FastDeploy/fastdeploy/input/ernie4_5_processor.py

"""
# Copyright (c) 2025  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""

import os

import numpy as np
from paddleformers.generation import GenerationConfig

from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
from fastdeploy.input.text_processor import BaseDataProcessor
from fastdeploy.utils import data_processor_logger

_SAMPLING_EPS = 1e-5


class Ernie4_5Processor(BaseDataProcessor):
    """
    初始化模型实例。

    Args:
        model_name_or_path (str): 模型名称或路径。

    Attributes:
        model_name_or_path (str): 存储模型名称或路径。
        decode_status (dict): 存储解码状态信息。
        tokenizer (object): 存储分词器实例。
        eos_token_ids (list): 存储结束符号的token ID列表。
        eos_token_id_len (int): 存储结束符号的token ID列表的长度。
        pad_token_id (int): 存储填充符号的token ID。
    """

    def __init__(self, model_name_or_path, reasoning_parser_obj=None, tool_parser_obj=None):

        self.model_name_or_path = model_name_or_path
        data_processor_logger.info(f"model_name_or_path: {model_name_or_path}")

        # Generation config
        try:
            self.generation_config = GenerationConfig.from_pretrained(self.model_name_or_path)
        except Exception as e:
            data_processor_logger.warning(
                f"Can't find generation config, so it will not use "
                f"generation_config field in the model config, details={e}"
            )
            self.generation_config = None

        self.decode_status = dict()
        self.tool_parser_dict = dict()
        self.thinking_parser_dict = dict()
        self._load_tokenizer()
        data_processor_logger.info(
            f"tokenizer information: bos_token is {self.tokenizer.bos_token} \
                                   {self.tokenizer.bos_token_id}, \
                                   eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id} "
        )
        from paddleformers.trl.llm_utils import get_eos_token_id

        self.eos_token_ids = get_eos_token_id(self.tokenizer, self.generation_config)
        self.eos_token_id_len = len(self.eos_token_ids)
        self.pad_token_id = self.get_pad_id()
        self.reasoning_parser = None
        self.tool_parser_obj = tool_parser_obj
        if reasoning_parser_obj:
            self.reasoning_parser = reasoning_parser_obj(self.tokenizer)

    def process_request(self, request, max_model_len=None, **kwargs):
        """
        Preprocess the request

        Args:
            request (Dict): may contain text and messages fields

        Returns:
            bool: Whether preprocessing is successful
            str: error message
        """
        data_processor_logger.info(f"Start processing request: {request}")
        request = self._apply_default_parameters(request)
        if request.get("eos_token_ids") is None or len(request.eos_token_ids) == 0:
            request.eos_token_ids = self.eos_token_ids

        # processing stop_sequences
        stop_sequences = request.get("stop", [])
        if stop_sequences is not None and len(stop_sequences) != 0:
            stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
            request.set("stop_token_ids", stop_seqs)
            request.set("stop_seqs_len", stop_seqs_len)

        # processing bad_words
        bad_words = request.get("bad_words")
        bad_words_token_ids = request.get("bad_words_token_ids")
        if bad_words:
            bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
            request["bad_words_token_ids"] = bad_words_token_ids

        # processing prompt_token_ids
        if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
            if request.prompt is not None:
                # prompt = request.prompt if request.prompt is not None else request.messages[0]
                prompt = request.prompt
                assert isinstance(prompt, str) or (
                    isinstance(prompt, list) and all([isinstance(t, int) for t in prompt])
                ), f"prompt must be a string or a list of integers, but got {type(prompt)}"

                if isinstance(prompt, list):  # if prompt is a token id list
                    request.prompt_token_ids = prompt
                else:
                    tokens = self.tokenizer.tokenize(prompt)
                    token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
                    request.prompt_token_ids = token_ids
                    data_processor_logger.debug(
                        f"request_ids: {request.request_id}, prompt: {prompt}, tokens: {tokens}, token_ids: {token_ids}"
                    )
            elif request.messages is not None:
                task = request.to_dict()
                chat_template_kwargs = kwargs.get("chat_template_kwargs", {})
                if chat_template_kwargs:
                    if isinstance(chat_template_kwargs, dict):
                        for k, v in chat_template_kwargs.items():
                            if k not in task:
                                task[k] = v
                    else:
                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
                request.prompt_token_ids = self.messages2ids(task, **chat_template_kwargs)
            else:
                raise ValueError(f"The request should have `prompt_token_ids`, `prompt` or `messages`: {request}.")

        if len(request.prompt_token_ids) == 0:
            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")

