add e2e tests for checking functionality of resume from checkpoint (#865

)

* use tensorboard to see if resume from checkpoint works

* make sure e2e test is either fp16 or bf16

* set max_steps and save limit so we have the checkpoint when testing resuming

* fix test parameters

requirements.txt

@@ -32,3 +32,4 @@ pynvml
 art
 fschat==0.2.29
 gradio
+tensorboard

tests/e2e/test_lora_llama.py

@@ -101,6 +101,7 @@ def test_lora_packing(self, temp_dir):
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch",
                 "lr_scheduler": "cosine",
+                "bf16": True,
             }
         )
         normalize_config(cfg)

tests/e2e/test_resume.py

@@ -0,0 +1,95 @@
+"""
+E2E tests for resuming training
+"""
+
+import logging
+import os
+import re
+import subprocess
+import unittest
+from pathlib import Path
+
+from transformers.utils import is_torch_bf16_gpu_available
+
+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 most_recent_subdir, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestResumeLlama(unittest.TestCase):
+    """
+    Test case for resuming training of llama models
+    """
+
+    @with_temp_dir
+    def test_resume_qlora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "sample_packing": True,
+                "flash_attention": True,
+                "load_in_4bit": True,
+                "adapter": "qlora",
+                "lora_r": 32,
+                "lora_alpha": 64,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {},
+                "datasets": [
+                    {
+                        "path": "vicgalle/alpaca-gpt4",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 2,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch",
+                "lr_scheduler": "cosine",
+                "save_steps": 10,
+                "save_total_limit": 5,
+                "max_steps": 40,
+            }
+        )
+        if is_torch_bf16_gpu_available():
+            cfg.bf16 = True
+        else:
+            cfg.fp16 = True
+        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)
+
+        resume_cfg = cfg | DictDefault(
+            {
+                "resume_from_checkpoint": f"{temp_dir}/checkpoint-30/",
+            }
+        )
+        normalize_config(resume_cfg)
+        cli_args = TrainerCliArgs()
+
+        train(cfg=resume_cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "adapter_model.bin").exists()
+
+        tb_log_path_1 = most_recent_subdir(temp_dir + "/runs")
+        cmd = f"tensorboard --inspect  --logdir {tb_log_path_1}"
+        res = subprocess.run(
+            cmd, shell=True, text=True, capture_output=True, check=True
+        )
+        pattern = r"first_step\s+(\d+)"
+        first_steps = int(re.findall(pattern, res.stdout)[0])
+        assert first_steps == 31

tests/e2e/utils.py

@@ -1,10 +1,11 @@
 """
 helper utils for tests
 """
-
+import os
 import shutil
 import tempfile
 from functools import wraps
+from pathlib import Path
 
 
 def with_temp_dir(test_func):
@@ -20,3 +21,13 @@ def wrapper(*args, **kwargs):
             shutil.rmtree(temp_dir)
 
     return wrapper
+
+
+def most_recent_subdir(path):
+    base_path = Path(path)
+    subdirectories = [d for d in base_path.iterdir() if d.is_dir()]
+    if not subdirectories:
+        return None
+    subdir = max(subdirectories, key=os.path.getctime)
+
+    return subdir