diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index d9e7df74ad..48cfe5a988 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -247,7 +247,7 @@ jobs: poetry run mypy --install-types --non-interactive --strict app/ - name: Run tests and coverage run: | - poetry run pytest --cov app + poetry run pytest app --cov=app --cov-report term-missing generated-api-up-to-date: name: OpenAPI Clients diff --git a/machine-learning/app/main.py b/machine-learning/app/main.py index a4afa841ef..bde40f36e4 100644 --- a/machine-learning/app/main.py +++ b/machine-learning/app/main.py @@ -119,16 +119,12 @@ async def load(model: InferenceModel) -> InferenceModel: if model.loaded: return model - def _load() -> None: + def _load(model: InferenceModel) -> None: with lock: model.load() - loop = asyncio.get_running_loop() try: - if thread_pool is None: - model.load() - else: - await loop.run_in_executor(thread_pool, _load) + await run(_load, model) return model except (OSError, InvalidProtobuf, BadZipFile, NoSuchFile): log.warning( @@ -138,10 +134,7 @@ async def load(model: InferenceModel) -> InferenceModel: ) ) model.clear_cache() - if thread_pool is None: - model.load() - else: - await loop.run_in_executor(thread_pool, _load) + await run(_load, model) return model diff --git a/machine-learning/app/models/__init__.py b/machine-learning/app/models/__init__.py index 31651bdee5..18d75e1632 100644 --- a/machine-learning/app/models/__init__.py +++ b/machine-learning/app/models/__init__.py @@ -21,4 +21,4 @@ def from_model_type(model_type: ModelType, model_name: str, **model_kwargs: Any) case _: raise ValueError(f"Unknown model type {model_type}") - raise ValueError(f"Unknown ${model_type} model {model_name}") + raise ValueError(f"Unknown {model_type} model {model_name}") diff --git a/machine-learning/app/models/base.py b/machine-learning/app/models/base.py index df9a612e24..f9caeb78ae 100644 --- a/machine-learning/app/models/base.py +++ b/machine-learning/app/models/base.py @@ -1,6 +1,5 @@ from __future__ import annotations -import pickle from abc import ABC, abstractmethod from pathlib import Path from shutil import rmtree @@ -11,7 +10,6 @@ import onnxruntime as ort from huggingface_hub import snapshot_download from onnx.shape_inference import infer_shapes from onnx.tools.update_model_dims import update_inputs_outputs_dims -from typing_extensions import Buffer import ann.ann from app.models.constants import STATIC_INPUT_PROVIDERS, SUPPORTED_PROVIDERS @@ -200,7 +198,7 @@ class InferenceModel(ABC): @providers.setter def providers(self, providers: list[str]) -> None: - log.debug( + log.info( (f"Setting '{self.model_name}' execution providers to {providers}, " "in descending order of preference"), ) self._providers = providers @@ -217,7 +215,7 @@ class InferenceModel(ABC): @provider_options.setter def provider_options(self, provider_options: list[dict[str, Any]]) -> None: - log.info(f"Setting execution provider options to {provider_options}") + log.debug(f"Setting execution provider options to {provider_options}") self._provider_options = provider_options @property @@ -255,7 +253,7 @@ class InferenceModel(ABC): @property def sess_options_default(self) -> ort.SessionOptions: - sess_options = PicklableSessionOptions() + sess_options = ort.SessionOptions() sess_options.enable_cpu_mem_arena = False # avoid thread contention between models @@ -287,15 +285,3 @@ class InferenceModel(ABC): @property def preferred_runtime_default(self) -> ModelRuntime: return ModelRuntime.ARMNN if ann.ann.is_available and settings.ann else ModelRuntime.ONNX - - -# HF deep copies configs, so we need to make session options picklable -class PicklableSessionOptions(ort.SessionOptions): # type: ignore[misc] - def __getstate__(self) -> bytes: - return pickle.dumps([(attr, getattr(self, attr)) for attr in dir(self) if not callable(getattr(self, attr))]) - - def __setstate__(self, state: Buffer) -> None: - self.