def tensor_data(draw, numbers=floats(), shape=None):
if shape is None:
shape = draw(shapes())
size = int(minitorch.prod(shape))
data = draw(lists(numbers, min_size=size, max_size=size))
permute = draw(permutations(range(len(shape))))
permute_shape = tuple([shape[i] for i in permute])
reverse_permute = [a[0] for a in sorted(enumerate(permute), key=lambda a: a[1])]
td = minitorch.TensorData(data, permute_shape)
ret = td.permute(*reverse_permute)
assert ret.shape[0] == shape[0]
return ret
def matmul_tensors(
draw, numbers=floats(allow_nan=False, min_value=-100, max_value=100)
):
i, j, k = [draw(integers(min_value=1, max_value=10)) for _ in range(3)]
l1 = (i, j)
l2 = (j, k)
values = []
for shape in [l1, l2]:
size = int(minitorch.prod(shape))
data = draw(lists(numbers, min_size=size, max_size=size))
values.append(minitorch.Tensor(minitorch.TensorData(data, shape)))
return values
def tensor_data(draw, numbers=floats(), shape=None):
if shape is None:
shape = draw(shapes())
size = int(minitorch.prod(shape))
data = draw(lists(numbers, min_size=size, max_size=size))
return minitorch.TensorData(data, shape)
