==================================================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_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 2 items test_squeeze.py . [hook] pytest_runtest_teardown:test_squeeze_high_dimension[pynative] tests/st/mint/test_squeeze.py::test_squeeze_high_dimension[pynative],max_mem:2.0M [WARNING] PARSER(168411,ffffa9dcaf30,python3.9):2026-01-29-17:39:08.283.767 [mindspore/ccsrc/frontend/jit/ps/parse/data_converter.cc:661] CheckAPI] The mint interface squeeze was called, and the operators under this interface have different view capabilities on pynative and graph mode. Use this interface with caution in graph mode, as it may produce unexpected results. For more information, please refer to: https://www.mindspore.cn/docs/en/master/features/view.html TotalTime = 2.76012, [30] [bootstrap]: 0.00060432 [type_inference]: 2.51001 [event_method]: 2.241e-05 [auto_monad]: 0.00011895 [graph_reusing]: 7.28999e-06 [pre_auto_parallel]: 1.167e-05 [py_interpret_to_execute]: 0.00053833 [rewriter_before_opt_a]: 9.968e-05 [expand_dump_flag]: 3.56999e-06 [jit_opt_a]: 0.245569, [2] [Cycle 1]: 0.00435341, [27] [switch_simplify]: 7.037e-05 [loop_unroll]: 2.706e-05 [a_1]: 0.00070262 [with_stream_mark]: 0.00015789 [recompute_prepare]: 1.466e-05 [updatestate_depend_eliminate]: 5.12999e-06 [updatestate_assign_eliminate]: 4.07e-06 [updatestate_loads_eliminate]: 3.93001e-06 [parameter_eliminate]: 1.94999e-06 [specialize_transform]: 8.35001e-06 [updatestate_useless_node_eliminater]: 7.03e-06 [accelerated_algorithm]: 9.07001e-06 [meta_shard_fg_expand]: 2.54999e-06 [get_grad_eliminate_]: 7.38999e-06 [merge_forward]: 3.93001e-06 [cell_reuse_recompute_pass]: 1.74e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.237e-05 [j_node_and_user_rematch]: 1.162e-05 [meta_fg_expand]: 3.01001e-06 [replace_old_param]: 1.326e-05 [inline_without_move]: 7.99997e-06 [renormalize]: 0.0029173 [add_forward_monad_depend]: 9.31998e-06 [auto_monad_grad]: 3.14999e-06 [auto_monad_eliminator]: 2.19e-05 [cse]: 3.711e-05 [replace_applicator]: 2.16e-05 [Cycle 2]: 0.00045347, [27] [switch_simplify]: 7.23e-06 [loop_unroll]: 6.47001e-06 [a_1]: 0.00013052 [with_stream_mark]: 1.546e-05 [recompute_prepare]: 6.91001e-06 [updatestate_depend_eliminate]: 4.429e-05 [updatestate_assign_eliminate]: 3.46001e-06 [updatestate_loads_eliminate]: 2.79999e-06 [parameter_eliminate]: 2.17999e-06 [specialize_transform]: 6.35002e-06 [updatestate_useless_node_eliminater]: 5.94e-06 [accelerated_algorithm]: 6.39999e-06 [meta_shard_fg_expand]: 2.08998e-06 [get_grad_eliminate_]: 5.52001e-06 [merge_forward]: 3.83999e-06 [cell_reuse_recompute_pass]: 3.51999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.072e-05 [j_node_and_user_rematch]: 9.81998e-06 [meta_fg_expand]: 2.11998e-06 [replace_old_param]: 1.043e-05 [inline_without_move]: 6.02999e-06 [renormalize]: 6.00121e-08 [add_forward_monad_depend]: 1.66e-06 [auto_monad_grad]: 1.13001e-06 [auto_monad_eliminator]: 6.89999e-06 [cse]: 1.348e-05 [replace_applicator]: 5.63997e-06 [py_interpret_to_execute_after_opt_a]: 1.552e-05 [rewriter_after_opt_a]: 5.612e-05 [convert_after_rewriter]: 2.614e-05 [order_py_execute_after_rewriter]: 6.21e-06 [mutable_eliminate]: 0.00080672 [jit_opt_b]: 0.000108, [1] [Cycle 1]: 9.831e-05, [2] [frontend_op_eliminate]: 2.021e-05 [inline_after_opt_a]: 6.472e-05 [cconv]: 3.454e-05 [loop_unroll]: 0.00051904 [jit_opt_after_cconv]: 0.00017926, [1] [Cycle 1]: 