mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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luukunn
532 lines
22 KiB
Python
532 lines
22 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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"""Unified multimodal processor for all VL model types.
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Consolidates the four separate VL processor wrappers and four separate
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DataProcessor classes into a single class with pluggable Encoding strategies.
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"""
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import pickle
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from collections.abc import Mapping
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from typing import Any, Dict, Optional
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import numpy as np
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import zmq
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from fastdeploy.entrypoints.chat_utils import parse_chat_messages
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from fastdeploy.input.base_processor import BaseTextProcessor
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from fastdeploy.input.encodings import EncodingRegistry
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from fastdeploy.input.image_processors import ImageProcessorRegistry
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from fastdeploy.input.mm_model_config import MODEL_CONFIGS
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from fastdeploy.input.utils import IDS_TYPE_FLAG, process_stop_token_ids
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from fastdeploy.utils import data_processor_logger
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_DEFAULT_MM_LIMITS = {"image": 1, "video": 1, "audio": 1}
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_SAMPLING_EPS = 1e-5
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class MultiModalProcessor(BaseTextProcessor):
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"""Unified multimodal processor for all supported VL model types.
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Uses a composition pattern: model-type-specific encoding logic is
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delegated to ``self.enc`` (an Encoding instance), while common logic
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(tokenization loop, request processing, caching) lives here.
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"""
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def __init__(
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self,
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model_name_or_path: str,
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model_type: str,
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config=None,
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limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
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mm_processor_kwargs: Optional[Dict[str, Any]] = None,
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reasoning_parser_obj=None,
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tool_parser_obj=None,
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enable_processor_cache: bool = False,
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):
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if model_type not in MODEL_CONFIGS:
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raise ValueError(f"Unsupported model_type '{model_type}'. " f"Must be one of {sorted(MODEL_CONFIGS)}.")
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self.model_type = model_type
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self.config = config
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self.cfg = MODEL_CONFIGS[model_type]
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self.enable_processor_cache = enable_processor_cache
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super().__init__(
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model_name_or_path,
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tokenizer_type=self.cfg.tokenizer_type,
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reasoning_parser_obj=reasoning_parser_obj,
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tool_parser_obj=tool_parser_obj,
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)
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data_processor_logger.info(f"model_name_or_path: {model_name_or_path}")
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processor_kwargs = self._parse_processor_kwargs(mm_processor_kwargs)
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self._init_image_processor()
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self._init_role_prefixes()
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# Composition: create encoding strategy via registry
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enc_cls = EncodingRegistry.get(self.model_type)
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self.enc = enc_cls(self, processor_kwargs)
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self.limit_mm_per_prompt = self._parse_limits(limit_mm_per_prompt)
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def _load_tokenizer(self):
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"""Load the appropriate tokenizer based on model_type."""
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if self.tokenizer_type == "ernie4_5":
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import os
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from fastdeploy.input.ernie4_5_tokenizer import Ernie4_5Tokenizer
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vocab_file_names = ["tokenizer.model", "spm.model", "ernie_token_100k.model"]
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for name in vocab_file_names:
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if os.path.exists(os.path.join(self.model_name_or_path, name)):
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Ernie4_5Tokenizer.resource_files_names["vocab_file"] = name
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break
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tokenizer = Ernie4_5Tokenizer.from_pretrained(self.model_name_or_path)
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else:
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from paddleformers.transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(self.model_name_or_path, padding_side="left", use_fast=True)
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return tokenizer
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def _init_image_processor(self):
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"""Create the appropriate image processor."""
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cls = ImageProcessorRegistry.get(self.model_type)
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self.image_processor = cls.from_pretrained(self.model_name_or_path)
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def _init_role_prefixes(self):
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"""Set up role prefixes for message parsing."""
