452 lines
15 KiB
Python
452 lines
15 KiB
Python
# coding=utf-8
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# Copyright 2022 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 gc
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import math
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import tracemalloc
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import unittest
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import torch
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from diffusers import UNet2DConditionModel, UNet2DModel
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from diffusers.utils import floats_tensor, slow, torch_device
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from .test_modeling_common import ModelTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class UnetModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor([10]).to(torch_device)
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return {"sample": noise, "timestep": time_step}
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@property
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def input_shape(self):
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return (3, 32, 32)
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@property
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def output_shape(self):
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return (3, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"block_out_channels": (32, 64),
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"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
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"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
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"attention_head_dim": None,
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"out_channels": 3,
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"in_channels": 3,
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"layers_per_block": 2,
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"sample_size": 32,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
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# def test_output_pretrained(self):
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# model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
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# model.eval()
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#
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# torch.manual_seed(0)
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# if torch.cuda.is_available():
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# torch.cuda.manual_seed_all(0)
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#
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# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
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# time_step = torch.tensor([10])
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#
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# with torch.no_grad():
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# output = model(noise, time_step).sample
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#
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# output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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# expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
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# fmt: on
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# self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
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class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 4
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sizes = (32, 32)
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor([10]).to(torch_device)
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return {"sample": noise, "timestep": time_step}
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@property
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def input_shape(self):
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return (4, 32, 32)
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@property
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def output_shape(self):
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return (4, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"sample_size": 32,
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"in_channels": 4,
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"out_channels": 4,
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"layers_per_block": 2,
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"block_out_channels": (32, 64),
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"attention_head_dim": 32,
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"down_block_types": ("DownBlock2D", "DownBlock2D"),
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"up_block_types": ("UpBlock2D", "UpBlock2D"),
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_from_pretrained_hub(self):
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model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input).sample
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assert image is not None, "Make sure output is not None"
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@unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
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def test_from_pretrained_accelerate(self):
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model, _ = UNet2DModel.from_pretrained(
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"fusing/unet-ldm-dummy-update", output_loading_info=True, device_map="auto"
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)
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model.to(torch_device)
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image = model(**self.dummy_input).sample
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assert image is not None, "Make sure output is not None"
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@unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
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def test_from_pretrained_accelerate_wont_change_results(self):
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model_accelerate, _ = UNet2DModel.from_pretrained(
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"fusing/unet-ldm-dummy-update", output_loading_info=True, device_map="auto"
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)
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model_accelerate.to(torch_device)
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model_accelerate.eval()
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noise = torch.randn(
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1,
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model_accelerate.config.in_channels,
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model_accelerate.config.sample_size,
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model_accelerate.config.sample_size,
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generator=torch.manual_seed(0),
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)
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noise = noise.to(torch_device)
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time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)
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arr_accelerate = model_accelerate(noise, time_step)["sample"]
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# two models don't need to stay in the device at the same time
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del model_accelerate
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torch.cuda.empty_cache()
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gc.collect()
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model_normal_load, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
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model_normal_load.to(torch_device)
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model_normal_load.eval()
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arr_normal_load = model_normal_load(noise, time_step)["sample"]
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assert torch.allclose(arr_accelerate, arr_normal_load, rtol=1e-3)
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@unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
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def test_memory_footprint_gets_reduced(self):
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torch.cuda.empty_cache()
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gc.collect()
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tracemalloc.start()
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model_accelerate, _ = UNet2DModel.from_pretrained(
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"fusing/unet-ldm-dummy-update", output_loading_info=True, device_map="auto"
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)
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model_accelerate.to(torch_device)
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model_accelerate.eval()
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_, peak_accelerate = tracemalloc.get_traced_memory()
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del model_accelerate
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torch.cuda.empty_cache()
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gc.collect()
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model_normal_load, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
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model_normal_load.to(torch_device)
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model_normal_load.eval()
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_, peak_normal = tracemalloc.get_traced_memory()
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tracemalloc.stop()
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assert peak_accelerate < peak_normal
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def test_output_pretrained(self):
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model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
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model.eval()
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model.to(torch_device)
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noise = torch.randn(
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1,
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model.config.in_channels,
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model.config.sample_size,
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model.config.sample_size,
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generator=torch.manual_seed(0),
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)
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noise = noise.to(torch_device)
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time_step = torch.tensor([10] * noise.shape[0]).to(torch_device)
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with torch.no_grad():
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output = model(noise, time_step).sample
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output_slice = output[0, -1, -3:, -3:].flatten().cpu()
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# fmt: off
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expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
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class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DConditionModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 4
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sizes = (32, 32)
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor([10]).to(torch_device)
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encoder_hidden_states = floats_tensor((batch_size, 4, 32)).to(torch_device)
