==================================================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_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_batchnormext_bprop_cast.py . [hook] pytest_runtest_teardown:test_bn_ext_bprop_skip_cast[pynative] tests/st/ops/ascend/test_batchnormext_bprop_cast.py::test_bn_ext_bprop_skip_cast[pynative],max_mem:2.0M TotalTime = 0.876353, [30] [bootstrap]: 0.00060772 [type_inference]: 0.367003 [event_method]: 0.00017455 [auto_monad]: 0.0001701 [graph_reusing]: 8.31002e-06 [pre_auto_parallel]: 1.397e-05 [py_interpret_to_execute]: 5.873e-05 [rewriter_before_opt_a]: 0.0002272 [expand_dump_flag]: 3.93001e-06 [jit_opt_a]: 0.504359, [3] [Cycle 1]: 0.449196, [27] [switch_simplify]: 0.00011962 [loop_unroll]: 6.985e-05 [a_1]: 0.00149768 [with_stream_mark]: 4.665e-05 [recompute_prepare]: 3.455e-05 [updatestate_depend_eliminate]: 1.283e-05 [updatestate_assign_eliminate]: 9.81e-06 [updatestate_loads_eliminate]: 9.81e-06 [parameter_eliminate]: 3.65998e-06 [specialize_transform]: 2.37e-05 [updatestate_useless_node_eliminater]: 2.075e-05 [accelerated_algorithm]: 2.137e-05 [meta_shard_fg_expand]: 6.46e-06 [get_grad_eliminate_]: 2.104e-05 [merge_forward]: 1.189e-05 [cell_reuse_recompute_pass]: 1.46998e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.773e-05 [j_node_and_user_rematch]: 3.731e-05 [meta_fg_expand]: 0.20684 [replace_old_param]: 0.00014092 [inline_without_move]: 0.00011947 [renormalize]: 0.23858 [add_forward_monad_depend]: 3.415e-05 [auto_monad_grad]: 1.952e-05 [auto_monad_eliminator]: 0.00014614 [cse]: 0.0006037 [replace_applicator]: 0.00035007 [Cycle 2]: 0.0463776, [27] [switch_simplify]: 0.00010837 [loop_unroll]: 0.00010273 [a_1]: 0.00204977 [with_stream_mark]: 3.696e-05 [recompute_prepare]: 2.013e-05 [updatestate_depend_eliminate]: 9.86e-06 [updatestate_assign_eliminate]: 8.50001e-06 [updatestate_loads_eliminate]: 7.25e-06 [parameter_eliminate]: 2.86e-06 [specialize_transform]: 1.541e-05 [updatestate_useless_node_eliminater]: 1.393e-05 [accelerated_algorithm]: 1.493e-05 [meta_shard_fg_expand]: 4.60999e-06 [get_grad_eliminate_]: 1.4e-05 [merge_forward]: 8.04002e-06 [cell_reuse_recompute_pass]: 1.22999e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.163e-05 [j_node_and_user_rematch]: 2.329e-05 [meta_fg_expand]: 0.00017274 [replace_old_param]: 2.637e-05 [inline_without_move]: 1.498e-05 [renormalize]: 0.0430748 [add_forward_monad_depend]: 1.292e-05 [auto_monad_grad]: 3.05998e-06 [auto_monad_eliminator]: 3.841e-05 [cse]: 0.00026968 [replace_applicator]: 3.979e-05 [Cycle 3]: 0.00087838, [27] [switch_simplify]: 1.651e-05 [loop_unroll]: 1.536e-05 [a_1]: 0.00038703 [with_stream_mark]: 2.739e-05 [recompute_prepare]: 1.531e-05 [updatestate_depend_eliminate]: 8.99e-06 [updatestate_assign_eliminate]: 8.33001e-06 [updatestate_loads_eliminate]: 7.55e-06 [parameter_eliminate]: 2.29999e-06 [specialize_transform]: 1.588e-05 [updatestate_useless_node_eliminater]: 1.481e-05 [accelerated_algorithm]: 1.467e-05 [meta_shard_fg_expand]: 4.79998e-06 [get_grad_eliminate_]: 1.392e-05 [merge_forward]: 8.47998e-06 [cell_reuse_recompute_pass]: 