==================================================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/mint, configfile: ../../../../../../sault/virtual_test/virtualenv_005/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 2 items test_reshape.py . [hook] pytest_runtest_teardown:test_reshape_high_dimension[pynative] tests/st/mint/test_reshape.py::test_reshape_high_dimension[pynative],max_mem:2.0M [WARNING] PARSER(169979,ffff9951cf30,python3.9):2026-01-29-17:39:49.459.659 [mindspore/ccsrc/frontend/jit/ps/parse/data_converter.cc:661] CheckAPI] The mint interface reshape was called, and the operators under this interface have different view capabilities on pynative and graph mode. Use this interface with caution in graph mode, as it may produce unexpected results. For more information, please refer to: https://www.mindspore.cn/docs/en/master/features/view.html TotalTime = 1.61235, [30] [bootstrap]: 0.00057532 [type_inference]: 1.41628 [event_method]: 2.059e-05 [auto_monad]: 0.00011069 [graph_reusing]: 7.30998e-06 [pre_auto_parallel]: 1.235e-05 [py_interpret_to_execute]: 0.00028392 [rewriter_before_opt_a]: 9.83e-05 [expand_dump_flag]: 4.57e-06 [jit_opt_a]: 0.191451, [2] [Cycle 1]: 0.00450086, [27] [switch_simplify]: 8.023e-05 [loop_unroll]: 3.238e-05 [a_1]: 0.00074382 [with_stream_mark]: 2.902e-05 [recompute_prepare]: 1.184e-05 [updatestate_depend_eliminate]: 5.10001e-06 [updatestate_assign_eliminate]: 4.90999e-06 [updatestate_loads_eliminate]: 3.15998e-06 [parameter_eliminate]: 2.39999e-06 [specialize_transform]: 7.35e-06 [updatestate_useless_node_eliminater]: 6.36e-06 [accelerated_algorithm]: 7.05e-06 [meta_shard_fg_expand]: 3.69002e-06 [get_grad_eliminate_]: 6.43e-06 [merge_forward]: 4.55001e-06 [cell_reuse_recompute_pass]: 1.34e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.426e-05 [j_node_and_user_rematch]: 1.133e-05 [meta_fg_expand]: 2.66999e-06 [replace_old_param]: 1.239e-05 [inline_without_move]: 6.43e-06 [renormalize]: 0.00311141 [add_forward_monad_depend]: 1.813e-05 [auto_monad_grad]: 2.79001e-06 [auto_monad_eliminator]: 2.438e-05 [cse]: 3.651e-05 [replace_applicator]: 2.713e-05 [Cycle 2]: 0.00044017, [27] [switch_simplify]: 8.29002e-06 [loop_unroll]: 6.78998e-06 [a_1]: 0.00014865 [with_stream_mark]: 2.107e-05 [recompute_prepare]: 7.01999e-06 [updatestate_depend_eliminate]: 5.00999e-06 [updatestate_assign_eliminate]: 3.74002e-06 [updatestate_loads_eliminate]: 2.91999e-06 [parameter_eliminate]: 1.91998e-06 [specialize_transform]: 6.02999e-06 [updatestate_useless_node_eliminater]: 5.79999e-06 [accelerated_algorithm]: 6.07999e-06 [meta_shard_fg_expand]: 1.97999e-06 [get_grad_eliminate_]: 5.37001e-06 [merge_forward]: 4.27998e-06 [cell_reuse_recompute_pass]: 2.57001e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.743e-05 [j_node_and_user_rematch]: 1.031e-05 [meta_fg_expand]: 2.54001e-06 [replace_old_param]: 9.86e-06 [inline_without_move]: 5.54e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 1.54e-06 [auto_monad_grad]: 1.69998e-06 [auto_monad_eliminator]: 6.73998e-06 [cse]: 1.446e-05 [replace_applicator]: 6.23998e-06 [py_interpret_to_execute_after_opt_a]: 1.941e-05 [rewriter_after_opt_a]: 6.38e-05 [convert_after_rewriter]: 2.511e-05 [order_py_execute_after_rewriter]: 5.52001e-06 [mutable_eliminate]: 0.00088024 [jit_opt_b]: 6.592e-05, [1] [Cycle 1]: 5.527e-05, [2] [frontend_op_eliminate]: 2.138e-05 [inline_after_opt_a]: 2.029e-05 [cconv]: 4.468e-05 [loop_unroll]: 0.00058714 [jit_opt_after_cconv]: 0.00022228, [1] [Cycle 1]: 