==================================================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_003/sault/config/pytest.ini plugins: mock-3.14.0, hydra-core-1.3.2, forked-1.6.0, anyio-4.9.0, xdist-1.32.0 collected 2 items test_unsqueeze.py . [hook] pytest_runtest_teardown:test_unsqueeze_0d_tensor[pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_0d_tensor[pynative],max_mem:2.0M TotalTime = 0.2181, [30] [bootstrap]: 0.00061776 [type_inference]: 0.129654 [event_method]: 1.849e-05 [auto_monad]: 0.00017668 [graph_reusing]: 7.66001e-06 [pre_auto_parallel]: 1.285e-05 [py_interpret_to_execute]: 3.34e-05 [rewriter_before_opt_a]: 7.567e-05 [expand_dump_flag]: 5.14e-06 [jit_opt_a]: 0.0840898, [2] [Cycle 1]: 0.00218141, [27] [switch_simplify]: 7.275e-05 [loop_unroll]: 2.521e-05 [a_1]: 0.00057357 [with_stream_mark]: 3.456e-05 [recompute_prepare]: 1.058e-05 [updatestate_depend_eliminate]: 7.19001e-06 [updatestate_assign_eliminate]: 1.206e-05 [updatestate_loads_eliminate]: 5.07e-06 [parameter_eliminate]: 2.16e-06 [specialize_transform]: 1.373e-05 [updatestate_useless_node_eliminater]: 1.263e-05 [accelerated_algorithm]: 8.62e-06 [meta_shard_fg_expand]: 3.18e-06 [get_grad_eliminate_]: 8.19998e-06 [merge_forward]: 5.64e-06 [cell_reuse_recompute_pass]: 1.34998e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.431e-05 [j_node_and_user_rematch]: 1.463e-05 [meta_fg_expand]: 3.32002e-06 [replace_old_param]: 1.355e-05 [inline_without_move]: 7.92e-06 [renormalize]: 0.00098688 [add_forward_monad_depend]: 8.21002e-06 [auto_monad_grad]: 2.92002e-06 [auto_monad_eliminator]: 2.635e-05 [cse]: 5.234e-05 [replace_applicator]: 1.644e-05 [Cycle 2]: 0.00052348, [27] [switch_simplify]: 9.91e-06 [loop_unroll]: 8.03999e-06 [a_1]: 0.00017908 [with_stream_mark]: 1.284e-05 [recompute_prepare]: 8.09997e-06 [updatestate_depend_eliminate]: 5.18002e-06 [updatestate_assign_eliminate]: 4.24002e-06 [updatestate_loads_eliminate]: 4.17e-06 [parameter_eliminate]: 9.09989e-07 [specialize_transform]: 8.25e-06 [updatestate_useless_node_eliminater]: 1.115e-05 [accelerated_algorithm]: 8.33001e-06 [meta_shard_fg_expand]: 2.49001e-06 [get_grad_eliminate_]: 7.63001e-06 [merge_forward]: 4.84e-06 [cell_reuse_recompute_pass]: 1.47001e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.894e-05 [j_node_and_user_rematch]: 1.394e-05 [meta_fg_expand]: 3.23998e-06 [replace_old_param]: 1.139e-05 [inline_without_move]: 7.87e-06 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 1.86e-06 [auto_monad_grad]: 1.52001e-06 [auto_monad_eliminator]: 1.34e-05 [cse]: 1.844e-05 [replace_applicator]: 8.17e-06 [py_interpret_to_execute_after_opt_a]: 1.571e-05 [rewriter_after_opt_a]: 0.00057296 [convert_after_rewriter]: 1.418e-05 [order_py_execute_after_rewriter]: 7.41001e-06 [mutable_eliminate]: 0.0007038 [jit_opt_b]: 7.135e-05, [1] [Cycle 1]: 6.318e-05, [2] [frontend_op_eliminate]: 2.523e-05 [inline_after_opt_a]: 2.48e-05 [cconv]: 3.125e-05 [loop_unroll]: 0.0004447 [jit_opt_after_cconv]: 0.00019837, [1] [Cycle 1]: 0.00019157, [11] [c_1]: 4.621e-05 [parameter_eliminate]: 3.13e-06 [updatestate_depend_eliminate]: 8.3e-06 [updatestate_assign_eliminate]: 4.48999e-06 [updatestate_loads_eliminate]: 4.70001e-06 [cse]: 2.533e-05 [call_graph_tuple_transform]: 2.392e-05 [tuple_list_get_item_eliminator]: 8.99003e-06 [none_parameter_eliminate]: 1.47001e-06 [renormalize]: 6.19999e-07 [switch_simplify]: 8.92e-06 [remove_dup_value]: 1.992e-05 [partial_unused_args_eliminate]: 3.13e-06 [environ_conv]: 1.963e-05 [add_recomputation]: 7.775e-05 [cse_after_recomputation]: 2.768e-05, [1] [Cycle 1]: 2.178e-05, [1] [cse]: 1.569e-05 [auto_monad_reorder]: 3.48e-05 [get_jit_bprop_graph]: 