|
""" |
|
E2E tests for mixtral |
|
""" |
|
|
|
import logging |
|
import os |
|
import unittest |
|
from pathlib import Path |
|
|
|
import torch |
|
from transformers.utils import is_torch_bf16_gpu_available |
|
|
|
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 with_temp_dir |
|
|
|
LOG = logging.getLogger("axolotl.tests.e2e") |
|
os.environ["WANDB_DISABLED"] = "true" |
|
|
|
|
|
class TestMixtral(unittest.TestCase): |
|
""" |
|
Test case for Llama models using LoRA |
|
""" |
|
|
|
@with_temp_dir |
|
def test_qlora_w_fa2(self, temp_dir): |
|
|
|
cfg = DictDefault( |
|
{ |
|
"base_model": "hf-internal-testing/Mixtral-tiny", |
|
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
|
"flash_attention": True, |
|
"sequence_len": 1024, |
|
"load_in_4bit": True, |
|
"adapter": "qlora", |
|
"lora_r": 4, |
|
"lora_alpha": 8, |
|
"lora_dropout": 0.1, |
|
"lora_target_modules": [ |
|
"o_proj", |
|
"w3", |
|
"k_proj", |
|
"v_proj", |
|
"w1", |
|
"q_proj", |
|
"w2", |
|
], |
|
"val_set_size": 0.1, |
|
"special_tokens": {}, |
|
"datasets": [ |
|
{ |
|
"path": "mhenrichsen/alpaca_2k_test", |
|
"type": "alpaca", |
|
}, |
|
], |
|
"num_epochs": 2, |
|
"micro_batch_size": 2, |
|
"gradient_accumulation_steps": 1, |
|
"output_dir": temp_dir, |
|
"learning_rate": 0.00001, |
|
"optimizer": "adamw_bnb_8bit", |
|
"lr_scheduler": "cosine", |
|
"max_steps": 20, |
|
"save_steps": 10, |
|
"eval_steps": 10, |
|
} |
|
) |
|
normalize_config(cfg) |
|
cli_args = TrainerCliArgs() |
|
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
|
model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
|
assert ( |
|
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
|
== torch.float32 |
|
) |
|
assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
|
@with_temp_dir |
|
def test_qlora_wo_fa2(self, temp_dir): |
|
|
|
cfg = DictDefault( |
|
{ |
|
"base_model": "hf-internal-testing/Mixtral-tiny", |
|
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
|
"flash_attention": False, |
|
"sequence_len": 1024, |
|
"load_in_4bit": True, |
|
"adapter": "qlora", |
|
"lora_r": 4, |
|
"lora_alpha": 8, |
|
"lora_dropout": 0.1, |
|
"lora_target_modules": [ |
|
"o_proj", |
|
"w3", |
|
"k_proj", |
|
"v_proj", |
|
"w1", |
|
"q_proj", |
|
"w2", |
|
], |
|
"val_set_size": 0.1, |
|
"special_tokens": {}, |
|
"datasets": [ |
|
{ |
|
"path": "mhenrichsen/alpaca_2k_test", |
|
"type": "alpaca", |
|
}, |
|
], |
|
"num_epochs": 2, |
|
"micro_batch_size": 2, |
|
"gradient_accumulation_steps": 1, |
|
"output_dir": temp_dir, |
|
"learning_rate": 0.00001, |
|
"optimizer": "adamw_bnb_8bit", |
|
"lr_scheduler": "cosine", |
|
"max_steps": 20, |
|
"save_steps": 10, |
|
"eval_steps": 10, |
|
} |
|
) |
|
normalize_config(cfg) |
|
cli_args = TrainerCliArgs() |
|
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
|
model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
|
assert ( |
|
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
|
== torch.float32 |
|
) |
|
assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
|
@with_temp_dir |
|
def test_16bit_lora_w_fa2(self, temp_dir): |
|
|
|
cfg = DictDefault( |
|
{ |
|
"base_model": "hf-internal-testing/Mixtral-tiny", |
|
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
|
"flash_attention": True, |
|
"sequence_len": 1024, |
|
