==================================================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_007/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_non_contiguous[pynative] tests/st/mint/test_reshape.py::test_reshape_non_contiguous[pynative],max_mem:2.0M [WARNING] PARSER(160890,ffffa9218f30,python3.9):2026-01-29-17:38:10.121.466 [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 = 0.889581, [30] [bootstrap]: 0.00567251 [type_inference]: 0.851838 [event_method]: 2.477e-05 [auto_monad]: 0.0001243 [graph_reusing]: 7.17002e-06 [pre_auto_parallel]: 1.216e-05 [py_interpret_to_execute]: 0.00039854 [rewriter_before_opt_a]: 0.00010695 [expand_dump_flag]: 3.59002e-06 [jit_opt_a]: 0.0157332, [2] [Cycle 1]: 0.00373686, [27] [switch_simplify]: 7.761e-05 [loop_unroll]: 3.398e-05 [a_1]: 0.00065403 [with_stream_mark]: 2.162e-05 [recompute_prepare]: 9.62999e-06 [updatestate_depend_eliminate]: 4.51002e-06 [updatestate_assign_eliminate]: 3.87002e-06 [updatestate_loads_eliminate]: 3.12002e-06 [parameter_eliminate]: 2.06e-06 [specialize_transform]: 8.54e-06 [updatestate_useless_node_eliminater]: 6.28002e-06 [accelerated_algorithm]: 6.71999e-06 [meta_shard_fg_expand]: 2.37001e-06 [get_grad_eliminate_]: 7.35e-06 [merge_forward]: 4.17e-06 [cell_reuse_recompute_pass]: 1.23002e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.951e-05 [j_node_and_user_rematch]: 1.183e-05 [meta_fg_expand]: 3.11999e-06 [replace_old_param]: 1.271e-05 [inline_without_move]: 6.79999e-06 [renormalize]: 0.00249026 [add_forward_monad_depend]: 1.737e-05 [auto_monad_grad]: 3.31001e-06 [auto_monad_eliminator]: 1.831e-05 [cse]: 4.505e-05 [replace_applicator]: 2.529e-05 [Cycle 2]: 0.00045486, [27] [switch_simplify]: 8.14002e-06 [loop_unroll]: 6.56999e-06 [a_1]: 0.00015473 [with_stream_mark]: 1.876e-05 [recompute_prepare]: 7.3e-06 [updatestate_depend_eliminate]: 4.05e-06 [updatestate_assign_eliminate]: 3.70998e-06 [updatestate_loads_eliminate]: 3.06999e-06 [parameter_eliminate]: 2.00002e-06 [specialize_transform]: 6.63e-06 [updatestate_useless_node_eliminater]: 6.22001e-06 [accelerated_algorithm]: 6.59001e-06 [meta_shard_fg_expand]: 1.99e-06 [get_grad_eliminate_]: 5.84999e-06 [merge_forward]: 5.00999e-06 [cell_reuse_recompute_pass]: 2.73998e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.752e-05 [j_node_and_user_rematch]: 1.02e-05 [meta_fg_expand]: 2.37999e-06 [replace_old_param]: 9.92001e-06 [inline_without_move]: 6.21e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 1.96e-06 [auto_monad_grad]: 1.61002e-06 [auto_monad_eliminator]: 9.54999e-06 [cse]: 1.708e-05 [replace_applicator]: 7.87e-06 [py_interpret_to_execute_after_opt_a]: 1.614e-05 [rewriter_after_opt_a]: 5.468e-05 [convert_after_rewriter]: 8.54e-06 [order_py_execute_after_rewriter]: 5.19e-06 [mutable_eliminate]: 0.00077261 [jit_opt_b]: 6.48e-05, [1] [Cycle 1]: 5.556e-05, [2] [frontend_op_eliminate]: 2.142e-05 [inline_after_opt_a]: 1.961e-05 [cconv]: 3.574e-05 [loop_unroll]: 0.00056522 [jit_opt_after_cconv]: 0.00019536, [1] [Cycle 1]: 0.00018862, [11] [c_1]: 3.059e-05 [parameter_eliminate]: 6.46999e-06 [updatestate_depend_eliminate]: 1.187e-05 [updatestate_assign_eliminate]: 4.16001e-06 [updatestate_loads_eliminate]: 2.61e-06 [cse]: 3.391e-05 [call_graph_tuple_transform]: 2.561e-05 [tuple_list_get_item_eliminator]: 6.86001e-06 [none_parameter_eliminate]: 2.29999e-06 [renormalize]: 5.50004e-07 [switch_simplify]: 7.87998e-06 [remove_dup_value]: 1.765e-05 [partial_unused_args_eliminate]: 2.74001e-06 [environ_conv]: 2.479e-05 [add_recomputation]: 6.037e-05 [cse_after_recomputation]: 3.007e-05, [1] [Cycle 1]: 2.382e-05, [1] [cse]: 1.549e-05 [auto_monad_reorder]: 2.598e-05 [get_jit_bprop_graph]: 2.56998e-06 [rewriter_after_jit_bprop_graph]: 0.0122522 [opt_after_jit_grad]: 0.00091903 [symbol_engine_optimizer]: 0.00011415, [1] [Cycle 1]: 0.00010298, [6] [build]: 7.46999e-06 [elim_shapecalc]: 1.265e-05 [elim_not_effective]: 2.356e-05 [opt_reshape]: 1.237e-05 [fold_const_symbol]: 1.084e-05 [renormalize]: 1.15001e-06 [validate]: 9.71e-05 Sums bootstrap : 0.005673s : 0.65% type_inference : 0.851838s : 97.12% event_method : 0.000025s : 0.00% auto_monad : 0.000124s : 0.01% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000012s : 0.00% py_interpret_to_execute : 0.000399s : 0.05% rewriter_before_opt_a : 0.000107s : 0.01% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000086s : 0.01% jit_opt_a.loop_unroll : 0.000041s : 0.00% jit_opt_a.a_1 : 0.000809s : 0.09% jit_opt_a.with_stream_mark : 0.000040s : 0.00% jit_opt_a.recompute_prepare : 0.000017s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000008s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000006s : 0.00% jit_opt_a.parameter_eliminate : 0.000004s : 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 : 0.000013s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000004s : 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 : 0.000037s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000022s : 0.00% jit_opt_a.meta_fg_expand : 0.000005s : 0.00% jit_opt_a.replace_old_param : 0.000023s : 0.00% jit_opt_a.inline_without_move : 0.000013s : 0.00% jit_opt_a.renormalize : 0.002490s : 0.28% 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 : 0.000028s : 0.00% jit_opt_a.cse : 0.000062s : 0.01% jit_opt_a.replace_applicator : 0.000033s : 0.00% py_interpret_to_execute_after_opt_a : 0.000016s : 0.00% rewriter_after_opt_a : 0.000055s : 0.01% convert_after_rewriter : 0.000009s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000773s : 0.09% jit_opt_b.frontend_op_eliminate : 0.000021s : 0.00% jit_opt_b.inline_after_opt_a : 0.000020s : 0.00% cconv : 0.000036s : 0.00% loop_unroll : 0.000565s : 0.06% jit_opt_after_cconv.c_1 : 0.000031s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000012s : 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 : 0.000034s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000026s : 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 : 0.000008s : 0.00% remove_dup_value : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000025s : 0.00% add_recomputation : 0.000060s : 0.01% cse_after_recomputation.cse : 0.000015s : 0.00% auto_monad_reorder : 0.000026s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.012252s : 1.40% opt_after_jit_grad : 0.000919s : 0.10% symbol_engine_optimizer.build : 0.000007s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000013s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000024s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000012s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000011s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000097s : 0.01% Time group info: ------[substitution.] 