==================================================Ascend ============================= test session starts ============================== platform linux -- Python 3.9.19, pytest-6.2.5, py-1.11.0, pluggy-1.5.0 rootdir: /home/jenkins/mindspore/testcases/testcases/tests/st/mint, configfile: ../../../../../../sault/virtual_test/virtualenv_006/sault/config/pytest.ini plugins: mock-3.14.0, hydra-core-1.3.2, forked-1.6.0, anyio-4.9.0, xdist-1.32.0 collected 2 items test_unsqueeze.py . [hook] pytest_runtest_teardown:test_unsqueeze_view_backward[pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_view_backward[pynative],max_mem:2.0M TotalTime = 0.572825, [30] [bootstrap]: 0.00054006 [type_inference]: 0.302738 [event_method]: 0.0001059 [auto_monad]: 0.00031198 [graph_reusing]: 1.086e-05 [pre_auto_parallel]: 1.49e-05 [py_interpret_to_execute]: 6.825e-05 [rewriter_before_opt_a]: 0.00019689 [expand_dump_flag]: 4.58001e-06 [jit_opt_a]: 0.251155, [3] [Cycle 1]: 0.225457, [27] [switch_simplify]: 0.00012309 [loop_unroll]: 7.617e-05 [a_1]: 0.126582 [with_stream_mark]: 4.92e-05 [recompute_prepare]: 3.771e-05 [updatestate_depend_eliminate]: 1.554e-05 [updatestate_assign_eliminate]: 1.502e-05 [updatestate_loads_eliminate]: 1.207e-05 [parameter_eliminate]: 3.94002e-06 [specialize_transform]: 3.123e-05 [updatestate_useless_node_eliminater]: 2.91e-05 [accelerated_algorithm]: 2.143e-05 [meta_shard_fg_expand]: 9.41998e-06 [get_grad_eliminate_]: 2.097e-05 [merge_forward]: 1.333e-05 [cell_reuse_recompute_pass]: 1.12e-06 [cell_reuse_handle_not_recompute_node_pass]: 5.103e-05 [j_node_and_user_rematch]: 5.104e-05 [meta_fg_expand]: 0.0833101 [replace_old_param]: 0.00023502 [inline_without_move]: 0.00016334 [renormalize]: 0.013383 [add_forward_monad_depend]: 2.205e-05 [auto_monad_grad]: 1.271e-05 [auto_monad_eliminator]: 0.00012576 [cse]: 0.00044618 [replace_applicator]: 0.0002372 [Cycle 2]: 0.0178536, [27] [switch_simplify]: 0.00012399 [loop_unroll]: 9.52e-05 [a_1]: 0.0136603 [with_stream_mark]: 4.417e-05 [recompute_prepare]: 2.104e-05 [updatestate_depend_eliminate]: 4.295e-05 [updatestate_assign_eliminate]: 7.10998e-06 [updatestate_loads_eliminate]: 6.19999e-06 [parameter_eliminate]: 2.98e-06 [specialize_transform]: 1.326e-05 [updatestate_useless_node_eliminater]: 1.612e-05 [accelerated_algorithm]: 1.113e-05 [meta_shard_fg_expand]: 3.82998e-06 [get_grad_eliminate_]: 1.019e-05 [merge_forward]: 8.32e-06 [cell_reuse_recompute_pass]: 2.81e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.466e-05 [j_node_and_user_rematch]: 1.827e-05 [meta_fg_expand]: 0.00020593 [replace_old_param]: 2.472e-05 [inline_without_move]: 1.189e-05 [renormalize]: 0.00169801 [add_forward_monad_depend]: 6.81001e-06 [auto_monad_grad]: 2.49999e-06 [auto_monad_eliminator]: 2.887e-05 [cse]: 0.00015945 [replace_applicator]: 2.118e-05 [Cycle 3]: 0.00072054, [27] [switch_simplify]: 2.526e-05 [loop_unroll]: 1.201e-05 [a_1]: 0.00026055 [with_stream_mark]: 2.314e-05 [recompute_prepare]: 1.155e-05 [updatestate_depend_eliminate]: 7.58999e-06 [updatestate_assign_eliminate]: 6.64999e-06 [updatestate_loads_eliminate]: 5.79e-06 [parameter_eliminate]: 1.81e-06 [specialize_transform]: 1.073e-05 [updatestate_useless_node_eliminater]: 1.386e-05 [accelerated_algorithm]: 1.143e-05 [meta_shard_fg_expand]: 