| """ |
| E2E tests for lora llama |
| """ |
|
|
| import logging |
| import os |
| import unittest |
| from pathlib import Path |
|
|
| import pytest |
|
|
| 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" |
|
|
|
|
| @pytest.mark.skip(reason="doesn't seem to work on modal") |
| class TestPhi(unittest.TestCase): |
| """ |
| Test case for Phi2 models |
| """ |
|
|
| @with_temp_dir |
| def test_phi_ft(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "microsoft/phi-1_5", |
| "model_type": "AutoModelForCausalLM", |
| "tokenizer_type": "AutoTokenizer", |
| "sequence_len": 2048, |
| "sample_packing": False, |
| "load_in_8bit": False, |
| "adapter": None, |
| "val_set_size": 0.1, |
| "special_tokens": { |
| "pad_token": "<|endoftext|>", |
| }, |
| "datasets": [ |
| { |
| "path": "mhenrichsen/alpaca_2k_test", |
| "type": "alpaca", |
| }, |
| ], |
| "dataset_shard_num": 10, |
| "dataset_shard_idx": 0, |
| "num_epochs": 1, |
| "micro_batch_size": 1, |
| "gradient_accumulation_steps": 1, |
| "output_dir": temp_dir, |
| "learning_rate": 0.00001, |
| "optimizer": "paged_adamw_8bit", |
| "lr_scheduler": "cosine", |
| "flash_attention": True, |
| "max_steps": 10, |
| "save_steps": 10, |
| "eval_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) / "pytorch_model.bin").exists() |
|
|
| @with_temp_dir |
| def test_phi_qlora(self, temp_dir): |
| |
| cfg = DictDefault( |
| { |
| "base_model": "microsoft/phi-1_5", |
| "model_type": "AutoModelForCausalLM", |
| "tokenizer_type": "AutoTokenizer", |
| "sequence_len": 2048, |
| "sample_packing": False, |
| "load_in_8bit": False, |
| "adapter": "qlora", |
| "lora_r": 64, |
| "lora_alpha": 32, |
| "lora_dropout": 0.05, |
| "lora_target_linear": True, |
| "val_set_size": 0.1, |
| "special_tokens": { |
| "pad_token": "<|endoftext|>", |
| }, |
| "datasets": [ |
| { |
| "path": "mhenrichsen/alpaca_2k_test", |
| "type": "alpaca", |
| }, |
| ], |
| "dataset_shard_num": 10, |
| "dataset_shard_idx": 0, |
| "num_epochs": 1, |
| "micro_batch_size": 1, |
| "gradient_accumulation_steps": 1, |
| "output_dir": temp_dir, |
| "learning_rate": 0.00001, |
| "optimizer": "paged_adamw_8bit", |
| "lr_scheduler": "cosine", |
| "flash_attention": True, |
| "max_steps": 10, |
| "save_steps": 10, |
| "eval_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() |
|
|
