==================================================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/ops/ascend, configfile: ../../../../../../../sault/virtual_test/virtualenv_002/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_nsa_compress.py TotalTime = 4.67294, [30] [bootstrap]: 0.00070584 [type_inference]: 4.17315 [event_method]: 3.222e-05 [auto_monad]: 0.00017163 [graph_reusing]: 6.70002e-06 [pre_auto_parallel]: 1.135e-05 [py_interpret_to_execute]: 0.00320113 [rewriter_before_opt_a]: 0.00018918 [expand_dump_flag]: 5.29998e-06 [jit_opt_a]: 0.490985, [2] [Cycle 1]: 0.343678, [27] [switch_simplify]: 9.04e-05 [loop_unroll]: 3.685e-05 [a_1]: 0.00083325 [with_stream_mark]: 3.409e-05 [recompute_prepare]: 1.269e-05 [updatestate_depend_eliminate]: 5.94e-06 [updatestate_assign_eliminate]: 3.90998e-06 [updatestate_loads_eliminate]: 3.56999e-06 [parameter_eliminate]: 2.04e-06 [specialize_transform]: 9.57001e-06 [updatestate_useless_node_eliminater]: 7.74002e-06 [accelerated_algorithm]: 8.27998e-06 [meta_shard_fg_expand]: 3.21999e-06 [get_grad_eliminate_]: 8.99e-06 [merge_forward]: 4.85001e-06 [cell_reuse_recompute_pass]: 1.34e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.393e-05 [j_node_and_user_rematch]: 1.308e-05 [meta_fg_expand]: 3.2e-06 [replace_old_param]: 1.517e-05 [inline_without_move]: 8.87e-06 [renormalize]: 0.342082 [add_forward_monad_depend]: 2.413e-05 [auto_monad_grad]: 3.9e-06 [auto_monad_eliminator]: 3.318e-05 [cse]: 6.483e-05 [replace_applicator]: 3.541e-05 [Cycle 2]: 0.00053953, [27] [switch_simplify]: 1.138e-05 [loop_unroll]: 9.42001e-06 [a_1]: 0.00018029 [with_stream_mark]: 2.386e-05 [recompute_prepare]: 1.026e-05 [updatestate_depend_eliminate]: 5.29e-06 [updatestate_assign_eliminate]: 4e-06 [updatestate_loads_eliminate]: 3.68999e-06 [parameter_eliminate]: 2.11e-06 [specialize_transform]: 8.24998e-06 [updatestate_useless_node_eliminater]: 7.98001e-06 [accelerated_algorithm]: 8.97e-06 [meta_shard_fg_expand]: 3.25e-06 [get_grad_eliminate_]: 7.90998e-06 [merge_forward]: 5.05999e-06 [cell_reuse_recompute_pass]: 2.99999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.831e-05 [j_node_and_user_rematch]: 1.346e-05 [meta_fg_expand]: 4.08999e-06 [replace_old_param]: 1.405e-05 [inline_without_move]: 8.17e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 1.84998e-06 [auto_monad_grad]: 1.29e-06 [auto_monad_eliminator]: 1.012e-05 [cse]: 2.196e-05 [replace_applicator]: 8.55001e-06 [py_interpret_to_execute_after_opt_a]: 2.091e-05 [rewriter_after_opt_a]: 8.941e-05 [convert_after_rewriter]: 2.245e-05 [order_py_execute_after_rewriter]: 5.76e-06 [mutable_eliminate]: 0.00137753 [jit_opt_b]: 8.135e-05, [1] [Cycle 1]: 6.935e-05, [2] [frontend_op_eliminate]: 2.983e-05 [inline_after_opt_a]: 2.589e-05 [cconv]: 4.419e-05 [loop_unroll]: 0.00051805 [jit_opt_after_cconv]: 0.00022396, [1] [Cycle 1]: 0.00021611, [11] [c_1]: 3.387e-05 [parameter_eliminate]: 5.69999e-06 [updatestate_depend_eliminate]: 1.298e-05 [updatestate_assign_eliminate]: 3.83999e-06 [updatestate_loads_eliminate]: 2.96999e-06 [cse]: 4.675e-05 [call_graph_tuple_transform]: 3.184e-05 [tuple_list_get_item_eliminator]: 8.67998e-06 [none_parameter_eliminate]: 1.61998e-06 [renormalize]: 8.70001e-07 [switch_simplify]: 8.44002e-06 [remove_dup_value]: 2.355e-05 [partial_unused_args_eliminate]: 4.13999e-06 [environ_conv]: 3.801e-05 [add_recomputation]: 7.512e-05 [cse_after_recomputation]: 3.376e-05, [1] [Cycle 1]: 2.663e-05, [1] [cse]: 1.94e-05 [auto_monad_reorder]: 2.933e-05 [get_jit_bprop_graph]: 2.49999e-06 [rewriter_after_jit_bprop_graph]: 