==================================================Ascend ============================= test session starts ============================== platform linux -- Python 3.9.19, pytest-6.2.5, py-1.11.0, pluggy-1.5.0 rootdir: /home/jenkins/mindspore/testcases/testcases/tests/st/mint, configfile: ../../../../../../sault/virtual_test/virtualenv_001/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_reshape.py . [hook] pytest_runtest_teardown:test_reshape_view_backward[pynative] tests/st/mint/test_reshape.py::test_reshape_view_backward[pynative],max_mem:2.0M TotalTime = 1.82533, [30] [bootstrap]: 0.00100594 [type_inference]: 1.36335 [event_method]: 0.00056941 [auto_monad]: 0.00055146 [graph_reusing]: 1.086e-05 [pre_auto_parallel]: 1.509e-05 [py_interpret_to_execute]: 5.495e-05 [rewriter_before_opt_a]: 0.00029899 [expand_dump_flag]: 5.62001e-06 [jit_opt_a]: 0.451186, [3] [Cycle 1]: 0.353175, [27] [switch_simplify]: 0.00016203 [loop_unroll]: 6.791e-05 [a_1]: 0.0660361 [with_stream_mark]: 5.247e-05 [recompute_prepare]: 4.365e-05 [updatestate_depend_eliminate]: 1.635e-05 [updatestate_assign_eliminate]: 1.219e-05 [updatestate_loads_eliminate]: 1.231e-05 [parameter_eliminate]: 4.18999e-06 [specialize_transform]: 2.691e-05 [updatestate_useless_node_eliminater]: 2.923e-05 [accelerated_algorithm]: 2.518e-05 [meta_shard_fg_expand]: 1.06e-05 [get_grad_eliminate_]: 2.366e-05 [merge_forward]: 1.431e-05 [cell_reuse_recompute_pass]: 1.66998e-06 [cell_reuse_handle_not_recompute_node_pass]: 5.657e-05 [j_node_and_user_rematch]: 4.393e-05 [meta_fg_expand]: 0.0777823 [replace_old_param]: 0.00018344 [inline_without_move]: 0.00017887 [renormalize]: 0.206992 [add_forward_monad_depend]: 3.191e-05 [auto_monad_grad]: 1.922e-05 [auto_monad_eliminator]: 0.00014452 [cse]: 0.00049486 [replace_applicator]: 0.0002827 [Cycle 2]: 0.0869385, [27] [switch_simplify]: 0.0443165 [loop_unroll]: 0.00016389 [a_1]: 0.00209252 [with_stream_mark]: 4.532e-05 [recompute_prepare]: 2.881e-05 [updatestate_depend_eliminate]: 5.049e-05 [updatestate_assign_eliminate]: 7.89997e-06 [updatestate_loads_eliminate]: 6.44001e-06 [parameter_eliminate]: 3.21001e-06 [specialize_transform]: 1.697e-05 [updatestate_useless_node_eliminater]: 2.378e-05 [accelerated_algorithm]: 1.568e-05 [meta_shard_fg_expand]: 4.13999e-06 [get_grad_eliminate_]: 1.276e-05 [merge_forward]: 9.50001e-06 [cell_reuse_recompute_pass]: 1.97999e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.042e-05 [j_node_and_user_rematch]: 2.197e-05 [meta_fg_expand]: 0.00022683 [replace_old_param]: 3.188e-05 [inline_without_move]: 1.429e-05 [renormalize]: 0.0391131 [add_forward_monad_depend]: 1.45e-05 [auto_monad_grad]: 2.71e-06 [auto_monad_eliminator]: 4.171e-05 [cse]: 0.00023675 [replace_applicator]: 4.03e-05 [Cycle 3]: 0.00079873, [27] [switch_simplify]: 1.469e-05 [loop_unroll]: 1.339e-05 [a_1]: 0.00032625 [with_stream_mark]: 3.101e-05 [recompute_prepare]: 1.272e-05 [updatestate_depend_eliminate]: 9.48002e-06 [updatestate_assign_eliminate]: 7.41999e-06 [updatestate_loads_eliminate]: 6.45002e-06 [parameter_eliminate]: 2.38002e-06 [specialize_transform]: 1.328e-05 [updatestate_useless_node_eliminater]: 1.527e-05 [accelerated_algorithm]: 1.452e-05 [meta_shard_fg_expand]: 3.91999e-06 [get_grad_eliminate_]: 1.164e-05 [merge_forward]: 7.67998e-06 [cell_reuse_recompute_pass]: 