diff --git a/Dockerfile b/Dockerfile index 59442ae..cd3fc39 100644 --- a/Dockerfile +++ b/Dockerfile @@ -18,4 +18,13 @@ COPY embeddings ./embeddings # Install package into system python, includes "marimo-launcher" script RUN uv pip install --system . -ENTRYPOINT ["embeddings"] +# Download the model and include in the Docker image +# NOTE: The env vars "TE_MODEL_URI" and "TE_MODEL_DOWNLOAD_PATH" are set here to support +# the downloading of the model into this image build, but persist in the container and +# effectively also set this as the default model. +ENV HF_HUB_DISABLE_PROGRESS_BARS=true +ENV TE_MODEL_URI=opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte +ENV TE_MODEL_DOWNLOAD_PATH=/model +RUN python -m embeddings.cli --verbose download-model + +ENTRYPOINT ["python", "-m", "embeddings.cli"] diff --git a/README.md b/README.md index 7026d28..054aa7a 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,20 @@ TE_MODEL_DOWNLOAD_PATH=# Download location for model HF_HUB_DISABLE_PROGRESS_BARS=#boolean to use progress bars for HuggingFace model downloads; defaults to 'true' in deployed contexts ``` +## Configuring an Embedding Model + +This CLI application is designed to create embeddings for input texts. To do this, a pre-trained model must be identified and configured for use. + +To this end, there is a base embedding class `BaseEmbeddingModel` that is designed to be extended and customized for a particular embedding model. + +Once an embedding class has been created, the preferred approach is to set env vars `TE_MODEL_URI` and `TE_MODEL_DOWNLOAD_PATH` directly in the `Dockerfile` to a) download a local snapshot of the model during image build, and b) set this model as the default for the CLI. + +This allows invoking the CLI without specifying a model URI or local location, allowing this model to serve as the default, e.g.: + +```shell +uv run --env-file .env embeddings test-model-load +``` + ## CLI Commands For local development, all CLI commands should be invoked with the following format to pickup environment variables from `.env`: diff --git a/embeddings/cli.py b/embeddings/cli.py index 8611292..9f5b84d 100644 --- a/embeddings/cli.py +++ b/embeddings/cli.py @@ -1,5 +1,6 @@ import functools import logging +import os import time from collections.abc import Callable from datetime import timedelta @@ -90,6 +91,25 @@ def download_model(model_uri: str, output: Path) -> None: click.echo(result_path) +@main.command() +def test_model_load() -> None: + """Test loading of embedding class and local model based on env vars. + + In a deployed context, the following env vars are expected: + - TE_MODEL_URI + - TE_MODEL_DOWNLOAD_PATH + + With these set, the embedding class should be registered successfully and initialized, + and the model loaded from a local copy. + """ + # load embedding model class + model_class = get_model_class(os.environ["TE_MODEL_URI"]) + model = model_class() + + model.load(os.environ["TE_MODEL_DOWNLOAD_PATH"]) + click.echo("OK") + + @main.command() @model_required def create_embeddings(_model_uri: str) -> None: diff --git a/embeddings/models/base.py b/embeddings/models/base.py index 51e6cd7..e64a5c2 100644 --- a/embeddings/models/base.py +++ b/embeddings/models/base.py @@ -28,9 +28,17 @@ def model_uri(self) -> str: return self.MODEL_URI @abstractmethod - def download(self, output_path: Path) -> Path: + def download(self, output_path: str | Path) -> Path: """Download and prepare model, saving to output_path. Args: output_path: Path where the model zip should be saved. """ + + @abstractmethod + def load(self, model_path: str | Path) -> None: + """Load model from local, downloaded instance. + + Args: + model_path: Path of local model directory. + """ diff --git a/embeddings/models/os_neural_sparse_doc_v3_gte.py b/embeddings/models/os_neural_sparse_doc_v3_gte.py index c6fcd10..b4f6dee 100644 --- a/embeddings/models/os_neural_sparse_doc_v3_gte.py +++ b/embeddings/models/os_neural_sparse_doc_v3_gte.py @@ -1,10 +1,24 @@ """OpenSearch Neural Sparse Doc v3 GTE model.""" +import json import logging +import shutil +import tempfile +import time from pathlib import Path +from typing import TYPE_CHECKING + +from huggingface_hub import snapshot_download +from transformers import AutoModelForMaskedLM, AutoTokenizer from embeddings.models.base import BaseEmbeddingModel +if TYPE_CHECKING: + from transformers import PreTrainedModel + from transformers.models.distilbert.tokenization_distilbert_fast import ( + DistilBertTokenizerFast, + ) + logger = logging.getLogger(__name__) @@ -16,11 +30,134 @@ class OSNeuralSparseDocV3GTE(BaseEmbeddingModel): MODEL_URI = "opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte" - def download(self, output_path: Path) -> Path: + def __init__(self) -> None: + """Initialize the model.""" + super().