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https://github.com/nomic-ai/gpt4all
synced 2024-11-02 09:40:42 +00:00
Working efficient chat context.
This commit is contained in:
Adam Treat
parent
c763d4737d
commit
e1677cb353
98
gptj.cpp
98
gptj.cpp
@ -687,14 +687,7 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
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std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(d_ptr->vocab, prompt);
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n_predict = std::min(n_predict, d_ptr->model.hparams.n_ctx - (int) embd_inp.size());
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ctx.n_past = std::min(ctx.n_past, 1024);
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// n_batch = embd_inp.size();
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std::cout << "The past was: " << ctx.n_past;
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fflush(stdout);
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std::vector<gpt_vocab::id> embd;
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std::vector<gpt_vocab::id> resp;
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ctx.n_past = std::min(ctx.n_past, d_ptr->model.hparams.n_ctx);
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// determine the required inference memory per token:
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static bool initialized = false;
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@ -704,69 +697,50 @@ void GPTJ::prompt(const std::string &prompt, std::function<bool(const std::strin
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initialized = true;
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}
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for (int i = embd.size(); i < embd_inp.size() + n_predict; i++) {
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// predict
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if (embd.size() > 0) {
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const int64_t t_start_us = ggml_time_us();
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// process the prompt in batches
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size_t i = 0;
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const int64_t t_start_prompt_us = ggml_time_us();
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while (i < embd_inp.size()) {
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size_t batch_end = std::min(i + n_batch, embd_inp.size());
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std::vector<gpt_vocab::id> batch(embd_inp.begin() + i, embd_inp.begin() + batch_end);
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if (!gptj_eval(d_ptr->model, d_ptr->n_threads, ctx.n_past, batch, ctx.logits, mem_per_token)) {
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std::cerr << "GPT-J ERROR: Failed to process prompt\n";
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return;
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}
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ctx.n_past += batch.size();
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i = batch_end;
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}
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t_prompt_us += ggml_time_us() - t_start_prompt_us;
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if (!gptj_eval(d_ptr->model, d_ptr->n_threads, ctx.n_past, embd, ctx.logits, mem_per_token)) {
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std::cerr << "GPT-J ERROR: Failed to predict\n";
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return;
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}
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// predict next tokens
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int32_t totalPredictions = 0;
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for (int i = 0; i < n_predict; i++) {
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t_predict_us += ggml_time_us() - t_start_us;
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// sample next token
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const int n_vocab = d_ptr->model.hparams.n_vocab;
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gpt_vocab::id id = 0;
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{
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const int64_t t_start_sample_us = ggml_time_us();
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id = gpt_sample_top_k_top_p(d_ptr->vocab, ctx.logits.data() + (ctx.logits.size() - n_vocab),
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top_k, top_p, temp, d_ptr->rng);
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t_sample_us += ggml_time_us() - t_start_sample_us;
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}
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ctx.n_past += embd.size();
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embd.clear();
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resp.clear();
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if (i >= embd_inp.size()) {
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t_prompt_us += ggml_time_us() - t_main_start_us;
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// sample next token
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const int n_vocab = d_ptr->model.hparams.n_vocab;
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gpt_vocab::id id = 0;
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{
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const int64_t t_start_sample_us = ggml_time_us();
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id = gpt_sample_top_k_top_p(d_ptr->vocab, ctx.logits.data() + (ctx.logits.size() - n_vocab), top_k, top_p, temp, d_ptr->rng);
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t_sample_us += ggml_time_us() - t_start_sample_us;
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}
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// add it to the context
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embd.push_back(id);
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if (id != 50256)
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resp.push_back(id);
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} else {
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// if here, it means we are still processing the input prompt
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for (int k = i; k < embd_inp.size(); k++) {
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embd.push_back(embd_inp[k]);
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if (embd.size() > n_batch) {
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break;
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}
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}
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i += embd.size() - 1;
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const int64_t t_start_predict_us = ggml_time_us();
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if (!gptj_eval(d_ptr->model, d_ptr->n_threads, ctx.n_past, { id }, ctx.logits, mem_per_token)) {
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std::cerr << "GPT-J ERROR: Failed to predict next token\n";
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return;
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}
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t_predict_us += ggml_time_us() - t_start_predict_us;
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ctx.n_past += 1;
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// display text
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for (auto id : resp) {
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if (!response(d_ptr->vocab.id_to_token[id]))
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goto stop_generating;
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}
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// end of text token
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if (embd.back() == 50256) {
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++totalPredictions;
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if (id == 50256 /*end of text*/ || !response(d_ptr->vocab.id_to_token[id]))
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break;
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}
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}
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stop_generating:
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#if 0
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#if 1
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// report timing
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{
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const int64_t t_main_end_us = ggml_time_us();
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@ -774,7 +748,7 @@ stop_generating:
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std::cout << "GPT-J INFO: mem per token = " << mem_per_token << " bytes\n";
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std::cout << "GPT-J INFO: sample time = " << t_sample_us/1000.0f << " ms\n";
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std::cout << "GPT-J INFO: prompt time = " << t_prompt_us/1000.0f << " ms\n";
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std::cout << "GPT-J INFO: predict time = " << t_predict_us/1000.0f << " ms / " << t_predict_us/1000.0f/n_past << " ms per token\n";
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std::cout << "GPT-J INFO: predict time = " << t_predict_us/1000.0f << " ms / " << t_predict_us/1000.0f/totalPredictions << " ms per token\n";
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std::cout << "GPT-J INFO: total time = " << (t_main_end_us - t_main_start_us)/1000.0f << " ms\n";
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fflush(stdout);
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}
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8
main.qml
8
main.qml
@ -210,11 +210,9 @@ Window {
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chatModel.append({"name": qsTr("Prompt: "), "currentResponse": false, "value": textInput.text})
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chatModel.append({"name": qsTr("Response: "), "currentResponse": true, "value": "", "prompt": prompt})
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// var contextPrompt = ""
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// for (var i = 0; i < chatModel.count; ++i) {
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// var listElement = chatModel.get(i)
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// contextPrompt += listElement.value + "\n";
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// }
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// var contextPrompt;
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// for (var i = 0; i < chatModel.count; ++i)
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// contextPrompt += chatModel.get(i).value + "\n";
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// prompt = contextPrompt + textInput.text + "\n"
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LLM.resetResponse()
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