==================================================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_002/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_non_contiguous[pynative] tests/st/mint/test_squeeze.py::test_squeeze_non_contiguous[pynative],max_mem:2.0M [WARNING] PARSER(166411,ffff8fb56f30,python3.9):2026-01-29-17:41:20.330.425 [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 = 2.11924, [30] [bootstrap]: 0.00073215 [type_inference]: 1.96488 [event_method]: 2.382e-05 [auto_monad]: 0.00013205 [graph_reusing]: 6.98e-06 [pre_auto_parallel]: 1.255e-05 [py_interpret_to_execute]: 0.00062286 [rewriter_before_opt_a]: 0.00012352 [expand_dump_flag]: 4.79002e-06 [jit_opt_a]: 0.148912, [2] [Cycle 1]: 0.138347, [27] [switch_simplify]: 7.956e-05 [loop_unroll]: 2.838e-05 [a_1]: 0.00060869 [with_stream_mark]: 3.451e-05 [recompute_prepare]: 1.092e-05 [updatestate_depend_eliminate]: 4.95999e-06 [updatestate_assign_eliminate]: 3.86999e-06 [updatestate_loads_eliminate]: 3.17002e-06 [parameter_eliminate]: 2.54001e-06 [specialize_transform]: 7.83001e-06 [updatestate_useless_node_eliminater]: 7.19001e-06 [accelerated_algorithm]: 7.26001e-06 [meta_shard_fg_expand]: 3.01001e-06 [get_grad_eliminate_]: 7.56001e-06 [merge_forward]: 4.94e-06 [cell_reuse_recompute_pass]: 1.54e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.199e-05 [j_node_and_user_rematch]: 1.337e-05 [meta_fg_expand]: 3.11001e-06 [replace_old_param]: 1.264e-05 [inline_without_move]: 6.76e-06 [renormalize]: 0.137065 [add_forward_monad_depend]: 2.091e-05 [auto_monad_grad]: 3.06999e-06 [auto_monad_eliminator]: 2.625e-05 [cse]: 5.244e-05 [replace_applicator]: 2.943e-05 [Cycle 2]: 0.00051519, [27] [switch_simplify]: 1.107e-05 [loop_unroll]: 8.06001e-06 [a_1]: 0.00016994 [with_stream_mark]: 1.992e-05 [recompute_prepare]: 8.97e-06 [updatestate_depend_eliminate]: 4.80001e-06 [updatestate_assign_eliminate]: 3.56001e-06 [updatestate_loads_eliminate]: 3.31999e-06 [parameter_eliminate]: 2.41e-06 [specialize_transform]: 8.07998e-06 [updatestate_useless_node_eliminater]: 7.00998e-06 [accelerated_algorithm]: 8.29998e-06 [meta_shard_fg_expand]: 2.43e-06 [get_grad_eliminate_]: 7.55e-06 [merge_forward]: 5.37999e-06 [cell_reuse_recompute_pass]: 3.38e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.567e-05 [j_node_and_user_rematch]: 1.235e-05 [meta_fg_expand]: 2.67001e-06 [replace_old_param]: 1.249e-05 [inline_without_move]: 7.16001e-06 [renormalize]: 9.00181e-08 [add_forward_monad_depend]: 1.87999e-06 [auto_monad_grad]: 1.35999e-06 [auto_monad_eliminator]: 9.57001e-06 [cse]: 1.82e-05 [replace_applicator]: 8.40999e-06 [py_interpret_to_execute_after_opt_a]: 2.247e-05 [rewriter_after_opt_a]: 6.649e-05 [convert_after_rewriter]: 8.74e-06 [order_py_execute_after_rewriter]: 5.91e-06 [mutable_eliminate]: 0.0008752 [jit_opt_b]: 7.448e-05, [1] [Cycle 1]: 6.312e-05, [2] [frontend_op_eliminate]: 2.426e-05 [inline_after_opt_a]: 2.292e-05 [cconv]: 4.688e-05 [loop_unroll]: 0.00057167 [jit_opt_after_cconv]: 0.00031552, [1] [Cycle 1]: 0.00030583, [11] [c_1]: 3.029e-05 [parameter_eliminate]: 7.33e-06 [updatestate_depend_eliminate]: 9.097e-05 [updatestate_assign_eliminate]: 4.89003e-06 [updatestate_loads_eliminate]: 3.75e-06 [cse]: 4.797e-05 [call_graph_tuple_transform]: 3.282e-05 [tuple_list_get_item_eliminator]: 7.88999e-06 [none_parameter_eliminate]: 1.99999e-06 [renormalize]: 1.10999e-06 [switch_simplify]: 7.97998e-06 [remove_dup_value]: 2.156e-05 [partial_unused_args_eliminate]: 2.84999e-06 [environ_conv]: 8.411e-05 [add_recomputation]: 7.622e-05 [cse_after_recomputation]: 3.854e-05, [1] [Cycle 1]: 3.027e-05, [1] [cse]: 2.019e-05 [auto_monad_reorder]: 3.089e-05 [get_jit_bprop_graph]: 2.62001e-06 [rewriter_after_jit_bprop_graph]: 0.0001726 [opt_after_jit_grad]: 0.00077099 [symbol_engine_optimizer]: 0.00010208, [1] [Cycle 1]: 9.256e-05, [6] [build]: 5.59e-06 [elim_shapecalc]: 1.06e-05 [elim_not_effective]: 1.961e-05 [opt_reshape]: 8.76002e-06 [fold_const_symbol]: 1.155e-05 [renormalize]: 1.40001e-06 [validate]: 9.394e-05 Sums bootstrap : 0.000732s : 0.03% type_inference : 1.964884s : 93.20% event_method : 0.000024s : 0.00% auto_monad : 0.000132s : 0.01% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000013s : 0.00% py_interpret_to_execute : 0.000623s : 0.03% rewriter_before_opt_a : 0.000124s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000091s : 0.00% jit_opt_a.loop_unroll : 0.000036s : 0.00% jit_opt_a.a_1 : 0.000779s : 0.04% jit_opt_a.with_stream_mark : 0.000054s : 0.00% jit_opt_a.recompute_prepare : 0.000020s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000010s : 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 : 0.000016s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000014s : 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 : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000048s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000026s : 0.00% jit_opt_a.meta_fg_expand : 0.000006s : 0.00% jit_opt_a.replace_old_param : 0.000025s : 0.00% jit_opt_a.inline_without_move : 0.000014s : 0.00% jit_opt_a.renormalize : 0.137065s : 6.50% jit_opt_a.add_forward_monad_depend : 0.000023s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000036s : 0.00% jit_opt_a.cse : 0.000071s : 0.00% jit_opt_a.replace_applicator : 0.000038s : 0.00% py_interpret_to_execute_after_opt_a : 0.000022s : 0.00% rewriter_after_opt_a : 0.000066s : 0.00% convert_after_rewriter : 0.000009s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.000875s : 0.04% jit_opt_b.frontend_op_eliminate : 0.000024s : 0.00% jit_opt_b.inline_after_opt_a : 0.000023s : 0.00% cconv : 0.000047s : 0.00% loop_unroll : 0.000572s : 0.03% jit_opt_after_cconv.c_1 : 0.000030s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000091s : 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 : 0.000048s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000033s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000008s : 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 : 0.000008s : 0.00% remove_dup_value : 0.000022s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000084s : 0.00% add_recomputation : 0.000076s : 0.00% cse_after_recomputation.cse : 0.000020s : 0.00% auto_monad_reorder : 0.000031s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000173s : 0.01% opt_after_jit_grad : 0.000771s : 0.04% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000020s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000012s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000094s : 0.00% Time group info: ------[substitution.] 