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self.role_prefixes = {
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"system": "",
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"user": "User: ",
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"bot": "Assistant: ",
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"assistant": "Assistant: ",
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}
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if self.cfg.has_tool_role:
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self.role_prefixes["tool"] = "Tool: "
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def _parse_processor_kwargs(self, kwargs: Optional[dict]) -> dict:
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if not kwargs:
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return {}
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try:
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if not isinstance(kwargs, dict):
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raise ValueError("mm-processor-kwargs must be a dictionary")
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data_processor_logger.info(f"Processing kwargs: {kwargs}")
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expected_types = self.cfg.expected_kwargs
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for key, value in kwargs.items():
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if key in expected_types and not isinstance(value, expected_types[key]):
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raise ValueError(
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f"Invalid type for {key}: expected "
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f"{expected_types[key].__name__}, got {type(value).__name__}"
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)
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return kwargs
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except Exception as e:
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data_processor_logger.warning(f"Invalid mm-processor-kwargs format: {e}")
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return {}
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def _parse_limits(self, limits: Optional[dict]) -> dict:
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if not limits:
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return dict(_DEFAULT_MM_LIMITS)
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try:
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if not isinstance(limits, dict):
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raise ValueError("limit-mm-per-prompt must be a dictionary")
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data_processor_logger.info(f"_parse_limits:{limits}")
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return {**_DEFAULT_MM_LIMITS, **limits}
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except Exception as e:
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data_processor_logger.warning(f"Invalid limit-mm-per-prompt format: {e}, using default limits")
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return dict(_DEFAULT_MM_LIMITS)
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def _check_mm_limits(self, item):
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if isinstance(item, dict):
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mm_data = item
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else:
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mm_data = {"image": [], "video": []}
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for message in item:
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if isinstance(message.get("content"), list):
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for part in message["content"]:
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part_type = part.get("type")
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if part_type in ("image_url", "image"):
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mm_data["image"].append(part)
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elif part_type in ("video_url", "video"):
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mm_data["video"].append(part)
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for modality, data in mm_data.items():
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if modality in self.limit_mm_per_prompt:
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limit = self.limit_mm_per_prompt[modality]
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if len(data) > limit:
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raise ValueError(f"Too many {modality} items in prompt, " f"got {len(data)} but limit is {limit}")
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def get_mm_max_tokens_per_item(self, seq_len: int) -> Optional[Mapping[str, int]]:
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return self.enc.get_mm_max_tokens_per_item(seq_len)
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def _extract_mm_items(self, request):
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"""Extract images/videos from request messages, handling processor cache."""
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messages = parse_chat_messages(request.get("messages"))
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mm_items = []
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for msg in messages:
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role = msg.get("role")
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if role not in self.role_prefixes:
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raise ValueError(f"Unsupported role: {role}")
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content = msg.get("content")
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if not isinstance(content, list):
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content = [content]
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for item in content:
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if item.get("type") in ["image", "video"]:
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mm_items.append(item)
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missing_hashes, missing_idx = [], []
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for idx, item in enumerate(mm_items):
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if not item.get("data"):
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missing_hashes.append(item.get("uuid"))
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missing_idx.append(idx)
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if len(missing_hashes) > 0 and not self.enable_processor_cache:
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raise ValueError("Missing items cannot be retrieved without processor cache.")
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dealer = None
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if self.enable_processor_cache:
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context = zmq.Context()
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dealer = context.socket(zmq.DEALER)
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dealer.connect("ipc:///dev/shm/processor_cache.ipc")
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missing_items = self.get_processor_cache(dealer, missing_hashes)
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for idx in range(len(missing_items)):
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if not missing_items[idx]:
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raise ValueError(f"Missing item {idx} not found in processor cache")
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mm_items[missing_idx[idx]]["data"] = missing_items[idx]
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images, videos = [], []
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image_uuid, video_uuid = [], []
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for item in mm_items:
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if item.get("type") == "image":
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images.append(item["data"])
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image_uuid.append(item["uuid"])
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elif item.get("type") == "video":
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videos.append(item["data"])
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video_uuid.append(item["uuid"])
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else:
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raise ValueError(f"Unsupported multimodal type: {item.get('type')}")
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return images, videos, image_uuid, video_uuid, dealer, missing_idx, mm_items
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def text2ids(self, text, images=None, videos=None, image_uuid=None, video_uuid=None):
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"""Convert text with image/video placeholders into model inputs."""
