forked from tinygrad/tinygrad
35 lines
862 B
Python
35 lines
862 B
Python
import numpy as np
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import torch
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from tinygrad.tensor import Tensor
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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 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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print(x,y)
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np.testing.assert_allclose(x, y, atol=1e-5)
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