3.25e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.201e-05 [j_node_and_user_rematch]: 2.409e-05 [meta_fg_expand]: 5.69e-06 [replace_old_param]: 2.302e-05 [inline_without_move]: 1.515e-05 [renormalize]: 7.99773e-08 [add_forward_monad_depend]: 2.34999e-06 [auto_monad_grad]: 1.72999e-06 [auto_monad_eliminator]: 1.606e-05 [cse]: 4.746e-05 [replace_applicator]: 1.569e-05 [py_interpret_to_execute_after_opt_a]: 2.698e-05 [rewriter_after_opt_a]: 0.00014254 [convert_after_rewriter]: 1.601e-05 [order_py_execute_after_rewriter]: 1.044e-05 [mutable_eliminate]: 0.0008141 [jit_opt_b]: 0.00013436, [1] [Cycle 1]: 0.00012132, [2] [frontend_op_eliminate]: 4.439e-05 [inline_after_opt_a]: 6.288e-05 [cconv]: 5.557e-05 [loop_unroll]: 0.00047763 [jit_opt_after_cconv]: 0.00031983, [1] [Cycle 1]: 0.00031173, [11] [c_1]: 6.484e-05 [parameter_eliminate]: 3.73001e-06 [updatestate_depend_eliminate]: 1.595e-05 [updatestate_assign_eliminate]: 8.09997e-06 [updatestate_loads_eliminate]: 7.48e-06 [cse]: 6.416e-05 [call_graph_tuple_transform]: 4.22e-05 [tuple_list_get_item_eliminator]: 2.713e-05 [none_parameter_eliminate]: 2.09999e-06 [renormalize]: 1.10001e-06 [switch_simplify]: 1.539e-05 [remove_dup_value]: 8.176e-05 [partial_unused_args_eliminate]: 2.49001e-06 [environ_conv]: 2.406e-05 [add_recomputation]: 0.00011213 [cse_after_recomputation]: 5.728e-05, [1] [Cycle 1]: 5.03e-05, [1] [cse]: 3.992e-05 [auto_monad_reorder]: 3.925e-05 [get_jit_bprop_graph]: 2.24001e-06 [rewriter_after_jit_bprop_graph]: 0.00025567 [opt_after_jit_grad]: 0.00055626 [symbol_engine_optimizer]: 0.00014296, [1] [Cycle 1]: 0.00013565, [6] [build]: 1.585e-05 [elim_shapecalc]: 1.94e-05 [elim_not_effective]: 2.858e-05 [opt_reshape]: 1.593e-05 [fold_const_symbol]: 2.346e-05 [renormalize]: 7.7e-07 [validate]: 8.92e-05 Sums bootstrap : 0.000608s : 0.07% type_inference : 0.367003s : 42.32% event_method : 0.000175s : 0.02% auto_monad : 0.000170s : 0.02% graph_reusing : 0.000008s : 0.00% pre_auto_parallel : 0.000014s : 0.00% py_interpret_to_execute : 0.000059s : 0.01% rewriter_before_opt_a : 0.000227s : 0.03% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000245s : 0.03% jit_opt_a.loop_unroll : 0.000188s : 0.02% jit_opt_a.a_1 : 0.003934s : 0.45% jit_opt_a.with_stream_mark : 0.000111s : 0.01% jit_opt_a.recompute_prepare : 0.000070s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000032s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000027s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000025s : 0.00% jit_opt_a.parameter_eliminate : 0.000009s : 0.00% jit_opt_a.specialize_transform : 0.000055s : 0.01% jit_opt_a.updatestate_useless_node_eliminater : 0.000049s : 0.01% jit_opt_a.accelerated_algorithm : 0.000051s : 0.01% jit_opt_a.meta_shard_fg_expand : 0.000016s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000049s : 0.01% jit_opt_a.merge_forward : 0.000028s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000006s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000111s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000085s : 0.01% jit_opt_a.meta_fg_expand : 0.207018s : 23.87% jit_opt_a.replace_old_param : 0.000190s : 