==================================================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_002/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_very_large_tensors[pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_very_large_tensors[pynative],max_mem:2.0M TotalTime = 0.432115, [30] [bootstrap]: 0.00065851 [type_inference]: 0.237929 [event_method]: 1.723e-05 [auto_monad]: 0.00017042 [graph_reusing]: 7.6e-06 [pre_auto_parallel]: 1.322e-05 [py_interpret_to_execute]: 3.853e-05 [rewriter_before_opt_a]: 6.647e-05 [expand_dump_flag]: 3.4e-06 [jit_opt_a]: 0.189534, [2] [Cycle 1]: 0.00212103, [27] [switch_simplify]: 9.028e-05 [loop_unroll]: 2.333e-05 [a_1]: 0.00054467 [with_stream_mark]: 3.342e-05 [recompute_prepare]: 1.273e-05 [updatestate_depend_eliminate]: 7.58001e-06 [updatestate_assign_eliminate]: 7.26999e-06 [updatestate_loads_eliminate]: 5.84e-06 [parameter_eliminate]: 2.05002e-06 [specialize_transform]: 9.10999e-06 [updatestate_useless_node_eliminater]: 1.175e-05 [accelerated_algorithm]: 9.76e-06 [meta_shard_fg_expand]: 3.50003e-06 [get_grad_eliminate_]: 8.23001e-06 [merge_forward]: 5.68002e-06 [cell_reuse_recompute_pass]: 1.49e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.484e-05 [j_node_and_user_rematch]: 1.453e-05 [meta_fg_expand]: 3.43e-06 [replace_old_param]: 1.346e-05 [inline_without_move]: 8.22e-06 [renormalize]: 0.00092278 [add_forward_monad_depend]: 1.392e-05 [auto_monad_grad]: 2.66999e-06 [auto_monad_eliminator]: 2.419e-05 [cse]: 5.102e-05 [replace_applicator]: 2.02e-05 [Cycle 2]: 0.00049107, [27] [switch_simplify]: 9.57999e-06 [loop_unroll]: 7.68999e-06 [a_1]: 0.00017217 [with_stream_mark]: 1.481e-05 [recompute_prepare]: 7.82e-06 [updatestate_depend_eliminate]: 5.81998e-06 [updatestate_assign_eliminate]: 4.74998e-06 [updatestate_loads_eliminate]: 4.2e-06 [parameter_eliminate]: 1.15999e-06 [specialize_transform]: 8.05e-06 [updatestate_useless_node_eliminater]: 1.09e-05 [accelerated_algorithm]: 7.83001e-06 [meta_shard_fg_expand]: 2.27001e-06 [get_grad_eliminate_]: 7.58999e-06 [merge_forward]: 5.35001e-06 [cell_reuse_recompute_pass]: 3.06999e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.745e-05 [j_node_and_user_rematch]: 1.313e-05 [meta_fg_expand]: 2.82002e-06 [replace_old_param]: 1.077e-05 [inline_without_move]: 7.28e-06 [renormalize]: 1.00001e-07 [add_forward_monad_depend]: 1.33002e-06 [auto_monad_grad]: 9.00007e-07 [auto_monad_eliminator]: 1.135e-05 [cse]: 1.775e-05 [replace_applicator]: 7.45e-06 [py_interpret_to_execute_after_opt_a]: 1.651e-05 [rewriter_after_opt_a]: 0.00062978 [convert_after_rewriter]: 1.593e-05 [order_py_execute_after_rewriter]: 7.77e-06 [mutable_eliminate]: 0.00080665 [jit_opt_b]: 7.625e-05, [1] [Cycle 1]: 6.519e-05, [2] [frontend_op_eliminate]: 2.626e-05 [inline_after_opt_a]: 2.491e-05 [cconv]: 3.809e-05 [loop_unroll]: 0.00047525 [jit_opt_after_cconv]: 0.00022001, [1] [Cycle 1]: 0.00021204, [11] [c_1]: 5.045e-05 [parameter_eliminate]: 4.2e-06 [updatestate_depend_eliminate]: 1.035e-05 [updatestate_assign_eliminate]: 5.57999e-06 [updatestate_loads_eliminate]: 4.25999e-06 [cse]: 3.686e-05 [call_graph_tuple_transform]: 2.353e-05 [tuple_list_get_item_eliminator]: 8.97e-06 [none_parameter_eliminate]: 1.50001e-06 [renormalize]: 5.8001e-07 [switch_simplify]: 9.00999e-06 [remove_dup_value]: 2.05e-05 [partial_unused_args_eliminate]: 2.25002e-06 [environ_conv]: 2.278e-05 [add_recomputation]: 8.309e-05 [cse_after_recomputation]: 3.227e-05, [1] [Cycle 1]: 2.436e-05, [1] [cse]: 1.655e-05 [auto_monad_reorder]: 3.745e-05 [get_jit_bprop_graph]: 2.51e-06 [rewriter_after_jit_bprop_graph]: 