dd78f2e0cc
* fix unintentional falsy checks * chore: lint --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
"""
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Simple end-to-end test for Liger integration
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"""
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import pytest
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from axolotl.common.datasets import load_datasets
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
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from axolotl.utils.dict import DictDefault
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from tests.e2e.utils import check_model_output_exists, require_torch_2_4_1
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class LigerIntegrationTestCase:
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"""
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e2e tests for liger integration with Axolotl
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"""
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@require_torch_2_4_1
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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": "HuggingFaceTB/SmolLM2-135M",
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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_glu_activation": 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": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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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": 2,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"save_safetensors": True,
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"bf16": "auto",
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"max_steps": 5,
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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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@require_torch_2_4_1
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@pytest.mark.parametrize(
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"liger_use_token_scaling",
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[True, False],
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)
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def test_llama_w_flce(self, temp_dir, liger_use_token_scaling):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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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_glu_activation": True,
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"liger_cross_entropy": False,
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"liger_fused_linear_cross_entropy": True,
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"liger_use_token_scaling": liger_use_token_scaling,
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"sequence_len": 1024,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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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": 2,
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"gradient_accumulation_steps": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"save_safetensors": True,
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"bf16": "auto",
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"max_steps": 5,
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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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