601 lines
22 KiB
Python
601 lines
22 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 (
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floats_tensor,
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load_hf_numpy,
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logging,
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require_torch_gpu,
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slow,
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torch_all_close,
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torch_device,
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)
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from parameterized import parameterized
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from ..test_modeling_common import ModelTesterMixin
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logger = logging.get_logger(__name__)
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torch.backends.cuda.matmul.allow_tf32 = False
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class Unet2DModelTests(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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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("fusing/unet-ldm-dummy-update", output_loading_info=True)
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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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# by defautl model loading will use accelerate as `fast_load=True`
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model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
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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(
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"fusing/unet-ldm-dummy-update", output_loading_info=True, fast_init=False
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)
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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_all_close(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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# by defautl model loading will use accelerate as `fast_load=True`
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model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
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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(
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"fusing/unet-ldm-dummy-update", output_loading_info=True, fast_init=False
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)
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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_all_close(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_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
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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_all_close(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_all_close(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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@slow
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class UNet2DConditionModelIntegrationTests(unittest.TestCase):
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def get_file_format(self, seed, shape):
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return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
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dtype = torch.float16 if fp16 else torch.float32
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image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
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return image
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def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
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revision = "fp16" if fp16 else None
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torch_dtype = torch.float16 if fp16 else torch.float32
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model = UNet2DConditionModel.from_pretrained(
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model_id, subfolder="unet", torch_dtype=torch_dtype, revision=revision
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)
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model.to(torch_device).eval()
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return model
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def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):
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dtype = torch.float16 if fp16 else torch.float32
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hidden_states = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
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return hidden_states
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@parameterized.expand(
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[
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# fmt: off
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[33, 4, [-0.4424, 0.1510, -0.1937, 0.2118, 0.3746, -0.3957, 0.0160, -0.0435]],
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[47, 0.55, [-0.1508, 0.0379, -0.3075, 0.2540, 0.3633, -0.0821, 0.1719, -0.0207]],
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[21, 0.89, [-0.6479, 0.6364, -0.3464, 0.8697, 0.4443, -0.6289, -0.0091, 0.1778]],
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[9, 1000, [0.8888, -0.5659, 0.5834, -0.7469, 1.1912, -0.3923, 1.1241, -0.4424]],
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# fmt: on
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]
