==================================================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_004/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_large_tensors[pynative] tests/st/mint/test_unsqueeze.py::test_unsqueeze_large_tensors[pynative],max_mem:2.0M TotalTime = 0.23308, [30] [bootstrap]: 0.00060305 [type_inference]: 0.218116 [event_method]: 1.987e-05 [auto_monad]: 0.00020946 [graph_reusing]: 6.44999e-06 [pre_auto_parallel]: 1.086e-05 [py_interpret_to_execute]: 3.342e-05 [rewriter_before_opt_a]: 7.127e-05 [expand_dump_flag]: 2.86e-06 [jit_opt_a]: 0.0102993, [2] [Cycle 1]: 0.00195326, [27] [switch_simplify]: 6.751e-05 [loop_unroll]: 2.346e-05 [a_1]: 0.0004873 [with_stream_mark]: 2.837e-05 [recompute_prepare]: 9.64e-06 [updatestate_depend_eliminate]: 6.09001e-06 [updatestate_assign_eliminate]: 5.72001e-06 [updatestate_loads_eliminate]: 4.90999e-06 [parameter_eliminate]: 1.76e-06 [specialize_transform]: 8.62998e-06 [updatestate_useless_node_eliminater]: 1.055e-05 [accelerated_algorithm]: 8e-06 [meta_shard_fg_expand]: 2.56998e-06 [get_grad_eliminate_]: 7.51001e-06 [merge_forward]: 5.09003e-06 [cell_reuse_recompute_pass]: 1.22999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.262e-05 [j_node_and_user_rematch]: 1.393e-05 [meta_fg_expand]: 3.71999e-06 [replace_old_param]: 1.261e-05 [inline_without_move]: 7.51999e-06 [renormalize]: 0.00088696 [add_forward_monad_depend]: 1.264e-05 [auto_monad_grad]: 2.93e-06 [auto_monad_eliminator]: 2.672e-05 [cse]: 4.112e-05 [replace_applicator]: 1.814e-05 [Cycle 2]: 0.00050662, [27] [switch_simplify]: 9.28002e-06 [loop_unroll]: 7.65998e-06 [a_1]: 0.00016676 [with_stream_mark]: 1.47e-05 [recompute_prepare]: 9.19e-06 [updatestate_depend_eliminate]: 5.34e-06 [updatestate_assign_eliminate]: 4.05998e-06 [updatestate_loads_eliminate]: 4.47998e-06 [parameter_eliminate]: 1.27999e-06 [specialize_transform]: 8.21002e-06 [updatestate_useless_node_eliminater]: 1.069e-05 [accelerated_algorithm]: 7.48e-06 [meta_shard_fg_expand]: 2.07001e-06 [get_grad_eliminate_]: 7.6e-06 [merge_forward]: 4.75001e-06 [cell_reuse_recompute_pass]: 1.61998e-06 [cell_reuse_handle_not_recompute_node_pass]: 1.663e-05 [j_node_and_user_rematch]: 1.354e-05 [meta_fg_expand]: 2.56998e-06 [replace_old_param]: 1.02e-05 [inline_without_move]: 7.50998e-06 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 2.14e-06 [auto_monad_grad]: 1.80001e-06 [auto_monad_eliminator]: 1.622e-05 [cse]: 2.04e-05 [replace_applicator]: 9.96e-06 [py_interpret_to_execute_after_opt_a]: 1.542e-05 [rewriter_after_opt_a]: 0.00059355 [convert_after_rewriter]: 2.805e-05 [order_py_execute_after_rewriter]: 6.64999e-06 [mutable_eliminate]: 0.00077778 [jit_opt_b]: 7.48e-05, [1] [Cycle 1]: 6.437e-05, [2] [frontend_op_eliminate]: 2.49e-05 [inline_after_opt_a]: 2.558e-05 [cconv]: 3.624e-05 [loop_unroll]: 0.00050996 [jit_opt_after_cconv]: 0.00022925, [1] [Cycle 1]: 0.0002215, [11] [c_1]: 5.348e-05 [parameter_eliminate]: 5.34e-06 [updatestate_depend_eliminate]: 1.261e-05 [updatestate_assign_eliminate]: 4.90001e-06 [updatestate_loads_eliminate]: 5.40999e-06 [cse]: 3.917e-05 [call_graph_tuple_transform]: 2.396e-05 [tuple_list_get_item_eliminator]: 8.10999e-06 [none_parameter_eliminate]: 1.96e-06 [renormalize]: 1.09e-06 [switch_simplify]: 8.74e-06 [remove_dup_value]: 1.953e-05 [partial_unused_args_eliminate]: 3.14001e-06 [environ_conv]: 2.69e-05 [add_recomputation]: 8.196e-05 [cse_after_recomputation]: 3.194e-05, [1] [Cycle 1]: 2.564e-05, [1] [cse]: 1.665e-05 [auto_monad_reorder]: 3.637e-05 [get_jit_bprop_graph]: 2.42001e-06 [rewriter_after_jit_bprop_graph]: 0.0001454 [opt_after_jit_grad]: 0.00058461 [symbol_engine_optimizer]: 0.00010026, [1] [Cycle 1]: 