Archives
/
gpt4all
mirror of https://github.com/nomic-ai/gpt4all
2
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Back out the prompt/response finding in gptj since it doesn't seem to help.

Browse Source 
Guard against reaching the end of the context window which we don't handle
gracefully except for avoiding a crash.
pull/520/head
Adam Treat 1 year ago
parent 154f35ce53
commit bb78ee0025
2 changed files with 45 additions and 68 deletions
Show Stats Download Patch File Download Diff File

78
gptj.cpp
Unescape Escape View File

@ -684,7 +684,7 @@ bool GPTJ::isModelLoaded() const
}
void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::string&)> response,
PromptContext &ctx, int32_t n_predict, int32_t top_k, float top_p, float temp, int32_t n_batch) {
PromptContext &promptCtx, int32_t n_predict, int32_t top_k, float top_p, float temp, int32_t n_batch) {
if (!isModelLoaded()) {
std::cerr << "GPT-J ERROR: prompt won't work with an unloaded model!\n";
@ -700,8 +700,10 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(d_ptr->vocab, prompt);
n_predict = std::min(n_predict, d_ptr->model.hparams.n_ctx - (int) embd_inp.size());
ctx.n_past = std::min(ctx.n_past, d_ptr->model.hparams.n_ctx);
const int n_ctx = d_ptr->model.hparams.n_ctx;
n_predict = std::min(n_predict, n_ctx - (int) embd_inp.size());
promptCtx.n_past = std::min(promptCtx.n_past, n_ctx);
// determine the required inference memory per token:
static bool initialized = false;
@ -709,9 +711,7 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
static std::vector<gpt_vocab::id> r_instruct;
size_t mem_per_token = 0;
if (!initialized) {
gptj_eval(d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, ctx.logits, mem_per_token);
p_instruct = ::gpt_tokenize(d_ptr->vocab, "### Prompt:");
r_instruct = ::gpt_tokenize(d_ptr->vocab, "### Response:");
gptj_eval(d_ptr->model, d_ptr->n_threads, 0, { 0, 1, 2, 3 }, promptCtx.logits, mem_per_token);
initialized = true;
}
@ -721,7 +721,15 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
while (i < embd_inp.size()) {
size_t batch_end = std::min(i + n_batch, embd_inp.size());
std::vector<gpt_vocab::id> batch(embd_inp.begin() + i, embd_inp.begin() + batch_end);
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, ctx.n_past, batch, ctx.logits, mem_per_token)) {
// Check if the context has run out...
if (promptCtx.n_past + batch.size() > n_ctx) {
// FIXME: will produce gibberish after this
promptCtx.n_past = std::min(promptCtx.n_past, int(n_ctx - batch.size()));
std::cerr << "GPT-J WARNING: reached the end of the context window!\n";
}
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, promptCtx.n_past, batch, promptCtx.logits, mem_per_token)) {
std::cerr << "GPT-J ERROR: Failed to process prompt\n";
return;
}
@ -730,7 +738,7 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
for (size_t t = 0; t < tokens; ++t)
if (!response(""))
return;
ctx.n_past += batch.size();
promptCtx.n_past += batch.size();
i = batch_end;
}
t_prompt_us += ggml_time_us() - t_start_prompt_us;
@ -738,8 +746,6 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
int p_instructFound = 0;
int r_instructFound = 0;
std::vector<gpt_vocab::id> cachedTokens;
// predict next tokens
int32_t totalPredictions = 0;
for (int i = 0; i < n_predict; i++) {
@ -749,52 +755,30 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
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, ctx.logits.data() + (ctx.logits.size() - n_vocab),
id = gpt_sample_top_k_top_p(d_ptr->vocab, promptCtx.logits.data() + (promptCtx.logits.size() - n_vocab),
top_k, top_p, temp, d_ptr->rng);
t_sample_us += ggml_time_us() - t_start_sample_us;
}
// Check if the context has run out...
if (promptCtx.n_past + 1 > n_ctx) {
// FIXME: will produce gibberish after this
promptCtx.n_past = std::min(promptCtx.n_past, n_ctx - 1);
std::cerr << "GPT-J WARNING: reached the end of the context window!\n";
}
const int64_t t_start_predict_us = ggml_time_us();
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, ctx.n_past, { id }, ctx.logits, mem_per_token)) {
if (!gptj_eval(d_ptr->model, d_ptr->n_threads, promptCtx.n_past, { id }, promptCtx.logits, mem_per_token)) {
std::cerr << "GPT-J ERROR: Failed to predict next token\n";
return;
}
cachedTokens.emplace_back(id);
// Check if this token is next token for p_instruct or r_instruct
if (p_instruct.at(p_instructFound) == id) {
++p_instructFound;
if (p_instructFound == p_instruct.size()) {
fprintf(stderr, "Warning: Tried to generate \"### Prompt:\" stopping.\n");
fflush(stderr);
goto stop_generating;
}
continue;
} else
p_instructFound = 0;
if (r_instruct.at(r_instructFound) == id) {
++r_instructFound;
if (r_instructFound == r_instruct.size()) {
fprintf(stderr, "Warning: Tried to generate \"### Response:\" stopping.\n");
fflush(stderr);
goto stop_generating;
}
continue;
} else
r_instructFound = 0;
t_predict_us += ggml_time_us() - t_start_predict_us;
for (int j = 0; j < cachedTokens.size(); ++j) {
gpt_vocab::id cachedToken = cachedTokens.at(j);
ctx.n_past += 1;
// display text
++totalPredictions;
if (id == 50256 /*end of text*/ || !response(d_ptr->vocab.id_to_token[cachedToken]))
goto stop_generating;
}
cachedTokens.clear();
promptCtx.n_past += 1;
// display text
++totalPredictions;
if (id == 50256 /*end of text*/ || !response(d_ptr->vocab.id_to_token[id]))
goto stop_generating;
}
stop_generating:

