Fix run time error in ROCm platform

python/sglang/srt/layers/elementwise.py

@@ -4,6 +4,10 @@
 import triton
 import triton.language as tl
 
+from sglang.srt.utils import is_hip
+
+_is_hip = is_hip()
+
 fused_softcap_autotune = triton.autotune(
     configs=[
         triton.Config(kwargs={"BLOCK_SIZE": 128}, num_warps=4),
@@ -185,6 +189,9 @@ def fused_dual_residual_rmsnorm(x, residual, weight1, weight2, eps, autotune=Fal
     assert x.shape == residual.shape and x.dtype == residual.dtype
     output, mid = torch.empty_like(x), torch.empty_like(x)
     bs, hidden_dim = x.shape
+
+    min_num_warps = 16 if _is_hip else 32
+
     if autotune:
         fused_dual_residual_rmsnorm_kernel_autotune[(bs,)](
             output, mid, x, residual, weight1, weight2, eps=eps, hidden_dim=hidden_dim
@@ -193,7 +200,10 @@ def fused_dual_residual_rmsnorm(x, residual, weight1, weight2, eps, autotune=Fal
         config = {
             "BLOCK_SIZE": triton.next_power_of_2(hidden_dim),
             "num_warps": max(
-                min(triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), 32), 4
+                min(
+                    triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), min_num_warps
+                ),
+                4,
             ),
         }
 
@@ -250,10 +260,13 @@ def fused_rmsnorm(x, weight, eps, autotune=False, inplace=False):
     else:
         output = torch.empty_like(x)
     bs, hidden_dim = x.shape
+
+    min_num_warps = 16 if _is_hip else 32
+
     config = {
         "BLOCK_SIZE": triton.next_power_of_2(hidden_dim),
         "num_warps": max(
-            min(triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), 32), 4
+            min(triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), min_num_warps), 4
         ),
     }
 

python/sglang/srt/layers/moe/router.py

@@ -5,6 +5,9 @@
 import triton.language as tl
 
 from sglang.srt.layers.moe.topk import fused_topk
+from sglang.srt.utils import is_hip
+
+_is_hip = is_hip()
 
 
 @triton.jit
@@ -116,10 +119,13 @@ def fused_moe_router_impl(
     topk_ids = torch.empty((bs, topk), dtype=torch.int32, device=x.device)
 
     grid = lambda meta: (bs,)
+
+    min_num_warps = 16 if _is_hip else 32
+
     config = {
         "BLOCK_SIZE": triton.next_power_of_2(hidden_dim),
         "num_warps": max(
-            min(triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), 32), 4
+            min(triton.next_power_of_2(triton.cdiv(hidden_dim, 256)), min_num_warps), 4
         ),
     }
 

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

@@ -171,6 +171,7 @@ def input_to_float8(
     amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
     fp8_max = finfo.max
     if _is_hip:
+        dtype = torch.float8_e4m3fnuz
         fp8_max = 224.0
     scale = fp8_max / amax
     x_scl_sat = (x * scale).clamp(min=-fp8_max, max=fp8_max)