File size: 9,201 Bytes
2778af7 | 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 | """Inference engine for Pluto checkpoints."""
from __future__ import annotations
import json
import os
import time
from contextlib import nullcontext
from typing import Any, Iterable
import numpy as np
import torch
import torch.nn.functional as F
from .calibration import ConformalGate
from .model import load_pluto
from .prompt import OPTION_MARKER, SPECIAL_TOKENS, option_texts, render_prompt
from .schema import KIND_TO_ID, Question, normalize_state, parse_questions
class PlutoEngine:
def __init__(self, path: str, device: str | None = None):
if device is None:
device = "cuda" if torch.cuda.is_available() and torch.cuda.device_count() > 0 else "cpu"
self.path = path
self.device = torch.device(device)
self.model, self.config = load_pluto(path, device=self.device)
from transformers import AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(path)
missing = [t for t in SPECIAL_TOKENS if t not in self.tokenizer.get_vocab()]
if missing:
self.tokenizer.add_special_tokens({"additional_special_tokens": missing})
self.marker_id = self.tokenizer.convert_tokens_to_ids(OPTION_MARKER)
self.model_name = self.config.get("model_name", "pluto")
cal = self.config.get("calibration", {})
self.temperatures = {
k: float(cal.get("temperature", {}).get(k, 1.0))
for k in KIND_TO_ID
}
self.gates = {}
for k, v in cal.get("gates", {}).items():
try:
self.gates[k] = ConformalGate.from_dict(v)
except Exception:
pass
self.train_max_len = int(self.config.get("training", {}).get("max_len", 640))
@property
def info(self) -> dict:
return {
"name": self.model_name,
"path": self.path,
"device": str(self.device),
"train_max_len": self.train_max_len,
"temperatures": self.temperatures,
"gates": {k: v.to_dict() for k, v in self.gates.items()},
"params": int(sum(p.numel() for p in self.model.parameters())),
}
def _tokenize_one(self, state: str, q: Question, max_len: int) -> tuple[list[int], list[int]]:
prompt = render_prompt(state, q)
ids = self.tokenizer(prompt, add_special_tokens=True)["input_ids"]
if len(ids) > max_len:
s = normalize_state(state)
best = None
for frac in (0.75, 0.55, 0.38, 0.25, 0.12):
keep = max(48, int(len(s) * frac))
head = keep // 2
tail = keep - head
clipped = s[:head] + "\n...\n" + (s[-tail:] if tail else "")
cand = render_prompt(clipped, q)
cand_ids = self.tokenizer(cand, add_special_tokens=True)["input_ids"]
best = cand_ids
if len(cand_ids) <= max_len:
ids = cand_ids
break
else:
ids = best
positions = [i for i, t in enumerate(ids) if t == self.marker_id]
if len(positions) != q.n_options:
raise ValueError(
f"prompt for question {q.name!r} has {len(positions)} option markers, "
f"expected {q.n_options}; shorten the state or the option descriptions"
)
return ids, positions
@torch.no_grad()
def _forward(self, encoded: list[tuple[list[int], list[int], int]]) -> tuple[np.ndarray, np.ndarray]:
max_len = max(len(ids) for ids, _, _ in encoded)
max_opt = max(len(pos) for _, pos, _ in encoded)
b = len(encoded)
input_ids = torch.zeros((b, max_len), dtype=torch.long)
attention = torch.zeros((b, max_len), dtype=torch.long)
marker = torch.zeros((b, max_opt), dtype=torch.long)
n_options = torch.zeros((b,), dtype=torch.long)
kinds = torch.zeros((b,), dtype=torch.long)
for i, (ids, pos, kind) in enumerate(encoded):
input_ids[i, : len(ids)] = torch.tensor(ids, dtype=torch.long)
attention[i, : len(ids)] = 1
marker[i, : len(pos)] = torch.tensor(pos, dtype=torch.long)
