==================================================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_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 10 items test_unsqueeze.py . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[inf-pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[inf-pynative],max_mem:2.0M TotalTime = 5.9047, [33] [bootstrap]: 0.00072284 [type_inference]: 0.228688 [event_method]: 2.771e-05 [auto_monad]: 0.00017252 [graph_reusing]: 6.46e-06 [pre_auto_parallel]: 1.453e-05 [py_interpret_to_execute]: 3.327e-05 [rewriter_before_opt_a]: 7.506e-05 [expand_dump_flag]: 3.62002e-06 [jit_opt_a]: 0.0110083, [2] [Cycle 1]: 0.00198752, [27] [switch_simplify]: 6.768e-05 [loop_unroll]: 2.342e-05 [a_1]: 0.00049926 [with_stream_mark]: 3.213e-05 [recompute_prepare]: 1.318e-05 [updatestate_depend_eliminate]: 7.41999e-06 [updatestate_assign_eliminate]: 7.3e-06 [updatestate_loads_eliminate]: 4.99e-06 [parameter_eliminate]: 2.07001e-06 [specialize_transform]: 9.42001e-06 [updatestate_useless_node_eliminater]: 1.16e-05 [accelerated_algorithm]: 8.1e-06 [meta_shard_fg_expand]: 2.61e-06 [get_grad_eliminate_]: 7.45e-06 [merge_forward]: 5.40999e-06 [cell_reuse_recompute_pass]: 1.20001e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.456e-05 [j_node_and_user_rematch]: 1.839e-05 [meta_fg_expand]: 3.91999e-06 [replace_old_param]: 1.281e-05 [inline_without_move]: 7.77e-06 [renormalize]: 0.00087189 [add_forward_monad_depend]: 1.262e-05 [auto_monad_grad]: 2.66e-06 [auto_monad_eliminator]: 2.583e-05 [cse]: 3.844e-05 [replace_applicator]: 1.971e-05 [Cycle 2]: 0.00053122, [27] [switch_simplify]: 8.87e-06 [loop_unroll]: 7.3e-06 [a_1]: 0.00016723 [with_stream_mark]: 1.64e-05 [recompute_prepare]: 8.84998e-06 [updatestate_depend_eliminate]: 5.79e-06 [updatestate_assign_eliminate]: 4.97e-06 [updatestate_loads_eliminate]: 3.95998e-06 [parameter_eliminate]: 1.60001e-06 [specialize_transform]: 7.78001e-06 [updatestate_useless_node_eliminater]: 1.079e-05 [accelerated_algorithm]: 8.12e-06 [meta_shard_fg_expand]: 2.29999e-06 [get_grad_eliminate_]: 7.28999e-06 [merge_forward]: 4.92e-06 [cell_reuse_recompute_pass]: 2.01998e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.682e-05 [j_node_and_user_rematch]: 1.327e-05 [meta_fg_expand]: 3.79002e-06 [replace_old_param]: 1.117e-05 [inline_without_move]: 7.19001e-06 [renormalize]: 7.00238e-08 [add_forward_monad_depend]: 2.11e-06 [auto_monad_grad]: 8.30012e-07 [auto_monad_eliminator]: 3.475e-05 [cse]: 2.056e-05 [replace_applicator]: 9.17999e-06 [py_interpret_to_execute_after_opt_a]: 1.865e-05 [rewriter_after_opt_a]: 0.00064441 [convert_after_rewriter]: 3.192e-05 [order_py_execute_after_rewriter]: 7.4e-06 [mutable_eliminate]: 0.00075728 [jit_opt_b]: 7.14e-05, [1] [Cycle 1]: 6.268e-05, [2] [frontend_op_eliminate]: 2.402e-05 [inline_after_opt_a]: 2.501e-05 [cconv]: 3.498e-05 [loop_unroll]: 0.00047356 [jit_opt_after_cconv]: 0.00022612, [1] [Cycle 1]: 0.00021889, [11] [c_1]: 5.076e-05 [parameter_eliminate]: 3.63999e-06 [updatestate_depend_eliminate]: 1.093e-05 [updatestate_assign_eliminate]: 4.88001e-06 [updatestate_loads_eliminate]: 4.3e-06 [cse]: 4.572e-05 [call_graph_tuple_transform]: 2.361e-05 [tuple_list_get_item_eliminator]: 8.52e-06 [none_parameter_eliminate]: 1.54998e-06 [renormalize]: 8.39995e-07 [switch_simplify]: 8.55001e-06 [remove_dup_value]: 1.867e-05 [partial_unused_args_eliminate]: 2.36e-06 [environ_conv]: 1.848e-05 [add_recomputation]: 7.657e-05 [cse_after_recomputation]: 3.196e-05, [1] [Cycle 1]: 2.576e-05, [1] [cse]: 1.95e-05 [auto_monad_reorder]: 3.741e-05 [get_jit_bprop_graph]: 2.24999e-06 [rewriter_after_jit_bprop_graph]: 0.00013489 [opt_after_jit_grad]: 0.00049909 [symbol_engine_optimizer]: 9.759e-05, [1] [Cycle 1]: 9.085e-05, [6] [build]: 6.64999e-06 [elim_shapecalc]: 1.185e-05 [elim_not_effective]: 1.92e-05 [opt_reshape]: 9.00001e-06 [fold_const_symbol]: 1.312e-05 [renormalize]: 5.00004e-07 [validate]: 7.055e-05 [backend_pass]: 1.19998e-06 [task_emit]: 5.66029 [execute]: 1.279e-05 Sums bootstrap : 0.000723s : 0.01% type_inference : 0.228688s : 3.88% event_method : 0.000028s : 0.00% auto_monad : 0.000173s : 0.00% graph_reusing : 0.000006s : 0.00% pre_auto_parallel : 0.000015s : 0.00% py_interpret_to_execute : 0.000033s : 0.00% rewriter_before_opt_a : 0.000075s : 0.00% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000077s : 0.00% jit_opt_a.loop_unroll : 0.000031s : 0.00% jit_opt_a.a_1 : 0.000666s : 0.01% jit_opt_a.with_stream_mark : 0.000049s : 0.00% jit_opt_a.recompute_prepare : 0.000022s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000013s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000012s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000009s : 0.00% jit_opt_a.parameter_eliminate : 0.000004s : 0.00% jit_opt_a.specialize_transform : 0.000017s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000022s : 0.00% jit_opt_a.accelerated_algorithm : 0.000016s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000005s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000015s : 0.00% jit_opt_a.merge_forward : 0.000010s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000003s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000051s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000032s : 0.00% jit_opt_a.meta_fg_expand : 0.000008s : 0.00% jit_opt_a.replace_old_param : 0.000024s : 0.00% jit_opt_a.inline_without_move : 0.000015s : 0.00% jit_opt_a.renormalize : 0.000872s : 0.01% jit_opt_a.add_forward_monad_depend : 0.000015s : 0.00% jit_opt_a.auto_monad_grad : 0.000003s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000061s : 0.00% jit_opt_a.cse : 0.000059s : 0.00% jit_opt_a.replace_applicator : 0.000029s : 0.00% py_interpret_to_execute_after_opt_a : 0.000019s : 0.00% rewriter_after_opt_a : 0.000644s : 0.01% convert_after_rewriter : 0.000032s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000757s : 0.01% jit_opt_b.frontend_op_eliminate : 0.000024s : 0.00% jit_opt_b.inline_after_opt_a : 0.000025s : 0.00% cconv : 0.000035s : 0.00% loop_unroll : 0.000474s : 0.01% jit_opt_after_cconv.c_1 : 0.000051s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000011s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.cse : 0.000046s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000009s : 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 : 0.000002s : 0.00% environ_conv : 0.000018s : 0.00% add_recomputation : 0.000077s : 0.00% cse_after_recomputation.cse : 0.000020s : 0.00% auto_monad_reorder : 0.000037s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000135s : 0.00% opt_after_jit_grad : 0.000499s : 0.01% symbol_engine_optimizer.build : 0.000007s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000012s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000019s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000013s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000071s : 0.00% backend_pass : 0.000001s : 0.00% task_emit : 5.660287s : 96.01% execute : 0.000013s : 0.00% Time group info: ------[substitution.] 0.000250 44 4.25% : 0.000011s : 2: substitution.depend_value_elim 1.49% : 0.000004s : 4: substitution.elim_not_effective 0.75% : 0.000002s : 4: substitution.fold_const_symbol 2.89% : 0.000007s : 5: substitution.graph_param_transform 69.68% : 0.000174s : 3: substitution.inline 2.24% : 0.000006s : 8: substitution.j_node_and_user_rematch 7.01% : 0.000018s : 8: substitution.remove_not_recompute_node 2.42% : 0.000006s : 2: substitution.replace_old_param 5.05% : 0.000013s : 3: substitution.updatestate_pure_node_eliminater 4.21% : 0.000011s : 5: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.228611 2 99.65% : 0.227811s : 1: type_inference.infer 0.35% : 0.000800s : 1: type_inference.specialize ------[replace.] 