add e2e smoke test for using activation/gradient checkpointing with offload (#2565)

* add e2e smoke test for using activation/gradient checkpointing with offload

* disable duplicate code check for the test

* fix relative import

* seq len too small to test this dataset with packing

* Fix checkpoint ptaching for tests

tests/e2e/patched/test_activation_checkpointing.py

@@ -0,0 +1,77 @@
+"""
+E2E tests for activation checkpointing
+"""
+
+import pytest
+import transformers
+from torch.utils.checkpoint import checkpoint
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from ..utils import check_model_output_exists
+
+
+@pytest.fixture()
+def fix_checkpoint_after_test():
+    yield
+    transformers.modeling_utils.checkpoint = checkpoint
+
+
+class TestActivationCheckpointing:
+    """
+    E2E tests for activation checkpointing
+    """
+
+    def test_activation_checkpointing_offload(
+        self,
+        temp_dir,
+        fix_checkpoint_after_test,  # pylint: disable=unused-argument,redefined-outer-name
+    ):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sequence_len": 1024,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                    "eos_token": "<|im_end|>",
+                },
+                "datasets": [
+                    {
+                        "chat_template": "chatml",
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "split": "train[:10%]",
+                        "field_messages": "conversations",
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": True,
+                "bf16": True,
+                "save_safetensors": True,
+                "gradient_checkpointing": "offload",
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)