9.225e-05, [6] [build]: 7.1e-06 [elim_shapecalc]: 1.121e-05 [elim_not_effective]: 1.966e-05 [opt_reshape]: 8.88002e-06 [fold_const_symbol]: 1.445e-05 [renormalize]: 7.2e-07 [validate]: 6.936e-05 Sums bootstrap : 0.000603s : 0.27% type_inference : 0.218116s : 97.20% event_method : 0.000020s : 0.01% auto_monad : 0.000209s : 0.09% graph_reusing : 0.000006s : 0.00% pre_auto_parallel : 0.000011s : 0.00% py_interpret_to_execute : 0.000033s : 0.01% rewriter_before_opt_a : 0.000071s : 0.03% expand_dump_flag : 0.000003s : 0.00% jit_opt_a.switch_simplify : 0.000077s : 0.03% jit_opt_a.loop_unroll : 0.000031s : 0.01% jit_opt_a.a_1 : 0.000654s : 0.29% jit_opt_a.with_stream_mark : 0.000043s : 0.02% jit_opt_a.recompute_prepare : 0.000019s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000011s : 0.01% 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 : 0.000003s : 0.00% jit_opt_a.specialize_transform : 0.000017s : 0.01% jit_opt_a.updatestate_useless_node_eliminater : 0.000021s : 0.01% jit_opt_a.accelerated_algorithm : 0.000015s : 0.01% jit_opt_a.meta_shard_fg_expand : 0.000005s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000015s : 0.01% jit_opt_a.merge_forward : 0.000010s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000003s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000039s : 0.02% jit_opt_a.j_node_and_user_rematch : 0.000027s : 0.01% jit_opt_a.meta_fg_expand : 0.000006s : 0.00% jit_opt_a.replace_old_param : 0.000023s : 0.01% jit_opt_a.inline_without_move : 0.000015s : 0.01% jit_opt_a.renormalize : 0.000887s : 0.40% jit_opt_a.add_forward_monad_depend : 0.000015s : 0.01% jit_opt_a.auto_monad_grad : 0.000005s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000043s : 0.02% jit_opt_a.cse : 0.000062s : 0.03% jit_opt_a.replace_applicator : 0.000028s : 0.01% py_interpret_to_execute_after_opt_a : 0.000015s : 0.01% rewriter_after_opt_a : 0.000594s : 0.26% convert_after_rewriter : 0.000028s : 0.01% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000778s : 0.35% jit_opt_b.frontend_op_eliminate : 0.000025s : 0.01% jit_opt_b.inline_after_opt_a : 0.000026s : 0.01% cconv : 0.000036s : 0.02% loop_unroll : 0.000510s : 0.23% jit_opt_after_cconv.c_1 : 0.000053s : 0.02% jit_opt_after_cconv.parameter_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000013s : 0.01% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000039s : 0.02% jit_opt_after_cconv.call_graph_tuple_transform : 0.000024s : 0.01% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000008s : 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 : 0.000009s : 0.00% remove_dup_value : 0.000020s : 0.01% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000027s : 0.01% add_recomputation : 0.000082s : 0.04% cse_after_recomputation.cse : 0.000017s : 0.01% auto_monad_reorder : 0.000036s : 0.02% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000145s : 0.06% opt_after_jit_grad : 0.000585s : 0.26% symbol_engine_optimizer.build : 0.000007s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000011s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000020s : 0.01% symbol_engine_optimizer.opt_reshape : 0.000009s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000014s : 0.01% symbol_engine_optimizer.renormalize : 0.000001s : 0.00% validate : 0.000069s : 0.03% Time group info: ------[substitution.] 0.000234 44 4.31% : 0.000010s : 2: substitution.depend_value_elim 1.40% : 0.000003s : 4: substitution.elim_not_effective 1.09% : 0.000003s : 4: substitution.fold_const_symbol 3.34% : 0.000008s : 5: substitution.graph_param_transform 73.38% : 0.000171s : 3: substitution.inline 2.03% : 0.000005s : 8: substitution.j_node_and_user_rematch 2.89% : 0.000007s : 8: substitution.remove_not_recompute_node 2.19% : 0.000005s : 2: substitution.replace_old_param 5.26% : 0.000012s : 3: substitution.updatestate_pure_node_eliminater 4.11% : 0.000010s : 5: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.218001 2 55.60% : 0.121211s : 1: type_inference.infer 44.40% : 0.096790s : 1: type_inference.specialize ------[replace.] 