[Feature] Integrate SM100 DeepGEMM support (#20087)

tests/kernels/moe/test_block_fp8.py

@@ -15,13 +15,13 @@ from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
 from vllm.model_executor.layers.fused_moe.fused_moe import (
     fused_topk, modular_triton_fused_moe)
 from vllm.platforms import current_platform
+from vllm.utils import has_deep_gemm
+from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
-dg_available = False
-try:
-    import deep_gemm
-    dg_available = True
-except ImportError:
-    pass
+dg_available = has_deep_gemm()
+
+if dg_available:
+    from deep_gemm import get_m_alignment_for_contiguous_layout
 
 if current_platform.get_device_capability() < (9, 0):
     pytest.skip("FP8 Triton requires CUDA 9.0 or higher",
@@ -224,6 +224,7 @@ def test_w8a8_block_fp8_fused_moe(M, N, K, E, topk, block_size, dtype, seed,
 @pytest.mark.parametrize("topk", TOP_KS)
 @pytest.mark.parametrize("seed", SEEDS)
 @pytest.mark.skipif(not dg_available, reason="DeepGemm kernels not available.")
+@pytest.mark.skipif(is_blackwell_deep_gemm_used(), reason="Not E8M0 scale MOE")
 @torch.inference_mode()
 def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed,
                                             monkeypatch):
@@ -238,8 +239,7 @@ def test_w8a8_block_fp8_deep_gemm_fused_moe(M, N, K, E, topk, seed,
     torch.manual_seed(seed)
 
     monkeypatch.setenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(chunk_size))
-
-    block_m = deep_gemm.get_m_alignment_for_contiguous_layout()
+    block_m = get_m_alignment_for_contiguous_layout()
     block_size = [block_m, block_m]
     dtype = torch.bfloat16