==================================================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_008/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_narrow.py . [hook] pytest_runtest_teardown:test_narrow_non_contiguous[pynative] tests/st/mint/test_narrow.py::test_narrow_non_contiguous[pynative],max_mem:2.0M TotalTime = 0.673958, [30] [bootstrap]: 0.00070545 [type_inference]: 0.54138 [event_method]: 1.691e-05 [auto_monad]: 0.00016464 [graph_reusing]: 5.84e-06 [pre_auto_parallel]: 1.361e-05 [py_interpret_to_execute]: 0.00012128 [rewriter_before_opt_a]: 7.665e-05 [expand_dump_flag]: 3.28998e-06 [jit_opt_a]: 0.126913, [2] [Cycle 1]: 0.117654, [27] [switch_simplify]: 5.987e-05 [loop_unroll]: 2.285e-05 [a_1]: 0.00043872 [with_stream_mark]: 2.946e-05 [recompute_prepare]: 1.111e-05 [updatestate_depend_eliminate]: 7.28e-06 [updatestate_assign_eliminate]: 8.64998e-06 [updatestate_loads_eliminate]: 5.23002e-06 [parameter_eliminate]: 1.82999e-06 [specialize_transform]: 9.89999e-06 [updatestate_useless_node_eliminater]: 1.273e-05 [accelerated_algorithm]: 9.14998e-06 [meta_shard_fg_expand]: 3.11999e-06 [get_grad_eliminate_]: 8.77e-06 [merge_forward]: 6.66999e-06 [cell_reuse_recompute_pass]: 1.32e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.628e-05 [j_node_and_user_rematch]: 1.452e-05 [meta_fg_expand]: 4.47e-06 [replace_old_param]: 1.347e-05 [inline_without_move]: 9.03002e-06 [renormalize]: 0.116533 [add_forward_monad_depend]: 1.695e-05 [auto_monad_grad]: 2.89001e-06 [auto_monad_eliminator]: 3.419e-05 [cse]: 5.676e-05 [replace_applicator]: 2.945e-05 [Cycle 2]: 0.000542, [27] [switch_simplify]: 1.102e-05 [loop_unroll]: 9.20999e-06 [a_1]: 0.00018759 [with_stream_mark]: 2.048e-05 [recompute_prepare]: 9.61e-06 [updatestate_depend_eliminate]: 6.78e-06 [updatestate_assign_eliminate]: 5.90002e-06 [updatestate_loads_eliminate]: 5.00999e-06 [parameter_eliminate]: 1.90001e-06 [specialize_transform]: 8.64e-06 [updatestate_useless_node_eliminater]: 1.164e-05 [accelerated_algorithm]: 8.90001e-06 [meta_shard_fg_expand]: 2.65002e-06 [get_grad_eliminate_]: 8.3e-06 [merge_forward]: 6.34001e-06 [cell_reuse_recompute_pass]: 3.95998e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.992e-05 [j_node_and_user_rematch]: 1.282e-05 [meta_fg_expand]: 3.31001e-06 [replace_old_param]: 1.246e-05 [inline_without_move]: 7.78999e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 1.82999e-06 [auto_monad_grad]: 1.55001e-06 [auto_monad_eliminator]: 1.386e-05 [cse]: 2.255e-05 [replace_applicator]: 8.98002e-06 [py_interpret_to_execute_after_opt_a]: 1.983e-05 [rewriter_after_opt_a]: 0.00109342 [convert_after_rewriter]: 2.208e-05 [order_py_execute_after_rewriter]: 8.37e-06 [mutable_eliminate]: 0.00081258 [jit_opt_b]: 7.956e-05, [1] [Cycle 1]: 6.938e-05, [2] [frontend_op_eliminate]: 2.523e-05 [inline_after_opt_a]: 2.953e-05 [cconv]: 4.12e-05 [loop_unroll]: 0.00050044 [jit_opt_after_cconv]: 0.0002501, [1] [Cycle 1]: 0.00024244, [11] [c_1]: 5.547e-05 [parameter_eliminate]: 6.68998e-06 [updatestate_depend_eliminate]: 1.403e-05 [updatestate_assign_eliminate]: 5.81998e-06 [updatestate_loads_eliminate]: 5.40001e-06 [cse]: 4.941e-05 [call_graph_tuple_transform]: 2.54e-05 [tuple_list_get_item_eliminator]: 9.31e-06 [none_parameter_eliminate]: 1.75001e-06 [renormalize]: 9.79984e-07 [switch_simplify]: 9.30001e-06 [remove_dup_value]: 3.076e-05 [partial_unused_args_eliminate]: 2.44999e-06 [environ_conv]: 4.837e-05 [add_recomputation]: 8.99e-05 [cse_after_recomputation]: 3.295e-05, [1] [Cycle 1]: 2.608e-05, [1] [cse]: 1.909e-05 [auto_monad_reorder]: 3.964e-05 [get_jit_bprop_graph]: 