0.000034 3 100.00% : 0.000034s : 3: replace.inline ------[match.] 0.000169 3 100.00% : 0.000169s : 3: match.inline ------[predicate.] 0.000160 825 1.24% : 0.000002s : 12: predicate.accumulaten_eliminater 1.58% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 1.05% : 0.000002s : 12: predicate.addn_check_dump 2.09% : 0.000003s : 12: predicate.addn_zero_filter 1.99% : 0.000003s : 12: predicate.arithmetic_simplify 1.23% : 0.000002s : 12: predicate.cast_eliminate 0.58% : 0.000001s : 5: predicate.check_bprop_eliminate 1.01% : 0.000002s : 12: predicate.compare_switch_simplify 1.28% : 0.000002s : 12: predicate.depend_value_elim 0.99% : 0.000002s : 12: predicate.dict_get_item_const_eliminator 1.04% : 0.000002s : 12: predicate.dict_get_item_eliminator 1.06% : 0.000002s : 12: predicate.dict_set_item_eliminator 5.87% : 0.000009s : 5: predicate.dumpgradient_eliminate 0.36% : 0.000001s : 5: predicate.elim_not_effective 0.74% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.00% : 0.000002s : 12: predicate.environ_add_const_eliminate 0.98% : 0.000002s : 12: predicate.environ_get_add_eliminate 0.98% : 0.000002s : 12: predicate.environ_get_depend_swap 1.28% : 0.000002s : 12: predicate.environ_get_eliminate 1.05% : 0.000002s : 12: predicate.environ_get_set_eliminate 0.26% : 0.000000s : 5: predicate.fold_const_symbol 1.28% : 0.000002s : 10: predicate.get_grad_eliminate 0.29% : 0.000000s : 5: predicate.graph_param_transform 5.22% : 0.000008s : 25: predicate.inline 1.09% : 0.000002s : 10: predicate.inline_without_move 0.47% : 0.000001s : 10: predicate.j_node_and_user_rematch 1.21% : 0.000002s : 10: predicate.less_batch_normalization 1.08% : 0.000002s : 12: predicate.list_to_tuple_eliminator_ 2.01% : 0.000003s : 17: predicate.load_eliminater 1.98% : 0.000003s : 5: predicate.loop_unroll_after_grad 2.77% : 0.000004s : 25: predicate.loop_unroll_before_grad 2.01% : 0.000003s : 17: predicate.make_slice_get_slice_eliminator 1.08% : 0.000002s : 12: predicate.merge_addn 1.01% : 0.000002s : 12: predicate.minmaximum_grad 1.92% : 0.000003s : 5: predicate.mutable_eliminate 0.60% : 0.000001s : 5: predicate.opt_reshape 1.81% : 0.000003s : 17: predicate.partial_eliminate 1.24% : 0.000002s : 12: predicate.print_const_string_wrapper 1.48% : 0.000002s : 12: predicate.reduce_eliminate 1.20% : 0.000002s : 12: predicate.redundant_stop_gradient_eliminater 0.64% : 0.000001s : 10: predicate.remove_not_recompute_node 1.59% : 0.000003s : 22: predicate.replace_applicator 0.84% : 0.000001s : 10: predicate.replace_old_param 0.61% : 0.000001s : 5: predicate.reset_defer_inline 1.09% : 0.000002s : 12: predicate.reshape_eliminate 1.16% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 1.23% : 0.000002s : 5: predicate.row_tensor_eliminate 1.17% : 0.000002s : 12: predicate.same_eliminate 0.62% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.21% : 0.000002s : 10: predicate.special_op_eliminate 1.31% : 0.000002s : 10: predicate.specialize_transform 1.44% : 0.000002s : 12: predicate.split_environ_get_set_with_tuple_value 1.27% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.61% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.70% : 0.000003s : 15: predicate.switch_defer_inline 1.47% : 0.000002s : 15: predicate.switch_layer_defer_inline 5.98% : 0.000010s : 45: predicate.switch_simplify 1.17% : 0.000002s : 12: predicate.tile_eliminate 