0.000247 23 1.22% : 0.000003s : 2: substitution.elim_not_effective 0.86% : 0.000002s : 2: substitution.fold_const_symbol 2.94% : 0.000007s : 4: substitution.graph_param_transform 81.91% : 0.000202s : 4: substitution.inline 2.21% : 0.000005s : 4: substitution.j_node_and_user_rematch 2.71% : 0.000007s : 4: substitution.remove_not_recompute_node 3.36% : 0.000008s : 2: substitution.replace_old_param 4.79% : 0.000012s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.964754 2 95.95% : 1.885257s : 1: type_inference.infer 4.05% : 0.079497s : 1: type_inference.specialize ------[replace.] 0.000060 5 84.54% : 0.000051s : 4: replace.inline 15.46% : 0.000009s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000210 5 94.92% : 0.000200s : 4: match.inline 5.08% : 0.000011s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000170 801 1.16% : 0.000002s : 12: predicate.accumulaten_eliminater 1.53% : 0.000003s : 4: predicate.ad_related_special_op_eliminate 0.98% : 0.000002s : 12: predicate.addn_check_dump 1.47% : 0.000003s : 12: predicate.addn_zero_filter 2.14% : 0.000004s : 12: predicate.arithmetic_simplify 1.19% : 0.000002s : 12: predicate.cast_eliminate 0.40% : 0.000001s : 4: predicate.check_bprop_eliminate 1.05% : 0.000002s : 12: predicate.compare_switch_simplify 1.09% : 0.000002s : 12: predicate.depend_value_elim 1.21% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.13% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.18% : 0.000002s : 12: predicate.dict_set_item_eliminator 1.26% : 0.000002s : 4: predicate.dumpgradient_eliminate 0.41% : 0.000001s : 4: predicate.elim_not_effective 0.51% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 1.44% : 0.000002s : 12: predicate.environ_add_const_eliminate 1.18% : 0.000002s : 12: predicate.environ_get_add_eliminate 1.36% : 0.000002s : 12: predicate.environ_get_depend_swap 1.39% : 0.000002s : 12: predicate.environ_get_eliminate 1.24% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.23% : 0.000000s : 4: predicate.fold_const_symbol 1.07% : 0.000002s : 8: predicate.get_grad_eliminate 0.22% : 0.000000s : 4: predicate.graph_param_transform 6.33% : 0.000011s : 25: predicate.inline 0.93% : 0.000002s : 8: predicate.inline_without_move 0.39% : 0.000001s : 8: predicate.j_node_and_user_rematch 1.25% : 0.000002s : 8: predicate.less_batch_normalization 1.36% : 0.000002s : 13: predicate.list_to_tuple_eliminator_ 1.69% : 0.000003s : 17: predicate.load_eliminater 1.19% : 0.000002s : 4: predicate.loop_unroll_after_grad 2.85% : 0.000005s : 28: predicate.loop_unroll_before_grad 1.96% : 0.000003s : 16: predicate.make_slice_get_slice_eliminator 1.01% : 0.000002s : 12: predicate.merge_addn 1.00% : 0.000002s : 12: predicate.minmaximum_grad 2.86% : 0.000005s : 4: predicate.mutable_eliminate 0.51% : 0.000001s : 4: predicate.opt_reshape 2.06% : 0.000003s : 17: predicate.partial_eliminate 1.07% : 0.000002s : 12: predicate.print_const_string_wrapper 1.86% : 0.000003s : 12: predicate.reduce_eliminate 1.68% : 0.000003s : 13: predicate.redundant_stop_gradient_eliminater 0.59% : 0.000001s : 8: predicate.remove_not_recompute_node 1.84% : 0.000003s : 21: predicate.replace_applicator 0.91% : 0.000002s : 8: predicate.replace_old_param 0.43% : 0.000001s : 4: predicate.reset_defer_inline 3.16% : 0.000005s : 12: predicate.reshape_eliminate 1.11% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.98% : 0.000002s : 4: predicate.row_tensor_eliminate 1.00% : 0.000002s : 12: predicate.same_eliminate 0.60% : 0.000001s : 8: predicate.set_cell_output_no_recompute 1.05% : 0.000002s : 8: predicate.special_op_eliminate 1.08% : 0.000002s : 8: predicate.specialize_transform 1.29% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.16% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.47% : 0.000001s : 4: predicate.switch_call_monad_eliminater 1.87% : 0.000003s : 17: predicate.switch_defer_inline 1.89% : 0.000003s : 17: predicate.switch_layer_defer_inline 6.45% : 0.000011s : 49: predicate.switch_simplify 1.24% : 0.000002s : 12: predicate.tile_eliminate 1.04% : 0.000002s : 12: predicate.transpose_eliminate 1.52% : 0.000003s : 12: predicate.tuple_list_convert_item_index_to_positive 1.84% : 0.000003s : 12: predicate.tuple_list_get_item_depend_reorder 3.75% : 0.000006s : 21: predicate.tuple_list_get_item_eliminator 1.72% : 0.000003s : 12: predicate.tuple_list_set_item_eliminator 1.23% : 0.000002s : 13: predicate.tuple_to_list_eliminator_ 1.73% : 0.000003s : 17: predicate.updatestate_pure_node_eliminater 2.66% : 0.000005s : 25: predicate.updatestate_useless_node_eliminater 1.64% : 0.000003s : 12: predicate.value_based_eliminate 0.38% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.52% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.002821 18 58.64% : 0.001654s : 12: func_graph_cloner_run.FuncGraphClonerGraph 41.36% : 0.001167s : 6: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 2.257319 72 0.00% : 0.000081s : 1: add_recomputation 0.01% : 0.000137s : 1: auto_monad 0.00% : 0.000034s : 1: auto_monad_reorder 0.03% : 0.000761s : 1: bootstrap 0.00% : 0.000050s : 1: cconv 0.00% : 0.000011s : 1: convert_after_rewriter 0.00% : 0.000041s : 1: cse_after_recomputation 0.00% : 0.000088s : 1: environ_conv 0.00% : 0.000032s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000010s : 1: graph_reusing 6.60% : 0.148915s : 1: jit_opt_a 0.01% : 0.000319s : 1: jit_opt_after_cconv 0.00% : 0.000079s : 1: jit_opt_b 0.03% : 0.000586s : 1: loop_unroll 0.04% : 0.000893s : 1: mutable_eliminate 0.05% : 0.001078s : 26: opt.transform.jit_opt_a 0.00% : 0.000075s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000038s : 4: opt.transform.jit_opt_b 0.00% : 0.000018s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000027s : 1: opt.transform.mutable_eliminate 0.00% : 0.000037s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000046s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000791s : 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 0.03% : 0.000631s : 1: py_interpret_to_execute 0.00% : 0.000025s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000025s : 1: remove_dup_value 6.00% : 0.135549s : 1: renormalize.infer 0.07% : 0.001497s : 1: renormalize.specialize 0.01% : 0.000177s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000070s : 1: rewriter_after_opt_a 0.01% : 0.000131s : 1: rewriter_before_opt_a 0.00% : 0.000105s : 1: symbol_engine_optimizer 87.05% : 1.964920s : 1: type_inference TotalTime = 1.67445, [30] [bootstrap]: 0.00060402 [type_inference]: 1.44697 [event_method]: 0.00077681 [auto_monad]: 0.00020441 [graph_reusing]: 1.115e-05 [pre_auto_parallel]: 4.04002e-06 [py_interpret_to_execute]: 5.886e-05 [rewriter_before_opt_a]: 0.00019016 [expand_dump_flag]: 4.37998e-06 [jit_opt_a]: 0.222375, [2] [Cycle 1]: 0.216146, [27] [switch_simplify]: 0.00025479 [loop_unroll]: 6.547e-05 [a_1]: 0.00155483 [with_stream_mark]: 4.25e-05 [recompute_prepare]: 3.454e-05 [updatestate_depend_eliminate]: 1.169e-05 [updatestate_assign_eliminate]: 7.18998e-06 [updatestate_loads_eliminate]: 7.06001e-06 [parameter_eliminate]: 4.23999e-06 [specialize_transform]: 1.963e-05 [updatestate_useless_node_eliminater]: 1.603e-05 [accelerated_algorithm]: 1.675e-05 [meta_shard_fg_expand]: 5.03002e-06 [get_grad_eliminate_]: 1.485e-05 [merge_forward]: 9.92001e-06 [cell_reuse_recompute_pass]: 1.79e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.541e-05 [j_node_and_user_rematch]: 3.001e-05 [meta_fg_expand]: 0.00250102 [replace_old_param]: 8.176e-05 [inline_without_move]: 6.984e-05 [renormalize]: 0.210921 [add_forward_monad_depend]: 2.069e-05 [auto_monad_grad]: 3.01999e-06 [auto_monad_eliminator]: 2.372e-05 [cse]: 3.834e-05 [replace_applicator]: 2.929e-05 [Cycle 2]: 0.00053791, [27] [switch_simplify]: 7.55e-06 [loop_unroll]: 5.88002e-06 [a_1]: 7e-05 [with_stream_mark]: 1.843e-05 [recompute_prepare]: 5.60001e-06 [updatestate_depend_eliminate]: 3.83001e-06 [updatestate_assign_eliminate]: 2.53e-06 [updatestate_loads_eliminate]: 2.27001e-06 [parameter_eliminate]: 1.82001e-06 [specialize_transform]: 4.27e-06 [updatestate_useless_node_eliminater]: 4.48001e-06 [accelerated_algorithm]: 4.43001e-06 [meta_shard_fg_expand]: 2.27999e-06 [get_grad_eliminate_]: 4.51002e-06 [merge_forward]: 3.43999e-06 [cell_reuse_recompute_pass]: 3.68e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.975e-05 [j_node_and_user_rematch]: 8.43001e-06 [meta_fg_expand]: 0.00017606 [replace_old_param]: 9.19e-06 [inline_without_move]: 4.72998e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 3.06999e-06 [auto_monad_grad]: 1.66998e-06 [auto_monad_eliminator]: 8.10999e-06 [cse]: 1.672e-05 [replace_applicator]: 4.92e-06 [py_interpret_to_execute_after_opt_a]: 2.091e-05 [rewriter_after_opt_a]: 0.00039631 [convert_after_rewriter]: 1.513e-05 [order_py_execute_after_rewriter]: 5.49e-06 [mutable_eliminate]: 0.00087197 [jit_opt_b]: 5.791e-05, [1] [Cycle 1]: 4.818e-05, [2] [frontend_op_eliminate]: 1.572e-05 [inline_after_opt_a]: 1.85e-05 [cconv]: 3.925e-05 [loop_unroll]: 0.00052604 [jit_opt_after_cconv]: 0.00019564, [1] [Cycle 1]: 0.00018655, [11] [c_1]: 1.91e-05 [parameter_eliminate]: 5.15999e-06 [updatestate_depend_eliminate]: 9.25001e-06 [updatestate_assign_eliminate]: 4.93001e-06 [updatestate_loads_eliminate]: 2.48e-06 [cse]: 3.53e-05 [call_graph_tuple_transform]: 2.266e-05 [tuple_list_get_item_eliminator]: 5.46e-06 [none_parameter_eliminate]: 1.49e-06 [renormalize]: 1.36998e-06 [switch_simplify]: 6.48e-06 [remove_dup_value]: 1.801e-05 [partial_unused_args_eliminate]: 2.91e-06 [environ_conv]: 6.63e-06 [add_recomputation]: 4.886e-05 [cse_after_recomputation]: 2.601e-05, [1] [Cycle 1]: 1.895e-05, [1] [cse]: 1.156e-05 [auto_monad_reorder]: 1.522e-05 [get_jit_bprop_graph]: 2.79001e-06 [rewriter_after_jit_bprop_graph]: 1.162e-05 [opt_after_jit_grad]: 0.00058423 [symbol_engine_optimizer]: 8.429e-05, [1] [Cycle 1]: 7.687e-05, [6] [build]: 4.99e-06 [elim_shapecalc]: 7.38e-06 [elim_not_effective]: 1.43e-05 [opt_reshape]: 7.19001e-06 [fold_const_symbol]: 7.92e-06 [renormalize]: 5.59987e-07 [validate]: 