hygon-hip-kernel-optimizer

name

hygon-hip-kernel-optimizer

description

Iteratively optimize Hygon DCU HIP / CK Tile kernels against a Python reference using hipprof, DTK tools, dccobjdump ISA verification, roofline-style budgeting, branch selection, ablation attribution, and gfx936/gfx938-aware optimization references. Use when the user asks to optimize HIP kernels, Hygon DCU kernels, gfx936/gfx938 kernels, CK Tile kernels, port CUDA kernel tuning workflows to DCU, validate DCU ISA patterns, or reason about DCU-specific inline assembly and source-backed HCU/AMDGPU builtins.

Hygon HIP Kernel Iterative Optimizer

What this skill does

Optimize a Hygon DCU HIP or CK Tile kernel against a Python reference by running a measured loop:

1. validate environment and baseline/reference contract,
2. profile the current best kernel with hipprof,
3. classify compute, memory, and latency gaps into an axis budget,
4. select optimization methods from the DCU method registry,
5. generate K branch kernels with different implementation parameters,
6. compile, validate, and benchmark branches,
7. profile the champion, ablate selected methods, and verify ISA with dccobjdump,
8. update state and emit a final summary.

Use deterministic scripts for environment checks, profiling, benchmarking, ablation, ISA checks, state updates, and summaries. Use agent reasoning only for method selection, code changes, and repair.

Key points

1. Roofline-style axis budget: allocate compute, memory, and latency method slots from measured DCU counters and timing.
2. Branch-and-select: generate several variants for the same method set, benchmark all valid branches, and keep the fastest champion.
3. Ablation attribution: keep a method only when removing it measurably hurts the champion.
4. DCU ISA verification: use dccobjdump patterns from references/dcu_isa_signatures.json; final proof is generated ISA, not source intent.
5. Source-backed builtin discipline: HCU or AMD-named builtins are candidates only when the exact call shape is backed by DCU KB source, a compile probe, or existing project code. __has_builtin failure alone is not enough to reject a source-backed builtin.
6. CK Tile first: prefer CK Tile for GEMM/conv/norm/MoE template work; do not port CUTLASS assumptions directly.
7. Deep search on ambiguity: for unclear hardware errors, unexplained performance regressions, or compiler/tool behavior that does not match expectation, search the local DCU knowledge base and source-backed reference projects before guessing. Web search is allowed when local references are insufficient.

Inputs

Have these before starting:

• baseline kernel file: .hip, .cu, .cpp, .cc, .cxx, or .py
• Python reference file exposing reference(**kwargs)
• dimension JSON such as {"N":1048576} or {"M":4096,"N":4096,"K":4096}

If the user only provides a reference file and shape, first use the sibling hygon-hip-baseline-generator skill to generate and correctness-validate kernel.hip plus the benchmark-compatible ref.py. Do not begin optimization iterations until that generated baseline passes preflight and benchmark correctness.

Optional:

• iterations, default 3
• --ncu-num, retained as the top-K DCU metric count for CUDA skill compatibility
• branches per iteration, default 4
• --ptr-size when benchmark allocation needs an explicit element count
• warmup and repeat counts for benchmark stability

If a required input is missing and cannot be inferred, ask once briefly.

Environment

Run DCU validation through the project remote workflow when the target DCU is remote. The current workflow enters the compute node directly after loading DTK/modules; it does not use Docker. Keep source edits local and sync them before remote execution.

hygon_tmp/ is only a temporary scratch area for ad-hoc probes, smoke-test cases, generated traces, pulled logs, and other validation artifacts. Do not make fixed filenames under hygon_tmp/ part of the skill contract, and do not treat any generated run there as a project source asset.

Required or expected tools:

• hipcc
• hipprof
• dccobjdump
• rocminfo
• rocm-smi
• Python 3.10+ with the project benchmark dependencies
• CK Tile headers for CK Tile kernels
• optional DTK analysis tools under /opt/dtk, including PMC/SQTT-related tools when available

Probe the environment first:

python <skill>/scripts/check_env.py --out ./env.json

The probe records gfx target, DTK tools, hipprof availability, dccobjdump, CK Tile include discovery, and degraded profiling flags. If counters are unavailable, continue with timing, source inspection, SQTT when useful, code-object resource analysis, and ISA evidence, but tell the user that profiling is degraded.