0.00014536 [opt_after_jit_grad]: 0.00053845 [symbol_engine_optimizer]: 0.00010015, [1] [Cycle 1]: 9.259e-05, [6] [build]: 6.42001e-06 [elim_shapecalc]: 1.101e-05 [elim_not_effective]: 1.925e-05 [opt_reshape]: 9.00999e-06 [fold_const_symbol]: 1.456e-05 [renormalize]: 8.2e-07 [validate]: 7.697e-05 Sums bootstrap : 0.000659s : 0.27% type_inference : 0.237929s : 97.37% event_method : 0.000017s : 0.01% auto_monad : 0.000170s : 0.07% graph_reusing : 0.000008s : 0.00% pre_auto_parallel : 0.000013s : 0.01% py_interpret_to_execute : 0.000039s : 0.02% rewriter_before_opt_a : 0.000066s : 0.03% expand_dump_flag : 0.000003s : 0.00% jit_opt_a.switch_simplify : 0.000100s : 0.04% jit_opt_a.loop_unroll : 0.000031s : 0.01% jit_opt_a.a_1 : 0.000717s : 0.29% jit_opt_a.with_stream_mark : 0.000048s : 0.02% jit_opt_a.recompute_prepare : 0.000021s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000013s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000012s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000010s : 0.00% jit_opt_a.parameter_eliminate : 0.000003s : 0.00% jit_opt_a.specialize_transform : 0.000017s : 0.01% jit_opt_a.updatestate_useless_node_eliminater : 0.000023s : 0.01% jit_opt_a.accelerated_algorithm : 0.000018s : 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 : 0.000011s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000042s : 0.02% jit_opt_a.j_node_and_user_rematch : 0.000028s : 0.01% jit_opt_a.meta_fg_expand : 0.000006s : 0.00% jit_opt_a.replace_old_param : 0.000024s : 0.01% jit_opt_a.inline_without_move : 0.000016s : 0.01% jit_opt_a.renormalize : 0.000923s : 0.38% jit_opt_a.add_forward_monad_depend : 0.000015s : 0.01% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000036s : 0.01% jit_opt_a.cse : 0.000069s : 0.03% jit_opt_a.replace_applicator : 0.000028s : 0.01% py_interpret_to_execute_after_opt_a : 0.000017s : 0.01% rewriter_after_opt_a : 0.000630s : 0.26% convert_after_rewriter : 0.000016s : 0.01% order_py_execute_after_rewriter : 0.000008s : 0.00% mutable_eliminate : 0.000807s : 0.33% jit_opt_b.frontend_op_eliminate : 0.000026s : 0.01% jit_opt_b.inline_after_opt_a : 0.000025s : 0.01% cconv : 0.000038s : 0.02% loop_unroll : 0.000475s : 0.19% jit_opt_after_cconv.c_1 : 0.000050s : 0.02% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000010s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.cse : 0.000037s : 0.02% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 0.01% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000009s : 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 : 0.000009s : 0.00% remove_dup_value : 0.000021s : 0.01% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000023s : 0.01% add_recomputation : 0.000083s : 0.03% cse_after_recomputation.cse : 0.000017s : 0.01% auto_monad_reorder : 0.000037s : 0.02% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000145s : 0.06% opt_after_jit_grad : 0.000538s : 0.22% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000019s : 0.01% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.01% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000077s : 0.03% Time group info: ------[substitution.] 0.000268 44 3.57% : 0.000010s : 2: substitution.depend_value_elim 1.07% : 0.000003s : 4: substitution.elim_not_effective 0.90% : 0.000002s : 4: substitution.fold_const_symbol 2.76% : 0.000007s : 5: substitution.graph_param_transform 75.74% : 0.000203s : 3: substitution.inline 1.89% : 0.000005s : 8: substitution.j_node_and_user_rematch 2.76% : 0.000007s : 8: substitution.remove_not_recompute_node 2.33% : 0.000006s : 2: substitution.replace_old_param 4.85% : 0.000013s : 3: substitution.updatestate_pure_node_eliminater 4.14% : 0.000011s : 5: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.237850 2 99.69% : 0.237123s : 1: type_inference.infer 0.31% : 0.000727s : 1: type_inference.specialize ------[replace.] 