==================================================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_unsqueeze.py TotalTime = 0.841097, [30] [bootstrap]: 0.00065883 [type_inference]: 0.234707 [event_method]: 1.677e-05 [auto_monad]: 0.00016211 [graph_reusing]: 6.79001e-06 [pre_auto_parallel]: 1.301e-05 [py_interpret_to_execute]: 3.972e-05 [rewriter_before_opt_a]: 7.94e-05 [expand_dump_flag]: 3.39001e-06 [jit_opt_a]: 0.324102, [2] [Cycle 1]: 0.316038, [27] [switch_simplify]: 5.988e-05 [loop_unroll]: 2.332e-05 [a_1]: 0.00047632 [with_stream_mark]: 2.868e-05 [recompute_prepare]: 1.315e-05 [updatestate_depend_eliminate]: 6.86999e-06 [updatestate_assign_eliminate]: 7.55998e-06 [updatestate_loads_eliminate]: 5.00001e-06 [parameter_eliminate]: 2.32999e-06 [specialize_transform]: 8.23999e-06 [updatestate_useless_node_eliminater]: 1.067e-05 [accelerated_algorithm]: 8.02998e-06 [meta_shard_fg_expand]: 2.56998e-06 [get_grad_eliminate_]: 7.28e-06 [merge_forward]: 5.17e-06 [cell_reuse_recompute_pass]: 1.19e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.183e-05 [j_node_and_user_rematch]: 1.336e-05 [meta_fg_expand]: 3.51001e-06 [replace_old_param]: 1.226e-05 [inline_without_move]: 7.38999e-06 [renormalize]: 0.31487 [add_forward_monad_depend]: 1.951e-05 [auto_monad_grad]: 2.59001e-06 [auto_monad_eliminator]: 3.33e-05 [cse]: 8.377e-05 [replace_applicator]: 3.118e-05 [Cycle 2]: 0.00056201, [27] [switch_simplify]: 1.095e-05 [loop_unroll]: 8.15e-06 [a_1]: 0.00018398 [with_stream_mark]: 1.987e-05 [recompute_prepare]: 9.01998e-06 [updatestate_depend_eliminate]: 5.80002e-06 [updatestate_assign_eliminate]: 5.48002e-06 [updatestate_loads_eliminate]: 4.75999e-06 [parameter_eliminate]: 1.79998e-06 [specialize_transform]: 7.85e-06 [updatestate_useless_node_eliminater]: 1.036e-05 [accelerated_algorithm]: 8.51002e-06 [meta_shard_fg_expand]: 2.68e-06 [get_grad_eliminate_]: 8.32e-06 [merge_forward]: 6.47001e-06 [cell_reuse_recompute_pass]: 3.89997e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.877e-05 [j_node_and_user_rematch]: 1.355e-05 [meta_fg_expand]: 3.12002e-06 [replace_old_param]: 1.238e-05 [inline_without_move]: 7.88999e-06 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 2.15002e-06 [auto_monad_grad]: 1.48002e-06 [auto_monad_eliminator]: 2.468e-05 [cse]: 1.64e-05 [replace_applicator]: 7.97e-06 [py_interpret_to_execute_after_opt_a]: 2.341e-05 [rewriter_after_opt_a]: 0.00060714 [convert_after_rewriter]: 1.503e-05 [order_py_execute_after_rewriter]: 6.53e-06 [mutable_eliminate]: 0.000727 [jit_opt_b]: 7.1e-05, [1] [Cycle 1]: 6.193e-05, [2] [frontend_op_eliminate]: 2.486e-05 [inline_after_opt_a]: 2.358e-05 [cconv]: 4.061e-05 [loop_unroll]: 0.00045396 [jit_opt_after_cconv]: 0.00024772, [1] [Cycle 1]: 0.00023966, [11] [c_1]: 5.043e-05 [parameter_eliminate]: 4.23999e-06 [updatestate_depend_eliminate]: 9.76998e-06 [updatestate_assign_eliminate]: 5.00999e-06 [updatestate_loads_eliminate]: 4.79e-06 [cse]: 5.014e-05 [call_graph_tuple_transform]: 3.707e-05 [tuple_list_get_item_eliminator]: 8.89e-06 [none_parameter_eliminate]: 2.29999e-06 [renormalize]: 7.30011e-07 [switch_simplify]: 9.00999e-06 [remove_dup_value]: 2.016e-05 [partial_unused_args_eliminate]: 2.76999e-06 [environ_conv]: 2.026e-05 [add_recomputation]: 8.104e-05 [cse_after_recomputation]: 4.383e-05, [1] [Cycle 1]: 3.776e-05, [1] [cse]: 3.009e-05 [auto_monad_reorder]: 4.086e-05 [get_jit_bprop_graph]: 2.79999e-06 [rewriter_after_jit_bprop_graph]: 5.10001e-06 [opt_after_jit_grad]: 0.00055219 [symbol_engine_optimizer]: 9.81e-05, [1] [Cycle 1]: 9.115e-05, [6] [build]: 