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authorGravatar A. Unique TensorFlower <gardener@tensorflow.org>2017-05-22 17:10:14 -0700
committerGravatar TensorFlower Gardener <gardener@tensorflow.org>2017-05-22 17:14:16 -0700
commit95719e869c61c78a4b0ac0407e1fb04e60daca35 (patch)
tree0e310309b9af8222a484155ccd1df9e5f3e38d30 /RELEASE.md
parentac9fee249c52de7abb113372f093a6bb620dee9c (diff)
[XLA] Teach Executable to do its own profiling (patch 1/4).
Presently, ExecuteOnStreamWrapper is a method on xla::Service, where it doesn't really conceptually belong -- note that it doesn't use anything from the containing Service object, but it does have an Executable object as its first parameter that it could easily be a method on instead. The only reason that it needs to be on Service is that it needs to access a Backend object in order to call backend->compiler()->shape_size_function(), and simply moving that into Executable would introduce a dependency cycle. Thus, this patch (the first part of a sequence to address this) teaches Executable and its derivatives to compute shape_size_function. In the CPU cases, this is simply a static function. However, in the GPU case, we need to pass in the shape_size_function to the constructor, since it depends on a pointer size computed in the GpuCompiler. PiperOrigin-RevId: 156807318
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