From f2be2a4a7ba00f4484090222838898ce73e7d682 Mon Sep 17 00:00:00 2001 From: Andrey Vasnetsov Date: Thu, 6 Oct 2022 19:50:37 +0200 Subject: [PATCH] Switch head (#180) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * support caching of metadata * upd version * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add tests for switchencoderandhead * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Upd version * `poetry lock` versins Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: M. Yusuf Sarıgöz --- poetry.lock | 700 ++++++------------ pyproject.toml | 4 +- quaterion/dataset/train_collator.py | 16 +- quaterion/train/cache/cache_encoder.py | 25 +- quaterion/train/cache/cache_model.py | 8 +- quaterion/train/cache/cache_train_collator.py | 16 +- .../train/cache/in_memory_cache_encoder.py | 28 +- quaterion/train/cache_mixin.py | 3 + quaterion/train/trainable_model.py | 5 + tests/cache/test_cache_dataloader.py | 17 +- tests/model_fixtures.py | 52 +- tests/test_switch_encoder.py | 22 + 12 files changed, 378 insertions(+), 518 deletions(-) create mode 100644 tests/test_switch_encoder.py diff --git a/poetry.lock b/poetry.lock index 565f22fc..5956ca82 100644 --- a/poetry.lock +++ b/poetry.lock @@ -8,7 +8,7 @@ python-versions = ">=3.6" [[package]] name = "aiohttp" -version = "3.8.1" +version = "3.8.3" description = "Async http client/server framework (asyncio)" category = "main" optional = false @@ -92,10 +92,10 @@ optional = false python-versions = ">=3.5" [package.extras] -dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "mypy (>=0.900,!=0.940)", "pre-commit", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "sphinx", "sphinx-notfound-page", "zope.interface"] -docs = ["furo", "sphinx", "sphinx-notfound-page", "zope.interface"] -tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "zope.interface"] -tests_no_zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "mypy (>=0.900,!=0.940)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins"] +dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] +docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] +tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] [[package]] name = "babel" @@ -118,7 +118,7 @@ python-versions = "*" [[package]] name = "black" -version = "22.6.0" +version = "22.8.0" description = "The uncompromising code formatter." category = "dev" optional = false @@ -148,7 +148,7 @@ python-versions = "~=3.7" [[package]] name = "certifi" -version = "2022.6.15" +version = "2022.9.24" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false @@ -213,12 +213,15 @@ python-versions = ">=3.7" [[package]] name = "executing" -version = "1.0.0" +version = "1.1.0" description = "Get the currently executing AST node of a frame, and other information" category = "dev" optional = false python-versions = "*" +[package.extras] +tests = ["rich", "littleutils", "pytest", "asttokens"] + [[package]] name = "frozenlist" version = "1.3.1" @@ -229,14 +232,14 @@ python-versions = ">=3.7" [[package]] name = "fsspec" -version = "2022.7.1" +version = "2022.8.2" description = "File-system specification" category = "main" optional = false python-versions = ">=3.7" [package.dependencies] -aiohttp = {version = "*", optional = true, markers = "extra == \"http\""} +aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""} requests = {version = "*", optional = true, markers = "extra == \"http\""} [package.extras] @@ -244,7 +247,7 @@ abfs = ["adlfs"] adl = ["adlfs"] arrow = ["pyarrow (>=1)"] dask = ["dask", "distributed"] -dropbox = ["dropbox", "dropboxdrivefs", "requests"] +dropbox = ["dropboxdrivefs", "requests", "dropbox"] entrypoints = ["importlib-metadata"] fuse = ["fusepy"] gcs = ["gcsfs"] @@ -253,7 +256,7 @@ github = ["requests"] gs = ["gcsfs"] gui = ["panel"] hdfs = ["pyarrow (>=1)"] -http = ["aiohttp", "requests"] +http = ["requests", "aiohttp (!