Refa: PARALLEL_DEVICES is a static parameter. (#6168)

### What problem does this PR solve?


### Type of change

- [x] Refactoring

deepdoc/parser/pdf_parser.py

@@ -37,13 +37,15 @@ from rag.nlp import rag_tokenizer
 from copy import deepcopy
 from huggingface_hub import snapshot_download
 
+from rag.settings import PARALLEL_DEVICES
+
 LOCK_KEY_pdfplumber = "global_shared_lock_pdfplumber"
 if LOCK_KEY_pdfplumber not in sys.modules:
     sys.modules[LOCK_KEY_pdfplumber] = threading.Lock()
 
 
 class RAGFlowPdfParser:
-    def __init__(self, parallel_devices: int | None = None):
+    def __init__(self):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
 
@@ -56,11 +58,10 @@ class RAGFlowPdfParser:
 
         """
         
-        self.ocr = OCR(parallel_devices = parallel_devices)
-        self.parallel_devices = parallel_devices
+        self.ocr = OCR()
         self.parallel_limiter = None
-        if parallel_devices is not None and parallel_devices > 1:
-            self.parallel_limiter = [trio.CapacityLimiter(1) for _ in range(parallel_devices)]
+        if PARALLEL_DEVICES is not None and PARALLEL_DEVICES > 1:
+            self.parallel_limiter = [trio.CapacityLimiter(1) for _ in range(PARALLEL_DEVICES)]
         
         if hasattr(self, "model_speciess"):
             self.layouter = LayoutRecognizer("layout." + self.model_speciess)
@@ -1018,7 +1019,6 @@ class RAGFlowPdfParser:
             self.pdf.close()
         if not self.outlines:
             logging.warning("Miss outlines")
-        
 
         logging.debug("Images converted.")
         self.is_english = [re.search(r"[a-zA-Z0-9,/¸;:'\[\]\(\)!@#$%^&*\"?<>._-]{30,}", "".join(
@@ -1066,8 +1066,8 @@ class RAGFlowPdfParser:
                     for i, img in enumerate(self.page_images):
                         chars = __ocr_preprocess()
 
-                        nursery.start_soon(__img_ocr, i, i % self.parallel_devices, img, chars,
-                                        self.parallel_limiter[i % self.parallel_devices])
+                        nursery.start_soon(__img_ocr, i, i % PARALLEL_DEVICES, img, chars,
+                                        self.parallel_limiter[i % PARALLEL_DEVICES])
                         await trio.sleep(0.1)
             else:
                 for i, img in enumerate(self.page_images):

deepdoc/vision/ocr.py

@@ -22,6 +22,7 @@ import os
 from huggingface_hub import snapshot_download
 
 from api.utils.file_utils import get_project_base_directory
+from rag.settings import PARALLEL_DEVICES
 from .operators import *  # noqa: F403
 from . import operators
 import math
@@ -509,7 +510,7 @@ class TextDetector:
 
 
 class OCR:
-    def __init__(self, model_dir=None, parallel_devices: int | None = None):
+    def __init__(self, model_dir=None):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!
 
@@ -528,10 +529,10 @@ class OCR:
                         "rag/res/deepdoc")
                 
                 # Append muti-gpus task to the list
-                if parallel_devices is not None and parallel_devices > 0:
+                if PARALLEL_DEVICES is not None and PARALLEL_DEVICES > 0:
                     self.text_detector = []
                     self.text_recognizer = []
-                    for device_id in range(parallel_devices):
+                    for device_id in range(PARALLEL_DEVICES):
                         self.text_detector.append(TextDetector(model_dir, device_id))
                         self.text_recognizer.append(TextRecognizer(model_dir, device_id))
                 else:
@@ -543,11 +544,11 @@ class OCR:
                                               local_dir=os.path.join(get_project_base_directory(), "rag/res/deepdoc"),
                                               local_dir_use_symlinks=False)
                 
