==================================================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_reshape.py . [hook] pytest_runtest_teardown:test_reshape_large_tensors[pynative] tests/st/mint/test_reshape.py::test_reshape_large_tensors[pynative],max_mem:2.0M [WARNING] PARSER(171422,ffffb7843f30,python3.9):2026-01-29-17:42:03.515.703 [mindspore/ccsrc/frontend/jit/ps/parse/data_converter.cc:661] CheckAPI] The mint interface reshape was called, and the operators under this interface have different view capabilities on pynative and graph mode. Use this interface with caution in graph mode, as it may produce unexpected results. For more information, please refer to: https://www.mindspore.cn/docs/en/master/features/view.html TotalTime = 2.01045, [30] [bootstrap]: 0.00129226 [type_inference]: 1.83776 [event_method]: 1.815e-05 [auto_monad]: 0.00010675 [graph_reusing]: 6.63e-06 [pre_auto_parallel]: 1.42e-05 [py_interpret_to_execute]: 0.00084552 [rewriter_before_opt_a]: 9.323e-05 [expand_dump_flag]: 4.42e-06 [jit_opt_a]: 0.166517, [2] [Cycle 1]: 0.00426617, [27] [switch_simplify]: 8.549e-05 [loop_unroll]: 3.341e-05 [a_1]: 0.00069433 [with_stream_mark]: 3.361e-05 [recompute_prepare]: 1.412e-05 [updatestate_depend_eliminate]: 4.85999e-06 [updatestate_assign_eliminate]: 3.43e-06 [updatestate_loads_eliminate]: 3.06001e-06 [parameter_eliminate]: 2.23002e-06 [specialize_transform]: 7.49002e-06 [updatestate_useless_node_eliminater]: 6.27001e-06 [accelerated_algorithm]: 6.96001e-06 [meta_shard_fg_expand]: 2.57001e-06 [get_grad_eliminate_]: 6.01998e-06 [merge_forward]: 4.45999e-06 [cell_reuse_recompute_pass]: 1.24e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.371e-05 [j_node_and_user_rematch]: 1.146e-05 [meta_fg_expand]: 2.98998e-06 [replace_old_param]: 1.291e-05 [inline_without_move]: 6.38e-06 [renormalize]: 0.00291247 [add_forward_monad_depend]: 1.788e-05 [auto_monad_grad]: 3.66001e-06 [auto_monad_eliminator]: 1.885e-05 [cse]: 5.181e-05 [replace_applicator]: 2.325e-05 [Cycle 2]: 0.00044676, [27] [switch_simplify]: 7.75998e-06 [loop_unroll]: 6.41e-06 [a_1]: 0.00014621 [with_stream_mark]: 1.608e-05 [recompute_prepare]: 7.11999e-06 [updatestate_depend_eliminate]: 4.82998e-06 [updatestate_assign_eliminate]: 3.09999e-06 [updatestate_loads_eliminate]: 3.08e-06 [parameter_eliminate]: 2.09e-06 [specialize_transform]: 6.46e-06 [updatestate_useless_node_eliminater]: 5.77999e-06 [accelerated_algorithm]: 6.31e-06 [meta_shard_fg_expand]: 2.09999e-06 [get_grad_eliminate_]: 5.84999e-06 [merge_forward]: 4.27e-06 [cell_reuse_recompute_pass]: 3.65e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.617e-05 [j_node_and_user_rematch]: 1.078e-05 [meta_fg_expand]: 2.76e-06 [replace_old_param]: 1.069e-05 [inline_without_move]: 5.88002e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 1.60001e-06 [auto_monad_grad]: 1.37999e-06 [auto_monad_eliminator]: 9.85002e-06 [cse]: 1.828e-05 [replace_applicator]: 8.1e-06 [py_interpret_to_execute_after_opt_a]: 1.646e-05 [rewriter_after_opt_a]: 7.78e-05 [convert_after_rewriter]: 1.058e-05 [order_py_execute_after_rewriter]: 5.23002e-06 [mutable_eliminate]: 0.00111654 [jit_opt_b]: 6.661e-05, [1] [Cycle 1]: 5.698e-05, [2] [frontend_op_eliminate]: 2.125e-05 [inline_after_opt_a]: 2.122e-05 [cconv]: 3.744e-05 [loop_unroll]: 0.00061192 [jit_opt_after_cconv]: 0.00020948, [1] [Cycle 1]: 0.00019889, [11] [c_1]: 2.862e-05 [parameter_eliminate]: 6.34001e-06 [updatestate_depend_eliminate]: 1.174e-05 [updatestate_assign_eliminate]: 