743ba62bd5
* bump transformers and trl * fix: update trainer.log signature * fix trl trainer.log interfaces * broken 🦥 with latest transformers * skip parent, call grandparent - yeah, super janky * update HF HUB env var and fix reward trainer log since it doesn't directly override log * also bump accelerate * patches for llama ga * detab the code to check * fix whitespace for patch check * play nicely with CI tests since we patch everytime * fix pop default in case it doesn't exist * more tweaks to make patches nicer in CI * fix detab for when there are possibly multiple patches --------- Co-authored-by: NanoCode012 <nano@axolotl.ai>
187 lines
6.0 KiB
Python
187 lines
6.0 KiB
Python
"""
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e2e tests for unsloth qlora
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"""
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import logging
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import os
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from pathlib import Path
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import pytest
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from axolotl.cli import load_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 check_tensorboard
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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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# pylint: disable=duplicate-code
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class TestUnslothQLoRA:
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"""
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Test class for Unsloth QLoRA Llama models
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"""
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@pytest.mark.parametrize(
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"sample_packing",
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[True, False],
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)
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def test_unsloth_llama_qlora_fa2(self, temp_dir, sample_packing):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 1024,
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"sample_packing": sample_packing,
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"flash_attention": True,
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"unsloth_lora_mlp": True,
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"unsloth_lora_qkv": True,
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"unsloth_lora_o": True,
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"load_in_4bit": True,
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"adapter": "qlora",
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"lora_r": 16,
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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": 0.05,
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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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"save_steps": 10,
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"micro_batch_size": 4,
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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": "adamw_8bit",
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"lr_scheduler": "cosine",
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"use_tensorboard": True,
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"bf16": "auto",
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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, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(temp_dir) / "adapter_model.bin").exists()
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
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)
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def test_unsloth_llama_qlora_unpacked(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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"sequence_len": 1024,
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"unsloth_lora_mlp": True,
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"unsloth_lora_qkv": True,
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"unsloth_lora_o": True,
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"sample_packing": False,
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"load_in_4bit": True,
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"adapter": "qlora",
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"lora_r": 16,
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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": 0.05,
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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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"save_steps": 10,
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"micro_batch_size": 4,
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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": "adamw_8bit",
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"lr_scheduler": "cosine",
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"use_tensorboard": True,
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"bf16": "auto",
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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, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(temp_dir) / "adapter_model.bin").exists()
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
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)
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@pytest.mark.parametrize(
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"sdp_attention",
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[True, False],
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)
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def test_unsloth_llama_qlora_unpacked_no_fa2_fp16(self, temp_dir, sdp_attention):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"sequence_len": 1024,
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"unsloth_lora_mlp": True,
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"unsloth_lora_qkv": True,
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"unsloth_lora_o": True,
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"sample_packing": False,
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"load_in_4bit": True,
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"adapter": "qlora",
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"lora_r": 16,
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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": 0.05,
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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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"save_steps": 10,
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"micro_batch_size": 4,
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"gradient_accumulation_steps": 2,
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"sdp_attention": sdp_attention,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_8bit",
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"lr_scheduler": "cosine",
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"use_tensorboard": True,
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"fp16": 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_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) / "adapter_model.bin").exists()
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check_tensorboard(
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temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
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)
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