7.35e-06 [elim_shapecalc]: 1.122e-05 [elim_not_effective]: 1.875e-05 [opt_reshape]: 8.51002e-06 [fold_const_symbol]: 1.466e-05 [renormalize]: 7.50006e-07 [validate]: 7.927e-05 Sums bootstrap : 0.000659s : 0.12% type_inference : 0.234707s : 42.30% event_method : 0.000017s : 0.00% auto_monad : 0.000162s : 0.03% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000013s : 0.00% py_interpret_to_execute : 0.000040s : 0.01% rewriter_before_opt_a : 0.000079s : 0.01% expand_dump_flag : 0.000003s : 0.00% jit_opt_a.switch_simplify : 0.000071s : 0.01% jit_opt_a.loop_unroll : 0.000031s : 0.01% jit_opt_a.a_1 : 0.000660s : 0.12% jit_opt_a.with_stream_mark : 0.000049s : 0.01% jit_opt_a.recompute_prepare : 0.000022s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000013s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000013s : 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 : 0.000016s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000021s : 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_ : 0.000016s : 0.00% jit_opt_a.merge_forward : 0.000012s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000041s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000027s : 0.00% jit_opt_a.meta_fg_expand : 0.000007s : 0.00% jit_opt_a.replace_old_param : 0.000025s : 0.00% jit_opt_a.inline_without_move : 0.000015s : 0.00% jit_opt_a.renormalize : 0.314870s : 56.75% jit_opt_a.add_forward_monad_depend : 0.000022s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000058s : 0.01% jit_opt_a.cse : 0.000100s : 0.02% jit_opt_a.replace_applicator : 0.000039s : 0.01% py_interpret_to_execute_after_opt_a : 0.000023s : 0.00% rewriter_after_opt_a : 0.000607s : 0.11% convert_after_rewriter : 0.000015s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000727s : 0.13% jit_opt_b.frontend_op_eliminate : 0.000025s : 0.00% jit_opt_b.inline_after_opt_a : 0.000024s : 0.00% cconv : 0.000041s : 0.01% loop_unroll : 0.000454s : 0.08% jit_opt_after_cconv.c_1 : 0.000050s : 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 : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000050s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000037s : 0.01% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000009s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000009s : 0.00% remove_dup_value : 0.000020s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000020s : 0.00% add_recomputation : 0.000081s : 0.01% cse_after_recomputation.cse : 0.000030s : 0.01% auto_monad_reorder : 0.000041s : 0.01% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000005s : 0.00% opt_after_jit_grad : 0.000552s : 0.10% symbol_engine_optimizer.build : 0.000007s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000019s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000079s : 0.01% Time group info: ------[substitution.] 0.000245 44 4.84% : 0.000012s : 2: substitution.depend_value_elim 1.18% : 0.000003s : 4: substitution.elim_not_effective 0.97% : 0.000002s : 4: substitution.fold_const_symbol 7.80% : 0.000019s : 5: substitution.graph_param_transform 67.22% : 0.000164s : 3: substitution.inline 2.18% : 0.000005s : 8: substitution.j_node_and_user_rematch 3.02% : 0.000007s : 8: substitution.remove_not_recompute_node 3.10% : 0.000008s : 2: substitution.replace_old_param 5.18% : 0.000013s : 3: substitution.updatestate_pure_node_eliminater 4.52% : 0.000011s : 5: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.234632 2 99.65% : 0.233814s : 1: type_inference.infer 0.35% : 0.000818s : 1: type_inference.specialize ------[replace.] 