cf17649ef3
* misc fixes for garbage collection and L40S w NCCL P2P * patch bnb fix for triton check * chore: lint * change up import * try patching differently * remove patch for bnb fix for now * more verbose checks and tweak train loss threshold
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
"""
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E2E tests for process reward model w/ lora llama
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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 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, 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 TestProcessRewardSmolLM2(unittest.TestCase):
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"""
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Test case for Llama process reward models using LoRA
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"""
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@with_temp_dir
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def test_prm(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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"model_type": "AutoModelForTokenClassification",
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"num_labels": 2,
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"process_reward_model": True,
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"sequence_len": 512,
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"val_set_size": 0.0,
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"datasets": [
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{
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"path": "trl-lib/math_shepherd",
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"type": "stepwise_supervised",
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"step_separator": "\n",
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"split": "train[:10%]",
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},
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],
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"max_steps": 100,
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"num_epochs": 1,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.0005,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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"gradient_checkpointing": True,
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"warmup_ratio": 0.1,
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"use_tensorboard": True,
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"special_tokens": {"pad_token": "<|endoftext|>"},
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"seed": 42,
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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_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.7, "Train Loss (%s) is too high"
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)
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check_model_output_exists(temp_dir, cfg)
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