7523d1f557
* cleanup dpo to be a little more extensible, add zephyr/nectar strategy * fix eos slash * support for eval split * fix kwargs * handle empty evals * don't load peft model for dpo * ensure dpo traning args gets bf16 for peft if applicable * fix duplicate kwargs for bf16 * make sure to respect the configured lr scheduler * supprt trainer callback to push config to wandb * set dataloader preload args * ensure that we are loading the lora when merging * Update src/axolotl/utils/data.py Co-authored-by: Agus <agustin.piqueres@gmail.com> * support local datasets for dpo Co-authored-by: Agus <agustin.piqueres@gmail.com> * chore: lint * dpo/kto/ipo smoke tests w lora, simplify dpo dataset type names * add split to dpo tests * fix rebase/merging error * handle edge case w logging * use accelerator for dpo datasets so it doesn't break the logger * missing args * validate checkpoint is an adapter for now * log warning when dataset strategy is not loadable --------- Co-authored-by: Agus <agustin.piqueres@gmail.com>
158 lines
5.3 KiB
Python
158 lines
5.3 KiB
Python
"""
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E2E tests for 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 pathlib import Path
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from axolotl.cli import load_rl_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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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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class TestDPOLlamaLora(unittest.TestCase):
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"""
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Test case for DPO Llama models using LoRA
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"""
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@with_temp_dir
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def test_dpo_lora(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": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 64,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"lora_target_linear": True,
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"special_tokens": {},
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"rl": "dpo",
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"datasets": [
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{
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"path": "Intel/orca_dpo_pairs",
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"type": "chatml.intel",
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"split": "train",
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},
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],
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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": 0.00001,
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"optimizer": "paged_adamw_8bit",
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"lr_scheduler": "cosine",
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"max_steps": 20,
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"save_steps": 10,
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"warmup_steps": 5,
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"gradient_checkpointing": True,
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"gradient_checkpointing_kwargs": {"use_reentrant": 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_rl_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) / "checkpoint-20/adapter_model.safetensors").exists()
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@with_temp_dir
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def test_kto_pair_lora(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": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 64,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"lora_target_linear": True,
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"special_tokens": {},
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"rl": "kto_pair",
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"datasets": [
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{
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"path": "Intel/orca_dpo_pairs",
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"type": "chatml.intel",
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"split": "train",
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},
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],
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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": 0.00001,
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"optimizer": "paged_adamw_8bit",
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"lr_scheduler": "cosine",
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"max_steps": 20,
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"save_steps": 10,
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"warmup_steps": 5,
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"gradient_checkpointing": True,
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"gradient_checkpointing_kwargs": {"use_reentrant": 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_rl_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) / "checkpoint-20/adapter_model.safetensors").exists()
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@with_temp_dir
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def test_ipo_lora(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": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 64,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"lora_target_linear": True,
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"special_tokens": {},
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"rl": "ipo",
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"datasets": [
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{
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"path": "Intel/orca_dpo_pairs",
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"type": "chatml.intel",
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"split": "train",
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},
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],
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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": 0.00001,
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"optimizer": "paged_adamw_8bit",
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"lr_scheduler": "cosine",
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"max_steps": 20,
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"save_steps": 10,
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"warmup_steps": 5,
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"gradient_checkpointing": True,
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"gradient_checkpointing_kwargs": {"use_reentrant": 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_rl_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) / "checkpoint-20/adapter_model.safetensors").exists()
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