==================================================Ascend ============================= test session starts ============================== platform linux -- Python 3.9.19, pytest-6.2.5, py-1.11.0, pluggy-1.5.0 rootdir: /home/jenkins/mindspore/testcases/testcases/tests/st/mint, configfile: ../../../../../../sault/virtual_test/virtualenv_002/sault/config/pytest.ini plugins: mock-3.14.0, hydra-core-1.3.2, forked-1.6.0, anyio-4.9.0, xdist-1.32.0 collected 2 items test_squeeze.py . [hook] pytest_runtest_teardown:test_squeeze_view_backward[pynative] tests/st/mint/test_squeeze.py::test_squeeze_view_backward[pynative],max_mem:2.0M TotalTime = 3.06697, [30] [bootstrap]: 0.028373 [type_inference]: 2.48247 [event_method]: 0.00076345 [auto_monad]: 0.00038159 [graph_reusing]: 1.199e-05 [pre_auto_parallel]: 2.077e-05 [py_interpret_to_execute]: 7.395e-05 [rewriter_before_opt_a]: 0.00021318 [expand_dump_flag]: 5.53002e-06 [jit_opt_a]: 0.551075, [3] [Cycle 1]: 0.414586, [27] [switch_simplify]: 0.000142 [loop_unroll]: 7.141e-05 [a_1]: 0.00186554 [with_stream_mark]: 4.639e-05 [recompute_prepare]: 3.213e-05 [updatestate_depend_eliminate]: 1.407e-05 [updatestate_assign_eliminate]: 1.633e-05 [updatestate_loads_eliminate]: 1.179e-05 [parameter_eliminate]: 3.83001e-06 [specialize_transform]: 2.233e-05 [updatestate_useless_node_eliminater]: 2.654e-05 [accelerated_algorithm]: 2.171e-05 [meta_shard_fg_expand]: 5.67999e-06 [get_grad_eliminate_]: 2.237e-05 [merge_forward]: 1.273e-05 [cell_reuse_recompute_pass]: 1.15999e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.749e-05 [j_node_and_user_rematch]: 3.682e-05 [meta_fg_expand]: 0.23602 [replace_old_param]: 0.00016791 [inline_without_move]: 0.00015341 [renormalize]: 0.174595 [add_forward_monad_depend]: 3.296e-05 [auto_monad_grad]: 1.245e-05 [auto_monad_eliminator]: 0.00012664 [cse]: 0.00045376 [replace_applicator]: 0.00024707 [Cycle 2]: 0.00429162, [27] [switch_simplify]: 9.834e-05 [loop_unroll]: 9.395e-05 [a_1]: 0.00138961 [with_stream_mark]: 3.104e-05 [recompute_prepare]: 1.717e-05 [updatestate_depend_eliminate]: 4.387e-05 [updatestate_assign_eliminate]: 7.33999e-06 [updatestate_loads_eliminate]: 5.87999e-06 [parameter_eliminate]: 2.69999e-06 [specialize_transform]: 1.289e-05 [updatestate_useless_node_eliminater]: 1.657e-05 [accelerated_algorithm]: 1.268e-05 [meta_shard_fg_expand]: 3.16999e-06 [get_grad_eliminate_]: 1.087e-05 [merge_forward]: 6.95002e-06 [cell_reuse_recompute_pass]: 2.21e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.341e-05 [j_node_and_user_rematch]: 1.771e-05 [meta_fg_expand]: 0.00027053 [replace_old_param]: 2.681e-05 [inline_without_move]: 1.193e-05 [renormalize]: 0.00170052 [add_forward_monad_depend]: 8.52e-06 [auto_monad_grad]: 2.53e-06 [auto_monad_eliminator]: 2.874e-05 [cse]: 0.00020144 [replace_applicator]: 2.681e-05 [Cycle 3]: 0.00069999, [27] [switch_simplify]: 1.259e-05 [loop_unroll]: 1.088e-05 [a_1]: 0.00026961 [with_stream_mark]: 2.188e-05 [recompute_prepare]: 1.216e-05 [updatestate_depend_eliminate]: 7.50998e-06 [updatestate_assign_eliminate]: 6.02999e-06 [updatestate_loads_eliminate]: 5.59998e-06 [parameter_eliminate]: 1.72001e-06 [specialize_transform]: 1.155e-05 [updatestate_useless_node_eliminater]: 1.448e-05 [accelerated_algorithm]: 1.112e-05 [meta_shard_fg_expand]: 2.63e-06 [get_grad_eliminate_]: 1.1e-05 [merge_forward]: 5.76e-06 [cell_reuse_recompute_pass]: 1.99e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.218e-05 [j_node_and_user_rematch]: 1.743e-05 [meta_fg_expand]: 4.35e-06 [replace_old_param]: 1.622e-05 [inline_without_move]: 1.112e-05 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 1.52001e-06 [auto_monad_grad]: 2.16e-06 [auto_monad_eliminator]: 1.528e-05 [cse]: 5.999e-05 [replace_applicator]: 1.211e-05 [py_interpret_to_execute_after_opt_a]: 2.131e-05 [rewriter_after_opt_a]: 9.222e-05 [convert_after_rewriter]: 1.159e-05 [order_py_execute_after_rewriter]: 7.55998e-06 [mutable_eliminate]: 0.00090911 [jit_opt_b]: 8.93e-05, [1] [Cycle 1]: 7.968e-05, [2] [frontend_op_eliminate]: 3.405e-05 [inline_after_opt_a]: 3.181e-05 [cconv]: 3.828e-05 [loop_unroll]: 0.00050005 [jit_opt_after_cconv]: 0.00026992, [1] [Cycle 1]: 0.00026188, [11] [c_1]: 6.691e-05 [parameter_eliminate]: 3.66999e-06 [updatestate_depend_eliminate]: 1.295e-05 [updatestate_assign_eliminate]: 6.17001e-06 [updatestate_loads_eliminate]: 6.53998e-06 [cse]: 4.956e-05 [call_graph_tuple_transform]: 3.198e-05 [tuple_list_get_item_eliminator]: 1.071e-05 [none_parameter_eliminate]: 2.15002e-06 [renormalize]: 6.69999e-07 [switch_simplify]: 1.042e-05 [remove_dup_value]: 2.18e-05 [partial_unused_args_eliminate]: 2.27001e-06 [environ_conv]: 1.972e-05 [add_recomputation]: 9.17e-05 [cse_after_recomputation]: 4.584e-05, [1] [Cycle 1]: 3.962e-05, [1] [cse]: 3.167e-05 [auto_monad_reorder]: 3.558e-05 [get_jit_bprop_graph]: 2.44001e-06 [rewriter_after_jit_bprop_graph]: 0.00022837 [opt_after_jit_grad]: 0.00057194 [symbol_engine_optimizer]: 0.00011282, [1] [Cycle 1]: 0.00010563, [6] [build]: 6.17001e-06 [elim_shapecalc]: 1.548e-05 [elim_not_effective]: 2.261e-05 [opt_reshape]: 1.297e-05 [fold_const_symbol]: 1.664e-05 [renormalize]: 7.29982e-07 [validate]: 8.548e-05 Sums bootstrap : 0.028373s : 0.97% type_inference : 2.482474s : 84.61% event_method : 0.000763s : 0.03% auto_monad : 0.000382s : 0.01% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000021s : 0.00% py_interpret_to_execute : 0.000074s : 0.00% rewriter_before_opt_a : 0.000213s : 0.01% expand_dump_flag : 0.000006s : 0.00% jit_opt_a.switch_simplify : 0.000253s : 0.01% jit_opt_a.loop_unroll : 0.000176s : 0.01% jit_opt_a.a_1 : 0.003525s : 0.12% jit_opt_a.with_stream_mark : 0.000099s : 0.00% jit_opt_a.recompute_prepare : 0.000061s : 0.00% jit_opt_a.updatestate_depend_eliminate : 0.000065s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000030s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000023s : 0.00% jit_opt_a.parameter_eliminate : 0.000008s : 0.00% jit_opt_a.specialize_transform : 0.000047s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000058s : 0.00% jit_opt_a.accelerated_algorithm : 0.000046s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000011s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000044s : 0.00% jit_opt_a.merge_forward : 0.000025s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000005s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000093s : 0.00% jit_opt_a.j_node_and_user_rematch : 0.000072s : 0.00% jit_opt_a.meta_fg_expand : 0.236295s : 8.05% jit_opt_a.replace_old_param : 0.000211s : 0.01% jit_opt_a.inline_without_move : 0.000176s : 0.01% jit_opt_a.renormalize : 0.176296s : 6.01% jit_opt_a.add_forward_monad_depend : 