        # truncate prompts that exceed the length limit
        if max_model_len is not None and len(request.prompt_token_ids) > max_model_len:
            request.prompt_token_ids = request.prompt_token_ids[: max_model_len - 1]
        if request.get("max_tokens") is None:
            request.set("max_tokens", max(1, max_model_len - len(request.prompt_token_ids)))
        if request.get("temperature") < _SAMPLING_EPS:
            # zero temperature is equivalent to greedy sampling
            request.set("temperature", 1)
        if request.get("top_p") < _SAMPLING_EPS:
            request.set("top_p", _SAMPLING_EPS)
        if self.reasoning_parser and self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser":
            request.enable_thinking = True

        data_processor_logger.info(f"Processed request: {request}")
        return request

    def process_request_dict(self, request, max_model_len=None):
        """
        Preprocess the request

        Args:
            request (Dict): may contain text and messages fields

        Returns:
            bool: Whether preprocessing is successful
            str: error message
        """
        data_processor_logger.info(f"Start processing request dict: {request}")
        request = self._apply_default_parameters(request)
        if not request.get("eos_token_ids"):
            request["eos_token_ids"] = self.eos_token_ids

        # processing stop_sequences
        stop_sequences = request.get("stop", [])
        if stop_sequences:
            stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
            request["stop_token_ids"] = stop_seqs
            request["stop_seqs_len"] = stop_seqs_len

        # processing bad_words
        bad_words = request.get("bad_words")
        bad_words_token_ids = request.get("bad_words_token_ids")
        if bad_words:
            bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
            request["bad_words_token_ids"] = bad_words_token_ids

        # processing prompt_token_ids
        if not request.get("prompt_token_ids"):
            if request.get("prompt"):
                prompt = request.get("prompt")
                assert isinstance(prompt, str) or (
                    isinstance(prompt, list) and all([isinstance(t, int) for t in prompt])
                ), f"prompt must be a string or a list of integers, but got {type(prompt)}"
                if isinstance(prompt, list):  # if prompt is a token id list
                    request["prompt_token_ids"] = prompt
                else:
                    request["text_after_process"] = prompt
                    tokens = self.tokenizer.tokenize(prompt)
                    token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
                    request["prompt_token_ids"] = token_ids
                    req_id = request.get("request_id", None)
                    data_processor_logger.info(f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
            elif request.get("messages"):
                chat_template_kwargs = request.get("chat_template_kwargs", {})
                if chat_template_kwargs:
                    if isinstance(chat_template_kwargs, dict):
                        for k, v in chat_template_kwargs.items():
                            if k not in request:
                                request[k] = v
                    else:
                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
                request["prompt_token_ids"] = self.messages2ids(request, **chat_template_kwargs)
            else:
                raise ValueError(f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}")

        if len(request["prompt_token_ids"]) == 0:
            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")

        # truncate prompts that exceed the length limit
        if max_model_len is not None and len(request["prompt_token_ids"]) > max_model_len:
            request["prompt_token_ids"] = request["prompt_token_ids"][: max_model_len - 1]
        if request.get("max_tokens") is None:
            request["max_tokens"] = max(1, max_model_len - len(request["prompt_token_ids"]))
        if request.get("temperature") < _SAMPLING_EPS:
            # zero temperature is equivalent to greedy sampling
            request["temperature"] = 1
        if request.get("top_p") < _SAMPLING_EPS:
            request["top_p"] = _SAMPLING_EPS
        if self.reasoning_parser and self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser":
            request["enable_thinking"] = True

        data_processor_logger.info(f"Processed request dict: {request}")
        return request

    def process_response(self, response_dict, **kwargs):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        req_id = response_dict.request_id
        token_ids = response_dict.outputs.token_ids

        response_dict.usage = {"completion_tokens": response_dict.outputs.index + 1}
        if token_ids[-1] == self.tokenizer.eos_token_id:
            token_ids = token_ids[:-1]
        full_text = self.tokenizer.decode(token_ids)
        if self.reasoning_parser:
            reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
            response_dict.outputs.text = text
            response_dict.outputs.reasoning_content = reasoning_content
        else:
            response_dict.outputs.text = full_text
        if self.tool_parser_obj:
            tool_parser = self.tool_parser_obj(self.tokenizer)
            tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
            if tool_call_info.tools_called:
                response_dict.outputs.tool_calls = tool_call_info.tool_calls
                response_dict.outputs.text = tool_call_info.content
        data_processor_logger.info(f"req_id:{req_id}, token_ids: {token_ids}")
        if response_dict.outputs.text == "" and response_dict.outputs.reasoning_content == "":
            return None
        return response_dict

    def process_response_dict(self, response_dict, stream, **kwargs):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        if stream:
            return self.process_response_dict_streaming(response_dict, **kwargs)
        else:
            return self.process_response_dict_normal(response_dict, **kwargs)

    def process_response_dict_normal(self, response_dict, **kwargs):
        """
        Preprocess the response

