90090fa9e8
* Support loss_type/loss_weights DPO * Validate dpo loss type/weights only set for dpo * Tests: Update ipo tests to use new path * Docs: Update docs for new ipo path * PR fixes - typo/validation * PR nit - warning * chore: fix warnings arg --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
388 lines
14 KiB
Python
388 lines
14 KiB
Python
"""E2E tests for lora llama"""
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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.args import TrainerCliArgs
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from axolotl.common.datasets import load_preference_datasets
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from .utils import check_model_output_exists, with_temp_dir
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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": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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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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"pad_token": "<|endoftext|>",
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},
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"rl": "dpo",
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"datasets": [
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{
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"path": "arcee-ai/distilabel-intel-orca-dpo-pairs-binarized",
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"type": "chatml.ultra",
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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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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_preference_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(Path(temp_dir) / "checkpoint-20", cfg)
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@with_temp_dir
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def test_dpo_use_weighting(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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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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"pad_token": "<|endoftext|>",
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},
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"rl": "dpo",
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"dpo_use_weighting": True,
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"datasets": [
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{
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"path": "arcee-ai/distilabel-intel-orca-dpo-pairs-binarized",
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"type": "chatml.ultra",
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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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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_preference_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(Path(temp_dir) / "checkpoint-20", cfg)
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@with_temp_dir
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def test_rpo(self, temp_dir):
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# For TRL >= 0.29, loss_type=["sigmoid", "sft"], loss_weights=[1, alpha]
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# replaces loss_type="rpo", rpo_alpha=alpha.
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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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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"pad_token": "<|endoftext|>",
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},
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"rl": "dpo",
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"dpo_loss_type": ["sigmoid", "sft"],
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"dpo_loss_weights": [1.0, 1.0],
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"datasets": [
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{
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"path": "arcee-ai/distilabel-intel-orca-dpo-pairs-binarized",
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"type": "chatml.ultra",
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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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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_preference_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(Path(temp_dir) / "checkpoint-20", cfg)
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@pytest.mark.skip("kto_pair no longer supported in trl")
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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": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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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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"pad_token": "<|endoftext|>",
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},
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"rl": "kto_pair",
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"datasets": [
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{
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"path": "arcee-ai/distilabel-intel-orca-dpo-pairs-binarized",
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"type": "chatml.ultra",
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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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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_preference_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(Path(temp_dir) / "checkpoint-20", cfg)
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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": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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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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"pad_token": "<|endoftext|>",
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},
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"rl": "dpo",
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"dpo_loss_type": ["ipo"],
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"datasets": [
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{
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"path": "arcee-ai/distilabel-intel-orca-dpo-pairs-binarized",
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"type": "chatml.ultra",
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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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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_preference_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(Path(temp_dir) / "checkpoint-20", cfg)
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@pytest.mark.skip(reason="TRL ORPO trainer has internal zip() length mismatch bug")
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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": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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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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"pad_token": "<|endoftext|>",
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},
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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/distilabel-capybara-dpo-7k-binarized",
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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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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_preference_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(Path(temp_dir) / "checkpoint-20", cfg)
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@pytest.mark.skip(reason="Fix the implementation")
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@with_temp_dir
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def test_kto_lora(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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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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"pad_token": "<|endoftext|>",
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},
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"rl": "kto",
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"rl_beta": 0.5,
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"kto_desirable_weight": 1.0,
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"kto_undesirable_weight": 1.0,
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"remove_unused_columns": False,
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"datasets": [
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# {
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# "path": "argilla/kto-mix-15k",
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# "type": "chatml.argilla_chat",
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# "split": "train",
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# },
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{
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"path": "argilla/ultrafeedback-binarized-preferences-cleaned-kto",
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"type": "chatml.ultra",
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"split": "train",
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},
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# {
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# "path": "argilla/kto-mix-15k",
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# "type": "llama3.argilla_chat",
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# "split": "train",
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# },
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{
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"path": "argilla/ultrafeedback-binarized-preferences-cleaned-kto",
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"type": "llama3.ultra",
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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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"save_first_step": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_preference_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(Path(temp_dir) / "checkpoint-20", cfg)
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