0.000037 3 100.00% : 0.000037s : 3: replace.inline ------[match.] 0.000171 3 100.00% : 0.000171s : 3: match.inline ------[predicate.] 0.000151 825 1.14% : 0.000002s : 12: predicate.accumulaten_eliminater 1.29% : 0.000002s : 5: predicate.ad_related_special_op_eliminate 1.37% : 0.000002s : 12: predicate.addn_check_dump 1.25% : 0.000002s : 12: predicate.addn_zero_filter 2.03% : 0.000003s : 12: predicate.arithmetic_simplify 1.23% : 0.000002s : 12: predicate.cast_eliminate 0.63% : 0.000001s : 5: predicate.check_bprop_eliminate 1.04% : 0.000002s : 12: predicate.compare_switch_simplify 1.72% : 0.000003s : 12: predicate.depend_value_elim 1.10% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.12% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.07% : 0.000002s : 12: predicate.dict_set_item_eliminator 1.31% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.54% : 0.000001s : 5: predicate.elim_not_effective 0.72% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.33% : 0.000002s : 12: predicate.environ_add_const_eliminate 1.01% : 0.000002s : 12: predicate.environ_get_add_eliminate 1.03% : 0.000002s : 12: predicate.environ_get_depend_swap 1.35% : 0.000002s : 12: predicate.environ_get_eliminate 1.00% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.27% : 0.000000s : 5: predicate.fold_const_symbol 1.25% : 0.000002s : 10: predicate.get_grad_eliminate 0.42% : 0.000001s : 5: predicate.graph_param_transform 5.58% : 0.000008s : 25: predicate.inline 1.28% : 0.000002s : 10: predicate.inline_without_move 0.50% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.40% : 0.000002s : 10: predicate.less_batch_normalization 1.18% : 0.000002s : 12: predicate.list_to_tuple_eliminator_ 1.70% : 0.000003s : 17: predicate.load_eliminater 1.64% : 0.000002s : 5: predicate.loop_unroll_after_grad 2.87% : 0.000004s : 25: predicate.loop_unroll_before_grad 2.10% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 1.37% : 0.000002s : 12: predicate.merge_addn 1.09% : 0.000002s : 12: predicate.minmaximum_grad 2.71% : 0.000004s : 5: predicate.mutable_eliminate 0.63% : 0.000001s : 5: predicate.opt_reshape 2.21% : 0.000003s : 17: predicate.partial_eliminate 1.38% : 0.000002s : 12: predicate.print_const_string_wrapper 1.51% : 0.000002s : 12: predicate.reduce_eliminate 1.21% : 0.000002s : 12: predicate.redundant_stop_gradient_eliminater 0.89% : 0.000001s : 10: predicate.remove_not_recompute_node 1.80% : 0.000003s : 22: predicate.replace_applicator 0.84% : 0.000001s : 10: predicate.replace_old_param 0.68% : 0.000001s : 5: predicate.reset_defer_inline 1.19% : 0.000002s : 12: predicate.reshape_eliminate 1.17% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.86% : 0.000001s : 5: predicate.row_tensor_eliminate 1.09% : 0.000002s : 12: predicate.same_eliminate 0.78% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.40% : 0.000002s : 10: predicate.special_op_eliminate 1.51% : 0.000002s : 10: predicate.specialize_transform 1.40% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.21% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.69% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.70% : 0.000003s : 15: predicate.switch_defer_inline 1.49% : 0.000002s : 15: predicate.switch_layer_defer_inline 6.65% : 0.000010s : 45: predicate.switch_simplify 1.25% : 0.000002s : 12: predicate.tile_eliminate 1.15% : 0.000002s : 12: predicate.transpose_eliminate 1.72% : 0.000003s : 12: predicate.tuple_list_convert_item_index_to_positive 1.26% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 3.22% : 0.000005s : 22: predicate.tuple_list_get_item_eliminator 1.38% : 0.000002s : 12: predicate.tuple_list_set_item_eliminator 1.39% : 0.000002s : 12: predicate.tuple_to_list_eliminator_ 1.59% : 0.000002s : 17: predicate.updatestate_pure_node_eliminater 3.38% : 0.000005s : 27: predicate.updatestate_useless_node_eliminater 1.53% : 0.000002s : 12: predicate.value_based_eliminate 0.47% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.72% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000400 6 5.62% : 0.000022s : 1: func_graph_cloner_run.FuncGraphClonerGraph 94.38% : 0.000377s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 5.906607 76 0.00% : 0.000080s : 1: add_recomputation 0.00% : 0.000177s : 1: auto_monad 0.00% : 0.000040s : 1: auto_monad_reorder 0.00% : 0.000004s : 1: backend_pass 0.01% : 0.000750s : 1: bootstrap 0.00% : 0.000038s : 1: cconv 0.00% : 0.000036s : 1: convert_after_rewriter 0.00% : 0.000034s : 1: cse_after_recomputation 0.00% : 0.000021s : 1: environ_conv 0.00% : 0.000034s : 1: event_method 0.00% : 0.000019s : 1: execute 0.00% : 0.000006s : 1: expand_dump_flag 0.00% : 0.000004s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 0.19% : 0.011012s : 1: jit_opt_a 0.00% : 0.000230s : 1: jit_opt_after_cconv 0.00% : 0.000075s : 1: jit_opt_b 0.01% : 0.000482s : 1: loop_unroll 0.01% : 0.000766s : 1: mutable_eliminate 0.02% : 0.000969s : 26: opt.transform.jit_opt_a 0.00% : 0.000087s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000041s : 4: opt.transform.jit_opt_b 0.00% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000023s : 1: opt.transform.mutable_eliminate 0.00% : 0.000030s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000049s : 4: opt.transform.symbol_engine_opt 0.01% : 0.000507s : 1: opt_after_jit_grad 0.00% : 0.000010s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000017s : 1: pre_auto_parallel 0.00% : 0.000036s : 1: py_interpret_to_execute 0.00% : 0.000022s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000021s : 1: remove_dup_value 0.01% : 0.000497s : 1: renormalize.infer 0.01% : 0.000365s : 1: renormalize.specialize 0.00% : 0.000139s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000653s : 1: rewriter_after_opt_a 0.00% : 0.000078s : 1: rewriter_before_opt_a 0.00% : 0.000100s : 1: symbol_engine_optimizer 95.83% : 5.660321s : 1: task_emit 3.87% : 0.228706s : 1: type_inference 0.00% : 0.000099s : 1: validate TotalTime = 1.51212, [33] [bootstrap]: 0.00058478 [type_inference]: 0.424754 [event_method]: 0.00015053 [auto_monad]: 0.00032449 [graph_reusing]: 1.151e-05 [pre_auto_parallel]: 4.33999e-06 [py_interpret_to_execute]: 5.878e-05 [rewriter_before_opt_a]: 0.00016259 [expand_dump_flag]: 4.67998e-06 [jit_opt_a]: 0.828703, [3] [Cycle 1]: 0.634732, [27] [switch_simplify]: 0.00025721 [loop_unroll]: 6.382e-05 [a_1]: 0.00149627 [with_stream_mark]: 5.01e-05 [recompute_prepare]: 3.057e-05 [updatestate_depend_eliminate]: 1.429e-05 [updatestate_assign_eliminate]: 1.056e-05 [updatestate_loads_eliminate]: 1.085e-05 [parameter_eliminate]: 3.22002e-06 [specialize_transform]: 1.969e-05 [updatestate_useless_node_eliminater]: 2.317e-05 [accelerated_algorithm]: 1.82e-05 [meta_shard_fg_expand]: 7.78999e-06 [get_grad_eliminate_]: 1.799e-05 [merge_forward]: 1.166e-05 [cell_reuse_recompute_pass]: 1.10001e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.908e-05 [j_node_and_user_rematch]: 3.419e-05 [meta_fg_expand]: 0.231283 [replace_old_param]: 0.00014156 [inline_without_move]: 0.00013172 [renormalize]: 0.399758 [add_forward_monad_depend]: 0.00010308 [auto_monad_grad]: 1.23e-05 [auto_monad_eliminator]: 9.174e-05 [cse]: 0.00047751 [replace_applicator]: 0.00023829 [Cycle 2]: 0.00456129, [27] [switch_simplify]: 6.155e-05 [loop_unroll]: 5.522e-05 [a_1]: 0.00122427 [with_stream_mark]: 3.157e-05 [recompute_prepare]: 1.592e-05 [updatestate_depend_eliminate]: 0.00010635 [updatestate_assign_eliminate]: 6.98e-06 [updatestate_loads_eliminate]: 4.77e-06 [parameter_eliminate]: 2.67001e-06 [specialize_transform]: 1.142e-05 [updatestate_useless_node_eliminater]: 