Fix spladepp parallelism (#169)

* fix: add get_worker_class implementation to spladepp

* fix: add tests for parallel embed for spladepp

fastembed/sparse/splade_pp.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Union, Type
 
 import numpy as np
 
@@ -114,6 +114,10 @@ class SpladePP(SparseTextEmbeddingBase, OnnxModel[SparseEmbedding]):
             parallel=parallel,
         )
 
+    @classmethod
+    def _get_worker_class(cls) -> Type[EmbeddingWorker]:
+        return SpladePPEmbeddingWorker
+
 
 class SpladePPEmbeddingWorker(EmbeddingWorker):
     def init_embedding(

tests/test_sparse_embeddings.py

@@ -71,3 +71,28 @@ def test_single_embedding():
 
         for i, value in enumerate(result.values):
             assert pytest.approx(value, abs=0.001) == expected_result["values"][i]
+
+
+def test_parallel_processing():
+    import numpy as np
+
+    model = SparseTextEmbedding(
+        model_name="prithivida/Splade_PP_en_v1",
+    )
+    docs = ["hello world", "flag embedding"] * 30
+    sparse_embeddings_duo = list(model.embed(docs, batch_size=10, parallel=2))
+    sparse_embeddings_all = list(model.embed(docs, batch_size=10, parallel=0))
+    sparse_embeddings = list(model.embed(docs, batch_size=10, parallel=None))
+
+    assert len(sparse_embeddings) == len(sparse_embeddings_duo) == len(sparse_embeddings_all) == len(docs)
+
+    for sparse_embedding, sparse_embedding_duo, sparse_embedding_all in zip(
+        sparse_embeddings, sparse_embeddings_duo, sparse_embeddings_all
+    ):
+        assert (
+            sparse_embedding.indices.tolist()
+            == sparse_embedding_duo.indices.tolist()
+            == sparse_embedding_all.indices.tolist()
+        )
+        assert np.allclose(sparse_embedding.values, sparse_embedding_duo.values, atol=1e-3)
+        assert np.allclose(sparse_embedding.values, sparse_embedding_all.values, atol=1e-3)