diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index dac6b1f8a..58efec377 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -166,7 +166,6 @@ jobs: - name: Install dependencies run: | poetry install --with dev - poetry run pip install --no-deps -r requirements.txt - name: Lint with ruff run: | poetry run ruff check --format=github app diff --git a/machine-learning/Dockerfile b/machine-learning/Dockerfile index bd65975fb..d43855320 100644 --- a/machine-learning/Dockerfile +++ b/machine-learning/Dockerfile @@ -10,9 +10,8 @@ RUN poetry config installer.max-workers 10 && \ RUN python -m venv /opt/venv ENV VIRTUAL_ENV="/opt/venv" PATH="/opt/venv/bin:${PATH}" -COPY poetry.lock pyproject.toml requirements.txt ./ +COPY poetry.lock pyproject.toml ./ RUN poetry install --sync --no-interaction --no-ansi --no-root --only main -RUN pip install --no-deps -r requirements.txt FROM python:3.11-slim-bookworm diff --git a/machine-learning/app/conftest.py b/machine-learning/app/conftest.py index 41fc82535..3bbb89c52 100644 --- a/machine-learning/app/conftest.py +++ b/machine-learning/app/conftest.py @@ -1,5 +1,6 @@ import json -from typing import Any, Iterator, TypeAlias +from pathlib import Path +from typing import Any, Iterator from unittest import mock import numpy as np @@ -8,8 +9,7 @@ from fastapi.testclient import TestClient from PIL import Image from .main import app, init_state - -ndarray: TypeAlias = np.ndarray[int, np.dtype[np.float32]] +from .schemas import ndarray_f32 @pytest.fixture @@ -18,13 +18,13 @@ def pil_image() -> Image.Image: @pytest.fixture -def cv_image(pil_image: Image.Image) -> ndarray: +def cv_image(pil_image: Image.Image) -> ndarray_f32: return np.asarray(pil_image)[:, :, ::-1] # PIL uses RGB while cv2 uses BGR @pytest.fixture def mock_get_model() -> Iterator[mock.Mock]: - with mock.patch("app.models.cache.InferenceModel.from_model_type", autospec=True) as mocked: + with mock.patch("app.models.cache.from_model_type", autospec=True) as mocked: yield mocked @@ -37,3 +37,25 @@ def deployed_app() -> TestClient: @pytest.fixture(scope="session") def responses() -> dict[str, Any]: return json.load(open("responses.json", "r")) + + +@pytest.fixture(scope="session") +def clip_model_cfg() -> dict[str, Any]: + return { + "embed_dim": 512, + "vision_cfg": {"image_size": 224, "layers": 12, "width": 768, "patch_size": 32}, + "text_cfg": {"context_length": 77, "vocab_size": 49408, "width": 512, "heads": 8, "layers": 12}, + } + + +@pytest.fixture(scope="session") +def clip_preprocess_cfg() -> dict[str, Any]: + return { + "size": [224, 224], + "mode": "RGB", + "mean": [0.48145466, 0.4578275, 0.40821073], + "std": [0.26862954, 0.26130258, 0.27577711], + "interpolation": "bicubic", + "resize_mode": "shortest", + "fill_color": 0, + } diff --git a/machine-learning/app/models/__init__.py b/machine-learning/app/models/__init__.py index 7d1dd0c71..a8df0050d 100644 --- a/machine-learning/app/models/__init__.py +++ b/machine-learning/app/models/__init__.py @@ -1,3 +1,25 @@ -from .clip import CLIPEncoder +from typing import Any + +from app.schemas import ModelType + +from .base import InferenceModel +from .clip import MCLIPEncoder, OpenCLIPEncoder, is_mclip, is_openclip from .facial_recognition import FaceRecognizer from .image_classification import ImageClassifier + + +def from_model_type(model_type: ModelType, model_name: str, **model_kwargs: Any) -> InferenceModel: + match model_type: + case ModelType.CLIP: + if is_openclip(model_name): + return OpenCLIPEncoder(model_name, **model_kwargs) + elif is_mclip(model_name): + return MCLIPEncoder(model_name, **model_kwargs) + else: + raise ValueError(f"Unknown CLIP model {model_name}") + case ModelType.FACIAL_RECOGNITION: + return FaceRecognizer(model_name, **model_kwargs) + case ModelType.IMAGE_CLASSIFICATION: + return ImageClassifier(model_name, **model_kwargs) + case _: + raise ValueError(f"Unknown model type {model_type}") diff --git a/machine-learning/app/models/base.py b/machine-learning/app/models/base.py index 5f7f7c6e0..4f597d876 100644 --- a/machine-learning/app/models/base.py +++ b/machine-learning/app/models/base.py @@ -25,7 +25,7 @@ class InferenceModel(ABC): ) -> None: self.model_name = model_name self.loaded = False - self._cache_dir = Path(cache_dir) if cache_dir is not None else get_cache_dir(model_name, self.model_type) + self._cache_dir = Path(cache_dir) if cache_dir is not None else None self.providers = model_kwargs.pop("providers", ["CPUExecutionProvider"]) # don't pre-allocate more memory than needed self.provider_options = model_kwargs.pop( @@ -92,7 +92,7 @@ class InferenceModel(ABC): @property def cache_dir(self) -> Path: - return self._cache_dir + return self._cache_dir if self._cache_dir is not None else get_cache_dir(self.model_name, self.model_type) @cache_dir.setter def cache_dir(self, cache_dir: Path) -> None: diff --git a/machine-learning/app/models/cache.py b/machine-learning/app/models/cache.py index 0e37c4aa6..bd8b59b3e 100644 --- a/machine-learning/app/models/cache.py +++ b/machine-learning/app/models/cache.py @@ -4,6 +4,8 @@ from aiocache.backends.memory import SimpleMemoryCache from