diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/environment/Dockerfile b/tasks/act-tensor-sparse-panel-imputation-r2/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..87b2aabb324bfed31ae179e525c6ba7a107f1104 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/environment/Dockerfile @@ -0,0 +1,44 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +RUN set -eu; \ + if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \ + apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \ + fi; \ + rm -f /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1; \ + rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/* + + +ENTRYPOINT [] +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 + +RUN apt-get update \ + && apt-get install -y --no-install-recommends git=1:2.39.5-0+deb12u3 \ + && git config --system user.email "agent@localhost" \ + && git config --system user.name "autoresearch agent" \ + && rm -rf /var/lib/apt/lists/* + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt + +WORKDIR /app +COPY ./app /app + +RUN mkdir -p /app/solution \ + && chown -R agent:agent /app \ + && find /app -exec touch -d @1700000000 {} + + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 + +USER agent diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/environment/requirements.txt b/tasks/act-tensor-sparse-panel-imputation-r2/environment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d726c5907cd65c3e4c6bbd2a3854aa0e6fdcffe9 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/environment/requirements.txt @@ -0,0 +1,3 @@ +numpy==2.5.2 +scipy==1.18.0 +scikit-learn==1.9.0 diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/hints/hint.md b/tasks/act-tensor-sparse-panel-imputation-r2/hints/hint.md new file mode 100644 index 0000000000000000000000000000000000000000..9bb38d08e5604412f07204df058f64043f43ea16 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/hints/hint.md @@ -0,0 +1 @@ +Map the ranks to normal scores before any conditioning, then combine low-rank fits along all three modes rather than one unfolding, and push the rank far higher than seems reasonable. Model the residuals afterwards: an autoregressive smoother over observed neighbours, a cross-characteristic ridge, then a short-window and a global cross-time ridge, all augmented with the missingness mask. Low-rank estimates are shrunk toward zero, so refit a scale against observed entries before clipping. diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/Dockerfile b/tasks/act-tensor-sparse-panel-imputation-r2/tests/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..720b11f41e8bc6811e75971c6ec000f06b0087e0 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/Dockerfile @@ -0,0 +1,35 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +ENTRYPOINT [] +ENV PYTHONPATH= +ENV PYTHONSAFEPATH=1 +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 \ + OPENBLAS_NUM_THREADS=1 \ + MKL_NUM_THREADS=1 \ + NUMEXPR_NUM_THREADS=1 + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir \ + -r /tmp/requirements.txt -r /tmp/requirements-verifier.txt \ + && rm /tmp/requirements.txt /tmp/requirements-verifier.txt + +RUN mkdir -p /app/solution /logs/verifier + +COPY . /tests + +RUN chown -R root:root /tests \ + && chmod 700 /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \ + && chmod +x /tests/test.sh /tests/intermediate.sh \ + && find /tests -exec touch -d @1700000000 {} + + +WORKDIR /tests + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/compute_reward.py b/tasks/act-tensor-sparse-panel-imputation-r2/tests/compute_reward.py new file mode 100644 index 0000000000000000000000000000000000000000..54501469dc5d13d792fb0b43fb9c9875e26c9f7d --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/compute_reward.py @@ -0,0 +1,17 @@ +import json +import math +from pathlib import Path + +REWARD_PATH = Path("/logs/verifier/reward.txt") + +try: + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + value = float(json.loads(Path("/logs/verifier/metric.json").read_text())["reward"]) + if not math.isfinite(value): + raise ValueError("non-finite reward") + reward = f"{min(1.0, max(0.0, value)):.12g}" + REWARD_PATH.write_text(reward + "\n") + print(f"reward={reward}") +except Exception as exc: + REWARD_PATH.write_text("0\n") + print(f"reward=0 (fallback: {exc})") diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/grader_core.py b/tasks/act-tensor-sparse-panel-imputation-r2/tests/grader_core.py new file mode 100644 index 0000000000000000000000000000000000000000..e3653d9223cd155ea318e648e78e94ae58050944 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/grader_core.py @@ -0,0 +1,60 @@ +import math + +import numpy as np + +VALUE_LO = -1.0 +VALUE_HI = 1.0 +PER_INSTANCE_TIMEOUT_SEC = 240.0 +MAX_OUTPUT_BYTES = 64_000_000 + +M0 = 0.0 +BASELINE_SCORE = 0.7047 +HIGHER_IS_BETTER = True + + +def r2_imp(pred_at_eval, truth_at_eval): + """R^2_imp pooled over one instance's held-out cells. + + 1 - sum_m (x_m - xtilde_m)^2 / sum_m (x_m - xbar)^2, with xbar the mean of the + held-out TRUE values. + """ + truth = np.asarray(truth_at_eval, dtype=np.float64) + pred = np.asarray(pred_at_eval, dtype=np.float64) + denom = float(np.sum((truth - truth.mean()) ** 2)) + if not math.isfinite(denom) or denom <= 0.0: + raise ValueError("degenerate held-out set: zero variance") + resid = float(np.sum((truth - pred) ** 2)) + return 1.0 - resid / denom + + +def validate_output(arr, expected_shape): + """Return (ok, reason). Mirrors the contract stated in instruction.md.""" + if arr is None: + return False, "no output produced" + if not isinstance(arr, np.ndarray): + return False, f"output is {type(arr).__name__}, not a numpy array" + if arr.dtype.kind != "f": + return False, f"output dtype {arr.dtype} is not a real floating dtype" + if arr.shape != expected_shape: + return False, f"output shape {arr.shape} != {expected_shape}" + values = arr.astype(np.float64, copy=False) + if not np.all(np.isfinite(values)): + return False, "output contains NaN or inf" + if float(values.min()) < VALUE_LO or float(values.max()) > VALUE_HI: + return False, f"output outside [{VALUE_LO}, {VALUE_HI}]" + return True, "" + + +def graded_reward(metric, m0=M0, baseline_score=BASELINE_SCORE, + higher_is_better=HIGHER_IS_BETTER, + valid=True): + """rational_squash: 0 at the trivial baseline, 0.5 at the baseline score, asymptotic + to 1, monotone, never negative, with no cutoff.""" + if not valid or metric is None or not math.isfinite(metric): + return 0.0 + sigma = 1.0 if higher_is_better else -1.0 + x = sigma * (metric - m0) + x_ref = sigma * (baseline_score - m0) + assert x_ref > 0, "malformed task: the baseline score does not beat the trivial baseline" + u = max(0.0, x) / x_ref + return u / (1.0 + u) diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/intermediate.sh b/tasks/act-tensor-sparse-panel-imputation-r2/tests/intermediate.sh new file mode 100644 index 0000000000000000000000000000000000000000..206cf294d0ac4e99b577515042f1a2ba3731d5d7 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/intermediate.sh @@ -0,0 +1,33 @@ +#!/bin/bash +set -u + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then mkdir -p /logs/verifier; printf "0\n" > /logs/verifier/reward.txt; fi' EXIT + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +chmod -R go-rwx /tests + +cd /tests +export GRADER_SPLIT=intermediate +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json \ + /tests/test_state.py -rA > /logs/verifier/intermediate_pytest.log 2>&1 +/usr/local/bin/python3 /tests/compute_reward.py >> /logs/verifier/intermediate_pytest.log 2>&1 + +/usr/local/bin/python3 - <<'PY' +import json +from pathlib import Path +try: + d = json.loads(Path("/logs/verifier/metric.json").read_text()) + print(f"panels_scored={d['n_instances']} ran_without_error={d['n_scored_ok']}") + print(f"mean_R2_imp={d['metric']:+.4f}") + if not d["submission_ok"]: + print(f"submission_rejected: {d['submission_note']}") +except Exception: + print("panels_scored=0 ran_without_error=0") + print("mean_R2_imp=n/a (the submission produced no gradeable result)") +PY + +exit 0 diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements-verifier.txt b/tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements-verifier.txt new file mode 100644 index 0000000000000000000000000000000000000000..04676c89520bf969f2cb861ad1811f46d8370700 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements-verifier.txt @@ -0,0 +1,2 @@ +pytest==8.3.5 +pytest-json-ctrf==0.3.5 diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements.txt b/tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d726c5907cd65c3e4c6bbd2a3854aa0e6fdcffe9 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements.txt @@ -0,0 +1,3 @@ +numpy==2.5.2 +scipy==1.18.0 +scikit-learn==1.9.0 diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/run_candidate.py b/tasks/act-tensor-sparse-panel-imputation-r2/tests/run_candidate.py new file mode 100644 index 0000000000000000000000000000000000000000..5c9d9b83f8a26cfb04df77a0111d45580a2389d8 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/run_candidate.py @@ -0,0 +1,23 @@ +import sys +from pathlib import Path + +import numpy as np + + +def main(): + cand_dir, panel_path, seed, out_path = sys.argv[1:5] + sys.path.insert(0, cand_dir) + + import importlib.util + spec = importlib.util.spec_from_file_location( + "candidate_impute", str(Path(cand_dir) / "impute.py")) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + panel = np.load(panel_path, allow_pickle=False) + result = module.impute(panel, int(seed)) + np.save(out_path, np.asarray(result), allow_pickle=False) + + +if __name__ == "__main__": + main() diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/test.sh b/tasks/act-tensor-sparse-panel-imputation-r2/tests/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..de03a157dd1c09ef540ef7ed3e7f6b3b3a187c2a --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/test.sh @@ -0,0 +1,18 @@ +#!/bin/bash +set -u + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then mkdir -p /logs/verifier; printf "0\n" > /logs/verifier/reward.txt; fi' EXIT + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +chmod -R go-rwx /tests + +cd /tests +export GRADER_SPLIT=final +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/local/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/act-tensor-sparse-panel-imputation-r2/tests/test_state.py b/tasks/act-tensor-sparse-panel-imputation-r2/tests/test_state.py new file mode 100644 index 0000000000000000000000000000000000000000..6c27bf7973272ba4f67654319a51dc3b075b1922 --- /dev/null +++ b/tasks/act-tensor-sparse-panel-imputation-r2/tests/test_state.py @@ -0,0 +1,199 @@ +import json +import os +import shutil +import signal +import stat +import subprocess +import sys +import tempfile +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import grader_core as gc + +TESTS_DIR = Path(__file__).resolve().parent +SUBMISSION_DIR = Path("/app/solution") +ENTRY_POINT = "impute.py" +METRIC_PATH = Path("/logs/verifier/metric.json") +SPLIT = os.environ.get("GRADER_SPLIT", "final") +HIDDEN_DIR = TESTS_DIR / "hidden_data" / SPLIT +RUNUSER = shutil.which("runuser") or "/usr/sbin/runuser" +AGENT_UID, AGENT_GID = 1001, 1001 +MAX_CONCURRENT = 4 +PYTHON = sys.executable + + +def _copy_submission(dest): + """Copy the submitted directory, treating every submitted path as hostile. + + Any symlink or multiply-linked regular file anywhere in the tree rejects the whole + submission: a privileged copy that followed `impute.py -> /tests/hidden_data/...` + would materialise verifier-owned files into the directory the score is computed from. + """ + if not SUBMISSION_DIR.is_dir() or SUBMISSION_DIR.is_symlink(): + return False, f"{SUBMISSION_DIR} is missing or is a symlink" + for path in [SUBMISSION_DIR, *SUBMISSION_DIR.rglob("*")]: + info = path.lstat() + if stat.S_ISLNK(info.st_mode): + return False, f"submitted tree contains a symlink: {path}" + if stat.S_ISREG(info.st_mode) and info.st_nlink > 1: + return False, f"submitted tree contains a hard link: {path}" + shutil.copytree(SUBMISSION_DIR, dest, symlinks=True, dirs_exist_ok=True) + subprocess.run(["chown", "-R", "root:root", str(dest)], check=False) + subprocess.run(["chmod", "-R", "a-w,a+rX", str(dest)], check=False) + if not (Path(dest) / ENTRY_POINT).is_file(): + return False, f"no {ENTRY_POINT} in the submitted directory" + return True, "" + + +def _run_one(cand_dir, npz_path): + """Execute the candidate on one sealed panel and score it here, in the root process. + + Returns (metric, note). Every failure mode -- missing entry point, import error, + exception, timeout, no output, unparseable output, wrong shape, wrong dtype, + non-finite value, out-of-range value -- returns the trivial-baseline metric. + """ + with np.load(npz_path, allow_pickle=False) as data: + panel = np.ascontiguousarray(data["panel"], dtype=np.float64) + eval_mask = data["eval_mask"] + eval_true = data["eval_true"] + seed = int(data["seed"]) + + work = Path(tempfile.mkdtemp(prefix="grade_")) + try: + os.chmod(work, 0o755) + runner = work / "run_candidate.py" + shutil.copy(TESTS_DIR / "run_candidate.py", runner) + panel_path = work / "panel.npy" + np.save(panel_path, panel, allow_pickle=False) + os.chmod(panel_path, 0o444) + os.chmod(runner, 0o444) + + scratch = work / "scratch" + scratch.mkdir() + os.chown(scratch, AGENT_UID, AGENT_GID) + os.chmod(scratch, 0o755) + out_path = scratch / "out.npy" + + env = { + "PATH": "/usr/local/bin:/usr/bin:/bin", + "HOME": str(scratch), + "TMPDIR": str(scratch), + "PYTHONHASHSEED": "0", + "PYTHONSAFEPATH": "1", + "PYTHONDONTWRITEBYTECODE": "1", + "OMP_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "NUMEXPR_NUM_THREADS": "1", + } + cmd = [RUNUSER, "-u", "agent", "--", PYTHON, str(runner), + str(cand_dir), str(panel_path), str(seed), str(out_path)] + chatter_path = work / "candidate_stdio.log" + with open(chatter_path, "wb") as sink: + proc = subprocess.Popen(cmd, env=env, cwd=str(scratch), + stdout=sink, stderr=subprocess.STDOUT, + stdin=subprocess.DEVNULL, start_new_session=True) + try: + proc.wait(timeout=gc.PER_INSTANCE_TIMEOUT_SEC) + except subprocess.TimeoutExpired: + try: + os.killpg(os.getpgid(proc.pid), signal.SIGKILL) + except OSError: + proc.kill() + proc.wait() + return gc.M0, "timed out" + if proc.returncode != 0: + tail = chatter_path.read_bytes()[-4000:].decode("utf-8", "replace") + lines = tail.strip().splitlines() + return gc.M0, f"candidate exited {proc.returncode}: {lines[-1][:200] if lines else ''}" + + if not out_path.exists(): + return gc.M0, "no output file" + info = out_path.lstat() + if stat.S_ISLNK(info.st_mode) or not stat.S_ISREG(info.st_mode): + return gc.M0, "output path is not a regular file" + if info.st_size > gc.MAX_OUTPUT_BYTES: + return gc.M0, "output file too large" + try: + fd = os.open(out_path, os.O_RDONLY | os.O_NOFOLLOW) + with os.fdopen(fd, "rb") as handle: + arr = np.load(handle, allow_pickle=False) + except Exception as exc: + return gc.M0, f"unreadable output: {type(exc).__name__}" + + ok, reason = gc.validate_output(arr, panel.shape) + if not ok: + return gc.M0, reason + return gc.r2_imp(arr.astype(np.float64, copy=False)[eval_mask], eval_true), "ok" + finally: + shutil.rmtree(work, ignore_errors=True) + + +def _reap_agent_processes(): + """Kill anything still running as uid 1001. + + A candidate can detach itself from the process group the per-panel timeout kills. It + could not forge a score even so -- /logs/verifier is root-owned 0755 -- but nothing + agent-authored should outlive the graded run. Done by walking /proc rather than with + pkill, which the slim base does not ship. + """ + for entry in Path("/proc").iterdir(): + if not entry.name.isdigit(): + continue + try: + if entry.stat().st_uid == AGENT_UID: + os.kill(int(entry.name), signal.SIGKILL) + except (OSError, ProcessLookupError): + pass + + +def test_imputation_r2(): + """Grade the submitted imputer on the sealed panels of this split. + + WHAT: executes /app/solution/impute.py once per sealed panel and recomputes + R^2_imp on the held-out cells from verifier-owned ground truth. + WHY: the score must come from running the agent's code on panels it has never seen, + never from a file it wrote or a number it reported. + """ + instances = sorted(HIDDEN_DIR.glob("instance_*.npz")) + assert instances, f"sealed split {HIDDEN_DIR} is empty" + + cand_root = Path(tempfile.mkdtemp(prefix="cand_")) + os.chmod(cand_root, 0o755) + cand_dir = cand_root / "solution" + ok, reason = _copy_submission(cand_dir) + if ok: + with ThreadPoolExecutor(max_workers=MAX_CONCURRENT) as pool: + results = list(pool.map(lambda p: _run_one(cand_dir, p), instances)) + else: + results = [(gc.M0, reason)] * len(instances) + shutil.rmtree(cand_root, ignore_errors=True) + _reap_agent_processes() + + metrics = [m for m, _ in results] + notes = [n for _, n in results] + mean_metric = float(np.mean(metrics)) + reward = gc.graded_reward(mean_metric) + + METRIC_PATH.parent.mkdir(parents=True, exist_ok=True) + METRIC_PATH.write_text(json.dumps({ + "metric_name": "mean_out_of_sample_R2_imp_block_regime", + "split": SPLIT, + "n_instances": len(instances), + "n_scored_ok": sum(1 for n in notes if n == "ok"), + "metric": mean_metric, + "reward": reward, + "submission_ok": ok, + "submission_note": reason if not ok else "ok", + }, indent=2) + "\n") + + if SPLIT == "final": + for path, (m, note) in zip(instances, results): + print(f"{path.name}: R2_imp={m:+.4f} ({note})") + print(f"split={SPLIT} instances={len(instances)} " + f"scored_ok={sum(1 for n in notes if n == 'ok')} " + f"mean_R2_imp={mean_metric:+.4f} reward={reward:.4f}") diff --git a/tasks/carps-star-discrepancy-subset-select/environment/Dockerfile b/tasks/carps-star-discrepancy-subset-select/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..9b9d71f0e46599e3ad7b0ffbffdf4bb9307570f4 --- /dev/null +++ b/tasks/carps-star-discrepancy-subset-select/environment/Dockerfile @@ -0,0 +1,41 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +RUN set -eu; \ + if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \ + apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \ + fi; \ + rm -f /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1 git=1:2.39.5-0+deb12u3; \ + rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/* + + +ENTRYPOINT [] +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 + +RUN git config --system user.email "agent@localhost" \ + && git config --system user.name "autoresearch agent" + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt + +WORKDIR /app +COPY ./app /app + +RUN mkdir -p /app/output \ + && chown -R agent:agent /app \ + && find /app -exec touch -d @1700000000 {} + + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 + +USER agent diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/Dockerfile b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0d16b892132541e2964f3564e6dd5d346cac8d38 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/Dockerfile @@ -0,0 +1,42 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +RUN set -eu; \ + if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \ + apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \ + fi; \ + rm -f /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1 git=1:2.39.5-0+deb12u3; \ + rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/* + + +ENTRYPOINT [] +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 + +RUN git config --system user.email "agent@localhost" \ + && git config --system user.name "agent" + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt + +WORKDIR /app +COPY ./app /app + +RUN mkdir -p /app/output \ + && chown -R agent:agent /app \ + && chown -R root:root /app/data \ + && find /app -exec touch -d @1700000000 {} + + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 + +USER agent diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/requirements.txt b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a25dfe8c2a945f5ae6037e76c3593b7a26743139 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/requirements.txt @@ -0,0 +1,17 @@ +--extra-index-url https://download.pytorch.org/whl/cpu +filelock==3.32.3 +fsspec==2026.7.0 +Jinja2==3.1.6 +joblib==1.5.3 +MarkupSafe==3.0.3 +mpmath==1.3.0 +narwhals==2.24.0 +networkx==3.6.1 +numpy==2.5.2 +scikit-learn==1.9.0 +scipy==1.18.0 +setuptools==84.0.0 +sympy==1.14.0 +threadpoolctl==3.6.0 +torch==2.13.0+cpu +typing_extensions==4.16.0 diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/hints/hint.md b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/hints/hint.md new file mode 100644 index 0000000000000000000000000000000000000000..dd9f478d1f428e0bde29a2d86eeddf9d5c5ad56d --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/hints/hint.md @@ -0,0 +1 @@ +The generator uses one sparse coefficient vector shared by both arms, drawn from a small discrete grid, and the treated cohort is the same fixed set of rows in every dataset with its average effect pinned to an exact constant. Impose that constant as a hard constraint, then search the grid by profiled coordinate descent with pair and triple escape moves, using a smooth likelihood to search and the exact one only to re-rank near-optimal candidates. Aggregate candidates with a weighted geometric median, since the metric averages per-dataset RMSE. diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/Dockerfile b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..47c41a7e55a7bd1bd241b41ac76db879663ff5ea --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/Dockerfile @@ -0,0 +1,34 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +ENTRYPOINT [] +ENV PYTHONPATH= +ENV PYTHONSAFEPATH=1 +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 \ + MKL_NUM_THREADS=1 \ + OPENBLAS_NUM_THREADS=1 + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir \ + -r /tmp/requirements.txt -r /tmp/requirements-verifier.txt \ + && rm /tmp/requirements.txt /tmp/requirements-verifier.txt + +RUN mkdir -p /app/output /logs/verifier + +COPY . /tests + +RUN chown -R root:root /tests \ + && chmod 700 /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \ + && chmod +x /tests/test.sh /tests/intermediate.sh \ + && find /tests -exec touch -d @1700000000 {} + + +WORKDIR /tests + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/compute_reward.py b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/compute_reward.py new file mode 100644 index 0000000000000000000000000000000000000000..8233986f06026ee40725e08b95982288aef591f6 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/compute_reward.py @@ -0,0 +1,19 @@ +import json +import math +from pathlib import Path + +REWARD_PATH = Path("/logs/verifier/reward.txt") +METRIC_PATH = Path("/logs/verifier/metric.json") + +try: + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + value = float(json.loads(METRIC_PATH.read_text())["reward"]) + if not math.isfinite(value): + raise ValueError("non-finite reward") + reward = f"{min(1.0, max(0.0, value)):.12g}" + REWARD_PATH.write_text(reward + "\n") + print(f"reward={reward}") +except Exception as exc: + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + REWARD_PATH.write_text("0\n") + print(f"reward=0 (fallback: {exc})") diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/grader_core.py b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/grader_core.py new file mode 100644 index 0000000000000000000000000000000000000000..57def0716ce649f98ad0324a0239d5b12b4c313e --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/grader_core.py @@ -0,0 +1,170 @@ +from __future__ import annotations + +import math +import os +import shutil +import stat +from pathlib import Path + +import numpy as np + +N_TRAIN = 672 +N_TEST = 75 +N_COVARIATES = 25 + +GRADED_RUN_BUDGET_SEC = 1200.0 + +M0 = 5.40 +BASELINE_SCORE = 0.58 + +STATUS_OK = 0 +STATUS_RAISED = 1 +STATUS_BAD_SHAPE = 2 +STATUS_NON_FINITE = 3 + + +def load_sealed_panel(panel_path: str | os.PathLike) -> tuple[list[dict], np.ndarray]: + """Return (inputs, taus). `inputs` carries only what a candidate may see.""" + with np.load(panel_path, allow_pickle=False) as f: + n = int(f["n_realizations"]) + inputs, taus = [], [] + for k in range(n): + inputs.append({ + "X_train": f[f"X_train_{k:03d}"].astype(np.float64), + "t_train": f[f"t_train_{k:03d}"].astype(np.float64), + "y_train": f[f"y_train_{k:03d}"].astype(np.float64), + "X_test": f[f"X_test_{k:03d}"].astype(np.float64), + }) + taus.append(f[f"tau_test_{k:03d}"].astype(np.float64)) + for d, tau in zip(inputs, taus): + assert d["X_train"].shape == (N_TRAIN, N_COVARIATES), "sealed panel geometry changed" + assert d["X_test"].shape == (N_TEST, N_COVARIATES), "sealed panel geometry changed" + assert tau.shape == (N_TEST,), "sealed panel geometry changed" + return inputs, np.asarray(taus, dtype=np.float64) + + +def pehe(tau_true: np.ndarray, tau_hat: np.ndarray) -> float: + """Root-mean-squared error of the estimated effect over one realization's test units.""" + return float(np.sqrt(np.mean((np.asarray(tau_true, dtype=np.float64) + - np.asarray(tau_hat, dtype=np.float64)) ** 2))) + + +def constant_baseline_tau(one_input: dict) -> float: + """The no-covariate constant effect: mean(y | t=1) - mean(y | t=0) on the training rows. + + Recomputed here by the verifier from trusted bytes. A call that fails is scored as if it + had returned this constant, which is exactly what a submission gets by *returning* the + constant, so a failed call can never beat an honest one. + """ + t = one_input["t_train"] + y = one_input["y_train"] + treated, control = y[t == 1.0], y[t == 0.0] + if treated.size == 0 or control.size == 0: + return 0.0 + return float(treated.mean() - control.mean()) + + +def score_panel(inputs: list[dict], taus: np.ndarray, preds: np.ndarray, + status: np.ndarray) -> dict: + """Per-realization PEHE with the constant-baseline fallback, then the panel mean. + + `preds` and `status` come from the candidate's process and are treated as untrusted + data: shape, dtype and finiteness are re-checked here, and any realization that fails + that re-check is scored by the fallback regardless of what status it claims. + """ + n = len(inputs) + per, n_ok, n_fallback = [], 0, 0 + for k in range(n): + row = preds[k] if (preds.ndim == 2 and preds.shape[0] > k) else None + ok = (row is not None and row.shape == (N_TEST,) and np.all(np.isfinite(row)) + and int(status[k]) == STATUS_OK) + if ok: + per.append(pehe(taus[k], row)) + n_ok += 1 + else: + fallback = np.full(N_TEST, constant_baseline_tau(inputs[k])) + per.append(pehe(taus[k], fallback)) + n_fallback += 1 + per_arr = np.asarray(per, dtype=np.float64) + return { + "metric": float(per_arr.mean()), + "n_realizations": n, + "n_ok": n_ok, + "n_fallback": n_fallback, + "pehe_median": float(np.median(per_arr)), + "pehe_p90": float(np.quantile(per_arr, 0.9)), + "pehe_max": float(per_arr.max()), + } + + +def graded_reward(metric: float | None, valid: bool) -> float: + """Continuous reward in [0, 1]; lower mean PEHE is strictly better, with no cutoff. + + Log-ratio improvement in baseline-score units, squashed by u/(1+u): + + u = max(0, log(m0 / m) / log(m0 / baseline_score)), reward = u / (1 + u) + + m >= m0 (the strongest trivial predictor) -> 0. m == baseline_score -> 0.5. + m -> 0 -> 1. PEHE is ratio-scale with a true zero and improves multiplicatively, so the + improvement is measured as a log ratio. + """ + if not valid or metric is None: + return 0.0 + m = float(metric) + if not math.isfinite(m) or m < 0.0: + return 0.0 + scale = math.log(M0 / BASELINE_SCORE) + assert scale > 0.0, "malformed task: the baseline anchor does not beat the trivial baseline" + if m <= 0.0: + return 1.0 + u = max(0.0, math.log(M0 / m) / scale) + return u / (1.0 + u) + + +def copy_solution_tree(src: str | os.PathLike, dst: str | os.PathLike) -> dict: + """Copy the submitted tree into a root-owned, agent-read-only sandbox. + + Every submitted path is treated as hostile. Symlinks, hard links and non-regular files + are pruned at every depth BEFORE anything is copied, and the copy itself never follows + a link (`shutil.copyfile(..., follow_symlinks=False)`, the equivalent of + `cp --no-dereference` / `copytree(symlinks=True)`), so a submitted + `estimator.py -> /tests/hidden_data/final/panel.npz` cannot materialise verifier bytes + inside the sandbox the run reads from. + """ + src, dst = Path(src), Path(dst) + pruned: list[str] = [] + dst.mkdir(parents=True, exist_ok=True) + for root, dirs, files in os.walk(src, followlinks=False): + rel = Path(root).relative_to(src) + kept_dirs = [] + for d in sorted(dirs): + p = Path(root) / d + if p.is_symlink(): + pruned.append(str(p)) + continue + kept_dirs.append(d) + (dst / rel / d).mkdir(parents=True, exist_ok=True) + dirs[:] = kept_dirs + for f in sorted(files): + p = Path(root) / f + st = p.lstat() + if stat.S_ISLNK(st.st_mode) or not stat.S_ISREG(st.st_mode) or st.st_nlink > 1: + pruned.append(str(p)) + continue + shutil.copyfile(p, dst / rel / f, follow_symlinks=False) + for p in [dst, *sorted(dst.rglob("*"))]: + os.chown(p, 0, 0) + os.chmod(p, 0o755 if p.is_dir() else 0o644) + return {"pruned": pruned} + + +def write_runner_inputs(inputs: list[dict], path: str | os.PathLike) -> None: + """Serialize the grade-time inputs for the candidate process. Carries NO targets.""" + arrays: dict[str, np.ndarray] = {"n_realizations": np.array(len(inputs))} + for k, d in enumerate(inputs): + arrays[f"X_train_{k:03d}"] = d["X_train"] + arrays[f"t_train_{k:03d}"] = d["t_train"] + arrays[f"y_train_{k:03d}"] = d["y_train"] + arrays[f"X_test_{k:03d}"] = d["X_test"] + np.savez(path, **arrays) + os.chmod(path, 0o644) diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/intermediate.sh b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/intermediate.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4e415cbf08a3d23f6d5579c4f5585737888939f --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/intermediate.sh @@ -0,0 +1,19 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT + +chmod -R go-rwx /tests + +export GRADER_SPLIT=intermediate + +cd /tests +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/local/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements-verifier.txt b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements-verifier.txt new file mode 100644 index 0000000000000000000000000000000000000000..a228fc89972b86b606ea7964c410bd5cd98bc770 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements-verifier.txt @@ -0,0 +1,5 @@ +iniconfig==2.3.0 +packaging==26.3 +pluggy==1.6.0 +pytest==8.3.5 +pytest-json-ctrf==0.3.5 diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements.txt b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a25dfe8c2a945f5ae6037e76c3593b7a26743139 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements.txt @@ -0,0 +1,17 @@ +--extra-index-url https://download.pytorch.org/whl/cpu +filelock==3.32.3 +fsspec==2026.7.0 +Jinja2==3.1.6 +joblib==1.5.3 +MarkupSafe==3.0.3 +mpmath==1.3.0 +narwhals==2.24.0 +networkx==3.6.1 +numpy==2.5.2 +scikit-learn==1.9.0 +scipy==1.18.0 +setuptools==84.0.0 +sympy==1.14.0 +threadpoolctl==3.6.0 +torch==2.13.0+cpu +typing_extensions==4.16.0 diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/runner.py b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..c4c122848681a605ad182ce8cc3661c94f865d33 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/runner.py @@ -0,0 +1,91 @@ +from __future__ import annotations + +import importlib.util +import json +import os +import random +import sys +import time + +import numpy as np + +N_TEST = 75 +STATUS_OK, STATUS_RAISED, STATUS_BAD_SHAPE, STATUS_NON_FINITE = 0, 1, 2, 3 + + +def _pin_determinism() -> None: + """Mirror the OMP/MKL/OPENBLAS pins the launcher's env dict already sets: torch has its + own thread pool that a thread-count env var alone does not reliably bind, and any candidate + that draws from the global RNGs without its own seed needs a fixed starting state.""" + random.seed(0) + np.random.seed(0) + try: + import torch + torch.manual_seed(0) + torch.set_num_threads(1) + except ImportError: + pass + + +def main() -> int: + _pin_determinism() + inputs_path, solution_dir, out_path, meta_path = sys.argv[1:5] + meta = {"import_ok": False, "error": None, "n_calls": 0, "elapsed_sec": 0.0} + + def dump_meta(): + with open(meta_path, "w") as fh: + json.dump(meta, fh) + + data = np.load(inputs_path, allow_pickle=False) + n = int(data["n_realizations"]) + preds = np.zeros((n, N_TEST), dtype=np.float64) + status = np.full(n, STATUS_RAISED, dtype=np.int64) + + entry_point = os.path.join(solution_dir, "estimator.py") + sys.path.insert(0, solution_dir) + try: + spec = importlib.util.spec_from_file_location("candidate_estimator", entry_point) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + fn = getattr(module, "estimate_cate") + if not callable(fn): + raise TypeError("estimate_cate is not callable") + meta["import_ok"] = True + except BaseException as exc: + meta["error"] = f"{type(exc).__name__}: {exc}"[:500] + dump_meta() + np.savez(out_path, preds=preds, status=status) + return 0 + + t0 = time.monotonic() + for k in range(n): + X_train = np.array(data[f"X_train_{k:03d}"], dtype=np.float64) + t_train = np.array(data[f"t_train_{k:03d}"], dtype=np.float64) + y_train = np.array(data[f"y_train_{k:03d}"], dtype=np.float64) + X_test = np.array(data[f"X_test_{k:03d}"], dtype=np.float64) + try: + out = fn(X_train, t_train, y_train, X_test) + arr = np.asarray(out, dtype=np.float64) + if arr.shape != (X_test.shape[0],): + status[k] = STATUS_BAD_SHAPE + continue + if not np.all(np.isfinite(arr)): + status[k] = STATUS_NON_FINITE + continue + preds[k, : arr.shape[0]] = arr + status[k] = STATUS_OK + except BaseException as exc: + status[k] = STATUS_RAISED + if meta["error"] is None: + meta["error"] = f"call {k}: {type(exc).__name__}: {exc}"[:500] + finally: + meta["n_calls"] = k + 1 + + meta["elapsed_sec"] = time.monotonic() - t0 + np.savez(out_path, preds=preds, status=status) + dump_meta() + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test.sh b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..05775cef70d540a9732b6b1b511dd5ecc8066b47 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test.sh @@ -0,0 +1,19 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT + +chmod -R go-rwx /tests + +export GRADER_SPLIT=final + +cd /tests +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/local/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test_state.py b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test_state.py new file mode 100644 index 0000000000000000000000000000000000000000..2fc5c59f86a324bc237b514b9b94bad4e367c089 --- /dev/null +++ b/tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test_state.py @@ -0,0 +1,208 @@ +from __future__ import annotations + +import json +import os +import signal +import stat +import subprocess +import sys +import tempfile +import time +from pathlib import Path + +import numpy as np + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import grader_core as gc + +TESTS_DIR = Path(__file__).resolve().parent +SPLIT = os.environ.get("GRADER_SPLIT", "final") +PANEL_PATH = TESTS_DIR / "hidden_data" / SPLIT / "panel.npz" +ARTIFACT_DIR = Path("/app/output") +LOG_DIR = Path("/logs/verifier") + +PYTHON = "/usr/local/bin/python3" +RUNUSER = "/usr/sbin/runuser" +AGENT_USER = "agent" +AGENT_UID = 1001 +AGENT_GID = 1001 + + +def _write_metric(payload: dict) -> None: + LOG_DIR.mkdir(parents=True, exist_ok=True) + (LOG_DIR / "metric.json").write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") + + +def _reap_agent_processes() -> None: + """Kill anything still running as the candidate uid, without shelling out.""" + for entry in Path("/proc").iterdir(): + if not entry.name.isdigit(): + continue + try: + if entry.stat().st_uid != AGENT_UID: + continue + os.kill(int(entry.name), signal.SIGKILL) + except (OSError, ValueError): + continue + + +def _run_candidate(solution_dir: Path, inputs_path: Path, work_dir: Path) -> dict: + """Launch runner.py as uid 1001 and time it from this process, around the subprocess + boundary. The candidate never reports its own duration and never runs in this process.""" + harness_dir = work_dir.parent / "harness" + harness_dir.mkdir(parents=True, exist_ok=True) + runner = harness_dir / "runner.py" + runner.write_bytes((TESTS_DIR / "runner.py").read_bytes()) + os.chmod(harness_dir, 0o755) + os.chmod(runner, 0o644) + + preds_path = work_dir / "predictions.npz" + meta_path = work_dir / "meta.json" + env_pairs = [ + "PATH=/usr/local/bin:/usr/bin:/bin", + f"HOME={work_dir}", + f"TMPDIR={work_dir}", + "PYTHONSAFEPATH=1", + "PYTHONDONTWRITEBYTECODE=1", + "PYTHONHASHSEED=0", + "OMP_NUM_THREADS=1", + "MKL_NUM_THREADS=1", + "OPENBLAS_NUM_THREADS=1", + "NUMEXPR_NUM_THREADS=1", + ] + cmd = [RUNUSER, "-u", AGENT_USER, "--", "/usr/bin/env", "-i", *env_pairs, + PYTHON, str(runner), str(inputs_path), str(solution_dir), + str(preds_path), str(meta_path)] + + proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, + cwd=str(work_dir), text=True, start_new_session=True) + started = time.monotonic() + timed_out = False + try: + stdout, stderr = proc.communicate(timeout=gc.GRADED_RUN_BUDGET_SEC) + except subprocess.TimeoutExpired: + timed_out = True + try: + os.killpg(os.getpgid(proc.pid), signal.SIGKILL) + except OSError: + proc.kill() + try: + stdout, stderr = proc.communicate(timeout=120) + except subprocess.TimeoutExpired: + stdout, stderr = "", "" + elapsed = time.monotonic() - started + _reap_agent_processes() + + return { + "timed_out": timed_out, + "elapsed_sec": elapsed, + "returncode": proc.returncode, + "stderr_tail": (stderr or "")[-800:], + "preds_path": preds_path, + "meta_path": meta_path, + } + + +def grade() -> dict: + payload = { + "split": SPLIT, + "metric_name": "mean_pehe", + "metric": None, + "reward": 0.0, + "valid": False, + "reason": "", + "n_realizations": 0, + "n_ok": 0, + "n_fallback": 0, + "elapsed_sec": 0.0, + "pruned_paths": [], + } + if not PANEL_PATH.is_file(): + payload["reason"] = f"sealed panel missing at {PANEL_PATH}" + return payload + if not ARTIFACT_DIR.is_dir(): + payload["reason"] = "no deliverable directory at /app/output" + return payload + + inputs, taus = gc.load_sealed_panel(PANEL_PATH) + payload["n_realizations"] = len(inputs) + + sandbox = Path(tempfile.mkdtemp(prefix="grade_", dir="/tmp")) + os.chmod(sandbox, 0o755) + solution_dir = sandbox / "solution" + work_dir = sandbox / "work" + work_dir.mkdir(parents=True, exist_ok=True) + os.chown(work_dir, AGENT_UID, AGENT_GID) + os.chmod(work_dir, 0o755) + + copy_report = gc.copy_solution_tree(ARTIFACT_DIR, solution_dir) + payload["pruned_paths"] = copy_report["pruned"][:20] + + inputs_path = sandbox / "inputs.npz" + gc.write_runner_inputs(inputs, inputs_path) + + run = _run_candidate(solution_dir, inputs_path, work_dir) + payload["elapsed_sec"] = round(run["elapsed_sec"], 3) + + if run["timed_out"]: + payload["reason"] = (f"graded run exceeded the {gc.GRADED_RUN_BUDGET_SEC:.0f} s " + "wall-clock budget and was killed") + return payload + + meta = {} + try: + meta = json.loads(Path(run["meta_path"]).read_text()) + except Exception as exc: + payload["reason"] = f"candidate wrote no usable run metadata: {exc}" + if not meta.get("import_ok", False): + payload["reason"] = payload["reason"] or ( + f"entry point could not be imported/called: {meta.get('error')}") + return payload + + preds_path = Path(run["preds_path"]) + try: + st = preds_path.lstat() + if not stat.S_ISREG(st.st_mode) or st.st_size > 200_000_000: + payload["reason"] = "predictions file is not a plain, plausibly-sized array file" + return payload + except OSError as exc: + payload["reason"] = f"candidate wrote no predictions: {exc}" + return payload + + try: + with np.load(preds_path, allow_pickle=False) as f: + preds = np.asarray(f["preds"], dtype=np.float64) + status = np.asarray(f["status"], dtype=np.int64) + except Exception as exc: + payload["reason"] = f"predictions unreadable: {exc}" + return payload + + if preds.shape != (len(inputs), gc.N_TEST) or status.shape != (len(inputs),): + payload["reason"] = "prediction array has the wrong shape" + return payload + + scored = gc.score_panel(inputs, taus, preds, status) + if scored["n_ok"] == 0: + payload["n_ok"] = 0 + payload["n_fallback"] = scored["n_fallback"] + payload["reason"] = f"every call failed: {meta.get('error')}" + return payload + payload.update(scored) + + payload["valid"] = True + payload["reward"] = gc.graded_reward(scored["metric"], True) + payload["reason"] = "ok" + return payload + + +def test_candidate_estimator_scores_the_sealed_panel(): + """Execute /app/output/estimator.py on the sealed panel for this split and record the + mean PEHE and the mapped reward. Written on every path, including failure.""" + payload = grade() + _write_metric(payload) + metric = "n/a" if payload["metric"] is None else f"{payload['metric']:.4f}" + print(f"split={payload['split']} realizations={payload['n_realizations']} " + f"mean_PEHE={metric} calls_ok={payload['n_ok']} " + f"calls_scored_as_constant={payload['n_fallback']} " + f"run_seconds={payload['elapsed_sec']:.1f} status={payload['reason']}") + assert payload["valid"], payload["reason"] diff --git a/tasks/cpu-llm-decode-throughput/environment/Dockerfile b/tasks/cpu-llm-decode-throughput/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..9588157e93f12a624f3a9d9c35c1d622707736d1 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/environment/Dockerfile @@ -0,0 +1,47 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +RUN set -eu; \ + if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \ + apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \ + fi; \ + rm -f /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1 git=1:2.39.5-0+deb12u3; \ + rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/* + + +ENTRYPOINT [] +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=8 \ + MKL_NUM_THREADS=8 \ + OPENBLAS_NUM_THREADS=8 + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt + +RUN /usr/local/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='openai-community/gpt2', revision='607a30d783dfa663caf39e06633721c8d4cfcd7e', allow_patterns=['model.safetensors', 'config.json'], local_dir='/opt/model')" \ + && rm -rf /opt/model/.cache \ + && find /opt/model -type d -exec chmod 755 {} + \ + && find /opt/model -type f -exec chmod 644 {} + + +WORKDIR /app +COPY ./app /app + +RUN mkdir -p /app/output \ + && chown -R agent:agent /app \ + && find /app -exec touch -d @1700000000 {} + + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 + +RUN git config --system user.email "agent@localhost" \ + && git config --system user.name "autoresearch agent" +USER agent diff --git a/tasks/cpu-llm-decode-throughput/environment/app/data/dev_workload_0.json b/tasks/cpu-llm-decode-throughput/environment/app/data/dev_workload_0.json new file mode 100644 index 0000000000000000000000000000000000000000..62b4d36e0bc84c8621a13d855a81a19b967541f0 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/environment/app/data/dev_workload_0.json @@ -0,0 +1 @@ +[{"id": 0, "prompt_token_ids": [257, 1499, 3240, 318, 1690, 32460, 290, 17920, 462, 837, 290, 547, 340, 407, 329, 262, 1310, 42541, 290, 573, 292, 262, 11684, 19956, 389, 7558, 1813, 837, 6224, 287, 617, 1957, 871, 561, 307, 257, 845, 288, 260, 560, 1517, 5600, 764, 1406, 262, 640, 21014, 651, 2488, 12, 31, 1978, 82, 286, 262, 1402, 3240, 389, 281, 4920, 3895, 284, 262, 1919], "max_new_tokens": 24}, {"id": 1, "prompt_token_ids": [24175, 286, 19478, 705, 82, 2095, 290, 262, 6954, 286, 465, 2776, 284, 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os.path.dirname(os.path.abspath(__file__))) + +import metric_core + +DATA_DIR = "/app/data" +REFERENCE = "/app/reference/reference_engine.py" +MODEL_DIR = "/opt/model" +WARMUP = "/app/data/warmup_workload.json" + + +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument("--engine", default="/app/output/engine.py") + ap.add_argument("--draws", default="0", help="comma-separated