mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-27 02:20:31 +08:00
347 lines
11 KiB
Python
347 lines
11 KiB
Python
"""
|
|
# 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 traceback
|
|
from concurrent.futures import ThreadPoolExecutor
|
|
|
|
from fastdeploy.config import ErnieArchitectures, FDConfig
|
|
from fastdeploy.engine.request import Request
|
|
from fastdeploy.utils import llm_logger
|
|
|
|
|
|
class LogitsProcessorBase:
|
|
"""
|
|
Abstract base class for logits processors in guided decoding.
|
|
|
|
This class defines the interface for logits processors that modify token probabilities
|
|
during generation to enforce schema constraints. Subclasses should implement all
|
|
abstract methods to provide specific constraint enforcement logic.
|
|
|
|
Attributes:
|
|
None (all state should be managed by subclasses)
|
|
"""
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def fill_token_bitmask(self, token_bitmask, idx):
|
|
"""
|
|
Fill the vocabulary mask.
|
|
|
|
Args:
|
|
token_bitmask (tensor): The vocabulary mask tensor.
|
|
idx (tensor): The tensor index.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def apply_token_mask(self, logits, token_bitmask):
|
|
"""
|
|
Apply the vocabulary mask to logits.
|
|
|
|
Args:
|
|
logits (tensor): The logits tensor.
|
|
token_bitmask (tensor): The vocabulary mask tensor.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def allocate_token_bitmask(self, batch_size, vocab_size):
|
|
"""
|
|
Allocate a token bitmask for the given batch size and vocabulary size.
|
|
|
|
Args:
|
|
batch_size (int): The batch size.
|
|
vocab_size (int): The vocabulary size.
|
|
|
|
Returns:
|
|
tensor: The allocated token bitmask.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def accept_token(self, token):
|
|
"""
|
|
Accept tokens based on the token bitmask
|
|
|
|
Args:
|
|
token (int): The token id.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def is_terminated(self):
|
|
"""
|
|
Check if the processor has been terminated.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def reset(self):
|
|
"""
|
|
Reset the matcher state.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def copy(self):
|
|
"""
|
|
Create a copy of the backend instance.
|
|
|
|
Returns:
|
|
BackendBase: A copy of the backend instance.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
|
|
class BackendBase:
|
|
"""
|
|
Abstract base class for guided decoding backends.
|
|
|
|
This class provides the core infrastructure for managing schema processors and
|
|
their caching. It handles:
|
|
- Processor creation and caching
|
|
- Tokenizer initialization
|
|
- Thread pool management for async operations
|
|
|
|
Attributes:
|
|
cache (dict): Cache of schema processors
|
|
fd_config (FDConfig): FastDeploy configuration
|
|
executor (ThreadPoolExecutor): Thread pool for async operations
|
|
max_cache_size (int): Maximum number of processors to cache
|
|
hf_tokenizer: HuggingFace tokenizer instance
|
|
"""
|
|
|
|
def __init__(self, fd_config: FDConfig):
|
|
self.cache = {}
|
|
self.fd_config = fd_config
|
|
self.executor = ThreadPoolExecutor()
|
|
self.max_cache_size = 2048
|
|
|
|
self.hf_tokenizer = self._get_tokenizer_hf()
|
|
|
|
def _create_processor(self):
|
|
"""
|
|
Create a specific logits processor instance.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def _json_processor(self, schemata):
|
|
"""
|
|
Process JSON schemata.
|
|
|
|
Args:
|
|
schemata (str): The schemata string.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def _regex_processor(self, schemata):
|
|
"""
|
|
Process regular expression schemata.
|
|
|
|
Args:
|
|
schemata (str): The schemata string.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def _grammar_processor(self, schemata):
|
|
"""
|
|
Process grammar schemata.
|
|
|
|
Args:
|
|
schemata (str): The schemata string.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def _structural_tag_processor(self, schemata):
|
|
"""
|
|
Process structural tag schemata.
|
|
|
|
Args:
|
|
schemata (str): The schemata string.
|
|
|
|
Raises:
|
|
NotImplementedError: This method should be implemented in subclasses.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
def _unsupported_processor_type(self, key_type, schemata):
|
|
"""
|
|
Process unsupported type.
|
|
|
|
Args:
|
|
key_type (str): The key type string.
|
|
schemata (str): The schemata string.
|
|
"""
|
|
raise Exception(f"Unsupported processor type {key_type}.")
|
|
|
|
def _init_logits_processor(self, schemata_key: tuple[str, str]) -> LogitsProcessorBase:
|
|
"""
|
|
init logits processor by type and schemata.
