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770 lines
30 KiB
770 lines
30 KiB
1 month ago
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import subprocess
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import numpy as np
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import torch
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import unittest, copy, mmap, random, math, array
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from tinygrad import Tensor, Device, dtypes
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from tinygrad.engine.schedule import create_schedule
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from tinygrad.helpers import getenv, temp, _METADATA, mv_address
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from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
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from hypothesis import given, settings, strategies as strat
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from tinygrad.device import is_dtype_supported
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settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
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settings.load_profile("my_profile")
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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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gradient = np.random.randn(1,3).astype(np.float32)
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class TestTinygrad(unittest.TestCase):
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def test_zerodim_initialization(self):
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self.assertEqual(Tensor(55).shape, ())
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self.assertEqual(Tensor(3.14).shape, ())
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def test_plus_equals(self):
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a = Tensor.randn(10,10)
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b = Tensor.randn(10,10)
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c = a + b
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val1 = c.numpy()
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a += b
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val2 = a.numpy()
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np.testing.assert_allclose(val1, val2)
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def test_backward_pass(self):
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def test_tinygrad():
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x = Tensor(x_init, requires_grad=True)
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W = Tensor(W_init, requires_grad=True)
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m = Tensor(m_init)
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out = x.dot(W).relu()
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out = out.log_softmax()
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out = out.mul(m).add(m).sum()
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out.backward()
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return out.numpy(), x.grad.numpy(), W.grad.numpy()
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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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# A simple test is to check that we can accumulate gradients (run backward twice or more times)
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# This will only work if retain_graph works.
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def test_retain_graph(self):
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x = Tensor(x_init, requires_grad=True)
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W = Tensor(W_init, requires_grad=True)
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m = Tensor(m_init)
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out = x.dot(W).relu()
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out = out.log_softmax()
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out = out.mul(m).add(m).sum()
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out.backward(retain_graph=True)
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xgrad,wgrad = x.grad, W.grad
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out.backward(retain_graph=True)
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xgrad2,wgrad2 = x.grad, W.grad
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out.backward() # no need to retain again since we will not re-run backward
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xgrad3,wgrad3 = x.grad, W.grad
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np.testing.assert_allclose(xgrad3.numpy(), xgrad.numpy() * 3., atol=1e-6)
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np.testing.assert_allclose(wgrad3.numpy(), wgrad.numpy() * 3., atol=1e-6)
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np.testing.assert_allclose(xgrad2.numpy(), xgrad.numpy() * 2., atol=1e-6)