0.00017099, [11] [c_1]: 2.879e-05 [parameter_eliminate]: 4.48001e-06 [updatestate_depend_eliminate]: 8.84e-06 [updatestate_assign_eliminate]: 3.08e-06 [updatestate_loads_eliminate]: 2.84999e-06 [cse]: 2.992e-05 [call_graph_tuple_transform]: 2.332e-05 [tuple_list_get_item_eliminator]: 6.92002e-06 [none_parameter_eliminate]: 1.67001e-06 [renormalize]: 9.60019e-07 [switch_simplify]: 6.64001e-06 [remove_dup_value]: 1.757e-05 [partial_unused_args_eliminate]: 3.07997e-06 [environ_conv]: 6.139e-05 [add_recomputation]: 6.167e-05 [cse_after_recomputation]: 2.917e-05, [1] [Cycle 1]: 2.232e-05, [1] [cse]: 1.39e-05 [auto_monad_reorder]: 2.488e-05 [get_jit_bprop_graph]: 2.59001e-06 [rewriter_after_jit_bprop_graph]: 0.00013819 [opt_after_jit_grad]: 0.00055697 [symbol_engine_optimizer]: 8.31e-05, [1] [Cycle 1]: 7.6e-05, [6] [build]: 4.74e-06 [elim_shapecalc]: 1.004e-05 [elim_not_effective]: 1.485e-05 [opt_reshape]: 7.23999e-06 [fold_const_symbol]: 9.42001e-06 [renormalize]: 4.00003e-07 [validate]: 6.471e-05 Sums bootstrap : 0.000604s : 0.02% type_inference : 2.510015s : 99.66% event_method : 0.000022s : 0.00% auto_monad : 0.000119s : 0.00% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000012s : 0.00% py_interpret_to_execute : 0.000538s : 0.02% rewriter_before_opt_a : 0.000100s : 0.00% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000078s : 0.00% jit_opt_a.loop_unroll : 0.000034s : 0.00% jit_opt_a.a_1 : 0.000833s : 0.03% jit_opt_a.with_stream_mark : 0.000173s : 0.01% jit_opt_a.recompute_prepare : 0.000022s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000049s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000008s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000007s : 0.00% jit_opt_a.parameter_eliminate : 0.000004s : 0.00% jit_opt_a.specialize_transform : 0.000015s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000013s : 0.00% jit_opt_a.accelerated_algorithm : 0.000015s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000005s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000013s : 0.00% jit_opt_a.merge_forward : 0.000008s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000043s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000021s : 0.00% jit_opt_a.meta_fg_expand : 0.000005s : 0.00% jit_opt_a.replace_old_param : 0.000024s : 0.00% jit_opt_a.inline_without_move : 0.000014s : 0.00% jit_opt_a.renormalize : 0.002917s : 0.12% jit_opt_a.add_forward_monad_depend : 0.000011s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000029s : 0.00% jit_opt_a.cse : 0.000051s : 0.00% jit_opt_a.replace_applicator : 0.000027s : 0.00% py_interpret_to_execute_after_opt_a : 0.000016s : 0.00% rewriter_after_opt_a : 0.000056s : 0.00% convert_after_rewriter : 0.000026s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.000807s : 0.03% jit_opt_b.frontend_op_eliminate : 0.000020s : 0.00% jit_opt_b.inline_after_opt_a : 0.000065s : 0.00% cconv : 0.000035s : 0.00% loop_unroll : 0.000519s : 0.02% jit_opt_after_cconv.c_1 : 0.000029s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000030s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000023s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000007s : 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 : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000061s : 0.00% add_recomputation : 0.000062s : 0.00% cse_after_recomputation.cse : 0.000014s : 0.00% auto_monad_reorder : 0.000025s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000138s : 0.01% opt_after_jit_grad : 0.000557s : 0.02% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000010s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000015s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000007s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000009s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000065s : 0.00% Time group info: ------[substitution.] 