=4.0.0a0,!=4.0.0a1)"] libarchive = ["libarchive-c"] oci = ["ocifs"] s3 = ["s3fs"] @@ -264,7 +267,7 @@ tqdm = ["tqdm"] [[package]] name = "google-auth" -version = "2.11.0" +version = "2.12.0" description = "Google Authentication Library" category = "main" optional = false @@ -277,7 +280,7 @@ rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""} six = ">=1.9.0" [package.extras] -aiohttp = ["aiohttp (>=3.6.2,<4.0.0dev)", "requests (>=2.20.0,<3.0.0dev)"] +aiohttp = ["requests (>=2.20.0,<3.0.0dev)", "aiohttp (>=3.6.2,<4.0.0dev)"] enterprise_cert = ["cryptography (==36.0.2)", "pyopenssl (==22.0.0)"] pyopenssl = ["pyopenssl (>=20.0.0)"] reauth = ["pyu2f (>=0.1.5)"] @@ -299,21 +302,21 @@ tool = ["click (>=6.0.0)"] [[package]] name = "grpcio" -version = "1.48.0" +version = "1.49.1" description = "HTTP/2-based RPC framework" category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] six = ">=1.5.2" [package.extras] -protobuf = ["grpcio-tools (>=1.48.0)"] +protobuf = ["grpcio-tools (>=1.49.1)"] [[package]] name = "idna" -version = "3.3" +version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" category = "main" optional = false @@ -329,7 +332,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "importlib-metadata" -version = "4.12.0" +version = "5.0.0" description = "Read metadata from Python packages" category = "main" optional = false @@ -339,9 +342,9 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["jaraco.packaging (>=9)", "rst.linker (>=1.9)", "sphinx"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)"] perf = ["ipython"] -testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"] [[package]] name = "iniconfig" @@ -366,7 +369,7 @@ toml = {version = ">=0.10.2", markers = "python_version > \"3.6\""} [[package]] name = "ipython" -version = "8.4.0" +version = "8.5.0" description = "IPython: Productive Interactive Computing" category = "dev" optional = false @@ -381,13 +384,13 @@ jedi = ">=0.16" matplotlib-inline = "*" pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} pickleshare = "*" -prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0" +prompt-toolkit = ">3.0.1,<3.1.0" pygments = ">=2.4.0" stack-data = "*" traitlets = ">=5" [package.extras] -all = ["Sphinx (>=1.3)", "black", "curio", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.19)", "pandas", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "testpath", "trio"] +all = ["black", "Sphinx (>=1.3)", "ipykernel", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.19)", "pandas", "trio"] black = ["black"] doc = ["Sphinx (>=1.3)"] kernel = ["ipykernel"] @@ -397,7 +400,7 @@ notebook = ["ipywidgets", "notebook"] parallel = ["ipyparallel"] qtconsole = ["qtconsole"] test = ["pytest (<7.1)", "pytest-asyncio", "testpath"] -test_extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.19)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"] +test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.19)", "pandas", "trio"] [[package]] name = "jedi" @@ -430,11 +433,11 @@ i18n = ["Babel (>=2.7)"] [[package]] name = "joblib" -version = "1.1.0" +version = "1.2.0" description = "Lightweight pipelining with Python functions" category = "main" optional = true -python-versions = ">=3.6" +python-versions = ">=3.7" [[package]] name = "loguru" @@ -449,7 +452,7 @@ colorama = {version = ">=0.3.4", markers = "sys_platform == \"win32\""} win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""} [package.extras] -dev = ["Sphinx (>=2.2.1)", "black (>=19.10b0)", "codecov (>=2.0.15)", "colorama (>=0.3.4)", "flake8 (>=3.7.7)", "isort (>=5.1.1)", "pytest (>=4.6.2)", "pytest-cov (>=2.7.1)", "sphinx-autobuild (>=0.7.1)", "sphinx-rtd-theme (>=0.4.3)", "tox (>=3.9.0)", "tox-travis (>=0.12)"] +dev = ["codecov (>=2.0.15)", "colorama (>=0.3.4)", "flake8 (>=3.7.7)", "tox (>=3.9.0)", "tox-travis (>=0.12)", "pytest (>=4.6.2)", "pytest-cov (>=2.7.1)", "Sphinx (>=2.2.1)", "sphinx-autobuild (>=0.7.1)", "sphinx-rtd-theme (>=0.4.3)", "black (>=19.10b0)", "isort (>=5.1.1)"] [[package]] name = "markdown" @@ -510,7 +513,7 @@ python-versions = "*" [[package]] name = "numpy" -version = "1.23.2" +version = "1.23.3" description = "NumPy is the fundamental package for array computing with Python." category = "main" optional = false @@ -518,7 +521,7 @@ python-versions = ">=3.8" [[package]] name = "oauthlib" -version = "3.2.0" +version = "3.2.1" description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic" category = "main" optional = false @@ -554,11 +557,11 @@ testing = ["docopt", "pytest (<6.0.0)"] [[package]] name = "pathspec" -version = "0.9.0" +version = "0.10.1" description = "Utility library for gitignore style pattern matching of file paths." category = "dev" optional = false -python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7" +python-versions = ">=3.7" [[package]] name = "pexpect" @@ -600,8 +603,8 @@ optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx (>=4)", "sphinx-autodoc-typehints (>=1.12)"] -test = ["appdirs (==1.4.4)", "pytest (>=6)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)"] +docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)", "sphinx (>=4)"] +test = ["appdirs (==1.4.4)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)", "pytest (>=6)"] [[package]] name = "pluggy" @@ -617,7 +620,7 @@ testing = ["pytest", "pytest-benchmark"] [[package]] name = "prompt-toolkit" -version = "3.0.30" +version = "3.0.31" description = "Library for building powerful interactive command lines in Python" category = "dev" optional = false @@ -628,7 +631,7 @@ wcwidth = "*" [[package]] name = "protobuf" -version = "3.19.4" +version = "3.19.6" description = "Protocol Buffers" category = "main" optional = false @@ -708,7 +711,7 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["jinja2", "railroad-diagrams"] +diagrams = ["railroad-diagrams", "jinja2"] [[package]] name = "pytest" @@ -733,7 +736,7 @@ testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xm [[package]] name = "pytorch-lightning" -version = "1.7.3" +version = "1.7.7" description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate." category = "main" optional = false @@ -752,21 +755,21 @@ tqdm = ">=4.57.0" typing-extensions = ">=4.0.0" [package.extras] -all = ["cloudpickle (>=1.3)", "codecov (>=2.1)", "comet-ml (>=3.1.12)", "coverage (>=6.4)", "deepspeed (>=0.6.0)", "fairscale (>=0.4.5)", "fastapi", "gcsfs (>=2021.5.0)", "gym[classic_control] (>=0.17.0)", "hivemind (>=1.0.1)", "horovod (>=0.21.2,!=0.24.0)", "hydra-core (>=1.0.5)", "ipython", "jsonargparse[signatures] (>=4.12.0)", "matplotlib (>3.1)", "mlflow (>=1.0.0)", "mypy (==0.971)", "neptune-client (>=0.10.0)", "omegaconf (>=2.0.5)", "onnxruntime", "pandas (>1.0)", "pre-commit (>=1.0)", "psutil", "pytest (>=7.0)", "pytest-cov", "pytest-forked", "pytest-rerunfailures (>=10.2)", "rich (>=10.14.0,!=10.15.0.a)", "scikit-learn (>0.22.1)", "test-tube (>=0.7.5)", "torchtext (>=0.10)", "torchvision (>=0.10)", "uvicorn", "wandb (>=0.10.22)"] +all = ["matplotlib (>3.1)", "torchtext (>=0.10)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.12.0)", "gcsfs (>=2021.5.0)", "rich (>=10.14.0,!=10.15.0.a)", "protobuf (<=3.20.1)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "wandb (>=0.10.22)", "coverage (>=6.4)", "codecov (>=2.1)", "pytest (>=7.0)", "pytest-cov", "pytest-forked", "pytest-rerunfailures (>=10.2)", "pre-commit (>=1.0)", "mypy (==0.971)", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "psutil", "pandas (>1.0)", "fastapi", "uvicorn", "torchvision (>=0.10)", "gym[classic_control] (>=0.17.0)", "ipython", "fairscale (>=0.4.5)", "deepspeed (>=0.6.0)", "horovod (>=0.21.2,!