File size: 2,341 Bytes
0deb31c | 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 | """Inspect Jeff's pinned, trained GLiFormer decision boundary using a real request."""
from __future__ import annotations
import json
from pathlib import Path
import torch
from huggingface_hub import snapshot_download
from jeff.backends.torch_backend import TorchBackend
from jeff.core.backend import Group
SOURCE_REPO = "knowledgator/gliformer-large-v1"
SOURCE_REVISION = "d0a4e53d09cebe6bc963dd9be319d4279084bb2d"
def main() -> None:
torch.set_num_threads(2)
checkpoint = snapshot_download(SOURCE_REPO, revision=SOURCE_REVISION)
backend = TorchBackend(checkpoint, device="cpu", dtype="float32", attn_kernel="eager", batch_size=1)
text = "The invoice was charged twice and the customer asks for a refund."
group = Group(
key="route",
labels=("billing: invoice or payment issue", "support: technical product issue"),
name="Choose the correct support queue",
)
native = backend.score([text], [[group]])[0]
tokens, _, _ = backend.model.prepare_inputs([text])
batch = backend._collator(
[
{
"tokenized_text": tokens[0],
"classification": [
{
"name": group.name,
"description": group.description,
"all_labels": list(group.labels),
"true_labels": [],
}
],
}
]
)
report = {
"source_repo": SOURCE_REPO,
"source_revision": SOURCE_REVISION,
"backend": backend.info(),
"scores": native.scores,
"input_tokens": native.input_tokens,
"model_parameters": sum(parameter.numel() for parameter in backend.model.model.parameters()),
"batch_tensors": {
name: {"shape": list(value.shape), "dtype": str(value.dtype)}
for name, value in batch.items()
if isinstance(value, torch.Tensor)
},
"batch_other": {name: str(type(value)) for name, value in batch.items() if not isinstance(value, torch.Tensor)},
}
output = Path("build/native-probe.json")
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(report, indent=2) + "\n")
print(json.dumps(report, indent=2), flush=True)
if __name__ == "__main__":
main()
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