使用python创建mxnet操作符(网络层)

对cuda了解不多，所以使用python创建新的操作层是个不错的选择，当然这个性能不如cuda编写的代码。

在MXNET源码的example/numpy-ops/下有官方提供的使用python编写新操作符的实例。分别跑ndarray_softmax.py、numpy_softmax.py和custom_softmax.py 发现ndarray_softmax.py中训练速度将近其他两种方法的3倍，分析发现ndarray_softmax.py中调用cuda核，而其他两种方法都是numpy在cpu上的运行。

这里总结一下我对ndarray_softmax.py的理解。

分析一下ndarray_softmax.py源码,重写了父类的一些基本方法，其中最重要的是前向和后向操作：

 def forward(self, in_data, out_data):
       x = in_data[0]
       y = out_data[0]
       if self.fwd_kernel is None:
           self.fwd_kernel = mx.rtc('softmax', [('x', x)], [('y', y)], """
   int i = threadIdx.x + blockIdx.x*blockDim.x;
   float max_x = x[i*x_dims[1]];
   for (int j = 1; j < x_dims[1]; ++j) {
       if (max_x < x[i*x_dims[1]+j]) {
           max_x = x[i*x_dims[1]+j];
       }
   }
   float sum = 0.0f;
   for (int j = 0; j < x_dims[1]; ++j) {
       sum += expf(x[i*x_dims[1]+j]-max_x);
   }
   for (int j = 0; j < x_dims[1]; ++j) {
       y[i*x_dims[1]+j] = expf(x[i*x_dims[1]+j]-max_x)/sum;
   }
   """)
       self.fwd_kernel.push([x], [y], (1, 1, 1), (x.shape[0], 1, 1))
   ​
   def backward(self, out_grad, in_data, out_data, in_grad):
       l = in_data[1]
       y = out_data[0]
       dx = in_grad[0]
       if self.bwd_kernel is None:
           self.bwd_kernel = mx.rtc('softmax_grad', [('y', y), ('l', l)], [('dx', dx)], """
   int i = blockIdx.x;
   int j = threadIdx.x;
   int k = static_cast<int>(l[i]);
   if (j == k) {
       dx[i*dx_dims[1]+j] = y[i*dx_dims[1]+j] - 1.0f;
   } else {
       dx[i*dx_dims[1]+j] = y[i*dx_dims[1]+j];
   }
   """)
       self.bwd_kernel.push([y,l], [dx], (y.shape[0],1,1), (y.shape[1], 1, 1))

使用mx.rtc(...)定义的就是cuda 编译相关内容了，查看/python/mxnet/rtc.py中Rtc类的定义，其参数部分描述如下：

 """MXRtc object in mxnet.
       This class allow you to write CUDA kernels in Python
       and call them with NDArray.
   ​
       Parameters
       ----------
       name : str
           Name of the kernel.
       inputs : tuple of (str, mxnet.ndarray)
           List of input names and ndarray.
       outputs : tuple of (str, mxnet.ndarray)
           List of output names and ndarray.
       kernel : str
           The actual kernel code.
           Note that this is only the body of the kernel, i.e.
           after { and before }. Rtc will decorate the kernel.
           For example, if ``name = "mykernel"`` and
           inputs = [('x', mx.nd.zeros((10,)))]
           outputs = [('y', mx.nd.zeros((10,)))]
           kernel = "y[threadIdx.x] = x[threadIdx.x];",
           then the compiled kernel will be:
           extern "C" __global__ mykernel(float *x, float *y) {
               const int x_ndim = 1;
               const int x_dims = { 10 };
               const int y_ndim = 1;
               const int y_dims = { 10 };
   ​
               y[threadIdx.x] = x[threadIdx.x];
           }
       """

以ndarray_softmax.py为例， softmax层输入数据shape=(100,10),输出数据shape=(100,10),那么forward方法里的x_dim=(100,10), 第三个参数即cuda编译的要执行的语句。 在forward方法中看到最后一句push方法，gridDim={'x':1,'y':1,'z':1}, blockDim={'x':100,'y':1,'z':1} (cuda存储参见cudaMemcpy与kernel)，于是每一个线程操作一个sample的10个elements，threadIdx.x表示线程在block块中的索引，那么threadIdx.x+blockIdx.x*blockDim.x就是对应线程总的索引，blockDim对应的是block中threads的个数，然后后面softmax前向计算就容易理解了。

再看backward方法，这个kernel将gradDim划分成(100,1,1), blockDim划分成(10,1,1)，即每一个element对应着一个线程，然后在每一个线程中计算该element对应的梯度。

example：

实现一个reorganize层，也就是yolo中特征重组层，具体功能YOLO v2 reorg 当然，最清楚的方式是看darknet中源码如何实现。

这个例子只是想继承mx.operator.NDArrayOp实现新的操作层，该操作层中没有权重参数，对于有权重的层要在forward和backward中操作对应的值。

