Memory Reduction (#1092)

backend/danswer/background/indexing/job_client.py

@@ -8,13 +8,15 @@ from collections.abc import Callable
 from dataclasses import dataclass
 from typing import Any
 from typing import Literal
-
+from typing import Optional
-from torch import multiprocessing
+from typing import TYPE_CHECKING
 
 from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
+if TYPE_CHECKING:
+    from torch.multiprocessing import Process
 
 JobStatusType = (
     Literal["error"]
@@ -30,7 +32,7 @@ class SimpleJob:
     """Drop in replacement for `dask.distributed.Future`"""
 
     id: int
-    process: multiprocessing.Process | None = None
+    process: Optional["Process"] = None
 
     def cancel(self) -> bool:
         return self.release()
@@ -87,6 +89,8 @@ class SimpleJobClient:
 
     def submit(self, func: Callable, *args: Any, pure: bool = True) -> SimpleJob | None:
         """NOTE: `pure` arg is needed so this can be a drop in replacement for Dask"""
+        from torch.multiprocessing import Process
+
         self._cleanup_completed_jobs()
         if len(self.jobs) >= self.n_workers:
             logger.debug("No available workers to run job")
@@ -95,7 +99,7 @@ class SimpleJobClient:
         job_id = self.job_id_counter
         self.job_id_counter += 1
 
-        process = multiprocessing.Process(target=func, args=args, daemon=True)
+        process = Process(target=func, args=args, daemon=True)
         job = SimpleJob(id=job_id, process=process)
         process.start()
 

backend/danswer/background/indexing/run_indexing.py

@@ -4,7 +4,6 @@ from datetime import datetime
 from datetime import timedelta
 from datetime import timezone
 
-import torch
 from sqlalchemy.orm import Session
 
 from danswer.background.indexing.checkpointing import get_time_windows_for_index_attempt
@@ -286,6 +285,8 @@ def run_indexing_entrypoint(index_attempt_id: int, num_threads: int) -> None:
     """Entrypoint for indexing run when using dask distributed.
     Wraps the actual logic in a `try` block so that we can catch any exceptions
     and mark the attempt as failed."""
+    import torch
+
     try:
         # set the indexing attempt ID so that all log messages from this process
         # will have it added as a prefix

backend/danswer/background/update.py

@@ -3,7 +3,6 @@ import time
 from datetime import datetime
 
 import dask
-import torch
 from dask.distributed import Client
 from dask.distributed import Future
 from distributed import LocalCluster
@@ -61,6 +60,8 @@ def _get_num_threads() -> int:
     """Get # of "threads" to use for ML models in an indexing job. By default uses
     the torch implementation, which returns the # of physical cores on the machine.
     """
+    import torch
+
     return max(MIN_THREADS_ML_MODELS, torch.get_num_threads())
 
 
@@ -457,6 +458,8 @@ def update__main() -> None:
     # needed for CUDA to work with multiprocessing
     # NOTE: needs to be done on application startup
     # before any other torch code has been run
+    import torch
+
     if not DASK_JOB_CLIENT_ENABLED:
         torch.multiprocessing.set_start_method("spawn")
 

backend/danswer/indexing/chunker.py

@@ -1,8 +1,6 @@
 import abc
 from collections.abc import Callable
-
+from typing import TYPE_CHECKING
-from llama_index.text_splitter import SentenceSplitter
-from transformers import AutoTokenizer  # type:ignore
 
 from danswer.configs.app_configs import BLURB_SIZE
 from danswer.configs.app_configs import CHUNK_OVERLAP
@@ -16,10 +14,15 @@ from danswer.search.search_nlp_models import get_default_tokenizer
 from danswer.utils.text_processing import shared_precompare_cleanup
 
 
+if TYPE_CHECKING:
+    from transformers import AutoTokenizer  # type:ignore
+
 ChunkFunc = Callable[[Document], list[DocAwareChunk]]
 
 
 def extract_blurb(text: str, blurb_size: int) -> str:
+    from llama_index.text_splitter import SentenceSplitter
+
     token_count_func = get_default_tokenizer().tokenize
     blurb_splitter = SentenceSplitter(
         tokenizer=token_count_func, chunk_size=blurb_size, chunk_overlap=0
@@ -33,11 +36,13 @@ def chunk_large_section(
     section_link_text: str,
     document: Document,
     start_chunk_id: int,
-    tokenizer: AutoTokenizer,
+    tokenizer: "AutoTokenizer",
     chunk_size: int = CHUNK_SIZE,
     chunk_overlap: int = CHUNK_OVERLAP,
     blurb_size: int = BLURB_SIZE,
 ) -> list[DocAwareChunk]:
+    from llama_index.text_splitter import SentenceSplitter
+
     blurb = extract_blurb(section_text, blurb_size)
 
     sentence_aware_splitter = SentenceSplitter(
@@ -155,6 +160,8 @@ def chunk_document(
 def split_chunk_text_into_mini_chunks(
     chunk_text: str, mini_chunk_size: int = MINI_CHUNK_SIZE
 ) -> list[str]:
+    from llama_index.text_splitter import SentenceSplitter
+
     token_count_func = get_default_tokenizer().tokenize
     sentence_aware_splitter = SentenceSplitter(
         tokenizer=token_count_func, chunk_size=mini_chunk_size, chunk_overlap=0

backend/danswer/main.py

@@ -2,7 +2,7 @@ from typing import Any
 from typing import cast
 
 import nltk  # type:ignore
-import torch
+import torch  # Import here is fine, API server needs torch anyway and nothing imports main.py
 import uvicorn
 from fastapi import APIRouter
 from fastapi import FastAPI

backend/danswer/search/danswer_helper.py

@@ -1,4 +1,4 @@
-from transformers import AutoTokenizer  # type:ignore
+from typing import TYPE_CHECKING
 
 from danswer.search.models import QueryFlow
 from danswer.search.models import SearchType
@@ -10,8 +10,11 @@ from danswer.utils.logger import setup_logger
 
 logger = setup_logger()
 
+if TYPE_CHECKING:
+    from transformers import AutoTokenizer  # type:ignore
 
-def count_unk_tokens(text: str, tokenizer: AutoTokenizer) -> int:
+
+def count_unk_tokens(text: str, tokenizer: "AutoTokenizer") -> int:
     """Unclear if the wordpiece tokenizer used is actually tokenizing anything as the [UNK] token
     It splits up even foreign characters and unicode emojis without using UNK"""
     tokenized_text = tokenizer.tokenize(text)