mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
9ac1ad40d6
* copy tensors to and from gpu * add on GPU * adding works * we stick shapes in * works on cpu and gpu * test changes, not passing yet * something else * op tests pass * add, mean, and sum have working forward/backward * mul ops test * no gpu support, no problem * test pass, clean up later * gpu cleanup * cleanup test ops, don't let div fail * revert more * aimpler dispatcher * clean up grad * GPU and * grad is a Tensor now * gate test on GPU * cleanups * late loading gpu * GPU as input option * last cleanups
69 lines
2.2 KiB
Python
69 lines
2.2 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.gradcheck import numerical_jacobian, jacobian, gradcheck
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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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class TestTinygrad(unittest.TestCase):
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def test_backward_pass(self):
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def test_tinygrad():
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x = Tensor(x_init)
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W = Tensor(W_init)
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m = Tensor(m_init)
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out = x.dot(W).relu()
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out = out.logsoftmax()
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out = out.mul(m).add(m).sum()
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out.backward()
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return out.data, x.grad.data, W.grad.data
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def test_pytorch():
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x = torch.tensor(x_init, requires_grad=True)
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W = torch.tensor(W_init, requires_grad=True)
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m = torch.tensor(m_init)
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out = x.matmul(W).relu()
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out = torch.nn.functional.log_softmax(out, dim=1)
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out = out.mul(m).add(m).sum()
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out.backward()
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return out.detach().numpy(), x.grad, W.grad
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for x,y in zip(test_tinygrad(), test_pytorch()):
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np.testing.assert_allclose(x, y, atol=1e-5)
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def test_jacobian(self):
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W = np.random.RandomState(1337).random((10, 5))
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x = np.random.RandomState(7331).random((1, 10)) - 0.5
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torch_x = torch.tensor(x, requires_grad=True)
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torch_W = torch.tensor(W, requires_grad=True)
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torch_func = lambda x: torch.nn.functional.log_softmax(x.matmul(torch_W).relu(), dim=1)
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PJ = torch.autograd.functional.jacobian(torch_func, torch_x).squeeze().numpy()
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tiny_x = Tensor(x)
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tiny_W = Tensor(W)
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tiny_func = lambda x: x.dot(tiny_W).relu().logsoftmax()
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J = jacobian(tiny_func, tiny_x)
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NJ = numerical_jacobian(tiny_func, tiny_x)
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np.testing.assert_allclose(PJ, J, atol = 1e-5)
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np.testing.assert_allclose(PJ, NJ, atol = 1e-5)
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def test_gradcheck(self):
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W = np.random.RandomState(1337).random((10, 5))
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x = np.random.RandomState(7331).random((1, 10)) - 0.5
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tiny_x = Tensor(x)
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tiny_W = Tensor(W)
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tiny_func = lambda x: x.dot(tiny_W).relu().logsoftmax()
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self.assertTrue(gradcheck(tiny_func, tiny_x))
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# coarse approx. since a "big" eps and the non-linearities of the model
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self.assertFalse(gradcheck(tiny_func, tiny_x, eps = 0.1))
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if __name__ == '__main__':
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unittest.main()
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