kd sample packing

src/axolotl/core/trainer_builder.py

@@ -824,9 +824,15 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 kwargs.pop("pad_to_multiple_of", None)
                 kwargs.pop("padding", None)
             elif self.cfg.kd_trainer:
-                from axolotl.integrations.kd.collator import DataCollatorForKD
+                from axolotl.integrations.kd.collator import (
+                    DataCollatorForKD,
+                    KDBatchSamplerDataCollatorForSeq2Seq,
+                )
 
-                collator = DataCollatorForKD
+                if self.cfg.sample_packing:
+                    collator = KDBatchSamplerDataCollatorForSeq2Seq
+                else:
+                    collator = DataCollatorForKD
             else:
                 collator = DataCollatorForSeq2Seq
 

src/axolotl/integrations/kd/chat_template.py

@@ -120,6 +120,11 @@ class ChatTemplateStrategyWithKD(ChatTemplateStrategy):
             target_logprobs.append(position_logprobs_scaled)
             target_token_ids.append(position_token_ids)
 
+        # since we started at index 1 for causal, we need one more padding token
+        target_logprobs.append([-float("inf")] * top_k)
+        target_token_ids.append(list(range(top_k)))
+        target_mask.append([0] * top_k)
+
         # Update sample with transformed logprobs
         sample["target_logprobs"] = target_logprobs
         sample["target_token_ids"] = target_token_ids

src/axolotl/integrations/kd/collator.py

@@ -186,3 +186,70 @@ class DataCollatorForKD(DataCollatorForSeq2Seq):
             features["decoder_input_ids"] = decoder_input_ids
 
         return features
+
+
+class KDBatchSamplerDataCollatorForSeq2Seq(DataCollatorForKD):
+    """
+    Collator for multipack (batch of sub-batches) specifically for KD.
+    Adapts DataCollatorForKD so it can pack multiple sequences in a single batch item.
+    """
+
+    def __call__(self, features, return_tensors=None):
+        """
+        Expects that `features` could be either:
+          - a single list of dicts, OR
+          - a list of lists of dicts (the "sub-batches" to be packed).
+        """
+        # 1) If we are *not* dealing with multiple sequences per batch element,
+        #    just pass straight to parent.
+        if not isinstance(features[0], list):
+            return super().__call__(features, return_tensors=return_tensors)
+
+        # 2) Otherwise, we *are* dealing with multiple sequences in each batch item.
+        #    We want to produce a single "merged" feature dict for each sub-batch.
+        out_features = [{} for _ in features]
+
+        for i, sub_features in enumerate(features):
+            # sub_features is a list of dicts, each dict = one sequence’s features
+            # We'll merge them into out_features[i].
+            #
+            # NOTE: You can customize how you combine fields as needed (e.g. summation
+            # or offset for attention_mask). Below is a straightforward concatenation/extension.
+
+            for field_name in sub_features[0].keys():
+                # Some fields you might want to skip or treat specially:
+                if field_name == "length":
+                    continue
+
+                # If it’s a KD field that’s a list-of-lists (e.g. target_logprobs),
+                # you typically just want to flatten them by extending.
+                if field_name in ["target_logprobs", "target_token_ids", "target_mask"]:
+                    combined = []
+                    for feat in sub_features:
+                        combined.extend(feat[field_name])
+                    out_features[i][field_name] = combined
+
+                elif field_name == "attention_mask":
+                    # Here we apply the (j+1) factor to differentiate each sub-sample
+                    # within this merged batch item.
+                    arrays = []
+                    for j, feat in enumerate(sub_features):
+                        if field_name in feat:
+                            arrays.append((j + 1) * np.array(feat[field_name]))
+                    out_features[i][field_name] = np.concatenate(arrays)
+                else:
+                    # By default, just concatenate them if they are arrays
+                    # or extend them if they are lists.
+                    # For example, input_ids or labels are often arrays.
+                    arrays = []
+                    for feat in sub_features:
+                        if field_name in feat:
+                            arr = np.array(feat[field_name])
+                            arrays.append(arr)
+                    out_features[i][field_name] = np.concatenate(arrays)
+
+        # 3) Now call the parent collator, which will do:
+        #    - padding of labels/position_ids
+        #    - KD-specific padding for target_logprobs, target_token_ids, etc.
+        #    - final conversion to return_tensors
+        return super().__call__(out_features, return_tensors=return_tensors)