support for mamba (#915)

* support for mamba

* more mamba fixes

* use fork for mamba kwargs fix

* grad checkpointing doesn't work

* fix extras for mamaba

* mamba loss fix

* use fp32 and remove verbose logging

* mamba fixes

* fix collator for mamba

* set model_type on training_args

* don't save safetensors for mamba

* update mamba config to disable safetensor checkpooints, install for tests

* no evals for mamba tests

* handle save_pretrained

* handle unused safetensors arg

tests/e2e/test_mamba.py

@@ -0,0 +1,65 @@
+"""
+E2E tests for lora llama
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+from axolotl.cli import load_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestMistral(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    @with_temp_dir
+    def test_fft(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "state-spaces/mamba-130m",
+                "model_type": "MambaLMHeadModel",
+                "tokenizer_type": "AutoTokenizer",
+                "tokenizer_config": "EleutherAI/gpt-neox-20b",
+                "flash_attention": False,
+                "sequence_len": 1024,
+                "load_in_8bit": False,
+                "val_set_size": 0.0,
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "gradient_checkpointing": False,
+                "num_epochs": 2,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "eval_steps": None,
+                "save_safetensors": False,
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "pytorch_model.bin").exists()