2.44001e-06 [rewriter_after_jit_bprop_graph]: 0.00012098 [opt_after_jit_grad]: 0.00050153 [symbol_engine_optimizer]: 9.585e-05, [1] [Cycle 1]: 8.889e-05, [6] [build]: 5.07e-06 [elim_shapecalc]: 1.114e-05 [elim_not_effective]: 1.836e-05 [opt_reshape]: 9.20001e-06 [fold_const_symbol]: 1.424e-05 [renormalize]: 2.60014e-07 [validate]: 7e-05 Sums bootstrap : 0.000618s : 0.45% type_inference : 0.129654s : 95.44% event_method : 0.000018s : 0.01% auto_monad : 0.000177s : 0.13% graph_reusing : 0.000008s : 0.01% pre_auto_parallel : 0.000013s : 0.01% py_interpret_to_execute : 0.000033s : 0.02% rewriter_before_opt_a : 0.000076s : 0.06% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000083s : 0.06% jit_opt_a.loop_unroll : 0.000033s : 0.02% jit_opt_a.a_1 : 0.000753s : 0.55% jit_opt_a.with_stream_mark : 0.000047s : 0.03% jit_opt_a.recompute_prepare : 0.000019s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000012s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000016s : 0.01% jit_opt_a.updatestate_loads_eliminate : 0.000009s : 0.01% jit_opt_a.parameter_eliminate : 0.000003s : 0.00% jit_opt_a.specialize_transform : 0.000022s : 0.02% jit_opt_a.updatestate_useless_node_eliminater : 0.000024s : 0.02% jit_opt_a.accelerated_algorithm : 0.000017s : 0.01% jit_opt_a.meta_shard_fg_expand : 0.000006s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000016s : 0.01% jit_opt_a.merge_forward : 0.000010s : 0.01% jit_opt_a.cell_reuse_recompute_pass : 0.000003s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000063s : 0.05% jit_opt_a.j_node_and_user_rematch : 0.000029s : 0.02% jit_opt_a.meta_fg_expand : 0.000007s : 0.00% jit_opt_a.replace_old_param : 0.000025s : 0.02% jit_opt_a.inline_without_move : 0.000016s : 0.01% jit_opt_a.renormalize : 0.000987s : 0.73% jit_opt_a.add_forward_monad_depend : 0.000010s : 0.01% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000040s : 0.03% jit_opt_a.cse : 0.000071s : 0.05% jit_opt_a.replace_applicator : 0.000025s : 0.02% py_interpret_to_execute_after_opt_a : 0.000016s : 0.01% rewriter_after_opt_a : 0.000573s : 0.42% convert_after_rewriter : 0.000014s : 0.01% order_py_execute_after_rewriter : 0.000007s : 0.01% mutable_eliminate : 0.000704s : 0.52% jit_opt_b.frontend_op_eliminate : 0.000025s : 0.02% jit_opt_b.inline_after_opt_a : 0.000025s : 0.02% cconv : 0.000031s : 0.02% loop_unroll : 0.000445s : 0.33% jit_opt_after_cconv.c_1 : 0.000046s : 0.03% jit_opt_after_cconv.parameter_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000008s : 0.01% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000025s : 0.02% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 0.02% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000009s : 0.01% jit_opt_after_cconv.none_parameter_eliminate : 0.000001s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000009s : 0.01% remove_dup_value : 0.000020s : 0.01% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000020s : 0.01% add_recomputation : 0.000078s : 0.06% cse_after_recomputation.cse : 0.000016s : 0.01% auto_monad_reorder : 0.000035s : 0.03% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000121s : 0.09% opt_after_jit_grad : 0.000502s : 0.37% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.01% symbol_engine_optimizer.elim_not_effective : 0.000018s : 0.01% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.01% symbol_engine_optimizer.fold_const_symbol : 0.000014s : 0.01% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000070s : 0.05% Time group info: ------[substitution.] 