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np.testing.assert_allclose(wgrad2.numpy(), wgrad.numpy() * 2., atol=1e-6)
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def test_second_order_backward_pass(self):
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def test_pytorch():
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x_val = torch.tensor([2.0], requires_grad=True)
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f = x_val**3
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first_derivative = torch.autograd.grad(outputs=f, inputs=x_val, create_graph=True)[0]
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second_derivative = torch.autograd.grad(outputs=first_derivative, inputs=x_val)[0]
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# d^2f/dx^2 = 6x = 6*2 = 12
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return second_derivative.numpy()
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def test_tinygrad():
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x_val = Tensor(2.0)
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f = x_val**3
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first_derivative = f.gradient(x_val)[0]
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second_derivative = first_derivative.gradient(x_val)[0]
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return second_derivative.numpy()
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np.testing.assert_allclose(test_tinygrad(), test_pytorch(), atol=1e-5)
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# passing `gradient` to backward
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def test_backward_pass_vjp(self):
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def test_tinygrad():
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x = Tensor(x_init, requires_grad=True)
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W = Tensor(W_init, requires_grad=True)
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m = Tensor(m_init)
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out = x.dot(W).relu()
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out = out.log_softmax()
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out = out.mul(m).add(m)
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out.backward(Tensor(gradient))
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return out.numpy(), x.grad.numpy(), W.grad.numpy()
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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)
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out.backward(torch.tensor(gradient))
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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, requires_grad=True)
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v = Tensor(V_init, requires_grad=True)
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w = 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 = out.log_softmax()
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out = out.sum()
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out.backward()
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return out.numpy(), u.grad.numpy(), v.grad.numpy(), w.grad.numpy()
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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, rtol=1e-6)
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def test_nograd(self):
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x = Tensor(x_init, requires_grad=False)
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m = Tensor(m_init, requires_grad=False)
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W = Tensor(W_init, requires_grad=True)
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tmp = x.mul(m)
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mm = tmp.matmul(W)
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out = mm.relu()
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out = out.sum()
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out.backward()
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assert x.grad is None
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assert m.grad is None
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assert tmp.grad is None
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assert mm.grad is not None
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assert W.grad is not None
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def test_dropout(self):
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with Tensor.train():
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n, rate = 1_000_000, 0.1
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w = Tensor.ones(n).dropout(rate)
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non_zeros = np.count_nonzero(w.numpy())
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expected = n * (1 - rate)
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np.testing.assert_allclose(non_zeros, expected, rtol=2e-3)
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def test_jacobian(self):