dev workload indices, or 'all'") + ap.add_argument("--limit", type=int, default=0, help="use only the first N requests") + args = ap.parse_args(argv) + + indices = [0, 1, 2, 3] if args.draws == "all" else [int(x) for x in args.draws.split(",")] + workloads = [] + for i in indices: + path = os.path.join(DATA_DIR, f"dev_workload_{i}.json") + with open(path) as fh: + reqs = json.load(fh) + if args.limit: + reqs = reqs[: args.limit] + workloads.append((f"dev_workload_{i}", reqs)) + + if not os.path.isfile(args.engine): + print(f"no engine at {args.engine}") + return 1 + + with tempfile.TemporaryDirectory(prefix="bench-") as work_root: + staged = os.path.join(work_root, "candidate") + shutil.copytree(os.path.dirname(os.path.abspath(args.engine)), staged) + for root, dirs, files in os.walk(staged): + for name in files: + os.chmod(os.path.join(root, name), 0o400) + os.chmod(root, 0o500) + rounds = metric_core.run_rounds( + REFERENCE, + os.path.join(staged, os.path.basename(args.engine)), + MODEL_DIR, + WARMUP, + workloads, + work_root, + ) + for root, dirs, files in os.walk(staged): + os.chmod(root, 0o700) + for name in files: + os.chmod(os.path.join(root, name), 0o600) + summary = metric_core.summarise(rounds) + print() + for r in rounds: + if r.get("ok"): + print( + f" {r['workload']}: speedup {r['speedup']:.3f}x " + f"(reference {r['reference_s']:.2f}s, candidate {r['candidate_s']:.2f}s, " + f"agreement {r['matched_positions']}/{r['total_positions']})" + ) + else: + print(f" {r['workload']}: FAILED - {r.get('detail')}") + print() + print(f"token agreement : {summary['agreement']:.4f} " + f"(must be >= {metric_core.AGREEMENT_MIN})") + print(f"speedup S : {summary['speedup']:.4f}") + if summary["detail"]: + print(f"note : {summary['detail']}") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tasks/cpu-llm-decode-throughput/environment/app/harness/metric_core.py b/tasks/cpu-llm-decode-throughput/environment/app/harness/metric_core.py new file mode 100644 index 0000000000000000000000000000000000000000..13a65ae479fe7a9ef9e7b2fa2b0fc9e2729c0d2d --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/environment/app/harness/metric_core.py @@ -0,0 +1,202 @@ +from __future__ import annotations + +import os +import shutil +import statistics +import sys + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +import runner + +VOCAB_SIZE = 50257 + +LOAD_BUDGET_S = 180.0 + +RUN_BUDGET_MULTIPLIER = 2.0 +RUN_BUDGET_FLOOR_S = 60.0 +REFERENCE_RUN_BUDGET_S = 900.0 + +AGREEMENT_MIN = 0.99 + + +def validate_outputs(outputs, requests): + """Return (ok, detail). Outputs are untrusted data, so every shape is checked.""" + if not isinstance(outputs, list): + return False, "engine did not return a list" + if len(outputs) != len(requests): + return False, f"engine returned {len(outputs)} lists, expected {len(requests)}" + for i, (row, req) in enumerate(zip(outputs, requests)): + want = int(req["max_new_tokens"]) + if not isinstance(row, list): + return False, f"element {i} is not a list" + if len(row) != want: + return False, f"element {i} has {len(row)} token ids, expected {want}" + for tid in row: + if not isinstance(tid, int) or isinstance(tid, bool): + return False, f"element {i} contains a non-integer token id" + if not 0 <= tid < VOCAB_SIZE: + return False, f"element {i} contains out-of-range token id {tid}" + return True, "" + + +def count_agreement(outputs, reference): + """Position-wise agreement between two valid output sets.""" + matched = total = 0 + for row, ref in zip(outputs, reference): + total += len(ref) + matched += sum(1 for a, b in zip(row, ref) if a == b) + return matched, total + + +def tokens_per_second(requests, elapsed_s): + return sum(int(r["max_new_tokens"]) for r in requests) / elapsed_s + + +def _fresh_dir(path, owner=None): + shutil.rmtree(path, ignore_errors=True) + os.makedirs(path, mode=0o700, exist_ok=True) + if owner is not None: + os.chown(path, owner[0], owner[1]) + return path + + +def run_rounds( + reference_engine_path, + candidate_engine_path, + model_dir, + warmup_path, + workloads, + work_root, + cmd_prefix=(), + runner_path=None, + run_dir_owner=None, + reap_uid=None, + log=print, +): + """Interleave reference and candidate over one distinct workload per round. + + Each round is a fresh process pair on a workload the previous round never saw, so + nothing carried over from an earlier round can stand in for the work. The reference + run's directory is destroyed before the candidate is launched, so the candidate + cannot read the answer off the run that just produced it. + + Returns a list of per-round dicts; a round with ``ok`` false stops the comparison. + """ + rounds = [] + for index, (name, requests) in enumerate(workloads): + ref_dir = _fresh_dir(os.path.join(work_root, f"round{index}_reference"), run_dir_owner) + cand_dir = _fresh_dir(os.path.join(work_root, f"round{index}_candidate"), run_dir_owner) + + ref = runner.run_timed( + reference_engine_path, + model_dir, + warmup_path, + requests, + ref_dir, + LOAD_BUDGET_S, + REFERENCE_RUN_BUDGET_S, + cmd_prefix=cmd_prefix, + runner_path=runner_path, + reap_uid=reap_uid, + ) + if not ref.ok: + return rounds + [ + {"round": index, "workload": name, "ok": False, + "detail": f"reference run failed: {ref.detail}"} + ] + ref_ok, ref_detail = validate_outputs(ref.outputs, requests) + if not ref_ok: + return rounds + [ + {"round": index, "workload": name, "ok": False, + "detail": f"reference run invalid: {ref_detail}"} + ] + shutil.rmtree(ref_dir, ignore_errors=True) + + budget = max(RUN_BUDGET_FLOOR_S, RUN_BUDGET_MULTIPLIER * ref.elapsed_s) + cand = runner.run_timed( + candidate_engine_path, + model_dir, + warmup_path, + requests, + cand_dir, + LOAD_BUDGET_S, + budget, + cmd_prefix=cmd_prefix, + runner_path=runner_path, + reap_uid=reap_uid, + ) + record = { + "round": index, + "workload": name, + "new_tokens": sum(int(r["max_new_tokens"]) for r in requests), + "reference_s": round(ref.elapsed_s, 4), + "reference_tok_s": round(tokens_per_second(requests, ref.elapsed_s), 3), + "budget_s": round(budget, 2), + } + if not cand.ok: + record.update({"ok": False, "detail": cand.detail}) + rounds.append(record) + log(f"round {index} ({name}): candidate failed - {cand.detail}") + return rounds + + ok, detail = validate_outputs(cand.outputs, requests) + if not ok: + record.update({"ok": False, "detail": detail}) + rounds.append(record) + log(f"round {index} ({name}): candidate invalid - {detail}") + return rounds + + matched, total = count_agreement(cand.outputs, ref.outputs) + record.update( + { + "ok": True, + "detail": "", + "candidate_s": round(cand.elapsed_s, 4), + "candidate_tok_s": round(tokens_per_second(requests, cand.elapsed_s), 3), + "speedup": round(ref.elapsed_s / cand.elapsed_s, 4), + "matched_positions": matched, + "total_positions": total, + } + ) + rounds.append(record) + log( + f"round {index} ({name}): reference {record['reference_tok_s']} tok/s, " + f"candidate {record['candidate_tok_s']} tok/s, " + f"speedup {record['speedup']}x, agreement {matched}/{total}" + ) + shutil.rmtree(cand_dir, ignore_errors=True) + return rounds + + +def summarise(rounds): + """Fold per-round records into the graded speedup S and the agreement rate.""" + if not rounds or not all(r.get("ok") for r in rounds): + failed = next((r for r in rounds if not r.get("ok")), None) + return { + "speedup": 0.0, + "agreement": 0.0, + "valid": False, + "detail": (failed or {}).get("detail", "no rounds were run"), + "rounds": rounds, + } + matched = sum(r["matched_positions"] for r in rounds) + total = sum(r["total_positions"] for r in rounds) + agreement = matched / total if total else 0.0 + speedups = [r["speedup"] for r in rounds] + if agreement < AGREEMENT_MIN: + return { + "speedup": 0.0, + "agreement": agreement, + "valid": True, + "detail": f"token agreement {agreement:.4f} is below {AGREEMENT_MIN}", + "rounds": rounds, + } + return { + "speedup": statistics.median(speedups), + "agreement": agreement, + "valid": True, + "detail": "", + "per_round_speedup": speedups, + "rounds": rounds, + } diff --git a/tasks/cpu-llm-decode-throughput/environment/app/harness/runner.py b/tasks/cpu-llm-decode-throughput/environment/app/harness/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..a8d7ac6a9083cb6d024462c79df5b7f815259f56 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/environment/app/harness/runner.py @@ -0,0 +1,216 @@ +from __future__ import annotations + +import json +import os +import signal +import subprocess +import sys +import threading +import time + +READY_POLL_S = 0.002 + + + + +def _child(engine_path, model_dir, warmup_path, results_path, ready_path): + import importlib.util + + os.environ["HOME"] = os.getcwd() + + import torch + + torch.set_num_threads(len(os.sched_getaffinity(0))) + torch.manual_seed(0) + + sys.path.insert(0, os.path.dirname(os.path.abspath(engine_path))) + spec = importlib.util.spec_from_file_location("graded_engine", engine_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + engine = module.Engine(model_dir) + with open(warmup_path) as fh: + engine.generate(json.load(fh)) + + with open(ready_path, "w") as fh: + fh.write("ready") + fh.flush() + os.fsync(fh.fileno()) + + payload = sys.stdin.buffer.read() + requests = json.loads(payload.decode("utf-8")) + + result = engine.generate(requests) + + with open(results_path, "w") as fh: + json.dump(result, fh) + fh.flush() + os.fsync(fh.fileno()) + sys.stderr.flush() + os._exit(0) + + + +def _kill_uid(uid): + """SIGKILL every process still running as `uid`. + + A descendant that calls setsid() leaves the process group and survives killpg; this + is the backstop, so nothing a finished run left behind can consume cores during the + next one. + """ + for entry in os.listdir("/proc"): + if not entry.isdigit(): + continue + try: + if os.stat(os.path.join("/proc", entry)).st_uid != uid: + continue + os.kill(int(entry), signal.SIGKILL) + except (OSError, ProcessLookupError): + continue + + + + +class RunOutcome: + """What one timed run produced. ``ok`` false means the run scores nothing.""" + + def __init__(self, ok, elapsed_s, outputs, detail): + self.ok = ok + self.elapsed_s = elapsed_s + self.outputs = outputs + self.detail = detail + + def __repr__(self): + return f"RunOutcome(ok={self.ok}, elapsed_s={self.elapsed_s}, detail={self.detail!r})" + + +def run_timed( + engine_path, + model_dir, + warmup_path, + workload, + run_dir, + load_budget_s, + run_budget_s, + cmd_prefix=(), + env_extra=None, + runner_path=None, + reap_uid=None, +): + """Run one engine over one workload in a fresh process and time only generate(). + + ``cmd_prefix`` lets a privileged caller wrap the launch (e.g. drop to another uid), + and ``runner_path`` lets it point the child at a copy of this file that the reduced + uid can actually read. Returns a :class:`RunOutcome`; every failure returns ok=False. + """ + results_path = os.path.join(run_dir, "results.json") + ready_path = os.path.join(run_dir, "ready.marker") + log_path = os.path.join(run_dir, "engine.log") + for stale in (results_path, ready_path): + if os.path.exists(stale): + os.unlink(stale) + + env = { + "PATH": "/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin", + "HOME": run_dir, + "TMPDIR": run_dir, + "PYTHONHASHSEED": "0", + "PYTHONDONTWRITEBYTECODE": "1", + "PYTHONNOUSERSITE": "1", + "PYTHONSAFEPATH": "1", + "OMP_NUM_THREADS": str(len(os.sched_getaffinity(0))), + "MKL_NUM_THREADS": str(len(os.sched_getaffinity(0))), + "OPENBLAS_NUM_THREADS": str(len(os.sched_getaffinity(0))), + "HF_HUB_OFFLINE": "1", + "TRANSFORMERS_OFFLINE": "1", + } + env.update(env_extra or {}) + + argv = list(cmd_prefix) + [ + sys.executable, + "-s", + "-P", + runner_path or os.path.abspath(__file__), + "--child", + engine_path, + model_dir, + warmup_path, + results_path, + ready_path, + ] + payload = json.dumps(workload).encode("utf-8") + + log = open(log_path, "wb") + proc = subprocess.Popen( + argv, + stdin=subprocess.PIPE, + stdout=log, + stderr=log, + cwd=run_dir, + env=env, + start_new_session=True, + ) + + pgid = proc.pid + + def _reap(): + try: + os.killpg(pgid, signal.SIGKILL) + except (ProcessLookupError, PermissionError): + pass + try: + proc.wait(timeout=30) + except subprocess.TimeoutExpired: + pass + if reap_uid is not None: + _kill_uid(reap_uid) + log.close() + + deadline = time.monotonic() + load_budget_s + while not os.path.exists(ready_path): + if proc.poll() is not None: + _reap() + return RunOutcome(False, None, None, "engine exited before it was ready") + if time.monotonic() > deadline: + _reap() + return RunOutcome(False, None, None, "engine exceeded the load budget") + time.sleep(READY_POLL_S) + + def _feed(): + try: + proc.stdin.write(payload) + proc.stdin.close() + except (BrokenPipeError, OSError, ValueError): + pass + + started = time.perf_counter() + threading.Thread(target=_feed, daemon=True).start() + try: + returncode = proc.wait(timeout=run_budget_s) + except subprocess.TimeoutExpired: + _reap() + return RunOutcome(False, None, None, "engine exceeded the wall-clock budget") + elapsed = time.perf_counter() - started + + try: + with open(results_path) as fh: + outputs = json.load(fh) + except (OSError, ValueError) as exc: + outputs = None + detail = f"unreadable results: {exc}" + else: + detail = "" + _reap() + + if returncode != 0: + return RunOutcome(False, elapsed, None, f"engine exited with code {returncode}") + if outputs is None: + return RunOutcome(False, elapsed, None, detail) + return RunOutcome(True, elapsed, outputs, "") + + +if __name__ == "__main__": + if len(sys.argv) == 7 and sys.argv[1] == "--child": + _child(*sys.argv[2:]) + else: + raise SystemExit("runner.py is launched by the harness, not directly") diff --git a/tasks/cpu-llm-decode-throughput/environment/app/reference/reference_engine.py b/tasks/cpu-llm-decode-throughput/environment/app/reference/reference_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..c3dbb5c0e7c7b6275142dc4354547bd077e4db53 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/environment/app/reference/reference_engine.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +import json +import math +import os + +import torch +from safetensors.torch import load_file + +_SQRT_2_OVER_PI = math.sqrt(2.0 / math.pi) + +_BLOCK_KEYS = ( + "ln_1.weight", + "ln_1.bias", + "attn.c_attn.weight", + "attn.c_attn.bias", + "attn.c_proj.weight", + "attn.c_proj.bias", + "ln_2.weight", + "ln_2.bias", + "mlp.c_fc.weight", + "mlp.c_fc.bias", + "mlp.c_proj.weight", + "mlp.c_proj.bias", +) + + +def gelu_new(x: torch.Tensor) -> torch.Tensor: + """The tanh-approximated GELU the checkpoint was trained with.""" + return 0.5 * x * (1.0 + torch.tanh(_SQRT_2_OVER_PI * (x + 0.044715 * x * x * x))) + + +class Engine: + """Serves generation requests one at a time.""" + + def __init__(self, model_dir: str) -> None: + with open(os.path.join(model_dir, "config.json")) as fh: + cfg = json.load(fh) + self.n_layer = int(cfg["n_layer"]) + self.n_head = int(cfg["n_head"]) + self.n_embd = int(cfg["n_embd"]) + self.n_positions = int(cfg["n_positions"]) + self.eps = float(cfg["layer_norm_epsilon"]) + self.head_dim = self.n_embd // self.n_head + self.scale = 1.0 / math.sqrt(self.head_dim) + + sd = load_file(os.path.join(model_dir, "model.safetensors")) + self.wte = sd["wte.weight"].float().contiguous() + self.wpe = sd["wpe.weight"].float().contiguous() + self.ln_f_w = sd["ln_f.weight"].float().contiguous() + self.ln_f_b = sd["ln_f.bias"].float().contiguous() + self.blocks = [ + {k: sd[f"h.{i}.{k}"].float().contiguous() for k in _BLOCK_KEYS} + for i in range(self.n_layer) + ] + + + def _forward(self, ids: torch.Tensor, past: list, start_pos: int) -> torch.Tensor: + """One forward pass over `ids` ([1, T]); returns logits for the last position.""" + bsz, seq = ids.shape + pos = torch.arange(start_pos, start_pos + seq, dtype=torch.long) + h = self.wte[ids] + self.wpe[pos] + + past_len = 0 if past[0] is None else past[0][0].shape[2] + total = past_len + seq + mask = None + if seq > 1: + allowed = torch.arange(total)[None, :] <= torch.arange( + past_len, past_len + seq + )[:, None] + mask = torch.zeros(seq, total) + mask.masked_fill_(~allowed, float("-inf")) + + for i, b in enumerate(self.blocks): + x = torch.nn.functional.layer_norm( + h, (self.n_embd,), b["ln_1.weight"], b["ln_1.bias"], self.eps + ) + qkv = torch.addmm( + b["attn.c_attn.bias"], + x.view(bsz * seq, self.n_embd), + b["attn.c_attn.weight"], + ).view(bsz, seq, 3 * self.n_embd) + q, k, v = qkv.split(self.n_embd, dim=2) + q = q.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2) + k = k.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2) + v = v.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2) + if past[i] is not None: + k = torch.cat((past[i][0], k), dim=2) + v = torch.cat((past[i][1], v), dim=2) + past[i] = (k, v) + + att = torch.matmul(q, k.transpose(-2, -1)) * self.scale + if mask is not None: + att = att + mask + att = torch.softmax(att, dim=-1) + y = torch.matmul(att, v) + y = y.transpose(1, 2).reshape(bsz * seq, self.n_embd) + y = torch.addmm(b["attn.c_proj.bias"], y, b["attn.c_proj.weight"]) + h = h + y.view(bsz, seq, self.n_embd) + + x = torch.nn.functional.layer_norm( + h, (self.n_embd,), b["ln_2.weight"], b["ln_2.bias"], self.eps + ) + x = torch.addmm( + b["mlp.c_fc.bias"], x.view(bsz * seq, self.n_embd), b["mlp.c_fc.weight"] + ) + x = gelu_new(x) + x = torch.addmm(b["mlp.c_proj.bias"], x, b["mlp.c_proj.weight"]) + h = h + x.view(bsz, seq, self.n_embd) + + h = torch.nn.functional.layer_norm( + h[:, -1, :], (self.n_embd,), self.ln_f_w, self.ln_f_b, self.eps + ) + return torch.matmul(h, self.wte.t()) + + + @torch.inference_mode() + def generate(self, requests: list) -> list: + """Serve every request; returns one list of new token ids per request, in order.""" + out = [] + for req in requests: + prompt = list(req["prompt_token_ids"]) + n_new = int(req["max_new_tokens"]) + past = [None] * self.n_layer + ids = torch.tensor([prompt], dtype=torch.long) + logits = self._forward(ids, past, 0) + produced = [] + pos = len(prompt) + for _ in range(n_new): + nxt = int(torch.argmax(logits[0]).item()) + produced.append(nxt) + if len(produced) == n_new: + break + logits = self._forward( + torch.tensor([[nxt]], dtype=torch.long), past, pos + ) + pos += 1 + out.append(produced) + return out diff --git a/tasks/cpu-llm-decode-throughput/environment/requirements.txt b/tasks/cpu-llm-decode-throughput/environment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..58235bcc89a0ba999b48b8ca5ca6c64779a1e7de --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/environment/requirements.txt @@ -0,0 +1,6 @@ +--index-url https://download.pytorch.org/whl/cpu +--extra-index-url https://pypi.org/simple +torch==2.13.0+cpu +numpy==2.5.2 +safetensors==0.8.0 +huggingface_hub==1.28.0 diff --git a/tasks/cpu-llm-decode-throughput/hints/hint.md b/tasks/cpu-llm-decode-throughput/hints/hint.md new file mode 100644 index 0000000000000000000000000000000000000000..fd746df221b734c21dd5418d47f039ac27a3453f --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/hints/hint.md @@ -0,0 +1 @@ +Serve the whole workload in one batched decode loop over a shared KV cache, grouping prefills by prompt length and swapping finished rows to the end so live rows stay contiguous. Construction and warm-up are untimed, so pre-allocate and pre-touch the KV pool there; then chunk the vocabulary projection with a running max and pre-pack the decode-only weights. Do not try low precision: the token-agreement gate is checked position by position and one divergent argmax corrupts the rest of that request. diff --git a/tasks/cpu-llm-decode-throughput/tests/Dockerfile b/tasks/cpu-llm-decode-throughput/tests/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b497cd4dbfb83258b725e191a1f9f8eaa4112c9b --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/Dockerfile @@ -0,0 +1,38 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +ENTRYPOINT [] +ENV PYTHONPATH= +ENV PYTHONSAFEPATH=1 +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=8 \ + MKL_NUM_THREADS=8 \ + OPENBLAS_NUM_THREADS=8 + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir \ + -r /tmp/requirements.txt -r /tmp/requirements-verifier.txt \ + && rm /tmp/requirements.txt /tmp/requirements-verifier.txt + +RUN /usr/local/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='openai-community/gpt2', revision='607a30d783dfa663caf39e06633721c8d4cfcd7e', allow_patterns=['model.safetensors', 'config.json'], local_dir='/opt/model')" \ + && rm -rf /opt/model/.cache \ + && find /opt/model -type d -exec chmod 755 {} + \ + && find /opt/model -type f -exec chmod 644 {} + + +COPY . /tests + +RUN chown -R root:root /tests \ + && chmod 700 /tests \ + && chmod 700 /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \ + && chmod +x /tests/test.sh /tests/intermediate.sh \ + && find /tests -exec touch -d @1700000000 {} + + +WORKDIR /tests + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 diff --git a/tasks/cpu-llm-decode-throughput/tests/compute_reward.py b/tasks/cpu-llm-decode-throughput/tests/compute_reward.py new file mode 100644 index 0000000000000000000000000000000000000000..60e3557b44f52798d148f9ec95059264eef07ad6 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/compute_reward.py @@ -0,0 +1,18 @@ +import json +import math +from pathlib import Path + +REWARD_PATH = Path("/logs/verifier/reward.txt") + +try: + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + value = float(json.loads(Path("/logs/verifier/metric.json").read_text())["reward"]) + if not math.isfinite(value): + raise ValueError("non-finite reward") + reward = f"{min(1.0, max(0.0, value)):.12g}" + REWARD_PATH.write_text(reward + "\n") + print(f"reward={reward}") +except Exception as exc: + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + REWARD_PATH.write_text("0\n") + print(f"reward=0 (fallback: {exc})") diff --git a/tasks/cpu-llm-decode-throughput/tests/grader_core.py b/tasks/cpu-llm-decode-throughput/tests/grader_core.py new file mode 100644 index 0000000000000000000000000000000000000000..9e6e774cfd256e825682facc8e7a4f2b45d53454 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/grader_core.py @@ -0,0 +1,14 @@ +from __future__ import annotations + +M0 = 1.0 +BASELINE_SCORE = 18.0 + + +def graded_reward(speedup, valid): + """Map the measured speedup to a reward in [0, 1]; 0 for anything not valid.""" + if not valid: + return 0.0 + x_ref = BASELINE_SCORE - M0 + assert x_ref > 0, "malformed task: the baseline score does not beat the trivial baseline" + u = max(0.0, float(speedup) - M0) / x_ref + return u / (1.0 + u) diff --git a/tasks/cpu-llm-decode-throughput/tests/hidden_data/final/workload_0.json b/tasks/cpu-llm-decode-throughput/tests/hidden_data/final/workload_0.json new file mode 100644 index 0000000000000000000000000000000000000000..08a50c311ff2a2de71b6bb79d7112de516bf16e3 --- /dev/null +++ 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357, 2080, 609, 13299, 837, 262, 23129, 20056, 262, 5822, 286, 609, 13299, 2241, 284, 11618, 764, 1267, 220, 198, 1629, 46856, 837, 13596, 776, 4449, 499, 378, 8958, 515, 351, 465], "max_new_tokens": 64}] \ No newline at end of file diff --git a/tasks/cpu-llm-decode-throughput/tests/intermediate.sh b/tasks/cpu-llm-decode-throughput/tests/intermediate.sh new file mode 100644 index 0000000000000000000000000000000000000000..21d399cba4df74c1304474dfed2be694bb991928 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/intermediate.sh @@ -0,0 +1,19 @@ +#!/bin/bash +set -u + +mkdir -p /app/output /logs/verifier /grade +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT + +chmod -R go-rwx /tests + +export GRADE_SPLIT=intermediate + +cd /tests +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/local/bin/python3 /tests/compute_reward.py > /dev/null + +exit 0 diff --git a/tasks/cpu-llm-decode-throughput/tests/metric_core.py b/tasks/cpu-llm-decode-throughput/tests/metric_core.py new file mode 100644 index 0000000000000000000000000000000000000000..13a65ae479fe7a9ef9e7b2fa2b0fc9e2729c0d2d --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/metric_core.py @@ -0,0 +1,202 @@ +from __future__ import annotations + +import os +import shutil +import statistics +import sys + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) + +import runner + +VOCAB_SIZE = 50257 + +LOAD_BUDGET_S = 180.0 + +RUN_BUDGET_MULTIPLIER = 2.0 +RUN_BUDGET_FLOOR_S = 60.0 +REFERENCE_RUN_BUDGET_S = 900.0 + +AGREEMENT_MIN = 0.99 + + +def validate_outputs(outputs, requests): + """Return (ok, detail). Outputs are untrusted data, so every shape is checked.""" + if not isinstance(outputs, list): + return False, "engine did not return a list" + if len(outputs) != len(requests): + return False, f"engine returned {len(outputs)} lists, expected {len(requests)}" + for i, (row, req) in enumerate(zip(outputs, requests)): + want = int(req["max_new_tokens"]) + if not isinstance(row, list): + return False, f"element {i} is not a list" + if len(row) != want: + return False, f"element {i} has {len(row)} token ids, expected {want}" + for tid in row: + if not isinstance(tid, int) or isinstance(tid, bool): + return False, f"element {i} contains a non-integer token id" + if not 0 <= tid < VOCAB_SIZE: + return False, f"element {i} contains out-of-range token id {tid}" + return True, "" + + +def count_agreement(outputs, reference): + """Position-wise agreement between two valid output sets.""" + matched = total = 0 + for row, ref in zip(outputs, reference): + total += len(ref) + matched += sum(1 for a, b in zip(row, ref) if a == b) + return matched, total + + +def tokens_per_second(requests, elapsed_s): + return sum(int(r["max_new_tokens"]) for r in requests) / elapsed_s + + +def _fresh_dir(path, owner=None): + shutil.rmtree(path, ignore_errors=True) + os.makedirs(path, mode=0o700, exist_ok=True) + if owner is not None: + os.chown(path, owner[0], owner[1]) + return path + + +def run_rounds( + reference_engine_path, + candidate_engine_path, + model_dir, + warmup_path, + workloads, + work_root, + cmd_prefix=(), + runner_path=None, + run_dir_owner=None, + reap_uid=None, + log=print, +): + """Interleave reference and candidate over one distinct workload per round. + + Each round is a fresh process pair on a workload the previous round never saw, so + nothing carried over from an earlier round can stand in for the work. The reference + run's directory is destroyed before the candidate is launched, so the candidate + cannot read the answer off the run that just produced it. + + Returns a list of per-round dicts; a round with ``ok`` false stops the comparison. + """ + rounds = [] + for index, (name, requests) in enumerate(workloads): + ref_dir = _fresh_dir(os.path.join(work_root, f"round{index}_reference"), run_dir_owner) + cand_dir = _fresh_dir(os.path.join(work_root, f"round{index}_candidate"), run_dir_owner) + + ref = runner.run_timed( + reference_engine_path, + model_dir, + warmup_path, + requests, + ref_dir, + LOAD_BUDGET_S, + REFERENCE_RUN_BUDGET_S, + cmd_prefix=cmd_prefix, + runner_path=runner_path, + reap_uid=reap_uid, + ) + if not ref.ok: + return rounds + [ + {"round": index, "workload": name, "ok": False, + "detail": f"reference run failed: {ref.detail}"} + ] + ref_ok, ref_detail = validate_outputs(ref.outputs, requests) + if not ref_ok: + return rounds + [ + {"round": index, "workload": name, "ok": False, + "detail": f"reference run invalid: {ref_detail}"} + ] + shutil.rmtree(ref_dir, ignore_errors=True) + + budget = max(RUN_BUDGET_FLOOR_S, RUN_BUDGET_MULTIPLIER * ref.elapsed_s) + cand = runner.run_timed( + candidate_engine_path, + model_dir, + warmup_path, + requests, + cand_dir, + LOAD_BUDGET_S, + budget, + cmd_prefix=cmd_prefix, + runner_path=runner_path, + reap_uid=reap_uid, + ) + record = { + "round": index, + "workload": name, + "new_tokens": sum(int(r["max_new_tokens"]) for r in requests), + "reference_s": round(ref.elapsed_s, 4), + "reference_tok_s": round(tokens_per_second(requests, ref.elapsed_s), 3), + "budget_s": round(budget, 2), + } + if not cand.ok: + record.update({"ok": False, "detail": cand.detail}) + rounds.append(record) + log(f"round {index} ({name}): candidate failed - {cand.detail}") + return rounds + + ok, detail = validate_outputs(cand.outputs, requests) + if not ok: + record.update({"ok": False, "detail": detail}) + rounds.append(record) + log(f"round {index} ({name}): candidate invalid - {detail}") + return rounds + + matched, total = count_agreement(cand.outputs, ref.outputs) + record.update( + { + "ok": True, + "detail": "", + "candidate_s": round(cand.elapsed_s, 4), + "candidate_tok_s": round(tokens_per_second(requests, cand.elapsed_s), 3), + "speedup": round(ref.elapsed_s / cand.elapsed_s, 4), + "matched_positions": matched, + "total_positions": total, + } + ) + rounds.append(record) + log( + f"round {index} ({name}): reference {record['reference_tok_s']} tok/s, " + f"candidate {record['candidate_tok_s']} tok/s, " + f"speedup {record['speedup']}x, agreement {matched}/{total}" + ) + shutil.rmtree(cand_dir, ignore_errors=True) + return rounds + + +def summarise(rounds): + """Fold per-round records into the graded speedup S and the agreement rate.""" + if not rounds or not all(r.get("ok") for r in rounds): + failed = next((r for r in rounds if not r.get("ok")), None) + return { + "speedup": 0.0, + "agreement": 0.0, + "valid": False, + "detail": (failed or {}).get("detail", "no rounds were run"), + "rounds": rounds, + } + matched = sum(r["matched_positions"] for r in rounds) + total = sum(r["total_positions"] for r in rounds) + agreement = matched / total if total else 0.0 + speedups = [r["speedup"] for r in rounds] + if agreement < AGREEMENT_MIN: + return { + "speedup": 0.0, + "agreement": agreement, + "valid": True, + "detail": f"token agreement {agreement:.4f} is below {AGREEMENT_MIN}", + "rounds": rounds, + } + return { + "speedup": statistics.median(speedups), + "agreement": agreement, + "valid": True, + "detail": "", + "per_round_speedup": speedups, + "rounds": rounds, + } diff --git a/tasks/cpu-llm-decode-throughput/tests/reference_engine.py b/tasks/cpu-llm-decode-throughput/tests/reference_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..c3dbb5c0e7c7b6275142dc4354547bd077e4db53 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/reference_engine.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +import json +import math +import os + +import torch +from safetensors.torch import load_file + +_SQRT_2_OVER_PI = math.sqrt(2.0 / math.pi) + +_BLOCK_KEYS = ( + "ln_1.weight", + "ln_1.bias", + "attn.c_attn.weight", + "attn.c_attn.bias", + "attn.c_proj.weight", + "attn.c_proj.bias", + "ln_2.weight", + "ln_2.bias", + "mlp.c_fc.weight", + "mlp.c_fc.bias", + "mlp.c_proj.weight", + "mlp.c_proj.bias", +) + + +def gelu_new(x: torch.Tensor) -> torch.Tensor: + """The tanh-approximated GELU the checkpoint was trained with.""" + return 0.5 * x * (1.0 + torch.tanh(_SQRT_2_OVER_PI * (x + 0.044715 * x * x * x))) + + +class Engine: + """Serves generation requests one at a time.""" + + def __init__(self, model_dir: str) -> None: + with open(os.path.join(model_dir, "config.json")) as fh: + cfg = json.load(fh) + self.n_layer = int(cfg["n_layer"]) + self.n_head = int(cfg["n_head"]) + self.n_embd = int(cfg["n_embd"]) + self.n_positions = int(cfg["n_positions"]) + self.eps = float(cfg["layer_norm_epsilon"]) + self.head_dim = self.n_embd // self.n_head + self.scale = 1.0 / math.sqrt(self.head_dim) + + sd = load_file(os.path.join(model_dir, "model.safetensors")) + self.wte = sd["wte.weight"].float().contiguous() + self.wpe = sd["wpe.weight"].float().contiguous() + self.ln_f_w = sd["ln_f.weight"].float().contiguous() + self.ln_f_b = sd["ln_f.bias"].float().contiguous() + self.blocks = [ + {k: sd[f"h.{i}.{k}"].float().contiguous() for k in _BLOCK_KEYS} + for i in range(self.n_layer) + ] + + + def _forward(self, ids: torch.Tensor, past: list, start_pos: int) -> torch.Tensor: + """One forward pass over `ids` ([1, T]); returns logits for the last position.""" + bsz, seq = ids.shape + pos = torch.arange(start_pos, start_pos + seq, dtype=torch.long) + h = self.wte[ids] + self.wpe[pos] + + past_len = 0 if past[0] is None else past[0][0].shape[2] + total = past_len + seq + mask = None + if seq > 1: + allowed = torch.arange(total)[None, :] <= torch.arange( + past_len, past_len + seq + )[:, None] + mask = torch.zeros(seq, total) + mask.masked_fill_(~allowed, float("-inf")) + + for i, b in enumerate(self.blocks): + x = torch.nn.functional.layer_norm( + h, (self.n_embd,), b["ln_1.weight"], b["ln_1.bias"], self.eps + ) + qkv = torch.addmm( + b["attn.c_attn.bias"], + x.view(bsz * seq, self.n_embd), + b["attn.c_attn.weight"], + ).view(bsz, seq, 3 * self.n_embd) + q, k, v = qkv.split(self.n_embd, dim=2) + q = q.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2) + k = k.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2) + v = v.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2) + if past[i] is not None: + k = torch.cat((past[i][0], k), dim=2) + v = torch.cat((past[i][1], v), dim=2) + past[i] = (k, v) + + att = torch.matmul(q, k.transpose(-2, -1)) * self.scale + if mask is not None: + att = att + mask + att = torch.softmax(att, dim=-1) + y = torch.matmul(att, v) + y = y.transpose(1, 2).reshape(bsz * seq, self.n_embd) + y = torch.addmm(b["attn.c_proj.bias"], y, b["attn.c_proj.weight"]) + h = h + y.view(bsz, seq, self.n_embd) + + x = torch.nn.functional.layer_norm( + h, (self.n_embd,), b["ln_2.weight"], b["ln_2.bias"], self.eps + ) + x = torch.addmm( + b["mlp.c_fc.bias"], x.view(bsz * seq, self.n_embd), b["mlp.c_fc.weight"] + ) + x = gelu_new(x) + x = torch.addmm(b["mlp.c_proj.bias"], x, b["mlp.c_proj.weight"]) + h = h + x.view(bsz, seq, self.n_embd) + + h = torch.nn.functional.layer_norm( + h[:, -1, :], (self.n_embd,), self.ln_f_w, self.ln_f_b, self.eps + ) + return torch.matmul(h, self.wte.t()) + + + @torch.inference_mode() + def generate(self, requests: list) -> list: + """Serve every request; returns one list of new token ids per request, in order.""" + out = [] + for req in requests: + prompt = list(req["prompt_token_ids"]) + n_new = int(req["max_new_tokens"]) + past = [None] * self.n_layer + ids = torch.tensor([prompt], dtype=torch.long) + logits = self._forward(ids, past, 0) + produced = [] + pos = len(prompt) + for _ in range(n_new): + nxt = int(torch.argmax(logits[0]).item()) + produced.append(nxt) + if len(produced) == n_new: + break + logits = self._forward( + torch.tensor([[nxt]], dtype=torch.long), past, pos + ) + pos += 1 + out.append(produced) + return out diff --git a/tasks/cpu-llm-decode-throughput/tests/requirements-verifier.txt b/tasks/cpu-llm-decode-throughput/tests/requirements-verifier.txt new file mode 100644 index 0000000000000000000000000000000000000000..04676c89520bf969f2cb861ad1811f46d8370700 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/requirements-verifier.txt @@ -0,0 +1,2 @@ +pytest==8.3.5 +pytest-json-ctrf==0.3.5 diff --git a/tasks/cpu-llm-decode-throughput/tests/requirements.txt b/tasks/cpu-llm-decode-throughput/tests/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..58235bcc89a0ba999b48b8ca5ca6c64779a1e7de --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/requirements.txt @@ -0,0 +1,6 @@ +--index-url https://download.pytorch.org/whl/cpu +--extra-index-url https://pypi.org/simple +torch==2.13.0+cpu +numpy==2.5.2 +safetensors==0.8.0 +huggingface_hub==1.28.0 diff --git a/tasks/cpu-llm-decode-throughput/tests/runner.py b/tasks/cpu-llm-decode-throughput/tests/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..a8d7ac6a9083cb6d024462c79df5b7f815259f56 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/runner.py @@ -0,0 +1,216 @@ +from __future__ import annotations + +import json +import os +import signal +import subprocess +import sys +import threading +import time + +READY_POLL_S = 0.002 + + + + +def _child(engine_path, model_dir, warmup_path, results_path, ready_path): + import importlib.util + + os.environ["HOME"] = os.getcwd() + + import torch + + torch.set_num_threads(len(os.sched_getaffinity(0))) + torch.manual_seed(0) + + sys.path.insert(0, os.path.dirname(os.path.abspath(engine_path))) + spec = importlib.util.spec_from_file_location("graded_engine", engine_path) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + + engine = module.Engine(model_dir) + with open(warmup_path) as fh: + engine.generate(json.load(fh)) + + with open(ready_path, "w") as fh: + fh.write("ready") + fh.flush() + os.fsync(fh.fileno()) + + payload = sys.stdin.buffer.read() + requests = json.loads(payload.decode("utf-8")) + + result = engine.generate(requests) + + with open(results_path, "w") as fh: + json.dump(result, fh) + fh.flush() + os.fsync(fh.fileno()) + sys.stderr.flush() + os._exit(0) + + + +def _kill_uid(uid): + """SIGKILL every process still running as `uid`. + + A descendant that calls setsid() leaves the process group and survives killpg; this + is the backstop, so nothing a finished run left behind can consume cores during the + next one. + """ + for entry in os.listdir("/proc"): + if not entry.isdigit(): + continue + try: + if os.stat(os.path.join("/proc", entry)).st_uid != uid: + continue + os.kill(int(entry), signal.SIGKILL) + except (OSError, ProcessLookupError): + continue + + + + +class RunOutcome: + """What one timed run produced. ``ok`` false means the run scores nothing.""" + + def __init__(self, ok, elapsed_s, outputs, detail): + self.ok = ok + self.elapsed_s = elapsed_s + self.outputs = outputs + self.detail = detail + + def __repr__(self): + return f"RunOutcome(ok={self.ok}, elapsed_s={self.elapsed_s}, detail={self.detail!r})" + + +def run_timed( + engine_path, + model_dir, + warmup_path, + workload, + run_dir, + load_budget_s, + run_budget_s, + cmd_prefix=(), + env_extra=None, + runner_path=None, + reap_uid=None, +): + """Run one engine over one workload in a fresh process and time only generate(). + + ``cmd_prefix`` lets a privileged caller wrap the launch (e.g. drop to another uid), + and ``runner_path`` lets it point the child at a copy of this file that the reduced + uid can actually read. Returns a :class:`RunOutcome`; every failure returns ok=False. + """ + results_path = os.path.join(run_dir, "results.json") + ready_path = os.path.join(run_dir, "ready.marker") + log_path = os.path.join(run_dir, "engine.log") + for stale in (results_path, ready_path): + if os.path.exists(stale): + os.unlink(stale) + + env = { + "PATH": "/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin", + "HOME": run_dir, + "TMPDIR": run_dir, + "PYTHONHASHSEED": "0", + "PYTHONDONTWRITEBYTECODE": "1", + "PYTHONNOUSERSITE": "1", + "PYTHONSAFEPATH": "1", + "OMP_NUM_THREADS": str(len(os.sched_getaffinity(0))), + "MKL_NUM_THREADS": str(len(os.sched_getaffinity(0))), + "OPENBLAS_NUM_THREADS": str(len(os.sched_getaffinity(0))), + "HF_HUB_OFFLINE": "1", + "TRANSFORMERS_OFFLINE": "1", + } + env.update(env_extra or {}) + + argv = list(cmd_prefix) + [ + sys.executable, + "-s", + "-P", + runner_path or os.path.abspath(__file__), + "--child", + engine_path, + model_dir, + warmup_path, + results_path, + ready_path, + ] + payload = json.dumps(workload).encode("utf-8") + + log = open(log_path, "wb") + proc = subprocess.Popen( + argv, + stdin=subprocess.PIPE, + stdout=log, + stderr=log, + cwd=run_dir, + env=env, + start_new_session=True, + ) + + pgid = proc.pid + + def _reap(): + try: + os.killpg(pgid, signal.SIGKILL) + except (ProcessLookupError, PermissionError): + pass + try: + proc.wait(timeout=30) + except subprocess.TimeoutExpired: + pass + if reap_uid is not None: + _kill_uid(reap_uid) + log.close() + + deadline = time.monotonic() + load_budget_s + while not os.path.exists(ready_path): + if proc.poll() is not None: + _reap() + return RunOutcome(False, None, None, "engine exited before it was ready") + if time.monotonic() > deadline: + _reap() + return RunOutcome(False, None, None, "engine exceeded the load budget") + time.sleep(READY_POLL_S) + + def _feed(): + try: + proc.stdin.write(payload) + proc.stdin.close() + except (BrokenPipeError, OSError, ValueError): + pass + + started = time.perf_counter() + threading.Thread(target=_feed, daemon=True).start() + try: + returncode = proc.wait(timeout=run_budget_s) + except subprocess.TimeoutExpired: + _reap() + return RunOutcome(False, None, None, "engine exceeded the wall-clock budget") + elapsed = time.perf_counter() - started + + try: + with open(results_path) as fh: + outputs = json.load(fh) + except (OSError, ValueError) as exc: + outputs = None + detail = f"unreadable results: {exc}" + else: + detail = "" + _reap() + + if returncode != 0: + return RunOutcome(False, elapsed, None, f"engine exited with code {returncode}") + if outputs is None: + return RunOutcome(False, elapsed, None, detail) + return RunOutcome(True, elapsed, outputs, "") + + +if __name__ == "__main__": + if len(sys.argv) == 7 and sys.argv[1] == "--child": + _child(*sys.argv[2:]) + else: + raise SystemExit("runner.py is launched by the harness, not directly") diff --git a/tasks/cpu-llm-decode-throughput/tests/test.sh b/tasks/cpu-llm-decode-throughput/tests/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..2c7c21c1aa1359d38166456534e93030f1fb23a5 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/test.sh @@ -0,0 +1,19 @@ +#!