0.000268 44 3.12% : 0.000008s : 2: substitution.depend_value_elim 1.04% : 0.000003s : 4: substitution.elim_not_effective 0.95% : 0.000003s : 4: substitution.fold_const_symbol 2.90% : 0.000008s : 5: substitution.graph_param_transform 77.10% : 0.000207s : 3: substitution.inline 1.98% : 0.000005s : 8: substitution.j_node_and_user_rematch 3.15% : 0.000008s : 8: substitution.remove_not_recompute_node 2.51% : 0.000007s : 2: substitution.replace_old_param 3.67% : 0.000010s : 3: substitution.updatestate_pure_node_eliminater 3.58% : 0.000010s : 5: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.129576 2 99.42% : 0.128827s : 1: type_inference.infer 0.58% : 0.000749s : 1: type_inference.specialize ------[replace.] 0.000038 3 100.00% : 0.000038s : 3: replace.inline ------[match.] 0.000204 3 100.00% : 0.000204s : 3: match.inline ------[predicate.] 0.000148 825 1.14% : 0.000002s : 12: predicate.accumulaten_eliminater 1.44% : 0.000002s : 5: predicate.ad_related_special_op_eliminate 1.14% : 0.000002s : 12: predicate.addn_check_dump 1.25% : 0.000002s : 12: predicate.addn_zero_filter 1.96% : 0.000003s : 12: predicate.arithmetic_simplify 1.36% : 0.000002s : 12: predicate.cast_eliminate 0.60% : 0.000001s : 5: predicate.check_bprop_eliminate 1.04% : 0.000002s : 12: predicate.compare_switch_simplify 1.27% : 0.000002s : 12: predicate.depend_value_elim 1.09% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.23% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.11% : 0.000002s : 12: predicate.dict_set_item_eliminator 1.04% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.39% : 0.000001s : 5: predicate.elim_not_effective 0.76% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.48% : 0.000002s : 12: predicate.environ_add_const_eliminate 1.06% : 0.000002s : 12: predicate.environ_get_add_eliminate 1.10% : 0.000002s : 12: predicate.environ_get_depend_swap 1.20% : 0.000002s : 12: predicate.environ_get_eliminate 1.16% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.31% : 0.000000s : 5: predicate.fold_const_symbol 1.29% : 0.000002s : 10: predicate.get_grad_eliminate 0.30% : 0.000000s : 5: predicate.graph_param_transform 5.52% : 0.000008s : 25: predicate.inline 1.22% : 0.000002s : 10: predicate.inline_without_move 0.49% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.42% : 0.000002s : 10: predicate.less_batch_normalization 1.28% : 0.000002s : 12: predicate.list_to_tuple_eliminator_ 1.69% : 0.000002s : 17: predicate.load_eliminater 1.58% : 0.000002s : 5: predicate.loop_unroll_after_grad 3.11% : 0.000005s : 25: predicate.loop_unroll_before_grad 2.08% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 1.12% : 0.000002s : 12: predicate.merge_addn 1.10% : 0.000002s : 12: predicate.minmaximum_grad 1.94% : 0.000003s : 5: predicate.mutable_eliminate 0.58% : 0.000001s : 5: predicate.opt_reshape 2.23% : 0.000003s : 17: predicate.partial_eliminate 1.18% : 0.000002s : 12: predicate.print_const_string_wrapper 1.74% : 0.000003s : 12: predicate.reduce_eliminate 1.21% : 0.000002s : 12: predicate.redundant_stop_gradient_eliminater 0.76% : 0.000001s : 10: predicate.remove_not_recompute_node 1.57% : 0.000002s : 22: predicate.replace_applicator 0.81% : 0.000001s : 10: predicate.replace_old_param 0.39% : 0.000001s : 5: predicate.reset_defer_inline 1.51% : 0.000002s : 12: predicate.reshape_eliminate 1.69% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.87% : 0.000001s : 5: predicate.row_tensor_eliminate 1.18% : 0.000002s : 12: predicate.same_eliminate 0.66% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.15% : 0.000002s : 10: predicate.special_op_eliminate 1.31% : 0.000002s : 10: predicate.specialize_transform 1.39% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.28% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.62% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.76% : 