0.000035 3 100.00% : 0.000035s : 3: replace.inline ------[match.] 0.000162 3 100.00% : 0.000162s : 3: match.inline ------[predicate.] 0.000158 825 1.37% : 0.000002s : 12: predicate.accumulaten_eliminater 1.42% : 0.000002s : 5: predicate.ad_related_special_op_eliminate 1.03% : 0.000002s : 12: predicate.addn_check_dump 1.36% : 0.000002s : 12: predicate.addn_zero_filter 2.39% : 0.000004s : 12: predicate.arithmetic_simplify 1.26% : 0.000002s : 12: predicate.cast_eliminate 0.58% : 0.000001s : 5: predicate.check_bprop_eliminate 1.07% : 0.000002s : 12: predicate.compare_switch_simplify 1.34% : 0.000002s : 12: predicate.depend_value_elim 1.02% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.23% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.21% : 0.000002s : 12: predicate.dict_set_item_eliminator 1.16% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.46% : 0.000001s : 5: predicate.elim_not_effective 0.72% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.19% : 0.000002s : 12: predicate.environ_add_const_eliminate 1.02% : 0.000002s : 12: predicate.environ_get_add_eliminate 1.06% : 0.000002s : 12: predicate.environ_get_depend_swap 1.37% : 0.000002s : 12: predicate.environ_get_eliminate 1.08% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.28% : 0.000000s : 5: predicate.fold_const_symbol 1.26% : 0.000002s : 10: predicate.get_grad_eliminate 0.65% : 0.000001s : 5: predicate.graph_param_transform 5.05% : 0.000008s : 25: predicate.inline 1.16% : 0.000002s : 10: predicate.inline_without_move 0.48% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.52% : 0.000002s : 10: predicate.less_batch_normalization 1.30% : 0.000002s : 12: predicate.list_to_tuple_eliminator_ 1.67% : 0.000003s : 17: predicate.load_eliminater 1.77% : 0.000003s : 5: predicate.loop_unroll_after_grad 2.81% : 0.000004s : 25: predicate.loop_unroll_before_grad 2.13% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 1.07% : 0.000002s : 12: predicate.merge_addn 1.04% : 0.000002s : 12: predicate.minmaximum_grad 2.42% : 0.000004s : 5: predicate.mutable_eliminate 0.55% : 0.000001s : 5: predicate.opt_reshape 2.08% : 0.000003s : 17: predicate.partial_eliminate 1.34% : 0.000002s : 12: predicate.print_const_string_wrapper 1.69% : 0.000003s : 12: predicate.reduce_eliminate 1.23% : 0.000002s : 12: predicate.redundant_stop_gradient_eliminater 0.81% : 0.000001s : 10: predicate.remove_not_recompute_node 1.70% : 0.000003s : 22: predicate.replace_applicator 0.88% : 0.000001s : 10: predicate.replace_old_param 0.32% : 0.000001s : 5: predicate.reset_defer_inline 1.56% : 0.000002s : 12: predicate.reshape_eliminate 1.25% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.88% : 0.000001s : 5: predicate.row_tensor_eliminate 1.25% : 0.000002s : 12: predicate.same_eliminate 0.59% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.12% : 0.000002s : 10: predicate.special_op_eliminate 1.16% : 0.000002s : 10: predicate.specialize_transform 1.48% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.20% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.57% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.92% : 0.000003s : 15: predicate.switch_defer_inline 1.53% : 0.000002s : 15: predicate.switch_layer_defer_inline 6.16% : 0.000010s : 45: predicate.switch_simplify 1.31% : 0.000002s : 12: predicate.tile_eliminate 1.24% : 0.000002s : 12: predicate.transpose_eliminate 1.60% : 0.000003s : 12: predicate.tuple_list_convert_item_index_to_positive 1.22% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 3.61% : 0.000006s : 22: predicate.tuple_list_get_item_eliminator 1.98% : 0.000003s : 12: predicate.tuple_list_set_item_eliminator 1.36% : 0.000002s : 12: predicate.tuple_to_list_eliminator_ 1.83% : 0.000003s : 17: predicate.updatestate_pure_node_eliminater 3.45% : 0.000005s : 27: predicate.updatestate_useless_node_eliminater 1.82% : 0.000003s : 12: predicate.value_based_eliminate 0.46% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.91% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000501 6 4.95% : 0.000025s : 1: func_graph_cloner_run.FuncGraphClonerGraph 95.05% : 0.000476s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.879084 72 0.01% : 0.000085s : 1: add_recomputation 0.02% : 0.000167s : 1: auto_monad 0.00% : 0.000044s : 1: auto_monad_reorder 0.08% : 0.000687s : 1: bootstrap 0.00% : 0.000043s : 1: cconv 0.00% : 0.000019s : 1: convert_after_rewriter 0.01% : 0.000046s : 1: cse_after_recomputation 0.00% : 0.000023s : 1: environ_conv 0.00% : 0.000022s : 1: event_method 0.00% : 0.000006s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000010s : 1: graph_reusing 36.87% : 0.324106s : 1: jit_opt_a 0.03% : 0.000251s : 1: jit_opt_after_cconv 0.01% : 0.000074s : 1: jit_opt_b 0.05% : 0.000464s : 1: loop_unroll 0.08% : 0.000739s : 1: mutable_eliminate 0.11% : 0.000955s : 26: opt.transform.jit_opt_a 0.01% : 0.000101s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000041s : 4: opt.transform.jit_opt_b 0.00% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000022s : 1: opt.transform.mutable_eliminate 0.00% : 0.000033s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000050s : 4: opt.transform.symbol_engine_opt 0.06% : 0.000563s : 1: opt_after_jit_grad 0.00% : 0.000009s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000016s : 1: pre_auto_parallel 0.00% : 0.000044s : 1: py_interpret_to_execute 0.00% : 0.000026s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000023s : 1: remove_dup_value 0.05% : 0.000465s : 1: renormalize.infer 35.76% : 0.314392s : 1: renormalize.specialize 0.00% : 0.000007s : 1: rewriter_after_jit_bprop_graph 0.07% : 0.000614s : 1: rewriter_after_opt_a 0.01% : 0.000083s : 1: rewriter_before_opt_a 0.01% : 0.000102s : 1: symbol_engine_optimizer 26.70% : 0.234725s : 1: type_inference TotalTime = 1.10263, [30] [bootstrap]: 0.00057558 [type_inference]: 0.514593 [event_method]: 0.00019779 [auto_monad]: 0.00058302 [graph_reusing]: 1.187e-05 [pre_auto_parallel]: 4.25999e-06 [py_interpret_to_execute]: 6.167e-05 [rewriter_before_opt_a]: 0.00031008 [expand_dump_flag]: 5.96e-06 [jit_opt_a]: 0.583259, [3] [Cycle 1]: 0.345822, [27] [switch_simplify]: 0.0002437 [loop_unroll]: 6.615e-05 [a_1]: 0.00144531 [with_stream_mark]: 4.421e-05 [recompute_prepare]: 3.295e-05 [updatestate_depend_eliminate]: 1.314e-05 [updatestate_assign_eliminate]: 1.13e-05 [updatestate_loads_eliminate]: 9.82001e-06 [parameter_eliminate]: 4.18999e-06 [specialize_transform]: 2.145e-05 [updatestate_useless_node_eliminater]: 2.417e-05 [accelerated_algorithm]: 1.862e-05 [meta_shard_fg_expand]: 5.84e-06 [get_grad_eliminate_]: 1.871e-05 [merge_forward]: 1.303e-05 [cell_reuse_recompute_pass]: 1.34e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.228e-05 [j_node_and_user_rematch]: 3.854e-05 [meta_fg_expand]: 0.326689 [replace_old_param]: 0.00012095 [inline_without_move]: 0.00011552 [renormalize]: 0.0158772 [add_forward_monad_depend]: 2.936e-05 [auto_monad_grad]: 9.42001e-06 [auto_monad_eliminator]: 8.661e-05 [cse]: 0.00028878 [replace_applicator]: 0.00020653 [Cycle 2]: 0.232111, [27] [switch_simplify]: 6.114e-05 [loop_unroll]: 5.539e-05 [a_1]: 0.00103433 [with_stream_mark]: 2.578e-05 [recompute_prepare]: 