0.02% jit_opt_a.inline_without_move : 0.000150s : 0.02% jit_opt_a.renormalize : 0.281655s : 32.48% jit_opt_a.add_forward_monad_depend : 0.000049s : 0.01% jit_opt_a.auto_monad_grad : 0.000024s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000201s : 0.02% jit_opt_a.cse : 0.000921s : 0.11% jit_opt_a.replace_applicator : 0.000406s : 0.05% py_interpret_to_execute_after_opt_a : 0.000027s : 0.00% rewriter_after_opt_a : 0.000143s : 0.02% convert_after_rewriter : 0.000016s : 0.00% order_py_execute_after_rewriter : 0.000010s : 0.00% mutable_eliminate : 0.000814s : 0.09% jit_opt_b.frontend_op_eliminate : 0.000044s : 0.01% jit_opt_b.inline_after_opt_a : 0.000063s : 0.01% cconv : 0.000056s : 0.01% loop_unroll : 0.000478s : 0.06% jit_opt_after_cconv.c_1 : 0.000065s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000016s : 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 : 0.000064s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000042s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000027s : 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 : 0.000015s : 0.00% remove_dup_value : 0.000082s : 0.01% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000024s : 0.00% add_recomputation : 0.000112s : 0.01% cse_after_recomputation.cse : 0.000040s : 0.00% auto_monad_reorder : 0.000039s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000256s : 0.03% opt_after_jit_grad : 0.000556s : 0.06% symbol_engine_optimizer.build : 0.000016s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000019s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000029s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000016s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000023s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000089s : 0.01% Time group info: ------[substitution.] 0.001821 235 0.20% : 0.000004s : 6: substitution.elim_not_effective 0.16% : 0.000003s : 6: substitution.fold_const_symbol 36.25% : 0.000660s : 5: substitution.getattr_setattr_resolve 0.62% : 0.000011s : 10: substitution.graph_param_transform 38.20% : 0.000696s : 15: substitution.inline 1.73% : 0.000032s : 3: substitution.inline_without_move 0.86% : 0.000016s : 21: substitution.j_node_and_user_rematch 1.70% : 0.000031s : 18: substitution.minmaximum_grad 1.05% : 0.000019s : 6: substitution.partial_eliminate 0.99% : 0.000018s : 21: substitution.remove_not_recompute_node 5.26% : 0.000096s : 21: substitution.replace_applicator 1.48% : 0.000027s : 30: substitution.replace_old_param 0.17% : 0.000003s : 1: substitution.set_cell_output_no_recompute 3.16% : 0.000058s : 18: substitution.tuple_list_convert_item_index_to_positive 2.08% : 0.000038s : 18: substitution.tuple_list_get_item_depend_reorder 6.08% : 0.000111s : 36: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.366866 2 99.25% : 0.364130s : 1: type_inference.infer 0.75% : 0.002736s : 1: type_inference.specialize ------[replace.] 0.000557 36 13.89% : 0.000077s : 4: replace.getattr_setattr_resolve 46.01% : 0.000256s : 15: replace.inline 16.22% : 0.000090s : 5: replace.replace_applicator 23.87% : 0.000133s : 12: replace.tuple_list_get_item_eliminator ------[match.] 