3.6e-06 [updatestate_loads_eliminate]: 3.43e-06 [cse]: 4.088e-05 [call_graph_tuple_transform]: 2.977e-05 [tuple_list_get_item_eliminator]: 6.97002e-06 [none_parameter_eliminate]: 1.77999e-06 [renormalize]: 6.50005e-07 [switch_simplify]: 7.4e-06 [remove_dup_value]: 1.763e-05 [partial_unused_args_eliminate]: 2.28998e-06 [environ_conv]: 3.035e-05 [add_recomputation]: 6.492e-05 [cse_after_recomputation]: 3.302e-05, [1] [Cycle 1]: 2.476e-05, [1] [cse]: 1.569e-05 [auto_monad_reorder]: 2.637e-05 [get_jit_bprop_graph]: 2.84999e-06 [rewriter_after_jit_bprop_graph]: 0.00021603 [opt_after_jit_grad]: 0.00068191 [symbol_engine_optimizer]: 9.568e-05, [1] [Cycle 1]: 8.476e-05, [6] [build]: 5.49e-06 [elim_shapecalc]: 1.013e-05 [elim_not_effective]: 1.681e-05 [opt_reshape]: 9.51e-06 [fold_const_symbol]: 1.024e-05 [renormalize]: 1.32e-06 [validate]: 8.508e-05 Sums bootstrap : 0.001292s : 0.07% type_inference : 1.837764s : 99.46% event_method : 0.000018s : 0.00% auto_monad : 0.000107s : 0.01% graph_reusing : 0.000007s : 0.00% pre_auto_parallel : 0.000014s : 0.00% py_interpret_to_execute : 0.000846s : 0.05% rewriter_before_opt_a : 0.000093s : 0.01% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000093s : 0.01% jit_opt_a.loop_unroll : 0.000040s : 0.00% jit_opt_a.a_1 : 0.000841s : 0.05% jit_opt_a.with_stream_mark : 0.000050s : 0.00% jit_opt_a.recompute_prepare : 0.000021s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000010s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000007s : 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 : 0.000012s : 0.00% jit_opt_a.accelerated_algorithm : 0.000013s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000005s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000012s : 0.00% jit_opt_a.merge_forward : 0.000009s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000040s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000022s : 0.00% jit_opt_a.meta_fg_expand : 0.000006s : 0.00% jit_opt_a.replace_old_param : 0.000024s : 0.00% jit_opt_a.inline_without_move : 0.000012s : 0.00% jit_opt_a.renormalize : 0.002913s : 0.16% jit_opt_a.add_forward_monad_depend : 0.000019s : 0.00% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000029s : 0.00% jit_opt_a.cse : 0.000070s : 0.00% jit_opt_a.replace_applicator : 0.000031s : 0.00% py_interpret_to_execute_after_opt_a : 0.000016s : 0.00% rewriter_after_opt_a : 0.000078s : 0.00% convert_after_rewriter : 0.000011s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.001117s : 0.06% jit_opt_b.frontend_op_eliminate : 0.000021s : 0.00% jit_opt_b.inline_after_opt_a : 0.000021s : 0.00% cconv : 0.000037s : 0.00% loop_unroll : 0.000612s : 0.03% jit_opt_after_cconv.c_1 : 0.000029s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000012s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.cse : 0.000041s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000030s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000007s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000007s : 0.00% remove_dup_value : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000030s : 0.00% add_recomputation : 0.000065s : 0.00% cse_after_recomputation.cse : 0.000016s : 0.00% auto_monad_reorder : 0.000026s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000216s : 0.01% opt_after_jit_grad : 0.000682s : 0.04% symbol_engine_optimizer.build : 0.000005s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000010s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000017s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000010s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000010s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000085s : 0.00% Time group info: ------[substitution.] 