Fast path commands

Use orchestrate.py for normal runs:

python <skill>/scripts/orchestrate.py setup \
  --baseline ./kernel.hip \
  --ref ./ref.py \
  --iterations 2 \
  --branches 2 \
  --ptr-size 1048576 \
  --dims '{"N":1048576}'

python <skill>/scripts/orchestrate.py open-iter \
  --run-dir ./run_YYYYMMDD_HHMMSS \
  --iter 1

# Agent writes iterv1/methods.json, analysis.md, and branch kernels.

python <skill>/scripts/orchestrate.py close-iter \
  --run-dir ./run_YYYYMMDD_HHMMSS \
  --iter 1

python <skill>/scripts/orchestrate.py finalize \
  --run-dir ./run_YYYYMMDD_HHMMSS

setup runs environment/preflight/state initialization and seeds the baseline. open-iter profiles the current best and writes roofline.json. close-iter validates methods, explores branches, profiles the champion, ablates, runs ISA checks, and updates state. finalize writes summary.md.

Detailed loop

Step 0: preflight

Use:

python <skill>/scripts/preflight.py \
  --baseline ./kernel.hip \
  --ref ./ref.py \
  --dims '{"N":1048576}'

Surface contract failures directly. Do not begin optimization if the reference cannot run or the baseline cannot be compiled/benchmarked.

Step 1: initialize and seed baseline

Normal path:

python <skill>/scripts/orchestrate.py setup --baseline ... --ref ... --dims ...

Manual path:

python <skill>/scripts/state.py init --baseline ./kernel.hip --ref ./ref.py --dims '{"N":1048576}' --env ./env.json
python <skill>/scripts/run_iteration.py seed-baseline --state ./run_*/state.json

The run folder is run_YYYYMMDD_HHMMSS/ beside the baseline and contains state.json, copied baseline artifacts, and benchmark results.

Step 2: profile current best and budget methods

Use:

python <skill>/scripts/profile_hipprof.py \
  --state ./run_*/state.json \
  --iter 1 \
  --which best_input \
  --pmc-mode all

python <skill>/scripts/roofline.py \
  --state ./run_*/state.json \
  --iter 1

Read:

• iterv{i}/dcu_top.json
• iterv{i}/roofline.json
• state.json
• current best_file

If all gaps are near peak, stop early and summarize. Otherwise use the axis_budget to decide how many methods to select per axis.

If the optimizer has completed three consecutive iterations without a material additional improvement over the previous best, trigger an SQTT/tooling triage before selecting more source changes. "Material" normally means exceeding the configured noise threshold in state.json (default 2%) and being explainable by profiler/ISA evidence, not just a single noisy timing sample. The triage should:

1. run profile_hipprof.py on the current best or latest champion with --pmc-mode none --sqtt-type 1 --sqtt-output-type 0 --sqtt-data-dir <itervN>/sqtt_json/;
2. analyze artifacts with scripts/analyze_sqtt.py;
3. if perfetto is available locally or remotely, analyze representative thread_trace_*.json files with scripts/analyze_perfetto_trace.py;
4. use the SQTT/Perfetto evidence to decide whether the next methods should target waitcnt placement, issue stalls, branch divergence, LDS/bank behavior, cache/global memory pressure, or whether the kernel is already near the practical ceiling.

You may run SQTT earlier for ambiguous hardware errors or unexplained regressions, but do not make it a mandatory every-iteration cost.

Step 3: select methods

Read these references in order, loading only the needed parts:

1. references/optimization_catalog.md for method intent, triggers, skip rules, and combining rules.
2. references/method_registry.json for machine-validated method ids, axes, priorities, requirements, and expected ISA signatures.
3. references/dcu_metrics_guide.md for metric-to-cause mapping and hipprof/PMC interpretation.
4. references/dcu_isa_signatures.json for final dccobjdump pattern names.

Selection rule:

1. For each axis with positive budget, scan methods by priority.
2. Skip methods already tried unless the bottleneck has changed materially.
3. Skip methods blocked by target architecture, datatype, layout, or previous implementation failure.
4. Prefer methods with direct evidence in dcu_top.json, roofline.json, or source inspection.
5. Select exactly sum(axis_budget) methods unless no valid method exists; if fewer are available, explain every missing slot in analysis.md.
6. Keep selected methods mutually compatible. Avoid choosing two methods that are just the same pipeline or tiling change in different words.

Write:

• iterv{i}/methods.json matching templates/methods.schema.json
• iterv{i}/analysis.md following templates/iteration_report.md

Validate before generating branches:

python <skill>/scripts/validate_methods.py \
  --methods ./run_*/iterv1/methods.json \
  --state ./run_*/state.json

Step 4: generate branch kernels

Generate K branches under iterv{i}/branches/b1..bK/. All branches should implement the same selected method set, but vary implementation details:

• tile sizes and vector width,
• wave/block mapping,
• LDS layout and bank-conflict strategy,
• pipeline stage count,
• CK Tile policy names and template parameters,
• direct load/store path versus LDS staging,
• inline asm or builtin form when compiler output must be forced.

