==================================================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/compiler/stream_event, 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 1 item test_with_stream.py [WARNING] ME(170857:281473286545200,MainProcess):2026-01-29-17:38:06.811.149 [mindspore/context.py:1334] For 'context.set_context', the parameter 'save_graphs' will be deprecated and removed in a future version. Please use the env MS_DEV_SAVE_GRAPHS instead. [WARNING] ME(170857:281473286545200,MainProcess):2026-01-29-17:38:06.811.707 [mindspore/context.py:1334] For 'context.set_context', the parameter 'save_graphs_path' will be deprecated and removed in a future version. Please use the env MS_DEV_SAVE_GRAPHS_PATH instead. TotalTime = 0.278263, [21] [bootstrap]: 0.00062697 [type_inference]: 0.116011 [event_method]: 7.868e-05 [auto_monad]: 0.00021201 [graph_reusing]: 1.112e-05 [inline]: 3.26001e-06 [add_attr]: 0.0482434, [1] [add_attr_with_inline]: 0.0482279, [1] [Cycle 1]: 0.00015514, [2] [tag_attr]: 5.101e-05 [meta_addattr_fg_expand]: 1.161e-05 [parallel-infer-symbol]: 3.93999e-06 [pre_auto_parallel]: 8.941e-05 [insert-virtual-dataset]: 3.68e-06 [parallel-infer-symbol-second]: 1.15001e-06 [dataset_repeat_opt]: 2.32001e-06 [pipeline_split]: 1.64998e-06 [optimize]: 0.0751761, [53] [py_interpret_to_execute]: 6.02e-05 [rewriter_before_opt_a]: 0.00019422 [opt_a]: 0.0717167, [3] [Cycle 1]: 0.0676412, [45] [expand_dump_flag]: 5.81998e-06 [switch_simplify]: 0.0001613 [loop_unroll]: 6.858e-05 [a_1]: 0.0633324 [with_stream_mark]: 0.0001209 [recompute_prepare]: 3.723e-05 [updatestate_depend_eliminate]: 1.259e-05 [updatestate_assign_eliminate]: 1.073e-05 [updatestate_loads_eliminate]: 9.96998e-06 [parameter_eliminate]: 5.04e-06 [a_2]: 0.00074597 [accelerated_algorithm]: 5.628e-05 [shard]: 2.46e-06 [meta_shard_fg_expand]: 7.25e-06 [shard_inline]: 1.591e-05 [merge_send_recv]: 2.88e-05 [auto_parallel]: 1.836e-05 [parallel]: 5.283e-05 [flash_sp]: 2.24e-05 [merge_comm]: 1.038e-05 [allreduce_fusion]: 9.62999e-06 [matmul_add_comm_reduction]: 2.148e-05 [allreduce_slice_to_reducescatter]: 6.19999e-07 [virtual_shard_identity]: 2.264e-05 [virtual_dataset]: 1.486e-05 [get_grad_eliminate_]: 1.426e-05 [virtual_output]: 1.448e-05 [merge_forward]: 1.072e-05 [cell_reuse_recompute_pass]: 1.79998e-06 [offload_activation]: 2.074e-05 [cell_reuse_handle_not_recompute_node_pass]: 4.101e-05 [merge_recompute_call_nodes]: 1.72001e-06 [before_grad]: 2.873e-05 [set_forward_comm_id_for_comm_node_pass]: 1.273e-05 [meta_fg_expand]: 7.51999e-06 [flash_sp_send_recv_attached]: 6.04999e-06 [receive_attached]: 1.178e-05 [after_resolve]: 2.066e-05 [a_after_grad]: 2.542e-05 [renormalize]: 0.00172045 [add_forward_monad_depend]: 1.836e-05 [auto_monad_grad]: 2.98e-06 [auto_monad_eliminator]: 3.462e-05 [cse]: 0.00018315 [a_3]: 0.00012102 [Cycle 2]: 0.00269289, [45] [expand_dump_flag]: 2.77002e-06 [switch_simplify]: 1.602e-05 [loop_unroll]: 1.344e-05 [a_1]: 0.00038067 [with_stream_mark]: 2.549e-05 [recompute_prepare]: 1.435e-05 [updatestate_depend_eliminate]: 9.40001e-06 [updatestate_assign_eliminate]: 7.73999e-06 [updatestate_loads_eliminate]: 7.95998e-06 [parameter_eliminate]: 2.69001e-06 [a_2]: 0.00046643 [accelerated_algorithm]: 2.443e-05 [shard]: 2.78e-06 [meta_shard_fg_expand]: 3.56001e-06 [shard_inline]: 1.107e-05 [merge_send_recv]: 1.573e-05 [auto_parallel]: 1.545e-05 [parallel]: 9.12999e-06 [flash_sp]: 5.54e-06 [merge_comm]: 7.38e-06 [allreduce_fusion]: 6.84001e-06 [matmul_add_comm_reduction]: 1.571e-05 [allreduce_slice_to_reducescatter]: 1.30999e-06 [virtual_shard_identity]: 1.284e-05 [virtual_dataset]: 