feat: load the model once and keep it loaded

lib/Service.py

@@ -7,7 +7,6 @@
 import json
 import logging
 import os
-from contextlib import contextmanager
 from copy import deepcopy
 from time import perf_counter
 from typing import TypedDict
@@ -33,39 +32,6 @@ class TranslateRequest(TypedDict):
     ctranslate2.set_random_seed(420)
 
 
-@contextmanager
-def translate_context(config: dict):
-    try:
-        tokenizer = SentencePieceProcessor()
-        tokenizer.Load(os.path.join(config["loader"]["model_path"], config["tokenizer_file"]))
-
-        translator = ctranslate2.Translator(
-            **{
-                # only NVIDIA GPUs are supported by CTranslate2 for now
-                "device": "cuda" if os.getenv("COMPUTE_DEVICE") == "CUDA" else "cpu",
-                **config["loader"],
-            }
-        )
-    except KeyError as e:
-        raise ServiceException(
-            "Incorrect config file, ensure all required keys are present from the default config"
-        ) from e
-    except Exception as e:
-        raise ServiceException("Error loading the translation model") from e
-
-    try:
-        start = perf_counter()
-        yield (tokenizer, translator)
-        elapsed = perf_counter() - start
-        logger.info(f"time taken: {elapsed:.2f}s")
-    except Exception as e:
-        raise ServiceException("Error translating the input text") from e
-    finally:
-        del tokenizer
-        # todo: offload to cpu?
-        del translator
-
-
 class Service:
     def __init__(self, config: dict):
         global logger
@@ -94,12 +60,34 @@ def load_config(self, config: dict):
 
         self.config = config_copy
 
+    def load_model(self):
+        try:
+            self.tokenizer = SentencePieceProcessor()
+            self.tokenizer.Load(os.path.join(self.config["loader"]["model_path"], self.config["tokenizer_file"]))
+
+            self.translator = ctranslate2.Translator(
+                **{
+                    "device": "cuda" if os.getenv("COMPUTE_DEVICE") == "CUDA" else "cpu",
+                    **self.config["loader"],
+                }
+            )
+        except KeyError as e:
+            raise ServiceException(
+                "Incorrect config file, ensure all required keys are present from the default config"
+            ) from e
+        except Exception as e:
+            raise ServiceException("Error loading the translation model") from e
+
     def translate(self, data: TranslateRequest) -> str:
         logger.debug(f"translating text to: {data['target_language']}")
 
-        with translate_context(self.config) as (tokenizer, translator):
-            input_tokens = tokenizer.Encode(f"<2{data['target_language']}> {clean_text(data['input'])}", out_type=str)
-            results = translator.translate_batch(
+        try:
+            start = perf_counter()
+            input_tokens = self.tokenizer.Encode(
+                f"<2{data['target_language']}> {clean_text(data['input'])}",
+                out_type=str,
+            )
+            results = self.translator.translate_batch(
                 [input_tokens],
                 batch_type="tokens",
                 **self.config["inference"],
@@ -109,7 +97,11 @@ def translate(self, data: TranslateRequest) -> str:
                 raise ServiceException("Empty result returned from translator")
 
             # todo: handle multiple hypotheses
-            translation = tokenizer.Decode(results[0].hypotheses[0])
+            translation = self.tokenizer.Decode(results[0].hypotheses[0])
+            elapsed = perf_counter() - start
+            logger.info(f"time taken: {elapsed:.2f}s")
+        except Exception as e:
+            raise ServiceException("Error translating the input text") from e
 
         logger.debug(f"Translated string: {translation}")
         return translation

lib/main.py

@@ -122,6 +122,7 @@ async def _(request: Request, exc: Exception):
 def task_fetch_thread(service: Service):
     global app_enabled
 
+    service.load_model()
     nc = NextcloudApp()
     while True:
         if not app_enabled.is_set():