File size: 7,098 Bytes
59022ca | 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 | """Real (tiny) model backends, small enough to run on a laptop CPU.
Two of them, because they trade off differently:
* :class:`TextPlannerBrain` - a ~0.5B *text* model over the OCR index. No vision,
no big download, and the element index does all the grounding, which is why it
works at this size at all.
* :class:`VLMBrain` - a tiny *vision* model that sees the screenshot itself
(256M-class models can read a button label from pixels, but reason poorly, so the
menu still carries most of the decision).
Both inherit menu building, prompt shape and lenient parsing from
:class:`~tiny_computer_agent.brain.ModelBrain`, so swapping weights never changes the
harness contract.
"""
from __future__ import annotations
import os
from typing import Any
from tiny_computer_agent.brain import BrainError, ModelBrain
from tiny_computer_agent.observe import Observation
DEFAULT_TEXT_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
DEFAULT_VISION_MODEL = "HuggingFaceTB/SmolVLM-256M-Instruct"
def resolve_device(prefer: str | None = None) -> str:
"""Pick a torch device: explicit preference, then CUDA, then CPU."""
try:
import torch
except ImportError: # pragma: no cover - depends on the machine
raise BrainError("model backends need torch: pip install 'tiny-computer-agent[vision]'")
if prefer:
return prefer
return "cuda" if torch.cuda.is_available() else "cpu"
def pick_dtype(device: str) -> Any:
import torch
if device.startswith("cuda"):
return torch.bfloat16
return torch.float32
def model_dependencies() -> tuple[bool, str]:
"""(available, detail) for preflight and the doctor command."""
try:
import transformers
except ImportError:
return False, "transformers is not installed (pip install 'tiny-computer-agent[vision]')"
try:
import torch
except ImportError:
return False, "torch is not installed (pip install 'tiny-computer-agent[vision]')"
return True, f"transformers {transformers.__version__}, torch {torch.__version__}"
def model_cached(model_id: str) -> bool:
"""True when the weights are already in the local Hugging Face cache."""
try:
from huggingface_hub import constants
except ImportError: # pragma: no cover
return False
folder = os.path.join(constants.HF_HUB_CACHE, "models--" + model_id.replace("/", "--"))
return os.path.isdir(folder)
class TextPlannerBrain(ModelBrain):
"""Causal-LM planner over the OCR index: no pixels in, one menu number out."""
name = "text-planner"
requires_index = True
def __init__(self, model_id: str = DEFAULT_TEXT_MODEL, *, device: str | None = None,
**kwargs: Any) -> None:
super().__init__(model_id, **kwargs)
self.device = device
self._tokenizer: Any = None
self._model: Any = None
def _load(self) -> None:
if self._model is not None:
return
try:
from transformers import AutoModelForCausalLM, AutoTokenizer
except ImportError as exc: # pragma: no cover - depends on the machine
raise BrainError(
"text planner needs transformers and torch: "
"pip install 'tiny-computer-agent[vision]'"
) from exc
self.device = resolve_device(self.device)
self._tokenizer = AutoTokenizer.from_pretrained(self.model_id)
self._model = AutoModelForCausalLM.from_pretrained(
self.model_id, dtype=pick_dtype(self.device)
).to(self.device).eval()
def unload(self) -> None:
self._model = None
self._tokenizer = None
def _generate(self, prompt: str, observation: Observation) -> str:
import torch
self._load()
messages = [{"role": "user", "content": prompt}]
text = self._tokenizer.apply_chat_template(
messages, add_generation_prompt=True, tokenize=False
)
inputs = self._tokenizer(text, return_tensors="pt").to(self.device)
with torch.inference_mode():
output = self._model.generate(
**inputs,
max_new_tokens=self.max_new_tokens,
do_sample=False,
pad_token_id=self._tokenizer.eos_token_id,
)
return self._tokenizer.decode(
output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
).strip()
class VLMBrain(ModelBrain):
"""Tiny vision-language model: sees the screenshot, answers with a menu number."""
name = "vlm"
requires_index = False
def __init__(self, model_id: str = DEFAULT_VISION_MODEL, *, device: str | None = None,
**kwargs: Any) -> None:
super().__init__(model_id, **kwargs)
self.device = device
self._processor: Any = None
self._model: Any = None
def _load(self) -> None:
if self._model is not None:
return
try:
from transformers import AutoModelForImageTextToText, AutoProcessor
except ImportError as exc: # pragma: no cover - depends on the machine
raise BrainError(
"the vision backend needs transformers and torch: "
"pip install 'tiny-computer-agent[vision]'"
) from exc
self.device = resolve_device(self.device)
self._processor = AutoProcessor.from_pretrained(self.model_id)
self._model = AutoModelForImageTextToText.from_pretrained(
self.model_id, dtype=pick_dtype(self.device)
).to(self.device).eval()
def unload(self) -> None:
self._model = None
self._processor = None
def _generate(self, prompt: str, observation: Observation) -> str:
import torch
image = observation.frame
if image is None or isinstance(image, str):
raise BrainError(
"the vision backend needs a real screenshot but the observation carries "
f"{type(image).__name__} - run with --live, or use the text planner"
)
self._load()
messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": prompt}]}]
text = self._processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = self._processor(text=text, images=[image], return_tensors="pt").to(self.device)
with torch.inference_mode():
output = self._model.generate(
**inputs, max_new_tokens=self.max_new_tokens, do_sample=False
)
return self._processor.batch_decode(
output[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True
)[0].strip()
MODEL_BRAINS = {"text-planner": TextPlannerBrain, "vlm": VLMBrain}
def make_model_brain(name: str = "text-planner", **kwargs: Any) -> ModelBrain:
"""Build a model brain by name, with an actionable error for unknown names."""
if name not in MODEL_BRAINS:
raise BrainError(f"unknown brain {name!r}; expected one of {tuple(MODEL_BRAINS)}")
return MODEL_BRAINS[name](**kwargs)
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