==================================================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_squeeze.py . [hook] pytest_runtest_teardown:test_squeeze_large_tensors[pynative] tests/st/mint/test_squeeze.py::test_squeeze_large_tensors[pynative],max_mem:2.0M [WARNING] PARSER(158492,ffff9ab99f30,python3.9):2026-01-29-17:38:02.154.373 [mindspore/ccsrc/frontend/jit/ps/parse/data_converter.cc:661] CheckAPI] The mint interface squeeze 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 = 2.26889, [30] [bootstrap]: 0.00056053 [type_inference]: 2.07523 [event_method]: 2.058e-05 [auto_monad]: 0.00011155 [graph_reusing]: 6.57002e-06 [pre_auto_parallel]: 1.155e-05 [py_interpret_to_execute]: 0.00051979 [rewriter_before_opt_a]: 9.82e-05 [expand_dump_flag]: 3.53e-06 [jit_opt_a]: 0.18926, [2] [Cycle 1]: 0.180465, [27] [switch_simplify]: 7.246e-05 [loop_unroll]: 0.176416 [a_1]: 0.00062765 [with_stream_mark]: 3.119e-05 [recompute_prepare]: 1.076e-05 [updatestate_depend_eliminate]: 4.86002e-06 [updatestate_assign_eliminate]: 3.45e-06 [updatestate_loads_eliminate]: 3.4e-06 [parameter_eliminate]: 2.66999e-06 [specialize_transform]: 7.22002e-06 [updatestate_useless_node_eliminater]: 6.46e-06 [accelerated_algorithm]: 7.42002e-06 [meta_shard_fg_expand]: 2.89001e-06 [get_grad_eliminate_]: 6.56e-06 [merge_forward]: 4.38999e-06 [cell_reuse_recompute_pass]: 1.14998e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.393e-05 [j_node_and_user_rematch]: 1.181e-05 [meta_fg_expand]: 3.4e-06 [replace_old_param]: 1.194e-05 [inline_without_move]: 6.48e-06 [renormalize]: 0.0027584 [add_forward_monad_depend]: 1.699e-05 [auto_monad_grad]: 3.30998e-06 [auto_monad_eliminator]: 2.223e-05 [cse]: 3.604e-05 [replace_applicator]: 4.185e-05 [Cycle 2]: 0.00042547, [27] [switch_simplify]: 8.3e-06 [loop_unroll]: 6.49999e-06 [a_1]: 0.00012958 [with_stream_mark]: 1.908e-05 [recompute_prepare]: 6.85998e-06 [updatestate_depend_eliminate]: 4.12e-06 [updatestate_assign_eliminate]: 3.2e-06 [updatestate_loads_eliminate]: 2.76e-06 [parameter_eliminate]: 2.48e-06 [specialize_transform]: 6.39999e-06 [updatestate_useless_node_eliminater]: 6.25002e-06 [accelerated_algorithm]: 6.40002e-06 [meta_shard_fg_expand]: 2.13002e-06 [get_grad_eliminate_]: 6.17999e-06 [merge_forward]: 4.33001e-06 [cell_reuse_recompute_pass]: 2.72001e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.954e-05 [j_node_and_user_rematch]: 1.06e-05 [meta_fg_expand]: 2.34001e-06 [replace_old_param]: 1.09e-05 [inline_without_move]: 6.16e-06 [renormalize]: 7.00238e-08 [add_forward_monad_depend]: 1.59e-06 [auto_monad_grad]: 1.58002e-06 [auto_monad_eliminator]: 8.84998e-06 [cse]: 1.646e-05 [replace_applicator]: 6.16e-06 [py_interpret_to_execute_after_opt_a]: 1.592e-05 [rewriter_after_opt_a]: 5.8e-05 [convert_after_rewriter]: 8.85001e-06 [order_py_execute_after_rewriter]: 5.93998e-06 [mutable_eliminate]: 0.00079153 [jit_opt_b]: 6.263e-05, [1] [Cycle 1]: 5.358e-05, [2] [frontend_op_eliminate]: 2.117e-05 [inline_after_opt_a]: 1.979e-05 [cconv]: 3.605e-05 [loop_unroll]: 0.00050221 [jit_opt_after_cconv]: 0.00018669, [1] [Cycle 1]: 0.00017864, [11] [c_1]: 2.879e-05 [parameter_eliminate]: 4.53001e-06 [updatestate_depend_eliminate]: 9.61e-06 [updatestate_assign_eliminate]: 3.2e-06 [updatestate_loads_eliminate]: 2.53e-06 [cse]: 3.232e-05 [call_graph_tuple_transform]: 2.595e-05 [tuple_list_get_item_eliminator]: 7.05998e-06 [none_parameter_eliminate]: 1.47999e-06 [renormalize]: 9.19972e-07 [switch_simplify]: 7.16999e-06 [remove_dup_value]: 1.879e-05 [partial_unused_args_eliminate]: 2.71999e-06 [environ_conv]: 6.946e-05 [add_recomputation]: 6.535e-05 [cse_after_recomputation]: 3.065e-05, [1] [Cycle 1]: 2.399e-05, [1] [cse]: 1.609e-05 [auto_monad_reorder]: 2.543e-05 [get_jit_bprop_graph]: 2.56e-06 [rewriter_after_jit_bprop_graph]: 0.00015408 [opt_after_jit_grad]: 0.00055693 [symbol_engine_optimizer]: 8.5e-05, [1] [Cycle 1]: 7.757e-05, [6] [build]: 4.50999e-06 [elim_shapecalc]: 9.46e-06 [elim_not_effective]: 1.542e-05 [opt_reshape]: 7.00998e-06 [fold_const_symbol]: 1.011e-05 [renormalize]: 5.60016e-07 [validate]: 6.916e-05 Sums bootstrap : 0.000561s : 0.02% type_inference : 2.075226s : 91.84% event_method : 0.000021s : 0.00% auto_monad : 0.000112s : 0.00% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000012s : 0.00% py_interpret_to_execute : 0.000520s : 0.02% rewriter_before_opt_a : 0.000098s : 0.00% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000081s : 0.00% jit_opt_a.loop_unroll : 0.176423s : 7.81% jit_opt_a.a_1 : 0.000757s : 0.03% jit_opt_a.with_stream_mark : 0.000050s : 0.00% jit_opt_a.recompute_prepare : 0.000018s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000007s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000006s : 0.00% jit_opt_a.parameter_eliminate : 0.000005s : 0.00% jit_opt_a.specialize_transform : 0.000014s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000013s : 0.00% jit_opt_a.accelerated_algorithm : 0.000014s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000005s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000013s : 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 : 0.000043s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000022s : 0.00% jit_opt_a.meta_fg_expand : 0.000006s : 0.00% jit_opt_a.replace_old_param : 0.000023s : 0.00% jit_opt_a.inline_without_move : 0.000013s : 0.00% jit_opt_a.renormalize : 0.002758s : 0.12% jit_opt_a.add_forward_monad_depend : 0.000019s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000031s : 0.00% jit_opt_a.cse : 0.000052s : 0.00% jit_opt_a.replace_applicator : 0.000048s : 0.00% py_interpret_to_execute_after_opt_a : 0.000016s : 0.00% rewriter_after_opt_a : 0.000058s : 0.00% convert_after_rewriter : 0.000009s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.000792s : 0.04% jit_opt_b.frontend_op_eliminate : 0.000021s : 0.00% jit_opt_b.inline_after_opt_a : 0.000020s : 0.00% cconv : 0.000036s : 0.00% loop_unroll : 0.000502s : 0.02% jit_opt_after_cconv.c_1 : 0.000029s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000010s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000032s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000026s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000007s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000001s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000007s : 0.00% remove_dup_value : 0.000019s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000069s : 0.00% add_recomputation : 0.000065s : 0.00% cse_after_recomputation.cse : 0.000016s : 0.00% auto_monad_reorder : 0.000025s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000154s : 0.01% opt_after_jit_grad : 0.000557s : 0.02% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000009s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000015s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000007s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000010s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000069s : 0.00% Time group info: ------[substitution.] 