4.685e-05 Sums bootstrap : 0.000604s : 0.04% type_inference : 1.446971s : 86.76% event_method : 0.000777s : 0.05% auto_monad : 0.000204s : 0.01% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000059s : 0.00% rewriter_before_opt_a : 0.000190s : 0.01% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000262s : 0.02% jit_opt_a.loop_unroll : 0.000071s : 0.00% jit_opt_a.a_1 : 0.001625s : 0.10% jit_opt_a.with_stream_mark : 0.000061s : 0.00% jit_opt_a.recompute_prepare : 0.000040s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000016s : 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 : 0.000024s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000021s : 0.00% jit_opt_a.accelerated_algorithm : 0.000021s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000007s : 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 : 0.000055s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000038s : 0.00% jit_opt_a.meta_fg_expand : 0.002677s : 0.16% jit_opt_a.replace_old_param : 0.000091s : 0.01% jit_opt_a.inline_without_move : 0.000075s : 0.00% jit_opt_a.renormalize : 0.210922s : 12.65% jit_opt_a.add_forward_monad_depend : 0.000024s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000032s : 0.00% jit_opt_a.cse : 0.000055s : 0.00% jit_opt_a.replace_applicator : 0.000034s : 0.00% py_interpret_to_execute_after_opt_a : 0.000021s : 0.00% rewriter_after_opt_a : 0.000396s : 0.02% convert_after_rewriter : 0.000015s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000872s : 0.05% jit_opt_b.frontend_op_eliminate : 0.000016s : 0.00% jit_opt_b.inline_after_opt_a : 0.000019s : 0.00% cconv : 0.000039s : 0.00% loop_unroll : 0.000526s : 0.03% jit_opt_after_cconv.c_1 : 0.000019s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.cse : 0.000035s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000023s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000005s : 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 : 0.000006s : 0.00% remove_dup_value : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000007s : 0.00% add_recomputation : 0.000049s : 0.00% cse_after_recomputation.cse : 0.000012s : 0.00% auto_monad_reorder : 0.000015s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000012s : 0.00% opt_after_jit_grad : 0.000584s : 0.04% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000007s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000014s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000007s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000008s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000047s : 0.00% Time group info: ------[substitution.] 0.000520 65 0.40% : 0.000002s : 1: substitution.elim_not_effective 0.25% : 0.000001s : 1: substitution.fold_const_symbol 1.14% : 0.000006s : 1: substitution.graph_param_transform 74.07% : 0.000385s : 13: substitution.inline 4.92% : 0.000026s : 2: substitution.inline_without_move 1.80% : 0.000009s : 9: substitution.j_node_and_user_rematch 0.80% : 0.000004s : 2: substitution.minmaximum_grad 1.82% : 0.000009s : 9: substitution.partial_eliminate 1.90% : 0.000010s : 9: substitution.remove_not_recompute_node 0.61% : 0.000003s : 1: substitution.replace_applicator 1.37% : 0.000007s : 6: substitution.replace_old_param 0.94% : 0.000005s : 1: substitution.set_cell_output_no_recompute 3.54% : 0.000018s : 3: substitution.switch_simplify 1.70% : 0.000009s : 2: substitution.tuple_list_convert_item_index_to_positive 1.28% : 0.000007s : 2: substitution.tuple_list_get_item_depend_reorder 3.45% : 0.000018s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.446817 2 99.63% : 1.441440s : 1: type_inference.infer 0.37% : 0.005377s : 1: type_inference.specialize ------[replace.] 