hyperloom-workload-optimizer

name

hyperloom-workload-optimizer

description

Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs and reports a validated gain, using the Hyperloom multi-agent optimizer. Given a model, framework, workload (TP/EP, concurrency, ISL/OSL, precision), an objective and a time budget, it explores per-workload which levers to pull (serving/config parameters and env, framework enablement and source patches, and hot GPU-kernel rewrites), benchmarks each candidate, and returns the optimization stack that produced the gain. Use when the user wants to make a model serve faster, raise tokens/sec or throughput, optimize or tune vLLM or SGLang on MI300X/MI325X/MI355X, run Hyperloom, run the kernel-agent, quantize-then-optimize with Quark, set up Hyperloom from scratch, or resume a Hyperloom session. Do not use to stand up a server for plain serving, diagnose a broken ROCm install, or run a one-off kernel/benchmark or trace analysis without the optimization loop.

Hyperloom Workload Optimizer

You are the catalog entry point for Hyperloom optimization on AMD Instinct GPUs. Bootstrap the workspace, prepare the runtime environment, collect workload parameters, then install, launch, and monitor the optimizer. This skill owns the orchestration and the launcher gates; environment prep and workload intake are delegated to the skills the Hyperloom wheel installs, and @${HYPERLOOM_SKILL_PATH} (inference_optimizer) is the execution baseline.

Do not manually optimize inside chat unless debugging.

Prerequisites

• AMD Instinct GPU host (MI300X / MI325X / MI355X) with ROCm
• /dev/kfd and /dev/dri present; amd-smi or rocm-smi works
• Python 3.10+ and network access to install the Hyperloom wheel
• Anthropic (or compatible) LLM credentials for agent backends
• A dedicated agent workspace directory

Every command in this skill runs on that GPU host. Confirm the shell you are in is on it before Phase 0, so a bootstrap does not land on a machine with no GPU.

The Hyperloom runtime ships via pip install of the published wheel.

What Hyperloom runs

The CLI starts a Python Coordinator that coordinates:

• Orchestration — baseline, explore, specialist, integrate_patch, sweep
• Kernel — trace_analyze, run_optimization, integrate
• Critic — proposal review (default --critic-agent)
• Robustness — health monitoring and RCA (default --robustness-agent)

State lives under a session directory per run; run-state root is $USER_DATA_PATH (default /workspace/hyperloom), independent of the install directory (INSTALL_DIR, where the wheel and .env live) and may point to shared storage. Layout: $USER_DATA_PATH/runtime/ (install.sh outputs, kernel-agent.env.sh), logs/, and <model_basename>/<UTC_ts>/ per session holding manifest.json, state.json, runs/, reports/, optimizer_runs/.

Workflow overview

Match hyperloom-custom-advanced section order — do not ask workload questions while writing .env or during /hyperloom-setup.

• Phase 0 Bootstrap — pip install, /hyperloom-setup → .env (credentials + run mode only)
• Phase 1 Environment — custom-advanced §Setup Configuration (baremetal: confirm host; docker: start container + setup inside, contract in setup.md)
• Phase 2 Workload intake — custom-advanced §Advanced Configuration → Model Resolution → show launch plan → user confirms
• Phase 3 Execute — install.sh → preflight → launch → monitor → report

Load hyperloom-custom-advanced at Phase 1 and follow its sections in order (discovery: .cursor/ / .claude/ / .agents/skills/hyperloom-custom-advanced/SKILL.md). If it is not on disk, stop and tell the user to restart the agent so the newly installed skills are picked up — do not improvise the environment or workload sections from memory, since the wheel is the source of truth for both. For deeper optimizer behavior read @${HYPERLOOM_SKILL_PATH} (inference_optimizer); Iron Rules + CLI reference: reference.md.

Iron Rules (launcher gates)

Run order is always IR-2 → IR-1 → launch. Full text in reference.md.

