==================================================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_squeeze.py . [hook] pytest_runtest_teardown:test_squeeze_jit_mode[pynative] tests/st/mint/test_squeeze.py::test_squeeze_jit_mode[pynative],max_mem:2.0M [WARNING] PARSER(171265,ffff88f4df30,python3.9):2026-01-29-17:40:39.841.671 [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 = 4.16005, [30] [bootstrap]: 0.00077678 [type_inference]: 3.48495 [event_method]: 2.384e-05 [auto_monad]: 0.00108827 [graph_reusing]: 5.92999e-06 [pre_auto_parallel]: 0.00012784 [py_interpret_to_execute]: 0.00169531 [rewriter_before_opt_a]: 0.00021264 [expand_dump_flag]: 4.4e-06 [jit_opt_a]: 0.660971, [2] [Cycle 1]: 0.00830902, [27] [switch_simplify]: 0.00015083 [loop_unroll]: 2.847e-05 [a_1]: 0.00073065 [with_stream_mark]: 3.196e-05 [recompute_prepare]: 1.397e-05 [updatestate_depend_eliminate]: 4.84e-06 [updatestate_assign_eliminate]: 3.71999e-06 [updatestate_loads_eliminate]: 3.14001e-06 [parameter_eliminate]: 1.99e-06 [specialize_transform]: 7.83001e-06 [updatestate_useless_node_eliminater]: 6.31998e-06 [accelerated_algorithm]: 7.45e-06 [meta_shard_fg_expand]: 2.94999e-06 [get_grad_eliminate_]: 7.22002e-06 [merge_forward]: 4.43001e-06 [cell_reuse_recompute_pass]: 2.12999e-06 [cell_reuse_handle_not_recompute_node_pass]: 0.00017466 [j_node_and_user_rematch]: 1.278e-05 [meta_fg_expand]: 3.2e-06 [replace_old_param]: 1.248e-05 [inline_without_move]: 6.44001e-06 [renormalize]: 0.0063503 [add_forward_monad_depend]: 0.0001344 [auto_monad_grad]: 3.08e-06 [auto_monad_eliminator]: 2.216e-05 [cse]: 3.977e-05 [replace_applicator]: 2.721e-05 [Cycle 2]: 0.00044036, [27] [switch_simplify]: 7.48999e-06 [loop_unroll]: 6.38e-06 [a_1]: 0.00014048 [with_stream_mark]: 1.667e-05 [recompute_prepare]: 6.54001e-06 [updatestate_depend_eliminate]: 3.95e-06 [updatestate_assign_eliminate]: 1.535e-05 [updatestate_loads_eliminate]: 3.14999e-06 [parameter_eliminate]: 1.95001e-06 [specialize_transform]: 6.37001e-06 [updatestate_useless_node_eliminater]: 6.76e-06 [accelerated_algorithm]: 7.09001e-06 [meta_shard_fg_expand]: 2.03002e-06 [get_grad_eliminate_]: 6.02999e-06 [merge_forward]: 7.38999e-06 [cell_reuse_recompute_pass]: 4.13999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.149e-05 [j_node_and_user_rematch]: 1.046e-05 [meta_fg_expand]: 2.73998e-06 [replace_old_param]: 1.058e-05 [inline_without_move]: 6.61e-06 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 1.30001e-06 [auto_monad_grad]: 9.80013e-07 [auto_monad_eliminator]: 6.30002e-06 [cse]: 1.236e-05 [replace_applicator]: 5.83002e-06 [py_interpret_to_execute_after_opt_a]: 1.832e-05 [rewriter_after_opt_a]: 0.0009994 [convert_after_rewriter]: 3.432e-05 [order_py_execute_after_rewriter]: 5.69999e-06 [mutable_eliminate]: 0.00157678 [jit_opt_b]: 7.357e-05, [1] [Cycle 1]: 6.212e-05, [2] [frontend_op_eliminate]: 2.531e-05 [inline_after_opt_a]: 2.305e-05 [cconv]: 0.00080809 [loop_unroll]: 0.00066833 [jit_opt_after_cconv]: 0.00023399, [1] [Cycle 1]: 0.00022397, [11] [c_1]: 3.782e-05 [parameter_eliminate]: 6.95002e-06 [updatestate_depend_eliminate]: 1.312e-05 [updatestate_assign_eliminate]: 4.09997e-06 [updatestate_loads_eliminate]: 4.23999e-06 [cse]: 4.653e-05 [call_graph_tuple_transform]: 3.268e-05 [tuple_list_get_item_eliminator]: 9.27001e-06 [none_parameter_eliminate]: 1.69998e-06 [renormalize]: 5.19998e-07 [switch_simplify]: 