0.00021364, [11] [c_1]: 2.901e-05 [parameter_eliminate]: 8.3e-06 [updatestate_depend_eliminate]: 1.402e-05 [updatestate_assign_eliminate]: 3.76999e-06 [updatestate_loads_eliminate]: 3.5e-06 [cse]: 4.805e-05 [call_graph_tuple_transform]: 2.971e-05 [tuple_list_get_item_eliminator]: 6.79001e-06 [none_parameter_eliminate]: 1.72001e-06 [renormalize]: 7.7e-07 [switch_simplify]: 8.95999e-06 [remove_dup_value]: 1.918e-05 [partial_unused_args_eliminate]: 3.36999e-06 [environ_conv]: 2.773e-05 [add_recomputation]: 6.465e-05 [cse_after_recomputation]: 3.03e-05, [1] [Cycle 1]: 2.302e-05, [1] [cse]: 1.483e-05 [auto_monad_reorder]: 2.729e-05 [get_jit_bprop_graph]: 3.36999e-06 [rewriter_after_jit_bprop_graph]: 0.0002233 [opt_after_jit_grad]: 0.00065312 [symbol_engine_optimizer]: 9.867e-05, [1] [Cycle 1]: 8.891e-05, [6] [build]: 6.29999e-06 [elim_shapecalc]: 9.72999e-06 [elim_not_effective]: 1.644e-05 [opt_reshape]: 1.229e-05 [fold_const_symbol]: 1.113e-05 [renormalize]: 8.40024e-07 [validate]: 9.214e-05 Sums bootstrap : 0.000575s : 0.04% type_inference : 1.416281s : 99.39% event_method : 0.000021s : 0.00% auto_monad : 0.000111s : 0.01% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000012s : 0.00% py_interpret_to_execute : 0.000284s : 0.02% rewriter_before_opt_a : 0.000098s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000089s : 0.01% jit_opt_a.loop_unroll : 0.000039s : 0.00% jit_opt_a.a_1 : 0.000892s : 0.06% jit_opt_a.with_stream_mark : 0.000050s : 0.00% jit_opt_a.recompute_prepare : 0.000019s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000010s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000009s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000006s : 0.00% jit_opt_a.parameter_eliminate : 0.000004s : 0.00% jit_opt_a.specialize_transform : 0.000013s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000012s : 0.00% jit_opt_a.accelerated_algorithm : 0.000013s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000006s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000012s : 0.00% jit_opt_a.merge_forward : 0.000009s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000052s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000022s : 0.00% jit_opt_a.meta_fg_expand : 0.000005s : 0.00% jit_opt_a.replace_old_param : 0.000022s : 0.00% jit_opt_a.inline_without_move : 0.000012s : 0.00% jit_opt_a.renormalize : 0.003111s : 0.22% jit_opt_a.add_forward_monad_depend : 0.000020s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000031s : 0.00% jit_opt_a.cse : 0.000051s : 0.00% jit_opt_a.replace_applicator : 0.000033s : 0.00% py_interpret_to_execute_after_opt_a : 0.000019s : 0.00% rewriter_after_opt_a : 0.000064s : 0.00% convert_after_rewriter : 0.000025s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.000880s : 0.06% jit_opt_b.frontend_op_eliminate : 0.000021s : 0.00% jit_opt_b.inline_after_opt_a : 0.000020s : 0.00% cconv : 0.000045s : 0.00% loop_unroll : 0.000587s : 0.04% jit_opt_after_cconv.c_1 : 0.000029s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000014s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000048s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000030s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000007s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000009s : 0.00% remove_dup_value : 0.000019s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000028s : 0.00% add_recomputation : 0.000065s : 0.00% cse_after_recomputation.cse : 0.000015s : 0.00% auto_monad_reorder : 0.000027s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000223s : 0.02% opt_after_jit_grad : 0.000653s : 0.05% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000010s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000016s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000012s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000011s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000092s : 0.01% Time group info: ------[substitution.] 