0.000207 23 1.27% : 0.000003s : 2: substitution.elim_not_effective 0.61% : 0.000001s : 2: substitution.fold_const_symbol 3.17% : 0.000007s : 4: substitution.graph_param_transform 81.15% : 0.000168s : 4: substitution.inline 2.06% : 0.000004s : 4: substitution.j_node_and_user_rematch 2.71% : 0.000006s : 4: substitution.remove_not_recompute_node 3.36% : 0.000007s : 2: substitution.replace_old_param 5.66% : 0.000012s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 2.509914 2 99.89% : 2.507063s : 1: type_inference.infer 0.11% : 0.002851s : 1: type_inference.specialize ------[replace.] 0.000056 5 77.25% : 0.000043s : 4: replace.inline 22.75% : 0.000013s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000176 5 93.98% : 0.000166s : 4: match.inline 6.02% : 0.000011s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000150 801 1.22% : 0.000002s : 12: predicate.accumulaten_eliminater 1.07% : 0.000002s : 4: predicate.ad_related_special_op_eliminate 1.20% : 0.000002s : 12: predicate.addn_check_dump 1.61% : 0.000002s : 12: predicate.addn_zero_filter 2.48% : 0.000004s : 12: predicate.arithmetic_simplify 1.44% : 0.000002s : 12: predicate.cast_eliminate 0.46% : 0.000001s : 4: predicate.check_bprop_eliminate 1.28% : 0.000002s : 12: predicate.compare_switch_simplify 1.23% : 0.000002s : 12: predicate.depend_value_elim 1.14% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.19% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.56% : 0.000002s : 12: predicate.dict_set_item_eliminator 0.86% : 0.000001s : 4: predicate.dumpgradient_eliminate 0.47% : 0.000001s : 4: predicate.elim_not_effective 0.56% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 1.45% : 0.000002s : 12: predicate.environ_add_const_eliminate 1.44% : 0.000002s : 12: predicate.environ_get_add_eliminate 1.18% : 0.000002s : 12: predicate.environ_get_depend_swap 1.54% : 0.000002s : 12: predicate.environ_get_eliminate 1.24% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.25% : 0.000000s : 4: predicate.fold_const_symbol 1.02% : 0.000002s : 8: predicate.get_grad_eliminate 0.26% : 0.000000s : 4: predicate.graph_param_transform 4.94% : 0.000007s : 25: predicate.inline 1.08% : 0.000002s : 8: predicate.inline_without_move 0.45% : 0.000001s : 8: predicate.j_node_and_user_rematch 1.20% : 0.000002s : 8: predicate.less_batch_normalization 1.68% : 0.000003s : 13: predicate.list_to_tuple_eliminator_ 1.82% : 0.000003s : 17: predicate.load_eliminater 1.48% : 0.000002s : 4: predicate.loop_unroll_after_grad 2.97% : 0.000004s : 28: predicate.loop_unroll_before_grad 2.07% : 0.000003s : 16: predicate.make_slice_get_slice_eliminator 1.04% : 0.000002s : 12: predicate.merge_addn 1.10% : 0.000002s : 12: predicate.minmaximum_grad 2.09% : 0.000003s : 4: predicate.mutable_eliminate 0.55% : 0.000001s : 4: predicate.opt_reshape 2.02% : 0.000003s : 17: predicate.partial_eliminate 1.24% : 0.000002s : 12: predicate.print_const_string_wrapper 1.72% : 0.000003s : 12: predicate.reduce_eliminate 1.45% : 0.000002s : 13: predicate.redundant_stop_gradient_eliminater 0.67% : 