0.000043s : 0.00% jit_opt_a.auto_monad_grad : 0.000017s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000171s : 0.01% jit_opt_a.cse : 0.000715s : 0.02% jit_opt_a.replace_applicator : 0.000286s : 0.01% py_interpret_to_execute_after_opt_a : 0.000021s : 0.00% rewriter_after_opt_a : 0.000092s : 0.00% convert_after_rewriter : 0.000012s : 0.00% order_py_execute_after_rewriter : 0.000008s : 0.00% mutable_eliminate : 0.000909s : 0.03% jit_opt_b.frontend_op_eliminate : 0.000034s : 0.00% jit_opt_b.inline_after_opt_a : 0.000032s : 0.00% cconv : 0.000038s : 0.00% loop_unroll : 0.000500s : 0.02% jit_opt_after_cconv.c_1 : 0.000067s : 0.00% jit_opt_after_cconv.parameter_eliminate : 0.000004s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000013s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000006s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.cse : 0.000050s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000032s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000011s : 0.00% jit_opt_after_cconv.none_parameter_eliminate : 0.000002s : 0.00% jit_opt_after_cconv.renormalize : 0.000001s : 0.00% jit_opt_after_cconv.switch_simplify : 0.000010s : 0.00% remove_dup_value : 0.000022s : 0.00% partial_unused_args_eliminate : 0.000002s : 0.00% environ_conv : 0.000020s : 0.00% add_recomputation : 0.000092s : 0.00% cse_after_recomputation.cse : 0.000032s : 0.00% auto_monad_reorder : 0.000036s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000228s : 0.01% opt_after_jit_grad : 0.000572s : 0.02% symbol_engine_optimizer.build : 0.000006s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000015s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000023s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000013s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000017s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000085s : 0.00% Time group info: ------[substitution.] 0.002302 197 0.71% : 0.000016s : 1: substitution.arithmetic_simplify 1.31% : 0.000030s : 8: substitution.depend_value_elim 0.14% : 0.000003s : 5: substitution.elim_not_effective 0.10% : 0.000002s : 5: substitution.fold_const_symbol 55.45% : 0.001276s : 4: substitution.getattr_setattr_resolve 0.36% : 0.000008s : 8: substitution.graph_param_transform 27.53% : 0.000634s : 16: substitution.inline 2.01% : 0.000046s : 5: substitution.inline_without_move 0.54% : 0.000012s : 23: substitution.j_node_and_user_rematch 0.46% : 0.000011s : 6: substitution.minmaximum_grad 0.71% : 0.000016s : 3: substitution.partial_eliminate 0.72% : 0.000017s : 23: substitution.remove_not_recompute_node 2.98% : 0.000069s : 19: substitution.replace_applicator 0.89% : 0.000020s : 23: substitution.replace_old_param 0.12% : 0.000003s : 1: substitution.set_cell_output_no_recompute 0.98% : 0.000023s : 6: substitution.tuple_list_convert_item_index_to_positive 0.66% : 0.000015s : 6: substitution.tuple_list_get_item_depend_reorder 2.04% : 0.000047s : 14: substitution.tuple_list_get_item_eliminator 0.80% : 0.000019s : 8: substitution.updatestate_pure_node_eliminater 1.49% : 0.000034s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 2.482284 2 99.82% : 2.477811s : 1: type_inference.infer 0.18% : 0.004472s : 1: type_inference.specialize ------[replace.] 