From f87b8eab23e49fada0aab5488ff94942b35b8979 Mon Sep 17 00:00:00 2001
From: fzyzcjy <5236035+fzyzcjy@users.noreply.github.com>
Date: Mon, 1 Dec 2025 14:45:27 +0800
Subject: [PATCH] Tiny fix transform_scale_ue8m0 wrong output in some scenarios
 (#14003)

---
 .../srt/layers/quantization/fp8_utils.py      | 46 +++++++++++++++++--
 1 file changed, 42 insertions(+), 4 deletions(-)

diff --git a/python/sglang/srt/layers/quantization/fp8_utils.py b/python/sglang/srt/layers/quantization/fp8_utils.py
index 5ad8b502d..f8a98e98f 100644
--- a/python/sglang/srt/layers/quantization/fp8_utils.py
+++ b/python/sglang/srt/layers/quantization/fp8_utils.py
@@ -575,21 +575,59 @@ def transform_scale_ue8m0_inplace(param, mn):
 
 
 # NOTE copy and modified from DeepGEMM
-def transform_scale_ue8m0(sf, mn):
+def transform_scale_ue8m0(sf, mn, use_torch_impl: bool = False):
     import deep_gemm.utils.layout
 
+    get_mn_major_tma_aligned_packed_ue8m0_tensor = (
+        _get_mn_major_tma_aligned_packed_ue8m0_tensor_torch_impl
+        if use_torch_impl
+        else deep_gemm.utils.layout.get_mn_major_tma_aligned_packed_ue8m0_tensor
+    )
+
     sf = sf.index_select(-2, torch.arange(mn, device=sf.device) // 128)
-    sf = deep_gemm.utils.layout.get_mn_major_tma_aligned_packed_ue8m0_tensor(sf)
+    sf = get_mn_major_tma_aligned_packed_ue8m0_tensor(sf)
     return sf
 
 
+# Copied from DeepGEMM tests
+def _get_mn_major_tma_aligned_packed_ue8m0_tensor_torch_impl(
+    x: torch.Tensor,
+) -> torch.Tensor:
+    from deep_gemm.utils import align, get_tma_aligned_size
+
+    assert x.dtype == torch.float and x.dim() in (2, 3)
+
+    # First, convert into UE8M0 `uint8_t`
+    ue8m0_tensor = (x.view(torch.int) >> 23).to(torch.uint8)
+
+    # Second, make padded packed tensors
+    mn, k = x.shape[-2], x.shape[-1]
+    remove_dim = False
+    if x.dim() == 2:
+        x, remove_dim = x.unsqueeze(0), True
+    b = x.shape[0]
+    aligned_mn = get_tma_aligned_size(mn, 4)
+    aligned_k = align(k, 4)
+    padded = torch.zeros((b, aligned_mn, aligned_k), device=x.device, dtype=torch.uint8)
+    padded[:, :mn, :k] = ue8m0_tensor
+    padded = padded.view(-1).view(dtype=torch.int).view(b, aligned_mn, aligned_k // 4)
+
+    # Finally, transpose
+    transposed = torch.zeros(
+        (b, aligned_k // 4, aligned_mn), device=x.device, dtype=torch.int
+    ).mT
+    transposed[:, :, :] = padded
+    aligned_x = transposed[:, :mn, :]
+    return aligned_x.squeeze(0) if remove_dim else aligned_x
+
+
 def inverse_transform_scale_ue8m0(sf_packed, mn):
     sf_fp32 = _inverse_transform_scale_ue8m0_impl(sf_packed)
     # Can call consistency check every time since this is only called on startup
-    sf_packed_recreated = transform_scale_ue8m0(sf_fp32, mn=mn)
+    sf_packed_recreated = transform_scale_ue8m0(sf_fp32, mn=mn, use_torch_impl=True)
     assert torch.all(
         sf_packed == sf_packed_recreated
-    ), f"{sf_packed=} {sf_packed_recreated}"
+    ), f"{sf_packed=} {sf_packed_recreated=} {sf_fp32=}"
     return sf_fp32