6.76999e-06 [remove_dup_value]: 1.952e-05 [partial_unused_args_eliminate]: 3.15998e-06 [environ_conv]: 0.00082374 [add_recomputation]: 7.135e-05 [cse_after_recomputation]: 3.83e-05, [1] [Cycle 1]: 2.982e-05, [1] [cse]: 2.03e-05 [auto_monad_reorder]: 2.865e-05 [get_jit_bprop_graph]: 2.51e-06 [rewriter_after_jit_bprop_graph]: 0.00294916 [opt_after_jit_grad]: 0.00089244 [symbol_engine_optimizer]: 0.00010393, [1] [Cycle 1]: 9.226e-05, [6] [build]: 6.91001e-06 [elim_shapecalc]: 1.005e-05 [elim_not_effective]: 1.936e-05 [opt_reshape]: 9.49e-06 [fold_const_symbol]: 1.288e-05 [renormalize]: 7.50006e-07 [validate]: 0.00039899 Sums bootstrap : 0.000777s : 0.02% type_inference : 3.484949s : 99.38% event_method : 0.000024s : 0.00% auto_monad : 0.001088s : 0.03% graph_reusing : 0.000006s : 0.00% pre_auto_parallel : 0.000128s : 0.00% py_interpret_to_execute : 0.001695s : 0.05% rewriter_before_opt_a : 0.000213s : 0.01% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000158s : 0.00% jit_opt_a.loop_unroll : 0.000035s : 0.00% jit_opt_a.a_1 : 0.000871s : 0.02% jit_opt_a.with_stream_mark : 0.000049s : 0.00% jit_opt_a.recompute_prepare : 0.000021s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000019s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000006s : 0.00% jit_opt_a.parameter_eliminate : 0.000004s : 0.00% jit_opt_a.specialize_transform : 0.000014s : 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 : 0.000012s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000006s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000196s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000023s : 0.00% jit_opt_a.meta_fg_expand : 0.000006s : 0.00% jit_opt_a.replace_old_param : 0.000023s : 0.00% jit_opt_a.inline_without_move : 0.000013s : 0.00% jit_opt_a.renormalize : 0.006350s : 0.18% jit_opt_a.add_forward_monad_depend : 0.000136s : 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 : 0.000052s : 0.00% jit_opt_a.replace_applicator : 0.000033s : 0.00% py_interpret_to_execute_after_opt_a : 0.000018s : 0.00% rewriter_after_opt_a : 0.000999s : 0.03% convert_after_rewriter : 0.000034s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.001577s : 0.04% jit_opt_b.frontend_op_eliminate : 0.000025s : 0.00% jit_opt_b.inline_after_opt_a : 0.000023s : 0.00% cconv : 0.000808s : 0.02% loop_unroll : 0.000668s : 0.02% jit_opt_after_cconv.c_1 : 0.000038s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000013s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.cse : 0.000047s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000033s : 0.00% 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 : 0.000007s : 0.00% remove_dup_value : 0.000020s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000824s : 0.02% add_recomputation : 0.000071s : 0.00% cse_after_recomputation.cse : 0.000020s : 0.00% auto_monad_reorder : 0.000029s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.002949s : 0.08% opt_after_jit_grad : 0.000892s : 0.03% symbol_engine_optimizer.build : 0.000007s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000010s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000019s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000013s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000399s : 0.01% Time group info: ------[substitution.] 