move relora to the patched tests suite

tests/e2e/patched/test_relora_llama.py

@@ -0,0 +1,88 @@
+"""
+E2E tests for relora llama
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+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
+from axolotl.utils.dict import DictDefault
+
+from .utils import check_model_output_exists, check_tensorboard, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestReLoraLlama(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    @with_temp_dir
+    def test_relora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "sequence_len": 2048,
+                "sample_packing": True,
+                "pad_to_sequence_len": True,
+                "flash_attention": True,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_modules": ["q_proj", "v_proj"],
+                "relora_steps": 100,
+                "relora_warmup_steps": 20,
+                "relora_anneal_steps": 10,
+                "relora_prune_ratio": 0.9,
+                "relora_cpu_offload": True,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "chat_template": "chatml",
+                "datasets": [
+                    {
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "split": "train[:10%]",
+                        "field_messages": "conversations",
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    },
+                ],
+                "warmup_steps": 20,
+                "num_epochs": 2,
+                "max_steps": 205,  # at least 2x relora_steps
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "use_tensorboard": True,
+            }
+        )
+        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(Path(temp_dir) / "checkpoint-100/adapter", cfg)
+        assert (
+            Path(temp_dir) / "checkpoint-100/relora/model.safetensors"
+        ).exists(), "Relora model checkpoint not found"
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/grad_norm", 0.2, "grad_norm is too high"
+        )