[BugFix] fix paddleocr prefix cache bug (#4625)

* fix paddleocr prefix cache bug

* disable prefix-caching in ocr

fastdeploy/engine/args_utils.py

@@ -452,6 +452,8 @@ class EngineArgs:
 
         if "PaddleOCR" in get_model_architecture(self.model, self.model_config_name):
             envs.FD_ENABLE_MAX_PREFILL = 1
+            self.enable_prefix_caching = False
+            self.max_encoder_cache = 0
 
     @staticmethod
     def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:

fastdeploy/input/paddleocr_vl_processor/paddleocr_vl_processor.py

@@ -47,6 +47,7 @@ class PaddleOCRVLProcessor(TextProcessor):
         mm_processor_kwargs=None,
         reasoning_parser_obj=None,
         tool_parser_obj=None,
+        enable_processor_cache=False,
     ):
         """
         Initialize PaddleOCRVLProcessor instance.
@@ -65,6 +66,7 @@ class PaddleOCRVLProcessor(TextProcessor):
         processor_kwargs = self._parse_processor_kwargs(mm_processor_kwargs)
         self.processor = DataProcessor(
             model_path=model_name_or_path,
+            enable_processor_cache=enable_processor_cache,
             tokens_per_second=config.vision_config.tokens_per_second,
             tokenizer=self.tokenizer,
             **processor_kwargs,
@@ -252,7 +254,7 @@ class PaddleOCRVLProcessor(TextProcessor):
 
         return request
 
-    def append_generated_tokens(self, outputs, generated_token_ids):
+    def append_generated_tokens(self, multimodal_inputs, generated_token_ids):
         """
         Append generated tokens to existing outputs.
 
@@ -260,19 +262,13 @@ class PaddleOCRVLProcessor(TextProcessor):
             outputs: Current model outputs
             generated_token_ids: Generated tokens to append
         """
-        out = {"input_ids": [], "token_type_ids": [], "position_ids": [], "cur_position": outputs["cur_position"]}
-        self.processor._add_text(generated_token_ids, out)
+        num_tokens = len(generated_token_ids)
+        multimodal_inputs["input_ids"].extend(generated_token_ids)
+        multimodal_inputs["token_type_ids"].extend([0] * num_tokens)
 
-        outputs["input_ids"] = np.concatenate(
-            [outputs["input_ids"], np.array(out["input_ids"], dtype=np.int64)], axis=0
-        )
-        outputs["token_type_ids"] = np.concatenate(
-            [outputs["token_type_ids"], np.array(out["token_type_ids"], dtype=np.int64)], axis=0
-        )
-        outputs["position_ids"] = np.concatenate(
-            [outputs["position_ids"], out["position_ids"][0]], axis=1, dtype=np.int64
-        )
-        outputs["cur_position"] = out["cur_position"]
+        pos_ids = self.processor._compute_text_positions(multimodal_inputs["cur_position"], num_tokens)
+        multimodal_inputs["position_ids"].append(pos_ids)
+        multimodal_inputs["cur_position"] += num_tokens
 
     def pack_outputs(self, outputs):
         """
@@ -284,6 +280,22 @@ class PaddleOCRVLProcessor(TextProcessor):
         Returns:
             dict: Packed output dictionary with all required fields
         """
+        if not outputs["images"]:
+            outputs["images"] = None  # No images case
+            outputs["grid_thw"] = None  # No spatial dimensions
+            outputs["image_type_ids"] = None  # No type IDs
+        else:
+            outputs["images"] = np.vstack(outputs["images"])  # Stack image features vertically
+            outputs["grid_thw"] = np.vstack(outputs["grid_thw"])  # Stack spatial dimensions
+            outputs["image_type_ids"] = np.array(outputs["image_type_ids"])  # Convert to numpy array
+
+        # Convert all outputs to numpy arrays with appropriate types
+        outputs["input_ids"] = np.array(outputs["input_ids"], dtype=np.int64)  # Token IDs as int64
+        outputs["token_type_ids"] = np.array(outputs["token_type_ids"], dtype=np.int64)  # Type IDs as int64
+        outputs["position_ids"] = np.concatenate(
+            outputs["position_ids"], axis=1, dtype=np.int64
+        )  # Concatenate position ID
+
         outputs["image_patch_id"] = self.processor.image_token_id
         outputs["video_patch_id"] = self.processor.video_token_id
         outputs["position_ids"] = outputs["position_ids"].transpose(1, 0)

fastdeploy/input/paddleocr_vl_processor/process.py

@@ -15,16 +15,23 @@
 # limitations under the License.
 """
 