__init__() # type: ignore[misc] - attrs: list[tuple[str, Any]] = pickle.loads(state) - for attr, val in attrs: - setattr(self, attr, val) diff --git a/machine-learning/app/models/cache.py b/machine-learning/app/models/cache.py index 1d6a0fc763..62afd05a09 100644 --- a/machine-learning/app/models/cache.py +++ b/machine-learning/app/models/cache.py @@ -80,20 +80,3 @@ class RevalidationPlugin(BasePlugin): # type: ignore[misc] key = client.build_key(key, namespace) if key in client._handlers: await client.expire(key, client.ttl) - - async def post_multi_get( - self, - client: SimpleMemoryCache, - keys: list[str], - ret: list[Any] | None = None, - namespace: str | None = None, - **kwargs: Any, - ) -> None: - if ret is None: - return - - for key, val in zip(keys, ret): - if namespace is not None: - key = client.build_key(key, namespace) - if val is not None and key in client._handlers: - await client.expire(key, client.ttl) diff --git a/machine-learning/app/models/clip.py b/machine-learning/app/models/clip.py index 469789155e..b0d73175a9 100644 --- a/machine-learning/app/models/clip.py +++ b/machine-learning/app/models/clip.py @@ -144,9 +144,7 @@ class OpenCLIPEncoder(BaseCLIPEncoder): def _load(self) -> None: super()._load() - text_cfg: dict[str, Any] = self.model_cfg["text_cfg"] - context_length: int = text_cfg.get("context_length", 77) - pad_token: int = self.tokenizer_cfg["pad_token"] + self._load_tokenizer() size: list[int] | int = self.preprocess_cfg["size"] self.size = size[0] if isinstance(size, list) else size @@ -155,11 +153,19 @@ class OpenCLIPEncoder(BaseCLIPEncoder): self.mean = np.array(self.preprocess_cfg["mean"], dtype=np.float32) self.std = np.array(self.preprocess_cfg["std"], dtype=np.float32) + def _load_tokenizer(self) -> Tokenizer: log.debug(f"Loading tokenizer for CLIP model '{self.model_name}'") + + text_cfg: dict[str, Any] = self.model_cfg["text_cfg"] + context_length: int = text_cfg.get("context_length", 77) + pad_token: str = self.tokenizer_cfg["pad_token"] + self.tokenizer: Tokenizer = Tokenizer.from_file(self.tokenizer_file_path.as_posix()) + pad_id: int = self.tokenizer.token_to_id(pad_token) self.tokenizer.enable_padding(length=context_length, pad_token=pad_token, pad_id=pad_id) self.tokenizer.enable_truncation(max_length=context_length) + log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'") def tokenize(self, text: str) -> dict[str, NDArray[np.int32]]: diff --git a/machine-learning/app/test_main.py b/machine-learning/app/test_main.py index c43e661f49..cf941c1bbf 100644 --- a/machine-learning/app/test_main.py +++ b/machine-learning/app/test_main.py @@ -1,7 +1,8 @@ import json -import pickle from io import BytesIO from pathlib import Path +from random import randint +from types import SimpleNamespace from typing import Any, Callable from unittest import mock @@ -13,10 +14,12 @@ from fastapi.testclient import TestClient from PIL import Image from pytest_mock import MockerFixture +from app.main import load + from .config import log, settings -from .models.base import InferenceModel, PicklableSessionOptions +from .models.base import InferenceModel from .models.cache import ModelCache -from .models.clip import OpenCLIPEncoder +from .models.clip import MCLIPEncoder, OpenCLIPEncoder from .models.facial_recognition import FaceRecognizer from .schemas import ModelRuntime, ModelType @@ -72,6 +75,17 @@ class TestBase: {"arena_extend_strategy": "kSameAsRequested"}, ] + def test_sets_openvino_device_id_if_possible(self, mocker: MockerFixture) -> None: + mocked = mocker.patch("app.models.base.ort.capi._pybind_state") + mocked.get_available_openvino_device_ids.return_value = ["GPU.0", "CPU"] + + encoder = OpenCLIPEncoder("ViT-B-32__openai", providers=["OpenVINOExecutionProvider", "CPUExecutionProvider"]) + + assert encoder.provider_options == [ + {"device_id": "GPU.0"}, + {"arena_extend_strategy": "kSameAsRequested"}, + ] + def test_sets_provider_options_kwarg(self) -> None: encoder = OpenCLIPEncoder( "ViT-B-32__openai", @@ -119,7 +133,7 @@ class TestBase: def test_sets_default_cache_dir(self) -> None: encoder = OpenCLIPEncoder("ViT-B-32__openai") - assert encoder.cache_dir == Path("/cache/clip/ViT-B-32__openai") + assert encoder.cache_dir == Path(settings.cache_folder) / "clip" / "ViT-B-32__openai" def test_sets_cache_dir_kwarg(self) -> None: cache_dir = Path("/test_cache") @@ -170,7 +184,7 @@ class TestBase: encoder.clear_cache() mock_rmtree.assert_called_once_with(encoder.cache_dir) - assert info.call_count == 2 + info.assert_called_with(f"Cleared cache directory for model '{encoder.model_name}'.") def test_clear_cache_warns_if_path_does_not_exist(self, mocker: MockerFixture) -> None: mock_rmtree = mocker.patch("app.models.base.rmtree", autospec=True) @@ -267,7 +281,7 @@ class TestBase: def test_download(self, mocker: MockerFixture) -> None: mock_snapshot_download = mocker.patch("app.models.base.snapshot_download") - encoder = OpenCLIPEncoder("ViT-B-32__openai") + encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir="/path/to/cache") encoder.download() mock_snapshot_download.assert_called_once_with( @@ -348,6 +362,60 @@ class TestCLIP: assert embedding.dtype == np.float32 mocked.run.assert_called_once() + def test_openclip_tokenizer( + self, + mocker: MockerFixture, + clip_model_cfg: dict[str, Any], + clip_preprocess_cfg: Callable[[Path], dict[str, Any]], + clip_tokenizer_cfg: Callable[[Path], dict[str, Any]], + ) -> None: + mocker.patch.object(OpenCLIPEncoder, "download") + mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg) + mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg) + mocker.patch.object(OpenCLIPEncoder, "tokenizer_cfg", clip_tokenizer_cfg) + mock_tokenizer = mocker.patch("app.models.clip.Tokenizer.from_file", autospec=True).return_value + mock_ids = [randint(0, 50000) for _ in range(77)] + mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids) + + clip_encoder = OpenCLIPEncoder("ViT-B-32__openai", cache_dir="test_cache", mode="text") + clip_encoder._load_tokenizer() + tokens = clip_encoder.tokenize("test search query") + + assert "text" in tokens + assert isinstance(tokens["text"], np.ndarray) + assert tokens["text"].shape == (1, 77) + assert tokens["text"].dtype == np.int32 + assert np.allclose(tokens["text"], np.array([mock_ids], dtype=np.int32), atol=0) + + def test_mclip_tokenizer( + self, + mocker: MockerFixture, + clip_model_cfg: dict[str, Any], + clip_preprocess_cfg: Callable[[Path], dict[str, Any]], + clip_tokenizer_cfg: Callable[[Path], dict[str, Any]], + ) -> None: + mocker.patch.object(OpenCLIPEncoder, "download") + mocker.patch.object(OpenCLIPEncoder, "model_cfg", clip_model_cfg) + mocker.patch.object(OpenCLIPEncoder, "preprocess_cfg", clip_preprocess_cfg) + mocker.patch.object(OpenCLIPEncoder, "tokenizer_cfg", clip_tokenizer_cfg) + mock_tokenizer = mocker.patch("app.models.clip.Tokenizer.from_file", autospec=True).return_value + mock_ids = [randint(0, 50000) for _ in range(77)] + mock_attention_mask = [randint(0, 1) for _ in range(77)] + mock_tokenizer.encode.return_value = SimpleNamespace(ids=mock_ids, attention_mask=mock_attention_mask) + + clip_encoder = MCLIPEncoder("ViT-B-32__openai", cache_dir="test_cache", mode="text") + clip_encoder._load_tokenizer() + tokens = clip_encoder.tokenize("test search query") + + assert "input_ids" in tokens + assert "attention_mask" in