==================================================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/compiler/mutable, configfile: ../../../../../../../sault/virtual_test/virtualenv_006/sault/config/pytest.ini plugins: mock-3.14.0, hydra-core-1.3.2, forked-1.6.0, anyio-4.9.0, xdist-1.32.0 collected 1 item test_mutable_in_graph.py TotalTime = 0.390847, [30] [bootstrap]: 0.00099966 [type_inference]: 0.274701 [event_method]: 0.00049517 [auto_monad]: 0.00050435 [graph_reusing]: 0.00035263 [pre_auto_parallel]: 0.00036625 [py_interpret_to_execute]: 0.00037985 [rewriter_before_opt_a]: 0.00042423 [expand_dump_flag]: 0.0003574 [jit_opt_a]: 0.10351, [2] [Cycle 1]: 0.00227506, [27] [switch_simplify]: 4.896e-05 [loop_unroll]: 1.694e-05 [a_1]: 0.00050235 [with_stream_mark]: 2.977e-05 [recompute_prepare]: 1.314e-05 [updatestate_depend_eliminate]: 6.09999e-06 [updatestate_assign_eliminate]: 4.97999e-06 [updatestate_loads_eliminate]: 4.80001e-06 [parameter_eliminate]: 2.37001e-06 [specialize_transform]: 1.02e-05 [updatestate_useless_node_eliminater]: 9.82001e-06 [accelerated_algorithm]: 2.758e-05 [meta_shard_fg_expand]: 2.60002e-06 [get_grad_eliminate_]: 9.05999e-06 [merge_forward]: 5.56e-06 [cell_reuse_recompute_pass]: 1.59998e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.618e-05 [j_node_and_user_rematch]: 1.635e-05 [meta_fg_expand]: 3.23998e-06 [replace_old_param]: 9.15999e-06 [inline_without_move]: 8.68001e-06 [renormalize]: 0.00116365 [add_forward_monad_depend]: 1.354e-05 [auto_monad_grad]: 2.86e-06 [auto_monad_eliminator]: 2.218e-05 [cse]: 6.591e-05 [replace_applicator]: 2.105e-05 [Cycle 2]: 0.00052405, [27] [switch_simplify]: 1.099e-05 [loop_unroll]: 8.62e-06 [a_1]: 0.00019302 [with_stream_mark]: 1.278e-05 [recompute_prepare]: 8.23999e-06 [updatestate_depend_eliminate]: 5.52001e-06 [updatestate_assign_eliminate]: 4.53999e-06 [updatestate_loads_eliminate]: 3.98001e-06 [parameter_eliminate]: 1.56998e-06 [specialize_transform]: 8.85999e-06 [updatestate_useless_node_eliminater]: 8.83001e-06 [accelerated_algorithm]: 1.62e-05 [meta_shard_fg_expand]: 2.37001e-06 [get_grad_eliminate_]: 8.33001e-06 [merge_forward]: 4.95999e-06 [cell_reuse_recompute_pass]: 1.96003e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.881e-05 [j_node_and_user_rematch]: 1.4e-05 [meta_fg_expand]: 3.4e-06 [replace_old_param]: 8.1e-06 [inline_without_move]: 8.73001e-06 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 1.59e-06 [auto_monad_grad]: 1.27e-06 [auto_monad_eliminator]: 1.04e-05 [cse]: 2.395e-05 [replace_applicator]: 1.044e-05 [py_interpret_to_execute_after_opt_a]: 0.00032672 [rewriter_after_opt_a]: 0.000407 [convert_after_rewriter]: 0.0003502 [order_py_execute_after_rewriter]: 0.00029454 [mutable_eliminate]: 0.00109845 [jit_opt_b]: 0.00029993, [1] [Cycle 1]: 7.041e-05, [2] [frontend_op_eliminate]: 2.692e-05 [inline_after_opt_a]: 2.461e-05 [cconv]: 0.00024525 [loop_unroll]: 0.00080814 [jit_opt_after_cconv]: 0.00041925, [1] [Cycle 1]: 0.00018542, [11] [c_1]: 3.548e-05 [parameter_eliminate]: 4e-06 [updatestate_depend_eliminate]: 6.24001e-06 [updatestate_assign_eliminate]: 3.53e-06 [updatestate_loads_eliminate]: 2.93e-06 [cse]: 3.068e-05 [call_graph_tuple_transform]: 2.55e-05 [tuple_list_get_item_eliminator]: 7.68001e-06 [none_parameter_eliminate]: 1.65001e-06 [renormalize]: 1.05999e-06 [switch_simplify]: 6.93998e-06 [remove_dup_value]: 0.00023152 [partial_unused_args_eliminate]: 0.00020338 [environ_conv]: 0.00025668 [add_recomputation]: 