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[tests] Fix group offloading and layerwise casting test interaction #11796

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Merged
merged 4 commits into from
Jun 24, 2025
Merged

[tests] Fix group offloading and layerwise casting test interaction #11796

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44f64d2
update
a-r-r-o-w Jun 24, 2025
4335679
update
a-r-r-o-w Jun 24, 2025
2e7ad09
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a-r-r-o-w Jun 24, 2025
2914a0f
Merge branch 'main' into cleanup-and-fix-go-lc-tests
a-r-r-o-w Jun 24, 2025
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18 changes: 10 additions & 8 deletions src/diffusers/models/autoencoders/autoencoder_kl_cosmos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,8 +110,11 @@ def __init__(self, patch_size: int = 1, patch_method: str = "haar") -> None:
self.patch_size = patch_size
self.patch_method = patch_method

self.register_buffer("wavelets", _WAVELETS[patch_method], persistent=False)
self.register_buffer("_arange", torch.arange(_WAVELETS[patch_method].shape[0]), persistent=False)
wavelets = _WAVELETS.get(patch_method).clone()
arange = torch.arange(wavelets.shape[0])

self.register_buffer("wavelets", wavelets, persistent=False)
self.register_buffer("_arange", arange, persistent=False)

def _dwt(self, hidden_states: torch.Tensor, mode: str = "reflect", rescale=False) -> torch.Tensor:
dtype = hidden_states.dtype
Expand Down Expand Up @@ -185,12 +188,11 @@ def __init__(self, patch_size: int = 1, patch_method: str = "haar"):
self.patch_size = patch_size
self.patch_method = patch_method

self.register_buffer("wavelets", _WAVELETS[patch_method], persistent=False)
self.register_buffer(
"_arange",
torch.arange(_WAVELETS[patch_method].shape[0]),
persistent=False,
)
wavelets = _WAVELETS.get(patch_method).clone()
arange = torch.arange(wavelets.shape[0])

self.register_buffer("wavelets", wavelets, persistent=False)
self.register_buffer("_arange", arange, persistent=False)

def _idwt(self, hidden_states: torch.Tensor, rescale: bool = False) -> torch.Tensor:
device = hidden_states.device
Expand Down
15 changes: 7 additions & 8 deletions tests/models/test_modeling_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1528,14 +1528,16 @@ def test_fn(storage_dtype, compute_dtype):
test_fn(torch.float8_e5m2, torch.float32)
test_fn(torch.float8_e4m3fn, torch.bfloat16)

@torch.no_grad()
def test_layerwise_casting_inference(self):
from diffusers.hooks.layerwise_casting import DEFAULT_SKIP_MODULES_PATTERN, SUPPORTED_PYTORCH_LAYERS

torch.manual_seed(0)
config, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**config).eval()
model = model.to(torch_device)
base_slice = model(**inputs_dict)[0].flatten().detach().cpu().numpy()
model = self.model_class(**config)
model.eval()
model.to(torch_device)
base_slice = model(**inputs_dict)[0].detach().flatten().cpu().numpy()

def check_linear_dtype(module, storage_dtype, compute_dtype):
patterns_to_check = DEFAULT_SKIP_MODULES_PATTERN
Expand Down Expand Up @@ -1573,6 +1575,7 @@ def test_layerwise_casting(storage_dtype, compute_dtype):
test_layerwise_casting(torch.float8_e4m3fn, torch.bfloat16)

@require_torch_accelerator
@torch.no_grad()
def test_layerwise_casting_memory(self):
MB_TOLERANCE = 0.2
LEAST_COMPUTE_CAPABILITY = 8.0
Expand Down Expand Up @@ -1706,10 +1709,6 @@ def test_group_offloading_with_disk(self, record_stream, offload_type):
if not self.model_class._supports_group_offloading:
pytest.skip("Model does not support group offloading.")

torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)

torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
Expand All @@ -1725,7 +1724,7 @@ def test_group_offloading_with_disk(self, record_stream, offload_type):
**additional_kwargs,
)
has_safetensors = glob.glob(f"{tmpdir}/*.safetensors")
assert has_safetensors, "No safetensors found in the directory."
self.assertTrue(len(has_safetensors) > 0, "No safetensors found in the offload directory.")
_ = model(**inputs_dict)[0]

def test_auto_model(self, expected_max_diff=5e-5):
Expand Down