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import unittest
import os
from pathlib import Path
from unittest.mock import patch
from omegaconf import OmegaConf
from experiments import exp_base
class DummyExperiment(exp_base.BaseLightningExperiment):
compatible_algorithms = {}
compatible_datasets = {}
def _build_training_loader(self):
return None
def _build_validation_loader(self):
return None
def _build_test_loader(self):
return None
class DummyTrainer:
init_calls = []
fit_calls = []
validate_calls = []
test_calls = []
save_checkpoint_calls = []
def __init__(self, *args, **kwargs):
self.init_calls.append({"args": args, "kwargs": kwargs})
def fit(self, *args, **kwargs):
self.fit_calls.append({"args": args, "kwargs": kwargs})
def validate(self, *args, **kwargs):
self.validate_calls.append({"args": args, "kwargs": kwargs})
def test(self, *args, **kwargs):
self.test_calls.append({"args": args, "kwargs": kwargs})
def save_checkpoint(self, path):
self.save_checkpoint_calls.append(path)
def _root_cfg(auto_resuming=True, algorithm_name="dummy", zero_init_gate=False, only_tune_memory=False):
return OmegaConf.create(
{
"debug": False,
"_auto_resuming": auto_resuming,
"customized_load": True,
"seperate_load": True,
"zero_init_gate": zero_init_gate,
"only_tune_memory": only_tune_memory,
"diffusion_model_path": "oasis500m.safetensors",
"vae_path": "vit-l-20.safetensors",
"algorithm": {"_name": algorithm_name},
"dataset": {"_name": "dummy"},
"experiment": {
"debug": False,
"num_nodes": 1,
"tasks": ["training"],
"training": {
"compile": False,
"precision": 32,
"batch_size": 1,
"max_epochs": 1,
"max_steps": 1,
"max_time": None,
"data": {"shuffle": False, "num_workers": 0},
"optim": {"gradient_clip_val": 0, "accumulate_grad_batches": 1},
},
"validation": {
"compile": False,
"precision": 32,
"inference_mode": True,
"val_every_n_step": None,
"val_every_n_epoch": None,
"limit_batch": 0,
"batch_size": 1,
"data": {"shuffle": False, "num_workers": 0},
},
"test": {
"compile": False,
"precision": 32,
"inference_mode": True,
"limit_batch": 0,
"batch_size": 1,
"data": {"shuffle": False, "num_workers": 0},
},
},
}
)
class ResumeCheckpointLogicTests(unittest.TestCase):
def test_dememwm_rejects_zero_init_gate(self):
with self.assertRaisesRegex(ValueError, "zero_init_gate.*dememwm_base"):
DummyExperiment(_root_cfg(algorithm_name="dememwm_base", zero_init_gate=True), logger=None)
def test_dememwm_rejects_only_tune_memory(self):
with self.assertRaisesRegex(ValueError, "only_tune_memory.*dememwm_base"):
DummyExperiment(_root_cfg(algorithm_name="dememwm_base", only_tune_memory=True), logger=None)
def test_non_dememwm_keeps_stale_flag_behavior(self):
experiment = DummyExperiment(
_root_cfg(algorithm_name="dummy", zero_init_gate=True, only_tune_memory=True),
logger=None,
)
self.assertTrue(experiment.zero_init_gate)
self.assertTrue(experiment.only_tune_memory)
def test_auto_resume_takes_priority_over_custom_separate_load(self):
DummyTrainer.fit_calls = []
experiment = DummyExperiment(_root_cfg(auto_resuming=True), logger=None, ckpt_path="/tmp/last.ckpt")
experiment.algo = object()
with patch.object(exp_base.pl, "Trainer", DummyTrainer), patch.object(
exp_base,
"load_custom_checkpoint",
side_effect=AssertionError("custom load should not run during auto-resume"),
):
experiment.training()
self.assertEqual(DummyTrainer.fit_calls[0]["kwargs"]["ckpt_path"], "/tmp/last.ckpt")
def test_curriculum_updates_stage_config_and_checkpoint_handoff(self):
DummyTrainer.init_calls = []
DummyTrainer.fit_calls = []
DummyTrainer.save_checkpoint_calls = []
cfg = _root_cfg(auto_resuming=True)
cfg.experiment.training.curriculum = {
"enabled": True,
"stages": [
{
"name": "near",
"until_step": 3,
"dataset": {"wo_updown": True, "memory_selection": {"pose_similarity_radius": 2.0}},
"algorithm": {"memory_selection": {"pose_similarity_radius": 2.0}},
},
{
"name": "full",
"until_step": 5,
