Add PaddleOCR-VL script for document processing with vLLM support

paddleocr-vl.py

@@ -0,0 +1,676 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets",
+#     "huggingface-hub[hf_transfer]",
+#     "pillow",
+#     "vllm",
+#     "tqdm",
+#     "toolz",
+#     "torch",
+# ]
+#
+# [[tool.uv.index]]
+# url = "https://wheels.vllm.ai/nightly"
+#
+# [tool.uv]
+# prerelease = "allow"
+# ///
+
+"""
+Convert document images to text/tables/formulas using PaddleOCR-VL with vLLM.
+
+PaddleOCR-VL is a compact 0.9B OCR model with task-specific capabilities for
+document parsing. It combines a NaViT-style dynamic resolution visual encoder
+with the ERNIE-4.5-0.3B language model for accurate element recognition.
+
+Features:
+- 🎯 Ultra-compact: Only 0.9B parameters (smallest OCR model)
+- 📝 OCR mode: General text extraction to markdown
+- 📊 Table mode: HTML table recognition and extraction
+- 📐 Formula mode: LaTeX mathematical notation
+- 📈 Chart mode: Structured chart analysis
+- 🌍 Multilingual support
+- ⚡ Fast initialization due to small size
+- 🔧 Based on ERNIE-4.5 (different from Qwen-based models)
+
+Model: PaddlePaddle/PaddleOCR-VL
+vLLM: Requires nightly build for full support
+"""
+
+import argparse
+import base64
+import io
+import json
+import logging
+import math
+import os
+import sys
+from typing import Any, Dict, List, Union
+from datetime import datetime
+
+import torch
+from datasets import load_dataset
+from huggingface_hub import DatasetCard, login
+from PIL import Image
+from toolz import partition_all
+from tqdm.auto import tqdm
+from vllm import LLM, SamplingParams
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Task mode configurations from official PaddleOCR-VL documentation
+TASK_MODES = {
+    "ocr": "OCR:",
+    "table": "Table Recognition:",
+    "formula": "Formula Recognition:",
+    "chart": "Chart Recognition:",
+}
+
+# Task descriptions for dataset card
+TASK_DESCRIPTIONS = {
+    "ocr": "General text extraction to markdown format",
+    "table": "Table extraction to HTML format",
+    "formula": "Mathematical formula recognition to LaTeX",
+    "chart": "Chart and diagram analysis",
+}
+
+
+def check_cuda_availability():
+    """Check if CUDA is available and exit if not."""
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        logger.error("Please run on a machine with a CUDA-capable GPU.")
+        sys.exit(1)
+    else:
+        logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name(0)}")
+
+
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 28,
+    min_pixels: int = 28 * 28 * 130,
+    max_pixels: int = 28 * 28 * 1280,
+) -> tuple[int, int]:
+    """
+    PaddleOCR-VL's intelligent resize logic.
+    
+    Rescales the image so that:
+    1. Both dimensions are divisible by 'factor' (28)
+    2. Total pixels are within [min_pixels, max_pixels]
+    3. Aspect ratio is maintained as closely as possible
+    
+    Args:
+        height: Original image height
+        width: Original image width
+        factor: Dimension divisibility factor (default: 28)
+        min_pixels: Minimum total pixels (default: 100,880)
+        max_pixels: Maximum total pixels (default: 1,003,520)
+    
+    Returns:
+        Tuple of (new_height, new_width)
+    """
+    if height < factor:
+        width = round((width * factor) / height)
+        height = factor
+
+    if width < factor:
+        height = round((height * factor) / width)
+        width = factor
+
+    if max(height, width) / min(height, width) > 200:
+        logger.warning(
+            f"Extreme aspect ratio detected: {max(height, width) / min(height, width):.1f}"
+        )
+        # Continue anyway, but warn about potential issues
+    
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = math.floor(height / beta / factor) * factor
+        w_bar = math.floor(width / beta / factor) * factor
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    
+    return h_bar, w_bar
+
+
+def make_ocr_message(
+    image: Union[Image.Image, Dict[str, Any], str],
+    task_mode: str = "ocr",
+    apply_smart_resize: bool = True,
+) -> List[Dict]:
+    """
+    Create chat message for PaddleOCR-VL processing.
+    
+    PaddleOCR-VL expects a specific format with the task prefix after the image.
