==================================================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_004/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_reshape.py . [hook] pytest_runtest_teardown:test_reshape_std[pynative] tests/st/mint/test_reshape.py::test_reshape_std[pynative],max_mem:2.0M [WARNING] PARSER(170253,ffff811a4f30,python3.9):2026-01-29-17:40:09.428.616 [mindspore/ccsrc/frontend/jit/ps/parse/data_converter.cc:661] CheckAPI] The mint interface reshape was called, and the operators under this interface have different view capabilities on pynative and graph mode. Use this interface with caution in graph mode, as it may produce unexpected results. For more information, please refer to: https://www.mindspore.cn/docs/en/master/features/view.html TotalTime = 4.87894, [33] [bootstrap]: 0.00083431 [type_inference]: 0.949517 [event_method]: 2.553e-05 [auto_monad]: 0.00011509 [graph_reusing]: 6.88e-06 [pre_auto_parallel]: 1.426e-05 [py_interpret_to_execute]: 0.00040631 [rewriter_before_opt_a]: 0.0001025 [expand_dump_flag]: 4.95001e-06 [jit_opt_a]: 0.100583, [2] [Cycle 1]: 0.0906481, [27] [switch_simplify]: 7.822e-05 [loop_unroll]: 0.0857842 [a_1]: 0.00080889 [with_stream_mark]: 3.175e-05 [recompute_prepare]: 1.173e-05 [updatestate_depend_eliminate]: 5.46e-06 [updatestate_assign_eliminate]: 3.58999e-06 [updatestate_loads_eliminate]: 3.31001e-06 [parameter_eliminate]: 2.94999e-06 [specialize_transform]: 8.59998e-06 [updatestate_useless_node_eliminater]: 7.09001e-06 [accelerated_algorithm]: 9.15001e-06 [meta_shard_fg_expand]: 2.86e-06 [get_grad_eliminate_]: 7.38e-06 [merge_forward]: 5.02999e-06 [cell_reuse_recompute_pass]: 1.09998e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.693e-05 [j_node_and_user_rematch]: 1.3e-05 [meta_fg_expand]: 3.40998e-06 [replace_old_param]: 1.2e-05 [inline_without_move]: 6.68e-06 [renormalize]: 0.00339002 [add_forward_monad_depend]: 1.778e-05 [auto_monad_grad]: 3.01999e-06 [auto_monad_eliminator]: 2.244e-05 [cse]: 4.893e-05 [replace_applicator]: 2.377e-05 [Cycle 2]: 0.0004371, [27] [switch_simplify]: 8.60001e-06 [loop_unroll]: 6.81001e-06 [a_1]: 0.00015209 [with_stream_mark]: 1.852e-05 [recompute_prepare]: 6.88e-06 [updatestate_depend_eliminate]: 4.55001e-06 [updatestate_assign_eliminate]: 3.66001e-06 [updatestate_loads_eliminate]: 2.76e-06 [parameter_eliminate]: 2.09999e-06 [specialize_transform]: 6.11998e-06 [updatestate_useless_node_eliminater]: 5.81e-06 [accelerated_algorithm]: 7.41001e-06 [meta_shard_fg_expand]: 2.32001e-06 [get_grad_eliminate_]: 5.62999e-06 [merge_forward]: 4.44998e-06 [cell_reuse_recompute_pass]: 2.80997e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.618e-05 [j_node_and_user_rematch]: 9.86e-06 [meta_fg_expand]: 2.47001e-06 [replace_old_param]: 9.44e-06 [inline_without_move]: 6.09001e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 1.32e-06 [auto_monad_grad]: 1.62999e-06 [auto_monad_eliminator]: 7.55e-06 [cse]: 1.361e-05 [replace_applicator]: 6.38998e-06 [py_interpret_to_execute_after_opt_a]: 1.593e-05 [rewriter_after_opt_a]: 7.099e-05 [convert_after_rewriter]: 1.01e-05 [order_py_execute_after_rewriter]: 5.52001e-06 [mutable_eliminate]: 0.00081195 [jit_opt_b]: 6.523e-05, [1] [Cycle 1]: 5.652e-05, [2] [frontend_op_eliminate]: 2.301e-05 [inline_after_opt_a]: 1.979e-05 [cconv]: 3.568e-05 [loop_unroll]: 0.00050421 [jit_opt_after_cconv]: 0.00024057, [1] [Cycle 1]: 0.00023217, [11] [c_1]: 2.895e-05 [parameter_eliminate]: 4.89003e-06 [updatestate_depend_eliminate]: 9.14e-06 [updatestate_assign_eliminate]: 4.13999e-06 [updatestate_loads_eliminate]: 2.95998e-06 [cse]: 3.551e-05 [call_graph_tuple_transform]: 4.255e-05 [tuple_list_get_item_eliminator]: 7.3e-06 [none_parameter_eliminate]: 1.85001e-06 [renormalize]: 6.89994e-07 [switch_simplify]: 8.69e-06 [remove_dup_value]: 1.921e-05 [partial_unused_args_eliminate]: 2.73e-06 [environ_conv]: 2.559e-05 [add_recomputation]: 6.549e-05 [cse_after_recomputation]: 2.955e-05, [1] [Cycle 1]: 2.214e-05, [1] [cse]: 1.425e-05 [auto_monad_reorder]: 2.483e-05 [get_jit_bprop_graph]: 2.11e-06 [rewriter_after_jit_bprop_graph]: 0.00022557 [opt_after_jit_grad]: 0.00074327 [symbol_engine_optimizer]: 9.854e-05, [1] [Cycle 1]: 9.039e-05, [6] [build]: 4.50001e-06 [elim_shapecalc]: 1.007e-05 [elim_not_effective]: 1.768e-05 [opt_reshape]: 1.254e-05 [fold_const_symbol]: 1.182e-05 [renormalize]: 3.09985e-07 [validate]: 7.417e-05 [backend_pass]: 1.09e-06 [task_emit]: 3.82383 [execute]: 1.058e-05 Sums bootstrap : 0.000834s : 0.02% type_inference : 0.949517s : 19.50% event_method : 0.000026s : 0.00% auto_monad : 0.000115s : 0.00% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000014s : 0.00% py_interpret_to_execute : 0.000406s : 0.01% rewriter_before_opt_a : 0.000102s : 0.00% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000087s : 0.00% jit_opt_a.loop_unroll : 0.085791s : 1.76% jit_opt_a.a_1 : 0.000961s : 0.02% jit_opt_a.with_stream_mark : 0.000050s : 0.00% jit_opt_a.recompute_prepare : 0.000019s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000010s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000007s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000006s : 0.00% jit_opt_a.parameter_eliminate : 0.000005s : 0.00% jit_opt_a.specialize_transform : 0.000015s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000013s : 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_ : 0.000013s : 0.00% jit_opt_a.merge_forward : 0.000009s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000043s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000023s : 0.00% jit_opt_a.meta_fg_expand : 0.000006s : 0.00% jit_opt_a.replace_old_param : 0.000021s : 0.00% jit_opt_a.inline_without_move : 0.000013s : 0.00% jit_opt_a.renormalize : 0.003390s : 0.07% jit_opt_a.add_forward_monad_depend : 0.000019s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000030s : 0.00% jit_opt_a.cse : 0.000063s : 0.00% jit_opt_a.replace_applicator : 0.000030s : 0.00% py_interpret_to_execute_after_opt_a : 0.000016s : 0.00% rewriter_after_opt_a : 0.000071s : 0.00% convert_after_rewriter : 0.000010s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.000812s : 0.02% jit_opt_b.frontend_op_eliminate : 0.000023s : 0.00% jit_opt_b.inline_after_opt_a : 0.000020s : 0.00% cconv : 0.000036s : 0.00% loop_unroll : 0.000504s : 0.01% jit_opt_after_cconv.c_1 : 0.000029s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000036s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000043s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000007s : 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 : 0.000019s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000026s : 0.00% add_recomputation : 0.000065s : 0.00% cse_after_recomputation.cse : 0.000014s : 0.00% auto_monad_reorder : 0.000025s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000226s : 0.00% opt_after_jit_grad : 0.000743s : 0.02% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000010s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000018s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000013s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000012s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000074s : 0.00% backend_pass : 0.000001s : 0.00% task_emit : 3.823827s : 78.54% execute : 0.000011s : 0.00% Time group info: ------[substitution.] 