Put the XGLM embedding patch behind a version check

aiserver.py

@@ -1015,27 +1015,28 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
         import transformers.generation_utils
         from transformers import __version__ as transformers_version
 
-        # Temporary fix for XGLM positional embedding issues until
+        # Some versions of transformers 4.17.0.dev0 are affected by
         # https://github.com/huggingface/transformers/issues/15736
-        # is resolved
-        try:
-            from transformers.models.xglm.modeling_xglm import XGLMSinusoidalPositionalEmbedding
-        except ImportError:
-            pass
-        else:
-            @torch.no_grad()
-            def new_forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0):
-                bsz, seq_len = inputs_embeds.size()[:-1]
-                input_shape = inputs_embeds.size()[:-1]
-                sequence_length = input_shape[1]
-                position_ids = torch.arange(
-                    past_key_values_length + self.padding_idx + 1, past_key_values_length + sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
-                ).unsqueeze(0).expand(input_shape).contiguous()
-                max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
-                if max_pos > self.weights.size(0):
-                    self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
-                return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
-            XGLMSinusoidalPositionalEmbedding.forward = new_forward
+        # This is a workaround for those versions of transformers.
+        if(transformers_version == "4.17.0.dev0"):
+            try:
+                from transformers.models.xglm.modeling_xglm import XGLMSinusoidalPositionalEmbedding
+            except ImportError:
+                pass
+            else:
+                @torch.no_grad()
+                def new_forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0):
+                    bsz, seq_len = inputs_embeds.size()[:-1]
+                    input_shape = inputs_embeds.size()[:-1]
+                    sequence_length = input_shape[1]
+                    position_ids = torch.arange(
+                        past_key_values_length + self.padding_idx + 1, past_key_values_length + sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device
+                    ).unsqueeze(0).expand(input_shape).contiguous()
+                    max_pos = self.padding_idx + 1 + seq_len + past_key_values_length
+                    if max_pos > self.weights.size(0):
+                        self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx)
+                    return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach()
+                XGLMSinusoidalPositionalEmbedding.forward = new_forward
 
         # Patch transformers to use our soft prompt
         def patch_causallm(cls):