server : allow to specify tokens as strings in logit_bias (#5003)
* server: allow to specify tokens as strings in logit_bias * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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@ -185,7 +185,7 @@ node index.js
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`ignore_eos`: Ignore end of stream token and continue generating (default: false).
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`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced (default: []).
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`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. (default: []).
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`n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
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@ -626,18 +626,36 @@ struct llama_server_context
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const int n_vocab = llama_n_vocab(model);
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for (const auto &el : *logit_bias)
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{
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if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
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if (el.is_array() && el.size() == 2)
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{
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llama_token tok = el[0].get<llama_token>();
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if (tok >= 0 && tok < n_vocab)
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float bias;
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if (el[1].is_number())
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{
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if (el[1].is_number())
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bias = el[1].get<float>();
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}
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else if (el[1].is_boolean() && !el[1].get<bool>())
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{
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bias = -INFINITY;
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}
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else
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{
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continue;
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}
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if (el[0].is_number_integer())
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{
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llama_token tok = el[0].get<llama_token>();
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if (tok >= 0 && tok < n_vocab)
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{
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slot->sparams.logit_bias[tok] = el[1].get<float>();
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slot->sparams.logit_bias[tok] = bias;
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}
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else if (el[1].is_boolean() && !el[1].get<bool>())
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}
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else if (el[0].is_string())
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{
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auto toks = llama_tokenize(model, el[0].get<std::string>(), false);
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for (auto tok : toks)
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{
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slot->sparams.logit_bias[tok] = -INFINITY;
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slot->sparams.logit_bias[tok] = bias;
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}
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}
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}
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