4.20999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.768e-05 [j_node_and_user_rematch]: 2.098e-05 [meta_fg_expand]: 4.87998e-06 [replace_old_param]: 1.692e-05 [inline_without_move]: 1.161e-05 [renormalize]: 8.9989e-08 [add_forward_monad_depend]: 2.56998e-06 [auto_monad_grad]: 2.32001e-06 [auto_monad_eliminator]: 1.908e-05 [cse]: 4.184e-05 [replace_applicator]: 1.419e-05 [py_interpret_to_execute_after_opt_a]: 2.974e-05 [rewriter_after_opt_a]: 0.00019339 [convert_after_rewriter]: 3.799e-05 [order_py_execute_after_rewriter]: 9.07999e-06 [mutable_eliminate]: 0.00505602 [jit_opt_b]: 0.00011192, [1] [Cycle 1]: 9.965e-05, [2] [frontend_op_eliminate]: 4.34e-05 [inline_after_opt_a]: 4.026e-05 [cconv]: 5.139e-05 [loop_unroll]: 0.00054852 [jit_opt_after_cconv]: 0.00033371, [1] [Cycle 1]: 0.00032541, [11] [c_1]: 7.872e-05 [parameter_eliminate]: 6.76e-06 [updatestate_depend_eliminate]: 1.877e-05 [updatestate_assign_eliminate]: 9.62999e-06 [updatestate_loads_eliminate]: 6.42001e-06 [cse]: 8.162e-05 [call_graph_tuple_transform]: 3.699e-05 [tuple_list_get_item_eliminator]: 1.212e-05 [none_parameter_eliminate]: 2.06e-06 [renormalize]: 5.19998e-07 [switch_simplify]: 1.205e-05 [remove_dup_value]: 5.932e-05 [partial_unused_args_eliminate]: 3.87998e-06 [environ_conv]: 2.478e-05 [add_recomputation]: 0.00011059 [cse_after_recomputation]: 5.378e-05, [1] [Cycle 1]: 4.618e-05, [1] [cse]: 3.715e-05 [auto_monad_reorder]: 4.791e-05 [get_jit_bprop_graph]: 2.83998e-06 [rewriter_after_jit_bprop_graph]: 0.00024952 [opt_after_jit_grad]: 0.00063799 [symbol_engine_optimizer]: 0.00013504, [1] [Cycle 1]: 0.00012702, [6] [build]: 8.92999e-06 [elim_shapecalc]: 1.712e-05 [elim_not_effective]: 2.732e-05 [opt_reshape]: 1.882e-05 [fold_const_symbol]: 1.982e-05 [renormalize]: 9.79984e-07 [validate]: 0.00014831 Sums bootstrap : 0.001006s : 0.06% type_inference : 1.363346s : 75.18% event_method : 0.000569s : 0.03% auto_monad : 0.000551s : 0.03% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000015s : 0.00% py_interpret_to_execute : 0.000055s : 0.00% rewriter_before_opt_a : 0.000299s : 0.02% expand_dump_flag : 0.000006s : 0.00% jit_opt_a.switch_simplify : 0.044493s : 2.45% jit_opt_a.loop_unroll : 0.000245s : 0.01% jit_opt_a.a_1 : 0.068455s : 3.77% jit_opt_a.with_stream_mark : 0.000129s : 0.01% jit_opt_a.recompute_prepare : 0.000085s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000076s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000028s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000025s : 0.00% jit_opt_a.parameter_eliminate : 0.000010s : 0.00% jit_opt_a.specialize_transform : 0.000057s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000068s : 0.00% jit_opt_a.accelerated_algorithm : 0.000055s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000019s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000048s : 0.00% jit_opt_a.merge_forward : 0.000031s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000008s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000115s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000087s : 0.00% jit_opt_a.meta_fg_expand : 0.078014s : 4.30% jit_opt_a.replace_old_param : 0.000232s : 0.01% jit_opt_a.inline_without_move : 0.000205s : 0.01% jit_opt_a.renormalize : 0.246105s : 13.57% jit_opt_a.add_forward_monad_depend : 