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W = np.random.RandomState(42069).random((10, 5)).astype(np.float32)
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x = np.random.RandomState(69420).random((1, 10)).astype(np.float32)
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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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def torch_func(x): return 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, requires_grad=True)
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tiny_W = Tensor(W, requires_grad=True)
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def tiny_func(x): return x.dot(tiny_W).relu().log_softmax()
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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-3)
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def test_gradcheck(self):
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W = np.random.RandomState(1337).random((10, 5)).astype(np.float32)
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x = np.random.RandomState(7331).random((1, 10)).astype(np.float32)
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tiny_x = Tensor(x, requires_grad=True)
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tiny_W = Tensor(W, requires_grad=True)
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def tiny_func(x): return x.dot(tiny_W).relu().log_softmax()
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self.assertTrue(gradcheck(tiny_func, tiny_x, eps = 1e-3))
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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 = 1e-5))
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def test_random_fns_are_deterministic_with_seed(self):
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for random_fn in [Tensor.randn, Tensor.normal, Tensor.uniform, Tensor.scaled_uniform, Tensor.glorot_uniform, Tensor.kaiming_normal]:
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with self.subTest(msg=f"Tensor.{random_fn.__name__}"):
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Tensor.manual_seed(1337)
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a = random_fn(10,10).realize()
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Tensor.manual_seed(1337)
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b = random_fn(10,10).realize()
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np.testing.assert_allclose(a.numpy(), b.numpy())
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def test_randn_isnt_inf_on_zero(self):
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# simulate failure case of rand handing a zero to randn
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original_rand, Tensor.rand = Tensor.rand, Tensor.zeros
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try: self.assertNotIn(np.inf, Tensor.randn(16).numpy())
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except: raise
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finally: Tensor.rand = original_rand
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def test_zeros_like_has_same_dtype_and_shape(self):
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for datatype in [dtypes.float16, dtypes.float32, dtypes.int8, dtypes.int32, dtypes.int64, dtypes.uint8]:
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a = Tensor([1, 2, 3], dtype=datatype)
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b = Tensor.zeros_like(a)
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assert a.dtype == b.dtype, f"dtype mismatch {a.dtype=} != {b.dtype}"
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assert a.shape == b.shape, f"shape mismatch {a.shape} != {b.shape}"
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a = Tensor([1, 2, 3])
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b = Tensor.zeros_like(a, dtype=dtypes.int8)
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assert a.dtype == dtypes.default_int and b.dtype == dtypes.int8, "a.dtype should be int and b.dtype should be char"
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assert a.shape == b.shape, f"shape mismatch {a.shape} != {b.shape}"
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def test_ones_like_has_same_dtype_and_shape(self):
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for datatype in [dtypes.float16, dtypes.float32, dtypes.int8, dtypes.int32, dtypes.int64, dtypes.uint8]:
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a = Tensor([1, 2, 3], dtype=datatype)
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b = Tensor.ones_like(a)
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assert a.dtype == b.dtype, f"dtype mismatch {a.dtype=} != {b.dtype}"
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assert a.shape == b.shape, f"shape mismatch {a.shape} != {b.shape}"
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a = Tensor([1, 2, 3])
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b = Tensor.ones_like(a, dtype=dtypes.int8)
