From ececd2cd52373fe339c561bf33a83f412592d86a Mon Sep 17 00:00:00 2001 From: Vik Paruchuri Date: Tue, 28 May 2024 10:35:57 -0700 Subject: [PATCH] Remove ray to enable python 3.12 compatibility --- README.md | 2 +- convert.py | 74 +++---- convert_single.py | 1 + marker/convert.py | 6 +- marker/models.py | 2 +- marker/ocr/heuristics.py | 12 +- marker/ocr/recognition.py | 10 - poetry.lock | 447 +++++++++----------------------------- pyproject.toml | 3 +- 9 files changed, 152 insertions(+), 405 deletions(-) diff --git a/README.md b/README.md index edda4998..2e7f343f 100644 --- a/README.md +++ b/README.md @@ -169,7 +169,7 @@ Peak GPU memory usage during the benchmark is `4.2GB` for nougat, and `4.1GB` fo **Throughput** -Marker takes about 4.5GB of VRAM on average per task, so you can convert 10 documents in parallel on an A6000. +Marker takes about 4GB of VRAM on average per task, so you can convert 12 documents in parallel on an A6000. ![Benchmark results](data/images/per_doc.png) diff --git a/convert.py b/convert.py index 8a5012ef..4d927456 100755 --- a/convert.py +++ b/convert.py @@ -1,8 +1,11 @@ -import argparse import os -from typing import Dict, Optional -import ray +os.environ["IN_STREAMLIT"] = "true" # Avoid multiprocessing inside surya +os.environ["PDFTEXT_CPU_WORKERS"] = "1" # Avoid multiprocessing inside pdftext + +import pypdfium2 # Needs to be at the top to avoid warnings +import argparse +import torch.multiprocessing as mp from tqdm import tqdm import math @@ -19,8 +22,19 @@ configure_logging() -@ray.remote(num_cpus=settings.RAY_CORES_PER_WORKER, num_gpus=.05 if settings.CUDA else 0) -def process_single_pdf(filepath: str, out_folder: str, model_refs, metadata: Optional[Dict] = None, min_length: Optional[int] = None): +def worker_init(shared_model): + global model_refs + model_refs = shared_model + + +def worker_exit(): + global model_refs + del model_refs + + +def process_single_pdf(args): + filepath, out_folder, metadata, min_length = args + fname = os.path.basename(filepath) if markdown_exists(out_folder, fname): return @@ -86,45 +100,27 @@ def main(): total_processes = min(len(files_to_convert), args.workers) - ray.init( - num_cpus=total_processes, - num_gpus=1 if settings.CUDA else 0, - storage=settings.RAY_CACHE_PATH, - _temp_dir=settings.RAY_CACHE_PATH, - log_to_driver=settings.DEBUG - ) + # Dynamically set GPU allocation per task based on GPU ram + if settings.CUDA: + tasks_per_gpu = settings.INFERENCE_RAM // settings.VRAM_PER_TASK if settings.CUDA else 0 + total_processes = min(tasks_per_gpu, total_processes) + mp.set_start_method('spawn') # Required for CUDA, forkserver doesn't work model_lst = load_all_models() - model_refs = ray.put(model_lst) - - # Dynamically set GPU allocation per task based on GPU ram - gpu_frac = settings.VRAM_PER_TASK / settings.INFERENCE_RAM if settings.CUDA else 0 + for model in model_lst: + if model: + model.share_memory() print(f"Converting {len(files_to_convert)} pdfs in chunk {args.chunk_idx + 1}/{args.num_chunks} with {total_processes} processes, and storing in {out_folder}") - futures = [ - process_single_pdf.options(num_gpus=gpu_frac).remote( - filepath, - out_folder, - model_refs, - metadata=metadata.get(os.path.basename(filepath)), - min_length=args.min_length - ) for filepath in files_to_convert - ] - - # Run all ray conversion tasks - progress_bar = tqdm(total=len(futures)) - while len(futures) > 0: - finished, futures = ray.wait( - futures, timeout=7.0 - ) - finished_lst = ray.get(finished) - if isinstance(finished_lst, list): - progress_bar.update(len(finished_lst)) - else: - progress_bar.update(1) + task_args = [(f, out_folder, metadata.get(os.path.basename(f)), args.min_length) for f in files_to_convert] + + with mp.Pool(processes=total_processes, initializer=worker_init, initargs=(model_lst,)) as pool: + list(tqdm(pool.imap(process_single_pdf, task_args), total=len(task_args), desc="Processing PDFs", unit="pdf")) + + pool._worker_handler.terminate = worker_exit - # Shutdown ray to free