[DataProcessor] Refactor multimodal processor: extract encoding strategies and unify MM processing pipeline (#7298)

* merge mm processor

fastdeploy/input/utils/common.py

@@ -0,0 +1,105 @@
+"""
+# 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.
+"""
+
+__all__ = [
+    "IDS_TYPE_FLAG",
+    "MAX_IMAGE_DIMENSION",
+]
+
+import os
+import socket
+from typing import Any, Callable, Dict, List, Tuple
+from urllib.parse import urlparse
+
+from fastdeploy.utils import console_logger
+
+IDS_TYPE_FLAG = {"text": 0, "image": 1, "video": 2, "audio": 3}
+
+MAX_IMAGE_DIMENSION = 9999999
+
+# Hub endpoints for connectivity check, keyed by DOWNLOAD_SOURCE value
+_HUB_ENDPOINTS = {
+    "huggingface": ("huggingface.co", 443),
+    "modelscope": ("modelscope.cn", 443),
+}
+
+
+def _get_hub_endpoint():
+    """Return (host, port, hub_name) for the active download hub."""
+    source = os.environ.get("DOWNLOAD_SOURCE", "huggingface")
+    if source == "aistudio":
+        url = os.environ.get("AISTUDIO_ENDPOINT", "http://git.aistudio.baidu.com")
+        parsed = urlparse(url)
+        host = parsed.hostname or "git.aistudio.baidu.com"
+        port = parsed.port or (443 if parsed.scheme == "https" else 80)
+        return host, port, "aistudio"
+    host, port = _HUB_ENDPOINTS.get(source, ("huggingface.co", 443))
+    return host, port, source
+
+
+def validate_model_path(model_name_or_path):
+    """
+    Validate model path before from_pretrained calls.
+    Give immediate feedback instead of letting users wait 50s+ for timeout.
+    """
+    if os.path.isdir(model_name_or_path) or os.path.isfile(model_name_or_path):
+        return  # Local path exists, no network needed
+
+    host, port, hub_name = _get_hub_endpoint()
+
+    console_logger.warning(
+        f"Model path '{model_name_or_path}' is not a local directory or file, "
+        f"will try to download from {hub_name} hub."
+    )
+
+    # Quick connectivity check — fail fast instead of waiting 50s
+    try:
+        sock = socket.create_connection((host, port), timeout=3)
+        sock.close()
+    except OSError:
+        console_logger.warning(
+            f"Cannot reach {host}. If the model is stored locally, "
+            f"please check the path '{model_name_or_path}'. Otherwise check "
+            f"network/proxy settings (DOWNLOAD_SOURCE={hub_name})."
+        )
+
+
+def process_stop_token_ids(
+    request: Dict[str, Any],
+    update_stop_seq_fn: Callable[[List[str]], Tuple[List[List[int]], List[int]]],
+) -> None:
+    stop_token_ids_final = []
+
+    if request.get("stop_token_ids") is not None:
+        stop_token_ids = request.get("stop_token_ids")
+        if isinstance(stop_token_ids, list) and len(stop_token_ids) > 0:
+            if isinstance(stop_token_ids[0], int):
+                # List[int] -> List[List[int]]
+                stop_token_ids_final.extend([[t] for t in stop_token_ids])
+            elif isinstance(stop_token_ids[0], list):
+                # Already List[List[int]]
+                stop_token_ids_final.extend(stop_token_ids)
+
+    stop_sequences = request.get("stop", [])
+    if stop_sequences:
+        stop_seqs, _ = update_stop_seq_fn(stop_sequences)
+        stop_token_ids_final.extend(stop_seqs)
+
+    # Update request
+    if stop_token_ids_final:
+        stop_seqs_len = [len(seq) for seq in stop_token_ids_final]
+        request["stop_token_ids"] = stop_token_ids_final
+        request["stop_seqs_len"] = stop_seqs_len