Enable modelopt gemma3 nvfp4/fp8, make workflow more robust (#22771)

Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com>
Signed-off-by: Michael Goin <mgoin64@gmail.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>

tests/kernels/moe/test_modular_kernel_combinations.py

@@ -11,7 +11,8 @@ import pytest
 import torch
 
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
-from vllm.config import VllmConfig, current_platform, set_current_vllm_config
+from vllm.config import VllmConfig, set_current_vllm_config
+from vllm.platforms import current_platform
 from vllm.utils import has_deep_ep, has_deep_gemm, has_pplx
 from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
 

vllm/compilation/backends.py

@@ -31,8 +31,11 @@ logger = init_logger(__name__)
 
 def make_compiler(compilation_config: CompilationConfig) -> CompilerInterface:
     if compilation_config.use_inductor:
-        if envs.VLLM_USE_STANDALONE_COMPILE and is_torch_equal_or_newer(
-                "2.8.0.dev"):
+        # Use standalone compile only if requested, version is new enough,
+        # and the symbol actually exists in this PyTorch build.
+        if (envs.VLLM_USE_STANDALONE_COMPILE
+                and is_torch_equal_or_newer("2.8.0.dev")
+                and hasattr(torch._inductor, "standalone_compile")):
             logger.debug("Using InductorStandaloneAdaptor")
             return InductorStandaloneAdaptor()
         else:

vllm/config/model.py

@@ -964,6 +964,9 @@ class ModelConfig:
                 "modelopt",
                 "modelopt_fp4",
                 "petit_nvfp4",
+                # Ensure heavy backends are probed last to avoid unnecessary
+                # imports during override detection (e.g., MXFP4 imports Triton)
+                "mxfp4",
             ]
             quantization_methods = [
                 q for q in supported_quantization if q not in overrides

vllm/model_executor/layers/fused_moe/gpt_oss_triton_kernels_moe.py

@@ -20,10 +20,10 @@ if has_triton_kernels():
         from triton_kernels.matmul_ogs import (FnSpecs, FusedActivation,
                                                matmul_ogs)
         from triton_kernels.routing import routing
-    except ModuleNotFoundError:
+    except (ModuleNotFoundError, AttributeError) as e:
         logger.error(
             "Failed to import Triton kernels. Please make sure your triton "
-            "version is compatible.")
+            "version is compatible. Error: %s", e)
 
 
 def triton_kernel_moe_forward(

vllm/model_executor/layers/quantization/modelopt.py

@@ -160,6 +160,7 @@ class ModelOptFp8Config(QuantizationConfig):
     def is_layer_excluded(self, prefix: str) -> bool:
         """
         Check if a layer should be excluded from quantization.
+        Handles both exact matching (for fused layers) and substring matching.
 
         This method handles both regular models and multimodal models that use
         the language_model prefix. For multimodal models, it checks if the
@@ -168,11 +169,18 @@ class ModelOptFp8Config(QuantizationConfig):
         if self.exclude_modules is None:
             return False
 
-        # Check if any excluded module matches the prefix
+        # First check exact matching with fused layer support
+        if is_layer_skipped(prefix, self.exclude_modules,
+                            self.packed_modules_mapping):
+            return True
+
+        # Then check substring matching for patterns not caught by exact match
         for module in self.exclude_modules:
-            if (module in prefix
-                    or (prefix.startswith("language_model.")
-                        and module in prefix.removeprefix("language_model."))):
+            # Skip exact matches already handled above
+            if (module != prefix and
+                (module in prefix or
+                 (prefix.startswith("language_model.")
+                  and module in prefix.removeprefix("language_model.")))):
                 return True
         return False
 
