==================================================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/ops/host/view, configfile: ../../../../../../../../sault/virtual_test/virtualenv_008/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 1 item test_view_ops.py TotalTime = 5.40507, [21] [bootstrap]: 0.00063898 [type_inference]: 5.38568 [event_method]: 1.541e-05 [auto_monad]: 0.00019512 [graph_reusing]: 6.12001e-06 [inline]: 2.79999e-06 [add_attr]: 0.0088034, [1] [add_attr_with_inline]: 0.00879093, [1] [Cycle 1]: 8.747e-05, [2] [tag_attr]: 1.681e-05 [meta_addattr_fg_expand]: 4.62998e-06 [parallel-infer-symbol]: 3.43e-06 [pre_auto_parallel]: 3.868e-05 [insert-virtual-dataset]: 0.00021477 [parallel-infer-symbol-second]: 2.10002e-06 [dataset_repeat_opt]: 2.37999e-06 [pipeline_split]: 2.09e-06 [optimize]: 0.00859308, [53] [py_interpret_to_execute]: 2.158e-05 [rewriter_before_opt_a]: 9.152e-05 [opt_a]: 0.00580155, [2] [Cycle 1]: 0.00491272, [45] [expand_dump_flag]: 3.39001e-06 [switch_simplify]: 3.159e-05 [loop_unroll]: 1.415e-05 [a_1]: 0.00037352 [with_stream_mark]: 1.757e-05 [recompute_prepare]: 1.033e-05 [updatestate_depend_eliminate]: 6.69001e-06 [updatestate_assign_eliminate]: 5.56e-06 [updatestate_loads_eliminate]: 5.02e-06 [parameter_eliminate]: 1.97999e-06 [a_2]: 0.00013513 [accelerated_algorithm]: 1.011e-05 [shard]: 2.51998e-06 [meta_shard_fg_expand]: 2.59001e-06 [shard_inline]: 9.41e-06 [merge_send_recv]: 2.123e-05 [auto_parallel]: 9.25001e-06 [parallel]: 4.52e-05 [flash_sp]: 1.856e-05 [merge_comm]: 6.19999e-06 [allreduce_fusion]: 5.05999e-06 [matmul_add_comm_reduction]: 1.281e-05 [allreduce_slice_to_reducescatter]: 8.70001e-07 [virtual_shard_identity]: 1.089e-05 [virtual_dataset]: 9.29998e-06 [get_grad_eliminate_]: 9.42999e-06 [virtual_output]: 9.10001e-06 [merge_forward]: 5.79999e-06 [cell_reuse_recompute_pass]: 1.62001e-06 [offload_activation]: 1.357e-05 [cell_reuse_handle_not_recompute_node_pass]: 2.6e-05 [merge_recompute_call_nodes]: 2.01e-06 [before_grad]: 1.52e-05 [set_forward_comm_id_for_comm_node_pass]: 5.51e-06 [meta_fg_expand]: 4.41002e-06 [flash_sp_send_recv_attached]: 2.51998e-06 [receive_attached]: 2.09999e-06 [after_resolve]: 1.368e-05 [a_after_grad]: 1.403e-05 [renormalize]: 0.00363037 [add_forward_monad_depend]: 6.19999e-06 [auto_monad_grad]: 3.08e-06 [auto_monad_eliminator]: 2.233e-05 [cse]: 4.025e-05 [a_3]: 7.118e-05 [Cycle 2]: 0.00087651, [45] [expand_dump_flag]: 1.71e-06 [switch_simplify]: 1.086e-05 [loop_unroll]: 8.81002e-06 [a_1]: 0.00020584 [with_stream_mark]: 1.596e-05 [recompute_prepare]: 9.39998e-06 [updatestate_depend_eliminate]: 7.05e-06 [updatestate_assign_eliminate]: 4.52e-06 [updatestate_loads_eliminate]: 4.45e-06 [parameter_eliminate]: 1.37999e-06 [a_2]: 0.00011973 [accelerated_algorithm]: 9.03002e-06 [shard]: 1.82999e-06 [meta_shard_fg_expand]: 2.14999e-06 [shard_inline]: 8.71002e-06 [merge_send_recv]: 7.56999e-06 [auto_parallel]: 9.04e-06 [parallel]: 6.01998e-06 [flash_sp]: 3.9e-06 [merge_comm]: 5.06997e-06 [allreduce_fusion]: 4.37e-06 [matmul_add_comm_reduction]: 8.20999e-06 [allreduce_slice_to_reducescatter]: 5.89993e-07 [virtual_shard_identity]: 9.44e-06 [virtual_dataset]: 8.43999e-06 [get_grad_eliminate_]: 9.51e-06 [virtual_output]: 8.18999e-06 [merge_forward]: 4.65999e-06 [cell_reuse_recompute_pass]: 2.26e-06 [offload_activation]: 1.198e-05 [cell_reuse_handle_not_recompute_node_pass]: 1.828e-05 [merge_recompute_call_nodes]: 1.24e-06 [before_grad]: 1.461e-05 [set_forward_comm_id_for_comm_node_pass]: 6.06e-06 [meta_fg_expand]: 3.65e-06 [flash_sp_send_recv_attached]: 1.24e-06 [receive_attached]: 2.11e-06 [after_resolve]: 1.275e-05 [a_after_grad]: 1.345e-05 [renormalize]: 1.00001e-07 [add_forward_monad_depend]: 1.32999e-06 [auto_monad_grad]: 1.35999e-06 [auto_monad_eliminator]: 1.081e-05 [cse]: 2.21e-05 [a_3]: 5.7e-05 [py_interpret_to_execute_after_opt_a]: 1.286e-05 [slice_cell_reuse_recomputed_activation]: 2.02001e-06 [rewriter_after_opt_a]: 0.00028129 [convert_after_rewriter]: 1.496e-05 [order_py_execute_after_rewriter]: 7.8e-06 [mutable_eliminate]: 0.00065817 [opt_b]: 0.00030111, [1] [Cycle 1]: 0.00029399, [7] [b_1]: 0.0002038 [b_2]: 1.087e-05 [updatestate_depend_eliminate]: 7.63999e-06 [updatestate_assign_eliminate]: 4.48001e-06 [updatestate_loads_eliminate]: 3.98999e-06 [renormalize]: 4.60015e-07 [cse]: 2.555e-05 [optimize_parallel_all_gather_comm]: 2.205e-05 [overlap_param_gather]: 4.39998e-06 [cconv]: 2.725e-05 [loop_unroll]: 0.00048616 [opt_after_cconv]: 0.00014429, [1] [Cycle 1]: 0.00013841, [7] [c_1]: 5.698e-05 [parameter_eliminate]: 2.54001e-06 [updatestate_depend_eliminate]: 8.03001e-06 [updatestate_assign_eliminate]: 4.68001e-06 [updatestate_loads_eliminate]: 4.17e-06 [cse]: 2.521e-05 [renormalize]: 5.09986e-07 [remove_dup_value]: 1.853e-05 [tuple_transform]: 9.159e-05, [1] [Cycle 1]: 8.703e-05, [4] [d_1]: 5.765e-05 [none_parameter_eliminate]: 1.81e-06 [renormalize]: 2.60014e-07 [switch_simplify]: 9.69999e-06 [partial_unused_args_eliminate]: 2.03002e-06 [add_recomputation]: 7.222e-05 [cse_after_recomputation]: 2.929e-05, [1] [Cycle 1]: 2.437e-05, [1] [cse]: 1.872e-05 [environ_conv]: 2.199e-05 [swap_dp_allreduce_reducescatter]: 7.43e-06 [bias_add_comm_swap]: 3.01001e-06 [label_micro_interleaved_index]: 4.77998e-06 [label_fine_grained_interleaved_index]: 3.30998e-06 [merge_cast_opt]: 1.73997e-06 [slice_recompute_activation]: 2.14e-06 [micro_interleaved_order_control]: 2.23002e-06 [assign_add_opt]: 1.74e-06 [ForceFp32Comm]: 9.50007e-07 [remove_cast_before_assign_add]: 1.43002e-06 [full_micro_interleaved_order_control]: 2.49999e-06 [reorder_send_recv_between_fp_bp]: 2.89001e-06 [comm_op_add_attrs]: 1.12e-06 [add_comm_op_reuse_tag]: 1.06002e-06 [interleave_split_concat_branches]: 1.45999e-06 [interleave_parallel_branches]: 1.12999e-06 [overlap_opt_shard_in_pipeline]: 1.599e-05 [overlap_opt_shard_grad_in_pipeline]: 1.92001e-06 [control_data_broadcast_order]: 1.808e-05 [grouped_pairwise_exchange_alltoall]: 1.80001e-06 [offloading_packed_experts]: 5.29e-06 [overlap_recompute_and_grad_model_parallel]: 6.14999e-06 [overlap_grad_matmul_and_grad_allreduce]: 1.40001e-06 [overlap_recompute_allgather_and_fa_grad]: 1.63002e-06 [overlap_recompute_comm]: 2.63e-06 [overlap_grad_ring_attention]: 5.09998e-06 [overlap_grad_flash_sp]: 2.545e-05 [begin_end_overlap_inline]: 6.09987e-07 [split_matmul_comm_elemetwise]: 2.68e-06 [split_layernorm_comm]: 2.61e-06 [handle_group_info]: 1.45999e-06 [symbol_engine_optimizer]: 0.00010479, [1] [Cycle 1]: 0.00010002, [6] [build]: 1.255e-05 [elim_shapecalc]: 1.513e-05 [elim_not_effective]: 1.805e-05 [opt_reshape]: 1.035e-05 [fold_const_symbol]: 1.476e-05 [renormalize]: 2.80008e-07 [detach_backward]: 2.23002e-06 [pipeline_parallel_scheduler]: 1.83002e-06 [auto_monad_reorder]: 3.552e-05 [get_jit_bprop_graph]: 1.49998e-06 [rewriter_after_jit_bprop_graph]: 3.56001e-06 [opt_after_jit_grad]: 0.00056256 [validate]: 7.852e-05 Sums bootstrap : 0.000639s : 0.01% type_inference : 5.385682s : 99.82% event_method : 0.000015s : 0.00% auto_monad : 0.000195s : 0.00% graph_reusing : 0.000006s : 0.00% inline : 0.000003s : 0.00% add_attr.add_attr_with_inline.tag_attr : 0.000017s : 0.00% add_attr.add_attr_with_inline.meta_addattr_fg_expand : 0.000005s : 0.00% parallel-infer-symbol : 0.000003s : 0.00% pre_auto_parallel : 0.000039s : 0.00% insert-virtual-dataset : 0.000215s : 0.00% parallel-infer-symbol-second : 0.000002s : 0.00% dataset_repeat_opt : 0.000002s : 0.00% pipeline_split : 0.000002s : 0.00% optimize.py_interpret_to_execute : 0.000022s : 0.00% optimize.rewriter_before_opt_a : 0.000092s : 0.00% optimize.opt_a.expand_dump_flag : 0.000005s : 0.00% optimize.opt_a.switch_simplify : 0.000042s : 0.00% optimize.opt_a.loop_unroll : 0.000023s : 0.00% optimize.opt_a.a_1 : 0.000579s : 0.01% optimize.opt_a.with_stream_mark : 0.000034s : 0.00% optimize.opt_a.recompute_prepare : 0.000020s : 0.00% optimize.opt_a.updatestate_depend_eliminate : 0.000014s : 0.00% optimize.opt_a.updatestate_assign_eliminate : 0.000010s : 0.00% optimize.opt_a.updatestate_loads_eliminate : 0.000009s : 0.00% optimize.opt_a.parameter_eliminate : 0.000003s : 0.00% optimize.opt_a.a_2 : 0.000255s : 0.00% optimize.opt_a.accelerated_algorithm : 0.000019s : 0.00% optimize.opt_a.shard : 0.000004s : 0.00% optimize.opt_a.meta_shard_fg_expand : 0.000005s : 0.00% optimize.opt_a.shard_inline : 0.000018s : 0.00% optimize.opt_a.merge_send_recv : 0.000029s : 0.00% optimize.opt_a.auto_parallel : 0.000018s : 0.00% optimize.opt_a.parallel : 0.000051s : 0.00% optimize.opt_a.flash_sp : 0.000022s : 0.00% optimize.opt_a.merge_comm : 0.000011s : 0.00% optimize.opt_a.allreduce_fusion : 0.000009s : 0.00% optimize.opt_a.matmul_add_comm_reduction : 0.000021s : 0.00% optimize.opt_a.allreduce_slice_to_reducescatter : 0.000001s : 0.00% optimize.opt_a.virtual_shard_identity : 0.000020s : 0.00% optimize.opt_a.virtual_dataset : 0.000018s : 0.00% optimize.opt_a.get_grad_eliminate_ : 0.000019s : 0.00% optimize.opt_a.virtual_output : 0.000017s : 0.00% optimize.opt_a.merge_forward : 0.000010s : 0.00% optimize.opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% optimize.opt_a.offload_activation : 0.000026s : 