Add DeepSeek-OCR-2 standalone scripts and project deps · oscillatory.net/ocr-to-typst@27b6351

+5

pyproject.toml

··· 8 8 "datasets>=2.19", 9 9 "Pillow>=10", 10 10 "torch>=2.3", 11 + "addict", 12 + "matplotlib", 11 13 "editdistance", 12 14 "tqdm", 13 15 "numpy", ··· 15 17 "fastapi>=0.111", 16 18 "uvicorn[standard]>=0.29", 17 19 "python-multipart>=0.0.9", 20 + "einops>=0.8.2", 21 + "easydict>=1.13", 18 22 ] 19 23 20 24 [project.scripts] ··· 26 30 generate-typeset = "src.generate_typeset:main" 27 31 probe = "src.probe:main" 28 32 app = "src.app:main" 33 + probe-deepseek = "src.probe_deepseek:main" 29 34 eff-mer-evaluate = "src.eff_mer.infer:main" 30 35 31 36 [build-system]

+122

scripts/probe_deepseek.py

··· 1 + #!/usr/bin/env python3 2 + # /// script 3 + # requires-python = ">=3.13" 4 + # dependencies = [ 5 + # "torch==2.6.0", 6 + # "torchvision", 7 + # "transformers==4.46.3", 8 + # "accelerate", 9 + # "einops", 10 + # "addict", 11 + # "easydict", 12 + # "pillow", 13 + # ] 14 + # /// 15 + """ 16 + Standalone DeepSeek-OCR-2 probe. Managed by uv as an isolated script env. 17 + 18 + Usage: 19 + uv run scripts/probe_deepseek.py --images a.png b.png 20 + uv run scripts/probe_deepseek.py --test-dir ../eff-mer/data/raster/mathwriting_Test --n 5 21 + """ 22 + 23 + import argparse 24 + import json 25 + import os 26 + import tempfile 27 + from pathlib import Path 28 + 29 + import torch 30 + from PIL import Image 31 + from transformers import AutoModel, AutoTokenizer 32 + 33 + MODEL_ID = "deepseek-ai/DeepSeek-OCR-2" 34 + DEFAULT_PROMPT = "<image>\nConvert this mathematical expression to Typst math notation. " 35 + 36 + 37 + def load_model(model_id: str = MODEL_ID): 38 + print(f"Loading {model_id} ...") 39 + tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) 40 + model = AutoModel.from_pretrained( 41 + model_id, 42 + trust_remote_code=True, 43 + use_safetensors=True, 44 + torch_dtype=torch.bfloat16, 45 + device_map="auto", 46 + ) 47 + model.eval() 48 + print("Model ready.\n") 49 + return model, tokenizer 50 + 51 + 52 + def run_image(img: Image.Image, model, tokenizer, prompt: str) -> str: 53 + with tempfile.TemporaryDirectory() as tmpdir: 54 + img_path = os.path.join(tmpdir, "input.png") 55 + out_dir = os.path.join(tmpdir, "out") 56 + os.makedirs(out_dir) 57 + img.save(img_path) 58 + res = model.infer( 59 + tokenizer, 60 + prompt=prompt, 61 + image_file=img_path, 62 + output_path=out_dir, 63 + base_size=1024, 64 + image_size=768, 65 + crop_mode=False, 66 + save_results=False, 67 + eval_mode=True, 68 + ) 69 + return str(res).strip() if res is not None else "" 70 + 71 + 72 + def load_test_records(test_dir: Path, n: int) -> list[dict]: 73 + manifest = test_dir / "manifest.jsonl" 74 + records = [] 75 + for line in manifest.read_text().splitlines(): 76 + r = json.loads(line) 77 + if r.get("typst", "").startswith("ERROR:") or not r.get("typst"): 78 + continue 79 + records.append({ 80 + "image_path": str(test_dir / r["image"]), 81 + "typst": r["typst"], 82 + }) 83 + if len(records) >= n: 84 + break 85 + return records 86 + 87 + 88 + def main(): 89 + parser = argparse.ArgumentParser() 90 + parser.add_argument("--model", default=MODEL_ID) 91 + parser.add_argument("--prompt", default=DEFAULT_PROMPT) 92 + parser.add_argument("--images", nargs="+", metavar="IMG", 93 + help="Arbitrary image files") 94 + parser.add_argument("--test-dir", metavar="DIR", 95 + help="Path to a manifest.jsonl split dir (e.g. mathwriting_Test)") 96 + parser.add_argument("--n", type=int, default=5) 97 + args = parser.parse_args() 98 + 99 + model, tokenizer = load_model(args.model) 100 + 101 + if args.images: 102 + for path in args.images: 103 + img = Image.open(path).convert("RGB") 104 + pred = run_image(img, model, tokenizer, args.prompt) 105 + print(f"{path}:\n {pred}\n") 106 + 107 + elif args.test_dir: 108 + records = load_test_records(Path(args.test_dir), args.n) 109 + for i, r in enumerate(records): 110 + img = Image.open(r["image_path"]).convert("RGB") 111 + pred = run_image(img, model, tokenizer, args.prompt) 112 + print(f"\n{'='*60}") 113 + print(f"[{i}] {Path(r['image_path']).name}") 114 + print(f" EXPECTED : {repr(r['typst'])}") 115 + print(f" PREDICTED: {repr(pred)}") 116 + 117 + else: 118 + parser.error("Provide --images or --test-dir") 119 + 120 + 121 + if __name__ == "__main__": 122 + main()

