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d4eb935 03d97ac d4eb935 | 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 | """Compare a converted Kev package with the merged PyTorch checkpoint."""
from __future__ import annotations
import argparse
import json
import statistics
import time
from pathlib import Path
import coremltools as ct
import numpy as np
import torch
from assets import load_model
from export_model import KevExport
from preprocessing import Shape, prepare_inputs
COMPUTE_UNITS = {
"all": ct.ComputeUnit.ALL,
"cpu": ct.ComputeUnit.CPU_ONLY,
"cpu-gpu": ct.ComputeUnit.CPU_AND_GPU,
"cpu-ne": ct.ComputeUnit.CPU_AND_NE,
}
def fixtures() -> list[dict]:
return [
{
"state": "The piece leaves one hole beneath it and creates a small bump on top.",
"questions": {
"q": {
"type": "choice",
"instructions": "Classify the placement.",
"criteria": {
"clean": "No buried holes and a flat surface",
"risky": "Creates a cavity or awkward surface",
},
"label": "risky",
"src": "coreml-fixture",
}
},
},
{
"state": "URGENT: verify your account at http://unknown.example and enter your password.",
"questions": {
"q": {
"type": "noul",
"instructions": "Is this message phishing?",
"criteria": {"false": "legitimate", "true": "phishing"},
"label": True,
"src": "coreml-fixture",
}
},
},
{
"state": "The customer was charged twice and wants the duplicate transaction reversed.",
"questions": {
"q": {
"type": "choice",
"instructions": "Route this support ticket.",
"criteria": {
"billing": "Payments and charges",
"technical": "Product malfunction",
"sales": "Buying a product",
},
"label": "billing",
"src": "coreml-fixture",
}
},
},
{
"state": "The order arrived two weeks late and the outer box was damaged.",
"questions": {
"q": {
"type": "score",
"instructions": "Rate the delivery issue severity.",
"criteria": ["Low impact", "Moderate impact", "High impact"],
"label": 2,
"src": "coreml-fixture",
}
},
},
]
def suite_requests(path: Path, limit: int) -> list[dict]:
requests = []
per_suite: dict[str, int] = {}
for line in path.read_text().splitlines():
row = json.loads(line)
if per_suite.get(row["suite"], 0) >= 2:
continue
if row["type"] == "choice":
question = {
"type": "choice",
"instructions": row["instructions"],
"criteria": {key: description for key, description in row["options"]},
"label": row["options"][row["gold"]][0],
}
else:
question = {
"type": "noul",
"instructions": row["instructions"],
"criteria": (
{key: description for key, description in row["options"]} if row["options"] else None
),
"label": bool(row["gold"]),
}
question["src"] = row["suite"]
requests.append({"state": json.loads(row["state"]), "questions": {"q": question}})
per_suite[row["suite"]] = per_suite.get(row["suite"], 0) + 1
if len(requests) == limit:
break
return requests
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("package", type=Path)
parser.add_argument("--length", type=int, default=128)
parser.add_argument("--max-options", type=int, default=32)
parser.add_argument("--units", choices=COMPUTE_UNITS, default="all")
parser.add_argument("--max-probability-error", type=float, default=0.02)
parser.add_argument("--suite", type=Path)
parser.add_argument("--suite-cases", type=int, default=20)
args = parser.parse_args()
_, tokenizer, decision_model = load_model()
decision_model.eval()
shape = Shape(args.length, args.max_options)
wrapper = KevExport(decision_model, shape.length, shape.max_options).eval()
started = time.perf_counter()
coreml = ct.models.MLModel(str(args.package), compute_units=COMPUTE_UNITS[args.units])
load_seconds = time.perf_counter() - started
errors: list[float] = []
times: list[float] = []
agreements = 0
evaluated = 0
requests = fixtures()
if args.suite:
requests.extend(suite_requests(args.suite, args.suite_cases))
for index, request in enumerate(requests):
try:
arrays, encoded = prepare_inputs(decision_model, tokenizer, request, shape)
except ValueError as error:
print(f"fixture {index}: skipped ({error})")
continue
evaluated += 1
tensors = tuple(torch.from_numpy(value) for value in arrays.values())
with torch.no_grad():
_, reference = wrapper(*tensors)
started = time.perf_counter()
output = coreml.predict(arrays)
times.append((time.perf_counter() - started) * 1000)
options = len(encoded["opt_idx"][0])
expected = reference[0, :options].numpy()
actual = np.asarray(output["probabilities"])[0, :options]
error = float(np.max(np.abs(expected - actual)))
errors.append(error)
agreement = int(expected.argmax()) == int(actual.argmax())
agreements += agreement
print(f"fixture {index}: options={options} argmax={agreement} max_probability_error={error:.6f}")
p95 = sorted(times)[max(0, int(0.95 * len(times)) - 1)]
print(
f"{agreements}/{evaluated} argmax; max_probability_error={max(errors):.6f}; "
f"p50={statistics.median(times):.2f} ms; p95={p95:.2f} ms; load={load_seconds:.2f} s"
)
if agreements != evaluated or max(errors) > args.max_probability_error:
raise SystemExit(1)
if __name__ == "__main__":
main()
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