0.000287 28 0.89% : 0.000003s : 2: substitution.elim_not_effective 0.53% : 0.000002s : 2: substitution.fold_const_symbol 2.72% : 0.000008s : 4: substitution.graph_param_transform 76.11% : 0.000219s : 5: substitution.inline 1.53% : 0.000004s : 4: substitution.j_node_and_user_rematch 0.47% : 0.000001s : 1: substitution.opt_reshape 1.97% : 0.000006s : 4: substitution.remove_not_recompute_node 2.55% : 0.000007s : 2: substitution.replace_old_param 10.04% : 0.000029s : 3: substitution.reshape_eliminate 3.17% : 0.000009s : 1: substitution.tuple_list_get_item_eliminator ------[type_inference.] 1.837664 2 99.90% : 1.835851s : 1: type_inference.infer 0.10% : 0.001813s : 1: type_inference.specialize ------[replace.] 0.000071 6 82.61% : 0.000059s : 5: replace.inline 17.39% : 0.000012s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000223 6 96.42% : 0.000215s : 5: match.inline 3.58% : 0.000008s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000169 867 1.21% : 0.000002s : 13: predicate.accumulaten_eliminater 1.63% : 0.000003s : 4: predicate.ad_related_special_op_eliminate 1.23% : 0.000002s : 13: predicate.addn_check_dump 1.20% : 0.000002s : 13: predicate.addn_zero_filter 2.19% : 0.000004s : 13: predicate.arithmetic_simplify 1.20% : 0.000002s : 13: predicate.cast_eliminate 0.66% : 0.000001s : 4: predicate.check_bprop_eliminate 1.07% : 0.000002s : 13: predicate.compare_switch_simplify 1.50% : 0.000003s : 13: predicate.depend_value_elim 1.05% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 1.14% : 0.000002s : 13: predicate.dict_get_item_eliminator 1.13% : 0.000002s : 13: predicate.dict_set_item_eliminator 1.43% : 0.000002s : 4: predicate.dumpgradient_eliminate 0.40% : 0.000001s : 4: predicate.elim_not_effective 0.67% : 0.000001s : 4: predicate.elim_shapecalc_of_broadcastargs 1.32% : 0.000002s : 13: predicate.environ_add_const_eliminate 1.25% : 0.000002s : 13: predicate.environ_get_add_eliminate 0.99% : 0.000002s : 13: predicate.environ_get_depend_swap 1.26% : 0.000002s : 13: predicate.environ_get_eliminate 1.29% : 0.000002s : 13: predicate.environ_get_set_eliminate 0.24% : 0.000000s : 4: predicate.fold_const_symbol 0.83% : 0.000001s : 8: predicate.get_grad_eliminate 0.34% : 0.000001s : 4: predicate.graph_param_transform 5.21% : 0.000009s : 27: predicate.inline 0.70% : 0.000001s : 8: predicate.inline_without_move 0.38% : 0.000001s : 8: predicate.j_node_and_user_rematch 1.29% : 0.000002s : 8: predicate.less_batch_normalization 1.77% : 0.000003s : 14: predicate.list_to_tuple_eliminator_ 1.65% : 0.000003s : 18: predicate.load_eliminater 1.71% : 0.000003s : 4: predicate.loop_unroll_after_grad 3.97% : 0.000007s : 37: predicate.loop_unroll_before_grad 1.81% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 0.96% : 0.000002s : 13: predicate.merge_addn 1.05% : 0.000002s : 13: predicate.minmaximum_grad 2.16% : 0.000004s : 4: predicate.mutable_eliminate 0.51% : 0.000001s : 4: predicate.opt_reshape 1.76% : 0.000003s : 18: predicate.partial_eliminate 1.78% : 0.000003s : 13: predicate.print_const_string_wrapper 1.58% : 0.000003s : 13: predicate.reduce_eliminate 1.44% : 0.000002s : 14: predicate.redundant_stop_gradient_eliminater 0.54% : 0.000001s : 8: predicate.remove_not_recompute_node 1.73% : 0.000003s : 22: predicate.replace_applicator 0.51% : 0.000001s : 8: predicate.replace_old_param 0.36% : 0.000001s : 4: predicate.reset_defer_inline 2.44% : 0.000004s : 13: predicate.reshape_eliminate 1.18% : 0.000002s : 13: predicate.row_tensor_add_zeros_like 1.09% : 0.000002s : 4: predicate.row_tensor_eliminate 1.25% : 0.000002s : 13: predicate.same_eliminate 0.54% : 0.000001s : 8: predicate.set_cell_output_no_recompute 0.79% : 0.000001s : 8: predicate.special_op_eliminate 0.77% : 0.000001s : 8: predicate.specialize_transform 1.26% : 0.000002s : 13: predicate.split_environ_get_set_with_tuple_value 1.28% : 0.000002s : 13: predicate.stack_unstack_eliminate 0.48% : 0.000001s : 4: predicate.switch_call_monad_eliminater 2.32% : 0.000004s : 19: predicate.switch_defer_inline 2.12% : 0.000004s : 19: predicate.switch_layer_defer_inline 7.55% : 0.000013s : 60: predicate.switch_simplify 1.29% : 0.000002s : 13: predicate.tile_eliminate 1.21% : 0.000002s : 13: predicate.transpose_eliminate 1.20% : 0.000002s : 13: predicate.tuple_list_convert_item_index_to_positive 1.45% : 0.000002s : 13: predicate.tuple_list_get_item_depend_reorder 3.79% : 0.000006s : 22: predicate.tuple_list_get_item_eliminator 1.34% : 0.000002s : 13: predicate.tuple_list_set_item_eliminator 1.62% : 0.000003s : 14: predicate.tuple_to_list_eliminator_ 1.48% : 0.000003s : 18: predicate.updatestate_pure_node_eliminater 2.70% : 0.000005s : 26: predicate.updatestate_useless_node_eliminater 1.87% : 0.000003s : 13: predicate.value_based_eliminate 0.31% : 0.000001s : 4: predicate.virtual_view_grad_eliminate 0.61% : 0.000001s : 4: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.003673 23 72.66% : 0.002669s : 16: func_graph_cloner_run.FuncGraphClonerGraph 27.34% : 0.001004s : 7: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 2.014426 72 0.00% : 0.000070s : 1: add_recomputation 0.01% : 0.000111s : 1: auto_monad 0.00% : 0.000030s : 1: auto_monad_reorder 0.07% : 0.001324s : 1: bootstrap 0.00% : 0.000040s : 1: cconv 0.00% : 0.000013s : 1: convert_after_rewriter 0.00% : 0.000036s : 1: cse_after_recomputation 0.00% : 0.000034s : 1: environ_conv 0.00% : 0.000023s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000009s : 1: graph_reusing 8.27% : 0.166522s : 1: jit_opt_a 0.01% : 0.000213s : 1: jit_opt_after_cconv 0.00% : 0.000070s : 1: jit_opt_b 0.03% : 0.000627s : 1: loop_unroll 0.06% : 0.001134s : 1: mutable_eliminate 0.06% : 0.001117s : 26: opt.transform.jit_opt_a 0.00% : 0.000068s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000034s : 4: opt.transform.jit_opt_b 0.00% : 0.000019s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000025s : 1: opt.transform.mutable_eliminate 0.00% : 0.000035s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000043s : 4: opt.transform.symbol_engine_opt 0.03% : 0.000704s : 1: opt_after_jit_grad 0.00% : 0.000008s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000017s : 1: pre_auto_parallel 0.04% : 0.000856s : 1: py_interpret_to_execute 0.00% : 0.000019s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000021s : 1: remove_dup_value 0.09% : 0.001798s : 1: renormalize.infer 0.05% : 0.001102s : 1: renormalize.specialize 0.01% : 0.000223s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000084s : 1: rewriter_after_opt_a 0.00% : 0.000098s : 1: rewriter_before_opt_a 0.00% : 0.000099s : 1: symbol_engine_optimizer 91.23% : 1.837783s : 1: type_inference TotalTime = 1.05781, [30] [bootstrap]: 0.00052709 [type_inference]: 0.775046 [event_method]: 0.00062526 [auto_monad]: 0.00028462 [graph_reusing]: 1.139e-05 [pre_auto_parallel]: 4.33999e-06 [py_interpret_to_execute]: 6.307e-05 [rewriter_before_opt_a]: 0.00018917 [expand_dump_flag]: 4.71002e-06 [jit_opt_a]: 0.277794, [2] [Cycle 1]: 0.272115, [27] [switch_simplify]: 0.00052082 [loop_unroll]: 8.471e-05 [a_1]: 0.00173595 [with_stream_mark]: 3.704e-05 [recompute_prepare]: 3.333e-05 [updatestate_depend_eliminate]: 1.125e-05 [updatestate_assign_eliminate]: 7.16999e-06 [updatestate_loads_eliminate]: 7.05998e-06 [parameter_eliminate]: 3.13998e-06 [specialize_transform]: 1.752e-05 [updatestate_useless_node_eliminater]: 1.615e-05 [accelerated_algorithm]: 2.011e-05 [meta_shard_fg_expand]: 8.33001e-06 [get_grad_eliminate_]: 1.769e-05 [merge_forward]: 1.072e-05 [cell_reuse_recompute_pass]: 1.74998e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.369e-05 [j_node_and_user_rematch]: 4.318e-05 [meta_fg_expand]: 0.135941 [replace_old_param]: 8.478e-05 [inline_without_move]: 7.34e-05 [renormalize]: 0.132938 [add_forward_monad_depend]: 1.843e-05 [auto_monad_grad]: 2.49001e-06 [auto_monad_eliminator]: 1.727e-05 [cse]: 3.28e-05 [replace_applicator]: 2.582e-05 [Cycle 2]: 0.00056339, [27] [switch_simplify]: 5.62001e-06 [loop_unroll]: 4.48001e-06 [a_1]: 6.033e-05 [with_stream_mark]: 1.668e-05 [recompute_prepare]: 4.17e-06 [updatestate_depend_eliminate]: 3.4e-06 [updatestate_assign_eliminate]: 2.47001e-06 [updatestate_loads_eliminate]: 2.17001e-06 [parameter_eliminate]: 1.69e-06 [specialize_transform]: 4.25e-06 [updatestate_useless_node_eliminater]: 3.84002e-06 [accelerated_algorithm]: 4.65999e-06 [meta_shard_fg_expand]: 2.32999e-06 [get_grad_eliminate_]: 3.65e-06 [merge_forward]: 3.91001e-06 [cell_reuse_recompute_pass]: 3.43999e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.817e-05 [j_node_and_user_rematch]: 7e-06 [meta_fg_expand]: 0.00023749 [replace_old_param]: 8.55999e-06 [inline_without_move]: 4.33001e-06 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 2.04e-06 [auto_monad_grad]: 1.82001e-06 [auto_monad_eliminator]: 5.83002e-06 [cse]: 1.628e-05 [replace_applicator]: 4.75999e-06 [py_interpret_to_execute_after_opt_a]: 1.471e-05 [rewriter_after_opt_a]: 0.00045126 [convert_after_rewriter]: 1.173e-05 [order_py_execute_after_rewriter]: 5.13002e-06 [mutable_eliminate]: 0.00084063 [jit_opt_b]: 5.497e-05, [1] [Cycle 1]: 4.506e-05, [2] [frontend_op_eliminate]: 1.397e-05 [inline_after_opt_a]: 1.682e-05 [cconv]: 2.815e-05 [loop_unroll]: 0.00056201 [jit_opt_after_cconv]: 0.00016635, [1] [Cycle 1]: 0.00015709, [11] [c_1]: 1.747e-05 [parameter_eliminate]: 4.22e-06 [updatestate_depend_eliminate]: 7.85998e-06 [updatestate_assign_eliminate]: 2.89999e-06 [updatestate_loads_eliminate]: 2.31e-06 [cse]: 3.041e-05 [call_graph_tuple_transform]: 1.926e-05 [tuple_list_get_item_eliminator]: 4.70001e-06 [none_parameter_eliminate]: 1.52999e-06 [renormalize]: 6.29982e-07 [switch_simplify]: 5.87999e-06 [remove_dup_value]: 1.82e-05 [partial_unused_args_eliminate]: 2.29999e-06 [environ_conv]: 6.27001e-06 [add_recomputation]: 4.662e-05 [cse_after_recomputation]: 5.967e-05, [1] [Cycle 1]: 4.836e-05, [1] [cse]: 9.47999e-06 [auto_monad_reorder]: 1.762e-05 [get_jit_bprop_graph]: 2.23002e-06 [rewriter_after_jit_bprop_graph]: 7.56999e-06 [opt_after_jit_grad]: 0.00059244 [symbol_engine_optimizer]: 8.374e-05, [1] [Cycle 1]: 7.537e-05, [6] [build]: 5.59e-06 [elim_shapecalc]: 7.56999e-06 [elim_not_effective]: 1.408e-05 [opt_reshape]: 5.92001e-06 [fold_const_symbol]: 8.25999e-06 [renormalize]: 6.19999e-07 [validate]: 4.052e-05 Sums bootstrap : 0.000527s : 0.05% type_inference : 0.775046s : 73.69% event_method : 0.000625s : 0.06% auto_monad : 0.000285s : 0.03% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000063s : 0.01% rewriter_before_opt_a : 0.000189s : 0.02% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000526s : 0.05% jit_opt_a.loop_unroll : 0.000089s : 0.01% jit_opt_a.a_1 : 0.001796s : 0.17% jit_opt_a.with_stream_mark : 0.000054s : 0.01% jit_opt_a.recompute_prepare : 0.000037s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000015s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000010s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000009s : 0.00% jit_opt_a.parameter_eliminate : 0.000005s : 0.00% jit_opt_a.specialize_transform : 0.000022s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000020s : 0.00% jit_opt_a.accelerated_algorithm : 0.000025s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000011s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000021s : 0.00% jit_opt_a.merge_forward : 0.000015s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000052s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000050s : 0.00% jit_opt_a.meta_fg_expand : 0.136179s : 12.95% jit_opt_a.replace_old_param : 0.000093s : 0.01% jit_opt_a.inline_without_move : 0.000078s : 0.01% jit_opt_a.renormalize : 0.132938s : 12.64% jit_opt_a.add_forward_monad_depend : 0.000020s : 0.00% jit_opt_a.auto_monad_grad : 0.000004s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000023s : 0.00% jit_opt_a.cse : 0.000049s : 0.00% jit_opt_a.replace_applicator : 0.000031s : 0.00% py_interpret_to_execute_after_opt_a : 0.000015s : 0.00% rewriter_after_opt_a : 0.000451s : 0.04% convert_after_rewriter : 0.000012s : 0.00% order_py_execute_after_rewriter : 0.000005s : 0.00% mutable_eliminate : 0.000841s : 0.08% jit_opt_b.frontend_op_eliminate : 0.000014s : 0.00% jit_opt_b.inline_after_opt_a : 0.000017s : 0.00% cconv : 0.000028s : 0.00% loop_unroll : 0.000562s : 0.05% jit_opt_after_cconv.c_1 : 0.000017s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000003s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.cse : 0.000030s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000019s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000005s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000006s : 0.00% remove_dup_value : 0.000018s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000006s : 0.00% add_recomputation : 0.000047s : 0.00% cse_after_recomputation.cse : 0.000009s : 0.00% auto_monad_reorder : 0.000018s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000008s : 0.00% opt_after_jit_grad : 0.000592s : 0.06% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000008s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000014s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000006s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000008s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000041s : 0.00% Time group info: ------[substitution.] 