==================================================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/host/view, 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_view_ops.py TotalTime = 1.19537, [30] [bootstrap]: 0.00068988 [type_inference]: 0.998463 [event_method]: 0.111312 [auto_monad]: 0.00042024 [graph_reusing]: 9.42999e-06 [pre_auto_parallel]: 1.556e-05 [py_interpret_to_execute]: 0.00054568 [rewriter_before_opt_a]: 0.00015838 [expand_dump_flag]: 5.09998e-06 [jit_opt_a]: 0.0150496, [2] [Cycle 1]: 0.00596673, [27] [switch_simplify]: 0.00019466 [loop_unroll]: 4.421e-05 [a_1]: 0.00115379 [with_stream_mark]: 4.073e-05 [recompute_prepare]: 2.666e-05 [updatestate_depend_eliminate]: 5.282e-05 [updatestate_assign_eliminate]: 9.41003e-06 [updatestate_loads_eliminate]: 8.39002e-06 [parameter_eliminate]: 3.68e-06 [specialize_transform]: 2e-05 [updatestate_useless_node_eliminater]: 2.414e-05 [accelerated_algorithm]: 1.563e-05 [meta_shard_fg_expand]: 3.51001e-06 [get_grad_eliminate_]: 1.507e-05 [merge_forward]: 8.94e-06 [cell_reuse_recompute_pass]: 2.39999e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.126e-05 [j_node_and_user_rematch]: 2.627e-05 [meta_fg_expand]: 5.89999e-06 [replace_old_param]: 2.024e-05 [inline_without_move]: 1.591e-05 [renormalize]: 0.00367595 [add_forward_monad_depend]: 1.918e-05 [auto_monad_grad]: 2.46e-06 [auto_monad_eliminator]: 4.072e-05 [cse]: 0.00015437 [replace_applicator]: 3.66e-05 [Cycle 2]: 0.00091922, [27] [switch_simplify]: 1.78e-05 [loop_unroll]: 1.475e-05 [a_1]: 0.0003705 [with_stream_mark]: 2.975e-05 [recompute_prepare]: 1.915e-05 [updatestate_depend_eliminate]: 1.021e-05 [updatestate_assign_eliminate]: 8.55001e-06 [updatestate_loads_eliminate]: 7.55998e-06 [parameter_eliminate]: 2.43e-06 [specialize_transform]: 1.569e-05 [updatestate_useless_node_eliminater]: 2.098e-05 [accelerated_algorithm]: 1.618e-05 [meta_shard_fg_expand]: 3.98001e-06 [get_grad_eliminate_]: 1.426e-05 [merge_forward]: 9.26002e-06 [cell_reuse_recompute_pass]: 2.54999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.983e-05 [j_node_and_user_rematch]: 2.389e-05 [meta_fg_expand]: 5.51e-06 [replace_old_param]: 1.892e-05 [inline_without_move]: 1.549e-05 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 2.82002e-06 [auto_monad_grad]: 1.62999e-06 [auto_monad_eliminator]: 2.686e-05 [cse]: 5.392e-05 [replace_applicator]: 1.976e-05 [py_interpret_to_execute_after_opt_a]: 2.556e-05 [rewriter_after_opt_a]: 0.00026296 [convert_after_rewriter]: 3.464e-05 [order_py_execute_after_rewriter]: 1.191e-05 [mutable_eliminate]: 0.00082561 [jit_opt_b]: 0.00011753, [1] [Cycle 1]: 0.00010685, [2] [frontend_op_eliminate]: 4.386e-05 [inline_after_opt_a]: 4.818e-05 [cconv]: 4.354e-05 [loop_unroll]: 0.00050382 [jit_opt_after_cconv]: 0.00035736, [1] [Cycle 1]: 0.00034991, [11] [c_1]: 9.109e-05 [parameter_eliminate]: 6.27001e-06 [updatestate_depend_eliminate]: 1.778e-05 [updatestate_assign_eliminate]: 8.32e-06 [updatestate_loads_eliminate]: 7.73001e-06 [cse]: 8.464e-05 [call_graph_tuple_transform]: 4.161e-05 [tuple_list_get_item_eliminator]: 1.548e-05 [none_parameter_eliminate]: 2.09999e-06 [renormalize]: 7.20029e-07 [switch_simplify]: 1.572e-05 [remove_dup_value]: 9.06e-05 [partial_unused_args_eliminate]: 2.81999e-06 [environ_conv]: 3.088e-05 [add_recomputation]: 0.00011091 [cse_after_recomputation]: 5.318e-05, [1] [Cycle 1]: 4.612e-05, [1] [cse]: 3.539e-05 [auto_monad_reorder]: 4.671e-05 [get_jit_bprop_graph]: 2.31e-06 [rewriter_after_jit_bprop_graph]: 5.25999e-06 [opt_after_jit_grad]: 