[LLM] First commit the llm deployment code

fastdeploy/input/text_processor.py

@@ -0,0 +1,533 @@
+"""
+# 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
+from abc import ABC, abstractmethod
+
+import numpy as np
+from paddlenlp.generation import GenerationConfig
+from paddlenlp.transformers import Llama3Tokenizer, LlamaTokenizer
+
+from fastdeploy.utils import data_processor_logger
+
+
+class BaseDataProcessor(ABC):
+    """base class for data processor"""
+
+    def __init__(self):
+        """
+        Returns:
+            None
+        """
+        self.tokenizer = self._load_tokenizer()
+        self.tokenizer.bos_token_id = self.tokenizer._convert_token_to_id(
+            self.tokenizer.bos_token)
+        self.tokenizer.cls_token_id = self.tokenizer._convert_token_to_id(
+            self.tokenizer.cls_token)
+        self.tokenizer.sep_token_id = self.tokenizer._convert_token_to_id(
+            self.tokenizer.sep_token)
+        self.tokenizer.eos_token_id = self.tokenizer._convert_token_to_id(
+            self.tokenizer.eos_token)
+        self.tokenizer.mask_token_id = self.tokenizer._convert_token_to_id(
+            self.tokenizer.mask_token)
+        data_processor_logger.info((
+            f"tokenizer information: bos_token is {self.tokenizer.bos_token}, {self.tokenizer.bos_token_id}, ",
+            f"cls_token is {self.tokenizer.cls_token}, {self.tokenizer.cls_token_id}, "
+            f"sep_token is {self.tokenizer.sep_token}, {self.tokenizer.sep_token_id}, "
+            f"eos_token is {self.tokenizer.eos_token}, {self.tokenizer.eos_token_id}, "
+            f"mask_token is {self.tokenizer.mask_token}, {self.tokenizer.mask_token_id}"
+        ))
+
+    @abstractmethod
+    def process_request(self, request, **kwargs):
+        """
+        Preprocess the request
+
+        Args:
+            request (Dict): may contain text and messages fields
+            **kwargs: others
+
+        Returns:
+            bool: Whether preprocessing is successful
+            str: error message
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def process_response(self, response_dict):
+        """
+        Preprocess the response
+
+        Args:
+            response_dict (Dict): response for engine, contain ids fields
+
+        Returns:
+            Dict: response contain text fields
+        """
+        raise NotImplementedError
+
+    def text2ids(self, text, max_model_len=None):
+        """
+        text to token ids
+
+        Args:
+            text (str): text
+
+        Returns:
+            List[int]: token ids list
+        """
+        raise NotImplementedError
+
+    def messages2ids(self, messages):
+        """
+        Convert multi-turn messages into ID sequences.
+
+        Args:
+            messages (List[List[Dict[str, Any]]]): multi-turn messages.
+
+        Returns:
+            List[int]: ID sequences
+        """
+        raise NotImplementedError
+
+    def ids2tokens(self, token_id, task_id=None):
+        """
+        token ids to strings
+
+        Args:
+            token_id (List[int]): token id
+			task_id (str): task id
+
+        Returns:
+            List[str]: strings
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def _load_tokenizer(self):
+        """
+        load tokenizer
+
+        Returns:
+            tokenizer (AutoTokenizer)
+        """
+        raise NotImplementedError
+
+
+class DataProcessor(BaseDataProcessor):
+
+    def __init__(self, model_name_or_path):
+        """
+            Initializes the DecodeStatus object.
+
+        Args:
+            model_name_or_path (str): The name or path of the pre-trained model to be loaded.
+                Can also be a path to a directory containing the pre-trained model file.
+
+        Returns:
+            None.
+
+        Raises:
+            None.
+        """
+
+        self.model_name_or_path = model_name_or_path
+        self._init_config()
+
+        self.decode_status = dict()
+        self.tokenizer = 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 paddlenlp.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.tokenizer.pad_token_id = self.pad_token_id
+
+    def _init_config(self):
+        """
+            初始化配置，包括模型名称、使用Hugging Face Tokenizer等。
+
+        Args:
+            无参数，但是会从环境变量中获取一些配置信息。
+
+        Returns:
+            无返回值，直接修改了类的属性。
+
+        Raises:
+            无异常抛出。
+        """
+        self.use_hf_tokenizer = int(os.getenv("USE_HF_TOKENIZER", "0")) == 1
+
+        # 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: {e}, so it will not use generation_config field in the model config"
+            )
+            self.generation_config = None
+
+    def process_request(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
+        """
+        if request.get("eos_token_ids") is None or len(
+                request.eos_token_ids) == 0:
+            request.eos_token_ids = self.eos_token_ids
+
+        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)
+
+        if request.prompt_token_ids is None or len(
+                request.prompt_token_ids) == 0:
+            if request.prompt is not None:
+                request.prompt_token_ids = self.text2ids(
+                    request.prompt, max_model_len, request.raw_request)
+            elif request.messages is not None:
+                if self.tokenizer.chat_template is None:
+                    raise ValueError(
+                        "This model does not support chat_template.")
+                request.prompt_token_ids = self.messages2ids(request.messages)
+            else:
+                raise ValueError(
+                    f"The request should have `input_ids`, `text` or `messages`: {request}."
+                )
+
+        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]
+        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
+        """
+        if not request.get('eos_token_ids'):
+            request['eos_token_ids'] = self.eos_token_ids
+
+        # 处理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
+
+        # 处理prompt_token_ids
+        if not request.get('prompt_token_ids'):
+            if 'prompt' in request:
+                raw_request = request.get('raw_request', True)
+                request['prompt_token_ids'] = self.text2ids(
+                    request['prompt'], max_model_len, raw_request).tolist()
+            elif 'messages' in request:
+                if self.tokenizer.chat_template is None:
+                    raise ValueError(
+                        "This model does not support chat_template.")
+                request['prompt_token_ids'] = self.messages2ids(
+                    request['messages']).tolist()
+            else:
+                raise ValueError(
+                    f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}"
+                )
+
+        # 截断超过长度限制的prompt
+        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]
+
+        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
+        """
+        is_end = response_dict.finished
+        req_id = response_dict.request_id
+
+        token_ids = response_dict.outputs.token_ids
+        response_dict.outputs.text = self.ids2tokens(token_ids, req_id)
+        response_dict.usage = {
+            "completion_tokens": response_dict.outputs.index + 1
+        }
+
+        if is_end:
+            self.clear_request_status(req_id)
+            data_processor_logger.debug(
+                "Request id: {} has been completed.".format(token_ids))
+            response_dict.outputs.text = self.ids2tokens(token_ids, req_id)
+            self.clear_request_status(req_id)
+        return response_dict
+
+    def process_response_dict(self, response_dict, stream=True):
+        """
+        Preprocess the response
+
+        Args:
+            response_dict (Dict): response for engine, contain ids fields
+
+        Returns:
+            Dict: response contain text fields
+        """
+        is_end = response_dict["finished"]
+        req_id = response_dict["request_id"]
+
+        token_ids = response_dict["outputs"]["token_ids"]
+
+        if is_end:
+            data_processor_logger.debug(
+                "Request id: {} has been completed.".format(token_ids))
+            full_text = self.clear_request_status(req_id)
+            if not stream:
+                response_dict["outputs"]["text"] = full_text
+            else:
+                response_dict["outputs"]["text"] = ""
+        else:
+            response_dict["outputs"]["text"] = self.ids2tokens(
+                token_ids, req_id)
+        return response_dict
+
+    def text2ids(self, text, max_model_len, raw_request=True):
+        """
+        text to token ids
+
+        Args:
+            text (str): text
+
+        Returns:
+            List[int]: token ids list
+        """
+        if self.use_hf_tokenizer:
+            tokens = self.tokenizer(
+                text,
+                return_tensors="np",
+                padding=True,
+                truncation=True,
+            )
+        else:
+            if not raw_request or self.tokenizer.chat_template is None:
+                text = [text] if isinstance(text, str) else text
+                chat_template = False
+            elif self.tokenizer.chat_template is not None:
+                text = [text] if isinstance(text, str) else text
+                text = [
+                    self.tokenizer.apply_chat_template(sentence,
+                                                       tokenize=False)
+                    for sentence in text
+                ]
+                chat_template = True
+            tokens = self.tokenizer(
+                text,
+                return_tensors="np",
+                padding=True,
+                truncation=True,
+                max_length=max_model_len,
+                add_special_tokens=chat_template,
+            )
+        return tokens["input_ids"][0]
+
+    def messages2ids(self, messages):
+        """
+        Convert multi-turn messages into ID sequences.
+
+        Args:
+            messages (List[List[Dict[str, Any]]]): multi-turn messages.
+
+        Returns:
+            List[int]: ID sequences
+        """
+        message_result = self.tokenizer.apply_chat_template(
+            messages, return_tensors="pd")
+        return np.array(message_result["input_ids"][0])
+
+    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 self.use_hf_tokenizer:
+            if task_id not in self.decode_status:
+                # history token ids & history token strings & befer decode str
+                self.decode_status[task_id] = [[], [], ""]
+
+            previous_token_ids = self.decode_status[task_id][0]
+            decode_str = self.tokenizer.batch_decode(
+                [previous_token_ids + token_id],
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False)
+            if isinstance(decode_str, list) and len(decode_str):
+                new_str = decode_str[0].replace(self.decode_status[task_id][2],
+                                                "", 1)
+                self.decode_status[task_id][1].append(new_str)
+                self.decode_status[task_id][2] = decode_str[0]
+            else:
+                new_str = ""
+            self.decode_status[task_id][0] += token_id
+            return new_str
+        else:
+            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]
+            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].append(decode_str)
+            return decode_str
+
+    def _load_tokenizer(self):
+        """
+        load tokenizer
+
+        Returns:
+            tokenizer (AutoTokenizer)
+        """
+
+        if self.use_hf_tokenizer:
+            from transformers import AutoTokenizer
+            return AutoTokenizer.from_pretrained(self.model_name_or_path,
+                                                 use_fast=False)
+        else:
+            from paddlenlp.transformers import AutoTokenizer
+            return AutoTokenizer.from_pretrained(self.model_name_or_path,
+                                                 padding_side="left",
+                                                 use_fast=True)
+
+    def clear_request_status(self, task_id):
+        """
+        clear request status
+
+        Args:
+            task_id (str): task id
+
+        Returns:
+            results_all (str): all token strings
+        """
+        results_all = ""
+        if task_id in self.decode_status:
+            if self.use_hf_tokenizer:
+                results_all = self.decode_status[task_id][2]
+            else:
+                results_all = "".join(self.decode_status[task_id][3])
+            del self.decode_status[task_id]
+        return results_all
+
+    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 = []
+        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