Merge pull request #94 from weaviate/update-dependencies-to-latest-ve…

…rsions

Update dependencies to latest versions, format all python files with black

app.py

@@ -6,9 +6,9 @@
 
 
 app = FastAPI()
-vec : Vectorizer
-meta_config : Meta
-logger = getLogger('uvicorn')
+vec: Vectorizer
+meta_config: Meta
+logger = getLogger("uvicorn")
 
 
 @app.on_event("startup")
@@ -20,16 +20,22 @@ def startup_event():
     cuda_per_process_memory_fraction = 1.0
     if "CUDA_PER_PROCESS_MEMORY_FRACTION" in os.environ:
         try:
-            cuda_per_process_memory_fraction = float(os.getenv("CUDA_PER_PROCESS_MEMORY_FRACTION"))
+            cuda_per_process_memory_fraction = float(
+                os.getenv("CUDA_PER_PROCESS_MEMORY_FRACTION")
+            )
         except ValueError:
-            logger.error(f"Invalid CUDA_PER_PROCESS_MEMORY_FRACTION (should be between 0.0-1.0)")
+            logger.error(
+                f"Invalid CUDA_PER_PROCESS_MEMORY_FRACTION (should be between 0.0-1.0)"
+            )
     if 0.0 <= cuda_per_process_memory_fraction <= 1.0:
-        logger.info(f"CUDA_PER_PROCESS_MEMORY_FRACTION set to {cuda_per_process_memory_fraction}")
-    cuda_support=False
-    cuda_core=""
+        logger.info(
+            f"CUDA_PER_PROCESS_MEMORY_FRACTION set to {cuda_per_process_memory_fraction}"
+        )
+    cuda_support = False
+    cuda_core = ""
 
     if cuda_env is not None and cuda_env == "true" or cuda_env == "1":
-        cuda_support=True
+        cuda_support = True
         cuda_core = os.getenv("CUDA_CORE")
         if cuda_core is None or cuda_core == "":
             cuda_core = "cuda:0"
@@ -40,10 +46,15 @@ def startup_event():
     # Batch text tokenization enabled by default
     direct_tokenize = False
     transformers_direct_tokenize = os.getenv("T2V_TRANSFORMERS_DIRECT_TOKENIZE")
-    if transformers_direct_tokenize is not None and transformers_direct_tokenize == "true" or transformers_direct_tokenize == "1":
+    if (
+        transformers_direct_tokenize is not None
+        and transformers_direct_tokenize == "true"
+        or transformers_direct_tokenize == "1"
+    ):
         direct_tokenize = True
 
     model_dir = "./models/model"
+
     def get_model_directory() -> (str, bool):
         if os.path.exists(f"{model_dir}/model_name"):
             with open(f"{model_dir}/model_name", "r") as f:
@@ -65,17 +76,27 @@ def log_info_about_onnx(onnx_runtime: bool):
             if os.path.exists(f"{model_dir}/onnx_quantization_info"):
                 with open(f"{model_dir}/onnx_quantization_info", "r") as f:
                     onnx_quantization_info = f.read()
-            logger.info(f"Running ONNX vectorizer with quantized model for {onnx_quantization_info}")
+            logger.info(
+                f"Running ONNX vectorizer with quantized model for {onnx_quantization_info}"
+            )
 
     model_name, use_sentence_transformer_vectorizer = get_model_directory()
     onnx_runtime = get_onnx_runtime()
     log_info_about_onnx(onnx_runtime)
 
     meta_config = Meta(model_dir, model_name, use_sentence_transformer_vectorizer)
-    vec = Vectorizer(model_dir, cuda_support, cuda_core, cuda_per_process_memory_fraction,
-                     meta_config.get_model_type(), meta_config.get_architecture(), 
-                     direct_tokenize, onnx_runtime, use_sentence_transformer_vectorizer,
-                     model_name)
+    vec = Vectorizer(
+        model_dir,
+        cuda_support,
+        cuda_core,
+        cuda_per_process_memory_fraction,
+        meta_config.get_model_type(),
+        meta_config.get_architecture(),
+        direct_tokenize,
+        onnx_runtime,
+        use_sentence_transformer_vectorizer,
+        model_name,
+    )
 
 
 @app.get("/.well-known/live", response_class=Response)
@@ -96,8 +117,6 @@ async def read_item(item: VectorInput, response: Response):
         vector = await vec.vectorize(item.text, item.config)
         return {"text": item.text, "vector": vector.tolist(), "dim": len(vector)}
     except Exception as e:
-        logger.exception(
-            'Something went wrong while vectorizing data.'
-        )
+        logger.exception("Something went wrong while vectorizing data.")
         response.status_code = status.HTTP_500_INTERNAL_SERVER_ERROR
         return {"error": str(e)}

custom_prerequisites.py

@@ -2,4 +2,4 @@
 
 import nltk
 
-nltk.download('punkt')
+nltk.download("punkt")

download.py

@@ -91,7 +91,9 @@ def quantization_config(onnx_cpu_arch: str):
         os.remove(f"{model_dir}/model.onnx")
     # Save information about ONNX runtime
     save_to_file(f"{model_dir}/onnx_runtime", onnx_runtime)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_name, trust_remote_code=trust_remote_code
+    )
     tokenizer.save_pretrained(onnx_path)
 