+347

scripts/train_deepseek.py

··· 1 + #!/usr/bin/env python3 2 + # /// script 3 + # requires-python = ">=3.13" 4 + # dependencies = [ 5 + # "torch==2.6.0", 6 + # "torchvision", 7 + # "transformers==4.46.3", 8 + # "accelerate", 9 + # "peft", 10 + # "bitsandbytes", 11 + # "einops", 12 + # "addict", 13 + # "easydict", 14 + # "pillow", 15 + # "tqdm", 16 + # ] 17 + # /// 18 + """ 19 + QLoRA fine-tuning of DeepSeek-OCR-2 for Typst math OCR. 20 + 21 + The LM backbone (LlamaFlashAttention2 / LlamaAttention) is targeted with LoRA; 22 + the visual encoder runs frozen (the model's forward pass wraps it in no_grad). 23 + 24 + Usage: 25 + uv run scripts/train_deepseek.py \\ 26 + --train-manifest ../eff-mer/data/raster/mathwriting_Train/manifest.jsonl \\ 27 + --val-manifest ../eff-mer/data/raster/mathwriting_Val/manifest.jsonl \\ 28 + --output checkpoints/deepseek-typst \\ 29 + --steps 5000 30 + 31 + Optional: 32 + --lora-r 32 LoRA rank (default 32) 33 + --lr 2e-4 Learning rate 34 + --grad-accum 8 Gradient accumulation steps 35 + --save-steps 500 Checkpoint every N steps 36 + --val-steps 100 Validate every N steps (0 = disable) 37 + --n-val 64 Number of val examples per eval 38 + --bits 4 Quantisation: 4 (NF4) or 8 (INT8) 39 + --list-modules Print trainable module names and exit 40 + """ 41 + 42 + import argparse 43 + import json 44 + import math 45 + import os 46 + import random 47 + from pathlib import Path 48 + 49 + import torch 50 + import torch.nn as nn 51 + from PIL import Image, ImageOps 52 + from torchvision import transforms 53 + from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig 54 + from peft import LoraConfig, get_peft_model, PeftModel 55 + from tqdm import tqdm 56 + 57 + # --------------------------------------------------------------------------- 58 + # Constants matching infer() with crop_mode=False, image_size=768 59 + # --------------------------------------------------------------------------- 60 + MODEL_ID = "deepseek-ai/DeepSeek-OCR-2" 61 + IMAGE_TOKEN_ID = 128815 62 + BOS_ID = 0 63 + EOS_ID = 1 64 + IMAGE_SIZE = 768 # global view resolution 65 + BASE_SIZE = 1024 # base_size arg (only needed for zero-pad of unused crop tensor) 66 + PATCH_SIZE = 16 67 + DOWNSAMPLE_RATIO = 4 68 + # num_queries = ceil((IMAGE_SIZE // PATCH_SIZE) / DOWNSAMPLE_RATIO) = 12 69 + NUM_QUERIES = math.ceil((IMAGE_SIZE // PATCH_SIZE) / DOWNSAMPLE_RATIO) 70 + # image tokens per image: 12*12 + 1 = 145 71 + N_IMAGE_TOKENS = NUM_QUERIES * NUM_QUERIES + 1 72 + 73 + PROMPT_SUFFIX = "\nConvert this mathematical expression to Typst math notation. " 74 + 75 + _IMAGE_TRANSFORM = transforms.Compose([ 76 + transforms.ToTensor(), 77 + transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]), 78 + ]) 79 + _PAD_COLOR = (127, 127, 127) # int(0.5 * 255) 80 + 81 + 82 + # --------------------------------------------------------------------------- 83 + # Preprocessing 84 + # --------------------------------------------------------------------------- 85 + 86 + def make_sample(image_path: str, typst: str, tokenizer) -> dict: 87 + """ 88 + Build a training sample. 