Function make_context

tensorrt_llm/models/qwen/utils.py:17–85

(
    tokenizer,
    query,
    history,
    system,
    max_input_length,
    max_window_size: int = 6144,
    chat_format: str = "chatml",
)

def make_context(
    tokenizer,
    query,
    history,
    system,
    max_input_length,
    max_window_size: int = 6144,
    chat_format: str = "chatml",
):
    if history is None:
        history = []

    if chat_format == "chatml":
        im_start, im_end = "<|im_start|>", "<|im_end|>"
        im_start_tokens = [tokenizer.im_start_id]
        im_end_tokens = [tokenizer.im_end_id]
        nl_tokens = tokenizer.encode("\n")

        def _tokenize_str(role, content):
            return (f"{role}\n{content}",
                    tokenizer.encode(
                        role,
                        allowed_special=set(),
                    ) + nl_tokens + tokenizer.encode(
                        content,
                        allowed_special=set(),
                    ))

        system_text, system_tokens_part = _tokenize_str("system", system)
        system_tokens = im_start_tokens + system_tokens_part + im_end_tokens
        raw_text = ""
        context_tokens = []

        for turn_query, turn_response in reversed(history):
            query_text, query_tokens_part = _tokenize_str("user", turn_query)
            query_tokens = im_start_tokens + query_tokens_part + im_end_tokens

            response_text, response_tokens_part = _tokenize_str(
                "assistant", turn_response)
            response_tokens = im_start_tokens + response_tokens_part + im_end_tokens
            next_context_tokens = nl_tokens + query_tokens + nl_tokens + response_tokens
            prev_chat = (
                f"\n{im_start}{query_text}{im_end}\n{im_start}{response_text}{im_end}"
            )

            current_context_size = (len(system_tokens) +
                                    len(next_context_tokens) +
                                    len(context_tokens))
            if current_context_size < max_window_size:
                context_tokens = next_context_tokens + context_tokens
                raw_text = prev_chat + raw_text
            else:
                break

        context_tokens = system_tokens + context_tokens
        raw_text = f"{im_start}{system_text}{im_end}" + raw_text
        context_tokens += (nl_tokens + im_start_tokens +
                           _tokenize_str("user", query)[1] + im_end_tokens +