test_autodiff_cpu.py 文件源码

def test_matmul_two_vars():
    x2 = ad.Variable(name='x2')
    x3 = ad.Variable(name='x3')
    y = ad.matmul(x2, x3)

    grad_x2, grad_x3 = ad.gradients(y, [x2, x3])
    executor = ad.Executor([y, grad_x2, grad_x3])
    x2_val = np.array([[1, 2], [3, 4], [5, 6]])  # 3x2
    x3_val = np.array([[7, 8, 9], [10, 11, 12]])  # 2x3

    y_val, grad_x2_val, grad_x3_val = executor.run(feed_shapes={x2: x2_val, x3: x3_val})

    expected_yval = np.matmul(x2_val, x3_val)
    expected_grad_x2_val = np.matmul(np.ones_like(expected_yval), np.transpose(x3_val))
    expected_grad_x3_val = np.matmul(np.transpose(x2_val), np.ones_like(expected_yval))

    assert isinstance(y, ad.Node)
    assert np.array_equal(y_val, expected_yval)
    assert np.array_equal(grad_x2_val, expected_grad_x2_val)
    assert np.array_equal(grad_x3_val, expected_grad_x3_val)