/bin/bash +set -u + +mkdir -p /app/output /logs/verifier /grade +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT + +chmod -R go-rwx /tests + +export GRADE_SPLIT=final + +cd /tests +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/local/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/cpu-llm-decode-throughput/tests/test_state.py b/tasks/cpu-llm-decode-throughput/tests/test_state.py new file mode 100644 index 0000000000000000000000000000000000000000..e04fc50c2fcf66da2f45112ec696e6b1f760e327 --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/test_state.py @@ -0,0 +1,231 @@ +from __future__ import annotations + +import json +import os +import pwd +import shutil +import stat +import subprocess +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import grader_core +import metric_core + +TESTS_DIR = Path(__file__).resolve().parent +DELIVERABLE_DIR = Path("/app/output") +ENTRY_POINT = "engine.py" +MODEL_DIR = "/opt/model" +GRADE_ROOT = Path("/grade") +LOG_DIR = Path("/logs/verifier") +SPLIT = os.environ.get("GRADE_SPLIT", "final") +MAX_STAGED_BYTES = 4 * 1024 * 1024 * 1024 + +_RESULT = None + + + + +def _agent_ids(): + entry = pwd.getpwnam("agent") + return entry.pw_uid, entry.pw_gid + + +def _reject_unsafe_paths(root: Path): + """Refuse symlinks, hard links and special files anywhere under the deliverable. + + Checked at every depth BEFORE anything is copied, and the copy itself never + follows a link, so a submitted path cannot materialise a verifier-owned file + into the tree the score is computed from. + """ + total = 0 + for dirpath, dirnames, filenames in os.walk(root, followlinks=False): + for name in list(dirnames) + list(filenames): + path = Path(dirpath) / name + st = path.lstat() + if stat.S_ISLNK(st.st_mode): + return f"deliverable contains a symlink: {path}" + if stat.S_ISDIR(st.st_mode): + continue + if not stat.S_ISREG(st.st_mode): + return f"deliverable contains a special file: {path}" + if st.st_nlink > 1: + return f"deliverable contains a hard link: {path}" + total += st.st_size + if total > MAX_STAGED_BYTES: + return "deliverable is larger than the staging budget" + return "" + + +def _clear_grade_root(): + """Empty the staging tree without removing the directory itself.""" + GRADE_ROOT.mkdir(mode=0o755, parents=True, exist_ok=True) + for child in GRADE_ROOT.iterdir(): + if child.is_dir() and not child.is_symlink(): + shutil.rmtree(child, ignore_errors=True) + else: + child.unlink(missing_ok=True) + + +def _stage(): + """Build the root-owned read-only tree every timed run is launched from.""" + _clear_grade_root() + + if not DELIVERABLE_DIR.is_dir(): + return None, f"no deliverable directory at {DELIVERABLE_DIR}" + problem = _reject_unsafe_paths(DELIVERABLE_DIR) + if problem: + return None, problem + entry = DELIVERABLE_DIR / ENTRY_POINT + if not entry.is_file() or entry.stat().st_size == 0: + return None, f"no entry point at {DELIVERABLE_DIR / ENTRY_POINT}" + + candidate = GRADE_ROOT / "candidate" + shutil.copytree(DELIVERABLE_DIR, candidate, symlinks=True) + reference = GRADE_ROOT / "reference" + reference.mkdir() + shutil.copy2(TESTS_DIR / "reference_engine.py", reference / "reference_engine.py") + harness = GRADE_ROOT / "harness" + harness.mkdir() + shutil.copy2(TESTS_DIR / "runner.py", harness / "runner.py") + shutil.copy2(TESTS_DIR / "warmup_workload.json", GRADE_ROOT / "warmup_workload.json") + + subprocess.run( + ["chown", "-R", "root:root", str(GRADE_ROOT)], check=True, timeout=120 + ) + subprocess.run(["chmod", "-R", "a=rX", str(GRADE_ROOT)], check=True, timeout=120) + subprocess.run(["chmod", "755", str(GRADE_ROOT)], check=True, timeout=60) + + work = GRADE_ROOT / "work" + work.mkdir(mode=0o755) + return { + "candidate_engine": str(candidate / ENTRY_POINT), + "reference_engine": str(reference / "reference_engine.py"), + "runner": str(harness / "runner.py"), + "warmup": str(GRADE_ROOT / "warmup_workload.json"), + "work": str(work), + }, "" + + +def _workloads(): + directory = TESTS_DIR / "hidden_data" / SPLIT + out = [] + for path in sorted(directory.glob("workload_*.json")): + with open(path) as fh: + out.append((path.stem, json.load(fh))) + assert out, f"no sealed workloads under {directory}" + return out + + + + +def _write_metric(payload): + LOG_DIR.mkdir(parents=True, exist_ok=True) + with open(LOG_DIR / "metric.json", "w") as fh: + json.dump(payload, fh, indent=2, sort_keys=True) + + +def _run_grade(): + started = time.monotonic() + staged, problem = _stage() + if staged is None: + return { + "split": SPLIT, + "valid": False, + "detail": problem, + "metric_name": "speedup", + "metric": 0.0, + "speedup": 0.0, + "agreement": 0.0, + "reward": 0.0, + "rounds": [], + } + + uid, gid = _agent_ids() + runuser = shutil.which("runuser", path="/usr/sbin:/sbin:/usr/bin:/bin") + assert runuser, "runuser is missing from the verifier image" + prefix = [runuser, "-u", "agent", "--"] + round_log = (lambda *a, **k: None) if SPLIT == "intermediate" else print + rounds = metric_core.run_rounds( + staged["reference_engine"], + staged["candidate_engine"], + MODEL_DIR, + staged["warmup"], + _workloads(), + staged["work"], + cmd_prefix=prefix, + runner_path=staged["runner"], + run_dir_owner=(uid, gid), + reap_uid=uid, + log=round_log, + ) + summary = metric_core.summarise(rounds) + reward = grader_core.graded_reward(summary["speedup"], summary["valid"]) + return { + "split": SPLIT, + "valid": bool(summary["valid"]), + "detail": summary["detail"], + "metric_name": "speedup", + "metric": float(summary["speedup"]), + "speedup": float(summary["speedup"]), + "agreement": float(summary["agreement"]), + "agreement_minimum": metric_core.AGREEMENT_MIN, + "per_round_speedup": summary.get("per_round_speedup", []), + "reward": float(reward), + "rounds": rounds, + "grading_seconds": round(time.monotonic() - started, 2), + } + + +def _grade(): + global _RESULT + if _RESULT is None: + try: + _RESULT = _run_grade() + except Exception as exc: + _RESULT = { + "split": SPLIT, + "valid": False, + "detail": f"grader error: {type(exc).__name__}: {exc}", + "metric_name": "speedup", + "metric": 0.0, + "speedup": 0.0, + "agreement": 0.0, + "reward": 0.0, + "rounds": [], + } + _write_metric(_RESULT) + _clear_grade_root() + return _RESULT + + + + +def test_deliverable_is_present_and_safe(): + """The submitted engine exists, is a plain file tree, and loads and runs at all.""" + result = _grade() + assert result["rounds"], f"the engine never produced a timed run: {result['detail']}" + + +def test_outputs_match_the_reference(): + """Returned token ids reproduce the reference engine's, position by position.""" + result = _grade() + assert result["agreement"] >= metric_core.AGREEMENT_MIN, ( + f"token agreement {result['agreement']:.4f} < {metric_core.AGREEMENT_MIN}: " + f"{result['detail']}" + ) + + +def test_speedup_is_measured_and_recorded(): + """A speedup over the reference was measured on every sealed workload.""" + result = _grade() + assert (LOG_DIR / "metric.json").is_file(), "metric.json was not written" + assert result["speedup"] > 0.0, f"no speedup was measured: {result['detail']}" + print( + f"split={result['split']} speedup_S={result['speedup']:.4f} " + f"token_agreement={result['agreement']:.4f} " + f"per_round={[round(s, 3) for s in result.get('per_round_speedup', [])]}" + ) diff --git a/tasks/cpu-llm-decode-throughput/tests/warmup_workload.json b/tasks/cpu-llm-decode-throughput/tests/warmup_workload.json new file mode 100644 index 0000000000000000000000000000000000000000..2a2b4e6bdd8ba62c6f5397ab308b9307eeae6d7c --- /dev/null +++ b/tasks/cpu-llm-decode-throughput/tests/warmup_workload.json @@ -0,0 +1 @@ +[{"id": 0, "prompt_token_ids": [290, 4987, 326, 262, 2551, 284, 787, 683, 609, 38453, 373, 3148, 764, 27812, 3000, 7311, 635, 16476, 319, 428, 2478, 837, 4585, 911, 1134, 46893, 705, 82, 22106, 355, 366, 281, 7485, 4293, 366, 530, 286, 262, 11330, 286, 262, 10389, 764, 554, 257, 8840, 2423, 286, 4471, 17501, 837, 287, 543, 262, 4365, 284, 19818, 37527, 468, 4054, 290, 262, 1866, 286, 911, 1134, 46893, 705, 82, 1074, 547, 19475, 6686, 837, 28730, 42086, 262, 366, 1049, 2589, 366, 287, 262, 4471, 287, 543, 911, 1134, 46893, 2540, 284, 3960, 706, 4673, 465, 2460, 481, 8551, 837, 290, 6875, 326, 339, 481, 307, 257, 1365, 3554, 329, 262, 11060, 286, 465, 2460, 764, 49755, 11058, 705, 82, 10799, 3998, 13943, 465, 366, 15163, 7654, 366, 837, 4585, 683, 366, 530, 286, 262, 749, 13899, 3435, 287, 262, 2168, 366, 290, 366, 530, 286, 262, 1178, 4988, 10457, 8486, 287, 427, 13090, 38572, 11984, 366, 764, 22254, 49902, 260], "max_new_tokens": 4}, {"id": 1, "prompt_token_ids": [1871, 883, 1944, 287, 262, 41756, 547, 8187, 276, 3701, 25656, 544, 290, 2520, 5510, 40020, 8213, 837, 355, 880, 355, 25656, 666, 6093, 3701, 4809, 5795, 290, 4664, 3790, 422, 584, 2422, 4991, 764, 4176, 290, 1811, 1230, 14138, 635, 9141, 837, 1390, 350, 509, 5719, 4587, 347, 2645, 837, 262, 763, 2488, 12, 31, 4139, 286, 8708, 10665, 764, 220, 198, 11325, 1757, 390, 18133, 2921, 257, 39362, 36706, 644, 339, 3417, 355, 257, 366, 21815, 3101, 9550, 366, 422, 11292, 764, 366, 15658, 508, 6622, 13626, 262, 16247, 286, 1692, 1204, 460, 307], "max_new_tokens": 4}, {"id": 2, "prompt_token_ids": [2756, 2620, 422, 2813, 284, 2211, 764, 220, 198, 796, 796, 3948, 590, 796, 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python3 +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd +from sklearn.metrics import roc_auc_score +from sklearn.model_selection import StratifiedShuffleSplit + +DEV_CSV = Path("/app/data/dev.csv") +FEATURES = [f"f{i}" for i in range(9)] +LABEL = "y" + +N_TRAIN = 1_785 +N_EVAL = 800 +N_UNLABELED = 2_413 + + +def _take(y: np.ndarray, n: int, seed: int) -> tuple[np.ndarray, np.ndarray]: + sss = StratifiedShuffleSplit(n_splits=1, train_size=n, random_state=seed) + return next(sss.split(np.zeros(len(y)), y)) + + +def make_local_panel(draw: int, dev_csv: Path = DEV_CSV) -> dict: + """Build local draw number `draw`. Deterministic in `draw`.""" + dev = pd.read_csv(dev_csv) + y = dev[LABEL].to_numpy() + + tr_idx, rest = _take(y, N_TRAIN, 100_000 + draw) + ev_rel, rest2 = _take(y[rest], N_EVAL, 200_000 + draw) + ev_idx = rest[ev_rel] + unl_rel, _ = _take(y[rest[rest2]], N_UNLABELED, 300_000 + draw) + unl_idx = rest[rest2][unl_rel] + + train_X = dev.iloc[tr_idx][FEATURES].reset_index(drop=True) + train_y = dev.iloc[tr_idx][LABEL].to_numpy(dtype=np.int8) + eval_X = dev.iloc[ev_idx][FEATURES].reset_index(drop=True) + eval_y = dev.iloc[ev_idx][LABEL].to_numpy(dtype=np.int8) + + pool_X = pd.concat( + [dev.iloc[unl_idx][FEATURES], eval_X], ignore_index=True + ) + rng = np.random.default_rng(400_000 + draw) + pool_X = pool_X.iloc[rng.permutation(len(pool_X))].reset_index(drop=True) + + return { + "train_X": train_X, + "train_y": train_y, + "pool_X": pool_X, + "eval_X": eval_X, + "eval_y": eval_y, + "seed": draw, + } + + +def main() -> int: + n_draws = int(sys.argv[1]) if len(sys.argv) > 1 else 6 + sys.path.insert(0, "/app/output") + import solution + + aucs = [] + for draw in range(n_draws): + p = make_local_panel(draw) + scores = np.asarray( + solution.fit_predict( + p["train_X"], p["train_y"], p["pool_X"], p["eval_X"], p["seed"] + ), + dtype=np.float64, + ) + if scores.shape != (N_EVAL,): + raise SystemExit(f"draw {draw}: expected shape ({N_EVAL},), got {scores.shape}") + auc = roc_auc_score(p["eval_y"], scores) + aucs.append(auc) + print(f"draw {draw:2d} roc_auc={auc:.4f}", flush=True) + print(f"mean over {n_draws} draws: {float(np.mean(aucs)):.4f} " + f"(min {min(aucs):.4f}, max {max(aucs):.4f})") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tasks/dctabeval-aeac-pooled-cat-statistics/tests/hidden_data/final/evalpool.csv b/tasks/dctabeval-aeac-pooled-cat-statistics/tests/hidden_data/final/evalpool.csv new file mode 100644 index 0000000000000000000000000000000000000000..cc04ab4e90a4b6cf3142e972a88228e27ff36de5 --- /dev/null +++ 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index 0000000000000000000000000000000000000000..147250ff16bfa04fd7fd36a7b84fa424ebab992b --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/environment/Dockerfile @@ -0,0 +1,57 @@ +FROM --platform=linux/amd64 gcr.io/kaggle-gpu-images/python@sha256:37c64f7dd9c54116ecd1bcc88817c5469b88387388fade02bfa8bf3fc647d461 + +ENV PYTHONHASHSEED=0 +ENV PYTHONDONTWRITEBYTECODE=1 +ENV PIP_NO_CACHE_DIR=1 +ENV PIP_DISABLE_PIP_VERSION_CHECK=1 +ENV OMP_NUM_THREADS=1 +ENV MKL_NUM_THREADS=1 +ENV OPENBLAS_NUM_THREADS=1 +ENV NUMEXPR_NUM_THREADS=1 +ENV CUBLAS_WORKSPACE_CONFIG=:4096:8 +ENV HF_HUB_DISABLE_PROGRESS_BARS=1 +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 + +ENTRYPOINT [] + +RUN useradd --create-home --uid 1001 agent + +RUN command -v tmux >/dev/null 2>&1 \ + || (apt-get update \ + && apt-get install -y --no-install-recommends tmux \ + && rm -rf /var/lib/apt/lists/*) + +RUN HF_HUB_OFFLINE=0 TRANSFORMERS_OFFLINE=0 /usr/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='openai-community/gpt2', revision='607a30d783dfa663caf39e06633721c8d4cfcd7e', local_dir='/opt/assets/gpt2', allow_patterns=['config.json','generation_config.json','merges.txt','vocab.json','tokenizer.json','model.safetensors']); snapshot_download(repo_id='EleutherAI/pythia-160m', revision='50f5173d932e8e61f858120bcb800b97af589f46', local_dir='/opt/assets/pythia-160m', allow_patterns=['config.json','generation_config.json','tokenizer.json','tokenizer_config.json','special_tokens_map.json','model.safetensors'])" && rm -rf /opt/assets/gpt2/.cache /opt/assets/pythia-160m/.cache + +COPY ./build_data.py /tmp/build_data.py +RUN set -eux; \ + REV=1588ec454efa1a09f29cd18ddd04fe05fc8653a2; \ + B="https://huggingface.co/datasets/allenai/c4/resolve/$REV"; \ + mkdir -p /tmp/corpus /app/data; \ + curl -sSL -o /tmp/corpus/val0.json.gz "$B/en/c4-validation.00000-of-00008.json.gz"; \ + curl -sSL -o /tmp/corpus/tr0.json.gz "$B/en/c4-train.00512-of-01024.json.gz"; \ + curl -sSL -o /tmp/corpus/tr1.json.gz "$B/en/c4-train.00513-of-01024.json.gz"; \ + curl -sSL -o /tmp/corpus/nc0.json.gz "$B/en.noclean/c4-train.00512-of-07168.json.gz"; \ + TOKENIZERS_PARALLELISM=true /usr/bin/python3 /tmp/build_data.py eval \ + --shards /tmp/corpus/val0.json.gz --out /app/data/dev_eval \ + --tokenizer /opt/assets/gpt2/tokenizer.json --target-tokens 2000000; \ + TOKENIZERS_PARALLELISM=true /usr/bin/python3 /tmp/build_data.py pool \ + --shards /tmp/corpus/tr0.json.gz /tmp/corpus/tr1.json.gz \ + --shards-b /tmp/corpus/nc0.json.gz \ + --target-tokens 250000000 --target-tokens-b 62500000 \ + --out /app/data/dev_pool --tokenizer /opt/assets/gpt2/tokenizer.json \ + --guard /app/data/dev_eval/guard.json; \ + rm -rf /tmp/corpus /tmp/build_data.py /app/data/dev_pool/guard.json /app/data/dev_eval/guard.json + +WORKDIR /app +COPY ./app /app + +RUN mkdir -p /app/output /app/scratch \ + && chown -R agent:agent /app \ + && find /app -exec touch -d @1700000000 {} + + +RUN git config --system user.email "agent@localhost" \ + && git config --system user.name "agent" + +USER agent diff --git a/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/dev_cycle.py b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/dev_cycle.py new file mode 100644 index 0000000000000000000000000000000000000000..8d2863237b82f1b44a9db44b2baaa304200a093c --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/dev_cycle.py @@ -0,0 +1,78 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import argparse +import importlib.util +import json +import math +import os +import sys +import time +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import pretrain_harness as H + +DEFAULT_POOL = "/app/data/dev_pool" +DEFAULT_EVAL = "/app/data/dev_eval" +DEFAULT_CONFIG = "/app/harness/train_config.json" +BUDGET_TOKENS = 125_000_000 + + +def load_selector(path: str): + spec = importlib.util.spec_from_file_location("dev_select", path) + module = importlib.util.module_from_spec(spec) + sys.path.insert(0, str(Path(path).resolve().parent)) + spec.loader.exec_module(module) + return module.select + + +def main() -> int: + ap = argparse.ArgumentParser() + ap.add_argument("--selector", default="/app/output/select.py") + ap.add_argument("--pool", default=DEFAULT_POOL) + ap.add_argument("--eval", default=DEFAULT_EVAL) + ap.add_argument("--config", default=DEFAULT_CONFIG) + ap.add_argument("--budget-tokens", type=int, default=BUDGET_TOKENS) + ap.add_argument("--workdir", default="/app/scratch") + ap.add_argument("--reference", action="store_true", + help="train on the entire pool (the percent-reduction denominator)") + ap.add_argument("--out", default=None, help="also write the result as JSON here") + args = ap.parse_args() + + os.makedirs(args.workdir, exist_ok=True) + cfg = H.load_config(args.config) + pool = H.Pool(args.pool) + eval_blocks = H.load_eval_blocks(args.eval, cfg["block_size"]) + print(f"pool: {pool.n_docs} documents, {int(pool.offsets[-1])} tokens") + print(f"eval: {eval_blocks.shape[0]} blocks of {cfg['block_size']} tokens") + + if args.reference: + doc_ids = list(range(pool.n_docs)) + stats = {"n_accepted": pool.n_docs, "tokens_used": int(pool.offsets[-1])} + else: + select = load_selector(args.selector) + started = time.monotonic() + raw = select(args.pool, args.budget_tokens, args.workdir) + elapsed = time.monotonic() - started + print(f"select() returned in {elapsed:.1f}s") + doc_ids, _used, stats = H.sanitize_selection(raw, pool.ntokens, args.budget_tokens) + print(f"selection: {json.dumps(stats)}") + if not doc_ids: + print("selection is empty -- this scores 0") + return 1 + + out = H.run_cycle(cfg, pool, doc_ids, eval_blocks) + out["selection_stats"] = stats + print(json.dumps({k: v for k, v in out.items() if k != "selection_stats"}, indent=2)) + print(f"perplexity = {out['perplexity']:.6f} (log-perplexity {math.log(out['perplexity']):.6f})") + if args.out: + out_path = Path(args.out) + out_path.parent.mkdir(parents=True, exist_ok=True) + out_path.write_text(json.dumps(out, indent=2)) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/pretrain_harness.py b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/pretrain_harness.py new file mode 100644 index 0000000000000000000000000000000000000000..0078ac8c3ae7c967942dac0e8cc4a29ba7ea3f4e --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/pretrain_harness.py @@ -0,0 +1,340 @@ +from __future__ import annotations + +import json +import math +import os +import random +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from torch.nn import functional as F + +EOT_TOKEN = 50256 + + + +def set_determinism(seed: int) -> None: + """Pin every source of run-to-run variation this harness can reach.""" + os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8") + os.environ["PYTHONHASHSEED"] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + torch.set_num_threads(1) + + + +class Pool: + """A token pool laid out as docs.jsonl + tokens.npy + offsets.npy.""" + + def __init__(self, pool_dir: str | os.PathLike): + self.dir = Path(pool_dir) + self.offsets = np.load(self.dir / "offsets.npy", mmap_mode="r") + self.tokens = np.load(self.dir / "tokens.npy", mmap_mode="r") + self.n_docs = int(self.offsets.shape[0] - 1) + self.ntokens = np.diff(np.asarray(self.offsets, dtype=np.int64)) + + def span(self, doc_id: int) -> np.ndarray: + a = int(self.offsets[doc_id]) + b = int(self.offsets[doc_id + 1]) + return np.asarray(self.tokens[a:b], dtype=np.int64) + + +def sanitize_selection(raw, ntokens: np.ndarray, budget_tokens: int): + """Turn an untrusted return value into the accepted document list. + + Contract (identical to the one stated in the task instructions): + * the value must be a list (or tuple) of ints; + * ids outside [0, N) are dropped; + * a repeat of an already-accepted id is dropped; + * documents are walked IN THE ORDER RETURNED and accepted whenever the running + token total plus that document's ntokens stays within budget_tokens; a document + that would overflow is skipped, never fatal. + + Returns (accepted_ids, tokens_used, stats). Raises ValueError when the value is not + a list of ints at all -- that is the invalid path, not a zero-length selection. + """ + if isinstance(raw, tuple): + raw = list(raw) + if not isinstance(raw, list): + raise ValueError(f"select() must return a list of ints, got {type(raw).__name__}") + n = int(ntokens.shape[0]) + accepted: list[int] = [] + seen: set[int] = set() + used = 0 + n_bad_type = 0 + n_out_of_range = 0 + n_duplicate = 0 + n_overflow = 0 + for item in raw: + if isinstance(item, bool) or not isinstance(item, (int, np.integer)): + n_bad_type += 1 + continue + i = int(item) + if i < 0 or i >= n: + n_out_of_range += 1 + continue + if i in seen: + n_duplicate += 1 + continue + cost = int(ntokens[i]) + if used + cost > budget_tokens: + n_overflow += 1 + continue + seen.add(i) + accepted.append(i) + used += cost + stats = { + "n_returned": len(raw), + "n_accepted": len(accepted), + "tokens_used": used, + "n_bad_type": n_bad_type, + "n_out_of_range": n_out_of_range, + "n_duplicate": n_duplicate, + "n_overflow": n_overflow, + } + return accepted, used, stats + + +def build_block_stream(pool: Pool, doc_ids, block_size: int, perm_seed: int) -> np.ndarray: + """Concatenate the selected documents in the order given, chunk into contiguous + blocks of block_size tokens, drop the final partial block, then permute the blocks + once with numpy.random.default_rng(perm_seed).""" + if len(doc_ids) == 0: + return np.zeros((0, block_size), dtype=np.uint16) + total = int(sum(int(pool.ntokens[i]) for i in doc_ids)) + flat = np.empty(total, dtype=np.uint16) + at = 0 + for i in doc_ids: + a = int(pool.offsets[i]) + b = int(pool.offsets[i + 1]) + k = b - a + flat[at:at + k] = pool.tokens[a:b] + at += k + n_blocks = total // block_size + if n_blocks == 0: + return np.zeros((0, block_size), dtype=np.uint16) + blocks = flat[: n_blocks * block_size].reshape(n_blocks, block_size) + perm = np.random.default_rng(perm_seed).permutation(n_blocks) + return np.ascontiguousarray(blocks[perm]) + + +def load_eval_blocks(eval_dir: str | os.PathLike, block_size: int) -> np.ndarray: + """The evaluation shard, already concatenated document-by-document at build time + (each document followed by one EOT), chunked into contiguous blocks with the final + partial block dropped. No permutation: the evaluation set is a fixed set.""" + toks = np.load(Path(eval_dir) / "tokens.npy", mmap_mode="r") + n_blocks = int(toks.shape[0]) // block_size + arr = np.asarray(toks[: n_blocks * block_size], dtype=np.uint16) + return arr.reshape(n_blocks, block_size) + + + +class CausalSelfAttention(nn.Module): + def __init__(self, cfg): + super().__init__() + assert cfg["n_embd"] % cfg["n_head"] == 0 + self.n_head = cfg["n_head"] + self.n_embd = cfg["n_embd"] + self.c_attn = nn.Linear(cfg["n_embd"], 3 * cfg["n_embd"], bias=cfg["bias"]) + self.c_proj = nn.Linear(cfg["n_embd"], cfg["n_embd"], bias=cfg["bias"]) + + def forward(self, x): + B, T, C = x.size() + q, k, v = self.c_attn(x).split(self.n_embd, dim=2) + hs = C // self.n_head + q = q.view(B, T, self.n_head, hs).transpose(1, 2) + k = k.view(B, T, self.n_head, hs).transpose(1, 2) + v = v.view(B, T, self.n_head, hs).transpose(1, 2) + y = F.scaled_dot_product_attention(q, k, v, is_causal=True) + y = y.transpose(1, 2).contiguous().view(B, T, C) + return self.c_proj(y) + + +class MLP(nn.Module): + def __init__(self, cfg): + super().__init__() + self.c_fc = nn.Linear(cfg["n_embd"], 4 * cfg["n_embd"], bias=cfg["bias"]) + self.c_proj = nn.Linear(4 * cfg["n_embd"], cfg["n_embd"], bias=cfg["bias"]) + + def forward(self, x): + return self.c_proj(F.gelu(self.c_fc(x), approximate="tanh")) + + +class Block(nn.Module): + def __init__(self, cfg): + super().__init__() + self.ln_1 = nn.LayerNorm(cfg["n_embd"], bias=cfg["bias"]) + self.attn = CausalSelfAttention(cfg) + self.ln_2 = nn.LayerNorm(cfg["n_embd"], bias=cfg["bias"]) + self.mlp = MLP(cfg) + + def forward(self, x): + x = x + self.attn(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class GPT(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.wte = nn.Embedding(cfg["vocab_size"], cfg["n_embd"]) + self.wpe = nn.Embedding(cfg["block_size"], cfg["n_embd"]) + self.h = nn.ModuleList([Block(cfg) for _ in range(cfg["n_layer"])]) + self.ln_f = nn.LayerNorm(cfg["n_embd"], bias=cfg["bias"]) + self.lm_head = nn.Linear(cfg["n_embd"], cfg["vocab_size"], bias=False) + self.lm_head.weight = self.wte.weight + self.apply(self._init_weights) + scale = 0.02 / math.sqrt(2 * cfg["n_layer"]) + for name, p in self.named_parameters(): + if name.endswith("c_proj.weight"): + torch.nn.init.normal_(p, mean=0.0, std=scale) + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + + def forward(self, idx): + B, T = idx.size() + pos = torch.arange(0, T, dtype=torch.long, device=idx.device) + x = self.wte(idx) + self.wpe(pos) + for block in self.h: + x = block(x) + return self.lm_head(self.ln_f(x)) + + +def n_parameters(model: GPT) -> int: + seen, total = set(), 0 + for p in model.parameters(): + if id(p) in seen: + continue + seen.add(id(p)) + total += p.numel() + return total + + + +def _lr_at(step: int, cfg) -> float: + warm = cfg["warmup_steps"] + if step < warm: + return cfg["learning_rate"] * (step + 1) / warm + prog = (step - warm) / max(1, cfg["max_steps"] - warm) + prog = min(1.0, max(0.0, prog)) + coeff = 0.5 * (1.0 + math.cos(math.pi * prog)) + return cfg["min_lr"] + coeff * (cfg["learning_rate"] - cfg["min_lr"]) + + +def train_model(cfg, blocks: np.ndarray, device: str = "cuda", log=print) -> GPT: + """Train a fresh model from random initialisation on `blocks`, consumed cyclically. + + Deterministic: seeded init, seeded data order, deterministic kernels, no TF32. + """ + set_determinism(cfg["seed"]) + torch.manual_seed(cfg["seed"]) + model = GPT(cfg).to(device) + log(f"[train] parameters={n_parameters(model)}") + + decay, nodecay = [], [] + for _, p in model.named_parameters(): + if not p.requires_grad: + continue + (decay if p.dim() >= 2 else nodecay).append(p) + optim = torch.optim.AdamW( + [{"params": decay, "weight_decay": cfg["weight_decay"]}, + {"params": nodecay, "weight_decay": 0.0}], + lr=cfg["learning_rate"], betas=(cfg["beta1"], cfg["beta2"]), eps=1e-8, fused=False, + ) + + micro = cfg["micro_batch_size"] + seqs_per_step = cfg["tokens_per_step"] // cfg["block_size"] + assert seqs_per_step % micro == 0, "tokens_per_step/block_size must be a multiple of micro_batch_size" + accum = seqs_per_step // micro + n_blocks = int(blocks.shape[0]) + if n_blocks == 0: + raise ValueError("empty training stream") + + cursor = 0 + model.train() + for step in range(cfg["max_steps"]): + lr = _lr_at(step, cfg) + for group in optim.param_groups: + group["lr"] = lr + optim.zero_grad(set_to_none=True) + for _ in range(accum): + idx = (cursor + np.arange(micro)) % n_blocks + cursor = (cursor + micro) % n_blocks + batch = torch.from_numpy(blocks[idx].astype(np.int64)).to(device, non_blocking=False) + x, y = batch[:, :-1], batch[:, 1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = model(x) + loss = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), y.reshape(-1) + ) + (loss / accum).backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), cfg["grad_clip"]) + optim.step() + if step % 100 == 0 or step == cfg["max_steps"] - 1: + log(f"[train] step {step}/{cfg['max_steps']} lr {lr:.3e} loss {loss.item():.4f}") + return model + + +@torch.no_grad() +def eval_perplexity(model: GPT, eval_blocks: np.ndarray, cfg, device: str = "cuda"): + """Token-weighted mean cross-entropy over predicted positions 1..block_size-1. + + Position 0 of each block has no context and is excluded. Perplexity is + exp(total_ce / total_predicted_positions) -- one mean over the whole shard, not a + mean of per-block perplexities. + """ + model.eval() + micro = cfg["micro_batch_size"] + total_ce = 0.0 + total_pos = 0 + for start in range(0, int(eval_blocks.shape[0]), micro): + chunk = eval_blocks[start:start + micro] + batch = torch.from_numpy(chunk.astype(np.int64)).to(device) + x, y = batch[:, :-1], batch[:, 1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = model(x) + ce = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), y.reshape(-1), reduction="sum" + ) + total_ce += float(ce.item()) + total_pos += int(y.numel()) + if total_pos == 0: + raise ValueError("empty evaluation set") + mean_ce = total_ce / total_pos + return {"total_ce": total_ce, "n_positions": total_pos, + "mean_ce": mean_ce, "perplexity": math.exp(mean_ce)} + + +def load_config(path: str | os.PathLike) -> dict: + with open(path, "r", encoding="utf-8") as fh: + return json.load(fh) + + +def run_cycle(cfg, pool: Pool, doc_ids, eval_blocks: np.ndarray, device="cuda", log=print): + """One full train-then-evaluate cycle over the given document ids.""" + blocks = build_block_stream(pool, doc_ids, cfg["block_size"], cfg["block_permutation_seed"]) + log(f"[cycle] docs={len(doc_ids)} blocks={blocks.shape[0]}") + model = train_model(cfg, blocks, device=device, log=log) + out = eval_perplexity(model, eval_blocks, cfg, device=device) + del model + torch.cuda.empty_cache() + out["n_blocks"] = int(blocks.shape[0]) + out["n_docs"] = len(doc_ids) + log(f"[cycle] perplexity={out['perplexity']:.6f}") + return out diff --git a/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/train_config.json b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/train_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6817b260d074bfce4020eae2d4a05defc470acd3 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/harness/train_config.json @@ -0,0 +1,20 @@ +{ + "beta1": 0.9, + "beta2": 0.95, + "bias": true, + "block_permutation_seed": 1234, + "block_size": 1024, + "grad_clip": 1.0, + "learning_rate": 0.0006, + "max_steps": 1500, + "micro_batch_size": 16, + "min_lr": 6e-05, + "n_embd": 768, + "n_head": 12, + "n_layer": 12, + "seed": 1234, + "tokens_per_step": 131072, + "vocab_size": 50257, + "warmup_steps": 100, + "weight_decay": 0.1 +} diff --git a/tasks/less-is-more-pretrain-token-budget-selector/environment/app/output/select.py b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/output/select.py new file mode 100644 index 0000000000000000000000000000000000000000..80e9ca3f38e6e91ecb85f121cfe75b2a1d0855e5 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/environment/app/output/select.py @@ -0,0 +1,7 @@ +import numpy as np + + +def select(pool_dir: str, budget_tokens: int, workdir: str) -> list[int]: + offsets = np.load(f"{pool_dir}/offsets.npy", mmap_mode="r") + n_docs = int(offsets.shape[0]) - 1 + return list(range(n_docs)) diff --git a/tasks/less-is-more-pretrain-token-budget-selector/environment/build_data.py b/tasks/less-is-more-pretrain-token-budget-selector/environment/build_data.py new file mode 100644 index 0000000000000000000000000000000000000000..4f019289465883a1cc93ed2b97fe28d86ac42865 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/environment/build_data.py @@ -0,0 +1,222 @@ +from __future__ import annotations + +import argparse +import gzip +import hashlib +import json +import re +import sys +from pathlib import Path + +import numpy as np +from tokenizers import Tokenizer + +EOT_TOKEN = 50256 +_WS = re.compile(r"\s+") + + +def norm_hash(text: str) -> str: + return hashlib.sha256(_WS.sub(" ", text).strip().lower().encode("utf-8")).hexdigest()[:32] + + +def host_of(url: str) -> str: + u = url.split("://", 1)[-1] + host = u.split("/", 1)[0].split("?", 1)[0].split("@")[-1].split(":")[0].lower() + if host.startswith("www."): + host = host[4:] + return host or "unknown" + + +def read_docs(paths, skip_docs: int = 0): + seen = 0 + for path in paths: + with gzip.open(path, "rt", encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if not line: + continue + seen += 1 + if seen <= skip_docs: + continue + yield json.loads(line) + + +def encode_stream(tokenizer_json: str, docs_iter, batch: int = 2000): + tok = Tokenizer.from_file(tokenizer_json) + buf = [] + for rec in docs_iter: + buf.append(rec) + if len(buf) == batch: + for rec, enc in zip(buf, tok.encode_batch([r["text"] for r in buf])): + yield rec, enc.ids + buf = [] + if buf: + for rec, enc in zip(buf, tok.encode_batch([r["text"] for r in buf])): + yield rec, enc.ids + + +def build_eval(args): + out = Path(args.out) + out.mkdir(parents=True, exist_ok=True) + chunks, hashes, urls = [], [], [] + total = 0 + n_docs = 0 + for rec, ids in encode_stream(args.tokenizer, read_docs(args.shards, args.skip_docs)): + ids = ids + [EOT_TOKEN] + chunks.append(np.asarray(ids, dtype=np.uint16)) + hashes.append(norm_hash(rec["text"])) + urls.append(rec.get("url", "")) + total += len(ids) + n_docs += 1 + if total >= args.target_tokens: + break + flat = np.concatenate(chunks)[: args.target_tokens] + np.save(out / "tokens.npy", flat) + meta = {"kind": "eval", "n_docs": n_docs, "n_tokens": int(flat.shape[0]), + "shards": [Path(s).name for s in args.shards]} + (out / "meta.json").write_text(json.dumps(meta, indent=2, sort_keys=True)) + (out / "guard.json").write_text(json.dumps( + {"hashes": sorted(set(hashes)), "urls": sorted(set(urls))})) + print(f"[eval] {meta}", flush=True) + + +def _merged_stream(args): + """Read the pool's corpus files. + + A pool may be drawn from two corpus variants of the same crawl -- a filtered one and + a minimally-filtered one -- in which case documents are interleaved deterministically + in proportion to their token targets, before any grouping or ordering happens. + """ + a = encode_stream(args.tokenizer, read_docs(args.shards, args.skip_docs)) + if not args.shards_b: + yield from a + return + b = encode_stream(args.tokenizer, read_docs(args.shards_b, args.skip_docs_b)) + want_a = args.target_tokens - args.target_tokens_b + want_b = args.target_tokens_b + got_a = got_b = 0 + a_done = b_done = False + while not (a_done and b_done): + take_b = (not b_done) and (b_done or a_done or + got_b * max(1, want_a) <= got_a * max(1, want_b)) + if take_b: + try: + rec, ids = next(b) + got_b += len(ids) + 1 + yield rec, ids + continue + except StopIteration: + b_done = True + continue + try: + rec, ids = next(a) + got_a += len(ids) + 1 + yield rec, ids + except StopIteration: + a_done = True + + +def build_pool(args): + out = Path(args.out) + out.mkdir(parents=True, exist_ok=True) + guard_hashes, guard_urls = set(), set() + for g in args.guard or []: + blob = json.loads(Path(g).read_text()) + guard_hashes.update(blob["hashes"]) + guard_urls.update(blob["urls"]) + + kept, seen, seen_urls = [], set(), set() + total = 0 + n_seen = n_guard_hash = n_guard_url = n_dup = n_empty = n_dup_url = 0 + for rec, ids in _merged_stream(args): + n_seen += 1 + text = rec["text"] + if not ids: + n_empty += 1 + continue + h = norm_hash(text) + url = rec.get("url", "") + if h in guard_hashes: + n_guard_hash += 1 + continue + if url and url in guard_urls: + n_guard_url += 1 + continue + if h in seen: + n_dup += 1 + continue + if url and url in seen_urls: + n_dup_url += 1 + continue + seen.add(h) + if url: + seen_urls.add(url) + ids = ids + [EOT_TOKEN] + kept.append((url, text, np.asarray(ids, dtype=np.uint16))) + total += len(ids) + if total >= args.target_tokens: + break + + by_host: dict[str, list[int]] = {} + host_tokens: dict[str, int] = {} + for i, (url, _text, ids) in enumerate(kept): + host = host_of(url) + by_host.setdefault(host, []).append(i) + host_tokens[host] = host_tokens.get(host, 0) + int(ids.shape[0]) + order: list[int] = [] + for host in sorted(host_tokens, key=lambda h: (-host_tokens[h], h)): + order.extend(by_host[host]) + + offsets = np.zeros(len(order) + 1, dtype=np.int64) + with open(out / "docs.jsonl", "w", encoding="utf-8") as fh: + for new_id, old in enumerate(order): + url, text, ids = kept[old] + offsets[new_id + 1] = offsets[new_id] + int(ids.shape[0]) + fh.write(json.dumps({"id": new_id, "text": text, "url": url, + "ntokens": int(ids.shape[0])}, ensure_ascii=False) + "\n") + flat = np.empty(int(offsets[-1]), dtype=np.uint16) + for new_id, old in enumerate(order): + flat[offsets[new_id]:offsets[new_id + 1]] = kept[old][2] + np.save(out / "tokens.npy", flat) + np.save(out / "offsets.npy", offsets) + + meta = {"kind": "pool", "n_docs": len(order), "n_tokens": int(offsets[-1]), + "n_hosts": len(host_tokens), "n_seen": n_seen, + "dropped_eval_hash": n_guard_hash, "dropped_eval_url": n_guard_url, + "dropped_internal_dup": n_dup, "dropped_internal_dup_url": n_dup_url, + "dropped_empty": n_empty, + "shards": [Path(s).name for s in args.shards], "skip_docs": args.skip_docs} + (out / "meta.json").write_text(json.dumps(meta, indent=2, sort_keys=True)) + (out / "guard.json").write_text(json.dumps( + {"hashes": sorted(seen), "urls": sorted(u for u in seen_urls if u)})) + print(f"[pool] {meta}", flush=True) + + pool_hashes = seen + assert not (pool_hashes & guard_hashes), "pool/eval text-hash overlap survived the guard" + print(f"[pool] split integrity OK: {len(pool_hashes)} pool hashes, " + f"{len(guard_hashes)} eval hashes, intersection 0", flush=True) + + +def main(argv=None): + ap = argparse.ArgumentParser() + ap.add_argument("kind", choices=["eval", "pool"]) + ap.add_argument("--shards", nargs="+", required=True) + ap.add_argument("--out", required=True) + ap.add_argument("--tokenizer", required=True, help="path to a HF tokenizer.json") + ap.add_argument("--target-tokens", type=int, required=True) + ap.add_argument("--shards-b", nargs="*", default=None, + help="second corpus variant to interleave into the pool") + ap.add_argument("--target-tokens-b", type=int, default=0, + help="how many of --target-tokens come from --shards-b") + ap.add_argument("--skip-docs-b", type=int, default=0) + ap.add_argument("--skip-docs", type=int, default=0, + help="skip this many leading documents of the shard list (disjoint draws)") + ap.add_argument("--guard", nargs="*", default=None, + help="eval guard.json files whose documents must not enter the pool") + args = ap.parse_args(argv) + (build_eval if args.kind == "eval" else build_pool)(args) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tasks/less-is-more-pretrain-token-budget-selector/hints/hint.md b/tasks/less-is-more-pretrain-token-budget-selector/hints/hint.md new file mode 100644 index 0000000000000000000000000000000000000000..6e977b8d5278c367b322c2a24eb49c6f4f09cdde --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/hints/hint.md @@ -0,0 +1 @@ +Surface-quality filters rank clean commercial prose highly while it teaches almost nothing, so score documents by loss under a small pretrained model or by similarity to a high-quality reference distribution in embedding space. You are measured against training on the entire pool, so the filter only wins if what it drops actively hurts. Cost is dominated by JSON decoding across the pool rather than by scoring, so an expensive scorer is affordable. diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/Dockerfile b/tasks/less-is-more-pretrain-token-budget-selector/tests/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..54dfbc537d7eaabee396d24dbb1d8b1bf4a357d5 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/Dockerfile @@ -0,0 +1,65 @@ +FROM --platform=linux/amd64 gcr.io/kaggle-gpu-images/python@sha256:37c64f7dd9c54116ecd1bcc88817c5469b88387388fade02bfa8bf3fc647d461 + +ENTRYPOINT [] +ENV PYTHONPATH= +ENV PYTHONSAFEPATH=1 +ENV PYTHONHASHSEED=0 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 \ + MKL_NUM_THREADS=1 \ + OPENBLAS_NUM_THREADS=1 \ + NUMEXPR_NUM_THREADS=1 \ + CUBLAS_WORKSPACE_CONFIG=:4096:8 \ + HF_HUB_DISABLE_PROGRESS_BARS=1 + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt +RUN /usr/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements-verifier.txt \ + && rm /tmp/requirements-verifier.txt + +RUN /usr/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='openai-community/gpt2', revision='607a30d783dfa663caf39e06633721c8d4cfcd7e', local_dir='/opt/assets/gpt2', allow_patterns=['config.json','generation_config.json','merges.txt','vocab.json','tokenizer.json','model.safetensors']); snapshot_download(repo_id='EleutherAI/pythia-160m', revision='50f5173d932e8e61f858120bcb800b97af589f46', local_dir='/opt/assets/pythia-160m', allow_patterns=['config.json','generation_config.json','tokenizer.json','tokenizer_config.json','special_tokens_map.json','model.safetensors'])" && rm -rf /opt/assets/gpt2/.cache /opt/assets/pythia-160m/.cache + +COPY . /tests + +RUN set -eux; \ + REV=1588ec454efa1a09f29cd18ddd04fe05fc8653a2; \ + B="https://huggingface.co/datasets/allenai/c4/resolve/$REV"; \ + TK=/opt/assets/gpt2/tokenizer.json; \ + mkdir -p /tmp/corpus /tests/hidden_data; \ + curl -sSL -o /tmp/corpus/valF.json.gz "$B/en/c4-validation.00003-of-00008.json.gz"; \ + curl -sSL -o /tmp/corpus/valI.json.gz "$B/en/c4-validation.00005-of-00008.json.gz"; \ + curl -sSL -o /tmp/corpus/trF0.json.gz "$B/en/c4-train.00777-of-01024.json.gz"; \ + curl -sSL -o /tmp/corpus/trF1.json.gz "$B/en/c4-train.00778-of-01024.json.gz"; \ + curl -sSL -o /tmp/corpus/ncF.json.gz "$B/en.noclean/c4-train.00777-of-07168.json.gz"; \ + curl -sSL -o /tmp/corpus/trI0.json.gz "$B/en/c4-train.00888-of-01024.json.gz"; \ + curl -sSL -o /tmp/corpus/trI1.json.gz "$B/en/c4-train.00889-of-01024.json.gz"; \ + curl -sSL -o /tmp/corpus/ncI.json.gz "$B/en.noclean/c4-train.00888-of-07168.json.gz"; \ + export TOKENIZERS_PARALLELISM=true; \ + /usr/bin/python3 /tests/build_data.py eval --shards /tmp/corpus/valF.json.gz \ + --out /tests/hidden_data/final/eval --tokenizer $TK --target-tokens 2000000; \ + /usr/bin/python3 /tests/build_data.py pool --shards /tmp/corpus/trF0.json.gz /tmp/corpus/trF1.json.gz \ + --shards-b /tmp/corpus/ncF.json.gz --target-tokens 250000000 --target-tokens-b 62500000 \ + --out /tests/hidden_data/final/pool --tokenizer $TK \ + --guard /tests/hidden_data/final/eval/guard.json; \ + /usr/bin/python3 /tests/build_data.py eval --shards /tmp/corpus/valI.json.gz \ + --out /tests/hidden_data/intermediate/eval --tokenizer $TK --target-tokens 2000000; \ + /usr/bin/python3 /tests/build_data.py pool --shards /tmp/corpus/trI0.json.gz /tmp/corpus/trI1.json.gz \ + --shards-b /tmp/corpus/ncI.json.gz --target-tokens 250000000 --target-tokens-b 62500000 \ + --out /tests/hidden_data/intermediate/pool --tokenizer $TK \ + --guard /tests/hidden_data/intermediate/eval/guard.json; \ + rm -rf /tmp/corpus + +RUN mkdir -p /app/output /logs/verifier + +RUN chown -R root:root /tests \ + && chmod 700 /tests/hidden_data \ + && chmod +x /tests/test.sh /tests/intermediate.sh \ + && find /tests -exec touch -d @1700000000 {} + + +WORKDIR /tests + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/build_data.py b/tasks/less-is-more-pretrain-token-budget-selector/tests/build_data.py new file mode 100644 index 0000000000000000000000000000000000000000..4f019289465883a1cc93ed2b97fe28d86ac42865 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/build_data.py @@ -0,0 +1,222 @@ +from __future__ import annotations + +import argparse +import gzip +import hashlib +import json +import re +import sys +from pathlib import Path + +import numpy as np +from tokenizers import Tokenizer + +EOT_TOKEN = 50256 +_WS = re.compile(r"\s+") + + +def norm_hash(text: str) -> str: + return hashlib.sha256(_WS.sub(" ", text).strip().lower().encode("utf-8")).hexdigest()[:32] + + +def host_of(url: str) -> str: + u = url.split("://", 1)[-1] + host = u.split("/", 1)[0].split("?", 1)[0].split("@")[-1].split(":")[0].lower() + if host.startswith("www."): + host = host[4:] + return host or "unknown" + + +def read_docs(paths, skip_docs: int = 0): + seen = 0 + for path in paths: + with gzip.open(path, "rt", encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if not line: + continue + seen += 1 + if seen <= skip_docs: + continue + yield json.loads(line) + + +def encode_stream(tokenizer_json: str, docs_iter, batch: int = 2000): + tok = Tokenizer.from_file(tokenizer_json) + buf = [] + for rec in docs_iter: + buf.append(rec) + if len(buf) == batch: + for rec, enc in zip(buf, tok.encode_batch([r["text"] for r in buf])): + yield rec, enc.ids + buf = [] + if buf: + for rec, enc in zip(buf, tok.encode_batch([r["text"] for r in buf])): + yield rec, enc.ids + + +def build_eval(args): + out = Path(args.out) + out.mkdir(parents=True, exist_ok=True) + chunks, hashes, urls = [], [], [] + total = 0 + n_docs = 0 + for rec, ids in encode_stream(args.tokenizer, read_docs(args.shards, args.skip_docs)): + ids = ids + [EOT_TOKEN] + chunks.append(np.asarray(ids, dtype=np.uint16)) + hashes.append(norm_hash(rec["text"])) + urls.append(rec.get("url", "")) + total += len(ids) + n_docs += 1 + if total >= args.target_tokens: + break + flat = np.concatenate(chunks)[: args.target_tokens] + np.save(out / "tokens.npy", flat) + meta = {"kind": "eval", "n_docs": n_docs, "n_tokens": int(flat.shape[0]), + "shards": [Path(s).name for s in args.shards]} + (out / "meta.json").write_text(json.dumps(meta, indent=2, sort_keys=True)) + (out / "guard.json").write_text(json.dumps( + {"hashes": sorted(set(hashes)), "urls": sorted(set(urls))})) + print(f"[eval] {meta}", flush=True) + + +def _merged_stream(args): + """Read the pool's corpus files. + + A pool may be drawn from two corpus variants of the same crawl -- a filtered one and + a minimally-filtered one -- in which case documents are interleaved deterministically + in proportion to their token targets, before any grouping or ordering happens. + """ + a = encode_stream(args.tokenizer, read_docs(args.shards, args.skip_docs)) + if not args.shards_b: + yield from a + return + b = encode_stream(args.tokenizer, read_docs(args.shards_b, args.skip_docs_b)) + want_a = args.target_tokens - args.target_tokens_b + want_b = args.target_tokens_b + got_a = got_b = 0 + a_done = b_done = False + while not (a_done and b_done): + take_b = (not b_done) and (b_done or a_done or + got_b * max(1, want_a) <= got_a * max(1, want_b)) + if take_b: + try: + rec, ids = next(b) + got_b += len(ids) + 1 + yield rec, ids + continue + except StopIteration: + b_done = True + continue + try: + rec, ids = next(a) + got_a += len(ids) + 1 + yield rec, ids + except StopIteration: + a_done = True + + +def build_pool(args): + out = Path(args.out) + out.mkdir(parents=True, exist_ok=True) + guard_hashes, guard_urls = set(), set() + for g in args.guard or []: + blob = json.loads(Path(g).read_text()) + guard_hashes.update(blob["hashes"]) + guard_urls.update(blob["urls"]) + + kept, seen, seen_urls = [], set(), set() + total = 0 + n_seen = n_guard_hash = n_guard_url = n_dup = n_empty = n_dup_url = 0 + for rec, ids in _merged_stream(args): + n_seen += 1 + text = rec["text"] + if not ids: + n_empty += 1 + continue + h = norm_hash(text) + url = rec.get("url", "") + if h in guard_hashes: + n_guard_hash += 1 + continue + if url and url in guard_urls: + n_guard_url += 1 + continue + if h in seen: + n_dup += 1 + continue + if url and url in seen_urls: + n_dup_url += 1 + continue + seen.add(h) + if url: + seen_urls.add(url) + ids = ids + [EOT_TOKEN] + kept.append((url, text, np.asarray(ids, dtype=np.uint16))) + total += len(ids) + if total >= args.target_tokens: + break + + by_host: dict[str, list[int]] = {} + host_tokens: dict[str, int] = {} + for i, (url, _text, ids) in enumerate(kept): + host = host_of(url) + by_host.setdefault(host, []).append(i) + host_tokens[host] = host_tokens.get(host, 0) + int(ids.shape[0]) + order: list[int] = [] + for host in sorted(host_tokens, key=lambda h: (-host_tokens[h], h)): + order.extend(by_host[host]) + + offsets = np.zeros(len(order) + 1, dtype=np.int64) + with open(out / "docs.jsonl", "w", encoding="utf-8") as fh: + for new_id, old in enumerate(order): + url, text, ids = kept[old] + offsets[new_id + 1] = offsets[new_id] + int(ids.shape[0]) + fh.write(json.dumps({"id": new_id, "text": text, "url": url, + "ntokens": int(ids.shape[0])}, ensure_ascii=False) + "\n") + flat = np.empty(int(offsets[-1]), dtype=np.uint16) + for new_id, old in enumerate(order): + flat[offsets[new_id]:offsets[new_id + 1]] = kept[old][2] + np.save(out / "tokens.npy", flat) + np.save(out / "offsets.npy", offsets) + + meta = {"kind": "pool", "n_docs": len(order), "n_tokens": int(offsets[-1]), + "n_hosts": len(host_tokens), "n_seen": n_seen, + "dropped_eval_hash": n_guard_hash, "dropped_eval_url": n_guard_url, + "dropped_internal_dup": n_dup, "dropped_internal_dup_url": n_dup_url, + "dropped_empty": n_empty, + "shards": [Path(s).name for s in args.shards], "skip_docs": args.skip_docs} + (out / "meta.json").write_text(json.dumps(meta, indent=2, sort_keys=True)) + (out / "guard.json").write_text(json.dumps( + {"hashes": sorted(seen), "urls": sorted(u for u in seen_urls if u)})) + print(f"[pool] {meta}", flush=True) + + pool_hashes = seen + assert not (pool_hashes & guard_hashes), "pool/eval text-hash overlap survived the guard" + print(f"[pool] split integrity OK: {len(pool_hashes)} pool hashes, " + f"{len(guard_hashes)} eval hashes, intersection 0", flush=True) + + +def main(argv=None): + ap = argparse.ArgumentParser() + ap.add_argument("kind", choices=["eval", "pool"]) + ap.add_argument("--shards", nargs="+", required=True) + ap.add_argument("--out", required=True) + ap.add_argument("--tokenizer", required=True, help="path to a HF tokenizer.json") + ap.add_argument("--target-tokens", type=int, required=True) + ap.add_argument("--shards-b", nargs="*", default=None, + help="second corpus variant to interleave into the pool") + ap.add_argument("--target-tokens-b", type=int, default=0, + help="how many of --target-tokens come from --shards-b") + ap.add_argument("--skip-docs-b", type=int, default=0) + ap.add_argument("--skip-docs", type=int, default=0, + help="skip this many leading documents of the shard list (disjoint draws)") + ap.add_argument("--guard", nargs="*", default=None, + help="eval guard.json files whose documents must not enter the pool") + args = ap.parse_args(argv) + (build_eval if args.kind == "eval" else build_pool)(args) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/candidate_runner.py b/tasks/less-is-more-pretrain-token-budget-selector/tests/candidate_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..c569b2c4fe5827a34c2af181038fc4d6a5092ed9 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/candidate_runner.py @@ -0,0 +1,62 @@ +import importlib.util +import json +import random +import sys +import traceback + +import numpy as np +import torch + + +def _transport(item): + """ints travel as ints; anything else travels as a marker the grader drops.""" + if isinstance(item, bool): + return {"__bad__": "bool"} + if isinstance(item, int): + return int(item) + typ = type(item).