File size: 4,824 Bytes
613264a | 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 | """Validate Core AI against full upstream BF16 text and image inference."""
import argparse
import asyncio
import json
import sys
import time
from pathlib import Path
import numpy as np
import torch
from coreai_d3 import D3CoreAI
from validation_cases import CASES
async def main():
p = argparse.ArgumentParser()
p.add_argument("name")
a = p.parse_args()
root = Path("upstream") / a.name
bundle = Path("bundles") / (a.name + "-CoreAI")
sys.path.insert(0, str(root.resolve()))
from d3_runtime import D3
torch.set_num_threads(4)
reference = D3.from_pretrained(root, device="cpu", verify="full")
engine = await D3CoreAI.load(bundle)
cases = [(s, q, None, None) for s, q in CASES]
cases.append(
(
"refund " * 350,
{
"type": "choice",
"instructions": "What topic?",
"criteria": {"refund": None, "shipping": None},
},
None,
None,
)
)
cases.append(
(
"A receipt.",
{
"type": "choice",
"instructions": "Which item appears on the receipt?",
"criteria": {"blender": None, "laptop": None, "bicycle": None},
},
[root / "assets/example-receipt.png"],
None,
)
)
cases.append(
(
"A receipt.",
{"type": "noul", "instructions": "Does this receipt list a blender?"},
[root / "assets/example-receipt.png"],
None,
)
)
cases.append(
(
"Two receipts.",
{"type": "noul", "instructions": "Do the receipts list a blender?"},
[root / "assets/example-receipt.png"] * 2,
None,
)
)
cases.append(
(
"A camera clip.",
{
"type": "choice",
"instructions": "Which way does the square move?",
"criteria": {
"right": "From left to right",
"left": "From right to left",
"still": "It does not move",
},
},
None,
[root / "assets/example-video.mp4"],
)
)
records = []
for state, q, images, videos in cases:
start = time.monotonic()
logits, keys, encoded = await engine.logits(state, q, images, videos)
actual = torch.tensor(logits).softmax(-1).numpy()
if images or videos:
# Compare identical resized pixels, prompts, positions and the source BF16 model.
inputs = {
k: torch.from_numpy(np.asarray(v))
for k, v in encoded.items()
if k
in [
"input_ids",
"attention_mask",
"pixel_values",
"pixel_values_videos",
"image_grid_thw",
"video_grid_thw",
"mm_token_type_ids",
]
}
with torch.inference_mode():
h = reference.backbone(**inputs, use_cache=False).last_hidden_state[
:, -1
]
scores = (h.float() @ reference.readout.T)[0, : len(keys)]
expected = scores.softmax(-1).numpy()
else:
seq = encoded["input_ids"][0].tolist()
expected = np.array(reference.probabilities([seq], [len(keys)])[0])
r = {
"type": q["type"],
"modality": "video"
if videos
else "images"
if images and len(images) > 1
else "image"
if images
else "text",
"tokens": len(encoded["input_ids"][0]),
"options": len(keys),
"coreai_choice": int(actual.argmax()),
"upstream_choice": int(expected.argmax()),
"max_probability_error": float(abs(expected - actual).max()),
"total_seconds": time.monotonic() - start,
}
print(json.dumps(r), flush=True)
records.append(r)
if not np.isfinite(actual).all():
raise AssertionError("Nonfinite Core AI probabilities")
if r["coreai_choice"] != r["upstream_choice"]:
raise AssertionError(r)
(bundle / "validation.json").write_text(json.dumps(records, indent=2) + "\n")
try:
await engine.logits("refund " * 1000, CASES[0][1])
except ValueError:
pass
else:
raise AssertionError("Oversized request was accepted")
print(
"PASS: selected answers agree; probability drift is recorded, not treated as strict parity",
flush=True,
)
if __name__ == "__main__":
asyncio.run(main())
|