0.00% optimize.opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000044s : 0.00% optimize.opt_a.merge_recompute_call_nodes : 0.000003s : 0.00% optimize.opt_a.before_grad : 0.000030s : 0.00% optimize.opt_a.set_forward_comm_id_for_comm_node_pass : 0.000012s : 0.00% optimize.opt_a.meta_fg_expand : 0.000008s : 0.00% optimize.opt_a.flash_sp_send_recv_attached : 0.000004s : 0.00% optimize.opt_a.receive_attached : 0.000004s : 0.00% optimize.opt_a.after_resolve : 0.000026s : 0.00% optimize.opt_a.a_after_grad : 0.000027s : 0.00% optimize.opt_a.renormalize : 0.003630s : 0.07% optimize.opt_a.add_forward_monad_depend : 0.000008s : 0.00% optimize.opt_a.auto_monad_grad : 0.000004s : 0.00% optimize.opt_a.auto_monad_eliminator : 0.000033s : 0.00% optimize.opt_a.cse : 0.000062s : 0.00% optimize.opt_a.a_3 : 0.000128s : 0.00% optimize.py_interpret_to_execute_after_opt_a : 0.000013s : 0.00% optimize.slice_cell_reuse_recomputed_activation : 0.000002s : 0.00% optimize.rewriter_after_opt_a : 0.000281s : 0.01% optimize.convert_after_rewriter : 0.000015s : 0.00% optimize.order_py_execute_after_rewriter : 0.000008s : 0.00% optimize.mutable_eliminate : 0.000658s : 0.01% optimize.opt_b.b_1 : 0.000204s : 0.00% optimize.opt_b.b_2 : 0.000011s : 0.00% optimize.opt_b.updatestate_depend_eliminate : 0.000008s : 0.00% optimize.opt_b.updatestate_assign_eliminate : 0.000004s : 0.00% optimize.opt_b.updatestate_loads_eliminate : 0.000004s : 0.00% optimize.opt_b.renormalize : 0.000000s : 0.00% optimize.opt_b.cse : 0.000026s : 0.00% optimize.optimize_parallel_all_gather_comm : 0.000022s : 0.00% optimize.overlap_param_gather : 0.000004s : 0.00% optimize.cconv : 0.000027s : 0.00% optimize.loop_unroll : 0.000486s : 0.01% optimize.opt_after_cconv.c_1 : 0.000057s : 0.00% optimize.opt_after_cconv.parameter_eliminate : 0.000003s : 0.00% optimize.opt_after_cconv.updatestate_depend_eliminate : 0.000008s : 0.00% optimize.opt_after_cconv.updatestate_assign_eliminate : 0.000005s : 0.00% optimize.opt_after_cconv.updatestate_loads_eliminate : 0.000004s : 0.00% optimize.opt_after_cconv.cse : 0.000025s : 0.00% optimize.opt_after_cconv.renormalize : 0.000001s : 0.00% optimize.remove_dup_value : 0.000019s : 0.00% optimize.tuple_transform.d_1 : 0.000058s : 0.00% optimize.tuple_transform.none_parameter_eliminate : 0.000002s : 0.00% optimize.tuple_transform.renormalize : 0.000000s : 0.00% optimize.tuple_transform.switch_simplify : 0.000010s : 0.00% optimize.partial_unused_args_eliminate : 0.000002s : 0.00% optimize.add_recomputation : 0.000072s : 0.00% optimize.cse_after_recomputation.cse : 0.000019s : 0.00% optimize.environ_conv : 0.000022s : 0.00% optimize.swap_dp_allreduce_reducescatter : 0.000007s : 0.00% optimize.bias_add_comm_swap : 0.000003s : 0.00% optimize.label_micro_interleaved_index : 0.000005s : 0.00% optimize.label_fine_grained_interleaved_index : 0.000003s : 0.00% optimize.merge_cast_opt : 0.000002s : 0.00% optimize.slice_recompute_activation : 0.000002s : 0.00% optimize.micro_interleaved_order_control : 0.000002s : 0.00% optimize.assign_add_opt : 0.000002s : 0.00% optimize.ForceFp32Comm : 