+    """
+    # Convert to PIL Image if needed
+    if isinstance(image, Image.Image):
+        pil_img = image
+    elif isinstance(image, dict) and "bytes" in image:
+        pil_img = Image.open(io.BytesIO(image["bytes"]))
+    elif isinstance(image, str):
+        pil_img = Image.open(image)
+    else:
+        raise ValueError(f"Unsupported image type: {type(image)}")
+
+    # Convert to RGB
+    pil_img = pil_img.convert("RGB")
+    
+    # Apply smart resize if requested
+    if apply_smart_resize:
+        original_size = pil_img.size
+        new_width, new_height = smart_resize(pil_img.height, pil_img.width)
+        if (new_width, new_height) != (pil_img.width, pil_img.height):
+            pil_img = pil_img.resize((new_width, new_height), Image.Resampling.LANCZOS)
+            logger.debug(f"Resized image from {original_size} to {pil_img.size}")
+
+    # Convert to base64 data URI
+    buf = io.BytesIO()
+    pil_img.save(buf, format="PNG")
+    data_uri = f"data:image/png;base64,{base64.b64encode(buf.getvalue()).decode()}"
+
+    # PaddleOCR-VL message format: image first, then task prefix
+    return [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": data_uri}},
+                {"type": "text", "text": TASK_MODES[task_mode]},
+            ],
+        }
+    ]
+
+
+def create_dataset_card(
+    source_dataset: str,
+    model: str,
+    task_mode: str,
+    num_samples: int,
+    processing_time: str,
+    batch_size: int,
+    max_model_len: int,
+    max_tokens: int,
+    gpu_memory_utilization: float,
+    temperature: float,
+    apply_smart_resize: bool,
+    image_column: str = "image",
+    split: str = "train",
+) -> str:
+    """Create a dataset card documenting the OCR process."""
+    task_description = TASK_DESCRIPTIONS[task_mode]
+    
+    return f"""---
+tags:
+- ocr
+- document-processing
+- paddleocr-vl
+- {task_mode}
+- uv-script
+- generated
+---
+
+# Document Processing using PaddleOCR-VL ({task_mode.upper()} mode)
+
+This dataset contains {task_mode.upper()} results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using PaddleOCR-VL, an ultra-compact 0.9B OCR model.
+
+## Processing Details
+
+- **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
+- **Model**: [{model}](https://huggingface.co/{model})
+- **Task Mode**: `{task_mode}` - {task_description}
+- **Number of Samples**: {num_samples:,}
+- **Processing Time**: {processing_time}
+- **Processing Date**: {datetime.now().strftime("%Y-%m-%d %H:%M UTC")}
+
+### Configuration
+
+- **Image Column**: `{image_column}`
+- **Output Column**: `paddleocr_{task_mode}`
+- **Dataset Split**: `{split}`
+- **Batch Size**: {batch_size}
+- **Smart Resize**: {"Enabled" if apply_smart_resize else "Disabled"}
+- **Max Model Length**: {max_model_len:,} tokens
+- **Max Output Tokens**: {max_tokens:,}
+- **Temperature**: {temperature}
+- **GPU Memory Utilization**: {gpu_memory_utilization:.1%}
+
+## Model Information
+
+PaddleOCR-VL is a state-of-the-art, resource-efficient model tailored for document parsing:
+- 🎯 **Ultra-compact** - Only 0.9B parameters (smallest OCR model)
+- 📝 **OCR mode** - General text extraction
+- 📊 **Table mode** - HTML table recognition
+- 📐 **Formula mode** - LaTeX mathematical notation
+- 📈 **Chart mode** - Structured chart analysis
+- 🌍 **Multilingual** - Support for multiple languages
+- ⚡ **Fast** - Quick initialization and inference
+- 🔧 **ERNIE-4.5 based** - Different architecture from Qwen models
+
+### Task Modes
+
+- **OCR**: Extract text content to markdown format
+- **Table Recognition**: Extract tables to HTML format
+- **Formula Recognition**: Extract mathematical formulas to LaTeX
+- **Chart Recognition**: Analyze and describe charts/diagrams