aiocache.lock import OptimisticLock from aiocache.plugins import BasePlugin, TimingPlugin +from app.models import from_model_type + from ..schemas import ModelType from .base import InferenceModel @@ -50,7 +52,7 @@ class ModelCache: async with OptimisticLock(self.cache, key) as lock: model = await self.cache.get(key) if model is None: - model = InferenceModel.from_model_type(model_type, model_name, **model_kwargs) + model = from_model_type(model_type, model_name, **model_kwargs) await lock.cas(model, ttl=self.ttl) return model diff --git a/machine-learning/app/models/clip.py b/machine-learning/app/models/clip.py index c1ce1801b..da0381d3a 100644 --- a/machine-learning/app/models/clip.py +++ b/machine-learning/app/models/clip.py @@ -1,23 +1,24 @@ -import os -import zipfile +import json +from abc import abstractmethod +from functools import cached_property from io import BytesIO +from pathlib import Path from typing import Any, Literal +import numpy as np import onnxruntime as ort -import torch -from clip_server.model.clip import BICUBIC, _convert_image_to_rgb -from clip_server.model.clip_onnx import _MODELS, _S3_BUCKET_V2, CLIPOnnxModel, download_model -from clip_server.model.pretrained_models import _VISUAL_MODEL_IMAGE_SIZE -from clip_server.model.tokenization import Tokenizer +from huggingface_hub import snapshot_download from PIL import Image -from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor +from transformers import AutoTokenizer + +from app.config import log +from app.models.transforms import crop, get_pil_resampling, normalize, resize, to_numpy +from app.schemas import ModelType, ndarray_f32, ndarray_i32, ndarray_i64 -from ..config import log -from ..schemas import ModelType from .base import InferenceModel -class CLIPEncoder(InferenceModel): +class BaseCLIPEncoder(InferenceModel): _model_type = ModelType.CLIP def __init__( @@ -27,48 +28,29 @@ class CLIPEncoder(InferenceModel): mode: Literal["text", "vision"] | None = None, **model_kwargs: Any, ) -> None: - if mode is not None and mode not in ("text", "vision"): - raise ValueError(f"Mode must be 'text', 'vision', or omitted; got '{mode}'") - if model_name not in _MODELS: - raise ValueError(f"Unknown model name {model_name}.") self.mode = mode super().__init__(model_name, cache_dir, **model_kwargs) - def _download(self) -> None: - models: tuple[tuple[str, str], tuple[str, str]] = _MODELS[self.model_name] - text_onnx_path = self.cache_dir / "textual.onnx" - vision_onnx_path = self.cache_dir / "visual.onnx" - - if not text_onnx_path.is_file(): - self._download_model(*models[0]) - - if not vision_onnx_path.is_file(): - self._download_model(*models[1]) - def _load(self) -> None: if self.mode == "text" or self.mode is None: log.debug(f"Loading clip text model '{self.model_name}'") + self.text_model = ort.InferenceSession( - self.cache_dir / "textual.onnx", + self.textual_path.as_posix(), sess_options=self.sess_options, providers=self.providers, provider_options=self.provider_options, ) - self.text_outputs = [output.name for output in self.text_model.get_outputs()] - self.tokenizer = Tokenizer(self.model_name) if self.mode == "vision" or self.mode is None: log.debug(f"Loading clip vision model '{self.model_name}'") + self.vision_model = ort.InferenceSession( - self.cache_dir / "visual.onnx", + self.visual_path.as_posix(), sess_options=self.sess_options, providers=self.providers, provider_options=self.provider_options, ) - self.vision_outputs = [output.name for output in self.vision_model.get_outputs()] - - image_size = _VISUAL_MODEL_IMAGE_SIZE[CLIPOnnxModel.get_model_name(self.model_name)] - self.transform = _transform_pil_image(image_size) def _predict(self, image_or_text: Image.Image | str) -> list[float]: if isinstance(image_or_text, bytes): @@ -78,55 +60,163 @@ class CLIPEncoder(InferenceModel): case Image.Image(): if self.mode == "text": raise TypeError("Cannot encode image as text-only model") - pixel_values = self.transform(image_or_text) - assert isinstance(pixel_values, torch.Tensor) - pixel_values = torch.unsqueeze(pixel_values, 0).numpy() - outputs = self.vision_model.run(self.vision_outputs, {"pixel_values": pixel_values}) + + outputs = self.vision_model.run(None, self.transform(image_or_text)) case str(): if self.mode == "vision": raise TypeError("Cannot encode text as vision-only model") - text_inputs: dict[str, torch.Tensor] = self.tokenizer(image_or_text) - inputs = { - "input_ids": text_inputs["input_ids"].int().numpy(), - "attention_mask": text_inputs["attention_mask"].int().numpy(), - } - outputs = self.text_model.run(self.text_outputs, inputs) + + outputs = self.text_model.run(None, self.tokenize(image_or_text)) case _: raise TypeError(f"Expected Image or str, but got: {type(image_or_text)}") return outputs[0][0].tolist() - def _download_model(self, model_name: str, model_md5: str) -> bool: - # downloading logic is adapted from clip-server's CLIPOnnxModel class - download_model( - url=_S3_BUCKET_V2 + model_name, - target_folder=self.cache_dir.as_posix(), - md5sum=model_md5, - with_resume=True, - ) - file = self.cache_dir / model_name.split("/")[1] - if file.suffix == ".zip": - with