==================================================Ascend ============================= test session starts ============================== platform linux -- Python 3.9.19, pytest-6.2.5, py-1.11.0, pluggy-1.5.0 rootdir: /home/jenkins/mindspore/testcases/testcases/tests/st/mint, configfile: ../../../../../../sault/virtual_test/virtualenv_005/sault/config/pytest.ini plugins: mock-3.14.0, hydra-core-1.3.2, forked-1.6.0, anyio-4.9.0, xdist-1.32.0 collected 2 items test_addmm.py . [hook] pytest_runtest_teardown:test_addmm_input_with_first_dim_1[pynative] tests/st/mint/test_addmm.py::test_addmm_input_with_first_dim_1[pynative],max_mem:2.0M TotalTime = 0.590044, [30] [bootstrap]: 0.00103668 [type_inference]: 0.573969 [event_method]: 2.057e-05 [auto_monad]: 0.00011126 [graph_reusing]: 5.71003e-06 [pre_auto_parallel]: 1.298e-05 [py_interpret_to_execute]: 0.00012158 [rewriter_before_opt_a]: 8.919e-05 [expand_dump_flag]: 3.36001e-06 [jit_opt_a]: 0.0115123, [2] [Cycle 1]: 0.00241939, [27] [switch_simplify]: 7.14e-05 [loop_unroll]: 2.911e-05 [a_1]: 0.0005737 [with_stream_mark]: 3.058e-05 [recompute_prepare]: 1.058e-05 [updatestate_depend_eliminate]: 4.92999e-06 [updatestate_assign_eliminate]: 3.85e-06 [updatestate_loads_eliminate]: 3.25998e-06 [parameter_eliminate]: 2.17001e-06 [specialize_transform]: 8.1e-06 [updatestate_useless_node_eliminater]: 6.75998e-06 [accelerated_algorithm]: 7.7e-06 [meta_shard_fg_expand]: 2.49001e-06 [get_grad_eliminate_]: 6.88e-06 [merge_forward]: 4.33001e-06 [cell_reuse_recompute_pass]: 1.12e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.095e-05 [j_node_and_user_rematch]: 1.138e-05 [meta_fg_expand]: 2.64001e-06 [replace_old_param]: 1.485e-05 [inline_without_move]: 7.18e-06 [renormalize]: 0.00124195 [add_forward_monad_depend]: 1.752e-05 [auto_monad_grad]: 3.18998e-06 [auto_monad_eliminator]: 2.002e-05 [cse]: 4.407e-05 [replace_applicator]: 2.196e-05 [Cycle 2]: 0.00045114, [27] [switch_simplify]: 8.08001e-06 [loop_unroll]: 7.10998e-06 [a_1]: 0.00014789 [with_stream_mark]: 1.711e-05 [recompute_prepare]: 7.09001e-06 [updatestate_depend_eliminate]: 4.28001e-06 [updatestate_assign_eliminate]: 3.51001e-06 [updatestate_loads_eliminate]: 3.31999e-06 [parameter_eliminate]: 1.88997e-06 [specialize_transform]: 6.68e-06 [updatestate_useless_node_eliminater]: 6.51e-06 [accelerated_algorithm]: 7.36001e-06 [meta_shard_fg_expand]: 2.33002e-06 [get_grad_eliminate_]: 6.24001e-06 [merge_forward]: 4.39002e-06 [cell_reuse_recompute_pass]: 2.37001e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.944e-05 [j_node_and_user_rematch]: 1.033e-05 [meta_fg_expand]: 2.32999e-06 [replace_old_param]: 1.306e-05 [inline_without_move]: 7.04001e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 2.01998e-06 [auto_monad_grad]: 1.76e-06 [auto_monad_eliminator]: 8.74e-06 [cse]: 1.696e-05 [replace_applicator]: 7.50998e-06 [py_interpret_to_execute_after_opt_a]: 1.795e-05 [rewriter_after_opt_a]: 7.235e-05 [convert_after_rewriter]: 1.083e-05 [order_py_execute_after_rewriter]: 6.58e-06 [mutable_eliminate]: 0.00078459 [jit_opt_b]: 6.884e-05, [1] [Cycle 1]: 5.965e-05, [2] [frontend_op_eliminate]: 2.353e-05 [inline_after_opt_a]: 2.189e-05 [cconv]: 4.058e-05 [loop_unroll]: 0.00047147 [jit_opt_after_cconv]: 0.00020684, [1] [Cycle 1]: 0.00019936, [11] [c_1]: 3.136e-05 [parameter_eliminate]: 5.35999e-06 [updatestate_depend_eliminate]: 1.065e-05 [updatestate_assign_eliminate]: 3.75e-06 [updatestate_loads_eliminate]: 3.14001e-06 [cse]: 4.374e-05 [call_graph_tuple_transform]: 2.636e-05 [tuple_list_get_item_eliminator]: 