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assert a.dtype == dtypes.default_int and b.dtype == dtypes.int8, "a.dtype should be int and b.dtype should be char"
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assert a.shape == b.shape, f"shape mismatch {a.shape} != {b.shape}"
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def test_rand_like_device(self):
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a = Tensor.ones(3, 3, device="CLANG")
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b = Tensor.rand_like(a)
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self.assertEqual(b.device, a.device)
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def test_ndim(self):
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assert Tensor(1).ndim == 0
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assert Tensor.randn(1).ndim == 1
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assert Tensor.randn(2,2,2).ndim == 3
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assert Tensor.randn(1,1,1,1,1,1).ndim == 6
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def test_argfix(self):
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for f in [Tensor.zeros, Tensor.ones, Tensor.rand, Tensor.randn, Tensor.empty]:
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self.assertEqual(f().shape, ())
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self.assertEqual(f(1).shape, (1,))
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self.assertEqual(f(10,20,40).shape, (10,20,40))
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self.assertEqual(f([]).shape, ())
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self.assertEqual(f([1]).shape, (1,))
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self.assertEqual(f([10,20,40]).shape, (10,20,40))
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self.assertEqual(f(()).shape, ())
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self.assertEqual(f((1,)).shape, (1,))
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self.assertEqual(f((10,20,40)).shape, (10,20,40))
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with self.assertRaises(ValueError): f((2, 2), 2, 2)
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with self.assertRaises(ValueError): f((2, 2), (2, 2))
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with self.assertRaises(ValueError): f((128, 128), 0.0, 0.01)
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def test_numel(self):
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assert Tensor.randn(10, 10).numel() == 100
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assert Tensor.randn(1,2,5).numel() == 10
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assert Tensor.randn(1,1,1,1,1,1).numel() == 1
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assert Tensor([]).numel() == 0
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assert Tensor.randn(1,0,2,5).numel() == 0
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assert Tensor(3).numel() == 1
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def test_len(self):
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assert len(torch.zeros(7)) == len(Tensor.zeros(7))
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assert len(torch.zeros(10,20)) == len(Tensor.zeros(10,20))
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assert len(torch.zeros(10,20)) == len(Tensor.zeros(10,20,30))
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assert len(torch.zeros(1).flatten()) == len(Tensor.zeros(1).flatten())
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with self.assertRaises(TypeError): len(Tensor(3))
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def test_size(self):
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t1, t2 = torch.zeros(10,20), Tensor.zeros(10,20)
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assert t1.size() == t2.size()
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assert t1.size(0) == t2.size(0)
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assert t1.size(1) == t2.size(1)
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assert t1.size(-1) == t2.size(-1)
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assert t1.size(-2) == t2.size(-2)
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with self.assertRaises(IndexError): t2.size(2)
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def test_tolist(self):
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# NOTE: float16 Tensor.tolist() requires python 3.12
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for arr in [[1,2,3], [1.5,2,3], [[1,2,3], [4,5,6]], 3]:
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assert Tensor(arr).tolist() == torch.tensor(arr).tolist() == arr
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def test_element_size(self):
|
||
|
|
for _, dtype in dtypes.fields().items():
|
||
|
|
assert dtype.itemsize == Tensor.randn(3, dtype=dtype).element_size(), f"Tensor.element_size() not matching Tensor.dtype.itemsize for {dtype}"
|
||
|
|
|
||
|
|
def test_deepwalk_ctx_check(self):
|
||
|
|
layer = Tensor.uniform(1, 1, requires_grad=True)
|
||
