[MoE Refactor][15/N] Apply Refactor to Fp8 (#31415)

tests/quantization/test_fp8.py

@@ -15,7 +15,6 @@ from vllm.model_executor.layers.quantization.fp8 import (
     Fp8Config,
     Fp8KVCacheMethod,
     Fp8LinearMethod,
-    Fp8MoeBackend,
     Fp8MoEMethod,
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
@@ -278,8 +277,18 @@ def test_scaled_fp8_quant(dtype) -> None:
 # this is the case for marlin as well as per-tensor Fp8MoEMethod
 @pytest.mark.parametrize("use_marlin", [False])  # skip True
 def test_fp8_reloading(
-    method_cls, is_checkpoint_fp8_serialized, weight_block_size, use_marlin, dist_init
+    method_cls,
+    is_checkpoint_fp8_serialized,
+    weight_block_size,
+    use_marlin,
+    dist_init,
+    monkeypatch,
 ):
+    # NOTE(rob): this test fails when using DeepGEMM because the
+    # shapes are invalid. Previously the test was passing because
+    # we set fp8_backend to None, which sidestepped the issue.
+    monkeypatch.setenv("VLLM_USE_DEEP_GEMM", "0")
+
     if is_checkpoint_fp8_serialized is False:
         pytest.skip("FP8 weight reloading does not support online quantization")
 
@@ -307,6 +316,7 @@ def test_fp8_reloading(
                 params_dtype=torch.bfloat16,
                 weight_loader=default_weight_loader,
             )
+            method.use_marlin = use_marlin
 
         else:
             layer = FusedMoE(
@@ -325,11 +335,6 @@ def test_fp8_reloading(
                 weight_loader=default_weight_loader,
             )
 
-        # Fp8LinearMethod uses use_marlin
-        # Fp8MoEMethod uses fp8_backend
-        method.use_marlin = use_marlin
-        method.fp8_backend = Fp8MoeBackend.MARLIN if use_marlin else None
-
     # capture weights format during loading
     original_metadata = [
         (name, param.shape, getattr(param, "weight_loader", default_weight_loader))