7.75e-06 [none_parameter_eliminate]: 2.15002e-06 [renormalize]: 6.69999e-07 [switch_simplify]: 8.05e-06 [remove_dup_value]: 1.883e-05 [partial_unused_args_eliminate]: 2.89001e-06 [environ_conv]: 2.196e-05 [add_recomputation]: 6.578e-05 [cse_after_recomputation]: 3.031e-05, [1] [Cycle 1]: 2.338e-05, [1] [cse]: 1.671e-05 [auto_monad_reorder]: 3.008e-05 [get_jit_bprop_graph]: 2.51e-06 [rewriter_after_jit_bprop_graph]: 0.00019642 [opt_after_jit_grad]: 0.00055942 [symbol_engine_optimizer]: 9.034e-05, [1] [Cycle 1]: 8.27e-05, [6] [build]: 6.45002e-06 [elim_shapecalc]: 1.052e-05 [elim_not_effective]: 1.529e-05 [opt_reshape]: 8.38001e-06 [fold_const_symbol]: 1.058e-05 [renormalize]: 6.00005e-07 [validate]: 7.181e-05 Sums bootstrap : 0.001037s : 0.18% type_inference : 0.573969s : 98.87% event_method : 0.000021s : 0.00% auto_monad : 0.000111s : 0.02% graph_reusing : 0.000006s : 0.00% pre_auto_parallel : 0.000013s : 0.00% py_interpret_to_execute : 0.000122s : 0.02% rewriter_before_opt_a : 0.000089s : 0.02% expand_dump_flag : 0.000003s : 0.00% jit_opt_a.switch_simplify : 0.000079s : 0.01% jit_opt_a.loop_unroll : 0.000036s : 0.01% jit_opt_a.a_1 : 0.000722s : 0.12% jit_opt_a.with_stream_mark : 0.000048s : 0.01% jit_opt_a.recompute_prepare : 0.000018s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000007s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000007s : 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 : 0.000015s : 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 : 0.000003s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000050s : 0.01% 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 : 0.000028s : 0.00% jit_opt_a.inline_without_move : 0.000014s : 0.00% jit_opt_a.renormalize : 0.001242s : 0.21% jit_opt_a.add_forward_monad_depend : 0.000020s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000029s : 0.00% jit_opt_a.cse : 0.000061s : 0.01% jit_opt_a.replace_applicator : 0.000029s : 0.01% py_interpret_to_execute_after_opt_a : 0.000018s : 0.00% rewriter_after_opt_a : 0.000072s : 0.01% convert_after_rewriter : 0.000011s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000785s : 0.14% jit_opt_b.frontend_op_eliminate : 0.000024s : 0.00% jit_opt_b.inline_after_opt_a : 0.000022s : 0.00% cconv : 0.000041s : 0.01% loop_unroll : 0.000471s : 0.08% jit_opt_after_cconv.c_1 : 0.000031s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000011s : 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 : 0.000044s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000026s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000008s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000008s : 0.00% remove_dup_value : 0.000019s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000022s : 0.00% add_recomputation : 0.000066s : 0.01% cse_after_recomputation.cse : 0.000017s : 0.00% auto_monad_reorder : 0.000030s : 0.01% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000196s : 0.03% opt_after_jit_grad : 0.000559s : 0.10% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000015s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000008s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000011s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000072s : 0.01% Time group info: ------[substitution.] 