__init__() + self._model: PreTrainedModel | None = None + self._tokenizer: DistilBertTokenizerFast | None = None + self._special_token_ids: list | None = None + self._id_to_token: list | None = None + + def download(self, output_path: str | Path) -> Path: """Download and prepare model, saving to output_path. Args: - output_path: Path where the model zip should be saved. + output_path: Path where the model should be saved. """ - logger.info(f"Downloading model: { self.model_uri}, saving to: {output_path}.") - raise NotImplementedError + start_time = time.perf_counter() + + output_path = Path(output_path) + logger.info(f"Downloading model: {self.model_uri}, saving to: {output_path}.") + + with tempfile.TemporaryDirectory() as temp_dir: + temp_path = Path(temp_dir) + + # download snapshot of HuggingFace model + snapshot_download(repo_id=self.model_uri, local_dir=temp_path) + logger.debug("Model download complete.") + + # patch local model with files from dependency model "Alibaba-NLP/new-impl" + self._patch_local_model_with_alibaba_new_impl(temp_path) + + # compress model directory as a zip file + if output_path.suffix.lower() == ".zip": + logger.debug("Creating zip file of model contents.") + shutil.make_archive(str(output_path.with_suffix("")), "zip", temp_path) + + # copy to output directory without zipping + else: + logger.debug(f"Copying model contents to {output_path}") + if output_path.exists(): + shutil.rmtree(output_path) + shutil.copytree(temp_path, output_path) + + logger.info(f"Model downloaded successfully, {time.perf_counter() - start_time}s") + return output_path + + def _patch_local_model_with_alibaba_new_impl(self, model_temp_path: Path) -> None: + """Patch downloaded model with required assets from Alibaba-NLP/new-impl. + + Our main model, opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte, + has configurations that attempt dynamic downloading of another model for files. + This can be seen here: https://huggingface.co/opensearch-project/opensearch- + neural-sparse-encoding-doc-v3-gte/blob/main/config.json#L6-L14. + + To avoid our deployed CLI application making requests to the HuggingFace API to + retrieve these required files, which is problematic during high concurrency, we + manually download these files and patch the model during our local download and + save. + + This allows us to load the primary model without any HuggingFace API calls. + """ + logger.info("Downloading custom code from Alibaba-NLP/new-impl") + with tempfile.TemporaryDirectory() as temp_dir: + temp_path = Path(temp_dir) + snapshot_download( + repo_id="Alibaba-NLP/new-impl", + local_dir=str(temp_path), + ) + + logger.info("Copying Alibaba code and updating config.json") + shutil.copy(temp_path / "modeling.py", model_temp_path / "modeling.py") + shutil.copy( + temp_path / "configuration.py", + model_temp_path / "configuration.py", + ) + + with open(model_temp_path / "config.json") as f: + config_json = json.load(f) + config_json["auto_map"] = { + "AutoConfig": "configuration.NewConfig", + "AutoModel": "modeling.NewModel", + "AutoModelForMaskedLM": "modeling.NewForMaskedLM", + "AutoModelForMultipleChoice": "modeling.NewForMultipleChoice", + "AutoModelForQuestionAnswering": "modeling.NewForQuestionAnswering", + "AutoModelForSequenceClassification": ( + "modeling.NewForSequenceClassification" + ), + "AutoModelForTokenClassification": ( + "modeling.NewForTokenClassification" + ), + } + with open(model_temp_path / "config.json", "w") as f: + f.write(json.dumps(config_json)) + + logger.debug("Dependency model Alibaba-NLP/new-impl downloaded and used.") + + def load(self, model_path: str | Path) -> None: + """Load the model from the specified path. + + Args: + model_path: Path to the model directory. + """ + start_time = time.perf_counter() + logger.info(f"Loading model from: {model_path}") + model_path = Path(model_path) + + # ensure model exists locally + if not model_path.exists(): + raise FileNotFoundError(f"Model not