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Fix OpenAI server sampling w.r.t. temp and seed #4668

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Dec 28, 2023
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Fix OpenAI server sampling w.r.t. temp and seed #4668

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31 changes: 19 additions & 12 deletions examples/server/server.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -441,7 +441,6 @@ struct llama_client_slot
}

images.clear();
// llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
}

bool has_budget(gpt_params &global_params) {
Expand Down Expand Up @@ -921,6 +920,7 @@ struct llama_server_context
llama_sampling_free(slot->ctx_sampling);
}
slot->ctx_sampling = llama_sampling_init(slot->sparams);
llama_set_rng_seed(ctx, slot->params.seed);
slot->command = LOAD_PROMPT;

all_slots_are_idle = false;
Expand Down Expand Up @@ -1215,7 +1215,7 @@ struct llama_server_context
{"n_ctx", slot.n_ctx},
{"model", params.model_alias},
{"seed", slot.params.seed},
{"temp", slot.sparams.temp},
{"temperature", slot.sparams.temp},
{"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p},
Expand Down Expand Up @@ -2437,26 +2437,33 @@ json oaicompat_completion_params_parse(
llama_params["__oaicompat"] = true;

// Map OpenAI parameters to llama.cpp parameters
//
// For parameters that are defined by the OpenAI documentation (e.g.
// temperature), we explicitly specify OpenAI's intended default; we
// need to do that because sometimes OpenAI disagrees with llama.cpp
//
// https://platform.openai.com/docs/api-reference/chat/create
llama_sampling_params default_sparams;
llama_params["model"] = json_value(body, "model", std::string("uknown"));
llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.8);
llama_params["top_k"] = json_value(body, "top_k", 40);
llama_params["top_p"] = json_value(body, "top_p", 0.95);
llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
llama_params["top_p"] = json_value(body, "top_p", 1.0);
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
llama_params["seed"] = json_value(body, "seed", 0);
llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
llama_params["stream"] = json_value(body, "stream", false);
llama_params["mirostat"] = json_value(body, "mirostat", false);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", false);
llama_params["typical_p"] = json_value(body, "typical_p", 0.0);
llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);

if (body.count("grammar") != 0) {
llama_params["grammar"] = json_value(body, "grammar", json::object());
Expand Down