1.043e-05 [get_grad_eliminate_]: 9.70002e-06 [virtual_output]: 1.038e-05 [merge_forward]: 8.57e-06 [cell_reuse_recompute_pass]: 3.18e-06 [offload_activation]: 1.612e-05 [cell_reuse_handle_not_recompute_node_pass]: 2.734e-05 [merge_recompute_call_nodes]: 1.76e-06 [before_grad]: 1.935e-05 [set_forward_comm_id_for_comm_node_pass]: 8.22e-06 [meta_fg_expand]: 4.77998e-06 [flash_sp_send_recv_attached]: 2.07999e-06 [receive_attached]: 2.58e-06 [after_resolve]: 1.568e-05 [a_after_grad]: 1.656e-05 [renormalize]: 0.00077471 [add_forward_monad_depend]: 8.00999e-06 [auto_monad_grad]: 2.31e-06 [auto_monad_eliminator]: 2.445e-05 [cse]: 8.609e-05 [a_3]: 9.829e-05 [Cycle 3]: 0.00135889, [45] [expand_dump_flag]: 2.76e-06 [switch_simplify]: 1.284e-05 [loop_unroll]: 1.035e-05 [a_1]: 0.00030779 [with_stream_mark]: 2.105e-05 [recompute_prepare]: 1.228e-05 [updatestate_depend_eliminate]: 7.83999e-06 [updatestate_assign_eliminate]: 6.16e-06 [updatestate_loads_eliminate]: 5.42999e-06 [parameter_eliminate]: 2.04999e-06 [a_2]: 0.00019662 [accelerated_algorithm]: 2.147e-05 [shard]: 2.05002e-06 [meta_shard_fg_expand]: 3.21001e-06 [shard_inline]: 1.097e-05 [merge_send_recv]: 1.258e-05 [auto_parallel]: 1.402e-05 [parallel]: 8.65001e-06 [flash_sp]: 1.53002e-06 [merge_comm]: 6.61e-06 [allreduce_fusion]: 6.77002e-06 [matmul_add_comm_reduction]: 1.386e-05 [allreduce_slice_to_reducescatter]: 5.89993e-07 [virtual_shard_identity]: 1.282e-05 [virtual_dataset]: 1.038e-05 [get_grad_eliminate_]: 1.052e-05 [virtual_output]: 1.04e-05 [merge_forward]: 7.73001e-06 [cell_reuse_recompute_pass]: 2.37001e-06 [offload_activation]: 1.472e-05 [cell_reuse_handle_not_recompute_node_pass]: 2.822e-05 [merge_recompute_call_nodes]: 1.56998e-06 [before_grad]: 2.022e-05 [set_forward_comm_id_for_comm_node_pass]: 9.59e-06 [meta_fg_expand]: 4.46002e-06 [flash_sp_send_recv_attached]: 1.52001e-06 [receive_attached]: 2.14e-06 [after_resolve]: 1.58e-05 [a_after_grad]: 1.558e-05 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 3.86999e-06 [auto_monad_grad]: 2.19001e-06 [auto_monad_eliminator]: 1.887e-05 [cse]: 4.58e-05 [a_3]: 8.096e-05 [py_interpret_to_execute_after_opt_a]: 2.473e-05 [slice_cell_reuse_recomputed_activation]: 5.29e-06 [rewriter_after_opt_a]: 7.973e-05 [convert_after_rewriter]: 2.732e-05 [order_py_execute_after_rewriter]: 1.093e-05 [mutable_eliminate]: 0.00079313 [opt_b]: 0.00043377, [1] [Cycle 1]: 0.00042054, [7] [b_1]: 0.00026924 [b_2]: 1.193e-05 [updatestate_depend_eliminate]: 1.283e-05 [updatestate_assign_eliminate]: 6.49999e-06 [updatestate_loads_eliminate]: 5.97999e-06 [renormalize]: 8.89995e-07 [cse]: 5.435e-05 [optimize_parallel_all_gather_comm]: 3.304e-05 [overlap_param_gather]: 4.84e-06 [cconv]: 4.002e-05 [loop_unroll]: 0.00055344 [opt_after_cconv]: 0.0001961, [1] [Cycle 1]: 0.00018395, [7] [c_1]: 5.363e-05 [parameter_eliminate]: 6.33e-06 [updatestate_depend_eliminate]: 1.123e-05 [updatestate_assign_eliminate]: 5.66e-06 [updatestate_loads_eliminate]: 5.47001e-06 [cse]: 4.164e-05 [renormalize]: 1.04e-06 [remove_dup_value]: 6.554e-05 [tuple_transform]: 0.00013402, [1] [Cycle 1]: 0.00012536, [4] [d_1]: 7.971e-05 [none_parameter_eliminate]: 1.95001e-06 [renormalize]: 2.69996e-07 [switch_simplify]: 1.131e-05 [partial_unused_args_eliminate]: 5.11002e-06 [add_recomputation]: 9.179e-05 [cse_after_recomputation]: 4.619e-05, [1] [Cycle 1]: 3.77e-05, [1] [cse]: 2.623e-05 [environ_conv]: 2.238e-05 [swap_dp_allreduce_reducescatter]: 1.274e-05 [bias_add_comm_swap]: 5.80002e-06 [label_micro_interleaved_index]: 8.32e-06 [label_fine_grained_interleaved_index]: 6.16e-06 [merge_cast_opt]: 3.78001e-06 [slice_recompute_activation]: 4.85001e-06 [micro_interleaved_order_control]: 4.84e-06 [assign_add_opt]: 3.8e-06 [ForceFp32Comm]: 3.46001e-06 [remove_cast_before_assign_add]: 3.35003e-06 [full_micro_interleaved_order_control]: 5.25999e-06 [reorder_send_recv_between_fp_bp]: 5.49998e-06 [comm_op_add_attrs]: 3.83999e-06 [add_comm_op_reuse_tag]: 3.31001e-06 [interleave_split_concat_branches]: 3.81999e-06 [interleave_parallel_branches]: 3.73999e-06 [overlap_opt_shard_in_pipeline]: 2.688e-05 [overlap_opt_shard_grad_in_pipeline]: 4.35e-06 [control_data_broadcast_order]: 2.863e-05 [grouped_pairwise_exchange_alltoall]: 4.42e-06 [offloading_packed_experts]: 9.34e-06 [overlap_recompute_and_grad_model_parallel]: 1.035e-05 [overlap_grad_matmul_and_grad_allreduce]: 3.91999e-06 [overlap_recompute_allgather_and_fa_grad]: 4.50001e-06 [overlap_recompute_comm]: 5.29e-06 [overlap_grad_ring_attention]: 1.049e-05 [overlap_grad_flash_sp]: 4.171e-05 [begin_end_overlap_inline]: 3.43e-06 [split_matmul_comm_elemetwise]: 5.31998e-06 [split_layernorm_comm]: 4.35e-06 [handle_group_info]: 3.55e-06 [symbol_engine_optimizer]: 0.00014166, [1] [Cycle 1]: 0.00013341, [6] [build]: 6.01998e-06 [elim_shapecalc]: 1.928e-05 [elim_not_effective]: 2.483e-05 [opt_reshape]: 1.203e-05 [fold_const_symbol]: 1.853e-05 [renormalize]: 2.29978e-07 [detach_backward]: 4.92e-06 [pipeline_parallel_scheduler]: 2.02001e-06 [auto_monad_reorder]: 4.015e-05 [get_jit_bprop_graph]: 1.94999e-06 [rewriter_after_jit_bprop_graph]: 6.48e-06 [opt_after_jit_grad]: 0.00067848 [validate]: 0.0362094 Sums bootstrap : 0.000627s : 0.28% type_inference : 0.116011s : 51.03% event_method : 0.000079s : 0.03% auto_monad : 0.000212s : 0.09% graph_reusing : 0.000011s : 0.00% inline : 0.000003s : 0.00% add_attr.add_attr_with_inline.tag_attr : 0.000051s : 0.02% add_attr.add_attr_with_inline.meta_addattr_fg_expand : 0.000012s : 0.01% parallel-infer-symbol : 0.000004s : 0.00% pre_auto_parallel : 0.000089s : 0.04% insert-virtual-dataset : 0.000004s : 0.00% parallel-infer-symbol-second : 0.000001s : 0.00% dataset_repeat_opt : 0.000002s : 0.00% pipeline_split : 0.000002s : 0.00% optimize.py_interpret_to_execute : 0.000060s : 0.03% optimize.rewriter_before_opt_a : 0.000194s : 0.09% optimize.opt_a.expand_dump_flag : 0.000011s : 0.00% optimize.opt_a.switch_simplify : 0.000190s : 0.08% optimize.opt_a.loop_unroll : 0.000092s : 0.04% optimize.opt_a.a_1 : 0.064021s : 28.16% optimize.opt_a.with_stream_mark : 0.000167s : 0.07% optimize.opt_a.recompute_prepare : 0.000064s : 0.03% optimize.opt_a.updatestate_depend_eliminate : 0.000030s : 0.01% optimize.opt_a.updatestate_assign_eliminate : 0.000025s : 0.01% optimize.opt_a.updatestate_loads_eliminate : 0.000023s : 0.01% optimize.opt_a.parameter_eliminate : 0.000010s : 0.00% optimize.opt_a.a_2 : 0.001409s : 0.62% optimize.opt_a.accelerated_algorithm : 0.000102s : 0.04% optimize.opt_a.shard : 0.000007s : 0.00% optimize.opt_a.meta_shard_fg_expand : 0.000014s : 0.01% optimize.opt_a.shard_inline : 0.000038s : 0.02% optimize.opt_a.merge_send_recv : 0.000057s : 0.03% optimize.opt_a.auto_parallel : 0.000048s : 0.02% optimize.opt_a.parallel : 0.000071s : 0.03% optimize.opt_a.flash_sp : 0.000029s : 0.01% optimize.opt_a.merge_comm : 0.000024s : 0.01% optimize.opt_a.allreduce_fusion : 0.000023s : 0.01% optimize.opt_a.matmul_add_comm_reduction : 0.000051s : 0.02% optimize.opt_a.allreduce_slice_to_reducescatter : 0.000003s : 0.00% optimize.opt_a.virtual_shard_identity : 0.000048s : 0.02% optimize.opt_a.virtual_dataset : 0.000036s : 0.02% optimize.opt_a.get_grad_eliminate_ : 0.000034s : 0.02% optimize.opt_a.virtual_output : 0.000035s : 