0.000240 23 0.86% : 0.000002s : 2: substitution.elim_not_effective 0.61% : 0.000001s : 2: substitution.fold_const_symbol 2.88% : 0.000007s : 4: substitution.graph_param_transform 82.68% : 0.000198s : 4: substitution.inline 2.25% : 0.000005s : 4: substitution.j_node_and_user_rematch 2.34% : 0.000006s : 4: substitution.remove_not_recompute_node 3.14% : 0.000008s : 2: substitution.replace_old_param 5.25% : 0.000013s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 2.075138 2 99.88% : 2.072643s : 1: type_inference.infer 0.12% : 0.002494s : 1: type_inference.specialize ------[replace.] 0.000059 5 85.23% : 0.000050s : 4: replace.inline 14.77% : 0.000009s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000207 5 94.29% : 0.000195s : 4: match.inline 5.71% : 0.000012s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000175 801 1.30% : 0.000002s : 12: predicate.accumulaten_eliminater 1.07% : 0.000002s : 4: predicate.ad_related_special_op_eliminate 0.96% : 0.000002s : 12: predicate.addn_check_dump 1.32% : 0.000002s : 12: predicate.addn_zero_filter 1.92% : 0.000003s : 12: predicate.arithmetic_simplify 1.32% : 0.000002s : 12: predicate.cast_eliminate 0.46% : 0.000001s : 4: predicate.check_bprop_eliminate 0.99% : 0.000002s : 12: predicate.compare_switch_simplify 1.12% : 0.000002s : 12: predicate.depend_value_elim 1.05% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.39% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.12% : 0.000002s : 12: predicate.dict_set_item_eliminator 0.84% : 0.000001s : 4: predicate.dumpgradient_eliminate 0.38% : 0.000001s : 4: predicate.elim_not_effective 0.57% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 1.09% : 0.000002s : 12: predicate.environ_add_const_eliminate 0.99% : 0.000002s : 12: predicate.environ_get_add_eliminate 0.89% : 0.000002s : 12: predicate.environ_get_depend_swap 1.08% : 0.000002s : 12: predicate.environ_get_eliminate 1.16% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.22% : 0.000000s : 4: predicate.fold_const_symbol 0.95% : 0.000002s : 8: predicate.get_grad_eliminate 0.33% : 0.000001s : 4: predicate.graph_param_transform 4.69% : 0.000008s : 25: predicate.inline 0.96% : 0.000002s : 8: predicate.inline_without_move 0.36% : 0.000001s : 8: predicate.j_node_and_user_rematch 1.15% : 0.000002s : 8: predicate.less_batch_normalization 4.81% : 0.000008s : 13: predicate.list_to_tuple_eliminator_ 1.59% : 0.000003s : 17: predicate.load_eliminater 1.35% : 0.000002s : 4: predicate.loop_unroll_after_grad 9.33% : 0.000016s : 28: predicate.loop_unroll_before_grad 1.68% : 0.000003s : 16: predicate.make_slice_get_slice_eliminator 0.90% : 0.000002s : 12: predicate.merge_addn 0.92% : 0.000002s : 12: predicate.minmaximum_grad 1.71% : 0.000003s : 4: predicate.mutable_eliminate 0.45% : 0.000001s : 4: predicate.opt_reshape 1.67% : 0.000003s : 17: predicate.partial_eliminate 1.11% : 0.000002s : 12: predicate.print_const_string_wrapper 1.75% : 0.000003s : 12: predicate.reduce_eliminate 1.47% : 0.000003s : 13: predicate.redundant_stop_gradient_eliminater 0.57% : 0.000001s : 8: predicate.remove_not_recompute_node 1.68% : 0.000003s : 21: predicate.replace_applicator 0.75% : 0.000001s : 8: predicate.replace_old_param 0.39% : 0.000001s : 4: predicate.reset_defer_inline 1.36% : 0.000002s : 12: predicate.reshape_eliminate 1.21% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 1.02% : 0.000002s : 4: predicate.row_tensor_eliminate 1.29% : 0.000002s : 12: predicate.same_eliminate 0.72% : 0.000001s : 8: predicate.set_cell_output_no_recompute 0.81% : 0.000001s : 8: predicate.special_op_eliminate 0.88% : 0.000002s : 8: predicate.specialize_transform 1.29% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.26% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.38% : 0.000001s : 4: predicate.switch_call_monad_eliminater 2.00% : 0.000003s : 17: predicate.switch_defer_inline 1.83% : 0.000003s : 17: predicate.switch_layer_defer_inline 6.37% : 0.000011s : 49: predicate.switch_simplify 1.09% : 0.000002s : 12: predicate.tile_eliminate 1.21% : 0.000002s : 12: predicate.transpose_eliminate 1.12% : 0.000002s : 12: predicate.tuple_list_convert_item_index_to_positive 1.19% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 3.76% : 0.000007s : 21: predicate.tuple_list_get_item_eliminator 1.52% : 0.000003s : 12: predicate.tuple_list_set_item_eliminator 1.24% : 0.000002s : 13: predicate.tuple_to_list_eliminator_ 1.32% : 0.000002s : 17: predicate.updatestate_pure_node_eliminater 2.58% : 0.000005s : 25: predicate.updatestate_useless_node_eliminater 1.83% : 0.000003s : 12: predicate.value_based_eliminate 0.27% : 0.000000s : 4: predicate.virtual_view_grad_eliminate 0.68% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.002414 18 69.40% : 0.001675s : 12: func_graph_cloner_run.FuncGraphClonerGraph 30.60% : 0.000739s : 6: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 2.449037 72 0.00% : 0.000069s : 1: add_recomputation 0.00% : 0.000116s : 1: auto_monad 0.00% : 0.000028s : 1: auto_monad_reorder 0.02% : 0.000581s : 1: bootstrap 0.00% : 0.000039s : 1: cconv 0.00% : 0.000011s : 1: convert_after_rewriter 0.00% : 0.000033s : 1: cse_after_recomputation 0.00% : 0.000073s : 1: environ_conv 0.00% : 0.000026s : 1: event_method 0.00% : 0.000006s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 7.73% : 0.189264s : 1: jit_opt_a 0.01% : 0.000190s : 1: jit_opt_after_cconv 0.00% : 0.000066s : 1: jit_opt_b 0.02% : 0.000513s : 1: loop_unroll 0.03% : 0.000808s : 1: mutable_eliminate 7.24% : 0.177425s : 26: opt.transform.jit_opt_a 0.00% : 0.000065s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000034s : 4: opt.transform.jit_opt_b 0.00% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000021s : 1: opt.transform.mutable_eliminate 0.00% : 0.000028s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000038s : 4: opt.transform.symbol_engine_opt 0.02% : 0.000569s : 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% : 0.000014s : 1: pre_auto_parallel 0.02% : 0.000531s : 1: py_interpret_to_execute 0.00% : 0.000018s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000022s : 1: remove_dup_value 0.08% : 0.001932s : 1: renormalize.infer 0.03% : 0.000814s : 1: renormalize.specialize 0.01% : 0.000158s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000062s : 1: rewriter_after_opt_a 0.00% : 0.000103s : 1: rewriter_before_opt_a 0.00% : 0.000088s : 1: symbol_engine_optimizer 84.74% : 2.075248s : 1: type_inference TotalTime = 1.33814, [30] [bootstrap]: 0.00047097 [type_inference]: 1.20646 [event_method]: 