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Add e2e test for ia3 ft

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This commit is contained in:
Wing Lian
2023-10-19 09:27:55 -04:00
parent 0bd89b38c6
commit d0b534292f
1 changed files with 99 additions and 0 deletions
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99
tests/e2e/test_lora_llama.py
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@@ -24,6 +24,10 @@ class TestLoraLlama(unittest.TestCase):
"""
def test_lora(self):
"""
support for legacy lora_ configs
:return:
"""
# pylint: disable=duplicate-code
output_dir = tempfile.mkdtemp()
cfg = DictDefault(
@@ -66,6 +70,101 @@ class TestLoraLlama(unittest.TestCase):
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(output_dir) / "adapter_model.bin").exists()
def test_lora_peft(self):
"""
support for legacy lora_ configs
:return:
"""
# pylint: disable=duplicate-code
output_dir = tempfile.mkdtemp()
cfg = DictDefault(
{
"base_model": "JackFram/llama-68m",
"base_model_config": "JackFram/llama-68m",
"tokenizer_type": "LlamaTokenizer",
"sequence_len": 1024,
"load_in_8bit": True,
"adapter": "lora",
"peft_r": 32,
"peft_alpha": 64,
"peft_dropout": 0.05,
"peft_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",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 8,
"gradient_accumulation_steps": 1,
"output_dir": output_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
}
)
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(output_dir) / "adapter_model.bin").exists()
def test_ia3_peft(self):
"""
support for IA3 peft
:return:
"""
# pylint: disable=duplicate-code
output_dir = tempfile.mkdtemp()
cfg = DictDefault(
{
"base_model": "JackFram/llama-68m",
"base_model_config": "JackFram/llama-68m",
"tokenizer_type": "LlamaTokenizer",
"sequence_len": 1024,
"load_in_8bit": True,
"adapter": "ia3",
"peft_r": 32,
"peft_alpha": 64,
"peft_dropout": 0.05,
"peft_target_modules": ["k_proj", "v_proj", "down_proj"],
"peft_feedforward_modules": ["down_proj"],
"val_set_size": 0.1,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 8,
"gradient_accumulation_steps": 1,
"output_dir": output_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
}
)
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(output_dir) / "adapter_model.bin").exists()
def test_lora_packing(self):
# pylint: disable=duplicate-code
output_dir = tempfile.mkdtemp()