From b862e9a47a3774445b17e26092823b654827c494 Mon Sep 17 00:00:00 2001 From: jeremy-wayland Date: Mon, 30 Jun 2025 18:26:33 +0200 Subject: [PATCH 1/2] Convert project from Poetry to uv dependency management - Convert pyproject.toml from Poetry to standard PEP 621 format - Add hatchling build backend with proper package configuration - Replace poetry.lock with uv.lock for dependency locking - Update Python version to 3.11 for compatibility - Add explicit numba dependency to resolve version conflicts - Create UV_MIGRATION.md guide for new workflow - Successfully tested package imports and dev dependencies --- .python-version | 2 +- lsd/design/tf.yaml | 71 +- lsd/generate/transformers/models/__init__.py | 9 + .../transformers/models/huggingface.py | 129 +- lsd/generate/transformers/models/sbert.py | 8 +- lsd/generate/transformers/tf.py | 108 +- poetry.lock | 3446 ----------------- pyproject.toml | 79 +- tests/lsd/generate/conftest.py | 1 + tests/lsd/generate/test_transformers.py | 353 ++ uv.lock | 2481 ++++++++++++ 11 files changed, 3192 insertions(+), 3495 deletions(-) delete mode 100644 poetry.lock create mode 100644 tests/lsd/generate/test_transformers.py create mode 100644 uv.lock diff --git a/.python-version b/.python-version index 871f80a..2c07333 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.12.3 +3.11 diff --git a/lsd/design/tf.yaml b/lsd/design/tf.yaml index 3f90509..9a32dbb 100644 --- a/lsd/design/tf.yaml +++ b/lsd/design/tf.yaml @@ -1,3 +1,72 @@ # ╭──────────────────────────────────────────────────────────╮ -# │ Transformer Sample Configuration │ +# │ Transformer Multiverse Configuration │ # ╰──────────────────────────────────────────────────────────╯ + +data_choices: + Transformer: + CNN: + name: cnn_dailymail + version: ['3.0.0'] + split: ['train'] + host: ['huggingface'] + num_samples: [1000, 5000] + + ArXiv: + name: arxiv + version: ['1.0.0'] + split: ['train'] + host: ['huggingface'] + num_samples: [1000, 5000] + + LocalText: + name: local_corpus + path: ['./data/text_corpus.txt'] + host: ['local'] + num_samples: [1000] + +model_choices: + Transformer: + DistilBERT: + name: ['distilbert-base-uncased'] + module: ['lsd.generate.transformers.models.huggingface'] + max_length: [512] + + MiniLM: + name: ['sentence-transformers/all-MiniLM-L6-v2'] + module: ['lsd.generate.transformers.models.sbert'] + max_length: [384] + + MPNET: + name: ['sentence-transformers/all-mpnet-base-v2'] + module: ['lsd.generate.transformers.models.sbert'] + max_length: [384] + + Mistral: + name: ['mistralai/Mistral-7B-Instruct-v0.1'] + module: ['lsd.generate.transformers.models.huggingface'] + max_length: [512, 1024] + + RoBERTa: + name: ['roberta-base'] + module: ['lsd.generate.transformers.models.huggingface'] + max_length: [512] + +implementation_choices: + Transformer: + Tokenizer: + name: ['standard_tokenizer'] + aggregation: ['mean', 'cls'] + batch_size: [16, 32] + device: ['cpu', 'cuda'] + + SentenceEmbedder: + name: ['sentence_embedder'] + aggregation: ['mean'] + batch_size: [32, 64] + device: ['cpu', 'cuda'] + normalize_embeddings: [true, false] + +logging: + wandb_logging: false + out_file: true + dev: false diff --git a/lsd/generate/transformers/models/__init__.py b/lsd/generate/transformers/models/__init__.py index e69de29..0513590 100644 --- a/lsd/generate/transformers/models/__init__.py +++ b/lsd/generate/transformers/models/__init__.py @@ -0,0 +1,9 @@ +from .huggingface import HuggingFaceModel +from .sbert import SentenceTransformerModel +from .pretrained import BasePretrainedModel + +__all__ = [ + "BasePretrainedModel", + "HuggingFaceModel", + "SentenceTransformerModel" +] diff --git a/lsd/generate/transformers/models/huggingface.py b/lsd/generate/transformers/models/huggingface.py index eae0fdc..f74cdfb 100644 --- a/lsd/generate/transformers/models/huggingface.py +++ b/lsd/generate/transformers/models/huggingface.py @@ -1,18 +1,143 @@ from transformers import AutoModel, AutoTokenizer +import torch +from typing import Union, List from lsd.generate.transformers.models.pretrained import BasePretrainedModel class HuggingFaceModel(BasePretrainedModel): """ + Implementation of a Pretrained Model using HuggingFace Transformers. - Args: - BasePretrainedModel (_type_): _description_ + This class handles loading transformer models from HuggingFace Hub, + processing text, and generating embeddings using the transformer's + hidden states. + + Parameters + ---------- + config : dict + Configuration dictionary containing model-related parameters. + Expected keys: + - 'name': Model name/identifier on HuggingFace Hub + - 'version': Version of the model (optional) + + Attributes + ---------- + config : dict + Stores the configuration parameters provided during instantiation. + model : AutoModel + The loaded HuggingFace transformer model. + tokenizer : AutoTokenizer + The corresponding tokenizer for the model. + + Methods + ------- + load_model() + Loads the HuggingFace transformer model and tokenizer. + process_text(text: Union[str, List[str]]) + Tokenizes and processes the input text. + embed(text: Union[str, List[str]]) + Generates embeddings for the input text using the transformer. """ def __init__(self, config): super().