Add tests for 2D UNet blocks (#1945)

* test unet blocks 2d * change to randn_tensor * mps flaky
2023-01-16 03:53:05 -08:00 · 2023-01-16 03:53:05 -08:00 · cc2cc00d20
parent 8f58159159
commit cc2cc00d20
2 changed files with 464 additions and 0 deletions
--- a/tests/test_unet_2d_blocks.py
+++ b/tests/test_unet_2d_blocks.py
@ -0,0 +1,343 @@
+# coding=utf-8
+# Copyright 2022 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 unittest
+
+from diffusers.models.unet_2d_blocks import *  # noqa F403
+from diffusers.utils import torch_device
+
+from .test_unet_blocks_common import UNetBlockTesterMixin
+
+
+class DownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = DownBlock2D  # noqa F405
+    block_type = "down"
+
+    def test_output(self):
+        expected_slice = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
+        super().test_output(expected_slice)
+
+
+class ResnetDownsampleBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = ResnetDownsampleBlock2D  # noqa F405
+    block_type = "down"
+
+    def test_output(self):
+        expected_slice = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
+        super().test_output(expected_slice)
+
+
+class AttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = AttnDownBlock2D  # noqa F405
+    block_type = "down"
+
+    def test_output(self):
+        expected_slice = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
+        super().test_output(expected_slice)
+
+
+class CrossAttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = CrossAttnDownBlock2D  # noqa F405
+    block_type = "down"
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
+        init_dict["cross_attention_dim"] = 32
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [0.2440, -0.6953, -0.2140, -0.3874, 0.1966, 1.2077, 0.0441, -0.7718, 0.2800]
+        super().test_output(expected_slice)
+
+
+class SimpleCrossAttnDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = SimpleCrossAttnDownBlock2D  # noqa F405
+    block_type = "down"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_encoder_hidden_states=True)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
+        init_dict["cross_attention_dim"] = 32
+        return init_dict, inputs_dict
+
+    @unittest.skipIf(torch_device == "mps", "MPS result is not consistent")
+    def test_output(self):
+        expected_slice = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
+        super().test_output(expected_slice)
+
+
+class SkipDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = SkipDownBlock2D  # noqa F405
+    block_type = "down"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_skip_sample=True)
+
+    def test_output(self):
+        expected_slice = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
+        super().test_output(expected_slice)
+
+
+class AttnSkipDownBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = AttnSkipDownBlock2D  # noqa F405
+    block_type = "down"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_skip_sample=True)
+
+    def test_output(self):
+        expected_slice = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
+        super().test_output(expected_slice)
+
+
+class DownEncoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = DownEncoderBlock2D  # noqa F405
+    block_type = "down"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_temb=False)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "in_channels": 32,
+            "out_channels": 32,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
+        super().test_output(expected_slice)
+
+
+class AttnDownEncoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = AttnDownEncoderBlock2D  # noqa F405
+    block_type = "down"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_temb=False)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "in_channels": 32,
+            "out_channels": 32,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
+        super().test_output(expected_slice)
+
+
+class UNetMidBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = UNetMidBlock2D  # noqa F405
+    block_type = "mid"
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "in_channels": 32,
+            "temb_channels": 128,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
+        super().test_output(expected_slice)
+
+
+class UNetMidBlock2DCrossAttnTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = UNetMidBlock2DCrossAttn  # noqa F405
+    block_type = "mid"
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
+        init_dict["cross_attention_dim"] = 32
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [0.1879, 2.2653, 0.5987, 1.1568, -0.8454, -1.6109, -0.8919, 0.8306, 1.6758]
+        super().test_output(expected_slice)
+
+
+class UNetMidBlock2DSimpleCrossAttnTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = UNetMidBlock2DSimpleCrossAttn  # noqa F405
+    block_type = "mid"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_encoder_hidden_states=True)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
+        init_dict["cross_attention_dim"] = 32
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
+        super().test_output(expected_slice)
+
+
+class UpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = UpBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_res_hidden_states_tuple=True)
+
+    def test_output(self):
+        expected_slice = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
+        super().test_output(expected_slice)
+
+
+class ResnetUpsampleBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = ResnetUpsampleBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_res_hidden_states_tuple=True)
+
+    def test_output(self):
+        expected_slice = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
+        super().test_output(expected_slice)
+
+
+class CrossAttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = CrossAttnUpBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_res_hidden_states_tuple=True)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
+        init_dict["cross_attention_dim"] = 32
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [-0.2796, -0.4364, -0.1067, -0.2693, 0.1894, 0.3869, -0.3470, 0.4584, 0.5091]
+        super().test_output(expected_slice)
+
+
