==================================================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_003/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_column_major_views[pynative] tests/st/mint/test_addmm.py::test_addmm_column_major_views[pynative],max_mem:2.0M TotalTime = 11.1792, [33] [bootstrap]: 0.00076775 [type_inference]: 0.7682 [event_method]: 2.091e-05 [auto_monad]: 9.979e-05 [graph_reusing]: 6.66999e-06 [pre_auto_parallel]: 1.317e-05 [py_interpret_to_execute]: 0.00013179 [rewriter_before_opt_a]: 8.072e-05 [expand_dump_flag]: 3.30003e-06 [jit_opt_a]: 0.257825, [2] [Cycle 1]: 0.00265519, [27] [switch_simplify]: 7.949e-05 [loop_unroll]: 2.963e-05 [a_1]: 0.0006608 [with_stream_mark]: 3.361e-05 [recompute_prepare]: 1.409e-05 [updatestate_depend_eliminate]: 5.83002e-06 [updatestate_assign_eliminate]: 3.88999e-06 [updatestate_loads_eliminate]: 3.88001e-06 [parameter_eliminate]: 2.16e-06 [specialize_transform]: 8.08999e-06 [updatestate_useless_node_eliminater]: 6.88e-06 [accelerated_algorithm]: 7.67002e-06 [meta_shard_fg_expand]: 3.51999e-06 [get_grad_eliminate_]: 7.15e-06 [merge_forward]: 4.82e-06 [cell_reuse_recompute_pass]: 1.40999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.507e-05 [j_node_and_user_rematch]: 1.189e-05 [meta_fg_expand]: 2.79001e-06 [replace_old_param]: 1.595e-05 [inline_without_move]: 7.3e-06 [renormalize]: 0.00133568 [add_forward_monad_depend]: 1.788e-05 [auto_monad_grad]: 2.78998e-06 [auto_monad_eliminator]: 2.481e-05 [cse]: 5.568e-05 [replace_applicator]: 2.508e-05 [Cycle 2]: 0.00053762, [27] [switch_simplify]: 9.76998e-06 [loop_unroll]: 8.02998e-06 [a_1]: 0.00015549 [with_stream_mark]: 1.962e-05 [recompute_prepare]: 7.08e-06 [updatestate_depend_eliminate]: 6.04999e-06 [updatestate_assign_eliminate]: 4.48999e-06 [updatestate_loads_eliminate]: 3.59002e-06 [parameter_eliminate]: 2.44001e-06 [specialize_transform]: 8.90001e-06 [updatestate_useless_node_eliminater]: 7.16001e-06 [accelerated_algorithm]: 7.48999e-06 [meta_shard_fg_expand]: 3.03e-06 [get_grad_eliminate_]: 6.73e-06 [merge_forward]: 5.77001e-06 [cell_reuse_recompute_pass]: 2.99001e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.341e-05 [j_node_and_user_rematch]: 1.129e-05 [meta_fg_expand]: 2.87002e-06 [replace_old_param]: 1.343e-05 [inline_without_move]: 6.34999e-06 [renormalize]: 1.19995e-07 [add_forward_monad_depend]: 1.66e-06 [auto_monad_grad]: 1.35999e-06 [auto_monad_eliminator]: 9.22001e-06 [cse]: 2.401e-05 [replace_applicator]: 8.03001e-06 [py_interpret_to_execute_after_opt_a]: 2.146e-05 [rewriter_after_opt_a]: 7.615e-05 [convert_after_rewriter]: 9.71998e-06 [order_py_execute_after_rewriter]: 2.151e-05 [mutable_eliminate]: 0.00089556 [jit_opt_b]: 7.313e-05, [1] [Cycle 1]: 6.173e-05, [2] [frontend_op_eliminate]: 2.432e-05 [inline_after_opt_a]: 2.303e-05 [cconv]: 3.986e-05 [loop_unroll]: 0.00052513 [jit_opt_after_cconv]: 0.00027855, [1] [Cycle 1]: 0.00027044, [11] [c_1]: 3.186e-05 [parameter_eliminate]: 6.76999e-06 [updatestate_depend_eliminate]: 1.134e-05 [updatestate_assign_eliminate]: 3.46999e-06 [updatestate_loads_eliminate]: 3.6e-06 [cse]: 8.703e-05 [call_graph_tuple_transform]: 4.581e-05 [tuple_list_get_item_eliminator]: 7.78001e-06 [none_parameter_eliminate]: 2.98e-06 [renormalize]: 7.50006e-07 [switch_simplify]: 7.4e-06 [remove_dup_value]: 2.231e-05 [partial_unused_args_eliminate]: 2.52001e-06 [environ_conv]: 9.235e-05 [add_recomputation]: 7.465e-05 [cse_after_recomputation]: 3.582e-05, [1] [Cycle 1]: 2.815e-05, [1] [cse]: 2.063e-05 [auto_monad_reorder]: 3.216e-05 [get_jit_bprop_graph]: 2.61999e-06 [rewriter_after_jit_bprop_graph]: 0.00015925 [opt_after_jit_grad]: 0.0007039 [symbol_engine_optimizer]: 9.663e-05, [1] [Cycle 1]: 8.754e-05, [6] [build]: 5.44e-06 [elim_shapecalc]: 1.08e-05 [elim_not_effective]: 2.002e-05 [opt_reshape]: 8.44998e-06 [fold_const_symbol]: 1.104e-05 [renormalize]: 5.50004e-07 [validate]: 7.211e-05 [backend_pass]: 1.05999e-06 [task_emit]: 10.1484 [execute]: 1.085e-05 Sums bootstrap : 0.000768s : 0.01% type_inference : 0.768200s : 7.03% event_method : 0.000021s : 0.00% auto_monad : 0.000100s : 0.00% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000013s : 0.00% py_interpret_to_execute : 0.000132s : 0.00% rewriter_before_opt_a : 0.000081s : 0.00% expand_dump_flag : 0.000003s : 0.00% jit_opt_a.switch_simplify : 0.000089s : 0.00% jit_opt_a.loop_unroll : 0.000038s : 0.00% jit_opt_a.a_1 : 0.000816s : 0.01% jit_opt_a.with_stream_mark : 0.000053s : 0.00% jit_opt_a.recompute_prepare : 0.000021s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000012s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000008s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000007s : 0.00% jit_opt_a.parameter_eliminate : 0.000005s : 0.00% jit_opt_a.specialize_transform : 0.000017s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000014s : 0.00% jit_opt_a.accelerated_algorithm : 0.000015s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000007s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000014s : 0.00% jit_opt_a.merge_forward : 0.000011s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000048s : 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 : 0.000029s : 0.00% jit_opt_a.inline_without_move : 0.000014s : 0.00% jit_opt_a.renormalize : 0.001336s : 0.01% 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 : 0.000034s : 0.00% jit_opt_a.cse : 0.000080s : 0.00% jit_opt_a.replace_applicator : 0.000033s : 0.00% py_interpret_to_execute_after_opt_a : 0.000021s : 0.00% rewriter_after_opt_a : 0.000076s : 0.00% convert_after_rewriter : 0.000010s : 0.00% order_py_execute_after_rewriter : 0.000022s : 0.00% mutable_eliminate : 0.000896s : 0.01% jit_opt_b.frontend_op_eliminate : 0.000024s : 0.00% jit_opt_b.inline_after_opt_a : 0.000023s : 0.00% cconv : 0.000040s : 0.00% loop_unroll : 0.000525s : 0.00% jit_opt_after_cconv.c_1 : 0.000032s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000011s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.cse : 0.000087s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000046s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000008s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000007s : 0.00% remove_dup_value : 0.000022s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000092s : 0.00% add_recomputation : 0.000075s : 0.00% cse_after_recomputation.cse : 0.000021s : 0.00% auto_monad_reorder : 0.000032s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000159s : 0.00% opt_after_jit_grad : 0.000704s : 0.01% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000020s : 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 : 0.00% backend_pass : 0.000001s : 0.00% task_emit : 10.148416s : 92.90% execute : 0.000011s : 0.00% Time group info: ------[substitution.] 0.000260 29 1.05% : 0.000003s : 2: substitution.elim_not_effective 0.57% : 0.000001s : 2: substitution.fold_const_symbol 7.58% : 0.000020s : 5: substitution.graph_param_transform 76.38% : 0.000199s : 3: substitution.inline 1.90% : 0.000005s : 4: substitution.j_node_and_user_rematch 2.15% : 0.000006s : 4: substitution.remove_not_recompute_node 3.69% : 0.000010s : 6: substitution.replace_old_param 6.68% : 0.000017s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.768053 2 99.80% : 0.766509s : 1: type_inference.infer 0.20% : 0.001544s : 1: type_inference.specialize ------[replace.] 