89 + 90 + Returns a dict with: 91 + input_ids LongTensor (seq_len,) 92 + labels LongTensor (seq_len,) prompt positions = -100 93 + image_ori bfloat16 Tensor (3, IMAGE_SIZE, IMAGE_SIZE) 94 + images_seq_mask bool Tensor (seq_len,) 95 + images_spatial_crop LongTensor (1, 2) 96 + """ 97 + image = Image.open(image_path).convert("RGB") 98 + global_view = ImageOps.pad(image, (IMAGE_SIZE, IMAGE_SIZE), color=_PAD_COLOR) 99 + image_ori = _IMAGE_TRANSFORM(global_view).to(torch.bfloat16) # (3, 768, 768) 100 + 101 + image_tokens = [IMAGE_TOKEN_ID] * N_IMAGE_TOKENS # 145 tokens 102 + 103 + prompt_ids = tokenizer.encode(PROMPT_SUFFIX, add_special_tokens=False) 104 + response_ids = tokenizer.encode(typst, add_special_tokens=False) + [EOS_ID] 105 + 106 + # Full sequence: BOS | image_tokens | prompt_suffix | response | EOS-already-in-response_ids 107 + input_ids = [BOS_ID] + image_tokens + prompt_ids + response_ids 108 + # Mask everything before the response with -100 109 + n_prompt = 1 + N_IMAGE_TOKENS + len(prompt_ids) 110 + labels = [-100] * n_prompt + response_ids 111 + 112 + images_seq_mask = ( 113 + [False] # BOS 114 + + [True] * N_IMAGE_TOKENS # image tokens 115 + + [False] * (len(prompt_ids) + len(response_ids)) 116 + ) 117 + 118 + return { 119 + "input_ids": torch.tensor(input_ids, dtype=torch.long), 120 + "labels": torch.tensor(labels, dtype=torch.long), 121 + "image_ori": image_ori, 122 + "images_seq_mask": torch.tensor(images_seq_mask, dtype=torch.bool), 123 + "images_spatial_crop": torch.tensor([[1, 1]], dtype=torch.long), 124 + } 125 + 126 + 127 + # --------------------------------------------------------------------------- 128 + # Data loading 129 + # --------------------------------------------------------------------------- 130 + 131 + def load_manifest(manifest_path: str) -> list[dict]: 132 + records = [] 133 + base = Path(manifest_path).parent 134 + for line in Path(manifest_path).read_text().splitlines(): 135 + r = json.loads(line) 136 + if not r.get("typst") or r["typst"].startswith("ERROR:"): 137 + continue 138 + records.append({ 139 + "image_path": str(base / r["image"]), 140 + "typst": r["typst"], 141 + }) 142 + return records 143 + 144 + 145 + # --------------------------------------------------------------------------- 146 + # Model loading 147 + # --------------------------------------------------------------------------- 148 + 149 + def load_model(model_id: str, bits: int): 150 + tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) 151 + 152 + if bits == 4: 153 + bnb_cfg = BitsAndBytesConfig( 154 + load_in_4bit=True, 155 + bnb_4bit_quant_type="nf4", 156 + bnb_4bit_compute_dtype=torch.bfloat16, 157 + bnb_4bit_use_double_quant=True, 158 + ) 159 + elif bits == 8: 160 + bnb_cfg = BitsAndBytesConfig(load_in_8bit=True) 161 + else: 162 + bnb_cfg = None 163 + 164 + kwargs = dict(trust_remote_code=True, use_safetensors=True) 165 + if bnb_cfg is not None: 166 + model = AutoModel.from_pretrained(model_id, quantization_config=bnb_cfg, **kwargs) 167 + else: 168 + model = AutoModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, 169 + device_map="auto", **kwargs) 170 + 171 + return model, tokenizer 