==================================================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_003/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_unsqueeze.py . [hook] pytest_runtest_teardown:test_unsqueeze_zero_bias[pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_zero_bias[pynative],max_mem:2.0M TotalTime = 0.807831, [30] [bootstrap]: 0.00256519 [type_inference]: 0.666599 [event_method]: 1.91e-05 [auto_monad]: 0.00015134 [graph_reusing]: 6.09999e-06 [pre_auto_parallel]: 1.798e-05 [py_interpret_to_execute]: 4.297e-05 [rewriter_before_opt_a]: 5.998e-05 [expand_dump_flag]: 3.25e-06 [jit_opt_a]: 0.0144797, [2] [Cycle 1]: 0.00218402, [27] [switch_simplify]: 6.37e-05 [loop_unroll]: 2.265e-05 [a_1]: 0.00054207 [with_stream_mark]: 3.547e-05 [recompute_prepare]: 1.288e-05 [updatestate_depend_eliminate]: 6.56e-06 [updatestate_assign_eliminate]: 7.66001e-06 [updatestate_loads_eliminate]: 5.44e-06 [parameter_eliminate]: 2.24001e-06 [specialize_transform]: 9.46e-06 [updatestate_useless_node_eliminater]: 1.253e-05 [accelerated_algorithm]: 8.53001e-06 [meta_shard_fg_expand]: 2.89001e-06 [get_grad_eliminate_]: 7.87e-06 [merge_forward]: 6.29999e-06 [cell_reuse_recompute_pass]: 1.24003e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.369e-05 [j_node_and_user_rematch]: 1.303e-05 [meta_fg_expand]: 3.26001e-06 [replace_old_param]: 1.247e-05 [inline_without_move]: 7.28e-06 [renormalize]: 0.00100905 [add_forward_monad_depend]: 1.367e-05 [auto_monad_grad]: 2.86e-06 [auto_monad_eliminator]: 2.796e-05 [cse]: 5.298e-05 [replace_applicator]: 2.053e-05 [Cycle 2]: 0.00055297, [27] [switch_simplify]: 8.35999e-06 [loop_unroll]: 8.08001e-06 [a_1]: 0.00016467 [with_stream_mark]: 5.599e-05 [recompute_prepare]: 1.129e-05 [updatestate_depend_eliminate]: 7.2e-06 [updatestate_assign_eliminate]: 4.05e-06 [updatestate_loads_eliminate]: 4.18001e-06 [parameter_eliminate]: 1.60999e-06 [specialize_transform]: 8.33001e-06 [updatestate_useless_node_eliminater]: 1.298e-05 [accelerated_algorithm]: 7.99997e-06 [meta_shard_fg_expand]: 1.74e-06 [get_grad_eliminate_]: 6.86999e-06 [merge_forward]: 4.67e-06 [cell_reuse_recompute_pass]: 1.55999e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.649e-05 [j_node_and_user_rematch]: 1.231e-05 [meta_fg_expand]: 3.13e-06 [replace_old_param]: 1.127e-05 [inline_without_move]: 7.38999e-06 [renormalize]: 6.00121e-08 [add_forward_monad_depend]: 1.62999e-06 [auto_monad_grad]: 1.95001e-06 [auto_monad_eliminator]: 1.508e-05 [cse]: 2.152e-05 [replace_applicator]: 9.33002e-06 [py_interpret_to_execute_after_opt_a]: 1.767e-05 [rewriter_after_opt_a]: 0.00061041 [convert_after_rewriter]: 1.866e-05 [order_py_execute_after_rewriter]: 6.97002e-06 [mutable_eliminate]: 0.00079735 [jit_opt_b]: 7.211e-05, [1] [Cycle 1]: 6.203e-05, [2] [frontend_op_eliminate]: 2.428e-05 [inline_after_opt_a]: 2.329e-05 [cconv]: 3.434e-05 [loop_unroll]: 0.00051456 [jit_opt_after_cconv]: 0.00021292, [1] [Cycle 1]: 0.00020463, [11] [c_1]: 4.943e-05 [parameter_eliminate]: 4.37e-06 [updatestate_depend_eliminate]: 1.02e-05 [updatestate_assign_eliminate]: 4.97e-06 [updatestate_loads_eliminate]: 4.23999e-06 [cse]: 3.445e-05 [call_graph_tuple_transform]: 2.175e-05 [tuple_list_get_item_eliminator]: 7.8e-06 [none_parameter_eliminate]: 1.64e-06 [renormalize]: 9.5999e-07 [switch_simplify]: 8.08001e-06 [remove_dup_value]: 1.836e-05 [partial_unused_args_eliminate]: 2.32001e-06 [environ_conv]: 2.113e-05 [add_recomputation]: 8.086e-05 [cse_after_recomputation]: 2.99e-05, [1] [Cycle 1]: 2.3e-05, [1] [cse]: 1.64e-05 [auto_monad_reorder]: 3.606e-05 [get_jit_bprop_graph]: 