-from typing import Any, Dict, List, Union
+import pickle
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
+import zmq
 from paddleformers.transformers import AutoTokenizer
+from PIL import Image
 
+from fastdeploy.engine.request import ImagePosition
 from fastdeploy.entrypoints.chat_utils import parse_chat_messages
+from fastdeploy.input.ernie4_5_vl_processor import read_video_decord
 from fastdeploy.input.utils import IDS_TYPE_FLAG
+from fastdeploy.multimodal.hasher import MultimodalHasher
 from fastdeploy.utils import data_processor_logger
 
 from .image_processor import ImageProcessor
+from .process_video import sample_frames
 
 
 class DataProcessor:
@@ -48,8 +55,11 @@ class DataProcessor:
     def __init__(
         self,
         model_path: str,
+        enable_processor_cache: bool = False,
         video_min_frames: int = 4,
         video_max_frames: int = 768,
+        video_target_frames: int = -1,
+        video_fps: int = -1,
         tokens_per_second: int = 2,
         tokenizer=None,
         **kwargs,
@@ -66,6 +76,8 @@ class DataProcessor:
         """
         self.min_frames = video_min_frames
         self.max_frames = video_max_frames
+        self.target_frames = video_target_frames
+        self.fps = video_fps
 
         # Initialize tokenizer with left padding and fast tokenizer
         if tokenizer is None:
@@ -74,13 +86,13 @@ class DataProcessor:
         else:
             self.tokenizer = tokenizer
         self.image_processor = ImageProcessor.from_pretrained(model_path)  # Initialize image processor
+        self.enable_processor_cache = enable_processor_cache
 
         # Convolution sizes for patch aggregation
         self.spatial_conv_size = self.image_processor.merge_size
         self.temporal_conv_size = self.image_processor.temporal_patch_size
 
         # Special tokens and IDs
-
         self.image_token = "<|IMAGE_PLACEHOLDER|>"
         self.video_token = "<|video_pad|>"
 
@@ -100,41 +112,7 @@ class DataProcessor:
             "assistant": "Assistant: ",
         }
 
-    def _pack_outputs(self, outputs):
-        """
-        Pack and convert all output data into numpy arrays with appropriate types.
-
-        Args:
-            outputs (dict): Dictionary containing model outputs with keys:
-                - images: List of visual features
-                - grid_thw: List of spatial dimensions
-                - image_type_ids: List of content type indicators
-                - input_ids: List of token IDs
-                - token_type_ids: List of type identifiers
-                - position_ids: List of position embeddings
-
-        Returns:
-            dict: Processed outputs with all values converted to numpy arrays
-        """
-        # Process visual outputs - stack if exists or set to None if empty
-        if not outputs["images"]:
-            outputs["images"] = None  # No images case
-            outputs["grid_thw"] = None  # No spatial dimensions
-            outputs["image_type_ids"] = None  # No type IDs
-        else:
-            outputs["images"] = np.vstack(outputs["images"])  # Stack image features vertically
-            outputs["grid_thw"] = np.vstack(outputs["grid_thw"])  # Stack spatial dimensions
-            outputs["image_type_ids"] = np.array(outputs["image_type_ids"])  # Convert to numpy array
-
-        # Convert all outputs to numpy arrays with appropriate types
-        outputs["input_ids"] = np.array(outputs["input_ids"], dtype=np.int64)  # Token IDs as int64
-        outputs["token_type_ids"] = np.array(outputs["token_type_ids"], dtype=np.int64)  # Type IDs as int64
-        outputs["position_ids"] = np.concatenate(
-            outputs["position_ids"], axis=1, dtype=np.int64
-        )  # Concatenate position IDs
-        return outputs
-
-    def text2ids(self, text, images=None, videos=None):
+    def text2ids(self, text, images=None, videos=None, image_uuid=None, video_uuid=None):
         """
         Convert text with image/video placeholders into model inputs.
 
