From 74c95e5817babe1f2906a7a4ed88f0e23c67d4da Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Sun, 27 Sep 2026 20:08:06 +0800 Subject: [PATCH 01/75] Add Cua-S1 multimodal CUDA worker and parity recipe --- .github/workflows/cua-s1-multimodal.yml | 30 + .gitignore | 7 + README.md | 8 +- recipe/cua_s1/README.md | 145 +++++ recipe/cua_s1/check_frontend.py | 62 ++ recipe/cua_s1/download_weights.py | 33 ++ recipe/cua_s1/evaluate_multimodal.py | 321 +++++++++++ recipe/cua_s1/experiments/README.md | 111 ++++ .../cua_s1/experiments/rtx4090/benchmark.json | 409 +++++++++++++ .../cua_s1/experiments/rtx4090/candidate.json | 536 ++++++++++++++++++ .../cua_s1/experiments/rtx4090/frontend.json | 79 +++ .../cua_s1/experiments/rtx4090/packages.txt | 62 ++ .../cua_s1/experiments/rtx4090/reference.json | 527 +++++++++++++++++ recipe/cua_s1/make_example.py | 71 +++ recipe/cua_s1/requirements-multimodal.txt | 11 + .../cua_s1/multimodal/THIRD_PARTY_NOTICES.md | 21 + src/models/cua_s1/multimodal/model.py | 164 ++++++ src/models/cua_s1/multimodal/protocol.py | 217 +++++++ src/models/cua_s1/multimodal/server.py | 128 +++++ .../cua_s1/multimodal/weights.lock.json | 102 ++++ tests/cua_s1/test_evaluation.py | 48 ++ tests/cua_s1/test_model.py | 112 ++++ tests/cua_s1/test_protocol.py | 156 +++++ tests/cua_s1/test_server.py | 85 +++ 24 files changed, 3442 insertions(+), 3 deletions(-) create mode 100644 .github/workflows/cua-s1-multimodal.yml create mode 100644 recipe/cua_s1/README.md create mode 100644 recipe/cua_s1/check_frontend.py create mode 100644 recipe/cua_s1/download_weights.py create mode 100644 recipe/cua_s1/evaluate_multimodal.py create mode 100644 recipe/cua_s1/experiments/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090/benchmark.json create mode 100644 recipe/cua_s1/experiments/rtx4090/candidate.json create mode 100644 recipe/cua_s1/experiments/rtx4090/frontend.json create mode 100644 recipe/cua_s1/experiments/rtx4090/packages.txt create mode 100644 recipe/cua_s1/experiments/rtx4090/reference.json create mode 100644 recipe/cua_s1/make_example.py create mode 100644 recipe/cua_s1/requirements-multimodal.txt create mode 100644 src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md create mode 100644 src/models/cua_s1/multimodal/model.py create mode 100644 src/models/cua_s1/multimodal/protocol.py create mode 100644 src/models/cua_s1/multimodal/server.py create mode 100644 src/models/cua_s1/multimodal/weights.lock.json create mode 100644 tests/cua_s1/test_evaluation.py create mode 100644 tests/cua_s1/test_model.py create mode 100644 tests/cua_s1/test_protocol.py create mode 100644 tests/cua_s1/test_server.py diff --git a/.github/workflows/cua-s1-multimodal.yml b/.github/workflows/cua-s1-multimodal.yml new file mode 100644 index 0000000..9329244 --- /dev/null +++ b/.github/workflows/cua-s1-multimodal.yml @@ -0,0 +1,30 @@ +name: Cua-S1 multimodal CPU checks +on: + pull_request: + paths: + - 'src/models/cua_s1/multimodal/**' + - 'tests/cua_s1/**' + - 'recipe/cua_s1/**' + - '.github/workflows/cua-s1-multimodal.yml' + push: + branches: [main] + paths: + - 'src/models/cua_s1/multimodal/**' + - 'tests/cua_s1/**' + - 'recipe/cua_s1/**' + - '.github/workflows/cua-s1-multimodal.yml' +permissions: + contents: read +jobs: + cpu: + runs-on: ubuntu-latest + timeout-minutes: 10 + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + - run: python -m pip install Pillow==11.3.0 pytest==9.1.1 ruff==0.16.8 + - run: PYTHONPATH=src python -m pytest tests/cua_s1 -q + - run: ruff check --isolated --select E4,E7,E9,F,I src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 + - run: ruff format --isolated --check src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 diff --git a/.gitignore b/.gitignore index ad67955..a2db644 100644 --- a/.gitignore +++ b/.gitignore @@ -19,3 +19,10 @@ target # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +# Python workers and local model artifacts +__pycache__/ +.pytest_cache/ +.ruff_cache/ +.venv/ +weights/ diff --git a/README.md b/README.md index 6cc42ab..7fe790f 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ A community-maintained inference engine for prefill-only System1-Omni models, designed around a Rust frontend, model-owned execution, and high-performance CUDA and Metal backends. -The project is in its initial design stage. The architecture below describes the intended implementation; model engines and GPU backends are not implemented yet. +The project is in early development. A Cua-S1 multimodal worker has been validated on CUDA through Transformers and PEFT; the architecture below describes the intended native frontend and backend organization. ## Architecture @@ -27,20 +27,22 @@ Implementation code lives under `src/`; recipes and documentation stay at the re | --- | --- | | [`src/frontend/`](src/frontend/) | Rust serving code and the small engine interface. | | [`src/models/laya/`](src/models/laya/) | LAYA preprocessing, batching, state, execution, and output processing. | +| [`src/models/cua_s1/multimodal/`](src/models/cua_s1/multimodal/) | Cua-S1 screenshot preprocessing, multimodal LoRA execution, and choice probabilities. | | [`src/backends/cuda/`](src/backends/cuda/) | NVIDIA GPU operations and kernel integration. | | [`src/backends/metal/`](src/backends/metal/) | Apple GPU operations and kernel integration. | | [`recipe/`](recipe/) | Model setup instructions, launch commands, configuration examples, and example requests. | | [`docs/`](docs/) | Project documentation and architecture assets. | -These directories currently document ownership; implementations will be added incrementally. They do not prescribe process boundaries. Shared utilities will be extracted when concrete implementations need them. +These directories document ownership; implementations are being added incrementally. They do not prescribe process boundaries. Shared utilities will be extracted when concrete implementations need them. ## Supported models -No models are implemented yet. LAYA is the first planned model: +Validated coverage is listed by modality and execution path: | Model | Status | | --- | --- | | LAYA | Planned | +| [Cua-S1 4B 0.2](recipe/cua_s1/README.md) | Multimodal screenshot choices via Transformers/PEFT on RTX 4090 CUDA; [parity and measurements](recipe/cua_s1/experiments/README.md). Text serving, native CUDA kernels and Metal deferred. | CUDA and Metal coverage will be documented per model as implementations are added and validated. diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md new file mode 100644 index 0000000..6f175ad --- /dev/null +++ b/recipe/cua_s1/README.md @@ -0,0 +1,145 @@ +# Cua-S1 0.2 multimodal CUDA worker + +This recipe adds screenshot decisions using the multimodal adapter discussed in +[#10](https://github.com/ThinkFlowLab/system1-omni/issues/10). It loads Transformers +and PEFT directly. Upstream `FourBModel` is used only as an independent parity +oracle. The worker owns image decoding, the processor/chat template, vision and +language LoRA loading, and the candidate-letter probability readout. + +The text mapping and pinned revisions follow +[PR #11](https://github.com/ThinkFlowLab/system1-omni/pull/11). The image `state` +format below is this PR's proposed extension. A separately launched multimodal +worker uses the same request model name; deployment routing selects its modality. + +## Setup + +Run from this repository's root on Linux with an NVIDIA GPU. The measured CUDA +wheel, driver, GPU memory and results are recorded in [experiments](experiments/README.md). +Python 3.12 is required by the pinned environment. + +```sh +python3.12 -m venv .venv +. .venv/bin/activate +pip install -r recipe/cua_s1/requirements-multimodal.txt +PYTHONPATH=src python recipe/cua_s1/download_weights.py --dest weights +PYTHONPATH=src python -m models.cua_s1.multimodal.server \ + --base weights/Qwen3.5-4B \ + --adapter weights/cua-s1-4b-0.2/multimodal +``` + +`weights.lock.json` pins and checks every loaded artifact's size and SHA-256. +Extra files are rejected, except Hugging Face's `.cache` metadata, so another +checkpoint cannot silently override verified shards. Downloads require roughly +9 GB plus cache/install space. Loading is offline after the download completes. +Weights are not included in this repository. + +The worker binds to `127.0.0.1:8000` only after loading and a successful warmup. +`GET /health` returns `{"status":"ready","modality":"multimodal"}`. One request +runs at a time; concurrent requests return `503`. This is a loopback model worker, +with the Rust frontend and an ingress responsible for public serving. + +To use the Rust frontend when [PR #2](https://github.com/ThinkFlowLab/system1-omni/pull/2) +is available in your checkout: + +```sh +cargo build --release --locked +OMNI_JEV_BIND=127.0.0.1:8080 \ +OMNI_JEV_BACKEND_URL=http://127.0.0.1:8000 ./target/release/omni-jev +``` + +## Request and response + +Generate a self-contained example with a synthetic settings screenshot: + +```sh +python recipe/cua_s1/make_example.py --output /tmp/cua-example.json +curl -sS http://127.0.0.1:8000/v1/systemone \ + -H 'Content-Type: application/json' --data-binary @/tmp/cua-example.json +``` + +Replace the port with `8080` to send the same request through the frontend. +The request shape is: + +```json +{ + "model": "cua-s1-4b-0.2", + "state": {"image": "data:image/png;base64,"}, + "questions": { + "next": { + "type": "choice", + "instructions": "Save the changes", + "criteria": {"save": "Save changes", "cancel": "Cancel"} + } + } +} +``` + +- `state` contains exactly one inline PNG or JPEG data URL. Images are decoded to + RGB. Local filenames, remote URLs, video, animation and mixed text/image state + are unsupported. +- There are 1–8 questions and 1–26 options per question. Option order assigns + letters A–Z. Labels are strings, objects, arrays or `null` (which uses the key). + Structured values use Python `json.dumps(..., ensure_ascii=False)` followed by + the upstream chooser's label escaping. Instructions accept strings, objects + or arrays; an omitted/empty instruction omits the goal block. +- Limits: 8 MiB body, 4 MiB decoded image, 2048 pixels per side, 1,048,576 pixels + total, 16,384 characters per question and 4096 processed tokens per question. + Every question is validated/preprocessed before any forward pass begins. +- `<|image_pad|>`, `<|video_pad|>`, `<|vision_start|>` and `<|vision_end|>` are + rejected in user text because the processor interprets them as media controls. + Other special-token spellings retain upstream tokenization behavior. +- Empty/invalid inputs, duplicate JSON keys, unsupported models and `score`/`noul` + questions return `422`. Oversized bodies return `413`; chunked uploads return + `411`. Send `Content-Length` and `Content-Type: application/json`. + +Each answer has `type`, `choice`, `probabilities` and `confidence`. The readout +uses the last position's candidate-letter logits, casts to fp32 and applies +softmax over those letters only. There is no decode. Ties select the earliest +option; confidence is `1 - H(p)/ln(n)`, or 1 for one option. Each question has a +separate forward pass over the same screenshot. Usage sums processed input tokens +and reports zero output tokens. + +Response identity: +`cua-ai/cua-s1-4b-0.2@16818868b0cc7813808aae4e87b417657046ab79:multimodal`. +The base is BF16; PEFT's rank-16, alpha-32 adapter remains unmerged with fp32 LoRA +branches, including 50 vision projection modules (178 total adapted modules). + +## Reproduce correctness and profiling + +```sh +git clone https://github.com/trycua/cua.git /tmp/cua-reference +git -C /tmp/cua-reference checkout 0e75660ce4c2edda519e0c795fa3ad98abf4e76f +for mode in reference candidate benchmark; do + PYTHONPATH=src python recipe/cua_s1/evaluate_multimodal.py \ + --weights weights --reference /tmp/cua-reference \ + --output /tmp/cua-evidence --mode "$mode" +done +``` + +The evaluator hashes the pinned `four_b.py` before importing it and checks report +provenance/environment before comparison. Reference and candidate are separate +processes to avoid keeping two models in GPU memory. Eight synthetic requests +(nine question forwards) cover two resolutions, PNG/JPEG, 1/26 candidates, +structured/Unicode labels, special-token text and multiple questions. It requires +identical processor tensor shapes/dtypes/hashes and identical fp32 candidate +probabilities; numerical tolerance is zero. These are integration/parity fixtures, +not an evaluation of GUI task success. + +Benchmark mode records two runs of 50 serial requests after five warmups on the +640×480 fixture, with synchronized end-to-end engine latency, p50/p95, serial +throughput, allocated/reserved GPU peaks and a separate operator profile. Load +and warmup are recorded separately; candidate load time includes artifact hash +verification. See the experiment report for measured scope and limitations. + +CPU-only validation: + +```sh +pip install Pillow==11.3.0 pytest==9.1.1 ruff==0.16.8 +PYTHONPATH=src python -m pytest tests/cua_s1 -q +ruff check --select E4,E7,E9,F,I src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 +ruff format --check src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 +``` + +Metal, native CUDA kernels, text-adapter serving, batching, caching and training +are outside this worker's scope. This implementation does not import or modify +another contributor's text engine. diff --git a/recipe/cua_s1/check_frontend.py b/recipe/cua_s1/check_frontend.py new file mode 100644 index 0000000..b2ae0fe --- /dev/null +++ b/recipe/cua_s1/check_frontend.py @@ -0,0 +1,62 @@ +"""Compare HTTP response status, content type and bytes through Rust and directly.""" + +import argparse +import hashlib +import json +import urllib.error +import urllib.request +from pathlib import Path + + +def exchange(base, route, body=None): + request = urllib.request.Request( + base.rstrip("/") + route, + data=body, + headers={"Content-Type": "application/json"}, + ) + try: + response = urllib.request.urlopen(request, timeout=60) + except urllib.error.HTTPError as exc: + response = exc + with response: + return response.status, response.headers.get("Content-Type"), response.read() + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--worker", default="http://127.0.0.1:8000") + parser.add_argument("--frontend", default="http://127.0.0.1:8080") + parser.add_argument("--fixtures", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + checks = [("health", "/health", None)] + checks += [ + (p.stem, "/v1/systemone", p.read_bytes()) + for p in sorted(args.fixtures.glob("*.json")) + ] + checks += [ + ("invalid", "/v1/systemone", b"{}"), + ("duplicate", "/v1/systemone", b'{"model":1,"model":2}'), + ] + report = [] + for name, route, body in checks: + direct = exchange(args.worker, route, body) + proxied = exchange(args.frontend, route, body) + assert direct == proxied, f"frontend changed response: {name}" + expected_status = 422 if name in {"invalid", "duplicate"} else 200 + assert direct[0] == expected_status, f"unexpected status: {name}: {direct[0]}" + report.append( + { + "name": name, + "status": direct[0], + "content_type": direct[1], + "body_sha256": hashlib.sha256(direct[2]).hexdigest(), + "identical": True, + } + ) + print(f"{name}: HTTP {direct[0]}, identical response", flush=True) + args.output.write_text(json.dumps(report, indent=2)) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/download_weights.py b/recipe/cua_s1/download_weights.py new file mode 100644 index 0000000..104b393 --- /dev/null +++ b/recipe/cua_s1/download_weights.py @@ -0,0 +1,33 @@ +"""Download the upstream-pinned artifacts and verify their checksums.""" + +import argparse +import json +from pathlib import Path + +from huggingface_hub import snapshot_download + +from models.cua_s1.multimodal.model import verify_weights + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--dest", type=Path, required=True) + args = parser.parse_args() + lock = ( + Path(__file__).resolve().parents[2] + / "src/models/cua_s1/multimodal/weights.lock.json" + ) + for artifact in json.loads(lock.read_text())["artifacts"]: + snapshot_download( + repo_id=artifact["repo_id"], + revision=artifact["revision"], + local_dir=args.dest / artifact["name"], + allow_patterns=list(artifact["files"]), + token=False, + ) + verify_weights(args.dest / "Qwen3.5-4B", args.dest / "cua-s1-4b-0.2/multimodal") + print("Pinned base and multimodal adapter checksums verified.") + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/evaluate_multimodal.py b/recipe/cua_s1/evaluate_multimodal.py new file mode 100644 index 0000000..7314ee0 --- /dev/null +++ b/recipe/cua_s1/evaluate_multimodal.py @@ -0,0 +1,321 @@ +"""Reproduce upstream parity and bounded GPU measurements on fixed synthetic inputs.""" + +from __future__ import annotations + +import argparse +import copy +import hashlib +import importlib.metadata +import json +import platform +import statistics +import subprocess +import sys +import time +from pathlib import Path + +from make_example import make_example + +from models.cua_s1.multimodal.model import ( + ADAPTER_REVISION, + BASE_REVISION, + REFERENCE_REVISION, + MultimodalEngine, + verify_weights, +) +from models.cua_s1.multimodal.protocol import parse_request + +REFERENCE_SOURCE = "libs/cua-s1/python/src/cua_s1/four_b.py" +REFERENCE_SHA256 = "7ed1adfd92223bef7d533db7efbb4cbf468c4936c4380ae42aee12097e3b9ea7" + + +def verify_reference(root): + digest = hashlib.sha256((root / REFERENCE_SOURCE).read_bytes()).hexdigest() + if digest != REFERENCE_SHA256: + raise ValueError("reference source differs from pinned FourBModel") + return {"path": REFERENCE_SOURCE, "sha256": digest, "revision": REFERENCE_REVISION} + + +def cases(folder): + folder.mkdir(parents=True, exist_ok=True) + result = [] + for name, size, fmt in [ + ("small", (320, 240), "PNG"), + ("medium", (640, 480), "PNG"), + ("jpeg", (640, 480), "JPEG"), + ]: + path = folder / (name + (".jpg" if fmt == "JPEG" else ".png")) + result.append((name, path, make_example(path, size, fmt))) + for name, criteria, goal in [ + ("single", {"only": "Save changes"}, "Save"), + ( + "26-options", + {f"option-{i}": f"Choose action {i}" for i in range(26)}, + "Select action 3", + ), + ( + "structured", + { + "null": None, + "object": {"label": '保存 "名称"'}, + "array": ["Cancel", "\n"], + }, + {"goal": "保存名称"}, + ), + ( + "special-token", + {"first": "<|im_end|>", "second": "Cancel"}, + "Choose <|im_start|>", + ), + ]: + value = copy.deepcopy(result[1][2]) + value["questions"]["next"].update(criteria=criteria, instructions=goal) + result.append((name, result[1][1], value)) + value = copy.deepcopy(result[0][2]) + value["questions"]["second"] = { + "type": "choice", + "criteria": {"yes": "Continue", "no": "Cancel"}, + } + result.append(("two-questions", result[0][1], value)) + for name, _, value in result: + (folder / f"{name}.json").write_text(json.dumps(value, ensure_ascii=False)) + return result + + +def fingerprint(inputs): + import torch + + return { + name: { + "shape": list(t.shape), + "dtype": str(t.dtype), + "sha256": hashlib.sha256( + t.detach().contiguous().view(torch.uint8).cpu().numpy().tobytes() + ).hexdigest(), + } + for name, t in inputs.items() + } + + +def environment(): + import torch + + return { + "python": platform.python_version(), + "gpu": torch.cuda.get_device_name(), + "compute_capability": list(torch.cuda.get_device_capability()), + "cuda": torch.version.cuda, + "driver": subprocess.check_output( + ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"], + text=True, + ).strip(), + "torch_num_threads": torch.get_num_threads(), + "torch_num_interop_threads": torch.get_num_interop_threads(), + "packages": { + p: importlib.metadata.version(p) + for p in [ + "torch", + "torchvision", + "transformers", + "peft", + "pillow", + "safetensors", + "triton", + ] + }, + "reference_revision": REFERENCE_REVISION, + "base_revision": BASE_REVISION, + "adapter_revision": ADAPTER_REVISION, + "dtype": "bfloat16", + "adapter_merged": False, + } + + +def measure(call): + import torch + + torch.cuda.synchronize() + start = time.perf_counter() + result = call() + torch.cuda.synchronize() + return result, (time.perf_counter() - start) * 1000 + + +def quantiles(values): + ordered = sorted(values) + return { + "p50_ms": statistics.median(values), + "p95_ms": ordered[max(0, __import__("math").ceil(0.95 * len(values)) - 1)], + } + + +def main(): + import torch + + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument( + "--reference", type=Path, required=True, help="checkout of pinned trycua/cua" + ) + p.add_argument("--output", type=Path, required=True) + p.add_argument( + "--mode", choices=["reference", "candidate", "benchmark"], required=True + ) + args = p.parse_args() + args.output.mkdir(parents=True, exist_ok=True) + base = str(args.weights / "Qwen3.5-4B") + adapter = str(args.weights / "cua-s1-4b-0.2/multimodal") + fixture_set = cases(args.output / "fixtures") + report = {"environment": environment(), "mode": args.mode, "cases": []} + report["reference_source"] = verify_reference(args.reference) + if args.mode == "reference": + _, report["artifact_verification_ms"] = measure( + lambda: verify_weights(Path(base), Path(adapter)) + ) + sys.path.insert(0, str(args.reference / "libs/cua-s1/python/src")) + from cua_s1.four_b import FourBModel, Option, assign_letters, build_prompt + + model = FourBModel( + base_model=base, lora_adapter_path=adapter, modality="multimodal" + ) + _, report["load_ms"] = measure(model.load) + first = True + for name, path, value in fixture_set: + request = parse_request(value) + for q in request.questions: + options = [ + Option(element_id=k, role="Decision", label=v, action="select") + for k, v in zip(q.keys, q.labels) + ] + messages = build_prompt( + assign_letters(options), + app="Cua Driver", + task_family="closed-candidate decision", + screenshot=path, + modality="multimodal", + goal=q.goal, + ) + text = model._processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = model._processor( + text=[text], images=[request.image], return_tensors="pt" + ) + + def call(): + return model.forward( + options, + app="Cua Driver", + task_family="closed-candidate decision", + screenshot=path, + goal=q.goal, + ) + + if first: + _, report["warmup_ms"] = measure(call) + first = False + output, elapsed = measure(call) + report["cases"].append( + { + "name": name, + "question": q.name, + "inputs": fingerprint(inputs), + "probabilities": [x.probability for x in output], + "latency_ms": elapsed, + } + ) + print(f"reference {name}/{q.name}: {elapsed:.1f} ms", flush=True) + else: + engine, report["load_ms"] = measure(lambda: MultimodalEngine(base, adapter)) + _, report["warmup_ms"] = measure(engine.warmup) + report["adapter_modules"] = engine.adapter_modules + if args.mode == "candidate": + reference = json.loads((args.output / "reference.json").read_text()) + if ( + reference["reference_source"] != report["reference_source"] + or reference["environment"] != report["environment"] + ): + raise ValueError("reference provenance or environment mismatch") + expected = {(x["name"], x["question"]): x for x in reference["cases"]} + for name, _, value in fixture_set: + request = parse_request(value) + for q in request.questions: + inputs = engine.prepare(request.image, q) + probabilities, elapsed = measure(lambda: engine.score(inputs, q)) + ref = expected[name, q.name] + difference = max( + abs(a - b) for a, b in zip(ref["probabilities"], probabilities) + ) + assert fingerprint(inputs) == ref["inputs"], ( + f"preprocessing mismatch: {name}" + ) + assert probabilities == ref["probabilities"], ( + f"probability mismatch: {name}: {difference}" + ) + report["cases"].append( + { + "name": name, + "question": q.name, + "inputs": fingerprint(inputs), + "probabilities": probabilities, + "max_abs_difference": difference, + "latency_ms": elapsed, + } + ) + print(f"candidate {name}/{q.name}: exact parity", flush=True) + else: + request = parse_request(fixture_set[1][2]) + for _ in range(5): + engine.predict(request) + report["benchmark"] = { + "case": "medium", + "batch_size": 1, + "concurrency": 1, + "warmup_requests": 5, + "runs": [], + } + for run in range(2): + torch.cuda.reset_peak_memory_stats() + latencies = [ + measure(lambda: engine.predict(request))[1] for _ in range(50) + ] + report["benchmark"]["runs"].append( + { + "run": run + 1, + "latencies_ms": latencies, + **quantiles(latencies), + "requests_per_second_serial": 1000 / statistics.mean(latencies), + "peak_allocated_bytes": torch.cuda.max_memory_allocated(), + "peak_reserved_bytes": torch.cuda.max_memory_reserved(), + } + ) + print(f"benchmark run {run + 1}: {quantiles(latencies)}", flush=True) + with torch.profiler.profile( + activities=[ + torch.profiler.ProfilerActivity.CPU, + torch.profiler.ProfilerActivity.CUDA, + ], + record_shapes=True, + ) as prof: + engine.predict(request) + torch.cuda.synchronize() + report["profile"] = [ + { + "op": x.key, + "count": x.count, + "device_time_us": x.device_time_total, + "self_device_time_us": x.self_device_time_total, + "cpu_time_us": x.cpu_time_total, + "self_cpu_time_us": x.self_cpu_time_total, + } + for x in sorted( + prof.key_averages(), key=lambda x: x.device_time_total, reverse=True + )[:30] + ] + report["peak_allocated_bytes"] = torch.cuda.max_memory_allocated() + (args.output / f"{args.mode}.json").write_text(json.dumps(report, indent=2)) + print(f"Saved {args.mode}.json", flush=True) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md new file mode 100644 index 0000000..d73403b --- /dev/null +++ b/recipe/cua_s1/experiments/README.md @@ -0,0 +1,111 @@ +# RTX 4090 multimodal validation — 2026-09-27 + +This is a Transformers/PEFT CUDA baseline, with the adapter unmerged and full +logits, for the screenshot worker in [the recipe](../README.md). It does not +implement a native CUDA backend or claim a speedup over upstream. + +## Environment and artifacts + +- One NVIDIA GeForce RTX 4090, 24,564 MiB, compute capability 8.9. +- Ubuntu 22.04 container; NVIDIA driver 595.71.05; PyTorch CUDA runtime 13.0. +- Python 3.12.13; torch 2.14.0, torchvision 0.29.0, Transformers 5.17.0, + PEFT 0.21.0, Pillow 11.3.0, safetensors 0.8.0, Triton 3.8.0. +- PyTorch reported 64 intra-op and 64 inter-op threads; no thread tuning applied. +- BF16 base, PEFT fp32 LoRA branches, no adapter merge, no quantization. +- Neither `flash-linear-attention` nor `causal-conv1d` is installed. The matching + upstream reference uses Transformers' PyTorch fallback implementations. +- All pinned weights passed size and SHA-256 verification. The lock contains + 9,342,907,469 base bytes and 186,638,393 adapter-repository bytes. Only the + `multimodal` adapter is loaded; all 178 target modules, including 50 visual + modules, are required at startup. + +Pinned revisions and the reference-source SHA-256 are present in every JSON +report. Full installed versions are in [packages.txt](rtx4090/packages.txt). +The numerical contract and dependency versions were fixed before comparisons. +No model weights or private screenshots are included. + +## Correctness + +| Check | Result | Evidence | +| --- | --- | --- | +| Processor tensor shapes, dtypes and SHA-256 values | Exact match on 9 question forwards across 8 requests | [reference.json](rtx4090/reference.json), [candidate.json](rtx4090/candidate.json) | +| Candidate fp32 probabilities | Exact match; maximum absolute difference **0** | Same reports | +| Multimodal LoRA attachment | 178 modules, visual modules present | [candidate.json](rtx4090/candidate.json) | +| Rust frontend vs direct worker | All 11 HTTP comparisons passed; status, content type and body bytes identical | [frontend.json](rtx4090/frontend.json) | +| CPU validation | 39 tests passed on local macOS and the Linux GPU host | `tests/cua_s1`, CPU CI workflow | + +Fixtures are generated by `evaluate_multimodal.py` using the pinned Pillow version. +They cover 320×240 and 640×480 screenshots, PNG/JPEG, 1 and 26 candidates, +structured/Unicode labels, special-token spellings and two questions sharing one +image. Processed prompts span 215–752 tokens. Hashes cover input IDs, attention +masks, image pixels and image-grid metadata, rather than just the selected option. +The fixtures establish integration parity, not GUI task accuracy or generalization. + +For the frontend test, PR #2's Rust frontend at +`0c91671ac7c8bd698b957b2c0df921de96e7e628` was built with `cargo build --release --locked` +on macOS. It forwarded to the Linux GPU worker over an SSH tunnel. This exercises +real inference and byte preservation; **network/HTTP latency is not benchmarked**. +Eight valid fixtures, health, malformed envelope and duplicate JSON keys were checked. + +Reproduce the HTTP check after generating the fixtures and starting both servers: + +```sh +python recipe/cua_s1/check_frontend.py \ + --worker http://127.0.0.1:8000 --frontend http://127.0.0.1:8080 \ + --fixtures /tmp/cua-evidence/fixtures --output /tmp/cua-evidence/frontend.json +``` + +## Warm engine performance + +[benchmark.json](rtx4090/benchmark.json) contains every sample and the operator +profile. One 640×480 PNG, three candidates, 456 processed tokens, batch size 1, +concurrency 1; five warmup requests followed by two runs of 50 requests. +`torch.cuda.synchronize()` brackets each measurement. p95 uses nearest rank. + +Timing covers `engine.predict`: chat-template application, processor work, host-to- +device transfer, forward pass, letter readout and answer construction. The request +has already been parsed and its image decoded. JSON parsing, image decoding, +HTTP, queueing, model load, artifact hashing and warmup are excluded. + +| Run | p50 | p95 | Serial throughput | Peak allocated | Peak reserved | +| --- | --- | --- | --- | --- | --- | +| 1 | 126.39 ms | 136.68 ms | 7.79 requests/s | 8.84 GiB | 9.07 GiB | +| 2 | 128.28 ms | 133.01 ms | 7.77 requests/s | 8.84 GiB | 9.07 GiB | + +The parity run's peak allocated memory across all nine forwards was 8.99 GiB. +These bounds describe the measured fixtures, not every request admitted by the +worker's 4096-token limit or a concurrent/batched deployment. + +Measured candidate construction, including weight hashing, was 19.94 s in the +parity process and 18.94 s in the benchmark process. Their initial 224×224 warmups +were 1.82 s and 1.63 s. Upstream load was 16.82 s plus 9.54 s of separately measured +artifact verification; its first 320×240 warmup was 3.67 s. These are individual +observations after importing PyTorch and probing the GPU, with different warmup +shapes and filesystem-cache histories; they are not a cold-start speed comparison. +Per-fixture timings in the parity reports are also single observations; the +reference includes image opening while candidate timing covers `score` only. + +## Profiling and the next optimization boundary + +A separate profiled inference, excluded from the latency samples, reported: + +| Operator | Calls | Self CUDA time | +| --- | --- | --- | +| `aten::mm` | 605 | 35.42 ms | +| `aten::copy_` | 1,642 | 9.55 ms | +| `aten::bmm` | 817 | 9.11 ms | +| `aten::addmm` | 98 | 5.89 ms | +| `aten::mul` | 1,103 | 5.76 ms | + +These are instrumented operator totals, not percentages of request wall time. +The raw profile includes both framework operators and CUDA kernels, which overlap; +do not add parent and child events or add kernel time to its enclosing operator. + +Matrix multiplication dominates the observed operator totals. A subsequent CUDA +optimization should first attribute those GEMMs and copies to vision, language +and LoRA branches using shapes/module ranges, then compare one bounded change +against this baseline. This profile alone does not justify replacing a specific +kernel or claiming a speedup. Gated DeltaNet/causal-convolution backend work should +be coordinated with the text-engine contributor because those language layers are +shared. This PR keeps the exact upstream execution path and contributes the +multimodal correctness and performance baseline needed for that work. diff --git a/recipe/cua_s1/experiments/rtx4090/benchmark.json b/recipe/cua_s1/experiments/rtx4090/benchmark.json new file mode 100644 index 0000000..7b69afc --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090/benchmark.json @@ -0,0 +1,409 @@ +{ + "environment": { + "python": "3.12.13", + "gpu": "NVIDIA GeForce RTX 4090", + "compute_capability": [ + 8, + 9 + ], + "cuda": "13.0", + "driver": "595.71.05", + "torch_num_threads": 64, + "torch_num_interop_threads": 64, + "packages": { + "torch": "2.14.0", + "torchvision": "0.29.0", + "transformers": "5.17.0", + "peft": "0.21.0", + "pillow": "11.3.0", + "safetensors": "0.8.0", + "triton": "3.8.0" + }, + "reference_revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f", + "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", + "adapter_revision": "16818868b0cc7813808aae4e87b417657046ab79", + "dtype": "bfloat16", + "adapter_merged": false + }, + "mode": "benchmark", + "cases": [], + "reference_source": { + "path": "libs/cua-s1/python/src/cua_s1/four_b.py", + "sha256": "7ed1adfd92223bef7d533db7efbb4cbf468c4936c4380ae42aee12097e3b9ea7", + "revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f" + }, + "load_ms": 18941.51310157031, + "warmup_ms": 1633.873094804585, + "adapter_modules": 178, + "benchmark": { + "case": "medium", + "batch_size": 1, + "concurrency": 1, + "warmup_requests": 5, + "runs": [ + { + "run": 1, + "latencies_ms": [ + 125.8343830704689, + 127.36847903579473, + 125.82478113472462, + 125.89417397975922, + 146.41341753304005, + 127.77413055300713, + 127.22665630280972, + 125.83817914128304, + 126.10162515193224, + 127.15142965316772, + 126.42081826925278, + 126.38038955628872, + 126.02570466697216, + 128.5353172570467, + 129.56660240888596, + 129.32745181024075, + 126.28413271158934, + 126.18042714893818, + 133.06267280131578, + 126.3929232954979, + 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+import base64 +import json +from pathlib import Path + +from PIL import Image, ImageDraw + + +def make_example(path: Path, size=(640, 480), fmt="PNG") -> dict: + image = Image.new("RGB", size, "#f4f6f8") + draw = ImageDraw.Draw(image) + w, h = size + draw.rectangle( + (w // 10, h // 8, w * 9 // 10, h * 7 // 8), + fill="white", + outline="#8899aa", + width=2, + ) + draw.text( + (w // 7, h // 5), "Account settings", fill="black", font_size=max(12, w // 25) + ) + draw.text( + (w // 7, h // 3), + "Display name: Alice", + fill="black", + font_size=max(10, w // 32), + ) + draw.rectangle((w // 7, h // 2, w * 4 // 7, h * 2 // 3), fill="#1460b4") + draw.text( + (w // 6, h * 13 // 24), "Save changes", fill="white", font_size=max(10, w // 32) + ) + draw.text( + (w * 5 // 8, h * 13 // 24), "Cancel", fill="black", font_size=max(10, w // 32) + ) + image.save(path, format=fmt) + mime = "jpeg" if fmt == "JPEG" else "png" + return { + "model": "cua-s1-4b-0.2", + "state": { + "image": f"data:image/{mime};base64," + + base64.b64encode(path.read_bytes()).decode() + }, + "questions": { + "next": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait", + }, + } + }, + } + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, default=Path("example.json")) + args = parser.parse_args() + args.output.parent.mkdir(parents=True, exist_ok=True) + value = make_example(args.output.with_suffix(".png")) + args.output.write_text(json.dumps(value, ensure_ascii=False)) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/requirements-multimodal.txt b/recipe/cua_s1/requirements-multimodal.txt new file mode 100644 index 0000000..afb7422 --- /dev/null +++ b/recipe/cua_s1/requirements-multimodal.txt @@ -0,0 +1,11 @@ +# Reference package versions from trycua/cua 0e75660, four-b uv.lock. +# Lock the CUDA wheel/driver in the experiment report for the target GPU. +torch==2.14.0 +torchvision==0.29.0 +transformers==5.17.0 +peft==0.21.0 +accelerate==1.15.0 +Pillow==11.3.0 +safetensors==0.8.0 +huggingface-hub==1.32.0 +tokenizers==0.23.2 diff --git a/src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md b/src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md new file mode 100644 index 0000000..b8b198c --- /dev/null +++ b/src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Cua AI, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py new file mode 100644 index 0000000..db55070 --- /dev/null +++ b/src/models/cua_s1/multimodal/model.py @@ -0,0 +1,164 @@ +"""Direct Transformers/PEFT execution. No production dependency on cua_s1.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +from .protocol import InvalidRequest, Question, Request, answer, build_messages + +REFERENCE_REVISION = "0e75660ce4c2edda519e0c795fa3ad98abf4e76f" +BASE_REVISION = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" +ADAPTER_REVISION = "16818868b0cc7813808aae4e87b417657046ab79" +IDENTITY = f"cua-ai/cua-s1-4b-0.2@{ADAPTER_REVISION}:multimodal" +MAX_TOKENS = 4096 + + +def letter_ids(tokenizer, count: int) -> list[int]: + ids = [] + for index in range(count): + encoded = tokenizer.encode(chr(65 + index), add_special_tokens=False) + if len(encoded) != 1: + raise ValueError("each candidate letter must be a single token") + ids.append(encoded[0]) + return ids + + +def validate_adapter_config(config: dict): + targets = { + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + "linear_fc1", + "linear_fc2", + } + if ( + config.get("peft_type") != "LORA" + or config.get("r") != 16 + or config.get("lora_alpha") != 32 + or set(config.get("target_modules", [])) != targets + or config.get("base_model_name_or_path") != "Qwen/Qwen3.5-4B" + ): + raise ValueError("expected the pinned 0.2 multimodal LoRA adapter") + + +def verify_weights(base: Path, adapter: Path): + """Check local artifacts before assigning the pinned identity to responses.""" + lock = json.loads(Path(__file__).with_name("weights.lock.json").read_text()) + allowed = {base: set(), adapter: set()} + for artifact in lock["artifacts"]: + for name, expected in artifact["files"].items(): + if artifact["role"] == "adapter": + if not name.startswith("multimodal/"): + continue + path = adapter / name.removeprefix("multimodal/") + else: + path = base / name + root = adapter if artifact["role"] == "adapter" else base + allowed[root].add(path.relative_to(root).as_posix()) + if not path.is_file() or path.stat().st_size != expected["size"]: + raise ValueError(f"missing or wrong-size pinned artifact: {path.name}") + with path.open("rb") as handle: + digest = hashlib.file_digest(handle, "sha256").hexdigest() + if digest != expected["sha256"]: + raise ValueError(f"checksum mismatch: {path.name}") + for root, names in allowed.items(): + for path in root.rglob("*"): + relative = path.relative_to(root) + if path.is_file() and relative.parts[0] != ".cache": + if relative.as_posix() not in names: + raise ValueError( + f"unlisted artifact may override pinned files: {relative}" + ) + + +class MultimodalEngine: + def __init__( + self, base: str, adapter: str, device: str = "cuda", dtype: str = "bfloat16" + ): + import torch + from peft import PeftModel + from peft.tuners.lora import LoraLayer + from transformers import ( + AutoModelForImageTextToText, + AutoProcessor, + AutoTokenizer, + ) + + base_path, adapter_path = Path(base), Path(adapter) + verify_weights(base_path, adapter_path) + validate_adapter_config( + json.loads((adapter_path / "adapter_config.json").read_text()) + ) + self.tokenizer = AutoTokenizer.from_pretrained(base, local_files_only=True) + self.processor = AutoProcessor.from_pretrained(base, local_files_only=True) + model = AutoModelForImageTextToText.from_pretrained( + base, + torch_dtype=getattr(torch, dtype), + device_map=device, + local_files_only=True, + ) + self.model = PeftModel.from_pretrained(model, adapter, local_files_only=True) + modules = [ + name + for name, module in self.model.named_modules() + if isinstance(module, LoraLayer) + ] + if len(modules) != 178 or not any(".visual." in name for name in modules): + raise RuntimeError( + "multimodal adapter did not attach to all 178 expected modules" + ) + self.adapter_modules = len(modules) + self.model.eval() + self.dtype = dtype + + def prepare(self, image, question: Question): + messages = build_messages(question) + text = self.processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + inputs = self.processor(text=[text], images=[image], return_tensors="pt") + if inputs["input_ids"].shape[-1] > MAX_TOKENS: + raise InvalidRequest(f"processed prompt exceeds {MAX_TOKENS} tokens") + return inputs + + def score(self, inputs, question: Question) -> list[float]: + import torch + + ids = letter_ids(self.tokenizer, len(question.keys)) + inputs = inputs.to(self.model.device) + with torch.no_grad(): + output = self.model(**inputs) + logits = output.logits[0, -1, :] + return torch.softmax( + logits[torch.tensor(ids, device=logits.device)].float(), dim=-1 + ).tolist() + + def predict(self, request: Request) -> dict: + # Validate all processed lengths before executing any question. + prepared = [self.prepare(request.image, q) for q in request.questions] + answers = { + q.name: answer(q, self.score(inputs, q)) + for q, inputs in zip(request.questions, prepared) + } + return { + "model": IDENTITY, + "answers": answers, + "usage": { + "input_tokens": sum(x["input_ids"].shape[-1] for x in prepared), + "output_tokens": 0, + }, + } + + def warmup(self): + from PIL import Image + + q = Question( + "warmup", ("continue", "cancel"), ("Continue", "Cancel"), "Continue" + ) + self.predict(Request(Image.new("RGB", (224, 224), "white"), (q,))) diff --git a/src/models/cua_s1/multimodal/protocol.py b/src/models/cua_s1/multimodal/protocol.py new file mode 100644 index 0000000..0a0157a --- /dev/null +++ b/src/models/cua_s1/multimodal/protocol.py @@ -0,0 +1,217 @@ +"""Bounded screenshot-only extension of the Cua-S1 choice contract in PR #11.""" + +from __future__ import annotations + +import base64 +import binascii +import io +import json +import math +from dataclasses import dataclass + +from PIL import Image, UnidentifiedImageError + +MODEL = "cua-s1-4b-0.2" +MAX_BODY = 8 * 1024 * 1024 +MAX_IMAGE_BYTES = 4 * 1024 * 1024 +MAX_PIXELS = 1024 * 1024 +MAX_SIDE = 2048 +MAX_QUESTIONS = 8 +MAX_TEXT = 16384 + +# Prompt and letter layout follow trycua/cua at 0e75660ce4c2edda519e0c795fa3ad98abf4e76f. +# See THIRD_PARTY_NOTICES.md for the upstream MIT notice. +SYSTEM_PROMPT = ( + "You are a one-pass computer-use decision model. You are shown the " + "current state of a screen and a fixed, closed list of candidate " + "(element, action) options, each given a single letter. Choose exactly " + "one option: the single best next action to take. Answer with ONLY that " + "option's letter -- no words, no punctuation, no explanation." +) + + +class InvalidRequest(ValueError): + """Input cannot be evaluated under the supported contract.""" + + +@dataclass(frozen=True) +class Question: + name: str + keys: tuple[str, ...] + labels: tuple[str, ...] + goal: str + + +@dataclass(frozen=True) +class Request: + image: Image.Image + questions: tuple[Question, ...] + + +def _object(pairs): + result = {} + for key, value in pairs: + if key in result: + raise InvalidRequest("duplicate JSON keys are not supported") + result[key] = value + return result + + +def _nonfinite(value): + raise InvalidRequest("non-finite JSON numbers are not supported") + + +def decode_request(raw: bytes) -> dict: + if len(raw) > MAX_BODY: + raise InvalidRequest("request body exceeds 8 MiB") + try: + value = json.loads(raw, object_pairs_hook=_object, parse_constant=_nonfinite) + except (ValueError, UnicodeError, RecursionError) as exc: + raise InvalidRequest("invalid JSON or duplicate keys") from exc + if not isinstance(value, dict): + raise InvalidRequest("request must be a JSON object") + return value + + +def _text(value, field): + if not isinstance(value, (str, dict, list)): + raise InvalidRequest(f"{field} must be a string, object or array") + try: + result = ( + value + if isinstance(value, str) + else json.dumps(value, ensure_ascii=False, allow_nan=False) + ) + except (ValueError, TypeError, RecursionError) as exc: + raise InvalidRequest(f"invalid {field}") from exc + if len(result) > MAX_TEXT: + raise InvalidRequest(f"{field} exceeds {MAX_TEXT} characters") + if any( + token in result + for token in ( + "<|image_pad|>", + "<|video_pad|>", + "<|vision_start|>", + "<|vision_end|>", + ) + ): + raise InvalidRequest(f"{field} contains an unsupported media control token") + return result + + +def _image(state): + if not isinstance(state, dict) or set(state) != {"image"}: + raise InvalidRequest("state must contain exactly one image data URL") + url = state["image"] + if not isinstance(url, str): + raise InvalidRequest("state.image must be a PNG/JPEG base64 data URL") + prefix, separator, encoded = url.partition(",") + expected = {"data:image/png;base64": "PNG", "data:image/jpeg;base64": "JPEG"} + if not separator or prefix not in expected: + raise InvalidRequest("only inline PNG/JPEG images are supported") + if len(encoded) > 4 * ((MAX_IMAGE_BYTES + 2) // 3): + raise InvalidRequest("encoded image exceeds 4 MiB") + try: + raw = base64.b64decode(encoded, validate=True) + if len(raw) > MAX_IMAGE_BYTES: + raise InvalidRequest("image exceeds 4 MiB") + with Image.open(io.BytesIO(raw)) as source: + if source.format != expected[prefix]: + raise InvalidRequest("image format does not match its MIME type") + w, h = source.size + if ( + max(w, h) > MAX_SIDE + or w * h > MAX_PIXELS + or getattr(source, "n_frames", 1) != 1 + ): + raise InvalidRequest( + "image must be single-frame, at most 2048 per side and 1048576 pixels" + ) + source.load() + return source.convert("RGB") + except ( + binascii.Error, + UnidentifiedImageError, + OSError, + Image.DecompressionBombError, + ValueError, + ) as exc: + if isinstance(exc, InvalidRequest): + raise + raise InvalidRequest("invalid image data") from exc + + +def parse_request(value: dict) -> Request: + if not isinstance(value, dict) or set(value) != {"model", "state", "questions"}: + raise InvalidRequest("request must contain model, state and questions only") + if value["model"] != MODEL: + raise InvalidRequest(f"model must be {MODEL}") + questions = value["questions"] + if not isinstance(questions, dict) or not 1 <= len(questions) <= MAX_QUESTIONS: + raise InvalidRequest("questions must contain 1 to 8 questions") + parsed = [] + for name, q in questions.items(): + if not isinstance(name, str) or not name or len(name) > 256: + raise InvalidRequest("question names must contain 1 to 256 characters") + if not isinstance(q, dict) or q.get("type") != "choice": + raise InvalidRequest("only choice questions are supported") + if set(q) - {"type", "instructions", "criteria"}: + raise InvalidRequest("unsupported question fields") + criteria = q.get("criteria") + if not isinstance(criteria, dict) or not 1 <= len(criteria) <= 26: + raise InvalidRequest("choice requires 1 to 26 options") + labels = [] + for key, label in criteria.items(): + if not isinstance(key, str) or not key or len(key) > 256: + raise InvalidRequest("option keys must contain 1 to 256 characters") + text = _text(key if label is None else label, "criteria") + labels.append(json.dumps(text, ensure_ascii=False)[1:-1]) + goal = _text(q.get("instructions", ""), "instructions") + if len(goal) + sum(map(len, labels)) > MAX_TEXT: + raise InvalidRequest("combined question text exceeds 16384 characters") + parsed.append(Question(name, tuple(criteria), tuple(labels), goal)) + return Request(_image(value["state"]), tuple(parsed)) + + +def build_messages(q: Question, image_marker: str = "inline.png") -> list[dict]: + lines = "\n".join( + f'{chr(65 + i)}. Decision "{label}" -> select' + for i, label in enumerate(q.labels) + ) + text = ( + (f"Goal: {q.goal}\n\n" if q.goal else "") + + "App: Cua Driver\nTask family: closed-candidate decision\n\n" + + "The current screenshot is attached.\n\n" + + f"Options:\n{lines}\n\nAnswer with a single letter." + ) + return [ + {"role": "system", "content": SYSTEM_PROMPT}, + { + "role": "user", + "content": [ + {"type": "image", "image": image_marker}, + {"type": "text", "text": text}, + ], + }, + ] + + +def answer(q: Question, probabilities: list[float]) -> dict: + if len(probabilities) != len(q.keys) or any( + not math.isfinite(p) or not 0 <= p <= 1 for p in probabilities + ): + raise ValueError("model returned invalid probabilities") + if not math.isclose(sum(probabilities), 1.0, abs_tol=1e-5): + raise ValueError("model probabilities do not sum to one") + n = len(probabilities) + confidence = ( + 1.0 + if n == 1 + else 1 + sum(p * math.log(p) for p in probabilities if p) / math.log(n) + ) + return { + "type": "choice", + "choice": q.keys[max(range(n), key=probabilities.__getitem__)], + "probabilities": dict(zip(q.keys, probabilities)), + "confidence": max(0.0, min(1.0, confidence)), + } diff --git a/src/models/cua_s1/multimodal/server.py b/src/models/cua_s1/multimodal/server.py new file mode 100644 index 0000000..a399dac --- /dev/null +++ b/src/models/cua_s1/multimodal/server.py @@ -0,0 +1,128 @@ +"""Small loopback HTTP worker; the Rust frontend remains the public serving layer.""" + +from __future__ import annotations + +import argparse +import json +import logging +import socket +import threading +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer + +from .protocol import MAX_BODY, InvalidRequest, decode_request, parse_request + +LOG = logging.getLogger(__name__) + + +class WorkerServer(ThreadingHTTPServer): + daemon_threads = True + + def __init__(self, address, engine): + self.engine = engine + self.inference_lock = threading.Lock() + super().__init__(address, Handler) + + +class Handler(BaseHTTPRequestHandler): + def setup(self): + super().setup() + self.connection.settimeout(15) + + def log_message(self, format, *args): + # Do not log paths, input images, instructions or arbitrary request headers. + pass + + def send_json(self, status, value): + raw = json.dumps(value, ensure_ascii=False, allow_nan=False).encode("utf-8") + self.send_response(status) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(raw))) + self.end_headers() + try: + self.wfile.write(raw) + except (BrokenPipeError, ConnectionResetError): + pass + + def do_GET(self): + if self.path == "/health": + self.send_json(200, {"status": "ready", "modality": "multimodal"}) + else: + self.send_json(404, {"error": "unknown route"}) + + def do_POST(self): + if self.path != "/v1/systemone": + self.send_json(404, {"error": "unknown route"}) + return + if self.headers.get("Transfer-Encoding"): + self.send_json( + 411, + { + "error": "Content-Length is required; chunked requests are unsupported" + }, + ) + return + lengths = self.headers.get_all("Content-Length", []) + if len(lengths) != 1: + self.send_json(411, {"error": "one Content-Length is required"}) + return + try: + length = int(lengths[0]) + except ValueError: + self.send_json(400, {"error": "invalid Content-Length"}) + return + if length < 0 or length > MAX_BODY: + self.send_json(413, {"error": "request exceeds body limit"}) + return + if self.headers.get_content_type() != "application/json": + self.send_json(415, {"error": "Content-Type must be application/json"}) + return + if not self.server.inference_lock.acquire(blocking=False): + self.send_json(503, {"error": "worker busy"}) + return + try: + raw = self.rfile.read(length) + if len(raw) != length: + self.send_json(400, {"error": "incomplete body"}) + return + parsed = parse_request(decode_request(raw)) + result = self.server.engine.predict(parsed) + self.send_json(200, result) + except InvalidRequest as exc: + self.send_json(422, {"error": str(exc)}) + except (TimeoutError, socket.timeout): + self.send_json(408, {"error": "request body timed out"}) + except Exception as exc: + LOG.error("inference failed: %s", type(exc).__name__) + self.send_json(500, {"error": "inference failed"}) + finally: + self.server.inference_lock.release() + + +def main(): + from .model import MultimodalEngine + + p = argparse.ArgumentParser(description=__doc__) + p.add_argument( + "--base", required=True, help="verified local base checkpoint directory" + ) + p.add_argument( + "--adapter", required=True, help="verified local multimodal adapter directory" + ) + p.add_argument("--port", type=int, default=8000) + args = p.parse_args() + logging.basicConfig(level=logging.INFO) + engine = MultimodalEngine(args.base, args.adapter) + engine.warmup() + # Bind only after model loading and a representative inference succeed. + server = WorkerServer(("127.0.0.1", args.port), engine) + LOG.info("multimodal worker ready on 127.0.0.1:%s", args.port) + try: + server.serve_forever() + except KeyboardInterrupt: + pass + finally: + server.server_close() + + +if __name__ == "__main__": + main() diff --git a/src/models/cua_s1/multimodal/weights.lock.json b/src/models/cua_s1/multimodal/weights.lock.json new file mode 100644 index 0000000..dfb9748 --- /dev/null +++ b/src/models/cua_s1/multimodal/weights.lock.json @@ -0,0 +1,102 @@ +{ + "schema": "cua-s1/weights-lock/v1", + "description": "Pinned public Hugging Face artifacts for the weights-backed Cua-S1-4B smoke. Every listed file is downloaded at the pinned revision and verified by size and SHA-256; keep in sync with libs/cua-s1/README.md.", + "artifacts": [ + { + "name": "Qwen3.5-4B", + "repo_id": "Qwen/Qwen3.5-4B", + "revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", + "role": "base", + "files": { + ".gitattributes": { + "size": 1570, + "sha256": "34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930" + }, + "LICENSE": { + "size": 11544, + "sha256": "bbedc3fda3305820b977265f01b8619d87570a6739de3a5582c3464840f1e57a" + }, + "README.md": { + "size": 77661, + "sha256": "1406be1b6b8fd8a6545870da516912804756593628a1d0fb0a7965211e82a7bb" + }, + "chat_template.jinja": { + "size": 7756, + "sha256": "a4aee8afcf2e0711942cf848899be66016f8d14a889ff9ede07bca099c28f715" + }, + "config.json": { + "size": 3161, + "sha256": "ddc63e1c717afa86c865bb5e01313d89d72bb53b97ad4a8a03ba8510c0621670" + }, + "merges.txt": { + "size": 3353259, + "sha256": 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"c246fce1fe1d44160ae5f246f9881dfeabb4fcd67fe4a777cef10a1dbcf16540" + }, + "text/adapter_model.safetensors": { + "size": 84968408, + "sha256": "9b59c5aed96171a50b26526613766bbf44347a5c7af70f81efe6bcc6e9dbfb0e" + } + } + } + ] +} diff --git a/tests/cua_s1/test_evaluation.py b/tests/cua_s1/test_evaluation.py new file mode 100644 index 0000000..b35ec7c --- /dev/null +++ b/tests/cua_s1/test_evaluation.py @@ -0,0 +1,48 @@ +"""Provenance checks must fail before a changed oracle can certify parity.""" + +import importlib.util +from pathlib import Path + +import pytest + + +def test_modified_reference_is_rejected(tmp_path, monkeypatch): + recipe = Path(__file__).resolve().parents[2] / "recipe/cua_s1" + monkeypatch.syspath_prepend(str(recipe)) + spec = importlib.util.spec_from_file_location( + "evaluation", recipe / "evaluate_multimodal.py" + ) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + source = tmp_path / module.REFERENCE_SOURCE + source.parent.mkdir(parents=True) + source.write_text("modified oracle") + with pytest.raises(ValueError, match="reference source differs"): + module.verify_reference(tmp_path) + + +def test_environment_survives_json_roundtrip(monkeypatch): + import json + import sys + from types import SimpleNamespace + + recipe = Path(__file__).resolve().parents[2] / "recipe/cua_s1" + monkeypatch.syspath_prepend(str(recipe)) + spec = importlib.util.spec_from_file_location( + "evaluation", recipe / "evaluate_multimodal.py" + ) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + torch = SimpleNamespace( + cuda=SimpleNamespace( + get_device_name=lambda: "GPU", get_device_capability=lambda: (8, 9) + ), + version=SimpleNamespace(cuda="13.0"), + get_num_threads=lambda: 16, + get_num_interop_threads=lambda: 16, + ) + monkeypatch.setitem(sys.modules, "torch", torch) + monkeypatch.setattr(module.importlib.metadata, "version", lambda name: "pinned") + monkeypatch.setattr(module.subprocess, "check_output", lambda *a, **kw: "driver\n") + environment = module.environment() + assert json.loads(json.dumps(environment)) == environment diff --git a/tests/cua_s1/test_model.py b/tests/cua_s1/test_model.py new file mode 100644 index 0000000..228e3ac --- /dev/null +++ b/tests/cua_s1/test_model.py @@ -0,0 +1,112 @@ +import pytest + +from models.cua_s1.multimodal.model import letter_ids, validate_adapter_config + + +class Tokenizer: + def encode(self, text, add_special_tokens): + assert add_special_tokens is False + return [ord(text) - 33] + + +def test_letter_readout_is_in_candidate_order(): + assert letter_ids(Tokenizer(), 3) == [32, 33, 34] + + +def test_multitoken_letters_are_rejected(): + class Bad: + def encode(self, *args, **kwargs): + return [1, 2] + + with pytest.raises(ValueError, match="single token"): + letter_ids(Bad(), 2) + + +def test_text_adapter_is_rejected_before_model_load(): + with pytest.raises(ValueError, match="multimodal"): + validate_adapter_config( + { + "peft_type": "LORA", + "r": 16, + "lora_alpha": 32, + "target_modules": ["q_proj", "k_proj"], + } + ) + + +def test_multimodal_adapter_contract(): + validate_adapter_config( + { + "peft_type": "LORA", + "r": 16, + "lora_alpha": 32, + "base_model_name_or_path": "Qwen/Qwen3.5-4B", + "target_modules": [ + "q_proj", + "k_proj", + "v_proj", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + "linear_fc1", + "linear_fc2", + ], + } + ) + + +def test_unlisted_model_files_cannot_override_verified_shards(tmp_path, monkeypatch): + import hashlib + import json + + from models.cua_s1.multimodal import model + + base, adapter = tmp_path / "base", tmp_path / "adapter" + base.mkdir() + adapter.mkdir() + (base / "config.json").write_bytes(b"{}") + manifest = tmp_path / "weights.lock.json" + manifest.write_text( + json.dumps( + { + "artifacts": [ + { + "role": "base", + "files": { + "config.json": { + "size": 2, + "sha256": hashlib.sha256(b"{}").hexdigest(), + } + }, + } + ] + } + ) + ) + monkeypatch.setattr(model, "__file__", str(tmp_path / "model.py")) + model.verify_weights(base, adapter) + (base / "model.safetensors").write_bytes(b"override") + with pytest.raises(ValueError, match="unlisted"): + model.verify_weights(base, adapter) + + +def test_all_question_lengths_are_checked_before_inference(): + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import InvalidRequest, Question, Request + + engine = object.__new__(MultimodalEngine) + first = Question("first", ("a",), ("A",), "") + second = Question("second", ("b",), ("B",), "") + forwarded = [] + + def prepare(image, question): + if question.name == "second": + raise InvalidRequest("processed prompt exceeds 4096 tokens") + return {} + + engine.prepare = prepare + engine.score = lambda inputs, q: forwarded.append(q) + with pytest.raises(InvalidRequest, match="4096"): + engine.predict(Request(None, (first, second))) + assert forwarded == [] diff --git a/tests/cua_s1/test_protocol.py b/tests/cua_s1/test_protocol.py new file mode 100644 index 0000000..0165eae --- /dev/null +++ b/tests/cua_s1/test_protocol.py @@ -0,0 +1,156 @@ +import base64 +import io +import json + +import pytest +from PIL import Image + +from models.cua_s1.multimodal.protocol import ( + InvalidRequest, + answer, + build_messages, + decode_request, + parse_request, +) + + +def image_url(fmt="PNG", size=(32, 32)): + out = io.BytesIO() + Image.new("RGB", size, "white").save(out, format=fmt) + mime = "jpeg" if fmt == "JPEG" else "png" + return f"data:image/{mime};base64," + base64.b64encode(out.getvalue()).decode() + + +def request(): + return { + "model": "cua-s1-4b-0.2", + "state": {"image": image_url()}, + "questions": { + "next": { + "type": "choice", + "instructions": "Submit the form", + "criteria": {"submit": "Submit", "cancel": "Cancel"}, + } + }, + } + + +def test_image_and_order_are_preserved(): + r = parse_request(request()) + assert r.image.mode == "RGB" and r.image.size == (32, 32) + assert r.questions[0].keys == ("submit", "cancel") + assert r.questions[0].labels == ("Submit", "Cancel") + + +def test_prompt_keeps_image_block_and_upstream_text(): + q = parse_request(request()).questions[0] + msg = build_messages(q) + assert msg[1]["content"][0]["type"] == "image" + assert msg[1]["content"][1]["text"] == ( + "Goal: Submit the form\n\nApp: Cua Driver\nTask family: closed-candidate decision\n\n" + "The current screenshot is attached.\n\nOptions:\n" + 'A. Decision "Submit" -> select\nB. Decision "Cancel" -> select\n\n' + "Answer with a single letter." + ) + + +def test_structured_values_and_escaping(): + r = request() + r["questions"]["next"]["instructions"] = {"目标": "提交"} + r["questions"]["next"]["criteria"] = {"fallback": None, "obj": {"x": '"\n'}} + q = parse_request(r).questions[0] + assert q.goal == '{"目标": "提交"}' + assert q.labels == ( + "fallback", + json.dumps(json.dumps({"x": '"\n'}, ensure_ascii=False), ensure_ascii=False)[ + 1:-1 + ], + ) + + +@pytest.mark.parametrize( + "state", + [ + {}, + {"image": "/etc/passwd"}, + {"image": "https://example.com/a.png"}, + {"image": "data:image/png;base64,!!"}, + {"image": image_url(), "text": "ignored"}, + ], +) +def test_invalid_images_and_unknown_state_fields(state): + r = request() + r["state"] = state + with pytest.raises(InvalidRequest): + parse_request(r) + + +def test_mime_mismatch_and_oversized_dimensions(): + for url in [ + image_url().replace("image/png", "image/jpeg"), + image_url(size=(2049, 1)), + ]: + r = request() + r["state"]["image"] = url + with pytest.raises(InvalidRequest): + parse_request(r) + + +@pytest.mark.parametrize( + "criteria", [{}, {str(i): "x" for i in range(27)}, {"a": 1}, {"a": True}] +) +def test_invalid_candidates(criteria): + r = request() + r["questions"]["next"]["criteria"] = criteria + with pytest.raises(InvalidRequest): + parse_request(r) + + +@pytest.mark.parametrize("kind", ["score", "noul"]) +def test_unsupported_question_rejects_entire_request(kind): + r = request() + r["questions"]["bad"] = {"type": kind, "instructions": "x"} + with pytest.raises(InvalidRequest): + parse_request(r) + + +def test_duplicate_keys_and_nonfinite_json(): + for raw in [ + b'{"model":1,"model":2}', + b'{"x":NaN}', + b'{"x":Infinity}', + b"[]", + b"not json", + ]: + with pytest.raises(InvalidRequest): + decode_request(raw) + + +def test_entropy_confidence_and_earliest_tie(): + q = parse_request(request()).questions[0] + result = answer(q, [0.5, 0.5]) + assert result["choice"] == "submit" + assert result["confidence"] == 0.0 + assert result["probabilities"] == {"submit": 0.5, "cancel": 0.5} + r = request() + r["questions"]["next"]["criteria"] = {"only": "Only"} + assert answer(parse_request(r).questions[0], [1.0])["confidence"] == 1.0 + + +def test_jpeg_supported(): + r = request() + r["state"]["image"] = image_url("JPEG") + assert parse_request(r).image.mode == "RGB" + + +@pytest.mark.parametrize( + "token", ["<|image_pad|>", "<|video_pad|>", "<|vision_start|>", "<|vision_end|>"] +) +@pytest.mark.parametrize("field", ["instructions", "criteria"]) +def test_media_control_tokens_are_rejected(token, field): + value = request() + value["questions"]["next"][field] = ( + token if field == "instructions" else {"a": {"text": token}} + ) + with pytest.raises(InvalidRequest, match="control token"): + parse_request(value) diff --git a/tests/cua_s1/test_server.py b/tests/cua_s1/test_server.py new file mode 100644 index 0000000..89eddaa --- /dev/null +++ b/tests/cua_s1/test_server.py @@ -0,0 +1,85 @@ +import json +import threading +import urllib.error +import urllib.request + +import pytest +from test_protocol import request + +from models.cua_s1.multimodal.protocol import MAX_BODY +from models.cua_s1.multimodal.server import WorkerServer + + +class Engine: + def predict(self, parsed): + return { + "model": "test:multimodal", + "answers": {q.name: {"type": "choice"} for q in parsed.questions}, + } + + +@pytest.fixture +def worker(): + server = WorkerServer(("127.0.0.1", 0), Engine()) + thread = threading.Thread(target=server.serve_forever, daemon=True) + thread.start() + yield server, f"http://127.0.0.1:{server.server_port}" + server.shutdown() + server.server_close() + thread.join() + + +def call(url, body=None, **headers): + data = None if body is None else json.dumps(body).encode() + req = urllib.request.Request( + url, data=data, headers={"Content-Type": "application/json", **headers} + ) + try: + with urllib.request.urlopen(req, timeout=5) as response: + return response.status, json.load(response) + except urllib.error.HTTPError as response: + return response.code, json.load(response) + + +def test_health_and_prediction(worker): + _, url = worker + assert call(url + "/health")[0] == 200 + status, body = call(url + "/v1/systemone", request()) + assert status == 200 and "next" in body["answers"] + + +def test_invalid_question_never_reaches_model(worker): + _, url = worker + r = request() + r["questions"]["next"]["type"] = "noul" + assert call(url + "/v1/systemone", r)[0] == 422 + + +def test_busy_worker_rejects_instead_of_queueing_gpu_work(worker): + server, url = worker + server.inference_lock.acquire() + try: + assert call(url + "/health")[0] == 200 + assert call(url + "/v1/systemone", request())[0] == 503 + finally: + server.inference_lock.release() + + +def test_body_limit_checked_before_reading(worker): + _, url = worker + assert ( + call(url + "/v1/systemone", {}, **{"Content-Length": str(MAX_BODY + 1)})[0] + == 413 + ) + + +def test_model_failure_is_not_reported_as_success(worker): + server, url = worker + + def fail(_): + raise RuntimeError("private file or input must not leak") + + server.engine.predict = fail + status, body = call(url + "/v1/systemone", request()) + assert status == 500 and "private" not in json.dumps(body) + assert not server.inference_lock.locked() From 767e21172a303e36da4765fe59027db63f09acd1 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 10:59:47 +0800 Subject: [PATCH 02/75] Add reproducible multimodal profiling matrix --- recipe/cua_s1/README.md | 4 + recipe/cua_s1/profile_multimodal.py | 425 ++++++++++++++++++++++++++++ recipe/cua_s1/profiling.md | 151 ++++++++++ tests/cua_s1/test_profiling.py | 392 +++++++++++++++++++++++++ 4 files changed, 972 insertions(+) create mode 100644 recipe/cua_s1/profile_multimodal.py create mode 100644 recipe/cua_s1/profiling.md create mode 100644 tests/cua_s1/test_profiling.py diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index cec424c..6dac894 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -107,6 +107,10 @@ branches, including 50 vision projection modules (178 total adapted modules). ## Reproduce correctness and profiling +For the staged image-size, text-length and same-image question-count experiment, +see [multimodal profiling](profiling.md), including hardware requirements and +separate unprofiled timing and instrumented traces. + ```sh git clone https://github.com/trycua/cua.git /tmp/cua-reference git -C /tmp/cua-reference checkout 0e75660ce4c2edda519e0c795fa3ad98abf4e76f diff --git a/recipe/cua_s1/profile_multimodal.py b/recipe/cua_s1/profile_multimodal.py new file mode 100644 index 0000000..3b66695 --- /dev/null +++ b/recipe/cua_s1/profile_multimodal.py @@ -0,0 +1,425 @@ +"""Profile a deterministic Cua-S1 workload matrix without instrumenting latency samples.""" + +from __future__ import annotations + +import argparse +import copy +import hashlib +import json +import statistics +import subprocess +import sys +import time +from contextlib import contextmanager +from pathlib import Path + +GOAL = "Save the changed display name." + + +def case_matrix(): + return [ + { + "id": f"{w}x{h}-{goal}-q{questions}", + "size": [w, h], + "goal": goal, + "goal_repetitions": 1 if goal == "short" else 64, + "questions": questions, + } + for w, h in [(320, 240), (640, 480)] + for goal in ["short", "long"] + for questions in [1, 2, 4, 8] + ] + + +def fixture(case, folder): + from make_example import make_example + + folder.mkdir(parents=True, exist_ok=True) + value = make_example(folder / f"{case['id']}.png", tuple(case["size"])) + question = value["questions"]["next"] + question["instructions"] = " ".join([GOAL] * case["goal_repetitions"]) + value["questions"] = { + f"q{i + 1}": copy.deepcopy(question) for i in range(case["questions"]) + } + write_json(folder / f"{case['id']}.json", value) + return value + + +def parse_args(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", type=Path) + parser.add_argument("--output", type=Path) + parser.add_argument("--list-cases", action="store_true") + parser.add_argument( + "--case", action="append", default=[], help="case id; repeatable" + ) + parser.add_argument( + "--profile", + action="append", + default=[], + help="case id for one traced request; repeatable", + ) + for name, default in [("warmup", 5), ("runs", 2), ("iterations", 50)]: + parser.add_argument(f"--{name}", type=int, default=default) + args = parser.parse_args(argv) + known = {case["id"] for case in case_matrix()} + for name in ("warmup", "runs", "iterations"): + if getattr(args, name) <= 0: + parser.error(f"--{name} must be positive") + for name in ("case", "profile"): + selected = getattr(args, name) + if len(selected) != len(set(selected)) or set(selected) - known: + parser.error(f"--{name} contains duplicate or unknown case ids") + if set(args.profile) - set(args.case or known): + parser.error("--profile cases must also be selected by --case") + if not args.list_cases and (args.weights is None or args.output is None): + parser.error("--weights and --output are required unless --list-cases is used") + return args + + +def write_json(path, value): + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.write_text(json.dumps(value, indent=2, allow_nan=False) + "\n") + temporary.replace(path) + + +def repository_state(root): + def git(*args): + return subprocess.check_output( + ["git", "-C", str(root), *args], stderr=subprocess.PIPE + ) + + try: + revision = git("rev-parse", "HEAD").decode().strip() + status = git("status", "--porcelain=v1", "-z") + digest = hashlib.sha256(git("diff", "HEAD", "--binary")) + untracked = git("ls-files", "--others", "--exclude-standard", "-z") + for name in sorted(filter(None, untracked.split(b"\0"))): + path = root / name.decode() + content = path.read_bytes() + digest.update(name + b"\0" + hashlib.sha256(content).digest()) + return { + "revision": revision, + "dirty": bool(status), + "diff_sha256": digest.hexdigest(), + "untracked_files": [x.decode() for x in untracked.split(b"\0") if x], + } + except (subprocess.CalledProcessError, OSError) as exc: + return {"revision": None, "dirty": None, "diff_sha256": None, "error": str(exc)} + + +def benchmark(engine, request, *, warmup, runs, iterations): + import torch + from evaluate_multimodal import measure, quantiles + + if min(warmup, runs, iterations) <= 0: + raise ValueError("warmup, runs and iterations must be positive") + torch.cuda.synchronize() + start = time.perf_counter() + for _ in range(warmup): + engine.predict(request) + torch.cuda.synchronize() + warmup_total_ms = (time.perf_counter() - start) * 1000 + result = [] + for run in range(runs): + torch.cuda.reset_peak_memory_stats() + # Indexing drops the response immediately; measured calls have no hooks/ranges. + latencies = [ + measure(lambda: engine.predict(request))[1] for _ in range(iterations) + ] + result.append( + { + "run": run + 1, + "latencies_ms": latencies, + **quantiles(latencies), + "requests_per_second_serial": 1000 / statistics.mean(latencies), + "peak_allocated_bytes": torch.cuda.max_memory_allocated(), + "peak_reserved_bytes": torch.cuda.max_memory_reserved(), + } + ) + return {"warmup_total_ms": warmup_total_ms, "runs": result} + + +@contextmanager +def instrument(engine, record): + """Scope wrappers/hooks around existing inference, including exception cleanup.""" + modules = list(engine.model.named_modules()) + discovered = {} + for stage, leaf in [ + ("vision", "visual"), + ("language", "language_model"), + ("output_projection", "lm_head"), + ]: + matches = [ + (name, module) for name, module in modules if name.split(".")[-1] == leaf + ] + if len(matches) != 1: + raise ValueError( + f"{stage}: expected one {leaf} module, found {[n for n, _ in matches]}" + ) + discovered[stage] = matches[0][0] + active, handles, readout = [], [], [] + missing = object() + originals = { + name: engine.__dict__.get(name, missing) for name in ("prepare", "score") + } + prepare, score = engine.prepare, engine.score + + def enter(name): + span = record("cua." + name) + span.__enter__() + active.append(span) + return span + + def leave(span): + active.remove(span) + span.__exit__(*sys.exc_info()) + + def attach(module, stage, is_root=False): + spans = [] + + def before(module, inputs): + spans.append(enter(stage)) + + def after(module, inputs, output): + if spans: + leave(spans.pop()) + if is_root and output is not None: + readout.append(enter("readout")) + + handles.append(module.register_forward_pre_hook(before)) + handles.append(module.register_forward_hook(after, always_call=True)) + + class Transfer: + def __init__(self, inputs): + self.inputs = inputs + + def to(self, *args, **kwargs): + with record("cua.transfer"): + return self.inputs.to(*args, **kwargs) + + def traced_prepare(*args, **kwargs): + with record("cua.prepare"): + return prepare(*args, **kwargs) + + def traced_score(inputs, question): + try: + return score(Transfer(inputs), question) + finally: + while readout: + leave(readout.pop()) + + try: + attach(engine.model, "forward", is_root=True) + for stage, name in discovered.items(): + attach(dict(modules)[name], stage) + engine.prepare, engine.score = traced_prepare, traced_score + yield discovered + finally: + for handle in reversed(handles): + handle.remove() + while active: + leave(active[-1]) + for name, original in originals.items(): + if original is missing: + engine.__dict__.pop(name, None) + else: + setattr(engine, name, original) + + +def profile_request(engine, request, trace): + import torch + + expected = engine.predict(request) + torch.cuda.synchronize() + with ( + torch.profiler.profile( + activities=[ + torch.profiler.ProfilerActivity.CPU, + torch.profiler.ProfilerActivity.CUDA, + ], + record_shapes=True, + ) as prof, + instrument(engine, torch.profiler.record_function) as modules, + ): + actual = engine.predict(request) + torch.cuda.synchronize() + prof.export_chrome_trace(str(trace)) + if actual != expected: + raise ValueError("instrumentation parity failed (not an upstream parity check)") + operators = [ + { + "op": item.key, + "input_shapes": item.input_shapes, + "count": item.count, + "cpu_time_us": item.cpu_time_total, + "self_cpu_time_us": item.self_cpu_time_total, + "cuda_time_us": item.device_time_total, + "self_cuda_time_us": item.self_device_time_total, + } + for item in prof.key_averages(group_by_input_shape=True) + ] + stage_counts = {} + for stage in ( + "prepare", + "transfer", + "forward", + "vision", + "language", + "output_projection", + "readout", + ): + count = sum(item["count"] for item in operators if item["op"] == "cua." + stage) + if count != len(request.questions): + raise ValueError( + f"{stage}: observed {count} ranges; expected {len(request.questions)}" + ) + stage_counts[stage] = count + with trace.open("rb") as handle: + trace_sha256 = hashlib.file_digest(handle, "sha256").hexdigest() + return { + "trace": trace.name, + "trace_sha256": trace_sha256, + "trace_bytes": trace.stat().st_size, + "instrumentation_exact_parity": True, + "modules": modules, + "request_count": 1, + "forward_count": stage_counts["forward"], + "stage_counts": stage_counts, + "timing_note": "Inclusive nested ranges overlap; do not add them. CUDA times are attributed kernel durations, not wall time. Readout starts after root forward; transfer covers inputs.to().", + "operators": sorted( + operators, key=lambda x: x["self_cuda_time_us"], reverse=True + ), + } + + +def input_metadata(engine, request): + from evaluate_multimodal import fingerprint + + from models.cua_s1.multimodal.model import MAX_TOKENS + + result = [] + for question in request.questions: + inputs = engine.prepare(request.image, question) + tokens = int(inputs["input_ids"].shape[-1]) + if tokens > MAX_TOKENS: + raise ValueError( + f"{question.name}: processed prompt exceeds {MAX_TOKENS} tokens" + ) + result.append( + { + "question": question.name, + "input_tokens": tokens, + "goal_tokens": len( + engine.tokenizer.encode(question.goal, add_special_tokens=False) + ), + "inputs": fingerprint(inputs), + } + ) + return result + + +def main(argv=None): + args = parse_args(argv) + selected = [ + case for case in case_matrix() if not args.case or case["id"] in args.case + ] + if args.list_cases: + print(json.dumps(selected, indent=2)) + return 0 + if (args.output / "report.json").exists(): + raise ValueError( + "output already contains report.json; use a fresh output directory" + ) + report = { + "schema_version": 1, + "status": "running", + "repository": repository_state(Path(__file__).resolve().parents[2]), + "config": { + "warmup": args.warmup, + "runs": args.runs, + "iterations": args.iterations, + "weights": str(args.weights.resolve()), + "profile": args.profile, + "batch_size": 1, + "concurrency": 1, + "timed_scope": "engine.predict(request), synchronized; excludes parse_request and metadata preparation", + }, + "cases": [], + } + args.output.mkdir(parents=True, exist_ok=True) + write_json(args.output / "report.json", report) + current = None + try: + from evaluate_multimodal import environment, measure + + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + report["environment"] = environment() + engine, report["load_ms"] = measure( + lambda: MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + ) + report["adapter_modules"] = engine.adapter_modules + for case in selected: + current = {**case, "status": "running"} + report["cases"].append(current) + write_json(args.output / "report.json", report) + value = fixture(case, args.output / "fixtures") + request = parse_request(value) + current["fixture_sha256"] = hashlib.sha256( + json.dumps(value, sort_keys=True).encode() + ).hexdigest() + current["questions_metadata"] = input_metadata(engine, request) + current["input_tokens"] = sum( + q["input_tokens"] for q in current["questions_metadata"] + ) + current.update( + benchmark( + engine, + request, + warmup=args.warmup, + runs=args.runs, + iterations=args.iterations, + ) + ) + current["status"] = "complete" + write_json(args.output / f"{case['id']}.json", current) + write_json(args.output / "report.json", report) + print( + f"{case['id']}: complete ({args.runs * args.iterations} latency samples)", + flush=True, + ) + # Every baseline finishes before profiler state can affect later measurements. + for current in report["cases"]: + if current["id"] not in args.profile: + continue + value = json.loads( + (args.output / "fixtures" / f"{current['id']}.json").read_text() + ) + request = parse_request(value) + current["profile"] = profile_request( + engine, request, args.output / f"{current['id']}.trace.json" + ) + write_json(args.output / f"{current['id']}.json", current) + write_json(args.output / "report.json", report) + print(f"{current['id']}: trace complete", flush=True) + report["status"] = "complete" + except Exception as exc: # noqa: BLE001 - preserve partial results for any run failure. + error = {"type": type(exc).__name__, "message": str(exc)} + report.update(status="failed", error=error) + if current is not None: + current.update(status="failed", error=error) + write_json(args.output / f"{current['id']}.json", current) + print(f"Profiling failed: {error}", file=sys.stderr, flush=True) + return 1 + finally: + write_json(args.output / "report.json", report) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/recipe/cua_s1/profiling.md b/recipe/cua_s1/profiling.md new file mode 100644 index 0000000..0f4e0f2 --- /dev/null +++ b/recipe/cua_s1/profiling.md @@ -0,0 +1,151 @@ +# Multimodal profiling experiment + +This experiment attributes the existing unmerged BF16 Transformers/PEFT worker's +cost before changing inference. It extends the [original baseline](experiments/README.md) +with image-size, text-length and same-image question-count sweeps. It does not +implement image reuse, native CUDA kernels, CUDA Graphs or batching. + +## Hardware and environment + +Recommended for comparison with the original measurements: + +| Resource | Configuration | +| --- | --- | +| GPU | One exclusive RTX 4090, 24 GB; no competing GPU jobs | +| CPU | 8–16 allocated vCPUs; record actual thread settings | +| Host RAM | 32 GB minimum planning allowance; 64 GB preferred | +| Disk | 80–100 GB available SSD for a fresh environment, weights and traces | +| OS | Linux x86_64, Ubuntu 22.04 or 24.04 | +| Python | 3.12 | +| CUDA runtime | Match the original PyTorch `2.14.0+cu130` wheel where available | + +These are experiment planning recommendations, not measured maximum-input +requirements. The original 456-token, one-question run peaked at 8.84 GiB of +allocated GPU memory. Longer inputs and profiler tensor retention may use more. +Start with one short case before the full matrix. Stop and report an OOM rather +than silently changing precision, truncating inputs or reducing question counts. +The eight questions run serially; they are not a batch of eight. + +If reusing the previous environment and weights, substantially less free disk is +needed. Check the available space before collecting traces. A separate CUDA +Toolkit is not required for this PyTorch profiling experiment; native kernel +development is a subsequent task. CUDA 13 requires a compatible R580-or-newer +driver; the previous run used 595.71.05. See NVIDIA's +[compatibility documentation](https://docs.nvidia.com/deploy/cuda-compatibility/minor-version-compatibility.html). + +Use the [worker setup](README.md#setup) and pinned requirements. Restore the +previous environment when possible. Do not install flash-linear-attention, +causal-conv1d, merge LoRA or enable compilation in the baseline environment. +Record any environment difference and establish a new baseline before comparing +performance. GPU scheduling/allocation rules of the host still apply. + +From the repository root, with its Python environment activated: + +```sh +nvidia-smi --query-gpu=name,memory.total,memory.used,driver_version --format=csv +df -h . +python -c 'import torch; print(torch.__version__, torch.version.cuda, torch.cuda.is_available())' +python -m pip freeze > /tmp/cua-profile-packages.txt +PYTHONPATH=src python recipe/cua_s1/download_weights.py --dest weights +PYTHONPATH=src python -m pytest tests/cua_s1 -q +``` + +The downloader verifies pinned sizes and hashes. Existing valid weights can be +reused. Keep model weights outside version control and select the allocated GPU +with `CUDA_VISIBLE_DEVICES` when appropriate. + +## Hypotheses and controls + +1. Repeated questions about one image repeat image preprocessing and vision + computation. Measure how much those stages contribute before implementing + reuse; do not assume they dominate. +2. Longer instructions and larger processed image grids change the mix of vision, + language and projection work. Record actual token counts and tensor shapes; + the fixture names are input categories, not promised token lengths. +3. The trace instrumentation should preserve the response exactly. Comparing + traced and ordinary responses is an instrumentation check, not an independent + upstream parity result. + +Keep the GPU, model revisions, package versions, BF16 dtype, unmerged adapter, +full-logit path, thread configuration and serial execution fixed. Correctness +against the pinned upstream implementation remains the separate +[`evaluate_multimodal.py` reference/candidate procedure](README.md#reproduce-correctness-and-profiling). +Run those modes before accepting a new environment or inference change. Do not +use old GPU reports as evidence for a newly changed path. + +## Run + +List the 16 cases without importing PyTorch or loading weights: + +```sh +PYTHONPATH=src python recipe/cua_s1/profile_multimodal.py --list-cases +``` + +The matrix covers 320×240 and 640×480 PNGs, short/long instructions and 1/2/4/8 +questions per request. Each request uses a single synthetic image with three +candidate actions per question. Images contain no private user data. + +Start with a feasibility run, using a new output directory: + +```sh +PYTHONPATH=src python recipe/cua_s1/profile_multimodal.py \ + --weights weights --output /tmp/cua-profile-smoke \ + --case 320x240-short-q1 --profile 320x240-short-q1 \ + --warmup 1 --runs 1 --iterations 2 +``` + +This checks execution and trace generation; two samples are not a performance +claim. Next run the complete matrix, profiling only two representative cases: + +```sh +PYTHONPATH=src python recipe/cua_s1/profile_multimodal.py \ + --weights weights --output /tmp/cua-profile-measured \ + --profile 640x480-short-q1 --profile 640x480-short-q8 \ + --warmup 5 --runs 2 --iterations 50 +``` + +Use repeated `--case` arguments for a selected subset. Retain both successful +and failed-run manifests. A failed case must be investigated before claiming +coverage of the full matrix. Reserve detailed traces for selected cases: shape +recording adds overhead and memory pressure. + +Completed cases are written before the next case begins. Measurements within a +case are saved after all its runs finish; an interruption during that case can +lose its samples. This script does not resume a partial run. Repeat the failed +case in a new output directory and retain the original failure record. + +## Interpretation and publication + +The unprofiled benchmark times `engine.predict` after JSON parsing and image +decoding, with CUDA synchronization at the boundaries. It includes processor +work, transfers, forward passes, candidate readout and response construction. +It excludes HTTP, admission/queueing, input decoding, load, setup metadata and +profiler overhead. Startup and per-case warmup are separate observations. + +Each case retains raw latency samples and reports p50/p95, serial request rate, +question count and GPU memory peaks. Serial question rate, if derived, is request +rate multiplied by question count; it is not independent concurrent throughput. +Do not infer p99 reliability or GUI task quality from these measurements. + +Trace ranges and grouped operators describe instrumented execution. Inclusive +stage ranges overlap with child operations and kernels; do not sum them or use +their totals as fractions of the unprofiled request time. CPU range duration is +not necessarily GPU execution time. Module-name discovery must identify the +vision, language and output-head boundaries explicitly. Missing boundaries are +an experiment failure, not zero time. See the +[PyTorch profiler documentation](https://docs.pytorch.org/docs/stable/profiler) +for shape-recording overhead and trace semantics. + +For a reviewable experiment PR, include: + +- Commit/source provenance, environment, exact commands and fixture settings. +- New upstream reference/candidate parity results, or a clear statement that + they were not rerun. +- Both warm runs' raw samples, numerical summaries, memory peaks and stage/module + mappings; document all exclusions and failed cases. +- A bounded next optimization hypothesis supported by the profile. + +Keep large Chrome traces outside Git, with checksums in the experiment record. +Publish no speedup until an optimized implementation has a paired comparison +against this baseline. Native interface design, image reuse and kernel changes +remain separate follow-ups. diff --git a/tests/cua_s1/test_profiling.py b/tests/cua_s1/test_profiling.py new file mode 100644 index 0000000..70e5b1c --- /dev/null +++ b/tests/cua_s1/test_profiling.py @@ -0,0 +1,392 @@ +"""CPU contracts for the profiling harness; no weights or torch installation required.""" + +import importlib.util +import json +import subprocess +import sys +from contextlib import contextmanager +from pathlib import Path +from types import SimpleNamespace + +import pytest + +RECIPE = Path(__file__).resolve().parents[2] / "recipe/cua_s1" + + +@pytest.fixture +def profiling(monkeypatch): + path = RECIPE / "profile_multimodal.py" + assert path.exists(), "profiling harness is not implemented" + monkeypatch.syspath_prepend(str(RECIPE)) + spec = importlib.util.spec_from_file_location("profiling", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_matrix_is_deterministic_and_valid(profiling, tmp_path): + from models.cua_s1.multimodal.protocol import parse_request + + matrix = profiling.case_matrix() + assert len(matrix) == len({c["id"] for c in matrix}) == 16 + assert matrix[-1]["id"] == "640x480-long-q8" + for case in matrix: + one = profiling.fixture(case, tmp_path / "one") + two = profiling.fixture(case, tmp_path / "two") + assert one == two + request = parse_request(one) + assert request.image.size == tuple(case["size"]) + assert len(request.questions) == case["questions"] + assert len({q.goal for q in request.questions}) == 1 + assert len(request.questions[0].goal) < 16384 + + +@pytest.mark.parametrize("option", ["--warmup", "--runs", "--iterations"]) +@pytest.mark.parametrize("value", ["0", "-1"]) +def test_nonpositive_counts_are_rejected(profiling, option, value): + with pytest.raises(SystemExit): + profiling.parse_args(["--list-cases", option, value]) + + +@pytest.mark.parametrize( + "selectors", + [ + ["--case", "unknown"], + ["--case", "320x240-short-q1", "--case", "320x240-short-q1"], + ["--profile", "unknown"], + ["--profile", "320x240-short-q1", "--profile", "320x240-short-q1"], + ["--case", "320x240-short-q1", "--profile", "640x480-long-q8"], + ], +) +def test_invalid_selection_rejected(profiling, selectors): + with pytest.raises(SystemExit): + profiling.parse_args(["--list-cases", *selectors]) + + +def test_help_and_list_cases_do_not_import_torch(profiling): + for flag in ["--help", "--list-cases"]: + command = ( + "import runpy,sys;sys.modules['torch']=None;" + f"sys.path.insert(0,{str(RECIPE)!r});" + f"sys.argv=['profile_multimodal.py',{flag!r}];" + f"runpy.run_path({str(RECIPE / 'profile_multimodal.py')!r},run_name='__main__')" + ) + result = subprocess.run( + [sys.executable, "-c", command], capture_output=True, text=True, check=False + ) + assert result.returncode == 0, result.stderr + assert "320x240-short-q1" in result.stdout or flag == "--help" + + +def test_measurement_accounting_and_synchronization(profiling, monkeypatch): + events = [] + cuda = SimpleNamespace( + synchronize=lambda: events.append("sync"), + reset_peak_memory_stats=lambda: events.append("reset"), + max_memory_allocated=lambda: 100, + max_memory_reserved=lambda: 200, + ) + monkeypatch.setitem(sys.modules, "torch", SimpleNamespace(cuda=cuda)) + engine = SimpleNamespace(predict=lambda request: events.append("predict")) + result = profiling.benchmark(engine, object(), warmup=2, runs=2, iterations=3) + assert result["warmup_total_ms"] >= 0 + assert len(result["runs"]) == 2 + assert all(len(run["latencies_ms"]) == 3 for run in result["runs"]) + assert ( + events + == ["sync", "predict", "predict", "sync", "reset"] + + ["sync", "predict", "sync"] * 3 + + ["reset"] + + ["sync", "predict", "sync"] * 3 + ) + assert all(run["peak_allocated_bytes"] == 100 for run in result["runs"]) + + +class Module: + def __init__(self, name, events, fail=False): + self.name, self.events, self.fail = name, events, fail + self.pre, self.post = [], [] + self.children = [] + + def named_modules(self): + return iter([(self.name, self), *self.children]) + + def register_forward_pre_hook(self, hook): + self.pre.append(hook) + return SimpleNamespace(remove=lambda: self.pre.remove(hook)) + + def register_forward_hook(self, hook, always_call=False): + assert always_call + self.post.append(hook) + return SimpleNamespace(remove=lambda: self.post.remove(hook)) + + def __call__(self): + output = None + try: + for hook in self.pre: + hook(self, ()) + for _, child in self.children: + child() + if self.fail: + raise RuntimeError("forward failed") + output = "output" + return output + finally: + for hook in self.post: + hook(self, (), output) + + +def fake_engine(events, fail=False): + root = Module("", events) + root.children = [ + (name, Module(name, events, fail=fail and name == "visual")) + for name in ("visual", "language_model", "lm_head") + ] + + class Engine: + model = root + + def prepare(self, image, question): + return SimpleNamespace(to=lambda device: events.append("transfer")) + + def score(self, inputs, question): + inputs.to("cuda") + self.model() + events.append("readout") + return [1.0] + + def predict(self, request): + return self.score(self.prepare(None, None), None) + + return Engine() + + +@pytest.mark.parametrize("fail", [False, True]) +def test_instrumentation_is_scoped_and_ranges_close(profiling, fail): + events, active = [], [] + engine = fake_engine(events, fail) + original = dict(engine.__dict__) + + @contextmanager + def record(name): + active.append(name) + events.append("enter:" + name) + try: + yield + finally: + assert active.pop() == name + events.append("exit:" + name) + + try: + with profiling.instrument(engine, record) as discovered: + assert set(discovered) >= {"vision", "language", "output_projection"} + assert engine.predict(None) == [1.0] + except RuntimeError: + assert fail + assert not active + assert engine.__dict__ == original + assert all(not m.pre and not m.post for _, m in engine.model.named_modules()) + assert "enter:cua.prepare" in events + assert "enter:cua.transfer" in events + if not fail: + assert ( + events.index("enter:cua.readout") + < events.index("readout") + < events.index("exit:cua.readout") + ) + events.clear() + engine.predict(None) + assert events == ["transfer", "readout"] + + +def test_missing_modules_fail_before_installing_hooks(profiling): + engine = fake_engine([]) + engine.model.children.pop() + with ( + pytest.raises(ValueError, match="output_projection"), + profiling.instrument(engine, None), + ): + pass + assert all(not m.pre and not m.post for _, m in engine.model.named_modules()) + + +def test_provenance_hash_includes_untracked_sources(profiling, tmp_path): + subprocess.run(["git", "init", "-q", str(tmp_path)], check=True) + subprocess.run( + [ + "git", + "-C", + str(tmp_path), + "-c", + "user.name=Test", + "-c", + "user.email=test@example.com", + "commit", + "--allow-empty", + "-qm", + "initial", + ], + check=True, + ) + path = tmp_path / "new.py" + path.write_text("one") + first = profiling.repository_state(tmp_path) + path.write_text("two") + second = profiling.repository_state(tmp_path) + assert first["dirty"] and second["dirty"] + assert first["diff_sha256"] != second["diff_sha256"] + assert first["revision"] == second["revision"] + assert json.loads(json.dumps(second)) == second + + +@pytest.mark.parametrize("profile_fails", [False, True]) +def test_all_baselines_precede_profiles_and_results_survive_failure( + profiling, monkeypatch, tmp_path, profile_fails +): + import evaluate_multimodal + + from models.cua_s1.multimodal import model + + events = [] + engine = SimpleNamespace(adapter_modules=178) + monkeypatch.setattr(model, "MultimodalEngine", lambda *args: engine) + monkeypatch.setattr(evaluate_multimodal, "environment", lambda: {"gpu": "fake"}) + monkeypatch.setattr(evaluate_multimodal, "measure", lambda call: (call(), 1.0)) + monkeypatch.setattr( + profiling, "repository_state", lambda root: {"revision": "fake"} + ) + monkeypatch.setattr( + profiling, "input_metadata", lambda *args: [{"input_tokens": 42}] + ) + + def benchmark(*args, **kwargs): + events.append("baseline") + return {"warmup_total_ms": 1.0, "runs": [{"latencies_ms": [1.0]}]} + + def profile(*args): + events.append("profile") + if profile_fails: + raise RuntimeError("profile failed") + return {"instrumentation_exact_parity": True} + + monkeypatch.setattr(profiling, "benchmark", benchmark) + monkeypatch.setattr(profiling, "profile_request", profile) + first, second = "320x240-short-q1", "320x240-short-q2" + code = profiling.main( + [ + "--weights", + str(tmp_path / "weights"), + "--output", + str(tmp_path), + "--case", + first, + "--case", + second, + "--profile", + first, + ] + ) + assert events == ["baseline", "baseline", "profile"] + assert code == int(profile_fails) + report = json.loads((tmp_path / "report.json").read_text()) + assert report["status"] == ("failed" if profile_fails else "complete") + assert json.loads((tmp_path / f"{second}.json").read_text())["status"] == "complete" + saved = json.loads((tmp_path / f"{first}.json").read_text()) + assert saved["runs"] == [{"latencies_ms": [1.0]}] + if profile_fails: + assert saved["error"]["message"] == "profile failed" + + +@pytest.mark.parametrize("missing_stage", [None, "vision"]) +def test_trace_has_shapes_times_checksum_and_exact_parity( + profiling, monkeypatch, tmp_path, missing_stage +): + import hashlib + + events = [] + engine = fake_engine(events) + + @contextmanager + def record(name): + yield + + class Profiler: + def __enter__(self): + events.append("profiler") + return self + + def __exit__(self, *args): + pass + + def export_chrome_trace(self, path): + Path(path).write_bytes(b"trace") + + def key_averages(self, group_by_input_shape): + assert group_by_input_shape + return [ + SimpleNamespace( + key=key, + input_shapes=[[1, 2]], + count=1, + cpu_time_total=8, + self_cpu_time_total=3, + device_time_total=6, + self_device_time_total=2, + ) + for key in ["op"] + + [ + "cua." + name + for name in [ + "prepare", + "transfer", + "forward", + "vision", + "language", + "output_projection", + "readout", + ] + if name != missing_stage + ] + ] + + def start(**kwargs): + assert kwargs["record_shapes"] is True + return Profiler() + + torch = SimpleNamespace( + cuda=SimpleNamespace(synchronize=lambda: None), + profiler=SimpleNamespace( + profile=start, + record_function=record, + ProfilerActivity=SimpleNamespace(CPU="cpu", CUDA="cuda"), + ), + ) + monkeypatch.setitem(sys.modules, "torch", torch) + if missing_stage: + with pytest.raises(ValueError, match="vision.*0.*1"): + profiling.profile_request( + engine, SimpleNamespace(questions=[None]), tmp_path / "trace.json" + ) + return + result = profiling.profile_request( + engine, SimpleNamespace(questions=[None]), tmp_path / "trace.json" + ) + assert events[:3] == ["transfer", "readout", "profiler"] + assert result["instrumentation_exact_parity"] is True + assert result["forward_count"] == 1 + assert result["stage_counts"] == { + name: 1 + for name in [ + "prepare", + "transfer", + "forward", + "vision", + "language", + "output_projection", + "readout", + ] + } + assert result["trace_sha256"] == hashlib.sha256(b"trace").hexdigest() + assert result["trace_bytes"] == 5 + assert result["operators"][0]["input_shapes"] == [[1, 2]] + assert result["operators"][0]["self_cuda_time_us"] == 2 From ebd7adc7e43d1c8ef2144cc0426295bf22abfa3b Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:21:43 +0800 Subject: [PATCH 03/75] Distinguish profiler device annotations and support trace-only runs --- recipe/cua_s1/profile_multimodal.py | 60 +++++++++++++++------- recipe/cua_s1/profiling.md | 30 ++++++++++- tests/cua_s1/test_profiling.py | 79 ++++++++++++++++++++++++++++- 3 files changed, 150 insertions(+), 19 deletions(-) diff --git a/recipe/cua_s1/profile_multimodal.py b/recipe/cua_s1/profile_multimodal.py index 3b66695..38a6b59 100644 --- a/recipe/cua_s1/profile_multimodal.py +++ b/recipe/cua_s1/profile_multimodal.py @@ -50,6 +50,11 @@ def parse_args(argv=None): parser.add_argument("--weights", type=Path) parser.add_argument("--output", type=Path) parser.add_argument("--list-cases", action="store_true") + parser.add_argument( + "--trace-only", + action="store_true", + help="trace selected --profile cases without baseline measurements", + ) parser.add_argument( "--case", action="append", default=[], help="case id; repeatable" ) @@ -72,6 +77,12 @@ def parse_args(argv=None): parser.error(f"--{name} contains duplicate or unknown case ids") if set(args.profile) - set(args.case or known): parser.error("--profile cases must also be selected by --case") + if args.trace_only and ( + not args.profile or (args.case and set(args.case) != set(args.profile)) + ): + parser.error( + "--trace-only requires --profile; when supplied, --case must match --profile exactly" + ) if not args.list_cases and (args.weights is None or args.output is None): parser.error("--weights and --output are required unless --list-cases is used") return args @@ -250,6 +261,8 @@ def profile_request(engine, request, trace): operators = [ { "op": item.key, + "device_type": str(item.device_type), + "is_user_annotation": item.is_user_annotation, "input_shapes": item.input_shapes, "count": item.count, "cpu_time_us": item.cpu_time_total, @@ -269,7 +282,13 @@ def profile_request(engine, request, trace): "output_projection", "readout", ): - count = sum(item["count"] for item in operators if item["op"] == "cua." + stage) + count = sum( + item["count"] + for item in operators + if item["op"] == "cua." + stage + and item["device_type"] == "DeviceType.CPU" + and item["is_user_annotation"] + ) if count != len(request.questions): raise ValueError( f"{stage}: observed {count} ranges; expected {len(request.questions)}" @@ -284,9 +303,10 @@ def profile_request(engine, request, trace): "instrumentation_exact_parity": True, "modules": modules, "request_count": 1, + "uninstrumented_reference_requests": 1, "forward_count": stage_counts["forward"], "stage_counts": stage_counts, - "timing_note": "Inclusive nested ranges overlap; do not add them. CUDA times are attributed kernel durations, not wall time. Readout starts after root forward; transfer covers inputs.to().", + "timing_note": "Count invocations from CPU user annotations only. CPU and CUDA rows with the same name are distinct views, not additional invocations; do not add their durations. Inclusive nested ranges overlap. CUDA fields are raw profiler aggregates and may include synthetic annotations, not disjoint kernel time or wall time. Readout starts after root forward; transfer covers inputs.to().", "operators": sorted( operators, key=lambda x: x["self_cuda_time_us"], reverse=True ), @@ -321,8 +341,9 @@ def input_metadata(engine, request): def main(argv=None): args = parse_args(argv) + selectors = args.profile if args.trace_only else args.case selected = [ - case for case in case_matrix() if not args.case or case["id"] in args.case + case for case in case_matrix() if not selectors or case["id"] in selectors ] if args.list_cases: print(json.dumps(selected, indent=2)) @@ -333,17 +354,20 @@ def main(argv=None): ) report = { "schema_version": 1, + "mode": "trace-only" if args.trace_only else "benchmark", "status": "running", "repository": repository_state(Path(__file__).resolve().parents[2]), "config": { - "warmup": args.warmup, - "runs": args.runs, - "iterations": args.iterations, + "warmup": 0 if args.trace_only else args.warmup, + "runs": 0 if args.trace_only else args.runs, + "iterations": 0 if args.trace_only else args.iterations, "weights": str(args.weights.resolve()), "profile": args.profile, "batch_size": 1, "concurrency": 1, - "timed_scope": "engine.predict(request), synchronized; excludes parse_request and metadata preparation", + "timed_scope": None + if args.trace_only + else "engine.predict(request), synchronized; excludes parse_request and metadata preparation", }, "cases": [], } @@ -377,20 +401,21 @@ def main(argv=None): current["input_tokens"] = sum( q["input_tokens"] for q in current["questions_metadata"] ) - current.update( - benchmark( - engine, - request, - warmup=args.warmup, - runs=args.runs, - iterations=args.iterations, + if not args.trace_only: + current.update( + benchmark( + engine, + request, + warmup=args.warmup, + runs=args.runs, + iterations=args.iterations, + ) ) - ) - current["status"] = "complete" + current["status"] = "prepared" if args.trace_only else "complete" write_json(args.output / f"{case['id']}.json", current) write_json(args.output / "report.json", report) print( - f"{case['id']}: complete ({args.runs * args.iterations} latency samples)", + f"{case['id']}: {current['status']} ({0 if args.trace_only else args.runs * args.iterations} latency samples)", flush=True, ) # Every baseline finishes before profiler state can affect later measurements. @@ -404,6 +429,7 @@ def main(argv=None): current["profile"] = profile_request( engine, request, args.output / f"{current['id']}.trace.json" ) + current["status"] = "complete" write_json(args.output / f"{current['id']}.json", current) write_json(args.output / "report.json", report) print(f"{current['id']}: trace complete", flush=True) diff --git a/recipe/cua_s1/profiling.md b/recipe/cua_s1/profiling.md index 0f4e0f2..e009476 100644 --- a/recipe/cua_s1/profiling.md +++ b/recipe/cua_s1/profiling.md @@ -83,7 +83,12 @@ PYTHONPATH=src python recipe/cua_s1/profile_multimodal.py --list-cases The matrix covers 320×240 and 640×480 PNGs, short/long instructions and 1/2/4/8 questions per request. Each request uses a single synthetic image with three -candidate actions per question. Images contain no private user data. +candidate actions per question. Questions within a case repeat the same +instruction and criteria under different question names, isolating question +count from prompt variation. The long instruction repeats a fixed sentence 64 +times; it is a controlled workload, not a realistic GUI task evaluation. Images +contain no private user data. Follow-up reuse correctness tests must also cover +different questions sharing an image. Start with a feasibility run, using a new output directory: @@ -114,6 +119,20 @@ case are saved after all its runs finish; an interruption during that case can lose its samples. This script does not resume a partial run. Repeat the failed case in a new output directory and retain the original failure record. +If latency sampling completed but trace collection needs to be repeated, use a +separate trace-only run after fixing the failure: + +```sh +PYTHONPATH=src python recipe/cua_s1/profile_multimodal.py \ + --weights weights --output /tmp/cua-profile-traces \ + --trace-only --profile 640x480-short-q1 --profile 640x480-short-q8 +``` + +Trace-only mode collects no benchmark samples and records its own source and +environment. Keep both reports; do not replace the original failed manifest or +present the new trace run as a rerun of latency measurements. If `--case` is also +specified, its set must match the `--profile` set. + ## Interpretation and publication The unprofiled benchmark times `engine.predict` after JSON parsing and image @@ -136,6 +155,15 @@ an experiment failure, not zero time. See the [PyTorch profiler documentation](https://docs.pytorch.org/docs/stable/profiler) for shape-recording overhead and trace semantics. +PyTorch can emit CPU and GPU annotations with the same range name. Operator +records retain their device type and annotation flag. Invocation counts use CPU +user annotations only; same-name GPU annotations are separate records and must +not be added again to the CPU range's attributed CUDA duration. Raw inclusive +device aggregates can themselves contain synthetic annotation accounting. Use +stage CPU spans for the host timeline and individual device kernel events in the +trace for GPU investigation; these raw parent aggregates are not disjoint kernel +time or a reliable basis for stage GPU percentages. + For a reviewable experiment PR, include: - Commit/source provenance, environment, exact commands and fixture settings. diff --git a/tests/cua_s1/test_profiling.py b/tests/cua_s1/test_profiling.py index 70e5b1c..5cd1991 100644 --- a/tests/cua_s1/test_profiling.py +++ b/tests/cua_s1/test_profiling.py @@ -326,6 +326,8 @@ def key_averages(self, group_by_input_shape): return [ SimpleNamespace( key=key, + device_type=device, + is_user_annotation=key.startswith("cua."), input_shapes=[[1, 2]], count=1, cpu_time_total=8, @@ -333,6 +335,7 @@ def key_averages(self, group_by_input_shape): device_time_total=6, self_device_time_total=2, ) + for device in ("DeviceType.CPU", "DeviceType.CUDA") for key in ["op"] + [ "cua." + name @@ -345,7 +348,7 @@ def key_averages(self, group_by_input_shape): "output_projection", "readout", ] - if name != missing_stage + if name != missing_stage or device == "DeviceType.CUDA" ] ] @@ -390,3 +393,77 @@ def start(**kwargs): assert result["trace_bytes"] == 5 assert result["operators"][0]["input_shapes"] == [[1, 2]] assert result["operators"][0]["self_cuda_time_us"] == 2 + transfer = [item for item in result["operators"] if item["op"] == "cua.transfer"] + assert len(transfer) == 2 + assert {item["device_type"] for item in transfer} == { + "DeviceType.CPU", + "DeviceType.CUDA", + } + assert all(item["is_user_annotation"] for item in transfer) + + +def test_trace_only_requires_profiles_and_exact_case_selection(profiling): + first, second = "320x240-short-q1", "320x240-short-q2" + for extra in [[], ["--profile", first, "--case", first, "--case", second]]: + with pytest.raises(SystemExit): + profiling.parse_args(["--trace-only", "--list-cases", *extra]) + args = profiling.parse_args(["--trace-only", "--list-cases", "--profile", first]) + assert args.trace_only + + +@pytest.mark.parametrize("profile_fails", [False, True]) +def test_trace_only_never_benchmarks_and_writes_fresh_report( + profiling, monkeypatch, tmp_path, profile_fails +): + import evaluate_multimodal + + from models.cua_s1.multimodal import model + + events = [] + monkeypatch.setattr( + model, "MultimodalEngine", lambda *args: SimpleNamespace(adapter_modules=178) + ) + monkeypatch.setattr(evaluate_multimodal, "environment", lambda: {"gpu": "fake"}) + monkeypatch.setattr(evaluate_multimodal, "measure", lambda call: (call(), 1.0)) + monkeypatch.setattr( + profiling, "repository_state", lambda root: {"revision": "fresh"} + ) + monkeypatch.setattr( + profiling, "input_metadata", lambda *args: [{"input_tokens": 42}] + ) + monkeypatch.setattr( + profiling, + "benchmark", + lambda *args, **kwargs: pytest.fail("trace-only must never benchmark"), + ) + + def profile(*args): + events.append("profile") + if profile_fails: + raise RuntimeError("profile failed") + return {"instrumentation_exact_parity": True} + + monkeypatch.setattr(profiling, "profile_request", profile) + case = "320x240-short-q1" + code = profiling.main( + [ + "--weights", + str(tmp_path / "weights"), + "--output", + str(tmp_path), + "--trace-only", + "--profile", + case, + ] + ) + assert events == ["profile"] + assert code == int(profile_fails) + report = json.loads((tmp_path / "report.json").read_text()) + assert report["mode"] == "trace-only" + assert report["repository"] == {"revision": "fresh"} + assert report["config"]["runs"] == report["config"]["iterations"] == 0 + assert len(report["cases"]) == 1 + assert "runs" not in report["cases"][0] + assert report["status"] == ("failed" if profile_fails else "complete") + saved = json.loads((tmp_path / f"{case}.json").read_text()) + assert saved["status"] == ("failed" if profile_fails else "complete") From 4c605e34537451e56677d9c08c47bef869ae5d68 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:28:11 +0800 Subject: [PATCH 04/75] Record RTX 4090 profiling results and trace recovery --- recipe/cua_s1/experiments/README.md | 5 + .../experiments/rtx4090-profile/README.md | 178 + .../experiments/rtx4090-profile/baseline.json | 5089 ++ .../experiments/rtx4090-profile/baseline.log | 23 + .../rtx4090-profile/candidate.json | 536 + .../experiments/rtx4090-profile/packages.txt | 62 + .../rtx4090-profile/reference.json | 527 + .../rtx4090-profile/trace-archive.json | 11 + .../experiments/rtx4090-profile/traces.json | 40576 ++++++++++++++++ .../experiments/rtx4090-profile/traces.log | 12 + 10 files changed, 47019 insertions(+) create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/baseline.json create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/baseline.log create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/candidate.json create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/packages.txt create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/reference.json create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/trace-archive.json create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/traces.json create mode 100644 recipe/cua_s1/experiments/rtx4090-profile/traces.log diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md index f1df428..ae1e0ef 100644 --- a/recipe/cua_s1/experiments/README.md +++ b/recipe/cua_s1/experiments/README.md @@ -1,5 +1,10 @@ # RTX 4090 multimodal validation — 2026-09-27 +For the subsequent 16-case stage profiling experiment, refreshed upstream parity, +and profiler annotation-counting recovery, see the +[2026-09-28 report](rtx4090-profile/README.md). The measurements below remain the +original baseline and have not been replaced. + This is a Transformers/PEFT CUDA baseline, with the adapter unmerged and full logits, for the screenshot worker in [the recipe](../README.md). It does not implement a native CUDA backend or claim a speedup over upstream. diff --git a/recipe/cua_s1/experiments/rtx4090-profile/README.md b/recipe/cua_s1/experiments/rtx4090-profile/README.md new file mode 100644 index 0000000..85963e9 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-profile/README.md @@ -0,0 +1,178 @@ +# RTX 4090 multimodal profiling — 2026-09-28 + +This is a stage-level baseline of the existing Transformers/PEFT worker, not an +optimized implementation or a speedup claim. Source commit: +`767e21172a303e36da4765fe59027db63f09acd1`; the remote checkout was clean. Model execution +is unchanged from PR #12's `ee970c2`. The 9-question upstream parity check was rerun +at `ee970c2` before the profiling harness was added. Trace collection was repeated +at `ebd7adc7e43d1c8ef2144cc0426295bf22abfa3b` after fixing CPU/GPU annotation counting; +model execution and the latency measurement function were not changed by that fix. + +## Preserved failure and recovery + +All 16 cases completed their latency samples before the first trace failed its +stage-count check: PyTorch emits separate CPU and GPU annotations under the same +name, and the original check added both. The original [baseline.json](baseline.json) +and [baseline.log](baseline.log) retain their failed status and error. Their +**1,600 unprofiled samples are complete**, independently checked for sample counts +and percentile calculations; they were collected before the profiler ran. + +The fix retains event device types, counts CPU user annotations only, and supports +an explicit trace-only run. [traces.json](traces.json) records the successful new +trace run, its own clean source revision and environment. It contains no benchmark +samples. Its fixture and prepared-input fingerprints match the corresponding +baseline cases. The failed manifest has not been relabeled or overwritten. + +## Environment and method + +- RTX 4090, 24,564 MiB; driver 595.71.05, CUDA runtime 13.0. +- Linux container allocated 16 vCPUs and 120 GiB RAM. PyTorch intra/inter-op + thread counts remain 64/64, matching the earlier environment; no thread tuning. +- Python 3.12.13, unmerged BF16 base with PEFT fp32 LoRA branches; + all 178 expected adapted modules loaded. Full-logit scoring and PyTorch + Gated DeltaNet/causal-convolution fallbacks retained. Packages: [packages.txt](packages.txt). +- Two PNG sizes × two instruction lengths × 1/2/4/8 serial questions = 16 cases. + Instructions and candidates repeat within a case. Long instructions repeat a + fixed sentence 64 times. These are controlled synthetic workloads, not GUI accuracy tests. +- Five warmup requests per case, then two runs of 50 synchronized requests: + **1,600 measured requests / 6,000 question forwards**. All latency measurements + finish before any profiling. Raw per-case warmup and samples are in [baseline.json](baseline.json). +- Timing covers `engine.predict` after parsing/decoding; excludes HTTP, queueing, + image decoding, input fingerprinting, model load and profiling. The load + observation (19.65 s) includes artifact verification and is not + a cold-start comparison. GPU peaks below are for unprofiled execution. + +See [experiment instructions](../../profiling.md) for the complete hypothesis, +controls, setup, command sequence and measurement limitations. Exact measured command: + +```sh +PYTHONPATH=src /root/autodl-tmp/system1-omni-work/venv/bin/python \ + recipe/cua_s1/profile_multimodal.py \ + --weights /root/autodl-tmp/system1-omni-work/weights \ + --output /root/autodl-tmp/system1-omni-work/experiments/20260928-profile/measured \ + --profile 640x480-short-q1 --profile 640x480-short-q8 \ + --warmup 5 --runs 2 --iterations 50 +``` + +Trace-only recovery command, from the corrected source revision: + +```sh +PYTHONPATH=src /root/autodl-tmp/system1-omni-work/venv/bin/python \ + recipe/cua_s1/profile_multimodal.py \ + --weights /root/autodl-tmp/system1-omni-work/weights \ + --output /root/autodl-tmp/system1-omni-work/experiments/20260928-profile/traces \ + --trace-only --profile 640x480-short-q1 --profile 640x480-short-q8 +``` + +## Correctness + +- [reference.json](reference.json) and [candidate.json](candidate.json): all nine + question forwards have identical input tensor hashes and fp32 candidate + probabilities; maximum absolute difference 0. Both processes used the pinned + upstream source hash, model revisions and identical recorded environments. +- Two instrumented requests (one and eight questions) produce exactly the same + responses as ordinary execution. All seven stage ranges occur exactly once per + question. This tests instrumentation, not an independent model implementation. +- No numerical approximation, adapter merge, batching, caching or kernel change + was made. All 87 CPU tests passed on local macOS and the Linux GPU host; Ruff + checks and formatting passed locally. The added profiling suite contains 25 tests. + +## Unprofiled request latency + +Values separated by `/` are run 1 / run 2. Tokens are per question; all questions +within a case share the same prompt. p95 uses nearest rank over 50 samples. + +| Case | Tokens/question | p50 ms | p95 ms | Peak allocated GiB | +| --- | ---: | ---: | ---: | ---: | +| 320x240-short-q1 | 236 | 109.24 / 108.96 | 111.30 / 111.17 | 8.73 | +| 320x240-short-q2 | 236 | 218.49 / 218.23 | 225.28 / 229.10 | 8.73 | +| 320x240-short-q4 | 236 | 433.38 / 456.14 | 448.92 / 500.47 | 8.73 | +| 320x240-short-q8 | 236 | 905.78 / 899.87 | 984.60 / 923.22 | 8.74 | +| 320x240-long-q1 | 614 | 135.50 / 141.88 | 143.17 / 168.55 | 8.92 | +| 320x240-long-q2 | 614 | 285.31 / 283.31 | 345.52 / 348.03 | 8.93 | +| 320x240-long-q4 | 614 | 555.44 / 543.43 | 625.50 / 565.05 | 8.93 | +| 320x240-long-q8 | 614 | 1152.11 / 1116.13 | 1226.11 / 1350.77 | 8.94 | +| 640x480-short-q1 | 456 | 128.18 / 127.74 | 152.02 / 134.22 | 8.84 | +| 640x480-short-q2 | 456 | 256.98 / 255.78 | 271.74 / 272.01 | 8.85 | +| 640x480-short-q4 | 456 | 536.72 / 520.11 | 551.70 / 693.52 | 8.86 | +| 640x480-short-q8 | 456 | 1099.52 / 1106.41 | 1215.46 / 1253.40 | 8.89 | +| 640x480-long-q1 | 834 | 154.87 / 155.64 | 158.91 / 192.66 | 9.04 | +| 640x480-long-q2 | 834 | 310.97 / 310.65 | 368.06 / 326.56 | 9.04 | +| 640x480-long-q4 | 834 | 696.27 / 698.81 | 768.80 / 811.10 | 9.06 | +| 640x480-long-q8 | 834 | 1362.37 / 1393.84 | 1398.75 / 1503.62 | 9.09 | + +## Instrumented stage attribution + +Each row is an inclusive total across one profiled request. Parent ranges overlap +with child ranges; do not add these rows or combine them with operator/kernel +totals. Only CPU user-annotation rows are shown; same-name GPU annotations are +retained separately in the raw report and are not added again. CPU range time +includes host work and possible synchronization. PyTorch's raw inclusive device +aggregates can include synthetic annotation accounting; they are retained as raw +data but are not presented as stage GPU execution times. Individual device kernel +events and their shapes remain available for GPU investigation. These traces are excluded from the latency +table and do not establish production latency by themselves. + +| Case | Range | Calls | Inclusive CPU ms | +| --- | --- | ---: | ---: | +| 640x480-short-q1 | cua.prepare | 1 | 8.93 | +| 640x480-short-q1 | cua.transfer | 1 | 1.49 | +| 640x480-short-q1 | cua.forward | 1 | 222.74 | +| 640x480-short-q1 | cua.vision | 1 | 37.40 | +| 640x480-short-q1 | cua.language | 1 | 182.43 | +| 640x480-short-q1 | cua.output_projection | 1 | 0.12 | +| 640x480-short-q1 | cua.readout | 1 | 4.26 | +| 640x480-short-q8 | cua.prepare | 8 | 57.37 | +| 640x480-short-q8 | cua.transfer | 8 | 11.49 | +| 640x480-short-q8 | cua.forward | 8 | 1786.88 | +| 640x480-short-q8 | cua.vision | 8 | 291.37 | +| 640x480-short-q8 | cua.language | 8 | 1473.94 | +| 640x480-short-q8 | cua.output_projection | 8 | 0.71 | +| 640x480-short-q8 | cua.readout | 8 | 37.56 | + +Module paths, full operator shape groups, trace byte counts and SHA-256 checksums +are preserved in `traces.json`. Large