2.44999e-06 [rewriter_after_jit_bprop_graph]: 0.00012871 [opt_after_jit_grad]: 0.120603 [symbol_engine_optimizer]: 0.00011813, [1] [Cycle 1]: 0.00010541, [6] [build]: 8.08001e-06 [elim_shapecalc]: 1.383e-05 [elim_not_effective]: 2.461e-05 [opt_reshape]: 8.93002e-06 [fold_const_symbol]: 1.492e-05 [renormalize]: 1.55999e-06 [validate]: 8.523e-05 Sums bootstrap : 0.002565s : 0.32% type_inference : 0.666599s : 83.84% event_method : 0.000019s : 0.00% auto_monad : 0.000151s : 0.02% graph_reusing : 0.000006s : 0.00% pre_auto_parallel : 0.000018s : 0.00% py_interpret_to_execute : 0.000043s : 0.01% rewriter_before_opt_a : 0.000060s : 0.01% expand_dump_flag : 0.000003s : 0.00% jit_opt_a.switch_simplify : 0.000072s : 0.01% jit_opt_a.loop_unroll : 0.000031s : 0.00% jit_opt_a.a_1 : 0.000707s : 0.09% jit_opt_a.with_stream_mark : 0.000091s : 0.01% jit_opt_a.recompute_prepare : 0.000024s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000014s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000012s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000010s : 0.00% jit_opt_a.parameter_eliminate : 0.000004s : 0.00% jit_opt_a.specialize_transform : 0.000018s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000026s : 0.00% jit_opt_a.accelerated_algorithm : 0.000017s : 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 : 0.000011s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000003s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000040s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000025s : 0.00% jit_opt_a.meta_fg_expand : 0.000006s : 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 : 0.001009s : 0.13% jit_opt_a.add_forward_monad_depend : 0.000015s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000043s : 0.01% jit_opt_a.cse : 0.000074s : 0.01% jit_opt_a.replace_applicator : 0.000030s : 0.00% py_interpret_to_execute_after_opt_a : 0.000018s : 0.00% rewriter_after_opt_a : 0.000610s : 0.08% convert_after_rewriter : 0.000019s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000797s : 0.10% jit_opt_b.frontend_op_eliminate : 0.000024s : 0.00% jit_opt_b.inline_after_opt_a : 0.000023s : 0.00% cconv : 0.000034s : 0.00% loop_unroll : 0.000515s : 0.06% jit_opt_after_cconv.c_1 : 0.000049s : 0.01% 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 : 0.000034s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000022s : 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 : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000021s : 0.00% add_recomputation : 0.000081s : 0.01% cse_after_recomputation.cse : 0.000016s : 0.00% auto_monad_reorder : 0.000036s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000129s : 0.02% opt_after_jit_grad : 0.120603s : 15.17% symbol_engine_optimizer.build : 0.000008s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000014s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000025s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.00% symbol_engine_optimizer.renormalize : 0.000002s : 0.00% validate : 0.000085s : 0.01% Time group info: ------[substitution.] 0.000276 44 3.62% : 0.000010s : 2: substitution.depend_value_elim 1.07% : 0.000003s : 4: substitution.elim_not_effective 0.76% : 0.000002s : 4: substitution.fold_const_symbol 2.67% : 0.000007s : 5: substitution.graph_param_transform 77.20% : 0.000213s : 3: substitution.inline 1.61% : 0.000004s : 8: substitution.j_node_and_user_rematch 2.53% : 0.000007s : 8: substitution.remove_not_recompute_node 2.24% : 0.000006s : 2: substitution.replace_old_param 4.62% : 0.000013s : 3: substitution.updatestate_pure_node_eliminater 3.67% : 0.000010s : 5: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.666509 2 77.91% : 0.519285s : 1: type_inference.infer 22.09% : 0.147224s : 1: type_inference.specialize ------[replace.] 