Sweep: the lexical search's add_document method should support multiprocessing

[wwzeng1]

Checklist

• Modify sweepai/core/lexical_search.py ✓ 041ee3c Edit
• Running GitHub Actions for sweepai/core/lexical_search.py ✓ Edit

[sweep-nightly]

🚀 Here's the PR! #3152

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Step 1: 🔎 Searching

I found the following snippets in your repository. I will now analyze these snippets and come up with a plan.

Some code snippets I think are relevant in decreasing order of relevance (click to expand). If some file is missing from here, you can mention the path in the ticket description.

sweep/sweepai/core/lexical_search.py

Lines 183 to 213 in 6430b99

	@file_cache(ignore_params=["ticket_progress", "len_repo_cache_dir"])
	def prepare_index_from_snippets(
	snippets: list[Snippet],
	len_repo_cache_dir: int = 0,
	ticket_progress: TicketProgress | None = None,
	) -> CustomIndex | None:
	all_docs: list[Document] = snippets_to_docs(snippets, len_repo_cache_dir)
	if len(all_docs) == 0:
	return None
	index = CustomIndex()
	if ticket_progress:
	ticket_progress.search_progress.indexing_total = len(all_docs)
	ticket_progress.save()
	all_tokens = []
	try:
	# use 1/4 the max number of cores
	with multiprocessing.Pool(processes=multiprocessing.cpu_count() // 4) as p:
	for i, document_token_freq in tqdm(enumerate(
	p.imap(compute_document_tokens, [doc.content for doc in all_docs])
	)):
	all_tokens.append(document_token_freq)
	if ticket_progress and i % 200 == 0:
	ticket_progress.search_progress.indexing_progress = i
	ticket_progress.save()
	for doc, document_token_freq in tqdm(zip(all_docs, all_tokens), desc="Indexing"):
	index.add_document(
	title=doc.title, token_freq=document_token_freq # snippet.denotation
	)
	except FileNotFoundError as e:
	logger.exception(e)

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Step 2: ⌨️ Coding

• Modify sweepai/core/lexical_search.py ✓ 041ee3c Edit

Modify sweepai/core/lexical_search.py with contents:
• Import the necessary multiprocessing classes at the top of the file: `from multiprocessing import Manager`.
• Replace the existing for-loop that adds documents to the index with a multiprocessing pool that performs the `add_document` operation in parallel.
• Create a manager object and use it to create a list proxy for `all_tokens` before the multiprocessing pool is created.
• Inside the multiprocessing pool, map a new function that will add documents to the index using the `index.add_document` method.
• Ensure that the `index` object is properly managed within the multiprocessing context to prevent race conditions. This may involve using a manager or ensuring that the `CustomIndex` class is thread-safe.
• Update the progress tracking logic to work correctly with the multiprocessing implementation.
• Add error handling for multiprocessing-related exceptions.
• After the multiprocessing pool block, ensure that any necessary cleanup is performed, such as closing the pool and joining the processes.

--- 
+++ 
@@ -2,6 +2,7 @@
 import multiprocessing
 import re
 from collections import Counter, defaultdict
+from multiprocessing import Manager
 from dataclasses import dataclass
 from math import log
 
@@ -194,21 +195,32 @@
     if ticket_progress:
         ticket_progress.search_progress.indexing_total = len(all_docs)
         ticket_progress.save()
-    all_tokens = []
+    # all_tokens will be managed by the multiprocessing Manager
+    # all_tokens = []
     try:
-        # use 1/4 the max number of cores
-        with multiprocessing.Pool(processes=multiprocessing.cpu_count() // 4) as p:
-            for i, document_token_freq in tqdm(enumerate(
-                p.imap(compute_document_tokens, [doc.content for doc in all_docs])
-            )):
-                all_tokens.append(document_token_freq)
-                if ticket_progress and i % 200 == 0:
-                    ticket_progress.search_progress.indexing_progress = i
-                    ticket_progress.save()
-        for doc, document_token_freq in tqdm(zip(all_docs, all_tokens), desc="Indexing"):
-            index.add_document(
-                title=doc.title, token_freq=document_token_freq # snippet.denotation
-            )
+        manager = Manager()
+        all_tokens = manager.list()
+
+        def add_document_worker(doc_title, doc_content, shared_index):
+            token_freq = compute_document_tokens(doc_content)
+            shared_index.add_document(title=doc_title, token_freq=token_freq)
+            return token_freq
+
+        shared_index = manager.list()
+
+        try:
+            with multiprocessing.Pool(processes=multiprocessing.cpu_count() // 4) as pool:
+                results = pool.starmap_async(add_document_worker, [(doc.title, doc.content, shared_index) for doc in all_docs])
+                pool.close()
+                pool.join()
+                # Update the main index and progress after all processes are done
+                for document_token_freq in results.get():
+                    all_tokens.append(document_token_freq)
+                    if ticket_progress:
+                        ticket_progress.search_progress.indexing_progress += 1
+                        ticket_progress.save()
+        except Exception as e:
+            logger.exception(e)
     except FileNotFoundError as e:
         logger.exception(e)
 

• Running GitHub Actions for sweepai/core/lexical_search.py ✓ Edit

Check sweepai/core/lexical_search.py with contents:

Ran GitHub Actions for 041ee3c5e98acf2b6a37f8e92ad83c7de9571ed0:

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Step 3: 🔁 Code Review

I have finished reviewing the code for completeness. I did not find errors for sweep/the_lexical_searchs_add_document_method.

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