0.00056451 [symbol_engine_optimizer]: 0.00012876, [1] [Cycle 1]: 0.00012214, [6] [build]: 7.72002e-06 [elim_shapecalc]: 1.77e-05 [elim_not_effective]: 2.937e-05 [opt_reshape]: 1.486e-05 [fold_const_symbol]: 2.213e-05 [renormalize]: 4.59986e-07 [validate]: 9.65e-05 Sums bootstrap : 0.000690s : 0.06% type_inference : 0.998463s : 89.05% event_method : 0.111312s : 9.93% auto_monad : 0.000420s : 0.04% graph_reusing : 0.000009s : 0.00% pre_auto_parallel : 0.000016s : 0.00% py_interpret_to_execute : 0.000546s : 0.05% rewriter_before_opt_a : 0.000158s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000212s : 0.02% jit_opt_a.loop_unroll : 0.000059s : 0.01% jit_opt_a.a_1 : 0.001524s : 0.14% jit_opt_a.with_stream_mark : 0.000070s : 0.01% jit_opt_a.recompute_prepare : 0.000046s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000063s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000018s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000016s : 0.00% jit_opt_a.parameter_eliminate : 0.000006s : 0.00% jit_opt_a.specialize_transform : 0.000036s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000045s : 0.00% jit_opt_a.accelerated_algorithm : 0.000032s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000007s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000029s : 0.00% jit_opt_a.merge_forward : 0.000018s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000071s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000050s : 0.00% jit_opt_a.meta_fg_expand : 0.000011s : 0.00% jit_opt_a.replace_old_param : 0.000039s : 0.00% jit_opt_a.inline_without_move : 0.000031s : 0.00% jit_opt_a.renormalize : 0.003676s : 0.33% jit_opt_a.add_forward_monad_depend : 0.000022s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000068s : 0.01% jit_opt_a.cse : 0.000208s : 0.02% jit_opt_a.replace_applicator : 0.000056s : 0.01% py_interpret_to_execute_after_opt_a : 0.000026s : 0.00% rewriter_after_opt_a : 0.000263s : 0.02% convert_after_rewriter : 0.000035s : 0.00% order_py_execute_after_rewriter : 0.000012s : 0.00% mutable_eliminate : 0.000826s : 0.07% jit_opt_b.frontend_op_eliminate : 0.000044s : 0.00% jit_opt_b.inline_after_opt_a : 0.000048s : 0.00% cconv : 0.000044s : 0.00% loop_unroll : 0.000504s : 0.04% jit_opt_after_cconv.c_1 : 0.000091s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000018s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.cse : 0.000085s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000042s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000015s : 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 : 0.000016s : 0.00% remove_dup_value : 0.000091s : 0.01% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000031s : 0.00% add_recomputation : 0.000111s : 0.01% cse_after_recomputation.cse : 0.000035s : 0.00% auto_monad_reorder : 0.000047s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000005s : 0.00% opt_after_jit_grad : 0.000565s : 0.05% symbol_engine_optimizer.build : 0.000008s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000018s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000029s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000015s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000022s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000096s : 0.01% Time group info: ------[substitution.] 