0.000039 3 100.00% : 0.000039s : 3: replace.inline ------[match.] 0.000210 3 100.00% : 0.000210s : 3: match.inline ------[predicate.] 0.000155 825 1.22% : 0.000002s : 12: predicate.accumulaten_eliminater 3.36% : 0.000005s : 5: predicate.ad_related_special_op_eliminate 1.85% : 0.000003s : 12: predicate.addn_check_dump 1.47% : 0.000002s : 12: predicate.addn_zero_filter 2.13% : 0.000003s : 12: predicate.arithmetic_simplify 1.18% : 0.000002s : 12: predicate.cast_eliminate 0.56% : 0.000001s : 5: predicate.check_bprop_eliminate 1.14% : 0.000002s : 12: predicate.compare_switch_simplify 1.25% : 0.000002s : 12: predicate.depend_value_elim 1.00% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.22% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.20% : 0.000002s : 12: predicate.dict_set_item_eliminator 2.14% : 0.000003s : 5: predicate.dumpgradient_eliminate 0.64% : 0.000001s : 5: predicate.elim_not_effective 0.90% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.19% : 0.000002s : 12: predicate.environ_add_const_eliminate 1.29% : 0.000002s : 12: predicate.environ_get_add_eliminate 1.20% : 0.000002s : 12: predicate.environ_get_depend_swap 1.22% : 0.000002s : 12: predicate.environ_get_eliminate 1.03% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.28% : 0.000000s : 5: predicate.fold_const_symbol 1.31% : 0.000002s : 10: predicate.get_grad_eliminate 0.32% : 0.000000s : 5: predicate.graph_param_transform 4.61% : 0.000007s : 25: predicate.inline 1.11% : 0.000002s : 10: predicate.inline_without_move 0.49% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.48% : 0.000002s : 10: predicate.less_batch_normalization 1.28% : 0.000002s : 12: predicate.list_to_tuple_eliminator_ 1.64% : 0.000003s : 17: predicate.load_eliminater 1.56% : 0.000002s : 5: predicate.loop_unroll_after_grad 3.06% : 0.000005s : 25: predicate.loop_unroll_before_grad 2.48% : 0.000004s : 17: predicate.make_slice_get_slice_eliminator 1.01% : 0.000002s : 12: predicate.merge_addn 1.11% : 0.000002s : 12: predicate.minmaximum_grad 1.98% : 0.000003s : 5: predicate.mutable_eliminate 0.72% : 0.000001s : 5: predicate.opt_reshape 1.98% : 0.000003s : 17: predicate.partial_eliminate 1.11% : 0.000002s : 12: predicate.print_const_string_wrapper 1.47% : 0.000002s : 12: predicate.reduce_eliminate 1.40% : 0.000002s : 12: predicate.redundant_stop_gradient_eliminater 0.83% : 0.000001s : 10: predicate.remove_not_recompute_node 1.45% : 0.000002s : 22: predicate.replace_applicator 0.76% : 0.000001s : 10: predicate.replace_old_param 0.40% : 0.000001s : 5: predicate.reset_defer_inline 1.14% : 0.000002s : 12: predicate.reshape_eliminate 1.17% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.96% : 0.000001s : 5: predicate.row_tensor_eliminate 1.19% : 0.000002s : 12: predicate.same_eliminate 0.69% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.14% : 0.000002s : 10: predicate.special_op_eliminate 1.12% : 0.000002s : 10: predicate.specialize_transform 1.28% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.17% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.69% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.97% : 0.000003s : 15: predicate.switch_defer_inline 1.50% : 0.000002s : 15: predicate.switch_layer_defer_inline 5.62% : 0.000009s : 45: predicate.switch_simplify 1.21% : 0.000002s : 12: predicate.tile_eliminate 1.06% : 0.000002s : 12: predicate.transpose_eliminate 1.31% : 