0.000222 17 58.46% : 0.000130s : 13: replace.inline 36.39% : 0.000081s : 3: replace.switch_simplify 5.15% : 0.000011s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000397 17 94.85% : 0.000377s : 13: match.inline 4.02% : 0.000016s : 3: match.switch_simplify 1.14% : 0.000005s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000279 1539 1.41% : 0.000004s : 26: predicate.accumulaten_eliminater 0.54% : 0.000002s : 1: predicate.ad_related_special_op_eliminate 1.39% : 0.000004s : 26: predicate.addn_check_dump 1.66% : 0.000005s : 26: predicate.addn_zero_filter 2.28% : 0.000006s : 26: predicate.arithmetic_simplify 1.42% : 0.000004s : 26: predicate.cast_eliminate 0.18% : 0.000000s : 1: predicate.check_bprop_eliminate 1.24% : 0.000003s : 26: predicate.compare_switch_simplify 1.35% : 0.000004s : 26: predicate.depend_value_elim 1.30% : 0.000004s : 26: predicate.dict_get_item_const_eliminator 1.34% : 0.000004s : 26: predicate.dict_get_item_eliminator 1.35% : 0.000004s : 26: predicate.dict_set_item_eliminator 0.33% : 0.000001s : 1: predicate.dumpgradient_eliminate 0.08% : 0.000000s : 1: predicate.elim_not_effective 0.26% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.52% : 0.000004s : 26: predicate.environ_add_const_eliminate 1.37% : 0.000004s : 26: predicate.environ_get_add_eliminate 1.26% : 0.000004s : 26: predicate.environ_get_depend_swap 1.44% : 0.000004s : 26: predicate.environ_get_eliminate 1.27% : 0.000004s : 26: predicate.environ_get_set_eliminate 0.04% : 0.000000s : 1: predicate.fold_const_symbol 0.95% : 0.000003s : 13: predicate.get_grad_eliminate 0.11% : 0.000000s : 1: predicate.graph_param_transform 5.23% : 0.000015s : 42: predicate.inline 2.60% : 0.000007s : 35: predicate.inline_without_move 0.44% : 0.000001s : 13: predicate.j_node_and_user_rematch 1.34% : 0.000004s : 13: predicate.less_batch_normalization 1.46% : 0.000004s : 27: predicate.list_to_tuple_eliminator_ 1.66% : 0.000005s : 28: predicate.load_eliminater 0.60% : 0.000002s : 1: predicate.loop_unroll_after_grad 3.86% : 0.000011s : 67: predicate.loop_unroll_before_grad 2.00% : 0.000006s : 27: predicate.make_slice_get_slice_eliminator 1.25% : 0.000003s : 26: predicate.merge_addn 1.30% : 0.000004s : 26: predicate.minmaximum_grad 1.04% : 0.000003s : 1: predicate.mutable_eliminate 0.14% : 0.000000s : 1: predicate.opt_reshape 2.13% : 0.000006s : 28: predicate.partial_eliminate 1.48% : 0.000004s : 26: predicate.print_const_string_wrapper 1.67% : 0.000005s : 26: predicate.reduce_eliminate 1.54% : 0.000004s : 27: predicate.redundant_stop_gradient_eliminater 0.55% : 0.000002s : 13: predicate.remove_not_recompute_node 1.49% : 0.000004s : 29: predicate.replace_applicator 1.38% : 0.000004s : 35: predicate.replace_old_param 0.23% : 0.000001s : 1: predicate.reset_defer_inline 1.35% : 0.000004s : 26: predicate.reshape_eliminate 1.69% : 0.000005s : 26: predicate.row_tensor_add_zeros_like 0.35% : 0.000001s : 1: predicate.row_tensor_eliminate 