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A bidirectional encoder reads the state, the typed question and every candidate
option. Each option is scored at its own marker token. A small permutation-
equivariant interaction stack compares options globally, then a scalar head
returns one logit per option. A second head predicts the teacher's confidence so
the runtime can abstain under a calibrated risk target.
"""
from __future__ import annotations
import json
import math
import os
from dataclasses import asdict, dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors.torch import load_file, save_file
from transformers import AutoConfig, AutoModel
from .schema import KIND_TO_ID
IGNORE_INDEX = -1
@dataclass
class PlutoArch:
hidden: int = 768
interaction_layers: int = 2
interaction_heads: int = 8
dropout: float = 0.05
max_options: int = 192
max_score_levels: int = 12
class PlutoForDecisions(nn.Module):
def __init__(
self,
backbone_name: str | None = None,
backbone_config: AutoConfig | None = None,
arch: PlutoArch | None = None,
vocab_size: int | None = None,
freeze_layers: int = 0,
):
super().__init__()
self.arch = arch or PlutoArch()
if backbone_config is None:
self.backbone = AutoModel.from_pretrained(
backbone_name, attn_implementation="sdpa"
)
else:
self.backbone = AutoModel.from_config(
backbone_config, attn_implementation="sdpa"
)
if vocab_size is not None and vocab_size != self.backbone.config.vocab_size:
self.backbone.resize_token_embeddings(vocab_size)
self.arch.hidden = self.backbone.config.hidden_size
h = self.arch.hidden
self.kind_embedding = nn.Embedding(len(KIND_TO_ID), h)
self.score_position = nn.Embedding(self.arch.max_score_levels, h)
self.option_in = nn.Sequential(nn.Linear(h, h), nn.LayerNorm(h))
self.global_in = nn.Sequential(nn.Linear(h, h), nn.LayerNorm(h))
layer = nn.TransformerEncoderLayer(
d_model=h,
nhead=self.arch.interaction_heads,
dim_feedforward=2 * h,
dropout=self.arch.dropout,
activation="gelu",
batch_first=True,
norm_first=True,
)
self.interaction = nn.TransformerEncoder(
layer, num_layers=self.arch.interaction_layers, enable_nested_tensor=False
)
self.option_norm = nn.LayerNorm(h)
self.scorer = nn.Sequential(
nn.Linear(h, h),
nn.GELU(),
nn.Dropout(self.arch.dropout),
nn.LayerNorm(h),
nn.Linear(h, 1),
)
self.confidence_head = nn.Sequential(
nn.Linear(h, h // 2), nn.GELU(), nn.Linear(h // 2, 1)
)
if freeze_layers > 0:
for p in self.backbone.embeddings.parameters():
p.requires_grad = False
for layer_mod in self.backbone.layers[:freeze_layers]:
for p in layer_mod.parameters():
p.requires_grad = False
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
marker_positions: torch.Tensor,
n_options: torch.Tensor,
kind_ids: torch.Tensor,
) -> dict[str, torch.Tensor]:
hidden = self.backbone(
input_ids=input_ids, attention_mask=attention_mask
).last_hidden_state
b, nmax = marker_positions.shape
h = hidden.shape[-1]
safe_positions = marker_positions.clamp(min=0).unsqueeze(-1).expand(-1, -1, h)
gathered = torch.gather(hidden, 1, safe_positions)
option_ids = torch.arange(nmax, device=input_ids.device).unsqueeze(0)
valid = option_ids < n_options.unsqueeze(1)
kind_vec = self.kind_embedding(kind_ids)
options = self.option_in(gathered) + kind_vec.unsqueeze(1)
score_rows = kind_ids == KIND_TO_ID["score"]
if score_rows.any():
pos = torch.arange(nmax, device=input_ids.device).clamp(
max=self.arch.max_score_levels - 1
)