found at path: {model_path}") + + # load local model and tokenizer + self._model = AutoModelForMaskedLM.from_pretrained( + model_path, + trust_remote_code=True, + local_files_only=True, + ) + self._tokenizer = AutoTokenizer.from_pretrained( # type: ignore[no-untyped-call] + model_path, + local_files_only=True, + ) + + # setup special tokens + self._special_token_ids = [ + self._tokenizer.vocab[str(token)] + for token in self._tokenizer.special_tokens_map.values() + ] + + # setup id_to_token mapping + self._id_to_token = ["" for _ in range(self._tokenizer.vocab_size)] + for token, token_id in self._tokenizer.vocab.items(): + self._id_to_token[token_id] = token + + logger.info(f"Model loaded successfully, {time.perf_counter()-start_time}s") diff --git a/pyproject.toml b/pyproject.toml index be5445b..3e0bb33 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -12,6 +12,8 @@ dependencies = [ "huggingface-hub>=0.26.0", "sentry-sdk>=2.34.1", "timdex-dataset-api", + "torch>=2.9.0", + "transformers>=4.57.1", ] [dependency-groups] @@ -32,7 +34,10 @@ line-length = 90 [tool.mypy] disallow_untyped_calls = true disallow_untyped_defs = true -exclude = ["tests/"] +exclude = [ + "tests/", + "output/" +] [tool.pytest.ini_options] log_level = "INFO" @@ -101,3 +106,6 @@ embeddings = "embeddings.cli:main" [build-system] requires = ["setuptools>=61"] build-backend = "setuptools.build_meta" + +[tool.setuptools] +py-modules = ["embeddings"] diff --git a/tests/conftest.py b/tests/conftest.py index 5ec25e9..7eb9262 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,3 +1,5 @@ +import json +import logging import zipfile from pathlib import Path @@ -6,6 +8,8 @@ from embeddings.models.base import BaseEmbeddingModel +logger = logging.getLogger(__name__) + @pytest.fixture(autouse=True) def _test_env(monkeypatch): @@ -32,8 +36,125 @@ def download(self, output_path: Path) -> Path: zf.writestr("tokenizer.json", '{"version": "1.0"}') return output_path + def load(self, model_path: str | Path) -> None: # noqa: ARG002 + logger.info("Model loaded successfully, 1.5s") + @pytest.fixture def mock_model(): """Fixture providing a MockEmbeddingModel instance.""" return MockEmbeddingModel() + + +@pytest.fixture +def neural_sparse_doc_v3_gte_fake_model_directory(tmp_path): + """Create a fake downloaded model directory with required files.""" + model_dir = tmp_path / "fake_model" + model_dir.mkdir() + + # create config.json + config_json = { + "model_type": "distilbert", + "vocab_size": 30000, + "auto_map": { + "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig", + "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", + }, + } + (model_dir / "config.json").write_text(json.dumps(config_json)) + + # create modeling.py and configuration.py + (model_dir / "modeling.py").write_text("# mock modeling code") + (model_dir / "configuration.py").write_text("# mock configuration code") + + # create tokenizer files + (model_dir / "tokenizer.json").write_text('{"version": "1.0"}') + (model_dir / "vocab.txt").write_text("word1\nword2\n") + + return model_dir + + +@pytest.fixture +def neural_sparse_doc_v3_gte_mock_huggingface_snapshot(monkeypatch, tmp_path): + """Mock snapshot_download to create fake model files locally.""" + + def mock_snapshot(repo_id, local_dir, **kwargs): + """Create fake model files based on repo_id.""" + local_path = Path(local_dir) + local_path.mkdir(parents=True, exist_ok=True) + + if repo_id == "opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte": + # create main model files + config_json = { + "model_type": "distilbert", + "vocab_size": 30000, + "auto_map": { + "AutoConfig": "Alibaba-NLP/new-impl--configuration.NewConfig", + "AutoModel": "Alibaba-NLP/new-impl--modeling.NewModel", + }, + } + (local_path / "config.json").write_text(json.dumps(config_json)) + (local_path / "pytorch_model.bin").write_bytes(b"fake weights") + (local_path / "tokenizer.json").write_text('{"version": "1.0"}') + + elif repo_id == "Alibaba-NLP/new-impl": + # create alibaba dependency files + (local_path / "modeling.py").write_text("# Alibaba modeling code") + (local_path / "configuration.py").write_text("# Alibaba configuration code") + + return str(local_path) + + monkeypatch.setattr( + "embeddings.models.os_neural_sparse_doc_v3_gte.snapshot_download", mock_snapshot + ) + return mock_snapshot + + +@pytest.fixture +def neural_sparse_doc_v3_gte_mock_transformers_models(monkeypatch): + """Mock