05f03b541a
* hf offline decorator for tests to workaround rate limits * fail quicker so we can see logs * try new cache name * limit files downloaded * phi mini predownload * offline decorator for phi tokenizer * handle meta llama 8b offline too * make sure to return fixtures if they are wrapped too * more fixes * more things offline * more offline things * fix the env var * fix the model name * handle gemma also * force reload of modules to recheck offline status * prefetch mistral too * use reset_sessions so hub picks up offline mode * more fixes * rename so it doesn't seem like a context manager * fix backoff * switch out tinyshakespeare dataset since it runs a py script to fetch data and doesn't work offline * include additional dataset * more fixes * more fixes * replace tiny shakespeaere dataset * skip some tests for now * use more robust check using snapshot download to determine if a dataset name is on the hub * typo for skip reason * use local_files_only * more fixtures * remove local only * use tiny shakespeare as pretrain dataset and streaming can't be offline even if precached * make sure fixtures aren't offline improve the offline reset try bumping version of datasets reorder reloading and setting prime a new cache run the tests now with fresh cache try with a static cache * now run all the ci again with hopefully a correct cache * skip wonky tests for now * skip wonky tests for now * handle offline mode for model card creation
134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
"""
|
||
E2E tests for deepseekv3
|
||
"""
|
||
|
||
import logging
|
||
import os
|
||
from pathlib import Path
|
||
|
||
import pytest
|
||
from utils import enable_hf_offline
|
||
|
||
from axolotl.cli.args import TrainerCliArgs
|
||
from axolotl.common.datasets import load_datasets
|
||
from axolotl.train import train
|
||
from axolotl.utils.config import normalize_config, validate_config
|
||
from axolotl.utils.dict import DictDefault
|
||
|
||
LOG = logging.getLogger("axolotl.tests.e2e")
|
||
os.environ["WANDB_DISABLED"] = "true"
|
||
|
||
|
||
class TestDeepseekV3:
|
||
"""
|
||
Test case for DeepseekV3 models
|
||
"""
|
||
|
||
@enable_hf_offline
|
||
@pytest.mark.parametrize(
|
||
"sample_packing",
|
||
[True, False],
|
||
)
|
||
def test_lora_deepseekv3(self, temp_dir, sample_packing):
|
||
# pylint: disable=duplicate-code
|
||
cfg = DictDefault(
|
||
{
|
||
"base_model": "axolotl-ai-co/DeepSeek-V3-11M",
|
||
"trust_remote_code": True,
|
||
"sample_packing": sample_packing,
|
||
"flash_attention": True,
|
||
"sequence_len": 2048,
|
||
"adapter": "lora",
|
||
"lora_r": 8,
|
||
"lora_alpha": 16,
|
||
"lora_dropout": 0.05,
|
||
"lora_target_linear": True,
|
||
"val_set_size": 0,
|
||
"datasets": [
|
||
{
|
||
"path": "mlabonne/FineTome-100k",
|
||
"type": "chat_template",
|
||
"field_messages": "conversations",
|
||
"message_property_mappings": {
|
||
"role": "from",
|
||
"content": "value",
|
||
},
|
||
"drop_system_message": True,
|
||
"split": "train[:1%]",
|
||
},
|
||
],
|
||
"special_tokens": {
|
||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||
"eos_token": "<｜end▁of▁sentence｜>",
|
||
},
|
||
"chat_template": "deepseek_v3",
|
||
"num_epochs": 1,
|
||
"micro_batch_size": 1,
|
||
"gradient_accumulation_steps": 4,
|
||
"output_dir": temp_dir,
|
||
"learning_rate": 0.00001,
|
||
"optimizer": "adamw_bnb_8bit",
|
||
"lr_scheduler": "cosine",
|
||
"max_steps": 5,
|
||
"save_safetensors": True,
|
||
"bf16": True,
|
||
}
|
||
)
|
||
cfg = validate_config(cfg)
|
||
normalize_config(cfg)
|
||
cli_args = TrainerCliArgs()
|
||
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
|
||
|
||
train(cfg=cfg, dataset_meta=dataset_meta)
|
||
assert (Path(temp_dir) / "adapter_model.safetensors").exists()
|
||
|
||
@enable_hf_offline
|
||
@pytest.mark.parametrize(
|
||
"sample_packing",
|
||
[True, False],
|
||
)
|
||
def test_fft_deepseekv3(self, temp_dir, sample_packing):
|
||
# pylint: disable=duplicate-code
|
||
cfg = DictDefault(
|
||
{
|
||
"base_model": "axolotl-ai-co/DeepSeek-V3-11M",
|
||
"trust_remote_code": True,
|
||
"sample_packing": sample_packing,
|
||
"flash_attention": True,
|
||
"sequence_len": 2048,
|
||
"val_set_size": 0,
|
||
"datasets": [
|
||
{
|
||
"path": "mlabonne/FineTome-100k",
|
||
"type": "chat_template",
|
||
"field_messages": "conversations",
|
||
"message_field_role": "from",
|
||
"message_field_content": "value",
|
||
"split": "train[:1%]",
|
||
},
|
||
],
|
||
"chat_template": "deepseek_v3",
|
||
"special_tokens": {
|
||
"bos_token": "<｜begin▁of▁sentence｜>",
|
||
"eos_token": "<｜end▁of▁sentence｜>",
|
||
},
|
||
"num_epochs": 1,
|
||
"micro_batch_size": 1,
|
||
"gradient_accumulation_steps": 4,
|
||
"output_dir": temp_dir,
|
||
"learning_rate": 0.00001,
|
||
"optimizer": "adamw_bnb_8bit",
|
||
"lr_scheduler": "cosine",
|
||
"max_steps": 5,
|
||
"save_safetensors": True,
|
||
"bf16": True,
|
||
}
|
||
)
|
||
cfg = validate_config(cfg)
|
||
normalize_config(cfg)
|
||
cli_args = TrainerCliArgs()
|
||
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
|
||
|
||
train(cfg=cfg, dataset_meta=dataset_meta)
|
||
assert (Path(temp_dir) / "model.safetensors").exists()
|