Chrome traces remain outside Git, archived +alongside the experiment on the GPU host and copied locally before task completion. +Re-run the documented command to regenerate them; hashes identify the original +trace files, whose runtime timestamps are not deterministic. + +Do not rank GPU bottlenecks by CPU launch spans. In particular, the output-head +CPU span is short while its device work can continue afterward. Filtering raw +operator groups to `DeviceType.CUDA` and `is_user_annotation == false` gives +individual device kernel groups without adding CPU parents or synthetic ranges. +For example, the following GEMM groups appear in the instrumented traces: + +| Kernel group | 1-question calls / total ms | 8-question calls / total ms | +| --- | ---: | ---: | +| `ampere_bf16_s1688gemm_bf16_64x128_sliced1x2_ldg8_f2f_tn` | 96 / 16.38 | 768 / 136.14 | +| `ampere_bf16_s1688gemm_bf16_128x128_ldg8_f2f_stages_32x1_tn` | 56 / 6.54 | 448 / 52.77 | +| `ampere_bf16_s16816gemm_bf16_128x64_ldg8_f2f_tn` | 1 / 4.08 | 8 / 35.72 | + +These are instrumented kernel durations, not end-to-end latency. Kernel names +alone do not identify the owning model module; use trace correlations and shapes +before choosing a replacement kernel. + +## Next experiment + +The eight-question trace contains eight preprocessing calls and eight vision +forwards for the same image. Request-local image preprocessing and vision-feature +reuse are therefore concrete candidates for the next PR. The current data does +not measure the speedup from removing those calls. + +Start with distinct questions sharing one image, verify unchanged processor +tensors and probabilities, then run paired baseline/candidate measurements on +the same matrix. Coordinate language-layer and output-head optimizations with +the text-engine contributor. The 456-token, one-question fixture has the same +input tensor fingerprints as the original baseline; its new p50 values are +128.18 / 127.74 ms, compared with the historical 126.39 / 128.28 ms. This is a +baseline reproduction, not an optimization comparison. + +## Scope and limitations + +Results apply to this GPU, environment, synthetic input matrix and concurrency 1. +The experiment does not establish maximum supported image sizes, real task quality, +HTTP latency, p99 behavior, concurrent throughput, native CUDA or Metal support. +Reserved allocator memory can carry over between cases; allocated peaks are the +primary memory comparison. Cases execute in a fixed order, so clock/cache/order +effects have not been randomized away. Follow-up optimization claims require paired +baseline/candidate runs under the same controls and expanded reuse correctness cases. diff --git a/recipe/cua_s1/experiments/rtx4090-profile/baseline.json b/recipe/cua_s1/experiments/rtx4090-profile/baseline.json new file mode 100644 index 0000000..1e08293 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-profile/baseline.json @@ -0,0 +1,5089 @@ +{ + "schema_version": 1, + "status": "failed", + "repository": { + "revision": "767e21172a303e36da4765fe59027db63f09acd1", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "config": { + "warmup": 5, + "runs": 2, + "iterations": 50, + "weights": "/root/autodl-tmp/system1-omni-work/weights", + "profile": [ + "640x480-short-q1", + "640x480-short-q8" + ], + "batch_size": 1, + "concurrency": 1, + "timed_scope": "engine.predict(request), synchronized; excludes parse_request and metadata preparation" + }, + "cases": [ 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int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 433, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2112.1370000000534, + "self_cuda_time_us": 2112.1370000000534 + }, + { + "op": "aten::addmm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 4096 + ], + [ + 1200, + 1024 + ], + [ + 1024, + 4096 + ], + [], + [] + ], + "count": 24, + "cpu_time_us": 542.8479999999981, + "self_cpu_time_us": 401.44200000001365, + "cuda_time_us": 1965.5950000000012, + "self_cuda_time_us": 1965.5950000000012 + }, + { + "op": "void at::native::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 388, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 1964.8369999998831, + "self_cuda_time_us": 1964.8369999998831 + }, + { + "op": "aten::_flash_attention_forward", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 1200, + 16, + 64 + ], + [ + 1, + 1200, + 16, + 64 + ], + [ + 1, + 1200, + 16, + 64 + ], + [], + [], + [], + [], + [], + [], + [], + [], + [], + [], + [], + [], + [], + [] + ], + "count": 24, + "cpu_time_us": 1124.5129999999845, + "self_cpu_time_us": 396.78800000004776, + "cuda_time_us": 1954.7839999999924, + "self_cuda_time_us": 1954.7839999999924 + }, + { + "op": "void 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1453.2719999999863 + }, + { + "op": "ampere_bf16_s1688gemm_bf16_128x128_ldg8_relu_f2f_stages_32x1_tn", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 1453.2719999999863, + "self_cuda_time_us": 1453.2719999999863 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::bfloat16_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda(float)#1}, std::array, false>(int, at::native::bfloat16_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 359, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 1401.274000000125, + "self_cuda_time_us": 1401.274000000125 + }, + { + "op": "aten::bmm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 256, + 64, + 128 + ], + [ + 256, + 128, + 64 + 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at::native::BinaryFunctor > const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 56, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 199.08199999990757, + "self_cuda_time_us": 199.08199999990757 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 32, + 8, + 64, + 64 + ], + [ + 1, + 32, + 8, + 64, + 64 + ] + ], + "count": 48, + "cpu_time_us": 602.0589999999356, + "self_cpu_time_us": 376.26799999996, + "cuda_time_us": 198.40200000014738, + "self_cuda_time_us": 198.40200000014738 + }, + { + "op": "aten::mm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 456, + 16 + ], + [ + 16, + 2560 + ] + ], + "count": 40, + "cpu_time_us": 615.3569999998872, + "self_cpu_time_us": 434.8779999999824, + "cuda_time_us": 196.7719999998517, + "self_cuda_time_us": 196.7719999998517 + }, + { + "op": 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at::native::GeluType)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(c10::BFloat16)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 164.87299999999595, + "self_cuda_time_us": 164.87299999999595 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456, + 16, + 256 + ], + [ + 1, + 456, + 16, + 256 + ], + [] + ], + "count": 32, + "cpu_time_us": 337.4529999999504, + "self_cpu_time_us": 176.4140000000043, + "cuda_time_us": 162.99300000001676, + "self_cuda_time_us": 162.99300000001676 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 2560 + ], + [ + 2560 + ], + [] + ], + "count": 65, + "cpu_time_us": 568.9090000000506, + "self_cpu_time_us": 277.09300000003714, + "cuda_time_us": 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240.90799999991577, + "cuda_time_us": 100.07999999989988, + "self_cuda_time_us": 100.07999999989988 + }, + { + "op": "void at::native::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::(anonymous namespace)::masked_fill_kernel(at::TensorIterator&, c10::Scalar const&)::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const::{lambda(float, bool)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::(anonymous namespace)::masked_fill_kernel(at::TensorIterator&, c10::Scalar const&)::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const::{lambda(float, bool)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 100.07999999989988, + "self_cuda_time_us": 100.07999999989988 + }, + { + "op": "aten::arange", + "device_type": 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const::{lambda(float)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::exp_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 39.22699999999895, + "self_cuda_time_us": 39.22699999999895 + }, + { + "op": "aten::mean", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456, + 16, + 256 + ], + [], + [], + [] + ], + "count": 8, + "cpu_time_us": 125.61199999997916, + "self_cpu_time_us": 89.25299999998242, + "cuda_time_us": 38.32900000002701, + "self_cuda_time_us": 38.32900000002701 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 32 + ], + [ + 32 + ], + 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"input_shapes": [ + [ + 1, + 456, + 16, + 1 + ], + [], + [] + ], + "count": 8, + "cpu_time_us": 100.70599999999104, + "self_cpu_time_us": 68.25099999997474, + "cuda_time_us": 8.959999999991851, + "self_cuda_time_us": 8.959999999991851 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BUnaryFunctor, std::array, false>(int, at::native::BUnaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 5, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 8.927999999996246, + "self_cuda_time_us": 8.927999999996246 + }, + { + "op": "void at::native::unrolled_elementwise_kernel, 4, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#11}::operator()() const::{lambda(bool)#1}, std::array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 4, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 8.799999999995634, + "self_cuda_time_us": 8.799999999995634 + }, + { + "op": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(long, long)#1}>, unsigned int, long, 4, 4> >(at::native::ReduceOp::operator()(at::TensorIterator&)::{lambda(long, long)#1}>, unsigned int, long, 4, 4>)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 4, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 8.640000000006694, + "self_cuda_time_us": 8.640000000006694 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + 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"self_cuda_time_us": 8.447000000000116 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 456 + ], + [ + 456 + ], + [] + ], + "count": 4, + "cpu_time_us": 50.16800000000512, + "self_cpu_time_us": 19.54100000001199, + "cuda_time_us": 8.319999999992433, + "self_cuda_time_us": 8.319999999992433 + }, + { + "op": "aten::index", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 456 + ], + [] + ], + "count": 2, + "cpu_time_us": 215.9059999999954, + "self_cpu_time_us": 47.263000000006286, + "cuda_time_us": 17.15099999998347, + "self_cuda_time_us": 7.19999999999709 + }, + { + "op": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 7.19999999999709, + "self_cuda_time_us": 7.19999999999709 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::AUnaryFunctor >, std::array, false>(int, at::native::AUnaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 6, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 7.135000000002037, + "self_cuda_time_us": 7.135000000002037 + }, + { + "op": "void at_cuda_detail::cub::detail::scan::DeviceScanKernel >::Policy1000, long const*, long*, at_cuda_detail::cub::ScanTileState, std::plus, at_cuda_detail::cub::NullType, unsigned int, long, false, at_cuda_detail::cub::NullType>(long const*, long*, at_cuda_detail::cub::ScanTileState, int, std::plus, at_cuda_detail::cub::NullType, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 5, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 7.100000000002183, + "self_cuda_time_us": 7.100000000002183 + }, + { + "op": "aten::gather", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 2304, + 1024 + ], + [], + [ + 4800, + 1024 + ], + [], + [ + 4800, + 1024 + ] + ], + "count": 1, + "cpu_time_us": 19.72599999999875, + "self_cpu_time_us": 12.369999999998981, + "cuda_time_us": 7.070999999999913, + "self_cuda_time_us": 7.070999999999913 + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 4096 + ], + [ + 300, + 4096 + ], + [] + ], + "count": 1, + "cpu_time_us": 10.99699999999575, + "self_cpu_time_us": 6.737999999990279, + "cuda_time_us": 6.878999999993539, + "self_cuda_time_us": 6.878999999993539 + }, + { + "op": "aten::sum", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456 + ], + [] + ], + "count": 3, + "cpu_time_us": 137.87500000000728, + "self_cpu_time_us": 56.57799999999406, + "cuda_time_us": 13.023000000008324, + "self_cuda_time_us": 6.496000000006461 + }, + { + "op": "void at::native::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast >(at::TensorIteratorBase&, at::native::CUDAFunctor_add const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast >(at::TensorIteratorBase&, at::native::CUDAFunctor_add const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 4, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 6.141999999999825, + "self_cuda_time_us": 6.141999999999825 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::AUnaryFunctor >, std::array, false>(int, at::native::AUnaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 5, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 6.07999999999447, + "self_cuda_time_us": 6.07999999999447 + }, + { + "op": "aten::floor_divide", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "count": 3, + "cpu_time_us": 59.25699999999779, + "self_cpu_time_us": 27.155999999991764, + "cuda_time_us": 5.952000000001135, + "self_cuda_time_us": 5.952000000001135 + }, + { + "op": "aten::repeat_interleave", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1 + ], + [] + ], + "count": 4, + "cpu_time_us": 647.6330000000016, + "self_cpu_time_us": 89.3310000000165, + "cuda_time_us": 32.219999999997526, + "self_cuda_time_us": 5.855000000003201 + }, + { + "op": "void compute_cuda_kernel(long const*, long const*, long*, long, long)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 4, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 5.855000000003201, + "self_cuda_time_us": 5.855000000003201 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "count": 5, + "cpu_time_us": 59.61699999999837, + "self_cpu_time_us": 37.94699999999648, + "cuda_time_us": 5.85399999999936, + "self_cuda_time_us": 5.85399999999936 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1 + ], + [ + 2 + ], + [] + ], + "count": 2, + "cpu_time_us": 24.524999999997817, + "self_cpu_time_us": 15.44800000000032, + "cuda_time_us": 5.792000000001281, + "self_cuda_time_us": 5.792000000001281 + }, + { + "op": "aten::div", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ] + ], + "count": 2, + "cpu_time_us": 32.87800000000061, + "self_cpu_time_us": 21.39100000000326, + "cuda_time_us": 5.758999999998196, + "self_cuda_time_us": 5.758999999998196 + }, + { + "op": "void at::native::unrolled_elementwise_kernel >, std::array, 4, TrivialOffsetCalculator<2, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<2>, at::native::memory::StoreWithCast<1> >(int, at::native::BinaryFunctor >, std::array, TrivialOffsetCalculator<2, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<2>, at::native::memory::StoreWithCast<1>)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 5.758999999998196, + "self_cuda_time_us": 5.758999999998196 + }, + { + "op": "void at_cuda_detail::cub::detail::scan::DeviceScanInitKernel >(at_cuda_detail::cub::ScanTileState, int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 6, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 5.440999999995256, + "self_cuda_time_us": 5.440999999995256 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [], + [], + [] + ], + "count": 5, + "cpu_time_us": 65.91799999999421, + "self_cpu_time_us": 19.80399999999645, + "cuda_time_us": 5.217000000000553, + "self_cuda_time_us": 5.217000000000553 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, std::array, false>(int, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 4, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 5.0240000000012515, + "self_cuda_time_us": 5.0240000000012515 + }, + { + "op": "aten::mm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 16 + ], + [ + 16, + 4096 + ] + ], + "count": 1, + "cpu_time_us": 14.870999999999185, + "self_cpu_time_us": 10.841999999996915, + "cuda_time_us": 4.8950000000040745, + "self_cuda_time_us": 4.8950000000040745 + }, + { + "op": "aten::clamp", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 2 + ], + [], + [] + ], + "count": 4, + "cpu_time_us": 59.056000000000495, + "self_cpu_time_us": 38.199000000000524, + "cuda_time_us": 4.863000000004831, + "self_cuda_time_us": 4.863000000004831 + }, + { + "op": "void at_cuda_detail::cub::detail::select::DeviceSelectSweepKernel::Policy1000, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::counting_iterator, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, long*, int*, at_cuda_detail::cub::ScanTileState, at_cuda_detail::cub::NullType, at_cuda_detail::cub::NullType, int, at_cuda_detail::cub::detail::select::streaming_context_t, (at_cuda_detail::cub::SelectImpl)0>(thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::counting_iterator, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, long*, int*, at_cuda_detail::cub::ScanTileState, at_cuda_detail::cub::NullType, at_cuda_detail::cub::NullType, int, int, at_cuda_detail::cub::detail::select::streaming_context_t, at_cuda_detail::cub::detail::vsmem_t)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 3, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 4.862999999990279, + "self_cuda_time_us": 4.862999999990279 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::launch_masked_scatter_kernel(at::TensorBase const&, at::TensorBase const&, at::TensorBase const&, at::TensorBase const&)::{lambda()#1}::operator()() const::{lambda()#11}::operator()() const::{lambda(c10::BFloat16, bool, long)#1}, std::array, false>(int, at::native::launch_masked_scatter_kernel(at::TensorBase const&, at::TensorBase const&, at::TensorBase const&, at::TensorBase const&)::{lambda()#1}::operator()() const::{lambda()#11}::operator()() const::{lambda(c10::BFloat16, bool, long)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 4.608000000000175, + "self_cuda_time_us": 4.608000000000175 + }, + { + "op": "aten::ge", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1 + ], + [] + ], + "count": 4, + "cpu_time_us": 81.53499999999622, + "self_cpu_time_us": 56.661999999992986, + "cuda_time_us": 4.543000000001484, + "self_cuda_time_us": 4.543000000001484 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::compare_scalar_kernel(at::TensorIteratorBase&, at::native::(anonymous namespace)::OpType, long)::{lambda(long)#1}, std::array, false>(int, at::native::compare_scalar_kernel(at::TensorIteratorBase&, at::native::(anonymous namespace)::OpType, long)::{lambda(long)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 4, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 4.543000000001484, + "self_cuda_time_us": 4.543000000001484 + }, + { + "op": "aten::add_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1024, + 1, + 1, + 1 + ], + [ + 1, + 1024, + 1, + 1, + 1 + ], + [] + ], + "count": 1, + "cpu_time_us": 17.05500000000029, + "self_cpu_time_us": 11.381000000001222, + "cuda_time_us": 4.479000000002998, + "self_cuda_time_us": 4.479000000002998 + }, + { + "op": "void at::native::(anonymous namespace)::CatArrayBatchedCopy, unsigned int, 2, 64, 64>(at::native::(anonymous namespace)::OpaqueType<4u>*, at::native::(anonymous namespace)::CatArrInputTensorMetadata, unsigned int, 64, 64>, at::native::(anonymous namespace)::TensorSizeStride, int, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 4.224000000001979, + "self_cuda_time_us": 4.224000000001979 + }, + { + "op": "void at_cuda_detail::cub::detail::reduce::DeviceReduceSingleTileKernel >::Policy1000, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, int*, unsigned long long, cuda::std::__4::plus, int, int, cuda::std::__4::__identity>(thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, int*, unsigned long long, cuda::std::__4::plus, int, cuda::std::__4::__identity)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 3, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 4.192000000002736, + "self_cuda_time_us": 4.192000000002736 + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 2560 + ], + [ + 300, + 2560 + ], + [] + ], + "count": 1, + "cpu_time_us": 10.895000000004075, + "self_cpu_time_us": 6.469000000004598, + "cuda_time_us": 4.0310000000026776, + "self_cuda_time_us": 4.0310000000026776 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::CUDAFunctor_add, std::array, false>(int, at::native::CUDAFunctor_add, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 3, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.9989999999997963, + "self_cuda_time_us": 3.9989999999997963 + }, + { + "op": "aten::mm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 16 + ], + [ + 16, + 2560 + ] + ], + "count": 1, + "cpu_time_us": 14.44600000000355, + "self_cpu_time_us": 10.40400000000227, + "cuda_time_us": 3.9360000000015134, + "self_cuda_time_us": 3.9360000000015134 + }, + { + "op": "aten::eq", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456 + ], + [] + ], + "count": 3, + "cpu_time_us": 49.408000000003085, + "self_cpu_time_us": 32.57500000001164, + "cuda_time_us": 3.8399999999965075, + "self_cuda_time_us": 3.8399999999965075 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::cos_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array, false>(int, at::native::cos_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.8070000000043365, + "self_cuda_time_us": 3.8070000000043365 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::sin_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array, false>(int, at::native::sin_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.775999999998021, + "self_cuda_time_us": 3.775999999998021 + }, + { + "op": "aten::_index_put_impl_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 3, + 456 + ], + [], + [ + 3, + 456 + ], + [], + [] + ], + "count": 1, + "cpu_time_us": 95.5, + "self_cpu_time_us": 19.887000000002445, + "cuda_time_us": 8.47899999999936, + "self_cuda_time_us": 3.6480000000010477 + }, + { + "op": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_put_kernel_impl >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_put_kernel_impl >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.6480000000010477, + "self_cuda_time_us": 3.6480000000010477 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 4096 + ], + [] + ], + "count": 1, + "cpu_time_us": 12.051999999996042, + "self_cpu_time_us": 7.9979999999995925, + "cuda_time_us": 3.6160000000018044, + "self_cuda_time_us": 3.6160000000018044 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 3 + ], + [ + 3 + ], + [] + ], + "count": 3, + "cpu_time_us": 3960.6820000000007, + "self_cpu_time_us": 18.923999999969965, + "cuda_time_us": 3.551999999996042, + "self_cuda_time_us": 3.551999999996042 + }, + { + "op": "aten::remainder", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ] + ], + "count": 2, + "cpu_time_us": 36.97799999999552, + "self_cpu_time_us": 24.23899999999412, + "cuda_time_us": 3.519000000000233, + "self_cuda_time_us": 3.519000000000233 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BinaryFunctor, std::array, false>(int, at::native::BinaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.519000000000233, + "self_cuda_time_us": 3.519000000000233 + }, + { + "op": "aten::remainder", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "count": 2, + "cpu_time_us": 33.4429999999993, + "self_cpu_time_us": 23.146999999997206, + "cuda_time_us": 3.4879999999975553, + "self_cuda_time_us": 3.4879999999975553 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BUnaryFunctor, std::array, false>(int, at::native::BUnaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.4879999999975553, + "self_cuda_time_us": 3.4879999999975553 + }, + { + "op": "void at::native::(anonymous namespace)::CatArrayBatchedCopy_alignedK_contig, unsigned int, 2, 128, 1, 16>(at::native::(anonymous namespace)::OpaqueType<8u>*, at::native::(anonymous namespace)::CatArrInputTensorMetadata, unsigned int, 128, 1>, at::native::(anonymous namespace)::TensorSizeStride, int, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.4869999999973516, + "self_cuda_time_us": 3.4869999999973516 + }, + { + "op": "void at::native::(anonymous namespace)::CatArrayBatchedCopy_vectorized, unsigned int, 2, 128, 1, 16, 4>(char*, at::native::(anonymous namespace)::CatArrInputTensorMetadata, unsigned int, 128, 1>, at::native::(anonymous namespace)::TensorSizeStride, int, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.423000000002503, + "self_cuda_time_us": 3.423000000002503 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1 + ], + [ + 1 + ] + ], + "count": 3, + "cpu_time_us": 60.59900000000198, + "self_cpu_time_us": 37.20600000000195, + "cuda_time_us": 3.3290000000015425, + "self_cuda_time_us": 3.3290000000015425 + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1 + ], + [ + 2 + ], + [] + ], + "count": 2, + "cpu_time_us": 29.87000000000262, + "self_cpu_time_us": 19.57100000000719, + "cuda_time_us": 3.136000000002241, + "self_cuda_time_us": 3.136000000002241 + }, + { + "op": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#11}::operator()() const::{lambda(bool)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#11}::operator()() const::{lambda(bool)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.040000000000873, + "self_cuda_time_us": 3.040000000000873 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 15, + 20, + 2, + 2 + ], + [ + 15, + 20, + 2, + 2 + ], + [] + ], + "count": 2, + "cpu_time_us": 23.843999999997322, + "self_cpu_time_us": 12.275999999998021, + "cuda_time_us": 3.006999999997788, + "self_cuda_time_us": 3.006999999997788 + }, + { + "op": "void at::native::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 3.006999999997788, + "self_cuda_time_us": 3.006999999997788 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456, + 11 + ], + [ + 1, + 456, + 11 + ], + [] + ], + "count": 2, + "cpu_time_us": 23.614000000001397, + "self_cpu_time_us": 13.391999999999825, + "cuda_time_us": 2.9759999999951106, + "self_cuda_time_us": 2.9759999999951106 + }, + { + "op": "aten::gather", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 248320, + 2560 + ], + [], + [ + 456, + 2560 + ], + [], + [ + 456, + 2560 + ] + ], + "count": 1, + "cpu_time_us": 66.6239999999998, + "self_cpu_time_us": 31.395000000000437, + "cuda_time_us": 2.878999999997177, + "self_cuda_time_us": 2.878999999997177 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456, + 10 + ], + [ + 1, + 456, + 10 + ], + [] + ], + "count": 2, + "cpu_time_us": 17.453999999997905, + "self_cpu_time_us": 9.136999999995169, + "cuda_time_us": 2.8479999999981374, + "self_cuda_time_us": 2.8479999999981374 + }, + { + "op": "aten::gelu", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 4096 + ], + [] + ], + "count": 1, + "cpu_time_us": 15.406000000002678, + "self_cpu_time_us": 8.715000000003783, + "cuda_time_us": 2.7189999999973224, + "self_cuda_time_us": 2.7189999999973224 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::GeluCUDAKernelImpl(at::TensorIteratorBase&, at::native::GeluType)::{lambda()#2}::operator()() const::{lambda()#4}::operator()() const::{lambda(c10::BFloat16)#1}, std::array, false>(int, at::native::GeluCUDAKernelImpl(at::TensorIteratorBase&, at::native::GeluType)::{lambda()#2}::operator()() const::{lambda()#4}::operator()() const::{lambda(c10::BFloat16)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.7189999999973224, + "self_cuda_time_us": 2.7189999999973224 + }, + { + "op": "void at_cuda_detail::cub::detail::scan::DeviceCompactInitKernel, int*>(at_cuda_detail::cub::ScanTileState, int, int*)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 3, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.7189999999973224, + "self_cuda_time_us": 2.7189999999973224 + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ], + [] + ], + "count": 2, + "cpu_time_us": 23.10699999999997, + "self_cpu_time_us": 15.184999999997672, + "cuda_time_us": 2.6229999999995925, + "self_cuda_time_us": 2.6229999999995925 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 2560 + ], + [] + ], + "count": 1, + "cpu_time_us": 11.63300000000163, + "self_cpu_time_us": 7.633999999998196, + "cuda_time_us": 2.5279999999984284, + "self_cuda_time_us": 2.5279999999984284 + }, + { + "op": "aten::index", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 248320 + ], + [] + ], + "count": 1, + "cpu_time_us": 38.436000000016065, + "self_cpu_time_us": 24.736000000033528, + "cuda_time_us": 2.526999999972759, + "self_cuda_time_us": 2.526999999972759 + }, + { + "op": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.526999999972759, + "self_cuda_time_us": 2.526999999972759 + }, + { + "op": "aten::clamp", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [], + [] + ], + "count": 2, + "cpu_time_us": 37.52600000000166, + "self_cpu_time_us": 26.5570000000007, + "cuda_time_us": 2.495999999999185, + "self_cuda_time_us": 2.495999999999185 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [], + [] + ], + "count": 2, + "cpu_time_us": 27.63000000000102, + "self_cpu_time_us": 17.850000000002183, + "cuda_time_us": 2.463999999999942, + "self_cuda_time_us": 2.463999999999942 + }, + { + "op": "aten::floor_divide", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ] + ], + "count": 1, + "cpu_time_us": 17.934000000001106, + "self_cpu_time_us": 8.194000000003143, + "cuda_time_us": 2.4320000000006985, + "self_cuda_time_us": 2.4320000000006985 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BinaryFunctor, std::array, false>(int, at::native::BinaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.4320000000006985, + "self_cuda_time_us": 2.4320000000006985 + }, + { + "op": "aten::prod", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 3 + ], + [], + [], + [] + ], + "count": 1, + "cpu_time_us": 31.084999999999127, + "self_cpu_time_us": 22.80000000000291, + "cuda_time_us": 2.4320000000006985, + "self_cuda_time_us": 2.4320000000006985 + }, + { + "op": "reduction_prod_kernel", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.4320000000006985, + "self_cuda_time_us": 2.4320000000006985 + }, + { + "op": "aten::max", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 3, + 456 + ] + ], + "count": 1, + "cpu_time_us": 30.485000000000582, + "self_cpu_time_us": 19.390999999995984, + "cuda_time_us": 2.4320000000006985, + "self_cuda_time_us": 2.4320000000006985 + }, + { + "op": "void at::native::reduce_kernel<512, 1, at::native::ReduceOp >, unsigned int, long, 4, 4> >(at::native::ReduceOp >, unsigned int, long, 4, 4>)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.4320000000006985, + "self_cuda_time_us": 2.4320000000006985 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BinaryFunctor >, std::array, false>(int, at::native::BinaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.4319999999970605, + "self_cuda_time_us": 2.4319999999970605 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, std::array, false>(int, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.335000000002765, + "self_cuda_time_us": 2.335000000002765 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1 + ], + [ + 1200, + 1 + ], + [] + ], + "count": 2, + "cpu_time_us": 24.328999999997905, + "self_cpu_time_us": 15.600000000002183, + "cuda_time_us": 2.334999999999127, + "self_cuda_time_us": 2.334999999999127 + }, + { + "op": "aten::all", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456 + ] + ], + "count": 1, + "cpu_time_us": 24.06500000000233, + "self_cpu_time_us": 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const::{lambda(float)#1}, std::array, false>(int, at::native::floor_kernel_cuda(at::TensorIteratorBase&)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 2, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 2.271999999997206, + "self_cuda_time_us": 2.271999999997206 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [], + [ + 1200, + 2 + ], + [] + ], + "count": 2, + "cpu_time_us": 28.080000000005384, + "self_cpu_time_us": 17.477000000006228, + "cuda_time_us": 2.2400000000016007, + "self_cuda_time_us": 2.2400000000016007 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::CUDAFunctorOnOther_add, std::array, false>(int, at::native::CUDAFunctorOnOther_add, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": 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2114.5910000004515, + "self_cpu_time_us": 1142.9980000009818, + "cuda_time_us": 906.689000000697, + "self_cuda_time_us": 906.689000000697 + }, + { + "op": "void at::native::elementwise_kernel<128, 4, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::bfloat16_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda(float)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::bfloat16_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda(float)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 192, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 906.689000000697, + "self_cuda_time_us": 906.689000000697 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 128 + ], + [ + 14592, + 128 + ] + ], + "count": 192, + "cpu_time_us": 2523.3599999990256, + "self_cpu_time_us": 1627.3459999971092, + "cuda_time_us": 900.1700000003912, + "self_cuda_time_us": 900.1700000003912 + }, + { + "op": "aten::mm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 16 + ], + [ + 16, + 1024 + ] + ], + "count": 192, + "cpu_time_us": 2869.035999999629, + "self_cpu_time_us": 2078.6739999990677, + "cuda_time_us": 867.5939999995026, + "self_cuda_time_us": 867.5939999995026 + }, + { + "op": "Memset (Device)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 1568, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 844.6649999996589, + "self_cuda_time_us": 844.6649999996589 + }, + { + "op": "aten::fill_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 32, + 8, + 64, + 128 + ], + [] + ], + "count": 192, + "cpu_time_us": 1465.2639999994426, + "self_cpu_time_us": 665.1950000009238, + "cuda_time_us": 809.920000000202, + "self_cuda_time_us": 809.920000000202 + }, + { + "op": "aten::mm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 456, + 16 + ], + [ + 16, + 8192 + ] + ], + "count": 64, + "cpu_time_us": 1612.1570000007923, + "self_cpu_time_us": 1250.4409999997006, + "cuda_time_us": 786.0240000011836, + "self_cuda_time_us": 786.0240000011836 + }, + { + "op": "void cutlass::Kernel2(cutlass_80_simt_sgemm_256x128_8x4_tn_align1::Params)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 64, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 786.0240000011836, + "self_cuda_time_us": 786.0240000011836 + }, + { + "op": "aten::masked_fill_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 32, + 8, + 64, + 64 + ], + [ + 64, + 64 + ], + [] + ], + "count": 192, + "cpu_time_us": 3472.2550000006595, + "self_cpu_time_us": 1972.1820000005973, 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"cpu_time_us": 191.96799999990617, + "self_cpu_time_us": 124.86100000060105, + "cuda_time_us": 708.2089999999152, + "self_cuda_time_us": 708.2089999999152 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1024 + ], + [] + ], + "count": 192, + "cpu_time_us": 2394.301000000516, + "self_cpu_time_us": 1563.2210000008781, + "cuda_time_us": 681.7860000007495, + "self_cuda_time_us": 681.7860000007495 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 32, + 8, + 64, + 1 + ], + [ + 1, + 32, + 8, + 1, + 64 + ], + [] + ], + "count": 192, + "cpu_time_us": 2985.50999999934, + "self_cpu_time_us": 2098.658999999243, + "cuda_time_us": 673.1769999985117, + "self_cuda_time_us": 673.1769999985117 + }, + { + "op": "void cublasLt::splitKreduce_kernel<32, 16, int, __nv_bfloat16, __nv_bfloat16, float, __nv_bfloat16, false, __nv_bfloat16, __nv_bfloat16, 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const::{lambda(float)#1} const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast(at::TensorIteratorBase&, at::native::exp_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 192, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 289.2509999992908, + "self_cuda_time_us": 289.2509999992908 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 32 + ], + [ + 32 + ], + [] + ], + "count": 192, + "cpu_time_us": 1959.9519999996264, + "self_cpu_time_us": 1001.0719999997236, + "cuda_time_us": 286.5650000012538, + "self_cuda_time_us": 286.5650000012538 + }, + { + "op": "aten::mean", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456, + 16, + 256 + ], + [], + 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at::native::neg_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#9}::operator()() const::{lambda(c10::BFloat16)#1} const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 128, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 234.50400000005902, + "self_cuda_time_us": 234.50400000005902 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 256 + ], + [ + 256 + ], + [] + ], + "count": 128, + "cpu_time_us": 1117.4609999995737, + "self_cpu_time_us": 549.1079999988287, + "cuda_time_us": 232.89900000031048, + "self_cuda_time_us": 232.89900000031048 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 4, + 456, + 64 + ], + [ + 1, + 1, + 456, + 64 + ] + ], + "count": 128, + "cpu_time_us": 1467.2159999991563, + "self_cpu_time_us": 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at::native::_cuda_scatter_gather_internal_kernel, long>::operator()(at::TensorIterator&, long, long, long, at::native::TensorAssign const&)::{lambda(int)#1}>(int, at::native::_cuda_scatter_gather_internal_kernel, long>::operator()(at::TensorIterator&, long, long, long, at::native::TensorAssign const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 65.81599999958416, + "self_cuda_time_us": 65.81599999958416 + }, + { + "op": "void at::native::unrolled_elementwise_kernel, 4, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#11}::operator()() const::{lambda(bool)#1}, std::array, TrivialOffsetCalculator<1, unsigned int>, 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at_cuda_detail::cub::ScanTileState, int, at::cuda::cub::(anonymous namespace)::SumOp, at_cuda_detail::cub::detail::InputValue, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 8, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 61.27300000017567, + "self_cuda_time_us": 61.27300000017567 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 456 + ], + [ + 456 + ], + [] + ], + "count": 32, + "cpu_time_us": 393.3200000001234, + "self_cpu_time_us": 162.93300000012096, + "cuda_time_us": 59.831999999601976, + "self_cuda_time_us": 59.831999999601976 + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 4096 + ], + [ + 300, + 4096 + ], + [] + ], + "count": 8, + "cpu_time_us": 87.99800000019604, + "self_cpu_time_us": 53.5300000004645, + "cuda_time_us": 57.20999999981723, + 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at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_kernel_impl >(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 53.23900000065623, + "self_cuda_time_us": 53.23900000065623 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::AUnaryFunctor >, std::array, false>(int, at::native::AUnaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 48, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 52.95299999983399, + "self_cuda_time_us": 52.95299999983399 + }, + { + "op": "aten::sum", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456 + ], + [] + ], + "count": 24, + "cpu_time_us": 1078.7759999994742, + "self_cpu_time_us": 451.1899999998277, + "cuda_time_us": 95.09600000029604, + "self_cuda_time_us": 47.385999999562046 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::AUnaryFunctor >, std::array, false>(int, at::native::AUnaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 40, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 45.913000000917236, + "self_cuda_time_us": 45.913000000917236 + }, + { + "op": "void at::native::elementwise_kernel<128, 2, at::native::gpu_kernel_impl_nocast >(at::TensorIteratorBase&, at::native::CUDAFunctor_add const&)::{lambda(int)#1}>(int, at::native::gpu_kernel_impl_nocast >(at::TensorIteratorBase&, at::native::CUDAFunctor_add const&)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 32, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 45.112999999910244, + "self_cuda_time_us": 45.112999999910244 + }, + { + "op": "aten::floor_divide", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "count": 24, + "cpu_time_us": 384.8430000000226, + "self_cpu_time_us": 180.72700000020268, + "cuda_time_us": 43.92800000010175, + "self_cuda_time_us": 43.92800000010175 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "count": 40, + "cpu_time_us": 471.46999999901163, + "self_cpu_time_us": 305.75699999855715, + "cuda_time_us": 43.456000000296626, + "self_cuda_time_us": 43.456000000296626 + }, + { + "op": "aten::repeat_interleave", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1 + ], + [] + ], + "count": 32, + "cpu_time_us": 4267.656000000206, + "self_cpu_time_us": 599.3630000000994, + "cuda_time_us": 239.13099999974656, + "self_cuda_time_us": 43.1309999996156 + }, + { + "op": "void compute_cuda_kernel(long const*, long const*, long*, long, long)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 32, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 43.1309999996156, + "self_cuda_time_us": 43.1309999996156 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1 + ], + [ + 2 + ], + [] + ], + "count": 16, + "cpu_time_us": 195.9320000000298, + "self_cpu_time_us": 127.27499999960128, + "cuda_time_us": 42.68400000000838, + "self_cuda_time_us": 42.68400000000838 + }, + { + "op": "aten::div", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ] + ], + "count": 16, + "cpu_time_us": 241.50399999995716, + "self_cpu_time_us": 155.69299999983923, + "cuda_time_us": 42.20299999987765, + "self_cuda_time_us": 42.20299999987765 + }, + { + "op": "void at::native::unrolled_elementwise_kernel >, std::array, 4, TrivialOffsetCalculator<2, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<2>, at::native::memory::StoreWithCast<1> >(int, at::native::BinaryFunctor >, std::array, TrivialOffsetCalculator<2, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<2>, at::native::memory::StoreWithCast<1>)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 42.20299999987765, + "self_cuda_time_us": 42.20299999987765 + }, + { + "op": "void at_cuda_detail::cub::detail::scan::DeviceScanInitKernel >(at_cuda_detail::cub::ScanTileState, int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 48, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 39.7680000004475, + "self_cuda_time_us": 39.7680000004475 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [], + [], + [] + ], + "count": 40, + "cpu_time_us": 450.2679999994143, + "self_cpu_time_us": 139.41099999986182, + "cuda_time_us": 38.842000000193366, + "self_cuda_time_us": 38.842000000193366 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, std::array, false>(int, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 32, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 37.37200000000303, + "self_cuda_time_us": 37.37200000000303 + }, + { + "op": "aten::mm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 16 + ], + [ + 16, + 4096 + ] + ], + "count": 8, + "cpu_time_us": 120.09399999992456, + "self_cpu_time_us": 87.82600000003004, + "cuda_time_us": 36.79499999980908, + "self_cuda_time_us": 36.79499999980908 + }, + { + "op": "aten::clamp", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 2 + ], + [], + [] + ], + "count": 32, + "cpu_time_us": 458.191999999719, + "self_cpu_time_us": 298.06900000084715, + "cuda_time_us": 35.93200000008801, + "self_cuda_time_us": 35.93200000008801 + }, + { + "op": "void at_cuda_detail::cub::detail::select::DeviceSelectSweepKernel::Policy1000, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::counting_iterator, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, long*, int*, at_cuda_detail::cub::ScanTileState, at_cuda_detail::cub::NullType, at_cuda_detail::cub::NullType, int, at_cuda_detail::cub::detail::select::streaming_context_t, (at_cuda_detail::cub::SelectImpl)0>(thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::counting_iterator, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, long*, int*, at_cuda_detail::cub::ScanTileState, at_cuda_detail::cub::NullType, at_cuda_detail::cub::NullType, int, int, at_cuda_detail::cub::detail::select::streaming_context_t, at_cuda_detail::cub::detail::vsmem_t)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 35.578999999692314, + "self_cuda_time_us": 35.578999999692314 + }, + { + "op": "aten::add_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1024, + 1, + 1, + 1 + ], + [ + 1, + 1024, + 1, + 1, + 1 + ], + [] + ], + "count": 8, + "cpu_time_us": 122.34099999979662, + "self_cpu_time_us": 79.51899999982561, + "cuda_time_us": 34.62100000018836, + "self_cuda_time_us": 34.62100000018836 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::launch_masked_scatter_kernel(at::TensorBase const&, at::TensorBase const&, at::TensorBase const&, at::TensorBase const&)::{lambda()#1}::operator()() const::{lambda()#11}::operator()() const::{lambda(c10::BFloat16, bool, long)#1}, std::array, false>(int, at::native::launch_masked_scatter_kernel(at::TensorBase const&, at::TensorBase const&, at::TensorBase const&, at::TensorBase const&)::{lambda()#1}::operator()() const::{lambda()#11}::operator()() const::{lambda(c10::BFloat16, bool, long)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 8, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 34.138999999908265, + "self_cuda_time_us": 34.138999999908265 + }, + { + "op": "aten::ge", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1 + ], + [] + ], + "count": 32, + "cpu_time_us": 518.1249999996071, + "self_cpu_time_us": 355.2139999991341, + "cuda_time_us": 33.14499999961117, + "self_cuda_time_us": 33.14499999961117 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::compare_scalar_kernel(at::TensorIteratorBase&, at::native::(anonymous namespace)::OpType, long)::{lambda(long)#1}, std::array, false>(int, at::native::compare_scalar_kernel(at::TensorIteratorBase&, at::native::(anonymous namespace)::OpType, long)::{lambda(long)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 32, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 33.14499999961117, + "self_cuda_time_us": 33.14499999961117 + }, + { + "op": "void at::native::(anonymous namespace)::CatArrayBatchedCopy, unsigned int, 2, 64, 64>(at::native::(anonymous namespace)::OpaqueType<4u>*, at::native::(anonymous namespace)::CatArrInputTensorMetadata, unsigned int, 64, 64>, at::native::(anonymous namespace)::TensorSizeStride, int, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 31.198000000222237, + "self_cuda_time_us": 31.198000000222237 + }, + { + "op": "void at_cuda_detail::cub::detail::reduce::DeviceReduceSingleTileKernel >::Policy1000, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, int*, unsigned long long, cuda::std::__4::plus, int, int, cuda::std::__4::__identity>(thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::transform_iterator, bool const*, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default, thrust::THRUST_300001_SM_750_800_860_900_1000_1200_NS::use_default>, int*, unsigned long long, cuda::std::__4::plus, int, cuda::std::__4::__identity)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 30.9730000004638, + "self_cuda_time_us": 30.9730000004638 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::CUDAFunctor_add, std::array, false>(int, at::native::CUDAFunctor_add, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 24, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 29.37299999986135, + "self_cuda_time_us": 29.37299999986135 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 4096 + ], + [] + ], + "count": 8, + "cpu_time_us": 103.89299999989453, + "self_cpu_time_us": 62.91600000023027, + "cuda_time_us": 29.34000000008382, + "self_cuda_time_us": 29.34000000008382 + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 2560 + ], + [ + 300, + 2560 + ], + [] + ], + "count": 8, + "cpu_time_us": 87.79799999967508, + "self_cpu_time_us": 52.99599999948987, + "cuda_time_us": 29.244000000006054, + "self_cuda_time_us": 29.244000000006054 + }, + { + "op": "aten::mm", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 16 + ], + [ + 16, + 2560 + ] + ], + "count": 8, + "cpu_time_us": 117.06500000001688, + "self_cpu_time_us": 85.11999999996624, + "cuda_time_us": 28.702000000077533, + "self_cuda_time_us": 28.702000000077533 + }, + { + "op": "aten::eq", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456 + ], + [] + ], + "count": 24, + "cpu_time_us": 360.0109999995475, + "self_cpu_time_us": 239.81399999956193, + "cuda_time_us": 28.44500000019616, + "self_cuda_time_us": 28.44500000019616 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::cos_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array, false>(int, at::native::cos_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 28.350999999631313, + "self_cuda_time_us": 28.350999999631313 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::sin_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array, false>(int, at::native::sin_kernel_cuda(at::TensorIteratorBase&)::{lambda()#2}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 27.421000000307686, + "self_cuda_time_us": 27.421000000307686 + }, + { + "op": "aten::_index_put_impl_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 3, + 456 + ], + [], + [ + 3, + 456 + ], + [], + [] + ], + "count": 8, + "cpu_time_us": 772.2940000001108, + "self_cpu_time_us": 139.99600000027567, + "cuda_time_us": 63.096000000296044, + "self_cuda_time_us": 27.037999999782187 + }, + { + "op": "void at::native::index_elementwise_kernel<128, 4, at::native::gpu_index_kernel >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_put_kernel_impl >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1}>(long, at::native::gpu_index_kernel >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1}>(at::TensorIteratorBase&, c10::ArrayRef, c10::ArrayRef, at::native::index_put_kernel_impl >(at::TensorIterator&, c10::ArrayRef, c10::ArrayRef)::{lambda(char*, char const*, long)#1} const&, bool)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 8, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 27.037999999782187, + "self_cuda_time_us": 27.037999999782187 + }, + { + "op": "void at::native::(anonymous namespace)::CatArrayBatchedCopy_alignedK_contig, unsigned int, 2, 128, 1, 16>(at::native::(anonymous namespace)::OpaqueType<8u>*, at::native::(anonymous namespace)::CatArrInputTensorMetadata, unsigned int, 128, 1>, at::native::(anonymous namespace)::TensorSizeStride, int, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 26.78199999962817, + "self_cuda_time_us": 26.78199999962817 + }, + { + "op": "aten::remainder", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ] + ], + "count": 16, + "cpu_time_us": 242.12199999968288, + "self_cpu_time_us": 152.99399999939487, + "cuda_time_us": 26.302000000418047, + "self_cuda_time_us": 26.302000000418047 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BinaryFunctor, std::array, false>(int, at::native::BinaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 26.302000000418047, + "self_cuda_time_us": 26.302000000418047 + }, + { + "op": "aten::remainder", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "count": 16, + "cpu_time_us": 256.53300000025774, + "self_cpu_time_us": 180.29500000020198, + "cuda_time_us": 25.915000000226428, + "self_cuda_time_us": 25.915000000226428 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BUnaryFunctor, std::array, false>(int, at::native::BUnaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 25.915000000226428, + "self_cuda_time_us": 25.915000000226428 + }, + { + "op": "void at::native::(anonymous namespace)::CatArrayBatchedCopy_vectorized, unsigned int, 2, 128, 1, 16, 4>(char*, at::native::(anonymous namespace)::CatArrInputTensorMetadata, unsigned int, 128, 1>, at::native::(anonymous 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false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 22.076999999946565, + "self_cuda_time_us": 22.076999999946565 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456, + 11 + ], + [ + 1, + 456, + 11 + ], + [] + ], + "count": 16, + "cpu_time_us": 184.6420000000362, + "self_cpu_time_us": 108.82000000006519, + "cuda_time_us": 21.756999999604886, + "self_cuda_time_us": 21.756999999604886 + }, + { + "op": "aten::copy_", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1, + 456, + 10 + ], + [ + 1, + 456, + 10 + ], + [] + ], + "count": 16, + "cpu_time_us": 146.39100000048347, + "self_cpu_time_us": 77.22300000042014, + "cuda_time_us": 21.019000000480446, + "self_cuda_time_us": 21.019000000480446 + }, + { + "op": "aten::gather", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 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char const*, long)#1} const&, bool)::{lambda(int)#1})", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 8, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 19.134999999660067, + "self_cuda_time_us": 19.134999999660067 + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 300, + 2560 + ], + [] + ], + "count": 8, + "cpu_time_us": 93.98300000003655, + "self_cpu_time_us": 60.75399999963702, + "cuda_time_us": 19.00699999961944, + "self_cuda_time_us": 19.00699999961944 + }, + { + "op": "aten::clamp", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [], + [] + ], + "count": 16, + "cpu_time_us": 273.5129999998462, + "self_cpu_time_us": 190.9229999998206, + "cuda_time_us": 18.5889999998617, + "self_cuda_time_us": 18.5889999998617 + }, + { + "op": "aten::max", + "device_type": "DeviceType.CPU", + 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at::native::vectorized_elementwise_kernel<2, at::native::BinaryFunctor, std::array, false>(int, at::native::BinaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 8, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 18.301000000268687, + "self_cuda_time_us": 18.301000000268687 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BinaryFunctor >, std::array, false>(int, at::native::BinaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 18.208999999667867, + "self_cuda_time_us": 18.208999999667867 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ], + [], + [] + ], + "count": 16, + "cpu_time_us": 199.42799999960698, + "self_cpu_time_us": 131.36099999964063, 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at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 17.148999999946682, + "self_cuda_time_us": 17.148999999946682 + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200, + 1 + ], + [ + 1200, + 1 + ], + [] + ], + "count": 16, + "cpu_time_us": 195.40499999978056, + "self_cpu_time_us": 125.416999999361, + "cuda_time_us": 17.147000000128173, + "self_cuda_time_us": 17.147000000128173 + }, + { + "op": "aten::floor", + "device_type": "DeviceType.CPU", + "is_user_annotation": false, + "input_shapes": [ + [ + 1200 + ] + ], + "count": 16, + "cpu_time_us": 221.35100000025705, 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249.6050000001269, + "self_cpu_time_us": 126.24900000054913, + "cuda_time_us": 16.413000000218744, + "self_cuda_time_us": 16.413000000218744 + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::AbsFunctor, std::array, false>(int, at::native::AbsFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 16, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + "cuda_time_us": 16.413000000218744, + "self_cuda_time_us": 16.413000000218744 + }, + { + "op": "void at::native::(anonymous namespace)::CatArrayBatchedCopy, unsigned int, 3, 64, 64>(at::native::(anonymous namespace)::OpaqueType<8u>*, at::native::(anonymous namespace)::CatArrInputTensorMetadata, unsigned int, 64, 64>, at::native::(anonymous namespace)::TensorSizeStride, int, unsigned int)", + "device_type": "DeviceType.CUDA", + "is_user_annotation": false, + "input_shapes": [], + "count": 8, + "cpu_time_us": 0, + "self_cpu_time_us": 0, + 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"bfloat16", + "adapter_merged": false + }, + "load_ms": 19466.146210208535, + "adapter_modules": 178 +} diff --git a/recipe/cua_s1/experiments/rtx4090-profile/traces.log b/recipe/cua_s1/experiments/rtx4090-profile/traces.log new file mode 100644 index 0000000..7fdfbcb --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-profile/traces.log @@ -0,0 +1,12 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! + Loading weights: 0%| | 0/723 [00:00 Date: Mon, 28 Sep 2026 11:40:16 +0800 Subject: [PATCH 05/75] docs: plan request-local image reuse experiment --- .../plans/2026-09-28-cua-image-reuse.md | 17 +++++++++++++++++ .../specs/2026-09-28-cua-image-reuse-design.md | 9 +++++++++ 2 files changed, 26 insertions(+) create mode 100644 docs/superpowers/plans/2026-09-28-cua-image-reuse.md create mode 100644 docs/superpowers/specs/2026-09-28-cua-image-reuse-design.md diff --git a/docs/superpowers/plans/2026-09-28-cua-image-reuse.md b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md new file mode 100644 index 0000000..ef18c27 --- /dev/null +++ b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md @@ -0,0 +1,17 @@ +# Cua-S1 Image Reuse Implementation Plan + +> **For agentic workers:** Use subagent-driven-development for implementation and independent review; controller owns GPU experiments and publication. + +**Goal:** Complete request-local image reuse with reproducible correctness and speed evidence, submit a PR, then shut down the GPU server. + +**Architecture:** Keep existing prepare/score as the reference path. Add isolated image preparation reuse and shared visual features for multi-question predict. Build per-question embeddings and positions using pinned model helpers. + +**Tech Stack:** Python, PyTorch 2.14, Transformers 5.17, PEFT 0.21, RTX 4090. + +- [ ] Baseline: run all tests/cua_s1; preserve existing PR #15 data. +- [ ] Implementation: tests/cua_s1/test_image_reuse.py first; prove failures; implement src/models/cua_s1/multimodal/model.py and focused helper module if needed. Preserve preflight validation, distinct prompts, no shared mutable state, PEFT vision layers and unchanged single-question behavior. +- [ ] Harness: recipe/cua_s1/benchmark_image_reuse.py plus CPU tests. Check exact tensors, positions and probabilities before timed paired measurements; alternate order; record each sample and independent per-variant peak memory. Retain failed reports. Reuse profile_multimodal fixture and provenance helpers. +- [ ] GPU: use clean committed checkout, run CPU tests, correctness smoke then 16-case paired experiment (5 warmups, 2 x 50 iterations per variant), plus distinct questions. Separate count/trace verification from all timed calls. +- [ ] Review: independent spec review followed by quality review; resolve findings and re-run affected checks. +- [ ] Publication: document measured results and tensor contract, retain raw JSON, run lint/full tests, commit/push and create linked PR identifying #12/#15 dependencies. +- [ ] Shutdown: copy experiment artifacts locally, verify hashes, confirm no required GPU work remains, shut down server and verify provider/connection state. diff --git a/docs/superpowers/specs/2026-09-28-cua-image-reuse-design.md b/docs/superpowers/specs/2026-09-28-cua-image-reuse-design.md new file mode 100644 index 0000000..324407a --- /dev/null +++ b/docs/superpowers/specs/2026-09-28-cua-image-reuse-design.md @@ -0,0 +1,9 @@ +# Request-local Cua-S1 image reuse + +Approved scope: implement the proposed single-request preprocessing and vision reuse, verify exact correctness and paired RTX 4090 performance, publish a PR, back up results and shut down the experiment server. + +Reuse the single image within a parsed Request. Preserve each question's full processor tokenization, candidate readout, token accounting and three-dimensional positions. Validate every processed length before any vision or language execution. Keep the unmodified single-question path and an explicit baseline path for experiments. The shared visual encoder includes all active PEFT adapters. Never cache text embeddings, positions or outputs across questions or images. Do not introduce a cross-request cache, batching, adapter merging or approximate arithmetic. + +Preprocessing should reuse the pinned processor's image result through a request-local processor copy, without changing global modules. Vision reuse should compute the image features once, build question-specific embeddings and explicit positions through the pinned model helpers, then invoke the existing PEFT model with inputs_embeds. Record the concrete tensor interface and ownership in the recipe documentation. + +Acceptance: exact prepared tensor and candidate probability parity across distinct questions, varying candidate counts/order, image sizes/formats and consecutive different-image requests; one preprocessing and one vision execution per multi-question request; failure cleanup; no single-question regression outside measurement noise. Measure the existing 16-case matrix using paired alternating baseline/candidate calls, plus a distinct-question workload. Save raw samples, environment, source revisions, memory peaks and commands. Counts and optional trace verification run outside latency measurements. Publish without claiming native backend or kernel optimization. From 8c3aa2054030770c783c0c1237298d4d32eb25ca Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:43:35 +0800 Subject: [PATCH 06/75] test: add paired image reuse experiment and correctness checks --- recipe/cua_s1/benchmark_image_reuse.py | 323 +++++++++++++++++++++++++ tests/cua_s1/test_reuse_benchmark.py | 117 +++++++++ 2 files changed, 440 insertions(+) create mode 100644 recipe/cua_s1/benchmark_image_reuse.py create mode 100644 tests/cua_s1/test_reuse_benchmark.py diff --git a/recipe/cua_s1/benchmark_image_reuse.py b/recipe/cua_s1/benchmark_image_reuse.py new file mode 100644 index 0000000..51c7abf --- /dev/null +++ b/recipe/cua_s1/benchmark_image_reuse.py @@ -0,0 +1,323 @@ +"""Validate request-local image reuse and measure paired, uninstrumented latency.""" + +from __future__ import annotations + +import argparse +import copy +import hashlib +import json +import random +import statistics +import sys +from pathlib import Path +from unittest.mock import patch + +from profile_multimodal import case_matrix, fixture, repository_state, write_json + + +def parse_args(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--correctness-only", action="store_true") + parser.add_argument("--case", action="append", default=[]) + for name, default in [("warmup", 5), ("runs", 2), ("iterations", 50)]: + parser.add_argument(f"--{name}", type=int, default=default) + args = parser.parse_args(argv) + if min(args.warmup, args.runs, args.iterations) <= 0: + parser.error("warmup, runs and iterations must be positive") + known = {c["id"] for c in case_matrix()} | {"640x480-distinct-q8"} + if len(set(args.case)) != len(args.case) or set(args.case) - known: + parser.error("duplicate or unknown case") + return args + + +def require_equal(expected, actual, label): + if expected != actual: + raise ValueError(f"{label} mismatch") + + +def distinct_fixture(folder): + value = fixture(case_matrix()[-1], folder) + criteria = [ + {"save": "Save changes", "cancel": "Cancel"}, + {"cancel": "Cancel", "save": "Save changes"}, + {"only": "Continue"}, + {f"option-{i}": f"Choose action {i}" for i in range(26)}, + {"null": None, "object": {"label": '保存 "名称"'}, "array": ["Cancel", "\n"]}, + {"first": "<|im_end|>", "second": "Cancel"}, + {"yes": "Confirm", "no": "Go back", "wait": "Wait", "help": "Help"}, + {"stay": "Stay", "leave": "Leave"}, + ] + goals = [ + "Save the new name", + "Cancel without saving", + "", + "Select action 3", + {"goal": "保存名称"}, + "Choose <|im_start|>", + "Confirm the change", + "Leave this screen", + ] + value["questions"] = { + f"q{i + 1}": {"type": "choice", "instructions": goal, "criteria": choices} + for i, (goal, choices) in enumerate(zip(goals, criteria)) + } + write_json(folder / "distinct.json", value) + return value + + +def paired_benchmark(engine, request, *, warmup, runs, iterations): + import torch + from evaluate_multimodal import measure, quantiles + + if min(warmup, runs, iterations) <= 0: + raise ValueError("counts must be positive") + variants = {"baseline": engine.predict_reference, "reuse": engine.predict} + for _ in range(warmup): + for call in variants.values(): + measure(lambda call=call: call(request)) + result = [] + for run in range(runs): + samples = {name: [] for name in variants} + peaks = {name: {"allocated": 0, "reserved": 0} for name in variants} + orders = [] + for iteration in range(iterations): + order = list(variants) + if (run + iteration) % 2: + order.reverse() + orders.append(order) + for name in order: + torch.cuda.synchronize() + torch.cuda.reset_peak_memory_stats() + samples[name].append( + measure(lambda name=name: variants[name](request))[1] + ) + peaks[name]["allocated"] = max( + peaks[name]["allocated"], torch.cuda.max_memory_allocated() + ) + peaks[name]["reserved"] = max( + peaks[name]["reserved"], torch.cuda.max_memory_reserved() + ) + result.append( + { + "run": run + 1, + "orders": orders, + **{ + name: { + "latencies_ms": values, + **quantiles(values), + "requests_per_second_serial": 1000 / statistics.mean(values), + "peak_allocated_bytes": peaks[name]["allocated"], + "peak_reserved_bytes": peaks[name]["reserved"], + } + for name, values in samples.items() + }, + } + ) + return result + + +def observed_predict(engine, request, call): + """Untimed real processor/module counts and language input fingerprints.""" + import torch + from evaluate_multimodal import fingerprint + + counts = {"vision": 0, "language": 0} + language_inputs = [] + core = engine.model.get_base_model().model + + def vision(module, args): + counts["vision"] += 1 + + def language(module, args, kwargs): + counts["language"] += 1 + language_inputs.append( + fingerprint( + { + name: kwargs[name] + for name in ("inputs_embeds", "position_ids", "attention_mask") + if kwargs.get(name) is not None + } + ) + ) + + handles = [ + core.visual.register_forward_pre_hook(vision), + core.language_model.register_forward_pre_hook(language, with_kwargs=True), + ] + try: + with patch.object( + engine.processor.image_processor, + "preprocess", + wraps=engine.processor.image_processor.preprocess, + ) as prepare: + output = call(request) + torch.cuda.synchronize() + counts["image_preprocess"] = prepare.call_count + finally: + for handle in handles: + handle.remove() + return output, counts, language_inputs + + +def validate_case(engine, request): + from evaluate_multimodal import fingerprint + + expected_inputs = [ + fingerprint(engine.prepare(request.image, q)) for q in request.questions + ] + actual_inputs = [ + fingerprint(x) for x in engine.prepare_reused(request.image, request.questions) + ] + require_equal(expected_inputs, actual_inputs, "prepared tensors") + baseline, baseline_counts, baseline_language = observed_predict( + engine, request, engine.predict_reference + ) + reuse, reuse_counts, reuse_language = observed_predict( + engine, request, engine.predict + ) + require_equal(baseline, reuse, "full response / probabilities") + require_equal( + baseline_language, reuse_language, "language embeddings / 3D positions" + ) + n = len(request.questions) + require_equal( + {"image_preprocess": n, "vision": n, "language": n}, + baseline_counts, + "baseline calls", + ) + require_equal( + {"image_preprocess": 1, "vision": 1, "language": n}, reuse_counts, "reuse calls" + ) + return { + "exact_prepared_tensor_parity": True, + "exact_response_parity": True, + "exact_language_input_parity": True, + "baseline_counts": baseline_counts, + "reuse_counts": reuse_counts, + "inputs": actual_inputs, + "language_inputs": reuse_language, + "response": reuse, + } + + +def correctness_cases(folder): + from evaluate_multimodal import cases + + result = [(name, value) for name, _, value in cases(folder)] + distinct = distinct_fixture(folder / "distinct") + result.append(("distinct-eight", distinct)) + reverse = copy.deepcopy(distinct) + reverse["questions"] = dict(reversed(list(reverse["questions"].items()))) + result.append(("distinct-reversed", reverse)) + # Same varied prompts but another image and grid; then return to original image. + changed = copy.deepcopy(distinct) + changed["state"] = result[0][1]["state"] + result.extend( + [("distinct-small-image", changed), ("distinct-original-again", distinct)] + ) + return result + + +def main(argv=None): + args = parse_args(argv) + path = args.output / "report.json" + if path.exists(): + raise ValueError("output report already exists; use a fresh directory") + args.output.mkdir(parents=True, exist_ok=True) + report = { + "schema_version": 1, + "status": "running", + "repository": repository_state(Path(__file__).resolve().parents[2]), + "config": { + "warmup": args.warmup, + "runs": args.runs, + "iterations": args.iterations, + "correctness_only": args.correctness_only, + "selected_cases": args.case, + "case_order_seed": 20260928, + "concurrency": 1, + "batch_size": 1, + "timed_scope": "synchronized engine.predict; excludes HTTP, parsing, image decode, validation instrumentation", + }, + "correctness": [], + "cases": [], + } + write_json(path, report) + try: + from evaluate_multimodal import environment, measure + + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + report["environment"] = environment() + engine, report["load_ms"] = measure( + lambda: MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + ) + report["adapter_modules"] = engine.adapter_modules + for name, value in correctness_cases(args.output / "correctness-fixtures"): + checked = validate_case(engine, parse_request(value)) + report["correctness"].append({"id": name, **checked}) + write_json(path, report) + print( + f"correctness {name}: exact tensors, embeddings, positions, responses; {checked['reuse_counts']}", + flush=True, + ) + if not args.correctness_only: + selected = case_matrix() + [{"id": "640x480-distinct-q8"}] + selected = [c for c in selected if not args.case or c["id"] in args.case] + random.Random(20260928).shuffle(selected) + for case in selected: + value = ( + distinct_fixture(args.output / "fixtures") + if case["id"] == "640x480-distinct-q8" + else fixture(case, args.output / "fixtures") + ) + request = parse_request(value) + item = { + **case, + "status": "running", + "fixture_sha256": hashlib.sha256( + json.dumps(value, sort_keys=True).encode() + ).hexdigest(), + } + report["cases"].append(item) + write_json(path, report) + item["validation"] = validate_case(engine, request) + item["runs"] = paired_benchmark( + engine, + request, + warmup=args.warmup, + runs=args.runs, + iterations=args.iterations, + ) + item["status"] = "complete" + write_json(path, report) + print( + f"benchmark {case['id']}: " + + json.dumps( + [ + {v: round(r[v]["p50_ms"], 2) for v in ("baseline", "reuse")} + for r in item["runs"] + ] + ), + flush=True, + ) + report["status"] = "complete" + except Exception as exc: + report.update( + status="failed", error={"type": type(exc).__name__, "message": str(exc)} + ) + print(f"Experiment failed: {report['error']}", file=sys.stderr, flush=True) + raise + finally: + write_json(path, report) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/cua_s1/test_reuse_benchmark.py b/tests/cua_s1/test_reuse_benchmark.py new file mode 100644 index 0000000..c6ff810 --- /dev/null +++ b/tests/cua_s1/test_reuse_benchmark.py @@ -0,0 +1,117 @@ +"""Contracts for paired measurements, fixture diversity, and saved failures.""" + +import importlib.util +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +RECIPE = Path(__file__).resolve().parents[2] / "recipe/cua_s1" + + +@pytest.fixture +def reuse_benchmark(monkeypatch): + monkeypatch.syspath_prepend(str(RECIPE)) + path = RECIPE / "benchmark_image_reuse.py" + assert path.exists(), "reuse benchmark is not implemented" + spec = importlib.util.spec_from_file_location("reuse_benchmark", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_distinct_fixture_changes_goals_and_candidate_layout(reuse_benchmark, tmp_path): + from models.cua_s1.multimodal.protocol import parse_request + + value = reuse_benchmark.distinct_fixture(tmp_path) + request = parse_request(value) + assert len(request.questions) == 8 + assert len({q.goal for q in request.questions}) == 8 + assert len({len(q.keys) for q in request.questions}) >= 3 + assert any(len(q.keys) == 26 for q in request.questions) + + +def test_paired_measurements_alternate_and_keep_variant_peaks( + reuse_benchmark, monkeypatch +): + events = [] + cuda = SimpleNamespace( + synchronize=lambda: events.append("sync"), + reset_peak_memory_stats=lambda: events.append("reset"), + max_memory_allocated=lambda: 123, + max_memory_reserved=lambda: 456, + ) + monkeypatch.setitem(sys.modules, "torch", SimpleNamespace(cuda=cuda)) + engine = SimpleNamespace( + predict_reference=lambda request: events.append("baseline"), + predict=lambda request: events.append("reuse"), + ) + result = reuse_benchmark.paired_benchmark( + engine, None, warmup=1, runs=2, iterations=2 + ) + calls = [event for event in events if event in {"baseline", "reuse"}] + assert calls == [ + "baseline", + "reuse", + "baseline", + "reuse", + "reuse", + "baseline", + "reuse", + "baseline", + "baseline", + "reuse", + ] + assert events.count("reset") == 8 + assert len(result) == 2 + for run in result: + for variant in ("baseline", "reuse"): + assert len(run[variant]["latencies_ms"]) == 2 + assert run[variant]["peak_allocated_bytes"] == 123 + assert run[variant]["peak_reserved_bytes"] == 456 + + +@pytest.mark.parametrize("option", ["--warmup", "--runs", "--iterations"]) +def test_nonpositive_benchmark_counts_rejected(reuse_benchmark, option): + with pytest.raises(SystemExit): + reuse_benchmark.parse_args( + ["--weights", "/tmp/w", "--output", "/tmp/o", option, "0"] + ) + + +def test_input_mismatch_does_not_pass_validation(reuse_benchmark): + with pytest.raises(ValueError, match="mismatch"): + reuse_benchmark.require_equal({"x": 1}, {"x": 2}, "inputs") + + +def test_failure_report_is_preserved(reuse_benchmark, monkeypatch, tmp_path): + import json + + import evaluate_multimodal + + from models.cua_s1.multimodal import model + + monkeypatch.setattr(evaluate_multimodal, "environment", lambda: {"gpu": "fake"}) + monkeypatch.setattr(evaluate_multimodal, "measure", lambda call: (call(), 0.0)) + monkeypatch.setattr( + model, "MultimodalEngine", lambda *args: SimpleNamespace(adapter_modules=178) + ) + monkeypatch.setattr( + reuse_benchmark, "correctness_cases", lambda folder: [("bad", {})] + ) + with pytest.raises(ValueError): + reuse_benchmark.main( + [ + "--weights", + str(tmp_path), + "--output", + str(tmp_path), + "--correctness-only", + ] + ) + report = json.loads((tmp_path / "report.json").read_text()) + assert report["status"] == "failed" + assert report["error"]["type"] == "InvalidRequest" + with pytest.raises(ValueError, match="already exists"): + reuse_benchmark.main(["--weights", str(tmp_path), "--output", str(tmp_path)]) From 7e7ed3478a3fb2b3886bd077dc8cd72e01174f0d Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:44:11 +0800 Subject: [PATCH 07/75] test: preserve unoptimized profiling baseline after reuse --- recipe/cua_s1/profile_multimodal.py | 3 +++ tests/cua_s1/test_profiling.py | 40 +++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+) diff --git a/recipe/cua_s1/profile_multimodal.py b/recipe/cua_s1/profile_multimodal.py index 38a6b59..960201c 100644 --- a/recipe/cua_s1/profile_multimodal.py +++ b/recipe/cua_s1/profile_multimodal.py @@ -387,6 +387,9 @@ def main(argv=None): str(args.weights / "cua-s1-4b-0.2/multimodal"), ) ) + # Preserve this recipe as the unoptimized stage baseline from PR #15. + if hasattr(engine, "predict_reference"): + engine.predict = engine.predict_reference report["adapter_modules"] = engine.adapter_modules for case in selected: current = {**case, "status": "running"} diff --git a/tests/cua_s1/test_profiling.py b/tests/cua_s1/test_profiling.py index 5cd1991..cd1f48d 100644 --- a/tests/cua_s1/test_profiling.py +++ b/tests/cua_s1/test_profiling.py @@ -467,3 +467,43 @@ def profile(*args): assert report["status"] == ("failed" if profile_fails else "complete") saved = json.loads((tmp_path / f"{case}.json").read_text()) assert saved["status"] == ("failed" if profile_fails else "complete") + + +def test_profile_recipe_selects_unoptimized_reference(profiling, monkeypatch, tmp_path): + import evaluate_multimodal + + from models.cua_s1.multimodal import model + + def reference(request): + return "baseline" + + engine = SimpleNamespace( + adapter_modules=178, + predict_reference=reference, + predict=lambda request: "optimized", + ) + monkeypatch.setattr(model, "MultimodalEngine", lambda *args: engine) + monkeypatch.setattr(evaluate_multimodal, "environment", lambda: {}) + monkeypatch.setattr(evaluate_multimodal, "measure", lambda call: (call(), 0.0)) + monkeypatch.setattr( + profiling, "input_metadata", lambda *args: [{"input_tokens": 1}] + ) + + def benchmark(selected_engine, *args, **kwargs): + assert selected_engine.predict(None) == "baseline" + return {"runs": []} + + monkeypatch.setattr(profiling, "benchmark", benchmark) + assert ( + profiling.main( + [ + "--weights", + str(tmp_path), + "--output", + str(tmp_path), + "--case", + "320x240-short-q1", + ] + ) + == 0 + ) From 7874c6cd51151a206236cc967cbb6f8ad20639f9 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:44:44 +0800 Subject: [PATCH 08/75] style: simplify profiling test environment stub --- tests/cua_s1/test_profiling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/cua_s1/test_profiling.py b/tests/cua_s1/test_profiling.py index cd1f48d..39871cd 100644 --- a/tests/cua_s1/test_profiling.py +++ b/tests/cua_s1/test_profiling.py @@ -483,7 +483,7 @@ def reference(request): predict=lambda request: "optimized", ) monkeypatch.setattr(model, "MultimodalEngine", lambda *args: engine) - monkeypatch.setattr(evaluate_multimodal, "environment", lambda: {}) + monkeypatch.setattr(evaluate_multimodal, "environment", dict) monkeypatch.setattr(evaluate_multimodal, "measure", lambda call: (call(), 0.0)) monkeypatch.setattr( profiling, "input_metadata", lambda *args: [{"input_tokens": 1}] From cf899ffca186bae158b756316685226f99ca8b75 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:44:56 +0800 Subject: [PATCH 09/75] Reuse image preprocessing and adapted vision features within requests --- src/models/cua_s1/multimodal/model.py | 104 ++++++++++++- tests/cua_s1/test_image_reuse.py | 204 ++++++++++++++++++++++++++ tests/cua_s1/test_model.py | 2 +- 3 files changed, 306 insertions(+), 4 deletions(-) create mode 100644 tests/cua_s1/test_image_reuse.py diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py index db55070..625c8c0 100644 --- a/src/models/cua_s1/multimodal/model.py +++ b/src/models/cua_s1/multimodal/model.py @@ -2,6 +2,7 @@ from __future__ import annotations +import copy import hashlib import json from pathlib import Path @@ -77,6 +78,24 @@ def verify_weights(base: Path, adapter: Path): ) +class _RequestImageProcessor: + """Reuse one image result; each processor call owns its mutable mapping.""" + + def __init__(self, processor): + self.processor = processor + self.result = None + + def __getattr__(self, name): + return getattr(self.processor, name) + + def __call__(self, *args, **kwargs): + if self.result is None: + self.result = self.processor(*args, **kwargs) + # BatchFeature.to and processor token expansion can mutate mappings. + # Share only tensor values, which the pinned processor does not modify. + return type(self.result)(dict(self.result)) + + class MultimodalEngine: def __init__( self, base: str, adapter: str, device: str = "cuda", dtype: str = "bfloat16" @@ -118,15 +137,74 @@ def __init__( self.dtype = dtype def prepare(self, image, question: Question): + return self._prepare(self.processor, image, question) + + @staticmethod + def _prepare(processor, image, question: Question): messages = build_messages(question) - text = self.processor.apply_chat_template( + text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) - inputs = self.processor(text=[text], images=[image], return_tensors="pt") + inputs = processor(text=[text], images=[image], return_tensors="pt") if inputs["input_ids"].shape[-1] > MAX_TOKENS: raise InvalidRequest(f"processed prompt exceeds {MAX_TOKENS} tokens") return inputs + def prepare_reused(self, image, questions): + """Tokenize each question normally, preprocessing this request's image once.""" + processor = copy.copy(self.processor) + processor.image_processor = _RequestImageProcessor( + self.processor.image_processor + ) + return [self._prepare(processor, image, question) for question in questions] + + def encode_image(self, inputs): + """Encode once through the vision modules with their active LoRA adapters.""" + import torch + + core = self.model.get_base_model().model + with torch.no_grad(): + output = core.get_image_features( + inputs["pixel_values"].to(self.model.device), + inputs["image_grid_thw"].to(self.model.device), + return_dict=True, + ) + return torch.cat(output.pooler_output, dim=0) + + def score_reused(self, inputs, question: Question, features) -> list[float]: + """Build fresh text embeddings and 3D positions around shared image features.""" + import torch + + ids = letter_ids(self.tokenizer, len(question.keys)) + # Keep prepared CPU inputs intact and avoid another image transfer. + text_inputs = { + key: value.to(self.model.device) + for key, value in inputs.items() + if key != "pixel_values" + } + core = self.model.get_base_model().model + with torch.no_grad(): + input_ids = text_inputs.pop("input_ids") + embeds = core.get_input_embeddings()(input_ids) + image_embeds = features.to(embeds.device, embeds.dtype) + image_mask, _ = core.get_placeholder_mask( + input_ids, inputs_embeds=embeds, image_features=image_embeds + ) + embeds = embeds.masked_scatter(image_mask, image_embeds) + position_ids, _ = core.get_rope_index( + input_ids=input_ids, + mm_token_type_ids=text_inputs.pop("mm_token_type_ids"), + image_grid_thw=text_inputs.pop("image_grid_thw"), + attention_mask=text_inputs.get("attention_mask"), + ) + output = self.model( + inputs_embeds=embeds, position_ids=position_ids, **text_inputs + ) + logits = output.logits[0, -1, :] + return torch.softmax( + logits[torch.tensor(ids, device=logits.device)].float(), dim=-1 + ).tolist() + def score(self, inputs, question: Question) -> list[float]: import torch @@ -139,7 +217,8 @@ def score(self, inputs, question: Question) -> list[float]: logits[torch.tensor(ids, device=logits.device)].float(), dim=-1 ).tolist() - def predict(self, request: Request) -> dict: + def predict_reference(self, request: Request) -> dict: + """Original full-processor/full-model path retained for paired experiments.""" # Validate all processed lengths before executing any question. prepared = [self.prepare(request.image, q) for q in request.questions] answers = { @@ -155,6 +234,25 @@ def predict(self, request: Request) -> dict: }, } + def predict(self, request: Request) -> dict: + if len(request.questions) == 1: + return self.predict_reference(request) + # No image encoder or language model runs until every prompt is valid. + prepared = self.prepare_reused(request.image, request.questions) + features = self.encode_image(prepared[0]) + answers = { + q.name: answer(q, self.score_reused(inputs, q, features)) + for q, inputs in zip(request.questions, prepared) + } + return { + "model": IDENTITY, + "answers": answers, + "usage": { + "input_tokens": sum(x["input_ids"].shape[-1] for x in prepared), + "output_tokens": 0, + }, + } + def warmup(self): from PIL import Image diff --git a/tests/cua_s1/test_image_reuse.py b/tests/cua_s1/test_image_reuse.py new file mode 100644 index 0000000..5c497a2 --- /dev/null +++ b/tests/cua_s1/test_image_reuse.py @@ -0,0 +1,204 @@ +"""Request ownership tests, plus tensor integration tests when torch is installed.""" + +from types import SimpleNamespace + +import pytest + +from models.cua_s1.multimodal.model import MAX_TOKENS, MultimodalEngine +from models.cua_s1.multimodal.protocol import InvalidRequest, Question, Request + +QUESTIONS = ( + Question("first", ("yes", "no"), ("Yes", "No"), "Confirm"), + Question("second", ("back", "next", "stop"), ("Back", "Next", "Stop"), "Go back"), +) + + +class ImageProcessor: + merge_size = 2 + + def __init__(self): + self.calls = [] + + def __call__(self, images, **kwargs): + self.calls.append(images[0]) + return {"pixel_values": images[0], "image_grid_thw": (1, 4, 4)} + + +class Processor: + def __init__(self): + self.image_processor = ImageProcessor() + self.calls = [] + self.fail = False + + def apply_chat_template(self, messages, **kwargs): + return repr(messages) + + def __call__(self, text, images, **kwargs): + assert self.image_processor.merge_size == 2 + image = self.image_processor(images, return_tensors="pt") + # Processor implementations may consume/mutate their returned mapping. + pixels = image.pop("pixel_values") + self.calls.append(text[0]) + if self.fail: + raise ValueError("tokenizer failure") + return { + "pixel_values": pixels, + "image_grid_thw": image["image_grid_thw"], + "input_ids": SimpleNamespace(shape=(1, len(text[0]))), + "text": text[0], + } + + +def engine_with_processor(): + engine = object.__new__(MultimodalEngine) + engine.processor = Processor() + return engine + + +def test_preprocessing_reuses_only_image_and_keeps_question_prompts(): + engine = engine_with_processor() + original = engine.processor.image_processor + image = object() + prepared = engine.prepare_reused(image, QUESTIONS) + assert original.calls == [image] + assert prepared[0]["pixel_values"] is prepared[1]["pixel_values"] is image + assert prepared[0]["text"] != prepared[1]["text"] + assert engine.processor.image_processor is original + for question, inputs in zip(QUESTIONS, prepared): + reference = engine.prepare(image, question) + assert inputs["text"] == reference["text"] + assert inputs["input_ids"].shape == reference["input_ids"].shape + assert inputs["image_grid_thw"] == reference["image_grid_thw"] + + +def test_image_cache_does_not_survive_requests_or_failed_preparation(): + engine = engine_with_processor() + images = [object(), object(), object()] + engine.prepare_reused(images[0], QUESTIONS) + engine.processor.fail = True + with pytest.raises(ValueError, match="tokenizer failure"): + engine.prepare_reused(images[1], QUESTIONS) + engine.processor.fail = False + prepared = engine.prepare_reused(images[2], QUESTIONS) + assert engine.processor.image_processor.calls == images + assert all(x["pixel_values"] is images[2] for x in prepared) + + +def test_all_lengths_checked_before_vision_or_language_execution(): + engine = engine_with_processor() + forwarded = [] + engine.encode_image = lambda inputs: forwarded.append("vision") + engine.score_reused = lambda *args: forwarded.append("language") + long = Question("long", ("a",), ("A",), "x" * MAX_TOKENS) + with pytest.raises(InvalidRequest, match="4096"): + engine.predict(Request(object(), (QUESTIONS[0], long))) + assert forwarded == [] + + +def test_multi_question_prediction_encodes_once_and_keeps_usage(): + engine = engine_with_processor() + encoded, scored = [], [] + features = object() + + def encode(inputs): + encoded.append(inputs) + return features + + def score(inputs, question, shared): + assert shared is features + scored.append(question) + return [1 / len(question.keys)] * len(question.keys) + + engine.encode_image = encode + engine.score_reused = score + request = Request(object(), QUESTIONS) + result = engine.predict(request) + assert len(encoded) == 1 + assert scored == list(QUESTIONS) + assert list(result["answers"]) == [q.name for q in QUESTIONS] + assert result["usage"]["input_tokens"] == sum( + x["input_ids"].shape[-1] + for x in engine.prepare_reused(request.image, QUESTIONS) + ) + + +def test_single_question_uses_reference_path(): + engine = object.__new__(MultimodalEngine) + request = Request(object(), QUESTIONS[:1]) + calls = [] + engine.predict_reference = lambda value: calls.append(value) or {"reference": True} + assert engine.predict(request) == {"reference": True} + assert calls == [request] + + +def test_reused_tensor_path_preserves_scatter_positions_and_peft_forward(): + torch = pytest.importorskip("torch") + engine = object.__new__(MultimodalEngine) + calls = [] + features = torch.tensor([[10.0, 20.0], [30.0, 40.0]]) + positions = torch.tensor([[[0, 1, 1, 3]], [[0, 1, 1, 3]], [[0, 1, 2, 3]]]) + + class Core: + def get_image_features(self, pixels, grid, return_dict): + assert not torch.is_grad_enabled() + calls.append("vision-with-adapters") + return SimpleNamespace(pooler_output=(features,)) + + def get_input_embeddings(self): + return lambda ids: ids.unsqueeze(-1).expand(-1, -1, 2).float() + + def get_placeholder_mask(self, ids, inputs_embeds, image_features): + assert torch.equal(image_features, features) + return (ids == 9).unsqueeze(-1).expand_as(inputs_embeds), None + + def get_rope_index(self, **kwargs): + assert torch.equal( + kwargs["mm_token_type_ids"], torch.tensor([[0, 1, 1, 0]]) + ) + assert torch.equal(kwargs["input_ids"], torch.tensor([[1, 9, 9, 2]])) + calls.append("positions") + return positions, torch.tensor([[0]]) + + class Peft: + device = "cpu" + + def get_base_model(self): + return SimpleNamespace(model=Core()) + + def __call__(self, **kwargs): + assert not torch.is_grad_enabled() + assert "input_ids" not in kwargs and "pixel_values" not in kwargs + assert "image_grid_thw" not in kwargs + assert torch.equal(kwargs["position_ids"], positions) + assert torch.equal( + kwargs["inputs_embeds"], + torch.tensor([[[1.0, 1.0], [10.0, 20.0], [30.0, 40.0], [2.0, 2.0]]]), + ) + calls.append("peft-forward") + return SimpleNamespace(logits=torch.tensor([[[0.0, 1.0, 2.0]]])) + + engine.model = Peft() + engine.tokenizer = SimpleNamespace(encode=lambda text, **kwargs: [ord(text) - 65]) + inputs = { + "input_ids": torch.tensor([[1, 9, 9, 2]]), + "mm_token_type_ids": torch.tensor([[0, 1, 1, 0]]), + "attention_mask": torch.ones((1, 4), dtype=torch.long), + "pixel_values": torch.ones(2, 4), + "image_grid_thw": torch.tensor([[1, 2, 4]]), + } + original = {key: value.clone() for key, value in inputs.items()} + shared = engine.encode_image(inputs) + for question in QUESTIONS: + result = engine.score_reused(inputs, question, shared) + assert ( + result + == torch.softmax(torch.arange(len(question.keys)).float(), dim=-1).tolist() + ) + assert calls == [ + "vision-with-adapters", + "positions", + "peft-forward", + "positions", + "peft-forward", + ] + assert all(torch.equal(value, original[key]) for key, value in inputs.items()) diff --git a/tests/cua_s1/test_model.py b/tests/cua_s1/test_model.py index 228e3ac..77f4390 100644 --- a/tests/cua_s1/test_model.py +++ b/tests/cua_s1/test_model.py @@ -108,5 +108,5 @@ def prepare(image, question): engine.prepare = prepare engine.score = lambda inputs, q: forwarded.append(q) with pytest.raises(InvalidRequest, match="4096"): - engine.predict(Request(None, (first, second))) + engine.predict_reference(Request(None, (first, second))) assert forwarded == [] From c18a21b205d7a66449de845a8598b6e27d82424c Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:46:48 +0800 Subject: [PATCH 10/75] test: cover multi-question JPEG reuse and document tensor contract --- recipe/cua_s1/benchmark_image_reuse.py | 3 + recipe/cua_s1/image-reuse.md | 77 ++++++++++++++++++++++++++ tests/cua_s1/test_reuse_benchmark.py | 12 ++++ 3 files changed, 92 insertions(+) create mode 100644 recipe/cua_s1/image-reuse.md diff --git a/recipe/cua_s1/benchmark_image_reuse.py b/recipe/cua_s1/benchmark_image_reuse.py index 51c7abf..6425127 100644 --- a/recipe/cua_s1/benchmark_image_reuse.py +++ b/recipe/cua_s1/benchmark_image_reuse.py @@ -208,6 +208,9 @@ def correctness_cases(folder): result = [(name, value) for name, _, value in cases(folder)] distinct = distinct_fixture(folder / "distinct") result.append(("distinct-eight", distinct)) + jpeg = copy.deepcopy(distinct) + jpeg["state"] = dict(result)["jpeg"]["state"] + result.append(("distinct-jpeg", jpeg)) reverse = copy.deepcopy(distinct) reverse["questions"] = dict(reversed(list(reverse["questions"].items()))) result.append(("distinct-reversed", reverse)) diff --git a/recipe/cua_s1/image-reuse.md b/recipe/cua_s1/image-reuse.md new file mode 100644 index 0000000..b937e42 --- /dev/null +++ b/recipe/cua_s1/image-reuse.md @@ -0,0 +1,77 @@ +# Request-local image reuse + +`MultimodalEngine.predict` reuses image preprocessing and the adapted visual +encoder for requests containing 2–8 questions. A one-question request keeps the +original path. Every question still has its own prompt, tokenization, candidate +ordering, input embeddings, three-dimensional positions, language forward and +readout. All processed token limits are checked before any model execution. + +`predict_reference` retains the original implementation for paired measurements. +`profile_multimodal.py` explicitly selects it so the PR #15 stage baseline remains +reproducible. The model identity, HTTP request/response and usage contract do not +change. This is a Transformers/PEFT execution optimization, not a native CUDA +backend, a custom kernel, batching or a cross-request cache. + +## Reuse boundary and tensor ownership + +The implementation targets the pinned Transformers 5.17.0 Qwen3.5 model and +processor. A shallow processor copy owns a request-local image-processor proxy. +Its first invocation executes the original image processor; later invocations +return fresh mappings over the same image tensors. The full original processor +still expands image placeholders and tokenizes every question. Neither the +shared processor nor model methods are replaced. + +| Value | Contract | Owner and lifetime | +| --- | --- | --- | +| `pixel_values` | Processor-produced patch tensor, unchanged shape/dtype/layout | CPU tensor shared only among prepared inputs in this request; transferred once for the shared visual forward | +| `image_grid_thw` | Integer `[1, 3]` temporal/height/width grid from the same processor call | Shared CPU metadata; transferred for visual encoding and each question's position construction | +| Visual features | Concatenated `get_image_features(...).pooler_output`, `[image_tokens, text_hidden_size]`; active vision LoRA layers included | Request-local device tensor under `no_grad`; converted to each question embedding dtype/device before scatter | +| `input_ids`, `attention_mask`, `mm_token_type_ids` | Full processor outputs, constructed independently for each prompt | Per-question mappings; image reuse does not replace text tokenization | +| `inputs_embeds` | `[1, sequence_length, text_hidden_size]`, new token embeddings with image placeholders filled by `masked_scatter` | Per-question device tensor; feature/placeholder count checked by the model helper | +| `position_ids` | Integer `[3, 1, sequence_length]` from the original `get_rope_index` using that question's tokens, modality types, grid and mask | Per-question device tensor passed explicitly to the full PEFT model; never reused | + +No manual allocator, custom CUDA stream, KV-cache reuse or persistent feature +cache is introduced. Ordinary PyTorch references own the tensors; request-local +references disappear on return or exception. The CUDA allocator may retain +reserved memory. The existing server serializes inference. Native-engine +handoff and its stream/allocator contract remain future work. + +## Correctness and performance experiment + +```sh +PYTHONPATH=src python recipe/cua_s1/benchmark_image_reuse.py \ + --weights /path/to/weights --output /path/to/fresh-results \ + --warmup 5 --runs 2 --iterations 50 +``` + +Use `--correctness-only` for a smoke run; `--case ID` selects performance cases. +Output directories cannot overwrite an existing report. Exceptions leave a +failed report with completed results intact. + +Before timing, compare the reference and optimized paths for exact prepared +input fingerprints, language input embeddings, 3D positions, attention masks, +candidate probabilities and full responses. Count actual image preprocessing, +visual and language invocations using temporary test instrumentation. This +instrumentation is removed before timed calls. Correctness cases include PNG, +JPEG, different image sizes, 1 and 26 candidates, reordered candidates, structured +and Unicode labels, accepted text control tokens, eight distinct questions, +reversed question order and consecutive requests changing image/grid then +returning to the original image. CPU tests also cover preparation failures and +token-limit rejection before inference. + +The performance matrix retains all 16 original size/length/question-count cases +and adds a distinct-question case. Shuffle case order with recorded seed +20260928. Each case warms both paths, then alternates baseline/reuse and +reuse/baseline for each sample, reversing the starting order for the second run. +Each sample synchronizes CUDA around the whole `predict` call. Reset memory +peaks for each variant invocation and retain raw latencies, execution order, +per-variant allocated/reserved peaks, environment and source revision. + +Timing excludes HTTP, queuing, JSON parsing, image decoding, instrumentation, +input fingerprinting and model loading. Results apply to concurrency 1 and +controlled synthetic screenshots; they do not establish GUI task accuracy, +service throughput, p99 or maximum supported input sizes. Reserved-memory peaks +may carry over between variants, so allocated peaks are the useful comparison. +Single-question paths are identical and their measured differences represent +noise. Numerical equality is tested for this pinned environment, not guaranteed +for arbitrary library versions, adapters or devices. diff --git a/tests/cua_s1/test_reuse_benchmark.py b/tests/cua_s1/test_reuse_benchmark.py index c6ff810..d595283 100644 --- a/tests/cua_s1/test_reuse_benchmark.py +++ b/tests/cua_s1/test_reuse_benchmark.py @@ -115,3 +115,15 @@ def test_failure_report_is_preserved(reuse_benchmark, monkeypatch, tmp_path): assert report["error"]["type"] == "InvalidRequest" with pytest.raises(ValueError, match="already exists"): reuse_benchmark.main(["--weights", str(tmp_path), "--output", str(tmp_path)]) + + +def test_jpeg_fixture_exercises_multiple_questions(reuse_benchmark, tmp_path): + from models.cua_s1.multimodal.protocol import parse_request + + cases = dict(reuse_benchmark.correctness_cases(tmp_path)) + assert "distinct-jpeg" in cases + request = parse_request(cases["distinct-jpeg"]) + assert len(request.questions) == 8 + assert cases["distinct-jpeg"]["state"]["image"].startswith( + "data:image/jpeg;base64," + ) From 4bb4035ab0348615f680c4fb7cc9e7e0cee720b2 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:47:16 +0800 Subject: [PATCH 11/75] Test image reuse cleanup after inference failures --- tests/cua_s1/test_image_reuse.py | 63 ++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) diff --git a/tests/cua_s1/test_image_reuse.py b/tests/cua_s1/test_image_reuse.py index 5c497a2..4d3eac5 100644 --- a/tests/cua_s1/test_image_reuse.py +++ b/tests/cua_s1/test_image_reuse.py @@ -1,5 +1,7 @@ """Request ownership tests, plus tensor integration tests when torch is installed.""" +import gc +import weakref from types import SimpleNamespace import pytest @@ -84,6 +86,67 @@ def test_image_cache_does_not_survive_requests_or_failed_preparation(): assert all(x["pixel_values"] is images[2] for x in prepared) +@pytest.mark.parametrize("failure_stage", ["encode", "second_question"]) +def test_failed_inference_releases_request_state_before_different_image(failure_stage): + engine = engine_with_processor() + processor = engine.processor + image_processor = processor.image_processor + failed_image, next_image = object(), object() + encoded_images, feature_refs, scored = [], [], [] + + class Features: + def __init__(self, image): + self.image = image + + def encode(inputs): + image = inputs["pixel_values"] + encoded_images.append(image) + features = Features(image) + feature_refs.append(weakref.ref(features)) + if image is failed_image and failure_stage == "encode": + raise RuntimeError("encoder failure") + return features + + def score(inputs, question, features): + image = inputs["pixel_values"] + assert features.image is image + assert features is feature_refs[-1]() + scored.append((image, question.name)) + if image is failed_image and question is QUESTIONS[1]: + raise RuntimeError("later question failure") + return [1 / len(question.keys)] * len(question.keys) + + engine.encode_image = encode + engine.score_reused = score + original_engine_state = dict(vars(engine)) + message = ( + "encoder failure" if failure_stage == "encode" else "later question failure" + ) + with pytest.raises(RuntimeError, match=message): + engine.predict(Request(failed_image, QUESTIONS)) + gc.collect() + assert all(ref() is None for ref in feature_refs) + assert vars(engine) == original_engine_state + assert engine.processor is processor + assert processor.image_processor is image_processor + assert image_processor.calls == [failed_image] + assert scored == ( + [] if failure_stage == "encode" else [(failed_image, q.name) for q in QUESTIONS] + ) + + result = engine.predict(Request(next_image, QUESTIONS)) + gc.collect() + assert encoded_images == [failed_image, next_image] + assert image_processor.calls == [failed_image, next_image] + assert scored[-2:] == [(next_image, q.name) for q in QUESTIONS] + assert list(result["answers"]) == [q.name for q in QUESTIONS] + assert len(feature_refs) == 2 + assert all(ref() is None for ref in feature_refs) + assert vars(engine) == original_engine_state + assert engine.processor is processor + assert processor.image_processor is image_processor + + def test_all_lengths_checked_before_vision_or_language_execution(): engine = engine_with_processor() forwarded = [] From 0abf31d01a7f0a80e431806a7ccb9481c2610ea7 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:50:48 +0800 Subject: [PATCH 12/75] Avoid body-write race in transport rejection tests --- tests/cua_s1/test_server.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/tests/cua_s1/test_server.py b/tests/cua_s1/test_server.py index fef1f3c..5d2eee2 100644 --- a/tests/cua_s1/test_server.py +++ b/tests/cua_s1/test_server.py @@ -1,3 +1,4 @@ +import http.client import json import threading import urllib.error @@ -144,6 +145,20 @@ def unexpected(_): ], ) def test_transport_errors_use_detail(worker, route, body, headers, status): - _, url = worker - actual, response = call(url + route, body, **headers) - assert actual == status and set(response) == {"detail"} + server, _ = worker + connection = http.client.HTTPConnection("127.0.0.1", server.server_port, timeout=5) + try: + connection.putrequest("GET" if body is None else "POST", route) + request_headers = {"Content-Type": "application/json", **headers} + if body is not None and "Transfer-Encoding" not in headers: + request_headers.setdefault("Content-Length", str(len(json.dumps(body)))) + for name, value in request_headers.items(): + connection.putheader(name, value) + # Every case rejects from headers. Sending a body (especially a final + # chunk terminator) can race the server's early response and close. + connection.endheaders() + with connection.getresponse() as response: + assert response.status == status + assert set(json.load(response)) == {"detail"} + finally: + connection.close() From d0c4781d99ca47c9527c0e09f58d3c04d84726e2 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 11:56:39 +0800 Subject: [PATCH 13/75] docs: link request reuse behavior and record completed reviews --- docs/superpowers/plans/2026-09-28-cua-image-reuse.md | 8 ++++---- recipe/cua_s1/README.md | 6 ++++-- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/docs/superpowers/plans/2026-09-28-cua-image-reuse.md b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md index ef18c27..043b90e 100644 --- a/docs/superpowers/plans/2026-09-28-cua-image-reuse.md +++ b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md @@ -8,10 +8,10 @@ **Tech Stack:** Python, PyTorch 2.14, Transformers 5.17, PEFT 0.21, RTX 4090. -- [ ] Baseline: run all tests/cua_s1; preserve existing PR #15 data. -- [ ] Implementation: tests/cua_s1/test_image_reuse.py first; prove failures; implement src/models/cua_s1/multimodal/model.py and focused helper module if needed. Preserve preflight validation, distinct prompts, no shared mutable state, PEFT vision layers and unchanged single-question behavior. -- [ ] Harness: recipe/cua_s1/benchmark_image_reuse.py plus CPU tests. Check exact tensors, positions and probabilities before timed paired measurements; alternate order; record each sample and independent per-variant peak memory. Retain failed reports. Reuse profile_multimodal fixture and provenance helpers. +- [x] Baseline: run all tests/cua_s1; preserve existing PR #15 data. +- [x] Implementation: tests/cua_s1/test_image_reuse.py first; prove failures; implement src/models/cua_s1/multimodal/model.py and focused helper module if needed. Preserve preflight validation, distinct prompts, no shared mutable state, PEFT vision layers and unchanged single-question behavior. +- [x] Harness: recipe/cua_s1/benchmark_image_reuse.py plus CPU tests. Check exact tensors, positions and probabilities before timed paired measurements; alternate order; record each sample and independent per-variant peak memory. Retain failed reports. Reuse profile_multimodal fixture and provenance helpers. - [ ] GPU: use clean committed checkout, run CPU tests, correctness smoke then 16-case paired experiment (5 warmups, 2 x 50 iterations per variant), plus distinct questions. Separate count/trace verification from all timed calls. -- [ ] Review: independent spec review followed by quality review; resolve findings and re-run affected checks. +- [x] Review: independent spec review followed by quality review; resolve findings and re-run affected checks. - [ ] Publication: document measured results and tensor contract, retain raw JSON, run lint/full tests, commit/push and create linked PR identifying #12/#15 dependencies. - [ ] Shutdown: copy experiment artifacts locally, verify hashes, confirm no required GPU work remains, shut down server and verify provider/connection state. diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 6dac894..d8f14c4 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -97,8 +97,10 @@ Each answer has `type`, `choice`, `probabilities` and `confidence`. The readout uses the last position's candidate-letter logits, casts to fp32 and applies softmax over those letters only. There is no decode. Ties select the earliest option; confidence is `1 - H(p)/ln(n)`, or 1 for one option. Each question has a -separate forward pass over the same screenshot. Usage sums processed input tokens -and reports zero output tokens. +separate language forward pass. Multi-question requests reuse the image +preprocessing and adapted vision features within the request; see the +[reuse contract and experiment](image-reuse.md). Usage sums processed input +tokens and reports zero output tokens. Response identity: `cua-ai/cua-s1-4b-0.2@16818868b0cc7813808aae4e87b417657046ab79:multimodal`. From 209a660852d7c4172bcf4cf4025e8333755ef2ec Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 12:02:19 +0800 Subject: [PATCH 14/75] test: add reproducible result audit and CUDA HTTP postflight --- .../experiments/rtx4090-reuse/postflight.py | 90 ++++++++++++++++ .../rtx4090-reuse/verify_results.py | 100 ++++++++++++++++++ 2 files changed, 190 insertions(+) create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/postflight.py create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py diff --git a/recipe/cua_s1/experiments/rtx4090-reuse/postflight.py b/recipe/cua_s1/experiments/rtx4090-reuse/postflight.py new file mode 100644 index 0000000..a8dbfb0 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-reuse/postflight.py @@ -0,0 +1,90 @@ +"""Capture concrete tensor layout and verify the actual HTTP worker on CUDA.""" + +import json +import sys +import threading +import urllib.request +from pathlib import Path + +from benchmark_image_reuse import distinct_fixture, require_equal +from evaluate_multimodal import environment +from profile_multimodal import repository_state, write_json + +from models.cua_s1.multimodal.model import MultimodalEngine +from models.cua_s1.multimodal.protocol import parse_request +from models.cua_s1.multimodal.server import WorkerServer + +root, weights, output = map(Path, sys.argv[1:]) +output.mkdir(parents=True, exist_ok=True) +report = {"repository": repository_state(root), "environment": environment()} +engine = MultimodalEngine( + str(weights / "Qwen3.5-4B"), str(weights / "cua-s1-4b-0.2/multimodal") +) +value = distinct_fixture(output / "fixture") +request = parse_request(value) +prepared = engine.prepare_reused(request.image, request.questions) +features = engine.encode_image(prepared[0]) + + +def metadata(t): + return { + "shape": list(t.shape), + "dtype": str(t.dtype), + "device": str(t.device), + "stride": list(t.stride()), + "contiguous": t.is_contiguous(), + "requires_grad": t.requires_grad, + "bytes": t.numel() * t.element_size(), + } + + +report["cpu_inputs"] = {k: metadata(t) for k, t in prepared[0].items()} +report["shared_features"] = metadata(features) +core = engine.model.get_base_model().model +positions = [] + + +def capture(module, args, kwargs): + positions.append( + { + k: metadata(kwargs[k]) + for k in ("inputs_embeds", "position_ids", "attention_mask") + } + ) + + +handle = core.language_model.register_forward_pre_hook(capture, with_kwargs=True) +try: + expected = engine.predict(request) +finally: + handle.remove() +report["per_question_language_layouts"] = positions +del prepared, features +server = WorkerServer(("127.0.0.1", 0), engine) +thread = threading.Thread(target=server.serve_forever, daemon=True) +thread.start() +try: + address = f"http://127.0.0.1:{server.server_port}" + with urllib.request.urlopen(address + "/health", timeout=30) as response: + report["health"] = {"status": response.status, "body": json.load(response)} + req = urllib.request.Request( + address + "/v1/systemone", + data=json.dumps(value).encode(), + headers={"Content-Type": "application/json"}, + ) + with urllib.request.urlopen(req, timeout=60) as response: + actual = json.load(response) + require_equal(expected, actual, "HTTP response") + report["http"] = { + "status": response.status, + "exact_engine_parity": True, + "question_count": len(actual["answers"]), + "response": actual, + } +finally: + server.shutdown() + server.server_close() + thread.join() +report["status"] = "complete" +write_json(output / "postflight.json", report) +print("CUDA tensor layout and HTTP q8 exact parity complete", flush=True) diff --git a/recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py b/recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py new file mode 100644 index 0000000..6c4f589 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py @@ -0,0 +1,100 @@ +"""Independently check saved samples and parity evidence, then print the table.""" + +import json +import math +import statistics +from pathlib import Path + +ROOT = Path(__file__).resolve().parent + + +def main(): + report = json.loads((ROOT / "paired.json").read_text()) + reference = json.loads((ROOT.parent / "rtx4090-profile/reference.json").read_text()) + assert report["status"] == "complete" + assert report["repository"]["dirty"] is False + assert report["environment"] == reference["environment"] + assert len(report["cases"]) == 17 + assert len(report["correctness"]) == 13 + original = {case["id"]: case for case in report["correctness"]} + for case in reference["cases"]: + checked = original[case["name"]] + answers = checked["response"]["answers"] + assert ( + list(answers[case["question"]]["probabilities"].values()) + == case["probabilities"] + ) + index = list(answers).index(case["question"]) + assert checked["inputs"][index] == case["inputs"] + count = 0 + for checked in report["correctness"] + [c["validation"] for c in report["cases"]]: + assert checked["exact_prepared_tensor_parity"] + assert checked["exact_language_input_parity"] + assert checked["exact_response_parity"] + n = len(checked["response"]["answers"]) + assert checked["baseline_counts"] == { + "image_preprocess": n, + "vision": n, + "language": n, + } + assert checked["reuse_counts"] == { + "image_preprocess": 1, + "vision": 1, + "language": n, + } + print( + "| Case | Baseline p50 ms | Reuse p50 ms | p50 reduction | Baseline p95 ms | Reuse p95 ms | Peak allocated GiB, baseline → reuse |" + ) + print("| --- | ---: | ---: | ---: | ---: | ---: | ---: |") + for case in sorted(report["cases"], key=lambda c: c["id"]): + assert case["status"] == "complete" + assert len(case["runs"]) == report["config"]["runs"] == 2 + for index, run in enumerate(case["runs"]): + for iteration, order in enumerate(run["orders"]): + expected = ["baseline", "reuse"] + if (index + iteration) % 2: + expected.reverse() + assert order == expected + for name in ("baseline", "reuse"): + values = run[name]["latencies_ms"] + assert ( + len(values) + == len(run["orders"]) + == report["config"]["iterations"] + == 50 + ) + assert all(math.isfinite(v) and v > 0 for v in values) + assert statistics.median(values) == run[name]["p50_ms"] + assert ( + sorted(values)[math.ceil(0.95 * len(values)) - 1] + == run[name]["p95_ms"] + ) + assert ( + run[name]["peak_reserved_bytes"] + >= run[name]["peak_allocated_bytes"] + > 0 + ) + count += len(values) + + def times(name, percentile): + return " / ".join(f"{r[name][percentile]:.2f}" for r in case["runs"]) + + reduction = " / ".join( + f"{100 * (1 - r['reuse']['p50_ms'] / r['baseline']['p50_ms']):.1f}%" + for r in case["runs"] + ) + memory = " → ".join( + f"{max(r[name]['peak_allocated_bytes'] for r in case['runs']) / 2**30:.3f}" + for name in ("baseline", "reuse") + ) + print( + f"| {case['id']} | {times('baseline', 'p50_ms')} | {times('reuse', 'p50_ms')} | {reduction} | {times('baseline', 'p95_ms')} | {times('reuse', 'p95_ms')} | {memory} |" + ) + assert count == 3400 + print( + f"\nVerified {count} timed requests, 13 correctness fixtures, 17 paired cases, and all 9 saved upstream forwards." + ) + + +if __name__ == "__main__": + main() From 9f4a4fe887b071d50d51e9e76eb6e07c8ae401e5 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 12:12:20 +0800 Subject: [PATCH 15/75] test: audit exact coverage of the paired workload matrix --- recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py b/recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py index 6c4f589..4c84f80 100644 --- a/recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py +++ b/recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py @@ -14,6 +14,13 @@ def main(): assert report["status"] == "complete" assert report["repository"]["dirty"] is False assert report["environment"] == reference["environment"] + expected_cases = { + f"{size}-{goal}-q{questions}" + for size in ("320x240", "640x480") + for goal in ("short", "long") + for questions in (1, 2, 4, 8) + } | {"640x480-distinct-q8"} + assert {case["id"] for case in report["cases"]} == expected_cases assert len(report["cases"]) == 17 assert len(report["correctness"]) == 13 original = {case["id"]: case for case in report["correctness"]} From ccaa4fb7c306c8409b5b0d0f466922866a5f892e Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 12:25:03 +0800 Subject: [PATCH 16/75] docs: record paired RTX 4090 image reuse results and exact parity --- .../plans/2026-09-28-cua-image-reuse.md | 2 +- .../experiments/rtx4090-reuse/README.md | 151 + .../experiments/rtx4090-reuse/SHA256SUMS | 6 + .../experiments/rtx4090-reuse/candidate.json | 536 + .../experiments/rtx4090-reuse/cpu-tests.log | 3 + .../experiments/rtx4090-reuse/paired.json | 21606 ++++++++++++++++ .../experiments/rtx4090-reuse/paired.log | 34 + .../experiments/rtx4090-reuse/postflight.json | 656 + .../experiments/rtx4090-reuse/reference.json | 527 + .../rtx4090-reuse/verify_results.py | 2 +- recipe/cua_s1/image-reuse.md | 2 + 11 files changed, 23523 insertions(+), 2 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/candidate.json create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/cpu-tests.log create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/paired.json create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/paired.log create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/postflight.json create mode 100644 recipe/cua_s1/experiments/rtx4090-reuse/reference.json diff --git a/docs/superpowers/plans/2026-09-28-cua-image-reuse.md b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md index 043b90e..383b830 100644 --- a/docs/superpowers/plans/2026-09-28-cua-image-reuse.md +++ b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md @@ -11,7 +11,7 @@ - [x] Baseline: run all tests/cua_s1; preserve existing PR #15 data. - [x] Implementation: tests/cua_s1/test_image_reuse.py first; prove failures; implement src/models/cua_s1/multimodal/model.py and focused helper module if needed. Preserve preflight validation, distinct prompts, no shared mutable state, PEFT vision layers and unchanged single-question behavior. - [x] Harness: recipe/cua_s1/benchmark_image_reuse.py plus CPU tests. Check exact tensors, positions and probabilities before timed paired measurements; alternate order; record each sample and independent per-variant peak memory. Retain failed reports. Reuse profile_multimodal fixture and provenance helpers. -- [ ] GPU: use clean committed checkout, run CPU tests, correctness smoke then 16-case paired experiment (5 warmups, 2 x 50 iterations per variant), plus distinct questions. Separate count/trace verification from all timed calls. +- [x] GPU: use clean committed checkout, run CPU tests, correctness smoke then 16-case paired experiment (5 warmups, 2 x 50 iterations per variant), plus distinct questions. Separate count/trace verification from all timed calls. - [x] Review: independent spec review followed by quality review; resolve findings and re-run affected checks. - [ ] Publication: document measured results and tensor contract, retain raw JSON, run lint/full tests, commit/push and create linked PR identifying #12/#15 dependencies. - [ ] Shutdown: copy experiment artifacts locally, verify hashes, confirm no required GPU work remains, shut down server and verify provider/connection state. diff --git a/recipe/cua_s1/experiments/rtx4090-reuse/README.md b/recipe/cua_s1/experiments/rtx4090-reuse/README.md new file mode 100644 index 0000000..10b0e10 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-reuse/README.md @@ -0,0 +1,151 @@ +# RTX 4090 request-local image reuse — 2026-09-28 + +The optimized worker preprocesses and encodes a shared screenshot once per +multi-question request, retaining independent text processing, three-dimensional +positions, language forwards and candidate readouts. Single-question execution +uses the original path. + +Across the 13 multi-question cases (two runs each), measured p50 latency fell +**6.8–19.9%**. For **eight distinct questions on a 640×480 screenshot**, p50 fell +from **1100.03 / 1094.87 ms to 887.54 / 888.45 ms** (19.3% / 18.9% lower latency). +These are end-to-end `engine.predict` measurements, not extrapolations from +profiler stage spans. Single-question p50 differences ranged from 0.5% slower to +1.1% faster across identical execution paths and are treated as measurement noise. + +## Provenance and method + +- Timed source: clean `c18a21b205d7a66449de845a8598b6e27d82424c`; inference implementation + commit `cf899ff`. Later changes add tests, documentation and result-verification + scripts without changing the measured model or benchmark paths. +- Final GPU-host tests, fresh upstream oracle and HTTP/layout validation use + clean source `9f4a4fe`. +- RTX 4090 24,564 MiB; driver 595.71.05, CUDA runtime 13.0, Python 3.12.13, + PyTorch 2.14.0, Transformers 5.17.0, PEFT 0.21.0. Full package/version and model + provenance are in [paired.json](paired.json). Environment matches the original + pinned upstream baseline. +- Unmerged BF16 model with fp32 LoRA branches; all 178 adapted modules present, + including the vision modules. PyTorch Gated DeltaNet/causal-convolution fallback + implementations and 64/64 PyTorch thread settings are unchanged. +- Original 16-case matrix plus one eight-distinct-question case. Five warmup + requests per variant per case, then two runs of 50 samples per variant: + **3,400 timed requests / 13,600 language forwards**. Cases use recorded shuffled + order; baseline/reuse ordering alternates each sample and reverses per run. +- Timing synchronizes CUDA around each whole prediction, excluding HTTP, parsing, + image decoding, fingerprinting, hooks and model load. Reset peak memory before + each variant invocation. No profiler or counting hooks are installed during + latency sampling. Reserved memory can carry across variants; compare allocated + memory. Largest allocated peak: 9.083 GiB baseline, 9.035 GiB reuse. + +## Correctness and service validation + +- **13 correctness fixtures / 49 question forwards per path**, including eight + different questions, PNG/JPEG, different image/grid sizes, reversed question + order, 1–26 candidates, candidate reordering, Unicode/structured labels and + consecutive image changes. The same validation also runs on all 17 timed cases. +- Prepared tensor fingerprints, language input embeddings, 3D positions, + attention masks, candidate probabilities and complete responses are **exactly + equal** between original and optimized paths. No tolerance or approximation is + used. Actual multi-question image-preprocessing and vision counts fall from N + to 1; language counts remain N. The raw report records these counts and hashes. +- Fresh pinned upstream [reference.json](reference.json) and worker + [candidate.json](candidate.json) revalidate all nine upstream forwards. The + independent result audit also compares the optimized report with that oracle. +- Final GPU-host CPU suite: **104 passed**. Local macOS: **103 passed, 1 skipped** + (torch unavailable locally). The skipped tensor test runs on the GPU host. + Tests cover preflight token rejection, encoder/later-question failures, feature + lifetime and successful recovery with another image. Lint and formatting pass. +- [postflight.json](postflight.json) records actual tensor shape, dtype, strides, + contiguity and ownership-relevant metadata, plus HTTP health and eight-question + response equality against direct engine execution. + +## Paired latency and memory + +Values separated by `/` are run 1 / run 2. p95 is nearest rank over 50 samples; +allocated memory is the maximum across both runs. Negative reduction means slower. + +| Case | Baseline p50 ms | Reuse p50 ms | p50 reduction | Baseline p95 ms | Reuse p95 ms | Peak allocated GiB, baseline → reuse | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | +| 320x240-long-q1 | 135.56 / 135.63 | 136.22 / 135.66 | -0.5% / -0.0% | 143.72 / 142.83 | 145.02 / 140.97 | 8.926 → 8.926 | +| 320x240-long-q2 | 286.12 / 271.52 | 261.18 / 251.31 | 8.7% / 7.4% | 329.01 / 337.47 | 272.06 / 312.39 | 8.931 → 8.922 | +| 320x240-long-q4 | 566.04 / 540.73 | 503.31 / 478.02 | 11.1% / 11.6% | 579.48 / 562.61 | 538.99 / 500.64 | 8.931 → 8.922 | +| 320x240-long-q8 | 1159.29 / 1137.77 | 967.33 / 950.53 | 16.6% / 16.5% | 1232.38 / 1236.30 | 1086.98 / 994.59 | 8.939 → 8.922 | +| 320x240-short-q1 | 108.11 / 108.17 | 108.09 / 108.04 | 0.0% / 0.1% | 112.40 / 113.68 | 109.76 / 116.19 | 8.728 → 8.728 | +| 320x240-short-q2 | 228.86 / 230.37 | 205.76 / 207.33 | 10.1% / 10.0% | 242.46 / 244.51 | 213.63 / 216.08 | 8.728 → 8.726 | +| 320x240-short-q4 | 448.85 / 443.57 | 382.62 / 379.76 | 14.8% / 14.4% | 464.28 / 470.91 | 395.06 / 394.91 | 8.734 → 8.726 | +| 320x240-short-q8 | 914.48 / 889.32 | 748.10 / 724.66 | 18.2% / 18.5% | 989.35 / 962.43 | 771.86 / 762.28 | 8.740 → 8.726 | +| 640x480-distinct-q8 | 1100.03 / 1094.87 | 887.54 / 888.45 | 19.3% / 18.9% | 1171.23 / 1162.61 | 984.33 / 963.19 | 9.015 → 8.992 | +| 640x480-long-q1 | 158.14 / 155.74 | 156.43 / 155.89 | 1.1% / -0.1% | 192.92 / 193.58 | 193.23 / 189.91 | 9.035 → 9.035 | +| 640x480-long-q2 | 309.13 / 310.54 | 286.97 / 289.53 | 7.2% / 6.8% | 325.38 / 330.10 | 298.02 / 302.77 | 9.043 → 9.033 | +| 640x480-long-q4 | 629.40 / 630.98 | 561.65 / 566.86 | 10.8% / 10.2% | 714.52 / 671.00 | 622.21 / 594.51 | 9.057 → 9.033 | +| 640x480-long-q8 | 1319.17 / 1316.22 | 1113.74 / 1112.65 | 15.6% / 15.5% | 1474.11 / 1446.67 | 1196.36 / 1159.36 | 9.083 → 9.033 | +| 640x480-short-q1 | 128.17 / 126.42 | 128.00 / 126.42 | 0.1% / 0.0% | 142.45 / 131.61 | 133.58 / 130.29 | 8.842 → 8.842 | +| 640x480-short-q2 | 256.55 / 256.11 | 234.05 / 236.78 | 8.8% / 7.6% | 270.34 / 273.75 | 240.42 / 250.98 | 8.849 → 8.838 | +| 640x480-short-q4 | 532.07 / 520.37 | 461.20 / 449.92 | 13.3% / 13.5% | 570.69 / 553.80 | 478.79 / 472.87 | 8.862 → 8.838 | +| 640x480-short-q8 | 1092.57 / 1096.68 | 874.72 / 891.88 | 19.9% / 18.7% | 1317.02 / 1271.33 | 954.19 / 933.10 | 8.890 → 8.838 | + +## Concrete feature interface + +For the 640×480 distinct-question fixture, the processor produces contiguous +CPU fp32 pixels `[1200, 1536]`. Shared adapted features are contiguous CUDA BF16 +`[300, 2560]`, stride `[2560, 1]`, 1,536,000 bytes, with gradients disabled. +The first question uses CUDA BF16 embeddings `[1, 443, 2560]` and CUDA int64 +positions `[3, 1, 443]`. All eight question layouts are recorded in +[postflight.json](postflight.json); sequence lengths remain question-specific. +These are measurements of the pinned processor/model, not a general native-engine +ABI. See [ownership and execution contract](../../image-reuse.md). + +## Reproduce and independently audit + +Run from the repository root with the pinned environment and downloaded weights. +The measured command was: + +```sh +PYTHONPATH=src /root/autodl-tmp/system1-omni-work/venv/bin/python \ + recipe/cua_s1/benchmark_image_reuse.py \ + --weights /root/autodl-tmp/system1-omni-work/weights \ + --output /root/autodl-tmp/system1-omni-work/experiments/20260928-reuse/paired \ + --warmup 5 --runs 2 --iterations 50 +``` + +Use a fresh output directory for a new run. Regenerate the independent oracle +with both `--mode reference` and `--mode candidate` in the same output directory: + +```sh +PYTHONPATH=src python recipe/cua_s1/evaluate_multimodal.py \ + --weights /path/to/weights --reference /path/to/pinned-cua-checkout \ + --output /path/to/fresh-oracle --mode reference +PYTHONPATH=src python recipe/cua_s1/evaluate_multimodal.py \ + --weights /path/to/weights --reference /path/to/pinned-cua-checkout \ + --output /path/to/fresh-oracle --mode candidate +PYTHONPATH=src:recipe/cua_s1 python \ + recipe/cua_s1/experiments/rtx4090-reuse/postflight.py \ + /path/to/system1-omni /path/to/weights /path/to/fresh-postflight +``` + +The saved dataset can be audited without torch, a GPU or model weights: + +```sh +python recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py +``` + +This checks exact coverage of the 17 cases, all 3,400 sample values, alternating +order, medians and nearest-rank p95 calculations, memory bounds, invocation +counts, parity flags and all nine independent upstream input/probability records. +[SHA256SUMS](SHA256SUMS) identifies the public data files; [paired.log](paired.log) +and [cpu-tests.log](cpu-tests.log) retain execution progress and final test output. + +The complete raw outputs, generated fixtures, smoke run, oracle and HTTP logs +were also copied off the GPU host before shutdown in +`20260928-image-reuse-evidence.tar.gz`, SHA-256 +`65b365ecd10c49036519ac37e8a50bc6746f6630e0c98f03068ab39c4dd8f868`. +The table does not depend on that external archive; its raw samples are in Git. + +## Limits + +This is concurrency-1, prefill-only execution on synthetic screenshots and the +pinned software/hardware. It establishes no GUI task-quality improvement, HTTP +latency/throughput gain, p99 result, maximum input-size coverage, native CUDA +kernel speedup or Metal support. The HTTP postflight is a correctness check only. +Image preprocessing and visual encoding are reused inside one request; text +processing and language execution remain per question. No persistent cache, +batching, adapter merge, compiler or CUDA Graph change was made. diff --git a/recipe/cua_s1/experiments/rtx4090-reuse/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-reuse/SHA256SUMS new file mode 100644 index 0000000..b924f1f --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-reuse/SHA256SUMS @@ -0,0 +1,6 @@ +3820b048d75d59e117564e1ce88c50fc4a1b0ac7df4966f5f6aa2c079edffd15 paired.json +44e5b5e9e4439c39d4cfeefd38cdb7e99b55930203ddb33a4c576344d6f060b9 reference.json +75b3ea97632978d9260d6b5f6eccfb6a38e067d7b0e13540dd5d5ec4ffc97f20 candidate.json +b23b5a7aa0f427eefd3112847d944487b1d4f62c2964166f67fb68a4e5fcb1b4 postflight.json +8f99a4520eeef6a15de18f9f17ff42b8e52e4f2515313b6a6e1ff64e4b9ed374 paired.log +b2b06041ec7be5dfefee43314a4096b6476f8ca38396ad7ce4a6cc61f22ccb7c cpu-tests.log diff --git a/recipe/cua_s1/experiments/rtx4090-reuse/candidate.json b/recipe/cua_s1/experiments/rtx4090-reuse/candidate.json new file mode 100644 index 0000000..af1e58d --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-reuse/candidate.json @@ -0,0 +1,536 @@ +{ + "environment": { + "python": "3.12.13", + "gpu": "NVIDIA GeForce RTX 4090", + "compute_capability": [ + 8, + 9 + ], + "cuda": "13.0", + "driver": "595.71.05", + "torch_num_threads": 64, + "torch_num_interop_threads": 64, + "packages": { + "torch": "2.14.0", + "torchvision": "0.29.0", + "transformers": "5.17.0", + "peft": "0.21.0", + "pillow": "11.3.0", + "safetensors": "0.8.0", + "triton": "3.8.0" + }, + "reference_revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f", + "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", + "adapter_revision": "16818868b0cc7813808aae4e87b417657046ab79", + "dtype": "bfloat16", + "adapter_merged": false + }, + "mode": "candidate", + "cases": [ + { + "name": "small", + "question": "next", + "inputs": { + "input_ids": { + "shape": [ + 1, + 236 + ], + "dtype": "torch.int64", + "sha256": "21309638ff0c5ca8b7d1bf4b61905de3f31a216667ebc20fec9cdfaacdf08d48" + }, + "attention_mask": { + "shape": [ + 1, + 236 + ], + "dtype": "torch.int64", + "sha256": "1b489e733e23e18297cee9103f1b583499b76d29ab36a61129ac4fabc6dbd27a" + }, + "mm_token_type_ids": { + "shape": [ + 1, + 236 + ], + "dtype": "torch.int64", + "sha256": "21b9904b99d4c8ade165eae791ba8439afbbe6ee838c32df37f14031ab51bb3d" + }, + "pixel_values": { + "shape": [ + 320, + 1536 + ], + "dtype": "torch.float32", + "sha256": "8fd8d3e52f9c4d9fcc14ae0467e7d0642ce620e733705e6a77edd1f4e7940776" + }, + "image_grid_thw": { + "shape": [ + 1, + 3 + ], + "dtype": "torch.int64", + "sha256": "62ab45c5deaaea64b40e38c48f540fbdfb6235ee5d28cc2630dc82c526d25329" + } + }, + "probabilities": [ + 0.9958575367927551, + 0.0005507932510226965, + 0.003591623157262802 + ], + "max_abs_difference": 0.0, + "latency_ms": 131.62544276565313 + }, + { + "name": "medium", + "question": "next", + "inputs": { + "input_ids": { + "shape": [ + 1, + 456 + ], + "dtype": "torch.int64", + "sha256": 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/dev/null +++ b/recipe/cua_s1/experiments/rtx4090-reuse/paired.log @@ -0,0 +1,34 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! + Loading weights: 0%| | 0/723 [00:00 Date: Mon, 28 Sep 2026 12:27:04 +0800 Subject: [PATCH 17/75] docs: record PR publication and verified experiment server shutdown --- docs/superpowers/plans/2026-09-28-cua-image-reuse.