zipfile.ZipFile(file, "r") as zip_ref: - zip_ref.extractall(self.cache_dir) - os.remove(file) - return True + @abstractmethod + def tokenize(self, text: str) -> dict[str, ndarray_i32]: + pass + + @abstractmethod + def transform(self, image: Image.Image) -> dict[str, ndarray_f32]: + pass + + @property + def textual_dir(self) -> Path: + return self.cache_dir / "textual" + + @property + def visual_dir(self) -> Path: + return self.cache_dir / "visual" + + @property + def model_cfg_path(self) -> Path: + return self.cache_dir / "config.json" + + @property + def textual_path(self) -> Path: + return self.textual_dir / "model.onnx" + + @property + def visual_path(self) -> Path: + return self.visual_dir / "model.onnx" + + @property + def preprocess_cfg_path(self) -> Path: + return self.visual_dir / "preprocess_cfg.json" @property def cached(self) -> bool: - return (self.cache_dir / "textual.onnx").is_file() and (self.cache_dir / "visual.onnx").is_file() + return self.textual_path.is_file() and self.visual_path.is_file() -# same as `_transform_blob` without `_blob2image` -def _transform_pil_image(n_px: int) -> Compose: - return Compose( - [ - Resize(n_px, interpolation=BICUBIC), - CenterCrop(n_px), - _convert_image_to_rgb, - ToTensor(), - Normalize( - (0.48145466, 0.4578275, 0.40821073), - (0.26862954, 0.26130258, 0.27577711), - ), - ] - ) +class OpenCLIPEncoder(BaseCLIPEncoder): + def __init__( + self, + model_name: str, + cache_dir: str | None = None, + mode: Literal["text", "vision"] | None = None, + **model_kwargs: Any, + ) -> None: + super().__init__(_clean_model_name(model_name), cache_dir, mode, **model_kwargs) + + def _download(self) -> None: + snapshot_download( + f"immich-app/{self.model_name}", + cache_dir=self.cache_dir, + local_dir=self.cache_dir, + local_dir_use_symlinks=False, + ) + + def _load(self) -> None: + super()._load() + + self.tokenizer = AutoTokenizer.from_pretrained(self.textual_dir) + self.sequence_length = self.model_cfg["text_cfg"]["context_length"] + + self.size = ( + self.preprocess_cfg["size"][0] if type(self.preprocess_cfg["size"]) == list else self.preprocess_cfg["size"] + ) + self.resampling = get_pil_resampling(self.preprocess_cfg["interpolation"]) + self.mean = np.array(self.preprocess_cfg["mean"], dtype=np.float32) + self.std = np.array(self.preprocess_cfg["std"], dtype=np.float32) + + def tokenize(self, text: str) -> dict[str, ndarray_i32]: + input_ids: ndarray_i64 = self.tokenizer( + text, + max_length=self.sequence_length, + return_tensors="np", + return_attention_mask=False, + padding="max_length", + truncation=True, + ).input_ids + return {"text": input_ids.astype(np.int32)} + + def transform(self, image: Image.Image) -> dict[str, ndarray_f32]: + image = resize(image, self.size) + image = crop(image, self.size) + image_np = to_numpy(image) + image_np = normalize(image_np, self.mean, self.std) + return {"image": np.expand_dims(image_np.transpose(2, 0, 1), 0)} + + @cached_property + def model_cfg(self) -> dict[str, Any]: + return json.load(self.model_cfg_path.open()) + + @cached_property + def preprocess_cfg(self) -> dict[str, Any]: + return json.load(self.preprocess_cfg_path.open()) + + +class MCLIPEncoder(OpenCLIPEncoder): + def tokenize(self, text: str) -> dict[str, ndarray_i32]: + tokens: dict[str, ndarray_i64] = self.tokenizer(text, return_tensors="np") + return {k: v.astype(np.int32) for k, v in tokens.items()} + + +_OPENCLIP_MODELS = { + "RN50__openai", + "RN50__yfcc15m", + "RN50__cc12m", + "RN101__openai", + "RN101__yfcc15m", + "RN50x4__openai", + "RN50x16__openai", + "RN50x64__openai", + "ViT-B-32__openai", + "ViT-B-32__laion2b_e16", + "ViT-B-32__laion400m_e31", + "ViT-B-32__laion400m_e32", + "ViT-B-32__laion2b-s34b-b79k", + "ViT-B-16__openai", + "ViT-B-16__laion400m_e31", + "ViT-B-16__laion400m_e32", + "ViT-B-16-plus-240__laion400m_e31", + "ViT-B-16-plus-240__laion400m_e32", + "ViT-L-14__openai", + "ViT-L-14__laion400m_e31", + "ViT-L-14__laion400m_e32", + "ViT-L-14__laion2b-s32b-b82k", + "ViT-L-14-336__openai", + "ViT-H-14__laion2b-s32b-b79k", + "ViT-g-14__laion2b-s12b-b42k", +} + + +_MCLIP_MODELS = { + "LABSE-Vit-L-14", + "XLM-Roberta-Large-Vit-B-32", + "XLM-Roberta-Large-Vit-B-16Plus", + "XLM-Roberta-Large-Vit-L-14", +} + + +def _clean_model_name(model_name: str) -> str: + return model_name.split("/")[-1].replace("::", "__") + + +def is_openclip(model_name: str) -> bool: + return _clean_model_name(model_name) in _OPENCLIP_MODELS + + +def is_mclip(model_name: str) -> bool: + return _clean_model_name(model_name) in _MCLIP_MODELS diff --git a/machine-learning/app/models/facial_recognition.py b/machine-learning/app/models/facial_recognition.py index cd4be2308..2ea7fdf67 100644 --- a/machine-learning/app/models/facial_recognition.py +++ b/machine-learning/app/models/facial_recognition.py @@ -9,7 +9,8 @@ from insightface.model_zoo import ArcFaceONNX, RetinaFace from insightface.utils.face_align import norm_crop from insightface.utils.storage import BASE_REPO_URL, download_file -from ..schemas import ModelType +from app.schemas import ModelType, ndarray_f32 + from .base import InferenceModel @@ -68,7 +69,7 @@ class