0.000309 28 0.85% : 0.000003s : 2: substitution.elim_not_effective 0.62% : 0.000002s : 2: substitution.fold_const_symbol 2.75% : 0.000008s : 4: substitution.graph_param_transform 74.41% : 0.000230s : 5: substitution.inline 1.49% : 0.000005s : 4: substitution.j_node_and_user_rematch 0.55% : 0.000002s : 1: substitution.opt_reshape 5.89% : 0.000018s : 4: substitution.remove_not_recompute_node 2.48% : 0.000008s : 2: substitution.replace_old_param 8.09% : 0.000025s : 3: substitution.reshape_eliminate 2.87% : 0.000009s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.416182 2 99.86% : 1.414209s : 1: type_inference.infer 0.14% : 0.001973s : 1: type_inference.specialize ------[replace.] 0.000070 6 85.28% : 0.000060s : 5: replace.inline 14.72% : 0.000010s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000235 6 96.52% : 0.000227s : 5: match.inline 3.48% : 0.000008s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000163 867 1.14% : 0.000002s : 13: predicate.accumulaten_eliminater 1.53% : 0.000002s : 4: predicate.ad_related_special_op_eliminate 1.00% : 0.000002s : 13: predicate.addn_check_dump 1.27% : 0.000002s : 13: predicate.addn_zero_filter 1.79% : 0.000003s : 13: predicate.arithmetic_simplify 1.29% : 0.000002s : 13: predicate.cast_eliminate 0.46% : 0.000001s : 4: predicate.check_bprop_eliminate 1.07% : 0.000002s : 13: predicate.compare_switch_simplify 1.28% : 0.000002s : 13: predicate.depend_value_elim 1.17% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 1.19% : 0.000002s : 13: predicate.dict_get_item_eliminator 1.23% : 0.000002s : 13: predicate.dict_set_item_eliminator 0.90% : 0.000001s : 4: predicate.dumpgradient_eliminate 0.47% : 0.000001s : 4: predicate.elim_not_effective 0.50% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 1.19% : 0.000002s : 13: predicate.environ_add_const_eliminate 1.08% : 0.000002s : 13: predicate.environ_get_add_eliminate 1.02% : 0.000002s : 13: predicate.environ_get_depend_swap 1.24% : 0.000002s : 13: predicate.environ_get_eliminate 1.22% : 0.000002s : 13: predicate.environ_get_set_eliminate 0.23% : 0.000000s : 4: predicate.fold_const_symbol 1.06% : 0.000002s : 8: predicate.get_grad_eliminate 0.42% : 0.000001s : 4: predicate.graph_param_transform 5.42% : 0.000009s : 27: predicate.inline 0.78% : 0.000001s : 8: predicate.inline_without_move 0.39% : 0.000001s : 8: predicate.j_node_and_user_rematch 1.26% : 0.000002s : 8: predicate.less_batch_normalization 1.32% : 0.000002s : 14: predicate.list_to_tuple_eliminator_ 1.53% : 0.000002s : 18: predicate.load_eliminater 2.28% : 0.000004s : 4: predicate.loop_unroll_after_grad 3.35% : 0.000005s : 37: predicate.loop_unroll_before_grad 2.30% : 0.000004s : 17: predicate.make_slice_get_slice_eliminator 0.99% : 0.000002s : 13: predicate.merge_addn 1.06% : 0.000002s : 13: predicate.minmaximum_grad 2.94% : 0.000005s : 4: predicate.mutable_eliminate 0.89% : 0.000001s : 4: predicate.opt_reshape 2.06% : 0.000003s : 18: predicate.partial_eliminate 1.51% : 0.000002s : 13: predicate.print_const_string_wrapper 1.99% : 0.000003s : 13: predicate.reduce_eliminate 