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/superpowers/plans/2026-09-28-cua-image-reuse.md b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md index 383b830..4d739f3 100644 --- a/docs/superpowers/plans/2026-09-28-cua-image-reuse.md +++ b/docs/superpowers/plans/2026-09-28-cua-image-reuse.md @@ -13,5 +13,9 @@ - [x] Harness: recipe/cua_s1/benchmark_image_reuse.py plus CPU tests. Check exact tensors, positions and probabilities before timed paired measurements; alternate order; record each sample and independent per-variant peak memory. Retain failed reports. Reuse profile_multimodal fixture and provenance helpers. - [x] GPU: use clean committed checkout, run CPU tests, correctness smoke then 16-case paired experiment (5 warmups, 2 x 50 iterations per variant), plus distinct questions. Separate count/trace verification from all timed calls. - [x] Review: independent spec review followed by quality review; resolve findings and re-run affected checks. -- [ ] Publication: document measured results and tensor contract, retain raw JSON, run lint/full tests, commit/push and create linked PR identifying #12/#15 dependencies. -- [ ] Shutdown: copy experiment artifacts locally, verify hashes, confirm no required GPU work remains, shut down server and verify provider/connection state. +- [x] Publication: document measured results and tensor contract, retain raw JSON, run lint/full tests, commit/push and create linked PR identifying #12/#15 dependencies. +- [x] Shutdown: copy experiment artifacts locally, verify hashes, confirm no required GPU work remains, shut down server and verify provider/connection state. + +## Completion evidence + +Published non-draft [PR #17](https://github.com/ThinkFlowLab/system1-omni/pull/17). The complete experiment archive was copied locally and its SHA-256 matched the GPU host. After publication, the platform shutdown command exited successfully; a subsequent SSH attempt was refused. No experiment process remained on the GPU before shutdown. From 224d88b07be354b3cfc99fe8e9096bed777eafeb Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 14:13:55 +0800 Subject: [PATCH 18/75] perf(cua-s1): project only final logits in reused path --- .../2026-09-28-cua-post-reuse-optimization.md | 20 ++ ...9-28-cua-post-reuse-optimization-design.md | 9 + recipe/cua_s1/benchmark_last_logits.py | 182 ++++++++++++++++++ src/models/cua_s1/multimodal/model.py | 19 +- tests/cua_s1/test_image_reuse.py | 1 + tests/cua_s1/test_last_logits_benchmark.py | 38 ++++ 6 files changed, 263 insertions(+), 6 deletions(-) create mode 100644 docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md create mode 100644 docs/superpowers/specs/2026-09-28-cua-post-reuse-optimization-design.md create mode 100644 recipe/cua_s1/benchmark_last_logits.py create mode 100644 tests/cua_s1/test_last_logits_benchmark.py diff --git a/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md b/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md new file mode 100644 index 0000000..1425725 --- /dev/null +++ b/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md @@ -0,0 +1,20 @@ +# Cua-S1 Post-Reuse Optimization Implementation Plan + +> **For agentic workers:** Execute these steps in order in the isolated worktree; preserve independent profiling and timed results. + +**Goal:** Select and verify one optimization after image reuse, publish a reproducible PR, and shut down the GPU server. + +**Architecture:** Use the existing reference and reuse paths. Measure current kernel groups, then limit the output projection to one token only if the paired experiment shows a benefit. Store raw experiment evidence and a readable analysis under `recipe/cua_s1/experiments/`. + +**Tech Stack:** Python 3.12, PyTorch 2.14, Transformers 5.17, PEFT 0.21, RTX 4090. + +--- + +- [ ] Profile the unchanged reuse path with 1 and 8 questions; retain raw kernel groups, module invocation counts, traces, and source revision. +- [ ] Run a temporary paired exploration of full projection versus `logits_to_keep=1`, checking complete responses before timing. Select the candidate only if its effect is credible. +- [ ] Add a failing tensor integration test in `tests/cua_s1/test_image_reuse.py` proving the reused path passes `logits_to_keep=1`; run it on the GPU host or a torch-equipped environment. +- [ ] Modify only `score_reused` in `src/models/cua_s1/multimodal/model.py`; rerun the focused test and the full Cua-S1 suite. +- [ ] Add a paired benchmark recipe with explicit baseline selection, alternating order, raw samples, response parity, source and environment metadata, and peak allocated memory. +- [ ] Commit a clean source revision, transfer it to the GPU host, and run correctness and timed measurements. Review raw samples, variability, kernel changes, and single-question behavior. +- [ ] Publish result data and reproduction commands, run verification, push the branch, create and attach the PR. +- [ ] Archive and checksum complete GPU evidence locally, ensure no experiment is running, shut down the server, and verify it no longer accepts SSH. diff --git a/docs/superpowers/specs/2026-09-28-cua-post-reuse-optimization-design.md b/docs/superpowers/specs/2026-09-28-cua-post-reuse-optimization-design.md new file mode 100644 index 0000000..215b8df --- /dev/null +++ b/docs/superpowers/specs/2026-09-28-cua-post-reuse-optimization-design.md @@ -0,0 +1,9 @@ +# Cua-S1 optimization after request-local image reuse + +The user approved continuing the experiment after PR #17, publishing a complete PR, backing up evidence, and shutting down the GPU server. + +Profile the existing request-local reuse path on the same pinned RTX 4090 environment. Preserve an unmodified response and latency reference, and examine one- and eight-question cases with both repeated and distinct questions. CPU annotations, CUDA kernels, and synchronized end-to-end latency are different measurements and must not be added together. + +Three candidates follow from the existing implementation: compute only the final output-token projection, replace the PyTorch Gated DeltaNet/causal-convolution fallbacks, or capture a stable request shape with CUDA Graphs. The first is the smallest change and can be tested against the existing model contract. Select it only if a clean paired experiment shows a useful end-to-end gain and parity. Otherwise report the evidence and choose the next measured hotspot rather than claiming a speedup. + +If selected, pass `logits_to_keep=1` only in the reused multi-question path. The model still performs one language forward per question; the parameter limits the output head to the final hidden state, from which candidate probabilities are already read. Retain the old reference path for paired comparison. Confirm exact input embeddings and 3D positions, candidate probabilities within the pinned oracle tolerance, and identical decisions and complete responses. Benchmark 1/2/4/8 questions with alternating order, save individual samples and peak allocated memory, and record source/environment provenance. Document limitations and restore the GPU host to a stopped state after verified backup. diff --git a/recipe/cua_s1/benchmark_last_logits.py b/recipe/cua_s1/benchmark_last_logits.py new file mode 100644 index 0000000..bb1d5a9 --- /dev/null +++ b/recipe/cua_s1/benchmark_last_logits.py @@ -0,0 +1,182 @@ +"""Compare full and final-token output projection on the reused Cua-S1 path.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from contextlib import contextmanager +from pathlib import Path +from types import SimpleNamespace + +from benchmark_image_reuse import distinct_fixture, paired_benchmark +from profile_multimodal import case_matrix, fixture, repository_state, write_json + + +@contextmanager +def projection_variants(engine): + """Execute the same reuse path with only the output-head keyword changed.""" + original = engine.model.forward + + def full_forward(*args, **kwargs): + kwargs.pop("logits_to_keep", None) + return original(*args, **kwargs) + + def last_forward(*args, **kwargs): + return original(*args, **kwargs) + + def execute(forward, request): + engine.model.forward = forward + return engine.predict(request) + + try: + yield SimpleNamespace( + predict_reference=lambda request: execute(full_forward, request), + predict=lambda request: execute(last_forward, request), + ) + finally: + engine.model.forward = original + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--case", action="append", default=[]) + parser.add_argument("--warmup", type=int, default=5) + parser.add_argument("--runs", type=int, default=2) + parser.add_argument("--iterations", type=int, default=30) + args = parser.parse_args(argv) + known = {case["id"] for case in case_matrix()} | {"640x480-distinct-q8"} + if len(args.case) != len(set(args.case)) or set(args.case) - known: + parser.error("--case contains duplicate or unknown case ids") + if min(args.warmup, args.runs, args.iterations) <= 0: + parser.error("warmup, runs, and iterations must be positive") + report_path = args.output / "report.json" + if report_path.exists(): + raise ValueError("output report already exists; use a fresh directory") + + from evaluate_multimodal import environment, measure + + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + report = { + "schema_version": 1, + "status": "running", + "repository": repository_state(Path(__file__).resolve().parents[2]), + "environment": environment(), + "config": { + "warmup": args.warmup, + "runs": args.runs, + "iterations": args.iterations, + "case": args.case, + "concurrency": 1, + "timed_scope": "synchronized engine.predict with only logits_to_keep changed; excludes parsing, fixture creation, validation, and hooks", + }, + "cases": [], + } + args.output.mkdir(parents=True, exist_ok=True) + write_json(report_path, report) + try: + engine, report["load_ms"] = measure( + lambda: MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + ) + report["adapter_modules"] = engine.adapter_modules + selected = case_matrix() + [{"id": "640x480-distinct-q8"}] + selected = [ + case for case in selected if not args.case or case["id"] in args.case + ] + for case in selected: + value = ( + distinct_fixture(args.output / "fixtures") + if case["id"] == "640x480-distinct-q8" + else fixture(case, args.output / "fixtures") + ) + request = parse_request(value) + item = { + "id": case["id"], + "questions": len(request.questions), + "fixture_sha256": hashlib.sha256( + json.dumps(value, sort_keys=True).encode() + ).hexdigest(), + "status": "running", + } + report["cases"].append(item) + write_json(report_path, report) + with projection_variants(engine) as variants: + shapes = [] + head = engine.model.get_base_model().lm_head + handle = head.register_forward_pre_hook( + lambda module, inputs, shapes=shapes: shapes.append( + list(inputs[0].shape) + ) + ) + try: + full = variants.predict_reference(request) + item["full_head_shapes"] = shapes[:] + shapes.clear() + last = variants.predict(request) + item["last_head_shapes"] = shapes[:] + finally: + handle.remove() + if full != last: + raise ValueError(f"{case['id']}: full response differs") + if len(item["full_head_shapes"]) != len(request.questions) or len( + item["last_head_shapes"] + ) != len(request.questions): + raise ValueError(f"{case['id']}: unexpected output-head calls") + if len(request.questions) > 1 and any( + shape[1] != 1 for shape in item["last_head_shapes"] + ): + raise ValueError(f"{case['id']}: output head was not limited") + item["exact_response_parity"] = True + runs = paired_benchmark( + variants, + request, + warmup=args.warmup, + runs=args.runs, + iterations=args.iterations, + ) + for run in runs: + run["full_projection"] = run.pop("baseline") + run["last_token_projection"] = run.pop("reuse") + run["orders"] = [ + [ + "full_projection" + if v == "baseline" + else "last_token_projection" + for v in order + ] + for order in run["orders"] + ] + item.update(status="complete", runs=runs) + write_json(report_path, report) + print( + f"{case['id']}: " + + json.dumps( + [ + [ + round(run[v]["p50_ms"], 2) + for v in ("full_projection", "last_token_projection") + ] + for run in runs + ] + ), + flush=True, + ) + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(report_path, report) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py index 625c8c0..d99773c 100644 --- a/src/models/cua_s1/multimodal/model.py +++ b/src/models/cua_s1/multimodal/model.py @@ -71,11 +71,14 @@ def verify_weights(base: Path, adapter: Path): for root, names in allowed.items(): for path in root.rglob("*"): relative = path.relative_to(root) - if path.is_file() and relative.parts[0] != ".cache": - if relative.as_posix() not in names: - raise ValueError( - f"unlisted artifact may override pinned files: {relative}" - ) + if ( + path.is_file() + and relative.parts[0] != ".cache" + and relative.as_posix() not in names + ): + raise ValueError( + f"unlisted artifact may override pinned files: {relative}" + ) class _RequestImageProcessor: @@ -197,8 +200,12 @@ def score_reused(self, inputs, question: Question, features) -> list[float]: image_grid_thw=text_inputs.pop("image_grid_thw"), attention_mask=text_inputs.get("attention_mask"), ) + # Candidate scoring reads only the final position. output = self.model( - inputs_embeds=embeds, position_ids=position_ids, **text_inputs + inputs_embeds=embeds, + position_ids=position_ids, + logits_to_keep=1, + **text_inputs, ) logits = output.logits[0, -1, :] return torch.softmax( diff --git a/tests/cua_s1/test_image_reuse.py b/tests/cua_s1/test_image_reuse.py index 4d3eac5..4de22d7 100644 --- a/tests/cua_s1/test_image_reuse.py +++ b/tests/cua_s1/test_image_reuse.py @@ -232,6 +232,7 @@ def __call__(self, **kwargs): assert not torch.is_grad_enabled() assert "input_ids" not in kwargs and "pixel_values" not in kwargs assert "image_grid_thw" not in kwargs + assert kwargs["logits_to_keep"] == 1 assert torch.equal(kwargs["position_ids"], positions) assert torch.equal( kwargs["inputs_embeds"], diff --git a/tests/cua_s1/test_last_logits_benchmark.py b/tests/cua_s1/test_last_logits_benchmark.py new file mode 100644 index 0000000..a417d13 --- /dev/null +++ b/tests/cua_s1/test_last_logits_benchmark.py @@ -0,0 +1,38 @@ +"""The paired experiment must compare only output projection scope.""" + +import importlib.util +from pathlib import Path +from types import SimpleNamespace + + +def test_projection_variants_change_only_forward_keyword(monkeypatch): + recipe = Path(__file__).resolve().parents[2] / "recipe/cua_s1" + monkeypatch.syspath_prepend(str(recipe)) + spec = importlib.util.spec_from_file_location( + "benchmark_last_logits", recipe / "benchmark_last_logits.py" + ) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + calls = [] + + def forward(**kwargs): + calls.append(kwargs) + return kwargs + + model = SimpleNamespace(forward=forward) + engine = SimpleNamespace(model=model) + engine.predict = lambda request: engine.model.forward( + inputs_embeds="same", logits_to_keep=1 + ) + with module.projection_variants(engine) as variants: + assert variants.predict_reference(None) == {"inputs_embeds": "same"} + assert variants.predict(None) == { + "inputs_embeds": "same", + "logits_to_keep": 1, + } + assert model.forward is forward + assert calls == [ + {"inputs_embeds": "same"}, + {"inputs_embeds": "same", "logits_to_keep": 1}, + ] From 89e35e9909f5f9959208c0231ea90a1619c58840 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 14:41:23 +0800 Subject: [PATCH 19/75] docs(cua-s1): publish RTX 4090 final-logits experiment --- .../2026-09-28-cua-post-reuse-optimization.md | 12 +- .../experiments/rtx4090-last-logits/README.md | 125 + .../rtx4090-last-logits/SHA256SUMS | 7 + .../rtx4090-last-logits/correctness.json | 4458 ++++ .../rtx4090-last-logits/correctness.log | 17 + .../rtx4090-last-logits/paired.json | 7851 +++++++ .../rtx4090-last-logits/paired.log | 21 + .../rtx4090-last-logits/postflight.json | 656 + .../rtx4090-last-logits/postflight.log | 5 + .../rtx4090-last-logits/reuse-profile.json | 17741 ++++++++++++++++ .../rtx4090-last-logits/verify_results.py | 118 + 11 files changed, 31005 insertions(+), 6 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/correctness.json create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/correctness.log create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/paired.json create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/paired.log create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/postflight.json create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/postflight.log create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/reuse-profile.json create mode 100644 recipe/cua_s1/experiments/rtx4090-last-logits/verify_results.py diff --git a/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md b/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md index 1425725..261e072 100644 --- a/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md +++ b/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md @@ -10,11 +10,11 @@ --- -- [ ] Profile the unchanged reuse path with 1 and 8 questions; retain raw kernel groups, module invocation counts, traces, and source revision. -- [ ] Run a temporary paired exploration of full projection versus `logits_to_keep=1`, checking complete responses before timing. Select the candidate only if its effect is credible. -- [ ] Add a failing tensor integration test in `tests/cua_s1/test_image_reuse.py` proving the reused path passes `logits_to_keep=1`; run it on the GPU host or a torch-equipped environment. -- [ ] Modify only `score_reused` in `src/models/cua_s1/multimodal/model.py`; rerun the focused test and the full Cua-S1 suite. -- [ ] Add a paired benchmark recipe with explicit baseline selection, alternating order, raw samples, response parity, source and environment metadata, and peak allocated memory. -- [ ] Commit a clean source revision, transfer it to the GPU host, and run correctness and timed measurements. Review raw samples, variability, kernel changes, and single-question behavior. +- [x] Profile the unchanged reuse path with 1 and 8 questions; retain raw kernel groups, module invocation counts, traces, and source revision. +- [x] Run a temporary paired exploration of full projection versus `logits_to_keep=1`, checking complete responses before timing. Select the candidate only if its effect is credible. +- [x] Add a failing tensor integration test in `tests/cua_s1/test_image_reuse.py` proving the reused path passes `logits_to_keep=1`; run it on the GPU host or a torch-equipped environment. +- [x] Modify `score_reused` in `src/models/cua_s1/multimodal/model.py` and clean up a nearby Ruff guard; rerun the focused test and the full Cua-S1 suite. +- [x] Add a paired benchmark recipe with explicit baseline selection, alternating order, raw samples, response parity, source and environment metadata, and peak allocated memory. +- [x] Commit a clean source revision, transfer it to the GPU host, and run correctness and timed measurements. Review raw samples, variability, kernel changes, and single-question behavior. - [ ] Publish result data and reproduction commands, run verification, push the branch, create and attach the PR. - [ ] Archive and checksum complete GPU evidence locally, ensure no experiment is running, shut down the server, and verify it no longer accepts SSH. diff --git a/recipe/cua_s1/experiments/rtx4090-last-logits/README.md b/recipe/cua_s1/experiments/rtx4090-last-logits/README.md new file mode 100644 index 0000000..5fcaa12 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-last-logits/README.md @@ -0,0 +1,125 @@ +# RTX 4090 final-token projection after image reuse — 2026-09-28 + +The request-local reuse path previously projected every language hidden state to +the 248,320-word vocabulary, then read only the last position to score choices. +Passing `logits_to_keep=1` to the pinned Transformers model projects that final +position alone. This change is limited to multi-question requests; the +single-question path remains the existing reference path. + +Across 13 multi-question cases and two runs each, synchronized `engine.predict` +p50 fell **0.5–5.3%** (median reduction **3.4%**). The 640×480 screenshot with +eight different questions fell from **852.40 / 881.26 ms** to **822.68 / 851.65 +ms**. Four single-question controls changed by **−0.4% to +0.2%** and are treated +as measurement noise. All 17 cases returned exactly the same full response with +the full and limited projections; no numerical tolerance was needed. + +## Provenance and measurement + +- Clean measured source: `224d88b07be354b3cfc99fe8e9096bed777eafeb`. + [paired.json](paired.json) records its revision, clean status, environment, + every sample, alternating call order, p50/p95 and allocated/reserved peaks. +- NVIDIA RTX 4090 24,564 MiB, driver 595.71.05, CUDA runtime 13.0, Python + 3.12.13, PyTorch 2.14.0, Transformers 5.17.0, PEFT 0.21.0, BF16 base with + unmerged multimodal LoRA. The 178 adapted modules and fallback Gated DeltaNet + and causal-convolution paths match PR #17. No package or model revision changed. +- Original 16-case matrix (two screenshot sizes, short/long instructions, + 1/2/4/8 repeated questions) plus eight distinct questions on a 640×480 image. + Five warmup calls per variant and case, then two runs of 30 calls per variant: + **2,040 timed predictions**. The order alternates each pair and reverses in + the second run. CUDA is synchronized around each prediction. Hooks and the + profiler are absent from timed samples. +- Both timed variants use the **same request-local image reuse path**. A temporary + benchmark wrapper removes `logits_to_keep` for the full-projection comparison; + the candidate retains it. Thus this experiment isolates output projection, + unlike PR #17's before/after image-reuse comparison. +- Timing includes processor work and engine inference after request parsing. + It excludes image decoding, HTTP, queueing, model load, correctness checks and + profiling. Measurements are serial, concurrency 1. + +| Case | Full p50 ms, runs 1 / 2 | Final-token p50 ms, runs 1 / 2 | p50 reduction | Peak allocated GiB, full → final | +| --- | ---: | ---: | ---: | ---: | +| 320×240 short, 1 question | 107.78 / 107.98 | 107.88 / 107.72 | −0.1% / +0.2% | 8.728 → 8.728 | +| 320×240 short, 8 questions | 716.83 / 715.95 | 711.50 / 705.99 | 0.7% / 1.4% | 8.726 → 8.677 | +| 320×240 long, 8 questions | 957.85 / 929.54 | 924.68 / 892.63 | 3.5% / 4.0% | 8.920 → 8.776 | +| 640×480 short, 1 question | 126.08 / 125.59 | 125.95 / 125.65 | +0.1% / −0.0% | 8.843 → 8.843 | +| 640×480 short, 8 questions | 857.32 / 901.78 | 830.27 / 875.32 | 3.2% / 2.9% | 8.838 → 8.739 | +| 640×480 long, 8 questions | 1086.47 / 1126.04 | 1029.76 / 1066.46 | 5.2% / 5.3% | 9.034 → 8.840 | +| 640×480 distinct, 8 questions | 852.40 / 881.26 | 822.68 / 851.65 | 3.5% / 3.4% | 8.994 → 8.812 | + +The independent [verification script](verify_results.py) recomputes all 17 rows, +including p95, from every saved sample; the displayed rows are representative. +For 640×480 long, eight questions, peak allocated memory fell by **0.194 GiB**. +Reserved memory may persist across variants, so allocated memory is the useful +peak comparison here. + +## Correctness and hotspot evidence + +- [correctness.json](correctness.json): 13 fixtures / 49 question forwards per + path. Prepared tensors, language embeddings, masks, three-dimensional positions, + candidate probabilities and full responses are exactly equal to the original + execution path. Vision executes once and language executes once per question. + The fixtures include different goals, candidate counts and ordering, Unicode, + PNG/JPEG and consecutive image changes. Unit tests cover failure recovery. +- [paired.json](paired.json): exact full-response parity for each of the 17 timed + cases. Its output-head pre-hooks record an input change from `[1, 456, 2560]` + to `[1, 1, 2560]` for every question in the 640×480 short, eight-question case; + for eight distinct questions, `[1, 443, 2560]` becomes `[1, 1, 2560]` for + the first question. Single-question head shapes are unchanged. +- [reuse-profile.json](reuse-profile.json): an **untimed** trace on the prior + reused path at clean source `9f4a4fe` shows one visual forward, eight language + forwards and eight output projections. Its shape-grouped `aten::mm` with + inputs `[456, 2560]` and + `[2560, 248320]` occurs eight times and records 35.93 ms of device time. This + identifies the targeted output projection; profiled kernel time is not an + additive estimate of end-to-end savings. Full Chrome traces are retained in + the separate evidence archive because they are hundreds of MiB. +- [postflight.json](postflight.json): actual CUDA layout, HTTP health 200 and + eight-question worker response 200, exactly equal to direct engine output. + The clean GPU-host Cua-S1 suite passed **105 tests**; local macOS passed + **104**, with one torch-dependent tensor test skipped locally. Ruff lint and + formatting checks passed. + +## Reproduce + +Run from a clean checkout with the pinned environment and verified weights: + +```sh +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_last_logits.py \ + --weights /path/to/weights --output /path/to/fresh-output \ + --warmup 5 --runs 2 --iterations 30 +python recipe/cua_s1/experiments/rtx4090-last-logits/verify_results.py +``` + +For the full reference-versus-reuse correctness sweep and real HTTP check: + +```sh +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_image_reuse.py \ + --weights /path/to/weights --output /path/to/fresh-correctness \ + --correctness-only +PYTHONPATH=src:recipe/cua_s1 python \ + recipe/cua_s1/experiments/rtx4090-reuse/postflight.py \ + /path/to/clean-repository /path/to/weights /path/to/fresh-postflight +``` + +The checked-in audit reads this directory's `paired.json`. To audit a fresh +run, copy its `report.json` to `paired.json` in a copy of this directory. The +profiling groups and Chrome traces are retained as separate evidence from the +timed benchmark. [SHA256SUMS](SHA256SUMS) identifies the checked-in data files +and logs. + +The complete experiment directory, including all three large Chrome traces, +generated fixtures, logs and the measured source bundle, was copied to the +local ignored `work/20260928-post-reuse-evidence.tar.gz` before server shutdown. +The server and local archive SHA-256 matched: +`774d952e89a9586709419c6c8583e4aa7ee2d27b4bf836253c8c01881c4dccd9` +(25,231,406 compressed bytes). The checked-in JSON is sufficient to audit the +latency claims without this archive. + +## Limits and next step + +These numbers describe synthetic screenshots and serial, prefill-only engine +calls on one pinned RTX 4090 configuration. They do not establish HTTP +throughput, p99, GUI-task quality, native-engine support, CUDA Graph benefits or +Metal performance. The output projection is now bounded by the last token; +the remaining repeated language forwards and PyTorch fallback kernels are the +next candidates for measurement and validation. diff --git a/recipe/cua_s1/experiments/rtx4090-last-logits/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-last-logits/SHA256SUMS new file mode 100644 index 0000000..18e4a3f --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-last-logits/SHA256SUMS @@ -0,0 +1,7 @@ +2128f2904081f8d2353a87e4fb5b2197411a85298b228b404e6682ed4ee2d8c2 paired.json +624c05398d013a84e4f17bd7fd35c99575354fabd59aec8d98a59e0a1ee13a4f correctness.json +b72315ff79097d05dba9f93094a68bea25cda9d9d013041b5eac6464688406fd postflight.json +591662d886215a4d629373c7da55b88287189ef8b56468816eed328c6b56c3e8 reuse-profile.json +6a40b0d4b17c925aa1a98422fe8d89c1cdfbc1e2409d8a33f73780737f243555 paired.log +e3f9b7223191a04f9e2b34201bda02aa559ceaf246af24852787ba514ec49a9b correctness.log 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std::array)", + "device_type": "DeviceType.CUDA", + "count": 12, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 18.025000000034197, + "annotation": false + }, + { + "op": "aten::remainder", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "cpu_us": 23.305999999996857, + "gpu_us": 3.138999999999214, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BUnaryFunctor, std::array, false>(int, at::native::BUnaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "count": 2, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 3.138999999999214, + "annotation": false + }, + { + "op": "aten::mul", + "device_type": "DeviceType.CPU", + "count": 5, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "cpu_us": 37.818999999999505, + "gpu_us": 5.283000000003085, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::AUnaryFunctor >, std::array, false>(int, at::native::AUnaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "count": 20, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 21.42099999983111, + "annotation": false + }, + { + "op": "aten::floor_divide", + "device_type": "DeviceType.CPU", + "count": 1, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ] + ], + "cpu_us": 8.067999999999302, + "gpu_us": 2.2739999999976135, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::BinaryFunctor, std::array, false>(int, at::native::BinaryFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "count": 1, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 2.2739999999976135, + "annotation": false + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ], + [] + ], + "cpu_us": 15.70400000000518, + "gpu_us": 2.3689999999987776, + "annotation": false + }, + { + "op": "aten::to", + "device_type": 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const::{lambda(float)#1}, std::array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "device_type": "DeviceType.CUDA", + "count": 3093, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 13343.530000008068, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::AUnaryFunctor >, std::array, false>(int, at::native::AUnaryFunctor >, std::array)", + "device_type": "DeviceType.CUDA", + "count": 1286, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 8156.365000001715, + "annotation": false + }, + { + "op": "aten::sub", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200 + ], + [], + [] + ], + "cpu_us": 15.919000000001688, + "gpu_us": 2.24199999999837, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::CUDAFunctorOnSelf_add, std::array, false>(int, at::native::CUDAFunctorOnSelf_add, std::array)", + "device_type": "DeviceType.CUDA", + "count": 58, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 58.660999999789055, + "annotation": false + }, + { + "op": "aten::clamp", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200 + ], + [], + [] + ], + "cpu_us": 27.568000000010215, + "gpu_us": 2.2410000000018044, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<2, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, std::array, false>(int, at::native::(anonymous namespace)::launch_clamp_scalar(at::TensorIteratorBase&, c10::Scalar, c10::Scalar, at::native::detail::ClampLimits)::{lambda()#1}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "count": 4, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 4.547000000005937, + "annotation": false + }, + { + "op": "aten::div", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200 + ], + [ + 1200 + ] + ], + "cpu_us": 20.650000000001455, + "gpu_us": 5.186999999998079, + "annotation": false + }, + { + "op": "void at::native::unrolled_elementwise_kernel >, std::array, 4, TrivialOffsetCalculator<2, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<2>, at::native::memory::StoreWithCast<1> >(int, at::native::BinaryFunctor >, std::array, TrivialOffsetCalculator<2, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<2>, at::native::memory::StoreWithCast<1>)", + "device_type": "DeviceType.CUDA", + "count": 2, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 5.186999999998079, + "annotation": false + }, + { + "op": "aten::floor", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200 + ] + ], + "cpu_us": 20.816999999999098, + "gpu_us": 2.0160000000032596, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::floor_kernel_cuda(at::TensorIteratorBase&)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array, false>(int, at::native::floor_kernel_cuda(at::TensorIteratorBase&)::{lambda()#1}::operator()() const::{lambda()#2}::operator()() const::{lambda(float)#1}, std::array)", + "device_type": "DeviceType.CUDA", + "count": 2, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 2.0160000000032596, + "annotation": false + }, + { + "op": "aten::arange", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [], + [], + [], + [], + [], + [] + ], + "cpu_us": 6.9539999999942665, + "gpu_us": 0.0, + "annotation": false + }, + { + "op": "void at::native::unrolled_elementwise_kernel, 4, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1> >(int, at::native::direct_copy_kernel_cuda(at::TensorIteratorBase&)::{lambda()#3}::operator()() const::{lambda()#4}::operator()() const::{lambda(long)#1}, std::array, TrivialOffsetCalculator<1, unsigned int>, TrivialOffsetCalculator<1, unsigned int>, at::native::memory::LoadWithCast<1>, at::native::memory::StoreWithCast<1>)", + "device_type": "DeviceType.CUDA", + "count": 26, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 51.51899999982197, + "annotation": false + }, + { + "op": "aten::unsqueeze", + "device_type": "DeviceType.CPU", + "count": 8, + "input_shapes": [ + [ + 1200 + ], + [] + ], + "cpu_us": 19.272000000000844, + "gpu_us": 0, + "annotation": false + }, + { + "op": "aten::add", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200, + 1 + ], + [ + 2 + ], + [] + ], + "cpu_us": 18.424999999999272, + "gpu_us": 2.848999999998341, + 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"count": 194, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 566.3139999990817, + "annotation": false + }, + { + "op": "aten::abs", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200, + 2 + ] + ], + "cpu_us": 9.867999999998574, + "gpu_us": 0.0, + "annotation": false + }, + { + "op": "aten::abs", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200, + 2 + ], + [ + 0 + ] + ], + "cpu_us": 18.23399999999674, + "gpu_us": 2.0499999999956344, + "annotation": false + }, + { + "op": "void at::native::vectorized_elementwise_kernel<4, at::native::AbsFunctor, std::array, false>(int, at::native::AbsFunctor, std::array)", + "device_type": "DeviceType.CUDA", + "count": 2, + "input_shapes": [], + "cpu_us": 0, + "gpu_us": 2.0499999999956344, + "annotation": false + }, + { + "op": "aten::rsub", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [ + [ + 1200, + 2 + ], + [], + [] + ], + "cpu_us": 7.166000000001077, + "gpu_us": 0.0, 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+ [], + [], + [], + [] + ], + "cpu_us": 11.299999999901047, + "gpu_us": 0.0, + "annotation": false + }, + { + "op": "Buffer Flush", + "device_type": "DeviceType.CPU", + "count": 5, + "input_shapes": [], + "cpu_us": 484.454000000027, + "gpu_us": 0, + "annotation": false + }, + { + "op": "cudaDeviceSynchronize", + "device_type": "DeviceType.CPU", + "count": 2, + "input_shapes": [], + "cpu_us": 10.06899999990128, + "gpu_us": 0, + "annotation": false + } + ] +} \ No newline at end of file diff --git a/recipe/cua_s1/experiments/rtx4090-last-logits/verify_results.py b/recipe/cua_s1/experiments/rtx4090-last-logits/verify_results.py new file mode 100644 index 0000000..09ec43c --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-last-logits/verify_results.py @@ -0,0 +1,118 @@ +"""Independently audit every saved final-token projection sample.""" + +import json +import math +import statistics +from pathlib import Path + +ROOT = Path(__file__).resolve().parent +VARIANTS = ("full_projection", "last_token_projection") + + +def main(): + report = json.loads((ROOT / "paired.json").read_text()) + correctness = json.loads((ROOT / "correctness.json").read_text()) + postflight = json.loads((ROOT / "postflight.json").read_text()) + assert report["status"] == "complete" + assert report["repository"]["dirty"] is False + assert correctness["status"] == postflight["status"] == "complete" + for checked in (correctness, postflight): + assert checked["repository"]["revision"] == report["repository"]["revision"] + assert checked["repository"]["dirty"] is False + assert checked["environment"] == report["environment"] + assert postflight["health"]["status"] == postflight["http"]["status"] == 200 + assert postflight["http"]["exact_engine_parity"] + assert postflight["http"]["question_count"] == 8 + assert len(correctness["correctness"]) == 13 + assert ( + sum(len(case["response"]["answers"]) for case in correctness["correctness"]) + == 49 + ) + for case in correctness["correctness"]: + assert case["exact_prepared_tensor_parity"] + assert case["exact_language_input_parity"] + assert case["exact_response_parity"] + n = len(case["response"]["answers"]) + assert case["baseline_counts"] == { + "image_preprocess": n, + "vision": n, + "language": n, + } + assert case["reuse_counts"] == { + "image_preprocess": 1, + "vision": 1, + "language": n, + } + assert report["config"]["case"] == [] + assert (report["config"]["runs"], report["config"]["iterations"]) == (2, 30) + expected = { + f"{size}-{goal}-q{questions}" + for size in ("320x240", "640x480") + for goal in ("short", "long") + for questions in (1, 2, 4, 8) + } | {"640x480-distinct-q8"} + cases = report["cases"] + assert len(cases) == 17 + assert {case["id"] for case in cases} == expected + total = 0 + print( + "| Case | Full p50 ms | Last p50 ms | p50 reduction | Full p95 ms | Last p95 ms | Peak allocated GiB, full → last |" + ) + print("| --- | ---: | ---: | ---: | ---: | ---: | ---: |") + for case in sorted(cases, key=lambda value: value["id"]): + assert case["status"] == "complete" and case["exact_response_parity"] + n = case["questions"] + assert len(case["full_head_shapes"]) == len(case["last_head_shapes"]) == n + for full, last in zip(case["full_head_shapes"], case["last_head_shapes"]): + assert len(full) == len(last) == 3 + assert full[0] == last[0] == 1 and full[2] == last[2] == 2560 + assert full[1] > 1 + assert last[1] == (full[1] if n == 1 else 1) + assert len(case["runs"]) == 2 + for run_index, run in enumerate(case["runs"]): + assert run["run"] == run_index + 1 + assert len(run["orders"]) == 30 + for iteration, order in enumerate(run["orders"]): + selected = list(VARIANTS) + if (run_index + iteration) % 2: + selected.reverse() + assert order == selected + for name in VARIANTS: + values = run[name]["latencies_ms"] + assert len(values) == 30 + assert all(math.isfinite(value) and value > 0 for value in values) + assert statistics.median(values) == run[name]["p50_ms"] + assert ( + sorted(values)[math.ceil(0.95 * len(values)) - 1] + == run[name]["p95_ms"] + ) + assert ( + run[name]["peak_reserved_bytes"] + >= run[name]["peak_allocated_bytes"] + > 0 + ) + total += len(values) + + def times(name, metric, case=case): + return " / ".join(f"{run[name][metric]:.2f}" for run in case["runs"]) + + reduction = " / ".join( + f"{100 * (1 - run['last_token_projection']['p50_ms'] / run['full_projection']['p50_ms']):.1f}%" + for run in case["runs"] + ) + memory = " → ".join( + f"{max(run[name]['peak_allocated_bytes'] for run in case['runs']) / 2**30:.3f}" + for name in VARIANTS + ) + print( + f"| {case['id']} | {times('full_projection', 'p50_ms')} | " + f"{times('last_token_projection', 'p50_ms')} | {reduction} | " + f"{times('full_projection', 'p95_ms')} | " + f"{times('last_token_projection', 'p95_ms')} | {memory} |" + ) + assert total == 2040 + print(f"Verified {len(cases)} cases and {total} timed predictions.") + + +if __name__ == "__main__": + main() From c522e00a79a67343a9a1b72a28243f794c576b8f Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 14:43:22 +0800 Subject: [PATCH 20/75] docs(cua-s1): record experiment publication and server shutdown --- .../plans/2026-09-28-cua-post-reuse-optimization.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md b/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md index 261e072..1a9742e 100644 --- a/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md +++ b/docs/superpowers/plans/2026-09-28-cua-post-reuse-optimization.md @@ -16,5 +16,9 @@ - [x] Modify `score_reused` in `src/models/cua_s1/multimodal/model.py` and clean up a nearby Ruff guard; rerun the focused test and the full Cua-S1 suite. - [x] Add a paired benchmark recipe with explicit baseline selection, alternating order, raw samples, response parity, source and environment metadata, and peak allocated memory. - [x] Commit a clean source revision, transfer it to the GPU host, and run correctness and timed measurements. Review raw samples, variability, kernel changes, and single-question behavior. -- [ ] Publish result data and reproduction commands, run verification, push the branch, create and attach the PR. -- [ ] Archive and checksum complete GPU evidence locally, ensure no experiment is running, shut down the server, and verify it no longer accepts SSH. +- [x] Publish result data and reproduction commands, run verification, push the branch, create and attach the PR. +- [x] Archive and checksum complete GPU evidence locally, ensure no experiment is running, shut down the server, and verify it no longer accepts SSH. + +## Completion evidence + +Published [PR #18](https://github.com/ThinkFlowLab/system1-omni/pull/18) with the 17-case, 2,040-prediction dataset. The independent audit recomputed every saved p50/p95 and passed the 13-fixture, 49-question exact correctness and eight-question HTTP checks. Clean measured source `224d88b` passed 105 Cua-S1 tests on the GPU host; local tests passed 104 with one torch-dependent skip. The full trace archive SHA-256 matched on the server and local host: `774d952e89a9586709419c6c8583e4aa7ee2d27b4bf836253c8c01881c4dccd9`. The platform shutdown command exited successfully, and a subsequent SSH attempt was refused. From a2abe7e7fc776d6fb6c095f8b951c5e10698795f Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 15:15:22 +0800 Subject: [PATCH 21/75] bench(cua-s1): measure multimodal CUDA Graph language forward --- recipe/cua_s1/benchmark_multimodal_graph.py | 383 ++++++++++++++++++ .../cua_s1/test_multimodal_graph_benchmark.py | 64 +++ 2 files changed, 447 insertions(+) create mode 100644 recipe/cua_s1/benchmark_multimodal_graph.py create mode 100644 tests/cua_s1/test_multimodal_graph_benchmark.py diff --git a/recipe/cua_s1/benchmark_multimodal_graph.py b/recipe/cua_s1/benchmark_multimodal_graph.py new file mode 100644 index 0000000..7f9d548 --- /dev/null +++ b/recipe/cua_s1/benchmark_multimodal_graph.py @@ -0,0 +1,383 @@ +"""Compare eager and CUDA Graph language forwards on the reused multimodal path.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import statistics +import time +from dataclasses import replace +from pathlib import Path + +from benchmark_image_reuse import distinct_fixture +from profile_multimodal import case_matrix, fixture, repository_state, write_json + + +def language_inputs(engine, inputs, features): + """Prepare per-question tensors exactly as the production reuse path does.""" + core = engine.model.get_base_model().model + text = { + key: value.to(engine.model.device) + for key, value in inputs.items() + if key != "pixel_values" + } + ids = text.pop("input_ids") + embeds = core.get_input_embeddings()(ids) + image_embeds = features.to(embeds.device, embeds.dtype) + mask, _ = core.get_placeholder_mask( + ids, inputs_embeds=embeds, image_features=image_embeds + ) + embeds = embeds.masked_scatter(mask, image_embeds) + positions, _ = core.get_rope_index( + input_ids=ids, + mm_token_type_ids=text.pop("mm_token_type_ids"), + image_grid_thw=text.pop("image_grid_thw"), + attention_mask=text.get("attention_mask"), + ) + return { + "inputs_embeds": embeds, + "position_ids": positions, + "attention_mask": text["attention_mask"], + } + + +class GraphForward: + """One exact-shape graph; static buffers remain alive for every replay.""" + + def __init__(self, model, example): + import torch + + self.model = model + self.buffers = {name: value.clone() for name, value in example.items()} + self.signatures = { + name: tensor_signature(value) for name, value in example.items() + } + self.capture_ms = 0.0 + torch.cuda.synchronize() + start = time.perf_counter() + allocated_before = torch.cuda.memory_allocated() + reserved_before = torch.cuda.memory_reserved() + stream = torch.cuda.Stream() + stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(stream), torch.no_grad(): + for _ in range(3): + self._forward() + torch.cuda.current_stream().wait_stream(stream) + torch.cuda.synchronize() + self.graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(self.graph), torch.no_grad(): + self.static_logits = self._forward() + torch.cuda.synchronize() + self.capture_ms = (time.perf_counter() - start) * 1000 + self.capture_allocated_delta_bytes = ( + torch.cuda.memory_allocated() - allocated_before + ) + self.capture_reserved_delta_bytes = ( + torch.cuda.memory_reserved() - reserved_before + ) + + def _forward(self): + return self.model(**self.buffers, logits_to_keep=1, use_cache=False).logits[ + 0, -1, : + ] + + def forward(self, values): + for name, value in values.items(): + if tensor_signature(value) != self.signatures[name]: + raise ValueError(f"graph tensor layout changed for {name}") + self.buffers[name].copy_(value) + self.graph.replay() + return self.static_logits + + +def tensor_signature(value): + return ( + tuple(value.shape), + tuple(value.stride()), + str(value.dtype), + str(value.device), + ) + + +class Scorer: + def __init__(self, engine, graph=False): + self.engine = engine + self.graph = graph + self.runners = {} + self.capture_ms = [] + + def score(self, inputs, question, features): + import torch + + from models.cua_s1.multimodal.model import letter_ids + + with torch.no_grad(): + values = language_inputs(self.engine, inputs, features) + if self.graph: + key = tuple( + (name, tensor_signature(value)) for name, value in values.items() + ) + if key not in self.runners: + self.runners[key] = GraphForward(self.engine.model, values) + self.capture_ms.append(self.runners[key].capture_ms) + logits = self.runners[key].forward(values) + else: + logits = self.engine.model( + **values, logits_to_keep=1, use_cache=False + ).logits[0, -1, :] + ids = letter_ids(self.engine.tokenizer, len(question.keys)) + return torch.softmax( + logits[torch.tensor(ids, device=logits.device)].float(), dim=-1 + ).tolist() + + +def run_variant(engine, scorer, request): + original = engine.score_reused + engine.score_reused = scorer.score + try: + return engine.predict(request) + finally: + engine.score_reused = original + + +def measure(call): + import torch + + torch.cuda.synchronize() + start = time.perf_counter() + value = call() + torch.cuda.synchronize() + return value, (time.perf_counter() - start) * 1000 + + +def summarize(values): + ordered = sorted(values) + return { + "samples_ms": values, + "p50_ms": statistics.median(values), + "p95_ms": ordered[min(len(ordered) - 1, int(len(ordered) * 0.95))], + } + + +def response_difference(expected, actual): + if expected["model"] != actual["model"] or expected["usage"] != actual["usage"]: + raise ValueError("model identity or usage changed") + if expected["answers"].keys() != actual["answers"].keys(): + raise ValueError("answer keys changed") + worst = 0.0 + for name, before in expected["answers"].items(): + after = actual["answers"][name] + if before["type"] != after["type"] or before["choice"] != after["choice"]: + raise ValueError(f"{name}: selected choice changed") + if before["probabilities"].keys() != after["probabilities"].keys(): + raise ValueError(f"{name}: candidate keys changed") + worst = max( + worst, + *( + abs(value - after["probabilities"][key]) + for key, value in before["probabilities"].items() + ), + ) + return worst + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", required=True, type=Path) + parser.add_argument("--output", required=True, type=Path) + parser.add_argument("--case", action="append", default=[]) + parser.add_argument("--warmup", type=int, default=3) + parser.add_argument("--runs", type=int, default=2) + parser.add_argument("--iterations", type=int, default=20) + args = parser.parse_args(argv) + known = {case["id"] for case in case_matrix()} | {"640x480-distinct-q8"} + if len(args.case) != len(set(args.case)) or set(args.case) - known: + parser.error("duplicate or unknown case") + if not args.case: + parser.error("select at least one --case for this bounded experiment") + if min(args.warmup, args.runs, args.iterations) <= 0: + parser.error("warmup, runs and iterations must be positive") + if (args.output / "report.json").exists(): + parser.error("report already exists; use a fresh output directory") + + import torch + from evaluate_multimodal import environment + + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + args.output.mkdir(parents=True, exist_ok=True) + report_path = args.output / "report.json" + report = { + "status": "running", + "repository": repository_state(Path(__file__).resolve().parents[2]), + "environment": environment(), + "config": { + "warmup": args.warmup, + "runs": args.runs, + "iterations": args.iterations, + "cases": args.case, + "scope": "synchronized engine.predict; Graph captures model forward after vision, text embeddings and 3D positions; both variants use_cache=False", + }, + "cases": [], + } + write_json(report_path, report) + try: + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + selected = case_matrix() + [{"id": "640x480-distinct-q8"}] + selected = [case for case in selected if case["id"] in args.case] + for case in selected: + value = ( + distinct_fixture(args.output / "fixtures") + if case["id"] == "640x480-distinct-q8" + else fixture(case, args.output / "fixtures") + ) + request = parse_request(value) + item = {"id": case["id"], "status": "running", "runs": []} + report["cases"].append(item) + write_json(report_path, report) + if len(request.questions) == 1: + item["status"] = "skipped_single_question" + write_json(report_path, report) + continue + eager, graph = Scorer(engine), Scorer(engine, graph=True) + baseline = engine.predict(request) + eager_response = run_variant(engine, eager, request) + graph_response = run_variant(engine, graph, request) + if baseline != eager_response: + item["parity_diagnostic"] = { + "baseline": baseline, + "eager": eager_response, + "graph": graph_response, + } + write_json(report_path, report) + raise ValueError(f"{case['id']}: eager response differs") + item["max_graph_probability_difference"] = response_difference( + baseline, graph_response + ) + if item["max_graph_probability_difference"] > 0.002: + item["parity_diagnostic"] = { + "baseline": baseline, + "graph": graph_response, + "graph_shapes": [list(key) for key in graph.runners], + } + item["status"] = "rejected_graph" + write_json(report_path, report) + raise ValueError( + f"{case['id']}: graph probability difference exceeds 0.002" + ) + item["exact_eager_response_parity"] = True + # Replay the same graph after a substantial image change, then return + # to the original input. This detects stale captured input pointers. + from PIL import Image + + changed = replace( + request, image=Image.new("RGB", request.image.size, "black") + ) + changed_eager = run_variant(engine, eager, changed) + graphs_before = len(graph.runners) + changed_graph = run_variant(engine, graph, changed) + original_again = run_variant(engine, graph, request) + if len(graph.runners) != graphs_before: + raise ValueError( + f"{case['id']}: image change unexpectedly changed graph shapes" + ) + item["changed_image_max_probability_difference"] = response_difference( + changed_eager, changed_graph + ) + if item["changed_image_max_probability_difference"] > 0.002: + raise ValueError(f"{case['id']}: changed-image graph response differs") + if response_difference(baseline, original_again) > 0.002: + raise ValueError(f"{case['id']}: original input was not restored") + if changed_eager == baseline: + raise ValueError( + f"{case['id']}: changed image did not change eager response" + ) + item["changed_image_replay_checked"] = True + item["fixture_sha256"] = hashlib.sha256( + json.dumps(value, sort_keys=True).encode() + ).hexdigest() + item["capture_ms"] = graph.capture_ms + item["graph_count"] = len(graph.runners) + item["capture_allocated_delta_bytes"] = [ + runner.capture_allocated_delta_bytes + for runner in graph.runners.values() + ] + item["capture_reserved_delta_bytes"] = [ + runner.capture_reserved_delta_bytes for runner in graph.runners.values() + ] + for _ in range(args.warmup): + run_variant(engine, eager, request) + run_variant(engine, graph, request) + for run in range(args.runs): + samples = {"eager": [], "graph": []} + orders = [] + peaks = {"eager": 0, "graph": 0} + for iteration in range(args.iterations): + order = ["eager", "graph"] + if (run + iteration) % 2: + order.reverse() + orders.append(order) + for name in order: + scorer = eager if name == "eager" else graph + torch.cuda.reset_peak_memory_stats() + response, elapsed = measure( + lambda scorer=scorer, request=request: run_variant( + engine, scorer, request + ) + ) + if name == "eager" and response != baseline: + raise ValueError( + f"{case['id']}: timed eager response differs" + ) + if ( + name == "graph" + and response_difference(baseline, response) > 0.002 + ): + raise ValueError( + f"{case['id']}: timed graph response differs" + ) + samples[name].append(elapsed) + peaks[name] = max( + peaks[name], torch.cuda.max_memory_allocated() + ) + item["runs"].append( + { + "orders": orders, + **{ + name: { + **summarize(values), + "peak_allocated_bytes": peaks[name], + } + for name, values in samples.items() + }, + } + ) + write_json(report_path, report) + item["status"] = "complete" + del graph + torch.cuda.empty_cache() + write_json(report_path, report) + print( + case["id"], + [ + (round(x["eager"]["p50_ms"], 2), round(x["graph"]["p50_ms"], 2)) + for x in item["runs"] + ], + flush=True, + ) + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(report_path, report) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/cua_s1/test_multimodal_graph_benchmark.py b/tests/cua_s1/test_multimodal_graph_benchmark.py new file mode 100644 index 0000000..3b47da1 --- /dev/null +++ b/tests/cua_s1/test_multimodal_graph_benchmark.py @@ -0,0 +1,64 @@ +"""Guard the graph experiment's response comparison against changed choices.""" + +import importlib.util +from copy import deepcopy +from pathlib import Path +from types import SimpleNamespace + +import pytest + + +@pytest.fixture +def graph_benchmark(monkeypatch): + recipe = Path(__file__).resolve().parents[2] / "recipe/cua_s1" + monkeypatch.syspath_prepend(str(recipe)) + spec = importlib.util.spec_from_file_location( + "benchmark_multimodal_graph", recipe / "benchmark_multimodal_graph.py" + ) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def response(): + return { + "model": "pinned", + "usage": {"input_tokens": 3, "output_tokens": 0}, + "answers": { + "next": { + "type": "choice", + "choice": "save", + "probabilities": {"save": 0.8, "cancel": 0.2}, + } + }, + } + + +def test_graph_comparison_tracks_probability_difference(graph_benchmark): + actual = deepcopy(response()) + actual["answers"]["next"]["probabilities"]["save"] = 0.799 + assert graph_benchmark.response_difference(response(), actual) == pytest.approx( + 0.001 + ) + + +def test_graph_comparison_rejects_changed_choice(graph_benchmark): + actual = deepcopy(response()) + actual["answers"]["next"]["choice"] = "cancel" + with pytest.raises(ValueError, match="selected choice changed"): + graph_benchmark.response_difference(response(), actual) + + +def test_graph_signature_includes_layout_and_dtype(graph_benchmark): + value = SimpleNamespace( + shape=(1, 4, 8), + stride=lambda: (32, 8, 1), + dtype="bfloat16", + device="cuda:0", + ) + assert graph_benchmark.tensor_signature(value) == ( + (1, 4, 8), + (32, 8, 1), + "bfloat16", + "cuda:0", + ) From 809cacc70979b1b4e3a55b8b89be4691d09b0ce4 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 15:23:18 +0800 Subject: [PATCH 22/75] bench(cua-s1): validate graph replay with changed question text --- recipe/cua_s1/benchmark_multimodal_graph.py | 48 +++++++++++++++++++++ 1 file changed, 48 insertions(+) diff --git a/recipe/cua_s1/benchmark_multimodal_graph.py b/recipe/cua_s1/benchmark_multimodal_graph.py index 7f9d548..67e3b87 100644 --- a/recipe/cua_s1/benchmark_multimodal_graph.py +++ b/recipe/cua_s1/benchmark_multimodal_graph.py @@ -182,6 +182,28 @@ def response_difference(expected, actual): return worst +def changed_text_request(engine, request): + """Find different words that retain every prepared question's tensor shape.""" + import torch + + before = engine.prepare_reused(request.image, request.questions) + for verb in ("Open", "Close", "View", "Read", "Edit", "Keep"): + questions = tuple( + replace(question, goal=question.goal.replace("Save", verb)) + for question in request.questions + ) + if questions == request.questions: + continue + after = engine.prepare_reused(request.image, questions) + if all( + old["input_ids"].shape == new["input_ids"].shape + and not torch.equal(old["input_ids"], new["input_ids"]) + for old, new in zip(before, after) + ): + return replace(request, questions=questions), verb + raise ValueError("no same-shape changed-text fixture available") + + def main(argv=None): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--weights", required=True, type=Path) @@ -298,6 +320,32 @@ def main(argv=None): f"{case['id']}: changed image did not change eager response" ) item["changed_image_replay_checked"] = True + changed_text, verb = changed_text_request(engine, request) + text_eager = run_variant(engine, eager, changed_text) + graphs_before = len(graph.runners) + text_graph = run_variant(engine, graph, changed_text) + original_again = run_variant(engine, graph, request) + if len(graph.runners) != graphs_before: + raise ValueError( + f"{case['id']}: text change unexpectedly changed graph shapes" + ) + item["changed_text_verb"] = verb + item["changed_text_max_probability_difference"] = response_difference( + text_eager, text_graph + ) + if item["changed_text_max_probability_difference"] > 0.002: + item["status"] = "rejected_graph" + write_json(report_path, report) + raise ValueError(f"{case['id']}: changed-text graph response differs") + if response_difference(baseline, original_again) > 0.002: + raise ValueError( + f"{case['id']}: original input was not restored after text change" + ) + if text_eager == baseline: + raise ValueError( + f"{case['id']}: changed text did not change eager response" + ) + item["changed_text_replay_checked"] = True item["fixture_sha256"] = hashlib.sha256( json.dumps(value, sort_keys=True).encode() ).hexdigest() From 74b8c9e80005b4531b6dce7d5ae93934b3b0d583 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 15:31:14 +0800 Subject: [PATCH 23/75] docs(cua-s1): publish multimodal CUDA Graph evidence --- recipe/cua_s1/experiments/README.md | 4 + .../experiments/rtx4090-graph/README.md | 97 ++ .../experiments/rtx4090-graph/SHA256SUMS | 4 + .../rtx4090-graph/distinct-rejected.json | 631 ++++++++++++ .../rtx4090-graph/long-rejected.json | 61 ++ .../experiments/rtx4090-graph/paired.json | 948 ++++++++++++++++++ .../rtx4090-graph/verify_results.py | 70 ++ 7 files changed, 1815 insertions(+) create mode 100644 recipe/cua_s1/experiments/rtx4090-graph/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-graph/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-graph/distinct-rejected.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph/long-rejected.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph/paired.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph/verify_results.py diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md index ae1e0ef..d3ed0f6 100644 --- a/recipe/cua_s1/experiments/README.md +++ b/recipe/cua_s1/experiments/README.md @@ -1,5 +1,9 @@ # RTX 4090 multimodal validation — 2026-09-27 +For the later bounded CUDA Graph experiment after image reuse and last-token +projection, see the [Graph feasibility report](rtx4090-graph/README.md). It +includes both successful fixed-shape cases and correctness-gated rejections. + For the subsequent 16-case stage profiling experiment, refreshed upstream parity, and profiler annotation-counting recovery, see the [2026-09-28 report](rtx4090-profile/README.md). The measurements below remain the diff --git a/recipe/cua_s1/experiments/rtx4090-graph/README.md b/recipe/cua_s1/experiments/rtx4090-graph/README.md new file mode 100644 index 0000000..eb161da --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph/README.md @@ -0,0 +1,97 @@ +# Cua-S1 multimodal CUDA Graph feasibility — RTX 4090, 2026-09-28 + +CUDA Graph replay of the language forward reduces synchronized `engine.predict` +latency for these **fixed-shape, short-instruction** requests. This is an +experiment on the Transformers/PEFT multimodal worker after the image reuse and +last-token projection changes in #17 and #18. It does not add a production Graph +path or make a claim about the separate native text worker in #19. + +The graph captures `model(inputs_embeds, position_ids, attention_mask, +logits_to_keep=1, use_cache=False)` after image encoding, text embedding, image +insertion, and three-dimensional position construction. Both measured variants +use `use_cache=False`; the eager variant returns exactly the original worker +response. Graph replay copies the current tensors into long-lived buffers whose +shape, stride, dtype, and device match capture. A graph is keyed by that tensor +layout. Image processing, image encoding, prompt preparation, candidate scoring, +and response assembly run normally on every request. + +## Result and correctness boundary + +The table gives p50 milliseconds from two alternating runs of 20 calls per +variant and case (240 timed predictions total). Each case used three warmup calls +per variant. Timings synchronize CUDA around each whole `engine.predict`; they +exclude request parsing, fixture creation, model load, graph capture, HTTP, and +concurrent traffic. Percentages compare Graph to eager within the same run. + +| Synthetic case | Eager p50, runs 1 / 2 | Graph p50, runs 1 / 2 | Reduction, runs 1 / 2 | First capture with 3 warmups | +| --- | ---: | ---: | ---: | ---: | +| 320×240, short instruction, 2 repeated questions | 181.42 / 183.28 | 100.31 / 100.38 | 44.7% / 45.2% | 355 ms | +| 320×240, short instruction, 8 repeated questions | 669.81 / 684.26 | 337.57 / 338.21 | 49.6% / 50.6% | 347 ms | +| 640×480, short instruction, 8 repeated questions | 825.78 / 786.77 | 562.90 / 561.72 | 31.8% / 28.6% | 416 ms | + +For all three rows, eager produced the original response exactly. Graph retained +the selected choices, model identity, usage, and answer keys. The largest +candidate probability difference from the original response was 0.000421; +the experiment's absolute probability threshold was 0.002. Replaying after +replacing the image with a same-size black image, then returning to the original, +passed with at most 0.000781 probability difference. Replacing “Save” with a +different same-token-length verb, then returning to the original, also passed. +The saved reports include these checks for each case. The post-capture +`torch.cuda.memory_allocated()` increase was 9–17 MiB for one graph. This +allocator measure is not a total GPU-card memory measurement. + +Two additional cases **failed the correctness gate** and were not timed as +supported Graph paths: + +| Case | Observed failure | +| --- | --- | +| 640×480, 8 distinct questions | Seven graph layouts were captured. Candidate probability difference reached **0.065181** on the original input, despite the same chosen options. | +| 640×480, long instruction, 8 repeated questions | The original input and changed image were within 0.002, but a same-length changed question produced **0.051902** maximum probability difference. | + +Those deviations exceed the 0.002 experimental gate. The first appears during +the original distinct-question inference; the second appears only after changing +text. The cause is not yet isolated. This evidence supports a bounded +fixed-shape study and argues for tracing the captured language kernels before +adding Graph replay to a production worker. It does not establish correctness +for arbitrary questions, prompt lengths, GPUs, concurrent requests, or the +native engine. In particular, the fixed-input long-instruction speedup observed +during exploratory timing is not counted as usable. + +## Provenance and reproduction + +- Clean measured source: `809cacc70979b1b4e3a55b8b89be4691d09b0ce4`, on + the #18 stack. All three reports record this revision with `dirty: false`. +- One NVIDIA GeForce RTX 4090 (24,564 MiB, sm_89), driver 595.71.05, CUDA + runtime 13.0, Python 3.12.13, PyTorch 2.14.0, Transformers 5.17.0, PEFT + 0.21.0. Pinned BF16 base and unmerged multimodal LoRA were used. The + `causal_conv1d` and `flash-linear-attention` optional packages were absent, + so Transformers used its reference fallback kernels, as in #18. +- [paired.json](paired.json) contains every elapsed sample, run order, p50/p95, + capture cost, allocator measures, and correctness checks. The two rejected + reports, [distinct-rejected.json](distinct-rejected.json) and + [long-rejected.json](long-rejected.json), preserve the numerical failures. + [verify_results.py](verify_results.py) independently recomputes all 240 + samples and checks both rejections. +- The Cua-S1 test suite passed **108 tests** on this GPU host. Ruff lint and + formatting checks passed. [SHA256SUMS](SHA256SUMS) identifies the published + report files. +- A complete 186,201-byte evidence archive with fixtures, error logs, package + versions, and a Git source bundle was copied off the GPU host and verified + against server SHA-256 + `11831981ae8ea1c414075d563284b40b378a3471a8164c26b9a281986a12bee0`. + +Run from the measured source with the pinned weights and environment: + +```sh +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_multimodal_graph.py \ + --weights /path/to/weights --output /path/to/new-output \ + --case 320x240-short-q2 --case 320x240-short-q8 \ + --case 640x480-short-q8 --warmup 3 --runs 2 --iterations 20 +python recipe/cua_s1/experiments/rtx4090-graph/verify_results.py +``` + +To reproduce a rejection, run the same command with only +`--case 640x480-distinct-q8` or `--case 640x480-long-q8`, and use a fresh output +directory. Both intentionally exit nonzero after saving `report.json`. The +verification script reads the committed report files; copy fresh reports to a +separate copy of this directory to audit a new run. diff --git a/recipe/cua_s1/experiments/rtx4090-graph/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-graph/SHA256SUMS new file mode 100644 index 0000000..4ae3174 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph/SHA256SUMS @@ -0,0 +1,4 @@ +b511dff19890233ecfb2e516ad00e992d01475aaf1805a7e5a901ce90304d487 paired.json +63375b26136a1200f7df7d9504e5386323807fa66f974b87ffc390ed33533c78 distinct-rejected.json +5f4efc03af87bcd5eb1e6f57f0d523f6597c40a9bd713761ee7758b7985ba28f long-rejected.json +758053d2103a264792fe2ded0e5eb6519f76e392961aa5639d94887d47abb2ea verify_results.py diff --git a/recipe/cua_s1/experiments/rtx4090-graph/distinct-rejected.json b/recipe/cua_s1/experiments/rtx4090-graph/distinct-rejected.json new file mode 100644 index 0000000..2763aea --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph/distinct-rejected.json @@ -0,0 +1,631 @@ +{ + "status": "failed", + "repository": { + "revision": "809cacc70979b1b4e3a55b8b89be4691d09b0ce4", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "environment": { + "python": "3.12.13", + "gpu": "NVIDIA GeForce RTX 4090", + "compute_capability": [ + 8, + 9 + ], + "cuda": "13.0", + "driver": "595.71.05", + "torch_num_threads": 64, + "torch_num_interop_threads": 64, + "packages": { + "torch": "2.14.0", + "torchvision": "0.29.0", + "transformers": "5.17.0", + "peft": "0.21.0", + "pillow": "11.3.0", + "safetensors": "0.8.0", + "triton": "3.8.0" + }, + "reference_revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f", + "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", + "adapter_revision": "16818868b0cc7813808aae4e87b417657046ab79", + "dtype": "bfloat16", + "adapter_merged": false + }, + "config": { + "warmup": 1, + "runs": 1, + "iterations": 2, + "cases": [ + "640x480-distinct-q8" 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"capture_allocated_delta_bytes": [ + 17536000 + ], + "capture_reserved_delta_bytes": [ + -31457280 + ] + } + ] +} diff --git a/recipe/cua_s1/experiments/rtx4090-graph/verify_results.py b/recipe/cua_s1/experiments/rtx4090-graph/verify_results.py new file mode 100644 index 0000000..a7f84cf --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph/verify_results.py @@ -0,0 +1,70 @@ +"""Recompute the published CUDA Graph experiment from saved raw samples.""" + +import json +import statistics +from pathlib import Path + +ROOT = Path(__file__).resolve().parent + + +def check(): + report = json.loads((ROOT / "paired.json").read_text()) + rejected = json.loads((ROOT / "distinct-rejected.json").read_text()) + long_rejected = json.loads((ROOT / "long-rejected.json").read_text()) + assert report["status"] == "complete" + assert report["repository"]["dirty"] is False + assert len(report["cases"]) == 3 + assert report["config"]["runs"] == 2 + assert report["config"]["iterations"] == 20 + count = 0 + for case in report["cases"]: + assert case["status"] == "complete" + assert case["exact_eager_response_parity"] + assert case["changed_image_replay_checked"] + assert case["changed_text_replay_checked"] + assert case["graph_count"] == 1 + assert len(case["capture_ms"]) == 1 + assert len(case["capture_allocated_delta_bytes"]) == 1 + assert len(case["capture_reserved_delta_bytes"]) == 1 + assert case["max_graph_probability_difference"] <= 0.002 + assert case["changed_image_max_probability_difference"] <= 0.002 + assert case["changed_text_max_probability_difference"] <= 0.002 + assert len(case["runs"]) == 2 + for run_index, run in enumerate(case["runs"]): + assert len(run["orders"]) == 20 + for sample_index, order in enumerate(run["orders"]): + expected = ["eager", "graph"] + if (run_index + sample_index) % 2: + expected.reverse() + assert order == expected + for variant in ("eager", "graph"): + values = run[variant]["samples_ms"] + assert len(values) == 20 and all(value > 0 for value in values) + ordered = sorted(values) + assert run[variant]["p50_ms"] == statistics.median(values) + assert run[variant]["p95_ms"] == ordered[19] + assert run[variant]["peak_allocated_bytes"] > 0 + count += len(values) + + assert rejected["status"] == "failed" + assert rejected["repository"]["dirty"] is False + assert len(rejected["cases"]) == 1 + failure = rejected["cases"][0] + assert failure["id"] == "640x480-distinct-q8" + assert failure["status"] == "rejected_graph" + assert failure["max_graph_probability_difference"] > 0.002 + assert len(failure["parity_diagnostic"]["graph_shapes"]) > 1 + assert long_rejected["status"] == "failed" + assert long_rejected["repository"]["dirty"] is False + assert len(long_rejected["cases"]) == 1 + failure = long_rejected["cases"][0] + assert failure["id"] == "640x480-long-q8" + assert failure["status"] == "rejected_graph" + assert failure["max_graph_probability_difference"] <= 0.002 + assert failure["changed_image_max_probability_difference"] <= 0.002 + assert failure["changed_text_max_probability_difference"] > 0.002 + print(f"Verified {count} paired samples across three cases and two rejected cases.") + + +if __name__ == "__main__": + check() From de000560ce927a0db58f4928e91234d95df2ae23 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 16:13:09 +0800 Subject: [PATCH 24/75] feat(cua-s1): add bounded segmented CUDA Graph runtime --- .../plans/2026-09-28-cua-graph-runtime.md | 45 +++ recipe/cua_s1/README.md | 21 +- recipe/cua_s1/benchmark_graph_runtime.py | 197 ++++++++++ recipe/cua_s1/check_graph_cache.py | 70 ++++ recipe/cua_s1/experiments/README.md | 2 + .../rtx4090-graph-runtime/README.md | 78 ++++ .../rtx4090-graph-runtime/verify_results.py | 72 ++++ src/models/cua_s1/multimodal/graph_runtime.py | 335 ++++++++++++++++++ src/models/cua_s1/multimodal/model.py | 36 +- src/models/cua_s1/multimodal/server.py | 23 +- tests/cua_s1/test_graph_runtime.py | 73 ++++ tests/cua_s1/test_image_reuse.py | 12 + tests/cua_s1/test_server.py | 6 +- 13 files changed, 958 insertions(+), 12 deletions(-) create mode 100644 docs/superpowers/plans/2026-09-28-cua-graph-runtime.md create mode 100644 recipe/cua_s1/benchmark_graph_runtime.py create mode 100644 recipe/cua_s1/check_graph_cache.py create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-runtime/verify_results.py create mode 100644 src/models/cua_s1/multimodal/graph_runtime.py create mode 100644 tests/cua_s1/test_graph_runtime.py diff --git a/docs/superpowers/plans/2026-09-28-cua-graph-runtime.md b/docs/superpowers/plans/2026-09-28-cua-graph-runtime.md new file mode 100644 index 0000000..16c49f9 --- /dev/null +++ b/docs/superpowers/plans/2026-09-28-cua-graph-runtime.md @@ -0,0 +1,45 @@ +# Cua-S1 Multimodal Graph Runtime Implementation Plan + +> Inline execution in this task; the user authorized implementation, GPU experiments, PR submission, and server shutdown. + +**Goal:** Serve multimodal language forwards through reusable CUDA Graphs while preserving the existing worker's candidate probabilities. + +**Architecture:** The pinned Qwen3.5 model has runs of three Gated DeltaNet layers separated by full attention layers. Capture only each three-layer run; execute full attention, rotary positions, normalization, and final projection eagerly. Cache all eight graph segments as one exact-layout entry, with limits and eager fallback. + +**Tech Stack:** Python 3.12, PyTorch 2.14 CUDA Graphs, Transformers 5.17, PEFT 0.21, pytest, RTX 4090. + +--- + +### Task 1: Diagnose numerical divergence + +- [x] Reproduce the two rejected cases from #20. +- [x] Compare per-layer eager and full-graph outputs; identify the first divergent operation. +- [x] Prototype segmented capture and compare complete candidate logits to the original forward. + +### Task 2: Build the bounded runtime + +**Files:** `src/models/cua_s1/multimodal/graph_runtime.py`, `tests/cua_s1/test_graph_runtime.py`. + +- [x] Add failing tests for configuration limits, exact tensor signatures, and LRU eviction. +- [x] Implement static input buffers, side-stream warmup, capture, and replay for contiguous linear-attention layers. +- [x] Build the exact model loop with eager full attention and the original final norm and head. +- [x] Validate a newly captured shape against the original forward before caching it. +- [x] Bound the cache by layout count and retained allocation; fall back to eager on unsupported inputs and resource limits. + +### Task 3: Integrate the worker + +**Files:** `src/models/cua_s1/multimodal/model.py`, `src/models/cua_s1/multimodal/server.py`, `tests/cua_s1/test_image_reuse.py`. + +- [x] Add a failing test that an enabled runtime receives the prepared multimodal tensors. +- [x] Route only the reused multi-question path through the runtime; retain existing default behavior. +- [x] Add server flags for opt-in Graph mode and limits; keep the server's inference lock. + +### Task 4: GPU acceptance and PR + +**Files:** `recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md`, `recipe/cua_s1/README.md`. + +- [x] On RTX 4090, verify eager versus Graph for repeated, distinct, changed-image, changed-text, and long prompts, including the two previously rejected cases. +- [x] Measure synchronized whole-request p50/p95, capture cost, retained GPU memory, and fallback counts. +- [x] Run the full Cua-S1 test suite, Ruff, and format checks. +- [ ] Inspect the diff, commit, push, and open a PR. +- [ ] Stop GPU processes and shut down the user-provided server after evidence is saved. diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index d8f14c4..362be31 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -38,6 +38,25 @@ The worker binds to `127.0.0.1:8000` only after loading and a successful warmup. runs at a time; concurrent requests return `503`. This is a loopback model worker, with the Rust frontend and an ingress responsible for public serving. +To enable the optional CUDA Graph runtime for multi-question requests, add +`--graph` to the worker command. It captures each run of Gated DeltaNet layers +at its actual token length and keeps full-attention layers eager. The first +request at a layout runs eagerly; the second captures and checks all output +logits against eager before the layout enters the cache. The defaults retain at +most eight layouts and 1 GiB of graph buffers; `--graph-max-shapes`, +`--graph-max-memory-mib`, `--graph-min-uses`, and `--graph-max-tokens` adjust +these limits. Inputs above 2,048 tokens, rejected layouts, or layouts that +exceed the graph memory budget use eager inference. Cache entries own their +static buffers and are evicted together. The loaded model must remain immutable; +call `graph_runtime.invalidate()` before changing its weights or adapters. +Single-question requests continue through the reference path. + +Whole-model capture changed BF16 attention results on the measured RTX 4090; +the earlier [Graph feasibility report](experiments/rtx4090-graph/README.md) +records those failures. The segmented runtime preserves eager attention +behavior. See [the runtime experiment](experiments/rtx4090-graph-runtime/README.md) +for the measured correctness and latency scope. + To use the Rust frontend included in this repository: ```sh @@ -147,6 +166,6 @@ ruff check --select E4,E7,E9,F,I src/models/cua_s1/multimodal recipe/cua_s1/*.py ruff format --check src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 ``` -Metal, native CUDA kernels, text-adapter serving, batching, caching and training +Metal, native CUDA kernels, text-adapter serving, batching and training are outside this worker's scope. This implementation does not import or modify another contributor's text engine. diff --git a/recipe/cua_s1/benchmark_graph_runtime.py b/recipe/cua_s1/benchmark_graph_runtime.py new file mode 100644 index 0000000..e5d4b38 --- /dev/null +++ b/recipe/cua_s1/benchmark_graph_runtime.py @@ -0,0 +1,197 @@ +"""Validate and time the serving worker's segmented CUDA Graph runtime.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +from dataclasses import replace +from pathlib import Path + +from benchmark_image_reuse import distinct_fixture +from benchmark_multimodal_graph import ( + changed_text_request, + measure, + response_difference, + summarize, +) +from profile_multimodal import case_matrix, fixture, repository_state, write_json + + +def main(argv=None): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + p.add_argument("--case", action="append", required=True) + p.add_argument("--warmup", type=int, default=3) + p.add_argument("--runs", type=int, default=2) + p.add_argument("--iterations", type=int, default=20) + p.add_argument("--graph-max-tokens", type=int, default=2048) + args = p.parse_args(argv) + if min(args.warmup, args.runs, args.iterations, args.graph_max_tokens) <= 0: + p.error("run limits must be positive") + known = {case["id"] for case in case_matrix()} | {"640x480-distinct-q8"} + if len(args.case) != len(set(args.case)) or set(args.case) - known: + p.error("duplicate or unknown case") + if (args.output / "report.json").exists(): + p.error("report already exists; select a fresh directory") + + import torch + from evaluate_multimodal import environment + + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + args.output.mkdir(parents=True, exist_ok=True) + report_path = args.output / "report.json" + report = { + "status": "running", + "repository": repository_state(Path(__file__).resolve().parents[2]), + "environment": environment(), + "config": vars(args) + | {"weights": str(args.weights), "output": str(args.output)}, + "cases": [], + } + write_json(report_path, report) + try: + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + selected = case_matrix() + [{"id": "640x480-distinct-q8"}] + for case in selected: + if case["id"] not in args.case: + continue + raw = ( + distinct_fixture(args.output / "fixtures") + if case["id"] == "640x480-distinct-q8" + else fixture(case, args.output / "fixtures") + ) + request = parse_request(raw) + if len(request.questions) == 1: + raise ValueError(f"{case['id']}: single-question path is outside scope") + item = { + "id": case["id"], + "status": "running", + "fixture_sha256": hashlib.sha256( + json.dumps(raw, sort_keys=True).encode() + ).hexdigest(), + "runs": [], + } + report["cases"].append(item) + write_json(report_path, report) + engine.graph_runtime = None + eager_response = engine.predict(request) + runtime = GraphRuntime( + engine.model, + GraphConfig(max_tokens=args.graph_max_tokens), + ) + engine.graph_runtime = runtime + graph_response = engine.predict(request) + # A second call crosses the default two-use capture threshold. + graph_response = engine.predict(request) + item["original_max_probability_difference"] = response_difference( + eager_response, graph_response + ) + if item["original_max_probability_difference"] != 0: + raise ValueError(f"{case['id']}: Graph changed original response") + from PIL import Image + + changed = replace( + request, image=Image.new("RGB", request.image.size, "black") + ) + engine.graph_runtime = None + changed_eager = engine.predict(changed) + engine.graph_runtime = runtime + changed_graph = engine.predict(changed) + item["changed_image_max_probability_difference"] = response_difference( + changed_eager, changed_graph + ) + if item["changed_image_max_probability_difference"] != 0: + raise ValueError(f"{case['id']}: Graph changed image response") + if case["id"] == "640x480-distinct-q8": + # This fixture intentionally has unrelated prompts, so change + # only its first question while keeping every tensor layout. + first = request.questions[0] + changed_text = replace( + request, + questions=( + replace(first, goal=first.goal.replace("Save", "Open")), + *request.questions[1:], + ), + ) + old = engine.prepare_reused(request.image, request.questions) + new = engine.prepare_reused(request.image, changed_text.questions) + if not all( + before["input_ids"].shape == after["input_ids"].shape + for before, after in zip(old, new) + ) or torch.equal(old[0]["input_ids"], new[0]["input_ids"]): + raise ValueError("distinct changed text did not retain layouts") + verb = "Open" + else: + changed_text, verb = changed_text_request(engine, request) + engine.graph_runtime = None + text_eager = engine.predict(changed_text) + engine.graph_runtime = runtime + text_graph = engine.predict(changed_text) + item["changed_text_verb"] = verb + item["changed_text_max_probability_difference"] = response_difference( + text_eager, text_graph + ) + if item["changed_text_max_probability_difference"] != 0: + raise ValueError(f"{case['id']}: Graph changed text response") + item["stats_after_validation"] = dict(runtime.stats) + item["cache_bytes_after_validation"] = runtime.cache.bytes + item["cache_shapes_after_validation"] = len(runtime.cache) + for _ in range(args.warmup): + engine.graph_runtime = None + engine.predict(request) + engine.graph_runtime = runtime + engine.predict(request) + for run in range(args.runs): + samples = {"eager": [], "graph": []} + for iteration in range(args.iterations): + order = ["eager", "graph"] + if (run + iteration) % 2: + order.reverse() + for name in order: + engine.graph_runtime = None if name == "eager" else runtime + result, elapsed = measure(lambda: engine.predict(request)) + if response_difference(eager_response, result) != 0: + raise ValueError( + f"{case['id']}: timed {name} response changed" + ) + samples[name].append(elapsed) + item["runs"].append( + {name: summarize(values) for name, values in samples.items()} + ) + write_json(report_path, report) + item["stats_final"] = dict(runtime.stats) + item["cache_bytes_final"] = runtime.cache.bytes + item["cache_shapes_final"] = len(runtime.cache) + item["status"] = "complete" + engine.graph_runtime = None + runtime.invalidate() + del runtime + torch.cuda.empty_cache() + write_json(report_path, report) + print( + case["id"], + [ + (run["eager"]["p50_ms"], run["graph"]["p50_ms"]) + for run in item["runs"] + ], + flush=True, + ) + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(report_path, report) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/recipe/cua_s1/check_graph_cache.py b/recipe/cua_s1/check_graph_cache.py new file mode 100644 index 0000000..10b5427 --- /dev/null +++ b/recipe/cua_s1/check_graph_cache.py @@ -0,0 +1,70 @@ +"""GPU check of atomic shape eviction and memory-budget eager fallback.""" + +from __future__ import annotations + +import argparse +from dataclasses import replace +from pathlib import Path + +from benchmark_image_reuse import distinct_fixture +from benchmark_multimodal_graph import response_difference +from profile_multimodal import write_json + + +def main(argv=None): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + args = p.parse_args(argv) + if args.output.exists(): + p.error("output already exists; select a fresh path") + + import torch + + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + request = parse_request(distinct_fixture(args.output.parent / "cache-fixtures")) + request = replace(request, questions=request.questions[:2]) + baseline = engine.predict(request) + report = {} + for name, config in ( + ("shape_eviction", GraphConfig(max_shapes=1, max_bytes=512 << 20, min_uses=1)), + ("memory_fallback", GraphConfig(max_shapes=1, max_bytes=1 << 20, min_uses=1)), + ): + runtime = GraphRuntime(engine.model, config) + engine.graph_runtime = runtime + first = engine.predict(request) + second = engine.predict(request) + assert response_difference(baseline, first) == 0 + assert response_difference(baseline, second) == 0 + if name == "shape_eviction": + assert runtime.stats["evictions"] >= 1 + assert len(runtime.cache) == 1 + else: + assert runtime.stats["memory_budget"] >= 1 + assert runtime.stats["disabled"] >= 1 + assert len(runtime.cache) == 0 + assert runtime.cache.bytes <= config.max_bytes + report[name] = { + "stats": dict(runtime.stats), + "cache_shapes": len(runtime.cache), + "cache_bytes": runtime.cache.bytes, + "allocated_bytes": torch.cuda.memory_allocated(), + "exact_response_parity": True, + } + engine.graph_runtime = None + runtime.invalidate() + del runtime + torch.cuda.empty_cache() + write_json(args.output, report) + print(report) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md index d3ed0f6..0f98d1a 100644 --- a/recipe/cua_s1/experiments/README.md +++ b/recipe/cua_s1/experiments/README.md @@ -3,6 +3,8 @@ For the later bounded CUDA Graph experiment after image reuse and last-token projection, see the [Graph feasibility report](rtx4090-graph/README.md). It includes both successful fixed-shape cases and correctness-gated rejections. +The [segmented Graph runtime report](rtx4090-graph-runtime/README.md) describes +the subsequent worker integration and its correctness and latency results. For the subsequent 16-case stage profiling experiment, refreshed upstream parity, and profiler annotation-counting recovery, see the diff --git a/recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md b/recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md new file mode 100644 index 0000000..b0840b9 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md @@ -0,0 +1,78 @@ +# Segmented CUDA Graph runtime — RTX 4090, 2026-09-28 + +This experiment exercises the serving worker's optional `--graph` path on the +Transformers/PEFT Cua-S1 multimodal adapter. The worker captures runs of three +Gated DeltaNet decoder layers and executes each intervening full-attention layer +eagerly. Image preprocessing, the adapted vision encoder, token embedding, 3D +position construction, candidate scoring, and response assembly remain in the +normal request path. + +## Why the graph is segmented + +The whole-language-forward experiment in [#20](../rtx4090-graph/README.md) +rejected an eight-distinct-question case with a 0.065181 candidate-probability +difference and a long-instruction text-change case with a 0.051902 difference. +On the pinned RTX 4090 environment, the whole-graph replay is deterministic, +and an eager forward before and after capture is bitwise identical. Inspecting +every decoder layer for the 26-choice question found identical outputs through +layers 0–2. The first difference occurs in layer 3's full-attention output; +its input layer norm is identical. Differences then accumulate across later +layers. Forcing PyTorch's math SDPA backend makes its own eager and graph +forwards identical, but changes the 26-choice candidate probabilities by +0.045445 relative to the worker's normal attention backend. These observations +localize the problem to the full-attention calculation under capture; they do +not establish which CUDA kernel or rounding rule causes it. + +Segmenting at full-attention layers keeps that calculation on the original +eager path. A manual layer loop using the pinned Qwen3.5 model was compared to +the original PEFT forward before graphing; logits were bitwise identical for +the four distinct-question shapes checked, including the 26-choice question. +Capturing only linear-attention runs retained bitwise-identical logits for +those inputs while holding 8–32 graph segments in memory across the four +shapes. The worker integration repeats the exact-logit comparison when each +layout is first captured. A failing layout falls back to eager. + +## Runtime and measurement scope + +The runtime keys entries by model identity, attention implementation, and the +shape, stride, dtype, and device of embeddings, 3D positions, and attention +mask. Each entry owns all eight contiguous linear-attention captures for one +layout. On replay it copies fresh hidden states and the recurrent mask into +static buffers. Full attention uses freshly generated rotary embeddings and +causal mask. The final norm and output projection run eagerly, producing a +fresh logits tensor before the entry can be replayed again. The worker's +existing request lock prevents concurrent inference. + +The first use of a layout runs eagerly. At the second use, the runtime captures +and compares all vocabulary logits to the worker's eager forward before caching +the entry. The default cache retains up to eight layouts and 1 GiB of measured +live graph allocations. Unsupported layouts, oversized entries, capture errors +with a healthy CUDA context, and numerical mismatches use eager. The memory +limit covers retained allocations, not the temporary peak during capture or +PyTorch's reserved allocator pool. + +The synchronized timing covers `engine.predict` from prepared request object to +response dict. It includes preprocessing, vision and language inference, and +response assembly. It excludes request parsing, HTTP, model loading, initial +capture, and concurrency. Each case alternates eager and Graph predictions. +The candidate responses are compared exactly for the original image, a black +replacement image of the same size, and a same-token-length text change. The +distinct-question case changes its first question's text while preserving all +eight layouts. + +Reproduce from this branch's clean source and the pinned weights: + +```sh +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_runtime.py \ + --weights /path/to/weights --output /path/to/fresh-output \ + --case 320x240-short-q2 --case 320x240-short-q8 \ + --case 640x480-short-q8 --case 640x480-long-q8 \ + --case 640x480-distinct-q8 --graph-max-tokens 4096 \ + --warmup 3 --runs 2 --iterations 20 +``` + +The raw report records all samples, capture counters and cost, retained graph +allocations, package versions, fixture hashes, and repository revision. The +`--graph-max-tokens 4096` override exercises the long prompt in this +experiment; the worker's default is 2048 tokens, with longer prompts going +eager. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-runtime/verify_results.py b/recipe/cua_s1/experiments/rtx4090-graph-runtime/verify_results.py new file mode 100644 index 0000000..952046c --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-runtime/verify_results.py @@ -0,0 +1,72 @@ +"""Independently recompute the segmented Graph report's timing and gates.""" + +from __future__ import annotations + +import argparse +import json +import statistics +from pathlib import Path + +CASES = { + "320x240-short-q2", + "320x240-short-q8", + "640x480-short-q8", + "640x480-long-q8", + "640x480-distinct-q8", +} + + +def verify(report, cache_report): + assert report["status"] == "complete" + assert report["repository"]["revision"] + assert report["repository"]["dirty"] is False + assert {case["id"] for case in report["cases"]} == CASES + assert report["config"]["runs"] == 2 + assert report["config"]["iterations"] == 20 + total = 0 + for case in report["cases"]: + assert case["status"] == "complete" + for field in ( + "original_max_probability_difference", + "changed_image_max_probability_difference", + "changed_text_max_probability_difference", + ): + assert case[field] == 0, (case["id"], field) + assert case["stats_final"]["captures"] >= 1 + assert case["stats_final"]["numerical_mismatch"] == 0 + assert case["stats_final"]["capture_error"] == 0 + assert case["cache_bytes_final"] <= 1024 * 1024 * 1024 + assert len(case["runs"]) == 2 + for run in case["runs"]: + for name in ("eager", "graph"): + item = run[name] + samples = item["samples_ms"] + assert len(samples) == 20 + assert all(value > 0 for value in samples) + assert item["p50_ms"] == statistics.median(samples) + ordered = sorted(samples) + assert item["p95_ms"] == ordered[19] + total += len(samples) + + for name in ("shape_eviction", "memory_fallback"): + assert cache_report[name]["exact_response_parity"] is True + assert cache_report["shape_eviction"]["stats"]["evictions"] >= 1 + assert cache_report["shape_eviction"]["cache_shapes"] == 1 + assert cache_report["memory_fallback"]["stats"]["memory_budget"] >= 1 + assert cache_report["memory_fallback"]["cache_shapes"] == 0 + return total + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--report", type=Path, required=True) + p.add_argument("--cache-report", type=Path, required=True) + args = p.parse_args() + report = json.loads(args.report.read_text()) + cache_report = json.loads(args.cache_report.read_text()) + total = verify(report, cache_report) + print(f"verified {len(report['cases'])} cases and {total} timed predictions") + + +if __name__ == "__main__": + main() diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py new file mode 100644 index 0000000..89c61f4 --- /dev/null +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -0,0 +1,335 @@ +"""Bounded CUDA Graph replay for Qwen3.5 multimodal linear-attention runs. + +Full-attention SDPA changes BF16 results under whole-model Graph capture on the +pinned RTX 4090 stack. Keep those layers eager and capture the intervening +Gated DeltaNet runs, whose replay matches the original forward bit for bit. +""" + +from __future__ import annotations + +import gc +import threading +import time +from collections import OrderedDict +from dataclasses import dataclass, field + + +def tensor_signature(value): + if value is None: + return None + return ( + tuple(value.shape), + tuple(value.stride()), + str(value.dtype), + str(value.device), + ) + + +@dataclass(frozen=True) +class GraphConfig: + max_shapes: int = 8 + max_bytes: int = 1024 * 1024 * 1024 + min_uses: int = 2 + max_tokens: int = 2048 + + def __post_init__(self): + if min(self.max_shapes, self.max_bytes, self.min_uses, self.max_tokens) <= 0: + raise ValueError("graph limits must be positive") + + +class GraphCache: + """LRU cache whose entries own all segments for one tensor layout.""" + + def __init__(self, max_shapes: int, max_bytes: int): + self.max_shapes = max_shapes + self.max_bytes = max_bytes + self.entries = OrderedDict() + self.bytes = 0 + + def __len__(self): + return len(self.entries) + + def get(self, key): + value = self.entries.get(key) + if value is not None: + self.entries.move_to_end(key) + return value + + def put(self, key, value): + if value.bytes > self.max_bytes: + return None + removed = [] + if key in self.entries: + previous = self.entries.pop(key) + self.bytes -= previous.bytes + removed.append(previous) + self.entries[key] = value + self.bytes += value.bytes + while len(self.entries) > self.max_shapes or self.bytes > self.max_bytes: + _, previous = self.entries.popitem(last=False) + self.bytes -= previous.bytes + removed.append(previous) + return removed + + def clear(self): + removed = list(self.entries.values()) + self.entries.clear() + self.bytes = 0 + return removed + + +class _GraphSegment: + """One fixed-layout capture of adjacent linear-attention decoder layers.""" + + def __init__(self, layers, start, end, hidden, mask): + import torch + + self.layers = layers + self.start = start + self.end = end + self.input_signature = tensor_signature(hidden) + self.mask_signature = tensor_signature(mask) + before = torch.cuda.memory_allocated(hidden.device) + self.static_hidden = hidden.clone() + self.static_mask = mask.clone() if mask is not None else None + + stream = torch.cuda.Stream(device=hidden.device) + stream.wait_stream(torch.cuda.current_stream(hidden.device)) + with torch.cuda.stream(stream), torch.no_grad(): + for _ in range(3): + self._forward() + torch.cuda.current_stream(hidden.device).wait_stream(stream) + + self.graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(self.graph), torch.no_grad(): + self.static_output = self._forward() + self.bytes = max(0, torch.cuda.memory_allocated(hidden.device) - before) + + def _forward(self): + hidden = self.static_hidden + for index in range(self.start, self.end): + hidden = self.layers[index]( + hidden, + position_embeddings=None, + attention_mask=self.static_mask, + position_ids=None, + past_key_values=None, + use_cache=False, + ) + return hidden + + def replay(self, hidden, mask): + if ( + tensor_signature(hidden) != self.input_signature + or tensor_signature(mask) != self.mask_signature + ): + raise ValueError("CUDA Graph segment layout changed") + self.static_hidden.copy_(hidden) + if self.static_mask is not None: + self.static_mask.copy_(mask) + self.graph.replay() + return self.static_output + + +@dataclass +class _ShapeEntry: + blocks: dict = field(default_factory=dict) + bytes: int = 0 + + +class GraphRuntime: + """Exact-length graph cache for a loaded, immutable multimodal model.""" + + def __init__(self, model, config: GraphConfig | None = None): + self.model = model + self.config = config or GraphConfig() + self.cache = GraphCache(self.config.max_shapes, self.config.max_bytes) + self.uses = OrderedDict() + self.disabled = OrderedDict() + self.lock = threading.Lock() + self.stats = { + "eager": 0, + "unsupported": 0, + "warmup": 0, + "disabled": 0, + "replays": 0, + "captures": 0, + "capture_ms": 0.0, + "evictions": 0, + "rejected": 0, + "memory_budget": 0, + "capture_oom": 0, + "capture_error": 0, + "numerical_mismatch": 0, + } + + def invalidate(self): + """Call after replacing model weights or adapters.""" + with self.lock: + retired = self.cache.clear() + self.uses.clear() + self.disabled.clear() + del retired + gc.collect() + + def _eager(self, values): + self.stats["eager"] += 1 + return self.model(**values, logits_to_keep=1, use_cache=False).logits[0, -1, :] + + def _supported(self, values): + hidden = values["inputs_embeds"] + if ( + self.model.training + or hidden.device.type != "cuda" + or hidden.ndim != 3 + or hidden.shape[0] != 1 + or hidden.shape[1] > self.config.max_tokens + or values["position_ids"].shape != (3, 1, hidden.shape[1]) + ): + return False + core = self.model.get_base_model().model + text = core.language_model + types = text.config.layer_types + return len(types) == text.config.num_hidden_layers == len(text.layers) and set( + types + ) == {"linear_attention", "full_attention"} + + def _run_segments(self, values, entry): + from transformers.masking_utils import ( + create_causal_mask, + create_recurrent_attention_mask, + ) + + core = self.model.get_base_model().model + text = core.language_model + hidden = values["inputs_embeds"] + kwargs = { + "config": text.config, + "inputs_embeds": hidden, + "attention_mask": values["attention_mask"], + "past_key_values": None, + "position_ids": None, + } + masks = { + "full_attention": create_causal_mask(**kwargs), + "linear_attention": create_recurrent_attention_mask(**kwargs), + } + rope = text.rotary_emb(hidden, values["position_ids"]) + index = 0 + while index < text.config.num_hidden_layers: + kind = text.config.layer_types[index] + if kind == "full_attention": + hidden = text.layers[index]( + hidden, + position_embeddings=rope, + attention_mask=masks[kind], + position_ids=None, + past_key_values=None, + use_cache=False, + ) + index += 1 + continue + end = index + 1 + while ( + end < text.config.num_hidden_layers + and text.config.layer_types[end] == "linear_attention" + ): + end += 1 + block = entry.blocks.get((index, end)) + if block is None: + block = _GraphSegment( + text.layers, index, end, hidden, masks["linear_attention"] + ) + entry.blocks[(index, end)] = block + entry.bytes += block.bytes + hidden = block.replay(hidden, masks["linear_attention"]) + index = end + hidden = text.norm(hidden) + return self.model.get_base_model().lm_head(hidden[:, -1:, :])[0, -1, :] + + def forward(self, values): + """Return fresh logits; no static graph output escapes the runtime lock.""" + import torch + + with self.lock, torch.no_grad(): + if not self._supported(values): + self.stats["unsupported"] += 1 + return self._eager(values) + key = ( + id(self.model), + self.model.get_base_model().model.language_model.config._attn_implementation, + *( + tensor_signature(values[name]) + for name in ("inputs_embeds", "position_ids", "attention_mask") + ), + ) + if key in self.disabled: + self.stats["disabled"] += 1 + return self._eager(values) + entry = self.cache.get(key) + if entry is not None: + output = self._run_segments(values, entry) + self.stats["replays"] += 1 + return output + + uses = self.uses.get(key, 0) + 1 + self.uses[key] = uses + self.uses.move_to_end(key) + while len(self.uses) > 128: + self.uses.popitem(last=False) + if uses < self.config.min_uses: + self.stats["warmup"] += 1 + return self._eager(values) + + reference = self._eager(values) + candidate = _ShapeEntry() + try: + torch.cuda.synchronize(values["inputs_embeds"].device) + capture_start = time.perf_counter() + output = self._run_segments(values, candidate) + torch.cuda.synchronize(values["inputs_embeds"].device) + capture_ms = (time.perf_counter() - capture_start) * 1000 + except torch.cuda.OutOfMemoryError: + self.stats["capture_oom"] += 1 + del candidate + gc.collect() + torch.cuda.empty_cache() + self._disable(key) + return reference + except RuntimeError: + # Only a healthy CUDA context can continue serving eagerly. + torch.cuda.synchronize(values["inputs_embeds"].device) + self.stats["capture_error"] += 1 + del candidate + self._disable(key) + gc.collect() + return reference + + if not torch.equal(reference, output): + self.stats["numerical_mismatch"] += 1 + del candidate, output + self._disable(key) + gc.collect() + return reference + retired = self.cache.put(key, candidate) + if retired is None: + self.stats["memory_budget"] += 1 + del candidate + self._disable(key) + gc.collect() + return reference + self.stats["captures"] += 1 + self.stats["capture_ms"] += capture_ms + self.stats["evictions"] += len(retired) + if retired: + del retired + gc.collect() + else: + del retired + return output + + def _disable(self, key): + self.stats["rejected"] += 1 + self.disabled[key] = None + while len(self.disabled) > 128: + self.disabled.popitem(last=False) diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py index d99773c..40470c2 100644 --- a/src/models/cua_s1/multimodal/model.py +++ b/src/models/cua_s1/multimodal/model.py @@ -101,7 +101,12 @@ def __call__(self, *args, **kwargs): class MultimodalEngine: def __init__( - self, base: str, adapter: str, device: str = "cuda", dtype: str = "bfloat16" + self, + base: str, + adapter: str, + device: str = "cuda", + dtype: str = "bfloat16", + graph_config=None, ): import torch from peft import PeftModel @@ -138,6 +143,11 @@ def __init__( self.adapter_modules = len(modules) self.model.eval() self.dtype = dtype + self.graph_runtime = None + if graph_config is not None: + from .graph_runtime import GraphRuntime + + self.graph_runtime = GraphRuntime(self.model, graph_config) def prepare(self, image, question: Question): return self._prepare(self.processor, image, question) @@ -201,13 +211,23 @@ def score_reused(self, inputs, question: Question, features) -> list[float]: attention_mask=text_inputs.get("attention_mask"), ) # Candidate scoring reads only the final position. - output = self.model( - inputs_embeds=embeds, - position_ids=position_ids, - logits_to_keep=1, - **text_inputs, - ) - logits = output.logits[0, -1, :] + graph_runtime = getattr(self, "graph_runtime", None) + if graph_runtime is None: + output = self.model( + inputs_embeds=embeds, + position_ids=position_ids, + logits_to_keep=1, + **text_inputs, + ) + logits = output.logits[0, -1, :] + else: + logits = graph_runtime.forward( + { + "inputs_embeds": embeds, + "position_ids": position_ids, + **text_inputs, + } + ) return torch.softmax( logits[torch.tensor(ids, device=logits.device)].float(), dim=-1 ).tolist() diff --git a/src/models/cua_s1/multimodal/server.py b/src/models/cua_s1/multimodal/server.py index c414bc9..e0de6a4 100644 --- a/src/models/cua_s1/multimodal/server.py +++ b/src/models/cua_s1/multimodal/server.py @@ -107,6 +107,7 @@ def do_POST(self): def main(): + from .graph_runtime import GraphConfig from .model import MultimodalEngine p = argparse.ArgumentParser(description=__doc__) @@ -117,9 +118,29 @@ def main(): "--adapter", required=True, help="verified local multimodal adapter directory" ) p.add_argument("--port", type=int, default=8000) + p.add_argument( + "--graph", action="store_true", help="enable segmented CUDA Graph replay" + ) + p.add_argument("--graph-max-shapes", type=int, default=8) + p.add_argument("--graph-max-memory-mib", type=int, default=1024) + p.add_argument("--graph-min-uses", type=int, default=2) + p.add_argument("--graph-max-tokens", type=int, default=2048) args = p.parse_args() + try: + graph_config = ( + GraphConfig( + max_shapes=args.graph_max_shapes, + max_bytes=args.graph_max_memory_mib * 1024 * 1024, + min_uses=args.graph_min_uses, + max_tokens=args.graph_max_tokens, + ) + if args.graph + else None + ) + except ValueError as exc: + p.error(str(exc)) logging.basicConfig(level=logging.INFO) - engine = MultimodalEngine(args.base, args.adapter) + engine = MultimodalEngine(args.base, args.adapter, graph_config=graph_config) engine.warmup() # Bind only after model loading and a representative inference succeed. server = WorkerServer(("127.0.0.1", args.port), engine) diff --git a/tests/cua_s1/test_graph_runtime.py b/tests/cua_s1/test_graph_runtime.py new file mode 100644 index 0000000..20b7599 --- /dev/null +++ b/tests/cua_s1/test_graph_runtime.py @@ -0,0 +1,73 @@ +"""Policy tests for the bounded CUDA Graph runtime (no GPU required).""" + +from types import SimpleNamespace + +import pytest + +from models.cua_s1.multimodal.graph_runtime import ( + GraphCache, + GraphConfig, + GraphRuntime, + tensor_signature, +) + + +@pytest.mark.parametrize( + "setting", + [ + {"max_shapes": 0}, + {"max_bytes": 0}, + {"min_uses": 0}, + {"max_tokens": 0}, + ], +) +def test_graph_config_rejects_nonpositive_limits(setting): + with pytest.raises(ValueError, match="positive"): + GraphConfig(**setting) + + +def test_tensor_signature_includes_layout_dtype_and_device(): + value = SimpleNamespace( + shape=(1, 128, 2560), + stride=lambda: (327680, 2560, 1), + dtype="bfloat16", + device="cuda:0", + ) + assert tensor_signature(value) == ( + (1, 128, 2560), + (327680, 2560, 1), + "bfloat16", + "cuda:0", + ) + + +def test_cache_evicts_least_recently_used_shape_atomically(): + cache = GraphCache(max_shapes=2, max_bytes=70) + first = SimpleNamespace(bytes=20) + second = SimpleNamespace(bytes=25) + third = SimpleNamespace(bytes=30) + assert cache.put("a", first) == [] + assert cache.put("b", second) == [] + assert cache.get("a") is first + assert cache.put("c", third) == [second] + assert cache.get("b") is None + assert cache.get("a") is first + assert cache.get("c") is third + assert cache.bytes == 50 + + +def test_cache_rejects_one_shape_exceeding_budget(): + cache = GraphCache(max_shapes=2, max_bytes=40) + assert cache.put("huge", SimpleNamespace(bytes=41)) is None + assert len(cache) == 0 + + +def test_graph_runtime_falls_back_if_model_is_training(): + runtime = GraphRuntime(SimpleNamespace(training=True)) + values = { + "inputs_embeds": SimpleNamespace( + device=SimpleNamespace(type="cuda"), ndim=3, shape=(1, 4, 2560) + ), + "position_ids": SimpleNamespace(shape=(3, 1, 4)), + } + assert runtime._supported(values) is False diff --git a/tests/cua_s1/test_image_reuse.py b/tests/cua_s1/test_image_reuse.py index 4de22d7..6ba724f 100644 --- a/tests/cua_s1/test_image_reuse.py +++ b/tests/cua_s1/test_image_reuse.py @@ -266,3 +266,15 @@ def __call__(self, **kwargs): "peft-forward", ] assert all(torch.equal(value, original[key]) for key, value in inputs.items()) + + captured = [] + engine.graph_runtime = SimpleNamespace( + forward=lambda values: captured.append(values) or torch.tensor([0.0, 1.0, 2.0]) + ) + assert ( + engine.score_reused(inputs, QUESTIONS[0], shared) + == torch.softmax(torch.tensor([0.0, 1.0]), dim=-1).tolist() + ) + assert len(captured) == 1 + assert torch.equal(captured[0]["position_ids"], positions) + assert torch.equal(captured[0]["inputs_embeds"][0, 1:3], features) diff --git a/tests/cua_s1/test_server.py b/tests/cua_s1/test_server.py index 5d2eee2..2973217 100644 --- a/tests/cua_s1/test_server.py +++ b/tests/cua_s1/test_server.py @@ -86,7 +86,8 @@ def fail(_): server.engine.predict = fail status, body = call(url + "/v1/systemone", request()) assert status == 500 and body == {"detail": "inference failed"} - assert not server.inference_lock.locked() + assert server.inference_lock.acquire(timeout=1) + server.inference_lock.release() @pytest.mark.parametrize( @@ -116,7 +117,8 @@ def unexpected(_): server.engine.predict = unexpected status, body = call(url + "/v1/systemone", raw) assert status == 400 and set(body) == {"detail"} - assert not server.inference_lock.locked() + assert server.inference_lock.acquire(timeout=1) + server.inference_lock.release() def test_missing_instructions_rejects_whole_request(worker): From 03c3acc5ecf84eb28a0f9343e6a13ab7f893d0a1 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 16:23:21 +0800 Subject: [PATCH 25/75] docs(cua-s1): publish segmented Graph runtime results --- .../rtx4090-graph-runtime/README.md | 50 +- .../rtx4090-graph-runtime/SHA256SUMS | 2 + .../rtx4090-graph-runtime/cache.json | 44 + .../rtx4090-graph-runtime/report.json | 817 ++++++++++++++++++ 4 files changed, 909 insertions(+), 4 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-runtime/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-runtime/cache.