0.000648 69 0.34% : 0.000002s : 1: substitution.elim_not_effective 0.23% : 0.000001s : 1: substitution.fold_const_symbol 0.84% : 0.000005s : 1: substitution.graph_param_transform 71.69% : 0.000464s : 14: substitution.inline 4.18% : 0.000027s : 2: substitution.inline_without_move 3.28% : 0.000021s : 9: substitution.j_node_and_user_rematch 0.73% : 0.000005s : 2: substitution.minmaximum_grad 5.40% : 0.000035s : 9: substitution.partial_eliminate 1.22% : 0.000008s : 9: substitution.remove_not_recompute_node 0.49% : 0.000003s : 1: substitution.replace_applicator 1.22% : 0.000008s : 7: substitution.replace_old_param 1.80% : 0.000012s : 2: substitution.reshape_eliminate 0.60% : 0.000004s : 1: substitution.set_cell_output_no_recompute 3.12% : 0.000020s : 3: substitution.switch_simplify 1.36% : 0.000009s : 2: substitution.tuple_list_convert_item_index_to_positive 1.05% : 0.000007s : 2: substitution.tuple_list_get_item_depend_reorder 2.45% : 0.000016s : 3: substitution.tuple_list_get_item_eliminator ------[type_inference.] 0.774872 2 99.34% : 0.769745s : 1: type_inference.infer 0.66% : 0.005126s : 1: type_inference.specialize ------[replace.] 0.000439 18 31.19% : 0.000137s : 14: replace.inline 65.50% : 0.000288s : 3: replace.switch_simplify 3.31% : 0.000015s : 1: replace.tuple_list_get_item_eliminator ------[match.] 0.000448 18 95.23% : 0.000426s : 14: match.inline 4.04% : 0.000018s : 3: match.switch_simplify 0.73% : 0.000003s : 1: match.tuple_list_get_item_eliminator ------[predicate.] 0.000311 1710 1.34% : 0.000004s : 29: predicate.accumulaten_eliminater 0.50% : 0.000002s : 1: predicate.ad_related_special_op_eliminate 1.21% : 0.000004s : 29: predicate.addn_check_dump 1.33% : 0.000004s : 29: predicate.addn_zero_filter 10.68% : 0.000033s : 29: predicate.arithmetic_simplify 1.31% : 0.000004s : 29: predicate.cast_eliminate 0.26% : 0.000001s : 1: predicate.check_bprop_eliminate 1.15% : 0.000004s : 29: predicate.compare_switch_simplify 1.21% : 0.000004s : 29: predicate.depend_value_elim 1.20% : 0.000004s : 29: predicate.dict_get_item_const_eliminator 1.19% : 0.000004s : 29: predicate.dict_get_item_eliminator 1.38% : 0.000004s : 29: predicate.dict_set_item_eliminator 0.37% : 0.000001s : 1: predicate.dumpgradient_eliminate 0.22% : 0.000001s : 1: predicate.elim_not_effective 0.17% : 0.000001s : 1: predicate.elim_shapecalc_of_broadcastargs 1.29% : 0.000004s : 29: predicate.environ_add_const_eliminate 1.17% : 0.000004s : 29: predicate.environ_get_add_eliminate 1.24% : 0.000004s : 29: predicate.environ_get_depend_swap 1.30% : 0.000004s : 29: predicate.environ_get_eliminate 1.23% : 0.000004s : 29: predicate.environ_get_set_eliminate 0.03% : 0.000000s : 1: predicate.fold_const_symbol 0.88% : 0.000003s : 14: predicate.get_grad_eliminate 0.11% : 0.000000s : 1: predicate.graph_param_transform 3.89% : 0.000012s : 46: predicate.inline 2.17% : 0.000007s : 38: predicate.inline_without_move 0.34% : 0.000001s : 14: predicate.j_node_and_user_rematch 1.31% : 0.000004s : 14: predicate.less_batch_normalization 1.30% : 0.000004s : 30: predicate.list_to_tuple_eliminator_ 1.35% : 0.000004s : 31: predicate.load_eliminater 0.56% : 0.000002s : 1: predicate.loop_unroll_after_grad 4.23% : 0.000013s : 78: predicate.loop_unroll_before_grad 1.68% : 0.000005s : 30: predicate.make_slice_get_slice_eliminator 1.21% : 0.000004s : 29: predicate.merge_addn 1.49% : 0.000005s : 29: predicate.minmaximum_grad 0.65% : 0.000002s : 1: predicate.mutable_eliminate 0.13% : 0.000000s : 1: predicate.opt_reshape 1.71% : 0.000005s : 31: predicate.partial_eliminate 1.23% : 0.000004s : 29: predicate.print_const_string_wrapper 1.65% : 0.000005s : 29: predicate.reduce_eliminate 1.42% : 0.000004s : 30: predicate.redundant_stop_gradient_eliminater 0.54% : 0.000002s : 14: predicate.remove_not_recompute_node 1.43% : 0.000004s : 32: predicate.replace_applicator 1.22% : 0.000004s : 38: predicate.replace_old_param 0.11% : 0.000000s : 