1.58% : 0.000004s : 26: predicate.same_eliminate 0.49% : 0.000001s : 13: predicate.set_cell_output_no_recompute 0.66% : 0.000002s : 2: predicate.special_op_eliminate 0.99% : 0.000003s : 13: predicate.specialize_transform 1.65% : 0.000005s : 26: predicate.split_environ_get_set_with_tuple_value 1.31% : 0.000004s : 26: predicate.stack_unstack_eliminate 0.10% : 0.000000s : 1: predicate.switch_call_monad_eliminater 2.72% : 0.000008s : 40: predicate.switch_defer_inline 2.31% : 0.000006s : 40: predicate.switch_layer_defer_inline 7.72% : 0.000022s : 114: predicate.switch_simplify 1.35% : 0.000004s : 26: predicate.tile_eliminate 1.47% : 0.000004s : 26: predicate.transpose_eliminate 1.72% : 0.000005s : 26: predicate.tuple_list_convert_item_index_to_positive 1.68% : 0.000005s : 26: predicate.tuple_list_get_item_depend_reorder 3.15% : 0.000009s : 29: predicate.tuple_list_get_item_eliminator 2.04% : 0.000006s : 26: predicate.tuple_list_set_item_eliminator 1.61% : 0.000005s : 27: predicate.tuple_to_list_eliminator_ 1.58% : 0.000004s : 28: predicate.updatestate_pure_node_eliminater 2.86% : 0.000008s : 41: predicate.updatestate_useless_node_eliminater 1.66% : 0.000005s : 26: predicate.value_based_eliminate 0.12% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.35% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.004457 37 80.92% : 0.003606s : 21: func_graph_cloner_run.FuncGraphClonerGraph 19.08% : 0.000850s : 16: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.887709 72 0.00% : 0.000053s : 1: add_recomputation 0.01% : 0.000217s : 1: auto_monad 0.00% : 0.000018s : 1: auto_monad_reorder 0.03% : 0.000628s : 1: bootstrap 0.00% : 0.000042s : 1: cconv 0.00% : 0.000019s : 1: convert_after_rewriter 0.00% : 0.000029s : 1: cse_after_recomputation 0.00% : 0.000009s : 1: environ_conv 0.04% : 0.000792s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000006s : 1: get_jit_bprop_graph 0.00% : 0.000014s : 1: graph_reusing 11.78% : 0.222380s : 1: jit_opt_a 0.01% : 0.000199s : 1: jit_opt_after_cconv 0.00% : 0.000061s : 1: jit_opt_b 0.03% : 0.000536s : 1: loop_unroll 0.05% : 0.000887s : 1: mutable_eliminate 0.12% : 0.002324s : 26: opt.transform.jit_opt_a 0.00% : 0.000049s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000024s : 4: opt.transform.jit_opt_b 0.00% : 0.000013s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000022s : 1: opt.transform.mutable_eliminate 0.00% : 0.000022s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000032s : 4: opt.transform.symbol_engine_opt 0.03% : 0.000596s : 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% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000062s : 1: py_interpret_to_execute 0.00% : 0.000024s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000021s : 1: remove_dup_value 11.12% : 0.209911s : 1: renormalize.infer 0.05% : 0.000984s : 1: renormalize.specialize 0.00% : 0.000014s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000406s : 1: rewriter_after_opt_a 0.01% : 0.000194s : 1: rewriter_before_opt_a 0.00% : 0.000087s : 1: symbol_engine_optimizer 76.65% : 1.447006s : 1: type_inference . [hook] pytest_runtest_teardown:test_squeeze_non_contiguous[KBK] tests/st/mint/test_squeeze.py::test_squeeze_non_contiguous[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 279.83s (0:04:39) ==================