• IR-1 — GPU unoccupied. Before every optimize (fresh or --resume), every visible GPU must have zero foreign serving PIDs (sglang.launch_server / vllm.entrypoints / Magpie) and ≲ 500 MiB VRAM in use.
• IR-2 — install.sh before launch. Run install.sh and source kernel-agent.env.sh in the same shell that spawns optimize.
• Resume carve-out: --resume may skip install only when install.sh exited 0 earlier in the same shell, kernel-agent.env.sh is still sourced, and the session's manifest.json exists. Any failure → re-run install.sh.

Phase discipline (do not skip)

One phase at a time. Each phase asks only its own questions, waits for the user's answers, completes its exit condition, then moves on. Never batch questions from different phases into one prompt. In particular, never ask workload questions (model, framework, TP/EP, precision, ISL/OSL, hours…) during Phase 0 or Phase 1 — those belong to Phase 2 only.

Phase 0 — Bootstrap

Skip completed steps (idempotent). Ask only about the install directory and credentials/run mode here. Do not ask about the model or workload yet.

Confirm the install directory

The wheel installs into a target directory with pip install --target <dir>, which also holds .env and runtime artifacts. Do not silently use the current directory. Show the resolved current directory (pwd) and confirm it with the user, or let them choose another dedicated path. Wait for the answer, then cd into the chosen directory before installing.

Install the Hyperloom wheel

Skip when hyperloom/ (wheel) or src/hyperloom/ (source) already exists in the confirmed directory.

The runtime is published to PyPI as hyperloom-inference-optimizer. List the releases, tell the user the newest one, and ask whether to install it or a version they name.

List with --pre so prereleases are visible, and install an exact == version so a later bootstrap installs the same runtime.

cd "$INSTALL_DIR"   # the directory confirmed above
pip index versions hyperloom-inference-optimizer --pre
pip install hyperloom-inference-optimizer==<version the user approved> --target .

Confirm hyperloom/inference_optimizer/assets/install.sh exists. Restart the agent if wheel skills are not visible.

Credentials and run mode

Run /hyperloom-setup (installed to .cursor/skills/hyperloom-setup/). It writes .env, sets USER_DATA_PATH, HYPERLOOM_RUN_MODE, and HYPERLOOM_SKILL_PATH, and on bare metal runs install_baremetal.sh.

Phase 0 is done when all hold:

• hyperloom/inference_optimizer/assets/install.sh exists
• .env exists with non-placeholder LLM secrets
• USER_DATA_PATH, HYPERLOOM_RUN_MODE, and HYPERLOOM_SKILL_PATH are set

More bootstrap detail: setup.md.

Phase 1 — Environment prep

Load hyperloom-custom-advanced and follow its Setup Configuration section only.

Baremetal (HYPERLOOM_RUN_MODE=baremetal): confirm install_baremetal.sh finished and the serving framework from setup is importable. Do not ask workload questions yet.

Docker (HYPERLOOM_RUN_MODE=docker): image choice, docker run, and the in-container setup are owned entirely by custom-advanced Setup Configuration — follow it, do not restate its commands or flags here. Do not ask workload questions until the container is up and in-container setup succeeded, and never run optimize on the host.

Phase 1 is done when the target environment (host or container) is ready.

Phase 2 — Workload intake

Enter only after Phase 0 and Phase 1 exit conditions hold. This is the first and only phase that asks workload questions.

Now follow custom-advanced Advanced Configuration, Default Values, and Model Resolution. Use the agent's structured question UI when available. Never copy API keys into chat output.

Field	CLI flag	Default	Notes
Model path	--model	required	Local dir with config.json, or HF cache
Framework	--framework	sglang	or vllm; prefer .env FRAMEWORK when set
TP / EP	--tp / --ep	1 / 1	tensor / expert parallel
CONC	--conc	64	client concurrency
ISL / OSL	--isl / --osl	1024 / 1024	input / output seq lengths
PRECISION	--precision	bf16	match checkpoint; fp8 for FP8 models
MAX_HOURS	--max-hours	CLI 2.0	offer 3 (quick) or 12 (full); see below
TARGET_GAIN	--target-gain	30	desired % gain

Optional: --no-explore, --no-enable-conc-sweep, --gpu-type, --server-args, --compare-against-gpu, --quantize prelude.

