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ebd72ff437
* Split tests Split tests into "Test CPU" and "Test GPU". Add test flag "TEST_DEVICES" which is a comma separated list of devices: CPU,GPU,ANE * Run tests based on provided TEST_DEVICES flag By default will run all "CPU,GPU,ANE" * fix bad quote * Revert changes and use GPU=1 This is done through setting the default Tensor Device to Device.CPU of GPU=1 is set. Run GPU tests: GPU=1 pytest -s -v
75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
import numpy as np
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import torch
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import unittest
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from tinygrad.tensor import Tensor
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from tinygrad.optim import Adam, SGD, RMSprop
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from extra.utils import get_parameters
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x_init = np.random.randn(1,3).astype(np.float32)
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W_init = np.random.randn(3,3).astype(np.float32)
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m_init = np.random.randn(1,3).astype(np.float32)
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def step_tinygrad(optim, kwargs={}):
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net = TinyNet()
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optim = optim([net.x, net.W], **kwargs)
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out = net.forward()
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out.backward()
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optim.step()
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return net.x.cpu().data, net.W.cpu().data
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def step_pytorch(optim, kwargs={}):
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net = TorchNet()
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optim = optim([net.x, net.W], **kwargs)
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out = net.forward()
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out.backward()
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optim.step()
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return net.x.detach().numpy(), net.W.detach().numpy()
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class TinyNet():
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def __init__(self):
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self.x = Tensor(x_init.copy())
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self.W = Tensor(W_init.copy())
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self.m = Tensor(m_init.copy())
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def forward(self):
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out = self.x.dot(self.W).relu()
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out = out.logsoftmax()
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out = out.mul(self.m).add(self.m).sum()
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return out
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class TorchNet():
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def __init__(self):
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self.x = torch.tensor(x_init.copy(), requires_grad=True)
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self.W = torch.tensor(W_init.copy(), requires_grad=True)
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self.m = torch.tensor(m_init.copy())
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def forward(self):
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out = self.x.matmul(self.W).relu()
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out = torch.nn.functional.log_softmax(out, dim=1)
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out = out.mul(self.m).add(self.m).sum()
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return out
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class TestOptim(unittest.TestCase):
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def test_adam(self):
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for x,y in zip(step_tinygrad(Adam),
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step_pytorch(torch.optim.Adam)):
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np.testing.assert_allclose(x, y, atol=1e-4)
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def test_sgd(self):
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for x,y in zip(step_tinygrad(SGD, kwargs={'lr': 0.001}),
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step_pytorch(torch.optim.SGD, kwargs={'lr': 0.001})):
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np.testing.assert_allclose(x, y, atol=1e-5)
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def test_rmsprop(self):
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for x,y in zip(step_tinygrad(RMSprop, kwargs={'lr': 0.001, 'decay': 0.99}),
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step_pytorch(torch.optim.RMSprop,
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kwargs={'lr': 0.001, 'alpha': 0.99})):
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np.testing.assert_allclose(x, y, atol=1e-5)
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if __name__ == '__main__':
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unittest.main()
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