unify is_cuda and is_hip (#4321)

python/sglang/srt/custom_op.py

 import torch
 from torch import nn
 
-_is_cuda = torch.cuda.is_available() and torch.version.cuda
-_is_rocm = torch.cuda.is_available() and torch.version.hip
+from sglang.srt.utils import is_cuda, is_hip
+
+_is_cuda = is_cuda()
+_is_hip = is_hip()
 
 
 class CustomOp(nn.Module):
@@ -34,7 +36,7 @@ class CustomOp(nn.Module):
     def dispatch_forward(self):
         if _is_cuda:
             return self.forward_cuda
-        elif _is_rocm:
+        elif _is_hip:
             return self.forward_hip
         else:
             return self.forward_native

python/sglang/srt/distributed/device_communicators/custom_all_reduce.py

@@ -22,15 +22,16 @@ from sglang.srt.utils import cuda_device_count_stateless, is_cuda, is_hip
 
 logger = logging.getLogger(__name__)
 
-is_hip_ = is_hip()
+_is_cuda = is_cuda()
+_is_hip = is_hip()
 
-if is_cuda():
+if _is_cuda:
     try:
         import pynvml
     except ImportError as e:
         logger.warning("Failed to import pynvml with %r", e)
 
-if is_hip_:
+if _is_hip:
     try:
         from amdsmi import (
             AmdSmiException,
@@ -43,7 +44,7 @@ if is_hip_:
         logger.warning("Failed to import amdsmi with %r", e)
 
 try:
-    if ops.use_vllm_custom_allreduce and not is_hip_:
+    if ops.use_vllm_custom_allreduce and not _is_hip:
         # Use vLLM custom allreduce
         ops.meta_size()
     else:
@@ -63,7 +64,7 @@ _R = TypeVar("_R")
 def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
     @wraps(fn)
     def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
-        if is_hip_:
+        if _is_hip:
             try:
                 amdsmi_init()
                 return fn(*args, **kwargs)
@@ -81,7 +82,7 @@ def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
 
 @with_nvml_context
 def is_full_nvlink(physical_device_ids: List[int], world_size: int) -> bool:
-    if is_hip_:
+    if _is_hip:
         """
         query if the set of gpus are fully connected by xgmi (1 hop)
         """
@@ -145,7 +146,7 @@ def is_weak_contiguous(inp: torch.Tensor):
 class CustomAllreduce:
     _SUPPORTED_WORLD_SIZES = [2, 4, 6, 8]
     _MAX_CAR_SIZE = 8192 * 1024
-    if is_hip_:
+    if _is_hip:
         # crossover is at 16MB buffer size for ROCm
         _MAX_CAR_SIZE = 2 * 8192 * 1024
 
@@ -229,7 +230,7 @@ class CustomAllreduce:
         # test nvlink first, this will filter out most of the cases
         # where custom allreduce is not supported
         # this checks hardware and driver support for NVLink
-        if is_cuda() or is_hip_:
+        if _is_cuda or _is_hip:
             full_nvlink = is_full_nvlink(physical_device_ids, world_size)
 
         if world_size > 2 and not full_nvlink:
@@ -243,7 +244,7 @@ class CustomAllreduce:
         # this is expensive to compute at the first time
         # then we cache the result
         # On AMD GPU, p2p is always enabled between XGMI connected GPUs
-        if not is_hip_ and not _can_p2p(rank, world_size):
+        if not _is_hip and not _can_p2p(rank, world_size):
             logger.warning(
                 "Custom allreduce is disabled because your platform lacks "
                 "GPU P2P capability or P2P test failed. To silence this "
@@ -256,7 +257,7 @@ class CustomAllreduce:
         self.world_size = world_size
         self.full_nvlink = full_nvlink
 
