20620771f1
* fix for pretrain with packing * fix model name and loss expected * make sure to check with micro batch size for pretraining * change loss threshholds based on parametrization * make tests smaller for CI * fix pretrain packing * fix pretrain packing test * address pr feedback
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
"""
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E2E tests for llama pretrain
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"""
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import logging
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import os
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import pytest
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from axolotl.cli.args import TrainerCliArgs
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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
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from axolotl.utils.dict import DictDefault
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from .utils import check_model_output_exists, check_tensorboard
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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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class TestPretrainLlama:
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"""
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Test case for Llama models w pretraining
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"""
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@pytest.mark.parametrize(
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"sample_packing",
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[True, False],
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)
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@pytest.mark.parametrize(
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"pretrain_multipack_attn",
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[True, False],
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)
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def test_pretrain(self, temp_dir, sample_packing, pretrain_multipack_attn):
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if not sample_packing and pretrain_multipack_attn:
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return
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"flash_attention": True,
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"sequence_len": 1024,
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"sample_packing": sample_packing,
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"pretrain_multipack_attn": pretrain_multipack_attn,
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"dataset_processes": 1,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"pretraining_dataset": [
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{
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"path": "allenai/c4",
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"name": "en",
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"type": "pretrain",
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}
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],
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"max_steps": 5,
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"num_epochs": 1,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 1,
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"val_set_size": 0.0,
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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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"use_tensorboard": True,
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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, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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loss_threshold = 3.5
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if sample_packing and not pretrain_multipack_attn:
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loss_threshold = 6.5
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check_tensorboard(
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temp_dir + "/runs",
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"train/train_loss",
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loss_threshold,
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"Train Loss is too high",
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)
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