@@ -142,6 +120,8 @@ class DataProcessor:
             text: Input text with <|image@placeholder|> and <|video@placeholder|> markers
             images: List of PIL Images corresponding to image placeholders
             videos: List of video data corresponding to video placeholders
+            image_uuid: List of unique identifiers for each image, used for caching or hashing.
+            video_uuid: List of unique identifiers for each video, used for caching or hashing.
 
         Returns:
             Dict containing:
@@ -162,11 +142,14 @@ class DataProcessor:
             "image_type_ids": [],
             "labels": [],
             "cur_position": 0,
-            "pic_cnt": 0,
             "video_cnt": 0,
+            "fps": [],
+            "mm_positions": [],
+            "mm_hashes": [],
             "vit_seqlen": [],
             "vit_position_ids": [],
         }
+
         # Define placeholders and their lengths
         IMAGE_PLACEHOLDER = self.image_token
         VIDEO_PLACEHOLDER = self.video_token
@@ -188,23 +171,30 @@ class DataProcessor:
                 break
 
             if ed == image_pos:
-                outputs["pic_cnt"] += 1
-                self._add_image(images[image_idx], outputs)
+                image = images[image_idx]
+                uuid = image_uuid[image_idx] if image_uuid else None
+                if not isinstance(image, tuple):
+                    self._add_image(image, outputs, uuid)
+                else:
+                    self._add_processed_image(image, outputs, uuid)
                 image_idx += 1
                 st = ed + IMAGE_PLACEHOLDER_LEN
             else:
                 item = videos[video_idx]
-                if isinstance(item, dict):
-                    frames, meta = self._load_and_process_video(item["video"], item)
+                uuid = video_uuid[video_idx] if video_uuid else None
+                if not isinstance(item, tuple):
+                    if isinstance(item, dict):
+                        frames, meta = self._load_and_process_video(item["video"], item)
+                    else:
+                        frames, meta = self._load_and_process_video(item, {})
+                    self._add_video(frames, meta, outputs, uuid)
                 else:
-                    frames, meta = self._load_and_process_video(item, {})
-
-                outputs["video_cnt"] += 1
-                self._add_video(frames, meta, outputs)
+                    # cached frames are already processed
+                    self._add_processed_video(item, outputs, uuid)
                 video_idx += 1
                 st = ed + VIDEO_PLACEHOLDER_LEN
 
-        return self._pack_outputs(outputs)
+        return outputs
 
     def request2ids(
         self, request: Dict[str, Any], tgts: List[str] = None
@@ -222,76 +212,84 @@ class DataProcessor:
             Dict with same structure as text2ids() output
         """
 
-        outputs = {
-            "input_ids": [],
-            "token_type_ids": [],
-            "position_ids": [],
-            "images": [],
-            "grid_thw": [],
-            "image_type_ids": [],
-            "labels": [],
-            "cur_position": 0,
-            "pic_cnt": 0,
-            "video_cnt": 0,
-            "vit_seqlen": [],
-            "vit_position_ids": [],
-        }
-
         # Parse and validate chat messages
         messages = parse_chat_messages(request.get("messages"))
-        image_message_list = []  # Store visual content messages
-
+        mm_items = []
         for msg in messages:
             role = msg.get("role")
             assert role in self.role_prefixes, f"Unsupported role: {role}"
 
