Merge pull request #73 from lightonai/fix_loading

Add safetensor OR bin loading logic + add loading tests

pylate/models/Dense.py

@@ -2,6 +2,7 @@
 import os
 
 import torch
+from safetensors import safe_open
 from safetensors.torch import load_model as load_safetensors_model
 from sentence_transformers.models import Dense as DenseSentenceTransformer
 from sentence_transformers.util import import_from_string
@@ -110,26 +111,49 @@ def from_stanford_weights(
         """
         # Check if the model is locally available
         if not (os.path.exists(os.path.join(model_name_or_path))):
-            # Else download the model/use the cached version
-            model_name_or_path = cached_file(
-                model_name_or_path,
-                filename="pytorch_model.bin",
-                cache_dir=cache_folder,
-                revision=revision,
-                local_files_only=local_files_only,
-                token=token,
-                use_auth_token=use_auth_token,
-            )
-        # If the model a local folder, load the PyTorch model
+            # Else download the model/use the cached version. We first try to use the safetensors version and fall back to bin if not existing. All the recent stanford-nlp models are safetensors but we keep bin for compatibility.
+            try:
+                model_name_or_path = cached_file(
+                    model_name_or_path,
+                    filename="model.safetensors",
+                    cache_dir=cache_folder,
+                    revision=revision,
+                    local_files_only=local_files_only,
+                    token=token,
+                    use_auth_token=use_auth_token,
+                )
+            except EnvironmentError:
+                print("No safetensor model found, falling back to bin.")
+                model_name_or_path = cached_file(
+                    model_name_or_path,
+                    filename="pytorch_model.bin",
+                    cache_dir=cache_folder,
+                    revision=revision,
+                    local_files_only=local_files_only,
+                    token=token,
+                    use_auth_token=use_auth_token,
+                )
+        # If the model a local folder, load the safetensor
+        # Again, we first try to load the safetensors version and fall back to bin if not existing.
+        else:
+            if os.path.exists(os.path.join(model_name_or_path, "model.safetensors")):
+                model_name_or_path = os.path.join(
+                    model_name_or_path, "model.safetensors"
+                )
+            else:
+                print("No safetensor model found, falling back to bin.")
+                model_name_or_path = os.path.join(
+                    model_name_or_path, "pytorch_model.bin"
+                )
+        if model_name_or_path.endswith("safetensors"):
+            with safe_open(model_name_or_path, framework="pt", device="cpu") as f:
+                state_dict = {"linear.weight": f.get_tensor("linear.weight")}
         else:
-            model_name_or_path = os.path.join(model_name_or_path, "pytorch_model.bin")
-
-        # Load the state dict using torch.load instead of safe_open
-        state_dict = {
-            "linear.weight": torch.load(model_name_or_path, map_location="cpu")[
-                "linear.weight"
-            ]
-        }
+            state_dict = {
+                "linear.weight": torch.load(model_name_or_path, map_location="cpu")[
+                    "linear.weight"
+                ]
+            }
 
         # Determine input and output dimensions
         in_features = state_dict["linear.weight"].shape[1]

tests/test_model.py

@@ -0,0 +1,90 @@
+import math
+
+import torch
+
+from pylate import models, rank
+
+
+def test_model_creation(**kwargs) -> None:
+    """Test the creation of different models."""
+    query = ["fruits are healthy."]
+    documents = [["fruits are healthy.", "fruits are good for health."]]
+    torch.manual_seed(42)
+    # Creation from a base encoder
+    model = models.ColBERT(model_name_or_path="bert-base-uncased")
+    # We don't test the embeddings of newly initied models for now as we need to make it deterministic
+    # queries_embeddings = model.encode(sentences=query, is_query=True)
+    # documents_embeddings = model.encode(sentences=documents, is_query=False)
+    # reranked_documents = rank.rerank(
+    #     documents_ids=[["1", "2"]],
+    #     queries_embeddings=queries_embeddings,
+    #     documents_embeddings=documents_embeddings,
+    # )
+    # assert math.isclose(
+    #     reranked_documents[0][0]["score"], 25.92, rel_tol=0.01, abs_tol=0.01
+    # )
+    # assert math.isclose(reranked_documents[0][1]["score"], 23.7, rel_tol=0.01, abs_tol=0.01)
+
+    # Creation from a base sentence-transformer
+    model = models.ColBERT(model_name_or_path="sentence-transformers/all-MiniLM-L6-v2")
+    # We don't test the embeddings of newly initied models for now as we need to make it deterministic
+    # queries_embeddings = model.encode(sentences=query, is_query=True)
+    # documents_embeddings = model.encode(sentences=documents, is_query=False)
+    # reranked_documents = rank.rerank(
+    #     documents_ids=[["1", "2"]],
+    #     queries_embeddings=queries_embeddings,
+    #     documents_embeddings=documents_embeddings,
+    # )
+    # assert math.isclose(
+    #     reranked_documents[0][0]["score"], 18.77, rel_tol=0.01, abs_tol=0.01
+    # )
+    # assert math.isclose(
+    #     reranked_documents[0][1]["score"], 18.63, rel_tol=0.01, abs_tol=0.01
+
+    # Creation from stanford-nlp (safetensor)
+    model = models.ColBERT(model_name_or_path="answerdotai/answerai-colbert-small-v1")
+    queries_embeddings = model.encode(sentences=query, is_query=True)
+    documents_embeddings = model.encode(sentences=documents, is_query=False)
+    reranked_documents = rank.rerank(
+        documents_ids=[["1", "2"]],
+        queries_embeddings=queries_embeddings,
+        documents_embeddings=documents_embeddings,
+    )
+    assert math.isclose(
+        reranked_documents[0][0]["score"], 31.71, rel_tol=0.01, abs_tol=0.01
+    )
+    assert math.isclose(
+        reranked_documents[0][1]["score"], 31.64, rel_tol=0.01, abs_tol=0.01
+    )
+
+    # Creation from stanford-nlp (bin)
+    model = models.ColBERT(model_name_or_path="Crystalcareai/Colbertv2")
+    queries_embeddings = model.encode(sentences=query, is_query=True)
+    documents_embeddings = model.encode(sentences=documents, is_query=False)
+    reranked_documents = rank.rerank(
+        documents_ids=[["1", "2"]],
+        queries_embeddings=queries_embeddings,
+        documents_embeddings=documents_embeddings,
+    )
+    assert math.isclose(
+        reranked_documents[0][0]["score"], 31.15, rel_tol=0.01, abs_tol=0.01
+    )
+    assert math.isclose(
+        reranked_documents[0][1]["score"], 30.61, rel_tol=0.01, abs_tol=0.01
+    )
+
+    # Creation from PyLate
+    model = models.ColBERT(model_name_or_path="lightonai/colbertv2.0")
+    queries_embeddings = model.encode(sentences=query, is_query=True)
+    documents_embeddings = model.encode(sentences=documents, is_query=False)
+    reranked_documents = rank.rerank(
+        documents_ids=[["1", "2"]],
+        queries_embeddings=queries_embeddings,
+        documents_embeddings=documents_embeddings,
+    )
+    assert math.isclose(
+        reranked_documents[0][0]["score"], 30.01, rel_tol=0.01, abs_tol=0.01
+    )
+    assert math.isclose(
+        reranked_documents[0][1]["score"], 26.98, rel_tol=0.01, abs_tol=0.01
+    )