commit | 9005c7fb4572a12ea4d68f8995585f8435284f72 | [log] [tgz] |
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author | A. Unique TensorFlower <gardener@tensorflow.org> | Sat Aug 17 10:19:48 2019 -0700 |
committer | TensorFlower Gardener <gardener@tensorflow.org> | Sat Aug 17 10:25:05 2019 -0700 |
tree | 0e1ee5dfe8bb237cc5061ec64573753d2b5e3d48 | |
parent | ed14b5da3f7e9105bea3f7314706d04a68969edc [diff] |
Add spirv::GlobalVariableOp that allows module level definition of variables FuncOps in MLIR use explicit capture. So global variables defined in module scope need to have a symbol name and this should be used to refer to the variable within the function. This deviates from SPIR-V spec, which assigns an SSA value to variables at all scopes that can be used to refer to the variable, which requires SPIR-V functions to allow implicit capture. To handle this add a new op, spirv::GlobalVariableOp that can be used to define module scope variables. Since instructions need an SSA value, an new spirv::AddressOfOp is added to convert a symbol reference to an SSA value for use with other instructions. This also means the spirv::EntryPointOp instruction needs to change to allow initializers to be specified using symbol reference instead of SSA value The current spirv::VariableOp which returns an SSA value (as defined by SPIR-V spec) can still be used to define function-scope variables. PiperOrigin-RevId: 263951109
Documentation |
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
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