0.000038 3 100.00% : 0.000038s : 3: replace.inline ------[match.] 0.000200 3 100.00% : 0.000200s : 3: match.inline ------[predicate.] 0.000165 825 1.74% : 0.000003s : 12: predicate.accumulaten_eliminater 1.57% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 1.49% : 0.000002s : 12: predicate.addn_check_dump 1.26% : 0.000002s : 12: predicate.addn_zero_filter 2.77% : 0.000005s : 12: predicate.arithmetic_simplify 1.12% : 0.000002s : 12: predicate.cast_eliminate 0.79% : 0.000001s : 5: predicate.check_bprop_eliminate 1.05% : 0.000002s : 12: predicate.compare_switch_simplify 1.46% : 0.000002s : 12: predicate.depend_value_elim 1.05% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.16% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.11% : 0.000002s : 12: predicate.dict_set_item_eliminator 1.34% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.35% : 0.000001s : 5: predicate.elim_not_effective 0.71% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.34% : 0.000002s : 12: predicate.environ_add_const_eliminate 0.91% : 0.000002s : 12: predicate.environ_get_add_eliminate 1.12% : 0.000002s : 12: predicate.environ_get_depend_swap 1.07% : 0.000002s : 12: predicate.environ_get_eliminate 1.02% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.27% : 0.000000s : 5: predicate.fold_const_symbol 1.10% : 0.000002s : 10: predicate.get_grad_eliminate 0.27% : 0.000000s : 5: predicate.graph_param_transform 5.62% : 0.000009s : 25: predicate.inline 1.08% : 0.000002s : 10: predicate.inline_without_move 0.48% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.63% : 0.000003s : 10: predicate.less_batch_normalization 1.11% : 0.000002s : 12: predicate.list_to_tuple_eliminator_ 1.61% : 0.000003s : 17: predicate.load_eliminater 1.88% : 0.000003s : 5: predicate.loop_unroll_after_grad 2.78% : 0.000005s : 25: predicate.loop_unroll_before_grad 1.90% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 0.97% : 0.000002s : 12: predicate.merge_addn 1.10% : 0.000002s : 12: predicate.minmaximum_grad 2.29% : 0.000004s : 5: predicate.mutable_eliminate 0.62% : 0.000001s : 5: predicate.opt_reshape 2.06% : 0.000003s : 17: predicate.partial_eliminate 1.16% : 0.000002s : 12: predicate.print_const_string_wrapper 1.65% : 0.000003s : 12: predicate.reduce_eliminate 1.18% : 0.000002s : 12: predicate.redundant_stop_gradient_eliminater 0.94% : 0.000002s : 10: predicate.remove_not_recompute_node 1.43% : 0.000002s : 22: predicate.replace_applicator 0.60% : 0.000001s : 10: predicate.replace_old_param 0.39% : 0.000001s : 5: predicate.reset_defer_inline 1.31% : 0.000002s : 12: predicate.reshape_eliminate 1.26% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.97% : 0.000002s : 5: predicate.row_tensor_eliminate 1.16% : 0.000002s : 12: predicate.same_eliminate 0.61% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.21% : 0.000002s : 10: predicate.special_op_eliminate 1.18% : 0.000002s : 10: predicate.specialize_transform 1.23% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.02% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.62% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.83% : 0.000003s : 15: predicate.switch_defer_inline 1.55% : 0.000003s : 15: predicate.switch_layer_defer_inline 6.55% : 0.000011s : 45: predicate.switch_simplify 1.08% : 0.000002s : 