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use smaller pretrained models for ci (#3620) [skip ci]

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* use smaller pretrained models for ci

* more steps for loss check

* fix tests

* more train steps

* fix losses
This commit is contained in:
Wing Lian
2026-04-27 13:22:56 -04:00
committed by GitHub
parent 798c8fba89
commit ac77da96da
24 changed files with 716 additions and 288 deletions
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116
tests/e2e/test_mixtral.py
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@@ -12,7 +12,11 @@ from axolotl.train import train
from axolotl.utils.config import normalize_config, validate_config
from axolotl.utils.dict import DictDefault
from .utils import check_model_output_exists, with_temp_dir
from .utils import (
check_model_output_exists,
check_tensorboard_loss_decreased,
with_temp_dir,
)
class TestMixtral(unittest.TestCase):
@@ -24,8 +28,7 @@ class TestMixtral(unittest.TestCase):
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",
"base_model": "axolotl-ai-co/tiny-mixtral-30m",
"flash_attention": True,
"sequence_len": 1024,
"load_in_4bit": True,
@@ -51,16 +54,18 @@ class TestMixtral(unittest.TestCase):
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"micro_batch_size": 4,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"learning_rate": 2e-4,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"max_steps": 50,
"logging_steps": 1,
"save_steps": 50,
"eval_steps": 50,
"save_first_step": False,
"use_tensorboard": True,
}
)
@@ -74,13 +79,19 @@ class TestMixtral(unittest.TestCase):
== torch.float32
)
check_model_output_exists(temp_dir, cfg)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=5,
final_window=5,
max_initial=5.0,
max_final=4.7,
)
@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",
"base_model": "axolotl-ai-co/tiny-mixtral-30m",
"flash_attention": False,
"sequence_len": 1024,
"load_in_4bit": True,
@@ -106,16 +117,18 @@ class TestMixtral(unittest.TestCase):
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"micro_batch_size": 4,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"learning_rate": 2e-4,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"max_steps": 50,
"logging_steps": 1,
"save_steps": 50,
"eval_steps": 50,
"save_first_step": False,
"use_tensorboard": True,
}
)
@@ -129,13 +142,19 @@ class TestMixtral(unittest.TestCase):
== torch.float32
)
check_model_output_exists(temp_dir, cfg)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=5,
final_window=5,
max_initial=5.0,
max_final=4.7,
)
@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",
"base_model": "axolotl-ai-co/tiny-mixtral-30m",
"flash_attention": True,
"sequence_len": 1024,
"adapter": "lora",
@@ -160,16 +179,18 @@ class TestMixtral(unittest.TestCase):
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"micro_batch_size": 4,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"learning_rate": 2e-4,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"max_steps": 50,
"logging_steps": 1,
"save_steps": 50,
"eval_steps": 50,
"save_first_step": False,
"use_tensorboard": True,
}
)
if is_torch_bf16_gpu_available():
@@ -187,13 +208,19 @@ class TestMixtral(unittest.TestCase):
== torch.float32
)
check_model_output_exists(temp_dir, cfg)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=5,
final_window=5,
max_initial=5.0,
max_final=4.7,
)
@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",
"base_model": "axolotl-ai-co/tiny-mixtral-30m",
"flash_attention": False,
"sequence_len": 1024,
"adapter": "lora",
@@ -218,16 +245,18 @@ class TestMixtral(unittest.TestCase):
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"micro_batch_size": 4,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"learning_rate": 2e-4,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"max_steps": 50,
"logging_steps": 1,
"save_steps": 50,
"eval_steps": 50,
"save_first_step": False,
"use_tensorboard": True,
}
)
@@ -245,13 +274,19 @@ class TestMixtral(unittest.TestCase):
== torch.float32
)
check_model_output_exists(temp_dir, cfg)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=5,
final_window=5,
max_initial=5.0,
max_final=4.7,
)
@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",
"base_model": "axolotl-ai-co/tiny-mixtral-30m",
"flash_attention": True,
"sequence_len": 1024,
"val_set_size": 0.02,
@@ -263,16 +298,18 @@ class TestMixtral(unittest.TestCase):
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"micro_batch_size": 4,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"learning_rate": 2e-4,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"max_steps": 50,
"logging_steps": 1,
"save_steps": 50,
"eval_steps": 50,
"save_first_step": False,
"use_tensorboard": True,
}
)
if is_torch_bf16_gpu_available():
@@ -286,3 +323,10 @@ class TestMixtral(unittest.TestCase):
train(cfg=cfg, dataset_meta=dataset_meta)
check_model_output_exists(temp_dir, cfg)
check_tensorboard_loss_decreased(
temp_dir + "/runs",
initial_window=5,
final_window=5,
max_initial=5.0,
max_final=4.7,
)