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `paddleocr_{task_mode}`: The extracted content based on task mode
+- `inference_info`: JSON list tracking all OCR models applied to this dataset
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+# Load the dataset
+dataset = load_dataset("{{output_dataset_id}}", split="{split}")
+
+# Access the extracted content
+for example in dataset:
+    print(example["paddleocr_{task_mode}"])
+    break
+
+# View all OCR models applied to this dataset
+inference_info = json.loads(dataset[0]["inference_info"])
+for info in inference_info:
+    print(f"Task: {{info['task_mode']}} - Model: {{info['model_id']}}")
+```
+
+## Reproduction
+
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) PaddleOCR-VL script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \\
+    {source_dataset} \\
+    <output-dataset> \\
+    --task-mode {task_mode} \\
+    --image-column {image_column} \\
+    --batch-size {batch_size} \\
+    --max-model-len {max_model_len} \\
+    --max-tokens {max_tokens} \\
+    --gpu-memory-utilization {gpu_memory_utilization}
+```
+
+## Performance
+
+- **Model Size**: 0.9B parameters (smallest among OCR models)
+- **Processing Speed**: ~{num_samples / (float(processing_time.split()[0]) * 60):.2f} images/second
+- **Architecture**: NaViT visual encoder + ERNIE-4.5-0.3B language model
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+"""
+
+
+def main(
+    input_dataset: str,
+    output_dataset: str,
+    image_column: str = "image",
+    batch_size: int = 16,
+    task_mode: str = "ocr",
+    max_model_len: int = 8192,
+    max_tokens: int = 4096,
+    temperature: float = 0.0,
+    gpu_memory_utilization: float = 0.8,
+    apply_smart_resize: bool = True,
+    hf_token: str = None,
+    split: str = "train",
+    max_samples: int = None,
+    private: bool = False,
+    shuffle: bool = False,
+    seed: int = 42,
+    output_column: str = None,
+):
+    """Process images from HF dataset through PaddleOCR-VL model."""
+    
+    # Check CUDA availability first
+    check_cuda_availability()
+    
+    # Track processing start time
+    start_time = datetime.now()
+    
+    # Enable HF_TRANSFER for faster downloads
+    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+    
+    # Login to HF if token provided
+    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+    
+    # Validate task mode
+    if task_mode not in TASK_MODES:
+        raise ValueError(
+            f"Invalid task_mode '{task_mode}'. Choose from: {list(TASK_MODES.keys())}"
+        )
+    
+    # Auto-generate output column name based on task mode
+    if output_column is None:
+        output_column = f"paddleocr_{task_mode}"
+    
+    logger.info(f"Using task mode: {task_mode} - {TASK_DESCRIPTIONS[task_mode]}")
+    logger.info(f"Output will be written to column: {output_column}")
+    
+    # Load dataset
+    logger.info(f"Loading dataset: {input_dataset}")
+    dataset = load_dataset(input_dataset, split=split)
+    
+    # Validate image column
+    if image_column not in dataset.column_names:
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+    
+    # Shuffle if requested
+    if shuffle:
+        logger.info(f"Shuffling dataset with seed {seed}")
+        dataset = dataset.shuffle(seed=seed)
+    
+    # Limit samples if requested
+    if max_samples:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+        logger.info(f"Limited to {len(dataset)} samples")
+    
+    # Initialize vLLM model
+    model_name = "PaddlePaddle/PaddleOCR-VL"
+    logger.info(f"Initializing vLLM with {model_name}")
+    logger.info("This may take a minute on first run (model is only 0.9B)...")