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-runtime/report.json diff --git a/recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md b/recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md index b0840b9..a865dc3 100644 --- a/recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md +++ b/recipe/cua_s1/experiments/rtx4090-graph-runtime/README.md @@ -55,10 +55,52 @@ The synchronized timing covers `engine.predict` from prepared request object to response dict. It includes preprocessing, vision and language inference, and response assembly. It excludes request parsing, HTTP, model loading, initial capture, and concurrency. Each case alternates eager and Graph predictions. -The candidate responses are compared exactly for the original image, a black -replacement image of the same size, and a same-token-length text change. The -distinct-question case changes its first question's text while preserving all -eight layouts. +Model identity, usage, choice, and candidate probabilities are compared for the +original image, a black replacement image of the same size, and a +same-token-length text change. The distinct-question case changes its first +question's text while preserving all eight layouts. Every recorded candidate +probability difference is **zero**. + +## Clean-source result + +The measured source was commit `de000560ce927a0db58f4928e91234d95df2ae23` +with a clean checkout. One RTX 4090 (24,564 MiB, sm_89) ran Python 3.12.13, +PyTorch 2.14.0+cu130, Transformers 5.17.0, and PEFT 0.21.0 with the pinned BF16 +base and unmerged multimodal LoRA. Optional `causal_conv1d` and +`flash-linear-attention` packages were absent, matching #20's fallback path. +The five cases each have two alternating runs of 20 timed predictions per +variant: **400 timed predictions** total. Each case had three warmups per +variant before timing. The table gives synchronized whole-request p50 +milliseconds; the raw report also contains every sample and p95. + +| Case | Eager p50, runs 1 / 2 | Graph p50, runs 1 / 2 | Reduction | Capture cost | Retained graph allocation | +| --- | ---: | ---: | ---: | ---: | ---: | +| 320×240, 2 repeated questions | 189.57 / 192.80 | 102.74 / 102.99 | 45.8% / 46.6% | 346 ms, 1 layout | 92.0 MiB | +| 320×240, 8 repeated questions | 702.32 / 701.88 | 349.60 / 349.04 | 50.2% / 50.3% | 329 ms, 1 layout | 92.0 MiB | +| 640×480, 8 repeated questions | 859.35 / 852.85 | 576.29 / 575.22 | 32.9% / 32.6% | 507 ms, 1 layout | 108.9 MiB | +| 640×480, 8 long-instruction questions | 1024.08 / 1026.02 | 971.69 / 971.54 | 5.1% / 5.3% | 618 ms, 1 layout | 130.3 MiB | +| 640×480, 8 distinct questions | 863.12 / 824.02 | 597.37 / 591.87 | 30.8% / 28.2% | 3493 ms, 7 layouts | 286.8 MiB | + +The distinct-question case requires seven captures because two questions have +the same tensor layout. Its total capture cost is higher than the repeated +question cases. Capturing also runs one eager forward for an exact-logit gate; +neither this validation nor capture is included in the timed samples. These +measurements are serial and do not show HTTP throughput or concurrency. + +The [raw report](report.json) records the repository SHA, fixtures, exact +comparison results, counters, allocation estimates, and all 400 samples. +[Cache checks](cache.json) exercised a one-layout LRU limit and a 1 MiB budget. +The first evicted three complete layouts over two requests while preserving +candidate probabilities. The second rejected both layouts, recorded the +budget fallback, kept zero cached layouts, and preserved candidate +probabilities. A real HTTP worker started with `--graph --graph-min-uses 1`, +returned a ready health response, and served two identical two-question +requests with byte-identical JSON bodies. On the clean checkout, all 116 +`tests/cua_s1` tests passed; Ruff lint and format checks passed. + +The [independent verifier](verify_results.py) recomputes p50/p95 from every +sample, checks the five correctness gates and cache limits, and requires a +clean measured revision. `SHA256SUMS` records the raw report hashes. Reproduce from this branch's clean source and the pinned weights: diff --git a/recipe/cua_s1/experiments/rtx4090-graph-runtime/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-graph-runtime/SHA256SUMS new file mode 100644 index 0000000..577074b --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-runtime/SHA256SUMS @@ -0,0 +1,2 @@ +d19216757f55d0fc7ec26860f7ce55bed7ba6396d2ea6d3c4b9028702501e04a report.json +a97bc0e02b4a57caab2327e76540c9b2852a489119ec47cb07794f258c10def5 cache.json diff --git a/recipe/cua_s1/experiments/rtx4090-graph-runtime/cache.json b/recipe/cua_s1/experiments/rtx4090-graph-runtime/cache.json new file mode 100644 index 0000000..97b6181 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-runtime/cache.json @@ -0,0 +1,44 @@ +{ + "shape_eviction": { + "stats": { + "eager": 4, + "unsupported": 0, + "warmup": 0, + "disabled": 0, + "replays": 0, + "captures": 4, + "capture_ms": 1722.1994018182158, + "evictions": 3, + "rejected": 0, + "memory_budget": 0, + 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"rejected": 0, + "memory_budget": 0, + "capture_oom": 0, + "capture_error": 0, + "numerical_mismatch": 0 + }, + "cache_bytes_final": 300692480, + "cache_shapes_final": 7 + } + ] +} From 9a818a72680fa0679af4e0c86a7fde3cbef302ef Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 16:24:50 +0800 Subject: [PATCH 26/75] docs(cua-s1): mark Graph runtime rollout complete --- docs/superpowers/plans/2026-09-28-cua-graph-runtime.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/superpowers/plans/2026-09-28-cua-graph-runtime.md b/docs/superpowers/plans/2026-09-28-cua-graph-runtime.md index 16c49f9..476db70 100644 --- a/docs/superpowers/plans/2026-09-28-cua-graph-runtime.md +++ b/docs/superpowers/plans/2026-09-28-cua-graph-runtime.md @@ -41,5 +41,5 @@ - [x] On RTX 4090, verify eager versus Graph for repeated, distinct, changed-image, changed-text, and long prompts, including the two previously rejected cases. - [x] Measure synchronized whole-request p50/p95, capture cost, retained GPU memory, and fallback counts. - [x] Run the full Cua-S1 test suite, Ruff, and format checks. -- [ ] Inspect the diff, commit, push, and open a PR. -- [ ] Stop GPU processes and shut down the user-provided server after evidence is saved. +- [x] Inspect the diff, commit, push, and open PR #22. +- [x] Stop GPU processes and shut down the user-provided server after evidence is saved. From 1019581bc80866d84e95d214a01da0bec70c9d1a Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 16:43:39 +0800 Subject: [PATCH 27/75] Add mixed-shape CUDA Graph cost benchmark --- recipe/cua_s1/benchmark_graph_mixed_shapes.py | 208 ++++++++++++++++++ .../test_graph_mixed_shapes_benchmark.py | 39 ++++ 2 files changed, 247 insertions(+) create mode 100644 recipe/cua_s1/benchmark_graph_mixed_shapes.py create mode 100644 tests/cua_s1/test_graph_mixed_shapes_benchmark.py diff --git a/recipe/cua_s1/benchmark_graph_mixed_shapes.py b/recipe/cua_s1/benchmark_graph_mixed_shapes.py new file mode 100644 index 0000000..031b2dd --- /dev/null +++ b/recipe/cua_s1/benchmark_graph_mixed_shapes.py @@ -0,0 +1,208 @@ +"""Measure cold and warm segmented Graph behavior on changing prompt lengths.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import statistics +from dataclasses import replace +from itertools import chain +from pathlib import Path + + +def schedules(): + """Exercise a fitting hot set and a working set larger than the cache.""" + return { + "hot_four": [1, 2, 4, 8] * 6, + "churn_twelve": list(range(1, 13)) * 3, + } + + +def summarize_events(events): + eager_total = sum(item["eager_ms"] for item in events) + graph_total = sum(item["graph_ms"] for item in events) + eager_so_far = graph_so_far = 0.0 + break_even = None + for index, item in enumerate(events, 1): + eager_so_far += item["eager_ms"] + graph_so_far += item["graph_ms"] + if break_even is None and graph_so_far <= eager_so_far: + break_even = index + return { + "eager_total_ms": eager_total, + "graph_total_ms": graph_total, + "total_reduction_percent": 100 * (1 - graph_total / eager_total), + "eager_p50_ms": statistics.median(x["eager_ms"] for x in events), + "graph_p50_ms": statistics.median(x["graph_ms"] for x in events), + "first_break_even_request": break_even, + } + + +def main(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--runs", type=int, default=2) + args = parser.parse_args(argv) + if args.runs <= 0: + parser.error("--runs must be positive") + if (args.output / "report.json").exists(): + parser.error("report already exists; choose a fresh output directory") + + import torch + from benchmark_multimodal_graph import measure, response_difference + from evaluate_multimodal import environment + from profile_multimodal import ( + GOAL, + case_matrix, + fixture, + repository_state, + write_json, + ) + + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + args.output.mkdir(parents=True, exist_ok=True) + report_path = args.output / "report.json" + report = { + "status": "running", + "repository": repository_state(Path(__file__).resolve().parents[2]), + "environment": environment(), + "config": { + "runs": args.runs, + "graph": { + "max_shapes": 8, + "max_bytes": 1024**3, + "min_uses": 2, + "max_tokens": 2048, + }, + "timing": "synchronized engine.predict, including first use and capture", + "scope": "two questions per request, one 320x240 synthetic image, serial, no HTTP", + }, + "workloads": {}, + } + write_json(report_path, report) + try: + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + case = next(x for x in case_matrix() if x["id"] == "320x240-short-q2") + raw = fixture(case, args.output / "fixtures") + base = parse_request(raw) + repetitions = sorted(set(chain.from_iterable(schedules().values()))) + requests = { + count: replace( + base, + questions=tuple( + replace(question, goal=" ".join([GOAL] * count)) + for question in base.questions + ), + ) + for count in repetitions + } + token_counts = { + count: int( + engine.prepare_reused(request.image, request.questions)[0][ + "input_ids" + ].shape[-1] + ) + for count, request in requests.items() + } + if ( + len(set(token_counts.values())) != len(token_counts) + or max(token_counts.values()) > 2048 + ): + raise ValueError("workload lengths must be distinct and Graph-supported") + report["fixture_sha256"] = hashlib.sha256( + json.dumps(raw, sort_keys=True).encode() + ).hexdigest() + report["token_counts"] = token_counts + + # Initialize kernels and the vision encoder without creating Graph entries. + engine.graph_runtime = None + for _ in range(3): + engine.predict(requests[1]) + torch.cuda.synchronize() + for workload_name, schedule in schedules().items(): + report["workloads"][workload_name] = [] + for run_index in range(args.runs): + runtime = GraphRuntime(engine.model, GraphConfig()) + record = { + "run": run_index + 1, + "schedule": schedule, + "events": [], + "status": "running", + } + report["workloads"][workload_name].append(record) + write_json(report_path, report) + for index, count in enumerate(schedule): + request = requests[count] + before = dict(runtime.stats) + samples = {} + results = {} + order = ["eager", "graph"] + if (run_index + index) % 2: + order.reverse() + for variant in order: + engine.graph_runtime = None if variant == "eager" else runtime + if variant == "graph": + torch.cuda.reset_peak_memory_stats() + results[variant], samples[variant] = measure( + lambda request=request: engine.predict(request) + ) + if variant == "graph": + peak_allocated = torch.cuda.max_memory_allocated() + peak_reserved = torch.cuda.max_memory_reserved() + allocated = torch.cuda.memory_allocated() + reserved = torch.cuda.memory_reserved() + difference = response_difference(results["eager"], results["graph"]) + if difference != 0: + raise ValueError( + f"{workload_name} run {run_index + 1} request {index + 1}: response changed" + ) + delta = { + name: runtime.stats[name] - value + for name, value in before.items() + } + event = { + "index": index + 1, + "goal_repetitions": count, + "tokens_per_question": token_counts[count], + "order": order, + "eager_ms": samples["eager"], + "graph_ms": samples["graph"], + "max_probability_difference": difference, + "graph_stats_delta": delta, + "cache_shapes": len(runtime.cache), + "cache_bytes": runtime.cache.bytes, + "allocated_bytes": allocated, + "reserved_bytes": reserved, + "peak_allocated_bytes": peak_allocated, + "peak_reserved_bytes": peak_reserved, + } + record["events"].append(event) + write_json(report_path, report) + record["summary"] = summarize_events(record["events"]) + record["stats_final"] = dict(runtime.stats) + record["status"] = "complete" + engine.graph_runtime = None + runtime.invalidate() + del runtime + torch.cuda.empty_cache() + write_json(report_path, report) + print(workload_name, run_index + 1, record["summary"], flush=True) + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(report_path, report) + + +if __name__ == "__main__": + main() diff --git a/tests/cua_s1/test_graph_mixed_shapes_benchmark.py b/tests/cua_s1/test_graph_mixed_shapes_benchmark.py new file mode 100644 index 0000000..ecffead --- /dev/null +++ b/tests/cua_s1/test_graph_mixed_shapes_benchmark.py @@ -0,0 +1,39 @@ +"""Check the mixed-shape experiment's workload and amortized cost accounting.""" + +import importlib.util +from pathlib import Path + + +def load_benchmark(): + path = ( + Path(__file__).resolve().parents[2] + / "recipe/cua_s1/benchmark_graph_mixed_shapes.py" + ) + spec = importlib.util.spec_from_file_location("benchmark_graph_mixed_shapes", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_workloads_have_hot_reuse_and_cache_churn(): + schedules = load_benchmark().schedules() + assert schedules["hot_four"] == [1, 2, 4, 8] * 6 + assert schedules["churn_twelve"] == list(range(1, 13)) * 3 + assert len(set(schedules["hot_four"])) <= 8 + assert len(set(schedules["churn_twelve"])) > 8 + + +def test_summary_includes_capture_cost_and_reports_first_break_even(): + summarize = load_benchmark().summarize_events + events = [ + {"eager_ms": 10.0, "graph_ms": 30.0}, + {"eager_ms": 10.0, "graph_ms": 5.0}, + {"eager_ms": 10.0, "graph_ms": 5.0}, + {"eager_ms": 10.0, "graph_ms": 5.0}, + {"eager_ms": 10.0, "graph_ms": 5.0}, + ] + result = summarize(events) + assert result["eager_total_ms"] == 50.0 + assert result["graph_total_ms"] == 50.0 + assert result["first_break_even_request"] == 5 + assert summarize(events[:4])["first_break_even_request"] is None From 6e0a432a6aa6fb3f1bed746ef8c68289bee20425 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 16:48:36 +0800 Subject: [PATCH 28/75] Parameterize Graph capture threshold for mixed workload comparison --- recipe/cua_s1/benchmark_graph_mixed_shapes.py | 18 +++++++++++++----- .../test_graph_mixed_shapes_benchmark.py | 16 ++++++++++++++++ 2 files changed, 29 insertions(+), 5 deletions(-) diff --git a/recipe/cua_s1/benchmark_graph_mixed_shapes.py b/recipe/cua_s1/benchmark_graph_mixed_shapes.py index 031b2dd..c79680e 100644 --- a/recipe/cua_s1/benchmark_graph_mixed_shapes.py +++ b/recipe/cua_s1/benchmark_graph_mixed_shapes.py @@ -39,16 +39,22 @@ def summarize_events(events): } -def main(argv=None): +def parse_args(argv=None): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--weights", type=Path, required=True) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--runs", type=int, default=2) + parser.add_argument("--graph-min-uses", type=int, default=2) args = parser.parse_args(argv) - if args.runs <= 0: - parser.error("--runs must be positive") + if min(args.runs, args.graph_min_uses) <= 0: + parser.error("--runs and --graph-min-uses must be positive") if (args.output / "report.json").exists(): parser.error("report already exists; choose a fresh output directory") + return args + + +def main(argv=None): + args = parse_args(argv) import torch from benchmark_multimodal_graph import measure, response_difference @@ -76,7 +82,7 @@ def main(argv=None): "graph": { "max_shapes": 8, "max_bytes": 1024**3, - "min_uses": 2, + "min_uses": args.graph_min_uses, "max_tokens": 2048, }, "timing": "synchronized engine.predict, including first use and capture", @@ -130,7 +136,9 @@ def main(argv=None): for workload_name, schedule in schedules().items(): report["workloads"][workload_name] = [] for run_index in range(args.runs): - runtime = GraphRuntime(engine.model, GraphConfig()) + runtime = GraphRuntime( + engine.model, GraphConfig(min_uses=args.graph_min_uses) + ) record = { "run": run_index + 1, "schedule": schedule, diff --git a/tests/cua_s1/test_graph_mixed_shapes_benchmark.py b/tests/cua_s1/test_graph_mixed_shapes_benchmark.py index ecffead..34fa4a8 100644 --- a/tests/cua_s1/test_graph_mixed_shapes_benchmark.py +++ b/tests/cua_s1/test_graph_mixed_shapes_benchmark.py @@ -3,6 +3,8 @@ import importlib.util from pathlib import Path +import pytest + def load_benchmark(): path = ( @@ -37,3 +39,17 @@ def test_summary_includes_capture_cost_and_reports_first_break_even(): assert result["graph_total_ms"] == 50.0 assert result["first_break_even_request"] == 5 assert summarize(events[:4])["first_break_even_request"] is None + + +def test_capture_threshold_must_be_positive(tmp_path): + with pytest.raises(SystemExit, match="2"): + load_benchmark().parse_args( + [ + "--weights", + str(tmp_path / "weights"), + "--output", + str(tmp_path / "results"), + "--graph-min-uses", + "0", + ] + ) From 2336a085fc090c1c6ac3a297f91d826b66949f30 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 16:56:59 +0800 Subject: [PATCH 29/75] Publish mixed-length CUDA Graph capture cost evidence --- recipe/cua_s1/README.md | 6 +- recipe/cua_s1/experiments/README.md | 2 + .../rtx4090-graph-mixed-shapes/README.md | 97 + .../rtx4090-graph-mixed-shapes/SHA256SUMS | 3 + .../rtx4090-graph-mixed-shapes/default.json | 4271 +++++++++++++++++ .../rtx4090-graph-mixed-shapes/min4.json | 4271 +++++++++++++++++ .../rtx4090-graph-mixed-shapes/min8.json | 4271 +++++++++++++++++ .../verify_results.py | 117 + 8 files changed, 13036 insertions(+), 2 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/default.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/min4.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/min8.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/verify_results.py diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 362be31..5953745 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -41,7 +41,7 @@ with the Rust frontend and an ingress responsible for public serving. To enable the optional CUDA Graph runtime for multi-question requests, add `--graph` to the worker command. It captures each run of Gated DeltaNet layers at its actual token length and keeps full-attention layers eager. The first -request at a layout runs eagerly; the second captures and checks all output +use of a layout runs eagerly; the second use captures and checks all output logits against eager before the layout enters the cache. The defaults retain at most eight layouts and 1 GiB of graph buffers; `--graph-max-shapes`, `--graph-max-memory-mib`, `--graph-min-uses`, and `--graph-max-tokens` adjust @@ -55,7 +55,9 @@ Whole-model capture changed BF16 attention results on the measured RTX 4090; the earlier [Graph feasibility report](experiments/rtx4090-graph/README.md) records those failures. The segmented runtime preserves eager attention behavior. See [the runtime experiment](experiments/rtx4090-graph-runtime/README.md) -for the measured correctness and latency scope. +for fixed-layout correctness and latency, and the +[mixed-length experiment](experiments/rtx4090-graph-mixed-shapes/README.md) for +capture and eviction costs under changing lengths. To use the Rust frontend included in this repository: diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md index 0f98d1a..0c801c7 100644 --- a/recipe/cua_s1/experiments/README.md +++ b/recipe/cua_s1/experiments/README.md @@ -5,6 +5,8 @@ projection, see the [Graph feasibility report](rtx4090-graph/README.md). It includes both successful fixed-shape cases and correctness-gated rejections. The [segmented Graph runtime report](rtx4090-graph-runtime/README.md) describes the subsequent worker integration and its correctness and latency results. +The [mixed-length Graph report](rtx4090-graph-mixed-shapes/README.md) includes +capture and eviction costs across changing prompt lengths. For the subsequent 16-case stage profiling experiment, refreshed upstream parity, and profiler annotation-counting recovery, see the diff --git a/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md new file mode 100644 index 0000000..53aab7d --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md @@ -0,0 +1,97 @@ +# Mixed-length segmented CUDA Graph cost — RTX 4090, 2026-09-28 + +The existing [segmented Graph runtime](../rtx4090-graph-runtime/README.md) was +measured after capture on fixed inputs. This experiment includes first use, +capture, replay, and eviction in request latency. It tests whether the current +exact-length cache helps when prompt lengths change between requests. No serving +policy or model code was changed. + +## Workload and measurement + +One pinned BF16 Cua-S1 multimodal adapter ran on an RTX 4090 (sm_89), with +PyTorch 2.14.0, Transformers 5.17.0, and PEFT 0.21.0. The optional +`causal_conv1d` and `flash-linear-attention` packages were absent. The base and +adapter revisions, full package versions, CUDA version, GPU, and source commit +are recorded in each raw report. + +Every request contains the same synthetic 320×240 screenshot and two identical +choice questions. Repeating the instruction produces 12 distinct processed +lengths, 236–302 tokens per question. The **hot four** schedule repeats lengths +1, 2, 4, and 8 six times (24 requests). The **churn twelve** schedule repeats +all 12 lengths three times (36 requests), exceeding the cache's eight-layout +limit. These are controlled stress cases, not a measured production traffic +distribution. + +For each threshold and schedule, two runs start with an empty Graph cache. Every +request is timed once through eager and once through Graph, in alternating +order. Synchronization brackets `engine.predict`, so the totals include image +preprocessing, vision, language, scoring, and any eager gate or capture that +occurs during a request. Initial model load, three eager kernel warmups, request +parsing, HTTP, and concurrency are excluded. The complete response is compared +per pair; every candidate probability, choice, usage count, and model identity +matched exactly. There are **360 paired requests, 720 timed predictions**. + +`min_uses` counts question forwards, not requests. With two repeated questions, +the default threshold of 2 captures on the first request for a new length. The +Graph cache holds at most eight layouts and 1 GiB of measured live allocations; +the reports separately record PyTorch's peak reserved memory, which includes +allocator pooling and capture-time allocations. + +## Results + +The percentage is the reduction in **total synchronized request time for the +whole schedule**, including first captures; a negative value means Graph was +slower. Each cell shows runs 1 / 2. + +| Capture threshold | Hot four, 24 requests | Churn twelve, 36 requests | Captures / evictions per churn run | +| --- | ---: | ---: | ---: | +| 2 (worker default) | **10.2% / 9.6% faster** | **246.0% / 303.6% slower** | 36 / 28 | +| 4 | 2.9% / 1.8% faster | 157.2% / 166.8% slower | 24 / 16 | +| 8 | 13.3% / 12.8% slower | 3.7% / 3.1% faster* | 0 / 0 | + +\* At threshold 8, the churn schedule never reaches capture: all Graph-path +forwards fall back to eager. Its small measured difference is run-to-run timing +variation, not Graph acceleration. + +The default threshold captures each of the four hot lengths once. Its 24-request +Graph total first drops below eager at request 19 in both runs. For churn it +captures all 12 lengths on each pass, evicting 28 entries per run; capture alone +takes 14.8 and 19.1 seconds across the 36 requests. Raising the threshold to 4 +reduces capture frequency but also delays hot-set replay. Raising it to 8 +prevents capture under churn, while the hot schedule has too few subsequent +replays to repay its four captures. The maximum observed PyTorch reserved memory +was about 12.1 GiB for hot four and 16.1 GiB for churn with captures, versus +9.3 GiB for churn with no captures. This is allocator memory, not the Graph +cache's measured entry size. + +The result shows a workload-dependent admission problem. It does not establish +that padding several lengths into one bucket is numerically safe: the current +runtime keys 3D positions, masks, and exact embedding layouts, and Gated +DeltaNet may consume padded tokens differently. A follow-up should measure +real length reuse and test a bounded admission or bucketing policy against +exact eager outputs before changing the worker default. + +## Evidence and reproduction + +[`default.json`](default.json) was measured from clean source commit `1019581`; +[`min4.json`](min4.json) and [`min8.json`](min8.json) were measured from clean +source commit `6e0a432`, which adds only the benchmark's threshold option. +Each report contains every latency sample, request order, runtime counter delta, +capture time, cache occupancy, and memory observation. The independent +[`verify_results.py`](verify_results.py) recomputes the totals and medians, +checks schedule coverage and cache limits, and requires exact response parity. +It verified all three reports and 360 paired requests. On the GPU host, all +118 Cua-S1 tests passed; Ruff lint and format checks passed for the new +benchmark. SHA-256 hashes are in [`SHA256SUMS`](SHA256SUMS). + +With this PR's source, pinned weights, and the GPU environment described above: + +```sh +for threshold in 2 4 8; do + PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_mixed_shapes.py \ + --weights /path/to/weights --output "/path/to/fresh-output-$threshold" \ + --runs 2 --graph-min-uses "$threshold" +done +python recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/verify_results.py \ + recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes +``` diff --git a/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/SHA256SUMS new file mode 100644 index 0000000..e72b0fb --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/SHA256SUMS @@ -0,0 +1,3 @@ +ab9c5c117eff80fff1447e10708d0e045f8fa5e74db9d38cb82517dc5165d003 default.json +3a77dab58071232ad64f0de4a7dbcaacb45d4967acf4e89d949ed5e7040fdab2 min4.json +6a836d084481b5ba27607ae37b48df3b6f44cc6937c6332b8f795dc072b6fbcf min8.json diff --git a/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/default.json b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/default.json new file mode 100644 index 0000000..7c2b44c --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/default.json @@ -0,0 +1,4271 @@ +{ + "status": "complete", + "repository": { + "revision": "1019581bc80866d84e95d214a01da0bec70c9d1a", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "environment": { + "python": "3.12.13", + "gpu": "NVIDIA GeForce RTX 4090", + "compute_capability": [ + 8, + 9 + ], + "cuda": "13.0", + "driver": "595.71.05", + "torch_num_threads": 16, + "torch_num_interop_threads": 64, + "packages": { + "torch": "2.14.0", + "torchvision": "0.29.0", + "transformers": "5.17.0", + "peft": "0.21.0", + "pillow": "11.3.0", + "safetensors": "0.8.0", + 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0, + "capture_ms": 0.0, + "evictions": 0, + "rejected": 0, + "memory_budget": 0, + "capture_oom": 0, + "capture_error": 0, + "numerical_mismatch": 0 + } + } + ] + }, + "fixture_sha256": "d32998f61c3f875804c0161fe9eaca360d5eae5eb6e8adebde55b969ebbf666c", + "token_counts": { + "1": 236, + "2": 242, + "3": 248, + "4": 254, + "5": 260, + "6": 266, + "7": 272, + "8": 278, + "9": 284, + "10": 290, + "11": 296, + "12": 302 + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/verify_results.py b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/verify_results.py new file mode 100644 index 0000000..6c03200 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/verify_results.py @@ -0,0 +1,117 @@ +"""Independently audit the three mixed-length CUDA Graph reports.""" + +from __future__ import annotations + +import argparse +import json +import math +import statistics +from pathlib import Path + +SCHEDULES = { + "hot_four": [1, 2, 4, 8] * 6, + "churn_twelve": list(range(1, 13)) * 3, +} +REPORTS = {"default.json": 2, "min4.json": 4, "min8.json": 8} + + +def check_report(report, min_uses): + assert report["status"] == "complete" + assert report["repository"]["revision"] + assert report["repository"]["dirty"] is False + assert report["config"]["runs"] == 2 + assert report["config"]["graph"] == { + "max_shapes": 8, + "max_bytes": 1024**3, + "min_uses": min_uses, + "max_tokens": 2048, + } + tokens = {int(k): v for k, v in report["token_counts"].items()} + assert sorted(tokens) == list(range(1, 13)) + assert len(set(tokens.values())) == 12 + assert max(tokens.values()) <= 2048 + assert set(report["workloads"]) == set(SCHEDULES) + count = 0 + for name, schedule in SCHEDULES.items(): + runs = report["workloads"][name] + assert len(runs) == 2 + for run_number, run in enumerate(runs, 1): + assert run["status"] == "complete" + assert run["run"] == run_number + assert run["schedule"] == schedule + events = run["events"] + assert len(events) == len(schedule) + eager_total = graph_total = 0.0 + first_break_even = None + deltas = {field: 0 for field in run["stats_final"]} + for index, (event, repetition) in enumerate(zip(events, schedule), 1): + assert event["index"] == index + assert event["goal_repetitions"] == repetition + assert event["tokens_per_question"] == tokens[repetition] + assert event["order"] == ( + ["eager", "graph"] + if (run_number - 1 + index - 1) % 2 == 0 + else ["graph", "eager"] + ) + assert event["max_probability_difference"] == 0 + assert event["eager_ms"] > 0 and event["graph_ms"] > 0 + assert 0 <= event["cache_shapes"] <= 8 + assert 0 <= event["cache_bytes"] <= 1024**3 + assert 0 < event["allocated_bytes"] <= event["peak_allocated_bytes"] + assert 0 < event["reserved_bytes"] <= event["peak_reserved_bytes"] + assert event["graph_stats_delta"].keys() == deltas.keys() + for field, value in event["graph_stats_delta"].items(): + assert value >= 0 + deltas[field] += value + eager_total += event["eager_ms"] + graph_total += event["graph_ms"] + if first_break_even is None and graph_total <= eager_total: + first_break_even = index + count += 1 + assert all( + math.isclose(deltas[field], value, abs_tol=1e-5) + for field, value in run["stats_final"].items() + ) + assert run["stats_final"]["numerical_mismatch"] == 0 + assert run["stats_final"]["capture_error"] == 0 + assert run["stats_final"]["capture_oom"] == 0 + assert run["stats_final"]["rejected"] == 0 + summary = run["summary"] + assert math.isclose(summary["eager_total_ms"], eager_total) + assert math.isclose(summary["graph_total_ms"], graph_total) + assert math.isclose( + summary["total_reduction_percent"], + 100 * (1 - graph_total / eager_total), + ) + assert math.isclose( + summary["eager_p50_ms"], + statistics.median(x["eager_ms"] for x in events), + ) + assert math.isclose( + summary["graph_p50_ms"], + statistics.median(x["graph_ms"] for x in events), + ) + assert summary["first_break_even_request"] == first_break_even + return count + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("directory", type=Path) + args = parser.parse_args() + reports = {} + total = 0 + for filename, min_uses in REPORTS.items(): + report = json.loads((args.directory / filename).read_text()) + total += check_report(report, min_uses) + reports[filename] = report + assert len({r["fixture_sha256"] for r in reports.values()}) == 1 + assert ( + len({json.dumps(r["token_counts"], sort_keys=True) for r in reports.values()}) + == 1 + ) + print(f"verified {len(REPORTS)} configurations and {total} paired requests") + + +if __name__ == "__main__": + main() From af969d6e85da1c7db9cff6e4c3062f96ed435597 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:20:42 +0800 Subject: [PATCH 30/75] docs: plan request-aware Graph admission and validation --- .../plans/2026-09-28-cua-graph-admission.md | 17 +++++++++++++++++ .../specs/2026-09-28-cua-graph-admission.md | 7 +++++++ 2 files changed, 24 insertions(+) create mode 100644 docs/superpowers/plans/2026-09-28-cua-graph-admission.md create mode 100644 docs/superpowers/specs/2026-09-28-cua-graph-admission.md diff --git a/docs/superpowers/plans/2026-09-28-cua-graph-admission.md b/docs/superpowers/plans/2026-09-28-cua-graph-admission.md new file mode 100644 index 0000000..b060348 --- /dev/null +++ b/docs/superpowers/plans/2026-09-28-cua-graph-admission.md @@ -0,0 +1,17 @@ +# CUDA Graph Admission Implementation Plan + +> **For agentic workers:** Use subagent-driven-development for the isolated benchmark task and independent review. Main agent owns runtime integration and GPU execution. + +**Goal:** Prevent low-reuse requests from repeatedly paying Graph capture costs and publish reproducible end-to-end comparisons. + +**Architecture:** A pure Python request-aware admission policy feeds the existing exact-layout Graph runtime. Request lifetime is explicit; cache hits bypass capture admission, and failed capture attempts count against the sliding budget. + +**Tech Stack:** Python, PyTorch CUDA Graphs, Transformers/PEFT, pytest, RTX 4090. + +- [ ] Add failing policy tests for repeated questions, sparse lengths, eviction cooldown, metadata bounds, count/time budgets and reset; run `PYTHONPATH=src python3 -m pytest tests/cua_s1/test_graph_admission.py -q`. +- [ ] Implement `graph_admission.py`; integrate request context, capture accounting, invalidation and CLI in runtime/model/server. Run new and existing CPU tests. +- [ ] Fix mixed-shape summary naming and full-response equality; preserve old raw evidence, distinguish 118/119 historical test counts. Add extended benchmark and verifier with raw responses, parity and complete schedules. Run benchmark unit tests. +- [ ] Review specification coverage, then code quality; resolve findings before final measurement. +- [ ] Transfer committed clean source to a separate server checkout. Run complete Cua-S1 tests, Ruff and GPU smoke. Compare legacy/new/eager on extended workloads, changed inputs, cache memory fallback, and HTTP. +- [ ] Independently verify raw results and hashes; document cost boundaries and limitations. Back up evidence and source locally. +- [ ] Commit/push and create PR with measured source SHA and validation. Attach PR; execute server shutdown and verify connectivity stops. diff --git a/docs/superpowers/specs/2026-09-28-cua-graph-admission.md b/docs/superpowers/specs/2026-09-28-cua-graph-admission.md new file mode 100644 index 0000000..e93645e --- /dev/null +++ b/docs/superpowers/specs/2026-09-28-cua-graph-admission.md @@ -0,0 +1,7 @@ +# Request-aware CUDA Graph admission + +User-approved scope: fix #24 review findings, implement admission/cooldown/capture budget, validate on the RTX 4090, publish a PR and evidence, then shut down the server. + +The optional Graph worker keeps exact layouts and segmented capture. Count a layout once per predict request, require two request observations no more than eight requests apart, and forget old sparse observations. Cached hits are always usable. On eviction discard accumulated heat and impose a 32-request cooldown. Bound metadata to 128 layouts. Budget at most four capture attempts and 2000 ms of completed capture-attempt work in any sliding 32-request window. One synchronous capture cannot be preempted; its elapsed time may overshoot the time budget, after which subsequent attempts use eager. Failures consume budget too. Model invalidation clears policy state. Explicit request contexts serialize complete Graph predictions and restore state on errors; standalone forward calls without context fall back eagerly. + +Keep --graph opt-in; expose policy limits and clarify min-uses now counts requests. No padding, new kernels, concurrency, or upstream full-attention numerical fix. Compare eager, legacy #24 admission, and new admission with cold caches, identical inputs, alternating order, all-response parity, and captured/uncaptured layout evidence. Extend churn beyond the former eight-use blind spot. Report total/p50/p95, post-capture sustained break-even, captures/evictions, fallback causes and memory. Small differences without graph replay are timing variation. From 7babb09fda6d4f318d20f49a168689d5eb7e0cd0 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:25:36 +0800 Subject: [PATCH 31/75] perf(cua-s1): gate Graph capture by request reuse and bounded work --- .../cua_s1/multimodal/graph_admission.py | 71 ++++++ src/models/cua_s1/multimodal/graph_runtime.py | 175 ++++++++----- src/models/cua_s1/multimodal/model.py | 6 + src/models/cua_s1/multimodal/server.py | 17 +- tests/cua_s1/test_graph_admission.py | 232 ++++++++++++++++++ 5 files changed, 444 insertions(+), 57 deletions(-) create mode 100644 src/models/cua_s1/multimodal/graph_admission.py create mode 100644 tests/cua_s1/test_graph_admission.py diff --git a/src/models/cua_s1/multimodal/graph_admission.py b/src/models/cua_s1/multimodal/graph_admission.py new file mode 100644 index 0000000..fe8ad60 --- /dev/null +++ b/src/models/cua_s1/multimodal/graph_admission.py @@ -0,0 +1,71 @@ +"""Bounded request-frequency admission and sliding capture-work budget.""" + +from collections import OrderedDict, deque + + +class AdmissionPolicy: + """Called under the runtime lock; counters advance once per predict request. + + A completed attempt can exceed the time budget because capture is synchronous. + Every subsequent attempt waits for enough budget to expire. Cache hits never + spend capture budget. History is intentionally bounded and forgetting is cold. + """ + + def __init__(self, config): + self.config = config + self.history = OrderedDict() + self.cooldowns = OrderedDict() + self.attempts = deque() + self.request_index = 0 + + def reset(self): + self.history.clear() + self.cooldowns.clear() + self.attempts.clear() + self.request_index = 0 + + def begin_request(self): + self.request_index += 1 + while self.attempts and ( + self.request_index - self.attempts[0][0] >= self.config.capture_window + ): + self.attempts.popleft() + + def reason(self, key): + """Observe a cache miss and return its eager fallback reason, if any.""" + if self.request_index <= self.cooldowns.get(key, -1): + return "cooldown" + self.cooldowns.pop(key, None) + last, count = self.history.get(key, (-self.config.admission_window, 0)) + if self.request_index - last > self.config.admission_window: + count = 0 + if last != self.request_index: + count += 1 + self.history[key] = (self.request_index, count) + self.history.move_to_end(key) + while len(self.history) > 128: + self.history.popitem(last=False) + if count < self.config.min_uses: + return "warmup" + if ( + len(self.attempts) >= self.config.max_captures + or sum(item[1] for item in self.attempts) >= self.config.capture_budget_ms + ): + return "capture_budget" + return None + + def start_capture(self): + ticket = [self.request_index, 0.0] + self.attempts.append(ticket) + return ticket + + @staticmethod + def finish_capture(ticket, elapsed_ms): + ticket[1] = elapsed_ms + + def evict(self, key): + self.history.pop(key, None) + self.cooldowns[key] = self.request_index + self.config.cooldown_requests + self.cooldowns.move_to_end(key) + while len(self.cooldowns) > 128: + self.cooldowns.popitem(last=False) diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py index 89c61f4..3c0ace7 100644 --- a/src/models/cua_s1/multimodal/graph_runtime.py +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -8,11 +8,15 @@ from __future__ import annotations import gc +import math import threading import time from collections import OrderedDict +from contextlib import contextmanager from dataclasses import dataclass, field +from .graph_admission import AdmissionPolicy + def tensor_signature(value): if value is None: @@ -31,9 +35,29 @@ class GraphConfig: max_bytes: int = 1024 * 1024 * 1024 min_uses: int = 2 max_tokens: int = 2048 + admission_window: int = 8 + cooldown_requests: int = 32 + capture_window: int = 32 + max_captures: int = 4 + capture_budget_ms: float = 2000.0 def __post_init__(self): - if min(self.max_shapes, self.max_bytes, self.min_uses, self.max_tokens) <= 0: + if not math.isfinite(self.capture_budget_ms): + raise ValueError("capture time budget must be finite") + if ( + min( + self.max_shapes, + self.max_bytes, + self.min_uses, + self.max_tokens, + self.admission_window, + self.cooldown_requests, + self.capture_window, + self.max_captures, + self.capture_budget_ms, + ) + <= 0 + ): raise ValueError("graph limits must be positive") @@ -133,6 +157,7 @@ def replay(self, hidden, mask): @dataclass class _ShapeEntry: + key: tuple | None = None blocks: dict = field(default_factory=dict) bytes: int = 0 @@ -144,10 +169,17 @@ def __init__(self, model, config: GraphConfig | None = None): self.model = model self.config = config or GraphConfig() self.cache = GraphCache(self.config.max_shapes, self.config.max_bytes) - self.uses = OrderedDict() + self.admission = AdmissionPolicy(self.config) + self._in_request = False self.disabled = OrderedDict() - self.lock = threading.Lock() + self.lock = threading.RLock() self.stats = { + "requests": 0, + "no_request": 0, + "cooldown": 0, + "capture_budget": 0, + "capture_attempts": 0, + "capture_attempt_ms": 0.0, "eager": 0, "unsupported": 0, "warmup": 0, @@ -163,11 +195,27 @@ def __init__(self, model, config: GraphConfig | None = None): "numerical_mismatch": 0, } + @contextmanager + def request(self): + """Serialize a complete prediction and count duplicate layouts only once.""" + with self.lock: + if self._in_request: + raise RuntimeError("nested Graph requests are unsupported") + self._in_request = True + self.admission.begin_request() + self.stats["requests"] += 1 + try: + yield + finally: + self._in_request = False + def invalidate(self): """Call after replacing model weights or adapters.""" with self.lock: + if self._in_request: + raise RuntimeError("cannot invalidate during a Graph request") retired = self.cache.clear() - self.uses.clear() + self.admission.reset() self.disabled.clear() del retired gc.collect() @@ -252,6 +300,9 @@ def forward(self, values): import torch with self.lock, torch.no_grad(): + if not self._in_request: + self.stats["no_request"] += 1 + return self._eager(values) if not self._supported(values): self.stats["unsupported"] += 1 return self._eager(values) @@ -272,61 +323,73 @@ def forward(self, values): self.stats["replays"] += 1 return output - uses = self.uses.get(key, 0) + 1 - self.uses[key] = uses - self.uses.move_to_end(key) - while len(self.uses) > 128: - self.uses.popitem(last=False) - if uses < self.config.min_uses: - self.stats["warmup"] += 1 + reason = self.admission.reason(key) + if reason is not None: + self.stats[reason] += 1 return self._eager(values) - reference = self._eager(values) - candidate = _ShapeEntry() + ticket = self.admission.start_capture() + self.stats["capture_attempts"] += 1 + attempt_start = time.perf_counter() try: - torch.cuda.synchronize(values["inputs_embeds"].device) - capture_start = time.perf_counter() - output = self._run_segments(values, candidate) - torch.cuda.synchronize(values["inputs_embeds"].device) - capture_ms = (time.perf_counter() - capture_start) * 1000 - except torch.cuda.OutOfMemoryError: - self.stats["capture_oom"] += 1 - del candidate - gc.collect() - torch.cuda.empty_cache() - self._disable(key) - return reference - except RuntimeError: - # Only a healthy CUDA context can continue serving eagerly. - torch.cuda.synchronize(values["inputs_embeds"].device) - self.stats["capture_error"] += 1 - del candidate - self._disable(key) - gc.collect() - return reference - - if not torch.equal(reference, output): - self.stats["numerical_mismatch"] += 1 - del candidate, output - self._disable(key) - gc.collect() - return reference - retired = self.cache.put(key, candidate) - if retired is None: - self.stats["memory_budget"] += 1 - del candidate - self._disable(key) - gc.collect() - return reference - self.stats["captures"] += 1 - self.stats["capture_ms"] += capture_ms - self.stats["evictions"] += len(retired) - if retired: - del retired - gc.collect() - else: - del retired - return output + return self._capture(values, key) + finally: + elapsed = (time.perf_counter() - attempt_start) * 1000 + self.admission.finish_capture(ticket, elapsed) + self.stats["capture_attempt_ms"] += elapsed + + def _capture(self, values, key): + import torch + + reference = self._eager(values) + candidate = _ShapeEntry(key=key) + try: + torch.cuda.synchronize(values["inputs_embeds"].device) + capture_start = time.perf_counter() + output = self._run_segments(values, candidate) + torch.cuda.synchronize(values["inputs_embeds"].device) + capture_ms = (time.perf_counter() - capture_start) * 1000 + except torch.cuda.OutOfMemoryError: + self.stats["capture_oom"] += 1 + del candidate + gc.collect() + torch.cuda.empty_cache() + self._disable(key) + return reference + except RuntimeError: + # Only a healthy CUDA context can continue serving eagerly. + torch.cuda.synchronize(values["inputs_embeds"].device) + self.stats["capture_error"] += 1 + del candidate + self._disable(key) + gc.collect() + return reference + + if not torch.equal(reference, output): + self.stats["numerical_mismatch"] += 1 + del candidate, output + self._disable(key) + gc.collect() + return reference + retired = self.cache.put(key, candidate) + if retired is None: + self.stats["memory_budget"] += 1 + del candidate + self._disable(key) + gc.collect() + return reference + self.stats["captures"] += 1 + self.stats["capture_ms"] += capture_ms + self.stats["evictions"] += len(retired) + if retired: + for entry in retired: + self.admission.evict(entry.key) + del entry + del retired + gc.collect() + else: + del retired + return output def _disable(self, key): self.stats["rejected"] += 1 diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py index 40470c2..91cb659 100644 --- a/src/models/cua_s1/multimodal/model.py +++ b/src/models/cua_s1/multimodal/model.py @@ -5,6 +5,7 @@ import copy import hashlib import json +from contextlib import nullcontext from pathlib import Path from .protocol import InvalidRequest, Question, Request, answer, build_messages @@ -262,6 +263,11 @@ def predict_reference(self, request: Request) -> dict: } def predict(self, request: Request) -> dict: + runtime = getattr(self, "graph_runtime", None) + with runtime.request() if runtime is not None else nullcontext(): + return self._predict(request) + + def _predict(self, request: Request) -> dict: if len(request.questions) == 1: return self.predict_reference(request) # No image encoder or language model runs until every prompt is valid. diff --git a/src/models/cua_s1/multimodal/server.py b/src/models/cua_s1/multimodal/server.py index e0de6a4..79c7f01 100644 --- a/src/models/cua_s1/multimodal/server.py +++ b/src/models/cua_s1/multimodal/server.py @@ -123,8 +123,18 @@ def main(): ) p.add_argument("--graph-max-shapes", type=int, default=8) p.add_argument("--graph-max-memory-mib", type=int, default=1024) - p.add_argument("--graph-min-uses", type=int, default=2) + p.add_argument( + "--graph-min-uses", + type=int, + default=2, + help="distinct requests needed before capture", + ) p.add_argument("--graph-max-tokens", type=int, default=2048) + p.add_argument("--graph-admission-window", type=int, default=8) + p.add_argument("--graph-cooldown-requests", type=int, default=32) + p.add_argument("--graph-capture-window", type=int, default=32) + p.add_argument("--graph-max-captures", type=int, default=4) + p.add_argument("--graph-capture-budget-ms", type=float, default=2000.0) args = p.parse_args() try: graph_config = ( @@ -133,6 +143,11 @@ def main(): max_bytes=args.graph_max_memory_mib * 1024 * 1024, min_uses=args.graph_min_uses, max_tokens=args.graph_max_tokens, + admission_window=args.graph_admission_window, + cooldown_requests=args.graph_cooldown_requests, + capture_window=args.graph_capture_window, + max_captures=args.graph_max_captures, + capture_budget_ms=args.graph_capture_budget_ms, ) if args.graph else None diff --git a/tests/cua_s1/test_graph_admission.py b/tests/cua_s1/test_graph_admission.py new file mode 100644 index 0000000..d036d09 --- /dev/null +++ b/tests/cua_s1/test_graph_admission.py @@ -0,0 +1,232 @@ +"""Admission invariants independent of CUDA timing and model internals.""" + +import pytest + +from models.cua_s1.multimodal import graph_runtime + + +def policy(**kwargs): + from models.cua_s1.multimodal.graph_admission import AdmissionPolicy + + return AdmissionPolicy(graph_runtime.GraphConfig(**kwargs)) + + +def test_duplicate_questions_are_one_request_observation(): + p = policy() + p.begin_request() + assert p.reason("a") == "warmup" + assert p.reason("a") == "warmup" + p.begin_request() + assert p.reason("a") is None + + +def test_rotating_twelve_lengths_never_qualify_even_after_six_rounds(): + p = policy() + for key in list(range(12)) * 6: + p.begin_request() + assert p.reason(key) == "warmup" + assert p.reason(key) == "warmup" + + +def test_eviction_requires_cooldown_then_fresh_request_reuse(): + p = policy(cooldown_requests=2) + p.begin_request() + p.reason("a") + p.begin_request() + assert p.reason("a") is None + p.evict("a") + for _ in range(2): + p.begin_request() + assert p.reason("a") == "cooldown" + p.begin_request() + assert p.reason("a") == "warmup" + p.begin_request() + assert p.reason("a") is None + + +def test_attempt_count_budget_includes_failed_attempts_and_expires(): + p = policy(min_uses=1, max_captures=2, capture_window=3) + p.begin_request() + for key in ("a", "b"): + assert p.reason(key) is None + ticket = p.start_capture() + p.finish_capture(ticket, 1) + assert p.reason("c") == "capture_budget" + for _ in range(2): + p.begin_request() + assert p.reason("c") == "capture_budget" + p.begin_request() + assert p.reason("c") is None + + +def test_time_budget_blocks_next_attempt_after_nonpreemptible_overshoot(): + p = policy(min_uses=1, capture_budget_ms=10) + p.begin_request() + ticket = p.start_capture() + p.finish_capture(ticket, 11) + assert p.reason("b") == "capture_budget" + + +def test_history_and_cooldown_metadata_are_bounded_and_resettable(): + p = policy() + for key in range(200): + p.begin_request() + p.reason(key) + p.evict(key) + assert len(p.history) <= 128 + assert len(p.cooldowns) <= 128 + ticket = p.start_capture() + p.finish_capture(ticket, 3000) + p.reset() + assert not p.history and not p.cooldowns and not p.attempts + assert p.request_index == 0 + + +@pytest.mark.parametrize( + "field", + [ + "admission_window", + "cooldown_requests", + "capture_window", + "max_captures", + "capture_budget_ms", + ], +) +def test_new_limits_must_be_positive(field): + with pytest.raises(ValueError, match="positive"): + graph_runtime.GraphConfig(**{field: 0}) + + +@pytest.mark.parametrize("value", [float("inf"), float("nan")]) +def test_capture_time_budget_must_be_finite(value): + with pytest.raises(ValueError, match="finite"): + graph_runtime.GraphConfig(capture_budget_ms=value) + + +def runtime_fixture(monkeypatch, **config): + import sys + from contextlib import nullcontext + from types import SimpleNamespace + + monkeypatch.setitem( + sys.modules, + "torch", + SimpleNamespace( + no_grad=nullcontext, + equal=lambda a, b: a == b, + cuda=SimpleNamespace( + synchronize=lambda *a: None, OutOfMemoryError=MemoryError + ), + ), + ) + core = SimpleNamespace( + model=SimpleNamespace( + language_model=SimpleNamespace( + config=SimpleNamespace(_attn_implementation="sdpa") + ) + ) + ) + r = graph_runtime.GraphRuntime( + SimpleNamespace(get_base_model=lambda: core), + graph_runtime.GraphConfig(**config), + ) + r._supported = lambda v: True + + def eager(v): + r.stats["eager"] += 1 + return 1 + + r._eager = eager + r._run_segments = lambda v, entry: 1 + + def values(length): + tensor = SimpleNamespace( + shape=(1, length, 2), + stride=lambda: (length * 2, 2, 1), + dtype="bf16", + device="cuda:0", + ) + return dict(inputs_embeds=tensor, position_ids=tensor, attention_mask=tensor) + + return r, values + + +def test_runtime_capture_replay_and_standalone_forward(monkeypatch): + r, values = runtime_fixture(monkeypatch) + r.forward(values(3)) + assert r.stats["no_request"] == 1 + with r.request(): + r.forward(values(3)) + r.forward(values(3)) + assert r.stats["captures"] == 0 + with r.request(): + r.forward(values(3)) + r.forward(values(3)) + assert r.stats["captures"] == 1 + assert r.stats["replays"] == 1 + assert r.stats["capture_attempts"] == 1 + + +def test_runtime_evicted_key_is_cooled_and_cache_hits_ignore_budget(monkeypatch): + r, values = runtime_fixture(monkeypatch, min_uses=1, max_shapes=1, max_captures=2) + with r.request(): + r.forward(values(1)) + r.forward(values(2)) + r.forward(values(1)) + r.forward(values(2)) + r.forward(values(3)) + assert r.stats["evictions"] == 1 + assert r.stats["cooldown"] == 1 + assert r.stats["replays"] == 1 + assert r.stats["capture_budget"] == 1 + + +def test_runtime_failed_capture_spends_budget(monkeypatch): + r, values = runtime_fixture(monkeypatch, min_uses=1, max_captures=1) + + def fail(v, entry): + raise RuntimeError("recoverable capture failure") + + r._run_segments = fail + with r.request(): + assert r.forward(values(1)) == 1 + assert r.forward(values(2)) == 1 + assert r.stats["capture_attempts"] == 1 + assert r.stats["capture_error"] == 1 + assert r.stats["capture_budget"] == 1 + assert len(r.admission.attempts) == 1 + + +def test_request_error_restores_scope_and_invalidation_resets_policy(monkeypatch): + r, values = runtime_fixture(monkeypatch) + with pytest.raises(ValueError): + with r.request(): + r.forward(values(1)) + raise ValueError("request failed") + assert not r._in_request + with r.request(): + with pytest.raises(RuntimeError, match="nested"): + with r.request(): + pass + with pytest.raises(RuntimeError, match="invalidate"): + r.invalidate() + r.invalidate() + assert r.admission.request_index == 0 + assert not r.admission.history and not r.cache.entries + + +def test_engine_predict_opens_one_context_for_entire_request(monkeypatch): + from models.cua_s1.multimodal.model import MultimodalEngine + + r, _ = runtime_fixture(monkeypatch) + engine = object.__new__(MultimodalEngine) + engine.graph_runtime = r + + def predict(request): + assert r._in_request + return "response" + + engine._predict = predict + assert engine.predict(object()) == "response" + assert r.stats["requests"] == 1 + assert not r._in_request From 780ac4d2f1bb3fff394696dde04864e7959e1956 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:26:56 +0800 Subject: [PATCH 32/75] docs: explain request-aware Graph admission limits --- recipe/cua_s1/README.md | 41 +++++++++++++++++++++++++++++++---------- 1 file changed, 31 insertions(+), 10 deletions(-) diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 5953745..9990445 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -40,16 +40,37 @@ with the Rust frontend and an ingress responsible for public serving. To enable the optional CUDA Graph runtime for multi-question requests, add `--graph` to the worker command. It captures each run of Gated DeltaNet layers -at its actual token length and keeps full-attention layers eager. The first -use of a layout runs eagerly; the second use captures and checks all output -logits against eager before the layout enters the cache. The defaults retain at -most eight layouts and 1 GiB of graph buffers; `--graph-max-shapes`, -`--graph-max-memory-mib`, `--graph-min-uses`, and `--graph-max-tokens` adjust -these limits. Inputs above 2,048 tokens, rejected layouts, or layouts that -exceed the graph memory budget use eager inference. Cache entries own their -static buffers and are evicted together. The loaded model must remain immutable; -call `graph_runtime.invalidate()` before changing its weights or adapters. -Single-question requests continue through the reference path. +at its actual token length and keeps full-attention layers eager. A layout must +appear in two distinct `predict` requests, at most eight requests apart, before +capture. Repeated questions inside one request count once. The captured logits +must exactly match eager before the layout enters the cache. The defaults retain +at most eight layouts and 1 GiB of measured live Graph allocations. + +`--graph-min-uses` now counts **requests**, not question forwards. Use +`--graph-admission-window` to change the maximum gap between observations. +Evicted layouts lose their heat and wait `--graph-cooldown-requests` (default 32) +before accumulating new observations. History and cooldown tables retain at +most 128 layouts each. Every prediction, including the single-question reference +path, advances the request clock while Graph is enabled. + +Capture is limited to `--graph-max-captures` (default 4) attempts per sliding +`--graph-capture-window` (default 32 requests). `--graph-capture-budget-ms` +(default 2000) also limits the accumulated elapsed attempt work, including the +eager correctness gate, failures and cleanup. A synchronous capture cannot be +interrupted; one attempt may overshoot this time budget and later attempts then +fall back to eager until budget expires. This is a request-count window, not a +wall-clock rate limit. Cached layouts can still replay when capture is paused. + +`--graph-max-shapes`, `--graph-max-memory-mib` and `--graph-max-tokens` adjust +cache and token limits. Inputs above 2,048 tokens, rejected layouts, insufficient +reuse and exhausted budgets use eager inference. The memory budget covers +retained live allocations, not capture-time peaks or allocator reservations. +Cache entries own their static buffers and are evicted together. The loaded model +must remain immutable; call `graph_runtime.invalidate()` before changing its +weights or adapters. Invalidation clears admission and cache state, while lifetime +statistics remain cumulative. `predict` opens a serialized Graph request context; +standalone runtime `forward` calls without that context use eager. Single-question +requests continue through the reference path. Whole-model capture changed BF16 attention results on the measured RTX 4090; the earlier [Graph feasibility report](experiments/rtx4090-graph/README.md) From c07de3d2f4ed3218eb0d22fc8c8bf8c74d5454d7 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:27:52 +0800 Subject: [PATCH 33/75] Add capture-inclusive admission benchmark and independent verifier --- recipe/cua_s1/benchmark_graph_admission.py | 256 ++++++++++++++++++ recipe/cua_s1/benchmark_graph_mixed_shapes.py | 31 ++- .../rtx4090-graph-mixed-shapes/README.md | 11 +- recipe/cua_s1/graph-admission.md | 42 +++ recipe/cua_s1/verify_graph_admission.py | 227 ++++++++++++++++ .../cua_s1/test_graph_admission_benchmark.py | 188 +++++++++++++ .../test_graph_mixed_shapes_benchmark.py | 21 +- 7 files changed, 763 insertions(+), 13 deletions(-) create mode 100644 recipe/cua_s1/benchmark_graph_admission.py create mode 100644 recipe/cua_s1/graph-admission.md create mode 100644 recipe/cua_s1/verify_graph_admission.py create mode 100644 tests/cua_s1/test_graph_admission_benchmark.py diff --git a/recipe/cua_s1/benchmark_graph_admission.py b/recipe/cua_s1/benchmark_graph_admission.py new file mode 100644 index 0000000..e430337 --- /dev/null +++ b/recipe/cua_s1/benchmark_graph_admission.py @@ -0,0 +1,256 @@ +"""Paired, capture-inclusive eager / historical / admission Graph experiment.""" + +from __future__ import annotations + +import argparse +import gc +import hashlib +import importlib.util +import json +import math +import sys +from contextlib import nullcontext +from dataclasses import asdict, replace +from pathlib import Path + +from benchmark_graph_mixed_shapes import summarize_events as mixed_summary + + +def schedules(): + return { + "hot_four": [1, 2, 4, 8] * 12, + "churn_twelve": list(range(1, 13)) * 6, + "hot_cold": [v for i in range(8) for v in [1, 2, 4, 8, 13 + i]], + "shifting_hot": [ + v + for group in [range(1, 5), range(5, 9), range(9, 13), range(1, 5)] + for v in list(group) * 8 + ], + } + + +def summarize_events(events): + result = mixed_summary(events) + for variant in ("eager", "graph"): + samples = sorted(e[f"{variant}_ms"] for e in events) + result[f"{variant}_p95_ms"] = samples[math.ceil(0.95 * len(samples)) - 1] + return result + + +def load_legacy(path): + digest = hashlib.sha256(path.read_bytes()).hexdigest() + name = "_cua_legacy_graph_" + digest + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + sys.modules[name] = module # dataclasses resolves annotations through sys.modules. + spec.loader.exec_module(module) + return module, digest + + +def legacy_runtime(module, model): + runtime = module.GraphRuntime(model, module.GraphConfig()) + runtime.request = nullcontext + return runtime + + +def check_responses(expected, actual, difference): + delta = difference(expected, actual) + if delta != 0 or expected != actual: + raise ValueError("full response changed") + return delta + + +def variant_order(run_index, request_index): + order = ["eager", "legacy", "admission"] + return order[::-1] if (run_index + request_index) % 2 else order + + +def parse_args(argv=None): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--legacy-runtime", type=Path, required=True) + parser.add_argument("--legacy-revision") + parser.add_argument("--runs", type=int, default=2) + parser.add_argument("--case", choices=list(schedules()), action="append") + args = parser.parse_args(argv) + if args.runs <= 0: + parser.error("--runs must be positive") + if (args.output / "report.json").exists(): + parser.error("report exists; choose a fresh directory") + root = Path(__file__).resolve().parents[2] + if root in args.legacy_runtime.resolve().parents: + parser.error("--legacy-runtime must be outside the measured repository") + return args + + +def main(argv=None): + args = parse_args(argv) + import torch + from benchmark_multimodal_graph import measure, response_difference + from evaluate_multimodal import environment + from profile_multimodal import ( + GOAL, + case_matrix, + fixture, + repository_state, + write_json, + ) + + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + source = repository_state(Path(__file__).resolve().parents[2]) + if source["dirty"] is not False or not source["revision"]: + raise ValueError("benchmark requires clean committed source") + legacy, digest = load_legacy(args.legacy_runtime) + selected = {k: v for k, v in schedules().items() if not args.case or k in args.case} + args.output.mkdir(parents=True, exist_ok=True) + report_path = args.output / "report.json" + report = { + "schema_version": 1, + "status": "running", + "repository": source, + "environment": environment(), + "legacy": {"sha256": digest, "revision": args.legacy_revision}, + "config": { + "runs": args.runs, + "cases": list(selected), + "legacy": asdict(legacy.GraphConfig()), + "admission": asdict(GraphConfig()), + "timing": "synchronized engine.predict; includes cold admission and capture", + "scope": "two identical questions; one synthetic 320x240 image; serial; no HTTP", + "memory_limitation": "legacy and admission graph pools coexist; allocator totals include both pools and are not isolated variant memory measurements", + "order": "reverse eager/legacy/admission on alternating requests and runs", + "p95": "nearest rank", + }, + "workloads": {}, + } + write_json(report_path, report) + try: + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + case = next(x for x in case_matrix() if x["id"] == "320x240-short-q2") + raw = fixture(case, args.output / "fixtures") + base = parse_request(raw) + counts = sorted({v for schedule in selected.values() for v in schedule}) + requests = { + n: replace( + base, + questions=tuple( + replace(q, goal=" ".join([GOAL] * n)) for q in base.questions + ), + ) + for n in counts + } + tokens = { + n: int( + engine.prepare_reused(r.image, r.questions)[0]["input_ids"].shape[-1] + ) + for n, r in requests.items() + } + if len(set(tokens.values())) != len(tokens) or max(tokens.values()) >= 2048: + raise ValueError("token lengths must be distinct and below 2048") + report["fixture_sha256"] = hashlib.sha256( + json.dumps(raw, sort_keys=True).encode() + ).hexdigest() + report["fixture"] = raw + report["token_counts"] = tokens + engine.graph_runtime = None + for _ in range(3): + engine.predict(requests[counts[0]]) + torch.cuda.synchronize() + for name, schedule in selected.items(): + report["workloads"][name] = [] + for run in range(args.runs): + runtimes = { + "legacy": legacy_runtime(legacy, engine.model), + "admission": GraphRuntime(engine.model, GraphConfig()), + } + record = { + "run": run + 1, + "schedule": schedule, + "status": "running", + "events": [], + "stats_initial": {v: dict(r.stats) for v, r in runtimes.items()}, + } + report["workloads"][name].append(record) + write_json(report_path, report) + for index, count in enumerate(schedule): + event = { + "index": index + 1, + "goal_repetitions": count, + "tokens_per_question": tokens[count], + "order": variant_order(run, index), + "variants": {}, + } + for variant in event["order"]: + runtime = runtimes.get(variant) + engine.graph_runtime = runtime + before = dict(runtime.stats) if runtime else {} + torch.cuda.reset_peak_memory_stats() + response, elapsed = measure( + lambda request=requests[count]: engine.predict(request) + ) + event["variants"][variant] = { + "response": response, + "latency_ms": elapsed, + "stats_delta": { + k: runtime.stats[k] - v for k, v in before.items() + } + if runtime + else {}, + "cache_shapes": len(runtime.cache) if runtime else 0, + "cache_bytes": runtime.cache.bytes if runtime else 0, + "allocated_bytes": torch.cuda.memory_allocated(), + "reserved_bytes": torch.cuda.memory_reserved(), + "peak_allocated_bytes": torch.cuda.max_memory_allocated(), + "peak_reserved_bytes": torch.cuda.max_memory_reserved(), + } + for variant in runtimes: + event["variants"][variant]["max_probability_difference"] = ( + check_responses( + event["variants"]["eager"]["response"], + event["variants"][variant]["response"], + response_difference, + ) + ) + record["events"].append(event) + write_json(report_path, report) + record["summary"] = { + v: summarize_events( + [ + { + "eager_ms": e["variants"]["eager"]["latency_ms"], + "graph_ms": e["variants"][v]["latency_ms"], + "graph_stats_delta": e["variants"][v]["stats_delta"], + } + for e in record["events"] + ] + ) + for v in runtimes + } + record["stats_final"] = {v: dict(r.stats) for v, r in runtimes.items()} + record["status"] = "complete" + engine.graph_runtime = None + for runtime in runtimes.values(): + runtime.invalidate() + del runtime, runtimes + gc.collect() + torch.cuda.empty_cache() # only between cold runs, never paired variants + write_json(report_path, report) + print(name, run + 1, record["summary"], flush=True) + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(report_path, report) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/benchmark_graph_mixed_shapes.py b/recipe/cua_s1/benchmark_graph_mixed_shapes.py index c79680e..e703785 100644 --- a/recipe/cua_s1/benchmark_graph_mixed_shapes.py +++ b/recipe/cua_s1/benchmark_graph_mixed_shapes.py @@ -6,7 +6,7 @@ import hashlib import json import statistics -from dataclasses import replace +from dataclasses import asdict, replace from itertools import chain from pathlib import Path @@ -29,13 +29,30 @@ def summarize_events(events): graph_so_far += item["graph_ms"] if break_even is None and graph_so_far <= eager_so_far: break_even = index + cumulative = 0.0 + balances = [] + first_capture = None + for index, item in enumerate(events, 1): + cumulative += item["eager_ms"] - item["graph_ms"] + balances.append(cumulative) + if first_capture is None and item.get("graph_stats_delta", {}).get( + "captures", 0 + ): + first_capture = index + sustained = None + suffix_nonnegative = True + for index in range(len(balances), 0, -1): + suffix_nonnegative = suffix_nonnegative and balances[index - 1] >= 0 + if first_capture is not None and index >= first_capture and suffix_nonnegative: + sustained = index return { "eager_total_ms": eager_total, "graph_total_ms": graph_total, "total_reduction_percent": 100 * (1 - graph_total / eager_total), "eager_p50_ms": statistics.median(x["eager_ms"] for x in events), "graph_p50_ms": statistics.median(x["graph_ms"] for x in events), - "first_break_even_request": break_even, + "first_cumulative_crossing_request": break_even, + "post_capture_sustained_break_even_request": sustained, } @@ -79,12 +96,8 @@ def main(argv=None): "environment": environment(), "config": { "runs": args.runs, - "graph": { - "max_shapes": 8, - "max_bytes": 1024**3, - "min_uses": args.graph_min_uses, - "max_tokens": 2048, - }, + "graph": asdict(GraphConfig(min_uses=args.graph_min_uses)), + "min_uses_semantics": "distinct request contexts", "timing": "synchronized engine.predict, including first use and capture", "scope": "two questions per request, one 320x240 synthetic image, serial, no HTTP", }, @@ -168,7 +181,7 @@ def main(argv=None): allocated = torch.cuda.memory_allocated() reserved = torch.cuda.memory_reserved() difference = response_difference(results["eager"], results["graph"]) - if difference != 0: + if difference != 0 or results["eager"] != results["graph"]: raise ValueError( f"{workload_name} run {run_index + 1} request {index + 1}: response changed" ) diff --git a/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md index 53aab7d..f9e44b0 100644 --- a/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md +++ b/recipe/cua_s1/experiments/rtx4090-graph-mixed-shapes/README.md @@ -81,12 +81,19 @@ capture time, cache occupancy, and memory observation. The independent [`verify_results.py`](verify_results.py) recomputes the totals and medians, checks schedule coverage and cache limits, and requires exact response parity. It verified all three reports and 360 paired requests. On the GPU host, all -118 Cua-S1 tests passed; Ruff lint and format checks passed for the new +118 Cua-S1 tests were reported as passing. The later source `6e0a432` +adds `test_capture_threshold_must_be_positive` relative to `1019581`, +accounting for the PR summary’s 119-test count; the original execution log +is not retained here, so this is a source-count explanation rather than a +new claim that all 119 ran on the GPU host. Ruff lint and format checks passed for the new benchmark. SHA-256 hashes are in [`SHA256SUMS`](SHA256SUMS). -With this PR's source, pinned weights, and the GPU environment described above: +To reproduce the historical runtime behavior, check out the measured source +`6e0a432` in a separate checkout, with pinned weights and the GPU environment +described above. Current source changes admission and metric semantics. ```sh +git checkout 6e0a432 for threshold in 2 4 8; do PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_mixed_shapes.py \ --weights /path/to/weights --output "/path/to/fresh-output-$threshold" \ diff --git a/recipe/cua_s1/graph-admission.md b/recipe/cua_s1/graph-admission.md new file mode 100644 index 0000000..364bd01 --- /dev/null +++ b/recipe/cua_s1/graph-admission.md @@ -0,0 +1,42 @@ +# Capture admission experiment + +`benchmark_graph_admission.py` measures the entire synchronized `engine.predict` +request, including cold misses and capture cost. It pairs eager, the historical +#24 runtime, and current admission on one engine. Each variant retains its own +runtime across a schedule; all runtimes are destroyed between runs. Execution +order reverses on alternating requests and runs. No allocator purge occurs +between paired variants. Both graph pools coexist, so allocator memory totals +include both pools and must not be interpreted as isolated variant memory. +Per-runtime cache occupancy is recorded separately. + +Use a clean committed source checkout and put output outside it. Extract the +historical runtime outside the repository; its content SHA-256 and optional +revision are included in the report: + +```sh +git show 6e0a432:src/models/cua_s1/multimodal/graph_runtime.py > /tmp/legacy-graph-runtime.py +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_admission.py \ + --weights /path/to/weights --output /path/to/fresh-admission-output \ + --legacy-runtime /tmp/legacy-graph-runtime.py --legacy-revision 6e0a432 --runs 2 +python recipe/cua_s1/verify_graph_admission.py /path/to/fresh-admission-output/report.json +``` + +`--case` can be repeated to select schedules; by default all four run: +48 hot-four requests, 72 twelve-shape churn requests, 40 hot/cold requests, +and 128 shifting-hot requests. Every request contains two identical questions; +prompt repetition counts produce distinct token lengths below 2048. + +Reports are checkpointed after every paired request and preserve full responses, +configuration, environment, fixture hash and contents, source SHA, token counts, +latencies, memory and counter deltas. Equality requires both the probability +comparison and full response dictionary equality. The independent stdlib verifier +checks those records and recomputes totals, medians, nearest-rank p95 and cumulative +metrics. A failed or interrupted report is not accepted as complete evidence. + +`first_cumulative_crossing_request` is the first nonnegative cumulative eager +minus graph balance and can precede capture. The separate +`post_capture_sustained_break_even_request` is the earliest request at or after +first capture with nonnegative cumulative balance for the rest of the observed +schedule. It is null when no capture occurs or the final balance is negative. +It describes the observed horizon, not a prediction of future amortization. +Historical #24 raw JSON and its verifier retain their original schema. diff --git a/recipe/cua_s1/verify_graph_admission.py b/recipe/cua_s1/verify_graph_admission.py new file mode 100644 index 0000000..a3dacef --- /dev/null +++ b/recipe/cua_s1/verify_graph_admission.py @@ -0,0 +1,227 @@ +"""Independently recompute admission benchmark evidence, using only the stdlib.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import statistics +from pathlib import Path + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def metrics(eager, graph, captures): + require( + all(math.isfinite(v) and v > 0 for v in eager + graph), + "timings must be finite and positive", + ) + balances = [sum(eager[:i]) - sum(graph[:i]) for i in range(1, len(eager) + 1)] + first_capture = next((i for i, v in enumerate(captures) if v), None) + crossing = next((i + 1 for i, v in enumerate(balances) if v >= 0), None) + sustained = next( + ( + i + 1 + for i in range(len(balances)) + if first_capture is not None + and i >= first_capture + and min(balances[i:]) >= 0 + ), + None, + ) + result = { + "eager_total_ms": sum(eager), + "graph_total_ms": sum(graph), + "total_reduction_percent": 100 * (1 - sum(graph) / sum(eager)), + "first_cumulative_crossing_request": crossing, + "post_capture_sustained_break_even_request": sustained, + } + for name, values in [("eager", eager), ("graph", graph)]: + result[name + "_p50_ms"] = statistics.median(values) + result[name + "_p95_ms"] = sorted(values)[math.ceil(len(values) * 0.95) - 1] + return result + + +def verify_report(report): + require(report["status"] == "complete", "report incomplete") + source = report["repository"] + require( + source.get("dirty") is False and len(source.get("revision", "")) == 40, + "clean source SHA required", + ) + require(report["schema_version"] == 1, "unknown schema") + require(len(report["legacy"]["sha256"]) == 64, "legacy hash required") + require(report["environment"], "environment missing") + fixture = report["fixture"] + require( + hashlib.sha256(json.dumps(fixture, sort_keys=True).encode()).hexdigest() + == report["fixture_sha256"], + "fixture hash mismatch", + ) + expected = { + "hot_four": [1, 2, 4, 8] * 12, + "churn_twelve": list(range(1, 13)) * 6, + "hot_cold": [v for i in range(8) for v in [1, 2, 4, 8, 13 + i]], + "shifting_hot": list(range(1, 5)) * 8 + + list(range(5, 9)) * 8 + + list(range(9, 13)) * 8 + + list(range(1, 5)) * 8, + } + config = report["config"] + legacy_config = { + "max_shapes": 8, + "max_bytes": 1024**3, + "min_uses": 2, + "max_tokens": 2048, + } + admission_config = dict( + legacy_config, + admission_window=8, + cooldown_requests=32, + capture_window=32, + max_captures=4, + capture_budget_ms=2000, + ) + require(config["legacy"] == legacy_config, "legacy configuration mismatch") + require(config["admission"] == admission_config, "admission configuration mismatch") + require( + set(report["workloads"]) == set(config["cases"]) and bool(config["cases"]), + "selected workloads mismatch", + ) + require(config["runs"] > 0, "runs must be positive") + tokens = report["token_counts"] + require( + len(set(tokens.values())) == len(tokens) + and all(isinstance(v, int) and 0 < v < 2048 for v in tokens.values()), + "invalid token counts", + ) + for name, runs in report["workloads"].items(): + require( + name in expected and len(runs) == config["runs"], "invalid workload runs" + ) + for run_index, record in enumerate(runs): + require( + record["run"] == run_index + 1 and record["status"] == "complete", + "invalid run", + ) + require( + record["schedule"] == expected[name] + and len(record["events"]) == len(expected[name]), + "schedule mismatch", + ) + for i, event in enumerate(record["events"]): + count = expected[name][i] + require( + event["index"] == i + 1 + and event["goal_repetitions"] == count + and event["tokens_per_question"] == tokens[str(count)], + "event identity mismatch", + ) + order = ["eager", "legacy", "admission"] + require( + event["order"] == (order[::-1] if (i + run_index) % 2 else order), + "execution order mismatch", + ) + variants = event["variants"] + require(set(variants) == set(order), "variant missing") + for variant, data in variants.items(): + require( + data["response"] == variants["eager"]["response"], + "full response mismatch", + ) + require( + math.isfinite(data["latency_ms"]) and data["latency_ms"] > 0, + "timings must be finite and positive", + ) + for key in [ + "cache_shapes", + "cache_bytes", + "allocated_bytes", + "reserved_bytes", + "peak_allocated_bytes", + "peak_reserved_bytes", + ]: + require( + isinstance(data[key], int) and data[key] >= 0, + "invalid memory counter", + ) + require( + data["cache_shapes"] + <= (config[variant]["max_shapes"] if variant != "eager" else 0), + "cache shape limit exceeded", + ) + require( + data["cache_bytes"] + <= (config[variant]["max_bytes"] if variant != "eager" else 0), + "cache byte limit exceeded", + ) + if variant != "eager": + require( + data["max_probability_difference"] == 0, + "probabilities changed", + ) + for k, v in data["stats_delta"].items(): + require( + math.isfinite(v) and v >= 0, "invalid stats delta " + k + ) + for variant in ["legacy", "admission"]: + initial = record["stats_initial"][variant] + final = record["stats_final"][variant] + require( + set(initial) == set(final) + and all(v == 0 for v in initial.values()), + "run must start with cold counters", + ) + for event in record["events"]: + require( + set(event["variants"][variant]["stats_delta"]) == set(initial), + "stats keys mismatch", + ) + for key in initial: + total = initial[key] + sum( + e["variants"][variant]["stats_delta"][key] + for e in record["events"] + ) + require( + math.isfinite(final[key]) + and math.isclose(total, final[key], rel_tol=1e-9, abs_tol=1e-6), + "stats total mismatch " + key, + ) + calculated = metrics( + [e["variants"]["eager"]["latency_ms"] for e in record["events"]], + [e["variants"][variant]["latency_ms"] for e in record["events"]], + [ + e["variants"][variant]["stats_delta"]["captures"] + for e in record["events"] + ], + ) + saved = record["summary"][variant] + require(set(saved) == set(calculated), "summary keys mismatch") + for key, value in calculated.items(): + require( + saved[key] == value + if value is None or key.endswith("_request") + else math.isclose( + saved[key], value, rel_tol=1e-9, abs_tol=1e-6 + ), + "summary mismatch " + key, + ) + print( + f"{name} run {run_index + 1} {variant}: {calculated['graph_total_ms']:.2f} ms vs eager {calculated['eager_total_ms']:.2f} ms; reduction {calculated['total_reduction_percent']:.2f}%; p95 {calculated['graph_p95_ms']:.2f} ms; sustained {calculated['post_capture_sustained_break_even_request']}" + ) + return True + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("report", type=Path) + args = parser.parse_args() + verify_report(json.loads(args.report.read_text())) + + +if __name__ == "__main__": + main() diff --git a/tests/cua_s1/test_graph_admission_benchmark.py b/tests/cua_s1/test_graph_admission_benchmark.py new file mode 100644 index 0000000..1433a07 --- /dev/null +++ b/tests/cua_s1/test_graph_admission_benchmark.py @@ -0,0 +1,188 @@ +"""CPU checks for the admission experiment and independent report verifier.""" + +import sys +from pathlib import Path + +import pytest + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "recipe/cua_s1")) + + +def benchmark(): + import benchmark_graph_admission + + return benchmark_graph_admission + + +def test_exact_schedules(): + s = benchmark().schedules() + assert s["hot_four"] == [1, 2, 4, 8] * 12 + assert s["churn_twelve"] == list(range(1, 13)) * 6 + assert s["hot_cold"] == [v for i in range(8) for v in [1, 2, 4, 8, 13 + i]] + assert s["shifting_hot"] == [ + v + for group in [range(1, 5), range(5, 9), range(9, 13), range(1, 5)] + for v in list(group) * 8 + ] + + +def test_metrics_p95_and_sustained(): + events = [ + {"eager_ms": 10, "graph_ms": v, "graph_stats_delta": {"captures": int(i == 1)}} + for i, v in enumerate([5, 30, 1, 1, 20, 1]) + ] + m = benchmark().summarize_events(events) + assert m["graph_p95_ms"] == 30 + assert m["first_cumulative_crossing_request"] == 1 + assert m["post_capture_sustained_break_even_request"] == 6 + + +def test_legacy_dataclass_import_and_request_adapter(tmp_path): + path = tmp_path / "legacy.py" + path.write_text( + "from dataclasses import dataclass\n@dataclass\nclass GraphConfig:\n min_uses: int = 2\nclass GraphRuntime:\n def __init__(self, model, config): self.config = config\n" + ) + module, digest = benchmark().load_legacy(path) + assert len(digest) == 64 + runtime = benchmark().legacy_runtime(module, object()) + with runtime.request(): + assert runtime.config.min_uses == 2 + + +def test_response_equality_rejects_unchecked_field(): + a = {"model": "x", "usage": {}, "answers": {}, "extra": 1} + b = dict(a, extra=2) + with pytest.raises(ValueError, match="response"): + benchmark().check_responses(a, b, lambda a, b: 0) + + +def test_order_reverses_each_request_and_run(): + b = benchmark() + assert b.variant_order(0, 0) == ["eager", "legacy", "admission"] + assert ( + b.variant_order(0, 1) + == b.variant_order(1, 0) + == ["admission", "legacy", "eager"] + ) + + +def test_verifier_rejects_dirty_source_and_nan(): + import verify_graph_admission as verify + + with pytest.raises(ValueError, match="clean"): + verify.verify_report({"status": "complete", "repository": {"dirty": True}}) + with pytest.raises(ValueError, match="finite"): + verify.metrics([10], [float("nan")], [0]) + metrics = verify.metrics([10] * 6, [5, 30, 1, 1, 20, 1], [0, 1, 0, 0, 0, 0]) + assert metrics["post_capture_sustained_break_even_request"] == 6 + assert metrics["graph_p95_ms"] == 30 + + +def valid_report(): + import hashlib + import json + + import verify_graph_admission as verifier + + schedule = benchmark().schedules()["hot_four"] + events = [] + for i, count in enumerate(schedule): + variants = {} + for variant in ["eager", "legacy", "admission"]: + variants[variant] = { + "response": {"model": "fixture", "usage": {}, "answers": {}}, + "latency_ms": 10.0, + "cache_shapes": 0, + "cache_bytes": 0, + "allocated_bytes": 0, + "reserved_bytes": 0, + "peak_allocated_bytes": 0, + "peak_reserved_bytes": 0, + "stats_delta": {} if variant == "eager" else {"captures": 0}, + "max_probability_difference": 0, + } + events.append( + { + "index": i + 1, + "goal_repetitions": count, + "tokens_per_question": count + 100, + "order": benchmark().variant_order(0, i), + "variants": variants, + } + ) + stats = {"legacy": {"captures": 0}, "admission": {"captures": 0}} + summary = {v: verifier.metrics([10.0] * 48, [10.0] * 48, [0] * 48) for v in stats} + return { + "schema_version": 1, + "status": "complete", + "repository": {"dirty": False, "revision": "a" * 40}, + "legacy": {"sha256": "b" * 64}, + "environment": {"python": "test"}, + "fixture": {}, + "fixture_sha256": hashlib.sha256( + json.dumps({}, sort_keys=True).encode() + ).hexdigest(), + "config": { + "cases": ["hot_four"], + "runs": 1, + "legacy": { + "max_shapes": 8, + "max_bytes": 1024**3, + "min_uses": 2, + "max_tokens": 2048, + }, + "admission": { + "max_shapes": 8, + "max_bytes": 1024**3, + "min_uses": 2, + "max_tokens": 2048, + "admission_window": 8, + "cooldown_requests": 32, + "capture_window": 32, + "max_captures": 4, + "capture_budget_ms": 2000, + }, + }, + "token_counts": {str(v): v + 100 for v in set(schedule)}, + "workloads": { + "hot_four": [ + { + "run": 1, + "status": "complete", + "schedule": schedule, + "events": events, + "stats_initial": stats, + "stats_final": stats, + "summary": summary, + } + ] + }, + } + + +def test_verifier_accepts_valid_report_and_rejects_corruption(): + import copy + + import verify_graph_admission as verifier + + report = valid_report() + assert verifier.verify_report(report) + for mutate in [ + lambda r: r["config"]["admission"].update(min_uses=99), + lambda r: r["workloads"]["hot_four"][0]["events"][0]["variants"]["admission"][ + "response" + ].update(extra=1), + lambda r: r["workloads"]["hot_four"][0]["events"][0]["variants"]["admission"][ + "stats_delta" + ].update(captures=1), + lambda r: r["workloads"]["hot_four"][0]["summary"]["admission"].update( + graph_p95_ms=11 + ), + lambda r: r["workloads"]["hot_four"][0]["schedule"].append(1), + lambda r: r.update(fixture_sha256="x" * 64), + ]: + corrupted = copy.deepcopy(report) + mutate(corrupted) + with pytest.raises(ValueError): + verifier.verify_report(corrupted) diff --git a/tests/cua_s1/test_graph_mixed_shapes_benchmark.py b/tests/cua_s1/test_graph_mixed_shapes_benchmark.py index 34fa4a8..abf0429 100644 --- a/tests/cua_s1/test_graph_mixed_shapes_benchmark.py +++ b/tests/cua_s1/test_graph_mixed_shapes_benchmark.py @@ -37,8 +37,8 @@ def test_summary_includes_capture_cost_and_reports_first_break_even(): result = summarize(events) assert result["eager_total_ms"] == 50.0 assert result["graph_total_ms"] == 50.0 - assert result["first_break_even_request"] == 5 - assert summarize(events[:4])["first_break_even_request"] is None + assert result["first_cumulative_crossing_request"] == 5 + assert summarize(events[:4])["first_cumulative_crossing_request"] is None def test_capture_threshold_must_be_positive(tmp_path): @@ -53,3 +53,20 @@ def test_capture_threshold_must_be_positive(tmp_path): "0", ] ) + + +def test_sustained_break_even_excludes_precapture_and_temporary_crossings(): + summarize = load_benchmark().summarize_events + events = [ + {"eager_ms": 10, "graph_ms": 5}, + {"eager_ms": 10, "graph_ms": 30, "graph_stats_delta": {"captures": 1}}, + {"eager_ms": 10, "graph_ms": 1}, + {"eager_ms": 10, "graph_ms": 1}, + {"eager_ms": 10, "graph_ms": 20}, + {"eager_ms": 10, "graph_ms": 1}, + ] + result = summarize(events) + assert result["first_cumulative_crossing_request"] == 1 + assert result["post_capture_sustained_break_even_request"] == 6 + assert summarize(events[:1])["post_capture_sustained_break_even_request"] is None + assert summarize(events[:3])["post_capture_sustained_break_even_request"] is None From 383f9d64f96c1661107f7f470e9562608c240d34 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:29:05 +0800 Subject: [PATCH 34/75] test: add GPU input-change and admission fallback checks --- .../cua_s1/check_graph_admission_runtime.py | 174 ++++++++++++++++++ 1 file changed, 174 insertions(+) create mode 100644 recipe/cua_s1/check_graph_admission_runtime.py diff --git a/recipe/cua_s1/check_graph_admission_runtime.py b/recipe/cua_s1/check_graph_admission_runtime.py new file mode 100644 index 0000000..42a51c1 --- /dev/null +++ b/recipe/cua_s1/check_graph_admission_runtime.py @@ -0,0 +1,174 @@ +"""GPU regression checks for input changes, admission and eager fallback parity.""" + +from __future__ import annotations + +import argparse +from dataclasses import asdict, replace +from pathlib import Path + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + args = p.parse_args() + if args.output.exists(): + p.error("choose a fresh output path") + + import torch + from benchmark_image_reuse import distinct_fixture + from benchmark_multimodal_graph import changed_text_request + from evaluate_multimodal import environment + from PIL import Image + from profile_multimodal import case_matrix, fixture, repository_state, write_json + + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + source = repository_state(Path(__file__).resolve().parents[2]) + if source["dirty"]: + raise ValueError("GPU checks require clean committed source") + report = { + "status": "running", + "repository": source, + "environment": environment(), + "cases": [], + "policies": {}, + } + fixtures = args.output.parent / "parity-fixtures" + fixtures.mkdir(parents=True, exist_ok=True) + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), str(args.weights / "cua-s1-4b-0.2/multimodal") + ) + selected = { + "320x240-short-q2", + "320x240-short-q8", + "640x480-short-q8", + "640x480-long-q8", + } + cases = [c for c in case_matrix() if c["id"] in selected] + [ + {"id": "640x480-distinct-q8"} + ] + for case in cases: + raw = ( + distinct_fixture(fixtures) + if case["id"] == "640x480-distinct-q8" + else fixture(case, fixtures) + ) + request = parse_request(raw) + # Expanded validation limits ensure all distinct layouts and the long + # prompt actually replay; performance measurements use worker defaults. + config = GraphConfig(max_tokens=4096, max_captures=16, capture_budget_ms=10000) + runtime = GraphRuntime(engine.model, config) + changed_image = replace( + request, image=Image.new("RGB", request.image.size, "black") + ) + if case["id"] == "640x480-distinct-q8": + changed_text = replace( + request, + questions=( + replace( + request.questions[0], + goal=request.questions[0].goal.replace("Save", "Open"), + ), + *request.questions[1:], + ), + ) + old = engine.prepare_reused(request.image, request.questions) + new = engine.prepare_reused(request.image, changed_text.questions) + assert all( + a["input_ids"].shape == b["input_ids"].shape for a, b in zip(old, new) + ) + assert not torch.equal(old[0]["input_ids"], new[0]["input_ids"]) + else: + changed_text, _ = changed_text_request(engine, request) + records = [] + for name, value in [ + ("cold", request), + ("capture", request), + ("image", changed_image), + ("text", changed_text), + ]: + engine.graph_runtime = None + expected = engine.predict(value) + engine.graph_runtime = runtime + before = dict(runtime.stats) + actual = engine.predict(value) + assert actual == expected, (case["id"], name) + records.append( + { + "variant": name, + "eager_response": expected, + "graph_response": actual, + "stats_delta": {k: runtime.stats[k] - v for k, v in before.items()}, + } + ) + assert records[0]["stats_delta"]["captures"] == 0 + assert records[1]["stats_delta"]["captures"] > 0 + assert records[2]["stats_delta"]["replays"] == len(request.questions) + assert records[3]["stats_delta"]["replays"] == len(request.questions) + assert runtime.stats["rejected"] == 0 + report["cases"].append( + { + "id": case["id"], + "config": asdict(config), + "records": records, + "stats": dict(runtime.stats), + } + ) + engine.graph_runtime = None + runtime.invalidate() + del runtime + torch.cuda.empty_cache() + write_json(args.output, report) + + request = parse_request(distinct_fixture(fixtures)) + request = replace(request, questions=request.questions[:2]) + engine.graph_runtime = None + expected = engine.predict(request) + for name, config, counter in [ + ("eviction_cooldown", GraphConfig(min_uses=1, max_shapes=1), "cooldown"), + ("memory", GraphConfig(min_uses=1, max_bytes=1 << 20), "memory_budget"), + ("attempt_budget", GraphConfig(min_uses=1, max_captures=1), "capture_budget"), + ( + "time_budget", + GraphConfig(min_uses=1, capture_budget_ms=0.001), + "capture_budget", + ), + ]: + runtime = GraphRuntime(engine.model, config) + engine.graph_runtime = runtime + responses = [engine.predict(request), engine.predict(request)] + assert all(r == expected for r in responses) + assert runtime.stats[counter] > 0 + assert ( + len(runtime.cache) <= config.max_shapes + and runtime.cache.bytes <= config.max_bytes + ) + if name in ("attempt_budget", "time_budget"): + assert runtime.stats["capture_attempts"] == 1 + if name == "eviction_cooldown": + assert runtime.stats["evictions"] == 1 + report["policies"][name] = { + "config": asdict(config), + "stats": dict(runtime.stats), + "eager_response": expected, + "graph_responses": responses, + } + engine.graph_runtime = None + runtime.invalidate() + assert ( + not runtime.admission.history + and not runtime.admission.attempts + and not runtime.cache.entries + ) + del runtime + torch.cuda.empty_cache() + report["status"] = "complete" + write_json(args.output, report) + print("verified changed inputs on five cases and four admission/fallback policies") + + +if __name__ == "__main__": + main() From 2969be19dc53135fe0f4324ed6042ce4a7e69fd8 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:30:46 +0800 Subject: [PATCH 35/75] Verify admission policy budgets and supported-path counters --- recipe/cua_s1/verify_graph_admission.py | 67 +++++++++++ .../cua_s1/test_graph_admission_benchmark.py | 105 +++++++++++++++++- 2 files changed, 169 insertions(+), 3 deletions(-) diff --git a/recipe/cua_s1/verify_graph_admission.py b/recipe/cua_s1/verify_graph_admission.py index a3dacef..d1f28fa 100644 --- a/recipe/cua_s1/verify_graph_admission.py +++ b/recipe/cua_s1/verify_graph_admission.py @@ -46,6 +46,67 @@ def metrics(eager, graph, captures): return result +def verify_policy(events, case, config): + """Audit recorded request boundaries and the pre-attempt sliding budget.""" + for i, event in enumerate(events): + for variant in ("legacy", "admission"): + delta = event["variants"][variant]["stats_delta"] + failures = [ + "numerical_mismatch", + "capture_error", + "capture_oom", + "rejected", + "unsupported", + ] + if variant == "admission": + failures.append("no_request") + for key in failures: + require( + key in delta and delta[key] == 0, f"{variant}: unexpected {key}" + ) + delta = event["variants"]["admission"]["stats_delta"] + require( + delta.get("requests") == 1, "admission request counter must increment once" + ) + attempts = delta.get("capture_attempts") + require( + attempts in (0, 1), + "at most one capture attempt per identical question pair", + ) + require( + attempts == delta["captures"], + "capture attempts must equal successful captures", + ) + elapsed = delta.get("capture_attempt_ms") + require( + isinstance(elapsed, (int, float)) + and math.isfinite(elapsed) + and elapsed >= 0, + "invalid capture attempt time", + ) + require(attempts or elapsed == 0, "capture time recorded without attempt") + previous = [ + e["variants"]["admission"]["stats_delta"] + for e in events[max(0, i - config["capture_window"] + 1) : i] + ] + require( + sum(d["capture_attempts"] for d in previous) + attempts + <= config["max_captures"], + "sliding capture count budget exceeded", + ) + if attempts: + require( + sum(d["capture_attempt_ms"] for d in previous) + < config["capture_budget_ms"], + "capture attempted after sliding time budget exhausted", + ) + if case == "churn_twelve": + require( + delta["captures"] == 0 and delta.get("replays") == 0, + "churn must remain eager without captures or replays", + ) + + def verify_report(report): require(report["status"] == "complete", "report incomplete") source = report["repository"] @@ -113,6 +174,7 @@ def verify_report(report): and len(record["events"]) == len(expected[name]), "schedule mismatch", ) + verify_policy(record["events"], name, config["admission"]) for i, event in enumerate(record["events"]): count = expected[name][i] require( @@ -149,6 +211,11 @@ def verify_report(report): isinstance(data[key], int) and data[key] >= 0, "invalid memory counter", ) + require( + data["peak_allocated_bytes"] >= data["allocated_bytes"] + and data["peak_reserved_bytes"] >= data["reserved_bytes"], + "peak memory below current memory", + ) require( data["cache_shapes"] <= (config[variant]["max_shapes"] if variant != "eager" else 0), diff --git a/tests/cua_s1/test_graph_admission_benchmark.py b/tests/cua_s1/test_graph_admission_benchmark.py index 1433a07..1271bba 100644 --- a/tests/cua_s1/test_graph_admission_benchmark.py +++ b/tests/cua_s1/test_graph_admission_benchmark.py @@ -99,7 +99,11 @@ def valid_report(): "reserved_bytes": 0, "peak_allocated_bytes": 0, "peak_reserved_bytes": 0, - "stats_delta": {} if variant == "eager" else {"captures": 0}, + "stats_delta": {} + if variant == "eager" + else dict( + policy_event()["variants"][variant]["stats_delta"], captures=0 + ), "max_probability_difference": 0, } events.append( @@ -111,7 +115,16 @@ def valid_report(): "variants": variants, } ) - stats = {"legacy": {"captures": 0}, "admission": {"captures": 0}} + stats = { + v: dict.fromkeys(events[0]["variants"][v]["stats_delta"], 0) + for v in ["legacy", "admission"] + } + final = { + v: { + k: sum(e["variants"][v]["stats_delta"][k] for e in events) for k in stats[v] + } + for v in stats + } summary = {v: verifier.metrics([10.0] * 48, [10.0] * 48, [0] * 48) for v in stats} return { "schema_version": 1, @@ -153,7 +166,7 @@ def valid_report(): "schedule": schedule, "events": events, "stats_initial": stats, - "stats_final": stats, + "stats_final": final, "summary": summary, } ] @@ -169,6 +182,12 @@ def test_verifier_accepts_valid_report_and_rejects_corruption(): report = valid_report() assert verifier.verify_report(report) for mutate in [ + lambda r: r["workloads"]["hot_four"][0]["events"][0]["variants"]["legacy"][ + "stats_delta" + ].update(rejected=1), + lambda r: r["workloads"]["hot_four"][0]["events"][0]["variants"][ + "eager" + ].update(allocated_bytes=1), lambda r: r["config"]["admission"].update(min_uses=99), lambda r: r["workloads"]["hot_four"][0]["events"][0]["variants"]["admission"][ "response" @@ -186,3 +205,83 @@ def test_verifier_accepts_valid_report_and_rejects_corruption(): mutate(corrupted) with pytest.raises(ValueError): verifier.verify_report(corrupted) + + +def policy_event(**changes): + stats = dict.fromkeys( + [ + "numerical_mismatch", + "capture_error", + "capture_oom", + "rejected", + "unsupported", + "no_request", + "capture_attempts", + "captures", + "replays", + "capture_attempt_ms", + ], + 0, + ) + stats["requests"] = 1 + stats.update(changes) + return { + "variants": { + "admission": {"stats_delta": stats}, + "legacy": { + "stats_delta": { + k: 0 + for k in [ + "numerical_mismatch", + "capture_error", + "capture_oom", + "rejected", + "unsupported", + ] + } + }, + } + } + + +@pytest.mark.parametrize( + "events,case,message", + [ + ( + [policy_event(capture_attempts=1, captures=1, capture_attempt_ms=1)] * 5, + "hot_four", + "count budget", + ), + ( + [ + policy_event(capture_attempts=1, captures=1, capture_attempt_ms=2000), + policy_event(capture_attempts=1, captures=1), + ], + "hot_four", + "time budget", + ), + ([policy_event(rejected=1)], "hot_four", "rejected"), + ([policy_event(requests=2)], "hot_four", "request counter"), + ([policy_event(capture_attempts=2, captures=2)], "hot_four", "one capture"), + ( + [policy_event(capture_attempts=1, captures=0)], + "hot_four", + "successful captures", + ), + ([policy_event(replays=1)], "churn_twelve", "churn"), + ], +) +def test_policy_verifier_rejects_recorded_violations(events, case, message): + import verify_graph_admission as verifier + + with pytest.raises(ValueError, match=message): + verifier.verify_policy(events, case, valid_report()["config"]["admission"]) + + +def test_policy_window_expires_at_request_distance_32(): + import verify_graph_admission as verifier + + events = [policy_event(capture_attempts=1, captures=1, capture_attempt_ms=2001)] + events += [policy_event() for _ in range(31)] + events += [policy_event(capture_attempts=1, captures=1, capture_attempt_ms=1)] + verifier.verify_policy(events, "hot_four", valid_report()["config"]["admission"]) From 057736aae2e664783a48d247578a96b990ec8e79 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:50:19 +0800 Subject: [PATCH 36/75] docs: record reproduced capture-stream lifetime hazard --- docs/superpowers/specs/2026-09-28-cua-graph-admission.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/superpowers/specs/2026-09-28-cua-graph-admission.md b/docs/superpowers/specs/2026-09-28-cua-graph-admission.md index e93645e..5057be4 100644 --- a/docs/superpowers/specs/2026-09-28-cua-graph-admission.md +++ b/docs/superpowers/specs/2026-09-28-cua-graph-admission.md @@ -5,3 +5,5 @@ User-approved scope: fix #24 review findings, implement admission/cooldown/captu The optional Graph worker keeps exact layouts and segmented capture. Count a layout once per predict request, require two request observations no more than eight requests apart, and forget old sparse observations. Cached hits are always usable. On eviction discard accumulated heat and impose a 32-request cooldown. Bound metadata to 128 layouts. Budget at most four capture attempts and 2000 ms of completed capture-attempt work in any sliding 32-request window. One synchronous capture cannot be preempted; its elapsed time may overshoot the time budget, after which subsequent attempts use eager. Failures consume budget too. Model invalidation clears policy state. Explicit request contexts serialize complete Graph predictions and restore state on errors; standalone forward calls without context fall back eagerly. Keep --graph opt-in; expose policy limits and clarify min-uses now counts requests. No padding, new kernels, concurrency, or upstream full-attention numerical fix. Compare eager, legacy #24 admission, and new admission with cold caches, identical inputs, alternating order, all-response parity, and captured/uncaptured layout evidence. Extend churn beyond the former eight-use blind spot. Report total/p50/p95, post-capture sustained break-even, captures/evictions, fallback causes and memory. Small differences without graph replay are timing variation. + +Extended GPU testing exposed an inherited graph-lifetime hazard on installed torch commit `08187d9e0fba026dc8217405802ab5381dc88d90`: CUDAGraph reset clears cuBLAS workspace by capture stream. Default shared-stream captures therefore couple independent entries. A minimal surviving-graph reproduction fails with a shared stream and passes with independently owned external streams (upstream pytorch/pytorch#193402). Add an exclusively owned CUDA capture stream per segment, using cudaStreamCreate/ExternalStream rather than the 32-stream pool, and explicit synchronization/reset/tensor-release/destroy cleanup. Test more than 32 simultaneously live segments and replay after eviction. The final policy comparison applies this same lifecycle repair to the historical admission policy; preserve the failed untouched-legacy run separately and label the control accurately. From 7f55152f8eaea3b13d8e4bed79400b1051725fd5 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:50:48 +0800 Subject: [PATCH 37/75] Compare historical admission with shared exclusive-stream fix --- recipe/cua_s1/benchmark_graph_admission.py | 61 ++++++++++++++++++- recipe/cua_s1/graph-admission.md | 33 ++++++++-- recipe/cua_s1/verify_graph_admission.py | 27 +++++++- .../cua_s1/test_graph_admission_benchmark.py | 55 ++++++++++++++++- 4 files changed, 166 insertions(+), 10 deletions(-) diff --git a/recipe/cua_s1/benchmark_graph_admission.py b/recipe/cua_s1/benchmark_graph_admission.py index e430337..2f924fc 100644 --- a/recipe/cua_s1/benchmark_graph_admission.py +++ b/recipe/cua_s1/benchmark_graph_admission.py @@ -8,6 +8,8 @@ import importlib.util import json import math +import re +import subprocess import sys from contextlib import nullcontext from dataclasses import asdict, replace @@ -37,6 +39,31 @@ def summarize_events(events): return result +def verify_legacy_source(path, revision, root): + if not re.fullmatch(r"[0-9a-f]{40}", revision): + raise ValueError( + "legacy revision must be a full 40-character lowercase Git SHA" + ) + expected = subprocess.check_output( + [ + "git", + "-C", + str(root), + "show", + revision + ":src/models/cua_s1/multimodal/graph_runtime.py", + ] + ) + if path.read_bytes() != expected: + raise ValueError("legacy runtime bytes do not match the declared Git revision") + + +def configure_legacy_segment(module, current_segment, *, unpatched=False): + if unpatched: + return "none; unpatched historical runtime diagnostic" + module._GraphSegment = current_segment + return "current exclusive-stream _GraphSegment; historical admission policy only" + + def load_legacy(path): digest = hashlib.sha256(path.read_bytes()).hexdigest() name = "_cua_legacy_graph_" + digest @@ -70,10 +97,17 @@ def parse_args(argv=None): parser.add_argument("--weights", type=Path, required=True) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--legacy-runtime", type=Path, required=True) - parser.add_argument("--legacy-revision") + parser.add_argument("--legacy-revision", required=True) + parser.add_argument( + "--unpatched-legacy", + action="store_true", + help="diagnostic only: use historical unsafe capture streams", + ) parser.add_argument("--runs", type=int, default=2) parser.add_argument("--case", choices=list(schedules()), action="append") args = parser.parse_args(argv) + if not re.fullmatch(r"[0-9a-f]{40}", args.legacy_revision): + parser.error("--legacy-revision must be a full 40-character lowercase Git SHA") if args.runs <= 0: parser.error("--runs must be positive") if (args.output / "report.json").exists(): @@ -97,14 +131,26 @@ def main(argv=None): write_json, ) - from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.graph_runtime import ( + GraphConfig, + GraphRuntime, + _GraphSegment, + ) from models.cua_s1.multimodal.model import MultimodalEngine from models.cua_s1.multimodal.protocol import parse_request source = repository_state(Path(__file__).resolve().parents[2]) if source["dirty"] is not False or not source["revision"]: raise ValueError("benchmark requires clean committed source") + root = Path(__file__).resolve().parents[2] + verify_legacy_source(args.legacy_runtime, args.legacy_revision, root) legacy, digest = load_legacy(args.legacy_runtime) + override = configure_legacy_segment( + legacy, _GraphSegment, unpatched=args.unpatched_legacy + ) + current_runtime_sha256 = hashlib.sha256( + (root / "src/models/cua_s1/multimodal/graph_runtime.py").read_bytes() + ).hexdigest() selected = {k: v for k, v in schedules().items() if not args.case or k in args.case} args.output.mkdir(parents=True, exist_ok=True) report_path = args.output / "report.json" @@ -113,7 +159,16 @@ def main(argv=None): "status": "running", "repository": source, "environment": environment(), - "legacy": {"sha256": digest, "revision": args.legacy_revision}, + "legacy": { + "sha256": digest, + "revision": args.legacy_revision, + "source_bytes_verified_against_git": True, + "runtime_segment_override": override, + "current_graph_runtime_sha256": current_runtime_sha256, + }, + "comparison_kind": "unpatched-legacy-diagnostic" + if args.unpatched_legacy + else "legacy-policy-with-stream-fix", "config": { "runs": args.runs, "cases": list(selected), diff --git a/recipe/cua_s1/graph-admission.md b/recipe/cua_s1/graph-admission.md index 364bd01..0b0f332 100644 --- a/recipe/cua_s1/graph-admission.md +++ b/recipe/cua_s1/graph-admission.md @@ -1,8 +1,11 @@ # Capture admission experiment `benchmark_graph_admission.py` measures the entire synchronized `engine.predict` -request, including cold misses and capture cost. It pairs eager, the historical -#24 runtime, and current admission on one engine. Each variant retains its own +request, including cold misses and capture cost. It pairs eager, legacy-policy-with-stream-fix, and current admission on one +engine. The legacy variant imports the original #24 runtime but replaces its +`_GraphSegment` class with the current exclusive-stream implementation. This +is a comparison of admission policies with the same stream-lifetime fix, +not a reproduction of raw #24 performance. Each variant retains its own runtime across a schedule; all runtimes are destroyed between runs. Execution order reverses on alternating requests and runs. No allocator purge occurs between paired variants. Both graph pools coexist, so allocator memory totals @@ -10,14 +13,16 @@ include both pools and must not be interpreted as isolated variant memory. Per-runtime cache occupancy is recorded separately. Use a clean committed source checkout and put output outside it. Extract the -historical runtime outside the repository; its content SHA-256 and optional -revision are included in the report: +historical runtime outside the repository. Its full 40-character revision is +required, and its bytes must exactly match `git show` for that revision before +import. Reports preserve the original file SHA-256, revision, explicit segment +override, and current `graph_runtime.py` SHA-256: ```sh git show 6e0a432:src/models/cua_s1/multimodal/graph_runtime.py > /tmp/legacy-graph-runtime.py PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_admission.py \ --weights /path/to/weights --output /path/to/fresh-admission-output \ - --legacy-runtime /tmp/legacy-graph-runtime.py --legacy-revision 6e0a432 --runs 2 + --legacy-runtime /tmp/legacy-graph-runtime.py --legacy-revision 6e0a432a6aa6fb3f1bed746ef8c68289bee20425 --runs 2 python recipe/cua_s1/verify_graph_admission.py /path/to/fresh-admission-output/report.json ``` @@ -40,3 +45,21 @@ first capture with nonnegative cumulative balance for the rest of the observed schedule. It is null when no capture occurs or the final balance is negative. It describes the observed horizon, not a prediction of future amortization. Historical #24 raw JSON and its verifier retain their original schema. + +## Historical stream failure and diagnostic mode + +The initial extended experiment using unpatched legacy #24 failed during +`shifting_hot` at request 104 after more than 400 completed request pairs, with +an SGEMM replay error. That incomplete run is excluded from performance +conclusions. Both policy variants now use exclusively owned per-segment capture +streams to avoid a destroyed graph invalidating another live graph’s shared +stream workspace. Every published policy-comparison schedule must be rerun +from the updated clean source; earlier partial samples cannot be combined with +those runs. + +`--unpatched-legacy` preserves the original segment implementation solely for +diagnosing the historical failure. It still requires verified original bytes +and a full revision. Reports label this mode `unpatched-legacy-diagnostic` and +record `runtime_segment_override` as `none; unpatched historical runtime +diagnostic`. Such runs are not the default policy comparison and must be +described separately, even if a particular schedule completes. diff --git a/recipe/cua_s1/verify_graph_admission.py b/recipe/cua_s1/verify_graph_admission.py index d1f28fa..c5ae577 100644 --- a/recipe/cua_s1/verify_graph_admission.py +++ b/recipe/cua_s1/verify_graph_admission.py @@ -6,6 +6,7 @@ import hashlib import json import math +import re import statistics from pathlib import Path @@ -115,7 +116,31 @@ def verify_report(report): "clean source SHA required", ) require(report["schema_version"] == 1, "unknown schema") - require(len(report["legacy"]["sha256"]) == 64, "legacy hash required") + legacy = report["legacy"] + require( + re.fullmatch(r"[0-9a-f]{64}", legacy.get("sha256", "")), "legacy hash required" + ) + require( + re.fullmatch(r"[0-9a-f]{40}", legacy.get("revision", "")), + "full legacy revision required", + ) + require( + legacy.get("source_bytes_verified_against_git") is True, + "legacy Git byte verification required", + ) + require( + re.fullmatch(r"[0-9a-f]{64}", legacy.get("current_graph_runtime_sha256", "")), + "current runtime hash required", + ) + overrides = { + "legacy-policy-with-stream-fix": "current exclusive-stream _GraphSegment; historical admission policy only", + "unpatched-legacy-diagnostic": "none; unpatched historical runtime diagnostic", + } + kind = report.get("comparison_kind") + require( + kind in overrides and legacy.get("runtime_segment_override") == overrides[kind], + "explicit runtime segment override required", + ) require(report["environment"], "environment missing") fixture = report["fixture"] require( diff --git a/tests/cua_s1/test_graph_admission_benchmark.py b/tests/cua_s1/test_graph_admission_benchmark.py index 1271bba..532bb5d 100644 --- a/tests/cua_s1/test_graph_admission_benchmark.py +++ b/tests/cua_s1/test_graph_admission_benchmark.py @@ -130,7 +130,14 @@ def valid_report(): "schema_version": 1, "status": "complete", "repository": {"dirty": False, "revision": "a" * 40}, - "legacy": {"sha256": "b" * 64}, + "legacy": { + "sha256": "b" * 64, + "revision": "c" * 40, + "source_bytes_verified_against_git": True, + "current_graph_runtime_sha256": "d" * 64, + "runtime_segment_override": "current exclusive-stream _GraphSegment; historical admission policy only", + }, + "comparison_kind": "legacy-policy-with-stream-fix", "environment": {"python": "test"}, "fixture": {}, "fixture_sha256": hashlib.sha256( @@ -285,3 +292,49 @@ def test_policy_window_expires_at_request_distance_32(): events += [policy_event() for _ in range(31)] events += [policy_event(capture_attempts=1, captures=1, capture_attempt_ms=1)] verifier.verify_policy(events, "hot_four", valid_report()["config"]["admission"]) + + +def test_legacy_source_provenance_checks_git_bytes(tmp_path): + import subprocess + + revision = subprocess.check_output( + ["git", "rev-parse", "6e0a432"], cwd=ROOT, text=True + ).strip() + source = subprocess.check_output( + ["git", "show", revision + ":src/models/cua_s1/multimodal/graph_runtime.py"], + cwd=ROOT, + ) + path = tmp_path / "legacy.py" + path.write_bytes(source) + benchmark().verify_legacy_source(path, revision, ROOT) + path.write_bytes(source + b"\n# changed\n") + with pytest.raises(ValueError, match="bytes"): + benchmark().verify_legacy_source(path, revision, ROOT) + with pytest.raises(ValueError, match="40"): + benchmark().verify_legacy_source(path, "6e0a432", ROOT) + + +def test_legacy_segment_override_and_diagnostic(): + from types import SimpleNamespace + + original, current = object(), object() + module = SimpleNamespace(_GraphSegment=original) + marker = benchmark().configure_legacy_segment(module, current) + assert module._GraphSegment is current + assert ( + marker + == "current exclusive-stream _GraphSegment; historical admission policy only" + ) + module = SimpleNamespace(_GraphSegment=original) + marker = benchmark().configure_legacy_segment(module, current, unpatched=True) + assert module._GraphSegment is original + assert marker == "none; unpatched historical runtime diagnostic" + + +def test_verifier_requires_explicit_segment_provenance(): + import verify_graph_admission as verifier + + report = valid_report() + report["legacy"]["runtime_segment_override"] = "unknown" + with pytest.raises(ValueError, match="segment override"): + verifier.verify_report(report) From f285131be8ebfe6dd99a9cefd79c6ed69ff11fb3 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:51:06 +0800 Subject: [PATCH 38/75] test: stress surviving graph replay across stream retirement --- recipe/cua_s1/check_graph_capture_stream.py | 69 +++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 recipe/cua_s1/check_graph_capture_stream.py diff --git a/recipe/cua_s1/check_graph_capture_stream.py b/recipe/cua_s1/check_graph_capture_stream.py new file mode 100644 index 0000000..2731f55 --- /dev/null +++ b/recipe/cua_s1/check_graph_capture_stream.py @@ -0,0 +1,69 @@ +"""GPU regression: >32 owned capture streams, eviction, surviving replay.""" + +import argparse +import json +from pathlib import Path + + +def main(): + import torch + + from models.cua_s1.multimodal.graph_runtime import _GraphSegment + + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--output", type=Path, required=True) + args = p.parse_args() + if args.output.exists(): + p.error("choose a fresh output") + torch.manual_seed(42) + + class Layer: + def __init__(self): + self.weight = torch.randn((640, 640), device="cuda", dtype=torch.bfloat16) + + def __call__(self, hidden, **kwargs): + return hidden @ self.weight + + layers = [Layer()] + hidden = torch.randn((1, 512, 640), device="cuda", dtype=torch.bfloat16) + expected = layers[0](hidden) + segments = [_GraphSegment(layers, 0, 1, hidden, None) for _ in range(40)] + handles = [segment.capture_stream.cuda_stream for segment in segments] + assert len(set(handles)) == 40 + for segment in segments: + assert torch.equal(segment.replay(hidden, None), expected) + # Release alternating graphs and their pools, then exercise survivors with + # changed inputs. A pooled 32-stream workaround cannot satisfy uniqueness. + for segment in segments[::2]: + segment.close() + segment.close() + torch.cuda.empty_cache() + changed = hidden + 1 + expected_changed = layers[0](changed) + for _ in range(3): + for segment in segments[1::2]: + assert torch.equal(segment.replay(changed, None), expected_changed) + for segment in segments: + segment.close() + torch.cuda.empty_cache() + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text( + json.dumps( + { + "status": "complete", + "torch": torch.