             # Normalize content to list format
-            content_items = msg.get("content")
-            if not isinstance(content_items, list):
-                content_items = [content_items]
-
+            content = msg.get("content")
+            if not isinstance(content, list):
+                content = [content]
             # Collect all visual content items
-            for item in content_items:
-                if isinstance(item, dict) and item.get("type") in ["image", "video"]:
-                    image_message_list.append(item)
+            for item in content:
+                if item.get("type") in ["image", "video"]:
+                    mm_items.append(item)
 
-        raw_messages = request["messages"]
-        request["messages"] = messages
+        missing_hashes, missing_idx = [], []
+        for idx, item in enumerate(mm_items):
+            if not item.get("data"):
+                # raw data not provided, should be retrieved from processor cache
+                missing_hashes.append(item.get("uuid"))
+                missing_idx.append(idx)
 
-        prompt_token_ids = self.apply_chat_template(request)
-        if len(prompt_token_ids) == 0:
-            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
-        request["messages"] = raw_messages
+        if len(missing_hashes) > 0 and not self.enable_processor_cache:
+            raise ValueError("Missing items cannot be retrieved without processor cache.")
 
-        vision_start_index = 0
-        vision_message_index = 0
-        for i in range(len(prompt_token_ids)):
-            if prompt_token_ids[i] == self.vision_start_id:
-                self._add_text(prompt_token_ids[vision_start_index : i + 1], outputs)
+        if self.enable_processor_cache:
+            context = zmq.Context()
+            dealer = context.socket(zmq.DEALER)
+            dealer.connect("ipc:///dev/shm/processor_cache.ipc")
 
-                vision_start_index = i + 1
-                image_message = image_message_list[vision_message_index]
+            missing_items = self.get_processor_cache(dealer, missing_hashes)
+            for idx in range(len(missing_items)):
+                if not missing_items[idx]:
+                    raise ValueError(f"Missing item {idx} not found in processor cache")
+                mm_items[missing_idx[idx]]["data"] = missing_items[idx]
 
-                if image_message["type"] == "image":
-                    img = image_message.get("image")
-                    if img is None:
-                        continue
-                    outputs["pic_cnt"] += 1
-                    self._add_image(img, outputs)
+        images, videos = [], []
+        image_uuid, video_uuid = [], []
+        for item in mm_items:
+            if item.get("type") == "image":
+                images.append(item["data"])
+                image_uuid.append(item["uuid"])
+            elif item.get("type") == "video":
+                videos.append(item["data"])
+                video_uuid.append(item["uuid"])
+            else:
+                raise ValueError(f"Unsupported multimodal type: {item.get('type')}")
 
-                elif image_message["type"] == "video":
-                    video_bytes = image_message.get("video")
-                    if video_bytes is None:
-                        continue
-                    frames, meta = self._load_and_process_video(video_bytes, image_message)
+        if self.tokenizer.chat_template is None:
+            raise ValueError("This model does not support chat template.")
 
-                    outputs["video_cnt"] += 1
-                    self._add_video(frames, meta, outputs)
+        chat_template_kwargs = request.get("chat_template_kwargs", {})
+        prompt = self.tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=request.get("add_generation_prompt", True),
+            **chat_template_kwargs,
+        )
+        request["prompt_tokens"] = prompt
 
-                vision_message_index += 1
+        outputs = self.text2ids(prompt, images, videos, image_uuid, video_uuid)
 
-        self._add_text(prompt_token_ids[vision_start_index:], outputs)
-        return self._pack_outputs(outputs)
+        if self.enable_processor_cache:
+            missing_idx = set(missing_idx)
+            hashes_to_cache, items_to_cache = [], []
+            for idx in range(len(mm_items)):
+                if idx in missing_idx:
+                    continue
+                meta = {}
+                t, h, w = outputs["grid_thw"][idx]
+                meta["thw"] = (t, h, w)
+                meta["fps"] = outputs["fps"][idx]
+                hashes_to_cache.append(outputs["mm_hashes"][idx])
+                items_to_cache.append((outputs["images"][idx], meta))
+            self.update_processor_cache(dealer, hashes_to_cache, items_to_cache)
+
+        return outputs
 
     def _add_text(self, tokens, outputs: Dict) -> None:
         """
@@ -316,9 +314,9 @@ class DataProcessor:
         outputs["input_ids"].extend(tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["text"]] * num_tokens)
 
