378 lines
13 KiB
Python
378 lines
13 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 HuggingFace Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import math
|
|
import unittest
|
|
|
|
import torch
|
|
|
|
from diffusers import UNet2DConditionModel, UNet2DModel
|
|
from diffusers.utils import floats_tensor, slow, torch_device
|
|
|
|
from .test_modeling_common import ModelTesterMixin
|
|
|
|
|
|
torch.backends.cuda.matmul.allow_tf32 = False
|
|
|
|
|
|
class UnetModelTests(ModelTesterMixin, unittest.TestCase):
|
|
model_class = 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}
|
|
|
|
@property
|
|
def input_shape(self):
|
|
return (3, 32, 32)
|
|
|
|
@property
|
|
def output_shape(self):
|
|
return (3, 32, 32)
|
|
|
|
def prepare_init_args_and_inputs_for_common(self):
|
|
init_dict = {
|
|
"block_out_channels": (32, 64),
|
|
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
|
|
"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
|
|
"attention_head_dim": None,
|
|
"out_channels": 3,
|
|
"in_channels": 3,
|
|
"layers_per_block": 2,
|
|
"sample_size": 32,
|
|
}
|
|
inputs_dict = self.dummy_input
|
|
return init_dict, inputs_dict
|
|
|
|
|
|
# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
|
|
# def test_output_pretrained(self):
|
|
# model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
|
|
# model.eval()
|
|
#
|
|
# torch.manual_seed(0)
|
|
# if torch.cuda.is_available():
|
|
# torch.cuda.manual_seed_all(0)
|
|
#
|
|
# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
|
|
# time_step = torch.tensor([10])
|
|
#
|
|
# with torch.no_grad():
|
|
# output = model(noise, time_step).sample
|
|
#
|
|
# output_slice = output[0, -1, -3:, -3:].flatten()
|
|
# fmt: off
|
|
# expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
|
|
# fmt: on
|
|
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
|
|
|
|
|
class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
|
|
model_class = 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}
|
|
|
|
@property
|
|
def input_shape(self):
|
|
return (4, 32, 32)
|
|
|
|
@property
|
|
def output_shape(self):
|
|
return (4, 32, 32)
|
|
|
|
def prepare_init_args_and_inputs_for_common(self):
|
|
init_dict = {
|
|
"sample_size": 32,
|
|
"in_channels": 4,
|
|
"out_channels": 4,
|
|
"layers_per_block": 2,
|
|
"block_out_channels": (32, 64),
|
|
"attention_head_dim": 32,
|
|
"down_block_types": ("DownBlock2D", "DownBlock2D"),
|
|
"up_block_types": ("UpBlock2D", "UpBlock2D"),
|
|
}
|
|
inputs_dict = self.dummy_input
|
|
return init_dict, inputs_dict
|
|
|
|
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)
|
|
|
|
model.to(torch_device)
|
|
image = model(**self.dummy_input).sample
|
|
|
|
assert image is not None, "Make sure output is not None"
|
|
|
|
def test_output_pretrained(self):
|
|
model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
|
|
model.eval()
|
|
model.to(torch_device)
|
|
|
|
noise = torch.randn(
|
|
1,
|
|
model.config.in_channels,
|
|
model.config.sample_size,
|
|
model.config.sample_size,
|
|
generator=torch.manual_seed(0),
|
|
)
|
|
noise = noise.to(torch_device)
|
|
time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)
|
|
|
|
with torch.no_grad():
|
|
output = model(noise, time_step).sample
|
|
|
|
output_slice = output[0, -1, -3:, -3:].flatten().cpu()
|
|
# 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
|
|
|
|
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
|
|
|
|
|
|
class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
|
|
model_class = UNet2DConditionModel
|
|
|
|
@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)
|
|
encoder_hidden_states = floats_tensor((batch_size, 4, 32)).to(torch_device)
|
|
|
|
return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
|
|
|
|
@property
|
|
def input_shape(self):
|
|
return (4, 32, 32)
|
|
|
|
@property
|
|
def output_shape(self):
|
|
return (4, 32, 32)
|
|
|
|
def prepare_init_args_and_inputs_for_common(self):
|
|
init_dict = {
|
|
"block_out_channels": (32, 64),
|
|
"down_block_types": ("CrossAttnDownBlock2D", "DownBlock2D"),
|
|
"up_block_types": ("UpBlock2D", "CrossAttnUpBlock2D"),
|
|
"cross_attention_dim": 32,
|
|
"attention_head_dim": 8,
|
|
"out_channels": 4,
|
|
"in_channels": 4,
|
|
"layers_per_block": 2,
|
|
"sample_size": 32,
|
|
}
|
|
inputs_dict = self.dummy_input
|
|
return init_dict, inputs_dict
|
|
|
|
def test_gradient_checkpointing(self):
|
|
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
|
|
model = self.model_class(**init_dict).eval()
|
|
model.to(torch_device)
|
|
|
|
out = model(**inputs_dict).sample
|
|
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
|
|
# we won't calculate the loss and rather backprop on out.sum()
|
|
model.zero_grad()
|
|
out.sum().backward()
