[CI Failure] Fix torchao dep failure for Quantization Test (#26824)

Signed-off-by: mgoin <mgoin64@gmail.com>

.buildkite/test-amd.yaml

@@ -603,7 +603,8 @@ steps:
   # since torchao nightly is only compatible with torch nightly currently
   # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
   # we can only upgrade after this is resolved
-  - pip install --pre torchao==0.13.0.dev20250814 --index-url https://download.pytorch.org/whl/nightly/cu128
+  # TODO(jerryzh168): resolve the above comment
+  - uv pip install --system torchao==0.13.0
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/
 
 - label: LM Eval Small Models # 53min

.buildkite/test-pipeline.yaml

@@ -527,7 +527,8 @@ steps:
   # since torchao nightly is only compatible with torch nightly currently
   # https://github.com/pytorch/ao/issues/2919, we'll have to skip new torchao tests for now
   # we can only upgrade after this is resolved
-  - pip install --pre torchao==0.13.0.dev20250814 --index-url https://download.pytorch.org/whl/nightly/cu128
+  # TODO(jerryzh168): resolve the above comment
+  - uv pip install --system torchao==0.13.0
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/
 
 - label: LM Eval Small Models # 53min

tests/quantization/test_compressed_tensors.py

@@ -697,7 +697,8 @@ def test_compressed_tensors_2of4_sparse_compressed(vllm_runner, args_2of4):
 @pytest.mark.parametrize(
     "args",
     [
-        ("nm-testing/TinyLlama-1.1B-Chat-v1.0-NVFP4A16", CompressedTensorsW4A16Fp4),
+        # TODO: Enable once model is available again
+        # ("nm-testing/TinyLlama-1.1B-Chat-v1.0-NVFP4A16", CompressedTensorsW4A16Fp4),
         ("nm-testing/TinyLlama-1.1B-Chat-v1.0-NVFP4", CompressedTensorsW4A4Fp4),
     ],
 )

vllm/model_executor/layers/quantization/rtn.py

@@ -15,6 +15,7 @@ from vllm.model_executor.layers.fused_moe.config import (
     FusedMoEConfig,
     FusedMoEQuantConfig,
 )
+from vllm.model_executor.layers.fused_moe.fused_marlin_moe import fused_marlin_moe
 from vllm.model_executor.layers.fused_moe.layer import FusedMoE, FusedMoEMethodBase
 from vllm.model_executor.layers.linear import (
     LinearBase,
@@ -396,7 +397,7 @@ class RTNMoEMethod(FusedMoEMethodBase):
             indices_type=self.topk_indices_dtype,
         )
 
-        return torch.ops.vllm.fused_marlin_moe(
+        return fused_marlin_moe(
             x,
             layer.w13_weight,
             layer.w2_weight,