[MoE Refactor][9/N] Use modular kernel for unquantized Triton MoE (#31052)

Signed-off-by: Yongye Zhu <zyy1102000@gmail.com>

tests/kernels/moe/test_moe.py

@@ -60,6 +60,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import quantize_w
 from vllm.model_executor.models.mixtral import MixtralMoE
 from vllm.platforms import current_platform
 from vllm.scalar_type import ScalarType, scalar_types
+from vllm.v1.worker.workspace import init_workspace_manager
 
 NUM_EXPERTS = [8, 64, 192]
 EP_SIZE = [1, 4]
@@ -487,6 +488,7 @@ def test_mixtral_moe(
     monkeypatch.setenv("MASTER_ADDR", "localhost")
     monkeypatch.setenv("MASTER_PORT", "12345")
     init_distributed_environment()
+    init_workspace_manager(torch.cuda.current_device())
 
     # Instantiate our and huggingface's MoE blocks
     vllm_config.compilation_config.static_forward_context = dict()
@@ -533,6 +535,11 @@ def test_mixtral_moe(
             torch.cuda.synchronize()
             torch.cuda.empty_cache()
 
+        # FIXME (zyongye) fix this after we move self.kernel
+        # assignment in FusedMoE.__init__
+
+        vllm_moe.experts.quant_method.process_weights_after_loading(vllm_moe.experts)
+
         # Run forward passes for both MoE blocks
         hf_states, _ = hf_moe.forward(hf_inputs)
         vllm_states = vllm_moe.forward(vllm_inputs)