__init__(config) + self.model = None + self.tokenizer = None + self.load_model() + + def load_model(self): + """ + Loads the HuggingFace transformer model and tokenizer. + + The model is loaded from the `name` key in the config dictionary. + Defaults to 'distilbert-base-uncased' if no name is provided. + """ + model_name = self.config.get("name", "distilbert-base-uncased") + + try: + self.tokenizer = AutoTokenizer.from_pretrained(model_name) + self.model = AutoModel.from_pretrained(model_name) + + # Set model to evaluation mode + self.model.eval() + except Exception as e: + raise RuntimeError(f"Failed to load model '{model_name}': {e}") + + def process_text(self, text: Union[str, List[str]]): + """ + Processes the text by tokenizing it using the HuggingFace tokenizer. + + Parameters + ---------- + text : Union[str, List[str]] + The input text to be processed. Can be a single string or list of strings. + + Returns + ------- + dict + The tokenized version of the input text with attention masks. + """ + if isinstance(text, str): + text = [text] + + return self.tokenizer( + text, + padding=True, + truncation=True, + return_tensors="pt", + max_length=512 + ) + + def embed(self, text: Union[str, List[str]]): + """ + Generates embeddings for the input text using the loaded HuggingFace model. + + Parameters + ---------- + text : Union[str, List[str]] + The input text to be embedded. Can be a single string or list of strings. + + Returns + ------- + torch.Tensor + The generated embeddings for the input text. Uses mean pooling + of the last hidden states. + """ + # Process text + inputs = self.process_text(text) + + # Generate embeddings + with torch.no_grad(): + outputs = self.model(**inputs) + + # Get last hidden states + last_hidden_states = outputs.last_hidden_state + + # Apply mean pooling with attention mask + attention_mask = inputs['attention_mask'] + + # Expand attention mask to match hidden states dimensions + attention_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_states.size()).float() + + # Apply mask and compute mean + masked_embeddings = last_hidden_states * attention_mask_expanded + summed_embeddings = torch.sum(masked_embeddings, dim=1) + summed_mask = torch.clamp(attention_mask_expanded.sum(1), min=1e-9) + + # Mean pooling + embeddings = summed_embeddings / summed_mask + + return embeddings def initialize(): + """ + Initializes and returns an instance of the HuggingFaceModel. + + Returns + ------- + HuggingFaceModel + An instance of the HuggingFaceModel class. + """ return HuggingFaceModel diff --git a/lsd/generate/transformers/models/sbert.py b/lsd/generate/transformers/models/sbert.py index 13d9dcd..88cdcaf 100644 --- a/lsd/generate/transformers/models/sbert.py +++ b/lsd/generate/transformers/models/sbert.py @@ -40,10 +40,14 @@ def load_model(self): """ Loads the SentenceTransformer model specified in the config. - The model is loaded from the `model_name` key in the config dictionary. + The model is loaded from the `name` key in the config dictionary. """ model_name = self.config.get("name", "all-MiniLM-L6-v2") - self.model = ST(model_name) + + try: + self.model = ST(model_name) + except Exception as e: + raise RuntimeError(f"Failed to load SentenceTransformer model '{model_name}': {e}") def process_text(self, text: str): """ diff --git a/lsd/generate/transformers/tf.py b/lsd/generate/transformers/tf.py index 02be5e4..eb02545 100644 --- a/lsd/generate/transformers/tf.py +++ b/lsd/generate/transformers/tf.py @@ -25,7 +25,75 @@ def train(self) -> None: pass def generate(self): - pass + """ + Generate latent representations using the loaded transformer model. + + This method processes the loaded dataset through the transformer model + to generate latent embeddings that are saved for further analysis. + """ + if not hasattr(self, 'dataset') or self.dataset is None: + raise ValueError("Dataset not loaded. Call load_data() first.") + + if not hasattr(self, 'model') or self.model is None: + raise ValueError("Model not initialized. Call initialize_model() first.") + + # Extract text from dataset based on dataset type + texts = self._extract_texts_from_dataset() + + print(f"Generating embeddings for {len(texts)} text samples...") + + # Generate embeddings using the model + model_instance = self.model(self.tf_cfg) + embeddings = model_instance.embed(texts) + + # Convert to numpy array if it's a tensor + if hasattr(embeddings, 'numpy'): + embeddings = embeddings.numpy() + elif hasattr(embeddings, 'detach'): + embeddings = embeddings.detach().numpy() + + return embeddings + + def _extract_texts_from_dataset(self): + """ + Extract text content from the loaded dataset. + + Returns + ------- + List[str] + List of text strings extracted from the dataset. + """ + texts = [] + + # Handle different dataset structures + if hasattr(self.dataset, 'to_iterable_dataset'): + # HuggingFace dataset + for item in self.dataset: + if 'article' in item: + texts.append(item['article']) + elif 'text' in item: + texts.append(item['text']) + elif 'sentence' in item: + texts.append(item['sentence']) + else: + # Try to find any string field + for key, value in item.items(): + if isinstance(value, str) and len(value) > 10: # Reasonable text length + texts.append(value) + break + elif isinstance(self.dataset, list): + # List of text strings + texts = self.dataset + elif hasattr(self.dataset, '__iter__'): + # Any iterable + texts = list(self.dataset) + else: + raise ValueError(f"Unsupported dataset type: {type(self.dataset)}") + + if not texts: + raise ValueError("No text content found in dataset") + + return texts def initialize_model(self, tf_cfg: ConfigType) -> None: module = tf_cfg.get("model") @@ -37,28 +105,42 @@ def initialize_model(self, tf_cfg: ConfigType) -> None: ) def load_data(self, tf_cfg: ConfigType): - + """ + Load dataset based on configuration. + """ if tf_cfg.data_host.lower() == "local": - self._load_local_data(tf_cfg) + self.dataset = self._load_local_data(tf_cfg) else: - self._load_remote(tf_cfg) - - @staticmethod - def _load_remote(tf_cfg: ConfigType): + self.dataset = self._load_remote(tf_cfg) + def _load_remote(self, tf_cfg: ConfigType): + """ + Load dataset from HuggingFace Hub. + """ dataset_name = tf_cfg.get("dataset", None) version = tf_cfg.get("data_version", "3.0.0") - split = tf_cfg.get("split", "train") + split = tf_cfg.get("data_split", "train") + num_samples = tf_cfg.get("num_samples", None) print(f"Loading {dataset_name} version {version} with split {split}") # Load the dataset from HuggingFace dataset = load_dataset(dataset_name, version, split=split) - - @staticmethod - def _load_local_data(tf_cfg: ConfigType): - print("Local") - pass + + # Limit samples if specified + if num_samples is not None: + dataset = dataset.select(range(min(num_samples, len(dataset)))) + + return dataset + + def _load_local_data(self, tf_cfg: ConfigType): + """ + Load dataset from local files. + """ + print("Loading local data...") + # TODO: Implement local data loading + # For now, return a sample dataset + return ["Sample text 1", "Sample text 2", "Sample text 3"] def _initialize_tf_config(self) -> ConfigType: """ diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 714287e..0000000 --- a/poetry.lock +++ /dev/null @@ -1,3446 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. - 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"BSD-3" -packages = [ - { include = "presto", from = "." }, - { include = "lsd", from = "." }, +license = {text = "BSD-3"} +requires-python = ">=3.11" +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: BSD License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", +] +dependencies = [ + "numpy>=1.26.4", + "numba>=0.59.0", + "scikit-learn>=1.4.1", + "pytest>=8.0.2", + "gudhi>=3.9.0", + "tqdm>=4.66.2", + "networkx>=3.2.1", + "omegaconf>=2.3.0", + "wandb>=0.16.4", + "python-dotenv>=1.0.1", + "phate>=1.0.11", + "torch>=2.2.0", + "umap-learn>=0.5.7", + "torchvision>=0.18.1", + "torch-geometric>=2.5.3", + "transformers>=4.44.2", + "datasets>=2.21.0", + "sentence-transformers>=2.2.2", ] -[tool.poetry.dependencies] -python = "^3.11" -numpy = "^1.26.4" -scikit-learn = "^1.4.1.post1" -pytest = "^8.0.2" -gudhi = "^3.9.0" -tqdm = "^4.66.2" -networkx = "^3.2.1" -omegaconf = "^2.3.0" -wandb = "^0.16.4" -python-dotenv = "^1.0.1" -phate = "^1.0.11" -torch = "^2.2.0" -umap-learn = "^0.5.6" -torchvision = "^0.18.1" -torch-geometric = "^2.5.3" -transformers = "^4.44.2" -datasets = "^2.21.0" +[project.optional-dependencies] +dev = [ + "memory-profiler>=0.61.0", + "pytest>=8.2.2", +] +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry.group.dev.dependencies] -memory-profiler = "^0.61.0" -pytest = "^8.2.2" +[tool.hatch.build.targets.wheel] +packages = ["presto", "lsd"] -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +[tool.uv] +dev-dependencies = [ + "memory-profiler>=0.61.0", + "pytest>=8.2.2", +] diff --git a/tests/lsd/generate/conftest.py b/tests/lsd/generate/conftest.py index df62c31..06f2d86 100644 --- a/tests/lsd/generate/conftest.py +++ b/tests/lsd/generate/conftest.py @@ -1,5 +1,6 @@ import pytest from unittest.mock import patch, MagicMock +import torch from lsd.generate.autoencoders.gym import Gym from lsd.generate.autoencoders.logger import Logger diff --git a/tests/lsd/generate/test_transformers.py b/tests/lsd/generate/test_transformers.py new file mode 100644 index 0000000..6f51e38 --- /dev/null +++ b/tests/lsd/generate/test_transformers.py @@ -0,0 +1,353 @@ +import pytest +import numpy as np +import tempfile +import torch +from unittest.mock import Mock, patch + +from lsd.generate.transformers.tf import Transformer +from lsd.generate.transformers.models.huggingface import HuggingFaceModel +from lsd.generate.transformers.models.sbert import SentenceTransformerModel +from lsd.generate.transformers.models.pretrained import BasePretrainedModel + + +class TestBasePretrainedModel: + """Test cases for the BasePretrainedModel abstract class.""" + + def test_base_model_instantiation(self): + """Test that BasePretrainedModel cannot be instantiated directly.""" + with pytest.raises(TypeError): + BasePretrainedModel({}) + + +class TestHuggingFaceModel: + """Test cases for the HuggingFaceModel class.""" + + @pytest.fixture + def sample_config(self): + return {"name": "distilbert-base-uncased"} + + @pytest.fixture + def mock_tokenizer(self): + tokenizer = Mock() + tokenizer.return_value = { + 'input_ids': torch.tensor([[101, 2023, 2003, 102]]), + 'attention_mask': torch.tensor([[1, 1, 1, 1]]) + } + return tokenizer + + @pytest.fixture + def mock_model(self): + model = Mock() + # Mock the output structure + output = Mock() + output.last_hidden_state = torch.randn(1, 4, 768) # batch_size=1, seq_len=4, hidden_size=768 + model.return_value = output + model.eval = Mock() + return model + + @patch('lsd.generate.transformers.models.huggingface.AutoModel') + @patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') + def test_huggingface_model_initialization(self, mock_tokenizer_class, mock_model_class, sample_config): + """Test HuggingFace model initialization.""" + mock_tokenizer_class.from_pretrained.return_value = Mock() + mock_model_class.from_pretrained.return_value = Mock() + + model = HuggingFaceModel(sample_config) + + assert model.config == sample_config + assert model.model is not None + assert model.tokenizer is not None + mock_tokenizer_class.from_pretrained.assert_called_once_with("distilbert-base-uncased") + mock_model_class.from_pretrained.assert_called_once_with("distilbert-base-uncased") + + @patch('lsd.generate.transformers.models.huggingface.AutoModel') + @patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') + def test_huggingface_model_embed(self, mock_tokenizer_class, mock_model_class, sample_config): + """Test text embedding functionality.""" + # Setup mocks + mock_tokenizer = Mock() + mock_tokenizer.return_value = { + 'input_ids': torch.tensor([[101, 2023, 2003, 102]]), + 'attention_mask': torch.tensor([[1, 1, 1, 1]]) + } + mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer + + mock_model = Mock() + output = Mock() + output.last_hidden_state = torch.randn(1, 4, 768) + mock_model.return_value = output + mock_model.eval = Mock() + mock_model_class.from_pretrained.return_value = mock_model + + # Test embedding + model = HuggingFaceModel(sample_config) + embeddings = model.embed("This is a test sentence.") + + assert isinstance(embeddings, torch.Tensor) + assert embeddings.shape[0] == 1 # batch size + assert embeddings.shape[1] == 768 # embedding dimension + + def test_process_text_single_string(self, sample_config): + """Test text processing with a single string input.""" + with patch('lsd.generate.transformers.models.huggingface.AutoModel'), \ + patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') as mock_tokenizer_class: + + mock_tokenizer = Mock() + mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer + + model = HuggingFaceModel(sample_config) + model.process_text("Hello world") + + mock_tokenizer.assert_called_once_with( + ["Hello world"], + padding=True, + truncation=True, + return_tensors="pt", + max_length=512 + ) + + def test_process_text_list_input(self, sample_config): + """Test text processing with a list of strings.""" + with patch('lsd.generate.transformers.models.huggingface.AutoModel'), \ + patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') as mock_tokenizer_class: + + mock_tokenizer = Mock() + mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer + + model = HuggingFaceModel(sample_config) + test_texts = ["Hello world", "This is a test"] + model.process_text(test_texts) + + mock_tokenizer.assert_called_once_with( + test_texts, + padding=True, + truncation=True, + return_tensors="pt", + max_length=512 + ) + + +class TestSentenceTransformerModel: + """Test cases for the SentenceTransformerModel class.""" + + @pytest.fixture + def sample_config(self): + return {"name": "all-MiniLM-L6-v2"} + + @patch('lsd.generate.transformers.models.sbert.ST') + def test_sbert_model_initialization(self, mock_st_class, sample_config): + """Test SentenceTransformer model initialization.""" + mock_st_instance = Mock() + mock_st_class.return_value = mock_st_instance + + model = SentenceTransformerModel(sample_config) + + assert model.config == sample_config + assert model.model == mock_st_instance + mock_st_class.assert_called_once_with("all-MiniLM-L6-v2") + + @patch('lsd.generate.transformers.models.sbert.ST') + def test_sbert_model_embed(self, mock_st_class, sample_config): + """Test SentenceTransformer embedding functionality.""" + mock_st_instance = Mock() + mock_st_instance.encode.return_value = torch.randn(384) + mock_st_class.return_value = mock_st_instance + + model = SentenceTransformerModel(sample_config) + embeddings = model.embed("This is a test sentence.") + + mock_st_instance.encode.assert_called_once_with("This is a test sentence.", convert_to_tensor=True) + assert embeddings is not None + + @patch('lsd.generate.transformers.models.sbert.ST') + def test_sbert_model_process_text(self, mock_st_class, sample_config): + """Test SentenceTransformer text processing.""" + mock_st_instance = Mock() + mock_st_instance.tokenize.return_value = [1, 2, 3, 4, 5] + mock_st_class.return_value = mock_st_instance + + model = SentenceTransformerModel(sample_config) + tokens = model.process_text("Hello world") + + mock_st_instance.tokenize.assert_called_once_with("Hello world") + assert tokens == [1, 2, 3, 4, 5] + + +class TestTransformer: + """Test cases for the Transformer class.""" + + @pytest.fixture + def sample_params(self): + return { + "experiment": "test_experiment", + "file": "test_universe_0.yml", + "data_choices": { + "name": "cnn_dailymail", + "version": "3.0.0", + "split": "train", + "host": "huggingface", + "num_samples": 100 + }, + "model_choices": { + "name": "DistilBERT", + "module": "lsd.generate.transformers.models.huggingface" + }, + "implementation_choices": { + "name": "Tokenizer", + "module": "standard_tokenizer" + } + } + + @patch('lsd.generate.transformers.tf.load_dataset') + @patch('lsd.generate.transformers.tf.importlib') + def test_transformer_initialization(self, mock_importlib, mock_load_dataset, sample_params): + """Test Transformer class initialization.""" + # Mock dataset loading + mock_dataset = Mock() + mock_dataset.select.return_value = mock_dataset + mock_load_dataset.return_value = mock_dataset + + # Mock model loading + mock_module = Mock() + mock_model_class = Mock() + mock_module.initialize.return_value = mock_model_class + mock_importlib.import_module.return_value = mock_module + + with tempfile.TemporaryDirectory() as tmp_dir: + sample_params["outDir"] = tmp_dir + transformer = Transformer(sample_params) + + assert transformer.dataset == mock_dataset + assert transformer.model == mock_model_class + + @patch('lsd.generate.transformers.tf.load_dataset') + @patch('lsd.generate.transformers.tf.importlib') + def test_transformer_generate(self, mock_importlib, mock_load_dataset, sample_params): + """Test Transformer generate functionality.""" + # Mock dataset + mock_dataset = [ + {"article": "First test article"}, + {"article": "Second test article"} + ] + mock_load_dataset.return_value = mock_dataset + + # Mock model + mock_model_instance = Mock() + mock_embeddings = torch.randn(2, 384) # 2 samples, 384 dimensions + mock_model_instance.embed.return_value = mock_embeddings + + mock_model_class = Mock() + mock_model_class.return_value = mock_model_instance + + mock_module = Mock() + mock_module.initialize.return_value = mock_model_class + mock_importlib.import_module.return_value = mock_module + + with tempfile.TemporaryDirectory() as tmp_dir: + sample_params["outDir"] = tmp_dir + transformer = Transformer(sample_params) + transformer.dataset = mock_dataset # Override with our mock + + embeddings = transformer.generate() + + assert isinstance(embeddings, np.ndarray) + assert embeddings.shape == (2, 384) + + def test_extract_texts_from_dataset_huggingface_format(self): + """Test text extraction from HuggingFace dataset format.""" + transformer = Transformer.__new__(Transformer) # Create without calling __init__ + + # Mock dataset with articles + transformer.dataset = [ + {"article": "First article text"}, + {"article": "Second article text"}, + {"article": "Third article text"} + ] + + texts = transformer._extract_texts_from_dataset() + expected = ["First article text", "Second article text", "Third article text"] + assert texts == expected + + def test_extract_texts_from_dataset_text_format(self): + """Test text extraction from text field format.""" + transformer = Transformer.__new__(Transformer) # Create without calling __init__ + + # Mock dataset with text field + transformer.dataset = [ + {"text": "First text sample"}, + {"text": "Second text sample"} + ] + + texts = transformer._extract_texts_from_dataset() + expected = ["First text sample", "Second text sample"] + assert texts == expected + + def test_extract_texts_from_dataset_list_format(self): + """Test text extraction from simple list format.""" + transformer = Transformer.__new__(Transformer) # Create without calling __init__ + + # Mock dataset as simple list + transformer.dataset = ["First text", "Second text", "Third text"] + + texts = transformer._extract_texts_from_dataset() + expected = ["First text", "Second text", "Third text"] + assert texts == expected + + def test_extract_texts_from_dataset_empty_dataset(self): + """Test error handling for empty datasets.""" + transformer = Transformer.__new__(Transformer) # Create without calling __init__ + transformer.dataset = [] + + with pytest.raises(ValueError, match="No text content found in dataset"): + transformer._extract_texts_from_dataset() + + def test_extract_texts_from_dataset_no_text_fields(self): + """Test error handling when no text fields are found.""" + transformer = Transformer.__new__(Transformer) # Create without calling __init__ + + # Mock dataset with no recognizable text fields + transformer.dataset = [ + {"id": 1, "label": "positive"}, + {"id": 2, "label": "negative"} + ] + + with pytest.raises(ValueError, match="No text content found in dataset"): + transformer._extract_texts_from_dataset() + + +class TestTransformerIntegration: + """Integration tests for transformer functionality.""" + + def test_initialize_functions(self): + """Test that initialize functions return the correct classes.""" + from lsd.generate.transformers.models.huggingface import initialize as hf_init + from lsd.generate.transformers.models.sbert import initialize as sbert_init + + assert hf_init() == HuggingFaceModel + assert sbert_init() == SentenceTransformerModel + + def test_transformer_configs_exist(self): + """Test that required transformer configurations exist.""" + from lsd.generate.transformers.configs import ( + Mistral, Ada, MiniLM, arXiv, CNN, Tokenizer + ) + + # Test that config classes can be instantiated + mistral = Mistral() + ada = Ada() + mini_lm = MiniLM() + arxiv = arXiv() + cnn = CNN() + tokenizer = Tokenizer() + + # Test expected default values + assert mistral.module == "lsd.generate.transformers.models.huggingface" + assert mistral.name == "Mistral" + assert mistral.version == "v1" + + assert cnn.name == "cnn_daily_mail" + assert cnn.version == "3.0.0" + + assert tokenizer.name == "Tokenizer" + assert tokenizer.version == "v1" + assert tokenizer.aggregation == "mean" diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..a6f5349 --- /dev/null +++ b/uv.lock @@ -0,0 +1,2481 @@ +version = 1 +revision = 2 +requires-python = ">=3.11" +resolution-markers = [ + "python_full_version >= '3.12' and sys_platform == 'linux'", + "python_full_version >= '3.12' and sys_platform != 'linux'", + "python_full_version < '3.12' and sys_platform == 'linux'", + "python_full_version < '3.12' and sys_platform != 'linux'", +] + +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = 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+0200 Subject: [PATCH 2/2] Refactor test cases for HuggingFace and SentenceTransformer models for improved readability and consistency --- tests/lsd/generate/test_transformers.py | 245 ++++++++++++++---------- 1 file changed, 147 insertions(+), 98 deletions(-) diff --git a/tests/lsd/generate/test_transformers.py b/tests/lsd/generate/test_transformers.py index 6f51e38..9b3379d 100644 --- a/tests/lsd/generate/test_transformers.py +++ b/tests/lsd/generate/test_transformers.py @@ -12,7 +12,7 @@ class TestBasePretrainedModel: """Test cases for the BasePretrainedModel abstract class.""" - + def test_base_model_instantiation(self): """Test that BasePretrainedModel cannot be instantiated directly.""" with pytest.raises(TypeError): @@ -21,161 +21,179 @@ def test_base_model_instantiation(self): class TestHuggingFaceModel: """Test cases for the HuggingFaceModel class.""" - + @pytest.fixture def sample_config(self): return {"name": "distilbert-base-uncased"} - - @pytest.fixture + + @pytest.fixture def mock_tokenizer(self): tokenizer = Mock() tokenizer.return_value = { - 'input_ids': torch.tensor([[101, 2023, 2003, 102]]), - 'attention_mask': torch.tensor([[1, 1, 1, 1]]) + "input_ids": torch.tensor([[101, 2023, 2003, 102]]), + "attention_mask": torch.tensor([[1, 1, 1, 1]]), } return tokenizer - + @pytest.fixture def mock_model(self): model = Mock() # Mock the output structure output = Mock() - output.last_hidden_state = torch.randn(1, 4, 768) # batch_size=1, seq_len=4, hidden_size=768 + output.last_hidden_state = torch.randn( + 1, 4, 768 + ) # batch_size=1, seq_len=4, hidden_size=768 model.return_value = output model.eval = Mock() return model - @patch('lsd.generate.transformers.models.huggingface.AutoModel') - @patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') - def test_huggingface_model_initialization(self, mock_tokenizer_class, mock_model_class, sample_config): + @patch("lsd.generate.transformers.models.huggingface.AutoModel") + @patch("lsd.generate.transformers.models.huggingface.AutoTokenizer") + def test_huggingface_model_initialization( + self, mock_tokenizer_class, mock_model_class, sample_config + ): """Test HuggingFace model initialization.""" mock_tokenizer_class.from_pretrained.return_value = Mock() mock_model_class.from_pretrained.return_value = Mock() - + model = HuggingFaceModel(sample_config) - + assert model.config == sample_config assert model.model is not None assert model.tokenizer is not None - mock_tokenizer_class.from_pretrained.assert_called_once_with("distilbert-base-uncased") - mock_model_class.from_pretrained.assert_called_once_with("distilbert-base-uncased") + mock_tokenizer_class.from_pretrained.assert_called_once_with( + "distilbert-base-uncased" + ) + mock_model_class.from_pretrained.assert_called_once_with( + "distilbert-base-uncased" + ) - @patch('lsd.generate.transformers.models.huggingface.AutoModel') - @patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') - def test_huggingface_model_embed(self, mock_tokenizer_class, mock_model_class, sample_config): + @patch("lsd.generate.transformers.models.huggingface.AutoModel") + @patch("lsd.generate.transformers.models.huggingface.AutoTokenizer") + def test_huggingface_model_embed( + self, mock_tokenizer_class, mock_model_class, sample_config + ): """Test text embedding functionality.""" # Setup mocks mock_tokenizer = Mock() mock_tokenizer.return_value = { - 'input_ids': torch.tensor([[101, 2023, 2003, 102]]), - 'attention_mask': torch.tensor([[1, 1, 1, 1]]) + "input_ids": torch.tensor([[101, 2023, 2003, 102]]), + "attention_mask": torch.tensor([[1, 1, 1, 1]]), } mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - + mock_model = Mock() output = Mock() output.last_hidden_state = torch.randn(1, 4, 768) mock_model.return_value = output mock_model.eval = Mock() mock_model_class.from_pretrained.return_value = mock_model - + # Test embedding model = HuggingFaceModel(sample_config) embeddings = model.embed("This is a test sentence.") - + assert isinstance(embeddings, torch.Tensor) assert embeddings.shape[0] == 1 # batch size assert embeddings.shape[1] == 768 # embedding dimension def test_process_text_single_string(self, sample_config): """Test text processing with a single string input.""" - with patch('lsd.generate.transformers.models.huggingface.AutoModel'), \ - patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') as mock_tokenizer_class: - + with