  # -*- coding: utf-8 -*-
   import mxnet as mx
   import numpy as np
   import logging
   ​
   class NDArrayReorg(mx.operator.NDArrayOp):
       def __init__(self, stride=2):
           super(NDArrayReorg, self).__init__(True)
           self.stride = stride
           self.fwd_kernel = None
           self.bwd_kernel = None

       def list_arguments(self):
           return ['data']

       def list_outputs(self):
           return ['output']

       def infer_shape(self, in_shape):
           data_shape = in_shape[0]
           output_shape = [in_shape[0][0], in_shape[0][1]*4
                           , in_shape[0][2]/self.stride, in_shape[0][3]/self.stride]

           return [data_shape], [output_shape]

       def forward(self, in_data, out_data):
           x = in_data[0]
           y = out_data[0]
           if self.fwd_kernel is None:
               self.fwd_kernel = mx.rtc('reorg',[('x',x)],[('y',y)],"""
               int i = threadIdx.x + blockIdx.x*blockDim.x ;
               int yw=y_dims[3];
               int yh = y_dims[2];
               int N = yw*yh;
               int xw=x_dims[3];
               int xh = x_dims[2];
               int len_block = x_dims[2]*x_dims[3];
               for(int j =0; j<xh; j+=2)
                   for(int k=0; k<xw; k+=2)
                   {   int t=j/2;
                       y[i*len_block+t*yw+k/2] = x[i*len_block+j*xw+k];
                       y[i*len_block+t*yw+k/2+N] = x[i*len_block + j*xw+k+1];
                       y[i*len_block+t*yw+k/2+2*N] = x[i*len_block +(j+1)*xw+k];
                       y[i*len_block+t*yw+k/2+3*N] = x[i*len_block +(j+1)*xw+k+1];
                   }
               """)
           self.fwd_kernel.push([x],[y],(x.shape[0]*x.shape[1],1,1),(1,1,1))

       def backward(self, out_grad, in_data, out_data, in_grad):
           y = out_grad[0]
           dx = in_grad[0]
           if self.bwd_kernel is None:
               self.bwd_kernel = mx.rtc('reorg_grad',[('y',y)],[('dx', dx)],"""
               int i = threadIdx.x + blockIdx.x * blockDim.x;
               int yh = y_dims[2];
               int yw = y_dims[3];
               int N = yw*yh;
               int old_block = dx_dims[2]*dx_dims[3];
               for(int k=0;k<4;++k)
                   for(int j=0; j<yw; ++j)
                       for(int t=0; t<yh; ++t){
                           dx[i*old_block+2*j*yw+t*2+k]=y[i*old_block+k*N+j*yw+t];
                   }
               """)
           self.bwd_kernel.push([y],[dx],(y.shape[0]*y.shape[1]/4,1,1),(1,1,1))

   mnist = mx.test_utils.get_mnist()
   batch_size = 100
   train_iter = mx.io.NDArrayIter(mnist['train_data'], mnist['train_label'], batch_size, shuffle=True)
   val_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
   ​
   ​
   data = mx.sym.var('data')
   conv1 = mx.sym.Convolution(data=data, kernel=(5,5), num_filter=20)
   tanh1 = mx.sym.Activation(data=conv1, act_type="tanh")
   # pool1 = mx.sym.Pooling(data=tanh1, pool_type="max", kernel=(2,2), stride=(2,2))
   ​
   reorg = NDArrayReorg(stride=2)
   reg = reorg(data=tanh1, name='reorg')
 conv2 = mx.sym.Convolution(data=reg, kernel=(5,5), num_filter=20)
 tanh2 = mx.sym.Activation(data=conv2, act_type="tanh") # 80x8x8
 ​
 conv2 = mx.sym.Convolution(data=tanh2, kernel=(5,5), num_filter=50)
 tanh2 = mx.sym.Activation(data=conv2, act_type="tanh")
 # pool2 = mx.sym.Pooling(data=tanh2, pool_type="max", kernel=(2,2), stride=(2,2))
 ​
 flatten = mx.sym.flatten(data=tanh2)
 fc1 = mx.sym.FullyConnected(data=flatten,num_hidden=500)
 tanh3 = mx.sym.Activation(data=fc1, act_type="tanh")
 ​
 fc2 = mx.sym.FullyConnected(data=tanh3, num_hidden=10)
 ​
 mynet = mx.sym.SoftmaxOutput(data=fc2, name='softmax')
 ​
 print(mynet.infer_shape(data=(100,1,28,28)))
 mynet_model = mx.mod.Module(symbol=mynet, context=mx.gpu())
 ​
 mynet_model.fit(train_iter,
 eval_data=val_iter,
 optimizer='sgd',
 optimizer_params = {'learning_rate':0.1},
 eval_metric='acc',
 batch_end_callback=mx.callback.Speedometer(100,100),
 num_epoch=10)  

 test_iter = mx.io.NDArrayIter(mnist['test_data'], None, batch_size)
 prob = mynet_model.predict(test_iter)
 test_iter = mx.io.NDArrayIter(mnist['test_data'], mnist['test_label'], batch_size)
 # predict accuracy for lenet
 acc = mx.metric.Accuracy()
 mynet_model.score(test_iter, acc)
 print(acc) # 网络是随便构建的，参数也是随便选的，所以出来的值并没有什么参考价值，只是为了验证调用mx.rtc创建cuda的kernel

因此，对于定制的层，可是使用类似的方法定义，该方法显然比使用numpy要快的多。