-        if ops.use_vllm_custom_allreduce and not is_hip_:
+        if ops.use_vllm_custom_allreduce and not _is_hip:
             # Buffers memory are owned by this Python class and passed to C++.
             # Meta data composes of two parts: meta data for synchronization and a
             # temporary buffer for storing intermediate allreduce results.
@@ -279,7 +280,7 @@ class CustomAllreduce:
             )
             ops.register_buffer(self._ptr, self.buffer_ptrs)
         else:
-            if is_hip_:
+            if _is_hip:
                 # meta data buffers need to be "uncached" for signal on MI200
                 self.meta = ops.allocate_meta_buffer(ops.meta_size() + max_size)
                 self.buffer = torch.empty(
@@ -418,7 +419,7 @@ class CustomAllreduce:
         ops.register_buffer(self._ptr, inp, handles, offsets)
 
     def register_graph_buffers(self):
-        if is_hip_:
+        if _is_hip:
             handle, offset = ops.get_graph_buffer_ipc_meta(self._ptr)
             handles, offsets = self._gather_ipc_meta((bytes(handle), offset))
             logger.info("Registering %d cuda graph addresses", len(offset))
@@ -454,12 +455,12 @@ class CustomAllreduce:
             return False
         # for 4 or more non NVLink-capable GPUs, custom allreduce provides
         # little performance improvement over NCCL.
-        if ops.use_vllm_custom_allreduce and not is_hip_:
+        if ops.use_vllm_custom_allreduce and not _is_hip:
             if self.world_size == 2 or self.full_nvlink:
                 return inp_size < self.max_size
             return False
 
-        if is_hip_:
+        if _is_hip:
             if self.full_nvlink:
                 if self.world_size == 8:
                     if self.MSCCL:
@@ -532,7 +533,7 @@ class CustomAllreduce:
             return None
         if self._IS_CAPTURING:
             if torch.cuda.is_current_stream_capturing():
-                if is_hip_:
+                if _is_hip:
                     return self.all_reduce_reg(input)
                 else:
                     return self.all_reduce(input, registered=True)
@@ -541,7 +542,7 @@ class CustomAllreduce:
                 # allreduce is out-of-place.
                 return torch.empty_like(input)
         else:
-            if is_hip_:
+            if _is_hip:
                 # note: outside of cuda graph context,
                 # custom allreduce incurs a cost of cudaMemcpy, which should
                 # be small(<=1% of overall latency) compared to the performance
@@ -556,7 +557,7 @@ class CustomAllreduce:
             if ops.use_vllm_custom_allreduce:
                 self.free_shared_buffer(self.meta_ptrs)
                 self.free_shared_buffer(self.buffer_ptrs)
-            elif is_cuda():
+            elif _is_cuda:
                 self.free_shared_buffer(self.buffer_ptrs)
                 self.free_shared_buffer(self.tmp_result_buffer_ptrs)
                 self.free_shared_buffer(self.barrier_in_ptrs)

python/sglang/srt/layers/attention/triton_ops/decode_attention.py

@@ -27,7 +27,7 @@ import triton.language as tl
 
 from sglang.srt.utils import is_hip
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 logger = logging.getLogger(__name__)
 
@@ -180,7 +180,7 @@ def _decode_att_m_fwd(
 ):
     BLOCK = 64
     # [TODO] work around SGPR limit on MI3xx
-    if is_hip_:
+    if _is_hip:
         BLOCK = 8
     NUM_KV_SPLITS = num_kv_splits
     Lk = k_buffer.shape[-1]
@@ -195,7 +195,7 @@ def _decode_att_m_fwd(
         num_warps = 4
     else:
         num_warps = 2
-        if is_hip_:
+        if _is_hip:
             num_warps = 1
 
     BLOCK_DMODEL = triton.next_power_of_2(Lk)
@@ -406,7 +406,7 @@ def _decode_grouped_att_m_fwd(
     Lv = v_buffer.shape[-1]
 
     # [TODO] work around shmem limit on MI3xx
-    if is_hip_ and Lk >= 576:
+    if _is_hip and Lk >= 576:
         BLOCK = 16
 
     if Lk == 576:
@@ -433,7 +433,7 @@ def _decode_grouped_att_m_fwd(
 
     extra_kargs = {}
     num_stages = 2
-    if is_hip_:
+    if _is_hip:
         # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
         # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
         extra_kargs = {"waves_per_eu": 1, "matrix_instr_nonkdim": 16, "kpack": 2}
@@ -546,7 +546,7 @@ def _decode_softmax_reducev_fwd(
     NUM_KV_SPLITS = num_kv_splits
 
     extra_kargs = {}
-    if is_hip_:
+    if _is_hip:
         # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
         # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
         extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}

python/sglang/srt/layers/attention/triton_ops/double_sparsity_attention.py

@@ -9,7 +9,7 @@ is_cuda_available = torch.cuda.is_available()
 if is_cuda_available:
     CUDA_CAPABILITY = torch.cuda.get_device_capability()
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 if global_server_args_dict.get("attention_reduce_in_fp32", False):
     REDUCE_TRITON_TYPE = tl.float32
@@ -1032,7 +1032,7 @@ def extend_attention_fwd(
         BLOCK_DPE = 0
     BLOCK_DV = triton.next_power_of_2(Lv)
 
