[Feature] Integrate SM100 DeepGEMM support (#20087)

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -34,6 +34,7 @@ from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
 from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 from vllm.utils import direct_register_custom_op
+from vllm.utils.deep_gemm import is_blackwell_deep_gemm_used
 
 from .rocm_aiter_fused_moe import is_rocm_aiter_moe_enabled
 
@@ -1171,9 +1172,15 @@ def fused_experts(
         allow_cutlass_block_scaled_grouped_gemm: bool = False) -> torch.Tensor:
     # For now, disable DeepGemm for small N (<= 512) until better
     # permute/unpermute ops are available.
+    # However, on B200, we use DeepGemm for all cases becuase they only support
+    # E8M0 scale, which means we requantize the weight and input to the specific
+    # scale. Fallen back to cutlass or triton for some cases would cause
+    # accuracy issue.
     N = w1.size(1)
-    if (allow_deep_gemm and use_fp8_w8a8 and N > 512
-            and _valid_deep_gemm(hidden_states, w1, w2)):
+    should_use_deep_gemm = ((N > 512
+                             and _valid_deep_gemm(hidden_states, w1, w2))
+                            or is_blackwell_deep_gemm_used())
+    if (allow_deep_gemm and use_fp8_w8a8 and should_use_deep_gemm):
         assert apply_router_weight_on_input is False
         return deep_gemm_moe_fp8(
             hidden_states=hidden_states,
@@ -1363,7 +1370,6 @@ def fused_experts_impl(
 
         curr_topk_ids = topk_ids[begin_chunk_idx:end_chunk_idx]
         curr_topk_weights = topk_weights[begin_chunk_idx:end_chunk_idx]
-
         qcurr_hidden_states, a1q_scale = moe_kernel_quantize_input(
             A=curr_hidden_states,
             A_scale=a1_scale,