1.303e-05 [updatestate_depend_eliminate]: 2.63e-05 [updatestate_assign_eliminate]: 5.99e-06 [updatestate_loads_eliminate]: 5.27001e-06 [parameter_eliminate]: 2.38002e-06 [specialize_transform]: 1.016e-05 [updatestate_useless_node_eliminater]: 1.172e-05 [accelerated_algorithm]: 8.26002e-06 [meta_shard_fg_expand]: 2.58003e-06 [get_grad_eliminate_]: 7.97e-06 [merge_forward]: 5.87999e-06 [cell_reuse_recompute_pass]: 1.19e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.995e-05 [j_node_and_user_rematch]: 1.352e-05 [meta_fg_expand]: 0.00010995 [replace_old_param]: 1.575e-05 [inline_without_move]: 8.2e-06 [renormalize]: 0.230304 [add_forward_monad_depend]: 1.369e-05 [auto_monad_grad]: 3.26001e-06 [auto_monad_eliminator]: 3.52e-05 [cse]: 5.442e-05 [replace_applicator]: 2.933e-05 [Cycle 3]: 0.0005252, [27] [switch_simplify]: 9.36e-06 [loop_unroll]: 9.48002e-06 [a_1]: 0.00018703 [with_stream_mark]: 2.097e-05 [recompute_prepare]: 8.1e-06 [updatestate_depend_eliminate]: 6.86001e-06 [updatestate_assign_eliminate]: 5.12999e-06 [updatestate_loads_eliminate]: 5.16002e-06 [parameter_eliminate]: 1.89999e-06 [specialize_transform]: 7.9e-06 [updatestate_useless_node_eliminater]: 1.035e-05 [accelerated_algorithm]: 8.65001e-06 [meta_shard_fg_expand]: 2.46998e-06 [get_grad_eliminate_]: 7.66999e-06 [merge_forward]: 5.65001e-06 [cell_reuse_recompute_pass]: 3.63e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.048e-05 [j_node_and_user_rematch]: 1.412e-05 [meta_fg_expand]: 3.53999e-06 [replace_old_param]: 1.312e-05 [inline_without_move]: 7.6e-06 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 2.20002e-06 [auto_monad_grad]: 1.32e-06 [auto_monad_eliminator]: 1.004e-05 [cse]: 1.988e-05 [replace_applicator]: 8.60999e-06 [py_interpret_to_execute_after_opt_a]: 2.437e-05 [rewriter_after_opt_a]: 0.00027823 [convert_after_rewriter]: 1.543e-05 [order_py_execute_after_rewriter]: 7.64002e-06 [mutable_eliminate]: 0.00080979 [jit_opt_b]: 7.355e-05, [1] [Cycle 1]: 6.503e-05, [2] [frontend_op_eliminate]: 2.641e-05 [inline_after_opt_a]: 2.497e-05 [cconv]: 3.763e-05 [loop_unroll]: 0.00045132 [jit_opt_after_cconv]: 0.00021616, [1] [Cycle 1]: 0.00020939, [11] [c_1]: 5.087e-05 [parameter_eliminate]: 3.28e-06 [updatestate_depend_eliminate]: 1.172e-05 [updatestate_assign_eliminate]: 5.86e-06 [updatestate_loads_eliminate]: 4.59998e-06 [cse]: 3.751e-05 [call_graph_tuple_transform]: 2.406e-05 [tuple_list_get_item_eliminator]: 8.47e-06 [none_parameter_eliminate]: 2.45002e-06 [renormalize]: 5.00004e-07 [switch_simplify]: 8.15e-06 [remove_dup_value]: 2.08e-05 [partial_unused_args_eliminate]: 2.48e-06 [environ_conv]: 9.25001e-06 [add_recomputation]: 8.044e-05 [cse_after_recomputation]: 3.216e-05, [1] [Cycle 1]: 2.607e-05, [1] [cse]: 1.964e-05 [auto_monad_reorder]: 2.942e-05 [get_jit_bprop_graph]: 2.68e-06 [rewriter_after_jit_bprop_graph]: 6.11e-06 [opt_after_jit_grad]: 0.00051701 [symbol_engine_optimizer]: 9.748e-05, [1] [Cycle 1]: 8.998e-05, [6] [build]: 6.51e-06 [elim_shapecalc]: 1.181e-05 [elim_not_effective]: 1.899e-05 [opt_reshape]: 8.42e-06 [fold_const_symbol]: 1.378e-05 [renormalize]: 6.19999e-07 [validate]: 6.127e-05 Sums bootstrap : 0.000576s : 0.05% type_inference : 0.514593s : 46.92% event_method : 0.000198s : 0.02% auto_monad : 0.000583s : 0.05% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000062s : 0.01% rewriter_before_opt_a : 0.000310s : 0.03% expand_dump_flag : 0.000006s : 0.00% jit_opt_a.switch_simplify : 0.000314s : 0.03% jit_opt_a.loop_unroll : 0.000131s : 0.01% jit_opt_a.a_1 : 0.002667s : 0.24% jit_opt_a.with_stream_mark : 0.000091s : 0.01% jit_opt_a.recompute_prepare : 0.000054s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000046s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000022s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000020s : 0.00% jit_opt_a.parameter_eliminate : 0.000008s : 0.00% jit_opt_a.specialize_transform : 0.000040s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000046s : 0.00% jit_opt_a.accelerated_algorithm : 0.000036s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000011s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000034s : 0.00% jit_opt_a.merge_forward : 0.000025s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000006s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000083s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000066s : 0.01% jit_opt_a.meta_fg_expand : 0.326802s : 29.80% jit_opt_a.replace_old_param : 0.000150s : 0.01% jit_opt_a.inline_without_move : 0.000131s : 0.01% jit_opt_a.renormalize : 0.246182s : 22.45% jit_opt_a.add_forward_monad_depend : 0.000045s : 0.00% jit_opt_a.auto_monad_grad : 0.000014s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000132s : 0.01% jit_opt_a.cse : 0.000363s : 0.03% jit_opt_a.replace_applicator : 0.000244s : 0.02% py_interpret_to_execute_after_opt_a : 0.000024s : 0.00% rewriter_after_opt_a : 0.000278s : 0.03% convert_after_rewriter : 0.000015s : 0.00% order_py_execute_after_rewriter : 0.000008s : 0.00% mutable_eliminate : 0.000810s : 0.07% jit_opt_b.frontend_op_eliminate : 0.000026s : 0.00% jit_opt_b.inline_after_opt_a : 0.000025s : 0.00% cconv : 0.000038s : 0.00% loop_unroll : 0.000451s : 0.04% jit_opt_after_cconv.c_1 : 0.000051s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000012s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000038s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 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 : 0.000021s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000009s : 0.00% add_recomputation : 0.000080s : 0.01% cse_after_recomputation.cse : 0.000020s : 0.00% auto_monad_reorder : 0.000029s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000006s : 0.00% opt_after_jit_grad : 0.000517s : 0.05% symbol_engine_optimizer.build : 0.000007s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000012s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000019s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000008s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000014s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000061s : 0.01% Time group info: ------[substitution.] 0.001712 170 1.57% : 0.000027s : 8: substitution.depend_value_elim 0.16% : 0.000003s : 4: substitution.elim_not_effective 0.14% : 0.000002s : 4: substitution.fold_const_symbol 49.66% : 0.000850s : 4: substitution.getattr_setattr_resolve 0.44% : 0.000007s : 5: substitution.graph_param_transform 30.71% : 0.000526s : 16: substitution.inline 2.10% : 0.000036s : 4: substitution.inline_without_move 0.82% : 0.000014s : 20: substitution.j_node_and_user_rematch 0.54% : 0.000009s : 5: substitution.minmaximum_grad 0.45% : 0.000008s : 9: substitution.partial_eliminate 0.98% : 0.000017s : 20: substitution.remove_not_recompute_node 2.79% : 0.000048s : 12: substitution.replace_applicator 1.07% : 0.000018s : 16: substitution.replace_old_param 0.24% : 0.000004s : 1: substitution.set_cell_output_no_recompute 0.88% : 0.000015s : 3: substitution.switch_simplify 1.14% : 0.000019s : 5: substitution.tuple_list_convert_item_index_to_positive 0.84% : 0.000014s : 5: substitution.tuple_list_get_item_depend_reorder 2.07% : 0.000036s : 8: substitution.tuple_list_get_item_eliminator 1.25% : 0.000021s : 8: substitution.updatestate_pure_node_eliminater 2.15% : 0.000037s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.514478 2 99.55% : 0.512171s : 1: type_inference.infer 0.45% : 0.002307s : 1: type_inference.specialize ------[replace.] 