Automatic Code Generation TVM Stack
Automatic Code Generation TVM Stack
CSE 599W Spring
TVM stack is an active project by saml.cs.washington.edu and many partners in the open source community
The Gap between Framework and Hardware
Frameworks
CNTK
Each backend to a new software stack on top of it!
Compiler's Perspective to this Problem
Frameworks
CNTK
Express computation
Intermediate Representation (s)
Code generation
Reusable Optimizations
Hardware
Computational Graph as IR
Represent High level Deep Learning Computations
data
w1
conv2d
relu
w2
conv2d
relu
flatten
w3
dense
shape=(1,10)
softmax
attributes
channels=32, kernel_size=(3,3), padding=(1,1), use_bias=0
operation
inputs dataflow dependency
Effective Equivalent Transformations to Optimize the Graph
conv2d bn relu
fused-conv2dbn-relu
Approach taken by: TensorFlow XLA, Intel NGraph, Nvidia TensorRT
XLA: Tensorflow Compiler
! Constant shape dimension ! Data layout is specific ! Operations are low level tensor primitives
Map Broadcast Reduce Convolution ReduceWindow ...
Source: Google
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