[enh] Throw error if StaticEmbedding-based model is trained with in…

…compatible loss (#2990)

sentence_transformers/losses/CachedGISTEmbedLoss.py

@@ -10,7 +10,7 @@
 from torch.utils.checkpoint import get_device_states, set_device_states
 
 from sentence_transformers import SentenceTransformer
-from sentence_transformers.models import Transformer
+from sentence_transformers.models import StaticEmbedding, Transformer
 
 
 class RandContext:
@@ -139,6 +139,11 @@ def __init__(
                 trainer.train()
         """
         super().__init__()
+        if isinstance(model[0], StaticEmbedding):
+            raise ValueError(
+                "CachedGISTEmbedLoss is not compatible with a SentenceTransformer model based on a StaticEmbedding. "
+                "Consider using GISTEmbedLoss instead."
+            )
         self.model = model
         self.guide = guide
         self.temperature = temperature

sentence_transformers/losses/CachedMultipleNegativesRankingLoss.py

@@ -10,6 +10,7 @@
 from torch.utils.checkpoint import get_device_states, set_device_states
 
 from sentence_transformers import SentenceTransformer, util
+from sentence_transformers.models import StaticEmbedding
 
 
 class RandContext:
@@ -145,6 +146,12 @@ def __init__(
                 trainer.train()
         """
         super().__init__()
+        if isinstance(model[0], StaticEmbedding):
+            raise ValueError(
+                "CachedMultipleNegativesRankingLoss is not compatible with a SentenceTransformer model based on a StaticEmbedding. "
+                "Consider using MultipleNegativesRankingLoss instead."
+            )
+
         self.model = model
         self.scale = scale
         self.similarity_fct = similarity_fct

sentence_transformers/losses/CachedMultipleNegativesSymmetricRankingLoss.py

@@ -10,6 +10,7 @@
 
 from sentence_transformers import SentenceTransformer, util
 from sentence_transformers.losses.CachedMultipleNegativesRankingLoss import RandContext
+from sentence_transformers.models import StaticEmbedding
 
 
 def _backward_hook(
@@ -114,6 +115,12 @@ def __init__(
             - Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup: https://arxiv.org/pdf/2101.06983.pdf
         """
         super().__init__()
+        if isinstance(model[0], StaticEmbedding):
+            raise ValueError(
+                "CachedMultipleNegativesSymmetricRankingLoss is not compatible with a SentenceTransformer model based on a StaticEmbedding. "
+                "Consider using MultipleNegativesSymmetricRankingLoss instead."
+            )
+
         self.model = model
         self.scale = scale
         self.similarity_fct = similarity_fct

sentence_transformers/losses/DenoisingAutoEncoderLoss.py

@@ -7,6 +7,7 @@
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, PreTrainedModel
 
 from sentence_transformers import SentenceTransformer
+from sentence_transformers.models import StaticEmbedding
 
 logger = logging.getLogger(__name__)
 
@@ -73,6 +74,12 @@ def __init__(
                 )
         """
         super().__init__()
+
+        if isinstance(model[0], StaticEmbedding):
+            raise ValueError(
+                "DenoisingAutoEncoderLoss is not compatible with a SentenceTransformer model based on a StaticEmbedding."
+            )
+
         self.encoder = model  # This will be the final model used during the inference time.
         self.tokenizer_encoder = model.tokenizer
 

sentence_transformers/losses/GISTEmbedLoss.py

@@ -5,7 +5,7 @@
 import torch
 from torch import Tensor, nn
 
-from sentence_transformers.models import Transformer
+from sentence_transformers.models import StaticEmbedding, Transformer
 from sentence_transformers.SentenceTransformer import SentenceTransformer
 
 
@@ -91,6 +91,12 @@ def __init__(
         if self.must_retokenize:
             self.tokenizer = self.model.tokenizer
 
+            if isinstance(self.model[0], StaticEmbedding):
+                raise ValueError(
+                    "If we must retokenize because the guide model has a different tokenizer, "
+                    "then the Sentence Transformer model must not be based on a StaticEmbedding."
+                )
+
     def sim_matrix(self, embed1: Tensor, embed2: Tensor) -> Tensor:
         return self.similarity_fct(embed1.unsqueeze(1), embed2.unsqueeze(0))