=0.24.0)", "hivemind (>=1.0.1)"] deepspeed = ["deepspeed (>=0.6.0)"] -dev = ["cloudpickle (>=1.3)", "codecov (>=2.1)", "comet-ml (>=3.1.12)", "coverage (>=6.4)", "fastapi", "gcsfs (>=2021.5.0)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.12.0)", "matplotlib (>3.1)", "mlflow (>=1.0.0)", "mypy (==0.971)", "neptune-client (>=0.10.0)", "omegaconf (>=2.0.5)", "onnxruntime", "pandas (>1.0)", "pre-commit (>=1.0)", "psutil", "pytest (>=7.0)", "pytest-cov", "pytest-forked", "pytest-rerunfailures (>=10.2)", "rich (>=10.14.0,!=10.15.0.a)", "scikit-learn (>0.22.1)", "test-tube (>=0.7.5)", "torchtext (>=0.10)", "uvicorn", "wandb (>=0.10.22)"] -examples = ["gym[classic_control] (>=0.17.0)", "ipython", "torchvision (>=0.10)"] -extra = ["gcsfs (>=2021.5.0)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.12.0)", "matplotlib (>3.1)", "omegaconf (>=2.0.5)", "rich (>=10.14.0,!=10.15.0.a)", "torchtext (>=0.10)"] +dev = ["matplotlib (>3.1)", "torchtext (>=0.10)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.12.0)", "gcsfs (>=2021.5.0)", "rich (>=10.14.0,!=10.15.0.a)", "protobuf (<=3.20.1)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "wandb (>=0.10.22)", "coverage (>=6.4)", "codecov (>=2.1)", "pytest (>=7.0)", "pytest-cov", "pytest-forked", "pytest-rerunfailures (>=10.2)", "pre-commit (>=1.0)", "mypy (==0.971)", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "psutil", "pandas (>1.0)", "fastapi", "uvicorn"] +examples = ["torchvision (>=0.10)", "gym[classic_control] (>=0.17.0)", "ipython"] +extra = ["matplotlib (>3.1)", "torchtext (>=0.10)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.12.0)", "gcsfs (>=2021.5.0)", "rich (>=10.14.0,!=10.15.0.a)", "protobuf (<=3.20.1)"] fairscale = ["fairscale (>=0.4.5)"] hivemind = ["hivemind (>=1.0.1)"] horovod = ["horovod (>=0.21.2,!=0.24.0)"] -loggers = ["comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "neptune-client (>=0.10.0)", "test-tube (>=0.7.5)", "wandb (>=0.10.22)"] -strategies = ["deepspeed (>=0.6.0)", "fairscale (>=0.4.5)", "hivemind (>=1.0.1)", "horovod (>=0.21.2,!=0.24.0)"] -test = ["cloudpickle (>=1.3)", "codecov (>=2.1)", "coverage (>=6.4)", "fastapi", "mypy (==0.971)", "onnxruntime", "pandas (>1.0)", "pre-commit (>=1.0)", "psutil", "pytest (>=7.0)", "pytest-cov", "pytest-forked", "pytest-rerunfailures (>=10.2)", "scikit-learn (>0.22.1)", "uvicorn"] +loggers = ["neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "wandb (>=0.10.22)"] +strategies = ["fairscale (>=0.4.5)", "deepspeed (>=0.6.0)", "horovod (>=0.21.2,!=0.24.0)", "hivemind (>=1.0.1)"] +test = ["coverage (>=6.4)", "codecov (>=2.1)", "pytest (>=7.0)", "pytest-cov", "pytest-forked", "pytest-rerunfailures (>=10.2)", "pre-commit (>=1.0)", "mypy (==0.971)", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "psutil", "pandas (>1.0)", "fastapi", "uvicorn"] [[package]] name = "pytorch-metric-learning" -version = "1.5.2" +version = "1.6.2" description = "The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch." category = "main" optional = true @@ -780,14 +783,14 @@ torchvision = "*" tqdm = "*" [package.extras] -dev = ["black", "flake8", "isort", "nbqa"] +dev = ["black", "isort", "nbqa", "flake8"] docs = ["mkdocs-material"] -with-hooks = ["faiss-gpu (>=1.6.3)", "record-keeper (>=0.9.32)", "tensorboard"] -with-hooks-cpu = ["faiss-cpu (>=1.6.3)", "record-keeper (>=0.9.32)", "tensorboard"] +with-hooks = ["record-keeper (>=0.9.32)", "faiss-gpu (>=1.6.3)", "tensorboard"] +with-hooks-cpu = ["record-keeper (>=0.9.32)", "faiss-cpu (>=1.6.3)", "tensorboard"] [[package]] name = "pytz" -version = "2022.2.1" +version = "2022.4" description = "World timezone definitions, modern and historical" category = "dev" optional = false @@ -821,7 +824,7 @@ resolved_reference = "a90cdd5925783c2b0ed3b8d39897cd4eaf942e2a" [[package]] name = "quaterion-models" -version = "0.1.16" +version = "0.1.17" description = "The collection of building blocks to build fine-tunable similarity learning models" category = "main" optional = false @@ -869,7 +872,7 @@ rsa = ["oauthlib[signedtoken] (>=3.0.0)"] [[package]] name = "rich" -version = "12.5.1" +version = "12.6.0" description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" category = "main" optional = false @@ -909,10 +912,10 @@ scipy = ">=1.3.2" threadpoolctl = ">=2.0.0" [package.extras] -benchmark = ["matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"] -docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.2)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] -examples = ["matplotlib (>=3.1.2)", "pandas (>=1.0.5)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"] -tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.2)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", 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"similarity-learning", "metric-learning", "similarity", python = ">=3.8,<3.11" torch = ">=1.8.2" pytorch-lightning = "^1.6.4" -quaterion-models = "^0.1.16" +quaterion-models = "0.1.17" loguru = "^0.5.3" mmh3 = "^3.0.0" pytorch-metric-learning = {version = "^1.3.0", optional = true} diff --git a/quaterion/dataset/train_collator.py b/quaterion/dataset/train_collator.py index 883939c3..8f62c735 100644 --- a/quaterion/dataset/train_collator.py +++ b/quaterion/dataset/train_collator.py @@ -2,7 +2,8 @@ from typing import Any, Callable, Dict, List, Tuple, Union -from quaterion_models.types import CollateFnType +from quaterion_models.types import CollateFnType, MetaExtractorFnType +from quaterion_models.utils.meta import merge_meta from quaterion.dataset import SimilarityGroupSample, SimilarityPairSample @@ -22,10 +23,14 @@ class TrainCollator: """ def __init__( - self, pre_collate_fn: Callable, encoder_collates: Dict[str, CollateFnType] + self, + pre_collate_fn: Callable, + encoder_collates: Dict[str, CollateFnType], + meta_extractors: Dict[str, MetaExtractorFnType], ): self.pre_collate_fn = pre_collate_fn self.encoder_collates = encoder_collates + self.meta_extractors = meta_extractors def pre_encoder_collate( self, features: List[Any], ids: List[int] = None, encoder_name: str = None @@ -35,6 +40,9 @@ def pre_encoder_collate( """ return features + def process_meta(self, meta: Dict[str, List]) -> Any: + return merge_meta(meta) + def __call__( self, batch: List[Tuple[int, Union[SimilarityPairSample, SimilarityGroupSample]]], @@ -42,8 +50,10 @@ def __call__( ids, features, labels = self.pre_collate_fn(batch) encoder_collate_result = {} + meta = {} for encoder_name, collate_fn in self.encoder_collates.items(): encoder_features = self.pre_encoder_collate(features, ids, encoder_name) encoder_collate_result[encoder_name] = collate_fn(encoder_features) + meta[encoder_name] = self.meta_extractors[encoder_name](encoder_features) - return encoder_collate_result, labels + return {"data": encoder_collate_result, "meta": self.process_meta(meta)}, labels diff --git a/quaterion/train/cache/cache_encoder.py b/quaterion/train/cache/cache_encoder.py index 66c505b3..47c2b425 100644 --- a/quaterion/train/cache/cache_encoder.py +++ b/quaterion/train/cache/cache_encoder.py @@ -2,7 +2,7 @@ from typing import Any, Callable, Hashable, List, Tuple, Union from quaterion_models.encoders import Encoder -from quaterion_models.types import CollateFnType, TensorInterchange +from quaterion_models.types import CollateFnType, MetaExtractorFnType, TensorInterchange from torch import Tensor KeyExtractorType = Callable[[Any], Hashable] @@ -58,6 +58,24 @@ def embedding_size(self) -> int: """ return self._encoder.embedding_size + def cache_extract_meta(self, batch: List[Any]) -> List[dict]: + """Extracts meta information from batch. + + Args: + batch: batch of data + Returns: + List[dict]: list of meta information + """ + raise NotImplementedError() + + def get_meta_extractor(self) -> MetaExtractorFnType: + """Provides function that extracts meta information from batch. + + Returns: + MetaExtractorFnType: meta extractor function + """ + return self.cache_extract_meta + def cache_collate( self, batch: Union[Tuple[List[Hashable], List[Any]], List[Hashable]] ) -> "CacheCollateReturnType": @@ -120,13 +138,16 @@ def is_filled(self) -> bool: """Check if cache already filled""" raise NotImplementedError() - def fill_cache(self, keys: List[Hashable], data: "TensorInterchange") -> None: + def fill_cache( + self, keys: List[Hashable], data: "TensorInterchange", meta: List[Any] + ) -> None: """Apply wrapped encoder to data and store processed data on corresponding device. Args: keys: Hash keys which should be associated with resulting vectors data: Tuple of keys and batches suitable for encoder + meta: List of batch meta information """ raise NotImplementedError() diff --git a/quaterion/train/cache/cache_model.py b/quaterion/train/cache/cache_model.py index f074a0f8..10fbd6fe 100644 --- a/quaterion/train/cache/cache_model.py +++ b/quaterion/train/cache/cache_model.py @@ -47,14 +47,16 @@ def predict_step( torch.Tensor: loss mock """ features, _labels = batch + features_data = features["data"] + features_meta = features["meta"] for encoder_name, encoder in self.encoders.items(): - if encoder_name not in features: + if encoder_name not in features_data: continue - keys, encoder_features = features.get(encoder_name) + keys, encoder_features = features_data.get(encoder_name) if len(keys) == 0: # empty batch possible if all unique object already cached continue - encoder.fill_cache(keys, encoder_features) + encoder.fill_cache(keys, encoder_features, features_meta[encoder_name]) return torch.Tensor([1]) diff --git a/quaterion/train/cache/cache_train_collator.py b/quaterion/train/cache/cache_train_collator.py index 9c809c53..7cc09a24 100644 --- a/quaterion/train/cache/cache_train_collator.py +++ b/quaterion/train/cache/cache_train_collator.py @@ -1,7 +1,7 @@ from collections import defaultdict from typing import Any, Dict, Hashable, List -from quaterion_models.types import CollateFnType +from quaterion_models.types import CollateFnType, MetaExtractorFnType from quaterion.dataset.train_collator import TrainCollator from quaterion.train.cache.cache_config import KeyExtractorType @@ -15,11 +15,12 @@ def __init__( self, pre_collate_fn, encoder_collates: Dict[str, "CollateFnType"], + meta_extractors: Dict[str, "MetaExtractorFnType"], key_extractors: Dict[str, "KeyExtractorType"], cachable_encoders: List[str], mode: CacheMode, ): - super().__init__(pre_collate_fn, encoder_collates) + super().__init__(pre_collate_fn, encoder_collates, meta_extractors) self.cachable_encoders = cachable_encoders self.mode = mode self.key_extractors = key_extractors @@ -78,3 +79,14 @@ def pre_encoder_collate( return keys raise NotImplementedError(f"Cache mode {self.mode} is not implemented") + + def process_meta(self, meta: Dict[str, List]) -> Any: + if self.mode == CacheMode.FILL: + # On the cache fill stage we need to know meta per encoder mapping + # To make proper cache filling + return meta + elif self.mode == CacheMode.TRAIN: + # On the train stage we fall back to the default behavior + return super().process_meta(meta) + else: + raise NotImplementedError(f"Cache mode {self.mode} is not implemented") diff --git a/quaterion/train/cache/in_memory_cache_encoder.py b/quaterion/train/cache/in_memory_cache_encoder.py index a911fde0..29d4657a 100644 --- a/quaterion/train/cache/in_memory_cache_encoder.py +++ b/quaterion/train/cache/in_memory_cache_encoder.py @@ -1,5 +1,5 @@ import pickle -from typing import Hashable, List +from typing import Any, Hashable, List, Union import torch from quaterion_models.encoders import Encoder @@ -23,6 +23,7 @@ def __init__( ): super().