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FrogAi
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
#!/usr/bin/env python
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import unittest
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import numpy as np
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from tinygrad.tensor import Tensor
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from models.rnnt import LSTM
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import torch
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class TestRNNT(unittest.TestCase):
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def test_lstm(self):
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BS, SQ, IS, HS, L = 2, 20, 40, 128, 2
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# create in torch
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with torch.no_grad():
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torch_layer = torch.nn.LSTM(IS, HS, L)
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# create in tinygrad
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layer = LSTM(IS, HS, L, 0.0)
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# copy weights
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with torch.no_grad():
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layer.cells[0].weights_ih.assign(Tensor(torch_layer.weight_ih_l0.numpy()))
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layer.cells[0].weights_hh.assign(Tensor(torch_layer.weight_hh_l0.numpy()))
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layer.cells[0].bias_ih.assign(Tensor(torch_layer.bias_ih_l0.numpy()))
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layer.cells[0].bias_hh.assign(Tensor(torch_layer.bias_hh_l0.numpy()))
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layer.cells[1].weights_ih.assign(Tensor(torch_layer.weight_ih_l1.numpy()))
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layer.cells[1].weights_hh.assign(Tensor(torch_layer.weight_hh_l1.numpy()))
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layer.cells[1].bias_ih.assign(Tensor(torch_layer.bias_ih_l1.numpy()))
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layer.cells[1].bias_hh.assign(Tensor(torch_layer.bias_hh_l1.numpy()))
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# test initial hidden
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for _ in range(3):
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x = Tensor.randn(SQ, BS, IS)
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z, hc = layer(x, None)
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torch_x = torch.tensor(x.numpy())
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torch_z, torch_hc = torch_layer(torch_x)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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# test passing hidden
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for _ in range(3):
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x = Tensor.randn(SQ, BS, IS)
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z, hc = layer(x, hc)
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torch_x = torch.tensor(x.numpy())
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torch_z, torch_hc = torch_layer(torch_x, torch_hc)
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np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-3, rtol=5e-3)
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if __name__ == '__main__':
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unittest.main()
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