|
|
x = Tensor.randn(1, 1, 1)
|
||
|
|
x.dot(layer).mean().backward()
|
||
|
|
x = Tensor.randn(1, 1, 1)
|
||
|
|
x.dot(layer).mean().backward()
|
||
|
|
|
||
|
|
def test_zerosized_tensors(self):
|
||
|
|
np.testing.assert_equal(Tensor([]).numpy(), np.array([]))
|
||
|
|
np.testing.assert_equal(Tensor(None).numpy(), np.array([]))
|
||
|
|
|
||
|
|
def test_tensor_ndarray_dtype(self):
|
||
|
|
arr = np.array([1]) # where dtype is implicitly int64
|
||
|
|
assert Tensor(arr).dtype == dtypes.int64
|
||
|
|
assert Tensor(arr, dtype=dtypes.float32).dtype == dtypes.float32 # check if ndarray correctly casts to Tensor dtype
|
||
|
|
assert Tensor(arr, dtype=dtypes.float64).dtype == dtypes.float64 # check that it works for something else
|
||
|
|
|
||
|
|
def test_tensor_from_blob(self):
|
||
|
|
x = memoryview(bytearray(16)).cast('I')
|
||
|
|
|
||
|
|
t = Tensor.from_blob(mv_address(x), (4,), dtype=dtypes.int, device="CLANG")
|
||
|
|
z = (t+1)
|
||
|
|
np.testing.assert_equal(z.numpy(), [1, 1, 1, 1])
|
||
|
|
|
||
|
|
x[:] = array.array('I', [0, 1, 2, 3])
|
||
|
|
z = (t+1)
|
||
|
|
np.testing.assert_equal(z.numpy(), [1, 2, 3, 4])
|
||
|
|
|
||
|
|
def test_tensor_list_dtype(self):
|
||
|
|
for arr in ([1], [[[1]]], [[1,1],[1,1]], [[[1,1],[1,1]],[[1,1],[1,1]]]):
|
||
|
|
assert Tensor(arr).dtype == dtypes.default_int
|
||
|
|
assert Tensor(arr, dtype=dtypes.float32).dtype == dtypes.float32
|
||
|
|
assert Tensor(arr, dtype=dtypes.float64).dtype == dtypes.float64
|
||
|
|
|
||
|
|
for arr in ([True], [[[False]]], [[True,False],[True,False]], [[[False,True],[False,False]],[[True,True],[False,True]]]):
|
||
|
|
assert Tensor(arr).dtype == dtypes.bool
|
||
|
|
assert Tensor(arr, dtype=dtypes.float32).dtype == dtypes.float32
|
||
|
|
assert Tensor(arr, dtype=dtypes.float64).dtype == dtypes.float64
|
||
|
|
|
||
|
|
# empty tensor defaults
|
||
|
|
for arr in ([], [[[]]], [[],[]]):
|
||
|
|
t = Tensor(arr)
|
||
|
|
assert t.dtype == dtypes.default_float
|
||
|
|
np.testing.assert_allclose(t.numpy(), np.array(arr))
|
||
|
|
|
||
|
|
# mixture of bool and int
|
||
|
|
for arr in ([True, 3], [[True],[3]], [[[True]], [[3]]], [[True, 3], [3, True]]):
|
||
|
|
t = Tensor(arr)
|
||
|
|
assert t.dtype == dtypes.default_int
|
||
|
|
np.testing.assert_allclose(t.numpy(), np.array(arr))
|
||
|
|
|
||
|
|
# mixture of bool, int and float
|
||
|
|
for arr in ([[True,True],[3.,True]], [[0,1],[3.,4]], [[[0],[1]],[[3.],[4]]], [[[True],[1]],[[3.],[4]]]):
|
||
|
|
t = Tensor(arr)
|
||
|
|
assert t.dtype == dtypes.default_float
|
||
|
|
np.testing.assert_allclose(t.numpy(), np.array(arr))
|
||
|
|
|
||
|
|
def test_tensor_list_shapes(self):
|
||
|
|
self.assertEqual(Tensor([[[]]]).shape, (1,1,0))
|
||
|
|
self.assertEqual(Tensor([[],[]]).shape, (2,0))
|
||
|
|
self.assertEqual(Tensor([[[[]],[[]]], [[[]],[[]]], [[[]],[[]]]]).shape, (3,2,1,0))
|
||
|
|
|
||
|
|
def test_tensor_list_errors(self):
|
||
|
|
# inhomogeneous shape
|
||
|
|
with self.assertRaises(ValueError): Tensor([[],[[]]])
|
||
|
|
with self.assertRaises(ValueError): Tensor([[1],[]])
|
||
|
|
with self.assertRaises(ValueError): Tensor([[1],[1],1])
|
||
|
|
with self.assertRaises(ValueError): Tensor([[[1,1,1],[1,1]]])
|
||
|
|
with self.assertRaises(ValueError): Tensor([[1,1,1],[[1,1,1]]])
|
||
|
|
|
||
|
|
def test_tensor_mixed_list_tuple(self):
|
||
|
|
def _list_or_tuple(): return list if random.random() < 0.5 else tuple
|
||
|
|
def _generate_data(depth):
|
||
|
|
if depth == 0: return _list_or_tuple()()
|
||
|
|
if depth == 1: return _list_or_tuple()([random.random(), random.random()])
|
||
|
|
return _list_or_tuple()([_generate_data(depth-1), _generate_data(depth-1)])
|
||
|
|
|
||
|
|
for depth in range(7):
|
||
|
|
for _ in range(20):
|
||
|
|
data = _generate_data(depth)
|
||
|
|
np.testing.assert_allclose(Tensor(data).numpy(), np.array(data))
|
||
|
|
|
||
|
|
def test_tensor_list_special_values(self):
|
||
|
|
if is_dtype_supported(dtypes.float16):
|
||
|
|
data = [math.nan, -math.inf, 65504, 65519, 65519.999, 65520, 65520.1]
|
||
|
|
data = data + [-x for x in data]
|
||
|
|
with np.errstate(over='ignore'): np.testing.assert_allclose(Tensor(data, dtype=dtypes.float16).numpy(), np.array(data).astype(np.float16))
|
||
|
|
|
||
|
|
# uint32
|
||
|
|
data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
|
||
|
|
data = data + [-x for x in data]
|
||
|
|
np.testing.assert_allclose(Tensor(data, dtype=dtypes.uint32).numpy(), np.array(data).astype(np.uint32))
|
||
|
|
|
||
|
|
# int32
|
||
|
|
data = [1 << 33, 1 << 32, 1 << 32 - 1, 1]
|
||
|
|
data = data + [-x for x in data]
|
||
|
|
np.testing.assert_allclose(Tensor(data, dtype=dtypes.int32).numpy(), np.array(data).astype(np.int32))
|
||
|
|
|
||
|
|
def test_tensor_list_ndarray(self):
|
||
|
|
data = [np.array([1, 2, 3]), np.array([1, 2, 3]), np.array([1, 2, 3])]
|
||
|
|
np.testing.assert_equal(Tensor(data).numpy(), np.array(data))
|
||
|
|
data = [np.array([1.0, 2.0, 3.0]), np.array([1, 2, 3]), np.array([1, 2, 3])]
|
||
|
|
np.testing.assert_equal(Tensor(data).numpy(), np.array(data))
|
||
|
|
data = [np.array(1.0), np.array(2.0), np.array(3.0)]
