1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
|
//===- SparseTensorPipelines.cpp - Pipelines for sparse tensor code -------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/SparseTensor/Pipelines/Passes.h"
#include "mlir/Conversion/GPUToNVVM/GPUToNVVMPass.h"
#include "mlir/Conversion/Passes.h"
#include "mlir/Dialect/Arith/Transforms/Passes.h"
#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
#include "mlir/Dialect/Bufferization/Transforms/OneShotAnalysis.h"
#include "mlir/Dialect/Bufferization/Transforms/Passes.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/GPU/IR/GPUDialect.h"
#include "mlir/Dialect/GPU/Transforms/Passes.h"
#include "mlir/Dialect/LLVMIR/NVVMDialect.h"
#include "mlir/Dialect/Linalg/Passes.h"
#include "mlir/Dialect/MemRef/Transforms/Passes.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
#include "mlir/Dialect/SparseTensor/Transforms/Passes.h"
#include "mlir/Pass/PassManager.h"
#include "mlir/Transforms/Passes.h"
using namespace mlir;
using namespace mlir::sparse_tensor;
/// Return configuration options for One-Shot Bufferize.
static bufferization::OneShotBufferizationOptions
getBufferizationOptions(bool analysisOnly) {
using namespace bufferization;
OneShotBufferizationOptions options;
options.bufferizeFunctionBoundaries = true;
// TODO(springerm): To spot memory leaks more easily, returning dense allocs
// should be disallowed.
options.allowReturnAllocs = true;
options.setFunctionBoundaryTypeConversion(LayoutMapOption::IdentityLayoutMap);
options.unknownTypeConverterFn = [](Value value, Attribute memorySpace,
const BufferizationOptions &options) {
return getMemRefTypeWithStaticIdentityLayout(
cast<TensorType>(value.getType()), memorySpace);
};
if (analysisOnly) {
options.testAnalysisOnly = true;
options.printConflicts = true;
}
return options;
}
//===----------------------------------------------------------------------===//
// Pipeline implementation.
//===----------------------------------------------------------------------===//
void mlir::sparse_tensor::buildSparseCompiler(
OpPassManager &pm, const SparseCompilerOptions &options) {
pm.addNestedPass<func::FuncOp>(createLinalgGeneralizationPass());
pm.addPass(createSparsificationAndBufferizationPass(
getBufferizationOptions(options.testBufferizationAnalysisOnly),
options.sparsificationOptions(), options.sparseTensorConversionOptions(),
options.createSparseDeallocs, options.enableRuntimeLibrary,
options.enableBufferInitialization, options.vectorLength,
/*enableVLAVectorization=*/options.armSVE,
/*enableSIMDIndex32=*/options.force32BitVectorIndices));
if (options.testBufferizationAnalysisOnly)
return;
pm.addNestedPass<func::FuncOp>(createCanonicalizerPass());
pm.addNestedPass<func::FuncOp>(
mlir::bufferization::createFinalizingBufferizePass());
// GPU code generation.
const bool gpuCodegen = options.gpuTriple.hasValue();
if (gpuCodegen) {
pm.addPass(createSparseGPUCodegenPass());
pm.addNestedPass<gpu::GPUModuleOp>(createStripDebugInfoPass());
pm.addNestedPass<gpu::GPUModuleOp>(createConvertSCFToCFPass());
pm.addNestedPass<gpu::GPUModuleOp>(createLowerGpuOpsToNVVMOpsPass());
}
// TODO(springerm): Add sparse support to the BufferDeallocation pass and add
// it to this pipeline.
pm.addNestedPass<func::FuncOp>(createConvertLinalgToLoopsPass());
pm.addNestedPass<func::FuncOp>(createConvertVectorToSCFPass());
pm.addNestedPass<func::FuncOp>(createConvertSCFToCFPass());
pm.addPass(memref::createExpandStridedMetadataPass());
pm.addPass(createLowerAffinePass());
pm.addPass(createConvertVectorToLLVMPass(options.lowerVectorToLLVMOptions()));
pm.addPass(createFinalizeMemRefToLLVMConversionPass());
pm.addNestedPass<func::FuncOp>(createConvertComplexToStandardPass());
pm.addNestedPass<func::FuncOp>(arith::createArithExpandOpsPass());
pm.addNestedPass<func::FuncOp>(createConvertMathToLLVMPass());
pm.addPass(createConvertMathToLibmPass());
pm.addPass(createConvertComplexToLibmPass());
// Repeat convert-vector-to-llvm.
pm.addPass(createConvertVectorToLLVMPass(options.lowerVectorToLLVMOptions()));
pm.addPass(createConvertComplexToLLVMPass());
pm.addPass(createConvertVectorToLLVMPass(options.lowerVectorToLLVMOptions()));
pm.addPass(createConvertFuncToLLVMPass());
// Finalize GPU code generation.
if (gpuCodegen) {
#if MLIR_GPU_TO_CUBIN_PASS_ENABLE
pm.addNestedPass<gpu::GPUModuleOp>(createGpuSerializeToCubinPass(
options.gpuTriple, options.gpuChip, options.gpuFeatures));
#endif
pm.addPass(createGpuToLLVMConversionPass());
}
pm.addPass(createReconcileUnrealizedCastsPass());
}
//===----------------------------------------------------------------------===//
// Pipeline registration.
//===----------------------------------------------------------------------===//
void mlir::sparse_tensor::registerSparseTensorPipelines() {
PassPipelineRegistration<SparseCompilerOptions>(
"sparse-compiler",
"The standard pipeline for taking sparsity-agnostic IR using the"
" sparse-tensor type, and lowering it to LLVM IR with concrete"
" representations and algorithms for sparse tensors.",
buildSparseCompiler);
}
|