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return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
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@property
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def input_shape(self):
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return (4, 32, 32)
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@property
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def output_shape(self):
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return (4, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"block_out_channels": (32, 64),
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"down_block_types": ("CrossAttnDownBlock2D", "DownBlock2D"),
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"up_block_types": ("UpBlock2D", "CrossAttnUpBlock2D"),
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"cross_attention_dim": 32,
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"attention_head_dim": 8,
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"out_channels": 4,
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"in_channels": 4,
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"layers_per_block": 2,
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"sample_size": 32,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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@unittest.skipIf(torch_device == "mps", "Gradient checkpointing skipped on MPS")
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def test_gradient_checkpointing(self):
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# enable deterministic behavior for gradient checkpointing
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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assert not model.is_gradient_checkpointing and model.training
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out = model(**inputs_dict).sample
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# run the backwards pass on the model. For backwards pass, for simplicity purpose,
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# we won't calculate the loss and rather backprop on out.sum()
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model.zero_grad()
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labels = torch.randn_like(out)
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loss = (out - labels).mean()
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loss.backward()
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# re-instantiate the model now enabling gradient checkpointing
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model_2 = self.model_class(**init_dict)
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# clone model
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model_2.load_state_dict(model.state_dict())
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model_2.to(torch_device)
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model_2.enable_gradient_checkpointing()
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assert model_2.is_gradient_checkpointing and model_2.training
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out_2 = model_2(**inputs_dict).sample
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# run the backwards pass on the model. For backwards pass, for simplicity purpose,
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# we won't calculate the loss and rather backprop on out.sum()
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model_2.zero_grad()
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loss_2 = (out_2 - labels).mean()
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loss_2.backward()
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# compare the output and parameters gradients
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self.assertTrue((loss - loss_2).abs() < 1e-5)
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named_params = dict(model.named_parameters())
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named_params_2 = dict(model_2.named_parameters())
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for name, param in named_params.items():
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self.assertTrue(torch.allclose(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
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# TODO(Patrick) - Re-add this test after having cleaned up LDM
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# def test_output_pretrained_spatial_transformer(self):
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# model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
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# model.eval()
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#
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# torch.manual_seed(0)
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# if torch.cuda.is_available():
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# torch.cuda.manual_seed_all(0)
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#
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# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
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# context = torch.ones((1, 16, 64), dtype=torch.float32)
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# time_step = torch.tensor([10] * noise.shape[0])
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#
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# with torch.no_grad():
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# output = model(noise, time_step, context=context)
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#
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# output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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# expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
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# fmt: on
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#
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# self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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#
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class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DModel
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@property
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def dummy_input(self, sizes=(32, 32)):
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batch_size = 4
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num_channels = 3
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor(batch_size * [10]).to(dtype=torch.int32, device=torch_device)
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return {"sample": noise, "timestep": time_step}
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@property
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def input_shape(self):
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return (3, 32, 32)
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@property
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def output_shape(self):
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return (3, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"block_out_channels": [32, 64, 64, 64],
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"in_channels": 3,
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"layers_per_block": 1,
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"out_channels": 3,
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"time_embedding_type": "fourier",
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"norm_eps": 1e-6,
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"mid_block_scale_factor": math.sqrt(2.0),
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"norm_num_groups": None,
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"down_block_types": [
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"SkipDownBlock2D",
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"AttnSkipDownBlock2D",
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"SkipDownBlock2D",
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"SkipDownBlock2D",
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],
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"up_block_types": [
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"SkipUpBlock2D",
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"SkipUpBlock2D",
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"AttnSkipUpBlock2D",
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"SkipUpBlock2D",
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],
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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@slow
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def test_from_pretrained_hub(self):
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model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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inputs = self.dummy_input
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noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
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inputs["sample"] = noise
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image = model(**inputs)
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assert image is not None, "Make sure output is not None"
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@slow
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def test_output_pretrained_ve_mid(self):
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model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
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model.to(torch_device)
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torch.manual_seed(0)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(0)
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batch_size = 4
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num_channels = 3
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sizes = (256, 256)
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noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
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with torch.no_grad():
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output = model(noise, time_step).sample
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output_slice = output[0, -3:, -3:, -1].flatten().cpu()
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# fmt: off
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expected_output_slice = torch.tensor([-4836.2231, -6487.1387, -3816.7969, -7964.9253, -10966.2842, -20043.6016, 8137.0571, 2340.3499, 544.6114])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
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def test_output_pretrained_ve_large(self):
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model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
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model.to(torch_device)
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torch.manual_seed(0)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(0)
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
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with torch.no_grad():
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output = model(noise, time_step).sample
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output_slice = output[0, -3:, -3:, -1].flatten().cpu()
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# fmt: off
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expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
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# fmt: on
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
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def test_forward_with_norm_groups(self):
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# not required for this model
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pass
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