mirror of https://github.com/commaai/tinygrad.git
103 lines
4.0 KiB
Python
103 lines
4.0 KiB
Python
import math
|
|
import unittest
|
|
import numpy as np
|
|
import torch
|
|
from tinygrad.tensor import Tensor
|
|
import tinygrad.nn as nn
|
|
|
|
# https://gist.github.com/devries/11405101
|
|
def ksprob(a):
|
|
fac, total, termbf = 2.0, 0.0, 0.0
|
|
a2 = -2.0 * a * a
|
|
for j in range(1, 101):
|
|
term = fac * math.exp(a2 * j * j)
|
|
total += term
|
|
if math.fabs(term) <= 0.001 * termbf or math.fabs(term) <= 1e-8 * total:
|
|
return total
|
|
fac = -fac
|
|
termbf = math.fabs(term)
|
|
return 1.0
|
|
|
|
def kstest(l1, l2):
|
|
n1, n2 = len(l1), len(l2)
|
|
l1.sort()
|
|
l2.sort()
|
|
j1, j2, d, fn1, fn2 = 0, 0, 0.0, 0.0, 0.0
|
|
while j1 < n1 and j2 < n2:
|
|
d1, d2 = l1[j1], l2[j2]
|
|
if d1 <= d2:
|
|
fn1 = (float(j1) + 1.0) / float(n1)
|
|
j1 += 1
|
|
if d2 <= d1:
|
|
fn2 = (float(j2) + 1.0) / float(n2)
|
|
j2 += 1
|
|
dtemp = math.fabs(fn2 - fn1)
|
|
if dtemp > d:
|
|
d = dtemp
|
|
ne = float(n1 * n2) / float(n1 + n2)
|
|
nesq = math.sqrt(ne)
|
|
prob = ksprob((nesq + 0.12 + 0.11 / nesq) * d)
|
|
return prob
|
|
|
|
def normal_test(func, shape=(20, 23), alpha=0.05):
|
|
Tensor.manual_seed(1337)
|
|
np.random.seed(1337)
|
|
x = func(*shape).cpu().numpy().flatten()
|
|
y = np.random.randn(*shape).flatten()
|
|
return kstest(x, y) >= alpha
|
|
|
|
def equal_distribution(tiny_func, torch_func, numpy_func=None, shape=(20, 23), alpha=0.05):
|
|
Tensor.manual_seed(1337)
|
|
torch.manual_seed(1337)
|
|
np.random.seed(1337)
|
|
x = tiny_func(*shape).cpu().numpy().flatten()
|
|
if numpy_func is not None: y = numpy_func(shape).flatten()
|
|
z = torch_func(shape).numpy().flatten()
|
|
return (numpy_func is None or kstest(x, y) >= alpha) and kstest(x, z) >= alpha
|
|
|
|
class TestRandomness(unittest.TestCase):
|
|
def test_rand(self):
|
|
self.assertFalse(normal_test(Tensor.rand))
|
|
self.assertTrue(equal_distribution(Tensor.rand, torch.rand, lambda x: np.random.rand(*x)))
|
|
|
|
def test_randn(self):
|
|
self.assertTrue(normal_test(Tensor.randn))
|
|
self.assertTrue(equal_distribution(Tensor.randn, torch.randn, lambda x: np.random.randn(*x)))
|
|
|
|
def test_uniform(self):
|
|
self.assertFalse(normal_test(Tensor.uniform))
|
|
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)))
|
|
|
|
def test_scaled_uniform(self):
|
|
self.assertFalse(normal_test(Tensor.scaled_uniform))
|
|
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))))
|
|
|
|
def test_glorot_uniform(self):
|
|
self.assertFalse(normal_test(Tensor.glorot_uniform))
|
|
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:])))))
|
|
|
|
def test_kaiming_uniform(self):
|
|
Tensor.manual_seed(1337)
|
|
torch.manual_seed(1337)
|
|
np.random.seed(1337)
|
|
for shape in [(128, 64, 3, 3), (20, 24)]:
|
|
self.assertTrue(equal_distribution(Tensor.kaiming_uniform, lambda x: torch.nn.init.kaiming_uniform_(torch.empty(x)), shape=shape))
|
|
|
|
def test_conv2d_init(self):
|
|
params = (128, 256, (3,3))
|
|
assert equal_distribution(lambda *_: nn.Conv2d(*params).weight, lambda _: torch.nn.Conv2d(*params).weight.detach())
|
|
assert equal_distribution(lambda *_: nn.Conv2d(*params).bias, lambda _: torch.nn.Conv2d(*params).bias.detach())
|
|
|
|
def test_linear_init(self):
|
|
params = (64, 64)
|
|
assert equal_distribution(lambda *_: nn.Linear(*params).weight, lambda _: torch.nn.Linear(*params).weight.detach())
|
|
assert equal_distribution(lambda *_: nn.Linear(*params).bias, lambda _: torch.nn.Linear(*params).bias.detach())
|
|
|
|
def test_bn_init(self):
|
|
params = (64,)
|
|
assert equal_distribution(lambda *_: nn.BatchNorm2d(*params).weight, lambda _: torch.nn.BatchNorm2d(*params).weight.detach())
|
|
assert equal_distribution(lambda *_: nn.BatchNorm2d(*params).bias, lambda _: torch.nn.BatchNorm2d(*params).bias.detach())
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|