def test_matmul(self, run_eagerly):
a = np.array([[5, 5], [5, 5]]).astype(np.int32)
b = np.array([[6, 6], [6, 6]]).astype(np.int32)
expected = a.dot(b)
context = tf_execution_context(run_eagerly)
with context.scope():
a_var = big_import(a)
b_var = big_import(b)
c_var = big_matmul(a_var, b_var)
c_str = big_export(c_var, tf.int32)
np.testing.assert_equal(context.evaluate(c_str), expected)
def test_add(self, run_eagerly):
a = "5453452435245245245242534"
b = "1424132412341234123412341234134"
expected = int(a) + int(b)
context = tf_execution_context(run_eagerly)
with context.scope():
a_var = big_import([[a]])
b_var = big_import([[b]])
c_var = big_add(a_var, b_var)
c_str = big_export(c_var, tf.string)
np.testing.assert_equal(int(context.evaluate(c_str)), expected)
def test_mod(self, run_eagerly):
x = np.array([[123, 234], [345, 456]]).astype(np.int32)
n = np.array([[37]]).astype(np.int32)
expected = x % n
context = tf_execution_context(run_eagerly)
with context.scope():
x_big = big_import(x)
n_big = big_import(n)
y_big = big_mod(x_big, n_big)
y_str = big_export(y_big, tf.int32)
np.testing.assert_equal(context.evaluate(y_str), expected)
def test_pow(self, run_eagerly, secure):
base = "54"
exp = "3434"
modulus = "35"
expected = pow(54, 3434, 35)
context = tf_execution_context(run_eagerly)
with context.scope():
base_var = big_import([[base]])
exp_var = big_import([[exp]])
mod_var = big_import([[modulus]])
out = big_pow(base_var, exp_var, mod_var, secure=secure)
out_str = big_export(out, tf.string)
np.testing.assert_equal(int(context.evaluate(out_str)), expected)
def _import_tensor_tensorflow(tensor):
if tensor.dtype in [tf.int64]:
tensor = tf.as_string(tensor)
elif tensor.dtype not in [tf.uint8, tf.int32, tf.string]:
raise ValueError("Unsupported dtype '{}'".format(tensor.dtype))
if len(tensor.shape) != 2:
raise ValueError("Tensor must have rank 2.")
return Tensor(ops.big_import(tensor))
def _import_tensor_numpy(tensor):
tensor = _convert_to_numpy_tensor(tensor)
if np.issubdtype(tensor.dtype, np.int64) or np.issubdtype(tensor.dtype, np.object_):
tensor = tensor.astype(np.string_)
elif not (
np.issubdtype(tensor.dtype, np.int32)
or np.issubdtype(tensor.dtype, np.string_)
or np.issubdtype(tensor.dtype, np.unicode_)
):
raise ValueError("Unsupported dtype '{}'.".format(tensor.dtype))
if len(tensor.shape) != 2:
raise ValueError("Tensors must have rank 2.")
return Tensor(ops.big_import(tensor))
def test_import_export(self, run_eagerly, raw, dtype):
context = tf_execution_context(run_eagerly)
with context.scope():
variant = big_import(raw)
output = big_export(variant, dtype)
assert context.evaluate(output) == raw