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static llama_context ** g_ctx; | |
static llama_model ** g_model; | |
static common_sampler ** g_smpl; | |
static common_params * g_params; | |
static std::vector<llama_token> * g_input_tokens; | |
static std::ostringstream * g_output_ss; | |
static std::vector<llama_token> * g_output_tokens; | |
static bool is_interacting = false; | |
static void write_logfile( | |
const llama_context * ctx, const common_params & params, const llama_model * model, | |
const std::vector<llama_token> & input_tokens, const std::string & output, | |
const std::vector<llama_token> & output_tokens | |
) { | |
if (params.logdir.empty()) { | |
return; | |
} | |
const std::string timestamp = string_get_sortable_timestamp(); | |
const bool success = fs_create_directory_with_parents(params.logdir); | |
if (!success) { | |
LOG_ERR("%s: warning: failed to create logdir %s, cannot write logfile\n", | |
__func__, params.logdir.c_str()); | |
return; | |
} | |
const std::string logfile_path = params.logdir + timestamp + ".yml"; | |
FILE * logfile = fopen(logfile_path.c_str(), "w"); | |
if (logfile == NULL) { | |
LOG_ERR("%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); | |
return; | |
} | |
fprintf(logfile, "binary: infill\n"); | |
char model_desc[128]; | |
llama_model_desc(model, model_desc, sizeof(model_desc)); | |
yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); | |
fprintf(logfile, "\n"); | |
fprintf(logfile, "######################\n"); | |
fprintf(logfile, "# Generation Results #\n"); | |
fprintf(logfile, "######################\n"); | |
fprintf(logfile, "\n"); | |
yaml_dump_string_multiline(logfile, "output", output.c_str()); | |
yaml_dump_vector_int(logfile, "output_tokens", output_tokens); | |
llama_perf_dump_yaml(logfile, ctx); | |
fclose(logfile); | |
} | |
static void sigint_handler(int signo) { | |
if (signo == SIGINT) { | |
if (!is_interacting) { | |
is_interacting = true; | |
} else { | |
console::cleanup(); | |
LOG("\n"); | |
common_perf_print(*g_ctx, *g_smpl); | |
write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); | |
// make sure all logs are flushed | |
LOG("Interrupted by user\n"); | |
common_log_pause(common_log_main()); | |
_exit(130); | |
} | |
} | |
} | |
int main(int argc, char ** argv) { | |
common_params params; | |
g_params = ¶ms; | |
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_INFILL)) { | |
return 1; | |
} | |
common_init(); | |
auto & sparams = params.sparams; | |
console::init(params.simple_io, params.use_color); | |
atexit([]() { console::cleanup(); }); | |
if (params.logits_all) { | |
LOG_ERR("\n************\n"); | |
LOG_ERR("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__); | |
LOG_ERR("************\n\n"); | |
return 0; | |
} | |
if (params.embedding) { | |
LOG_ERR("\n************\n"); | |
LOG_ERR("%s: please use the 'embedding' tool for embedding calculations\n", __func__); | |
LOG_ERR("************\n\n"); | |
return 0; | |
} | |
if (params.n_ctx != 0 && params.n_ctx < 8) { | |
LOG_WRN("%s: minimum context size is 8, using minimum size.\n", __func__); | |
params.n_ctx = 8; | |
} | |
if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) { | |
LOG_ERR("\n************\n"); | |
LOG_ERR("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__); | |
LOG_ERR("************\n\n"); | |
return 0; | |
} | |
if (params.rope_freq_base != 0.0) { | |
LOG_WRN("%s: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); | |
} | |
if (params.rope_freq_scale != 0.0) { | |
LOG_WRN("%s: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale); | |
} | |
LOG_INF("%s: llama backend init\n", __func__); | |
llama_backend_init(); | |
llama_numa_init(params.numa); | |
llama_model * model = nullptr; | |
llama_context * ctx = nullptr; | |
common_sampler * smpl = nullptr; | |
g_model = &model; | |
g_ctx = &ctx; | |
g_smpl = &smpl; | |
// load the model and apply lora adapter, if any | |
LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); | |
common_init_result llama_init = common_init_from_params(params); | |
model = llama_init.model; | |
ctx = llama_init.context; | |
if (model == NULL) { | |
LOG_ERR("%s: unable to load model\n", __func__); | |
return 1; | |
} | |
const int n_ctx_train = llama_n_ctx_train(model); | |
const int n_ctx = llama_n_ctx(ctx); | |
LOG_DBG("n_ctx: %d\n", n_ctx); | |
if (n_ctx > n_ctx_train) { | |
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx); | |
} | |
// print system information | |
{ | |
LOG_INF("\n"); | |