1.64% : 0.000003s : 14: predicate.redundant_stop_gradient_eliminater 0.47% : 0.000001s : 8: predicate.remove_not_recompute_node 1.88% : 0.000003s : 22: predicate.replace_applicator 0.65% : 0.000001s : 8: predicate.replace_old_param 0.27% : 0.000000s : 4: predicate.reset_defer_inline 1.79% : 0.000003s : 13: predicate.reshape_eliminate 1.25% : 0.000002s : 13: predicate.row_tensor_add_zeros_like 0.73% : 0.000001s : 4: predicate.row_tensor_eliminate 1.72% : 0.000003s : 13: predicate.same_eliminate 0.57% : 0.000001s : 8: predicate.set_cell_output_no_recompute 0.94% : 0.000002s : 8: predicate.special_op_eliminate 0.82% : 0.000001s : 8: predicate.specialize_transform 1.42% : 0.000002s : 13: predicate.split_environ_get_set_with_tuple_value 1.22% : 0.000002s : 13: predicate.stack_unstack_eliminate 0.39% : 0.000001s : 4: predicate.switch_call_monad_eliminater 2.25% : 0.000004s : 19: predicate.switch_defer_inline 1.83% : 0.000003s : 19: predicate.switch_layer_defer_inline 6.96% : 0.000011s : 60: predicate.switch_simplify 1.17% : 0.000002s : 13: predicate.tile_eliminate 1.41% : 0.000002s : 13: predicate.transpose_eliminate 1.60% : 0.000003s : 13: predicate.tuple_list_convert_item_index_to_positive 1.22% : 0.000002s : 13: predicate.tuple_list_get_item_depend_reorder 3.87% : 0.000006s : 22: predicate.tuple_list_get_item_eliminator 1.68% : 0.000003s : 13: predicate.tuple_list_set_item_eliminator 1.36% : 0.000002s : 14: predicate.tuple_to_list_eliminator_ 1.62% : 0.000003s : 18: predicate.updatestate_pure_node_eliminater 2.87% : 0.000005s : 26: predicate.updatestate_useless_node_eliminater 1.74% : 0.000003s : 13: predicate.value_based_eliminate 0.30% : 0.000000s : 4: predicate.virtual_view_grad_eliminate 0.54% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.003133 23 77.01% : 0.002412s : 16: func_graph_cloner_run.FuncGraphClonerGraph 22.99% : 0.000720s : 7: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.616591 72 0.00% : 0.000068s : 1: add_recomputation 0.01% : 0.000115s : 1: auto_monad 0.00% : 0.000030s : 1: auto_monad_reorder 0.04% : 0.000600s : 1: bootstrap 0.00% : 0.000048s : 1: cconv 0.00% : 0.000028s : 1: convert_after_rewriter 0.00% : 0.000033s : 1: cse_after_recomputation 0.00% : 0.000031s : 1: environ_conv 0.00% : 0.000025s : 1: event_method 0.00% : 0.000009s : 1: expand_dump_flag 0.00% : 0.000006s : 1: get_jit_bprop_graph 0.00% : 0.000010s : 1: graph_reusing 11.84% : 0.191456s : 1: jit_opt_a 0.01% : 0.000226s : 1: jit_opt_after_cconv 0.00% : 0.000069s : 1: jit_opt_b 0.04% : 0.000600s : 1: loop_unroll 0.06% : 0.000899s : 1: mutable_eliminate 0.07% : 0.001181s : 26: opt.transform.jit_opt_a 0.00% : 0.000070s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000034s : 4: opt.transform.jit_opt_b 0.00% : 0.000019s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000022s : 1: opt.transform.mutable_eliminate 0.00% : 0.000033s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000045s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000669s : 1: opt_after_jit_grad 0.00% : 0.000008s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000015s : 1: pre_auto_parallel 0.02% : 0.000290s : 1: py_interpret_to_execute 0.00% : 0.000022s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000022s : 1: remove_dup_value 0.13% : 0.002123s : 1: renormalize.infer 0.06% : 0.000974s : 1: renormalize.specialize 0.01% : 0.000230s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000068s : 1: rewriter_after_opt_a 0.01% : 0.000104s : 1: rewriter_before_opt_a 