0.001367 36 44.52% : 0.000608s : 4: match.getattr_setattr_resolve 50.14% : 0.000685s : 15: match.inline 2.75% : 0.000038s : 5: match.replace_applicator 2.60% : 0.000035s : 12: match.tuple_list_get_item_eliminator ------[predicate.] 0.000689 4444 1.39% : 0.000010s : 68: predicate.accumulaten_eliminater 0.43% : 0.000003s : 10: predicate.ad_related_special_op_eliminate 1.37% : 0.000009s : 68: predicate.addn_check_dump 1.41% : 0.000010s : 68: predicate.addn_zero_filter 1.93% : 0.000013s : 68: predicate.arithmetic_simplify 1.43% : 0.000010s : 68: predicate.cast_eliminate 0.24% : 0.000002s : 10: predicate.check_bprop_eliminate 1.28% : 0.000009s : 68: predicate.compare_switch_simplify 1.35% : 0.000009s : 68: predicate.depend_value_elim 1.36% : 0.000009s : 68: predicate.dict_get_item_const_eliminator 1.40% : 0.000010s : 68: predicate.dict_get_item_eliminator 1.38% : 0.000009s : 68: predicate.dict_set_item_eliminator 0.32% : 0.000002s : 10: predicate.dumpgradient_eliminate 0.14% : 0.000001s : 10: predicate.elim_not_effective 0.29% : 0.000002s : 10: predicate.elim_shapecalc_of_broadcastargs 1.39% : 0.000010s : 68: predicate.environ_add_const_eliminate 1.33% : 0.000009s : 68: predicate.environ_get_add_eliminate 1.30% : 0.000009s : 68: predicate.environ_get_depend_swap 1.37% : 0.000009s : 68: predicate.environ_get_eliminate 1.36% : 0.000009s : 68: predicate.environ_get_set_eliminate 0.12% : 0.000001s : 10: predicate.fold_const_symbol 0.90% : 0.000006s : 36: predicate.get_grad_eliminate 1.22% : 0.000008s : 31: predicate.getattr_setattr_resolve 0.12% : 0.000001s : 10: predicate.graph_param_transform 4.54% : 0.000031s : 115: predicate.inline 2.61% : 0.000018s : 99: predicate.inline_without_move 0.43% : 0.000003s : 36: predicate.j_node_and_user_rematch 1.07% : 0.000007s : 36: predicate.less_batch_normalization 1.63% : 0.000011s : 80: predicate.list_to_tuple_eliminator_ 1.91% : 0.000013s : 90: predicate.load_eliminater 0.43% : 0.000003s : 10: predicate.loop_unroll_after_grad 3.77% : 0.000026s : 177: predicate.loop_unroll_before_grad 1.75% : 0.000012s : 78: predicate.make_slice_get_slice_eliminator 1.33% : 0.000009s : 68: predicate.merge_addn 1.38% : 0.000009s : 68: predicate.minmaximum_grad 0.56% : 0.000004s : 10: predicate.mutable_eliminate 0.25% : 0.000002s : 10: predicate.opt_reshape 2.37% : 0.000016s : 90: predicate.partial_eliminate 1.35% : 0.000009s : 68: predicate.print_const_string_wrapper 1.87% : 0.000013s : 68: predicate.reduce_eliminate 1.67% : 0.000011s : 80: predicate.redundant_stop_gradient_eliminater 0.44% : 0.000003s : 36: predicate.remove_not_recompute_node 3.31% : 0.000023s : 211: predicate.replace_applicator 1.36% : 0.000009s : 99: predicate.replace_old_param 0.18% : 0.000001s : 10: predicate.reset_defer_inline 1.40% : 0.000010s : 68: predicate.reshape_eliminate 1.35% : 0.000009s : 68: predicate.row_tensor_add_zeros_like 0.31% : 0.000002s : 10: predicate.row_tensor_eliminate 1.32% : 0.000009s : 68: predicate.same_eliminate 0.50% : 0.000003s : 36: predicate.set_cell_output_no_recompute 0.54% : 0.000004s : 20: predicate.special_op_eliminate 1.00% : 0.000007s : 36: predicate.specialize_transform 1.73% : 0.000012s : 68: predicate.split_environ_get_set_with_tuple_value 1.33% : 0.000009s : 68: predicate.stack_unstack_eliminate 0.22% : 0.000002s : 10: predicate.switch_call_monad_eliminater 2.46% : 0.000017s : 95: predicate.switch_defer_inline 2.12% : 0.000015s : 95: predicate.switch_layer_defer_inline 6.65% : 0.000046s : 282: predicate.switch_simplify 1.35% : 0.000009s : 68: predicate.tile_eliminate 1.39% : 0.000010s : 68: predicate.transpose_eliminate 1.81% : 0.000012s : 68: predicate.tuple_list_convert_item_index_to_positive 1.73% : 0.000012s : 68: predicate.tuple_list_get_item_depend_reorder 3.48% : 0.000024s : 100: predicate.tuple_list_get_item_eliminator 1.76% : 0.000012s : 68: predicate.tuple_list_set_item_eliminator 1.65% : 0.000011s : 80: predicate.tuple_to_list_eliminator_ 1.85% : 0.000013s : 90: predicate.updatestate_pure_node_eliminater 2.82% : 0.000019s : 126: predicate.updatestate_useless_node_eliminater 1.69% : 0.000012s : 68: predicate.value_based_eliminate 0.20% : 0.000001s : 10: predicate.virtual_view_grad_eliminate 0.32% : 0.000002s : 10: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.066654 66 36.65% : 0.024425s : 38: func_graph_cloner_run.FuncGraphClonerGraph 63.35% : 0.042229s : 28: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.164443 89 0.01% : 0.000116s : 1: add_recomputation 0.02% : 0.000177s : 1: auto_monad 0.00% : 0.000043s : 1: auto_monad_reorder 0.05% : 0.000632s : 1: bootstrap 0.01% : 0.000059s : 1: cconv 0.00% : 0.000019s : 1: convert_after_rewriter 0.01% : 0.000060s : 1: cse_after_recomputation 0.00% : 0.000027s : 1: environ_conv 0.02% : 0.000184s : 1: event_method 0.00% : 0.000006s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000012s : 1: graph_reusing 43.31% : 0.504363s : 1: jit_opt_a 0.03% : 0.000323s : 1: jit_opt_after_cconv 0.01% : 0.000137s : 1: jit_opt_b 0.04% : 0.000486s : 1: loop_unroll 0.07% : 0.000827s : 1: mutable_eliminate 0.47% : 0.005503s : 39: opt.transform.jit_opt_a 0.01% : 0.000145s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000098s : 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% : 0.000052s : 1: opt.transform.opt_after_jit_grad 0.07% : 0.000812s : 2: opt.transform.opt_resolve 0.01% : 0.000083s : 4: opt.transform.symbol_engine_opt 0.05% : 0.000568s : 1: opt_after_jit_grad 0.00% : 0.000013s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000016s : 1: pre_auto_parallel 0.01% : 0.000062s : 1: py_interpret_to_execute 0.00% : 0.000030s : 1: py_interpret_to_execute_after_opt_a 0.01% : 0.000086s : 1: remove_dup_value 20.18% : 0.235011s : 2: renormalize.infer 4.00% : 0.046613s : 2: renormalize.specialize 0.02% : 0.000260s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000147s : 1: rewriter_after_opt_a 0.02% : 0.000231s : 1: rewriter_before_opt_a 0.01% : 0.000146s : 1: symbol_engine_optimizer 31.52% : 0.367030s : 1: type_inference . [hook] pytest_runtest_teardown:test_bn_ext_bprop_skip_cast[kbk] tests/st/ops/ascend/test_batchnormext_bprop_cast.py::test_bn_ext_bprop_skip_cast[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 69.50s (0:01:09) ===================