1.462e-05 [accelerated_algorithm]: 1.001e-05 [meta_shard_fg_expand]: 3.48e-06 [get_grad_eliminate_]: 9.59e-06 [merge_forward]: 5.39998e-06 [cell_reuse_recompute_pass]: 1.30001e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.957e-05 [j_node_and_user_rematch]: 1.301e-05 [meta_fg_expand]: 0.00019231 [replace_old_param]: 1.902e-05 [inline_without_move]: 8.87e-06 [renormalize]: 0.00240865 [add_forward_monad_depend]: 8.88002e-06 [auto_monad_grad]: 2.48002e-06 [auto_monad_eliminator]: 2.554e-05 [cse]: 4.8e-05 [replace_applicator]: 3.377e-05 [Cycle 3]: 0.00067535, [27] [switch_simplify]: 9.99001e-06 [loop_unroll]: 8.26002e-06 [a_1]: 0.00030209 [with_stream_mark]: 2.261e-05 [recompute_prepare]: 1.118e-05 [updatestate_depend_eliminate]: 7.01001e-06 [updatestate_assign_eliminate]: 6.59001e-06 [updatestate_loads_eliminate]: 4.67e-06 [parameter_eliminate]: 1.69e-06 [specialize_transform]: 8.97e-06 [updatestate_useless_node_eliminater]: 1.233e-05 [accelerated_algorithm]: 8.51997e-06 [meta_shard_fg_expand]: 2.56e-06 [get_grad_eliminate_]: 9.03002e-06 [merge_forward]: 5.94e-06 [cell_reuse_recompute_pass]: 4.15e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.064e-05 [j_node_and_user_rematch]: 1.486e-05 [meta_fg_expand]: 3.16999e-06 [replace_old_param]: 1.332e-05 [inline_without_move]: 8.12e-06 [renormalize]: 7.99773e-08 [add_forward_monad_depend]: 3.78001e-06 [auto_monad_grad]: 1.45001e-06 [auto_monad_eliminator]: 1.225e-05 [cse]: 2.834e-05 [replace_applicator]: 8.93002e-06 [py_interpret_to_execute_after_opt_a]: 2.027e-05 [rewriter_after_opt_a]: 0.00073284 [convert_after_rewriter]: 1.637e-05 [order_py_execute_after_rewriter]: 6.56e-06 [mutable_eliminate]: 0.00109457 [jit_opt_b]: 7.931e-05, [1] [Cycle 1]: 7.014e-05, [2] [frontend_op_eliminate]: 3.089e-05 [inline_after_opt_a]: 2.401e-05 [cconv]: 3.436e-05 [loop_unroll]: 0.00059169 [jit_opt_after_cconv]: 0.0120206, [1] [Cycle 1]: 0.0120071, [11] [c_1]: 5.423e-05 [parameter_eliminate]: 3.96001e-06 [updatestate_depend_eliminate]: 1.034e-05 [updatestate_assign_eliminate]: 4.71002e-06 [updatestate_loads_eliminate]: 4.18999e-06 [cse]: 3.632e-05 [call_graph_tuple_transform]: 0.0116956 [tuple_list_get_item_eliminator]: 3.661e-05 [none_parameter_eliminate]: 6.83e-06 [renormalize]: 1.14e-06 [switch_simplify]: 1.535e-05 [remove_dup_value]: 5.691e-05 [partial_unused_args_eliminate]: 4.94e-06 [environ_conv]: 1.7e-05 [add_recomputation]: 0.00012007 [cse_after_recomputation]: 7.433e-05, [1] [Cycle 1]: 6.086e-05, [1] [cse]: 5.067e-05 [auto_monad_reorder]: 3.364e-05 [get_jit_bprop_graph]: 3.6e-06 [rewriter_after_jit_bprop_graph]: 1.335e-05 [opt_after_jit_grad]: 0.00130025 [symbol_engine_optimizer]: 0.00012579, [1] [Cycle 1]: 0.00011642, [6] [build]: 1.131e-05 [elim_shapecalc]: 1.437e-05 [elim_not_effective]: 3.175e-05 [opt_reshape]: 1.042e-05 [fold_const_symbol]: 1.454e-05 [renormalize]: 9.70002e-07 [validate]: 0.0501002 [backend_pass]: 2.39001e-06 [task_emit]: 0.190492 [execute]: 1.02e-05 Sums bootstrap : 0.000585s : 0.04% type_inference : 0.424754s : 32.13% event_method : 0.000151s : 0.01% auto_monad : 0.000324s : 0.02% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000059s : 0.00% rewriter_before_opt_a : 0.000163s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000329s : 0.02% jit_opt_a.loop_unroll : 0.000127s : 0.01% jit_opt_a.a_1 : 0.003023s : 0.23% jit_opt_a.with_stream_mark : 0.000104s : 0.01% jit_opt_a.recompute_prepare : 0.000058s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000128s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000024s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000020s : 0.00% jit_opt_a.parameter_eliminate : 0.000008s : 0.00% jit_opt_a.specialize_transform : 0.000040s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000050s : 0.00% jit_opt_a.accelerated_algorithm : 