refactor: minor refactors regarding multimodal processing (#6187)

python/sglang/srt/hf_transformers_utils.py

@@ -19,6 +19,7 @@ import warnings
 from pathlib import Path
 from typing import Dict, Optional, Type, Union
 
+import torch
 from huggingface_hub import snapshot_download
 from transformers import (
     AutoConfig,
@@ -65,6 +66,43 @@ def download_from_hf(model_path: str):
     return snapshot_download(model_path, allow_patterns=["*.json", "*.bin", "*.model"])
 
 
+def get_hf_text_config(config: PretrainedConfig):
+    """Get the "sub" config relevant to llm for multi modal models.
+    No op for pure text models.
+    """
+    if config.architectures is not None:
+        class_name = config.architectures[0]
+        if class_name.startswith("Llava") and class_name.endswith("ForCausalLM"):
+            # We support non-hf version of llava models, so we do not want to
+            # read the wrong values from the unused default text_config.
+            # NOTE(HandH1998): We set `torch_dtype` of config to `torch.float16` for the weights, as
+            # `torch.float16` is default used for image features in `python/sglang/srt/models/llava.py`.
+            setattr(config, "torch_dtype", torch.float16)
+            return config
+
+    if hasattr(config, "text_config"):
+        # The code operates under the assumption that text_config should have
+        # `num_attention_heads` (among others). Assert here to fail early
+        # if transformers config doesn't align with this assumption.
+        assert hasattr(config.text_config, "num_attention_heads")
+        return config.text_config
+    if hasattr(config, "language_config"):
+        return config.language_config
+    if hasattr(config, "thinker_config"):
+        # qwen2.5 omni
+        thinker_config = config.thinker_config
+        if hasattr(thinker_config, "text_config"):
+            setattr(
+                thinker_config.text_config,
+                "torch_dtype",
+                getattr(thinker_config, "torch_dtype", None),
+            )
+            return thinker_config.text_config
+        return thinker_config
+    else:
+        return config
+
+
 def get_config(
     model: str,
     trust_remote_code: bool,
@@ -80,13 +118,12 @@ def get_config(
     config = AutoConfig.from_pretrained(
         model, trust_remote_code=trust_remote_code, revision=revision, **kwargs
     )
+    text_config = get_hf_text_config(config=config)
 
-    # FIXME: Pour contents of janus-pro's langauge_config to first-level
-    if isinstance(model, str) and model.lower().startswith("deepseek-ai/janus-pro"):
-        assert hasattr(config, "language_config")
-        for key, val in config.language_config.__dict__.items():
-            setattr(config, key, val)
-        setattr(config, "architectures", ["MultiModalityCausalLM"])
+    if isinstance(model, str) and text_config is not None:
+        for key, val in text_config.__dict__.items():
+            if not hasattr(config, key) and getattr(text_config, key, None) is not None:
+                setattr(config, key, val)
 
     if config.model_type in _CONFIG_REGISTRY:
         config_class = _CONFIG_REGISTRY[config.model_type]
@@ -99,6 +136,9 @@ def get_config(
             if not hasattr(config, key):
                 setattr(config, key, val)
 
+    if config.model_type == "multi_modality":
+        config.update({"architectures": ["MultiModalityCausalLM"]})
+
     if model_override_args:
         config.update(model_override_args)