0.02% optimize.opt_a.merge_forward : 0.000027s : 0.01% optimize.opt_a.cell_reuse_recompute_pass : 0.000007s : 0.00% optimize.opt_a.offload_activation : 0.000052s : 0.02% optimize.opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000097s : 0.04% optimize.opt_a.merge_recompute_call_nodes : 0.000005s : 0.00% optimize.opt_a.before_grad : 0.000068s : 0.03% optimize.opt_a.set_forward_comm_id_for_comm_node_pass : 0.000031s : 0.01% optimize.opt_a.meta_fg_expand : 0.000017s : 0.01% optimize.opt_a.flash_sp_send_recv_attached : 0.000010s : 0.00% optimize.opt_a.receive_attached : 0.000016s : 0.01% optimize.opt_a.after_resolve : 0.000052s : 0.02% optimize.opt_a.a_after_grad : 0.000058s : 0.03% optimize.opt_a.renormalize : 0.002495s : 1.10% optimize.opt_a.add_forward_monad_depend : 0.000030s : 0.01% optimize.opt_a.auto_monad_grad : 0.000007s : 0.00% optimize.opt_a.auto_monad_eliminator : 0.000078s : 0.03% optimize.opt_a.cse : 0.000315s : 0.14% optimize.opt_a.a_3 : 0.000300s : 0.13% optimize.py_interpret_to_execute_after_opt_a : 0.000025s : 0.01% optimize.slice_cell_reuse_recomputed_activation : 0.000005s : 0.00% optimize.rewriter_after_opt_a : 0.000080s : 0.04% optimize.convert_after_rewriter : 0.000027s : 0.01% optimize.order_py_execute_after_rewriter : 0.000011s : 0.00% optimize.mutable_eliminate : 0.000793s : 0.35% optimize.opt_b.b_1 : 0.000269s : 0.12% optimize.opt_b.b_2 : 0.000012s : 0.01% optimize.opt_b.updatestate_depend_eliminate : 0.000013s : 0.01% optimize.opt_b.updatestate_assign_eliminate : 0.000006s : 0.00% optimize.opt_b.updatestate_loads_eliminate : 0.000006s : 0.00% optimize.opt_b.renormalize : 0.000001s : 0.00% optimize.opt_b.cse : 0.000054s : 0.02% optimize.optimize_parallel_all_gather_comm : 0.000033s : 0.01% optimize.overlap_param_gather : 0.000005s : 0.00% optimize.cconv : 0.000040s : 0.02% optimize.loop_unroll : 0.000553s : 0.24% optimize.opt_after_cconv.c_1 : 0.000054s : 0.02% optimize.opt_after_cconv.parameter_eliminate : 0.000006s : 0.00% optimize.opt_after_cconv.updatestate_depend_eliminate : 0.000011s : 0.00% optimize.opt_after_cconv.updatestate_assign_eliminate : 0.000006s : 0.00% optimize.opt_after_cconv.updatestate_loads_eliminate : 0.000005s : 0.00% optimize.opt_after_cconv.cse : 0.000042s : 0.02% optimize.opt_after_cconv.renormalize : 0.000001s : 0.00% optimize.remove_dup_value : 0.000066s : 0.03% optimize.tuple_transform.d_1 : 0.000080s : 0.04% optimize.tuple_transform.none_parameter_eliminate : 0.000002s : 0.00% optimize.tuple_transform.renormalize : 0.000000s : 0.00% optimize.tuple_transform.switch_simplify : 0.000011s : 0.00% optimize.partial_unused_args_eliminate : 0.000005s : 0.00% optimize.add_recomputation : 0.000092s : 0.04% optimize.cse_after_recomputation.cse : 0.000026s : 0.01% optimize.environ_conv : 0.000022s : 0.01% optimize.swap_dp_allreduce_reducescatter : 0.000013s : 0.01% optimize.bias_add_comm_swap : 0.000006s : 0.00% optimize.label_micro_interleaved_index : 0.000008s : 0.00% optimize.label_fine_grained_interleaved_index : 0.000006s : 0.00% optimize.merge_cast_opt : 0.000004s : 0.00% optimize.slice_recompute_activation : 0.000005s : 0.00% optimize.micro_interleaved_order_control : 0.000005s : 0.00% optimize.assign_add_opt : 0.000004s : 0.00% optimize.ForceFp32Comm : 0.000003s : 0.00% optimize.remove_cast_before_assign_add : 0.000003s : 0.00% optimize.full_micro_interleaved_order_control : 0.000005s : 0.00% optimize.reorder_send_recv_between_fp_bp : 0.000005s : 0.00% optimize.comm_op_add_attrs : 0.000004s : 0.00% optimize.add_comm_op_reuse_tag : 0.000003s : 0.00% optimize.interleave_split_concat_branches : 0.000004s : 