[Perf] Use Triton instead of Torch for DeepGEMM Per Token Group Quant (#20841)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

tests/kernels/moe/test_deepgemm.py

@@ -13,9 +13,10 @@ import torch
 
 # vLLM fused-expert reference (Triton fallback + DeepGEMM option)
 from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    per_token_group_quant_fp8)
 from vllm.utils import has_deep_gemm
-from vllm.utils.deep_gemm import (calc_diff, per_block_cast_to_fp8,
-                                  per_token_group_cast_to_fp8)
+from vllm.utils.deep_gemm import calc_diff, per_block_cast_to_fp8
 
 BLOCK_SIZE = [128, 128]
 
@@ -81,7 +82,7 @@ def run_single_case(m, n, k, topk, num_experts, block_size):
     """
     tokens_bf16 = torch.randn(
         m, k, device="cuda", dtype=torch.bfloat16).clamp_min_(-1).clamp_max_(1)
-    _, a1_scale = per_token_group_cast_to_fp8(tokens_bf16, block_size[1])
+    _, a1_scale = per_token_group_quant_fp8(tokens_bf16, block_size[1])
 
     # expert weight tensors
     w1, w2, w1_s, w2_s = make_block_quant_fp8_weights(num_experts, n, k,