TheRock/rocm-libraries/projects/composablekernel/dispatcher/examples/grouped_conv/python

Grouped Convolution — Python Examples

Examples and test harnesses for the grouped convolution dispatcher (forward, bwd_data, bwd_weight) using the Python JIT codegen + hipcc workflow.

Run scripts from this directory:

cd dispatcher/examples/grouped_conv/python
python3 -u <script.py>          # use -u for unbuffered logs

GPU arch is auto-detected (detect_gpu_arch()); pass --arch gfx950 to override.

Examples

Script	Purpose
01_basic_grouped_conv.py	End-to-end smoke test: build + run forward kernel, verify output.
02_forward.py	Forward variant (NHWGC / GKYXC), small 2D problem.
03_bwd_data.py	Backward-data variant. Runner contract: run(dY, W, prob).
04_bwd_weight.py	Backward-weight variant. Runner contract: run(X, dY, prob).
05_benchmark.py	Multi-kernel sweep + timing (slow; runs many configs).
06_registry_json.py	Build a registry from a JSON config file.
09_ml_heuristic.py	Demo of LightGBM heuristic (requires lightgbm); see ML heuristic below.
10_test_all_pipelines.py	For each variant, test all 8 pipelines with intrawave.
11_test_schedulers.py	For each variant, test all 8 pipelines × {intrawave, interwave}.
12_test_config_options.py	Test the 5 config options (see Config-options harness below).

Runner argument contract

runner.run(input_np, weight_np, prob) — order matters per variant:

Variant	input_np	weight_np
forward	X (NHWGC)	W (GKYXC)
bwd_data	dY	W
bwd_weight	X	dY

Pipelines & schedulers

All 8 pipelines: basic_v1, mem, compv3, compv4, compv5, compv6, comp_async, basic_async_v1.

• compv4 and comp_async require double_smem_buffer=True (loud static_assert otherwise).
• Not every pipeline supports both intrawave and interwave. 11_test_schedulers.py treats a pipeline as supported if at least one scheduler runs successfully.

Config-options harness (12_test_config_options.py)

Verifies the 5 GroupedConvKernelConfig options:

1. double_smem_buffer — LDS ping-pong (required for compv4 / comp_async).
2. num_groups_to_merge — fuse groups into one tile.
3. split_image — split spatial dims for large tensors.
4. explicit_gemm — explicit GEMM path (experimental).
5. two_stage — two-stage bwd_weight with fp32 workspace.

Each test is run in its own subprocess (--single-test '<json>' mode) so a single GPU page fault doesn’t take down the whole sweep — failing combinations are reported as CRASH and the run continues.

Test problem sizes are kept small (e.g. 2D: N=1, G=2, C=K=64, Hi=Wi=8, 3×3) to avoid OOM / aperture violations on the test GPU.

ML heuristic (09_ml_heuristic.py)

LightGBM regression model that predicts kernel TFLOPS and selects a kernel for a given problem. Requires the lightgbm Python package.

• Models live in dispatcher/heuristics/models/grouped_conv_<variant>_bf16_<arch>/ (forward, bwd_data, bwd_weight all available).
• Feature engine: dispatcher/heuristics/feature_engine_grouped_conv.py.
• Training entry point: dispatcher/heuristics/train.py.
• Prediction: dispatcher/heuristics/predict.py (use Predictor with GroupedConvFeatureEngine; build the candidate kernel pool from a training/holdout parquet via df["kernel_name"].unique()).

Typical training flow:

# 1. Benchmark to CSV (slow)
cd tile_engine/ops/grouped_conv
python3 -u grouped_conv_full_benchmark.py configs/forward_bf16.json \
  --arch gfx950 --problems forward_training \
  --csv benchmark_forward_bf16_gfx950.csv --workers 8

# 2. CSV → Parquet
cd ../../../dispatcher/heuristics
python3 convert_csv_to_parquet.py \
  --input ../../tile_engine/ops/grouped_conv/benchmark_forward_bf16_gfx950.csv \
  --output data/grouped_conv_forward_bf16_gfx950.parquet --arch gfx950

# 3. Train
python3 train.py --data_dir data \
  --out_dir models/grouped_conv_forward_bf16_gfx950 \
  --op grouped_conv --dtype bf16 --arch gfx950 --targets tflops --n_splits 5

To add a new pipeline (e.g. compv6) update: dispatcher/codegen/grouped_config_rules.py (VARIANT_PIPELINES), dispatcher/heuristics/feature_engine_grouped_conv.py (add the is_<name> flag), and the relevant tile_engine/ops/grouped_conv/configs/*.json. Then re-run the benchmark + train flow above.

Notes

• Use python3 -u for any long-running script so logs aren’t buffered.
• Kernels are compiled once and cached under /tmp/dispatcher/; subsequent runs reuse the cached .so.
• This repo has 1 GPU — do not run benchmarks in parallel.