File size: 3,061 Bytes
3265ba1 | 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 | """Decision Index engine for Dinah-0 (https://github.com/apolinario/decision-index).
python -m decision_index run --engine decision_index_engine:DinahEngine \
--option revision=<commit> --edition 0.2.1 --suite-dir suite-0.2 --out runs/dinah-0-apache
Put this file next to `dinah.py` (both are in the model repository) and on PYTHONPATH.
Kit rules: nothing is truncated and no option is dropped. A request that does not fit in 8,192 tokens,
or a question type the model does not support, raises `Unsupported`.
One call = one request (a state and several questions); all questions of a request go through the
encoder together, 16 at a time. Probabilities are the softmax over the option logits; the confidence
of a `choice` answer comes from the confidence head.
"""
from __future__ import annotations
import sys
from pathlib import Path
from decision_index.engines import Engine, Unsupported
sys.path.insert(0, str(Path(__file__).resolve().parent))
from dinah import Dinah, RequestTooLong # noqa: E402
class DinahEngine(Engine):
name = "dinah-0-apache"
latency = ("In-process request wall time: tokenization plus one encoder pass per request "
"(all questions of the request batched together); excludes model loading.")
def __init__(self, repo: str = "Lukitaduarte/dinah-0-apache", revision: str | None = None, device: str | None = None,
dtype: str | None = None, batch_size: int = 16, calibrate: str | bool = True, **options):
super().__init__(**options)
self.model = Dinah.from_pretrained(repo, device=device, dtype=dtype, revision=revision)
self.calibrated = str(calibrate).lower() not in ("0", "false", "no", "off")
if not self.calibrated: # raw logits and raw confidence head, for comparison with the calibrated run
self.model.temperature, self.model.platt = None, None
self.batch_size = int(batch_size)
self.provenance = {"repo": repo, "revision": revision or "main", "device": self.model.device,
"dtype": self.model.dtype, "max_length": self.model.max_length, "calibrated": self.calibrated,
"truncation": "none: requests above 8,192 tokens raise Unsupported"}
def __call__(self, state, questions):
keys = list(questions)
batch = []
for key in keys:
q = questions[key]
if q["type"] not in ("choice", "noul"):
raise Unsupported(f"question type {q['type']}")
batch.append({"type": q["type"], "state": state, "instructions": q.get("instructions"),
"criteria": q.get("criteria")})
try:
answers = self.model.predict(batch, batch_size=self.batch_size)
except RequestTooLong as e:
raise Unsupported(f"context window: {e}")
return {"model": "dinah-0-apache", "answers": dict(zip(keys, answers))}, None
def synchronize(self):
if self.model.device.startswith("cuda"):
self.model.torch.cuda.synchronize()
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