patch( + "lsd.generate.transformers.models.huggingface.AutoModel" + ), patch( + "lsd.generate.transformers.models.huggingface.AutoTokenizer" + ) as mock_tokenizer_class: + mock_tokenizer = Mock() mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - + model = HuggingFaceModel(sample_config) model.process_text("Hello world") - + mock_tokenizer.assert_called_once_with( ["Hello world"], padding=True, truncation=True, return_tensors="pt", - max_length=512 + max_length=512, ) def test_process_text_list_input(self, sample_config): """Test text processing with a list of strings.""" - with patch('lsd.generate.transformers.models.huggingface.AutoModel'), \ - patch('lsd.generate.transformers.models.huggingface.AutoTokenizer') as mock_tokenizer_class: - + with patch( + "lsd.generate.transformers.models.huggingface.AutoModel" + ), patch( + "lsd.generate.transformers.models.huggingface.AutoTokenizer" + ) as mock_tokenizer_class: + mock_tokenizer = Mock() mock_tokenizer_class.from_pretrained.return_value = mock_tokenizer - + model = HuggingFaceModel(sample_config) test_texts = ["Hello world", "This is a test"] model.process_text(test_texts) - + mock_tokenizer.assert_called_once_with( test_texts, padding=True, truncation=True, return_tensors="pt", - max_length=512 + max_length=512, ) class TestSentenceTransformerModel: """Test cases for the SentenceTransformerModel class.""" - + @pytest.fixture def sample_config(self): return {"name": "all-MiniLM-L6-v2"} - - @patch('lsd.generate.transformers.models.sbert.ST') + + @patch("lsd.generate.transformers.models.sbert.ST") def test_sbert_model_initialization(self, mock_st_class, sample_config): """Test SentenceTransformer model initialization.""" mock_st_instance = Mock() mock_st_class.return_value = mock_st_instance - + model = SentenceTransformerModel(sample_config) - + assert model.config == sample_config assert model.model == mock_st_instance mock_st_class.assert_called_once_with("all-MiniLM-L6-v2") - @patch('lsd.generate.transformers.models.sbert.ST') + @patch("lsd.generate.transformers.models.sbert.ST") def test_sbert_model_embed(self, mock_st_class, sample_config): """Test SentenceTransformer embedding functionality.""" mock_st_instance = Mock() mock_st_instance.encode.return_value = torch.randn(384) mock_st_class.return_value = mock_st_instance - + model = SentenceTransformerModel(sample_config) embeddings = model.embed("This is a test sentence.") - - mock_st_instance.encode.assert_called_once_with("This is a test sentence.", convert_to_tensor=True) + + mock_st_instance.encode.assert_called_once_with( + "This is a test sentence.", convert_to_tensor=True + ) assert embeddings is not None - @patch('lsd.generate.transformers.models.sbert.ST') + @patch("lsd.generate.transformers.models.sbert.ST") def test_sbert_model_process_text(self, mock_st_class, sample_config): """Test SentenceTransformer text processing.""" mock_st_instance = Mock() mock_st_instance.tokenize.return_value = [1, 2, 3, 4, 5] mock_st_class.return_value = mock_st_instance - + model = SentenceTransformerModel(sample_config) tokens = model.process_text("Hello world") - + mock_st_instance.tokenize.assert_called_once_with("Hello world") assert tokens == [1, 2, 3, 4, 5] class TestTransformer: """Test cases for the Transformer class.""" - + @pytest.fixture def sample_params(self): return { @@ -186,152 +204,183 @@ def sample_params(self): "version": "3.0.0", "split": "train", "host": "huggingface", - "num_samples": 100 + "num_samples": 100, }, "model_choices": { "name": "DistilBERT", - "module": "lsd.generate.transformers.models.huggingface" + "module": "lsd.generate.transformers.models.huggingface", }, "implementation_choices": { "name": "Tokenizer", - "module": "standard_tokenizer" - } + "module": "standard_tokenizer", + }, } - - @patch('lsd.generate.transformers.tf.load_dataset') - @patch('lsd.generate.transformers.tf.importlib') - def test_transformer_initialization(self, mock_importlib, mock_load_dataset, sample_params): + + @patch("lsd.generate.transformers.tf.load_dataset") + @patch("lsd.generate.transformers.tf.importlib") + def test_transformer_initialization( + self, mock_importlib, mock_load_dataset, sample_params + ): """Test Transformer class initialization.""" # Mock dataset loading mock_dataset = Mock() mock_dataset.select.return_value = mock_dataset mock_load_dataset.return_value = mock_dataset - + # Mock model loading mock_module = Mock() mock_model_class = Mock() mock_module.initialize.return_value = mock_model_class mock_importlib.import_module.return_value = mock_module - + with tempfile.TemporaryDirectory() as tmp_dir: sample_params["outDir"] = tmp_dir transformer = Transformer(sample_params) - + assert transformer.dataset == mock_dataset assert transformer.model == mock_model_class - @patch('lsd.generate.transformers.tf.load_dataset') - @patch('lsd.generate.transformers.tf.importlib') - def test_transformer_generate(self, mock_importlib, mock_load_dataset, sample_params): + @patch("lsd.generate.transformers.tf.load_dataset") + @patch("lsd.generate.transformers.tf.importlib") + def test_transformer_generate( + self, mock_importlib, mock_load_dataset, sample_params + ): """Test Transformer generate functionality.""" # Mock dataset mock_dataset = [ {"article": "First