cleanup deps 2/n (#4464)

python/sglang/srt/layers/activation.py

@@ -23,7 +23,9 @@ import torch.nn.functional as F
 
 from sglang.srt.utils import is_cuda_available
 
-if is_cuda_available():
+_is_cuda = is_cuda_available()
+
+if _is_cuda:
     from sgl_kernel import gelu_and_mul, gelu_tanh_and_mul, silu_and_mul
 
 from sglang.srt.custom_op import CustomOp
@@ -165,7 +167,7 @@ def get_act_fn(
     return act_fn
 
 
-if not is_cuda_available():
+if not _is_cuda:
     logger.info(
         "sgl-kernel is not available on Non-NV platforms. Fallback to other kernel libraries."
     )

python/sglang/srt/layers/layernorm.py

@@ -21,7 +21,9 @@ import torch.nn as nn
 
 from sglang.srt.utils import is_cuda_available
 
-if is_cuda_available():
+_is_cuda = is_cuda_available()
+
+if _is_cuda:
     from sgl_kernel import (
         fused_add_rmsnorm,
         gemma_fused_add_rmsnorm,
@@ -117,7 +119,7 @@ class GemmaRMSNorm(CustomOp):
         return out
 
 
-if not is_cuda_available():
+if not _is_cuda:
     logger.info(
         "sgl-kernel is not available on Non-NV platforms. Fallback to other kernel libraries."
     )

python/sglang/srt/models/deepseek_nextn.py

@@ -41,9 +41,13 @@ from sglang.srt.managers.schedule_batch import global_server_args_dict
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 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
+from sglang.srt.utils import add_prefix, is_cuda, is_hip
 
 _is_hip = is_hip()
+_is_cuda = is_cuda()
+
+if _is_cuda:
+    from sgl_kernel import awq_dequantize
 
 
 class DeepseekModelNextN(nn.Module):
@@ -261,14 +265,21 @@ class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):
             self_attn = self.model.decoder.self_attn
             if hasattr(self_attn.kv_b_proj, "qweight"):
                 # AWQ compatible
-                w = ops.awq_dequantize(
-                    self_attn.kv_b_proj.qweight,
-                    self_attn.kv_b_proj.scales,
-                    self_attn.kv_b_proj.qzeros,
-                    0,
-                    0,
-                    0,
-                ).T
+                if _is_cuda:
+                    w = awq_dequantize(
+                        self_attn.kv_b_proj.qweight,
+                        self_attn.kv_b_proj.scales,
+                        self_attn.kv_b_proj.qzeros,
+                    ).T
+                else:
+                    w = ops.awq_dequantize(
+                        self_attn.kv_b_proj.qweight,
+                        self_attn.kv_b_proj.scales,
+                        self_attn.kv_b_proj.qzeros,
+                        0,
+                        0,
+                        0,
+                    ).T
             else:
                 w = self_attn.kv_b_proj.weight
             # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.

python/sglang/srt/models/deepseek_v2.py

@@ -68,12 +68,13 @@ from sglang.srt.layers.vocab_parallel_embedding import (
 from sglang.srt.managers.schedule_batch import global_server_args_dict
 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
+from sglang.srt.utils import add_prefix, is_cuda, is_cuda_available, is_hip
 
 _is_hip = is_hip()
+_is_cuda = is_cuda()
 
-if is_cuda_available():
-    from sgl_kernel import bmm_fp8
+if _is_cuda:
+    from sgl_kernel import awq_dequantize, bmm_fp8
 
 
 class DeepseekV2MLP(nn.Module):
@@ -1174,14 +1175,21 @@ class DeepseekV2ForCausalLM(nn.Module):
                 self_attn = self.model.layers[layer_id].self_attn
                 if hasattr(self_attn.kv_b_proj, "qweight"):
                     # AWQ compatible
-                    w = ops.awq_dequantize(
-                        self_attn.kv_b_proj.qweight,
-                        self_attn.kv_b_proj.scales,
-                        self_attn.kv_b_proj.qzeros,
-                        0,
-                        0,
-                        0,
-                    ).T
+                    if _is_cuda:
+                        w = awq_dequantize(
+                            self_attn.kv_b_proj.qweight,
+                            self_attn.kv_b_proj.scales,
+                            self_attn.kv_b_proj.qzeros,
+                        ).T
+                    else:
+                        w = ops.awq_dequantize(
+                            self_attn.kv_b_proj.qweight,
+                            self_attn.kv_b_proj.scales,
+                            self_attn.kv_b_proj.qzeros,
+                            0,
+                            0,
+                            0,
+                        ).T
                 else:
                     w = self_attn.kv_b_proj.weight
                 # NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.