"adapter": "lora", |
|
"lora_r": 4, |
|
"lora_alpha": 8, |
|
"lora_dropout": 0.1, |
|
"lora_target_modules": [ |
|
"o_proj", |
|
"w3", |
|
"k_proj", |
|
"v_proj", |
|
"w1", |
|
"q_proj", |
|
"w2", |
|
], |
|
"val_set_size": 0.1, |
|
"special_tokens": {}, |
|
"datasets": [ |
|
{ |
|
"path": "mhenrichsen/alpaca_2k_test", |
|
"type": "alpaca", |
|
}, |
|
], |
|
"num_epochs": 2, |
|
"micro_batch_size": 2, |
|
"gradient_accumulation_steps": 1, |
|
"output_dir": temp_dir, |
|
"learning_rate": 0.00001, |
|
"optimizer": "adamw_bnb_8bit", |
|
"lr_scheduler": "cosine", |
|
"max_steps": 20, |
|
"save_steps": 10, |
|
"eval_steps": 10, |
|
} |
|
) |
|
if is_torch_bf16_gpu_available(): |
|
cfg.bf16 = True |
|
else: |
|
cfg.fp16 = True |
|
normalize_config(cfg) |
|
cli_args = TrainerCliArgs() |
|
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
|
model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
|
assert ( |
|
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
|
== torch.float32 |
|
) |
|
assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
|
@with_temp_dir |
|
def test_16bit_lora_wo_fa2(self, temp_dir): |
|
|
|
cfg = DictDefault( |
|
{ |
|
"base_model": "hf-internal-testing/Mixtral-tiny", |
|
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
|
"flash_attention": False, |
|
"sequence_len": 1024, |
|
"adapter": "lora", |
|
"lora_r": 4, |
|
"lora_alpha": 8, |
|
"lora_dropout": 0.1, |
|
"lora_target_modules": [ |
|
"o_proj", |
|
"w3", |
|
"k_proj", |
|
"v_proj", |
|
"w1", |
|
"q_proj", |
|
"w2", |
|
], |
|
"val_set_size": 0.1, |
|
"special_tokens": {}, |
|
"datasets": [ |
|
{ |
|
"path": "mhenrichsen/alpaca_2k_test", |
|
"type": "alpaca", |
|
}, |
|
], |
|
"num_epochs": 2, |
|
"micro_batch_size": 2, |
|
"gradient_accumulation_steps": 1, |
|
"output_dir": temp_dir, |
|
"learning_rate": 0.00001, |
|
"optimizer": "adamw_bnb_8bit", |
|
"lr_scheduler": "cosine", |
|
"max_steps": 20, |
|
"save_steps": 10, |
|
"eval_steps": 10, |
|
} |
|
) |
|
normalize_config(cfg) |
|
if is_torch_bf16_gpu_available(): |
|
cfg.bf16 = True |
|
else: |
|
cfg.fp16 = True |
|
cli_args = TrainerCliArgs() |
|
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args) |
|
|
|
model, _ = train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta) |
|
assert ( |
|
model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype |
|
== torch.float32 |
|
) |
|
assert (Path(temp_dir) / "adapter_model.bin").exists() |
|
|
|
@with_temp_dir |
|
def test_ft(self, temp_dir): |
|
|
|
cfg = DictDefault( |
|
{ |
|
"base_model": "hf-internal-testing/Mixtral-tiny", |
|
"tokenizer_config": "LoneStriker/Mixtral-8x7B-v0.1-HF", |
|
"flash_attention": True, |
|
"sequence_len": 1024, |
|
"val_set_size": 0.1, |
|
"special_tokens": {}, |
|
"datasets": [ |
|
{ |
|
"path": "mhenrichsen/alpaca_2k_test", |
|
"type": "alpaca", |
|
}, |
|
], |
|
"num_epochs": 2, |
|
"micro_batch_size": 2, |
|
"gradient_accumulation_steps": 1, |
|
"output_dir": temp_dir, |
|
"learning_rate": 0.00001, |
|
"optimizer": "adamw_bnb_8bit", |
|
"lr_scheduler": "cosine", |
|
"max_steps": 20, |
|
"save_steps": 10, |
|
"eval_steps": 10, |
|
} |
|
) |
|
if is_torch_bf16_gpu_available(): |
|
cfg.bf16 = True |
|
else: |
|
cfg.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) / "pytorch_model.bin").exists() |
|
|
