wire up squash_position_ids

* make multipack sampler patch explicit

* combining

requirements.txt

@@ -13,8 +13,8 @@ liger-kernel==0.6.1
 packaging==23.2
 
 huggingface_hub>=0.33.0
-peft==0.17.0
-transformers==4.55.2
+peft>=0.17.0
+transformers==4.55.3
 tokenizers>=0.21.1
 accelerate==1.10.0
 datasets==4.0.0

src/axolotl/common/datasets.py

@@ -6,7 +6,6 @@ from dataclasses import dataclass
 
 from datasets import Dataset
 
-import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
 from axolotl.cli.args import PreprocessCliArgs, TrainerCliArgs
 from axolotl.loaders import load_processor, load_tokenizer
 from axolotl.utils.data import prepare_datasets, prepare_preference_datasets

src/axolotl/core/builders/causal.py

@@ -476,6 +476,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 )
             ):
                 collator = V2BatchSamplerDataCollatorForSeq2Seq
+                if self.cfg.squash_position_ids:
+                    kwargs["squash_position_ids"] = True
             else:
                 collator = BatchSamplerDataCollatorForSeq2Seq
         else:

src/axolotl/loaders/patch_manager.py

@@ -277,6 +277,14 @@ class PatchManager:
                 has_remote_code=has_remote_code,
             )
 
+        if self.cfg.sample_packing:
+            from axolotl.monkeypatch.data.batch_dataset_fetcher import (
+                apply_multipack_dataloader_patch,
+            )
+
+            LOG.info("Applying multipack dataloader patch for sample packing...")
+            apply_multipack_dataloader_patch()
+
     def _apply_fsdp2_bnb_patches(self):
         """Apply FSDP2 BNB patches."""
         if (

src/axolotl/monkeypatch/data/batch_dataset_fetcher.py

@@ -1,4 +1,4 @@
-"""monkey patches for the dataset fetcher to handle batches of packed indexes"""
+"""Monkey patches for the dataset fetcher to handle batches of packed indexes."""
 
 # pylint: disable=protected-access
 
@@ -6,10 +6,20 @@ import torch
 from torch.utils.data._utils.fetch import _BaseDatasetFetcher
 from torch.utils.data._utils.worker import _worker_loop
 
+_ORIGINAL_MAP_DATASET_FETCHER = None
+_ORIGINAL_WORKER_LOOP = None
+_IS_PATCHED = False
+
 
 class _MapDatasetFetcher(_BaseDatasetFetcher):
+    """
+    Custom dataset fetcher that handles nested batch structures from
+    MultipackBatchSampler.
+    """
+
     def fetch(self, possibly_batched_index):
         if isinstance(possibly_batched_index[0], list):
+            # Handle nested structure from MultipackBatchSampler
             data = [None for i in possibly_batched_index]
             for i, possibly_batched_index_ in enumerate(possibly_batched_index):
                 if self.auto_collation:
@@ -23,6 +33,7 @@ class _MapDatasetFetcher(_BaseDatasetFetcher):
                 else:
                     data[i] = self.dataset[possibly_batched_index_]
         else:
+            # Standard batch handling
             if self.auto_collation:
                 if hasattr(self.dataset, "__getitems__") and self.dataset.__getitems__:
                     data = self.dataset.__getitems__(possibly_batched_index)
@@ -34,14 +45,54 @@ class _MapDatasetFetcher(_BaseDatasetFetcher):
 
 
 def patch_fetchers():
+    """Apply patches to PyTorch's DataLoader components."""
     torch.utils.data._utils.fetch._MapDatasetFetcher = _MapDatasetFetcher
     torch.utils.data.dataloader._utils.fetch._MapDatasetFetcher = _MapDatasetFetcher
 
 
 def patched_worker_loop(*args, **kwargs):
+    """Worker loop that ensures patches are applied in worker processes."""
     patch_fetchers()
     return _worker_loop(*args, **kwargs)
 
 
-torch.utils.data._utils.worker._worker_loop = patched_worker_loop
-patch_fetchers()
+def apply_multipack_dataloader_patch():
+    """
+    This patch allows DataLoader to correctly process batches that contain multiple bins
+    of packed sequences.
+    """
+    # pylint: disable=global-statement
+    global _ORIGINAL_MAP_DATASET_FETCHER, _ORIGINAL_WORKER_LOOP, _IS_PATCHED
+
+    if _IS_PATCHED:
+        return
+
+    # Store original implementations
+    _ORIGINAL_MAP_DATASET_FETCHER = torch.utils.data._utils.fetch._MapDatasetFetcher
+    _ORIGINAL_WORKER_LOOP = torch.utils.data._utils.worker._worker_loop
+
+    # Apply patches
+    patch_fetchers()
+    torch.utils.data._utils.worker._worker_loop = patched_worker_loop
+
+    _IS_PATCHED = True
+
+
+def remove_multipack_dataloader_patch():
+    """Remove the monkeypatch and restore original PyTorch DataLoader behavior."""
+    # pylint: disable=global-statement
+    global _IS_PATCHED
+
+    if not _IS_PATCHED:
+        return
+
+    if _ORIGINAL_MAP_DATASET_FETCHER:
+        torch.utils.data._utils.fetch._MapDatasetFetcher = _ORIGINAL_MAP_DATASET_FETCHER
+        torch.utils.data.dataloader._utils.fetch._MapDatasetFetcher = (
+            _ORIGINAL_MAP_DATASET_FETCHER
+        )
+
+    if _ORIGINAL_WORKER_LOOP:
+        torch.utils.data._utils.worker._worker_loop = _ORIGINAL_WORKER_LOOP
+
+    _IS_PATCHED = False

src/axolotl/utils/schemas/config.py

@@ -459,6 +459,12 @@ class AxolotlInputConfig(
             "description": "The multiprocessing start method to use for packing. Should be 'fork', 'spawn' or 'forkserver'"
         },
     )
+    squash_position_ids: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to squash position_ids for packing, effectively extending context length."
+        },
+    )
     eval_sample_packing: bool | None = Field(
         default=None,
         json_schema_extra={

tests/test_packed_batch_sampler.py

@@ -48,7 +48,13 @@ class TestBatchedSamplerPacking:
         max_seq_length,
         sequential,
     ):
-        import axolotl.monkeypatch.data.batch_dataset_fetcher  # pylint: disable=unused-import  # noqa: F401
+        from axolotl.monkeypatch.data.batch_dataset_fetcher import (
+            apply_multipack_dataloader_patch,
+            remove_multipack_dataloader_patch,
+        )
+
+        # Apply the patch for multipack handling
+        apply_multipack_dataloader_patch()
 
         dataset = dataset_winglian_tiny_shakespeare["train"]
 
@@ -101,10 +107,14 @@ class TestBatchedSamplerPacking:
             for pack in batch:
                 batch_idxs.extend(pack)
 
-        for batch in loader:
-            assert batch["input_ids"].numel() <= batch_size * max_seq_length
-            assert batch["input_ids"].shape[1] == max_seq_length
+        try:
+            for batch in loader:
+                assert batch["input_ids"].numel() <= batch_size * max_seq_length
+                assert batch["input_ids"].shape[1] == max_seq_length
 
-        original_idxs = set(range(len(train_dataset)))
-        assert original_idxs == set(batch_idxs)
-        assert len(batch_idxs) == len(set(batch_idxs))
+            original_idxs = set(range(len(train_dataset)))
+            assert original_idxs == set(batch_idxs)
+            assert len(batch_idxs) == len(set(batch_idxs))
+        finally:
+            # Clean up: remove the patch after the test
+            remove_multipack_dataloader_patch()