Modelopt quant config adaptation (#8829)

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -111,18 +111,52 @@ class ModelOptFp8Config(QuantizationConfig):
 
     @classmethod
     def from_config(cls, config: Dict[str, Any]) -> ModelOptFp8Config:
-        quant_method = cls.get_from_keys(config, ["quantization"]).get("quant_algo")
-        kv_cache_quant_method = cls.get_from_keys(config, ["quantization"]).get(
-            "kv_cache_quant_algo"
+        # Handle two different config formats:
+        # 1. hf_quant_config.json format: {"quantization": {"quant_algo": "FP8", ...}}
+        # 2. config.json quantization_config format: {"quant_algo": "FP8", ...}
+        # In future modelopt will deprecate hf_quant_config.json, and only keep config.json.
+        # For legacy reasons, we keep hf_quant_config.json for now.
+
+        # Initialize variables
+        kv_cache_quant_method = None
+        exclude_modules = None
+
+        # Try flat format first (config.json quantization_config - preferred format)
+        quant_method = config.get("quant_algo")
+        if quant_method is not None:
+            # Flat format (config.json quantization_config)
+            # For kv_cache, check if kv_cache_scheme exists and extract algo
+            kv_cache_scheme = config.get("kv_cache_scheme")
+            if (
+                kv_cache_scheme
+                and kv_cache_scheme.get("type") == "float"
+                and kv_cache_scheme.get("num_bits") == 8
+            ):
+                kv_cache_quant_method = "FP8"
+
+            # Map 'ignore' field to 'exclude_modules'
+            exclude_modules = config.get("ignore")
+        else:
+            # Fall back to nested format (hf_quant_config.json - legacy format)
+            try:
+                quantization_section = cls.get_from_keys(config, ["quantization"])
+                quant_method = quantization_section.get("quant_algo")
+                kv_cache_quant_method = quantization_section.get("kv_cache_quant_algo")
+                exclude_modules = quantization_section.get("exclude_modules")
+            except ValueError:
+                raise ValueError(
+                    "Cannot find 'quant_algo' in the model's quantization config. "
+                    "Expected either flat format (config.json) or nested format (hf_quant_config.json)."
                 )
-        exclude_modules = cls.get_from_keys(config, ["quantization"]).get(
-            "exclude_modules"
+        if quant_method is None:
+            raise ValueError(
+                "Cannot find 'quant_algo' in the model's quantization config. "
             )
-
         if "FP8" not in quant_method:
             raise ValueError(
-                "ModelOpt only supports static FP8 quantization in SGLang. "
-                "Check the `hf_quant_config.json` file for your model's configuration."
+                "ModelOptFp8Config only supports static FP8 quantization in SGLang. "
+                "For FP4 quantization, use ModelOptFp4Config. "
+                "Check the quantization config for your model's configuration."
             )
 
         return cls(
@@ -485,22 +519,63 @@ class ModelOptFp4Config(QuantizationConfig):
 
     @classmethod
     def from_config(cls, config: Dict[str, Any]) -> ModelOptFp4Config:
+        # Handle two different config formats:
+        # 1. hf_quant_config.json format: {"quantization": {"quant_algo": "NVFP4", ...}}
+        # 2. config.json quantization_config format: {"quant_algo": "NVFP4", ...}
+        # In future modelopt will deprecate hf_quant_config.json, and only keep config.json.
+        # For legacy reasons, we keep hf_quant_config.json for now.
+
+        # Initialize variables
+        kv_cache_quant_algo = None
+        group_size = None
+        exclude_modules = []
+
+        # Try flat format first (config.json quantization_config - preferred format)
+        quant_method = config.get("quant_algo")
+        if quant_method is not None:
+            # Flat format (config.json quantization_config)
+            # Note: FP4 models in config.json format may not have all the detailed fields
+            # that are present in hf_quant_config.json, so we need to handle defaults
+            kv_cache_quant_algo = config.get("kv_cache_quant_algo")
+            if not kv_cache_quant_algo:
+                # For config.json format, derive from kv_cache_scheme if available
+                kv_cache_scheme = config.get("kv_cache_scheme")
+                if (
+                    kv_cache_scheme
+                    and kv_cache_scheme.get("type") == "float"
+                    and kv_cache_scheme.get("num_bits") == 8
+                ):
+                    kv_cache_quant_algo = "FP8"
+                else:
+                    kv_cache_quant_algo = "auto"
+
+            group_size = config.get("group_size")
+            exclude_modules = config.get("ignore", [])
+        else:
+            # Fall back to nested format (hf_quant_config.json - legacy format)
+            try:
                 quant_config = cls.get_from_keys(config, ["quantization"])
                 quant_method = quant_config["quant_algo"]
+                kv_cache_quant_algo = quant_config.get("kv_cache_quant_algo")
+                if not kv_cache_quant_algo:
+                    kv_cache_quant_algo = "auto"
+                group_size = quant_config.get("group_size")
+                exclude_modules = quant_config.get("exclude_modules", [])
+            except (ValueError, KeyError):
+                raise ValueError(
+                    "Cannot find 'quant_algo' in the model's quantization config. "
+                    "Expected either flat format (config.json) or nested format (hf_quant_config.json)."
+                )
+
         if not quant_method in ["FP8", "NVFP4"]:
             raise ValueError(
                 f"ModelOpt currently only supports: FP8, NVFP4"
                 " quantizations in sglang. Please check the "
-                "`hf_quant_config.json` file for your model's "
-                "quant configuration."
+                "quantization config for your model's configuration."
             )
         is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
-        kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
-        if not kv_cache_quant_algo:
-            kv_cache_quant_algo = "auto"
-        group_size = quant_config["group_size"]
-        exclude_modules = quant_config["exclude_modules"]
-        if not (group_size and kv_cache_quant_algo and exclude_modules):
+
+        if not (group_size and kv_cache_quant_algo) or exclude_modules is None:
             logger.warning(
                 f"group_size: {group_size},"
                 f"kv_cache_quant_algo: {kv_cache_quant_algo},"
@@ -508,8 +583,7 @@ class ModelOptFp4Config(QuantizationConfig):
             )
             raise ValueError(
                 "NVFP4 quantization requires group size and "
-                "kv_cache_quant_algo specified in "
-                "hf_quant_config.json"
+                "kv_cache_quant_algo specified in the quantization config"
             )
         return cls(
             is_checkpoint_nvfp4_serialized,