0.000415 23 1.00% : 0.000004s : 2: substitution.elim_not_effective 0.59% : 0.000002s : 2: substitution.fold_const_symbol 2.15% : 0.000009s : 4: substitution.graph_param_transform 52.32% : 0.000217s : 4: substitution.inline 1.13% : 0.000005s : 4: substitution.j_node_and_user_rematch 38.30% : 0.000159s : 4: substitution.remove_not_recompute_node 1.72% : 0.000007s : 2: substitution.replace_old_param 2.79% : 0.000012s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 3.484385 2 99.82% : 3.478095s : 1: type_inference.infer 0.18% : 0.006290s : 1: type_inference.specialize ------[replace.] 0.000064 5 83.19% : 0.000053s : 4: replace.inline 16.81% : 0.000011s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000224 5 95.21% : 0.000214s : 4: match.inline 4.79% : 0.000011s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000261 801 1.30% : 0.000003s : 12: predicate.accumulaten_eliminater 0.93% : 0.000002s : 4: predicate.ad_related_special_op_eliminate 0.66% : 0.000002s : 12: predicate.addn_check_dump 0.75% : 0.000002s : 12: predicate.addn_zero_filter 1.46% : 0.000004s : 12: predicate.arithmetic_simplify 0.81% : 0.000002s : 12: predicate.cast_eliminate 0.27% : 0.000001s : 4: predicate.check_bprop_eliminate 0.62% : 0.000002s : 12: predicate.compare_switch_simplify 0.84% : 0.000002s : 12: predicate.depend_value_elim 0.62% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 0.62% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.41% : 0.000004s : 12: predicate.dict_set_item_eliminator 0.80% : 0.000002s : 4: predicate.dumpgradient_eliminate 0.38% : 0.000001s : 4: predicate.elim_not_effective 0.34% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 0.72% : 0.000002s : 12: predicate.environ_add_const_eliminate 0.61% : 0.000002s : 12: predicate.environ_get_add_eliminate 0.68% : 0.000002s : 12: predicate.environ_get_depend_swap 0.69% : 0.000002s : 12: predicate.environ_get_eliminate 0.56% : 0.000001s : 12: predicate.environ_get_set_eliminate 0.15% : 0.000000s : 4: predicate.fold_const_symbol 0.64% : 0.000002s : 8: predicate.get_grad_eliminate 0.22% : 0.000001s : 4: predicate.graph_param_transform 2.98% : 0.000008s : 25: predicate.inline 0.61% : 0.000002s : 8: predicate.inline_without_move 0.27% : 0.000001s : 8: predicate.j_node_and_user_rematch 0.66% : 0.000002s : 8: predicate.less_batch_normalization 1.18% : 0.000003s : 13: predicate.list_to_tuple_eliminator_ 0.97% : 0.000003s : 17: predicate.load_eliminater 0.86% : 0.000002s : 4: predicate.loop_unroll_after_grad 1.99% : 0.000005s : 28: predicate.loop_unroll_before_grad 1.45% : 0.000004s : 16: predicate.make_slice_get_slice_eliminator 1.02% : 0.000003s : 12: predicate.merge_addn 0.61% : 0.000002s : 12: predicate.minmaximum_grad 1.82% : 0.000005s : 4: predicate.mutable_eliminate 0.31% : 0.000001s : 4: predicate.opt_reshape 1.07% : 0.000003s : 17: predicate.partial_eliminate 0.66% : 0.000002s : 12: predicate.print_const_string_wrapper 1.10% : 0.000003s : 12: predicate.reduce_eliminate 0.82% : 0.000002s : 13: predicate.redundant_stop_gradient_eliminater 0.39% : 0.000001s : 8: predicate.remove_not_recompute_node 1.07% : 0.000003s : 21: predicate.replace_applicator 0.41% : 0.000001s : 8: predicate.replace_old_param 0.28% : 0.000001s : 4: predicate.reset_defer_inline 0.75% : 0.000002s : 12: predicate.reshape_eliminate 0.74% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.65% : 0.000002s : 4: predicate.row_tensor_eliminate 1.03% : 0.000003s : 12: predicate.same_eliminate 0.34% : 0.000001s : 