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8cd99ae | 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 | """Inference for an EmbeddingGemma 2 decision model (Clef-style head).
Usage:
from decision_model import DecisionModel
dm = DecisionModel("username/embeddinggemma2-decision") # or a local folder
dm.decide("Hi, I was charged twice...", {
"team": {"type": "choice", "instructions": "Which team should handle this?",
"criteria": {"billing": "payments, refunds", "technical": "bugs", "sales": "pricing"}},
"refund": {"type": "noul", "instructions": "The customer asks for a refund."},
"urgency": {"type": "score", "instructions": "How urgent is this?",
"criteria": ["Can wait", "Normal queue", "This week", "Today"]},
})
Run in float32 or bfloat16 only. EmbeddingGemma 2 returns NaN in float16.
"""
import os
import numpy as np
import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer
class DecisionHead(nn.Module):
"""Score of an option = MLP(option vector, question vector)."""
def __init__(self, d, hidden=512):
super().__init__()
self.norm = nn.LayerNorm(d)
self.w_opt, self.w_q = nn.Linear(d, hidden), nn.Linear(d, hidden)
self.w_int = nn.Linear(d, hidden, bias=False)
self.out = nn.Sequential(nn.GELU(), nn.Dropout(0.1), nn.Linear(hidden, 1))
def forward(self, opt_vecs, q_vec):
o, q = self.norm(opt_vecs), self.norm(q_vec).unsqueeze(0)
z = self.w_opt(o) + self.w_q(q) + self.w_int(o * q)
return self.out(z).squeeze(-1)
def option_list(spec):
"""choice: criteria {key: description}; score: criteria [level descriptions]; noul: false/true."""
t, crit = spec.get("type", "choice"), spec.get("criteria")
if isinstance(crit, dict) and crit:
keys = list(crit.keys())
return keys, [str(crit[k]) for k in keys]
if isinstance(crit, list) and crit:
return [str(i) for i in range(len(crit))], [str(c) for c in crit]
if t == "noul":
return ["false", "true"], ["No.", "Yes."]
if t == "score":
n = int(spec.get("levels", 4))
return [str(i) for i in range(n)], [f"Level {i}" for i in range(n)]
raise ValueError(f"Cannot determine options for question: {spec}")
def question_text(qtype, instructions):
if qtype == "noul":
return "\nstatement (true or false): " + instructions
if qtype == "score":
return "\nrating question: " + instructions
return "\nquestion: " + instructions
def option_text(key, desc):
return f"{key}: {desc}" if desc and desc != key else str(key)
def temp_for(temps, domain, qtype):
for g in (domain, "typed", "extra"):
if f"{g}|{qtype}" in temps:
return temps[f"{g}|{qtype}"]
return 1.0
def softmax_np(x, T=1.0):
z = x / T
z = z - z.max()
e = np.exp(z)
return e / e.sum()
class DecisionModel:
def __init__(self, path_or_repo, device=None, token=None):
path = path_or_repo
if not os.path.isdir(path):
from huggingface_hub import snapshot_download
path = snapshot_download(path_or_repo, token=token)
ck = torch.load(os.path.join(path, "head.pt"), map_location="cpu")
cfg = ck.get("config", {})
self.prefix, self.max_len, self.temps = ck["prefix"], int(cfg.get("max_len", 1024)), ck.get("temps", {})
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
self.bf16 = self.device == "cuda" and torch.cuda.is_bf16_supported()
self.tok = AutoTokenizer.from_pretrained(os.path.join(path, "encoder"))
self.enc = AutoModel.from_pretrained(os.path.join(path, "encoder"), torch_dtype=torch.float32)
self.enc.to(self.device).eval()
self.head = DecisionHead(ck["d_model"])
self.head.load_state_dict(ck["head"])
self.head.to(self.device).eval()
self.lead, self.trail = self._special_wrap()
self.pad_id = self.tok.pad_token_id if self.tok.pad_token_id is not None else 0
self.mark = self._ids("\n- ")
def _ids(self, s):
return self.tok(s, add_special_tokens=False)["input_ids"]
def _special_wrap(self):
full = self.tok("hello world")["input_ids"]
core = self._ids("hello world")
for p in range(len(full) - len(core) + 1):
if full[p:p + len(core)] == core:
return full[:p], full[p + len(core):]
return [], []
def _build(self, text, qs):
parts = []
for q in qs:
qids = self._ids(question_text(q["type"], q["instructions"]))
opts = [self._ids(option_text(k, d)) for k, d in zip(q["keys"], q["descs"])]
parts.append((qids, opts))
q_total = sum(len(qi) + sum(len(self.mark) + len(o) for o in os_) for qi, os_ in parts)
prefix = self._ids(self.prefix)
budget = self.max_len - len(self.lead) - len(self.trail) - len(prefix) - q_total
if budget < 16:
raise ValueError("Questions are too long for max_len")
ids = list(self.lead) + prefix + self._ids(text)[:budget] # cut the end of the input, keep questions
q_spans, o_spans = [], []
for qids, opts in parts:
s = len(ids)
ids += qids
q_spans.append((s, len(ids)))
spans = []
for o in opts:
ids += self.mark
s = len(ids)
ids += o
spans.append((s, len(ids)))
o_spans.append(spans)
return ids + list(self.trail), q_spans, o_spans
@torch.no_grad()
def decide(self, text, questions, domain="extra"):
"""domain: "extra" for natural text (messages, emails), "typed" for JSON state like typed-decisions."""
qs = []
for name, spec in questions.items():
keys, descs = option_list(spec)
qs.append(dict(name=name, type=spec.get("type", "choice"),
instructions=spec.get("instructions", ""), keys=keys, descs=descs))
ids, q_spans, o_spans = self._build(text if isinstance(text, str) else str(text), qs)
x = torch.tensor([ids], device=self.device)
with torch.autocast("cuda", dtype=torch.bfloat16, enabled=self.bf16):
out = self.enc(input_ids=x, attention_mask=torch.ones_like(x))
H = (out.last_hidden_state if getattr(out, "last_hidden_state", None) is not None else out[0])[0].float()
res = {}
for q, (qs_, qe), spans in zip(qs, q_spans, o_spans):
logits = self.head(torch.stack([H[s:e].mean(0) for s, e in spans]), H[qs_:qe].mean(0))
p = softmax_np(logits.cpu().numpy(), temp_for(self.temps, domain, q["type"]))
i = int(p.argmax())
ans = q["keys"][i]
if q["type"] == "noul":
ans = ans == "true"
elif q["type"] == "score":
ans = int(ans)
res[q["name"]] = {"answer": ans, "confidence": float(p[i]),
"probabilities": {k: round(float(v), 4) for k, v in zip(q["keys"], p)}}
return res
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