6.45002e-06 [opt_after_jit_grad]: 0.00058998 [symbol_engine_optimizer]: 0.00018115, [1] [Cycle 1]: 0.00017342, [6] [build]: 5.70001e-06 [elim_shapecalc]: 1.083e-05 [elim_not_effective]: 1.734e-05 [opt_reshape]: 9.49e-06 [fold_const_symbol]: 1.973e-05 [renormalize]: 1.06002e-06 [validate]: 0.00013824 Sums bootstrap : 0.000706s : 0.02% type_inference : 4.173149s : 92.23% event_method : 0.000032s : 0.00% auto_monad : 0.000172s : 0.00% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000011s : 0.00% py_interpret_to_execute : 0.003201s : 0.07% rewriter_before_opt_a : 0.000189s : 0.00% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000102s : 0.00% jit_opt_a.loop_unroll : 0.000046s : 0.00% jit_opt_a.a_1 : 0.001014s : 0.02% jit_opt_a.with_stream_mark : 0.000058s : 0.00% jit_opt_a.recompute_prepare : 0.000023s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000011s : 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 : 0.000004s : 0.00% jit_opt_a.specialize_transform : 0.000018s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000016s : 0.00% jit_opt_a.accelerated_algorithm : 0.000017s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000006s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000017s : 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 : 0.000052s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000027s : 0.00% jit_opt_a.meta_fg_expand : 0.000007s : 0.00% jit_opt_a.replace_old_param : 0.000029s : 0.00% jit_opt_a.inline_without_move : 0.000017s : 0.00% jit_opt_a.renormalize : 0.342082s : 7.56% jit_opt_a.add_forward_monad_depend : 0.000026s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000043s : 0.00% jit_opt_a.cse : 0.000087s : 0.00% jit_opt_a.replace_applicator : 0.000044s : 0.00% py_interpret_to_execute_after_opt_a : 0.000021s : 0.00% rewriter_after_opt_a : 0.000089s : 0.00% convert_after_rewriter : 0.000022s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.001378s : 0.03% jit_opt_b.frontend_op_eliminate : 0.000030s : 0.00% jit_opt_b.inline_after_opt_a : 0.000026s : 0.00% cconv : 0.000044s : 0.00% loop_unroll : 0.000518s : 0.01% jit_opt_after_cconv.c_1 : 0.000034s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000013s : 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 : 0.000047s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000032s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000009s : 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 : 0.000024s : 0.00% partial_unused_args_eliminate : 0.000004s : 0.00% environ_conv : 0.000038s : 0.00% add_recomputation : 0.000075s : 0.00% cse_after_recomputation.cse : 0.000019s : 0.00% auto_monad_reorder : 0.000029s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000006s : 0.00% opt_after_jit_grad : 0.000590s : 0.01% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000017s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000020s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000138s : 0.00% Time group info: ------[substitution.] 0.000306 29 0.75% : 0.000002s : 2: substitution.elim_not_effective 1.10% : 0.000003s : 2: substitution.fold_const_symbol 3.10% : 0.000009s : 5: substitution.graph_param_transform 82.32% : 0.000252s : 4: substitution.inline 1.88% : 0.000006s : 4: substitution.j_node_and_user_rematch 2.05% : 0.000006s : 4: substitution.remove_not_recompute_node 2.88% : 0.000009s : 4: substitution.replace_old_param 5.92% : 0.000018s : 4: substitution.tuple_list_get_item_eliminator ------[type_inference.] 4.172939 2 99.50% : 4.152018s : 1: type_inference.infer 0.50% : 0.020921s : 1: type_inference.specialize ------[replace.] 