3.04999e-06 [rewriter_after_jit_bprop_graph]: 0.00017193 [opt_after_jit_grad]: 0.00071574 [symbol_engine_optimizer]: 0.0001091, [1] [Cycle 1]: 0.00010023, [6] [build]: 9.77001e-06 [elim_shapecalc]: 1.227e-05 [elim_not_effective]: 2.308e-05 [opt_reshape]: 9.12999e-06 [fold_const_symbol]: 1.434e-05 [renormalize]: 9.89996e-07 [validate]: 0.00015151 Sums bootstrap : 0.000705s : 0.11% type_inference : 0.541380s : 81.49% event_method : 0.000017s : 0.00% auto_monad : 0.000165s : 0.02% graph_reusing : 0.000006s : 0.00% pre_auto_parallel : 0.000014s : 0.00% py_interpret_to_execute : 0.000121s : 0.02% rewriter_before_opt_a : 0.000077s : 0.01% expand_dump_flag : 0.000003s : 0.00% jit_opt_a.switch_simplify : 0.000071s : 0.01% jit_opt_a.loop_unroll : 0.000032s : 0.00% jit_opt_a.a_1 : 0.000626s : 0.09% jit_opt_a.with_stream_mark : 0.000050s : 0.01% jit_opt_a.recompute_prepare : 0.000021s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000014s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000015s : 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 : 0.000019s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000024s : 0.00% jit_opt_a.accelerated_algorithm : 0.000018s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000006s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000017s : 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 : 0.000046s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000027s : 0.00% jit_opt_a.meta_fg_expand : 0.000008s : 0.00% jit_opt_a.replace_old_param : 0.000026s : 0.00% jit_opt_a.inline_without_move : 0.000017s : 0.00% jit_opt_a.renormalize : 0.116533s : 17.54% jit_opt_a.add_forward_monad_depend : 0.000019s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000048s : 0.01% jit_opt_a.cse : 0.000079s : 0.01% jit_opt_a.replace_applicator : 0.000038s : 0.01% py_interpret_to_execute_after_opt_a : 0.000020s : 0.00% rewriter_after_opt_a : 0.001093s : 0.16% convert_after_rewriter : 0.000022s : 0.00% order_py_execute_after_rewriter : 0.000008s : 0.00% mutable_eliminate : 0.000813s : 0.12% jit_opt_b.frontend_op_eliminate : 0.000025s : 0.00% jit_opt_b.inline_after_opt_a : 0.000030s : 0.00% cconv : 0.000041s : 0.01% loop_unroll : 0.000500s : 0.08% jit_opt_after_cconv.c_1 : 0.000055s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000014s : 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 : 0.000049s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000025s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000009s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000009s : 0.00% remove_dup_value : 0.000031s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000048s : 0.01% add_recomputation : 0.000090s : 0.01% cse_after_recomputation.cse : 0.000019s : 0.00% auto_monad_reorder : 0.000040s : 0.01% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000172s : 0.03% opt_after_jit_grad : 0.000716s : 0.11% symbol_engine_optimizer.build : 0.000010s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000012s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000023s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000014s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000152s : 0.02% Time group info: ------[substitution.] 0.000231 43 5.16% : 0.000012s : 2: substitution.depend_value_elim 1.56% : 0.000004s : 4: substitution.elim_not_effective 1.00% : 0.000002s : 4: substitution.fold_const_symbol 3.54% : 0.000008s : 5: substitution.graph_param_transform 68.91% : 0.000159s : 2: substitution.inline 2.18% : 0.000005s : 8: substitution.j_node_and_user_rematch 3.41% : 0.000008s : 8: substitution.remove_not_recompute_node 3.03% : 0.000007s : 2: substitution.replace_old_param 6.16% : 0.000014s : 3: substitution.updatestate_pure_node_eliminater 5.05% : 0.000012s : 5: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.541292 2 99.81% : 0.540238s : 1: type_inference.infer 0.19% : 0.001053s : 1: type_inference.specialize ------[replace.] 