Merge pull request #97 from weaviate/add-support-for-trust-remote-cod…

…e-setting

Add support for trust remote code setting

.github/workflows/main.yaml

@@ -140,6 +140,7 @@ jobs:
       MODEL_TAG_NAME: ${{matrix.model_tag_name}}
       ONNX_RUNTIME: ${{matrix.onnx_runtime}}
       USE_SENTENCE_TRANSFORMERS_VECTORIZER: ${{matrix.use_sentence_transformers_vectorizer}}
+      TRUST_REMOTE_CODE: ${{matrix.trust_remote_code}}
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4

Dockerfile

@@ -16,6 +16,8 @@ ARG TARGETARCH
 ARG MODEL_NAME
 ARG ONNX_RUNTIME
 ENV ONNX_CPU=${TARGETARCH}
+ARG TRUST_REMOTE_CODE
+ARG USE_SENTENCE_TRANSFORMERS_VECTORIZER
 RUN mkdir nltk_data
 COPY download.py .
 RUN ./download.py

app.py

@@ -1,6 +1,8 @@
 import os
 from logging import getLogger
 from fastapi import FastAPI, Response, status
+from typing import Union
+from config import TRUST_REMOTE_CODE
 from vectorizer import Vectorizer, VectorInput
 from meta import Meta
 
@@ -55,7 +57,7 @@ def startup_event():
 
     model_dir = "./models/model"
 
-    def get_model_directory() -> (str, bool):
+    def get_model_name() -> Union[str, bool]:
         if os.path.exists(f"{model_dir}/model_name"):
             with open(f"{model_dir}/model_name", "r") as f:
                 model_name = f.read()
@@ -70,6 +72,13 @@ def get_onnx_runtime() -> bool:
                 return onnx_runtime == "true"
         return False
 
+    def get_trust_remote_code() -> bool:
+        if os.path.exists(f"{model_dir}/trust_remote_code"):
+            with open(f"{model_dir}/trust_remote_code", "r") as f:
+                trust_remote_code = f.read()
+                return trust_remote_code == "true"
+        return TRUST_REMOTE_CODE
+
     def log_info_about_onnx(onnx_runtime: bool):
         if onnx_runtime:
             onnx_quantization_info = "missing"
@@ -80,11 +89,17 @@ def log_info_about_onnx(onnx_runtime: bool):
                 f"Running ONNX vectorizer with quantized model for {onnx_quantization_info}"
             )
 
-    model_name, use_sentence_transformer_vectorizer = get_model_directory()
+    model_name, use_sentence_transformer_vectorizer = get_model_name()
     onnx_runtime = get_onnx_runtime()
+    trust_remote_code = get_trust_remote_code()
     log_info_about_onnx(onnx_runtime)
 
-    meta_config = Meta(model_dir, model_name, use_sentence_transformer_vectorizer)
+    meta_config = Meta(
+        model_dir,
+        model_name,
+        use_sentence_transformer_vectorizer,
+        trust_remote_code,
+    )
     vec = Vectorizer(
         model_dir,
         cuda_support,
@@ -96,6 +111,7 @@ def log_info_about_onnx(onnx_runtime: bool):
         onnx_runtime,
         use_sentence_transformer_vectorizer,
         model_name,
+        trust_remote_code,
     )
 
 
@@ -112,7 +128,7 @@ def meta():
 
 @app.post("/vectors")
 @app.post("/vectors/")
-async def read_item(item: VectorInput, response: Response):
+async def vectorize(item: VectorInput, response: Response):
     try:
         vector = await vec.vectorize(item.text, item.config)
         return {"text": item.text, "vector": vector.tolist(), "dim": len(vector)}

cicd/build.sh

@@ -5,8 +5,12 @@ set -eou pipefail
 local_repo=${LOCAL_REPO?Variable LOCAL_REPO is required}
 model_name=${MODEL_NAME?Variable MODEL_NAME is required}
 onnx_runtime=${ONNX_RUNTIME?Variable ONNX_RUNTIME is required}
+trust_remote_code=${TRUST_REMOTE_CODE:-false}
+use_sentence_transformers_vectorizer=${USE_SENTENCE_TRANSFORMERS_VECTORIZER:-false}
 
 docker build \
   --build-arg "MODEL_NAME=$model_name" \
   --build-arg "ONNX_RUNTIME=$onnx_runtime" \
+  --build-arg "TRUST_REMOTE_CODE=$trust_remote_code" \
+  --build-arg "USE_SENTENCE_TRANSFORMERS_VECTORIZER=$use_sentence_transformers_vectorizer" \
   -t "$local_repo" .

