ppl

我们今天继续分析著名的attention is all you need 论文的pytorch实现的源码解析。 由于项目很大，所以我们会分开几讲来进行讲解。 上一讲连接在此： Attention is all you need pytorch实现 源码解析01 - 数据预处理、词表的构建 - https://blog.csdn.net/weixin_42744102/article/details/87006081 先上github源码： https://github.com/Eathoublu/attention-is-all-you-need-pytorch 项目结构： -transfomer —__init__.py —Beam.py —Constants.py —Layers.py —Models.py —Module.py —Optim.py —SubLayers.py —Translator.py datasets.py preprocess.py train.py translate.py 今天是第二讲，我们讲一讲模型的训练。模型的训练我将会用两节来讲解，第一节讲的是模型总体的训练的代码（也就是这一节） train.py ，下一节我们讲一讲模型的构建以及结构， 也就是transformer目录下的Models.py 。 下面我们来看一下train.py的源码以及解析：

I have a question about the Microsoft PPL library, and parallel programming in general. I am using FFTW to perform a large set (100,000) of 64 x 64 x 64 FFTs and inverse FFTs. In my current implementation, I use a parallel for loop and allocate the storage arrays within the loop. I have noticed that my CPU usage only tops out at about 60-70% in these cases. (Note this is still better utilization than the built in threaded FFTs provided by FFTW which I have tested). Since I am using fftw_malloc, is it possible that excessive locking is occurring which is preventing full usage? In light of this,

Is there a simple function in the ppl library for C++ where you can do something like Concurrency::max(vec) where vec is a vector of numbers? I can write my own, but I was hoping I could save myself the work. Edit: Sorry I was not clear enough maybe. I need the max function to utilize parallelization. There is nothing built in, but it's straightforward with combinable (a reduction variable) and a parallel loop (here parallel_for_each). However if the work you are doing is only 'max' of numbers unless the amount of numbers you are looking at is very large, it may be difficult to see speedups.

In the below example, a call to HandleChangesAsync is made from within an asynchronous task, and via an event handler. Question - Is there a way to ensure that only one thread can execute the HandleChangesAsync create_task + task continuation blocks at a time (even if the task continuation blocks invoke other async functions)? Note that I can't just use a synchronization primitive because HandleChangesAsync can return before the async operations complete. void MyClass::DoStuffAsync() { WeakReference weakThis(this); create_task(DoMoreStuffAsync()) .then([weakThis]()) { auto strongThis =

I'm planning to start "playing" with task-based parallelism for a cross-platform project. I wanted to use Intel Threading Building Blocks. I'm starting with Windows and Visual Studio. As I just want to prototype for the moment, I'm thinking about "playing" only on windows, then have enough knowledge to use the library on all compatible platforms. I've learned that since VS2010, Microsoft provide a similar library, Parallel Processing Library, that have (almost) the same interface than Intel TBB. Some sources suggest, including TBB's team blog, that they build it together and that it's the same