-        position_ids = self._compute_text_positions(outputs["cur_position"], num_tokens)
-        outputs["position_ids"].append(position_ids)
-        outputs["cur_position"] = position_ids.max() + 1
+        pos_ids = self._compute_text_positions(outputs["cur_position"], num_tokens)
+        outputs["position_ids"].append(pos_ids)
+        outputs["cur_position"] = pos_ids.max() + 1
 
     def _compute_text_positions(self, start_pos: int, num_tokens: int) -> np.ndarray:
         """
@@ -336,7 +334,7 @@ class DataProcessor:
         position = text_index + start_pos
         return position
 
-    def _add_image(self, img, outputs: Dict) -> None:
+    def _add_image(self, img, outputs: Dict, uuid: Optional[str]) -> None:
         """
         Add image data to model inputs dictionary.
 
@@ -353,23 +351,49 @@ class DataProcessor:
         num_tokens = ret["grid_thw"].prod() // self.image_processor.merge_size**2
         grid_thw = ret["grid_thw"].tolist()
 
+        outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.image_token_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["image"]] * num_tokens)
 
         outputs["images"].append(ret["pixel_values"])
+        if not uuid:
+            outputs["mm_hashes"].append(MultimodalHasher.hash_features(ret["pixel_values"]))
+        else:
+            outputs["mm_hashes"].append(uuid)
         outputs["grid_thw"].append(grid_thw)
         outputs["image_type_ids"].append(0)
 
         # position_ids
         t, h, w = grid_thw
-        position_ids = self._compute_vision_positions(outputs["cur_position"], t, h, w, 0)
-        outputs["position_ids"].append(position_ids)
-        outputs["cur_position"] = position_ids.max() + 1
+        pos_ids = self._compute_vision_positions(outputs["cur_position"], t, h, w, 0)
+        outputs["position_ids"].append(pos_ids)
+        outputs["cur_position"] = pos_ids.max() + 1
+        outputs["fps"].append(0)
         numel = h * w
         outputs["vit_seqlen"].append(numel)
         outputs["vit_position_ids"].append(np.arange(numel) % numel)
 
-    def _add_video(self, frames, meta: Dict, outputs: Dict) -> None:
+    def _add_processed_image(self, img_cache: Tuple[np.ndarray, dict], outputs: Dict, uuid: str) -> None:
+        img, meta = img_cache
+        num_tokens = img.shape[0] // self.image_processor.merge_size**2
+
+        outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
+        outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
+        outputs["token_type_ids"].extend([IDS_TYPE_FLAG["image"]] * num_tokens)
+
+        _, h, w = meta["thw"]
+        pos_ids = self._compute_vision_positions(outputs["cur_position"], 1, h, w, 0)
+        outputs["position_ids"].append(pos_ids)
+        outputs["cur_position"] = pos_ids.max() + 1
+
+        outputs["images"].append(img)
+        outputs["mm_hashes"].append(uuid)
+        outputs["grid_thw"].append(np.array([[1, h, w]]))
+        outputs["image_type_ids"].append(0)
+
+        outputs["fps"].append(0)
+
+    def _add_video(self, frames, meta: Dict, outputs: Dict, uuid: Optional[str]) -> None:
         """
         Add video data to model inputs dictionary.
 