0.000064 6 68.25% : 0.000043s : 3: replace.inline 31.75% : 0.000020s : 3: replace.tuple_list_get_item_eliminator ------[match.] 0.000212 6 92.51% : 0.000196s : 3: match.inline 7.49% : 0.000016s : 3: match.tuple_list_get_item_eliminator ------[predicate.] 0.000162 922 1.69% : 0.000003s : 13: predicate.accumulaten_eliminater 1.59% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 1.06% : 0.000002s : 13: predicate.addn_check_dump 1.65% : 0.000003s : 13: predicate.addn_zero_filter 1.92% : 0.000003s : 13: predicate.arithmetic_simplify 1.14% : 0.000002s : 13: predicate.cast_eliminate 0.69% : 0.000001s : 5: predicate.check_bprop_eliminate 0.93% : 0.000002s : 13: predicate.compare_switch_simplify 1.03% : 0.000002s : 13: predicate.depend_value_elim 1.06% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 1.27% : 0.000002s : 13: predicate.dict_get_item_eliminator 1.44% : 0.000002s : 13: predicate.dict_set_item_eliminator 1.00% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.53% : 0.000001s : 5: predicate.elim_not_effective 0.63% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 0.95% : 0.000002s : 13: predicate.environ_add_const_eliminate 1.22% : 0.000002s : 13: predicate.environ_get_add_eliminate 0.91% : 0.000001s : 13: predicate.environ_get_depend_swap 1.32% : 0.000002s : 13: predicate.environ_get_eliminate 1.38% : 0.000002s : 13: predicate.environ_get_set_eliminate 0.27% : 0.000000s : 5: predicate.fold_const_symbol 1.13% : 0.000002s : 10: predicate.get_grad_eliminate 0.28% : 0.000000s : 5: predicate.graph_param_transform 5.69% : 0.000009s : 29: predicate.inline 0.88% : 0.000001s : 10: predicate.inline_without_move 0.46% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.26% : 0.000002s : 10: predicate.less_batch_normalization 1.41% : 0.000002s : 16: predicate.list_to_tuple_eliminator_ 1.79% : 0.000003s : 21: predicate.load_eliminater 1.54% : 0.000002s : 5: predicate.loop_unroll_after_grad 2.91% : 0.000005s : 32: predicate.loop_unroll_before_grad 1.99% : 0.000003s : 18: predicate.make_slice_get_slice_eliminator 1.19% : 0.000002s : 13: predicate.merge_addn 0.95% : 0.000002s : 13: predicate.minmaximum_grad 2.09% : 0.000003s : 5: predicate.mutable_eliminate 0.80% : 0.000001s : 5: predicate.opt_reshape 2.11% : 0.000003s : 21: predicate.partial_eliminate 1.15% : 0.000002s : 13: predicate.print_const_string_wrapper 1.57% : 0.000003s : 13: predicate.reduce_eliminate 1.88% : 0.000003s : 16: predicate.redundant_stop_gradient_eliminater 0.70% : 0.000001s : 10: predicate.remove_not_recompute_node 1.96% : 0.000003s : 26: predicate.replace_applicator 0.66% : 0.000001s : 10: predicate.replace_old_param 0.34% : 0.000001s : 5: predicate.reset_defer_inline 1.51% : 0.000002s : 13: predicate.reshape_eliminate 1.16% : 0.000002s : 13: predicate.row_tensor_add_zeros_like 1.20% : 0.000002s : 5: predicate.row_tensor_eliminate 1.27% : 0.000002s : 13: predicate.same_eliminate 0.70% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.10% : 0.000002s : 10: predicate.special_op_eliminate 1.04% : 0.000002s : 10: predicate.specialize_transform 1.27% : 0.000002s : 13: predicate.split_environ_get_set_with_tuple_value 1.04% : 0.000002s : 13: predicate.stack_unstack_eliminate 0.42% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.83% : 0.000003s : 19: predicate.switch_defer_inline 1.74% : 0.000003s : 19: predicate.switch_layer_defer_inline 7.29% : 