1.11% : 0.000002s : 12: predicate.transpose_eliminate 1.48% : 0.000002s : 12: predicate.tuple_list_convert_item_index_to_positive 1.21% : 0.000002s : 12: predicate.tuple_list_get_item_depend_reorder 3.18% : 0.000005s : 22: predicate.tuple_list_get_item_eliminator 1.71% : 0.000003s : 12: predicate.tuple_list_set_item_eliminator 1.23% : 0.000002s : 12: predicate.tuple_to_list_eliminator_ 1.67% : 0.000003s : 17: predicate.updatestate_pure_node_eliminater 3.12% : 0.000005s : 27: predicate.updatestate_useless_node_eliminater 1.45% : 0.000002s : 12: predicate.value_based_eliminate 0.52% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.78% : 0.000001s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000341 6 6.44% : 0.000022s : 1: func_graph_cloner_run.FuncGraphClonerGraph 93.56% : 0.000319s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.234940 72 0.04% : 0.000086s : 1: add_recomputation 0.09% : 0.000214s : 1: auto_monad 0.02% : 0.000040s : 1: auto_monad_reorder 0.27% : 0.000627s : 1: bootstrap 0.02% : 0.000039s : 1: cconv 0.01% : 0.000032s : 1: convert_after_rewriter 0.01% : 0.000034s : 1: cse_after_recomputation 0.01% : 0.000030s : 1: environ_conv 0.01% : 0.000026s : 1: event_method 0.00% : 0.000005s : 1: expand_dump_flag 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000010s : 1: graph_reusing 4.39% : 0.010303s : 1: jit_opt_a 0.10% : 0.000233s : 1: jit_opt_after_cconv 0.03% : 0.000078s : 1: jit_opt_b 0.22% : 0.000521s : 1: loop_unroll 0.34% : 0.000793s : 1: mutable_eliminate 0.40% : 0.000940s : 26: opt.transform.jit_opt_a 0.04% : 0.000090s : 4: opt.transform.jit_opt_after_cconv 0.02% : 0.000042s : 4: opt.transform.jit_opt_b 0.01% : 0.000022s : 1: opt.transform.loop_unroll_optimizer 0.01% : 0.000022s : 1: opt.transform.mutable_eliminate 0.02% : 0.000045s : 1: opt.transform.opt_after_jit_grad 0.02% : 0.000051s : 4: opt.transform.symbol_engine_opt 0.25% : 0.000597s : 1: opt_after_jit_grad 0.00% : 0.000009s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.01% : 0.000014s : 1: pre_auto_parallel 0.02% : 0.000036s : 1: py_interpret_to_execute 0.01% : 0.000018s : 1: py_interpret_to_execute_after_opt_a 0.01% : 0.000022s : 1: remove_dup_value 0.22% : 0.000515s : 1: renormalize.infer 0.15% : 0.000362s : 1: renormalize.specialize 0.06% : 0.000150s : 1: rewriter_after_jit_bprop_graph 0.26% : 0.000603s : 1: rewriter_after_opt_a 0.03% : 0.000074s : 1: rewriter_before_opt_a 0.04% : 0.000103s : 1: symbol_engine_optimizer 92.85% : 0.218142s : 1: type_inference TotalTime = 1.05984, [30] [bootstrap]: 0.00057851 [type_inference]: 0.49664 [event_method]: 0.00013837 [auto_monad]: 0.00029367 [graph_reusing]: 1.179e-05 [pre_auto_parallel]: 3.92998e-06 [py_interpret_to_execute]: 5.662e-05 [rewriter_before_opt_a]: 0.00015859 [expand_dump_flag]: 4.62e-06 [jit_opt_a]: 0.558327, [3] [Cycle 1]: 0.38069, [27] [switch_simplify]: 0.00025446 [loop_unroll]: 6.647e-05 [a_1]: 0.0696531 [with_stream_mark]: 7.468e-05 [recompute_prepare]: 8.467e-05 [updatestate_depend_eliminate]: 1.698e-05 [updatestate_assign_eliminate]: 1.652e-05 [updatestate_loads_eliminate]: 1.128e-05 [parameter_eliminate]: 5.51e-06 [specialize_transform]: 2.313e-05 [updatestate_useless_node_eliminater]: 2.968e-05 [accelerated_algorithm]: 2.079e-05 [meta_shard_fg_expand]: 1.63e-05 [get_grad_eliminate_]: 1.926e-05 [merge_forward]: 1.258e-05 [cell_reuse_recompute_pass]: 1.57999e-06 [cell_reuse_handle_not_recompute_node_pass]: 4.335e-05 [j_node_and_user_rematch]: 3.587e-05 [meta_fg_expand]: 0.289088 [replace_old_param]: 0.00015385 [inline_without_move]: 0.00014004 [renormalize]: 0.0199156 [add_forward_monad_depend]: 2.268e-05 [auto_monad_grad]: 9.76e-06 [auto_monad_eliminator]: 