3.55e-06 [get_grad_eliminate_]: 1.05e-05 [merge_forward]: 6.11998e-06 [cell_reuse_recompute_pass]: 2.59999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.44e-05 [j_node_and_user_rematch]: 1.709e-05 [meta_fg_expand]: 4.12998e-06 [replace_old_param]: 1.578e-05 [inline_without_move]: 9.86998e-06 [renormalize]: 9.00181e-08 [add_forward_monad_depend]: 3.14001e-06 [auto_monad_grad]: 2.88e-06 [auto_monad_eliminator]: 1.892e-05 [cse]: 6.482e-05 [replace_applicator]: 1.283e-05 [py_interpret_to_execute_after_opt_a]: 2.153e-05 [rewriter_after_opt_a]: 0.00013611 [convert_after_rewriter]: 1.51e-05 [order_py_execute_after_rewriter]: 8.62e-06 [mutable_eliminate]: 0.00074246 [jit_opt_b]: 8.611e-05, [1] [Cycle 1]: 7.808e-05, [2] [frontend_op_eliminate]: 3.391e-05 [inline_after_opt_a]: 3.115e-05 [cconv]: 3.551e-05 [loop_unroll]: 0.0145295 [jit_opt_after_cconv]: 0.00036967, [1] [Cycle 1]: 0.00035644, [11] [c_1]: 7.658e-05 [parameter_eliminate]: 7.23999e-06 [updatestate_depend_eliminate]: 1.9e-05 [updatestate_assign_eliminate]: 1.147e-05 [updatestate_loads_eliminate]: 8e-06 [cse]: 7.693e-05 [call_graph_tuple_transform]: 3.532e-05 [tuple_list_get_item_eliminator]: 1.242e-05 [none_parameter_eliminate]: 2.32999e-06 [renormalize]: 8.10018e-07 [switch_simplify]: 1.179e-05 [remove_dup_value]: 2.358e-05 [partial_unused_args_eliminate]: 3.13e-06 [environ_conv]: 2.028e-05 [add_recomputation]: 0.00010404 [cse_after_recomputation]: 4.665e-05, [1] [Cycle 1]: 3.886e-05, [1] [cse]: 3.061e-05 [auto_monad_reorder]: 4.405e-05 [get_jit_bprop_graph]: 2.43998e-06 [rewriter_after_jit_bprop_graph]: 0.0002562 [opt_after_jit_grad]: 0.00066244 [symbol_engine_optimizer]: 0.00012189, [1] [Cycle 1]: 0.00011369, [6] [build]: 8.81002e-06 [elim_shapecalc]: 1.671e-05 [elim_not_effective]: 2.425e-05 [opt_reshape]: 1.146e-05 [fold_const_symbol]: 1.827e-05 [renormalize]: 6.60017e-07 [validate]: 7.707e-05 Sums bootstrap : 0.000540s : 0.10% type_inference : 0.302738s : 53.77% event_method : 0.000106s : 0.02% auto_monad : 0.000312s : 0.06% graph_reusing : 0.000011s : 0.00% pre_auto_parallel : 0.000015s : 0.00% py_interpret_to_execute : 0.000068s : 0.01% rewriter_before_opt_a : 0.000197s : 0.03% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000272s : 0.05% jit_opt_a.loop_unroll : 0.000183s : 0.03% jit_opt_a.a_1 : 0.140503s : 24.95% jit_opt_a.with_stream_mark : 0.000117s : 0.02% jit_opt_a.recompute_prepare : 0.000070s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000066s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000029s : 0.01% jit_opt_a.updatestate_loads_eliminate : 0.000024s : 0.00% jit_opt_a.parameter_eliminate : 0.000009s : 0.00% jit_opt_a.specialize_transform : 0.000055s : 0.01% jit_opt_a.updatestate_useless_node_eliminater : 0.000059s : 0.01% jit_opt_a.accelerated_algorithm : 0.000044s : 0.01% jit_opt_a.meta_shard_fg_expand : 0.000017s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000042s : 0.01% jit_opt_a.merge_forward : 0.000028s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000100s : 0.02% jit_opt_a.j_node_and_user_rematch : 0.000086s : 0.02% jit_opt_a.meta_fg_expand : 0.083520s : 14.83% jit_opt_a.replace_old_param : 0.000276s : 0.05% jit_opt_a.inline_without_move : 0.000185s : 0.03% jit_opt_a.renormalize : 0.015081s : 2.68% jit_opt_a.add_forward_monad_depend : 0.000032s : 0.01% jit_opt_a.auto_monad_grad : 0.000018s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000174s : 0.03% jit_opt_a.cse : 0.000670s : 0.12% jit_opt_a.replace_applicator : 0.000271s : 0.05% py_interpret_to_execute_after_opt_a : 0.000022s : 0.00% rewriter_after_opt_a : 0.000136s : 0.02% convert_after_rewriter : 0.000015s : 0.00% order_py_execute_after_rewriter : 0.000009s : 0.00% mutable_eliminate : 0.000742s : 0.13% jit_opt_b.frontend_op_eliminate : 0.000034s : 0.01% jit_opt_b.inline_after_opt_a : 0.000031s : 0.01% cconv : 0.000036s : 0.01% loop_unroll : 0.014529s : 2.58% jit_opt_after_cconv.c_1 : 0.000077s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000019s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000011s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.cse : 0.000077s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000035s : 0.01% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000012s : 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 : 0.000012s : 0.00% remove_dup_value : 0.000024s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000020s : 0.00% add_recomputation : 0.000104s : 0.02% cse_after_recomputation.cse : 0.000031s : 0.01% auto_monad_reorder : 0.000044s : 0.01% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000256s : 0.05% opt_after_jit_grad : 0.000662s : 0.12% symbol_engine_optimizer.build : 0.000009s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000017s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000024s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000011s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000018s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000077s : 0.01% Time group info: ------[substitution.] 0.002558 216 1.21% : 0.000031s : 1: substitution.arithmetic_simplify 1.05% : 0.000027s : 8: substitution.depend_value_elim 0.14% : 0.000003s : 6: substitution.elim_not_effective 0.11% : 0.000003s : 6: substitution.fold_const_symbol 34.84% : 0.000891s : 4: substitution.getattr_setattr_resolve 0.35% : 0.000009s : 8: substitution.graph_param_transform 46.63% : 0.001193s : 20: substitution.inline 1.92% : 0.000049s : 6: substitution.inline_without_move 1.04% : 0.000027s : 26: substitution.j_node_and_user_rematch 0.47% : 0.000012s : 7: substitution.minmaximum_grad 0.93% : 0.000024s : 3: substitution.partial_eliminate 0.73% : 0.000019s : 26: substitution.remove_not_recompute_node 2.47% : 0.000063s : 20: substitution.replace_applicator 0.87% : 0.000022s : 23: substitution.replace_old_param 0.15% : 0.000004s : 1: substitution.set_cell_output_no_recompute 1.08% : 0.000028s : 7: substitution.tuple_list_convert_item_index_to_positive 0.71% : 0.000018s : 7: substitution.tuple_list_get_item_depend_reorder 2.76% : 0.000071s : 16: substitution.tuple_list_get_item_eliminator 1.03% : 0.000026s : 8: substitution.updatestate_pure_node_eliminater 1.51% : 0.000039s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.302596 2 99.13% : 0.299957s : 1: type_inference.infer 0.87% : 0.002639s : 1: type_inference.specialize ------[replace.] 