0.00076918 [auto_monad]: 0.00021499 [graph_reusing]: 1.161e-05 [pre_auto_parallel]: 4.28999e-06 [py_interpret_to_execute]: 6.367e-05 [rewriter_before_opt_a]: 0.00017595 [expand_dump_flag]: 4.52998e-06 [jit_opt_a]: 0.127058, [2] [Cycle 1]: 0.121332, [27] [switch_simplify]: 0.00028626 [loop_unroll]: 6.724e-05 [a_1]: 0.00154878 [with_stream_mark]: 4.751e-05 [recompute_prepare]: 3.025e-05 [updatestate_depend_eliminate]: 9.91e-06 [updatestate_assign_eliminate]: 7.92e-06 [updatestate_loads_eliminate]: 6.73998e-06 [parameter_eliminate]: 3.97e-06 [specialize_transform]: 1.729e-05 [updatestate_useless_node_eliminater]: 1.41e-05 [accelerated_algorithm]: 1.497e-05 [meta_shard_fg_expand]: 7.68001e-06 [get_grad_eliminate_]: 1.516e-05 [merge_forward]: 9.32999e-06 [cell_reuse_recompute_pass]: 1.32e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.267e-05 [j_node_and_user_rematch]: 2.791e-05 [meta_fg_expand]: 0.00251818 [replace_old_param]: 8.009e-05 [inline_without_move]: 7.063e-05 [renormalize]: 0.11611 [add_forward_monad_depend]: 1.346e-05 [auto_monad_grad]: 3.50998e-06 [auto_monad_eliminator]: 1.649e-05 [cse]: 3.209e-05 [replace_applicator]: 2.121e-05 [Cycle 2]: 0.00063488, [27] [switch_simplify]: 6.22001e-06 [loop_unroll]: 4.57e-06 [a_1]: 5.638e-05 [with_stream_mark]: 1.242e-05 [recompute_prepare]: 4.07998e-06 [updatestate_depend_eliminate]: 3.51999e-06 [updatestate_assign_eliminate]: 2.34001e-06 [updatestate_loads_eliminate]: 2.28998e-06 [parameter_eliminate]: 1.79998e-06 [specialize_transform]: 3.75998e-06 [updatestate_useless_node_eliminater]: 3.71999e-06 [accelerated_algorithm]: 4.33999e-06 [meta_shard_fg_expand]: 2.09e-06 [get_grad_eliminate_]: 3.81999e-06 [merge_forward]: 3.41999e-06 [cell_reuse_recompute_pass]: 3.55998e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.613e-05 [j_node_and_user_rematch]: 6.88998e-06 [meta_fg_expand]: 0.00031548 [replace_old_param]: 9.67999e-06 [inline_without_move]: 4.96002e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 3.14999e-06 [auto_monad_grad]: 1.17e-06 [auto_monad_eliminator]: 7.31001e-06 [cse]: 1.45e-05 [replace_applicator]: 4.78001e-06 [py_interpret_to_execute_after_opt_a]: 1.357e-05 [rewriter_after_opt_a]: 0.00037659 [convert_after_rewriter]: 1.398e-05 [order_py_execute_after_rewriter]: 5.04e-06 [mutable_eliminate]: 0.00075667 [jit_opt_b]: 6.228e-05, [1] [Cycle 1]: 3.987e-05, [2] [frontend_op_eliminate]: 1.302e-05 [inline_after_opt_a]: 1.43e-05 [cconv]: 3.349e-05 [loop_unroll]: 0.00049586 [jit_opt_after_cconv]: 0.0001414, [1] [Cycle 1]: 0.0001333, [11] [c_1]: 1.506e-05 [parameter_eliminate]: 2.65002e-06 [updatestate_depend_eliminate]: 5.83002e-06 [updatestate_assign_eliminate]: 2.08002e-06 [updatestate_loads_eliminate]: 2.24999e-06 [cse]: 1.998e-05 [call_graph_tuple_transform]: 1.742e-05 [tuple_list_get_item_eliminator]: 4.42e-06 [none_parameter_eliminate]: 1.82999e-06 [renormalize]: 6.69999e-07 [switch_simplify]: 4.27998e-06 [remove_dup_value]: 1.756e-05 [partial_unused_args_eliminate]: 2.56998e-06 [environ_conv]: 6.39999e-06 [add_recomputation]: 4.361e-05 [cse_after_recomputation]: 2.138e-05, [1] [Cycle 1]: 1.534e-05, [1] [cse]: 8.37e-06 [auto_monad_reorder]: 1.692e-05 [get_jit_bprop_graph]: 2.86999e-06 [rewriter_after_jit_bprop_graph]: 5.37999e-06 [opt_after_jit_grad]: 0.00051592 [symbol_engine_optimizer]: 7.878e-05, [1] [Cycle 1]: 7.155e-05, [6] [build]: 4.36002e-06 [elim_shapecalc]: 7.38e-06 [elim_not_effective]: 1.201e-05 [opt_reshape]: 5.71e-06 [fold_const_symbol]: 8.91997e-06 [renormalize]: 4.49974e-07 [validate]: 4.186e-05 Sums bootstrap : 0.000471s : 0.04% type_inference : 1.206458s : 90.56% event_method : 0.000769s : 0.06% auto_monad : 0.000215s : 0.02% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000064s : 0.00% rewriter_before_opt_a : 0.000176s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000292s : 0.02% jit_opt_a.loop_unroll : 0.000072s : 0.01% jit_opt_a.a_1 : 0.001605s : 0.12% jit_opt_a.with_stream_mark : 0.000060s : 0.00% jit_opt_a.recompute_prepare : 0.000034s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000013s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000010s : 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 : 0.000021s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000018s : 0.00% jit_opt_a.accelerated_algorithm : 0.000019s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000010s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000019s : 0.00% jit_opt_a.merge_forward : 0.000013s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000049s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000035s : 0.00% jit_opt_a.meta_fg_expand : 0.002834s : 0.21% jit_opt_a.replace_old_param : 0.000090s : 0.01% jit_opt_a.inline_without_move : 0.000076s : 0.01% jit_opt_a.renormalize : 0.116110s : 8.72% jit_opt_a.add_forward_monad_depend : 0.000017s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000024s : 0.00% jit_opt_a.cse : 0.000047s : 0.00% jit_opt_a.replace_applicator : 0.000026s : 0.00% py_interpret_to_execute_after_opt_a : 0.000014s : 0.00% rewriter_after_opt_a : 0.000377s : 0.03% convert_after_rewriter : 0.000014s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000757s : 0.06% jit_opt_b.frontend_op_eliminate : 0.000013s : 0.00% jit_opt_b.inline_after_opt_a : 0.000014s : 0.00% cconv : 0.000033s : 0.00% loop_unroll : 0.000496s : 0.04% jit_opt_after_cconv.c_1 : 0.000015s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.cse : 0.000020s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000017s : 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 : 0.000004s : 0.00% remove_dup_value : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000006s : 0.00% add_recomputation : 0.000044s : 0.00% cse_after_recomputation.cse : 0.000008s : 0.00% auto_monad_reorder : 0.000017s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000005s : 0.00% opt_after_jit_grad : 0.000516s : 0.04% symbol_engine_optimizer.build : 0.000004s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000007s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000012s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000006s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000009s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000042s : 0.00% Time group info: ------[substitution.] 