0.000234 29 0.95% : 0.000002s : 2: substitution.elim_not_effective 0.61% : 0.000001s : 2: substitution.fold_const_symbol 3.01% : 0.000007s : 5: substitution.graph_param_transform 76.35% : 0.000179s : 3: substitution.inline 1.87% : 0.000004s : 4: substitution.j_node_and_user_rematch 6.90% : 0.000016s : 4: substitution.remove_not_recompute_node 3.64% : 0.000009s : 6: substitution.replace_old_param 6.67% : 0.000016s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.573854 2 99.74% : 0.572354s : 1: type_inference.infer 0.26% : 0.001500s : 1: type_inference.specialize ------[replace.] 0.000060 6 68.67% : 0.000041s : 3: replace.inline 31.33% : 0.000019s : 3: replace.tuple_list_get_item_eliminator ------[match.] 0.000191 6 92.71% : 0.000177s : 3: match.inline 7.29% : 0.000014s : 3: match.tuple_list_get_item_eliminator ------[predicate.] 0.000153 922 1.21% : 0.000002s : 13: predicate.accumulaten_eliminater 1.63% : 0.000002s : 5: predicate.ad_related_special_op_eliminate 0.95% : 0.000001s : 13: predicate.addn_check_dump 1.20% : 0.000002s : 13: predicate.addn_zero_filter 1.72% : 0.000003s : 13: predicate.arithmetic_simplify 1.09% : 0.000002s : 13: predicate.cast_eliminate 0.57% : 0.000001s : 5: predicate.check_bprop_eliminate 1.04% : 0.000002s : 13: predicate.compare_switch_simplify 1.58% : 0.000002s : 13: predicate.depend_value_elim 0.97% : 0.000001s : 13: predicate.dict_get_item_const_eliminator 1.41% : 0.000002s : 13: predicate.dict_get_item_eliminator 1.13% : 0.000002s : 13: predicate.dict_set_item_eliminator 1.06% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.50% : 0.000001s : 5: predicate.elim_not_effective 0.88% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.17% : 0.000002s : 13: predicate.environ_add_const_eliminate 0.96% : 0.000001s : 13: predicate.environ_get_add_eliminate 1.28% : 0.000002s : 13: predicate.environ_get_depend_swap 1.19% : 0.000002s : 13: predicate.environ_get_eliminate 1.06% : 0.000002s : 13: predicate.environ_get_set_eliminate 0.29% : 0.000000s : 5: predicate.fold_const_symbol 1.05% : 0.000002s : 10: predicate.get_grad_eliminate 0.29% : 0.000000s : 5: predicate.graph_param_transform 5.89% : 0.000009s : 29: predicate.inline 1.13% : 0.000002s : 10: predicate.inline_without_move 0.47% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.67% : 0.000003s : 10: predicate.less_batch_normalization 1.45% : 0.000002s : 16: predicate.list_to_tuple_eliminator_ 1.74% : 0.000003s : 21: predicate.load_eliminater 1.34% : 0.000002s : 5: predicate.loop_unroll_after_grad 2.98% : 0.000005s : 32: predicate.loop_unroll_before_grad 2.03% : 0.000003s : 18: predicate.make_slice_get_slice_eliminator 1.13% : 0.000002s : 13: predicate.merge_addn 0.97% : 0.000001s : 13: predicate.minmaximum_grad 2.17% : 0.000003s : 5: predicate.mutable_eliminate 0.64% : 0.000001s : 5: predicate.opt_reshape 2.08% : 0.000003s : 21: predicate.partial_eliminate 1.08% : 0.000002s : 13: predicate.print_const_string_wrapper 1.32% : 0.000002s : 13: predicate.reduce_eliminate 1.63% : 0.000002s : 16: predicate.redundant_stop_gradient_eliminater 0.86% : 0.000001s : 10: predicate.remove_not_recompute_node 2.05% : 0.000003s : 26: predicate.replace_applicator 0.96% : 0.000001s : 10: predicate.replace_old_param 0.54% : 0.000001s : 5: predicate.reset_defer_inline 1.49% : 0.000002s : 13: predicate.reshape_eliminate 1.23% : 0.000002s : 13: predicate.row_tensor_add_zeros_like 0.91% : 0.000001s : 5: predicate.row_tensor_eliminate 1.08% : 0.000002s : 13: predicate.same_eliminate 0.67% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.21% : 0.000002s : 10: predicate.special_op_eliminate 1.08% : 0.000002s : 10: predicate.specialize_transform 1.30% : 0.000002s : 13: predicate.split_environ_get_set_with_tuple_value 1.13% : 0.000002s : 13: predicate.stack_unstack_eliminate 0.48% : 0.000001s : 5: predicate.switch_call_monad_eliminater 2.27% : 0.000003s : 19: predicate.switch_defer_inline 1.84% : 0.000003s : 19: predicate.switch_layer_defer_inline 6.90% : 0.000011s : 56: predicate.switch_simplify 1.57% : 0.000002s : 13: predicate.tile_eliminate 1.20% : 0.000002s : 13: predicate.transpose_eliminate 1.23% : 0.000002s : 13: predicate.tuple_list_convert_item_index_to_positive 1.20% : 0.000002s : 13: predicate.tuple_list_get_item_depend_reorder 4.60% : 0.000007s : 26: predicate.tuple_list_get_item_eliminator 1.32% : 0.000002s : 13: predicate.tuple_list_set_item_eliminator 1.50% : 0.000002s : 16: predicate.tuple_to_list_eliminator_ 1.72% : 0.000003s : 21: predicate.updatestate_pure_node_eliminater 3.01% : 0.000005s : 31: predicate.updatestate_useless_node_eliminater 1.44% : 0.000002s : 13: predicate.value_based_eliminate 0.53% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.73% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.001123 10 55.35% : 0.000622s : 5: func_graph_cloner_run.FuncGraphClonerGraph 44.65% : 0.000502s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.592211 72 0.01% : 0.000069s : 1: add_recomputation 0.02% : 0.000116s : 1: auto_monad 0.01% : 0.000033s : 1: auto_monad_reorder 0.18% : 0.001064s : 1: bootstrap 0.01% : 0.000044s : 1: cconv 0.00% : 0.000014s : 1: convert_after_rewriter 0.01% : 0.000033s : 1: cse_after_recomputation 0.00% : 0.000025s : 1: environ_conv 0.00% : 0.000025s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000008s : 1: graph_reusing 1.94% : 0.011516s : 1: jit_opt_a 0.04% : 0.000210s : 1: jit_opt_after_cconv 0.01% : 0.000073s : 1: jit_opt_b 0.08% : 0.000482s : 1: loop_unroll 0.13% : 0.000798s : 1: mutable_eliminate 0.17% : 0.001009s : 26: opt.transform.jit_opt_a 0.01% : 0.000070s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000038s : 4: opt.transform.jit_opt_b 0.00% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000023s : 1: opt.transform.mutable_eliminate 0.01% : 0.000035s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000041s : 4: opt.transform.symbol_engine_opt 0.10% : 0.000573s : 1: opt_after_jit_grad 0.00% : 0.000009s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000015s : 1: pre_auto_parallel 0.02% : 0.000125s : 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 0.11% : 0.000643s : 1: renormalize.infer 0.10% : 0.000588s : 1: renormalize.specialize 0.03% : 0.000201s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000076s : 1: rewriter_after_opt_a 0.02% : 0.000093s : 1: rewriter_before_opt_a 0.02% : 0.000094s : 1: symbol_engine_optimizer 96.92% : 0.573989s : 1: type_inference TotalTime = 1.0907, [30] [bootstrap]: 0.0006338 [type_inference]: 0.73952 [event_method]: 8.721e-05 [auto_monad]: 0.00017123 [graph_reusing]: 1.053e-05 [pre_auto_parallel]: 3.74002e-06 [py_interpret_to_execute]: 5.777e-05 [rewriter_before_opt_a]: 0.0002075 [expand_dump_flag]: 4.33999e-06 [jit_opt_a]: 0.198449, [3] [Cycle 1]: 0.188324, [27] [switch_simplify]: 0.00024793 [loop_unroll]: 8.247e-05 [a_1]: 0.00190652 [with_stream_mark]: 4.129e-05 [recompute_prepare]: 3.082e-05 [updatestate_depend_eliminate]: 1.228e-05 [updatestate_assign_eliminate]: 9.10001e-06 [updatestate_loads_eliminate]: 8.84e-06 [parameter_eliminate]: 4.4e-06 [specialize_transform]: 2.052e-05 [updatestate_useless_node_eliminater]: 1.963e-05 [accelerated_algorithm]: 3.656e-05 [meta_shard_fg_expand]: 4.36002e-06 [get_grad_eliminate_]: 1.863e-05 [merge_forward]: 1.133e-05 [cell_reuse_recompute_pass]: 1.10001e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.66e-05 [j_node_and_user_rematch]: 3.181e-05 [meta_fg_expand]: 0.00214566 [replace_old_param]: 8.875e-05 [inline_without_move]: 7.222e-05 [renormalize]: 0.182639 [add_forward_monad_depend]: 2.923e-05 [auto_monad_grad]: 7.87e-06 [auto_monad_eliminator]: 7.595e-05 [cse]: 0.00030856 [replace_applicator]: 0.00010915 [Cycle 2]: 0.00464967, [27] [switch_simplify]: 8.69e-05 [loop_unroll]: 5.28e-05 [a_1]: 0.00181783 [with_stream_mark]: 3.285e-05 [recompute_prepare]: 2.179e-05 [updatestate_depend_eliminate]: 8.45001e-06 [updatestate_assign_eliminate]: 7.31999e-06 [updatestate_loads_eliminate]: 5.92999e-06 [parameter_eliminate]: 2.79001e-06 [specialize_transform]: 1.41e-05 [updatestate_useless_node_eliminater]: 1.268e-05 [accelerated_algorithm]: 4.606e-05 [meta_shard_fg_expand]: 3.63e-06 [get_grad_eliminate_]: 1.261e-05 [merge_forward]: 7.62998e-06 [cell_reuse_recompute_pass]: 1.89e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.707e-05 [j_node_and_user_rematch]: 1.915e-05 [meta_fg_expand]: 0.0002461 [replace_old_param]: 3.024e-05 [inline_without_move]: 1.192e-05 [renormalize]: 0.0016842 [add_forward_monad_depend]: 1.097e-05 [auto_monad_grad]: 3.08e-06 [auto_monad_eliminator]: 2.88e-05 [cse]: 0.00013768 [replace_applicator]: 3.017e-05 [Cycle 3]: 0.0011416, [27] [switch_simplify]: 1.291e-05 [loop_unroll]: 1.167e-05 [a_1]: 0.00028084 [with_stream_mark]: 2.244e-05 [recompute_prepare]: 1.358e-05 [updatestate_depend_eliminate]: 6.76999e-06 [updatestate_assign_eliminate]: 6.29999e-06 [updatestate_loads_eliminate]: 6.23e-06 [parameter_eliminate]: 2.55997e-06 [specialize_transform]: 1.304e-05 [updatestate_useless_node_eliminater]: 1.184e-05 [accelerated_algorithm]: 2.172e-05 [meta_shard_fg_expand]: 3.31999e-06 [get_grad_eliminate_]: 1.081e-05 [merge_forward]: 7.2e-06 [cell_reuse_recompute_pass]: 2.58e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.6e-05 [j_node_and_user_rematch]: 1.921e-05 [meta_fg_expand]: 3.85998e-06 [replace_old_param]: 1.667e-05 [inline_without_move]: 1.007e-05 [renormalize]: 9.00181e-08 [add_forward_monad_depend]: 2.41998e-06 [auto_monad_grad]: 1.91e-06 [auto_monad_eliminator]: 1.762e-05 [cse]: 4.532e-05 [replace_applicator]: 2.575e-05 [py_interpret_to_execute_after_opt_a]: 2.728e-05 [rewriter_after_opt_a]: 0.00027515 [convert_after_rewriter]: 1.949e-05 [order_py_execute_after_rewriter]: 8.28999e-06 [mutable_eliminate]: 0.00085108 [jit_opt_b]: 9.631e-05, [1] [Cycle 1]: 8.693e-05, [2] [frontend_op_eliminate]: 3.71e-05 [inline_after_opt_a]: 3.525e-05 [cconv]: 4.279e-05 [loop_unroll]: 0.00052315 [jit_opt_after_cconv]: 0.00028349, [1] [Cycle 1]: 0.00027481, [11] [c_1]: 4.651e-05 [parameter_eliminate]: 5.29e-06 [updatestate_depend_eliminate]: 1.485e-05 [updatestate_assign_eliminate]: 6.37001e-06 [updatestate_loads_eliminate]: 5.59e-06 [cse]: 7.621e-05 [call_graph_tuple_transform]: 3.692e-05 [tuple_list_get_item_eliminator]: 1.17e-05 [none_parameter_eliminate]: 1.65001e-06 [renormalize]: 7.00005e-07 [switch_simplify]: 1.235e-05 [remove_dup_value]: 7.842e-05 [partial_unused_args_eliminate]: 2.93e-06 [environ_conv]: 1.429e-05 [add_recomputation]: 9.117e-05 [cse_after_recomputation]: 5.609e-05, [1] [Cycle 1]: 4.68e-05, [1] [cse]: 3.534e-05 [auto_monad_reorder]: 2.953e-05 [get_jit_bprop_graph]: 2.34001e-06 [rewriter_after_jit_bprop_graph]: 8.25999e-06 [opt_after_jit_grad]: 0.148624 [symbol_engine_optimizer]: 0.00014641, [1] [Cycle 1]: 0.00013439, [6] [build]: 2.041e-05 [elim_shapecalc]: 1.566e-05 [elim_not_effective]: 3.263e-05 [opt_reshape]: 1.191e-05 [fold_const_symbol]: 