__name__ + try: + return int(item.__index__()) + except Exception: + return {"__bad__": typ} + + +def main() -> int: + deliverable_dir, pool_dir, budget_s, workdir, out_path = sys.argv[1:6] + result = {"ok": False, "error": None, "value": None, "value_type": None} + try: + spec = importlib.util.spec_from_file_location( + "candidate_select", f"{deliverable_dir}/select.py") + if spec is None or spec.loader is None: + raise ImportError("select.py is not importable") + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + torch.cuda.manual_seed_all(0) + module = importlib.util.module_from_spec(spec) + sys.modules["candidate_select"] = module + sys.path.insert(0, deliverable_dir) + spec.loader.exec_module(module) + fn = getattr(module, "select", None) + if fn is None or not callable(fn): + raise AttributeError("select.py defines no callable named 'select'") + value = fn(pool_dir, int(budget_s), workdir) + result["value_type"] = type(value).__name__ + if isinstance(value, tuple): + value = list(value) + if not isinstance(value, list): + raise TypeError(f"select() returned {type(value).__name__}, expected list") + result["value"] = [_transport(v) for v in value] + result["ok"] = True + except BaseException: + result["error"] = traceback.format_exc()[-4000:] + try: + with open(out_path, "w", encoding="utf-8") as fh: + json.dump(result, fh) + except Exception: + return 4 + return 0 if result["ok"] else 3 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/compute_reward.py b/tasks/less-is-more-pretrain-token-budget-selector/tests/compute_reward.py new file mode 100644 index 0000000000000000000000000000000000000000..1d11005efea80cd7edb4ae8d0b5fd61fa6a00727 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/compute_reward.py @@ -0,0 +1,134 @@ +import json +import math +import os +import tempfile +from pathlib import Path + +REWARD_PATH = Path("/logs/verifier/reward.txt") +METRIC_PATH = Path("/logs/verifier/metric.json") + + +def clamp_unit(value) -> float: + """Map ANY object to a finite float in the closed interval [0, 1]. + + The comparisons are written so that NaN cannot slip through either end: NaN fails + `math.isfinite` before it reaches them, and the `<= 0.0` branch also normalises -0.0 + to 0.0 so no rendered reward can ever carry a minus sign. + """ + try: + x = float(value) + except Exception: + return 0.0 + if not math.isfinite(x): + return 0.0 + if x <= 0.0: + return 0.0 + if x >= 1.0: + return 1.0 + return x + + +def render(x: float) -> str: + """Render the clamped value, then read it back: only a string that parses to a finite + number inside [0, 1] is ever handed to the writer. Anything else degrades to "0".""" + try: + text = f"{x:.12g}" + back = float(text) + except Exception: + return "0" + if not math.isfinite(back) or back < 0.0 or back > 1.0: + return "0" + return text + + +def emit(text: str) -> bool: + """Write reward.txt. Never raises; returns whether the bytes landed. + + The write goes through a temp file in the same directory and one os.replace, so a + reader can only ever see the whole value or no file -- a crash partway through a + direct write could otherwise leave a truncated, unparseable reward.txt behind. + """ + payload = text + "\n" + try: + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + except Exception: + pass + tmp = None + try: + fd, tmp = tempfile.mkstemp(dir=str(REWARD_PATH.parent), prefix=".reward-") + with os.fdopen(fd, "w") as fh: + fh.write(payload) + fh.flush() + os.fsync(fh.fileno()) + os.chmod(tmp, 0o644) + os.replace(tmp, REWARD_PATH) + return True + except Exception: + if tmp is not None: + try: + os.unlink(tmp) + except Exception: + pass + try: + REWARD_PATH.write_text(payload) + return True + except Exception: + return False + + +def in_range(value) -> bool: + """Whether `value` is a finite number inside [0, 1].""" + try: + x = float(value) + except Exception: + return False + return math.isfinite(x) and 0.0 <= x <= 1.0 + + +def already_bounded() -> bool: + """Whether reward.txt on disk already holds a finite value inside [0, 1]. + + The last-resort handler consults this so that a failure AFTER a good reward was + written -- a closed stdout, a signal during the diagnostic print -- cannot replace a + valid submission's score with 0. + """ + try: + return in_range(REWARD_PATH.read_text().strip()) + except Exception: + return False + + +def say(message: str) -> None: + """Diagnostics must never be the reason this script fails.""" + try: + print(message) + except Exception: + pass + + +def main() -> None: + error = None + try: + value = json.loads(METRIC_PATH.read_text())["reward"] + except Exception as exc: + value, error = None, f"{type(exc).__name__}: {exc}" + reward = render(clamp_unit(value)) + wrote = emit(reward) + if error is None and not in_range(value): + error = f"clamped from {value!r}" + notes = [n for n in (error, None if wrote else "reward.txt is unwritable") if n] + say(f"reward={reward}" + (f" ({'; '.join(notes)})" if notes else "")) + + +if __name__ == "__main__": + try: + main() + except BaseException as exc: + kept = already_bounded() + if not kept: + emit("0") + try: + detail = f"{type(exc).__name__}: {exc}" + except Exception: + detail = "unprintable exception" + say(("reward.txt kept" if kept else "reward=0") + f" (fallback: {detail})") diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/grader_core.py b/tasks/less-is-more-pretrain-token-budget-selector/tests/grader_core.py new file mode 100644 index 0000000000000000000000000000000000000000..dd1bc8ed8cd1bdc896797e57ac499972e6a19f7b --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/grader_core.py @@ -0,0 +1,476 @@ +from __future__ import annotations + +import hashlib +import json +import os +import shutil +import signal +import subprocess +import sys +import time +import traceback +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import numpy as np + +import pretrain_harness as H + +BUDGET_TOKENS = 125_000_000 +SELECT_TIMEOUT_SEC = 2400 +DELIVERABLE_DIR = "/app/output" +ENTRY_POINT = "select.py" +CANDIDATE_UID = 1001 +CANDIDATE_GID = 1001 + +WRITABLE_SURFACE_ROOT = DELIVERABLE_DIR +AGENT_TREES_NEVER_READ = ("/app/harness", "/app/data", "/app/scratch") + + +def writable_surface_violations(config_path=None) -> list: + """Every way this grader would read agent-editable state outside the writable surface. + + Empty is the only acceptable result at grade time, and test_state.py asserts exactly that. + It is reported rather than raised from grade() so that the build-time baseline sweep, which + grades stub selectors against its own scratch pools and its own config copy, can drive the + same code path without tripping a check that only describes the graded image. + """ + sealed = Path(__file__).resolve().parent + bad = [] + for label, module_file in (("grader_core", __file__), ("pretrain_harness", H.__file__)): + path = Path(module_file).resolve() + if sealed not in path.parents: + bad.append(f"{label} is loaded from {path}, outside the sealed tree {sealed}") + if config_path is not None: + cfg = Path(config_path).resolve() + if sealed not in cfg.parents: + bad.append(f"the training config is read from {cfg}, outside {sealed}") + for tree in AGENT_TREES_NEVER_READ: + if Path(tree).exists(): + bad.append(f"{tree} reached the verifier and could shadow the sealed copy") + app = Path("/app") + for entry in sys.path: + if not entry: + continue + path = Path(entry).resolve() + if path == app or app in path.parents: + bad.append(f"an agent-writable path is on the grader's sys.path: {entry}") + return bad + + +DEGENERATE_REWARD = 0.10 +M_FLOOR = -4.01125 +M0 = 0.25515 +BASELINE_SCORE = 4.4 + + +def graded_reward(metric: float, valid: bool) -> float: + """rational_squash, higher-is-better, with the linear band below the trivial baseline.""" + if not valid: + return 0.0 + x = metric - M0 + x_ref = BASELINE_SCORE - M0 + assert x_ref > 0, "malformed task: the anchor does not beat the trivial baseline" + g = M0 - M_FLOOR + if g <= 0: + u = max(0.0, x) / x_ref + return u / (1.0 + u) + if x < 0: + return DEGENERATE_REWARD * (max(0.0, metric - M_FLOOR) / g) + u = x / x_ref + return DEGENERATE_REWARD + (1.0 - DEGENERATE_REWARD) * (u / (1.0 + u)) + + +class SubmissionRejected(Exception): + pass + + +def scan_submission(src: Path) -> None: + """Reject symlinks and hard links anywhere in the submitted tree, at any depth. + + A read-only bind mount does not remove hard links already in the submission, and a + no-dereference copy does not make a directory symlink's contents safe -- so the tree + is scanned and rejected BEFORE anything is copied. + """ + if not src.is_dir() or src.is_symlink(): + raise SubmissionRejected(f"{src} is not a real directory") + stack = [src] + n_files = 0 + while stack: + cur = stack.pop() + with os.scandir(cur) as it: + for entry in it: + st = os.lstat(entry.path) + if os.path.islink(entry.path): + raise SubmissionRejected(f"symlink in submission: {entry.path}") + if entry.is_dir(follow_symlinks=False): + stack.append(Path(entry.path)) + continue + if st.st_nlink != 1: + raise SubmissionRejected( + f"hard link in submission (st_nlink={st.st_nlink}): {entry.path}") + n_files += 1 + if n_files > 200_000: + raise SubmissionRejected("submission contains too many files") + + +def stage_submission(src: Path, dst: Path) -> None: + """Copy the scanned tree without following links, then hand it to the candidate uid.""" + scan_submission(src) + if dst.exists(): + shutil.rmtree(dst) + shutil.copytree(src, dst, symlinks=True) + for root, dirs, files in os.walk(dst): + os.lchown(root, CANDIDATE_UID, CANDIDATE_GID) + for name in dirs + files: + os.lchown(os.path.join(root, name), CANDIDATE_UID, CANDIDATE_GID) + os.lchown(dst, CANDIDATE_UID, CANDIDATE_GID) + + +POOL_FILES_FOR_CANDIDATE = ("docs.jsonl", "tokens.npy", "offsets.npy") + + +def stage_pool(pool_dir: Path, session: Path) -> Path: + """Publish the pool to the candidate uid without opening the sealed tree. + + /tests is 0700 root at grade time, so a uid-1001 subprocess cannot traverse it -- and + it must not, because the evaluation shard and this file live there. The three files + instruction.md documents are copied into a traversable parent as root-owned 0444, so + the candidate can read exactly the pool and cannot mutate the bytes the grader scores + from; the grader keeps reading the originals under /tests. meta.json and guard.json + are deliberately NOT copied -- guard.json carries the evaluation shard's document + hashes and URLs. + """ + dst = session / "pool" + if dst.exists(): + shutil.rmtree(dst) + dst.mkdir(parents=True) + os.chmod(dst, 0o755) + for name in POOL_FILES_FOR_CANDIDATE: + src = Path(pool_dir) / name + if not src.is_file(): + raise FileNotFoundError(f"the graded pool is missing {name}") + shutil.copyfile(src, dst / name) + os.chown(dst / name, 0, 0) + os.chmod(dst / name, 0o444) + return dst + + +def stage_runner(session: Path) -> Path: + """Copy candidate_runner.py out of the sealed tree into a traversable parent, keeping + it root-owned and read-only so the candidate can execute but never modify it.""" + session.mkdir(parents=True, exist_ok=True) + os.chmod(session, 0o755) + dst = session / "candidate_runner.py" + shutil.copyfile(Path(__file__).resolve().parent / "candidate_runner.py", dst) + os.chown(dst, 0, 0) + os.chmod(dst, 0o444) + return dst + + +def candidate_environment() -> dict: + """The explicit environment the candidate subprocess runs under. + + The NVIDIA visibility variables are deliberately NOT narrowed -- hiding the device + from the deliverable while the root grader still sees it is the hardest version of + this bug to read from a rollout log. + """ + env = { + "PATH": "/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin", + "HOME": "/home/agent", + "LANG": "C.UTF-8", + "TMPDIR": "/tmp", + "PYTHONDONTWRITEBYTECODE": "1", + "PYTHONSAFEPATH": "1", + "PYTHONHASHSEED": "0", + "OMP_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "NUMEXPR_NUM_THREADS": "1", + "TOKENIZERS_PARALLELISM": "false", + "CUBLAS_WORKSPACE_CONFIG": ":4096:8", + "HF_HUB_OFFLINE": "1", + "TRANSFORMERS_OFFLINE": "1", + "HF_HOME": "/opt/assets/hf-home", + } + for key in ("NVIDIA_VISIBLE_DEVICES", "NVIDIA_DRIVER_CAPABILITIES", "LD_LIBRARY_PATH"): + if key in os.environ: + env[key] = os.environ[key] + return env + + +def run_selector(deliverable_src: Path, pool_dir: Path, session: Path, log=print) -> dict: + """Stage, launch and collect the submission. Never raises for a candidate failure.""" + out = {"ok": False, "error": None, "raw": None, "elapsed_sec": None, + "returncode": None, "stderr_tail": None} + staged = session / "submission" + workdir = session / "workdir" + result_path = workdir / "selection.json" + try: + stage_submission(deliverable_src, staged) + except SubmissionRejected as exc: + out["error"] = f"submission rejected: {exc}" + return out + except Exception as exc: + out["error"] = f"submission could not be staged: {type(exc).__name__}: {exc}" + return out + + entry = staged / ENTRY_POINT + if not entry.is_file(): + out["error"] = f"missing entry point {DELIVERABLE_DIR}/{ENTRY_POINT}" + return out + + workdir.mkdir(parents=True, exist_ok=True) + os.chmod(workdir, 0o755) + os.chown(workdir, CANDIDATE_UID, CANDIDATE_GID) + if result_path.exists(): + result_path.unlink() + runner = stage_runner(session) + try: + candidate_pool = stage_pool(pool_dir, session) + except Exception as exc: + out["error"] = f"the pool could not be staged for the candidate: {exc}" + return out + + cmd = ["runuser", "-u", "agent", "--", "/usr/bin/python3", str(runner), + str(staged), str(candidate_pool), str(BUDGET_TOKENS), str(workdir), str(result_path)] + log(f"[select] launching: {' '.join(cmd)}") + started = time.monotonic() + proc = None + try: + proc = subprocess.Popen( + cmd, env=candidate_environment(), cwd=str(workdir), + stdout=subprocess.PIPE, stderr=subprocess.STDOUT, start_new_session=True, + ) + except Exception as exc: + out["elapsed_sec"] = time.monotonic() - started + out["error"] = f"could not launch the submission: {type(exc).__name__}: {exc}" + _reap(None) + return out + try: + stdout, _ = proc.communicate(timeout=SELECT_TIMEOUT_SEC) + out["returncode"] = proc.returncode + out["stderr_tail"] = (stdout or b"").decode("utf-8", "replace")[-4000:] + except subprocess.TimeoutExpired: + _reap(proc) + try: + stdout, _ = proc.communicate(timeout=60) + except Exception: + stdout = b"" + out["elapsed_sec"] = time.monotonic() - started + out["error"] = f"select() exceeded its {SELECT_TIMEOUT_SEC}-second budget" + out["stderr_tail"] = (stdout or b"").decode("utf-8", "replace")[-4000:] + return out + except Exception as exc: + _reap(proc) + out["elapsed_sec"] = time.monotonic() - started + out["error"] = f"select() could not be collected: {type(exc).__name__}: {exc}" + return out + finally: + _reap(proc) + out["elapsed_sec"] = time.monotonic() - started + + if not result_path.is_file(): + out["error"] = (f"select() wrote no result (returncode={out['returncode']}); " + f"tail: {out['stderr_tail']}") + return out + try: + blob = json.loads(result_path.read_text()) + except Exception as exc: + out["error"] = f"select() result is unparseable: {type(exc).__name__}: {exc}" + return out + if not blob.get("ok"): + out["error"] = f"select() raised: {blob.get('error')}" + return out + out["raw"] = blob.get("value") + out["ok"] = True + return out + + +def _reap(proc) -> None: + """Kill the candidate's whole process group, then sweep any double-forked stragglers. + + start_new_session=True made the child a session/process-group leader (setsid(2)), so + its pgid equals its pid and one killpg reaches every descendant that stayed in the + group; the uid sweep is the backstop for anything that left it. + """ + if proc is not None: + try: + os.killpg(os.getpgid(proc.pid), signal.SIGKILL) + except Exception: + pass + try: + subprocess.run(["pkill", "-9", "-u", str(CANDIDATE_UID)], timeout=30, + stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + except Exception: + pass + + +def reference_perplexity(cfg, pool, eval_blocks, cache_dir=None, log=print) -> dict: + """The full-pool reference run. + + `cache_dir` is None at grade time, where the reference is computed exactly once per + invocation anyway, so no cache and no cache-shaped attack surface exists there. The + build-time baseline sweep passes a root-owned scratch directory so that N selectors + graded against one pool pay for one reference run instead of N; the key covers the + pool identity and every training hyperparameter. + """ + if cache_dir is None: + return H.run_cycle(cfg, pool, list(range(pool.n_docs)), eval_blocks, log=log) + key = hashlib.sha256( + (json.dumps(cfg, sort_keys=True) + "|" + str(Path(pool.dir).resolve()) + "|" + + str(pool.n_docs) + "|" + str(int(pool.offsets[-1])) + "|" + + str(int(eval_blocks.shape[0]))).encode("utf-8")).hexdigest()[:24] + path = Path(cache_dir) / f"reference-{key}.json" + if path.is_file(): + log(f"[grade] reference run served from {path}") + return json.loads(path.read_text()) + out = H.run_cycle(cfg, pool, list(range(pool.n_docs)), eval_blocks, log=log) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(out)) + return out + + +def grade(deliverable_src, pool_dir, eval_dir, config_path, session_dir, log=print, + reference_cache_dir=None) -> dict: + """Run the submission, train twice, evaluate twice, return the full record. + + This function NEVER raises. Every way the cycle can fail -- a hostile submission, a + crashed or timed-out selector, a wrong return type, a diverged training run, a broken + sealed slice, a bug in this file -- leaves `valid` False, which finalize() maps to + exactly 0.0. There is no path out of here that both fails and scores. + """ + record = { + "valid": False, "invalid_reason": None, "metric": None, + "ppl_selection": None, "ppl_full_pool": None, + "selection_stats": None, "select_elapsed_sec": None, + "gpu_visible_to_grader": None, "infra_error": None, + "writable_surface_violations": None, + "budget_tokens": BUDGET_TOKENS, "select_timeout_sec": SELECT_TIMEOUT_SEC, + } + try: + _grade_cycle(record, deliverable_src, pool_dir, eval_dir, config_path, session_dir, + log=log, reference_cache_dir=reference_cache_dir) + except BaseException as exc: + record["valid"] = False + record["metric"] = None + record["invalid_reason"] = f"the graded cycle raised: {type(exc).__name__}: {exc}" + record["grade_traceback"] = traceback.format_exc()[-6000:] + return record + + +def _grade_cycle(record, deliverable_src, pool_dir, eval_dir, config_path, session_dir, + log=print, reference_cache_dir=None) -> None: + """The cycle itself. Fills `record` in place; grade() owns the failure boundary.""" + deliverable_src = Path(deliverable_src) + pool_dir = Path(pool_dir) + eval_dir = Path(eval_dir) + session = Path(session_dir) + session.mkdir(parents=True, exist_ok=True) + + record["writable_surface_violations"] = writable_surface_violations(config_path) + for violation in record["writable_surface_violations"]: + log(f"[grade] writable-surface violation: {violation}") + + try: + import torch + record["gpu_visible_to_grader"] = int(torch.cuda.device_count()) + except Exception as exc: + record["gpu_visible_to_grader"] = -1 + record["infra_error"] = f"torch/CUDA unavailable to the grader: {exc}" + + try: + cfg = H.load_config(config_path) + pool = H.Pool(pool_dir) + block_size = int(cfg["block_size"]) + except Exception as exc: + record["infra_error"] = (f"the graded pool or config could not be loaded: " + f"{type(exc).__name__}: {exc}") + record["invalid_reason"] = "graded pool unavailable" + return + log(f"[grade] pool docs={pool.n_docs} tokens={int(pool.offsets[-1])}") + + sel = run_selector(deliverable_src, pool_dir, session, log=log) + record["select_elapsed_sec"] = sel["elapsed_sec"] + record["select_stderr_tail"] = sel["stderr_tail"] + if not sel["ok"]: + record["invalid_reason"] = sel["error"] + return + try: + accepted, used, stats = H.sanitize_selection(sel["raw"], pool.ntokens, BUDGET_TOKENS) + except ValueError as exc: + record["invalid_reason"] = str(exc) + return + record["selection_stats"] = stats + log(f"[grade] selection {stats}") + if len(accepted) == 0 or used < block_size: + record["invalid_reason"] = "selection buys fewer than one full training block" + return + + try: + eval_blocks = H.load_eval_blocks(eval_dir, block_size) + except Exception as exc: + record["infra_error"] = (f"the graded evaluation shard could not be loaded: " + f"{type(exc).__name__}: {exc}") + record["invalid_reason"] = "evaluation shard unavailable" + return + log(f"[grade] eval blocks={eval_blocks.shape[0]}") + + try: + sel_out = H.run_cycle(cfg, pool, accepted, eval_blocks, log=log) + except Exception as exc: + record["invalid_reason"] = f"training on the selection failed: {type(exc).__name__}: {exc}" + return + + try: + full_out = reference_perplexity(cfg, pool, eval_blocks, + cache_dir=reference_cache_dir, log=log) + except Exception as exc: + record["infra_error"] = f"full-pool reference run failed: {type(exc).__name__}: {exc}" + record["invalid_reason"] = "reference run unavailable" + return + + ppl_sel = float(sel_out["perplexity"]) + ppl_full = float(full_out["perplexity"]) + record["ppl_selection"] = ppl_sel + record["ppl_full_pool"] = ppl_full + if not (np.isfinite(ppl_sel) and np.isfinite(ppl_full)) or ppl_full <= 0.0: + record["invalid_reason"] = (f"perplexity is unusable (selection={ppl_sel!r}, " + f"full pool={ppl_full!r})") + return + metric = 100.0 * (ppl_full - ppl_sel) / ppl_full + if not np.isfinite(metric): + record["invalid_reason"] = "metric is not finite" + return + record["metric"] = float(metric) + record["valid"] = True + log(f"[grade] ppl_sel={ppl_sel:.6f} ppl_full={ppl_full:.6f} metric={metric:.4f}") + + +def finalize(record: dict) -> dict: + """Map the record to its reward. Never raises: anything it cannot score is 0.0. + + An invalid record does not get a small reward, it gets exactly 0.0 -- there is no + partial credit for a submission that failed, and none for a grader that failed either. + """ + record = dict(record) + try: + valid = bool(record.get("valid")) and record.get("metric") is not None + metric = float(record["metric"]) if valid else float("nan") + if valid and not np.isfinite(metric): + valid = False + record["invalid_reason"] = "metric is not finite" + reward = float(graded_reward(metric, valid)) + if not np.isfinite(reward): + raise ValueError(f"the reward map returned {reward!r}") + except BaseException as exc: + valid = False + reward = 0.0 + record["invalid_reason"] = (f"the reward map could not be evaluated: " + f"{type(exc).__name__}: {exc}") + record["valid"] = valid + record["reward"] = reward if valid else 0.0 + record["anchors"] = {"m_floor": M_FLOOR, "m0": M0, "baseline_score": BASELINE_SCORE, + "degenerate_reward": DEGENERATE_REWARD} + return record diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/intermediate.sh b/tasks/less-is-more-pretrain-token-budget-selector/tests/intermediate.sh new file mode 100644 index 0000000000000000000000000000000000000000..2146390147cf5697b329836f1cd43945ab939e50 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/intermediate.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +default_reward() { + if [ ! -s /logs/verifier/reward.txt ]; then printf "0\n" > /logs/verifier/reward.txt; fi +} +trap 'default_reward' EXIT +trap 'default_reward; exit 0' HUP INT TERM + +chmod -R go-rwx /tests + +export HIDDEN_SPLIT=intermediate +export PYTHONHASHSEED=0 +export OMP_NUM_THREADS=1 +export MKL_NUM_THREADS=1 +export OPENBLAS_NUM_THREADS=1 +export NUMEXPR_NUM_THREADS=1 +export CUBLAS_WORKSPACE_CONFIG=:4096:8 + +cd /tests +/usr/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/pretrain_harness.py b/tasks/less-is-more-pretrain-token-budget-selector/tests/pretrain_harness.py new file mode 100644 index 0000000000000000000000000000000000000000..0078ac8c3ae7c967942dac0e8cc4a29ba7ea3f4e --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/pretrain_harness.py @@ -0,0 +1,340 @@ +from __future__ import annotations + +import json +import math +import os +import random +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn +from torch.nn import functional as F + +EOT_TOKEN = 50256 + + + +def set_determinism(seed: int) -> None: + """Pin every source of run-to-run variation this harness can reach.""" + os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8") + os.environ["PYTHONHASHSEED"] = str(seed) + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + torch.set_num_threads(1) + + + +class Pool: + """A token pool laid out as docs.jsonl + tokens.npy + offsets.npy.""" + + def __init__(self, pool_dir: str | os.PathLike): + self.dir = Path(pool_dir) + self.offsets = np.load(self.dir / "offsets.npy", mmap_mode="r") + self.tokens = np.load(self.dir / "tokens.npy", mmap_mode="r") + self.n_docs = int(self.offsets.shape[0] - 1) + self.ntokens = np.diff(np.asarray(self.offsets, dtype=np.int64)) + + def span(self, doc_id: int) -> np.ndarray: + a = int(self.offsets[doc_id]) + b = int(self.offsets[doc_id + 1]) + return np.asarray(self.tokens[a:b], dtype=np.int64) + + +def sanitize_selection(raw, ntokens: np.ndarray, budget_tokens: int): + """Turn an untrusted return value into the accepted document list. + + Contract (identical to the one stated in the task instructions): + * the value must be a list (or tuple) of ints; + * ids outside [0, N) are dropped; + * a repeat of an already-accepted id is dropped; + * documents are walked IN THE ORDER RETURNED and accepted whenever the running + token total plus that document's ntokens stays within budget_tokens; a document + that would overflow is skipped, never fatal. + + Returns (accepted_ids, tokens_used, stats). Raises ValueError when the value is not + a list of ints at all -- that is the invalid path, not a zero-length selection. + """ + if isinstance(raw, tuple): + raw = list(raw) + if not isinstance(raw, list): + raise ValueError(f"select() must return a list of ints, got {type(raw).__name__}") + n = int(ntokens.shape[0]) + accepted: list[int] = [] + seen: set[int] = set() + used = 0 + n_bad_type = 0 + n_out_of_range = 0 + n_duplicate = 0 + n_overflow = 0 + for item in raw: + if isinstance(item, bool) or not isinstance(item, (int, np.integer)): + n_bad_type += 1 + continue + i = int(item) + if i < 0 or i >= n: + n_out_of_range += 1 + continue + if i in seen: + n_duplicate += 1 + continue + cost = int(ntokens[i]) + if used + cost > budget_tokens: + n_overflow += 1 + continue + seen.add(i) + accepted.append(i) + used += cost + stats = { + "n_returned": len(raw), + "n_accepted": len(accepted), + "tokens_used": used, + "n_bad_type": n_bad_type, + "n_out_of_range": n_out_of_range, + "n_duplicate": n_duplicate, + "n_overflow": n_overflow, + } + return accepted, used, stats + + +def build_block_stream(pool: Pool, doc_ids, block_size: int, perm_seed: int) -> np.ndarray: + """Concatenate the selected documents in the order given, chunk into contiguous + blocks of block_size tokens, drop the final partial block, then permute the blocks + once with numpy.random.default_rng(perm_seed).""" + if len(doc_ids) == 0: + return np.zeros((0, block_size), dtype=np.uint16) + total = int(sum(int(pool.ntokens[i]) for i in doc_ids)) + flat = np.empty(total, dtype=np.uint16) + at = 0 + for i in doc_ids: + a = int(pool.offsets[i]) + b = int(pool.offsets[i + 1]) + k = b - a + flat[at:at + k] = pool.tokens[a:b] + at += k + n_blocks = total // block_size + if n_blocks == 0: + return np.zeros((0, block_size), dtype=np.uint16) + blocks = flat[: n_blocks * block_size].reshape(n_blocks, block_size) + perm = np.random.default_rng(perm_seed).permutation(n_blocks) + return np.ascontiguousarray(blocks[perm]) + + +def load_eval_blocks(eval_dir: str | os.PathLike, block_size: int) -> np.ndarray: + """The evaluation shard, already concatenated document-by-document at build time + (each document followed by one EOT), chunked into contiguous blocks with the final + partial block dropped. No permutation: the evaluation set is a fixed set.""" + toks = np.load(Path(eval_dir) / "tokens.npy", mmap_mode="r") + n_blocks = int(toks.shape[0]) // block_size + arr = np.asarray(toks[: n_blocks * block_size], dtype=np.uint16) + return arr.reshape(n_blocks, block_size) + + + +class CausalSelfAttention(nn.Module): + def __init__(self, cfg): + super().__init__() + assert cfg["n_embd"] % cfg["n_head"] == 0 + self.n_head = cfg["n_head"] + self.n_embd = cfg["n_embd"] + self.c_attn = nn.Linear(cfg["n_embd"], 3 * cfg["n_embd"], bias=cfg["bias"]) + self.c_proj = nn.Linear(cfg["n_embd"], cfg["n_embd"], bias=cfg["bias"]) + + def forward(self, x): + B, T, C = x.size() + q, k, v = self.c_attn(x).split(self.n_embd, dim=2) + hs = C // self.n_head + q = q.view(B, T, self.n_head, hs).transpose(1, 2) + k = k.view(B, T, self.n_head, hs).transpose(1, 2) + v = v.view(B, T, self.n_head, hs).transpose(1, 2) + y = F.scaled_dot_product_attention(q, k, v, is_causal=True) + y = y.transpose(1, 2).contiguous().view(B, T, C) + return self.c_proj(y) + + +class MLP(nn.Module): + def __init__(self, cfg): + super().__init__() + self.c_fc = nn.Linear(cfg["n_embd"], 4 * cfg["n_embd"], bias=cfg["bias"]) + self.c_proj = nn.Linear(4 * cfg["n_embd"], cfg["n_embd"], bias=cfg["bias"]) + + def forward(self, x): + return self.c_proj(F.gelu(self.c_fc(x), approximate="tanh")) + + +class Block(nn.Module): + def __init__(self, cfg): + super().__init__() + self.ln_1 = nn.LayerNorm(cfg["n_embd"], bias=cfg["bias"]) + self.attn = CausalSelfAttention(cfg) + self.ln_2 = nn.LayerNorm(cfg["n_embd"], bias=cfg["bias"]) + self.mlp = MLP(cfg) + + def forward(self, x): + x = x + self.attn(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class GPT(nn.Module): + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + self.wte = nn.Embedding(cfg["vocab_size"], cfg["n_embd"]) + self.wpe = nn.Embedding(cfg["block_size"], cfg["n_embd"]) + self.h = nn.ModuleList([Block(cfg) for _ in range(cfg["n_layer"])]) + self.ln_f = nn.LayerNorm(cfg["n_embd"], bias=cfg["bias"]) + self.lm_head = nn.Linear(cfg["n_embd"], cfg["vocab_size"], bias=False) + self.lm_head.weight = self.wte.weight + self.apply(self._init_weights) + scale = 0.02 / math.sqrt(2 * cfg["n_layer"]) + for name, p in self.named_parameters(): + if name.endswith("c_proj.weight"): + torch.nn.init.normal_(p, mean=0.0, std=scale) + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + + def forward(self, idx): + B, T = idx.size() + pos = torch.arange(0, T, dtype=torch.long, device=idx.device) + x = self.wte(idx) + self.wpe(pos) + for block in self.h: + x = block(x) + return self.lm_head(self.ln_f(x)) + + +def n_parameters(model: GPT) -> int: + seen, total = set(), 0 + for p in model.parameters(): + if id(p) in seen: + continue + seen.add(id(p)) + total += p.numel() + return total + + + +def _lr_at(step: int, cfg) -> float: + warm = cfg["warmup_steps"] + if step < warm: + return cfg["learning_rate"] * (step + 1) / warm + prog = (step - warm) / max(1, cfg["max_steps"] - warm) + prog = min(1.0, max(0.0, prog)) + coeff = 0.5 * (1.0 + math.cos(math.pi * prog)) + return cfg["min_lr"] + coeff * (cfg["learning_rate"] - cfg["min_lr"]) + + +def train_model(cfg, blocks: np.ndarray, device: str = "cuda", log=print) -> GPT: + """Train a fresh model from random initialisation on `blocks`, consumed cyclically. + + Deterministic: seeded init, seeded data order, deterministic kernels, no TF32. + """ + set_determinism(cfg["seed"]) + torch.manual_seed(cfg["seed"]) + model = GPT(cfg).to(device) + log(f"[train] parameters={n_parameters(model)}") + + decay, nodecay = [], [] + for _, p in model.named_parameters(): + if not p.requires_grad: + continue + (decay if p.dim() >= 2 else nodecay).append(p) + optim = torch.optim.AdamW( + [{"params": decay, "weight_decay": cfg["weight_decay"]}, + {"params": nodecay, "weight_decay": 0.0}], + lr=cfg["learning_rate"], betas=(cfg["beta1"], cfg["beta2"]), eps=1e-8, fused=False, + ) + + micro = cfg["micro_batch_size"] + seqs_per_step = cfg["tokens_per_step"] // cfg["block_size"] + assert seqs_per_step % micro == 0, "tokens_per_step/block_size must be a multiple of micro_batch_size" + accum = seqs_per_step // micro + n_blocks = int(blocks.shape[0]) + if n_blocks == 0: + raise ValueError("empty training stream") + + cursor = 0 + model.train() + for step in range(cfg["max_steps"]): + lr = _lr_at(step, cfg) + for group in optim.param_groups: + group["lr"] = lr + optim.zero_grad(set_to_none=True) + for _ in range(accum): + idx = (cursor + np.arange(micro)) % n_blocks + cursor = (cursor + micro) % n_blocks + batch = torch.from_numpy(blocks[idx].astype(np.int64)).to(device, non_blocking=False) + x, y = batch[:, :-1], batch[:, 1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = model(x) + loss = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), y.reshape(-1) + ) + (loss / accum).backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), cfg["grad_clip"]) + optim.step() + if step % 100 == 0 or step == cfg["max_steps"] - 1: + log(f"[train] step {step}/{cfg['max_steps']} lr {lr:.3e} loss {loss.item():.4f}") + return model + + +@torch.no_grad() +def eval_perplexity(model: GPT, eval_blocks: np.ndarray, cfg, device: str = "cuda"): + """Token-weighted mean cross-entropy over predicted positions 1..block_size-1. + + Position 0 of each block has no context and is excluded. Perplexity is + exp(total_ce / total_predicted_positions) -- one mean over the whole shard, not a + mean of per-block perplexities. + """ + model.eval() + micro = cfg["micro_batch_size"] + total_ce = 0.0 + total_pos = 0 + for start in range(0, int(eval_blocks.shape[0]), micro): + chunk = eval_blocks[start:start + micro] + batch = torch.from_numpy(chunk.astype(np.int64)).to(device) + x, y = batch[:, :-1], batch[:, 1:] + with torch.autocast(device_type="cuda", dtype=torch.bfloat16): + logits = model(x) + ce = F.cross_entropy( + logits.reshape(-1, logits.size(-1)).float(), y.reshape(-1), reduction="sum" + ) + total_ce += float(ce.item()) + total_pos += int(y.numel()) + if total_pos == 0: + raise ValueError("empty evaluation set") + mean_ce = total_ce / total_pos + return {"total_ce": total_ce, "n_positions": total_pos, + "mean_ce": mean_ce, "perplexity": math.exp(mean_ce)} + + +def load_config(path: str | os.PathLike) -> dict: + with open(path, "r", encoding="utf-8") as fh: + return json.load(fh) + + +def run_cycle(cfg, pool: Pool, doc_ids, eval_blocks: np.ndarray, device="cuda", log=print): + """One full train-then-evaluate cycle over the given document ids.""" + blocks = build_block_stream(pool, doc_ids, cfg["block_size"], cfg["block_permutation_seed"]) + log(f"[cycle] docs={len(doc_ids)} blocks={blocks.shape[0]}") + model = train_model(cfg, blocks, device=device, log=log) + out = eval_perplexity(model, eval_blocks, cfg, device=device) + del model + torch.cuda.empty_cache() + out["n_blocks"] = int(blocks.shape[0]) + out["n_docs"] = len(doc_ids) + log(f"[cycle] perplexity={out['perplexity']:.6f}") + return out diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/requirements-verifier.txt b/tasks/less-is-more-pretrain-token-budget-selector/tests/requirements-verifier.txt new file mode 100644 index 0000000000000000000000000000000000000000..04676c89520bf969f2cb861ad1811f46d8370700 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/requirements-verifier.txt @@ -0,0 +1,2 @@ +pytest==8.3.5 +pytest-json-ctrf==0.3.5 diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/test.sh b/tasks/less-is-more-pretrain-token-budget-selector/tests/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..0b45b02a58a5afd4d762ebe145fdb47442bdbd24 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/test.sh @@ -0,0 +1,29 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +default_reward() { + if [ ! -s /logs/verifier/reward.txt ]; then printf "0\n" > /logs/verifier/reward.txt; fi +} +trap 'default_reward' EXIT +trap 'default_reward; exit 0' HUP INT TERM + +chmod -R go-rwx /tests + +export HIDDEN_SPLIT=final +export PYTHONHASHSEED=0 +export OMP_NUM_THREADS=1 +export MKL_NUM_THREADS=1 +export OPENBLAS_NUM_THREADS=1 +export NUMEXPR_NUM_THREADS=1 +export CUBLAS_WORKSPACE_CONFIG=:4096:8 + +cd /tests +/usr/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/test_state.py b/tasks/less-is-more-pretrain-token-budget-selector/tests/test_state.py new file mode 100644 index 0000000000000000000000000000000000000000..61c45123fc95a42ee424179b9b4c196bb82742dc --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/test_state.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +import json +import os +import sys +import traceback +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import grader_core +import pretrain_harness + +METRIC_PATH = Path("/logs/verifier/metric.json") +SPLIT = os.environ.get("HIDDEN_SPLIT", "final") +HIDDEN = Path(__file__).resolve().parent / "hidden_data" / SPLIT +CONFIG = Path(__file__).resolve().parent / "train_config.json" +SESSION = Path("/tmp/grade-session") + + +def _write(record: dict) -> None: + METRIC_PATH.parent.mkdir(parents=True, exist_ok=True) + METRIC_PATH.write_text(json.dumps(record, indent=2, sort_keys=True, default=str)) + + +def test_split_integrity(): + """The graded pool and the graded evaluation shard share no document. + + Asserted here, at grade time, on the shipped bytes -- not only in the builder that + produced them. The duplicate-key fixture rows make the check meaningful: a set-based + intersection over unique keys cannot detect grouped leakage, so the fixture below + deliberately repeats a key on each side and the assertion still has to hold. + """ + guard = json.loads((HIDDEN / "eval" / "guard.json").read_text()) + eval_hashes = set(guard["hashes"]) + eval_urls = set(guard["urls"]) + pool_meta = json.loads((HIDDEN / "pool" / "meta.json").read_text()) + pool_guard = json.loads((HIDDEN / "pool" / "guard.json").read_text()) + pool_hashes = set(pool_guard["hashes"]) + pool_urls = set(pool_guard["urls"]) + assert pool_hashes, "the graded pool shipped no document hashes" + assert eval_hashes, "the graded evaluation shard shipped no document hashes" + assert not (pool_hashes & eval_hashes), "pool/eval text overlap" + assert not (pool_urls & eval_urls), "pool/eval URL overlap" + assert pool_meta["n_tokens"] > 200_000_000, pool_meta + + fix_pool = ["k1", "k1", "k2", "k3", "k3"] + fix_eval = ["k4", "k4", "k5"] + assert not (set(fix_pool) & set(fix_eval)) + assert len(fix_pool) != len(set(fix_pool)) and len(fix_eval) != len(set(fix_eval)) + leaky_eval = ["k2", "k2", "k9"] + assert set(fix_pool) & set(leaky_eval), "the disjointness assertion cannot detect leakage" + + +def test_writable_surface(): + """The verifier reads exactly the files instruction.md lets a solution change. + + instruction.md, "Files you may change", names `/app/output/select.py` plus any further file + under `/app/output/` as the complete writable surface, and states that the harness, the + config and everything else under `/app` are development material the grader never reads. + This asserts the verifier honours that same list: the deliverable it stages is `/app/output` + and its entry point is `select.py`; the harness, the config and the graded data come from + the sealed tree beside this file; and no agent-editable path is reachable as an import or + present in this image. It scores nothing -- no submission can influence it. + """ + sealed = Path(__file__).resolve().parent + assert grader_core.DELIVERABLE_DIR == "/app/output" + assert grader_core.ENTRY_POINT == "select.py" + assert grader_core.WRITABLE_SURFACE_ROOT == grader_core.DELIVERABLE_DIR + assert Path(pretrain_harness.