0.011944 35 0.06% : 0.000008s : 1: replace.arithmetic_simplify 0.59% : 0.000071s : 3: replace.getattr_setattr_resolve 95.65% : 0.011424s : 20: replace.inline 0.30% : 0.000036s : 1: replace.replace_applicator 3.18% : 0.000380s : 9: replace.tuple_list_get_item_eliminator 0.21% : 0.000025s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.002115 35 1.38% : 0.000029s : 1: match.arithmetic_simplify 39.70% : 0.000840s : 3: match.getattr_setattr_resolve 55.79% : 0.001180s : 20: match.inline 0.63% : 0.000013s : 1: match.replace_applicator 1.90% : 0.000040s : 9: match.tuple_list_get_item_eliminator 0.60% : 0.000013s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000755 4435 1.31% : 0.000010s : 67: predicate.accumulaten_eliminater 0.49% : 0.000004s : 8: predicate.ad_related_special_op_eliminate 1.14% : 0.000009s : 67: predicate.addn_check_dump 1.31% : 0.000010s : 67: predicate.addn_zero_filter 2.06% : 0.000016s : 68: predicate.arithmetic_simplify 1.40% : 0.000011s : 68: predicate.cast_eliminate 0.22% : 0.000002s : 8: predicate.check_bprop_eliminate 1.19% : 0.000009s : 67: predicate.compare_switch_simplify 1.42% : 0.000011s : 67: predicate.depend_value_elim 1.28% : 0.000010s : 68: predicate.dict_get_item_const_eliminator 1.64% : 0.000012s : 68: predicate.dict_get_item_eliminator 1.26% : 0.000009s : 68: predicate.dict_set_item_eliminator 0.30% : 0.000002s : 8: predicate.dumpgradient_eliminate 0.13% : 0.000001s : 8: predicate.elim_not_effective 0.25% : 0.000002s : 8: predicate.elim_shapecalc_of_broadcastargs 1.32% : 0.000010s : 68: predicate.environ_add_const_eliminate 1.23% : 0.000009s : 68: predicate.environ_get_add_eliminate 1.20% : 0.000009s : 68: predicate.environ_get_depend_swap 1.41% : 0.000011s : 68: predicate.environ_get_eliminate 1.27% : 0.000010s : 68: predicate.environ_get_set_eliminate 0.09% : 0.000001s : 8: predicate.fold_const_symbol 0.76% : 0.000006s : 36: predicate.get_grad_eliminate 0.94% : 0.000007s : 20: predicate.getattr_setattr_resolve 0.12% : 0.000001s : 8: predicate.graph_param_transform 4.95% : 0.000037s : 114: predicate.inline 2.67% : 0.000020s : 118: predicate.inline_without_move 0.34% : 0.000003s : 36: predicate.j_node_and_user_rematch 0.81% : 0.000006s : 36: predicate.less_batch_normalization 1.50% : 0.000011s : 77: predicate.list_to_tuple_eliminator_ 1.59% : 0.000012s : 85: predicate.load_eliminater 1.02% : 0.000008s : 8: predicate.loop_unroll_after_grad 3.70% : 0.000028s : 201: predicate.loop_unroll_before_grad 1.63% : 0.000012s : 76: predicate.make_slice_get_slice_eliminator 1.18% : 0.000009s : 67: predicate.merge_addn 1.25% : 0.000009s : 68: predicate.minmaximum_grad 0.46% : 0.000003s : 8: predicate.mutable_eliminate 0.19% : 0.000001s : 8: predicate.opt_reshape 2.13% : 0.000016s : 85: predicate.partial_eliminate 1.42% : 0.000011s : 67: predicate.print_const_string_wrapper 1.79% : 0.000013s : 68: predicate.reduce_eliminate 1.47% : 0.000011s : 77: predicate.redundant_stop_gradient_eliminater 0.43% : 0.000003s : 36: predicate.remove_not_recompute_node 2.65% : 0.000020s : 202: predicate.replace_applicator 1.38% : 0.000010s : 118: predicate.replace_old_param 0.11% : 0.000001s : 8: predicate.reset_defer_inline 1.37% : 0.000010s : 68: predicate.reshape_eliminate 1.33% : 0.000010s : 67: predicate.row_tensor_add_zeros_like 0.26% : 0.000002s : 8: predicate.row_tensor_eliminate 1.32% : 0.000010s : 67: predicate.same_eliminate 0.44% : 0.000003s : 38: predicate.set_cell_output_no_recompute 0.43% : 