0.01% : 0.000102s : 1: symbol_engine_optimizer 87.61% : 1.416304s : 1: type_inference TotalTime = 1.57539, [30] [bootstrap]: 0.00048962 [type_inference]: 1.21531 [event_method]: 0.00053484 [auto_monad]: 0.00019061 [graph_reusing]: 1.12e-05 [pre_auto_parallel]: 4.27e-06 [py_interpret_to_execute]: 5.735e-05 [rewriter_before_opt_a]: 0.00022963 [expand_dump_flag]: 5.11002e-06 [jit_opt_a]: 0.354994, [2] [Cycle 1]: 0.349601, [27] [switch_simplify]: 0.00027245 [loop_unroll]: 9.654e-05 [a_1]: 0.00163744 [with_stream_mark]: 4.305e-05 [recompute_prepare]: 2.768e-05 [updatestate_depend_eliminate]: 1.059e-05 [updatestate_assign_eliminate]: 7.97e-06 [updatestate_loads_eliminate]: 6.70998e-06 [parameter_eliminate]: 3.55998e-06 [specialize_transform]: 1.768e-05 [updatestate_useless_node_eliminater]: 1.579e-05 [accelerated_algorithm]: 1.728e-05 [meta_shard_fg_expand]: 8.54e-06 [get_grad_eliminate_]: 1.611e-05 [merge_forward]: 9.82001e-06 [cell_reuse_recompute_pass]: 1.27999e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.182e-05 [j_node_and_user_rematch]: 2.776e-05 [meta_fg_expand]: 0.166421 [replace_old_param]: 8.642e-05 [inline_without_move]: 7.77e-05 [renormalize]: 0.180333 [add_forward_monad_depend]: 2.19e-05 [auto_monad_grad]: 3.18e-06 [auto_monad_eliminator]: 2.101e-05 [cse]: 3.658e-05 [replace_applicator]: 2.975e-05 [Cycle 2]: 0.00066522, [27] [switch_simplify]: 6.87002e-06 [loop_unroll]: 5.14e-06 [a_1]: 7.133e-05 [with_stream_mark]: 1.94e-05 [recompute_prepare]: 4.92999e-06 [updatestate_depend_eliminate]: 3.46001e-06 [updatestate_assign_eliminate]: 3.26001e-06 [updatestate_loads_eliminate]: 2.29999e-06 [parameter_eliminate]: 2.22999e-06 [specialize_transform]: 4.41002e-06 [updatestate_useless_node_eliminater]: 4.21001e-06 [accelerated_algorithm]: 4.92e-06 [meta_shard_fg_expand]: 2.22001e-06 [get_grad_eliminate_]: 4.30999e-06 [merge_forward]: 4.03001e-06 [cell_reuse_recompute_pass]: 3.56999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.215e-05 [j_node_and_user_rematch]: 8.30999e-06 [meta_fg_expand]: 0.00029809 [replace_old_param]: 9.58002e-06 [inline_without_move]: 5.42001e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 2.76999e-06 [auto_monad_grad]: 2.06e-06 [auto_monad_eliminator]: 8.77999e-06 [cse]: 1.786e-05 [replace_applicator]: 5.37999e-06 [py_interpret_to_execute_after_opt_a]: 2.115e-05 [rewriter_after_opt_a]: 0.00054923 [convert_after_rewriter]: 1.756e-05 [order_py_execute_after_rewriter]: 5.22e-06 [mutable_eliminate]: 0.00090758 [jit_opt_b]: 6.365e-05, [1] [Cycle 1]: 5.313e-05, [2] [frontend_op_eliminate]: 1.609e-05 [inline_after_opt_a]: 2.069e-05 [cconv]: 4.615e-05 [loop_unroll]: 0.00052777 [jit_opt_after_cconv]: 0.00018471, [1] [Cycle 1]: 0.00017582, [11] [c_1]: 1.968e-05 [parameter_eliminate]: 5.92999e-06 [updatestate_depend_eliminate]: 1.128e-05 [updatestate_assign_eliminate]: 3.61999e-06 [updatestate_loads_eliminate]: 2.56998e-06 [cse]: 3.94e-05 [call_graph_tuple_transform]: 2.408e-05 [tuple_list_get_item_eliminator]: 4.35e-06 [none_parameter_eliminate]: 1.75001e-06 [renormalize]: 6.69999e-07 [switch_simplify]: 5.39998e-06 [remove_dup_value]: 1.745e-05 [partial_unused_args_eliminate]: 2.36e-06 [environ_conv]: 7.46999e-06 [add_recomputation]: 4.862e-05 [cse_after_recomputation]: 2.522e-05, [1] [Cycle 1]: 1.81e-05, [1] [cse]: 9.09e-06 [auto_monad_reorder]: 1.604e-05 [get_jit_bprop_graph]: 3.28998e-06 [rewriter_after_jit_bprop_graph]: 9.16998e-06 [opt_after_jit_grad]: 0.00068124 [symbol_engine_optimizer]: 0.00011641, [1] [Cycle 