Infer PRECISION from the model name when obvious (e.g. an FP8 model implies --precision fp8) and confirm it — do not silently keep the bf16 default.

Budget and flags — offer these three

Offer all three and let the user pick one. The flags in each are a set: pass them together, and do not ask for a budget and then ask separately which phases to run. The two demos take the workload and flags of the Hyperloom demo skill of the same budget — treat those as given and skip the table above. The user may name their own model instead of the demo's; for the 3-hour demo keep it at 8B or below. Confirm everything in the launch plan. Only Custom collects workload answers.

1. 3-hour demo (hyperloom-qwen3-8b-3h) — Qwen/Qwen3-8B unless the user names another 8B-or-smaller model, TP=1, CONC=64, ISL=OSL=1024, --precision bf16, serving and config parameters only, no kernel rewrites. Resolve the model per custom-advanced Model Resolution; download it from Hugging Face when it is not already local. Match --precision to the chosen checkpoint. Expect a modest validated gain, or an honest 0% when the workload has no parameter headroom.

--max-hours 3 --precision bf16
--no-framework-agent --no-kernel --no-enable-conc-sweep --no-enable-roofline
--max-minutes-explore-pct 0.39 --max-minutes-sweep-pct 0.01
--explore-force-exit-budget-pct 0.01 --explore-force-exit-hours-remaining 0.05

2. 12-hour demo (hyperloom-qwen3-14b-fp8-12h) — Qwen/Qwen3-14B-FP8 unless the user names another model, TP=1, CONC=64, ISL=OSL=1024, --precision fp8 matched to the chosen checkpoint, every lever with kernel rewrites included. The kernel agent needs room to profile, rewrite and revalidate, which is where the larger gains come from.

--max-hours 12 --precision fp8
--max-minutes-framework-pct 0.01 --max-minutes-explore-pct 0.42
--max-minutes-kernel-pct 0.42

3. Custom — the user brings their own model or workload instead of taking a demo. Walk through the fields in the table above and the phase toggles, one question at a time, and derive the flags from the answers rather than asking for flags. Whichever levers they pick, a budget of 3 hours or less keeps the 3-hour demo's flag set. Optional flags come from the list above; show the full flag list in the launch plan either way.

Confirmation gate (required before Phase 3)

The Coordinator has no in-loop setup / classify — a value not asked here is silently lost to its default. Before running any Phase 3 command, present the full launch plan (including defaulted fields) and get explicit user confirmation.

Print the plan in the reply body as this aligned block:

Launch plan — please confirm:
  MODEL_PATH    /wekafs/models/Qwen3-14B-FP8
  FRAMEWORK     vllm
  TP=1  EP=1  CONC=64
  ISL=1024  OSL=1024
  PRECISION=fp8
  MAX_HOURS=3     TARGET_GAIN=20%
  profile       3-hour demo — no kernel, no framework agent, no roofline
  flags         --no-framework-agent --no-kernel --no-enable-conc-sweep
                --no-enable-roofline
                --max-minutes-explore-pct 0.39 --max-minutes-sweep-pct 0.01
                --explore-force-exit-budget-pct 0.01
                --explore-force-exit-hours-remaining 0.05
  RUN_MODE      baremetal

Never put the plan inside the confirmation prompt itself. A prompt renders as one wrapped paragraph, which collapses the alignment above into an unreadable blob the user has to search for MAX_HOURS in. Keep the prompt to a single short question such as Approve this launch plan?, and if you offer a "change something" option, name the field to change rather than making the user retype it as free text.

Do not run install.sh or launch optimize until the user approves this plan.