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outputs = self.enc._make_outputs()
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IMAGE_PLACEHOLDER = self.cfg.image_placeholder
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VIDEO_PLACEHOLDER = self.cfg.video_placeholder
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IMAGE_PLACEHOLDER_LEN = len(IMAGE_PLACEHOLDER)
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VIDEO_PLACEHOLDER_LEN = len(VIDEO_PLACEHOLDER)
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st, image_idx, video_idx = 0, 0, 0
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while st < len(text):
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image_pos = text.find(IMAGE_PLACEHOLDER, st)
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image_pos = len(text) if image_pos == -1 else image_pos
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video_pos = text.find(VIDEO_PLACEHOLDER, st)
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video_pos = len(text) if video_pos == -1 else video_pos
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ed = min(image_pos, video_pos)
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self._add_text(text[st:ed], outputs)
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if ed == len(text):
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break
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if ed == image_pos:
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image = images[image_idx]
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uuid = image_uuid[image_idx] if image_uuid else None
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if not isinstance(image, tuple):
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self.enc.add_image(image, outputs, uuid)
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else:
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self.enc.add_processed_image(image, outputs, uuid)
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image_idx += 1
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st = ed + IMAGE_PLACEHOLDER_LEN
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else:
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item = videos[video_idx]
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uuid = video_uuid[video_idx] if video_uuid else None
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if not isinstance(item, tuple):
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if isinstance(item, dict):
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frames, meta = self.enc.load_video(item["video"], item)
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else:
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frames, meta = self.enc.load_video(item, {})
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self.enc.add_video(frames, outputs, uuid, meta=meta)
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else:
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self.enc.add_processed_video(item, outputs, uuid)
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video_idx += 1
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st = ed + VIDEO_PLACEHOLDER_LEN
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return outputs
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def request2ids(self, request):
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"""Convert chat request with multimodal messages into model inputs."""
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images, videos, image_uuid, video_uuid, dealer, missing_idx, mm_items = self._extract_mm_items(request)
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if self.tokenizer.chat_template is None:
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raise ValueError("This model does not support chat template.")
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chat_template_kwargs = request.get("chat_template_kwargs", {})
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if self.cfg.chat_template_pass_request:
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# ernie: pass full request to apply_chat_template
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prompt = self.tokenizer.apply_chat_template(
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request,
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tokenize=False,
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add_generation_prompt=request.get("add_generation_prompt", True),
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**chat_template_kwargs,
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)
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else:
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messages = parse_chat_messages(request.get("messages"))
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prompt = self.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=request.get("add_generation_prompt", True),
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**chat_template_kwargs,
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)
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request["prompt_tokens"] = prompt
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outputs = self.text2ids(prompt, images, videos, image_uuid, video_uuid)
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if self.enable_processor_cache:
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self._update_mm_cache(dealer, missing_idx, mm_items, outputs)
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return outputs
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def _process_prompt_token_ids(self, request):
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"""Handle the prompt_token_ids tokenisation path.
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Mirrors ``request2ids`` in structure: Processor owns extract/cache,
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Encoding only does pure encoding.
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"""
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prompt_token_ids = request.get("prompt_token_ids", [])
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if not request.get("messages"):
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return self.enc.prompt_token_ids2outputs(prompt_token_ids)
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images, videos, image_uuid, video_uuid, dealer, missing_idx, mm_items = self._extract_mm_items(request)
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outputs = self.enc.prompt_token_ids2outputs(prompt_token_ids, mm_items)
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if self.enable_processor_cache:
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self._update_mm_cache(dealer, missing_idx, mm_items, outputs)
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return outputs
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def _update_mm_cache(self, dealer, missing_idx, mm_items, outputs):
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"""Write newly-processed multimodal items to the processor cache."""
