mirror of https://github.com/llvm/torch-mlir
64 lines
2.6 KiB
TableGen
64 lines
2.6 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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def NumpyRefinePublicReturn : Pass<"numpy-refine-public-return", "ModuleOp"> {
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let summary = "Refine public return";
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let constructor = "mlir::NPCOMP::Numpy::createRefinePublicReturnPass()";
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let description = [{
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Refines types of values return from public functions based on
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intraprocedural information.
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This pass effectively encodes an assumption by the pass pipeline author that
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the public calling convention of the module can have its types refined,
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without causing ABI mismatches. This is frequently true -- for example, in
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many systems, `tensor<?x?xf32>`, `tensor<3x3xf32>` and
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`tensor<*x!numpy.any_dtype>` are all the same data structure on calling
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convention boundaries.
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This pass is expected to run after shape refinement has occurred to
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otherwise resolve shapes, and is currently mainly useful to convert
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rank/dtype-erased function boundaries to ranked, dtyped code for
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compiler backends.
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}];
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}
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#endif // NPCOMP_NUMPY_PASSES
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