0.000001s : 0.00% optimize.remove_cast_before_assign_add : 0.000001s : 0.00% optimize.full_micro_interleaved_order_control : 0.000002s : 0.00% optimize.reorder_send_recv_between_fp_bp : 0.000003s : 0.00% optimize.comm_op_add_attrs : 0.000001s : 0.00% optimize.add_comm_op_reuse_tag : 0.000001s : 0.00% optimize.interleave_split_concat_branches : 0.000001s : 0.00% optimize.interleave_parallel_branches : 0.000001s : 0.00% optimize.overlap_opt_shard_in_pipeline : 0.000016s : 0.00% optimize.overlap_opt_shard_grad_in_pipeline : 0.000002s : 0.00% optimize.control_data_broadcast_order : 0.000018s : 0.00% optimize.grouped_pairwise_exchange_alltoall : 0.000002s : 0.00% optimize.offloading_packed_experts : 0.000005s : 0.00% optimize.overlap_recompute_and_grad_model_parallel : 0.000006s : 0.00% optimize.overlap_grad_matmul_and_grad_allreduce : 0.000001s : 0.00% optimize.overlap_recompute_allgather_and_fa_grad : 0.000002s : 0.00% optimize.overlap_recompute_comm : 0.000003s : 0.00% optimize.overlap_grad_ring_attention : 0.000005s : 0.00% optimize.overlap_grad_flash_sp : 0.000025s : 0.00% optimize.begin_end_overlap_inline : 0.000001s : 0.00% optimize.split_matmul_comm_elemetwise : 0.000003s : 0.00% optimize.split_layernorm_comm : 0.000003s : 0.00% optimize.handle_group_info : 0.000001s : 0.00% optimize.symbol_engine_optimizer.build : 0.000013s : 0.00% optimize.symbol_engine_optimizer.elim_shapecalc : 0.000015s : 0.00% optimize.symbol_engine_optimizer.elim_not_effective : 0.000018s : 0.00% optimize.symbol_engine_optimizer.opt_reshape : 0.000010s : 0.00% optimize.symbol_engine_optimizer.fold_const_symbol : 0.000015s : 0.00% optimize.symbol_engine_optimizer.renormalize : 0.000000s : 0.00% detach_backward : 0.000002s : 0.00% pipeline_parallel_scheduler : 0.000002s : 0.00% auto_monad_reorder : 0.000036s : 0.00% get_jit_bprop_graph : 0.000001s : 0.00% rewriter_after_jit_bprop_graph : 0.000004s : 0.00% opt_after_jit_grad : 0.000563s : 0.01% validate : 0.000079s : 0.00% Time group info: ------[substitution.] 0.000160 49 5.15% : 0.000008s : 2: substitution.depend_value_elim 1.77% : 0.000003s : 4: substitution.elim_not_effective 1.30% : 0.000002s : 4: substitution.fold_const_symbol 4.53% : 0.000007s : 6: substitution.graph_param_transform 54.52% : 0.000087s : 1: substitution.inline 3.08% : 0.000005s : 8: substitution.j_node_and_user_rematch 10.11% : 0.000016s : 8: substitution.remove_not_recompute_node 2.65% : 0.000004s : 2: substitution.replace_old_param 8.76% : 0.000014s : 6: substitution.updatestate_pure_node_eliminater 8.14% : 0.000013s : 8: substitution.updatestate_useless_node_eliminater ------[type_inference.] 5.385588 2 99.95% : 5.383012s : 1: type_inference.infer 0.05% : 0.002576s : 1: type_inference.specialize ------[replace.] 0.000017 1 100.00% : 0.000017s : 1: replace.inline ------[match.] 0.000086 1 100.00% : 0.000086s : 1: match.inline ------[predicate.] 