252 lines
8.4 KiB
Python
252 lines
8.4 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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import tinygrad.nn as nn
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from tinygrad.helpers import dtypes
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from functools import partial
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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 equal_distribution(
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tiny_func, torch_func=None, numpy_func=None, shape=(20, 23), alpha=0.05
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):
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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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assert not (
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torch_func is None and numpy_func is None
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), "no function to compare with"
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x = tiny_func(*shape).numpy().flatten()
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if numpy_func is not None:
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y = numpy_func(shape).flatten()
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if torch_func is not None:
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z = torch_func(shape).numpy().flatten()
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return (numpy_func is None or kstest(x, y) >= alpha) and (
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torch_func is None or kstest(x, z) >= alpha
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)
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def normal_test(func, shape=(20, 23), alpha=0.05):
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return equal_distribution(
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func, numpy_func=lambda x: np.random.randn(*x), shape=shape, alpha=alpha
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)
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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(
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equal_distribution(Tensor.rand, torch.rand, lambda x: np.random.rand(*x))
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)
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def test_randn(self):
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self.assertTrue(normal_test(Tensor.randn))
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self.assertTrue(
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equal_distribution(Tensor.randn, torch.randn, lambda x: np.random.randn(*x))
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)
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def test_normal(self):
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self.assertTrue(normal_test(Tensor.normal))
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self.assertTrue(
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equal_distribution(
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Tensor.normal,
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lambda x: torch.nn.init.normal_(torch.empty(x), mean=0, std=1),
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lambda x: np.random.normal(loc=0, scale=1, size=x),
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)
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)
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def test_uniform(self):
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self.assertFalse(normal_test(Tensor.uniform))
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self.assertTrue(
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equal_distribution(
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Tensor.uniform,
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lambda x: torch.nn.init.uniform_(torch.empty(x)),
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lambda x: np.random.uniform(size=x),
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)
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)
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self.assertTrue(
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equal_distribution(
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partial(Tensor.uniform, low=-100, high=100, dtype=dtypes.int32),
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numpy_func=lambda x: np.random.randint(low=-100, high=100, size=x),
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)
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)
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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(
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equal_distribution(
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Tensor.scaled_uniform,
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lambda x: torch.nn.init.uniform_(torch.empty(x), a=-1, b=1)
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/ math.sqrt(math.prod(x)),
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lambda x: np.random.uniform(-1, 1, size=x) / math.sqrt(math.prod(x)),
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)
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)
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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(
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equal_distribution(
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Tensor.glorot_uniform,
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lambda x: torch.nn.init.xavier_uniform_(torch.empty(x)),
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lambda x: np.random.uniform(-1, 1, size=x)
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* math.sqrt(6 / (x[0] + math.prod(x[1:]))),
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)
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)
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def test_kaiming_uniform(self):
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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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for shape in [(128, 64, 3, 3), (20, 24)]:
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self.assertTrue(
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equal_distribution(
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Tensor.kaiming_uniform,
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lambda x: torch.nn.init.kaiming_uniform_(torch.empty(x)),
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shape=shape,
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)
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)
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def test_kaiming_normal(self):
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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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for shape in [(128, 64, 3, 3), (20, 24)]:
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self.assertTrue(
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equal_distribution(
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Tensor.kaiming_normal,
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lambda x: torch.nn.init.kaiming_normal_(torch.empty(x)),
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shape=shape,
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)
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)
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def test_multinomial(self):
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self.assertRaises(
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AssertionError, lambda: Tensor(2).multinomial(1, replacement=False)
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)
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self.assertRaises(
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AssertionError, lambda: Tensor([1, 9]).multinomial(0, replacement=False)
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)
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def _check_with_torch(w, num_samples, replacement):
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tiny_res = Tensor(w).multinomial(num_samples, replacement=replacement)
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torch_res = torch.tensor(w).multinomial(
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num_samples, replacement=replacement
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)
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self.assertEqual(tiny_res.shape, torch_res.shape)
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if torch_res.ndim == 1:
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tiny_res = tiny_res.unsqueeze(0)
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torch_res = torch_res.unsqueeze(0)
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for i in range(torch_res.shape[0]):
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self.assertTrue(
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equal_distribution(lambda *_: tiny_res[i], lambda _: torch_res[i])
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)
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_check_with_torch(w=[0.231, 0.0, 1.0, 0.5], num_samples=2000, replacement=True)
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_check_with_torch(
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w=[[0.2, 0.8]], num_samples=2000, replacement=True
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) # 2D but only 1 row
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_check_with_torch(
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w=[[0.453, 0.0, 1.0, 0.81], [0.1, 0.8, 0.0, 0.1]],
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num_samples=2000,
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replacement=True,
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)
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# no-replacement isn't supported, unless taking only one sample
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w = [0.1, 0.9]
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self.assertRaises(
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AssertionError, lambda: Tensor(w).multinomial(100, replacement=False)
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)
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tiny_samples = [
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Tensor(w).multinomial(1, replacement=False).numpy().item()
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for _ in range(1000)
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]
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torch_samples = [
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torch.tensor(w).multinomial(1, replacement=False).item()
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for _ in range(1000)
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]
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self.assertTrue(
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equal_distribution(
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lambda *_: Tensor(tiny_samples), lambda _: torch.tensor(torch_samples)
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)
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)
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def test_multinomial_counterexample(self):
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tiny_res = Tensor([0.3, 0.6, 0.1]).multinomial(2000, replacement=True)
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torch_res = torch.tensor([0.3, 0.6, 0.1]).multinomial(2000, replacement=True)
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self.assertTrue(equal_distribution(lambda *_: tiny_res, lambda _: torch_res))
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torch_res = torch.tensor([0.2, 0.7, 0.1]).multinomial(2000, replacement=True)
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self.assertFalse(equal_distribution(lambda *_: tiny_res, lambda _: torch_res))
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def test_conv2d_init(self):
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params = (128, 256, (3, 3))
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assert equal_distribution(
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lambda *_: nn.Conv2d(*params).weight,
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lambda _: torch.nn.Conv2d(*params).weight.detach(),
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)
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assert equal_distribution(
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lambda *_: nn.Conv2d(*params).bias,
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lambda _: torch.nn.Conv2d(*params).bias.detach(),
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)
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def test_linear_init(self):
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params = (64, 64)
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assert equal_distribution(
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lambda *_: nn.Linear(*params).weight,
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lambda _: torch.nn.Linear(*params).weight.detach(),
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)
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assert equal_distribution(
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lambda *_: nn.Linear(*params).bias,
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lambda _: torch.nn.Linear(*params).bias.detach(),
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)
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def test_bn_init(self):
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params = (64,)
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assert equal_distribution(
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lambda *_: nn.BatchNorm2d(*params).weight,
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lambda _: torch.nn.BatchNorm2d(*params).weight.detach(),
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)
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assert equal_distribution(
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lambda *_: nn.BatchNorm2d(*params).bias,
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lambda _: torch.nn.BatchNorm2d(*params).bias.detach(),
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)
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if __name__ == "__main__":
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unittest.main()
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