[ROCm][Bugfix] fix exception related to trust_remote_code for MiniMax-M2.1-MXFP4

tests/quantization/test_quark_maybe_update_config.py

@@ -0,0 +1,63 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Unit tests for QuarkConfig.maybe_update_config.
+
+Fetches real HF configs (metadata only, no model weights) to verify
+that dynamic_mxfp4_quant is only enabled for DeepSeek-V3-family models.
+
+Run: pytest tests/quantization/test_quark_maybe_update_config.py -v
+"""
+
+import pytest
+from transformers import AutoConfig
+
+from vllm.model_executor.layers.quantization.quark.quark import QuarkConfig
+
+
+def _make_quark_config() -> QuarkConfig:
+    """Create a minimal QuarkConfig for testing."""
+    return QuarkConfig(quant_config={}, kv_cache_group=[], pack_method="reorder")
+
+
+# ---------------------------------------------------------------------------
+# Non-deepseek models must not flip dynamic_mxfp4_quant
+# ---------------------------------------------------------------------------
+@pytest.mark.parametrize(
+    "model_name",
+    ["amd/MiniMax-M2.1-MXFP4"],
+)
+def test_non_deepseek_model_stays_false(model_name: str):
+    """Non-deepseek_v3 models must not enable dynamic_mxfp4_quant."""
+    hf_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    qcfg = _make_quark_config()
+
+    qcfg.maybe_update_config(model_name, hf_config=hf_config)
+
+    assert qcfg.dynamic_mxfp4_quant is False
+
+
+# ---------------------------------------------------------------------------
+# DeepSeek-V3 family + fp4 must enable dynamic_mxfp4_quant
+# ---------------------------------------------------------------------------
+@pytest.mark.parametrize(
+    "model_name",
+    ["amd/DeepSeek-R1-MXFP4-ASQ"],
+)
+def test_deepseek_family_fp4_enables_flag(model_name: str):
+    hf_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    qcfg = _make_quark_config()
+
+    qcfg.maybe_update_config(model_name, hf_config=hf_config)
+
+    assert qcfg.dynamic_mxfp4_quant is True
+
+
+# ---------------------------------------------------------------------------
+# Missing hf_config → warn and stay False
+# ---------------------------------------------------------------------------
+def test_missing_hf_config_stays_false():
+    qcfg = _make_quark_config()
+
+    qcfg.maybe_update_config("some/model")
+
+    assert qcfg.dynamic_mxfp4_quant is False

vllm/config/vllm.py

@@ -526,7 +526,10 @@ def _get_quantization_config(
                     f"method {model_config.quantization}. Supported dtypes: "
                     f"{supported_dtypes}"
                 )
-            quant_config.maybe_update_config(model_config.model)
+            quant_config.maybe_update_config(
+                model_config.model,
+                hf_config=model_config.hf_config,
+            )
             return quant_config
         return None
 

vllm/model_executor/layers/quantization/awq.py

@@ -5,6 +5,7 @@
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
+from transformers import PretrainedConfig
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
@@ -146,7 +147,12 @@ def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
                 self.modules_to_not_convert
             )
 
-    def maybe_update_config(self, model_name: str, revision: str | None = None):
+    def maybe_update_config(
+        self,
+        model_name: str,
+        hf_config: PretrainedConfig | None = None,
+        revision: str | None = None,
+    ):
         if self.modules_to_not_convert:
             return
 

vllm/model_executor/layers/quantization/awq_marlin.py

@@ -6,6 +6,7 @@
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
 from torch.nn import Parameter
+from transformers import PretrainedConfig
 
 import vllm.model_executor.layers.fused_moe  # noqa
 from vllm import _custom_ops as ops
@@ -332,7 +333,12 @@ def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
                 self.modules_to_not_convert
             )
 
-    def maybe_update_config(self, model_name: str, revision: str | None = None):
+    def maybe_update_config(
+        self,
+        model_name: str,
+        hf_config: PretrainedConfig | None = None,
+        revision: str | None = None,
+    ):
         if self.modules_to_not_convert:
             return
 

vllm/model_executor/layers/quantization/base_config.py

@@ -7,6 +7,7 @@
 
 import torch
 from torch import nn
+from transformers import PretrainedConfig
 
 if TYPE_CHECKING:
     from vllm.model_executor.layers.quantization import QuantizationMethods
@@ -168,10 +169,23 @@ def apply_vllm_mapper(  # noqa: B027
         # TODO (@kylesayrs): add implementations for all subclasses
         pass
 