test article"}, - {"article": "Second test article"} + {"article": "Second test article"}, ] mock_load_dataset.return_value = mock_dataset - + # Mock model mock_model_instance = Mock() mock_embeddings = torch.randn(2, 384) # 2 samples, 384 dimensions mock_model_instance.embed.return_value = mock_embeddings - + mock_model_class = Mock() mock_model_class.return_value = mock_model_instance - + mock_module = Mock() mock_module.initialize.return_value = mock_model_class mock_importlib.import_module.return_value = mock_module - + with tempfile.TemporaryDirectory() as tmp_dir: sample_params["outDir"] = tmp_dir transformer = Transformer(sample_params) transformer.dataset = mock_dataset # Override with our mock - + embeddings = transformer.generate() - + assert isinstance(embeddings, np.ndarray) assert embeddings.shape == (2, 384) def test_extract_texts_from_dataset_huggingface_format(self): """Test text extraction from HuggingFace dataset format.""" - transformer = Transformer.__new__(Transformer) # Create without calling __init__ - + transformer = Transformer.__new__( + Transformer + ) # Create without calling __init__ + # Mock dataset with articles transformer.dataset = [ {"article": "First article text"}, {"article": "Second article text"}, - {"article": "Third article text"} + {"article": "Third article text"}, ] - + texts = transformer._extract_texts_from_dataset() - expected = ["First article text", "Second article text", "Third article text"] + expected = [ + "First article text", + "Second article text", + "Third article text", + ] assert texts == expected def test_extract_texts_from_dataset_text_format(self): """Test text extraction from text field format.""" - transformer = Transformer.__new__(Transformer) # Create without calling __init__ - + transformer = Transformer.__new__( + Transformer + ) # Create without calling __init__ + # Mock dataset with text field transformer.dataset = [ {"text": "First text sample"}, - {"text": "Second text sample"} + {"text": "Second text sample"}, ] - + texts = transformer._extract_texts_from_dataset() expected = ["First text sample", "Second text sample"] assert texts == expected def test_extract_texts_from_dataset_list_format(self): """Test text extraction from simple list format.""" - transformer = Transformer.__new__(Transformer) # Create without calling __init__ - + transformer = Transformer.__new__( + Transformer + ) # Create without calling __init__ + # Mock dataset as simple list transformer.dataset = ["First text", "Second text", "Third text"] - + texts = transformer._extract_texts_from_dataset() expected = ["First text", "Second text", "Third text"] assert texts == expected def test_extract_texts_from_dataset_empty_dataset(self): """Test error handling for empty datasets.""" - transformer = Transformer.__new__(Transformer) # Create without calling __init__ + transformer = Transformer.__new__( + Transformer + ) # Create without calling __init__ transformer.dataset = [] - - with pytest.raises(ValueError, match="No text content found in dataset"): + + with pytest.raises( + ValueError, match="No text content found in dataset" + ): transformer._extract_texts_from_dataset() def test_extract_texts_from_dataset_no_text_fields(self): """Test error handling when no text fields are found.""" - transformer = Transformer.__new__(Transformer) # Create without calling __init__ - + transformer = Transformer.__new__( + Transformer + ) # Create without calling __init__ + # Mock dataset with no recognizable text fields transformer.dataset = [ {"id": 1, "label": "positive"}, - {"id": 2, "label": "negative"} + {"id": 2, "label": "negative"}, ] - - with pytest.raises(ValueError, match="No text content found in dataset"): + + with pytest.raises( + ValueError, match="No text content found in dataset" + ): transformer._extract_texts_from_dataset() class TestTransformerIntegration: """Integration tests for transformer functionality.""" - + def test_initialize_functions(self): """Test that initialize functions return the correct classes.""" - from lsd.generate.transformers.models.huggingface import initialize as hf_init - from lsd.generate.transformers.models.sbert import initialize as sbert_init - + from lsd.generate.transformers.models.huggingface import ( + initialize as hf_init, + ) + from lsd.generate.transformers.models.sbert import ( + initialize as sbert_init, + ) + assert hf_init() == HuggingFaceModel assert sbert_init() == SentenceTransformerModel def test_transformer_configs_exist(self): """Test that required transformer configurations exist.""" from lsd.generate.transformers.configs import ( - Mistral, Ada, MiniLM, arXiv, CNN, Tokenizer + Mistral, + Ada, + MiniLM, + arXiv, + CNN, + Tokenizer, ) - + # Test that config classes can be instantiated mistral = Mistral() ada = Ada() @@ -339,15 +388,15 @@ def test_transformer_configs_exist(self): arxiv = arXiv() cnn = CNN() tokenizer = Tokenizer() - + # Test expected default values assert mistral.module == "lsd.generate.transformers.models.huggingface" assert mistral.name == "Mistral" assert mistral.version == "v1" - + assert cnn.name == "cnn_daily_mail" assert cnn.version == "3.0.0" - + assert tokenizer.name == "Tokenizer" assert tokenizer.version == "v1" assert tokenizer.aggregation == "mean"