|
|
|
|
# now we save the output and parameter gradients that we will use for comparison purposes with
|
|
# the non-checkpointed run.
|
|
output_not_checkpointed = out.data.clone()
|
|
grad_not_checkpointed = {}
|
|
for name, param in model.named_parameters():
|
|
grad_not_checkpointed[name] = param.grad.data.clone()
|
|
|
|
model.enable_gradient_checkpointing()
|
|
out = model(**inputs_dict).sample
|
|
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
|
|
# we won't calculate the loss and rather backprop on out.sum()
|
|
model.zero_grad()
|
|
out.sum().backward()
|
|
|
|
# now we save the output and parameter gradients that we will use for comparison purposes with
|
|
# the non-checkpointed run.
|
|
output_checkpointed = out.data.clone()
|
|
grad_checkpointed = {}
|
|
for name, param in model.named_parameters():
|
|
grad_checkpointed[name] = param.grad.data.clone()
|
|
|
|
# compare the output and parameters gradients
|
|
self.assertTrue((output_checkpointed == output_not_checkpointed).all())
|
|
for name in grad_checkpointed:
|
|
self.assertTrue(torch.allclose(grad_checkpointed[name], grad_not_checkpointed[name], atol=5e-5))
|
|
|
|
|
|
# TODO(Patrick) - Re-add this test after having cleaned up LDM
|
|
# def test_output_pretrained_spatial_transformer(self):
|
|
# model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
|
|
# model.eval()
|
|
#
|
|
# torch.manual_seed(0)
|
|
# if torch.cuda.is_available():
|
|
# torch.cuda.manual_seed_all(0)
|
|
#
|
|
# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
|
|
# context = torch.ones((1, 16, 64), dtype=torch.float32)
|
|
# time_step = torch.tensor([10] * noise.shape[0])
|
|
#
|
|
# with torch.no_grad():
|
|
# output = model(noise, time_step, context=context)
|
|
#
|
|
# output_slice = output[0, -1, -3:, -3:].flatten()
|
|
# fmt: off
|
|
# expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
|
|
# fmt: on
|
|
#
|
|
# self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
|
|
#
|
|
|
|
|
|
class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
|
|
model_class = 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}
|
|
|
|
@property
|
|
def input_shape(self):
|
|
return (3, 32, 32)
|
|
|
|
@property
|
|
def output_shape(self):
|
|
return (3, 32, 32)
|
|
|
|
def prepare_init_args_and_inputs_for_common(self):
|
|
init_dict = {
|
|
"block_out_channels": [32, 64, 64, 64],
|
|
"in_channels": 3,
|
|
"layers_per_block": 1,
|
|
"out_channels": 3,
|
|
"time_embedding_type": "fourier",
|
|
"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",
|
|
],
|
|
}
|
|
inputs_dict = self.dummy_input
|
|
return init_dict, inputs_dict
|
|
|
|
@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)
|
|
|
|
model.to(torch_device)
|
|
inputs = self.dummy_input
|
|
noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
|
|
inputs["sample"] = noise
|
|
image = model(**inputs)
|
|
|
|
assert image is not None, "Make sure output is not None"
|
|
|
|
@slow
|
|
def test_output_pretrained_ve_mid(self):
|
|
model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
|
|
model.to(torch_device)
|
|
|
|
torch.manual_seed(0)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.manual_seed_all(0)
|
|
|
|
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)
|
|
|
|
with torch.no_grad():
|
|
output = model(noise, time_step).sample
|
|
|
|
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
|
|
# fmt: off
|
|
expected_output_slice = torch.tensor([-4836.2231, -6487.1387, -3816.7969, -7964.9253, -10966.2842, -20043.6016, 8137.0571, 2340.3499, 544.6114])
|
|
# fmt: on
|
|
|
|
self.assertTrue(torch.allclose(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)
|
|
|
|
torch.manual_seed(0)
|
|
if torch.cuda.is_available():
|
|
torch.cuda.manual_seed_all(0)
|
|
|
|
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)
|
|
|
|
with torch.no_grad():
|
|
output = model(noise, time_step).sample
|
|
|
|
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
|
|
# 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
|
|
|
|
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
|
|
|
def test_forward_with_norm_groups(self):
|
|
# not required for this model
|
|
pass
|