8.003e-05 [cse]: 0.00031577 [replace_applicator]: 0.00020341 [Cycle 2]: 0.17265, [27] [switch_simplify]: 6.346e-05 [loop_unroll]: 5.82e-05 [a_1]: 0.169924 [with_stream_mark]: 5.084e-05 [recompute_prepare]: 2.272e-05 [updatestate_depend_eliminate]: 7.556e-05 [updatestate_assign_eliminate]: 6.24001e-06 [updatestate_loads_eliminate]: 5.27999e-06 [parameter_eliminate]: 2.89999e-06 [specialize_transform]: 1.071e-05 [updatestate_useless_node_eliminater]: 1.479e-05 [accelerated_algorithm]: 1.081e-05 [meta_shard_fg_expand]: 4.48001e-06 [get_grad_eliminate_]: 1.015e-05 [merge_forward]: 6.34999e-06 [cell_reuse_recompute_pass]: 4.55999e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.201e-05 [j_node_and_user_rematch]: 1.401e-05 [meta_fg_expand]: 0.00019561 [replace_old_param]: 1.799e-05 [inline_without_move]: 9.24998e-06 [renormalize]: 0.00175589 [add_forward_monad_depend]: 9.44e-06 [auto_monad_grad]: 3.01001e-06 [auto_monad_eliminator]: 2.851e-05 [cse]: 4.894e-05 [replace_applicator]: 2.643e-05 [Cycle 3]: 0.00060592, [27] [switch_simplify]: 1.002e-05 [loop_unroll]: 9.16998e-06 [a_1]: 0.00019202 [with_stream_mark]: 1.932e-05 [recompute_prepare]: 9.17001e-06 [updatestate_depend_eliminate]: 6.71999e-06 [updatestate_assign_eliminate]: 4.4e-06 [updatestate_loads_eliminate]: 4.15e-06 [parameter_eliminate]: 2.56e-06 [specialize_transform]: 8.57e-06 [updatestate_useless_node_eliminater]: 1.132e-05 [accelerated_algorithm]: 8.86002e-06 [meta_shard_fg_expand]: 2.21e-06 [get_grad_eliminate_]: 8.17e-06 [merge_forward]: 4.63999e-06 [cell_reuse_recompute_pass]: 2.51998e-06 [cell_reuse_handle_not_recompute_node_pass]: 2.288e-05 [j_node_and_user_rematch]: 5.611e-05 [meta_fg_expand]: 4.17e-06 [replace_old_param]: 1.405e-05 [inline_without_move]: 9.46998e-06 [renormalize]: 1.00001e-07 [add_forward_monad_depend]: 2.29001e-06 [auto_monad_grad]: 1.72001e-06 [auto_monad_eliminator]: 1.621e-05 [cse]: 2.719e-05 [replace_applicator]: 9.04998e-06 [py_interpret_to_execute_after_opt_a]: 2.059e-05 [rewriter_after_opt_a]: 0.00030514 [convert_after_rewriter]: 1.772e-05 [order_py_execute_after_rewriter]: 7.41001e-06 [mutable_eliminate]: 0.00094595 [jit_opt_b]: 0.00014158, [1] [Cycle 1]: 0.00012876, [2] [frontend_op_eliminate]: 7.491e-05 [inline_after_opt_a]: 2.938e-05 [cconv]: 4.587e-05 [loop_unroll]: 0.00060015 [jit_opt_after_cconv]: 0.0002579, [1] [Cycle 1]: 0.00024862, [11] [c_1]: 6.046e-05 [parameter_eliminate]: 7.25e-06 [updatestate_depend_eliminate]: 1.514e-05 [updatestate_assign_eliminate]: 5.46002e-06 [updatestate_loads_eliminate]: 5.06997e-06 [cse]: 4.894e-05 [call_graph_tuple_transform]: 2.698e-05 [tuple_list_get_item_eliminator]: 8.3e-06 [none_parameter_eliminate]: 1.82999e-06 [renormalize]: 8.39995e-07 [switch_simplify]: 9.46998e-06 [remove_dup_value]: 2.328e-05 [partial_unused_args_eliminate]: 2.59999e-06 [environ_conv]: 1.123e-05 [add_recomputation]: 8.276e-05 [cse_after_recomputation]: 4.047e-05, [1] [Cycle 1]: 3.264e-05, [1] [cse]: 2.304e-05 [auto_monad_reorder]: 3.307e-05 [get_jit_bprop_graph]: 2.31e-06 [rewriter_after_jit_bprop_graph]: 9.18002e-06 [opt_after_jit_grad]: 0.00062633 [symbol_engine_optimizer]: 0.00011433, [1] [Cycle 1]: 0.00010526, [6] [build]: 9.29e-06 [elim_shapecalc]: 1.395e-05 [elim_not_effective]: 2.366e-05 [opt_reshape]: 9.76e-06 [fold_const_symbol]: 1.539e-05 [renormalize]: 1.80007e-07 [validate]: 6.269e-05 Sums bootstrap : 0.000579s : 0.05% type_inference : 0.496640s : 47.11% event_method : 0.000138s : 0.01% auto_monad : 0.000294s : 0.03% graph_reusing : 0.000012s : 0.00% pre_auto_parallel : 0.000004s : 0.00% py_interpret_to_execute : 0.000057s : 0.01% rewriter_before_opt_a : 0.000159s : 0.02% expand_dump_flag : 0.000005s : 