__file__).resolve().parent == sealed + assert CONFIG.is_file() and CONFIG.resolve().parent == sealed + assert str(HIDDEN.resolve()).startswith(str(sealed) + os.sep) + for name in ("pool", "eval"): + assert (HIDDEN / name).is_dir(), f"the sealed {name} is missing" + assert grader_core.writable_surface_violations(CONFIG) == [] + + +def test_grade(): + """Execute the submission, recompute the metric from the sealed slice, write it out.""" + record = {"reward": 0.0, "valid": False, "invalid_reason": "grader did not complete", + "split": SPLIT} + try: + try: + import torch + record["grader_cuda_device_count"] = int(torch.cuda.device_count()) + record["grader_cuda_available"] = bool(torch.cuda.is_available()) + except Exception as exc: + record["grader_cuda_device_count"] = -1 + record["infra_error"] = f"the grader itself cannot reach CUDA: {exc}" + + raw = grader_core.grade( + deliverable_src=grader_core.DELIVERABLE_DIR, + pool_dir=HIDDEN / "pool", + eval_dir=HIDDEN / "eval", + config_path=CONFIG, + session_dir=SESSION, + log=lambda msg: print(msg, flush=True), + ) + raw["split"] = SPLIT + record = grader_core.finalize(raw) + except BaseException: + record = {"reward": 0.0, "valid": False, "split": SPLIT, + "invalid_reason": "grader raised", + "traceback": traceback.format_exc()[-6000:]} + finally: + _write(record) + + print(json.dumps({k: v for k, v in record.items() + if k not in ("select_stderr_tail", "traceback", "grade_traceback")}, + indent=2, sort_keys=True, default=str), flush=True) + assert isinstance(record["reward"], float) and 0.0 <= record["reward"] <= 1.0 diff --git a/tasks/less-is-more-pretrain-token-budget-selector/tests/train_config.json b/tasks/less-is-more-pretrain-token-budget-selector/tests/train_config.json new file mode 100644 index 0000000000000000000000000000000000000000..6817b260d074bfce4020eae2d4a05defc470acd3 --- /dev/null +++ b/tasks/less-is-more-pretrain-token-budget-selector/tests/train_config.json @@ -0,0 +1,20 @@ +{ + "beta1": 0.9, + "beta2": 0.95, + "bias": true, + "block_permutation_seed": 1234, + "block_size": 1024, + "grad_clip": 1.0, + "learning_rate": 0.0006, + "max_steps": 1500, + "micro_batch_size": 16, + "min_lr": 6e-05, + "n_embd": 768, + "n_head": 12, + "n_layer": 12, + "seed": 1234, + "tokens_per_step": 131072, + "vocab_size": 50257, + "warmup_steps": 100, + "weight_decay": 0.1 +} diff --git a/tasks/svdquant-w4a4-psnr/environment/Dockerfile b/tasks/svdquant-w4a4-psnr/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..144dff821b949e849cb565ba96c3d7aa1865090f --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/environment/Dockerfile @@ -0,0 +1,34 @@ +FROM --platform=linux/amd64 gcr.io/kaggle-gpu-images/python@sha256:37c64f7dd9c54116ecd1bcc88817c5469b88387388fade02bfa8bf3fc647d461 +ENV PYTHONHASHSEED=0 PYTHONDONTWRITEBYTECODE=1 PIP_NO_CACHE_DIR=1 PIP_DISABLE_PIP_VERSION_CHECK=1 OMP_NUM_THREADS=1 + +ENTRYPOINT [] + +RUN useradd --create-home --uid 1001 agent +COPY ./requirements.txt /tmp/requirements.txt +RUN /usr/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt +RUN /usr/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download('PixArt-alpha/PixArt-Sigma-XL-2-1024-MS', revision='e102b3591cc82e97071b8b4cb90d834d0c487207', local_dir='/opt/pixart-sigma', ignore_patterns=['asset/*', 'README.md', '.gitattributes'])" \ + && rm -rf /root/.cache/huggingface + +WORKDIR /app +COPY ./app/data/calibration_prompts.jsonl /app/data/calibration_prompts.jsonl +COPY ./entrypoint.sh /usr/local/bin/task-entrypoint +RUN chown -R agent:agent /app \ + && chmod +x /usr/local/bin/task-entrypoint \ + && find /app -exec touch -d @1700000000 {} + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 + +RUN command -v git >/dev/null 2>&1 \ + || (apt-get update \ + && apt-get install -y --no-install-recommends git \ + && rm -rf /var/lib/apt/lists/*) +RUN git config --system user.email "agent@localhost" \ + && git config --system user.name "agent" + +RUN command -v tmux >/dev/null 2>&1 \ + || (apt-get update \ + && apt-get install -y --no-install-recommends tmux \ + && rm -rf /var/lib/apt/lists/*) +USER agent +ENTRYPOINT ["/usr/local/bin/task-entrypoint"] diff --git a/tasks/svdquant-w4a4-psnr/environment/app/data/calibration_prompts.jsonl b/tasks/svdquant-w4a4-psnr/environment/app/data/calibration_prompts.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3900fcdf3fef2359718278a38853f021045cd3aa --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/environment/app/data/calibration_prompts.jsonl @@ -0,0 +1,128 @@ +{"prompt": "New Campervan in a corn field. photo", "category": "vehicles"} +{"prompt": "STYLE Closeup Shot GENRE Gourmet EMOTION Tempting SCENE A table of American food TAGS Highend food photography, clean composition, dramatic lighting, luxurious, elegant, mouthwatering, indulgent, gourmet CAMERA Nikon Z7 FOCAL LENGTH 105mm SHOT TYPE Closeup COMPOSITION Centered LIGHTING Soft directional light PRODUCTION Food Stylist TIME Evening LOCATION TYPE Interior", "category": "food"} +{"prompt": "tattoo design, sleeve, project, original, 4 ideas, japanese style,8k,samurai", "category": "art"} +{"prompt": "an old and creepy beggar on the side of an antique cobbled road near a forest, with a lightning storm in the sky, middleage, dark atmosphere, photograph", "category": "people"} +{"prompt": "very beautiful oil painting of polson montana on flathead lake by artist Ted Winger, HDR, fine brush strokes", "category": "landscape"} +{"prompt": "halfbody, expressive eyes, ORANGE FROG IN SPACEX SPACESUIT IN scifi fantasy BACKGROUND landscape with a Mars scenario background, HE is based on the god Thor. He wears a cloak on top of the spacesuit. athletic build, toned muscles. You cant imagine how beautiful he is. Human proportions, semirealistic. 32k, rtx on", "category": "art"} +{"prompt": "a sea of cars and trucks on a highway with lots of traffic, highways and traffic everywhere, Birds Eye view, minimal design, symmetric composition, David Lynch, coloring in style of the movie The Joker, offcenter, 35 mm film still shot on Kodachrome, inside, night time, retro. instant capture, 8k, ultra detailed, ultra realistic, Contax T3 SLR, 16mm lens , accent lighting", "category": "vehicles"} +{"prompt": "Harry Potter is training in the gym on a power rack, Canon EOS R5, photorealistic, super detail skin, hyper detailed", "category": "indoor"} +{"prompt": "a realistic photo of Chris Patten eating a eggtart at a busy street of hong kong in the early evening, with disire, professional cinematic photography", "category": "food"} +{"prompt": "preraphaelite, In the painting, a woman is depicted as the personification of Scorpio, with piercing eyes and an intense expression on her face. The womans clothing and the background of the painting are dark and mysterious, evoking the signs association with depth and hidden truths. She may be holding a scorpion, the symbol of the Scorpio zodiac sign, or surrounded by other elements that represent the sign, such as a serpent or a phoenix. The womans gaze is direct and unwavering, conveying a sense of power and emotional depth that is often associated with Scorpio. Overall, the painting captures the enigmatic and transformative energy of Scorpio", "category": "art"} +{"prompt": "minecraft castle on hill", "category": "landscape"} +{"prompt": "ginger girl with long straight hair, a rose in one of her eyes, holding a celt carnyx, rosebushes around her, petals flying with the wind, protecting a giant three behind her, rose tattoos in her shoulders and legs, cute outfit", "category": "art"} +{"prompt": "an epic oldschool US Astronaut walks through the streets of Lagos, Nigeria, Emmanuel Lubezki Photography, modern neon lights and screens, difused lighting, Professional Commercial Photography Cinematic full body, DSLR, Canon EOS 5D and Fujifilm Superia X TRA 400 film, iso 100, 120mm anamorphic lens flare Photorealistic, ultrarealistic color grading, fine tune square shape edges, fix eyes lips face fingers body geometry incorrections, Make it a wide cinematic shot, ultra detailed, film lighting, moody, 35mm, candid, wide angle, in the style of davide sorrenti, DSLR, Synthology Nicheartunion, Cinematic, Color Grading, portrait Photography, Ultra Wide Angle, Depth of Field, hyper detailed, beautifully coded by color, insane detail, intricate detail, beautifully colored, Unreal Engine, Cinematic, Color Grading, Editorial Photography, Photography, Photoshoot, Depth of Field, DOF, Tilt Blur, White Balance, 32k, Super Resolution, Megapixel, ProPhoto RGB, VR, Halfrear Lighting, Backlight, Natural Lighting, Incandescent, Fiber Optic, Moody Lighting, Cinematic Lighting, Studio Lighting, Soft Lighting, Volumetric, Contre Jour, Beautiful Lighting, Accent Lighting, Global Illumination, Screen Space Global Illumination, Ray Tracing Global Illumination, Optical, Scattering, Glowing, Shadows, Rough, Shimmer, Ra y Tracing Reflections, Lumen Reflections, Screen Space Reflections, Diffraction Gradation, Chromatic aberration, GB displacement, scan lines, ray tracing, ray tracing ambient occlusion, anti aliasing, FK AA, TXAA, RTX, SSAO, Shaders", "category": "fashion"} +{"prompt": "a hot dog in the forest,", "category": "food"} +{"prompt": "Victoian Gothic, Butterfly Specimen, creepy, Parchment Paper, Display, cinematic", "category": "art"} +{"prompt": "Un parque bajo un puente vehicular en la ciudad, gente haciendo ejercicio bajo el puente, gente caminando, una parada de autobus, edificios en el horizonte, ciudad de Tuxtla Gutiérrez, una ciclovia con ciclistas, muchos arboles, la vista es bajo el puente, hyperrealism, 8k, full hd, HyperDetailed, Photorealistic, UHD, HDR, HD, Natural Lighting, Canon EOS R3, F1,4, ISO 200, 1160s, 32K, RAW, Pro Photo RGB, Bokeh, High Quality, 400MP, Megapixel, SuperResolution", "category": "vehicles"} +{"prompt": "a monkey wearing a helmet playing a guitart", "category": "animals"} +{"prompt": "a gunmetal colored 370z batmobile with an upgraded exoskeloton parked in a modern museum with neon lights", "category": "vehicles"} +{"prompt": "Modern minimalistic vibrant logo of zodiac libra,", "category": "logo"} +{"prompt": "Epic metamorphasis desert landscape with massive storm. Dont look at the esoteric red cirlce. v", "category": "landscape"} +{"prompt": "advertising photography of hamburger inspired by salvadore dali with 4 layers of well cooked beef meat. Ingredients include lettuce, tomato, oignon, pickles, peppers, white melting cheese, orange dripping sauce, mushrooms and bacon. There is a beautiful hard light coming from the back. The hamburger is sitting on a modern marble table, In the background we can see a blurry window and modern architecture at f1.2. bright light 5", "category": "food"} +{"prompt": "An intense and realistic battle scene, inspired by Game of Thrones Battle of the Bastards content fierce woman warrior, scantly clad , fantastical backdrop, fearlessly facing an undead army medium digital art style a combination of contemporary fantasy art and cinematic visuals lighting low and atmospheric, with flickering firelight reflecting off her battered armor colors a bold mix of deep blues, grays, and vivid warm tones from the surrounding flames composition a dynamic side view of the woman warrior in the foreground, poised for combat, with the relentless undead horde advancing in the background", "category": "art"} +{"prompt": "if tori kelly was black and jhene aiko was nerdy white girl, puppy", "category": "people"} +{"prompt": "combined samurai and viking style", "category": "people"} +{"prompt": "a woman performing a concert at dusk in new mexico surrounded by a huge crowd of fans, 8k, full HD", "category": "people"} +{"prompt": "coloring white sheet, beautiful mountain landscape, the monster truck with very high chassis, huge springs and shocks visible, converted from a 1983 Ford Ranger, highresolution white background, highresolution ink details, and outlines", "category": "vehicles"} +{"prompt": "a Cinematic, scene of two symbolemages fighting", "category": "indoor"} +{"prompt": "modern realisitic drawing art sketch of scabiosa flowers, clean straight lines, black and white, a lot of white space to color, coloring book style", "category": "plants"} +{"prompt": "nile river of chaos, anarchy, liberty, independence, force and energy, very detailed, photo taken with Hasselblad X1D, ISO 100, national geographic", "category": "vehicles"} +{"prompt": "Photo of an archeofusturist scene, rusty but futurist atmosphere, amidst the ruins, life still persists. A group of scavengers search through the debris, looking for something of value, wide angle, lots of details, UHD, 32k", "category": "indoor"} +{"prompt": "old 90s television with flicker on shelf horror dark style 169 super realistic", "category": "indoor"} +{"prompt": "hd photograph of various plants, flowers, shrubs, and gardening tools, overhead view, detailed, ar 169", "category": "plants"} +{"prompt": "Photograph of Superman, cinematic", "category": "fashion"} +{"prompt": "a camel, dressed as rock musician , with sunglasses ,by Bill Gekas", "category": "animals"} +{"prompt": "A group of friends laughing while sharing stories over dinner A cozy, dimly lit restaurant in Paris Warm candlelight 35mm, photorealistic, Canon EOS 5D Mark IV DSLR, f5.6 aperture, 1125 second shutter speed, ISO 100", "category": "people"} +{"prompt": "comeback of warrior, video game cover, 4k, ultra high quality, ultra detailed", "category": "art"} +{"prompt": "caminhos, man arquitetura, love, inside train, vacations, The resolution should be 8k", "category": "indoor"} +{"prompt": "Abel Makkonen Tesfaye in full growth in sunglasses Cyberpunk style in tokyo with massive fireworks in the background", "category": "fashion"} +{"prompt": "red sand mars and sky", "category": "landscape"} +{"prompt": "Girl, avatar, anime, face in shadow, sword, in a pose slightly leaning forward with a sword, yellow glowing eyes with a heart pattern", "category": "art"} +{"prompt": "An early summer forest with tiny wild strawberries, fern fronds, white blossoms in the photographic style of Paul Barson,", "category": "plants"} +{"prompt": "A plate with sushi on it is running fast on the rail", "category": "food"} +{"prompt": "a street scene on a dark hot summer night of the 1950s a very beautiful tall blonde in a silk dress with her hair blowing in the wind is looking and leaning into the camera her lips slightly separated with behind her little italy restaurants in the 1950s the houses connected with strings of italian light bulbs hanging across the street5", "category": "fashion"} +{"prompt": "A forest where a group of animals live, deer, elephant, giraffe, rabbit, fox, pig happy and free in harmony. Pieces, highly detailed cinematic, childrens book style ,, No Text super detailed vibrant", "category": "animals"} +{"prompt": "Digital Transformation Concept System Engineering Binary Code Programming. Highly detailed, ultra realistic, trending on artstation, rendered in octane, illuminated by brilliant led lights", "category": "logo"} +{"prompt": "A supersportscar prototype with retro elements on a coastal road, with the sea in the background, automotive photography, sunset, Canon 5D, DSLR, 50mm portrait, DOF, Vray render, 8K, ray tracing, golden hour lighting, uplight, hard rim lighting, photoshopped", "category": "vehicles"} +{"prompt": "Single character and background, setting is Fantasy, semirealistic proportions, a female Shadow Assassin with two daggers, lurking in the shadows of a dark alley, anime facial features, human proportions, semirealistic, by neoartcore, rtx on", "category": "fashion"} +{"prompt": "The Avenger She is a vengeful warrior, with a personal vendetta against the villain. She is often consumed by rage and hatred, but her desire for revenge is what fuels her strength.", "category": "fashion"} +{"prompt": "ancient iron coin, zoom in, fantasy, runes in a circle, ultrareal, 4k", "category": "logo"} +{"prompt": "coucher de soleil dans lespace sur saturne, cinematic lighting", "category": "landscape"} +{"prompt": "immense victorian era formal livingroom at night. dark. couches. dirty. roaring fire in the fireplace", "category": "indoor"} +{"prompt": "award winning architectural digest photograph of upscale living room in mcmansion, solid gold furniture with luxurious cushions, skylight, marble flooring, stone and hardwood walls, vaulted ceiling, ritzy, tasteful, gold, marble, wealthy, golden sunset light streaming in through cut crystal window", "category": "indoor"} +{"prompt": "La mano de Eva para llegar a la manzana prohibida. Una serpiente está colgando del árbol. ilustración digital, alta definición.", "category": "art"} +{"prompt": "Capital Letter L in Spencerian and blackletter form in 3D with spikes and roses, black background, details, striking details and dynamic range, colorful lighting", "category": "logo"} +{"prompt": "birds eye view of an island golf course, 8k quality", "category": "landscape"} +{"prompt": "a carrier pigeon wearing a flight suit with 100 stacks", "category": "fashion"} +{"prompt": "cinematic still of Agronomist observing a rose in a rose growing field, natural lights, sunlight, dramatic lighting, volumetric lighting, cinematic color tones, muted color tones, shot on Alexa 65, shot on 24mm lens, extreme wide angle, film grain, photorealistic, cinematography, depth of field, bokeh, ornate details, maximum resolution, Afga Vista 400, natural lighting", "category": "fashion"} +{"prompt": "An AIpowered flying car, soaring through the skies above a bustling Vancouver city, showcasing the freedom and innovation of transportation in the future", "category": "vehicles"} +{"prompt": "a ultramodern desert home built into the cliffs, built by Frank Lloyd Wright,futuristic,earth tones,military overtones,sandstone,polished aluminium,prairie style.elliptical minimalism,Cinematic, natural lighting, rich colors, magazine photography", "category": "indoor"} +{"prompt": "An award winning water color painting of a dynamic multi layered landscape including water and mountains with Japanese influences in the style of Roberto Nezzi", "category": "landscape"} +{"prompt": "professional photo of a campari spritz. italy theme.", "category": "fashion"} +{"prompt": "evil apocalypse ultrahighqualitycinematic hdr ray tracing", "category": "vehicles"} +{"prompt": "do a logo with letters F and C being part of a triangle, without background, metalic, 2D, company style, black and gold, volcano edit", "category": "logo"} +{"prompt": "a grass seed piercing through the soil, by mike lasove on 500px, in the style of andreas achenbach, oliver jeffers, ominous vibe, uhd image, pegi nicol macleod, stock photo, tumblewave", "category": "plants"} +{"prompt": "Spray bottle with a label sprays water on green coffee beans on a stool, black pepper, vanilla pods, bay leaf, parsley in the form of a mocap lie around 4320 x 4320 px size", "category": "plants"} +{"prompt": "Ash,Bodies Women,Hell,4k,Hight Resolution,Hight Texture,19201080 Pixels", "category": "art"} +{"prompt": "Film scene. An 8yearold boy and an 11yearold girl are making a very small wooden toy water wheel by a small stream. Bright forest. Rays of light break through the branches. Cinematic lighting. Wide angle shot", "category": "people"} +{"prompt": "Create an image of a young traveler man enjoying the beauty of Reykjadalur hot thermal spring in Iceland. The background should consist of a mild northern lights sky and a green mountain to create a sense of wonder and awe. The man should be the main focus of the image, taking up at least onethird of the area of the image, preferably twothirds. Use a sunny and clear weather setting to enhance the vibrancy of the hot spring. Use a closeup shot to make the man and the lagoon prominent, with the man in the center of the image and his head reaching the upper border. This image should be designed to serve as a YouTube thumbnail.", "category": "people"} +{"prompt": "3 oddly shaped UFOs hovering in the night sky, casting an eerie glow across the surrounding area. metallic exterior is unlike anything seen on Earth, with intricate patterns etched into the surface that seem to pulse with otherworldly energy. The multicolored lights, flicker and dance around the crafts a bright beam of blue light shoots down from the bottom of the ship, illuminating the ground below. As the light touches down on the ground, a cow grazing nearby becomes enveloped in the beam, lifted off the ground, and suspended in midair. casting a brilliant glow across the surrounding fields and countryside. the cow is carried up with it,", "category": "vehicles"} +{"prompt": "purple yellow and pink columbines looking up towards the sky from the grass or a meadow, in the style of light skyblue and light pink, naturalist aesthetic, dragonfly, light skyblue and green, vibrant florals, light red and green, ricoh ff9d", "category": "plants"} +{"prompt": "shitzu dog with glasses and seduction face", "category": "animals"} +{"prompt": "epic, exclusive, dark coloured background, black rolls royce, luxury, class, realistic,", "category": "vehicles"} +{"prompt": "Production still from a 1980s action movie, night, a fancy inerior of a condominium with large windows and a city scape in the background, dark, night, cinematic,", "category": "indoor"} +{"prompt": "affluent handsome young Latino man with a mustache and jheri curl mullet in the 1980s, high fashion, High fashion editorial shoot, expensive commercial photo, cinematic lighting, professionally retouched, dramatic lighting, emotional, highly detailed, intricate, elite, luxury, cinematic hypermaximalist, elegant, ornate, hyper textured, colored, insane details, depth of field background, artful bokeh depth of field, artstation, photorealistic ultra detailed cinematic scene smooth sharp features sharp focus, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, clear glass reflection, subsurface scattering, photo realism, Photography, hypermaximalist, artful bokeh depth of field, Natural Lighting, intricate details, epic movie scene, professional photography, visualization most artistic way, breathtaking composition, photography, highly detailed, intricate details, Ultra Quality Awardwinning Photography, Ray Tracing Reflections", "category": "fashion"} +{"prompt": "a mindful magnolia on a balcony overlooking the country side peaceful serene ponderous", "category": "plants"} +{"prompt": "if the Last Of Us was a romcom", "category": "people"} +{"prompt": "3D illustration of a cellphone displaying a movie script written on the screen. apple product, product design, 3D,", "category": "logo"} +{"prompt": "An old man with a white beard wearing pirate attire sitting on a river bank by sunset talking to Heath Ledger wearing medieval attire", "category": "people"} +{"prompt": "watercolor wildflower border on white background", "category": "plants"} +{"prompt": "the tiles of the pool, in the style of alasdair mclellan, psychedelic color schemes, leon spilliaert, opaque resin panels, crossprocessed film, earthy color palette, stanisław ignacy witkiewicz", "category": "indoor"} +{"prompt": "hyper realistic viburnum, fill page, coloring book style, high detail, vector lines, 8k, modern cartoon, no shading, easy to color, printable coloring book page, black and white", "category": "plants"} +{"prompt": "white background, with a modern, minimalist sign, elegant neon gold color with the letters R", "category": "logo"} +{"prompt": "nightmare darkart dramatic sky, cinematic", "category": "landscape"} +{"prompt": "electric motorcycle 8k, Canon EOS R5, F1. 2, ISO100, 35MM, Realistic,", "category": "vehicles"} +{"prompt": "lemon and nearby crystal mephedrone, hashish, marijuana", "category": "food"} +{"prompt": "dungeons dragons anime a stone bridge takes to a squared fortress on a mountain mesa,", "category": "landscape"} +{"prompt": "Beautiful website pure water vector graphics conservation, special effect, ui, ux, ui ux, website 8k", "category": "logo"} +{"prompt": "Milwaukee, in the style of luis Royo, red glow, dim cinematic lighting, 8k udd, hyper realistic, extremely detailed, photograph", "category": "art"} +{"prompt": "DONALD TRUMP SAVING A CHILD FROM BURNING BUILDING", "category": "people"} +{"prompt": "An early summer forest with wild strawberries, fern fronds, white blossoms in the photographic style of Paul Barson", "category": "plants"} +{"prompt": "wet cobbled street photo tram tracks victorian", "category": "vehicles"} +{"prompt": "a slice of cheesecake with strawberries, expresso on the side,8k,high detail,", "category": "food"} +{"prompt": "photorealistic, 3d, transparent background, Blue Diamond , engraved with Level 1", "category": "logo"} +{"prompt": "A stylish Swahili muslim grandfather dressed in a Gucci suit is reading a newspaper through the streets of Lamu, magazine photography, editoria, cinematic lights, 8K kodak gold 200 details,", "category": "people"} +{"prompt": "photo in the style of Mario Sorrenti, Lachlan Bailey, photography, background in focus, ultra realistic face, full length photo of a person, girl, 35mm, kodak portra, kodak portra 800, 35 mm f1. 8, 8k,", "category": "people"} +{"prompt": "golden hour nature, morning nature photography", "category": "plants"} +{"prompt": "black Caribbean man walking balance front his fate chaos anarchy liberty independence force energy torahans the Galaxy hypermaximalist sculpted anatomically correct cinematic global illumination darkness shafts of iridescent smoke they glow coming from their intricate filigree", "category": "fashion"} +{"prompt": "Apollo american astronaut sitting on a salt flat at dusk, blue hour, dust blowing around, desolate, mountains in the far off background, Ford Raptor in background, hyper realistic, hyper detailed, 8k, photography", "category": "vehicles"} +{"prompt": "3 fashionalble girls in their 20s, with colorful magenta and blonde hair, looking at the camera, smiling, having fun, excited, wearing short skirts, during sunset, festival background, HD ultra quality, megapixel, realistic, 8k 16k, Canon EOS 5D Mar IV camera with a canon EF 2470mm f2.8 L II USM lens, aspect 11", "category": "fashion"} +{"prompt": "stunning, vibrant, multicolored, Energetic Positivity Unleashed playlist cover, highresolution, digital illustration", "category": "art"} +{"prompt": "Old brasilain couple at the jesus statue in Rio, year 2023, photography, superphotoreal, hyperreal, photorealistic, high def, ultradetailed, ultra realistic, medium format, wes anderson movie, pastel colors,delicate light, cinematic light 8k", "category": "people"} +{"prompt": "Magic The Gathering inspired Crest", "category": "logo"} +{"prompt": "full body, an black man with the wisdom of Solomon and the courage of Samson save the world with the help of cybernetic artificial intelligence , chaos, anarchy, liberty, independence, force and energy, very detailed, photo taken with Hasselblad X1D, ISO 100, national geographic", "category": "fashion"} +{"prompt": "an anthropomorphic white rabbit dressed as a wizard", "category": "animals"} +{"prompt": "An exquisite image capturing the essence of a luxurious seafood feast, set against the majestic beauty of the Antarctic. The seafood platter brimming with fresh lobster, scallops, mussels, and clams is displayed on the deck of a small yacht, with colossal icebergs bathed in the golden light of the sunset creating a mesmerizing background. Nikon D850, 50mm lens, ISO 200, f4, 1160s", "category": "food"} +{"prompt": "a black couple Caribbean man and woman with children walking balance front his fate, chaos, anarchy, liberty, independence, force, energy, torah, waving caustics deep below mens skulls head 4k cinematic lighting, atmosphere, light fog, foggy industrial environment, dim volumetric lighting, extreme detail, trending on artstation, 3 5 mm, f 3 2, golden angle,oil painting, whole body, very detailed, photo taken with Hasselblad X1D50c, ISO 100, national geographic", "category": "people"} +{"prompt": "five steampunk winemakers wine tasting 8k", "category": "people"} +{"prompt": "a redhaired kitten with a fulllength tail stands on a white background", "category": "animals"} +{"prompt": "film scene, teenager hacker doing works on computer n an abandoned factory, dimly lighting style, korean film style,", "category": "indoor"} +{"prompt": "food stylist photography of stylish chocolate pudding with sausages in a high class restaurant. haute cuisine. Mamiya 645, fstop 2.8, Kodak Portra.", "category": "food"} +{"prompt": "professional photo of a lemonade cocktail on the counter of a stylish new york bar", "category": "food"} +{"prompt": "obiwan Kenobi in a 80s Bollywood movie dark , brown skin.", "category": "people"} +{"prompt": "This is an ultra realistic photo a glass bowl full of tasty ripe strawberries with the top layer in the bowl covered with cream on top. There is a glass of strawberry milkshake next to the bowl with strawberries. The scene is set in a rustic outdoor setting in the countryside, surrounded by lush greenery and a small lake with light blue water sparkling in the sun in the background. In the foreground, there is a wooden table with a glass bowl full of fresh and ripe strawberries that have been covered in a generous layer of creamy white cream. The strawberries look plump and juicy, with their vibrant red color standing out against the pale cream. The bowl is placed at an angle, with some of the strawberries spilling over the edge onto the wooden table, adding a touch of playfulness and spontaneity to the scene. The lighting is sharp, with the sun shining through the trees and casting shadows on the scene. The colors are rich and vibrant, with the light green of the trees, blue water on the lake and the vivid red of the strawberries creating a beautiful contrast. In the background, the small lake adds an extra touch of tranquility to the scene, with its calm waters reflecting the greenery and blue sky above. The photo has a bright and airy feel, with a focus on the bright vivid colors and textures of the food. The scene should evoke a desire for fresh, organic food. This photo is captured by Marte Marie Forsberg with a Canon EOS R5 camera, paired with a macro lens such as the Canon EF 100mm f2.8L Macro IS USM, a wide aperture such as f2.8 to achieve a shallow depth of field and blur the background slightly, creating a sense of depth and focus on the strawberries. A shutter speed of 1125s will help to freeze any movement and ensure a sharp image. Finally, using a ...", "category": "food"} +{"prompt": "Thai pretty girl, 25 years old, wearing v neck gown, long hair, enjoying herself in a night club environment, dancing to the music and smiling towards the camera.", "category": "people"} +{"prompt": "full shot, a mysterious ocean, edge of beach, epic waves, greek mythological, cinematic, rich colors, daytime, surreal, artistic photography", "category": "landscape"} +{"prompt": "barbados as a transformer in an action pose", "category": "vehicles"} +{"prompt": "extremely simple flowers, smiling, childrens book illustration style, multiple poses and expressions, single black and white drawing, line art, white space, coloring page, wide angle, white background", "category": "plants"} +{"prompt": "halloween candy packages from another world, knolling, white background", "category": "food"} +{"prompt": "closeup food photography of a shoyu ramen, creamy chicken broth, soy sauce, black garlic mayu oil, thin organic wheat noodle, Thinly sliced spring onions ,Thinly sliced chicken meat, Narutomaki , Nori seaweed, sliced into strips,half Softboiled egg, ,Roasted sesame seeds. , ultra realistic, low angle, on table, canon 5d, dslr, 50mm portrait, DOF, vray render, 8k, ray tracing, golden hour lighting, up lighting, rim lighting rigid, photoshopped", "category": "food"} +{"prompt": "facebook logo, dry, broken", "category": "logo"} +{"prompt": "Create a mouthwatering Chocolate Protein Pudding with Whipped Cream and Berries that will make your taste buds tingle ultrarealistic", "category": "food"} +{"prompt": "small bucolic backyard with Japanese tree and koi pond", "category": "indoor"} +{"prompt": "Detox Diets,Shot using a Hasselblad camera, ISO 100. Professional color grading. Soft shadows. Clean sharp focus. High end retouching. Food magazine photography. Award winning photography. Advertising photography. Commercial photography.", "category": "food"} +{"prompt": "cactus ,sun shining through the old window an artist studio, nice weather, realistic, super detailed, photography, detailed, atmosphere, realistic, cinematic, photorealistic", "category": "indoor"} +{"prompt": "troupial 4K hyper realistic1 brave1", "category": "animals"} +{"prompt": "shubniggurath in a dark age grove, ultra fine details, ultrarealistic 4k, ultra photo realisitc 4k", "category": "art"} +{"prompt": "realistic wolf looking towards me on mountaintop with a sunset in background", "category": "animals"} +{"prompt": "realistic photography,Inside a selfdriving car,Sony Alpha 1,FE C 1635mmT3.1 G,", "category": "vehicles"} diff --git a/tasks/svdquant-w4a4-psnr/environment/entrypoint.sh b/tasks/svdquant-w4a4-psnr/environment/entrypoint.sh new file mode 100644 index 0000000000000000000000000000000000000000..1a354de8f29dc0c6d815541d2f2ae89fbbe9b670 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/environment/entrypoint.sh @@ -0,0 +1,5 @@ +#!/bin/sh +set -eu + +mkdir -p /app/output +exec "$@" diff --git a/tasks/svdquant-w4a4-psnr/environment/requirements.txt b/tasks/svdquant-w4a4-psnr/environment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3eb7b818f0c853966150e30553d1f8e371f94a42 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/environment/requirements.txt @@ -0,0 +1,4 @@ +diffusers==0.39.0 +huggingface-hub==1.11.0 +safetensors==0.8.0 +sentencepiece==0.2.1 diff --git a/tasks/svdquant-w4a4-psnr/hints/hint.md b/tasks/svdquant-w4a4-psnr/hints/hint.md new file mode 100644 index 0000000000000000000000000000000000000000..f76163b7e07c05a96ed03bae5b7f64ddd35334fe --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/hints/hint.md @@ -0,0 +1 @@ +Alternate: quantise the weight minus the current low-rank term, refit that term to the new residual, repeat a few passes. Derive per-channel activation smoothing from every calibration prompt you are allowed, not a subset. The submission directory is checked for being a single file, so a generated bytecode directory scores zero with correct quantisation code in place - clear it before every grading. diff --git a/tasks/svdquant-w4a4-psnr/tests/Dockerfile b/tasks/svdquant-w4a4-psnr/tests/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..09c7a3dfb6cc7c0e0a6e9c74f2fd6508b4816048 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/Dockerfile @@ -0,0 +1,42 @@ +FROM --platform=linux/amd64 gcr.io/kaggle-gpu-images/python@sha256:37c64f7dd9c54116ecd1bcc88817c5469b88387388fade02bfa8bf3fc647d461 + +ENTRYPOINT [] +ENV PYTHONPATH= +ENV PYTHONSAFEPATH=1 +ENV PYTHONNOUSERSITE=1 +ENV PYTHONHASHSEED=0 \ + PYTHONDONTWRITEBYTECODE=1 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 \ + MKL_NUM_THREADS=1 \ + OPENBLAS_NUM_THREADS=1 \ + CUBLAS_WORKSPACE_CONFIG=:4096:8 + +RUN useradd --create-home --uid 1001 agent +COPY ./requirements.txt /tmp/requirements.txt +RUN /usr/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt +RUN /usr/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download('PixArt-alpha/PixArt-Sigma-XL-2-1024-MS', revision='e102b3591cc82e97071b8b4cb90d834d0c487207', local_dir='/opt/pixart-sigma', ignore_patterns=['asset/*', 'README.md', '.gitattributes'])" \ + && rm -rf /root/.cache/huggingface + +COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt +RUN /usr/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements-verifier.txt \ + && rm /tmp/requirements-verifier.txt +RUN mkdir -p /app/output /logs/verifier /tests/hidden_data/intermediate /tests/hidden_data/final +COPY ./test.sh /tests/test.sh +COPY ./intermediate.sh /tests/intermediate.sh +COPY ./test_state.py /tests/test_state.py +COPY ./compute_reward.py /tests/compute_reward.py +COPY ./grader_core.py /tests/grader_core.py +COPY ./candidate_runner.py /tests/candidate_runner.py +COPY ./hidden_data/intermediate/prompts.jsonl /tests/hidden_data/intermediate/prompts.jsonl +COPY ./hidden_data/final/prompts.jsonl /tests/hidden_data/final/prompts.jsonl +COPY ./app_data/calibration_prompts.jsonl /app/data/calibration_prompts.jsonl +RUN chown -R root:root /tests /logs/verifier /opt/pixart-sigma \ + && chmod 700 /tests /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \ + && chmod +x /tests/test.sh /tests/intermediate.sh \ + && find /tests /app/data -exec touch -d @1700000000 {} + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 +WORKDIR /tests diff --git a/tasks/svdquant-w4a4-psnr/tests/app_data/calibration_prompts.jsonl b/tasks/svdquant-w4a4-psnr/tests/app_data/calibration_prompts.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3900fcdf3fef2359718278a38853f021045cd3aa --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/app_data/calibration_prompts.jsonl @@ -0,0 +1,128 @@ +{"prompt": "New Campervan in a corn field. photo", "category": "vehicles"} +{"prompt": "STYLE Closeup Shot GENRE Gourmet EMOTION Tempting SCENE A table of American food TAGS Highend food photography, clean composition, dramatic lighting, luxurious, elegant, mouthwatering, indulgent, gourmet CAMERA Nikon Z7 FOCAL LENGTH 105mm SHOT TYPE Closeup COMPOSITION Centered LIGHTING Soft directional light PRODUCTION Food Stylist TIME Evening LOCATION TYPE Interior", "category": "food"} +{"prompt": "tattoo design, sleeve, project, original, 4 ideas, japanese style,8k,samurai", "category": "art"} +{"prompt": "an old and creepy beggar on the side of an antique cobbled road near a forest, with a lightning storm in the sky, middleage, dark atmosphere, photograph", "category": "people"} +{"prompt": "very beautiful oil painting of polson montana on flathead lake by artist Ted Winger, HDR, fine brush strokes", "category": "landscape"} +{"prompt": "halfbody, expressive eyes, ORANGE FROG IN SPACEX SPACESUIT IN scifi fantasy BACKGROUND landscape with a Mars scenario background, HE is based on the god Thor. He wears a cloak on top of the spacesuit. athletic build, toned muscles. You cant imagine how beautiful he is. Human proportions, semirealistic. 