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4be6a52 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | """Bounded real-weight GPU training/reload gate; never manages other workloads.
Run from the repository with uv run --locked --extra ml python scripts/probe_clef_training.py.
The parent process must impose a wall-clock timeout and restore borrowed GPU services.
"""
import argparse
import gc
import hashlib
import json
import time
from pathlib import Path
import torch
from stackcraft.clef import ClefPlayer, encode_observation
from stackcraft.data import audit_dataset
from stackcraft.players import observe
from stackcraft.provenance import source_identity
from stackcraft.schema import GameState
from stackcraft.training import (
decision_loss,
load_checkpoint,
parameter_hashes,
prepare_trainable,
save_checkpoint,
)
def observation(row):
raw = row["observation"]
return observe(
GameState(tuple(tuple(r) for r in raw["board"]), 0, 0, raw["current"], raw["next_piece"])
)
def write_json(path, value):
path.write_text(json.dumps(value, indent=2, allow_nan=False) + "\n")
def probabilities(player, rows):
return [player.choose(observation(row)).probabilities for row in rows]
def train_steps(player, rows, *, steps, learning_rate):
model = player.model
model.train()
if model._stackcraft_training["mode"] == "head":
model.language_model.eval()
parameters = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.AdamW(parameters, lr=learning_rate)
events = []
for index in range(steps):
row = rows[index % len(rows)]
encoded = encode_observation(
observation(row), player.processor.tokenizer, player.native, player.max_length
)
batch = player.native.collate_records(
[encoded], player.processor.tokenizer.pad_token_id, torch.device("cuda")
)
optimizer.zero_grad(set_to_none=True)
started = time.monotonic()
logits = model(batch)[0][0]
loss = decision_loss(logits, encoded, row["action_id"])
if not torch.isfinite(loss):
raise RuntimeError("training loss is nonfinite")
loss.backward()
gradient_sums = {"head": 0.0, "lora": 0.0}
for name, param in model.named_parameters():
if param.grad is None:
continue
if not torch.isfinite(param.grad).all():
raise RuntimeError(f"nonfinite gradient: {name}")
group = "lora" if "lora_" in name else "head"
gradient_sums[group] += float(param.grad.detach().abs().sum())
if gradient_sums["head"] <= 0:
raise RuntimeError("no nonzero decision-head gradients")
if any("lora_" in name for name, p in model.named_parameters() if p.requires_grad):
if gradient_sums["lora"] <= 0:
raise RuntimeError("no nonzero LoRA gradients")
torch.nn.utils.clip_grad_norm_(parameters, 1.0, error_if_nonfinite=True)
optimizer.step()
torch.cuda.synchronize()
event = {
"step": index + 1,
"row_id": row["id"],
"tokens": len(encoded.input_ids),
"loss": float(loss.detach()),
"gradient_abs_sums": gradient_sums,
"seconds": time.monotonic() - started,
"peak_allocated_bytes": torch.cuda.max_memory_allocated(),
"peak_reserved_bytes": torch.cuda.max_memory_reserved(),
}
events.append(event)
print(json.dumps(event), flush=True)
del optimizer
model.zero_grad(set_to_none=True)
model.eval()
torch.cuda.empty_cache()
return events
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--dataset", type=Path, default=Path("data/study-v1"))
parser.add_argument("--reload", type=Path)
args = parser.parse_args()
if args.output.exists():
parser.error("output already exists; choose a new directory")
args.output.mkdir(parents=True)
started = time.monotonic()
report = {"status": "running", "reload": bool(args.reload)}
write_json(args.output / "report.json", report)
try:
if not torch.cuda.is_available():
raise RuntimeError("CUDA unavailable; this gate requires real GPU training")
free, total = torch.cuda.mem_get_info()
if free < 25 * 1024**3:
raise RuntimeError(f"requires at least25GiB free before loading; available={free}")
torch.manual_seed(42)
torch.set_num_threads(8)
torch.backends.cuda.matmul.allow_tf32 = False
manifest_path = args.dataset / "manifest.json"
manifest = json.loads(manifest_path.read_text())
records = {
split: [
json.loads(line)
for line in (args.dataset / f"{split}.jsonl").read_text().splitlines()
]
for split in ("train", "validation")
}
audit_dataset(records, manifest)