8: predicate.set_cell_output_no_recompute 0.75% : 0.000002s : 8: predicate.special_op_eliminate 0.54% : 0.000001s : 8: predicate.specialize_transform 0.81% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 0.90% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.23% : 0.000001s : 4: predicate.switch_call_monad_eliminater 1.22% : 0.000003s : 17: predicate.switch_defer_inline 1.04% : 0.000003s : 17: predicate.switch_layer_defer_inline 4.59% : 0.000012s : 49: predicate.switch_simplify 1.22% : 0.000003s : 12: predicate.tile_eliminate 36.24% : 0.000095s : 12: predicate.transpose_eliminate 1.14% : 0.000003s : 12: predicate.tuple_list_convert_item_index_to_positive 1.10% : 0.000003s : 12: predicate.tuple_list_get_item_depend_reorder 2.56% : 0.000007s : 21: predicate.tuple_list_get_item_eliminator 0.90% : 0.000002s : 12: predicate.tuple_list_set_item_eliminator 0.89% : 0.000002s : 13: predicate.tuple_to_list_eliminator_ 1.00% : 0.000003s : 17: predicate.updatestate_pure_node_eliminater 1.78% : 0.000005s : 25: predicate.updatestate_useless_node_eliminater 2.42% : 0.000006s : 12: predicate.value_based_eliminate 0.20% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.36% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.005923 18 73.37% : 0.004345s : 12: func_graph_cloner_run.FuncGraphClonerGraph 26.63% : 0.001577s : 6: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 4.167309 72 0.00% : 0.000075s : 1: add_recomputation 0.03% : 0.001093s : 1: auto_monad 0.00% : 0.000032s : 1: auto_monad_reorder 0.02% : 0.000806s : 1: bootstrap 0.02% : 0.000820s : 1: cconv 0.00% : 0.000038s : 1: convert_after_rewriter 0.00% : 0.000041s : 1: cse_after_recomputation 0.02% : 0.000830s : 1: environ_conv 0.00% : 0.000029s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 15.86% : 0.660975s : 1: jit_opt_a 0.01% : 0.000237s : 1: jit_opt_after_cconv 0.00% : 0.000077s : 1: jit_opt_b 0.02% : 0.000680s : 1: loop_unroll 0.04% : 0.001598s : 1: mutable_eliminate 0.03% : 0.001305s : 26: opt.transform.jit_opt_a 0.00% : 0.000080s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000038s : 4: opt.transform.jit_opt_b 0.00% : 0.000017s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000022s : 1: opt.transform.mutable_eliminate 0.00% : 0.000037s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000047s : 4: opt.transform.symbol_engine_opt 0.02% : 0.000904s : 1: opt_after_jit_grad 0.00% : 0.000008s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000130s : 1: pre_auto_parallel 0.04% : 0.001709s : 1: py_interpret_to_execute 0.00% : 0.000022s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000022s : 1: remove_dup_value 0.12% : 0.005033s : 1: renormalize.infer 0.03% : 0.001302s : 1: renormalize.specialize 0.07% : 0.002961s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.001009s : 1: rewriter_after_opt_a 0.01% : 0.000218s : 1: rewriter_before_opt_a 0.00% : 0.000107s : 1: symbol_engine_optimizer 83.63% : 3.484980s : 1: type_inference TotalTime = 2.21775, [30] [bootstrap]: 0.00076438 [type_inference]: 2.02205 [event_method]: 0.00110258 [auto_monad]: 0.000306 [graph_reusing]: 1.174e-05 [pre_auto_parallel]: 5.19e-06 [py_interpret_to_execute]: 7.116e-05 [rewriter_before_opt_a]: 0.00018342 [expand_dump_flag]: 5.34e-06 [jit_opt_a]: 0.189617, [2] [Cycle 1]: 0.183612, [27] [switch_simplify]: 0.00027517 [loop_unroll]: 6.02e-05 [a_1]: 0.00154153 [with_stream_mark]: 