mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
Sync v2.0 version of code to github repo
This commit is contained in:
Jiang-Jia-Jun
444
fastdeploy/input/ernie_processor.py
Normal file
444
fastdeploy/input/ernie_processor.py
Normal file
@@ -0,0 +1,444 @@
|
||||
"""
|
||||
# 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 import envs
|
||||
from fastdeploy.utils import data_processor_logger
|
||||
from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
|
||||
|
||||
from fastdeploy.input.text_processor import BaseDataProcessor
|
||||
|
||||
_SAMPLING_EPS = 1e-5
|
||||
|
||||
class ErnieProcessor(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):
|
||||
|
||||
self.model_name_or_path = model_name_or_path
|
||||
data_processor_logger.info(f"model_name_or_path: {model_name_or_path}")
|
||||
self._init_config()
|
||||
|
||||
self.decode_status = 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} "
|
||||
)
|
||||
self.eos_token_ids = [self.tokenizer.eos_token_id]
|
||||
self.eos_token_id_len = len(self.eos_token_ids)
|
||||
self.pad_token_id = self.get_pad_id()
|
||||
self.reasoning_parser = None
|
||||
if reasoning_parser_obj:
|
||||
self.reasoning_parser = reasoning_parser_obj(self.tokenizer)
|
||||
|
||||
def _init_config(self):
|
||||
self.use_hf_tokenizer = int(envs.FD_USE_HF_TOKENIZER) == 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, so it will not use "
|
||||
f"generation_config field in the model config, details={e}")
|
||||
self.generation_config = None
|
||||
|
||||
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
|
||||
"""
|
||||
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
|
||||
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:
|
||||
system = request.get("system")
|
||||
if request.prompt is None and request.messages is None:
|
||||
raise ValueError(
|
||||
f"The request should have `input_ids`, `text` or `messages`: {request}.")
|
||||
if request.prompt is not None or not request.raw_request:
|
||||
prompt = request.prompt if request.prompt is not None else request.messages[0]
|
||||
prompt = prompt[0] if isinstance(prompt, list) else prompt
|
||||
tokens = self.tokenizer.tokenize(prompt)
|
||||
token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
|
||||
request.prompt_token_ids = token_ids
|
||||
data_processor_logger.info(f"req_id:{request.request_id}, tokens:{tokens}, token_ids: {token_ids}")
|
||||
else:
|
||||
request.prompt_token_ids = self.messages2ids(request.to_dict())
|
||||
|
||||
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)
|
||||
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
|
||||
"""
|
||||
request = self._apply_default_parameters(request)
|
||||
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
|
||||
|
||||
system = request.get("system")
|
||||
# 处理prompt_token_ids
|
||||
if not request.get('prompt_token_ids'):
|
||||
if request.get('prompt') is None and request.get(
|
||||
'messages') is None:
|
||||
raise ValueError(
|
||||
f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}"
|
||||
)
|
||||
if request.get('prompt'):
|
||||
prompt = request.get('prompt')
|
||||
prompt = prompt[0] if isinstance(prompt, list) else 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}"
|
||||
)
|
||||
else:
|
||||
request['prompt_token_ids'] = self.messages2ids(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]
|
||||
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
|
||||
data_processor_logger.info(f"Processed request {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
|
||||
data_processor_logger.info(f"req_id:{req_id}, token)ids: {token_ids}")
|
||||
if response_dict.outputs.text == "" and \
|
||||
response_dict.outputs.reasoning_content == "" and \
|
||||
response_dict.outputs.tool_call_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
|
||||
"""
|
||||
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:
|
||||
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:
|
||||
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
|
||||
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:
|
||||
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)
|
||||
if enable_thinking and self.reasoning_parser:
|
||||
reasoning_content, text = 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"]["text"] = text
|
||||
response_dict["outputs"]["reasoning_content"] = reasoning_content
|
||||
else:
|
||||
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]
|
||||
return response_dict
|
||||
|
||||
def messages2ids(self, request_or_messages):
|
||||
"""
|
||||
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)
|
||||
|
||||
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])):
|
||||
ErnieBotTokenizer.resource_files_names[
|
||||
"vocab_file"] = vocab_file_names[i]
|
||||
break
|
||||
self.tokenizer = ErnieBotTokenizer.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 = []
|
||||
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
|
||||
Reference in New Issue
Block a user