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* mistral e2e tests * make sure to enable flash attention for the e2e tests * use latest transformers full sha * uninstall first
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""" | ||
E2E tests for lora llama | ||
""" | ||
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import logging | ||
import os | ||
import tempfile | ||
import unittest | ||
from pathlib import Path | ||
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from transformers.utils import is_torch_bf16_gpu_available | ||
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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 | ||
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LOG = logging.getLogger("axolotl.tests.e2e") | ||
os.environ["WANDB_DISABLED"] = "true" | ||
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class TestMistral(unittest.TestCase): | ||
""" | ||
Test case for Llama models using LoRA | ||
""" | ||
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def test_lora(self): | ||
# pylint: disable=duplicate-code | ||
output_dir = tempfile.mkdtemp() | ||
cfg = DictDefault( | ||
{ | ||
"base_model": "openaccess-ai-collective/tiny-mistral", | ||
"base_model_config": "openaccess-ai-collective/tiny-mistral", | ||
"flash_attention": True, | ||
"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, | ||
"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": 2, | ||
"gradient_accumulation_steps": 1, | ||
"output_dir": output_dir, | ||
"learning_rate": 0.00001, | ||
"optimizer": "adamw_torch", | ||
"lr_scheduler": "cosine", | ||
"max_steps": 20, | ||
"save_steps": 10, | ||
"eval_steps": 10, | ||
} | ||
) | ||
normalize_config(cfg) | ||
cli_args = TrainerCliArgs() | ||
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) | ||
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) | ||
assert (Path(output_dir) / "adapter_model.bin").exists() | ||
|
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def test_lora_packing(self): | ||
# pylint: disable=duplicate-code | ||
output_dir = tempfile.mkdtemp() | ||
cfg = DictDefault( | ||
{ | ||
"base_model": "openaccess-ai-collective/tiny-mistral", | ||
"base_model_config": "openaccess-ai-collective/tiny-mistral", | ||
"flash_attention": True, | ||
"sample_packing": True, | ||
"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, | ||
"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": 2, | ||
"gradient_accumulation_steps": 1, | ||
"output_dir": output_dir, | ||
"learning_rate": 0.00001, | ||
"optimizer": "adamw_torch", | ||
"lr_scheduler": "cosine", | ||
"max_steps": 20, | ||
"save_steps": 10, | ||
"eval_steps": 10, | ||
} | ||
) | ||
normalize_config(cfg) | ||
cli_args = TrainerCliArgs() | ||
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) | ||
|
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) | ||
assert (Path(output_dir) / "adapter_model.bin").exists() | ||
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def test_ft(self): | ||
# pylint: disable=duplicate-code | ||
output_dir = tempfile.mkdtemp() | ||
cfg = DictDefault( | ||
{ | ||
"base_model": "openaccess-ai-collective/tiny-mistral", | ||
"base_model_config": "openaccess-ai-collective/tiny-mistral", | ||
"flash_attention": True, | ||
"sequence_len": 1024, | ||
"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": 2, | ||
"gradient_accumulation_steps": 1, | ||
"output_dir": output_dir, | ||
"learning_rate": 0.00001, | ||
"optimizer": "adamw_torch", | ||
"lr_scheduler": "cosine", | ||
"max_steps": 20, | ||
"save_steps": 10, | ||
"eval_steps": 10, | ||
} | ||
) | ||
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) | ||
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) | ||
assert (Path(output_dir) / "pytorch_model.bin").exists() | ||
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def test_ft_packing(self): | ||
# pylint: disable=duplicate-code | ||
output_dir = tempfile.mkdtemp() | ||
cfg = DictDefault( | ||
{ | ||
"base_model": "openaccess-ai-collective/tiny-mistral", | ||
"base_model_config": "openaccess-ai-collective/tiny-mistral", | ||
"flash_attention": True, | ||
"sample_packing": True, | ||
"sequence_len": 1024, | ||
"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": 2, | ||
"gradient_accumulation_steps": 1, | ||
"output_dir": output_dir, | ||
"learning_rate": 0.00001, | ||
"optimizer": "adamw_torch", | ||
"lr_scheduler": "cosine", | ||
"max_steps": 20, | ||
"save_steps": 10, | ||
"eval_steps": 10, | ||
} | ||
) | ||
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) | ||
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) | ||
assert (Path(output_dir) / "pytorch_model.bin").exists() |