LOG_INF("%s\n", common_params_get_system_info(params).c_str()); | |
} | |
const bool add_bos = llama_add_bos_token(model); | |
GGML_ASSERT(!llama_add_eos_token(model)); | |
std::vector<llama_token> embd_inp; | |
std::vector<llama_token> embd_end; | |
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false); | |
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false); | |
GGML_ASSERT(llama_token_fim_pre(model) >= 0); | |
GGML_ASSERT(llama_token_fim_suf(model) >= 0); | |
inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model)); | |
inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model)); | |
embd_inp = params.spm_infill ? inp_sfx : inp_pfx; | |
embd_end = params.spm_infill ? inp_pfx : inp_sfx; | |
if (add_bos) { | |
embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); | |
} | |
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); | |
const llama_token middle_token = llama_token_fim_mid(model); | |
if (middle_token >= 0) { | |
embd_inp.push_back(middle_token); | |
} | |
LOG_DBG("add_bos: %d\n", add_bos); | |
LOG_DBG("prefix: \"%s\"\n", params.input_prefix.c_str()); | |
LOG_DBG("suffix: \"%s\"\n", params.input_suffix.c_str()); | |
LOG_DBG("tokens: %s\n", string_from(ctx, embd_inp).c_str()); | |
// Should not run without any tokens | |
if (embd_inp.empty()) { | |
embd_inp.push_back(llama_token_bos(model)); | |
LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str()); | |
} | |
if ((int) embd_inp.size() > n_ctx - 4) { | |
LOG_ERR("%s: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); | |
return 1; | |
} | |
// number of tokens to keep when resetting context | |
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) { | |
params.n_keep = (int)embd_inp.size(); | |
} | |
LOG_INF("inp_pfx: %s\n", string_from(ctx, inp_pfx).c_str()); | |
LOG_INF("inp_sfx: %s\n", string_from(ctx, inp_sfx).c_str()); | |
// enable interactive mode if interactive start is specified | |
if (params.interactive_first) { | |
params.interactive = true; | |
} | |
if (params.verbose_prompt) { | |
LOG_INF("\n"); | |
LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); | |
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); | |
for (int i = 0; i < (int) embd_inp.size(); i++) { | |
LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str()); | |
} | |
if (params.n_keep > 0) { | |
LOG_INF("%s: static prompt based on n_keep: '", __func__); | |
for (int i = 0; i < params.n_keep; i++) { | |
LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str()); | |
} | |
LOG_CNT("'\n"); | |
} | |
LOG_INF("\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); | |
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL { | |
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false; | |
}; | |
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true); | |
LOG_INF("%s: interactive mode on.\n", __func__); | |
if (params.input_prefix_bos) { | |
LOG_INF("Input prefix with BOS\n"); | |
} | |
if (!params.input_prefix.empty()) { | |
LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str()); | |
} | |
if (!params.input_suffix.empty()) { | |
LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str()); | |
} | |
} | |
smpl = common_sampler_init(model, sparams); | |
LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl)); | |
LOG_INF("sampler params: \n%s\n", sparams.print().c_str()); | |
LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str()); | |
LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); | |
LOG_INF("\n"); | |
LOG_INF("\n##### Infill mode #####\n\n"); | |
if (params.interactive) { | |
const char *control_message; | |
if (params.multiline_input) { | |
control_message = " - To return control to LLaMA, end your input with '\\'.\n" | |
" - To return control without starting a new line, end your input with '/'.\n"; | |
} else { | |
control_message = " - Press Return to return control to LLaMA.\n" | |
" - To return control without starting a new line, end your input with '/'.\n" | |
" - If you want to submit another line, end your input with '\\'.\n"; | |
} | |
LOG_INF("== Running in interactive mode. ==\n"); | |
LOG_INF( " - Press Ctrl+C to interject at any time.\n"); | |
LOG_INF( "%s\n", control_message); | |
is_interacting = params.interactive_first; | |
} | |
bool input_echo = true; | |
int n_past = 0; | |
int n_remain = params.n_predict; | |
int n_consumed = 0; | |
std::vector<int> input_tokens; g_input_tokens = &input_tokens; | |
std::vector<int> output_tokens; g_output_tokens = &output_tokens; | |