1.829e-05 [renormalize]: 6.79982e-07 [validate]: 8.664e-05 Sums bootstrap : 0.000634s : 0.06% type_inference : 0.739520s : 68.17% event_method : 0.000087s : 0.01% auto_monad : 0.000171s : 0.02% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000058s : 0.01% rewriter_before_opt_a : 0.000208s : 0.02% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000348s : 0.03% jit_opt_a.loop_unroll : 0.000147s : 0.01% jit_opt_a.a_1 : 0.004005s : 0.37% jit_opt_a.with_stream_mark : 0.000097s : 0.01% jit_opt_a.recompute_prepare : 0.000066s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000028s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000023s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000021s : 0.00% jit_opt_a.parameter_eliminate : 0.000010s : 0.00% jit_opt_a.specialize_transform : 0.000048s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000044s : 0.00% jit_opt_a.accelerated_algorithm : 0.000104s : 0.01% jit_opt_a.meta_shard_fg_expand : 0.000011s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000042s : 0.00% jit_opt_a.merge_forward : 0.000026s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000006s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000090s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000070s : 0.01% jit_opt_a.meta_fg_expand : 0.002396s : 0.22% jit_opt_a.replace_old_param : 0.000136s : 0.01% jit_opt_a.inline_without_move : 0.000094s : 0.01% jit_opt_a.renormalize : 0.184323s : 16.99% jit_opt_a.add_forward_monad_depend : 0.000043s : 0.00% jit_opt_a.auto_monad_grad : 0.000013s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000122s : 0.01% jit_opt_a.cse : 0.000492s : 0.05% jit_opt_a.replace_applicator : 0.000165s : 0.02% py_interpret_to_execute_after_opt_a : 0.000027s : 0.00% rewriter_after_opt_a : 0.000275s : 0.03% convert_after_rewriter : 0.000019s : 0.00% order_py_execute_after_rewriter : 0.000008s : 0.00% mutable_eliminate : 0.000851s : 0.08% jit_opt_b.frontend_op_eliminate : 0.000037s : 0.00% jit_opt_b.inline_after_opt_a : 0.000035s : 0.00% cconv : 0.000043s : 0.00% loop_unroll : 0.000523s : 0.05% jit_opt_after_cconv.c_1 : 0.000047s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000015s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.cse : 0.000076s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000037s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000012s : 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 : 0.000012s : 0.00% remove_dup_value : 0.000078s : 0.01% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000014s : 0.00% add_recomputation : 0.000091s : 0.01% cse_after_recomputation.cse : 0.000035s : 0.00% auto_monad_reorder : 0.000030s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000008s : 0.00% opt_after_jit_grad : 0.148624s : 13.70% symbol_engine_optimizer.build : 0.000020s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000016s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000033s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000012s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000018s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000087s : 0.01% Time group info: ------[substitution.] 