0.000497 30 1.23% : 0.000006s : 1: replace.arithmetic_simplify 18.54% : 0.000092s : 3: replace.getattr_setattr_resolve 31.10% : 0.000155s : 16: replace.inline 7.55% : 0.000038s : 1: replace.replace_applicator 36.59% : 0.000182s : 8: replace.tuple_list_get_item_eliminator 4.99% : 0.000025s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001896 30 0.80% : 0.000015s : 1: match.arithmetic_simplify 63.47% : 0.001203s : 3: match.getattr_setattr_resolve 32.93% : 0.000624s : 16: match.inline 0.83% : 0.000016s : 1: match.replace_applicator 1.27% : 0.000024s : 8: match.tuple_list_get_item_eliminator 0.71% : 0.000013s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000656 4213 1.41% : 0.000009s : 63: predicate.accumulaten_eliminater 0.37% : 0.000002s : 8: predicate.ad_related_special_op_eliminate 1.38% : 0.000009s : 63: predicate.addn_check_dump 1.36% : 0.000009s : 63: predicate.addn_zero_filter 1.93% : 0.000013s : 64: predicate.arithmetic_simplify 1.51% : 0.000010s : 64: predicate.cast_eliminate 0.20% : 0.000001s : 8: predicate.check_bprop_eliminate 1.44% : 0.000009s : 63: predicate.compare_switch_simplify 1.49% : 0.000010s : 63: predicate.depend_value_elim 1.35% : 0.000009s : 64: predicate.dict_get_item_const_eliminator 1.42% : 0.000009s : 64: predicate.dict_get_item_eliminator 1.48% : 0.000010s : 64: predicate.dict_set_item_eliminator 0.36% : 0.000002s : 8: predicate.dumpgradient_eliminate 0.15% : 0.000001s : 8: predicate.elim_not_effective 0.26% : 0.000002s : 8: predicate.elim_shapecalc_of_broadcastargs 1.31% : 0.000009s : 64: predicate.environ_add_const_eliminate 1.32% : 0.000009s : 64: predicate.environ_get_add_eliminate 1.40% : 0.000009s : 64: predicate.environ_get_depend_swap 1.31% : 0.000009s : 64: predicate.environ_get_eliminate 1.26% : 0.000008s : 64: predicate.environ_get_set_eliminate 0.10% : 0.000001s : 8: predicate.fold_const_symbol 1.13% : 0.000007s : 36: predicate.get_grad_eliminate 1.26% : 0.000008s : 20: predicate.getattr_setattr_resolve 0.10% : 0.000001s : 8: predicate.graph_param_transform 3.89% : 0.000026s : 105: predicate.inline 3.01% : 0.000020s : 114: predicate.inline_without_move 0.42% : 0.000003s : 36: predicate.j_node_and_user_rematch 0.96% : 0.000006s : 36: predicate.less_batch_normalization 1.44% : 0.000009s : 72: predicate.list_to_tuple_eliminator_ 1.89% : 0.000012s : 80: predicate.load_eliminater 0.59% : 0.000004s : 8: predicate.loop_unroll_after_grad 4.24% : 0.000028s : 196: predicate.loop_unroll_before_grad 1.77% : 0.000012s : 72: predicate.make_slice_get_slice_eliminator 1.28% : 0.000008s : 63: predicate.merge_addn 1.40% : 0.000009s : 64: predicate.minmaximum_grad 0.51% : 0.000003s : 8: predicate.mutable_eliminate 0.25% : 0.000002s : 8: predicate.opt_reshape 1.95% : 0.000013s : 80: predicate.partial_eliminate 1.35% : 0.000009s : 63: predicate.print_const_string_wrapper 1.69% : 0.000011s : 64: predicate.reduce_eliminate 1.51% : 0.000010s : 72: predicate.redundant_stop_gradient_eliminater 0.46% : 0.000003s : 36: predicate.remove_not_recompute_node 2.92% : 0.000019s : 198: predicate.replace_applicator 1.60% : 0.000010s : 114: predicate.replace_old_param 0.17% : 0.000001s : 8: predicate.reset_defer_inline 1.38% : 0.000009s : 64: predicate.reshape_eliminate 1.35% : 0.000009s : 63: predicate.row_tensor_add_zeros_like 0.26% : 0.000002s : 8: predicate.row_tensor_eliminate 1.35% : 0.000009s : 63: predicate.same_eliminate 0.53% : 0.000003s : 38: predicate.set_cell_output_no_recompute 0.38% : 0.000003s : 16: predicate.special_op_eliminate 0.94% : 0.000006s : 36: predicate.specialize_transform 1.49% : 0.000010s : 63: predicate.split_environ_get_set_with_tuple_value 2.55% : 0.000017s : 63: predicate.stack_unstack_eliminate 0.20% : 0.000001s : 8: predicate.switch_call_monad_eliminater 2.48% : 0.000016s : 89: predicate.switch_defer_inline 2.12% : 0.000014s : 89: predicate.switch_layer_defer_inline 6.85% : 0.000045s : 293: predicate.switch_simplify 1.39% : 0.000009s : 64: predicate.tile_eliminate 1.56% : 0.000010s : 64: predicate.transpose_eliminate 1.64% : 0.000011s : 64: predicate.tuple_list_convert_item_index_to_positive 1.43% : 0.000009s : 64: predicate.tuple_list_get_item_depend_reorder 2.98% : 0.000020s : 88: predicate.tuple_list_get_item_eliminator 1.76% : 0.000012s : 64: predicate.tuple_list_set_item_eliminator 1.63% : 0.000011s : 72: predicate.tuple_to_list_eliminator_ 1.84% : 0.000012s : 80: predicate.updatestate_pure_node_eliminater 3.12% : 0.000020s : 117: predicate.updatestate_useless_node_eliminater 1.75% : 0.000012s : 63: predicate.value_based_eliminate 0.15% : 0.000001s : 8: predicate.virtual_view_grad_eliminate 0.28% : 0.000002s : 8: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.006619 69 74.15% : 0.004908s : 37: func_graph_cloner_run.FuncGraphClonerGraph 25.85% : 0.001711s : 32: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 3.249696 89 0.00% : 0.000095s : 1: add_recomputation 0.01% : 0.000399s : 1: auto_monad 0.00% : 0.000039s : 1: auto_monad_reorder 0.87% : 0.028415s : 1: bootstrap 0.00% : 0.000041s : 1: cconv 0.00% : 0.000014s : 1: convert_after_rewriter 0.00% : 0.000048s : 1: cse_after_recomputation 0.00% : 0.000022s : 1: environ_conv 0.02% : 0.000779s : 1: event_method 0.00% : 0.000008s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000016s : 1: graph_reusing 16.96% : 0.551079s : 1: jit_opt_a 0.01% : 0.000273s : 1: jit_opt_after_cconv 0.00% : 0.000092s : 1: jit_opt_b 0.02% : 0.000509s : 1: loop_unroll 0.03% : 0.000923s : 1: mutable_eliminate 0.15% : 0.004968s : 39: opt.transform.jit_opt_a 0.00% : 0.000116s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000058s : 4: opt.transform.jit_opt_b 0.00% : 0.000023s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000027s : 1: opt.transform.mutable_eliminate 0.00% : 0.000043s : 1: opt.transform.opt_after_jit_grad 0.04% : 0.001453s : 2: opt.transform.opt_resolve 0.00% : 0.000064s : 4: opt.transform.symbol_engine_opt 0.02% : 0.000582s : 1: opt_after_jit_grad 0.00% : 0.000010s : 1: order_py_execute_after_rewriter 0.00% : 0.000004s : 1: partial_unused_args_eliminate 0.00% : 0.000023s : 1: pre_auto_parallel 0.00% : 0.000078s : 1: py_interpret_to_execute 0.00% : 0.000024s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000024s : 1: remove_dup_value 5.29% : 0.171748s : 2: renormalize.infer 0.14% : 0.004520s : 2: renormalize.specialize 0.01% : 0.000234s : 1: rewriter_after_jit_bprop_graph 0.00% : 0.000097s : 1: rewriter_after_opt_a 0.01% : 0.000220s : 1: rewriter_before_opt_a 0.00% : 0.000116s : 1: symbol_engine_optimizer 76.39% : 2.482507s : 1: type_inference . [hook] pytest_runtest_teardown:test_squeeze_view_backward[KBK] tests/st/mint/test_squeeze.py::test_squeeze_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 193.15s (0:03:13) ==================