AutoModelForMaskedLM and AutoTokenizer.""" + + class MockTokenizer: + """Mock tokenizer with necessary attributes.""" + + def __init__(self, *args, **kwargs): # noqa: ARG002 + self.vocab = { + "[CLS]": 0, + "[SEP]": 1, + "[PAD]": 2, + "word1": 3, + "word2": 4, + } + self.vocab_size = len(self.vocab) + self.special_tokens_map = { + "cls_token": "[CLS]", + "sep_token": "[SEP]", + "pad_token": "[PAD]", + } + + class MockModel: + """Mock model with necessary attributes.""" + + def __init__(self, *args, **kwargs): # noqa: ARG002 + self.config = {"vocab_size": 30000} + + class MockAutoTokenizer: + @staticmethod + def from_pretrained(*args, **kwargs): # noqa: ARG004 + return MockTokenizer() + + class MockAutoModelForMaskedLM: + @staticmethod + def from_pretrained(*args, **kwargs): # noqa: ARG004 + return MockModel() + + monkeypatch.setattr( + "embeddings.models.os_neural_sparse_doc_v3_gte.AutoTokenizer", + MockAutoTokenizer, + ) + monkeypatch.setattr( + "embeddings.models.os_neural_sparse_doc_v3_gte.AutoModelForMaskedLM", + MockAutoModelForMaskedLM, + ) + + return {"tokenizer": MockTokenizer, "model": MockModel} diff --git a/tests/test_os_neural_sparse_doc_v3_gte.py b/tests/test_os_neural_sparse_doc_v3_gte.py new file mode 100644 index 0000000..06268fd --- /dev/null +++ b/tests/test_os_neural_sparse_doc_v3_gte.py @@ -0,0 +1,220 @@ +"""Tests for OSNeuralSparseDocV3GTE embedding model.""" + +# ruff: noqa: SLF001, PLR2004 + +import json +from pathlib import Path + +import pytest + +from embeddings.models.os_neural_sparse_doc_v3_gte import OSNeuralSparseDocV3GTE + + +def test_init(): + """Test model initialization.""" + model = OSNeuralSparseDocV3GTE() + assert model._model is None + assert model._tokenizer is None + assert model._special_token_ids is None + assert model._id_to_token is None + + +def test_model_uri(): + """Test model_uri property returns correct HuggingFace URI.""" + model = OSNeuralSparseDocV3GTE() + assert ( + model.model_uri + == "opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte" + ) + assert ( + model.MODEL_URI + == "opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte" + ) + + +def test_download_to_directory( + neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path +): + """Test download to directory (not zip).""" + model = OSNeuralSparseDocV3GTE() + output_path = tmp_path / "model_output" + + result = model.download(output_path) + + assert result == output_path + assert output_path.exists() + assert (output_path / "config.json").exists() + assert (output_path / "pytorch_model.bin").exists() + assert (output_path / "tokenizer.json").exists() + + +def test_download_to_zip_file( + neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path +): + """Test download creates zip when path ends in .zip.""" + model = OSNeuralSparseDocV3GTE() + output_path = tmp_path / "model.zip" + + result = model.download(output_path) + + assert result == output_path + assert output_path.exists() + assert output_path.suffix == ".zip" + + +def test_download_calls_patch_method( + neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path, monkeypatch +): + """Test that download calls the Alibaba patching method.""" + model = OSNeuralSparseDocV3GTE() + output_path = tmp_path / "model_output" + + patch_called = False + + def mock_patch(temp_path): + nonlocal patch_called + patch_called = True + + monkeypatch.setattr(model, "_patch_local_model_with_alibaba_new_impl", mock_patch) + + model.download(output_path) + + assert patch_called + + +def test_download_returns_path( + neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path +): + """Test download returns the output path.""" + model = OSNeuralSparseDocV3GTE() + output_path = tmp_path / "model_output" + + result = model.download(output_path) + + assert result == output_path + assert isinstance(result, Path) + + +def test_patch_downloads_alibaba_model( + neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path +): + """Test patch method downloads Alibaba-NLP/new-impl.""" + model = OSNeuralSparseDocV3GTE() + model_temp_path = tmp_path / "temp_model" + model_temp_path.mkdir() + (model_temp_path / "config.json").write_text('{"model_type": "test"}') + + model._patch_local_model_with_alibaba_new_impl(model_temp_path) + + assert (model_temp_path / "modeling.py").exists() + assert (model_temp_path / "configuration.py").exists() + + +def