1: predicate.reset_defer_inline 1.50% : 0.000005s : 29: predicate.reshape_eliminate 1.33% : 0.000004s : 29: predicate.row_tensor_add_zeros_like 0.37% : 0.000001s : 1: predicate.row_tensor_eliminate 1.37% : 0.000004s : 29: predicate.same_eliminate 0.43% : 0.000001s : 14: predicate.set_cell_output_no_recompute 0.25% : 0.000001s : 2: predicate.special_op_eliminate 0.78% : 0.000002s : 14: predicate.specialize_transform 1.44% : 0.000004s : 29: predicate.split_environ_get_set_with_tuple_value 1.40% : 0.000004s : 29: predicate.stack_unstack_eliminate 0.14% : 0.000000s : 1: predicate.switch_call_monad_eliminater 3.29% : 0.000010s : 44: predicate.switch_defer_inline 2.23% : 0.000007s : 44: predicate.switch_layer_defer_inline 8.32% : 0.000026s : 129: predicate.switch_simplify 1.25% : 0.000004s : 29: predicate.tile_eliminate 1.37% : 0.000004s : 29: predicate.transpose_eliminate 1.44% : 0.000004s : 29: predicate.tuple_list_convert_item_index_to_positive 1.37% : 0.000004s : 29: predicate.tuple_list_get_item_depend_reorder 2.79% : 0.000009s : 32: predicate.tuple_list_get_item_eliminator 1.53% : 0.000005s : 29: predicate.tuple_list_set_item_eliminator 1.41% : 0.000004s : 30: predicate.tuple_to_list_eliminator_ 1.52% : 0.000005s : 31: predicate.updatestate_pure_node_eliminater 2.27% : 0.000007s : 45: predicate.updatestate_useless_node_eliminater 1.58% : 0.000005s : 29: predicate.value_based_eliminate 0.07% : 0.000000s : 1: predicate.virtual_view_grad_eliminate 0.16% : 0.000001s : 1: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.005120 47 82.91% : 0.004245s : 30: func_graph_cloner_run.FuncGraphClonerGraph 17.09% : 0.000875s : 17: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.193557 72 0.00% : 0.000050s : 1: add_recomputation 0.02% : 0.000296s : 1: auto_monad 0.00% : 0.000021s : 1: auto_monad_reorder 0.05% : 0.000546s : 1: bootstrap 0.00% : 0.000031s : 1: cconv 0.00% : 0.000015s : 1: convert_after_rewriter 0.01% : 0.000063s : 1: cse_after_recomputation 0.00% : 0.000008s : 1: environ_conv 0.05% : 0.000641s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000015s : 1: graph_reusing 23.27% : 0.277798s : 1: jit_opt_a 0.01% : 0.000170s : 1: jit_opt_after_cconv 0.00% : 0.000058s : 1: jit_opt_b 0.05% : 0.000573s : 1: loop_unroll 0.07% : 0.000853s : 1: mutable_eliminate 0.23% : 0.002789s : 26: opt.transform.jit_opt_a 0.00% : 0.000043s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000021s : 4: opt.transform.jit_opt_b 0.00% : 0.000013s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000017s : 1: opt.transform.mutable_eliminate 0.00% : 0.000024s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000031s : 4: opt.transform.symbol_engine_opt 0.05% : 0.000606s : 1: opt_after_jit_grad 0.00% : 0.000008s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000007s : 1: pre_auto_parallel 0.01% : 0.000066s : 1: py_interpret_to_execute 0.00% : 0.000017s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000021s : 1: remove_dup_value 11.05% : 0.131938s : 1: renormalize.infer 0.08% : 0.000985s : 1: renormalize.specialize 0.00% : 0.000010s : 1: rewriter_after_jit_bprop_graph 0.04% : 0.000458s : 1: rewriter_after_opt_a 0.02% : 0.000193s : 1: rewriter_before_opt_a 0.01% : 0.000087s : 1: symbol_engine_optimizer 64.94% : 0.775069s : 1: type_inference . [hook] pytest_runtest_teardown:test_reshape_large_tensors[KBK] tests/st/mint/test_reshape.py::test_reshape_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 319.17s (0:05:19) ==================