-    if is_hip_:
+    if _is_hip:
         BLOCK_M, BLOCK_N = (64, 64)
         num_warps = 4
 
@@ -1062,7 +1062,7 @@ def extend_attention_fwd(
     num_stages = 1
 
     extra_kargs = {}
-    if is_hip_:
+    if _is_hip:
         extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}
 
     _fwd_kernel[grid](

python/sglang/srt/layers/attention/triton_ops/extend_attention.py

@@ -29,7 +29,7 @@ is_cuda_available = torch.cuda.is_available()
 if is_cuda_available:
     CUDA_CAPABILITY = torch.cuda.get_device_capability()
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 
 @triton.jit
@@ -330,7 +330,7 @@ def extend_attention_fwd(
         BLOCK_DPE = 0
     BLOCK_DV = triton.next_power_of_2(Lv)
 
-    if is_hip_:
+    if _is_hip:
         BLOCK_M, BLOCK_N = (64, 64)
         num_warps = 4
 
@@ -364,7 +364,7 @@ def extend_attention_fwd(
     num_stages = 1
 
     extra_kargs = {}
-    if is_hip_:
+    if _is_hip:
         extra_kargs = {"waves_per_eu": 1, "matrix_instr_nonkdim": 16, "kpack": 2}
 
     _fwd_kernel[grid](
@@ -403,7 +403,7 @@ def extend_attention_fwd(
         Lv=Lv,
         USE_CUSTOM_MASK=USE_CUSTOM_MASK,
         SKIP_PREFIX_CUSTOM_MASK=SKIP_PREFIX_CUSTOM_MASK,
-        STORE_TRANSPOSE=is_hip_,
+        STORE_TRANSPOSE=_is_hip,
         num_warps=num_warps,
         num_stages=num_stages,
         **extra_kargs,

python/sglang/srt/layers/attention/triton_ops/rocm_mla_decode_rope.py

@@ -32,7 +32,7 @@ def is_hip():
     return triton.runtime.driver.active.get_current_target().backend == "hip"
 
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 
 @triton.jit
@@ -333,7 +333,7 @@ def _decode_grouped_att_m_fwd_rope(
     BLOCK = 32
 
     # # [TODO] work around shmem limit on MI3xx
-    # if is_hip_ and kv_lora_rank >= 576:
+    # if _is_hip and kv_lora_rank >= 576:
     #     BLOCK = 16
 
     qk_rope_head_dim = k_buffer.shape[-1] - kv_lora_rank
@@ -353,7 +353,7 @@ def _decode_grouped_att_m_fwd_rope(
 
     extra_kargs = {}
     num_stages = 2
-    if is_hip_:
+    if _is_hip:
         # https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
         # https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
         extra_kargs = {"waves_per_eu": 1, "matrix_instr_nonkdim": 16, "kpack": 2}

python/sglang/srt/layers/moe/ep_moe/kernels.py

@@ -6,8 +6,9 @@ import triton
 import triton.language as tl
 
 from sglang.srt.layers.quantization.fp8_kernel import per_token_group_quant_fp8
+from sglang.srt.utils import is_cuda
 
-_is_cuda = torch.cuda.is_available() and torch.version.cuda
+_is_cuda = is_cuda()
 if _is_cuda:
     from sglang.srt.layers.quantization.fp8_kernel import (
         sglang_per_token_group_quant_fp8,

python/sglang/srt/layers/moe/ep_moe/layer.py

@@ -30,6 +30,8 @@ from sglang.srt.utils import is_hip, set_weight_attrs
 
 logger = logging.getLogger(__name__)
 