0.000034 2 100.00% : 0.000034s : 2: replace.inline ------[match.] 0.000157 2 100.00% : 0.000157s : 2: match.inline ------[predicate.] 0.000158 767 1.13% : 0.000002s : 11: predicate.accumulaten_eliminater 1.79% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 0.92% : 0.000001s : 11: predicate.addn_check_dump 1.43% : 0.000002s : 11: predicate.addn_zero_filter 1.87% : 0.000003s : 11: predicate.arithmetic_simplify 1.27% : 0.000002s : 11: predicate.cast_eliminate 0.70% : 0.000001s : 5: predicate.check_bprop_eliminate 1.01% : 0.000002s : 11: predicate.compare_switch_simplify 1.33% : 0.000002s : 11: predicate.depend_value_elim 1.10% : 0.000002s : 11: predicate.dict_get_item_const_eliminator 1.26% : 0.000002s : 11: predicate.dict_get_item_eliminator 1.10% : 0.000002s : 11: predicate.dict_set_item_eliminator 1.80% : 0.000003s : 5: predicate.dumpgradient_eliminate 0.47% : 0.000001s : 5: predicate.elim_not_effective 1.01% : 0.000002s : 5: predicate.elim_shapecalc_of_broadcastargs 1.13% : 0.000002s : 11: predicate.environ_add_const_eliminate 0.94% : 0.000001s : 11: predicate.environ_get_add_eliminate 1.01% : 0.000002s : 11: predicate.environ_get_depend_swap 1.18% : 0.000002s : 11: predicate.environ_get_eliminate 1.00% : 0.000002s : 11: predicate.environ_get_set_eliminate 0.39% : 0.000001s : 5: predicate.fold_const_symbol 1.46% : 0.000002s : 10: predicate.get_grad_eliminate 0.41% : 0.000001s : 5: predicate.graph_param_transform 4.99% : 0.000008s : 23: predicate.inline 1.39% : 0.000002s : 10: predicate.inline_without_move 0.51% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.76% : 0.000003s : 10: predicate.less_batch_normalization 1.42% : 0.000002s : 11: predicate.list_to_tuple_eliminator_ 1.77% : 0.000003s : 16: predicate.load_eliminater 1.88% : 0.000003s : 5: predicate.loop_unroll_after_grad 2.41% : 0.000004s : 20: predicate.loop_unroll_before_grad 2.79% : 0.000004s : 16: predicate.make_slice_get_slice_eliminator 1.08% : 0.000002s : 11: predicate.merge_addn 1.16% : 0.000002s : 11: predicate.minmaximum_grad 2.52% : 0.000004s : 5: predicate.mutable_eliminate 0.58% : 0.000001s : 5: predicate.opt_reshape 1.95% : 0.000003s : 16: predicate.partial_eliminate 1.27% : 0.000002s : 11: predicate.print_const_string_wrapper 1.72% : 0.000003s : 11: predicate.reduce_eliminate 1.17% : 0.000002s : 11: predicate.redundant_stop_gradient_eliminater 0.83% : 0.000001s : 10: predicate.remove_not_recompute_node 1.73% : 0.000003s : 21: predicate.replace_applicator 0.82% : 0.000001s : 10: predicate.replace_old_param 1.11% : 0.000002s : 5: predicate.reset_defer_inline 1.21% : 0.000002s : 11: predicate.reshape_eliminate 1.08% : 0.000002s : 11: predicate.row_tensor_add_zeros_like 0.97% : 0.000002s : 5: predicate.row_tensor_eliminate 1.14% : 0.000002s : 11: predicate.same_eliminate 0.60% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.30% : 0.000002s : 10: predicate.special_op_eliminate 1.36% : 0.000002s : 10: predicate.specialize_transform 1.37% : 0.000002s : 11: predicate.split_environ_get_set_with_tuple_value 1.11% : 0.000002s : 11: predicate.stack_unstack_eliminate 0.70% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.58% : 0.000002s : 13: predicate.switch_defer_inline 1.48% : 0.000002s : 13: predicate.switch_layer_defer_inline 