Enzyme – High-performance automatic differentiation of LLVM
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Re: Enzyme – High-performance automatic differentiation of LLVM
#2Funny, I worked on Tapenade (one of the compared automatic differentiation software). I'm happy that it still reaches 60% of the performance of something written directly inside an optimizing compiler.
Re: Enzyme – High-performance automatic differentiation of LLVM
#3Also see the Julia package that makes it acessible with a high level interface and probably one of the easier ways to play with it: https://github.com/wsmoses/Enzyme.jl.
Re: Enzyme – High-performance automatic differentiation of LLVM
#4> The Enzyme project is a tool for performing reverse-mode automatic differentiation (AD) of statically-analyzable LLVM IR. This allows developers to use Enzyme to automatically create gradients of their source code without much additional work.
Can someone please explain applications of creating gradients of my source code?
Re: Enzyme – High-performance automatic differentiation of LLVM
#5> The Enzyme project is a tool for performing reverse-mode automatic differentiation (AD) of statically-analyzable LLVM IR. This allows developers to use Enzyme to automatically create gradients of their source code without much additional work. Can someone please explain applications of creating gradients of my source code?
AFAIK, It's mainly used for implementing gradient descent, which is used for training neural networks.
Frameworks like pytorch, tensorflow, probably used back propagation to calculate the gradient of a multidimensional function. But in involves tracing, and storing the network state during the forward pass.
Static automatic differentiation should be faster and should look a lot like differentiation is done mathematically rather than numerically.
Of course there are more applications to AD in scientific computing.
Re: Enzyme – High-performance automatic differentiation of LLVM
#6> The Enzyme project is a tool for performing reverse-mode automatic differentiation (AD) of statically-analyzable LLVM IR. This allows developers to use Enzyme to automatically create gradients of their source code without much additional work. Can someone please explain applications of creating gradients of my source code?
Optimization. Then again, one could probably calculate gradients numerically.
Re: Enzyme – High-performance automatic differentiation of LLVM
#7> The Enzyme project is a tool for performing reverse-mode automatic differentiation (AD) of statically-analyzable LLVM IR. This allows developers to use Enzyme to automatically create gradients of their source code without much additional work. Can someone please explain applications of creating gradients of my source code?
Optimization. Then again, one could probably calculate gradients numerically.
One could, but automatic differentiation is much more efficient than numerical differentiation, thus for high performance applications it is preferable to use automatic differentiation.
Re: Enzyme – High-performance automatic differentiation of LLVM
#8> The Enzyme project is a tool for performing reverse-mode automatic differentiation (AD) of statically-analyzable LLVM IR. This allows developers to use Enzyme to automatically create gradients of their source code without much additional work. Can someone please explain applications of creating gradients of my source code?
AFAIK, It's mainly used for implementing gradient descent, which is used for training neural networks. Frameworks like pytorch, tensorflow, probably used back propagation to calculate the gradient of a multidimensional function. But in involves tracing, and storing the network state during the forward pass. Static automatic differentiation should be faster and should look a lot like differentiation is done mathematic…
I think Swift is also going into this direction of backing in directly into the compiler and provide it as a higher level language construction.
https://github.com/apple/swift/blob/main/docs/Differentiable...
Which leads to "Swift for Tensorflow" that unlike other languages like Java, Go or Python is not just about bindings to the C++ tensorflow library.
Re: Enzyme – High-performance automatic differentiation of LLVM
#9> The Enzyme project is a tool for performing reverse-mode automatic differentiation (AD) of statically-analyzable LLVM IR. This allows developers to use Enzyme to automatically create gradients of their source code without much additional work. Can someone please explain applications of creating gradients of my source code?
It constructs an analytical gradient from the code. The reason is that you can compute the gradient directly. This can enable optimizations such as avoiding caching big matrices because you don't need to keep track of states/trace the graph, or you can compute the 2nd, 3rd, 4th... and so on derivatives because you have an analytical gradient.
For example in an affine function, the gradient of the bias/intercept is the gradient of the loss wrt the activation function and for the weights, it's the product of loss wrt activation function and the input to the layer.
With automatic graph construction e.g. eager Tensorflow/Pytorch, the layer needs to cache the input of the layer, so that it can compute the gradient of the weights. If the layer receives inputs multiple times within the computation graph, you end up caching it multiple times.
With analytical gradients, you may be able to save memory by finding optimizations because you have the analytical gradient, e.g. above you can sum the inputs ie (dL/dz)input1 + (dL/dz)input2 = (dL/dz)(input1+input2).
Re: Enzyme – High-performance automatic differentiation of LLVM
#10> The Enzyme project is a tool for performing reverse-mode automatic differentiation (AD) of statically-analyzable LLVM IR. This allows developers to use Enzyme to automatically create gradients of their source code without much additional work. Can someone please explain applications of creating gradients of my source code?
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