Persist the plan (required — shells do not share exports)

Agent shells do not persist exports between calls, so write the confirmed values to $RUN_DIR/workload.env right after approval. Every Phase 3 block sources it; without this, launch silently falls back to ${TP:-1} / ${CONC:-64} defaults and --model "". Fill each value from the approved plan.

export USER_DATA_PATH="${USER_DATA_PATH:?run /hyperloom-setup first}"
export RUN_DIR="${USER_DATA_PATH}/optimizer_runs"
mkdir -p "$RUN_DIR"
# Quoted heredoc (<<'EOF'): values are written literally, so a MODEL_PATH with
# spaces, $, or $(...) is not expanded or executed. Edit each value to the plan.
cat > "$RUN_DIR/workload.env" <<'EOF'
export MODEL_PATH=/wekafs/models/Qwen3-14B-FP8
export FRAMEWORK=vllm
export TP=1
export EP=1
export CONC=64
export ISL=1024
export OSL=1024
export PRECISION=fp8
export MAX_HOURS=3
export TARGET_GAIN=20
# The whole flag set for the approved profile, space-separated. The 3-hour
# demo is shown; a 12-hour run swaps in its own set.
export OPT_FLAGS="--no-framework-agent --no-kernel --no-enable-conc-sweep --no-enable-roofline --max-minutes-explore-pct 0.39 --max-minutes-sweep-pct 0.01 --explore-force-exit-budget-pct 0.01 --explore-force-exit-hours-remaining 0.05"
EOF

Phase 3 — Install (IR-2)

INSTALL_DIR is the directory confirmed in Phase 0, the one holding hyperloom/ and .env. Every Phase 3 block below rebuilds it from the current directory, so run them from there; the check refuses a directory that is not it.

Resolve paths for wheel or source layout:

export INSTALL_DIR="$(pwd -P)"
[ -d "${INSTALL_DIR}/hyperloom" ] || [ -d "${INSTALL_DIR}/src/hyperloom" ] || {
  echo "ERROR: ${INSTALL_DIR} holds no hyperloom/ -- cd to the Phase 0 install directory" >&2; exit 1; }
set -a; . "${INSTALL_DIR}/.env"; set +a
export USER_DATA_PATH="${USER_DATA_PATH:?USER_DATA_PATH missing}"
. "${USER_DATA_PATH}/optimizer_runs/workload.env"   # confirmed Phase 2 values
export PYTHONPATH="${INSTALL_DIR}:${PYTHONPATH:-}"
ulimit -Sn 65536 || true

INSTALL_SH="${INSTALL_DIR}/hyperloom/inference_optimizer/assets/install.sh"
[ -f "$INSTALL_SH" ] || INSTALL_SH="${INSTALL_DIR}/src/hyperloom/inference_optimizer/assets/install.sh"

bash "$INSTALL_SH"
. "${KERNEL_AGENT_ENV:-${USER_DATA_PATH}/runtime/kernel-agent.env.sh}"
export PYTHONPATH="${INSTALL_DIR}:${PYTHONPATH:-}"

In Docker mode, run this inside the container.

Phase 3 — Preflight (IR-1)

install.sh exports $PYTHON; the fallback below covers agent sandboxes that do not persist exports between shell calls.

export SKILL_DIR="${SKILL_DIR:?absolute path of the directory holding this SKILL.md}"
. "${USER_DATA_PATH}/optimizer_runs/workload.env"   # confirmed Phase 2 values
export PYTHON="${PYTHON:-$(command -v python3)}"
"$PYTHON" "${SKILL_DIR}/scripts/preflight.py"

The gate exits non-zero — do not launch — when MODEL_PATH is missing or has no config.json, torch sees no GPU, a foreign serving process still holds a card, or any GPU holds more than IR1_VRAM_LIMIT_MIB (default 500) MiB.

It also blocks when VRAM cannot be read at all: no amd-smi/rocm-smi on PATH, a probe that exits non-zero, or output it cannot parse. An unreadable probe cannot rule out a busy GPU, and a foreign process holding VRAM under a different name would slip through. Confirm the GPUs are idle by hand before re-running with IR1_ALLOW_UNVERIFIED_VRAM=1.

Never print API keys or tokens. scripts/tests/test_preflight.py covers the probe shapes this gate must reject.

