mirror of https://github.com/llvm/torch-mlir
42 lines
1.7 KiB
TableGen
42 lines
1.7 KiB
TableGen
//===-- Passes.td - Pass definition file -------------------*- tablegen -*-===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#ifndef NPCOMP_NUMPY_PASSES
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#define NPCOMP_NUMPY_PASSES
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include "mlir/Pass/PassBase.td"
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//===----------------------------------------------------------------------===//
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// TypeInference
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//===----------------------------------------------------------------------===//
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def NumpyPublicFunctionsToTensor : Pass<"numpy-public-functions-to-tensor", "ModuleOp"> {
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let summary = "Converts public functions to operate on tensors (instead of ndarray)";
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let constructor = "mlir::NPCOMP::Numpy::createPublicFunctionsToTensorPass()";
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}
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def NumpyArrayToTensor : Pass<"numpy-array-to-tensor", "FuncOp"> {
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let summary = "Replace arrays with tensors where possible (optimization only).";
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let description = [{
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This pass is analogous to an SSA-formation pass in a
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traditional compiler, with the added complication that arrays can alias
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each other in interesting ways.
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The current code doesn't implement any fancy algorithm, and is intended
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to be just sufficient for a first e2e spike. An algorithm inspired by the
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SSA formation literature will need to be implemented.
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Also, this pass doesn't currently handle interprocedural rewriting
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(of private functions), which is even more complex.
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}];
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let constructor = "mlir::NPCOMP::Numpy::createArrayToTensorPass()";
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}
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#endif // NPCOMP_NUMPY_PASSES
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