Method sample

benchmarks/benchmark_dataset.py:222–262

(
        self,
        num_requests: int,
        lora_path: Optional[str] = None,
        max_loras: Optional[int] = None,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        **kwargs,
    )

            random.shuffle(self.data)

    def sample(
        self,
        num_requests: int,
        lora_path: Optional[str] = None,
        max_loras: Optional[int] = None,
        output_len: Optional[int] = None,
        enable_multimodal_chat: bool = False,
        **kwargs,
    ) -> list:
        samples: list = []
        cnt = 1
        for entry in self.data:
            if len(samples) >= num_requests:
                break
            json_data = entry

            prompt = entry["text"]
            self.temperature = float(entry.get("temperature", 1))
            self.repetition_penalty = float(entry.get("penalty_score", 0))
            self.frequency_penalty = float(entry.get("frequency_score", 0))
            self.presence_penalty = float(entry.get("presence_score", 0))
            self.top_p = float(entry.get("topp", 1))
            self.prompt_len = int(entry.get("input_token_num", 0))
            new_output_len = int(entry.get("max_dec_len", 0))

            if enable_multimodal_chat:
                prompt = self.apply_multimodal_chat_transformation(prompt, None)
            samples.append(
                SampleRequest(
                    no=cnt,
                    json_data=json_data,
                    prompt=prompt,
                    prompt_len=self.prompt_len,
                    history_QA=[],
                    expected_output_len=new_output_len,
                )
            )
            cnt += 1

        self.maybe_oversample_requests(samples, num_requests)
        return samples


class EBChatDataset(BenchmarkDataset):