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
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
arg.target_host: None arg.result_file: results.tsv arg.transfer_tune: None args.search_type: default
network_args = { “network”: resnet_50, “batch_size”: 1 }
tuning_args = { “eval_only”: false, “continue_tuning”: false, “n_trials”: 200 “num_measures_per_round”: 10 “log_file”: resnet_50-B1-cuda-a100.json “run_timeout”: 25, “cost_model”: pam, “load_model”: None, “n_lines”: None, “psa_model_type”: a100_40 }
TaskScheduler tasks: tasks objective_func = lambda costs: sum(c * w for c, w in zip(costs, task_weights)) strategy: gradient load_log_file: None load_model_file: None alpha: 0.2 beta: 2 gamma: 0.5 backward_window_size: 3 callbacks: [PrintTableInfo(), LogEstimateLatency(“total_latency.tsv”)] task_cts = [0 for _ in range(len(self.tasks))] 记录任务i被调优多少次 task_best_cts = [0 for _ in range(len(self.tasks))] 记录任务i当前最佳延迟 task_costs_history = [[] for _ in range(len(self.tasks))] 记录任务i历史延迟记录 best_costs = 1e10 * np.ones(len(self.tasks)) 记录任务i最佳延迟 cur_score = self._compute_score(self.best_costs) tune_option、measurer、search_policies、ct、best_ct、best_score、tic、num_measures_per_round: None dead_tasks = set()
task_tags、tag_to_groud_id、group_task_ids、flop_cts
search_policy = ‘sketch.pam’ psa_model_type = a100_40 search_policy_params = None num_measure_per_round = 10 tune_option.verbose = 1 load_model_file = None load_log_file = None adaptive_training = false disable_cost_model_update = false
cost_model = PAMModel(disable_update, few_shot_learning) cost_model_psa = PSAModel(peak_performance=19490, glbmem_bandwidth=1555, vec_len=11, activate_blocks_per_sm=1, sm_nums=108, arm_sm_partition=4, arch_warp_size=32) init_search_callbacks = [PreloadMeasuredStates(load_log_file)] search_policies = [SketchPolicy(task, cost_model, cost_model_psa, params, verbose, init_search_callbacks) for task in tasks]
PAMModel BufferNameMap、BufferCache PerStoreFeatureExtractorPAM: VisitStmt_(AttrStmtNode)、VisitStmt_(BufferRealizeNode)
========== Task 0 (workload key: [“9847f8cc0b305137f4…) ========== placeholder = PLACEHOLDER [1, 2048] placeholder = PLACEHOLDER [1000, 2048] T_dense(i, j) += (placeholder[i, k]*placeholder[j, k]) placeholder = PLACEHOLDER [1000] T_add(ax0, ax1) = (T_dense[ax0, ax1] + placeholder[ax1])
*ret = SerializeFeaturePAM(features, fea_sizes, kmp_indexes, normalized_throughputs, task_ids, min_costs, byte_data)
features: 三维特征向量数组(Nbuffer_seqBuffer_Embedding_Dim)
pam_model.update dataset.update_from_measure_pairs → get_per_store_features_from_measure_pairs_pam → load_task_data fit_base → _fit_a_model → register_new_task → make_net
pytorch版本问题的解决 按照pruner的要求下载相应的pytorch，但是原来下载也是pruner要求的但是运行的时候在training model会报错，然后用下面的指令下载pruner指定的版本则不会报错 pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
best_costs[i]: 任务i的当前最佳延迟 task_cts[i]: 任务i被调优的次数 task_costs_history[i]: 任务i的历史延迟记录 flop_cts[i]: 任务i的浮点运算次数 alpha: 历史vs预期权重参数 beta: 相似任务性能差异阈值 FLOPS = flop_cts / best_costs task_best_cts[i]: 记录任务i发现最佳结果的轮次
TaskScheduler的tune方法 梯度策略的核心： 在有限时间内，应该优先调优哪个任务，才能让任务整体性能提升最大 Chain Gradient(链式梯度): 评估该任务对整体目标的重要性 Backward Gradient(向后梯度): 评估该任务最近的表现 Forward Gradient(向前梯度): 评估该任务改进空间
task_tag: 任务标签 tag_to_group_id: 标签到group ID的映射 group_task_ids: 每个组包含的任务ID映射
segment_sizes_normal: 记录每个样本的特征段长度 flatten_normal_features: 存储所有普通特征 flatten_gemm_features: 存储GEMM相关的缓冲区特征
extractor(stmt) → 调用operator() operator() → 调用VisitStmt() VisitStmt() → 初始化vtable，调用vtable() vtable() → 根据type_index查找函数 type_index() → 返回180(AttrStmtNode的ID) func_[180] → 调用对应的lambda函数 lambda函数 → 类型转换并调用VisitStmt_ VisitStmt_(AttrStmtNode*) → 目标函数
te::InferBound 前馈图FeedGraph: 张量→消费者操作的映射 附加路径AttachPath: 操作→附加点的映射
PerStoreFeatureExtractorPAM类 ExtractBufferAccessFeature方法: cur_compute_ops: 当前计算操作数 compute_ops_list: 输出参数，各层循环的计算操作数列表 mem_bytes_list: 输出参数，各层循环的内存访问字节数列表 for_touch_regions_: 存储每个for循环的内存访问区域信息 buffer_regions_map: 当前循环层的buffer区域映射 tuple<BufferAccessTypePAM, int64_t, int>: 访问类型，访问元素数，单元素字节数
ComputeRegionPAM ElementProduct GetLoopExtentPAM ComputeStridePAM ComputeReusePAM
PAMDataLoader:
