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""" |
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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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import pytest |
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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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@pytest.mark.skip(reason="doesn't seem to work on modal") |
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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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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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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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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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@with_temp_dir |
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def test_orpo_lora(self, temp_dir): |
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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": "orpo", |
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"orpo_alpha": 0.1, |
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"remove_unused_columns": False, |
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"chat_template": "chatml", |
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"datasets": [ |
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{ |
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"path": "argilla/ultrafeedback-binarized-preferences-cleaned", |
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"type": "chat_template.argilla", |
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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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