-    def maybe_update_config(self, model_name: str):  # noqa: B027
+    def maybe_update_config(  # noqa: B027
+        self,
+        model_name: str,
+        hf_config: PretrainedConfig | None = None,
+        revision: str | None = None,
+    ):
         """
         Interface to update values after config initialization.
+
+        Args:
+            model_name: The name of the model
+            hf_config: The Hugging Face config of the model
+            revision: The revision of the model
+        Returns:
         """
+        # TODO: revision is never passed currently in vllm.py,
+        # but is used in subclasses, should we remove this parameter?
         pass
 
     def is_mxfp4_quant(self, prefix: str, layer: torch.nn.Module) -> bool:

vllm/model_executor/layers/quantization/cpu_wna16.py

@@ -5,6 +5,7 @@
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
+from transformers import PretrainedConfig
 
 from vllm._custom_ops import (
     cpu_gemm_wna16,
@@ -133,7 +134,12 @@ def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
                 self.modules_to_not_convert
             )
 
-    def maybe_update_config(self, model_name: str, revision: str | None = None):
+    def maybe_update_config(
+        self,
+        model_name: str,
+        hf_config: PretrainedConfig | None = None,
+        revision: str | None = None,
+    ):
         if self.modules_to_not_convert:
             return
 

vllm/model_executor/layers/quantization/gptq.py

@@ -9,6 +9,7 @@
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
 from torch.nn.parameter import Parameter
+from transformers import PretrainedConfig
 
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
@@ -193,7 +194,12 @@ def apply_vllm_mapper(self, hf_to_vllm_mapper: "WeightsMapper"):
                 self.modules_in_block_to_quantize
             )
 
-    def maybe_update_config(self, model_name: str, revision: str | None = None):
+    def maybe_update_config(
+        self,
+        model_name: str,
+        hf_config: PretrainedConfig | None = None,
+        revision: str | None = None,
+    ):
         if self.modules_in_block_to_quantize:
             if is_list_of(self.modules_in_block_to_quantize, list):
                 # original modules_in_block_to_quantize: list[list[str]]

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -6,6 +6,7 @@
 
 import torch
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
+from transformers import PretrainedConfig
 
 import vllm.model_executor.layers.fused_moe  # noqa
 from vllm import _custom_ops as ops
@@ -299,7 +300,12 @@ def apply_vllm_mapper(self, hf_to_vllm_mapper):
                 self.modules_in_block_to_quantize
             )
 
-    def maybe_update_config(self, model_name: str, revision: str | None = None):
+    def maybe_update_config(
+        self,
+        model_name: str,
+        hf_config: PretrainedConfig | None = None,
+        revision: str | None = None,
+    ):
         if self.modules_in_block_to_quantize:
             if is_list_of(self.modules_in_block_to_quantize, list):
                 # original modules_in_block_to_quantize: list[list[str]]

vllm/model_executor/layers/quantization/quark/quark.py

@@ -5,6 +5,7 @@
 from typing import TYPE_CHECKING, Any, cast
 
 import torch
+from transformers import PretrainedConfig
 
 from vllm.logger import init_logger
 from vllm.model_executor.layers.attention import Attention
@@ -36,7 +37,6 @@
 )
 from vllm.model_executor.models.utils import WeightsMapper
 from vllm.platforms import current_platform
-from vllm.transformers_utils.config import get_config
 
 if TYPE_CHECKING:
     from vllm.model_executor.models.utils import WeightsMapper
@@ -45,6 +45,10 @@
 
 logger = init_logger(__name__)
 
+# model_type values that use dynamic MXFP4 re-quantization for
+# OCP MX fp4 Quark checkpoints
+_DEEPSEEK_V3_FAMILY_MODEL_TYPES = frozenset({"deepseek_v3"})
+
 
 class QuarkConfig(QuantizationConfig):
     def __init__(
@@ -63,19 +67,28 @@ def __init__(
         self.pack_method = pack_method
         self.dynamic_mxfp4_quant = False
 
-    def maybe_update_config(self, model_name: str, revision: str | None = None):
-        self.hf_config = get_config(
-            model=model_name,
-            trust_remote_code=False,  # or get from model_config if available
-            revision=revision,
-            config_format="auto",
-        )
+    def maybe_update_config(
+        self,
+        model_name: str,
+        hf_config: PretrainedConfig | None = None,
+        revision: str | None = None,
+    ):
+        """Enable dynamic MXFP4 only for DeepSeek-V3-family + fp4 Quark checkpoints."""
 
-        quant_config = getattr(self.hf_config, "quantization_config", None)
+        if (
+            getattr(hf_config, "model_type", None)
+            not in _DEEPSEEK_V3_FAMILY_MODEL_TYPES
+        ):
+            return
+
+        quant_config = getattr(hf_config, "quantization_config", None)
         if quant_config is not None:
-            quant_dtype = quant_config["global_quant_config"]["weight"]["dtype"]
-            model_type = self.hf_config.model_type
-            if quant_dtype == "fp4" and model_type == "deepseek_v3":
+            quant_dtype = (
+                quant_config.get("global_quant_config", {})
+                .get("weight", {})
+                .get("dtype")
+            )
+            if quant_dtype == "fp4":
                 self.dynamic_mxfp4_quant = True
 
     def get_linear_method(self) -> "QuarkLinearMethod":