For GEMM/conv/norm/MoE-style kernels, prefer CK Tile strategies and known fast paths such as TLS, MLS, WASP, cshuffle, wavelet, persistent, split-k, preshuffle, and DS-read matrix variants when the operation shape fits.

Step 5: branch explore and repair

Use:

python <skill>/scripts/branch_explore.py \
  --state ./run_*/state.json \
  --iter 1

or let orchestrate.py close-iter run it. If all branches fail, inspect branch bench.json, bench.stderr.txt, compiler logs, and validation errors. Repair the branch sources and rerun. Do not mark a method ineffective when the branch never compiled or never passed correctness.

Step 6: profile champion, ablate, verify ISA

Use:

python <skill>/scripts/profile_hipprof.py \
  --state ./run_*/state.json \
  --iter 1 \
  --which kernel

python <skill>/scripts/ablate.py \
  --state ./run_*/state.json \
  --iter 1

python <skill>/scripts/sass_check.py \
  --state ./run_*/state.json \
  --iter 1

sass_check.py is named for CUDA compatibility, but on this skill it runs DCU ISA verification with dccobjdump and DCU signature patterns.

Step 7: update state and summarize

Use:

python <skill>/scripts/state.py update \
  --state ./run_*/state.json \
  --iter 1 \
  --kernel ./run_*/iterv1/kernel.hip \
  --bench ./run_*/iterv1/bench.json \
  --methods-json ./run_*/iterv1/methods.json \
  --attribution ./run_*/iterv1/attribution.json \
  --sass-check ./run_*/iterv1/isa_check.json

python <skill>/scripts/summarize.py \
  --state ./run_*/state.json \
  --out ./run_*/summary.md

State rules:

• Add every attempted method to selected_methods.
• Add a method to effective_methods only when attribution is positive beyond noise and expected ISA evidence is present.
• Add a method to ineffective_methods when ISA evidence is present but attribution is not positive.
• Add a method to implementation_failed_methods when the code compiled but expected ISA evidence is missing from a relevant dump.
• If a branch is faster but ISA evidence for a claimed method is missing, keep the faster kernel if correct, but record that method as implementation-failed.

Hygon-specific hard rules

• Treat wavefront size as 64. Recheck every CUDA warp-size assumption.
• Use CK Tile instead of CUTLASS for DCU template kernels.
• Use hipprof --pmc --pmc-type 3 for regular PMC-style data when available. Use SQTT/stat-stall tooling when PMC cannot explain stalls and the environment supports it.
• Use hipprof --pmc-read --pmc-type 3 and hipprof --pmc-write --pmc-type 3 in addition to regular --pmc when memory-read/write behavior matters. The bundled profiler defaults to --pmc-mode all and merges those CSVs into dcu_top.json.
• Use hipprof --codeobj-analyze <elf-or-so-file> after compilation to inspect VGPR/SGPR/LDS pressure. Treat high register pressure as a first-class signal for register control, occupancy, and latency decisions.
• Use SQTT for ambiguous stalls or instruction-flow questions, and automatically consider it after three consecutive no-material-improvement iterations: hipprof --sqtt --sqtt-type 1, stat_stall, stat_valu, or all depending on trace size. Prefer --sqtt-output-type 0 for JSON and --sqtt-data-dir <dir>/ when traces are large. Analyze generated thread_trace_*.json with scripts/analyze_sqtt.py; when the Python perfetto package is available, use scripts/analyze_perfetto_trace.py for PerfettoSQL summaries. Keep large temporary traces under hygon_tmp/ when they are diagnostic probes rather than run artifacts.
• SQTT export may require llvm-objdump in PATH because hipprof calls it internally while creating trace JSON. This is not a replacement for dccobjdump: the optimizer's DCU ISA verification and pattern checks must still use DTK dccobjdump.
• Use dccobjdump --inputs=<binary> --show-sass --show-instruction-encoding --separate-functions plus resource/symbol dumps when instruction, register, LDS, or occupancy evidence is needed.
• If dccobjdump fails or produces no instruction lines, scripts/sass_check.py falls back to compiling the kernel source with hipcc -save-temps=obj and reads the generated device .s files. Treat this as a recovery path for compiler-lowered ISA text, not as a replacement for a successful final code-object dump.

