[BugFix] replace the input_to_float8 used in dsv2 (#11612)

Signed-off-by: Liu-congo <1502632128@qq.com>

python/sglang/srt/models/deepseek_v2.py

@@ -92,7 +92,6 @@ from sglang.srt.layers.quantization.fp8_utils import (
     block_quant_dequant,
     block_quant_to_tensor_quant,
     channel_quant_to_tensor_quant,
-    input_to_float8,
     normalize_e4m3fn_to_e4m3fnuz,
     quant_weight_ue8m0,
     requant_weight_ue8m0_inplace,
@@ -1623,14 +1622,14 @@ class DeepseekV2AttentionMLA(nn.Module):
                     self.w_kc.to(torch.bfloat16) * self.w_scale,
                 )
         elif self.w_kc.dtype == torch.float8_e4m3fn:
-            # TODO fix the per_tensor_quant_mla_fp8 for cublas 12.9
-            if _is_cublas_ge_129:
-                q_nope_val, q_nope_scale = input_to_float8(
-                    q_nope.transpose(0, 1), torch.float8_e4m3fn
-                )
-            else:
+            # fix bmm_fp8 error under cublas12.9 caused by bumpallocator, detail in pr#11612
             q_nope_val, q_nope_scale = per_tensor_quant_mla_fp8(
-                    q_nope.transpose(0, 1), zero_allocator.allocate(1)
+                q_nope.transpose(0, 1),
+                (
+                    torch.zeros((1,), dtype=torch.float32, device=q_nope.device)
+                    if _is_cublas_ge_129
+                    else zero_allocator.allocate(1)
+                ),
             )
             q_nope_out = bmm_fp8(
                 q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
@@ -1772,13 +1771,13 @@ class DeepseekV2AttentionMLA(nn.Module):
                 attn_bmm_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
 
         elif self.w_vc.dtype == torch.float8_e4m3fn:
-            if _is_cublas_ge_129:
-                attn_output_val, attn_output_scale = input_to_float8(
-                    attn_output.transpose(0, 1), torch.float8_e4m3fn
-                )
-            else:
             attn_output_val, attn_output_scale = per_tensor_quant_mla_fp8(
-                    attn_output.transpose(0, 1), zero_allocator.allocate(1)
+                attn_output.transpose(0, 1),
+                (
+                    torch.zeros((1,), dtype=torch.float32, device=attn_output.device)
+                    if _is_cublas_ge_129
+                    else zero_allocator.allocate(1)
+                ),
             )
             attn_bmm_output = bmm_fp8(
                 attn_output_val,