File size: 9,672 Bytes
f340984 | 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 | from __future__ import annotations
import argparse
import json
import sys
import traceback
from pathlib import Path
from .constants import MODEL_REVISION, VERSION
from .errors import AppError, ModelIntegrityError
from .infer import RunOptions, default_output_path, run_inference
from .model_store import prepare_model, verify_model
from .paths import default_cache_dir, default_model_dir
from .runtime import inspect_runtime, runtime_issues
def bounded_int(minimum: int, maximum: int):
def parse(value: str) -> int:
number = int(value)
if not minimum <= number <= maximum:
raise argparse.ArgumentTypeError(f"must be between {minimum} and {maximum}")
return number
return parse
def _add_output_flags(parser: argparse.ArgumentParser, *, suppress_defaults: bool = False) -> None:
default = argparse.SUPPRESS if suppress_defaults else False
parser.add_argument("--json", action="store_true", default=default, help="emit structured JSON")
parser.add_argument("-q", "--quiet", action="store_true", default=default, help="suppress progress diagnostics")
parser.add_argument("--debug", action="store_true", default=default, help="show tracebacks for unexpected failures")
parser.add_argument(
"--no-color", action="store_true", default=default, help="disable color (currently the default)"
)
def build_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(
prog="unlimited-ocr-rdna4",
description="Run Baidu Unlimited-OCR locally on a single AMD RDNA 4 GPU.",
epilog="Examples: unlimited-ocr-rdna4 prepare; unlimited-ocr-rdna4 run --input page.png",
)
_add_output_flags(parser)
parser.add_argument("--version", action="version", version=VERSION)
subparsers = parser.add_subparsers(dest="command")
prepare = subparsers.add_parser("prepare", help="download and verify the pinned Baidu model")
_add_output_flags(prepare, suppress_defaults=True)
prepare.add_argument("--model-dir", type=Path, default=default_model_dir(), help="prepared model destination")
prepare.add_argument("--cache-dir", type=Path, default=default_cache_dir(), help="persistent download cache")
prepare.add_argument("-n", "--dry-run", action="store_true", help="show the plan without downloading")
doctor = subparsers.add_parser("doctor", help="inspect ROCm, PyTorch, RDNA 4, and model readiness")
_add_output_flags(doctor, suppress_defaults=True)
doctor.add_argument("--device", help="ROCm ordinal or stable ROCr UUID; omit to inspect all visible GPUs")
doctor.add_argument("--model-dir", type=Path, default=default_model_dir(), help="prepared model directory")
doctor.add_argument("--require-model", action="store_true", help="fail when the prepared model is absent")
run = subparsers.add_parser("run", help="parse one image or PDF into Markdown")
_add_output_flags(run, suppress_defaults=True)
run.add_argument("--input", required=True, type=Path, help="input image or PDF")
run.add_argument("-o", "--output", type=Path, help="Markdown output path")
run.add_argument(
"--device",
default=None,
help="ROCm ordinal or stable ROCr UUID (default: UNLIMITED_OCR_DEVICE or 0)",
)
run.add_argument("--model-dir", type=Path, default=default_model_dir(), help="prepared model directory")
run.add_argument("--mode", choices=("gundam", "base"), default="gundam", help="single-page image profile")
run.add_argument("--max-length", type=bounded_int(512, 32768), default=4096, help="total sequence limit")
run.add_argument("--dpi", type=bounded_int(72, 400), default=200, help="PDF rendering resolution")
run.add_argument("--start-page", type=bounded_int(1, 100000), default=1, help="first PDF page, 1-based")
run.add_argument("--max-pages", type=bounded_int(1, 100), default=20, help="maximum PDF pages per run")
run.add_argument(
"--max-page-pixels",
type=bounded_int(1_000_000, 200_000_000),
default=60_000_000,
help="maximum rendered pixels for one PDF page",
)
run.add_argument(
"--max-total-pixels",
type=bounded_int(1_000_000, 2_000_000_000),
default=400_000_000,
help="maximum aggregate rendered pixels for a PDF run",
)
run.add_argument(
"--max-page-rendered-mib",
type=bounded_int(1, 2048),
default=512,
help="maximum temporary size of one rendered PDF page",
)
run.add_argument(
"--max-rendered-mib",