0.000398 96 5.45% : 0.000022s : 10: substitution.depend_value_elim 0.97% : 0.000004s : 7: substitution.elim_not_effective 0.79% : 0.000003s : 7: substitution.fold_const_symbol 3.05% : 0.000012s : 12: substitution.graph_param_transform 65.89% : 0.000262s : 7: substitution.inline 2.09% : 0.000008s : 14: substitution.j_node_and_user_rematch 2.76% : 0.000011s : 14: substitution.remove_not_recompute_node 2.05% : 0.000008s : 2: substitution.replace_old_param 5.93% : 0.000024s : 1: substitution.switch_simplify 5.53% : 0.000022s : 9: substitution.updatestate_pure_node_eliminater 5.50% : 0.000022s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.998365 2 99.75% : 0.995820s : 1: type_inference.infer 0.25% : 0.002545s : 1: type_inference.specialize ------[replace.] 0.000122 8 56.14% : 0.000068s : 7: replace.inline 43.86% : 0.000053s : 1: replace.switch_simplify ------[match.] 0.000279 8 91.92% : 0.000257s : 7: match.inline 8.08% : 0.000023s : 1: match.switch_simplify ------[predicate.] 0.000334 2280 1.43% : 0.000005s : 36: predicate.accumulaten_eliminater 1.01% : 0.000003s : 11: predicate.ad_related_special_op_eliminate 1.27% : 0.000004s : 36: predicate.addn_check_dump 1.43% : 0.000005s : 36: predicate.addn_zero_filter 1.94% : 0.000006s : 36: predicate.arithmetic_simplify 1.36% : 0.000005s : 36: predicate.cast_eliminate 0.51% : 0.000002s : 12: predicate.check_bprop_eliminate 1.24% : 0.000004s : 36: predicate.compare_switch_simplify 1.55% : 0.000005s : 36: predicate.depend_value_elim 1.29% : 0.000004s : 36: predicate.dict_get_item_const_eliminator 1.38% : 0.000005s : 36: predicate.dict_get_item_eliminator 1.30% : 0.000004s : 36: predicate.dict_set_item_eliminator 0.75% : 0.000003s : 11: predicate.dumpgradient_eliminate 0.31% : 0.000001s : 11: predicate.elim_not_effective 0.57% : 0.000002s : 11: predicate.elim_shapecalc_of_broadcastargs 1.32% : 0.000004s : 36: predicate.environ_add_const_eliminate 1.27% : 0.000004s : 36: predicate.environ_get_add_eliminate 1.35% : 0.000005s : 36: predicate.environ_get_depend_swap 1.30% : 0.000004s : 36: predicate.environ_get_eliminate 1.32% : 0.000004s : 36: predicate.environ_get_set_eliminate 0.26% : 0.000001s : 11: predicate.fold_const_symbol 1.05% : 0.000004s : 24: predicate.get_grad_eliminate 0.33% : 0.000001s : 12: predicate.graph_param_transform 4.93% : 0.000016s : 67: predicate.inline 1.34% : 0.000004s : 24: predicate.inline_without_move 0.54% : 0.000002s : 24: predicate.j_node_and_user_rematch 1.25% : 0.000004s : 24: predicate.less_batch_normalization 1.43% : 0.000005s : 36: predicate.list_to_tuple_eliminator_ 1.79% : 0.000006s : 48: predicate.load_eliminater 0.90% : 0.000003s : 12: predicate.loop_unroll_after_grad 2.47% : 0.000008s : 54: predicate.loop_unroll_before_grad 1.96% : 0.000007s : 48: predicate.make_slice_get_slice_eliminator 1.29% : 0.000004s : 36: predicate.merge_addn 1.33% : 0.000004s : 36: predicate.minmaximum_grad 1.56% : 0.000005s : 12: predicate.mutable_eliminate 0.50% : 0.000002s : 11: predicate.opt_reshape 2.39% : 0.000008s : 48: predicate.partial_eliminate 1.31% : 0.000004s : 36: predicate.print_const_string_wrapper 1.85% : 0.000006s : 36: predicate.reduce_eliminate 1.51% : 0.000005s : 36: predicate.redundant_stop_gradient_eliminater 0.77% : 0.000003s : 24: predicate.remove_not_recompute_node 2.27% : 0.000008s : 60: predicate.replace_applicator 0.62% : 0.000002s : 24: predicate.replace_old_param 0.45% : 0.000002s : 12: predicate.reset_defer_inline 1.32% : 0.000004s : 36: predicate.reshape_eliminate 1.49% : 0.000005s : 36: predicate.row_tensor_add_zeros_like 0.73% : 0.000002s : 12: predicate.row_tensor_eliminate 1.33% : 0.000004s : 36: predicate.same_eliminate 0.84% : 0.000003s : 26: predicate.set_cell_output_no_recompute 0.99% : 0.000003s : 23: predicate.special_op_eliminate 1.33% : 0.000004s : 24: predicate.specialize_transform 1.52% : 0.000005s : 36: predicate.split_environ_get_set_with_tuple_value 1.34% : 0.000004s : 36: predicate.stack_unstack_eliminate 0.56% : 0.000002s : 12: predicate.switch_call_monad_eliminater 2.03% : 0.000007s : 43: predicate.switch_defer_inline 1.76% : 0.000006s : 43: predicate.switch_layer_defer_inline 6.23% : 0.000021s : 111: predicate.switch_simplify 1.45% : 0.000005s : 36: predicate.tile_eliminate 1.53% : 0.000005s : 36: predicate.transpose_eliminate 1.56% : 0.000005s : 36: predicate.tuple_list_convert_item_index_to_positive 1.47% : 0.000005s : 36: predicate.tuple_list_get_item_depend_reorder 3.48% : 0.000012s : 60: predicate.tuple_list_get_item_eliminator 1.56% : 0.000005s : 36: predicate.tuple_list_set_item_eliminator 1.36% : 0.000005s : 36: predicate.tuple_to_list_eliminator_ 1.87% : 0.000006s : 48: predicate.updatestate_pure_node_eliminater 3.39% : 0.000011s : 72: predicate.updatestate_useless_node_eliminater 1.71% : 0.000006s : 36: predicate.value_based_eliminate 0.42% : 0.000001s : 11: predicate.virtual_view_grad_eliminate 0.70% : 0.000002s : 12: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.064161 38 98.58% : 0.063251s : 29: func_graph_cloner_run.FuncGraphClonerGraph 1.42% : 0.000911s : 9: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.136372 72 0.01% : 0.000115s : 1: add_recomputation 0.04% : 0.000432s : 1: auto_monad 0.00% : 0.000051s : 1: auto_monad_reorder 0.06% : 0.000719s : 1: bootstrap 0.00% : 0.000047s : 1: cconv 0.00% : 0.000040s : 1: convert_after_rewriter 0.00% : 0.000055s : 1: cse_after_recomputation 0.00% : 0.000034s : 1: environ_conv 9.80% : 0.111344s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000014s : 1: graph_reusing 1.32% : 0.015054s : 1: jit_opt_a 0.03% : 0.000361s : 1: jit_opt_after_cconv 0.01% : 0.000121s : 1: jit_opt_b 0.05% : 0.000513s : 1: loop_unroll 0.07% : 0.000840s : 1: mutable_eliminate 0.19% : 0.002175s : 26: opt.transform.jit_opt_a 0.01% : 0.000160s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000084s : 4: opt.transform.jit_opt_b 0.00% : 0.000024s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000034s : 1: opt.transform.mutable_eliminate 0.00% : 0.000052s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000080s : 4: opt.transform.symbol_engine_opt 0.05% : 0.000573s : 1: opt_after_jit_grad 0.00% : 0.000015s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000019s : 1: pre_auto_parallel 0.05% : 0.000556s : 1: py_interpret_to_execute 0.00% : 0.000030s : 1: py_interpret_to_execute_after_opt_a 0.01% : 0.000094s : 1: remove_dup_value 0.22% : 0.002549s : 1: renormalize.infer 0.10% : 0.001112s : 1: renormalize.specialize 0.00% : 0.000007s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000270s : 1: rewriter_after_opt_a 0.01% : 0.000167s : 1: rewriter_before_opt_a 0.01% : 0.000132s : 1: symbol_engine_optimizer 87.87% : 0.998483s : 1: type_inference . [hook] pytest_runtest_teardown:test_graph_view_to_aclop tests/st/ops/host/view/test_view_ops.py::test_graph_view_to_aclop,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") test_view_ops.py::test_graph_view_to_aclop /usr/local/Ascend/cann-8.5.0/python/site-packages/asc_op_compile_base/asc_op_compiler/ascendc_compile_gen_code.py:161: DeprecationWarning: invalid escape sequence \w match = re.search(f'{option}=(\w+)', ' '.join(compile_options)) -- Docs: https://docs.pytest.org/en/stable/warnings.html ================== 1 passed, 26 warnings in 125.42s (0:02:05) ==================