02ce520b7e
* upgrade liger to 0.3.1 * update docs and example * skip duplicate code check * Update src/axolotl/integrations/liger/args.py Co-authored-by: NanoCode012 <nano@axolotl.ai> * Update README.md Co-authored-by: NanoCode012 <nano@axolotl.ai> * add logging * chore: lint * add test case * upgrade liger and transformers * also upgrade accelerate * use kwargs to support patch release * make sure prepared path is empty for test * use transfromers 4.46.1 since 4.46.2 breaks fsdp --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
110 lines
3.6 KiB
Python
110 lines
3.6 KiB
Python
"""
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Simple end-to-end test for Liger integration
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"""
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import unittest
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from pathlib import Path
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from axolotl.cli import load_datasets
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import train
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from axolotl.utils.config import normalize_config
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from axolotl.utils.dict import DictDefault
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from ..utils import with_temp_dir
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class LigerIntegrationTestCase(unittest.TestCase):
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"""
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e2e tests for liger integration with Axolotl
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"""
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@with_temp_dir
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def test_llama_wo_flce(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"plugins": [
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"axolotl.integrations.liger.LigerPlugin",
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],
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"liger_rope": True,
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"liger_rms_norm": True,
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"liger_swiglu": True,
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"liger_cross_entropy": True,
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"liger_fused_linear_cross_entropy": False,
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"sequence_len": 1024,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"micro_batch_size": 8,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"save_safetensors": True,
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"bf16": "auto",
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}
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)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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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)
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assert (Path(temp_dir) / "model.safetensors").exists()
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@with_temp_dir
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def test_llama_w_flce(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"plugins": [
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"axolotl.integrations.liger.LigerPlugin",
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],
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"liger_rope": True,
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"liger_rms_norm": True,
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"liger_swiglu": True,
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"liger_cross_entropy": False,
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"liger_fused_linear_cross_entropy": True,
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"sequence_len": 1024,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"micro_batch_size": 8,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"save_safetensors": True,
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"bf16": "auto",
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}
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)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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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)
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assert (Path(temp_dir) / "model.safetensors").exists()
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