0.000003s : 15: predicate.switch_defer_inline 1.56% : 0.000002s : 15: predicate.switch_layer_defer_inline 6.90% : 0.000010s : 45: predicate.switch_simplify 1.29% : 0.000002s : 12: predicate.tile_eliminate 1.34% : 0.000002s : 12: predicate.transpose_eliminate 1.47% : 0.000002s : 12: predicate.tuple_list_convert_item_index_to_positive 1.20% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 3.62% : 0.000005s : 22: predicate.tuple_list_get_item_eliminator 1.83% : 0.000003s : 12: predicate.tuple_list_set_item_eliminator 1.29% : 0.000002s : 12: predicate.tuple_to_list_eliminator_ 1.69% : 0.000002s : 17: predicate.updatestate_pure_node_eliminater 3.76% : 0.000006s : 27: predicate.updatestate_useless_node_eliminater 1.56% : 0.000002s : 12: predicate.value_based_eliminate 0.59% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.76% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000401 6 5.46% : 0.000022s : 1: func_graph_cloner_run.FuncGraphClonerGraph 94.54% : 0.000380s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.220100 72 0.04% : 0.000081s : 1: add_recomputation 0.08% : 0.000183s : 1: auto_monad 0.02% : 0.000038s : 1: auto_monad_reorder 0.29% : 0.000643s : 1: bootstrap 0.02% : 0.000034s : 1: cconv 0.01% : 0.000017s : 1: convert_after_rewriter 0.01% : 0.000030s : 1: cse_after_recomputation 0.01% : 0.000022s : 1: environ_conv 0.01% : 0.000024s : 1: event_method 0.00% : 0.000009s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000011s : 1: graph_reusing 38.21% : 0.084094s : 1: jit_opt_a 0.09% : 0.000201s : 1: jit_opt_after_cconv 0.03% : 0.000074s : 1: jit_opt_b 0.21% : 0.000454s : 1: loop_unroll 0.32% : 0.000713s : 1: mutable_eliminate 0.49% : 0.001074s : 26: opt.transform.jit_opt_a 0.04% : 0.000084s : 4: opt.transform.jit_opt_after_cconv 0.02% : 0.000043s : 4: opt.transform.jit_opt_b 0.01% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.01% : 0.000018s : 1: opt.transform.mutable_eliminate 0.01% : 0.000032s : 1: opt.transform.opt_after_jit_grad 0.02% : 0.000049s : 4: opt.transform.symbol_engine_opt 0.23% : 0.000510s : 1: opt_after_jit_grad 0.00% : 0.000010s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.01% : 0.000016s : 1: pre_auto_parallel 0.02% : 0.000036s : 1: py_interpret_to_execute 0.01% : 0.000018s : 1: py_interpret_to_execute_after_opt_a 0.01% : 0.000023s : 1: remove_dup_value 0.26% : 0.000568s : 1: renormalize.infer 0.19% : 0.000410s : 1: renormalize.specialize 0.06% : 0.000124s : 1: rewriter_after_jit_bprop_graph 0.26% : 0.000578s : 1: rewriter_after_opt_a 0.04% : 0.000079s : 1: rewriter_before_opt_a 0.04% : 0.000099s : 1: symbol_engine_optimizer 58.92% : 0.129675s : 1: type_inference TotalTime = 0.948431, [30] [bootstrap]: 0.00059302 [type_inference]: 0.412378 [event_method]: 0.00032429 [auto_monad]: 0.000349 [graph_reusing]: 1.355e-05 [pre_auto_parallel]: 4.23999e-06 [py_interpret_to_execute]: 7.32e-05 [rewriter_before_opt_a]: 0.00020345 [expand_dump_flag]: 5.59998e-06 [jit_opt_a]: 0.494154, [3] [Cycle 1]: 0.39255, [27] [switch_simplify]: 0.00030419 [loop_unroll]: 7.245e-05 [a_1]: 0.00183758 [with_stream_mark]: 5.353e-05 [recompute_prepare]: 3.58e-05 [updatestate_depend_eliminate]: 1.647e-05 [updatestate_assign_eliminate]: 1.199e-05 [updatestate_loads_eliminate]: 1.106e-05 [parameter_eliminate]: 4.13001e-06 [specialize_transform]: 2.173e-05 [updatestate_useless_node_eliminater]: 5.105e-05 [accelerated_algorithm]: 2.216e-05 [meta_shard_fg_expand]: 1.172e-05 [get_grad_eliminate_]: 2.004e-05 [merge_forward]: 1.497e-05 [cell_reuse_recompute_pass]: 1.24e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.228e-05 [j_node_and_user_rematch]: 3.655e-05 [meta_fg_expand]: 