+    
+    llm = LLM(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_memory_utilization,
+        limit_mm_per_prompt={"image": 1},
+    )
+    
+    # Sampling parameters - deterministic for OCR
+    sampling_params = SamplingParams(
+        temperature=temperature,
+        max_tokens=max_tokens,
+    )
+    
+    logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
+    if apply_smart_resize:
+        logger.info("Smart resize enabled (PaddleOCR-VL's adaptive resolution)")
+    
+    # Process images in batches
+    all_outputs = []
+    
+    for batch_indices in tqdm(
+        partition_all(batch_size, range(len(dataset))),
+        total=(len(dataset) + batch_size - 1) // batch_size,
+        desc=f"PaddleOCR-VL {task_mode.upper()} processing",
+    ):
+        batch_indices = list(batch_indices)
+        batch_images = [dataset[i][image_column] for i in batch_indices]
+        
+        try:
+            # Create messages for batch with task-specific prefix
+            batch_messages = [
+                make_ocr_message(img, task_mode=task_mode, apply_smart_resize=apply_smart_resize)
+                for img in batch_images
+            ]
+            
+            # Process with vLLM
+            outputs = llm.chat(batch_messages, sampling_params)
+            
+            # Extract outputs
+            for output in outputs:
+                text = output.outputs[0].text.strip()
+                all_outputs.append(text)
+        
+        except Exception as e:
+            logger.error(f"Error processing batch: {e}")
+            # Add error placeholders for failed batch
+            all_outputs.extend([f"[{task_mode.upper()} ERROR]"] * len(batch_images))
+    
+    # Calculate processing time
+    processing_duration = datetime.now() - start_time
+    processing_time_str = f"{processing_duration.total_seconds() / 60:.1f} min"
+    
+    # Add output column to dataset
+    logger.info(f"Adding '{output_column}' column to dataset")
+    dataset = dataset.add_column(output_column, all_outputs)
+    
+    # Handle inference_info tracking (for multi-model comparisons)
+    inference_entry = {
+        "model_id": model_name,
+        "model_name": "PaddleOCR-VL",
+        "model_size": "0.9B",
+        "task_mode": task_mode,
+        "column_name": output_column,
+        "timestamp": datetime.now().isoformat(),
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+        "smart_resize": apply_smart_resize,
+    }
+    
+    if "inference_info" in dataset.column_names:
+        # Append to existing inference info
+        logger.info("Updating existing inference_info column")
+        
+        def update_inference_info(example):
+            try:
+                existing_info = json.loads(example["inference_info"]) if example["inference_info"] else []
+            except (json.JSONDecodeError, TypeError):
+                existing_info = []
+            
+            existing_info.append(inference_entry)
+            return {"inference_info": json.dumps(existing_info)}
+        
+        dataset = dataset.map(update_inference_info)
+    else:
+        # Create new inference_info column
+        logger.info("Creating new inference_info column")
+        inference_list = [json.dumps([inference_entry])] * len(dataset)
+        dataset = dataset.add_column("inference_info", inference_list)
+    
+    # Push to hub
+    logger.info(f"Pushing to {output_dataset}")
+    dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
+    
+    # Create and push dataset card
+    logger.info("Creating dataset card")
+    card_content = create_dataset_card(
+        source_dataset=input_dataset,
+        model=model_name,
+        task_mode=task_mode,
+        num_samples=len(dataset),
+        processing_time=processing_time_str,
+        batch_size=batch_size,
+        max_model_len=max_model_len,
+        max_tokens=max_tokens,
+        gpu_memory_utilization=gpu_memory_utilization,
+        temperature=temperature,
+        apply_smart_resize=apply_smart_resize,
+        image_column=image_column,
+        split=split,
+    )
+    
+    card = DatasetCard(card_content)
+    card.push_to_hub(output_dataset, token=HF_TOKEN)
+    
+    logger.info("✅ PaddleOCR-VL processing complete!")
+    logger.info(f"Dataset available at: https://huggingface.co/datasets/{output_dataset}")
+    logger.info(f"Processing time: {processing_time_str}")
+    logger.info(f"Task mode: {task_mode} - {TASK_DESCRIPTIONS[task_mode]}")
+
+
+if __name__ == "__main__":
+    # Show example usage if no arguments
+    if len(sys.argv) == 1:
+        print("=" * 80)
+        print("PaddleOCR-VL Document Processing")
+        print("=" * 80)
+        print("\nUltra-compact 0.9B OCR model with task-specific capabilities")
+        print("\nFeatures:")
+        print("- 🎯 Smallest OCR model - Only 0.9B parameters")
+        print("- 📝 OCR mode - General text extraction")
+        print("- 📊 Table mode - HTML table recognition")
+        print("- 📐 Formula mode - LaTeX mathematical notation")
+        print("- 📈 Chart mode - Structured chart analysis")
+        print("- 🌍 Multilingual support")
+        print("- ⚡ Fast initialization and inference")
+        print("- 🔧 Based on ERNIE-4.5 (unique architecture)")