cicd/docker_push.sh

@@ -7,6 +7,8 @@ model_name=${MODEL_NAME?Variable MODEL_NAME is required}
 docker_username=${DOCKER_USERNAME?Variable DOCKER_USERNAME is required}
 docker_password=${DOCKER_PASSWORD?Variable DOCKER_PASSWORD is required}
 onnx_runtime=${ONNX_RUNTIME?Variable ONNX_RUNTIME is required}
+trust_remote_code=${TRUST_REMOTE_CODE:-false}
+use_sentence_transformers_vectorizer=${USE_SENTENCE_TRANSFORMERS_VECTORIZER:-false}
 original_model_name=$model_name
 git_tag=$GITHUB_REF_NAME
 
@@ -16,6 +18,7 @@ function main() {
   echo "git ref name is $GITHUB_REF_NAME"
   echo "git tag is $git_tag"
   echo "onnx_runtime is $onnx_runtime"
+  echo "trust_remote_code is $trust_remote_code"
   push_tag
 }
 
@@ -46,6 +49,8 @@ function push_tag() {
     docker buildx build --platform=linux/arm64,linux/amd64 \
       --build-arg "MODEL_NAME=$original_model_name" \
       --build-arg "ONNX_RUNTIME=$onnx_runtime" \
+      --build-arg "TRUST_REMOTE_CODE=$trust_remote_code" \
+      --build-arg "USE_SENTENCE_TRANSFORMERS_VECTORIZER=$use_sentence_transformers_vectorizer" \
       --push \
       --tag "$tag_git" \
       --tag "$tag_latest" \

download.py

@@ -3,6 +3,7 @@
 import os
 import sys
 import nltk
+import json
 from transformers import (
     AutoModel,
     AutoTokenizer,
@@ -98,6 +99,18 @@ def quantization_config(onnx_cpu_arch: str):
 
 
 def download_model(model_name: str, model_dir: str, trust_remote_code: bool = False):
+    def save_model_name(model_name: str):
+        with open(f"{model_dir}/model_name", "w") as f:
+            f.write(model_name)
+
+    def save_trust_remote_code(trust_remote_code: bool):
+        with open(f"{model_dir}/trust_remote_code", "w") as f:
+            f.write(f"{trust_remote_code}")
+
+    def save_model_config(model_config):
+        with open(f"{model_dir}/model_config", "w") as f:
+            f.write(json.dumps(model_config))
+
     print(
         f"Downloading model {model_name} from huggingface model hub ({trust_remote_code=})"
     )
@@ -107,9 +120,11 @@ def download_model(model_name: str, model_dir: str, trust_remote_code: bool = Fa
     if (
         model_type is not None and model_type == "t5"
     ) or use_sentence_transformers_vectorizer.lower() == "true":
-        SentenceTransformer(model_name, cache_folder=model_dir)
-        with open(f"{model_dir}/model_name", "w") as f:
-            f.write(model_name)
+        SentenceTransformer(
+            model_name, cache_folder=model_dir, trust_remote_code=trust_remote_code
+        )
+        save_model_name(model_name)
+        save_model_config(config.to_dict())
     else:
         if config.architectures and not force_automodel:
             print(f"Using class {config.architectures[0]} to load model weights")
@@ -136,6 +151,8 @@ def download_model(model_name: str, model_dir: str, trust_remote_code: bool = Fa
         model.save_pretrained(model_dir)
         tokenizer.save_pretrained(model_dir)
 
+    save_trust_remote_code(trust_remote_code)
+
     nltk.download("punkt", download_dir=nltk_dir)
     nltk.download("punkt_tab", download_dir=nltk_dir)
 

meta.py

@@ -1,22 +1,25 @@
+import json
+import os
 from transformers import AutoConfig
 
-from config import TRUST_REMOTE_CODE
-
 
 class Meta:
-    config: AutoConfig
-
     def __init__(
         self,
         model_path: str,
         model_name: str,
         use_sentence_transformer_vectorizer: bool,
+        trust_remote_code: bool,
     ):
         if use_sentence_transformer_vectorizer:
-            self.config = {"model_name": model_name, "model_type": None}
+            if os.path.exists(f"{model_path}/model_config"):
+                with open(f"{model_path}/model_config", "r") as f:
+                    self.config = json.loads(f.read())
+            else:
+                self.config = {"model_name": model_name, "model_type": None}
         else:
             self.config = AutoConfig.from_pretrained(
-                model_path, trust_remote_code=TRUST_REMOTE_CODE
+                model_path, trust_remote_code=trust_remote_code
             ).to_dict()
 
     def get(self):

smoke_test.py

@@ -37,9 +37,15 @@ def test_meta(self):
         self.assertIsInstance(res.json(), dict)
 
     def test_vectorizing(self):
-        def try_to_vectorize(url):
-            print(f"url: {url}")
+        def get_req_body(task_type: str = ""):
             req_body = {"text": "The London Eye is a ferris wheel at the River Thames."}
+            if task_type != "":
+                req_body["config"] = {"task_type": task_type}
+            return req_body
+
+        def try_to_vectorize(url, task_type: str = ""):
+            print(f"url: {url}")
+            req_body = get_req_body(task_type)
 
             res = requests.post(url, json=req_body)
             resBody = res.json()