+_is_hip = is_hip()
+
 
 class GroupedGemmRunner(torch.nn.Module):
     flashinfer_gemm_warpper = None
@@ -703,7 +705,7 @@ class Fp8EPMoEMethod(Fp8MoEMethod):
         # If checkpoint is fp16, quantize in place.
         if not self.quant_config.is_checkpoint_fp8_serialized:
             # If rocm, use float8_e4m3fnuz as dtype
-            fp8_dtype = torch.float8_e4m3fnuz if is_hip() else torch.float8_e4m3fn
+            fp8_dtype = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
             w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype)
             w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)
 

python/sglang/srt/layers/moe/fused_moe_triton/fused_moe.py

@@ -23,10 +23,11 @@ from sglang.srt.utils import (
     direct_register_custom_op,
     get_bool_env_var,
     get_device_name,
+    is_cuda,
     is_hip,
 )
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 
 logger = logging.getLogger(__name__)
@@ -36,8 +37,7 @@ enable_moe_align_block_size_triton = bool(
     int(os.getenv("ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON", "0"))
 )
 
-_is_cuda = torch.cuda.is_available() and torch.version.cuda
-_is_rocm = torch.cuda.is_available() and torch.version.hip
+_is_cuda = is_cuda()
 
 if _is_cuda:
     from sgl_kernel import gelu_and_mul, silu_and_mul
@@ -46,7 +46,7 @@ if _is_cuda:
         sglang_per_token_group_quant_fp8,
     )
 
-if _is_cuda or _is_rocm:
+if _is_cuda or _is_hip:
     from sgl_kernel import moe_align_block_size as sgl_moe_align_block_size
 
 
@@ -679,7 +679,7 @@ def get_default_config(
                 "BLOCK_SIZE_K": 128,
                 "GROUP_SIZE_M": 32,
                 "num_warps": 8,
-                "num_stages": 2 if is_hip_ else 4,
+                "num_stages": 2 if _is_hip else 4,
             }
             if M <= E:
                 config = {
@@ -688,7 +688,7 @@ def get_default_config(
                     "BLOCK_SIZE_K": 128,
                     "GROUP_SIZE_M": 1,
                     "num_warps": 4,
-                    "num_stages": 2 if is_hip_ else 4,
+                    "num_stages": 2 if _is_hip else 4,
                 }
         else:
             # Block-wise quant: BLOCK_SIZE_K must be divisable by block_shape[1]
@@ -698,7 +698,7 @@ def get_default_config(
                 "BLOCK_SIZE_K": block_shape[1],
                 "GROUP_SIZE_M": 32,
                 "num_warps": 4,
-                "num_stages": 2 if is_hip_ else 3,
+                "num_stages": 2 if _is_hip else 3,
             }
     else:
         config = {
@@ -976,7 +976,7 @@ def fused_experts_impl(
     if (
         not (use_fp8_w8a8 or use_int8_w8a8)
         or block_shape is not None
-        or (is_hip_ and get_bool_env_var("CK_MOE"))
+        or (_is_hip and get_bool_env_var("CK_MOE"))
     ):
         padded_size = 0
 
@@ -1131,7 +1131,7 @@ def fused_experts_impl(
 
         if no_combine:
             pass
-        elif is_hip_:
+        elif _is_hip:
             ops.moe_sum(
                 intermediate_cache3.view(*intermediate_cache3.shape),
                 out_hidden_states[begin_chunk_idx:end_chunk_idx],

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -27,9 +27,9 @@ else:
 
 import logging
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
-if is_hip_:
+if _is_hip:
     from aiter import ck_moe
 
 logger = logging.getLogger(__name__)
@@ -102,7 +102,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         set_weight_attrs(w2_weight, extra_weight_attrs)
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        if is_hip_ and get_bool_env_var("CK_MOE"):
+        if _is_hip and get_bool_env_var("CK_MOE"):
             layer.w13_weight = torch.nn.Parameter(
                 permute_weight(layer.w13_weight.data),
                 requires_grad=False,
@@ -175,7 +175,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             correction_bias=correction_bias,
         )
 
-        if is_hip_ and get_bool_env_var("CK_MOE"):
+        if _is_hip and get_bool_env_var("CK_MOE"):
             assert not no_combine, "unsupported"
             return ck_moe(
                 x,
@@ -514,7 +514,7 @@ class FusedMoE(torch.nn.Module):
         # Case input scale: input_scale loading is only supported for fp8
         if "input_scale" in weight_name:
             # INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust input_scale for e4m3fnuz (AMD)
-            if is_hip_ and get_bool_env_var("USE_INT4_WEIGHT"):
+            if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
                 loaded_weight = loaded_weight * 2.0
 
