MXNET源码中NDArray数据的获取和打印

虽然本人也很想写一个系列的分析文章，奈何水平不足，零零碎碎学到一点就写一点吧

本人是想学习MXNET的源码，首先想要添加一些打印，debug一下，第一个问题是如何在C++源码中打印出NDArray结构的值，

今天尝试如下，可以打印出来，

文件 incubator-mxnet/src/c_api/c_api.cc 中，函数MXNDArraySlice修改如下：

int MXNDArraySlice(NDArrayHandle handle,
                   mx_uint slice_begin,
                   mx_uint slice_end,
                   NDArrayHandle *out) {
  NDArray *ptr = new NDArray();
  API_BEGIN();
  std::cout << "slice_begin:" << slice_begin << std::endl;
  std::cout << "slice_end:" << slice_end << std::endl;
  *ptr = static_cast<NDArray*>(handle)->SliceWithRecord(
      slice_begin, slice_end);
  *out = ptr;
  float *p = (float *)ptr->data().dptr_;
  std::cout << "p[0] = " << p[0] << std::endl;
  std::cout << "p[1] = " << p[1] << std::endl;
  API_END_HANDLE_ERROR(delete ptr);
}

Python测试代码如下

from mxnet import autograd, nd
import mxnet
print(mxnet.__version__)

x = nd.arange(2, 7).reshape((5, 1))
print(x[2:4].asnumpy())

打印结果为：

#python3 mxnet_test.py 
1.5.0
slice_begin:2
slice_end:4
p[0] = 4
p[1] = 5
[[4.]
 [5.]]

Great，可以验证出来实际的数值就是在NDArray的data()函数的dptr_指针中，

____________________________________________

但是在操作时有时会无法得到预期的结果，如同文件中函数MXNDArrayGetGrad，如果按照上面的代码进行打印的话，会发现打印出的值全为0，这时需要在代码中添加一行WaitToRead，如下可正常打印

int MXNDArrayGetGrad(NDArrayHandle handle, NDArrayHandle *out) {
  API_BEGIN();
  NDArray *arr = static_cast<NDArray*>(handle);
  NDArray ret = arr->grad();
  if (ret.is_none()) {
    *out = NULL;
  } else {
    std::cout << "ret.shape().ndim() = " << ret.shape().ndim() << std::endl;
    std::cout << "ret.shape()[0] = " << ret.shape()[0] << std::endl;
    std::cout << "ret.shape()[1] = " << ret.shape()[1] << std::endl;
    *out = new NDArray(ret);

    ret.WaitToRead();

    float *p_float = (float *)(ret.data().dptr_);
    for (int i = 0; i < ret.shape()[0] * ret.shape()[1]; i++){
     std::cout << "p_float[" << i << "] = " << p_float[i] << std::endl;
    }
  }
  API_END();
}

Python 测试代码为：

from mxnet import autograd, nd
import mxnet
print(mxnet.__version__)

x = nd.arange(2, 7).reshape((5, 1))

x.attach_grad()


with autograd.record():
    y = 2 * nd.dot(x.T, x)

y.backward()


# assert (x.grad - 4 * x).norm().asscalar() == 0
print(x.grad)

输出为：

# python3 autograd_test.py 
1.5.0
ret.shape().ndim() = 2
ret.shape()[0] = 5
ret.shape()[1] = 1
p_float[0] = 8
p_float[1] = 12
p_float[2] = 16
p_float[3] = 20
p_float[4] = 24

[[ 8.]
 [12.]
 [16.]
 [20.]
 [24.]]
<NDArray 5x1 @cpu(0)>