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from __future__ import annotations
from pathlib import Path
import torch
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.models import SparseStaticEmbedding
from tests.sparse_encoder.utils import sparse_allclose
def test_sparse_static_embedding_padding_ignored(inference_free_splade_bert_tiny_model: SparseEncoder) -> None:
model = inference_free_splade_bert_tiny_model
input_texts = ["This is a test input", "This is a considerably longer test input to check padding behavior."]
# Encode the input texts
batch_embeddings = model.encode_query(input_texts, save_to_cpu=True)
single_embeddings = [model.encode_query(text, save_to_cpu=True) for text in input_texts]
single_embeddings = torch.stack(single_embeddings)
# Check that the batch embeddings match the single embeddings
assert sparse_allclose(
batch_embeddings, single_embeddings, atol=1e-6
), "Batch encoding does not match single encoding."
def test_sparse_static_embedding_save_load(
inference_free_splade_bert_tiny_model: SparseEncoder, tmp_path: Path
) -> None:
model = inference_free_splade_bert_tiny_model
# Define test inputs
test_inputs = ["This is a simple test.", "Another example text for testing."]
# Get embeddings before saving
original_embeddings = model.encode_query(test_inputs, save_to_cpu=True)
# Save the model
save_path = tmp_path / "test_sparse_static_embedding_model"
model.save_pretrained(save_path)
# Load the model
loaded_model = SparseEncoder(str(save_path))
# Get embeddings after loading
loaded_embeddings = loaded_model.encode_query(test_inputs, save_to_cpu=True)
# Check if embeddings are the same before and after save/load
assert sparse_allclose(original_embeddings, loaded_embeddings, atol=1e-6), "Embeddings changed after save and load"
# Check if SparseStaticEmbedding weights are maintained after loading
assert isinstance(
loaded_model[0].query_0_SparseStaticEmbedding, SparseStaticEmbedding
), "SparseStaticEmbedding component missing after loading"
assert torch.allclose(
model[0].query_0_SparseStaticEmbedding.weight, loaded_model[0].query_0_SparseStaticEmbedding.weight
), "SparseStaticEmbedding weights changed after save and load"
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