0.000003s : 16: predicate.special_op_eliminate 0.83% : 0.000006s : 36: predicate.specialize_transform 1.69% : 0.000013s : 67: predicate.split_environ_get_set_with_tuple_value 1.30% : 0.000010s : 67: predicate.stack_unstack_eliminate 0.19% : 0.000001s : 8: predicate.switch_call_monad_eliminater 3.68% : 0.000028s : 98: predicate.switch_defer_inline 2.20% : 0.000017s : 98: predicate.switch_layer_defer_inline 9.40% : 0.000071s : 307: predicate.switch_simplify 1.35% : 0.000010s : 68: predicate.tile_eliminate 1.29% : 0.000010s : 68: predicate.transpose_eliminate 1.77% : 0.000013s : 68: predicate.tuple_list_convert_item_index_to_positive 1.95% : 0.000015s : 68: predicate.tuple_list_get_item_depend_reorder 3.21% : 0.000024s : 93: predicate.tuple_list_get_item_eliminator 1.78% : 0.000013s : 68: predicate.tuple_list_set_item_eliminator 1.53% : 0.000012s : 77: predicate.tuple_to_list_eliminator_ 1.62% : 0.000012s : 85: predicate.updatestate_pure_node_eliminater 2.68% : 0.000020s : 122: predicate.updatestate_useless_node_eliminater 1.57% : 0.000012s : 67: predicate.value_based_eliminate 0.18% : 0.000001s : 8: predicate.virtual_view_grad_eliminate 0.24% : 0.000002s : 8: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.005844 66 57.63% : 0.003368s : 30: func_graph_cloner_run.FuncGraphClonerGraph 5.25% : 0.000307s : 3: func_graph_cloner_run.FuncGraphClonerNode 37.12% : 0.002169s : 33: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.731068 89 0.01% : 0.000107s : 1: add_recomputation 0.04% : 0.000320s : 1: auto_monad 0.01% : 0.000047s : 1: auto_monad_reorder 0.08% : 0.000561s : 1: bootstrap 0.01% : 0.000038s : 1: cconv 0.00% : 0.000018s : 1: convert_after_rewriter 0.01% : 0.000049s : 1: cse_after_recomputation 0.00% : 0.000023s : 1: environ_conv 0.02% : 0.000116s : 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 34.36% : 0.251159s : 1: jit_opt_a 0.05% : 0.000376s : 1: jit_opt_after_cconv 0.01% : 0.000089s : 1: jit_opt_b 1.99% : 0.014550s : 1: loop_unroll 0.10% : 0.000752s : 1: mutable_eliminate 19.43% : 0.142060s : 39: opt.transform.jit_opt_a 0.02% : 0.000132s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000058s : 4: opt.transform.jit_opt_b 0.01% : 0.000045s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000025s : 1: opt.transform.mutable_eliminate 0.01% : 0.000048s : 1: opt.transform.opt_after_jit_grad 0.14% : 0.001026s : 2: opt.transform.opt_resolve 0.01% : 0.000067s : 4: opt.transform.symbol_engine_opt 0.09% : 0.000673s : 1: opt_after_jit_grad 0.00% : 0.000011s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000018s : 1: pre_auto_parallel 0.01% : 0.000072s : 1: py_interpret_to_execute 0.00% : 0.000024s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000026s : 1: remove_dup_value 1.42% : 0.010382s : 2: renormalize.infer 0.64% : 0.004673s : 2: renormalize.specialize 0.04% : 0.000262s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000141s : 1: rewriter_after_opt_a 0.03% : 0.000201s : 1: rewriter_before_opt_a 0.02% : 0.000125s : 1: symbol_engine_optimizer 41.41% : 0.302760s : 1: type_inference . [hook] pytest_runtest_teardown:test_unsqueeze_view_backward[KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_view_backward[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 84.06s (0:01:24) ===================