Add triton version as a fused_moe_triton config search key to avoid performace decrease in different Triton version (#5955)

python/sglang/srt/layers/moe/fused_moe_triton/configs/README

@@ -3,6 +3,9 @@ For different settings of
 - E (number of experts)
 - N (intermediate size)
 - device_name (torch.cuda.get_device_name())
+- dtype: The data type used by the fused MoE kernel for computation. Supported types include fp8_w8a8, int8_w8a8, int8_w8a16, int4_w4a16, etc. This determines the precision and quantization scheme for both weights and activations.
+- block_shape: The block quantization shape introduced starting from DeepSeek V3/R1 models. This parameter defines the granularity for block-wise quantization, typically specified as `[block_n, block_k]` where `block_n` and `block_k` represent the block dimensions. For example, DeepSeek V3 commonly uses `[128, 128]` block shapes for efficient block-wise FP8 quantization.
+
 the JSON file contains a mapping from M (batch size) to the chosen configuration.
 
 The example configurations provided are for the Mixtral model for TP2 on H100