0.00% optimize.interleave_parallel_branches : 0.000004s : 0.00% optimize.overlap_opt_shard_in_pipeline : 0.000027s : 0.01% optimize.overlap_opt_shard_grad_in_pipeline : 0.000004s : 0.00% optimize.control_data_broadcast_order : 0.000029s : 0.01% optimize.grouped_pairwise_exchange_alltoall : 0.000004s : 0.00% optimize.offloading_packed_experts : 0.000009s : 0.00% optimize.overlap_recompute_and_grad_model_parallel : 0.000010s : 0.00% optimize.overlap_grad_matmul_and_grad_allreduce : 0.000004s : 0.00% optimize.overlap_recompute_allgather_and_fa_grad : 0.000005s : 0.00% optimize.overlap_recompute_comm : 0.000005s : 0.00% optimize.overlap_grad_ring_attention : 0.000010s : 0.00% optimize.overlap_grad_flash_sp : 0.000042s : 0.02% optimize.begin_end_overlap_inline : 0.000003s : 0.00% optimize.split_matmul_comm_elemetwise : 0.000005s : 0.00% optimize.split_layernorm_comm : 0.000004s : 0.00% optimize.handle_group_info : 0.000004s : 0.00% optimize.symbol_engine_optimizer.build : 0.000006s : 0.00% optimize.symbol_engine_optimizer.elim_shapecalc : 0.000019s : 0.01% optimize.symbol_engine_optimizer.elim_not_effective : 0.000025s : 0.01% optimize.symbol_engine_optimizer.opt_reshape : 0.000012s : 0.01% optimize.symbol_engine_optimizer.fold_const_symbol : 0.000019s : 0.01% optimize.symbol_engine_optimizer.renormalize : 0.000000s : 0.00% detach_backward : 0.000005s : 0.00% pipeline_parallel_scheduler : 0.000002s : 0.00% auto_monad_reorder : 0.000040s : 0.02% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000006s : 0.00% opt_after_jit_grad : 0.000678s : 0.30% validate : 0.036209s : 15.93% Time group info: ------[substitution.] 0.001147 184 30.92% : 0.000355s : 56: substitution.arithmetic_simplify 1.30% : 0.000015s : 6: substitution.depend_value_elim 0.31% : 0.000004s : 7: substitution.elim_not_effective 0.26% : 0.000003s : 7: substitution.fold_const_symbol 0.83% : 0.000009s : 8: substitution.graph_param_transform 55.84% : 0.000640s : 21: substitution.inline 1.27% : 0.000015s : 25: substitution.j_node_and_user_rematch 3.40% : 0.000039s : 15: substitution.less_batch_normalization 0.33% : 0.000004s : 4: substitution.redundant_stop_gradient_eliminater 1.67% : 0.000019s : 25: substitution.remove_not_recompute_node 0.27% : 0.000003s : 2: substitution.replace_applicator 0.81% : 0.000009s : 3: substitution.replace_old_param 0.43% : 0.000005s : 2: substitution.set_cell_output_no_recompute 0.83% : 0.000010s : 2: substitution.specialize_transform 1.56% : 0.000018s : 1: substitution.switch_simplify ------[type_inference.] 0.115899 2 98.22% : 0.113835s : 1: type_inference.infer 1.78% : 0.002064s : 1: type_inference.specialize ------[replace.] 0.000300 20 10.16% : 0.000030s : 2: replace.depend_value_elim 73.35% : 0.000220s : 17: replace.inline 16.50% : 0.000049s : 1: replace.switch_simplify ------[match.] 0.000644 20 0.54% : 0.000003s : 2: match.depend_value_elim 96.84% : 0.000624s : 17: match.inline 2.62% : 0.000017s : 1: match.switch_simplify ------[predicate.] 0.000703 4446 1.12% : 0.000008s : 49: predicate.accumulaten_eliminater 0.43% : 0.000003s : 8: predicate.ad_related_special_op_eliminate 1.07% : 0.000008s : 56: predicate.addn_check_dump 1.11% : 0.000008s : 49: predicate.addn_zero_filter 0.96% : 0.000007s : 49: predicate.adjust_all_reduce_mul_add 3.02% : 0.000021s : 99: predicate.arithmetic_simplify 1.04% : 0.000007s : 49: predicate.cast_eliminate 0.54% : 0.000004s : 26: predicate.check_bprop_eliminate 1.12% : 0.000008s : 56: predicate.compare_switch_simplify 0.09% : 0.000001s : 8: predicate.const_output_eliminate 1.15% : 0.000008s : 51: predicate.depend_value_elim 1.03% : 0.000007s : 49: predicate.dict_get_item_const_eliminator 1.10% : 0.000008s : 49: predicate.dict_get_item_eliminator 0.98% : 0.000007s : 49: predicate.dict_set_item_eliminator 0.46% : 0.000003s : 16: predicate.dumpgradient_eliminate 0.11% : 0.000001s : 8: predicate.elim_not_effective 0.20% : 0.000001s : 8: predicate.elim_shapecalc_of_broadcastargs 1.19% : 0.000008s : 57: predicate.environ_add_const_eliminate 1.09% : 0.000008s : 57: predicate.environ_get_add_eliminate 1.09% : 0.000008s : 57: predicate.environ_get_depend_swap 2.26% : 0.000016s : 107: predicate.environ_get_eliminate 1.14% : 0.000008s : 57: predicate.environ_get_set_eliminate 1.33% : 0.000009s : 64: predicate.exchange_switch_depend_value 2.14% : 0.000015s : 64: predicate.float_depend_g_call 1.11% : 0.000008s : 56: predicate.float_environ_get_switch 1.29% : 0.000009s : 64: predicate.float_tuple_getitem_switch 0.08% : 0.000001s : 8: predicate.fold_const_symbol 0.64% : 0.000004s : 28: predicate.get_grad_eliminate 0.14% : 0.000001s : 8: predicate.graph_param_transform 1.06% : 0.000007s : 50: predicate.incorporate_call 0.95% : 0.000007s : 50: predicate.incorporate_call_switch 6.34% : 0.000045s : 211: predicate.inline 0.91% : 0.000006s : 28: predicate.inline_without_move 0.27% : 0.000002s : 28: predicate.j_node_and_user_rematch 0.97% : 0.000007s : 28: predicate.less_batch_normalization 1.46% : 0.000010s : 65: predicate.list_to_tuple_eliminator_ 2.22% : 0.000016s : 114: predicate.load_eliminater 0.54% : 0.000004s : 8: predicate.loop_unroll_after_grad 1.91% : 0.000013s : 95: predicate.loop_unroll_before_grad 1.40% : 0.000010s : 65: predicate.make_slice_get_slice_eliminator 1.16% : 0.000008s : 56: predicate.merge_addn 0.54% : 0.000004s : 26: predicate.micro_step_allgather_replace 0.58% : 0.000004s : 26: predicate.mini_step_allgather_replace 0.97% : 0.000007s : 49: predicate.minmaximum_grad 0.49% : 0.000003s : 8: predicate.mutable_eliminate 0.23% : 0.000002s : 8: predicate.opt_reshape 0.19% : 0.000001s : 8: predicate.parallel_virtual_node 1.85% : 0.000013s : 64: predicate.partial_defer_inline 1.27% : 0.000009s : 57: predicate.partial_eliminate 0.94% : 0.000007s : 49: predicate.print_const_string_wrapper 1.02% : 0.000007s : 48: predicate.reduce_all_const_elim 1.35% : 0.000009s : 49: predicate.reduce_eliminate 2.40% : 0.000017s : 114: predicate.redundant_stop_gradient_eliminater 0.43% : 0.000003s : 28: predicate.remove_not_recompute_node 1.16% : 0.000008s : 75: predicate.replace_applicator 0.36% : 0.000003s : 28: predicate.replace_old_param 0.20% : 0.000001s : 8: predicate.reset_defer_inline 1.00% : 0.000007s : 49: predicate.reshape_eliminate 0.58% : 0.000004s : 26: predicate.row_tensor_add_zeros_like 0.20% : 0.000001s : 8: predicate.row_tensor_eliminate 0.73% : 0.000005s : 26: predicate.same_eliminate 0.45% : 0.000003s : 36: predicate.set_cell_output_no_recompute 0.73% : 0.000005s : 28: predicate.shard_identity_eliminate 0.38% : 0.000003s : 16: predicate.special_op_eliminate 1.49% : 0.000010s : 56: predicate.specialize_transform 0.87% : 0.000006s : 26: predicate.split_environ_get_set_with_tuple_value 0.76% : 0.000005s : 28: predicate.stack_unstack_eliminate 0.20% : 0.000001s : 8: predicate.switch_call_monad_eliminater 1.35% : 0.000009s : 64: predicate.switch_defer_inline 1.84% : 0.000013s : 90: predicate.switch_layer_defer_inline 4.82% : 0.000034s : 225: predicate.switch_simplify 0.98% : 0.000007s : 49: predicate.tile_eliminate 0.99% : 0.000007s : 49: predicate.transpose_eliminate 1.50% : 0.000011s : 65: predicate.tuple_list_convert_item_index_to_positive 1.44% : 0.000010s : 65: predicate.tuple_list_get_item_const_eliminator 