mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-24 06:18:01 -05:00
7086d77db1
it might be coming from an expand, and resetting results incorrect stride. caught by interpreted backend
61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
#!/usr/bin/env python
|
|
import numpy as np
|
|
import unittest
|
|
from tinygrad.lazy import LazyBuffer
|
|
from tinygrad import Device
|
|
from tinygrad.tensor import Tensor
|
|
|
|
class TestLazyBuffer(unittest.TestCase):
|
|
@unittest.skip("it doesn't work like this anymore")
|
|
def test_fromcpu_buffer_sharing(self):
|
|
a = np.arange(8)
|
|
assert LazyBuffer.fromCPU(a).realized._buf is a
|
|
|
|
def test_fromcpu_shape_tracker(self):
|
|
def helper(a: np.ndarray):
|
|
print(a.shape, a.strides, a.flags.c_contiguous)
|
|
b = LazyBuffer.fromCPU(a)
|
|
#assert b.st.contiguous == a.flags.c_contiguous
|
|
assert b.st.shape == a.shape
|
|
np.testing.assert_equal(a, Tensor(b).numpy())
|
|
|
|
for ndims in range(1, 4):
|
|
a = np.random.randn(*(4,)*ndims).astype(np.float32)
|
|
for stride in [-2, 1, 2]:
|
|
for start in [0, 1]:
|
|
helper(a[(slice(start, None, stride),)*ndims])
|
|
|
|
def test_shuffle_pad_ops_cmpeq(self):
|
|
y = Tensor([1]).cat(Tensor([1]) == 0).numpy()
|
|
z = Tensor([1, 0]).numpy()
|
|
np.testing.assert_allclose(y, z)
|
|
|
|
def test_shuffle_pad_ops_div(self):
|
|
y = Tensor([1]).cat(Tensor([1]).div(Tensor([2.0]))).numpy()
|
|
z = Tensor([1, 0.5]).numpy()
|
|
np.testing.assert_allclose(y, z)
|
|
|
|
def test_shuffle_pad_ops_log(self):
|
|
y = Tensor([1]).cat(Tensor([1]).log()).numpy()
|
|
z = Tensor([1, 0]).numpy()
|
|
np.testing.assert_allclose(y, z)
|
|
|
|
def test_shuffle_pad_ops_exp(self):
|
|
y = Tensor([1]).cat(Tensor([1]).exp()).numpy()
|
|
z = Tensor([1, np.e]).numpy()
|
|
np.testing.assert_allclose(y, z)
|
|
|
|
def test_device_0_is_the_same_device(self):
|
|
a = Tensor([1, 2, 3], f"{Device.DEFAULT}")
|
|
b = Tensor([1, 2, 3], f"{Device.DEFAULT}:0")
|
|
assert a.device == b.device
|
|
|
|
def test_shrink_const_into_zero(self):
|
|
a = Tensor.zeros(4,4,4).shrink((None, (0,0), None))
|
|
b = Tensor.zeros(4,1,4)
|
|
c = a.cat(b, dim=1)
|
|
np.testing.assert_allclose(c.numpy(), np.concatenate((a.numpy(), b.numpy()), axis=1))
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|