haraldwolff/llama.cpp
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server : allow json array in prompt or content for direct token input (#2306)

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* server: allow json array in prompt or content

We accept an array of strings and numbers representing tokens,
in addition to the current string valued prompt or content.

This allows direct token input, so that any special tokens
can be processed and used at the frontend during the construction
of the json data, before sending to the server. And the server
does not need to know or parse special tokens from textual input.

With this, we can use EOS and BOS used in llama-2-chat models.

* server: use tokenizePrompt(json) and default "" if empty prompt

* server: fix prompt check

* server: tokenize endpoint no longer adds BOS
This commit is contained in:
Xiao-Yong Jin 2023-08-23 02:12:12 -05:00 committed by GitHub
parent f5fe98d11b
commit b8ad1b66b2
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2 changed files with 74 additions and 8 deletions
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2
examples/server/README.md
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@ -126,7 +126,7 @@ node .
`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
`prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. A space is inserted in the front like main.cpp does.
`prompt`: Provide a prompt as a string, or as an array of strings and numbers representing tokens. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. If the prompt is a string, or an array with the first element given as a string, a space is inserted in the front like main.cpp does.
`stop`: Specify a JSON array of stopping strings.
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).

80
examples/server/server.cpp
View file

@ -190,6 +190,7 @@ struct llama_server_context
size_t n_past = 0;
size_t n_remain = 0;
json prompt;
std::vector<llama_token> embd;
std::vector<llama_token> last_n_tokens;
@ -267,6 +268,53 @@ struct llama_server_context
return true;
}
std::vector<llama_token> tokenize(json json_prompt, bool add_bos)
{
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
// or the first element of the json_prompt array is a string.
std::vector<llama_token> prompt_tokens;
if (json_prompt.is_array())
{
bool first = true;
for (const auto& p : json_prompt)
{
if (p.is_string())
{
auto s = p.template get<std::string>();
std::vector<llama_token> p;
if (first)
{
s.insert(0, 1, ' '); // add a space if it's the first
p = ::llama_tokenize(ctx, s, add_bos);
first = false;
}
else
{
p = ::llama_tokenize(ctx, s, false);
}
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
}
else
{
if (first)
{
first = false;
}
prompt_tokens.push_back(p.template get<llama_token>());
}
}
}
else
{
auto s = json_prompt.template get<std::string>();
s.insert(0, 1, ' '); // always add a first space
prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
}
return prompt_tokens;
}
bool loadGrammar()
{
if (!params.grammar.empty()) {
@ -294,8 +342,8 @@ struct llama_server_context
void loadPrompt()
{
params.prompt.insert(0, 1, ' '); // always add a first space
std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
auto prompt_tokens = tokenize(prompt, true); // always add BOS
num_prompt_tokens = prompt_tokens.size();
if (params.n_keep < 0)
@ -1016,7 +1064,7 @@ static json format_final_response(llama_server_context &llama, const std::string
{"tokens_predicted", llama.num_tokens_predicted},
{"tokens_evaluated", llama.num_prompt_tokens},
{"generation_settings", format_generation_settings(llama)},
{"prompt", llama.params.prompt},
{"prompt", llama.prompt},
{"truncated", llama.truncated},
{"stopped_eos", llama.stopped_eos},
{"stopped_word", llama.stopped_word},
@ -1085,10 +1133,18 @@ static void parse_options_completion(const json &body, llama_server_context &lla
llama.params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl);
llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep);
llama.params.seed = json_value(body, "seed", default_params.seed);
llama.params.prompt = json_value(body, "prompt", default_params.prompt);
llama.params.grammar = json_value(body, "grammar", default_params.grammar);
llama.params.n_probs = json_value(body, "n_probs", default_params.n_probs);
if (body.count("prompt") != 0)
{
llama.prompt = body["prompt"];
}
else
{
llama.prompt = "";
}
llama.params.logit_bias.clear();
if (json_value(body, "ignore_eos", false))
{
@ -1345,8 +1401,11 @@ int main(int argc, char **argv)
auto lock = llama.lock();
const json body = json::parse(req.body);
const std::string content = json_value<std::string>(body, "content", "");
const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
std::vector<llama_token> tokens;
if (body.count("content") != 0)
{
tokens = llama.tokenize(body["content"], false);
}
const json data = format_tokenizer_response(tokens);
return res.set_content(data.dump(), "application/json"); });
@ -1358,7 +1417,14 @@ int main(int argc, char **argv)
llama.rewind();
llama_reset_timings(llama.ctx);
llama.params.prompt = json_value<std::string>(body, "content", "");
if (body.count("content") != 0)
{
llama.prompt = body["content"];
}
else
{
llama.prompt = "";
}
llama.params.n_predict = 0;
llama.loadPrompt();
llama.beginCompletion();