mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-04-29 03:00:14 -04:00
3075988468
* added kaiming_uniform init for conv2d and linear layers * fix: set getattr * up * fix: set getattr * fix comments * better does not mean it is good * more nonlinearities * added test checks the distribution of default relu option * prettier * fix kernel size * edit distribution of returned tensor * complete tests and fix fan_mode * added higher dim test * prettier test * fix silly blank * just leaky_relu mode * default fan in and leaky relu * update params * fix test * shorter * generalize Tensor.uniform and adjust kaiming init - added low and high parameters to Tensor.uniform function, so it can have a specific range (default is 0 to 1) - adjusted return line of kaiming_uniform * range from -1 to 1 * delete comment * adjusted test_uniform * fixed * delete comment
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
import math
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import unittest
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import numpy as np
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import torch
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from tinygrad.tensor import Tensor
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# https://gist.github.com/devries/11405101
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def ksprob(a):
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fac, total, termbf = 2.0, 0.0, 0.0
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a2 = -2.0 * a * a
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for j in range(1, 101):
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term = fac * math.exp(a2 * j * j)
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total += term
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if math.fabs(term) <= 0.001 * termbf or math.fabs(term) <= 1e-8 * total:
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return total
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fac = -fac
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termbf = math.fabs(term)
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return 1.0
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def kstest(l1, l2):
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n1, n2 = len(l1), len(l2)
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l1.sort()
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l2.sort()
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j1, j2, d, fn1, fn2 = 0, 0, 0.0, 0.0, 0.0
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while j1 < n1 and j2 < n2:
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d1, d2 = l1[j1], l2[j2]
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if d1 <= d2:
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fn1 = (float(j1) + 1.0) / float(n1)
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j1 += 1
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if d2 <= d1:
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fn2 = (float(j2) + 1.0) / float(n2)
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j2 += 1
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dtemp = math.fabs(fn2 - fn1)
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if dtemp > d:
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d = dtemp
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ne = float(n1 * n2) / float(n1 + n2)
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nesq = math.sqrt(ne)
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prob = ksprob((nesq + 0.12 + 0.11 / nesq) * d)
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return prob
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def normal_test(func, shape=(20, 23), alpha=0.05):
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x = func(*shape).cpu().numpy().flatten()
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y = np.random.randn(*shape).flatten()
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return kstest(x, y) >= alpha
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def equal_distribution(tiny_func, torch_func, numpy_func, shape=(20, 23), alpha=0.05):
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Tensor.manual_seed(1337)
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torch.manual_seed(1337)
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np.random.seed(1337)
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x = tiny_func(*shape).cpu().numpy().flatten()
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y = numpy_func(shape).flatten()
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z = torch_func(shape).numpy().flatten()
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return kstest(x, y) >= alpha and kstest(x, z) >= alpha
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class TestRandomness(unittest.TestCase):
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def test_rand(self):
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self.assertFalse(normal_test(Tensor.rand))
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self.assertTrue(equal_distribution(Tensor.rand, torch.rand, lambda x: np.random.rand(*x)))
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def test_randn(self):
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self.assertTrue(normal_test(Tensor.randn))
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self.assertTrue(equal_distribution(Tensor.randn, torch.randn, lambda x: np.random.randn(*x)))
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def test_uniform(self):
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self.assertFalse(normal_test(Tensor.uniform))
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self.assertTrue(equal_distribution(Tensor.uniform, lambda x: torch.nn.init.uniform_(torch.empty(x), a=-1, b=1), lambda x: np.random.uniform(low=-1, high=1, size=x)))
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def test_scaled_uniform(self):
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self.assertFalse(normal_test(Tensor.scaled_uniform))
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self.assertTrue(equal_distribution(Tensor.scaled_uniform, lambda x: torch.nn.init.uniform_(torch.empty(x), a=-1, b=1) / math.sqrt(math.prod(x)), lambda x: (np.random.rand(*x) * 2 - 1) / math.sqrt(math.prod(x))))
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def test_glorot_uniform(self):
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self.assertFalse(normal_test(Tensor.glorot_uniform))
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self.assertTrue(equal_distribution(Tensor.glorot_uniform, lambda x: torch.nn.init.xavier_uniform_(torch.empty(x)), lambda x: (np.random.rand(*x) * 2 - 1) * math.sqrt(6 / (x[0] + math.prod(x[1:])))))
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def test_kaiming_uniform(self, shape=(20, 23), a=0.01):
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Tensor.manual_seed(1337)
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torch.manual_seed(1337)
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np.random.seed(1337)
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bound = (math.sqrt(3.0) * (math.sqrt(2.0 / (1 + a ** 2)) / math.sqrt(shape[1] * np.prod(shape[2:]))))
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self.assertTrue(equal_distribution(Tensor.kaiming_uniform, lambda x: torch.nn.init.kaiming_uniform_(torch.empty(x)), lambda x: np.random.uniform(low=-bound, high=bound, size=shape)))
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if __name__ == "__main__":
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unittest.main()
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