tokens + assert isinstance(tokens["input_ids"], np.ndarray) + assert isinstance(tokens["attention_mask"], np.ndarray) + assert tokens["input_ids"].shape == (1, 77) + assert tokens["attention_mask"].shape == (1, 77) + assert np.allclose(tokens["input_ids"], np.array([mock_ids], dtype=np.int32), atol=0) + assert np.allclose(tokens["attention_mask"], np.array([mock_attention_mask], dtype=np.int32), atol=0) + class TestFaceRecognition: def test_set_min_score(self, mocker: MockerFixture) -> None: @@ -420,12 +488,75 @@ class TestCache: mock_lock_cls.return_value.__aenter__.return_value.cas.assert_called_with(mock.ANY, ttl=100) @mock.patch("app.models.cache.SimpleMemoryCache.expire") - async def test_revalidate(self, mock_cache_expire: mock.Mock, mock_get_model: mock.Mock) -> None: + async def test_revalidate_get(self, mock_cache_expire: mock.Mock, mock_get_model: mock.Mock) -> None: model_cache = ModelCache(ttl=100, revalidate=True) await model_cache.get("test_model_name", ModelType.FACIAL_RECOGNITION) await model_cache.get("test_model_name", ModelType.FACIAL_RECOGNITION) mock_cache_expire.assert_called_once_with(mock.ANY, 100) + async def test_profiling(self, mock_get_model: mock.Mock) -> None: + model_cache = ModelCache(ttl=100, profiling=True) + await model_cache.get("test_model_name", ModelType.FACIAL_RECOGNITION) + profiling = await model_cache.get_profiling() + assert isinstance(profiling, dict) + assert profiling == model_cache.cache.profiling + + async def test_loads_mclip(self) -> None: + model_cache = ModelCache() + + model = await model_cache.get("XLM-Roberta-Large-Vit-B-32", ModelType.CLIP, mode="text") + + assert isinstance(model, MCLIPEncoder) + assert model.model_name == "XLM-Roberta-Large-Vit-B-32" + + async def test_raises_exception_if_invalid_model_type(self) -> None: + invalid: Any = SimpleNamespace(value="invalid") + model_cache = ModelCache() + + with pytest.raises(ValueError): + await model_cache.get("XLM-Roberta-Large-Vit-B-32", invalid, mode="text") + + async def test_raises_exception_if_unknown_model_name(self) -> None: + model_cache = ModelCache() + + with pytest.raises(ValueError): + await model_cache.get("test_model_name", ModelType.CLIP, mode="text") + + +@pytest.mark.asyncio +class TestLoad: + async def test_load(self) -> None: + mock_model = mock.Mock(spec=InferenceModel) + mock_model.loaded = False + + res = await load(mock_model) + + assert res is mock_model + mock_model.load.assert_called_once() + mock_model.clear_cache.assert_not_called() + + async def test_load_returns_model_if_loaded(self) -> None: + mock_model = mock.Mock(spec=InferenceModel) + mock_model.loaded = True + + res = await load(mock_model) + + assert res is mock_model + mock_model.load.assert_not_called() + + async def test_load_clears_cache_and_retries_if_os_error(self) -> None: + mock_model = mock.Mock(spec=InferenceModel) + mock_model.model_name = "test_model_name" + mock_model.model_type = ModelType.CLIP + mock_model.load.side_effect = [OSError, None] + mock_model.loaded = False + + res = await load(mock_model) + + assert res is mock_model + mock_model.clear_cache.assert_called_once() + assert mock_model.load.call_count == 2 + @pytest.mark.skipif( not settings.test_full, @@ -437,15 +568,21 @@ class TestEndpoints: ) -> None: byte_image = BytesIO() pil_image.save(byte_image, format="jpeg") + expected = responses["clip"]["image"] + response = deployed_app.post( "http://localhost:3003/predict", data={"modelName": "ViT-B-32__openai", "modelType": "clip", "options": json.dumps({"mode": "vision"})}, files={"image": byte_image.getvalue()}, ) + + actual = response.json() assert response.status_code == 200 - assert response.json() == responses["clip"]["image"] + assert np.allclose(expected, actual) def