common : refactor cli arg parsing (#7675)
* common : gpt_params_parse do not print usage * common : rework usage print (wip) * common : valign * common : rework print_usage * infill : remove cfg support * common : reorder args * server : deduplicate parameters ggml-ci * common : add missing header ggml-ci * common : remote --random-prompt usages ggml-ci * examples : migrate to gpt_params ggml-ci * batched-bench : migrate to gpt_params * retrieval : migrate to gpt_params * common : change defaults for escape and n_ctx * common : remove chatml and instruct params ggml-ci * common : passkey use gpt_params
This commit is contained in:
Georgi Gerganov
GitHub
parent
554c247caf
commit
1442677f92
34 changed files with 899 additions and 1455 deletions
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@ -107,6 +107,7 @@ int main(int argc, char ** argv) {
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g_params = ¶ms;
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if (!gpt_params_parse(argc, argv, params)) {
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gpt_params_print_usage(argc, argv, params);
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return 1;
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}
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@ -139,27 +140,6 @@ int main(int argc, char ** argv) {
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LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
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params.n_ctx = 8;
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}
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if (params.instruct) {
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printf("\n************\n");
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printf("%s: please use the 'main' tool for instruct mode\n", __func__);
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printf("************\n\n");
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return 0;
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}
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if (params.chatml) {
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printf("\n************\n");
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printf("%s: please use the 'main' tool for chatml mode\n", __func__);
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printf("************\n\n");
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return 0;
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}
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if (!params.antiprompt.empty()) {
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printf("\n************\n");
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printf("%s: please use the 'main' tool for antiprompt mode\n", __func__);
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printf("************\n\n");
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return 0;
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}
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if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) {
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printf("\n************\n");
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printf("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__);
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@ -167,20 +147,6 @@ int main(int argc, char ** argv) {
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return 0;
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}
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if (params.random_prompt) {
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printf("\n************\n");
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printf("%s: please use the 'main' tool for random prompt mode\n", __func__);
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printf("************\n\n");
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return 0;
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}
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if (!params.path_prompt_cache.empty()) {
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printf("\n************\n");
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printf("%s: infill does not support prompt caching\n", __func__);
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printf("************\n\n");
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return 0;
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}
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if (params.rope_freq_base != 0.0) {
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LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
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@ -207,17 +173,13 @@ int main(int argc, char ** argv) {
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llama_model * model;
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llama_context * ctx;
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llama_context * ctx_guidance = NULL;
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g_model = &model;
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g_ctx = &ctx;
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// load the model and apply lora adapter, if any
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LOG("%s: load the model and apply lora adapter, if any\n", __func__);
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (sparams.cfg_scale > 1.f) {
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struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
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ctx_guidance = llama_new_context_with_model(model, lparams);
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}
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if (model == NULL) {
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LOG_TEE("%s: error: unable to load model\n", __func__);
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@ -273,25 +235,6 @@ int main(int argc, char ** argv) {
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LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
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}
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// Tokenize negative prompt
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std::vector<llama_token> guidance_inp;
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int guidance_offset = 0;
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int original_prompt_len = 0;
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if (ctx_guidance) {
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LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
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guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true);
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LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
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std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
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LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
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original_prompt_len = original_inp.size();
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guidance_offset = (int)guidance_inp.size() - original_prompt_len;
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LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
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LOG("guidance_offset: %s", log_tostr(guidance_offset));
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}
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if ((int) embd_inp.size() > n_ctx - 4) {
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LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
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return 1;
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@ -319,15 +262,6 @@ int main(int argc, char ** argv) {
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LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
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}
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if (ctx_guidance) {
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LOG_TEE("\n");
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LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
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LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
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for (int i = 0; i < (int) guidance_inp.size(); i++) {
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LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
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}
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}
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if (params.n_keep > 0) {
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LOG_TEE("%s: static prompt based on n_keep: '", __func__);
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for (int i = 0; i < params.n_keep; i++) {
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@ -395,12 +329,11 @@ int main(int argc, char ** argv) {
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is_interacting = params.interactive_first;
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}
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bool input_echo = true;
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bool input_echo = true;
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int n_past = 0;
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int n_remain = params.n_predict;
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int n_consumed = 0;
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int n_past_guidance = 0;
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int n_past = 0;
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int n_remain = params.n_predict;
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int n_consumed = 0;
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std::vector<int> input_tokens; g_input_tokens = &input_tokens;
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std::vector<int> output_tokens; g_output_tokens = &output_tokens;
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@ -410,7 +343,6 @@ int main(int argc, char ** argv) {
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console::set_display(console::prompt);
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std::vector<llama_token> embd;
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std::vector<llama_token> embd_guidance;
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struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
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@ -436,7 +368,7 @@ int main(int argc, char ** argv) {
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// if we run out of context:
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// - take the n_keep first tokens from the original prompt (via n_past)
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// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
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if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
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if (n_past + (int) embd.size() > n_ctx) {
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if (params.n_predict == -2) {
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LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
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break;
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@ -453,11 +385,7 @@ int main(int argc, char ** argv) {
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n_past -= n_discard;
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if (ctx_guidance) {
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n_past_guidance -= n_discard;
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}
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LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
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LOG("after swap: n_past = %d\n", n_past);
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LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
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@ -465,45 +393,6 @@ int main(int argc, char ** argv) {
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// evaluate tokens in batches
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// embd is typically prepared beforehand to fit within a batch, but not always
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if (ctx_guidance) {
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int input_size = 0;
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llama_token * input_buf = NULL;
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if (n_past_guidance < (int) guidance_inp.size()) {
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// Guidance context should have the same data with these modifications:
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//
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// * Replace the initial prompt
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// * Shift everything by guidance_offset
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embd_guidance = guidance_inp;
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if (embd.begin() + original_prompt_len < embd.end()) {
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embd_guidance.insert(
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embd_guidance.end(),
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embd.begin() + original_prompt_len,
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embd.end()
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);
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}
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input_buf = embd_guidance.data();
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input_size = embd_guidance.size();
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LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());
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} else {
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input_buf = embd.data();
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input_size = embd.size();
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}
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for (int i = 0; i < input_size; i += params.n_batch) {
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int n_eval = std::min(input_size - i, params.n_batch);
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if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) {
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LOG_TEE("%s : failed to eval\n", __func__);
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return 1;
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}
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n_past_guidance += n_eval;
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}
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}
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for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
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int n_eval = (int) embd.size() - i;
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if (n_eval > params.n_batch) {
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@ -525,11 +414,9 @@ int main(int argc, char ** argv) {
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}
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embd.clear();
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embd_guidance.clear();
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if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
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const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
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const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr);
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llama_sampling_accept(ctx_sampling, ctx, id, true);
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@ -583,7 +470,6 @@ int main(int argc, char ** argv) {
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// if not currently processing queued inputs;
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if ((int) embd_inp.size() <= n_consumed) {
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// deal with eot token in infill mode
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if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
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if (is_interacting && !params.interactive_first) {
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@ -644,7 +530,6 @@ int main(int argc, char ** argv) {
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embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
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embd_inp.push_back(llama_token_middle(model));
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embd.clear();
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embd_guidance.clear();
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n_remain = params.n_predict;
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n_past = 0;
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n_consumed = 0;
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@ -751,7 +636,6 @@ int main(int argc, char ** argv) {
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llama_print_timings(ctx);
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write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
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if (ctx_guidance) { llama_free(ctx_guidance); }
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llama_free(ctx);
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llama_free_model(model);
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