0.000002s : 12: predicate.tuple_list_convert_item_index_to_positive 1.28% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 3.65% : 0.000006s : 22: predicate.tuple_list_get_item_eliminator 1.38% : 0.000002s : 12: predicate.tuple_list_set_item_eliminator 1.61% : 0.000002s : 12: predicate.tuple_to_list_eliminator_ 1.73% : 0.000003s : 17: predicate.updatestate_pure_node_eliminater 3.28% : 0.000005s : 27: predicate.updatestate_useless_node_eliminater 1.43% : 0.000002s : 12: predicate.value_based_eliminate 0.48% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.91% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000343 6 6.47% : 0.000022s : 1: func_graph_cloner_run.FuncGraphClonerGraph 93.53% : 0.000320s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.809735 72 0.01% : 0.000084s : 1: add_recomputation 0.02% : 0.000157s : 1: auto_monad 0.00% : 0.000039s : 1: auto_monad_reorder 0.32% : 0.002605s : 1: bootstrap 0.00% : 0.000037s : 1: cconv 0.00% : 0.000022s : 1: convert_after_rewriter 0.00% : 0.000032s : 1: cse_after_recomputation 0.00% : 0.000024s : 1: environ_conv 0.00% : 0.000025s : 1: event_method 0.00% : 0.000005s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 1.79% : 0.014483s : 1: jit_opt_a 0.03% : 0.000216s : 1: jit_opt_after_cconv 0.01% : 0.000075s : 1: jit_opt_b 0.06% : 0.000525s : 1: loop_unroll 0.10% : 0.000810s : 1: mutable_eliminate 0.12% : 0.000990s : 26: opt.transform.jit_opt_a 0.01% : 0.000084s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000040s : 4: opt.transform.jit_opt_b 0.00% : 0.000017s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000022s : 1: opt.transform.mutable_eliminate 0.01% : 0.000057s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000059s : 4: opt.transform.symbol_engine_opt 14.90% : 0.120631s : 1: opt_after_jit_grad 0.00% : 0.000009s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000021s : 1: pre_auto_parallel 0.01% : 0.000047s : 1: py_interpret_to_execute 0.00% : 0.000020s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000021s : 1: remove_dup_value 0.07% : 0.000563s : 1: renormalize.infer 0.05% : 0.000435s : 1: renormalize.specialize 0.02% : 0.000132s : 1: rewriter_after_jit_bprop_graph 0.08% : 0.000617s : 1: rewriter_after_opt_a 0.01% : 0.000063s : 1: rewriter_before_opt_a 0.02% : 0.000122s : 1: symbol_engine_optimizer 82.33% : 0.666627s : 1: type_inference TotalTime = 1.20064, [30] [bootstrap]: 0.00059628 [type_inference]: 0.506261 [event_method]: 0.00014713 [auto_monad]: 0.00030295 [graph_reusing]: 1.115e-05 [pre_auto_parallel]: 3.74002e-06 [py_interpret_to_execute]: 6.075e-05 [rewriter_before_opt_a]: 0.00015264 [expand_dump_flag]: 4.58001e-06 [jit_opt_a]: 0.529322, [3] [Cycle 1]: 0.520296, [27] [switch_simplify]: 0.00023842 [loop_unroll]: 5.982e-05 [a_1]: 0.00141236 [with_stream_mark]: 4.145e-05 [recompute_prepare]: 2.588e-05 [updatestate_depend_eliminate]: 0.00023168 [updatestate_assign_eliminate]: 1.262e-05 [updatestate_loads_eliminate]: 9.81998e-06 [parameter_eliminate]: 4.55001e-06 [specialize_transform]: 3.484e-05 [updatestate_useless_node_eliminater]: 2.745e-05 [accelerated_algorithm]: 1.826e-05 [meta_shard_fg_expand]: 6.44001e-06 [get_grad_eliminate_]: 1.842e-05 [merge_forward]: 1.351e-05 [cell_reuse_recompute_pass]: 1.83002e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.06e-05 [j_node_and_user_rematch]: 4.555e-05 [meta_fg_expand]: 0.327515 [replace_old_param]: 0.00015514 [inline_without_move]: 0.0002432 [renormalize]: 0.188953 [add_forward_monad_depend]: 3.206e-05 [auto_monad_grad]: 1.205e-05 [auto_monad_eliminator]: 8.957e-05 [cse]: 0.00037832 [replace_applicator]: 0.00024085 [Cycle 2]: 0.00412689, [27] [switch_simplify]: 6.149e-05 [loop_unroll]: 5.646e-05 [a_1]: 0.00136118 [with_stream_mark]: 3.831e-05 [recompute_prepare]: 2.432e-05 [updatestate_depend_eliminate]: 5.357e-05 [updatestate_assign_eliminate]: 7.82e-06 [updatestate_loads_eliminate]: 4.72e-06 [parameter_eliminate]: 3.64002e-06 [specialize_transform]: 1.361e-05 [updatestate_useless_node_eliminater]: 2.047e-05 [accelerated_algorithm]: 1.122e-05 [meta_shard_fg_expand]: 3.40998e-06 [get_grad_eliminate_]: 1.004e-05 [merge_forward]: 6.31e-06 [cell_reuse_recompute_pass]: 1.86998e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.243e-05 [j_node_and_user_rematch]: 1.729e-05 [meta_fg_expand]: 0.00018843 [replace_old_param]: 2.819e-05 [inline_without_move]: 1.068e-05 [renormalize]: 0.00175525 [add_forward_monad_depend]: 1.176e-05 [auto_monad_grad]: 3.44001e-06 [auto_monad_eliminator]: 2.983e-05 [cse]: 4.678e-05 [replace_applicator]: 3.169e-05 [Cycle 3]: 0.00063253, [27] [switch_simplify]: 1.004e-05 [loop_unroll]: 8.89e-06 [a_1]: 0.00018717 [with_stream_mark]: 2.19e-05 [recompute_prepare]: 1.165e-05 [updatestate_depend_eliminate]: 6.54001e-06 [updatestate_assign_eliminate]: 5.52001e-06 [updatestate_loads_eliminate]: 5.12e-06 [parameter_eliminate]: 2.17999e-06 [specialize_transform]: 9.84999e-06 [updatestate_useless_node_eliminater]: 1.211e-05 [accelerated_algorithm]: 9.89999e-06 [meta_shard_fg_expand]: 2.86999e-06 [get_grad_eliminate_]: 8.12e-06 [merge_forward]: 7.43e-06 [cell_reuse_recompute_pass]: 3.30998e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.175e-05 [j_node_and_user_rematch]: 1.507e-05 [meta_fg_expand]: 3.03998e-06 [replace_old_param]: 1.342e-05 [inline_without_move]: 7.97e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 2.92002e-06 [auto_monad_grad]: 2.04999e-06 [auto_monad_eliminator]: 5.232e-05 [cse]: 3.26e-05 [replace_applicator]: 1.186e-05 [py_interpret_to_execute_after_opt_a]: 2.4e-05 [rewriter_after_opt_a]: 0.00029214 [convert_after_rewriter]: 1.778e-05 [order_py_execute_after_rewriter]: 7.7e-06 [mutable_eliminate]: 0.00104134 [jit_opt_b]: 8.067e-05, [1] [Cycle 1]: 6.945e-05, [2] [frontend_op_eliminate]: 2.853e-05 [inline_after_opt_a]: 2.619e-05 [cconv]: 4.534e-05 [loop_unroll]: 0.16035 [jit_opt_after_cconv]: 0.00031049, [1] [Cycle 1]: 0.00029734, [11] [c_1]: 7.817e-05 [parameter_eliminate]: 8.55001e-06 [updatestate_depend_eliminate]: 1.795e-05 [updatestate_assign_eliminate]: 6.96001e-06 [updatestate_loads_eliminate]: 5.25001e-06 [cse]: 6.402e-05 [call_graph_tuple_transform]: 2.821e-05 [tuple_list_get_item_eliminator]: 1.047e-05 [none_parameter_eliminate]: 2.19999e-06 [renormalize]: 3.4002e-07 [switch_simplify]: 9.22999e-06 [remove_dup_value]: 2.858e-05 [partial_unused_args_eliminate]: 2.93e-06 [environ_conv]: 1.102e-05 [add_recomputation]: 8.787e-05 [cse_after_recomputation]: 3.929e-05, [1] [Cycle 1]: 3.141e-05, [1] [cse]: 2.35e-05 [auto_monad_reorder]: 3.273e-05 [get_jit_bprop_graph]: 3.38e-06 [rewriter_after_jit_bprop_graph]: 1.214e-05 [opt_after_jit_grad]: 0.00090028 [symbol_engine_optimizer]: 0.00011553, [1] [Cycle 1]: 0.00010688, [6] [build]: 7.43e-06 [elim_shapecalc]: 1.332e-05 [elim_not_effective]: 2.381e-05 [opt_reshape]: 1.122e-05 [fold_const_symbol]: 1.482e-05 [renormalize]: 6.69999e-07 [validate]: 6.451e-05 Sums bootstrap : 0.000596s : 0.05% type_inference : 0.506261s : 42.36% event_method : 0.000147s : 0.01% auto_monad : 0.000303s : 0.03% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000061s : 0.01% rewriter_before_opt_a : 0.000153s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000310s : 0.03% jit_opt_a.loop_unroll : 0.000125s : 0.01% jit_opt_a.a_1 : 0.002961s : 0.25% jit_opt_a.with_stream_mark : 0.000102s : 0.01% jit_opt_a.recompute_prepare : 0.000062s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000292s : 0.02% jit_opt_a.updatestate_assign_eliminate : 0.000026s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000020s : 0.00% jit_opt_a.parameter_eliminate : 0.000010s : 0.00% jit_opt_a.specialize_transform : 0.000058s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000060s : 0.01% jit_opt_a.accelerated_algorithm : 0.000039s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000013s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000037s : 0.00% jit_opt_a.merge_forward : 0.000027s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000085s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000078s : 0.01% jit_opt_a.meta_fg_expand : 0.327707s : 27.42% jit_opt_a.replace_old_param : 0.000197s : 0.02% jit_opt_a.inline_without_move : 0.000262s : 0.02% jit_opt_a.renormalize : 0.190708s : 15.96% jit_opt_a.add_forward_monad_depend : 0.000047s : 0.00% jit_opt_a.auto_monad_grad : 0.000018s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000172s : 0.01% jit_opt_a.cse : 0.000458s : 0.04% jit_opt_a.replace_applicator : 0.000284s : 0.02% py_interpret_to_execute_after_opt_a : 0.000024s : 0.00% rewriter_after_opt_a : 0.000292s : 0.02% convert_after_rewriter : 0.000018s : 0.00% order_py_execute_after_rewriter : 0.000008s : 0.00% mutable_eliminate : 0.001041s : 0.09% jit_opt_b.frontend_op_eliminate : 0.000029s : 0.00% jit_opt_b.inline_after_opt_a : 0.000026s : 0.00% cconv : 0.000045s : 0.00% loop_unroll : 0.160350s : 13.42% jit_opt_after_cconv.c_1 : 0.000078s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000009s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000018s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000064s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000028s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000010s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000000s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000009s : 0.00% remove_dup_value : 0.000029s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000011s : 0.00% add_recomputation : 0.000088s : 0.01% cse_after_recomputation.cse : 0.000024s : 0.00% auto_monad_reorder : 0.000033s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000012s : 0.00% opt_after_jit_grad : 0.000900s : 0.08% symbol_engine_optimizer.build : 0.000007s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000013s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000024s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000011s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000065s : 0.01% Time group info: ------[substitution.] 0.002156 170 1.31% : 0.000028s : 8: substitution.depend_value_elim 0.14% : 0.000003s : 4: substitution.elim_not_effective 0.13% : 0.000003s : 4: substitution.fold_const_symbol 50.36% : 0.001085s : 4: substitution.getattr_setattr_resolve 0.34% : 0.000007s : 5: substitution.graph_param_transform 30.31% : 0.000653s : 16: substitution.inline 2.30% : 0.000050s : 4: substitution.inline_without_move 1.15% : 0.000025s : 20: substitution.j_node_and_user_rematch 0.44% : 0.000010s : 5: substitution.minmaximum_grad 1.48% : 0.000032s : 9: substitution.partial_eliminate 0.74% : 0.000016s : 20: substitution.remove_not_recompute_node 2.93% : 0.000063s : 12: substitution.replace_applicator 0.90% : 0.000019s : 16: substitution.replace_old_param 0.13% : 0.000003s : 1: substitution.set_cell_output_no_recompute 0.76% : 0.000016s : 3: substitution.switch_simplify 1.00% : 0.000022s : 5: substitution.tuple_list_convert_item_index_to_positive 0.71% : 0.000015s : 5: substitution.tuple_list_get_item_depend_reorder 1.76% : 0.000038s : 8: substitution.tuple_list_get_item_eliminator 1.18% : 0.000025s : 8: substitution.updatestate_pure_node_eliminater 1.92% : 0.000041s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.506132 2 70.11% : 0.354848s : 1: type_inference.infer 29.89% : 0.151283s : 1: type_inference.specialize ------[replace.] 0.000576 27 27.65% : 0.000159s : 3: replace.getattr_setattr_resolve 25.74% : 0.000148s : 16: replace.inline 11.67% : 0.000067s : 1: replace.replace_applicator 13.80% : 0.000080s : 3: replace.switch_simplify 15.96% : 0.000092s : 3: replace.tuple_list_get_item_eliminator 5.18% : 0.000030s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001702 27 58.62% : 0.000998s : 3: match.getattr_setattr_resolve 37.70% : 0.000642s : 16: match.inline 1.33% : 0.000023s : 1: match.replace_applicator 0.82% : 0.000014s : 3: match.switch_simplify 0.58% : 0.000010s : 3: match.tuple_list_get_item_eliminator 0.94% : 0.000016s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000630 3164 1.25% : 0.000008s : 50: predicate.accumulaten_eliminater 0.51% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 1.08% : 0.000007s : 50: predicate.addn_check_dump 1.14% : 0.000007s : 50: predicate.addn_zero_filter 1.82% : 0.000011s : 50: predicate.arithmetic_simplify 1.18% : 0.000007s : 50: predicate.cast_eliminate 0.14% : 0.000001s : 5: predicate.check_bprop_eliminate 1.04% : 0.000007s : 50: predicate.compare_switch_simplify 1.32% : 0.000008s : 50: predicate.depend_value_elim 1.08% : 0.000007s : 50: predicate.dict_get_item_const_eliminator 1.15% : 0.000007s : 50: predicate.dict_get_item_eliminator 1.20% : 0.000008s : 50: predicate.dict_set_item_eliminator 0.42% : 0.000003s : 5: predicate.dumpgradient_eliminate 0.13% : 0.000001s : 5: predicate.elim_not_effective 0.21% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.06% : 0.000007s : 50: predicate.environ_add_const_eliminate 1.04% : 0.000007s : 50: predicate.environ_get_add_eliminate 1.08% : 0.000007s : 50: predicate.environ_get_depend_swap 1.13% : 0.000007s : 50: predicate.environ_get_eliminate 1.06% : 0.000007s : 50: predicate.environ_get_set_eliminate 0.08% : 0.000000s : 5: predicate.fold_const_symbol 0.76% : 0.000005s : 26: predicate.get_grad_eliminate 1.56% : 0.000010s : 20: predicate.getattr_setattr_resolve 0.12% : 0.000001s : 5: predicate.graph_param_transform 3.68% : 0.000023s : 80: predicate.inline 18.45% : 0.000116s : 87: predicate.inline_without_move 0.32% : 0.000002s : 26: predicate.j_node_and_user_rematch 0.92% : 0.000006s : 26: predicate.less_batch_normalization 1.23% : 0.000008s : 53: predicate.list_to_tuple_eliminator_ 1.54% : 0.000010s : 58: predicate.load_eliminater 1.37% : 0.000009s : 5: predicate.loop_unroll_after_grad 3.05% : 0.000019s : 132: predicate.loop_unroll_before_grad 1.48% : 0.000009s : 55: predicate.make_slice_get_slice_eliminator 1.03% : 0.000006s : 50: predicate.merge_addn 1.11% : 0.000007s : 50: predicate.minmaximum_grad 0.74% : 0.000005s : 5: predicate.mutable_eliminate 0.23% : 0.000001s : 5: predicate.opt_reshape 1.62% : 0.000010s : 58: predicate.partial_eliminate 1.13% : 0.000007s : 50: predicate.print_const_string_wrapper 1.59% : 0.000010s : 50: predicate.reduce_eliminate 1.24% : 0.000008s : 53: predicate.redundant_stop_gradient_eliminater 0.41% : 0.000003s : 26: predicate.remove_not_recompute_node 2.16% : 0.000014s : 126: predicate.replace_applicator 1.42% : 0.000009s : 87: predicate.replace_old_param 0.12% : 0.000001s : 5: predicate.reset_defer_inline 1.11% : 0.000007s : 50: predicate.reshape_eliminate 1.21% : 0.000008s : 50: predicate.row_tensor_add_zeros_like 0.40% : 0.000003s : 5: predicate.row_tensor_eliminate 1.12% : 0.000007s : 50: predicate.same_eliminate 0.46% : 0.000003s : 28: predicate.set_cell_output_no_recompute 0.37% : 0.000002s : 10: predicate.special_op_eliminate 0.88% : 0.000006s : 26: predicate.specialize_transform 1.56% : 0.000010s : 50: predicate.split_environ_get_set_with_tuple_value 1.10% : 0.000007s : 50: predicate.stack_unstack_eliminate 0.14% : 0.000001s : 5: predicate.switch_call_monad_eliminater 2.07% : 0.000013s : 70: predicate.switch_defer_inline 1.83% : 0.000012s : 70: predicate.switch_layer_defer_inline 5.54% : 0.000035s : 213: predicate.switch_simplify 1.14% : 0.000007s : 50: predicate.tile_eliminate 1.15% : 0.000007s : 50: predicate.transpose_eliminate 1.32% : 0.000008s : 50: predicate.tuple_list_convert_item_index_to_positive 1.25% : 0.000008s : 50: predicate.tuple_list_get_item_depend_reorder 2.51% : 0.000016s : 63: predicate.tuple_list_get_item_eliminator 1.30% : 0.000008s : 50: predicate.tuple_list_set_item_eliminator 1.17% : 0.000007s : 53: predicate.tuple_to_list_eliminator_ 1.57% : 0.000010s : 58: predicate.updatestate_pure_node_eliminater 2.54% : 0.000016s : 85: predicate.updatestate_useless_node_eliminater 1.51% : 0.000010s : 50: predicate.value_based_eliminate 0.12% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.25% : 0.000002s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.163936 47 99.05% : 0.162374s : 20: func_graph_cloner_run.FuncGraphClonerGraph 0.95% : 0.001562s : 27: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.397575 89 0.01% : 0.000092s : 1: add_recomputation 0.02% : 0.000311s : 1: auto_monad 0.00% : 0.000036s : 1: auto_monad_reorder 0.05% : 0.000630s : 1: bootstrap 0.00% : 0.000049s : 1: cconv 0.00% : 0.000021s : 1: convert_after_rewriter 0.00% : 0.000042s : 1: cse_after_recomputation 0.00% : 0.000015s : 1: environ_conv 0.01% : 0.000157s : 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 37.87% : 0.529328s : 1: jit_opt_a 0.02% : 0.000317s : 1: jit_opt_after_cconv 0.01% : 0.000084s : 1: jit_opt_b 11.48% : 0.160381s : 1: loop_unroll 0.08% : 0.001057s : 1: mutable_eliminate 0.32% : 0.004473s : 39: opt.transform.jit_opt_a 0.01% : 0.000121s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000046s : 4: opt.transform.jit_opt_b 0.00% : 0.000054s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000035s : 1: opt.transform.mutable_eliminate 0.00% : 0.000046s : 1: opt.transform.opt_after_jit_grad 0.11% : 0.001582s : 2: opt.transform.opt_resolve 0.00% : 0.000058s : 4: opt.transform.symbol_engine_opt 0.07% : 0.000914s : 1: opt_after_jit_grad 0.00% : 0.000010s : 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% : 0.000064s : 1: py_interpret_to_execute 0.00% : 0.000027s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000032s : 1: remove_dup_value 13.36% : 0.186698s : 2: renormalize.infer 0.28% : 0.003975s : 2: renormalize.specialize 0.00% : 0.000015s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000300s : 1: rewriter_after_opt_a 0.01% : 0.000157s : 1: rewriter_before_opt_a 0.01% : 0.000119s : 1: symbol_engine_optimizer 36.23% : 0.506287s : 1: type_inference . [hook] pytest_runtest_teardown:test_unsqueeze_zero_bias[KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_zero_bias[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 285.84s (0:04:45) ==================