!initialized) {
gptj_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, promptCtx.logits,
gptj_eval(*d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits,
d_ptr->mem_per_token);
initialized = true;
}
// process the prompt in batches
size_t i = 0;
while (i < embd_inp.size()) {
size_t batch_end = std::min(i + promptCtx.n_batch, embd_inp.size());
std::vector<gpt_vocab::id> batch(embd_inp.begin() + i, embd_inp.begin() + batch_end);
// Check if the context has run out...
if (promptCtx.n_past + int32_t(batch.size()) > promptCtx.n_ctx) {
const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
// Erase the first percentage of context from the tokens...
std::cerr << "GPTJ: reached the end of the context window so resizing\n";
promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
promptCtx.n_past = promptCtx.tokens.size();
recalculateContext(promptCtx, recalculateCallback);
assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
}
if (!evalTokens(promptCtx, batch)) {
std::cerr << "GPT-J ERROR: Failed to process prompt\n";
return;
}
size_t tokens = batch_end - i;
for (size_t t = 0; t < tokens; ++t) {
if (int32_t(promptCtx.tokens.size()) == promptCtx.n_ctx)
promptCtx.tokens.erase(promptCtx.tokens.begin());
promptCtx.tokens.push_back(batch.at(t));
if (!promptCallback(batch.at(t)))
return;
}
promptCtx.n_past += batch.size();
i = batch_end;
}
std::string cachedResponse;
std::vector<gpt_vocab::id> cachedTokens;
std::unordered_set<std::string> reversePrompts
= { "### Instruction", "### Prompt", "### Response", "### Human", "### Assistant", "### Context" };
// predict next tokens
for (int i = 0; i < promptCtx.n_predict; i++) {
// sample next token
const int n_vocab = d_ptr->model->hparams.n_vocab;
gpt_vocab::id id = 0;
{
const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
id = gpt_sample_top_k_top_p(n_vocab,
promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
n_prev_toks,
promptCtx.logits,
promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
promptCtx.repeat_penalty,
d_ptr->rng);
}
// Check if the context has run out...
if (promptCtx.n_past + 1 > promptCtx.n_ctx) {
const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
// Erase the first percentage of context from the tokens...
std::cerr << "GPTJ: reached the end of the context window so resizing\n";
promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
promptCtx.n_past = promptCtx.tokens.size();
recalculateContext(promptCtx, recalculateCallback);
assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
}
if (!evalTokens(promptCtx, { id })) {
std::cerr << "GPT-J ERROR: Failed to predict next token\n";
return;
}
promptCtx.n_past += 1;
// display text
if (id == 50256 /*end of text*/)
return;
const std::string str = d_ptr->vocab.id_to_token[id];
// Check if the provided str is part of our reverse prompts
bool foundPartialReversePrompt = false;
const std::string completed = cachedResponse + str;
if (reversePrompts.find(completed) != reversePrompts.end())
return;
// Check if it partially matches our reverse prompts and if so, cache
for (const auto &s : reversePrompts) {
if (s.compare(0, completed.size(), completed) == 0) {
foundPartialReversePrompt = true;
cachedResponse = completed;
break;
}
}
// Regardless the token gets added to our cache
cachedTokens.push_back(id);
// Continue if we have found a partial match
if (foundPartialReversePrompt)
continue;
// Empty the cache
for (auto t : cachedTokens) {
if (int32_t(promptCtx.tokens.size()) == promptCtx.n_ctx)
promptCtx.tokens.erase(promptCtx.tokens.begin());
promptCtx.tokens.push_back(t);
if (!responseCallback(t, d_ptr->vocab.id_to_token[t]))
return;
}
cachedTokens.clear();
}
return gptj_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
}
bool GPTJ::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens)
int32_t GPTJ::contextLength() const
{
return gptj_eval(*d_ptr->model, d_ptr->n_threads, ctx.n_past, tokens, ctx.logits, d_ptr->mem_per_token);
return d_ptr->model->hparams.n_ctx;
}
const std::vector<LLModel::Token> &GPTJ::endTokens() const
{
static const std::vector<LLModel::Token> fres = {50256};
return fres;
}
#if defined(_WIN32)