0ee98a0309
* fix token state json and mistral tokenizer issue * centralize constants * forgot to commit constants file * Fix weakref in pickling relora state dict * make curl a bit quieter so it doesn't log 2K lines * fix path traversal for olmoe test * more test fixes that weren't flagged previously * chore: lint * skip tests that fail b/c of OutOfResources * scattermoe as slow tests * update fbgemm-genai for torch 2.10
344 lines
11 KiB
Python
344 lines
11 KiB
Python
"""
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E2E tests for custom optimizers using Llama
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"""
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import unittest
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import pytest
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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, validate_config
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from axolotl.utils.dict import DictDefault
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from .utils import (
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check_model_output_exists,
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require_torch_2_5_1,
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require_torch_2_6_0,
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require_torch_2_7_0,
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with_temp_dir,
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)
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class TestCustomOptimizers(unittest.TestCase):
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"""
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Test case for Llama models using LoRA
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"""
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@with_temp_dir
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def test_optimi_adamw(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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"model_type": "AutoModelForCausalLM",
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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": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.02,
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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": "mhenrichsen/alpaca_2k_test",
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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": 8,
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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": "optimi_adamw",
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"max_steps": 5,
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"lr_scheduler": "cosine",
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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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dataset_meta = load_datasets(cfg=cfg)
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_, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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assert trainer.optimizer.optimizer.__class__.__name__ == "AdamW"
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@with_temp_dir
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@require_torch_2_5_1
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def test_adopt_adamw(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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"model_type": "AutoModelForCausalLM",
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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": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.02,
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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": "mhenrichsen/alpaca_2k_test",
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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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"max_steps": 5,
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"micro_batch_size": 8,
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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": "adopt_adamw",
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"lr_scheduler": "cosine",
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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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dataset_meta = load_datasets(cfg=cfg)
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_, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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assert "ADOPT" in trainer.optimizer.optimizer.__class__.__name__
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@with_temp_dir
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@require_torch_2_5_1
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def test_muon(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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"model_type": "AutoModelForCausalLM",
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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": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.02,
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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": "mhenrichsen/alpaca_2k_test",
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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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"max_steps": 5,
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"micro_batch_size": 8,
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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": "muon",
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"lr_scheduler": "cosine",
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"weight_decay": 0.01,
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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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dataset_meta = load_datasets(cfg=cfg)
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_, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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assert "Muon" in trainer.optimizer.optimizer.__class__.__name__
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@with_temp_dir
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@require_torch_2_7_0
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def test_dion(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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"model_type": "AutoModelForCausalLM",
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"tokenizer_type": "AutoTokenizer",
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"sequence_len": 1024,
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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": "mhenrichsen/alpaca_2k_test",
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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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"max_steps": 5,
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"micro_batch_size": 8,
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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": "dion",
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"dion_lr": 0.01,
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"dion_momentum": 0.95,
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"lr_scheduler": "cosine",
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"weight_decay": 0.01,
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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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dataset_meta = load_datasets(cfg=cfg)
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_, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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assert "Dion" in trainer.optimizer.optimizer.__class__.__name__
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@with_temp_dir
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def test_fft_schedule_free_adamw(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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"model_type": "AutoModelForCausalLM",
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"sequence_len": 1024,
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"val_set_size": 0.01,
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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": "mhenrichsen/alpaca_2k_test",
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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": 2,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "schedule_free_adamw",
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"lr_scheduler": "constant",
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"max_steps": 10,
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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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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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@with_temp_dir
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@require_torch_2_6_0
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def test_came_pytorch(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": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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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": 8,
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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": "came_pytorch",
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"adam_beta3": 0.9999,
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"adam_epsilon2": 1e-16,
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"max_steps": 5,
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"lr_scheduler": "cosine",
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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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dataset_meta = load_datasets(cfg=cfg)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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@require_torch_2_7_0
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@pytest.mark.parametrize(
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"optimizer_name,expected_class,learning_rate",
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[
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("flash_adamw", "FlashAdamW", 0.00001),
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("flash_adam", "FlashAdam", 0.00001),
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("flash_sgd", "FlashSGD", 0.01),
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("flash_sgdw", "FlashSGDW", 0.01),
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("flash_lion", "FlashLion", 0.0001),
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],
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)
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def test_flash_optimizers(tmp_path, optimizer_name, expected_class, learning_rate):
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pytest.importorskip("flashoptim")
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temp_dir = str(tmp_path)
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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": "AutoModelForCausalLM",
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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": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.02,
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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": "mhenrichsen/alpaca_2k_test",
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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": 8,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": learning_rate,
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"optimizer": optimizer_name,
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"max_steps": 5,
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"lr_scheduler": "cosine",
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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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dataset_meta = load_datasets(cfg=cfg)
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_, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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assert trainer.optimizer.optimizer.__class__.__name__ == expected_class
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