FaceRecognizer(InferenceModel): ) self.rec_model.prepare(ctx_id=0) - def _predict(self, image: np.ndarray[int, np.dtype[Any]] | bytes) -> list[dict[str, Any]]: + def _predict(self, image: ndarray_f32 | bytes) -> list[dict[str, Any]]: if isinstance(image, bytes): image = cv2.imdecode(np.frombuffer(image, np.uint8), cv2.IMREAD_COLOR) bboxes, kpss = self.det_model.detect(image) diff --git a/machine-learning/app/models/transforms.py b/machine-learning/app/models/transforms.py new file mode 100644 index 000000000..93b98d0ef --- /dev/null +++ b/machine-learning/app/models/transforms.py @@ -0,0 +1,35 @@ +import numpy as np +from PIL import Image + +from app.schemas import ndarray_f32 + +_PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in Image.Resampling} + + +def resize(img: Image.Image, size: int) -> Image.Image: + if img.width < img.height: + return img.resize((size, int((img.height / img.width) * size)), resample=Image.BICUBIC) + else: + return img.resize((int((img.width / img.height) * size), size), resample=Image.BICUBIC) + + +# https://stackoverflow.com/a/60883103 +def crop(img: Image.Image, size: int) -> Image.Image: + left = int((img.size[0] / 2) - (size / 2)) + upper = int((img.size[1] / 2) - (size / 2)) + right = left + size + lower = upper + size + + return img.crop((left, upper, right, lower)) + + +def to_numpy(img: Image.Image) -> ndarray_f32: + return np.asarray(img.convert("RGB")).astype(np.float32) / 255.0 + + +def normalize(img: ndarray_f32, mean: float | ndarray_f32, std: float | ndarray_f32) -> ndarray_f32: + return (img - mean) / std + + +def get_pil_resampling(resample: str) -> Image.Resampling: + return _PIL_RESAMPLING_METHODS[resample.lower()] diff --git a/machine-learning/app/schemas.py b/machine-learning/app/schemas.py index cad279687..ad1faac8c 100644 --- a/machine-learning/app/schemas.py +++ b/machine-learning/app/schemas.py @@ -1,5 +1,7 @@ from enum import StrEnum +from typing import TypeAlias +import numpy as np from pydantic import BaseModel @@ -31,3 +33,8 @@ class ModelType(StrEnum): IMAGE_CLASSIFICATION = "image-classification" CLIP = "clip" FACIAL_RECOGNITION = "facial-recognition" + + +ndarray_f32: TypeAlias = np.ndarray[int, np.dtype[np.float32]] +ndarray_i64: TypeAlias = np.ndarray[int, np.dtype[np.int64]] +ndarray_i32: TypeAlias = np.ndarray[int, np.dtype[np.int32]] diff --git a/machine-learning/app/test_main.py b/machine-learning/app/test_main.py index 3e21389da..0b28f8234 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 typing import Any, TypeAlias +from pathlib import Path +from typing import Any, Callable from unittest import mock import cv2 @@ -14,13 +15,11 @@ from pytest_mock import MockerFixture from .config import settings from .models.base import PicklableSessionOptions from .models.cache import ModelCache -from .models.clip import CLIPEncoder +from .models.clip import OpenCLIPEncoder from .models.facial_recognition import FaceRecognizer from .models.image_classification import ImageClassifier from .schemas import ModelType -ndarray: TypeAlias = np.ndarray[int, np.dtype[np.float32]] - class TestImageClassifier: classifier_preds = [ @@ -56,30 +55,50 @@ class TestImageClassifier: class TestCLIP: embedding = np.random.rand(512).astype(np.float32) + cache_dir = Path("test_cache") - def test_basic_image(self, pil_image: Image.Image, mocker: MockerFixture) -> None: - mocker.patch.object(CLIPEncoder, "download") + def test_basic_image( + self, + pil_image: Image.Image, + mocker: MockerFixture, + clip_model_cfg: dict[str, Any], + clip_preprocess_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("app.models.clip.AutoTokenizer.from_pretrained", autospec=True) mocked = mocker.patch("app.models.clip.ort.InferenceSession", autospec=True) mocked.return_value.run.return_value = [[self.embedding]] - clip_encoder = CLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="vision") - assert clip_encoder.mode == "vision" + + clip_encoder = OpenCLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="vision") embedding = clip_encoder.predict(pil_image) + assert clip_encoder.mode == "vision" assert isinstance(embedding, list) - assert len(embedding) == 512 + assert len(embedding) == clip_model_cfg["embed_dim"] assert all([isinstance(num, float) for num in embedding]) clip_encoder.vision_model.run.assert_called_once() - def test_basic_text(self, mocker: MockerFixture) -> None: - mocker.patch.object(CLIPEncoder, "download") + def test_basic_text( + self, + mocker: MockerFixture, + clip_model_cfg: dict[str, Any], + clip_preprocess_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("app.models.clip.AutoTokenizer.from_pretrained", autospec=True) mocked = mocker.patch("app.models.clip.ort.InferenceSession", autospec=True) mocked.return_value.run.return_value = [[self.embedding]] - clip_encoder = CLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="text") - assert clip_encoder.mode == "text" + + clip_encoder = OpenCLIPEncoder("ViT-B-32::openai", cache_dir="test_cache", mode="text") embedding = clip_encoder.predict("test search query") + assert clip_encoder.mode == "text" assert isinstance(embedding, list) - assert len(embedding) == 