0.000037s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000014s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000037s : 0.00% jit_opt_a.merge_forward : 0.000023s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000079s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000062s : 0.00% jit_opt_a.meta_fg_expand : 0.231478s : 17.51% jit_opt_a.replace_old_param : 0.000174s : 0.01% jit_opt_a.inline_without_move : 0.000149s : 0.01% jit_opt_a.renormalize : 0.402167s : 30.42% jit_opt_a.add_forward_monad_depend : 0.000116s : 0.01% jit_opt_a.auto_monad_grad : 0.000016s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000130s : 0.01% jit_opt_a.cse : 0.000554s : 0.04% jit_opt_a.replace_applicator : 0.000281s : 0.02% py_interpret_to_execute_after_opt_a : 0.000020s : 0.00% rewriter_after_opt_a : 0.000733s : 0.06% convert_after_rewriter : 0.000016s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.001095s : 0.08% jit_opt_b.frontend_op_eliminate : 0.000031s : 0.00% jit_opt_b.inline_after_opt_a : 0.000024s : 0.00% cconv : 0.000034s : 0.00% loop_unroll : 0.000592s : 0.04% jit_opt_after_cconv.c_1 : 0.000054s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000010s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.cse : 0.000036s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.011696s : 0.88% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000037s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000015s : 0.00% remove_dup_value : 0.000057s : 0.00% partial_unused_args_eliminate : 0.000005s : 0.00% environ_conv : 0.000017s : 0.00% add_recomputation : 0.000120s : 0.01% cse_after_recomputation.cse : 0.000051s : 0.00% auto_monad_reorder : 0.000034s : 0.00% get_jit_bprop_graph : 0.000004s : 0.00% rewriter_after_jit_bprop_graph : 0.000013s : 0.00% opt_after_jit_grad : 0.001300s : 0.10% symbol_engine_optimizer.build : 0.000011s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000014s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000032s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000010s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.050100s : 3.79% backend_pass : 0.000002s : 0.00% task_emit : 0.190492s : 14.41% execute : 0.000010s : 0.00% Time group info: ------[substitution.] 0.002081 170 1.22% : 0.000025s : 8: substitution.depend_value_elim 0.17% : 0.000003s : 4: substitution.elim_not_effective 0.11% : 0.000002s : 4: substitution.fold_const_symbol 48.40% : 0.001007s : 4: substitution.getattr_setattr_resolve 0.95% : 0.000020s : 5: substitution.graph_param_transform 33.32% : 0.000693s : 16: substitution.inline 2.03% : 0.000042s : 4: substitution.inline_without_move 0.60% : 0.000012s : 20: substitution.j_node_and_user_rematch 0.48% : 0.000010s : 5: substitution.minmaximum_grad 0.37% : 0.000008s : 9: substitution.partial_eliminate 0.77% : 0.000016s : 20: substitution.remove_not_recompute_node 3.06% : 0.000064s : 12: substitution.replace_applicator 0.84% : 0.000017s : 16: substitution.replace_old_param 0.15% : 0.000003s : 1: substitution.set_cell_output_no_recompute 0.88% : 0.000018s : 3: substitution.switch_simplify 1.05% : 0.000022s : 5: substitution.tuple_list_convert_item_index_to_positive 0.73% : 0.000015s : 5: substitution.tuple_list_get_item_depend_reorder 1.59% : 0.000033s : 8: substitution.tuple_list_get_item_eliminator 1.21% : 0.000025s : 8: substitution.updatestate_pure_node_eliminater 2.07% : 0.000043s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.424616 2 99.14% : 0.420979s : 1: type_inference.infer 0.86% : 0.003637s : 1: type_inference.specialize ------[replace.] 