test_clip_text_endpoint(self, responses: dict[str, Any], deployed_app: TestClient) -> None: + expected = responses["clip"]["text"] + response = deployed_app.post( "http://localhost:3003/predict", data={ @@ -455,12 +592,15 @@ class TestEndpoints: "options": json.dumps({"mode": "text"}), }, ) + + actual = response.json() assert response.status_code == 200 - assert response.json() == responses["clip"]["text"] + assert np.allclose(expected, actual) def test_face_endpoint(self, pil_image: Image.Image, responses: dict[str, Any], deployed_app: TestClient) -> None: byte_image = BytesIO() pil_image.save(byte_image, format="jpeg") + expected = responses["facial-recognition"] response = deployed_app.post( "http://localhost:3003/predict", @@ -471,15 +611,13 @@ class TestEndpoints: }, files={"image": byte_image.getvalue()}, ) + + actual = response.json() assert response.status_code == 200 - assert response.json() == responses["facial-recognition"] - - -def test_sess_options() -> None: - sess_options = PicklableSessionOptions() - sess_options.intra_op_num_threads = 1 - sess_options.inter_op_num_threads = 1 - pickled = pickle.dumps(sess_options) - unpickled = pickle.loads(pickled) - assert unpickled.intra_op_num_threads == 1 - assert unpickled.inter_op_num_threads == 1 + assert len(expected) == len(actual) + for expected_face, actual_face in zip(expected, actual): + assert expected_face["imageHeight"] == actual_face["imageHeight"] + assert expected_face["imageWidth"] == actual_face["imageWidth"] + assert expected_face["boundingBox"] == actual_face["boundingBox"] + assert np.allclose(expected_face["embedding"], actual_face["embedding"]) + assert np.allclose(expected_face["score"], actual_face["score"]) diff --git a/machine-learning/responses.json b/machine-learning/responses.json index 10c581674b..209f05cffb 100644 --- a/machine-learning/responses.json +++ b/machine-learning/responses.json @@ -1,1570 +1,329 @@ { - "image-classification": [ - "matchstick", - "nematode", - "nematode worm", - "roundworm", - "theater curtain", - "theatre curtain", - "spotlight", - "spot", - "digital clock" + "clip": { + "image": [ + -0.013126737, -0.022995953, -0.0493738, -0.0063057775, 0.013601424, + -0.003761688, -0.03379882, 0.11106285, 0.024760082, 0.023903701, + 0.04418207, -0.013594999, 0.030850016, 0.0012876489, -0.012471005, + 0.009750715, 0.0095717255, 0.013320666, 0.0027921356, 0.03240264, + 0.033538498, 0.013624318, -0.0069946186, -0.0036184592, -0.009846507, + -0.017311024, -0.036686428, -0.0041808123, 0.030871637, -0.028624479, + -0.016515259, 0.014418001, -0.024542322, -0.0025438748, -0.049111884, + -0.023928944, 0.012270045, -0.016418075, 0.004895335, -0.15801854, + -0.026325515, 0.03166467, -0.017224329, 0.0411128, -0.022944424, + 0.015693054, -0.020919899, -0.010764121, -0.008499815, -0.020263294, + -0.009743323, -0.035395622, 0.03474742, 0.003049183, 0.009424564, + 0.010707678, 0.01664117, -0.0059027374, -0.013055344, 0.0018035833, + -0.003976456, -0.04325922, 0.014407317, 0.035728276, 0.002226939, + -0.006235411, -0.0073032235, -0.035129357, 0.001095443, -0.028552389, + -0.044300288, -0.012959393, 0.02259977, 0.017141517, -0.029432472, + -0.017583484, 0.010974336, 0.018141218, 0.0015389329, -0.008220305, + -0.0060965014, 0.004929384, 0.019477025, -0.033071984, 0.025183259, + 0.013607688, 0.01836233, 0.04586782, 0.0103442725, -0.036077496, + -0.029715508, 0.007203621, -0.7949153, 0.046866275, 0.026910711, + -0.0047834567, 0.033243995, 0.009379981, -0.03749048, -0.055274535, + -0.01955359, 0.012887587, 0.00922838, -0.0032776103, -0.011456734, + 0.0045412215, -0.11506394, 0.0348558, 0.029478835, -0.011811103, + -0.00483158, -0.010586126, -0.018853206, -0.01591496, 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