mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
104 lines
3.2 KiB
Python
104 lines
3.2 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, W.grad
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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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class TestOps(unittest.TestCase):
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def test_conv2d(self):
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x = torch.randn((5,2,10,7), requires_grad=True)
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w = torch.randn((4,2,3,3), requires_grad=True)
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xt = Tensor(x.detach().numpy())
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wt = Tensor(w.detach().numpy())
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out = torch.nn.functional.conv2d(x,w)
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ret = Tensor.conv2d(xt, wt)
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np.testing.assert_allclose(ret.data, out.detach().numpy(), atol=1e-5)
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out.mean().backward()
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ret.mean().backward()
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np.testing.assert_allclose(w.grad, wt.grad, atol=1e-7)
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np.testing.assert_allclose(x.grad, xt.grad, atol=1e-7)
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def test_maxpool2x2(self):
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x = torch.randn((5,2,10,8), requires_grad=True)
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xt = Tensor(x.detach().numpy())
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# in tinygrad
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ret = xt.maxpool2x2()
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assert ret.shape == (5,2,10//2,8//2)
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ret.mean().backward()
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# in torch
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out = torch.nn.MaxPool2d((2,2))(x)
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out.mean().backward()
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# forward and backward the same
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np.testing.assert_allclose(ret.data, out.detach().numpy(), atol=1e-5)
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np.testing.assert_allclose(x.grad, xt.grad, atol=1e-5)
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if __name__ == '__main__':
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unittest.main()
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