0.000012s : 56: predicate.switch_simplify 1.24% : 0.000002s : 13: predicate.tile_eliminate 1.41% : 0.000002s : 13: predicate.transpose_eliminate 1.14% : 0.000002s : 13: predicate.tuple_list_convert_item_index_to_positive 1.09% : 0.000002s : 13: predicate.tuple_list_get_item_depend_reorder 4.22% : 0.000007s : 26: predicate.tuple_list_get_item_eliminator 1.56% : 0.000003s : 13: predicate.tuple_list_set_item_eliminator 1.48% : 0.000002s : 16: predicate.tuple_to_list_eliminator_ 1.98% : 0.000003s : 21: predicate.updatestate_pure_node_eliminater 3.23% : 0.000005s : 31: predicate.updatestate_useless_node_eliminater 1.58% : 0.000003s : 13: predicate.value_based_eliminate 0.40% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.69% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.001064 10 58.27% : 0.000620s : 5: func_graph_cloner_run.FuncGraphClonerGraph 41.73% : 0.000444s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 11.181747 76 0.00% : 0.000078s : 1: add_recomputation 0.00% : 0.000104s : 1: auto_monad 0.00% : 0.000036s : 1: auto_monad_reorder 0.00% : 0.000004s : 1: backend_pass 0.01% : 0.000793s : 1: bootstrap 0.00% : 0.000043s : 1: cconv 0.00% : 0.000013s : 1: convert_after_rewriter 0.00% : 0.000038s : 1: cse_after_recomputation 0.00% : 0.000096s : 1: environ_conv 0.00% : 0.000026s : 1: event_method 0.00% : 0.000017s : 1: execute 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 2.31% : 0.257830s : 1: jit_opt_a 0.00% : 0.000282s : 1: jit_opt_after_cconv 0.00% : 0.000076s : 1: jit_opt_b 0.00% : 0.000536s : 1: loop_unroll 0.01% : 0.000912s : 1: mutable_eliminate 0.01% : 0.001121s : 26: opt.transform.jit_opt_a 0.00% : 0.000089s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000039s : 4: opt.transform.jit_opt_b 0.00% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000024s : 1: opt.transform.mutable_eliminate 0.00% : 0.000038s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000047s : 4: opt.transform.symbol_engine_opt 0.01% : 0.000717s : 1: opt_after_jit_grad 0.00% : 0.000024s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000015s : 1: pre_auto_parallel 0.00% : 0.000136s : 1: py_interpret_to_execute 0.00% : 0.000024s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000025s : 1: remove_dup_value 0.01% : 0.000715s : 1: renormalize.infer 0.01% : 0.000610s : 1: renormalize.specialize 0.00% : 0.000163s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000082s : 1: rewriter_after_opt_a 0.00% : 0.000084s : 1: rewriter_before_opt_a 0.00% : 0.000099s : 1: symbol_engine_optimizer 90.76% : 10.148446s : 1: task_emit 6.87% : 0.768220s : 1: type_inference 0.00% : 0.000104s : 1: validate TotalTime = 1.82395, [33] [bootstrap]: 0.00068461 [type_inference]: 1.15662 [event_method]: 0.00011219 [auto_monad]: 0.00021622 [graph_reusing]: 1.179e-05 [pre_auto_parallel]: 3.93001e-06 [py_interpret_to_execute]: 6.454e-05 [rewriter_before_opt_a]: 0.00022837 [expand_dump_flag]: 5.39e-06 [jit_opt_a]: 0.276144, [3] [Cycle 1]: 0.265839, [27] [switch_simplify]: 0.0003294 [loop_unroll]: 8.485e-05 [a_1]: 0.00204141 [with_stream_mark]: 4.791e-05 [recompute_prepare]: 3.576e-05 [updatestate_depend_eliminate]: 1.255e-05 [updatestate_assign_eliminate]: 9.61e-06 [updatestate_loads_eliminate]: 9.19e-06 [parameter_eliminate]: 3.88001e-06 [specialize_transform]: 2.026e-05 [updatestate_useless_node_eliminater]: 1.868e-05 [accelerated_algorithm]: 4.055e-05 [meta_shard_fg_expand]: 7.13e-06 [get_grad_eliminate_]: 1.964e-05 [merge_forward]: 1.249e-05 [cell_reuse_recompute_pass]: 9.79984e-07 [cell_reuse_handle_not_recompute_node_pass]: 