Replace ONNXMiniLM_L6_V2._init_model_and_tokenizer with tokenizer and…

… model cached properties (chroma-core#1194)

## Description of changes

*Summarize the changes made by this PR.*
 - Improvements & Bug fixes
- Fixes chroma-core#1193: race condition in
`ONNXMiniLM_L6_V2._init_model_and_tokenizer`

## Test plan
*How are these changes tested?*

- [x] Tests pass locally with `pytest` for python, `yarn test` for js

chromadb/utils/embedding_functions.py

@@ -1,5 +1,6 @@
 import hashlib
 import logging
+from functools import cached_property
 
 from tenacity import stop_after_attempt, wait_random, retry, retry_if_exception
 
@@ -18,20 +19,23 @@
 import os
 import tarfile
 import requests
-from typing import Any, Dict, List, Mapping, Union, cast
+from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Union, cast
 import numpy as np
 import numpy.typing as npt
 import importlib
 import inspect
 import json
 import sys
-from typing import Optional
 
 try:
     from chromadb.is_thin_client import is_thin_client
 except ImportError:
     is_thin_client = False
 
+if TYPE_CHECKING:
+    from onnxruntime import InferenceSession
+    from tokenizers import Tokenizer
+
 logger = logging.getLogger(__name__)
 
 
@@ -361,8 +365,6 @@ class ONNXMiniLM_L6_V2(EmbeddingFunction[Documents]):
         "https://chroma-onnx-models.s3.amazonaws.com/all-MiniLM-L6-v2/onnx.tar.gz"
     )
     _MODEL_SHA256 = "913d7300ceae3b2dbc2c50d1de4baacab4be7b9380491c27fab7418616a16ec3"
-    tokenizer = None
-    model = None
 
     # https://github.com/python/mypy/issues/7291 mypy makes you type the constructor if
     # no args
@@ -440,8 +442,6 @@ def _normalize(self, v: npt.NDArray) -> npt.NDArray:  # type: ignore
     # type: ignore
     def _forward(self, documents: List[str], batch_size: int = 32) -> npt.NDArray:
         # We need to cast to the correct type because the type checker doesn't know that init_model_and_tokenizer will set the values
-        self.tokenizer = cast(self.Tokenizer, self.tokenizer)  # type: ignore
-        self.model = cast(self.ort.InferenceSession, self.model)  # type: ignore
         all_embeddings = []
         for i in range(0, len(documents), batch_size):
             batch = documents[i : i + batch_size]
@@ -469,46 +469,45 @@ def _forward(self, documents: List[str], batch_size: int = 32) -> npt.NDArray:
             all_embeddings.append(embeddings)
         return np.concatenate(all_embeddings)
 
-    def _init_model_and_tokenizer(self) -> None:
-        if self.model is None and self.tokenizer is None:
-            self.tokenizer = self.Tokenizer.from_file(
-                os.path.join(
-                    self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "tokenizer.json"
-                )
+    @cached_property
+    def tokenizer(self) -> "Tokenizer":
+        tokenizer = self.Tokenizer.from_file(
+            os.path.join(
+                self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "tokenizer.json"
             )
-            # max_seq_length = 256, for some reason sentence-transformers uses 256 even though the HF config has a max length of 128
-            # https://github.com/UKPLab/sentence-transformers/blob/3e1929fddef16df94f8bc6e3b10598a98f46e62d/docs/_static/html/models_en_sentence_embeddings.html#LL480
-            self.tokenizer.enable_truncation(max_length=256)
-            self.tokenizer.enable_padding(pad_id=0, pad_token="[PAD]", length=256)
-
-            if self._preferred_providers is None or len(self._preferred_providers) == 0:
-                if len(self.ort.get_available_providers()) > 0:
-                    logger.debug(
-                        f"WARNING: No ONNX providers provided, defaulting to available providers: "
-                        f"{self.ort.get_available_providers()}"
-                    )
-                self._preferred_providers = self.ort.get_available_providers()
-            elif not set(self._preferred_providers).issubset(
-                set(self.ort.get_available_providers())
-            ):
-                raise ValueError(
-                    f"Preferred providers must be subset of available providers: {self.ort.get_available_providers()}"
+        )
+        # max_seq_length = 256, for some reason sentence-transformers uses 256 even though the HF config has a max length of 128
+        # https://github.com/UKPLab/sentence-transformers/blob/3e1929fddef16df94f8bc6e3b10598a98f46e62d/docs/_static/html/models_en_sentence_embeddings.html#LL480
+        tokenizer.enable_truncation(max_length=256)
+        tokenizer.enable_padding(pad_id=0, pad_token="[PAD]", length=256)
+        return tokenizer
+
+    @cached_property
+    def model(self) -> "InferenceSession":
+        if self._preferred_providers is None or len(self._preferred_providers) == 0:
+            if len(self.ort.get_available_providers()) > 0:
+                logger.debug(
+                    f"WARNING: No ONNX providers provided, defaulting to available providers: "
+                    f"{self.ort.get_available_providers()}"
                 )
-            self.model = self.ort.InferenceSession(
-                os.path.join(
-                    self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "model.onnx"
-                ),
-                # Since 1.9 onnyx runtime requires providers to be specified when there are multiple available - https://onnxruntime.ai/docs/api/python/api_summary.html
-                # This is probably not ideal but will improve DX as no exceptions will be raised in multi-provider envs
-                providers=self._preferred_providers,
+            self._preferred_providers = self.ort.get_available_providers()
+        elif not set(self._preferred_providers).issubset(
+            set(self.ort.get_available_providers())
+        ):
+            raise ValueError(
+                f"Preferred providers must be subset of available providers: {self.ort.get_available_providers()}"
             )
+        return self.ort.InferenceSession(
+            os.path.join(self.DOWNLOAD_PATH, self.EXTRACTED_FOLDER_NAME, "model.onnx"),
+            # Since 1.9 onnyx runtime requires providers to be specified when there are multiple available - https://onnxruntime.ai/docs/api/python/api_summary.html
+            # This is probably not ideal but will improve DX as no exceptions will be raised in multi-provider envs
+            providers=self._preferred_providers,
+        )
 
     def __call__(self, input: Documents) -> Embeddings:
         # Only download the model when it is actually used
         self._download_model_if_not_exists()
-        self._init_model_and_tokenizer()
-        res = cast(Embeddings, self._forward(input).tolist())
-        return res
+        return cast(Embeddings, self._forward(input).tolist())
 
     def _download_model_if_not_exists(self) -> None:
         onnx_files = [