Fix sgl-kernel benchmark dead code (#11022)

sgl-kernel/benchmark/bench_per_tensor_quant_fp8.py

@@ -1,5 +1,6 @@
 import itertools
 import math
+import os
 from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
@@ -7,11 +8,26 @@ import torch
 import triton
 import triton.testing
 from sgl_kernel import sgl_per_tensor_quant_fp8
-from vllm import _custom_ops as ops
+
+# Optional imports
+try:
+    from vllm import _custom_ops as ops
+
+    VLLM_AVAILABLE = True
+except ImportError:
+    ops = None
+    VLLM_AVAILABLE = False
 
 from sglang.srt.utils import is_hip
 
 _is_hip = is_hip()
+
+# CI environment detection
+IS_CI = (
+    os.getenv("CI", "false").lower() == "true"
+    or os.getenv("GITHUB_ACTIONS", "false").lower() == "true"
+)
+
 fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
 
 
@@ -19,6 +35,9 @@ def vllm_scaled_fp8_quant(
     input: torch.Tensor,
     scale: Optional[torch.Tensor] = None,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
+    if not VLLM_AVAILABLE:
+        # Fallback to SGLang implementation
+        return sglang_scaled_fp8_quant(input, scale)
     return ops.scaled_fp8_quant(input, scale)
 
 
@@ -42,6 +61,10 @@ def calculate_diff(batch_size: int, seq_len: int):
     device = torch.device("cuda")
     x = torch.rand((batch_size, seq_len), dtype=torch.float16, device=device)
 
+    if not VLLM_AVAILABLE:
+        print("⚠️ vLLM not available, skipping comparison")
+        return
+
     vllm_out, vllm_scale = vllm_scaled_fp8_quant(x)
     sglang_out, sglang_scale = sglang_scaled_fp8_quant(x)
 
@@ -56,8 +79,13 @@ def calculate_diff(batch_size: int, seq_len: int):
         print("❌ Implementations differ")
 
 
-batch_size_range = [16, 32, 64, 128]
-seq_len_range = [64, 128, 256, 512, 1024, 2048]
+# CI environment uses simplified parameters
+if IS_CI:
+    batch_size_range = [16]  # Single batch size for CI
+    seq_len_range = [64]  # Single sequence length for CI
+else:
+    batch_size_range = [16, 32, 64, 128]
+    seq_len_range = [64, 128, 256, 512, 1024, 2048]
 
 configs = list(itertools.product(batch_size_range, seq_len_range))