1]: 0.00010715, [6] [build]: 6.02999e-06 [elim_shapecalc]: 8.05e-06 [elim_not_effective]: 1.609e-05 [opt_reshape]: 8.42e-06 [fold_const_symbol]: 1.124e-05 [renormalize]: 1.02e-06 [validate]: 4.841e-05 Sums bootstrap : 0.000490s : 0.03% type_inference : 1.215307s : 77.42% event_method : 0.000535s : 0.03% auto_monad : 0.000191s : 0.01% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000057s : 0.00% rewriter_before_opt_a : 0.000230s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000279s : 0.02% jit_opt_a.loop_unroll : 0.000102s : 0.01% jit_opt_a.a_1 : 0.001709s : 0.11% jit_opt_a.with_stream_mark : 0.000062s : 0.00% jit_opt_a.recompute_prepare : 0.000033s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000014s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000011s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000009s : 0.00% jit_opt_a.parameter_eliminate : 0.000006s : 0.00% jit_opt_a.specialize_transform : 0.000022s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000020s : 0.00% jit_opt_a.accelerated_algorithm : 0.000022s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000011s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000020s : 0.00% jit_opt_a.merge_forward : 0.000014s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000054s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000036s : 0.00% jit_opt_a.meta_fg_expand : 0.166720s : 10.62% jit_opt_a.replace_old_param : 0.000096s : 0.01% jit_opt_a.inline_without_move : 0.000083s : 0.01% jit_opt_a.renormalize : 0.180333s : 11.49% jit_opt_a.add_forward_monad_depend : 0.000025s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000030s : 0.00% jit_opt_a.cse : 0.000054s : 0.00% jit_opt_a.replace_applicator : 0.000035s : 0.00% py_interpret_to_execute_after_opt_a : 0.000021s : 0.00% rewriter_after_opt_a : 0.000549s : 0.03% convert_after_rewriter : 0.000018s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000908s : 0.06% jit_opt_b.frontend_op_eliminate : 0.000016s : 0.00% jit_opt_b.inline_after_opt_a : 0.000021s : 0.00% cconv : 0.000046s : 0.00% loop_unroll : 0.000528s : 0.03% jit_opt_after_cconv.c_1 : 0.000020s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000011s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000039s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000004s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000005s : 0.00% remove_dup_value : 0.000017s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000007s : 0.00% add_recomputation : 0.000049s : 0.00% cse_after_recomputation.cse : 0.000009s : 0.00% auto_monad_reorder : 0.000016s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000009s : 0.00% opt_after_jit_grad : 0.000681s : 0.04% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000008s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000016s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000008s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000011s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000048s : 0.00% Time group info: ------[substitution.] 