Work queue coallescing

pdelfin/beakerpipeline.py

@@ -33,6 +33,7 @@ def compute_workgroup_sha1(work_group: list[str]) -> str:
         sha1.update(pdf.encode('utf-8'))
     return sha1.hexdigest()
 
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='Manager for running millions of PDFs through a batch inference pipeline')
     parser.add_argument('workspace', help='The S3 path where work will be done e.g., s3://bucket/prefix/')
@@ -76,12 +77,18 @@ if __name__ == '__main__':
 
         all_pdfs = set(all_pdfs)
         logger.info(f"Found {len(all_pdfs):,} total pdf paths")
-        
+
         existing_lines = download_zstd_csv(workspace_s3, index_file_s3_path)
 
         # Parse existing work items into groups
-        existing_groups = [line.strip().split(",") for line in existing_lines if line.strip()]
-        existing_pdf_set = set(pdf for group in existing_groups for pdf in group)
+        existing_groups = {}
+        for line in existing_lines:
+            if line.strip():
+                parts = line.strip().split(",")
+                group_hash = parts[0]
+                group_pdfs = parts[1:]
+                existing_groups[group_hash] = group_pdfs
+        existing_pdf_set = set(pdf for group_pdfs in existing_groups.values() for pdf in group_pdfs)
 
         logger.info(f"Loaded {len(existing_pdf_set):,} existing pdf paths from the workspace")
 
@@ -96,18 +103,22 @@ if __name__ == '__main__':
         for pdf in sorted(new_pdfs):  # Sort for consistency
             current_group.append(pdf)
             if len(current_group) == args.group_size:
-                new_groups.append(current_group)
+                group_hash = compute_workgroup_sha1(current_group)
+                new_groups.append((group_hash, current_group))
                 current_group = []
         if current_group:
-            new_groups.append(current_group)
+            group_hash = compute_workgroup_sha1(current_group)
+            new_groups.append((group_hash, current_group))
 
         logger.info(f"Created {len(new_groups):,} new work groups")
 
         # Combine existing groups with new groups
-        combined_groups = existing_groups + new_groups
+        combined_groups = existing_groups.copy()
+        for group_hash, group_pdfs in new_groups:
+            combined_groups[group_hash] = group_pdfs
 
         # Prepare lines to write back
-        combined_lines = [",".join(group) for group in combined_groups]
+        combined_lines = [",".join([group_hash] + group_pdfs) for group_hash, group_pdfs in combined_groups.items()]
 
         # Upload the combined work items back to S3
         if new_groups:
@@ -119,9 +130,9 @@ if __name__ == '__main__':
     # If there is a beaker flag, then your job is to trigger this script with N replicas on beaker
     # If not, then your job is to do the actual work
 
-    # Donwload the model from the best place available
+    # Download the model from the best place available
     model_cache_dir = os.path.join(os.path.expanduser('~'), '.cache', 'pdelfin', 'model')
-    #download_directory(args.model, model_cache_dir)
+    download_directory(args.model, model_cache_dir)
 
     # Start up the sglang server
     sglang_process = subprocess.Popen([
@@ -131,7 +142,6 @@ if __name__ == '__main__':
             "--context-length", str(args.model_max_context),
         ])
 
-
     # Register atexit function and signal handlers to guarantee process termination
     def terminate_processes():
         print("Terminating child processes...")
@@ -153,11 +163,28 @@ if __name__ == '__main__':
     signal.signal(signal.SIGTERM, signal_handler)
 
     # Read in the work queue from s3
-    work_queue = download_zstd_csv(workspace_s3, index_file_s3_path)
-    work_queue = {compute_workgroup_sha1(pdfs): pdfs for pdfs in work_queue}
+    work_queue_lines = download_zstd_csv(workspace_s3, index_file_s3_path)
+    work_queue = {}
+    for line in work_queue_lines:
+        if line.strip():
+            parts = line.strip().split(",")
+            group_hash = parts[0]
+            group_pdfs = parts[1:]
+            work_queue[group_hash] = group_pdfs
 
     # Read in the done items from the s3 workspace
-    done_work_items = expand_s3_glob(workspace_s3, f"{args.workspace}/dolma_documents/*.jsonl")
+    done_work_items = expand_s3_glob(workspace_s3, f"{args.workspace}/dolma_documents/output_*.jsonl")
+    done_work_hashes = set()
+    for item in done_work_items:
+        filename = os.path.basename(item)
+        if filename.startswith('output_') and filename.endswith('.jsonl'):
+            group_hash = filename[len('output_'):-len('.jsonl')]
+            done_work_hashes.add(group_hash)
+
+    remaining_work_hashes = set(work_queue.keys()) - done_work_hashes
+    remaining_work_queue = {hash: work_queue[hash] for hash in remaining_work_hashes}
+
+    logger.info(f"Remaining work items: {len(remaining_work_queue)}")
 
     # TODO
     # Spawn up to N workers to do:
@@ -178,4 +205,4 @@ if __name__ == '__main__':
                 logger.error(f"Sglang server exited with code {sglang_process.returncode} exiting.")
     except KeyboardInterrupt:
         logger.info("Got keyboard interrupt, exiting everything")
-        sys.exit(1)
+        sys.exit(1)

pyproject.toml

@@ -32,6 +32,8 @@ dependencies = [
   "markdown2",
   "filelock",
   "orjson",
+  "requests",
+  "zstandard",
 ]
 license = {file = "LICENSE"}