0.001130 216 3.90% : 0.000044s : 4: substitution.arithmetic_simplify 0.40% : 0.000004s : 6: substitution.elim_not_effective 0.25% : 0.000003s : 6: substitution.fold_const_symbol 0.86% : 0.000010s : 8: substitution.graph_param_transform 65.39% : 0.000739s : 21: substitution.inline 1.95% : 0.000022s : 2: substitution.inline_without_move 1.25% : 0.000014s : 23: substitution.j_node_and_user_rematch 2.11% : 0.000024s : 7: substitution.less_batch_normalization 1.69% : 0.000019s : 15: substitution.minmaximum_grad 0.90% : 0.000010s : 10: substitution.partial_eliminate 1.53% : 0.000017s : 23: substitution.remove_not_recompute_node 3.32% : 0.000038s : 9: substitution.replace_applicator 1.59% : 0.000018s : 19: substitution.replace_old_param 0.29% : 0.000003s : 1: substitution.set_cell_output_no_recompute 1.17% : 0.000013s : 3: substitution.switch_simplify 3.91% : 0.000044s : 15: substitution.tuple_list_convert_item_index_to_positive 2.46% : 0.000028s : 15: substitution.tuple_list_get_item_depend_reorder 7.05% : 0.000080s : 29: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.739362 2 79.62% : 0.588655s : 1: type_inference.infer 20.38% : 0.150707s : 1: type_inference.specialize ------[replace.] 0.000391 38 54.56% : 0.000213s : 21: replace.inline 18.72% : 0.000073s : 3: replace.switch_simplify 26.72% : 0.000104s : 14: replace.tuple_list_get_item_eliminator ------[match.] 0.000771 38 94.23% : 0.000727s : 21: match.inline 1.49% : 0.000012s : 3: match.switch_simplify 4.27% : 0.000033s : 14: match.tuple_list_get_item_eliminator ------[predicate.] 0.000726 4528 1.37% : 0.000010s : 75: predicate.accumulaten_eliminater 0.97% : 0.000007s : 8: predicate.ad_related_special_op_eliminate 1.44% : 0.000010s : 75: predicate.addn_check_dump 1.36% : 0.000010s : 75: predicate.addn_zero_filter 1.71% : 0.000012s : 75: predicate.arithmetic_simplify 1.62% : 0.000012s : 75: predicate.cast_eliminate 0.20% : 0.000001s : 8: predicate.check_bprop_eliminate 1.25% : 0.000009s : 75: predicate.compare_switch_simplify 1.43% : 0.000010s : 75: predicate.depend_value_elim 1.25% : 0.000009s : 75: predicate.dict_get_item_const_eliminator 1.39% : 0.000010s : 75: predicate.dict_get_item_eliminator 1.27% : 0.000009s : 75: predicate.dict_set_item_eliminator 0.57% : 0.000004s : 8: predicate.dumpgradient_eliminate 0.21% : 0.000002s : 8: predicate.elim_not_effective 0.25% : 0.000002s : 8: predicate.elim_shapecalc_of_broadcastargs 1.36% : 0.000010s : 75: predicate.environ_add_const_eliminate 1.34% : 0.000010s : 75: predicate.environ_get_add_eliminate 1.29% : 0.000009s : 75: predicate.environ_get_depend_swap 1.30% : 0.000009s : 75: predicate.environ_get_eliminate 1.30% : 0.000009s : 75: predicate.environ_get_set_eliminate 0.09% : 0.000001s : 8: predicate.fold_const_symbol 0.74% : 0.000005s : 34: predicate.get_grad_eliminate 0.09% : 0.000001s : 8: predicate.graph_param_transform 4.27% : 0.000031s : 126: predicate.inline 1.62% : 0.000012s : 65: predicate.inline_without_move 0.37% : 0.000003s : 34: predicate.j_node_and_user_rematch 1.25% : 0.000009s : 34: predicate.less_batch_normalization 1.69% : 0.000012s : 89: predicate.list_to_tuple_eliminator_ 1.77% : 0.000013s : 97: predicate.load_eliminater 0.55% : 0.000004s : 8: predicate.loop_unroll_after_grad 2.94% : 0.000021s : 152: predicate.loop_unroll_before_grad 1.66% : 0.000012s : 83: predicate.make_slice_get_slice_eliminator 1.36% : 0.000010s : 75: predicate.merge_addn 1.37% : 0.000010s : 75: predicate.minmaximum_grad 0.60% : 0.000004s : 8: predicate.mutable_eliminate 0.20% : 0.000001s : 8: predicate.opt_reshape 2.05% : 0.000015s : 97: predicate.partial_eliminate 1.28% : 0.000009s : 75: predicate.print_const_string_wrapper 1.62% : 0.000012s : 75: predicate.reduce_eliminate 1.64% : 0.000012s : 89: predicate.redundant_stop_gradient_eliminater 0.47% : 0.000003s : 34: predicate.remove_not_recompute_node 2.37% : 0.000017s : 157: predicate.replace_applicator 0.88% : 