9871fa060b
* optim e2e tests to run a bit faster * run prequant w/o lora_modules_to_save * use smollm2
75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
"""
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E2E tests for packed training
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"""
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import logging
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import os
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import unittest
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from tbparse import SummaryReader
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from transformers.utils import is_torch_bf16_gpu_available
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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 most_recent_subdir, with_temp_dir
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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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class TestPackedLlama(unittest.TestCase):
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"""
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Test case for Packed training of llama models
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"""
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@with_temp_dir
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def test_loss_packed(self, temp_dir):
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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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"sequence_len": 1024,
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"sample_packing": True,
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"flash_attention": True,
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"val_set_size": 0.0,
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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": "vicgalle/alpaca-gpt4",
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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": 4,
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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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"max_steps": 5,
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"use_tensorboard": True,
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}
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)
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if is_torch_bf16_gpu_available():
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cfg.bf16 = True
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else:
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cfg.fp16 = True
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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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tb_log_path = most_recent_subdir(temp_dir + "/runs")
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event_file = os.path.join(tb_log_path, sorted(os.listdir(tb_log_path))[0])
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reader = SummaryReader(event_file)
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df = reader.scalars # pylint: disable=invalid-name
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df = df[(df.tag == "train/train_loss")] # pylint: disable=invalid-name
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assert df.value.values[-1] < 2.0, "Loss is too high"
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