+        print("\nTask Modes:")
+        for mode, description in TASK_DESCRIPTIONS.items():
+            print(f"  {mode:8} - {description}")
+        print("\nExample usage:")
+        print("\n1. Basic OCR (default mode):")
+        print("   uv run paddleocr-vl.py input-dataset output-dataset")
+        print("\n2. Table extraction:")
+        print("   uv run paddleocr-vl.py docs tables-extracted --task-mode table")
+        print("\n3. Formula recognition:")
+        print("   uv run paddleocr-vl.py papers formulas --task-mode formula --batch-size 32")
+        print("\n4. Chart analysis:")
+        print("   uv run paddleocr-vl.py diagrams charts-analyzed --task-mode chart")
+        print("\n5. Test with small sample:")
+        print("   uv run paddleocr-vl.py dataset test --max-samples 10 --shuffle")
+        print("\n6. Running on HF Jobs:")
+        print("   hf jobs uv run --flavor l4x1 \\")
+        print("     -e HF_TOKEN=$(python3 -c \"from huggingface_hub import get_token; print(get_token())\") \\")
+        print("     -e HF_HUB_ENABLE_HF_TRANSFER=1 \\")
+        print("     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl.py \\")
+        print("       input-dataset output-dataset --task-mode ocr")
+        print("\n" + "=" * 80)
+        print("\nFor full help, run: uv run paddleocr-vl.py --help")
+        sys.exit(0)
+    
+    parser = argparse.ArgumentParser(
+        description="Document processing using PaddleOCR-VL (0.9B task-specific model)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Task Modes:
+  ocr       General text extraction to markdown (default)
+  table     Table extraction to HTML format
+  formula   Mathematical formula recognition to LaTeX
+  chart     Chart and diagram analysis
+
+Examples:
+  # Basic text OCR
+  uv run paddleocr-vl.py my-docs analyzed-docs
+
+  # Extract tables from documents
+  uv run paddleocr-vl.py papers tables --task-mode table
+
+  # Recognize mathematical formulas
+  uv run paddleocr-vl.py textbooks formulas --task-mode formula
+
+  # Analyze charts and diagrams
+  uv run paddleocr-vl.py reports charts --task-mode chart
+
+  # Test with random sampling
+  uv run paddleocr-vl.py large-dataset test --max-samples 50 --shuffle --task-mode ocr
+
+  # Disable smart resize for original resolution
+  uv run paddleocr-vl.py images output --no-smart-resize
+        """,
+    )
+    
+    parser.add_argument("input_dataset", help="Input dataset ID from Hugging Face Hub")
+    parser.add_argument("output_dataset", help="Output dataset ID for Hugging Face Hub")
+    parser.add_argument(
+        "--image-column",
+        default="image",
+        help="Column containing images (default: image)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=16,
+        help="Batch size for processing (default: 16)",
+    )
+    parser.add_argument(
+        "--task-mode",
+        choices=list(TASK_MODES.keys()),
+        default="ocr",
+        help="Task type: ocr (default), table, formula, or chart",
+    )
+    parser.add_argument(
+        "--max-model-len",
+        type=int,
+        default=8192,
+        help="Maximum model context length (default: 8192)",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=4096,
+        help="Maximum tokens to generate (default: 4096)",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.0,
+        help="Sampling temperature (default: 0.0 for deterministic)",
+    )
+    parser.add_argument(
+        "--gpu-memory-utilization",
+        type=float,
+        default=0.8,
+        help="GPU memory utilization (default: 0.8)",
+    )
+    parser.add_argument(
+        "--no-smart-resize",
+        action="store_true",
+        help="Disable PaddleOCR-VL's smart resize, use original image size",
+    )
+    parser.add_argument("--hf-token", help="Hugging Face API token")
+    parser.add_argument(
+        "--split", default="train", help="Dataset split to use (default: train)"
+    )
+    parser.add_argument(
+        "--max-samples",
+        type=int,
+        help="Maximum number of samples to process (for testing)",
+    )
+    parser.add_argument(
+        "--private", action="store_true", help="Make output dataset private"
+    )
+    parser.add_argument(
+        "--shuffle", action="store_true", help="Shuffle dataset before processing"
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for shuffling (default: 42)",
+    )
+    parser.add_argument(
+        "--output-column",
+        help="Column name for output (default: paddleocr_[task_mode])",
+    )
+    
+    args = parser.parse_args()
+    
+    main(
+        input_dataset=args.input_dataset,
+        output_dataset=args.output_dataset,
+        image_column=args.image_column,
+        batch_size=args.batch_size,
+        task_mode=args.task_mode,
+        max_model_len=args.max_model_len,
+        max_tokens=args.max_tokens,
+        temperature=args.temperature,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+        apply_smart_resize=not args.no_smart_resize,
+        hf_token=args.hf_token,
+        split=args.split,
+        max_samples=args.max_samples,
+        private=args.private,
+        shuffle=args.shuffle,
+        seed=args.seed,
+        output_column=args.output_column,
+    )