0.000006s : 65: predicate.replace_old_param 0.15% : 0.000001s : 8: predicate.reset_defer_inline 1.40% : 0.000010s : 75: predicate.reshape_eliminate 1.41% : 0.000010s : 75: predicate.row_tensor_add_zeros_like 0.30% : 0.000002s : 8: predicate.row_tensor_eliminate 1.37% : 0.000010s : 75: predicate.same_eliminate 0.54% : 0.000004s : 34: predicate.set_cell_output_no_recompute 0.36% : 0.000003s : 16: predicate.special_op_eliminate 0.84% : 0.000006s : 34: predicate.specialize_transform 1.53% : 0.000011s : 75: predicate.split_environ_get_set_with_tuple_value 1.34% : 0.000010s : 75: predicate.stack_unstack_eliminate 0.16% : 0.000001s : 8: predicate.switch_call_monad_eliminater 2.75% : 0.000020s : 110: predicate.switch_defer_inline 2.23% : 0.000016s : 110: predicate.switch_layer_defer_inline 12.35% : 0.000090s : 276: predicate.switch_simplify 1.31% : 0.000009s : 75: predicate.tile_eliminate 1.34% : 0.000010s : 75: predicate.transpose_eliminate 1.79% : 0.000013s : 75: predicate.tuple_list_convert_item_index_to_positive 1.67% : 0.000012s : 75: predicate.tuple_list_get_item_depend_reorder 3.22% : 0.000023s : 105: predicate.tuple_list_get_item_eliminator 1.89% : 0.000014s : 75: predicate.tuple_list_set_item_eliminator 1.63% : 0.000012s : 89: predicate.tuple_to_list_eliminator_ 1.80% : 0.000013s : 97: predicate.updatestate_pure_node_eliminater 2.77% : 0.000020s : 131: predicate.updatestate_useless_node_eliminater 1.61% : 0.000012s : 75: predicate.value_based_eliminate 0.17% : 0.000001s : 8: predicate.virtual_view_grad_eliminate 0.32% : 0.000002s : 8: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.215937 47 99.32% : 0.214479s : 22: func_graph_cloner_run.FuncGraphClonerGraph 0.68% : 0.001459s : 25: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.280485 87 0.01% : 0.000097s : 1: add_recomputation 0.01% : 0.000178s : 1: auto_monad 0.00% : 0.000033s : 1: auto_monad_reorder 0.05% : 0.000668s : 1: bootstrap 0.00% : 0.000046s : 1: cconv 0.00% : 0.000024s : 1: convert_after_rewriter 0.00% : 0.000059s : 1: cse_after_recomputation 0.00% : 0.000017s : 1: environ_conv 0.01% : 0.000095s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000013s : 1: graph_reusing 15.50% : 0.198454s : 1: jit_opt_a 0.02% : 0.000287s : 1: jit_opt_after_cconv 0.01% : 0.000100s : 1: jit_opt_b 0.04% : 0.000535s : 1: loop_unroll 0.07% : 0.000864s : 1: mutable_eliminate 0.41% : 0.005291s : 39: opt.transform.jit_opt_a 0.01% : 0.000104s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000063s : 4: opt.transform.jit_opt_b 0.00% : 0.000025s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000030s : 1: opt.transform.mutable_eliminate 0.01% : 0.000076s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000075s : 4: opt.transform.symbol_engine_opt 11.61% : 0.148649s : 1: opt_after_jit_grad 0.00% : 0.000011s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000061s : 1: py_interpret_to_execute 0.00% : 0.000030s : 1: py_interpret_to_execute_after_opt_a 0.01% : 0.000082s : 1: remove_dup_value 14.15% : 0.181162s : 2: renormalize.infer 0.24% : 0.003133s : 2: renormalize.specialize 0.00% : 0.000010s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000281s : 1: rewriter_after_opt_a 0.02% : 0.000211s : 1: rewriter_before_opt_a 0.01% : 0.000151s : 1: symbol_engine_optimizer 57.76% : 0.739548s : 1: type_inference . [hook] pytest_runtest_teardown:test_addmm_input_with_first_dim_1[KBK] tests/st/mint/test_addmm.py::test_addmm_input_with_first_dim_1[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 173.72s (0:02:53) ==================