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# Copyright 2024 ByteDance and/or its affiliates.
#
# 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 math
import time
import unittest
import torch
from protenix.model.modules.transformer import DiffusionTransformer
class TestDiffusionTransformer(unittest.TestCase):
def setUp(self) -> None:
self._start_time = time.time()
self.device = "cuda" if torch.cuda.is_available() else "cpu"
super().setUp()
def get_model(
self,
c_a: int = 128,
c_s: int = 384,
c_z: int = 64,
n_blocks: int = 3,
n_heads: int = 4,
):
model = DiffusionTransformer(
c_a=c_a, c_s=c_s, c_z=c_z, n_blocks=n_blocks, n_heads=n_heads
).to(self.device)
return model
def test_shape(self) -> None:
n_heads = 2
c_a = 13 * n_heads
c_s = 23
c_z = 17
N = 45
bs_dims = (2, 3)
inputs = {
"a": torch.rand(size=(*bs_dims, N, c_a)).to(self.device),
"s": torch.rand(size=(*bs_dims, N, c_s)).to(self.device),
"z": torch.rand(size=(*bs_dims, N, N, c_z)).to(self.device),
"n_queries": None,
"n_keys": None,
}
model = self.get_model(c_a=c_a, c_s=c_s, c_z=c_z, n_heads=n_heads)
out = model(**inputs)
target_shape = (*bs_dims, N, c_a)
self.assertEqual(out.shape, out.reshape(target_shape).shape)
N_q = 32
N_k = 128
N_blocks = math.ceil(N / N_q)
inputs = {
"a": torch.rand(size=(*bs_dims, N, c_a)).to(self.device),
"s": torch.rand(size=(*bs_dims, N, c_s)).to(self.device),
"z": torch.rand(size=(*bs_dims, N_blocks, N_q, N_k, c_z)).to(self.device),
"n_queries": 32,
"n_keys": 128,
}
out = model(**inputs)
target_shape = (*bs_dims, N, c_a)
self.assertEqual(out.shape, out.reshape(target_shape).shape)
def tearDown(self):
elapsed_time = time.time() - self._start_time
print(f"Test {self.id()} took {elapsed_time:.6f}s")
if __name__ == "__main__":
unittest.main()
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