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())