225 lines
8.4 KiB
Python
225 lines
8.4 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, Device
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from tinygrad.helpers import dtypes
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from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
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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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def test_zerodim_initialization(self):
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a = Tensor(55)
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b = Tensor(3.14)
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self.assertEqual(a.shape, ())
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self.assertEqual(b.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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@unittest.skipIf(Device.DEFAULT == "WEBGPU", "this test uses more than 8 bufs which breaks webgpu") #TODO: remove after #1461
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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)
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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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Tensor.training = True
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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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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, requires_grad=True)
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tiny_W = Tensor(W, requires_grad=True)
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tiny_func = lambda x: 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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tiny_func = lambda x: 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(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"a.dtype and b.dtype should be {datatype}"
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assert a.shape == b.shape, f"shape mismatch (Tensor.zeros_like){a.shape} != (torch){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 != b.dtype and a.dtype == dtypes.float32 and b.dtype == dtypes.int8, "a.dtype should be float and b.dtype should be char"
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assert a.shape == b.shape, f"shape mismatch (Tensor.zeros_like){a.shape} != (torch){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"a.dtype and b.dtype should be {datatype}"
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assert a.shape == b.shape, f"shape mismatch (Tensor.ones_like){a.shape} != (torch){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 != b.dtype and a.dtype == dtypes.float32 and b.dtype == dtypes.int8, "a.dtype should be float and b.dtype should be char"
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assert a.shape == b.shape, f"shape mismatch (Tensor.ones_like){a.shape} != (torch){b.shape}"
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def test_ndim(self):
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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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self.assertEqual(Tensor.zeros().shape, ())
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self.assertEqual(Tensor.ones().shape, ())
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self.assertEqual(Tensor.zeros([]).shape, ())
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self.assertEqual(Tensor.ones([]).shape, ())
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self.assertEqual(Tensor.zeros(tuple()).shape, ())
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self.assertEqual(Tensor.ones(tuple()).shape, ())
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self.assertEqual(Tensor.zeros(1).shape, (1,))
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self.assertEqual(Tensor.ones(1).shape, (1,))
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self.assertEqual(Tensor.zeros(1,10,20).shape, (1,10,20))
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self.assertEqual(Tensor.ones(1,10,20).shape, (1,10,20))
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self.assertEqual(Tensor.zeros([1]).shape, (1,))
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self.assertEqual(Tensor.ones([1]).shape, (1,))
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self.assertEqual(Tensor.zeros([10,20,40]).shape, (10,20,40))
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self.assertEqual(Tensor.ones([10,20,40]).shape, (10,20,40))
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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) == 0 # TODO: fix empty tensors
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def test_element_size(self):
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for _, dtype in dtypes.fields().items():
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assert dtype.itemsize == Tensor.randn(3, dtype=dtype).element_size(), f"Tensor.element_size() not matching Tensor.dtype.itemsize for {dtype}"
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def test_deepwalk_ctx_check(self):
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layer = Tensor.uniform(1, 1, requires_grad=True)
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x = Tensor.randn(1, 1, 1)
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x.dot(layer).mean().backward()
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x = Tensor.randn(1, 1, 1)
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x.dot(layer).mean().backward()
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if __name__ == '__main__':
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unittest.main()
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