12: predicate.tile_eliminate 1.39% : 0.000002s : 12: predicate.transpose_eliminate 1.42% : 0.000002s : 12: predicate.tuple_list_convert_item_index_to_positive 1.26% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 4.40% : 0.000007s : 22: predicate.tuple_list_get_item_eliminator 1.91% : 0.000003s : 12: predicate.tuple_list_set_item_eliminator 1.52% : 0.000003s : 12: predicate.tuple_to_list_eliminator_ 1.90% : 0.000003s : 17: predicate.updatestate_pure_node_eliminater 3.28% : 0.000005s : 27: predicate.updatestate_useless_node_eliminater 1.40% : 0.000002s : 12: predicate.value_based_eliminate 0.50% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.91% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000396 6 6.15% : 0.000024s : 1: func_graph_cloner_run.FuncGraphClonerGraph 93.85% : 0.000372s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.434053 72 0.02% : 0.000086s : 1: add_recomputation 0.04% : 0.000176s : 1: auto_monad 0.01% : 0.000040s : 1: auto_monad_reorder 0.16% : 0.000680s : 1: bootstrap 0.01% : 0.000041s : 1: cconv 0.00% : 0.000019s : 1: convert_after_rewriter 0.01% : 0.000035s : 1: cse_after_recomputation 0.01% : 0.000026s : 1: environ_conv 0.01% : 0.000023s : 1: event_method 0.00% : 0.000005s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000011s : 1: graph_reusing 43.67% : 0.189538s : 1: jit_opt_a 0.05% : 0.000223s : 1: jit_opt_after_cconv 0.02% : 0.000079s : 1: jit_opt_b 0.11% : 0.000485s : 1: loop_unroll 0.19% : 0.000819s : 1: mutable_eliminate 0.24% : 0.001024s : 26: opt.transform.jit_opt_a 0.02% : 0.000088s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000044s : 4: opt.transform.jit_opt_b 0.00% : 0.000017s : 1: opt.transform.loop_unroll_optimizer 0.01% : 0.000022s : 1: opt.transform.mutable_eliminate 0.01% : 0.000033s : 1: opt.transform.opt_after_jit_grad 0.01% : 0.000050s : 4: opt.transform.symbol_engine_opt 0.13% : 0.000548s : 1: opt_after_jit_grad 0.00% : 0.000010s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000016s : 1: pre_auto_parallel 0.01% : 0.000042s : 1: py_interpret_to_execute 0.00% : 0.000019s : 1: py_interpret_to_execute_after_opt_a 0.01% : 0.000023s : 1: remove_dup_value 0.12% : 0.000519s : 1: renormalize.infer 0.09% : 0.000393s : 1: renormalize.specialize 0.03% : 0.000149s : 1: rewriter_after_jit_bprop_graph 0.15% : 0.000636s : 1: rewriter_after_opt_a 0.02% : 0.000070s : 1: rewriter_before_opt_a 0.02% : 0.000103s : 1: symbol_engine_optimizer 54.82% : 0.237951s : 1: type_inference TotalTime = 6.8029, [30] [bootstrap]: 0.00536984 [type_inference]: 5.13756 [event_method]: 0.00012301 [auto_monad]: 0.00043076 [graph_reusing]: 1.053e-05 [pre_auto_parallel]: 3.84002e-06 [py_interpret_to_execute]: 4.623e-05 [rewriter_before_opt_a]: 0.00022878 [expand_dump_flag]: 4.23999e-06 [jit_opt_a]: 1.44774, [3] [Cycle 1]: 0.648343, [27] [switch_simplify]: 0.00235005 [loop_unroll]: 6.032e-05 [a_1]: 0.00242666 [with_stream_mark]: 3.612e-05 [recompute_prepare]: 2.515e-05 [updatestate_depend_eliminate]: 1.312e-05 [updatestate_assign_eliminate]: 9.94001e-06 [updatestate_loads_eliminate]: 9.39998e-06 [parameter_eliminate]: 3.01999e-06 [specialize_transform]: 1.884e-05 [updatestate_useless_node_eliminater]: 2.303e-05 [accelerated_algorithm]: 1.851e-05 [meta_shard_fg_expand]: 4.63001e-06 [get_grad_eliminate_]: 1.728e-05 [merge_forward]: 1.073e-05 [cell_reuse_recompute_pass]: 9.50007e-07 [cell_reuse_handle_not_recompute_node_pass]: 5.191e-05 [j_node_and_user_rematch]: 0.00056452 [meta_fg_expand]: 0.427924 [replace_old_param]: 0.0001283 [inline_without_move]: 0.00011981 [renormalize]: 0.213457 [add_forward_monad_depend]: 3.074e-05 [auto_monad_grad]: 1.28e-05 [auto_monad_eliminator]: 9.025e-05 [cse]: 0.00032985 [replace_applicator]: 0.00025766 [Cycle 2]: 0.00336067, [27] [switch_simplify]: 6.157e-05 [loop_unroll]: 5.773e-05 [a_1]: 0.00115476 [with_stream_mark]: 3.019e-05 [recompute_prepare]: 1.538e-05 [updatestate_depend_eliminate]: 3.595e-05 [updatestate_assign_eliminate]: 6.54999e-06 [updatestate_loads_eliminate]: 5.65001e-06 [parameter_eliminate]: 2.66e-06 [specialize_transform]: 1.09e-05 [updatestate_useless_node_eliminater]: 1.24e-05 [accelerated_algorithm]: 9.28002e-06 [meta_shard_fg_expand]: 2.78003e-06 [get_grad_eliminate_]: 8.46002e-06 [merge_forward]: 5.38002e-06 [cell_reuse_recompute_pass]: 1.96e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.059e-05 [j_node_and_user_rematch]: 1.375e-05 [meta_fg_expand]: 0.00012603 [replace_old_param]: 1.915e-05 [inline_without_move]: 9.96e-06 [renormalize]: 0.0013877 [add_forward_monad_depend]: 9.66998e-06 [auto_monad_grad]: 2.71e-06 [auto_monad_eliminator]: 2.644e-05 [cse]: 4.291e-05 [replace_applicator]: 2.431e-05 [Cycle 3]: 0.0005307, [27] [switch_simplify]: 1.019e-05 [loop_unroll]: 7.92e-06 [a_1]: 0.00018551 [with_stream_mark]: 1.868e-05 [recompute_prepare]: 8.69e-06 [updatestate_depend_eliminate]: 6.51e-06 [updatestate_assign_eliminate]: 4.80999e-06 [updatestate_loads_eliminate]: 4.82998e-06 [parameter_eliminate]: 1.66e-06 [specialize_transform]: 8.59e-06 [updatestate_useless_node_eliminater]: 1.114e-05 [accelerated_algorithm]: 8.45001e-06 [meta_shard_fg_expand]: 2.85002e-06 [get_grad_eliminate_]: 7.53999e-06 [merge_forward]: 5.32001e-06 [cell_reuse_recompute_pass]: 2.29001e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.156e-05 [j_node_and_user_rematch]: 1.46e-05 [meta_fg_expand]: 3.2e-06 [replace_old_param]: 1.174e-05 [inline_without_move]: 7.78001e-06 [renormalize]: 8.9989e-08 [add_forward_monad_depend]: 2.09999e-06 [auto_monad_grad]: 1.67001e-06 [auto_monad_eliminator]: 1.203e-05 [cse]: 2.202e-05 [replace_applicator]: 8.75999e-06 [py_interpret_to_execute_after_opt_a]: 1.683e-05 [rewriter_after_opt_a]: 0.00029308 [convert_after_rewriter]: 1.769e-05 [order_py_execute_after_rewriter]: 6.77002e-06 [mutable_eliminate]: 0.00080225 [jit_opt_b]: 7.551e-05, [1] [Cycle 1]: 6.634e-05, [2] [frontend_op_eliminate]: 2.588e-05 [inline_after_opt_a]: 2.604e-05 [cconv]: 3.229e-05 [loop_unroll]: 0.208333 [jit_opt_after_cconv]: 0.0003315, [1] [Cycle 1]: 0.00031899, [11] [c_1]: 6.511e-05 [parameter_eliminate]: 6.98e-06 [updatestate_depend_eliminate]: 1.452e-05 [updatestate_assign_eliminate]: 7.13998e-06 [updatestate_loads_eliminate]: 5.19e-06 [cse]: 0.0001104 [call_graph_tuple_transform]: 2.698e-05 [tuple_list_get_item_eliminator]: 1.048e-05 [none_parameter_eliminate]: 1.87001e-06 [renormalize]: 8.10018e-07 [switch_simplify]: 9.05001e-06 [remove_dup_value]: 2.569e-05 [partial_unused_args_eliminate]: 2.89999e-06 [environ_conv]: 1.186e-05 [add_recomputation]: 8.602e-05 [cse_after_recomputation]: 3.861e-05, [1] [Cycle 1]: 3.052e-05, [1] [cse]: 2.246e-05 [auto_monad_reorder]: 2.891e-05 [get_jit_bprop_graph]: 2.51e-06 [rewriter_after_jit_bprop_graph]: 1.157e-05 [opt_after_jit_grad]: 0.0006812 [symbol_engine_optimizer]: 0.00011343, [1] [Cycle 1]: 0.00010402, [6] [build]: 8.79e-06 [elim_shapecalc]: 1.367e-05 [elim_not_effective]: 2.39e-05 [opt_reshape]: 9.54e-06 [fold_const_symbol]: 1.466e-05 [renormalize]: 5.69999e-07 [validate]: 6.587e-05 Sums bootstrap : 0.005370s : 0.09% type_inference : 5.137564s : 85.54% event_method : 0.000123s : 0.00% auto_monad : 0.000431s : 