debug

PrunerCode

Code Reproduction

Posted by Treaseven on March 26, 2025

on-line cost model Pruner: 1.69ms Used time 5563s Estimated total latency: 1.476ms MoA-Pruner: 1.70ms Used time 4978s Estimated total latency: 1.457ms Ansor: 3.96ms Used time 6691s Estimated total latency: 1.592ms

off-line cost model Pruner-offline: 1.71ms Used time 4212s Estimated total latency: 1.469ms TensetMLP: 1.79ms Used time 5469s Estimated total latency: 1.621ms

源码修改
inlcude
tvm/auto_scheduler
feature_pam.h、feature_psa.h

python
tvm/auto_scheduler
cost_model/pam_model.py、psa_model.py
修改 dataset.py、feature.py、search_policy.py、task_scheduler.py


src
auto_scheduler
feature_pam.cc、feature_psa.cc
修改 search_policy sketch_policy.cc、sketch_policy.cc

测试脚本 search with Pruner python3 tune_network.py –network resnet_50 –n-trials 2000 –cost-model pam –target “cuda –model=a100” –psa a100_40

search with the MoA-Pruner python3 tune_network.py –network resnet_50 –n-trials 2000 –cost-model pam-siamese-update –load-model pam_k80_1500.pkl –target “cuda –model=a100” –psa a100_40

search with Ansor python3 tune_network.py –network resnet_50 –n-trials 2000 –cost-model mlp –target “cuda –model=a100”

auto_scheduler源码分析—ansor Extract search tasks auto_scheduler.extract_tasks

get_tuning_option auto_scheduler.LocalRPCMeasureContext auto_scheduler.TuningOptions auto_scheduler.RecordToFile

Run search auto_scheduler.TaskScheduler

evaluate results local_search、default_search from tvm.auto_scheduler.measure_record import load_records, save_records from tvm.auto_scheduler.utils import decode_workload_key from tvm.auto_scheduler.measure import MeasureInput auto_scheduler.ApplyHistoryBest tvm.transform.PassContext relay.build tvm.context runtime.GraphModule

注册的全局函数 feature_pam.cc: GetPerStoreFeaturesFromStatePAM、GetPerStoreFeaturesFromMeasurePairsPAM feature_psa.cc: GetPerStoreFeaturesFromStatePSA、GetPerStoreFeautresFromMeasurePairsPSA

measure.cc: EmptyBuilder、EmptyRunner

auto_scheduler 88+1521+174+1698+576+483+477+176+1880=7073 1764+975=2739

search_policy 126+124+1242+790+503=2785

python 63+23+280+539+471+478+619+355+1321+393+281+596+837+450+281=6987 26+201+66+915+747+147+200+1255+741=4298

编译流程
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从relay_integration.py文件中 grc.codegen -> GraphRuntimeCodegen.Codegen -> heads_ = VisitExpr(func->body) -> std::vector VisitExpr_(const CallNode* call_node) -> GraphAddCallNode -> CachedFunc lowered_func = (*pf1)(compile_engine_, key)

在compile_engine.cc文件中 relay.backend._CompileEngineLower -> self.Lower -> LowerInternal -> CreateSchedule -> ScheduleGetter.Create -> 调用python端的auto_scheduler.relay_integration.auto_scheduler_topi_compute

一个问题就是 self._mod = _build_module._GraphRuntimeCodegen() self._init = self._mod[“init”] 上面这两行代码里面会涉及到去调用C++运行文件graph_runtime_codegen.cc文件类GraphRuntimeCodegenModule中的GetFunction方法从而保证上面两行代码的正确性 这是因为在TVM的python里面tvm/_ffi/_ctypes/moudle.py文件中 Python Module类中def getitem(self, name): return self.get_function(name)

self.callbacks = [PrintTableInfo(), LogEstimatedLatency(“total_latency.tsv”)]

python中namedtuple和OrderedDict namedtuple: 用于创建一个带有命名字段的元组，普通的元组只能通过索引访问其元素，元组是不可变的，创建后不能修改、添加或删除元素 OrderedDict: 特殊的字典类型，记住了键值对添加的顺序，普通的字典不保证元素的顺序

PAMModel predict函数 get_per_store_features_from_states_pam -> _ffi_api.GetPerStoreFeaturesFromStatesPAM -> unpack_feature_apm -> PAMDataset.create_one_task -> load_task_data -> self.model.predict -> self._predict_a_dataset -> self._predict_a_task -> PAMDataLoader

update函数 PAMDataset.update_from_measure_pairs -> get_per_store_features_from_measure_pairs_pam -> _ffi_api.GetPerStoreFeaturesFromMeasurePairsPAM -> unpack_feature_pam -> self.model.fit_base -> self._fit_a_model -> self.register_new_task -> PAMDataLoader -> self.make_net -> PAMModule

auto_scheduler.GetPerStoreFeaturesFromStatesPAM GetPerStoreFeatureFromStatesPAM -> ExtractAndPaddingFeaturePAM -> GetPerStoreFeaturesWorkerFuncPAM -> GetPerStoreFeaturePAM -> PerStoreFatureExtractorPAM -> KMP -> FeatureSetPAM -> AnnotationPosTypePAM -> BufferAccessTypePAM -> ReuseTypePAM -> BufferFeatureSetPAM -> BufferAccessFeaturePAM -> LocationTypePAM ->

GetPerStoreFeatureworkerFuncPAM函数的作用 用于在TVM中为自动调度提取每个存储操作特征

GetPerStoreFeaturePAM函数的作用 从TVM语句中提取性能相关特征，这些特征会被用于性能模型，以预测TVM调度的执行时间

SerializeFeaturesPAM函数 将提取的特征数据序列化为字节数组，以便在C++和Python之间传递数据

GetPerStoreFeaturePAM PerStoreFeatureExtractorPAM(类)、KMP、FeatureSetPAM(结构体)、AnnotationPosTypePAM(类)、BufferAccessTypePAM(类)、ReuseTypePAM(类)、BufferFeatureSetPAM(结构体)、BufferAccessFeaturePAM(结构体)、LocationTypePAM(类)、

ComputeRegionPAM、ComputeReusePAM、ComputeStridePAM、CoefficientExtractorPAM、BufferAccessExtractorPAM、MathOpCounterPAM(类)、GetLoopExtentPAM(函数)、GetAnnotationPosEncodingPAM、VarInExprPAM、BufferAccessPAM

feature_pam.cc文件 PerStoreFeatureExtractorPAM: VisitStmt_(BufferRealizeNode): StorageScope -> runtime::DefaultStorageRank/StorageScope::Create -> StmtExprVisitor::VisitStmt_(node) -> ExtractAllocationFeature

VisitStmt_(BufferStoreNode): MathOpCounterPAM -> ExtractComputationFeature -> ExtractBufferAccessFeature -> ExtractArithmeticIntensityFeature -> ExtractOuterScopeFeature

VisitStmt_(ForNode): GetLoopExtentPAM -> StmtExprVisitor::VisitStmt_

VisitStmt_(AttrStmtNode): StmtExprVisitor::VisitStmt_

Pruner的网络模型架构 平常特征 self.segment_encoder

矩阵乘特征 self.gemm_encoder self.attention

output = torch.cat([segment_sum, gemm_mha_output], dim=1) self.fuse

decoder self.norm self.l0 self.l1 self.decoder

sketch_rules: rule_add_cache_read_stage、rule_special_compute_location_gpu、rule_always_inline、rule_simplify_compute_with_const_tensor、rule_cross_thread_reduction、rule_add_cache_write_stage、rule_multi_level_tiling_with_fusion、rule_multi_level_tiling、rule_skip_stage

gdb –args python tune_network.py –network resnet_50 –n-trials 200 –cost-model pam –target “cuda –model=a100” –psa a100_40

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