Upload glm-ocr.py with huggingface_hub

glm-ocr.py

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+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets>=3.1.0",
+#     "pyarrow>=17.0.0,<18.0.0",
+#     "huggingface-hub",
+#     "pillow",
+#     "vllm",
+#     "tqdm",
+#     "toolz",
+#     "torch",
+# ]
+#
+# [[tool.uv.index]]
+# url = "https://wheels.vllm.ai/nightly"
+#
+# [tool.uv]
+# prerelease = "allow"
+# override-dependencies = ["transformers @ git+https://github.com/huggingface/transformers.git"]
+# ///
+
+"""
+Convert document images to markdown using GLM-OCR with vLLM.
+
+GLM-OCR is a compact 0.9B parameter OCR model achieving 94.62% on OmniDocBench V1.5.
+Uses CogViT visual encoder with GLM-0.5B language decoder and Multi-Token Prediction
+(MTP) loss for fast, accurate document parsing.
+
+NOTE: Requires vLLM nightly wheels and transformers from git for GLM-OCR support.
+First run may take a few minutes to download and install dependencies.
+
+Features:
+- 0.9B parameters (ultra-compact)
+- 94.62% on OmniDocBench V1.5 (SOTA for sub-1B models)
+- Text recognition with markdown output
+- LaTeX formula recognition
+- Table extraction (HTML format)
+- Multilingual: zh, en, fr, es, ru, de, ja, ko
+- MIT licensed
+
+Model: zai-org/GLM-OCR
+vLLM: Requires vLLM nightly build + transformers from git
+Performance: 94.62% on OmniDocBench V1.5
+"""
+
+import argparse
+import base64
+import io
+import json
+import logging
+import os
+import sys
+from datetime import datetime
+from typing import Any, Dict, List, Union
+
+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__)
+
+MODEL = "zai-org/GLM-OCR"
+
+# Task prompts as specified by the model
+TASK_PROMPTS = {
+    "ocr": "Text Recognition:",
+    "formula": "Formula Recognition:",
+    "table": "Table Recognition:",
+}
+
+
+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 make_ocr_message(
+    image: Union[Image.Image, Dict[str, Any], str],
+    task: str = "ocr",
+) -> List[Dict]:
+    """
+    Create chat message for OCR processing.
+
+    GLM-OCR uses a chat format with an image and a task prompt prefix.
+    Supported tasks: ocr, formula, table.
+    """
+    # 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")
+
+    # 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()}"
+
+    prompt_text = TASK_PROMPTS.get(task, TASK_PROMPTS["ocr"])
+
+    return [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image_url", "image_url": {"url": data_uri}},
+                {"type": "text", "text": prompt_text},
+            ],
+        }
+    ]
+
+
+def create_dataset_card(
+    source_dataset: str,
+    model: str,
+    num_samples: int,
+    processing_time: str,
+    batch_size: int,
+    max_model_len: int,
+    max_tokens: int,
+    gpu_memory_utilization: float,
+    temperature: float,
+    top_p: float,
+    task: str,
+    image_column: str = "image",
+    split: str = "train",
+) -> str:
+    """Create a dataset card documenting the OCR process."""