0.231209 [replace_old_param]: 0.00014024 [inline_without_move]: 0.00012834 [renormalize]: 0.157432 [add_forward_monad_depend]: 2.929e-05 [auto_monad_grad]: 1.394e-05 [auto_monad_eliminator]: 9.099e-05 [cse]: 0.00036103 [replace_applicator]: 0.00021158 [Cycle 2]: 0.0040729, [27] [switch_simplify]: 6.501e-05 [loop_unroll]: 6.063e-05 [a_1]: 0.00117494 [with_stream_mark]: 2.946e-05 [recompute_prepare]: 1.705e-05 [updatestate_depend_eliminate]: 3.463e-05 [updatestate_assign_eliminate]: 6.63e-06 [updatestate_loads_eliminate]: 5.00999e-06 [parameter_eliminate]: 2.98e-06 [specialize_transform]: 1.1e-05 [updatestate_useless_node_eliminater]: 1.37e-05 [accelerated_algorithm]: 1.021e-05 [meta_shard_fg_expand]: 3.55e-06 [get_grad_eliminate_]: 9.74e-06 [merge_forward]: 6.31e-06 [cell_reuse_recompute_pass]: 2.14e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.461e-05 [j_node_and_user_rematch]: 1.501e-05 [meta_fg_expand]: 0.00021503 [replace_old_param]: 2.568e-05 [inline_without_move]: 9.52999e-06 [renormalize]: 0.00199038 [add_forward_monad_depend]: 9.24998e-06 [auto_monad_grad]: 2.88e-06 [auto_monad_eliminator]: 2.55e-05 [cse]: 4.623e-05 [replace_applicator]: 2.811e-05 [Cycle 3]: 0.00059236, [27] [switch_simplify]: 1.158e-05 [loop_unroll]: 9.14998e-06 [a_1]: 0.00021608 [with_stream_mark]: 2.113e-05 [recompute_prepare]: 1.071e-05 [updatestate_depend_eliminate]: 7.50998e-06 [updatestate_assign_eliminate]: 5.26998e-06 [updatestate_loads_eliminate]: 5.23002e-06 [parameter_eliminate]: 2.71999e-06 [specialize_transform]: 1.068e-05 [updatestate_useless_node_eliminater]: 1.105e-05 [accelerated_algorithm]: 1.017e-05 [meta_shard_fg_expand]: 2.99001e-06 [get_grad_eliminate_]: 8.70001e-06 [merge_forward]: 6.38e-06 [cell_reuse_recompute_pass]: 3.95e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.359e-05 [j_node_and_user_rematch]: 1.478e-05 [meta_fg_expand]: 3.38e-06 [replace_old_param]: 1.431e-05 [inline_without_move]: 8.71002e-06 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 2.63998e-06 [auto_monad_grad]: 1.92999e-06 [auto_monad_eliminator]: 1.206e-05 [cse]: 2.297e-05 [replace_applicator]: 9.61e-06 [py_interpret_to_execute_after_opt_a]: 1.918e-05 [rewriter_after_opt_a]: 0.036869 [convert_after_rewriter]: 3.776e-05 [order_py_execute_after_rewriter]: 8.59e-06 [mutable_eliminate]: 0.0009793 [jit_opt_b]: 9.683e-05, [1] [Cycle 1]: 8.567e-05, [2] [frontend_op_eliminate]: 3.503e-05 [inline_after_opt_a]: 3.231e-05 [cconv]: 4.843e-05 [loop_unroll]: 0.00056251 [jit_opt_after_cconv]: 0.00028648, [1] [Cycle 1]: 0.00027755, [11] [c_1]: 6.67e-05 [parameter_eliminate]: 7.30003e-06 [updatestate_depend_eliminate]: 1.688e-05 [updatestate_assign_eliminate]: 7.4e-06 [updatestate_loads_eliminate]: 5.14003e-06 [cse]: 5.973e-05 [call_graph_tuple_transform]: 3.007e-05 [tuple_list_get_item_eliminator]: 1.04e-05 [none_parameter_eliminate]: 2.29999e-06 [renormalize]: 1.15999e-06 [switch_simplify]: 1.012e-05 [remove_dup_value]: 3.013e-05 [partial_unused_args_eliminate]: 3.09001e-06 [environ_conv]: 1.159e-05 [add_recomputation]: 9.353e-05 [cse_after_recomputation]: 4.188e-05, [1] [Cycle 1]: 3.385e-05, [1] [cse]: 2.563e-05 [auto_monad_reorder]: 3.408e-05 [get_jit_bprop_graph]: 3.21001e-06 [rewriter_after_jit_bprop_graph]: 1.08e-05 [opt_after_jit_grad]: 0.00067817 [symbol_engine_optimizer]: 0.00012272, [1] [Cycle 1]: 0.00011274, [6] [build]: 8.43999e-06 [elim_shapecalc]: 1.403e-05 [elim_not_effective]: 2.685e-05 [opt_reshape]: 1.076e-05 [fold_const_symbol]: 1.619e-05 [renormalize]: 1.00999e-06 [validate]: 8.345e-05 Sums bootstrap : 0.000593s : 0.07% type_inference : 0.412378s : 48.50% event_method : 0.000324s : 0.04% auto_monad : 0.000349s : 0.04% graph_reusing : 0.000014s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000073s : 0.01% rewriter_before_opt_a : 0.000203s : 0.02% expand_dump_flag : 0.000006s : 0.00% jit_opt_a.switch_simplify : 0.000381s : 0.04% jit_opt_a.loop_unroll : 0.000142s : 0.02% jit_opt_a.a_1 : 0.003229s : 0.38% jit_opt_a.with_stream_mark : 0.000104s : 0.01% jit_opt_a.recompute_prepare : 0.000064s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000059s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000024s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000021s : 0.00% jit_opt_a.parameter_eliminate : 0.000010s : 0.00% jit_opt_a.specialize_transform : 0.000043s : 0.01% jit_opt_a.updatestate_useless_node_eliminater : 0.000076s : 0.01% jit_opt_a.accelerated_algorithm : 0.000043s : 0.01% jit_opt_a.meta_shard_fg_expand : 0.000018s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000038s : 0.00% jit_opt_a.merge_forward : 0.000028s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000090s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000066s : 0.01% jit_opt_a.meta_fg_expand : 0.231428s : 27.22% jit_opt_a.replace_old_param : 0.000180s : 0.02% jit_opt_a.inline_without_move : 0.000147s : 0.02% jit_opt_a.renormalize : 0.159422s : 18.75% jit_opt_a.add_forward_monad_depend : 0.000041s : 0.00% jit_opt_a.auto_monad_grad : 0.000019s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000129s : 0.02% jit_opt_a.cse : 0.000430s : 0.05% jit_opt_a.replace_applicator : 0.000249s : 0.03% py_interpret_to_execute_after_opt_a : 0.000019s : 0.00% rewriter_after_opt_a : 0.036869s : 4.34% convert_after_rewriter : 0.000038s : 0.00% order_py_execute_after_rewriter : 0.000009s : 0.00% mutable_eliminate : 0.000979s : 0.12% jit_opt_b.frontend_op_eliminate : 0.000035s : 0.00% jit_opt_b.inline_after_opt_a : 0.000032s : 0.00% cconv : 0.000048s : 0.01% loop_unroll : 0.000563s : 0.07% jit_opt_after_cconv.c_1 : 0.000067s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000017s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000060s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000030s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000010s : 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 : 0.000010s : 0.00% remove_dup_value : 0.000030s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000012s : 0.00% add_recomputation : 0.000094s : 0.01% cse_after_recomputation.cse : 0.000026s : 0.00% auto_monad_reorder : 0.000034s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000011s : 0.00% opt_after_jit_grad : 0.000678s : 0.08% symbol_engine_optimizer.build : 0.000008s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000014s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000027s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000011s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000016s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000083s : 0.01% Time group info: ------[substitution.] 0.002046 170 1.33% : 0.000027s : 8: substitution.depend_value_elim 0.19% : 0.000004s : 4: substitution.elim_not_effective 0.15% : 0.000003s : 4: substitution.fold_const_symbol 45.45% : 0.000930s : 4: substitution.getattr_setattr_resolve 0.40% : 0.000008s : 5: substitution.graph_param_transform 34.79% : 0.000712s : 16: substitution.inline 1.98% : 0.000041s : 4: substitution.inline_without_move 0.69% : 0.000014s : 20: substitution.j_node_and_user_rematch 0.49% : 0.000010s : 5: substitution.minmaximum_grad 0.52% : 0.000011s : 9: substitution.partial_eliminate 0.89% : 0.000018s : 20: substitution.remove_not_recompute_node 2.42% : 0.000050s : 12: substitution.replace_applicator 1.03% : 0.000021s : 16: substitution.replace_old_param 0.17% : 0.000003s : 1: substitution.set_cell_output_no_recompute 1.07% : 0.000022s : 3: substitution.switch_simplify 2.89% : 0.000059s : 5: substitution.tuple_list_convert_item_index_to_positive 0.74% : 0.000015s : 5: substitution.tuple_list_get_item_depend_reorder 1.90% : 0.000039s : 8: substitution.tuple_list_get_item_eliminator 1.13% : 0.000023s : 8: substitution.updatestate_pure_node_eliminater 1.78% : 0.000036s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.412205 2 93.72% : 0.386307s : 1: type_inference.infer 6.28% : 0.025898s : 1: type_inference.specialize ------[replace.] 0.000499 27 14.03% : 0.000070s : 3: replace.getattr_setattr_resolve 31.91% : 0.000159s : 16: replace.inline 10.42% : 0.000052s : 1: replace.replace_applicator 21.05% : 0.000105s : 3: replace.switch_simplify 17.51% : 0.000087s : 3: replace.tuple_list_get_item_eliminator 5.07% : 0.000025s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001613 27 53.61% : 0.000865s : 3: match.getattr_setattr_resolve 43.32% : 0.000699s : 16: match.inline 0.72% : 0.000012s : 1: match.replace_applicator 1.24% : 0.000020s : 3: match.switch_simplify 0.49% : 0.000008s : 3: match.tuple_list_get_item_eliminator 0.62% : 0.000010s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000523 3164 1.45% : 0.000008s : 50: predicate.accumulaten_eliminater 0.58% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 1.26% : 0.000007s : 50: predicate.addn_check_dump 1.44% : 0.000008s : 50: predicate.addn_zero_filter 2.08% : 0.000011s : 50: predicate.arithmetic_simplify 1.37% : 0.000007s : 50: predicate.cast_eliminate 0.22% : 0.000001s : 5: predicate.check_bprop_eliminate 1.34% : 0.000007s : 50: predicate.compare_switch_simplify 1.56% : 0.000008s : 50: predicate.depend_value_elim 1.28% : 0.000007s : 50: predicate.dict_get_item_const_eliminator 1.43% : 0.000007s : 50: predicate.dict_get_item_eliminator 1.30% : 0.000007s : 50: predicate.dict_set_item_eliminator 0.55% : 0.000003s : 5: predicate.dumpgradient_eliminate 0.15% : 0.000001s : 5: predicate.elim_not_effective 0.24% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.39% : 0.000007s : 50: predicate.environ_add_const_eliminate 1.28% : 0.000007s : 50: predicate.environ_get_add_eliminate 1.31% : 0.000007s : 50: predicate.environ_get_depend_swap 1.34% : 0.000007s : 50: predicate.environ_get_eliminate 1.26% : 0.000007s : 50: predicate.environ_get_set_eliminate 0.09% : 0.000000s : 5: predicate.fold_const_symbol 0.87% : 0.000005s : 26: predicate.get_grad_eliminate 1.28% : 0.000007s : 20: predicate.getattr_setattr_resolve 0.13% : 0.000001s : 5: predicate.graph_param_transform 4.51% : 0.000024s : 80: predicate.inline 3.03% : 0.000016s : 87: predicate.inline_without_move 0.45% : 0.000002s : 26: predicate.j_node_and_user_rematch 1.20% : 0.000006s : 26: predicate.less_batch_normalization 1.63% : 0.000009s : 53: predicate.list_to_tuple_eliminator_ 1.66% : 0.000009s : 58: predicate.load_eliminater 0.74% : 0.000004s : 5: predicate.loop_unroll_after_grad 3.99% : 0.000021s : 132: predicate.loop_unroll_before_grad 1.72% : 0.000009s : 55: predicate.make_slice_get_slice_eliminator 1.28% : 0.000007s : 50: predicate.merge_addn 1.36% : 0.000007s : 50: predicate.minmaximum_grad 0.72% : 0.000004s : 5: predicate.mutable_eliminate 0.18% : 0.000001s : 5: predicate.opt_reshape 1.99% : 0.000010s : 58: predicate.partial_eliminate 1.34% : 0.000007s : 50: predicate.print_const_string_wrapper 1.77% : 0.000009s : 50: predicate.reduce_eliminate 1.48% : 0.000008s : 53: predicate.redundant_stop_gradient_eliminater 0.43% : 0.000002s : 26: predicate.remove_not_recompute_node 2.49% : 0.000013s : 126: predicate.replace_applicator 