+class SimpleCrossAttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = SimpleCrossAttnUpBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_res_hidden_states_tuple=True, include_encoder_hidden_states=True)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict, inputs_dict = super().prepare_init_args_and_inputs_for_common()
+        init_dict["cross_attention_dim"] = 32
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        if torch_device == "mps":
+            expected_slice = [0.4327, 0.5538, 0.3919, 0.5682, 0.2704, 0.1573, -0.8768, -0.4615, -0.4146]
+        else:
+            expected_slice = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
+        super().test_output(expected_slice)
+
+
+class AttnUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = AttnUpBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_res_hidden_states_tuple=True)
+
+    @unittest.skipIf(torch_device == "mps", "MPS result is not consistent")
+    def test_output(self):
+        expected_slice = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
+        super().test_output(expected_slice)
+
+
+class SkipUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = SkipUpBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_res_hidden_states_tuple=True)
+
+    def test_output(self):
+        expected_slice = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
+        super().test_output(expected_slice)
+
+
+class AttnSkipUpBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = AttnSkipUpBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_res_hidden_states_tuple=True)
+
+    def test_output(self):
+        expected_slice = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
+        super().test_output(expected_slice)
+
+
+class UpDecoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = UpDecoderBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_temb=False)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {"in_channels": 32, "out_channels": 32}
+
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        expected_slice = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
+        super().test_output(expected_slice)
+
+
+class AttnUpDecoderBlock2DTests(UNetBlockTesterMixin, unittest.TestCase):
+    block_class = AttnUpDecoderBlock2D  # noqa F405
+    block_type = "up"
+
+    @property
+    def dummy_input(self):
+        return super().get_dummy_input(include_temb=False)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {"in_channels": 32, "out_channels": 32}
+
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        if torch_device == "mps":
+            expected_slice = [-0.3669, -0.3387, 0.1029, -0.6564, 0.2728, -0.3233, 0.5977, -0.1784, 0.5482]
+        else:
+            expected_slice = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
+        super().test_output(expected_slice)
--- a/tests/test_unet_blocks_common.py
+++ b/tests/test_unet_blocks_common.py
@ -0,0 +1,121 @@
+# coding=utf-8
+# Copyright 2022 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 unittest
+from typing import Tuple
+
+import torch
+
+from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
+from diffusers.utils.testing_utils import require_torch
+
+
+@require_torch
+class UNetBlockTesterMixin:
+    @property
+    def dummy_input(self):
+        return self.get_dummy_input()
+
+    @property
+    def output_shape(self):
+        if self.block_type == "down":
+            return (4, 32, 16, 16)
+        elif self.block_type == "mid":
+            return (4, 32, 32, 32)
+        elif self.block_type == "up":
+            return (4, 32, 64, 64)
+
+        raise ValueError(f"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.")
+
+    def get_dummy_input(
+        self,
+        include_temb=True,
+        include_res_hidden_states_tuple=False,
+        include_encoder_hidden_states=False,
+        include_skip_sample=False,
+    ):
+        batch_size = 4
+        num_channels = 32
+        sizes = (32, 32)
+
+        generator = torch.manual_seed(0)
+        device = torch.device(torch_device)
+        shape = (batch_size, num_channels) + sizes
+        hidden_states = randn_tensor(shape, generator=generator, device=device)
+        dummy_input = {"hidden_states": hidden_states}
+
+        if include_temb:
+            temb_channels = 128
+            dummy_input["temb"] = randn_tensor((batch_size, temb_channels), generator=generator, device=device)
+
+        if include_res_hidden_states_tuple:
+            generator_1 = torch.manual_seed(1)
+            dummy_input["res_hidden_states_tuple"] = (randn_tensor(shape, generator=generator_1, device=device),)
+
+        if include_encoder_hidden_states:
+            dummy_input["encoder_hidden_states"] = floats_tensor((batch_size, 32, 32)).to(torch_device)
+
+        if include_skip_sample:
+            dummy_input["skip_sample"] = randn_tensor(((batch_size, 3) + sizes), generator=generator, device=device)
+
+        return dummy_input
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "in_channels": 32,
+            "out_channels": 32,
+            "temb_channels": 128,
+        }
+        if self.block_type == "up":
+            init_dict["prev_output_channel"] = 32
+
+        if self.block_type == "mid":
+            init_dict.pop("out_channels")
+
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_output(self, expected_slice):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        unet_block = self.block_class(**init_dict)
+        unet_block.to(torch_device)
+        unet_block.eval()
+
+        with torch.no_grad():
+            output = unet_block(**inputs_dict)
+
+        if isinstance(output, Tuple):
+            output = output[0]
+
+        self.assertEqual(output.shape, self.output_shape)
+
+        output_slice = output[0, -1, -3:, -3:]
+        expected_slice = torch.tensor(expected_slice).to(torch_device)
+        assert torch_all_close(output_slice.flatten(), expected_slice, atol=5e-3)
+
+    @unittest.skipIf(torch_device == "mps", "Training is not supported in mps")
+    def test_training(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.block_class(**init_dict)
+        model.to(torch_device)
+        model.train()
+        output = model(**inputs_dict)
+
+        if isinstance(output, Tuple):
+            output = output[0]
+
+        device = torch.device(torch_device)
+        noise = randn_tensor(output.shape, device=device)
+        loss = torch.nn.functional.mse_loss(output, noise)
+        loss.backward()