0.00026379 [cse_after_recomputation]: 0.00025808, [1] [Cycle 1]: 3.57e-05, [1] [cse]: 2.284e-05 [auto_monad_reorder]: 0.0002437 [get_jit_bprop_graph]: 0.00020676 [rewriter_after_jit_bprop_graph]: 0.00021071 [opt_after_jit_grad]: 0.00089617 [symbol_engine_optimizer]: 0.00049959, [1] [Cycle 1]: 0.00019974, [6] [build]: 9.484e-05 [elim_shapecalc]: 1.592e-05 [elim_not_effective]: 2.747e-05 [opt_reshape]: 7.73999e-06 [fold_const_symbol]: 1.264e-05 [renormalize]: 8.80013e-07 [validate]: 0.00034618 Sums bootstrap : 0.001000s : 0.35% type_inference : 0.274701s : 95.46% event_method : 0.000495s : 0.17% auto_monad : 0.000504s : 0.18% graph_reusing : 0.000353s : 0.12% pre_auto_parallel : 0.000366s : 0.13% py_interpret_to_execute : 0.000380s : 0.13% rewriter_before_opt_a : 0.000424s : 0.15% expand_dump_flag : 0.000357s : 0.12% jit_opt_a.switch_simplify : 0.000060s : 0.02% jit_opt_a.loop_unroll : 0.000026s : 0.01% jit_opt_a.a_1 : 0.000695s : 0.24% jit_opt_a.with_stream_mark : 0.000043s : 0.01% jit_opt_a.recompute_prepare : 0.000021s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000012s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000010s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000009s : 0.00% jit_opt_a.parameter_eliminate : 0.000004s : 0.00% jit_opt_a.specialize_transform : 0.000019s : 0.01% jit_opt_a.updatestate_useless_node_eliminater : 0.000019s : 0.01% jit_opt_a.accelerated_algorithm : 0.000044s : 0.02% jit_opt_a.meta_shard_fg_expand : 0.000005s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000017s : 0.01% 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 : 0.000045s : 0.02% jit_opt_a.j_node_and_user_rematch : 0.000030s : 0.01% jit_opt_a.meta_fg_expand : 0.000007s : 0.00% jit_opt_a.replace_old_param : 0.000017s : 0.01% jit_opt_a.inline_without_move : 0.000017s : 0.01% jit_opt_a.renormalize : 0.001164s : 0.40% jit_opt_a.add_forward_monad_depend : 0.000015s : 0.01% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000033s : 0.01% jit_opt_a.cse : 0.000090s : 0.03% jit_opt_a.replace_applicator : 0.000031s : 0.01% py_interpret_to_execute_after_opt_a : 0.000327s : 0.11% rewriter_after_opt_a : 0.000407s : 0.14% convert_after_rewriter : 0.000350s : 0.12% order_py_execute_after_rewriter : 0.000295s : 0.10% mutable_eliminate : 0.001098s : 0.38% jit_opt_b.frontend_op_eliminate : 0.000027s : 0.01% jit_opt_b.inline_after_opt_a : 0.000025s : 0.01% cconv : 0.000245s : 0.09% loop_unroll : 0.000808s : 0.28% jit_opt_after_cconv.c_1 : 0.000035s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000006s : 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 : 0.000031s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000025s : 0.01% 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 : 0.000007s : 0.00% remove_dup_value : 0.000232s : 0.08% partial_unused_args_eliminate : 0.000203s : 0.07% environ_conv : 0.000257s : 0.09% add_recomputation : 0.000264s : 0.09% cse_after_recomputation.cse : 0.000023s : 0.01% auto_monad_reorder : 0.000244s : 0.08% get_jit_bprop_graph : 0.000207s : 0.07% rewriter_after_jit_bprop_graph : 0.000211s : 0.07% opt_after_jit_grad : 0.000896s : 0.31% symbol_engine_optimizer.build : 0.000095s : 0.03% symbol_engine_optimizer.elim_shapecalc : 0.000016s : 0.01% symbol_engine_optimizer.elim_not_effective : 0.000027s : 0.01% symbol_engine_optimizer.opt_reshape : 0.000008s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000013s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000346s : 0.12% Time group info: ------[substitution.] 