File size: 6,712 Bytes
5b31db0 | 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 | """Standalone runtime for the merged Pluto-2.1 decision model."""
from __future__ import annotations
import hashlib
import json
import os
import random
from pathlib import Path
from typing import Any, Mapping, Sequence
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
SYSTEM_PROMPT = (
"Choose exactly one supplied decision token. Return only that token. "
"Do not explain the answer."
)
DECISION_TYPES = frozenset({"choice", "score", "noul"})
def _canonical_json(value: Any) -> str:
return json.dumps(value, ensure_ascii=False, separators=(",", ":"), sort_keys=True)
def _compact_criteria(mapped_criteria: Sequence[Mapping[str, Any]]):
parsed_descriptions = []
description_fields = None
for criterion in mapped_criteria:
try:
parsed = json.loads(str(criterion["d"]))
except json.JSONDecodeError:
parsed = None
if not isinstance(parsed, dict) or not all(isinstance(key, str) for key in parsed):
description_fields = None
break
fields = sorted(parsed)
if description_fields is None:
description_fields = fields
elif fields != description_fields:
description_fields = None
break
parsed_descriptions.append(parsed)
if description_fields is not None and len(parsed_descriptions) == len(mapped_criteria):
rows = [
[criterion["t"], criterion["o"], criterion["n"], *(parsed[field] for field in description_fields)]
for criterion, parsed in zip(mapped_criteria, parsed_descriptions, strict=True)
]
return ["t", "o", "n", *description_fields], rows
return ["t", "o", "n", "d"], [
[criterion["t"], criterion["o"], criterion["n"], criterion["d"]]
for criterion in mapped_criteria
]
def render_decision_prompt(
*,
state: Any,
instructions: str,
candidates: Sequence[Mapping[str, Any]],
decision_type: str,
decision_tokens: Sequence[str],
seed: int,
row_id: str,
prompt_schema: str,
):
candidate_count = len(candidates)
if not 2 <= candidate_count <= 255:
raise ValueError("Pluto-2.1 supports 2 to 255 candidates")
if decision_type not in DECISION_TYPES:
raise ValueError("decision_type must be choice, score, or noul")
if len(decision_tokens) < candidate_count:
raise ValueError("not enough decision tokens")
presentation_order = list(range(candidate_count))
digest = hashlib.sha256(f"{seed}:{row_id}:candidate-order".encode()).digest()
random.Random(int.from_bytes(digest[:8], "big")).shuffle(presentation_order)
mapped = []
candidate_to_slot = {}
for slot, candidate_index in enumerate(presentation_order):
candidate = candidates[candidate_index]
candidate_id = str(candidate["id"])
candidate_to_slot[candidate_id] = slot
mapped.append(
{
"t": decision_tokens[slot],
"o": candidate_index,
"n": str(candidate["label"]),
"d": candidate.get("description") or "",
}
)
fields, criteria = _compact_criteria(mapped)
content = _canonical_json(
{
"schema": prompt_schema,
"state": state,
"question": {
"instructions": instructions,
"type": decision_type,
"criteria_fields": fields,
"criteria": criteria,
},
}
)
return content, presentation_order, candidate_to_slot
class Pluto21:
def __init__(self, path: str, device: str | None = "cuda"):
self.path = Path(path)
with open(self.path / "pluto21_config.json", encoding="utf-8") as handle:
self.config = json.load(handle)
self.model = AutoModelForCausalLM.from_pretrained(
path,
dtype=torch.bfloat16,
device_map=device,
).eval()
tokenizer_path = self.path / "tokenizer"
self.tokenizer = AutoTokenizer.from_pretrained(
str(tokenizer_path if tokenizer_path.is_dir() else path)
)
self.tokenizer.padding_side = "left"
self.decision_tokens = [
f"<|decision_{index:03d}|>"
for index in range(int(self.config["decision_token_count"]))
]
ids = [self.tokenizer.convert_tokens_to_ids(token) for token in self.decision_tokens]
if any(token_id is None for token_id in ids):
raise RuntimeError("tokenizer is missing the decision tokens")
self.decision_token_ids = torch.tensor(ids, dtype=torch.long)
def predict(
self,
*,
state: Any,
instructions: str,
candidates: Sequence[Mapping[str, Any]],
decision_type: str = "choice",
seed: int = 0,
row_id: str = "0",
) -> dict[str, Any]:
content, presentation_order, candidate_to_slot = render_decision_prompt(
state=state,
instructions=instructions,
candidates=candidates,
decision_type=decision_type,
decision_tokens=self.decision_tokens,
seed=seed,
row_id=row_id,
prompt_schema=self.config["prompt_schema"],
)
prompt = self.tokenizer.apply_chat_template(
[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": content},
],
tokenize=False,
add_generation_prompt=True,
enable_thinking=self.config.get("decision_prompt_enable_thinking", False),
)
encoded = self.tokenizer(str(prompt), return_tensors="pt", truncation=False)
device = self.model.device
ids = encoded["input_ids"].to(device)
mask = encoded["attention_mask"].to(device)
with torch.inference_mode():
logits = self.model(input_ids=ids, attention_mask=mask).logits[0, -1]
logits = logits.index_select(0, self.decision_token_ids.to(device))
slot_logits = logits[: len(candidates)].float()
slot_probs = torch.softmax(slot_logits, dim=-1)
probabilities = torch.empty_like(slot_probs)
for slot, candidate_index in enumerate(presentation_order):
probabilities[candidate_index] = slot_probs[slot]
return {
"content": content,
"prompt": str(prompt),
"candidate_to_slot": candidate_to_slot,
"slot_logits": slot_logits.detach().cpu().tolist(),
"probabilities": probabilities.detach().cpu().tolist(),
}
__all__ = ["Pluto21", "render_decision_prompt"]
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