             # this is needed for compressed-tensors only
@@ -556,7 +556,7 @@ class FusedMoE(torch.nn.Module):
             quant_method = getattr(param, "quant_method", None)
             if quant_method == FusedMoeWeightScaleSupported.CHANNEL.value:
                 # INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust INT4 column-wise scaling number to e4m3fnuz (AMD)
-                if is_hip_ and get_bool_env_var("USE_INT4_WEIGHT"):
+                if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
                     loaded_weight = loaded_weight * 0.5
 
                 self._load_per_channel_weight_scale(
@@ -579,7 +579,7 @@ class FusedMoE(torch.nn.Module):
                 )
             elif quant_method == FusedMoeWeightScaleSupported.TENSOR.value:
                 # INT4-FP8 (INT4 MoE Weight, FP8 Compute): Adjust FP8 per-tensor scaling number for e4m3fnuz (AMD)
-                if is_hip_ and get_bool_env_var("USE_INT4_WEIGHT"):
+                if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
                     loaded_weight = loaded_weight * 2.0
 
                 self._load_per_tensor_weight_scale(

python/sglang/srt/layers/quantization/fp8.py

@@ -54,9 +54,9 @@ from sglang.srt.utils import (
 
 ACTIVATION_SCHEMES = ["static", "dynamic"]
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
-if is_hip_:
+if _is_hip:
     from aiter.fused_moe_bf16_asm import asm_moe
     from aiter.ops.shuffle import shuffle_weight
 
@@ -175,7 +175,7 @@ class Fp8LinearMethod(LinearMethodBase):
         # kernel for fast weight-only FP8 quantization
         self.use_marlin = get_bool_env_var("SGLANG_FORCE_FP8_MARLIN")
         # Disable marlin for ROCm
-        if is_hip_:
+        if _is_hip:
             self.use_marlin = False
 
         self.block_quant = self.quant_config.weight_block_size is not None
@@ -287,7 +287,7 @@ class Fp8LinearMethod(LinearMethodBase):
         # Block quant doesn't need to process weights after loading
         if self.block_quant:
             # If ROCm, normalize the weights and scales to e4m3fnuz
-            if is_hip_:
+            if _is_hip:
                 # activation_scheme: dynamic
                 weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                     weight=layer.weight,
@@ -347,7 +347,7 @@ class Fp8LinearMethod(LinearMethodBase):
                 weight = layer.weight
                 weight_scale = layer.weight_scale
                 # If ROCm, normalize the weights and scales to e4m3fnuz
-                if is_hip_:
+                if _is_hip:
                     weight, weight_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
                         weight=weight,
                         weight_scale=weight_scale,
@@ -563,7 +563,7 @@ class Fp8MoEMethod:
             layer.register_parameter("w2_weight_scale", w2_weight_scale)
 
             if (
-                is_hip_
+                _is_hip
             ):  # and get_bool_env_var("CK_MOE"): TODO: add check back after triton kernel
                 # ROCm - using column scaling, duplicate scaling numbers in case per tensor scaling
                 w13_weight_scale1 = torch.nn.Parameter(
@@ -630,7 +630,7 @@ class Fp8MoEMethod:
         # Block quant doesn't need to process weights after loading
         if self.block_quant:
             # If ROCm, normalize the weights and scales to e4m3fnuz
-            if is_hip_:
+            if _is_hip:
                 # activation_scheme: dynamic
                 w13_weight, w13_weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                     weight=layer.w13_weight,
@@ -667,7 +667,7 @@ class Fp8MoEMethod:
         # If checkpoint is fp16 or bfloat16, quantize in place.
         if not self.quant_config.is_checkpoint_fp8_serialized:
             # If ROCm, use float8_e4m3fnuz instead (MI300x HW)
-            fp8_dtype = torch.float8_e4m3fnuz if is_hip_ else torch.float8_e4m3fn
+            fp8_dtype = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
             w13_weight = torch.empty_like(layer.w13_weight.data, dtype=fp8_dtype)
             w2_weight = torch.empty_like(layer.w2_weight.data, dtype=fp8_dtype)
 
@@ -689,7 +689,7 @@ class Fp8MoEMethod:
             layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
             layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
 
-            if is_hip_:
+            if _is_hip:
                 self.process_weights_hip_scale_padding(layer)
             return
 
@@ -721,7 +721,7 @@ class Fp8MoEMethod:
                 )
 