0.00% jit_opt_a.switch_simplify : 0.000328s : 0.03% jit_opt_a.loop_unroll : 0.000134s : 0.01% jit_opt_a.a_1 : 0.239769s : 22.74% jit_opt_a.with_stream_mark : 0.000145s : 0.01% jit_opt_a.recompute_prepare : 0.000117s : 0.01% jit_opt_a.updatestate_depend_eliminate : 0.000099s : 0.01% jit_opt_a.updatestate_assign_eliminate : 0.000027s : 0.00% jit_opt_a.updatestate_loads_eliminate : 0.000021s : 0.00% jit_opt_a.parameter_eliminate : 0.000011s : 0.00% jit_opt_a.specialize_transform : 0.000042s : 0.00% jit_opt_a.updatestate_useless_node_eliminater : 0.000056s : 0.01% jit_opt_a.accelerated_algorithm : 0.000040s : 0.00% jit_opt_a.meta_shard_fg_expand : 0.000023s : 0.00% jit_opt_a.get_grad_eliminate_ : 0.000038s : 0.00% jit_opt_a.merge_forward : 0.000024s : 0.00% jit_opt_a.cell_reuse_recompute_pass : 0.000009s : 0.00% jit_opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000088s : 0.01% jit_opt_a.j_node_and_user_rematch : 0.000106s : 0.01% jit_opt_a.meta_fg_expand : 0.289288s : 27.44% jit_opt_a.replace_old_param : 0.000186s : 0.02% jit_opt_a.inline_without_move : 0.000159s : 0.02% jit_opt_a.renormalize : 0.021672s : 2.06% jit_opt_a.add_forward_monad_depend : 0.000034s : 0.00% jit_opt_a.auto_monad_grad : 0.000014s : 0.00% jit_opt_a.auto_monad_eliminator : 0.000125s : 0.01% jit_opt_a.cse : 0.000392s : 0.04% jit_opt_a.replace_applicator : 0.000239s : 0.02% py_interpret_to_execute_after_opt_a : 0.000021s : 0.00% rewriter_after_opt_a : 0.000305s : 0.03% convert_after_rewriter : 0.000018s : 0.00% order_py_execute_after_rewriter : 0.000007s : 0.00% mutable_eliminate : 0.000946s : 0.09% jit_opt_b.frontend_op_eliminate : 0.000075s : 0.01% jit_opt_b.inline_after_opt_a : 0.000029s : 0.00% cconv : 0.000046s : 0.00% loop_unroll : 0.000600s : 0.06% jit_opt_after_cconv.c_1 : 0.000060s : 0.01% jit_opt_after_cconv.parameter_eliminate : 0.000007s : 0.00% jit_opt_after_cconv.updatestate_depend_eliminate : 0.000015s : 0.00% jit_opt_after_cconv.updatestate_assign_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% jit_opt_after_cconv.cse : 0.000049s : 0.00% jit_opt_after_cconv.call_graph_tuple_transform : 0.000027s : 0.00% jit_opt_after_cconv.tuple_list_get_item_eliminator : 0.000008s : 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 : 0.000009s : 0.00% remove_dup_value : 0.000023s : 0.00% partial_unused_args_eliminate : 0.000003s : 0.00% environ_conv : 0.000011s : 0.00% add_recomputation : 0.000083s : 0.01% cse_after_recomputation.cse : 0.000023s : 0.00% auto_monad_reorder : 0.000033s : 0.00% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000009s : 0.00% opt_after_jit_grad : 0.000626s : 0.06% symbol_engine_optimizer.build : 0.000009s : 0.00% symbol_engine_optimizer.elim_shapecalc : 0.000014s : 0.00% symbol_engine_optimizer.elim_not_effective : 0.000024s : 0.00% symbol_engine_optimizer.opt_reshape : 0.000010s : 0.00% symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.00% symbol_engine_optimizer.renormalize : 0.000000s : 0.00% validate : 0.000063s : 0.01% Time group info: ------[substitution.] 0.001825 170 1.73% : 0.000032s : 8: substitution.depend_value_elim 0.19% : 0.000004s : 4: substitution.elim_not_effective 0.15% : 0.000003s : 4: substitution.fold_const_symbol 49.18% : 0.000898s : 4: substitution.getattr_setattr_resolve 0.42% : 0.000008s : 5: substitution.graph_param_transform 29.96% : 0.000547s : 16: substitution.inline 2.27% : 0.000041s : 4: substitution.inline_without_move 0.76% : 0.000014s : 20: substitution.j_node_and_user_rematch 0.50% : 0.000009s : 5: substitution.minmaximum_grad 0.45% : 0.000008s : 9: substitution.partial_eliminate 0.91% : 0.000017s : 20: substitution.remove_not_recompute_node 2.63% : 0.000048s : 12: substitution.replace_applicator 1.02% : 0.000019s : 16: substitution.replace_old_param 1.74% : 0.000032s : 1: substitution.set_cell_output_no_recompute 0.93% : 0.000017s : 3: substitution.switch_simplify 1.11% : 0.000020s : 5: substitution.tuple_list_convert_item_index_to_positive 0.73% : 0.000013s : 5: substitution.tuple_list_get_item_depend_reorder 1.65% : 0.000030s : 8: substitution.tuple_list_get_item_eliminator 1.63% : 0.000030s : 8: substitution.updatestate_pure_node_eliminater 2.04% : 0.000037s : 13: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.496516 2 99.41% : 0.493597s : 1: type_inference.infer 0.59% : 0.002919s : 1: type_inference.specialize ------[replace.] 0.000420 27 16.76% : 0.000070s : 3: replace.getattr_setattr_resolve 29.56% : 0.000124s : 16: replace.inline 12.26% : 0.000051s : 1: replace.replace_applicator 18.95% : 0.000080s : 3: replace.switch_simplify 17.20% : 0.000072s : 3: replace.tuple_list_get_item_eliminator 5.28% : 0.000022s : 1: replace.updatestate_useless_node_eliminater ------[match.] 0.001413 27 58.83% : 0.000831s : 3: match.getattr_setattr_resolve 38.07% : 0.000538s : 16: match.inline 0.87% : 0.000012s : 1: match.replace_applicator 1.06% : 0.000015s : 3: match.switch_simplify 0.47% : 0.000007s : 3: match.tuple_list_get_item_eliminator 0.70% : 0.000010s : 1: match.updatestate_useless_node_eliminater ------[predicate.] 0.000568 3164 1.37% : 0.000008s : 50: predicate.accumulaten_eliminater 0.58% : 0.000003s : 5: predicate.ad_related_special_op_eliminate 1.44% : 0.000008s : 50: predicate.addn_check_dump 1.34% : 0.000008s : 50: predicate.addn_zero_filter 2.04% : 0.000012s : 50: predicate.arithmetic_simplify 1.60% : 0.000009s : 50: predicate.cast_eliminate 0.18% : 0.000001s : 5: predicate.check_bprop_eliminate 1.29% : 0.000007s : 50: predicate.compare_switch_simplify 1.38% : 0.000008s : 50: predicate.depend_value_elim 1.31% : 0.000007s : 50: predicate.dict_get_item_const_eliminator 3.79% : 0.000022s : 50: predicate.dict_get_item_eliminator 1.31% : 0.000007s : 50: predicate.dict_set_item_eliminator 0.27% : 0.000002s : 5: predicate.dumpgradient_eliminate 0.14% : 0.000001s : 5: predicate.elim_not_effective 0.19% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.25% : 0.000007s : 50: predicate.environ_add_const_eliminate 1.30% : 0.000007s : 50: predicate.environ_get_add_eliminate 1.25% : 0.000007s : 50: predicate.environ_get_depend_swap 1.30% : 0.000007s : 50: predicate.environ_get_eliminate 1.22% : 0.000007s : 50: predicate.environ_get_set_eliminate 0.12% : 0.000001s : 5: predicate.fold_const_symbol 0.89% : 0.000005s : 26: predicate.get_grad_eliminate 1.36% : 0.000008s : 20: predicate.getattr_setattr_resolve 0.11% : 0.000001s : 5: predicate.graph_param_transform 4.17% : 0.000024s : 80: predicate.inline 2.96% : 0.000017s : 87: predicate.inline_without_move 0.36% : 0.000002s : 26: predicate.j_node_and_user_rematch 1.24% : 0.000007s : 26: predicate.less_batch_normalization 1.51% : 0.000009s : 53: predicate.list_to_tuple_eliminator_ 1.63% : 0.000009s : 58: predicate.load_eliminater 0.56% : 0.000003s : 5: predicate.loop_unroll_after_grad 3.59% : 0.000020s : 132: predicate.loop_unroll_before_grad 1.57% : 0.000009s : 55: predicate.make_slice_get_slice_eliminator 1.22% : 0.000007s : 50: predicate.merge_addn 1.27% : 0.000007s : 50: predicate.minmaximum_grad 0.97% : 0.000005s : 5: predicate.mutable_eliminate 0.23% : 0.000001s : 5: predicate.opt_reshape 1.81% : 0.000010s : 58: predicate.partial_eliminate 1.44% : 0.000008s : 50: predicate.print_const_string_wrapper 2.06% : 0.000012s : 50: predicate.reduce_eliminate 1.52% : 0.000009s : 53: predicate.redundant_stop_gradient_eliminater 0.58% : 0.000003s : 26: predicate.remove_not_recompute_node 