Phase 3 — Launch

After IR-2 and IR-1 pass, launch. setsid nohup is required for runs longer than 5 minutes, so the run outlives the agent shell.

export INSTALL_DIR="$(pwd -P)"
export SKILL_DIR="${SKILL_DIR:?absolute path of the directory holding this SKILL.md}"
bash "${SKILL_DIR}/scripts/launch.sh"

Every workload value comes from the confirmed workload.env; the script has no ${VAR:-default} fallbacks, so a missing value fails loudly instead of launching a different config. Put any optional Phase 2 flags (--no-kernel, --no-explore, --gpu-type, --model-class, --server-args, --compare-against-gpu, --quantize, phase budget flags) into OPT_FLAGS in workload.env. OPT_FLAGS is word-split, so quote any flag value that contains spaces, e.g. export OPT_FLAGS='--server-args "--foo bar"'.

Launch health check (30 s after start)

Required after every launch and resume. The PID recorded at launch is the setsid wrapper, which exits immediately — it is NOT the optimizer. This reads the real .pid and .session_dir from the launch-info JSON, rewrites the PID file so the monitor watches the right process, and records both in $RUN_DIR/last_launch.env for the later phases.

export INSTALL_DIR="$(pwd -P)"
export SKILL_DIR="${SKILL_DIR:?absolute path of the directory holding this SKILL.md}"
bash "${SKILL_DIR}/scripts/launch_health.sh"

It exits non-zero when the launch-info JSON never appeared, no optimizer process can be found, or session_dir is still unset — inspect the reported run log in those cases. Never guess session_dir from a timestamp; concurrent sessions share USER_DATA_PATH.

Phase 3 — Monitor

Poll at most every 5 minutes unless debugging a startup failure. Use the state reader the wheel ships rather than parsing state.json by hand — it also prints the recent lifecycle events.

export INSTALL_DIR="$(pwd -P)"
. "${USER_DATA_PATH}/optimizer_runs/last_launch.env"   # SESSION_DIR from launch
STATE_TOOL="${INSTALL_DIR}/hyperloom/inference_optimizer/tools/read_optimizer_state.py"
[ -f "$STATE_TOOL" ] || STATE_TOOL="${INSTALL_DIR}/src/hyperloom/inference_optimizer/tools/read_optimizer_state.py"
"${PYTHON:-python3}" "$STATE_TOOL" "$SESSION_DIR"

For recent action counts grouped by category, the wheel also ships tools/event_counts.py, invoked the same way.

Report session id + log path, baseline_tput / current_best / cumulative_gain, explore accepted/rejected, last kernel opt (correctness, speedup, KEEP/REVERT), and process-alive vs stop_reason. See reference.md Report fields.

Resume

Resume runs in a fresh shell. Re-run the IR-2 and IR-1 gates first, exactly as for a fresh launch — the script does not re-check them.

export INSTALL_DIR="$(pwd -P)"
export SKILL_DIR="${SKILL_DIR:?absolute path of the directory holding this SKILL.md}"
bash "${SKILL_DIR}/scripts/resume.sh"
bash "${SKILL_DIR}/scripts/launch_health.sh"

It resumes the session recorded in last_launch.env and always passes --resume-from explicitly, because a bare --resume auto-picks the newest session and can target the wrong run. Resume writes its own log (run_resume-*.log) so the original run log is preserved. Reuse the IR-2 carve-out rules; re-run install.sh if the shell or env changed.

stop_reason	Action
time_exhausted	--resume same session
no_more_leverage	stop; resume only if user changes strategy
policy_loop	inspect policy_denial_history; clear stale prunes

Expected optimizer flow

1. Establish baseline_tput.
2. Coordinator runs roofline/profile analysis after baseline.
3. explore tests serving parameters incrementally.
4. Kernel-agent runs on hot paths with compile + correctness evidence.
5. sweep validates concurrency around the best candidate.
6. Final report under $SESSION_DIR/reports/.

When to defer

• Plain serving only — use serving-llms-on-instinct.
• ROCm driver broken — diagnose the ROCm stack first (e.g. a rocm-doctor skill if published); do not start the optimizer on a broken driver.
• Edge cases — read @${HYPERLOOM_SKILL_PATH} for multi-node, atom framework (IR-8), critic/robustness backends, cache topology, and the full failure matrix.

Further reading

• Bootstrap detail: setup.md
• Iron Rules + CLI reference: reference.md
• Authoritative runtime skill: hyperloom/inference_optimizer/SKILL.md