0.000351 28 0.75% : 0.000003s : 2: substitution.elim_not_effective 0.70% : 0.000002s : 2: substitution.fold_const_symbol 5.90% : 0.000021s : 4: substitution.graph_param_transform 77.35% : 0.000271s : 5: substitution.inline 1.51% : 0.000005s : 4: substitution.j_node_and_user_rematch 0.62% : 0.000002s : 1: substitution.opt_reshape 1.67% : 0.000006s : 4: substitution.remove_not_recompute_node 1.77% : 0.000006s : 2: substitution.replace_old_param 7.06% : 0.000025s : 3: substitution.reshape_eliminate 2.68% : 0.000009s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.949390 2 99.67% : 0.946233s : 1: type_inference.infer 0.33% : 0.003157s : 1: type_inference.specialize ------[replace.] 0.000083 6 86.21% : 0.000071s : 5: replace.inline 13.79% : 0.000011s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000277 6 96.86% : 0.000268s : 5: match.inline 3.14% : 0.000009s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.085886 867 0.00% : 0.000002s : 13: predicate.accumulaten_eliminater 0.00% : 0.000002s : 4: predicate.ad_related_special_op_eliminate 0.00% : 0.000002s : 13: predicate.addn_check_dump 0.00% : 0.000002s : 13: predicate.addn_zero_filter 0.00% : 0.000003s : 13: predicate.arithmetic_simplify 0.00% : 0.000003s : 13: predicate.cast_eliminate 0.00% : 0.000001s : 4: predicate.check_bprop_eliminate 0.00% : 0.000002s : 13: predicate.compare_switch_simplify 0.00% : 0.000002s : 13: predicate.depend_value_elim 0.00% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 0.00% : 0.000002s : 13: predicate.dict_get_item_eliminator 0.00% : 0.000002s : 13: predicate.dict_set_item_eliminator 0.00% : 0.000002s : 4: predicate.dumpgradient_eliminate 0.00% : 0.000001s : 4: predicate.elim_not_effective 0.00% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 0.00% : 0.000002s : 13: predicate.environ_add_const_eliminate 0.00% : 0.000002s : 13: predicate.environ_get_add_eliminate 0.00% : 0.000002s : 13: predicate.environ_get_depend_swap 0.00% : 0.000002s : 13: predicate.environ_get_eliminate 0.00% : 0.000002s : 13: predicate.environ_get_set_eliminate 0.00% : 0.000000s : 4: predicate.fold_const_symbol 0.00% : 0.000002s : 8: predicate.get_grad_eliminate 0.00% : 0.000001s : 4: predicate.graph_param_transform 0.01% : 0.000009s : 27: predicate.inline 0.00% : 0.000001s : 8: predicate.inline_without_move 0.00% : 0.000001s : 8: predicate.j_node_and_user_rematch 0.00% : 0.000003s : 8: predicate.less_batch_normalization 0.00% : 0.000002s : 14: predicate.list_to_tuple_eliminator_ 0.00% : 0.000003s : 18: predicate.load_eliminater 0.00% : 0.000002s : 4: predicate.loop_unroll_after_grad 99.80% : 0.085713s : 37: predicate.loop_unroll_before_grad 0.00% : 0.000004s : 17: predicate.make_slice_get_slice_eliminator 0.00% : 0.000002s : 13: predicate.merge_addn 0.00% : 0.000002s : 13: predicate.minmaximum_grad 0.00% : 0.000002s : 4: predicate.mutable_eliminate 0.00% : 0.000001s : 4: predicate.opt_reshape 0.00% : 0.000004s : 18: predicate.partial_eliminate 0.00% : 0.000002s : 13: predicate.print_const_string_wrapper 0.00% : 0.000003s : 13: predicate.reduce_eliminate 0.00% : 0.000002s : 14: predicate.redundant_stop_gradient_eliminater 0.00% : 0.000001s : 8: predicate.remove_not_recompute_node 0.00% : 0.000003s : 22: predicate.replace_applicator 0.00% : 0.000001s : 8: predicate.replace_old_param 0.00% : 0.000001s : 4: predicate.reset_defer_inline 0.00% : 0.000003s : 13: predicate.reshape_eliminate 0.00% : 0.000002s : 13: predicate.row_tensor_add_zeros_like 0.00% : 0.000002s : 4: predicate.row_tensor_eliminate 0.00% : 0.000002s : 13: predicate.same_eliminate 0.00% : 0.000001s : 8: predicate.set_cell_output_no_recompute 0.00% : 0.000002s : 8: predicate.special_op_eliminate 0.00% : 0.000002s : 8: predicate.specialize_transform 0.00% : 0.000003s : 13: predicate.split_environ_get_set_with_tuple_value 0.00% : 0.000003s : 13: predicate.stack_unstack_eliminate 0.00% : 0.000001s : 4: predicate.switch_call_monad_eliminater 0.01% : 0.000007s : 19: predicate.switch_defer_inline 0.00% : 0.000003s : 19: predicate.switch_layer_defer_inline 0.01% : 0.000012s : 60: predicate.switch_simplify 0.00% : 0.000002s : 13: predicate.tile_eliminate 0.00% : 0.000002s : 13: predicate.transpose_eliminate 0.00% : 0.000003s : 13: predicate.tuple_list_convert_item_index_to_positive 0.00% : 0.000003s : 13: predicate.tuple_list_get_item_depend_reorder 0.01% : 0.000007s : 22: predicate.tuple_list_get_item_eliminator 0.00% : 0.000003s : 13: predicate.tuple_list_set_item_eliminator 0.01% : 0.000009s : 14: predicate.tuple_to_list_eliminator_ 0.00% : 0.000003s : 18: predicate.updatestate_pure_node_eliminater 0.01% : 0.000005s : 26: predicate.updatestate_useless_node_eliminater 0.00% : 0.000004s : 13: predicate.value_based_eliminate 0.00% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.00% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.003255 23 74.91% : 0.002439s : 16: func_graph_cloner_run.FuncGraphClonerGraph 25.09% : 0.000817s : 7: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 4.969364 76 0.00% : 0.000069s : 1: add_recomputation 0.00% : 0.000119s : 1: auto_monad 0.00% : 0.000028s : 1: auto_monad_reorder 0.00% : 0.000004s : 1: backend_pass 0.02% : 0.000863s : 1: bootstrap 0.00% : 0.000039s : 1: cconv 0.00% : 0.000013s : 1: convert_after_rewriter 0.00% : 0.000032s : 1: cse_after_recomputation 0.00% : 0.000029s : 1: environ_conv 0.00% : 0.000031s : 1: event_method 0.00% : 0.000016s : 1: execute 0.00% : 0.000009s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 2.02% : 0.100587s : 1: jit_opt_a 0.00% : 0.000244s : 1: jit_opt_after_cconv 0.00% : 0.000068s : 1: jit_opt_b 0.01% : 0.000514s : 1: loop_unroll 0.02% : 0.000822s : 1: mutable_eliminate 1.75% : 0.086985s : 26: opt.transform.jit_opt_a 0.00% : 0.000083s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000034s : 4: opt.transform.jit_opt_b 0.00% : 0.000015s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000018s : 1: opt.transform.mutable_eliminate 0.00% : 0.000050s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000049s : 4: opt.transform.symbol_engine_opt 0.02% : 0.000756s : 1: opt_after_jit_grad 0.00% : 0.000008s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000017s : 1: pre_auto_parallel 0.01% : 0.000414s : 1: py_interpret_to_execute 0.00% : 0.000018s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000023s : 1: remove_dup_value 0.05% : 0.002436s : 1: renormalize.infer 0.02% : 0.000941s : 1: renormalize.specialize 0.00% : 0.000230s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000075s : 1: rewriter_after_opt_a 0.00% : 0.000108s : 1: rewriter_before_opt_a 0.00% : 0.000101s : 1: symbol_engine_optimizer 76.95% : 3.823857s : 1: task_emit 19.11% : 0.949541s : 1: type_inference 0.00% : 0.000099s : 1: validate