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9ecd924859
* [Tests] Correct PT2 * correct more * move versatile to nightly * up * up * again * Apply suggestions from code review
157 lines
6.6 KiB
Python
157 lines
6.6 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import tempfile
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import unittest
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import torch
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from diffusers import UNet2DConditionModel
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from diffusers.training_utils import EMAModel
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from diffusers.utils.testing_utils import skip_mps, torch_device
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class EMAModelTests(unittest.TestCase):
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model_id = "hf-internal-testing/tiny-stable-diffusion-pipe"
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batch_size = 1
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prompt_length = 77
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text_encoder_hidden_dim = 32
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num_in_channels = 4
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latent_height = latent_width = 64
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generator = torch.manual_seed(0)
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def get_models(self, decay=0.9999):
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unet = UNet2DConditionModel.from_pretrained(self.model_id, subfolder="unet")
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unet = unet.to(torch_device)
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ema_unet = EMAModel(unet.parameters(), decay=decay, model_cls=UNet2DConditionModel, model_config=unet.config)
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return unet, ema_unet
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def get_dummy_inputs(self):
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noisy_latents = torch.randn(
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self.batch_size, self.num_in_channels, self.latent_height, self.latent_width, generator=self.generator
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).to(torch_device)
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timesteps = torch.randint(0, 1000, size=(self.batch_size,), generator=self.generator).to(torch_device)
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encoder_hidden_states = torch.randn(
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self.batch_size, self.prompt_length, self.text_encoder_hidden_dim, generator=self.generator
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).to(torch_device)
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return noisy_latents, timesteps, encoder_hidden_states
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def simulate_backprop(self, unet):
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updated_state_dict = {}
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for k, param in unet.state_dict().items():
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updated_param = torch.randn_like(param) + (param * torch.randn_like(param))
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updated_state_dict.update({k: updated_param})
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unet.load_state_dict(updated_state_dict)
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return unet
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def test_optimization_steps_updated(self):
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unet, ema_unet = self.get_models()
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# Take the first (hypothetical) EMA step.
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ema_unet.step(unet.parameters())
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assert ema_unet.optimization_step == 1
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# Take two more.
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for _ in range(2):
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ema_unet.step(unet.parameters())
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assert ema_unet.optimization_step == 3
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def test_shadow_params_not_updated(self):
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unet, ema_unet = self.get_models()
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# Since the `unet` is not being updated (i.e., backprop'd)
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# there won't be any difference between the `params` of `unet`
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# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
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ema_unet.step(unet.parameters())
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orig_params = list(unet.parameters())
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for s_param, param in zip(ema_unet.shadow_params, orig_params):
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assert torch.allclose(s_param, param)
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# The above holds true even if we call `ema.step()` multiple times since
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# `unet` params are still not being updated.
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for _ in range(4):
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ema_unet.step(unet.parameters())
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for s_param, param in zip(ema_unet.shadow_params, orig_params):
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assert torch.allclose(s_param, param)
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def test_shadow_params_updated(self):
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unet, ema_unet = self.get_models()
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# Here we simulate the parameter updates for `unet`. Since there might
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# be some parameters which are initialized to zero we take extra care to
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# initialize their values to something non-zero before the multiplication.
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unet_pseudo_updated_step_one = self.simulate_backprop(unet)
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# Take the EMA step.
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ema_unet.step(unet_pseudo_updated_step_one.parameters())
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# Now the EMA'd parameters won't be equal to the original model parameters.
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orig_params = list(unet_pseudo_updated_step_one.parameters())
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for s_param, param in zip(ema_unet.shadow_params, orig_params):
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assert ~torch.allclose(s_param, param)
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# Ensure this is the case when we take multiple EMA steps.
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for _ in range(4):
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ema_unet.step(unet.parameters())
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for s_param, param in zip(ema_unet.shadow_params, orig_params):
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assert ~torch.allclose(s_param, param)
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def test_consecutive_shadow_params_updated(self):
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# If we call EMA step after a backpropagation consecutively for two times,
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# the shadow params from those two steps should be different.
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unet, ema_unet = self.get_models()
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# First backprop + EMA
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unet_step_one = self.simulate_backprop(unet)
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ema_unet.step(unet_step_one.parameters())
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step_one_shadow_params = ema_unet.shadow_params
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# Second backprop + EMA
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unet_step_two = self.simulate_backprop(unet_step_one)
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ema_unet.step(unet_step_two.parameters())
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step_two_shadow_params = ema_unet.shadow_params
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for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):
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assert ~torch.allclose(step_one, step_two)
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def test_zero_decay(self):
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# If there's no decay even if there are backprops, EMA steps
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# won't take any effect i.e., the shadow params would remain the
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# same.
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unet, ema_unet = self.get_models(decay=0.0)
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unet_step_one = self.simulate_backprop(unet)
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ema_unet.step(unet_step_one.parameters())
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step_one_shadow_params = ema_unet.shadow_params
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unet_step_two = self.simulate_backprop(unet_step_one)
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ema_unet.step(unet_step_two.parameters())
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step_two_shadow_params = ema_unet.shadow_params
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for step_one, step_two in zip(step_one_shadow_params, step_two_shadow_params):
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assert torch.allclose(step_one, step_two)
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@skip_mps
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def test_serialization(self):
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unet, ema_unet = self.get_models()
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noisy_latents, timesteps, encoder_hidden_states = self.get_dummy_inputs()
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with tempfile.TemporaryDirectory() as tmpdir:
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ema_unet.save_pretrained(tmpdir)
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loaded_unet = UNet2DConditionModel.from_pretrained(tmpdir, model_cls=UNet2DConditionModel)
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loaded_unet = loaded_unet.to(unet.device)
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# Since no EMA step has been performed the outputs should match.
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output = unet(noisy_latents, timesteps, encoder_hidden_states).sample
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output_loaded = loaded_unet(noisy_latents, timesteps, encoder_hidden_states).sample
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assert torch.allclose(output, output_loaded, atol=1e-4)
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