0.000582 69 0.45% : 0.000003s : 1: substitution.elim_not_effective 0.31% : 0.000002s : 1: substitution.fold_const_symbol 1.06% : 0.000006s : 1: substitution.graph_param_transform 74.59% : 0.000434s : 14: substitution.inline 5.05% : 0.000029s : 2: substitution.inline_without_move 1.50% : 0.000009s : 9: substitution.j_node_and_user_rematch 0.75% : 0.000004s : 2: substitution.minmaximum_grad 1.64% : 0.000010s : 9: substitution.partial_eliminate 1.48% : 0.000009s : 9: substitution.remove_not_recompute_node 0.68% : 0.000004s : 1: substitution.replace_applicator 1.24% : 0.000007s : 7: substitution.replace_old_param 2.19% : 0.000013s : 2: substitution.reshape_eliminate 0.58% : 0.000003s : 1: substitution.set_cell_output_no_recompute 3.11% : 0.000018s : 3: substitution.switch_simplify 1.47% : 0.000009s : 2: substitution.tuple_list_convert_item_index_to_positive 0.99% : 0.000006s : 2: substitution.tuple_list_get_item_depend_reorder 2.92% : 0.000017s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.215124 2 99.60% : 1.210273s : 1: type_inference.infer 0.40% : 0.004851s : 1: type_inference.specialize ------[replace.] 0.000224 18 55.47% : 0.000124s : 14: replace.inline 39.26% : 0.000088s : 3: replace.switch_simplify 5.27% : 0.000012s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000445 18 95.64% : 0.000426s : 14: match.inline 3.53% : 0.000016s : 3: match.switch_simplify 0.83% : 0.000004s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000282 1710 1.50% : 0.000004s : 29: predicate.accumulaten_eliminater 0.71% : 0.000002s : 1: predicate.ad_related_special_op_eliminate 1.25% : 0.000004s : 29: predicate.addn_check_dump 1.45% : 0.000004s : 29: predicate.addn_zero_filter 2.14% : 0.000006s : 29: predicate.arithmetic_simplify 1.34% : 0.000004s : 29: predicate.cast_eliminate 0.14% : 0.000000s : 1: predicate.check_bprop_eliminate 1.26% : 0.000004s : 29: predicate.compare_switch_simplify 1.36% : 0.000004s : 29: predicate.depend_value_elim 1.19% : 0.000003s : 29: predicate.dict_get_item_const_eliminator 1.34% : 0.000004s : 29: predicate.dict_get_item_eliminator 1.43% : 0.000004s : 29: predicate.dict_set_item_eliminator 0.78% : 0.000002s : 1: predicate.dumpgradient_eliminate 0.13% : 0.000000s : 1: predicate.elim_not_effective 0.20% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.32% : 0.000004s : 29: predicate.environ_add_const_eliminate 1.26% : 0.000004s : 29: predicate.environ_get_add_eliminate 1.33% : 0.000004s : 29: predicate.environ_get_depend_swap 1.30% : 0.000004s : 29: predicate.environ_get_eliminate 1.26% : 0.000004s : 29: predicate.environ_get_set_eliminate 0.06% : 0.000000s : 1: predicate.fold_const_symbol 0.78% : 0.000002s : 14: predicate.get_grad_eliminate 0.12% : 0.000000s : 1: predicate.graph_param_transform 4.45% : 0.000013s : 46: predicate.inline 2.79% : 0.000008s : 38: predicate.inline_without_move 0.37% : 0.000001s : 14: predicate.j_node_and_user_rematch 1.19% : 0.000003s : 14: predicate.less_batch_normalization 1.50% : 0.000004s : 30: predicate.list_to_tuple_eliminator_ 1.61% : 0.000005s : 31: predicate.load_eliminater 0.76% : 0.000002s : 1: predicate.loop_unroll_after_grad 4.58% : 0.000013s : 78: predicate.loop_unroll_before_grad 1.72% : 0.000005s : 30: predicate.make_slice_get_slice_eliminator 1.29% : 0.000004s : 29: predicate.merge_addn 1.33% : 0.000004s : 29: predicate.minmaximum_grad 1.42% : 0.000004s : 1: predicate.mutable_eliminate 0.24% : 0.000001s : 1: predicate.opt_reshape 2.08% : 0.000006s : 31: predicate.partial_eliminate 1.36% : 0.000004s : 29: predicate.print_const_string_wrapper 1.78% : 0.000005s : 29: predicate.reduce_eliminate 1.43% : 0.000004s : 30: predicate.redundant_stop_gradient_eliminater 0.47% : 0.000001s : 14: predicate.remove_not_recompute_node 1.62% : 0.000005s : 32: predicate.replace_applicator 1.40% : 0.000004s : 38: predicate.replace_old_param 0.25% : 0.000001s : 1: predicate.reset_defer_inline 1.66% : 0.000005s : 29: predicate.reshape_eliminate 1.40% : 