512 + assert len(embedding) == clip_model_cfg["embed_dim"] assert all([isinstance(num, float) for num in embedding]) clip_encoder.text_model.run.assert_called_once() diff --git a/machine-learning/export/Dockerfile b/machine-learning/export/Dockerfile new file mode 100644 index 000000000..1c74546bc --- /dev/null +++ b/machine-learning/export/Dockerfile @@ -0,0 +1,21 @@ +FROM mambaorg/micromamba:bookworm-slim as builder + +ENV NODE_ENV=production \ + TRANSFORMERS_CACHE=/cache \ + PYTHONDONTWRITEBYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + PATH="/opt/venv/bin:$PATH" \ + PYTHONPATH=/usr/src + +COPY --chown=$MAMBA_USER:$MAMBA_USER conda-lock.yml /tmp/conda-lock.yml +RUN micromamba install -y -n base -f /tmp/conda-lock.yml && \ + micromamba remove -y -n base cxx-compiler && \ + micromamba clean --all --yes + +WORKDIR /usr/src/app + +COPY --chown=$MAMBA_USER:$MAMBA_USER start.sh . +COPY --chown=$MAMBA_USER:$MAMBA_USER app . + +ENTRYPOINT ["/usr/local/bin/_entrypoint.sh"] +CMD ["./start.sh"] diff --git a/machine-learning/export/conda-lock.yml b/machine-learning/export/conda-lock.yml new file mode 100644 index 000000000..12dc35778 --- /dev/null +++ b/machine-learning/export/conda-lock.yml @@ -0,0 +1,3520 @@ +# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). DO NOT EDIT! +# +# A "lock file" contains a concrete list of package versions (with checksums) to be installed. Unlike +# e.g. `conda env create`, the resulting environment will not change as new package versions become +# available, unless you explicitly update the lock file. +# +# Install this environment as "YOURENV" with: +# conda-lock install -n YOURENV --file conda-lock.yml +# To update a single package to the latest version compatible with the version constraints in the source: +# conda-lock lock --lockfile conda-lock.yml --update PACKAGE +# To re-solve the entire environment, e.g. after changing a version constraint in the source file: +# conda-lock -f env.yaml --lockfile conda-lock.yml +version: 1 +metadata: + content_hash: + linux-64: d42204fa0b65bc7acfc3edfff2d027fb2c395335b1de603909316cf7971a602a + channels: + - url: conda-forge + used_env_vars: [] + - url: nvidia + used_env_vars: [] + - url: pytorch-nightly + used_env_vars: [] + platforms: + - linux-64 + sources: + - env.yaml +package: +- name: _libgcc_mutex + version: '0.1' + manager: conda + platform: linux-64 + dependencies: {} + 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linux-64 +dependencies: + - cxx-compiler + - onnx==1.* + - onnxruntime==1.* + - open-clip-torch==2.* + - orjson==3.* + - pip + - python==3.11.* + - pytorch + - rich==13.* + - safetensors==0.* + - setuptools==68.* + - torchvision + - transformers==4.* + - pip: + - multilingual-clip + - onnx-simplifier +category: main diff --git a/machine-learning/export/models/__init__.py b/machine-learning/export/models/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/machine-learning/export/models/mclip.py b/machine-learning/export/models/mclip.py new file mode 100644 index 000000000..565539016 --- /dev/null +++ b/machine-learning/export/models/mclip.py @@ -0,0 +1,67 @@ +import tempfile +import warnings +from pathlib import Path + +import torch +from multilingual_clip.pt_multilingual_clip import MultilingualCLIP +from transformers import AutoTokenizer + +from .openclip import OpenCLIPModelConfig +from .openclip import to_onnx as openclip_to_onnx +from .optimize import optimize +from .util import get_model_path + +_MCLIP_TO_OPENCLIP = { + "M-CLIP/XLM-Roberta-Large-Vit-B-32": OpenCLIPModelConfig("ViT-B-32", "openai"), + "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus": OpenCLIPModelConfig("ViT-B-16-plus-240", "laion400m_e32"), + "M-CLIP/LABSE-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"), + "M-CLIP/XLM-Roberta-Large-Vit-L-14": OpenCLIPModelConfig("ViT-L-14", "openai"), +} + + +def to_onnx( + model_name: str, + output_dir_visual: Path | str, + output_dir_textual: Path | str, +) -> None: + textual_path = get_model_path(output_dir_textual) + with tempfile.TemporaryDirectory() as tmpdir: + model = MultilingualCLIP.from_pretrained(model_name, cache_dir=tmpdir) + AutoTokenizer.from_pretrained(model_name).save_pretrained(output_dir_textual) + + for param in model.parameters(): + param.requires_grad_(False) + + export_text_encoder(model, textual_path) + openclip_to_onnx(_MCLIP_TO_OPENCLIP[model_name], output_dir_visual) + optimize(textual_path) + + +def export_text_encoder(model: MultilingualCLIP, output_path: Path | str) -> None: + output_path = Path(output_path) + + def forward(self: MultilingualCLIP, input_ids: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor: + embs = self.transformer(input_ids, attention_mask)[0] + embs = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None] + embs = self.LinearTransformation(embs) + return torch.nn.functional.normalize(embs, dim=-1) + + # unfortunately need to monkeypatch for tracing to work here + # otherwise it hits the 2GiB protobuf serialization limit + MultilingualCLIP.forward = forward + + args = (torch.ones(1, 77, dtype=torch.int32), torch.ones(1, 77, dtype=torch.int32)) + with warnings.catch_warnings(): + warnings.simplefilter("ignore", UserWarning) + torch.onnx.export( + model, + args, + output_path.as_posix(), + input_names=["input_ids", "attention_mask"], + output_names=["text_embedding"], + opset_version=17, + dynamic_axes={ + "input_ids": {0: "batch_size", 1: "sequence_length"}, + "attention_mask": {0: "batch_size", 1: "sequence_length"}, + }, + ) diff --git a/machine-learning/export/models/openclip.py b/machine-learning/export/models/openclip.py new file mode 100644 index 000000000..c29dafce7 --- /dev/null +++ b/machine-learning/export/models/openclip.py @@ -0,0 +1,109 @@ +import tempfile +import warnings +from dataclasses import dataclass, field +from pathlib import Path + +import open_clip +import torch +from transformers import AutoTokenizer + +from .optimize import optimize +from .util import get_model_path, save_config + + +@dataclass +class OpenCLIPModelConfig: + name: str + pretrained: str + image_size: int = field(init=False) + sequence_length: int = field(init=False) + + def __post_init__(self) -> None: + open_clip_cfg = open_clip.get_model_config(self.name) + if open_clip_cfg is None: + raise ValueError(f"Unknown model {self.name}") + self.image_size = open_clip_cfg["vision_cfg"]["image_size"] + self.sequence_length = open_clip_cfg["text_cfg"]["context_length"] + + +def to_onnx( + model_cfg: OpenCLIPModelConfig, + output_dir_visual: Path | str | None = None, + output_dir_textual: Path | str | None = None, +) -> None: + with tempfile.TemporaryDirectory() as tmpdir: + model = open_clip.create_model( + model_cfg.name, + pretrained=model_cfg.pretrained, + jit=False, + cache_dir=tmpdir, + require_pretrained=True, + ) + + text_vision_cfg = open_clip.get_model_config(model_cfg.name) + + for param in model.parameters(): + param.requires_grad_(False) + + if output_dir_visual is not None: + output_dir_visual = Path(output_dir_visual) + visual_path = get_model_path(output_dir_visual) + + save_config(open_clip.get_model_preprocess_cfg(model), output_dir_visual / "preprocess_cfg.json") + save_config(text_vision_cfg, output_dir_visual.parent / "config.json") + export_image_encoder(model, model_cfg, visual_path) + + optimize(visual_path) + + if output_dir_textual is not None: + output_dir_textual = Path(output_dir_textual) + textual_path = get_model_path(output_dir_textual) + + tokenizer_name = text_vision_cfg["text_cfg"].get("hf_tokenizer_name", "openai/clip-vit-base-patch32") + AutoTokenizer.from_pretrained(tokenizer_name).save_pretrained(output_dir_textual) + export_text_encoder(model, model_cfg, textual_path) + optimize(textual_path) + + +def export_image_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None: + output_path = Path(output_path) + + def encode_image(image: torch.Tensor) -> torch.Tensor: + return model.encode_image(image, normalize=True) + + args = (torch.randn(1, 3, model_cfg.image_size, model_cfg.image_size),) + traced = torch.jit.trace(encode_image, args) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", UserWarning) + torch.onnx.export( + traced, + args, + output_path.as_posix(), + input_names=["image"], + output_names=["image_embedding"], + opset_version=17, + dynamic_axes={"image": {0: "batch_size"}}, + ) + + +def export_text_encoder(model: open_clip.CLIP, model_cfg: OpenCLIPModelConfig, output_path: Path | str) -> None: + output_path = Path(output_path) + + def encode_text(text: torch.Tensor) -> torch.Tensor: + return model.encode_text(text, normalize=True) + + args = (torch.ones(1, model_cfg.sequence_length, dtype=torch.int32),) + traced = torch.jit.trace(encode_text, args) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", UserWarning) + torch.onnx.export( + traced, + args, + output_path.as_posix(), + input_names=["text"], + output_names=["text_embedding"], + opset_version=17, + dynamic_axes={"text": {0: "batch_size"}}, + ) diff --git a/machine-learning/export/models/optimize.py b/machine-learning/export/models/optimize.py new file mode 100644 index 000000000..b0ef1ee60 --- /dev/null +++ b/machine-learning/export/models/optimize.py @@ -0,0 +1,38 @@ +from pathlib import Path + +import onnx +import onnxruntime as ort +import onnxsim + + +def optimize_onnxsim(model_path: Path | str, output_path: Path | str) -> None: + model_path = Path(model_path) + output_path = Path(output_path) + model = onnx.load(model_path.as_posix()) + model, check = onnxsim.simplify(model, skip_shape_inference=True) + assert check, "Simplified ONNX model could not be validated" + onnx.save(model, output_path.as_posix()) + + +def optimize_ort( + model_path: Path | str, + output_path: Path | str, + level: ort.GraphOptimizationLevel = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC, +) -> None: + model_path = Path(model_path) + output_path = Path(output_path) + + sess_options = ort.SessionOptions() + sess_options.graph_optimization_level = level + sess_options.optimized_model_filepath = output_path.as_posix() + + ort.InferenceSession(model_path.as_posix(), providers=["CPUExecutionProvider"], sess_options=sess_options) + + +def optimize(model_path: Path | str) -> None: + model_path = Path(model_path) + + optimize_ort(model_path, model_path) + # onnxsim