0.000263 28 1.15% : 0.000003s : 2: substitution.elim_not_effective 0.68% : 0.000002s : 2: substitution.fold_const_symbol 2.65% : 0.000007s : 4: substitution.graph_param_transform 73.01% : 0.000192s : 5: substitution.inline 1.59% : 0.000004s : 4: substitution.j_node_and_user_rematch 0.61% : 0.000002s : 1: substitution.opt_reshape 2.01% : 0.000005s : 4: substitution.remove_not_recompute_node 2.27% : 0.000006s : 2: substitution.replace_old_param 13.00% : 0.000034s : 3: substitution.reshape_eliminate 3.02% : 0.000008s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.851691 2 95.03% : 0.809354s : 1: type_inference.infer 4.97% : 0.042336s : 1: type_inference.specialize ------[replace.] 0.000064 6 83.99% : 0.000054s : 5: replace.inline 16.01% : 0.000010s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000196 6 96.30% : 0.000188s : 5: match.inline 3.70% : 0.000007s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000166 867 1.39% : 0.000002s : 13: predicate.accumulaten_eliminater 2.03% : 0.000003s : 4: predicate.ad_related_special_op_eliminate 1.23% : 0.000002s : 13: predicate.addn_check_dump 1.29% : 0.000002s : 13: predicate.addn_zero_filter 1.88% : 0.000003s : 13: predicate.arithmetic_simplify 1.45% : 0.000002s : 13: predicate.cast_eliminate 0.42% : 0.000001s : 4: predicate.check_bprop_eliminate 1.14% : 0.000002s : 13: predicate.compare_switch_simplify 1.22% : 0.000002s : 13: predicate.depend_value_elim 1.13% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 1.34% : 0.000002s : 13: predicate.dict_get_item_eliminator 1.28% : 0.000002s : 13: predicate.dict_set_item_eliminator 1.13% : 0.000002s : 4: predicate.dumpgradient_eliminate 0.55% : 0.000001s : 4: predicate.elim_not_effective 0.57% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 1.20% : 0.000002s : 13: predicate.environ_add_const_eliminate 1.43% : 0.000002s : 13: predicate.environ_get_add_eliminate 1.20% : 0.000002s : 13: predicate.environ_get_depend_swap 1.24% : 0.000002s : 13: predicate.environ_get_eliminate 1.53% : 0.000003s : 13: predicate.environ_get_set_eliminate 0.21% : 0.000000s : 4: predicate.fold_const_symbol 0.99% : 0.000002s : 8: predicate.get_grad_eliminate 0.22% : 0.000000s : 4: predicate.graph_param_transform 5.68% : 0.000009s : 27: predicate.inline 0.96% : 0.000002s : 8: predicate.inline_without_move 0.40% : 0.000001s : 8: predicate.j_node_and_user_rematch 0.94% : 0.000002s : 8: predicate.less_batch_normalization 1.29% : 0.000002s : 14: predicate.list_to_tuple_eliminator_ 1.46% : 0.000002s : 18: predicate.load_eliminater 1.02% : 0.000002s : 4: predicate.loop_unroll_after_grad 3.49% : 0.000006s : 37: predicate.loop_unroll_before_grad 2.15% : 0.000004s : 17: predicate.make_slice_get_slice_eliminator 1.05% : 0.000002s : 13: predicate.merge_addn 1.02% : 0.000002s : 13: predicate.minmaximum_grad 1.44% : 0.000002s : 4: predicate.mutable_eliminate 0.76% : 0.000001s : 4: predicate.opt_reshape 1.91% : 0.000003s : 18: predicate.partial_eliminate 1.54% : 0.000003s : 13: predicate.print_const_string_wrapper 1.60% : 0.000003s : 13: predicate.reduce_eliminate 1.50% : 0.000002s : 14: predicate.redundant_stop_gradient_eliminater 0.44% : 0.000001s : 8: predicate.remove_not_recompute_node 2.03% : 0.000003s : 22: predicate.replace_applicator 0.79% : 0.000001s : 8: predicate.replace_old_param 0.26% : 0.000000s : 4: predicate.reset_defer_inline 1.76% : 0.000003s : 13: predicate.reshape_eliminate 1.61% : 0.000003s : 13: predicate.row_tensor_add_zeros_like 