0.01% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000046s : 0.00% rewriter_before_opt_a : 0.000229s : 0.00% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.002422s : 0.04% jit_opt_a.loop_unroll : 0.000126s : 0.00% jit_opt_a.a_1 : 0.003767s : 0.06% jit_opt_a.with_stream_mark : 0.000085s : 0.00% jit_opt_a.recompute_prepare : 0.000049s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000056s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000021s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000020s : 0.00% jit_opt_a.parameter_eliminate : 0.000007s : 0.00% jit_opt_a.specialize_transform : 0.000038s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000047s : 0.00% jit_opt_a.accelerated_algorithm : 0.000036s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000010s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000033s : 0.00% jit_opt_a.merge_forward : 0.000021s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000094s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000593s : 0.01% jit_opt_a.meta_fg_expand : 0.428053s : 7.13% jit_opt_a.replace_old_param : 0.000159s : 0.00% jit_opt_a.inline_without_move : 0.000138s : 0.00% jit_opt_a.renormalize : 0.214844s : 3.58% jit_opt_a.add_forward_monad_depend : 0.000043s : 0.00% jit_opt_a.auto_monad_grad : 0.000017s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000129s : 0.00% jit_opt_a.cse : 0.000395s : 0.01% jit_opt_a.replace_applicator : 0.000291s : 0.00% py_interpret_to_execute_after_opt_a : 0.000017s : 0.00% rewriter_after_opt_a : 0.000293s : 0.00% convert_after_rewriter : 0.000018s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000802s : 0.01% jit_opt_b.frontend_op_eliminate : 0.000026s : 0.00% jit_opt_b.inline_after_opt_a : 0.000026s : 0.00% cconv : 0.000032s : 0.00% loop_unroll : 0.208333s : 3.47% jit_opt_after_cconv.c_1 : 0.000065s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000015s : 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 : 0.000110s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000027s : 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 : 0.000009s : 0.00% remove_dup_value : 0.000026s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000012s : 0.00% add_recomputation : 0.000086s : 0.00% cse_after_recomputation.cse : 0.000022s : 0.00% auto_monad_reorder : 0.000029s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000012s : 0.00% opt_after_jit_grad : 0.000681s : 0.01% symbol_engine_optimizer.build : 0.000009s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000014s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000024s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000010s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000066s : 0.00% Time group info: ------[substitution.] 0.003190 170 0.76% : 0.000024s : 8: substitution.depend_value_elim 0.12% : 0.000004s : 4: substitution.elim_not_effective 0.08% : 0.000003s : 4: substitution.fold_const_symbol 33.01% : 0.001053s : 4: substitution.getattr_setattr_resolve 0.23% : 0.000007s : 5: substitution.graph_param_transform 17.72% : 0.000565s : 16: substitution.inline 1.19% : 0.000038s : 4: substitution.inline_without_move 16.75% : 0.000534s : 20: substitution.j_node_and_user_rematch 0.36% : 0.000011s : 5: substitution.minmaximum_grad 22.30% : 0.000711s : 9: substitution.partial_eliminate 0.52% : 0.000016s : 20: substitution.remove_not_recompute_node 1.63% : 0.000052s : 12: substitution.replace_applicator 0.54% : 0.000017s : 16: substitution.replace_old_param 0.08% : 0.000003s : 1: substitution.set_cell_output_no_recompute 0.53% : 0.000017s : 3: substitution.switch_simplify 0.69% : 0.000022s : 5: substitution.tuple_list_convert_item_index_to_positive 0.46% : 0.000015s : 5: substitution.tuple_list_get_item_depend_reorder 1.23% : 0.000039s : 8: substitution.tuple_list_get_item_eliminator 0.71% : 0.000022s : 8: substitution.updatestate_pure_node_eliminater 1.10% : 0.000035s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 5.137041 2 96.61% : 4.962684s : 1: type_inference.infer 3.39% : 0.174357s : 1: type_inference.specialize ------[replace.] 