==================================================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_006/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_very_large_tensors[pynative] tests/st/mint/test_reshape.py::test_reshape_very_large_tensors[pynative],max_mem:2.0M [WARNING] PARSER(171088,ffff8a0cef30,python3.9):2026-01-29-17:41:08.251.153 [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 = 7.75983, [30] [bootstrap]: 0.0135156 [type_inference]: 6.99412 [event_method]: 2.683e-05 [auto_monad]: 0.00115048 [graph_reusing]: 8.07998e-06 [pre_auto_parallel]: 0.00018397 [py_interpret_to_execute]: 0.00619231 [rewriter_before_opt_a]: 0.0003361 [expand_dump_flag]: 6.25002e-06 [jit_opt_a]: 0.720474, [2] [Cycle 1]: 0.0693999, [27] [switch_simplify]: 0.00023037 [loop_unroll]: 4.37e-05 [a_1]: 0.00076246 [with_stream_mark]: 3.093e-05 [recompute_prepare]: 1.306e-05 [updatestate_depend_eliminate]: 4.95999e-06 [updatestate_assign_eliminate]: 4.14002e-06 [updatestate_loads_eliminate]: 3.06001e-06 [parameter_eliminate]: 2.21e-06 [specialize_transform]: 9.92999e-06 [updatestate_useless_node_eliminater]: 6.85002e-06 [accelerated_algorithm]: 7.01001e-06 [meta_shard_fg_expand]: 2.83e-06 [get_grad_eliminate_]: 7.33999e-06 [merge_forward]: 4.53999e-06 [cell_reuse_recompute_pass]: 1.30001e-06 [cell_reuse_handle_not_recompute_node_pass]: 0.00017707 [j_node_and_user_rematch]: 1.231e-05 [meta_fg_expand]: 3.28e-06 [replace_old_param]: 1.327e-05 [inline_without_move]: 7.34002e-06 [renormalize]: 0.0672061 [add_forward_monad_depend]: 9.242e-05 [auto_monad_grad]: 2.61e-06 [auto_monad_eliminator]: 2.035e-05 [cse]: 3.353e-05 [replace_applicator]: 2.471e-05 [Cycle 2]: 0.01504, [27] [switch_simplify]: 8e-06 [loop_unroll]: 6.31998e-06 [a_1]: 0.014469 [with_stream_mark]: 2.507e-05 [recompute_prepare]: 1.429e-05 [updatestate_depend_eliminate]: 5.52001e-06 [updatestate_assign_eliminate]: 4.38999e-06 [updatestate_loads_eliminate]: 3.13e-06 [parameter_eliminate]: 2.25002e-06 [specialize_transform]: 7.92e-06 [updatestate_useless_node_eliminater]: 7.3e-06 [accelerated_algorithm]: 9.28002e-06 [meta_shard_fg_expand]: 2.79001e-06 [get_grad_eliminate_]: 0.00018708 [merge_forward]: 5.29e-06 [cell_reuse_recompute_pass]: 3.03e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.892e-05 [j_node_and_user_rematch]: 1.116e-05 [meta_fg_expand]: 2.96001e-06 [replace_old_param]: 1.082e-05 [inline_without_move]: 6.04999e-06 [renormalize]: 7.99773e-08 [add_forward_monad_depend]: 2.99999e-06 [auto_monad_grad]: 1.57001e-06 [auto_monad_eliminator]: 9.39e-06 [cse]: 3.539e-05 [replace_applicator]: 6.46e-06 [py_interpret_to_execute_after_opt_a]: 2.185e-05 [rewriter_after_opt_a]: 0.00059172 [convert_after_rewriter]: 3.498e-05 [order_py_execute_after_rewriter]: 5.37001e-06 [mutable_eliminate]: 0.00115251 [jit_opt_b]: 6.419e-05, [1] [Cycle 1]: 5.397e-05, [2] [frontend_op_eliminate]: 2.216e-05 [inline_after_opt_a]: 1.832e-05 [cconv]: 2.94e-05 [loop_unroll]: 0.00044998 [jit_opt_after_cconv]: 0.00087246, [1] [Cycle 1]: 0.00086507, [11] [c_1]: 2.706e-05 [parameter_eliminate]: 3.14999e-06 [updatestate_depend_eliminate]: 6.96001e-06 [updatestate_assign_eliminate]: 3.18e-06 [updatestate_loads_eliminate]: 2.46998e-06 [cse]: 2.364e-05 [call_graph_tuple_transform]: 2.611e-05 [tuple_list_get_item_eliminator]: 7.29001e-06 [none_parameter_eliminate]: 2.18998e-06 [renormalize]: 6.69999e-07 [switch_simplify]: 7.71999e-06 [remove_dup_value]: 1.851e-05 [partial_unused_args_eliminate]: 2.19999e-06 [environ_conv]: 0.00010458 [add_recomputation]: 5.73e-05 [cse_after_recomputation]: 2.689e-05, [1] [Cycle 1]: 2.051e-05, [1] [cse]: 1.426e-05 [auto_monad_reorder]: 2.513e-05 [get_jit_bprop_graph]: 2.07001e-06 [rewriter_after_jit_bprop_graph]: 0.00136276 [opt_after_jit_grad]: 0.0151824 [symbol_engine_optimizer]: 0.00011701, [1] [Cycle 1]: 0.00010427, [6] [build]: 8.55999e-06 [elim_shapecalc]: 1.039e-05 [elim_not_effective]: 2.525e-05 [opt_reshape]: 1.427e-05 [fold_const_symbol]: 1.208e-05 [renormalize]: 1.47001e-06 [validate]: 0.00072927 Sums bootstrap : 0.013516s : 0.19% type_inference : 6.994119s : 98.24% event_method : 0.000027s : 0.00% auto_monad : 0.001150s : 0.02% graph_reusing : 0.000008s : 0.00% pre_auto_parallel : 0.000184s : 0.00% py_interpret_to_execute : 0.006192s : 0.09% rewriter_before_opt_a : 0.000336s : 0.00% expand_dump_flag : 0.000006s : 0.00% jit_opt_a.switch_simplify : 0.000238s : 0.00% jit_opt_a.loop_unroll : 0.000050s : 0.00% jit_opt_a.a_1 : 0.015231s : 0.21% jit_opt_a.with_stream_mark : 0.000056s : 0.00% jit_opt_a.recompute_prepare : 0.000027s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000010s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000009s : 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 : 0.000018s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000014s : 0.00% jit_opt_a.accelerated_algorithm : 0.000016s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000006s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000194s : 0.00% jit_opt_a.merge_forward : 0.000010s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000196s : 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 : 0.000024s : 0.00% jit_opt_a.inline_without_move : 0.000013s : 0.00% jit_opt_a.renormalize : 0.067206s : 0.94% jit_opt_a.add_forward_monad_depend : 0.000095s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000030s : 0.00% jit_opt_a.cse : 0.000069s : 0.00% jit_opt_a.replace_applicator : 0.000031s : 0.00% py_interpret_to_execute_after_opt_a : 0.000022s : 0.00% rewriter_after_opt_a : 0.000592s : 0.01% convert_after_rewriter : 0.000035s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.001153s : 0.02% jit_opt_b.frontend_op_eliminate : 0.000022s : 0.00% jit_opt_b.inline_after_opt_a : 0.000018s : 0.00% cconv : 0.000029s : 0.00% loop_unroll : 0.000450s : 0.01% jit_opt_after_cconv.c_1 : 0.000027s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.cse : 0.000024s : 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 : 0.000019s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000105s : 0.00% add_recomputation : 0.000057s : 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 : 0.001363s : 0.02% opt_after_jit_grad : 0.015182s : 0.21% symbol_engine_optimizer.build : 0.000009s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000010s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000025s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000014s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000012s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000729s : 0.01% Time group info: ------[substitution.] 0.000480 28 0.69% : 0.000003s : 2: substitution.elim_not_effective 0.52% : 0.000003s : 2: substitution.fold_const_symbol 1.64% : 0.000008s : 4: substitution.graph_param_transform 52.00% : 0.000250s : 5: substitution.inline 1.19% : 0.000006s : 4: substitution.j_node_and_user_rematch 0.46% : 0.000002s : 1: substitution.opt_reshape 32.88% : 0.000158s : 4: substitution.remove_not_recompute_node 1.61% : 0.000008s : 2: substitution.replace_old_param 7.12% : 0.000034s : 3: substitution.reshape_eliminate 1.88% : 0.000009s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 6.993779 2 99.93% : 6.989006s : 1: type_inference.infer 0.07% : 0.004773s : 1: type_inference.specialize ------[replace.] 0.000074 6 82.24% : 0.000061s : 5: replace.inline 17.76% : 0.000013s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000254 6 96.80% : 0.000246s : 5: match.inline 3.20% : 0.000008s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000452 867 0.71% : 0.000003s : 13: predicate.accumulaten_eliminater 1.14% : 0.000005s : 4: predicate.ad_related_special_op_eliminate 0.66% : 0.000003s : 13: predicate.addn_check_dump 0.57% : 0.000003s : 13: predicate.addn_zero_filter 0.97% : 0.000004s : 13: predicate.arithmetic_simplify 0.59% : 0.000003s : 13: predicate.cast_eliminate 0.16% : 0.000001s : 4: predicate.check_bprop_eliminate 0.44% : 0.000002s : 13: predicate.compare_switch_simplify 0.49% : 0.000002s : 13: predicate.depend_value_elim 0.44% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 0.52% : 0.000002s : 13: predicate.dict_get_item_eliminator 0.51% : 0.000002s : 13: predicate.dict_set_item_eliminator 50.34% : 0.000227s : 4: predicate.dumpgradient_eliminate 0.20% : 0.000001s : 4: predicate.elim_not_effective 0.18% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 0.73% : 0.000003s : 13: predicate.environ_add_const_eliminate 0.50% : 0.000002s : 13: predicate.environ_get_add_eliminate 1.20% : 0.000005s : 13: predicate.environ_get_depend_swap 0.43% : 0.000002s : 13: predicate.environ_get_eliminate 0.54% : 0.000002s : 13: predicate.environ_get_set_eliminate 0.10% : 0.000000s : 4: predicate.fold_const_symbol 1.02% : 0.000005s : 8: predicate.get_grad_eliminate 0.18% : 0.000001s : 4: predicate.graph_param_transform 1.80% : 0.000008s : 27: predicate.inline 0.29% : 0.000001s : 8: predicate.inline_without_move 0.18% : 0.000001s : 8: predicate.j_node_and_user_rematch 0.55% : 0.000002s : 8: predicate.less_batch_normalization 7.02% : 0.000032s : 14: predicate.list_to_tuple_eliminator_ 0.65% : 0.000003s : 18: predicate.load_eliminater 0.41% : 0.000002s : 4: predicate.loop_unroll_after_grad 2.18% : 0.000010s : 37: predicate.loop_unroll_before_grad 0.66% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 0.42% : 0.000002s : 13: predicate.merge_addn 0.59% : 0.000003s : 13: predicate.minmaximum_grad 0.67% : 0.000003s : 4: predicate.mutable_eliminate 0.37% : 0.000002s : 4: predicate.opt_reshape 0.75% : 0.000003s : 18: predicate.partial_eliminate 0.49% : 0.000002s : 13: predicate.print_const_string_wrapper 0.99% : 0.000004s : 13: predicate.reduce_eliminate 0.66% : 0.000003s : 14: predicate.redundant_stop_gradient_eliminater 0.15% : 0.000001s : 8: predicate.remove_not_recompute_node 0.67% : 0.000003s : 22: predicate.replace_applicator 0.22% : 0.000001s : 8: predicate.replace_old_param 0.12% : 0.000001s : 4: predicate.reset_defer_inline 0.73% : 0.000003s : 13: predicate.reshape_eliminate 0.68% : 0.000003s : 13: predicate.row_tensor_add_zeros_like 0.43% : 0.000002s : 4: predicate.row_tensor_eliminate 0.68% : 0.000003s : 13: predicate.same_eliminate 0.20% : 0.000001s : 8: predicate.set_cell_output_no_recompute 0.43% : 0.000002s : 8: predicate.special_op_eliminate 0.28% : 0.000001s : 8: predicate.specialize_transform 0.92% : 0.000004s : 13: predicate.split_environ_get_set_with_tuple_value 0.47% : 0.000002s : 13: predicate.stack_unstack_eliminate 0.14% : 0.000001s : 4: predicate.switch_call_monad_eliminater 1.04% : 0.000005s : 19: predicate.switch_defer_inline 0.69% : 0.000003s : 19: predicate.switch_layer_defer_inline 3.55% : 0.000016s : 60: predicate.switch_simplify 0.48% : 0.000002s : 13: predicate.tile_eliminate 0.74% : 0.000003s : 13: predicate.transpose_eliminate 0.46% : 0.000002s : 13: predicate.tuple_list_convert_item_index_to_positive 0.55% : 0.000002s : 13: predicate.tuple_list_get_item_depend_reorder 1.29% : 0.000006s : 22: predicate.tuple_list_get_item_eliminator 0.47% : 0.000002s : 13: predicate.tuple_list_set_item_eliminator 0.54% : 0.000002s : 14: predicate.tuple_to_list_eliminator_ 0.57% : 0.000003s : 18: predicate.updatestate_pure_node_eliminater 1.01% : 0.000005s : 26: predicate.updatestate_useless_node_eliminater 0.77% : 0.000003s : 13: predicate.value_based_eliminate 0.16% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.23% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.013563 23 73.21% : 0.009929s : 16: func_graph_cloner_run.FuncGraphClonerGraph 26.79% : 0.003634s : 7: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 7.839912 72 0.00% : 0.000060s : 1: add_recomputation 0.01% : 0.001163s : 1: auto_monad 0.00% : 0.000028s : 1: auto_monad_reorder 0.17% : 0.013560s : 1: bootstrap 0.00% : 0.000032s : 1: cconv 0.00% : 0.000038s : 1: convert_after_rewriter 0.00% : 0.000029s : 1: cse_after_recomputation 0.00% : 0.000107s : 1: environ_conv 0.00% : 0.000032s : 1: event_method 0.00% : 0.000010s : 1: expand_dump_flag 0.00% : 0.000004s : 1: get_jit_bprop_graph 0.00% : 0.000011s : 1: graph_reusing 9.19% : 0.720480s : 1: jit_opt_a 0.01% : 0.000876s : 1: jit_opt_after_cconv 0.00% : 0.000067s : 1: jit_opt_b 0.01% : 0.000458s : 1: loop_unroll 0.01% : 0.001162s : 1: mutable_eliminate 0.20% : 0.015878s : 26: opt.transform.jit_opt_a 0.00% : 0.000064s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000033s : 4: opt.transform.jit_opt_b 0.00% : 0.000015s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000015s : 1: opt.transform.mutable_eliminate 0.00% : 0.000280s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000058s : 4: opt.transform.symbol_engine_opt 0.19% : 0.015217s : 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% : 0.000186s : 1: pre_auto_parallel 0.08% : 0.006219s : 1: py_interpret_to_execute 0.00% : 0.000024s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000022s : 1: remove_dup_value 0.82% : 0.064394s : 1: renormalize.infer 0.04% : 0.002798s : 1: renormalize.specialize 0.02% : 0.001367s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000597s : 1: rewriter_after_opt_a 0.00% : 0.000344s : 1: rewriter_before_opt_a 0.00% : 0.000122s : 1: symbol_engine_optimizer 89.21% : 6.994150s : 1: type_inference TotalTime = 1.03921, [30] [bootstrap]: 0.0005733 [type_inference]: 0.811701 [event_method]: 0.00046347 [auto_monad]: 0.00024285 [graph_reusing]: 1.114e-05 [pre_auto_parallel]: 3.97e-06 [py_interpret_to_execute]: 5.714e-05 [rewriter_before_opt_a]: 0.00024914 [expand_dump_flag]: 5.04e-06 [jit_opt_a]: 0.222457, [2] [Cycle 1]: 0.216567, [27] [switch_simplify]: 0.00026266 [loop_unroll]: 8.5e-05 [a_1]: 0.0893896 [with_stream_mark]: 4.307e-05 [recompute_prepare]: 3.114e-05 [updatestate_depend_eliminate]: 1.137e-05 [updatestate_assign_eliminate]: 8.02e-06 [updatestate_loads_eliminate]: 7.05002e-06 [parameter_eliminate]: 3.54002e-06 [specialize_transform]: 1.81e-05 [updatestate_useless_node_eliminater]: 1.478e-05 [accelerated_algorithm]: 1.538e-05 [meta_shard_fg_expand]: 8.47e-06 [get_grad_eliminate_]: 1.555e-05 [merge_forward]: 8.82e-06 [cell_reuse_recompute_pass]: 1.27e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.142e-05 [j_node_and_user_rematch]: 2.797e-05 [meta_fg_expand]: 0.0023403 [replace_old_param]: 7.696e-05 [inline_without_move]: 6.947e-05 [renormalize]: 0.123648 [add_forward_monad_depend]: 1.774e-05 [auto_monad_grad]: 2.52001e-06 [auto_monad_eliminator]: 2.244e-05 [cse]: 3.85e-05 [replace_applicator]: 2.608e-05 [Cycle 2]: 0.00055711, [27] [switch_simplify]: 5.96998e-06 [loop_unroll]: 5.32001e-06 [a_1]: 6.61e-05 [with_stream_mark]: 1.747e-05 [recompute_prepare]: 4.37998e-06 [updatestate_depend_eliminate]: 3.37002e-06 [updatestate_assign_eliminate]: 3.25e-06 [updatestate_loads_eliminate]: 2.39999e-06 [parameter_eliminate]: 2.47001e-06 [specialize_transform]: 4.3e-06 [updatestate_useless_node_eliminater]: 3.82002e-06 [accelerated_algorithm]: 4.90001e-06 [meta_shard_fg_expand]: 2.61e-06 [get_grad_eliminate_]: 4.39998e-06 [merge_forward]: 3.53e-06 [cell_reuse_recompute_pass]: 2.94001e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.113e-05 [j_node_and_user_rematch]: 7.8e-06 [meta_fg_expand]: 0.0001948 [replace_old_param]: 2.848e-05 [inline_without_move]: 4.71002e-06 [renormalize]: 7.00238e-08 [add_forward_monad_depend]: 2.79999e-06 [auto_monad_grad]: 1.22e-06 [auto_monad_eliminator]: 7.80998e-06 [cse]: 1.472e-05 [replace_applicator]: 4.58001e-06 [py_interpret_to_execute_after_opt_a]: 1.848e-05 [rewriter_after_opt_a]: 0.00056629 [convert_after_rewriter]: 1.813e-05 [order_py_execute_after_rewriter]: 5.57999e-06 [mutable_eliminate]: 0.00088797 [jit_opt_b]: 5.387e-05, [1] [Cycle 1]: 4.442e-05, [2] [frontend_op_eliminate]: 1.402e-05 [inline_after_opt_a]: 1.601e-05 [cconv]: 3.937e-05 [loop_unroll]: 0.00049348 [jit_opt_after_cconv]: 0.00033929, [1] [Cycle 1]: 0.00033137, [11] [c_1]: 1.624e-05 [parameter_eliminate]: 6.22001e-06 [updatestate_depend_eliminate]: 8.82e-06 [updatestate_assign_eliminate]: 2.56998e-06 [updatestate_loads_eliminate]: 2.37999e-06 [cse]: 7.116e-05 [call_graph_tuple_transform]: 2.402e-05 [tuple_list_get_item_eliminator]: 5.08002e-06 [none_parameter_eliminate]: 2.16e-06 [renormalize]: 6.60017e-07 [switch_simplify]: 6.53e-06 [remove_dup_value]: 1.87e-05 [partial_unused_args_eliminate]: 2.64001e-06 [environ_conv]: 6.76999e-06 [add_recomputation]: 4.594e-05 [cse_after_recomputation]: 2.398e-05, [1] [Cycle 1]: 1.692e-05, [1] [cse]: 1.007e-05 [auto_monad_reorder]: 1.453e-05 [get_jit_bprop_graph]: 2.72001e-06 [rewriter_after_jit_bprop_graph]: 7.63001e-06 [opt_after_jit_grad]: 0.00050825 [symbol_engine_optimizer]: 7.56e-05, [1] [Cycle 1]: 6.864e-05, [6] [build]: 4.12e-06 [elim_shapecalc]: 7.61001e-06 [elim_not_effective]: 1.24e-05 [opt_reshape]: 5.72999e-06 [fold_const_symbol]: 8.13999e-06 [renormalize]: 5.69999e-07 [validate]: 4.015e-05 Sums bootstrap : 0.000573s : 0.06% type_inference : 0.811701s : 78.59% event_method : 0.000463s : 0.04% auto_monad : 0.000243s : 0.02% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000057s : 0.01% rewriter_before_opt_a : 0.000249s : 0.02% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000269s : 0.03% jit_opt_a.loop_unroll : 0.000090s : 0.01% jit_opt_a.a_1 : 0.089456s : 8.66% jit_opt_a.with_stream_mark : 0.000061s : 0.01% jit_opt_a.recompute_prepare : 0.000036s : 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 : 0.000009s : 0.00% jit_opt_a.parameter_eliminate : 0.000006s : 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 : 0.000020s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000011s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000020s : 0.00% jit_opt_a.merge_forward : 0.000012s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000053s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000036s : 0.00% jit_opt_a.meta_fg_expand : 0.002535s : 0.25% jit_opt_a.replace_old_param : 0.000105s : 0.01% jit_opt_a.inline_without_move : 0.000074s : 0.01% jit_opt_a.renormalize : 0.123648s : 11.97% jit_opt_a.add_forward_monad_depend : 0.000021s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000030s : 0.00% jit_opt_a.cse : 0.000053s : 0.01% jit_opt_a.replace_applicator : 0.000031s : 0.00% py_interpret_to_execute_after_opt_a : 0.000018s : 0.00% rewriter_after_opt_a : 0.000566s : 0.05% convert_after_rewriter : 0.000018s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.000888s : 0.09% jit_opt_b.frontend_op_eliminate : 0.000014s : 0.00% jit_opt_b.inline_after_opt_a : 0.000016s : 0.00% cconv : 0.000039s : 0.00% loop_unroll : 0.000493s : 0.05% jit_opt_after_cconv.c_1 : 0.000016s : 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 : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.cse : 0.000071s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000005s : 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 : 0.000007s : 0.00% remove_dup_value : 0.000019s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000007s : 0.00% add_recomputation : 0.000046s : 0.00% cse_after_recomputation.cse : 0.000010s : 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 : 0.000508s : 0.05% symbol_engine_optimizer.build : 0.000004s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000008s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000012s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000006s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000008s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000040s : 0.00% Time group info: ------[substitution.] 0.000648 69 0.29% : 0.000002s : 1: substitution.elim_not_effective 0.19% : 0.000001s : 1: substitution.fold_const_symbol 0.83% : 0.000005s : 1: substitution.graph_param_transform 77.69% : 0.000504s : 14: substitution.inline 3.80% : 0.000025s : 2: substitution.inline_without_move 1.33% : 0.000009s : 9: substitution.j_node_and_user_rematch 0.73% : 0.000005s : 2: substitution.minmaximum_grad 2.35% : 0.000015s : 9: substitution.partial_eliminate 1.23% : 0.000008s : 9: substitution.remove_not_recompute_node 0.49% : 0.000003s : 1: substitution.replace_applicator 0.95% : 0.000006s : 7: substitution.replace_old_param 1.86% : 0.000012s : 2: substitution.reshape_eliminate 0.74% : 0.000005s : 1: substitution.set_cell_output_no_recompute 2.55% : 0.000017s : 3: substitution.switch_simplify 1.50% : 0.000010s : 2: substitution.tuple_list_convert_item_index_to_positive 1.04% : 0.000007s : 2: substitution.tuple_list_get_item_depend_reorder 2.44% : 0.000016s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.811489 2 99.42% : 0.806767s : 1: type_inference.infer 0.58% : 0.004722s : 1: type_inference.specialize ------[replace.] 0.087749 18 99.89% : 0.087654s : 14: replace.inline 0.10% : 0.000085s : 3: replace.switch_simplify 0.01% : 0.000010s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000511 18 96.57% : 0.000494s : 14: match.inline 2.87% : 0.000015s : 3: match.switch_simplify 0.56% : 0.000003s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000305 1710 1.56% : 0.000005s : 29: predicate.accumulaten_eliminater 0.50% : 0.000002s : 1: predicate.ad_related_special_op_eliminate 1.33% : 0.000004s : 29: predicate.addn_check_dump 1.61% : 0.000005s : 29: predicate.addn_zero_filter 1.96% : 0.000006s : 29: predicate.arithmetic_simplify 1.87% : 0.000006s : 29: predicate.cast_eliminate 0.32% : 0.000001s : 1: predicate.check_bprop_eliminate 1.21% : 0.000004s : 29: predicate.compare_switch_simplify 1.35% : 0.000004s : 29: predicate.depend_value_elim 1.29% : 0.000004s : 29: predicate.dict_get_item_const_eliminator 1.44% : 0.000004s : 29: predicate.dict_get_item_eliminator 1.46% : 0.000004s : 29: predicate.dict_set_item_eliminator 0.33% : 0.000001s : 1: predicate.dumpgradient_eliminate 0.14% : 0.000000s : 1: predicate.elim_not_effective 0.29% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.38% : 0.000004s : 29: predicate.environ_add_const_eliminate 1.40% : 0.000004s : 29: predicate.environ_get_add_eliminate 1.41% : 0.000004s : 29: predicate.environ_get_depend_swap 1.35% : 0.000004s : 29: predicate.environ_get_eliminate 1.25% : 0.000004s : 29: predicate.environ_get_set_eliminate 0.04% : 0.000000s : 1: predicate.fold_const_symbol 0.91% : 0.000003s : 14: predicate.get_grad_eliminate 0.17% : 0.000001s : 1: predicate.graph_param_transform 4.91% : 0.000015s : 46: predicate.inline 2.40% : 0.000007s : 38: predicate.inline_without_move 0.34% : 0.000001s : 14: predicate.j_node_and_user_rematch 1.09% : 0.000003s : 14: predicate.less_batch_normalization 2.08% : 0.000006s : 30: predicate.list_to_tuple_eliminator_ 1.70% : 0.000005s : 31: predicate.load_eliminater 0.53% : 0.000002s : 1: predicate.loop_unroll_after_grad 3.77% : 0.000011s : 78: predicate.loop_unroll_before_grad 1.56% : 0.000005s : 30: predicate.make_slice_get_slice_eliminator 1.29% : 0.000004s : 29: predicate.merge_addn 1.37% : 0.000004s : 29: predicate.minmaximum_grad 0.66% : 0.000002s : 1: predicate.mutable_eliminate 0.19% : 0.000001s : 1: predicate.opt_reshape 1.90% : 0.000006s : 31: predicate.partial_eliminate 1.49% : 0.000005s : 29: predicate.print_const_string_wrapper 1.81% : 0.000006s : 29: predicate.reduce_eliminate 1.56% : 0.000005s : 30: predicate.redundant_stop_gradient_eliminater 0.51% : 0.000002s : 14: predicate.remove_not_recompute_node 1.42% : 0.000004s : 32: predicate.replace_applicator 1.32% : 0.000004s : 38: predicate.replace_old_param 0.17% : 0.000001s : 1: predicate.reset_defer_inline 1.64% : 0.000005s : 29: predicate.reshape_eliminate 1.45% : 0.000004s : 29: predicate.row_tensor_add_zeros_like 0.37% : 