__version__, + "torch_git": torch.version.git_version, + "live_unique_streams": len(set(handles)), + "initial_exact_replays": 40, + "survivor_changed_input_exact_replays": 60, + "idempotent_cleanup": True, + }, + indent=2, + ) + + "\n" + ) + print("40 unique streams; 100 exact replays across graph retirement") + + +if __name__ == "__main__": + main() From 0cdaad4d63f27aaa40f0e4b1eede043a42dec24d Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:51:11 +0800 Subject: [PATCH 39/75] fix: give each CUDA graph an exclusively owned capture stream --- src/models/cua_s1/multimodal/graph_runtime.py | 63 ++++++++- tests/cua_s1/test_graph_capture_stream.py | 132 ++++++++++++++++++ 2 files changed, 191 insertions(+), 4 deletions(-) create mode 100644 tests/cua_s1/test_graph_capture_stream.py diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py index 3c0ace7..9c33a5e 100644 --- a/src/models/cua_s1/multimodal/graph_runtime.py +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -7,12 +7,13 @@ from __future__ import annotations +import ctypes import gc import math import threading import time from collections import OrderedDict -from contextlib import contextmanager +from contextlib import contextmanager, suppress from dataclasses import dataclass, field from .graph_admission import AdmissionPolicy @@ -108,6 +109,11 @@ class _GraphSegment: def __init__(self, layers, start, end, hidden, mask): import torch + self._torch = torch + self._device = hidden.device + self._stream_ptr = None + self.graph = None + self.capture_stream = None self.layers = layers self.start = start self.end = end @@ -124,11 +130,60 @@ def __init__(self, layers, start, end, hidden, mask): self._forward() torch.cuda.current_stream(hidden.device).wait_stream(stream) - self.graph = torch.cuda.CUDAGraph() - with torch.cuda.graph(self.graph), torch.no_grad(): - self.static_output = self._forward() + try: + # The pinned PyTorch build clears cuBLAS workspaces by capture + # stream when a graph is destroyed. Each live graph therefore needs + # an exclusive stream; torch.cuda.Stream cycles through a small pool. + with torch.cuda.device(self._device): + pointer = ctypes.c_void_p() + torch.cuda.check_error( + torch.cuda.cudart().cudaStreamCreate(ctypes.addressof(pointer)) + ) + self._stream_ptr = pointer.value + self.capture_stream = torch.cuda.ExternalStream( + self._stream_ptr, device=self._device + ) + self.graph = torch.cuda.CUDAGraph() + with ( + torch.cuda.graph(self.graph, stream=self.capture_stream), + torch.no_grad(), + ): + self.static_output = self._forward() + except BaseException: + # Preserve the actual capture failure if a poisoned CUDA context + # also prevents cleanup. The destructor makes a best-effort retry. + with suppress(Exception): + self.close() + raise self.bytes = max(0, torch.cuda.memory_allocated(hidden.device) - before) + def close(self): + """Release a graph before its exclusively owned capture stream.""" + if getattr(self, "_stream_ptr", None) is None: + return + torch = self._torch + with torch.cuda.device(self._device): + # Replays run on the caller's stream, not the capture stream. + torch.cuda.synchronize(self._device) + if self.graph is not None: + self.graph.reset() + self.graph = None + self.static_output = None + self.static_hidden = None + self.static_mask = None + torch.cuda.check_error( + torch.cuda.cudart().cudaStreamDestroy(self._stream_ptr) + ) + self._stream_ptr = None + self.capture_stream = None + + def __del__(self): + # Interpreter teardown and failed CUDA contexts cannot safely raise. + try: + self.close() + except Exception: # noqa: BLE001, S110 -- best-effort destructor cleanup + pass + def _forward(self): hidden = self.static_hidden for index in range(self.start, self.end): diff --git a/tests/cua_s1/test_graph_capture_stream.py b/tests/cua_s1/test_graph_capture_stream.py new file mode 100644 index 0000000..bf1121a --- /dev/null +++ b/tests/cua_s1/test_graph_capture_stream.py @@ -0,0 +1,132 @@ +"""CUDA stream ownership contracts, exercised without a GPU.""" + +import ctypes +import sys +import weakref +from contextlib import contextmanager, nullcontext +from types import SimpleNamespace + +import pytest + +from models.cua_s1.multimodal.graph_runtime import _GraphSegment + + +@pytest.fixture +def cuda(monkeypatch): + state = SimpleNamespace(events=[], next_handle=100, captures=[], fail=False) + + class Tensor: + shape = (1, 2, 3) + dtype = "bf16" + device = "cuda:0" + + def stride(self): + return (6, 3, 1) + + def clone(self): + return Tensor() + + class Stream: + def wait_stream(self, other): + pass + + class Graph: + def reset(self): + state.events.append("reset") + + def create(address): + state.next_handle += 1 + ctypes.c_void_p.from_address(address).value = state.next_handle + state.events.append("create") + return 0 + + def destroy(handle): + state.events.append(("destroy", handle)) + return 0 + + def check_error(result): + if result: + raise RuntimeError("CUDA failure") + + @contextmanager + def graph(g, stream=None): + state.captures.append(stream) + if state.fail: + raise ValueError("capture failed") + yield + + state.torch = SimpleNamespace( + no_grad=nullcontext, + cuda=SimpleNamespace( + memory_allocated=lambda device: 0, + Stream=lambda **kwargs: Stream(), + current_stream=lambda device: Stream(), + stream=lambda stream: nullcontext(), + device=lambda device: nullcontext(), + ExternalStream=lambda handle, **kwargs: SimpleNamespace(cuda_stream=handle), + cudart=lambda: SimpleNamespace( + cudaStreamCreate=create, cudaStreamDestroy=destroy + ), + check_error=check_error, + CUDAGraph=Graph, + graph=graph, + synchronize=lambda device: state.events.append("sync"), + ), + ) + monkeypatch.setitem(sys.modules, "torch", state.torch) + state.make = lambda: _GraphSegment( + [lambda *a, **kw: Tensor()], 0, 1, Tensor(), Tensor() + ) + return state + + +def test_live_segments_own_unique_capture_streams_beyond_pool_size(cuda): + segments = [cuda.make() for _ in range(40)] + assert all(stream is not None for stream in cuda.captures) + assert len({stream.cuda_stream for stream in cuda.captures}) == 40 + for segment in segments: + segment.close() + + +def test_close_waits_for_replay_then_resets_releases_and_destroys(cuda): + segment = cuda.make() + tensors = [ + weakref.ref(getattr(segment, name)) + for name in ("static_hidden", "static_mask", "static_output") + ] + handle = cuda.captures[0].cuda_stream + original_destroy = segment._torch.cuda.cudart().cudaStreamDestroy + + def destroy(value): + assert all(ref() is None for ref in tensors) + return original_destroy(value) + + cuda.torch.cuda.cudart = lambda: SimpleNamespace(cudaStreamDestroy=destroy) + cuda.events.clear() + segment.close() + assert cuda.events == ["sync", "reset", ("destroy", handle)] + segment.close() + assert cuda.events == ["sync", "reset", ("destroy", handle)] + + +def test_capture_exception_resets_graph_before_destroying_stream(cuda): + cuda.fail = True + with pytest.raises(ValueError, match="capture failed"): + cuda.make() + assert "reset" in cuda.events + assert cuda.events.index("sync") < cuda.events.index("reset") + assert cuda.events[-1][0] == "destroy" + + +def test_cleanup_failure_preserves_original_capture_exception(cuda): + cuda.fail = True + cuda.torch.cuda.synchronize = lambda device: (_ for _ in ()).throw( + RuntimeError("cleanup failed") + ) + with pytest.raises(ValueError, match="capture failed"): + cuda.make() + + +def test_destructor_tolerates_partial_initialization(): + segment = object.__new__(_GraphSegment) + segment.__del__() From b094c92a3a68742e5285c4637d29c0996ecd364d Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Mon, 28 Sep 2026 17:56:40 +0800 Subject: [PATCH 40/75] style: satisfy admission test lint rules --- tests/cua_s1/test_graph_admission.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/tests/cua_s1/test_graph_admission.py b/tests/cua_s1/test_graph_admission.py index d036d09..6ee562d 100644 --- a/tests/cua_s1/test_graph_admission.py +++ b/tests/cua_s1/test_graph_admission.py @@ -146,7 +146,7 @@ def values(length): dtype="bf16", device="cuda:0", ) - return dict(inputs_embeds=tensor, position_ids=tensor, attention_mask=tensor) + return {"inputs_embeds": tensor, "position_ids": tensor, "attention_mask": tensor} return r, values @@ -199,15 +199,13 @@ def fail(v, entry): def test_request_error_restores_scope_and_invalidation_resets_policy(monkeypatch): r, values = runtime_fixture(monkeypatch) - with pytest.raises(ValueError): - with r.request(): - r.forward(values(1)) - raise ValueError("request failed") + with pytest.raises(ValueError), r.request(): + r.forward(values(1)) + raise ValueError("request failed") assert not r._in_request with r.request(): - with pytest.raises(RuntimeError, match="nested"): - with r.request(): - pass + with pytest.raises(RuntimeError, match="nested"), r.request(): + pass with pytest.raises(RuntimeError, match="invalidate"): r.invalidate() r.invalidate() From 1b2b456da78e46ab92a34fb0ca080c8b26b07708 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 10:13:24 +0800 Subject: [PATCH 41/75] Share shape execution and pool accounting with legacy policy comparator --- recipe/cua_s1/benchmark_graph_admission.py | 21 +++++----- recipe/cua_s1/graph-admission.md | 26 ++++++++----- recipe/cua_s1/verify_graph_admission.py | 2 +- .../cua_s1/test_graph_admission_benchmark.py | 38 ++++++++++++------- 4 files changed, 52 insertions(+), 35 deletions(-) diff --git a/recipe/cua_s1/benchmark_graph_admission.py b/recipe/cua_s1/benchmark_graph_admission.py index 2f924fc..6c14cdf 100644 --- a/recipe/cua_s1/benchmark_graph_admission.py +++ b/recipe/cua_s1/benchmark_graph_admission.py @@ -57,11 +57,13 @@ def verify_legacy_source(path, revision, root): raise ValueError("legacy runtime bytes do not match the declared Git revision") -def configure_legacy_segment(module, current_segment, *, unpatched=False): +def configure_legacy_execution(module, current, *, unpatched=False): if unpatched: return "none; unpatched historical runtime diagnostic" - module._GraphSegment = current_segment - return "current exclusive-stream _GraphSegment; historical admission policy only" + module._GraphSegment = current._GraphSegment + module._ShapeEntry = current._ShapeEntry + module.GraphRuntime._run_segments = current.GraphRuntime._run_segments + return "current _GraphSegment, _ShapeEntry, and GraphRuntime._run_segments; shared per-shape pool, owned stream, and reserved-memory accounting; historical admission/cache policy only" def load_legacy(path): @@ -131,11 +133,8 @@ def main(argv=None): write_json, ) - from models.cua_s1.multimodal.graph_runtime import ( - GraphConfig, - GraphRuntime, - _GraphSegment, - ) + from models.cua_s1.multimodal import graph_runtime as current_runtime + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime from models.cua_s1.multimodal.model import MultimodalEngine from models.cua_s1.multimodal.protocol import parse_request @@ -145,8 +144,8 @@ def main(argv=None): root = Path(__file__).resolve().parents[2] verify_legacy_source(args.legacy_runtime, args.legacy_revision, root) legacy, digest = load_legacy(args.legacy_runtime) - override = configure_legacy_segment( - legacy, _GraphSegment, unpatched=args.unpatched_legacy + override = configure_legacy_execution( + legacy, current_runtime, unpatched=args.unpatched_legacy ) current_runtime_sha256 = hashlib.sha256( (root / "src/models/cua_s1/multimodal/graph_runtime.py").read_bytes() @@ -168,7 +167,7 @@ def main(argv=None): }, "comparison_kind": "unpatched-legacy-diagnostic" if args.unpatched_legacy - else "legacy-policy-with-stream-fix", + else "legacy-policy-with-execution-fix", "config": { "runs": args.runs, "cases": list(selected), diff --git a/recipe/cua_s1/graph-admission.md b/recipe/cua_s1/graph-admission.md index 0b0f332..88ef344 100644 --- a/recipe/cua_s1/graph-admission.md +++ b/recipe/cua_s1/graph-admission.md @@ -1,11 +1,14 @@ # Capture admission experiment `benchmark_graph_admission.py` measures the entire synchronized `engine.predict` -request, including cold misses and capture cost. It pairs eager, legacy-policy-with-stream-fix, and current admission on one -engine. The legacy variant imports the original #24 runtime but replaces its -`_GraphSegment` class with the current exclusive-stream implementation. This -is a comparison of admission policies with the same stream-lifetime fix, -not a reproduction of raw #24 performance. Each variant retains its own +request, including cold misses and capture cost. It pairs eager, +legacy-policy-with-execution-fix, and current admission on one engine. The legacy variant imports the original #24 runtime but replaces its +`_GraphSegment`, `_ShapeEntry`, and `GraphRuntime._run_segments` with their +current implementations. Both variants therefore use the same implementation +for per-shape graph pools, owned streams, and reserved-memory accounting. Historical admission and +cache policy remain unchanged. This is a comparison of admission policies +with the same execution and accounting fixes; it does not reproduce raw #24 +performance. Each variant retains its own runtime across a schedule; all runtimes are destroyed between runs. Execution order reverses on alternating requests and runs. No allocator purge occurs between paired variants. Both graph pools coexist, so allocator memory totals @@ -19,10 +22,10 @@ import. Reports preserve the original file SHA-256, revision, explicit segment override, and current `graph_runtime.py` SHA-256: ```sh -git show 6e0a432:src/models/cua_s1/multimodal/graph_runtime.py > /tmp/legacy-graph-runtime.py +git show 2336a085fc090c1c6ac3a297f91d826b66949f30:src/models/cua_s1/multimodal/graph_runtime.py > /tmp/legacy-graph-runtime.py PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_admission.py \ --weights /path/to/weights --output /path/to/fresh-admission-output \ - --legacy-runtime /tmp/legacy-graph-runtime.py --legacy-revision 6e0a432a6aa6fb3f1bed746ef8c68289bee20425 --runs 2 + --legacy-runtime /tmp/legacy-graph-runtime.py --legacy-revision 2336a085fc090c1c6ac3a297f91d826b66949f30 --runs 2 python recipe/cua_s1/verify_graph_admission.py /path/to/fresh-admission-output/report.json ``` @@ -51,9 +54,12 @@ Historical #24 raw JSON and its verifier retain their original schema. The initial extended experiment using unpatched legacy #24 failed during `shifting_hot` at request 104 after more than 400 completed request pairs, with an SGEMM replay error. That incomplete run is excluded from performance -conclusions. Both policy variants now use exclusively owned per-segment capture -streams to avoid a destroyed graph invalidating another live graph’s shared -stream workspace. Every published policy-comparison schedule must be rerun +conclusions. An intermediate exclusive-stream run from `b094c92` completed the +matrix but failed the publication verifier because `shifting_hot` recorded capture OOMs; +it is also excluded from performance conclusions. Both policy variants now +use per-shape pools and owned streams, with reserved-memory accounting, +to avoid both cross-shape workspace invalidation and unaccounted pool growth. +Every published policy-comparison schedule must be rerun from the updated clean source; earlier partial samples cannot be combined with those runs. diff --git a/recipe/cua_s1/verify_graph_admission.py b/recipe/cua_s1/verify_graph_admission.py index c5ae577..d97850e 100644 --- a/recipe/cua_s1/verify_graph_admission.py +++ b/recipe/cua_s1/verify_graph_admission.py @@ -133,7 +133,7 @@ def verify_report(report): "current runtime hash required", ) overrides = { - "legacy-policy-with-stream-fix": "current exclusive-stream _GraphSegment; historical admission policy only", + "legacy-policy-with-execution-fix": "current _GraphSegment, _ShapeEntry, and GraphRuntime._run_segments; shared per-shape pool, owned stream, and reserved-memory accounting; historical admission/cache policy only", "unpatched-legacy-diagnostic": "none; unpatched historical runtime diagnostic", } kind = report.get("comparison_kind") diff --git a/tests/cua_s1/test_graph_admission_benchmark.py b/tests/cua_s1/test_graph_admission_benchmark.py index 532bb5d..81a5412 100644 --- a/tests/cua_s1/test_graph_admission_benchmark.py +++ b/tests/cua_s1/test_graph_admission_benchmark.py @@ -135,9 +135,9 @@ def valid_report(): "revision": "c" * 40, "source_bytes_verified_against_git": True, "current_graph_runtime_sha256": "d" * 64, - "runtime_segment_override": "current exclusive-stream _GraphSegment; historical admission policy only", + "runtime_segment_override": "current _GraphSegment, _ShapeEntry, and GraphRuntime._run_segments; shared per-shape pool, owned stream, and reserved-memory accounting; historical admission/cache policy only", }, - "comparison_kind": "legacy-policy-with-stream-fix", + "comparison_kind": "legacy-policy-with-execution-fix", "environment": {"python": "test"}, "fixture": {}, "fixture_sha256": hashlib.sha256( @@ -317,18 +317,30 @@ def test_legacy_source_provenance_checks_git_bytes(tmp_path): def test_legacy_segment_override_and_diagnostic(): from types import SimpleNamespace - original, current = object(), object() - module = SimpleNamespace(_GraphSegment=original) - marker = benchmark().configure_legacy_segment(module, current) - assert module._GraphSegment is current - assert ( - marker - == "current exclusive-stream _GraphSegment; historical admission policy only" + original = object() + segment, entry, run = object(), object(), object() + current = SimpleNamespace( + _GraphSegment=segment, + _ShapeEntry=entry, + GraphRuntime=SimpleNamespace(_run_segments=run), ) - module = SimpleNamespace(_GraphSegment=original) - marker = benchmark().configure_legacy_segment(module, current, unpatched=True) - assert module._GraphSegment is original - assert marker == "none; unpatched historical runtime diagnostic" + for unpatched in (False, True): + runtime = SimpleNamespace(_run_segments=original, admission_policy=original) + module = SimpleNamespace( + _GraphSegment=original, _ShapeEntry=original, GraphRuntime=runtime + ) + marker = benchmark().configure_legacy_execution( + module, current, unpatched=unpatched + ) + assert module._GraphSegment is (original if unpatched else segment) + assert module._ShapeEntry is (original if unpatched else entry) + assert module.GraphRuntime._run_segments is (original if unpatched else run) + assert module.GraphRuntime.admission_policy is original + assert marker == ( + "none; unpatched historical runtime diagnostic" + if unpatched + else "current _GraphSegment, _ShapeEntry, and GraphRuntime._run_segments; shared per-shape pool, owned stream, and reserved-memory accounting; historical admission/cache policy only" + ) def test_verifier_requires_explicit_segment_provenance(): From 97913fb36ebcd7654dabc74ff14050fb17f0f2ac Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 10:16:10 +0800 Subject: [PATCH 42/75] fix: share graph pools per shape and bound reserved memory --- recipe/cua_s1/check_graph_pool.py | 112 +++++++++++++++ src/models/cua_s1/multimodal/graph_runtime.py | 127 ++++++++++++++---- tests/cua_s1/test_graph_capture_stream.py | 61 ++++++++- 3 files changed, 272 insertions(+), 28 deletions(-) create mode 100644 recipe/cua_s1/check_graph_pool.py diff --git a/recipe/cua_s1/check_graph_pool.py b/recipe/cua_s1/check_graph_pool.py new file mode 100644 index 0000000..6388361 --- /dev/null +++ b/recipe/cua_s1/check_graph_pool.py @@ -0,0 +1,112 @@ +"""GPU regression for sequential segments sharing one bounded shape pool.""" + +import argparse +import json +from pathlib import Path + + +def main(): + import torch + + from models.cua_s1.multimodal.graph_runtime import ( + GraphCache, + _GraphPool, + _GraphSegment, + _ShapeEntry, + ) + + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + if args.output.exists(): + parser.error("choose a fresh output") + torch.manual_seed(42) + + class Layer: + def __init__(self): + self.weight = ( + torch.randn((640, 640), device="cuda", dtype=torch.bfloat16) / 25 + ) + + def __call__(self, hidden, **kwargs): + return torch.relu(hidden @ self.weight) + + layers = [Layer() for _ in range(3)] + hidden = torch.randn((1, 512, 640), device="cuda", dtype=torch.bfloat16) + + def capture(): + entry = _ShapeEntry(pool=_GraphPool(hidden.device)) + value = hidden + for index in range(len(layers)): + block = _GraphSegment( + layers, index, index + 1, value, None, pool=entry.pool + ) + entry.blocks[index] = block + value = block.replay(value, None) + entry.update_bytes() + return entry + + def replay(entry, value): + for block in entry.blocks.values(): + value = block.replay(value, None) + return value + + entries = [capture() for _ in range(40)] + handles = [entry.pool.capture_stream.cuda_stream for entry in entries] + assert len(set(handles)) == 40 + reserved = [entry.pool.reserved_bytes() for entry in entries] + assert all(value > 0 for value in reserved) + for entry, size in zip(entries, reserved): + assert entry.bytes == size + sum( + block.external_bytes for block in entry.blocks.values() + ) + changed = hidden + 0.5 + expected = changed + for layer in layers: + expected = layer(expected) + for entry in entries: + assert torch.equal(replay(entry, changed), expected) + retired_ids = [entry.pool.pool_id for entry in entries[::2]] + for entry in entries[::2]: + entry.close() + torch.cuda.empty_cache() + assert all( + not torch.cuda.memory_snapshot(mempool_id=pool, include_traces=False) + for pool in retired_ids + ) + for entry in entries[1::2]: + assert torch.equal(replay(entry, changed), expected) + # Reserved footprint, not live tensor deltas, drives cache eviction. + cache = GraphCache(max_shapes=8, max_bytes=entries[1].bytes * 2) + evictions = 0 + for index, entry in enumerate(entries[1::2]): + retired = cache.put(index, entry) + assert retired is not None + evictions += len(retired) + for item in retired: + item.close() + assert cache.bytes <= cache.max_bytes + assert len(cache) <= 2 + for entry in entries: + entry.close() + torch.cuda.empty_cache() + result = { + "status": "complete", + "torch": torch.__version__, + "torch_git": torch.version.git_version, + "shape_count": 40, + "segments_per_shape": 3, + "unique_shape_streams": len(set(handles)), + "exact_changed_input_shape_replays": 60, + "pool_reserved_bytes": reserved, + "cache_budget_bytes": cache.max_bytes, + "budget_driven_evictions": evictions, + "retired_pools_released": True, + } + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(json.dumps(result, indent=2) + "\n") + print(json.dumps(result)) + + +if __name__ == "__main__": + main() diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py index 9c33a5e..0812ece 100644 --- a/src/models/cua_s1/multimodal/graph_runtime.py +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -103,15 +103,70 @@ def clear(self): return removed +class _GraphPool: + """One shape's private allocator pool and exclusively owned capture stream. + + Segments share scratch space only within a shape, in capture/replay order. + All graphs must be reset before this stream is destroyed. Other shapes use + separate streams because the pinned PyTorch clears cuBLAS workspaces by + capture stream when destroying a graph. + """ + + def __init__(self, device): + import torch + + self._torch = torch + self.device = device + self._stream_ptr = None + self.pool_id = torch.cuda.graph_pool_handle() + with torch.cuda.device(device): + pointer = ctypes.c_void_p() + torch.cuda.check_error( + torch.cuda.cudart().cudaStreamCreate(ctypes.addressof(pointer)) + ) + self._stream_ptr = pointer.value + self.capture_stream = torch.cuda.ExternalStream( + self._stream_ptr, device=device + ) + + def reserved_bytes(self): + # Allocated tensor deltas omit inactive graph scratch space, which stays + # reserved for replay. Query this pool directly, not process-wide deltas. + return sum( + segment["total_size"] + for segment in self._torch.cuda.memory_snapshot( + mempool_id=self.pool_id, include_traces=False + ) + ) + + def close(self): + if getattr(self, "_stream_ptr", None) is None: + return + torch = self._torch + with torch.cuda.device(self.device): + torch.cuda.check_error( + torch.cuda.cudart().cudaStreamDestroy(self._stream_ptr) + ) + self._stream_ptr = None + self.capture_stream = None + + def __del__(self): + try: + self.close() + except Exception: # noqa: BLE001, S110 -- best-effort destructor cleanup + pass + + class _GraphSegment: """One fixed-layout capture of adjacent linear-attention decoder layers.""" - def __init__(self, layers, start, end, hidden, mask): + def __init__(self, layers, start, end, hidden, mask, pool=None): import torch self._torch = torch self._device = hidden.device - self._stream_ptr = None + self._pool = None + self._owns_pool = pool is None self.graph = None self.capture_stream = None self.layers = layers @@ -119,9 +174,11 @@ def __init__(self, layers, start, end, hidden, mask): self.end = end self.input_signature = tensor_signature(hidden) self.mask_signature = tensor_signature(mask) - before = torch.cuda.memory_allocated(hidden.device) self.static_hidden = hidden.clone() self.static_mask = mask.clone() if mask is not None else None + self.external_bytes = self.static_hidden.untyped_storage().nbytes() + if self.static_mask is not None: + self.external_bytes += self.static_mask.untyped_storage().nbytes() stream = torch.cuda.Stream(device=hidden.device) stream.wait_stream(torch.cuda.current_stream(hidden.device)) @@ -131,21 +188,16 @@ def __init__(self, layers, start, end, hidden, mask): torch.cuda.current_stream(hidden.device).wait_stream(stream) try: - # The pinned PyTorch build clears cuBLAS workspaces by capture - # stream when a graph is destroyed. Each live graph therefore needs - # an exclusive stream; torch.cuda.Stream cycles through a small pool. with torch.cuda.device(self._device): - pointer = ctypes.c_void_p() - torch.cuda.check_error( - torch.cuda.cudart().cudaStreamCreate(ctypes.addressof(pointer)) - ) - self._stream_ptr = pointer.value - self.capture_stream = torch.cuda.ExternalStream( - self._stream_ptr, device=self._device - ) + self._pool = pool if pool is not None else _GraphPool(self._device) + self.capture_stream = self._pool.capture_stream self.graph = torch.cuda.CUDAGraph() with ( - torch.cuda.graph(self.graph, stream=self.capture_stream), + torch.cuda.graph( + self.graph, + stream=self.capture_stream, + pool=self._pool.pool_id, + ), torch.no_grad(), ): self.static_output = self._forward() @@ -155,11 +207,10 @@ def __init__(self, layers, start, end, hidden, mask): with suppress(Exception): self.close() raise - self.bytes = max(0, torch.cuda.memory_allocated(hidden.device) - before) def close(self): - """Release a graph before its exclusively owned capture stream.""" - if getattr(self, "_stream_ptr", None) is None: + """Retire a graph; shared-pool segments must all retire together.""" + if getattr(self, "_pool", None) is None: return torch = self._torch with torch.cuda.device(self._device): @@ -171,10 +222,9 @@ def close(self): self.static_output = None self.static_hidden = None self.static_mask = None - torch.cuda.check_error( - torch.cuda.cudart().cudaStreamDestroy(self._stream_ptr) - ) - self._stream_ptr = None + if self._owns_pool: + self._pool.close() + self._pool = None self.capture_stream = None def __del__(self): @@ -215,6 +265,28 @@ class _ShapeEntry: key: tuple | None = None blocks: dict = field(default_factory=dict) bytes: int = 0 + pool: _GraphPool | None = None + + def update_bytes(self): + self.bytes = self.pool.reserved_bytes() + sum( + block.external_bytes for block in self.blocks.values() + ) + + def close(self): + # No surviving segment may replay after the first reset clears the + # shared stream's workspace. The runtime retires an entire shape at once. + for block in self.blocks.values(): + block.close() + self.blocks.clear() + if self.pool is not None: + self.pool.close() + self.pool = None + + def __del__(self): + try: + self.close() + except Exception: # noqa: BLE001, S110 -- best-effort destructor cleanup + pass class GraphRuntime: @@ -340,11 +412,18 @@ def _run_segments(self, values, entry): end += 1 block = entry.blocks.get((index, end)) if block is None: + if entry.pool is None: + entry.pool = _GraphPool(hidden.device) block = _GraphSegment( - text.layers, index, end, hidden, masks["linear_attention"] + text.layers, + index, + end, + hidden, + masks["linear_attention"], + pool=entry.pool, ) entry.blocks[(index, end)] = block - entry.bytes += block.bytes + entry.update_bytes() hidden = block.replay(hidden, masks["linear_attention"]) index = end hidden = text.norm(hidden) diff --git a/tests/cua_s1/test_graph_capture_stream.py b/tests/cua_s1/test_graph_capture_stream.py index bf1121a..54531c0 100644 --- a/tests/cua_s1/test_graph_capture_stream.py +++ b/tests/cua_s1/test_graph_capture_stream.py @@ -13,7 +13,9 @@ @pytest.fixture def cuda(monkeypatch): - state = SimpleNamespace(events=[], next_handle=100, captures=[], fail=False) + state = SimpleNamespace( + events=[], next_handle=100, captures=[], pools=[], fail=False + ) class Tensor: shape = (1, 2, 3) @@ -26,6 +28,9 @@ def stride(self): def clone(self): return Tensor() + def untyped_storage(self): + return SimpleNamespace(nbytes=lambda: 12) + class Stream: def wait_stream(self, other): pass @@ -49,8 +54,9 @@ def check_error(result): raise RuntimeError("CUDA failure") @contextmanager - def graph(g, stream=None): + def graph(g, stream=None, pool=None): state.captures.append(stream) + state.pools.append(pool) if state.fail: raise ValueError("capture failed") yield @@ -59,6 +65,8 @@ def graph(g, stream=None): no_grad=nullcontext, cuda=SimpleNamespace( memory_allocated=lambda device: 0, + graph_pool_handle=lambda: (0, object()), + memory_snapshot=lambda **kwargs: [], Stream=lambda **kwargs: Stream(), current_stream=lambda device: Stream(), stream=lambda stream: nullcontext(), @@ -74,8 +82,8 @@ def graph(g, stream=None): ), ) monkeypatch.setitem(sys.modules, "torch", state.torch) - state.make = lambda: _GraphSegment( - [lambda *a, **kw: Tensor()], 0, 1, Tensor(), Tensor() + state.make = lambda **kwargs: _GraphSegment( + [lambda *a, **kw: Tensor()], 0, 1, Tensor(), Tensor(), **kwargs ) return state @@ -130,3 +138,48 @@ def test_cleanup_failure_preserves_original_capture_exception(cuda): def test_destructor_tolerates_partial_initialization(): segment = object.__new__(_GraphSegment) segment.__del__() + + +def test_shape_segments_share_pool_and_stream_but_shapes_are_isolated(cuda): + from models.cua_s1.multimodal.graph_runtime import _GraphPool, _ShapeEntry + + first = _ShapeEntry(pool=_GraphPool("cuda:0")) + second = _ShapeEntry(pool=_GraphPool("cuda:0")) + first.blocks[0] = cuda.make(pool=first.pool) + first.blocks[1] = cuda.make(pool=first.pool) + second.blocks[0] = cuda.make(pool=second.pool) + assert cuda.captures[0] is cuda.captures[1] + assert cuda.captures[0] is not cuda.captures[2] + assert cuda.pools[0] == cuda.pools[1] != cuda.pools[2] + cuda.events.clear() + first.close() + assert cuda.events.count("reset") == 2 + assert ( + len([e for e in cuda.events if isinstance(e, tuple) and e[0] == "destroy"]) == 1 + ) + assert cuda.events[-1][0] == "destroy" + assert second.blocks[0].graph is not None + second.close() + + +def test_shape_counts_all_reserved_pool_bytes_and_external_inputs_once(cuda): + from models.cua_s1.multimodal.graph_runtime import _GraphPool, _ShapeEntry + + entry = _ShapeEntry(pool=_GraphPool("cuda:0")) + entry.blocks[0] = cuda.make(pool=entry.pool) + entry.blocks[1] = cuda.make(pool=entry.pool) + calls = [] + + def snapshot(**kwargs): + calls.append(kwargs) + return [ + {"total_size": 1000, "allocated_size": 10}, + {"total_size": 2000, "allocated_size": 20}, + ] + + cuda.torch.cuda.memory_snapshot = snapshot + entry.update_bytes() + # Two independently cloned inputs and masks, each with 12 bytes of storage. + assert entry.bytes == 3000 + 4 * 12 + assert calls == [{"mempool_id": entry.pool.pool_id, "include_traces": False}] + entry.close() From dda1f0c49fa737dd7d1488fa8e9f15f88b3ffe04 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 10:18:37 +0800 Subject: [PATCH 43/75] fix: require native allocator for bounded graph memory accounting --- src/models/cua_s1/multimodal/graph_runtime.py | 4 ++++ tests/cua_s1/test_graph_capture_stream.py | 10 ++++++++++ 2 files changed, 14 insertions(+) diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py index 0812ece..377bdd6 100644 --- a/src/models/cua_s1/multimodal/graph_runtime.py +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -118,6 +118,10 @@ def __init__(self, device): self._torch = torch self.device = device self._stream_ptr = None + if torch.cuda.get_allocator_backend() != "native": + raise RuntimeError( + "graph memory accounting requires the native CUDA allocator" + ) self.pool_id = torch.cuda.graph_pool_handle() with torch.cuda.device(device): pointer = ctypes.c_void_p() diff --git a/tests/cua_s1/test_graph_capture_stream.py b/tests/cua_s1/test_graph_capture_stream.py index 54531c0..6553ea2 100644 --- a/tests/cua_s1/test_graph_capture_stream.py +++ b/tests/cua_s1/test_graph_capture_stream.py @@ -65,6 +65,7 @@ def graph(g, stream=None, pool=None): no_grad=nullcontext, cuda=SimpleNamespace( memory_allocated=lambda device: 0, + get_allocator_backend=lambda: "native", graph_pool_handle=lambda: (0, object()), memory_snapshot=lambda **kwargs: [], Stream=lambda **kwargs: Stream(), @@ -183,3 +184,12 @@ def snapshot(**kwargs): assert entry.bytes == 3000 + 4 * 12 assert calls == [{"mempool_id": entry.pool.pool_id, "include_traces": False}] entry.close() + + +def test_unsupported_allocator_fails_before_allocating_unaccounted_pool(cuda): + from models.cua_s1.multimodal.graph_runtime import _GraphPool + + cuda.torch.cuda.get_allocator_backend = lambda: "cudaMallocAsync" + with pytest.raises(RuntimeError, match="native CUDA allocator"): + _GraphPool("cuda:0") + assert not cuda.events From 9f2c7ef1cb1778b573d0c8f844c2b5be0710cf1a Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 10:21:58 +0800 Subject: [PATCH 44/75] test: reserve enough cache for distinct-layout replay coverage --- recipe/cua_s1/check_graph_admission_runtime.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/recipe/cua_s1/check_graph_admission_runtime.py b/recipe/cua_s1/check_graph_admission_runtime.py index 42a51c1..4e7e8e7 100644 --- a/recipe/cua_s1/check_graph_admission_runtime.py +++ b/recipe/cua_s1/check_graph_admission_runtime.py @@ -59,7 +59,12 @@ def main(): request = parse_request(raw) # Expanded validation limits ensure all distinct layouts and the long # prompt actually replay; performance measurements use worker defaults. - config = GraphConfig(max_tokens=4096, max_captures=16, capture_budget_ms=10000) + config = GraphConfig( + max_tokens=4096, + max_captures=16, + capture_budget_ms=10000, + max_bytes=4 * 1024**3, + ) runtime = GraphRuntime(engine.model, config) changed_image = replace( request, image=Image.new("RGB", request.image.size, "black") From fb8a47d4657f2dc3c3294ea4686a2342dc15c189 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 10:34:41 +0800 Subject: [PATCH 45/75] docs: publish verified RTX 4090 admission and pool evidence --- .../plans/2026-09-28-cua-graph-admission.md | 10 +- .../specs/2026-09-28-cua-graph-admission.md | 2 + recipe/cua_s1/README.md | 17 +- recipe/cua_s1/experiments/README.md | 5 +- .../rtx4090-graph-admission/README.md | 194 + .../rtx4090-graph-admission/SHA256SUMS | 22 + .../expanded-lint-before-final.log | 106 + .../diagnostics/failed-matrix.log | 77 + .../diagnostics/failed-report.json.gz | Bin 0 -> 76877 bytes .../diagnostics/oom-policy-report.json.gz | Bin 0 -> 94384 bytes .../diagnostics/workspace-owned.log | 1 + .../diagnostics/workspace-shared.log | 15 + .../diagnostics/workspace_repro.py.txt | 39 + .../rtx4090-graph-admission/http.json | 152 + .../http_postflight.py.txt | 46 + .../rtx4090-graph-admission/lint-baseline.log | 1 + .../rtx4090-graph-admission/lint-final.log | 2 + .../rtx4090-graph-admission/lint.log | 1 + .../rtx4090-graph-admission/packages.txt | 62 + .../rtx4090-graph-admission/parity.json | 4396 + .../rtx4090-graph-admission/pools.json | 54 + .../rtx4090-graph-admission/report.json | 96614 ++++++++++++++++ .../rtx4090-graph-admission/source.txt | 1 + .../rtx4090-graph-admission/streams.json | 9 + .../rtx4090-graph-admission/tests.log | 4 + .../rtx4090-graph-admission/verification.log | 17 + .../verify_publication.py | 54 + tests/cua_s1/test_graph_admission.py | 6 +- 28 files changed, 101896 insertions(+), 11 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/expanded-lint-before-final.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/failed-matrix.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/failed-report.json.gz create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/oom-policy-report.json.gz create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace-owned.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace-shared.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace_repro.py.txt create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/http.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/http_postflight.py.txt create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/lint-baseline.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/lint-final.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/lint.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/packages.txt create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/parity.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/pools.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/report.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/source.txt create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/streams.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/tests.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/verification.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-admission/verify_publication.py diff --git a/docs/superpowers/plans/2026-09-28-cua-graph-admission.md b/docs/superpowers/plans/2026-09-28-cua-graph-admission.md index b060348..ffd592b 100644 --- a/docs/superpowers/plans/2026-09-28-cua-graph-admission.md +++ b/docs/superpowers/plans/2026-09-28-cua-graph-admission.md @@ -8,10 +8,10 @@ **Tech Stack:** Python, PyTorch CUDA Graphs, Transformers/PEFT, pytest, RTX 4090. -- [ ] Add failing policy tests for repeated questions, sparse lengths, eviction cooldown, metadata bounds, count/time budgets and reset; run `PYTHONPATH=src python3 -m pytest tests/cua_s1/test_graph_admission.py -q`. -- [ ] Implement `graph_admission.py`; integrate request context, capture accounting, invalidation and CLI in runtime/model/server. Run new and existing CPU tests. -- [ ] Fix mixed-shape summary naming and full-response equality; preserve old raw evidence, distinguish 118/119 historical test counts. Add extended benchmark and verifier with raw responses, parity and complete schedules. Run benchmark unit tests. -- [ ] Review specification coverage, then code quality; resolve findings before final measurement. -- [ ] Transfer committed clean source to a separate server checkout. Run complete Cua-S1 tests, Ruff and GPU smoke. Compare legacy/new/eager on extended workloads, changed inputs, cache memory fallback, and HTTP. +- [x] Add failing policy tests for repeated questions, sparse lengths, eviction cooldown, metadata bounds, count/time budgets and reset; run `PYTHONPATH=src python3 -m pytest tests/cua_s1/test_graph_admission.py -q`. +- [x] Implement `graph_admission.py`; integrate request context, capture accounting, invalidation and CLI in runtime/model/server. Run new and existing CPU tests. +- [x] Fix mixed-shape summary naming and full-response equality; preserve old raw evidence, distinguish 118/119 historical test counts. Add extended benchmark and verifier with raw responses, parity and complete schedules. Run benchmark unit tests. +- [x] Review specification coverage, then code quality; resolve findings before final measurement. +- [x] Transfer committed clean source to a separate server checkout. Run complete Cua-S1 tests, Ruff and GPU smoke. Compare legacy/new/eager on extended workloads, changed inputs, cache memory fallback, and HTTP. - [ ] Independently verify raw results and hashes; document cost boundaries and limitations. Back up evidence and source locally. - [ ] Commit/push and create PR with measured source SHA and validation. Attach PR; execute server shutdown and verify connectivity stops. diff --git a/docs/superpowers/specs/2026-09-28-cua-graph-admission.md b/docs/superpowers/specs/2026-09-28-cua-graph-admission.md index 5057be4..07988da 100644 --- a/docs/superpowers/specs/2026-09-28-cua-graph-admission.md +++ b/docs/superpowers/specs/2026-09-28-cua-graph-admission.md @@ -7,3 +7,5 @@ The optional Graph worker keeps exact layouts and segmented capture. Count a lay Keep --graph opt-in; expose policy limits and clarify min-uses now counts requests. No padding, new kernels, concurrency, or upstream full-attention numerical fix. Compare eager, legacy #24 admission, and new admission with cold caches, identical inputs, alternating order, all-response parity, and captured/uncaptured layout evidence. Extend churn beyond the former eight-use blind spot. Report total/p50/p95, post-capture sustained break-even, captures/evictions, fallback causes and memory. Small differences without graph replay are timing variation. Extended GPU testing exposed an inherited graph-lifetime hazard on installed torch commit `08187d9e0fba026dc8217405802ab5381dc88d90`: CUDAGraph reset clears cuBLAS workspace by capture stream. Default shared-stream captures therefore couple independent entries. A minimal surviving-graph reproduction fails with a shared stream and passes with independently owned external streams (upstream pytorch/pytorch#193402). Add an exclusively owned CUDA capture stream per segment, using cudaStreamCreate/ExternalStream rather than the 32-stream pool, and explicit synchronization/reset/tensor-release/destroy cleanup. Test more than 32 simultaneously live segments and replay after eviction. The final policy comparison applies this same lifecycle repair to the historical admission policy; preserve the failed untouched-legacy run separately and label the control accurately. + +The complete September 29 matrix with per-segment owned streams exposed capture-OOM fallback despite exact responses. Live-tensor deltas understated private-pool reservations. Share one pool and exclusive stream across sequential segments of each shape, preserving capture/replay order and whole-shape retirement, while isolating different shapes. Account full private-pool reserved segments plus external static buffers against max_bytes. Native allocator is required for this accounting; unsupported allocators recover eagerly. Candidate capture peaks may exceed resident-cache limits. Apply the same _ShapeEntry/_run_segments/execution and accounting to the historical policy comparator and rerun all final evidence; retain OOM results only as diagnostics. diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 9990445..b3441cb 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -62,10 +62,15 @@ fall back to eager until budget expires. This is a request-count window, not a wall-clock rate limit. Cached layouts can still replay when capture is paused. `--graph-max-shapes`, `--graph-max-memory-mib` and `--graph-max-tokens` adjust -cache and token limits. Inputs above 2,048 tokens, rejected layouts, insufficient +cache and token limits. Graph pool accounting requires the native PyTorch CUDA +allocator; other allocator backends fall back eagerly. Inputs above 2,048 tokens, rejected layouts, insufficient reuse and exhausted budgets use eager inference. The memory budget covers -retained live allocations, not capture-time peaks or allocator reservations. -Cache entries own their static buffers and are evicted together. The loaded model +the full reservations of each retained Graph private pool plus external static +input buffers. It excludes the model, default allocator cache and capture-time +peaks; an in-progress candidate temporarily coexists with retained entries. +Segments of one shape share an exclusively owned stream and pool and replay +in capture order. Different shapes have independent owners. Cache entries own +their static buffers and all segments are retired together. The loaded model must remain immutable; call `graph_runtime.invalidate()` before changing its weights or adapters. Invalidation clears admission and cache state, while lifetime statistics remain cumulative. `predict` opens a serialized Graph request context; @@ -78,7 +83,11 @@ records those failures. The segmented runtime preserves eager attention behavior. See [the runtime experiment](experiments/rtx4090-graph-runtime/README.md) for fixed-layout correctness and latency, and the [mixed-length experiment](experiments/rtx4090-graph-mixed-shapes/README.md) for -capture and eviction costs under changing lengths. +capture and eviction costs under changing lengths. The subsequent +[request-aware admission report](experiments/rtx4090-graph-admission/README.md) +compares bounded capture admission against the old policy with the same owned-stream +fix, including cold requests and negative results. See the +[reproduction guide](graph-admission.md) for the complete schedules. To use the Rust frontend included in this repository: diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md index 0c801c7..46b515a 100644 --- a/recipe/cua_s1/experiments/README.md +++ b/recipe/cua_s1/experiments/README.md @@ -6,7 +6,10 @@ includes both successful fixed-shape cases and correctness-gated rejections. The [segmented Graph runtime report](rtx4090-graph-runtime/README.md) describes the subsequent worker integration and its correctness and latency results. The [mixed-length Graph report](rtx4090-graph-mixed-shapes/README.md) includes -capture and eviction costs across changing prompt lengths. +capture and eviction costs across changing prompt lengths. The subsequent +[request-aware admission report](rtx4090-graph-admission/README.md) tests +extended churn and changing hotspots with bounded capture work and exclusively +owned capture streams. For the subsequent 16-case stage profiling experiment, refreshed upstream parity, and profiler annotation-counting recovery, see the diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/README.md b/recipe/cua_s1/experiments/rtx4090-graph-admission/README.md new file mode 100644 index 0000000..40c217a --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/README.md @@ -0,0 +1,194 @@ +# Request-aware Graph admission — RTX 4090, 2026-09-29 + +This change counts reuse across distinct `predict` requests, resets sparse heat, +adds eviction cooldown, and limits capture work in a sliding request window. +Cache hits continue to replay when the capture budget is exhausted. It also gives +each shape an exclusively owned CUDA stream and a shared private pool for its +ordered segments. Cache accounting includes all reserved pool segments plus +external static input buffers, instead of only live tensor deltas. Whole-shape +retirement ordering addresses a separately reproduced stream/workspace lifetime +hazard while sharing temporary storage between that shape’s serial segments. +Graph execution remains opt-in. Admission bounds wasted work; it does not predict +whether a future workload will amortize capture. + +## Sources and measurement boundaries + +After the server endpoint changed, the interrupted September 28 matrix was +retained separately. The first September 29 matrix exposed capture-OOM fallback +and is diagnostic only. Final performance, parity and HTTP checks use clean commit +`9f2c7ef1cb1778b573d0c8f844c2b5be0710cf1a` after the pool fix. +The 164-test suite and pool/stream checks use `dda1f0c`; the only following change +before measurement raises the correctness-only cache budget to cover all seven +large distinct layouts. Runtime and performance code are identical. +Later commits format tests and add reports, publication checks and documentation, +not runtime or benchmark changes. + +The legacy comparator loads the original runtime bytes from PR #24 commit +`2336a085fc090c1c6ac3a297f91d826b66949f30`, verified against Git before import, +then substitutes the current `_GraphSegment`, `_ShapeEntry`, and `_run_segments` +implementations. Thus **both policies use the same execution, memory accounting, +and stream-lifetime fixes**, preserving historical admission/cache policy. These numbers compare admission policies; +they are not a rerun of unmodified #24. Original and current runtime SHA-256 values +are stored in the raw report and independently checked against the Git objects. + +One RTX 4090 (24,564 MiB), Python 3.12.13, PyTorch 2.14.0+cu130, Transformers +5.17.0 and PEFT 0.21.0; BF16 base with the original PEFT adapter. The installed +PyTorch Git revision is `08187d9e0fba026dc8217405802ab5381dc88d90`. +The native PyTorch CUDA allocator is required for per-pool reservation accounting. +Neither `flash-linear-attention` nor `causal-conv1d` is installed. This measures +the existing Transformers fallback implementation, not new custom kernels. + +All four schedules run twice from cold caches. There are 576 paired request +positions and **1,728 timed predictions** across eager, legacy and admission. +Each prediction contains two identical questions and one synthetic 320×240 image; +lengths vary with goal repetitions. Timing surrounds synchronized `engine.predict`, +including preparation, cold admission, capture, correctness gates, retirement and +replay. Model loading, parsing/HTTP and concurrent traffic are excluded. Execution +order reverses between adjacent positions and runs; legacy remains in the middle. +The two graph caches coexist on one engine: CUDA allocator totals include both +pools and are not isolated per-variant memory measurements. Raw cache occupancy +is recorded separately. There is no statistical significance or general GUI-task +accuracy claim from these two runs and synthetic fixtures. + +## Results + +| Workload (requests/run) | Eager total s | Legacy total s | New total s | New reduction % | +| --- | ---: | ---: | ---: | ---: | +| hot_four (48) | 11.71 / 11.35 | 7.58 / 7.30 | 7.73 / 7.58 | 33.95 / 33.23 | +| churn_twelve (72) | 17.46 / 17.58 | 51.34 / 51.96 | 17.08 / 17.08 | 2.14 / 2.88 | +| hot_cold (40) | 9.78 / 9.69 | 11.79 / 11.65 | 8.01 / 7.78 | 18.13 / 19.75 | +| shifting_hot (128) | 30.91 / 31.25 | 24.28 / 24.32 | 26.14 / 25.62 | 15.44 / 18.02 | + +| Workload | Eager p50 / p95 ms (run 1; run 2) | Legacy p50 / p95 ms | New p50 / p95 ms | +| --- | ---: | ---: | ---: | +| hot_four | 238.90 / 258.75; 234.34 / 246.69 | 109.21 / 619.87; 108.27 / 594.07 | 110.36 / 563.04; 108.45 / 539.90 | +| churn_twelve | 244.08 / 253.28; 246.51 / 253.64 | 723.02 / 804.66; 732.03 / 794.51 | 236.50 / 254.70; 238.00 / 247.66 | +| hot_cold | 242.43 / 261.83; 240.45 / 260.87 | 111.78 / 856.01; 111.08 / 845.56 | 122.85 / 556.48; 121.98 / 536.67 | +| shifting_hot | 242.69 / 252.46; 243.30 / 256.96 | 120.76 / 706.05; 121.27 / 720.38 | 123.61 / 705.66; 123.39 / 586.47 | + +| Workload | Legacy captures / evictions (run 1; run 2) | New captures / evictions | New sustained break-even request | +| --- | ---: | ---: | ---: | +| hot_four | 4 / 0; 4 / 0 | 4 / 0; 4 / 0 | 18 / 18 | +| churn_twelve | 72 / 64; 72 / 64 | 0 / 0; 0 / 0 | None / None | +| hot_cold | 12 / 5; 12 / 5 | 4 / 0; 4 / 0 | 22 / 22 | +| shifting_hot | 16 / 8; 16 / 8 | 16 / 8; 14 / 6 | 18 / 18 | + +Each pair below is run 1 / run 2. A positive reduction means faster than eager; +a negative value means slower. Total time includes every cold request and capture. +P95 uses the nearest-rank definition. The full raw report records all responses, +latencies, counters, configurations, lengths and memory samples. + +The new policy avoids all 72 captures and 64 evictions per run in the churn +schedule. Its 2.1–2.9% difference from eager is timing/order variation, **not a Graph +speedup**, because no replay occurs. The legacy policy takes about 2.94–2.96× eager +time there. In hot/cold traffic, captures fall from 12 to 4 and the new policy is +18.1–19.7% faster than eager, while legacy remains about 20% slower. + +Conservative admission is not universally better: hot-four is 33.2–34.0% faster +than eager but trails legacy's 35.3–35.6%; shifting-hot is 15.4–18.0% faster than +eager but trails legacy's 21.5–22.2%. Delayed capture and budget fallback trade +some high-reuse speed for protection from low-reuse capture churn. Capturing +workloads still have substantially higher p95 than eager. The time budget is +soft for one non-preemptible capture, not a per-request latency cap. + +Every final sample has exact full-response equality. Both policy variants record +zero capture OOMs, capture errors, numerical mismatches and rejected layouts. +Peak process reserved memory is 12,274,630,656 bytes (12.27 GB), including the +model and both policies' pools. Each runtime's retained cache stays within 1 GiB; +candidate/transient capture allocations and default allocator reservations are +outside that resident-cache limit. + +## Correctness and verification + +- `tests.log`: 164 Cua-S1 tests pass on Python 3.12.13. The baseline Ruff rule set + (`--isolated --select E4,E7,E9,F`) passes; the final local tree also passes + the exact CI rules (including import order) and format check. Expanded lint also reports nine existing + findings in historical benchmark/server files; they are not silently represented + as a clean expanded lint run. New admission test findings were fixed. +- `parity.json`: five complete-model cases, including 320×240 / 640×480, long + prompts and distinct questions. Black-image replacement and same-length question + replacement replay all questions and match the entire eager response dictionary. + Four further cases exercise eviction cooldown, a 1 MiB cache, capture-count and + capture-time budget fallback. Correctness-only limits are expanded to 4,096 + tokens, 16 captures, 10,000 ms and a 4 GiB cache so seven distinct question shapes actually + capture; performance uses the default limits, including 2,048 tokens and a 1 GiB cache. + The earlier 1 GiB correctness run preserved exact responses but evicted a large + distinct layout, so it could not demonstrate replay of all seven layouts. +- `streams.json`: 40 simultaneously live standalone capture streams, 40 exact initial + replays, then 60 exact changed-input survivor replays after retiring half the + graphs. Idempotent cleanup is exercised. This exceeds the ordinary 32-stream + pool and rules out merely rotating through pooled handles. +- `pools.json`: 40 independent shape owners, 120 sequential Graph segments and + 60 exact changed-input shape replays, including survivors after retirement. + Retired pool snapshots are empty; reserved-memory budgets force eviction. +- `http.json`: real worker health and three inference requests, including cold + admission, capture and replay, exactly match eager responses. This is a worker + HTTP smoke test; it is not a Rust-frontend or throughput benchmark. +- `verification.log`: independent stdlib verifier recomputes performance metrics, + compares complete response dictionaries, audits sliding capture budgets, and + validates memory/counter consistency. Separate publication checks require all + four cases × two runs and compare provenance hashes to the measured Git objects. + +The observed post-capture sustained break-even point must remain nonnegative +through the end of that run. First cumulative crossing can occur before capture, +so it is reported separately and is not evidence of amortization. No-capture and +net-negative runs have no post-capture break-even point. + +## Memory-pressure diagnostic + +The first complete matrix with owned per-segment streams returned exact responses +but failed the publication verifier: both policy variants entered capture-OOM +fallback in the shifting-hot workload. Reported cache tensor allocations were +about 0.73 GB per runtime while process reserved memory reached about 23.9 GB. +Those timings are diagnostic only (`diagnostics/oom-policy-report.json.gz`) and +are excluded from the final comparison. The discrepancy exposed unaccounted +Graph private-pool reservations and redundant per-segment workspaces. + +## Stream lifetime failure and limits of attribution + +An earlier full run with unpatched legacy segments stopped at `shifting_hot`, +run 1, request 104, with `CUBLAS_STATUS_EXECUTION_FAILED` in PEFT projection. +Its partial timings are excluded from the table. An isolated repeat with +`CUDA_LAUNCH_BLOCKING=1` completed, so that particular failure was not deterministic. +The incomplete report and logs are preserved under `diagnostics/` and in the +full local evidence backup. + +The installed PyTorch source clears cuBLAS workspace associated with a capture +stream when its graph is reset. A minimal shared-stream reproduction captures +forward/backward GEMMs, destroys temporary graphs on the same stream, and then +fails replaying the surviving graph with CUDA launch failure. Giving those graphs +independent owned streams makes the same reproduction pass with exact gradients. +See `diagnostics/workspace_repro.py.txt`, `workspace-shared.log` and +`workspace-owned.log`; execute the saved text with Python, with and without +`--owned`, in separate processes. This reproduces the shared-stream lifetime +hazard and supports the fix, but does not uniquely prove the cause of every +original model failure. The completed final matrix is the regression evidence. + +Shared per-shape pools follow [PyTorch’s ordered, non-concurrent replay contract](https://docs.pytorch.org/docs/2.14/notes/cuda.html#sharing-memory-across-captures). + +Primary references: [PyTorch issue #193402](https://github.com/pytorch/pytorch/issues/193402) +and the [installed wheel's CUDAGraph source](https://github.com/pytorch/pytorch/blob/08187d9e0fba026dc8217405802ab5381dc88d90/aten/src/ATen/cuda/CUDAGraph.cpp). +The installed wheel's Git SHA matters; a release tag may have different code. + +This does not fix whole-model BF16 attention differences by relaxing tolerances. +The pre-existing segmented executor still leaves full attention eager. No new +CUDA/Triton model kernel, native multimodal adapter, length bucketing, compilation, +dynamic batching or concurrent-throughput result is claimed. + +## Reproduction + +See [the experiment guide](../../graph-admission.md). Use the measured clean +commit above and the exact historical revision. Run the matrix, then: + +```sh +python recipe/cua_s1/verify_graph_admission.py /path/to/output/report.json +python recipe/cua_s1/experiments/rtx4090-graph-admission/verify_publication.py +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_graph_capture_stream.py --output /tmp/streams.json +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_graph_pool.py --output /tmp/pools.json +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_graph_admission_runtime.py --weights /path/to/weights --output /tmp/parity.json +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/experiments/rtx4090-graph-admission/http_postflight.py.txt --weights /path/to/weights --output /tmp/http.json +``` + +Use fresh output paths. `SHA256SUMS` covers the archived evidence; the raw original +failure is compressed to keep the review manageable. No model weights or credentials +are included. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-graph-admission/SHA256SUMS new file mode 100644 index 0000000..7df7fd0 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/SHA256SUMS @@ -0,0 +1,22 @@ +68e3de7845b3c53d38cd2a58bc2998c9d58a00e12076579f71daf6ad12ef892a README.md +f5e21b74dbe644016c8f222ddb6082c8d6aefc969e5a4822bdd6157dfd6874e2 diagnostics/expanded-lint-before-final.log +d76c3ff8460cb932a3d758027ef5628bdfe966bc63bae272d6fd64c50c99b69d diagnostics/failed-matrix.log +69dbce5d5dbb1a142c4f5942c38374b55df957d839f994500234653fb2b12d27 diagnostics/failed-report.json.gz +9c0a6dfe6a566180d0387fb916fd67b1636e9cda8536e728640a7bb4e5114d7e diagnostics/oom-policy-report.json.gz +66c2a370f4a5ab7daef088ce669910bd68415944b3231c02c678b511a00afed7 diagnostics/workspace-owned.log +9bbfe3abbc06174d4daa864a4d8866ade1cafc7d373eae790ee41250fc1682ed diagnostics/workspace-shared.log +b61b2afaf8b327ed1d4e775e6cfa4cf0e9618f24a267c77c91d4e5c92675f07e diagnostics/workspace_repro.py.txt +b1ee9328996f0c6b90a356ab07af9eb464aa8e55b0b6d3afdd1c99e1866de115 http.json +8977c6fee8ee4324635e8f7f2050ef48cb09770c453acc687f85bbc4fff5a1ad http_postflight.py.txt +82b3e6a6c090a57601d22943bd23fca9218d1031dbe5a7b754092f9a156b4f18 lint-baseline.log +f5d87cf8a96d00f2a59af8c24192a21c46ccdd200db52f57eda09a001d644542 lint-final.log +82b3e6a6c090a57601d22943bd23fca9218d1031dbe5a7b754092f9a156b4f18 lint.log +fee300879ec71f879ec200fd0a0ab3943caa08302d6c5efacff4717a40b89801 packages.txt +0af2718845a3b9a3f2911cd851f2331c9ba5f37739b07320e2e7914599993f0d parity.json +175da034c14c92cb9a8d7ead90087bce2d180a64d5c70455f40e37f4a432267e pools.json +63d217d385677b04982adee85e00e74fabaf7b5c31094a0960964a80bea53126 report.json +3b3cd7b587b90d5bc3fcee10da7f4a222938068fd09a7321243bae6d8119dba7 source.txt +9da88704a840797e036c1a0ed043a035aa3f81e6fde64e8f41137eab9965bd1e streams.json +eeaf94fd7d943f2780b77e8921c1619fe58f097f50b6cca8d357d58d3aed512c tests.log +02752e5281ec0bc61bcbcc7b5ba540a7381aac50011b3b5de108da9c745a4043 verification.log +8f2679642754466a8e405a8b365fc44e13f79a3ba4aaba0eb47c90ff80467527 verify_publication.py diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/expanded-lint-before-final.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/expanded-lint-before-final.log new file mode 100644 index 0000000..5306fd1 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/expanded-lint-before-final.log @@ -0,0 +1,106 @@ +B023 Function definition does not bind loop variable `request` + --> recipe/cua_s1/benchmark_graph_runtime.py:160:74 + | +158 | for name in order: +159 | engine.graph_runtime = None if name == "eager" else runtime +160 | result, elapsed = measure(lambda: engine.predict(request)) + | ^^^^^^^ +161 | if response_difference(eager_response, result) != 0: +162 | raise ValueError( + | + +B023 Function definition does not bind loop variable `options` + --> recipe/cua_s1/evaluate_multimodal.py:208:25 + | +206 | def call(): +207 | return model.forward( +208 | options, + | ^^^^^^^ +209 | app="Cua Driver", +210 | task_family="closed-candidate decision", + | + +B023 Function definition does not bind loop variable `path` + --> recipe/cua_s1/evaluate_multimodal.py:211:36 + | +209 | app="Cua Driver", +210 | task_family="closed-candidate decision", +211 | screenshot=path, + | ^^^^ +212 | goal=q.goal, +213 | ) + | + +B023 Function definition does not bind loop variable `q` + --> recipe/cua_s1/evaluate_multimodal.py:212:30 + | +210 | task_family="closed-candidate decision", +211 | screenshot=path, +212 | goal=q.goal, + | ^ +213 | ) + | + +B023 Function definition does not bind loop variable `inputs` + --> recipe/cua_s1/evaluate_multimodal.py:245:75 + | +243 | for q in request.questions: +244 | inputs = engine.prepare(request.image, q) +245 | probabilities, elapsed = measure(lambda: engine.score(inputs, q)) + | ^^^^^^ +246 | ref = expected[name, q.name] +247 | difference = max( + | + +B023 Function definition does not bind loop variable `q` + --> recipe/cua_s1/evaluate_multimodal.py:245:83 + | +243 | for q in request.questions: +244 | inputs = engine.prepare(request.image, q) +245 | probabilities, elapsed = measure(lambda: engine.score(inputs, q)) + | ^ +246 | ref = expected[name, q.name] +247 | difference = max( + | + +B023 Function definition does not bind loop variable `case` + --> recipe/cua_s1/experiments/rtx4090-reuse/verify_results.py:87:69 + | +86 | def times(name, percentile): +87 | return " / ".join(f"{r[name][percentile]:.2f}" for r in case["runs"]) + | ^^^^ +88 | +89 | reduction = " / ".join( + | + +UP041 [*] Replace aliased errors with `TimeoutError` + --> src/models/cua_s1/multimodal/server.py:100:16 + | + 98 | except InvalidRequest as exc: + 99 | self.send_json(422, {"detail": str(exc)}) +100 | except (TimeoutError, socket.timeout): + | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +101 | self.send_json(408, {"detail": "request body timed out"}) +102 | except Exception as exc: + | +help: Replace with builtin `TimeoutError` + | +99 | self.send_json(422, {"detail": str(exc)}) + - except (TimeoutError, socket.timeout): +100 + except TimeoutError: +101 | self.send_json(408, {"detail": "request body timed out"}) + | + +BLE001 Do not catch blind exception: `Exception` + --> src/models/cua_s1/multimodal/server.py:102:16 + | +100 | except (TimeoutError, socket.timeout): +101 | self.send_json(408, {"detail": "request body timed out"}) +102 | except Exception as exc: + | ^^^^^^^^^ +103 | LOG.error("inference failed: %s", type(exc).__name__) +104 | self.send_json(500, {"detail": "inference failed"}) + | + +Found 9 errors. +[*] 1 fixable with the `--fix` option. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/failed-matrix.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/failed-matrix.log new file mode 100644 index 0000000..585251b --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/failed-matrix.log @@ -0,0 +1,77 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! + Loading weights: 0%| | 0/723 [00:00 + main() + File "/root/autodl-tmp/system1-omni-work/graph-admission-repo/recipe/cua_s1/benchmark_graph_admission.py", line 195, in main + response, elapsed = measure( + ^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-admission-repo/recipe/cua_s1/benchmark_multimodal_graph.py", line 149, in measure + value = call() + ^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-admission-repo/recipe/cua_s1/benchmark_graph_admission.py", line 196, in + lambda request=requests[count]: engine.predict(request) + ^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-admission-repo/src/models/cua_s1/multimodal/model.py", line 268, in predict + return self._predict(request) + ^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-admission-repo/src/models/cua_s1/multimodal/model.py", line 277, in _predict + q.name: answer(q, self.score_reused(inputs, q, features)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-admission-repo/src/models/cua_s1/multimodal/model.py", line 225, in score_reused + logits = graph_runtime.forward( + ^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/legacy-graph-runtime-2336.py", line 271, in forward + output = self._run_segments(values, entry) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/legacy-graph-runtime-2336.py", line 222, in _run_segments + hidden = text.layers[index]( + ^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/transformers/modeling_layers.py", line 109, in __call__ + return super().__call__(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/transformers/models/qwen3_5/modeling_qwen3_5.py", line 897, in forward + hidden_states, _ = self.self_attn( + ^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/transformers/models/qwen3_5/modeling_qwen3_5.py", line 820, in forward + attn_output = self.o_proj(attn_output) + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/peft/tuners/lora/layer.py", line 1102, in forward + result = result + lora_B(lora_A(dropout(x))) * scaling + ^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1783, in _wrapped_call_impl + return self._call_impl(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1794, in _call_impl + return forward_call(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/nn/modules/linear.py", line 134, in forward + return F.linear(input, self.weight, self.bias) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)` diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/failed-report.json.gz b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/failed-report.json.gz new file mode 100644 index 0000000000000000000000000000000000000000..bef0689a424140af5d4794d5a80616735893e3a9 GIT binary patch literal 76877 zcmZTvbzD`^(!IbXMH=aDBqRl-ySqCjqy*{iZlt@ryFn$Tq*IV?>4xuMz4v~f|LMKn zv-j+owbrcJhbRmV4g~yxe0*zQZfRmnZ)aj_WA}lvZS)%I_K6E;kP+QKfv08TaSAOSft^6`2nc=r=|N`aA4x3lx+&XiG)*X^+nIyH*Og<^CepVrjNMItfJ<@n@U 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--git a/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace-shared.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace-shared.log new file mode 100644 index 0000000..2bbaca7 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace-shared.log @@ -0,0 +1,15 @@ +Traceback (most recent call last): + File "/root/autodl-tmp/system1-omni-work/experiments/graph-admission-final/workspace_repro.py", line 35, in + training.replay();torch.cuda.synchronize() + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/venv/lib/python3.12/site-packages/torch/cuda/__init__.py", line 1281, in synchronize + return torch._C._cuda_synchronize() + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +torch.AcceleratorError: CUDA error: unspecified launch failure +Search for `cudaErrorLaunchFailure' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information. +CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. +For debugging consider passing CUDA_LAUNCH_BLOCKING=1 +The CUDA driver logged these messages, which may provide useful details: +Returning 719 (CUDA_ERROR_LAUNCH_FAILED) from cuCtxSynchronize_v2 + + diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace_repro.py.txt b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace_repro.py.txt new file mode 100644 index 0000000..1523f80 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/diagnostics/workspace_repro.py.txt @@ -0,0 +1,39 @@ +import argparse,ctypes,gc,json +import torch +p=argparse.ArgumentParser();p.add_argument('--owned',action='store_true');a=p.parse_args() +torch.manual_seed(1) +left=torch.randn((32768,640),device='cuda',dtype=torch.bfloat16,requires_grad=True) +right=torch.randn((640,640),device='cuda',dtype=torch.bfloat16,requires_grad=True) +with torch.no_grad():reference=left@right +torch.cuda.synchronize() +handles=[] +def stream(): + if not a.owned:return None + h=ctypes.c_void_p();torch.cuda.check_error(torch.cuda.cudart().cudaStreamCreate(ctypes.addressof(h)));handles.append(h.value) + return torch.cuda.ExternalStream(h.value) +def capture(): + g=torch.cuda.CUDAGraph();s=stream() + with torch.cuda.graph(g,stream=s):y=left@right + g.replay();torch.cuda.synchronize();assert torch.equal(y,reference) + return g,y,s +training_stream=stream() if a.owned else torch.cuda.Stream() +if not a.owned: + def stream():return training_stream +with torch.cuda.stream(training_stream): + output=left@right + grad=torch.ones_like(output) + expected=torch.autograd.grad(output,(left,right),grad) + training=torch.cuda.CUDAGraph() + with torch.cuda.graph(training,stream=training_stream): + output=left@right + gradients=torch.autograd.grad(output,(left,right),grad) + training.replay() +torch.cuda.synchronize() +for _ in range(2): + temporary=capture() + temporary[0].reset();del temporary;gc.collect();torch.cuda.empty_cache() +training.replay();torch.cuda.synchronize() +assert all(torch.equal(a,b) for a,b in zip(expected,gradients)) +print(json.dumps({'owned':a.owned,'surviving_graph_exact':True,'torch_git':torch.version.git_version})) +training.reset();del training +for h in handles:torch.cuda.check_error(torch.cuda.cudart().cudaStreamDestroy(h)) diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/http.json b/recipe/cua_s1/experiments/rtx4090-graph-admission/http.json new file mode 100644 index 0000000..06e1eff --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/http.json @@ -0,0 +1,152 @@ +{ + "status": "complete", + "repository": { + "revision": "9f2c7ef1cb1778b573d0c8f844c2b5be0710cf1a", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "health": { + "status": "ready", + "modality": "multimodal" + }, + "eager_response": { + "model": "cua-ai/cua-s1-4b-0.2@16818868b0cc7813808aae4e87b417657046ab79:multimodal", + "answers": { + "q1": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + }, + "q2": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + } + }, + "usage": { + "input_tokens": 472, + "output_tokens": 0 + } + }, + "http_responses": [ + { + "model": "cua-ai/cua-s1-4b-0.2@16818868b0cc7813808aae4e87b417657046ab79:multimodal", + "answers": { + "q1": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + }, + "q2": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + } + }, + "usage": { + "input_tokens": 472, + "output_tokens": 0 + } + }, + { + "model": "cua-ai/cua-s1-4b-0.2@16818868b0cc7813808aae4e87b417657046ab79:multimodal", + "answers": { + "q1": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + }, + "q2": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + } + }, + "usage": { + "input_tokens": 472, + "output_tokens": 0 + } + }, + { + "model": "cua-ai/cua-s1-4b-0.2@16818868b0cc7813808aae4e87b417657046ab79:multimodal", + "answers": { + "q1": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + }, + "q2": { + "type": "choice", + "choice": "save", + "probabilities": { + "save": 0.9958575367927551, + "cancel": 0.0005507932510226965, + "wait": 0.003591623157262802 + }, + "confidence": 0.9740719299887141 + } + }, + "usage": { + "input_tokens": 472, + "output_tokens": 0 + } + } + ], + "stats": { + "requests": 3, + "no_request": 0, + "cooldown": 0, + "capture_budget": 0, + "capture_attempts": 1, + "capture_attempt_ms": 440.25929691269994, + "eager": 3, + "unsupported": 0, + "warmup": 2, + "disabled": 0, + "replays": 3, + "captures": 1, + "capture_ms": 350.8566820528358, + "evictions": 0, + "rejected": 0, + "memory_budget": 0, + "capture_oom": 0, + "capture_error": 0, + "numerical_mismatch": 0 + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/http_postflight.py.txt b/recipe/cua_s1/experiments/rtx4090-graph-admission/http_postflight.py.txt new file mode 100644 index 0000000..2aca628 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/http_postflight.py.txt @@ -0,0 +1,46 @@ +"""Run on the measured source with PYTHONPATH=src:recipe/cua_s1.""" +import argparse +import json +import threading +import urllib.request +from pathlib import Path + +from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime +from models.cua_s1.multimodal.model import MultimodalEngine +from models.cua_s1.multimodal.protocol import parse_request +from models.cua_s1.multimodal.server import WorkerServer +from profile_multimodal import case_matrix, fixture, repository_state, write_json + +p = argparse.ArgumentParser() +p.add_argument('--weights', type=Path, required=True) +p.add_argument('--output', type=Path, required=True) +a = p.parse_args() +if a.output.exists(): + raise ValueError('output exists') +engine = MultimodalEngine(str(a.weights/'Qwen3.5-4B'), str(a.weights/'cua-s1-4b-0.2/multimodal')) +raw = fixture(next(c for c in case_matrix() if c['id']=='320x240-short-q2'), a.output.parent/'http-fixtures') +expected = engine.predict(parse_request(raw)) +runtime = GraphRuntime(engine.model, GraphConfig()) +engine.graph_runtime = runtime +server = WorkerServer(('127.0.0.1',0), engine) +thread = threading.Thread(target=server.serve_forever, daemon=True) +thread.start() +base = 'http://127.0.0.1:'+str(server.server_address[1]) +try: + with urllib.request.urlopen(base+'/health',timeout=20) as r: + health = json.load(r) + responses=[] + for _ in range(3): + request=urllib.request.Request(base+'/v1/systemone', data=json.dumps(raw).encode(), headers={'Content-Type':'application/json'}) + with urllib.request.urlopen(request,timeout=60) as r: + assert r.status==200 + responses.append(json.load(r)) + assert all(r==expected for r in responses) + assert runtime.stats['captures']==1 and runtime.stats['replays']==3 + assert runtime.stats['requests']==3 + write_json(a.output, {'status':'complete','repository':repository_state(Path.cwd()),'health':health,'eager_response':expected,'http_responses':responses,'stats':dict(runtime.stats)}) + print('HTTP health, cold admission, capture and replay: exact eager parity') +finally: + server.shutdown() + thread.join() + server.server_close() diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/lint-baseline.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/lint-baseline.log new file mode 100644 index 0000000..1f5f344 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/lint-baseline.log @@ -0,0 +1 @@ +All checks passed! diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/lint-final.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/lint-final.log new file mode 100644 index 0000000..a981dc9 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/lint-final.log @@ -0,0 +1,2 @@ +All checks passed! +55 files already formatted diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/lint.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/lint.log new file mode 100644 index 0000000..1f5f344 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/lint.log @@ -0,0 +1 @@ +All checks passed! diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/packages.txt b/recipe/cua_s1/experiments/rtx4090-graph-admission/packages.txt new file mode 100644 index 0000000..ee593e5 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/packages.txt @@ -0,0 +1,62 @@ +Jinja2==3.1.6 +MarkupSafe==3.0.3 +PyYAML==6.0.3 +Pygments==2.21.0 +accelerate==1.15.0 +annotated-doc==0.0.5 +anyio==4.15.1 +certifi==2026.7.22 +click==8.5.0 +cuda-bindings==13.4.2 +cuda-pathfinder==1.8.2 +cuda-toolkit==13.0.3.0 +filelock==4.0.1 +fsspec==2026.9.0 +h11==0.16.0 +hf-xet==1.6.0 +httpcore==1.0.9 +httpx==0.28.1 +huggingface_hub==1.32.0 +idna==3.20 +iniconfig==2.3.0 +markdown-it-py==4.2.0 +mdurl==0.1.2 +mpmath==1.3.0 +networkx==3.6.1 +numpy==2.5.3 +nvidia-cublas==13.1.1.3 +nvidia-cuda-cupti==13.0.85 +nvidia-cuda-nvrtc==13.0.88 +nvidia-cuda-runtime==13.0.96 +nvidia-cudnn-cu13==9.24.0.43 +nvidia-cufft==12.0.0.61 +nvidia-cufile==1.15.1.6 +nvidia-curand==10.4.0.35 +nvidia-cusolver==12.0.4.66 +nvidia-cusparse==12.6.3.3 +nvidia-cusparselt-cu13==0.8.1 +nvidia-nccl-cu13==2.30.7 +nvidia-nvjitlink==13.4.92 +nvidia-nvshmem-cu13==3.4.5 +nvidia-nvtx==13.0.85 +packaging==26.3 +peft==0.21.0 +pillow==11.3.0 +pluggy==1.6.0 +psutil==7.2.2 +pytest==9.1.1 +regex==2026.9.10 +rich==15.0.0 +ruff==0.16.8 +safetensors==0.8.0 +setuptools==84.0.0 +shellingham==1.5.4 +sympy==1.14.0 +tokenizers==0.23.2 +torch==2.14.0 +torchvision==0.29.0 +tqdm==4.70.1 +transformers==5.17.0 +triton==3.8.0 +typer==0.27.2 +typing_extensions==4.16.0 diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/parity.json b/recipe/cua_s1/experiments/rtx4090-graph-admission/parity.json new file mode 100644 index 0000000..43416a9 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/parity.json @@ -0,0 +1,4396 @@ +{ + "status": "complete", + "repository": { + "revision": "9f2c7ef1cb1778b573d0c8f844c2b5be0710cf1a", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "environment": { + "python": "3.12.13", + "gpu": "NVIDIA GeForce RTX 4090", + "compute_capability": [ + 8, + 9 + ], + "cuda": "13.0", + "driver": "595.71.05", + "torch_num_threads": 64, + "torch_num_interop_threads": 64, + "packages": { + "torch": "2.14.0", + "torchvision": "0.29.0", + "transformers": "5.17.0", + "peft": "0.21.0", + "pillow": "11.3.0", + "safetensors": "0.8.0", + "triton": "3.8.0" + }, + "reference_revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f", + "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", + 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+ }, + "questions": { + "q1": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + }, + "q2": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + } + } + }, + "token_counts": { + "1": 236, + "2": 242, + "3": 248, + "4": 254, + "5": 260, + "6": 266, + "7": 272, + "8": 278, + "9": 284, + "10": 290, + "11": 296, + "12": 302, + "13": 308, + "14": 314, + "15": 320, + "16": 326, + "17": 332, + "18": 338, + "19": 344, + "20": 350 + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/source.txt b/recipe/cua_s1/experiments/rtx4090-graph-admission/source.txt new file mode 100644 index 0000000..5161920 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/source.txt @@ -0,0 +1 @@ +9f2c7ef1cb1778b573d0c8f844c2b5be0710cf1a diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/streams.json b/recipe/cua_s1/experiments/rtx4090-graph-admission/streams.json new file mode 100644 index 0000000..11ed05e --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/streams.json @@ -0,0 +1,9 @@ +{ + "status": "complete", + "torch": "2.14.0+cu130", + "torch_git": "08187d9e0fba026dc8217405802ab5381dc88d90", + "live_unique_streams": 40, + "initial_exact_replays": 40, + "survivor_changed_input_exact_replays": 60, + "idempotent_cleanup": true +} diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/tests.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/tests.log new file mode 100644 index 0000000..86a0832 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/tests.log @@ -0,0 +1,4 @@ +........................................................................ [ 43%] +........................................................................ [ 87%] +.................... [100%] +164 passed in 15.13s diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/verification.log b/recipe/cua_s1/experiments/rtx4090-graph-admission/verification.log new file mode 100644 index 0000000..56d6989 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/verification.log @@ -0,0 +1,17 @@ +hot_four run 1 legacy: 7577.37 ms vs eager 11710.46 ms; reduction 35.29%; p95 619.87 ms; sustained 16 +hot_four run 1 admission: 7734.34 ms vs eager 11710.46 ms; reduction 33.95%; p95 563.04 ms; sustained 18 +hot_four run 2 legacy: 7303.02 ms vs eager 11348.29 ms; reduction 35.65%; p95 594.07 ms; sustained 16 +hot_four run 2 admission: 7577.55 ms vs eager 11348.29 ms; reduction 33.23%; p95 539.90 ms; sustained 18 +churn_twelve run 1 legacy: 51336.25 ms vs eager 17455.10 ms; reduction -194.10%; p95 804.66 ms; sustained None +churn_twelve run 1 admission: 17082.38 ms vs eager 17455.10 ms; reduction 2.14%; p95 254.70 ms; sustained None +churn_twelve run 2 legacy: 51957.36 ms vs eager 17581.85 ms; reduction -195.52%; p95 794.51 ms; sustained None +churn_twelve run 2 admission: 17075.38 ms vs eager 17581.85 ms; reduction 2.88%; p95 247.66 ms; sustained None +hot_cold run 1 legacy: 11788.70 ms vs eager 9784.76 ms; reduction -20.48%; p95 856.01 ms; sustained None +hot_cold run 1 admission: 8010.80 ms vs eager 9784.76 ms; reduction 18.13%; p95 556.48 ms; sustained 22 +hot_cold run 2 legacy: 11647.41 ms vs eager 9688.87 ms; reduction -20.21%; p95 845.56 ms; sustained None +hot_cold run 2 admission: 7775.37 ms vs eager 9688.87 ms; reduction 19.75%; p95 536.67 ms; sustained 22 +shifting_hot run 1 legacy: 24278.16 ms vs eager 30910.87 ms; reduction 21.46%; p95 706.05 ms; sustained 16 +shifting_hot run 1 admission: 26138.76 ms vs eager 30910.87 ms; reduction 15.44%; p95 705.66 ms; sustained 18 +shifting_hot run 2 legacy: 24321.80 ms vs eager 31253.25 ms; reduction 22.18%; p95 720.38 ms; sustained 16 +shifting_hot run 2 admission: 25621.29 ms vs eager 31253.25 ms; reduction 18.02%; p95 586.47 ms; sustained 18 +Publication gate passed: four schedules x two runs; 576 triplets / 1728 predictions; Git hashes match diff --git a/recipe/cua_s1/experiments/rtx4090-graph-admission/verify_publication.py b/recipe/cua_s1/experiments/rtx4090-graph-admission/verify_publication.py new file mode 100644 index 0000000..8d68ae8 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-admission/verify_publication.py @@ -0,0 +1,54 @@ +"""Require the complete publication matrix and verify its Git source hashes.""" + +import hashlib +import json +import subprocess +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +ROOT = HERE.parents[3] +sys.path.insert(0, str(HERE.parents[1])) + + +def main(): + from verify_graph_admission import verify_report + + report = json.loads((HERE / "report.json").read_text()) + verify_report(report) + measured = "9f2c7ef1cb1778b573d0c8f844c2b5be0710cf1a" + historical = "2336a085fc090c1c6ac3a297f91d826b66949f30" + assert report["repository"]["revision"] == measured + assert report["legacy"]["revision"] == historical + assert report["comparison_kind"] == "legacy-policy-with-execution-fix" + assert report["config"]["runs"] == 2 + expected = {"hot_four": 48, "churn_twelve": 72, "hot_cold": 40, "shifting_hot": 128} + assert set(report["workloads"]) == set(expected) + count = 0 + for name, length in expected.items(): + runs = report["workloads"][name] + assert len(runs) == 2 + for run in runs: + assert len(run["events"]) == length + count += length + assert count == 576 + for revision, key in [ + (historical, "sha256"), + (measured, "current_graph_runtime_sha256"), + ]: + source = subprocess.check_output( + [ + "git", + "show", + revision + ":src/models/cua_s1/multimodal/graph_runtime.py", + ], + cwd=ROOT, + ) + assert hashlib.sha256(source).hexdigest() == report["legacy"][key] + print( + "Publication gate passed: four schedules x two runs; 576 triplets / 1728 predictions; Git hashes match" + ) + + +if __name__ == "__main__": + main() diff --git a/tests/cua_s1/test_graph_admission.py b/tests/cua_s1/test_graph_admission.py index 6ee562d..491e510 100644 --- a/tests/cua_s1/test_graph_admission.py +++ b/tests/cua_s1/test_graph_admission.py @@ -146,7 +146,11 @@ def values(length): dtype="bf16", device="cuda:0", ) - return {"inputs_embeds": tensor, "position_ids": tensor, "attention_mask": tensor} + return { + "inputs_embeds": tensor, + "position_ids": tensor, + "attention_mask": tensor, + } return r, values From 5376cac0d1350fba5f6176d13b5515b42c0ddba6 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 10:37:20 +0800 Subject: [PATCH 46/75] docs: record PR handoff and verified server shutdown --- docs/superpowers/plans/2026-09-28-cua-graph-admission.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/superpowers/plans/2026-09-28-cua-graph-admission.md b/docs/superpowers/plans/2026-09-28-cua-graph-admission.md index ffd592b..a1b5ef5 100644 --- a/docs/superpowers/plans/2026-09-28-cua-graph-admission.md +++ b/docs/superpowers/plans/2026-09-28-cua-graph-admission.md @@ -13,5 +13,7 @@ - [x] Fix mixed-shape summary naming and full-response equality; preserve old raw evidence, distinguish 118/119 historical test counts. Add extended benchmark and verifier with raw responses, parity and complete schedules. Run benchmark unit tests. - [x] Review specification coverage, then code quality; resolve findings before final measurement. - [x] Transfer committed clean source to a separate server checkout. Run complete Cua-S1 tests, Ruff and GPU smoke. Compare legacy/new/eager on extended workloads, changed inputs, cache memory fallback, and HTTP. -- [ ] Independently verify raw results and hashes; document cost boundaries and limitations. Back up evidence and source locally. -- [ ] Commit/push and create PR with measured source SHA and validation. Attach PR; execute server shutdown and verify connectivity stops. +- [x] Independently verify raw results and hashes; document cost boundaries and limitations. Back up evidence and source locally. +- [x] Commit/push and create PR with measured source SHA and validation. Attach PR; execute server shutdown and verify connectivity stops. + +Completed 2026-09-29: [PR #33](https://github.com/ThinkFlowLab/system1-omni/pull/33). Final measured source `9f2c7ef`, final implementation/evidence validation `fb8a47d`: 164 tests, CI lint/format, complete publication verifier. Local evidence archive SHA-256 `158d40ae84387cc3e246e0bcb81a3bd5dd03c1887801895c3600971d3142a80d`. Server shutdown returned successfully and the subsequent SSH connection was refused. From be44f57ed8a34c4122a6a03fb0adf33bf3000c51 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 11:30:30 +0800 Subject: [PATCH 47/75] Fix shallow-checkout provenance test and image aspect validation --- recipe/cua_s1/README.md | 4 +- src/models/cua_s1/multimodal/protocol.py | 4 ++ .../cua_s1/test_graph_admission_benchmark.py | 40 +++++++++++++++---- tests/cua_s1/test_protocol.py | 16 ++++++++ tests/cua_s1/test_server.py | 22 +++++++++- 5 files changed, 76 insertions(+), 10 deletions(-) diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index b3441cb..0cc3bd6 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -133,7 +133,9 @@ The request shape is: the upstream chooser's label escaping. Instructions accept strings, objects or arrays and must be present; an empty string or `null` omits the goal block. - Limits: 8 MiB body, 4 MiB decoded image, 2048 pixels per side, 1,048,576 pixels - total, 16,384 characters per question and 4096 processed tokens per question. + total, at most 200:1 image aspect ratio, 16,384 characters per question and + 4096 processed tokens per question. Unsupported image dimensions or aspect + ratios return `422` before model inference. Every question is validated/preprocessed before any forward pass begins. - `<|image_pad|>`, `<|video_pad|>`, `<|vision_start|>` and `<|vision_end|>` are rejected in user text because the processor interprets them as media controls. diff --git a/src/models/cua_s1/multimodal/protocol.py b/src/models/cua_s1/multimodal/protocol.py index d83c6fc..a11883f 100644 --- a/src/models/cua_s1/multimodal/protocol.py +++ b/src/models/cua_s1/multimodal/protocol.py @@ -16,6 +16,7 @@ MAX_IMAGE_BYTES = 4 * 1024 * 1024 MAX_PIXELS = 1024 * 1024 MAX_SIDE = 2048 +MAX_ASPECT_RATIO = 200 MAX_QUESTIONS = 8 MAX_TEXT = 16384 @@ -140,6 +141,9 @@ def _image(state): raise InvalidRequest( "image must be single-frame, at most 2048 per side and 1048576 pixels" ) + # Match the pinned Qwen image processor before entering inference. + if max(w, h) > MAX_ASPECT_RATIO * min(w, h): + raise InvalidRequest("image aspect ratio must be at most 200:1") source.load() return source.convert("RGB") except ( diff --git a/tests/cua_s1/test_graph_admission_benchmark.py b/tests/cua_s1/test_graph_admission_benchmark.py index 81a5412..bc8c84b 100644 --- a/tests/cua_s1/test_graph_admission_benchmark.py +++ b/tests/cua_s1/test_graph_admission_benchmark.py @@ -297,21 +297,45 @@ def test_policy_window_expires_at_request_distance_32(): def test_legacy_source_provenance_checks_git_bytes(tmp_path): import subprocess + # Keep this unit test independent of checkout depth and project history. + repo = tmp_path / "repo" + repo.mkdir() + subprocess.run(["git", "init", "-q", str(repo)], check=True) + relative = "src/models/cua_s1/multimodal/graph_runtime.py" + tracked = repo / relative + tracked.parent.mkdir(parents=True) + source = b"# Synthetic historical runtime for byte provenance validation.\n" + tracked.write_bytes(source) + subprocess.run(["git", "add", relative], cwd=repo, check=True) + subprocess.run( + [ + "git", + "-c", + "user.name=Provenance Test", + "-c", + "user.email=provenance@example.invalid", + "-c", + "commit.gpgsign=false", + "commit", + "-qm", + "Add synthetic runtime", + ], + cwd=repo, + check=True, + ) revision = subprocess.check_output( - ["git", "rev-parse", "6e0a432"], cwd=ROOT, text=True + ["git", "rev-parse", "HEAD"], cwd=repo, text=True ).strip() - source = subprocess.check_output( - ["git", "show", revision + ":src/models/cua_s1/multimodal/graph_runtime.py"], - cwd=ROOT, - ) + # Provenance must use the committed bytes, not the current working file. + tracked.write_bytes(source + b"# Uncommitted change\n") path = tmp_path / "legacy.py" path.write_bytes(source) - benchmark().verify_legacy_source(path, revision, ROOT) + benchmark().verify_legacy_source(path, revision, repo) path.write_bytes(source + b"\n# changed\n") with pytest.raises(ValueError, match="bytes"): - benchmark().verify_legacy_source(path, revision, ROOT) + benchmark().verify_legacy_source(path, revision, repo) with pytest.raises(ValueError, match="40"): - benchmark().verify_legacy_source(path, "6e0a432", ROOT) + benchmark().verify_legacy_source(path, revision[:7], repo) def test_legacy_segment_override_and_diagnostic(): diff --git a/tests/cua_s1/test_protocol.py b/tests/cua_s1/test_protocol.py index cafce3d..c518cf6 100644 --- a/tests/cua_s1/test_protocol.py +++ b/tests/cua_s1/test_protocol.py @@ -96,6 +96,22 @@ def test_mime_mismatch_and_oversized_dimensions(): parse_request(r) +@pytest.mark.parametrize("fmt", ["PNG", "JPEG"]) +@pytest.mark.parametrize("size", [(2048, 1), (1, 2048), (201, 1), (1, 201)]) +def test_unsupported_image_aspect_ratio_is_rejected(fmt, size): + r = request() + r["state"]["image"] = image_url(fmt, size) + with pytest.raises(InvalidRequest, match="aspect ratio"): + parse_request(r) + + +@pytest.mark.parametrize("size", [(200, 1), (1, 200), (199, 1), (1, 199)]) +def test_supported_image_aspect_ratio_boundary_is_preserved(size): + r = request() + r["state"]["image"] = image_url(size=size) + assert parse_request(r).image.size == size + + @pytest.mark.parametrize( "criteria", [{}, {str(i): "x" for i in range(27)}, {"a": 1}, {"a": True}] ) diff --git a/tests/cua_s1/test_server.py b/tests/cua_s1/test_server.py index 2973217..e434077 100644 --- a/tests/cua_s1/test_server.py +++ b/tests/cua_s1/test_server.py @@ -5,7 +5,7 @@ import urllib.request import pytest -from test_protocol import request +from test_protocol import image_url, request from models.cua_s1.multimodal.protocol import MAX_BODY from models.cua_s1.multimodal.server import WorkerServer @@ -59,6 +59,26 @@ def test_invalid_question_never_reaches_model(worker): assert status == 422 and set(body) == {"detail"} +@pytest.mark.parametrize("size", [(2048, 1), (1, 2048)]) +def test_unsupported_image_aspect_ratio_returns_422_without_inference(worker, size): + server, url = worker + calls = [] + + def unexpected(parsed): + calls.append(parsed) + raise AssertionError("invalid image reached inference") + + server.engine.predict = unexpected + value = request() + value["state"]["image"] = image_url(size=size) + status, body = call(url + "/v1/systemone", value) + assert status == 422 and set(body) == {"detail"} + assert "aspect ratio" in body["detail"] + assert not calls + assert server.inference_lock.acquire(timeout=1) + server.inference_lock.release() + + def test_busy_worker_rejects_instead_of_queueing_gpu_work(worker): server, url = worker server.inference_lock.acquire() From 3a3f2358d7d108cc5269b08862f46e9fc400e7e8 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:20:25 +0800 Subject: [PATCH 48/75] docs: scope segmented graph bucketing experiment --- .../plans/2026-09-29-cua-graph-buckets.md | 14 ++++++++++++++ .../specs/2026-09-29-cua-graph-buckets.md | 9 +++++++++ 2 files changed, 23 insertions(+) create mode 100644 docs/superpowers/plans/2026-09-29-cua-graph-buckets.md create mode 100644 docs/superpowers/specs/2026-09-29-cua-graph-buckets.md diff --git a/docs/superpowers/plans/2026-09-29-cua-graph-buckets.md b/docs/superpowers/plans/2026-09-29-cua-graph-buckets.md new file mode 100644 index 0000000..1ea8459 --- /dev/null +++ b/docs/superpowers/plans/2026-09-29-cua-graph-buckets.md @@ -0,0 +1,14 @@ +# Cua-S1 Graph Bucket Experiment Implementation Plan + +Execute inline in the isolated worktree. Scope is an experiment and reusable opt-in recipe runtime, not default production enablement. + +**Goal:** Determine whether causal segment padding can safely reduce length churn and end-to-end latency. +**Architecture:** A recipe-only GraphRuntime subclass reuses #33 capture, admission, allocator and stream ownership. It changes segment input packing and cache keys, adds per-real-length exact validation, and keeps full attention at original length. +**Tech Stack:** Pinned PyTorch 2.14.0+cu130, Transformers 5.17.0, PEFT 0.21.0; RTX 4090. + +- [ ] Establish baseline: bundle clean PR #33 source to a separate server checkout; run `PYTHONPATH=src python -m pytest tests/cua_s1 -q`. Preserve existing remote experiments. +- [ ] Add `tests/cua_s1/test_graph_buckets.py` before runtime code: bucket boundaries 63/64/65, invalid widths, prefix preservation/zero-tail reset, key sharing/separation, and per-length rejection without poisoning verified lengths. Run failures before implementing `recipe/cua_s1/graph_buckets.py`. +- [ ] Implement only dense batch-one/no-input-padding support. Use `_ShapeEntry` and `_GraphSegment` unchanged; pad each linear run then crop. Retain lock, request accounting, capture budget, strict full-logit capture gate and per-length validation/rejection metadata bounded by resident entries. +- [ ] Add `recipe/cua_s1/benchmark_graph_buckets.py`: clean revision/environment provenance, complete response comparison, cold-inclusive timing, balanced variant order and incremental report writes. First run correctness on 12 distinct lengths and changed image/text. If exact parity fails, keep diagnostics and investigate before timing claims. +- [ ] Run repeated mixed-length and stable-hot schedules. Separately measure each variant's reservation with no other graph runtime resident. Include padding ratio, p50/p95, total time, capture/validation counts, and regressions. +- [ ] Preserve raw evidence and a reproducible report under `recipe/cua_s1/experiments/rtx4090-graph-buckets/`; verify recomputed metrics and full responses, run CI-scoped lint/tests, and record limitations. diff --git a/docs/superpowers/specs/2026-09-29-cua-graph-buckets.md b/docs/superpowers/specs/2026-09-29-cua-graph-buckets.md new file mode 100644 index 0000000..10e981f --- /dev/null +++ b/docs/superpowers/specs/2026-09-29-cua-graph-buckets.md @@ -0,0 +1,9 @@ +# Cua-S1 segmented Graph bucketing experiment + +User approved starting the previously proposed length-bucketing experiment on the supplied RTX 4090 server. Base: PR #33 commit be44f57ed8a34c4122a6a03fb0adf33bf3000c51. Preserve the existing worker behavior; first implement an opt-in experimental runtime in recipe/cua_s1. + +Pad only causal linear-attention runs on the right to a multiple of 64 (the installed fallback DeltaNet chunk width). Crop each run's output before full attention. Full attention, rotary positions, final-token selection and usage remain at original length. The first experiment fixes image geometry and batch size one, with no padding in the original request. Reject other mask/layout forms. No recurrent state or KV cache survives a forward. + +Capture keys describe the padded segment layout; position values never enter captured linear layers. Bound caches, capture work, streams and pools using #33. Validate complete logits exactly against unpadded eager on capture and the first encounter of each real length in each resident bucket. Reject mismatching lengths and preserve eager fallback; changed image/text checks remain necessary because one-time validation is not a universal proof. + +Measure cold-inclusive synchronized predict latency against eager and unmodified exact-layout Graph runtime, with two questions per request and balanced variant order. Record first-length checks, captures, replay, padding, memory and complete responses. Warm replay must be distinguished from first-length validation. Keep negative results, and do not enable a worker option until this approach has evidence of correctness and benefit. From e8d73e8daf97acc76beeb2c7174f3e073740e633 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:24:15 +0800 Subject: [PATCH 49/75] experiment: bucket causal graph segments with strict per-length gates --- recipe/cua_s1/benchmark_graph_buckets.py | 207 +++++++++++++++++++++++ recipe/cua_s1/graph_buckets.py | 184 ++++++++++++++++++++ tests/cua_s1/test_graph_buckets.py | 106 ++++++++++++ 3 files changed, 497 insertions(+) create mode 100644 recipe/cua_s1/benchmark_graph_buckets.py create mode 100644 recipe/cua_s1/graph_buckets.py create mode 100644 tests/cua_s1/test_graph_buckets.py diff --git a/recipe/cua_s1/benchmark_graph_buckets.py b/recipe/cua_s1/benchmark_graph_buckets.py new file mode 100644 index 0000000..11db2c5 --- /dev/null +++ b/recipe/cua_s1/benchmark_graph_buckets.py @@ -0,0 +1,207 @@ +"""Capture-inclusive eager / exact / bucket experiment on the pinned model.""" + +from __future__ import annotations + +import argparse +import gc +import hashlib +import math +import statistics +from dataclasses import asdict, replace +from pathlib import Path + + +def schedules(): + return { + "probe": list(range(1, 13)) * 2, + "hot_four": [1, 2, 4, 8] * 12, + "churn_twelve": list(range(1, 13)) * 6, + } + + +def summary(events, variant): + samples = [event["variants"][variant]["latency_ms"] for event in events] + ordered = sorted(samples) + eager_total = sum(e["variants"]["eager"]["latency_ms"] for e in events) + return { + "requests": len(samples), + "total_ms": sum(samples), + "p50_ms": statistics.median(samples), + "p95_ms": ordered[math.ceil(len(samples) * 0.95) - 1], + "total_reduction_vs_eager_pct": 100 * (1 - sum(samples) / eager_total), + } + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + p.add_argument("--runs", type=int, default=2) + p.add_argument("--width", type=int, default=64) + p.add_argument("--case", choices=list(schedules()), action="append") + args = p.parse_args() + if args.output.exists(): + p.error("choose a fresh output directory") + if args.runs < 1: + p.error("runs must be positive") + + import torch + from benchmark_multimodal_graph import measure + from evaluate_multimodal import environment + from graph_buckets import BucketRuntime, bucket_length + from profile_multimodal import ( + GOAL, + case_matrix, + fixture, + repository_state, + write_json, + ) + + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + bucket_length(1, args.width) + root = Path(__file__).resolve().parents[2] + source = repository_state(root) + if source["dirty"] or not source["revision"]: + raise ValueError("clean committed source required") + args.output.mkdir(parents=True) + report_path = args.output / "report.json" + selected = {k: v for k, v in schedules().items() if k in (args.case or ["probe"])} + config = GraphConfig() + report = { + "status": "running", + "repository": source, + "environment": environment(), + "source_sha256": { + str(path.relative_to(root)): hashlib.sha256(path.read_bytes()).hexdigest() + for path in [Path(__file__), Path(__file__).with_name("graph_buckets.py")] + }, + "config": { + "graph": asdict(config), + "width": args.width, + "runs": args.runs, + "timing": "synchronized predict including cold capture and first-length checks", + "memory": "paired caches coexist; allocator totals are combined, cache bytes are per variant", + "scope": "serial, two identical questions, synthetic 320x240 image, no HTTP", + "order": "all six permutations cycle across requests, reversed across runs", + }, + "workloads": {}, + } + write_json(report_path, report) + try: + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + case = next(c for c in case_matrix() if c["id"] == "320x240-short-q2") + raw = fixture(case, args.output / "fixtures") + report["fixture"] = raw + base = parse_request(raw) + counts = sorted({n for s in selected.values() for n in s}) + requests = { + n: replace( + base, + questions=tuple( + replace(q, goal=" ".join([GOAL] * n)) for q in base.questions + ), + ) + for n in counts + } + tokens = { + n: int( + engine.prepare_reused(r.image, r.questions)[0]["input_ids"].shape[-1] + ) + for n, r in requests.items() + } + report["tokens"] = tokens + report["buckets"] = {n: bucket_length(v, args.width) for n, v in tokens.items()} + print("lengths", tokens, "buckets", report["buckets"], flush=True) + for _ in range(3): + engine.predict(requests[counts[0]]) + orders = [ + ("eager", "exact", "bucket"), + ("exact", "bucket", "eager"), + ("bucket", "eager", "exact"), + ("bucket", "exact", "eager"), + ("exact", "eager", "bucket"), + ("eager", "bucket", "exact"), + ] + for name, schedule in selected.items(): + report["workloads"][name] = [] + for run in range(args.runs): + runtimes = { + "exact": GraphRuntime(engine.model, config), + "bucket": BucketRuntime(engine.model, config, width=args.width), + } + record = {"run": run + 1, "status": "running", "events": []} + report["workloads"][name].append(record) + for index, n in enumerate(schedule): + order = orders[(index + run * 3) % len(orders)] + event = { + "index": index, + "goal_repetitions": n, + "tokens": tokens[n], + "bucket": bucket_length(tokens[n], args.width), + "order": order, + "variants": {}, + } + record["events"].append(event) + for variant in order: + runtime = runtimes.get(variant) + engine.graph_runtime = runtime + before = dict(runtime.stats) if runtime else {} + torch.cuda.reset_peak_memory_stats() + response, elapsed = measure(lambda: engine.predict(requests[n])) + event["variants"][variant] = { + "response": response, + "latency_ms": elapsed, + "stats_delta": { + k: runtime.stats[k] - v for k, v in before.items() + } + if runtime + else {}, + "cache_shapes": len(runtime.cache) if runtime else 0, + "cache_bytes": runtime.cache.bytes if runtime else 0, + "reserved_bytes": torch.cuda.memory_reserved(), + "peak_reserved_bytes": torch.cuda.max_memory_reserved(), + } + assert all( + v["response"] == event["variants"]["eager"]["response"] + for v in event["variants"].values() + ), (name, run, index) + write_json(report_path, report) + if index % 12 == 11: + print( + name, + run + 1, + index + 1, + {v: dict(r.stats) for v, r in runtimes.items()}, + flush=True, + ) + record["summary"] = { + v: summary(record["events"], v) + for v in ("eager", "exact", "bucket") + } + record["stats_final"] = {v: dict(r.stats) for v, r in runtimes.items()} + record["status"] = "complete" + engine.graph_runtime = None + for runtime in runtimes.values(): + runtime.invalidate() + del runtime, runtimes + gc.collect() + torch.cuda.empty_cache() + write_json(report_path, report) + print(name, run + 1, record["summary"], flush=True) + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(report_path, report) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/graph_buckets.py b/recipe/cua_s1/graph_buckets.py new file mode 100644 index 0000000..557b30d --- /dev/null +++ b/recipe/cua_s1/graph_buckets.py @@ -0,0 +1,184 @@ +"""Experimental right-padded linear segments; full attention stays exact-length. + +Not enabled by the worker. Only batch-one dense, cache-free prefill is supported. +The causal linear layers cannot read future padded tokens. Nevertheless GEMM +shapes can change rounding, so every real length receives a strict logits gate. +""" + +from __future__ import annotations + +import time + +from models.cua_s1.multimodal.graph_runtime import ( + GraphRuntime, + _GraphPool, + _GraphSegment, +) + + +def bucket_length(length, width): + if type(width) is not int or width <= 0 or width % 64: + raise ValueError("bucket width must be a positive integer multiple of 64") + return ((length + width - 1) // width) * width + + +def pack_hidden(hidden, width): + import torch.nn.functional as F + + length = hidden.shape[1] + return F.pad(hidden, (0, 0, 0, bucket_length(length, width) - length)) + + +class BucketRuntime(GraphRuntime): + """Recipe-only variant with the same ownership/admission policy as #33.""" + + def __init__(self, model, config=None, *, width=64): + bucket_length(1, width) + super().__init__(model, config) + self.width = width + self.stats.update( + length_checks=0, + length_rejections=0, + length_disabled=0, + length_check_ms=0.0, + real_tokens=0, + padded_tokens=0, + ) + + @staticmethod + def _dense(values): + hidden = values["inputs_embeds"] + mask = values["attention_mask"] + return ( + hidden.is_contiguous() + and mask is not None + and tuple(mask.shape) == tuple(hidden.shape[:2]) + and bool((mask == 1).all()) + ) + + def _key(self, values): + hidden = values["inputs_embeds"] + return ( + id(self.model), + self.model.get_base_model().model.language_model.config._attn_implementation, + bucket_length(hidden.shape[1], self.width), + hidden.shape[2], + str(hidden.dtype), + str(hidden.device), + ) + + def _validate_replay(self, values, entry, output): + import torch + + length = values["inputs_embeds"].shape[1] + if length in entry.verified_lengths: + return output + started = time.perf_counter() + reference = self._eager(values) + self.stats["length_checks"] += 1 + if torch.equal(reference, output): + entry.verified_lengths.add(length) + else: + entry.rejected_lengths.add(length) + self.stats["length_rejections"] += 1 + output = reference + self.stats["length_check_ms"] += (time.perf_counter() - started) * 1000 + return output + + def forward(self, values): + import torch + + with self.lock, torch.no_grad(): + if not self._in_request: + self.stats["no_request"] += 1 + return self._eager(values) + if not self._supported(values) or not self._dense(values): + self.stats["unsupported"] += 1 + return self._eager(values) + length = values["inputs_embeds"].shape[1] + if bucket_length(length, self.width) > self.config.max_tokens: + self.stats["unsupported"] += 1 + return self._eager(values) + key = self._key(values) + if key in self.disabled: + self.stats["disabled"] += 1 + return self._eager(values) + entry = self.cache.get(key) + if entry is not None: + if length in entry.rejected_lengths: + self.stats["length_disabled"] += 1 + return self._eager(values) + output = self._run_segments(values, entry) + self.stats["replays"] += 1 + return self._validate_replay(values, entry, output) + reason = self.admission.reason(key) + if reason is not None: + self.stats[reason] += 1 + return self._eager(values) + ticket = self.admission.start_capture() + self.stats["capture_attempts"] += 1 + attempt_start = time.perf_counter() + try: + output = self._capture(values, key) + entry = self.cache.get(key) + if entry is not None: + entry.verified_lengths = {length} + entry.rejected_lengths = set() + return output + finally: + elapsed = (time.perf_counter() - attempt_start) * 1000 + self.admission.finish_capture(ticket, elapsed) + self.stats["capture_attempt_ms"] += elapsed + + def _run_segments(self, values, entry): + from transformers.masking_utils import create_causal_mask + + core = self.model.get_base_model().model + text = core.language_model + hidden = values["inputs_embeds"] + length = hidden.shape[1] + self.stats["real_tokens"] += length + self.stats["padded_tokens"] += bucket_length(length, self.width) - length + mask = create_causal_mask( + config=text.config, + inputs_embeds=hidden, + attention_mask=values["attention_mask"], + past_key_values=None, + position_ids=None, + ) + rope = text.rotary_emb(hidden, values["position_ids"]) + index = 0 + while index < text.config.num_hidden_layers: + if text.config.layer_types[index] == "full_attention": + hidden = text.layers[index]( + hidden, + position_embeddings=rope, + attention_mask=mask, + position_ids=None, + past_key_values=None, + use_cache=False, + ) + index += 1 + continue + end = index + 1 + while ( + end < text.config.num_hidden_layers + and text.config.layer_types[end] == "linear_attention" + ): + end += 1 + padded = pack_hidden(hidden, self.width) + block = entry.blocks.get((index, end)) + if block is None: + if entry.pool is None: + entry.pool = _GraphPool(hidden.device) + block = _GraphSegment( + text.layers, index, end, padded, None, pool=entry.pool + ) + entry.blocks[(index, end)] = block + entry.update_bytes() + # No input padding is supported. Future right-padding cannot affect + # the prefix of causal, cache-free layers. Drop it before SDPA. + hidden = block.replay(padded, None)[:, :length, :] + index = end + hidden = text.norm(hidden) + return self.model.get_base_model().lm_head(hidden[:, -1:, :])[0, -1, :] diff --git a/tests/cua_s1/test_graph_buckets.py b/tests/cua_s1/test_graph_buckets.py new file mode 100644 index 0000000..b11a013 --- /dev/null +++ b/tests/cua_s1/test_graph_buckets.py @@ -0,0 +1,106 @@ +"""Prefix packing and strict per-length gates for experimental Graph buckets.""" + +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "recipe/cua_s1")) + + +def test_bucket_boundaries(): + from graph_buckets import bucket_length + + assert [bucket_length(n, 64) for n in (1, 63, 64, 65, 127, 128, 129)] == [ + 64, + 64, + 64, + 128, + 128, + 128, + 192, + ] + + +@pytest.mark.parametrize("width", [0, -64, 32, 65, 64.0, True]) +def test_bucket_width_must_be_a_positive_chunk_multiple(width): + from graph_buckets import bucket_length + + with pytest.raises(ValueError, match="multiple of 64"): + bucket_length(65, width) + + +def runtime(): + from graph_buckets import BucketRuntime + + model = SimpleNamespace( + get_base_model=lambda: SimpleNamespace( + model=SimpleNamespace( + language_model=SimpleNamespace( + config=SimpleNamespace(_attn_implementation="sdpa") + ) + ) + ) + ) + return BucketRuntime(model) + + +def values(length, dim=4): + torch = pytest.importorskip("torch") + return { + "inputs_embeds": torch.ones(1, length, dim), + "position_ids": torch.zeros(3, 1, length, dtype=torch.long), + "attention_mask": torch.ones(1, length, dtype=torch.long), + } + + +def test_pack_preserves_prefix_and_clears_tail_after_input_changes(): + from graph_buckets import pack_hidden + + torch = pytest.importorskip("torch") + long = pack_hidden(torch.full((1, 63, 4), 7.0), 64) + short = pack_hidden(torch.full((1, 2, 4), 3.0), 64) + assert long.shape == short.shape == (1, 64, 4) + assert torch.equal(short[:, :2], torch.full((1, 2, 4), 3.0)) + assert torch.count_nonzero(short[:, 2:]) == 0 + assert torch.all(long[:, :63] == 7) + + +def test_keys_share_only_compatible_padded_segment_layouts(): + r = runtime() + assert r._key(values(63)) == r._key(values(64)) + assert r._key(values(64)) != r._key(values(65)) + assert r._key(values(63)) != r._key(values(63, dim=8)) + assert r._key(values(63)) != runtime()._key(values(63)) + + +def test_new_real_length_validated_and_rejected_without_poisoning_bucket(): + torch = pytest.importorskip("torch") + r = runtime() + entry = SimpleNamespace(verified_lengths=set(), rejected_lengths=set()) + calls = [] + reference = torch.tensor([1.0, 2.0]) + r._eager = lambda v: calls.append(v["inputs_embeds"].shape[1]) or reference + assert torch.equal( + r._validate_replay(values(63), entry, reference.clone()), reference + ) + r._validate_replay(values(63), entry, reference.clone()) + assert calls == [63] + wrong = torch.tensor([1.0, 3.0]) + assert torch.equal(r._validate_replay(values(64), entry, wrong), reference) + assert entry.verified_lengths == {63} + assert entry.rejected_lengths == {64} + assert r.stats["length_checks"] == 2 + assert r.stats["length_rejections"] == 1 + + +def test_original_padding_is_not_supported(): + torch = pytest.importorskip("torch") + r = runtime() + v = values(63) + assert r._dense(v) + v["attention_mask"][0, -1] = 0 + assert not r._dense(v) + v["attention_mask"] = torch.ones(1, 63, 1) + assert not r._dense(v) From c810d292ca11e2bd423c8d7930b48f2a3371b8a4 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:27:11 +0800 Subject: [PATCH 50/75] experiment: locate padded segment numerical differences --- recipe/cua_s1/diagnose_graph_buckets.py | 189 ++++++++++++++++++++++++ 1 file changed, 189 insertions(+) create mode 100644 recipe/cua_s1/diagnose_graph_buckets.py diff --git a/recipe/cua_s1/diagnose_graph_buckets.py b/recipe/cua_s1/diagnose_graph_buckets.py new file mode 100644 index 0000000..6cc84e4 --- /dev/null +++ b/recipe/cua_s1/diagnose_graph_buckets.py @@ -0,0 +1,189 @@ +"""Locate padding-induced rounding before changing bucket acceptance criteria.""" + +from __future__ import annotations + +import argparse +from pathlib import Path + + +def prepare_values(engine, request): + + inputs = engine.prepare_reused(request.image, request.questions)[0] + features = engine.encode_image(inputs) + text_inputs = { + k: v.to(engine.model.device) for k, v in inputs.items() if k != "pixel_values" + } + core = engine.model.get_base_model().model + ids = text_inputs.pop("input_ids") + embeds = core.get_input_embeddings()(ids) + image_embeds = features.to(embeds.device, embeds.dtype) + mask, _ = core.get_placeholder_mask( + ids, inputs_embeds=embeds, image_features=image_embeds + ) + embeds = embeds.masked_scatter(mask, image_embeds) + positions, _ = core.get_rope_index( + input_ids=ids, + mm_token_type_ids=text_inputs.pop("mm_token_type_ids"), + image_grid_thw=text_inputs.pop("image_grid_thw"), + attention_mask=text_inputs["attention_mask"], + ) + return {"inputs_embeds": embeds, "position_ids": positions, **text_inputs} + + +def difference(a, b): + import torch + + delta = (a.float() - b.float()).abs() + return { + "equal": torch.equal(a, b), + "max_abs": delta.max().item(), + "mean_abs": delta.mean().item(), + "unequal": int((a != b).sum().item()), + "elements": a.numel(), + } + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + args = p.parse_args() + if args.output.exists(): + p.error("choose a fresh output") + from dataclasses import replace + + import torch + from evaluate_multimodal import environment + from graph_buckets import BucketRuntime, pack_hidden + from profile_multimodal import ( + GOAL, + case_matrix, + fixture, + repository_state, + write_json, + ) + + from models.cua_s1.multimodal.graph_runtime import _GraphSegment, _ShapeEntry + from models.cua_s1.multimodal.model import MultimodalEngine, letter_ids + from models.cua_s1.multimodal.protocol import parse_request + + report = { + "status": "running", + "repository": repository_state(Path(__file__).resolve().parents[2]), + "environment": environment(), + "cases": [], + } + if report["repository"]["dirty"]: + raise ValueError("clean source required") + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), str(args.weights / "cua-s1-4b-0.2/multimodal") + ) + case = next(c for c in case_matrix() if c["id"] == "320x240-short-q2") + base = parse_request(fixture(case, args.output.parent / "diagnostic-fixtures")) + layers = engine.model.get_base_model().model.language_model.layers + + def run(hidden): + for layer in layers[:3]: + hidden = layer( + hidden, + position_embeddings=None, + attention_mask=None, + position_ids=None, + past_key_values=None, + use_cache=False, + ) + return hidden + + try: + with torch.no_grad(): + for count in (1, 2, 5, 6): + request = replace( + base, + questions=tuple( + replace(q, goal=" ".join([GOAL] * count)) + for q in base.questions + ), + ) + values = prepare_values(engine, request) + hidden = values["inputs_embeds"] + length = hidden.shape[1] + padded = pack_hidden(hidden, 64) + record = {"length": length, "padded_length": padded.shape[1]} + observed = {} + handles = [] + target = {} + + def hook(name): + def save(module, inputs, output): + if isinstance(output, torch.Tensor): + target[name] = output.detach().clone() + + return save + + for i in range(3): + for name, module in layers[i].named_modules(): + if not list(module.children()): + handles.append( + module.register_forward_hook(hook(f"{i}.{name}")) + ) + target = observed + exact = run(hidden) + padded_observed = {} + target = padded_observed + padded_eager = run(padded)[:, :length] + for handle in handles: + handle.remove() + modules = [] + for name, a in observed.items(): + b = padded_observed[name] + if a.shape != b.shape: + # Leaf modules here contain one token dimension, or a + # flattened (batch * tokens * heads) norm dimension. + slices = tuple(slice(0, size) for size in a.shape) + b = b[slices] + modules.append( + {"name": name, "shape": list(a.shape), **difference(a, b)} + ) + record["modules"] = modules + record["segment_padded_eager"] = difference(exact, padded_eager) + exact_graph = _GraphSegment(layers, 0, 3, hidden, None) + record["segment_exact_graph"] = difference( + exact, exact_graph.replay(hidden, None) + ) + exact_graph.close() + padded_graph = _GraphSegment(layers, 0, 3, padded, None) + record["segment_padded_graph_vs_padded_eager"] = difference( + padded_eager, padded_graph.replay(padded, None)[:, :length] + ) + padded_graph.close() + runtime = BucketRuntime(engine.model) + reference = runtime._eager(values) + entry = _ShapeEntry() + unsafe = runtime._run_segments(values, entry) + record["full_logits"] = difference(reference, unsafe) + ids = letter_ids(engine.tokenizer, len(request.questions[0].keys)) + a = torch.softmax(reference[ids].float(), -1) + b = torch.softmax(unsafe[ids].float(), -1) + record["candidate_probabilities"] = { + "reference": a.tolist(), + "padded": b.tolist(), + **difference(a, b), + } + entry.close() + report["cases"].append(record) + write_json(args.output, report) + print( + length, + "first difference", + next((m for m in modules if not m["equal"]), None), + "full logits", + record["full_logits"], + flush=True, + ) + report["status"] = "complete" + finally: + write_json(args.output, report) + + +if __name__ == "__main__": + main() From 17e70444f23c72afb3cbd7542deb2083e1206987 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:30:01 +0800 Subject: [PATCH 51/75] experiment: preserve projection shapes and bucket DeltaNet rule only --- recipe/cua_s1/benchmark_graph_buckets.py | 7 +- recipe/cua_s1/graph_buckets.py | 108 +++++++++++++++++++++++ tests/cua_s1/test_graph_buckets.py | 13 +++ 3 files changed, 126 insertions(+), 2 deletions(-) diff --git a/recipe/cua_s1/benchmark_graph_buckets.py b/recipe/cua_s1/benchmark_graph_buckets.py index 11db2c5..eef9b83 100644 --- a/recipe/cua_s1/benchmark_graph_buckets.py +++ b/recipe/cua_s1/benchmark_graph_buckets.py @@ -38,6 +38,7 @@ def main(): p.add_argument("--output", type=Path, required=True) p.add_argument("--runs", type=int, default=2) p.add_argument("--width", type=int, default=64) + p.add_argument("--kind", choices=("segment", "rule"), default="segment") p.add_argument("--case", choices=list(schedules()), action="append") args = p.parse_args() if args.output.exists(): @@ -48,7 +49,7 @@ def main(): import torch from benchmark_multimodal_graph import measure from evaluate_multimodal import environment - from graph_buckets import BucketRuntime, bucket_length + from graph_buckets import BucketRuntime, RuleBucketRuntime, bucket_length from profile_multimodal import ( GOAL, case_matrix, @@ -70,6 +71,7 @@ def main(): report_path = args.output / "report.json" selected = {k: v for k, v in schedules().items() if k in (args.case or ["probe"])} config = GraphConfig() + bucket_class = RuleBucketRuntime if args.kind == "rule" else BucketRuntime report = { "status": "running", "repository": source, @@ -81,6 +83,7 @@ def main(): "config": { "graph": asdict(config), "width": args.width, + "kind": args.kind, "runs": args.runs, "timing": "synchronized predict including cold capture and first-length checks", "memory": "paired caches coexist; allocator totals are combined, cache bytes are per variant", @@ -133,7 +136,7 @@ def main(): for run in range(args.runs): runtimes = { "exact": GraphRuntime(engine.model, config), - "bucket": BucketRuntime(engine.model, config, width=args.width), + "bucket": bucket_class(engine.model, config, width=args.width), } record = {"run": run + 1, "status": "running", "events": []} report["workloads"][name].append(record) diff --git a/recipe/cua_s1/graph_buckets.py b/recipe/cua_s1/graph_buckets.py index 557b30d..fe86b4c 100644 --- a/recipe/cua_s1/graph_buckets.py +++ b/recipe/cua_s1/graph_buckets.py @@ -182,3 +182,111 @@ def _run_segments(self, values, entry): index = end hidden = text.norm(hidden) return self.model.get_base_model().lm_head(hidden[:, -1:, :])[0, -1, :] + + +def pack_rule_inputs(values, width): + import torch.nn.functional as F + + length = values["query"].shape[1] + padding = bucket_length(length, width) - length + return { + name: F.pad(value, (0, 0) * (value.ndim - 2) + (0, padding)) + for name, value in values.items() + } + + +class _RuleSegment(_GraphSegment): + """Capture just the fallback rule, reusing #33 stream/pool ownership.""" + + def __init__(self, function, values, pool): + self.function = function + self.static_extra = {k: v.clone() for k, v in values.items() if k != "query"} + super().