TensorFlow自定义分配器和从Tensor访问数据 [英] TensorFlow Custom Allocator and Accessing Data from Tensor
问题描述
在TensorFlow中，您可以出于各种原因创建自定义分配器（我正在为新硬件执行此操作）。由于设备的结构，我需要使用几个元素的结构作为我的数据指针，分配器返回 void * 。
In TensorFlow, you can create custom allocators for various reasons (I am doing it for new hardware). Due to the structure of the device, I need to use a struct of a few elements as my data pointer which the allocator returns as a void*.
在我写的内核中，我可以访问Tensors，但是我需要得到我写的指针结构。检查类，看起来我可以通过 tensor_t.buf _-> data（）
In the kernels that I am writing, I am given access to Tensors but I need t get the pointer struct that I wrote. Examining the classes, it seemed that I could get this struct by doing tensor_t.buf_->data()
问题是，我找不到这样的代码，我担心它是不安全的（很可能！）或有一个更标准的方法来做这个。
The problem is that I can't find code that does this and I am worried that it is unsafe (highly likely!) or there is a more standard way to do this.
有人可以确认这是一个好主意还是坏主意？
Can someone confirm if this is a good/bad idea? And provide an alternative if such exists?
推荐答案
四天后...
void* GetBase(const Tensor* src) {
return const_cast<void*>(DMAHelper::base(src));
}
来自 GPUUtils
DMAHelper :: base（） 是一个朋友类方法，可以使用私有 Tensor :: base（） 获取数据指针。
DMAHelper::base() is a friend class method that is given the ability to use the private Tensor::base() to get at the data pointer.
实施显示这只是一个包装，我想做一个又一个抽象。我猜这是一个更安全的方法来获取指针，应该改用。
The implementation shows that this is all just a wrapper around what I wanted to do after yet another abstraction. I am guessing it is a safer approach to getting the pointer and should be used instead.
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