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)
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def test_compvis_sd_v1_4(self, seed, timestep, expected_slice):
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model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4")
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latents = self.get_latents(seed)
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encoder_hidden_states = self.get_encoder_hidden_states(seed)
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with torch.no_grad():
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sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
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assert sample.shape == latents.shape
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
|
expected_output_slice = torch.tensor(expected_slice)
|
|
|
|
assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
# fmt: off
|
|
[83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
|
|
[17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
|
|
[8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
|
|
[3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
|
|
# fmt: on
|
|
]
|
|
)
|
|
@require_torch_gpu
|
|
def test_compvis_sd_v1_4_fp16(self, seed, timestep, expected_slice):
|
|
model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4", fp16=True)
|
|
latents = self.get_latents(seed, fp16=True)
|
|
encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
|
|
|
|
with torch.no_grad():
|
|
sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
|
|
|
|
assert sample.shape == latents.shape
|
|
|
|
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
|
expected_output_slice = torch.tensor(expected_slice)
|
|
|
|
assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
# fmt: off
|
|
[33, 4, [-0.4430, 0.1570, -0.1867, 0.2376, 0.3205, -0.3681, 0.0525, -0.0722]],
|
|
[47, 0.55, [-0.1415, 0.0129, -0.3136, 0.2257, 0.3430, -0.0536, 0.2114, -0.0436]],
|
|
[21, 0.89, [-0.7091, 0.6664, -0.3643, 0.9032, 0.4499, -0.6541, 0.0139, 0.1750]],
|
|
[9, 1000, [0.8878, -0.5659, 0.5844, -0.7442, 1.1883, -0.3927, 1.1192, -0.4423]],
|
|
# fmt: on
|
|
]
|
|
)
|
|
def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
|
|
model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5")
|
|
latents = self.get_latents(seed)
|
|
encoder_hidden_states = self.get_encoder_hidden_states(seed)
|
|
|
|
with torch.no_grad():
|
|
sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
|
|
|
|
assert sample.shape == latents.shape
|
|
|
|
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
|
expected_output_slice = torch.tensor(expected_slice)
|
|
|
|
assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
# fmt: off
|
|
[83, 4, [-0.2695, -0.1669, 0.0073, -0.3181, -0.1187, -0.1676, -0.1395, -0.5972]],
|
|
[17, 0.55, [-0.1290, -0.2588, 0.0551, -0.0916, 0.3286, 0.0238, -0.3669, 0.0322]],
|
|
[8, 0.89, [-0.5283, 0.1198, 0.0870, -0.1141, 0.9189, -0.0150, 0.5474, 0.4319]],
|
|
[3, 1000, [-0.5601, 0.2411, -0.5435, 0.1268, 1.1338, -0.2427, -0.0280, -1.0020]],
|
|
# fmt: on
|
|
]
|
|
)
|
|
@require_torch_gpu
|
|
def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
|
|
model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5", fp16=True)
|
|
latents = self.get_latents(seed, fp16=True)
|
|
encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
|
|
|
|
with torch.no_grad():
|
|
sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
|
|
|
|
assert sample.shape == latents.shape
|
|
|
|
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
|
expected_output_slice = torch.tensor(expected_slice)
|
|
|
|
assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
# fmt: off
|
|
[33, 4, [-0.7639, 0.0106, -0.1615, -0.3487, -0.0423, -0.7972, 0.0085, -0.4858]],
|
|
[47, 0.55, [-0.6564, 0.0795, -1.9026, -0.6258, 1.8235, 1.2056, 1.2169, 0.9073]],
|
|
[21, 0.89, [0.0327, 0.4399, -0.6358, 0.3417, 0.4120, -0.5621, -0.0397, -1.0430]],
|
|
[9, 1000, [0.1600, 0.7303, -1.0556, -0.3515, -0.7440, -1.2037, -1.8149, -1.8931]],
|
|
# fmt: on
|
|
]
|
|
)
|
|
def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
|
|
model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting")
|
|
latents = self.get_latents(seed, shape=(4, 9, 64, 64))
|
|
encoder_hidden_states = self.get_encoder_hidden_states(seed)
|
|
|
|
with torch.no_grad():
|
|
sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
|
|
|
|
assert sample.shape == (4, 4, 64, 64)
|
|
|
|
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
|
expected_output_slice = torch.tensor(expected_slice)
|
|
|
|
assert torch_all_close(output_slice, expected_output_slice, atol=1e-3)
|
|
|
|
@parameterized.expand(
|
|
[
|
|
# fmt: off
|
|
[83, 4, [-0.1047, -1.7227, 0.1067, 0.0164, -0.5698, -0.4172, -0.1388, 1.1387]],
|
|
[17, 0.55, [0.0975, -0.2856, -0.3508, -0.4600, 0.3376, 0.2930, -0.2747, -0.7026]],
|
|
[8, 0.89, [-0.0952, 0.0183, -0.5825, -0.1981, 0.1131, 0.4668, -0.0395, -0.3486]],
|
|
[3, 1000, [0.4790, 0.4949, -1.0732, -0.7158, 0.7959, -0.9478, 0.1105, -0.9741]],
|
|
# fmt: on
|
|
]
|
|
)
|
|
@require_torch_gpu
|
|
def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
|
|
model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting", fp16=True)
|
|
latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
|
|
encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
|
|
|
|
with torch.no_grad():
|
|
sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
|
|
|
|
assert sample.shape == (4, 4, 64, 64)
|
|
|
|
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
|
expected_output_slice = torch.tensor(expected_slice)
|
|
|
|
assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
|