@@ -180,9 +188,10 @@ class ModelOptFp8Config(QuantizationConfig):
                          prefix: str) -> Optional["QuantizeMethodBase"]:
         from vllm.attention.layer import Attention  # Avoid circular import
         if isinstance(layer, LinearBase):
-            if (is_layer_skipped(prefix, self.exclude_modules,
-                                 self.packed_modules_mapping)
-                    or self.is_layer_excluded(prefix)):
+            if self.is_layer_excluded(prefix):
+                return UnquantizedLinearMethod()
+            # Check if this is a vision model layer that should not be quantized
+            if ("vision_tower" in prefix or "vision_model" in prefix):
                 return UnquantizedLinearMethod()
             return ModelOptFp8LinearMethod(self)
         elif isinstance(layer, Attention):
@@ -778,22 +787,34 @@ class ModelOptNvFp4Config(QuantizationConfig):
         return cls(is_checkpoint_nvfp4_serialized, kv_cache_quant_algo,
                    exclude_modules, group_size)
 
-    def is_layer_excluded(self, prefix: str,
-                          exclude_modules: list[str]) -> bool:
+    def is_layer_excluded(self, prefix: str) -> bool:
+        """
+        Check if a layer should be excluded from quantization.
+        Handles both exact matching (for fused layers) and pattern matching.
+        """
+        # First check exact matching with fused layer support
+        if is_layer_skipped(prefix, self.exclude_modules,
+                            self.packed_modules_mapping):
+            return True
+
+        # Check regex pattern matching for patterns not caught by exact match
         import regex as re
-        for pattern in exclude_modules:
-            regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
-            if re.fullmatch(regex_str, prefix):
-                return True
+        for pattern in self.exclude_modules:
+            # Skip patterns that would be caught by exact matching
+            if '*' in pattern or '.' in pattern:
+                regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
+                if re.fullmatch(regex_str, prefix):
+                    return True
         return False
 
     def get_quant_method(self, layer: torch.nn.Module,
                          prefix: str) -> Optional["QuantizeMethodBase"]:
         from vllm.attention.layer import Attention  # Avoid circular import
         if isinstance(layer, LinearBase):
-            if (is_layer_skipped(prefix, self.exclude_modules,
-                                 self.packed_modules_mapping)
-                    or self.is_layer_excluded(prefix, self.exclude_modules)):
+            if self.is_layer_excluded(prefix):
+                return UnquantizedLinearMethod()
+            # Check if this is a vision model layer that should not be quantized
+            if ("vision_tower" in prefix or "vision_model" in prefix):
                 return UnquantizedLinearMethod()
             return ModelOptNvFp4LinearMethod(self)
         elif isinstance(layer, Attention):

vllm/model_executor/models/gemma3.py

@@ -446,6 +446,22 @@ class Gemma3Model(nn.Module):
                 weight_loader(param, loaded_weight)
                 loaded_params.add(scale_name)
                 continue
+
+            # Check if this is a scale parameter that needs remapping first
+            if name.endswith(
+                (".k_scale", ".v_scale", ".q_scale", ".prob_scale")):
+                # Try to remap the scale name first
+                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
+                if remapped_name is not None and remapped_name in params_dict:
+                    # Successfully remapped, use the remapped name
+                    param = params_dict[remapped_name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+                    loaded_params.add(remapped_name)
+                    continue
+                # If remapping failed, continue with normal processing
+
             for (param_name, shard_name, shard_id) in stacked_params_mapping:
                 if shard_name not in name:
                     continue

vllm/model_executor/models/siglip.py

@@ -20,7 +20,8 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
-from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader, maybe_remap_kv_scale_name)
 
 from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs
 
@@ -506,6 +507,21 @@ class SiglipVisionModel(nn.Module):
                 if layer_idx >= layer_count:
                     continue
 
+            # Check if this is a scale parameter that needs remapping first
+            if name.endswith(
+                (".k_scale", ".v_scale", ".q_scale", ".prob_scale")):
+                # Try to remap the scale name first
+                remapped_name = maybe_remap_kv_scale_name(name, params_dict)
+                if remapped_name is not None and remapped_name in params_dict:
+                    # Successfully remapped, use the remapped name
+                    param = params_dict[remapped_name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+                    weight_loader(param, loaded_weight)
+                    loaded_params.add(remapped_name)
+                    continue
+                # If remapping failed, continue with normal processing
+
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue