add tests for merging lora and validating the dtype

src/axolotl/cli/merge_lora.py

@@ -8,6 +8,7 @@ import transformers
 
 from axolotl.cli import do_merge_lora, load_cfg, print_axolotl_text_art
 from axolotl.common.cli import TrainerCliArgs
+from axolotl.utils.dict import DictDefault
 
 
 def do_cli(config: Path = Path("examples/"), **kwargs):
@@ -27,21 +28,26 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
         flash_attention=False,
         **kwargs,
     )
+    cfg = modify_cfg_for_merge(parsed_cfg)
 
-    if not parsed_cfg.lora_model_dir and parsed_cfg.output_dir:
+    do_merge_lora(cfg=cfg, cli_args=parsed_cli_args)
-        parsed_cfg.lora_model_dir = parsed_cfg.output_dir
+
-    if not Path(parsed_cfg.lora_model_dir).exists():
+
+def modify_cfg_for_merge(cfg: DictDefault) -> DictDefault:
+    if not cfg.lora_model_dir and cfg.output_dir:
+        cfg.lora_model_dir = cfg.output_dir
+    if not Path(cfg.lora_model_dir).exists():
         raise ValueError(
-            f"Target directory for merge: `{parsed_cfg.lora_model_dir}` does not exist."
+            f"Target directory for merge: `{cfg.lora_model_dir}` does not exist."
         )
 
-    parsed_cfg.load_in_4bit = False
+    cfg.load_in_4bit = False
-    parsed_cfg.load_in_8bit = False
+    cfg.load_in_8bit = False
-    parsed_cfg.flash_attention = False
+    cfg.flash_attention = False
-    parsed_cfg.deepspeed = None
+    cfg.deepspeed = None
-    parsed_cfg.fsdp = None
+    cfg.fsdp = None
 
-    do_merge_lora(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    return cfg
 
 
 if __name__ == "__main__":

tests/e2e/test_lora_llama.py

@@ -1,13 +1,16 @@
 """
 E2E tests for lora llama
 """
-
+import json
 import logging
 import os
 import unittest
 from pathlib import Path
 
-from axolotl.cli import load_datasets
+from transformers.utils import is_torch_bf16_gpu_available
+
+from axolotl.cli import do_merge_lora, load_datasets
+from axolotl.cli.merge_lora import modify_cfg_for_merge
 from axolotl.common.cli import TrainerCliArgs
 from axolotl.train import train
 from axolotl.utils.config import normalize_config
@@ -39,11 +42,6 @@ class TestLoraLlama(unittest.TestCase):
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
                 "val_set_size": 0.1,
-                "special_tokens": {
-                    "unk_token": "<unk>",
-                    "bos_token": "<s>",
-                    "eos_token": "</s>",
-                },
                 "datasets": [
                     {
                         "path": "mhenrichsen/alpaca_2k_test",
@@ -57,6 +55,7 @@ class TestLoraLlama(unittest.TestCase):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
+                "max_steps": 10,
             }
         )
         normalize_config(cfg)
@@ -65,3 +64,67 @@ class TestLoraLlama(unittest.TestCase):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+    @with_temp_dir
+    def test_lora_merge(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "max_steps": 10,
+                "bf16": "auto",
+            }
+        )
+        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) / "adapter_model.bin").exists()
+
+        cfg.lora_model_dir = cfg.output_dir
+        cfg.load_in_4bit = False
+        cfg.load_in_8bit = False
+        cfg.flash_attention = False
+        cfg.deepspeed = None
+        cfg.fsdp = None
+
+        cfg = modify_cfg_for_merge(cfg)
+        cfg.merge_lora = True
+
+        cli_args = TrainerCliArgs(merge_lora=True)
+
+        do_merge_lora(cfg=cfg, cli_args=cli_args)
+        assert (Path(temp_dir) / "merged/pytorch_model.bin").exists()
+
+        with open(
+            Path(temp_dir) / "merged/config.json", "r", encoding="utf-8"
+        ) as f_handle:
+            config = f_handle.read()
+        config = json.loads(config)
+        if is_torch_bf16_gpu_available():
+            assert config["torch_dtype"] == "torch.bfloat16"
+        else:
+            assert config["torch_dtype"] == "torch.float16"