chore: import upstream snapshot with attribution

tests/README.md

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+# FlagEmbedding Tests
+
+This directory contains tests for the FlagEmbedding library, including compatibility tests for Transformers 5.0.
+
+## Test Files
+
+- `test_imports_v5.py`: Tests that imports work with Transformers v5, particularly the compatibility layer for `is_torch_fx_available`.
+- `test_infer_embedder_basic.py`: Tests basic functionality of BGE embedder models with a small public checkpoint.
+- `test_infer_reranker_basic.py`: Tests basic functionality of reranker models.
+
+## Running Tests
+
+1. create a python venv `python -m venv pytest_venv`
+2. activate venv  `source pytest_venv/bin/activate`
+3. install pytest `pip install pytest`
+4. install flagembedding package in development mode: `pip install -e .`
+
+Then run the tests using pytest:
+
+```bash
+# Run all tests
+pytest tests/
+
+# Run a specific test file
+pytest tests/test_imports_v5.py
+
+# Run with verbose output
+pytest -v tests/
+```
+
+## Transformers 5.0 Compatibility
+
+The tests verify that FlagEmbedding works with Transformers 5.0, which removed the `is_torch_fx_available` function.
+The compatibility layer in `FlagEmbedding/utils/transformers_compat.py` provides this function for backward compatibility.
+
+**Note:** Transformers 5.0 requires Python 3.10 or higher. If you're using Python 3.9 or lower, you'll need to upgrade your Python version to test with Transformers 5.0.
+
+To test with a specific version of transformers (with Python 3.10+):
+
+```bash
+pip install transformers==5.0.0
+pytest tests/

tests/conftest.py

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+"""
+Common pytest fixtures and configuration for FlagEmbedding tests.
+"""
+
+import os
+import pytest
+import torch
+from packaging import version
+import transformers
+
+# Check if we're using transformers v5+
+TF_VER = version.parse(getattr(transformers, "__version__", "0.0.0"))
+IS_TF_V5_OR_HIGHER = TF_VER >= version.parse("5.0.0")
+
+
+@pytest.fixture(scope="session")
+def device():
+    """Return the device to use for tests."""
+    return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+@pytest.fixture(scope="session")
+def transformers_version():
+    """Return the transformers version."""
+    return TF_VER

tests/test_imports_v5.py

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+"""
+Test that imports work with Transformers v5.
+
+This test verifies that the compatibility layer in FlagEmbedding/utils/transformers_compat.py
+properly handles the the removal of is_torch_fx_available in Transformers v5
+"""
+
+import pytest
+import transformers
+from packaging import version
+
+# Import the compatibility layer
+from FlagEmbedding.utils.transformers_compat import is_torch_fx_available
+
+# Check if we're using transformers v5+
+TF_VER = version.parse(getattr(transformers, "__version__", "0.0.0"))
+IS_TF_V5_OR_HIGHER = TF_VER >= version.parse("5.0.0")
+
+
+# Import the files mentioned in issue #1561 that use is_torch_fx_available
+def test_import_modeling_minicpm_reranker_inference():
+    """Test importing the modeling_minicpm_reranker module from inference."""
+    from FlagEmbedding.inference.reranker.decoder_only.models.modeling_minicpm_reranker import (
+        LayerWiseMiniCPMForCausalLM,
+    )
+
+    assert LayerWiseMiniCPMForCausalLM is not None
+
+
+def test_import_modeling_minicpm_reranker_finetune():
+    """Test importing the modeling_minicpm_reranker module from finetune."""
+    from FlagEmbedding.finetune.reranker.decoder_only.layerwise.modeling_minicpm_reranker import (
+        LayerWiseMiniCPMForCausalLM,
+    )
+
+    assert LayerWiseMiniCPMForCausalLM is not None
+
+
+@pytest.mark.skipif(not IS_TF_V5_OR_HIGHER, reason="Only relevant for Transformers v5+")
+def test_is_torch_fx_available_v5():
+    """Test that is_torch_fx_available works with Transformers v5."""
+    # This should not raise an exception
+    result = is_torch_fx_available()
+    # The result depends on whether torch.fx is available, but the function should work
+    assert isinstance(result, bool)
+
+
+def test_transformers_version(transformers_version):
+    """Test that we can detect the transformers version."""
+    assert transformers_version is not None
+    print(f"Transformers version: {transformers_version}")