35
llamamodel.cpp
Unescape Escape View File

@ -43,7 +43,7 @@ bool LLamaModel::loadModel(const std::string &modelPath)
d_ptr->params = llama_context_default_params();
gpt_params params;
d_ptr->params.n_ctx = params.n_ctx;
d_ptr->params.n_ctx = 2048;
d_ptr->params.n_parts = params.n_parts;
d_ptr->params.seed = params.seed;
d_ptr->params.f16_kv = params.memory_f16;
@ -114,16 +114,18 @@ void LLamaModel::prompt(const std::string &prompt, std::function<bool(const std:
size_t batch_end = std::min(i + n_batch, embd_inp.size());
std::vector<llama_token> batch(embd_inp.begin() + i, embd_inp.begin() + batch_end);
// Check if the context has run out...
if (promptCtx.n_past + batch.size() > n_ctx) {
std::cerr << "eval n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl;
// FIXME: will produce gibberish after this
promptCtx.n_past = std::min(promptCtx.n_past, int(n_ctx - batch.size()));
std::cerr << "after n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl;
std::cerr << "LLAMA WARNING: reached the end of the context window!\n";
}
if (llama_eval(d_ptr->ctx, batch.data(), batch.size(), promptCtx.n_past, d_ptr->n_threads)) {
std::cerr << "LLAMA ERROR: Failed to process prompt\n";
return;
}
// We pass a null string for each token to see if the user has asked us to stop...
size_t tokens = batch_end - i;
for (size_t t = 0; t < tokens; ++t)
@ -133,37 +135,28 @@ void LLamaModel::prompt(const std::string &prompt, std::function<bool(const std:
i = batch_end;
}
std::vector<llama_token> cachedTokens;
// predict next tokens
int32_t totalPredictions = 0;
for (int i = 0; i < n_predict; i++) {
// sample next token
llama_token id = llama_sample_top_p_top_k(d_ptr->ctx, {}, 0, top_k, top_p, temp, 1.0f);
// Check if the context has run out...
if (promptCtx.n_past + 1 > n_ctx) {
std::cerr << "eval 2 n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl;
// FIXME: will produce gibberish after this
promptCtx.n_past = std::min(promptCtx.n_past, n_ctx - 1);
std::cerr << "after 2 n_ctx " << n_ctx << " n_past " << promptCtx.n_past << std::endl;
std::cerr << "LLAMA WARNING: reached the end of the context window!\n";
}
if (llama_eval(d_ptr->ctx, &id, 1, promptCtx.n_past, d_ptr->n_threads)) {
std::cerr << "LLAMA ERROR: Failed to predict next token\n";
return;
}
cachedTokens.emplace_back(id);
for (int j = 0; j < cachedTokens.size(); ++j) {
llama_token cachedToken = cachedTokens.at(j);
promptCtx.n_past += 1;
// display text
++totalPredictions;
if (id == llama_token_eos() || !response(llama_token_to_str(d_ptr->ctx, cachedToken)))
goto stop_generating;
}
cachedTokens.clear();
}
stop_generating:
return;
promptCtx.n_past += 1;
// display text
++totalPredictions;
if (id == llama_token_eos() || !response(llama_token_to_str(d_ptr->ctx, id)))
return;
}
}

Write Preview
Loading…
Cancel
Save