Unify sglang coding style (#2856)

Co-authored-by: Lin, Soga <soga.lin@amd.com>

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

 # Adapted from https://github.com/vllm-project/vllm/blob/a6221a144af772fd1a68fe7e627935dc53e81738/vllm/model_executor/layers/fused_moe/layer.py
 
-import os
 from abc import abstractmethod
 from enum import Enum
 from typing import Callable, List, Optional, Tuple
@@ -19,7 +18,7 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
-from sglang.srt.utils import is_hip, permute_weight, set_weight_attrs
+from sglang.srt.utils import get_bool_env_var, is_hip, permute_weight, set_weight_attrs
 
 if torch.cuda.is_available():
     from sglang.srt.layers.moe.fused_moe_triton.fused_moe import fused_experts
@@ -28,6 +27,8 @@ else:
 
 import logging
 
+is_hip_ = is_hip()
+
 logger = logging.getLogger(__name__)
 
 
@@ -99,7 +100,7 @@ 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 bool(int(os.getenv("CK_MOE", "0"))):
+        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,
@@ -163,7 +164,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
             correction_bias=correction_bias,
         )
 
-        if is_hip() and bool(int(os.getenv("CK_MOE", "0"))):
+        if is_hip_ and get_bool_env_var("CK_MOE"):
             import ater
             from ater.fused_moe import fused_experts_ck
 

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

 # Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/layers/quantization/fp8.py
 
 import logging
-import os
 from typing import Any, Callable, Dict, List, Optional
 
 import torch
@@ -47,6 +46,8 @@ from sglang.srt.utils import (
 
 ACTIVATION_SCHEMES = ["static", "dynamic"]
 
+is_hip_ = is_hip()
+
 logger = logging.getLogger(__name__)
 
 
@@ -162,7 +163,7 @@ 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
@@ -274,7 +275,7 @@ 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,
@@ -331,7 +332,7 @@ class Fp8LinearMethod(LinearMethodBase):
                 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,
@@ -568,7 +569,7 @@ 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,
@@ -595,7 +596,7 @@ 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)
 
@@ -617,8 +618,8 @@ 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 bool(int(os.getenv("CK_MOE", "0"))):
+            if is_hip_:
+                if get_bool_env_var("CK_MOE"):
                     layer.w13_weight = torch.nn.Parameter(
                         permute_weight(layer.w13_weight.data),
                         requires_grad=False,
@@ -629,7 +630,7 @@ class Fp8MoEMethod:
                         requires_grad=False,
                     )
                     torch.cuda.empty_cache()
-                elif bool(int(os.getenv("MOE_PADDING", "0"))):
+                elif get_bool_env_var("MOE_PADDING"):
                     # If ROCm, apply weight padding (min. Mem channel contention) only if set
                     layer.w13_weight = torch.nn.Parameter(
                         F.pad(layer.w13_weight.data, (0, padding_size), "constant", 0),
@@ -671,7 +672,7 @@ 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(
@@ -721,8 +722,8 @@ class Fp8MoEMethod:
                 max_w13_scales, requires_grad=False
             )
 
-            if is_hip():
-                if bool(int(os.getenv("CK_MOE", "0"))):
+            if is_hip_:
+                if get_bool_env_var("CK_MOE"):
                     layer.w13_weight = torch.nn.Parameter(
                         permute_weight(layer.w13_weight.data),
                         requires_grad=False,
@@ -733,7 +734,7 @@ class Fp8MoEMethod:
                         requires_grad=False,
                     )
                     torch.cuda.empty_cache()
-                elif bool(int(os.getenv("MOE_PADDING", "0"))):
+                elif get_bool_env_var("MOE_PADDING"):
                     # If ROCm, apply weight padding (min. Mem channel contention) only if set
                     layer.w13_weight = torch.nn.Parameter(
                         F.pad(layer.w13_weight.data, (0, padding_size), "constant", 0),
@@ -777,7 +778,7 @@ class Fp8MoEMethod:
             correction_bias=correction_bias,
         )
 
-        if is_hip() and bool(int(os.getenv("CK_MOE", "0"))):
+        if is_hip_ and get_bool_env_var("CK_MOE"):
             import ater
             from ater.fused_moe import fused_experts_ck