0.000221 1421 0.78% : 0.000002s : 13: predicate.accumulaten_eliminater 1.04% : 0.000002s : 6: predicate.ad_related_special_op_eliminate 0.70% : 0.000002s : 12: predicate.addn_check_dump 1.04% : 0.000002s : 13: predicate.addn_zero_filter 0.71% : 0.000002s : 13: predicate.adjust_all_reduce_mul_add 2.19% : 0.000005s : 25: predicate.arithmetic_simplify 0.94% : 0.000002s : 13: predicate.cast_eliminate 0.99% : 0.000002s : 12: predicate.check_bprop_eliminate 0.72% : 0.000002s : 12: predicate.compare_switch_simplify 0.23% : 0.000001s : 6: predicate.const_output_eliminate 0.84% : 0.000002s : 12: predicate.depend_value_elim 0.80% : 0.000002s : 13: predicate.dict_get_item_const_eliminator 0.91% : 0.000002s : 13: predicate.dict_get_item_eliminator 0.79% : 0.000002s : 13: predicate.dict_set_item_eliminator 1.10% : 0.000002s : 12: predicate.dumpgradient_eliminate 0.28% : 0.000001s : 6: predicate.elim_not_effective 0.59% : 0.000001s : 6: predicate.elim_shapecalc_of_broadcastargs 1.13% : 0.000003s : 19: predicate.environ_add_const_eliminate 1.09% : 0.000002s : 19: predicate.environ_get_add_eliminate 1.04% : 0.000002s : 19: predicate.environ_get_depend_swap 1.92% : 0.000004s : 31: predicate.environ_get_eliminate 1.08% : 0.000002s : 19: predicate.environ_get_set_eliminate 0.80% : 0.000002s : 14: predicate.exchange_switch_depend_value 1.69% : 0.000004s : 14: predicate.float_depend_g_call 0.71% : 0.000002s : 12: predicate.float_environ_get_switch 1.08% : 0.000002s : 18: predicate.float_tuple_getitem_switch 0.23% : 0.000001s : 6: predicate.fold_const_symbol 0.89% : 0.000002s : 12: predicate.get_grad_eliminate 0.37% : 0.000001s : 6: predicate.graph_param_transform 0.80% : 0.000002s : 12: predicate.incorporate_call 0.66% : 0.000001s : 12: predicate.incorporate_call_switch 5.72% : 0.000013s : 63: predicate.inline 1.02% : 0.000002s : 12: predicate.inline_without_move 0.44% : 0.000001s : 12: predicate.j_node_and_user_rematch 1.07% : 0.000002s : 12: predicate.less_batch_normalization 1.72% : 0.000004s : 25: predicate.list_to_tuple_eliminator_ 2.49% : 0.000006s : 38: predicate.load_eliminater 1.11% : 0.000002s : 6: predicate.loop_unroll_after_grad 1.17% : 0.000003s : 18: predicate.loop_unroll_before_grad 1.96% : 0.000004s : 25: predicate.make_slice_get_slice_eliminator 0.81% : 0.000002s : 12: predicate.merge_addn 0.76% : 0.000002s : 12: predicate.micro_step_allgather_replace 0.80% : 0.000002s : 12: predicate.mini_step_allgather_replace 0.69% : 0.000002s : 13: predicate.minmaximum_grad 1.23% : 0.000003s : 6: predicate.mutable_eliminate 0.47% : 0.000001s : 6: predicate.opt_reshape 0.47% : 0.000001s : 6: predicate.parallel_virtual_node 1.02% : 0.000002s : 14: predicate.partial_defer_inline 1.22% : 0.000003s : 19: predicate.partial_eliminate 0.81% : 0.000002s : 13: predicate.print_const_string_wrapper 0.77% : 0.000002s : 12: predicate.reduce_all_const_elim 1.23% : 0.000003s : 13: predicate.reduce_eliminate 2.30% : 0.000005s : 38: predicate.redundant_stop_gradient_eliminater 0.68% : 0.000002s : 12: predicate.remove_not_recompute_node 1.34% : 0.000003s : 25: predicate.replace_applicator 0.74% : 0.000002s : 12: predicate.replace_old_param 0.33% : 0.000001s : 6: predicate.reset_defer_inline 0.90% : 0.000002s : 13: predicate.reshape_eliminate 0.80% : 0.000002s : 12: predicate.row_tensor_add_zeros_like 0.46% : 0.000001s : 6: predicate.row_tensor_eliminate 1.09% : 0.000002s : 12: predicate.same_eliminate 0.55% : 0.000001s : 12: predicate.set_cell_output_no_recompute 0.97% : 0.000002s : 12: predicate.shard_identity_eliminate 0.90% : 0.000002s : 12: predicate.special_op_eliminate 0.90% : 0.000002s : 12: predicate.specialize_transform 1.44% : 0.000003s : 12: predicate.split_environ_get_set_with_tuple_value 0.93% : 0.000002s : 12: predicate.stack_unstack_eliminate 0.41% : 0.000001s : 6: predicate.switch_call_monad_eliminater 0.85% : 0.000002s : 14: predicate.switch_defer_inline 1.53% : 0.000003s : 26: predicate.switch_layer_defer_inline 3.76% : 0.000008s : 50: predicate.switch_simplify 0.92% : 0.000002s : 13: predicate.tile_eliminate 0.80% : 0.000002s : 13: predicate.transpose_eliminate 1.62% : 0.000004s : 25: predicate.tuple_list_convert_item_index_to_positive 1.71% : 0.000004s : 25: predicate.tuple_list_get_item_const_eliminator 1.49% : 0.000003s : 25: predicate.tuple_list_get_item_depend_reorder 3.01% : 0.000007s : 37: predicate.tuple_list_get_item_eliminator 1.53% : 0.000003s : 25: predicate.tuple_list_get_set_item_eliminator 2.55% : 0.000006s : 37: predicate.tuple_list_set_item_eliminator 1.68% : 0.000004s : 25: predicate.tuple_to_list_eliminator_ 2.29% : 0.000005s : 38: predicate.updatestate_pure_node_eliminater 3.29% : 0.000007s : 50: predicate.updatestate_useless_node_eliminater 0.44% : 0.000001s : 6: predicate.value_based_eliminate 0.80% : 0.000002s : 12: predicate.virtual_dataset_eliminate 0.83% : 0.000002s : 12: predicate.virtual_output_eliminate 0.41% : 0.000001s : 6: predicate.virtual_view_grad_eliminate 0.63% : 0.000001s : 6: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.003863 28 79.15% : 0.003058s : 25: func_graph_cloner_run.FuncGraphClonerGraph 20.85% : 0.000806s : 3: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 5.427540 192 0.00% : 0.000004s : 1: ForceFp32Comm 0.16% : 0.008809s : 1: add_attr 0.16% : 0.008795s : 1: add_attr_with_inline 0.00% : 0.000004s : 1: add_comm_op_reuse_tag 0.00% : 0.000077s : 1: add_recomputation 0.00% : 0.000005s : 1: assign_add_opt 0.00% : 0.000204s : 1: auto_monad 0.00% : 0.000040s : 1: auto_monad_reorder 0.00% : 0.000003s : 1: begin_end_overlap_inline 0.00% : 0.000006s : 1: bias_add_comm_swap 0.01% : 0.000672s : 1: bootstrap 0.00% : 0.000031s : 1: cconv 0.00% : 0.000005s : 1: comm_op_add_attrs 0.00% : 0.000021s : 1: control_data_broadcast_order 0.00% : 0.000019s : 1: convert_after_rewriter 0.00% : 0.000032s : 1: cse_after_recomputation 0.00% : 0.000006s : 1: dataset_repeat_opt 0.00% : 0.000006s : 1: detach_backward 0.00% : 0.000026s : 1: environ_conv 0.00% : 0.000023s : 1: event_method 0.00% : 0.000005s : 1: full_micro_interleaved_order_control 0.00% : 0.000005s : 1: get_jit_bprop_graph 0.00% : 0.000010s : 1: graph_reusing 0.00% : 0.000004s : 1: grouped_pairwise_exchange_alltoall 0.00% : 0.000004s : 1: handle_group_info 0.00% : 0.000006s : 1: inline 0.00% : 0.000228s : 1: insert-virtual-dataset 0.00% : 0.000004s : 1: interleave_parallel_branches 0.00% : 0.000005s : 1: interleave_split_concat_branches 0.00% : 0.000006s : 1: label_fine_grained_interleaved_index 0.00% : 0.000008s : 1: label_micro_interleaved_index 0.01% : 0.000495s : 1: loop_unroll 0.00% : 0.000005s : 1: merge_cast_opt 0.00% : 0.000005s : 1: micro_interleaved_order_control 0.01% : 0.000669s : 1: mutable_eliminate 0.00% : 0.000009s : 1: offloading_packed_experts 0.00% : 0.000019s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000020s : 1: opt.transform.mutable_eliminate 0.02% : 0.001192s : 78: opt.transform.opt_a 0.00% : 0.000056s : 1: opt.transform.opt_after_cconv 0.00% : 0.000035s : 1: opt.transform.opt_after_jit_grad 0.00% : 0.000182s : 28: opt.transform.opt_b 0.00% : 0.000065s : 2: opt.transform.opt_trans_graph 0.00% : 0.000055s : 4: opt.transform.symbol_engine_opt 0.11% : 0.005806s : 1: opt_a 0.00% : 0.000148s : 1: opt_after_cconv 0.01% : 0.000573s : 1: opt_after_jit_grad 0.01% : 0.000305s : 1: opt_b 0.16% : 0.008598s : 1: optimize 0.00% : 0.000026s : 1: optimize_parallel_all_gather_comm 0.00% : 0.000011s : 1: order_py_execute_after_rewriter 0.00% : 0.000029s : 1: overlap_grad_flash_sp 0.00% : 0.000004s : 1: overlap_grad_matmul_and_grad_allreduce 0.00% : 0.000008s : 1: overlap_grad_ring_attention 0.00% : 0.000005s : 1: overlap_opt_shard_grad_in_pipeline 0.00% : 0.000020s : 1: overlap_opt_shard_in_pipeline 0.00% : 0.000008s : 1: overlap_param_gather 0.00% : 0.000005s : 1: overlap_recompute_allgather_and_fa_grad 0.00% : 0.000009s : 1: overlap_recompute_and_grad_model_parallel 0.00% : 0.000005s : 1: overlap_recompute_comm 0.00% : 0.000007s : 1: parallel-infer-symbol 0.00% : 0.000006s : 1: parallel-infer-symbol-second 0.00% : 0.000005s : 1: partial_unused_args_eliminate 0.00% : 0.000005s : 1: pipeline_parallel_scheduler 0.00% : 0.000005s : 1: pipeline_split 0.00% : 0.000043s : 1: pre_auto_parallel 0.00% : 0.000026s : 1: py_interpret_to_execute 0.00% : 0.000016s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000004s : 1: remove_cast_before_assign_add 0.00% : 0.000022s : 1: remove_dup_value 0.05% : 0.002729s : 1: renormalize.infer 0.02% : 0.000891s : 1: renormalize.specialize 0.00% : 0.000006s : 1: reorder_send_recv_between_fp_bp 0.00% : 0.000007s : 1: rewriter_after_jit_bprop_graph 0.01% : 0.000289s : 1: rewriter_after_opt_a 0.00% : 0.000098s : 1: rewriter_before_opt_a 0.00% : 0.000005s : 1: slice_cell_reuse_recomputed_activation 0.00% : 0.000005s : 1: slice_recompute_activation 0.00% : 0.000005s : 1: split_layernorm_comm 0.00% : 0.000006s : 1: split_matmul_comm_elemetwise 0.00% : 0.000011s : 1: swap_dp_allreduce_reducescatter 0.00% : 0.000108s : 1: symbol_engine_optimizer 0.00% : 0.000095s : 1: tuple_transform 99.23% : 5.385710s : 1: type_inference . [hook] pytest_runtest_teardown:test_split_with_size_view_ascend tests/st/ops/host/view/test_view_ops.py::test_split_with_size_view_ascend,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 ================== 1 passed, 25 warnings in 200.29s (0:03:20) ==================