==================================================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_select.py . [hook] pytest_runtest_teardown:test_select_view_backward[pynative] tests/st/mint/test_select.py::test_select_view_backward[pynative],max_mem:2.0M [WARNING] PIPELINE(167412,ffff9d353f30,python3.9):2026-01-29-17:41:48.097.669 [mindspore/ccsrc/frontend/jit/ps/action.cc:1249] FindViewInplaceNode] There is an in-place modification to a Tensor view that requires gradients. However, computing gradients for in-place modified Tensor views is still an experimental feature, and the results may be inaccurate. It is recommended to replace the Tensor view here with a non-view Tensor. The code location is as follows: In file /home/jenkins/mindspore/testcases/testcases/tests/st/mint/test_select.py:705, 14~33 out = dst_view.copy_(src) ^~~~~~~~~~~~~~~~~~~ TotalTime = 1.50608, [30] [bootstrap]: 0.00088579 [type_inference]: 0.81062 [event_method]: 8.359e-05 [auto_monad]: 0.00064419 [graph_reusing]: 1.36e-05 [pre_auto_parallel]: 1.884e-05 [py_interpret_to_execute]: 9.005e-05 [rewriter_before_opt_a]: 0.00022593 [expand_dump_flag]: 4.17998e-06 [jit_opt_a]: 0.688896, [3] [Cycle 1]: 0.492787, [27] [switch_simplify]: 0.00011858 [loop_unroll]: 6.393e-05 [a_1]: 0.00172498 [with_stream_mark]: 5.298e-05 [recompute_prepare]: 4.333e-05 [updatestate_depend_eliminate]: 1.654e-05 [updatestate_assign_eliminate]: 6.505e-05 [updatestate_loads_eliminate]: 1.4e-05 [parameter_eliminate]: 4.95001e-06 [specialize_transform]: 3.001e-05 [updatestate_useless_node_eliminater]: 3.283e-05 [accelerated_algorithm]: 2.681e-05 [meta_shard_fg_expand]: 6.38e-06 [get_grad_eliminate_]: 2.569e-05 [merge_forward]: 1.614e-05 [cell_reuse_recompute_pass]: 1.47001e-06 [cell_reuse_handle_not_recompute_node_pass]: 5.741e-05 [j_node_and_user_rematch]: 6.006e-05 [meta_fg_expand]: 0.322228 [replace_old_param]: 0.00017963 [inline_without_move]: 0.00018668 [renormalize]: 0.166279 [add_forward_monad_depend]: 3.604e-05 [auto_monad_grad]: 2.114e-05 [auto_monad_eliminator]: 0.00019233 [cse]: 0.00051392 [replace_applicator]: 0.00032774 [Cycle 2]: 0.186135, [27] [switch_simplify]: 0.00014544 [loop_unroll]: 0.00013376 [a_1]: 0.00215403 [with_stream_mark]: 3.907e-05 [recompute_prepare]: 2.291e-05 [updatestate_depend_eliminate]: 4.243e-05 [updatestate_assign_eliminate]: 8.89e-06 [updatestate_loads_eliminate]: 6.83e-06 [parameter_eliminate]: 2.54999e-06 [specialize_transform]: 1.478e-05 [updatestate_useless_node_eliminater]: 1.951e-05 [accelerated_algorithm]: 1.425e-05 [meta_shard_fg_expand]: 5.27001e-06 [get_grad_eliminate_]: 1.273e-05 [merge_forward]: 8.38999e-06 [cell_reuse_recompute_pass]: 2.07999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.999e-05 [j_node_and_user_rematch]: 2.142e-05 [meta_fg_expand]: 2.203e-05 [replace_old_param]: 2.021e-05 [inline_without_move]: 1.236e-05 [renormalize]: 0.182783 [add_forward_monad_depend]: 1.705e-05 [auto_monad_grad]: 2.71e-06 [auto_monad_eliminator]: 5.34e-05 [cse]: 0.0002008 [replace_applicator]: 4.212e-05 [Cycle 3]: 0.00095017, [27] [switch_simplify]: 1.705e-05 [loop_unroll]: 1.318e-05 [a_1]: 0.00035849 [with_stream_mark]: 3.29e-05 [recompute_prepare]: 1.776e-05 [updatestate_depend_eliminate]: 1.037e-05 [updatestate_assign_eliminate]: 8.92e-06 [updatestate_loads_eliminate]: 7.13e-06 [parameter_eliminate]: 2.64001e-06 [specialize_transform]: 1.317e-05 [updatestate_useless_node_eliminater]: 2.203e-05 [accelerated_algorithm]: 1.556e-05 [meta_shard_fg_expand]: 3.76999e-06 [get_grad_eliminate_]: 1.298e-05 [merge_forward]: 8.99998e-06 [cell_reuse_recompute_pass]: 3.86999e-06 [cell_reuse_handle_not_recompute_node_pass]: 3.062e-05 [j_node_and_user_rematch]: 2.289e-05 [meta_fg_expand]: 5.10001e-06 [replace_old_param]: 2.132e-05 [inline_without_move]: 1.233e-05 [renormalize]: 8.00064e-08 [add_forward_monad_depend]: 3.39001e-06 [auto_monad_grad]: 2.19001e-06 [auto_monad_eliminator]: 3.1e-05 [cse]: 8.632e-05 [replace_applicator]: 1.901e-05 [py_interpret_to_execute_after_opt_a]: 3.21e-05 [rewriter_after_opt_a]: 0.00022489 [convert_after_rewriter]: 4.316e-05 [order_py_execute_after_rewriter]: 9.82001e-06 [mutable_eliminate]: 0.00094522 [jit_opt_b]: 0.00010948, [1] [Cycle 1]: 9.735e-05, [2] [frontend_op_eliminate]: 3.831e-05 [inline_after_opt_a]: 4.309e-05 [cconv]: 5.136e-05 [loop_unroll]: 0.00066604 [jit_opt_after_cconv]: 0.00035434, [1] [Cycle 1]: 0.00034547, [11] [c_1]: 8.552e-05 [parameter_eliminate]: 7.7e-06 [updatestate_depend_eliminate]: 2.194e-05 [updatestate_assign_eliminate]: 7.7e-06 [updatestate_loads_eliminate]: 8.05e-06 [cse]: 8.502e-05 [call_graph_tuple_transform]: 3.703e-05 [tuple_list_get_item_eliminator]: 1.339e-05 [none_parameter_eliminate]: 2.12999e-06 [renormalize]: 8.09989e-07 [switch_simplify]: 1.427e-05 [remove_dup_value]: 7.764e-05 [partial_unused_args_eliminate]: 3.11001e-06 [environ_conv]: 2.273e-05 [add_recomputation]: 0.00011715 [cse_after_recomputation]: 6.049e-05, [1] [Cycle 1]: 5.16e-05, [1] [cse]: 4.147e-05 [auto_monad_reorder]: 5.008e-05 [get_jit_bprop_graph]: 2.93e-06 [rewriter_after_jit_bprop_graph]: 0.00027812 [opt_after_jit_grad]: 0.00077736 [symbol_engine_optimizer]: 0.00013185, [1] [Cycle 1]: 0.00012312, [6] [build]: 1.054e-05 [elim_shapecalc]: 1.643e-05 [elim_not_effective]: 2.908e-05 [opt_reshape]: 1.341e-05 [fold_const_symbol]: 2.017e-05 [renormalize]: 9.89996e-07 [validate]: 0.00017864 Sums bootstrap : 0.000886s : 0.06% type_inference : 0.810620s : 54.20% event_method : 0.000084s : 0.01% auto_monad : 0.000644s : 0.04% graph_reusing : 0.000014s : 0.00% pre_auto_parallel : 0.000019s : 0.00% py_interpret_to_execute : 0.000090s : 0.01% rewriter_before_opt_a : 0.000226s : 0.02% expand_dump_flag : 0.000004s : 0.00% jit_opt_a.switch_simplify : 0.000281s : 0.02% jit_opt_a.loop_unroll : 0.000211s : 0.01% jit_opt_a.a_1 : 0.004238s : 0.28% jit_opt_a.with_stream_mark : 0.000125s : 0.01% jit_opt_a.recompute_prepare : 0.000084s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000069s : 0.00% jit_opt_a.updatestate_assign_eliminate : 0.000083s : 0.01% jit_opt_a.updatestate_loads_eliminate : 0.000028s : 0.00% jit_opt_a.parameter_eliminate : 0.000010s : 0.00% jit_opt_a.specialize_transform : 0.000058s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000074s : 0.00% jit_opt_a.accelerated_algorithm : 0.000057s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000015s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000051s : 0.00% jit_opt_a.merge_forward : 0.000034s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000118s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000104s : 0.01% jit_opt_a.meta_fg_expand : 0.322255s : 