@@ -387,24 +411,52 @@ class DataProcessor:
         num_tokens = ret["image_grid_thw"].prod() // self.image_processor.merge_size**2
         grid_thw = ret["image_grid_thw"].tolist()
 
+        outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
         outputs["input_ids"].extend([self.video_token_id] * num_tokens)
         outputs["token_type_ids"].extend([IDS_TYPE_FLAG["video"]] * num_tokens)
 
         outputs["images"].append(ret["pixel_values"])
+        if not uuid:
+            outputs["mm_hashes"].append(MultimodalHasher.hash_features(ret["pixel_values"]))
+        else:
+            outputs["mm_hashes"].append(uuid)
         outputs["grid_thw"].append(grid_thw)
         outputs["image_type_ids"].extend([1] * grid_thw[0])
 
         fps = meta["fps"]
         second_per_grid_t = self.temporal_conv_size / fps
         t, h, w = grid_thw
-        position_ids = self._compute_vision_positions(outputs["cur_position"], t, h, w, second_per_grid_t)
+        pos_ids = self._compute_vision_positions(outputs["cur_position"], t, h, w, second_per_grid_t)
 
-        outputs["position_ids"].append(position_ids)
-        outputs["cur_position"] = position_ids.max() + 1
+        outputs["position_ids"].append(pos_ids)
+        outputs["cur_position"] = pos_ids.max() + 1
+        outputs["fps"].append(fps)
         numel = h * w
         outputs["vit_seqlen"].append(numel)
         outputs["vit_position_ids"].append(np.arange(numel) % numel)
 
+    def _add_processed_video(self, frames_cache: Tuple[np.ndarray, dict], outputs: Dict, uuid: str) -> None:
+        frames, meta = frames_cache
+        num_tokens = frames.shape[0] // self.image_processor.merge_size**2
+
+        t, h, w = meta["thw"]
+        outputs["images"].append(frames)
+        outputs["mm_hashes"].append(uuid)
+        outputs["grid_thw"].append(np.array([[t, h, w]]))
+
+        outputs["mm_positions"].append(ImagePosition(len(outputs["input_ids"]), num_tokens))
+        outputs["input_ids"].extend([self.image_patch_id] * num_tokens)
+        outputs["token_type_ids"].extend([IDS_TYPE_FLAG["video"]] * num_tokens)
+        outputs["image_type_ids"].extend([1] * t)
+
+        fps = meta["fps"]
+        second_per_grid_t = self.temporal_conv_size / fps
+        pos_ids = self._compute_vision_positions(outputs["cur_position"], t, h, w, second_per_grid_t)
+        outputs["position_ids"].append(pos_ids)
+        outputs["cur_position"] = pos_ids.max() + 1
+
+        outputs["fps"].append(fps)
+
     def _compute_vision_positions(
         self, start_pos: int, t: int, h: int, w: int, second_per_grid_t: float
     ) -> np.ndarray:
@@ -438,6 +490,78 @@ class DataProcessor:
         position = np.stack([t_index, h_index, w_index]) + start_pos
         return position
 
+    def _load_and_process_video(self, url: str, item: Dict) -> Tuple[np.ndarray, Dict]:
+        """
+        Load and preprocess video into frames.
+
+        Args:
+            url: Video file path or bytes
+            item: Dictionary containing processing parameters
+
+        Returns:
+            tuple: (frames, metadata) where:
+                - frames: Processed video frames as numpy array
+                - metadata: Updated video metadata dictionary
+        """
+        reader, meta, _ = read_video_decord(url, save_to_disk=False)
+
+        # Apply frame sampling if fps or target_frames specified
+        fps = item.get("fps", self.fps)
+        num_frames = item.get("target_frames", self.target_frames)
+
+        frame_indices = list(range(meta["num_of_frame"]))
+        if fps > 0 or num_frames > 0:
+            # Get frame sampling constraints
+            min_frames = item.get("min_frames", self.min_frames)
+            max_frames = item.get("max_frames", self.max_frames)
+
+            # Sample frames according to specifications
+            frame_indices = sample_frames(
+                frame_factor=self.temporal_conv_size,  # Ensure divisible by temporal patch size
+                min_frames=min_frames,
+                max_frames=max_frames,
+                metadata=meta,
+                fps=fps,
+                num_frames=num_frames,
+            )
+
+            # Update metadata with new frame count and fps
+            meta["num_of_frame"] = len(frame_indices)
+            if fps is not None:
+                meta["fps"] = fps  # Use specified fps
+                meta["duration"] = len(frame_indices) / fps
+            else:
+                meta["fps"] = len(frame_indices) / meta["duration"]  # Calculate fps from sampled frames
+
+        frames = []
+        for idx in frame_indices:
+            frame = reader[idx].asnumpy()
+            image = Image.fromarray(frame, "RGB")
+            frames.append(image)
+        frames = np.stack([np.array(f.convert("RGB")) for f in frames], axis=0)
+
+        return frames, meta
+
+    def get_processor_cache(self, socket, mm_hashes: list[str]) -> list:
+        """
+        get cache correspond to given hash values
+        """
+        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: list[str], mm_items):
+        """
+        update cache data
+        """
+        req = pickle.dumps((mm_hashes, mm_items))
+        socket.send_multipart([b"", req])
+        data_processor_logger.info(f"Update cache of mm_hashes: {mm_hashes}")
+
     def apply_chat_template(self, request):
         """
         Apply chat template to convert messages into token sequence.

