Address review comments (benchmark logs, ids, and design comments)

Signed-off-by: Yufeng Zhu <yzhu802@gatech.edu>
Signed-off-by: princepride <wangzhipeng628@gmail.com>

docs/user_guide/diffusion/mot_config.md

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+This directory contains auto-tuned Triton kernel configurations for the
+MoT (Mixture-of-Tokens) GEMM and RMSNorm operators used by BAGEL and other
+MoT-architecture diffusion models.
+
+File naming convention:
+    device_name=<GPU_NAME>,dtype=<DTYPE>.json
+
+For example:
+    device_name=NVIDIA_A100-SXM4-80GB,dtype=w16a16.json
+
+Each JSON file maps (K, N) matrix shapes to a dictionary of batch sizes (M)
+and their optimal Triton tile configurations:
+
+    {
+        "3584_4608": {              // K=3584, N=4608 (QKV projection)
+            "1024": {
+                "BLOCK_SIZE_M": 64,
+                "BLOCK_SIZE_N": 128,
+                "BLOCK_SIZE_K": 64,
+                "GROUP_SIZE_M": 8,
+                "num_warps": 4,
+                "num_stages": 3
+            },
+            ...
+        },
+        "3584_3584": { ... }        // K=3584, N=3584 (output projection)
+    }
+
+Config loading order (3-tier, see ops/mot_gemm.py):
+    1. $VLLM_TUNED_CONFIG_FOLDER/<filename>  (env override)
+    2. This directory: vllm_omni/diffusion/layers/mot/configs/<filename>
+    3. Conservative default config (compiles everywhere, sub-optimal perf)
+
+If no config file matches the current device, a warning is printed with
+instructions to run the auto-tuning benchmark.
+
+To generate configs for your hardware:
+
+    python benchmarks/kernels/mot_linear_benchmarks.py \
+        --model ByteDance-Seed/BAGEL-7B-MoT \
+        --tp-size 1 --dtype w16a16 --tune \
+        --save-dir vllm_omni/diffusion/layers/mot/configs/
+
+For multi-GPU tuning (uses Ray for parallel search):
+
+    python benchmarks/kernels/mot_linear_benchmarks.py \
+        --model ByteDance-Seed/BAGEL-7B-MoT \
+        --tp-size 2 --tune
+
+See benchmarks/kernels/mot_linear_benchmarks.py for full options.