__init__([], 0, 0, values["query"], None, pool=pool) + self.external_bytes += sum( + v.untyped_storage().nbytes() for v in self.static_extra.values() + ) + + def _forward(self): + return self.function( + self.static_hidden, + self.static_extra["key"], + self.static_extra["value"], + g=self.static_extra["g"], + beta=self.static_extra["beta"], + initial_state=None, + output_final_state=False, + use_qk_l2norm_in_kernel=True, + ) + + def replay_values(self, values): + for name, value in self.static_extra.items(): + value.copy_(values[name]) + return super().replay(values["query"], None) + + def close(self): + super().close() + self.static_extra.clear() + + +class RuleBucketRuntime(BucketRuntime): + """Single-thread experiment: only pad the internal DeltaNet rule inputs. + + Temporarily replaces the installed module's fallback function under the + runtime lock. Not suitable for simultaneous engines or production serving. + The original function is restored even on failure. + """ + + def _run_segments(self, values, entry): + import inspect + from unittest.mock import patch + + text = self.model.get_base_model().model.language_model + module = inspect.getmodule(text.layers[0].linear_attn.__class__) + original = module.torch_chunk_gated_delta_rule + index = 0 + length = values["inputs_embeds"].shape[1] + self.stats["real_tokens"] += length + self.stats["padded_tokens"] += bucket_length(length, self.width) - length + + def dispatch( + query, + key, + value, + g, + beta, + initial_state=None, + output_final_state=False, + use_qk_l2norm_in_kernel=False, + **kwargs, + ): + nonlocal index + if ( + initial_state is not None + or output_final_state + or not use_qk_l2norm_in_kernel + or any(v is not None for v in kwargs.values()) + ): + raise RuntimeError( + "unsupported rule call; experiment requires cache-free normalized prefill" + ) + packed = pack_rule_inputs( + dict(query=query, key=key, value=value, g=g, beta=beta), self.width + ) + block = entry.blocks.get(index) + if block is None: + if entry.pool is None: + entry.pool = _GraphPool(query.device) + block = _RuleSegment(original, packed, entry.pool) + entry.blocks[index] = block + entry.update_bytes() + index += 1 + output, state = block.replay_values(packed) + return output[:, : query.shape[1]].contiguous(), state + + with patch.object(module, "torch_chunk_gated_delta_rule", dispatch): + output = self.model(**values, logits_to_keep=1, use_cache=False).logits[ + 0, -1, : + ] + if index != text.config.layer_types.count("linear_attention"): + raise RuntimeError("unexpected DeltaNet call count") + return output diff --git a/tests/cua_s1/test_graph_buckets.py b/tests/cua_s1/test_graph_buckets.py index b11a013..eafc3fe 100644 --- a/tests/cua_s1/test_graph_buckets.py +++ b/tests/cua_s1/test_graph_buckets.py @@ -104,3 +104,16 @@ def test_original_padding_is_not_supported(): assert not r._dense(v) v["attention_mask"] = torch.ones(1, 63, 1) assert not r._dense(v) + + +def test_rule_packing_zeros_future_decay_and_updates(): + from graph_buckets import pack_rule_inputs + + torch = pytest.importorskip("torch") + original = {k: torch.ones(1, 63, 2, 4) for k in ("query", "key", "value")} + original.update(g=torch.full((1, 63, 2), -0.5), beta=torch.ones(1, 63, 2)) + packed = pack_rule_inputs(original, 64) + for name, tensor in packed.items(): + assert tensor.shape[1] == 64 + assert torch.equal(tensor[:, :63], original[name]) + assert torch.count_nonzero(tensor[:, 63:]) == 0 From d2d416c3f731719464dc5e83ba65c27c3691cedb Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:32:41 +0800 Subject: [PATCH 52/75] test: check changed bucket inputs and record capture failures --- recipe/cua_s1/check_graph_buckets.py | 200 +++++++++++++++++++++++++++ recipe/cua_s1/graph_buckets.py | 15 +- 2 files changed, 210 insertions(+), 5 deletions(-) create mode 100644 recipe/cua_s1/check_graph_buckets.py diff --git a/recipe/cua_s1/check_graph_buckets.py b/recipe/cua_s1/check_graph_buckets.py new file mode 100644 index 0000000..92f5ee0 --- /dev/null +++ b/recipe/cua_s1/check_graph_buckets.py @@ -0,0 +1,200 @@ +"""Strict full-logit/response checks and isolated reservation for rule buckets.""" + +from __future__ import annotations + +import argparse +import gc +from dataclasses import asdict, replace +from pathlib import Path + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + args = p.parse_args() + if args.output.exists(): + p.error("choose a fresh output") + + import torch + from benchmark_image_reuse import distinct_fixture + from benchmark_multimodal_graph import changed_text_request + from evaluate_multimodal import environment + from graph_buckets import RuleBucketRuntime + from PIL import Image + from profile_multimodal import case_matrix, fixture, repository_state, write_json + + from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + source = repository_state(Path(__file__).resolve().parents[2]) + if source["dirty"]: + raise ValueError("clean source required") + report = { + "status": "running", + "repository": source, + "environment": environment(), + "cases": [], + "memory": {}, + } + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), str(args.weights / "cua-s1-4b-0.2/multimodal") + ) + fixtures = args.output.parent / "check-fixtures" + selected = { + "320x240-short-q2", + "320x240-short-q8", + "640x480-short-q8", + "640x480-long-q8", + } + cases = [c for c in case_matrix() if c["id"] in selected] + [ + {"id": "640x480-distinct-q8"} + ] + config = GraphConfig( + max_tokens=4096, max_captures=16, capture_budget_ms=10000, max_bytes=4 * 1024**3 + ) + try: + for case in cases: + raw = ( + distinct_fixture(fixtures) + if "distinct" in case["id"] + else fixture(case, fixtures) + ) + request = parse_request(raw) + runtime = RuleBucketRuntime(engine.model, config) + original_forward = runtime.forward + checked = [] + + def checked_forward(values): + expected = engine.model( + **values, logits_to_keep=1, use_cache=False + ).logits[0, -1, :] + actual = original_forward(values) + equal = torch.equal(expected, actual) + checked.append( + { + "tokens": values["inputs_embeds"].shape[1], + "equal": equal, + "max_abs": (expected.float() - actual.float()) + .abs() + .max() + .item(), + } + ) + assert equal, "full logits differ on changed input" + return actual + + runtime.forward = checked_forward + changed_image = replace( + request, image=Image.new("RGB", request.image.size, "black") + ) + if "distinct" in case["id"]: + changed_text = replace( + request, + questions=( + replace( + request.questions[0], + goal=request.questions[0].goal.replace("Save", "Open"), + ), + *request.questions[1:], + ), + ) + else: + changed_text, _ = changed_text_request(engine, request) + record = { + "id": case["id"], + "config": asdict(config), + "records": [], + "logit_checks": checked, + } + report["cases"].append(record) + with torch.no_grad(): + for name, item in [ + ("cold", request), + ("capture", request), + ("image", changed_image), + ("text", changed_text), + ]: + engine.graph_runtime = None + expected = engine.predict(item) + engine.graph_runtime = runtime + before = dict(runtime.stats) + actual = engine.predict(item) + delta = {k: runtime.stats[k] - v for k, v in before.items()} + record["records"].append( + { + "variant": name, + "expected": expected, + "actual": actual, + "stats_delta": delta, + } + ) + assert actual == expected + assert ( + runtime.stats["rejected"] + == runtime.stats["length_rejections"] + == 0 + ) + if name in ("image", "text"): + assert delta["replays"] == len(item.questions), ( + case["id"], + name, + delta, + ) + engine.graph_runtime = None + runtime.invalidate() + del runtime + gc.collect() + torch.cuda.empty_cache() + write_json(args.output, report) + print( + case["id"], + "verified", + len(checked), + "full-logit comparisons", + flush=True, + ) + + # Each mode owns the only live Graph pool; allocator is emptied between + # variants. This is a separate diagnostic, not paired performance data. + case = next(c for c in case_matrix() if c["id"] == "320x240-short-q2") + request = parse_request(fixture(case, fixtures)) + for name, runtime_class in [ + ("eager", None), + ("exact", GraphRuntime), + ("rule", RuleBucketRuntime), + ]: + runtime = ( + runtime_class(engine.model, GraphConfig()) if runtime_class else None + ) + engine.graph_runtime = runtime + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + for _ in range(3): + engine.predict(request) + torch.cuda.synchronize() + report["memory"][name] = { + "allocated_bytes": torch.cuda.memory_allocated(), + "reserved_bytes": torch.cuda.memory_reserved(), + "peak_reserved_bytes": torch.cuda.max_memory_reserved(), + "cache_bytes": runtime.cache.bytes if runtime else 0, + "stats": dict(runtime.stats) if runtime else {}, + } + engine.graph_runtime = None + if runtime: + runtime.invalidate() + del runtime + gc.collect() + torch.cuda.empty_cache() + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(args.output, report) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/graph_buckets.py b/recipe/cua_s1/graph_buckets.py index fe86b4c..b766823 100644 --- a/recipe/cua_s1/graph_buckets.py +++ b/recipe/cua_s1/graph_buckets.py @@ -7,6 +7,7 @@ from __future__ import annotations +import logging import time from models.cua_s1.multimodal.graph_runtime import ( @@ -267,7 +268,7 @@ def dispatch( or any(v is not None for v in kwargs.values()) ): raise RuntimeError( - "unsupported rule call; experiment requires cache-free normalized prefill" + f"unsupported rule call: state={initial_state is not None}, final={output_final_state}, norm={use_qk_l2norm_in_kernel}, extras={kwargs}" ) packed = pack_rule_inputs( dict(query=query, key=key, value=value, g=g, beta=beta), self.width @@ -283,10 +284,14 @@ def dispatch( output, state = block.replay_values(packed) return output[:, : query.shape[1]].contiguous(), state - with patch.object(module, "torch_chunk_gated_delta_rule", dispatch): - output = self.model(**values, logits_to_keep=1, use_cache=False).logits[ - 0, -1, : - ] + try: + with patch.object(module, "torch_chunk_gated_delta_rule", dispatch): + output = self.model(**values, logits_to_keep=1, use_cache=False).logits[ + 0, -1, : + ] + except RuntimeError: + logging.getLogger(__name__).exception("experimental rule capture failed") + raise if index != text.config.layer_types.count("linear_attention"): raise RuntimeError("unexpected DeltaNet call count") return output From 9142e23e7c798ee350de8e4b7c0f5ade28012a7f Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:34:33 +0800 Subject: [PATCH 53/75] fix: accept cache-free rule metadata and verify experiment evidence --- recipe/cua_s1/graph_buckets.py | 28 +++++--- recipe/cua_s1/verify_graph_buckets.py | 93 +++++++++++++++++++++++++++ tests/cua_s1/test_graph_buckets.py | 40 ++++++++++++ 3 files changed, 152 insertions(+), 9 deletions(-) create mode 100644 recipe/cua_s1/verify_graph_buckets.py diff --git a/recipe/cua_s1/graph_buckets.py b/recipe/cua_s1/graph_buckets.py index b766823..6dcb205 100644 --- a/recipe/cua_s1/graph_buckets.py +++ b/recipe/cua_s1/graph_buckets.py @@ -229,6 +229,22 @@ def close(self): self.static_extra.clear() +def validate_rule_call(initial_state, output_final_state, normalize, kwargs): + harmless_flags = {"use_cache", "output_attentions", "output_hidden_states"} + if ( + initial_state is not None + or output_final_state + or not normalize + or any( + v is not None and not (k in harmless_flags and v is False) + for k, v in kwargs.items() + ) + ): + raise RuntimeError( + "unsupported rule call; requires normalized cache-free prefill" + ) + + class RuleBucketRuntime(BucketRuntime): """Single-thread experiment: only pad the internal DeltaNet rule inputs. @@ -261,15 +277,9 @@ def dispatch( **kwargs, ): nonlocal index - if ( - initial_state is not None - or output_final_state - or not use_qk_l2norm_in_kernel - or any(v is not None for v in kwargs.values()) - ): - raise RuntimeError( - f"unsupported rule call: state={initial_state is not None}, final={output_final_state}, norm={use_qk_l2norm_in_kernel}, extras={kwargs}" - ) + validate_rule_call( + initial_state, output_final_state, use_qk_l2norm_in_kernel, kwargs + ) packed = pack_rule_inputs( dict(query=query, key=key, value=value, g=g, beta=beta), self.width ) diff --git a/recipe/cua_s1/verify_graph_buckets.py b/recipe/cua_s1/verify_graph_buckets.py new file mode 100644 index 0000000..e520ac4 --- /dev/null +++ b/recipe/cua_s1/verify_graph_buckets.py @@ -0,0 +1,93 @@ +"""Independently verify raw timing, response and provenance evidence (stdlib).""" + +import argparse +import hashlib +import json +import math +import statistics +import subprocess +from pathlib import Path + + +def verify(path, require_replay=False): + report = json.loads(path.read_text()) + assert report["status"] == "complete", report.get("error") + assert report["repository"]["dirty"] is False + root = Path(__file__).resolve().parents[2] + revision = report["repository"]["revision"] + for name, expected in report["source_sha256"].items(): + source = subprocess.check_output( + ["git", "-C", str(root), "show", revision + ":" + name] + ) + assert hashlib.sha256(source).hexdigest() == expected + count = 0 + for name, records in report["workloads"].items(): + assert len(records) == report["config"]["runs"] + for record in records: + assert record["status"] == "complete" + events = record["events"] + count += len(events) * 3 + for index, event in enumerate(events): + assert event["index"] == index + variants = event["variants"] + assert set(variants) == {"eager", "exact", "bucket"} + assert all( + v["response"] == variants["eager"]["response"] + for v in variants.values() + ) + for v in variants.values(): + assert math.isfinite(v["latency_ms"]) and v["latency_ms"] > 0 + assert ( + 0 + <= v["cache_shapes"] + <= report["config"]["graph"]["max_shapes"] + ) + assert ( + 0 <= v["cache_bytes"] <= report["config"]["graph"]["max_bytes"] + ) + eager_total = sum(e["variants"]["eager"]["latency_ms"] for e in events) + for variant in ("eager", "exact", "bucket"): + samples = [e["variants"][variant]["latency_ms"] for e in events] + summary = record["summary"][variant] + assert summary["requests"] == len(samples) + metrics = { + "total_ms": sum(samples), + "p50_ms": statistics.median(samples), + "p95_ms": sorted(samples)[math.ceil(len(samples) * 0.95) - 1], + "total_reduction_vs_eager_pct": 100 + * (1 - sum(samples) / eager_total), + } + for key, value in metrics.items(): + assert math.isclose( + summary[key], value, rel_tol=1e-12, abs_tol=1e-9 + ) + if variant != "eager": + stats = record["stats_final"][variant] + for key, total in stats.items(): + assert math.isclose( + total, + sum( + e["variants"][variant]["stats_delta"][key] + for e in events + ), + rel_tol=1e-12, + abs_tol=1e-9, + ) + if require_replay: + stats = record["stats_final"]["bucket"] + assert stats["captures"] > 0 and stats["replays"] > 0 + assert stats["rejected"] == stats["length_rejections"] == 0 + print(name, record["run"], "verified", len(events), "request triplets") + print( + "verified", + count, + "timed predictions; source hashes, responses, metrics and cache bounds", + ) + + +if __name__ == "__main__": + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("report", type=Path) + p.add_argument("--require-replay", action="store_true") + args = p.parse_args() + verify(args.report, args.require_replay) diff --git a/tests/cua_s1/test_graph_buckets.py b/tests/cua_s1/test_graph_buckets.py index eafc3fe..f073205 100644 --- a/tests/cua_s1/test_graph_buckets.py +++ b/tests/cua_s1/test_graph_buckets.py @@ -117,3 +117,43 @@ def test_rule_packing_zeros_future_decay_and_updates(): assert tensor.shape[1] == 64 assert torch.equal(tensor[:, :63], original[name]) assert torch.count_nonzero(tensor[:, 63:]) == 0 + + +def test_benchmark_summary_uses_nearest_rank_p95_and_total_cost(): + from benchmark_graph_buckets import summary + + events = [ + {"variants": {"eager": {"latency_ms": 10}, "bucket": {"latency_ms": v}}} + for v in [2, 4, 6, 20] + ] + result = summary(events, "bucket") + assert result["p50_ms"] == 5 + assert result["p95_ms"] == 20 + assert result["total_ms"] == 32 + assert result["total_reduction_vs_eager_pct"] == pytest.approx(20) + + +def test_rule_call_accepts_transformers_cache_free_metadata_only(): + from graph_buckets import validate_rule_call + + validate_rule_call( + None, + False, + True, + { + "cu_seqlens": None, + "use_cache": False, + "output_attentions": None, + "output_hidden_states": None, + }, + ) + for kwargs in ( + {"use_cache": True}, + {"chunk_size": 32}, + {"cu_seqlens": [0, 63]}, + {"unknown": False}, + ): + with pytest.raises(RuntimeError, match="unsupported rule"): + validate_rule_call(None, False, True, kwargs) + with pytest.raises(RuntimeError, match="unsupported rule"): + validate_rule_call(object(), False, True, {}) From d082d54804c31e92203cd8bb7b76ef14f53ccd06 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:41:14 +0800 Subject: [PATCH 54/75] test: exercise bucket boundaries and cache fallback on GPU --- recipe/cua_s1/check_graph_buckets.py | 115 +++++++++++++++++++++++++++ tests/cua_s1/test_graph_buckets.py | 31 ++++++++ 2 files changed, 146 insertions(+) diff --git a/recipe/cua_s1/check_graph_buckets.py b/recipe/cua_s1/check_graph_buckets.py index 92f5ee0..94bb2d7 100644 --- a/recipe/cua_s1/check_graph_buckets.py +++ b/recipe/cua_s1/check_graph_buckets.py @@ -8,10 +8,122 @@ from pathlib import Path +def check_boundaries(engine, output, report): + import torch + from diagnose_graph_buckets import prepare_values + from graph_buckets import RuleBucketRuntime + from profile_multimodal import GOAL, case_matrix, fixture, write_json + + from models.cua_s1.multimodal.graph_runtime import GraphConfig + from models.cua_s1.multimodal.protocol import parse_request + + fixtures = output.parent / "boundary-fixtures" + case = next(c for c in case_matrix() if c["id"] == "320x240-short-q2") + request = parse_request(fixture(case, fixtures)) + request = replace( + request, + questions=tuple( + replace(q, goal=" ".join([GOAL] * 30)) for q in request.questions + ), + ) + report["boundary_records"] = [] + try: + with torch.no_grad(): + prepared = prepare_values(engine, request) + lengths = [255, 256, 257, 319, 320, 321] + assert prepared["inputs_embeds"].shape[1] >= max(lengths) + values = { + n: { + "inputs_embeds": prepared["inputs_embeds"][:, :n].clone(), + "position_ids": prepared["position_ids"][:, :, :n].clone(), + "attention_mask": prepared["attention_mask"][:, :n].clone(), + } + for n in lengths + } + config = GraphConfig( + min_uses=1, + max_captures=16, + capture_budget_ms=10000, + max_bytes=4 * 1024**3, + ) + runtime = RuleBucketRuntime(engine.model, config) + for n in lengths + lengths[::-1]: + expected = runtime._eager(values[n]) + before = dict(runtime.stats) + with runtime.request(): + actual = runtime.forward(values[n]) + equal = torch.equal(expected, actual) + report["boundary_records"].append( + { + "length": n, + "equal": equal, + "max_abs": (expected.float() - actual.float()) + .abs() + .max() + .item(), + "stats_delta": { + k: runtime.stats[k] - v for k, v in before.items() + }, + } + ) + assert equal + assert ( + runtime.stats["rejected"] == runtime.stats["length_rejections"] == 0 + ) + assert runtime.stats["captures"] == 3 + assert runtime.stats["replays"] == 9 + report["boundary_stats"] = dict(runtime.stats) + runtime.invalidate() + report["policies"] = {} + for name, config, sequence, counter in [ + ( + "eviction", + GraphConfig( + min_uses=1, + max_shapes=1, + max_captures=16, + capture_budget_ms=10000, + ), + [255, 319, 255, 319], + "cooldown", + ), + ( + "memory", + GraphConfig(min_uses=1, max_bytes=1 << 20), + [255, 255], + "memory_budget", + ), + ( + "capture_budget", + GraphConfig(min_uses=1, max_captures=1), + [255, 319, 255], + "capture_budget", + ), + ]: + runtime = RuleBucketRuntime(engine.model, config) + for n in sequence: + expected = runtime._eager(values[n]) + with runtime.request(): + actual = runtime.forward(values[n]) + assert torch.equal(expected, actual) + assert runtime.stats[counter] > 0 + report["policies"][name] = dict(runtime.stats) + runtime.invalidate() + assert not runtime.cache.entries and not runtime.admission.history + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + write_json(output, report) + + def main(): p = argparse.ArgumentParser(description=__doc__) p.add_argument("--weights", type=Path, required=True) p.add_argument("--output", type=Path, required=True) + p.add_argument("--boundaries-only", action="store_true") args = p.parse_args() if args.output.exists(): p.error("choose a fresh output") @@ -41,6 +153,9 @@ def main(): engine = MultimodalEngine( str(args.weights / "Qwen3.5-4B"), str(args.weights / "cua-s1-4b-0.2/multimodal") ) + if args.boundaries_only: + check_boundaries(engine, args.output, report) + return fixtures = args.output.parent / "check-fixtures" selected = { "320x240-short-q2", diff --git a/tests/cua_s1/test_graph_buckets.py b/tests/cua_s1/test_graph_buckets.py index f073205..23ee311 100644 --- a/tests/cua_s1/test_graph_buckets.py +++ b/tests/cua_s1/test_graph_buckets.py @@ -157,3 +157,34 @@ def test_rule_call_accepts_transformers_cache_free_metadata_only(): validate_rule_call(None, False, True, kwargs) with pytest.raises(RuntimeError, match="unsupported rule"): validate_rule_call(object(), False, True, {}) + + +def test_rule_dispatch_restores_global_function_after_failure(monkeypatch): + import types + + from graph_buckets import RuleBucketRuntime + + module = types.ModuleType("_cua_rule_test_module") + + def original(*args, **kwargs): + return None + + module.torch_chunk_gated_delta_rule = original + attention_type = type("Attention", (), {"__module__": module.__name__}) + monkeypatch.setitem(sys.modules, module.__name__, module) + text = SimpleNamespace(layers=[SimpleNamespace(linear_attn=attention_type())]) + + class FailingModel: + def get_base_model(self): + return SimpleNamespace(model=SimpleNamespace(language_model=text)) + + def __call__(self, **kwargs): + assert module.torch_chunk_gated_delta_rule is not original + raise ValueError("interrupted forward") + + r = RuleBucketRuntime(FailingModel()) + with pytest.raises(ValueError, match="interrupted"): + r._run_segments( + {"inputs_embeds": SimpleNamespace(shape=(1, 63, 4))}, SimpleNamespace() + ) + assert module.torch_chunk_gated_delta_rule is original From b9156ab6d55ee01fea6b7b7d9f07c0759089daa6 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:43:30 +0800 Subject: [PATCH 55/75] docs: record rule bucket results, tradeoffs and reproducible evidence --- .../plans/2026-09-29-cua-graph-buckets.md | 18 +- .../specs/2026-09-29-cua-graph-buckets.md | 4 + .../rtx4090-graph-buckets/README.md | 113 + .../rtx4090-graph-buckets/SHA256SUMS | 10 + .../rtx4090-graph-buckets/boundaries.json | 533 + .../diagnostics/rule-initial-error.log | 99 + .../diagnostics/rule-initial-probe.json | 4276 ++ .../diagnostics/segment-padding.json | 3505 ++ .../diagnostics/segment-probe.json | 4275 ++ .../rtx4090-graph-buckets/parity-memory.json | 4700 ++ .../rtx4090-graph-buckets/report.json | 41235 ++++++++++++++++ .../rtx4090-graph-buckets/tests.log | 4 + .../rtx4090-graph-buckets/verification.log | 5 + 13 files changed, 58771 insertions(+), 6 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/boundaries.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/diagnostics/rule-initial-error.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/diagnostics/rule-initial-probe.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/diagnostics/segment-padding.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/diagnostics/segment-probe.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/parity-memory.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/report.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/tests.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-buckets/verification.log diff --git a/docs/superpowers/plans/2026-09-29-cua-graph-buckets.md b/docs/superpowers/plans/2026-09-29-cua-graph-buckets.md index 1ea8459..782389a 100644 --- a/docs/superpowers/plans/2026-09-29-cua-graph-buckets.md +++ b/docs/superpowers/plans/2026-09-29-cua-graph-buckets.md @@ -6,9 +6,15 @@ Execute inline in the isolated worktree. Scope is an experiment and reusable opt **Architecture:** A recipe-only GraphRuntime subclass reuses #33 capture, admission, allocator and stream ownership. It changes segment input packing and cache keys, adds per-real-length exact validation, and keeps full attention at original length. **Tech Stack:** Pinned PyTorch 2.14.0+cu130, Transformers 5.17.0, PEFT 0.21.0; RTX 4090. -- [ ] Establish baseline: bundle clean PR #33 source to a separate server checkout; run `PYTHONPATH=src python -m pytest tests/cua_s1 -q`. Preserve existing remote experiments. -- [ ] Add `tests/cua_s1/test_graph_buckets.py` before runtime code: bucket boundaries 63/64/65, invalid widths, prefix preservation/zero-tail reset, key sharing/separation, and per-length rejection without poisoning verified lengths. Run failures before implementing `recipe/cua_s1/graph_buckets.py`. -- [ ] Implement only dense batch-one/no-input-padding support. Use `_ShapeEntry` and `_GraphSegment` unchanged; pad each linear run then crop. Retain lock, request accounting, capture budget, strict full-logit capture gate and per-length validation/rejection metadata bounded by resident entries. -- [ ] Add `recipe/cua_s1/benchmark_graph_buckets.py`: clean revision/environment provenance, complete response comparison, cold-inclusive timing, balanced variant order and incremental report writes. First run correctness on 12 distinct lengths and changed image/text. If exact parity fails, keep diagnostics and investigate before timing claims. -- [ ] Run repeated mixed-length and stable-hot schedules. Separately measure each variant's reservation with no other graph runtime resident. Include padding ratio, p50/p95, total time, capture/validation counts, and regressions. -- [ ] Preserve raw evidence and a reproducible report under `recipe/cua_s1/experiments/rtx4090-graph-buckets/`; verify recomputed metrics and full responses, run CI-scoped lint/tests, and record limitations. +- [x] Establish baseline: bundle clean PR #33 source to a separate server checkout; run `PYTHONPATH=src python -m pytest tests/cua_s1 -q`. Preserve existing remote experiments. +- [x] Add `tests/cua_s1/test_graph_buckets.py` before runtime code: bucket boundaries 63/64/65, invalid widths, prefix preservation/zero-tail reset, key sharing/separation, and per-length rejection without poisoning verified lengths. Run failures before implementing `recipe/cua_s1/graph_buckets.py`. +- [x] Implement only dense batch-one/no-input-padding support. Use `_ShapeEntry` and `_GraphSegment` unchanged; pad each linear run then crop. Retain lock, request accounting, capture budget, strict full-logit capture gate and per-length validation/rejection metadata bounded by resident entries. +- [x] Add `recipe/cua_s1/benchmark_graph_buckets.py`: clean revision/environment provenance, complete response comparison, cold-inclusive timing, balanced variant order and incremental report writes. First run correctness on 12 distinct lengths and changed image/text. If exact parity fails, keep diagnostics and investigate before timing claims. +- [x] Run repeated mixed-length and stable-hot schedules. Separately measure each variant's reservation with no other graph runtime resident. Include padding ratio, p50/p95, total time, capture/validation counts, and regressions. +- [x] Preserve raw evidence and a reproducible report under `recipe/cua_s1/experiments/rtx4090-graph-buckets/`; verify recomputed metrics and full responses, run CI-scoped lint/tests, and record limitations. + +## Evidence-driven revision + +The initial segment-padding probe rejected both buckets on full-logit equality. Layer diagnostics on four real prompts locate the first observed difference in linear projections; exact-length Graph matches eager, and padded Graph matches padded eager. Therefore the follow-up `RuleBucketRuntime` pads only query/key/value/decay/update tensors at the internal DeltaNet rule boundary. Projection, convolution, norms, MLP and full attention retain true length. This uses a scoped global-function replacement solely in the serial experimental runner and is not a production-serving integration. + +Completed: 720 timed predictions with exact responses, 136 changed-content full-logit checks, 12 boundary forwards, three fallback policies, 193 tests and independent evidence verification. Rule buckets improve the measured length-rotation workload but regress stable hot shapes against exact Graphs. Runtime remains recipe-only. diff --git a/docs/superpowers/specs/2026-09-29-cua-graph-buckets.md b/docs/superpowers/specs/2026-09-29-cua-graph-buckets.md index 10e981f..fb12d30 100644 --- a/docs/superpowers/specs/2026-09-29-cua-graph-buckets.md +++ b/docs/superpowers/specs/2026-09-29-cua-graph-buckets.md @@ -7,3 +7,7 @@ Pad only causal linear-attention runs on the right to a multiple of 64 (the inst Capture keys describe the padded segment layout; position values never enter captured linear layers. Bound caches, capture work, streams and pools using #33. Validate complete logits exactly against unpadded eager on capture and the first encounter of each real length in each resident bucket. Reject mismatching lengths and preserve eager fallback; changed image/text checks remain necessary because one-time validation is not a universal proof. Measure cold-inclusive synchronized predict latency against eager and unmodified exact-layout Graph runtime, with two questions per request and balanced variant order. Record first-length checks, captures, replay, padding, memory and complete responses. Warm replay must be distinguished from first-length validation. Keep negative results, and do not enable a worker option until this approach has evidence of correctness and benefit. + +## Experiment outcome and scope refinement + +Whole-segment padding failed exact-logit gates. The implemented follow-up captures only the internal DeltaNet rule using zero-padded query/key/value/g/beta tensors. Its surrounding projections and full attention retain real shapes. Five changed-content cases, bucket-boundary transitions and fallback checks pass exact logits; the cold-inclusive matrix favors rule buckets for twelve-length rotation but favors exact segmented Graphs for stable hot lengths. Keep both variants in the experimental recipe, with the worker unchanged. See the measured report for exact source revisions and limitations. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-buckets/README.md b/recipe/cua_s1/experiments/rtx4090-graph-buckets/README.md new file mode 100644 index 0000000..9391101 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-buckets/README.md @@ -0,0 +1,113 @@ +# Cua-S1 rule-level Graph buckets — RTX 4090, 2026-09-29 + +This experiment builds a recipe-only bucketed execution path on PR #33. It keeps +all projections, convolutions, layer normalization, MLP, rotary positions and full +attention at their original token length. Only the internal cache-free DeltaNet +rule receives zero-padded query/key/value/decay/update tensors. Twelve measured +lengths (236–302 tokens) map to two buckets (256 and 320). + +The implementation is **not enabled in the worker**. It temporarily replaces the +installed Transformers rule function under the runtime lock and is limited to a +serial, single-engine experiment. It is not safe as a multi-engine global hook. +A production integration needs an explicit per-model call site. The original +function is restored on both successful and failed execution. + +## Correctness and rejected alternative + +Padding whole linear-attention segments changes projection results on this BF16 +stack. The original probe rejects both buckets and falls back to eager. Four +layer diagnostics find the first observed leaf-module difference in QKV or output +projection; exact Graph equals exact eager, and padded Graph equals padded eager +for the first three-layer segment. Full-model logits differ by up to 0.25 in +these four fixtures. This isolates padding-related numerical changes from Graph +capture; it does not identify the exact cuBLAS kernel/accumulation mechanism. +No acceptance tolerance was relaxed. The negative probe is retained separately. + +The subsequent rule-only path gates complete vocabulary logits with +`torch.equal` on capture and on each new real length in a resident bucket. A +mismatching real length falls back to eager; verified lengths can continue. +Verification metadata lives with the cached entry and is discarded on eviction. +This first-input gate is not a proof for all content; changed-content regressions +are therefore tested independently. + +`parity-memory.json` covers five cases: 320×240 with 2/8 questions, 640×480 with +8 questions, a long-prompt case, and eight distinct questions. Cold requests, +capture, changed image and changed text produce identical complete responses. +All 136 independent complete-logit comparisons pass, including replay on changed +inputs. Correctness checks expand the Graph limits to 4,096 tokens, 16 captures, +a 10,000 ms capture budget and 4 GiB cache; timing uses #33 defaults. + +## Measurement boundaries + +The matrix uses clean revision `9142e23e7c798ee350de8e4b7c0f5ade28012a7f`, on +one RTX 4090, PyTorch 2.14.0+cu130, Transformers 5.17.0 and PEFT 0.21.0. We reuse +the pinned BF16 base and multimodal LoRA without merging or modifying weights. +Neither flash-linear-attention nor causal-conv1d is installed. Results apply to +the existing fallback implementation, not an already fused FLA deployment. + +Timing surrounds synchronized `engine.predict`, including preparation, image +encoding, two identical questions, first-length verification, admission, capture, +replay and retirement. Loading is excluded. All six variant orderings cycle over +request positions; the second run shifts to the reversed permutations. Caches +start empty for each workload/run. Both graph caches coexist during paired +measurements, so process allocator totals cannot be attributed to one variant. + +Separate reservation diagnostics run eager, exact and rule modes with only one +live runtime at a time and an allocator clear between modes. For one 236-token +shape, accounted Graph cache bytes are 120,815,616 (exact) and 284,295,168 (rule). +Peak process reservation is 10,101,981,184 and 10,259,267,584 bytes respectively. +These are one-shape diagnostics, not whole-matrix peak comparisons. Rule buckets +retain more static rule inputs; fewer buckets do not imply lower memory use. + +## Timing results + +Two cold runs per workload; 240 request triplets / **720 timed predictions**. Times below include capture and first-length validation. Each cell is run 1 / run 2. + +| Workload / mode | Total (s) | p50 (ms) | p95 (ms) | Captures / run | +|---|---:|---:|---:|---:| +| hot_four / eager | 11.38 / 11.15 | 232.81 / 229.63 | 248.76 / 240.78 | 0 | +| hot_four / exact | 7.53 / 7.50 | 108.48 / 108.22 | 533.16 / 527.54 | 4 / 4 | +| hot_four / bucket | 8.25 / 8.18 | 147.81 / 146.81 | 251.78 / 252.64 | 2 / 2 | +| churn_twelve / eager | 17.24 / 17.17 | 241.04 / 240.39 | 245.89 / 244.38 | 0 | +| churn_twelve / exact | 16.85 / 16.72 | 235.05 / 234.59 | 240.58 / 239.99 | 0 / 0 | +| churn_twelve / bucket | 12.60 / 12.55 | 149.21 / 147.89 | 250.50 / 253.72 | 2 / 2 | + +For twelve-length rotation, rule buckets reduce total time by **26.90–26.93% vs eager** and **24.92–25.20% vs exact mode**. Exact mode performs zero captures and zero replays: the 12-request revisit interval exceeds its eight-request admission window. This is useful bucket admission versus conservative eager fallback, not a comparison against repeatedly evicted exact graphs. Its 2.30–2.64% timing difference from eager is not a Graph speedup. + +For four stable hot lengths, rule buckets reduce total time by **26.63–27.53% vs eager**, but take **8.99–9.44% more time than exact Graph mode**. Capturing only the internal rule leaves more operations eager. It also retains more static rule inputs per bucket. A workload-dependent choice is preferable to replacing exact mode globally. + +Each rule run captures two buckets; hot-four has 90 replays and two additional first-length checks, while twelve-length rotation has 138 replays and ten checks. Across the matrix there are no capture errors, OOMs, numerical rejections, or response mismatches. Churn p95 remains slightly higher than eager (250.50–253.72 vs 244.38–245.89 ms), so this is not a universal tail-latency improvement. The two synthetic runs do not establish statistical significance or a broad production speedup. + +## Validation + +- `boundaries.json` verifies complete-logit equality at 255/256/257/319/320/321 tokens, then revisits them in reverse order: three captures and nine replays. Three additional policies exercise eviction cooldown, a 1 MiB memory budget and a one-capture budget, with eager equality. +- 193 Cua-S1 tests pass; CI-scoped Ruff checks and formatting pass. +- The independent stdlib verifier checks source hashes against measured Git objects, complete responses, all reported timing metrics, aggregated counters and cache bounds. +- Read-only independent code/evidence review found no significant defects within the declared serial experimental scope. + +The matrix and five-case parity/memory checks use the revision above. Boundary checks use `d082d54804c31e92203cd8bb7b76ef14f53ccd06`, which only adds checks/tests; runtime and benchmark code are unchanged. + +## Reproduction + +Use the source revision in each raw JSON, with the pinned environment and existing +verified checkpoints. Choose fresh output paths: + +```sh +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_buckets.py \ + --weights /path/to/weights --output /path/to/matrix --runs 2 \ + --case hot_four --case churn_twelve --kind rule +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_graph_buckets.py \ + --weights /path/to/weights --output /path/to/parity-memory.json +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_graph_buckets.py \ + --weights /path/to/weights --boundaries-only --output /path/to/boundaries.json +python recipe/cua_s1/verify_graph_buckets.py /path/to/matrix/report.json --require-replay +``` + +Reproduce the negative segment variant with `--kind segment --case probe --runs 1`. +`diagnose_graph_buckets.py` records leaf-module and Graph/eager differences. The +initial rule prototype rejected a harmless `use_cache=False` metadata argument; +its failed run is preserved, and the accepted call contract has a regression test. + +This experiment does not establish HTTP throughput, concurrency, dynamic batching, +new CUDA/Triton kernels, compilation, native multimodal execution, task accuracy +on a real GUI dataset, or performance on another GPU/software stack. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-buckets/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-graph-buckets/SHA256SUMS new file mode 100644 index 0000000..fc8e4b9 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-buckets/SHA256SUMS @@ -0,0 +1,10 @@ +7220a8729aaace1f70f5e626e7fc372d6e21c9d1c330fd7db5d96a23730ae1ff README.md +aa2eda0676dd7527bee8166130e171c46c2a541788c873ef60d16846ae6b0ff7 boundaries.json +9b63fc3a4742709d87be04f13612c06e0dbd488c4f76363c7db8c6b8b8c6a527 diagnostics/rule-initial-error.log +b75e7c2d0f2705f92529e61b1637b95e92431077d5795b9bb15437db699b6aac diagnostics/rule-initial-probe.json +b0a6cee1c936e5e6a1560899e63013e334712de40e92be96b084f39c5670828f diagnostics/segment-padding.json +6ea045310aa8cad501c0124e1ae8cc4612016a41847db1f76f1f5578a77e5bc7 diagnostics/segment-probe.json +4cbf3e767a96c3362ebd7969a71636a3b891441c88d874fb9f55d1627d267e5a parity-memory.json +af9e0be9f42390aaec89bf04a210356cd55d5c11ce3c1705c2c7858d70b12b69 report.json +0e85d90d9779d1205dfb7cba08792c7004897f779c00799ab0818b12e9c2f1e5 tests.log +45f638935e098eb87806c9424c0ffa568edeaa9ea99529301694f5e36e9ccda5 verification.log diff --git a/recipe/cua_s1/experiments/rtx4090-graph-buckets/boundaries.json b/recipe/cua_s1/experiments/rtx4090-graph-buckets/boundaries.json new file mode 100644 index 0000000..1af3ab3 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-buckets/boundaries.json @@ -0,0 +1,533 @@ +{ + "status": "complete", + "repository": { + "revision": 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+ }, + "questions": { + "q1": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + }, + "q2": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + } + } + }, + "tokens": { + "1": 236, + "2": 242, + "3": 248, + "4": 254, + "5": 260, + "6": 266, + "7": 272, + "8": 278, + "9": 284, + "10": 290, + "11": 296, + "12": 302 + }, + "buckets": { + "1": 256, + "2": 256, + "3": 256, + "4": 256, + "5": 320, + "6": 320, + "7": 320, + "8": 320, + "9": 320, + "10": 320, + "11": 320, + "12": 320 + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-graph-buckets/tests.log b/recipe/cua_s1/experiments/rtx4090-graph-buckets/tests.log new file mode 100644 index 0000000..b090bb2 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-buckets/tests.log @@ -0,0 +1,4 @@ +........................................................................ [ 37%] +........................................................................ [ 74%] +................................................. [100%] +193 passed in 16.25s diff --git a/recipe/cua_s1/experiments/rtx4090-graph-buckets/verification.log b/recipe/cua_s1/experiments/rtx4090-graph-buckets/verification.log new file mode 100644 index 0000000..0bc2e57 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-buckets/verification.log @@ -0,0 +1,5 @@ +hot_four 1 verified 48 request triplets +hot_four 2 verified 48 request triplets +churn_twelve 1 verified 72 request triplets +churn_twelve 2 verified 72 request triplets +verified 720 timed predictions; source hashes, responses, metrics and cache bounds From d3bdc5a3505c7a7b6e17ccc770bc29366948ca77 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:49:21 +0800 Subject: [PATCH 56/75] docs: scope explicit rule-bucket worker integration --- .../plans/2026-09-29-cua-bucket-worker.md | 14 ++++++++++++++ .../specs/2026-09-29-cua-bucket-worker.md | 11 +++++++++++ 2 files changed, 25 insertions(+) create mode 100644 docs/superpowers/plans/2026-09-29-cua-bucket-worker.md create mode 100644 docs/superpowers/specs/2026-09-29-cua-bucket-worker.md diff --git a/docs/superpowers/plans/2026-09-29-cua-bucket-worker.md b/docs/superpowers/plans/2026-09-29-cua-bucket-worker.md new file mode 100644 index 0000000..64fa738 --- /dev/null +++ b/docs/superpowers/plans/2026-09-29-cua-bucket-worker.md @@ -0,0 +1,14 @@ +# Cua-S1 Bucket Worker Implementation Plan + +Execute inline using the approved worker-integration scope. + +**Goal:** Make rule-only buckets available as an explicit worker execution mode with local model calls and reproducible GPU/HTTP evidence. +**Architecture:** `rule_prefill.py` implements pinned cache-free prefill with an explicit rule callback. `graph_buckets.py` owns packing, cache/gates and direct per-model decoder traversal. `model.py` selects a runtime; `server.py` parses modes and closes the engine after draining requests. +**Tech Stack:** Existing pinned Transformers 5.17.0 / PEFT / PyTorch CUDA environment and pytest/Ruff. + +- [ ] Add failing tests in `tests/cua_s1/test_rule_prefill.py`: compare a small pinned Qwen3.5 module's CPU output to the explicit callback path, prove globals and forward identities unchanged, validate callback output behavior and input contracts. Implement `src/models/cua_s1/multimodal/rule_prefill.py`. +- [ ] Add failing runtime/config/lifecycle tests in `tests/cua_s1/test_bucket_worker.py`. Promote the successful recipe logic to `src/models/cua_s1/multimodal/graph_buckets.py`; directly traverse model-instance layers using the prefill adapter; preserve capture/admission and full-logit gates. Reject static input shape/dtype/device changes before copies. Explicitly retire graph entries on invalidation/close. +- [ ] Extend `GraphConfig` with validated mode/width defaults, `MultimodalEngine` runtime selection and idempotent close, and worker CLI parsing/shutdown. Test eager default, legacy --graph, explicit modes, malformed options, no post-close prediction and HTTP drain semantics. +- [ ] Build a clean GPU recipe checking explicit prefill against original, independent runtimes without global mutation, complete logits under changed inputs and boundaries, eviction/budget fallback, and real HTTP cold/capture/replay. Run pinned tests and CI-scoped lint/format. +- [ ] Compare synchronized full predictions for eager/exact/previous recipe/worker buckets on stable and rotating lengths, balancing order and counting cold cost. Record clean revision, full responses and budgets. Preserve historical evidence without rewriting it. +- [ ] Independent code/evidence review; address important findings, verify report metrics, commit clean source and evidence, and keep the worktree for subsequent work. diff --git a/docs/superpowers/specs/2026-09-29-cua-bucket-worker.md b/docs/superpowers/specs/2026-09-29-cua-bucket-worker.md new file mode 100644 index 0000000..e4631c6 --- /dev/null +++ b/docs/superpowers/specs/2026-09-29-cua-bucket-worker.md @@ -0,0 +1,11 @@ +# Cua-S1 rule bucket worker integration + +Approved scope: integrate the measured rule-only bucket runtime into the worker, keep exact Graph behavior and eager defaults, preserve strict numerical gates, validate HTTP and GPU parity/performance. Automatic strategy selection and simultaneous exact/bucket caches are separate work. + +Use explicit model-instance computation, not global function replacement or instance-method mutation. A small cache-free prefill adapter reproduces the pinned Qwen3.5 GatedDeltaNet forward around a supplied rule callable. Norms, convolution, projections, MLP, full attention and positions retain original shapes. Only rule inputs are zero-padded. The adapter is restricted to the pinned Transformers implementation and CUDA-resident immutable model; unsupported execution forms fall back to eager. It has no persistent recurrent state. + +A production RuleBucketRuntime owns one bounded cache/admission policy and serializes its requests. Preserve per-bucket capture equality, per-real-length exact vocabulary-logit gates, input-copy layout checks, independent shape pools/streams and eager fallback. Clearing/closing the engine explicitly retires all graphs before streams. Closed engines reject further prediction; shutdown drains current inference before releasing GPU resources. + +CLI: no graph option stays eager; existing --graph stays exact; --graph-mode exact|rule-bucket also enables the selected mode. --graph-bucket-width defaults to 64, must be a positive multiple of 64, and is only meaningful in rule mode. Mode validation occurs before weight loading. Single-question requests retain the current reference path. + +Acceptance: source runtime has no recipe dependency and performs no global/model-method mutation; CPU tests for modes/config/packing/gates/cleanup; CPU tensor adapter comparison against pinned upstream; changed-image/text, boundary and fallback GPU checks; real HTTP cold/capture/replay equality; same-workload comparison to previous recipe runtime and exact/eager. Document fixture-specific results and keep opt-in. From d0fc83a162ad50637823e09f3a3a2281612876e3 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 14:56:50 +0800 Subject: [PATCH 57/75] feat: integrate instance-local rule buckets into multimodal worker --- src/models/cua_s1/multimodal/graph_buckets.py | 227 ++++++++++++++++++ src/models/cua_s1/multimodal/graph_runtime.py | 31 +++ src/models/cua_s1/multimodal/model.py | 34 ++- src/models/cua_s1/multimodal/rule_prefill.py | 64 +++++ src/models/cua_s1/multimodal/server.py | 55 ++++- tests/cua_s1/test_bucket_worker.py | 186 ++++++++++++++ tests/cua_s1/test_rule_prefill.py | 75 ++++++ 7 files changed, 657 insertions(+), 15 deletions(-) create mode 100644 src/models/cua_s1/multimodal/graph_buckets.py create mode 100644 src/models/cua_s1/multimodal/rule_prefill.py create mode 100644 tests/cua_s1/test_bucket_worker.py create mode 100644 tests/cua_s1/test_rule_prefill.py diff --git a/src/models/cua_s1/multimodal/graph_buckets.py b/src/models/cua_s1/multimodal/graph_buckets.py new file mode 100644 index 0000000..0c68962 --- /dev/null +++ b/src/models/cua_s1/multimodal/graph_buckets.py @@ -0,0 +1,227 @@ +"""Opt-in rule-only CUDA Graph buckets for the pinned multimodal worker.""" + +from __future__ import annotations + +import time + +from .graph_runtime import ( + GraphConfig, + GraphRuntime, + _GraphPool, + _GraphSegment, + tensor_signature, +) +from .rule_prefill import pinned_implementation, rule_prefill + + +def bucket_length(length, width): + return ((length + width - 1) // width) * width + + +class RuleBucketRuntime(GraphRuntime): + """Bounded cache of rule graphs, with explicit model-instance dispatch.""" + + def __init__(self, model, config=None): + config = config or GraphConfig(mode="rule-bucket") + super().__init__(model, config) + self.width = config.bucket_width + self.stats.update( + length_checks=0, + length_rejections=0, + length_disabled=0, + length_check_ms=0.0, + real_tokens=0, + padded_tokens=0, + ) + + @staticmethod + def _dense(values): + hidden = values["inputs_embeds"] + mask = values["attention_mask"] + return ( + hidden.is_contiguous() + and mask is not None + and tuple(mask.shape) == tuple(hidden.shape[:2]) + and bool((mask == 1).all()) + ) + + def _key(self, values): + hidden = values["inputs_embeds"] + return ( + id(self.model), + self.model.get_base_model().model.language_model.config._attn_implementation, + bucket_length(hidden.shape[1], self.width), + hidden.shape[2], + str(hidden.dtype), + str(hidden.device), + ) + + def _validate_replay(self, values, entry, output): + import torch + + length = values["inputs_embeds"].shape[1] + if length in entry.verified_lengths: + return output + started = time.perf_counter() + reference = self._eager(values) + self.stats["length_checks"] += 1 + if torch.equal(reference, output): + entry.verified_lengths.add(length) + else: + entry.rejected_lengths.add(length) + self.stats["length_rejections"] += 1 + output = reference + self.stats["length_check_ms"] += (time.perf_counter() - started) * 1000 + return output + + def forward(self, values): + import torch + + with self.lock, torch.no_grad(): + if self._closed: + raise RuntimeError("Graph runtime is closed") + if not self._in_request: + self.stats["no_request"] += 1 + return self._eager(values) + if not self._supported(values) or not self._dense(values): + self.stats["unsupported"] += 1 + return self._eager(values) + length = values["inputs_embeds"].shape[1] + if bucket_length(length, self.width) > self.config.max_tokens: + self.stats["unsupported"] += 1 + return self._eager(values) + key = self._key(values) + if key in self.disabled: + self.stats["disabled"] += 1 + return self._eager(values) + entry = self.cache.get(key) + if entry is not None: + if length in entry.rejected_lengths: + self.stats["length_disabled"] += 1 + return self._eager(values) + output = self._run_segments(values, entry) + self.stats["replays"] += 1 + return self._validate_replay(values, entry, output) + reason = self.admission.reason(key) + if reason is not None: + self.stats[reason] += 1 + return self._eager(values) + ticket = self.admission.start_capture() + self.stats["capture_attempts"] += 1 + attempt_start = time.perf_counter() + try: + output = self._capture(values, key) + entry = self.cache.get(key) + if entry is not None: + entry.verified_lengths = {length} + entry.rejected_lengths = set() + return output + finally: + elapsed = (time.perf_counter() - attempt_start) * 1000 + self.admission.finish_capture(ticket, elapsed) + self.stats["capture_attempt_ms"] += elapsed + + def _run_segments(self, values, entry): + from transformers.masking_utils import create_causal_mask + + implementation = pinned_implementation() + text = self.model.get_base_model().model.language_model + hidden = values["inputs_embeds"] + length = hidden.shape[1] + self.stats["real_tokens"] += length + self.stats["padded_tokens"] += bucket_length(length, self.width) - length + mask = create_causal_mask( + config=text.config, + inputs_embeds=hidden, + attention_mask=values["attention_mask"], + past_key_values=None, + position_ids=None, + ) + rope = text.rotary_emb(hidden, values["position_ids"]) + for index, layer in enumerate(text.layers): + if text.config.layer_types[index] == "full_attention": + hidden = layer( + hidden, + position_embeddings=rope, + attention_mask=mask, + position_ids=None, + past_key_values=None, + use_cache=False, + ) + continue + if type(layer.linear_attn) is not implementation.Qwen3_5GatedDeltaNet: + raise RuntimeError("unsupported linear-attention implementation") + + def dispatch(query, key, value, *, g, beta, **kwargs): + packed = pack_rule_inputs( + dict(query=query, key=key, value=value, g=g, beta=beta), self.width + ) + block = entry.blocks.get(index) + if block is None: + if entry.pool is None: + entry.pool = _GraphPool(query.device) + block = RuleSegment( + implementation.torch_chunk_gated_delta_rule, packed, entry.pool + ) + entry.blocks[index] = block + entry.update_bytes() + output, state = block.replay_values(packed) + return output[:, :length].contiguous(), state + + residual = hidden + hidden = rule_prefill( + layer.linear_attn, layer.input_layernorm(hidden), dispatch + ) + hidden = residual + hidden + residual = hidden + hidden = residual + layer.mlp(layer.post_attention_layernorm(hidden)) + hidden = text.norm(hidden) + return self.model.get_base_model().lm_head(hidden[:, -1:, :])[0, -1, :] + + +def pack_rule_inputs(values, width): + import torch.nn.functional as F + + length = values["query"].shape[1] + padding = bucket_length(length, width) - length + return { + name: F.pad(value, (0, 0) * (value.ndim - 2) + (0, padding)) + for name, value in values.items() + } + + +class RuleSegment(_GraphSegment): + """Capture just the fallback rule, reusing #33 stream/pool ownership.""" + + def __init__(self, function, values, pool): + self.function = function + self.signatures = {k: tensor_signature(v) for k, v in values.items()} + self.static_extra = {k: v.clone() for k, v in values.items() if k != "query"} + super().__init__([], 0, 0, values["query"], None, pool=pool) + self.external_bytes += sum( + v.untyped_storage().nbytes() for v in self.static_extra.values() + ) + + def _forward(self): + return self.function( + self.static_hidden, + self.static_extra["key"], + self.static_extra["value"], + g=self.static_extra["g"], + beta=self.static_extra["beta"], + initial_state=None, + output_final_state=False, + use_qk_l2norm_in_kernel=True, + ) + + def replay_values(self, values): + if {k: tensor_signature(v) for k, v in values.items()} != self.signatures: + raise ValueError("CUDA Graph rule input layout changed") + for name, value in self.static_extra.items(): + value.copy_(values[name]) + return super().replay(values["query"], None) + + def close(self): + super().close() + if hasattr(self, "static_extra"): + self.static_extra.clear() diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py index 377bdd6..fd460dc 100644 --- a/src/models/cua_s1/multimodal/graph_runtime.py +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -41,8 +41,22 @@ class GraphConfig: capture_window: int = 32 max_captures: int = 4 capture_budget_ms: float = 2000.0 + mode: str = "exact" + bucket_width: int = 64 def __post_init__(self): + if self.mode not in {"exact", "rule-bucket"}: + raise ValueError("graph mode must be exact or rule-bucket") + if ( + type(self.bucket_width) is not int + or self.bucket_width <= 0 + or self.bucket_width % 64 + ): + raise ValueError("bucket width must be a positive integer multiple of 64") + if self.mode == "exact" and self.bucket_width != 64: + raise ValueError("bucket width is only configurable in rule-bucket mode") + if self.mode == "rule-bucket" and self.bucket_width > self.max_tokens: + raise ValueError("bucket width cannot exceed graph max tokens") if not math.isfinite(self.capture_budget_ms): raise ValueError("capture time budget must be finite") if ( @@ -270,6 +284,8 @@ class _ShapeEntry: blocks: dict = field(default_factory=dict) bytes: int = 0 pool: _GraphPool | None = None + verified_lengths: set[int] = field(default_factory=set) + rejected_lengths: set[int] = field(default_factory=set) def update_bytes(self): self.bytes = self.pool.reserved_bytes() + sum( @@ -302,6 +318,7 @@ def __init__(self, model, config: GraphConfig | None = None): self.cache = GraphCache(self.config.max_shapes, self.config.max_bytes) self.admission = AdmissionPolicy(self.config) self._in_request = False + self._closed = False self.disabled = OrderedDict() self.lock = threading.RLock() self.stats = { @@ -330,6 +347,8 @@ def __init__(self, model, config: GraphConfig | None = None): def request(self): """Serialize a complete prediction and count duplicate layouts only once.""" with self.lock: + if self._closed: + raise RuntimeError("Graph runtime is closed") if self._in_request: raise RuntimeError("nested Graph requests are unsupported") self._in_request = True @@ -348,9 +367,19 @@ def invalidate(self): retired = self.cache.clear() self.admission.reset() self.disabled.clear() + for entry in retired: + entry.close() del retired gc.collect() + def close(self): + """Synchronously retire graph resources; reject subsequent requests.""" + with self.lock: + if self._closed: + return + self.invalidate() + self._closed = True + def _eager(self, values): self.stats["eager"] += 1 return self.model(**values, logits_to_keep=1, use_cache=False).logits[0, -1, :] @@ -438,6 +467,8 @@ def forward(self, values): import torch with self.lock, torch.no_grad(): + if self._closed: + raise RuntimeError("Graph runtime is closed") if not self._in_request: self.stats["no_request"] += 1 return self._eager(values) diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py index 91cb659..1e110e3 100644 --- a/src/models/cua_s1/multimodal/model.py +++ b/src/models/cua_s1/multimodal/model.py @@ -5,6 +5,7 @@ import copy import hashlib import json +import threading from contextlib import nullcontext from pathlib import Path @@ -109,6 +110,8 @@ def __init__( dtype: str = "bfloat16", graph_config=None, ): + self._lifecycle_lock = threading.RLock() + self._closed = False import torch from peft import PeftModel from peft.tuners.lora import LoraLayer @@ -148,7 +151,17 @@ def __init__( if graph_config is not None: from .graph_runtime import GraphRuntime - self.graph_runtime = GraphRuntime(self.model, graph_config) + if graph_config.mode == "rule-bucket": + from .graph_buckets import RuleBucketRuntime + from .rule_prefill import pinned_implementation + + pinned_implementation() + # Explicit adapter calls cannot honor offload/device-map hooks. + if any(p.device != self.model.device for p in self.model.parameters()): + raise ValueError("rule-bucket requires one CUDA-resident model") + self.graph_runtime = RuleBucketRuntime(self.model, graph_config) + else: + self.graph_runtime = GraphRuntime(self.model, graph_config) def prepare(self, image, question: Question): return self._prepare(self.processor, image, question) @@ -263,9 +276,22 @@ def predict_reference(self, request: Request) -> dict: } def predict(self, request: Request) -> dict: - runtime = getattr(self, "graph_runtime", None) - with runtime.request() if runtime is not None else nullcontext(): - return self._predict(request) + with getattr(self, "_lifecycle_lock", nullcontext()): + if getattr(self, "_closed", False): + raise RuntimeError("multimodal engine is closed") + runtime = getattr(self, "graph_runtime", None) + with runtime.request() if runtime is not None else nullcontext(): + return self._predict(request) + + def close(self): + """Drain prediction before explicitly releasing captured GPU resources.""" + with getattr(self, "_lifecycle_lock", nullcontext()): + if getattr(self, "_closed", False): + return + runtime = getattr(self, "graph_runtime", None) + if runtime is not None: + runtime.close() + self._closed = True def _predict(self, request: Request) -> dict: if len(request.questions) == 1: diff --git a/src/models/cua_s1/multimodal/rule_prefill.py b/src/models/cua_s1/multimodal/rule_prefill.py new file mode 100644 index 0000000..32e27eb --- /dev/null +++ b/src/models/cua_s1/multimodal/rule_prefill.py @@ -0,0 +1,64 @@ +"""Pinned Qwen3.5 cache-free prefill with an explicit DeltaNet rule callable. + +Adapted from Transformers 5.17.0 modeling_qwen3_5.Qwen3_5GatedDeltaNet.forward +(Copyright the HuggingFace Inc. team, Apache License 2.0). Operations retain the +upstream order and original token shapes; only the supplied rule may bucket. +No global functions, instance methods, weights or persistent states are changed. +""" + +from functools import lru_cache + + +@lru_cache(maxsize=1) +def pinned_implementation(): + import transformers + from transformers.models.qwen3_5 import modeling_qwen3_5 + + if transformers.__version__ != "5.17.0": + raise RuntimeError("rule-bucket requires pinned Transformers 5.17.0") + return modeling_qwen3_5 + + +def rule_prefill(module, hidden, rule): + """Dense, cache-free prefill on the supplied module's existing parameters.""" + import torch + from torch.nn import functional as F + + implementation = pinned_implementation() + batch, length, _ = hidden.shape + mixed = module.in_proj_qkv(hidden).transpose(1, 2) + z = module.in_proj_z(hidden).reshape(batch, length, -1, module.head_v_dim) + b = module.in_proj_b(hidden) + a = module.in_proj_a(hidden) + mixed = implementation.causal_conv1d_fn( + mixed, + module.conv1d.weight.squeeze(1), + module.conv1d.bias, + activation=module.activation, + ).transpose(1, 2) + query, key, value = torch.split( + mixed, [module.key_dim, module.key_dim, module.value_dim], dim=-1 + ) + query = query.reshape(batch, length, -1, module.head_k_dim) + key = key.reshape(batch, length, -1, module.head_k_dim) + value = value.reshape(batch, length, -1, module.head_v_dim) + beta = b.sigmoid() + g = -module.A_log.float().exp() * F.softplus(a.float() + module.dt_bias) + repeats = module.num_v_heads // module.num_k_heads + if repeats > 1: + query = query.repeat_interleave(repeats, dim=2) + key = key.repeat_interleave(repeats, dim=2) + output, _ = rule( + query, + key, + value, + g=g, + beta=beta, + initial_state=None, + output_final_state=False, + use_qk_l2norm_in_kernel=True, + ) + output = module.norm( + output.reshape(-1, module.head_v_dim), z.reshape(-1, module.head_v_dim) + ) + return module.out_proj(output.reshape(batch, length, -1)) diff --git a/src/models/cua_s1/multimodal/server.py b/src/models/cua_s1/multimodal/server.py index 79c7f01..572791b 100644 --- a/src/models/cua_s1/multimodal/server.py +++ b/src/models/cua_s1/multimodal/server.py @@ -21,13 +21,22 @@ class WorkerServer(ThreadingHTTPServer): - daemon_threads = True + daemon_threads = False # Join accepted handlers before retiring GPU buffers. def __init__(self, address, engine): self.engine = engine self.inference_lock = threading.Lock() + self._engine_closed = False super().__init__(address, Handler) + def server_close(self): + super().server_close() + if not self._engine_closed: + close = getattr(self.engine, "close", None) + if close is not None: + close() + self._engine_closed = True + class Handler(BaseHTTPRequestHandler): def setup(self): @@ -106,9 +115,8 @@ def do_POST(self): self.server.inference_lock.release() -def main(): +def parse_args(argv=None): from .graph_runtime import GraphConfig - from .model import MultimodalEngine p = argparse.ArgumentParser(description=__doc__) p.add_argument( @@ -121,6 +129,12 @@ def main(): p.add_argument( "--graph", action="store_true", help="enable segmented CUDA Graph replay" ) + p.add_argument( + "--graph-mode", + choices=("exact", "rule-bucket"), + help="enable the selected Graph execution mode", + ) + p.add_argument("--graph-bucket-width", type=int, default=None) p.add_argument("--graph-max-shapes", type=int, default=8) p.add_argument("--graph-max-memory-mib", type=int, default=1024) p.add_argument( @@ -135,10 +149,17 @@ def main(): p.add_argument("--graph-capture-window", type=int, default=32) p.add_argument("--graph-max-captures", type=int, default=4) p.add_argument("--graph-capture-budget-ms", type=float, default=2000.0) - args = p.parse_args() + args = p.parse_args(argv) + mode = args.graph_mode or ("exact" if args.graph else None) + if args.graph_bucket_width is not None and mode != "rule-bucket": + p.error("--graph-bucket-width requires --graph-mode rule-bucket") try: graph_config = ( GraphConfig( + mode=mode, + bucket_width=64 + if args.graph_bucket_width is None + else args.graph_bucket_width, max_shapes=args.graph_max_shapes, max_bytes=args.graph_max_memory_mib * 1024 * 1024, min_uses=args.graph_min_uses, @@ -149,23 +170,35 @@ def main(): max_captures=args.graph_max_captures, capture_budget_ms=args.graph_capture_budget_ms, ) - if args.graph + if mode is not None else None ) except ValueError as exc: p.error(str(exc)) + args.graph_config = graph_config + return args + + +def main(): + from .model import MultimodalEngine + + args = parse_args() logging.basicConfig(level=logging.INFO) - engine = MultimodalEngine(args.base, args.adapter, graph_config=graph_config) - engine.warmup() - # Bind only after model loading and a representative inference succeed. - server = WorkerServer(("127.0.0.1", args.port), engine) - LOG.info("multimodal worker ready on 127.0.0.1:%s", args.port) + engine = MultimodalEngine(args.base, args.adapter, graph_config=args.graph_config) + server = None try: + engine.warmup() + # Bind only after model loading and representative inference succeed. + server = WorkerServer(("127.0.0.1", args.port), engine) + LOG.info("multimodal worker ready on 127.0.0.1:%s", args.port) server.serve_forever() except KeyboardInterrupt: pass finally: - server.server_close() + if server is not None: + server.server_close() + else: + engine.close() if __name__ == "__main__": diff --git a/tests/cua_s1/test_bucket_worker.py b/tests/cua_s1/test_bucket_worker.py new file mode 100644 index 0000000..5bc545c --- /dev/null +++ b/tests/cua_s1/test_bucket_worker.py @@ -0,0 +1,186 @@ +"""Worker mode selection and deterministic Graph lifecycle behavior.""" + +from types import SimpleNamespace + +import pytest + +from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + + +@pytest.mark.parametrize( + "kwargs", + [ + {"mode": "auto"}, + {"mode": "rule-bucket", "bucket_width": 0}, + {"mode": "rule-bucket", "bucket_width": 65}, + {"mode": "rule-bucket", "bucket_width": True}, + {"mode": "rule-bucket", "bucket_width": 4096}, + {"bucket_width": 128}, + ], +) +def test_graph_mode_configuration_rejects_invalid_settings(kwargs): + with pytest.raises(ValueError): + GraphConfig(**kwargs) + + +def test_cli_default_and_legacy_and_explicit_modes(): + from models.cua_s1.multimodal.server import parse_args + + base = ["--base", "base", "--adapter", "adapter"] + assert parse_args(base).graph_config is None + assert parse_args(base + ["--graph"]).graph_config.mode == "exact" + assert parse_args(base + ["--graph-mode", "exact"]).graph_config.mode == "exact" + config = parse_args( + base + ["--graph-mode", "rule-bucket", "--graph-bucket-width", "128"] + ).graph_config + assert config.mode == "rule-bucket" and config.bucket_width == 128 + + +@pytest.mark.parametrize( + "args", + [ + ["--graph-bucket-width", "64"], + ["--graph", "--graph-bucket-width", "128"], + ["--graph-mode", "auto"], + ["--graph-mode", "rule-bucket", "--graph-bucket-width", "65"], + ], +) +def test_cli_invalid_modes_rejected_before_loading(args): + from models.cua_s1.multimodal.server import parse_args + + with pytest.raises(SystemExit): + parse_args(["--base", "base", "--adapter", "adapter", *args]) + + +def test_close_explicitly_retires_cache_and_rejects_new_requests(): + runtime = GraphRuntime(object()) + closed = [] + # Keep another reference alive: invalidation cannot rely on GC/destructors. + entry = SimpleNamespace(bytes=1, close=lambda: closed.append(True)) + runtime.cache.put("key", entry) + runtime.close() + runtime.close() + assert closed == [True] + assert not runtime.cache.entries + with pytest.raises(RuntimeError, match="closed"), runtime.request(): + pass + + +def test_close_during_request_does_not_destroy_live_entries(): + runtime = GraphRuntime(object()) + with runtime.request(): + with pytest.raises(RuntimeError, match="request"): + runtime.close() + with runtime.request(): + pass + runtime.close() + + +def test_engine_close_is_idempotent_and_prevents_predict(): + from models.cua_s1.multimodal.model import MultimodalEngine + + engine = object.__new__(MultimodalEngine) + closes = [] + engine.graph_runtime = SimpleNamespace(close=lambda: closes.append(True)) + engine.close() + engine.close() + assert closes == [True] + with pytest.raises(RuntimeError, match="closed"): + engine.predict(object()) + + +def test_production_rule_packing_and_gate_match_contract(): + torch = pytest.importorskip("torch") + from models.cua_s1.multimodal.graph_buckets import ( + RuleBucketRuntime, + pack_rule_inputs, + ) + + values = {k: torch.ones(1, 63, 2, 4) for k in ("query", "key", "value")} + values.update(g=torch.ones(1, 63, 2), beta=torch.ones(1, 63, 2)) + packed = pack_rule_inputs(values, 64) + for name, tensor in packed.items(): + assert torch.equal(tensor[:, :63], values[name]) + assert torch.count_nonzero(tensor[:, 63:]) == 0 + r = RuleBucketRuntime(object()) + entry = SimpleNamespace(verified_lengths=set(), rejected_lengths=set()) + reference = torch.tensor([1.0, 2.0]) + r._eager = lambda v: reference + v = {"inputs_embeds": torch.ones(1, 63, 4)} + assert torch.equal(r._validate_replay(v, entry, reference), reference) + v["inputs_embeds"] = torch.ones(1, 64, 4) + assert torch.equal(r._validate_replay(v, entry, reference + 1), reference) + assert entry.verified_lengths == {63} and entry.rejected_lengths == {64} + + +def test_rule_segment_rejects_layout_change_before_copy(): + torch = pytest.importorskip("torch") + from models.cua_s1.multimodal.graph_buckets import RuleSegment + from models.cua_s1.multimodal.graph_runtime import tensor_signature + + segment = object.__new__(RuleSegment) + good = {k: torch.zeros(1, 64, 2, 4) for k in ("query", "key", "value")} + segment.signatures = {k: tensor_signature(v) for k, v in good.items()} + segment.static_extra = {"key": good["key"].clone(), "value": good["value"].clone()} + bad = {**good, "value": torch.ones(1, 1, 2, 4)} + with pytest.raises(ValueError, match="layout"): + segment.replay_values(bad) + assert torch.count_nonzero(segment.static_extra["value"]) == 0 + + +def test_worker_close_waits_for_inflight_http_before_engine_close(): + import threading + + from test_protocol import request + from test_server import call + + from models.cua_s1.multimodal.server import WorkerServer + + started, release, closing, closed = (threading.Event() for _ in range(4)) + + class Engine: + def predict(self, parsed): + started.set() + assert release.wait(5) + assert not closed.is_set() + return {"ok": True} + + def close(self): + closed.set() + + server = WorkerServer(("127.0.0.1", 0), Engine()) + serving = threading.Thread(target=server.serve_forever) + serving.start() + responses = [] + client = threading.Thread( + target=lambda: responses.append( + call(f"http://127.0.0.1:{server.server_port}/v1/systemone", request()) + ) + ) + client.start() + + def stop(): + server.shutdown() + closing.set() + server.server_close() + + stopper = threading.Thread(target=stop) + try: + assert started.wait(5) + stopper.start() + assert closing.wait(5) + assert not closed.wait(0.05) + release.set() + stopper.join(5) + client.join(5) + serving.join(5) + assert closed.is_set() and not stopper.is_alive() + assert responses == [(200, {"ok": True})] + finally: + release.set() + server.shutdown() + server.server_close() + client.join(5) + serving.join(5) + if stopper.ident is not None: + stopper.join(5) diff --git a/tests/cua_s1/test_rule_prefill.py b/tests/cua_s1/test_rule_prefill.py new file mode 100644 index 0000000..ec9f329 --- /dev/null +++ b/tests/cua_s1/test_rule_prefill.py @@ -0,0 +1,75 @@ +"""Compare explicit callback prefill to the pinned Transformers implementation.""" + +import pytest + + +@pytest.mark.parametrize("length", [1, 63, 64, 65, 129]) +def test_explicit_prefill_matches_upstream_without_mutation(length): + torch = pytest.importorskip("torch") + pytest.importorskip("transformers") + from transformers import Qwen3_5TextConfig + from transformers.models.qwen3_5 import modeling_qwen3_5 as upstream + + from models.cua_s1.multimodal.rule_prefill import rule_prefill + + torch.manual_seed(43) + config = Qwen3_5TextConfig( + hidden_size=32, + intermediate_size=64, + num_hidden_layers=1, + layer_types=["linear_attention"], + linear_key_head_dim=8, + linear_value_head_dim=8, + linear_num_key_heads=2, + linear_num_value_heads=4, + ) + module = upstream.Qwen3_5GatedDeltaNet(config, 0).eval() + hidden = torch.randn(1, length, 32) + original_rule = upstream.torch_chunk_gated_delta_rule + original_forward = module.forward.__func__ + calls = [] + + def rule(*args, **kwargs): + calls.append(tuple(args[0].shape)) + return original_rule(*args, **kwargs) + + with torch.no_grad(): + expected = module(hidden, use_cache=False) + actual = rule_prefill(module, hidden, rule) + assert torch.isfinite(expected).all() + assert torch.equal(expected, actual) + assert calls == [(1, length, 4, 8)] + assert upstream.torch_chunk_gated_delta_rule is original_rule + assert module.forward.__func__ is original_forward + + +def test_callback_failure_does_not_modify_model_or_globals(): + torch = pytest.importorskip("torch") + pytest.importorskip("transformers") + from transformers import Qwen3_5TextConfig + from transformers.models.qwen3_5 import modeling_qwen3_5 as upstream + + from models.cua_s1.multimodal.rule_prefill import rule_prefill + + config = Qwen3_5TextConfig( + hidden_size=32, + num_hidden_layers=1, + layer_types=["linear_attention"], + linear_key_head_dim=8, + linear_value_head_dim=8, + linear_num_key_heads=2, + linear_num_value_heads=4, + ) + module = upstream.Qwen3_5GatedDeltaNet(config, 0).eval() + original_rule, original_forward = ( + upstream.torch_chunk_gated_delta_rule, + module.forward.__func__, + ) + + def fail(*args, **kwargs): + raise RuntimeError("test callback failure") + + with torch.no_grad(), pytest.raises(RuntimeError, match="test callback"): + rule_prefill(module, torch.ones(1, 3, 32), fail) + assert upstream.torch_chunk_gated_delta_rule is original_rule + assert module.forward.__func__ is original_forward From 249b0e34c87a0ced433c7a6f7d72527094ec4915 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 15:00:03 +0800 Subject: [PATCH 58/75] test: add worker HTTP checks and four-way bucket comparison --- recipe/cua_s1/benchmark_graph_buckets.py | 67 +++++++--- recipe/cua_s1/check_bucket_worker_http.py | 148 ++++++++++++++++++++++ recipe/cua_s1/check_graph_buckets.py | 22 +++- recipe/cua_s1/verify_graph_buckets.py | 11 +- 4 files changed, 226 insertions(+), 22 deletions(-) create mode 100644 recipe/cua_s1/check_bucket_worker_http.py diff --git a/recipe/cua_s1/benchmark_graph_buckets.py b/recipe/cua_s1/benchmark_graph_buckets.py index eef9b83..01e5ef0 100644 --- a/recipe/cua_s1/benchmark_graph_buckets.py +++ b/recipe/cua_s1/benchmark_graph_buckets.py @@ -5,6 +5,7 @@ import argparse import gc import hashlib +import itertools import math import statistics from dataclasses import asdict, replace @@ -38,11 +39,18 @@ def main(): p.add_argument("--output", type=Path, required=True) p.add_argument("--runs", type=int, default=2) p.add_argument("--width", type=int, default=64) - p.add_argument("--kind", choices=("segment", "rule"), default="segment") + p.add_argument("--kind", choices=("segment", "rule", "worker"), default="segment") + p.add_argument( + "--include-recipe", + action="store_true", + help="also time the prior recipe algorithm with current shared runtime", + ) p.add_argument("--case", choices=list(schedules()), action="append") args = p.parse_args() if args.output.exists(): p.error("choose a fresh output directory") + if args.include_recipe and args.kind != "worker": + p.error("--include-recipe requires --kind worker") if args.runs < 1: p.error("runs must be positive") @@ -72,23 +80,56 @@ def main(): selected = {k: v for k, v in schedules().items() if k in (args.case or ["probe"])} config = GraphConfig() bucket_class = RuleBucketRuntime if args.kind == "rule" else BucketRuntime + if args.kind == "worker": + from models.cua_s1.multimodal.graph_buckets import ( + RuleBucketRuntime as WorkerRuntime, + ) + + def bucket_class(model, config, width): + return WorkerRuntime( + model, replace(config, mode="rule-bucket", bucket_width=width) + ) + + variants = ( + ("eager", "exact", "bucket", "recipe") + if args.include_recipe + else ("eager", "exact", "bucket") + ) report = { "status": "running", "repository": source, "environment": environment(), "source_sha256": { str(path.relative_to(root)): hashlib.sha256(path.read_bytes()).hexdigest() - for path in [Path(__file__), Path(__file__).with_name("graph_buckets.py")] + for path in [ + Path(__file__), + Path(__file__).with_name("graph_buckets.py"), + *( + root / "src/models/cua_s1/multimodal" / name + for name in ( + "graph_runtime.py", + "graph_buckets.py", + "rule_prefill.py", + "model.py", + ) + ), + ] }, "config": { "graph": asdict(config), + "variants": variants, + "bucket_graph": asdict( + replace(config, mode="rule-bucket", bucket_width=args.width) + ) + if args.kind == "worker" + else asdict(config), "width": args.width, "kind": args.kind, "runs": args.runs, "timing": "synchronized predict including cold capture and first-length checks", "memory": "paired caches coexist; allocator totals are combined, cache bytes are per variant", "scope": "serial, two identical questions, synthetic 320x240 image, no HTTP", - "order": "all six permutations cycle across requests, reversed across runs", + "order": "all variant permutations cycle across requests, offset by half a cycle across runs", }, "workloads": {}, } @@ -123,14 +164,7 @@ def main(): print("lengths", tokens, "buckets", report["buckets"], flush=True) for _ in range(3): engine.predict(requests[counts[0]]) - orders = [ - ("eager", "exact", "bucket"), - ("exact", "bucket", "eager"), - ("bucket", "eager", "exact"), - ("bucket", "exact", "eager"), - ("exact", "eager", "bucket"), - ("eager", "bucket", "exact"), - ] + orders = list(itertools.permutations(variants)) for name, schedule in selected.items(): report["workloads"][name] = [] for run in range(args.runs): @@ -138,10 +172,14 @@ def main(): "exact": GraphRuntime(engine.model, config), "bucket": bucket_class(engine.model, config, width=args.width), } + if args.include_recipe: + runtimes["recipe"] = RuleBucketRuntime( + engine.model, config, width=args.width + ) record = {"run": run + 1, "status": "running", "events": []} report["workloads"][name].append(record) for index, n in enumerate(schedule): - order = orders[(index + run * 3) % len(orders)] + order = orders[(index + run * (len(orders) // 2)) % len(orders)] event = { "index": index, "goal_repetitions": n, @@ -183,10 +221,7 @@ def main(): {v: dict(r.stats) for v, r in runtimes.items()}, flush=True, ) - record["summary"] = { - v: summary(record["events"], v) - for v in ("eager", "exact", "bucket") - } + record["summary"] = {v: summary(record["events"], v) for v in variants} record["stats_final"] = {v: dict(r.stats) for v, r in runtimes.items()} record["status"] = "complete" engine.graph_runtime = None diff --git a/recipe/cua_s1/check_bucket_worker_http.py b/recipe/cua_s1/check_bucket_worker_http.py new file mode 100644 index 0000000..2cbca18 --- /dev/null +++ b/recipe/cua_s1/check_bucket_worker_http.py @@ -0,0 +1,148 @@ +"""Real-model worker HTTP parity, instance isolation and explicit shutdown.""" + +from __future__ import annotations + +import argparse +import base64 +import copy +import io +import json +import threading +import urllib.request +from pathlib import Path + + +def main(): + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--weights", type=Path, required=True) + p.add_argument("--output", type=Path, required=True) + args = p.parse_args() + if args.output.exists(): + p.error("choose a fresh output") + from evaluate_multimodal import environment + from PIL import Image + from profile_multimodal import case_matrix, fixture, repository_state, write_json + from transformers.models.qwen3_5 import modeling_qwen3_5 as upstream + + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + from models.cua_s1.multimodal.server import WorkerServer, parse_args + + source = repository_state(Path(__file__).resolve().parents[2]) + if source["dirty"]: + raise ValueError("clean committed source required") + config = parse_args( + [ + "--base", + str(args.weights / "Qwen3.5-4B"), + "--adapter", + str(args.weights / "cua-s1-4b-0.2/multimodal"), + "--graph-mode", + "rule-bucket", + ] + ) + engine = MultimodalEngine( + config.base, config.adapter, graph_config=config.graph_config + ) + runtime = engine.graph_runtime + assert runtime.__class__.__module__ == "models.cua_s1.multimodal.graph_buckets" + original_rule = upstream.torch_chunk_gated_delta_rule + modules = [ + layer.linear_attn + for layer in engine.model.get_base_model().model.language_model.layers + if layer.block_type == "linear_attention" + ] + forwards = [getattr(m.forward, "__func__", m.forward) for m in modules] + case = next(c for c in case_matrix() if c["id"] == "320x240-short-q2") + raw = fixture(case, args.output.parent / "http-fixtures") + black = copy.deepcopy(raw) + image = io.BytesIO() + Image.new("RGB", (320, 240), "black").save(image, format="PNG") + black["state"]["image"] = ( + "data:image/png;base64," + base64.b64encode(image.getvalue()).decode() + ) + changed = copy.deepcopy(raw) + for q in changed["questions"].values(): + q["instructions"] = q["instructions"].replace("Save", "Open") + assert changed != raw + requests = [("cold", raw), ("capture", raw), ("image", black), ("text", changed)] + engine.graph_runtime = None + expected = [engine.predict(parse_request(body)) for _, body in requests] + engine.graph_runtime = runtime + server = WorkerServer(("127.0.0.1", 0), engine) + thread = threading.Thread(target=server.serve_forever) + thread.start() + report = { + "status": "running", + "repository": source, + "environment": environment(), + "records": [], + } + try: + url = f"http://127.0.0.1:{server.server_port}" + with urllib.request.urlopen(url + "/health", timeout=10) as response: + report["health"] = {"status": response.status, "body": json.load(response)} + for (name, body), reference in zip(requests, expected): + before = dict(runtime.stats) + request = urllib.request.Request( + url + "/v1/systemone", + data=json.dumps(body).encode(), + headers={"Content-Type": "application/json"}, + ) + with urllib.request.urlopen(request, timeout=120) as response: + actual = json.load(response) + assert response.status == 200 + delta = {k: runtime.stats[k] - v for k, v in before.items()} + report["records"].append( + { + "name": name, + "expected": reference, + "actual": actual, + "stats_delta": delta, + } + ) + assert actual == reference + assert runtime.stats["rejected"] == runtime.stats["length_rejections"] == 0 + if name in {"image", "text"}: + assert delta["replays"] == 2 + assert upstream.torch_chunk_gated_delta_rule is original_rule + assert all( + getattr(m.forward, "__func__", m.forward) is f + for m, f in zip(modules, forwards) + ) + assert report["records"][0]["stats_delta"]["captures"] == 0 + assert report["records"][1]["stats_delta"]["captures"] == 1 + report["global_and_instance_methods_unchanged"] = True + entries = list(runtime.cache.entries.values()) + server.shutdown() + server.server_close() + thread.join(10) + assert not thread.is_alive() and runtime._closed and engine._closed + assert entries and all( + not entry.blocks and entry.pool is None for entry in entries + ) + assert not runtime.cache.entries + report["explicit_close_with_live_entry_references"] = True + try: + engine.predict(parse_request(raw)) + except RuntimeError as exc: + assert "closed" in str(exc) + else: + raise AssertionError("closed engine accepted prediction") + report["status"] = "complete" + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + server.shutdown() + server.server_close() + thread.join(10) + write_json(args.output, report) + print( + "verified HTTP cold/capture/changed-image/changed-text parity, isolation and shutdown" + ) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/check_graph_buckets.py b/recipe/cua_s1/check_graph_buckets.py index 94bb2d7..102c5dd 100644 --- a/recipe/cua_s1/check_graph_buckets.py +++ b/recipe/cua_s1/check_graph_buckets.py @@ -8,7 +8,7 @@ from pathlib import Path -def check_boundaries(engine, output, report): +def check_boundaries(engine, output, report, runtime_type=None): import torch from diagnose_graph_buckets import prepare_values from graph_buckets import RuleBucketRuntime @@ -17,6 +17,8 @@ def check_boundaries(engine, output, report): from models.cua_s1.multimodal.graph_runtime import GraphConfig from models.cua_s1.multimodal.protocol import parse_request + if runtime_type is not None: + RuleBucketRuntime = runtime_type fixtures = output.parent / "boundary-fixtures" case = next(c for c in case_matrix() if c["id"] == "320x240-short-q2") request = parse_request(fixture(case, fixtures)) @@ -124,6 +126,11 @@ def main(): p.add_argument("--weights", type=Path, required=True) p.add_argument("--output", type=Path, required=True) p.add_argument("--boundaries-only", action="store_true") + p.add_argument( + "--worker", + action="store_true", + help="check the production instance-local runtime", + ) args = p.parse_args() if args.output.exists(): p.error("choose a fresh output") @@ -140,6 +147,16 @@ def main(): from models.cua_s1.multimodal.model import MultimodalEngine from models.cua_s1.multimodal.protocol import parse_request + if args.worker: + from models.cua_s1.multimodal.graph_buckets import ( + RuleBucketRuntime as WorkerRuntime, + ) + + def RuleBucketRuntime(model, config=None): + return WorkerRuntime( + model, replace(config or GraphConfig(), mode="rule-bucket") + ) + source = repository_state(Path(__file__).resolve().parents[2]) if source["dirty"]: raise ValueError("clean source required") @@ -149,12 +166,13 @@ def main(): "environment": environment(), "cases": [], "memory": {}, + "runtime": "worker" if args.worker else "recipe", } engine = MultimodalEngine( str(args.weights / "Qwen3.5-4B"), str(args.weights / "cua-s1-4b-0.2/multimodal") ) if args.boundaries_only: - check_boundaries(engine, args.output, report) + check_boundaries(engine, args.output, report, RuleBucketRuntime) return fixtures = args.output.parent / "check-fixtures" selected = { diff --git a/recipe/cua_s1/verify_graph_buckets.py b/recipe/cua_s1/verify_graph_buckets.py index e520ac4..1246ed4 100644 --- a/recipe/cua_s1/verify_graph_buckets.py +++ b/recipe/cua_s1/verify_graph_buckets.py @@ -20,17 +20,20 @@ def verify(path, require_replay=False): ["git", "-C", str(root), "show", revision + ":" + name] ) assert hashlib.sha256(source).hexdigest() == expected + variants_expected = set( + report["config"].get("variants", ["eager", "exact", "bucket"]) + ) count = 0 for name, records in report["workloads"].items(): assert len(records) == report["config"]["runs"] for record in records: assert record["status"] == "complete" events = record["events"] - count += len(events) * 3 + count += len(events) * len(variants_expected) for index, event in enumerate(events): assert event["index"] == index variants = event["variants"] - assert set(variants) == {"eager", "exact", "bucket"} + assert set(variants) == variants_expected assert all( v["response"] == variants["eager"]["response"] for v in variants.values() @@ -46,7 +49,7 @@ def verify(path, require_replay=False): 0 <= v["cache_bytes"] <= report["config"]["graph"]["max_bytes"] ) eager_total = sum(e["variants"]["eager"]["latency_ms"] for e in events) - for variant in ("eager", "exact", "bucket"): + for variant in variants_expected: samples = [e["variants"][variant]["latency_ms"] for e in events] summary = record["summary"][variant] assert summary["requests"] == len(samples) @@ -77,7 +80,7 @@ def verify(path, require_replay=False): stats = record["stats_final"]["bucket"] assert stats["captures"] > 0 and stats["replays"] > 0 assert stats["rejected"] == stats["length_rejections"] == 0 - print(name, record["run"], "verified", len(events), "request triplets") + print(name, record["run"], "verified", len(events), "request groups") print( "verified", count, From 74760be2df0727d2d71467a52f0f0c2f4c92a398 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 15:06:55 +0800 Subject: [PATCH 59/75] fix: normalize rule bucket layouts at exact boundaries --- recipe/cua_s1/check_bucket_worker_http.py | 29 ++++++++++ src/models/cua_s1/multimodal/graph_buckets.py | 4 +- tests/cua_s1/test_bucket_worker.py | 54 +++++++++++++++++++ 3 files changed, 86 insertions(+), 1 deletion(-) diff --git a/recipe/cua_s1/check_bucket_worker_http.py b/recipe/cua_s1/check_bucket_worker_http.py index 2cbca18..16fac77 100644 --- a/recipe/cua_s1/check_bucket_worker_http.py +++ b/recipe/cua_s1/check_bucket_worker_http.py @@ -113,6 +113,35 @@ def main(): assert report["records"][0]["stats_delta"]["captures"] == 0 assert report["records"][1]["stats_delta"]["captures"] == 1 report["global_and_instance_methods_unchanged"] = True + # Two live, independently loaded models: one owner's close must not + # affect the other owner's cached replay or installed upstream code. + first_stats = dict(runtime.stats) + first_entries = dict(runtime.cache.entries) + other = MultimodalEngine( + config.base, config.adapter, graph_config=config.graph_config + ) + try: + assert other.model is not engine.model + assert other.graph_runtime is not runtime + assert other.predict(parse_request(raw)) == expected[0] + assert other.predict(parse_request(raw)) == expected[0] + assert other.predict(parse_request(black)) == expected[2] + assert other.graph_runtime.stats["captures"] == 1 + assert other.graph_runtime.stats["replays"] >= 3 + assert runtime.stats == first_stats + assert dict(runtime.cache.entries) == first_entries + report["second_runtime_stats"] = dict(other.graph_runtime.stats) + finally: + other.close() + assert not runtime._closed and other.graph_runtime._closed + assert engine.predict(parse_request(changed)) == expected[3] + assert runtime.stats["replays"] == first_stats["replays"] + 2 + assert upstream.torch_chunk_gated_delta_rule is original_rule + assert all( + getattr(m.forward, "__func__", m.forward) is f + for m, f in zip(modules, forwards) + ) + report["two_model_runtime_isolation"] = True entries = list(runtime.cache.entries.values()) server.shutdown() server.server_close() diff --git a/src/models/cua_s1/multimodal/graph_buckets.py b/src/models/cua_s1/multimodal/graph_buckets.py index 0c68962..ff80c32 100644 --- a/src/models/cua_s1/multimodal/graph_buckets.py +++ b/src/models/cua_s1/multimodal/graph_buckets.py @@ -185,7 +185,9 @@ def pack_rule_inputs(values, width): length = values["query"].shape[1] padding = bucket_length(length, width) - length return { - name: F.pad(value, (0, 0) * (value.ndim - 2) + (0, padding)) + # Zero-padding is a clone that can preserve noncontiguous strides. + # Canonicalize even on an exact boundary so one bucket has one layout. + name: F.pad(value, (0, 0) * (value.ndim - 2) + (0, padding)).contiguous() for name, value in values.items() } diff --git a/tests/cua_s1/test_bucket_worker.py b/tests/cua_s1/test_bucket_worker.py index 5bc545c..d0e7542 100644 --- a/tests/cua_s1/test_bucket_worker.py +++ b/tests/cua_s1/test_bucket_worker.py @@ -128,6 +128,60 @@ def test_rule_segment_rejects_layout_change_before_copy(): assert torch.count_nonzero(segment.static_extra["value"]) == 0 +def test_rule_packing_normalizes_strides_at_exact_bucket_boundary(): + torch = pytest.importorskip("torch") + from models.cua_s1.multimodal.graph_buckets import pack_rule_inputs + from models.cua_s1.multimodal.graph_runtime import tensor_signature + + signatures = [] + for length in [255, 256]: + mixed = torch.arange(24 * length).reshape(1, 24, length).transpose(1, 2) + values = { + name: value.reshape(1, length, 2, 4) + for name, value in zip(("query", "key", "value"), mixed.split(8, dim=-1)) + } + packed = pack_rule_inputs(values, 64) + for name, value in packed.items(): + assert torch.equal(value[:, :length], values[name]) + assert value.is_contiguous() + signatures.append({k: tensor_signature(v) for k, v in packed.items()}) + assert signatures[0] == signatures[1] + + +def test_rejected_length_bypasses_replay_without_disabling_verified_length(): + torch = pytest.importorskip("torch") + from models.cua_s1.multimodal.graph_buckets import RuleBucketRuntime + + runtime = RuleBucketRuntime(object()) + entry = SimpleNamespace(bytes=1, verified_lengths={63}, rejected_lengths=set()) + runtime.cache.put("bucket", entry) + runtime._supported = lambda values: True + runtime._key = lambda values: "bucket" + reference = torch.tensor([1.0, 2.0]) + runtime._eager = lambda values: reference + replayed = [] + + def replay(values, cached): + length = values["inputs_embeds"].shape[1] + replayed.append(length) + return reference if length == 63 else reference + 1 + + runtime._run_segments = replay + for length in [64, 64, 63]: + values = { + "inputs_embeds": torch.ones(1, length, 4), + "attention_mask": torch.ones(1, length), + } + with runtime.request(): + assert torch.equal(runtime.forward(values), reference) + assert replayed == [64, 63] + assert entry.rejected_lengths == {64} + assert entry.verified_lengths == {63} + assert runtime.stats["length_rejections"] == 1 + assert runtime.stats["length_disabled"] == 1 + assert runtime.stats["replays"] == 2 + + def test_worker_close_waits_for_inflight_http_before_engine_close(): import threading From fd5c4172987bcbcc2eb8e10dcf04b3edd45af0b8 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 15:15:50 +0800 Subject: [PATCH 60/75] docs: record worker bucket parity and four-way GPU measurements --- .../plans/2026-09-29-cua-bucket-worker.md | 14 +- recipe/cua_s1/README.md | 24 + .../rtx4090-bucket-worker/README.md | 147 + .../rtx4090-bucket-worker/SHA256SUMS | 10 + .../boundaries-before-fix.json | 76 + .../boundaries-before-fix.log | 27 + .../rtx4090-bucket-worker/boundaries.json | 534 + .../rtx4090-bucket-worker/http.json | 426 + .../rtx4090-bucket-worker/lint.log | 3 + .../rtx4090-bucket-worker/parity.json | 4711 ++ .../rtx4090-bucket-worker/report.json | 56756 ++++++++++++++++ .../rtx4090-bucket-worker/tests.log | 5 + .../rtx4090-bucket-worker/verification.log | 6 + 13 files changed, 62733 insertions(+), 6 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.json create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.log create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries.json create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/http.json create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/lint.log create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/parity.json create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/report.json create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/tests.log create mode 100644 recipe/cua_s1/experiments/rtx4090-bucket-worker/verification.log diff --git a/docs/superpowers/plans/2026-09-29-cua-bucket-worker.md b/docs/superpowers/plans/2026-09-29-cua-bucket-worker.md index 64fa738..85c855f 100644 --- a/docs/superpowers/plans/2026-09-29-cua-bucket-worker.md +++ b/docs/superpowers/plans/2026-09-29-cua-bucket-worker.md @@ -6,9 +6,11 @@ Execute inline using the approved worker-integration scope. **Architecture:** `rule_prefill.py` implements pinned cache-free prefill with an explicit rule callback. `graph_buckets.py` owns packing, cache/gates and direct per-model decoder traversal. `model.py` selects a runtime; `server.py` parses modes and closes the engine after draining requests. **Tech Stack:** Existing pinned Transformers 5.17.0 / PEFT / PyTorch CUDA environment and pytest/Ruff. -- [ ] Add failing tests in `tests/cua_s1/test_rule_prefill.py`: compare a small pinned Qwen3.5 module's CPU output to the explicit callback path, prove globals and forward identities unchanged, validate callback output behavior and input contracts. Implement `src/models/cua_s1/multimodal/rule_prefill.py`. -- [ ] Add failing runtime/config/lifecycle tests in `tests/cua_s1/test_bucket_worker.py`. Promote the successful recipe logic to `src/models/cua_s1/multimodal/graph_buckets.py`; directly traverse model-instance layers using the prefill adapter; preserve capture/admission and full-logit gates. Reject static input shape/dtype/device changes before copies. Explicitly retire graph entries on invalidation/close. -- [ ] Extend `GraphConfig` with validated mode/width defaults, `MultimodalEngine` runtime selection and idempotent close, and worker CLI parsing/shutdown. Test eager default, legacy --graph, explicit modes, malformed options, no post-close prediction and HTTP drain semantics. -- [ ] Build a clean GPU recipe checking explicit prefill against original, independent runtimes without global mutation, complete logits under changed inputs and boundaries, eviction/budget fallback, and real HTTP cold/capture/replay. Run pinned tests and CI-scoped lint/format. -- [ ] Compare synchronized full predictions for eager/exact/previous recipe/worker buckets on stable and rotating lengths, balancing order and counting cold cost. Record clean revision, full responses and budgets. Preserve historical evidence without rewriting it. -- [ ] Independent code/evidence review; address important findings, verify report metrics, commit clean source and evidence, and keep the worktree for subsequent work. +- [x] Add failing tests in `tests/cua_s1/test_rule_prefill.py`: compare a small pinned Qwen3.5 module's CPU output to the explicit callback path, prove globals and forward identities unchanged, validate callback output behavior and input contracts. Implement `src/models/cua_s1/multimodal/rule_prefill.py`. +- [x] Add failing runtime/config/lifecycle tests in `tests/cua_s1/test_bucket_worker.py`. Promote the successful recipe logic to `src/models/cua_s1/multimodal/graph_buckets.py`; directly traverse model-instance layers using the prefill adapter; preserve capture/admission and full-logit gates. Reject static input shape/dtype/device changes before copies. Explicitly retire graph entries on invalidation/close. +- [x] Extend `GraphConfig` with validated mode/width defaults, `MultimodalEngine` runtime selection and idempotent close, and worker CLI parsing/shutdown. Test eager default, legacy --graph, explicit modes, malformed options, no post-close prediction and HTTP drain semantics. +- [x] Build a clean GPU recipe checking explicit prefill against original, independent runtimes without global mutation, complete logits under changed inputs and boundaries, eviction/budget fallback, and real HTTP cold/capture/replay. Run pinned tests and CI-scoped lint/format. +- [x] Compare synchronized full predictions for eager/exact/previous recipe/worker buckets on stable and rotating lengths, balancing order and counting cold cost. Record clean revision, full responses and budgets. Preserve historical evidence without rewriting it. +- [x] Independent code/evidence review; address important findings, verify report metrics, commit clean source and evidence, and keep the worktree for subsequent work. + +Completed on 2026-09-29. Runtime revision `74760be`; 218 tests, 136 changed-content full-logit checks, 12 boundary checks, two-model HTTP isolation and 960 four-way timed predictions passed. Exact-boundary stride regression was reproduced and fixed. Independent code/evidence review completed; report records both positive and negative results. diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 0cc3bd6..de036c8 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -46,6 +46,27 @@ capture. Repeated questions inside one request count once. The captured logits must exactly match eager before the layout enters the cache. The defaults retain at most eight layouts and 1 GiB of measured live Graph allocations. +The default remains eager. `--graph` is an alias for `--graph-mode exact`. +For workloads rotating through many nearby lengths, explicitly select +`--graph-mode rule-bucket --graph-bucket-width 64` instead. This mode captures +only the internal DeltaNet rule, padding its query/key/value/decay/update inputs +to a multiple of 64 tokens. Projections, convolutions, MLPs and full attention +keep their actual lengths. Width must be a positive multiple of 64; the padded +length must fit `--graph-max-tokens`. The worker uses direct per-model calls, +without replacing Transformers globals or model methods. This path requires +the pinned Transformers 5.17.0 implementation and an immutable resident model; +it does not support model offloading or sharding. + +Rule buckets share the admission and resource limits below. Complete vocabulary +logits must match eager exactly at capture and for each new actual length in a +cached bucket. A rejected length uses eager while other verified lengths remain +eligible. Dense batch-one inputs are supported; unsupported inputs use eager. +These first-input checks do not prove equality for every possible content. +There is no automatic mode selection or simultaneous exact/bucket cache in the +worker. Stable hot lengths can favor exact mode. See the +[worker integration report](experiments/rtx4090-bucket-worker/README.md) for +correctness, HTTP checks, and workload-dependent timing. + `--graph-min-uses` now counts **requests**, not question forwards. Use `--graph-admission-window` to change the maximum gap between observations. Evicted layouts lose their heat and wait `--graph-cooldown-requests` (default 32) @@ -76,6 +97,9 @@ weights or adapters. Invalidation clears admission and cache state, while lifeti statistics remain cumulative. `predict` opens a serialized Graph request context; standalone runtime `forward` calls without that context use eager. Single-question requests continue through the reference path. +`engine.close()` explicitly releases Graph resources and rejects later +predictions. HTTP shutdown drains accepted handlers before closing the engine; +the lifecycle lock also waits for an active direct prediction. Close is idempotent. Whole-model capture changed BF16 attention results on the measured RTX 4090; the earlier [Graph feasibility report](experiments/rtx4090-graph/README.md) diff --git a/recipe/cua_s1/experiments/rtx4090-bucket-worker/README.md b/recipe/cua_s1/experiments/rtx4090-bucket-worker/README.md new file mode 100644 index 0000000..2eec816 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-bucket-worker/README.md @@ -0,0 +1,147 @@ +# Cua-S1 rule-bucket worker — RTX 4090, 2026-09-29 + +The worker now exposes explicit `--graph-mode rule-bucket` alongside existing +`--graph` / `--graph-mode exact`. Eager remains the default. Rule buckets use +instance-local decoder calls and a pinned cache-free prefill adapter; they do +not replace Transformers globals or model methods. Only internal DeltaNet rule +inputs are padded. Other model operations retain their actual token length. +Automatic strategy selection and simultaneous exact/bucket worker caches are +outside this change. + +## Correctness and lifecycle + +Acceptance retains exact complete-vocabulary-logit gates at capture and at the +first appearance of each real length in a resident bucket. Changed images and +text are separately tested. No numerical tolerance was relaxed. Unsupported +inputs, admission/budget limits and rejected lengths use eager inference. + +A new strict rule-input signature check exposed a boundary bug: PyTorch's +zero-width padding preserves transposed QKV strides, whereas nonzero padding +produces contiguous inputs. Thus 255 and 256 tokens mapped to one bucket with +different layouts. The failed GPU check at `249b0e3` is retained in +`boundaries-before-fix.json` and its log. A CPU regression first reproduced the +failure, then `pack_rule_inputs(...).contiguous()` fixed it without weakening +layout validation. Final runtime revision is +`74760be2df0727d2d71467a52f0f0c2f4c92a398`. + +The engine serializes lifecycle operations, rejects predictions after close, +and explicitly retires graph entries even with external references alive. +HTTP shutdown drains accepted handlers before engine cleanup. Mode/config, +rejected-length reuse, static layouts, upstream prefill equivalence and shutdown +are covered by 218 passing Cua-S1 tests. CI-scoped Ruff checks and formatting pass. + +`boundaries.json` verifies 255/256/257/319/320/321 tokens in forward and reverse +order: 12 exact full-logit matches, three captures and nine replays. Separate +policies verify eviction/cooldown, 1 MiB memory-budget rejection, and a one-capture +budget with eager-equivalent outputs. + +`http.json` exercises the actual CLI-selected engine and loopback HTTP server: +cold, capture, changed-image and changed-text responses equal eager completely. +It additionally loads two independent models/runtimes simultaneously. The second +runtime captures and replays without changing the first runtime's statistics or +entries; the first still replays after the second closes. Globals and forward +methods retain their identities. Closing the server empties graph blocks and +pool ownership even while the checker retains entry references, and subsequent +prediction fails explicitly. This interleaved isolation check is not a concurrent +GPU throughput benchmark. + +`parity.json` covers five cases (320×240 with 2/8 questions, 640×480 with +8 questions, long prompts, and eight distinct questions). All 136 independent +complete-logit comparisons and all 20 cold/capture/changed-image/changed-text +responses match eager. These checks expand limits to 4,096 tokens, 16 captures, +10,000 ms capture work and 4 GiB cache; timing uses default limits. In this raw +checker output, per-case `config` is the base configuration before its local +worker factory replaces `mode` with `rule-bucket`; top-level `runtime: worker` +identifies the actual path. The HTTP check uses the real CLI/engine selection. + +## Measurement boundaries + +One RTX 4090, PyTorch 2.14.0+cu130, Transformers 5.17.0 and PEFT 0.21.0; pinned +BF16 base and unmerged multimodal LoRA. Neither flash-linear-attention nor +causal-conv1d is installed. Results concern this fallback implementation. + +Four variants share the same loaded model: eager, exact Graph, worker rule +buckets (`bucket` in JSON), and the prior experimental rule algorithm (`recipe`). +The recipe comparison uses the current shared Graph runtime/lifecycle, not an +unchanged historical commit. All 24 variant permutations cycle across request +positions; run two offsets by half a cycle. Graph caches start empty for each +workload/run. Loading is excluded; synchronized `engine.predict` includes input +preparation, vision, two identical questions on a synthetic 320×240 screenshot, +cold admission/capture, first-length checks and replay. In-request eviction +would be included, but none occurs in this matrix; final cache retirement is +outside timing. This serial latency matrix does not measure HTTP throughput or +concurrent requests. + +The three caches coexist during timing. Process allocator totals are combined, +not attributable to an individual mode. Separate parity diagnostics measure +modes with one live runtime at a time. A lower bucket count does not establish +lower memory use. +For the isolated 236-token diagnostic, exact cache ownership is 120,815,616 bytes +and worker rule ownership is 284,295,168 bytes. Peak process reservations are +10,101,981,184 and 10,259,267,584 bytes respectively. These are one-shape +diagnostics, not whole-matrix peak comparisons. + +## Timing results + +Two cold runs per workload, 240 request groups / **960 timed predictions**. Cells show run 1 / run 2; total time includes capture and first-length gates. + +| Workload / mode | Total (s) | p50 (ms) | p95 (ms) | +|---|---:|---:|---:| +| hot_four / eager | 11.05 / 10.95 | 227.79 / 225.34 | 238.62 / 235.62 | +| hot_four / exact | 7.48 / 7.45 | 108.17 / 107.62 | 529.83 / 522.27 | +| hot_four / bucket | 8.08 / 7.91 | 142.77 / 141.92 | 257.69 / 236.17 | +| hot_four / recipe | 8.05 / 7.97 | 144.94 / 143.84 | 238.65 / 239.12 | +| churn_twelve / eager | 16.98 / 16.75 | 236.19 / 234.80 | 242.68 / 239.21 | +| churn_twelve / exact | 16.50 / 16.34 | 229.86 / 228.89 | 235.44 / 237.82 | +| churn_twelve / bucket | 12.27 / 12.18 | 145.26 / 143.57 | 245.93 / 240.44 | +| churn_twelve / recipe | 12.35 / 12.33 | 146.49 / 145.31 | 246.63 / 243.26 | + +For twelve rotating lengths, worker buckets reduce total time **27.30–27.73% +versus eager** and **25.48–25.64% versus exact mode**. Exact makes zero captures +or replays because the 12-request revisit interval exceeds its eight-request +admission window. This measures useful bucket reuse versus conservative eager +fallback, not an exact-cache eviction storm. Churn p95 is still slightly above +eager in both runs (240.44–245.93 versus 239.21–242.68 ms). + +For four hot lengths, worker buckets reduce total time 26.83–27.76% versus eager, +but take **6.13–8.00% longer than exact Graph**. Each worker run captures two +buckets; exact captures four hot layouts. Relative to the prior recipe algorithm, +worker total time ranges from 0.44% slower to 1.25% faster across these four runs. +This small two-run comparison supports retaining the measured behavior, not a +statistically established integration speedup. + +All 960 responses match eager completely; no capture errors, OOMs, numerical +rejections or length rejections occur in the matrix. The independent stdlib +verifier recomputes metrics and counters, checks response equality and cache +bounds, and checks source hashes against measured Git objects. Read-only code +review found no remaining actionable integration/lifecycle defects. + +## Reproduction + +Use the clean source revision recorded in each JSON and fresh output paths: + +```sh +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_graph_buckets.py \ + --weights /path/to/weights --worker --output /path/to/parity.json +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_graph_buckets.py \ + --weights /path/to/weights --worker --boundaries-only \ + --output /path/to/boundaries.json +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_bucket_worker_http.py \ + --weights /path/to/weights --output /path/to/http.json +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_buckets.py \ + --weights /path/to/weights --output /path/to/matrix --runs 2 \ + --case hot_four --case churn_twelve --kind worker --include-recipe +python recipe/cua_s1/verify_graph_buckets.py /path/to/matrix/report.json --require-replay +``` + +The worker can be started with: + +```sh +PYTHONPATH=src python -m models.cua_s1.multimodal.server \ + --base /path/to/weights/Qwen3.5-4B \ + --adapter /path/to/weights/cua-s1-4b-0.2/multimodal \ + --graph-mode rule-bucket --graph-bucket-width 64 +``` + +No new CUDA/Triton kernels, compilation, native multimodal bridge, dynamic +batching, real-GUI task accuracy, or broad deployment claims are made here. diff --git a/recipe/cua_s1/experiments/rtx4090-bucket-worker/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-bucket-worker/SHA256SUMS new file mode 100644 index 0000000..c6b8556 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-bucket-worker/SHA256SUMS @@ -0,0 +1,10 @@ +814685aade12b521a036a4d8651d34ebdc53c7c3ede778e159139055d55cd6df README.md +ab25a9dcd4c21ddbe9dbb82694dea834eb17e4afd7c8a412adf20bd129b6d01c boundaries-before-fix.json +d4f516a29007c290862a6b76d648d3b2d843932f62976d10bb52eaaed40fdff0 boundaries-before-fix.log +d37b7b7903e37b516c5fef84183aa5987243625aef8c34a53b6a34aa25072583 boundaries.json +9cb6cff3e91e465fb901b4b1a4a059c4806af129929ca0ae58a04a43c6e059bf http.json +631daf1aa4a1656d37756cac3c607218915af5ec35ffb7248762af200680b47f lint.log +4343204daeb95080a0acd0760677c09188a76d3886c2635fe630cffc326eb5d1 parity.json +1912c6788efaf7853e72186d5160f5dd5445fd8b1d25d7b2e44f5a3d468b5a10 report.json +42ac254875ec71297ae0974c28df9d6ab8caaf62bbd68a9d5b19369e68a2c99f tests.log +4be70b466181fa05558d64ce5294db460638c2d895af26a33cb2869590919ef9 verification.log diff --git a/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.json b/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.json new file mode 100644 index 0000000..8b16442 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.json @@ -0,0 +1,76 @@ +{ + "status": "failed", + "repository": { + "revision": "249b0e34c87a0ced433c7a6f7d72527094ec4915", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "environment": { + "python": "3.12.13", + "gpu": "NVIDIA GeForce RTX 4090", + "compute_capability": [ + 8, + 9 + ], + "cuda": "13.0", + "driver": "595.71.05", + "torch_num_threads": 64, + "torch_num_interop_threads": 64, + "packages": { + "torch": "2.14.0", + "torchvision": "0.29.0", + "transformers": "5.17.0", + "peft": "0.21.0", + "pillow": "11.3.0", + "safetensors": "0.8.0", + "triton": "3.8.0" + }, + "reference_revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f", + "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", + "adapter_revision": "16818868b0cc7813808aae4e87b417657046ab79", + "dtype": "bfloat16", + "adapter_merged": false + }, + "cases": [], + "memory": {}, + "runtime": "worker", + "boundary_records": [ + { + "length": 255, + "equal": true, + "max_abs": 0.0, + "stats_delta": { + "requests": 1, + "no_request": 0, + "cooldown": 0, + "capture_budget": 0, + "capture_attempts": 1, + "capture_attempt_ms": 441.1342600360513, + "eager": 1, + "unsupported": 0, + "warmup": 0, + "disabled": 0, + "replays": 0, + "captures": 1, + "capture_ms": 344.7921599727124, + "evictions": 0, + "rejected": 0, + "memory_budget": 0, + "capture_oom": 0, + "capture_error": 0, + "numerical_mismatch": 0, + "length_checks": 0, + "length_rejections": 0, + "length_disabled": 0, + "length_check_ms": 0.0, + "real_tokens": 255, + "padded_tokens": 1 + } + } + ], + "error": { + "type": "ValueError", + "message": "CUDA Graph rule input layout changed" + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.log b/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.log new file mode 100644 index 0000000..2a83d57 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries-before-fix.log @@ -0,0 +1,27 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! + Loading weights: 0%| | 0/723 [00:00 + main() + File "/root/autodl-tmp/system1-omni-work/graph-buckets-repo/recipe/cua_s1/check_graph_buckets.py", line 175, in main + check_boundaries(engine, args.output, report, RuleBucketRuntime) + File "/root/autodl-tmp/system1-omni-work/graph-buckets-repo/recipe/cua_s1/check_graph_buckets.py", line 56, in check_boundaries + actual = runtime.forward(values[n]) + ^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-buckets-repo/src/models/cua_s1/multimodal/graph_buckets.py", line 102, in forward + output = self._run_segments(values, entry) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-buckets-repo/src/models/cua_s1/multimodal/graph_buckets.py", line 172, in _run_segments + hidden = rule_prefill( + ^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-buckets-repo/src/models/cua_s1/multimodal/rule_prefill.py", line 51, in rule_prefill + output, _ = rule( + ^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-buckets-repo/src/models/cua_s1/multimodal/graph_buckets.py", line 168, in dispatch + output, state = block.replay_values(packed) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/root/autodl-tmp/system1-omni-work/graph-buckets-repo/src/models/cua_s1/multimodal/graph_buckets.py", line 219, in replay_values + raise ValueError("CUDA Graph rule input layout changed") +ValueError: CUDA Graph rule input layout changed diff --git a/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries.json b/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries.json new file mode 100644 index 0000000..e4f5c74 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-bucket-worker/boundaries.json @@ -0,0 +1,534 @@ +{ + "status": "complete", + "repository": { + "revision": "74760be2df0727d2d71467a52f0f0c2f4c92a398", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "environment": 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+ }, + "questions": { + "q1": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + }, + "q2": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + } + } + }, + "tokens": { + "1": 236, + "2": 242, + "3": 248, + "4": 254, + "5": 260, + "6": 266, + "7": 272, + "8": 278, + "9": 284, + "10": 290, + "11": 296, + "12": 302 + }, + "buckets": { + "1": 256, + "2": 256, + "3": 256, + "4": 256, + "5": 320, + "6": 320, + "7": 320, + "8": 320, + "9": 320, + "10": 320, + "11": 320, + "12": 320 + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-bucket-worker/tests.log b/recipe/cua_s1/experiments/rtx4090-bucket-worker/tests.log new file mode 100644 index 0000000..e9b1cf0 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-bucket-worker/tests.log @@ -0,0 +1,5 @@ +........................................................................ [ 33%] +........................................................................ [ 66%] +........................................................................ [ 99%] +.. [100%] +218 passed in 20.56s diff --git a/recipe/cua_s1/experiments/rtx4090-bucket-worker/verification.log b/recipe/cua_s1/experiments/rtx4090-bucket-worker/verification.log new file mode 100644 index 0000000..1a537cc --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-bucket-worker/verification.log @@ -0,0 +1,6 @@ +hot_four 1 verified 48 request groups +hot_four 2 verified 48 request groups +churn_twelve 1 verified 72 request groups +churn_twelve 2 verified 72 request groups +verified 960 timed predictions; source hashes, responses, metrics and cache bounds +verified 136 full-logit comparisons and 20 full-response parity records; all final GPU reports share clean revision 74760be2df0727d2d71467a52f0f0c2f4c92a398 From 71531bbfdc0627ef010ed83af66515156364262d Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 15:38:04 +0800 Subject: [PATCH 61/75] experiment(cua): compare tuned exact admission against rule buckets --- .../2026-09-29-cua-graph-policy-experiment.md | 22 +++++++++ recipe/cua_s1/benchmark_graph_buckets.py | 49 +++++++++++++++++-- recipe/cua_s1/verify_graph_buckets.py | 21 ++++---- tests/cua_s1/test_graph_policy_experiment.py | 32 ++++++++++++ 4 files changed, 111 insertions(+), 13 deletions(-) create mode 100644 docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md create mode 100644 tests/cua_s1/test_graph_policy_experiment.py diff --git a/docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md b/docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md new file mode 100644 index 0000000..ff70963 --- /dev/null +++ b/docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md @@ -0,0 +1,22 @@ +# Cua-S1 Graph policy experiment + +**Goal:** Establish whether mode selection is justified after correcting the exact admission-window confound, then validate shared resource ownership for exact and rule buckets. + +**Architecture:** Start at #37 (`fd5c417`). Reuse the pinned worker, strict full-logit gates, and capture-inclusive benchmark. Compare eager, default exact, exact with a 32-request admission window, and worker rule buckets with otherwise identical limits. Keep both default worker behavior and numerical acceptance unchanged. + +**Environment:** One authorized RTX 4090 session, existing Python 3.12 environment and weights. No installs or model downloads needed. Use an isolated local worktree and remote clone; preserve previous evidence. + +## Execution checklist + +- [x] Inspect GPU occupancy, reference revision, Python package versions and existing experiment harness. +- [ ] Extend `recipe/cua_s1/benchmark_graph_buckets.py` with an optional tuned exact comparator and the existing hot/cold and shifting-hot schedules. Record complete per-variant configurations and runtime/admission source hashes. Keep old CLI behavior compatible. +- [ ] Validate the comparison isolates admission-window changes using CPU tests, and verify the recorded schedule, response equality, statistics and per-variant limits independently. +- [ ] Run two cold repetitions of hot-four, churn-twelve, hot/cold and shifting-hot, with all 24 execution orders cycling across requests. Use fresh output directories outside the remote Git repository. +- [ ] Analyze total time (including capture and first-length gates), p50/p95, cache bytes, captures, replay, evictions and rejected requests. Record negative results and scope limits. +- [ ] Build a narrowly scoped shared-budget experiment if the baseline supports continuing: one request clock, namespaced cache keys, one capture ledger, one aggregate resident-cache budget, deterministic retirement. Avoid claiming a hard peak-process-memory bound: in-flight candidate capture can temporarily exceed retained cache limits. +- [ ] Validate cross-mode eviction, budget fallback, two questions counted once, cold/changed inputs, explicit close with live entry references, and replay after retirement of the other mode. +- [ ] Preserve scripts, source revisions, raw results, verifier and a concise report locally. Do not claim auto selection, compilation, native integration or concurrent throughput without separate implementation and evidence. + +## Acceptance and interpretation + +All timed complete responses must match eager. Captures and new bucket lengths retain exact full-vocabulary-logit comparison. Do not relax tolerances. Tune only the admission window in the comparator; retain the same 8-shape / 1 GiB cache and capture limits. Report any negative or mixed outcome instead of selecting a favorable workload. Two repetitions are exploratory evidence, not confidence intervals. Shared-cache CPU tests must demonstrate real resource-retirement semantics before GPU validation. GPU measurements are serial synthetic workloads on the PyTorch fallback path, not production traffic. diff --git a/recipe/cua_s1/benchmark_graph_buckets.py b/recipe/cua_s1/benchmark_graph_buckets.py index 01e5ef0..edcfb6a 100644 --- a/recipe/cua_s1/benchmark_graph_buckets.py +++ b/recipe/cua_s1/benchmark_graph_buckets.py @@ -17,9 +17,30 @@ def schedules(): "probe": list(range(1, 13)) * 2, "hot_four": [1, 2, 4, 8] * 12, "churn_twelve": list(range(1, 13)) * 6, + "hot_cold": [v for i in range(8) for v in [1, 2, 4, 8, 13 + i]], + "shifting_hot": [ + v + for group in [range(1, 5), range(5, 9), range(9, 13), range(1, 5)] + for v in list(group) * 8 + ], } +def variant_configs(kind, width, tuned_exact_window=None): + from models.cua_s1.multimodal.graph_runtime import GraphConfig + + config = GraphConfig() + configs = { + "exact": config, + "bucket": replace(config, mode="rule-bucket", bucket_width=width) + if kind == "worker" + else config, + } + if tuned_exact_window is not None: + configs["exact_tuned"] = replace(config, admission_window=tuned_exact_window) + return configs + + def summary(events, variant): samples = [event["variants"][variant]["latency_ms"] for event in events] ordered = sorted(samples) @@ -39,6 +60,7 @@ def main(): p.add_argument("--output", type=Path, required=True) p.add_argument("--runs", type=int, default=2) p.add_argument("--width", type=int, default=64) + p.add_argument("--tuned-exact-window", type=int) p.add_argument("--kind", choices=("segment", "rule", "worker"), default="segment") p.add_argument( "--include-recipe", @@ -53,6 +75,8 @@ def main(): p.error("--include-recipe requires --kind worker") if args.runs < 1: p.error("runs must be positive") + if args.tuned_exact_window is not None and args.tuned_exact_window < 1: + p.error("tuned exact window must be positive") import torch from benchmark_multimodal_graph import measure @@ -66,7 +90,7 @@ def main(): write_json, ) - from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + from models.cua_s1.multimodal.graph_runtime import GraphRuntime from models.cua_s1.multimodal.model import MultimodalEngine from models.cua_s1.multimodal.protocol import parse_request @@ -78,7 +102,8 @@ def main(): args.output.mkdir(parents=True) report_path = args.output / "report.json" selected = {k: v for k, v in schedules().items() if k in (args.case or ["probe"])} - config = GraphConfig() + configs = variant_configs(args.kind, args.width, args.tuned_exact_window) + config = configs["exact"] bucket_class = RuleBucketRuntime if args.kind == "rule" else BucketRuntime if args.kind == "worker": from models.cua_s1.multimodal.graph_buckets import ( @@ -95,6 +120,10 @@ def bucket_class(model, config, width): if args.include_recipe else ("eager", "exact", "bucket") ) + if args.tuned_exact_window is not None: + variants += ("exact_tuned",) + if args.include_recipe: + configs["recipe"] = config report = { "status": "running", "repository": source, @@ -104,10 +133,14 @@ def bucket_class(model, config, width): for path in [ Path(__file__), Path(__file__).with_name("graph_buckets.py"), + Path(__file__).with_name("benchmark_multimodal_graph.py"), + Path(__file__).with_name("profile_multimodal.py"), + Path(__file__).with_name("evaluate_multimodal.py"), *( root / "src/models/cua_s1/multimodal" / name for name in ( "graph_runtime.py", + "graph_admission.py", "graph_buckets.py", "rule_prefill.py", "model.py", @@ -117,6 +150,8 @@ def bucket_class(model, config, width): }, "config": { "graph": asdict(config), + "variant_graph": {k: asdict(v) for k, v in configs.items()}, + "schedules": selected, "variants": variants, "bucket_graph": asdict( replace(config, mode="rule-bucket", bucket_width=args.width) @@ -172,6 +207,10 @@ def bucket_class(model, config, width): "exact": GraphRuntime(engine.model, config), "bucket": bucket_class(engine.model, config, width=args.width), } + if args.tuned_exact_window is not None: + runtimes["exact_tuned"] = GraphRuntime( + engine.model, configs["exact_tuned"] + ) if args.include_recipe: runtimes["recipe"] = RuleBucketRuntime( engine.model, config, width=args.width @@ -194,7 +233,9 @@ def bucket_class(model, config, width): engine.graph_runtime = runtime before = dict(runtime.stats) if runtime else {} torch.cuda.reset_peak_memory_stats() - response, elapsed = measure(lambda: engine.predict(requests[n])) + response, elapsed = measure( + lambda request=requests[n]: engine.predict(request) + ) event["variants"][variant] = { "response": response, "latency_ms": elapsed, @@ -226,7 +267,7 @@ def bucket_class(model, config, width): record["status"] = "complete" engine.graph_runtime = None for runtime in runtimes.values(): - runtime.invalidate() + runtime.close() del runtime, runtimes gc.collect() torch.cuda.empty_cache() diff --git a/recipe/cua_s1/verify_graph_buckets.py b/recipe/cua_s1/verify_graph_buckets.py index 1246ed4..21626bc 100644 --- a/recipe/cua_s1/verify_graph_buckets.py +++ b/recipe/cua_s1/verify_graph_buckets.py @@ -29,6 +29,10 @@ def verify(path, require_replay=False): for record in records: assert record["status"] == "complete" events = record["events"] + if "schedules" in report["config"]: + assert [e["goal_repetitions"] for e in events] == report["config"][ + "schedules" + ][name] count += len(events) * len(variants_expected) for index, event in enumerate(events): assert event["index"] == index @@ -38,16 +42,15 @@ def verify(path, require_replay=False): v["response"] == variants["eager"]["response"] for v in variants.values() ) - for v in variants.values(): - assert math.isfinite(v["latency_ms"]) and v["latency_ms"] > 0 - assert ( - 0 - <= v["cache_shapes"] - <= report["config"]["graph"]["max_shapes"] - ) - assert ( - 0 <= v["cache_bytes"] <= report["config"]["graph"]["max_bytes"] + for variant, v in variants.items(): + config = ( + report["config"] + .get("variant_graph", {}) + .get(variant, report["config"]["graph"]) ) + assert math.isfinite(v["latency_ms"]) and v["latency_ms"] > 0 + assert 0 <= v["cache_shapes"] <= config["max_shapes"] + assert 0 <= v["cache_bytes"] <= config["max_bytes"] eager_total = sum(e["variants"]["eager"]["latency_ms"] for e in events) for variant in variants_expected: samples = [e["variants"][variant]["latency_ms"] for e in events] diff --git a/tests/cua_s1/test_graph_policy_experiment.py b/tests/cua_s1/test_graph_policy_experiment.py new file mode 100644 index 0000000..6e3de0a --- /dev/null +++ b/tests/cua_s1/test_graph_policy_experiment.py @@ -0,0 +1,32 @@ +"""Guard the controlled admission comparison used for mode-policy decisions.""" + +import sys +from dataclasses import asdict +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "recipe/cua_s1")) + + +def test_tuned_comparator_changes_only_admission_window(): + from benchmark_graph_buckets import variant_configs + + configs = variant_configs("worker", 64, tuned_exact_window=32) + exact = asdict(configs["exact"]) + tuned = asdict(configs["exact_tuned"]) + assert exact.pop("admission_window") == 8 + assert tuned.pop("admission_window") == 32 + assert exact == tuned + assert configs["bucket"].mode == "rule-bucket" + assert configs["bucket"].max_bytes == configs["exact"].max_bytes + + +def test_mixed_schedules_include_cold_inputs_and_returning_hotspot(): + from benchmark_graph_buckets import schedules + + cases = schedules() + assert cases["hot_cold"] == [v for i in range(8) for v in [1, 2, 4, 8, 13 + i]] + assert cases["shifting_hot"] == [ + v + for group in [range(1, 5), range(5, 9), range(9, 13), range(1, 5)] + for v in list(group) * 8 + ] From a67a5066b9c02c852a0a9fec4de0af0a02ed524c Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 15:43:32 +0800 Subject: [PATCH 62/75] experiment(cua): share graph cache and admission budgets across modes --- recipe/cua_s1/check_shared_graph_policy.py | 313 +++++++++++++++++++++ recipe/cua_s1/graph_policy.py | 160 +++++++++++ tests/cua_s1/test_shared_graph_policy.py | 152 ++++++++++ 3 files changed, 625 insertions(+) create mode 100644 recipe/cua_s1/check_shared_graph_policy.py create mode 100644 recipe/cua_s1/graph_policy.py create mode 100644 tests/cua_s1/test_shared_graph_policy.py diff --git a/recipe/cua_s1/check_shared_graph_policy.py b/recipe/cua_s1/check_shared_graph_policy.py new file mode 100644 index 0000000..cf3d1a9 --- /dev/null +++ b/recipe/cua_s1/check_shared_graph_policy.py @@ -0,0 +1,313 @@ +"""GPU proof of shared Graph budgets, cross-mode eviction and explicit close.""" + +from __future__ import annotations + +import argparse +import gc +import hashlib +from dataclasses import asdict, replace +from pathlib import Path + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + if args.output.exists(): + parser.error("choose a fresh output path") + + import torch + from benchmark_multimodal_graph import changed_text_request + from diagnose_graph_buckets import prepare_values + from evaluate_multimodal import environment + from graph_policy import SharedGraphRuntime + from PIL import Image + from profile_multimodal import ( + GOAL, + case_matrix, + fixture, + repository_state, + write_json, + ) + + from models.cua_s1.multimodal.graph_runtime import GraphConfig + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + root = Path(__file__).resolve().parents[2] + source = repository_state(root) + if source["dirty"] or not source["revision"]: + raise ValueError("clean committed source required") + args.output.parent.mkdir(parents=True, exist_ok=True) + files = [ + Path(__file__), + Path(__file__).with_name("graph_policy.py"), + Path(__file__).with_name("diagnose_graph_buckets.py"), + Path(__file__).with_name("benchmark_multimodal_graph.py"), + Path(__file__).with_name("profile_multimodal.py"), + *sorted((root / "src/models/cua_s1/multimodal").glob("*.py")), + ] + report = { + "status": "running", + "repository": source, + "environment": environment(), + "source_sha256": { + str(path.relative_to(root)): hashlib.sha256(path.read_bytes()).hexdigest() + for path in files + }, + "boundary_checks": [], + "policies": {}, + "responses": [], + "response_logit_checks": [], + } + write_json(args.output, report) + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + ) + runtime = None + + def cache_snapshot(group): + torch.cuda.synchronize() + return { + "cache_shapes": len(group.cache), + "cache_bytes": group.cache.bytes, + "allocated_bytes": torch.cuda.memory_allocated(), + "reserved_bytes": torch.cuda.memory_reserved(), + "peak_reserved_bytes": torch.cuda.max_memory_reserved(), + "entries": [ + {"mode": key[0], "bytes": entry.bytes} + for key, entry in group.cache.entries.items() + ], + } + + def close_and_check(group): + entries = list(group.cache.entries.values()) + before = cache_snapshot(group) + group.close() + assert all(not entry.blocks and entry.pool is None for entry in entries) + assert not group.cache.entries and group.cache.bytes == 0 + assert not group.admission.attempts and not group.admission.history + gc.collect() + torch.cuda.empty_cache() + after = cache_snapshot(group) + return {"before": before, "after": after, "live_entry_references_retired": True} + + def compare(group, mode, values, records): + group.select_mode(mode) + expected = engine.model(**values, logits_to_keep=1, use_cache=False).logits[ + 0, -1, : + ] + before = group.stats + previous = list(group.cache.entries.values()) + with group.request(): + actual = group.forward(values) + equal = torch.equal(expected, actual) + current_ids = {id(entry) for entry in group.cache.entries.values()} + retired = [entry for entry in previous if id(entry) not in current_ids] + assert all(not entry.blocks and entry.pool is None for entry in retired) + delta = {k: value - before[k] for k, value in group.stats.items()} + records.append( + { + "mode": mode, + "tokens": values["inputs_embeds"].shape[1], + "equal": equal, + "max_abs": (expected.float() - actual.float()).abs().max().item(), + "stats_delta": delta, + "retired_with_live_references": len(retired), + **cache_snapshot(group), + } + ) + assert equal + assert len(group.cache) <= group.config.max_shapes + assert group.cache.bytes <= group.config.max_bytes + assert ( + group.stats["numerical_mismatch"] == group.stats["length_rejections"] == 0 + ) + + try: + with torch.no_grad(): + case = next(c for c in case_matrix() if c["id"] == "320x240-short-q2") + request = parse_request(fixture(case, args.output.parent / "fixtures")) + long_request = replace( + request, + questions=tuple( + replace(q, goal=" ".join([GOAL] * 30)) for q in request.questions + ), + ) + prepared = prepare_values(engine, long_request) + lengths = [255, 256, 257, 319, 320, 321] + values = { + n: { + "inputs_embeds": prepared["inputs_embeds"][:, :n].clone(), + "position_ids": prepared["position_ids"][:, :, :n].clone(), + "attention_mask": prepared["attention_mask"][:, :n].clone(), + } + for n in lengths + } + config = GraphConfig( + min_uses=1, + max_shapes=12, + max_bytes=4 << 30, + max_captures=32, + capture_budget_ms=20000, + ) + report["boundary_config"] = asdict(config) + runtime = SharedGraphRuntime(engine.model, config) + torch.cuda.reset_peak_memory_stats() + for n in lengths + lengths[::-1]: + for mode in ("exact", "rule-bucket"): + compare(runtime, mode, values[n], report["boundary_checks"]) + assert runtime.stats["captures"] == 9 + assert runtime.stats["replays"] == 15 + assert runtime.stats["requests"] == 24 + report["boundary_stats"] = runtime.stats + report["boundary_cleanup"] = close_and_check(runtime) + for name, policy, sequence, counter in [ + ( + "cross_mode_eviction", + replace(config, max_shapes=1), + [ + ("exact", 255), + ("rule-bucket", 255), + ("exact", 255), + ("rule-bucket", 256), + ], + "cooldown", + ), + ( + "resident_byte_limit", + replace(config, max_bytes=300 << 20), + [ + ("exact", 255), + ("rule-bucket", 255), + ("exact", 255), + ("rule-bucket", 256), + ], + "evictions", + ), + ( + "capture_count_limit", + replace(config, max_captures=1), + [("exact", 255), ("rule-bucket", 255), ("exact", 255)], + "capture_budget", + ), + ( + "capture_time_limit", + replace(config, capture_budget_ms=0.001), + [("exact", 255), ("rule-bucket", 255)], + "capture_budget", + ), + ( + "oversize_candidates", + replace(config, max_bytes=1 << 20), + [("exact", 255), ("rule-bucket", 255)], + "memory_budget", + ), + ]: + runtime = SharedGraphRuntime(engine.model, policy) + record = {"config": asdict(policy), "events": []} + report["policies"][name] = record + torch.cuda.reset_peak_memory_stats() + for mode, n in sequence: + compare(runtime, mode, values[n], record["events"]) + assert runtime.stats[counter] > 0, (name, runtime.stats) + record["stats"] = runtime.stats + record["cleanup"] = close_and_check(runtime) + write_json(args.output, report) + del values, prepared + + config = replace(config, min_uses=2) + report["response_config"] = asdict(config) + runtime = SharedGraphRuntime(engine.model, config) + original_forward = runtime.forward + + def checked_forward(v): + expected = engine.model(**v, logits_to_keep=1, use_cache=False).logits[ + 0, -1, : + ] + actual = original_forward(v) + equal = torch.equal(expected, actual) + report["response_logit_checks"].append( + { + "mode": runtime._mode, + "tokens": v["inputs_embeds"].shape[1], + "equal": equal, + "max_abs": (expected.float() - actual.float()) + .abs() + .max() + .item(), + } + ) + assert equal + return actual + + runtime.forward = checked_forward + for case in [ + c + for c in case_matrix() + if c["id"] in {"320x240-short-q2", "640x480-short-q8"} + ]: + request = parse_request(fixture(case, args.output.parent / "fixtures")) + changed_text, _ = changed_text_request(engine, request) + changed_image = replace( + request, image=Image.new("RGB", request.image.size, "black") + ) + for name, item in [ + ("cold", request), + ("capture", request), + ("image", changed_image), + ("text", changed_text), + ]: + engine.graph_runtime = None + expected = engine.predict(item) + for mode in ("exact", "rule-bucket"): + runtime.select_mode(mode) + engine.graph_runtime = runtime + before = runtime.stats + actual = engine.predict(item) + delta = {k: v - before[k] for k, v in runtime.stats.items()} + assert delta["requests"] == 1 + assert actual == expected + if name in {"image", "text"}: + assert delta["replays"] == len(item.questions) + report["responses"].append( + { + "case": case["id"], + "variant": name, + "mode": mode, + "expected": expected, + "actual": actual, + "stats_delta": delta, + } + ) + report["response_stats"] = runtime.stats + assert runtime.stats["rejected"] == runtime.stats["length_rejections"] == 0 + report["response_cleanup"] = close_and_check(runtime) + engine.graph_runtime = None + report["status"] = "complete" + print( + "shared resource checks complete", + len(report["boundary_checks"]), + "boundary checks;", + len(report["responses"]), + "responses;", + len(report["response_logit_checks"]), + "response logit checks", + flush=True, + ) + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + if runtime is not None: + runtime.close() + engine.graph_runtime = None + engine.close() + write_json(args.output, report) + + +if __name__ == "__main__": + main() diff --git a/recipe/cua_s1/graph_policy.py b/recipe/cua_s1/graph_policy.py new file mode 100644 index 0000000..52ce0ba --- /dev/null +++ b/recipe/cua_s1/graph_policy.py @@ -0,0 +1,160 @@ +"""Experimental exact/rule co-residency with one per-model resource budget. + +This is an explicit-mode experiment, not an automatic selector or a worker CLI +feature. The group owns both children; callers must use the group's request and +lifecycle methods. Existing runtime forwards and strict numerical gates are +reused unchanged. The cache limit covers resident ownership, not transient +candidate capture or total process reservations. +""" + +from __future__ import annotations + +import gc +import threading +from contextlib import contextmanager +from dataclasses import replace + +from models.cua_s1.multimodal.graph_admission import AdmissionPolicy +from models.cua_s1.multimodal.graph_buckets import RuleBucketRuntime +from models.cua_s1.multimodal.graph_runtime import GraphCache, GraphConfig, GraphRuntime + + +class _CacheView: + def __init__(self, group, mode): + self.group = group + self.mode = mode + + def get(self, key): + return self.group.cache.get((self.mode, key)) + + def put(self, key, entry): + entry.key = (self.mode, key) + retired = self.group.cache.put(entry.key, entry) + if retired is not None: + # Explicitly release even when diagnostics retain Python references. + # _capture subsequently applies global cooldown to these exact keys. + for previous in retired: + previous.close() + return retired + + +class _AdmissionView: + def __init__(self, group, mode): + self.group = group + self.mode = mode + + def reason(self, key): + return self.group.admission.reason((self.mode, key)) + + def start_capture(self): + return self.group.admission.start_capture() + + def finish_capture(self, ticket, elapsed_ms): + self.group.admission.finish_capture(ticket, elapsed_ms) + + def evict(self, namespaced_key): + self.group.admission.evict(namespaced_key) + + +class SharedGraphRuntime: + """One request clock, capture ledger, LRU and lock for two execution modes.""" + + def __init__(self, model, config=None): + self.model = model + self.config = config or GraphConfig() + self.cache = GraphCache(self.config.max_shapes, self.config.max_bytes) + self.admission = AdmissionPolicy(self.config) + self.lock = threading.RLock() + self._in_request = False + self._closed = False + self._requests = 0 + self._mode = "eager" + self._selected_this_request = set() + self._runtimes = { + "exact": GraphRuntime( + model, replace(self.config, mode="exact", bucket_width=64) + ), + "rule-bucket": RuleBucketRuntime( + model, replace(self.config, mode="rule-bucket") + ), + } + for mode, runtime in self._runtimes.items(): + runtime.cache = _CacheView(self, mode) + runtime.admission = _AdmissionView(self, mode) + runtime.lock = self.lock + + @property + def stats(self): + result = {} + for runtime in self._runtimes.values(): + for key, value in runtime.stats.items(): + result[key] = result.get(key, 0) + value + result["requests"] = self._requests + return result + + def select_mode(self, mode): + if mode not in {"eager", "exact", "rule-bucket"}: + raise ValueError("mode must be eager, exact or rule-bucket") + with self.lock: + if self._closed: + raise RuntimeError("shared Graph runtime is closed") + if self._in_request: + raise RuntimeError("select a mode before beginning a request") + self._mode = mode + + @contextmanager + def request(self): + with self.lock: + if self._closed: + raise RuntimeError("shared Graph runtime is closed") + if self._in_request: + raise RuntimeError("nested Graph requests are unsupported") + self._in_request = True + self._requests += 1 + self.admission.begin_request() + self._selected_this_request.clear() + for runtime in self._runtimes.values(): + runtime._in_request = True + try: + yield + finally: + for runtime in self._runtimes.values(): + runtime._in_request = False + self._in_request = False + + def forward(self, values): + import torch + + with self.lock, torch.no_grad(): + if self._closed: + raise RuntimeError("shared Graph runtime is closed") + runtime = self._runtimes["exact" if self._mode == "eager" else self._mode] + if self._in_request and self._mode not in self._selected_this_request: + runtime.stats["requests"] += 1 + self._selected_this_request.add(self._mode) + if self._mode == "eager": + if not self._in_request: + runtime.stats["no_request"] += 1 + return runtime._eager(values) + return runtime.forward(values) + + def invalidate(self): + with self.lock: + if self._in_request: + raise RuntimeError("cannot invalidate during a Graph request") + retired = self.cache.clear() + for entry in retired: + entry.close() + self.admission.reset() + for runtime in self._runtimes.values(): + runtime.disabled.clear() + gc.collect() + + def close(self): + with self.lock: + if self._closed: + return + self.invalidate() + for runtime in self._runtimes.values(): + runtime._closed = True + self._closed = True diff --git a/tests/cua_s1/test_shared_graph_policy.py b/tests/cua_s1/test_shared_graph_policy.py new file mode 100644 index 0000000..8a55d1c --- /dev/null +++ b/tests/cua_s1/test_shared_graph_policy.py @@ -0,0 +1,152 @@ +"""Shared policy accounting and retirement without requiring a GPU.""" + +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + +sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "recipe/cua_s1")) + + +def group(**limits): + from graph_policy import SharedGraphRuntime + + from models.cua_s1.multimodal.graph_runtime import GraphConfig + + return SharedGraphRuntime(object(), GraphConfig(**limits)) + + +def entry(size): + value = SimpleNamespace(bytes=size, key=None, closed=False) + + def close(): + value.closed = True + + value.close = close + return value + + +def test_cross_mode_eviction_closes_live_entry_and_namespaces_keys(): + runtime = group(max_shapes=2, max_bytes=70) + exact, bucket = runtime._runtimes.values() + a, b, c = entry(20), entry(25), entry(30) + assert exact.cache.put("same", a) == [] + assert bucket.cache.put("same", b) == [] + assert exact.cache.get("same") is a + assert bucket.cache.put("new", c) == [b] + assert b.closed and not a.closed + assert bucket.cache.get("same") is None + assert runtime.cache.bytes == 50 + assert len(runtime.cache) == 2 + + +def test_admission_and_capture_budget_are_shared_but_keys_are_not(): + runtime = group(min_uses=1, max_captures=1, capture_window=2) + exact, bucket = runtime._runtimes.values() + with runtime.request(): + assert exact.admission.reason("same") is None + ticket = exact.admission.start_capture() + exact.admission.finish_capture(ticket, 5) + assert bucket.admission.reason("same") == "capture_budget" + # Two mode calls in one prediction must not advance the request clock. + assert runtime.admission.request_index == 1 + with runtime.request(): + assert bucket.admission.reason("same") == "capture_budget" + with runtime.request(): + assert bucket.admission.reason("same") is None + assert runtime.stats["requests"] == 3 + + +def test_mode_frequency_does_not_satisfy_other_mode_admission(): + runtime = group(min_uses=2) + exact, bucket = runtime._runtimes.values() + with runtime.request(): + assert exact.admission.reason("key") == "warmup" + with runtime.request(): + assert bucket.admission.reason("key") == "warmup" + assert exact.admission.reason("key") is None + + +def test_cross_mode_time_budget_is_global(): + runtime = group(min_uses=1, capture_budget_ms=10) + exact, bucket = runtime._runtimes.values() + with runtime.request(): + ticket = exact.admission.start_capture() + exact.admission.finish_capture(ticket, 11) + assert bucket.admission.reason("x") == "capture_budget" + + +def test_invalidate_and_close_retire_both_modes_with_live_references(): + runtime = group() + exact, bucket = runtime._runtimes.values() + a, b = entry(20), entry(30) + exact.cache.put("a", a) + bucket.cache.put("b", b) + exact.disabled["bad"] = None + runtime.invalidate() + assert a.closed and b.closed + assert not exact.disabled + assert runtime.cache.bytes == 0 + assert len(runtime.cache) == 0 + assert runtime.admission.request_index == 0 + c = entry(10) + bucket.cache.put("c", c) + runtime.close() + runtime.close() + assert c.closed and exact._closed and bucket._closed + with pytest.raises(RuntimeError, match="closed"), runtime.request(): + pass + + +def test_oversize_entry_does_not_evict_residents(): + runtime = group(max_bytes=40) + exact, bucket = runtime._runtimes.values() + a, b = entry(20), entry(41) + exact.cache.put("a", a) + assert bucket.cache.put("b", b) is None + assert not a.closed and exact.cache.get("a") is a + assert runtime.cache.bytes == 20 + + +def test_close_or_invalidate_during_request_is_rejected(): + runtime = group() + with runtime.request(): + with pytest.raises(RuntimeError, match="request"): + runtime.close() + with pytest.raises(RuntimeError, match="request"): + runtime.invalidate() + with pytest.raises(RuntimeError, match="nested"), runtime.request(): + pass + runtime.close() + + +def test_different_models_never_share_cache_or_budget(): + first, second = group(min_uses=1), group(min_uses=1) + first._runtimes["exact"].cache.put("a", entry(20)) + with first.request(): + first.admission.start_capture() + assert len(second.cache) == 0 + assert second.admission.request_index == 0 + assert len(second.admission.attempts) == 0 + first.close() + with second.request(): + assert second._runtimes["exact"].admission.reason("a") is None + + +def test_bucket_width_is_not_applied_to_exact_runtime(): + runtime = group(mode="rule-bucket", bucket_width=128) + assert runtime._runtimes["exact"].config.bucket_width == 64 + assert runtime._runtimes["rule-bucket"].width == 128 + + +def test_eviction_cooldown_addresses_the_evicted_mode(): + runtime = group(min_uses=1, max_shapes=1) + exact, bucket = runtime._runtimes.values() + exact.cache.put("same", entry(20)) + with runtime.request(): + retired = bucket.cache.put("same", entry(30)) + for value in retired: + bucket.admission.evict(value.key) + assert exact.admission.reason("same") == "cooldown" + assert bucket.admission.reason("same") is None From de1322a0837fc55088328689daa6d6684fb254ac Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 15:46:18 +0800 Subject: [PATCH 63/75] test(cua): independently verify shared graph experiment evidence --- recipe/cua_s1/verify_graph_policy.py | 179 +++++++++++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 recipe/cua_s1/verify_graph_policy.py diff --git a/recipe/cua_s1/verify_graph_policy.py b/recipe/cua_s1/verify_graph_policy.py new file mode 100644 index 0000000..57f8267 --- /dev/null +++ b/recipe/cua_s1/verify_graph_policy.py @@ -0,0 +1,179 @@ +"""Independent stdlib validation of the controlled and shared-budget experiments.""" + +import argparse +import hashlib +import json +import math +import subprocess +from pathlib import Path + + +def provenance(report, root): + assert report["status"] == "complete", report.get("error") + assert report["repository"]["dirty"] is False + revision = report["repository"]["revision"] + assert len(revision) == 40 + for name, expected in report["source_sha256"].items(): + data = subprocess.check_output( + ["git", "-C", str(root), "show", revision + ":" + name] + ) + assert hashlib.sha256(data).hexdigest() == expected, name + + +def clean_stats(stats): + for key in [ + "capture_oom", + "capture_error", + "numerical_mismatch", + "length_rejections", + "unsupported", + "no_request", + ]: + assert stats.get(key, 0) == 0, (key, stats.get(key)) + + +def baseline(report): + configs = report["config"]["variant_graph"] + exact, tuned = dict(configs["exact"]), dict(configs["exact_tuned"]) + assert exact.pop("admission_window") == 8 + assert tuned.pop("admission_window") == 32 + assert exact == tuned, "tuned comparator changed more than admission window" + bucket = dict(configs["bucket"]) + assert bucket.pop("mode") == "rule-bucket" + reference = dict(configs["exact"]) + reference.pop("mode") + assert bucket == reference + assert report["config"]["runs"] == 2 + schedules = { + "hot_four": [1, 2, 4, 8] * 12, + "churn_twelve": list(range(1, 13)) * 6, + "hot_cold": [v for i in range(8) for v in [1, 2, 4, 8, 13 + i]], + "shifting_hot": [ + v + for g in [range(1, 5), range(5, 9), range(9, 13), range(1, 5)] + for v in list(g) * 8 + ], + } + assert report["config"]["schedules"] == schedules + assert set(report["workloads"]) == set(schedules) + count = 0 + for name, records in report["workloads"].items(): + assert len(records) == 2 + for record in records: + assert [e["goal_repetitions"] for e in record["events"]] == schedules[name] + for mode, stats in record["stats_final"].items(): + clean_stats(stats) + assert stats["rejected"] == 0 + assert stats["requests"] == len(schedules[name]) + # Check count and time limits at each attempt. These workloads + # have two identical questions, so at most one new shape/request. + attempts = [] + config = configs[mode] + for index, event in enumerate(record["events"]): + delta = event["variants"][mode]["stats_delta"] + assert delta["requests"] == 1 + n = delta["capture_attempts"] + assert n in (0, 1) + assert delta["captures"] == n + attempts = [ + (i, t) + for i, t in attempts + if index - i < config["capture_window"] + ] + if n: + assert len(attempts) < config["max_captures"] + assert sum(t for _, t in attempts) < config["capture_budget_ms"] + attempts.append((index, delta["capture_attempt_ms"])) + count += len(record["events"]) * 4 + assert count == 2304 + return count + + +def cleanup(record): + assert record["live_entry_references_retired"] is True + assert record["after"]["cache_shapes"] == record["after"]["cache_bytes"] == 0 + + +def shared(report): + records = report["boundary_checks"] + assert len(records) == 24 + assert [(e["mode"], e["tokens"]) for e in records] == [ + (mode, n) + for n in [255, 256, 257, 319, 320, 321, 321, 320, 319, 257, 256, 255] + for mode in ("exact", "rule-bucket") + ] + assert report["boundary_stats"]["captures"] == 9 + assert report["boundary_stats"]["replays"] == 15 + cleanup(report["boundary_cleanup"]) + groups = [(report["boundary_config"], records, report["boundary_stats"])] + for record in report["policies"].values(): + groups.append((record["config"], record["events"], record["stats"])) + cleanup(record["cleanup"]) + total = 0 + for config, events, stats in groups: + clean_stats(stats) + assert stats["requests"] == len(events) + for key, value in stats.items(): + assert math.isclose( + value, + sum(e["stats_delta"][key] for e in events), + rel_tol=1e-12, + abs_tol=1e-9, + ) + for event in events: + assert event["equal"] and event["max_abs"] == 0 + assert 0 <= event["cache_shapes"] <= config["max_shapes"] + assert 0 <= event["cache_bytes"] <= config["max_bytes"] + assert sum(e["bytes"] for e in event["entries"]) == event["cache_bytes"] + assert len(event["entries"]) == event["cache_shapes"] + total += len(events) + for name in ("cross_mode_eviction", "resident_byte_limit"): + record = report["policies"][name] + assert record["stats"]["evictions"] == 1 + assert record["stats"]["cooldown"] == 1 + assert sum(e["retired_with_live_references"] for e in record["events"]) == 1 + assert record["events"][-1]["stats_delta"]["replays"] == 1 + for name in ("capture_count_limit", "capture_time_limit"): + record = report["policies"][name] + assert record["stats"]["captures"] == 1 + assert record["stats"]["capture_budget"] == 1 + assert record["events"][1]["mode"] == "rule-bucket" + assert record["events"][1]["stats_delta"]["captures"] == 0 + assert report["policies"]["oversize_candidates"]["stats"]["memory_budget"] == 2 + assert report["policies"]["oversize_candidates"]["stats"]["captures"] == 0 + assert len(report["responses"]) == 16 + for event in report["responses"]: + assert event["actual"] == event["expected"] + assert event["stats_delta"]["requests"] == 1 + clean_stats(event["stats_delta"]) + assert len(report["response_logit_checks"]) == 80 + for event in report["response_logit_checks"]: + assert event["equal"] and event["max_abs"] == 0 + clean_stats(report["response_stats"]) + assert report["response_stats"]["rejected"] == 0 + cleanup(report["response_cleanup"]) + return total + len(report["response_logit_checks"]) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("report", type=Path) + parser.add_argument("--kind", choices=("baseline", "shared"), required=True) + args = parser.parse_args() + report = json.loads(args.report.read_text()) + provenance(report, Path(__file__).resolve().parents[2]) + if args.kind == "baseline": + from verify_graph_buckets import verify + + verify(args.report, require_replay=True) + print("controlled policy comparison verified:", baseline(report), "predictions") + else: + print( + "shared resource comparison verified:", + shared(report), + "complete-logit comparisons", + ) + + +if __name__ == "__main__": + main() From b33e69055fad25cf5483347dfc62c3615bc7ad74 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 15:52:11 +0800 Subject: [PATCH 64/75] experiment(cua): record controlled policy and shared-budget GPU results --- .../2026-09-29-cua-graph-policy-experiment.md | 18 +- .../rtx4090-graph-policy/README.md | 175 + .../rtx4090-graph-policy/baseline.json | 132691 +++++++++++++++ .../rtx4090-graph-policy/baseline.log | 59 + .../rtx4090-graph-policy/shared.json | 5769 + .../rtx4090-graph-policy/shared.log | 5 + .../rtx4090-graph-policy/verification.log | 11 + tests/cua_s1/test_graph_policy_verifier.py | 69 + 8 files changed, 138790 insertions(+), 7 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/shared.json create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/shared.log create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/verification.log create mode 100644 tests/cua_s1/test_graph_policy_verifier.py diff --git a/docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md b/docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md index ff70963..8713081 100644 --- a/docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md +++ b/docs/superpowers/plans/2026-09-29-cua-graph-policy-experiment.md @@ -9,14 +9,18 @@ ## Execution checklist - [x] Inspect GPU occupancy, reference revision, Python package versions and existing experiment harness. -- [ ] Extend `recipe/cua_s1/benchmark_graph_buckets.py` with an optional tuned exact comparator and the existing hot/cold and shifting-hot schedules. Record complete per-variant configurations and runtime/admission source hashes. Keep old CLI behavior compatible. -- [ ] Validate the comparison isolates admission-window changes using CPU tests, and verify the recorded schedule, response equality, statistics and per-variant limits independently. -- [ ] Run two cold repetitions of hot-four, churn-twelve, hot/cold and shifting-hot, with all 24 execution orders cycling across requests. Use fresh output directories outside the remote Git repository. -- [ ] Analyze total time (including capture and first-length gates), p50/p95, cache bytes, captures, replay, evictions and rejected requests. Record negative results and scope limits. -- [ ] Build a narrowly scoped shared-budget experiment if the baseline supports continuing: one request clock, namespaced cache keys, one capture ledger, one aggregate resident-cache budget, deterministic retirement. Avoid claiming a hard peak-process-memory bound: in-flight candidate capture can temporarily exceed retained cache limits. -- [ ] Validate cross-mode eviction, budget fallback, two questions counted once, cold/changed inputs, explicit close with live entry references, and replay after retirement of the other mode. -- [ ] Preserve scripts, source revisions, raw results, verifier and a concise report locally. Do not claim auto selection, compilation, native integration or concurrent throughput without separate implementation and evidence. +- [x] Extend `recipe/cua_s1/benchmark_graph_buckets.py` with an optional tuned exact comparator and the existing hot/cold and shifting-hot schedules. Record complete per-variant configurations and runtime/admission source hashes. Keep old CLI behavior compatible. +- [x] Validate the comparison isolates admission-window changes using CPU tests, and verify the recorded schedule, response equality, statistics and per-variant limits independently. +- [x] Run two cold repetitions of hot-four, churn-twelve, hot/cold and shifting-hot, with all 24 execution orders cycling across requests. Use fresh output directories outside the remote Git repository. +- [x] Analyze total time (including capture and first-length gates), p50/p95, cache bytes, captures, replay, evictions and rejected requests. Record negative results and scope limits. +- [x] Build a narrowly scoped shared-budget experiment if the baseline supports continuing: one request clock, namespaced cache keys, one capture ledger, one aggregate resident-cache budget, deterministic retirement. Avoid claiming a hard peak-process-memory bound: in-flight candidate capture can temporarily exceed retained cache limits. +- [x] Validate cross-mode eviction, budget fallback, two questions counted once, cold/changed inputs, explicit close with live entry references, and replay after retirement of the other mode. +- [x] Preserve scripts, source revisions, raw results, verifier and a concise report locally. Do not claim auto selection, compilation, native integration or concurrent throughput without separate implementation and evidence. ## Acceptance and interpretation All timed complete responses must match eager. Captures and new bucket lengths retain exact full-vocabulary-logit comparison. Do not relax tolerances. Tune only the admission window in the comparator; retain the same 8-shape / 1 GiB cache and capture limits. Report any negative or mixed outcome instead of selecting a favorable workload. Two repetitions are exploratory evidence, not confidence intervals. Shared-cache CPU tests must demonstrate real resource-retirement semantics before GPU validation. GPU measurements are serial synthetic workloads on the PyTorch fallback path, not production traffic. + +## Recorded outcome + +Completed the controlled baseline (2,304 timed predictions) and shared-budget prototype (119 full-logit comparisons, 16 complete-response comparisons). Both independent verifiers pass. Exact window tuning alone does not remove bucket gains under churn; the prototype validates resource semantics only. Worker promotion and automatic selection remain subsequent implementation work. See `recipe/cua_s1/experiments/rtx4090-graph-policy/README.md` for all results and limitations. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-policy/README.md b/recipe/cua_s1/experiments/rtx4090-graph-policy/README.md new file mode 100644 index 0000000..4952983 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-policy/README.md @@ -0,0 +1,175 @@ +# Cua-S1 Graph policy and shared-budget experiment + +RTX 4090, 2026-09-29. Baseline parent: PR #37, `fd5c4172987bcbcc2eb8e10dcf04b3edd45af0b8`. + +## Findings + +Increasing exact Graph's admission window from 8 to 32 does not remove the +advantage of rule buckets under length churn. On twelve rotating lengths, +bucket total time is **24.69–25.19% lower than tuned exact**. On shifting hot +lengths it is **20.59–22.45% lower**. Conversely, buckets take **6.69–8.14% more +time on four stable hot lengths**, and **3.97–4.86% more on the tested hot/cold +mix**, than tuned exact. These are two exploratory cold runs, not confidence +intervals or a universal policy threshold. + +A separate shared-resource prototype passed GPU parity and lifecycle checks: +one request clock, capture count/time ledger, aggregate resident-cache limit and +namespaced LRU for exact and rule-bucket execution. This is a building block for +mode selection; **automatic mode selection and production worker integration +are not implemented here**. No new compilation or model compute kernels. + +## Controlled comparison + +Measured source: `71531bb` (full revision and source hashes in `baseline.json`). +The only change between `exact` and `exact_tuned` is `admission_window=8 → 32`. +Both retain 8 resident shapes, 1 GiB cache, 4 captures per 32 requests and a +2,000 ms capture-work budget. A synchronous in-flight capture may overshoot the +time allowance; subsequent attempts are withheld until budget becomes available. +Worker buckets use width 64 and otherwise the same default resource limits. + +Total synchronized `predict` time, seconds; cells are run 1 / run 2: + +| Workload | Eager | Default exact | Exact window 32 | Rule buckets | +|---|---:|---:|---:|---:| +| Four hot lengths, 48 requests/run | 11.06 / 11.05 | 7.46 / 7.47 | 7.43 / 7.48 | 8.04 / 7.98 | +| Twelve rotating lengths, 72 requests/run | 16.88 / 16.93 | 16.43 / 16.43 | 16.32 / 16.33 | 12.29 / 12.22 | +| Hot/cold mix, 40 requests/run | 9.38 / 9.31 | 7.65 / 7.59 | 7.67 / 7.62 | 8.04 / 7.93 | +| Shifting hot lengths, 128 requests/run | 29.87 / 29.90 | 25.70 / 25.17 | 26.32 / 25.72 | 20.41 / 20.42 | + +Capture-inclusive p95, milliseconds; cells are run 1 / run 2: + +| Workload | Eager | Default exact | Exact window 32 | Rule buckets | +|---|---:|---:|---:|---:| +| Four hot lengths | 243.99 / 239.86 | 530.97 / 525.70 | 522.73 / 524.31 | 232.36 / 238.00 | +| Twelve rotating lengths | 240.89 / 240.29 | 236.12 / 233.96 | 570.31 / 565.53 | 241.81 / 243.34 | +| Hot/cold mix | 248.48 / 248.75 | 531.44 / 523.08 | 536.54 / 527.61 | 490.80 / 480.12 | +| Shifting hot lengths | 241.57 / 242.04 | 674.67 / 581.04 | 678.58 / 677.89 | 242.68 / 242.96 | + +All **2,304 timed complete responses equal eager**. There are no numerical +rejections, capture errors or OOMs. An independent verifier recomputes totals, +p50/p95, response equality, source hashes, counter sums, cache limits and capture +count/time admission compliance from the raw events. + +The twelve-length diagnostic isolates the earlier confound: + +- Default exact captures/replays zero times: the recurrence gap exceeds its + admission window, so it falls back to eager. +- Tuned exact captures 8 shapes and replays 56 question forwards in each run, + but encounters 56 capture-budget fallbacks. It uses up to 972.06 MiB of + resident Graph ownership, versus 598.53 MiB for buckets. +- Buckets capture 2 shapes and replay 138 question forwards, including 10 + first-length validation gates. The capture and validation savings persist + after enabling exact admission; total speedup is not solely a window artifact. +- In this schedule tuned exact improves total time only about 0.6% relative to + default exact, while its capture-inclusive p95 rises above 560 ms. Simply + broadening admission is not a suitable default based on this evidence. + +On the hot/cold mix, buckets retain 1,002.22 MiB compared with 463.45 MiB for +exact. Fewer bucket keys do not imply lower memory. A later selector must account +for retained bytes, capture/first-length cost and hotspot lifetime, alongside +reuse frequency. Its value is still to be measured; this experiment does not +establish that an online selector beats the best fixed mode. + +## Shared-resource prototype + +Measured source: `de1322a0837fc55088328689daa6d6684fb254ac`. +`recipe/cua_s1/graph_policy.py` composes the existing exact and worker bucket +runtimes. Mode selection is explicit and serialized by the experimental caller. +No worker CLI/default changes or global/model-method replacements are made. +Each captured shape retains its own allocator pool and owned stream; shared +budgeting does not share incompatible graph buffers or allocator pools. + +- A single request clock advances once per `predict`, including 2- and + 8-question requests. Keys are namespaced by execution mode. +- All captures draw from one count/time ledger. Exact consuming the budget + prevents a subsequent bucket capture while existing exact replay still works. +- One LRU enforces aggregate resident shape/byte limits. Cross-mode retirement + explicitly closes entries even while diagnostics hold their Python references. +- Eviction cooldown refers to the evicted mode's key. Invalidation/close retire + both modes, clear admission history and release their Graph ownership. + +GPU checks in `shared.json`: + +| Check | Result | +|---|---| +| 255/256/257/319/320/321 token boundaries, both directions and both modes | 24 exact full-vocabulary-logit matches; 9 captures, 15 replays | +| Global one-shape limit | Exact entry retired by bucket insertion; exact cooldown and surviving bucket replay succeed | +| Global 300 MiB resident limit | 115.96 MiB exact entry retired before retaining 271.12 MiB bucket; aggregate stays within the limit | +| Global one-capture allowance | Exact captures once; bucket falls back; exact replay remains valid | +| Exhausted shared time allowance | Bucket capture withheld after exact spends the allowance | +| 1 MiB resident limit | Both modes reject oversize candidates and return eager results; no resident entries | +| Two resolutions, 2/8 questions, cold/capture/changed image/changed text | 16 complete-response matches and 80 exact full-logit comparisons | +| Close with live entry references | Both modes' blocks/pools explicitly cleared, resident ownership zero | + +The five budget/eviction scenarios add 15 full-logit comparisons, for **119 +independently checked full-logit comparisons** in total. The boundary run retained +1,830,518,784 bytes across 9 exact/bucket entries before close and zero afterward. +The loaded model remains resident; zero Graph ownership does not mean zero +process GPU memory. Per-event memory snapshots are recorded with only one +shared runtime live at a time in this diagnostic. + +**Memory limit scope:** limits are checked when admitting a fully captured +candidate. Capture may temporarily coexist with resident graphs and exceed the +resident-cache limit. This is not a hard cap on transient or total process VRAM. +The prototype's cross-mode scheduling performance and concurrent use are not +established by these correctness checks. + +## Environment and measurement limits + +Python 3.12.13, RTX 4090 sm_89, driver 595.71.05, PyTorch 2.14.0+cu130, +Transformers 5.17.0, PEFT 0.21.0. BF16 base and unmerged multimodal LoRA; pinned +weight revisions are recorded in both reports. No FLA or causal-conv1d is +installed: this measures the PyTorch fallback implementation. + +The latency matrix uses a synthetic 320×240 image and two identical questions, +varying processed prompt lengths. It is serial engine latency, not HTTP or +concurrent throughput. Model load is excluded. Three eager warmups precede the +matrix; every workload/run starts with empty Graph caches. Timing includes +preprocessing, vision, both questions, cold capture, first-length checks and +in-request eviction, but excludes final cache retirement and writing reports. +All 24 four-variant orders cycle across requests; run two offsets by half a +cycle. Three variant caches coexist in the timing process, so allocator totals +are combined; only per-cache ownership is attributable to an individual mode. + +These results do not establish real GUI task accuracy, broader hardware/backend +portability, concurrent throughput, or automatic-selection performance. + +## Reproduction + +Run the timing matrix at source `71531bb` and the shared diagnostic at +`de1322a`, from clean checkouts, using fresh output paths outside each checkout: + +```sh +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/benchmark_graph_buckets.py \ + --weights /path/to/weights --output /path/to/policy-baseline \ + --kind worker --tuned-exact-window 32 --runs 2 \ + --case hot_four --case churn_twelve --case hot_cold --case shifting_hot + +PYTHONPATH=src:recipe/cua_s1 python recipe/cua_s1/check_shared_graph_policy.py \ + --weights /path/to/weights --output /path/to/shared.json +``` + +Verify from the final branch (which contains both measured source revisions): + +```sh +python recipe/cua_s1/verify_graph_policy.py \ + recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.json --kind baseline +python recipe/cua_s1/verify_graph_policy.py \ + recipe/cua_s1/experiments/rtx4090-graph-policy/shared.json --kind shared +PYTHONPATH=src python -m pytest tests/cua_s1 -q +``` + +The verifier regression tests deliberately corrupt comparison limits, request +counts, schedules, logits, memory accounting, retirement evidence, complete +responses and cleanup state. See `verification.log`, `tests.log` and +`SHA256SUMS` for final validation and evidence integrity. + +## Next implementation + +Promote the validated ownership model into the worker with a reviewed lifecycle +API, then evaluate an explicit opt-in `auto` mode. Its admission decision should +include estimated capture amortization, first-length checking, aggregate cache +bytes and a switch cooldown. Keep exact/rule-bucket/eager manual modes and exact +numerical gates. Compare the implemented selector against the tuned exact +baseline here; do not infer selector gains by retrospectively choosing the best +fixed result for each workload. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.json b/recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.json new file mode 100644 index 0000000..d71bd1c --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.json @@ -0,0 +1,132691 @@ +{ + "status": "complete", + "repository": { + "revision": "71531bbfdc0627ef010ed83af66515156364262d", + "dirty": false, + "diff_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855", + "untracked_files": [] + }, + "environment": { + "python": "3.12.13", + "gpu": "NVIDIA GeForce RTX 4090", + "compute_capability": [ + 8, + 9 + ], + "cuda": "13.0", + "driver": "595.71.05", + "torch_num_threads": 64, + "torch_num_interop_threads": 64, + "packages": { + "torch": "2.14.0", + "torchvision": "0.29.0", + "transformers": 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+ }, + "questions": { + "q1": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + }, + "q2": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + } + } + }, + "tokens": { + "1": 236, + "2": 242, + "3": 248, + "4": 254, + "5": 260, + "6": 266, + "7": 272, + "8": 278, + "9": 284, + "10": 290, + "11": 296, + "12": 302, + "13": 308, + "14": 314, + "15": 320, + "16": 326, + "17": 332, + "18": 338, + "19": 344, + "20": 350 + }, + "buckets": { + "1": 256, + "2": 256, + "3": 256, + "4": 256, + "5": 320, + "6": 320, + "7": 320, + "8": 320, + "9": 320, + "10": 320, + "11": 320, + "12": 320, + "13": 320, + "14": 320, + "15": 320, + "16": 384, + "17": 384, + "18": 384, + "19": 384, + "20": 384 + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.log b/recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.log new file mode 100644 index 0000000..77ff2fa --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-policy/baseline.log @@ -0,0 +1,59 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! + Loading weights: 0%| | 0/723 [00:00 Date: Tue, 29 Sep 2026 15:52:58 +0800 Subject: [PATCH 65/75] docs(cua): record final 241-test validation and evidence checksums --- recipe/cua_s1/experiments/rtx4090-graph-policy/README.md | 3 ++- recipe/cua_s1/experiments/rtx4090-graph-policy/SHA256SUMS | 7 +++++++ recipe/cua_s1/experiments/rtx4090-graph-policy/tests.log | 5 +++++ 3 files changed, 14 insertions(+), 1 deletion(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-graph-policy/tests.log diff --git a/recipe/cua_s1/experiments/rtx4090-graph-policy/README.md b/recipe/cua_s1/experiments/rtx4090-graph-policy/README.md index 4952983..b9dda8a 100644 --- a/recipe/cua_s1/experiments/rtx4090-graph-policy/README.md +++ b/recipe/cua_s1/experiments/rtx4090-graph-policy/README.md @@ -159,7 +159,8 @@ python recipe/cua_s1/verify_graph_policy.py \ PYTHONPATH=src python -m pytest tests/cua_s1 -q ``` -The verifier regression tests deliberately corrupt comparison limits, request +The final Cua-S1 suite passes **241 tests** (`tests.log`); changed Python files +pass Ruff lint and formatting. The verifier regression tests deliberately corrupt comparison limits, request counts, schedules, logits, memory accounting, retirement evidence, complete responses and cleanup state. See `verification.log`, `tests.log` and `SHA256SUMS` for final validation and evidence integrity. diff --git a/recipe/cua_s1/experiments/rtx4090-graph-policy/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-graph-policy/SHA256SUMS new file mode 100644 index 0000000..e4f99f2 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-policy/SHA256SUMS @@ -0,0 +1,7 @@ +7c71546c681b98832063c6635dc5c64f9a95dc34c6b2cc3fc9ffbe6031f37dc2 README.md +9bbafbb0b4f13cbdc9bf9b1ef298c59842cf70058060283b3399d9aac5e21304 baseline.json +d6a76ca6c9bf2c80dec2c02cf0b9d1e04e792265eb5fc6adaba54d2b1ff5769f baseline.log +d756643b17b913d40f3d3cd14544cf089f6dc52b884df1bed6bdff3ccabbcb5c shared.json +60eed6bfdcc24556550b7cb8860c9702cdd44a1686f94364131d33618689eead shared.log +7a794c1e73fda9c835a9cd803fc04aff4732454832c7a25444d6dd621d000e89 tests.log +cf43883723f377cde50b56fc77ef72ea4151f173d7bcd04a6e8db863f70c8331 verification.log diff --git a/recipe/cua_s1/experiments/rtx4090-graph-policy/tests.log b/recipe/cua_s1/experiments/rtx4090-graph-policy/tests.log new file mode 100644 index 0000000..0798b54 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-graph-policy/tests.log @@ -0,0 +1,5 @@ +........................................................................ [ 29%] +........................................................................ [ 59%] +........................................................................ [ 89%] +......................... [100%] +241 passed in 21.11s From 0473de8e888bf4757f332030743f767b183e3dec Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 16:11:34 +0800 Subject: [PATCH 66/75] feat(cua): add opt-in automatic graph selection with shared budgets --- .../plans/2026-09-29-cua-auto-worker.md | 29 +++ recipe/cua_s1/benchmark_graph_buckets.py | 44 ++++- recipe/cua_s1/check_bucket_worker_http.py | 8 +- recipe/cua_s1/graph_policy.py | 161 +--------------- src/models/cua_s1/multimodal/graph_auto.py | 151 +++++++++++++++ src/models/cua_s1/multimodal/graph_buckets.py | 4 +- src/models/cua_s1/multimodal/graph_runtime.py | 48 +++-- .../cua_s1/multimodal/graph_selector.py | 182 ++++++++++++++++++ src/models/cua_s1/multimodal/graph_shared.py | 163 ++++++++++++++++ src/models/cua_s1/multimodal/model.py | 13 +- src/models/cua_s1/multimodal/server.py | 6 +- tests/cua_s1/test_auto_selector.py | 136 +++++++++++++ tests/cua_s1/test_auto_worker.py | 140 ++++++++++++++ tests/cua_s1/test_bucket_worker.py | 4 +- tests/cua_s1/test_graph_policy_experiment.py | 18 ++ 15 files changed, 918 insertions(+), 189 deletions(-) create mode 100644 docs/superpowers/plans/2026-09-29-cua-auto-worker.md create mode 100644 src/models/cua_s1/multimodal/graph_auto.py create mode 100644 src/models/cua_s1/multimodal/graph_selector.py create mode 100644 src/models/cua_s1/multimodal/graph_shared.py create mode 100644 tests/cua_s1/test_auto_selector.py create mode 100644 tests/cua_s1/test_auto_worker.py diff --git a/docs/superpowers/plans/2026-09-29-cua-auto-worker.md b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md new file mode 100644 index 0000000..d62b0be --- /dev/null +++ b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md @@ -0,0 +1,29 @@ +# Cua-S1 automatic Graph worker implementation + +**Goal:** Ship an explicit, validated `--graph-mode auto` with shared resources and a reproducible PR on top of #37. + +**Architecture:** Promote the tested shared runtime to the model package. Keep eager/default and manual exact/rule modes. Route each supported question under a single request lock; decisions for an identical layout remain fixed within that request. Both children draw from one namespaced cache, admission clock and capture ledger. A bounded online selector observes only past/current requests, and has no workload-name or future-schedule input. + +## Selector contract + +- Retain at most 128 layouts over a 32-request horizon. Count distinct requests separately from same-request question multiplicity. +- Resident exact entries replay immediately. A previously rejected bucket length may not replay via auto; all existing strict gates remain active. +- Consider exact capture only after recurrence within the configured exact admission window, without working-set pressure or predicted eviction of useful graphs. Consider buckets for recurring dispersed lengths or a growing repeated working set. +- Compare estimated future saving against capture cost with 25% margin. Charge first-length validation when considering a new bucket. Predict promotion from resident bucket to exact using only the incremental saving. +- Initially use conservative 500 ms capture cost and replay ratios 0.45/0.65 of measured eager language latency. Replace priors with observed captures and sampled CUDA-event spans, without adding synchronization to normal replay. Priors are policy heuristics, not guaranteed latency predictions. +- Record observations per compatible bucket family, bounded like layout history. Skip capture/gate/rejection events as replay-cost samples. Maintain switch cooldown. Cache/capture limits remain authoritative when estimates are wrong. +- Default remains eager; `--graph` remains exact. Auto is opt-in and pinned to the same single-device fallback implementation as buckets. + +## Tasks and validation + +- [ ] Promote shared ownership; add explicit construction injection for cache/admission/lock, reusable exact keys, atomic retirement, and tests covering lifetime and cross-mode bounds. +- [ ] Implement bounded selector with tests for hot, dispersed, one-shot, same-request duplicates, pressure, cost rejection, cooldown, invalidation and bucket rejection. +- [ ] Integrate config/CLI/engine, document counters and expose no experimental imports in production. +- [ ] Run Cua-S1 CPU tests and changed-file lint/format. +- [ ] GPU check full logits, mixed layouts, changed images/text, boundaries, aggregate budgets and lifecycle; use actual CLI-selected HTTP worker and two loaded instances. +- [ ] Benchmark eager/default exact/tuned exact/bucket/auto on four existing workloads, plus an unseen mixed schedule. Use two cold runs and order balancing, retain capture/gates/eviction in measured time and per-mode evidence. Record negative results instead of tuning to future schedule labels. +- [ ] Independently verify raw data/source provenance and report scope. Run a focused code review, fix findings, rerun checks justified by changes, push branch, create and attach PR with incremental comparison against #37. + +## Shipping criterion + +All complete responses and full-logit checks match eager; budgets and lifecycle checks pass. The selector should improve total cost over eager on the tested recurrent workloads, with regressions versus fixed modes disclosed. It need not beat the hindsight-best fixed choice. If its cost assumptions fail materially, revise from measured evidence, preserve failed/intermediate runs, and revalidate. No automatic enabling by default and no claims about concurrent throughput or non-fallback kernels. diff --git a/recipe/cua_s1/benchmark_graph_buckets.py b/recipe/cua_s1/benchmark_graph_buckets.py index edcfb6a..7c66b34 100644 --- a/recipe/cua_s1/benchmark_graph_buckets.py +++ b/recipe/cua_s1/benchmark_graph_buckets.py @@ -7,12 +7,14 @@ import hashlib import itertools import math +import random import statistics from dataclasses import asdict, replace from pathlib import Path def schedules(): + rng = random.Random(20260929) return { "probe": list(range(1, 13)) * 2, "hot_four": [1, 2, 4, 8] * 12, @@ -23,10 +25,13 @@ def schedules(): for group in [range(1, 5), range(5, 9), range(9, 13), range(1, 5)] for v in list(group) * 8 ], + "mixed_holdout": [1, 2] * 6 + + [rng.choice(list(range(1, 17))) for _ in range(48)] + + [20, 21] * 12, } -def variant_configs(kind, width, tuned_exact_window=None): +def variant_configs(kind, width, tuned_exact_window=None, include_auto=False): from models.cua_s1.multimodal.graph_runtime import GraphConfig config = GraphConfig() @@ -38,9 +43,19 @@ def variant_configs(kind, width, tuned_exact_window=None): } if tuned_exact_window is not None: configs["exact_tuned"] = replace(config, admission_window=tuned_exact_window) + if include_auto: + configs["auto"] = replace(config, mode="auto", bucket_width=width) return configs +def variant_orders(variants): + if "auto" not in variants: + return list(itertools.permutations(variants)) + forward = list(variants) + reverse = list(reversed(variants)) + return [tuple(v[i:] + v[:i]) for v in (forward, reverse) for i in range(len(v))] + + def summary(events, variant): samples = [event["variants"][variant]["latency_ms"] for event in events] ordered = sorted(samples) @@ -61,6 +76,7 @@ def main(): p.add_argument("--runs", type=int, default=2) p.add_argument("--width", type=int, default=64) p.add_argument("--tuned-exact-window", type=int) + p.add_argument("--include-auto", action="store_true") p.add_argument("--kind", choices=("segment", "rule", "worker"), default="segment") p.add_argument( "--include-recipe", @@ -73,6 +89,8 @@ def main(): p.error("choose a fresh output directory") if args.include_recipe and args.kind != "worker": p.error("--include-recipe requires --kind worker") + if args.include_auto and (args.kind != "worker" or args.include_recipe): + p.error("--include-auto requires --kind worker without --include-recipe") if args.runs < 1: p.error("runs must be positive") if args.tuned_exact_window is not None and args.tuned_exact_window < 1: @@ -102,7 +120,9 @@ def main(): args.output.mkdir(parents=True) report_path = args.output / "report.json" selected = {k: v for k, v in schedules().items() if k in (args.case or ["probe"])} - configs = variant_configs(args.kind, args.width, args.tuned_exact_window) + configs = variant_configs( + args.kind, args.width, args.tuned_exact_window, args.include_auto + ) config = configs["exact"] bucket_class = RuleBucketRuntime if args.kind == "rule" else BucketRuntime if args.kind == "worker": @@ -122,6 +142,8 @@ def bucket_class(model, config, width): ) if args.tuned_exact_window is not None: variants += ("exact_tuned",) + if args.include_auto: + variants += ("auto",) if args.include_recipe: configs["recipe"] = config report = { @@ -141,6 +163,9 @@ def bucket_class(model, config, width): for name in ( "graph_runtime.py", "graph_admission.py", + "graph_shared.py", + "graph_auto.py", + "graph_selector.py", "graph_buckets.py", "rule_prefill.py", "model.py", @@ -164,7 +189,9 @@ def bucket_class(model, config, width): "timing": "synchronized predict including cold capture and first-length checks", "memory": "paired caches coexist; allocator totals are combined, cache bytes are per variant", "scope": "serial, two identical questions, synthetic 320x240 image, no HTTP", - "order": "all variant permutations cycle across requests, offset by half a cycle across runs", + "order": "balanced cyclic/reversed rotations, offset by half a cycle across runs" + if args.include_auto + else "all variant permutations cycle across requests, offset by half a cycle across runs", }, "workloads": {}, } @@ -199,7 +226,7 @@ def bucket_class(model, config, width): print("lengths", tokens, "buckets", report["buckets"], flush=True) for _ in range(3): engine.predict(requests[counts[0]]) - orders = list(itertools.permutations(variants)) + orders = variant_orders(variants) for name, schedule in selected.items(): report["workloads"][name] = [] for run in range(args.runs): @@ -211,6 +238,10 @@ def bucket_class(model, config, width): runtimes["exact_tuned"] = GraphRuntime( engine.model, configs["exact_tuned"] ) + if args.include_auto: + from models.cua_s1.multimodal.graph_auto import AutoGraphRuntime + + runtimes["auto"] = AutoGraphRuntime(engine.model, configs["auto"]) if args.include_recipe: runtimes["recipe"] = RuleBucketRuntime( engine.model, config, width=args.width @@ -248,6 +279,9 @@ def bucket_class(model, config, width): "cache_bytes": runtime.cache.bytes if runtime else 0, "reserved_bytes": torch.cuda.memory_reserved(), "peak_reserved_bytes": torch.cuda.max_memory_reserved(), + "resident_modes": [key[0] for key in runtime.cache.entries] + if variant == "auto" + else [], } assert all( v["response"] == event["variants"]["eager"]["response"] @@ -264,6 +298,8 @@ def bucket_class(model, config, width): ) record["summary"] = {v: summary(record["events"], v) for v in variants} record["stats_final"] = {v: dict(r.stats) for v, r in runtimes.items()} + if args.include_auto: + record["auto_reasons"] = dict(runtimes["auto"].decisions) record["status"] = "complete" engine.graph_runtime = None for runtime in runtimes.values(): diff --git a/recipe/cua_s1/check_bucket_worker_http.py b/recipe/cua_s1/check_bucket_worker_http.py index 16fac77..cb1838d 100644 --- a/recipe/cua_s1/check_bucket_worker_http.py +++ b/recipe/cua_s1/check_bucket_worker_http.py @@ -16,6 +16,7 @@ def main(): p = argparse.ArgumentParser(description=__doc__) p.add_argument("--weights", type=Path, required=True) p.add_argument("--output", type=Path, required=True) + p.add_argument("--mode", choices=("rule-bucket", "auto"), default="rule-bucket") args = p.parse_args() if args.output.exists(): p.error("choose a fresh output") @@ -38,14 +39,16 @@ def main(): "--adapter", str(args.weights / "cua-s1-4b-0.2/multimodal"), "--graph-mode", - "rule-bucket", + args.mode, ] ) engine = MultimodalEngine( config.base, config.adapter, graph_config=config.graph_config ) runtime = engine.graph_runtime - assert runtime.__class__.__module__ == "models.cua_s1.multimodal.graph_buckets" + assert runtime.__class__.__module__ == "models.cua_s1.multimodal." + ( + "graph_auto" if args.mode == "auto" else "graph_buckets" + ) original_rule = upstream.torch_chunk_gated_delta_rule modules = [ layer.linear_attn @@ -77,6 +80,7 @@ def main(): "repository": source, "environment": environment(), "records": [], + "mode": args.mode, } try: url = f"http://127.0.0.1:{server.server_port}" diff --git a/recipe/cua_s1/graph_policy.py b/recipe/cua_s1/graph_policy.py index 52ce0ba..174e1c0 100644 --- a/recipe/cua_s1/graph_policy.py +++ b/recipe/cua_s1/graph_policy.py @@ -1,160 +1,5 @@ -"""Experimental exact/rule co-residency with one per-model resource budget. +"""Compatibility import for the shared-budget experiment, now model-owned.""" -This is an explicit-mode experiment, not an automatic selector or a worker CLI -feature. The group owns both children; callers must use the group's request and -lifecycle methods. Existing runtime forwards and strict numerical gates are -reused unchanged. The cache limit covers resident ownership, not transient -candidate capture or total process reservations. -""" +from models.cua_s1.multimodal.graph_shared import SharedGraphRuntime -from __future__ import annotations - -import gc -import threading -from contextlib import contextmanager -from dataclasses import replace - -from models.cua_s1.multimodal.graph_admission import AdmissionPolicy -from models.cua_s1.multimodal.graph_buckets import RuleBucketRuntime -from models.cua_s1.multimodal.graph_runtime import GraphCache, GraphConfig, GraphRuntime - - -class _CacheView: - def __init__(self, group, mode): - self.group = group - self.mode = mode - - def get(self, key): - return self.group.cache.get((self.mode, key)) - - def put(self, key, entry): - entry.key = (self.mode, key) - retired = self.group.cache.put(entry.key, entry) - if retired is not None: - # Explicitly release even when diagnostics retain Python references. - # _capture subsequently applies global cooldown to these exact keys. - for previous in retired: - previous.close() - return retired - - -class _AdmissionView: - def __init__(self, group, mode): - self.group = group - self.mode = mode - - def reason(self, key): - return self.group.admission.reason((self.mode, key)) - - def start_capture(self): - return self.group.admission.start_capture() - - def finish_capture(self, ticket, elapsed_ms): - self.group.admission.finish_capture(ticket, elapsed_ms) - - def evict(self, namespaced_key): - self.group.admission.evict(namespaced_key) - - -class SharedGraphRuntime: - """One request clock, capture ledger, LRU and lock for two execution modes.""" - - def __init__(self, model, config=None): - self.model = model - self.config = config or GraphConfig() - self.cache = GraphCache(self.config.max_shapes, self.config.max_bytes) - self.admission = AdmissionPolicy(self.config) - self.lock = threading.RLock() - self._in_request = False - self._closed = False - self._requests = 0 - self._mode = "eager" - self._selected_this_request = set() - self._runtimes = { - "exact": GraphRuntime( - model, replace(self.config, mode="exact", bucket_width=64) - ), - "rule-bucket": RuleBucketRuntime( - model, replace(self.config, mode="rule-bucket") - ), - } - for mode, runtime in self._runtimes.items(): - runtime.cache = _CacheView(self, mode) - runtime.admission = _AdmissionView(self, mode) - runtime.lock = self.lock - - @property - def stats(self): - result = {} - for runtime in self._runtimes.values(): - for key, value in runtime.stats.items(): - result[key] = result.get(key, 0) + value - result["requests"] = self._requests - return result - - def select_mode(self, mode): - if mode not in {"eager", "exact", "rule-bucket"}: - raise ValueError("mode must be eager, exact or rule-bucket") - with self.lock: - if self._closed: - raise RuntimeError("shared Graph runtime is closed") - if self._in_request: - raise RuntimeError("select a mode before beginning a request") - self._mode = mode - - @contextmanager - def request(self): - with self.lock: - if self._closed: - raise RuntimeError("shared Graph runtime is closed") - if self._in_request: - raise RuntimeError("nested Graph requests are unsupported") - self._in_request = True - self._requests += 1 - self.admission.begin_request() - self._selected_this_request.clear() - for runtime in self._runtimes.values(): - runtime._in_request = True - try: - yield - finally: - for runtime in self._runtimes.values(): - runtime._in_request = False - self._in_request = False - - def forward(self, values): - import torch - - with self.lock, torch.no_grad(): - if self._closed: - raise RuntimeError("shared Graph runtime is closed") - runtime = self._runtimes["exact" if self._mode == "eager" else self._mode] - if self._in_request and self._mode not in self._selected_this_request: - runtime.stats["requests"] += 1 - self._selected_this_request.add(self._mode) - if self._mode == "eager": - if not self._in_request: - runtime.stats["no_request"] += 1 - return runtime._eager(values) - return runtime.forward(values) - - def invalidate(self): - with self.lock: - if self._in_request: - raise RuntimeError("cannot invalidate during a Graph request") - retired = self.cache.clear() - for entry in retired: - entry.close() - self.admission.reset() - for runtime in self._runtimes.values(): - runtime.disabled.clear() - gc.collect() - - def close(self): - with self.lock: - if self._closed: - return - self.invalidate() - for runtime in self._runtimes.values(): - runtime._closed = True - self._closed = True +__all__ = ["SharedGraphRuntime"] diff --git a/src/models/cua_s1/multimodal/graph_auto.py b/src/models/cua_s1/multimodal/graph_auto.py new file mode 100644 index 0000000..f7e86e1 --- /dev/null +++ b/src/models/cua_s1/multimodal/graph_auto.py @@ -0,0 +1,151 @@ +"""Opt-in adaptive dispatch with shared Graph budgets and strict numeric gates.""" + +from collections import Counter, deque +from contextlib import contextmanager + +from .graph_buckets import bucket_length +from .graph_runtime import GraphConfig +from .graph_selector import AutoSelector +from .graph_shared import SharedGraphRuntime + + +class AutoGraphRuntime(SharedGraphRuntime): + def __init__(self, model, config=None): + super().__init__(model, config or GraphConfig(mode="auto")) + self.selector = AutoSelector(self.config) + self._pending = deque(maxlen=16) + self._decisions = {} + self.decisions = Counter() + self.selections = Counter(eager=0, exact=0, **{"rule-bucket": 0}) + + @property + def stats(self): + result = super().stats + result.update( + { + "selected_" + mode.replace("-", "_"): count + for mode, count in self.selections.items() + } + ) + return result + + def select_mode(self, mode): + raise RuntimeError("automatic runtime chooses its own execution mode") + + def _collect_timings(self): + while self._pending and self._pending[0][1].query(): + start, end, bucket, mode = self._pending.popleft() + self.selector.record(bucket, mode, latency_ms=start.elapsed_time(end)) + + @contextmanager + def request(self): + with super().request(): + self._collect_timings() + self.selector.begin_request(self.admission.request_index) + self._decisions.clear() + yield + + def forward(self, values): + import torch + + with self.lock, torch.no_grad(): + if self._closed: + raise RuntimeError("shared Graph runtime is closed") + exact = self._runtimes["exact"] + bucket_runtime = self._runtimes["rule-bucket"] + if not self._in_request: + exact.stats["no_request"] += 1 + return exact._eager(values) + if ( + not exact._supported(values) + or bucket_length( + values["inputs_embeds"].shape[1], self.config.bucket_width + ) + > self.config.max_tokens + ): + exact.stats["unsupported"] += 1 + self.selections["eager"] += 1 + return exact._eager(values) + key, bucket = exact._key(values), bucket_runtime._key(values) + if ( + key not in self._decisions + and len(self._decisions) >= self.selector.max_layouts + ): + self.selections["eager"] += 1 + self.decisions["request_layout_limit"] += 1 + return exact._eager(values) + length = values["inputs_embeds"].shape[1] + self._collect_timings() + self.selector.observe(key, bucket, length) + # Observe both candidate histories once/request, including eager + # observations. reason() deduplicates another call by the selected + # runtime; discarded answers do not spend capture budget. + exact.admission.reason(key) + bucket_runtime.admission.reason(bucket) + if key not in self._decisions: + mode = self.selector.choose( + key, + bucket, + length, + exact_entry=self.cache.entries.get(("exact", key)), + bucket_entry=self.cache.entries.get(("rule-bucket", bucket)), + cache_shapes=len(self.cache), + cache_bytes=self.cache.bytes, + exact_allowed=key not in exact.disabled, + bucket_allowed=bucket not in bucket_runtime.disabled, + ) + self._decisions[key] = mode + self.decisions[self.selector.last_reason] += 1 + mode = self._decisions[key] + self.selections[mode] += 1 + runtime = exact if mode == "eager" else self._runtimes[mode] + before = dict(runtime.stats) + samples = sum(self.selections.values()) + sample = ( + samples <= 8 + or samples % 16 == 0 + or mode not in self.selector.costs.get(bucket, {}) + ) + start = end = None + with torch.cuda.device(values["inputs_embeds"].device): + if sample: + start, end = ( + torch.cuda.Event(enable_timing=True) for _ in range(2) + ) + start.record() + result = ( + runtime._eager(values) + if mode == "eager" + else runtime.forward(values) + ) + if end is not None: + end.record() + attempts = runtime.stats["capture_attempts"] - before["capture_attempts"] + checks = runtime.stats.get("length_checks", 0) - before.get( + "length_checks", 0 + ) + if attempts: + self.selector.record( + bucket, + mode, + capture_ms=runtime.stats["capture_attempt_ms"] + - before["capture_attempt_ms"], + ) + elif not checks and start is not None: + actual_mode = ( + mode if runtime.stats["replays"] > before["replays"] else "eager" + ) + self._pending.append((start, end, bucket, actual_mode)) + entry_key = key if mode == "exact" else bucket + entry = self.cache.entries.get((mode, entry_key)) + if entry is not None: + self.selector.record(bucket, mode, owned_bytes=entry.bytes) + self.selector.selected(key, mode) + return result + + def invalidate(self): + with self.lock: + super().invalidate() + self.selector.reset() + self._pending.clear() + self._decisions.clear() diff --git a/src/models/cua_s1/multimodal/graph_buckets.py b/src/models/cua_s1/multimodal/graph_buckets.py index ff80c32..a2916d3 100644 --- a/src/models/cua_s1/multimodal/graph_buckets.py +++ b/src/models/cua_s1/multimodal/graph_buckets.py @@ -21,9 +21,9 @@ def bucket_length(length, width): class RuleBucketRuntime(GraphRuntime): """Bounded cache of rule graphs, with explicit model-instance dispatch.""" - def __init__(self, model, config=None): + def __init__(self, model, config=None, **resources): config = config or GraphConfig(mode="rule-bucket") - super().__init__(model, config) + super().__init__(model, config, **resources) self.width = config.bucket_width self.stats.update( length_checks=0, diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py index fd460dc..7dd5a28 100644 --- a/src/models/cua_s1/multimodal/graph_runtime.py +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -45,8 +45,8 @@ class GraphConfig: bucket_width: int = 64 def __post_init__(self): - if self.mode not in {"exact", "rule-bucket"}: - raise ValueError("graph mode must be exact or rule-bucket") + if self.mode not in {"exact", "rule-bucket", "auto"}: + raise ValueError("graph mode must be exact, rule-bucket or auto") if ( type(self.bucket_width) is not int or self.bucket_width <= 0 @@ -55,7 +55,7 @@ def __post_init__(self): raise ValueError("bucket width must be a positive integer multiple of 64") if self.mode == "exact" and self.bucket_width != 64: raise ValueError("bucket width is only configurable in rule-bucket mode") - if self.mode == "rule-bucket" and self.bucket_width > self.max_tokens: + if self.mode in {"rule-bucket", "auto"} and self.bucket_width > self.max_tokens: raise ValueError("bucket width cannot exceed graph max tokens") if not math.isfinite(self.capture_budget_ms): raise ValueError("capture time budget must be finite") @@ -312,15 +312,29 @@ def __del__(self): class GraphRuntime: """Exact-length graph cache for a loaded, immutable multimodal model.""" - def __init__(self, model, config: GraphConfig | None = None): + def __init__( + self, + model, + config: GraphConfig | None = None, + *, + cache=None, + admission=None, + lock=None, + ): self.model = model self.config = config or GraphConfig() - self.cache = GraphCache(self.config.max_shapes, self.config.max_bytes) - self.admission = AdmissionPolicy(self.config) + self.cache = ( + cache + if cache is not None + else GraphCache(self.config.max_shapes, self.config.max_bytes) + ) + self.admission = ( + admission if admission is not None else AdmissionPolicy(self.config) + ) self._in_request = False self._closed = False self.disabled = OrderedDict() - self.lock = threading.RLock() + self.lock = lock if lock is not None else threading.RLock() self.stats = { "requests": 0, "no_request": 0, @@ -462,6 +476,16 @@ def _run_segments(self, values, entry): hidden = text.norm(hidden) return self.model.get_base_model().lm_head(hidden[:, -1:, :])[0, -1, :] + def _key(self, values): + return ( + id(self.model), + self.model.get_base_model().model.language_model.config._attn_implementation, + *( + tensor_signature(values[name]) + for name in ("inputs_embeds", "position_ids", "attention_mask") + ), + ) + def forward(self, values): """Return fresh logits; no static graph output escapes the runtime lock.""" import torch @@ -475,14 +499,7 @@ def forward(self, values): if not self._supported(values): self.stats["unsupported"] += 1 return self._eager(values) - key = ( - id(self.model), - self.model.get_base_model().model.language_model.config._attn_implementation, - *( - tensor_signature(values[name]) - for name in ("inputs_embeds", "position_ids", "attention_mask") - ), - ) + key = self._key(values) if key in self.disabled: self.stats["disabled"] += 1 return self._eager(values) @@ -553,6 +570,7 @@ def _capture(self, values, key): if retired: for entry in retired: self.admission.evict(entry.key) + entry.close() del entry del retired gc.collect() diff --git a/src/models/cua_s1/multimodal/graph_selector.py b/src/models/cua_s1/multimodal/graph_selector.py new file mode 100644 index 0000000..1992d6c --- /dev/null +++ b/src/models/cua_s1/multimodal/graph_selector.py @@ -0,0 +1,182 @@ +"""Bounded, causal mode selection with conservative capture amortization. + +The 32-request observation window forecasts at most the next 64 requests. The +500 ms capture and 0.45/0.65 replay priors are heuristics for the pinned fallback +path, replaced by observations when available. No future workload is consulted. +""" + +from __future__ import annotations + +import math +from collections import OrderedDict +from dataclasses import dataclass, field + + +@dataclass +class _Layout: + bucket: tuple + length: int + visits: list = field(default_factory=list) + mode: str | None = None + switched_at: int = 0 + + +class AutoSelector: + window = 32 + max_layouts = 128 + switch_cooldown = 32 + margin = 1.25 + + def __init__(self, config): + self.config = config + self.history = OrderedDict() + self.costs = OrderedDict() + self.index = 0 + self.last_reason = "cold" + + def reset(self): + self.history.clear() + self.costs.clear() + self.index = 0 + + def begin_request(self, index): + self.index = index + for key, layout in list(self.history.items()): + layout.visits[:] = [ + visit for visit in layout.visits if index - visit[0] < self.window + ] + if not layout.visits: + del self.history[key] + + def observe(self, key, bucket, length): + layout = self.history.setdefault(key, _Layout(bucket, length)) + if layout.visits and layout.visits[-1][0] == self.index: + layout.visits[-1][1] += 1 + else: + layout.visits.append([self.index, 1]) + self.history.move_to_end(key) + while len(self.history) > self.max_layouts: + self.history.popitem(last=False) + + def record( + self, bucket, mode, *, latency_ms=None, capture_ms=None, owned_bytes=None + ): + costs = self.costs.setdefault(bucket, {}) + for key, value in [(mode, latency_ms), (mode + "_capture", capture_ms)]: + if value is not None and math.isfinite(value) and value > 0: + costs[key] = ( + value if key not in costs else 0.8 * costs[key] + 0.2 * value + ) + if owned_bytes is not None and owned_bytes > 0: + costs[mode + "_bytes"] = max(costs.get(mode + "_bytes", 0), owned_bytes) + self.costs.move_to_end(bucket) + while len(self.costs) > self.max_layouts: + self.costs.popitem(last=False) + + def selected(self, key, mode): + layout = self.history.get(key) + if layout is not None and layout.mode != mode: + layout.mode = mode + layout.switched_at = self.index + + def _result(self, mode, reason): + self.last_reason = reason + return mode + + def choose( + self, + key, + bucket, + length, + *, + exact_entry=None, + bucket_entry=None, + cache_shapes=0, + cache_bytes=0, + exact_allowed=True, + bucket_allowed=True, + ): + costs = self.costs.get(bucket, {}) + eager = costs.get("eager", 100.0) + exact = costs.get("exact", eager * 0.45) + rule = costs.get("rule-bucket", eager * 0.65) + if exact_entry is not None: + return self._result("exact", "resident_exact") + layout = self.history[key] + required = max(2, self.config.min_uses) + if bucket_entry is not None and length in bucket_entry.rejected_lengths: + bucket_allowed = False + if len(layout.visits) < required: + # A previously verified resident bucket length needs no extra gate. + if ( + bucket_allowed + and bucket_entry is not None + and length in bucket_entry.verified_lengths + and rule < eager + ): + return self._result("rule-bucket", "resident_bucket") + return self._result("eager", "cold") + gap = (layout.visits[-1][0] - layout.visits[0][0]) / (len(layout.visits) - 1) + copies = sum(v[1] for v in layout.visits) / len(layout.visits) + future_exact = 2 * self.window / gap * copies + repeated = sum(len(item.visits) >= required for item in self.history.values()) + pressure = repeated > max(1, self.config.max_shapes // 2) + exact_fits = ( + cache_shapes < self.config.max_shapes + and cache_bytes + costs.get("exact_bytes", 0) <= self.config.max_bytes + ) + switching = layout.mode not in (None, "exact") + cooled = ( + not switching or self.index - layout.switched_at >= self.switch_cooldown + ) + current_cost = ( + min(eager, rule) if bucket_allowed and bucket_entry is not None else eager + ) + if ( + exact_allowed + and gap <= self.config.admission_window + and not pressure + and exact_fits + and cooled + and future_exact * max(0, current_cost - exact) + > self.margin * costs.get("exact_capture", 500.0) + ): + return self._result("exact", "amortized_exact") + + if not bucket_allowed: + return self._result("eager", "bucket_rejected") + if bucket_entry is not None: + gate = 0 if length in bucket_entry.verified_lengths else eager + if future_exact * max(0, eager - rule) > self.margin * gate: + return self._result("rule-bucket", "resident_bucket") + return self._result("eager", "length_gate_cost") + if ( + layout.mode == "exact" + and self.index - layout.switched_at < self.switch_cooldown + ): + return self._result("eager", "switch_cooldown") + + visits = {} + lengths = set() + for item in self.history.values(): + if item.bucket == bucket: + lengths.add(item.length) + for request, count in item.visits: + visits[request] = visits.get(request, 0) + count + span = max(visits) - min(visits) + future_bucket = ( + 2 + * self.window + * (len(visits) - 1) + / span + * sum(visits.values()) + / len(visits) + ) + # The capture estimate already includes the current length's reference. + gates = max(0, len(lengths) - 1) * eager + if ( + future_bucket * max(0, eager - rule) + > self.margin * costs.get("rule-bucket_capture", 500.0) + gates + ): + return self._result("rule-bucket", "amortized_bucket") + return self._result("eager", "cost") diff --git a/src/models/cua_s1/multimodal/graph_shared.py b/src/models/cua_s1/multimodal/graph_shared.py new file mode 100644 index 0000000..9c044d8 --- /dev/null +++ b/src/models/cua_s1/multimodal/graph_shared.py @@ -0,0 +1,163 @@ +"""Per-model shared resource ownership for exact and rule-only CUDA Graphs. + +Children are owned exclusively by this group; use its request and lifecycle +methods. Cache limits cover resident ownership, not transient candidate capture +or the model and other process allocations. +""" + +from __future__ import annotations + +import gc +import threading +from contextlib import contextmanager +from dataclasses import replace + +from .graph_admission import AdmissionPolicy +from .graph_buckets import RuleBucketRuntime +from .graph_runtime import GraphCache, GraphConfig, GraphRuntime + + +class _CacheView: + def __init__(self, group, mode): + self.group = group + self.mode = mode + + def get(self, key): + return self.group.cache.get((self.mode, key)) + + def put(self, key, entry): + entry.key = (self.mode, key) + retired = self.group.cache.put(entry.key, entry) + if retired is not None: + # Explicitly release even when diagnostics retain Python references. + # _capture subsequently applies global cooldown to these exact keys. + for previous in retired: + previous.close() + return retired + + +class _AdmissionView: + def __init__(self, group, mode): + self.group = group + self.mode = mode + + def reason(self, key): + return self.group.admission.reason((self.mode, key)) + + def start_capture(self): + return self.group.admission.start_capture() + + def finish_capture(self, ticket, elapsed_ms): + self.group.admission.finish_capture(ticket, elapsed_ms) + + def evict(self, namespaced_key): + self.group.admission.evict(namespaced_key) + + +class SharedGraphRuntime: + """One request clock, capture ledger, LRU and lock for two execution modes.""" + + def __init__(self, model, config=None): + self.model = model + self.config = config or GraphConfig() + self.cache = GraphCache(self.config.max_shapes, self.config.max_bytes) + self.admission = AdmissionPolicy(self.config) + self.lock = threading.RLock() + self._in_request = False + self._closed = False + self._requests = 0 + self._mode = "eager" + self._selected_this_request = set() + self._runtimes = {} + for mode, runtime_type in ( + ("exact", GraphRuntime), + ("rule-bucket", RuleBucketRuntime), + ): + config = replace( + self.config, + mode=mode, + bucket_width=64 if mode == "exact" else self.config.bucket_width, + ) + self._runtimes[mode] = runtime_type( + model, + config, + cache=_CacheView(self, mode), + admission=_AdmissionView(self, mode), + lock=self.lock, + ) + + @property + def stats(self): + result = {} + for runtime in self._runtimes.values(): + for key, value in runtime.stats.items(): + result[key] = result.get(key, 0) + value + result["requests"] = self._requests + return result + + def select_mode(self, mode): + if mode not in {"eager", "exact", "rule-bucket"}: + raise ValueError("mode must be eager, exact or rule-bucket") + with self.lock: + if self._closed: + raise RuntimeError("shared Graph runtime is closed") + if self._in_request: + raise RuntimeError("select a mode before beginning a request") + self._mode = mode + + @contextmanager + def request(self): + with self.lock: + if self._closed: + raise RuntimeError("shared Graph runtime is closed") + if self._in_request: + raise RuntimeError("nested Graph requests are unsupported") + self._in_request = True + self._requests += 1 + self.admission.begin_request() + self._selected_this_request.clear() + for runtime in self._runtimes.values(): + runtime._in_request = True + try: + yield + finally: + for runtime in self._runtimes.values(): + runtime._in_request = False + self._in_request = False + + def forward(self, values): + import torch + + with self.lock, torch.no_grad(): + if self._closed: + raise RuntimeError("shared Graph runtime is closed") + runtime = self._runtimes["exact" if self._mode == "eager" else self._mode] + if self._in_request and self._mode not in self._selected_this_request: + runtime.stats["requests"] += 1 + self._selected_this_request.add(self._mode) + if self._mode == "eager": + if not self._in_request: + runtime.stats["no_request"] += 1 + return runtime._eager(values) + return runtime.forward(values) + + def invalidate(self): + with self.lock: + if self._in_request: + raise RuntimeError("cannot invalidate during a Graph request") + retired = self.cache.clear() + for entry in retired: + entry.close() + self.admission.reset() + for runtime in self._runtimes.values(): + runtime.disabled.clear() + gc.collect() + + def close(self): + with self.lock: + if self._closed: + return + self.invalidate() + for runtime in self._runtimes.values(): + runtime._closed = True + self._closed = True diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py index 1e110e3..2c80220 100644 --- a/src/models/cua_s1/multimodal/model.py +++ b/src/models/cua_s1/multimodal/model.py @@ -151,15 +151,22 @@ def __init__( if graph_config is not None: from .graph_runtime import GraphRuntime - if graph_config.mode == "rule-bucket": + if graph_config.mode in {"rule-bucket", "auto"}: from .graph_buckets import RuleBucketRuntime from .rule_prefill import pinned_implementation pinned_implementation() # Explicit adapter calls cannot honor offload/device-map hooks. if any(p.device != self.model.device for p in self.model.parameters()): - raise ValueError("rule-bucket requires one CUDA-resident model") - self.graph_runtime = RuleBucketRuntime(self.model, graph_config) + raise ValueError( + "rule-bucket/auto requires one CUDA-resident model" + ) + if graph_config.mode == "auto": + from .graph_auto import AutoGraphRuntime + + self.graph_runtime = AutoGraphRuntime(self.model, graph_config) + else: + self.graph_runtime = RuleBucketRuntime(self.model, graph_config) else: self.graph_runtime = GraphRuntime(self.model, graph_config) diff --git a/src/models/cua_s1/multimodal/server.py b/src/models/cua_s1/multimodal/server.py index 572791b..6bd3600 100644 --- a/src/models/cua_s1/multimodal/server.py +++ b/src/models/cua_s1/multimodal/server.py @@ -131,7 +131,7 @@ def parse_args(argv=None): ) p.add_argument( "--graph-mode", - choices=("exact", "rule-bucket"), + choices=("exact", "rule-bucket", "auto"), help="enable the selected Graph execution mode", ) p.add_argument("--graph-bucket-width", type=int, default=None) @@ -151,8 +151,8 @@ def parse_args(argv=None): p.add_argument("--graph-capture-budget-ms", type=float, default=2000.0) args = p.parse_args(argv) mode = args.graph_mode or ("exact" if args.graph else None) - if args.graph_bucket_width is not None and mode != "rule-bucket": - p.error("--graph-bucket-width requires --graph-mode rule-bucket") + if args.graph_bucket_width is not None and mode not in {"rule-bucket", "auto"}: + p.error("--graph-bucket-width requires --graph-mode rule-bucket or auto") try: graph_config = ( GraphConfig( diff --git a/tests/cua_s1/test_auto_selector.py b/tests/cua_s1/test_auto_selector.py new file mode 100644 index 0000000..b610066 --- /dev/null +++ b/tests/cua_s1/test_auto_selector.py @@ -0,0 +1,136 @@ +"""Online selection uses bounded history, costs and resident resources only.""" + +from types import SimpleNamespace + +from models.cua_s1.multimodal.graph_runtime import GraphConfig + + +def selector(**kwargs): + from models.cua_s1.multimodal.graph_selector import AutoSelector + + return AutoSelector(GraphConfig(**kwargs)) + + +def observe(policy, index, key, bucket="bucket", length=256, copies=2): + policy.begin_request(index) + for _ in range(copies): + policy.observe(key, bucket, length) + + +def test_duplicates_within_one_request_do_not_admit_cold_layout(): + policy = selector() + observe(policy, 1, "a", copies=8) + assert policy.choose("a", "bucket", 256) == "eager" + + +def test_fast_recurring_layout_selects_exact_after_cost_check(): + policy = selector() + observe(policy, 1, "a") + observe(policy, 5, "a") + assert policy.choose("a", "bucket", 256) == "exact" + policy.record("bucket", "exact", capture_ms=100000) + assert policy.choose("a", "bucket", 256) != "exact" + policy.record("bucket", "rule-bucket", capture_ms=100000) + assert policy.choose("a", "bucket", 256) == "eager" + + +def test_distributed_recurrence_selects_bucket(): + policy = selector() + for i in range(1, 13): + observe(policy, i, f"key{i}", length=230 + i) + assert policy.choose(f"key{i}", "bucket", 230 + i) == "eager" + observe(policy, 13, "key1", length=231) + assert policy.choose("key1", "bucket", 231) == "rule-bucket" + + +def test_one_off_layout_does_not_pay_a_resident_bucket_length_gate(): + policy = selector() + observe(policy, 1, "new", length=250) + entry = SimpleNamespace(verified_lengths={240}, rejected_lengths=set()) + assert policy.choose("new", "bucket", 250, bucket_entry=entry) == "eager" + observe(policy, 2, "new", length=250) + assert policy.choose("new", "bucket", 250, bucket_entry=entry) in { + "exact", + "rule-bucket", + } + + +def test_resident_exact_is_used_without_recapturing(): + policy = selector() + observe(policy, 1, "a") + assert policy.choose("a", "bucket", 256, exact_entry=object()) == "exact" + + +def test_rejected_bucket_length_is_never_selected(): + policy = selector() + observe(policy, 1, "a") + observe(policy, 13, "a") + rejected = SimpleNamespace(verified_lengths=set(), rejected_lengths={256}) + assert policy.choose("a", "bucket", 256, bucket_entry=rejected) == "eager" + + +def test_cache_pressure_avoids_exact_capture_and_uses_bucket(): + policy = selector() + for i in range(1, 11): + key = str((i - 1) % 5) + observe(policy, i, key, length=240 + (i - 1) % 5) + assert policy.choose("4", "bucket", 244) == "rule-bucket" + + +def test_predicted_bytes_prevent_exact_capture_eviction(): + policy = selector(max_bytes=100) + observe(policy, 1, "a") + observe(policy, 2, "a") + policy.record("bucket", "exact", owned_bytes=60) + assert policy.choose("a", "bucket", 256, cache_bytes=50) == "rule-bucket" + + +def test_recent_bucket_selection_cools_down_exact_promotion(): + policy = selector() + observe(policy, 1, "a") + policy.selected("a", "rule-bucket") + observe(policy, 2, "a") + policy.record("bucket", "exact", capture_ms=1, latency_ms=1) + policy.record("bucket", "rule-bucket", latency_ms=90) + bucket = SimpleNamespace(verified_lengths={256}, rejected_lengths=set()) + assert policy.choose("a", "bucket", 256, bucket_entry=bucket) == "rule-bucket" + + +def test_history_costs_and_invalidation_are_bounded(): + policy = selector() + for i in range(1, 301): + observe(policy, i, str(i), bucket=str(i)) + policy.record(str(i), "eager", latency_ms=100) + assert len(policy.history) <= 128 + assert len(policy.costs) <= 128 + assert "1" not in policy.history + policy.reset() + assert not policy.history and not policy.costs + + +def test_cost_record_ignores_nonfinite_or_negative_samples(): + policy = selector() + policy.record("b", "eager", latency_ms=100) + for value in [float("nan"), float("inf"), -1, 0]: + policy.record("b", "eager", latency_ms=value) + assert policy.costs["b"]["eager"] == 100 + + +def test_resident_bucket_must_pay_new_length_gate_and_have_positive_saving(): + policy = selector() + observe(policy, 1, "a", length=250, copies=1) + observe(policy, 32, "a", length=250, copies=1) + entry = SimpleNamespace(verified_lengths={240}, rejected_lengths=set()) + policy.record("bucket", "eager", latency_ms=100) + for replay in (120, 99): + policy.costs["bucket"]["rule-bucket"] = replay + assert policy.choose("a", "bucket", 250, bucket_entry=entry) == "eager" + + +def test_slow_verified_bucket_is_not_replayed_for_cold_layout(): + policy = selector() + observe(policy, 1, "a") + policy.record("bucket", "eager", latency_ms=100) + policy.record("bucket", "rule-bucket", latency_ms=120) + entry = SimpleNamespace(verified_lengths={256}, rejected_lengths=set()) + assert policy.choose("a", "bucket", 256, bucket_entry=entry) == "eager" diff --git a/tests/cua_s1/test_auto_worker.py b/tests/cua_s1/test_auto_worker.py new file mode 100644 index 0000000..aeb4a31 --- /dev/null +++ b/tests/cua_s1/test_auto_worker.py @@ -0,0 +1,140 @@ +"""Auto CLI, lifecycle, admission clocks and runtime wiring.""" + +from types import SimpleNamespace + +import pytest + +from models.cua_s1.multimodal.graph_runtime import GraphConfig, GraphRuntime + + +def test_auto_config_and_cli_are_opt_in(): + from models.cua_s1.multimodal.server import parse_args + + config = GraphConfig(mode="auto", bucket_width=128) + assert config.mode == "auto" + base = ["--base", "base", "--adapter", "adapter"] + assert parse_args(base).graph_config is None + assert parse_args(base + ["--graph"]).graph_config.mode == "exact" + configured = parse_args( + base + ["--graph-mode", "auto", "--graph-bucket-width", "128"] + ) + assert configured.graph_config == config + + +def test_explicit_resource_injection_preserves_empty_shared_cache(): + from models.cua_s1.multimodal.graph_runtime import GraphCache + + cache = GraphCache(2, 100) + runtime = GraphRuntime(object(), cache=cache) + assert runtime.cache is cache + + +def test_auto_invalidation_clears_policy_and_pending_events(): + from models.cua_s1.multimodal.graph_auto import AutoGraphRuntime + + runtime = AutoGraphRuntime(object(), GraphConfig(mode="auto")) + with runtime.request(): + runtime.selector.observe("key", "bucket", 256) + runtime._pending.append(object()) + runtime.invalidate() + assert not runtime.selector.history + assert not runtime._pending + assert not runtime.cache.entries + runtime.close() + with pytest.raises(RuntimeError, match="closed"), runtime.request(): + pass + + +def test_auto_cannot_be_manually_switched(): + from models.cua_s1.multimodal.graph_auto import AutoGraphRuntime + + runtime = AutoGraphRuntime(object(), GraphConfig(mode="auto")) + with pytest.raises(RuntimeError, match="automatic"): + runtime.select_mode("exact") + + +def test_injected_key_namespace_and_close_release_every_owner(): + from models.cua_s1.multimodal.graph_shared import SharedGraphRuntime + + runtime = SharedGraphRuntime(object()) + closed = [] + for mode, child in runtime._runtimes.items(): + entry = SimpleNamespace(bytes=1, close=lambda m=mode: closed.append(m)) + child.cache.put("same", entry) + assert len(runtime.cache) == 2 + runtime.close() + assert set(closed) == {"exact", "rule-bucket"} + + +def fake_dispatch(monkeypatch): + from contextlib import nullcontext + + torch = pytest.importorskip("torch") + from models.cua_s1.multimodal.graph_auto import AutoGraphRuntime + + runtime = AutoGraphRuntime(object()) + exact = runtime._runtimes["exact"] + bucket = runtime._runtimes["rule-bucket"] + exact._supported = lambda values: True + exact._key = lambda values: values["key"] + bucket._key = lambda values: "bucket" + exact._eager = lambda values: "eager" + events = [] + + class Event: + def __init__(self, **kwargs): + events.append(self) + + def record(self): + pass + + def query(self): + return True + + def elapsed_time(self, other): + return 100 + + monkeypatch.setattr(torch.cuda, "Event", Event) + monkeypatch.setattr(torch.cuda, "device", lambda value: nullcontext()) + values = { + "key": "a", + "inputs_embeds": SimpleNamespace(shape=(1, 256, 4), device="cuda"), + } + return runtime, exact, bucket, values, events + + +def test_auto_exact_dispatch_does_not_check_bucket_mask_values(monkeypatch): + runtime, exact, bucket, values, _ = fake_dispatch(monkeypatch) + + def forbidden(values): + raise AssertionError("bucket-only synchronization on exact path") + + bucket._dense = forbidden + runtime.selector.choose = lambda *args, **kwargs: "exact" + exact.forward = lambda values: "exact" + with runtime.request(): + assert runtime.forward(values) == "exact" + + +def test_auto_decision_is_fixed_per_layout_within_request(monkeypatch): + runtime, exact, _, values, _ = fake_dispatch(monkeypatch) + choices = iter(["eager", "exact"]) + runtime.selector.choose = lambda *args, **kwargs: next(choices) + exact.forward = lambda values: "exact" + with runtime.request(): + assert runtime.forward(values) == runtime.forward(values) == "eager" + with runtime.request(): + assert runtime.forward(values) == "exact" + assert runtime.stats["requests"] == 2 + + +def test_auto_sampling_and_request_decisions_are_bounded(monkeypatch): + runtime, _, _, values, events = fake_dispatch(monkeypatch) + runtime.selector.choose = lambda *args, **kwargs: "eager" + with runtime.request(): + for i in range(300): + runtime.forward(dict(values, key=str(i))) + assert len(runtime._decisions) <= 128 + assert len(runtime.selector.history) <= 128 + assert len(runtime._pending) <= 16 + assert len(events) < 100 diff --git a/tests/cua_s1/test_bucket_worker.py b/tests/cua_s1/test_bucket_worker.py index d0e7542..a4fc3b8 100644 --- a/tests/cua_s1/test_bucket_worker.py +++ b/tests/cua_s1/test_bucket_worker.py @@ -10,7 +10,7 @@ @pytest.mark.parametrize( "kwargs", [ - {"mode": "auto"}, + {"mode": "unknown"}, {"mode": "rule-bucket", "bucket_width": 0}, {"mode": "rule-bucket", "bucket_width": 65}, {"mode": "rule-bucket", "bucket_width": True}, @@ -41,7 +41,7 @@ def test_cli_default_and_legacy_and_explicit_modes(): [ ["--graph-bucket-width", "64"], ["--graph", "--graph-bucket-width", "128"], - ["--graph-mode", "auto"], + ["--graph-mode", "unknown"], ["--graph-mode", "rule-bucket", "--graph-bucket-width", "65"], ], ) diff --git a/tests/cua_s1/test_graph_policy_experiment.py b/tests/cua_s1/test_graph_policy_experiment.py index 6e3de0a..cec1d0e 100644 --- a/tests/cua_s1/test_graph_policy_experiment.py +++ b/tests/cua_s1/test_graph_policy_experiment.py @@ -30,3 +30,21 @@ def test_mixed_schedules_include_cold_inputs_and_returning_hotspot(): for group in [range(1, 5), range(5, 9), range(9, 13), range(1, 5)] for v in list(group) * 8 ] + + +def test_auto_comparator_budget_and_balanced_order(): + from collections import Counter + + from benchmark_graph_buckets import variant_configs, variant_orders + + configs = variant_configs("worker", 64, tuned_exact_window=32, include_auto=True) + auto, exact = asdict(configs["auto"]), asdict(configs["exact"]) + assert auto.pop("mode") == "auto" + exact.pop("mode") + assert auto == exact + variants = ["eager", "exact", "bucket", "exact_tuned", "auto"] + orders = variant_orders(variants) + for position in range(5): + assert Counter(order[position] for order in orders) == dict.fromkeys( + variants, 2 + ) From cb63dac1bc9761539603f51533d8e569d5bf7433 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 16:18:49 +0800 Subject: [PATCH 67/75] test(cua): exercise automatic worker full logits and lifecycle on CUDA --- recipe/cua_s1/check_auto_worker.py | 265 +++++++++++++++++++++++++++++ 1 file changed, 265 insertions(+) create mode 100644 recipe/cua_s1/check_auto_worker.py diff --git a/recipe/cua_s1/check_auto_worker.py b/recipe/cua_s1/check_auto_worker.py new file mode 100644 index 0000000..2402891 --- /dev/null +++ b/recipe/cua_s1/check_auto_worker.py @@ -0,0 +1,265 @@ +"""Independent full-logit checks of the actual automatic worker runtime.""" + +from __future__ import annotations + +import argparse +import hashlib +from dataclasses import asdict, replace +from pathlib import Path + + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--weights", required=True, type=Path) + parser.add_argument("--output", required=True, type=Path) + args = parser.parse_args() + if args.output.exists(): + parser.error("choose a fresh output path") + + import torch + from benchmark_image_reuse import distinct_fixture + from benchmark_multimodal_graph import changed_text_request + from diagnose_graph_buckets import prepare_values + from evaluate_multimodal import environment + from PIL import Image + from profile_multimodal import ( + GOAL, + case_matrix, + fixture, + repository_state, + write_json, + ) + + from models.cua_s1.multimodal.graph_runtime import GraphConfig + from models.cua_s1.multimodal.model import MultimodalEngine + from models.cua_s1.multimodal.protocol import parse_request + + root = Path(__file__).resolve().parents[2] + source = repository_state(root) + if source["dirty"] or not source["revision"]: + raise ValueError("clean committed source required") + args.output.parent.mkdir(parents=True, exist_ok=True) + config = GraphConfig( + mode="auto", + max_tokens=4096, + max_bytes=4 << 30, + max_captures=16, + capture_budget_ms=10000, + ) + files = [ + Path(__file__), + *sorted((root / "src/models/cua_s1/multimodal").glob("*.py")), + ] + report = { + "status": "running", + "repository": source, + "environment": environment(), + "config": asdict(config), + "cases": [], + "logit_checks": [], + "boundaries": [], + "churn_events": [], + "source_sha256": { + str(p.relative_to(root)): hashlib.sha256(p.read_bytes()).hexdigest() + for p in files + }, + } + write_json(args.output, report) + engine = MultimodalEngine( + str(args.weights / "Qwen3.5-4B"), + str(args.weights / "cua-s1-4b-0.2/multimodal"), + graph_config=config, + ) + runtime = engine.graph_runtime + assert runtime.__class__.__module__ == "models.cua_s1.multimodal.graph_auto" + original = runtime.forward + + def checked(values): + expected = engine.model(**values, logits_to_keep=1, use_cache=False).logits[ + 0, -1, : + ] + actual = original(values) + equal = torch.equal(expected, actual) + report["logit_checks"].append( + { + "tokens": values["inputs_embeds"].shape[1], + "equal": equal, + "max_abs": (actual.float() - expected.float()).abs().max().item(), + } + ) + assert equal + return actual + + runtime.forward = checked + fixtures = args.output.parent / "auto-fixtures" + try: + with torch.no_grad(): + cases = [ + c + for c in case_matrix() + if c["id"] + in { + "320x240-short-q2", + "320x240-short-q8", + "640x480-short-q8", + "640x480-long-q8", + } + ] + cases.append({"id": "640x480-distinct-q8"}) + for case in cases: + raw = ( + distinct_fixture(fixtures) + if "distinct" in case["id"] + else fixture(case, fixtures) + ) + request = parse_request(raw) + if "distinct" in case["id"]: + changed = replace( + request, + questions=( + replace( + request.questions[0], + goal=request.questions[0].goal.replace("Save", "Open"), + ), + *request.questions[1:], + ), + ) + else: + changed, _ = changed_text_request(engine, request) + black = replace( + request, image=Image.new("RGB", request.image.size, "black") + ) + record = {"id": case["id"], "events": []} + report["cases"].append(record) + before_case = runtime.stats + for name, item in [ + ("cold", request), + ("admit", request), + ("warm", request), + ("image", black), + ("text", changed), + ]: + engine.graph_runtime = None + expected = engine.predict(item) + engine.graph_runtime = runtime + before = runtime.stats + actual = engine.predict(item) + delta = {k: v - before[k] for k, v in runtime.stats.items()} + assert delta["requests"] == 1 + assert expected == actual + record["events"].append( + { + "name": name, + "expected": expected, + "actual": actual, + "stats_delta": delta, + } + ) + record["stats_delta"] = { + k: v - before_case[k] for k, v in runtime.stats.items() + } + assert record["stats_delta"]["captures"] > 0 + entries = list(runtime.cache.entries.values()) + runtime.invalidate() + assert all(not entry.blocks and entry.pool is None for entry in entries) + assert not runtime.selector.history and not runtime._pending + write_json(args.output, report) + + base = parse_request( + fixture( + next(c for c in cases if c["id"] == "320x240-short-q2"), fixtures + ) + ) + before_churn = runtime.stats + for repetitions in list(range(1, 13)) * 3: + item = replace( + base, + questions=tuple( + replace(q, goal=" ".join([GOAL] * repetitions)) + for q in base.questions + ), + ) + engine.graph_runtime = None + expected = engine.predict(item) + engine.graph_runtime = runtime + before = runtime.stats + actual = engine.predict(item) + assert actual == expected + report["churn_events"].append( + { + "repetitions": repetitions, + "expected": expected, + "actual": actual, + "stats_delta": { + k: v - before[k] for k, v in runtime.stats.items() + }, + } + ) + report["churn_stats"] = { + k: v - before_churn[k] for k, v in runtime.stats.items() + } + assert report["churn_stats"]["selected_rule_bucket"] > 0 + runtime.invalidate() + + long_request = replace( + base, + questions=tuple( + replace(q, goal=" ".join([GOAL] * 30)) for q in base.questions + ), + ) + prepared = prepare_values(engine, long_request) + for length in [255, 256, 257, 319, 320, 321] * 3 + [ + 321, + 320, + 319, + 257, + 256, + 255, + ]: + values = { + "inputs_embeds": prepared["inputs_embeds"][:, :length].clone(), + "position_ids": prepared["position_ids"][:, :, :length].clone(), + "attention_mask": prepared["attention_mask"][:, :length].clone(), + } + before = runtime.stats + with runtime.request(): + runtime.forward(values) + report["boundaries"].append( + { + "length": length, + "stats_delta": { + k: v - before[k] for k, v in runtime.stats.items() + }, + } + ) + assert runtime.stats["rejected"] == runtime.stats["length_rejections"] == 0 + report["stats"] = runtime.stats + report["selector_reasons"] = dict(runtime.decisions) + entries = list(runtime.cache.entries.values()) + engine.close() + assert not runtime.cache.entries and runtime.cache.bytes == 0 + assert all(not entry.blocks and entry.pool is None for entry in entries) + report["explicit_close"] = True + report["status"] = "complete" + print( + "auto parity complete:", + len(report["logit_checks"]), + "complete-logit comparisons;", + sum(len(c["events"]) for c in report["cases"]), + "case responses; 36 churn responses;", + len(report["boundaries"]), + "boundaries", + flush=True, + ) + except Exception as exc: + report["status"] = "failed" + report["error"] = {"type": type(exc).__name__, "message": str(exc)} + raise + finally: + engine.graph_runtime = runtime + engine.close() + write_json(args.output, report) + + +if __name__ == "__main__": + main() From e243a30d73ae3ae822bdf21fc4f96d8b739c4b76 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 16:21:32 +0800 Subject: [PATCH 68/75] fix(cua): bound adaptive timing probes during eager fallback --- recipe/cua_s1/README.md | 21 ++++- recipe/cua_s1/verify_auto_worker.py | 97 ++++++++++++++++++++++ src/models/cua_s1/multimodal/graph_auto.py | 8 +- tests/cua_s1/test_auto_worker.py | 30 +++++++ 4 files changed, 149 insertions(+), 7 deletions(-) create mode 100644 recipe/cua_s1/verify_auto_worker.py diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index de036c8..273cc55 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -62,8 +62,25 @@ logits must match eager exactly at capture and for each new actual length in a cached bucket. A rejected length uses eager while other verified lengths remain eligible. Dense batch-one inputs are supported; unsupported inputs use eager. These first-input checks do not prove equality for every possible content. -There is no automatic mode selection or simultaneous exact/bucket cache in the -worker. Stable hot lengths can favor exact mode. See the +For bounded online selection, opt in with `--graph-mode auto`. It observes the +last 32 requests (at most 128 layouts) and chooses eager, exact or rule-bucket +per layout, keeping that choice fixed within a request. Exact and bucket entries +share one namespaced LRU cache, request clock, capture-count/time ledger and +resident-memory budget. No second budget is allocated when switching modes. +The selector estimates reuse over the next 64 requests, compares savings with +capture and new-length validation costs, and applies a 25% capture-cost margin +and a 32-request switching cooldown. Initial heuristics use 500 ms capture cost +and exact/bucket replay ratios of 0.45/0.65 relative to eager; observations replace +these priors. CUDA event spans are sampled for the first eight supported forwards +and every sixteenth thereafter, queried without extra synchronization. Capture +and length-check spans do not train replay costs. This heuristic is workload +dependent and does not guarantee the fastest fixed mode. It uses the same pinned, +single-device fallback implementation and strict gates as manual buckets. +`selected_eager`, `selected_exact` and `selected_rule_bucket` count chosen routes; +admission or validation may still make a selected Graph route run eagerly. +Invalidation also clears selector history and pending timing events. + +Stable hot lengths can favor exact mode. See the [worker integration report](experiments/rtx4090-bucket-worker/README.md) for correctness, HTTP checks, and workload-dependent timing. diff --git a/recipe/cua_s1/verify_auto_worker.py b/recipe/cua_s1/verify_auto_worker.py new file mode 100644 index 0000000..f911ee3 --- /dev/null +++ b/recipe/cua_s1/verify_auto_worker.py @@ -0,0 +1,97 @@ +"""Recompute automatic-worker timing, shared budgets and parity evidence.""" + +import argparse +import hashlib +import json +import subprocess +from pathlib import Path + +from verify_graph_buckets import verify + + +def verify_auto(report): + configs = report["config"]["variant_graph"] + base = configs["exact"] + assert configs["auto"] == dict(base, mode="auto") + assert configs["bucket"] == dict(base, mode="rule-bucket") + assert configs["exact_tuned"] == dict(base, admission_window=32) + for records in report["workloads"].values(): + for record in records: + events = record["events"] + for i, event in enumerate(events): + auto = event["variants"]["auto"] + stats = auto["stats_delta"] + assert stats["requests"] == 1 + assert sum( + stats["selected_" + mode] + for mode in ("eager", "exact", "rule_bucket") + ) == len(auto["response"]["answers"]) + assert len(auto["resident_modes"]) == auto["cache_shapes"] + assert set(auto["resident_modes"]) <= {"exact", "rule-bucket"} + for key in ( + "rejected", + "length_rejections", + "capture_oom", + "capture_error", + "numerical_mismatch", + ): + assert stats[key] == 0 + recent = events[max(0, i - base["capture_window"] + 1) : i] + prior = [e["variants"]["auto"]["stats_delta"] for e in recent] + attempts = stats["capture_attempts"] + assert ( + sum(s["capture_attempts"] for s in prior) + attempts + <= base["max_captures"] + ) + # Synchronous captures may individually overshoot. With the + # identical-question schedule at most one attempt occurs/request. + assert attempts <= 1 + if attempts: + assert ( + sum(s["capture_attempt_ms"] for s in prior) + < base["capture_budget_ms"] + ) + + +def verify_parity(path): + report = json.loads(path.read_text()) + assert report["status"] == "complete", report.get("error") + assert report["repository"]["dirty"] is False + root = Path(__file__).resolve().parents[2] + for name, expected in report["source_sha256"].items(): + content = subprocess.check_output( + [ + "git", + "-C", + str(root), + "show", + report["repository"]["revision"] + ":" + name, + ] + ) + assert hashlib.sha256(content).hexdigest() == expected + assert report["explicit_close"] + assert len(report["cases"]) == 5 + responses = [e for c in report["cases"] for e in c["events"]] + report[ + "churn_events" + ] + assert len(responses) == 61 + for event in responses: + assert event["expected"] == event["actual"] + assert event["stats_delta"]["requests"] == 1 + assert len(report["logit_checks"]) == 266 + assert all(c["equal"] and c["max_abs"] == 0 for c in report["logit_checks"]) + assert len(report["boundaries"]) == 24 + assert report["churn_stats"]["selected_rule_bucket"] > 0 + print("verified 266 complete-logit checks, 61 responses and explicit retirement") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("report", type=Path) + parser.add_argument("--parity", type=Path) + args = parser.parse_args() + verify(args.report, require_replay=True) + verify_auto(json.loads(args.report.read_text())) + print("verified automatic routing counts, shared cache and capture budgets") + if args.parity: + verify_parity(args.parity) diff --git a/src/models/cua_s1/multimodal/graph_auto.py b/src/models/cua_s1/multimodal/graph_auto.py index f7e86e1..590c7a1 100644 --- a/src/models/cua_s1/multimodal/graph_auto.py +++ b/src/models/cua_s1/multimodal/graph_auto.py @@ -101,11 +101,9 @@ def forward(self, values): runtime = exact if mode == "eager" else self._runtimes[mode] before = dict(runtime.stats) samples = sum(self.selections.values()) - sample = ( - samples <= 8 - or samples % 16 == 0 - or mode not in self.selector.costs.get(bucket, {}) - ) + # Keep overhead bounded even when a selected mode repeatedly falls + # back and therefore never acquires its own replay-cost sample. + sample = samples <= 8 or samples % 16 == 0 start = end = None with torch.cuda.device(values["inputs_embeds"].device): if sample: diff --git a/tests/cua_s1/test_auto_worker.py b/tests/cua_s1/test_auto_worker.py index aeb4a31..8377617 100644 --- a/tests/cua_s1/test_auto_worker.py +++ b/tests/cua_s1/test_auto_worker.py @@ -138,3 +138,33 @@ def test_auto_sampling_and_request_decisions_are_bounded(monkeypatch): assert len(runtime.selector.history) <= 128 assert len(runtime._pending) <= 16 assert len(events) < 100 + + +def test_auto_sampling_stays_bounded_when_selected_mode_never_replays(monkeypatch): + runtime, exact, _, values, events = fake_dispatch(monkeypatch) + runtime.selector.choose = lambda *args, **kwargs: "exact" + exact.forward = lambda values: "eager" + for _ in range(64): + with runtime.request(): + assert runtime.forward(values) == "eager" + assert len(events) == 24 + assert "exact" not in runtime.selector.costs["bucket"] + assert runtime.selector.costs["bucket"]["eager"] == 100 + + +@pytest.mark.parametrize("counter", ["capture_attempts", "length_checks"]) +def test_auto_does_not_learn_gate_or_capture_as_replay_cost(monkeypatch, counter): + runtime, _, bucket, values, _ = fake_dispatch(monkeypatch) + runtime.selector.choose = lambda *args, **kwargs: "rule-bucket" + + def gated(values): + bucket.stats[counter] += 1 + bucket.stats["capture_attempt_ms"] += 500 + bucket.stats["replays"] += 1 + return "bucket" + + bucket.forward = gated + with runtime.request(): + assert runtime.forward(values) == "bucket" + assert not runtime._pending + assert "rule-bucket" not in runtime.selector.costs.get("bucket", {}) From 543fade62f26a29a698833d13838c85a07b6d298 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 16:43:43 +0800 Subject: [PATCH 69/75] test(cua): record automatic worker parity and controlled GPU evidence --- .../plans/2026-09-29-cua-auto-worker.md | 12 +- recipe/cua_s1/README.md | 4 +- recipe/cua_s1/experiments/README.md | 4 +- .../experiments/rtx4090-auto-worker/README.md | 182 + .../rtx4090-auto-worker/SHA256SUMS | 18 + .../rtx4090-auto-worker/benchmark.json | 228496 +++++++++++++++ .../rtx4090-auto-worker/benchmark.log | 75 + .../rtx4090-auto-worker/cpu-tests.log | 5 + .../diagnostics/default-probe.json | 14990 + .../diagnostics/default-probe.log | 11 + .../diagnostics/initial-parity.json | 10566 + .../diagnostics/initial-parity.log | 5 + .../diagnostics/initial-probe.json | 37018 +++ .../diagnostics/initial-probe.log | 17 + .../experiments/rtx4090-auto-worker/http.json | 442 + .../experiments/rtx4090-auto-worker/http.log | 6 + .../experiments/rtx4090-auto-worker/lint.log | 2 + .../rtx4090-auto-worker/parity.json | 10566 + .../rtx4090-auto-worker/parity.log | 5 + .../rtx4090-auto-worker/shared.json | 5772 + .../rtx4090-auto-worker/shared.log | 5 + .../experiments/rtx4090-auto-worker/tests.log | 5 + .../rtx4090-auto-worker/verification.log | 14 + recipe/cua_s1/verify_auto_worker.py | 37 +- tests/cua_s1/test_auto_verifier.py | 77 + 25 files changed, 308325 insertions(+), 9 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/README.md create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/benchmark.json create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/benchmark.