0.000100 8 60.38% : 0.000060s : 4: replace.inline 39.62% : 0.000040s : 4: replace.tuple_list_get_item_eliminator ------[match.] 0.000264 8 94.02% : 0.000248s : 4: match.inline 5.98% : 0.000016s : 4: match.tuple_list_get_item_eliminator ------[predicate.] 0.000182 1039 1.33% : 0.000002s : 15: predicate.accumulaten_eliminater 1.04% : 0.000002s : 5: predicate.ad_related_special_op_eliminate 1.03% : 0.000002s : 15: predicate.addn_check_dump 1.40% : 0.000003s : 15: predicate.addn_zero_filter 1.97% : 0.000004s : 15: predicate.arithmetic_simplify 1.36% : 0.000002s : 15: predicate.cast_eliminate 0.52% : 0.000001s : 5: predicate.check_bprop_eliminate 1.06% : 0.000002s : 15: predicate.compare_switch_simplify 1.27% : 0.000002s : 15: predicate.depend_value_elim 1.14% : 0.000002s : 15: predicate.dict_get_item_const_eliminator 1.38% : 0.000003s : 15: predicate.dict_get_item_eliminator 1.02% : 0.000002s : 15: predicate.dict_set_item_eliminator 0.90% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.41% : 0.000001s : 5: predicate.elim_not_effective 0.58% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.10% : 0.000002s : 15: predicate.environ_add_const_eliminate 1.03% : 0.000002s : 15: predicate.environ_get_add_eliminate 0.94% : 0.000002s : 15: predicate.environ_get_depend_swap 1.06% : 0.000002s : 15: predicate.environ_get_eliminate 1.17% : 0.000002s : 15: predicate.environ_get_set_eliminate 0.32% : 0.000001s : 5: predicate.fold_const_symbol 0.85% : 0.000002s : 10: predicate.get_grad_eliminate 0.38% : 0.000001s : 5: predicate.graph_param_transform 5.08% : 0.000009s : 33: predicate.inline 1.03% : 0.000002s : 10: predicate.inline_without_move 0.45% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.42% : 0.000003s : 10: predicate.less_batch_normalization 1.51% : 0.000003s : 19: predicate.list_to_tuple_eliminator_ 2.10% : 0.000004s : 24: predicate.load_eliminater 1.71% : 0.000003s : 5: predicate.loop_unroll_after_grad 3.55% : 0.000006s : 38: predicate.loop_unroll_before_grad 1.88% : 0.000003s : 20: predicate.make_slice_get_slice_eliminator 1.11% : 0.000002s : 15: predicate.merge_addn 1.09% : 0.000002s : 15: predicate.minmaximum_grad 2.87% : 0.000005s : 5: predicate.mutable_eliminate 0.47% : 0.000001s : 5: predicate.opt_reshape 2.33% : 0.000004s : 24: predicate.partial_eliminate 1.33% : 0.000002s : 15: predicate.print_const_string_wrapper 1.79% : 0.000003s : 15: predicate.reduce_eliminate 1.55% : 0.000003s : 19: predicate.redundant_stop_gradient_eliminater 0.69% : 0.000001s : 10: predicate.remove_not_recompute_node 2.39% : 0.000004s : 29: predicate.replace_applicator 0.79% : 0.000001s : 10: predicate.replace_old_param 0.50% : 0.000001s : 5: predicate.reset_defer_inline 1.44% : 0.000003s : 15: predicate.reshape_eliminate 1.50% : 0.000003s : 15: predicate.row_tensor_add_zeros_like 1.05% : 0.000002s : 5: predicate.row_tensor_eliminate 1.11% : 0.000002s : 15: predicate.same_eliminate 0.62% : 0.000001s : 10: predicate.set_cell_output_no_recompute 0.94% : 0.000002s : 10: predicate.special_op_eliminate 0.88% : 0.000002s : 10: predicate.specialize_transform 1.35% : 0.000002s : 15: predicate.split_environ_get_set_with_tuple_value 1.21% : 0.000002s : 15: predicate.stack_unstack_eliminate 0.51% : 0.000001s : 5: predicate.switch_call_monad_eliminater 2.12% : 0.000004s : 23: predicate.switch_defer_inline 1.83% : 0.000003s : 23: predicate.switch_layer_defer_inline 6.92% : 0.000013s : 66: predicate.switch_simplify 1.36% : 0.000002s : 15: predicate.tile_eliminate 1.09% : 0.000002s : 15: predicate.transpose_eliminate 1.52% : 0.000003s : 15: predicate.tuple_list_convert_item_index_to_positive 1.30% : 0.000002s : 15: predicate.tuple_list_get_item_depend_reorder 4.02% : 0.000007s : 29: predicate.tuple_list_get_item_eliminator 1.64% : 0.000003s : 15: predicate.tuple_list_set_item_eliminator 1.67% : 0.000003s : 19: predicate.tuple_to_list_eliminator_ 1.73% : 0.000003s : 24: predicate.updatestate_pure_node_eliminater 2.73% : 0.000005s : 34: predicate.updatestate_useless_node_eliminater 1.61% : 0.000003s : 15: predicate.value_based_eliminate 0.35% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.64% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.409828 175 59.17% : 0.242481s : 169: func_graph_cloner_run.FuncGraphClonerGraph 40.83% : 0.167347s : 6: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 5.015750 72 0.00% : 0.000078s : 1: add_recomputation 0.00% : 0.000178s : 1: auto_monad 0.00% : 0.000032s : 1: auto_monad_reorder 0.01% : 0.000729s : 1: bootstrap 0.00% : 0.000047s : 1: cconv 0.00% : 0.000025s : 1: convert_after_rewriter 0.00% : 0.000037s : 1: cse_after_recomputation 0.00% : 0.000041s : 1: environ_conv 0.00% : 0.000038s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000011s : 1: graph_reusing 9.79% : 0.490992s : 1: jit_opt_a 0.00% : 0.000227s : 1: jit_opt_after_cconv 0.00% : 0.000085s : 1: jit_opt_b 0.01% : 0.000528s : 1: loop_unroll 0.03% : 0.001393s : 1: mutable_eliminate 0.03% : 0.001366s : 26: opt.transform.jit_opt_a 0.00% : 0.000079s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000047s : 4: opt.transform.jit_opt_b 0.00% : 0.000019s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000031s : 1: opt.transform.mutable_eliminate 0.00% : 0.000081s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000053s : 4: opt.transform.symbol_engine_opt 0.01% : 0.000601s : 1: opt_after_jit_grad 0.00% : 0.000008s : 1: order_py_execute_after_rewriter 0.00% : 0.000006s : 1: partial_unused_args_eliminate 0.00% : 0.000014s : 1: pre_auto_parallel 0.06% : 0.003222s : 1: py_interpret_to_execute 0.00% : 0.000024s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000026s : 1: remove_dup_value 3.47% : 0.174270s : 1: renormalize.infer 3.34% : 0.167764s : 1: renormalize.specialize 0.00% : 0.000009s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000095s : 1: rewriter_after_opt_a 0.00% : 0.000196s : 1: rewriter_before_opt_a 0.00% : 0.000185s : 1: symbol_engine_optimizer 83.20% : 4.173199s : 1: type_inference TotalTime = 7.10744, [30] [bootstrap]: 0.00075433 [type_inference]: 4.76787 [event_method]: 0.00413746 [auto_monad]: 0.00026313 [graph_reusing]: 1.108e-05 [pre_auto_parallel]: 5.43002e-06 [py_interpret_to_execute]: 0.00010247 [rewriter_before_opt_a]: 0.00030496 [expand_dump_flag]: 4.98001e-06 [jit_opt_a]: 2.33039, [3] [Cycle 1]: 1.90213, [27] [switch_simplify]: 0.00031897 [loop_unroll]: 8.213e-05 [a_1]: 0.00194339 [with_stream_mark]: 5.544e-05 [recompute_prepare]: 3.717e-05 [updatestate_depend_eliminate]: 1.441e-05 [updatestate_assign_eliminate]: 9.00001e-06 [updatestate_loads_eliminate]: 9.14e-06 [parameter_eliminate]: 3.93001e-06 [specialize_transform]: 2.677e-05 [updatestate_useless_node_eliminater]: 2.015e-05 [accelerated_algorithm]: 7.932e-05 [meta_shard_fg_expand]: 1.076e-05 [get_grad_eliminate_]: 2.04e-05 [merge_forward]: 1.327e-05 [cell_reuse_recompute_pass]: 1.55999e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.146e-05 [j_node_and_user_rematch]: 3.809e-05 [meta_fg_expand]: 0.00283922 [replace_old_param]: 0.00011789 [inline_without_move]: 8.966e-05 [renormalize]: 1.8941 [add_forward_monad_depend]: 2.761e-05 [auto_monad_grad]: 9.47001e-06 [auto_monad_eliminator]: 8.508e-05 [cse]: 0.00161066 [replace_applicator]: 0.00013971 [Cycle 2]: 0.213495, [27] [switch_simplify]: 5.888e-05 [loop_unroll]: 5.423e-05 [a_1]: 0.00190894 [with_stream_mark]: 3.583e-05 [recompute_prepare]: 1.759e-05 [updatestate_depend_eliminate]: 8.79e-06 [updatestate_assign_eliminate]: 6.49001e-06 [updatestate_loads_eliminate]: 5.55001e-06 [parameter_eliminate]: 3.13998e-06 [specialize_transform]: 1.395e-05 [updatestate_useless_node_eliminater]: 1.112e-05 [accelerated_algorithm]: 1.752e-05 [meta_shard_fg_expand]: 5.49e-06 [get_grad_eliminate_]: 1.091e-05 [merge_forward]: 7.01999e-06 [cell_reuse_recompute_pass]: 1.44e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.339e-05 [j_node_and_user_rematch]: 1.802e-05 [meta_fg_expand]: 0.00053644 [replace_old_param]: 3.277e-05 [inline_without_move]: 1.317e-05 [renormalize]: 0.210165 [add_forward_monad_depend]: 1.315e-05 [auto_monad_grad]: 2.71999e-06 [auto_monad_eliminator]: 3.067e-05 [cse]: 0.00019693 [replace_applicator]: 3.864e-05 [Cycle 3]: 0.00078536, [27] [switch_simplify]: 1.432e-05 [loop_unroll]: 1.159e-05 [a_1]: 0.00032295 [with_stream_mark]: 2.426e-05 [recompute_prepare]: 1.213e-05 [updatestate_depend_eliminate]: 8.60999e-06 [updatestate_assign_eliminate]: 7.38e-06 [updatestate_loads_eliminate]: 5.61e-06 [parameter_eliminate]: 2.58998e-06 [specialize_transform]: 1.154e-05 [updatestate_useless_node_eliminater]: 1.057e-05 [accelerated_algorithm]: 1.946e-05 [meta_shard_fg_expand]: 4.75999e-06 [get_grad_eliminate_]: 1.063e-05 [merge_forward]: 7.06999e-06 [cell_reuse_recompute_pass]: 3.6e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.524e-05 [j_node_and_user_rematch]: 1.924e-05 [meta_fg_expand]: 4.95001e-06 [replace_old_param]: 2.04e-05 [inline_without_move]: 1.088e-05 [renormalize]: 8.9989e-08 [add_forward_monad_depend]: 3.04999e-06 [auto_monad_grad]: 1.99999e-06 [auto_monad_eliminator]: 1.958e-05 [cse]: 4.715e-05 [replace_applicator]: 1.299e-05 [py_interpret_to_execute_after_opt_a]: 2.543e-05 [rewriter_after_opt_a]: 0.00023861 [convert_after_rewriter]: 1.606e-05 [order_py_execute_after_rewriter]: 9.04998e-06 [mutable_eliminate]: 0.00084216 [jit_opt_b]: 0.00011901, [1] [Cycle 1]: 0.00010872, [2] [frontend_op_eliminate]: 3.768e-05 [inline_after_opt_a]: 5.695e-05 [cconv]: 4.069e-05 [loop_unroll]: 0.00053175 [jit_opt_after_cconv]: 0.00034965, [1] [Cycle 1]: 0.00034188, [11] [c_1]: 0.00011175 [parameter_eliminate]: 5.75001e-06 [updatestate_depend_eliminate]: 1.421e-05 [updatestate_assign_eliminate]: 5.75001e-06 [updatestate_loads_eliminate]: 4.74998e-06 [cse]: 6.679e-05 [call_graph_tuple_transform]: 3.668e-05 [tuple_list_get_item_eliminator]: 2.113e-05 [none_parameter_eliminate]: 1.82999e-06 [renormalize]: 7.60017e-07 [switch_simplify]: 1.197e-05 [remove_dup_value]: 8.737e-05 [partial_unused_args_eliminate]: 2.34001e-06 [environ_conv]: 1.898e-05 [add_recomputation]: 9.033e-05 [cse_after_recomputation]: 4.922e-05, [1] [Cycle 1]: 4.193e-05, [1] [cse]: 3.379e-05 [auto_monad_reorder]: 2.745e-05 [get_jit_bprop_graph]: 3.16001e-06 [rewriter_after_jit_bprop_graph]: 8.97999e-06 [opt_after_jit_grad]: 0.00058518 [symbol_engine_optimizer]: 0.00012708, [1] [Cycle 1]: 0.00011833, [6] [build]: 1.713e-05 [elim_shapecalc]: 1.526e-05 [elim_not_effective]: 2.459e-05 [opt_reshape]: 1.342e-05 [fold_const_symbol]: 1.703e-05 [renormalize]: 7.00005e-07 [validate]: 0.00011232 Sums bootstrap : 0.000754s : 0.01% type_inference : 4.767871s : 69.18% event_method : 0.004137s : 0.06% auto_monad : 0.000263s : 0.00% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000005s : 0.00% py_interpret_to_execute : 0.000102s : 0.00% rewriter_before_opt_a : 0.000305s : 0.00% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000392s : 0.01% jit_opt_a.loop_unroll : 0.000148s : 0.00% jit_opt_a.a_1 : 0.004175s : 0.06% jit_opt_a.with_stream_mark : 0.000116s : 0.00% jit_opt_a.recompute_prepare : 0.000067s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000032s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000023s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000020s : 0.00% jit_opt_a.parameter_eliminate : 0.000010s : 0.00% jit_opt_a.specialize_transform : 0.000052s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000042s : 0.00% jit_opt_a.accelerated_algorithm : 0.000116s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000021s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000042s : 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 : 0.000090s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000075s : 0.00% jit_opt_a.meta_fg_expand : 0.003381s : 0.05% jit_opt_a.replace_old_param : 0.000171s : 0.00% jit_opt_a.inline_without_move : 0.000114s : 0.00% jit_opt_a.renormalize : 2.104270s : 30.53% jit_opt_a.add_forward_monad_depend : 0.000044s : 0.00% jit_opt_a.auto_monad_grad : 0.000014s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000135s : 0.00% jit_opt_a.cse : 0.001855s : 0.03% jit_opt_a.replace_applicator : 0.000191s : 0.00% py_interpret_to_execute_after_opt_a : 0.000025s : 0.00% rewriter_after_opt_a : 0.000239s : 0.00% convert_after_rewriter : 0.000016s : 0.00% order_py_execute_after_rewriter : 0.000009s : 0.00% mutable_eliminate : 0.000842s : 0.01% jit_opt_b.frontend_op_eliminate : 0.000038s : 0.00% jit_opt_b.inline_after_opt_a : 0.000057s : 0.00% cconv : 0.000041s : 0.00% loop_unroll : 0.000532s : 0.01% jit_opt_after_cconv.c_1 : 0.000112s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000014s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000067s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000037s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000021s : 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 : 0.000087s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000019s : 0.00% add_recomputation : 0.000090s : 0.00% cse_after_recomputation.cse : 0.000034s : 0.00% auto_monad_reorder : 0.000027s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000009s : 0.00% opt_after_jit_grad : 0.000585s : 0.01% symbol_engine_optimizer.build : 0.000017s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000015s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000025s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000013s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000017s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000112s : 0.00% Time group info: ------[substitution.] 0.001356 200 0.22% : 0.000003s : 5: substitution.elim_not_effective 0.18% : 0.000002s : 5: substitution.fold_const_symbol 0.69% : 0.000009s : 8: substitution.graph_param_transform 65.68% : 0.000891s : 19: substitution.inline 2.12% : 0.000029s : 2: substitution.inline_without_move 1.28% : 0.000017s : 20: substitution.j_node_and_user_rematch 4.32% : 0.000059s : 3: substitution.less_batch_normalization 2.20% : 0.000030s : 15: substitution.minmaximum_grad 1.04% : 0.000014s : 11: substitution.partial_eliminate 1.14% : 0.000015s : 20: substitution.remove_not_recompute_node 3.23% : 0.000044s : 9: substitution.replace_applicator 1.51% : 0.000020s : 18: substitution.replace_old_param 0.31% : 0.000004s : 1: substitution.set_cell_output_no_recompute 1.40% : 0.000019s : 3: substitution.switch_simplify 3.73% : 0.000051s : 15: substitution.tuple_list_convert_item_index_to_positive 2.49% : 0.000034s : 15: substitution.tuple_list_get_item_depend_reorder 8.47% : 0.000115s : 31: substitution.tuple_list_get_item_eliminator ------[type_inference.] 