Merge pull request #80 from VE-FORBRYDERNE/xglm-position-ids

Temporary fix for XGLM positional embedding issues

aiserver.py

@@ -764,6 +764,28 @@ 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
+        # 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
+
         # Patch transformers to use our soft prompt
         def patch_causallm(cls):
             old_forward = cls.forward