+    model_name = model.split("/")[-1]
+    task_desc = {"ocr": "text recognition", "formula": "formula recognition", "table": "table recognition"}
+
+    return f"""---
+tags:
+- ocr
+- document-processing
+- glm-ocr
+- markdown
+- uv-script
+- generated
+---
+
+# Document OCR using {model_name}
+
+This dataset contains OCR results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
+
+## Processing Details
+
+- **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
+- **Model**: [{model}](https://huggingface.co/{model})
+- **Task**: {task_desc.get(task, task)}
+- **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**: `markdown`
+- **Dataset Split**: `{split}`
+- **Batch Size**: {batch_size}
+- **Max Model Length**: {max_model_len:,} tokens
+- **Max Output Tokens**: {max_tokens:,}
+- **Temperature**: {temperature}
+- **Top P**: {top_p}
+- **GPU Memory Utilization**: {gpu_memory_utilization:.1%}
+
+## Model Information
+
+GLM-OCR is a compact, high-performance OCR model:
+- 0.9B parameters
+- 94.62% on OmniDocBench V1.5
+- CogViT visual encoder + GLM-0.5B language decoder
+- Multi-Token Prediction (MTP) loss for efficiency
+- Multilingual: zh, en, fr, es, ru, de, ja, ko
+- MIT licensed
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The extracted text in markdown format
+- `inference_info`: JSON list tracking all OCR models applied to this dataset
+
+## Reproduction
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \\
+    {source_dataset} \\
+    <output-dataset> \\
+    --image-column {image_column} \\
+    --batch-size {batch_size} \\
+    --task {task}
+```
+
+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,
+    max_model_len: int = 8192,
+    max_tokens: int = 16384,
+    temperature: float = 0.01,
+    top_p: float = 0.00001,
+    repetition_penalty: float = 1.1,
+    gpu_memory_utilization: float = 0.8,
+    task: str = "ocr",
+    hf_token: str = None,
+    split: str = "train",
+    max_samples: int = None,
+    private: bool = False,
+    shuffle: bool = False,
+    seed: int = 42,
+    output_column: str = "markdown",
+):
+    """Process images from HF dataset through GLM-OCR model."""
+
+    check_cuda_availability()
+
+    start_time = datetime.now()
+
+    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+
+    # Validate task
+    if task not in TASK_PROMPTS:
+        logger.error(f"Unknown task '{task}'. Supported: {list(TASK_PROMPTS.keys())}")
+        sys.exit(1)
+
+    logger.info(f"Using model: {MODEL}")
+    logger.info(f"Task: {task} (prompt: '{TASK_PROMPTS[task]}')")
+
+    # Load dataset
+    logger.info(f"Loading dataset: {input_dataset}")
+    dataset = load_dataset(input_dataset, split=split)
+
+    if image_column not in dataset.column_names:
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+
+    if shuffle:
+        logger.info(f"Shuffling dataset with seed {seed}")
+        dataset = dataset.shuffle(seed=seed)
+
+    if max_samples:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+        logger.info(f"Limited to {len(dataset)} samples")
+
+    # Initialize vLLM
+    logger.info("Initializing vLLM with GLM-OCR")
+    logger.info("This may take a few minutes on first run...")
+    llm = LLM(
+        model=MODEL,
+        trust_remote_code=True,
+        max_model_len=max_model_len,
+        gpu_memory_utilization=gpu_memory_utilization,
+        limit_mm_per_prompt={"image": 1},
+    )
+
+    # Sampling defaults from GLM-OCR SDK (github.com/zai-org/GLM-OCR)
+    # glmocr/config.py PageLoaderConfig: temperature=0.01, top_p=0.00001,
+    # top_k=1, repetition_penalty=1.1, max_tokens=16384
+    # generation_config.json on HF also sets do_sample=false (greedy)
+    sampling_params = SamplingParams(
+        temperature=temperature,
+        top_p=top_p,
+        max_tokens=max_tokens,
+        repetition_penalty=repetition_penalty,
+    )
+
+    logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
+    logger.info(f"Output will be written to column: {output_column}")
+
+    all_outputs = []
+
+    for batch_indices in tqdm(
+        partition_all(batch_size, range(len(dataset))),
+        total=(len(dataset) + batch_size - 1) // batch_size,
+        desc="GLM-OCR processing",
+    ):
+        batch_indices = list(batch_indices)
+        batch_images = [dataset[i][image_column] for i in batch_indices]
+
+        try:
+            batch_messages = [
+                make_ocr_message(img, task=task)
+                for img in batch_images
+            ]
+
+            outputs = llm.chat(batch_messages, sampling_params)
+
+            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}")
+            all_outputs.extend(["[OCR ERROR]"] * len(batch_images))
+