0.000436 27 15.91% : 0.000069s : 3: replace.getattr_setattr_resolve 29.67% : 0.000129s : 16: replace.inline 13.41% : 0.000059s : 1: replace.replace_applicator 18.63% : 0.000081s : 3: replace.switch_simplify 17.14% : 0.000075s : 3: replace.tuple_list_get_item_eliminator 5.25% : 0.000023s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001348 27 58.17% : 0.000784s : 3: match.getattr_setattr_resolve 38.26% : 0.000516s : 16: match.inline 0.95% : 0.000013s : 1: match.replace_applicator 0.99% : 0.000013s : 3: match.switch_simplify 0.58% : 0.000008s : 3: match.tuple_list_get_item_eliminator 1.05% : 0.000014s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000507 3164 1.39% : 0.000007s : 50: predicate.accumulaten_eliminater 0.42% : 0.000002s : 5: predicate.ad_related_special_op_eliminate 1.28% : 0.000007s : 50: predicate.addn_check_dump 1.41% : 0.000007s : 50: predicate.addn_zero_filter 1.94% : 0.000010s : 50: predicate.arithmetic_simplify 1.54% : 0.000008s : 50: predicate.cast_eliminate 0.16% : 0.000001s : 5: predicate.check_bprop_eliminate 1.29% : 0.000007s : 50: predicate.compare_switch_simplify 1.48% : 0.000007s : 50: predicate.depend_value_elim 1.27% : 0.000006s : 50: predicate.dict_get_item_const_eliminator 1.47% : 0.000007s : 50: predicate.dict_get_item_eliminator 1.55% : 0.000008s : 50: predicate.dict_set_item_eliminator 0.35% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.15% : 0.000001s : 5: predicate.elim_not_effective 0.22% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.47% : 0.000007s : 50: predicate.environ_add_const_eliminate 1.27% : 0.000006s : 50: predicate.environ_get_add_eliminate 1.39% : 0.000007s : 50: predicate.environ_get_depend_swap 1.45% : 0.000007s : 50: predicate.environ_get_eliminate 1.30% : 0.000007s : 50: predicate.environ_get_set_eliminate 0.09% : 0.000000s : 5: predicate.fold_const_symbol 0.97% : 0.000005s : 26: predicate.get_grad_eliminate 2.23% : 0.000011s : 20: predicate.getattr_setattr_resolve 0.09% : 0.000000s : 5: predicate.graph_param_transform 4.11% : 0.000021s : 80: predicate.inline 2.95% : 0.000015s : 87: predicate.inline_without_move 0.37% : 0.000002s : 26: predicate.j_node_and_user_rematch 0.99% : 0.000005s : 26: predicate.less_batch_normalization 1.57% : 0.000008s : 53: predicate.list_to_tuple_eliminator_ 1.76% : 0.000009s : 58: predicate.load_eliminater 0.50% : 0.000003s : 5: predicate.loop_unroll_after_grad 4.04% : 0.000021s : 132: predicate.loop_unroll_before_grad 1.73% : 0.000009s : 55: predicate.make_slice_get_slice_eliminator 1.24% : 0.000006s : 50: predicate.merge_addn 1.34% : 0.000007s : 50: predicate.minmaximum_grad 0.62% : 0.000003s : 5: predicate.mutable_eliminate 0.17% : 0.000001s : 5: predicate.opt_reshape 2.11% : 0.000011s : 58: predicate.partial_eliminate 1.32% : 0.000007s : 50: predicate.print_const_string_wrapper 1.76% : 0.000009s : 50: predicate.reduce_eliminate 1.60% : 0.000008s : 53: predicate.redundant_stop_gradient_eliminater 0.47% : 0.000002s : 26: predicate.remove_not_recompute_node 2.66% : 0.000014s : 126: predicate.replace_applicator 1.64% : 0.000008s : 87: predicate.replace_old_param 0.20% : 0.000001s : 5: predicate.reset_defer_inline 1.48% : 0.000007s : 50: predicate.reshape_eliminate 