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missing_idx_set = set(missing_idx)
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hashes_to_cache, items_to_cache = [], []
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for idx in range(len(mm_items)):
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if idx in missing_idx_set:
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continue
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meta = {}
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grid_thw = np.asarray(outputs["grid_thw"][idx])
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if grid_thw.ndim > 1:
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t, h, w = grid_thw[0]
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else:
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t, h, w = grid_thw
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meta["thw"] = (int(t), int(h), int(w))
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if "fps" in outputs:
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meta["fps"] = outputs["fps"][idx]
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hashes_to_cache.append(outputs["mm_hashes"][idx])
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items_to_cache.append((outputs["images"][idx], meta))
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if hashes_to_cache:
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self.update_processor_cache(dealer, hashes_to_cache, items_to_cache)
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return outputs
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def _add_text(self, tokens, outputs):
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"""Add text tokens to outputs, delegating position logic to enc."""
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if not tokens:
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return
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if isinstance(tokens, str):
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tokens_str = self.tokenizer.tokenize(tokens)
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tokens = self.tokenizer.convert_tokens_to_ids(tokens_str)
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num_tokens = len(tokens)
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outputs["input_ids"].extend(tokens)
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outputs["token_type_ids"].extend([IDS_TYPE_FLAG["text"]] * num_tokens)
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self.enc.add_text_positions(outputs, num_tokens)
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def process_request_dict(self, request, max_model_len=None):
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"""Process a request dictionary into model inputs."""
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cfg = self.cfg
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request = self._apply_default_parameters(request)
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if not request.get("eos_token_ids"):
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request["eos_token_ids"] = self.eos_token_ids
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# Stop tokens
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if cfg.stop_tokens_variant == "qwen3":
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stop_sequences = request.get("stop", [])
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if stop_sequences:
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stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
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request["stop_token_ids"] = stop_seqs
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request["stop_seqs_len"] = stop_seqs_len
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else:
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process_stop_token_ids(request, self.update_stop_seq)
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# Bad words
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if cfg.has_bad_words:
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bad_words = request.get("bad_words")
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bad_words_token_ids = request.get("bad_words_token_ids")
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if bad_words:
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bad_words_token_ids = self.update_bad_words(bad_words, bad_words_token_ids)
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request["bad_words_token_ids"] = bad_words_token_ids
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# Logits processor (ernie think)
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if cfg.has_logits_processor_think:
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logits_processors_args = self._prepare_think_stop_sentence(
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request.get("logits_processors_args") or {}, max_model_len
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)
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request["logits_processors_args"] = logits_processors_args
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# Tokenize
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outputs = self._tokenize_request(request)
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# Post-token handling
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self._process_post_tokens(request, outputs)
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# Force disable thinking for qwen_vl / qwen3_vl
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if cfg.force_disable_thinking:
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request["enable_thinking"] = False
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# Pack outputs
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outputs = self.pack_outputs(outputs)
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# Assign prompt_token_ids
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if cfg.preserve_prompt_token_ids and request.get("prompt_token_ids"):
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pass # preserve existing
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else:
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request["prompt_token_ids"] = outputs["input_ids"].tolist()
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request["multimodal_inputs"] = outputs
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# Truncation
