def test_matrix_multiply():
ctx = ndarray.gpu(0)
x = np.random.uniform(0, 10, size=(500, 700)).astype(np.float32)
y = np.random.uniform(0, 10, size=(700, 1000)).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.array(y, ctx=ctx)
arr_z = ndarray.empty((500, 1000), ctx=ctx)
gpu_op.matrix_multiply(arr_x, False, arr_y, False, arr_z)
z = arr_z.asnumpy()
np.testing.assert_allclose(np.dot(x, y), z, rtol=1e-5)
x = np.random.uniform(0, 10, size=(1000, 500)).astype(np.float32)
y = np.random.uniform(0, 10, size=(2000, 500)).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.array(y, ctx=ctx)
arr_z = ndarray.empty((1000, 2000), ctx=ctx)
gpu_op.matrix_multiply(arr_x, False, arr_y, True, arr_z)
z = arr_z.asnumpy()
np.testing.assert_allclose(np.dot(x, np.transpose(y)), z, rtol=1e-5)
x = np.random.uniform(0, 10, size=(500, 1000)).astype(np.float32)
y = np.random.uniform(0, 10, size=(2000, 500)).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.array(y, ctx=ctx)
arr_z = ndarray.empty((1000, 2000), ctx=ctx)
gpu_op.matrix_multiply(arr_x, True, arr_y, True, arr_z)
z = arr_z.asnumpy()
np.testing.assert_allclose(np.dot(np.transpose(x), np.transpose(y)),
z,
rtol=1e-5)
def test_softmax():
ctx = ndarray.gpu(0)
shape = (400, 1000)
x = np.random.uniform(-5, 5, shape).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.empty(shape, ctx=ctx)
gpu_op.softmax(arr_x, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(au.nn.softmax_func(x), y, rtol=1e-5)
def test_relu():
shape = (2000, 2500)
ctx = ndarray.gpu(0)
x = np.random.uniform(-1, 1, shape).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.empty(shape, ctx=ctx)
gpu_op.relu(arr_x, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(np.maximum(x, 0).astype(np.float32), y)
def test_matrix_elementwise_add_by_const():
shape = (2000, 3000)
ctx = ndarray.gpu(0)
x = np.random.uniform(0, 10, size=shape).astype(np.float32)
val = np.random.uniform(-5, 5)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.empty(shape, ctx=ctx)
gpu_op.matrix_elementwise_add_by_const(arr_x, val, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(x + val, y, rtol=1e-5)
def test_broadcast_to():
ctx = ndarray.gpu(0)
shape = (200, 300)
to_shape = (130, 200, 300)
x = np.random.uniform(-1, 1, shape).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.empty(to_shape, ctx=ctx)
gpu_op.broadcast_to(arr_x, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(np.broadcast_to(x, to_shape), y)
def test_matrix_elementwise_multiply():
ctx = ndarray.gpu(0)
shape = (500, 200)
x = np.random.uniform(0, 10, size=shape).astype(np.float32)
y = np.random.uniform(0, 10, size=shape).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.array(y, ctx=ctx)
arr_z = ndarray.empty(shape, ctx=ctx)
gpu_op.matrix_elementwise_multiply(arr_x, arr_y, arr_z)
z = arr_z.asnumpy()
np.testing.assert_allclose(x * y, z, rtol=1e-5)
def test_relu_gradient():
shape = (2000, 2500)
ctx = ndarray.gpu(0)
x = np.random.uniform(-1, 1, shape).astype(np.float32)
grad_x = np.random.uniform(-5, 5, shape).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_grad_x = ndarray.array(grad_x, ctx=ctx)
arr_y = ndarray.empty(shape, ctx=ctx)
gpu_op.relu_gradient(arr_x, arr_grad_x, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(((x > 0) * grad_x).astype(np.float32), y)
def test_array_set():
ctx = ndarray.gpu(0)
shape = (5000, 2000)
# oneslike
arr_x = ndarray.empty(shape, ctx=ctx)
gpu_op.array_set(arr_x, 1.)
x = arr_x.asnumpy()
np.testing.assert_allclose(np.ones(shape), x)
# zeroslike
gpu_op.array_set(arr_x, 0.)
x = arr_x.asnumpy()
np.testing.assert_allclose(np.zeros(shape), x)
def test_reduce_sum_axis_zero():
ctx = ndarray.gpu(0)
shape = (500, 200, 100)
to_shape = (200, 100)
x = np.random.uniform(0, 20, shape).astype(np.float32)
arr_x = ndarray.array(x, ctx=ctx)
arr_y = ndarray.empty(to_shape, ctx=ctx)
gpu_op.reduce_sum_axis_zero(arr_x, arr_y)
y = arr_y.asnumpy()
y_ = np.sum(x, axis=0)
for index, _ in np.ndenumerate(y):
v = y[index]
v_ = y_[index]
if abs((v - v_) / v_) > 1e-4:
print(index, v, v_)
np.testing.assert_allclose(np.sum(x, axis=0), y, rtol=1e-5)
def memory_plan(self, feed_shapes):
"""
:param feed_shapes:
:return:
"""
# topo_order = find_topo_sort(self.eval_node_list) # TODO (upul) cache this
# self.node_to_arr_map = {}
# for node in topo_order:
# self.node_to_arr_map[node] = ndarray.empty(self.node_to_shape_map[node], ctx=self.ctx)
if self.node_to_arr_map is None:
self.node_to_arr_map = {}
for node in self.topo_order:
if node in feed_shapes:
continue
self.node_to_arr_map[node] = ndarray.empty(self.node_to_shape_map[node], ctx=self.ctx)