+    processing_duration = datetime.now() - start_time
+    processing_time_str = f"{processing_duration.total_seconds() / 60:.1f} min"
+
+    logger.info(f"Adding '{output_column}' column to dataset")
+    dataset = dataset.add_column(output_column, all_outputs)
+
+    # Inference info tracking
+    inference_entry = {
+        "model_id": MODEL,
+        "model_name": "GLM-OCR",
+        "column_name": output_column,
+        "timestamp": datetime.now().isoformat(),
+        "task": task,
+        "temperature": temperature,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "max_tokens": max_tokens,
+    }
+
+    if "inference_info" in dataset.column_names:
+        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:
+        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,
+        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,
+        top_p=top_p,
+        task=task,
+        image_column=image_column,
+        split=split,
+    )
+
+    card = DatasetCard(card_content)
+    card.push_to_hub(output_dataset, token=HF_TOKEN)
+
+    logger.info("Done! GLM-OCR 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"Processing speed: {len(dataset) / processing_duration.total_seconds():.2f} images/sec")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) == 1:
+        print("=" * 70)
+        print("GLM-OCR Document Processing")
+        print("=" * 70)
+        print("\n0.9B OCR model - 94.62% on OmniDocBench V1.5")
+        print("\nTask modes:")
+        print("  ocr      - Text recognition (default)")
+        print("  formula  - LaTeX formula recognition")
+        print("  table    - Table extraction")
+        print("\nExamples:")
+        print("\n1. Basic OCR:")
+        print("   uv run glm-ocr.py input-dataset output-dataset")
+        print("\n2. Formula recognition:")
+        print("   uv run glm-ocr.py docs results --task formula")
+        print("\n3. Table extraction:")
+        print("   uv run glm-ocr.py docs results --task table")
+        print("\n4. Test with small sample:")
+        print("   uv run glm-ocr.py large-dataset test --max-samples 10 --shuffle")
+        print("\n5. Running on HF Jobs:")
+        print("   hf jobs uv run --flavor l4x1 \\")
+        print("     -s HF_TOKEN \\")
+        print("     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \\")
+        print("       input-dataset output-dataset --batch-size 16")
+        print("\nFor full help: uv run glm-ocr.py --help")
+        sys.exit(0)
+
+    parser = argparse.ArgumentParser(
+        description="Document OCR using GLM-OCR (0.9B, 94.62% OmniDocBench V1.5)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Task modes:
+  ocr      Text recognition to markdown (default)
+  formula  LaTeX formula recognition
+  table    Table extraction
+
+Examples:
+  uv run glm-ocr.py my-docs analyzed-docs
+  uv run glm-ocr.py docs results --task formula
+  uv run glm-ocr.py large-dataset test --max-samples 50 --shuffle
+        """,
+    )
+
+    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(
+        "--max-model-len",
+        type=int,
+        default=8192,
+        help="Maximum model context length (default: 8192)",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=16384,
+        help="Maximum tokens to generate (default: 16384)",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.01,
+        help="Sampling temperature (default: 0.01, near-greedy for OCR accuracy)",
+    )
+    parser.add_argument(
+        "--top-p",
+        type=float,
+        default=0.00001,
+        help="Top-p sampling parameter (default: 0.00001, near-greedy)",
+    )
+    parser.add_argument(
+        "--repetition-penalty",
+        type=float,
+        default=1.1,
+        help="Repetition penalty to prevent loops (default: 1.1)",
+    )
+    parser.add_argument(
+        "--gpu-memory-utilization",
+        type=float,
+        default=0.8,
+        help="GPU memory utilization (default: 0.8)",
+    )
+    parser.add_argument(
+        "--task",
+        choices=["ocr", "formula", "table"],
+        default="ocr",
+        help="OCR task mode (default: ocr)",
+    )
+    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",
+        default="markdown",
+        help="Column name for output text (default: markdown)",
+    )
+
+    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,
+        max_model_len=args.max_model_len,
+        max_tokens=args.max_tokens,
+        temperature=args.temperature,
+        top_p=args.top_p,
+        repetition_penalty=args.repetition_penalty,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+        task=args.task,
+        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,
+    )