1.60% : 0.000008s : 87: predicate.replace_old_param 0.24% : 0.000001s : 5: predicate.reset_defer_inline 1.36% : 0.000007s : 50: predicate.reshape_eliminate 1.38% : 0.000007s : 50: predicate.row_tensor_add_zeros_like 0.36% : 0.000002s : 5: predicate.row_tensor_eliminate 1.49% : 0.000008s : 50: predicate.same_eliminate 0.52% : 0.000003s : 28: predicate.set_cell_output_no_recompute 0.37% : 0.000002s : 10: predicate.special_op_eliminate 0.87% : 0.000005s : 26: predicate.specialize_transform 1.66% : 0.000009s : 50: predicate.split_environ_get_set_with_tuple_value 1.39% : 0.000007s : 50: predicate.stack_unstack_eliminate 0.17% : 0.000001s : 5: predicate.switch_call_monad_eliminater 2.77% : 0.000014s : 70: predicate.switch_defer_inline 2.22% : 0.000012s : 70: predicate.switch_layer_defer_inline 7.21% : 0.000038s : 213: predicate.switch_simplify 1.29% : 0.000007s : 50: predicate.tile_eliminate 1.39% : 0.000007s : 50: predicate.transpose_eliminate 1.78% : 0.000009s : 50: predicate.tuple_list_convert_item_index_to_positive 1.68% : 0.000009s : 50: predicate.tuple_list_get_item_depend_reorder 3.06% : 0.000016s : 63: predicate.tuple_list_get_item_eliminator 1.88% : 0.000010s : 50: predicate.tuple_list_set_item_eliminator 1.48% : 0.000008s : 53: predicate.tuple_to_list_eliminator_ 1.72% : 0.000009s : 58: predicate.updatestate_pure_node_eliminater 2.78% : 0.000015s : 85: predicate.updatestate_useless_node_eliminater 1.77% : 0.000009s : 50: predicate.value_based_eliminate 0.15% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.25% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.004479 47 62.01% : 0.002777s : 20: func_graph_cloner_run.FuncGraphClonerGraph 37.99% : 0.001701s : 27: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.113706 89 0.01% : 0.000099s : 1: add_recomputation 0.03% : 0.000359s : 1: auto_monad 0.00% : 0.000037s : 1: auto_monad_reorder 0.05% : 0.000611s : 1: bootstrap 0.00% : 0.000052s : 1: cconv 0.00% : 0.000044s : 1: convert_after_rewriter 0.00% : 0.000045s : 1: cse_after_recomputation 0.00% : 0.000015s : 1: environ_conv 0.03% : 0.000334s : 1: event_method 0.00% : 0.000009s : 1: expand_dump_flag 0.00% : 0.000006s : 1: get_jit_bprop_graph 0.00% : 0.000017s : 1: graph_reusing 44.37% : 0.494160s : 1: jit_opt_a 0.03% : 0.000291s : 1: jit_opt_after_cconv 0.01% : 0.000100s : 1: jit_opt_b 0.05% : 0.000575s : 1: loop_unroll 0.09% : 0.000994s : 1: mutable_eliminate 0.42% : 0.004668s : 39: opt.transform.jit_opt_a 0.01% : 0.000112s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000055s : 4: opt.transform.jit_opt_b 0.00% : 0.000025s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000033s : 1: opt.transform.mutable_eliminate 0.00% : 0.000047s : 1: opt.transform.opt_after_jit_grad 0.10% : 0.001073s : 2: opt.transform.opt_resolve 0.01% : 0.000063s : 4: opt.transform.symbol_engine_opt 0.06% : 0.000696s : 1: opt_after_jit_grad 0.00% : 0.000011s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000007s : 1: pre_auto_parallel 0.01% : 0.000077s : 1: py_interpret_to_execute 0.00% : 0.000023s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000034s : 1: remove_dup_value 13.90% : 0.154755s : 2: renormalize.infer 0.42% : 0.004633s : 2: renormalize.specialize 0.00% : 0.000013s : 1: rewriter_after_jit_bprop_graph 3.31% : 0.036890s : 1: rewriter_after_opt_a 0.02% : 0.000208s : 1: rewriter_before_opt_a 0.01% : 0.000126s : 1: symbol_engine_optimizer 37.03% : 0.412404s : 1: type_inference . [hook] pytest_runtest_teardown:test_unsqueeze_0d_tensor[KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_0d_tensor[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 94.12s (0:01:34) ===================