serializes large models as a blob, which uses much more memory when loading the model at runtime + if not any(file.name.startswith("Constant") for file in model_path.parent.iterdir()): + optimize_onnxsim(model_path, model_path) diff --git a/machine-learning/export/models/util.py b/machine-learning/export/models/util.py new file mode 100644 index 000000000..67e582af4 --- /dev/null +++ b/machine-learning/export/models/util.py @@ -0,0 +1,15 @@ +import json +from pathlib import Path +from typing import Any + + +def get_model_path(output_dir: Path | str) -> Path: + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + return output_dir / "model.onnx" + + +def save_config(config: Any, output_path: Path | str) -> None: + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + json.dump(config, output_path.open("w")) diff --git a/machine-learning/export/run.py b/machine-learning/export/run.py new file mode 100644 index 000000000..5ce32189e --- /dev/null +++ b/machine-learning/export/run.py @@ -0,0 +1,76 @@ +import gc +import os +from pathlib import Path +from tempfile import TemporaryDirectory + +from huggingface_hub import create_repo, login, upload_folder +from models import mclip, openclip +from rich.progress import Progress + +models = [ + "RN50::openai", + "RN50::yfcc15m", + "RN50::cc12m", + "RN101::openai", + "RN101::yfcc15m", + "RN50x4::openai", + "RN50x16::openai", + "RN50x64::openai", + "ViT-B-32::openai", + "ViT-B-32::laion2b_e16", + "ViT-B-32::laion400m_e31", + "ViT-B-32::laion400m_e32", + "ViT-B-32::laion2b-s34b-b79k", + "ViT-B-16::openai", + "ViT-B-16::laion400m_e31", + "ViT-B-16::laion400m_e32", + "ViT-B-16-plus-240::laion400m_e31", + "ViT-B-16-plus-240::laion400m_e32", + "ViT-L-14::openai", + "ViT-L-14::laion400m_e31", + "ViT-L-14::laion400m_e32", + "ViT-L-14::laion2b-s32b-b82k", + "ViT-L-14-336::openai", + "ViT-H-14::laion2b-s32b-b79k", + "ViT-g-14::laion2b-s12b-b42k", + "M-CLIP/LABSE-Vit-L-14", + "M-CLIP/XLM-Roberta-Large-Vit-B-32", + "M-CLIP/XLM-Roberta-Large-Vit-B-16Plus", + "M-CLIP/XLM-Roberta-Large-Vit-L-14", +] + +login(token=os.environ["HF_AUTH_TOKEN"]) + +with Progress() as progress: + task1 = progress.add_task("[green]Exporting models...", total=len(models)) + task2 = progress.add_task("[yellow]Uploading models...", total=len(models)) + + with TemporaryDirectory() as tmp: + tmpdir = Path(tmp) + for model in models: + model_name = model.split("/")[-1].replace("::", "__") + config_path = tmpdir / model_name / "config.json" + + def upload() -> None: + progress.update(task2, description=f"[yellow]Uploading {model_name}") + repo_id = f"immich-app/{model_name}" + + create_repo(repo_id, exist_ok=True) + upload_folder(repo_id=repo_id, folder_path=tmpdir / model_name) + progress.update(task2, advance=1) + + def export() -> None: + progress.update(task1, description=f"[green]Exporting {model_name}") + visual_dir = tmpdir / model_name / "visual" + textual_dir = tmpdir / model_name / "textual" + if model.startswith("M-CLIP"): + mclip.to_onnx(model, visual_dir, textual_dir) + else: + name, _, pretrained = model_name.partition("__") + openclip.to_onnx(openclip.OpenCLIPModelConfig(name, pretrained), visual_dir, textual_dir) + + progress.update(task1, advance=1) + gc.collect() + + export() + upload() diff --git a/machine-learning/locustfile.py b/machine-learning/locustfile.py index 0bf2a37d7..3577ca1bc 100644 --- a/machine-learning/locustfile.py +++ b/machine-learning/locustfile.py @@ -1,11 +1,12 @@ -from io import BytesIO import json +from argparse import ArgumentParser +from io import BytesIO from typing import Any from locust import HttpUser, events, task from locust.env import Environment from PIL import Image -from argparse import ArgumentParser + byte_image = BytesIO() @@ -14,11 +15,21 @@ def _(parser: ArgumentParser) -> None: parser.add_argument("--tag-model", type=str, default="microsoft/resnet-50") parser.add_argument("--clip-model", type=str, default="ViT-B-32::openai") parser.add_argument("--face-model", type=str, default="buffalo_l") - parser.add_argument("--tag-min-score", type=int, default=0.0, - help="Returns all tags at or above this score. The default returns all tags.") - parser.add_argument("--face-min-score", type=int, default=0.034, - help=("Returns all faces at or above this score. The default returns 1 face per request; " - "setting this to 0 blows up the number of faces to the thousands.")) + parser.add_argument( + "--tag-min-score", + type=int, + default=0.0, + help="Returns all tags at or above this score. The default returns all tags.", + ) + parser.add_argument( + "--face-min-score", + type=int, + default=0.034, + help=( + "Returns all faces at or above this score. The default returns 1 face per request; " + "setting this to 0 blows up the number of faces to the thousands." + ), + ) parser.add_argument("--image-size", type=int, default=1000) @@ -62,7 +73,7 @@ class CLIPTextFormDataLoadTest(InferenceLoadTest): ("modelName", self.environment.parsed_options.clip_model), ("modelType", "clip"), ("options", json.dumps({"mode": "text"})), - ("text", "test search query") + ("text", "test search query"), ] self.client.post("/predict", data=data) @@ -88,5 +99,5 @@ class RecognitionFormDataLoadTest(InferenceLoadTest): ("options", json.dumps({"minScore": self.environment.parsed_options.face_min_score})), ] files = {"image": self.data} - + self.client.post("/predict", data=data, files=files) diff --git a/machine-learning/poetry.lock b/machine-learning/poetry.lock index 528ac7046..2b5a11e8b 100644 --- a/machine-learning/poetry.lock +++ b/machine-learning/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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"pypi"}, - {markers = "platform_machine == 'amd64' or platform_machine == 'x86_64'", version = "=2.0.1", source = "pytorch-cpu"} + {markers = "platform_machine == 'arm64' or platform_machine == 'aarch64'", version = "=2.1.0", source = "pypi"}, + {markers = "platform_machine == 'amd64' or platform_machine == 'x86_64'", version = "=2.1.0", source = "pytorch-cpu"} ] transformers = "^4.29.2" onnxruntime = "^1.15.0" @@ -22,14 +22,9 @@ uvicorn = {extras = ["standard"], version = "^0.22.0"} pydantic = "^1.10.8" aiocache = "^0.12.1" optimum = "^1.9.1" -torchvision = [ - {markers = "platform_machine == 'arm64' or platform_machine == 'aarch64'", version = "=0.15.2", source = "pypi"}, - {markers = "platform_machine == 'amd64' or platform_machine == 'x86_64'", version = "=0.15.2", source = "pytorch-cpu"} -] rich = "^13.4.2" ftfy = "^6.1.1" setuptools = "^68.0.0" -open-clip-torch = "^2.20.0" python-multipart = "^0.0.6" orjson = "^3.9.5" safetensors = "0.3.2" @@ -63,6 +58,7 @@ warn_redundant_casts = true disallow_any_generics = true check_untyped_defs = true disallow_untyped_defs = true +ignore_missing_imports = true [tool.pydantic-mypy] init_forbid_extra = true @@ -70,30 +66,6 @@ init_typed = true warn_required_dynamic_aliases = true warn_untyped_fields = true -[[tool.mypy.overrides]] -module = [ - "huggingface_hub", - "transformers", - "gunicorn", - "cv2", - "insightface.model_zoo", - "insightface.utils.face_align", - "insightface.utils.storage", - "onnxruntime", - "optimum", - "optimum.pipelines", - "optimum.onnxruntime", - "clip_server.model.clip", - "clip_server.model.clip_onnx", - "clip_server.model.pretrained_models", - "clip_server.model.tokenization", - "torchvision.transforms", - "aiocache.backends.memory", - "aiocache.lock", - "aiocache.plugins" -] -ignore_missing_imports = true - [tool.ruff] line-length = 120 target-version = "py311" diff --git a/machine-learning/requirements.txt b/machine-learning/requirements.txt deleted file mode 100644 index c3caa0644..000000000 --- a/machine-learning/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -# requirements to be installed with `--no-deps` flag -clip-server==0.8.* \ No newline at end of file diff --git a/server/src/domain/smart-info/smart-info.service.spec.ts b/server/src/domain/smart-info/smart-info.service.spec.ts index 2a3dcd566..96e2af4b3 100644 --- a/server/src/domain/smart-info/smart-info.service.spec.ts +++ b/server/src/domain/smart-info/smart-info.service.spec.ts @@ -193,7 +193,7 @@ describe(SmartInfoService.name, () => { expect(machineMock.encodeImage).toHaveBeenCalledWith( 'http://immich-machine-learning:3003', { imagePath: 'path/to/resize.ext' }, - { enabled: true, modelName: 'ViT-B-32::openai' }, + { enabled: true, modelName: 'ViT-B-32__openai' }, ); expect(smartMock.upsert).toHaveBeenCalledWith({ assetId: 'asset-1', diff --git a/server/src/domain/system-config/system-config.core.ts b/server/src/domain/system-config/system-config.core.ts index 29f9c363d..df4ef374b 100644 --- a/server/src/domain/system-config/system-config.core.ts +++ b/server/src/domain/system-config/system-config.core.ts @@ -67,7 +67,7 @@ export const defaults = Object.freeze({ }, clip: { enabled: true, - modelName: 'ViT-B-32::openai', + modelName: 'ViT-B-32__openai', }, facialRecognition: { enabled: true, diff --git a/server/src/domain/system-config/system-config.service.spec.ts b/server/src/domain/system-config/system-config.service.spec.ts index f687e4506..c3808a8cd 100644 --- a/server/src/domain/system-config/system-config.service.spec.ts +++ b/server/src/domain/system-config/system-config.service.spec.ts @@ -68,7 +68,7 @@ const updatedConfig = Object.freeze({ }, clip: { enabled: true, - modelName: 'ViT-B-32::openai', + modelName: 'ViT-B-32__openai', }, facialRecognition: { enabled: true, diff --git a/web/src/lib/components/admin-page/settings/machine-learning-settings/machine-learning-settings.svelte b/web/src/lib/components/admin-page/settings/machine-learning-settings/machine-learning-settings.svelte index bba0233d4..78c37977d 100644 --- a/web/src/lib/components/admin-page/settings/machine-learning-settings/machine-learning-settings.svelte +++ b/web/src/lib/components/admin-page/settings/machine-learning-settings/machine-learning-settings.svelte @@ -140,9 +140,8 @@ isEdited={machineLearningConfig.clip.modelName !== savedConfig.clip.modelName} >

- The name of a CLIP model listed here. Note that you must re-run the 'Encode CLIP' job for all images upon changing a model. + The name of a CLIP model listed here. Note that + you must re-run the 'Encode CLIP' job for all images upon changing a model.