#!/usr/bin/env python3 # Copyright (c) 2021 Xiaomi Corporation (authors: Fangjun Kuang) # # See ../../../../LICENSE for clarification regarding multiple authors # # 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 torch from rnn_lm.model import RnnLmModel, make_pad_mask def test_makd_pad_mask(): lengths = torch.tensor([1, 3, 2]) mask = make_pad_mask(lengths) expected = torch.tensor( [ [False, True, True], [False, False, False], [False, False, True], ] ) assert torch.all(torch.eq(mask, expected)) assert (~expected).sum() == lengths.sum() def test_rnn_lm_model(): vocab_size = 4 model = RnnLmModel( vocab_size=vocab_size, embedding_dim=10, hidden_dim=10, num_layers=2 ) x = torch.tensor( [ [1, 3, 2, 2], [1, 2, 2, 0], [1, 2, 0, 0], ] ) y = torch.tensor( [ [3, 2, 2, 1], [2, 2, 1, 0], [2, 1, 0, 0], ] ) lengths = torch.tensor([4, 3, 2]) nll_loss = model(x, y, lengths) print(nll_loss) """ tensor([[1.1180, 1.3059, 1.2426, 1.7773], [1.4231, 1.2783, 1.7321, 0.0000], [1.4231, 1.6752, 0.0000, 0.0000]], grad_fn=) """ def test_rnn_lm_model_tie_weights(): model = RnnLmModel( vocab_size=10, embedding_dim=10, hidden_dim=10, num_layers=2, tie_weights=True, ) assert model.input_embedding.weight is model.output_linear.weight def main(): test_makd_pad_mask() test_rnn_lm_model() test_rnn_lm_model_tie_weights() if __name__ == "__main__": torch.manual_seed(20211122) main()