|
||
|
|
np.testing.assert_equal(Tensor(data).numpy(), np.array(data))
|
||
|
|
|
||
|
|
def test_tensor_dtype_errors(self):
|
||
|
|
with self.assertRaises(AttributeError): Tensor([3], dtype="typo")
|
||
|
|
with self.assertRaises(TypeError): Tensor([3], dtype=(dtypes.int,))
|
||
|
|
|
||
|
|
def test_tensor_bytes(self):
|
||
|
|
data = b"abc123"
|
||
|
|
t = Tensor(data)
|
||
|
|
assert t.dtype == dtypes.uint8
|
||
|
|
assert t.shape == (6,)
|
||
|
|
np.testing.assert_equal(t.numpy(), list(data))
|
||
|
|
|
||
|
|
def test_tensor_copy(self):
|
||
|
|
x = copy.deepcopy(Tensor.ones((3,3,3)))
|
||
|
|
np.testing.assert_allclose(x.numpy(), np.ones((3,3,3)))
|
||
|
|
|
||
|
|
def test_copy_from_disk(self):
|
||
|
|
t = Tensor.randn(30).to(f"disk:{temp('test_copy_from_disk')}")
|
||
|
|
a = t[10:20]
|
||
|
|
dev = a.to(Device.DEFAULT)
|
||
|
|
np.testing.assert_allclose(a.numpy(), dev.numpy())
|
||
|
|
|
||
|
|
# Regression test for https://github.com/tinygrad/tinygrad/issues/1751
|
||
|
|
def test_copy_from_numpy_unaligned(self):
|
||
|
|
# 2**15 is the minimum for repro
|
||
|
|
arr = np.random.randn(2**15).astype(np.float32)
|
||
|
|
fn = temp('test_copy_from_numpy_unaligned')
|
||
|
|
with open(fn, 'wb') as f: f.write(b't' + arr.tobytes())
|
||
|
|
with open(fn, "a+b") as f: memview = memoryview(mmap.mmap(f.fileno(), arr.nbytes + 1))
|
||
|
|
ua_arr = np.frombuffer(memview[1:], dtype=arr.dtype, count=arr.shape[0])
|
||
|
|
np.testing.assert_allclose(arr, ua_arr)
|
||
|
|
assert not ua_arr.flags.aligned
|
||
|
|
# force device copy - to() is opt'd away - Tensor(dev)/1 is ignored
|
||
|
|
np.testing.assert_allclose(ua_arr, (Tensor(ua_arr)/Tensor(1)).numpy())
|
||
|
|
|
||
|
|
def test_item_to_tensor_to_item(self):
|
||
|
|
for a in [0, 1, 2, 3, -1, -100, 100, -101.1, 2.345, 100.1, True, False]:
|
||
|
|
item = Tensor(a).item()
|
||
|
|
assert type(item) is type(a), a
|
||
|
|
np.testing.assert_allclose(item, a), a
|
||
|
|
buffered_item = Tensor([a]).item()
|
||
|
|
assert type(buffered_item) is type(a), a
|
||
|
|
np.testing.assert_allclose(buffered_item, a), a
|
||
|
|
reshaped_item = Tensor([a]).reshape((1, 1, 1, 1, 1)).item()
|
||
|
|
assert type(reshaped_item) is type(a), a
|
||
|
|
np.testing.assert_allclose(reshaped_item, a), a
|
||
|
|
|
||
|
|
def test_no_bool(self):
|
||
|
|
with self.assertRaises(TypeError):
|
||
|
|
if Tensor(3):
|
||
|
|
print("hi")
|
||
|
|
|
||
|
|
with self.assertRaises(TypeError):
|
||
|
|
_a = Tensor([3]) in [Tensor([3]), Tensor([4]), Tensor([5])]
|
||
|
|
|
||
|
|
def test_repr_with_grad(self):
|
||
|
|
a = Tensor([1], requires_grad=True)
|
||
|
|
b = Tensor([1])
|
||
|
|
c = (a + b).mean().backward()
|
||
|
|
print(a)
|
||
|
|
print(c)
|
||
|
|
|
||
|
|
def test_env_overwrite_default_device(self):
|
||
|
|
subprocess.run(['DISK=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT != \\"DISK\\""'],
|
||
|
|
shell=True, check=True)
|
||
|
|
subprocess.run(['NPY=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT != \\"NPY\\""'],
|
||
|
|
shell=True, check=True)
|
||
|
|
subprocess.run([f'{Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
|
||
|
|
shell=True, check=True)
|
||
|
|
subprocess.run([f'DISK=1 {Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
|
||
|
|
shell=True, check=True)
|
||
|
|
subprocess.run([f'NPY=1 {Device.DEFAULT}=1 python3 -c "from tinygrad import Device; assert Device.DEFAULT == \\"{Device.DEFAULT}\\""'],
|
||
|
|
shell=True, check=True)
|
||
|
|
|
||
|
|
@unittest.skip("this test is just flaky, sync issue")
|
||
|
|
class TestMoveTensor(unittest.TestCase):
|
||
|
|
d0, d1 = f"{Device.DEFAULT}:0", f"{Device.DEFAULT}:1"
|
||
|
|
@given(strat.sampled_from([d0, d1]), strat.sampled_from([d0, d1]),
|
||
|
|
strat.sampled_from([dtypes.float16, dtypes.float32]), strat.sampled_from([True, False, None]))
|
||
|
|
def test_to_preserves(self, src, dest, dtype, requires_grad):
|
||
|
|
if not is_dtype_supported(dtype):
|
||
|
|
return
|
||
|
|
s = Tensor([1, 2, 3], device=src, dtype=dtype, requires_grad=requires_grad)
|
||
|
|
if requires_grad: s.sum().backward()
|
||
|
|
t = s.to(dest)
|
||
|
|
np.testing.assert_equal(s.numpy(), t.numpy())
|
||
|
|
assert s.dtype == t.dtype
|
||
|
|
assert s.requires_grad == t.requires_grad
|
||
|
|
if requires_grad:
|
||
|
|
np.testing.assert_equal(s.grad.numpy(), t.grad.numpy())
|
||
|
|
|
||
|
|
@given(strat.sampled_from([dtypes.float16, dtypes.float32]), strat.sampled_from([True, False, None]))
|
||
|
|
def test_shard_preserves(self, dtype, requires_grad):
|
||
|
|
s = Tensor([1, 2, 3], dtype=dtype, requires_grad=requires_grad)
|
||
|
|
t = s.shard((f"{Device.DEFAULT}:0", f"{Device.DEFAULT}:1"))
|
||
|
|
np.testing.assert_equal(s.numpy(), t.numpy())
|
||
|
|
assert s.dtype == t.dtype
|
||
|
|
assert s.requires_grad == t.requires_grad
|
||
|
|
|
||
|
|
@given(strat.sampled_from([d0, d1]))
|
||
|
|
def test_same_dev(self, dev):
|
||
|
|
x = Tensor([1,2,3], device=dev)
|
||
|
|
y = x.to(dev)
|
||
|
|
assert x is y
|
||
|
|
|
||
|
|
def test_to_grad(self):
|
||
|
|
x = Tensor.eye(3, requires_grad=True, device=self.d0)