0.000575 65 0.40% : 0.000002s : 1: substitution.elim_not_effective 0.32% : 0.000002s : 1: substitution.fold_const_symbol 0.91% : 0.000005s : 1: substitution.graph_param_transform 76.07% : 0.000438s : 13: substitution.inline 4.88% : 0.000028s : 2: substitution.inline_without_move 1.83% : 0.000011s : 9: substitution.j_node_and_user_rematch 0.79% : 0.000005s : 2: substitution.minmaximum_grad 1.59% : 0.000009s : 9: substitution.partial_eliminate 1.51% : 0.000009s : 9: substitution.remove_not_recompute_node 0.70% : 0.000004s : 1: substitution.replace_applicator 1.26% : 0.000007s : 6: substitution.replace_old_param 0.64% : 0.000004s : 1: substitution.set_cell_output_no_recompute 3.69% : 0.000021s : 3: substitution.switch_simplify 1.51% : 0.000009s : 2: substitution.tuple_list_convert_item_index_to_positive 1.10% : 0.000006s : 2: substitution.tuple_list_get_item_depend_reorder 2.81% : 0.000016s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.206304 2 99.55% : 1.200903s : 1: type_inference.infer 0.45% : 0.005402s : 1: type_inference.specialize ------[replace.] 0.000221 17 51.70% : 0.000114s : 13: replace.inline 43.01% : 0.000095s : 3: replace.switch_simplify 5.29% : 0.000012s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000450 17 95.22% : 0.000428s : 13: match.inline 3.98% : 0.000018s : 3: match.switch_simplify 0.80% : 0.000004s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000261 1539 1.53% : 0.000004s : 26: predicate.accumulaten_eliminater 0.39% : 0.000001s : 1: predicate.ad_related_special_op_eliminate 1.30% : 0.000003s : 26: predicate.addn_check_dump 1.74% : 0.000005s : 26: predicate.addn_zero_filter 2.02% : 0.000005s : 26: predicate.arithmetic_simplify 1.44% : 0.000004s : 26: predicate.cast_eliminate 0.11% : 0.000000s : 1: predicate.check_bprop_eliminate 1.30% : 0.000003s : 26: predicate.compare_switch_simplify 1.43% : 0.000004s : 26: predicate.depend_value_elim 1.28% : 0.000003s : 26: predicate.dict_get_item_const_eliminator 1.46% : 0.000004s : 26: predicate.dict_get_item_eliminator 1.42% : 0.000004s : 26: predicate.dict_set_item_eliminator 0.37% : 0.000001s : 1: predicate.dumpgradient_eliminate 0.14% : 0.000000s : 1: predicate.elim_not_effective 0.21% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.47% : 0.000004s : 26: predicate.environ_add_const_eliminate 1.43% : 0.000004s : 26: predicate.environ_get_add_eliminate 1.29% : 0.000003s : 26: predicate.environ_get_depend_swap 1.46% : 0.000004s : 26: predicate.environ_get_eliminate 1.33% : 0.000003s : 26: predicate.environ_get_set_eliminate 0.05% : 0.000000s : 1: predicate.fold_const_symbol 1.00% : 0.000003s : 13: predicate.get_grad_eliminate 0.07% : 0.000000s : 1: predicate.graph_param_transform 4.40% : 0.000011s : 42: predicate.inline 2.52% : 0.000007s : 35: predicate.inline_without_move 0.38% : 0.000001s : 13: predicate.j_node_and_user_rematch 1.17% : 0.000003s : 13: predicate.less_batch_normalization 2.26% : 0.000006s : 27: predicate.list_to_tuple_eliminator_ 1.52% : 0.000004s : 28: predicate.load_eliminater 0.62% : 0.000002s : 1: predicate.loop_unroll_after_grad 4.96% : 0.000013s : 67: predicate.loop_unroll_before_grad 1.70% : 0.000004s : 27: predicate.make_slice_get_slice_eliminator 1.29% : 0.000003s : 26: predicate.merge_addn 1.51% : 0.000004s : 26: predicate.minmaximum_grad 0.61% : 0.000002s : 1: predicate.mutable_eliminate 0.19% : 0.000000s : 1: predicate.opt_reshape 1.92% : 0.000005s : 28: predicate.partial_eliminate 1.36% : 0.000004s : 26: predicate.print_const_string_wrapper 1.96% : 0.000005s : 26: predicate.reduce_eliminate 1.44% : 0.000004s : 27: predicate.redundant_stop_gradient_eliminater 0.59% : 0.000002s : 13: predicate.remove_not_recompute_node 1.52% : 0.000004s : 29: predicate.replace_applicator 1.46% : 0.000004s : 35: predicate.replace_old_param 0.10% : 0.000000s : 1: predicate.reset_defer_inline 1.49% : 