3.761e-05 [j_node_and_user_rematch]: 3.455e-05 [meta_fg_expand]: 0.00263662 [replace_old_param]: 0.00010205 [inline_without_move]: 7.982e-05 [renormalize]: 0.259375 [add_forward_monad_depend]: 2.398e-05 [auto_monad_grad]: 7.63001e-06 [auto_monad_eliminator]: 7.479e-05 [cse]: 0.00032378 [replace_applicator]: 0.00011048 [Cycle 2]: 0.00442499, [27] [switch_simplify]: 5.278e-05 [loop_unroll]: 7.571e-05 [a_1]: 0.00164584 [with_stream_mark]: 2.899e-05 [recompute_prepare]: 1.614e-05 [updatestate_depend_eliminate]: 8.29002e-06 [updatestate_assign_eliminate]: 6.92997e-06 [updatestate_loads_eliminate]: 5.91e-06 [parameter_eliminate]: 2.78998e-06 [specialize_transform]: 1.27e-05 [updatestate_useless_node_eliminater]: 1.168e-05 [accelerated_algorithm]: 2.074e-05 [meta_shard_fg_expand]: 3.31999e-06 [get_grad_eliminate_]: 1.002e-05 [merge_forward]: 6.79001e-06 [cell_reuse_recompute_pass]: 1.97999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.545e-05 [j_node_and_user_rematch]: 1.874e-05 [meta_fg_expand]: 0.00030788 [replace_old_param]: 3.123e-05 [inline_without_move]: 1.28e-05 [renormalize]: 0.00164266 [add_forward_monad_depend]: 9.14e-06 [auto_monad_grad]: 2.59999e-06 [auto_monad_eliminator]: 2.57e-05 [cse]: 0.00015561 [replace_applicator]: 3.175e-05 [Cycle 3]: 0.00071965, [27] [switch_simplify]: 1.32e-05 [loop_unroll]: 1.112e-05 [a_1]: 0.00027759 [with_stream_mark]: 2.422e-05 [recompute_prepare]: 1.186e-05 [updatestate_depend_eliminate]: 7.87e-06 [updatestate_assign_eliminate]: 6.16998e-06 [updatestate_loads_eliminate]: 5.59e-06 [parameter_eliminate]: 1.76e-06 [specialize_transform]: 1.105e-05 [updatestate_useless_node_eliminater]: 1.047e-05 [accelerated_algorithm]: 2.008e-05 [meta_shard_fg_expand]: 3.63999e-06 [get_grad_eliminate_]: 1.114e-05 [merge_forward]: 7.6e-06 [cell_reuse_recompute_pass]: 4.37e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.538e-05 [j_node_and_user_rematch]: 1.752e-05 [meta_fg_expand]: 4.58999e-06 [replace_old_param]: 1.687e-05 [inline_without_move]: 1.064e-05 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 2.37999e-06 [auto_monad_grad]: 2.22001e-06 [auto_monad_eliminator]: 1.645e-05 [cse]: 5.043e-05 [replace_applicator]: 1.307e-05 [py_interpret_to_execute_after_opt_a]: 2.613e-05 [rewriter_after_opt_a]: 0.00028761 [convert_after_rewriter]: 1.492e-05 [order_py_execute_after_rewriter]: 7.92e-06 [mutable_eliminate]: 0.00088223 [jit_opt_b]: 0.00011908, [1] [Cycle 1]: 0.00010858, [2] [frontend_op_eliminate]: 3.544e-05 [inline_after_opt_a]: 5.559e-05 [cconv]: 4.396e-05 [loop_unroll]: 0.00057631 [jit_opt_after_cconv]: 0.0003095, [1] [Cycle 1]: 0.00030029, [11] [c_1]: 5.249e-05 [parameter_eliminate]: 6.19001e-06 [updatestate_depend_eliminate]: 1.739e-05 [updatestate_assign_eliminate]: 7.68999e-06 [updatestate_loads_eliminate]: 6.63e-06 [cse]: 7.833e-05 [call_graph_tuple_transform]: 4.278e-05 [tuple_list_get_item_eliminator]: 1.24e-05 [none_parameter_eliminate]: 1.96e-06 [renormalize]: 5.8001e-07 [switch_simplify]: 1.236e-05 [remove_dup_value]: 0.248475 [partial_unused_args_eliminate]: 9.25001e-06 [environ_conv]: 2.419e-05 [add_recomputation]: 0.00011472 [cse_after_recomputation]: 0.00010286, [1] [Cycle 1]: 8.916e-05, [1] [cse]: 6.38e-05 [auto_monad_reorder]: 3.341e-05 [get_jit_bprop_graph]: 2.51998e-06 [rewriter_after_jit_bprop_graph]: 1.212e-05 [opt_after_jit_grad]: 0.00090531 [symbol_engine_optimizer]: 0.00013515, [1] [Cycle 1]: 0.00012782, [6] [build]: 1.581e-05 [elim_shapecalc]: 1.593e-05 [elim_not_effective]: 3.201e-05 [opt_reshape]: 1.217e-05 [fold_const_symbol]: 1.852e-05 [renormalize]: 