172 + 173 + 174 + # --------------------------------------------------------------------------- 175 + # LoRA setup 176 + # --------------------------------------------------------------------------- 177 + 178 + LORA_TARGETS = ["q_proj", "k_proj", "v_proj", "o_proj"] 179 + 180 + 181 + def apply_lora(model, lora_r: int) -> nn.Module: 182 + lora_cfg = LoraConfig( 183 + r=lora_r, 184 + lora_alpha=lora_r * 2, 185 + target_modules=LORA_TARGETS, 186 + lora_dropout=0.05, 187 + bias="none", 188 + ) 189 + model = get_peft_model(model, lora_cfg) 190 + model.print_trainable_parameters() 191 + return model 192 + 193 + 194 + # --------------------------------------------------------------------------- 195 + # Forward pass helper 196 + # --------------------------------------------------------------------------- 197 + 198 + def forward_sample(model, sample: dict, device: str = "cuda"): 199 + input_ids = sample["input_ids"].unsqueeze(0).to(device) 200 + labels = sample["labels"].unsqueeze(0).to(device) 201 + images_seq_mask = sample["images_seq_mask"].unsqueeze(0).to(device) 202 + images_spatial_crop = sample["images_spatial_crop"].to(device) 203 + image_ori = sample["image_ori"].unsqueeze(0).to(device) 204 + # Zeros for crop tensor: sum==0 triggers the no-crop branch in forward() 205 + images_crop = torch.zeros(1, 3, IMAGE_SIZE, IMAGE_SIZE, 206 + dtype=torch.bfloat16, device=device) 207 + 208 + with torch.autocast("cuda", dtype=torch.bfloat16): 209 + out = model( 210 + input_ids=input_ids, 211 + labels=labels, 212 + images=[(images_crop, image_ori)], 213 + images_seq_mask=images_seq_mask, 214 + images_spatial_crop=images_spatial_crop, 215 + use_cache=False, 216 + ) 217 + return out.loss 218 + 219 + 220 + # --------------------------------------------------------------------------- 221 + # Training loop 222 + # --------------------------------------------------------------------------- 223 + 224 + def train(args): 225 + print(f"Loading {args.model} at {args.bits}-bit ...") 226 + model, tokenizer = load_model(args.model, args.bits) 227 + print("Applying LoRA ...") 228 + model = apply_lora(model, args.lora_r) 229 + model.train() 230 + 231 + train_records = load_manifest(args.train_manifest) 232 + print(f"Training records: {len(train_records)}") 233 + val_records = [] 234 + if args.val_manifest: 235 + val_records = load_manifest(args.val_manifest) 236 + print(f"Val records: {len(val_records)}") 237 + 238 + optimizer = torch.optim.AdamW( 239 + (p for p in model.parameters() if p.requires_grad), 240 + lr=args.lr, 241 + weight_decay=0.01, 242 + ) 243 + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( 244 + optimizer, T_max=args.steps 245 + ) 246 + 247 + output_dir = Path(args.output) 248 + output_dir.mkdir(parents=True, exist_ok=True) 249 + 250 + step = 0 251 + accum_loss = 0.0 252 + optimizer.zero_grad() 253 + 254 + pbar = tqdm(total=args.steps, desc="training") 255 + 256 + while step < args.steps: 257 + random.shuffle(train_records) 258 + for r in train_records: 259 + if step >= args.steps: 260 + break 261 + try: 262 + sample = make_sample(r["image_path"], r["typst"], tokenizer) 