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        enable_thinking = kwargs.get("enable_thinking")
        token_ids = response_dict["outputs"]["token_ids"]
        is_end = response_dict["finished"]
        req_id = response_dict["request_id"]
        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
            if token_ids[-1] == self.tokenizer.eos_token_id:
                token_ids = token_ids[:-1]
        delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
        if is_end:
            full_text = previous_texts + delta_text
            if self.reasoning_parser and (
                enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
            ):
                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
                response_dict["outputs"]["text"] = text
                response_dict["outputs"]["reasoning_content"] = reasoning_content
            else:
                response_dict["outputs"]["text"] = full_text
            if self.tool_parser_obj:
                tool_parser = self.tool_parser_obj(self.tokenizer)
                tool_call_info = tool_parser.extract_tool_calls(full_text, response_dict)
                if tool_call_info.tools_called:
                    response_dict["outputs"]["tool_call"] = tool_call_info.tool_calls
                    response_dict["outputs"]["text"] = tool_call_info.content
            response_dict["outputs"]["raw_prediction"] = full_text
            data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
            del self.decode_status[req_id]
        return response_dict

    def process_response_dict_streaming(self, response_dict, **kwargs):
        """
        Preprocess the response streaming

        Args:
            response_dict (Dict): response for engine, contain ids fields

        Returns:
            Dict: response contain text fields
        """
        enable_thinking = kwargs.get("enable_thinking")
        is_end = response_dict["finished"]
        req_id = response_dict["request_id"]
        token_ids = response_dict["outputs"]["token_ids"]

        if is_end and len(token_ids) > 0 and not kwargs.get("include_stop_str_in_output"):
            if token_ids[-1] == self.tokenizer.eos_token_id:
                token_ids = token_ids[:-1]
        delta_text, previous_token_ids, previous_texts = self.ids2tokens(token_ids, req_id)
        response_dict["outputs"]["raw_prediction"] = delta_text
        if self.reasoning_parser and (
            enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
        ):
            reasoning_delta_message = self.reasoning_parser.extract_reasoning_content_streaming(
                previous_texts,
                previous_texts + delta_text,
                delta_text,
                previous_token_ids,
                previous_token_ids + token_ids,
                token_ids,
            )
            response_dict["outputs"]["delta_message"] = reasoning_delta_message
        if self.tool_parser_obj:
            if req_id not in self.tool_parser_dict:
                self.tool_parser_dict[req_id] = self.tool_parser_obj(self.tokenizer)
            tool_parser = self.tool_parser_dict[req_id]
            tool_call_delta_message = tool_parser.extract_tool_calls_streaming(
                previous_texts,
                previous_texts + delta_text,
                delta_text,
                previous_token_ids,
                previous_token_ids + token_ids,
                token_ids,
                response_dict,
            )
            if tool_call_delta_message is None or tool_call_delta_message.tool_calls:
                response_dict["outputs"]["delta_message"] = tool_call_delta_message
        response_dict["outputs"]["text"] = delta_text
        if is_end:
            data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
            del self.decode_status[req_id]
            if req_id in self.tool_parser_dict:
                del self.tool_parser_dict[req_id]
        return response_dict

    def messages2ids(self, request_or_messages, **kwargs):
        """
        Convert multi-turn messages into ID sequences.

        Args:
            request_or_messages: Either a request dict containing 'messages' field,
                                or a list of message dicts directly

        Returns:
            List of token IDs as strings (converted from token objects)
        """
        if self.tokenizer.chat_template is None:
            raise ValueError("This model does not support chat_template.")
        spliced_message = self.tokenizer.apply_chat_template(
            request_or_messages,
            tokenize=False,
            split_special_tokens=False,
            add_special_tokens=False,
            **kwargs,
        )
        request_or_messages["text_after_process"] = spliced_message
        req_id = None
        if isinstance(request_or_messages, dict):
            req_id = request_or_messages.get("request_id", None)
        tokens = self.tokenizer.tokenize(spliced_message)
        token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
        data_processor_logger.info(f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
        return token_ids

    def ids2tokens(self, token_id, task_id):
        """
        token ids to strings

        Args:
            token_ids (List[int]): token ids
                        task_id (str): task id

