[Docs] Clean up README_TUNING.md (#28088)

Signed-off-by: windsonsea <[email protected]>

Author: windsonsea

vllm/lora/ops/triton_ops/README_TUNING.md

@@ -1,60 +1,60 @@
 # Multi-LoRA Tuning
 
-**Note**: The LoRA configuration folder should be specified by exporting `VLLM_TUNED_CONFIG_FOLDER=/path/to/configs`. Without this, the shrink/expand kernels will use default configurations.
+**Note**: The LoRA configuration folder should be specified by exporting `VLLM_TUNED_CONFIG_FOLDER=/path/to/configs`.
+Without this, the shrink/expand kernels will use default configurations.
 
 ## Tuning Process
 
-Multi-lora shrink/expand Triton kernel tuning follows a similar methodology from [Triton MoE tuning](https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_moe.py).
+Multi-lora shrink/expand Triton kernel tuning follows a similar methodology from
+[Triton MoE tuning](https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_moe.py).
 
-**Step 1**
-Define the searching space. An example searching space:
+1. Define the searching space. Here is an example of searching space:
 
-```python
-block_m_range = [16, 32, 64, 128, 256]
-block_n_range = [32, 64, 128, 256]
-block_k_range = [32, 64, 128, 256]
-num_warps_range = [4, 8]
-num_stage_range = [2, 3, 4, 5]
-num_ctas_range = [1]
-split_k_range = [4, 8, 16, 32, 64]
-```
+   ```python
+   block_m_range = [16, 32, 64, 128, 256]
+   block_n_range = [32, 64, 128, 256]
+   block_k_range = [32, 64, 128, 256]
+   num_warps_range = [4, 8]
+   num_stage_range = [2, 3, 4, 5]
+   num_ctas_range = [1]
+   split_k_range = [4, 8, 16, 32, 64]
+   ```
 
-**Step 2**
-Get all hidden_state sizes and num_slices that the target model uses for a specific TP size.
+2. Get all hidden_state sizes and num_slices that the target model uses for a specific TP size.
 
-For example, we can aquire those info by simply checking [add_lora_linear](https://github.com/li2haipeng/vllm/blob/multi_lora_v01011/vllm/lora/punica_wrapper/punica_gpu.py#L192):
+   For example, you can acquire the info by simply checking
+   [add_lora_linear](https://github.com/vllm-project/vllm/blob/main/vllm/lora/punica_wrapper/punica_gpu.py#L181):
 
-```python
-print(f"x_shape: {x.view(-1, x.shape[-1]).shape}")
-print(f"num_sclises: {len(output_slices)}")
-for i in range(len(output_slices)):
-    print(f"a{i} shape: {lora_a_stacked[i].shape}")
-    print(f"b{i} shape: {lora_b_stacked[i].shape}")
-print("y_shape", y.shape)
-```
+   ```python
+   print(f"x_shape: {x.view(-1, x.shape[-1]).shape}")
+   print(f"num_slices: {len(output_slices)}")
+   for i in range(len(output_slices)):
+       print(f"a{i} shape: {lora_a_stacked[i].shape}")
+       print(f"b{i} shape: {lora_b_stacked[i].shape}")
+   print("y_shape", y.shape)
+   ```
 
-**Step 3**
-Benchmark the shrink/expand kernel runtime with different kernel configurations generated from the pre-defined search space by performing a grid search to find the optimal kernel configuration. vLLM's [benchmark_lora.py](https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_lora.py) can be used to search for configurations for different shapes.
+3. Benchmark the shrink/expand kernel runtime with different kernel configurations generated from the pre-defined search space
+   by performing a grid search to find the optimal kernel configuration.
+   vLLM's [benchmark_lora.py](https://github.com/vllm-project/vllm/blob/main/benchmarks/kernels/benchmark_lora.py)
+   can be used to search for configurations for different shapes.
 
 ## Config Files
 
-### File Name
+### File Naming
 
-For `shrink`, the config file is named as `{gpu_name}_SHRINK.json`, e.g. `NVIDIA_H200_SHRINK.json`.
+| Kernel Type               | File Name Template                          | Example                                     |
+|---------------------------|--------------------------------------------|---------------------------------------------|
+| shrink                    | `{gpu_name}_SHRINK.json`                   | `NVIDIA_H200_SHRINK.json`                  |
+| expand                    | `{gpu_name}_EXPAND_{add_input}.json`       | `NVIDIA_H200_EXPAND_TRUE.json`             |
+| fused_moe_lora_w13_shrink | `{gpu_name}_FUSED_MOE_LORA_W13_SHRINK.json` | `NVIDIA_H200_FUSED_MOE_LORA_W13_SHRINK.json` |
+| fused_moe_lora_w13_expand | `{gpu_name}_FUSED_MOE_LORA_W13_EXPAND.json` | `NVIDIA_H200_FUSED_MOE_LORA_W13_EXPAND.json` |
+| fused_moe_lora_w2_shrink  | `{gpu_name}_FUSED_MOE_LORA_W2_SHRINK.json`  | `NVIDIA_H200_FUSED_MOE_LORA_W2_SHRINK.json` |
+| fused_moe_lora_w2_expand  | `{gpu_name}_FUSED_MOE_LORA_W2_EXPAND.json`  | `NVIDIA_H200_FUSED_MOE_LORA_W2_EXPAND.json` |
 
-For `expand`, the config fileis named as `{gpu_name}_EXPAND_{add_input}.json`, e.g. `NVIDIA_H200_EXPAND_TRUE.json`.
+The `gpu_name` can be automatically detected by calling `torch.cuda.get_device_name()`.
 
-For `fused_moe_lora_w13_shrink`, the config file is named as `{gpu_name}_FUSED_MOE_LORA_W13_SHRINK.json`, e.g. `NVIDIA_H200_FUSED_MOE_LORA_W13_SHRINK.json`.
+### JSON Structure
 
-For `fused_moe_lora_w13_expand`, the config file is named as `{gpu_name}_FUSED_MOE_LORA_W13_EXPAND.json`, e.g. `NVIDIA_H200_FUSED_MOE_LORA_W13_EXPAND.json`.
-
-For `fused_moe_lora_w2_shrink`, the config file is named as `{gpu_name}_FUSED_MOE_LORA_W2_SHRINK.json`, e.g. `NVIDIA_H200_FUSED_MOE_LORA_W2_SHRINK.json`.
-
-For `fused_moe_lora_w2_expand`, the config file is named as `{gpu_name}_FUSED_MOE_LORA_W2_EXPAND.json`, e.g. `NVIDIA_H200_FUSED_MOE_LORA_W2_EXPAND.json`.
-
-The `gpu_name` can be automatically detected by calling `torch.cuda.get_device_name()`
-
-### Json Structure
-
-Optimal kernel configuration files are saved as JSON files with the structure `config_data[max_loras][num_slices][m][k][n][i]`
+Optimal kernel configuration files are saved as JSON files with the structure `config_data[max_loras][num_slices][m][k][n][i]`,
 where `i` is an optional dimension in the `fused_moe_lora` configuration, representing the intermediate size of the MoE layer.