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if max_model_len is not None and len(request["prompt_token_ids"]) > max_model_len:
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request["prompt_token_ids"] = request["prompt_token_ids"][: max_model_len - 1]
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request["prompt_token_ids_len"] = len(request["prompt_token_ids"])
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# Ernie: update thinking prompt state
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if cfg.has_logits_processor_think:
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logits_processors_args = self._update_thinking_prompt_state(
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request["prompt_token_ids"],
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request.get("logits_processors_args") or {},
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)
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request["logits_processors_args"] = logits_processors_args
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# max_tokens
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max_tokens = max_model_len - len(request["prompt_token_ids"])
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if request.get("max_tokens") is None:
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request["max_tokens"] = max(1, max_tokens)
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else:
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request["max_tokens"] = min(max_tokens, request["max_tokens"])
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# Ernie: default reasoning_max_tokens
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if cfg.set_default_reasoning_max_tokens and request.get("reasoning_max_tokens") is None:
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request["reasoning_max_tokens"] = max(int(request["max_tokens"] * 0.8), 1)
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# Clamp top_p
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if request.get("top_p") is not None and request.get("top_p") < _SAMPLING_EPS:
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request["top_p"] = _SAMPLING_EPS
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request["top_k"] = 1
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# Reasoning parser
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if self.reasoning_parser:
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self._apply_reasoning_parser(request)
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# Ernie: cap response_max_tokens
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if cfg.cap_response_max_tokens:
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if request.get("response_max_tokens") is not None and request.get("enable_thinking") is False:
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request["max_tokens"] = min(request["response_max_tokens"], request["max_tokens"])
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data_processor_logger.info(f"Processed request {request}")
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return request
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def _tokenize_request(self, request):
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cfg = self.cfg
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default_thinking = cfg.default_thinking
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if request.get("prompt_token_ids") and cfg.supports_prompt_token_ids:
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messages = request.get("messages")
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if messages:
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self._check_mm_limits(messages)
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request.setdefault("enable_thinking", default_thinking)
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return self._process_prompt_token_ids(request)
|
|
|
|
elif request.get("prompt"):
|
|
multimodal_data = request.get("multimodal_data") or {}
|
|
self._check_mm_limits(multimodal_data)
|
|
images = multimodal_data.get("image", None)
|
|
videos = multimodal_data.get("video", None)
|
|
request["prompt_tokens"] = request.get("prompt")
|
|
request.setdefault("enable_thinking", default_thinking)
|
|
return self.text2ids(request["prompt"], images, videos)
|
|
|
|
elif request.get("messages"):
|
|
messages = request["messages"]
|
|
self._check_mm_limits(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 or request[k] is None:
|
|
request[k] = v
|
|
else:
|
|
raise ValueError("Invalid input: chat_template_kwargs must be a dict")
|
|
request.setdefault("enable_thinking", default_thinking)
|
|
return self.request2ids(request)
|
|
|
|
else:
|
|
raise ValueError(f"Request must contain 'prompt', or 'messages': {request}")
|
|
|
|
def _process_post_tokens(self, request, outputs):
|
|
completion_token_ids = request.get("completion_token_ids") or request.get("generated_token_ids")
|
|
if completion_token_ids:
|
|
self.enc.append_completion_tokens(outputs, completion_token_ids)
|
|
|
|
def append_completion_tokens(self, multimodal_inputs, completion_token_ids):
|
|
"""Append completion tokens — delegates to enc."""
|
|
self.enc.append_completion_tokens(multimodal_inputs, completion_token_ids)
|
|
|
|
def pack_outputs(self, outputs):
|
|
"""Convert intermediate outputs to final packed format."""
|
|
if not outputs["images"]:
|
|
outputs["images"] = None
|
|
outputs["grid_thw"] = None
|
|
outputs["image_type_ids"] = None
|
|
else:
|
|
outputs["images"] = np.vstack(outputs["images"])
|
|
outputs["grid_thw"] = np.vstack(outputs["grid_thw"])
|
|
outputs["image_type_ids"] = np.array(outputs["image_type_ids"])
|
|
|
|
outputs["input_ids"] = np.array(outputs["input_ids"], dtype=np.int64)
|
|
outputs["token_type_ids"] = np.array(outputs["token_type_ids"], dtype=np.int64)
|
|
outputs["mm_num_token_func"] = self.enc.mm_num_tokens
|
|
|
|
# Position IDs: delegate to encoding strategy
|
|
self.enc.pack_position_ids(outputs)
|
|
|
|
return outputs
|
|
|
|
def get_processor_cache(self, socket, mm_hashes):
|
|
req = pickle.dumps(mm_hashes)
|
|
socket.send_multipart([b"", req])
|
|
_, resp = socket.recv_multipart()
|
|
mm_items = pickle.loads(resp)
|
|
data_processor_logger.info(f"Get cache of mm_hashes: {mm_hashes}")
|
|
return mm_items
|
|
|
|
def update_processor_cache(self, socket, mm_hashes, mm_items):
|
|
req = pickle.dumps((mm_hashes, mm_items))
|
|
socket.send_multipart([b"", req])
|
|
data_processor_logger.info(f"Update cache of mm_hashes: {mm_hashes}")
|