1.47% : 0.000007s : 50: predicate.row_tensor_add_zeros_like 0.34% : 0.000002s : 5: predicate.row_tensor_eliminate 1.49% : 0.000008s : 50: predicate.same_eliminate 0.53% : 0.000003s : 28: predicate.set_cell_output_no_recompute 0.35% : 0.000002s : 10: predicate.special_op_eliminate 0.92% : 0.000005s : 26: predicate.specialize_transform 1.60% : 0.000008s : 50: predicate.split_environ_get_set_with_tuple_value 1.48% : 0.000007s : 50: predicate.stack_unstack_eliminate 0.18% : 0.000001s : 5: predicate.switch_call_monad_eliminater 2.58% : 0.000013s : 70: predicate.switch_defer_inline 2.13% : 0.000011s : 70: predicate.switch_layer_defer_inline 7.01% : 0.000036s : 213: predicate.switch_simplify 1.39% : 0.000007s : 50: predicate.tile_eliminate 1.48% : 0.000007s : 50: predicate.transpose_eliminate 1.68% : 0.000008s : 50: predicate.tuple_list_convert_item_index_to_positive 1.66% : 0.000008s : 50: predicate.tuple_list_get_item_depend_reorder 2.86% : 0.000015s : 63: predicate.tuple_list_get_item_eliminator 1.79% : 0.000009s : 50: predicate.tuple_list_set_item_eliminator 1.53% : 0.000008s : 53: predicate.tuple_to_list_eliminator_ 1.78% : 0.000009s : 58: predicate.updatestate_pure_node_eliminater 2.79% : 0.000014s : 85: predicate.updatestate_useless_node_eliminater 1.74% : 0.000009s : 50: predicate.value_based_eliminate 0.13% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.29% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.003917 47 61.75% : 0.002419s : 20: func_graph_cloner_run.FuncGraphClonerGraph 38.25% : 0.001498s : 27: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.473855 89 0.01% : 0.000084s : 1: add_recomputation 0.04% : 0.000596s : 1: auto_monad 0.00% : 0.000033s : 1: auto_monad_reorder 0.04% : 0.000601s : 1: bootstrap 0.00% : 0.000041s : 1: cconv 0.00% : 0.000019s : 1: convert_after_rewriter 0.00% : 0.000035s : 1: cse_after_recomputation 0.00% : 0.000012s : 1: environ_conv 0.01% : 0.000208s : 1: event_method 0.00% : 0.000009s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 39.57% : 0.583263s : 1: jit_opt_a 0.01% : 0.000220s : 1: jit_opt_after_cconv 0.01% : 0.000077s : 1: jit_opt_b 0.03% : 0.000460s : 1: loop_unroll 0.06% : 0.000820s : 1: mutable_eliminate 0.27% : 0.003926s : 39: opt.transform.jit_opt_a 0.01% : 0.000087s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000044s : 4: opt.transform.jit_opt_b 0.00% : 0.000017s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000023s : 1: opt.transform.mutable_eliminate 0.00% : 0.000033s : 1: opt.transform.opt_after_jit_grad 8.21% : 0.121031s : 2: opt.transform.opt_resolve 0.00% : 0.000049s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000528s : 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% : 0.000007s : 1: pre_auto_parallel 0.00% : 0.000066s : 1: py_interpret_to_execute 0.00% : 0.000027s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000024s : 1: remove_dup_value 16.46% : 0.242573s : 2: renormalize.infer 0.24% : 0.003575s : 2: renormalize.specialize 0.00% : 0.000009s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000283s : 1: rewriter_after_opt_a 0.02% : 0.000319s : 1: rewriter_before_opt_a 0.01% : 0.000100s : 1: symbol_engine_optimizer 34.92% : 0.514621s : 1: type_inference . [hook] pytest_runtest_teardown:test_mint_unsqueeze_normal[0] tests/st/mint/test_unsqueeze.py::test_mint_unsqueeze_normal[0],max_mem:4.0M . [hook] pytest_runtest_teardown:test_mint_unsqueeze_normal[1] tests/st/mint/test_unsqueeze.py::test_mint_unsqueeze_normal[1],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 182.26s (0:03:02) ==================