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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 unittest
import torch
from protenix.model.modules.primitives import (
rearrange_qk_to_dense_trunk,
rearrange_to_dense_trunk,
)
def create_qkv(batch_size_dims, n_q, n_kv, d):
q = torch.rand(size=(*batch_size_dims, n_q, d))
k = torch.rand(size=(*batch_size_dims, n_kv, d))
v = torch.rand(size=(*batch_size_dims, n_kv, d))
return q, k, v
class TestUtils(unittest.TestCase):
def setUp(self):
return super().setUp()
def test_equivalence(self):
batch_size_dims = (3, 5)
n = 128 * 2 + 18
d = 9
n_queries = 32
n_keys = 128
inf = 10e10
torch.random.manual_seed(42)
q, k, v = create_qkv(batch_size_dims, n, n, d)
q_trunked, k_trunked, _, attn_bias_trunked, q_pad_length = (
rearrange_to_dense_trunk(
q,
k,
v,
n_queries,
n_keys,
inf=inf,
)
)
q_b, k_b, padding_info = rearrange_qk_to_dense_trunk(
q, k, dim_q=-2, dim_k=-2, n_queries=n_queries, n_keys=n_keys
)
self.assertTrue(
torch.allclose(
padding_info["mask_trunked"] > 0, attn_bias_trunked[0, 0] > -1
)
)
self.assertTrue(torch.allclose(q_b, q_trunked))
self.assertTrue(torch.allclose(k_b, k_trunked))
if __name__ == "__main__":
unittest.main()
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