0.001808 27 4.21% : 0.000076s : 3: replace.getattr_setattr_resolve 7.59% : 0.000137s : 16: replace.inline 5.24% : 0.000095s : 1: replace.replace_applicator 76.90% : 0.001391s : 3: replace.switch_simplify 4.66% : 0.000084s : 3: replace.tuple_list_get_item_eliminator 1.40% : 0.000025s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001531 27 60.41% : 0.000925s : 3: match.getattr_setattr_resolve 36.29% : 0.000556s : 16: match.inline 0.85% : 0.000013s : 1: match.replace_applicator 0.98% : 0.000015s : 3: match.switch_simplify 0.69% : 0.000010s : 3: match.tuple_list_get_item_eliminator 0.80% : 0.000012s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000848 3164 0.87% : 0.000007s : 50: predicate.accumulaten_eliminater 0.33% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 0.77% : 0.000007s : 50: predicate.addn_check_dump 0.83% : 0.000007s : 50: predicate.addn_zero_filter 1.15% : 0.000010s : 50: predicate.arithmetic_simplify 0.93% : 0.000008s : 50: predicate.cast_eliminate 0.10% : 0.000001s : 5: predicate.check_bprop_eliminate 0.82% : 0.000007s : 50: predicate.compare_switch_simplify 0.89% : 0.000008s : 50: predicate.depend_value_elim 0.84% : 0.000007s : 50: predicate.dict_get_item_const_eliminator 0.88% : 0.000007s : 50: predicate.dict_get_item_eliminator 0.81% : 0.000007s : 50: predicate.dict_set_item_eliminator 0.30% : 0.000003s : 5: predicate.dumpgradient_eliminate 0.08% : 0.000001s : 5: predicate.elim_not_effective 0.20% : 0.000002s : 5: predicate.elim_shapecalc_of_broadcastargs 0.83% : 0.000007s : 50: predicate.environ_add_const_eliminate 0.85% : 0.000007s : 50: predicate.environ_get_add_eliminate 0.77% : 0.000007s : 50: predicate.environ_get_depend_swap 0.83% : 0.000007s : 50: predicate.environ_get_eliminate 0.77% : 0.000007s : 50: predicate.environ_get_set_eliminate 0.05% : 0.000000s : 5: predicate.fold_const_symbol 0.55% : 0.000005s : 26: predicate.get_grad_eliminate 1.10% : 0.000009s : 20: predicate.getattr_setattr_resolve 0.07% : 0.000001s : 5: predicate.graph_param_transform 2.61% : 0.000022s : 80: predicate.inline 1.88% : 0.000016s : 87: predicate.inline_without_move 0.85% : 0.000007s : 26: predicate.j_node_and_user_rematch 0.75% : 0.000006s : 26: predicate.less_batch_normalization 1.09% : 0.000009s : 53: predicate.list_to_tuple_eliminator_ 1.09% : 0.000009s : 58: predicate.load_eliminater 0.81% : 0.000007s : 5: predicate.loop_unroll_after_grad 2.31% : 0.000020s : 132: predicate.loop_unroll_before_grad 1.18% : 0.000010s : 55: predicate.make_slice_get_slice_eliminator 0.81% : 0.000007s : 50: predicate.merge_addn 0.80% : 0.000007s : 50: predicate.minmaximum_grad 0.41% : 0.000003s : 5: predicate.mutable_eliminate 0.12% : 0.000001s : 5: predicate.opt_reshape 1.28% : 0.000011s : 58: predicate.partial_eliminate 0.92% : 0.000008s : 50: predicate.print_const_string_wrapper 1.07% : 0.000009s : 50: predicate.reduce_eliminate 0.96% : 0.000008s : 53: predicate.redundant_stop_gradient_eliminater 0.29% : 0.000002s : 26: predicate.remove_not_recompute_node 1.65% : 0.000014s : 126: predicate.replace_applicator 0.92% : 