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refactor unet_2d tests (#13901)

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Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
This commit is contained in:
Akshan Krithick
2026-06-09 21:30:25 -07:00
committed by GitHub
parent 0d56193cd1
commit f809080764
1 changed files with 119 additions and 189 deletions
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308
tests/models/unets/test_models_unet_2d.py
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@@ -15,12 +15,12 @@
import gc
import math
import unittest
import pytest
import torch
from diffusers import UNet2DModel
from diffusers.utils import logging
from diffusers.utils.torch_utils import randn_tensor
from ...testing_utils import (
backend_empty_cache,
@@ -31,39 +31,31 @@ from ...testing_utils import (
torch_all_close,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
from ..testing_utils import BaseModelTesterConfig, MemoryTesterMixin, ModelTesterMixin, TrainingTesterMixin
logger = logging.get_logger(__name__)
enable_full_determinism()
class Unet2DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = UNet2DModel
main_input_name = "sample"
class Unet2DModelTesterConfig(BaseModelTesterConfig):
@property
def model_class(self):
return UNet2DModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
def main_input_name(self) -> str:
return "sample"
@property
def input_shape(self):
def output_shape(self) -> tuple:
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def generator(self):
return torch.Generator("cpu").manual_seed(0)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
def get_init_dict(self) -> dict:
return {
"block_out_channels": (4, 8),
"norm_num_groups": 2,
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
@@ -74,110 +66,77 @@ class Unet2DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
"layers_per_block": 2,
"sample_size": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def get_dummy_inputs(self) -> dict:
noise = randn_tensor((4, 3, 32, 32), generator=self.generator, device=torch_device)
timestep = torch.tensor([10], device=torch_device)
return {"sample": noise, "timestep": timestep}
class TestUnet2DModel(Unet2DModelTesterConfig, ModelTesterMixin):
def test_mid_block_attn_groups(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict = self.get_init_dict()
init_dict["add_attention"] = True
init_dict["attn_norm_num_groups"] = 4
model = self.model_class(**init_dict).to(torch_device).eval()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
self.assertIsNotNone(
model.mid_block.attentions[0].group_norm, "Mid block Attention group norm should exist but does not."
assert model.mid_block.attentions[0].group_norm is not None, (
"Mid block Attention group norm should exist but does not."
)
self.assertEqual(
model.mid_block.attentions[0].group_norm.num_groups,
init_dict["attn_norm_num_groups"],
"Mid block Attention group norm does not have the expected number of groups.",
assert model.mid_block.attentions[0].group_norm.num_groups == init_dict["attn_norm_num_groups"], (
"Mid block Attention group norm does not have the expected number of groups."
)
with torch.no_grad():
output = model(**inputs_dict)
output = model(**self.get_dummy_inputs()).sample
if isinstance(output, dict):
output = output.to_tuple()[0]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
assert output.shape == self.get_dummy_inputs()["sample"].shape, "Input and output shapes do not match"
def test_mid_block_none(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
mid_none_init_dict, mid_none_inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict = self.get_init_dict()
mid_none_init_dict = self.get_init_dict()
mid_none_init_dict["mid_block_type"] = None
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
mid_none_model = self.model_class(**mid_none_init_dict)
mid_none_model.to(torch_device)
mid_none_model.eval()
self.assertIsNone(mid_none_model.mid_block, "Mid block should not exist.")
model = self.model_class(**init_dict).to(torch_device).eval()
mid_none_model = self.model_class(**mid_none_init_dict).to(torch_device).eval()
assert mid_none_model.mid_block is None, "Mid block should not exist."
with torch.no_grad():
output = model(**inputs_dict)
output = model(**self.get_dummy_inputs()).sample
mid_none_output = mid_none_model(**self.get_dummy_inputs()).sample
if isinstance(output, dict):
output = output.to_tuple()[0]
assert not torch.allclose(output, mid_none_output, rtol=1e-3), "outputs should be different."
with torch.no_grad():
mid_none_output = mid_none_model(**mid_none_inputs_dict)
if isinstance(mid_none_output, dict):
mid_none_output = mid_none_output.to_tuple()[0]
self.assertFalse(torch.allclose(output, mid_none_output, rtol=1e-3), "outputs should be different.")