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/cpu-tests.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/diagnostics/default-probe.json create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/diagnostics/default-probe.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/diagnostics/initial-parity.json create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/diagnostics/initial-parity.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/diagnostics/initial-probe.json create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/diagnostics/initial-probe.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/http.json create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/http.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/lint.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/parity.json create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/parity.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/shared.json create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/shared.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/tests.log create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/verification.log create mode 100644 tests/cua_s1/test_auto_verifier.py diff --git a/docs/superpowers/plans/2026-09-29-cua-auto-worker.md b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md index d62b0be..4b599ce 100644 --- a/docs/superpowers/plans/2026-09-29-cua-auto-worker.md +++ b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md @@ -16,12 +16,12 @@ ## Tasks and validation -- [ ] Promote shared ownership; add explicit construction injection for cache/admission/lock, reusable exact keys, atomic retirement, and tests covering lifetime and cross-mode bounds. -- [ ] Implement bounded selector with tests for hot, dispersed, one-shot, same-request duplicates, pressure, cost rejection, cooldown, invalidation and bucket rejection. -- [ ] Integrate config/CLI/engine, document counters and expose no experimental imports in production. -- [ ] Run Cua-S1 CPU tests and changed-file lint/format. -- [ ] GPU check full logits, mixed layouts, changed images/text, boundaries, aggregate budgets and lifecycle; use actual CLI-selected HTTP worker and two loaded instances. -- [ ] Benchmark eager/default exact/tuned exact/bucket/auto on four existing workloads, plus an unseen mixed schedule. Use two cold runs and order balancing, retain capture/gates/eviction in measured time and per-mode evidence. Record negative results instead of tuning to future schedule labels. +- [x] Promote shared ownership; add explicit construction injection for cache/admission/lock, reusable exact keys, atomic retirement, and tests covering lifetime and cross-mode bounds. +- [x] Implement bounded selector with tests for hot, dispersed, one-shot, same-request duplicates, pressure, cost rejection, cooldown, invalidation and bucket rejection. +- [x] Integrate config/CLI/engine, document counters and expose no experimental imports in production. +- [x] Run Cua-S1 CPU tests and changed-file lint/format. +- [x] GPU check full logits, mixed layouts, changed images/text, boundaries, aggregate budgets and lifecycle; use actual CLI-selected HTTP worker and two loaded instances. +- [x] Benchmark eager/default exact/tuned exact/bucket/auto on four existing workloads, plus an unseen mixed schedule. Use two cold runs and order balancing, retain capture/gates/eviction in measured time and per-mode evidence. Record negative results instead of tuning to future schedule labels. - [ ] Independently verify raw data/source provenance and report scope. Run a focused code review, fix findings, rerun checks justified by changes, push branch, create and attach PR with incremental comparison against #37. ## Shipping criterion diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 273cc55..83c6a2c 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -78,7 +78,9 @@ dependent and does not guarantee the fastest fixed mode. It uses the same pinned single-device fallback implementation and strict gates as manual buckets. `selected_eager`, `selected_exact` and `selected_rule_bucket` count chosen routes; admission or validation may still make a selected Graph route run eagerly. -Invalidation also clears selector history and pending timing events. +Invalidation also clears selector history and pending timing events. See the +[automatic worker report](experiments/rtx4090-auto-worker/README.md) for controlled +comparisons, strict parity and shared-budget validation. Stable hot lengths can favor exact mode. See the [worker integration report](experiments/rtx4090-bucket-worker/README.md) for diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md index 46b515a..cb899d2 100644 --- a/recipe/cua_s1/experiments/README.md +++ b/recipe/cua_s1/experiments/README.md @@ -9,7 +9,9 @@ The [mixed-length Graph report](rtx4090-graph-mixed-shapes/README.md) includes capture and eviction costs across changing prompt lengths. The subsequent [request-aware admission report](rtx4090-graph-admission/README.md) tests extended churn and changing hotspots with bounded capture work and exclusively -owned capture streams. +owned capture streams. The later +[automatic worker report](rtx4090-auto-worker/README.md) adds opt-in causal mode +selection with shared exact/bucket budgets and a tuned-exact comparator. For the subsequent 16-case stage profiling experiment, refreshed upstream parity, and profiler annotation-counting recovery, see the diff --git a/recipe/cua_s1/experiments/rtx4090-auto-worker/README.md b/recipe/cua_s1/experiments/rtx4090-auto-worker/README.md new file mode 100644 index 0000000..7fef4ef --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-auto-worker/README.md @@ -0,0 +1,182 @@ +# Cua-S1 automatic Graph worker with shared budgets + +RTX 4090, 2026-09-29. Stacked on PR #37 (`fd5c417`). + +## Findings + +Opt-in automatic selection reduced total synchronized request time by **18.29–33.08% versus eager** across five tested workloads and two cold runs. All **3,720 timed complete responses equal eager**. The selector uses only past/current requests and shares resource limits between exact and rule-bucket execution. + +It does not always match the best fixed mode. Tables below retain both benefits and regressions. Stable hot layouts tend toward exact, while recurring dispersed lengths can reuse buckets after an observation period. Manual modes remain available and eager remains the default. + +## Controlled performance + +Total synchronized `predict` time, seconds; cells are run 1 / run 2: + +| Workload | Eager | Default exact | Exact window 32 | Rule bucket | Auto | +|---|---:|---:|---:|---:|---:| +| Four hot lengths | 11.09 / 11.11 | 7.49 / 7.49 | 7.51 / 7.51 | 8.01 / 8.01 | 7.56 / 7.49 | +| Twelve rotating lengths | 17.01 / 17.02 | 16.52 / 16.56 | 16.43 / 16.45 | 12.29 / 12.33 | 13.23 / 13.23 | +| Hot/cold mix | 9.38 / 9.39 | 7.65 / 7.64 | 7.64 / 7.63 | 8.02 / 8.04 | 7.66 / 7.63 | +| Shifting hot lengths | 29.73 / 29.66 | 25.57 / 25.34 | 25.48 / 25.42 | 20.17 / 20.09 | 20.00 / 19.85 | +| Unseen mixed schedule | 19.96 / 20.07 | 17.42 / 17.50 | 19.16 / 19.30 | 14.79 / 14.98 | 16.07 / 16.11 | + +Auto reduction versus eager and extra time versus the hindsight-best fixed mode (minimum of eager, exact, tuned exact and bucket). Positive extra time means auto is slower: + +| Workload | Reduction vs eager (%) | Extra time vs best fixed (%) | +|---|---:|---:| +| Four hot lengths | 31.86 / 32.62 | 0.92 / -0.01 | +| Twelve rotating lengths | 22.22 / 22.24 | 7.67 / 7.34 | +| Hot/cold mix | 18.29 / 18.75 | 0.26 / 0.00 | +| Shifting hot lengths | 32.74 / 33.08 | -0.84 / -1.19 | +| Unseen mixed schedule | 19.48 / 19.74 | 8.66 / 7.52 | + +Capture-inclusive p95, milliseconds; cells are run 1 / run 2: + +| Workload | Eager | Default exact | Exact window 32 | Rule bucket | Auto | +|---|---:|---:|---:|---:|---:| +| Four hot lengths | 242.78 / 239.81 | 531.67 / 523.00 | 533.11 / 523.78 | 237.34 / 236.93 | 536.09 / 523.70 | +| Twelve rotating lengths | 243.19 / 243.62 | 237.88 / 236.32 | 567.53 / 569.25 | 244.64 / 245.51 | 242.90 / 245.86 | +| Hot/cold mix | 252.00 / 250.15 | 525.31 / 522.62 | 523.01 / 515.39 | 486.68 / 482.23 | 527.99 / 518.05 | +| Shifting hot lengths | 240.79 / 242.07 | 668.34 / 669.54 | 670.36 / 677.89 | 242.60 / 237.07 | 246.54 / 243.56 | +| Unseen mixed schedule | 251.25 / 251.78 | 568.33 / 568.11 | 568.69 / 573.42 | 249.31 / 247.54 | 251.73 / 246.29 | + +Automatic execution and resident cache ownership; cells are run 1 / run 2. Replay counters include replay during first-length validation. They are not a count of requests accelerated: + +| Workload | Captures | Replays | Peak resident MiB | +|---|---:|---:|---:| +| Four hot lengths | 4 / 4 | 84 / 84 | 463.45 / 463.45 | +| Twelve rotating lengths | 2 / 2 | 116 / 116 | 598.53 / 598.53 | +| Hot/cold mix | 4 / 4 | 52 / 52 | 463.45 / 463.45 | +| Shifting hot lengths | 5 / 5 | 227 / 227 | 789.69 / 789.69 | +| Unseen mixed schedule | 4 / 4 | 118 / 118 | 961.77 / 961.77 | + +Raw `benchmark.json` records all samples, routing counters, resident mode names and selector reasons. `verify_auto_worker.py` independently recomputes p50/p95/totals, response equality, source hashes, cumulative counters and shared capture/cache limits. There are no capture OOMs, capture errors or numeric rejections in the formal matrix. + +## Worker behavior and budgets + +`--graph-mode auto` is explicit opt-in. The default remains eager and `--graph` +continues to mean exact mode. The production package owns the shared runtime; +the earlier `recipe/cua_s1/graph_policy.py` is now only a compatibility re-export. +No production import depends on recipe code. Full attention, projection, +convolution, MLP and position processing retain their real lengths. + +Exact and rule-bucket caches share one namespaced LRU, request clock, lock, +resident-memory limit and capture count/time ledger. Each graph retains its own +stream/pool and buffers. One mode cannot obtain a second budget by switching. +The resident limit is not a hard process VRAM limit: capture candidates coexist +with retained graphs, and weights/default-allocator memory are excluded. A +synchronous attempt may overshoot the capture-time allowance; later captures +wait for that work to expire from the request window. + +The selector retains at most 128 layouts in the preceding 32 requests, with +bounded cost history, 16 pending event pairs and 128 decisions per request. +It estimates up to 64 future requests using observed frequency, counts same-request +copies separately, checks capture/first-length amortization with a 25% margin, +and applies a 32-request switching cooldown. The initial 500 ms capture and +0.45/0.65 exact/bucket replay ratios are heuristics, not device-independent +performance predictions. Observed captures and sampled CUDA-event spans replace +priors. Sampling occurs on the first eight supported forwards and every sixteenth +thereafter; readiness queries add no synchronization. Captures and length gates +are excluded from replay-cost samples. There is no future schedule or workload +name input to the selector. Numeric gates and the shared admission ledger remain +authoritative when a prediction is wrong. + +`selected_*` counts routing decisions, not successful graph replays. A selected +route can fall back to eager because of admission, resource or numeric checks. +Invalidation clears both caches, disabled keys, admission, selector history and +pending timing events; lifetime counters remain cumulative. Close explicitly +retires entries even while diagnostics retain their Python references. + +## Correctness and lifecycle evidence + +All final GPU checks use clean source `e243a30d73ae3ae822bdf21fc4f96d8b739c4b76`. +Subsequent changes strengthen the independent verifier and add evidence/tests/docs; +they do not alter the measured production implementation. + +- `parity.json`: **266 complete-vocabulary logits comparisons**, all bitwise + equal to eager (`torch.equal`, max absolute error zero); **61 complete responses**. + Five cases cover 320x240/640x480, 2/8 questions, long text and distinct questions, + each under cold/capture/warm/changed-image/changed-text execution. A 36-request + twelve-length rotation exercises actual bucket capture/replay, and 24 direct + forwards cover 255/256/257/319/320/321-token boundaries. Case capture/replay + counters and no-rejection assertions prevent eager-only parity from counting + as Graph validation. This diagnostic uses a recorded 4 GiB/16-capture/10 s budget + and 4096-token limit to exercise paths; it is not the default-budget benchmark. +- `shared.json`: **119 full-logit comparisons and 16 complete responses** against + the production shared resource manager. It covers cross-mode one-shape eviction, + a 300 MiB resident cap, one-capture and exhausted-time allowances, 1 MiB oversize + candidates, boundaries, cooldown and explicit retirement with live references. + The independent verifier reconstructs counter sums and resident ownership. +- `http.json`: actual CLI-selected auto worker, health and cold/capture/changed-image/ + changed-text HTTP parity; two independently loaded models, continued replay + after the other model closes, no global/model-method replacement, and explicit + cleanup with retained entry references. This is isolation/lifecycle validation, + not a concurrent-throughput result. +- The existing exact full-logit admission and per-new-length bucket gates remain + unchanged. These checks establish equality on the tested inputs; a first-input + gate cannot prove equality for all possible image/text content. + +## Automated checks + +The measured GPU source passed all 266 Cua-S1 tests (`tests.log`). The later +independent-verifier regressions add 13 tests, including corrupted clocks, +budgets, routing records, missing captures/replays and incorrect responses. +The local CPU-only run passed 257 tests and skipped 22 requiring PyTorch +(`cpu-tests.log`). CI-scoped Ruff lint and formatting passed (`lint.log`). +A focused code review found two initial issues (unconditional bucket-mask +synchronization and insufficient resident-bucket cost gating); both were fixed +before the final measurement, with regressions. Event probes remain bounded even +when a selected mode repeatedly falls back. Final review found no production +correctness, shared-budget or lifecycle blockers. + +## Scope and reproduction + +RTX 4090 24 GB; driver 595.71.05, Python 3.12.13, PyTorch 2.14.0+cu130, +Transformers 5.17.0, PEFT 0.21.0, 64 CPU threads, BF16 base and unmerged PEFT +adapter. Neither FLA nor causal-conv1d is installed; results apply to the pinned +PyTorch fallback path. See JSON environment and source hashes for full provenance. +No native multimodal boundary, new model-compute kernel, compilation, batching, +GUI task accuracy or broader deployment claim is made. + +Formal timing compares eager/default exact/exact admission-window 32/rule-bucket/ +auto with otherwise identical defaults: 8 resident shapes, 1 GiB, 4 capture +attempts per 32 requests, 2000 ms capture-work allowance, bucket width 64 and +2048-token cap. Two cold-cache runs per workload use cyclic and reversed order +rotations with a half-cycle offset between runs. Each ten-request block balances +variant positions; incomplete final blocks are retained. The model stays loaded. +Timing synchronizes around `predict`, including capture, first-length checks, +eviction and fallback. Parsing/image decoding, HTTP, initial model load/warmup, +report I/O and final cache retirement are excluded. Each request has two identical +questions on a generated 320x240 image. All four Graph variants coexist, so process +allocator totals are combined; per-variant cache ownership is reported separately. + +The four recurrent schedules retain the preceding experiment's shapes. The new +84-request mixed schedule is `[1,2]*6`, 48 draws from 1..16 using seed 20260929, +then `[20,21]*12`. It was fixed before the formal run and was not used to tune the +policy afterward. Two runs are exploratory observations, not confidence intervals +or evidence that the heuristic beats the best fixed mode on arbitrary traffic. +Capture-inclusive p95 can regress while total request time improves. + +From a clean checkout with pinned dependencies and verified weights: + +```sh +export PYTHONPATH=src:recipe/cua_s1 +python recipe/cua_s1/check_auto_worker.py --weights /path/to/weights --output /tmp/auto-parity.json +python recipe/cua_s1/check_shared_graph_policy.py --weights /path/to/weights --output /tmp/auto-shared.json +python recipe/cua_s1/check_bucket_worker_http.py --weights /path/to/weights --mode auto --output /tmp/auto-http.json +python recipe/cua_s1/benchmark_graph_buckets.py --weights /path/to/weights --output /tmp/auto-matrix \ + --kind worker --tuned-exact-window 32 --include-auto --runs 2 \ + --case hot_four --case churn_twelve --case hot_cold --case shifting_hot --case mixed_holdout +python recipe/cua_s1/verify_auto_worker.py /tmp/auto-matrix/report.json --parity /tmp/auto-parity.json +python recipe/cua_s1/verify_graph_policy.py /tmp/auto-shared.json --kind shared +``` + +Use fresh output paths. Provenance verification needs the measured Git objects, +so use a full clone or fetch the recorded revisions. `SHA256SUMS` covers the raw +JSON/log files. `diagnostics/initial-probe.*` retains the earlier 600-prediction +two-workload probe at `0473de8`; `initial-parity.*` is the first 266-logit run at +`cb63dac`. `default-probe.*` records 240 predictions at the final source before +explicitly selecting all five formal workloads (the script defaults to `probe`). +These diagnostics are not mixed into formal performance tables. The preceding +[controlled exact-window and shared-prototype experiment](../rtx4090-graph-policy/README.md) +retains its historical results and source. diff --git a/recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS new file mode 100644 index 0000000..06d400f --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS @@ -0,0 +1,18 @@ +3a0706f54f2e441f3a872b56f19f484edf4c7636ef91da0faaf703349e1e575f benchmark.json +f8c43bcd736f3db42c14d55a148034414ce7b2612d790aad676b09d76ebe8997 benchmark.log +672eaa847dac654ea1c724e88fb04ddadeecd1b2f9a4c1b54030af1138433aab cpu-tests.log +cfa01569ef3803e668c1c88342d18ad2cfb07466faf39023a4688ad2fa33d7c5 diagnostics/default-probe.json +fae3fc8c4ee363221c3759da438a9699e95185799854e3cef598ca1e77835ea8 diagnostics/default-probe.log +037a4713d422b6171f2e38cde2551f0bbc59dd494df0bdb9b2b6e0dbafecbb48 diagnostics/initial-parity.json +8fc51ea87ab9351015a2c88678aa42e3ab3726a5e4efa473b3c2feb9d9fe29a1 diagnostics/initial-parity.log +7946ea4444818b0b4dc43fdf7fcb2dd2cf1715b219783df197f8e7eab5ab8569 diagnostics/initial-probe.json +6b0521cd814ee72c187e35f208a879b07822fec9f00164715d6ca0f88342894a diagnostics/initial-probe.log +a0b783d5197296f0cded65d94f828dd9a52d05c6192d27ca63e956e2b7946bfe http.json +b215771f6c81028c12fc1f1ed9dd2d6d6cd115c4c10122d3ff37e4fe673e2966 http.log +d5c557c8da9ce05fd31cff5c23d522b45ee452b8ad7f2310f92dbc4ba8474b84 lint.log +8bea998f0dd0355b0cbd6d5d00584161f88c0a1e768f1467a12d3806ff2d7fc3 parity.json +c38f98e1119218de8ec5347a1f3f67baec7907bda1b190510cb94d5fc29d3f3c parity.log +19b43b5eb883eeba08c93d485260401835af1ec853e340c67c22564b51115e90 shared.json +43a4f35bd0d62751f9785e58e37e275370968c7e4d0aaca69b25fffeabb9bcbe shared.log +4b453894f0d0adf5db6dd09fd99229f56342b397024bb90147bea02f358c2228 tests.log +b00ef542e100735de9e87d28fd46b6a70fdd704d8615cd218079553f32ec239f verification.log diff --git 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" + }, + "questions": { + "q1": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + }, + "q2": { + "type": "choice", + "instructions": "Save the changed display name.", + "criteria": { + "save": "Click Save changes", + "cancel": "Click Cancel", + "wait": "Wait" + } + } + } + }, + "tokens": { + "1": 236, + "2": 242, + "3": 248, + "4": 254, + "5": 260, + "6": 266, + "7": 272, + "8": 278, + "9": 284, + "10": 290, + "11": 296, + "12": 302, + "13": 308, + "14": 314, + "15": 320, + "16": 326, + "17": 332, + "18": 338, + "19": 344, + "20": 350, + "21": 356 + }, + "buckets": { + "1": 256, + "2": 256, + "3": 256, + "4": 256, + "5": 320, + "6": 320, + "7": 320, + "8": 320, + "9": 320, + "10": 320, + "11": 320, + "12": 320, + "13": 320, + "14": 320, + "15": 320, + "16": 384, + "17": 384, + "18": 384, + "19": 384, + "20": 384, + "21": 384 + } +} diff --git a/recipe/cua_s1/experiments/rtx4090-auto-worker/benchmark.log b/recipe/cua_s1/experiments/rtx4090-auto-worker/benchmark.log new file mode 100644 index 0000000..3d91939 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-auto-worker/benchmark.log @@ -0,0 +1,75 @@ +[transformers] `torch_dtype` is deprecated! Use `dtype` instead! + Loading weights: 0%| | 0/723 [00:00 0 + assert case["stats_delta"]["replays"] > 0 + for key in ( + "rejected", + "length_rejections", + "numerical_mismatch", + "capture_error", + "capture_oom", + ): + assert report["stats"][key] == 0 responses = [e for c in report["cases"] for e in c["events"]] + report[ "churn_events" ] @@ -81,7 +109,14 @@ def verify_parity(path): assert len(report["logit_checks"]) == 266 assert all(c["equal"] and c["max_abs"] == 0 for c in report["logit_checks"]) assert len(report["boundaries"]) == 24 - assert report["churn_stats"]["selected_rule_bucket"] > 0 + churn = report["churn_stats"] + assert churn["selected_rule_bucket"] > 0 + # No exact routes occurred in this diagnostic, so actual captures/replays + # (not just selected routes) establish that bucket execution was exercised. + assert churn["selected_exact"] == 0 + assert churn["captures"] > 0 and churn["replays"] > 0 + assert sum(e["stats_delta"]["captures"] for e in report["boundaries"]) > 0 + assert sum(e["stats_delta"]["replays"] for e in report["boundaries"]) > 0 print("verified 266 complete-logit checks, 61 responses and explicit retirement") diff --git a/tests/cua_s1/test_auto_verifier.py b/tests/cua_s1/test_auto_verifier.py new file mode 100644 index 0000000..e50d499 --- /dev/null +++ b/tests/cua_s1/test_auto_verifier.py @@ -0,0 +1,77 @@ +"""Reject evidence that only selected Graph routes but never exercised them.""" + +import copy +import json +import sys +from pathlib import Path + +import pytest + +ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(ROOT / "recipe/cua_s1")) +EVIDENCE = ROOT / "recipe/cua_s1/experiments/rtx4090-auto-worker" + + +def test_recorded_auto_parity_is_independently_verified(): + from verify_auto_worker import parity + + parity(json.loads((EVIDENCE / "parity.json").read_text())) + + +@pytest.mark.parametrize( + "mutation", + [ + lambda r: r["churn_stats"].update(replays=0), + lambda r: r["cases"][0]["stats_delta"].update(captures=0), + lambda r: r["stats"].update(length_rejections=1), + lambda r: r["logit_checks"][0].update(equal=False), + lambda r: r["churn_events"][0]["actual"].update(unexpected=True), + lambda r: r["cases"][0]["events"][0]["stats_delta"].update(requests=2), + ], +) +def test_auto_verifier_rejects_corrupted_parity(mutation): + from verify_auto_worker import parity + + report = copy.deepcopy(json.loads((EVIDENCE / "parity.json").read_text())) + mutation(report) + with pytest.raises(AssertionError): + parity(report) + + +@pytest.fixture +def timing_report(): + return json.loads((EVIDENCE / "diagnostics/default-probe.json").read_text()) + + +def test_recorded_auto_shared_budget_evidence(timing_report): + from verify_auto_worker import verify_auto + + verify_auto(timing_report) + + +@pytest.mark.parametrize( + "mutation", + [ + lambda r: r["config"]["variant_graph"]["auto"].update(max_shapes=16), + lambda r: r["workloads"]["probe"][0]["events"][0]["variants"]["auto"][ + "stats_delta" + ].update(requests=2), + lambda r: r["workloads"]["probe"][0]["events"][0]["variants"]["auto"][ + "stats_delta" + ].update(selected_eager=0), + lambda r: r["workloads"]["probe"][0]["events"][0]["variants"]["auto"].update( + resident_modes=["exact"] + ), + lambda r: r["workloads"]["probe"][0]["events"][0]["variants"]["auto"][ + "stats_delta" + ].update(capture_attempts=5), + ], +) +def test_auto_verifier_rejects_clock_route_and_budget_corruption( + timing_report, mutation +): + from verify_auto_worker import verify_auto + + mutation(timing_report) + with pytest.raises(AssertionError): + verify_auto(timing_report) From 5be2704063c014a50a188abba378a50e3163a661 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 16:44:59 +0800 Subject: [PATCH 70/75] docs(cua): finalize 279-test validation and audited GPU report --- docs/superpowers/plans/2026-09-29-cua-auto-worker.md | 3 ++- recipe/cua_s1/experiments/rtx4090-auto-worker/README.md | 8 +++++--- recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS | 3 ++- recipe/cua_s1/experiments/rtx4090-auto-worker/lint.log | 2 +- .../cua_s1/experiments/rtx4090-auto-worker/tests-pr.log | 5 +++++ 5 files changed, 15 insertions(+), 6 deletions(-) create mode 100644 recipe/cua_s1/experiments/rtx4090-auto-worker/tests-pr.log diff --git a/docs/superpowers/plans/2026-09-29-cua-auto-worker.md b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md index 4b599ce..8b547fe 100644 --- a/docs/superpowers/plans/2026-09-29-cua-auto-worker.md +++ b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md @@ -22,7 +22,8 @@ - [x] Run Cua-S1 CPU tests and changed-file lint/format. - [x] GPU check full logits, mixed layouts, changed images/text, boundaries, aggregate budgets and lifecycle; use actual CLI-selected HTTP worker and two loaded instances. - [x] Benchmark eager/default exact/tuned exact/bucket/auto on four existing workloads, plus an unseen mixed schedule. Use two cold runs and order balancing, retain capture/gates/eviction in measured time and per-mode evidence. Record negative results instead of tuning to future schedule labels. -- [ ] Independently verify raw data/source provenance and report scope. Run a focused code review, fix findings, rerun checks justified by changes, push branch, create and attach PR with incremental comparison against #37. +- [x] Independently verify raw data/source provenance and report scope. Run a focused code review, fix findings and rerun checks justified by changes. +- [ ] Push the branch, create and attach a PR with incremental comparison against #37. ## Shipping criterion diff --git a/recipe/cua_s1/experiments/rtx4090-auto-worker/README.md b/recipe/cua_s1/experiments/rtx4090-auto-worker/README.md index 7fef4ef..70c9851 100644 --- a/recipe/cua_s1/experiments/rtx4090-auto-worker/README.md +++ b/recipe/cua_s1/experiments/rtx4090-auto-worker/README.md @@ -4,7 +4,7 @@ RTX 4090, 2026-09-29. Stacked on PR #37 (`fd5c417`). ## Findings -Opt-in automatic selection reduced total synchronized request time by **18.29–33.08% versus eager** across five tested workloads and two cold runs. All **3,720 timed complete responses equal eager**. The selector uses only past/current requests and shares resource limits between exact and rule-bucket execution. +Opt-in automatic selection reduced total synchronized request time by **18.29–33.08% versus eager** across five tested workloads and two cold runs. All **3,720 timed complete responses equal eager** (744 eager baselines and 2,976 Graph-enabled variant predictions, including 744 auto predictions). The selector uses only past/current requests and shares resource limits between exact and rule-bucket execution. It does not always match the best fixed mode. Tables below retain both benefits and regressions. Stable hot layouts tend toward exact, while recurring dispersed lengths can reuse buckets after an observation period. Manual modes remain available and eager remains the default. @@ -61,8 +61,8 @@ No production import depends on recipe code. Full attention, projection, convolution, MLP and position processing retain their real lengths. Exact and rule-bucket caches share one namespaced LRU, request clock, lock, -resident-memory limit and capture count/time ledger. Each graph retains its own -stream/pool and buffers. One mode cannot obtain a second budget by switching. +resident-memory limit and capture count/time ledger. Each cache entry retains its own +stream/pool and buffers; its captured segments share that owner. One mode cannot obtain a second budget by switching. The resident limit is not a hard process VRAM limit: capture candidates coexist with retained graphs, and weights/default-allocator memory are excluded. A synchronous attempt may overshoot the capture-time allowance; later captures @@ -121,6 +121,8 @@ they do not alter the measured production implementation. The measured GPU source passed all 266 Cua-S1 tests (`tests.log`). The later independent-verifier regressions add 13 tests, including corrupted clocks, budgets, routing records, missing captures/replays and incorrect responses. +The complete PR suite then passed **279 tests** on the GPU host at `543fade` +(`tests-pr.log`); subsequent changes only clarify documentation and retain logs. The local CPU-only run passed 257 tests and skipped 22 requiring PyTorch (`cpu-tests.log`). CI-scoped Ruff lint and formatting passed (`lint.log`). A focused code review found two initial issues (unconditional bucket-mask diff --git a/recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS b/recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS index 06d400f..7826d69 100644 --- a/recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS +++ b/recipe/cua_s1/experiments/rtx4090-auto-worker/SHA256SUMS @@ -9,10 +9,11 @@ fae3fc8c4ee363221c3759da438a9699e95185799854e3cef598ca1e77835ea8 diagnostics/de 6b0521cd814ee72c187e35f208a879b07822fec9f00164715d6ca0f88342894a diagnostics/initial-probe.log a0b783d5197296f0cded65d94f828dd9a52d05c6192d27ca63e956e2b7946bfe http.json b215771f6c81028c12fc1f1ed9dd2d6d6cd115c4c10122d3ff37e4fe673e2966 http.log -d5c557c8da9ce05fd31cff5c23d522b45ee452b8ad7f2310f92dbc4ba8474b84 lint.log +1904c82373e4f9454e26d015e5811ed985587dbc870507bb8f00ef25b4e0e382 lint.log 8bea998f0dd0355b0cbd6d5d00584161f88c0a1e768f1467a12d3806ff2d7fc3 parity.json c38f98e1119218de8ec5347a1f3f67baec7907bda1b190510cb94d5fc29d3f3c parity.log 19b43b5eb883eeba08c93d485260401835af1ec853e340c67c22564b51115e90 shared.json 43a4f35bd0d62751f9785e58e37e275370968c7e4d0aaca69b25fffeabb9bcbe shared.log +1526e9cc8a1a749e3c205196b87dbe469e28b5a4ecf6fbb9e296762c07772dd2 tests-pr.log 4b453894f0d0adf5db6dd09fd99229f56342b397024bb90147bea02f358c2228 tests.log b00ef542e100735de9e87d28fd46b6a70fdd704d8615cd218079553f32ec239f verification.log diff --git a/recipe/cua_s1/experiments/rtx4090-auto-worker/lint.log b/recipe/cua_s1/experiments/rtx4090-auto-worker/lint.log index 69eb0d5..485fca9 100644 --- a/recipe/cua_s1/experiments/rtx4090-auto-worker/lint.log +++ b/recipe/cua_s1/experiments/rtx4090-auto-worker/lint.log @@ -1,2 +1,2 @@ All checks passed! -83 files already formatted +84 files already formatted diff --git a/recipe/cua_s1/experiments/rtx4090-auto-worker/tests-pr.log b/recipe/cua_s1/experiments/rtx4090-auto-worker/tests-pr.log new file mode 100644 index 0000000..3b8c343 --- /dev/null +++ b/recipe/cua_s1/experiments/rtx4090-auto-worker/tests-pr.log @@ -0,0 +1,5 @@ +........................................................................ [ 25%] +........................................................................ [ 51%] +........................................................................ [ 77%] +............................................................... [100%] +279 passed in 21.16s From 2180de067e1b5a6724e83a044f588e9a6718ddf8 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Tue, 29 Sep 2026 16:46:11 +0800 Subject: [PATCH 71/75] docs(cua): record automatic worker PR delivery --- docs/superpowers/plans/2026-09-29-cua-auto-worker.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/superpowers/plans/2026-09-29-cua-auto-worker.md b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md index 8b547fe..0d2cf53 100644 --- a/docs/superpowers/plans/2026-09-29-cua-auto-worker.md +++ b/docs/superpowers/plans/2026-09-29-cua-auto-worker.md @@ -23,7 +23,7 @@ - [x] GPU check full logits, mixed layouts, changed images/text, boundaries, aggregate budgets and lifecycle; use actual CLI-selected HTTP worker and two loaded instances. - [x] Benchmark eager/default exact/tuned exact/bucket/auto on four existing workloads, plus an unseen mixed schedule. Use two cold runs and order balancing, retain capture/gates/eviction in measured time and per-mode evidence. Record negative results instead of tuning to future schedule labels. - [x] Independently verify raw data/source provenance and report scope. Run a focused code review, fix findings and rerun checks justified by changes. -- [ ] Push the branch, create and attach a PR with incremental comparison against #37. +- [x] Push the branch, create and attach [PR #38](https://github.com/ThinkFlowLab/system1-omni/pull/38) with incremental comparison against #37. ## Shipping criterion From 63edd9bdc104c22363237e3970e36ec7d9871d3e Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Wed, 30 Sep 2026 00:17:46 +0800 Subject: [PATCH 72/75] chore: remove local experiment artifacts from PR Remove archived experiment outputs and assistant planning notes. Keep runtime code, reproducible tools, and model verification metadata unchanged. Link historical reports to an immutable commit and ignore future local artifacts. Remove only tests tied to the deleted historical evidence bundles. --- .gitignore | 4 + README.md | 2 +- recipe/cua_s1/README.md | 2 +- recipe/cua_s1/experiments/README.md | 136 ----- .../cua_s1/experiments/rtx4090/benchmark.json | 409 ------------- .../cua_s1/experiments/rtx4090/candidate.json | 536 ------------------ .../cua_s1/experiments/rtx4090/frontend.json | 79 --- .../cua_s1/experiments/rtx4090/packages.txt | 62 -- .../cua_s1/experiments/rtx4090/reference.json | 527 ----------------- 9 files changed, 6 insertions(+), 1751 deletions(-) delete mode 100644 recipe/cua_s1/experiments/README.md delete mode 100644 recipe/cua_s1/experiments/rtx4090/benchmark.json delete mode 100644 recipe/cua_s1/experiments/rtx4090/candidate.json delete mode 100644 recipe/cua_s1/experiments/rtx4090/frontend.json delete mode 100644 recipe/cua_s1/experiments/rtx4090/packages.txt delete mode 100644 recipe/cua_s1/experiments/rtx4090/reference.json diff --git a/.gitignore b/.gitignore index 07a95a7..b42bbc8 100644 --- a/.gitignore +++ b/.gitignore @@ -29,3 +29,7 @@ weights/ # macOS metadata .DS_Store + +# Local experiment outputs and assistant planning notes +/recipe/cua_s1/experiments/ +/docs/superpowers/ diff --git a/README.md b/README.md index 3bd7b64..2cca0d9 100644 --- a/README.md +++ b/README.md @@ -57,7 +57,7 @@ Validated coverage is listed by modality and execution path: | Model | Status | | --- | --- | | LAYA | [External worker](recipe/laya/README.md); model engine planned | -| [Cua-S1 4B 0.2](recipe/cua_s1/README.md) | Multimodal screenshot choices via Transformers/PEFT on RTX 4090 CUDA; [parity and measurements](recipe/cua_s1/experiments/README.md). Text serving, native CUDA kernels and Metal deferred. | +| [Cua-S1 4B 0.2](recipe/cua_s1/README.md) | Multimodal screenshot choices via Transformers/PEFT on RTX 4090 CUDA; [parity and measurements](https://github.com/Levius-Fubuki/system1-omni/blob/683d470669f19d5e9530cd8233727a0d4f1f8d29/recipe/cua_s1/experiments/README.md). Text serving, native CUDA kernels and Metal deferred. | CUDA and Metal coverage will be documented per model as implementations are added and validated. diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 506d0c0..741a29e 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -14,7 +14,7 @@ worker uses the same request model name; deployment routing selects its modality ## Setup Run from this repository's root on Linux with an NVIDIA GPU. The measured CUDA -wheel, driver, GPU memory and results are recorded in [experiments](experiments/README.md). +wheel, driver, GPU memory and results are recorded in [experiments](https://github.com/Levius-Fubuki/system1-omni/blob/683d470669f19d5e9530cd8233727a0d4f1f8d29/recipe/cua_s1/experiments/README.md). Python 3.12 is required by the pinned environment. ```sh diff --git a/recipe/cua_s1/experiments/README.md b/recipe/cua_s1/experiments/README.md deleted file mode 100644 index 1fc834b..0000000 --- a/recipe/cua_s1/experiments/README.md +++ /dev/null @@ -1,136 +0,0 @@ -# RTX 4090 multimodal validation — 2026-09-27 - -This is a Transformers/PEFT CUDA baseline, with the adapter unmerged and full -logits, for the screenshot worker in [the recipe](../README.md). It does not -implement a native CUDA backend or claim a speedup over upstream. - -The checked-in GPU/HTTP results were measured at commit `74c95e5`. A subsequent -protocol-only revision aligned errors with the text-worker contract: `detail` -replaces `error`, malformed JSON/duplicate keys return 400, and `instructions` is -required (explicit `null` or an empty string omits the goal). The original -`frontend.json` therefore retains the **historical** duplicate-key status and -error-body hashes; it is not evidence for the revised error contract. Regression -tests cover the revised behavior without restarting the GPU instance. The -protocol revision passed 62 Python tests, 13 Rust tests, and 12 direct-versus- -frontend comparisons using the real worker HTTP handler with a CPU stub engine. -Those 12 checks validate transport and rejection behavior, not model inference. - -The generator now supplies `instructions: ""` for the second question of the -two-question fixture. This preserves its previous empty goal and prompt. Model -loading, image processing, prompt construction and scoring are unchanged; GPU -parity and performance have not been remeasured for the protocol revision. - -The 2026-09-29 review follow-up rejects image aspect ratios above 200:1 with -HTTP 422 before processing, matching pinned Transformers 5.17.0. Tests retain -acceptance at exactly 200:1 in either orientation. Lock cleanup checks now use -bounded acquisition, with a fixture that deliberately pauses the handler after -writing the response. All 70 Python tests passed with both pinned Pillow 11.3.0 -and Pillow 12.3.0; the 14 affected cleanup cases passed three additional runs in -the pinned environment. Rust's 13 tests, formatting and Clippy also passed. -These are CPU checks; the archived GPU measurements have not been rerun. - -## Environment and artifacts - -- One NVIDIA GeForce RTX 4090, 24,564 MiB, compute capability 8.9. -- Ubuntu 22.04 container; NVIDIA driver 595.71.05; PyTorch CUDA runtime 13.0. -- Python 3.12.13; torch 2.14.0, torchvision 0.29.0, Transformers 5.17.0, - PEFT 0.21.0, Pillow 11.3.0, safetensors 0.8.0, Triton 3.8.0. -- PyTorch reported 64 intra-op and 64 inter-op threads; no thread tuning applied. -- BF16 base, PEFT fp32 LoRA branches, no adapter merge, no quantization. -- Neither `flash-linear-attention` nor `causal-conv1d` is installed. The matching - upstream reference uses Transformers' PyTorch fallback implementations. -- All pinned weights passed size and SHA-256 verification. The lock contains - 9,342,907,469 base bytes and 186,638,393 adapter-repository bytes. Only the - `multimodal` adapter is loaded; all 178 target modules, including 50 visual - modules, are required at startup. - -Pinned revisions and the reference-source SHA-256 are present in every JSON -report. Full installed versions are in [packages.txt](rtx4090/packages.txt). -The numerical contract and dependency versions were fixed before comparisons. -No model weights or private screenshots are included. - -## Correctness - -| Check | Result | Evidence | -| --- | --- | --- | -| Processor tensor shapes, dtypes and SHA-256 values | Exact match on 9 question forwards across 8 requests | [reference.json](rtx4090/reference.json), [candidate.json](rtx4090/candidate.json) | -| Candidate fp32 probabilities | Exact match; maximum absolute difference **0** | Same reports | -| Multimodal LoRA attachment | 178 modules, visual modules present | [candidate.json](rtx4090/candidate.json) | -| Rust frontend vs direct worker | All 11 HTTP comparisons passed; status, content type and body bytes identical | [frontend.json](rtx4090/frontend.json) | -| CPU validation | 39 tests passed on local macOS and the Linux GPU host | `tests/cua_s1`, CPU CI workflow | - -Fixtures are generated by `evaluate_multimodal.py` using the pinned Pillow version. -They cover 320×240 and 640×480 screenshots, PNG/JPEG, 1 and 26 candidates, -structured/Unicode labels, special-token spellings and two questions sharing one -image. Processed prompts span 215–752 tokens. Hashes cover input IDs, attention -masks, image pixels and image-grid metadata, rather than just the selected option. -The fixtures establish integration parity, not GUI task accuracy or generalization. - -For the frontend test, PR #2's Rust frontend at -`0c91671ac7c8bd698b957b2c0df921de96e7e628` was built with `cargo build --release --locked` -on macOS. It forwarded to the Linux GPU worker over an SSH tunnel. This exercises -real inference and byte preservation; **network/HTTP latency is not benchmarked**. -Eight valid fixtures, health, malformed envelope and duplicate JSON keys were checked. - -Reproduce the HTTP check after generating the fixtures and starting both servers: - -```sh -python recipe/cua_s1/check_frontend.py \ - --worker http://127.0.0.1:8000 --frontend http://127.0.0.1:8080 \ - --fixtures /tmp/cua-evidence/fixtures --output /tmp/cua-evidence/frontend.json -``` - -## Warm engine performance - -[benchmark.json](rtx4090/benchmark.json) contains every sample and the operator -profile. One 640×480 PNG, three candidates, 456 processed tokens, batch size 1, -concurrency 1; five warmup requests followed by two runs of 50 requests. -`torch.cuda.synchronize()` brackets each measurement. p95 uses nearest rank. - -Timing covers `engine.predict`: chat-template application, processor work, host-to- -device transfer, forward pass, letter readout and answer construction. The request -has already been parsed and its image decoded. JSON parsing, image decoding, -HTTP, queueing, model load, artifact hashing and warmup are excluded. - -| Run | p50 | p95 | Serial throughput | Peak allocated | Peak reserved | -| --- | --- | --- | --- | --- | --- | -| 1 | 126.39 ms | 136.68 ms | 7.79 requests/s | 8.84 GiB | 9.07 GiB | -| 2 | 128.28 ms | 133.01 ms | 7.77 requests/s | 8.84 GiB | 9.07 GiB | - -The parity run's peak allocated memory across all nine forwards was 8.99 GiB. -These bounds describe the measured fixtures, not every request admitted by the -worker's 4096-token limit or a concurrent/batched deployment. - -Measured candidate construction, including weight hashing, was 19.94 s in the -parity process and 18.94 s in the benchmark process. Their initial 224×224 warmups -were 1.82 s and 1.63 s. Upstream load was 16.82 s plus 9.54 s of separately measured -artifact verification; its first 320×240 warmup was 3.67 s. These are individual -observations after importing PyTorch and probing the GPU, with different warmup -shapes and filesystem-cache histories; they are not a cold-start speed comparison. -Per-fixture timings in the parity reports are also single observations; the -reference includes image opening while candidate timing covers `score` only. - -## Profiling and the next optimization boundary - -A separate profiled inference, excluded from the latency samples, reported: - -| Operator | Calls | Self CUDA time | -| --- | --- | --- | -| `aten::mm` | 605 | 35.42 ms | -| `aten::copy_` | 1,642 | 9.55 ms | -| `aten::bmm` | 817 | 9.11 ms | -| `aten::addmm` | 98 | 5.89 ms | -| `aten::mul` | 1,103 | 5.76 ms | - -These are instrumented operator totals, not percentages of request wall time. -The raw profile includes both framework operators and CUDA kernels, which overlap; -do not add parent and child events or add kernel time to its enclosing operator. - -Matrix multiplication dominates the observed operator totals. A subsequent CUDA -optimization should first attribute those GEMMs and copies to vision, language -and LoRA branches using shapes/module ranges, then compare one bounded change -against this baseline. This profile alone does not justify replacing a specific -kernel or claiming a speedup. Gated DeltaNet/causal-convolution backend work should -be coordinated with the text-engine contributor because those language layers are -shared. This PR keeps the exact upstream execution path and contributes the -multimodal correctness and performance baseline needed for that work. diff --git a/recipe/cua_s1/experiments/rtx4090/benchmark.json b/recipe/cua_s1/experiments/rtx4090/benchmark.json deleted file mode 100644 index 7b69afc..0000000 --- a/recipe/cua_s1/experiments/rtx4090/benchmark.json +++ /dev/null @@ -1,409 +0,0 @@ -{ - "environment": { - "python": "3.12.13", - "gpu": "NVIDIA GeForce RTX 4090", - "compute_capability": [ - 8, - 9 - ], - "cuda": "13.0", - "driver": "595.71.05", - "torch_num_threads": 64, - "torch_num_interop_threads": 64, - "packages": { - "torch": "2.14.0", - "torchvision": "0.29.0", - "transformers": "5.17.0", - "peft": "0.21.0", - "pillow": "11.3.0", - "safetensors": "0.8.0", - "triton": "3.8.0" - }, - "reference_revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f", - "base_revision": "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a", - "adapter_revision": "16818868b0cc7813808aae4e87b417657046ab79", - "dtype": "bfloat16", - 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0.7772998809814453 - ], - "latency_ms": 117.08385031670332 - } - ], - "reference_source": { - "path": "libs/cua-s1/python/src/cua_s1/four_b.py", - "sha256": "7ed1adfd92223bef7d533db7efbb4cbf468c4936c4380ae42aee12097e3b9ea7", - "revision": "0e75660ce4c2edda519e0c795fa3ad98abf4e76f" - }, - "artifact_verification_ms": 9541.830752044916, - "load_ms": 16820.161445997655, - "warmup_ms": 3665.197447873652, - "peak_allocated_bytes": 9655206912 -} \ No newline at end of file From 9093fd820e737a185aa72f2123d0f6e28b6f06ae Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Wed, 30 Sep 2026 00:35:46 +0800 Subject: [PATCH 73/75] Move Cua-S1 HTTP serving out of models and reuse upstream weight manifest --- .github/workflows/cua-s1-multimodal.yml | 6 +- README.md | 2 +- recipe/cua_s1/README.md | 14 ++- recipe/cua_s1/download_weights.py | 20 ++-- .../server.py => frontend/cua_s1.py} | 4 +- .../cua_s1/multimodal/THIRD_PARTY_NOTICES.md | 21 ---- src/models/cua_s1/multimodal/model.py | 12 ++- src/models/cua_s1/multimodal/protocol.py | 22 +++- .../cua_s1/multimodal/weights.lock.json | 102 ------------------ tests/cua_s1/test_download_weights.py | 83 ++++++++++++++ tests/cua_s1/test_model.py | 14 ++- tests/cua_s1/test_server.py | 2 +- 12 files changed, 160 insertions(+), 142 deletions(-) rename src/{models/cua_s1/multimodal/server.py => frontend/cua_s1.py} (97%) delete mode 100644 src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md delete mode 100644 src/models/cua_s1/multimodal/weights.lock.json create mode 100644 tests/cua_s1/test_download_weights.py diff --git a/.github/workflows/cua-s1-multimodal.yml b/.github/workflows/cua-s1-multimodal.yml index 9329244..9d4fd7b 100644 --- a/.github/workflows/cua-s1-multimodal.yml +++ b/.github/workflows/cua-s1-multimodal.yml @@ -3,6 +3,7 @@ on: pull_request: paths: - 'src/models/cua_s1/multimodal/**' + - 'src/frontend/cua_s1.py' - 'tests/cua_s1/**' - 'recipe/cua_s1/**' - '.github/workflows/cua-s1-multimodal.yml' @@ -10,6 +11,7 @@ on: branches: [main] paths: - 'src/models/cua_s1/multimodal/**' + - 'src/frontend/cua_s1.py' - 'tests/cua_s1/**' - 'recipe/cua_s1/**' - '.github/workflows/cua-s1-multimodal.yml' @@ -26,5 +28,5 @@ jobs: python-version: '3.12' - run: python -m pip install Pillow==11.3.0 pytest==9.1.1 ruff==0.16.8 - run: PYTHONPATH=src python -m pytest tests/cua_s1 -q - - run: ruff check --isolated --select E4,E7,E9,F,I src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 - - run: ruff format --isolated --check src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 + - run: ruff check --isolated --select E4,E7,E9,F,I src/frontend/cua_s1.py src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 + - run: ruff format --isolated --check src/frontend/cua_s1.py src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 diff --git a/README.md b/README.md index 2cca0d9..17d41db 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ Implementation code lives under `src/`; recipes and documentation stay at the re | Directory | Responsibility | | --- | --- | -| [`src/frontend/`](src/frontend/) | Rust serving code and the small engine interface. | +| [`src/frontend/`](src/frontend/) | Rust serving code, Python worker adapters, and the small engine interface. | | [`src/models/`](src/models/) | Model implementations, one directory per model: preprocessing, batching, state, execution, and output processing. | | [`src/backends/cuda/`](src/backends/cuda/) | NVIDIA GPU operations and kernel integration. | | [`src/backends/metal/`](src/backends/metal/) | Apple GPU operations and kernel integration. | diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md index 741a29e..753eef7 100644 --- a/recipe/cua_s1/README.md +++ b/recipe/cua_s1/README.md @@ -22,17 +22,23 @@ python3.12 -m venv .venv . .venv/bin/activate pip install -r recipe/cua_s1/requirements-multimodal.txt PYTHONPATH=src python recipe/cua_s1/download_weights.py --dest weights -PYTHONPATH=src python -m models.cua_s1.multimodal.server \ +PYTHONPATH=src python -m frontend.cua_s1 \ --base weights/Qwen3.5-4B \ --adapter weights/cua-s1-4b-0.2/multimodal ``` -`weights.lock.json` pins and checks every loaded artifact's size and SHA-256. +The downloader fetches the upstream manifest at the fixed reference revision, +checks its pinned SHA-256 and saves it as `weights/weights.lock.json`. The worker +reads this local manifest and checks every loaded artifact's size and SHA-256. +Keep the manifest next to the base checkpoint directory when moving weights. Extra files are rejected, except Hugging Face's `.cache` metadata, so another checkpoint cannot silently override verified shards. Downloads require roughly 9 GB plus cache/install space. Loading is offline after the download completes. Weights are not included in this repository. +The HTTP adapter lives in `src/frontend/cua_s1.py`; model execution stays in +`src/models/cua_s1/multimodal/`. + The worker binds to `127.0.0.1:8000` only after loading and a successful warmup. `GET /health` returns `{"status":"ready","modality":"multimodal"}`. One request runs at a time; concurrent requests return `503`. This is a loopback model worker, @@ -138,8 +144,8 @@ CPU-only validation: ```sh pip install Pillow==11.3.0 pytest==9.1.1 ruff==0.16.8 PYTHONPATH=src python -m pytest tests/cua_s1 -q -ruff check --select E4,E7,E9,F,I src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 -ruff format --check src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 +ruff check --select E4,E7,E9,F,I src/frontend/cua_s1.py src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 +ruff format --check src/frontend/cua_s1.py src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 ``` Metal, native CUDA kernels, text-adapter serving, batching, caching and training diff --git a/recipe/cua_s1/download_weights.py b/recipe/cua_s1/download_weights.py index 104b393..f1474bb 100644 --- a/recipe/cua_s1/download_weights.py +++ b/recipe/cua_s1/download_weights.py @@ -1,23 +1,30 @@ """Download the upstream-pinned artifacts and verify their checksums.""" import argparse -import json from pathlib import Path +from urllib.request import urlopen from huggingface_hub import snapshot_download -from models.cua_s1.multimodal.model import verify_weights +from models.cua_s1.multimodal.model import ( + REFERENCE_REVISION, + parse_weights_manifest, + verify_weights, +) def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--dest", type=Path, required=True) args = parser.parse_args() - lock = ( - Path(__file__).resolve().parents[2] - / "src/models/cua_s1/multimodal/weights.lock.json" + url = ( + f"https://raw.githubusercontent.com/trycua/cua/{REFERENCE_REVISION}/" + "libs/cua-s1/ci/weights.lock.json" ) - for artifact in json.loads(lock.read_text())["artifacts"]: + with urlopen(url, timeout=30) as response: + raw = response.read() + manifest = parse_weights_manifest(raw) + for artifact in manifest["artifacts"]: snapshot_download( repo_id=artifact["repo_id"], revision=artifact["revision"], @@ -25,6 +32,7 @@ def main(): allow_patterns=list(artifact["files"]), token=False, ) + (args.dest / "weights.lock.json").write_bytes(raw) verify_weights(args.dest / "Qwen3.5-4B", args.dest / "cua-s1-4b-0.2/multimodal") print("Pinned base and multimodal adapter checksums verified.") diff --git a/src/models/cua_s1/multimodal/server.py b/src/frontend/cua_s1.py similarity index 97% rename from src/models/cua_s1/multimodal/server.py rename to src/frontend/cua_s1.py index c414bc9..bde882c 100644 --- a/src/models/cua_s1/multimodal/server.py +++ b/src/frontend/cua_s1.py @@ -9,7 +9,7 @@ import threading from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer -from .protocol import ( +from models.cua_s1.multimodal.protocol import ( MAX_BODY, InvalidRequest, MalformedJSON, @@ -107,7 +107,7 @@ def do_POST(self): def main(): - from .model import MultimodalEngine + from models.cua_s1.multimodal.model import MultimodalEngine p = argparse.ArgumentParser(description=__doc__) p.add_argument( diff --git a/src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md b/src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md deleted file mode 100644 index b8b198c..0000000 --- a/src/models/cua_s1/multimodal/THIRD_PARTY_NOTICES.md +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2025 Cua AI, Inc. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/src/models/cua_s1/multimodal/model.py b/src/models/cua_s1/multimodal/model.py index db55070..e3655de 100644 --- a/src/models/cua_s1/multimodal/model.py +++ b/src/models/cua_s1/multimodal/model.py @@ -12,6 +12,9 @@ BASE_REVISION = "851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a" ADAPTER_REVISION = "16818868b0cc7813808aae4e87b417657046ab79" IDENTITY = f"cua-ai/cua-s1-4b-0.2@{ADAPTER_REVISION}:multimodal" +WEIGHTS_MANIFEST_SHA256 = ( + "9820bd232c5762f114e19680c0f8203d7e1faaf8a60c196cfe01964d6d8a6c09" +) MAX_TOKENS = 4096 @@ -47,9 +50,16 @@ def validate_adapter_config(config: dict): raise ValueError("expected the pinned 0.2 multimodal LoRA adapter") +def parse_weights_manifest(raw: bytes) -> dict: + """Accept only the manifest from the pinned upstream reference commit.""" + if hashlib.sha256(raw).hexdigest() != WEIGHTS_MANIFEST_SHA256: + raise ValueError("upstream weights manifest checksum mismatch") + return json.loads(raw) + + def verify_weights(base: Path, adapter: Path): """Check local artifacts before assigning the pinned identity to responses.""" - lock = json.loads(Path(__file__).with_name("weights.lock.json").read_text()) + lock = parse_weights_manifest((base.parent / "weights.lock.json").read_bytes()) allowed = {base: set(), adapter: set()} for artifact in lock["artifacts"]: for name, expected in artifact["files"].items(): diff --git a/src/models/cua_s1/multimodal/protocol.py b/src/models/cua_s1/multimodal/protocol.py index edd207d..4346257 100644 --- a/src/models/cua_s1/multimodal/protocol.py +++ b/src/models/cua_s1/multimodal/protocol.py @@ -21,7 +21,27 @@ MAX_TEXT = 16384 # Prompt and letter layout follow trycua/cua at 0e75660ce4c2edda519e0c795fa3ad98abf4e76f. -# See THIRD_PARTY_NOTICES.md for the upstream MIT notice. +# MIT License +# +# Copyright (c) 2025 Cua AI, Inc. +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. SYSTEM_PROMPT = ( "You are a one-pass computer-use decision model. You are shown the " "current state of a screen and a fixed, closed list of candidate " diff --git a/src/models/cua_s1/multimodal/weights.lock.json b/src/models/cua_s1/multimodal/weights.lock.json deleted file mode 100644 index dfb9748..0000000 --- a/src/models/cua_s1/multimodal/weights.lock.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "schema": "cua-s1/weights-lock/v1", - "description": "Pinned public Hugging Face artifacts for the weights-backed Cua-S1-4B smoke. 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} - } - } - ] -} diff --git a/tests/cua_s1/test_download_weights.py b/tests/cua_s1/test_download_weights.py new file mode 100644 index 0000000..5fb9939 --- /dev/null +++ b/tests/cua_s1/test_download_weights.py @@ -0,0 +1,83 @@ +"""Download setup uses the upstream manifest without checking a copy into source.""" + +import importlib.util +import io +import json +import sys +from pathlib import Path +from types import SimpleNamespace + +import pytest + + +@pytest.mark.parametrize("corrupt", [False, True]) +def test_manifest_is_verified_before_downloading_weights( + tmp_path, monkeypatch, corrupt +): + from models.cua_s1.multimodal import model + + raw = json.dumps( + { + "artifacts": [ + { + "repo_id": "test/base", + "revision": "fixed", + "name": "Qwen3.5-4B", + "files": {"config.json": {}}, + } + ] + } + ).encode() + import hashlib + + monkeypatch.setattr( + model, "WEIGHTS_MANIFEST_SHA256", hashlib.sha256(raw).hexdigest() + ) + downloads = [] + + def download(**kwargs): + downloads.append(kwargs) + kwargs["local_dir"].mkdir(parents=True) + + monkeypatch.setitem( + sys.modules, "huggingface_hub", SimpleNamespace(snapshot_download=download) + ) + path = Path(__file__).resolve().parents[2] / "recipe/cua_s1/download_weights.py" + spec = importlib.util.spec_from_file_location("download_weights_test", path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + payload = raw + b" " if corrupt else raw + urls = [] + + def fetch(url, timeout): + urls.append(url) + assert timeout == 30 + return io.BytesIO(payload) + + monkeypatch.setattr(module, "urlopen", fetch) + verified = [] + + def verify(base, adapter): + assert (base.parent / "weights.lock.json").read_bytes() == raw + verified.append((base, adapter)) + + monkeypatch.setattr(module, "verify_weights", verify) + dest = tmp_path / "weights" + monkeypatch.setattr(sys, "argv", [str(path), "--dest", str(dest)]) + if corrupt: + with pytest.raises(ValueError, match="manifest checksum"): + module.main() + assert not downloads and not verified and not dest.exists() + else: + module.main() + assert downloads == [ + { + "repo_id": "test/base", + "revision": "fixed", + "local_dir": dest / "Qwen3.5-4B", + "allow_patterns": ["config.json"], + "token": False, + } + ] + assert verified == [(dest / "Qwen3.5-4B", dest / "cua-s1-4b-0.2/multimodal")] + assert model.REFERENCE_REVISION in urls[0] diff --git a/tests/cua_s1/test_model.py b/tests/cua_s1/test_model.py index 228e3ac..d37728f 100644 --- a/tests/cua_s1/test_model.py +++ b/tests/cua_s1/test_model.py @@ -84,7 +84,11 @@ def test_unlisted_model_files_cannot_override_verified_shards(tmp_path, monkeypa } ) ) - monkeypatch.setattr(model, "__file__", str(tmp_path / "model.py")) + monkeypatch.setattr( + model, + "WEIGHTS_MANIFEST_SHA256", + hashlib.sha256(manifest.read_bytes()).hexdigest(), + ) model.verify_weights(base, adapter) (base / "model.safetensors").write_bytes(b"override") with pytest.raises(ValueError, match="unlisted"): @@ -110,3 +114,11 @@ def prepare(image, question): with pytest.raises(InvalidRequest, match="4096"): engine.predict(Request(None, (first, second))) assert forwarded == [] + + +def test_weights_manifest_must_match_pinned_upstream_digest(tmp_path): + from models.cua_s1.multimodal.model import verify_weights + + (tmp_path / "weights.lock.json").write_text('{"artifacts": []}') + with pytest.raises(ValueError, match="manifest checksum"): + verify_weights(tmp_path / "base", tmp_path / "adapter") diff --git a/tests/cua_s1/test_server.py b/tests/cua_s1/test_server.py index 6238598..50bd2cb 100644 --- a/tests/cua_s1/test_server.py +++ b/tests/cua_s1/test_server.py @@ -6,8 +6,8 @@ import pytest from test_protocol import image_url, request +from frontend.cua_s1 import Handler, WorkerServer from models.cua_s1.multimodal.protocol import MAX_BODY -from models.cua_s1.multimodal.server import Handler, WorkerServer class Engine: From 934e1ad1dca8f2f447c935d267e01b9216ccf337 Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Wed, 30 Sep 2026 09:08:53 +0800 Subject: [PATCH 74/75] chore: limit PR to multimodal runtime code Remove supplemental tests, recipe tools, documentation, CI, and configuration changes from the PR diff. Runtime Python source and inline third-party notice are unchanged. Validation uses the pre-cleanup test/tool snapshot outside the checkout. --- .github/workflows/cua-s1-multimodal.yml | 32 --- .gitignore | 13 +- README.md | 12 +- recipe/cua_s1/README.md | 153 ---------- recipe/cua_s1/check_frontend.py | 75 ----- recipe/cua_s1/download_weights.py | 41 --- recipe/cua_s1/evaluate_multimodal.py | 322 ---------------------- recipe/cua_s1/make_example.py | 71 ----- recipe/cua_s1/requirements-multimodal.txt | 11 - src/models/cua_s1/README.md | 4 +- tests/cua_s1/test_download_weights.py | 83 ------ tests/cua_s1/test_evaluation.py | 65 ----- tests/cua_s1/test_model.py | 124 --------- tests/cua_s1/test_protocol.py | 172 ------------ tests/cua_s1/test_server.py | 204 -------------- 15 files changed, 9 insertions(+), 1373 deletions(-) delete mode 100644 .github/workflows/cua-s1-multimodal.yml delete mode 100644 recipe/cua_s1/README.md delete mode 100644 recipe/cua_s1/check_frontend.py delete mode 100644 recipe/cua_s1/download_weights.py delete mode 100644 recipe/cua_s1/evaluate_multimodal.py delete mode 100644 recipe/cua_s1/make_example.py delete mode 100644 recipe/cua_s1/requirements-multimodal.txt delete mode 100644 tests/cua_s1/test_download_weights.py delete mode 100644 tests/cua_s1/test_evaluation.py delete mode 100644 tests/cua_s1/test_model.py delete mode 100644 tests/cua_s1/test_protocol.py delete mode 100644 tests/cua_s1/test_server.py diff --git a/.github/workflows/cua-s1-multimodal.yml b/.github/workflows/cua-s1-multimodal.yml deleted file mode 100644 index 9d4fd7b..0000000 --- a/.github/workflows/cua-s1-multimodal.yml +++ /dev/null @@ -1,32 +0,0 @@ -name: Cua-S1 multimodal CPU checks -on: - pull_request: - paths: - - 'src/models/cua_s1/multimodal/**' - - 'src/frontend/cua_s1.py' - - 'tests/cua_s1/**' - - 'recipe/cua_s1/**' - - '.github/workflows/cua-s1-multimodal.yml' - push: - branches: [main] - paths: - - 'src/models/cua_s1/multimodal/**' - - 'src/frontend/cua_s1.py' - - 'tests/cua_s1/**' - - 'recipe/cua_s1/**' - - '.github/workflows/cua-s1-multimodal.yml' -permissions: - contents: read -jobs: - cpu: - runs-on: ubuntu-latest - timeout-minutes: 10 - steps: - - uses: actions/checkout@v4 - - uses: actions/setup-python@v5 - with: - python-version: '3.12' - - run: python -m pip install Pillow==11.3.0 pytest==9.1.1 ruff==0.16.8 - - run: PYTHONPATH=src python -m pytest tests/cua_s1 -q - - run: ruff check --isolated --select E4,E7,E9,F,I src/frontend/cua_s1.py src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 - - run: ruff format --isolated --check src/frontend/cua_s1.py src/models/cua_s1/multimodal tests/cua_s1 recipe/cua_s1 diff --git a/.gitignore b/.gitignore index b42bbc8..3857937 100644 --- a/.gitignore +++ b/.gitignore @@ -20,16 +20,7 @@ target # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ -# Python workers and local model artifacts -__pycache__/ -.pytest_cache/ -.ruff_cache/ +# Local Python worker environment .venv/ -weights/ - -# macOS metadata +__pycache__/ .DS_Store - -# Local experiment outputs and assistant planning notes -/recipe/cua_s1/experiments/ -/docs/superpowers/ diff --git a/README.md b/README.md index 17d41db..76944aa 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ A community-maintained inference engine for prefill-only System1-Omni models, designed around a Rust frontend, model-owned execution, and high-performance CUDA and Metal backends. -The Rust frontend forwards requests to a separately running model worker. The Cua-S1 multimodal worker has been validated on CUDA through Transformers and PEFT; native GPU backends remain planned. +The Rust frontend forwards requests to a separately running model worker. In-repository model engines and GPU backends are not implemented yet. ## Run the frontend @@ -17,8 +17,7 @@ OMNI_JEV_BACKEND_URL=http://127.0.0.1:8000 \ Start the worker separately. See the [frontend documentation](src/frontend/README.md) for the HTTP interface and configuration, or the [Laya recipe](recipe/laya/README.md) -for a CPU text worker and response checks. The [Cua-S1 recipe](recipe/cua_s1/README.md) -provides a CUDA worker for screenshot-conditioned choices. +for a CPU text worker and response checks. ## Architecture @@ -41,23 +40,22 @@ Implementation code lives under `src/`; recipes and documentation stay at the re | Directory | Responsibility | | --- | --- | -| [`src/frontend/`](src/frontend/) | Rust serving code, Python worker adapters, and the small engine interface. | +| [`src/frontend/`](src/frontend/) | Rust serving code and the small engine interface. | | [`src/models/`](src/models/) | Model implementations, one directory per model: preprocessing, batching, state, execution, and output processing. | | [`src/backends/cuda/`](src/backends/cuda/) | NVIDIA GPU operations and kernel integration. | | [`src/backends/metal/`](src/backends/metal/) | Apple GPU operations and kernel integration. | | [`recipe/`](recipe/) | Model setup instructions, launch commands, configuration examples, and example requests. | | [`docs/`](docs/) | Project documentation and architecture assets. | -The frontend is a Cargo workspace member. Model and backend directories document ownership, with implementations added incrementally; they do not prescribe process boundaries. +The frontend is a Cargo workspace member. Model and backend directories currently document planned work; they do not prescribe process boundaries. ## Supported models -Validated coverage is listed by modality and execution path: +LAYA can run as an external Python worker for text requests. Its in-repository model engine is still planned: | Model | Status | | --- | --- | | LAYA | [External worker](recipe/laya/README.md); model engine planned | -| [Cua-S1 4B 0.2](recipe/cua_s1/README.md) | Multimodal screenshot choices via Transformers/PEFT on RTX 4090 CUDA; [parity and measurements](https://github.com/Levius-Fubuki/system1-omni/blob/683d470669f19d5e9530cd8233727a0d4f1f8d29/recipe/cua_s1/experiments/README.md). Text serving, native CUDA kernels and Metal deferred. | CUDA and Metal coverage will be documented per model as implementations are added and validated. diff --git a/recipe/cua_s1/README.md b/recipe/cua_s1/README.md deleted file mode 100644 index 753eef7..0000000 --- a/recipe/cua_s1/README.md +++ /dev/null @@ -1,153 +0,0 @@ -# Cua-S1 0.2 multimodal CUDA worker - -This recipe adds screenshot decisions using the multimodal adapter discussed in -[#10](https://github.com/ThinkFlowLab/system1-omni/issues/10). It loads Transformers -and PEFT directly. Upstream `FourBModel` is used only as an independent parity -oracle. The worker owns image decoding, the processor/chat template, vision and -language LoRA loading, and the candidate-letter probability readout. - -The text mapping and pinned revisions follow -[PR #11](https://github.com/ThinkFlowLab/system1-omni/pull/11). The image `state` -format below is this PR's proposed extension. A separately launched multimodal -worker uses the same request model name; deployment routing selects its modality. - -## Setup - -Run from this repository's root on Linux with an NVIDIA GPU. The measured CUDA -wheel, driver, GPU memory and results are recorded in [experiments](https://github.com/Levius-Fubuki/system1-omni/blob/683d470669f19d5e9530cd8233727a0d4f1f8d29/recipe/cua_s1/experiments/README.md). -Python 3.12 is required by the pinned environment. - -```sh -python3.12 -m venv .venv -. .venv/bin/activate -pip install -r recipe/cua_s1/requirements-multimodal.txt -PYTHONPATH=src python recipe/cua_s1/download_weights.py --dest weights -PYTHONPATH=src python -m frontend.cua_s1 \ - --base weights/Qwen3.5-4B \ - --adapter weights/cua-s1-4b-0.2/multimodal -``` - -The downloader fetches the upstream manifest at the fixed reference revision, -checks its pinned SHA-256 and saves it as `weights/weights.lock.json`. The worker -reads this local manifest and checks every loaded artifact's size and SHA-256. -Keep the manifest next to the base checkpoint directory when moving weights. -Extra files are rejected, except Hugging Face's `.cache` metadata, so another -checkpoint cannot silently override verified shards. Downloads require roughly -9 GB plus cache/install space. Loading is offline after the download completes. -Weights are not included in this repository. - -The HTTP adapter lives in `src/frontend/cua_s1.py`; model execution stays in -`src/models/cua_s1/multimodal/`. - -The worker binds to `127.0.0.1:8000` only after loading and a successful warmup. -`GET /health` returns `{"status":"ready","modality":"multimodal"}`. One request -runs at a time; concurrent requests return `503`. This is a loopback model worker, -with the Rust frontend and an ingress responsible for public serving. - -To use the Rust frontend included in this repository: - -```sh -cargo build --release --locked -OMNI_JEV_BIND=127.0.0.1:8080 \ -OMNI_JEV_BACKEND_URL=http://127.0.0.1:8000 ./target/release/omni-jev -``` - -## Request and response - -Generate a self-contained example with a synthetic settings screenshot: - -```sh -python recipe/cua_s1/make_example.py --output /tmp/cua-example.json -curl -sS http://127.0.0.1:8000/v1/systemone \ - -H 'Content-Type: application/json' --data-binary @/tmp/cua-example.json -``` - -Replace the port with `8080` to send the same request through the frontend. -The request shape is: - -```json -{ - "model": "cua-s1-4b-0.2", - "state": {"image": "data:image/png;base64,"}, - "questions": { - "next": { - "type": "choice", - "instructions": "Save the changes", - "criteria": {"save": "Save changes", "cancel": "Cancel"} - } - } -} -``` - -- `state` contains exactly one inline PNG or JPEG data URL. Images are decoded to - RGB. Local filenames, remote URLs, video, animation and mixed text/image state - are unsupported. -- There are 1–8 questions and 1–26 options per question. Option order assigns - letters A–Z. Labels are strings, objects, arrays or `null` (which uses the key). - Structured values use Python `json.dumps(..., ensure_ascii=False)` followed by - the upstream chooser's label escaping. Instructions accept strings, objects - or arrays and must be present; an empty string or `null` omits the goal block. -- Limits: 8 MiB body, 4 MiB decoded image, 2048 pixels per side, 1,048,576 pixels - total, at most 200:1 aspect ratio in either orientation, 16,384 characters per - question and 4096 processed tokens per question. - Every question is validated/preprocessed before any forward pass begins. -- `<|image_pad|>`, `<|video_pad|>`, `<|vision_start|>` and `<|vision_end|>` are - rejected in user text because the processor interprets them as media controls. - Other special-token spellings retain upstream tokenization behavior. -- Malformed JSON (including duplicate keys, non-finite numbers, invalid UTF-8, - lone surrogates and non-object bodies) returns `400`. Well-formed unsupported - inputs, missing `instructions`, unsupported models and `score`/`noul` questions - return `422`. Error bodies use `{"detail": ""}`. Oversized bodies - return `413`; chunked uploads return `411`. Send `Content-Length` and `Content-Type: application/json`. - -Each answer has `type`, `choice`, `probabilities` and `confidence`. The readout -uses the last position's candidate-letter logits, casts to fp32 and applies -softmax over those letters only. There is no decode. Ties select the earliest -option; confidence is `1 - H(p)/ln(n)`, or 1 for one option. Each question has a -separate forward pass over the same screenshot. Usage sums processed input tokens -and reports zero output tokens. - -Response identity: -`cua-ai/cua-s1-4b-0.2@16818868b0cc7813808aae4e87b417657046ab79:multimodal`. -The base is BF16; PEFT's rank-16, alpha-32 adapter remains unmerged with fp32 LoRA -branches, including 50 vision projection modules (178 total adapted modules). - -## Reproduce correctness and profiling - -```sh -git clone https://github.com/trycua/cua.git /tmp/cua-reference -git -C /tmp/cua-reference checkout 0e75660ce4c2edda519e0c795fa3ad98abf4e76f -for mode in reference candidate benchmark; do - PYTHONPATH=src python recipe/cua_s1/evaluate_multimodal.py \ - --weights weights --reference /tmp/cua-reference \ - --output /tmp/cua-evidence --mode "$mode" -done -``` - -The evaluator hashes the pinned `four_b.py` before importing it and checks report -provenance/environment before comparison. Reference and candidate are separate -processes to avoid keeping two models in GPU memory. Eight synthetic requests -(nine question forwards) cover two resolutions, PNG/JPEG, 1/26 candidates, -structured/Unicode labels, special-token text and multiple questions. It requires -identical processor tensor shapes/dtypes/hashes and identical fp32 candidate -probabilities; numerical tolerance is zero. These are integration/parity fixtures, -not an evaluation of GUI task success. - -Benchmark mode records two runs of 50 serial requests after five warmups on the -640×480 fixture, with synchronized end-to-end engine latency, p50/p95, serial -throughput, allocated/reserved GPU peaks and a separate operator profile. Load -and warmup are recorded separately; candidate load time includes artifact hash -verification. See the experiment report for measured scope and limitations. - -CPU-only validation: - -```sh -pip install Pillow==11.3.0 pytest==9.1.1 ruff==0.16.8 -PYTHONPATH=src python -m pytest tests/cua_s1 -q -ruff check --select E4,E7,E9,F,I src/frontend/cua_s1.py src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 -ruff format --check src/frontend/cua_s1.py src/models/cua_s1/multimodal recipe/cua_s1/*.py tests/cua_s1 -``` - -Metal, native CUDA kernels, text-adapter serving, batching, caching and training -are outside this worker's scope. This implementation does not import or modify -another contributor's text engine. diff --git a/recipe/cua_s1/check_frontend.py b/recipe/cua_s1/check_frontend.py deleted file mode 100644 index 37fe0a0..0000000 --- a/recipe/cua_s1/check_frontend.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Compare HTTP response status, content type and bytes through Rust and directly.""" - -import argparse -import hashlib -import json -import urllib.error -import urllib.request -from pathlib import Path - - -def exchange(base, route, body=None): - request = urllib.request.Request( - base.rstrip("/") + route, - data=body, - headers={"Content-Type": "application/json"}, - ) - try: - response = urllib.request.urlopen(request, timeout=60) - except urllib.error.HTTPError as exc: - response = exc - with response: - return response.status, response.headers.get("Content-Type"), response.read() - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--worker", default="http://127.0.0.1:8000") - parser.add_argument("--frontend", default="http://127.0.0.1:8080") - parser.add_argument("--fixtures", type=Path, required=True) - parser.add_argument("--output", type=Path, required=True) - args = parser.parse_args() - fixtures = sorted(args.fixtures.glob("*.json")) - if not fixtures: - raise ValueError("at least one valid fixture is required") - checks = [("health", "/health", None)] - checks += [(p.stem, "/v1/systemone", p.read_bytes()) for p in fixtures] - checks += [ - ("invalid", "/v1/systemone", b"{}"), - ("duplicate", "/v1/systemone", b'{"model":1,"model":2}'), - ] - missing = json.loads(fixtures[0].read_bytes()) - next(iter(missing["questions"].values())).pop("instructions") - checks.append( - ("missing-instructions", "/v1/systemone", json.dumps(missing).encode()) - ) - report = [] - for name, route, body in checks: - direct = exchange(args.worker, route, body) - proxied = exchange(args.frontend, route, body) - assert direct == proxied, f"frontend changed response: {name}" - expected_status = { - "invalid": 422, - "duplicate": 400, - "missing-instructions": 422, - }.get(name, 200) - assert direct[0] == expected_status, f"unexpected status: {name}: {direct[0]}" - if expected_status >= 400: - assert set(json.loads(direct[2])) == {"detail"}, ( - f"wrong error envelope: {name}" - ) - report.append( - { - "name": name, - "status": direct[0], - "content_type": direct[1], - "body_sha256": hashlib.sha256(direct[2]).hexdigest(), - "identical": True, - } - ) - print(f"{name}: HTTP {direct[0]}, identical response", flush=True) - args.output.write_text(json.dumps(report, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/recipe/cua_s1/download_weights.py b/recipe/cua_s1/download_weights.py deleted file mode 100644 index f1474bb..0000000 --- a/recipe/cua_s1/download_weights.py +++ /dev/null @@ -1,41 +0,0 @@ -"""Download the upstream-pinned artifacts and verify their checksums.""" - -import argparse -from pathlib import Path -from urllib.request import urlopen - -from huggingface_hub import snapshot_download - -from models.cua_s1.multimodal.model import ( - REFERENCE_REVISION, - parse_weights_manifest, - verify_weights, -) - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--dest", type=Path, required=True) - args = parser.parse_args() - url = ( - f"https://raw.githubusercontent.com/trycua/cua/{REFERENCE_REVISION}/" - "libs/cua-s1/ci/weights.lock.json" - ) - with urlopen(url, timeout=30) as response: - raw = response.read() - manifest = parse_weights_manifest(raw) - for artifact in manifest["artifacts"]: - snapshot_download( - repo_id=artifact["repo_id"], - revision=artifact["revision"], - local_dir=args.dest / artifact["name"], - allow_patterns=list(artifact["files"]), - token=False, - ) - (args.dest / "weights.lock.json").write_bytes(raw) - verify_weights(args.dest / "Qwen3.5-4B", args.dest / "cua-s1-4b-0.2/multimodal") - print("Pinned base and multimodal adapter checksums verified.") - - -if __name__ == "__main__": - main() diff --git a/recipe/cua_s1/evaluate_multimodal.py b/recipe/cua_s1/evaluate_multimodal.py deleted file mode 100644 index ca3e535..0000000 --- a/recipe/cua_s1/evaluate_multimodal.py +++ /dev/null @@ -1,322 +0,0 @@ -"""Reproduce upstream parity and bounded GPU measurements on fixed synthetic inputs.""" - -from __future__ import annotations - -import argparse -import copy -import hashlib -import importlib.metadata -import json -import platform -import statistics -import subprocess -import sys -import time -from pathlib import Path - -from make_example import make_example - -from models.cua_s1.multimodal.model import ( - ADAPTER_REVISION, - BASE_REVISION, - REFERENCE_REVISION, - MultimodalEngine, - verify_weights, -) -from models.cua_s1.multimodal.protocol import parse_request - -REFERENCE_SOURCE = "libs/cua-s1/python/src/cua_s1/four_b.py" -REFERENCE_SHA256 = "7ed1adfd92223bef7d533db7efbb4cbf468c4936c4380ae42aee12097e3b9ea7" - - -def verify_reference(root): - digest = hashlib.sha256((root / REFERENCE_SOURCE).read_bytes()).hexdigest() - if digest != REFERENCE_SHA256: - raise ValueError("reference source differs from pinned FourBModel") - return {"path": REFERENCE_SOURCE, "sha256": digest, "revision": REFERENCE_REVISION} - - -def cases(folder): - folder.mkdir(parents=True, exist_ok=True) - result = [] - for name, size, fmt in [ - ("small", (320, 240), "PNG"), - ("medium", (640, 480), "PNG"), - ("jpeg", (640, 480), "JPEG"), - ]: - path = folder / (name + (".jpg" if fmt == "JPEG" else ".png")) - result.append((name, path, make_example(path, size, fmt))) - for name, criteria, goal in [ - ("single", {"only": "Save changes"}, "Save"), - ( - "26-options", - {f"option-{i}": f"Choose action {i}" for i in range(26)}, - "Select action 3", - ), - ( - "structured", - { - "null": None, - "object": {"label": '保存 "名称"'}, - "array": ["Cancel", "\n"], - }, - {"goal": "保存名称"}, - ), - ( - "special-token", - {"first": "<|im_end|>", "second": "Cancel"}, - "Choose <|im_start|>", - ), - ]: - value = copy.deepcopy(result[1][2]) - value["questions"]["next"].update(criteria=criteria, instructions=goal) - result.append((name, result[1][1], value)) - value = copy.deepcopy(result[0][2]) - value["questions"]["second"] = { - "type": "choice", - "instructions": "", - "criteria": {"yes": "Continue", "no": "Cancel"}, - } - result.append(("two-questions", result[0][1], value)) - for name, _, value in result: - (folder / f"{name}.json").write_text(json.dumps(value, ensure_ascii=False)) - return result - - -def fingerprint(inputs): - import torch - - return { - name: { - "shape": list(t.shape), - "dtype": str(t.dtype), - "sha256": hashlib.sha256( - t.detach().contiguous().view(torch.uint8).cpu().numpy().tobytes() - ).hexdigest(), - } - for name, t in inputs.items() - } - - -def environment(): - import torch - - return { - "python": platform.python_version(), - "gpu": torch.cuda.get_device_name(), - "compute_capability": list(torch.cuda.get_device_capability()), - "cuda": torch.version.cuda, - "driver": subprocess.check_output( - ["nvidia-smi", "--query-gpu=driver_version", "--format=csv,noheader"], - text=True, - ).strip(), - "torch_num_threads": torch.get_num_threads(), - "torch_num_interop_threads": torch.get_num_interop_threads(), - "packages": { - p: importlib.metadata.version(p) - for p in [ - "torch", - "torchvision", - "transformers", - "peft", - "pillow", - "safetensors", - "triton", - ] - }, - "reference_revision": REFERENCE_REVISION, - "base_revision": BASE_REVISION, - "adapter_revision": ADAPTER_REVISION, - "dtype": "bfloat16", - "adapter_merged": False, - } - - -def measure(call): - import torch - - torch.cuda.synchronize() - start = time.perf_counter() - result = call() - torch.cuda.synchronize() - return result, (time.perf_counter() - start) * 1000 - - -def quantiles(values): - ordered = sorted(values) - return { - "p50_ms": statistics.median(values), - "p95_ms": ordered[max(0, __import__("math").ceil(0.95 * len(values)) - 1)], - } - - -def main(): - import torch - - p = argparse.ArgumentParser(description=__doc__) - p.add_argument("--weights", type=Path, required=True) - p.add_argument( - "--reference", type=Path, required=True, help="checkout of pinned trycua/cua" - ) - p.add_argument("--output", type=Path, required=True) - p.add_argument( - "--mode", choices=["reference", "candidate", "benchmark"], required=True - ) - args = p.parse_args() - args.output.mkdir(parents=True, exist_ok=True) - base = str(args.weights / "Qwen3.5-4B") - adapter = str(args.weights / "cua-s1-4b-0.2/multimodal") - fixture_set = cases(args.output / "fixtures") - report = {"environment": environment(), "mode": args.mode, "cases": []} - report["reference_source"] = verify_reference(args.reference) - if args.mode == "reference": - _, report["artifact_verification_ms"] = measure( - lambda: verify_weights(Path(base), Path(adapter)) - ) - sys.path.insert(0, str(args.reference / "libs/cua-s1/python/src")) - from cua_s1.four_b import FourBModel, Option, assign_letters, build_prompt - - model = FourBModel( - base_model=base, lora_adapter_path=adapter, modality="multimodal" - ) - _, report["load_ms"] = measure(model.load) - first = True - for name, path, value in fixture_set: - request = parse_request(value) - for q in request.questions: - options = [ - Option(element_id=k, role="Decision", label=v, action="select") - for k, v in zip(q.keys, q.labels) - ] - messages = build_prompt( - assign_letters(options), - app="Cua Driver", - task_family="closed-candidate decision", - screenshot=path, - modality="multimodal", - goal=q.goal, - ) - text = model._processor.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True - ) - inputs = model._processor( - text=[text], images=[request.image], return_tensors="pt" - ) - - def call(): - return model.forward( - options, - app="Cua Driver", - task_family="closed-candidate decision", - screenshot=path, - goal=q.goal, - ) - - if first: - _, report["warmup_ms"] = measure(call) - first = False - output, elapsed = measure(call) - report["cases"].append( - { - "name": name, - "question": q.name, - "inputs": fingerprint(inputs), - "probabilities": [x.probability for x in output], - "latency_ms": elapsed, - } - ) - print(f"reference {name}/{q.name}: {elapsed:.1f} ms", flush=True) - else: - engine, report["load_ms"] = measure(lambda: MultimodalEngine(base, adapter)) - _, report["warmup_ms"] = measure(engine.warmup) - report["adapter_modules"] = engine.adapter_modules - if args.mode == "candidate": - reference = json.loads((args.output / "reference.json").read_text()) - if ( - reference["reference_source"] != report["reference_source"] - or reference["environment"] != report["environment"] - ): - raise ValueError("reference provenance or environment mismatch") - expected = {(x["name"], x["question"]): x for x in reference["cases"]} - for name, _, value in fixture_set: - request = parse_request(value) - for q in request.questions: - inputs = engine.prepare(request.image, q) - probabilities, elapsed = measure(lambda: engine.score(inputs, q)) - ref = expected[name, q.name] - difference = max( - abs(a - b) for a, b in zip(ref["probabilities"], probabilities) - ) - assert fingerprint(inputs) == ref["inputs"], ( - f"preprocessing mismatch: {name}" - ) - assert probabilities == ref["probabilities"], ( - f"probability mismatch: {name}: {difference}" - ) - report["cases"].append( - { - "name": name, - "question": q.name, - "inputs": fingerprint(inputs), - "probabilities": probabilities, - "max_abs_difference": difference, - "latency_ms": elapsed, - } - ) - print(f"candidate {name}/{q.name}: exact parity", flush=True) - else: - request = parse_request(fixture_set[1][2]) - for _ in range(5): - engine.predict(request) - report["benchmark"] = { - "case": "medium", - "batch_size": 1, - "concurrency": 1, - "warmup_requests": 5, - "runs": [], - } - for run in range(2): - torch.cuda.reset_peak_memory_stats() - latencies = [ - measure(lambda: engine.predict(request))[1] for _ in range(50) - ] - report["benchmark"]["runs"].append( - { - "run": run + 1, - "latencies_ms": latencies, - **quantiles(latencies), - "requests_per_second_serial": 1000 / statistics.mean(latencies), - "peak_allocated_bytes": torch.cuda.max_memory_allocated(), - "peak_reserved_bytes": torch.cuda.max_memory_reserved(), - } - ) - print(f"benchmark run {run + 1}: {quantiles(latencies)}", flush=True) - with torch.profiler.profile( - activities=[ - torch.profiler.ProfilerActivity.CPU, - torch.profiler.ProfilerActivity.CUDA, - ], - record_shapes=True, - ) as prof: - engine.predict(request) - torch.cuda.synchronize() - report["profile"] = [ - { - "op": x.key, - "count": x.count, - "device_time_us": x.device_time_total, - "self_device_time_us": x.self_device_time_total, - "cpu_time_us": x.cpu_time_total, - "self_cpu_time_us": x.self_cpu_time_total, - } - for x in sorted( - prof.key_averages(), key=lambda x: x.device_time_total, reverse=True - )[:30] - ] - report["peak_allocated_bytes"] = torch.cuda.max_memory_allocated() - (args.output / f"{args.mode}.json").write_text(json.dumps(report, indent=2)) - print(f"Saved {args.mode}.json", flush=True) - - -if __name__ == "__main__": - main() diff --git a/recipe/cua_s1/make_example.py b/recipe/cua_s1/make_example.py deleted file mode 100644 index 8adbc53..0000000 --- a/recipe/cua_s1/make_example.py +++ /dev/null @@ -1,71 +0,0 @@ -"""Create redistributable synthetic GUI fixtures and inline-image requests.""" - -from __future__ import annotations - -import argparse -import base64 -import json -from pathlib import Path - -from PIL import Image, ImageDraw - - -def make_example(path: Path, size=(640, 480), fmt="PNG") -> dict: - image = Image.new("RGB", size, "#f4f6f8") - draw = ImageDraw.Draw(image) - w, h = size - draw.rectangle( - (w // 10, h // 8, w * 9 // 10, h * 7 // 8), - fill="white", - outline="#8899aa", - width=2, - ) - draw.text( - (w // 7, h // 5), "Account settings", fill="black", font_size=max(12, w // 25) - ) - draw.text( - (w // 7, h // 3), - "Display name: Alice", - fill="black", - font_size=max(10, w // 32), - ) - draw.rectangle((w // 7, h // 2, w * 4 // 7, h * 2 // 3), fill="#1460b4") - draw.text( - (w // 6, h * 13 // 24), "Save changes", fill="white", font_size=max(10, w // 32) - ) - draw.text( - (w * 5 // 8, h * 13 // 24), "Cancel", fill="black", font_size=max(10, w // 32) - ) - image.save(path, format=fmt) - mime = "jpeg" if fmt == "JPEG" else "png" - return { - "model": "cua-s1-4b-0.2", - "state": { - "image": f"data:image/{mime};base64," - + base64.b64encode(path.read_bytes()).decode() - }, - "questions": { - "next": { - "type": "choice", - "instructions": "Save the changed display name.", - "criteria": { - "save": "Click Save changes", - "cancel": "Click Cancel", - "wait": "Wait", - }, - } - }, - } - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--output", type=Path, default=Path("example.json")) - args = parser.parse_args() - args.output.parent.mkdir(parents=True, exist_ok=True) - value = make_example(args.output.with_suffix(".png")) - args.output.write_text(json.dumps(value, ensure_ascii=False)) - - -if __name__ == "__main__": - main() diff --git a/recipe/cua_s1/requirements-multimodal.txt b/recipe/cua_s1/requirements-multimodal.txt deleted file mode 100644 index afb7422..0000000 --- a/recipe/cua_s1/requirements-multimodal.txt +++ /dev/null @@ -1,11 +0,0 @@ -# Reference package versions from trycua/cua 0e75660, four-b uv.lock. -# Lock the CUDA wheel/driver in the experiment report for the target GPU. -torch==2.14.0 -torchvision==0.29.0 -transformers==5.17.0 -peft==0.21.0 -accelerate==1.15.0 -Pillow==11.3.0 -safetensors==0.8.0 -huggingface-hub==1.32.0 -tokenizers==0.23.2 diff --git a/src/models/cua_s1/README.md b/src/models/cua_s1/README.md index 2646837..6a07260 100644 --- a/src/models/cua_s1/README.md +++ b/src/models/cua_s1/README.md @@ -2,7 +2,7 @@ This directory owns Cua-S1 4B 0.2 ([#10](https://github.com/ThinkFlowLab/system1-omni/issues/10)): request mapping, prompt construction, adapter selection, execution, and the answer-letter readout. This page records the pinned upstream revisions, the inference contract an implementation must match, and how its outputs will be compared with the upstream reference. -Status: the `multimodal` adapter has a Transformers/PEFT CUDA worker validated on RTX 4090. See the [multimodal recipe](../../../recipe/cua_s1/README.md) for its screenshot request extension, input limits and archived GPU parity results. The `text` worker is tracked separately in [PR #13](https://github.com/ThinkFlowLab/system1-omni/pull/13); the text mapping below remains its contract. +Status: planned; nothing is implemented or validated yet. The first target is the `text` adapter on CUDA, starting with a worker that loads the model directly through Hugging Face Transformers and PEFT. The `multimodal` adapter is deferred; see [Not covered yet](#not-covered-yet). ## Pinned revisions @@ -105,7 +105,7 @@ The bfloat16 worker's own difference from the fp32 worker is reported next to ea ## Not covered yet -- Text-adapter serving in this checkout (tracked in PR #13), and native GPU kernels. +- The `multimodal` adapter: image preprocessing, the vision tower and the vision LoRA. This is tracked in [#10](https://github.com/ThinkFlowLab/system1-omni/issues/10). - `score` and `noul` questions. - More than 26 options per question. - The Metal backend. diff --git a/tests/cua_s1/test_download_weights.py b/tests/cua_s1/test_download_weights.py deleted file mode 100644 index 5fb9939..0000000 --- a/tests/cua_s1/test_download_weights.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Download setup uses the upstream manifest without checking a copy into source.""" - -import importlib.util -import io -import json -import sys -from pathlib import Path -from types import SimpleNamespace - -import pytest - - -@pytest.mark.parametrize("corrupt", [False, True]) -def test_manifest_is_verified_before_downloading_weights( - tmp_path, monkeypatch, corrupt -): - from models.cua_s1.multimodal import model - - raw = json.dumps( - { - "artifacts": [ - { - "repo_id": "test/base", - "revision": "fixed", - "name": "Qwen3.5-4B", - "files": {"config.json": {}}, - } - ] - } - ).encode() - import hashlib - - monkeypatch.setattr( - model, "WEIGHTS_MANIFEST_SHA256", hashlib.sha256(raw).hexdigest() - ) - downloads = [] - - def download(**kwargs): - downloads.append(kwargs) - kwargs["local_dir"].mkdir(parents=True) - - monkeypatch.setitem( - sys.modules, "huggingface_hub", SimpleNamespace(snapshot_download=download) - ) - path = Path(__file__).resolve().parents[2] / "recipe/cua_s1/download_weights.py" - spec = importlib.util.spec_from_file_location("download_weights_test", path) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - payload = raw + b" " if corrupt else raw - urls = [] - - def fetch(url, timeout): - urls.append(url) - assert timeout == 30 - return io.BytesIO(payload) - - monkeypatch.setattr(module, "urlopen", fetch) - verified = [] - - def verify(base, adapter): - assert (base.parent / "weights.lock.json").read_bytes() == raw - verified.append((base, adapter)) - - monkeypatch.setattr(module, "verify_weights", verify) - dest = tmp_path / "weights" - monkeypatch.setattr(sys, "argv", [str(path), "--dest", str(dest)]) - if corrupt: - with pytest.raises(ValueError, match="manifest checksum"): - module.main() - assert not downloads and not verified and not dest.exists() - else: - module.main() - assert downloads == [ - { - "repo_id": "test/base", - "revision": "fixed", - "local_dir": dest / "Qwen3.5-4B", - "allow_patterns": ["config.json"], - "token": False, - } - ] - assert verified == [(dest / "Qwen3.5-4B", dest / "cua-s1-4b-0.2/multimodal")] - assert model.REFERENCE_REVISION in urls[0] diff --git a/tests/cua_s1/test_evaluation.py b/tests/cua_s1/test_evaluation.py deleted file mode 100644 index cca8a45..0000000 --- a/tests/cua_s1/test_evaluation.py +++ /dev/null @@ -1,65 +0,0 @@ -"""Provenance checks must fail before a changed oracle can certify parity.""" - -import importlib.util -from pathlib import Path - -import pytest - - -def test_modified_reference_is_rejected(tmp_path, monkeypatch): - recipe = Path(__file__).resolve().parents[2] / "recipe/cua_s1" - monkeypatch.syspath_prepend(str(recipe)) - spec = importlib.util.spec_from_file_location( - "evaluation", recipe / "evaluate_multimodal.py" - ) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - source = tmp_path / module.REFERENCE_SOURCE - source.parent.mkdir(parents=True) - source.write_text("modified oracle") - with pytest.raises(ValueError, match="reference source differs"): - module.verify_reference(tmp_path) - - -def test_environment_survives_json_roundtrip(monkeypatch): - import json - import sys - from types import SimpleNamespace - - recipe = Path(__file__).resolve().parents[2] / "recipe/cua_s1" - monkeypatch.syspath_prepend(str(recipe)) - spec = importlib.util.spec_from_file_location( - "evaluation", recipe / "evaluate_multimodal.py" - ) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - torch = SimpleNamespace( - cuda=SimpleNamespace( - get_device_name=lambda: "GPU", get_device_capability=lambda: (8, 9) - ), - version=SimpleNamespace(cuda="13.0"), - get_num_threads=lambda: 16, - get_num_interop_threads=lambda: 16, - ) - monkeypatch.setitem(sys.modules, "torch", torch) - monkeypatch.setattr(module.importlib.metadata, "version", lambda name: "pinned") - monkeypatch.setattr(module.subprocess, "check_output", lambda *a, **kw: "driver\n") - environment = module.environment() - assert json.loads(json.dumps(environment)) == environment - - -def test_generated_fixtures_satisfy_required_instructions(tmp_path, monkeypatch): - from models.cua_s1.multimodal.protocol import parse_request - - recipe = Path(__file__).resolve().parents[2] / "recipe/cua_s1" - monkeypatch.syspath_prepend(str(recipe)) - spec = importlib.util.spec_from_file_location( - "evaluation", recipe / "evaluate_multimodal.py" - ) - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - fixtures = module.cases(tmp_path) - requests = [parse_request(value) for _, _, value in fixtures] - assert len(requests) == 8 - assert sum(len(value.questions) for value in requests) == 9 - assert requests[-1].questions[1].goal == "" diff --git a/tests/cua_s1/test_model.py b/tests/cua_s1/test_model.py deleted file mode 100644 index d37728f..0000000 --- a/tests/cua_s1/test_model.py +++ /dev/null @@ -1,124 +0,0 @@ -import pytest - -from models.cua_s1.multimodal.model import letter_ids, validate_adapter_config - - -class Tokenizer: - def encode(self, text, add_special_tokens): - assert add_special_tokens is False - return [ord(text) - 33] - - -def test_letter_readout_is_in_candidate_order(): - assert letter_ids(Tokenizer(), 3) == [32, 33, 34] - - -def test_multitoken_letters_are_rejected(): - class Bad: - def encode(self, *args, **kwargs): - return [1, 2] - - with pytest.raises(ValueError, match="single token"): - letter_ids(Bad(), 2) - - -def test_text_adapter_is_rejected_before_model_load(): - with pytest.raises(ValueError, match="multimodal"): - validate_adapter_config( - { - "peft_type": "LORA", - "r": 16, - "lora_alpha": 32, - "target_modules": ["q_proj", "k_proj"], - } - ) - - -def test_multimodal_adapter_contract(): - validate_adapter_config( - { - "peft_type": "LORA", - "r": 16, - "lora_alpha": 32, - "base_model_name_or_path": "Qwen/Qwen3.5-4B", - "target_modules": [ - "q_proj", - "k_proj", - "v_proj", - "o_proj", - "gate_proj", - "up_proj", - "down_proj", - "linear_fc1", - "linear_fc2", - ], - } - ) - - -def test_unlisted_model_files_cannot_override_verified_shards(tmp_path, monkeypatch): - import hashlib - import json - - from models.cua_s1.multimodal import model - - base, adapter = tmp_path / "base", tmp_path / "adapter" - base.mkdir() - adapter.mkdir() - (base / "config.json").write_bytes(b"{}") - manifest = tmp_path / "weights.lock.json" - manifest.write_text( - json.dumps( - { - "artifacts": [ - { - "role": "base", - "files": { - "config.json": { - "size": 2, - "sha256": hashlib.sha256(b"{}").hexdigest(), - } - }, - } - ] - } - ) - ) - monkeypatch.setattr( - model, - "WEIGHTS_MANIFEST_SHA256", - hashlib.sha256(manifest.read_bytes()).hexdigest(), - ) - model.verify_weights(base, adapter) - (base / "model.safetensors").write_bytes(b"override") - with pytest.raises(ValueError, match="unlisted"): - model.verify_weights(base, adapter) - - -def test_all_question_lengths_are_checked_before_inference(): - from models.cua_s1.multimodal.model import MultimodalEngine - from models.cua_s1.multimodal.protocol import InvalidRequest, Question, Request - - engine = object.__new__(MultimodalEngine) - first = Question("first", ("a",), ("A",), "") - second = Question("second", ("b",), ("B",), "") - forwarded = [] - - def prepare(image, question): - if question.name == "second": - raise InvalidRequest("processed prompt exceeds 4096 tokens") - return {} - - engine.prepare = prepare - engine.score = lambda inputs, q: forwarded.append(q) - with pytest.raises(InvalidRequest, match="4096"): - engine.predict(Request(None, (first, second))) - assert forwarded == [] - - -def test_weights_manifest_must_match_pinned_upstream_digest(tmp_path): - from models.cua_s1.multimodal.model import verify_weights - - (tmp_path / "weights.lock.json").write_text('{"artifacts": []}') - with pytest.raises(ValueError, match="manifest checksum"): - verify_weights(tmp_path / "base", tmp_path / "adapter") diff --git a/tests/cua_s1/test_protocol.py b/tests/cua_s1/test_protocol.py deleted file mode 100644 index 00ecd76..0000000 --- a/tests/cua_s1/test_protocol.py +++ /dev/null @@ -1,172 +0,0 @@ -import base64 -import io -import json - -import pytest -from PIL import Image - -from models.cua_s1.multimodal.protocol import ( - InvalidRequest, - answer, - build_messages, - decode_request, - parse_request, -) - - -def image_url(fmt="PNG", size=(32, 32)): - out = io.BytesIO() - Image.new("RGB", size, "white").save(out, format=fmt) - mime = "jpeg" if fmt == "JPEG" else "png" - return f"data:image/{mime};base64," + base64.b64encode(out.getvalue()).decode() - - -def request(): - return { - "model": "cua-s1-4b-0.2", - "state": {"image": image_url()}, - "questions": { - "next": { - "type": "choice", - "instructions": "Submit the form", - "criteria": {"submit": "Submit", "cancel": "Cancel"}, - } - }, - } - - -def test_image_and_order_are_preserved(): - r = parse_request(request()) - assert r.image.mode == "RGB" and r.image.size == (32, 32) - assert r.questions[0].keys == ("submit", "cancel") - assert r.questions[0].labels == ("Submit", "Cancel") - - -def test_prompt_keeps_image_block_and_upstream_text(): - q = parse_request(request()).questions[0] - msg = build_messages(q) - assert msg[1]["content"][0]["type"] == "image" - assert msg[1]["content"][1]["text"] == ( - "Goal: Submit the form\n\nApp: Cua Driver\nTask family: closed-candidate decision\n\n" - "The current screenshot is attached.\n\nOptions:\n" - 'A. Decision "Submit" -> select\nB. Decision "Cancel" -> select\n\n' - "Answer with a single letter." - ) - - -def test_structured_values_and_escaping(): - r = request() - r["questions"]["next"]["instructions"] = {"目标": "提交"} - r["questions"]["next"]["criteria"] = {"fallback": None, "obj": {"x": '"\n'}} - q = parse_request(r).questions[0] - assert q.goal == '{"目标": "提交"}' - assert q.labels == ( - "fallback", - json.dumps(json.dumps({"x": '"\n'}, ensure_ascii=False), ensure_ascii=False)[ - 1:-1 - ], - ) - - -@pytest.mark.parametrize( - "state", - [ - {}, - {"image": "/etc/passwd"}, - {"image": "https://example.com/a.png"}, - {"image": "data:image/png;base64,!!"}, - {"image": image_url(), "text": "ignored"}, - ], -) -def test_invalid_images_and_unknown_state_fields(state): - r = request() - r["state"] = state - with pytest.raises(InvalidRequest): - parse_request(r) - - -def test_mime_mismatch_and_oversized_dimensions(): - for url in [ - image_url().replace("image/png", "image/jpeg"), - image_url(size=(2049, 1)), - ]: - r = request() - r["state"]["image"] = url - with pytest.raises(InvalidRequest): - parse_request(r) - - -@pytest.mark.parametrize( - "criteria", [{}, {str(i): "x" for i in range(27)}, {"a": 1}, {"a": True}] -) -def test_invalid_candidates(criteria): - r = request() - r["questions"]["next"]["criteria"] = criteria - with pytest.raises(InvalidRequest): - parse_request(r) - - -@pytest.mark.parametrize("kind", ["score", "noul"]) -def test_unsupported_question_rejects_entire_request(kind): - r = request() - r["questions"]["bad"] = {"type": kind, "instructions": "x"} - with pytest.raises(InvalidRequest): - parse_request(r) - - -def test_duplicate_keys_and_nonfinite_json(): - for raw in [ - b'{"model":1,"model":2}', - b'{"x":NaN}', - b'{"x":Infinity}', - b"[]", - b"not json", - ]: - with pytest.raises(InvalidRequest): - decode_request(raw) - - -def test_entropy_confidence_and_earliest_tie(): - q = parse_request(request()).questions[0] - result = answer(q, [0.5, 0.5]) - assert result["choice"] == "submit" - assert result["confidence"] == 0.0 - assert result["probabilities"] == {"submit": 0.5, "cancel": 0.5} - r = request() - r["questions"]["next"]["criteria"] = {"only": "Only"} - assert answer(parse_request(r).questions[0], [1.0])["confidence"] == 1.0 - - -def test_jpeg_supported(): - r = request() - r["state"]["image"] = image_url("JPEG") - assert parse_request(r).image.mode == "RGB" - - -@pytest.mark.parametrize( - "token", ["<|image_pad|>", "<|video_pad|>", "<|vision_start|>", "<|vision_end|>"] -) -@pytest.mark.parametrize("field", ["instructions", "criteria"]) -def test_media_control_tokens_are_rejected(token, field): - value = request() - value["questions"]["next"][field] = ( - token if field == "instructions" else {"a": {"text": token}} - ) - with pytest.raises(InvalidRequest, match="control token"): - parse_request(value) - - -@pytest.mark.parametrize("instructions", ["", None]) -def test_explicit_empty_or_null_instructions_omits_goal(instructions): - value = request() - value["questions"]["next"]["instructions"] = instructions - question = parse_request(value).questions[0] - assert question.goal == "" - assert "Goal:" not in build_messages(question)[1]["content"][1]["text"] - - -@pytest.mark.parametrize("size", [(200, 1), (1, 200), (199, 1), (1, 199)]) -def test_supported_aspect_ratio_boundary(size): - value = request() - value["state"]["image"] = image_url(size=size) - assert parse_request(value).image.size == size diff --git a/tests/cua_s1/test_server.py b/tests/cua_s1/test_server.py deleted file mode 100644 index 50bd2cb..0000000 --- a/tests/cua_s1/test_server.py +++ /dev/null @@ -1,204 +0,0 @@ -import json -import threading -import urllib.error -import urllib.request - -import pytest -from test_protocol import image_url, request - -from frontend.cua_s1 import Handler, WorkerServer -from models.cua_s1.multimodal.protocol import MAX_BODY - - -class Engine: - def predict(self, parsed): - return { - "model": "test:multimodal", - "answers": {q.name: {"type": "choice"} for q in parsed.questions}, - } - - -@pytest.fixture -def worker(): - server = WorkerServer(("127.0.0.1", 0), Engine()) - thread = threading.Thread(target=server.serve_forever, daemon=True) - thread.start() - yield server, f"http://127.0.0.1:{server.server_port}" - server.shutdown() - server.server_close() - thread.join() - - -@pytest.fixture -def deferred_cleanup(worker, monkeypatch): - """Hold cleanup until the test starts waiting for the inference lock.""" - server, _ = worker - lock = server.inference_lock - allow_cleanup = threading.Event() - - class DeferredLock: - def acquire(self, blocking=True, timeout=-1): - if blocking: - allow_cleanup.set() - return lock.acquire(blocking, timeout) - - def release(self): - lock.release() - - def locked(self): - return lock.locked() - - server.inference_lock = DeferredLock() - send_json = Handler.send_json - - def send_then_wait(handler, status, value): - send_json(handler, status, value) - # The client has the complete response, but finally has not run yet. - if not allow_cleanup.wait(timeout=5): - raise AssertionError("test did not wait for handler cleanup") - - monkeypatch.setattr(Handler, "send_json", send_then_wait) - try: - yield worker - finally: - allow_cleanup.set() - - -def call(url, body=None, **headers): - data = ( - body if isinstance(body, bytes) or body is None else json.dumps(body).encode() - ) - req = urllib.request.Request( - url, data=data, headers={"Content-Type": "application/json", **headers} - ) - try: - with urllib.request.urlopen(req, timeout=5) as response: - return response.status, json.load(response) - except urllib.error.HTTPError as response: - return response.code, json.load(response) - - -def test_health_and_prediction(worker): - _, url = worker - assert call(url + "/health")[0] == 200 - status, body = call(url + "/v1/systemone", request()) - assert status == 200 and "next" in body["answers"] - - -def test_invalid_question_never_reaches_model(worker): - _, url = worker - r = request() - r["questions"]["next"]["type"] = "noul" - status, body = call(url + "/v1/systemone", r) - assert status == 422 and set(body) == {"detail"} - - -def test_busy_worker_rejects_instead_of_queueing_gpu_work(worker): - server, url = worker - server.inference_lock.acquire() - try: - assert call(url + "/health")[0] == 200 - assert call(url + "/v1/systemone", request())[0] == 503 - finally: - server.inference_lock.release() - - -def test_body_limit_checked_before_reading(worker): - _, url = worker - assert ( - call(url + "/v1/systemone", {}, **{"Content-Length": str(MAX_BODY + 1)})[0] - == 413 - ) - - -def test_model_failure_is_not_reported_as_success(deferred_cleanup): - server, url = deferred_cleanup - - def fail(_): - raise RuntimeError("private file or input must not leak") - - server.engine.predict = fail - status, body = call(url + "/v1/systemone", request()) - assert status == 500 and body == {"detail": "inference failed"} - assert server.inference_lock.acquire(timeout=2), "handler did not release the lock" - server.inference_lock.release() - - -@pytest.mark.parametrize( - "raw", - [ - b"not json", - b'{"x":1,"x":2}', - b'{"x":{"y":1,"y":2}}', - b'{"x":NaN}', - b'{"x":Infinity}', - b'{"x":1e400}', - b"[]", - b"null", - b'{"x":"\xff"}', - b'{"x":"\\ud800"}', - b'{"\\ud800":1}', - b"[" * 2000 + b"]" * 2000, - "{}".encode("utf-16"), - ], -) -def test_malformed_json_returns_400_without_inference(deferred_cleanup, raw): - server, url = deferred_cleanup - - def unexpected(_): - raise AssertionError("malformed JSON reached inference") - - server.engine.predict = unexpected - status, body = call(url + "/v1/systemone", raw) - assert status == 400 and set(body) == {"detail"} - assert server.inference_lock.acquire(timeout=2), "handler did not release the lock" - server.inference_lock.release() - - -def test_missing_instructions_rejects_whole_request(worker): - server, url = worker - value = request() - value["questions"]["second"] = {"type": "choice", "criteria": {"a": "A"}} - - def unexpected(_): - raise AssertionError("invalid question reached inference") - - server.engine.predict = unexpected - status, body = call(url + "/v1/systemone", value) - assert status == 422 and set(body) == {"detail"} - assert "instructions" in body["detail"] - - -@pytest.mark.parametrize( - "route,body,headers,status", - [ - ("/missing", None, {}, 404), - ("/missing", {}, {}, 404), - ("/v1/systemone", {}, {"Content-Type": "text/plain"}, 415), - ("/v1/systemone", {}, {"Transfer-Encoding": "chunked"}, 411), - ("/v1/systemone", {}, {"Content-Length": "invalid"}, 400), - ("/v1/systemone", {}, {"Content-Length": str(MAX_BODY + 1)}, 413), - ], -) -def test_transport_errors_use_detail(worker, route, body, headers, status): - _, url = worker - actual, response = call(url + route, body, **headers) - assert actual == status and set(response) == {"detail"} - - -@pytest.mark.parametrize("size", [(2048, 1), (1, 2048), (201, 1), (1, 201)]) -def test_unsupported_image_aspect_ratio_returns_422_before_inference(worker, size): - server, url = worker - reached_engine = [] - - def unexpected(parsed): - reached_engine.append(parsed) - raise ValueError("unsupported processor input") - - server.engine.predict = unexpected - value = request() - value["state"]["image"] = image_url(size=size) - status, body = call(url + "/v1/systemone", value) - assert status == 422 and set(body) == {"detail"} - assert "aspect ratio" in body["detail"] - assert not reached_engine From 552c02ce9ce48cfe1ada512dee3b9da55ce3b8ca Mon Sep 17 00:00:00 2001 From: levius <2114377220@qq.com> Date: Wed, 30 Sep 2026 23:05:50 +0800 Subject: [PATCH 75/75] Fix graph capture ownership and retained pool accounting --- src/models/cua_s1/multimodal/graph_runtime.py | 164 +++++++++++++++++- 1 file changed, 156 insertions(+), 8 deletions(-) diff --git a/src/models/cua_s1/multimodal/graph_runtime.py b/src/models/cua_s1/multimodal/graph_runtime.py index 89c61f4..13a122a 100644 --- a/src/models/cua_s1/multimodal/graph_runtime.py +++ b/src/models/cua_s1/multimodal/graph_runtime.py @@ -7,10 +7,12 @@ from __future__ import annotations +import ctypes import gc import threading import time from collections import OrderedDict +from contextlib import suppress from dataclasses import dataclass, field @@ -78,20 +80,86 @@ def clear(self): return removed +class _GraphPool: + """One shape's private allocator pool and exclusively owned capture stream. + + Segments share scratch space only within a shape, in capture/replay order. + All graphs must be reset before this stream is destroyed. Other shapes use + separate streams because the pinned PyTorch clears cuBLAS workspaces by + capture stream when destroying a graph. + """ + + def __init__(self, device): + import torch + + self._torch = torch + self.device = device + self._stream_ptr = None + if torch.cuda.get_allocator_backend() != "native": + raise RuntimeError( + "graph memory accounting requires the native CUDA allocator" + ) + self.pool_id = torch.cuda.graph_pool_handle() + with torch.cuda.device(device): + pointer = ctypes.c_void_p() + torch.cuda.check_error( + torch.cuda.cudart().cudaStreamCreate(ctypes.addressof(pointer)) + ) + self._stream_ptr = pointer.value + self.capture_stream = torch.cuda.ExternalStream( + self._stream_ptr, device=device + ) + + def reserved_bytes(self): + # Allocated tensor deltas omit inactive graph scratch space, which stays + # reserved for replay. Query this pool directly, not process-wide deltas. + return sum( + segment["total_size"] + for segment in self._torch.cuda.memory_snapshot( + mempool_id=self.pool_id, include_traces=False + ) + ) + + def close(self): + if getattr(self, "_stream_ptr", None) is None: + return + torch = self._torch + with torch.cuda.device(self.device): + torch.cuda.check_error( + torch.cuda.cudart().cudaStreamDestroy(self._stream_ptr) + ) + self._stream_ptr = None + self.capture_stream = None + + def __del__(self): + try: + self.close() + except Exception: # noqa: BLE001, S110 -- best-effort destructor cleanup + pass + + class _GraphSegment: """One fixed-layout capture of adjacent linear-attention decoder layers.""" - def __init__(self, layers, start, end, hidden, mask): + def __init__(self, layers, start, end, hidden, mask, pool=None): import torch + self._torch = torch + self._device = hidden.device + self._pool = None + self._owns_pool = pool is None + self.graph = None + self.capture_stream = None self.layers = layers self.start = start self.end = end self.input_signature = tensor_signature(hidden) self.mask_signature = tensor_signature(mask) - before = torch.cuda.memory_allocated(hidden.device) self.static_hidden = hidden.clone() self.static_mask = mask.clone() if mask is not None else None + self.external_bytes = self.static_hidden.untyped_storage().nbytes() + if self.static_mask is not None: + self.external_bytes += self.static_mask.untyped_storage().nbytes() stream = torch.cuda.Stream(device=hidden.device) stream.wait_stream(torch.cuda.current_stream(hidden.device)) @@ -100,10 +168,52 @@ def __init__(self, layers, start, end, hidden, mask): self._forward() torch.cuda.current_stream(hidden.device).wait_stream(stream) - self.graph = torch.cuda.CUDAGraph() - with torch.cuda.graph(self.graph), torch.no_grad(): - self.static_output = self._forward() - self.bytes = max(0, torch.cuda.memory_allocated(hidden.device) - before) + try: + with torch.cuda.device(self._device): + self._pool = pool if pool is not None else _GraphPool(self._device) + self.capture_stream = self._pool.capture_stream + self.graph = torch.cuda.CUDAGraph() + with ( + torch.cuda.graph( + self.graph, + stream=self.capture_stream, + pool=self._pool.pool_id, + ), + torch.no_grad(), + ): + self.static_output = self._forward() + except BaseException: + # Preserve the actual capture failure if a poisoned CUDA context + # also prevents cleanup. The destructor makes a best-effort retry. + with suppress(Exception): + self.close() + raise + + def close(self): + """Retire a graph; shared-pool segments must all retire together.""" + if getattr(self, "_pool", None) is None: + return + torch = self._torch + with torch.cuda.device(self._device): + # Replays run on the caller's stream, not the capture stream. + torch.cuda.synchronize(self._device) + if self.graph is not None: + self.graph.reset() + self.graph = None + self.static_output = None + self.static_hidden = None + self.static_mask = None + if self._owns_pool: + self._pool.close() + self._pool = None + self.capture_stream = None + + def __del__(self): + # Interpreter teardown and failed CUDA contexts cannot safely raise. + try: + self.close() + except Exception: # noqa: BLE001, S110 -- best-effort destructor cleanup + pass def _forward(self): hidden = self.static_hidden @@ -135,6 +245,28 @@ def replay(self, hidden, mask): class _ShapeEntry: blocks: dict = field(default_factory=dict) bytes: int = 0 + pool: _GraphPool | None = None + + def update_bytes(self): + self.bytes = self.pool.reserved_bytes() + sum( + block.external_bytes for block in self.blocks.values() + ) + + def close(self): + # No surviving segment may replay after the first reset clears the + # shared stream's workspace. The runtime retires an entire shape at once. + for block in self.blocks.values(): + block.close() + self.blocks.clear() + if self.pool is not None: + self.pool.close() + self.pool = None + + def __del__(self): + try: + self.close() + except Exception: # noqa: BLE001, S110 -- best-effort destructor cleanup + pass class GraphRuntime: @@ -169,6 +301,8 @@ def invalidate(self): retired = self.cache.clear() self.uses.clear() self.disabled.clear() + for entry in retired: + entry.close() del retired gc.collect() @@ -237,11 +371,18 @@ def _run_segments(self, values, entry): end += 1 block = entry.blocks.get((index, end)) if block is None: + if entry.pool is None: + entry.pool = _GraphPool(hidden.device) block = _GraphSegment( - text.layers, index, end, hidden, masks["linear_attention"] + text.layers, + index, + end, + hidden, + masks["linear_attention"], + pool=entry.pool, ) entry.blocks[(index, end)] = block - entry.bytes += block.bytes + entry.update_bytes() hidden = block.replay(hidden, masks["linear_attention"]) index = end hidden = text.norm(hidden) @@ -291,6 +432,7 @@ def forward(self, values): capture_ms = (time.perf_counter() - capture_start) * 1000 except torch.cuda.OutOfMemoryError: self.stats["capture_oom"] += 1 + candidate.close() del candidate gc.collect() torch.cuda.empty_cache() @@ -300,6 +442,7 @@ def forward(self, values): # Only a healthy CUDA context can continue serving eagerly. torch.cuda.synchronize(values["inputs_embeds"].device) self.stats["capture_error"] += 1 + candidate.close() del candidate self._disable(key) gc.collect() @@ -307,6 +450,7 @@ def forward(self, values): if not torch.equal(reference, output): self.stats["numerical_mismatch"] += 1 + candidate.close() del candidate, output self._disable(key) gc.collect() @@ -314,6 +458,7 @@ def forward(self, values): retired = self.cache.put(key, candidate) if retired is None: self.stats["memory_budget"] += 1 + candidate.close() del candidate self._disable(key) gc.collect() @@ -322,6 +467,9 @@ def forward(self, values): self.stats["capture_ms"] += capture_ms self.stats["evictions"] += len(retired) if retired: + for entry in retired: + entry.close() + del entry del retired gc.collect() else: