==================================================Ascend ============================= test session starts ============================== platform linux -- Python 3.9.19, pytest-6.2.5, py-1.11.0, pluggy-1.5.0 rootdir: /home/jenkins/mindspore/testcases/testcases/tests/st/tools/profiler/daily_test, configfile: ../../../../../../../../sault/virtual_test/virtualenv_007/sault/config/pytest.ini plugins: mock-3.14.0, hydra-core-1.3.2, forked-1.6.0, anyio-4.9.0, xdist-1.32.0 collected 1 item test_legacy_profiler_parse.py [WARNING] ME(165505:281473477852976,MainProcess):2026-01-29-17:37:28.996.222 [mindspore/context.py:1334] For 'context.set_context', the parameter 'device_target' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead. [WARNING] ME(165505:281473477852976,MainProcess):2026-01-29-17:37:28.997.343 [mindspore/profiler/profiler.py:269] 'mindspore.Profiler' will be deprecated and removed in a future version. Please use the api 'mindspore.profiler.profile' instead. [WARNING] ME(165505:281473477852976,MainProcess):2026-01-29-17:37:30.274.072 [mindspore/profiler/schedule.py:225] Profiler won't be using warmup, this can skew profiler results TotalTime = 17.8564, [21] [bootstrap]: 0.0018231 [type_inference]: 14.4286 [event_method]: 0.00190891 [auto_monad]: 0.132622 [graph_reusing]: 0.00057958 [inline]: 4.68001e-06 [add_attr]: 0.0135653, [1] [add_attr_with_inline]: 0.0135401, [1] [Cycle 1]: 0.00229306, [2] [tag_attr]: 0.00148008 [meta_addattr_fg_expand]: 0.00068864 [parallel-infer-symbol]: 5.46e-06 [pre_auto_parallel]: 0.144429 [insert-virtual-dataset]: 3.75e-06 [parallel-infer-symbol-second]: 4.95999e-06 [dataset_repeat_opt]: 3.11001e-06 [pipeline_split]: 1.59e-06 [optimize]: 3.12974, [53] [py_interpret_to_execute]: 0.00288376 [rewriter_before_opt_a]: 0.0053304 [opt_a]: 2.91038, [2] [Cycle 1]: 2.72266, [45] [expand_dump_flag]: 0.00015847 [switch_simplify]: 0.00379716 [loop_unroll]: 0.0817686 [a_1]: 1.64266 [with_stream_mark]: 0.00072321 [recompute_prepare]: 0.00063518 [updatestate_depend_eliminate]: 0.00272076 [updatestate_assign_eliminate]: 0.0549098 [updatestate_loads_eliminate]: 0.00205174 [parameter_eliminate]: 4.36002e-06 [a_2]: 0.00812388 [accelerated_algorithm]: 0.00083049 [shard]: 2.59001e-06 [meta_shard_fg_expand]: 0.0261911 [shard_inline]: 0.0003048 [merge_send_recv]: 0.00022317 [auto_parallel]: 0.00014455 [parallel]: 0.00011979 [flash_sp]: 9.059e-05 [merge_comm]: 0.00014654 [allreduce_fusion]: 0.00013589 [matmul_add_comm_reduction]: 0.00019533 [allreduce_slice_to_reducescatter]: 7.39994e-07 [virtual_shard_identity]: 0.00030854 [virtual_dataset]: 0.00030568 [get_grad_eliminate_]: 0.00028253 [virtual_output]: 0.00027582 [merge_forward]: 0.00027093 [cell_reuse_recompute_pass]: 3.88999e-06 [offload_activation]: 0.0002191 [cell_reuse_handle_not_recompute_node_pass]: 0.0005611 [merge_recompute_call_nodes]: 2.16e-06 [before_grad]: 0.0124123 [set_forward_comm_id_for_comm_node_pass]: 0.00037938 [meta_fg_expand]: 0.00035474 [flash_sp_send_recv_attached]: 1.294e-05 [receive_attached]: 2.69001e-06 [after_resolve]: 0.00035859 [a_after_grad]: 0.00053629 [renormalize]: 0.812592 [add_forward_monad_depend]: 1.847e-05 [auto_monad_grad]: 3.4e-06 [auto_monad_eliminator]: 0.00155369 [cse]: 0.00173937 [a_3]: 0.0397751 [Cycle 2]: 0.187689, [45] [expand_dump_flag]: 4.67e-06 [switch_simplify]: 0.00024399 [loop_unroll]: 0.115409 [a_1]: 0.00798706 [with_stream_mark]: 0.0003338 [recompute_prepare]: 0.00024559 [updatestate_depend_eliminate]: 0.00017702 [updatestate_assign_eliminate]: 0.00014227 [updatestate_loads_eliminate]: 0.00015347 [parameter_eliminate]: 5.00001e-06 [a_2]: 0.0533551 [accelerated_algorithm]: 0.00030749 [shard]: 3.76999e-06 [meta_shard_fg_expand]: 0.00013571 [shard_inline]: 0.00025449 [merge_send_recv]: 0.000254 [auto_parallel]: 0.0001772 [parallel]: 1.418e-05 [flash_sp]: 5.67001e-06 [merge_comm]: 0.00017776 [allreduce_fusion]: 0.00017023 [matmul_add_comm_reduction]: 0.00025042 [allreduce_slice_to_reducescatter]: 9.39996e-07 [virtual_shard_identity]: 0.00028062 [virtual_dataset]: 0.00026142 [get_grad_eliminate_]: 0.00025758 [virtual_output]: 0.0002509 [merge_forward]: 0.00018078 [cell_reuse_recompute_pass]: 4.53999e-06 [offload_activation]: 0.00026268 [cell_reuse_handle_not_recompute_node_pass]: 0.00059286 [merge_recompute_call_nodes]: 3.25998e-06 [before_grad]: 0.00046834 [set_forward_comm_id_for_comm_node_pass]: 0.00022454 [meta_fg_expand]: 0.00025039 [flash_sp_send_recv_attached]: 3.13e-06 [receive_attached]: 3.11001e-06 [after_resolve]: 0.00034468 [a_after_grad]: 0.00043934 [renormalize]: 5.00004e-08 [add_forward_monad_depend]: 9.22999e-06 [auto_monad_grad]: 3.26999e-06 [auto_monad_eliminator]: 0.00047348 [cse]: 0.00088873 [a_3]: 0.00191299 [py_interpret_to_execute_after_opt_a]: 0.00034088 [slice_cell_reuse_recomputed_activation]: 3.55998e-06 [rewriter_after_opt_a]: 0.0274184 [convert_after_rewriter]: 0.00037024 [order_py_execute_after_rewriter]: 0.00015796 [mutable_eliminate]: 0.0982202 [opt_b]: 0.00769336, [1] [Cycle 1]: 0.0076774, [7] [b_1]: 0.00604628 [b_2]: 0.00024018 [updatestate_depend_eliminate]: 0.00022408 [updatestate_assign_eliminate]: 0.00014348 [updatestate_loads_eliminate]: 0.00015675 [renormalize]: 6.50005e-07 [cse]: 0.00075723 [optimize_parallel_all_gather_comm]: 0.00037999 [overlap_param_gather]: 6.02001e-06 [cconv]: 0.00013178 [loop_unroll]: 0.00107476 [opt_after_cconv]: 0.0652947, [1] [Cycle 1]: 0.0652785, [7] [c_1]: 0.00135291 [parameter_eliminate]: 6.78998e-06 [updatestate_depend_eliminate]: 0.00019758 [updatestate_assign_eliminate]: 0.0001481 [updatestate_loads_eliminate]: 0.00015316 [cse]: 0.0632822 [renormalize]: 1.20001e-06 [remove_dup_value]: 0.00138905 [tuple_transform]: 0.0025075, [1] [Cycle 1]: 0.00249047, [4] [d_1]: 0.00217823 [none_parameter_eliminate]: 5.96998e-06 [renormalize]: 6.19999e-07 [switch_simplify]: 0.00024677 [partial_unused_args_eliminate]: 4.33999e-06 [add_recomputation]: 0.00173858 [cse_after_recomputation]: 0.0005935, [1] [Cycle 1]: 0.00058077, [1] [cse]: 0.00052363 [environ_conv]: 0.00015765 [swap_dp_allreduce_reducescatter]: 0.00022988 [bias_add_comm_swap]: 3.53999e-06 [label_micro_interleaved_index]: 8e-06 [label_fine_grained_interleaved_index]: 2.89001e-06 [merge_cast_opt]: 1.45999e-06 [slice_recompute_activation]: 2.14999e-06 [micro_interleaved_order_control]: 2.80997e-06 [assign_add_opt]: 1.81998e-06 [ForceFp32Comm]: 9.99979e-07 [remove_cast_before_assign_add]: 1.22e-06 [full_micro_interleaved_order_control]: 2.27001e-06 [reorder_send_recv_between_fp_bp]: 2.73998e-06 [comm_op_add_attrs]: 1.49e-06 [add_comm_op_reuse_tag]: 9.79984e-07 [interleave_split_concat_branches]: 1.53002e-06 [interleave_parallel_branches]: 1.44998e-06 [overlap_opt_shard_in_pipeline]: 3.081e-05 [overlap_opt_shard_grad_in_pipeline]: 1.70001e-06 [control_data_broadcast_order]: 0.00033719 [grouped_pairwise_exchange_alltoall]: 1.94e-06 [offloading_packed_experts]: 8.048e-05 [overlap_recompute_and_grad_model_parallel]: 0.00010283 [overlap_grad_matmul_and_grad_allreduce]: 1.92001e-06 [overlap_recompute_allgather_and_fa_grad]: 1.50001e-06 [overlap_recompute_comm]: 2.63e-06 [overlap_grad_ring_attention]: 7.84e-05 [overlap_grad_flash_sp]: 0.00045074 [begin_end_overlap_inline]: 6.59988e-07 [split_matmul_comm_elemetwise]: 2.44001e-06 [split_layernorm_comm]: 1.67999e-06 [handle_group_info]: 1.30999e-06 [symbol_engine_optimizer]: 0.00168941, [1] [Cycle 1]: 0.00167768, [6] [build]: 0.00012911 [elim_shapecalc]: 0.00032552 [elim_not_effective]: 0.00047421 [opt_reshape]: 0.00027123 [fold_const_symbol]: 0.00040546 [renormalize]: 8.2e-07 [detach_backward]: 3.44001e-06 [pipeline_parallel_scheduler]: 1.87001e-06 [auto_monad_reorder]: 0.00049133 [get_jit_bprop_graph]: 2.81999e-06 [rewriter_after_jit_bprop_graph]: 8.32e-06 [opt_after_jit_grad]: 0.00173228 [validate]: 0.00051438 Sums bootstrap : 0.001823s : 0.01% type_inference : 14.428556s : 80.98% event_method : 0.001909s : 0.01% auto_monad : 0.132622s : 0.74% graph_reusing : 0.000580s : 0.00% inline : 0.000005s : 0.00% add_attr.add_attr_with_inline.tag_attr : 0.001480s : 0.01% add_attr.add_attr_with_inline.meta_addattr_fg_expand : 0.000689s : 0.00% parallel-infer-symbol : 0.000005s : 0.00% pre_auto_parallel : 0.144429s : 0.81% insert-virtual-dataset : 0.000004s : 0.00% parallel-infer-symbol-second : 0.000005s : 0.00% dataset_repeat_opt : 0.000003s : 0.00% pipeline_split : 0.000002s : 0.00% optimize.py_interpret_to_execute : 0.002884s : 0.02% optimize.rewriter_before_opt_a : 0.005330s : 0.03% optimize.opt_a.expand_dump_flag : 0.000163s : 0.00% optimize.opt_a.switch_simplify : 0.004041s : 0.02% optimize.opt_a.loop_unroll : 0.197178s : 1.11% optimize.opt_a.a_1 : 1.650650s : 9.26% optimize.opt_a.with_stream_mark : 0.001057s : 0.01% optimize.opt_a.recompute_prepare : 0.000881s : 0.00% optimize.opt_a.updatestate_depend_eliminate : 0.002898s : 0.02% optimize.opt_a.updatestate_assign_eliminate : 0.055052s : 0.31% optimize.opt_a.updatestate_loads_eliminate : 0.002205s : 0.01% optimize.opt_a.parameter_eliminate : 0.000009s : 0.00% optimize.opt_a.a_2 : 0.061479s : 0.35% optimize.opt_a.accelerated_algorithm : 0.001138s : 0.01% optimize.opt_a.shard : 0.000006s : 0.00% optimize.opt_a.meta_shard_fg_expand : 0.026327s : 0.15% optimize.opt_a.shard_inline : 0.000559s : 0.00% optimize.opt_a.merge_send_recv : 0.000477s : 0.00% optimize.opt_a.auto_parallel : 0.000322s : 0.00% optimize.opt_a.parallel : 0.000134s : 0.00% optimize.opt_a.flash_sp : 0.000096s : 0.00% optimize.opt_a.merge_comm : 0.000324s : 0.00% optimize.opt_a.allreduce_fusion : 0.000306s : 0.00% optimize.opt_a.matmul_add_comm_reduction : 0.000446s : 0.00% optimize.opt_a.allreduce_slice_to_reducescatter : 0.000002s : 0.00% optimize.opt_a.virtual_shard_identity : 0.000589s : 0.00% optimize.opt_a.virtual_dataset : 0.000567s : 0.00% optimize.opt_a.get_grad_eliminate_ : 0.000540s : 0.00% optimize.opt_a.virtual_output : 0.000527s : 0.00% optimize.opt_a.merge_forward : 0.000452s : 0.00% optimize.opt_a.cell_reuse_recompute_pass : 0.000008s : 0.00% optimize.opt_a.offload_activation : 0.000482s : 0.00% optimize.opt_a.cell_reuse_handle_not_recompute_node_pass : 0.001154s : 0.01% optimize.opt_a.merge_recompute_call_nodes : 0.000005s : 0.00% optimize.opt_a.before_grad : 0.012881s : 0.07% optimize.opt_a.set_forward_comm_id_for_comm_node_pass : 0.000604s : 0.00% optimize.opt_a.meta_fg_expand : 0.000605s : 0.00% optimize.opt_a.flash_sp_send_recv_attached : 0.000016s : 0.00% optimize.opt_a.receive_attached : 0.000006s : 0.00% optimize.opt_a.after_resolve : 0.000703s : 0.00% optimize.opt_a.a_after_grad : 0.000976s : 0.01% optimize.opt_a.renormalize : 0.812592s : 4.56% optimize.opt_a.add_forward_monad_depend : 0.000028s : 0.00% optimize.opt_a.auto_monad_grad : 0.000007s : 0.00% optimize.opt_a.auto_monad_eliminator : 0.002027s : 0.01% optimize.opt_a.cse : 0.002628s : 0.01% optimize.opt_a.a_3 : 0.041688s : 0.23% optimize.py_interpret_to_execute_after_opt_a : 0.000341s : 0.00% optimize.slice_cell_reuse_recomputed_activation : 0.000004s : 0.00% optimize.rewriter_after_opt_a : 0.027418s : 0.15% optimize.convert_after_rewriter : 0.000370s : 0.00% optimize.order_py_execute_after_rewriter : 0.000158s : 0.00% optimize.mutable_eliminate : 0.098220s : 0.55% optimize.opt_b.b_1 : 0.006046s : 0.03% optimize.opt_b.b_2 : 0.000240s : 0.00% optimize.opt_b.updatestate_depend_eliminate : 0.000224s : 0.00% optimize.opt_b.updatestate_assign_eliminate : 0.000143s : 0.00% optimize.opt_b.updatestate_loads_eliminate : 0.000157s : 0.00% optimize.opt_b.renormalize : 0.000001s : 0.00% optimize.opt_b.cse : 0.000757s : 0.00% optimize.optimize_parallel_all_gather_comm : 0.000380s : 0.00% optimize.overlap_param_gather : 0.000006s : 0.00% optimize.cconv : 0.000132s : 0.00% optimize.loop_unroll : 0.001075s : 0.01% optimize.opt_after_cconv.c_1 : 0.001353s : 0.01% optimize.opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% optimize.opt_after_cconv.updatestate_depend_eliminate : 0.000198s : 0.00% optimize.opt_after_cconv.updatestate_assign_eliminate : 0.000148s : 0.00% optimize.opt_after_cconv.updatestate_loads_eliminate : 0.000153s : 0.00% optimize.opt_after_cconv.cse : 0.063282s : 0.36% optimize.opt_after_cconv.renormalize : 0.000001s : 0.00% optimize.remove_dup_value : 0.001389s : 0.01% optimize.tuple_transform.d_1 : 0.002178s : 0.01% optimize.tuple_transform.none_parameter_eliminate : 0.000006s : 0.00% optimize.tuple_transform.renormalize : 0.000001s : 0.00% optimize.tuple_transform.switch_simplify : 0.000247s : 0.00% optimize.partial_unused_args_eliminate : 0.000004s : 0.00% optimize.add_recomputation : 0.001739s : 0.01% optimize.cse_after_recomputation.cse : 0.000524s : 0.00% optimize.environ_conv : 0.000158s : 0.00% optimize.swap_dp_allreduce_reducescatter : 0.000230s : 0.00% optimize.bias_add_comm_swap : 0.000004s : 0.00% optimize.label_micro_interleaved_index : 0.000008s : 0.00% optimize.label_fine_grained_interleaved_index : 0.000003s : 0.00% optimize.merge_cast_opt : 0.000001s : 0.00% optimize.slice_recompute_activation : 0.000002s : 0.00% optimize.micro_interleaved_order_control : 0.000003s : 0.00% optimize.assign_add_opt : 0.000002s : 0.00% optimize.ForceFp32Comm : 0.000001s : 0.00% optimize.remove_cast_before_assign_add : 0.000001s : 0.00% optimize.full_micro_interleaved_order_control : 0.000002s : 0.00% optimize.reorder_send_recv_between_fp_bp : 0.000003s : 0.00% optimize.comm_op_add_attrs : 0.000001s : 0.00% optimize.add_comm_op_reuse_tag : 0.000001s : 0.00% optimize.interleave_split_concat_branches : 0.000002s : 0.00% optimize.interleave_parallel_branches : 0.000001s : 0.00% optimize.overlap_opt_shard_in_pipeline : 0.000031s : 0.00% optimize.overlap_opt_shard_grad_in_pipeline : 0.000002s : 0.00% optimize.control_data_broadcast_order : 0.000337s : 0.00% optimize.grouped_pairwise_exchange_alltoall : 0.000002s : 0.00% optimize.offloading_packed_experts : 0.000080s : 0.00% optimize.overlap_recompute_and_grad_model_parallel : 0.000103s : 0.00% optimize.overlap_grad_matmul_and_grad_allreduce : 0.000002s : 0.00% optimize.overlap_recompute_allgather_and_fa_grad : 0.000002s : 0.00% optimize.overlap_recompute_comm : 0.000003s : 0.00% optimize.overlap_grad_ring_attention : 0.000078s : 0.00% optimize.overlap_grad_flash_sp : 0.000451s : 0.00% optimize.begin_end_overlap_inline : 0.000001s : 0.00% optimize.split_matmul_comm_elemetwise : 0.000002s : 0.00% optimize.split_layernorm_comm : 0.000002s : 0.00% optimize.handle_group_info : 0.000001s : 0.00% optimize.symbol_engine_optimizer.build : 0.000129s : 0.00% optimize.symbol_engine_optimizer.elim_shapecalc : 0.000326s : 0.00% optimize.symbol_engine_optimizer.elim_not_effective : 0.000474s : 0.00% optimize.symbol_engine_optimizer.opt_reshape : 0.000271s : 0.00% optimize.symbol_engine_optimizer.fold_const_symbol : 0.000405s : 0.00% optimize.symbol_engine_optimizer.renormalize : 0.000001s : 0.00% detach_backward : 0.000003s : 0.00% pipeline_parallel_scheduler : 0.000002s : 0.00% auto_monad_reorder : 0.000491s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000008s : 0.00% opt_after_jit_grad : 0.001732s : 0.01% validate : 0.000514s : 0.00% Time group info: ------[substitution.] 0.578567 10208 0.14% : 0.000834s : 160: substitution.arithmetic_simplify 0.03% : 0.000169s : 14: substitution.cast_eliminate 0.00% : 0.000027s : 9: substitution.depend_value_elim 0.01% : 0.000062s : 180: substitution.elim_not_effective 0.02% : 0.000093s : 100: substitution.float_tuple_getitem_switch 0.01% : 0.000056s : 180: substitution.fold_const_symbol 0.03% : 0.000158s : 227: substitution.graph_param_transform 92.06% : 0.532617s : 855: substitution.inline 0.03% : 0.000162s : 360: substitution.j_node_and_user_rematch 0.10% : 0.000550s : 44: substitution.less_batch_normalization 0.03% : 0.000150s : 228: substitution.load_eliminater 0.13% : 0.000737s : 503: substitution.minmaximum_grad 0.00% : 0.000010s : 23: substitution.opt_reshape 0.04% : 0.000259s : 360: substitution.remove_not_recompute_node 0.01% : 0.000046s : 72: substitution.replace_old_param 0.15% : 0.000849s : 150: substitution.reshape_eliminate 0.03% : 0.000201s : 73: substitution.switch_simplify 0.08% : 0.000449s : 135: substitution.transpose_eliminate 0.28% : 0.001635s : 553: substitution.tuple_list_convert_item_index_to_positive 0.17% : 0.000960s : 724: substitution.tuple_list_get_item_const_eliminator 0.48% : 0.002789s : 724: substitution.tuple_list_get_item_depend_reorder 0.49% : 0.002856s : 907: substitution.tuple_list_get_item_eliminator 2.64% : 0.015285s : 724: substitution.tuple_list_get_set_item_eliminator 0.23% : 0.001317s : 1446: substitution.updatestate_pure_node_eliminater 2.82% : 0.016296s : 1457: substitution.updatestate_useless_node_eliminater ------[type_inference.] 14.425037 2 91.87% : 13.252308s : 1: type_inference.infer 8.13% : 1.172728s : 1: type_inference.specialize ------[replace.] 0.185396 1246 0.09% : 0.000167s : 14: replace.cast_eliminate 0.04% : 0.000074s : 5: replace.depend_value_elim 97.15% : 0.180120s : 855: replace.inline 0.06% : 0.000113s : 12: replace.reshape_eliminate 0.54% : 0.001005s : 73: replace.switch_simplify 1.32% : 0.002450s : 171: replace.tuple_list_get_item_depend_reorder 0.76% : 0.001416s : 108: replace.tuple_list_get_item_eliminator 0.03% : 0.000051s : 8: replace.updatestate_useless_node_eliminater ------[match.] 0.534225 1246 0.03% : 0.000160s : 14: match.cast_eliminate 0.00% : 0.000003s : 5: match.depend_value_elim 99.56% : 0.531887s : 855: match.inline 0.01% : 0.000053s : 12: match.reshape_eliminate 0.03% : 0.000157s : 73: match.switch_simplify 0.29% : 0.001528s : 171: match.tuple_list_get_item_depend_reorder 0.08% : 0.000420s : 108: match.tuple_list_get_item_eliminator 0.00% : 0.000017s : 8: match.updatestate_useless_node_eliminater ------[predicate.] 0.081270175493 0.51% : 0.000413s : 2713: predicate.accumulaten_eliminater 0.06% : 0.000050s : 227: predicate.ad_related_special_op_eliminate 0.12% : 0.000100s : 703: predicate.addn_check_dump 0.53% : 0.000429s : 2713: predicate.addn_zero_filter 0.51% : 0.000415s : 2713: predicate.adjust_all_reduce_mul_add 0.95% : 0.000768s : 3416: predicate.arithmetic_simplify 0.58% : 0.000468s : 2739: predicate.cast_eliminate 0.18% : 0.000145s : 454: predicate.check_bprop_eliminate 0.12% : 0.000100s : 703: predicate.compare_switch_simplify 0.02% : 0.000017s : 227: predicate.const_output_eliminate 0.13% : 0.000103s : 707: predicate.depend_value_elim 0.54% : 0.000441s : 2739: predicate.dict_get_item_const_eliminator 0.60% : 0.000486s : 2739: predicate.dict_get_item_eliminator 0.50% : 0.000403s : 2739: predicate.dict_set_item_eliminator 0.08% : 0.000067s : 454: predicate.dumpgradient_eliminate 0.02% : 0.000017s : 227: predicate.elim_not_effective 0.05% : 0.000040s : 227: predicate.elim_shapecalc_of_broadcastargs 0.53% : 0.000432s : 2966: predicate.environ_add_const_eliminate 0.52% : 0.000425s : 2966: predicate.environ_get_add_eliminate 0.53% : 0.000432s : 2966: predicate.environ_get_depend_swap 0.66% : 0.000536s : 3669: predicate.environ_get_eliminate 0.52% : 0.000421s : 2966: predicate.environ_get_set_eliminate 0.70% : 0.000571s : 3881: predicate.exchange_switch_depend_value 1.11% : 0.000899s : 3881: predicate.float_depend_g_call 0.22% : 0.000176s : 703: predicate.float_environ_get_switch 0.20% : 0.000161s : 930: predicate.float_tuple_getitem_switch 0.02% : 0.000016s : 227: predicate.fold_const_symbol 0.09% : 0.000077s : 462: predicate.get_grad_eliminate 0.02% : 0.000019s : 227: predicate.graph_param_transform 0.13% : 0.000102s : 703: predicate.incorporate_call 0.12% : 0.000099s : 703: predicate.incorporate_call_switch 2.11% : 0.001712s : 8213: predicate.inline 0.12% : 0.000095s : 462: predicate.inline_without_move 0.04% : 0.000034s : 462: predicate.j_node_and_user_rematch 0.10% : 0.000084s : 462: predicate.less_batch_normalization 0.68% : 0.000550s : 3472: predicate.list_to_tuple_eliminator_ 1.13% : 0.000920s : 6185: predicate.load_eliminater 0.06% : 0.000050s : 227: predicate.loop_unroll_after_grad 1.00% : 0.000813s : 2096: predicate.loop_unroll_before_grad 0.65% : 0.000531s : 3364: predicate.make_slice_get_slice_eliminator 0.13% : 0.000103s : 703: predicate.merge_addn 0.09% : 0.000072s : 454: predicate.micro_step_allgather_replace 0.09% : 0.000072s : 454: predicate.mini_step_allgather_replace 0.54% : 0.000441s : 2713: predicate.minmaximum_grad 0.13% : 0.000109s : 227: predicate.mutable_eliminate 0.06% : 0.000047s : 227: predicate.opt_reshape 0.05% : 0.000037s : 227: predicate.parallel_virtual_node 1.88% : 0.001531s : 3881: predicate.partial_defer_inline 0.64% : 0.000519s : 3245: predicate.partial_eliminate 0.53% : 0.000432s : 2713: predicate.print_const_string_wrapper 0.12% : 0.000100s : 697: predicate.reduce_all_const_elim 0.78% : 0.000633s : 2713: predicate.reduce_eliminate 1.11% : 0.000905s : 6185: predicate.redundant_stop_gradient_eliminater 0.04% : 0.000035s : 462: predicate.remove_not_recompute_node 0.40% : 0.000326s : 3472: predicate.replace_applicator 0.04% : 0.000034s : 462: predicate.replace_old_param 0.02% : 0.000017s : 227: predicate.reset_defer_inline 0.57% : 0.000459s : 2725: predicate.reshape_eliminate 0.12% : 0.000097s : 454: predicate.row_tensor_add_zeros_like 0.05% : 0.000037s : 227: predicate.row_tensor_eliminate 46.64% : 0.037908s : 454: predicate.same_eliminate 0.07% : 0.000060s : 796: predicate.set_cell_output_no_recompute 0.10% : 0.000081s : 462: predicate.shard_identity_eliminate 0.08% : 0.000068s : 454: predicate.special_op_eliminate 0.14% : 0.000115s : 703: predicate.specialize_transform 0.09% : 0.000075s : 454: predicate.split_environ_get_set_with_tuple_value 0.09% : 0.000075s : 462: predicate.stack_unstack_eliminate 0.04% : 0.000032s : 227: predicate.switch_call_monad_eliminater 16.19% : 0.013154s : 3881: predicate.switch_defer_inline 0.89% : 0.000726s : 4335: predicate.switch_layer_defer_inline 1.79% : 0.001451s : 7053: predicate.switch_simplify 0.53% : 0.000432s : 2713: predicate.tile_eliminate 0.53% : 0.000429s : 2713: predicate.transpose_eliminate 0.67% : 0.000547s : 3193: predicate.tuple_list_convert_item_index_to_positive 0.82% : 0.000664s : 3364: predicate.tuple_list_get_item_const_eliminator 0.74% : 0.000603s : 3364: predicate.tuple_list_get_item_depend_reorder 1.14% : 0.000929s : 4175: predicate.tuple_list_get_item_eliminator 0.73% : 0.000591s : 3364: predicate.tuple_list_get_set_item_eliminator 0.92% : 0.000749s : 4067: predicate.tuple_list_set_item_eliminator 0.67% : 0.000548s : 3472: predicate.tuple_to_list_eliminator_ 1.15% : 0.000934s : 6185: predicate.updatestate_pure_node_eliminater 1.42% : 0.001152s : 6896: predicate.updatestate_useless_node_eliminater 0.04% : 0.000037s : 227: predicate.value_based_eliminate 0.10% : 0.000080s : 462: predicate.virtual_dataset_eliminate 0.09% : 0.000076s : 462: predicate.virtual_output_eliminate 0.04% : 0.000029s : 227: predicate.virtual_view_grad_eliminate 0.13% : 0.000107s : 227: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 1.095069 1066 49.72% : 0.544468s : 474: func_graph_cloner_run.FuncGraphClonerGraph 24.10% : 0.263936s : 227: func_graph_cloner_run.FuncGraphClonerNode 26.18% : 0.286666s : 365: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 23.896428 209 0.00% : 0.000004s : 1: ForceFp32Comm 0.06% : 0.013573s : 1: add_attr 0.06% : 0.013548s : 1: add_attr_with_inline 0.00% : 0.000005s : 1: add_comm_op_reuse_tag 0.01% : 0.001765s : 1: add_recomputation 0.00% : 0.000004s : 1: assign_add_opt 0.56% : 0.132671s : 1: auto_monad 0.00% : 0.000504s : 1: auto_monad_reorder 0.00% : 0.000006s : 1: begin_end_overlap_inline 0.00% : 0.000007s : 1: bias_add_comm_swap 0.01% : 0.001887s : 1: bootstrap 0.00% : 0.000139s : 1: cconv 0.00% : 0.000006s : 1: comm_op_add_attrs 0.00% : 0.000344s : 1: control_data_broadcast_order 0.00% : 0.000386s : 1: convert_after_rewriter 0.00% : 0.000600s : 1: cse_after_recomputation 0.00% : 0.000007s : 1: dataset_repeat_opt 0.00% : 0.000007s : 1: detach_backward 0.00% : 0.000167s : 1: environ_conv 0.01% : 0.001949s : 1: event_method 0.00% : 0.000005s : 1: full_micro_interleaved_order_control 0.00% : 0.000010s : 1: get_jit_bprop_graph 0.00% : 0.000602s : 1: graph_reusing 0.00% : 0.000007s : 1: grouped_pairwise_exchange_alltoall 0.00% : 0.000006s : 1: handle_group_info 0.00% : 0.000009s : 1: inline 0.00% : 0.000012s : 1: insert-virtual-dataset 0.00% : 0.000004s : 1: interleave_parallel_branches 0.00% : 0.000004s : 1: interleave_split_concat_branches 0.00% : 0.000009s : 1: label_fine_grained_interleaved_index 0.00% : 0.000011s : 1: label_micro_interleaved_index 0.00% : 0.001088s : 1: loop_unroll 0.00% : 0.000004s : 1: merge_cast_opt 0.00% : 0.000006s : 1: micro_interleaved_order_control 0.41% : 0.098246s : 1: mutable_eliminate 0.00% : 0.000088s : 1: offloading_packed_experts 0.00% : 0.000274s : 1: opt.transform.loop_unroll_optimizer 0.41% : 0.097288s : 1: opt.transform.mutable_eliminate 8.27% : 1.975124s : 95: opt.transform.opt_a 0.01% : 0.001350s : 1: opt.transform.opt_after_cconv 0.00% : 0.000751s : 1: opt.transform.opt_after_jit_grad 0.03% : 