class TestUnet2DModelTraining(Unet2DModelTesterConfig, TrainingTesterMixin):
def test_gradient_checkpointing_is_applied(self):
expected_set = {
"AttnUpBlock2D",
"AttnDownBlock2D",
"UNetMidBlock2D",
"UpBlock2D",
"DownBlock2D",
}
# NOTE: unlike UNet2DConditionModel, UNet2DModel does not currently support tuples for `attention_head_dim`
attention_head_dim = 8
block_out_channels = (16, 32)
super().test_gradient_checkpointing_is_applied(
expected_set=expected_set, attention_head_dim=attention_head_dim, block_out_channels=block_out_channels
)
expected_set = {"AttnUpBlock2D", "AttnDownBlock2D", "UNetMidBlock2D", "UpBlock2D", "DownBlock2D"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = UNet2DModel
main_input_name = "sample"
class TestUnet2DModelMemory(Unet2DModelTesterConfig, MemoryTesterMixin):
"""Memory optimization tests for UNet2DModel."""
class UNetLDMModelTesterConfig(BaseModelTesterConfig):
@property
def model_class(self):
return UNet2DModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 4
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"sample": noise, "timestep": time_step}
def main_input_name(self) -> str:
return "sample"
@property
def input_shape(self):
def output_shape(self) -> tuple:
return (4, 32, 32)
@property
def output_shape(self):
return (4, 32, 32)
def generator(self):
return torch.Generator("cpu").manual_seed(0)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
def get_init_dict(self) -> dict:
return {
"sample_size": 32,
"in_channels": 4,
"out_channels": 4,
@@ -187,26 +146,28 @@ class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
"down_block_types": ("DownBlock2D", "DownBlock2D"),
"up_block_types": ("UpBlock2D", "UpBlock2D"),
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def get_dummy_inputs(self) -> dict:
noise = randn_tensor((4, 4, 32, 32), generator=self.generator, device=torch_device)
timestep = torch.tensor([10], device=torch_device)
return {"sample": noise, "timestep": timestep}
class TestUNetLDMModel(UNetLDMModelTesterConfig, ModelTesterMixin):
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
assert model is not None
assert len(loading_info["missing_keys"]) == 0
model.to(torch_device)
image = model(**self.dummy_input).sample
image = model(**self.get_dummy_inputs()).sample
assert image is not None, "Make sure output is not None"
@require_torch_accelerator
def test_from_pretrained_accelerate(self):
model, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
model.to(torch_device)
image = model(**self.dummy_input).sample
image = model(**self.get_dummy_inputs()).sample
assert image is not None, "Make sure output is not None"
@require_torch_accelerator
@@ -264,45 +225,38 @@ class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
# fmt: off
expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
# fmt: on
assert torch_all_close(output_slice, expected_output_slice, rtol=1e-3)
self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3))
class TestUNetLDMModelTraining(UNetLDMModelTesterConfig, TrainingTesterMixin):
def test_gradient_checkpointing_is_applied(self):
expected_set = {"DownBlock2D", "UNetMidBlock2D", "UpBlock2D"}
# NOTE: unlike UNet2DConditionModel, UNet2DModel does not currently support tuples for `attention_head_dim`
attention_head_dim = 32
block_out_channels = (32, 64)
super().test_gradient_checkpointing_is_applied(
expected_set=expected_set, attention_head_dim=attention_head_dim, block_out_channels=block_out_channels
)
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = UNet2DModel
main_input_name = "sample"
class TestUNetLDMModelMemory(UNetLDMModelTesterConfig, MemoryTesterMixin):
"""Memory optimization tests for the LDM UNet2DModel config."""
class NCSNppModelTesterConfig(BaseModelTesterConfig):
@property
def model_class(self):
return UNet2DModel
@property
def dummy_input(self, sizes=(32, 32)):
batch_size = 4
num_channels = 3
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(dtype=torch.int32, device=torch_device)
return {"sample": noise, "timestep": time_step}
def main_input_name(self) -> str:
return "sample"
@property
def input_shape(self):
def output_shape(self) -> tuple:
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def generator(self):
return torch.Generator("cpu").manual_seed(0)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
def get_init_dict(self) -> dict:
return {
"block_out_channels": [32, 64, 64, 64],
"in_channels": 3,
"layers_per_block": 1,
@@ -311,34 +265,27 @@ class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
"norm_eps": 1e-6,
"mid_block_scale_factor": math.sqrt(2.0),
"norm_num_groups": None,
"down_block_types": [
"SkipDownBlock2D",
"AttnSkipDownBlock2D",
"SkipDownBlock2D",
"SkipDownBlock2D",
],
"up_block_types": [
"SkipUpBlock2D",
"SkipUpBlock2D",
"AttnSkipUpBlock2D",
"SkipUpBlock2D",
],
"down_block_types": ["SkipDownBlock2D", "AttnSkipDownBlock2D", "SkipDownBlock2D", "SkipDownBlock2D"],
"up_block_types": ["SkipUpBlock2D", "SkipUpBlock2D", "AttnSkipUpBlock2D", "SkipUpBlock2D"],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def get_dummy_inputs(self) -> dict:
noise = randn_tensor((4, 3, 32, 32), generator=self.generator, device=torch_device)
timestep = torch.tensor(4 * [10], dtype=torch.int32, device=torch_device)
return {"sample": noise, "timestep": timestep}
class TestNCSNppModel(NCSNppModelTesterConfig, ModelTesterMixin):
@slow
def test_from_pretrained_hub(self):
model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
assert model is not None
assert len(loading_info["missing_keys"]) == 0
model.to(torch_device)
inputs = self.dummy_input
noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
inputs["sample"] = noise
inputs = self.get_dummy_inputs()
inputs["sample"] = floats_tensor((4, 3) + (256, 256)).to(torch_device)
image = model(**inputs)
assert image is not None, "Make sure output is not None"
@slow
@@ -346,12 +293,8 @@ class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
model.to(torch_device)
batch_size = 4
num_channels = 3
sizes = (256, 256)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
noise = torch.ones((4, 3) + (256, 256)).to(torch_device)
time_step = torch.tensor(4 * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step).sample
@@ -360,19 +303,14 @@ class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
# fmt: off
expected_output_slice = torch.tensor([-4836.2178, -6487.1470, -3816.8196, -7964.9302, -10966.3037, -20043.5957, 8137.0513, 2340.3328, 544.6056])
# fmt: on
self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
assert torch_all_close(output_slice, expected_output_slice, rtol=1e-2)
def test_output_pretrained_ve_large(self):
model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
model.to(torch_device)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
noise = torch.ones((4, 3) + (32, 32)).to(torch_device)
time_step = torch.tensor(4 * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step).sample
@@ -381,36 +319,28 @@ class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
# fmt: off
expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
# fmt: on
assert torch_all_close(output_slice, expected_output_slice, rtol=1e-2)
self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
@unittest.skip("Test not supported.")
def test_forward_with_norm_groups(self):
# not required for this model
pass
class TestNCSNppModelTraining(NCSNppModelTesterConfig, TrainingTesterMixin):
def test_gradient_checkpointing_is_applied(self):
expected_set = {
"UNetMidBlock2D",
}
expected_set = {"UNetMidBlock2D"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
block_out_channels = (32, 64, 64, 64)
def test_gradient_checkpointing_equivalence(self):
super().test_gradient_checkpointing_equivalence(skip={"time_proj.weight"})
super().test_gradient_checkpointing_is_applied(
expected_set=expected_set, block_out_channels=block_out_channels
)
def test_effective_gradient_checkpointing(self):
super().test_effective_gradient_checkpointing(skip={"time_proj.weight"})
@unittest.skip(
"To make layerwise casting work with this model, we will have to update the implementation. Due to potentially low usage, we don't support it here."
)
def test_layerwise_casting_inference(self):
pass
@unittest.skip(
"To make layerwise casting work with this model, we will have to update the implementation. Due to potentially low usage, we don't support it here."
)
class TestNCSNppModelMemory(NCSNppModelTesterConfig, MemoryTesterMixin):
# Layerwise casting is not supported for this model.
@pytest.mark.skip("Layerwise casting is not supported for this model.")
def test_layerwise_casting_memory(self):
pass
@pytest.mark.skip("Layerwise casting is not supported for this model.")
def test_layerwise_casting_training(self):
pass
@pytest.mark.skip("Layerwise casting is not supported for this model.")
def test_group_offloading_with_layerwise_casting(self, *args, **kwargs):
pass