Minor update for ROCm variable style (#5562)

benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py

@@ -20,7 +20,7 @@ from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
 )
 from sglang.srt.utils import is_hip
 
-_is_hip_ = is_hip()
+_is_hip = is_hip()
 
 
 class BenchmarkConfig(TypedDict):
@@ -112,8 +112,8 @@ def benchmark_config(
             )
 
     if use_fp8_w8a8:
-        w1 = w1.to(torch.float8_e4m3fnuz if _is_hip_ else torch.float8_e4m3fn)
-        w2 = w2.to(torch.float8_e4m3fnuz if _is_hip_ else torch.float8_e4m3fn)
+        w1 = w1.to(torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn)
+        w2 = w2.to(torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn)
 
     input_gating = torch.empty(num_tokens, num_experts, dtype=torch.float32)
 
@@ -204,7 +204,7 @@ def get_configs_compute_bound() -> List[Dict[str, int]]:
     # TODO(woosuk): Increase the search space and use a performance model to
     # prune the search space.
     configs: List[BenchmarkConfig] = []
-    if _is_hip_:
+    if _is_hip:
         configs = get_rocm_configs_compute_bound()
     else:
         for num_stages in [2, 3, 4, 5]:

benchmark/kernels/quantization/tuning_block_wise_kernel.py

@@ -33,7 +33,7 @@ from sglang.srt.layers.quantization.fp8_kernel import (
 from sglang.srt.layers.quantization.int8_kernel import _w8a8_block_int8_matmul
 from sglang.srt.utils import get_device_core_count, get_device_name, is_hip
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 DTYPE_MAP = {
     "float32": torch.float32,
@@ -99,7 +99,7 @@ def w8a8_block_matmul(
     if A.dtype == torch.float8_e4m3fnuz or A.dtype == torch.float8_e4m3fn:
         kernel = (
             _w8a8_block_fp8_matmul_unrolledx4
-            if (is_hip_ == True and num_workgroups <= get_device_core_count())
+            if (_is_hip == True and num_workgroups <= get_device_core_count())
             else _w8a8_block_fp8_matmul
         )
     else:
@@ -157,7 +157,7 @@ def get_rocm_configs_compute_bound():
 
 def get_configs_compute_bound():
     configs = []
-    if is_hip_:
+    if _is_hip:
         configs = get_rocm_configs_compute_bound()
     else:
         for num_stages in [2, 3, 4, 5]:
@@ -244,7 +244,7 @@ def tune(M, N, K, block_size, out_dtype, search_space, input_type):
 
     if input_type == "fp8":
         fp8_info = torch.finfo(
-            torch.float8_e4m3fnuz if is_hip_ else torch.float8_e4m3fn
+            torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
         )
         fp8_max, fp8_min = fp8_info.max, fp8_info.min
 
@@ -252,14 +252,14 @@ def tune(M, N, K, block_size, out_dtype, search_space, input_type):
             (torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
         )
         A = A_fp32.clamp(min=fp8_min, max=fp8_max).to(
-            torch.float8_e4m3fnuz if is_hip_ else torch.float8_e4m3fn
+            torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
         )
 
         B_fp32 = (
             (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
         )
         B = B_fp32.clamp(min=fp8_min, max=fp8_max).to(
-            torch.float8_e4m3fnuz if is_hip_ else torch.float8_e4m3fn
+            torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
         )
     else:
         int8_info = torch.iinfo(torch.int8)