4.619e-05 [recompute_prepare]: 0.168192 [updatestate_depend_eliminate]: 3.268e-05 [updatestate_assign_eliminate]: 9.41e-06 [updatestate_loads_eliminate]: 7.85998e-06 [parameter_eliminate]: 1.246e-05 [specialize_transform]: 2.826e-05 [updatestate_useless_node_eliminater]: 1.799e-05 [accelerated_algorithm]: 1.854e-05 [meta_shard_fg_expand]: 1.422e-05 [get_grad_eliminate_]: 1.672e-05 [merge_forward]: 1.155e-05 [cell_reuse_recompute_pass]: 1.40999e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.736e-05 [j_node_and_user_rematch]: 2.935e-05 [meta_fg_expand]: 0.00276549 [replace_old_param]: 8.57e-05 [inline_without_move]: 8.097e-05 [renormalize]: 0.00981916 [add_forward_monad_depend]: 1.035e-05 [auto_monad_grad]: 2.99001e-06 [auto_monad_eliminator]: 2.074e-05 [cse]: 3.82e-05 [replace_applicator]: 3.136e-05 [Cycle 2]: 0.00061405, [27] [switch_simplify]: 8.12e-06 [loop_unroll]: 6.14001e-06 [a_1]: 7.506e-05 [with_stream_mark]: 1.776e-05 [recompute_prepare]: 6.59001e-06 [updatestate_depend_eliminate]: 3.98999e-06 [updatestate_assign_eliminate]: 3.35e-06 [updatestate_loads_eliminate]: 2.31e-06 [parameter_eliminate]: 1.94e-06 [specialize_transform]: 4.2e-06 [updatestate_useless_node_eliminater]: 4.27e-06 [accelerated_algorithm]: 4.43001e-06 [meta_shard_fg_expand]: 2.57001e-06 [get_grad_eliminate_]: 3.92998e-06 [merge_forward]: 4.18999e-06 [cell_reuse_recompute_pass]: 4.37e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.197e-05 [j_node_and_user_rematch]: 7.96001e-06 [meta_fg_expand]: 0.00023171 [replace_old_param]: 9.54e-06 [inline_without_move]: 4.97999e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 2.83e-06 [auto_monad_grad]: 1.54e-06 [auto_monad_eliminator]: 8.94998e-06 [cse]: 1.985e-05 [replace_applicator]: 6.39999e-06 [py_interpret_to_execute_after_opt_a]: 1.797e-05 [rewriter_after_opt_a]: 0.0004947 [convert_after_rewriter]: 1.694e-05 [order_py_execute_after_rewriter]: 5.72001e-06 [mutable_eliminate]: 0.00090425 [jit_opt_b]: 5.926e-05, [1] [Cycle 1]: 4.891e-05, [2] [frontend_op_eliminate]: 1.706e-05 [inline_after_opt_a]: 1.792e-05 [cconv]: 4.082e-05 [loop_unroll]: 0.00064002 [jit_opt_after_cconv]: 0.00018862, [1] [Cycle 1]: 0.00017766, [11] [c_1]: 2.066e-05 [parameter_eliminate]: 6.63e-06 [updatestate_depend_eliminate]: 8.85999e-06 [updatestate_assign_eliminate]: 2.31e-06 [updatestate_loads_eliminate]: 2.44001e-06 [cse]: 3.62e-05 [call_graph_tuple_transform]: 2.309e-05 [tuple_list_get_item_eliminator]: 5.70001e-06 [none_parameter_eliminate]: 2.29999e-06 [renormalize]: 7.40023e-07 [switch_simplify]: 6.17001e-06 [remove_dup_value]: 1.959e-05 [partial_unused_args_eliminate]: 3.63999e-06 [environ_conv]: 8.13999e-06 [add_recomputation]: 5.5e-05 [cse_after_recomputation]: 3.3e-05, [1] [Cycle 1]: 2.23e-05, [1] [cse]: 1.171e-05 [auto_monad_reorder]: 1.855e-05 [get_jit_bprop_graph]: 2.73e-06 [rewriter_after_jit_bprop_graph]: 7.99002e-06 [opt_after_jit_grad]: 0.00067051 [symbol_engine_optimizer]: 9.59e-05, [1] [Cycle 1]: 8.443e-05, [6] [build]: 6.12999e-06 [elim_shapecalc]: 1.018e-05 [elim_not_effective]: 1.609e-05 [opt_reshape]: 6.54001e-06 [fold_const_symbol]: 1.001e-05 [renormalize]: 5.20027e-07 [validate]: 4.938e-05 Sums bootstrap : 0.000764s : 0.03% type_inference : 2.022054s : 91.44% event_method : 0.001103s : 0.05% auto_monad : 0.000306s : 0.01% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000005s : 0.00% py_interpret_to_execute : 0.000071s : 0.00% rewriter_before_opt_a : 0.000183s : 0.01% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000283s : 0.01% jit_opt_a.loop_unroll : 0.000066s : 0.00% jit_opt_a.a_1 : 0.001617s : 0.07% jit_opt_a.with_stream_mark : 0.000064s : 0.00% jit_opt_a.recompute_prepare : 0.168199s : 7.61% jit_opt_a.updatestate_depend_eliminate : 0.000037s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000013s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000010s : 0.00% jit_opt_a.parameter_eliminate : 0.000014s : 0.00% jit_opt_a.specialize_transform : 0.000032s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000022s : 0.00% jit_opt_a.accelerated_algorithm : 0.000023s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000017s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000021s : 0.00% jit_opt_a.merge_forward : 0.000016s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000006s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000059s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000037s : 0.00% jit_opt_a.meta_fg_expand : 0.002997s : 0.14% jit_opt_a.replace_old_param : 0.000095s : 0.00% jit_opt_a.inline_without_move : 0.000086s : 0.00% jit_opt_a.renormalize : 0.009819s : 0.44% jit_opt_a.add_forward_monad_depend : 0.000013s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000030s : 0.00% jit_opt_a.cse : 0.000058s : 0.00% jit_opt_a.replace_applicator : 0.000038s : 0.00% py_interpret_to_execute_after_opt_a : 0.000018s : 0.00% rewriter_after_opt_a : 0.000495s : 0.02% convert_after_rewriter : 0.000017s : 0.00% order_py_execute_after_rewriter : 0.000006s : 0.00% mutable_eliminate : 0.000904s : 0.04% jit_opt_b.frontend_op_eliminate : 0.000017s : 0.00% jit_opt_b.inline_after_opt_a : 0.000018s : 0.00% cconv : 0.000041s : 0.00% loop_unroll : 0.000640s : 0.03% jit_opt_after_cconv.c_1 : 0.000021s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000009s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.cse : 0.000036s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000023s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000006s : 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 : 0.000006s : 0.00% remove_dup_value : 0.000020s : 0.00% partial_unused_args_eliminate : 0.000004s : 0.00% environ_conv : 0.000008s : 0.00% add_recomputation : 0.000055s : 0.00% cse_after_recomputation.cse : 0.000012s : 0.00% auto_monad_reorder : 0.000019s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000008s : 0.00% opt_after_jit_grad : 0.000671s : 0.03% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000010s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000016s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000007s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000010s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000049s : 0.00% Time group info: ------[substitution.] 0.000586 65 0.41% : 0.000002s : 1: substitution.elim_not_effective 0.34% : 0.000002s : 1: substitution.fold_const_symbol 0.91% : 0.000005s : 1: substitution.graph_param_transform 70.85% : 0.000415s : 13: substitution.inline 5.38% : 0.000032s : 2: substitution.inline_without_move 1.74% : 0.000010s : 9: substitution.j_node_and_user_rematch 1.22% : 0.000007s : 2: substitution.minmaximum_grad 1.65% : 0.000010s : 9: substitution.partial_eliminate 1.64% : 0.000010s : 9: substitution.remove_not_recompute_node 0.60% : 0.000004s : 1: substitution.replace_applicator 1.10% : 0.000006s : 6: substitution.replace_old_param 3.30% : 0.000019s : 1: substitution.set_cell_output_no_recompute 3.28% : 0.000019s : 3: substitution.switch_simplify 1.93% : 0.000011s : 2: substitution.tuple_list_convert_item_index_to_positive 1.89% : 0.000011s : 2: substitution.tuple_list_get_item_depend_reorder 3.74% : 0.000022s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 2.021899 2 99.64% : 2.014626s : 1: type_inference.infer 0.36% : 0.007273s : 1: type_inference.specialize ------[replace.] 0.000225 17 51.71% : 0.000117s : 13: replace.inline 42.32% : 0.000095s : 3: replace.switch_simplify 5.97% : 0.000013s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000429 17 94.91% : 0.000408s : 13: match.inline 3.73% : 0.000016s : 3: match.switch_simplify 1.36% : 0.000006s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000265 1539 1.39% : 0.000004s : 26: predicate.accumulaten_eliminater 0.49% : 0.000001s : 1: predicate.ad_related_special_op_eliminate 1.40% : 0.000004s : 26: predicate.addn_check_dump 1.30% : 0.000003s : 26: predicate.addn_zero_filter 2.15% : 0.000006s : 26: predicate.arithmetic_simplify 1.70% : 0.000005s : 26: predicate.cast_eliminate 0.28% : 0.000001s : 1: predicate.check_bprop_eliminate 1.25% : 0.000003s : 26: predicate.compare_switch_simplify 1.28% : 0.000003s : 26: predicate.depend_value_elim 1.28% : 0.000003s : 26: predicate.dict_get_item_const_eliminator 1.51% : 0.000004s : 26: predicate.dict_get_item_eliminator 1.32% : 0.000003s : 26: predicate.dict_set_item_eliminator 0.61% : 0.000002s : 1: predicate.dumpgradient_eliminate 0.15% : 0.000000s : 1: predicate.elim_not_effective 0.34% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.42% : 0.000004s : 26: predicate.environ_add_const_eliminate 1.21% : 0.000003s : 26: predicate.environ_get_add_eliminate 1.19% : 0.000003s : 26: predicate.environ_get_depend_swap 1.37% : 0.000004s : 26: predicate.environ_get_eliminate 1.45% : 0.000004s : 26: predicate.environ_get_set_eliminate 0.08% : 0.000000s : 1: predicate.fold_const_symbol 1.11% : 0.000003s : 13: predicate.get_grad_eliminate 0.06% : 0.000000s : 1: predicate.graph_param_transform 4.71% : 0.000012s : 42: predicate.inline 2.65% : 0.000007s : 35: predicate.inline_without_move 0.38% : 0.000001s : 13: predicate.j_node_and_user_rematch 1.71% : 0.000005s : 13: predicate.less_batch_normalization 1.57% : 0.000004s : 27: predicate.list_to_tuple_eliminator_ 1.62% : 0.000004s : 28: predicate.load_eliminater 0.75% : 0.000002s : 1: predicate.loop_unroll_after_grad 3.73% : 0.000010s : 67: predicate.loop_unroll_before_grad 1.82% : 0.000005s : 27: predicate.make_slice_get_slice_eliminator 1.27% : 0.000003s : 26: predicate.merge_addn 1.24% : 0.000003s : 26: predicate.minmaximum_grad 1.07% : 0.000003s : 1: predicate.mutable_eliminate 0.14% : 0.000000s : 1: predicate.opt_reshape 1.93% : 0.000005s : 28: predicate.partial_eliminate 1.31% : 0.000003s : 26: predicate.print_const_string_wrapper 2.08% : 0.000006s : 26: predicate.reduce_eliminate 1.47% : 0.000004s : 27: predicate.redundant_stop_gradient_eliminater 0.65% : 0.000002s : 13: predicate.remove_not_recompute_node 1.42% : 0.000004s : 29: predicate.replace_applicator 1.76% : 0.000005s : 35: predicate.replace_old_param 0.17% : 0.000000s : 1: predicate.reset_defer_inline 1.33% : 0.000004s : 26: predicate.reshape_eliminate 1.46% : 0.000004s : 