Olmocr runner implemented

olmocr/bench/convert.py

@@ -2,6 +2,8 @@ import argparse
 import glob
 import importlib
 import os
+import asyncio
+import inspect
 
 from tqdm import tqdm
 
@@ -32,6 +34,34 @@ def parse_method_arg(method_arg):
     return name, kwargs
 
 
+async def process_pdfs(config, pdf_directory, data_directory, repeats):
+    """Process PDFs with both sync and async functions"""
+    for candidate in config.keys():
+        print(f"Starting conversion using {candidate} with kwargs: {config[candidate]['kwargs']}")
+        candidate_output_dir = os.path.join(data_directory, candidate)
+        os.makedirs(candidate_output_dir, exist_ok=True)
+        
+        method = config[candidate]["method"]
+        kwargs = config[candidate]["kwargs"]
+        is_async = asyncio.iscoroutinefunction(method)
+        
+        for pdf_path in tqdm(glob.glob(os.path.join(pdf_directory, "*.pdf")), desc=candidate):
+            base_name = os.path.basename(pdf_path).replace(".pdf", "")
+            
+            for i in range(1, repeats + 1):
+                if is_async:
+                    # Run async function
+                    markdown = await method(pdf_path, page_num=1, **kwargs)
+                else:
+                    # Run synchronous function
+                    markdown = method(pdf_path, page_num=1, **kwargs)
+                
+                output_filename = f"{base_name}_{i}.md"
+                output_path = os.path.join(candidate_output_dir, output_filename)
+                with open(output_path, "w") as out_f:
+                    out_f.write(markdown)
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run PDF conversion using specified OCR methods and extra parameters.")
     parser.add_argument("methods", nargs="+", help="Methods to run in the format method[:key=value ...]. " "Example: gotocr mineru:temperature=2 marker:runs=3")
@@ -40,6 +70,7 @@ if __name__ == "__main__":
 
     # Mapping of method names to a tuple: (module path, function name)
     available_methods = {
+        "olmocr": ("olmocr.bench.runners.run_olmocr", "run_olmocr"),
         "gotocr": ("olmocr.bench.runners.run_gotocr", "run_gotocr"),
         "marker": ("olmocr.bench.runners.run_marker", "run_marker"),
         "mineru": ("olmocr.bench.runners.run_mineru", "run_mineru"),
@@ -61,18 +92,5 @@ if __name__ == "__main__":
     data_directory = os.path.join(os.path.dirname(__file__), "sample_data")
     pdf_directory = os.path.join(data_directory, "pdfs")
 
-    # Process each PDF using each specified method and repeat the conversion as needed.
+    # Run the async process function
-    for candidate in config.keys():
+    asyncio.run(process_pdfs(config, pdf_directory, data_directory, args.repeats))
-        print(f"Starting conversion using {candidate} with kwargs: {config[candidate]['kwargs']}")
-        candidate_output_dir = os.path.join(data_directory, candidate)
-        os.makedirs(candidate_output_dir, exist_ok=True)
-
-        for pdf_path in tqdm(glob.glob(os.path.join(pdf_directory, "*.pdf")), desc=candidate):
-            base_name = os.path.basename(pdf_path).replace(".pdf", "")
-            # Repeat the conversion as many times as specified.
-            for i in range(1, args.repeats + 1):
-                markdown = config[candidate]["method"](pdf_path, page_num=1, **config[candidate]["kwargs"])
-                output_filename = f"{base_name}_{i}.md"
-                output_path = os.path.join(candidate_output_dir, output_filename)
-                with open(output_path, "w") as out_f:
-                    out_f.write(markdown)

olmocr/bench/runners/run_olmocr.py

@@ -1,35 +1,106 @@
 import asyncio
-import glob
+import logging
-import json
 import os
-import shutil
+import tempfile
-import sys
+from dataclasses import dataclass
+from functools import partial
+import argparse
+from typing import Optional
+import json
 
-import olmocr.pipeline
+# Import necessary components from olmocr
+from olmocr.pipeline import (
+    sglang_server_host, 
+    sglang_server_ready, 
+    build_page_query,
+    apost,
+    SGLANG_SERVER_PORT,
+    MetricsKeeper,
+    WorkerTracker
+)
+from olmocr.prompts import PageResponse
 
-# Set sys.argv as if you were running the script from the command line.
 
