File size: 1,578 Bytes
ae9e4fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import numpy as np
from mla import Tensor, gradcheck
def _rand(*shape):
rng = np.random.default_rng(0)
return Tensor(rng.standard_normal(shape))
def test_add():
ok, err = gradcheck(lambda a, b: a.add(b), [_rand(3, 4), _rand(3, 4)])
assert ok, err
def test_add_broadcast():
ok, err = gradcheck(lambda a, b: a.add(b), [_rand(3, 4), _rand(4)])
assert ok, err
def test_mul():
ok, err = gradcheck(lambda a, b: a.mul(b), [_rand(3, 4), _rand(3, 4)])
assert ok, err
def test_mul_broadcast():
ok, err = gradcheck(lambda a, b: a.mul(b), [_rand(2, 3, 4), _rand(4)])
assert ok, err
def test_matmul():
ok, err = gradcheck(lambda a, b: a.matmul(b), [_rand(3, 5), _rand(5, 2)])
assert ok, err
def test_matmul_batched():
ok, err = gradcheck(lambda a, b: a.matmul(b), [_rand(2, 3, 5), _rand(5, 2)])
assert ok, err
def test_reshape():
ok, err = gradcheck(lambda a: a.reshape(4, 3), [_rand(2, 6)])
assert ok, err
def test_transpose():
ok, err = gradcheck(lambda a: a.transpose((0, 2, 1)), [_rand(2, 3, 4)])
assert ok, err
def test_sum_all():
ok, err = gradcheck(lambda a: a.sum(), [_rand(3, 4)])
assert ok, err
def test_sum_axis():
ok, err = gradcheck(lambda a: a.sum(axis=1), [_rand(3, 4)])
assert ok, err
def test_gather():
idx = np.array([0, 2, 2, 4, 1])
ok, err = gradcheck(lambda w: w.gather(idx), [_rand(5, 3)])
assert ok, err
def test_chain_add_mul_sum():
ok, err = gradcheck(lambda a, b: a.mul(b).add(a).sum(), [_rand(3, 4), _rand(3, 4)])
assert ok, err
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