0.000049s : 0.00% jit_opt_a.auto_monad_grad : 0.000024s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000205s : 0.01% jit_opt_a.cse : 0.000773s : 0.04% jit_opt_a.replace_applicator : 0.000337s : 0.02% py_interpret_to_execute_after_opt_a : 0.000030s : 0.00% rewriter_after_opt_a : 0.000193s : 0.01% convert_after_rewriter : 0.000038s : 0.00% order_py_execute_after_rewriter : 0.000009s : 0.00% mutable_eliminate : 0.005056s : 0.28% jit_opt_b.frontend_op_eliminate : 0.000043s : 0.00% jit_opt_b.inline_after_opt_a : 0.000040s : 0.00% cconv : 0.000051s : 0.00% loop_unroll : 0.000549s : 0.03% jit_opt_after_cconv.c_1 : 0.000079s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000019s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000010s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.cse : 0.000082s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000037s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000012s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000012s : 0.00% remove_dup_value : 0.000059s : 0.00% partial_unused_args_eliminate : 0.000004s : 0.00% environ_conv : 0.000025s : 0.00% add_recomputation : 0.000111s : 0.01% cse_after_recomputation.cse : 0.000037s : 0.00% auto_monad_reorder : 0.000048s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000250s : 0.01% opt_after_jit_grad : 0.000638s : 0.04% symbol_engine_optimizer.build : 0.000009s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000017s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000027s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000019s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000020s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000148s : 0.01% Time group info: ------[substitution.] 0.002493 226 0.70% : 0.000018s : 1: substitution.arithmetic_simplify 1.48% : 0.000037s : 8: substitution.depend_value_elim 0.18% : 0.000004s : 6: substitution.elim_not_effective 0.13% : 0.000003s : 6: substitution.fold_const_symbol 40.56% : 0.001011s : 4: substitution.getattr_setattr_resolve 0.39% : 0.000010s : 9: substitution.graph_param_transform 37.00% : 0.000923s : 14: substitution.inline 2.09% : 0.000052s : 6: substitution.inline_without_move 0.66% : 0.000016s : 26: substitution.j_node_and_user_rematch 0.62% : 0.000016s : 7: substitution.minmaximum_grad 0.08% : 0.000002s : 2: substitution.opt_reshape 0.67% : 0.000017s : 3: substitution.partial_eliminate 0.85% : 0.000021s : 26: substitution.remove_not_recompute_node 3.20% : 0.000080s : 22: substitution.replace_applicator 0.94% : 0.000024s : 26: substitution.replace_old_param 1.90% : 0.000047s : 8: substitution.reshape_eliminate 0.15% : 0.000004s : 1: substitution.set_cell_output_no_recompute 1.32% : 0.000033s : 7: substitution.tuple_list_convert_item_index_to_positive 0.84% : 0.000021s : 7: substitution.tuple_list_get_item_depend_reorder 2.92% : 0.000073s : 16: substitution.tuple_list_get_item_eliminator 1.29% : 0.000032s : 8: substitution.updatestate_pure_node_eliminater 2.01% : 0.000050s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 1.363189 2 99.77% : 1.360076s : 1: type_inference.infer 0.23% : 0.003113s : 1: type_inference.specialize ------[replace.] 