5.04% : 0.000008s : 38: predicate.switch_simplify 1.05% : 0.000002s : 11: predicate.tile_eliminate 1.18% : 0.000002s : 11: predicate.transpose_eliminate 1.35% : 0.000002s : 11: predicate.tuple_list_convert_item_index_to_positive 1.46% : 0.000002s : 11: predicate.tuple_list_get_item_depend_reorder 4.15% : 0.000007s : 21: predicate.tuple_list_get_item_eliminator 1.56% : 0.000002s : 11: predicate.tuple_list_set_item_eliminator 1.08% : 0.000002s : 11: predicate.tuple_to_list_eliminator_ 1.56% : 0.000002s : 16: predicate.updatestate_pure_node_eliminater 3.90% : 0.000006s : 26: predicate.updatestate_useless_node_eliminater 1.97% : 0.000003s : 11: predicate.value_based_eliminate 0.47% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.79% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000427 5 6.03% : 0.000026s : 1: func_graph_cloner_run.FuncGraphClonerGraph 93.97% : 0.000402s : 4: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.791364 72 0.01% : 0.000093s : 1: add_recomputation 0.02% : 0.000170s : 1: auto_monad 0.01% : 0.000043s : 1: auto_monad_reorder 0.09% : 0.000727s : 1: bootstrap 0.01% : 0.000044s : 1: cconv 0.00% : 0.000027s : 1: convert_after_rewriter 0.00% : 0.000035s : 1: cse_after_recomputation 0.01% : 0.000051s : 1: environ_conv 0.00% : 0.000023s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000006s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 16.04% : 0.126918s : 1: jit_opt_a 0.03% : 0.000253s : 1: jit_opt_after_cconv 0.01% : 0.000083s : 1: jit_opt_b 0.06% : 0.000509s : 1: loop_unroll 0.10% : 0.000821s : 1: mutable_eliminate 0.12% : 0.000933s : 26: opt.transform.jit_opt_a 0.01% : 0.000095s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000046s : 4: opt.transform.jit_opt_b 0.00% : 0.000021s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000027s : 1: opt.transform.mutable_eliminate 0.01% : 0.000042s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000055s : 4: opt.transform.symbol_engine_opt 0.09% : 0.000728s : 1: opt_after_jit_grad 0.00% : 0.000011s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000016s : 1: pre_auto_parallel 0.02% : 0.000125s : 1: py_interpret_to_execute 0.00% : 0.000023s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000034s : 1: remove_dup_value 14.66% : 0.115980s : 1: renormalize.infer 0.07% : 0.000538s : 1: renormalize.specialize 0.02% : 0.000176s : 1: rewriter_after_jit_bprop_graph 0.14% : 0.001102s : 1: rewriter_after_opt_a 0.01% : 0.000081s : 1: rewriter_before_opt_a 0.01% : 0.000112s : 1: symbol_engine_optimizer 68.41% : 0.541397s : 1: type_inference TotalTime = 0.9461, [30] [bootstrap]: 0.00063353 [type_inference]: 0.471335 [event_method]: 0.0002 [auto_monad]: 0.00038508 [graph_reusing]: 1.097e-05 [pre_auto_parallel]: 3.78001e-06 [py_interpret_to_execute]: 9.948e-05 [rewriter_before_opt_a]: 0.00020309 [expand_dump_flag]: 4.80001e-06 [jit_opt_a]: 0.47038, [3] [Cycle 1]: 0.448414, [27] [switch_simplify]: 0.00024444 [loop_unroll]: 5.771e-05 [a_1]: 0.125191 [with_stream_mark]: 4.438e-05 [recompute_prepare]: 3.379e-05 [updatestate_depend_eliminate]: 1.449e-05 [updatestate_assign_eliminate]: 1.153e-05 [updatestate_loads_eliminate]: 1.023e-05 [parameter_eliminate]: 4.18001e-06 [specialize_transform]: 2.105e-05 [updatestate_useless_node_eliminater]: 2.529e-05 [accelerated_algorithm]: 1.867e-05 [meta_shard_fg_expand]: 8.82e-06 [get_grad_eliminate_]: 1.842e-05 [merge_forward]: 1.27e-05 [cell_reuse_recompute_pass]: 2.05002e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.97e-05 [j_node_and_user_rematch]: 4.862e-05 [meta_fg_expand]: 0.193814 [replace_old_param]: 0.00012409 [inline_without_move]: 0.00011968 [renormalize]: 0.127589 [add_forward_monad_depend]: 2.28e-05 [auto_monad_grad]: 9.71e-06 [auto_monad_eliminator]: 8.672e-05 [cse]: 0.00029237 [replace_applicator]: 0.00020515 [Cycle 2]: 0.00309963, [27] [switch_simplify]: 6.095e-05 [loop_unroll]: 5.725e-05 [a_1]: 0.00109244 [with_stream_mark]: 2.568e-05 [recompute_prepare]: 1.853e-05 [updatestate_depend_eliminate]: 3.259e-05 [updatestate_assign_eliminate]: 6.51e-06 [updatestate_loads_eliminate]: 5.07e-06 [parameter_eliminate]: 3.81999e-06 [specialize_transform]: 1.09e-05 [updatestate_useless_node_eliminater]: 1.372e-05 [accelerated_algorithm]: 9.24e-06 [meta_shard_fg_expand]: 2.76999e-06 [get_grad_eliminate_]: 8.45001e-06 [merge_forward]: 6.51999e-06 [cell_reuse_recompute_pass]: 1.81e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.166e-05 [j_node_and_user_rematch]: 1.419e-05 [meta_fg_expand]: 0.00010473 [replace_old_param]: 1.907e-05 [inline_without_move]: 9.03002e-06 [renormalize]: 0.00124664 [add_forward_monad_depend]: 8.27e-06 [auto_monad_grad]: 2.86e-06 [auto_monad_eliminator]: 2.509e-05 [cse]: 7.256e-05 [replace_applicator]: 2.265e-05 [Cycle 3]: 0.00051845, [27] [switch_simplify]: 9.57999e-06 [loop_unroll]: 8.74e-06 [a_1]: 0.00017779 [with_stream_mark]: 1.716e-05 [recompute_prepare]: 8.88002e-06 [updatestate_depend_eliminate]: 6.93e-06 [updatestate_assign_eliminate]: 5.46e-06 [updatestate_loads_eliminate]: 4.62998e-06 [parameter_eliminate]: 1.71e-06 [specialize_transform]: 8.75001e-06 [updatestate_useless_node_eliminater]: 1.191e-05 [accelerated_algorithm]: 8.51002e-06 [meta_shard_fg_expand]: 1.94999e-06 [get_grad_eliminate_]: 7.73001e-06 [merge_forward]: 5.09e-06 [cell_reuse_recompute_pass]: 2.68e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.962e-05 [j_node_and_user_rematch]: 1.293e-05 [meta_fg_expand]: 2.86e-06 [replace_old_param]: 1.215e-05 [inline_without_move]: 8.05999e-06 [renormalize]: 1.00001e-07 [add_forward_monad_depend]: 1.65001e-06 [auto_monad_grad]: 1.42e-06 [auto_monad_eliminator]: 1.24e-05 [cse]: 2.893e-05 [replace_applicator]: 8.28999e-06 [py_interpret_to_execute_after_opt_a]: 1.917e-05 [rewriter_after_opt_a]: 0.00020139 [convert_after_rewriter]: 1.103e-05 [order_py_execute_after_rewriter]: 7.4e-06 [mutable_eliminate]: 0.00067741 [jit_opt_b]: 7.424e-05, [1] [Cycle 1]: 6.74e-05, [2] [frontend_op_eliminate]: 2.798e-05 [inline_after_opt_a]: 2.634e-05 [cconv]: 3.206e-05 [loop_unroll]: 0.00046956 [jit_opt_after_cconv]: 0.00021347, [1] [Cycle 1]: 0.00020697, [11] [c_1]: 4.68e-05 [parameter_eliminate]: 3.22002e-06 [updatestate_depend_eliminate]: 8.60001e-06 [updatestate_assign_eliminate]: 5.15999e-06 [updatestate_loads_eliminate]: 5.19998e-06 [cse]: 3.722e-05 [call_graph_tuple_transform]: 2.466e-05 [tuple_list_get_item_eliminator]: 8.79e-06 [none_parameter_eliminate]: 2.06998e-06 [renormalize]: 6.40022e-07 [switch_simplify]: 8.89e-06 [remove_dup_value]: 2.68e-05 [partial_unused_args_eliminate]: 2.36e-06 [environ_conv]: 7.75e-06 [add_recomputation]: 7.102e-05 [cse_after_recomputation]: 3.57e-05, [1] [Cycle 1]: 2.98e-05, [1] [cse]: 2.35e-05 [auto_monad_reorder]: 2.762e-05 [get_jit_bprop_graph]: 2.57001e-06 [rewriter_after_jit_bprop_graph]: 4.99998e-06 [opt_after_jit_grad]: 0.00047438 [symbol_engine_optimizer]: 9.594e-05, [1] [Cycle 1]: 8.925e-05, [6] [build]: 5.78002e-06 [elim_shapecalc]: 1.195e-05 [elim_not_effective]: 1.868e-05 [opt_reshape]: 9.07001e-06 [fold_const_symbol]: 1.372e-05 [renormalize]: 3.50003e-07 [validate]: 0.00015116 Sums bootstrap : 0.000634s : 0.07% type_inference : 0.471335s : 