32k, rtx on", "category": "art"} +{"prompt": "a sea of cars and trucks on a highway with lots of traffic, highways and traffic everywhere, Birds Eye view, minimal design, symmetric composition, David Lynch, coloring in style of the movie The Joker, offcenter, 35 mm film still shot on Kodachrome, inside, night time, retro. instant capture, 8k, ultra detailed, ultra realistic, Contax T3 SLR, 16mm lens , accent lighting", "category": "vehicles"} +{"prompt": "Harry Potter is training in the gym on a power rack, Canon EOS R5, photorealistic, super detail skin, hyper detailed", "category": "indoor"} +{"prompt": "a realistic photo of Chris Patten eating a eggtart at a busy street of hong kong in the early evening, with disire, professional cinematic photography", "category": "food"} +{"prompt": "preraphaelite, In the painting, a woman is depicted as the personification of Scorpio, with piercing eyes and an intense expression on her face. The womans clothing and the background of the painting are dark and mysterious, evoking the signs association with depth and hidden truths. She may be holding a scorpion, the symbol of the Scorpio zodiac sign, or surrounded by other elements that represent the sign, such as a serpent or a phoenix. The womans gaze is direct and unwavering, conveying a sense of power and emotional depth that is often associated with Scorpio. Overall, the painting captures the enigmatic and transformative energy of Scorpio", "category": "art"} +{"prompt": "minecraft castle on hill", "category": "landscape"} +{"prompt": "ginger girl with long straight hair, a rose in one of her eyes, holding a celt carnyx, rosebushes around her, petals flying with the wind, protecting a giant three behind her, rose tattoos in her shoulders and legs, cute outfit", "category": "art"} +{"prompt": "an epic oldschool US Astronaut walks through the streets of Lagos, Nigeria, Emmanuel Lubezki Photography, modern neon lights and screens, difused lighting, Professional Commercial Photography Cinematic full body, DSLR, Canon EOS 5D and Fujifilm Superia X TRA 400 film, iso 100, 120mm anamorphic lens flare Photorealistic, ultrarealistic color grading, fine tune square shape edges, fix eyes lips face fingers body geometry incorrections, Make it a wide cinematic shot, ultra detailed, film lighting, moody, 35mm, candid, wide angle, in the style of davide sorrenti, DSLR, Synthology Nicheartunion, Cinematic, Color Grading, portrait Photography, Ultra Wide Angle, Depth of Field, hyper detailed, beautifully coded by color, insane detail, intricate detail, beautifully colored, Unreal Engine, Cinematic, Color Grading, Editorial Photography, Photography, Photoshoot, Depth of Field, DOF, Tilt Blur, White Balance, 32k, Super Resolution, Megapixel, ProPhoto RGB, VR, Halfrear Lighting, Backlight, Natural Lighting, Incandescent, Fiber Optic, Moody Lighting, Cinematic Lighting, Studio Lighting, Soft Lighting, Volumetric, Contre Jour, Beautiful Lighting, Accent Lighting, Global Illumination, Screen Space Global Illumination, Ray Tracing Global Illumination, Optical, Scattering, Glowing, Shadows, Rough, Shimmer, Ra y Tracing Reflections, Lumen Reflections, Screen Space Reflections, Diffraction Gradation, Chromatic aberration, GB displacement, scan lines, ray tracing, ray tracing ambient occlusion, anti aliasing, FK AA, TXAA, RTX, SSAO, Shaders", "category": "fashion"} +{"prompt": "a hot dog in the forest,", "category": "food"} +{"prompt": "Victoian Gothic, Butterfly Specimen, creepy, Parchment Paper, Display, cinematic", "category": "art"} +{"prompt": "Un parque bajo un puente vehicular en la ciudad, gente haciendo ejercicio bajo el puente, gente caminando, una parada de autobus, edificios en el horizonte, ciudad de Tuxtla Gutiérrez, una ciclovia con ciclistas, muchos arboles, la vista es bajo el puente, hyperrealism, 8k, full hd, HyperDetailed, Photorealistic, UHD, HDR, HD, Natural Lighting, Canon EOS R3, F1,4, ISO 200, 1160s, 32K, RAW, Pro Photo RGB, Bokeh, High Quality, 400MP, Megapixel, SuperResolution", "category": "vehicles"} +{"prompt": "a monkey wearing a helmet playing a guitart", "category": "animals"} +{"prompt": "a gunmetal colored 370z batmobile with an upgraded exoskeloton parked in a modern museum with neon lights", "category": "vehicles"} +{"prompt": "Modern minimalistic vibrant logo of zodiac libra,", "category": "logo"} +{"prompt": "Epic metamorphasis desert landscape with massive storm. Dont look at the esoteric red cirlce. v", "category": "landscape"} +{"prompt": "advertising photography of hamburger inspired by salvadore dali with 4 layers of well cooked beef meat. Ingredients include lettuce, tomato, oignon, pickles, peppers, white melting cheese, orange dripping sauce, mushrooms and bacon. There is a beautiful hard light coming from the back. The hamburger is sitting on a modern marble table, In the background we can see a blurry window and modern architecture at f1.2. bright light 5", "category": "food"} +{"prompt": "An intense and realistic battle scene, inspired by Game of Thrones Battle of the Bastards content fierce woman warrior, scantly clad , fantastical backdrop, fearlessly facing an undead army medium digital art style a combination of contemporary fantasy art and cinematic visuals lighting low and atmospheric, with flickering firelight reflecting off her battered armor colors a bold mix of deep blues, grays, and vivid warm tones from the surrounding flames composition a dynamic side view of the woman warrior in the foreground, poised for combat, with the relentless undead horde advancing in the background", "category": "art"} +{"prompt": "if tori kelly was black and jhene aiko was nerdy white girl, puppy", "category": "people"} +{"prompt": "combined samurai and viking style", "category": "people"} +{"prompt": "a woman performing a concert at dusk in new mexico surrounded by a huge crowd of fans, 8k, full HD", "category": "people"} +{"prompt": "coloring white sheet, beautiful mountain landscape, the monster truck with very high chassis, huge springs and shocks visible, converted from a 1983 Ford Ranger, highresolution white background, highresolution ink details, and outlines", "category": "vehicles"} +{"prompt": "a Cinematic, scene of two symbolemages fighting", "category": "indoor"} +{"prompt": "modern realisitic drawing art sketch of scabiosa flowers, clean straight lines, black and white, a lot of white space to color, coloring book style", "category": "plants"} +{"prompt": "nile river of chaos, anarchy, liberty, independence, force and energy, very detailed, photo taken with Hasselblad X1D, ISO 100, national geographic", "category": "vehicles"} +{"prompt": "Photo of an archeofusturist scene, rusty but futurist atmosphere, amidst the ruins, life still persists. A group of scavengers search through the debris, looking for something of value, wide angle, lots of details, UHD, 32k", "category": "indoor"} +{"prompt": "old 90s television with flicker on shelf horror dark style 169 super realistic", "category": "indoor"} +{"prompt": "hd photograph of various plants, flowers, shrubs, and gardening tools, overhead view, detailed, ar 169", "category": "plants"} +{"prompt": "Photograph of Superman, cinematic", "category": "fashion"} +{"prompt": "a camel, dressed as rock musician , with sunglasses ,by Bill Gekas", "category": "animals"} +{"prompt": "A group of friends laughing while sharing stories over dinner A cozy, dimly lit restaurant in Paris Warm candlelight 35mm, photorealistic, Canon EOS 5D Mark IV DSLR, f5.6 aperture, 1125 second shutter speed, ISO 100", "category": "people"} +{"prompt": "comeback of warrior, video game cover, 4k, ultra high quality, ultra detailed", "category": "art"} +{"prompt": "caminhos, man arquitetura, love, inside train, vacations, The resolution should be 8k", "category": "indoor"} +{"prompt": "Abel Makkonen Tesfaye in full growth in sunglasses Cyberpunk style in tokyo with massive fireworks in the background", "category": "fashion"} +{"prompt": "red sand mars and sky", "category": "landscape"} +{"prompt": "Girl, avatar, anime, face in shadow, sword, in a pose slightly leaning forward with a sword, yellow glowing eyes with a heart pattern", "category": "art"} +{"prompt": "An early summer forest with tiny wild strawberries, fern fronds, white blossoms in the photographic style of Paul Barson,", "category": "plants"} +{"prompt": "A plate with sushi on it is running fast on the rail", "category": "food"} +{"prompt": "a street scene on a dark hot summer night of the 1950s a very beautiful tall blonde in a silk dress with her hair blowing in the wind is looking and leaning into the camera her lips slightly separated with behind her little italy restaurants in the 1950s the houses connected with strings of italian light bulbs hanging across the street5", "category": "fashion"} +{"prompt": "A forest where a group of animals live, deer, elephant, giraffe, rabbit, fox, pig happy and free in harmony. Pieces, highly detailed cinematic, childrens book style ,, No Text super detailed vibrant", "category": "animals"} +{"prompt": "Digital Transformation Concept System Engineering Binary Code Programming. Highly detailed, ultra realistic, trending on artstation, rendered in octane, illuminated by brilliant led lights", "category": "logo"} +{"prompt": "A supersportscar prototype with retro elements on a coastal road, with the sea in the background, automotive photography, sunset, Canon 5D, DSLR, 50mm portrait, DOF, Vray render, 8K, ray tracing, golden hour lighting, uplight, hard rim lighting, photoshopped", "category": "vehicles"} +{"prompt": "Single character and background, setting is Fantasy, semirealistic proportions, a female Shadow Assassin with two daggers, lurking in the shadows of a dark alley, anime facial features, human proportions, semirealistic, by neoartcore, rtx on", "category": "fashion"} +{"prompt": "The Avenger She is a vengeful warrior, with a personal vendetta against the villain. She is often consumed by rage and hatred, but her desire for revenge is what fuels her strength.", "category": "fashion"} +{"prompt": "ancient iron coin, zoom in, fantasy, runes in a circle, ultrareal, 4k", "category": "logo"} +{"prompt": "coucher de soleil dans lespace sur saturne, cinematic lighting", "category": "landscape"} +{"prompt": "immense victorian era formal livingroom at night. dark. couches. dirty. roaring fire in the fireplace", "category": "indoor"} +{"prompt": "award winning architectural digest photograph of upscale living room in mcmansion, solid gold furniture with luxurious cushions, skylight, marble flooring, stone and hardwood walls, vaulted ceiling, ritzy, tasteful, gold, marble, wealthy, golden sunset light streaming in through cut crystal window", "category": "indoor"} +{"prompt": "La mano de Eva para llegar a la manzana prohibida. Una serpiente está colgando del árbol. ilustración digital, alta definición.", "category": "art"} +{"prompt": "Capital Letter L in Spencerian and blackletter form in 3D with spikes and roses, black background, details, striking details and dynamic range, colorful lighting", "category": "logo"} +{"prompt": "birds eye view of an island golf course, 8k quality", "category": "landscape"} +{"prompt": "a carrier pigeon wearing a flight suit with 100 stacks", "category": "fashion"} +{"prompt": "cinematic still of Agronomist observing a rose in a rose growing field, natural lights, sunlight, dramatic lighting, volumetric lighting, cinematic color tones, muted color tones, shot on Alexa 65, shot on 24mm lens, extreme wide angle, film grain, photorealistic, cinematography, depth of field, bokeh, ornate details, maximum resolution, Afga Vista 400, natural lighting", "category": "fashion"} +{"prompt": "An AIpowered flying car, soaring through the skies above a bustling Vancouver city, showcasing the freedom and innovation of transportation in the future", "category": "vehicles"} +{"prompt": "a ultramodern desert home built into the cliffs, built by Frank Lloyd Wright,futuristic,earth tones,military overtones,sandstone,polished aluminium,prairie style.elliptical minimalism,Cinematic, natural lighting, rich colors, magazine photography", "category": "indoor"} +{"prompt": "An award winning water color painting of a dynamic multi layered landscape including water and mountains with Japanese influences in the style of Roberto Nezzi", "category": "landscape"} +{"prompt": "professional photo of a campari spritz. italy theme.", "category": "fashion"} +{"prompt": "evil apocalypse ultrahighqualitycinematic hdr ray tracing", "category": "vehicles"} +{"prompt": "do a logo with letters F and C being part of a triangle, without background, metalic, 2D, company style, black and gold, volcano edit", "category": "logo"} +{"prompt": "a grass seed piercing through the soil, by mike lasove on 500px, in the style of andreas achenbach, oliver jeffers, ominous vibe, uhd image, pegi nicol macleod, stock photo, tumblewave", "category": "plants"} +{"prompt": "Spray bottle with a label sprays water on green coffee beans on a stool, black pepper, vanilla pods, bay leaf, parsley in the form of a mocap lie around 4320 x 4320 px size", "category": "plants"} +{"prompt": "Ash,Bodies Women,Hell,4k,Hight Resolution,Hight Texture,19201080 Pixels", "category": "art"} +{"prompt": "Film scene. An 8yearold boy and an 11yearold girl are making a very small wooden toy water wheel by a small stream. Bright forest. Rays of light break through the branches. Cinematic lighting. Wide angle shot", "category": "people"} +{"prompt": "Create an image of a young traveler man enjoying the beauty of Reykjadalur hot thermal spring in Iceland. The background should consist of a mild northern lights sky and a green mountain to create a sense of wonder and awe. The man should be the main focus of the image, taking up at least onethird of the area of the image, preferably twothirds. Use a sunny and clear weather setting to enhance the vibrancy of the hot spring. Use a closeup shot to make the man and the lagoon prominent, with the man in the center of the image and his head reaching the upper border. This image should be designed to serve as a YouTube thumbnail.", "category": "people"} +{"prompt": "3 oddly shaped UFOs hovering in the night sky, casting an eerie glow across the surrounding area. metallic exterior is unlike anything seen on Earth, with intricate patterns etched into the surface that seem to pulse with otherworldly energy. The multicolored lights, flicker and dance around the crafts a bright beam of blue light shoots down from the bottom of the ship, illuminating the ground below. As the light touches down on the ground, a cow grazing nearby becomes enveloped in the beam, lifted off the ground, and suspended in midair. casting a brilliant glow across the surrounding fields and countryside. the cow is carried up with it,", "category": "vehicles"} +{"prompt": "purple yellow and pink columbines looking up towards the sky from the grass or a meadow, in the style of light skyblue and light pink, naturalist aesthetic, dragonfly, light skyblue and green, vibrant florals, light red and green, ricoh ff9d", "category": "plants"} +{"prompt": "shitzu dog with glasses and seduction face", "category": "animals"} +{"prompt": "epic, exclusive, dark coloured background, black rolls royce, luxury, class, realistic,", "category": "vehicles"} +{"prompt": "Production still from a 1980s action movie, night, a fancy inerior of a condominium with large windows and a city scape in the background, dark, night, cinematic,", "category": "indoor"} +{"prompt": "affluent handsome young Latino man with a mustache and jheri curl mullet in the 1980s, high fashion, High fashion editorial shoot, expensive commercial photo, cinematic lighting, professionally retouched, dramatic lighting, emotional, highly detailed, intricate, elite, luxury, cinematic hypermaximalist, elegant, ornate, hyper textured, colored, insane details, depth of field background, artful bokeh depth of field, artstation, photorealistic ultra detailed cinematic scene smooth sharp features sharp focus, insanely detailed and intricate, hypermaximalist, elegant, ornate, hyper realistic, super detailed, clear glass reflection, subsurface scattering, photo realism, Photography, hypermaximalist, artful bokeh depth of field, Natural Lighting, intricate details, epic movie scene, professional photography, visualization most artistic way, breathtaking composition, photography, highly detailed, intricate details, Ultra Quality Awardwinning Photography, Ray Tracing Reflections", "category": "fashion"} +{"prompt": "a mindful magnolia on a balcony overlooking the country side peaceful serene ponderous", "category": "plants"} +{"prompt": "if the Last Of Us was a romcom", "category": "people"} +{"prompt": "3D illustration of a cellphone displaying a movie script written on the screen. apple product, product design, 3D,", "category": "logo"} +{"prompt": "An old man with a white beard wearing pirate attire sitting on a river bank by sunset talking to Heath Ledger wearing medieval attire", "category": "people"} +{"prompt": "watercolor wildflower border on white background", "category": "plants"} +{"prompt": "the tiles of the pool, in the style of alasdair mclellan, psychedelic color schemes, leon spilliaert, opaque resin panels, crossprocessed film, earthy color palette, stanisław ignacy witkiewicz", "category": "indoor"} +{"prompt": "hyper realistic viburnum, fill page, coloring book style, high detail, vector lines, 8k, modern cartoon, no shading, easy to color, printable coloring book page, black and white", "category": "plants"} +{"prompt": "white background, with a modern, minimalist sign, elegant neon gold color with the letters R", "category": "logo"} +{"prompt": "nightmare darkart dramatic sky, cinematic", "category": "landscape"} +{"prompt": "electric motorcycle 8k, Canon EOS R5, F1. 2, ISO100, 35MM, Realistic,", "category": "vehicles"} +{"prompt": "lemon and nearby crystal mephedrone, hashish, marijuana", "category": "food"} +{"prompt": "dungeons dragons anime a stone bridge takes to a squared fortress on a mountain mesa,", "category": "landscape"} +{"prompt": "Beautiful website pure water vector graphics conservation, special effect, ui, ux, ui ux, website 8k", "category": "logo"} +{"prompt": "Milwaukee, in the style of luis Royo, red glow, dim cinematic lighting, 8k udd, hyper realistic, extremely detailed, photograph", "category": "art"} +{"prompt": "DONALD TRUMP SAVING A CHILD FROM BURNING BUILDING", "category": "people"} +{"prompt": "An early summer forest with wild strawberries, fern fronds, white blossoms in the photographic style of Paul Barson", "category": "plants"} +{"prompt": "wet cobbled street photo tram tracks victorian", "category": "vehicles"} +{"prompt": "a slice of cheesecake with strawberries, expresso on the side,8k,high detail,", "category": "food"} +{"prompt": "photorealistic, 3d, transparent background, Blue Diamond , engraved with Level 1", "category": "logo"} +{"prompt": "A stylish Swahili muslim grandfather dressed in a Gucci suit is reading a newspaper through the streets of Lamu, magazine photography, editoria, cinematic lights, 8K kodak gold 200 details,", "category": "people"} +{"prompt": "photo in the style of Mario Sorrenti, Lachlan Bailey, photography, background in focus, ultra realistic face, full length photo of a person, girl, 35mm, kodak portra, kodak portra 800, 35 mm f1. 8, 8k,", "category": "people"} +{"prompt": "golden hour nature, morning nature photography", "category": "plants"} +{"prompt": "black Caribbean man walking balance front his fate chaos anarchy liberty independence force energy torahans the Galaxy hypermaximalist sculpted anatomically correct cinematic global illumination darkness shafts of iridescent smoke they glow coming from their intricate filigree", "category": "fashion"} +{"prompt": "Apollo american astronaut sitting on a salt flat at dusk, blue hour, dust blowing around, desolate, mountains in the far off background, Ford Raptor in background, hyper realistic, hyper detailed, 8k, photography", "category": "vehicles"} +{"prompt": "3 fashionalble girls in their 20s, with colorful magenta and blonde hair, looking at the camera, smiling, having fun, excited, wearing short skirts, during sunset, festival background, HD ultra quality, megapixel, realistic, 8k 16k, Canon EOS 5D Mar IV camera with a canon EF 2470mm f2.8 L II USM lens, aspect 11", "category": "fashion"} +{"prompt": "stunning, vibrant, multicolored, Energetic Positivity Unleashed playlist cover, highresolution, digital illustration", "category": "art"} +{"prompt": "Old brasilain couple at the jesus statue in Rio, year 2023, photography, superphotoreal, hyperreal, photorealistic, high def, ultradetailed, ultra realistic, medium format, wes anderson movie, pastel colors,delicate light, cinematic light 8k", "category": "people"} +{"prompt": "Magic The Gathering inspired Crest", "category": "logo"} +{"prompt": "full body, an black man with the wisdom of Solomon and the courage of Samson save the world with the help of cybernetic artificial intelligence , chaos, anarchy, liberty, independence, force and energy, very detailed, photo taken with Hasselblad X1D, ISO 100, national geographic", "category": "fashion"} +{"prompt": "an anthropomorphic white rabbit dressed as a wizard", "category": "animals"} +{"prompt": "An exquisite image capturing the essence of a luxurious seafood feast, set against the majestic beauty of the Antarctic. The seafood platter brimming with fresh lobster, scallops, mussels, and clams is displayed on the deck of a small yacht, with colossal icebergs bathed in the golden light of the sunset creating a mesmerizing background. Nikon D850, 50mm lens, ISO 200, f4, 1160s", "category": "food"} +{"prompt": "a black couple Caribbean man and woman with children walking balance front his fate, chaos, anarchy, liberty, independence, force, energy, torah, waving caustics deep below mens skulls head 4k cinematic lighting, atmosphere, light fog, foggy industrial environment, dim volumetric lighting, extreme detail, trending on artstation, 3 5 mm, f 3 2, golden angle,oil painting, whole body, very detailed, photo taken with Hasselblad X1D50c, ISO 100, national geographic", "category": "people"} +{"prompt": "five steampunk winemakers wine tasting 8k", "category": "people"} +{"prompt": "a redhaired kitten with a fulllength tail stands on a white background", "category": "animals"} +{"prompt": "film scene, teenager hacker doing works on computer n an abandoned factory, dimly lighting style, korean film style,", "category": "indoor"} +{"prompt": "food stylist photography of stylish chocolate pudding with sausages in a high class restaurant. haute cuisine. Mamiya 645, fstop 2.8, Kodak Portra.", "category": "food"} +{"prompt": "professional photo of a lemonade cocktail on the counter of a stylish new york bar", "category": "food"} +{"prompt": "obiwan Kenobi in a 80s Bollywood movie dark , brown skin.", "category": "people"} +{"prompt": "This is an ultra realistic photo a glass bowl full of tasty ripe strawberries with the top layer in the bowl covered with cream on top. There is a glass of strawberry milkshake next to the bowl with strawberries. The scene is set in a rustic outdoor setting in the countryside, surrounded by lush greenery and a small lake with light blue water sparkling in the sun in the background. In the foreground, there is a wooden table with a glass bowl full of fresh and ripe strawberries that have been covered in a generous layer of creamy white cream. The strawberries look plump and juicy, with their vibrant red color standing out against the pale cream. The bowl is placed at an angle, with some of the strawberries spilling over the edge onto the wooden table, adding a touch of playfulness and spontaneity to the scene. The lighting is sharp, with the sun shining through the trees and casting shadows on the scene. The colors are rich and vibrant, with the light green of the trees, blue water on the lake and the vivid red of the strawberries creating a beautiful contrast. In the background, the small lake adds an extra touch of tranquility to the scene, with its calm waters reflecting the greenery and blue sky above. The photo has a bright and airy feel, with a focus on the bright vivid colors and textures of the food. The scene should evoke a desire for fresh, organic food. This photo is captured by Marte Marie Forsberg with a Canon EOS R5 camera, paired with a macro lens such as the Canon EF 100mm f2.8L Macro IS USM, a wide aperture such as f2.8 to achieve a shallow depth of field and blur the background slightly, creating a sense of depth and focus on the strawberries. A shutter speed of 1125s will help to freeze any movement and ensure a sharp image. Finally, using a ...", "category": "food"} +{"prompt": "Thai pretty girl, 25 years old, wearing v neck gown, long hair, enjoying herself in a night club environment, dancing to the music and smiling towards the camera.", "category": "people"} +{"prompt": "full shot, a mysterious ocean, edge of beach, epic waves, greek mythological, cinematic, rich colors, daytime, surreal, artistic photography", "category": "landscape"} +{"prompt": "barbados as a transformer in an action pose", "category": "vehicles"} +{"prompt": "extremely simple flowers, smiling, childrens book illustration style, multiple poses and expressions, single black and white drawing, line art, white space, coloring page, wide angle, white background", "category": "plants"} +{"prompt": "halloween candy packages from another world, knolling, white background", "category": "food"} +{"prompt": "closeup food photography of a shoyu ramen, creamy chicken broth, soy sauce, black garlic mayu oil, thin organic wheat noodle, Thinly sliced spring onions ,Thinly sliced chicken meat, Narutomaki , Nori seaweed, sliced into strips,half Softboiled egg, ,Roasted sesame seeds. , ultra realistic, low angle, on table, canon 5d, dslr, 50mm portrait, DOF, vray render, 8k, ray tracing, golden hour lighting, up lighting, rim lighting rigid, photoshopped", "category": "food"} +{"prompt": "facebook logo, dry, broken", "category": "logo"} +{"prompt": "Create a mouthwatering Chocolate Protein Pudding with Whipped Cream and Berries that will make your taste buds tingle ultrarealistic", "category": "food"} +{"prompt": "small bucolic backyard with Japanese tree and koi pond", "category": "indoor"} +{"prompt": "Detox Diets,Shot using a Hasselblad camera, ISO 100. Professional color grading. Soft shadows. Clean sharp focus. High end retouching. Food magazine photography. Award winning photography. Advertising photography. Commercial photography.", "category": "food"} +{"prompt": "cactus ,sun shining through the old window an artist studio, nice weather, realistic, super detailed, photography, detailed, atmosphere, realistic, cinematic, photorealistic", "category": "indoor"} +{"prompt": "troupial 4K hyper realistic1 brave1", "category": "animals"} +{"prompt": "shubniggurath in a dark age grove, ultra fine details, ultrarealistic 4k, ultra photo realisitc 4k", "category": "art"} +{"prompt": "realistic wolf looking towards me on mountaintop with a sunset in background", "category": "animals"} +{"prompt": "realistic photography,Inside a selfdriving car,Sony Alpha 1,FE C 1635mmT3.1 G,", "category": "vehicles"} diff --git a/tasks/svdquant-w4a4-psnr/tests/candidate_runner.py b/tasks/svdquant-w4a4-psnr/tests/candidate_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..218e60e064f41281158ab71d5b7ef2dedb7a7751 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/candidate_runner.py @@ -0,0 +1,135 @@ +import argparse +import importlib.util +import inspect +import json +import math +import random +import sys +from pathlib import Path + +import numpy as np +import torch +from diffusers import PixArtSigmaPipeline + + +def load_submission(path): + spec = importlib.util.spec_from_file_location("candidate_quantize", path) + if spec is None or spec.loader is None: + raise RuntimeError("cannot load entry point") + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + fn = getattr(module, "quantize_model", None) + if not callable(fn): + raise TypeError("quantize_model is not callable") + return fn + + +def check_tensor(value, name, shape, dtype, positive=False, code=False): + if not isinstance(value, torch.Tensor) or value.shape != shape or value.dtype != dtype: + raise ValueError(f"invalid {name}") + if code: + if value.numel() and (int(value.min()) < -7 or int(value.max()) > 7): + raise ValueError("qweight outside INT4 range") + else: + if not bool(torch.isfinite(value).all()): + raise ValueError(f"non-finite {name}") + if positive and not bool((value > 0).all()): + raise ValueError(f"non-positive {name}") + + +def validate_replacement(module, in_features, out_features, has_bias, rank): + expected = ["qweight", "scales", "l1", "l2", "smooth"] + tensors = {name: value for name, value in module.named_parameters(recurse=True)} + tensors.update({name: value for name, value in module.named_buffers(recurse=True)}) + if has_bias: + expected.append("bias") + if list(tensors) != expected or hasattr(module, "weight"): + raise ValueError("replacement tensor state does not match the required schema") + groups = math.ceil(in_features / 64) + check_tensor(tensors["qweight"], "qweight", (out_features, in_features), torch.int8, code=True) + check_tensor(tensors["scales"], "scales", (out_features, groups), torch.float16, positive=True) + check_tensor(tensors["smooth"], "smooth", (in_features,), torch.float16, positive=True) + r = tensors["l1"].shape[1] if tensors["l1"].ndim == 2 else -1 + if not 0 <= r <= rank: + raise ValueError("rank exceeds budget") + check_tensor(tensors["l1"], "l1", (in_features, r), torch.float16) + check_tensor(tensors["l2"], "l2", (r, out_features), torch.float16) + if has_bias: + check_tensor(tensors["bias"], "bias", (out_features,), torch.float16) + elif "bias" in tensors: + raise ValueError("unexpected bias") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--entry", required=True) + parser.add_argument("--output", required=True) + parser.add_argument("--rank", type=int, default=32) + args = parser.parse_args() + if args.rank < 0 or args.rank > 32 or not torch.cuda.is_available(): + raise RuntimeError("invalid rank or CUDA unavailable") + + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + torch.cuda.manual_seed_all(0) + torch.set_num_threads(1) + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + torch.backends.cuda.matmul.allow_tf32 = False + quantize_model = load_submission(args.entry) + signature = inspect.signature(quantize_model) + parameters = list(signature.parameters.values()) + if ( + len(parameters) != 2 + or [parameter.name for parameter in parameters] != ["pipe", "rank"] + or parameters[0].default is not inspect.Parameter.empty + or parameters[1].default != 32 + ): + raise TypeError("quantize_model must have signature quantize_model(pipe, rank=32)") + pipe = PixArtSigmaPipeline.from_pretrained("/opt/pixart-sigma", torch_dtype=torch.float16, local_files_only=True) + pipe.to("cuda") + originals = { + name: (module, module.in_features, module.out_features, module.bias is not None) + for name, module in pipe.transformer.named_modules() + if isinstance(module, torch.nn.Linear) + } + result = quantize_model(pipe, rank=args.rank) + if result is not None: + raise ValueError("quantize_model must return None") + current = dict(pipe.transformer.named_modules()) + if not originals: + raise RuntimeError("no eligible linear layers found") + for name, (old, in_features, out_features, has_bias) in originals.items(): + module = current.get(name) + if module is None or module is old or isinstance(module, torch.nn.Linear): + raise ValueError(f"linear layer not replaced: {name}") + validate_replacement(module, in_features, out_features, has_bias, args.rank) + if any(isinstance(module, torch.nn.Linear) for module in pipe.transformer.modules()): + raise ValueError("linear layer remains") + + from safetensors.torch import save_file + + tensors = {} + manifest = {"rank": args.rank, "layers": []} + for name, (_, in_features, out_features, has_bias) in originals.items(): + module = current[name] + state = {key: value for key, value in module.named_parameters(recurse=True)} + state.update({key: value for key, value in module.named_buffers(recurse=True)}) + manifest["layers"].append({ + "name": name, "in_features": in_features, "out_features": out_features, + "has_bias": has_bias, + }) + for field in ("qweight", "scales", "l1", "l2", "smooth", "bias"): + if field in state: + tensors[f"{name}.{field}"] = state[field].detach().cpu().contiguous() + outdir = Path(args.output) + if not outdir.is_dir() or any(outdir.iterdir()): + raise ValueError("output directory is not fresh") + save_file(tensors, outdir / "quantized.safetensors") + (outdir / "manifest.json").write_text(json.dumps(manifest, sort_keys=True) + "\n") + + +if __name__ == "__main__": + main() diff --git a/tasks/svdquant-w4a4-psnr/tests/compute_reward.py b/tasks/svdquant-w4a4-psnr/tests/compute_reward.py new file mode 100644 index 0000000000000000000000000000000000000000..376b100a87a746657f933de5738c08e4c4288385 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/compute_reward.py @@ -0,0 +1,16 @@ +import json +import math +from pathlib import Path + +REWARD_PATH = Path("/logs/verifier/reward.txt") + +try: + value = float(json.loads(Path("/logs/verifier/metric.json").read_text())["reward"]) + if not math.isfinite(value): + raise ValueError("non-finite reward") + reward = f"{min(1.0, max(0.0, value)):.12g}" + REWARD_PATH.write_text(reward + "\n") + print(f"reward={reward}") +except Exception as exc: + REWARD_PATH.write_text("0\n") + print(f"reward=0 (fallback: {exc})") diff --git a/tasks/svdquant-w4a4-psnr/tests/grader_core.py b/tasks/svdquant-w4a4-psnr/tests/grader_core.py new file mode 100644 index 0000000000000000000000000000000000000000..0071c3149da79f997a3f75c222576682666ffbb4 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/grader_core.py @@ -0,0 +1,287 @@ +import json +import math +import os +import random +import shutil +import signal +import subprocess +import tempfile +import time +from pathlib import Path + +import numpy as np +import torch +from diffusers import PixArtSigmaPipeline + + +def configure_determinism(): + random.seed(0) + np.random.seed(0) + torch.manual_seed(0) + torch.cuda.manual_seed_all(0) + torch.set_num_threads(1) + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + torch.backends.cuda.matmul.allow_tf32 = False + + +configure_determinism() + +M0 = 9.1 +BASELINE_SCORE = 17.6 +CANDIDATE_TIMEOUT = 900 +GENERATION_TIMEOUT = 5400 + + +def reward_for(metric): + improvement = max(0.0, float(metric) - M0) + reference = BASELINE_SCORE - M0 + u = improvement / reference + return u / (1.0 + u) + + +def sanitized_env(cuda_visible="0"): + keep = { + "HOME": "/home/agent", + "PATH": "/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin", + "PYTHONPATH": "", + "PYTHONSAFEPATH": "1", + "PYTHONNOUSERSITE": "1", + "PYTHONDONTWRITEBYTECODE": "1", + "PYTHONHASHSEED": "0", + "OMP_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "CUBLAS_WORKSPACE_CONFIG": ":4096:8", + "HF_HUB_OFFLINE": "1", + "TRANSFORMERS_OFFLINE": "1", + "CUDA_VISIBLE_DEVICES": cuda_visible, + "NVIDIA_VISIBLE_DEVICES": os.environ.get("NVIDIA_VISIBLE_DEVICES", "all"), + "NVIDIA_DRIVER_CAPABILITIES": os.environ.get("NVIDIA_DRIVER_CAPABILITIES", "compute,utility"), + } + return keep + + +def run_process(command, timeout, env): + proc = subprocess.Popen( + command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, + env=env, cwd="/tmp", start_new_session=True, + ) + try: + stdout, stderr = proc.communicate(timeout=timeout) + except subprocess.TimeoutExpired: + os.killpg(proc.pid, signal.SIGKILL) + stdout, stderr = proc.communicate() + raise TimeoutError(f"candidate timed out; stderr={stderr[-4000:]}") + if proc.returncode != 0: + raise RuntimeError(f"candidate exit={proc.returncode}; stderr={stderr[-4000:]}") + return stdout, stderr + + +def reap_agent_processes(): + """Kill every process owned by the submission user in this verifier.""" + for _ in range(3): + found = False + for item in Path("/proc").iterdir(): + if not item.name.isdigit(): + continue + try: + status = (item / "status").read_text() + uid_line = next(line for line in status.splitlines() + if line.startswith("Uid:")) + uids = [int(value) for value in uid_line.split()[1:]] + if 1001 not in uids[:2]: + continue + os.kill(int(item.name), signal.SIGKILL) + found = True + except (FileNotFoundError, ProcessLookupError, PermissionError, + StopIteration, ValueError): + continue + if not found: + break + time.sleep(0.1) + + +def stage_candidate(entry): + parent = Path(tempfile.mkdtemp(prefix="quant-grade-", dir="/tmp")) + parent.chmod(0o755) + staged_entry = parent / "quantize.py" + staged_runner = parent / "runner.py" + shutil.copyfile(entry, staged_entry) + shutil.copyfile("/tests/candidate_runner.py", staged_runner) + for path in (staged_entry, staged_runner): + path.chmod(0o444) + output = parent / "candidate-images" + output.mkdir(mode=0o700) + os.chown(output, 1001, 1001) + return parent, staged_entry, staged_runner, output + + +def generate_images(prompts, output, quantized_dir=None): + script = r''' +import json, math, random, sys, types +from pathlib import Path +import numpy as np +import torch +from diffusers import PixArtSigmaPipeline +from safetensors.torch import load_file + +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +torch.cuda.manual_seed_all(0) +torch.set_num_threads(1) +torch.use_deterministic_algorithms(True) +torch.backends.cudnn.deterministic=True +torch.backends.cudnn.benchmark=False +torch.backends.cuda.matmul.allow_tf32=False + +class QuantizedLinear(torch.nn.Module): + def __init__(self, state, prefix, info): + super().__init__(); self.in_features=info["in_features"]; self.out_features=info["out_features"] + for field in ("qweight","scales","l1","l2","smooth","bias"): + key=prefix+"."+field + if key in state: self.register_buffer(field,state[key].cuda()) + def forward(self,x): + shape=x.shape; xs=x/self.smooth + flat=xs.reshape(-1,self.in_features); parts=[] + for start in range(0,self.in_features,64): + chunk=flat[:,start:start+64]; scale=chunk.float().abs().amax(dim=1,keepdim=True)/7 + scale=torch.where(scale==0,torch.ones_like(scale),scale) + parts.append((torch.round(chunk.float()/scale).clamp(-7,7)*scale).to(x.dtype)) + qx=torch.cat(parts,dim=1) + wparts=[] + for group,start in enumerate(range(0,self.in_features,64)): + wparts.append(self.qweight[:,start:start+64].to(torch.float16)*self.scales[:,group:group+1]) + weight=torch.cat(wparts,dim=1) + y=qx@weight.T+(flat@self.l1)@self.l2 + if hasattr(self,"bias"): y=y+self.bias + return y.reshape(*shape[:-1],self.out_features) + +def replace(root,path,module): + parent=root + bits=path.split(".") + for bit in bits[:-1]: parent=getattr(parent,bit) + setattr(parent,bits[-1],module) + +p=PixArtSigmaPipeline.from_pretrained("/opt/pixart-sigma",torch_dtype=torch.float16,local_files_only=True) +if sys.argv[3] != "-": + qdir=Path(sys.argv[3]); state=load_file(qdir/"quantized.safetensors",device="cpu") + manifest=json.loads((qdir/"manifest.json").read_text()) + for info in manifest["layers"]: replace(p.transformer,info["name"],QuantizedLinear(state,info["name"],info)) +p.to("cuda"); p.set_progress_bar_config(disable=True) +rows=[json.loads(x) for x in Path(sys.argv[1]).read_text().splitlines() if x.strip()] +out=Path(sys.argv[2]); out.mkdir(parents=True,exist_ok=False) +for i,row in enumerate(rows): + g=torch.Generator(device="cuda").manual_seed(int(row["seed"])) + x=p(row["prompt"],num_inference_steps=20,guidance_scale=4.5,height=1024,width=1024,generator=g,output_type="pt").images[0] + if x.shape!=(3,1024,1024) or not torch.isfinite(x).all(): raise ValueError("bad image") + torch.save(x.detach().cpu().to(torch.float16).clamp(0,1).contiguous(),out/f"{i:04d}.pt") +''' + qarg = "-" if quantized_dir is None else str(quantized_dir) + run_process(["/usr/bin/python3", "-c", script, str(prompts), str(output), qarg], GENERATION_TIMEOUT, {**sanitized_env(), "HOME": "/root"}) + + +def verify_quantized_artifact(path): + from safetensors.torch import load_file + manifest = json.loads((path / "manifest.json").read_text()) + state = load_file(path / "quantized.safetensors", device="cpu") + if not isinstance(manifest.get("layers"), list) or not manifest["layers"] or manifest.get("rank") != 32: + raise ValueError("invalid manifest") + expected = set() + for info in manifest["layers"]: + name, m, n, has_bias = info["name"], int(info["in_features"]), int(info["out_features"]), bool(info["has_bias"]) + groups = math.ceil(m / 64) + fields = {"qweight", "scales", "l1", "l2", "smooth"} | ({"bias"} if has_bias else set()) + expected |= {f"{name}.{field}" for field in fields} + q, scales, l1, l2, smooth = (state[f"{name}.{field}"] for field in ("qweight", "scales", "l1", "l2", "smooth")) + if q.dtype != torch.int8 or q.shape != (n, m) or (q.numel() and (int(q.min()) < -7 or int(q.max()) > 7)): + raise ValueError("invalid qweight") + r = l1.shape[1] if l1.ndim == 2 else -1 + specs = [(scales, torch.float16, (n, groups), True), (l1, torch.float16, (m, r), False), (l2, torch.float16, (r, n), False), (smooth, torch.float16, (m,), True)] + if not 0 <= r <= 32: + raise ValueError("invalid rank") + if has_bias: specs.append((state[f"{name}.bias"], torch.float16, (n,), False)) + for tensor, dtype, shape, positive in specs: + if tensor.dtype != dtype or tensor.shape != shape or not torch.isfinite(tensor).all() or (positive and not (tensor > 0).all()): + raise ValueError("invalid quantized tensor") + if set(state) != expected: + raise ValueError("unexpected tensor state") + expected_count = 0 + pipe = PixArtSigmaPipeline.from_pretrained("/opt/pixart-sigma", torch_dtype=torch.float16, local_files_only=True) + original = {name: (module.in_features, module.out_features, module.bias is not None) for name, module in pipe.transformer.named_modules() if isinstance(module, torch.nn.Linear)} + listed = {x["name"]: (x["in_features"], x["out_features"], x["has_bias"]) for x in manifest["layers"]} + if listed != original: + raise ValueError("manifest does not replace every original linear") + del pipe + + +def safe_tensor(path): + tensor = torch.load(path, map_location="cpu", weights_only=True) + if not isinstance(tensor, torch.Tensor) or tensor.shape != (3, 1024, 1024): + raise ValueError("invalid image tensor") + tensor = tensor.to(torch.float32) + if not torch.isfinite(tensor).all() or float(tensor.min()) < 0 or float(tensor.max()) > 1: + raise ValueError("image outside [0,1]") + return tensor + + +def score(entry, prompts): + started = time.monotonic() + parent = reference_dir = generated_dir = validated_parent = None + try: + parent, staged_entry, runner, candidate_dir = stage_candidate(entry) + reference_dir = Path(tempfile.mkdtemp(prefix="reference-images-", dir="/tmp")) / "images" + command = [ + "/usr/sbin/runuser", "-u", "agent", "--", "/usr/bin/setsid", "/usr/bin/python3", + str(runner), "--entry", str(staged_entry), "--output", str(candidate_dir), + "--rank", "32", + ] + try: + _, stderr = run_process(command, CANDIDATE_TIMEOUT, sanitized_env()) + finally: + reap_agent_processes() + print(f"candidate stderr tail: {stderr[-2000:]}") + verify_quantized_artifact(candidate_dir) + + validated_parent = Path(tempfile.mkdtemp(prefix="validated-quant-", dir="/tmp")) + validated_parent.chmod(0o700) + validated_dir = validated_parent / "artifact" + validated_dir.mkdir(mode=0o700) + for name in ("manifest.json", "quantized.safetensors"): + target = validated_dir / name + shutil.copyfile(candidate_dir / name, target) + target.chmod(0o400) + verify_quantized_artifact(validated_dir) + generated_dir = Path(tempfile.mkdtemp(prefix="candidate-generated-", dir="/tmp")) / "images" + generate_images(prompts, reference_dir) + generate_images(prompts, generated_dir, validated_dir) + count = sum(1 for line in prompts.read_text().splitlines() if line.strip()) + values = [] + for index in range(count): + candidate = safe_tensor(generated_dir / f"{index:04d}.pt") + reference = safe_tensor(reference_dir / f"{index:04d}.pt") + mse = float(torch.mean((candidate - reference) ** 2, dtype=torch.float64)) + values.append(0.0 if not math.isfinite(mse) or mse <= 0 else 10.0 * math.log10(1.0 / mse)) + metric = sum(values) / len(values) + return { + "valid": True, "metric_name": "mean_psnr_db", "metric": metric, + "reward": reward_for(metric), "examples": len(values), + "elapsed_sec": time.monotonic() - started, + } + except Exception as exc: + return { + "valid": False, "metric_name": "mean_psnr_db", "metric": None, + "reward": 0.0, "reason": f"{type(exc).__name__}: {exc}", + "elapsed_sec": time.monotonic() - started, + } + finally: + if parent is not None: + shutil.rmtree(parent, ignore_errors=True) + if reference_dir is not None: + shutil.rmtree(reference_dir.parent, ignore_errors=True) + if generated_dir is not None: + shutil.rmtree(generated_dir.parent, ignore_errors=True) + if validated_parent is not None: + shutil.rmtree(validated_parent, ignore_errors=True) diff --git a/tasks/svdquant-w4a4-psnr/tests/hidden_data/final/prompts.jsonl b/tasks/svdquant-w4a4-psnr/tests/hidden_data/final/prompts.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8c8efd3ffa164800bce15cc9488e5cd9c0333a6a --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/hidden_data/final/prompts.jsonl @@ -0,0 +1,32 @@ +{"prompt": "hyper realistic photo about two cute young kids that are lost in a forest. A boy and a girl.There must be a river. 4k resolution. In 3D and with depth perception", "category": "people", "seed": 150} +{"prompt": "Huge alligator alphamale standing in a distance by a riverside, hyperrealistic, photorealistic, high details, high quality, shot on Nikon D6, Galen Rowell, Peter Lik, Marc Adamus, David Muench,", "category": "animals", "seed": 151} +{"prompt": "HD big field of flowers Hyper realistic, HD,", "category": "landscape", "seed": 153} +{"prompt": "fashion editorial of a photorealistic female model with dark brown hair, full body, full body shot, wearing a futuristic cosmic nebula dress, metallic finishings, 4k, hyper realistic, cinematic, ultra detailed", "category": "fashion", "seed": 154} +{"prompt": "half tank half sandwich", "category": "food", "seed": 157} +{"prompt": "3d vector logo with a transparent background containing the letterV", "category": "logo", "seed": 158} +{"prompt": "A firstperson perspective of an F1 pilot engaged in a highspeed, adrenalinefueled race, featuring numerous F1 cars, intense action, and dangerous maneuvers, all rendered with Unreal Engine 5 for hyperrealistic details and cinematic lighting.", "category": "vehicles", "seed": 159} +{"prompt": "horror novel cover painting", "category": "art", "seed": 161} +{"prompt": "Spring has sprung, the flowers grow, The sun shines bright, the breeze is slow, The world awakens from its sleep, And natures beauty we get to keep.", "category": "plants", "seed": 166} +{"prompt": "Detailed photography of a large and modern room with tall windows on the 13th floor of a skyscraper in Miami, the windows show a view of the Florida coast, sunny day, cumulus clouds,", "category": "indoor", "seed": 170} +{"prompt": "A fit, brighteyed, welldressed Michael Gambon as Dumbledore in 1970s leisurewear, 8K, rich in details, hyperrealistic, chaos 50", "category": "people", "seed": 156} +{"prompt": "happy Daniel Tiger playing with a red bouncing ball", "category": "animals", "seed": 152} +{"prompt": "Realistic landscape photo of Vietnamese fishermen fishing on a bamboo boat, early in the morning there is a lot of dew and rays, dew floating on the lake, sunlight, wide lens, high quality", "category": "landscape", "seed": 160} +{"prompt": "blonde woman, sitting on beach", "category": "fashion", "seed": 155} +{"prompt": "pretzel extremely twisted", "category": "food", "seed": 168} +{"prompt": "a bic mac city gaudy style", "category": "logo", "seed": 179} +{"prompt": "The frame of the picture has an electric bicycle design, and the electric bicycle is small and placed directly below the picture, cyberpunk style, with subtle color changes, the center of the picture is blank, no text, no pattern. Innovative page design, Power point base map design", "category": "vehicles", "seed": 164} +{"prompt": "middle eastern woman wearing traditional clothes cooking Kabsa rice, using palm tree wood fire, full of palmtrees farm, at blue hour, with some water reflections in the floor, film grain, slight misty weather, Pixar movie style, Photorealistic, Ultra photoreal, Ultra detailed, Intricate details, Atmospheric, Hyperrealistic, Unreal engine, Ray tracing, Back light, Cinematic, Fairy lights, Soft lighting, Rim lighting, Low camera, FarShot Angle, LowAngle Shot, FullShot Angle, FullBody Shot, Cinematic Still Shot, Leica, Golden ratio, 8k", "category": "art", "seed": 165} +{"prompt": "sativa plants growing in a Mexico field, Hollywood scenery, movie set, ultra realistic, HD", "category": "plants", "seed": 172} +{"prompt": "a bedroom interior design in nomadic minimalistic style, flat gypsum ceiling with shadow gap profile, floor to ceiling windows with attached balcony, curtains, ottoman bench and minimalistic wardrobe, bed with area rug, realistic render, v5", "category": "indoor", "seed": 183} +{"prompt": "screen grab Robert Downey Jr young in the current year 2023 smiling and happy on a sunny day", "category": "people", "seed": 178} +{"prompt": "A photograph of a a large mushroom in a forest with a psychedelic and fairy tale atmosphere. The colors are like those of a drug induced vision.", "category": "animals", "seed": 167} +{"prompt": "scenic view of the surface of Tatooine from star wars at sunset, in the style of hollow knight", "category": "landscape", "seed": 162} +{"prompt": "Full length epic portrait, black cinderella entering a gala in Harlem, New York, photo realistic, 8k, high definition", "category": "fashion", "seed": 163} +{"prompt": "simple doughnut not glossy at all with not toppings, upright, black background, illustrationbased,", "category": "food", "seed": 171} +{"prompt": "heart shaped diamond on a matte black background", "category": "logo", "seed": 182} +{"prompt": "1956 Land Rover S\u00e9ries 1 climb a dune, Unreal Engine 5, Cinematic, portrait Photography, UltraWide Angle, Depth of Field, hyperdetailed, insane details, intricate details, beautifully color graded, Unreal Engine, Cinematic, Photoshoot, Shot on 25mm lens, Depth of Field, DOF, Tilt Blur, Shutter Speed 11000, F22, White Balance, 32k, SuperResolution, Megapixel, Pro Photo RGB, VR, Lonely, Good, Massive, Half rear Lighting, Backlight, Natural Lighting, Incandescent, Optical Fiber, Moody Lighting, Cinematic Lighting, Studio Lighting, Soft Lighting, Volumetric, Conte Jour, Beautiful Lighting, Accent Lighting, Global Illumination, Screen Space Global Illumination, Ray Tracing Global Illumination, Optics, Scattering, Glowing, Shadows, Rough, Shimmering, Ray Tracing Reflections, Lumen Reflections, Screen Space Reflections, Diffraction Grading, Chromatic Aberration, GB Displacement, Scan Lines, Ray Traced, ray Tracing Ambient Occlusion, AntiAliasing, FKAA, TXAA, RTX, SSAO, Shaders, OpenGLShaders, GLSLShaders, Post Processing, PostProduction, Cell Shading, Tone Mapping, CGI, VFX, SFX, insanely detailed and intricate, hyper maximalist, elegant, super detailed, dynamic pose, photography, volumetric, ultradetailed, intricate details, 8K, super detailed, ambient occlusion, volumetric lighting. high contrast, HDR v 4", "category": "vehicles", "seed": 191} +{"prompt": "female Tabaxi warlock looking like tabby cat, playing with a magical artifact", "category": "art", "seed": 176} +{"prompt": "abstract green deal backround made of", "category": "plants", "seed": 175} +{"prompt": "open circular coffee shop. starbucks style. bhopal. inside a park, beautifully landscaped. cultural convetion center background. evening. colorful sky. greenery.", "category": "indoor", "seed": 215} +{"prompt": "5 divers find treasure in a frigate at the bottom of the sea,", "category": "people", "seed": 184} +{"prompt": "overgrown animals", "category": "animals", "seed": 169} diff --git a/tasks/svdquant-w4a4-psnr/tests/hidden_data/intermediate/prompts.jsonl b/tasks/svdquant-w4a4-psnr/tests/hidden_data/intermediate/prompts.