21.55% jit_opt_a.replace_old_param : 0.000221s : 0.01% jit_opt_a.inline_without_move : 0.000211s : 0.01% jit_opt_a.renormalize : 0.349062s : 23.34% jit_opt_a.add_forward_monad_depend : 0.000056s : 0.00% jit_opt_a.auto_monad_grad : 0.000026s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000277s : 0.02% jit_opt_a.cse : 0.000801s : 0.05% jit_opt_a.replace_applicator : 0.000389s : 0.03% py_interpret_to_execute_after_opt_a : 0.000032s : 0.00% rewriter_after_opt_a : 0.000225s : 0.02% convert_after_rewriter : 0.000043s : 0.00% order_py_execute_after_rewriter : 0.000010s : 0.00% mutable_eliminate : 0.000945s : 0.06% jit_opt_b.frontend_op_eliminate : 0.000038s : 0.00% jit_opt_b.inline_after_opt_a : 0.000043s : 0.00% cconv : 0.000051s : 0.00% loop_unroll : 0.000666s : 0.04% jit_opt_after_cconv.c_1 : 0.000086s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000022s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000008s : 0.00% jit_opt_after_cconv.cse : 0.000085s : 0.01% jit_opt_after_cconv.call_graph_tuple_transform : 0.000037s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000013s : 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 : 0.000014s : 0.00% remove_dup_value : 0.000078s : 0.01% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000023s : 0.00% add_recomputation : 0.000117s : 0.01% cse_after_recomputation.cse : 0.000041s : 0.00% auto_monad_reorder : 0.000050s : 0.00% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000278s : 0.02% opt_after_jit_grad : 0.000777s : 0.05% symbol_engine_optimizer.build : 0.000011s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000016s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000029s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000013s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000020s : 0.00% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000179s : 0.01% Time group info: ------[substitution.] 0.002362 253 2.97% : 0.000070s : 3: substitution.arithmetic_simplify 1.19% : 0.000028s : 8: substitution.depend_value_elim 0.19% : 0.000005s : 7: substitution.elim_not_effective 0.13% : 0.000003s : 7: substitution.fold_const_symbol 41.40% : 0.000978s : 4: substitution.getattr_setattr_resolve 0.44% : 0.000010s : 9: substitution.graph_param_transform 33.32% : 0.000787s : 15: substitution.inline 2.19% : 0.000052s : 7: substitution.inline_without_move 1.40% : 0.000033s : 29: substitution.j_node_and_user_rematch 0.71% : 0.000017s : 8: substitution.minmaximum_grad 1.23% : 0.000029s : 3: substitution.partial_eliminate 0.89% : 0.000021s : 29: substitution.remove_not_recompute_node 3.73% : 0.000088s : 25: substitution.replace_applicator 1.07% : 0.000025s : 30: substitution.replace_old_param 0.21% : 0.000005s : 1: substitution.set_cell_output_no_recompute 1.40% : 0.000033s : 8: substitution.tuple_list_convert_item_index_to_positive 0.95% : 0.000022s : 8: substitution.tuple_list_get_item_depend_reorder 2.83% : 0.000067s : 18: substitution.tuple_list_get_item_eliminator 1.42% : 0.000034s : 13: substitution.updatestate_pure_node_eliminater 2.32% : 0.000055s : 21: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.665647 2 99.60% : 0.662997s : 1: type_inference.infer 0.40% : 0.002650s : 1: type_inference.specialize ------[replace.] 