26: predicate.row_tensor_add_zeros_like 0.35% : 0.000001s : 1: predicate.row_tensor_eliminate 1.45% : 0.000004s : 26: predicate.same_eliminate 1.34% : 0.000004s : 13: predicate.set_cell_output_no_recompute 0.29% : 0.000001s : 2: predicate.special_op_eliminate 1.17% : 0.000003s : 13: predicate.specialize_transform 1.62% : 0.000004s : 26: predicate.split_environ_get_set_with_tuple_value 1.37% : 0.000004s : 26: predicate.stack_unstack_eliminate 0.17% : 0.000000s : 1: predicate.switch_call_monad_eliminater 3.19% : 0.000008s : 40: predicate.switch_defer_inline 2.42% : 0.000006s : 40: predicate.switch_layer_defer_inline 7.17% : 0.000019s : 114: predicate.switch_simplify 1.32% : 0.000003s : 26: predicate.tile_eliminate 1.31% : 0.000003s : 26: predicate.transpose_eliminate 1.77% : 0.000005s : 26: predicate.tuple_list_convert_item_index_to_positive 1.59% : 0.000004s : 26: predicate.tuple_list_get_item_depend_reorder 3.34% : 0.000009s : 29: predicate.tuple_list_get_item_eliminator 1.72% : 0.000005s : 26: predicate.tuple_list_set_item_eliminator 1.56% : 0.000004s : 27: predicate.tuple_to_list_eliminator_ 1.67% : 0.000004s : 28: predicate.updatestate_pure_node_eliminater 2.67% : 0.000007s : 41: predicate.updatestate_useless_node_eliminater 1.60% : 0.000004s : 26: predicate.value_based_eliminate 0.08% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.28% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.136866 37 99.34% : 0.135963s : 21: func_graph_cloner_run.FuncGraphClonerGraph 0.66% : 0.000903s : 16: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 2.398097 72 0.00% : 0.000060s : 1: add_recomputation 0.01% : 0.000321s : 1: auto_monad 0.00% : 0.000022s : 1: auto_monad_reorder 0.03% : 0.000792s : 1: bootstrap 0.00% : 0.000044s : 1: cconv 0.00% : 0.000021s : 1: convert_after_rewriter 0.00% : 0.000036s : 1: cse_after_recomputation 0.00% : 0.000012s : 1: environ_conv 0.05% : 0.001117s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 7.91% : 0.189622s : 1: jit_opt_a 0.01% : 0.000192s : 1: jit_opt_after_cconv 0.00% : 0.000062s : 1: jit_opt_b 0.03% : 0.000656s : 1: loop_unroll 0.04% : 0.000916s : 1: mutable_eliminate 7.11% : 0.170510s : 26: opt.transform.jit_opt_a 0.00% : 0.000051s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000025s : 4: opt.transform.jit_opt_b 0.00% : 0.000016s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000020s : 1: opt.transform.mutable_eliminate 0.00% : 0.000029s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000037s : 4: opt.transform.symbol_engine_opt 0.03% : 0.000685s : 1: opt_after_jit_grad 0.00% : 0.000008s : 1: order_py_execute_after_rewriter 0.00% : 0.000006s : 1: partial_unused_args_eliminate 0.00% : 0.000007s : 1: pre_auto_parallel 0.00% : 0.000074s : 1: py_interpret_to_execute 0.00% : 0.000021s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000023s : 1: remove_dup_value 0.36% : 0.008737s : 1: renormalize.infer 0.04% : 0.001064s : 1: renormalize.specialize 0.00% : 0.000011s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000503s : 1: rewriter_after_opt_a 0.01% : 0.000187s : 1: rewriter_before_opt_a 0.00% : 0.000099s : 1: symbol_engine_optimizer 84.32% : 2.022081s : 1: type_inference . [hook] pytest_runtest_teardown:test_squeeze_jit_mode[KBK] tests/st/mint/test_squeeze.py::test_squeeze_jit_mode[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 243.32s (0:04:03) ==================