0.000008s : 87: predicate.replace_old_param 0.08% : 0.000001s : 5: predicate.reset_defer_inline 37.96% : 0.000322s : 50: predicate.reshape_eliminate 0.83% : 0.000007s : 50: predicate.row_tensor_add_zeros_like 0.22% : 0.000002s : 5: predicate.row_tensor_eliminate 0.83% : 0.000007s : 50: predicate.same_eliminate 0.29% : 0.000002s : 28: predicate.set_cell_output_no_recompute 0.28% : 0.000002s : 10: predicate.special_op_eliminate 0.59% : 0.000005s : 26: predicate.specialize_transform 0.96% : 0.000008s : 50: predicate.split_environ_get_set_with_tuple_value 0.93% : 0.000008s : 50: predicate.stack_unstack_eliminate 0.13% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.50% : 0.000013s : 70: predicate.switch_defer_inline 1.30% : 0.000011s : 70: predicate.switch_layer_defer_inline 4.42% : 0.000037s : 213: predicate.switch_simplify 0.92% : 0.000008s : 50: predicate.tile_eliminate 0.83% : 0.000007s : 50: predicate.transpose_eliminate 1.10% : 0.000009s : 50: predicate.tuple_list_convert_item_index_to_positive 0.97% : 0.000008s : 50: predicate.tuple_list_get_item_depend_reorder 2.06% : 0.000017s : 63: predicate.tuple_list_get_item_eliminator 1.11% : 0.000009s : 50: predicate.tuple_list_set_item_eliminator 0.96% : 0.000008s : 53: predicate.tuple_to_list_eliminator_ 1.02% : 0.000009s : 58: predicate.updatestate_pure_node_eliminater 1.95% : 0.000017s : 85: predicate.updatestate_useless_node_eliminater 1.14% : 0.000010s : 50: predicate.value_based_eliminate 0.10% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.13% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.175022 47 99.13% : 0.173504s : 20: func_graph_cloner_run.FuncGraphClonerGraph 0.87% : 0.001519s : 27: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 7.026801 89 0.00% : 0.000090s : 1: add_recomputation 0.01% : 0.000439s : 1: auto_monad 0.00% : 0.000032s : 1: auto_monad_reorder 0.08% : 0.005509s : 1: bootstrap 0.00% : 0.000035s : 1: cconv 0.00% : 0.000021s : 1: convert_after_rewriter 0.00% : 0.000041s : 1: cse_after_recomputation 0.00% : 0.000015s : 1: environ_conv 0.00% : 0.000131s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000014s : 1: graph_reusing 20.60% : 1.447748s : 1: jit_opt_a 0.00% : 0.000336s : 1: jit_opt_after_cconv 0.00% : 0.000079s : 1: jit_opt_b 2.97% : 0.208354s : 1: loop_unroll 0.01% : 0.000814s : 1: mutable_eliminate 0.11% : 0.007715s : 39: opt.transform.jit_opt_a 0.00% : 0.000107s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000044s : 4: opt.transform.jit_opt_b 0.00% : 0.000040s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000023s : 1: opt.transform.mutable_eliminate 0.00% : 0.000042s : 1: opt.transform.opt_after_jit_grad 0.02% : 0.001202s : 2: opt.transform.opt_resolve 0.00% : 0.000057s : 4: opt.transform.symbol_engine_opt 0.01% : 0.000696s : 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% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000049s : 1: py_interpret_to_execute 0.00% : 0.000019s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000029s : 1: remove_dup_value 3.00% : 0.210789s : 2: renormalize.infer 0.06% : 0.004028s : 2: renormalize.specialize 0.00% : 0.000014s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000301s : 1: rewriter_after_opt_a 0.00% : 0.000233s : 1: rewriter_before_opt_a 0.00% : 0.000117s : 1: symbol_engine_optimizer 73.11% : 5.137604s : 1: type_inference . [hook] pytest_runtest_teardown:test_unsqueeze_very_large_tensors[KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_very_large_tensors[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 266.58s (0:04:26) ==================