0.000001s : 8: predicate.remove_not_recompute_node 2.03% : 0.000003s : 21: predicate.replace_applicator 0.88% : 0.000001s : 8: predicate.replace_old_param 0.49% : 0.000001s : 4: predicate.reset_defer_inline 1.54% : 0.000002s : 12: predicate.reshape_eliminate 1.29% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 1.02% : 0.000002s : 4: predicate.row_tensor_eliminate 1.27% : 0.000002s : 12: predicate.same_eliminate 0.69% : 0.000001s : 8: predicate.set_cell_output_no_recompute 0.82% : 0.000001s : 8: predicate.special_op_eliminate 1.23% : 0.000002s : 8: predicate.specialize_transform 1.69% : 0.000003s : 12: predicate.split_environ_get_set_with_tuple_value 1.35% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.41% : 0.000001s : 4: predicate.switch_call_monad_eliminater 2.38% : 0.000004s : 17: predicate.switch_defer_inline 1.97% : 0.000003s : 17: predicate.switch_layer_defer_inline 6.18% : 0.000009s : 49: predicate.switch_simplify 1.32% : 0.000002s : 12: predicate.tile_eliminate 1.24% : 0.000002s : 12: predicate.transpose_eliminate 1.43% : 0.000002s : 12: predicate.tuple_list_convert_item_index_to_positive 1.28% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 3.85% : 0.000006s : 21: predicate.tuple_list_get_item_eliminator 1.63% : 0.000002s : 12: predicate.tuple_list_set_item_eliminator 1.38% : 0.000002s : 13: predicate.tuple_to_list_eliminator_ 1.62% : 0.000002s : 17: predicate.updatestate_pure_node_eliminater 2.79% : 0.000004s : 25: predicate.updatestate_useless_node_eliminater 1.53% : 0.000002s : 12: predicate.value_based_eliminate 0.36% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.60% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.002648 18 73.53% : 0.001947s : 12: func_graph_cloner_run.FuncGraphClonerGraph 26.47% : 0.000701s : 6: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 2.764136 72 0.00% : 0.000065s : 1: add_recomputation 0.00% : 0.000123s : 1: auto_monad 0.00% : 0.000028s : 1: auto_monad_reorder 0.02% : 0.000633s : 1: bootstrap 0.00% : 0.000037s : 1: cconv 0.00% : 0.000029s : 1: convert_after_rewriter 0.00% : 0.000032s : 1: cse_after_recomputation 0.00% : 0.000065s : 1: environ_conv 0.00% : 0.000028s : 1: event_method 0.00% : 0.000006s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000010s : 1: graph_reusing 8.88% : 0.245573s : 1: jit_opt_a 0.01% : 0.000182s : 1: jit_opt_after_cconv 0.00% : 0.000111s : 1: jit_opt_b 0.02% : 0.000530s : 1: loop_unroll 0.03% : 0.000819s : 1: mutable_eliminate 0.04% : 0.001101s : 26: opt.transform.jit_opt_a 0.00% : 0.000062s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000078s : 4: opt.transform.jit_opt_b 0.00% : 0.000015s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000020s : 1: opt.transform.mutable_eliminate 0.00% : 0.000026s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000038s : 4: opt.transform.symbol_engine_opt 0.02% : 0.000568s : 1: opt_after_jit_grad 0.00% : 0.000009s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000014s : 1: pre_auto_parallel 0.02% : 0.000549s : 1: py_interpret_to_execute 0.00% : 0.000018s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000020s : 1: remove_dup_value 0.08% : 0.002088s : 1: renormalize.infer 0.03% : 0.000817s : 1: renormalize.specialize 0.01% : 0.000142s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000060s : 1: rewriter_after_opt_a 0.00% : 0.000105s : 1: rewriter_before_opt_a 0.00% : 0.000086s : 1: symbol_engine_optimizer 90.81% : 2.510042s : 1: type_inference TotalTime = 2.91112, [30] [bootstrap]: 0.00044713 [type_inference]: 2.69365 [event_method]: 0.00080614 [auto_monad]: 0.00017231 [graph_reusing]: 1.024e-05 [pre_auto_parallel]: 3.63e-06 [py_interpret_to_execute]: 4.835e-05 [rewriter_before_opt_a]: 0.00016018 [expand_dump_flag]: 4.28999e-06 [jit_opt_a]: 0.21285, [2] [Cycle 1]: 0.207754, [27] [switch_simplify]: 0.00021102 [loop_unroll]: 5.915e-05 [a_1]: 0.00147553 [with_stream_mark]: 0.00017318 [recompute_prepare]: 3.653e-05 [updatestate_depend_eliminate]: 1.164e-05 [updatestate_assign_eliminate]: 8.24002e-06 [updatestate_loads_eliminate]: 7.21999e-06 [parameter_eliminate]: 4.78001e-06 [specialize_transform]: 1.591e-05 [updatestate_useless_node_eliminater]: 1.416e-05 [accelerated_algorithm]: 1.559e-05 [meta_shard_fg_expand]: 6.63e-06 [get_grad_eliminate_]: 1.65e-05 [merge_forward]: 9.51998e-06 [cell_reuse_recompute_pass]: 1.37999e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.253e-05 [j_node_and_user_rematch]: 2.596e-05 [meta_fg_expand]: 0.00256262 [replace_old_param]: 7.947e-05 [inline_without_move]: 6.784e-05 [renormalize]: 0.202476 [add_forward_monad_depend]: 8.43999e-06 [auto_monad_grad]: 2.92002e-06 [auto_monad_eliminator]: 2.068e-05 [cse]: 3.278e-05 [replace_applicator]: 2.536e-05 [Cycle 2]: 0.00047488, [27] [switch_simplify]: 5.37999e-06 [loop_unroll]: 5.82001e-06 [a_1]: 6.245e-05 [with_stream_mark]: 1.496e-05 [recompute_prepare]: 4.34002e-06 [updatestate_depend_eliminate]: 4.21001e-06 [updatestate_assign_eliminate]: 2.78e-06 [updatestate_loads_eliminate]: 2.11e-06 [parameter_eliminate]: 2.44001e-06 [specialize_transform]: 3.98001e-06 [updatestate_useless_node_eliminater]: 3.77002e-06 [accelerated_algorithm]: 4.17e-06 [meta_shard_fg_expand]: 2.21e-06 [get_grad_eliminate_]: 3.81001e-06 [merge_forward]: 4.24997e-06 [cell_reuse_recompute_pass]: 3.34001e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.726e-05 [j_node_and_user_rematch]: 7.41999e-06 [meta_fg_expand]: 0.00015208 [replace_old_param]: 7.87998e-06 [inline_without_move]: 4.02e-06 [renormalize]: 1.10012e-07 [add_forward_monad_depend]: 2.52001e-06 [auto_monad_grad]: 1.49998e-06 [auto_monad_eliminator]: 7.55e-06 [cse]: 1.474e-05 [replace_applicator]: 4.05e-06 [py_interpret_to_execute_after_opt_a]: 1.486e-05 [rewriter_after_opt_a]: 0.00036748 [convert_after_rewriter]: 1.251e-05 [order_py_execute_after_rewriter]: 5.07999e-06 [mutable_eliminate]: 0.00079574 [jit_opt_b]: 6.819e-05, [1] [Cycle 1]: 5.855e-05, [2] [frontend_op_eliminate]: 1.337e-05 [inline_after_opt_a]: 1.791e-05 [cconv]: 3.46e-05 [loop_unroll]: 0.0004759 [jit_opt_after_cconv]: 0.00016142, [1] [Cycle 1]: 0.00015446, [11] [c_1]: 1.552e-05 [parameter_eliminate]: 4.35999e-06 [updatestate_depend_eliminate]: 7.85e-06 [updatestate_assign_eliminate]: 2.89001e-06 [updatestate_loads_eliminate]: 3.17997e-06 [cse]: 3.155e-05 [call_graph_tuple_transform]: 2.075e-05 [tuple_list_get_item_eliminator]: 4.52e-06 [none_parameter_eliminate]: 1.68002e-06 [renormalize]: 5.00004e-07 [switch_simplify]: 5.01002e-06 [remove_dup_value]: 1.884e-05 [partial_unused_args_eliminate]: 2.76e-06 [environ_conv]: 6.07999e-06 [add_recomputation]: 4.658e-05 [cse_after_recomputation]: 2.531e-05, [1] [Cycle 1]: 1.742e-05, [1] [cse]: 9.92999e-06 [auto_monad_reorder]: 1.452e-05 [get_jit_bprop_graph]: 2.66999e-06 [rewriter_after_jit_bprop_graph]: 7.86001e-06 [opt_after_jit_grad]: 0.00052896 [symbol_engine_optimizer]: 8.026e-05, [1] [Cycle 