Builtin and asm verification workflow

When a method needs a builtin or inline asm:

1. Search DCU KB first for the exact gfx target, builtin name, call signature, and source example.
2. If uncertain, create or update a minimal probe under a task-specific scratch directory such as hygon_tmp/<probe-name>/.
3. Run the probe remotely with the target --offload-arch, using the actual probe path you just created, for example:

python3 <probe-path> --arch gfx938

4. Remove unsupported call forms from the method implementation or mark them unavailable for that target.
5. Verify the final optimized kernel with dccobjdump; compile success alone is not enough.

Use this hierarchy for evidence:

1. benchmark correctness and timing,
2. hipprof/PMC/SQTT bottleneck evidence,
3. source-backed compile probe for builtin or asm availability,
4. final dccobjdump ISA and resource evidence.

Ambiguous hardware or performance behavior

When the optimizer hits unclear DCU behavior, do not stop at generic GPU advice. Continue investigation in this order:

1. Search the local DCU KB for the exact gfx target, tool output, mnemonic, builtin, compiler diagnostic, profiler counter, or CK Tile path.
2. Inspect source-backed reference projects found by the KB and copy only patterns whose call signatures, target guards, layout contracts, and wait rules are visible in source.
3. If local evidence is insufficient, search the web for ROCm, AMD GPU, HIP, LLVM AMDGPU, or CK Tile references. Mark those findings as analogies until Hygon hipcc and dccobjdump confirm them.
4. Build a minimal compile or runtime probe under a task-specific scratch directory in repository-root hygon_tmp/. Keep probe inputs, source, logs, PMC read/write outputs, SQTT JSON/HTML/stat files, code-object analysis logs, dumps, and summaries there, but do not reference those scratch filenames as stable workflow entry points.
5. Feed confirmed findings back into the branch implementation, method notes, or reference files. Remove or quarantine unsupported assumptions.

Use this path for unclear errors, unexpected slowdowns, profiler/tool contradictions, unsupported intrinsic questions, codegen surprises, and suspected waitcnt/LDS/MMAC hazards.

Failure modes

• hipprof writes degraded or empty metrics: continue with timing and ISA evidence, but disclose degraded profiling.
• Hardware-specific errors or unclear performance regressions: search DCU KB and reference projects deeply, optionally use web sources as analogies, then create a minimal probe in hygon_tmp/ before changing the main kernel.
• dccobjdump cannot find a binary or relevant function: inspect compile artifacts and symbol names before declaring a method failed.
• Expected ISA pattern is absent from a relevant dump: mark the method implementation-failed, even if the branch is fast.
• All branches fail correctness or compilation: repair source and retry; do not update method attribution from failed branches.
• Champion speedup comes only from hyperparameters: record methods with low attribution as ineffective even if the kernel is faster.
• Builtin exists in AMD material but not DCU source/probe: do not use it as a DCU claim.
• __has_builtin reports missing for source-backed HCU builtins: treat that probe as inconclusive and compile the exact call shape instead.
• Remote DCU access is unavailable: prepare local scripts/probes under hygon_tmp/ and ask the user to run them remotely, then process returned logs.

References

Load references only when needed:

• references/optimization_catalog.md: human-readable optimization catalog, triggers, skip rules, combining guidance, and DCU-specific strategies.
• references/method_registry.json: structured method ids, priorities, requirements, signatures, and validation metadata.
• references/dcu_metrics_guide.md: hipprof/PMC/SQTT metrics, bottleneck interpretation, and tool usage notes.
• references/dcu_isa_signatures.json: dccobjdump pattern groups for DCU ISA verification.
• examples/walkthrough.md: full walkthrough for debugging or demonstrating the workflow.

Output contract

Each run creates:

<baseline-dir>/run_YYYYMMDD_HHMMSS/
|-- env.json
|-- state.json
|-- baseline/
|   |-- <baseline copy>
|   `-- bench.json
|-- iterv1/
|   |-- analysis.md
|   |-- methods.json
|   |-- dcu_top.json
|   |-- roofline.json
|   |-- best_input.hipprof.csv
|   |-- best_input.hipprof.log
|   |-- best_input.hipprof.codeobj_analyze.log
|   |-- best_input.hipprof.sqtt_analysis.json
|   |-- kernel.<ext>
|   |-- kernel.hipprof.csv
|   |-- kernel.hipprof.log
|   |-- kernel.hipprof.codeobj_analyze.log
|   |-- kernel.hipprof.sqtt_analysis.json
|   |-- bench.json
|   |-- attribution.json
|   |-- isa_check.json
|   `-- branches/
|       |-- b1/
|       |-- b2/
|       `-- ...
|-- iterv2/
`-- summary.md

The final user-facing answer should report best speedup, champion path, effective methods, implementation-failed methods, profiling/ISA caveats, and any remote validation that could not be run.