0.000182 29 5.92% : 0.000011s : 2: substitution.elim_not_effective 1.86% : 0.000003s : 2: substitution.fold_const_symbol 3.94% : 0.000007s : 4: substitution.graph_param_transform 71.17% : 0.000130s : 1: substitution.inline 3.37% : 0.000006s : 8: substitution.j_node_and_user_rematch 7.79% : 0.000014s : 2: substitution.less_batch_normalization 1.98% : 0.000004s : 2: substitution.mutable_eliminate 3.98% : 0.000007s : 8: substitution.remove_not_recompute_node ------[type_inference.] 0.272851 2 99.72% : 0.272073s : 1: type_inference.infer 0.28% : 0.000777s : 1: type_inference.specialize ------[replace.] 0.000078 3 41.91% : 0.000033s : 1: replace.inline 58.09% : 0.000045s : 2: replace.mutable_eliminate ------[match.] 0.000130 3 98.74% : 0.000129s : 1: match.inline 1.26% : 0.000002s : 2: match.mutable_eliminate ------[predicate.] 0.000156 837 1.23% : 0.000002s : 13: predicate.accumulaten_eliminater 1.67% : 0.000003s : 4: predicate.ad_related_special_op_eliminate 1.08% : 0.000002s : 13: predicate.addn_check_dump 1.40% : 0.000002s : 13: predicate.addn_zero_filter 2.19% : 0.000003s : 13: predicate.arithmetic_simplify 1.20% : 0.000002s : 13: predicate.cast_eliminate 0.48% : 0.000001s : 4: predicate.check_bprop_eliminate 1.10% : 0.000002s : 13: predicate.compare_switch_simplify 1.23% : 0.000002s : 13: predicate.depend_value_elim 1.25% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 1.16% : 0.000002s : 13: predicate.dict_get_item_eliminator 1.15% : 0.000002s : 13: predicate.dict_set_item_eliminator 1.09% : 0.000002s : 4: predicate.dumpgradient_eliminate 0.42% : 0.000001s : 4: predicate.elim_not_effective 0.66% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 1.23% : 0.000002s : 13: predicate.environ_add_const_eliminate 1.11% : 0.000002s : 13: predicate.environ_get_add_eliminate 1.08% : 0.000002s : 13: predicate.environ_get_depend_swap 1.10% : 0.000002s : 13: predicate.environ_get_eliminate 1.11% : 0.000002s : 13: predicate.environ_get_set_eliminate 0.27% : 0.000000s : 4: predicate.fold_const_symbol 1.31% : 0.000002s : 12: predicate.get_grad_eliminate 0.40% : 0.000001s : 4: predicate.graph_param_transform 5.22% : 0.000008s : 22: predicate.inline 1.63% : 0.000003s : 12: predicate.inline_without_move 0.66% : 0.000001s : 12: predicate.j_node_and_user_rematch 1.92% : 0.000003s : 12: predicate.less_batch_normalization 1.27% : 0.000002s : 13: predicate.list_to_tuple_eliminator_ 1.64% : 0.000003s : 17: predicate.load_eliminater 1.69% : 0.000003s : 4: predicate.loop_unroll_after_grad 1.79% : 0.000003s : 15: predicate.loop_unroll_before_grad 1.86% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 1.09% : 0.000002s : 13: predicate.merge_addn 1.11% : 0.000002s : 13: predicate.minmaximum_grad 3.16% : 0.000005s : 7: predicate.mutable_eliminate 0.46% : 0.000001s : 4: predicate.opt_reshape 2.34% : 0.000004s : 17: predicate.partial_eliminate 1.27% : 0.000002s : 13: predicate.print_const_string_wrapper 1.59% : 0.000002s : 13: predicate.reduce_eliminate 1.29% : 0.000002s : 13: predicate.redundant_stop_gradient_eliminater 1.01% : 0.000002s : 12: predicate.remove_not_recompute_node 2.28% : 0.000004s : 25: predicate.replace_applicator 1.05% : 0.000002s : 12: predicate.replace_old_param 0.88% : 0.000001s : 4: predicate.reset_defer_inline 1.23% : 0.000002s : 13: predicate.reshape_eliminate 1.22% : 0.000002s : 13: predicate.row_tensor_add_zeros_like 0.82% : 0.000001s : 