def test_permute(data, t1):
permutation = data.draw(permutations(range(len(t1.shape))))
def permute(a):
return a.permute(*permutation)
grad_check(permute, t1)
def test_log_softmax(t):
q = minitorch.softmax(t, 3)
q2 = minitorch.logsoftmax(t, 3).exp()
for i in q._tensor.indices():
assert_close(q[i], q2[i])
minitorch.grad_check(lambda a: minitorch.logsoftmax(a, dim=2), t)
def test_custom():
fn = lambda t1, t2: t1 * t2
t1 = minitorch.tensor_fromlist([[1, 2], [4, 5]])
t2 = minitorch.tensor_fromlist([[1, 4]])
ts = [
t1,
t2,
]
minitorch.grad_check(fn, *ts)
def test_permute(backend, data):
"Check permutations for all backends."
t1 = data.draw(tensors(backend=shared[backend]))
permutation = data.draw(permutations(range(len(t1.shape))))
def permute(a):
return a.permute(*permutation)
minitorch.grad_check(permute, t1)
def test_softmax(t):
q = minitorch.softmax(t, 3)
x = q.sum(dim=3)
assert_close(x[0, 0, 0, 0], 1.0)
q = minitorch.softmax(t, 1)
x = q.sum(dim=1)
assert_close(x[0, 0, 0, 0], 1.0)
minitorch.grad_check(lambda a: minitorch.softmax(a, dim=2), t)
def test_max(t):
out = minitorch.nn.max(t, 2)
assert out[0, 0, 0] == max(t[0, 0, i] for i in range(4))
out = minitorch.nn.max(t, 1)
assert out[0, 0, 0] == max(t[0, i, 0] for i in range(3))
out = minitorch.nn.max(t, 0)
assert out[0, 0, 0] == max(t[i, 0, 0] for i in range(2))
rand_tensor = minitorch.rand(t.shape) * 1e-5
t = t + rand_tensor
minitorch.grad_check(lambda t: minitorch.nn.max(t, 2), t)
def test_mm2():
a = minitorch.rand((2, 3), backend=FastTensorBackend)
b = minitorch.rand((3, 4), backend=FastTensorBackend)
c = a @ b
c2 = (a.view(2, 3, 1) * b.view(1, 3, 4)).sum(1).view(2, 4)
for ind in c._tensor.indices():
assert_close(c[ind], c2[ind])
minitorch.grad_check(lambda a, b: a @ b, a, b)
def test_conv1d_simple2():
t = minitorch.tensor_fromlist([[1, 2, 1, 2, 3, 4],
[2, 1, 3, 2, 3, 2.]]).view(1, 2, 6)
t.requires_grad_(True)
t2 = minitorch.tensor_fromlist([[[3, 2, 1],
[1, 2, 3.]], [[3, 2, 1],
[1, 2, 3.]]]).view(2, 2, 3)
out = minitorch.Conv1dFun.apply(t, t2)
print(out)
assert out[0, 1, 2] == 26
minitorch.grad_check(minitorch.Conv1dFun.apply, t, t2)
def test_conv2():
t = minitorch.tensor_fromlist([[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3],
[0, 1, 2, 3]]).view(1, 1, 4, 4)
t.requires_grad_(True)
t2 = minitorch.tensor_fromlist([[1, 1], [1, 1]]).view(1, 1, 2, 2)
t2.requires_grad_(True)
out = minitorch.Conv2dFun.apply(t, t2)
out.sum().backward()
minitorch.grad_check(minitorch.Conv2dFun.apply, t, t2)
def test_avg(t):
out = minitorch.avgpool2d(t, (2, 2))
assert_close(
out[0, 0, 0, 0],
sum([t[0, 0, i, j] for i in range(2) for j in range(2)]) / 4.0)
out = minitorch.avgpool2d(t, (2, 1))
assert_close(
out[0, 0, 0, 0],
sum([t[0, 0, i, j] for i in range(2) for j in range(1)]) / 2.0)
out = minitorch.avgpool2d(t, (1, 2))
assert_close(
out[0, 0, 0, 0],
sum([t[0, 0, i, j] for i in range(1) for j in range(2)]) / 2.0)
minitorch.grad_check(lambda t: minitorch.avgpool2d(t, (2, 2)), t)
def test_two_grad_broadcast(fn, ts):
t1, t2 = ts
minitorch.grad_check(fn[1], t1, t2)
# broadcast check
minitorch.grad_check(fn[1], t1.sum(0), t2)
minitorch.grad_check(fn[1], t1, t2.sum(0))
def test_two_grad_broadcast(fn, ts):
name, base_fn, tensor_fn = fn
t1, t2 = ts
grad_check(tensor_fn, t1, t2)
# broadcast check
grad_check(tensor_fn, t1.sum(0), t2)
grad_check(tensor_fn, t1, t2.sum(0))
def test_softmax(t):
t = minitorch.tensor_fromlist([
[
[
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00]]]])
q = minitorch.softmax(t, 2)
print('=====t')
print(t)
print('=====q')
print(q)
x = q.sum(dim=3)
print('=====x')
print(x)
assert_close(x[0, 0, 0, 0], 1.0)
q = minitorch.softmax(t, 1)
x = q.sum(dim=1)
assert_close(x[0, 0, 0, 0], 1.0)
minitorch.grad_check(lambda a: minitorch.softmax(a, dim=2), t)
def test_two_grad_broadcast(fn, backend, data):
"Run backward for all two arg functions above with broadcast."
