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https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
fix: reduce and refactor changes
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parent
27cd254b89
commit
24a5588735
@ -1000,6 +1000,7 @@ async fn chat_completions(
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tools,
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tool_choice,
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tool_prompt,
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temperature
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..
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} = req;
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@ -1008,6 +1009,10 @@ async fn chat_completions(
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let logprobs = logprobs.unwrap_or(false);
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let tool_prompt = tool_prompt.unwrap_or_default();
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let stop = stop.unwrap_or_default();
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// rescale where 0 is deterministic and 1 is random (this is the opposite of other endpoints)
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let adjusted_temperature = temperature.map_or(1.0, |t| 1.0 - t);
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let do_sample = adjusted_temperature > 0.0;
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let temperature = Some(adjusted_temperature);
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// extract tool grammar if present
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let tool_grammar = match ToolGrammar::apply(tools, tool_choice) {
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@ -1054,13 +1059,13 @@ async fn chat_completions(
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inputs: inputs.to_string(),
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parameters: GenerateParameters {
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best_of: None,
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temperature: req.temperature.map(|t| 1.0 - t),
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temperature,
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repetition_penalty,
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frequency_penalty: req.frequency_penalty,
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top_k: None,
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top_p: req.top_p,
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typical_p: None,
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do_sample: req.temperature.map_or(true, |t| t > 0.0),
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do_sample,
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max_new_tokens,
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return_full_text: None,
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stop,
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@ -273,25 +273,23 @@ class HeterogeneousNextTokenChooser:
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else None
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)
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if any([x != 1.0 for x in temperature]):
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if any(x != 1.0 for x in temperature):
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do_sample = [
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# 1 and 0 both mean no sampling in different contexts
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sample or x == 1.0 or x == 0.0 or math.isclose(x, 0.0)
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for x, sample in zip(temperature, do_sample)
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sample or x != 1.0 for x, sample in zip(temperature, do_sample)
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]
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warpers.append(
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HeterogeneousTemperatureLogitsWarper(temperature, dtype, device)
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)
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if any([x != 0 for x in top_k]):
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if any(x != 0 for x in top_k):
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do_sample = [sample or x != 0 for x, sample in zip(top_k, do_sample)]
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warpers.append(HeterogeneousTopKLogitsWarper(top_k, device))
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if any([x < 1.0 for x in top_p]):
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if any(x < 1.0 for x in top_p):
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do_sample = [sample or x < 1.0 for x, sample in zip(top_p, do_sample)]
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warpers.append(HeterogeneousTopPLogitsWarper(top_p, dtype, device))
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if any([x < 1.0 for x in typical_p]):
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if any(x < 1.0 for x in typical_p):
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do_sample = [sample or x < 1.0 for x, sample in zip(typical_p, do_sample)]
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warpers.append(HeterogeneousTypicalLogitsWarper(typical_p, dtype, device))
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