# 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)