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

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The default values for tfs_z and typical_p were being set to zero, which
caused the token candidates array to get shrunk down to one element thus
preventing any sampling. Note this only applies to OpenAI API compatible
HTTP server requests.

The solution is to use the default values that OpenAI documents, as well
as ensuring we use the llama.cpp defaults for the rest. I've tested this
change still ensures deterministic output by default. If a "temperature"
greater than 0 is explicitly passed, then output is unique each time. If
"seed" is specified in addition to "temperature" then the output becomes
deterministic once more.

See mozilla-Ocho/llamafile#117
See mozilla-Ocho/llamafile@9e4bf29
This commit is contained in:
Justine Tunney 2023-12-28 11:20:00 -08:00 committed by GitHub
parent ea5497df5d
commit 65e5f6dadb
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1 changed files with 19 additions and 12 deletions
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31
examples/server/server.cpp
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@ -441,7 +441,6 @@ struct llama_client_slot
} }
images.clear(); images.clear();
// llama_set_rng_seed(ctx, params.seed); in batched the seed matter???????
} }
bool has_budget(gpt_params &global_params) { bool has_budget(gpt_params &global_params) {
@ -921,6 +920,7 @@ struct llama_server_context
llama_sampling_free(slot->ctx_sampling); llama_sampling_free(slot->ctx_sampling);
} }
slot->ctx_sampling = llama_sampling_init(slot->sparams); slot->ctx_sampling = llama_sampling_init(slot->sparams);
llama_set_rng_seed(ctx, slot->params.seed);
slot->command = LOAD_PROMPT; slot->command = LOAD_PROMPT;
all_slots_are_idle = false; all_slots_are_idle = false;
@ -1215,7 +1215,7 @@ struct llama_server_context
{"n_ctx", slot.n_ctx}, {"n_ctx", slot.n_ctx},
{"model", params.model_alias}, {"model", params.model_alias},
{"seed", slot.params.seed}, {"seed", slot.params.seed},
{"temp", slot.sparams.temp}, {"temperature", slot.sparams.temp},
{"top_k", slot.sparams.top_k}, {"top_k", slot.sparams.top_k},
{"top_p", slot.sparams.top_p}, {"top_p", slot.sparams.top_p},
{"min_p", slot.sparams.min_p}, {"min_p", slot.sparams.min_p},
@ -2437,26 +2437,33 @@ json oaicompat_completion_params_parse(
llama_params["__oaicompat"] = true; llama_params["__oaicompat"] = true;
// Map OpenAI parameters to llama.cpp parameters // 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["model"] = json_value(body, "model", std::string("uknown"));
llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' 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["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.8); llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_k"] = json_value(body, "top_k", 40); llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
llama_params["top_p"] = json_value(body, "top_p", 0.95); llama_params["top_p"] = json_value(body, "top_p", 1.0);
llama_params["n_predict"] = json_value(body, "max_tokens", -1); llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
llama_params["presence_penalty"] = json_value(body, "presence_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["stream"] = json_value(body, "stream", false);
llama_params["mirostat"] = json_value(body, "mirostat", false); llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0); llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0); llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", false); llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
llama_params["typical_p"] = json_value(body, "typical_p", 0.0); 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["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); 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) { if (body.count("grammar") != 0) {
llama_params["grammar"] = json_value(body, "grammar", json::object()); llama_params["grammar"] = json_value(body, "grammar", json::object());