-                if parallel_devices is not None:
-                    assert parallel_devices > 0 , "Number of devices must be >= 1"
+                if PARALLEL_DEVICES is not None:
+                    assert PARALLEL_DEVICES > 0, "Number of devices must be >= 1"
                     self.text_detector = []
                     self.text_recognizer = []
-                    for device_id in range(parallel_devices):
+                    for device_id in range(PARALLEL_DEVICES):
                         self.text_detector.append(TextDetector(model_dir, device_id))
                         self.text_recognizer.append(TextRecognizer(model_dir, device_id))
                 else:

deepdoc/vision/t_ocr.py

@@ -34,15 +34,15 @@ import trio
 os.environ['CUDA_VISIBLE_DEVICES'] = '0' #1 gpu
 # os.environ['CUDA_VISIBLE_DEVICES'] = '' #cpu
 
+
 def main(args):
     import torch.cuda
 
     cuda_devices = torch.cuda.device_count()
     limiter = [trio.CapacityLimiter(1) for _ in range(cuda_devices)] if cuda_devices > 1 else None
-    ocr = OCR(parallel_devices = cuda_devices)
+    ocr = OCR()
     images, outputs = init_in_out(args)
 
-
     def __ocr(i, id, img):
         print("Task {} start".format(i))
         bxs = ocr(np.array(img), id)

rag/app/naive.py

@@ -128,8 +128,8 @@ class Docx(DocxParser):
 
 
 class Pdf(PdfParser):
-    def __init__(self, parallel_devices = None):
-        super().__init__(parallel_devices)
+    def __init__(self):
+        super().__init__()
 
     def __call__(self, filename, binary=None, from_page=0,
                  to_page=100000, zoomin=3, callback=None):
@@ -197,7 +197,7 @@ class Markdown(MarkdownParser):
 
 
 def chunk(filename, binary=None, from_page=0, to_page=100000,
-          lang="Chinese", callback=None, parallel_devices=None, **kwargs):
+          lang="Chinese", callback=None, **kwargs):
     """
         Supported file formats are docx, pdf, excel, txt.
         This method apply the naive ways to chunk files.
@@ -237,7 +237,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         return res
 
     elif re.search(r"\.pdf$", filename, re.IGNORECASE):
-        pdf_parser = Pdf(parallel_devices)
+        pdf_parser = Pdf()
         if parser_config.get("layout_recognize", "DeepDOC") == "Plain Text":
             pdf_parser = PlainParser()
         sections, tables = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page,

rag/settings.py

@@ -39,6 +39,13 @@ SVR_CONSUMER_GROUP_NAME = "rag_flow_svr_task_broker"
 PAGERANK_FLD = "pagerank_fea"
 TAG_FLD = "tag_feas"
 
+PARALLEL_DEVICES = None
+try:
+    import torch.cuda
+    PARALLEL_DEVICES = torch.cuda.device_count()
+    logging.info(f"found {PARALLEL_DEVICES} gpus")
+except Exception:
+    logging.info("can't import package 'torch'")
 
 def print_rag_settings():
     logging.info(f"MAX_CONTENT_LENGTH: {DOC_MAXIMUM_SIZE}")

rag/svr/task_executor.py

@@ -100,13 +100,6 @@ MAX_CONCURRENT_CHUNK_BUILDERS = int(os.environ.get('MAX_CONCURRENT_CHUNK_BUILDER
 task_limiter = trio.CapacityLimiter(MAX_CONCURRENT_TASKS)
 chunk_limiter = trio.CapacityLimiter(MAX_CONCURRENT_CHUNK_BUILDERS)
 
-PARALLEL_DEVICES = None
-try:
-    import torch.cuda
-    PARALLEL_DEVICES = torch.cuda.device_count()
-    logging.info(f"found {PARALLEL_DEVICES} gpus")
-except Exception:
-    logging.info("can't import package 'torch'")
 
 # SIGUSR1 handler: start tracemalloc and take snapshot
 def start_tracemalloc_and_snapshot(signum, frame):
@@ -249,7 +242,7 @@ async def build_chunks(task, progress_callback):
     try:
         async with chunk_limiter:
             cks = await trio.to_thread.run_sync(lambda: chunker.chunk(task["name"], binary=binary, from_page=task["from_page"],
-                                to_page=task["to_page"], lang=task["language"], parallel_devices = PARALLEL_DEVICES, callback=progress_callback,
+                                to_page=task["to_page"], lang=task["language"], callback=progress_callback,
                                 kb_id=task["kb_id"], parser_config=task["parser_config"], tenant_id=task["tenant_id"]))
         logging.info("Chunking({}) {}/{} done".format(timer() - st, task["location"], task["name"]))
     except TaskCanceledException: