[Bugfix] Use HF config fields as fallback when loading Mistral config (#29239)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/test-amd.yaml

@@ -754,6 +754,7 @@ steps:
   torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
+  - vllm/transformers_utils/
   - tests/models/test_initialization.py
   commands:
     # Only when vLLM model source is modified - test initialization of a large

.buildkite/test-pipeline.yaml

@@ -691,6 +691,7 @@ steps:
   torch_nightly: true
   source_file_dependencies:
   - vllm/model_executor/models/
+  - vllm/transformers_utils/
   - tests/models/test_initialization.py
   commands:
     # Only when vLLM model source is modified - test initialization of a large

vllm/transformers_utils/config.py

@@ -204,7 +204,19 @@ class MistralConfigParser(ConfigParserBase):
 
         from vllm.transformers_utils.configs.mistral import adapt_config_dict
 
-        config = adapt_config_dict(config_dict)
+        # Get missing fields from HF config if available
+        try:
+            hf_config_dict, _ = PretrainedConfig.get_config_dict(
+                model,
+                revision=revision,
+                code_revision=code_revision,
+                token=_get_hf_token(),
+                **kwargs,
+            )
+        except OSError:  # Not found
+            hf_config_dict = {}
+
+        config = adapt_config_dict(config_dict, defaults=hf_config_dict)
 
         # Mistral configs may define sliding_window as list[int]. Convert it
         # to int and add the layer_types list[str] to make it HF compatible

vllm/transformers_utils/configs/mistral.py

@@ -9,14 +9,18 @@ from vllm.logger import init_logger
 logger = init_logger(__name__)
 
 
-def adapt_config_dict(config_dict: dict[str, Any], **kwargs) -> PretrainedConfig:
+def adapt_config_dict(
-    config_dict.update(kwargs)
+    config_dict: dict[str, Any],
+    defaults: dict[str, Any],
+) -> PretrainedConfig:
     config_dict = _remap_general_mistral_args(config_dict)
 
     if bool(config_dict.get("quantization")):
         config_dict = _remap_mistral_quantization_args(config_dict)
 
-    if bool(config_dict.get("moe")):
+    if config_dict.get("model_type") == "mamba":
+        config_dict["architectures"] = ["Mamba2ForCausalLM"]
+    elif bool(config_dict.get("moe")):
         config_dict["architectures"] = ["MixtralForCausalLM"]
     else:
         config_dict["architectures"] = ["MistralForCausalLM"]
@@ -52,6 +56,9 @@ def adapt_config_dict(config_dict: dict[str, Any], **kwargs) -> PretrainedConfig
     if is_audio:
         config_dict = _remap_mistral_audio_args(config_dict)
 
+    for k, v in defaults.items():
+        config_dict.setdefault(k, v)
+
     config = PretrainedConfig.from_dict(config_dict)
 
     logger.debug("Initialized config %s", config)