0.000004s : 29: predicate.row_tensor_add_zeros_like 0.55% : 0.000002s : 1: predicate.row_tensor_eliminate 1.37% : 0.000004s : 29: predicate.same_eliminate 0.55% : 0.000002s : 14: predicate.set_cell_output_no_recompute 0.30% : 0.000001s : 2: predicate.special_op_eliminate 0.78% : 0.000002s : 14: predicate.specialize_transform 1.59% : 0.000004s : 29: predicate.split_environ_get_set_with_tuple_value 1.29% : 0.000004s : 29: predicate.stack_unstack_eliminate 0.18% : 0.000001s : 1: predicate.switch_call_monad_eliminater 3.37% : 0.000010s : 44: predicate.switch_defer_inline 2.41% : 0.000007s : 44: predicate.switch_layer_defer_inline 8.25% : 0.000023s : 129: predicate.switch_simplify 1.43% : 0.000004s : 29: predicate.tile_eliminate 1.41% : 0.000004s : 29: predicate.transpose_eliminate 2.07% : 0.000006s : 29: predicate.tuple_list_convert_item_index_to_positive 1.71% : 0.000005s : 29: predicate.tuple_list_get_item_depend_reorder 3.16% : 0.000009s : 32: predicate.tuple_list_get_item_eliminator 1.64% : 0.000005s : 29: predicate.tuple_list_set_item_eliminator 1.40% : 0.000004s : 30: predicate.tuple_to_list_eliminator_ 1.59% : 0.000004s : 31: predicate.updatestate_pure_node_eliminater 2.57% : 0.000007s : 45: predicate.updatestate_useless_node_eliminater 1.66% : 0.000005s : 29: predicate.value_based_eliminate 0.07% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.27% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.169204 47 99.51% : 0.168373s : 30: func_graph_cloner_run.FuncGraphClonerGraph 0.49% : 0.000831s : 17: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.758209 72 0.00% : 0.000052s : 1: add_recomputation 0.01% : 0.000199s : 1: auto_monad 0.00% : 0.000019s : 1: auto_monad_reorder 0.03% : 0.000514s : 1: bootstrap 0.00% : 0.000049s : 1: cconv 0.00% : 0.000022s : 1: convert_after_rewriter 0.00% : 0.000028s : 1: cse_after_recomputation 0.00% : 0.000011s : 1: environ_conv 0.03% : 0.000547s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000006s : 1: get_jit_bprop_graph 0.00% : 0.000014s : 1: graph_reusing 20.19% : 0.354999s : 1: jit_opt_a 0.01% : 0.000188s : 1: jit_opt_after_cconv 0.00% : 0.000067s : 1: jit_opt_b 0.03% : 0.000539s : 1: loop_unroll 0.05% : 0.000923s : 1: mutable_eliminate 0.14% : 0.002456s : 26: opt.transform.jit_opt_a 0.00% : 0.000048s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000025s : 4: opt.transform.jit_opt_b 0.00% : 0.000014s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000025s : 1: opt.transform.mutable_eliminate 0.00% : 0.000029s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000038s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000697s : 1: opt_after_jit_grad 0.00% : 0.000007s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000061s : 1: py_interpret_to_execute 0.00% : 0.000024s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000020s : 1: remove_dup_value 10.19% : 0.179108s : 1: renormalize.infer 0.07% : 0.001205s : 1: renormalize.specialize 0.00% : 0.000011s : 1: rewriter_after_jit_bprop_graph 0.03% : 0.000560s : 1: rewriter_after_opt_a 0.01% : 0.000234s : 1: rewriter_before_opt_a 0.01% : 0.000119s : 1: symbol_engine_optimizer 69.12% : 1.215332s : 1: type_inference . [hook] pytest_runtest_teardown:test_reshape_high_dimension[KBK] tests/st/mint/test_reshape.py::test_reshape_high_dimension[KBK],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") -- Docs: https://docs.pytest.org/en/stable/warnings.html ================== 2 passed, 25 warnings in 185.32s (0:03:05) ==================