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perform flakey patched tests in individual runner

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Wing Lian
2024-12-12 23:22:28 -05:00
parent effc4dc409
commit 79612da5c8
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tests/e2e/each/test_unsloth_qlora.py Normal file
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"""
e2e tests for unsloth qlora
"""
import logging
import os
from pathlib import Path
import pytest
from axolotl.cli import load_datasets
from axolotl.common.cli import TrainerCliArgs
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.dict import DictDefault
from ..utils import check_tensorboard
LOG = logging.getLogger("axolotl.tests.e2e")
os.environ["WANDB_DISABLED"] = "true"
# pylint: disable=duplicate-code
class TestUnslothQLoRA:
"""
Test class for Unsloth QLoRA Llama models
"""
@pytest.mark.parametrize(
"sample_packing",
[True, False],
)
def test_unsloth_llama_qlora_fa2(self, temp_dir, sample_packing):
cfg = DictDefault(
{
"base_model": "HuggingFaceTB/SmolLM2-135M",
"sequence_len": 1024,
"sample_packing": sample_packing,
"flash_attention": True,
"unsloth_lora_mlp": True,
"unsloth_lora_qkv": True,
"unsloth_lora_o": True,
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 16,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.05,
"special_tokens": {
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 1,
"max_steps": 5,
"save_steps": 10,
"micro_batch_size": 4,
"gradient_accumulation_steps": 2,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_8bit",
"lr_scheduler": "cosine",
"use_tensorboard": True,
"bf16": "auto",
}
)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "adapter_model.bin").exists()
check_tensorboard(
temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
)
def test_unsloth_llama_qlora_unpacked(self, temp_dir):
cfg = DictDefault(
{
"base_model": "HuggingFaceTB/SmolLM2-135M",
"sequence_len": 1024,
"unsloth_lora_mlp": True,
"unsloth_lora_qkv": True,
"unsloth_lora_o": True,
"sample_packing": False,
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 16,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.05,
"special_tokens": {
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 1,
"max_steps": 5,
"save_steps": 10,
"micro_batch_size": 4,
"gradient_accumulation_steps": 2,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_8bit",
"lr_scheduler": "cosine",
"use_tensorboard": True,
"bf16": "auto",
}
)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "adapter_model.bin").exists()
check_tensorboard(
temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
)
@pytest.mark.parametrize(
"sdp_attention",
[True, False],
)
def test_unsloth_llama_qlora_unpacked_no_fa2_fp16(self, temp_dir, sdp_attention):
cfg = DictDefault(
{
"base_model": "HuggingFaceTB/SmolLM2-135M",
"sequence_len": 1024,
"unsloth_lora_mlp": True,
"unsloth_lora_qkv": True,
"unsloth_lora_o": True,
"sample_packing": False,
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 16,
"lora_alpha": 16,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.05,
"special_tokens": {
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 1,
"max_steps": 5,
"save_steps": 10,
"micro_batch_size": 4,
"gradient_accumulation_steps": 2,
"sdp_attention": sdp_attention,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_8bit",
"lr_scheduler": "cosine",
"use_tensorboard": True,
"fp16": True,
}
)
normalize_config(cfg)
cli_args = TrainerCliArgs()
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "adapter_model.bin").exists()
check_tensorboard(
temp_dir + "/runs", "train/train_loss", 2.0, "Train Loss is too high"
)