2.43% : 0.000014s : 126: predicate.replace_applicator 1.50% : 0.000009s : 87: predicate.replace_old_param 0.15% : 0.000001s : 5: predicate.reset_defer_inline 1.39% : 0.000008s : 50: predicate.reshape_eliminate 2.26% : 0.000013s : 50: predicate.row_tensor_add_zeros_like 0.43% : 0.000002s : 5: predicate.row_tensor_eliminate 1.47% : 0.000008s : 50: predicate.same_eliminate 0.49% : 0.000003s : 28: predicate.set_cell_output_no_recompute 0.53% : 0.000003s : 10: predicate.special_op_eliminate 0.98% : 0.000006s : 26: predicate.specialize_transform 2.07% : 0.000012s : 50: predicate.split_environ_get_set_with_tuple_value 1.41% : 0.000008s : 50: predicate.stack_unstack_eliminate 0.17% : 0.000001s : 5: predicate.switch_call_monad_eliminater 2.32% : 0.000013s : 70: predicate.switch_defer_inline 2.20% : 0.000013s : 70: predicate.switch_layer_defer_inline 6.32% : 0.000036s : 213: predicate.switch_simplify 1.55% : 0.000009s : 50: predicate.tile_eliminate 1.27% : 0.000007s : 50: predicate.transpose_eliminate 1.59% : 0.000009s : 50: predicate.tuple_list_convert_item_index_to_positive 1.43% : 0.000008s : 50: predicate.tuple_list_get_item_depend_reorder 2.61% : 0.000015s : 63: predicate.tuple_list_get_item_eliminator 1.62% : 0.000009s : 50: predicate.tuple_list_set_item_eliminator 1.43% : 0.000008s : 53: predicate.tuple_to_list_eliminator_ 1.61% : 0.000009s : 58: predicate.updatestate_pure_node_eliminater 2.62% : 0.000015s : 85: predicate.updatestate_useless_node_eliminater 2.04% : 0.000012s : 50: predicate.value_based_eliminate 0.12% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.30% : 0.000002s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.240202 47 99.36% : 0.238658s : 20: func_graph_cloner_run.FuncGraphClonerGraph 0.64% : 0.001543s : 27: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 1.323965 89 0.01% : 0.000087s : 1: add_recomputation 0.02% : 0.000304s : 1: auto_monad 0.00% : 0.000036s : 1: auto_monad_reorder 0.05% : 0.000608s : 1: bootstrap 0.00% : 0.000048s : 1: cconv 0.00% : 0.000023s : 1: convert_after_rewriter 0.00% : 0.000043s : 1: cse_after_recomputation 0.00% : 0.000014s : 1: environ_conv 0.01% : 0.000148s : 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 42.17% : 0.558333s : 1: jit_opt_a 0.02% : 0.000261s : 1: jit_opt_after_cconv 0.01% : 0.000146s : 1: jit_opt_b 0.05% : 0.000614s : 1: loop_unroll 0.07% : 0.000969s : 1: mutable_eliminate 18.22% : 0.241223s : 39: opt.transform.jit_opt_a 0.01% : 0.000100s : 4: opt.transform.jit_opt_after_cconv 0.01% : 0.000093s : 4: opt.transform.jit_opt_b 0.00% : 0.000023s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000033s : 1: opt.transform.mutable_eliminate 0.00% : 0.000042s : 1: opt.transform.opt_after_jit_grad 0.08% : 0.001047s : 2: opt.transform.opt_resolve 0.00% : 0.000058s : 4: opt.transform.symbol_engine_opt 0.05% : 0.000640s : 1: opt_after_jit_grad 0.00% : 0.000010s : 1: order_py_execute_after_rewriter 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000006s : 1: pre_auto_parallel 0.00% : 0.000061s : 1: py_interpret_to_execute 0.00% : 0.000023s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000026s : 1: remove_dup_value 1.35% : 0.017818s : 2: renormalize.infer 0.29% : 0.003823s : 2: renormalize.specialize 0.00% : 0.000011s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000314s : 1: rewriter_after_opt_a 0.01% : 0.000162s : 1: rewriter_before_opt_a 0.01% : 0.000117s : 1: symbol_engine_optimizer 37.51% : 0.496667s : 1: type_inference . [hook] pytest_runtest_teardown:test_unsqueeze_large_tensors[KBK] tests/st/mint/test_unsqueeze.py::test_unsqueeze_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 98.84s (0:01:38) ===================