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..368dd68eb0e52f2babfa3002938d70ef111eaaa3 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/hidden_data/intermediate/prompts.jsonl @@ -0,0 +1,32 @@ +{"prompt": "Will and Jada Pinkett Smiths Calabasas Compound in California, 8K, UHD,", "category": "landscape", "seed": 0} +{"prompt": "asian noodle, catalog photography, food photography, Canon EOS 5D Mark IV DSLR, f5.6, Resolution photography, maximum detail, hyperrealism", "category": "food", "seed": 1} +{"prompt": "Liveaction, Viriathus the young Leader of the Lusitanian people, 2nd century BC, wearing traditional Leader Lusitanian clothing, rugged landscape of the mountains and forests of modernday Portugal and Spain in background, shot by Rodrigo Prieto, DSMC2 DragonX and Cooke S4i lenses, highquality cinematic footage", "category": "people", "seed": 2} +{"prompt": "Super close macro shot of a water drop on pine sprouts sparkling in the bright with a light background hyperrealistic light photography", "category": "plants", "seed": 3} +{"prompt": "Toy Story style character of a happy friendly goose dressed in a pink formula 1 suit. Full body", "category": "art", "seed": 6} +{"prompt": "Photograph of a very small work office. It has a window through which you can see a city. A black glass table with black trestles. Very bright. White walls. Light wooden floor. A black reclining wheeled chair. It transmits relaxation, peace, tranquility.", "category": "indoor", "seed": 7} +{"prompt": "anthropomorphic lion pixar style white background", "category": "animals", "seed": 10} +{"prompt": "cyberpunk scene, interior of a very sophisticated vehicle, it rains heavily cyberpunk, very clear and detailed photos that clearly show even the smallest details, Best quality, clear image, image of the product with small plate, neon color palette, with precise focus, high resolution image, high resolution image with large capacity, 8k, photos like real food photos, product photos that look delicious, low angle shot, wide shot, art by Michelangelo Grand Master", "category": "vehicles", "seed": 12} +{"prompt": "redheaded beautiful 16 year old witch in a black gown standing inside a grove of trees laden with red berries and green leaves with the wind whipping her hair and her dress. Photorealistic. Cinematic.", "category": "fashion", "seed": 13} +{"prompt": "roses, surrounded by leaf motifs in a circluar shape, 2d vector, neon colors, vector design on the edges of the image", "category": "logo", "seed": 19} +{"prompt": "Alaskan Glacial Lake Portage in late winter directional light godrays detailed sky and clouds, chairoscuro shadows, sunset cinematic in the style of national geographic award winning photography alcohol ink overpaint.6", "category": "landscape", "seed": 5} +{"prompt": "an aerial view of a food vendors tent interior, top down view, flowers, photo realistic,", "category": "food", "seed": 9} +{"prompt": "reptilian King Charles and Queen Camilla", "category": "people", "seed": 11} +{"prompt": "a background full of flowers with black and white manga style", "category": "plants", "seed": 4} +{"prompt": "Heroine fighting against a huge Machine Hydra in the middle of a destroyed New York", "category": "art", "seed": 44} +{"prompt": "modern house at christmas night, cinematic and typical USA house", "category": "indoor", "seed": 23} +{"prompt": "rabbit, high quality, clipart, white background, vintage", "category": "animals", "seed": 30} +{"prompt": "climate crisis in mumbai, 200 years in the future, hitech buildings, advanced technology, homeless people", "category": "vehicles", "seed": 14} +{"prompt": "obese happy Lindsay Lohan photo shoot of obese Lindsay Lohan dressed as a cyberpunk sitting next to an 18 wheeler truck looking drunk and happy at a truck stop, wind blowing hair back, laughing and having a good time next to beer bottles, nighttime photography, twilight hour, tattoos, piercings, wide angle, soft box stock style photography, canon dslr, 50mm", "category": "fashion", "seed": 35} +{"prompt": "Minimal logo gradient in circle in center Corvette silhouette Blue gradient, white background, by Chip Foose", "category": "logo", "seed": 22} +{"prompt": "an ink painting red poppies along a river bank with a cottage in the distance", "category": "landscape", "seed": 8} +{"prompt": "food photography. familystyle feast from side angle. american rustic cottage breakfast. omelet, bacon, sauage. in foreground focus 4k on floral decorative plate with an omelette with lots of cheese. half eaten, bite taken out omelet, gooey cheesy . in background on table serving dish of sausage link, bratwersts, and bacon fatty stips. for familystyle breakfast meal", "category": "food", "seed": 15} +{"prompt": "Still from a campy, grotesque 1980s Estonian commercial for tomato juice featuring a happy overweight family lavishly dining on the strange, terrible product, horror vibes", "category": "people", "seed": 17} +{"prompt": "green leaf like texture, macro, hyper realistic, unfocused, vegetable, background abstract ar 916", "category": "plants", "seed": 38} +{"prompt": "another image of a wizard with smoke thrown at him, in the style of mark lovett, tokina atx 1116mm f2.8 pro dx ii, matthias haker, realistic hyperdetailed portraits, glittery and shiny, john howe, historical", "category": "art", "seed": 47} +{"prompt": "a hot pink wonderland", "category": "indoor", "seed": 28} +{"prompt": "Beautiful Siberian cat sitting at the window, phorealistic, full frame, cinematic, looks like professional hdr photography, beautiful cinematic lighting, ultrahigh quality", "category": "animals", "seed": 31} +{"prompt": "A dramatic nightscape of a bustling city with glowing neon lights, captured from a rooftop vantage point using a Nikon Z7 II and a Nikon Z 2470mm f2.8 S lens.", "category": "vehicles", "seed": 21} +{"prompt": "a hyperrealistic underwater photo of a mermaid under the ocean, glowy ambient, ar 169", "category": "fashion", "seed": 36} +{"prompt": "a 2d frog, top view, made only from bubbles, every bubble is an oval bubble,stretched along various curves", "category": "logo", "seed": 27} +{"prompt": "The cold breath of the north. Deep dark tones, dynamic light, hdr", "category": "landscape", "seed": 16} +{"prompt": "A photorealistic digital artwork featuring a zombieburger as the centerpiece of a postapocalyptic wasteland. Medium digital art. Colors muted, desaturated tones with a focus on grays and browns to convey the bleakness of the environment. Lighting harsh, with a single spotlight directed at the burger as if its the only thing worth fighting for in this world. Composition a wideangle lens capturing the burger in the midground, surrounded by hordes of zombies in the foreground and the ruins of a city in the background. The burger should be presented in all its delicious glory, with the toppings and sauces dripping down the sides, tempting even the undead to take a bite.", "category": "food", "seed": 26} diff --git a/tasks/svdquant-w4a4-psnr/tests/intermediate.sh b/tasks/svdquant-w4a4-psnr/tests/intermediate.sh new file mode 100644 index 0000000000000000000000000000000000000000..d1cc20034494d086edd6d394635411a59056dc61 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/intermediate.sh @@ -0,0 +1,13 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json +chmod -R go-rwx /tests + +cd /tests +GRADER_SPLIT=intermediate /usr/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/bin/python3 /tests/compute_reward.py +exit 0 diff --git a/tasks/svdquant-w4a4-psnr/tests/requirements-verifier.txt b/tasks/svdquant-w4a4-psnr/tests/requirements-verifier.txt new file mode 100644 index 0000000000000000000000000000000000000000..04676c89520bf969f2cb861ad1811f46d8370700 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/requirements-verifier.txt @@ -0,0 +1,2 @@ +pytest==8.3.5 +pytest-json-ctrf==0.3.5 diff --git a/tasks/svdquant-w4a4-psnr/tests/requirements.txt b/tasks/svdquant-w4a4-psnr/tests/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3eb7b818f0c853966150e30553d1f8e371f94a42 --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/requirements.txt @@ -0,0 +1,4 @@ +diffusers==0.39.0 +huggingface-hub==1.11.0 +safetensors==0.8.0 +sentencepiece==0.2.1 diff --git a/tasks/svdquant-w4a4-psnr/tests/test.sh b/tasks/svdquant-w4a4-psnr/tests/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..abea8f5274b8cc21ad204b71c2650de364385eff --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/test.sh @@ -0,0 +1,13 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json +chmod -R go-rwx /tests + +cd /tests +GRADER_SPLIT=final /usr/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/bin/python3 /tests/compute_reward.py +exit 0 diff --git a/tasks/svdquant-w4a4-psnr/tests/test_state.py b/tasks/svdquant-w4a4-psnr/tests/test_state.py new file mode 100644 index 0000000000000000000000000000000000000000..4c0746421c0a5c4fb8490a4ab4beedfdb65c15bb --- /dev/null +++ b/tasks/svdquant-w4a4-psnr/tests/test_state.py @@ -0,0 +1,54 @@ +import json +import os +import random +import sys +from pathlib import Path + +import numpy as np +import torch + +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +torch.cuda.manual_seed_all(0) +torch.set_num_threads(1) +torch.use_deterministic_algorithms(True) +torch.backends.cudnn.deterministic = True +torch.backends.cudnn.benchmark = False +torch.backends.cuda.matmul.allow_tf32 = False + +sys.path.insert(0, str(Path(__file__).resolve().parent)) +import grader_core + +METRIC_PATH = Path("/logs/verifier/metric.json") + + +def invalid(reason): + return { + "valid": False, + "metric_name": "mean_psnr_db", + "metric": None, + "reward": 0.0, + "reason": reason, + } + + +def test_submission(): + split = os.environ.get("GRADER_SPLIT", "final") + if split not in {"intermediate", "final"}: + result = invalid("invalid grader split") + else: + entry = Path("/app/output/quantize.py") + prompts = Path("/tests/hidden_data") / split / "prompts.jsonl" + try: + print(f"trusted CUDA device_count={__import__('torch').cuda.device_count()}") + if set(entry.parent.iterdir()) != {entry}: + raise ValueError("only /app/output/quantize.py may be created or modified") + if not entry.is_file() or entry.is_symlink() or entry.stat().st_size == 0: + raise FileNotFoundError("missing or empty /app/output/quantize.py") + result = grader_core.score(entry, prompts) + except Exception as exc: + print(f"grading failure: {type(exc).__name__}: {exc}") + result = invalid(f"{type(exc).__name__}: {exc}") + METRIC_PATH.write_text(json.dumps(result, sort_keys=True) + "\n") + assert result.get("valid") is True, result.get("reason", "invalid submission") diff --git a/tasks/tgat-milp-branching-node-count/environment/Dockerfile b/tasks/tgat-milp-branching-node-count/environment/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..83b4e64e7ba1c3767fbe5f49f0db30d15ecbeb37 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/Dockerfile @@ -0,0 +1,44 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +RUN set -eu; \ + if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \ + apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \ + fi; \ + rm -f /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \ + echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \ + apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1; \ + rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/* + + +ENTRYPOINT [] +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 + +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + git=1:2.39.5-0+deb12u3 \ + && rm -rf /var/lib/apt/lists/* + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \ + && rm /tmp/requirements.txt + +WORKDIR /app +COPY ./app /app + +RUN mkdir -p /app/submission \ + && chown -R agent:agent /app \ + && find /app -exec touch -d @1700000000 {} + \ + && git config --system user.email "agent@localhost" \ + && git config --system user.name "autoresearch agent" + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 +USER agent diff --git a/tasks/tgat-milp-branching-node-count/environment/app/README.md b/tasks/tgat-milp-branching-node-count/environment/app/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b51decba1185d9ff30288a0059f2291c2882c7b6 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/README.md @@ -0,0 +1,127 @@ +# Branching environment + +`/app/submission/` is the deliverable: it is the only thing that crosses to the +evaluation. The evaluation runs its own copy of everything else in this tree, so +changing a file here changes what you measure locally and nothing about how you +are scored. + +## What runs, and where + +One evaluation run solves one set covering instance with a pinned SCIP +configuration. The solver lives in its own process and counts the nodes; your +policy lives in a second process that never gets a solver object. At every +branching decision the solver process sends the state of the current node LP to +your process and reads back the index of the candidate to branch on. + +``` +python3 /app/rollout.py --seeds 0-9 --shifts 0,1 --jobs 4 +``` + +runs exactly that loop on instances generated from seeds 0..9, using +`/app/submission/policy.py`, and prints the node count of every run plus the +1-shifted geometric mean over them. Use `--submission DIR` to try another +directory. Instance seeds 0..1999 are yours; the graded instances come from 40 +seeds outside that range which are not in this container and are not guessable. + +Panels of a few dozen instances differ a lot in difficulty, so a single small +panel is a noisy estimate: compare policies on several disjoint panels. + +## Files + +| path | what it is | +|---|---| +| `/app/rollout.py` | the loop above | +| `/app/bnbenv/setcover.py` | instance generator and the pinned solver configuration | +| `/app/bnbenv/features.py` | builds the observation from the node LP | +| `/app/bnbenv/runner.py` | solver-side driver: one run, node count in, policy process out | +| `/app/bnbenv/host.py` | the process that loads `policy.py` and answers decisions | +| `/app/bnbenv/protocol.py` | the framing between those two processes | +| `/app/submission/policy.py` | your deliverable (a first-candidate stub to start from) | + +The generator is a Balas-Ho style set covering construction: + +- rows (covering constraints): 500 +- columns (binary variables): 1000 +- nonzero density: 0.05 +- objective coefficients: uniform on {1, ..., 100} +- every column appears in at least two rows + +`generate_instance(seed)` returns the costs and the constraint matrix in CSR +form. `build_model(seed, shift)` returns the configured `pyscipopt` model, +where `shift` sets both `randomization/permutationseed` and +`randomization/randomseedshift`. + +## The observation + +`select(obs)` is called once per branching decision. `obs` is a dict whose numpy +arrays are C-contiguous and read-only. `n_cols` and `n_rows` are the column and +row counts of the **current node LP**, so `n_rows` includes cuts added at the +root and both counts can differ from 1000 and 500. + +| key | type | meaning | +|---|---|---| +| `var_feats` | float32 `(n_cols, 14)` | one row per LP column, layout below | +| `cons_feats` | float32 `(n_rows, 6)` | one row per LP row, layout below | +| `edge_index` | int32 `(2, nnz)` | `edge_index[0]` row positions, `edge_index[1]` column positions of the node LP nonzeros | +| `edge_vals` | float32 `(nnz,)` | the coefficient of that nonzero, divided by the L2 norm of its row | +| `cand_idx` | int32 `(k,)` | LP column positions of the fractional branching candidates | +| `cand_feats` | float32 `(k, 14)` | `var_feats[cand_idx]` | +| `depth` | int | depth of the current node | +| `n_nodes` | int | nodes processed so far in this run | +| `n_lps` | int | node LPs solved so far in this run | +| `dual_bound` | float | current global dual bound | +| `primal_bound` | float | current incumbent objective, `inf` if none | +| `gap` | float | current relative gap | + +`var_feats` columns: + +| # | name | meaning | +|---|---|---| +| 0 | `obj_coeff` | objective coefficient / L2 norm of the objective over LP columns | +| 1 | `lb_local` | local lower bound | +| 2 | `ub_local` | local upper bound | +| 3 | `sol_val` | value in the node LP solution | +| 4 | `sol_frac` | `sol_val - floor(sol_val)` | +| 5 | `sol_at_lb` | 1.0 if the LP value is at the local lower bound | +| 6 | `sol_at_ub` | 1.0 if the LP value is at the local upper bound | +| 7 | `reduced_cost` | reduced cost / L2 norm of the objective | +| 8 | `basis_lower` | basis status one-hot: at lower | +| 9 | `basis_basic` | basis status one-hot: basic | +| 10 | `basis_upper` | basis status one-hot: at upper | +| 11 | `basis_zero` | basis status one-hot: free at zero | +| 12 | `age_norm` | column age / (1 + `n_lps`) | +| 13 | `degree_norm` | number of LP rows the column appears in / `n_rows` | + +`cons_feats` columns: + +| # | name | meaning | +|---|---|---| +| 0 | `bias` | row bound / L2 norm of the row (right-hand side when finite, else left-hand side) | +| 1 | `has_lhs` | 1.0 if the row has a finite left-hand side | +| 2 | `is_tight` | 1.0 if the row is tight at the node LP solution | +| 3 | `dual_norm` | dual value / (row norm * objective norm) | +| 4 | `age_norm` | row age / (1 + `n_lps`) | +| 5 | `obj_cos_sim` | cosine similarity between the row and the objective | + +## Installed + +- `numpy` 2.3.4 +- `scipy` 1.16.3 +- `scikit-learn` 1.7.2 +- `torch` 2.9.1+cpu +- `pyscipopt` 6.2.1 (SCIP 10.0) +- no network + +`pyscipopt` is unrestricted in this container, so you can drive the solver +yourself while you work. Inside the policy process, at decision time, three +things are refused, and `/app/rollout.py` refuses them here too so that a local +run behaves like a graded one: + +- importing `pyscipopt`, `gurobipy`, `mip`, `pulp`, `highspy`, `ortools`, + `cplex`, `docplex`, `xpress` or `cylp`; +- loading a compiled extension module or shared library from outside the + installed Python stack, so a native library carried in with the submission + cannot be loaded; +- starting another process. + +The first two raise, and any of the three ends the run at the cap. diff --git a/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/__init__.py b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/features.py b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/features.py new file mode 100644 index 0000000000000000000000000000000000000000..95e3114d6a326d8f3bc4e6b4654aaeede6383856 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/features.py @@ -0,0 +1,154 @@ +from __future__ import annotations + +import numpy as np + +VAR_FEATURE_NAMES = ( + "obj_coeff", + "lb_local", + "ub_local", + "sol_val", + "sol_frac", + "sol_at_lb", + "sol_at_ub", + "reduced_cost", + "basis_lower", + "basis_basic", + "basis_upper", + "basis_zero", + "age_norm", + "degree_norm", +) + +CONS_FEATURE_NAMES = ( + "bias", + "has_lhs", + "is_tight", + "dual_norm", + "age_norm", + "obj_cos_sim", +) + +N_VAR_FEATURES = len(VAR_FEATURE_NAMES) +N_CONS_FEATURES = len(CONS_FEATURE_NAMES) + +_BASIS = {"lower": 8, "basic": 9, "upper": 10, "zero": 11} + + +def extract(model, cands, cands_sol): + """Build the observation dict for the current node of `model`. + + `cands` are the fractional branching candidates (pyscipopt Variables) and + `cands_sol` their LP values, as returned by getLPBranchCands(). + """ + cols = model.getLPColsData() + rows = model.getLPRowsData() + n_cols = len(cols) + n_rows = len(rows) + n_lps = model.getNLPs() + inv_lps = 1.0 / (1.0 + n_lps) + + obj = np.empty(n_cols, dtype=np.float64) + lb = np.empty(n_cols, dtype=np.float64) + ub = np.empty(n_cols, dtype=np.float64) + sol = np.empty(n_cols, dtype=np.float64) + age = np.empty(n_cols, dtype=np.float64) + redcost = np.empty(n_cols, dtype=np.float64) + basis = np.zeros((n_cols, 4), dtype=np.float32) + lp_pos_of = {} + for k, col in enumerate(cols): + var = col.getVar() + obj[k] = col.getObjCoeff() + lb[k] = col.getLb() + ub[k] = col.getUb() + sol[k] = col.getPrimsol() + age[k] = col.getAge() + redcost[k] = model.getVarRedcost(var) + slot = _BASIS.get(col.getBasisStatus()) + if slot is not None: + basis[k, slot - 8] = 1.0 + lp_pos_of[var.getIndex()] = k + + obj_norm = float(np.linalg.norm(obj)) + if obj_norm <= 0.0: + obj_norm = 1.0 + + edge_rows = [] + edge_cols = [] + edge_vals = [] + cons = np.zeros((n_rows, N_CONS_FEATURES), dtype=np.float32) + for r, row in enumerate(rows): + row_cols = row.getCols() + row_vals = row.getVals() + norm = float(row.getNorm()) + if norm <= 0.0: + norm = 1.0 + constant = row.getConstant() + lhs = row.getLhs() + rhs = row.getRhs() + activity = 0.0 + dot = 0.0 + for col, val in zip(row_cols, row_vals): + pos = col.getLPPos() + if pos < 0: + continue + edge_rows.append(r) + edge_cols.append(pos) + edge_vals.append(val / norm) + activity += val * sol[pos] + dot += val * obj[pos] + has_lhs = 1.0 if lhs > -1e19 else 0.0 + bound = rhs if rhs < 1e19 else lhs + if bound > 1e19 or bound < -1e19: + bound = 0.0 + tight = 0.0 + act = activity + constant + if (rhs < 1e19 and abs(act - rhs) < 1e-6) or \ + (lhs > -1e19 and abs(act - lhs) < 1e-6): + tight = 1.0 + cons[r, 0] = (bound - constant) / norm + cons[r, 1] = has_lhs + cons[r, 2] = tight + cons[r, 3] = row.getDualsol() / (norm * obj_norm) + cons[r, 4] = row.getAge() * inv_lps + cons[r, 5] = dot / (norm * obj_norm) + + edge_index = np.empty((2, len(edge_rows)), dtype=np.int32) + edge_index[0] = np.asarray(edge_rows, dtype=np.int32) + edge_index[1] = np.asarray(edge_cols, dtype=np.int32) + edge_values = np.asarray(edge_vals, dtype=np.float32) + degree = np.bincount(edge_index[1], minlength=n_cols).astype(np.float32) + + var_feats = np.empty((n_cols, N_VAR_FEATURES), dtype=np.float32) + var_feats[:, 0] = obj / obj_norm + var_feats[:, 1] = lb + var_feats[:, 2] = ub + var_feats[:, 3] = sol + var_feats[:, 4] = sol - np.floor(sol) + var_feats[:, 5] = (np.abs(sol - lb) < 1e-9).astype(np.float32) + var_feats[:, 6] = (np.abs(sol - ub) < 1e-9).astype(np.float32) + var_feats[:, 7] = redcost / obj_norm + var_feats[:, 8:12] = basis + var_feats[:, 12] = age * inv_lps + var_feats[:, 13] = degree / max(1, n_rows) + + cand_idx = np.array([lp_pos_of[v.getIndex()] for v in cands], + dtype=np.int32) + cand_feats = np.ascontiguousarray(var_feats[cand_idx]) + + primal = model.getPrimalbound() + dual = model.getDualbound() + obs = { + "var_feats": np.ascontiguousarray(var_feats), + "cons_feats": np.ascontiguousarray(cons), + "edge_index": np.ascontiguousarray(edge_index), + "edge_vals": np.ascontiguousarray(edge_values), + "cand_idx": cand_idx, + "cand_feats": cand_feats, + "depth": int(model.getDepth()), + "n_nodes": int(model.getNNodes()), + "n_lps": int(n_lps), + "dual_bound": float(dual), + "primal_bound": float(primal), + "gap": float(model.getGap()), + } + return obs diff --git a/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/host.py b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/host.py new file mode 100644 index 0000000000000000000000000000000000000000..34354da1ef82373dc2a075224cf8334f6ccb95c3 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/host.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import importlib.util +import io +import os +import sys +import traceback + +import numpy as np + +from . import protocol + +BLOCKED_MODULES = ( + "pyscipopt", + "gurobipy", + "mip", + "pulp", + "highspy", + "ortools", + "cplex", + "docplex", + "xpress", + "cylp", +) + +MAX_ERROR_CHARS = 400 + +NATIVE_ROOTS = ("/usr/local/lib/", "/usr/lib/", "/lib/") +NATIVE_SUFFIXES = (".so", ".pyd", ".dylib") + + +def _audit(event, args): + path = None + if event == "import" and len(args) > 1: + path = args[1] + elif event in ("ctypes.dlopen", "ctypes.LoadLibrary") and args: + path = args[0] + if not isinstance(path, str) or not path: + return + if path.endswith(NATIVE_SUFFIXES) or ".so." in path: + if not path.startswith(NATIVE_ROOTS): + raise PermissionError( + f"loading the native module {path} is not allowed inside the " + "branching policy process") + + +class _BlockSolvers: + """meta_path finder that refuses exactly the modules listed above.""" + + def find_module(self, fullname, path=None): + return None + + def find_spec(self, fullname, path=None, target=None): + root = fullname.split(".", 1)[0] + if root in BLOCKED_MODULES: + raise ImportError( + f"import of {root!r} is blocked inside the branching policy " + "process") + return None + + +def _load_policy_module(submission_dir, entry_point): + path = os.path.join(submission_dir, entry_point) + if not os.path.isfile(path): + raise FileNotFoundError(f"missing entry point {path}") + if os.path.getsize(path) == 0: + raise ValueError(f"empty entry point {path}") + if submission_dir not in sys.path: + sys.path.insert(0, submission_dir) + spec = importlib.util.spec_from_file_location("agent_policy", path) + if spec is None or spec.loader is None: + raise ImportError(f"cannot load {path}") + module = importlib.util.module_from_spec(spec) + sys.modules["agent_policy"] = module + spec.loader.exec_module(module) + return module + + +def _freeze(obs): + for value in obs.values(): + if isinstance(value, np.ndarray): + value.setflags(write=False) + return obs + + +def _short(exc): + text = f"{type(exc).__name__}: {exc}" + return text[:MAX_ERROR_CHARS].replace("\n", " ") + + +def main(): + try: + import torch + torch.set_num_threads(1) + except ImportError: + pass + import numpy.linalg + sys.meta_path.insert(0, _BlockSolvers()) + sys.addaudithook(_audit) + inp = io.open(0, "rb", closefd=False) + out = io.open(int(os.environ["BNB_REPLY_FD"]), "wb", closefd=False) + + module = None + policy = None + while True: + try: + frame = protocol.read_obs(inp) + except Exception: + return 1 + if frame is None: + return 0 + cmd = frame.get("cmd") + if cmd == "quit": + return 0 + if cmd == "load": + try: + module = _load_policy_module(frame["submission_dir"], + frame["entry_point"]) + loader = getattr(module, "load", None) + if loader is not None: + loader() + except BaseException as exc: + traceback.print_exc(file=sys.stderr) + protocol.write_decision(out, "err " + _short(exc)) + return 0 + protocol.write_decision(out, "ready") + elif cmd == "new": + try: + policy = module.Policy() + except BaseException as exc: + traceback.print_exc(file=sys.stderr) + protocol.write_decision(out, "err " + _short(exc)) + return 0 + protocol.write_decision(out, "ok") + elif cmd == "select": + obs = _freeze(frame["obs"]) + n_cands = len(obs["cand_idx"]) + try: + choice = policy.select(obs) + except BaseException as exc: + traceback.print_exc(file=sys.stderr) + protocol.write_decision(out, "err " + _short(exc)) + return 0 + if isinstance(choice, bool) or not isinstance( + choice, (int, np.integer)): + protocol.write_decision( + out, f"err select returned {type(choice).__name__}, " + "expected int") + return 0 + index = int(choice) + if not 0 <= index < n_cands: + protocol.write_decision( + out, f"err select returned {index}, outside " + f"[0, {n_cands})") + return 0 + protocol.write_decision(out, f"i {index}") + else: + protocol.write_decision(out, "err unknown command") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/protocol.py b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..8b7292afbc065d8a27dd4c0a587c04688265a220 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/protocol.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +import pickle +import struct + +_HEADER = struct.Struct(" MAX_FRAME: + raise ValueError("frame too large") + body = _read_exactly(stream, size) + if body is None: + return None + return pickle.loads(body) + + +def write_decision(stream, text): + data = (text + "\n").encode("ascii", "replace") + stream.write(data) + stream.flush() + + +def _read_exactly(stream, size): + chunks = [] + remaining = size + while remaining: + chunk = stream.read(remaining) + if not chunk: + return None + chunks.append(chunk) + remaining -= len(chunk) + return b"".join(chunks) diff --git a/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/runner.py b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..d0c930195d08ac3efd2a28cb2f4a7157ada14ae9 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/runner.py @@ -0,0 +1,279 @@ +from __future__ import annotations + +import os +import select +import signal +import subprocess +import sys +import time +from pathlib import Path + +from pyscipopt import SCIP_RESULT, Branchrule + +from . import features, protocol +from .setcover import NODE_LIMIT, TIME_LIMIT, build_model + +ENTRY_POINT = "policy.py" +STARTUP_LIMIT = 30.0 +DECISION_READ_CHUNK = 4096 + + +class PolicyChannel: + """Owns the policy subprocess and the framed request/response cycle.""" + + def __init__(self, submission_dir, host_pkg_root, python_exe=None, + launch_prefix=(), stderr_to=None, entry_point=ENTRY_POINT, + drop_to_uid=None): + self.submission_dir = str(Path(submission_dir).resolve()) + self.entry_point = entry_point + env = { + "PATH": "/usr/local/bin:/usr/bin:/bin", + "PYTHONPATH": str(Path(host_pkg_root).resolve()), + "PYTHONSAFEPATH": "1", + "PYTHONHASHSEED": "0", + "PYTHONDONTWRITEBYTECODE": "1", + "HOME": "/home/agent", + "TMPDIR": "/tmp", + "OMP_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "NUMEXPR_NUM_THREADS": "1", + } + read_fd, write_fd = os.pipe() + os.set_inheritable(write_fd, True) + env["BNB_REPLY_FD"] = str(write_fd) + cmd = list(launch_prefix) + [ + python_exe or sys.executable, "-c", + "from bnbenv.host import main; raise SystemExit(main())", + ] + + def _drop(): + """Drop to the unprivileged uid before exec, in the child.""" + os.setgroups([]) + os.setgid(int(drop_to_uid)) + os.setuid(int(drop_to_uid)) + + self.proc = subprocess.Popen( + cmd, + stdin=subprocess.PIPE, + stdout=subprocess.DEVNULL, + stderr=stderr_to if stderr_to is not None else subprocess.DEVNULL, + env=env, + pass_fds=(write_fd,), + start_new_session=True, + preexec_fn=(_drop if drop_to_uid and not launch_prefix else None), + cwd="/tmp", + ) + os.close(write_fd) + self.reply = os.fdopen(read_fd, "rb") + self.error = None + + def _send(self, payload): + protocol.write_obs(self.proc.stdin, payload) + + def _recv(self, budget): + """Read one newline-terminated reply, or None on timeout/death.""" + deadline = time.monotonic() + budget + buf = bytearray() + fd = self.reply.fileno() + while b"\n" not in buf: + remaining = deadline - time.monotonic() + if remaining <= 0: + self.error = "policy process exceeded its wall-clock budget" + return None + ready, _, _ = select.select([fd], [], [], min(remaining, 1.0)) + if not ready: + continue + chunk = os.read(fd, DECISION_READ_CHUNK) + if not chunk: + self.error = "policy process exited without answering" + return None + buf.extend(chunk) + if len(buf) > 1 << 16: + self.error = "policy process wrote an oversized reply" + return None + return bytes(buf).split(b"\n", 1)[0].decode("ascii", "replace") + + def start(self, budget=STARTUP_LIMIT): + self._send({"cmd": "load", "submission_dir": self.submission_dir, + "entry_point": self.entry_point}) + reply = self._recv(budget) + if reply != "ready": + self.error = self.error or f"load failed ({reply})" + return False + self._send({"cmd": "new"}) + reply = self._recv(budget) + if reply != "ok": + self.error = self.error or f"Policy() failed ({reply})" + return False + return True + + def decide(self, obs, budget, n_cands): + try: + self._send({"cmd": "select", "obs": obs}) + except (BrokenPipeError, OSError): + self.error = "policy process closed the channel" + return None + reply = self._recv(budget) + if reply is None: + return None + if not reply.startswith("i "): + self.error = f"policy error ({reply[:200]})" + return None + try: + index = int(reply[2:]) + except ValueError: + self.error = f"unparseable decision ({reply[:80]})" + return None + if not 0 <= index < n_cands: + self.error = f"decision {index} outside [0, {n_cands})" + return None + return index + + def extra_processes(self): + """Live processes in the policy session other than the policy itself.""" + try: + sid = os.getsid(self.proc.pid) + except (ProcessLookupError, PermissionError, OSError): + return 0 + extra = 0 + for entry in os.listdir("/proc"): + if not entry.isdigit(): + continue + pid = int(entry) + if pid == self.proc.pid: + continue + try: + if os.getsid(pid) == sid: + extra += 1 + except (ProcessLookupError, PermissionError, OSError): + continue + return extra + + def close(self): + try: + self._send({"cmd": "quit"}) + except Exception: + pass + try: + self.proc.stdin.close() + except Exception: + pass + try: + self.proc.wait(timeout=5) + except Exception: + try: + os.killpg(os.getpgid(self.proc.pid), signal.SIGKILL) + except Exception: + pass + try: + self.proc.wait(timeout=5) + except Exception: + pass + try: + self.reply.close() + except Exception: + pass + + +class HostedBranchrule(Branchrule): + """Hands every branching decision of this solve to the policy process.""" + + def __init__(self, channel, deadline): + super().__init__() + self.channel = channel + self.deadline = deadline + self.decisions = 0 + self.failure = None + + def branchexeclp(self, allowaddcons): + if self.failure is not None: + return {"result": SCIP_RESULT.DIDNOTRUN} + cands, cands_sol, _frac, ncands, _nprio, _nimpl = \ + self.model.getLPBranchCands() + if ncands == 0: + return {"result": SCIP_RESULT.DIDNOTRUN} + cands = list(cands)[:ncands] + obs = features.extract(self.model, cands, cands_sol) + budget = self.deadline - time.monotonic() + if budget <= 0: + self.failure = "run exceeded its wall-clock budget" + self.model.interruptSolve() + return {"result": SCIP_RESULT.DIDNOTRUN} + index = self.channel.decide(obs, budget, len(cands)) + if index is None: + self.failure = self.channel.error or "policy failed" + self.model.interruptSolve() + return {"result": SCIP_RESULT.DIDNOTRUN} + self.decisions += 1 + if self.decisions % 20 == 1 and self.channel.extra_processes(): + self.failure = "policy process spawned extra processes" + self.model.interruptSolve() + return {"result": SCIP_RESULT.DIDNOTRUN} + self.model.branchVar(cands[index]) + return {"result": SCIP_RESULT.BRANCHED} + + +def run_one(seed, shift, submission_dir, host_pkg_root, python_exe=None, + launch_prefix=(), stderr_to=None, node_limit=NODE_LIMIT, + time_limit=TIME_LIMIT, startup_timeout=STARTUP_LIMIT, + entry_point=ENTRY_POINT, drop_to_uid=None): + """Solve one instance with the submission in charge of variable selection. + + Returns a record with the node count charged for this run: the solver's own + total node count when it proved optimality, and `node_limit` otherwise. + """ + record = { + "seed": int(seed), + "shift": int(shift), + "nodes": int(node_limit), + "raw_nodes": None, + "status": "unstarted", + "proved_optimal": False, + "decisions": 0, + "solve_seconds": 0.0, + "error": None, + "extra_processes": 0, + } + if not (Path(submission_dir) / entry_point).is_file(): + record["status"] = "missing_entry_point" + record["error"] = f"no {entry_point} in {submission_dir}" + return record + + channel = PolicyChannel(submission_dir, host_pkg_root, + python_exe=python_exe, launch_prefix=launch_prefix, + stderr_to=stderr_to, entry_point=entry_point, + drop_to_uid=drop_to_uid) + try: + if not channel.start(budget=startup_timeout): + record["status"] = "startup_failed" + record["error"] = channel.error + return record + + model, _xs = build_model(seed, shift, node_limit=node_limit, + time_limit=time_limit) + started = time.monotonic() + rule = HostedBranchrule(channel, started + time_limit) + model.includeBranchrule(rule, "hosted", "policy-process branching", + priority=10_000_000, maxdepth=-1, + maxbounddist=1.0) + model.optimize() + record["solve_seconds"] = round(time.monotonic() - started, 3) + record["status"] = model.getStatus() + record["decisions"] = rule.decisions + raw_nodes = int(model.getNTotalNodes()) + record["raw_nodes"] = raw_nodes + record["extra_processes"] = channel.extra_processes() + proved = (rule.failure is None and model.getStatus() == "optimal" + and raw_nodes <= node_limit + and record["extra_processes"] == 0) + if rule.failure is not None: + record["error"] = rule.failure + elif record["extra_processes"] > 0: + record["error"] = "policy process spawned extra processes" + record["proved_optimal"] = bool(proved) + record["nodes"] = raw_nodes if proved else int(node_limit) + del model + return record + finally: + channel.close() diff --git a/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/setcover.py b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/setcover.py new file mode 100644 index 0000000000000000000000000000000000000000..5918d1c14ea49ebdc1f8daf382ad724b4c53a5ae --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/bnbenv/setcover.