|
|
|
|
Args:
|
|
schemata_key (tuple[str, str]): Tuple containing processor type and schema string
|
|
|
|
Returns:
|
|
LogitsProcessorBase: Initialized logits processor instance
|
|
|
|
Raises:
|
|
ValueError: If processor type is not supported
|
|
"""
|
|
key_type, schemata = schemata_key
|
|
if key_type == "json":
|
|
return self._json_processor(schemata)
|
|
elif key_type == "regex":
|
|
return self._regex_processor(schemata)
|
|
elif key_type == "grammar":
|
|
return self._grammar_processor(schemata)
|
|
elif key_type == "structural_tag":
|
|
return self._structural_tag_processor(schemata)
|
|
else:
|
|
llm_logger.error(f"Unsupported processor type {key_type}.")
|
|
return None
|
|
|
|
def get_logits_processor(self, schemata_key: tuple[str, str]) -> tuple[LogitsProcessorBase, bool]:
|
|
"""
|
|
get logits processor by key from cache or create new one.
|
|
|
|
Args:
|
|
schemata_key (tuple[str, str]): Tuple containing processor type and schema string
|
|
|
|
Returns:
|
|
tuple[LogitsProcessorBase, bool]: Tuple containing:
|
|
- LogitsProcessorBase: The logits processor instance
|
|
- bool: True if processor was from cache, False if newly created
|
|
"""
|
|
value = self.cache.get(schemata_key, None)
|
|
if value:
|
|
return value.copy(), True
|
|
value = self.executor.submit(self._init_logits_processor, schemata_key)
|
|
return value, False
|
|
|
|
def _get_tokenizer_hf(self):
|
|
"""
|
|
Initialize and return a HuggingFace tokenizer instance.
|
|
|
|
This method handles special cases for Ernie models and falls back to standard
|
|
AutoTokenizer for other models. It also ensures fast tokenizer is used when possible.
|
|
|
|
Returns:
|
|
Tokenizer: Initialized HuggingFace tokenizer instance
|
|
|
|
Raises:
|
|
Exception: If tokenizer initialization fails
|
|
"""
|
|
try:
|
|
architectures = self.fd_config.model_config.architectures
|
|
if not ErnieArchitectures.contains_ernie_arch(architectures):
|
|
|
|
from transformers import AutoTokenizer, PreTrainedTokenizerFast
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(
|
|
self.fd_config.model_config.model,
|
|
use_fast=False,
|
|
)
|
|
|
|
if not isinstance(tokenizer, PreTrainedTokenizerFast):
|
|
tokenizer = PreTrainedTokenizerFast(__slow_tokenizer=tokenizer)
|
|
else:
|
|
from fastdeploy.model_executor.guided_decoding.ernie_tokenizer import (
|
|
Ernie4_5Tokenizer,
|
|
)
|
|
|
|
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.fd_config.model_config.model,
|
|
vocab_file_names[i],
|
|
)
|
|
):
|
|
Ernie4_5Tokenizer.vocab_files_names["vocab_file"] = vocab_file_names[i]
|
|
break
|
|
|
|
tokenizer = Ernie4_5Tokenizer.from_pretrained(self.fd_config.model_config.model)
|
|
|
|
return tokenizer
|
|
except Exception as e:
|
|
raise Exception(f"Fail to initialize hf tokenizer: {e}, {str(traceback.format_exc())}")
|
|
|
|
def add_cache(self, schemata_key: tuple[str, str], processor: LogitsProcessorBase) -> None:
|
|
"""
|
|
add logits processor to cache.
|
|
|
|
Args:
|
|
schemata_key (tuple[str, str]): Tuple containing processor type and schema string
|
|
processor (LogitsProcessorBase): Logits processor instance to cache
|
|
|
|
Returns:
|
|
None: No return value
|
|
"""
|
|
if len(self.cache) >= self.max_cache_size:
|
|
return
|
|
self.cache[schemata_key] = processor.copy()
|
|
|
|
|
|
class BaseChecker:
|
|
"""
|
|
Abstract base class for schema checkers.
|
|
|
|
This class defines the interface for validating and formatting schemas
|
|
before they are used by logits processors. Subclasses should implement
|
|
schema-specific validation and formatting logic.
|
|
|
|
Attributes:
|
|
None (all state should be managed by subclasses)
|
|
"""
|
|
|
|
def __init__(self):
|
|
pass
|
|
|
|
def schema_format(self, request: Request):
|
|
"""
|
|
format schema to backend specific format.
|
|
Args:
|
|
request (Request): request object.
|
|
|
|
Returns:
|
|
request (Request): request object with formatted schema.
|
|
"""
|
|
raise NotImplementedError
|