0.000661 29 1.01% : 0.000007s : 1: replace.arithmetic_simplify 11.86% : 0.000078s : 3: replace.getattr_setattr_resolve 27.33% : 0.000181s : 14: replace.inline 6.35% : 0.000042s : 1: replace.replace_applicator 48.13% : 0.000318s : 9: replace.tuple_list_get_item_eliminator 5.33% : 0.000035s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001925 29 0.86% : 0.000017s : 1: match.arithmetic_simplify 48.47% : 0.000933s : 3: match.getattr_setattr_resolve 47.35% : 0.000911s : 14: match.inline 0.70% : 0.000013s : 1: match.replace_applicator 1.68% : 0.000032s : 9: match.tuple_list_get_item_eliminator 0.93% : 0.000018s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000780 4540 1.22% : 0.000010s : 67: predicate.accumulaten_eliminater 0.52% : 0.000004s : 9: predicate.ad_related_special_op_eliminate 1.07% : 0.000008s : 67: predicate.addn_check_dump 1.25% : 0.000010s : 67: predicate.addn_zero_filter 2.00% : 0.000016s : 68: predicate.arithmetic_simplify 1.41% : 0.000011s : 68: predicate.cast_eliminate 0.25% : 0.000002s : 9: predicate.check_bprop_eliminate 1.15% : 0.000009s : 67: predicate.compare_switch_simplify 1.32% : 0.000010s : 67: predicate.depend_value_elim 1.12% : 0.000009s : 68: predicate.dict_get_item_const_eliminator 1.20% : 0.000009s : 68: predicate.dict_get_item_eliminator 1.19% : 0.000009s : 68: predicate.dict_set_item_eliminator 0.31% : 0.000002s : 9: predicate.dumpgradient_eliminate 0.13% : 0.000001s : 9: predicate.elim_not_effective 0.18% : 0.000001s : 9: predicate.elim_shapecalc_of_broadcastargs 1.20% : 0.000009s : 68: predicate.environ_add_const_eliminate 1.12% : 0.000009s : 68: predicate.environ_get_add_eliminate 1.11% : 0.000009s : 68: predicate.environ_get_depend_swap 1.23% : 0.000010s : 68: predicate.environ_get_eliminate 1.14% : 0.000009s : 68: predicate.environ_get_set_eliminate 0.09% : 0.000001s : 9: predicate.fold_const_symbol 0.87% : 0.000007s : 41: predicate.get_grad_eliminate 1.01% : 0.000008s : 20: predicate.getattr_setattr_resolve 0.11% : 0.000001s : 9: predicate.graph_param_transform 5.11% : 0.000040s : 110: predicate.inline 2.97% : 0.000023s : 136: predicate.inline_without_move 0.37% : 0.000003s : 41: predicate.j_node_and_user_rematch 1.18% : 0.000009s : 41: predicate.less_batch_normalization 1.44% : 0.000011s : 77: predicate.list_to_tuple_eliminator_ 1.71% : 0.000013s : 86: predicate.load_eliminater 0.53% : 0.000004s : 9: predicate.loop_unroll_after_grad 5.82% : 0.000045s : 206: predicate.loop_unroll_before_grad 1.55% : 0.000012s : 77: predicate.make_slice_get_slice_eliminator 1.17% : 0.000009s : 67: predicate.merge_addn 1.15% : 0.000009s : 68: predicate.minmaximum_grad 1.27% : 0.000010s : 9: predicate.mutable_eliminate 0.20% : 0.000002s : 9: predicate.opt_reshape 1.92% : 0.000015s : 86: predicate.partial_eliminate 1.17% : 0.000009s : 67: predicate.print_const_string_wrapper 1.98% : 0.000015s : 68: predicate.reduce_eliminate 1.55% : 0.000012s : 77: predicate.redundant_stop_gradient_eliminater 0.44% : 0.000003s : 41: predicate.remove_not_recompute_node 2.98% : 0.000023s : 223: predicate.replace_applicator 1.48% : 0.000012s : 136: predicate.replace_old_param 0.21% : 0.000002s : 9: predicate.reset_defer_inline 1.38% : 0.000011s : 68: predicate.reshape_eliminate 1.21% : 0.000009s : 67: predicate.row_tensor_add_zeros_like 0.30% : 0.000002s : 9: predicate.row_tensor_eliminate 1.22% : 0.000010s : 67: predicate.same_eliminate 0.57% : 0.000004s : 43: predicate.set_cell_output_no_recompute 