handle token/logprob shifting

src/axolotl/core/trainers/kd/__init__.py

@@ -41,7 +41,12 @@ def _set_signature_columns_if_needed(self):
                 self._signature_columns += columns_to_add
 
     def compute_loss(
-        self, model, inputs, return_outputs=False, num_items_in_batch=None
+        self,
+        model,
+        inputs,
+        return_outputs=False,
+        num_items_in_batch=None,
+        shift_targets=False,
     ):
         """
         How the loss is computed by Trainer. By default, all models return the loss in the first element.
@@ -65,16 +70,22 @@ def compute_loss(
         # FIXME: account for tokenizer.padding_side
         student_logits = outputs["logits"][:, :seq_len, :].contiguous()
 
-        shift_logits = student_logits[..., :-1, :].contiguous()
-        shift_target_logprobs = target_logprobs[..., 1:, :].contiguous()
-        shift_target_token_ids = target_token_ids[..., 1:, :].contiguous()
-        shift_target_mask = target_mask[..., 1:, :].contiguous()
+        if shift_targets:
+            shift_logits = student_logits[..., :-1, :].contiguous()
+            target_logprobs_for_loss = target_logprobs[..., 1:, :].contiguous()
+            target_token_ids_for_loss = target_token_ids[..., 1:, :].contiguous()
+            target_mask_for_loss = target_mask[..., 1:, :].contiguous()
+        else:
+            shift_logits = student_logits.contiguous()
+            target_logprobs_for_loss = target_logprobs.contiguous()
+            target_token_ids_for_loss = target_token_ids.contiguous()
+            target_mask_for_loss = target_mask.contiguous()
 
         loss_kd = topk_kd_loss(
             shift_logits,
-            shift_target_token_ids,
-            shift_target_logprobs,
-            shift_target_mask,
+            target_token_ids_for_loss,
+            target_logprobs_for_loss,
+            target_mask_for_loss,
             num_items_in_batch=num_items_in_batch,
             kd_temperature=self.args.kd_temperature,
         )

src/axolotl/prompt_strategies/chat_template.py

@@ -502,7 +502,7 @@ def transform_logprobs(self, sample):
 
         # fill with -inf for padding_len tokens for top_k tokens
         # extend target_logprobs with a padding_len x top_k 2D list filled with -inf
-        for _ in range(input_padding_len):
+        for _ in range(1, input_padding_len):  # start at 1 since this is causal
             target_logprobs.append([-float("inf")] * top_k)
             target_token_ids.append(list(range(top_k)))
             target_mask.append([0] * top_k)