|
||
|
|
y = Tensor([[2.0,0,-2.0]], requires_grad=True, device=self.d0)
|
||
|
|
z = y.matmul(x).to(self.d1).sum()
|
||
|
|
z.backward()
|
||
|
|
np.testing.assert_equal(x.grad.numpy(), [[2,2,2],[0,0,0],[-2,-2,-2]])
|
||
|
|
|
||
|
|
class TestZeroShapeTensor(unittest.TestCase):
|
||
|
|
def test_shape_stride(self):
|
||
|
|
t = Tensor.empty(3, 2, 0)
|
||
|
|
assert t.shape == (3, 2, 0)
|
||
|
|
# numpy has stride 0, 0, 0; torch has stride 2, 1, 1
|
||
|
|
assert t.lazydata.st.real_strides() == (0, 0, 0)
|
||
|
|
|
||
|
|
t = Tensor.empty(3, 0, 2)
|
||
|
|
assert t.shape == (3, 0, 2)
|
||
|
|
# numpy has stride 0, 0, 0; torch has stride 2, 2, 1
|
||
|
|
assert t.lazydata.st.real_strides() == (0, 0, 0)
|
||
|
|
|
||
|
|
t = Tensor.empty(0, 0, 0)
|
||
|
|
assert t.shape == (0, 0, 0)
|
||
|
|
# numpy has stride 0, 0, 0; torch has stride 1, 1, 1
|
||
|
|
assert t.lazydata.st.real_strides() == (0, 0, 0)
|
||
|
|
|
||
|
|
def test_rand(self):
|
||
|
|
t = Tensor.rand(3, 2, 0)
|
||
|
|
assert t.shape == (3, 2, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.zeros((3, 2, 0)))
|
||
|
|
t = Tensor.rand(0)
|
||
|
|
assert t.shape == (0,)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.zeros((0,)))
|
||
|
|
t = Tensor.rand(0, 0, 0)
|
||
|
|
assert t.shape == (0, 0, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.zeros((0, 0, 0)))
|
||
|
|
|
||
|
|
def test_full(self):
|
||
|
|
t = Tensor.zeros(3, 2, 0)
|
||
|
|
assert t.shape == (3, 2, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.zeros((3, 2, 0)))
|
||
|
|
t = Tensor.full((3, 2, 0), 12)
|
||
|
|
assert t.shape == (3, 2, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.full((3, 2, 0), 12))
|
||
|
|
|
||
|
|
def test_reshape(self):
|
||
|
|
t = Tensor.zeros(3, 2, 0)
|
||
|
|
a = t.reshape(7, 0)
|
||
|
|
assert a.shape == (7, 0)
|
||
|
|
np.testing.assert_equal(a.numpy(), np.zeros((7, 0)))
|
||
|
|
a = t.reshape(0)
|
||
|
|
assert a.shape == (0,)
|
||
|
|
np.testing.assert_equal(a.numpy(), np.zeros((0,)))
|
||
|
|
with self.assertRaises(ValueError):
|
||
|
|
# cannot reshape from size 0 to size 1
|
||
|
|
a = t.reshape(())
|
||
|
|
|
||
|
|
def test_expand(self):
|
||
|
|
t = Tensor.full((1, 2, 0), 12).expand((6, 2, 0))
|
||
|
|
assert t.shape == (6, 2, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.full((6, 2, 0), 12))
|
||
|
|
|
||
|
|
def test_pad(self):
|
||
|
|
t = Tensor.rand(3, 2, 0).pad((None, None, (1, 1)), value=1)
|
||
|
|
assert t.shape == (3, 2, 2)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.ones((3, 2, 2)))
|
||
|
|
|
||
|
|
t = Tensor.rand(3, 2, 0).pad((None, (1, 1), None), value=1)
|
||
|
|
assert t.shape == (3, 4, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.ones((3, 4, 0)))
|
||
|
|
|
||
|
|
t = Tensor.rand(3, 2, 0).pad(((1, 1), None, None), value=1)
|
||
|
|
assert t.shape == (5, 2, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.ones((5, 2, 0)))
|
||
|
|
|
||
|
|
def test_shrink_into_zero(self):
|
||
|
|
t = Tensor.rand(3, 4).realize()
|
||
|
|
assert t.shrink((None, (2, 2))).realize().shape == (3, 0)
|
||
|
|
assert t.shrink(((2, 2), None)).realize().shape == (0, 4)
|
||
|
|
assert t.shrink(((2, 2), (2, 2))).realize().shape == (0, 0)
|
||
|
|
|
||
|
|
def test_cat(self):
|
||
|
|
a = Tensor.rand(3, 2, 2)
|
||
|
|
b = Tensor.rand(3, 2, 0)
|
||
|
|
|
||
|
|
t = a.cat(b, dim=2)
|
||
|
|
assert t.shape == (3, 2, 2)
|
||
|
|
np.testing.assert_equal(t.numpy(), a.numpy())
|
||
|
|
|
||
|
|
t = b.cat(a, dim=2)
|
||
|
|
assert t.shape == (3, 2, 2)
|
||
|
|
np.testing.assert_equal(t.numpy(), a.numpy())
|
||
|
|
|
||
|
|
t = b.cat(b, dim=0)
|
||
|
|
assert t.shape == (6, 2, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.zeros((6, 2, 0)))
|
||
|
|
t = b.cat(b, dim=1)
|
||
|
|
assert t.shape == (3, 4, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.zeros((3, 4, 0)))
|
||
|
|
t = b.cat(b, dim=2)
|
||
|
|
assert t.shape == (3, 2, 0)
|
||
|
|
np.testing.assert_equal(t.numpy(), np.zeros((3, 2, 0)))
|
||
|
|
|
||
|
|
def test_elementwise(self):
|
||
|
|
a = Tensor.rand(3, 2, 0)
|
||
|
|
a_exp = a.exp()
|
||
|
|
assert a_exp.shape == (3, 2, 0)
|
||
|
|
np.testing.assert_equal(a_exp.numpy(), np.exp(a.numpy()))
|
||
|
|
|
||
|
|
b = Tensor.rand(3, 2, 0)
|
||
|
|
assert b.shape == (3, 2, 0)
|
||
|
|
ab = a * b
|
||
|
|
assert ab.shape == (3, 2, 0)
|
||
|
|
np.testing.assert_equal(ab.numpy(), a.numpy() * b.numpy())
|
||
|
|
|
||
|
|
mask = (Tensor.rand(3, 2, 0) > 0.5)
|
||
|
|
assert mask.shape == (3, 2, 0)
|
||
|
|
c = mask.where(a, b)
|
||
|
|
assert c.shape == (3, 2, 0)
|
||
|
|
np.testing.assert_equal(c.numpy(), np.where(mask.numpy(), a.numpy(), b.numpy()))
|
||
|
|
|
||
|
|
def test_reduce_over_non_zero(self):
|
||
|
|
a = Tensor.ones(3, 2, 0).sum(axis=1)
|
||
|
|
assert a.shape == (3, 0)
|
||
|
|
np.testing.assert_equal(a.numpy(), np.sum(np.zeros((3, 2, 0)), axis=1))
|
||
|
|
|
||
|
|
def test_reduce_over_zero(self):
|
||
|
|
a = Tensor.ones(3, 2, 0).sum(axis=2)
|
||
|
|
assert a.shape == (3, 2)
|
||
|
|
np.testing.assert_equal(a.numpy(), np.sum(np.zeros((3, 2, 0)), axis=2))
|
||
|
|
|
||
|
|
