[NVIDIA] Fix missing `get_col_major_tma_aligned_tensor` for Blackwell deepgemm in EpMoE (#8955)

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -55,6 +55,22 @@ if _use_aiter:
 logger = logging.getLogger(__name__)
 
 
+# TODO(kaixih@nvidia): ideally we should merge this logic into
+# `fill_gateup_input_triton_kernel` to directly generate e8m0 scale.
+@torch.compile
+def _cast_to_e8m0_with_rounding_up(x: torch.Tensor) -> torch.Tensor:
+    temp = x.to(torch.float32).view(torch.int32)
+    exp = torch.bitwise_right_shift(temp, 23)
+    mant = torch.bitwise_and(temp, 0x7FFFFF)
+    is_ru = torch.logical_and(
+        torch.logical_and((mant > 0), (exp != 0xFE)),
+        ~torch.logical_and((exp == 0), (mant <= 0x400000)),
+    )
+    exp = torch.where(is_ru, exp + 1, exp)
+    new_x = exp.to(torch.uint8).view(torch.int)
+    return new_x.transpose(1, 2).contiguous().transpose(1, 2)
+
+
 class EPMoE(FusedMoE):
     """
     MoE Expert Parallel Impl
@@ -204,10 +220,22 @@ class EPMoE(FusedMoE):
 
         dispose_tensor(hidden_states)
 
+        if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0:
+            b, s_mn, s_k = gateup_input_scale.shape
+            assert (
+                s_mn % 4 == 0 and s_k % 4 == 0
+            ), f"scales must be aligned to 4, but got ({b}, {s_mn}, {s_k})"
+
         # GroupGemm-0
         gateup_input_fp8 = (
             gateup_input,
-            deep_gemm_wrapper.get_col_major_tma_aligned_tensor(gateup_input_scale),
+            (
+                _cast_to_e8m0_with_rounding_up(gateup_input_scale)
+                if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
+                else deep_gemm_wrapper.get_col_major_tma_aligned_tensor(
+                    gateup_input_scale
+                )
+            ),
         )
         num_groups, m, k = gateup_input_fp8[0].size()
         n = self.w13_weight.size(1)
@@ -215,7 +243,12 @@ class EPMoE(FusedMoE):
             (num_groups, m, n), device=hidden_states_device, dtype=torch.bfloat16
         )
         deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_masked(
-            gateup_input_fp8, self.w13_weight_fp8, gateup_output, masked_m, expected_m
+            gateup_input_fp8,
+            self.w13_weight_fp8,
+            gateup_output,
+            masked_m,
+            expected_m,
+            recipe=(1, 128, 128) if deep_gemm_wrapper.DEEPGEMM_BLACKWELL else None,
         )
         del gateup_input
         del gateup_input_fp8
@@ -246,6 +279,7 @@ class EPMoE(FusedMoE):
             down_input_scale,
             scale_block_size,
             masked_m,
+            scale_ue8m0=deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0,
         )
         del gateup_output
 
@@ -253,13 +287,24 @@ class EPMoE(FusedMoE):
         n = self.w2_weight.size(1)
         down_input_fp8 = (
             down_input,
-            deep_gemm_wrapper.get_col_major_tma_aligned_tensor(down_input_scale),
+            (
+                down_input_scale
+                if deep_gemm_wrapper.DEEPGEMM_SCALE_UE8M0
+                else deep_gemm_wrapper.get_col_major_tma_aligned_tensor(
+                    down_input_scale
+                )
+            ),
         )
         down_output = torch.empty(
             (num_groups, m, n), device=hidden_states_device, dtype=torch.bfloat16
         )
         deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_masked(
-            down_input_fp8, self.w2_weight_fp8, down_output, masked_m, expected_m
+            down_input_fp8,
+            self.w2_weight_fp8,
+            down_output,
+            masked_m,
+            expected_m,
+            recipe=(1, 128, 128) if deep_gemm_wrapper.DEEPGEMM_BLACKWELL else None,
         )
         del down_input
         del down_input_fp8