7.50006e-07 [validate]: 7.432e-05 [backend_pass]: 1.18001e-06 [task_emit]: 0.137322 [execute]: 1.086e-05 Sums bootstrap : 0.000685s : 0.04% type_inference : 1.156619s : 63.64% event_method : 0.000112s : 0.01% auto_monad : 0.000216s : 0.01% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000065s : 0.00% rewriter_before_opt_a : 0.000228s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000395s : 0.02% jit_opt_a.loop_unroll : 0.000172s : 0.01% jit_opt_a.a_1 : 0.003965s : 0.22% jit_opt_a.with_stream_mark : 0.000101s : 0.01% jit_opt_a.recompute_prepare : 0.000064s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000029s : 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 : 0.000008s : 0.00% jit_opt_a.specialize_transform : 0.000044s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000041s : 0.00% jit_opt_a.accelerated_algorithm : 0.000081s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000014s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000041s : 0.00% jit_opt_a.merge_forward : 0.000027s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000088s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000071s : 0.00% jit_opt_a.meta_fg_expand : 0.002949s : 0.16% jit_opt_a.replace_old_param : 0.000150s : 0.01% jit_opt_a.inline_without_move : 0.000103s : 0.01% jit_opt_a.renormalize : 0.261018s : 14.36% jit_opt_a.add_forward_monad_depend : 0.000035s : 0.00% jit_opt_a.auto_monad_grad : 0.000012s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000117s : 0.01% jit_opt_a.cse : 0.000530s : 0.03% jit_opt_a.replace_applicator : 0.000155s : 0.01% py_interpret_to_execute_after_opt_a : 0.000026s : 0.00% rewriter_after_opt_a : 0.000288s : 0.02% convert_after_rewriter : 0.000015s : 0.00% order_py_execute_after_rewriter : 0.000008s : 0.00% mutable_eliminate : 0.000882s : 0.05% jit_opt_b.frontend_op_eliminate : 0.000035s : 0.00% jit_opt_b.inline_after_opt_a : 0.000056s : 0.00% cconv : 0.000044s : 0.00% loop_unroll : 0.000576s : 0.03% jit_opt_after_cconv.c_1 : 0.000052s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000017s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.cse : 0.000078s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000043s : 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 : 0.248475s : 13.67% partial_unused_args_eliminate : 0.000009s : 0.00% environ_conv : 0.000024s : 0.00% add_recomputation : 0.000115s : 0.01% cse_after_recomputation.cse : 0.000064s : 0.00% auto_monad_reorder : 0.000033s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000012s : 0.00% opt_after_jit_grad : 0.000905s : 0.05% symbol_engine_optimizer.build : 0.000016s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000016s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000032s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000012s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000019s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000074s : 0.00% backend_pass : 0.000001s : 0.00% task_emit : 0.137322s : 7.56% execute : 0.000011s : 0.00% Time group info: ------[substitution.] 