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7b5e901 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 | """WeMM-JEV text and vision inference."""
from __future__ import annotations
import contextlib
import gc
import os
import sys
import threading
from pathlib import Path
from ._compat import Engine, Unsupported
from . import _images as vimg
from ._images import resize_policy
os.environ.setdefault("TRITON_CACHE_AUTOTUNING", "1")
os.environ.setdefault("HF_HUB_DISABLE_XET", "1")
_LOAD_LOCK = threading.Lock()
class D3Engine(Engine):
name = "d3"
latency = (
"Device-synchronized in-process request wall time including prompt rendering and tokenization (and, "
"for image and video requests, their decoding and preprocessing); one request per call, its questions in "
"request order in batches of batch_size; excludes model loading."
)
def __init__(
self,
model: str | None = None,
revision: str | None = None,
device: str | None = None,
batch_size: int = 8,
verify: str = "fast",
model_name: str | None = "WeMM-JEV",
permutation_average: str | bool = False,
max_pixels: int | None = vimg.MAX_PIXELS,
image_root: str | None = None,
replicas: int | None = None,
full_warmup: str | bool = False,
enable_videos: str | bool = False,
max_batch_tokens: int = 8192,
**options,
):
if options:
raise TypeError(f"unknown engine options {sorted(options)}")
if not model:
model = str(Path(__file__).absolute().parent.parent)
flag = str(permutation_average).strip().lower()
if flag not in ("0", "1", "false", "true"):
raise ValueError("permutation_average must be 0 or 1")
averaged = flag in ("1", "true")
fw_flag = str(full_warmup).strip().lower()
if fw_flag not in ("0", "1", "false", "true"):
raise ValueError("full_warmup must be 0 or 1")
self.full_warmup = fw_flag in ("1", "true")
video_flag = str(enable_videos).strip().lower()
if video_flag not in ("0", "1", "false", "true"):
raise ValueError("enable_videos must be 0 or 1")
self.enable_videos = video_flag in ("1", "true")
import torch
self.torch = torch
self.model_id = str(model)
self.revision = revision
self.batch_size = int(batch_size)
self.max_batch_tokens = int(max_batch_tokens)
if self.batch_size < 1 or self.max_batch_tokens < 0:
raise ValueError("batch_size must be positive and max_batch_tokens nonnegative")
self.verify = verify
self.model_name = model_name
self.permutation_average = averaged
self.max_pixels = max_pixels
self.image_root = image_root
gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
if device in (None, "cuda"):
primary_device = "cuda:0" if gpus > 0 else "cpu"
self._multi_gpu = gpus > 1
self._num_gpus = max(1, gpus)
else:
primary_device = str(device)
self._multi_gpu = False
self._num_gpus = 1
super().__init__(
model=self.model_id,
revision=revision,
device=primary_device,
batch_size=self.batch_size,
verify=verify,
model_name=model_name,
max_pixels=max_pixels,
enable_videos=self.enable_videos,
max_batch_tokens=self.max_batch_tokens,
**({"permutation_average": 1} if averaged else {}),
)
hf_model, decision = self._load_replica(primary_device)
self.model = hf_model
self.decision = decision
self._slots = {0: (hf_model, decision, threading.Lock())}
self._local = threading.local()
self._next_slot = 0
self._slots_lock = threading.Lock()
if replicas is not None:
n_rep = int(replicas)
if n_rep > 1:
if gpus < n_rep:
raise RuntimeError(f"replicas={n_rep} needs {n_rep} CUDA devices, found {gpus}")
self._multi_gpu = True
self._num_gpus = n_rep
for idx in range(1, n_rep):
m_i, d_i = self._load_replica(f"cuda:{idx}")
self._slots[idx] = (m_i, d_i, threading.Lock())
self.provenance = {
**self.decision.provenance(),
"max_pixels": self.max_pixels,
"max_batch_tokens": self.max_batch_tokens,
"image_resize": resize_policy(self.max_pixels) + "; then the checkpoint's own processor",
}
def _load_replica(self, device_str: str):
with _LOAD_LOCK:
local = Path(os.fspath(self.model_id)).expanduser()
if local.is_dir():
pkg_dir = os.path.abspath(os.fspath(local))
if pkg_dir not in sys.path:
sys.path.insert(0, pkg_dir)
from transformers import AutoModel
if device_str.startswith("cuda"):
self.torch.cuda.set_device(device_str)
hf_model = AutoModel.from_pretrained(
self.model_id,
trust_remote_code=True,
revision=self.revision,
device=device_str,
batch_size=self.batch_size,
verify=self.verify,
)
decision = getattr(hf_model, "runtime", None)
if decision is None:
raise ValueError(
f"{self.model_id!r} did not load as a d3 decision package (missing decision_config.json)"
)
if self.model_name:
decision.model_name = self.model_name
if self.permutation_average:
decision.permutation_average = True
# Text/image evaluation uses PIL and needs no video decoder. This also
# prevents provenance().video_contract() from importing optional cv2.
if not self.enable_videos and getattr(decision, "video_unavailable", None) is None:
decision.video_unavailable = "disabled for text/image evaluation (enable_videos=0)"
return hf_model, decision
def _acquire_slot(self):
slot = getattr(self._local, "slot", None)
if slot is None:
with self._slots_lock:
slot = self._next_slot if self._multi_gpu else 0
self._next_slot = (self._next_slot + 1) % max(1, self._num_gpus)
self._local.slot = slot
entry = self._slots.get(slot)
if entry is None:
with self._slots_lock:
entry = self._slots.get(slot)
if entry is None:
hf_model, decision = self._load_replica(f"cuda:{slot}")
entry = (hf_model, decision, threading.Lock())
self._slots[slot] = entry
return entry
@contextlib.contextmanager
def _device_ctx(self, decision):
dev = getattr(decision, "device", None)
if dev is not None and getattr(dev, "type", None) == "cuda":
with self.torch.cuda.device(dev):
yield
else:
yield
def warmup(self):
with self._device_ctx(self.decision):
super().warmup()
if self.full_warmup or getattr(self.decision, "fast", None) is not None:
self.warmup_seconds = self.decision.warmup()
def runtime(self):
return self.decision.runtime_info()
def synchronize(self):
for _, decision, _ in list(self._slots.values()):
decision.synchronize()
def _prepare_images(self, images, decision):
if not images:
return None, None
from PIL import Image
pil_images = []
image_pixels = []
for item in images:
if isinstance(item, Image.Image):
im = item.convert("RGB")
if self.max_pixels and im.width * im.height > self.max_pixels:
im = im.resize(vimg.fit(im.width, im.height, self.max_pixels), Image.LANCZOS)
else:
im = vimg.pil(item, self.image_root, self.max_pixels).convert("RGB")
pil_images.append(im)
image_pixels.append(im.width * im.height)
image_tokens = None
proc = getattr(decision, "processor", None)
if proc is not None and getattr(proc, "image_processor", None) is not None:
ip = proc.image_processor
merge = getattr(ip, "merge_size", 2) ** 2
try:
image_tokens = sum(
ip.get_number_of_image_patches(im.height, im.width, {}) // merge
for im in pil_images
)
except Exception:
image_tokens = None
raw = {"image_pixels": image_pixels}
if image_tokens is not None:
raw["image_tokens"] = image_tokens
return pil_images, raw
def __call__(self, state, questions, images=None, videos=None, prompt=None):
if videos and not self.enable_videos:
raise Unsupported("video evaluation requires --option enable_videos=1 and a working video decoder")
_, decision, lock = self._acquire_slot()
pil_images, raw = self._prepare_images(images, decision)
with lock, self._device_ctx(decision):
prepared = decision.prepare(state, questions, pil_images, videos)
over = [
e["message"]
for e in prepared.errors.values()
if e["error"] == "max_length_exceeded"
]
if over:
raise Unsupported(over[0])
if prepared.errors:
raise ValueError(
"invalid questions: "
+ "; ".join(f"{k}: {e['message']}" for k, e in prepared.errors.items())
)
probabilities, tokens = self._run_prepared(decision, prepared)
response = decision.respond(prepared, probabilities, tokens)
failed = {
k: a["message"] for k, a in response["answers"].items() if "error" in a
}
if failed:
raise ValueError(f"invalid model output: {failed}")
return response, raw
def _run_prepared(self, decision, prepared):
"""Bound padded tokens per forward without truncating questions or context."""
original = decision.batch_size
sizes = list(getattr(prepared, "lengths", {}).values())
sizes.extend(len(s) for s in getattr(prepared, "sequences", {}).values())
sizes.extend(len(s) for s in getattr(prepared, "reversed_sequences", {}).values())
sizes.extend(getattr(prepared, "reversed_lengths", {}).values())
batch = original
if self.max_batch_tokens and sizes:
width = max(sizes) * (2 if self.permutation_average else 1)
batch = min(batch, max(1, self.max_batch_tokens // max(1, width)))
try:
while True:
decision.batch_size = batch
try:
return decision.run(prepared)
except self.torch.OutOfMemoryError:
if batch == 1:
raise
# Leave the except block first to release the failed forward's traceback/tensors.
batch = max(1, batch // 2)
gc.collect()
self.torch.cuda.empty_cache()
finally:
decision.batch_size = original
def close(self):
with self._slots_lock:
self._slots.clear()
self.model = None
self.decision = None
if self.torch.cuda.is_available():
self.torch.cuda.empty_cache()
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