0.000686 30 10.94% : 0.000075s : 3: replace.getattr_setattr_resolve 25.60% : 0.000176s : 15: replace.inline 6.23% : 0.000043s : 1: replace.replace_applicator 51.99% : 0.000357s : 10: replace.tuple_list_get_item_eliminator 5.24% : 0.000036s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001757 30 52.08% : 0.000915s : 3: match.getattr_setattr_resolve 44.19% : 0.000776s : 15: match.inline 0.81% : 0.000014s : 1: match.replace_applicator 1.95% : 0.000034s : 10: match.tuple_list_get_item_eliminator 0.98% : 0.000017s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000774 4708 1.53% : 0.000012s : 69: predicate.accumulaten_eliminater 0.56% : 0.000004s : 9: predicate.ad_related_special_op_eliminate 1.21% : 0.000009s : 69: predicate.addn_check_dump 1.30% : 0.000010s : 69: predicate.addn_zero_filter 2.05% : 0.000016s : 69: predicate.arithmetic_simplify 1.47% : 0.000011s : 69: predicate.cast_eliminate 0.21% : 0.000002s : 9: predicate.check_bprop_eliminate 1.23% : 0.000010s : 69: predicate.compare_switch_simplify 1.36% : 0.000011s : 69: predicate.depend_value_elim 1.37% : 0.000011s : 69: predicate.dict_get_item_const_eliminator 1.44% : 0.000011s : 69: predicate.dict_get_item_eliminator 1.34% : 0.000010s : 69: predicate.dict_set_item_eliminator 0.40% : 0.000003s : 9: predicate.dumpgradient_eliminate 0.16% : 0.000001s : 9: predicate.elim_not_effective 0.24% : 0.000002s : 9: predicate.elim_shapecalc_of_broadcastargs 1.16% : 0.000009s : 69: predicate.environ_add_const_eliminate 1.19% : 0.000009s : 69: predicate.environ_get_add_eliminate 1.19% : 0.000009s : 69: predicate.environ_get_depend_swap 1.32% : 0.000010s : 69: predicate.environ_get_eliminate 1.32% : 0.000010s : 69: predicate.environ_get_set_eliminate 0.10% : 0.000001s : 9: predicate.fold_const_symbol 0.91% : 0.000007s : 39: predicate.get_grad_eliminate 0.94% : 0.000007s : 20: predicate.getattr_setattr_resolve 0.14% : 0.000001s : 9: predicate.graph_param_transform 4.51% : 0.000035s : 123: predicate.inline 3.15% : 0.000024s : 151: predicate.inline_without_move 0.36% : 0.000003s : 39: predicate.j_node_and_user_rematch 1.08% : 0.000008s : 39: predicate.less_batch_normalization 1.90% : 0.000015s : 79: predicate.list_to_tuple_eliminator_ 1.86% : 0.000014s : 88: predicate.load_eliminater 0.69% : 0.000005s : 9: predicate.loop_unroll_after_grad 4.05% : 0.000031s : 225: predicate.loop_unroll_before_grad 1.71% : 0.000013s : 78: predicate.make_slice_get_slice_eliminator 1.32% : 0.000010s : 69: predicate.merge_addn 1.41% : 0.000011s : 69: predicate.minmaximum_grad 0.76% : 0.000006s : 9: predicate.mutable_eliminate 0.19% : 0.000001s : 9: predicate.opt_reshape 2.08% : 0.000016s : 88: predicate.partial_eliminate 1.42% : 0.000011s : 69: predicate.print_const_string_wrapper 1.67% : 0.000013s : 69: predicate.reduce_eliminate 1.69% : 0.000013s : 79: predicate.redundant_stop_gradient_eliminater 0.52% : 0.000004s : 39: predicate.remove_not_recompute_node 3.39% : 0.000026s : 246: predicate.replace_applicator 1.60% : 0.000012s : 151: predicate.replace_old_param 0.14% : 0.000001s : 9: predicate.reset_defer_inline 1.39% : 0.000011s : 69: predicate.reshape_eliminate 1.36% : 0.000011s : 69: predicate.row_tensor_add_zeros_like 0.31% : 0.000002s : 9: predicate.row_tensor_eliminate 1.49% : 0.000012s : 69: predicate.same_eliminate 0.54% : 0.000004s : 41: predicate.set_cell_output_no_recompute 0.40% : 0.000003s : 18: predicate.special_op_eliminate 