263 + except Exception as e: 264 + print(f" skip {r['image_path']}: {e}") 265 + continue 266 + 267 + loss = forward_sample(model, sample) / args.grad_accum 268 + loss.backward() 269 + accum_loss += loss.item() 270 + 271 + if (step + 1) % args.grad_accum == 0: 272 + torch.nn.utils.clip_grad_norm_( 273 + (p for p in model.parameters() if p.requires_grad), 1.0 274 + ) 275 + optimizer.step() 276 + scheduler.step() 277 + optimizer.zero_grad() 278 + 279 + pbar.set_postfix( 280 + loss=f"{accum_loss * args.grad_accum:.4f}", 281 + lr=f"{scheduler.get_last_lr()[0]:.2e}", 282 + ) 283 + step += 1 284 + pbar.update(1) 285 + 286 + if args.val_steps and step % args.val_steps == 0 and val_records: 287 + model.eval() 288 + subset = random.sample(val_records, min(args.n_val, len(val_records))) 289 + val_loss = 0.0 290 + with torch.no_grad(): 291 + for vr in subset: 292 + try: 293 + vs = make_sample(vr["image_path"], vr["typst"], tokenizer) 294 + val_loss += forward_sample(model, vs).item() 295 + except Exception: 296 + pass 297 + print(f"\n[step {step}] val_loss = {val_loss / len(subset):.4f}") 298 + model.train() 299 + accum_loss = 0.0 300 + 301 + if args.save_steps and step % args.save_steps == 0: 302 + ckpt = output_dir / f"step-{step}" 303 + model.save_pretrained(ckpt) 304 + tokenizer.save_pretrained(ckpt) 305 + print(f"\nSaved checkpoint → {ckpt}") 306 + 307 + pbar.close() 308 + final = output_dir / "final" 309 + model.save_pretrained(final) 310 + tokenizer.save_pretrained(final) 311 + print(f"Done. Final checkpoint → {final}") 312 + 313 + 314 + # --------------------------------------------------------------------------- 315 + # Entry point 316 + # --------------------------------------------------------------------------- 317 + 318 + def main(): 319 + parser = argparse.ArgumentParser() 320 + parser.add_argument("--model", default=MODEL_ID) 321 + parser.add_argument("--bits", type=int, default=4, choices=[4, 8, 16]) 322 + parser.add_argument("--lora-r", type=int, default=32) 323 + parser.add_argument("--lr", type=float, default=2e-4) 324 + parser.add_argument("--grad-accum", type=int, default=8) 325 + parser.add_argument("--steps", type=int, default=5000) 326 + parser.add_argument("--save-steps", type=int, default=500) 327 + parser.add_argument("--val-steps", type=int, default=200) 328 + parser.add_argument("--n-val", type=int, default=64) 329 + parser.add_argument("--train-manifest", required=True) 330 + parser.add_argument("--val-manifest", default=None) 331 + parser.add_argument("--output", default="checkpoints/deepseek-typst") 332 + parser.add_argument("--list-modules", action="store_true", 333 + help="Print model module names and exit") 334 + args = parser.parse_args() 335 + 336 + if args.list_modules: 337 + model, _ = load_model(args.model, args.bits) 338 + for name, mod in model.named_modules(): 339 + if isinstance(mod, nn.Linear): 340 + print(f" {name} [{mod.in_features} → {mod.out_features}]") 341 + return 342 + 343 + train(args) 344 + 345 + 346 + if __name__ == "__main__": 347 + main()

+366 -18

uv.lock

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