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# torchrun --nnodes=1 --nproc_per_node=1 -m pytest -s tests/trainer/test_sft_trainer.py
import json
import os
import pathlib
import tempfile
import time
import unittest
import pytest
from diffusers.utils import export_to_video
from parameterized import parameterized
from PIL import Image
from finetrainers import BaseArgs, ControlTrainer, TrainingType, get_logger
from finetrainers.trainer.control_trainer.config import ControlType
os.environ["WANDB_MODE"] = "disabled"
os.environ["FINETRAINERS_LOG_LEVEL"] = "INFO"
from ..models.cogview4.control_specification import DummyCogView4ControlModelSpecification # noqa
from ..models.wan.control_specification import DummyWanControlModelSpecification # noqa
logger = get_logger()
@pytest.fixture(autouse=True)
def slow_down_tests():
yield
# Sleep between each test so that process groups are cleaned and resources are released.
# Not doing so seems to randomly trigger some test failures, which wouldn't fail if run individually.
# !!!Look into this in future!!!
time.sleep(5)
class ControlTrainerFastTestsMixin:
model_specification_cls = None
num_data_files = 4
num_frames = 4
height = 64
width = 64
def setUp(self):
self.tmpdir = tempfile.TemporaryDirectory()
self.data_files = []
for i in range(self.num_data_files):
data_file = pathlib.Path(self.tmpdir.name) / f"{i}.mp4"
export_to_video(
[Image.new("RGB", (self.width, self.height))] * self.num_frames, data_file.as_posix(), fps=2
)
self.data_files.append(data_file.as_posix())
csv_filename = pathlib.Path(self.tmpdir.name) / "metadata.csv"
with open(csv_filename.as_posix(), "w") as f:
f.write("file_name,caption\n")
for i in range(self.num_data_files):
prompt = f"A cat ruling the world - {i}"
f.write(f'{i}.mp4,"{prompt}"\n')
dataset_config = {
"datasets": [
{
"data_root": self.tmpdir.name,
"dataset_type": "video",
"id_token": "TEST",
"video_resolution_buckets": [[self.num_frames, self.height, self.width]],
"reshape_mode": "bicubic",
}
]
}
self.dataset_config_filename = pathlib.Path(self.tmpdir.name) / "dataset_config.json"
with open(self.dataset_config_filename.as_posix(), "w") as f:
json.dump(dataset_config, f)
def tearDown(self):
self.tmpdir.cleanup()
def get_base_args(self) -> BaseArgs:
args = BaseArgs()
args.dataset_config = self.dataset_config_filename.as_posix()
args.train_steps = 10
args.max_data_samples = 25
args.batch_size = 1
args.gradient_checkpointing = True
args.output_dir = self.tmpdir.name
args.checkpointing_steps = 6
args.enable_precomputation = False
args.precomputation_items = self.num_data_files
args.precomputation_dir = os.path.join(self.tmpdir.name, "precomputed")
args.compile_scopes = "regional" # This will only be in effect when `compile_modules` is set
args.control_type = ControlType.CANNY
args.train_qk_norm = True
args.frame_conditioning_type = "random"
args.frame_conditioning_index = None
args.frame_conditioning_concatenate_mask = False
return args
def get_args(self) -> BaseArgs:
raise NotImplementedError("`get_args` must be implemented in the subclass.")
def _test_training(self, args: BaseArgs):
model_specification = self.model_specification_cls()
trainer = ControlTrainer(args, model_specification)
trainer.run()
class ControlTrainerLoRATestsMixin___PTD(ControlTrainerFastTestsMixin):
def get_args(self) -> BaseArgs:
args = self.get_base_args()
args.parallel_backend = "ptd"
args.training_type = TrainingType.CONTROL_LORA
args.rank = 4
args.lora_alpha = 4
args.target_modules = ["to_q", "to_k", "to_v", "to_out.0"]
return args
@parameterized.expand([(False,), (True,)])
def test___dp_degree_1___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 1
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_1___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 1
args.batch_size = 2
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_2___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_2___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 2
args.batch_size = 2
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_shards_2___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_shards = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_shards_2___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.dp_shards = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_2___dp_shards_2___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 2
args.dp_shards = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___tp_degree_2___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.tp_degree = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
class ControlTrainerFullFinetuneTestsMixin___PTD(ControlTrainerFastTestsMixin):
def get_args(self) -> BaseArgs:
args = self.get_base_args()
args.parallel_backend = "ptd"
args.training_type = TrainingType.CONTROL_FULL_FINETUNE
return args
@parameterized.expand([(False,), (True,)])
def test___dp_degree_1___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 1
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_1___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 1
args.batch_size = 2
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_2___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_2___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 2
args.batch_size = 2
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_shards_2___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_shards = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_shards_2___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.dp_shards = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___dp_degree_2___dp_shards_2___batch_size_1(self, enable_precomputation: bool):
args = self.get_args()
args.dp_degree = 2
args.dp_shards = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
@parameterized.expand([(False,), (True,)])
def test___tp_degree_2___batch_size_2(self, enable_precomputation: bool):
args = self.get_args()
args.tp_degree = 2
args.batch_size = 1
args.enable_precomputation = enable_precomputation
self._test_training(args)
class ControlTrainerCogView4LoRATests___PTD(ControlTrainerLoRATestsMixin___PTD, unittest.TestCase):
model_specification_cls = DummyCogView4ControlModelSpecification
class ControlTrainerCogView4FullFinetuneTests___PTD(ControlTrainerFullFinetuneTestsMixin___PTD, unittest.TestCase):
model_specification_cls = DummyCogView4ControlModelSpecification
class ControlTrainerWanLoRATests___PTD(ControlTrainerLoRATestsMixin___PTD, unittest.TestCase):
model_specification_cls = DummyWanControlModelSpecification
class ControlTrainerWanFullFinetuneTests___PTD(ControlTrainerFullFinetuneTestsMixin___PTD, unittest.TestCase):
model_specification_cls = DummyWanControlModelSpecification
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