type=bounded_int(1, 8192),
default=2048,
help="maximum aggregate size of rendered PDF pages",
)
run.add_argument("--prompt", default="<image>document parsing.", help="model prompt")
run.add_argument("-f", "--force", action="store_true", help="replace an existing output file")
return parser
def _emit(payload: dict, *, as_json: bool, quiet: bool = False) -> None:
if as_json:
print(json.dumps(payload, ensure_ascii=False, sort_keys=True))
return
if quiet:
primary = payload.get("output") or payload.get("model_dir") or payload.get("status")
if primary is not None:
print(primary)
return
for key, value in payload.items():
if isinstance(value, (dict, list, tuple)):
print(f"{key}={json.dumps(value, ensure_ascii=False, sort_keys=True)}")
else:
print(f"{key}={value}")
def _command_prepare(args: argparse.Namespace) -> int:
if not args.quiet:
action = "Would prepare" if args.dry_run else "Preparing"
print(f"{action} {MODEL_REVISION} at {args.model_dir}", file=sys.stderr, flush=True)
status = prepare_model(args.model_dir, args.cache_dir, dry_run=args.dry_run)
payload = {"schema_version": 1, **status.__dict__, "status": "dry-run" if args.dry_run else "ok"}
_emit(payload, as_json=args.json, quiet=args.quiet)
return 0
def _command_doctor(args: argparse.Namespace) -> int:
runtime = inspect_runtime(device=args.device, require_single=False, validate=False)
issues = runtime_issues(runtime, require_single=False)
model_payload: dict[str, object]
try:
model = verify_model(args.model_dir.expanduser().resolve(), full_weight_hash=False)
model_payload = {"prepared": True, "model_dir": model.model_dir, "revision": model.revision}
except ModelIntegrityError as exc:
if args.require_model:
raise
model_payload = {"prepared": False, "model_dir": str(args.model_dir), "detail": str(exc)}
payload = {
"schema_version": 1,
"status": "ok" if not issues else "error",
"runtime": runtime.to_dict(),
"runtime_issues": issues,
"model": model_payload,
}
_emit(payload, as_json=args.json, quiet=args.quiet)
return 0 if not issues else 3
def _command_run(args: argparse.Namespace) -> int:
import os
device = args.device or os.environ.get("UNLIMITED_OCR_DEVICE")
visibility_is_preconfigured = any(
name in os.environ for name in ("ROCR_VISIBLE_DEVICES", "HIP_VISIBLE_DEVICES", "CUDA_VISIBLE_DEVICES")
)
if device is None and not visibility_is_preconfigured:
device = "0"
runtime = inspect_runtime(device=device, require_single=True)
input_path = args.input.expanduser()
output = args.output.expanduser() if args.output else default_output_path(input_path)
if not args.quiet:
print(f"Loading pinned model on RDNA 4 device {device}; inference is offline", file=sys.stderr, flush=True)
result = run_inference(
RunOptions(
input_path=input_path,
output_path=output,
model_dir=args.model_dir,
mode=args.mode,
max_length=args.max_length,
dpi=args.dpi,
start_page=args.start_page,
max_pages=args.max_pages,
max_page_pixels=args.max_page_pixels,
max_total_pixels=args.max_total_pixels,
max_page_rendered_bytes=args.max_page_rendered_mib * 1024**2,
max_rendered_bytes=args.max_rendered_mib * 1024**2,
prompt=args.prompt,
force=args.force,
quiet=args.quiet,
),
runtime,
)
for warning in result.warnings:
print(f"warning: {warning}", file=sys.stderr)
payload = {"status": "ok", **result.to_dict()}
_emit(payload, as_json=args.json, quiet=args.quiet)
return 0
def run(argv: list[str] | None = None) -> int:
parser = build_parser()
args = parser.parse_args(argv)
if not args.command:
parser.print_help(sys.stderr)
return 2
try:
if args.command == "prepare":
return _command_prepare(args)
if args.command == "doctor":
return _command_doctor(args)
if args.command == "run":
return _command_run(args)
parser.error(f"unknown command: {args.command}")
except KeyboardInterrupt:
print("interrupted", file=sys.stderr)
return 130
except AppError as exc:
if args.json:
print(
json.dumps({"schema_version": 1, "status": "error", "error": str(exc), "exit_code": exc.exit_code}),
file=sys.stderr,
)
else:
print(f"error: {exc}", file=sys.stderr)
return exc.exit_code
except Exception as exc:
if args.debug:
traceback.print_exc()
else:
print(f"error: unexpected failure: {exc}; rerun with --debug", file=sys.stderr)
return 1
return 1
def main() -> None:
raise SystemExit(run())
|