Add interactive mode (#61)

* Initial work on interactive mode.

* Improve interactive mode. Make rev. prompt optional.

* Update README to explain interactive mode.

* Fix OS X build
pull/65/head
Matvey Soloviev 2 years ago committed by GitHub
parent 9661954835
commit 96ea727f47
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@ -183,6 +183,29 @@ The number of files generated for each model is as follows:
When running the larger models, make sure you have enough disk space to store all the intermediate files.
### Interactive mode
If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
In this mode, you can always interrupt generation by pressing Ctrl+C and enter one or more lines of text which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt which makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
Here is an example few-shot interaction, invoked with the command
```
./main -m ./models/13B/ggml-model-q4_0.bin -t 8 --repeat_penalty 1.2 --temp 0.9 --top_p 0.9 -n 256 \
--color -i -r "User:" \
-p \
"Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
User: Hello, Bob.
Bob: Hello. How may I help you today?
User: Please tell me the largest city in Europe.
Bob: Sure. The largest city in Europe is London, the capital of the United Kingdom.
User:"
```
Note the use of `--color` to distinguish between user input and generated text.
![image](https://user-images.githubusercontent.com/401380/224572787-d418782f-47b2-49c4-a04e-65bfa7ad4ec0.png)
## Limitations
- Not sure if my tokenizer is correct. There are a few places where we might have a mistake:

@ -11,6 +11,18 @@
#include <string>
#include <vector>
#include <signal.h>
#include <unistd.h>
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
#define ANSI_COLOR_BLUE "\x1b[34m"
#define ANSI_COLOR_MAGENTA "\x1b[35m"
#define ANSI_COLOR_CYAN "\x1b[36m"
#define ANSI_COLOR_RESET "\x1b[0m"
#define ANSI_BOLD "\x1b[1m"
// determine number of model parts based on the dimension
static const std::map<int, int> LLAMA_N_PARTS = {
{ 4096, 1 },
@ -733,6 +745,18 @@ bool llama_eval(
return true;
}
static bool is_interacting = false;
void sigint_handler(int signo) {
if (signo == SIGINT) {
if (!is_interacting) {
is_interacting=true;
} else {
_exit(130);
}
}
}
int main(int argc, char ** argv) {
ggml_time_init();
const int64_t t_main_start_us = ggml_time_us();
@ -787,6 +811,9 @@ int main(int argc, char ** argv) {
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
// tokenize the reverse prompt
std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
printf("\n");
printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
@ -794,6 +821,24 @@ int main(int argc, char ** argv) {
printf("%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
}
printf("\n");
if (params.interactive) {
struct sigaction sigint_action;
sigint_action.sa_handler = sigint_handler;
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
printf("%s: interactive mode on.\n", __func__);
if(antiprompt_inp.size()) {
printf("%s: reverse prompt: '%s'\n", __func__, params.antiprompt.c_str());
printf("%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
printf("%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
}
printf("\n");
}
}
printf("sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
printf("\n\n");
@ -807,7 +852,28 @@ int main(int argc, char ** argv) {
std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
if (params.interactive) {
printf("== Running in interactive mode. ==\n"
" - Press Ctrl+C to interject at any time.\n"
" - Press Return to return control to LLaMa.\n"
" - If you want to submit another line, end your input in '\\'.\n");
}
int remaining_tokens = params.n_predict;
int input_consumed = 0;
bool input_noecho = false;
// prompt user immediately after the starting prompt has been loaded
if (params.interactive_start) {
is_interacting = true;
}
if (params.use_color) {
printf(ANSI_COLOR_YELLOW);
}
while (remaining_tokens > 0) {
// predict
if (embd.size() > 0) {
const int64_t t_start_us = ggml_time_us();
@ -823,8 +889,8 @@ int main(int argc, char ** argv) {
n_past += embd.size();
embd.clear();
if (i >= embd_inp.size()) {
// sample next token
if (embd_inp.size() <= input_consumed) {
// out of input, sample next token
const float top_k = params.top_k;
const float top_p = params.top_p;
const float temp = params.temp;
@ -847,24 +913,74 @@ int main(int argc, char ** argv) {
// add it to the context
embd.push_back(id);
// echo this to console
input_noecho = false;
// decrement remaining sampling budget
--remaining_tokens;
} else {
// if here, it means we are still processing the input prompt
for (int k = i; k < embd_inp.size(); k++) {
embd.push_back(embd_inp[k]);
while (embd_inp.size() > input_consumed) {
embd.push_back(embd_inp[input_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[k]);
last_n_tokens.push_back(embd_inp[input_consumed]);
++input_consumed;
if (embd.size() > params.n_batch) {
break;
}
}
i += embd.size() - 1;
if (params.use_color && embd_inp.size() <= input_consumed) {
printf(ANSI_COLOR_RESET);
}
}
// display text
for (auto id : embd) {
printf("%s", vocab.id_to_token[id].c_str());
if (!input_noecho) {
for (auto id : embd) {
printf("%s", vocab.id_to_token[id].c_str());
}
fflush(stdout);
}
// in interactive mode, and not currently processing queued inputs;
// check if we should prompt the user for more
if (params.interactive && embd_inp.size() <= input_consumed) {
// check for reverse prompt
if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
// reverse prompt found
is_interacting = true;
}
if (is_interacting) {
// currently being interactive
bool another_line=true;
while (another_line) {
char buf[256] = {0};
int n_read;
if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
scanf("%255[^\n]%n%*c", buf, &n_read);
if(params.use_color) printf(ANSI_COLOR_RESET);
if (n_read > 0 && buf[n_read-1]=='\\') {
another_line = true;
buf[n_read-1] = '\n';
buf[n_read] = 0;
} else {
another_line = false;
buf[n_read] = '\n';
buf[n_read+1] = 0;
}
std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buf, false);
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
input_noecho = true; // do not echo this again
}
is_interacting = false;
}
}
fflush(stdout);
// end of text token
if (embd.back() == 2) {
@ -873,6 +989,7 @@ int main(int argc, char ** argv) {
}
}
// report timing
{
const int64_t t_main_end_us = ggml_time_us();

@ -49,6 +49,15 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.n_batch = std::stoi(argv[++i]);
} else if (arg == "-m" || arg == "--model") {
params.model = argv[++i];
} else if (arg == "-i" || arg == "--interactive") {
params.interactive = true;
} else if (arg == "--interactive-start") {
params.interactive = true;
params.interactive_start = true;
} else if (arg == "--color") {
params.use_color = true;
} else if (arg == "-r" || arg == "--reverse-prompt") {
params.antiprompt = argv[++i];
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, params);
exit(0);
@ -67,6 +76,11 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -i, --interactive run in interactive mode\n");
fprintf(stderr, " --interactive-start run in interactive mode and poll user input at startup\n");
fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT\n");
fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");

@ -28,6 +28,12 @@ struct gpt_params {
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string prompt;
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
bool interactive_start = false; // reverse prompt immediately
std::string antiprompt = ""; // string upon seeing which more user input is prompted
};
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);

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