"dataset": {"wo_updown": False, "memory_selection": {"pose_similarity_radius": 8.0}},
"algorithm": {"memory_selection": {"pose_similarity_radius": 8.0}},
},
],
}
experiment = DummyExperiment(cfg, logger=None, ckpt_path="/tmp/last.ckpt")
experiment.algo = object()
seen_dataset = []
def build_training_loader():
seen_dataset.append(
(
bool(experiment.root_cfg.dataset.wo_updown),
float(experiment.root_cfg.dataset.memory_selection.pose_similarity_radius),
)
)
return None
experiment._build_training_loader = build_training_loader
with patch.object(exp_base.pl, "Trainer", DummyTrainer), patch.object(
DummyExperiment,
"_curriculum_checkpoint_path",
return_value=Path("/tmp/curriculum_stage.ckpt"),
):
experiment.training()
self.assertEqual(seen_dataset, [(True, 2.0), (False, 8.0)])
self.assertEqual(
[call["kwargs"]["ckpt_path"] for call in DummyTrainer.fit_calls],
["/tmp/last.ckpt", Path("/tmp/curriculum_stage.ckpt")],
)
self.assertEqual(
[call["kwargs"]["max_steps"] for call in DummyTrainer.init_calls],
[3, 5],
)
self.assertEqual(DummyTrainer.save_checkpoint_calls, [Path("/tmp/curriculum_stage.ckpt")])
self.assertEqual(float(experiment.root_cfg.algorithm.memory_selection.pose_similarity_radius), 8.0)
def test_auto_resume_curriculum_skips_completed_stages(self):
DummyTrainer.init_calls = []
DummyTrainer.fit_calls = []
DummyTrainer.save_checkpoint_calls = []
cfg = _root_cfg(auto_resuming=True)
cfg.experiment.training.curriculum = {
"enabled": True,
"stages": [
{"name": "near", "until_step": 3, "dataset": {"wo_updown": True}},
{"name": "full", "until_step": 5, "dataset": {"wo_updown": False}},
],
}
experiment = DummyExperiment(cfg, logger=None, ckpt_path="/tmp/epoch1_step4.ckpt")
experiment.algo = object()
seen_dataset = []
def build_training_loader():
seen_dataset.append(bool(experiment.root_cfg.dataset.wo_updown))
return None
experiment._build_training_loader = build_training_loader
with patch.object(exp_base.pl, "Trainer", DummyTrainer), patch.object(
DummyExperiment,
"_curriculum_checkpoint_path",
return_value=Path("/tmp/curriculum_stage.ckpt"),
):
experiment.training()
self.assertEqual(seen_dataset, [False])
self.assertEqual(
[call["kwargs"]["ckpt_path"] for call in DummyTrainer.fit_calls],
["/tmp/epoch1_step4.ckpt"],
)
self.assertEqual(
[call["kwargs"]["max_steps"] for call in DummyTrainer.init_calls],
[5],
)
self.assertEqual(DummyTrainer.save_checkpoint_calls, [])
def test_validation_load_checkpoint_takes_priority_over_separate_load(self):
DummyTrainer.validate_calls = []
experiment = DummyExperiment(_root_cfg(auto_resuming=False), logger=None, ckpt_path="/tmp/trained.ckpt")
experiment.algo = object()
with patch.object(exp_base.pl, "Trainer", DummyTrainer), patch.object(exp_base, "load_custom_checkpoint") as load_mock:
experiment.validation()
load_mock.assert_called_once_with(algo=experiment.algo, checkpoint_path="/tmp/trained.ckpt")
self.assertIsNone(DummyTrainer.validate_calls[0]["kwargs"]["ckpt_path"])
def test_test_load_checkpoint_takes_priority_over_separate_load(self):
DummyTrainer.test_calls = []
experiment = DummyExperiment(_root_cfg(auto_resuming=False), logger=None, ckpt_path="/tmp/trained.ckpt")
experiment.algo = object()
with patch.object(exp_base.pl, "Trainer", DummyTrainer), patch.object(exp_base, "load_custom_checkpoint") as load_mock:
experiment.test()
load_mock.assert_called_once_with(algo=experiment.algo, checkpoint_path="/tmp/trained.ckpt")
self.assertIsNone(DummyTrainer.test_calls[0]["kwargs"]["ckpt_path"])
def test_pre_lightning_rank_uses_slurm_procid(self):
import main
import utils.distributed_utils as distributed_utils
old_env = os.environ.copy()
try:
os.environ.pop("RANK", None)
os.environ["SLURM_PROCID"] = "1"
self.assertEqual(main._process_rank(), 1)
reloaded = importlib.reload(distributed_utils)
self.assertFalse(reloaded.is_rank_zero)
finally:
os.environ.clear()
os.environ.update(old_env)
importlib.reload(distributed_utils)
if __name__ == "__main__":
unittest.main()
|