1]: 7.297e-05, [6] [build]: 4.94e-06 [elim_shapecalc]: 8.54998e-06 [elim_not_effective]: 1.254e-05 [opt_reshape]: 5.61e-06 [fold_const_symbol]: 7.61001e-06 [renormalize]: 4.10015e-07 [validate]: 4.202e-05 Sums bootstrap : 0.000447s : 0.02% type_inference : 2.693651s : 92.71% event_method : 0.000806s : 0.03% auto_monad : 0.000172s : 0.01% graph_reusing : 0.000010s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000048s : 0.00% rewriter_before_opt_a : 0.000160s : 0.01% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000216s : 0.01% jit_opt_a.loop_unroll : 0.000065s : 0.00% jit_opt_a.a_1 : 0.001538s : 0.05% jit_opt_a.with_stream_mark : 0.000188s : 0.01% jit_opt_a.recompute_prepare : 0.000041s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000016s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000011s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000009s : 0.00% jit_opt_a.parameter_eliminate : 0.000007s : 0.00% jit_opt_a.specialize_transform : 0.000020s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000018s : 0.00% jit_opt_a.accelerated_algorithm : 0.000020s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000009s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000020s : 0.00% jit_opt_a.merge_forward : 0.000014s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000050s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000033s : 0.00% jit_opt_a.meta_fg_expand : 0.002715s : 0.09% jit_opt_a.replace_old_param : 0.000087s : 0.00% jit_opt_a.inline_without_move : 0.000072s : 0.00% jit_opt_a.renormalize : 0.202476s : 6.97% jit_opt_a.add_forward_monad_depend : 0.000011s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000028s : 0.00% jit_opt_a.cse : 0.000048s : 0.00% jit_opt_a.replace_applicator : 0.000029s : 0.00% py_interpret_to_execute_after_opt_a : 0.000015s : 0.00% rewriter_after_opt_a : 0.000367s : 0.01% convert_after_rewriter : 0.000013s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000796s : 0.03% jit_opt_b.frontend_op_eliminate : 0.000013s : 0.00% jit_opt_b.inline_after_opt_a : 0.000018s : 0.00% cconv : 0.000035s : 0.00% loop_unroll : 0.000476s : 0.02% jit_opt_after_cconv.c_1 : 0.000016s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000032s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000021s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000005s : 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 : 0.000005s : 0.00% remove_dup_value : 0.000019s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000006s : 0.00% add_recomputation : 0.000047s : 0.00% cse_after_recomputation.cse : 0.000010s : 0.00% auto_monad_reorder : 0.000015s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000008s : 0.00% opt_after_jit_grad : 0.000529s : 0.02% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000009s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000013s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000006s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000008s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000042s : 0.00% Time group info: ------[substitution.] 