             # If ROCm, normalize the weights and scales to e4m3fnuz
-            if is_hip_:
+            if _is_hip:
                 # Normalize the weights and scales
                 w13_weight, w13_weight_scale, w13_input_scale = (
                     normalize_e4m3fn_to_e4m3fnuz(
@@ -771,7 +771,7 @@ class Fp8MoEMethod:
                 max_w13_scales, requires_grad=False
             )
 
-            if is_hip_:
+            if _is_hip:
                 self.process_weights_hip_scale_padding(layer)
             return
 
@@ -882,7 +882,7 @@ class Fp8MoEMethod:
             correction_bias=correction_bias,
         )
 
-        if is_hip_ and get_bool_env_var("USE_INT4_WEIGHT"):
+        if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
             # TODO: add triton kernel and add check get_bool_env_var("CK_MOE")
             assert not no_combine, f"{no_combine=} is not supported."
             return asm_moe(
@@ -895,7 +895,7 @@ class Fp8MoEMethod:
                 layer.w2_weight_scale1,
                 activation=activation,
             )
-        if is_hip_ and get_bool_env_var("CK_MOE"):
+        if _is_hip and get_bool_env_var("CK_MOE"):
             # TODO(CK_MOE): FP8 or FP8 block_quant only supports 'silu' for the time-being.
             assert (
                 activation == "silu"

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

@@ -22,12 +22,12 @@ import torch
 import triton
 import triton.language as tl
 
-from sglang.srt.utils import get_device_core_count, get_device_name, is_hip
+from sglang.srt.utils import get_device_core_count, get_device_name, is_cuda, is_hip
 
-is_hip_ = is_hip()
-fp8_type_ = torch.float8_e4m3fnuz if is_hip_ else torch.float8_e4m3fn
+_is_hip = is_hip()
+fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
 
-_is_cuda = torch.cuda.is_available() and torch.version.cuda
+_is_cuda = is_cuda()
 if _is_cuda:
     import deep_gemm
     from sgl_kernel import sgl_per_token_group_quant_fp8, sgl_per_token_quant_fp8
@@ -157,7 +157,7 @@ def per_token_group_quant_fp8(
     finfo = torch.finfo(dtype)
     fp8_max = finfo.max
 
-    if is_hip_:
+    if _is_hip:
         fp8_max = 224.0
 
     fp8_min = -fp8_max
@@ -332,7 +332,7 @@ def static_quant_fp8(
     finfo = torch.finfo(dtype)
     fp8_max = finfo.max
 
-    if is_hip_:
+    if _is_hip:
         fp8_max = 224.0
 
     fp8_min = -fp8_max
@@ -732,7 +732,7 @@ def w8a8_block_fp8_matmul(
     else:
         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
         )
 

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

@@ -17,8 +17,8 @@ from sglang.srt.utils import (
 
 use_vllm_cutlass_w8a8_fp8_kernel = get_bool_env_var("USE_VLLM_CUTLASS_W8A8_FP8_KERNEL")
 
-is_hip_ = is_hip()
-if is_hip_ and get_bool_env_var("CK_MOE"):
+_is_hip = is_hip()
+if _is_hip and get_bool_env_var("CK_MOE"):
     from aiter import gemm_a8w8_blockscale
 
 _is_cuda = is_cuda()
@@ -111,7 +111,7 @@ def apply_w8a8_block_fp8_linear(
         output = fp8_blockwise_scaled_mm(
             q_input, weight.T, x_scale, weight_scale.T, out_dtype=input.dtype
         )
-    elif is_hip_ and get_bool_env_var("CK_MOE"):
+    elif _is_hip and get_bool_env_var("CK_MOE"):
         q_input, x_scale = per_token_group_quant_fp8(
             input_2d, block_size[1], column_major_scales=False
         )
@@ -142,7 +142,7 @@ def input_to_float8(
     min_val, max_val = x.aminmax()
     amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
     fp8_max = finfo.max
-    if is_hip_:
+    if _is_hip:
         fp8_max = 224.0
     scale = fp8_max / amax
     x_scl_sat = (x * scale).clamp(min=-fp8_max, max=fp8_max)

python/sglang/srt/layers/quantization/w8a8_fp8.py

@@ -16,6 +16,8 @@ from sglang.srt.layers.quantization.fp8_utils import (
 )
 from sglang.srt.utils import is_hip
 
+_is_hip = is_hip()
+
 
 class W8A8Fp8Config(QuantizationConfig):
     """Config class for W8A8 FP8 Quantization.
@@ -71,7 +73,7 @@ class W8A8Fp8LinearMethod(LinearMethodBase):
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         weight = layer.weight
         weight_scale = layer.weight_scale.detach()
-        if is_hip():
+        if _is_hip:
             weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                 weight=weight, weight_scale=weight_scale
             )