 

requirements-test.txt

@@ -1,13 +1,14 @@
 requests==2.32.3
-transformers==4.42.4
-fastapi==0.112.0
-uvicorn==0.30.5
+transformers==4.44.2
+fastapi==0.115.0
+uvicorn==0.31.0
 nltk==3.9.1
-torch==2.4.0
+torch==2.4.1
 sentencepiece==0.2.0
-sentence-transformers==3.0.1
-optimum==1.21.2
-onnxruntime==1.18.1
-onnx==1.16.2
+sentence-transformers==3.1.1
+optimum==1.22.0
+onnxruntime==1.19.2
+onnx==1.17.0
 numpy==1.26.4
+einops==0.8.0
 pytest

requirements.txt

@@ -1,11 +1,12 @@
-transformers==4.42.4
-fastapi==0.112.0
-uvicorn==0.30.5
+transformers==4.44.2
+fastapi==0.115.0
+uvicorn==0.31.0
 nltk==3.9.1
-torch==2.4.0
+torch==2.4.1
 sentencepiece==0.2.0
-sentence-transformers==3.0.1
-optimum==1.21.2
-onnxruntime==1.18.1
-onnx==1.16.2
+sentence-transformers==3.1.1
+optimum==1.22.0
+onnxruntime==1.19.2
+onnx==1.17.0
 numpy==1.26.4
+einops==0.8.0

smoke_test.py

@@ -5,11 +5,11 @@
 
 class SmokeTest(unittest.TestCase):
     def setUp(self):
-        self.url = 'http://localhost:8000'
+        self.url = "http://localhost:8000"
 
         for i in range(0, 100):
             try:
-                res = requests.get(self.url + '/.well-known/ready')
+                res = requests.get(self.url + "/.well-known/ready")
                 if res.status_code == 204:
                     return
                 else:
@@ -21,25 +21,25 @@ def setUp(self):
         raise Exception("did not start up")
 
     def test_well_known_ready(self):
-        res = requests.get(self.url + '/.well-known/ready')
+        res = requests.get(self.url + "/.well-known/ready")
 
         self.assertEqual(res.status_code, 204)
 
     def test_well_known_live(self):
-        res = requests.get(self.url + '/.well-known/live')
+        res = requests.get(self.url + "/.well-known/live")
 
         self.assertEqual(res.status_code, 204)
 
     def test_meta(self):
-        res = requests.get(self.url + '/meta')
+        res = requests.get(self.url + "/meta")
 
         self.assertEqual(res.status_code, 200)
         self.assertIsInstance(res.json(), dict)
 
     def test_vectorizing(self):
         def try_to_vectorize(url):
             print(f"url: {url}")
-            req_body = {'text': 'The London Eye is a ferris wheel at the River Thames.'}
+            req_body = {"text": "The London Eye is a ferris wheel at the River Thames."}
 
             res = requests.post(url, json=req_body)
             resBody = res.json()
@@ -49,7 +49,7 @@ def try_to_vectorize(url):
             # below tests that what we deem a reasonable vector is returned. We are
             # aware of 384 and 768 dim vectors, which should both fall in that
             # range
-            self.assertTrue(len(resBody['vector']) > 100)
+            self.assertTrue(len(resBody["vector"]) > 100)
             print(f"vector dimensions are: {len(resBody['vector'])}")
 
         try_to_vectorize(self.url + "/vectors/")

test_app.py

@@ -11,16 +11,15 @@
 
 
 def wait_for_uvicorn_start():
-    url = 'http://localhost:8000/.well-known/ready'
+    url = "http://localhost:8000/.well-known/ready"
 
     for i in range(0, 100):
         try:
             res = requests.get(url)
             if res.status_code == 204:
                 return
             else:
-                raise Exception(
-                    "status code is {}".format(res.status_code))
+                raise Exception("status code is {}".format(res.status_code))
         except Exception as e:
             print("Attempt {}: {}".format(i, e))
             time.sleep(2)
@@ -32,10 +31,15 @@ def run_server():
     uvicorn.run(app)
 
 
-@pytest.fixture(params=["t5-small",
-                        "distilroberta-base",
-                        "vblagoje/dpr-ctx_encoder-single-lfqa-wiki",
-                        "vblagoje/dpr-question_encoder-single-lfqa-wiki"], scope="function")
+@pytest.fixture(
+    params=[
+        "t5-small",
+        "distilroberta-base",
+        "vblagoje/dpr-ctx_encoder-single-lfqa-wiki",
+        "vblagoje/dpr-question_encoder-single-lfqa-wiki",
+    ],
+    scope="function",
+)
 def server(request):
     os.environ["MODEL_NAME"] = request.param
     subprocess.call("python download.py", shell=True)
@@ -48,12 +52,12 @@ def server(request):
 
 def test_vectorizing(server):
     wait_for_uvicorn_start()
-    url = 'http://127.0.0.1:8000/vectors/'
-    req_body = {'text': 'The London Eye is a ferris wheel at the River Thames.'}
+    url = "http://127.0.0.1:8000/vectors/"
+    req_body = {"text": "The London Eye is a ferris wheel at the River Thames."}
 
     res = requests.post(url, json=req_body)
     resBody = res.json()
-    vectorized_text = resBody['vector']
+    vectorized_text = resBody["vector"]
 
     assert 200 == res.status_code
 
@@ -66,14 +70,15 @@ def test_vectorizing(server):
 
     # now let's try two sentences
 
-    req_body = {'text': 'The London Eye is a ferris wheel at the River Thames. Here is the second sentence.'}
+    req_body = {
+        "text": "The London Eye is a ferris wheel at the River Thames. Here is the second sentence."
+    }
     res = requests.post(url, json=req_body)
     resBody = res.json()
-    vectorized_text = resBody['vector']
+    vectorized_text = resBody["vector"]
 
     assert 200 == res.status_code
 
     assert type(vectorized_text) is list
 
     assert 128 <= len(vectorized_text) <= 1024
-