0.000004s : 26: predicate.reshape_eliminate 1.47% : 0.000004s : 26: predicate.row_tensor_add_zeros_like 0.40% : 0.000001s : 1: predicate.row_tensor_eliminate 1.39% : 0.000004s : 26: predicate.same_eliminate 0.48% : 0.000001s : 13: predicate.set_cell_output_no_recompute 0.31% : 0.000001s : 2: predicate.special_op_eliminate 1.04% : 0.000003s : 13: predicate.specialize_transform 1.68% : 0.000004s : 26: predicate.split_environ_get_set_with_tuple_value 1.38% : 0.000004s : 26: predicate.stack_unstack_eliminate 0.10% : 0.000000s : 1: predicate.switch_call_monad_eliminater 3.40% : 0.000009s : 40: predicate.switch_defer_inline 2.61% : 0.000007s : 40: predicate.switch_layer_defer_inline 7.92% : 0.000021s : 114: predicate.switch_simplify 1.31% : 0.000003s : 26: predicate.tile_eliminate 1.39% : 0.000004s : 26: predicate.transpose_eliminate 1.69% : 0.000004s : 26: predicate.tuple_list_convert_item_index_to_positive 1.59% : 0.000004s : 26: predicate.tuple_list_get_item_depend_reorder 3.00% : 0.000008s : 29: predicate.tuple_list_get_item_eliminator 1.86% : 0.000005s : 26: predicate.tuple_list_set_item_eliminator 1.55% : 0.000004s : 27: predicate.tuple_to_list_eliminator_ 1.43% : 0.000004s : 28: predicate.updatestate_pure_node_eliminater 2.50% : 0.000007s : 41: predicate.updatestate_useless_node_eliminater 1.90% : 0.000005s : 26: predicate.value_based_eliminate 0.12% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.26% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.003942 37 83.55% : 0.003293s : 21: func_graph_cloner_run.FuncGraphClonerGraph 16.45% : 0.000648s : 16: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.456558 72 0.00% : 0.000047s : 1: add_recomputation 0.02% : 0.000226s : 1: auto_monad 0.00% : 0.000020s : 1: auto_monad_reorder 0.03% : 0.000499s : 1: bootstrap 0.00% : 0.000036s : 1: cconv 0.00% : 0.000017s : 1: convert_after_rewriter 0.00% : 0.000024s : 1: cse_after_recomputation 0.00% : 0.000009s : 1: environ_conv 0.05% : 0.000785s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 8.72% : 0.127062s : 1: jit_opt_a 0.01% : 0.000145s : 1: jit_opt_after_cconv 0.00% : 0.000066s : 1: jit_opt_b 0.03% : 0.000505s : 1: loop_unroll 0.05% : 0.000766s : 1: mutable_eliminate 0.16% : 0.002309s : 26: opt.transform.jit_opt_a 0.00% : 0.000038s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000020s : 4: opt.transform.jit_opt_b 0.00% : 0.000011s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000012s : 1: opt.transform.mutable_eliminate 0.00% : 0.000019s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000030s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000525s : 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% : 0.000007s : 1: pre_auto_parallel 0.00% : 0.000067s : 1: py_interpret_to_execute 0.00% : 0.000016s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000020s : 1: remove_dup_value 7.92% : 0.115329s : 1: renormalize.infer 0.05% : 0.000767s : 1: renormalize.specialize 0.00% : 0.000007s : 1: rewriter_after_jit_bprop_graph 0.03% : 0.000383s : 1: rewriter_after_opt_a 0.01% : 0.000181s : 1: rewriter_before_opt_a 0.01% : 0.000082s : 1: symbol_engine_optimizer 82.83% : 1.206490s : 1: type_inference . [hook] pytest_runtest_teardown:test_squeeze_large_tensors[KBK] tests/st/mint/test_squeeze.py::test_squeeze_large_tensors[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 90.33s (0:01:30) ===================