t1, t2 = data.draw(shaped_tensors(2, backend=backend))
minitorch.grad_check(fn[1], t1, t2)
# broadcast check
minitorch.grad_check(fn[1], t1.sum(0), t2)
minitorch.grad_check(fn[1], t1, t2.sum(0))
def test_two_grad_broadcast(fn, backend, data):
"Run backward for all two arg functions above with broadcast."
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
name, base_fn, tensor_fn = fn
grad_check(tensor_fn, t1, t2)
# broadcast check
grad_check(tensor_fn, t1.sum(0), t2)
grad_check(tensor_fn, t1, t2.sum(0))
def test_max(t):
t_np = t.to_numpy()
compare_0 = t_np.max(axis=0)
out_0 = minitorch.Max.apply(t, 0)
assert out_0.shape == (1, 3, 4)
assert np.array_equal(compare_0.reshape(-1), out_0.to_numpy().reshape(-1))
minitorch.grad_check(lambda t: minitorch.Max.apply(t, 0),
t + (minitorch.rand(t.shape) * 1e-5))
compare_1 = t_np.max(axis=1)
out_1 = minitorch.Max.apply(t, 1)
assert out_1.shape == (2, 1, 4)
assert np.array_equal(compare_1.reshape(-1), out_1.to_numpy().reshape(-1))
minitorch.grad_check(lambda t: minitorch.Max.apply(t, 1),
t + (minitorch.rand(t.shape) * 1e-5))
compare_2 = t_np.max(axis=2)
out_2 = minitorch.Max.apply(t, 2)
assert out_2.shape == (2, 3, 1)
assert np.array_equal(compare_2.reshape(-1), out_2.to_numpy().reshape(-1))
minitorch.grad_check(lambda t: minitorch.Max.apply(t, 0),
t + (minitorch.rand(t.shape) * 1e-5))
def test_two_grad(fn, backend, data):
"Run backward for all two arg functions above."
t1, t2 = data.draw(shaped_tensors(2, backend=shared[backend]))
name, _, tensor_fn = fn
grad_check(tensor_fn, t1, t2)
def test_one_derivative(fn, backend, data):
"Run backward for all one arg functions above."
t1 = data.draw(tensors(backend=shared[backend]))
name, _, tensor_fn = fn
grad_check(tensor_fn, t1)
def test_conv1d(input, weight):
out = minitorch.Conv1dFun.apply(input, weight)
minitorch.grad_check(minitorch.Conv1dFun.apply, input, weight)
print('=============')
print(input, weight)
print(out)
def test_conv_batch(input, weight):
minitorch.grad_check(minitorch.Conv2dFun.apply, input, weight)
def test_two_grad(fn, ts):
t1, t2 = ts
minitorch.grad_check(fn[1], t1, t2)
def test_back_view(t1):
def view(a):
a = a.contiguous()
return a.view(a.size)
grad_check(view, t1)
def test_one_derivative(fn, t1):
name, _, tensor_fn = fn
grad_check(tensor_fn, t1)
def test_conv1d(input, weight):
minitorch.grad_check(minitorch.Conv1dFun.apply, input, weight)
def test_reduce(fn, t1):
name, _, tensor_fn = fn
grad_check(tensor_fn, t1)
def test_one_derivative(fn, t1):
minitorch.grad_check(fn[1], t1)
def test_reduce(fn, t1):
minitorch.grad_check(fn[1], t1)
def test_conv_channel(input, weight):
# Run several times for random seed
for _ in range(5):
minitorch.grad_check(minitorch.Conv2dFun.apply, input, weight)
def test_reduce(fn, backend, data):
"Run backward for all reduce functions above."
t1 = data.draw(tensors(backend=shared[backend]))
name, _, tensor_fn = fn
grad_check(tensor_fn, t1)
def test_two_grad(fn, ts):
name, _, tensor_fn = fn
t1, t2 = ts
grad_check(tensor_fn, t1, t2)