0.000494 27 17.32% : 0.000086s : 3: replace.getattr_setattr_resolve 28.84% : 0.000143s : 16: replace.inline 12.41% : 0.000061s : 1: replace.replace_applicator 18.24% : 0.000090s : 3: replace.switch_simplify 17.89% : 0.000088s : 3: replace.tuple_list_get_item_eliminator 5.30% : 0.000026s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001672 27 55.72% : 0.000932s : 3: match.getattr_setattr_resolve 40.90% : 0.000684s : 16: match.inline 0.94% : 0.000016s : 1: match.replace_applicator 0.97% : 0.000016s : 3: match.switch_simplify 0.37% : 0.000006s : 3: match.tuple_list_get_item_eliminator 1.09% : 0.000018s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000554 3164 1.82% : 0.000010s : 50: predicate.accumulaten_eliminater 0.93% : 0.000005s : 5: predicate.ad_related_special_op_eliminate 1.19% : 0.000007s : 50: predicate.addn_check_dump 1.47% : 0.000008s : 50: predicate.addn_zero_filter 1.93% : 0.000011s : 50: predicate.arithmetic_simplify 1.23% : 0.000007s : 50: predicate.cast_eliminate 0.28% : 0.000002s : 5: predicate.check_bprop_eliminate 1.27% : 0.000007s : 50: predicate.compare_switch_simplify 1.44% : 0.000008s : 50: predicate.depend_value_elim 1.27% : 0.000007s : 50: predicate.dict_get_item_const_eliminator 1.46% : 0.000008s : 50: predicate.dict_get_item_eliminator 1.20% : 0.000007s : 50: predicate.dict_set_item_eliminator 0.51% : 0.000003s : 5: predicate.dumpgradient_eliminate 0.18% : 0.000001s : 5: predicate.elim_not_effective 0.37% : 0.000002s : 5: predicate.elim_shapecalc_of_broadcastargs 1.26% : 0.000007s : 50: predicate.environ_add_const_eliminate 1.31% : 0.000007s : 50: predicate.environ_get_add_eliminate 1.21% : 0.000007s : 50: predicate.environ_get_depend_swap 1.47% : 0.000008s : 50: predicate.environ_get_eliminate 1.32% : 0.000007s : 50: predicate.environ_get_set_eliminate 0.07% : 0.000000s : 5: predicate.fold_const_symbol 1.17% : 0.000006s : 26: predicate.get_grad_eliminate 1.45% : 0.000008s : 20: predicate.getattr_setattr_resolve 0.11% : 0.000001s : 5: predicate.graph_param_transform 4.03% : 0.000022s : 80: predicate.inline 3.19% : 0.000018s : 87: predicate.inline_without_move 0.34% : 0.000002s : 26: predicate.j_node_and_user_rematch 1.07% : 0.000006s : 26: predicate.less_batch_normalization 1.57% : 0.000009s : 53: predicate.list_to_tuple_eliminator_ 1.47% : 0.000008s : 58: predicate.load_eliminater 0.43% : 0.000002s : 5: predicate.loop_unroll_after_grad 3.45% : 0.000019s : 132: predicate.loop_unroll_before_grad 1.70% : 0.000009s : 55: predicate.make_slice_get_slice_eliminator 1.26% : 0.000007s : 50: predicate.merge_addn 1.26% : 0.000007s : 50: predicate.minmaximum_grad 0.57% : 0.000003s : 5: predicate.mutable_eliminate 0.21% : 0.000001s : 5: predicate.opt_reshape 1.86% : 0.000010s : 58: predicate.partial_eliminate 1.38% : 0.000008s : 50: predicate.print_const_string_wrapper 2.49% : 0.000014s : 50: predicate.reduce_eliminate 1.98% : 0.000011s : 53: predicate.redundant_stop_gradient_eliminater 0.43% : 0.000002s : 26: predicate.remove_not_recompute_node 2.96% : 0.000016s : 126: predicate.replace_applicator 1.39% : 0.000008s : 87: predicate.replace_old_param 0.15% : 0.000001s : 5: predicate.reset_defer_inline 1.60% : 0.000009s : 50: predicate.reshape_eliminate 1.57% : 0.000009s : 50: predicate.row_tensor_add_zeros_like 0.35% : 0.000002s : 5: predicate.row_tensor_eliminate 1.30% : 0.000007s : 50: predicate.same_eliminate 0.45% : 0.000002s : 28: predicate.set_cell_output_no_recompute 0.83% : 0.000005s : 10: predicate.special_op_eliminate 0.84% : 0.000005s : 26: predicate.specialize_transform 1.83% : 0.000010s : 50: predicate.split_environ_get_set_with_tuple_value 1.24% : 0.000007s : 50: predicate.stack_unstack_eliminate 0.20% : 0.000001s : 5: predicate.switch_call_monad_eliminater 2.49% : 0.000014s : 70: predicate.switch_defer_inline 2.08% : 0.000012s : 70: predicate.switch_layer_defer_inline 6.75% : 0.000037s : 213: predicate.switch_simplify 1.63% : 0.000009s : 50: predicate.tile_eliminate 1.47% : 0.000008s : 50: predicate.transpose_eliminate 1.57% : 0.000009s : 50: predicate.tuple_list_convert_item_index_to_positive 