4: predicate.row_tensor_eliminate 1.18% : 0.000002s : 13: predicate.same_eliminate 0.93% : 0.000001s : 12: predicate.set_cell_output_no_recompute 1.08% : 0.000002s : 8: predicate.special_op_eliminate 1.51% : 0.000002s : 12: predicate.specialize_transform 1.36% : 0.000002s : 13: predicate.split_environ_get_set_with_tuple_value 1.16% : 0.000002s : 13: predicate.stack_unstack_eliminate 0.50% : 0.000001s : 4: predicate.switch_call_monad_eliminater 1.72% : 0.000003s : 14: predicate.switch_defer_inline 2.19% : 0.000003s : 14: predicate.switch_layer_defer_inline 5.10% : 0.000008s : 33: predicate.switch_simplify 1.24% : 0.000002s : 13: predicate.tile_eliminate 1.16% : 0.000002s : 13: predicate.transpose_eliminate 1.40% : 0.000002s : 13: predicate.tuple_list_convert_item_index_to_positive 1.30% : 0.000002s : 13: predicate.tuple_list_get_item_depend_reorder 3.65% : 0.000006s : 21: predicate.tuple_list_get_item_eliminator 1.54% : 0.000002s : 13: predicate.tuple_list_set_item_eliminator 1.24% : 0.000002s : 13: predicate.tuple_to_list_eliminator_ 1.59% : 0.000002s : 17: predicate.updatestate_pure_node_eliminater 3.89% : 0.000006s : 29: predicate.updatestate_useless_node_eliminater 1.49% : 0.000002s : 13: predicate.value_based_eliminate 0.47% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.93% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000684 7 58.52% : 0.000400s : 4: func_graph_cloner_run.FuncGraphClonerGraph 41.48% : 0.000284s : 3: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.392285 72 0.07% : 0.000270s : 1: add_recomputation 0.13% : 0.000511s : 1: auto_monad 0.06% : 0.000249s : 1: auto_monad_reorder 0.26% : 0.001017s : 1: bootstrap 0.06% : 0.000250s : 1: cconv 0.09% : 0.000357s : 1: convert_after_rewriter 0.07% : 0.000264s : 1: cse_after_recomputation 0.07% : 0.000263s : 1: environ_conv 0.13% : 0.000504s : 1: event_method 0.09% : 0.000364s : 1: expand_dump_flag 0.05% : 0.000212s : 1: get_jit_bprop_graph 0.09% : 0.000359s : 1: graph_reusing 26.39% : 0.103519s : 1: jit_opt_a 0.11% : 0.000426s : 1: jit_opt_after_cconv 0.08% : 0.000306s : 1: jit_opt_b 0.21% : 0.000815s : 1: loop_unroll 0.28% : 0.001107s : 1: mutable_eliminate 0.25% : 0.000996s : 26: opt.transform.jit_opt_a 0.02% : 0.000070s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000040s : 4: opt.transform.jit_opt_b 0.00% : 0.000018s : 1: opt.transform.loop_unroll_optimizer 0.02% : 0.000090s : 1: opt.transform.mutable_eliminate 0.01% : 0.000033s : 1: opt.transform.opt_after_jit_grad 0.02% : 0.000059s : 4: opt.transform.symbol_engine_opt 0.23% : 0.000904s : 1: opt_after_jit_grad 0.08% : 0.000301s : 1: order_py_execute_after_rewriter 0.05% : 0.000209s : 1: partial_unused_args_eliminate 0.10% : 0.000373s : 1: pre_auto_parallel 0.10% : 0.000387s : 1: py_interpret_to_execute 0.09% : 0.000334s : 1: py_interpret_to_execute_after_opt_a 0.06% : 0.000238s : 1: remove_dup_value 0.18% : 0.000716s : 1: renormalize.infer 0.11% : 0.000439s : 1: renormalize.specialize 0.06% : 0.000216s : 1: rewriter_after_jit_bprop_graph 0.11% : 0.000414s : 1: rewriter_after_opt_a 0.11% : 0.000431s : 1: rewriter_before_opt_a 0.13% : 0.000506s : 1: symbol_engine_optimizer 70.03% : 0.274716s : 1: type_inference . [hook] pytest_runtest_teardown:test_output_mutable_list tests/st/compiler/mutable/test_mutable_in_graph.py::test_output_mutable_list,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 ================== 1 passed, 25 warnings in 81.09s (0:01:21) ===================