0.93% : 0.000007s : 39: predicate.specialize_transform 1.71% : 0.000013s : 69: predicate.split_environ_get_set_with_tuple_value 1.24% : 0.000010s : 69: predicate.stack_unstack_eliminate 0.25% : 0.000002s : 9: predicate.switch_call_monad_eliminater 2.53% : 0.000020s : 95: predicate.switch_defer_inline 1.85% : 0.000014s : 95: predicate.switch_layer_defer_inline 6.87% : 0.000053s : 329: predicate.switch_simplify 1.28% : 0.000010s : 69: predicate.tile_eliminate 1.33% : 0.000010s : 69: predicate.transpose_eliminate 1.63% : 0.000013s : 69: predicate.tuple_list_convert_item_index_to_positive 1.57% : 0.000012s : 69: predicate.tuple_list_get_item_depend_reorder 3.15% : 0.000024s : 97: predicate.tuple_list_get_item_eliminator 1.53% : 0.000012s : 69: predicate.tuple_list_set_item_eliminator 1.54% : 0.000012s : 79: predicate.tuple_to_list_eliminator_ 1.77% : 0.000014s : 88: predicate.updatestate_pure_node_eliminater 3.01% : 0.000023s : 128: predicate.updatestate_useless_node_eliminater 1.74% : 0.000013s : 69: predicate.value_based_eliminate 0.51% : 0.000004s : 10: predicate.virtual_view_eliminate 0.17% : 0.000001s : 9: predicate.virtual_view_grad_eliminate 0.27% : 0.000002s : 9: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.006170 69 62.05% : 0.003829s : 33: func_graph_cloner_run.FuncGraphClonerGraph 37.95% : 0.002342s : 36: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.863543 93 0.01% : 0.000120s : 1: add_recomputation 0.04% : 0.000661s : 1: auto_monad 0.00% : 0.000054s : 1: auto_monad_reorder 0.05% : 0.000915s : 1: bootstrap 0.00% : 0.000056s : 1: cconv 0.00% : 0.000048s : 1: convert_after_rewriter 0.00% : 0.000063s : 1: cse_after_recomputation 0.00% : 0.000025s : 1: environ_conv 0.00% : 0.000093s : 1: event_method 0.00% : 0.000007s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000018s : 1: graph_reusing 36.97% : 0.688901s : 1: jit_opt_a 0.02% : 0.000358s : 1: jit_opt_after_cconv 0.01% : 0.000113s : 1: jit_opt_b 0.04% : 0.000679s : 1: loop_unroll 0.05% : 0.000965s : 1: mutable_eliminate 0.32% : 0.006000s : 39: opt.transform.jit_opt_a 0.01% : 0.000145s : 4: opt.transform.jit_opt_after_cconv 0.00% : 0.000072s : 4: opt.transform.jit_opt_b 0.00% : 0.000032s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000039s : 1: opt.transform.mutable_eliminate 0.00% : 0.000055s : 1: opt.transform.opt_after_jit_grad 0.06% : 0.001120s : 2: opt.transform.opt_resolve 0.00% : 0.000075s : 4: opt.transform.symbol_engine_opt 0.00% : 0.000056s : 2: opt.transform.view_inplace 0.04% : 0.000794s : 1: opt_after_jit_grad 0.00% : 0.000012s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000022s : 1: pre_auto_parallel 0.01% : 0.000099s : 1: py_interpret_to_execute 0.00% : 0.000035s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000082s : 1: remove_dup_value 8.69% : 0.162014s : 3: renormalize.infer 10.10% : 0.188267s : 3: renormalize.specialize 0.02% : 0.000285s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000239s : 1: rewriter_after_opt_a 0.01% : 0.000238s : 1: rewriter_before_opt_a 0.01% : 0.000135s : 1: symbol_engine_optimizer 43.50% : 0.810638s : 1: type_inference . [hook] pytest_runtest_teardown:test_select_view_backward[KBK] tests/st/mint/test_select.py::test_select_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 305.80s (0:05:05) ==================