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 sgd_update_cpu(w1, w2, w3):
w1_gpu = ndarray.empty(w1.shape, executor_ctx)
w1.copyto(w1_gpu)
w2_gpu = ndarray.empty(w2.shape, executor_ctx)
w2.copyto(w2_gpu)
sgd_update_gpu(w1_gpu, w2_gpu, w3)
w1_gpu.copyto(w1)
w2_gpu.copyto(w2)
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(autodiff.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_multiply_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_multiply_by_const(arr_x, val, arr_y)
y = arr_y.asnumpy()
np.testing.assert_allclose(x * val, y, rtol=1e-5)
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_reduce_sum_axis_n():
ctx = ndarray.gpu(0)
shape = (5)
to_shape = (1,)
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(arr_x, arr_y)
print(arr_x.shape[0])
print(arr_y.asnumpy())
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 = (500, 200)
# 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_dropout():
ctx = ndarray.gpu(0)
in_shape = (5, 1, 2)
input_arr_np = np.arange(10).reshape(in_shape)
arr_in = ndarray.array(input_arr_np, ctx=ctx)
arr_out = ndarray.empty(in_shape,ctx=ctx)
r = gpu_op.dropout_forward(arr_in,arr_out,"NCHW",0.2,1)
print(arr_in.asnumpy())
print(arr_out.asnumpy())
gpu_op.dropout_backward(arr_out, arr_in, "NCHW", 0.2,1, r)
print(arr_in.asnumpy())
def test_broadcast_to():
ctx = ndarray.gpu(0)
shape = (2, 3)
to_shape = ( 5,2,3)
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()
print(arr_x.asnumpy())
print(y)
np.testing.assert_allclose(np.broadcast_to(x, to_shape), y)
def test_softmax_cross_entropy():
ctx = ndarray.gpu(0)
shape = (400, 1000)
y = np.random.uniform(-5, 5, shape).astype(np.float32)
y_ = np.random.uniform(-5, 5, shape).astype(np.float32)
arr_y = ndarray.array(y, ctx=ctx)
arr_y_ = ndarray.array(y_, ctx=ctx)
arr_out = ndarray.empty((1,), ctx=ctx)
gpu_op.softmax_cross_entropy(arr_y, arr_y_, arr_out)
out = arr_out.asnumpy()
# numpy calculation
cross_entropy = np.mean(
-np.sum(y_ * np.log(autodiff.softmax_func(y)), axis=1), keepdims=True)
np.testing.assert_allclose(cross_entropy, out, rtol=1e-5)
def test_reduce_sum_axis_zero():
ctx = ndarray.gpu(0)
shape = (5)
to_shape = (1,)
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)
print(arr_x.asnumpy())
print(arr_y.asnumpy())
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 test_convolution_forward():
ctx = ndarray.gpu(0)
in_shape = (1,1,8)
filter_shape = (1, 1, 5)
out_shape = (1, 1, 8)
input_arr_np = np.arange(8).reshape(in_shape)
dinput_arr_np = np.arange(8).reshape(in_shape)
filter_arr_np = np.arange(5).reshape(filter_shape)
dfilter_arr_np = np.arange(5).reshape(filter_shape)
dinput=ndarray.array(dinput_arr_np, ctx=ctx)
dfilter=ndarray.array(dfilter_arr_np, ctx=ctx)
arr_in = ndarray.array(input_arr_np, ctx=ctx)
arr_filter = ndarray.array(filter_arr_np, ctx=ctx)
arr_out = ndarray.empty(out_shape, ctx=ctx)
gpu_op.convolution_1d_forward(arr_in,arr_filter,arr_out,"NCHW","SAME",1)
gpu_op.convolution_1d_backward(arr_in,arr_out,arr_filter,dfilter,dinput,"NCHW","SAME",1)
print(arr_out.asnumpy())
print(dfilter.asnumpy())
print(dinput.asnumpy())
print(arr_in.asnumpy())
