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add ds zero3 to multigpu biweekly tests (#1900)

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* add ds zero3 to multigpu biweekly tests

* fix for upstream api change

* use updated accelerate and fix deepspeed tests

* stringify the Path, and run multigpu tests if the multigpu tests change for a PR

* use correct json rather than yaml

* revert accelerate for deepspeed
This commit is contained in:
Wing Lian
2024-10-13 17:34:37 -04:00
committed by GitHub
parent 68b1369de9
commit ec4272c3a0
2 changed files with 115 additions and 1 deletions
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114
tests/e2e/multigpu/test_llama.py
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@@ -19,6 +19,8 @@ from ..utils import with_temp_dir
LOG = logging.getLogger("axolotl.tests.e2e.multigpu")
os.environ["WANDB_DISABLED"] = "true"
AXOLOTL_ROOT = Path(__file__).parent.parent.parent.parent
@pytest.fixture(scope="session", autouse=True)
def download_model():
@@ -346,3 +348,115 @@ class TestMultiGPULlama(unittest.TestCase):
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_ds_zero3_packed(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"sample_packing": True,
"eval_sample_packing": False,
"pad_to_sequence_len": True,
"sequence_len": 2048,
"val_set_size": 0.05,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
},
"datasets": [
{
"path": "tatsu-lab/alpaca",
"type": "alpaca",
},
],
"num_epochs": 1,
"max_steps": 100,
"micro_batch_size": 4,
"gradient_accumulation_steps": 4,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
"flash_attention": True,
"deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
}
)
# write cfg to yaml file
Path(temp_dir).mkdir(parents=True, exist_ok=True)
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
execute_subprocess_async(
[
"accelerate",
"launch",
"--num-processes",
"2",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)
@with_temp_dir
def test_ds_zero3_qlora_packed(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "TinyLlama/TinyLlama_v1.1",
"tokenizer_type": "LlamaTokenizer",
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 8,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_target_linear": True,
"sample_packing": True,
"eval_sample_packing": False,
"pad_to_sequence_len": True,
"sequence_len": 2048,
"val_set_size": 0.05,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
},
"datasets": [
{
"path": "tatsu-lab/alpaca",
"type": "alpaca",
},
],
"num_epochs": 1,
"max_steps": 100,
"micro_batch_size": 4,
"gradient_accumulation_steps": 4,
"output_dir": temp_dir,
"learning_rate": 0.0001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
"flash_attention": True,
"deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
}
)
# write cfg to yaml file
Path(temp_dir).mkdir(parents=True, exist_ok=True)
with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
execute_subprocess_async(
[
"accelerate",
"launch",
"--num-processes",
"2",
"-m",
"axolotl.cli.train",
str(Path(temp_dir) / "config.yaml"),
]
)