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -35,7 +35,7 @@ from sglang.srt.model_executor.forward_batch_info import (
 )
 from sglang.srt.utils import is_hip
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 if TYPE_CHECKING:
     from sglang.srt.model_executor.model_runner import ModelRunner
@@ -119,7 +119,7 @@ def get_batch_sizes_to_capture(model_runner: ModelRunner):
         else:
             capture_bs = list(range(1, 33))
 
-    if is_hip_:
+    if _is_hip:
         capture_bs += [i * 8 for i in range(21, 33)]
 
     if max(capture_bs) > model_runner.req_to_token_pool.size:

python/sglang/srt/models/deepseek_nextn.py

@@ -40,7 +40,7 @@ from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.models.deepseek_v2 import DeepseekV2DecoderLayer, DeepseekV3ForCausalLM
 from sglang.srt.utils import add_prefix, is_hip
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 
 class DeepseekModelNextN(nn.Module):
@@ -277,7 +277,7 @@ class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):
                 weight_block_size = self.quant_config.weight_block_size
                 if weight_block_size is not None:
                     assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
-                    if is_hip_:
+                    if _is_hip:
                         weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                             weight=w,
                             weight_scale=self_attn.kv_b_proj.weight_scale_inv,
@@ -301,7 +301,7 @@ class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):
                 and self_attn.w_scale is None
             ):
                 self_attn.w_scale = self_attn.kv_b_proj.weight_scale
-                if is_hip_:
+                if _is_hip:
                     self_attn.w_scale *= 2.0
 
 

python/sglang/srt/models/deepseek_v2.py

@@ -65,7 +65,7 @@ from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.model_loader.weight_utils import default_weight_loader
 from sglang.srt.utils import add_prefix, is_cuda_available, is_hip
 
-is_hip_ = is_hip()
+_is_hip = is_hip()
 
 if is_cuda_available():
     from sgl_kernel import bmm_fp8
@@ -571,7 +571,7 @@ class DeepseekV2AttentionMLA(nn.Module):
         if no_absorb():
             return self.forward_normal(positions, hidden_states, forward_batch)
         else:
-            if is_hip_:
+            if _is_hip:
                 if (
                     os.getenv("SGLANG_ROCM_FUSED_DECODE_MLA") == "1"
                     and forward_batch.forward_mode.is_decode()
@@ -1190,7 +1190,7 @@ class DeepseekV2ForCausalLM(nn.Module):
                     weight_block_size = self.quant_config.weight_block_size
                     if weight_block_size is not None:
                         assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
-                        if is_hip_:
+                        if _is_hip:
                             weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
                                 weight=w,
                                 weight_scale=self_attn.kv_b_proj.weight_scale_inv,
@@ -1230,7 +1230,7 @@ class DeepseekV2ForCausalLM(nn.Module):
                     and self_attn.w_scale is None
                 ):
                     self_attn.w_scale = self_attn.kv_b_proj.weight_scale
-                    if is_hip_:
+                    if _is_hip:
                         self_attn.w_scale *= 2.0
 
     def get_embed_and_head(self):

python/sglang/srt/utils.py

@@ -72,13 +72,17 @@ show_time_cost = False
 time_infos = {}
 
 
+# https://pytorch.org/docs/stable/notes/hip.html#checking-for-hip
 def is_hip() -> bool:
-    """Return whether it is HIP on the AMD ROCm platform."""
     return torch.version.hip is not None
 
 
+def is_rocm() -> bool:
+    return torch.cuda.is_available() and torch.version.hip
+
+
 def is_cuda():
-    return hasattr(torch, "cuda") and torch.version.cuda is not None
+    return torch.cuda.is_available() and torch.version.cuda
 
 
 def is_cuda_alike():
@@ -100,11 +104,11 @@ def is_flashinfer_available():
     """
     if not get_bool_env_var("SGLANG_IS_FLASHINFER_AVAILABLE", default="true"):
         return False
-    return torch.cuda.is_available() and torch.version.cuda
+    return is_cuda()
 
 
 def is_cuda_available():
-    return torch.cuda.is_available() and torch.version.cuda
+    return is_cuda()
 
 
 def enable_show_time_cost():