TotalTime = 1.39832, [33] [bootstrap]: 0.00058089 [type_inference]: 0.997483 [event_method]: 0.00066 [auto_monad]: 0.00021187 [graph_reusing]: 1.084e-05 [pre_auto_parallel]: 4.41002e-06 [py_interpret_to_execute]: 5.827e-05 [rewriter_before_opt_a]: 0.00018873 [expand_dump_flag]: 4.88001e-06 [jit_opt_a]: 0.395501, [2] [Cycle 1]: 0.178684, [27] [switch_simplify]: 0.00029529 [loop_unroll]: 9.862e-05 [a_1]: 0.00177541 [with_stream_mark]: 4.27e-05 [recompute_prepare]: 3.286e-05 [updatestate_depend_eliminate]: 1.215e-05 [updatestate_assign_eliminate]: 8.50999e-06 [updatestate_loads_eliminate]: 7.94002e-06 [parameter_eliminate]: 3.63e-06 [specialize_transform]: 1.846e-05 [updatestate_useless_node_eliminater]: 1.638e-05 [accelerated_algorithm]: 1.894e-05 [meta_shard_fg_expand]: 9.56998e-06 [get_grad_eliminate_]: 1.822e-05 [merge_forward]: 1.043e-05 [cell_reuse_recompute_pass]: 1.44e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.535e-05 [j_node_and_user_rematch]: 2.858e-05 [meta_fg_expand]: 0.00273775 [replace_old_param]: 0.0001014 [inline_without_move]: 7.789e-05 [renormalize]: 0.1729 [add_forward_monad_depend]: 1.122e-05 [auto_monad_grad]: 3.11999e-06 [auto_monad_eliminator]: 2.074e-05 [cse]: 3.401e-05 [replace_applicator]: 2.61e-05 [Cycle 2]: 0.00071494, [27] [switch_simplify]: 6.87002e-06 [loop_unroll]: 5.57999e-06 [a_1]: 8.113e-05 [with_stream_mark]: 1.689e-05 [recompute_prepare]: 4.57e-06 [updatestate_depend_eliminate]: 3.32002e-06 [updatestate_assign_eliminate]: 2.61999e-06 [updatestate_loads_eliminate]: 2.29001e-06 [parameter_eliminate]: 1.92999e-06 [specialize_transform]: 4.68999e-06 [updatestate_useless_node_eliminater]: 4.10998e-06 [accelerated_algorithm]: 4.71002e-06 [meta_shard_fg_expand]: 2.49999e-06 [get_grad_eliminate_]: 4e-06 [merge_forward]: 3.36001e-06 [cell_reuse_recompute_pass]: 3.46001e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.948e-05 [j_node_and_user_rematch]: 8.37e-06 [meta_fg_expand]: 0.00032462 [replace_old_param]: 1.286e-05 [inline_without_move]: 6.28e-06 [renormalize]: 2.89991e-07 [add_forward_monad_depend]: 3.11999e-06 [auto_monad_grad]: 1.45001e-06 [auto_monad_eliminator]: 9.31e-06 [cse]: 1.93e-05 [replace_applicator]: 5.95002e-06 [py_interpret_to_execute_after_opt_a]: 1.762e-05 [rewriter_after_opt_a]: 0.00050626 [convert_after_rewriter]: 1.39e-05 [order_py_execute_after_rewriter]: 5.09003e-06 [mutable_eliminate]: 0.00088569 [jit_opt_b]: 6.456e-05, [1] [Cycle 1]: 5.445e-05, [2] [frontend_op_eliminate]: 1.665e-05 [inline_after_opt_a]: 2.134e-05 [cconv]: 4.021e-05 [loop_unroll]: 0.00060714 [jit_opt_after_cconv]: 0.00019186, [1] [Cycle 1]: 0.00018318, [11] [c_1]: 2.023e-05 [parameter_eliminate]: 5.56998e-06 [updatestate_depend_eliminate]: 9.15001e-06 [updatestate_assign_eliminate]: 3.51001e-06 [updatestate_loads_eliminate]: 3.13e-06 [cse]: 3.745e-05 [call_graph_tuple_transform]: 2.385e-05 [tuple_list_get_item_eliminator]: 6.19999e-06 [none_parameter_eliminate]: 1.60001e-06 [renormalize]: 5.29981e-07 [switch_simplify]: 5.84999e-06 [remove_dup_value]: 1.774e-05 [partial_unused_args_eliminate]: 3.70003e-06 [environ_conv]: 6.93e-06 [add_recomputation]: 4.84e-05 [cse_after_recomputation]: 2.662e-05, [1] [Cycle 1]: 1.932e-05, [1] [cse]: 1.064e-05 [auto_monad_reorder]: 1.536e-05 [get_jit_bprop_graph]: 2.90002e-06 [rewriter_after_jit_bprop_graph]: 8.07003e-06 [opt_after_jit_grad]: 0.00065313 [symbol_engine_optimizer]: 0.00013082, [1] [Cycle 1]: 0.00012101, [6] [build]: 5.86e-06 [elim_shapecalc]: 9.47999e-06 [elim_not_effective]: 1.64e-05 [opt_reshape]: 6.48998e-06 [fold_const_symbol]: 4.165e-05 [renormalize]: 6.69999e-07 [validate]: 4.748e-05 [backend_pass]: 1.04998e-06 [task_emit]: 2.921e-05 [execute]: 1.35999e-06 Sums bootstrap : 0.000581s : 0.05% type_inference : 0.997483s : 84.44% event_method : 0.000660s : 0.06% auto_monad : 0.000212s : 0.02% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000058s : 0.00% rewriter_before_opt_a : 0.000189s : 0.02% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000302s : 0.03% jit_opt_a.loop_unroll : 0.000104s : 0.01% jit_opt_a.a_1 : 0.001857s : 0.16% jit_opt_a.with_stream_mark : 0.000060s : 0.01% jit_opt_a.recompute_prepare : 0.000037s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000015s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000011s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000010s : 0.00% jit_opt_a.parameter_eliminate : 0.000006s : 0.00% jit_opt_a.specialize_transform : 0.000023s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000020s : 0.00% jit_opt_a.accelerated_algorithm : 0.000024s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000012s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000022s : 0.00% jit_opt_a.merge_forward : 0.000014s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000055s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000037s : 0.00% jit_opt_a.meta_fg_expand : 0.003062s : 0.26% jit_opt_a.replace_old_param : 0.000114s : 0.01% jit_opt_a.inline_without_move : 0.000084s : 0.01% jit_opt_a.renormalize : 0.172901s : 14.64% jit_opt_a.add_forward_monad_depend : 0.000014s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000030s : 0.00% jit_opt_a.cse : 0.000053s : 0.00% jit_opt_a.replace_applicator : 0.000032s : 0.00% py_interpret_to_execute_after_opt_a : 0.000018s : 0.00% rewriter_after_opt_a : 0.000506s : 0.04% convert_after_rewriter : 0.000014s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000886s : 0.07% jit_opt_b.frontend_op_eliminate : 0.000017s : 0.00% jit_opt_b.inline_after_opt_a : 0.000021s : 0.00% cconv : 0.000040s : 0.00% loop_unroll : 0.000607s : 0.05% jit_opt_after_cconv.c_1 : 0.000020s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000037s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000006s : 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 : 0.000006s : 0.00% remove_dup_value : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000004s : 0.00% environ_conv : 0.000007s : 0.00% add_recomputation : 0.000048s : 0.00% cse_after_recomputation.cse : 0.000011s : 0.00% auto_monad_reorder : 0.000015s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000008s : 0.00% opt_after_jit_grad : 0.000653s : 0.06% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000009s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000016s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000006s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000042s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000047s : 0.00% backend_pass : 0.000001s : 0.00% task_emit : 0.000029s : 0.00% execute : 0.000001s : 0.00% Time group info: ------[substitution.] 