Created by: LDOUBLEV

本地环境：

cuda 9, cudnn7, python3.6

问题描述，希望实现的功能是：

y = f(x) dydx = fluid.gradient(y, x) dydxdy = fluid.gradient(dydx*x, y)

代码：

import paddle.fluid as fluid
import numpy as np

def test_grad_grad():
    """validation the validation of mul double grad"""
    train_program = fluid.Program()
    start_program = fluid.Program()
    place = fluid.CUDAPlace(0)
    #place = fluid.CPUPlace()
    with fluid.program_guard(train_program, start_program):
        rng = np.random.RandomState(0)
        rng = np.random
        inp_ = rng.uniform(-1, 1, [32, 24]).astype('float32')
        w1_ = rng.uniform(-1, 1, [24, 50]).astype('float32')
        w2_ = rng.uniform(-1, 1, [50, 14]).astype('float32')
        yg_ = rng.uniform(-1, 1, [2, ]).astype('float32')

        inp = fluid.layers.data('inp', [32, 24], append_batch_size=False)
        w1 = fluid.layers.data('w1', [24, 50], append_batch_size=False)
        w2 = fluid.layers.data('w2', [50, 14], append_batch_size=False)
        yg = fluid.layers.data('yg', [2, ], append_batch_size=False)

        inp.stop_gradient = False
        w1.stop_gradient = False
        w2.stop_gradient = False
        yg.stop_gradient = False

        y = fluid.layers.mul(fluid.layers.mul(inp, w1), w2)

        f = fluid.layers.reshape(y, [-1])

        x = [w1, w2]
        dfdx_f1 = fluid.gradients(f, x, f)

        # double gradient
        dfdx_x_ = fluid.gradients([dfdx_f1[0]*x[0], dfdx_f1[1]*x[1]], f)
        print(dfdx_x_)
        print( train_program, file=open("program.txt", 'wt'))
    exe = fluid.Executor(place)
    exe.run(program=fluid.default_startup_program())
    compiled_prog = fluid.compiler.CompiledProgram(train_program)

    res = exe.run(compiled_prog, feed={'inp':inp_, 'w1':w1_, 'w2':w2_, 'yg':yg_},
                  fetch_list=[dfdx_f1[0].name, dfdx_f1[1].name,
                              dfdx_x_[0].name])
    print(res)
test_grad_grad()
  

报错截图

计算第二次梯度的时候len(self.ops)和 len(ops_in_cpp)不相等；这段代码十月之前在paddle上还可以正常运行