0.006220s : 28: opt.transform.opt_b 0.01% : 0.002413s : 2: opt.transform.opt_trans_graph 0.01% : 0.001468s : 4: opt.transform.symbol_engine_opt 12.18% : 2.910386s : 1: opt_a 0.27% : 0.065302s : 1: opt_after_cconv 0.01% : 0.001747s : 1: opt_after_jit_grad 0.03% : 0.007701s : 1: opt_b 13.10% : 3.129748s : 1: optimize 0.00% : 0.000393s : 1: optimize_parallel_all_gather_comm 0.00% : 0.000166s : 1: order_py_execute_after_rewriter 0.00% : 0.000459s : 1: overlap_grad_flash_sp 0.00% : 0.000006s : 1: overlap_grad_matmul_and_grad_allreduce 0.00% : 0.000083s : 1: overlap_grad_ring_attention 0.00% : 0.000006s : 1: overlap_opt_shard_grad_in_pipeline 0.00% : 0.000036s : 1: overlap_opt_shard_in_pipeline 0.00% : 0.000011s : 1: overlap_param_gather 0.00% : 0.000006s : 1: overlap_recompute_allgather_and_fa_grad 0.00% : 0.000110s : 1: overlap_recompute_and_grad_model_parallel 0.00% : 0.000006s : 1: overlap_recompute_comm 0.00% : 0.000011s : 1: parallel-infer-symbol 0.00% : 0.000011s : 1: parallel-infer-symbol-second 0.00% : 0.000009s : 1: partial_unused_args_eliminate 0.00% : 0.000005s : 1: pipeline_parallel_scheduler 0.00% : 0.000005s : 1: pipeline_split 0.60% : 0.144477s : 1: pre_auto_parallel 0.01% : 0.002925s : 1: py_interpret_to_execute 0.00% : 0.000354s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000004s : 1: remove_cast_before_assign_add 0.01% : 0.001416s : 1: remove_dup_value 2.44% : 0.583155s : 1: renormalize.infer 0.96% : 0.229398s : 1: renormalize.specialize 0.00% : 0.000007s : 1: reorder_send_recv_between_fp_bp 0.00% : 0.000012s : 1: rewriter_after_jit_bprop_graph 0.11% : 0.027449s : 1: rewriter_after_opt_a 0.02% : 0.005368s : 1: rewriter_before_opt_a 0.00% : 0.000009s : 1: slice_cell_reuse_recomputed_activation 0.00% : 0.000006s : 1: slice_recompute_activation 0.00% : 0.000007s : 1: split_layernorm_comm 0.00% : 0.000006s : 1: split_matmul_comm_elemetwise 0.00% : 0.000237s : 1: swap_dp_allreduce_reducescatter 0.01% : 0.001695s : 1: symbol_engine_optimizer 0.01% : 0.002515s : 1: tuple_transform 60.38% : 14.428603s : 1: type_inference ..[165505] Start parsing profiling data in sync mode at: /tmp/tmp9rna7lpa/ascend216_165505_20260129093730277_ascend_ms [107867] Parsing: [ ] 0/3 AscendMsprofParser Elapsed: 0s [107867] Parsing: [####### ] 1/3 AscendMsprofParser Elapsed: 63s [107867] Parsing: [####### ] 1/3 FrameworkParser Elapsed: 63s [107867] Parsing: [####### ] 1/3 FrameworkParser Elapsed: 63s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 64s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 64s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 66s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 67s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 68s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 69s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 70s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 71s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 72s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 73s [107867] Parsing: [############# ] 2/3 FrameworkCannRelationParser Elapsed: 74s [107867] Parsing: [####################] 3/3 Done Elapsed: 74s . [hook] pytest_runtest_teardown:test_profiler_analyse_pretty_true_011 tests/st/tools/profiler/daily_test/test_legacy_profiler_parse.py::test_profiler_analyse_pretty_true_011,max_mem:4.0M =============================== warnings summary =============================== ../../../../../../../../../../usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/classifier/transdata/transdata_classifier.py:222 /usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/classifier/transdata/transdata_classifier.py:222: DeprecationWarning: invalid escape sequence \B """ ../../../../../../../../../../usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:143 /usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:143: DeprecationWarning: invalid escape sequence \c """ ../../../../../../../../../../usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:170 /usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:170: DeprecationWarning: invalid escape sequence \c """ ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero. setattr(self, word, getattr(machar, word).flat[0]) ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero. return self._float_to_str(self.smallest_subnormal) ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero. setattr(self, word, getattr(machar, word).flat[0]) ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero. return self._float_to_str(self.smallest_subnormal) ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2.py:57 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2.py:57: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("batchnorm_fold2") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad.py:56 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad.py:56: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("batchnorm_fold2_grad") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad_reduce.py:48 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad_reduce.py:48: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("batchnorm_fold2_grad_reduce") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul.py:51 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul.py:51: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("correction_mul") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:51 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:51: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("correction_mul_grad") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:143 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:143: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("correction_mul_grad_reduce") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perlayer") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad.py:92 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad.py:92: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perlayer_grad_d") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad_reduce.py:49 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad_reduce.py:49: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perlayer_grad_d_reduce") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perchannel") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad.py:91 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad.py:91: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perchannel_grad_d") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad_reduce.py:48 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad_reduce.py:48: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perchannel_grad_d_reduce") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel.py:52 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel.py:52: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_perchannel") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel_grad.py:81 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel_grad.py:81: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_perchannel_grad") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer.py:54 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer.py:54: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_per_layer") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer_grad.py:81 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer_grad.py:81: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_per_layer_grad") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perchannel.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perchannel.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("minmax_update_perchannel") ../../../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perlayer.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perlayer.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("minmax_update_perlayer") test_legacy_profiler_parse.py::test_profiler_analyse_pretty_true_011 /usr/local/Ascend/cann-8.5.0/python/site-packages/asc_op_compile_base/asc_op_compiler/ascendc_compile_gen_code.py:161: DeprecationWarning: invalid escape sequence \w match = re.search(f'{option}=(\w+)', ' '.join(compile_options)) -- Docs: https://docs.pytest.org/en/stable/warnings.html ================== 1 passed, 26 warnings in 337.20s (0:05:37) ==================