50.86% event_method : 0.000200s : 0.02% auto_monad : 0.000385s : 0.04% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000099s : 0.01% rewriter_before_opt_a : 0.000203s : 0.02% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000315s : 0.03% jit_opt_a.loop_unroll : 0.000124s : 0.01% jit_opt_a.a_1 : 0.126461s : 13.65% jit_opt_a.with_stream_mark : 0.000087s : 0.01% jit_opt_a.recompute_prepare : 0.000061s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000054s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000024s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000020s : 0.00% jit_opt_a.parameter_eliminate : 0.000010s : 0.00% jit_opt_a.specialize_transform : 0.000041s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000051s : 0.01% jit_opt_a.accelerated_algorithm : 0.000036s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000014s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000035s : 0.00% jit_opt_a.merge_forward : 0.000024s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000081s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000076s : 0.01% jit_opt_a.meta_fg_expand : 0.193922s : 20.93% jit_opt_a.replace_old_param : 0.000155s : 0.02% jit_opt_a.inline_without_move : 0.000137s : 0.01% jit_opt_a.renormalize : 0.128836s : 13.90% jit_opt_a.add_forward_monad_depend : 0.000033s : 0.00% jit_opt_a.auto_monad_grad : 0.000014s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000124s : 0.01% jit_opt_a.cse : 0.000394s : 0.04% jit_opt_a.replace_applicator : 0.000236s : 0.03% py_interpret_to_execute_after_opt_a : 0.000019s : 0.00% rewriter_after_opt_a : 0.000201s : 0.02% convert_after_rewriter : 0.000011s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000677s : 0.07% jit_opt_b.frontend_op_eliminate : 0.000028s : 0.00% jit_opt_b.inline_after_opt_a : 0.000026s : 0.00% cconv : 0.000032s : 0.00% loop_unroll : 0.000470s : 0.05% jit_opt_after_cconv.c_1 : 0.000047s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000003s : 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 : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000037s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000025s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000009s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000009s : 0.00% remove_dup_value : 0.000027s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000008s : 0.00% add_recomputation : 0.000071s : 0.01% cse_after_recomputation.cse : 0.000024s : 0.00% auto_monad_reorder : 0.000028s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000005s : 0.00% opt_after_jit_grad : 0.000474s : 0.05% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000012s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000019s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000014s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000151s : 0.02% Time group info: ------[substitution.] 0.002204 170 1.18% : 0.000026s : 8: substitution.depend_value_elim 0.13% : 0.000003s : 4: substitution.elim_not_effective 0.10% : 0.000002s : 4: substitution.fold_const_symbol 51.24% : 0.001129s : 4: substitution.getattr_setattr_resolve 0.32% : 0.000007s : 5: substitution.graph_param_transform 31.77% : 0.000700s : 15: substitution.inline 1.67% : 0.000037s : 4: substitution.inline_without_move 1.20% : 0.000026s : 20: substitution.j_node_and_user_rematch 0.48% : 0.000010s : 5: substitution.minmaximum_grad 1.55% : 0.000034s : 9: substitution.partial_eliminate 0.68% : 0.000015s : 20: substitution.remove_not_recompute_node 2.14% : 0.000047s : 12: substitution.replace_applicator 0.72% : 0.000016s : 17: substitution.replace_old_param 0.21% : 0.000005s : 1: substitution.set_cell_output_no_recompute 0.79% : 0.000017s : 3: substitution.switch_simplify 1.00% : 0.000022s : 5: substitution.tuple_list_convert_item_index_to_positive 0.68% : 0.000015s : 5: substitution.tuple_list_get_item_depend_reorder 1.66% : 0.000037s : 8: substitution.tuple_list_get_item_eliminator 0.91% : 0.000020s : 8: substitution.updatestate_pure_node_eliminater 1.59% : 0.000035s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.471217 2 99.33% : 0.468075s : 1: type_inference.infer 0.67% : 0.003143s : 1: type_inference.specialize ------[replace.] 0.000528 26 21.61% : 0.000114s : 3: replace.getattr_setattr_resolve 33.75% : 0.000178s : 15: replace.inline 10.27% : 0.000054s : 1: replace.replace_applicator 15.08% : 0.000080s : 3: replace.switch_simplify 14.78% : 0.000078s : 3: replace.tuple_list_get_item_eliminator 4.51% : 0.000024s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001757 26 58.04% : 0.001020s : 3: match.getattr_setattr_resolve 39.21% : 0.000689s : 15: match.inline 0.70% : 0.000012s : 1: match.replace_applicator 0.88% : 0.000016s : 3: match.switch_simplify 0.47% : 0.000008s : 3: match.tuple_list_get_item_eliminator 0.70% : 0.000012s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.123949 3150 0.01% : 0.000008s : 50: predicate.accumulaten_eliminater 0.00% : 0.000002s : 5: predicate.ad_related_special_op_eliminate 0.01% : 0.000007s : 50: predicate.addn_check_dump 0.01% : 0.000008s : 50: predicate.addn_zero_filter 0.01% : 0.000011s : 50: predicate.arithmetic_simplify 0.01% : 0.000007s : 50: predicate.cast_eliminate 0.00% : 0.000001s : 5: predicate.check_bprop_eliminate 0.01% : 0.000006s : 50: predicate.compare_switch_simplify 0.01% : 0.000008s : 50: predicate.depend_value_elim 0.01% : 0.000007s : 50: predicate.dict_get_item_const_eliminator 0.01% : 0.000007s : 50: predicate.dict_get_item_eliminator 0.01% : 0.000008s : 50: predicate.dict_set_item_eliminator 0.00% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.00% : 0.000001s : 5: predicate.elim_not_effective 0.00% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 0.01% : 0.000007s : 50: predicate.environ_add_const_eliminate 0.01% : 0.000007s : 50: predicate.environ_get_add_eliminate 0.01% : 0.000008s : 50: predicate.environ_get_depend_swap 0.01% : 0.000008s : 50: predicate.environ_get_eliminate 99.58% : 0.123427s : 50: predicate.environ_get_set_eliminate 0.00% : 0.000000s : 5: predicate.fold_const_symbol 0.00% : 0.000005s : 26: predicate.get_grad_eliminate 0.01% : 0.000009s : 20: predicate.getattr_setattr_resolve 0.00% : 0.000000s : 5: predicate.graph_param_transform 0.02% : 0.000023s : 79: predicate.inline 0.01% : 0.000015s : 87: predicate.inline_without_move 0.00% : 0.000002s : 26: predicate.j_node_and_user_rematch 0.00% : 0.000005s : 26: predicate.less_batch_normalization 0.01% : 0.000010s : 53: predicate.list_to_tuple_eliminator_ 0.01% : 0.000009s : 58: predicate.load_eliminater 0.00% : 0.000002s : 5: predicate.loop_unroll_after_grad 0.02% : 0.000019s : 127: predicate.loop_unroll_before_grad 0.01% : 0.000010s : 55: predicate.make_slice_get_slice_eliminator 0.01% : 0.000007s : 50: predicate.merge_addn 0.01% : 0.000007s : 50: predicate.minmaximum_grad 0.00% : 0.000003s : 5: predicate.mutable_eliminate 0.00% : 0.000001s : 5: predicate.opt_reshape 0.01% : 0.000011s : 58: predicate.partial_eliminate 0.01% : 0.000007s : 50: predicate.print_const_string_wrapper 0.01% : 0.000011s : 50: predicate.reduce_eliminate 0.01% : 0.000008s : 53: predicate.redundant_stop_gradient_eliminater 0.00% : 0.000003s : 26: predicate.remove_not_recompute_node 0.01% : 0.000013s : 126: predicate.replace_applicator 0.01% : 0.000008s : 87: predicate.replace_old_param 0.00% : 0.000001s : 5: predicate.reset_defer_inline 0.01% : 0.000008s : 50: predicate.reshape_eliminate 0.01% : 0.000008s : 50: predicate.row_tensor_add_zeros_like 0.00% : 0.000002s : 5: predicate.row_tensor_eliminate 0.01% : 0.000008s : 50: predicate.same_eliminate 0.00% : 0.000003s : 28: predicate.set_cell_output_no_recompute 0.00% : 0.000002s : 10: predicate.special_op_eliminate 0.00% : 0.000005s : 26: predicate.specialize_transform 0.01% : 0.000009s : 50: predicate.split_environ_get_set_with_tuple_value 0.01% : 0.000008s : 50: predicate.stack_unstack_eliminate 0.00% : 0.000001s : 5: predicate.switch_call_monad_eliminater 0.01% : 0.000016s : 69: predicate.switch_defer_inline 0.01% : 0.000012s : 69: predicate.switch_layer_defer_inline 0.03% : 0.000035s : 207: predicate.switch_simplify 0.01% : 0.000008s : 50: predicate.tile_eliminate 0.01% : 0.000007s : 50: predicate.transpose_eliminate 0.01% : 0.000009s : 50: predicate.tuple_list_convert_item_index_to_positive 0.01% : 0.000008s : 50: predicate.tuple_list_get_item_depend_reorder 0.02% : 0.000020s : 63: predicate.tuple_list_get_item_eliminator 0.01% : 0.000010s : 50: predicate.tuple_list_set_item_eliminator 0.01% : 0.000008s : 53: predicate.tuple_to_list_eliminator_ 0.01% : 0.000008s : 58: predicate.updatestate_pure_node_eliminater 0.01% : 0.000015s : 85: predicate.updatestate_useless_node_eliminater 0.01% : 0.000010s : 50: predicate.value_based_eliminate 0.00% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.00% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.004359 44 69.17% : 0.003015s : 18: func_graph_cloner_run.FuncGraphClonerGraph 30.83% : 0.001344s : 26: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.203983 89 0.01% : 0.000074s : 1: add_recomputation 0.03% : 0.000397s : 1: auto_monad 0.00% : 0.000031s : 1: auto_monad_reorder 0.05% : 0.000659s : 1: bootstrap 0.00% : 0.000035s : 1: cconv 0.00% : 0.000014s : 1: convert_after_rewriter 0.00% : 0.000038s : 1: cse_after_recomputation 0.00% : 0.000010s : 1: environ_conv 0.02% : 0.000209s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 39.07% : 0.470384s : 1: jit_opt_a 0.02% : 0.000216s : 1: jit_opt_after_cconv 0.01% : 0.000077s : 1: jit_opt_b 0.04% : 0.000477s : 1: loop_unroll 0.06% : 0.000685s : 1: mutable_eliminate 10.61% : 0.127740s : 39: opt.transform.jit_opt_a 0.01% : 0.000085s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000047s : 4: opt.transform.jit_opt_b 0.00% : 0.000017s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000020s : 1: opt.transform.mutable_eliminate 0.00% : 0.000031s : 1: opt.transform.opt_after_jit_grad 0.11% : 0.001316s : 2: opt.transform.opt_resolve 0.00% : 0.000049s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000482s : 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 0.01% : 0.000104s : 1: py_interpret_to_execute 0.00% : 0.000022s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000030s : 1: remove_dup_value 10.42% : 0.125473s : 2: renormalize.infer 0.28% : 0.003338s : 2: renormalize.specialize 0.00% : 0.000007s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000206s : 1: rewriter_after_opt_a 0.02% : 0.000208s : 1: rewriter_before_opt_a 0.01% : 0.000099s : 1: symbol_engine_optimizer 39.15% : 0.471356s : 1: type_inference . [hook] pytest_runtest_teardown:test_narrow_non_contiguous[KBK] tests/st/mint/test_narrow.py::test_narrow_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 268.38s (0:04:28) ==================