0.000482 65 0.44% : 0.000002s : 1: substitution.elim_not_effective 0.26% : 0.000001s : 1: substitution.fold_const_symbol 0.97% : 0.000005s : 1: substitution.graph_param_transform 74.46% : 0.000359s : 13: substitution.inline 5.25% : 0.000025s : 2: substitution.inline_without_move 1.71% : 0.000008s : 9: substitution.j_node_and_user_rematch 0.93% : 0.000005s : 2: substitution.minmaximum_grad 1.81% : 0.000009s : 9: substitution.partial_eliminate 1.74% : 0.000008s : 9: substitution.remove_not_recompute_node 0.59% : 0.000003s : 1: substitution.replace_applicator 1.37% : 0.000007s : 6: substitution.replace_old_param 1.01% : 0.000005s : 1: substitution.set_cell_output_no_recompute 2.83% : 0.000014s : 3: substitution.switch_simplify 1.70% : 0.000008s : 2: substitution.tuple_list_convert_item_index_to_positive 1.28% : 0.000006s : 2: substitution.tuple_list_get_item_depend_reorder 3.66% : 0.000018s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 2.693502 2 91.18% : 2.456058s : 1: type_inference.infer 8.82% : 0.237444s : 1: type_inference.specialize ------[replace.] 0.000179 17 58.49% : 0.000105s : 13: replace.inline 36.87% : 0.000066s : 3: replace.switch_simplify 4.64% : 0.000008s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000368 17 95.47% : 0.000351s : 13: match.inline 3.17% : 0.000012s : 3: match.switch_simplify 1.36% : 0.000005s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000256 1539 1.38% : 0.000004s : 26: predicate.accumulaten_eliminater 0.63% : 0.000002s : 1: predicate.ad_related_special_op_eliminate 1.28% : 0.000003s : 26: predicate.addn_check_dump 1.57% : 0.000004s : 26: predicate.addn_zero_filter 2.36% : 0.000006s : 26: predicate.arithmetic_simplify 1.73% : 0.000004s : 26: predicate.cast_eliminate 0.22% : 0.000001s : 1: predicate.check_bprop_eliminate 1.25% : 0.000003s : 26: predicate.compare_switch_simplify 1.38% : 0.000004s : 26: predicate.depend_value_elim 1.33% : 0.000003s : 26: predicate.dict_get_item_const_eliminator 1.42% : 0.000004s : 26: predicate.dict_get_item_eliminator 1.33% : 0.000003s : 26: predicate.dict_set_item_eliminator 0.52% : 0.000001s : 1: predicate.dumpgradient_eliminate 0.07% : 0.000000s : 1: predicate.elim_not_effective 0.25% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.42% : 0.000004s : 26: predicate.environ_add_const_eliminate 1.28% : 0.000003s : 26: predicate.environ_get_add_eliminate 1.30% : 0.000003s : 26: predicate.environ_get_depend_swap 1.37% : 0.000004s : 26: predicate.environ_get_eliminate 1.32% : 0.000003s : 26: predicate.environ_get_set_eliminate 0.05% : 0.000000s : 1: predicate.fold_const_symbol 0.97% : 0.000002s : 13: predicate.get_grad_eliminate 0.08% : 0.000000s : 1: predicate.graph_param_transform 4.25% : 0.000011s : 42: predicate.inline 2.59% : 0.000007s : 35: predicate.inline_without_move 0.38% : 0.000001s : 13: predicate.j_node_and_user_rematch 1.24% : 0.000003s : 13: predicate.less_batch_normalization 1.65% : 0.000004s : 27: predicate.list_to_tuple_eliminator_ 1.70% : 0.000004s : 28: predicate.load_eliminater 0.89% : 0.000002s : 1: predicate.loop_unroll_after_grad 4.18% : 0.000011s : 67: predicate.loop_unroll_before_grad 1.70% : 0.000004s : 27: predicate.make_slice_get_slice_eliminator 1.42% : 0.000004s : 26: predicate.merge_addn 1.62% : 0.000004s : 26: predicate.minmaximum_grad 0.80% : 0.000002s : 1: predicate.mutable_eliminate 0.16% : 0.000000s : 1: predicate.opt_reshape 1.97% : 0.000005s : 28: predicate.partial_eliminate 1.42% : 0.000004s : 26: predicate.print_const_string_wrapper 1.85% : 0.000005s : 26: predicate.reduce_eliminate 1.52% : 0.000004s : 27: predicate.redundant_stop_gradient_eliminater 0.54% : 0.000001s : 13: predicate.remove_not_recompute_node 1.70% : 0.000004s : 29: predicate.replace_applicator 1.48% : 0.000004s : 35: predicate.replace_old_param 0.25% : 0.000001s : 1: predicate.reset_defer_inline 1.50% : 0.000004s : 26: predicate.reshape_eliminate 1.43% : 0.000004s : 26: predicate.row_tensor_add_zeros_like 0.49% : 0.000001s : 1: predicate.row_tensor_eliminate 1.34% : 0.000003s : 26: predicate.same_eliminate 0.54% : 0.000001s : 13: predicate.set_cell_output_no_recompute 0.29% : 0.000001s : 2: predicate.special_op_eliminate 1.01% : 0.000003s : 13: predicate.specialize_transform 1.54% : 0.000004s : 26: predicate.split_environ_get_set_with_tuple_value 1.47% : 0.000004s : 26: predicate.stack_unstack_eliminate 0.16% : 0.000000s : 1: predicate.switch_call_monad_eliminater 2.92% : 0.000007s : 40: predicate.switch_defer_inline 3.00% : 0.000008s : 40: predicate.switch_layer_defer_inline 7.48% : 0.000019s : 114: predicate.switch_simplify 1.44% : 0.000004s : 26: predicate.tile_eliminate 1.53% : 0.000004s : 26: predicate.transpose_eliminate 1.77% : 0.000005s : 26: predicate.tuple_list_convert_item_index_to_positive 1.55% : 0.000004s : 26: predicate.tuple_list_get_item_depend_reorder 3.28% : 0.000008s : 29: predicate.tuple_list_get_item_eliminator 1.82% : 0.000005s : 26: predicate.tuple_list_set_item_eliminator 1.39% : 0.000004s : 27: predicate.tuple_to_list_eliminator_ 1.46% : 0.000004s : 28: predicate.updatestate_pure_node_eliminater 2.72% : 0.000007s : 41: predicate.updatestate_useless_node_eliminater 1.65% : 0.000004s : 26: predicate.value_based_eliminate 0.06% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.29% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.234096 37 99.67% : 0.233333s : 21: func_graph_cloner_run.FuncGraphClonerGraph 0.33% : 0.000763s : 16: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 3.115769 72 0.00% : 0.000051s : 1: add_recomputation 0.01% : 0.000179s : 1: auto_monad 0.00% : 0.000018s : 1: auto_monad_reorder 0.02% : 0.000469s : 1: bootstrap 0.00% : 0.000038s : 1: cconv 0.00% : 0.000016s : 1: convert_after_rewriter 0.00% : 0.000028s : 1: cse_after_recomputation 0.00% : 0.000009s : 1: environ_conv 0.03% : 0.000821s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000013s : 1: graph_reusing 6.83% : 0.212854s : 1: jit_opt_a 0.01% : 0.000165s : 1: jit_opt_after_cconv 0.00% : 0.000071s : 1: jit_opt_b 0.02% : 0.000486s : 1: loop_unroll 0.03% : 0.000807s : 1: mutable_eliminate 0.07% : 0.002163s : 26: opt.transform.jit_opt_a 0.00% : 0.000042s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000022s : 4: opt.transform.jit_opt_b 0.00% : 0.000013s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000018s : 1: opt.transform.mutable_eliminate 0.00% : 0.000021s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000030s : 4: opt.transform.symbol_engine_opt 0.02% : 0.000540s : 1: opt_after_jit_grad 0.00% : 0.000007s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000051s : 1: py_interpret_to_execute 0.00% : 0.000017s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000022s : 1: remove_dup_value 6.47% : 0.201717s : 1: renormalize.infer 0.02% : 0.000743s : 1: renormalize.specialize 0.00% : 0.000010s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000375s : 1: rewriter_after_opt_a 0.01% : 0.000164s : 1: rewriter_before_opt_a 0.00% : 0.000083s : 1: symbol_engine_optimizer 86.45% : 2.693683s : 1: type_inference . [hook] pytest_runtest_teardown:test_squeeze_high_dimension[KBK] tests/st/mint/test_squeeze.py::test_squeeze_high_dimension[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 153.11s (0:02:33) ==================