__init__(encoder) self._cache = None + self._meta_cache = {} self._offset_map = {} self._cache_type = cache_type self._original_device = None @@ -53,6 +54,16 @@ def forward(self, batch: "TensorInterchange") -> Tensor: return embeddings + def cache_extract_meta(self, batch: Union[tuple, List[Hashable]]) -> List[dict]: + if isinstance(batch, tuple): + # Cache filling phase + _keys, features = batch + return self._encoder.get_meta_extractor()(features) + else: + # Assume training phase. + # Only keys are provided here + return [self._meta_cache[key] for key in batch] + def get_collate_fn(self) -> "CollateFnType": """Provides function that converts raw data batch into suitable input. @@ -65,7 +76,9 @@ def get_collate_fn(self) -> "CollateFnType": def is_filled(self) -> bool: return self._cache is not None - def fill_cache(self, keys: List[Hashable], data: "TensorInterchange") -> None: + def fill_cache( + self, keys: List[Hashable], data: "TensorInterchange", meta: List[Any] + ) -> None: embeddings = self._encoder(data) if self.cache_type == CacheType.CPU: embeddings = embeddings.to("cpu") @@ -76,6 +89,9 @@ def fill_cache(self, keys: List[Hashable], data: "TensorInterchange") -> None: self._offset_map[key] = len(self._offset_map) self._tmp.append(embeddings) + for key, meta_item in zip(keys, meta): + self._meta_cache[key] = meta_item + def finish_fill(self): self._cache = torch.cat(self._tmp) self._tmp = [] @@ -83,14 +99,18 @@ def finish_fill(self): def reset_cache(self) -> None: """Resets cache.""" self._cache = None + self._meta_cache = {} self._offset_map = {} self._tmp = [] def save_cache(self, path): - pickle.dump([self._cache.to("cpu"), self._offset_map], open(path, "wb")) + pickle.dump( + [self._cache.to("cpu"), self._offset_map, self._meta_cache], + open(path, "wb"), + ) def load_cache(self, path): - self._cache, self._offset_map = pickle.load(open(path, "rb")) + self._cache, self._offset_map, self._meta_cache = pickle.load(open(path, "rb")) if self.cache_type != CacheType.CPU: device = self._encoder_device() self._cache = self._cache.to(device) diff --git a/quaterion/train/cache_mixin.py b/quaterion/train/cache_mixin.py index f5350422..d4f746f7 100644 --- a/quaterion/train/cache_mixin.py +++ b/quaterion/train/cache_mixin.py @@ -182,6 +182,9 @@ def _cache( encoder_collates={ name: encoder.get_collate_fn() for name, encoder in encoders.items() }, + meta_extractors={ + name: encoder.get_meta_extractor() for name, encoder in encoders.items() + }, key_extractors=key_extractors, cachable_encoders=list(cache_encoders.keys()), mode=CacheMode.TRAIN, diff --git a/quaterion/train/trainable_model.py b/quaterion/train/trainable_model.py index 81cf0f04..d0110c75 100644 --- a/quaterion/train/trainable_model.py +++ b/quaterion/train/trainable_model.py @@ -452,10 +452,15 @@ def setup_dataloader(self, dataloader: SimilarityDataLoader): (key, encoder.get_collate_fn()) for key, encoder in self.model.encoders.items() ) + meta_extractors = dict( + (key, encoder.get_meta_extractor()) + for key, encoder in self.model.encoders.items() + ) collator = TrainCollator( pre_collate_fn=dataloader.collate_fn, encoder_collates=encoder_collate_fns, + meta_extractors=meta_extractors, ) dataloader.collate_fn = collator diff --git a/tests/cache/test_cache_dataloader.py b/tests/cache/test_cache_dataloader.py index 0e4c9206..6bf656c2 100644 --- a/tests/cache/test_cache_dataloader.py +++ b/tests/cache/test_cache_dataloader.py @@ -40,15 +40,20 @@ def test_cache_dataloader(): assert isinstance(encoder, InMemoryCacheEncoder) assert len(encoder._cache) == len(dataset.data) * 2 - cached_ids, labels = next(iter(dataloader)) - print("cached_batch: ", cached_ids) + cached_batch, labels = next(iter(dataloader)) + cached_data = cached_batch["data"] + cached_meta = cached_batch["meta"] + print("cached_batch: ", cached_data) + print("cached_meta: ", cached_meta) + + assert len(cached_data[DEFAULT_ENCODER_KEY]) == len(cached_meta) # check that batch for cache contains only IDs - assert isinstance(cached_ids[DEFAULT_ENCODER_KEY], list) - assert len(cached_ids[DEFAULT_ENCODER_KEY]) == batch_size * 2 - assert isinstance(cached_ids[DEFAULT_ENCODER_KEY][0], int) + assert isinstance(cached_data[DEFAULT_ENCODER_KEY], list) + assert len(cached_data[DEFAULT_ENCODER_KEY]) == batch_size * 2 + assert isinstance(cached_data[DEFAULT_ENCODER_KEY][0], int) - cached_result = cache_trainable_model.model.forward(cached_ids) + cached_result = cache_trainable_model.model.forward(cached_batch) print("cached_result: ", cached_result) # Same, without cache diff --git a/tests/model_fixtures.py b/tests/model_fixtures.py index 93733114..4b545450 100644 --- a/tests/model_fixtures.py +++ b/tests/model_fixtures.py @@ -1,8 +1,8 @@ -from typing import Dict, Union +from typing import Any, Dict, List, Union import torch -from quaterion_models.encoders import Encoder -from quaterion_models.heads import EncoderHead, GatedHead +from quaterion_models.encoders import Encoder, SwitchEncoder +from quaterion_models.heads import EmptyHead, EncoderHead, GatedHead, SwitchHead from quaterion_models.types import TensorInterchange from torch import Tensor from torch.utils.data import Dataset @@ -30,6 +30,15 @@ def __init__(self): "mandarin ".strip(): torch.tensor([0.0, 1.0, 0.0, 0.0, 0.0, 0.0]), } + @classmethod + def extract_meta(cls, batch: List[Any]) -> List[dict]: + return [ + { + "first_letter": name[0], + } + for name in batch + ] + @property def trainable(self) -> bool: return False @@ -94,3 +103,40 @@ def configure_head(self, input_embedding_size: int) -> EncoderHead: def configure_optimizers(self): return torch.optim.Adam(params=self.model.parameters(), lr=0.001) + + +class FakeEncoderWithNegativeOutput(FakeEncoder): + def forward(self, batch: TensorInterchange) -> Tensor: + return -torch.stack([self.tensors[word] for word in batch]) + + @classmethod + def load(cls, input_path: str) -> "Encoder": + return FakeEncoderWithNegativeOutput() + + +class FakeSwitchEncoder(SwitchEncoder): + @classmethod + def encoder_selection(cls, record: Any) -> str: + if record.startswith("m"): + return "positive" + else: + return "negative" + + +class FakeTrainableModelWithSwitchEncoder(FakeTrainableModel): + def configure_encoders(self) -> Union[Encoder, Dict[str, Encoder]]: + return FakeSwitchEncoder( + options={ + "positive": FakeEncoder(), + "negative": FakeEncoderWithNegativeOutput(), + } + ) + + def configure_head(self, input_embedding_size: int) -> EncoderHead: + return SwitchHead( + { + "positive": GatedHead(input_embedding_size), + "negative": GatedHead(input_embedding_size), + }, + input_embedding_size, + ) diff --git a/tests/test_switch_encoder.py b/tests/test_switch_encoder.py new file mode 100644 index 00000000..a88b8f18 --- /dev/null +++ b/tests/test_switch_encoder.py @@ -0,0 +1,22 @@ +import pytorch_lightning as pl + +from quaterion import Quaterion +from quaterion.dataset import PairsSimilarityDataLoader + +from .model_fixtures import FakePairDataset, FakeTrainableModelWithSwitchEncoder + + +class TestSwitchEncoder: + def test_switch_encoder_and_head(self): + model = FakeTrainableModelWithSwitchEncoder() + dataset = FakePairDataset() + data_loader = PairsSimilarityDataLoader(dataset, batch_size=3) + trainer_args = Quaterion.trainer_defaults(model, data_loader) + trainer_args["callbacks"].pop(1) # remove EarlyStopping callback + trainer_args["accelerator"] = "cpu" + trainer_args["max_epochs"] = 1 + Quaterion.fit( + trainable_model=model, + trainer=pl.Trainer(**trainer_args), + train_dataloader=data_loader, + )