0.000001s : 1: predicate.row_tensor_eliminate 1.76% : 0.000005s : 29: predicate.same_eliminate 0.42% : 0.000001s : 14: predicate.set_cell_output_no_recompute 0.38% : 0.000001s : 2: predicate.special_op_eliminate 0.79% : 0.000002s : 14: predicate.specialize_transform 1.56% : 0.000005s : 29: predicate.split_environ_get_set_with_tuple_value 1.83% : 0.000006s : 29: predicate.stack_unstack_eliminate 0.08% : 0.000000s : 1: predicate.switch_call_monad_eliminater 3.84% : 0.000012s : 44: predicate.switch_defer_inline 2.37% : 0.000007s : 44: predicate.switch_layer_defer_inline 7.44% : 0.000023s : 129: predicate.switch_simplify 1.38% : 0.000004s : 29: predicate.tile_eliminate 1.44% : 0.000004s : 29: predicate.transpose_eliminate 2.02% : 0.000006s : 29: predicate.tuple_list_convert_item_index_to_positive 1.80% : 0.000005s : 29: predicate.tuple_list_get_item_depend_reorder 3.50% : 0.000011s : 32: predicate.tuple_list_get_item_eliminator 1.70% : 0.000005s : 29: predicate.tuple_list_set_item_eliminator 1.56% : 0.000005s : 30: predicate.tuple_to_list_eliminator_ 1.58% : 0.000005s : 31: predicate.updatestate_pure_node_eliminater 2.26% : 0.000007s : 45: predicate.updatestate_useless_node_eliminater 1.84% : 0.000006s : 29: predicate.value_based_eliminate 0.06% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.29% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.018332 47 94.54% : 0.017331s : 30: func_graph_cloner_run.FuncGraphClonerGraph 5.46% : 0.001001s : 17: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.253029 72 0.00% : 0.000049s : 1: add_recomputation 0.02% : 0.000256s : 1: auto_monad 0.00% : 0.000017s : 1: auto_monad_reorder 0.05% : 0.000600s : 1: bootstrap 0.00% : 0.000042s : 1: cconv 0.00% : 0.000023s : 1: convert_after_rewriter 0.00% : 0.000026s : 1: cse_after_recomputation 0.00% : 0.000009s : 1: environ_conv 0.04% : 0.000480s : 1: event_method 0.00% : 0.000009s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000014s : 1: graph_reusing 17.75% : 0.222462s : 1: jit_opt_a 0.03% : 0.000343s : 1: jit_opt_after_cconv 0.00% : 0.000057s : 1: jit_opt_b 0.04% : 0.000502s : 1: loop_unroll 0.07% : 0.000900s : 1: mutable_eliminate 7.19% : 0.090155s : 26: opt.transform.jit_opt_a 0.00% : 0.000046s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000021s : 4: opt.transform.jit_opt_b 0.00% : 0.000013s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000018s : 1: opt.transform.mutable_eliminate 0.00% : 0.000019s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000030s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000518s : 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% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000060s : 1: py_interpret_to_execute 0.00% : 0.000021s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000021s : 1: remove_dup_value 9.78% : 0.122551s : 1: renormalize.infer 0.09% : 0.001080s : 1: renormalize.specialize 0.00% : 0.000010s : 1: rewriter_after_jit_bprop_graph 0.05% : 0.000578s : 1: rewriter_after_opt_a 0.02% : 0.000259s : 1: rewriter_before_opt_a 0.01% : 0.000078s : 1: symbol_engine_optimizer 64.78% : 0.811735s : 1: type_inference . [hook] pytest_runtest_teardown:test_reshape_very_large_tensors[KBK] tests/st/mint/test_reshape.py::test_reshape_very_large_tensors[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 270.41s (0:04:30) ==================