tinygrad/test/test_tensor_variable.py

66 lines
1.9 KiB
Python

import unittest
import numpy as np
from tinygrad import Tensor, Variable
class TestTensorVariable(unittest.TestCase):
def test_add_tvar(self):
vv = Variable("a", 0, 10).bind(1)
ret = (Tensor(vv) + 3).item()
assert ret == 4
def test_inner_tvar_node(self):
vv = Variable("w", 0, 10).bind(2)
ret = Tensor.from_uop(vv * 4).item()
assert ret == 8
def test_inner_tvar_mul(self):
vv = Variable("w", 0, 10).bind(2)
assert (Tensor(3) * vv).item() == 6
def test_inner_tvar_mul_node(self):
vv = Variable("w", 0, 10).bind(2)
assert (Tensor(3) * (vv * 4)).item() == 24
def test_symbolic_mean(self):
vv = Variable("a", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
ret = t.mean().item()
assert ret == 1
def test_symbolic_mean_2d(self):
vv = Variable("a", 1, 10).bind(2)
vv2 = Variable("b", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
ret = t.mean().item()
assert ret == 1
def test_symbolic_mean_2d_axis_1(self):
vv = Variable("a", 1, 10).bind(2)
vv2 = Variable("b", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(vv2, vv)
ret = t.mean(axis=1).reshape(2, 1).numpy()
assert np.all(ret == 1)
def test_symbolic_mean_2d_add(self):
add_term = Variable("c", 0, 10).bind(1)
vv = Variable("a", 1, 10).bind(1)
vv2 = Variable("b", 1, 10).bind(1)
t = Tensor.ones(2, 2).contiguous().reshape(vv2+add_term, vv+add_term)
ret = t.mean().item()
assert ret == 1
def test_symbolic_var(self):
vv = Variable("a", 1, 10).bind(2)
t = Tensor.ones(2, 2).contiguous().reshape(2, vv)
ret = t.var().item()
assert ret == 0
@unittest.skip("symbolic arange isn't supported")
def test_symbolic_arange(self):
vv = Variable("a", 1, 10).bind(2)
ret = Tensor.arange(0, vv)
ret.realize()
if __name__ == '__main__':
unittest.main()