Fsdp grad accum monkeypatch (#2064)

requirements.txt

@@ -1,7 +1,7 @@
 --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
 packaging==23.2
 peft==0.13.2
-transformers==4.46.1
+transformers==4.46.2
 tokenizers>=0.20.1
 bitsandbytes==0.44.1
 accelerate==1.1.0

src/axolotl/monkeypatch/trainer_fsdp_grad_accum.py

@@ -0,0 +1,83 @@
+"""
+fix for FSDP gradient accumulation
+see https://github.com/huggingface/transformers/pull/34645
+"""
+import inspect
+
+from accelerate.logging import get_logger
+from transformers.trainer import Trainer
+
+from axolotl.monkeypatch.unsloth_ import detab_code
+
+LOG = get_logger("axolotl.monkeypatch.trainer_fsdp_grad_accumulation")
+
+ORIGINAL_CONTEXT_CODE = """
+                context = (
+                    functools.partial(self.accelerator.no_sync, model=model)
+                    if i == len(batch_samples) - 1
+                    else contextlib.nullcontext
+                )
+"""
+
+PATCHED_CONTEXT_CODE = """
+                context = (
+                    functools.partial(self.accelerator.no_sync, model=model)
+                    if i != len(batch_samples) - 1
+                    else contextlib.nullcontext
+                )
+"""
+
+
+def get_training_loop_code() -> str:
+    training_loop = inspect.getsource(
+        Trainer._inner_training_loop  # pylint: disable=protected-access
+    )
+    return training_loop
+
+
+def check_training_loop_is_patchable() -> bool:
+    train_loop = get_training_loop_code()
+    train_loop, _ = detab_code(train_loop)
+    return ORIGINAL_CONTEXT_CODE in train_loop
+
+
+def patch_training_loop_for_fsdp_grad_accum():
+    """
+    monkeypatch for fixing the training loop for FSDP gradient accumulation
+    """
+
+    train_loop = get_training_loop_code()
+    Trainer._original_inner_training_loop = (  # pylint: disable=protected-access
+        train_loop
+    )
+    train_loop, _ = detab_code(train_loop)
+    assert (
+        ORIGINAL_CONTEXT_CODE in train_loop
+    ), "Original _inner_training_loop code not found"
+
+    train_loop = train_loop.replace(ORIGINAL_CONTEXT_CODE, PATCHED_CONTEXT_CODE)
+    train_loop = train_loop.replace(
+        "def _inner_training_loop(",
+        "def _fixed_inner_training_loop(",
+        1,
+    )
+
+    # load imports necessary
+    import transformers.trainer
+
+    items_to_import = []
+    for item in dir(transformers.trainer):
+        if item in train_loop:
+            items_to_import.append(item)
+
+    exec(  # pylint: disable=exec-used  # nosec B102
+        "from transformers.trainer import ("
+        + ", ".join(x for x in items_to_import)
+        + ")",
+        globals(),
+    )
+    exec(train_loop, globals())  # pylint: disable=exec-used  # nosec B102
+    LOG.info("patching _inner_training_loop", main_process_only=True)
+    Trainer._inner_training_loop = (  # pylint: disable=protected-access
+        _fixed_inner_training_loop  # pylint: disable=undefined-variable  # noqa: F821
+    )

src/axolotl/utils/trainer.py

@@ -16,6 +16,9 @@
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
+from axolotl.monkeypatch.trainer_fsdp_grad_accum import (
+    patch_training_loop_for_fsdp_grad_accum,
+)
 from axolotl.utils.distributed import reduce_and_broadcast
 from axolotl.utils.environment import check_cuda_p2p_ib_support
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
@@ -493,6 +496,11 @@ def prepare_opinionated_env(cfg):
 def setup_trainer(
     cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps
 ):
+    if cfg.fsdp:
+        try:
+            patch_training_loop_for_fsdp_grad_accum()
+        except AssertionError:
+            pass
     if cfg.rl in ["dpo", "ipo", "orpo", "kto", "simpo"]:
         trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer, processor)
         trainer_builder.model_ref = model[1]

tests/e2e/patched/test_trainer_fsdp.py

@@ -0,0 +1,15 @@
+"""Test module for checking whether the integration of Unsloth with Hugging Face Transformers is working as expected."""
+import unittest
+
+from axolotl.monkeypatch.trainer_fsdp_grad_accum import check_training_loop_is_patchable
+
+
+class TestTrainerFSDPIntegration(unittest.TestCase):
+    """Unsloth monkeypatch integration tests."""
+
+    def test_train_loop_patchable(self):
+        # ensures the current version of transformers has loss code that matches our patching code
+        self.assertTrue(
+            check_training_loop_is_patchable(),
+            "HF transformers _inner_training_loop has changed and isn't patchable",
+        )