pos_vec = self.score_position(pos).unsqueeze(0)
options = options + pos_vec * score_rows.view(-1, 1, 1).to(options.dtype)
global_vec = self.global_in(hidden[:, 0]) + kind_vec
sequence = torch.cat([global_vec.unsqueeze(1), options], dim=1)
pad_mask = torch.cat(
[
torch.zeros(b, 1, dtype=torch.bool, device=input_ids.device),
~valid,
],
dim=1,
)
sequence = self.interaction(sequence, src_key_padding_mask=pad_mask)
logits = self.scorer(sequence[:, 1:]).squeeze(-1).float()
logits = logits.masked_fill(~valid, -1e9)
correct_logit = self.confidence_head(sequence[:, 0]).squeeze(-1).float()
return {
"logits": logits,
"correct_logit": correct_logit,
"valid": valid,
}
def decision_loss(
outputs: dict[str, torch.Tensor],
targets: torch.Tensor,
kind_ids: torch.Tensor,
weights: dict[str, float] | None = None,
) -> tuple[torch.Tensor, dict[str, float]]:
weights = weights or {"kl": 1.0, "ce": 0.5, "rps": 0.5, "conf": 0.1}
logits = outputs["logits"]
valid = outputs["valid"]
target = targets.clamp_min(0.0)
target = target / target.sum(-1, keepdim=True).clamp_min(1e-9)
log_probs = F.log_softmax(logits, dim=-1)
row_valid = valid.any(-1)
kind_w = torch.tensor([1.0, 0.8, 1.2], device=logits.device)[kind_ids]
kl = -(target * log_probs).sum(-1)
kl = (kl * kind_w)[row_valid].mean()
argmax = target.argmax(-1)
ce = F.nll_loss(log_probs, argmax, reduction="none")
ce = (ce * kind_w)[row_valid].mean()
score_rows = (kind_ids == KIND_TO_ID["score"]) & row_valid
rps = torch.zeros((), device=logits.device)
if score_rows.any():
probs = log_probs.exp()
cs = (probs.cumsum(-1) - target.cumsum(-1))[:, :-1]
row_rps = cs.pow(2).mean(-1)
rps = row_rps[score_rows].mean()
correct_target = (logits.argmax(-1) == argmax).float()
conf = F.binary_cross_entropy_with_logits(
outputs["correct_logit"], correct_target, reduction="none"
)
conf = conf[row_valid].mean()
total = (
weights["kl"] * kl
+ weights["ce"] * ce
+ weights["rps"] * rps
+ weights["conf"] * conf
)
parts = {
"kl": float(kl.detach()),
"ce": float(ce.detach()),
"rps": float(rps.detach()),
"correct": float(conf.detach()),
}
return total, parts
def save_pluto(
model: PlutoForDecisions,
tokenizer,
out_dir: str,
extra: dict,
) -> None:
os.makedirs(out_dir, exist_ok=True)
state = {k: v.detach().cpu() for k, v in model.state_dict().items()}
save_file(state, os.path.join(out_dir, "model.safetensors"))
tokenizer.save_pretrained(out_dir)
model.backbone.config.to_json_file(os.path.join(out_dir, "backbone_config.json"))
config = {
"format": "pluto-1",
"arch": asdict(model.arch),
"backbone_type": model.backbone.config.model_type,
"vocab_size": model.backbone.config.vocab_size,
**extra,
}
with open(os.path.join(out_dir, "pluto_config.json"), "w", encoding="utf-8") as f:
json.dump(config, f, indent=2)
def load_pluto(path: str, device: str | torch.device = "cpu") -> tuple[PlutoForDecisions, dict]:
with open(os.path.join(path, "pluto_config.json"), "r", encoding="utf-8") as f:
config = json.load(f)
backbone_config = AutoConfig.for_model(
**json.load(open(os.path.join(path, "backbone_config.json"), encoding="utf-8"))
)
arch = PlutoArch(**config["arch"])
model = PlutoForDecisions(
backbone_config=backbone_config,
arch=arch,
vocab_size=config["vocab_size"],
)
state = load_file(os.path.join(path, "model.safetensors"))
model.load_state_dict(state, strict=True)
model.to(device)
model.eval()
return model, config
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