4.767652 2 95.23% : 4.540282s : 1: type_inference.infer 4.77% : 0.227370s : 1: type_inference.specialize ------[replace.] 0.000477 34 51.47% : 0.000245s : 19: replace.inline 20.23% : 0.000096s : 3: replace.switch_simplify 28.30% : 0.000135s : 12: replace.tuple_list_get_item_eliminator ------[match.] 0.000926 34 94.73% : 0.000877s : 19: match.inline 1.87% : 0.000017s : 3: match.switch_simplify 3.40% : 0.000032s : 12: match.tuple_list_get_item_eliminator ------[predicate.] 0.000661 4383 1.45% : 0.000010s : 73: predicate.accumulaten_eliminater 0.39% : 0.000003s : 8: predicate.ad_related_special_op_eliminate 1.32% : 0.000009s : 73: predicate.addn_check_dump 1.48% : 0.000010s : 73: predicate.addn_zero_filter 2.26% : 0.000015s : 73: predicate.arithmetic_simplify 1.53% : 0.000010s : 73: predicate.cast_eliminate 0.19% : 0.000001s : 8: predicate.check_bprop_eliminate 1.34% : 0.000009s : 73: predicate.compare_switch_simplify 1.42% : 0.000009s : 73: predicate.depend_value_elim 1.54% : 0.000010s : 73: predicate.dict_get_item_const_eliminator 1.52% : 0.000010s : 73: predicate.dict_get_item_eliminator 1.42% : 0.000009s : 73: predicate.dict_set_item_eliminator 0.32% : 0.000002s : 8: predicate.dumpgradient_eliminate 0.15% : 0.000001s : 8: predicate.elim_not_effective 0.29% : 0.000002s : 8: predicate.elim_shapecalc_of_broadcastargs 1.40% : 0.000009s : 73: predicate.environ_add_const_eliminate 1.36% : 0.000009s : 73: predicate.environ_get_add_eliminate 1.33% : 0.000009s : 73: predicate.environ_get_depend_swap 1.39% : 0.000009s : 73: predicate.environ_get_eliminate 1.40% : 0.000009s : 73: predicate.environ_get_set_eliminate 0.10% : 0.000001s : 8: predicate.fold_const_symbol 0.81% : 0.000005s : 33: predicate.get_grad_eliminate 0.11% : 0.000001s : 8: predicate.graph_param_transform 4.52% : 0.000030s : 120: predicate.inline 1.93% : 0.000013s : 65: predicate.inline_without_move 0.37% : 0.000002s : 33: predicate.j_node_and_user_rematch 1.11% : 0.000007s : 33: predicate.less_batch_normalization 1.82% : 0.000012s : 85: predicate.list_to_tuple_eliminator_ 1.98% : 0.000013s : 93: predicate.load_eliminater 0.60% : 0.000004s : 8: predicate.loop_unroll_after_grad 3.17% : 0.000021s : 143: predicate.loop_unroll_before_grad 1.82% : 0.000012s : 81: predicate.make_slice_get_slice_eliminator 1.33% : 0.000009s : 73: predicate.merge_addn 1.49% : 0.000010s : 73: predicate.minmaximum_grad 0.61% : 0.000004s : 8: predicate.mutable_eliminate 0.24% : 0.000002s : 8: predicate.opt_reshape 2.41% : 0.000016s : 93: predicate.partial_eliminate 1.42% : 0.000009s : 73: predicate.print_const_string_wrapper 1.88% : 0.000012s : 73: predicate.reduce_eliminate 1.80% : 0.000012s : 85: predicate.redundant_stop_gradient_eliminater 0.45% : 0.000003s : 33: predicate.remove_not_recompute_node 2.67% : 0.000018s : 155: predicate.replace_applicator 0.95% : 0.000006s : 65: predicate.replace_old_param 0.14% : 0.000001s : 8: predicate.reset_defer_inline 1.53% : 0.000010s : 73: predicate.reshape_eliminate 1.47% : 0.000010s : 73: predicate.row_tensor_add_zeros_like 0.26% : 0.000002s : 8: predicate.row_tensor_eliminate 1.60% : 0.000011s : 73: predicate.same_eliminate 0.49% : 0.000003s : 33: predicate.set_cell_output_no_recompute 0.39% : 0.000003s : 16: predicate.special_op_eliminate 0.92% : 0.000006s : 33: predicate.specialize_transform 1.68% : 0.000011s : 73: predicate.split_environ_get_set_with_tuple_value 1.41% : 0.000009s : 73: predicate.stack_unstack_eliminate 0.21% : 0.000001s : 8: predicate.switch_call_monad_eliminater 3.05% : 0.000020s : 104: predicate.switch_defer_inline 2.43% : 0.000016s : 104: predicate.switch_layer_defer_inline 6.72% : 0.000044s : 261: predicate.switch_simplify 1.47% : 0.000010s : 73: predicate.tile_eliminate 1.60% : 0.000011s : 73: predicate.transpose_eliminate 1.87% : 0.000012s : 73: predicate.tuple_list_convert_item_index_to_positive 1.74% : 0.000011s : 73: predicate.tuple_list_get_item_depend_reorder 3.50% : 0.000023s : 101: predicate.tuple_list_get_item_eliminator 1.87% : 0.000012s : 73: predicate.tuple_list_set_item_eliminator 1.75% : 0.000012s : 85: predicate.tuple_to_list_eliminator_ 1.79% : 0.000012s : 93: predicate.updatestate_pure_node_eliminater 2.79% : 0.000018s : 126: predicate.updatestate_useless_node_eliminater 1.88% : 0.000012s : 73: predicate.value_based_eliminate 0.16% : 0.000001s : 8: predicate.virtual_view_grad_eliminate 0.24% : 0.000002s : 8: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 1.140034 282 81.88% : 0.933463s : 259: func_graph_cloner_run.FuncGraphClonerGraph 18.12% : 0.206571s : 23: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 9.217417 87 0.00% : 0.000095s : 1: add_recomputation 0.00% : 0.000273s : 1: auto_monad 0.00% : 0.000030s : 1: auto_monad_reorder 0.01% : 0.000782s : 1: bootstrap 0.00% : 0.000044s : 1: cconv 0.00% : 0.000020s : 1: convert_after_rewriter 0.00% : 0.000052s : 1: cse_after_recomputation 0.00% : 0.000022s : 1: environ_conv 0.05% : 0.004163s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000006s : 1: get_jit_bprop_graph 0.00% : 0.000014s : 1: graph_reusing 25.28% : 2.330394s : 1: jit_opt_a 0.00% : 0.000353s : 1: jit_opt_after_cconv 0.00% : 0.000122s : 1: jit_opt_b 0.01% : 0.000543s : 1: loop_unroll 0.01% : 0.000857s : 1: mutable_eliminate 0.06% : 0.005588s : 39: opt.transform.jit_opt_a 0.00% : 0.000121s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000086s : 4: opt.transform.jit_opt_b 0.00% : 0.000025s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000031s : 1: opt.transform.mutable_eliminate 0.00% : 0.000048s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000066s : 4: opt.transform.symbol_engine_opt 0.01% : 0.000596s : 1: opt_after_jit_grad 0.00% : 0.000011s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000007s : 1: pre_auto_parallel 0.00% : 0.000106s : 1: py_interpret_to_execute 0.00% : 0.000029s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000091s : 1: remove_dup_value 19.58% : 1.805142s : 2: renormalize.infer 3.24% : 0.299091s : 2: renormalize.specialize 0.00% : 0.000011s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000245s : 1: rewriter_after_opt_a 0.00% : 0.000309s : 1: rewriter_before_opt_a 0.00% : 0.000130s : 1: symbol_engine_optimizer 51.73% : 4.767902s : 1: type_inference . [hook] pytest_runtest_teardown:test_nsa_compress_bfloat16_large_dims[0] tests/st/ops/ascend/test_nsa_compress.py::test_nsa_compress_bfloat16_large_dims[0],max_mem:22.0M . [hook] pytest_runtest_teardown:test_nsa_compress_bfloat16_large_dims[1] tests/st/ops/ascend/test_nsa_compress.py::test_nsa_compress_bfloat16_large_dims[1],max_mem:24.0M =============================== 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 156.05s (0:02:36) ==================