resources - ray.shutdown() + # Delete all CUDA tensors + del model_lst if __name__ == "__main__": diff --git a/convert_single.py b/convert_single.py index aa3bdd97..78ee9c5f 100755 --- a/convert_single.py +++ b/convert_single.py @@ -1,3 +1,4 @@ +import pypdfium2 # Needs to be at the top to avoid warnings import argparse import os diff --git a/marker/convert.py b/marker/convert.py index eec53124..5ebbf964 100644 --- a/marker/convert.py +++ b/marker/convert.py @@ -1,7 +1,7 @@ import warnings warnings.filterwarnings("ignore", category=UserWarning) # Filter torch pytree user warnings -import pypdfium2 as pdfium +import pypdfium2 as pdfium # Needs to be at the top to avoid warnings from PIL import Image from marker.utils import flush_cuda_memory @@ -33,8 +33,8 @@ def convert_single_pdf( fname: str, model_lst: List, - max_pages=None, - metadata: Optional[Dict]=None, + max_pages: int = None, + metadata: Optional[Dict] = None, langs: Optional[List[str]] = None, batch_multiplier: int = 1 ) -> Tuple[str, Dict[str, Image.Image], Dict]: diff --git a/marker/models.py b/marker/models.py index 9b113621..7333e8d0 100644 --- a/marker/models.py +++ b/marker/models.py @@ -71,7 +71,7 @@ def load_all_models(langs=None, device=None, dtype=None, force_load_ocr=False): edit = load_editing_model(device, dtype) # Only load recognition model if we'll need it for all pdfs - ocr = setup_recognition_model(langs, device, dtype) if ((settings.OCR_ENGINE == "surya" and settings.OCR_ALL_PAGES) or force_load_ocr) else None + ocr = setup_recognition_model(langs, device, dtype) texify = setup_texify_model(device, dtype) model_lst = [texify, layout, order, edit, detection, ocr] return model_lst \ No newline at end of file diff --git a/marker/ocr/heuristics.py b/marker/ocr/heuristics.py index 916707f9..b3cf6b2b 100644 --- a/marker/ocr/heuristics.py +++ b/marker/ocr/heuristics.py @@ -8,7 +8,11 @@ def should_ocr_page(page: Page, no_text: bool): - detected_lines_found = detected_line_coverage(page) + detected_lines_found, total_lines = detected_line_coverage(page) + + # No reason to OCR page if it has no text lines + if total_lines == 0: + return False # OCR page if we got minimal text, or if we got too many spaces conditions = [ @@ -55,7 +59,6 @@ def no_text_found(pages: List[Page]): def detected_line_coverage(page: Page, intersect_thresh=.5, detection_thresh=.4): found_lines = 0 for detected_line in page.text_lines.bboxes: - # Get bbox and rescale to match dimensions of original page detected_bbox = detected_line.bbox detected_bbox = rescale_bbox(page.text_lines.image_bbox, page.bbox, detected_bbox) @@ -70,5 +73,6 @@ def detected_line_coverage(page: Page, intersect_thresh=.5, detection_thresh=.4) total_lines = len(page.text_lines.bboxes) if total_lines == 0: - return False - return found_lines / total_lines > detection_thresh + return True, 0 + + return found_lines / total_lines > detection_thresh, total_lines diff --git a/marker/ocr/recognition.py b/marker/ocr/recognition.py index 4fad04a4..20ff0fd8 100644 --- a/marker/ocr/recognition.py +++ b/marker/ocr/recognition.py @@ -48,17 +48,7 @@ def run_ocr(doc, pages: List[Page], langs: List[str], rec_model, batch_multiplie if ocr_method is None: return pages, {"ocr_pages": 0, "ocr_failed": 0, "ocr_success": 0, "ocr_engine": "none"} elif ocr_method == "surya": - # Load model just in time if we're not OCRing everything - del_rec_model = False - if rec_model is None: - lang_tokens = langs_to_ids(langs) - rec_model = setup_recognition_model(lang_tokens) - del_rec_model = True - new_pages = surya_recognition(doc, ocr_idxs, langs, rec_model, pages, batch_multiplier=batch_multiplier) - - if del_rec_model: - del rec_model elif ocr_method == "ocrmypdf": new_pages = tesseract_recognition(doc, ocr_idxs, langs) else: diff --git a/poetry.lock b/poetry.lock index a5ce1264..03527d66 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,19 +1,5 @@ # This file is automatically @generated by Poetry 1.5.0 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