std::ostringstream output_ss; g_output_ss = &output_ss; | |
// the first thing we will do is to output the prompt, so set color accordingly | |
console::set_display(console::prompt); | |
std::vector<llama_token> embd; | |
while (n_remain != 0 || params.interactive) { | |
// predict | |
if (!embd.empty()) { | |
// Note: n_ctx - 4 here is to match the logic for commandline prompt handling via | |
// --prompt or --file which uses the same value. | |
int max_embd_size = n_ctx - 4; | |
// Ensure the input doesn't exceed the context size by truncating embd if necessary. | |
if ((int) embd.size() > max_embd_size) { | |
const int skipped_tokens = (int) embd.size() - max_embd_size; | |
embd.resize(max_embd_size); | |
console::set_display(console::error); | |
LOG_WRN("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); | |
console::set_display(console::reset); | |
} | |
// infinite text generation via context swapping | |
// if we run out of context: | |
// - take the n_keep first tokens from the original prompt (via n_past) | |
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches | |
if (n_past + (int) embd.size() > n_ctx) { | |
if (params.n_predict == -2) { | |
LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); | |
break; | |
} | |
const int n_left = n_past - params.n_keep - 1; | |
const int n_discard = n_left/2; | |
LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n", | |
n_past, n_left, n_ctx, params.n_keep, n_discard); | |
llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1); | |
llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard); | |
n_past -= n_discard; | |
LOG_DBG("after swap: n_past = %d\n", n_past); | |
LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str()); | |
} | |
// evaluate tokens in batches | |
// embd is typically prepared beforehand to fit within a batch, but not always | |
for (int i = 0; i < (int) embd.size(); i += params.n_batch) { | |
int n_eval = (int) embd.size() - i; | |
if (n_eval > params.n_batch) { | |
n_eval = params.n_batch; | |
} | |
LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str()); | |
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) { | |
LOG_ERR("%s : failed to eval\n", __func__); | |
return 1; | |
} | |
n_past += n_eval; | |
LOG_DBG("n_past = %d\n", n_past); | |
} | |
} | |
embd.clear(); | |
if ((int) embd_inp.size() <= n_consumed && !is_interacting) { | |
const llama_token id = common_sampler_sample(smpl, ctx, -1); | |
common_sampler_accept(smpl, id, true); | |
// LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str()); | |
embd.push_back(id); | |
// echo this to console | |
input_echo = true; | |
// decrement remaining sampling budget | |
--n_remain; | |
LOG_DBG("n_remain: %d\n", n_remain); | |
} else { | |
// some user input remains from prompt or interaction, forward it to processing | |
LOG_DBG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); | |
while ((int) embd_inp.size() > n_consumed) { | |
embd.push_back(embd_inp[n_consumed]); | |
// push the prompt in the sampling context in order to apply repetition penalties later | |
// for the prompt, we don't apply grammar rules | |
common_sampler_accept(smpl, embd_inp[n_consumed], false); | |
++n_consumed; | |
if ((int) embd.size() >= params.n_batch) { | |
break; | |
} | |
} | |
} | |
// display text | |
if (input_echo) { | |
for (auto id : embd) { | |
const std::string token_str = common_token_to_piece(ctx, id); | |
LOG("%s", token_str.c_str()); | |
if (embd.size() > 1) { | |
input_tokens.push_back(id); | |
} else { | |
output_tokens.push_back(id); | |
output_ss << token_str; | |
} | |
} | |
} | |
// reset color to default if we there is no pending user input | |
if (input_echo && (int) embd_inp.size() == n_consumed) { | |
console::set_display(console::reset); | |
} | |
// if not currently processing queued inputs; | |
if ((int) embd_inp.size() <= n_consumed) { | |
// deal with eot token in infill mode | |
if ((common_sampler_last(smpl) == llama_token_eot(model) || is_interacting) && params.interactive){ | |
if (is_interacting && !params.interactive_first) { | |
// print an eot token | |
LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str()); | |
} | |
LOG("\n"); | |
console::set_display(console::user_input); | |
std::string buffer; | |
std::string line; | |
bool another_line=true; | |
// set a new prefix via stdin | |
do { | |
another_line = console::readline(line, params.multiline_input); | |
buffer += line; | |
} while (another_line); | |
// check if we got an empty line, if so we use the old input | |