-workspace_dir = "olmocr/bench/sample_data/olmocr/workspace"
+# Setup basic logging
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("olmocr_runner")
 
-sys.argv = [
+# Basic configuration
-    "pipeline.py",  # The script name (can be arbitrary)
+@dataclass
-    "olmocr/bench/sample_data/olmocr/workspace",  # Positional argument: workspace
+class Args:
-    "--pdfs",
+    model: str = "allenai/olmOCR-7B-0225-preview"
-    *list(glob.glob("olmocr/bench/sample_data/pdfs/*.pdf")),  # PDF paths
+    model_chat_template: str = "qwen2-vl"
-]
+    model_max_context: int = 8192
+    target_longest_image_dim: int = 1024
+    target_anchor_text_len: int = 6000
 
-# Call the async main() function.
+async def run_olmocr(pdf_path: str, page_num: int = 1, temperature: float = 0.8) -> str:
-asyncio.run(olmocr.pipeline.main())
+    """
-
+    Process a single page of a PDF using the olmocr pipeline.
-# Now, take a produced jsonl files and unpack them into mds
+    
-for jsonl_path in glob.glob(workspace_dir + "/results/*.jsonl"):
+    Args:
-    with open(jsonl_path, "r") as jsonl_f:
+        pdf_path: Path to the PDF file
-        for line in jsonl_f:
+        page_num: Page number to process (1-indexed)
-            data = json.loads(line)
+        temperature: Temperature parameter for the model
-
+        
-            name = os.path.basename(data["metadata"]["Source-File"])
+    Returns:
-
+        The extracted text from the page
-            with open(f"olmocr/bench/sample_data/olmocr/{name.replace('.pdf', '.md')}", "w") as out_f:
+    """
-                out_f.write(data["text"])
+    # Ensure global variables are initialized
-
+    global metrics, tracker
-shutil.rmtree(workspace_dir)
+    if 'metrics' not in globals() or metrics is None:
+        metrics = MetricsKeeper(window=60*5)
+    if 'tracker' not in globals() or tracker is None:
+        tracker = WorkerTracker()
+    
+    args = Args()
+    semaphore = asyncio.Semaphore(1)
+    
+    # Ensure server is running
+    server_task = None
+    try:
+        await asyncio.wait_for(sglang_server_ready(), timeout=5)
+        print("Using existing sglang server")
+    except Exception:
+        print("Starting new sglang server")
+        server_task = asyncio.create_task(sglang_server_host(args, semaphore))
+        await sglang_server_ready()
+    
+    try:
+        # Process the page
+        query = await build_page_query(
+            pdf_path, 
+            page_num, 
+            args.target_longest_image_dim, 
+            args.target_anchor_text_len
+        )
+        query["temperature"] = temperature
+        
+        # Make request and get response
+        url = f"http://localhost:{SGLANG_SERVER_PORT}/v1/chat/completions"
+        status_code, response_body = await apost(url, json_data=query)
+        
+        if status_code != 200:
+            return f"Error: HTTP status {status_code}"
+        
+        # Parse response
+        response_data = json.loads(response_body)
+        content = response_data["choices"][0]["message"]["content"]
+        model_json = json.loads(content)
+        page_response = PageResponse(**model_json)
+        
+        # Update metrics
+        metrics.add_metrics(
+            sglang_input_tokens=response_data["usage"].get("prompt_tokens", 0),
+            sglang_output_tokens=response_data["usage"].get("completion_tokens", 0)
+        )
+        
+        return page_response.natural_text
+        
+    except Exception as e:
+        return f"Error: {type(e).__name__} - {str(e)}"
+        
+    finally:
+        # We leave the server running for potential reuse
+        # This is more efficient if multiple pages will be processed
+        pass