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +import numpy as np + +N_ROWS = 500 +N_COLS = 1000 +DENSITY = 0.05 +MAX_COEF = 100 + +NODE_LIMIT = 20000 +TIME_LIMIT = 120.0 + + +def generate_instance(seed, n_rows=N_ROWS, n_cols=N_COLS, density=DENSITY, + max_coef=MAX_COEF): + """Return (costs, row_indptr, row_indices) for one set covering instance. + + `costs` is an int array of length n_cols. The constraint matrix is returned + in CSR form over rows: row r covers columns + `row_indices[row_indptr[r]:row_indptr[r + 1]]`. + """ + rng = np.random.default_rng(int(seed)) + nnzrs = int(n_rows * n_cols * density) + if nnzrs // n_rows < 2 or nnzrs // n_cols < 2: + raise ValueError("density too low for the requested shape") + + indices = rng.integers(0, n_rows, size=nnzrs) + col_of_nnz = rng.integers(0, n_cols, size=nnzrs) + col_of_nnz[: 2 * n_cols] = np.repeat(np.arange(n_cols), 2) + _, col_nnzs = np.unique(col_of_nnz, return_counts=True) + indices[:n_rows] = rng.permutation(n_rows) + + i = 0 + indptr = [0] + for n in col_nnzs: + if i >= n_rows: + indices[i:i + n] = rng.choice(n_rows, size=n, replace=False) + elif i + n > n_rows: + remaining = np.setdiff1d(np.arange(n_rows), indices[i:n_rows], + assume_unique=True) + indices[n_rows:i + n] = rng.choice( + remaining, size=i + n - n_rows, replace=False) + i += n + indptr.append(i) + + costs = rng.integers(1, max_coef + 1, size=n_cols) + + col_indptr = np.asarray(indptr, dtype=np.int64) + row_of_nnz = np.asarray(indices[:i], dtype=np.int64) + col_ids = np.repeat(np.arange(n_cols, dtype=np.int64), np.diff(col_indptr)) + order = np.lexsort((col_ids, row_of_nnz)) + row_sorted = row_of_nnz[order] + row_indices = col_ids[order] + row_counts = np.bincount(row_sorted, minlength=n_rows) + row_indptr = np.concatenate(([0], np.cumsum(row_counts))).astype(np.int64) + return costs.astype(np.int64), row_indptr, row_indices + + +def build_model(seed, shift, node_limit=NODE_LIMIT, time_limit=TIME_LIMIT, + **gen_kwargs): + """Build the pinned pyscipopt Model for (instance seed, seed shift).""" + from pyscipopt import Model, quicksum + + costs, row_indptr, row_indices = generate_instance(seed, **gen_kwargs) + n_cols = len(costs) + + model = Model(f"setcover-{seed}") + model.hideOutput() + xs = [model.addVar(vtype="B", obj=float(costs[j]), name=f"x{j}") + for j in range(n_cols)] + for r in range(len(row_indptr) - 1): + cols = row_indices[row_indptr[r]:row_indptr[r + 1]] + model.addCons(quicksum(xs[int(j)] for j in cols) >= 1, name=f"c{r}") + + model.setIntParam("separating/maxrounds", 0) + model.setIntParam("presolving/maxrestarts", 0) + model.setIntParam("randomization/permutationseed", int(shift)) + model.setIntParam("randomization/randomseedshift", int(shift)) + model.setIntParam("timing/clocktype", 2) + model.setLongintParam("limits/nodes", int(node_limit)) + model.setRealParam("limits/time", float(time_limit)) + model.setParam("display/verblevel", 0) + return model, xs diff --git a/tasks/tgat-milp-branching-node-count/environment/app/rollout.py b/tasks/tgat-milp-branching-node-count/environment/app/rollout.py new file mode 100644 index 0000000000000000000000000000000000000000..094c1da53bc80dab6041093c7fea3478277c8f2c --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/rollout.py @@ -0,0 +1,77 @@ +from __future__ import annotations + +import argparse +import json +import math +import sys +from concurrent.futures import ProcessPoolExecutor +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from bnbenv import runner +from bnbenv.runner import STARTUP_LIMIT +from bnbenv.setcover import NODE_LIMIT, TIME_LIMIT + +PKG_ROOT = str(Path(__file__).resolve().parent) + + +def parse_ints(text): + out = [] + for part in text.split(","): + part = part.strip() + if not part: + continue + if "-" in part[1:]: + lo, hi = part.split("-", 1) + out.extend(range(int(lo), int(hi) + 1)) + else: + out.append(int(part)) + return out + + +def _run(job): + seed, shift, submission, time_limit, startup = job + return runner.run_one(seed, shift, submission, PKG_ROOT, + time_limit=time_limit, startup_timeout=startup, + stderr_to=None) + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--seeds", default="0-4", + help="instance seeds, e.g. '0-19' or '0,3,7'") + ap.add_argument("--shifts", default="0,1", + help="solver seed shifts, e.g. '0,1'") + ap.add_argument("--submission", default="/app/submission") + ap.add_argument("--jobs", type=int, default=2) + ap.add_argument("--time-limit", type=float, default=TIME_LIMIT) + ap.add_argument("--startup-limit", type=float, default=STARTUP_LIMIT) + ap.add_argument("--json", default=None, help="also write records here") + args = ap.parse_args() + + jobs = [(s, k, args.submission, args.time_limit, args.startup_limit) + for s in parse_ints(args.seeds) for k in parse_ints(args.shifts)] + records = [] + with ProcessPoolExecutor(max_workers=max(1, args.jobs)) as pool: + for rec in pool.map(_run, jobs): + records.append(rec) + print(f"seed={rec['seed']} shift={rec['shift']} " + f"nodes={rec['nodes']} status={rec['status']} " + f"decisions={rec['decisions']} " + f"secs={rec['solve_seconds']}" + + (f" error={rec['error']}" if rec["error"] else ""), + flush=True) + + nodes = [r["nodes"] for r in records] + metric = math.exp(sum(math.log(n + 1.0) for n in nodes) / len(nodes)) - 1.0 + capped = sum(1 for r in records if not r["proved_optimal"]) + print(f"\nruns={len(nodes)} counted_at_cap={capped} " + f"(cap={NODE_LIMIT} nodes / {TIME_LIMIT:.0f}s)") + print(f"1-shifted geometric mean of nodes = {metric:.2f}") + if args.json: + Path(args.json).write_text(json.dumps(records, indent=1)) + + +if __name__ == "__main__": + main() diff --git a/tasks/tgat-milp-branching-node-count/environment/app/submission/policy.py b/tasks/tgat-milp-branching-node-count/environment/app/submission/policy.py new file mode 100644 index 0000000000000000000000000000000000000000..5914b632bc5c4b3141fc877d7b2b9acd7b527522 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/app/submission/policy.py @@ -0,0 +1,3 @@ +class Policy: + def select(self, obs): + return 0 diff --git a/tasks/tgat-milp-branching-node-count/environment/requirements.txt b/tasks/tgat-milp-branching-node-count/environment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a68a0bf90a8e747157198775aa58bb0fd3f472f --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/environment/requirements.txt @@ -0,0 +1,18 @@ +--index-url https://download.pytorch.org/whl/cpu +--extra-index-url https://pypi.org/simple +numpy==2.3.4 +scipy==1.16.3 +scikit-learn==1.7.2 +pyscipopt==6.2.1 +torch==2.9.1+cpu +filelock==3.32.3 +fsspec==2026.7.0 +jinja2==3.1.6 +joblib==1.5.3 +markupsafe==3.0.3 +mpmath==1.3.0 +networkx==3.6.1 +setuptools==84.0.0 +sympy==1.14.0 +threadpoolctl==3.6.0 +typing_extensions==4.16.0 diff --git a/tasks/tgat-milp-branching-node-count/hints/hint.md b/tasks/tgat-milp-branching-node-count/hints/hint.md new file mode 100644 index 0000000000000000000000000000000000000000..8510bbe5bc28b2bd981efe4e7bac6f5a45caa16d --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/hints/hint.md @@ -0,0 +1 @@ +Reconstruct the node LP from the observation and run real strong branching at grade time with the bundled linear-programming solver, scoring by the product of child bound improvements. Make it self-throttling: shortlist by a cheap score, cap time per decision and cumulatively, fall back when the remaining wall clock is low, and branch fully only near the root - a capped run is charged the full node cap and the score is a geometric mean. Disable presolve on the tiny child LPs, and accumulate per-variable pseudocosts across the instance as the cheap rule. diff --git a/tasks/tgat-milp-branching-node-count/tests/Dockerfile b/tasks/tgat-milp-branching-node-count/tests/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..12dd31a81502a2bf7d7b3a504e57e28ac9a061f1 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/Dockerfile @@ -0,0 +1,37 @@ +FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb + +ENTRYPOINT [] +ENV PYTHONPATH= +ENV PYTHONSAFEPATH=1 +ENV DEBIAN_FRONTEND=noninteractive \ + PYTHONHASHSEED=0 \ + PIP_NO_CACHE_DIR=1 \ + PIP_DISABLE_PIP_VERSION_CHECK=1 \ + OMP_NUM_THREADS=1 \ + MKL_NUM_THREADS=1 \ + OPENBLAS_NUM_THREADS=1 + +RUN useradd --create-home --uid 1001 agent + +COPY ./requirements.txt /tmp/requirements.txt +COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt +RUN /usr/local/bin/python3 -m pip install --no-cache-dir \ + -r /tmp/requirements.txt -r /tmp/requirements-verifier.txt \ + && rm /tmp/requirements.txt /tmp/requirements-verifier.txt + +RUN mkdir -p /app/submission /logs/verifier + +COPY ./bnbenv /opt/bnbenv + +COPY . /tests + +RUN chown -R root:root /tests \ + && chmod 700 /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \ + && chmod +x /tests/test.sh /tests/intermediate.sh \ + && find /tests -exec touch -d @1700000000 {} + \ + && chmod 0700 /usr/local/lib/python3.13/site-packages/pyscipopt + +WORKDIR /tests + +ENV HF_HUB_OFFLINE=1 +ENV TRANSFORMERS_OFFLINE=1 diff --git a/tasks/tgat-milp-branching-node-count/tests/bnbenv/__init__.py b/tasks/tgat-milp-branching-node-count/tests/bnbenv/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tasks/tgat-milp-branching-node-count/tests/bnbenv/features.py b/tasks/tgat-milp-branching-node-count/tests/bnbenv/features.py new file mode 100644 index 0000000000000000000000000000000000000000..95e3114d6a326d8f3bc4e6b4654aaeede6383856 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/bnbenv/features.py @@ -0,0 +1,154 @@ +from __future__ import annotations + +import numpy as np + +VAR_FEATURE_NAMES = ( + "obj_coeff", + "lb_local", + "ub_local", + "sol_val", + "sol_frac", + "sol_at_lb", + "sol_at_ub", + "reduced_cost", + "basis_lower", + "basis_basic", + "basis_upper", + "basis_zero", + "age_norm", + "degree_norm", +) + +CONS_FEATURE_NAMES = ( + "bias", + "has_lhs", + "is_tight", + "dual_norm", + "age_norm", + "obj_cos_sim", +) + +N_VAR_FEATURES = len(VAR_FEATURE_NAMES) +N_CONS_FEATURES = len(CONS_FEATURE_NAMES) + +_BASIS = {"lower": 8, "basic": 9, "upper": 10, "zero": 11} + + +def extract(model, cands, cands_sol): + """Build the observation dict for the current node of `model`. + + `cands` are the fractional branching candidates (pyscipopt Variables) and + `cands_sol` their LP values, as returned by getLPBranchCands(). + """ + cols = model.getLPColsData() + rows = model.getLPRowsData() + n_cols = len(cols) + n_rows = len(rows) + n_lps = model.getNLPs() + inv_lps = 1.0 / (1.0 + n_lps) + + obj = np.empty(n_cols, dtype=np.float64) + lb = np.empty(n_cols, dtype=np.float64) + ub = np.empty(n_cols, dtype=np.float64) + sol = np.empty(n_cols, dtype=np.float64) + age = np.empty(n_cols, dtype=np.float64) + redcost = np.empty(n_cols, dtype=np.float64) + basis = np.zeros((n_cols, 4), dtype=np.float32) + lp_pos_of = {} + for k, col in enumerate(cols): + var = col.getVar() + obj[k] = col.getObjCoeff() + lb[k] = col.getLb() + ub[k] = col.getUb() + sol[k] = col.getPrimsol() + age[k] = col.getAge() + redcost[k] = model.getVarRedcost(var) + slot = _BASIS.get(col.getBasisStatus()) + if slot is not None: + basis[k, slot - 8] = 1.0 + lp_pos_of[var.getIndex()] = k + + obj_norm = float(np.linalg.norm(obj)) + if obj_norm <= 0.0: + obj_norm = 1.0 + + edge_rows = [] + edge_cols = [] + edge_vals = [] + cons = np.zeros((n_rows, N_CONS_FEATURES), dtype=np.float32) + for r, row in enumerate(rows): + row_cols = row.getCols() + row_vals = row.getVals() + norm = float(row.getNorm()) + if norm <= 0.0: + norm = 1.0 + constant = row.getConstant() + lhs = row.getLhs() + rhs = row.getRhs() + activity = 0.0 + dot = 0.0 + for col, val in zip(row_cols, row_vals): + pos = col.getLPPos() + if pos < 0: + continue + edge_rows.append(r) + edge_cols.append(pos) + edge_vals.append(val / norm) + activity += val * sol[pos] + dot += val * obj[pos] + has_lhs = 1.0 if lhs > -1e19 else 0.0 + bound = rhs if rhs < 1e19 else lhs + if bound > 1e19 or bound < -1e19: + bound = 0.0 + tight = 0.0 + act = activity + constant + if (rhs < 1e19 and abs(act - rhs) < 1e-6) or \ + (lhs > -1e19 and abs(act - lhs) < 1e-6): + tight = 1.0 + cons[r, 0] = (bound - constant) / norm + cons[r, 1] = has_lhs + cons[r, 2] = tight + cons[r, 3] = row.getDualsol() / (norm * obj_norm) + cons[r, 4] = row.getAge() * inv_lps + cons[r, 5] = dot / (norm * obj_norm) + + edge_index = np.empty((2, len(edge_rows)), dtype=np.int32) + edge_index[0] = np.asarray(edge_rows, dtype=np.int32) + edge_index[1] = np.asarray(edge_cols, dtype=np.int32) + edge_values = np.asarray(edge_vals, dtype=np.float32) + degree = np.bincount(edge_index[1], minlength=n_cols).astype(np.float32) + + var_feats = np.empty((n_cols, N_VAR_FEATURES), dtype=np.float32) + var_feats[:, 0] = obj / obj_norm + var_feats[:, 1] = lb + var_feats[:, 2] = ub + var_feats[:, 3] = sol + var_feats[:, 4] = sol - np.floor(sol) + var_feats[:, 5] = (np.abs(sol - lb) < 1e-9).astype(np.float32) + var_feats[:, 6] = (np.abs(sol - ub) < 1e-9).astype(np.float32) + var_feats[:, 7] = redcost / obj_norm + var_feats[:, 8:12] = basis + var_feats[:, 12] = age * inv_lps + var_feats[:, 13] = degree / max(1, n_rows) + + cand_idx = np.array([lp_pos_of[v.getIndex()] for v in cands], + dtype=np.int32) + cand_feats = np.ascontiguousarray(var_feats[cand_idx]) + + primal = model.getPrimalbound() + dual = model.getDualbound() + obs = { + "var_feats": np.ascontiguousarray(var_feats), + "cons_feats": np.ascontiguousarray(cons), + "edge_index": np.ascontiguousarray(edge_index), + "edge_vals": np.ascontiguousarray(edge_values), + "cand_idx": cand_idx, + "cand_feats": cand_feats, + "depth": int(model.getDepth()), + "n_nodes": int(model.getNNodes()), + "n_lps": int(n_lps), + "dual_bound": float(dual), + "primal_bound": float(primal), + "gap": float(model.getGap()), + } + return obs diff --git a/tasks/tgat-milp-branching-node-count/tests/bnbenv/host.py b/tasks/tgat-milp-branching-node-count/tests/bnbenv/host.py new file mode 100644 index 0000000000000000000000000000000000000000..34354da1ef82373dc2a075224cf8334f6ccb95c3 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/bnbenv/host.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import importlib.util +import io +import os +import sys +import traceback + +import numpy as np + +from . import protocol + +BLOCKED_MODULES = ( + "pyscipopt", + "gurobipy", + "mip", + "pulp", + "highspy", + "ortools", + "cplex", + "docplex", + "xpress", + "cylp", +) + +MAX_ERROR_CHARS = 400 + +NATIVE_ROOTS = ("/usr/local/lib/", "/usr/lib/", "/lib/") +NATIVE_SUFFIXES = (".so", ".pyd", ".dylib") + + +def _audit(event, args): + path = None + if event == "import" and len(args) > 1: + path = args[1] + elif event in ("ctypes.dlopen", "ctypes.LoadLibrary") and args: + path = args[0] + if not isinstance(path, str) or not path: + return + if path.endswith(NATIVE_SUFFIXES) or ".so." in path: + if not path.startswith(NATIVE_ROOTS): + raise PermissionError( + f"loading the native module {path} is not allowed inside the " + "branching policy process") + + +class _BlockSolvers: + """meta_path finder that refuses exactly the modules listed above.""" + + def find_module(self, fullname, path=None): + return None + + def find_spec(self, fullname, path=None, target=None): + root = fullname.split(".", 1)[0] + if root in BLOCKED_MODULES: + raise ImportError( + f"import of {root!r} is blocked inside the branching policy " + "process") + return None + + +def _load_policy_module(submission_dir, entry_point): + path = os.path.join(submission_dir, entry_point) + if not os.path.isfile(path): + raise FileNotFoundError(f"missing entry point {path}") + if os.path.getsize(path) == 0: + raise ValueError(f"empty entry point {path}") + if submission_dir not in sys.path: + sys.path.insert(0, submission_dir) + spec = importlib.util.spec_from_file_location("agent_policy", path) + if spec is None or spec.loader is None: + raise ImportError(f"cannot load {path}") + module = importlib.util.module_from_spec(spec) + sys.modules["agent_policy"] = module + spec.loader.exec_module(module) + return module + + +def _freeze(obs): + for value in obs.values(): + if isinstance(value, np.ndarray): + value.setflags(write=False) + return obs + + +def _short(exc): + text = f"{type(exc).__name__}: {exc}" + return text[:MAX_ERROR_CHARS].replace("\n", " ") + + +def main(): + try: + import torch + torch.set_num_threads(1) + except ImportError: + pass + import numpy.linalg + sys.meta_path.insert(0, _BlockSolvers()) + sys.addaudithook(_audit) + inp = io.open(0, "rb", closefd=False) + out = io.open(int(os.environ["BNB_REPLY_FD"]), "wb", closefd=False) + + module = None + policy = None + while True: + try: + frame = protocol.read_obs(inp) + except Exception: + return 1 + if frame is None: + return 0 + cmd = frame.get("cmd") + if cmd == "quit": + return 0 + if cmd == "load": + try: + module = _load_policy_module(frame["submission_dir"], + frame["entry_point"]) + loader = getattr(module, "load", None) + if loader is not None: + loader() + except BaseException as exc: + traceback.print_exc(file=sys.stderr) + protocol.write_decision(out, "err " + _short(exc)) + return 0 + protocol.write_decision(out, "ready") + elif cmd == "new": + try: + policy = module.Policy() + except BaseException as exc: + traceback.print_exc(file=sys.stderr) + protocol.write_decision(out, "err " + _short(exc)) + return 0 + protocol.write_decision(out, "ok") + elif cmd == "select": + obs = _freeze(frame["obs"]) + n_cands = len(obs["cand_idx"]) + try: + choice = policy.select(obs) + except BaseException as exc: + traceback.print_exc(file=sys.stderr) + protocol.write_decision(out, "err " + _short(exc)) + return 0 + if isinstance(choice, bool) or not isinstance( + choice, (int, np.integer)): + protocol.write_decision( + out, f"err select returned {type(choice).__name__}, " + "expected int") + return 0 + index = int(choice) + if not 0 <= index < n_cands: + protocol.write_decision( + out, f"err select returned {index}, outside " + f"[0, {n_cands})") + return 0 + protocol.write_decision(out, f"i {index}") + else: + protocol.write_decision(out, "err unknown command") + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tasks/tgat-milp-branching-node-count/tests/bnbenv/protocol.py b/tasks/tgat-milp-branching-node-count/tests/bnbenv/protocol.py new file mode 100644 index 0000000000000000000000000000000000000000..8b7292afbc065d8a27dd4c0a587c04688265a220 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/bnbenv/protocol.py @@ -0,0 +1,45 @@ +from __future__ import annotations + +import pickle +import struct + +_HEADER = struct.Struct(" MAX_FRAME: + raise ValueError("frame too large") + body = _read_exactly(stream, size) + if body is None: + return None + return pickle.loads(body) + + +def write_decision(stream, text): + data = (text + "\n").encode("ascii", "replace") + stream.write(data) + stream.flush() + + +def _read_exactly(stream, size): + chunks = [] + remaining = size + while remaining: + chunk = stream.read(remaining) + if not chunk: + return None + chunks.append(chunk) + remaining -= len(chunk) + return b"".join(chunks) diff --git a/tasks/tgat-milp-branching-node-count/tests/bnbenv/runner.py b/tasks/tgat-milp-branching-node-count/tests/bnbenv/runner.py new file mode 100644 index 0000000000000000000000000000000000000000..d0c930195d08ac3efd2a28cb2f4a7157ada14ae9 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/bnbenv/runner.py @@ -0,0 +1,279 @@ +from __future__ import annotations + +import os +import select +import signal +import subprocess +import sys +import time +from pathlib import Path + +from pyscipopt import SCIP_RESULT, Branchrule + +from . import features, protocol +from .setcover import NODE_LIMIT, TIME_LIMIT, build_model + +ENTRY_POINT = "policy.py" +STARTUP_LIMIT = 30.0 +DECISION_READ_CHUNK = 4096 + + +class PolicyChannel: + """Owns the policy subprocess and the framed request/response cycle.""" + + def __init__(self, submission_dir, host_pkg_root, python_exe=None, + launch_prefix=(), stderr_to=None, entry_point=ENTRY_POINT, + drop_to_uid=None): + self.submission_dir = str(Path(submission_dir).resolve()) + self.entry_point = entry_point + env = { + "PATH": "/usr/local/bin:/usr/bin:/bin", + "PYTHONPATH": str(Path(host_pkg_root).resolve()), + "PYTHONSAFEPATH": "1", + "PYTHONHASHSEED": "0", + "PYTHONDONTWRITEBYTECODE": "1", + "HOME": "/home/agent", + "TMPDIR": "/tmp", + "OMP_NUM_THREADS": "1", + "MKL_NUM_THREADS": "1", + "OPENBLAS_NUM_THREADS": "1", + "NUMEXPR_NUM_THREADS": "1", + } + read_fd, write_fd = os.pipe() + os.set_inheritable(write_fd, True) + env["BNB_REPLY_FD"] = str(write_fd) + cmd = list(launch_prefix) + [ + python_exe or sys.executable, "-c", + "from bnbenv.host import main; raise SystemExit(main())", + ] + + def _drop(): + """Drop to the unprivileged uid before exec, in the child.""" + os.setgroups([]) + os.setgid(int(drop_to_uid)) + os.setuid(int(drop_to_uid)) + + self.proc = subprocess.Popen( + cmd, + stdin=subprocess.PIPE, + stdout=subprocess.DEVNULL, + stderr=stderr_to if stderr_to is not None else subprocess.DEVNULL, + env=env, + pass_fds=(write_fd,), + start_new_session=True, + preexec_fn=(_drop if drop_to_uid and not launch_prefix else None), + cwd="/tmp", + ) + os.close(write_fd) + self.reply = os.fdopen(read_fd, "rb") + self.error = None + + def _send(self, payload): + protocol.write_obs(self.proc.stdin, payload) + + def _recv(self, budget): + """Read one newline-terminated reply, or None on timeout/death.""" + deadline = time.monotonic() + budget + buf = bytearray() + fd = self.reply.fileno() + while b"\n" not in buf: + remaining = deadline - time.monotonic() + if remaining <= 0: + self.error = "policy process exceeded its wall-clock budget" + return None + ready, _, _ = select.select([fd], [], [], min(remaining, 1.0)) + if not ready: + continue + chunk = os.read(fd, DECISION_READ_CHUNK) + if not chunk: + self.error = "policy process exited without answering" + return None + buf.extend(chunk) + if len(buf) > 1 << 16: + self.error = "policy process wrote an oversized reply" + return None + return bytes(buf).split(b"\n", 1)[0].decode("ascii", "replace") + + def start(self, budget=STARTUP_LIMIT): + self._send({"cmd": "load", "submission_dir": self.submission_dir, + "entry_point": self.entry_point}) + reply = self._recv(budget) + if reply != "ready": + self.error = self.error or f"load failed ({reply})" + return False + self._send({"cmd": "new"}) + reply = self._recv(budget) + if reply != "ok": + self.error = self.error or f"Policy() failed ({reply})" + return False + return True + + def decide(self, obs, budget, n_cands): + try: + self._send({"cmd": "select", "obs": obs}) + except (BrokenPipeError, OSError): + self.error = "policy process closed the channel" + return None + reply = self._recv(budget) + if reply is None: + return None + if not reply.startswith("i "): + self.error = f"policy error ({reply[:200]})" + return None + try: + index = int(reply[2:]) + except ValueError: + self.error = f"unparseable decision ({reply[:80]})" + return None + if not 0 <= index < n_cands: + self.error = f"decision {index} outside [0, {n_cands})" + return None + return index + + def extra_processes(self): + """Live processes in the policy session other than the policy itself.""" + try: + sid = os.getsid(self.proc.pid) + except (ProcessLookupError, PermissionError, OSError): + return 0 + extra = 0 + for entry in os.listdir("/proc"): + if not entry.isdigit(): + continue + pid = int(entry) + if pid == self.proc.pid: + continue + try: + if os.getsid(pid) == sid: + extra += 1 + except (ProcessLookupError, PermissionError, OSError): + continue + return extra + + def close(self): + try: + self._send({"cmd": "quit"}) + except Exception: + pass + try: + self.proc.stdin.close() + except Exception: + pass + try: + self.proc.wait(timeout=5) + except Exception: + try: + os.killpg(os.getpgid(self.proc.pid), signal.SIGKILL) + except Exception: + pass + try: + self.proc.wait(timeout=5) + except Exception: + pass + try: + self.reply.close() + except Exception: + pass + + +class HostedBranchrule(Branchrule): + """Hands every branching decision of this solve to the policy process.""" + + def __init__(self, channel, deadline): + super().__init__() + self.channel = channel + self.deadline = deadline + self.decisions = 0 + self.failure = None + + def branchexeclp(self, allowaddcons): + if self.failure is not None: + return {"result": SCIP_RESULT.DIDNOTRUN} + cands, cands_sol, _frac, ncands, _nprio, _nimpl = \ + self.model.getLPBranchCands() + if ncands == 0: + return {"result": SCIP_RESULT.DIDNOTRUN} + cands = list(cands)[:ncands] + obs = features.extract(self.model, cands, cands_sol) + budget = self.deadline - time.monotonic() + if budget <= 0: + self.failure = "run exceeded its wall-clock budget" + self.model.interruptSolve() + return {"result": SCIP_RESULT.DIDNOTRUN} + index = self.channel.decide(obs, budget, len(cands)) + if index is None: + self.failure = self.channel.error or "policy failed" + self.model.interruptSolve() + return {"result": SCIP_RESULT.DIDNOTRUN} + self.decisions += 1 + if self.decisions % 20 == 1 and self.channel.extra_processes(): + self.failure = "policy process spawned extra processes" + self.model.interruptSolve() + return {"result": SCIP_RESULT.DIDNOTRUN} + self.model.branchVar(cands[index]) + return {"result": SCIP_RESULT.BRANCHED} + + +def run_one(seed, shift, submission_dir, host_pkg_root, python_exe=None, + launch_prefix=(), stderr_to=None, node_limit=NODE_LIMIT, + time_limit=TIME_LIMIT, startup_timeout=STARTUP_LIMIT, + entry_point=ENTRY_POINT, drop_to_uid=None): + """Solve one instance with the submission in charge of variable selection. + + Returns a record with the node count charged for this run: the solver's own + total node count when it proved optimality, and `node_limit` otherwise. + """ + record = { + "seed": int(seed), + "shift": int(shift), + "nodes": int(node_limit), + "raw_nodes": None, + "status": "unstarted", + "proved_optimal": False, + "decisions": 0, + "solve_seconds": 0.0, + "error": None, + "extra_processes": 0, + } + if not (Path(submission_dir) / entry_point).is_file(): + record["status"] = "missing_entry_point" + record["error"] = f"no {entry_point} in {submission_dir}" + return record + + channel = PolicyChannel(submission_dir, host_pkg_root, + python_exe=python_exe, launch_prefix=launch_prefix, + stderr_to=stderr_to, entry_point=entry_point, + drop_to_uid=drop_to_uid) + try: + if not channel.start(budget=startup_timeout): + record["status"] = "startup_failed" + record["error"] = channel.error + return record + + model, _xs = build_model(seed, shift, node_limit=node_limit, + time_limit=time_limit) + started = time.monotonic() + rule = HostedBranchrule(channel, started + time_limit) + model.includeBranchrule(rule, "hosted", "policy-process branching", + priority=10_000_000, maxdepth=-1, + maxbounddist=1.0) + model.optimize() + record["solve_seconds"] = round(time.monotonic() - started, 3) + record["status"] = model.getStatus() + record["decisions"] = rule.decisions + raw_nodes = int(model.getNTotalNodes()) + record["raw_nodes"] = raw_nodes + record["extra_processes"] = channel.extra_processes() + proved = (rule.failure is None and model.getStatus() == "optimal" + and raw_nodes <= node_limit + and record["extra_processes"] == 0) + if rule.failure is not None: + record["error"] = rule.failure + elif record["extra_processes"] > 0: + record["error"] = "policy process spawned extra processes" + record["proved_optimal"] = bool(proved) + record["nodes"] = raw_nodes if proved else int(node_limit) + del model + return record + finally: + channel.close() diff --git a/tasks/tgat-milp-branching-node-count/tests/bnbenv/setcover.py b/tasks/tgat-milp-branching-node-count/tests/bnbenv/setcover.py new file mode 100644 index 0000000000000000000000000000000000000000..5918d1c14ea49ebdc1f8daf382ad724b4c53a5ae --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/bnbenv/setcover.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +import numpy as np + +N_ROWS = 500 +N_COLS = 1000 +DENSITY = 0.05 +MAX_COEF = 100 + +NODE_LIMIT = 20000 +TIME_LIMIT = 120.0 + + +def generate_instance(seed, n_rows=N_ROWS, n_cols=N_COLS, density=DENSITY, + max_coef=MAX_COEF): + """Return (costs, row_indptr, row_indices) for one set covering instance. + + `costs` is an int array of length n_cols. The constraint matrix is returned + in CSR form over rows: row r covers columns + `row_indices[row_indptr[r]:row_indptr[r + 1]]`. + """ + rng = np.random.default_rng(int(seed)) + nnzrs = int(n_rows * n_cols * density) + if nnzrs // n_rows < 2 or nnzrs // n_cols < 2: + raise ValueError("density too low for the requested shape") + + indices = rng.integers(0, n_rows, size=nnzrs) + col_of_nnz = rng.integers(0, n_cols, size=nnzrs) + col_of_nnz[: 2 * n_cols] = np.repeat(np.arange(n_cols), 2) + _, col_nnzs = np.unique(col_of_nnz, return_counts=True) + indices[:n_rows] = rng.permutation(n_rows) + + i = 0 + indptr = [0] + for n in col_nnzs: + if i >= n_rows: + indices[i:i + n] = rng.choice(n_rows, size=n, replace=False) + elif i + n > n_rows: + remaining = np.setdiff1d(np.arange(n_rows), indices[i:n_rows], + assume_unique=True) + indices[n_rows:i + n] = rng.choice( + remaining, size=i + n - n_rows, replace=False) + i += n + indptr.append(i) + + costs = rng.integers(1, max_coef + 1, size=n_cols) + + col_indptr = np.asarray(indptr, dtype=np.int64) + row_of_nnz = np.asarray(indices[:i], dtype=np.int64) + col_ids = np.repeat(np.arange(n_cols, dtype=np.int64), np.diff(col_indptr)) + order = np.lexsort((col_ids, row_of_nnz)) + row_sorted = row_of_nnz[order] + row_indices = col_ids[order] + row_counts = np.bincount(row_sorted, minlength=n_rows) + row_indptr = np.concatenate(([0], np.cumsum(row_counts))).astype(np.int64) + return costs.astype(np.int64), row_indptr, row_indices + + +def build_model(seed, shift, node_limit=NODE_LIMIT, time_limit=TIME_LIMIT, + **gen_kwargs): + """Build the pinned pyscipopt Model for (instance seed, seed shift).""" + from pyscipopt import Model, quicksum + + costs, row_indptr, row_indices = generate_instance(seed, **gen_kwargs) + n_cols = len(costs) + + model = Model(f"setcover-{seed}") + model.hideOutput() + xs = [model.addVar(vtype="B", obj=float(costs[j]), name=f"x{j}") + for j in range(n_cols)] + for r in range(len(row_indptr) - 1): + cols = row_indices[row_indptr[r]:row_indptr[r + 1]] + model.addCons(quicksum(xs[int(j)] for j in cols) >= 1, name=f"c{r}") + + model.setIntParam("separating/maxrounds", 0) + model.setIntParam("presolving/maxrestarts", 0) + model.setIntParam("randomization/permutationseed", int(shift)) + model.setIntParam("randomization/randomseedshift", int(shift)) + model.setIntParam("timing/clocktype", 2) + model.setLongintParam("limits/nodes", int(node_limit)) + model.setRealParam("limits/time", float(time_limit)) + model.setParam("display/verblevel", 0) + return model, xs diff --git a/tasks/tgat-milp-branching-node-count/tests/compute_reward.py b/tasks/tgat-milp-branching-node-count/tests/compute_reward.py new file mode 100644 index 0000000000000000000000000000000000000000..066c6d74153cde4557431798eed29be5bdc7c615 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/compute_reward.py @@ -0,0 +1,22 @@ +from __future__ import annotations + +import json +import math +from pathlib import Path + +REWARD_PATH = Path("/logs/verifier/reward.txt") +METRIC_PATH = Path("/logs/verifier/metric.json") + +try: + payload = json.loads(METRIC_PATH.read_text()) + value = float(payload["reward"]) if payload.get("valid") else 0.0 + if not math.isfinite(value): + raise ValueError("non-finite reward") + reward = f"{min(1.0, max(0.0, value)):.12g}" + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + REWARD_PATH.write_text(reward + "\n") + print(f"reward={reward}") +except Exception as exc: + REWARD_PATH.parent.mkdir(parents=True, exist_ok=True) + REWARD_PATH.write_text("0\n") + print(f"reward=0 (fallback: {exc})") diff --git a/tasks/tgat-milp-branching-node-count/tests/grader_core.py b/tasks/tgat-milp-branching-node-count/tests/grader_core.py new file mode 100644 index 0000000000000000000000000000000000000000..35058f6f059d0af7945fc80261fffdf402b89743 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/grader_core.py @@ -0,0 +1,46 @@ +from __future__ import annotations + +import json +import math +from pathlib import Path + +NODE_CAP = 20000 + +M0_BY_PANEL = {"intermediate": 398.89, "final": 595.15} +M0_DEFAULT = 487.0 +BASELINE_SCORE = 126.0 + + +def m0_for(panel): + return M0_BY_PANEL.get(panel, M0_DEFAULT) + +MAX_SUBMISSION_BYTES = 200 * 1024 * 1024 + + +def shifted_geomean(nodes): + """exp(mean(log(n + 1))) - 1 over the run node counts.""" + values = [float(n) for n in nodes] + if not values: + raise ValueError("no runs to aggregate") + return math.exp(sum(math.log(v + 1.0) for v in values) / len(values)) - 1.0 + + +def reward_from_metric(metric, m0=M0_DEFAULT): + """Map a node-count metric to a reward in [0, 1]; lower metric is better.""" + if metric is None or not math.isfinite(metric) or metric < 0: + return 0.0 + x_ref = math.log1p(m0) - math.log1p(BASELINE_SCORE) + assert x_ref > 0, "malformed task: the baseline score does not beat the trivial rule" + x = math.log1p(m0) - math.log1p(metric) + u = max(0.0, x) / x_ref + return u / (1.0 + u) + + +def load_panel(panel_dir): + """Read one sealed panel: instance seeds and solver seed shifts.""" + spec = json.loads((Path(panel_dir) / "panel.json").read_text()) + seeds = [int(s) for s in spec["seeds"]] + shifts = [int(k) for k in spec["shifts"]] + if not seeds or not shifts: + raise ValueError(f"empty panel in {panel_dir}") + return seeds, shifts diff --git a/tasks/tgat-milp-branching-node-count/tests/hidden_data/final/panel.json b/tasks/tgat-milp-branching-node-count/tests/hidden_data/final/panel.json new file mode 100644 index 0000000000000000000000000000000000000000..ff73cb483b7f3e6c6cd7acac0853c77d824c1e6b --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/hidden_data/final/panel.json @@ -0,0 +1 @@ +{"seeds": [166168984713443, 166608692677627, 167335946794831, 168166293748666, 168203654674866, 168818544469219, 169926472391610, 172624471364011, 172942070534143, 173033243417267, 173831499122182, 173911671553598, 174636451767918, 175222218927612, 177885408025558, 178260741575725, 179025685820358, 180180769861047, 180616911470984, 181047369429824, 181938761321275, 182106013640696, 183087060150036, 183913877006466, 184625836524321, 185683452554815, 185999888802787, 188592428319028, 188838073799118, 190234398654864, 191188026842038, 191200837785332, 191926700239707, 192327986971286, 192621300834950, 193657742995583, 195110041367383, 195497169949370, 195581619515192, 195646848757014], "shifts": [0, 1]} diff --git a/tasks/tgat-milp-branching-node-count/tests/hidden_data/intermediate/panel.json b/tasks/tgat-milp-branching-node-count/tests/hidden_data/intermediate/panel.json new file mode 100644 index 0000000000000000000000000000000000000000..a67c77958cb83e69889ac0c45d79ba7b9c16b6f1 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/hidden_data/intermediate/panel.json @@ -0,0 +1 @@ +{"seeds": [140850428960565, 141873238652254, 142009945552195, 142112234229131, 143750526809517, 143865201938284, 144346250682454, 144420811669405, 144662001709460, 145430315660390, 145464561064255, 146514491074813, 146758217667556, 149717369692528, 149891128306633, 150753503905554, 150956559704529, 151496602785847, 152310221187048, 152685125717065, 152915148771590, 153366012179151, 153518571530594, 154159515906741, 156158417170012, 156698301178329, 156793614019320, 157889408108203, 157911854925942, 158505497075691, 160077600026441, 160316270683091, 161018456823700, 161729153373888, 163268697742743, 163923044430378, 164876754317166, 165913980076947, 166018908061055, 166158128576784], "shifts": [0, 1]} diff --git a/tasks/tgat-milp-branching-node-count/tests/intermediate.sh b/tasks/tgat-milp-branching-node-count/tests/intermediate.sh new file mode 100644 index 0000000000000000000000000000000000000000..c5a2d6d2fd76371c136ffa8982626bbe2397e825 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/intermediate.sh @@ -0,0 +1,19 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT + +chmod -R go-rwx /tests + +export GRADE_SPLIT=intermediate +cd /tests +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/local/bin/python3 /tests/report_intermediate.py +/usr/local/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/tgat-milp-branching-node-count/tests/report_intermediate.py b/tasks/tgat-milp-branching-node-count/tests/report_intermediate.py new file mode 100644 index 0000000000000000000000000000000000000000..e88d08a1dd09433705a34a0dbc1205b5ae17fa20 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/report_intermediate.py @@ -0,0 +1,24 @@ +from __future__ import annotations + +import json +import statistics +from pathlib import Path + +try: + payload = json.loads(Path("/logs/verifier/metric.json").read_text()) +except Exception as exc: + print(f"no grading result to report ({exc})") + raise SystemExit(0) + +if not payload.get("valid"): + print(f"submission rejected: {payload.get('invalid_reason')}") + raise SystemExit(0) + +nodes = sorted(r["nodes"] for r in payload["runs"]) +print("=== public evaluation panel ===") +print(f"runs : {payload['n_runs']}") +print(f"proved optimality : {payload['n_proved_optimal']}") +print(f"charged the cap : {payload['n_charged_cap']}") +print(f"nodes min / median / max : {nodes[0]} / " + f"{int(statistics.median(nodes))} / {nodes[-1]}") +print(f"1-shifted geometric mean : {payload['metric']}") diff --git a/tasks/tgat-milp-branching-node-count/tests/requirements-verifier.txt b/tasks/tgat-milp-branching-node-count/tests/requirements-verifier.txt new file mode 100644 index 0000000000000000000000000000000000000000..04676c89520bf969f2cb861ad1811f46d8370700 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/requirements-verifier.txt @@ -0,0 +1,2 @@ +pytest==8.3.5 +pytest-json-ctrf==0.3.5 diff --git a/tasks/tgat-milp-branching-node-count/tests/requirements.txt b/tasks/tgat-milp-branching-node-count/tests/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a68a0bf90a8e747157198775aa58bb0fd3f472f --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/requirements.txt @@ -0,0 +1,18 @@ +--index-url https://download.pytorch.org/whl/cpu +--extra-index-url https://pypi.org/simple +numpy==2.3.4 +scipy==1.16.3 +scikit-learn==1.7.2 +pyscipopt==6.2.1 +torch==2.9.1+cpu +filelock==3.32.3 +fsspec==2026.7.0 +jinja2==3.1.6 +joblib==1.5.3 +markupsafe==3.0.3 +mpmath==1.3.0 +networkx==3.6.1 +setuptools==84.0.0 +sympy==1.14.0 +threadpoolctl==3.6.0 +typing_extensions==4.16.0 diff --git a/tasks/tgat-milp-branching-node-count/tests/solve_run.py b/tasks/tgat-milp-branching-node-count/tests/solve_run.py new file mode 100644 index 0000000000000000000000000000000000000000..6e9526e20fd38ddadc590a2ca52b68989390cd1a --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/solve_run.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import argparse +import json +import signal +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +from bnbenv import runner + + +class _Expired(Exception): + pass + + +def _on_alarm(signum, frame): + raise _Expired("run driver exceeded its own hard limit") + + +def main(): + ap = argparse.ArgumentParser() + ap.add_argument("--seed", type=int, required=True) + ap.add_argument("--shift", type=int, required=True) + ap.add_argument("--submission", required=True) + ap.add_argument("--host-pkg-root", required=True) + ap.add_argument("--python", default="/usr/local/bin/python3") + ap.add_argument("--time-limit", type=float, required=True) + ap.add_argument("--startup-timeout", type=float, required=True) + ap.add_argument("--launch-prefix", default="") + ap.add_argument("--drop-to-uid", type=int, default=None) + args = ap.parse_args() + + prefix = [p for p in args.launch_prefix.split(",") if p] + signal.signal(signal.SIGALRM, _on_alarm) + signal.alarm(int(args.time_limit + args.startup_timeout + 60)) + try: + record = runner.run_one( + args.seed, args.shift, args.submission, args.host_pkg_root, + python_exe=args.python, launch_prefix=prefix, + time_limit=args.time_limit, + startup_timeout=args.startup_timeout, + drop_to_uid=args.drop_to_uid) + except BaseException as exc: + from bnbenv.setcover import NODE_LIMIT + record = { + "seed": args.seed, "shift": args.shift, "nodes": NODE_LIMIT, + "raw_nodes": None, "status": "run_driver_failed", + "proved_optimal": False, "decisions": 0, "solve_seconds": 0.0, + "error": f"{type(exc).__name__}: {exc}"[:200], + "extra_processes": 0, + } + finally: + signal.alarm(0) + print(json.dumps(record), flush=True) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tasks/tgat-milp-branching-node-count/tests/test.sh b/tasks/tgat-milp-branching-node-count/tests/test.sh new file mode 100644 index 0000000000000000000000000000000000000000..a592fd0f679078052c765b37ab7bc84f28a8cb7b --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/test.sh @@ -0,0 +1,18 @@ +#!/bin/bash +set -u + +mkdir -p /logs/verifier +chown root:root /logs/verifier 2>/dev/null || true +chmod 755 /logs/verifier 2>/dev/null || true +rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json + +trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT + +chmod -R go-rwx /tests + +export GRADE_SPLIT=final +cd /tests +/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA +/usr/local/bin/python3 /tests/compute_reward.py + +exit 0 diff --git a/tasks/tgat-milp-branching-node-count/tests/test_state.py b/tasks/tgat-milp-branching-node-count/tests/test_state.py new file mode 100644 index 0000000000000000000000000000000000000000..01d0bd209085343d938521ab39b36fd1750125c4 --- /dev/null +++ b/tasks/tgat-milp-branching-node-count/tests/test_state.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +import json +import os +import shutil +import subprocess +import sys +import time +from concurrent.futures import ProcessPoolExecutor, as_completed +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).resolve().parent)) + +import grader_core + +TESTS = Path(__file__).resolve().parent +SUBMISSION = Path("/app/submission") +ENTRY_POINT = "policy.py" +HOST_PKG_ROOT = "/opt" +PANEL = os.environ.get("GRADE_SPLIT", "final") +PANEL_DIR = TESTS / "hidden_data" / PANEL +METRIC_PATH = Path("/logs/verifier/metric.json") + +PYTHON = "/usr/local/bin/python3" +AGENT_UID = 1001 +SBIN = "/usr/sbin:/sbin:/usr/bin:/bin:/usr/local/bin" + + +def _launch_prefix(): + """How the policy process is dropped to uid 1001. + + Measured during the build: wrapping the launch in `setsid runuser -u agent` + inserts a PAM fork, so the pid the solver process holds is the wrapper and + the policy ends up in a different session. `killpg` on a hung policy then + misses it and the run leaks a process that competes with the rest of the + panel for the 8 CPUs. So the solver process drops privilege itself + (`drop_to_uid`, setgroups/setgid/setuid before exec) with + `start_new_session=True`, i.e. setsid(2) in the child: the policy process is + the session leader, uid 1001, and the whole session is killable by pid. + + An explicit prefix is still supported for a host without a root verifier; + `runuser` is resolved here so that path stays available. + """ + if os.geteuid() == 0: + return [] + runuser = shutil.which("runuser", path=SBIN) + setsid = shutil.which("setsid", path=SBIN) + if runuser and setsid: + return [setsid, runuser, "-u", "agent", "--"] + return [] + + +LAUNCH_PREFIX = _launch_prefix() + +WORKERS = 8 +SOLVE_TIMEOUT = 120.0 +STARTUP_TIMEOUT = 30.0 +PANEL_TIMEOUT = 1900.0 +RUN_HARD_TIMEOUT = SOLVE_TIMEOUT + STARTUP_TIMEOUT + 45.0 + +_RESULT = None + + +def _validate_submission(): + """Fail-closed contract checks on the deliverable directory.""" + if not SUBMISSION.is_dir(): + return False, f"{SUBMISSION} is not a directory" + entry = SUBMISSION / ENTRY_POINT + if not entry.is_file(): + return False, f"no {ENTRY_POINT} in {SUBMISSION}" + if entry.stat().st_size == 0: + return False, f"{entry} is empty" + total = 0 + root = SUBMISSION.resolve() + for dirpath, dirnames, filenames in os.walk(SUBMISSION, followlinks=False): + for name in list(dirnames) + list(filenames): + path = Path(dirpath) / name + if path.is_symlink(): + target = Path(os.path.realpath(path)) + if root not in target.parents and target != root: + return False, f"symlink escapes the submission: {path}" + else: + try: + total += path.stat().st_size + except OSError: + pass + if total > grader_core.MAX_SUBMISSION_BYTES: + return False, (f"submission is {total} bytes, over " + f"{grader_core.MAX_SUBMISSION_BYTES}") + return True, None + + +def _run_one(job): + """One graded run, in its own solver subprocess owned by the verifier.""" + seed, shift = job + cmd = [ + PYTHON, str(TESTS / "solve_run.py"), + "--seed", str(seed), "--shift", str(shift), + "--submission", str(SUBMISSION), + "--host-pkg-root", HOST_PKG_ROOT, + "--python", PYTHON, + "--time-limit", str(SOLVE_TIMEOUT), + "--startup-timeout", str(STARTUP_TIMEOUT), + "--launch-prefix", ",".join(LAUNCH_PREFIX), + "--drop-to-uid", str(AGENT_UID), + ] + started = time.monotonic() + try: + proc = subprocess.run(cmd, capture_output=True, text=True, + timeout=RUN_HARD_TIMEOUT) + record = json.loads(proc.stdout.strip().splitlines()[-1]) + except Exception as exc: + record = { + "seed": int(seed), "shift": int(shift), + "nodes": grader_core.NODE_CAP, "raw_nodes": None, + "status": "driver_failed", "proved_optimal": False, + "decisions": 0, "solve_seconds": 0.0, + "error": f"{type(exc).__name__}: {exc}"[:200], + "extra_processes": 0, + } + record["wall"] = round(time.monotonic() - started, 2) + return record + + +def _grade(): + """Grade once per pytest session and cache the outcome.""" + global _RESULT + if _RESULT is not None: + return _RESULT + + METRIC_PATH.parent.mkdir(parents=True, exist_ok=True) + payload = { + "panel": PANEL, + "valid": False, + "metric": None, + "reward": 0.0, + "invalid_reason": None, + "runs": [], + "launcher": LAUNCH_PREFIX or f"in-process setuid({AGENT_UID})", + } + try: + seeds, shifts = grader_core.load_panel(PANEL_DIR) + ok, reason = _validate_submission() + if not ok: + payload["invalid_reason"] = reason + else: + subprocess.run(["chmod", "-R", "go+rX", str(SUBMISSION)], + check=False, timeout=120) + jobs = [(s, k) for s in seeds for k in shifts] + deadline = time.monotonic() + PANEL_TIMEOUT + records = {} + with ProcessPoolExecutor(max_workers=WORKERS) as pool: + futures = {pool.submit(_run_one, job): job for job in jobs} + for fut in as_completed(futures): + rec = fut.result() + records[(rec["seed"], rec["shift"])] = rec + if time.monotonic() > deadline: + for pending in futures: + pending.cancel() + break + for job in jobs: + if job not in records: + records[job] = { + "seed": job[0], "shift": job[1], + "nodes": grader_core.NODE_CAP, "raw_nodes": None, + "status": "panel_budget_exhausted", + "proved_optimal": False, "decisions": 0, + "solve_seconds": 0.0, "error": None, + "extra_processes": 0, "wall": 0.0, + } + runs = [records[job] for job in jobs] + nodes = [r["nodes"] for r in runs] + metric = grader_core.shifted_geomean(nodes) + payload.update({ + "valid": True, + "metric": round(metric, 4), + "reward": grader_core.reward_from_metric( + metric, m0=grader_core.m0_for(PANEL)), + "runs": runs, + "n_runs": len(runs), + "n_proved_optimal": sum(1 for r in runs if r["proved_optimal"]), + "n_charged_cap": sum(1 for r in runs + if not r["proved_optimal"]), + "node_min": min(nodes), + "node_max": max(nodes), + "m0": grader_core.m0_for(PANEL), + "baseline_score": grader_core.BASELINE_SCORE, + }) + except Exception as exc: + payload["invalid_reason"] = f"grader error: {type(exc).__name__}: {exc}" + payload["valid"] = False + payload["reward"] = 0.0 + METRIC_PATH.write_text(json.dumps(payload, indent=1)) + _RESULT = payload + return payload + + +def test_deliverable_contract(): + """The submission directory satisfies the contract stated in the task. + + An entry point that is missing, empty, oversized, or reaching outside the + submission directory through a symlink is scored 0 and nothing is executed. + """ + result = _grade() + assert result["invalid_reason"] is None, result["invalid_reason"] + + +def test_graded_panel(): + """Every run of the sealed panel executed and the metric was aggregated. + + The node count of a run is the solver's own count, taken in a process that + never loaded submission code; a run that fails to prove optimality inside + the caps is charged the node cap. + """ + result = _grade() + assert result["valid"], result["invalid_reason"] + assert result["n_runs"] == len(result["runs"]) + assert result["metric"] is not None and result["metric"] > 0 + print(f"panel={result['panel']} metric={result['metric']} " + f"charged_cap={result['n_charged_cap']}/{result['n_runs']}") + + +def test_reward_is_bounded(): + """The reward derived from the metric is finite and inside [0, 1].""" + result = _grade() + assert 0.0 <= float(result["reward"]) <= 1.0