0.87% : 0.000001s : 4: predicate.row_tensor_eliminate 1.28% : 0.000002s : 13: predicate.same_eliminate 0.60% : 0.000001s : 8: predicate.set_cell_output_no_recompute 1.08% : 0.000002s : 8: predicate.special_op_eliminate 1.00% : 0.000002s : 8: predicate.specialize_transform 1.46% : 0.000002s : 13: predicate.split_environ_get_set_with_tuple_value 1.28% : 0.000002s : 13: predicate.stack_unstack_eliminate 0.51% : 0.000001s : 4: predicate.switch_call_monad_eliminater 2.05% : 0.000003s : 19: predicate.switch_defer_inline 2.00% : 0.000003s : 19: predicate.switch_layer_defer_inline 7.13% : 0.000012s : 60: predicate.switch_simplify 1.14% : 0.000002s : 13: predicate.tile_eliminate 2.22% : 0.000004s : 13: predicate.transpose_eliminate 1.60% : 0.000003s : 13: predicate.tuple_list_convert_item_index_to_positive 1.46% : 0.000002s : 13: predicate.tuple_list_get_item_depend_reorder 3.44% : 0.000006s : 22: predicate.tuple_list_get_item_eliminator 1.52% : 0.000003s : 13: predicate.tuple_list_set_item_eliminator 1.54% : 0.000003s : 14: predicate.tuple_to_list_eliminator_ 1.57% : 0.000003s : 18: predicate.updatestate_pure_node_eliminater 2.36% : 0.000004s : 26: predicate.updatestate_useless_node_eliminater 1.55% : 0.000003s : 13: predicate.value_based_eliminate 0.49% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.67% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.002766 23 75.86% : 0.002098s : 16: func_graph_cloner_run.FuncGraphClonerGraph 24.14% : 0.000668s : 7: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.893095 72 0.01% : 0.000064s : 1: add_recomputation 0.01% : 0.000129s : 1: auto_monad 0.00% : 0.000030s : 1: auto_monad_reorder 0.64% : 0.005707s : 1: bootstrap 0.00% : 0.000038s : 1: cconv 0.00% : 0.000011s : 1: convert_after_rewriter 0.00% : 0.000032s : 1: cse_after_recomputation 0.00% : 0.000028s : 1: environ_conv 0.00% : 0.000031s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000010s : 1: graph_reusing 1.76% : 0.015737s : 1: jit_opt_a 0.02% : 0.000198s : 1: jit_opt_after_cconv 0.01% : 0.000068s : 1: jit_opt_b 0.06% : 0.000575s : 1: loop_unroll 0.09% : 0.000784s : 1: mutable_eliminate 0.12% : 0.001086s : 26: opt.transform.jit_opt_a 0.01% : 0.000067s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000034s : 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% : 0.000043s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000055s : 4: opt.transform.symbol_engine_opt 0.11% : 0.000938s : 1: opt_after_jit_grad 0.00% : 0.000007s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000014s : 1: pre_auto_parallel 0.05% : 0.000405s : 1: py_interpret_to_execute 0.00% : 0.000019s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000020s : 1: remove_dup_value 0.19% : 0.001724s : 1: renormalize.infer 0.08% : 0.000753s : 1: renormalize.specialize 1.38% : 0.012281s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000058s : 1: rewriter_after_opt_a 0.01% : 0.000112s : 1: rewriter_before_opt_a 0.01% : 0.000118s : 1: symbol_engine_optimizer 95.38% : 0.851865s : 1: type_inference TotalTime = 1.25928, [30] [bootstrap]: 0.00073188 [type_inference]: 1.06554 [event_method]: 0.00068162 [auto_monad]: 0.00018423 [graph_reusing]: 1.239e-05 [pre_auto_parallel]: 3.73001e-06 [py_interpret_to_execute]: 5.557e-05 [rewriter_before_opt_a]: 0.00017605 [expand_dump_flag]: 3.97e-06 [jit_opt_a]: 0.189173, [2] [Cycle 1]: 0.18412, [27] [switch_simplify]: 0.00025489 [loop_unroll]: 8.768e-05 [a_1]: 0.00169604 [with_stream_mark]: 4.034e-05 [recompute_prepare]: 3.354e-05 [updatestate_depend_eliminate]: 1.191e-05 [updatestate_assign_eliminate]: 7.6e-06 [updatestate_loads_eliminate]: 7.48e-06 [parameter_eliminate]: 3.02002e-06 [specialize_transform]: 1.836e-05 [updatestate_useless_node_eliminater]: 1.557e-05 [accelerated_algorithm]: 1.642e-05 [meta_shard_fg_expand]: 4.60001e-06 [get_grad_eliminate_]: 1.577e-05 [merge_forward]: 1.027e-05 [cell_reuse_recompute_pass]: 1.40999e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.276e-05 [j_node_and_user_rematch]: 2.612e-05 [meta_fg_expand]: 0.0025267 [replace_old_param]: 0.0001161 [inline_without_move]: 8.483e-05 [renormalize]: 0.178723 [add_forward_monad_depend]: 1.774e-05 [auto_monad_grad]: 3.03e-06 [auto_monad_eliminator]: 1.411e-05 [cse]: 3.074e-05 [replace_applicator]: 2.243e-05 [Cycle 2]: 0.00048285, [27] [switch_simplify]: 5.89999e-06 [loop_unroll]: 4.34997e-06 [a_1]: 5.767e-05 [with_stream_mark]: 1.63e-05 [recompute_prepare]: 4.20999e-06 [updatestate_depend_eliminate]: 3.61999e-06 [updatestate_assign_eliminate]: 3.09001e-06 [updatestate_loads_eliminate]: 2.31e-06 [parameter_eliminate]: 2.12999e-06 [specialize_transform]: 3.97e-06 [updatestate_useless_node_eliminater]: 3.51001e-06 [accelerated_algorithm]: 4.27e-06 [meta_shard_fg_expand]: 1.85001e-06 [get_grad_eliminate_]: 3.86001e-06 [merge_forward]: 3.46999e-06 [cell_reuse_recompute_pass]: 2.82002e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.73e-05 [j_node_and_user_rematch]: 6.84001e-06 [meta_fg_expand]: 0.00017315 [replace_old_param]: 5.96e-06 [inline_without_move]: 4.42e-06 [renormalize]: 1.09983e-07 [add_forward_monad_depend]: 1.97999e-06 [auto_monad_grad]: 1.15001e-06 [auto_monad_eliminator]: 5.19e-06 [cse]: 1.324e-05 [replace_applicator]: 4.1e-06 [py_interpret_to_execute_after_opt_a]: 1.254e-05 [rewriter_after_opt_a]: 0.00037697 [convert_after_rewriter]: 1.197e-05 [order_py_execute_after_rewriter]: 5.22e-06 [mutable_eliminate]: 0.00068482 [jit_opt_b]: 4.697e-05, [1] [Cycle 1]: 3.889e-05, [2] [frontend_op_eliminate]: 1.158e-05 [inline_after_opt_a]: 1.431e-05 [cconv]: 3.053e-05 [loop_unroll]: 0.0004811 [jit_opt_after_cconv]: 0.00013579, [1] [Cycle 1]: 0.00012898, [11] [c_1]: 1.49e-05 [parameter_eliminate]: 2.62001e-06 [updatestate_depend_eliminate]: 5.86e-06 [updatestate_assign_eliminate]: 2.01e-06 [updatestate_loads_eliminate]: 1.99e-06 [cse]: 2.131e-05 [call_graph_tuple_transform]: 1.544e-05 [tuple_list_get_item_eliminator]: 4.1e-06 [none_parameter_eliminate]: 1.36002e-06 [renormalize]: 4.30009e-07 [switch_simplify]: 4.2e-06 [remove_dup_value]: 1.622e-05 [partial_unused_args_eliminate]: 2.20002e-06 [environ_conv]: 5.69e-06 [add_recomputation]: 4.429e-05 [cse_after_recomputation]: 2.103e-05, [1] [Cycle 1]: 1.525e-05, [1] [cse]: 8.84e-06 [auto_monad_reorder]: 1.376e-05 [get_jit_bprop_graph]: 2.01e-06 [rewriter_after_jit_bprop_graph]: 5.54e-06 [opt_after_jit_grad]: 0.00045171 [symbol_engine_optimizer]: 0.00010055, [1] [Cycle 1]: 9.415e-05, [6] [build]: 4.28001e-06 [elim_shapecalc]: 7.51999e-06 [elim_not_effective]: 3.676e-05 [opt_reshape]: 5.16998e-06 [fold_const_symbol]: 6.66e-06 [renormalize]: 3.7998e-07 [validate]: 3.917e-05 Sums bootstrap : 0.000732s : 0.06% type_inference : 1.065544s : 84.98% event_method : 0.000682s : 0.05% auto_monad : 0.000184s : 0.01% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000056s : 0.00% rewriter_before_opt_a : 0.000176s : 0.01% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000261s : 0.02% jit_opt_a.loop_unroll : 0.000092s : 0.01% jit_opt_a.a_1 : 0.001754s : 0.14% jit_opt_a.with_stream_mark : 0.000057s : 0.00% jit_opt_a.recompute_prepare : 0.000038s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000016s : 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 : 0.000005s : 0.00% jit_opt_a.specialize_transform : 0.000022s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000019s : 0.00% jit_opt_a.accelerated_algorithm : 0.000021s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000006s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000020s : 0.00% jit_opt_a.merge_forward : 0.000014s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000050s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000033s : 0.00% jit_opt_a.meta_fg_expand : 0.002700s : 0.22% jit_opt_a.replace_old_param : 0.000122s : 0.01% jit_opt_a.inline_without_move : 0.000089s : 0.01% jit_opt_a.renormalize : 0.178723s : 14.25% jit_opt_a.add_forward_monad_depend : 0.000020s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000019s : 0.00% jit_opt_a.cse : 0.000044s : 0.00% jit_opt_a.replace_applicator : 0.000027s : 0.00% py_interpret_to_execute_after_opt_a : 0.000013s : 0.00% rewriter_after_opt_a : 0.000377s : 0.03% convert_after_rewriter : 0.000012s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000685s : 0.05% jit_opt_b.frontend_op_eliminate : 0.000012s : 0.00% jit_opt_b.inline_after_opt_a : 0.000014s : 0.00% cconv : 0.000031s : 0.00% loop_unroll : 0.000481s : 0.04% jit_opt_after_cconv.c_1 : 0.000015s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.cse : 0.000021s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000015s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000004s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000001s : 0.00% jit_opt_after_cconv.renormalize : 0.000000s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000004s : 0.00% remove_dup_value : 0.000016s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000006s : 0.00% add_recomputation : 0.000044s : 0.00% cse_after_recomputation.cse : 0.000009s : 0.00% auto_monad_reorder : 0.000014s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000006s : 0.00% opt_after_jit_grad : 0.000452s : 0.04% symbol_engine_optimizer.build : 0.000004s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000008s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000037s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000005s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000007s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000039s : 0.00% Time group info: ------[substitution.] 0.000534 69 0.34% : 0.000002s : 1: substitution.elim_not_effective 0.21% : 0.000001s : 1: substitution.fold_const_symbol 0.80% : 0.000004s : 1: substitution.graph_param_transform 73.48% : 0.000392s : 14: substitution.inline 5.36% : 0.000029s : 2: substitution.inline_without_move 1.31% : 0.000007s : 9: substitution.j_node_and_user_rematch 0.88% : 0.000005s : 2: substitution.minmaximum_grad 1.76% : 0.000009s : 9: substitution.partial_eliminate 1.59% : 0.000008s : 9: substitution.remove_not_recompute_node 0.84% : 0.000004s : 1: substitution.replace_applicator 1.38% : 0.000007s : 7: substitution.replace_old_param 2.29% : 0.000012s : 2: substitution.reshape_eliminate 0.78% : 0.000004s : 1: substitution.set_cell_output_no_recompute 3.33% : 0.000018s : 3: substitution.switch_simplify 1.61% : 0.000009s : 2: substitution.tuple_list_convert_item_index_to_positive 1.05% : 0.000006s : 2: substitution.tuple_list_get_item_depend_reorder 2.98% : 0.000016s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.065369 2 89.26% : 0.950966s : 1: type_inference.infer 10.74% : 0.114403s : 1: type_inference.specialize ------[replace.] 0.000220 18 59.39% : 0.000130s : 14: replace.inline 35.45% : 0.000078s : 3: replace.switch_simplify 5.17% : 0.000011s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000404 18 95.17% : 0.000385s : 14: match.inline 3.94% : 0.000016s : 3: match.switch_simplify 0.89% : 0.000004s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000321 1710 1.27% : 0.000004s : 29: predicate.accumulaten_eliminater 0.29% : 0.000001s : 1: predicate.ad_related_special_op_eliminate 1.26% : 0.000004s : 29: predicate.addn_check_dump 1.38% : 0.000004s : 29: predicate.addn_zero_filter 1.66% : 0.000005s : 29: predicate.arithmetic_simplify 1.26% : 0.000004s : 29: predicate.cast_eliminate 0.11% : 0.000000s : 1: predicate.check_bprop_eliminate 1.28% : 0.000004s : 29: predicate.compare_switch_simplify 1.20% : 0.000004s : 29: predicate.depend_value_elim 1.14% : 0.000004s : 29: predicate.dict_get_item_const_eliminator 1.21% : 0.000004s : 29: predicate.dict_get_item_eliminator 1.26% : 0.000004s : 29: predicate.dict_set_item_eliminator 0.22% : 0.000001s : 1: predicate.dumpgradient_eliminate 0.12% : 0.000000s : 1: predicate.elim_not_effective 0.26% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.41% : 0.000005s : 29: predicate.environ_add_const_eliminate 13.14% : 0.000042s : 29: predicate.environ_get_add_eliminate 1.25% : 0.000004s : 29: predicate.environ_get_depend_swap 1.29% : 0.000004s : 29: predicate.environ_get_eliminate 1.35% : 0.000004s : 29: predicate.environ_get_set_eliminate 0.03% : 0.000000s : 1: predicate.fold_const_symbol 0.78% : 0.000003s : 14: predicate.get_grad_eliminate 0.10% : 0.000000s : 1: predicate.graph_param_transform 3.70% : 0.000012s : 46: predicate.inline 2.78% : 0.000009s : 38: predicate.inline_without_move 0.33% : 0.000001s : 14: predicate.j_node_and_user_rematch 0.97% : 0.000003s : 14: predicate.less_batch_normalization 1.48% : 0.000005s : 30: predicate.list_to_tuple_eliminator_ 1.34% : 0.000004s : 31: predicate.load_eliminater 0.38% : 0.000001s : 1: predicate.loop_unroll_after_grad 3.77% : 0.000012s : 78: predicate.loop_unroll_before_grad 1.55% : 0.000005s : 30: predicate.make_slice_get_slice_eliminator 1.16% : 0.000004s : 29: predicate.merge_addn 1.30% : 0.000004s : 29: predicate.minmaximum_grad 0.53% : 0.000002s : 1: predicate.mutable_eliminate 0.21% : 0.000001s : 1: predicate.opt_reshape 1.75% : 0.000006s : 31: predicate.partial_eliminate 1.37% : 0.000004s : 29: predicate.print_const_string_wrapper 1.68% : 0.000005s : 29: predicate.reduce_eliminate 1.38% : 0.000004s : 30: predicate.redundant_stop_gradient_eliminater 0.48% : 0.000002s : 14: predicate.remove_not_recompute_node 1.45% : 0.000005s : 32: predicate.replace_applicator 1.33% : 0.000004s : 38: predicate.replace_old_param 0.09% : 0.000000s : 1: predicate.reset_defer_inline 1.75% : 0.000006s : 29: predicate.reshape_eliminate 1.21% : 0.000004s : 29: predicate.row_tensor_add_zeros_like 0.21% : 0.000001s : 1: predicate.row_tensor_eliminate 1.46% : 0.000005s : 29: predicate.same_eliminate 0.53% : 0.000002s : 14: predicate.set_cell_output_no_recompute 0.26% : 0.000001s : 2: predicate.special_op_eliminate 0.75% : 0.000002s : 14: predicate.specialize_transform 1.48% : 0.000005s : 29: predicate.split_environ_get_set_with_tuple_value 1.23% : 0.000004s : 29: predicate.stack_unstack_eliminate 0.07% : 0.000000s : 1: predicate.switch_call_monad_eliminater 2.89% : 0.000009s : 44: predicate.switch_defer_inline 2.23% : 0.000007s : 44: predicate.switch_layer_defer_inline 6.85% : 0.000022s : 129: predicate.switch_simplify 1.22% : 0.000004s : 29: predicate.tile_eliminate 1.30% : 0.000004s : 29: predicate.transpose_eliminate 1.49% : 0.000005s : 29: predicate.tuple_list_convert_item_index_to_positive 1.49% : 0.000005s : 29: predicate.tuple_list_get_item_depend_reorder 3.08% : 0.000010s : 32: predicate.tuple_list_get_item_eliminator 1.61% : 0.000005s : 29: predicate.tuple_list_set_item_eliminator 1.35% : 0.000004s : 30: predicate.tuple_to_list_eliminator_ 1.37% : 0.000004s : 31: predicate.updatestate_pure_node_eliminater 2.21% : 0.000007s : 45: predicate.updatestate_useless_node_eliminater 1.45% : 0.000005s : 29: predicate.value_based_eliminate 0.06% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.17% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.137160 47 99.49% : 0.136454s : 30: func_graph_cloner_run.FuncGraphClonerGraph 0.51% : 0.000706s : 17: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.440525 72 0.00% : 0.000047s : 1: add_recomputation 0.01% : 0.000191s : 1: auto_monad 0.00% : 0.000016s : 1: auto_monad_reorder 0.05% : 0.000762s : 1: bootstrap 0.00% : 0.000033s : 1: cconv 0.00% : 0.000016s : 1: convert_after_rewriter 0.00% : 0.000023s : 1: cse_after_recomputation 0.00% : 0.000008s : 1: environ_conv 0.05% : 0.000695s : 1: event_method 0.00% : 0.000006s : 1: expand_dump_flag 0.00% : 0.000004s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 13.13% : 0.189177s : 1: jit_opt_a 0.01% : 0.000139s : 1: jit_opt_after_cconv 0.00% : 0.000050s : 1: jit_opt_b 0.03% : 0.000489s : 1: loop_unroll 0.05% : 0.000693s : 1: mutable_eliminate 0.17% : 0.002497s : 26: opt.transform.jit_opt_a 0.00% : 0.000034s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000018s : 4: opt.transform.jit_opt_b 0.00% : 0.000011s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000013s : 1: opt.transform.mutable_eliminate 0.00% : 0.000015s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000052s : 4: opt.transform.symbol_engine_opt 0.03% : 0.000459s : 1: opt_after_jit_grad 0.00% : 0.000007s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000059s : 1: py_interpret_to_execute 0.00% : 0.000015s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000019s : 1: remove_dup_value 12.35% : 0.177894s : 1: renormalize.infer 0.06% : 0.000813s : 1: renormalize.specialize 0.00% : 0.000008s : 1: rewriter_after_jit_bprop_graph 0.03% : 0.000383s : 1: rewriter_after_opt_a 0.01% : 0.000179s : 1: rewriter_before_opt_a 0.01% : 0.000103s : 1: symbol_engine_optimizer 73.97% : 1.065570s : 1: type_inference . [hook] pytest_runtest_teardown:test_reshape_non_contiguous[KBK] tests/st/mint/test_reshape.py::test_reshape_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 95.06s (0:01:35) ===================