File size: 10,112 Bytes
fd43906
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import torch

from diffusers import UNet1DModel
from diffusers.utils import floats_tensor, slow, torch_device

from ..test_modeling_common import ModelTesterMixin


torch.backends.cuda.matmul.allow_tf32 = False


class UNet1DModelTests(ModelTesterMixin, unittest.TestCase):
    model_class = UNet1DModel

    @property
    def dummy_input(self):
        batch_size = 4
        num_features = 14
        seq_len = 16

        noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
        time_step = torch.tensor([10] * batch_size).to(torch_device)

        return {"sample": noise, "timestep": time_step}

    @property
    def input_shape(self):
        return (4, 14, 16)

    @property
    def output_shape(self):
        return (4, 14, 16)

    def test_ema_training(self):
        pass

    def test_training(self):
        pass

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_determinism(self):
        super().test_determinism()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_outputs_equivalence(self):
        super().test_outputs_equivalence()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_from_save_pretrained(self):
        super().test_from_save_pretrained()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_from_save_pretrained_variant(self):
        super().test_from_save_pretrained_variant()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_model_from_pretrained(self):
        super().test_model_from_pretrained()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_output(self):
        super().test_output()

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "block_out_channels": (32, 64, 128, 256),
            "in_channels": 14,
            "out_channels": 14,
            "time_embedding_type": "positional",
            "use_timestep_embedding": True,
            "flip_sin_to_cos": False,
            "freq_shift": 1.0,
            "out_block_type": "OutConv1DBlock",
            "mid_block_type": "MidResTemporalBlock1D",
            "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
            "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
            "act_fn": "mish",
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_from_pretrained_hub(self):
        model, loading_info = UNet1DModel.from_pretrained(
            "bglick13/hopper-medium-v2-value-function-hor32", output_loading_info=True, subfolder="unet"
        )
        self.assertIsNotNone(model)
        self.assertEqual(len(loading_info["missing_keys"]), 0)

        model.to(torch_device)
        image = model(**self.dummy_input)

        assert image is not None, "Make sure output is not None"

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_output_pretrained(self):
        model = UNet1DModel.from_pretrained("bglick13/hopper-medium-v2-value-function-hor32", subfolder="unet")
        torch.manual_seed(0)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(0)

        num_features = model.in_channels
        seq_len = 16
        noise = torch.randn((1, seq_len, num_features)).permute(
            0, 2, 1
        )  # match original, we can update values and remove
        time_step = torch.full((num_features,), 0)

        with torch.no_grad():
            output = model(noise, time_step).sample.permute(0, 2, 1)

        output_slice = output[0, -3:, -3:].flatten()
        # fmt: off
        expected_output_slice = torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348])
        # fmt: on
        self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))

    def test_forward_with_norm_groups(self):
        # Not implemented yet for this UNet
        pass

    @slow
    def test_unet_1d_maestro(self):
        model_id = "harmonai/maestro-150k"
        model = UNet1DModel.from_pretrained(model_id, subfolder="unet")
        model.to(torch_device)

        sample_size = 65536
        noise = torch.sin(torch.arange(sample_size)[None, None, :].repeat(1, 2, 1)).to(torch_device)
        timestep = torch.tensor([1]).to(torch_device)

        with torch.no_grad():
            output = model(noise, timestep).sample

        output_sum = output.abs().sum()
        output_max = output.abs().max()

        assert (output_sum - 224.0896).abs() < 4e-2
        assert (output_max - 0.0607).abs() < 4e-4


class UNetRLModelTests(ModelTesterMixin, unittest.TestCase):
    model_class = UNet1DModel

    @property
    def dummy_input(self):
        batch_size = 4
        num_features = 14
        seq_len = 16

        noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
        time_step = torch.tensor([10] * batch_size).to(torch_device)

        return {"sample": noise, "timestep": time_step}

    @property
    def input_shape(self):
        return (4, 14, 16)

    @property
    def output_shape(self):
        return (4, 14, 1)

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_determinism(self):
        super().test_determinism()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_outputs_equivalence(self):
        super().test_outputs_equivalence()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_from_save_pretrained(self):
        super().test_from_save_pretrained()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_from_save_pretrained_variant(self):
        super().test_from_save_pretrained_variant()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_model_from_pretrained(self):
        super().test_model_from_pretrained()

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_output(self):
        # UNetRL is a value-function is different output shape
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            output = model(**inputs_dict)

            if isinstance(output, dict):
                output = output.sample

        self.assertIsNotNone(output)
        expected_shape = torch.Size((inputs_dict["sample"].shape[0], 1))
        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")

    def test_ema_training(self):
        pass

    def test_training(self):
        pass

    def prepare_init_args_and_inputs_for_common(self):
        init_dict = {
            "in_channels": 14,
            "out_channels": 14,
            "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
            "up_block_types": [],
            "out_block_type": "ValueFunction",
            "mid_block_type": "ValueFunctionMidBlock1D",
            "block_out_channels": [32, 64, 128, 256],
            "layers_per_block": 1,
            "downsample_each_block": True,
            "use_timestep_embedding": True,
            "freq_shift": 1.0,
            "flip_sin_to_cos": False,
            "time_embedding_type": "positional",
            "act_fn": "mish",
        }
        inputs_dict = self.dummy_input
        return init_dict, inputs_dict

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_from_pretrained_hub(self):
        value_function, vf_loading_info = UNet1DModel.from_pretrained(
            "bglick13/hopper-medium-v2-value-function-hor32", output_loading_info=True, subfolder="value_function"
        )
        self.assertIsNotNone(value_function)
        self.assertEqual(len(vf_loading_info["missing_keys"]), 0)

        value_function.to(torch_device)
        image = value_function(**self.dummy_input)

        assert image is not None, "Make sure output is not None"

    @unittest.skipIf(torch_device == "mps", "mish op not supported in MPS")
    def test_output_pretrained(self):
        value_function, vf_loading_info = UNet1DModel.from_pretrained(
            "bglick13/hopper-medium-v2-value-function-hor32", output_loading_info=True, subfolder="value_function"
        )
        torch.manual_seed(0)
        if torch.cuda.is_available():
            torch.cuda.manual_seed_all(0)

        num_features = value_function.in_channels
        seq_len = 14
        noise = torch.randn((1, seq_len, num_features)).permute(
            0, 2, 1
        )  # match original, we can update values and remove
        time_step = torch.full((num_features,), 0)

        with torch.no_grad():
            output = value_function(noise, time_step).sample

        # fmt: off
        expected_output_slice = torch.tensor([165.25] * seq_len)
        # fmt: on
        self.assertTrue(torch.allclose(output, expected_output_slice, rtol=1e-3))

    def test_forward_with_norm_groups(self):
        # Not implemented yet for this UNet
        pass