| name | kernel-perf-testing |
| description | Run TLX kernel performance benchmarks on Hopper, Blackwell, and AMD (gfx950/CDNA4, gfx1250) GPUs. Use when user asks to benchmark, profile, or measure performance of any TLX kernel (GEMM, Flash Attention, addmm+GLU, IKBO variants). Handles GPU selection, denoise wrapping (NVIDIA only), and version flags. Never run unless explicitly asked.
|
| disable-model-invocation | true |
Kernel Performance Testing
Never run performance tests unless the user explicitly asks.
Perf scripts live in third_party/tlx/tutorials/testing/test_<arch>_<op>_perf.py,
one per (op, input-contract, arch). Each takes [--version ...] to select a
kernel variant; with no --version it runs all variants for that op.
NVIDIA (Hopper / Blackwell)
GPU selection protocol
- Run
nvidia-smi to check GPU occupancy.
- Pick the GPU with the lowest memory usage.
- Set
CUDA_VISIBLE_DEVICES to that GPU.
Wrap benchmarks with denoise.sh (locks clocks/power) for stable results.
Hopper GPU
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_hopper_gemm_perf.py [--version {ws|pipelined}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_hopper_fa_perf.py [--version {ws|ws_pipelined|ws_pipelined_pingpong|ws_pipelined_pingpong_persistent}]
Blackwell GPU
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_blackwell_gemm_perf.py [--version {ws|clc|pipelined|2cta}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_blackwell_fa_perf.py [--version {ws|ws_persistent|ws_pipelined|ws_pipelined_persistent|clc}] [--mode fwd|bwd]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_blackwell_fa_mxfp8_perf.py
AMD (gfx950 / CDNA4, gfx1250)
denoise.sh works on AMD too: it runs the wrapped command with NUMA pinning. Its
GPU clock/power lock is implemented with nvidia-smi, so on AMD that part is
simply skipped (no clock lock) — expect a bit more run-to-run variance.
Pick a free GPU with rocm-smi (check GPU use (%) / VRAM). denoise.sh reads
CUDA_VISIBLE_DEVICES, which torch on ROCm also honors for device selection.
gfx950 (CDNA4 / MI350-class)
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_gemm_perf.py [--version {warp_pipeline|pipelined}] [--dtype fp16|bf16]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_fa_perf.py [--version {simple|prefetch|persistent}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_addmm_glu_perf.py [--version {tlx_baseline|tlx_simple_async|tlx_optimized_async|tlx_optimized|tlx_persistent}]
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_ikbo_fa_perf.py
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_ikbo_lce_perf.py
gfx1250
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/testing/test_amd_mxfp_gemm_perf.py [--version {tdm_pipelined}] [--transpose-b]
Each script self-gates (is_hip, is_hip_cdna4, or is_hip_gfx1250) and prints
"Skipping benchmarks" on the wrong hardware.
Other kernels
CUDA_VISIBLE_DEVICES=<gpu_id> third_party/tlx/denoise.sh python third_party/tlx/tutorials/<KERNEL.py>
If tests hang
Run third_party/tlx/killgpu.sh to kill GPU processes that have been running too long.
Interpreting results
- Output reports TFLOPS for each problem size and configuration.
- A reference baseline is printed alongside the Triton/TLX results: cuBLAS/rocBLAS
for GEMM, SDPA for FA, the PyTorch
pytorch_baseline for addmm+GLU.
- Higher TFLOPS = better. Look for regressions relative to previous runs.
- Check for consistency across runs — high variance suggests noisy measurements (ensure
denoise.sh is being used).
