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# coding=utf-8
# Copyright 2023 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 tempfile
import unittest
import torch
from diffusers import UNet2DConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import skip_mps, torch_device
class EMAModelTests(unittest.TestCase):
model_id = "hf-internal-testing/tiny-stable-diffusion-pipe"
batch_size = 1
prompt_length = 77
text_encoder_hidden_dim = 32
num_in_channels = 4
latent_height = latent_width = 64
generator = torch.manual_seed(0)
def get_models(self, decay=0.9999):
unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder="unet")
unet = unet.to(torch_device)
ema_unet = EMAModel(unet.parameters(), decay=decay, model_cls=UNet2DConditionModel, model_config=unet.config)
return unet, ema_unet
def get_dummy_inputs(self):
noisy_latents = torch.randn(
self.batch_size, self.num_in_channels, self.latent_height, self.latent_width, generator=self.generator
).to(torch_device)
timesteps = torch.randint(0, 1000, size=(self.batch_size,), generator=self.generator).to(torch_device)
encoder_hidden_states = torch.randn(
self.batch_size, self.prompt_length, self.text_encoder_hidden_dim, generator=self.generator
).to(torch_device)
return noisy_latents, timesteps, encoder_hidden_states
def simulate_backprop(self, unet):
updated_state_dict = {}
for k, param in unet.state_dict().items():
updated_param = torch.randn_like(param) + (param * torch.randn_like(param))
updated_state_dict.update({k: updated_param})
unet.load_state_dict(updated_state_dict)
return unet
def test_optimization_steps_updated(self):
unet, ema_unet = self.get_models()
# Take the first (hypothetical) EMA step.
ema_unet.step(unet.parameters())
assert ema_unet.optimization_step == 1
# Take two more.
for _ in range(2):
ema_unet.step(unet.parameters())
assert ema_unet.optimization_step == 3
def test_shadow_params_not_updated(self):
unet, ema_unet = self.get_models()
# Since the `unet` is not being updated (i.e., backprop'd)
# there won't be any difference between the `params` of `unet`
# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
ema_unet.step(unet.parameters())
orig_params = list(unet.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert torch.allclose(s_param, param)
# The above holds true even if we call `ema.step()` multiple times since
# `unet` params are still not being updated.
for _ in range(4):
ema_unet.step(unet.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert torch.allclose(s_param, param)
def test_shadow_params_updated(self):
unet, ema_unet = self.get_models()
# Here we simulate the parameter updates for `unet`. Since there might
# be some parameters which are initialized to zero we take extra care to
# initialize their values to something non-zero before the multiplication.
unet_pseudo_updated_step_one = self.simulate_backprop(unet)
# Take the EMA step.
ema_unet.step(unet_pseudo_updated_step_one.parameters())
# Now the EMA'd parameters won't be equal to the original model parameters.
orig_params = list(unet_pseudo_updated_step_one.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert ~torch.allclose(s_param, param)
# Ensure this is the case when we take multiple EMA steps.
for _ in range(4):
ema_unet.step(unet.parameters())
for s_param, param in zip(ema_unet.shadow_params, orig_params):
assert ~torch.allclose(s_param, param)
def test_consecutive_shadow_params_updated(self):
# If we call EMA step after a backpropagation consecutively for two times,
# the shadow params from those two steps should be different.
unet, ema_unet = self.get_models()
# First backprop + EMA
unet_step_one = self.simulate_backprop(unet)
ema_unet.step(unet_step_one.parameters())
step_one_shadow_params = ema_unet.shadow_params
# Second backprop + EMA
unet_step_two = self.simulate_backprop(unet_step_one)
ema_unet.step(unet_step_two.parameters())
step_two_shadow_params = ema_unet.shadow_params
for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):
assert ~torch.allclose(step_one, step_two)
def test_zero_decay(self):
# If there's no decay even if there are backprops, EMA steps
# won't take any effect i.e., the shadow params would remain the
# same.
unet, ema_unet = self.get_models(decay=0.0)
unet_step_one = self.simulate_backprop(unet)
ema_unet.step(unet_step_one.parameters())
step_one_shadow_params = ema_unet.shadow_params
unet_step_two = self.simulate_backprop(unet_step_one)
ema_unet.step(unet_step_two.parameters())
step_two_shadow_params = ema_unet.shadow_params
for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):
assert torch.allclose(step_one, step_two)
@skip_mps
def test_serialization(self):
unet, ema_unet = self.get_models()
noisy_latents, timesteps, encoder_hidden_states = self.get_dummy_inputs()
with tempfile.TemporaryDirectory() as tmpdir:
ema_unet.save_pretrained(tmpdir)
loaded_unet = UNet2DConditionModel.from_pretrained(tmpdir, model_cls=UNet2DConditionModel)
loaded_unet = loaded_unet.to(unet.device)
# Since no EMA step has been performed the outputs should match.
output = unet(noisy_latents, timesteps, encoder_hidden_states).sample
output_loaded = loaded_unet(noisy_latents, timesteps, encoder_hidden_states).sample
assert torch.allclose(output, output_loaded, atol=1e-4)
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