a = Tensor.ones(3, 2, 0).sum(axis=2, keepdim=True)
|
||
|
|
assert a.shape == (3, 2, 1)
|
||
|
|
np.testing.assert_equal(a.numpy(), np.sum(np.zeros((3, 2, 0)), axis=2, keepdims=True))
|
||
|
|
|
||
|
|
def test_clone(self):
|
||
|
|
a = Tensor.rand(16, 16).realize()
|
||
|
|
np.testing.assert_allclose(a.numpy(), a.clone().numpy())
|
||
|
|
|
||
|
|
a = Tensor.rand(16, 16).mul(5.0).add(5.0)
|
||
|
|
np.testing.assert_allclose(a.numpy(), a.clone().numpy())
|
||
|
|
|
||
|
|
def test_clone_with_grad(self):
|
||
|
|
a = Tensor.rand(16, 16, requires_grad=True)
|
||
|
|
a.mul(5.0).add(5.0).mean().backward()
|
||
|
|
b = a.clone()
|
||
|
|
assert a.grad is not None
|
||
|
|
assert b.grad is not None
|
||
|
|
np.testing.assert_allclose(a.grad.numpy(), b.grad.numpy())
|
||
|
|
|
||
|
|
def test_reduce_default(self):
|
||
|
|
np.testing.assert_equal(Tensor([]).max().numpy(), -float("inf"))
|
||
|
|
np.testing.assert_equal(Tensor([]).min().numpy(), float("inf"))
|
||
|
|
np.testing.assert_equal(Tensor([]).sum().numpy(), 0)
|
||
|
|
np.testing.assert_equal(Tensor([]).mean().numpy(), float("nan"))
|
||
|
|
|
||
|
|
class TestTensorCreationDevice(unittest.TestCase):
|
||
|
|
# test auxiliary tensors are created on the same device
|
||
|
|
def test_one_hot(self):
|
||
|
|
y = Tensor([1, 2, 3]).to("CLANG")
|
||
|
|
x = y.one_hot(10)
|
||
|
|
x.realize()
|
||
|
|
|
||
|
|
class TestTrainMode(unittest.TestCase):
|
||
|
|
def test_train_mode(self):
|
||
|
|
assert not Tensor.training
|
||
|
|
@Tensor.train()
|
||
|
|
def f():
|
||
|
|
assert Tensor.training
|
||
|
|
f()
|
||
|
|
assert not Tensor.training
|
||
|
|
|
||
|
|
class TestInferenceMode(unittest.TestCase):
|
||
|
|
def test_inference(self):
|
||
|
|
x = Tensor(x_init, requires_grad=True)
|
||
|
|
m = Tensor(m_init, requires_grad=True)
|
||
|
|
W = Tensor(W_init, requires_grad=True)
|
||
|
|
with Tensor.test():
|
||
|
|
tmp = x.mul(m)
|
||
|
|
mm = tmp.matmul(W)
|
||
|
|
out = mm.relu()
|
||
|
|
out = out.sum()
|
||
|
|
out.backward()
|
||
|
|
assert x.grad is None
|
||
|
|
assert m.grad is None
|
||
|
|
assert tmp.grad is None
|
||
|
|
assert mm.grad is None
|
||
|
|
assert W.grad is None
|
||
|
|
assert W.requires_grad
|
||
|
|
|
||
|
|
def test_no_grad_mode_context_manager(self):
|
||
|
|
x = Tensor(x_init, requires_grad=True)
|
||
|
|
m = Tensor(m_init, requires_grad=True)
|
||
|
|
W = Tensor(W_init, requires_grad=True)
|
||
|
|
@Tensor.test()
|
||
|
|
def f(x, m, W):
|
||
|
|
tmp = x.mul(m)
|
||
|
|
mm = tmp.matmul(W)
|
||
|
|
out = mm.relu()
|
||
|
|
out = out.sum()
|
||
|
|
out.backward()
|
||
|
|
assert x.grad is None
|
||
|
|
assert m.grad is None
|
||
|
|
assert tmp.grad is None
|
||
|
|
assert mm.grad is None
|
||
|
|
assert W.grad is None
|
||
|
|
f(x, m, W)
|
||
|
|
|
||
|
|
class TestTensorMetadata(unittest.TestCase):
|
||
|
|
def setUp(self) -> None: _METADATA.set(None)
|
||
|
|
def test_matmul(self):
|
||
|
|
x = Tensor.rand(3, requires_grad=True)
|
||
|
|
W = Tensor.rand(3, 3, requires_grad=True)
|
||
|
|
out = x.matmul(W)
|
||
|
|
self.assertEqual(out.lazydata.metadata.name, "matmul")
|
||
|
|
si = create_schedule([out.lazydata])[-1]
|
||
|
|
self.assertEqual(len(si.metadata), 1)
|
||
|
|
self.assertEqual(si.metadata[0].name, "matmul")
|
||
|
|
|
||
|
|
def test_relu(self):
|
||
|
|
x = Tensor.rand(3, requires_grad=True)
|
||
|
|
out = x.relu()
|
||
|
|
self.assertEqual(out.lazydata.metadata.name, "relu")
|
||
|
|
si = create_schedule([out.lazydata])[-1]
|
||
|
|
self.assertEqual(len(si.metadata), 1)
|
||
|
|
self.assertEqual(si.metadata[0].name, "relu")
|
||
|
|
|
||
|
|
def test_complex(self):
|
||
|
|
x = Tensor.rand(3, requires_grad=True)
|
||
|
|
y = Tensor.rand(3, requires_grad=True)
|
||
|
|
out = x.relu() * y.sigmoid()
|
||
|
|
self.assertEqual(out.lazydata.metadata.name, "__mul__")
|
||
|
|
self.assertEqual(out.lazydata.src[0].metadata.name, "relu")
|
||
|
|
self.assertEqual(out.lazydata.src[1].metadata.name, "sigmoid")
|
||
|
|
si = create_schedule([out.lazydata])[-1]
|
||
|
|
self.assertEqual(len(si.metadata), 3)
|
||
|
|
self.assertEqual(set(m.name for m in si.metadata), {"relu", "sigmoid", "__mul__"})
|
||
|
|
|
||
|
|
def test_complex_backward(self):
|
||
|
|
x = Tensor.rand(3, requires_grad=True)
|
||
|
|
y = Tensor.rand(3, requires_grad=True)
|
||
|
|
out = (x.relu() * y.sigmoid()).sum()
|
||
|
|
self.assertEqual(out.lazydata.metadata.name, "sum")
|
||
|
|
out.backward()
|
||
|
|
self.assertEqual(x.grad.lazydata.metadata.name, "relu")
|
||
|
|
self.assertTrue(x.grad.lazydata.metadata.backward)
|
||
|
|
self.assertEqual(y.grad.lazydata.metadata.name, "sigmoid")
|
||
|
|
self.assertTrue(y.grad.lazydata.metadata.backward)
|
||
|
|
si = create_schedule([out.lazydata, x.grad.lazydata, y.grad.lazydata])[-1]
|
||
|
|
self.assertEqual(len(si.metadata), 3, f"failed with {si.metadata}")
|
||
|
|
self.assertEqual(set(m.name for m in si.metadata), {"sigmoid", "sigmoid", "relu"})
|
||
|
|
bw = [m for m in si.metadata if m.backward]
|
||
|
|
self.assertEqual(len(bw), 1)
|
||
|
|
self.assertEqual(bw[0].name, "sigmoid")
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
unittest.main()
|