fastdeploy/input/paddleocr_vl_processor/process_video.py

@@ -0,0 +1,82 @@
+"""
+# 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 math
+from typing import Optional, Union
+
+import numpy as np
+
+
+def sample_frames(
+    frame_factor: int,
+    min_frames: int,
+    max_frames: int,
+    metadata: Optional[dict] = None,
+    fps: Optional[Union[int, float]] = None,
+    num_frames: Optional[int] = None,
+):
+    """
+    Sample frames from video according to specified criteria.
+
+    Args:
+        frame_factor: Ensure sampled frames are multiples of this factor
+        min_frames: Minimum number of frames to sample
+        max_frames: Maximum number of frames to sample
+        metadata: Video metadata containing fps information
+        fps: Target frames per second for sampling
+        num_frames: Exact number of frames to sample
+
+    Returns:
+        np.ndarray: Sampled video frames
+
+    Raises:
+        ValueError: If both fps and num_frames are specified,
+                   or if required metadata is missing,
+                   or if requested frames exceed available frames
+    """
+    if fps > 0 and num_frames > 0:
+        raise ValueError("`num_frames` and `fps` are mutually exclusive arguments, please use only one!")
+
+    total_num_frames = metadata["num_of_frame"]
+
+    # If num_frames is not given but fps is, calculate num_frames from fps
+    if num_frames > 0:
+        num_frames = round(num_frames / frame_factor) * frame_factor
+    elif fps > 0:
+        if metadata is None:
+            raise ValueError(
+                "Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. "
+                "Please pass in `VideoMetadata` object or use a fixed `num_frames` per input video"
+            )
+        max_frames = math.floor(min(max_frames, total_num_frames) / frame_factor) * frame_factor
+        num_frames = total_num_frames / metadata["fps"] * fps
+        num_frames = min(min(max(num_frames, min_frames), max_frames), total_num_frames)
+        num_frames = math.floor(num_frames / frame_factor) * frame_factor
+    if num_frames > total_num_frames:
+        raise ValueError(
+            f"Video can't be sampled. The inferred `num_frames={num_frames}` exceeds `total_num_frames={total_num_frames}`. "
+            "Decrease `num_frames` or `fps` for sampling."
+        )
+
+    # Calculate frame indices based on sampling strategy
+    if num_frames > 0:
+        # Evenly spaced sampling for target frame count
+        indices = np.arange(0, total_num_frames, total_num_frames / num_frames).astype(np.int32)
+    else:
+        # Keep all frames if no sampling requested
+        indices = np.arange(0, total_num_frames).astype(np.int32)
+
+    return indices

fastdeploy/worker/worker_process.py

@@ -814,6 +814,8 @@ def initialize_fd_config(args, ranks: int = 1, local_rank: int = 0) -> FDConfig:
     architecture = fd_config.model_config.architectures[0]
     if "PaddleOCR" in architecture:
         envs.FD_ENABLE_MAX_PREFILL = 1
+        fd_config.cache_config.enable_prefix_caching = False
+        fd_config.cache_config.max_encoder_cache = 0
     return fd_config