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9df612b cd5d868 9df612b | 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 | """Milo Pointer: a 149M on-device model that picks the next UI action from an accessibility tree.
from milo_pointer import MiloPointer
m = MiloPointer.load("milo-pointer") # folder with model.safetensors
a = m.predict(goal="Turn on dark mode", instruction="Open display settings",
elements=[{"cls": "TextView", "text": "Display", "b": [0, 400, 1080, 520], "click": 1}, ...],
width=1080, height=2400)
# {'action_type': 'click', 'element': 3, 'confidence': 0.998, 'defer': False}
Elements are dicts with: cls, text, desc, hint, rid (resource id), b = [left, top, right, bottom] in pixels, and 0/1 flags
click, long, edit, scroll, checked, sel. Only the first 220 elements are read. When `defer` is True the model is unsure
(confidence below the threshold registered for the AndroidControl router); send the step to a larger model instead.
"""
import re
from pathlib import Path
import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer
BASE = "answerdotai/ModernBERT-base"
TYPES = [
"click",
"long_press",
"input_text",
"scroll_up",
"scroll_down",
"scroll_left",
"scroll_right",
"navigate_back",
"navigate_home",
"open_app",
"wait",
]
DEFER_BELOW = 0.9854
MAXLEN = 3072
def _words(rid):
return " ".join(re.sub(r"([a-z])([A-Z])", r"\1 \2", rid).replace("_", " ").split()).lower()
def el_text(e, W, H): # identical to the training rendering
g = lambda k: e.get(k) or ""
main = (g("text") or g("desc") or g("hint"))[:80]
extra = [_words(g("rid"))] if g("rid") else []
if g("desc") and g("text"):
extra.append(g("desc")[:40])
if g("hint") and (g("text") or g("desc")):
extra.append("hint " + g("hint")[:30])
x = (e["b"][0] + e["b"][2]) / 2 / max(W, 1)
y = (e["b"][1] + e["b"][3]) / 2 / max(H, 1)
flags = [
f
for f, k in (
("tap", "click"),
("edit", "edit"),
("scroll", "scroll"),
("checked", "checked"),
("selected", "sel"),
)
if e.get(k)
]
return f"{g('cls')} {main} | {' | '.join(extra)} @{int(y*10)},{int(x*10)} {' '.join(flags)}"
class _Net(nn.Module):
def __init__(self, cfg):
super().__init__()
self.enc = AutoModel.from_config(cfg)
h = cfg.hidden_size
self.head_type = nn.Linear(h, len(TYPES))
self.pt = nn.Linear(h, 1)
self.se = nn.Linear(h, 2)
def forward(self, ids):
hs = self.enc(input_ids=ids).last_hidden_state[0]
return self.head_type(hs[0]), self.pt(hs).squeeze(-1), self.se(hs)
class MiloPointer:
def __init__(self, net, tok, device):
self.net, self.tok, self.device = net, tok, device
@classmethod
def load(cls, path, device=None):
from safetensors.torch import load_file
from transformers import AutoConfig
device = device or (
"cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
)
p = Path(path)
tok = AutoTokenizer.from_pretrained(p if (p / "tokenizer.json").exists() else BASE)
cfg = AutoConfig.from_pretrained(p if (p / "config.json").exists() else BASE)
net = _Net(cfg)
net.load_state_dict(
{k: v.float() for k, v in load_file(str(p / "model.safetensors")).items()}, strict=True
)
net = net.to(device).eval()
if device != "cpu":
net = net.half()
return cls(net, tok, device)
@torch.no_grad()
def predict(self, goal, instruction, elements, width, height, last_step="new step"):
tok = self.tok
prefix = f"{instruction} || {last_step} || goal: {goal}"
p = tok(prefix, add_special_tokens=False, return_offsets_mapping=True)
ids = [tok.cls_token_id] + p["input_ids"] + [tok.sep_token_id]
offs = [(0, 0)] + p["offset_mapping"] + [(0, 0)]
marks, kept = [], []
for k, e in enumerate(elements[:220]):
t = tok(el_text(e, width, height), add_special_tokens=False)["input_ids"][:28]
if len(ids) + len(t) + 2 > MAXLEN:
break
marks.append(len(ids))
ids += [tok.mask_token_id] + t
kept.append(k)
ids.append(tok.sep_token_id)
lt, lp, lse = (x.float() for x in self.net(torch.tensor([ids], device=self.device)))
pt = torch.softmax(lt, -1)
conf = float(pt.max())
ty = TYPES[int(pt.argmax())]
out = {"action_type": ty}
if ty in ("click", "long_press") and marks:
sm = torch.softmax(lp[marks], -1)
j = int(sm.argmax())
conf *= float(sm[j])
k = kept[j]
eb = elements[k]["b"]
out.update(element=k, x=(eb[0] + eb[2]) // 2, y=(eb[1] + eb[3]) // 2)
elif ty in ("input_text", "open_app"):
n = len(offs)
s = int(lse[:n, 0].argmax())
e = int(lse[s:n, 1].argmax()) + s
out["text" if ty == "input_text" else "app_name"] = prefix[offs[s][0] : offs[e][1]]
out["confidence"] = round(conf, 4)
out["defer"] = conf < DEFER_BELOW
return out
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