        Returns:
            List[str]: strings
        """

        if task_id not in self.decode_status:
            # prefix offset & read offset & history token ids & history token strings
            self.decode_status[task_id] = [0, 0, [], ""]

        prefix_offset = self.decode_status[task_id][0]
        read_offset = self.decode_status[task_id][1]
        previous_token_ids = self.decode_status[task_id][2]
        previous_texts = self.decode_status[task_id][3]
        decode_str, prefix_offset, read_offset = self.tokenizer.decode_token(
            previous_token_ids + token_id, prefix_offset, read_offset
        )
        self.decode_status[task_id][0] = prefix_offset
        self.decode_status[task_id][1] = read_offset
        self.decode_status[task_id][2] += token_id
        self.decode_status[task_id][3] += decode_str

        return decode_str, previous_token_ids, previous_texts

    def _load_tokenizer(self):
        """
        load tokenizer

        Returns:
            tokenizer (AutoTokenizer)
        """
        vocab_file_names = [
            "tokenizer.model",
            "spm.model",
            "ernie_token_100k.model",
        ]
        for i in range(len(vocab_file_names)):
            if os.path.exists(os.path.join(self.model_name_or_path, vocab_file_names[i])):
                Ernie4_5Tokenizer.resource_files_names["vocab_file"] = vocab_file_names[i]
                break
        self.tokenizer = Ernie4_5Tokenizer.from_pretrained(self.model_name_or_path)

    def get_pad_id(self):
        """
        get pad_token_id, if not pad_token_id, use eos_token

        Returns:
            int: pad_token_id
        """
        # if isinstance(self.tokenizer, (LlamaTokenizer, Llama3Tokenizer)) and not self.tokenizer.pad_token_id:
        #     return self.tokenizer.eos_token
        return self.tokenizer.pad_token_id

    def pad_batch_data(
        self,
        insts,
        pad_id=0,
        return_seq_len=False,
        return_array=True,
        pad_style="right",
    ):
        """Pad the instances to the max sequence length in batch."""
        if len(insts) == 0:
            padded_insts = np.array([[]], dtype=np.int64) if return_array else [[]]
            if return_seq_len:
                seq_len = np.array([], dtype=np.int64) if return_array else []
                return padded_insts, seq_len
            return padded_insts

        max_len = max(map(len, insts))
        if pad_style == "left":
            padded_insts = [[pad_id] * (max_len - len(inst)) + list(inst) for inst in insts]
        else:
            padded_insts = [list(inst) + [pad_id] * (max_len - len(inst)) for inst in insts]
        if return_array:
            padded_insts = np.array(padded_insts, dtype=np.int64).reshape([-1, max_len])

        if return_seq_len:
            seq_len = [len(inst) for inst in insts]
            if return_array:
                seq_len = np.array(seq_len, dtype=np.int64).reshape(-1, 1)
            return padded_insts, seq_len
        return padded_insts

    def update_stop_seq(self, stop_sequences):
        """
        Update stop sequences from request.
        """
        stop_seqs = []
        if isinstance(stop_sequences, str):
            stop_sequences = [stop_sequences]
        for seq in stop_sequences:
            if seq != self.tokenizer.eos_token_id:
                stop_seqs.append(self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(seq)))
        stop_seqs, stop_seqs_len = self.pad_batch_data(stop_seqs, pad_id=-1, return_seq_len=True, return_array=False)
        data_processor_logger.debug(f"processed stop_seqs: {stop_seqs}, {stop_seqs_len}")
        return stop_seqs, stop_seqs_len

    def process_logprob_response(self, token_ids, **kwargs):
        full_text = self.tokenizer.decode(token_ids, **kwargs)
        return full_text

    def update_bad_words(self, bad_words, bad_words_token_ids):
        """Support bad words"""

        token_ids = bad_words_token_ids

        if token_ids is None:
            token_ids = []
        for bad_word in bad_words:
            # To prohibit words both at the beginning
            # and in the middle of text
            # (related to add_prefix_space tokenizer parameter)
            for add_prefix_space in [False, True]:
                prefix = " " if add_prefix_space else ""
                prompt = prefix + bad_word.lstrip()
                prompt_token_ids = self.tokenizer.convert_tokens_to_ids(self.tokenizer.tokenize(prompt))
                data_processor_logger.debug(f"processed bad_words: {prompt}, {prompt_token_ids}")

                if len(prompt_token_ids) != 1:
                    if not add_prefix_space:
                        data_processor_logger.warning(
                            f"Skip bad_words: <{prompt}>."
                            f"Bad words should be a single token."
                            f"Got tokens: {prompt_token_ids}."
                        )
                    continue

                if prompt_token_ids[0] > self.tokenizer.vocab_size:
                    if not add_prefix_space:
                        data_processor_logger.warning(
                            f"Skip bad_words: <{prompt}>."
                            f"All token id values should be satisfying:"
                            f" 0 <= token_id < {self.tokenizer.vocab_size}."
                            f"Got token: {prompt_token_ids}."
                        )
                    continue

                if prompt_token_ids not in token_ids:
                    token_ids.extend(prompt_token_ids)
        return token_ids