test_patch_copies_files(neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path): + """Test patch copies modeling.py and configuration.py.""" + model = OSNeuralSparseDocV3GTE() + model_temp_path = tmp_path / "temp_model" + model_temp_path.mkdir() + (model_temp_path / "config.json").write_text('{"model_type": "test"}') + + model._patch_local_model_with_alibaba_new_impl(model_temp_path) + + modeling_content = (model_temp_path / "modeling.py").read_text() + config_content = (model_temp_path / "configuration.py").read_text() + + assert "Alibaba modeling code" in modeling_content + assert "Alibaba configuration code" in config_content + + +def test_patch_updates_config_json( + neural_sparse_doc_v3_gte_mock_huggingface_snapshot, tmp_path +): + """Test patch updates auto_map in config.json.""" + model = OSNeuralSparseDocV3GTE() + model_temp_path = tmp_path / "temp_model" + model_temp_path.mkdir() + initial_config = {"model_type": "test", "vocab_size": 30000} + (model_temp_path / "config.json").write_text(json.dumps(initial_config)) + + model._patch_local_model_with_alibaba_new_impl(model_temp_path) + + updated_config = json.loads((model_temp_path / "config.json").read_text()) + + assert "auto_map" in updated_config + assert updated_config["auto_map"]["AutoConfig"] == "configuration.NewConfig" + assert updated_config["auto_map"]["AutoModel"] == "modeling.NewModel" + assert updated_config["auto_map"]["AutoModelForMaskedLM"] == "modeling.NewForMaskedLM" + + +def test_load_success( + neural_sparse_doc_v3_gte_fake_model_directory, + neural_sparse_doc_v3_gte_mock_transformers_models, +): + """Test successful load from local path.""" + model = OSNeuralSparseDocV3GTE() + + model.load(neural_sparse_doc_v3_gte_fake_model_directory) + + assert model._model is not None + assert model._tokenizer is not None + + +def test_load_file_not_found(): + """Test load raises FileNotFoundError for missing path.""" + model = OSNeuralSparseDocV3GTE() + nonexistent_path = Path("/nonexistent/path") + + with pytest.raises(FileNotFoundError, match="Model not found at path"): + model.load(nonexistent_path) + + +def test_load_initializes_model_and_tokenizer( + neural_sparse_doc_v3_gte_fake_model_directory, + neural_sparse_doc_v3_gte_mock_transformers_models, +): + """Test load initializes _model and _tokenizer attributes.""" + model = OSNeuralSparseDocV3GTE() + + assert model._model is None + assert model._tokenizer is None + + model.load(neural_sparse_doc_v3_gte_fake_model_directory) + + assert model._model is not None + assert model._tokenizer is not None + + +def test_load_sets_up_special_token_ids( + neural_sparse_doc_v3_gte_fake_model_directory, + neural_sparse_doc_v3_gte_mock_transformers_models, +): + """Test load sets up _special_token_ids list.""" + model = OSNeuralSparseDocV3GTE() + + model.load(neural_sparse_doc_v3_gte_fake_model_directory) + + assert model._special_token_ids is not None + assert isinstance(model._special_token_ids, list) + assert len(model._special_token_ids) == 3 # CLS, SEP, PAD + assert 0 in model._special_token_ids # [CLS] token id + assert 1 in model._special_token_ids # [SEP] token id + assert 2 in model._special_token_ids # [PAD] token id + + +def test_load_sets_up_id_to_token_mapping( + neural_sparse_doc_v3_gte_fake_model_directory, + neural_sparse_doc_v3_gte_mock_transformers_models, +): + """Test load creates _id_to_token mapping correctly.""" + model = OSNeuralSparseDocV3GTE() + + model.load(neural_sparse_doc_v3_gte_fake_model_directory) + + assert model._id_to_token is not None + assert isinstance(model._id_to_token, list) + assert len(model._id_to_token) == 5 # vocab_size from mock + assert model._id_to_token[0] == "[CLS]" + assert model._id_to_token[1] == "[SEP]" + assert model._id_to_token[2] == "[PAD]" + assert model._id_to_token[3] == "word1" + assert model._id_to_token[4] == "word2" diff --git a/uv.lock b/uv.lock index e973a57..072bb83 100644 --- a/uv.lock +++ b/uv.lock @@ -538,6 +538,18 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/c0/5a/9cac0c82afec3d09ccd97c8b6502d48f165f9124db81b4bcb90b4af974ee/jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9", size = 1572278, upload-time = "2024-11-11T01:41:40.175Z" }, ] +[[package]] +name = "jinja2" +version = "3.1.6" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "markupsafe" }, +] +sdist = { url = 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