0.000668 69 0.26% : 0.000002s : 1: substitution.elim_not_effective 4.85% : 0.000032s : 1: substitution.fold_const_symbol 0.88% : 0.000006s : 1: substitution.graph_param_transform 72.48% : 0.000484s : 14: substitution.inline 4.41% : 0.000029s : 2: substitution.inline_without_move 1.23% : 0.000008s : 9: substitution.j_node_and_user_rematch 0.79% : 0.000005s : 2: substitution.minmaximum_grad 1.53% : 0.000010s : 9: substitution.partial_eliminate 1.29% : 0.000009s : 9: substitution.remove_not_recompute_node 0.52% : 0.000003s : 1: substitution.replace_applicator 1.25% : 0.000008s : 7: substitution.replace_old_param 2.05% : 0.000014s : 2: substitution.reshape_eliminate 0.71% : 0.000005s : 1: substitution.set_cell_output_no_recompute 2.64% : 0.000018s : 3: substitution.switch_simplify 1.31% : 0.000009s : 2: substitution.tuple_list_convert_item_index_to_positive 1.01% : 0.000007s : 2: substitution.tuple_list_get_item_depend_reorder 2.80% : 0.000019s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.997319 2 99.46% : 0.991944s : 1: type_inference.infer 0.54% : 0.005375s : 1: type_inference.specialize ------[replace.] 0.000261 18 57.18% : 0.000149s : 14: replace.inline 37.29% : 0.000097s : 3: replace.switch_simplify 5.53% : 0.000014s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000494 18 96.02% : 0.000475s : 14: match.inline 3.12% : 0.000015s : 3: match.switch_simplify 0.86% : 0.000004s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000293 1710 1.44% : 0.000004s : 29: predicate.accumulaten_eliminater 0.60% : 0.000002s : 1: predicate.ad_related_special_op_eliminate 1.18% : 0.000003s : 29: predicate.addn_check_dump 1.37% : 0.000004s : 29: predicate.addn_zero_filter 2.04% : 0.000006s : 29: predicate.arithmetic_simplify 1.32% : 0.000004s : 29: predicate.cast_eliminate 0.16% : 0.000000s : 1: predicate.check_bprop_eliminate 1.26% : 0.000004s : 29: predicate.compare_switch_simplify 1.33% : 0.000004s : 29: predicate.depend_value_elim 1.23% : 0.000004s : 29: predicate.dict_get_item_const_eliminator 1.35% : 0.000004s : 29: predicate.dict_get_item_eliminator 1.41% : 0.000004s : 29: predicate.dict_set_item_eliminator 0.64% : 0.000002s : 1: predicate.dumpgradient_eliminate 0.16% : 0.000000s : 1: predicate.elim_not_effective 0.37% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.28% : 0.000004s : 29: predicate.environ_add_const_eliminate 1.34% : 0.000004s : 29: predicate.environ_get_add_eliminate 1.39% : 0.000004s : 29: predicate.environ_get_depend_swap 1.66% : 0.000005s : 29: predicate.environ_get_eliminate 1.19% : 0.000004s : 29: predicate.environ_get_set_eliminate 0.06% : 0.000000s : 1: predicate.fold_const_symbol 1.23% : 0.000004s : 14: predicate.get_grad_eliminate 0.05% : 0.000000s : 1: predicate.graph_param_transform 4.76% : 0.000014s : 46: predicate.inline 2.73% : 0.000008s : 38: predicate.inline_without_move 0.37% : 0.000001s : 14: predicate.j_node_and_user_rematch 1.08% : 0.000003s : 14: predicate.less_batch_normalization 1.57% : 0.000005s : 30: predicate.list_to_tuple_eliminator_ 1.68% : 0.000005s : 31: predicate.load_eliminater 0.73% : 0.000002s : 1: predicate.loop_unroll_after_grad 4.01% : 0.000012s : 78: predicate.loop_unroll_before_grad 1.78% : 0.000005s : 30: predicate.make_slice_get_slice_eliminator 1.28% : 0.000004s : 29: predicate.merge_addn 1.27% : 0.000004s : 29: predicate.minmaximum_grad 0.95% : 0.000003s : 1: predicate.mutable_eliminate 0.18% : 0.000001s : 1: predicate.opt_reshape 2.09% : 0.000006s : 31: predicate.partial_eliminate 1.45% : 0.000004s : 29: predicate.print_const_string_wrapper 2.07% : 0.000006s : 29: predicate.reduce_eliminate 1.44% : 0.000004s : 30: predicate.redundant_stop_gradient_eliminater 0.52% : 0.000002s : 14: predicate.remove_not_recompute_node 1.74% : 0.000005s : 32: predicate.replace_applicator 1.34% : 0.000004s : 38: predicate.replace_old_param 0.18% : 0.000001s : 1: predicate.reset_defer_inline 1.81% : 0.000005s : 29: predicate.reshape_eliminate 1.45% : 0.000004s : 29: predicate.row_tensor_add_zeros_like 0.30% : 0.000001s : 1: predicate.row_tensor_eliminate 1.39% : 0.000004s : 29: predicate.same_eliminate 0.47% : 0.000001s : 14: predicate.set_cell_output_no_recompute 0.39% : 0.000001s : 2: predicate.special_op_eliminate 0.94% : 0.000003s : 14: predicate.specialize_transform 1.80% : 0.000005s : 29: predicate.split_environ_get_set_with_tuple_value 1.47% : 0.000004s : 29: predicate.stack_unstack_eliminate 0.14% : 0.000000s : 1: predicate.switch_call_monad_eliminater 3.78% : 0.000011s : 44: predicate.switch_defer_inline 2.33% : 0.000007s : 44: predicate.switch_layer_defer_inline 7.66% : 0.000022s : 129: predicate.switch_simplify 1.47% : 0.000004s : 29: predicate.tile_eliminate 1.36% : 0.000004s : 29: predicate.transpose_eliminate 1.94% : 0.000006s : 29: predicate.tuple_list_convert_item_index_to_positive 1.55% : 0.000005s : 29: predicate.tuple_list_get_item_depend_reorder 2.98% : 0.000009s : 32: predicate.tuple_list_get_item_eliminator 1.99% : 0.000006s : 29: predicate.tuple_list_set_item_eliminator 1.49% : 0.000004s : 30: predicate.tuple_to_list_eliminator_ 1.53% : 0.000004s : 31: predicate.updatestate_pure_node_eliminater 2.53% : 0.000007s : 45: predicate.updatestate_useless_node_eliminater 1.59% : 0.000005s : 29: predicate.value_based_eliminate 0.12% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.23% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.005433 47 83.45% : 0.004534s : 30: func_graph_cloner_run.FuncGraphClonerGraph 16.55% : 0.000899s : 17: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.574008 76 0.00% : 0.000051s : 1: add_recomputation 0.01% : 0.000221s : 1: auto_monad 0.00% : 0.000018s : 1: auto_monad_reorder 0.00% : 0.000004s : 1: backend_pass 0.04% : 0.000611s : 1: bootstrap 0.00% : 0.000043s : 1: cconv 0.00% : 0.000018s : 1: convert_after_rewriter 0.00% : 0.000029s : 1: cse_after_recomputation 0.00% : 0.000010s : 1: environ_conv 0.04% : 0.000677s : 1: event_method 0.00% : 0.000004s : 1: execute 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000014s : 1: graph_reusing 25.13% : 0.395506s : 1: jit_opt_a 0.01% : 0.000196s : 1: jit_opt_after_cconv 0.00% : 0.000068s : 1: jit_opt_b 0.04% : 0.000619s : 1: loop_unroll 0.06% : 0.000899s : 1: mutable_eliminate 0.17% : 0.002658s : 26: opt.transform.jit_opt_a 0.00% : 0.000050s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000026s : 4: opt.transform.jit_opt_b 0.00% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000021s : 1: opt.transform.mutable_eliminate 0.00% : 0.000026s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000068s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000668s : 1: opt_after_jit_grad 0.00% : 0.000007s : 1: order_py_execute_after_rewriter 0.00% : 0.000006s : 1: partial_unused_args_eliminate 0.00% : 0.000007s : 1: pre_auto_parallel 0.00% : 0.000061s : 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 10.90% : 0.171613s : 1: renormalize.infer 0.08% : 0.001270s : 1: renormalize.specialize 0.00% : 0.000010s : 1: rewriter_after_jit_bprop_graph 0.03% : 0.000515s : 1: rewriter_after_opt_a 0.01% : 0.000192s : 1: rewriter_before_opt_a 0.01% : 0.000134s : 1: symbol_engine_optimizer 0.00% : 0.000036s : 1: task_emit 63.37% : 0.997510s : 1: type_inference 0.00% : 0.000071s : 1: validate . [hook] pytest_runtest_teardown:test_reshape_std[KBK] tests/st/mint/test_reshape.py::test_reshape_std[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 202.75s (0:03:22) ==================