TheRock/rocm-libraries/projects/hipblaslt/utilities/QuickTune

hipBLASLt Offline Tuning Script

This tool aims to provide an easy-to-use script to perform GEMM tuning with hipblaslt-bench.

GEMM library hipBLASLt provides benchmark tool for hipBLASLt's supported operations. Please check the README for details.

The process of hipBLASLt GEMM tuning includes the followings

1. Extract hipBLASLt log
2. Pre-Tuning
3. GEMM Tuning
4. Apply Tuning Result

Quick Start

To run GEMM tuning with hipblaslt-bench on Qwen3-32B model.

nohup python gemm_tuning.py --input_file example/Qwen3-32B_hipblaslt.log --output_path test_tuning --requested_solution 128 > output.log 2>&1 &

The tuning generates some output files located at the output path "test_tuning".

    |testing_tuning
    |----> baseline_reproduce_commands.log       # logs that records commands to reproduce baseline result
    |----> tuning_reproduce_commands.log         # logs that records commands to reproduce tuning result
    |----> unique_Qwen3-32B_hipblaslt.log        # hipblaslt log without duplicate lines
    |----> tuning.txt                            # (optional) tuning result, if HIPBLASLT_TUNING_FILE is not defined
    |----> tuning_result.csv                     # includes info of each GEMM and the summary of both baseline and tuning data.

Extract hipBLASLt log

The hipBLASLt log consists of the hipblaslt-bench command lines for each call of hipBLASLt GEMM kernel. Follow the steps below to extract the hipBLASLt log.

1. Setup Environment Variables

   export HIPBLASLT_LOG_MASK=32
   export HIPBLASLT_LOG_FILE=<path/to/hipblaslt/log>
   

2. Run the model

   With the above environment variables, the hipBLASLt log will be automatically saved to the path of the export HIPBLASLT_LOG_MASK=32.

Pre-Tuning

Before start GEMM tuning, we need to do the following.

1. Setup Environment Variables

   export HIPBLASLT_LOG_MASK=32
   export HIP_VISIBLE_DEVICES=<gpu_id>
   export HIPBLASLT_TUNING_FILE=<path/to/tuning/result>
   

2. Remove duplicates in hipBLASLt log

   Since each hipBLASLt GEMM might be called many times when runing the model, the hipBLASLt log contains many duplicate lines. The parse_input_log() in remove_duplicate.py will remove the duplicate and count the occurance of each GEMM. The hipBLASLt log will be saved under the user-defined output path with name of "unique_<input_hipblaslt_log>".

   python remove_duplicate.py --input_file <input_log> --output_path <output_path>
   

3. Set-up stable GPU (Optional)

   A stable GPU frequency is critical for GEMM tuning. The following commands will set-up the GPU frequency relatively stable at 1900 MHz.

   # set-up GPU
   export HIP_FORCE_DEV_KERNARG=1
   sudo amd-smi set -g <gpu_id> --perf-determinism 1900
   
   # reset GPU
   unset HIP_FORCE_DEV_KERNARG
   sudo amd-smi reset -g <gpu_id> --perf-determinism
   

GEMM Tuning

The GEMM Tuning with hipblaslt-bench is to measure the performance of the candidate kernels within the searching space, and return the kernel with best latency. Runing gemm_tuning.py will start the GEMM tuning with hipblaslt-bench. The options of gemm_tuning.py are listed below.

1. input_file

   The input_file refers to the hipBLASLt log with the GEMMs to be tuned. The duplicate lines in the log will be removed when runing.

2. output_path

   The output_path refers to the directory that user defined to save the tuning result files.

3. swizzleA/B

   Enabling swizzleA/B will re-arrange the memory layout of matrix A/B to avoid bank conflict and boost the latency performance.

4. requested_solution

   The requested_solution define the size of searching space for GEMM tuning. The default value of requested_solution is 128. To measure the baseline latency of the original kernel, just set the request solution to be 1. To expand searching space to the entire kernel libarary, just set the requested solution to be -1.

5. cold_iters & iters

   The cold_iters refers to the warm-up iteration, and the iters refers to the runing iteration to measure kernel latency. The recommended value of cold_iters and iters will be (200, 100) for relative small GEMM, and (10, 2) for relative large GEMM. The default setting will be dynamic iteration, which will adjust the cold_iters and iters based on its size of mnk.

6. gpu_id

   The gpu_id determines which GPU you choose to run GEMM tuning. Recommand to use "amd-smi monitor" to find a idle GPU to run GEMM tuning for an accurate result.

7. stablize_gpu

   Enable stablize_gpu will provide a lower performance but more consistent GPU setting.

GEMM Tuning Analysis

The overall tuning speedup can be calculated by the individual kernel speedup and the time percentage of each kernel. We offer a script to help users to analyze the tuning result and calculate the overall speedup brought by GEMM tuning. The calculated overall speedup is only for hipBLASLt GEMMs, not the entire model.

python tuning_analysis.py --input_log <input hipblaslt log> --input_csv <tuning_csv> --output_csv <analysis csv file>

Apply Tuning Result

After GEMM tuning is done, the tuning result will be saved to the location that HIPBLASLT_TUNING_FILE pointing to. To apply the GEMM tuning result, you need to do the followings.

1. Set-up Environment Variables

   unset HIPBLASLT_TUNING_FILE
   export HIPBLASLT_LOG_MASK=32
   export HIP_VISIBLE_DEVICES=<gpu_id>
   export HIPBLASLT_TUNING_OVERRIDE_FILE=<path/to/tuning/result>
   

2. Run the Model

   Run the model again, the tuned GEMM will automatically pick the tuned kernel in the tuning result.