fix ray train and add fsdp2 smoke test for ray trainer (#3053)

* add fsdp2 smokle test for ray trainer

* fix raytrain with fsdp2

src/axolotl/cli/utils/train.py

@@ -123,6 +123,9 @@ def launch_training(
             _launch_torchrun_training(cfg_file, kwargs, launcher_args, use_exec)
         elif launcher == "python":
             _launch_python_training(cfg_file, kwargs)
+    elif launcher is None:
+        # handle ray train launch
+        _launch_python_training(cfg_file, kwargs)
 
 
 def _launch_cloud_training(

tests/e2e/multigpu/test_ray.py

@@ -10,7 +10,11 @@ from accelerate.test_utils import execute_subprocess_async
 
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import check_tensorboard, require_torch_lt_2_6_0
+from tests.e2e.utils import (
+    check_tensorboard,
+    require_torch_2_7_0,
+    require_torch_lt_2_6_0,
+)
 
 AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
 
@@ -139,3 +143,71 @@ class TestMultiGPURay:
         check_tensorboard(
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
         )
+
+    @require_torch_2_7_0
+    @pytest.mark.parametrize(
+        "gradient_accumulation_steps",
+        [1, 2],
+    )
+    def test_sft_fsdp2_packed(self, temp_dir, gradient_accumulation_steps):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sample_packing": True,
+                "pad_to_sequence_len": True,
+                "sequence_len": 1024,
+                "val_set_size": 0.01,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                        "split": "train[:10%]",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 2,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": gradient_accumulation_steps,
+                "output_dir": temp_dir,
+                "dataset_prepared_path": temp_dir + "/last_run_prepared",
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "fsdp_version": 2,
+                "fsdp_config": {
+                    "offload_params": False,
+                    "cpu_ram_efficient_loading": False,
+                    "transformer_layer_cls_to_wrap": "LlamaDecoderLayer",
+                    "state_dict_type": "FULL_STATE_DICT",
+                    "auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+                    "reshard_after_forward": True,
+                },
+                "use_tensorboard": True,
+                "save_first_step": False,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--use-ray",
+                "--ray-num-workers",
+                "2",
+            ]
+        )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss (%s) is too high"
+        )