if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { | |
params.input_prefix = buffer; | |
} | |
buffer.clear(); | |
// set a new suffix via stdin | |
do { | |
another_line = console::readline(line, params.multiline_input); | |
buffer += line; | |
} while (another_line); | |
// check if we got an empty line | |
if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) { | |
params.input_suffix = buffer; | |
} | |
buffer.clear(); | |
// done taking input, reset color | |
console::set_display(console::reset); | |
if (params.escape) { | |
//process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here | |
string_process_escapes(params.input_prefix); | |
string_process_escapes(params.input_suffix); | |
} | |
// tokenize new prefix and suffix | |
std::vector<llama_token> inp_pfx = common_tokenize(ctx, params.input_prefix, false); | |
std::vector<llama_token> inp_sfx = common_tokenize(ctx, params.input_suffix, false); | |
inp_pfx.insert(inp_pfx.begin(), llama_token_fim_pre(model)); | |
inp_sfx.insert(inp_sfx.begin(), llama_token_fim_suf(model)); | |
embd_inp = params.spm_infill ? inp_sfx : inp_pfx; | |
embd_end = params.spm_infill ? inp_pfx : inp_sfx; | |
if (add_bos) { | |
embd_inp.insert(embd_inp.begin(), llama_token_bos(model)); | |
} | |
embd_inp.insert(embd_inp.end(), embd_end.begin(), embd_end.end()); | |
if (middle_token >= 0) { | |
embd_inp.push_back(middle_token); | |
} | |
embd.clear(); | |
n_remain = params.n_predict; | |
n_past = 0; | |
n_consumed = 0; | |
is_interacting = false; | |
} | |
// deal with end of generation tokens in interactive mode | |
else if (llama_token_is_eog(model, common_sampler_last(smpl))) { | |
LOG_DBG("found EOS token\n"); | |
if (params.interactive) { | |
is_interacting = true; | |
LOG("\n"); | |
console::set_display(console::user_input); | |
} | |
} | |
if (n_past > 0 && is_interacting && !params.interactive) { | |
LOG_DBG("waiting for user input\n"); | |
if (params.input_prefix_bos) { | |
LOG_DBG("adding input prefix BOS token\n"); | |
embd_inp.push_back(llama_token_bos(model)); | |
} | |
std::string buffer; | |
if (!params.input_prefix.empty()) { | |
LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str()); | |
buffer += params.input_prefix; | |
LOG("%s", buffer.c_str()); | |
} | |
std::string line; | |
bool another_line = true; | |
do { | |
another_line = console::readline(line, params.multiline_input); | |
buffer += line; | |
} while (another_line); | |
// done taking input, reset color | |
console::set_display(console::reset); | |
// Add tokens to embd only if the input buffer is non-empty | |
// Entering a empty line lets the user pass control back | |
if (buffer.length() > 1) { | |
// append input suffix if any | |
if (!params.input_suffix.empty()) { | |
LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str()); | |
buffer += params.input_suffix; | |
LOG("%s", params.input_suffix.c_str()); | |
} | |
LOG_DBG("buffer: '%s'\n", buffer.c_str()); | |
const size_t original_size = embd_inp.size(); | |
const auto line_inp = common_tokenize(ctx, buffer, false); | |
LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str()); | |
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); | |
for (size_t i = original_size; i < embd_inp.size(); ++i) { | |
const llama_token token = embd_inp[i]; | |
output_tokens.push_back(token); | |
output_ss << common_token_to_piece(ctx, token); | |
} | |
n_remain -= line_inp.size(); | |
LOG_DBG("n_remain: %d\n", n_remain); | |
} else { | |
LOG_DBG("empty line, passing control back\n"); | |
} | |
input_echo = false; // do not echo this again | |
} | |
if (n_past > 0) { | |
if (is_interacting) { | |
common_sampler_reset(smpl); | |
} | |
is_interacting = false; | |
} | |
} | |
// end of generation | |
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) { | |
break; | |
} | |
// In interactive mode, respect the maximum number of tokens and drop back to user input when reached. | |
// We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size). | |
if (params.interactive && n_remain <= 0 && params.n_predict >= 0) { | |
n_remain = params.n_predict; | |
is_interacting = true; | |
} | |
} | |
if (!params.interactive && n_remain <= 0) { | |
LOG("%s", common_token_to_piece(ctx, llama_token_eot(model)).c_str()); | |
} | |
LOG("\n"); | |
common_perf_print(ctx, smpl); | |
write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); | |
llama_free(ctx); | |
llama_free_model(model); | |
common_sampler_free(smpl); | |
llama_backend_free(); | |
return 0; | |
} | |