1.43% : 0.000010s : 65: predicate.tuple_list_get_item_depend_reorder 2.98% : 0.000021s : 115: predicate.tuple_list_get_item_eliminator 1.54% : 0.000011s : 65: predicate.tuple_list_get_set_item_eliminator 2.79% : 0.000020s : 115: predicate.tuple_list_set_item_eliminator 1.34% : 0.000009s : 65: predicate.tuple_to_list_eliminator_ 2.20% : 0.000015s : 114: predicate.updatestate_pure_node_eliminater 3.33% : 0.000023s : 164: predicate.updatestate_useless_node_eliminater 0.19% : 0.000001s : 8: predicate.value_based_eliminate 0.67% : 0.000005s : 28: predicate.virtual_dataset_eliminate 0.64% : 0.000005s : 28: predicate.virtual_output_eliminate 0.17% : 0.000001s : 8: predicate.virtual_view_grad_eliminate 0.21% : 0.000001s : 8: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.001693 28 44.57% : 0.000755s : 8: func_graph_cloner_run.FuncGraphClonerGraph 5.22% : 0.000088s : 2: func_graph_cloner_run.FuncGraphClonerNode 50.21% : 0.000850s : 18: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.434535 267 0.00% : 0.000006s : 1: ForceFp32Comm 11.11% : 0.048257s : 1: add_attr 11.10% : 0.048232s : 1: add_attr_with_inline 0.00% : 0.000007s : 1: add_comm_op_reuse_tag 0.02% : 0.000096s : 1: add_recomputation 0.00% : 0.000006s : 1: assign_add_opt 0.05% : 0.000227s : 1: auto_monad 0.01% : 0.000048s : 1: auto_monad_reorder 0.00% : 0.000006s : 1: begin_end_overlap_inline 0.00% : 0.000009s : 1: bias_add_comm_swap 0.16% : 0.000702s : 1: bootstrap 0.01% : 0.000044s : 1: cconv 0.00% : 0.000007s : 1: comm_op_add_attrs 0.01% : 0.000032s : 1: control_data_broadcast_order 0.01% : 0.000031s : 1: convert_after_rewriter 0.01% : 0.000049s : 1: cse_after_recomputation 0.00% : 0.000008s : 1: dataset_repeat_opt 0.01% : 0.000027s : 1: detach_backward 0.01% : 0.000025s : 1: environ_conv 0.02% : 0.000094s : 1: event_method 0.00% : 0.000008s : 1: full_micro_interleaved_order_control 0.00% : 0.000008s : 1: get_jit_bprop_graph 0.00% : 0.000018s : 1: graph_reusing 0.00% : 0.000007s : 1: grouped_pairwise_exchange_alltoall 0.00% : 0.000006s : 1: handle_group_info 0.00% : 0.000009s : 1: inline 0.00% : 0.000014s : 1: insert-virtual-dataset 0.00% : 0.000007s : 1: interleave_parallel_branches 0.00% : 0.000006s : 1: interleave_split_concat_branches 0.00% : 0.000009s : 1: label_fine_grained_interleaved_index 0.00% : 0.000011s : 1: label_micro_interleaved_index 0.13% : 0.000560s : 1: loop_unroll 0.00% : 0.000006s : 1: merge_cast_opt 0.00% : 0.000008s : 1: micro_interleaved_order_control 0.18% : 0.000802s : 1: mutable_eliminate 0.00% : 0.000012s : 1: offloading_packed_experts 0.01% : 0.000023s : 1: opt.transform.loop_unroll_optimizer 0.01% : 0.000026s : 1: opt.transform.mutable_eliminate 15.24% : 0.066240s : 151: opt.transform.opt_a 0.01% : 0.000052s : 1: opt.transform.opt_after_cconv 0.01% : 0.000043s : 1: opt.transform.opt_after_jit_grad 0.05% : 0.000207s : 28: opt.transform.opt_b 0.02% : 0.000088s : 2: opt.transform.opt_trans_graph 0.02% : 0.000069s : 4: opt.transform.symbol_engine_opt 16.51% : 0.071720s : 1: opt_a 0.05% : 0.000200s : 1: opt_after_cconv 0.16% : 0.000690s : 1: opt_after_jit_grad 0.10% : 0.000438s : 1: opt_b 17.41% : 0.075636s : 1: optimize 0.01% : 0.000037s : 1: optimize_parallel_all_gather_comm 0.00% : 0.000014s : 1: order_py_execute_after_rewriter 0.01% : 0.000046s : 1: overlap_grad_flash_sp 0.00% : 0.000007s : 1: overlap_grad_matmul_and_grad_allreduce 0.00% : 0.000013s : 1: overlap_grad_ring_attention 0.00% : 0.000007s : 1: overlap_opt_shard_grad_in_pipeline 0.01% : 0.000031s : 1: overlap_opt_shard_in_pipeline 0.00% : 0.000008s : 1: overlap_param_gather 0.00% : 0.000007s : 1: overlap_recompute_allgather_and_fa_grad 0.00% : 0.000014s : 1: overlap_recompute_and_grad_model_parallel 0.00% : 0.000008s : 1: overlap_recompute_comm 0.00% : 0.000011s : 1: parallel-infer-symbol 0.00% : 0.000007s : 1: parallel-infer-symbol-second 0.00% : 0.000008s : 1: partial_unused_args_eliminate 0.00% : 0.000011s : 1: pipeline_parallel_scheduler 0.00% : 0.000007s : 1: pipeline_split 0.03% : 0.000128s : 1: pre_auto_parallel 0.02% : 0.000065s : 1: py_interpret_to_execute 0.01% : 0.000028s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000006s : 1: remove_cast_before_assign_add 0.02% : 0.000070s : 1: remove_dup_value 0.32% : 0.001393s : 2: renormalize.infer 0.25% : 0.001080s : 2: renormalize.specialize 0.00% : 0.000008s : 1: reorder_send_recv_between_fp_bp 0.00% : 0.000012s : 1: rewriter_after_jit_bprop_graph 0.02% : 0.000083s : 1: rewriter_after_opt_a 0.05% : 0.000200s : 1: rewriter_before_opt_a 0.00% : 0.000008s : 1: slice_cell_reuse_recomputed_activation 0.00% : 0.000008s : 1: slice_recompute_activation 0.00% : 0.000008s : 1: split_layernorm_comm 0.00% : 0.000008s : 1: split_matmul_comm_elemetwise 0.00% : 0.000016s : 1: swap_dp_allreduce_reducescatter 0.03% : 0.000145s : 1: symbol_engine_optimizer 0.03% : 0.000137s : 1: tuple_transform 26.71% : 0.116059s : 1: type_inference F [hook] pytest_runtest_teardown:test_basic_stream_block_annotation_1 tests/st/compiler/stream_event/test_with_stream.py::test_basic_stream_block_annotation_1,max_mem:6.0M =================================== FAILURES =================================== _____________________ test_basic_stream_block_annotation_1 _____________________ @arg_mark(plat_marks=['platform_ascend910b'], level_mark='level0', card_mark='onecard', essential_mark='essential', mem_peak=8.00) def test_basic_stream_block_annotation_1(): """ Feature: Support with stream. Description: Support with stream. Expectation: Run success. """ s1 = Stream() class MsJitStreamNet(nn.Cell): def construct(self, x, con): y = x * 2 with MsJitStreamCtx(s1): z = a + x if con < 5: z = z + 5 y = y + z y = y + z return y + z save_path = "./test_basic_stream_block_annotation_1" os.environ['MS_DEV_DUMP_IR_PASSES'] = 'validate' ms.set_context(jit_config={"jit_level": "O0"}, save_graphs=True, save_graphs_path=save_path) net = MsJitStreamNet() x = Tensor(np.ones([3, 3]), ms.float32) con = 0 result = net(x, con) os.unsetenv('MS_DEV_DUMP_IR_PASSES') > assert np.allclose(result, Tensor(np.ones([3, 3], dtype=np.float32)) * 23) E AssertionError: assert False E + where False = (Tensor(shape=[3, 3], dtype=Float32, value=\n[[ 1.60000000e+01, 1.60000000e+01, 1.60000000e+01],\n [ 1.60000000e+01, 1.60000000e+01, 1.60000000e+01],\n [ 1.60000000e+01, 1.60000000e+01, 1.60000000e+01]]), (Tensor(shape=[3, 3], dtype=Float32, value=\n[[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]) * 23)) E + where = np.allclose E + and Tensor(shape=[3, 3], dtype=Float32, value=\n[[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],\n [ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]) = Tensor(array([[1., 1., 1.],\n [1., 1., 1.],\n [1., 1., 1.]], dtype=float32)) E + where array([[1., 1., 1.],\n [1., 1., 1.],\n [1., 1., 1.]], dtype=float32) = ([3, 3], dtype=) E + where = np.ones E + and = np.float32 test_with_stream.py:252: AssertionError =============================== 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 =========================== short test summary info ============================ FAILED test_with_stream.py::test_basic_stream_block_annotation_1 - AssertionE... ================== 1 failed, 25 warnings in 93.16s (0:01:33) ===================