tests/test_infer_embedder_basic.py

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+"""
+Test basic functionality of BGE embedder models with Transformers v5.
+
+This test loads a small/public BGE checkpoint and runs a single encode on toy strings,
+verifying that the shape/dtype are correct and that cosine similarity is sane.
+"""
+import pytest
+import torch
+import numpy as np
+from FlagEmbedding import FlagModel
+
+def cosine_similarity(a, b):
+    """Compute cosine similarity between two vectors."""
+    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
+
+def test_bge_embedder_basic(device):
+    """Test basic functionality of BGE embedder."""
+    # Load a small BGE model
+    model_name = "BAAI/bge-base-en-v1.5"
+    model = FlagModel(model_name, device=device)
+    
+    # Test encoding single strings
+    query = "What is the capital of France?"
+    passage = "Paris is the capital and most populous city of France."
+    
+    # Get embeddings
+    query_embedding = model.encode(query)
+    passage_embedding = model.encode(passage)
+    
+    # Check shapes and types
+    assert isinstance(query_embedding, np.ndarray)
+    assert isinstance(passage_embedding, np.ndarray)
+    assert query_embedding.ndim == 1  # Should be a 1D vector
+    assert passage_embedding.ndim == 1  # Should be a 1D vector
+    
+    # Check that embeddings have reasonable values
+    assert not np.isnan(query_embedding).any()
+    assert not np.isnan(passage_embedding).any()
+    
+    # Check cosine similarity is reasonable (should be high for related texts)
+    similarity = cosine_similarity(query_embedding, passage_embedding)
+    assert 0 <= similarity <= 1  # Cosine similarity range
+    assert similarity > 0.5  # These texts should be somewhat similar
+
+def test_bge_embedder_batch(device):
+    """Test batch encoding with BGE embedder."""
+    # Load a small BGE model
+    model_name = "BAAI/bge-base-en-v1.5"
+    model = FlagModel(model_name, device=device)
+    
+    # Test batch encoding
+    queries = [
+        "What is the capital of France?",
+        "Who wrote Romeo and Juliet?"
+    ]
+    
+    # Get embeddings
+    embeddings = model.encode(queries)
+    
+    # Check shapes and types
+    assert isinstance(embeddings, np.ndarray)
+    assert embeddings.ndim == 2  # Should be a 2D array (batch_size x embedding_dim)
+    assert embeddings.shape[0] == len(queries)
+    
+    # Check that embeddings have reasonable values
+    assert not np.isnan(embeddings).any()

tests/test_infer_reranker_basic.py

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+"""
+Test basic functionality of reranker models with Transformers v5.
+
+This test instantiates a lightweight reranker and calls compute_score on query/doc pairs
+to validate the forward pass.
+"""
+
+import pytest
+import torch
+import numpy as np
+from FlagEmbedding import FlagReranker
+
+
+def test_reranker_basic(device):
+    """Test basic functionality of reranker."""
+    # Load a lightweight reranker model
+    model_name = "BAAI/bge-reranker-base"
+    model = FlagReranker(model_name, device=device)
+
+    # Test scoring a single query-document pair
+    query = "What is the capital of France?"
+    passage = "Paris is the capital and most populous city of France."
+
+    # Get score
+    pair = [(query, passage)]
+    scores = model.compute_score(pair)
+    score = scores[0]
+
+    # Check score type and range
+    assert isinstance(score, float)
+    # Scores are typically in a reasonable range (model-dependent)
+    assert -100 < score < 100
+
+
+def test_reranker_batch(device):
+    """Test batch scoring with reranker."""
+    # Load a lightweight reranker model
+    model_name = "BAAI/bge-reranker-base"
+    model = FlagReranker(model_name, device=device)
+
+    # Test batch scoring
+    query = "What is the capital of France?"
+    passages = [
+        "Paris is the capital and most populous city of France.",
+        "Berlin is the capital and largest city of Germany.",
+        "London is the capital and largest city of England and the United Kingdom.",
+    ]
+
+    # Create pairs for scoring
+    pairs = [(query, passage) for passage in passages]
+
+    # Get scores
+    scores = model.compute_score(pairs)
+
+    # Check scores shape and type
+    assert isinstance(scores, list)
+    assert len(scores) == len(passages)
+    assert all(isinstance(score, float) for score in scores)
+
+    # Check that Paris (correct answer) gets highest score
+    paris_score = scores[0]
+    assert paris_score == max(scores), "Paris should have the highest score"