0.001195 216 3.83% : 0.000046s : 4: substitution.arithmetic_simplify 0.34% : 0.000004s : 6: substitution.elim_not_effective 0.22% : 0.000003s : 6: substitution.fold_const_symbol 0.89% : 0.000011s : 8: substitution.graph_param_transform 65.32% : 0.000781s : 21: substitution.inline 2.25% : 0.000027s : 2: substitution.inline_without_move 1.24% : 0.000015s : 23: substitution.j_node_and_user_rematch 2.10% : 0.000025s : 7: substitution.less_batch_normalization 1.74% : 0.000021s : 15: substitution.minmaximum_grad 0.95% : 0.000011s : 10: substitution.partial_eliminate 1.36% : 0.000016s : 23: substitution.remove_not_recompute_node 2.88% : 0.000034s : 9: substitution.replace_applicator 1.60% : 0.000019s : 19: substitution.replace_old_param 0.39% : 0.000005s : 1: substitution.set_cell_output_no_recompute 1.65% : 0.000020s : 3: substitution.switch_simplify 3.71% : 0.000044s : 15: substitution.tuple_list_convert_item_index_to_positive 2.56% : 0.000031s : 15: substitution.tuple_list_get_item_depend_reorder 6.99% : 0.000084s : 29: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.156424 2 79.43% : 0.918556s : 1: type_inference.infer 20.57% : 0.237868s : 1: type_inference.specialize ------[replace.] 0.000474 38 53.39% : 0.000253s : 21: replace.inline 24.23% : 0.000115s : 3: replace.switch_simplify 22.38% : 0.000106s : 14: replace.tuple_list_get_item_eliminator ------[match.] 0.000821 38 93.50% : 0.000768s : 21: match.inline 2.18% : 0.000018s : 3: match.switch_simplify 4.32% : 0.000035s : 14: match.tuple_list_get_item_eliminator ------[predicate.] 0.000685 4528 1.56% : 0.000011s : 75: predicate.accumulaten_eliminater 0.43% : 0.000003s : 8: predicate.ad_related_special_op_eliminate 1.45% : 0.000010s : 75: predicate.addn_check_dump 1.58% : 0.000011s : 75: predicate.addn_zero_filter 2.01% : 0.000014s : 75: predicate.arithmetic_simplify 1.51% : 0.000010s : 75: predicate.cast_eliminate 0.24% : 0.000002s : 8: predicate.check_bprop_eliminate 1.40% : 0.000010s : 75: predicate.compare_switch_simplify 1.55% : 0.000011s : 75: predicate.depend_value_elim 1.46% : 0.000010s : 75: predicate.dict_get_item_const_eliminator 1.36% : 0.000009s : 75: predicate.dict_get_item_eliminator 1.54% : 0.000011s : 75: predicate.dict_set_item_eliminator 0.35% : 0.000002s : 8: predicate.dumpgradient_eliminate 0.18% : 0.000001s : 8: predicate.elim_not_effective 0.22% : 0.000002s : 8: predicate.elim_shapecalc_of_broadcastargs 1.43% : 0.000010s : 75: predicate.environ_add_const_eliminate 1.43% : 0.000010s : 75: predicate.environ_get_add_eliminate 1.34% : 0.000009s : 75: predicate.environ_get_depend_swap 1.42% : 0.000010s : 75: predicate.environ_get_eliminate 1.34% : 0.000009s : 75: predicate.environ_get_set_eliminate 0.09% : 0.000001s : 8: predicate.fold_const_symbol 0.89% : 0.000006s : 34: predicate.get_grad_eliminate 0.09% : 0.000001s : 8: predicate.graph_param_transform 4.51% : 0.000031s : 126: predicate.inline 1.70% : 0.000012s : 65: predicate.inline_without_move 0.37% : 0.000003s : 34: predicate.j_node_and_user_rematch 1.19% : 0.000008s : 34: predicate.less_batch_normalization 1.90% : 0.000013s : 89: predicate.list_to_tuple_eliminator_ 1.84% : 0.000013s : 97: predicate.load_eliminater 0.58% : 0.000004s : 8: predicate.loop_unroll_after_grad 3.49% : 0.000024s : 152: predicate.loop_unroll_before_grad 1.76% : 0.000012s : 83: predicate.make_slice_get_slice_eliminator 1.37% : 0.000009s : 75: predicate.merge_addn 1.43% : 0.000010s : 75: predicate.minmaximum_grad 0.68% : 0.000005s : 8: predicate.mutable_eliminate 0.28% : 0.000002s : 8: predicate.opt_reshape 2.36% : 0.000016s : 97: predicate.partial_eliminate 1.43% : 0.000010s : 75: predicate.print_const_string_wrapper 1.73% : 0.000012s : 75: predicate.reduce_eliminate 1.81% : 0.000012s : 89: predicate.redundant_stop_gradient_eliminater 0.46% : 0.000003s : 34: predicate.remove_not_recompute_node 2.39% : 0.000016s : 157: predicate.replace_applicator 0.97% : 0.000007s : 65: predicate.replace_old_param 0.21% : 0.000001s : 8: predicate.reset_defer_inline 1.52% : 0.000010s : 75: predicate.reshape_eliminate 1.46% : 0.000010s : 75: predicate.row_tensor_add_zeros_like 0.42% : 0.000003s : 8: predicate.row_tensor_eliminate 1.63% : 0.000011s : 75: predicate.same_eliminate 0.60% : 0.000004s : 34: predicate.set_cell_output_no_recompute 0.39% : 0.000003s : 16: predicate.special_op_eliminate 0.81% : 0.000006s : 34: predicate.specialize_transform 1.59% : 0.000011s : 75: predicate.split_environ_get_set_with_tuple_value 1.52% : 0.000010s : 75: predicate.stack_unstack_eliminate 0.21% : 0.000001s : 8: predicate.switch_call_monad_eliminater 2.82% : 0.000019s : 110: predicate.switch_defer_inline 2.44% : 0.000017s : 110: predicate.switch_layer_defer_inline 6.71% : 0.000046s : 276: predicate.switch_simplify 1.49% : 0.000010s : 75: predicate.tile_eliminate 1.56% : 0.000011s : 75: predicate.transpose_eliminate 1.68% : 0.000012s : 75: predicate.tuple_list_convert_item_index_to_positive 1.71% : 0.000012s : 75: predicate.tuple_list_get_item_depend_reorder 3.33% : 0.000023s : 105: predicate.tuple_list_get_item_eliminator 2.08% : 0.000014s : 75: predicate.tuple_list_set_item_eliminator 1.69% : 0.000012s : 89: predicate.tuple_to_list_eliminator_ 1.92% : 0.000013s : 97: predicate.updatestate_pure_node_eliminater 2.97% : 0.000020s : 131: predicate.updatestate_useless_node_eliminater 1.66% : 0.000011s : 75: predicate.value_based_eliminate 0.21% : 0.000001s : 8: predicate.virtual_view_grad_eliminate 0.23% : 0.000002s : 8: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.108500 47 98.66% : 0.107048s : 22: func_graph_cloner_run.FuncGraphClonerGraph 1.34% : 0.001452s : 25: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 2.090554 91 0.01% : 0.000120s : 1: add_recomputation 0.01% : 0.000228s : 1: auto_monad 0.00% : 0.000036s : 1: auto_monad_reorder 0.00% : 0.000004s : 1: backend_pass 0.03% : 0.000719s : 1: bootstrap 0.00% : 0.000048s : 1: cconv 0.00% : 0.000018s : 1: convert_after_rewriter 0.01% : 0.000106s : 1: cse_after_recomputation 0.00% : 0.000027s : 1: environ_conv 0.01% : 0.000124s : 1: event_method 0.00% : 0.000017s : 1: execute 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 13.21% : 0.276149s : 1: jit_opt_a 0.01% : 0.000312s : 1: jit_opt_after_cconv 0.01% : 0.000123s : 1: jit_opt_b 0.03% : 0.000588s : 1: loop_unroll 0.04% : 0.000899s : 1: mutable_eliminate 0.25% : 0.005301s : 39: opt.transform.jit_opt_a 0.01% : 0.000116s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000081s : 4: opt.transform.jit_opt_b 0.00% : 0.000026s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000032s : 1: opt.transform.mutable_eliminate 0.00% : 0.000053s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000074s : 4: opt.transform.symbol_engine_opt 0.04% : 0.000915s : 1: opt_after_jit_grad 0.00% : 0.000010s : 1: order_py_execute_after_rewriter 0.00% : 0.000015s : 1: partial_unused_args_eliminate 0.00% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000068s : 1: py_interpret_to_execute 0.00% : 0.000029s : 1: py_interpret_to_execute_after_opt_a 11.89% : 0.248510s : 1: remove_dup_value 12.33% : 0.257847s : 2: renormalize.infer 0.15% : 0.003145s : 2: renormalize.specialize 0.00% : 0.000014s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000295s : 1: rewriter_after_opt_a 0.01% : 0.000234s : 1: rewriter_before_opt_a 0.01% : 0.000138s : 1: symbol_engine_optimizer 6.57% : 0.137343s : 1: task_emit 55.33% : 1.156646s : 1: type_inference 0.01% : 0.000108s : 1: validate . [hook] pytest_runtest_teardown:test_addmm_column_major_views[KBK] tests/st/mint/test_addmm.py::test_addmm_column_major_views[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 227.24s (0:03:47) ==================