use sglang_per_token_group_quant_fp8 from sgl-kernel instead of trion kernel (#5473)

Co-authored-by: Zhang Kaihong <zhangkaihong.zkh@alibaba-inc.com>

python/sglang/srt/layers/quantization/fp8_kernel.py

@@ -275,6 +275,8 @@ def sglang_per_token_group_quant_fp8(
     x: torch.Tensor,
     group_size: int,
     eps: float = 1e-10,
+    column_major_scales: bool = False,
+    scale_tma_aligned: bool = False,
 ):
     assert (
         x.shape[-1] % group_size == 0
@@ -282,11 +284,28 @@ def sglang_per_token_group_quant_fp8(
     assert x.is_contiguous(), "`x` is not contiguous"
 
     x_q = torch.empty_like(x, device=x.device, dtype=_fp8_type)
+    if column_major_scales:
+        if scale_tma_aligned:
+            # aligned to 4 * sizeof(float)
+            aligned_size = (x.shape[-2] + 3) // 4 * 4
+            x_s = torch.empty(
+                x.shape[:-2] + (x.shape[-1] // group_size, aligned_size),
+                device=x.device,
+                dtype=torch.float32,
+            ).permute(-1, -2)[: x.shape[-2], :]
+        else:
+            x_s = torch.empty(
+                (x.shape[-1] // group_size,) + x.shape[:-1],
+                device=x.device,
+                dtype=torch.float32,
+            ).permute(-1, -2)
+    else:
         x_s = torch.empty(
             x.shape[:-1] + (x.shape[-1] // group_size,),
             device=x.device,
             dtype=torch.float32,
         )
+
     sgl_per_token_group_quant_fp8(x, x_q, x_s, group_size, eps, fp8_min, fp8_max)
 
     return x_q, x_s

python/sglang/srt/layers/quantization/fp8_utils.py

@@ -141,7 +141,7 @@ def apply_w8a8_block_fp8_linear(
         gemm_a8w8_blockscale(q_input, weight, x_scale, weight_scale, output)
     else:
         if _enable_jit_deepgemm:
-            q_input, x_scale = per_token_group_quant_fp8(
+            q_input, x_scale = sglang_per_token_group_quant_fp8(
                 input_2d,
                 block_size[1],
                 column_major_scales=True,