File size: 7,766 Bytes
ffa8431 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | """Decision 3.0 host adapter. All neural inference runs through Core AI."""
import json
from pathlib import Path
import numpy as np
from coreai.runtime import AIModel, NDArray, SpecializationOptions
from PIL import Image
from transformers import AutoProcessor, AutoTokenizer
from d3_format import SYSTEM_PROMPT, user_prompt
from d3_format import render as render_d3
from d3_runtime import load_image, load_video, normalize_question, product_answer
def pack(encoded, manifest, features=None):
ids = np.asarray(encoded["input_ids"], dtype=np.int32)
n = ids.shape[1]
length = manifest["sequence_length"]
hidden = manifest["hidden_size"]
if n > length:
raise ValueError(
f"Prompt needs {n} tokens; asset capacity is {length}; input was not truncated"
)
pad = length - n
ids = np.pad(ids, ((0, 0), (pad, 0)), constant_values=0)
mask = np.pad(np.ones((1, n), dtype=np.int32), ((0, 0), (pad, 0)))
pos = np.zeros((4, 1, length), dtype=np.int32)
pos[0, 0, pad:] = np.arange(n)
media_mask = np.zeros((1, length, 1), dtype=np.bool_)
media = np.zeros((1, length, hidden), dtype=np.float32)
# Qwen3.5 mRoPE: text increments all axes, media contributes its grid coordinates.
types = np.asarray(
encoded.get("mm_token_type_ids", np.zeros((1, n))), dtype=np.int32
)[0]
cursor = 0
offset = 0
feature_index = 0
grid_side = manifest["image_side"] // 16 // 2
while cursor < n:
if types[cursor] == 0:
end = cursor + 1
while end < n and types[end] == 0:
end += 1
pos[1:, 0, pad + cursor : pad + end] = np.arange(
offset, offset + end - cursor
)
offset += end - cursor
cursor = end
else:
count = grid_side**2
end = cursor + count
if end > n or not np.all(types[cursor:end] == types[cursor]):
raise ValueError("Unexpected visual grid")
pos[1, 0, pad + cursor : pad + end] = offset
pos[2, 0, pad + cursor : pad + end] = offset + np.repeat(
np.arange(grid_side), grid_side
)
pos[3, 0, pad + cursor : pad + end] = offset + np.tile(
np.arange(grid_side), grid_side
)
media_mask[0, pad + cursor : pad + end] = True
media[0, pad + cursor : pad + end] = features[feature_index]
feature_index += 1
offset += grid_side
cursor = end
return dict(
input_ids=ids,
attention_mask=mask,
position_ids=pos,
visual_embeddings=media,
visual_mask=media_mask,
)
class D3CoreAI:
@classmethod
async def load(cls, bundle, cpu_only=False):
self = cls()
self.root = Path(bundle)
self.manifest = json.loads((self.root / "manifest.json").read_text())
self.config = json.loads((self.root / "decision_config.json").read_text())
self.tokenizer = AutoTokenizer.from_pretrained(self.root)
self.processor = AutoProcessor.from_pretrained(self.root)
opts = (
SpecializationOptions.cpu_only()
if cpu_only
else SpecializationOptions.default()
)
self.model = await AIModel.load(self.root / "decision.aimodel", opts)
self.fn = self.model.load_function("main")
self.vision = await AIModel.load(self.root / "vision.aimodel", opts)
self.vfn = self.vision.load_function("main")
return self
async def logits(self, state, question, images=None, videos=None):
normalized = normalize_question(question)
rendered, keys = normalized.rendered, normalized.keys
features = []
if images or videos:
side = self.manifest["image_side"]
resized = [
load_image(x).resize((side, side), Image.Resampling.BICUBIC)
for x in (images or [])
]
decoded = [load_video(x) for x in (videos or [])]
frames = [
np.stack(
[
np.asarray(
Image.fromarray(frame).resize(
(side, side), Image.Resampling.BICUBIC
)
)
for frame in v.frames
]
)
for v in decoded
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [{"type": "image"} for _ in resized]
+ [{"type": "video"} for _ in decoded]
+ [
{
"type": "text",
"text": user_prompt(state, rendered, self.config["codes"]),
}
],
},
]
text = self.processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False,
)
args = {
"text": [text],
"return_tensors": "np",
"images_kwargs": {"do_resize": False},
"videos_kwargs": {
"do_resize": False,
"do_sample_frames": False,
"return_metadata": True,
},
}
if resized:
args["images"] = resized
if decoded:
args.update(
videos=frames, video_metadata=[v.metadata() for v in decoded]
)
encoded = self.processor(**args)
if len(encoded["input_ids"][0]) > self.manifest["sequence_length"]:
raise ValueError(
"Media prompt exceeds asset capacity; input was not truncated"
)
per_image = (side // 16) ** 2
for name in ["pixel_values", "pixel_values_videos"]:
if name not in encoded:
continue
patches = encoded[name]
for start in range(0, len(patches), per_image):
out = await self.vfn(
{
"pixel_values": NDArray(
patches[start : start + per_image].astype(np.float32)
)
}
)
features.append(out[self.manifest["vision_output"]].numpy())
else:
text = render_d3(self.tokenizer, state, rendered, self.config["codes"])
encoded = self.tokenizer(
[text], add_special_tokens=False, return_tensors="np"
)
arrays = pack(encoded, self.manifest, features)
out = await self.fn({k: NDArray(v) for k, v in arrays.items()})
return out[self.manifest["output"]].numpy()[0, : len(keys)], keys, encoded
async def system_one(self, *, state, questions, images=None, videos=None):
answers = {}
for key, q in questions.items():
logits, keys, _ = await self.logits(state, q, images, videos)
z = logits / self.config.get("temperature", 1.0)
probs = np.exp(z - z.max())
probs /= probs.sum()
if not np.isfinite(probs).all():
raise FloatingPointError("Core AI returned nonfinite probabilities")
answers[key] = product_answer(normalize_question(q), probs.tolist())
return {"model": self.manifest["source"], "answers": answers}
|