1.41% : 0.000008s : 50: predicate.tuple_list_get_item_depend_reorder 3.67% : 0.000020s : 63: predicate.tuple_list_get_item_eliminator 1.74% : 0.000010s : 50: predicate.tuple_list_set_item_eliminator 1.36% : 0.000008s : 53: predicate.tuple_to_list_eliminator_ 1.57% : 0.000009s : 58: predicate.updatestate_pure_node_eliminater 2.46% : 0.000014s : 85: predicate.updatestate_useless_node_eliminater 2.07% : 0.000011s : 50: predicate.value_based_eliminate 0.14% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.30% : 0.000002s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.005513 47 55.61% : 0.003066s : 20: func_graph_cloner_run.FuncGraphClonerGraph 44.39% : 0.002447s : 27: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.931709 93 0.01% : 0.000124s : 1: add_recomputation 0.02% : 0.000333s : 1: auto_monad 0.00% : 0.000036s : 1: auto_monad_reorder 0.00% : 0.000009s : 1: backend_pass 0.03% : 0.000608s : 1: bootstrap 0.00% : 0.000037s : 1: cconv 0.00% : 0.000020s : 1: convert_after_rewriter 0.00% : 0.000077s : 1: cse_after_recomputation 0.00% : 0.000020s : 1: environ_conv 0.01% : 0.000158s : 1: event_method 0.00% : 0.000016s : 1: execute 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000007s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 42.90% : 0.828708s : 1: jit_opt_a 0.62% : 0.012028s : 1: jit_opt_after_cconv 0.00% : 0.000082s : 1: jit_opt_b 0.03% : 0.000602s : 1: loop_unroll 0.06% : 0.001104s : 1: mutable_eliminate 0.23% : 0.004371s : 39: opt.transform.jit_opt_a 0.61% : 0.011787s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000048s : 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% : 0.000053s : 1: opt.transform.opt_after_jit_grad 0.06% : 0.001174s : 2: opt.transform.opt_resolve 0.00% : 0.000067s : 4: opt.transform.symbol_engine_opt 0.07% : 0.001321s : 1: opt_after_jit_grad 0.00% : 0.000009s : 1: order_py_execute_after_rewriter 0.00% : 0.000008s : 1: partial_unused_args_eliminate 0.00% : 0.000007s : 1: pre_auto_parallel 0.00% : 0.000063s : 1: py_interpret_to_execute 0.00% : 0.000023s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000061s : 1: remove_dup_value 20.50% : 0.395940s : 2: renormalize.infer 0.32% : 0.006195s : 2: renormalize.specialize 0.00% : 0.000015s : 1: rewriter_after_jit_bprop_graph 0.04% : 0.000739s : 1: rewriter_after_opt_a 0.01% : 0.000166s : 1: rewriter_before_opt_a 0.01% : 0.000129s : 1: symbol_engine_optimizer 9.86% : 0.190516s : 1: task_emit 21.99% : 0.424777s : 1: type_inference 2.60% : 0.050211s : 1: validate . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[inf-KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[inf-KBK],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[nan-pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[nan-pynative],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[nan-KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[nan-KBK],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[zero-pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[zero-pynative],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[zero-KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[zero-KBK],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[large-pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[large-pynative],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[large-KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[large-KBK],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[small-pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[small-pynative],max_mem:4.0M . [hook] pytest_runtest_teardown:test_unsqueeze_special_values[small-KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_special_values[small-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 ================= 10 passed, 25 warnings in 242.36s (0:04:02) ==================