# Development probe uses only the first four training positions, never test seeds.
rows = records["train"][:4]
report.update(
gpu=torch.cuda.get_device_name(),
total_vram=total,
initial_free_vram=free,
torch=torch.__version__,
dataset_manifest_sha256=hashlib.sha256(manifest_path.read_bytes()).hexdigest(),
row_ids=[row["id"] for row in rows],
)
report.update(source_identity(Path(__file__).resolve().parents[1]))
player = ClefPlayer.from_pretrained(trust_pinned_code=True)
torch.cuda.reset_peak_memory_stats()
if args.reload:
reference = json.loads((args.reload / "reference.json").read_text())
for key in ("row_ids", "dataset_manifest_sha256"):
if reference.get(key) != report[key]:
raise RuntimeError(f"reload reference {key} differs")
player.model = load_checkpoint(player.model, args.reload)
actual = probabilities(player, rows)
if len(reference["probabilities"]) != len(actual):
raise RuntimeError("reference record count differs")
delta = 0.0
for expected, observed in zip(reference["probabilities"], actual, strict=True):
if expected.keys() != observed.keys():
raise RuntimeError("reload probability option set differs")
delta = max(delta, *(abs(expected[k] - observed[k]) for k in expected))
if delta > 1e-4:
raise RuntimeError(f"fresh-process probability drift{delta} exceeds1e-4")
report.update(max_absolute_probability_difference=delta, tolerance=1e-4)
else:
native_probabilities = probabilities(player, rows)
report["native_probabilities"] = native_probabilities
report["runtime_config"] = player.runtime_config
report["load_and_native_seconds"] = time.monotonic() - started
write_json(args.output / "report.json", report)
prepare_trainable(player.model, mode="head")
wrapped = probabilities(player, rows)
report["fp32_head_initial_max_probability_drift"] = max(
abs(a[key] - b[key])
for a, b in zip(native_probabilities, wrapped, strict=True)
for key in a
)
before_head = parameter_hashes(player.model, trainable=True)
before_frozen = parameter_hashes(player.model, trainable=False)
report["head_steps"] = train_steps(player, rows, steps=3, learning_rate=1e-5)
if before_head == parameter_hashes(player.model, trainable=True):
raise RuntimeError("head parameters did not change")
if before_frozen != parameter_hashes(player.model, trainable=False):
raise RuntimeError("frozen parameters changed during head training")
save_checkpoint(
player.model,
args.output / "head-checkpoint",
extra_metadata={"probe_rows": report["row_ids"]},
)
write_json(
args.output / "head-checkpoint" / "reference.json",
{
"probabilities": probabilities(player, rows),
"row_ids": report["row_ids"],
"dataset_manifest_sha256": report["dataset_manifest_sha256"],
},
)
report["head_frozen_parameters_unchanged"] = True
write_json(args.output / "report.json", report)
del player
gc.collect()
torch.cuda.empty_cache()
player = ClefPlayer.from_pretrained(trust_pinned_code=True)
prepare_trainable(player.model, mode="lora", rank=4)
before_lora = parameter_hashes(player.model, trainable=True)
before_frozen = parameter_hashes(player.model, trainable=False)
report["lora_steps"] = train_steps(player, rows, steps=5, learning_rate=1e-5)
after_lora = parameter_hashes(player.model, trainable=True)
if not any(
"lora_" in name and value != after_lora[name] for name, value in before_lora.items()
):
raise RuntimeError("LoRA parameters did not change")
if before_frozen != parameter_hashes(player.model, trainable=False):
raise RuntimeError("frozen parameters changed during LoRA training")
checkpoint = args.output / "checkpoint"
save_checkpoint(
player.model, checkpoint, extra_metadata={"probe_rows": report["row_ids"]}
)
write_json(
checkpoint / "reference.json",
{
"probabilities": probabilities(player, rows),
"row_ids": report["row_ids"],
"dataset_manifest_sha256": report["dataset_manifest_sha256"],
},
)
report["checkpoint"] = str(checkpoint)
report["frozen_parameters_unchanged"] = True
report.update(status="passed", elapsed_seconds=time.monotonic() - started)
except Exception as error:
report.update(status="failed", error=f"{type(error).__name__}: {error}")
raise
finally:
report["elapsed_seconds"] = time.monotonic() - started
write_json(args.output / "report.json", report)
if __name__ == "__main__":
main()
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