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
|
#!/usr/bin/env python3
"""Audit and mechanically select the frozen Transformer P1 rates."""
import argparse
import hashlib
import json
import math
import os
import sys
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, ROOT)
from experiments.transformer_crossover_grid import p1_jobs
TRAIN_HASH = (
"6ec305602a99ac2802745a134e1f5e33e2231b4855525b00b9aebb730ac2626f")
VALIDATION_HASH = (
"d37d30cc0c8327c270d493299c3dca54135f6d5f1c9ef60cda78076e311204b1")
EXPECTED_TRAIN_TOKENS = 1000 * 32 * 64
EXPECTED_VALIDATION_TOKENS = 1742 * 64
def sha256(path):
digest = hashlib.sha256()
with open(path, "rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def read_json(path):
with open(path, encoding="utf-8") as handle:
return json.load(handle)
def audit_completed(job, manifest, expected_source):
record = read_json(job["output"])
assert manifest["output_sha256"] == sha256(job["output"])
assert record["provenance"]["git_commit"] == expected_source["git_commit"]
assert record["provenance"]["git_tracked_dirty"] is False
args = record["args"]
expected_args = {
"method": job["method"],
"depth": 4,
"width": 128,
"heads": 4,
"mlp_ratio": 4,
"context_length": 64,
"batch_size": 32,
"eval_batch_size": 32,
"train_steps": 1000,
"lr": job["rate"],
"schedule": "constant",
"weight_decay": 0.1,
"run_seed": 0,
"model_seed": 2027,
"loader_seed": 0,
"negative_seed": 5001,
"ep_sign_seed": 5002,
"traffic_ratio": 4.0,
"eval_every": 0,
"max_val_batches": 0,
}
for key, expected in expected_args.items():
assert args[key] == expected, (
f"{job['experiment_name']}: drift in {key}")
assert record["protocol_family"] == (
"transformer_local_learning_crossover")
assert record["dataset"]["train_sha256"] == TRAIN_HASH
assert record["dataset"]["validation_sha256"] == VALIDATION_HASH
assert record["architecture"]["forward_parameter_count"] == 813568
assert record["evaluation_protocol"]["split"] == "validation"
assert record["evaluation_protocol"]["test_evaluations"] == 0
assert record["evaluation_protocol"]["test_used_for_selection"] is False
assert record["first_nonfinite_step"] is None
assert record["work"]["ordinary_training_tokens"] == (
EXPECTED_TRAIN_TOKENS)
assert record["work"]["ordinary_validation_tokens"] == (
EXPECTED_VALIDATION_TOKENS)
assert record["work"]["completed_optimizer_steps"] == 1000
assert len(record["validation"]) == 1
assert record["final"]["tokens"] == EXPECTED_VALIDATION_TOKENS
assert record["final"]["step"] == 1000
assert record["hardware"]["peak_memory_allocated_bytes"] is not None
assert record["hardware"]["peak_memory_reserved_bytes"] is not None
nll = float(record["final"]["nll"])
finite = math.isfinite(nll)
return {
"finite": finite,
"final_validation_nll": nll,
"final_validation_perplexity":
float(record["final"]["perplexity"]),
"final_validation_accuracy":
float(record["final"]["accuracy"]),
"completed_optimizer_steps": 1000,
"peak_memory_allocated_bytes":
record["hardware"]["peak_memory_allocated_bytes"],
"total_wall_seconds": float(record["total_wall_seconds"]),
"work": record["work"],
}
def audit_job(job, expected_source):
manifest_path = job["output"] + ".manifest.json"
assert os.path.isfile(manifest_path), (
f"missing manifest for {job['experiment_name']}")
manifest = read_json(manifest_path)
for key in (
"stage", "method", "architecture", "rate", "experiment_name",
"output", "timeout_seconds", "command"):
assert manifest[key] == job[key], (
f"{job['experiment_name']}: drift in {key}")
assert manifest["source"] == expected_source
hardware = manifest["hardware_lock"]
assert hardware["physical_gpu_index"] in (5, 7)
assert hardware["physical_gpu_uuid"]
status = manifest["status"]
common = {
"method": job["method"],
"rate": job["rate"],
"experiment_name": job["experiment_name"],
"manifest": os.path.relpath(manifest_path, ROOT),
"status": status,
"driver_wall_seconds": manifest["driver_wall_seconds"],
"physical_gpu_index": hardware["physical_gpu_index"],
"physical_gpu_uuid": hardware["physical_gpu_uuid"],
"output_sha256": manifest["output_sha256"],
}
if status == "completed":
assert manifest["output_exists"] is True
return {
**common,
**audit_completed(job, manifest, expected_source),
}
assert status in {
"timeout", "nonzero_exit", "missing_output"}
return {
**common,
"finite": False,
"final_validation_nll": None,
"final_validation_perplexity": None,
"final_validation_accuracy": None,
"completed_optimizer_steps": None,
"peak_memory_allocated_bytes": None,
"total_wall_seconds": None,
"work": None,
}
def choose(candidates):
return min(candidates, key=lambda row: (
not row["finite"],
(
row["final_validation_nll"]
if row["final_validation_nll"] is not None else math.inf),
row["rate"],
))
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--out",
default="results/transformer_crossover/p1_selector.json")
parser.add_argument("--allow-partial", action="store_true")
args = parser.parse_args()
jobs = p1_jobs()
manifest_paths = [
job["output"] + ".manifest.json" for job in jobs
if os.path.isfile(job["output"] + ".manifest.json")]
missing = [
job["experiment_name"] for job in jobs
if not os.path.isfile(job["output"] + ".manifest.json")]
if missing and not args.allow_partial:
raise AssertionError(f"missing P1 jobs: {missing}")
sources = [read_json(path)["source"] for path in manifest_paths]
if sources:
expected_source = sources[0]
assert all(source == expected_source for source in sources)
else:
expected_source = None
records = [
audit_job(job, expected_source) for job in jobs
if os.path.isfile(job["output"] + ".manifest.json")]
selected = {}
if not missing:
for method in dict((job["method"], None) for job in jobs):
winner = choose([
record for record in records
if record["method"] == method])
selected[method] = {
"rate": winner["rate"],
"finite": winner["finite"],
"final_validation_nll":
winner["final_validation_nll"],
"experiment_name": winner["experiment_name"],
}
report = {
"gate": "pass" if not missing else "partial",
"stage": "transformer_crossover_p1",
"selection_rule":
"minimum finite final validation NLL, then lower learning rate; "
"failed/timeout records rank after finite records",
"source": expected_source,
"registry_sha256": hashlib.sha256(json.dumps(
jobs, sort_keys=True).encode("utf-8")).hexdigest(),
"num_expected_records": len(jobs),
"num_audited_records": len(records),
"missing_experiments": missing,
"test_policy": "none",
"records": records,
"selected": selected,
}
os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
with open(args.out, "w", encoding="utf-8") as handle:
json.dump(report, handle, indent=2, sort_keys=True)
handle.write("\n")
print(json.dumps({
"gate": report["gate"],
"num_audited_records": len(records),
"missing_experiments": missing,
"selected": selected,
}, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
|