n_options[i] = len(pos)
kinds[i] = kind
input_ids = input_ids.to(self.device)
attention = attention.to(self.device)
marker = marker.to(self.device)
n_options = n_options.to(self.device)
kinds = kinds.to(self.device)
ctx = (
torch.autocast(device_type="cuda", dtype=torch.bfloat16)
if self.device.type == "cuda"
else nullcontext()
)
with ctx:
outputs = self.model(input_ids, attention, marker, n_options, kinds)
logits = outputs["logits"].float().cpu()
correct_prob = torch.sigmoid(outputs["correct_logit"].float()).cpu()
temps = torch.tensor(
[self.temperatures[k] for k in ("choice", "noul", "score")], dtype=torch.float32
)[kinds.cpu()]
probs = F.softmax(logits / temps.unsqueeze(-1), dim=-1)
opt_ids = torch.arange(max_opt).unsqueeze(0)
probs = probs.masked_fill(opt_ids >= n_options.cpu().unsqueeze(1), 0.0)
probs = probs / probs.sum(-1, keepdim=True).clamp_min(1e-9)
return probs.numpy(), correct_prob.numpy()
def predict(
self,
state: Any,
questions: dict[str, Any] | list[Question],
max_len: int | None = None,
alpha: float | None = None,
) -> dict:
qs = questions if isinstance(questions, list) else parse_questions(questions)
state_s = normalize_state(state)
max_len = max_len or self.train_max_len
encoded = []
for q in qs:
ids, pos = self._tokenize_one(state_s, q, max_len)
encoded.append((ids, pos, KIND_TO_ID[q.kind]))
t0 = time.perf_counter()
probs, correct_prob = self._forward(encoded)
latency = (time.perf_counter() - t0) * 1000.0
answers = {}
escalate = False
for i, q in enumerate(qs):
p = probs[i, : q.n_options]
top = int(np.argmax(p))
confidence = float(p[top])
gate = self.gates.get(q.kind)
threshold = gate.threshold if gate else 0.5
accept_p = float(correct_prob[i])
item = {
"type": q.kind,
"confidence": confidence,
"accept_probability": accept_p,
"escalate": bool(accept_p < threshold),
"probabilities": {k: float(v) for k, v in zip(q.keys, p)},
}
if q.kind == "choice":
item["choice"] = q.keys[top]
elif q.kind == "noul":
item["noul"] = float(p[1])
else:
labels = option_texts(q)
expected = float(np.dot(np.arange(q.n_options), p))
item["score"] = expected
item["score_normalized"] = expected / max(1, q.n_options - 1)
item["labels"] = labels
answers[q.name] = item
escalate = escalate or item["escalate"]
return {
"answers": answers,
"escalate": escalate,
"routing": {
"model": self.model_name,
"device": str(self.device),
"latency_ms": round(latency, 3),
},
}
def predict_many(
self,
items: Iterable[tuple[Any, Question]],
batch_size: int = 64,
max_len: int | None = None,
) -> list[dict]:
items = list(items)
max_len = max_len or self.train_max_len
enc = []
for state, q in items:
ids, pos = self._tokenize_one(normalize_state(state), q, max_len)
enc.append((ids, pos, KIND_TO_ID[q.kind]))
order = sorted(range(len(enc)), key=lambda i: len(enc[i][0]))
out: list[dict | None] = [None] * len(enc)
for start in range(0, len(order), batch_size):
chunk_idx = order[start : start + batch_size]
chunk = [enc[i] for i in chunk_idx]
probs, correct_prob = self._forward(chunk)
for row, i in enumerate(chunk_idx):
q = items[i][1]
p = probs[row, : q.n_options]
top = int(np.argmax(p))
if q.kind == "choice":
value = q.keys[top]
elif q.kind == "noul":
value = float(p[1])
else:
value = float(np.dot(np.arange(q.n_options), p))
out[i] = {
"index": i,
"top": top,
"value": value,
"probs": p.astype(np.float32),
"confidence": float(p[top]),
"accept_probability": float(correct_prob[row]),
}
return [o for o in out if o is not None]
|