mirror of
https://github.com/tinygrad/tinygrad.git
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125 lines
3.9 KiB
Python
125 lines
3.9 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, GPU, Device
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from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
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from .config import ANE
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x_init = np.random.randn(1, 3).astype(np.float32)
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U_init = np.random.randn(3, 3).astype(np.float32)
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V_init = np.random.randn(3, 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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device = Device.CPU
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def test_backward_pass(self):
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def test_tinygrad():
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x = Tensor(x_init, device=self.device)
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W = Tensor(W_init, device=self.device)
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m = Tensor(m_init, device=self.device)
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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.cpu().data, x.grad.cpu().data, W.grad.cpu().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_backward_pass_diamond_model(self):
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def test_tinygrad():
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u = Tensor(U_init, device=self.device)
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v = Tensor(V_init, device=self.device)
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w = Tensor(W_init, device=self.device)
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x = u.mul(v).relu()
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y = u.mul(w).relu()
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out = x.add(y).mul(y).relu()
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out = out.logsoftmax()
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out = out.sum()
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out.backward()
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return out.cpu().data, u.cpu().grad.data, v.cpu().grad.data, w.cpu().grad.data
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def test_pytorch():
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u = torch.tensor(U_init, requires_grad=True)
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v = torch.tensor(V_init, requires_grad=True)
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w = torch.tensor(W_init, requires_grad=True)
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x = u.mul(v).relu()
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y = u.mul(w).relu()
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out = x.add(y).mul(y).relu()
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out = torch.nn.functional.log_softmax(out, dim=1)
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out = out.sum()
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out.backward()
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return out.detach().numpy(), u.grad, v.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(
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x.matmul(torch_W).relu(), dim=1
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)
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PJ = torch.autograd.functional.jacobian(torch_func, torch_x).squeeze().numpy()
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tiny_x = Tensor(x, device=self.device)
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tiny_W = Tensor(W, device=self.device)
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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, device=self.device)
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tiny_W = Tensor(W, device=self.device)
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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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@unittest.skipUnless(GPU, "Requires GPU")
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class TestTinygradGPU(TestTinygrad):
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device = Device.GPU
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@unittest.skip("float64 not supported on GPU")
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def test_jacobian(self):
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pass
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@unittest.skip("float64 not supported on GPU")
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def test_gradcheck(self):
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pass
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@unittest.skipUnless(ANE, "Requires ANE")
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class TestOpsANE(TestTinygrad):
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device = Device.ANE
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if __name__ == "__main__":
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unittest.main()
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