0.39% : 0.000003s : 18: predicate.special_op_eliminate 0.93% : 0.000007s : 41: predicate.specialize_transform 1.46% : 0.000011s : 67: predicate.split_environ_get_set_with_tuple_value 1.16% : 0.000009s : 67: predicate.stack_unstack_eliminate 0.19% : 0.000002s : 9: predicate.switch_call_monad_eliminater 2.59% : 0.000020s : 92: predicate.switch_defer_inline 1.83% : 0.000014s : 92: predicate.switch_layer_defer_inline 10.01% : 0.000078s : 307: predicate.switch_simplify 1.40% : 0.000011s : 68: predicate.tile_eliminate 1.22% : 0.000010s : 68: predicate.transpose_eliminate 1.60% : 0.000012s : 68: predicate.tuple_list_convert_item_index_to_positive 1.54% : 0.000012s : 68: predicate.tuple_list_get_item_depend_reorder 2.95% : 0.000023s : 95: predicate.tuple_list_get_item_eliminator 1.51% : 0.000012s : 68: predicate.tuple_list_set_item_eliminator 1.48% : 0.000012s : 77: predicate.tuple_to_list_eliminator_ 1.55% : 0.000012s : 86: predicate.updatestate_pure_node_eliminater 2.91% : 0.000023s : 128: predicate.updatestate_useless_node_eliminater 1.74% : 0.000014s : 67: predicate.value_based_eliminate 0.16% : 0.000001s : 9: predicate.virtual_view_grad_eliminate 0.25% : 0.000002s : 9: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.007633 75 71.20% : 0.005434s : 43: func_graph_cloner_run.FuncGraphClonerGraph 28.80% : 0.002198s : 32: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 2.186963 89 0.01% : 0.000114s : 1: add_recomputation 0.03% : 0.000564s : 1: auto_monad 0.00% : 0.000051s : 1: auto_monad_reorder 0.05% : 0.001030s : 1: bootstrap 0.00% : 0.000055s : 1: cconv 0.00% : 0.000041s : 1: convert_after_rewriter 0.00% : 0.000056s : 1: cse_after_recomputation 0.00% : 0.000027s : 1: environ_conv 0.03% : 0.000584s : 1: event_method 0.00% : 0.000009s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 20.63% : 0.451190s : 1: jit_opt_a 0.02% : 0.000337s : 1: jit_opt_after_cconv 0.01% : 0.000116s : 1: jit_opt_b 0.03% : 0.000558s : 1: loop_unroll 0.23% : 0.005078s : 1: mutable_eliminate 5.23% : 0.114344s : 39: opt.transform.jit_opt_a 0.01% : 0.000135s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000072s : 4: opt.transform.jit_opt_b 0.00% : 0.000026s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000059s : 1: opt.transform.mutable_eliminate 0.00% : 0.000050s : 1: opt.transform.opt_after_jit_grad 0.05% : 0.001166s : 2: opt.transform.opt_resolve 0.00% : 0.000078s : 4: opt.transform.symbol_engine_opt 0.03% : 0.000650s : 1: opt_after_jit_grad 0.00% : 0.000012s : 1: order_py_execute_after_rewriter 0.00% : 0.000006s : 1: partial_unused_args_eliminate 0.00% : 0.000018s : 1: pre_auto_parallel 0.00% : 0.000079s : 1: py_interpret_to_execute 0.00% : 0.000033s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000062s : 1: remove_dup_value 10.96% : 0.239741s : 2: renormalize.infer 0.29% : 0.006325s : 2: renormalize.specialize 0.01% : 0.000253s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000200s : 1: rewriter_after_opt_a 0.01% : 0.000306s : 1: rewriter_before_opt_a 0.01% : 0.000139s : 1: symbol_engine_optimizer 62.34% : 1.363377s : 1: type_inference . [hook] pytest_runtest_teardown:test_reshape_view_backward[KBK] tests/st/mint/test_reshape.py::test_reshape_view_backward[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 262.32s (0:04:22) ==================