==================================================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/ascend/test_aclnn_ops, 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_dequant_swiglu_quant.py [WARNING] ME(167844:281473624141616,MainProcess):2026-01-29-17:40:38.906.969 [mindspore/context.py:1334] For 'context.set_context', the parameter 'device_target' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead. TotalTime = 0.59093, [21] [bootstrap]: 0.00078678 [type_inference]: 0.29972 [event_method]: 2.105e-05 [auto_monad]: 0.00074664 [graph_reusing]: 6.31998e-06 [inline]: 2.93003e-06 [add_attr]: 0.272211, [1] [add_attr_with_inline]: 0.272052, [1] [Cycle 1]: 0.00055335, [2] [tag_attr]: 2.506e-05 [meta_addattr_fg_expand]: 5.24e-06 [parallel-infer-symbol]: 3.99002e-06 [pre_auto_parallel]: 0.00029679 [insert-virtual-dataset]: 3.14001e-06 [parallel-infer-symbol-second]: 1.13001e-06 [dataset_repeat_opt]: 1.97999e-06 [pipeline_split]: 1.58002e-06 [optimize]: 0.0159451, [53] [py_interpret_to_execute]: 0.00013957 [rewriter_before_opt_a]: 0.00024376 [opt_a]: 0.00756194, [2] [Cycle 1]: 0.00645902, [45] [expand_dump_flag]: 3.08e-06 [switch_simplify]: 3.915e-05 [loop_unroll]: 2.55e-05 [a_1]: 0.00176299 [with_stream_mark]: 2.498e-05 [recompute_prepare]: 8.535e-05 [updatestate_depend_eliminate]: 7.92e-06 [updatestate_assign_eliminate]: 6.24001e-06 [updatestate_loads_eliminate]: 1.418e-05 [parameter_eliminate]: 2.93e-06 [a_2]: 0.0001791 [accelerated_algorithm]: 2.981e-05 [shard]: 2.47001e-06 [meta_shard_fg_expand]: 3.42997e-06 [shard_inline]: 1.201e-05 [merge_send_recv]: 1.301e-05 [auto_parallel]: 1.159e-05 [parallel]: 0.00056974 [flash_sp]: 1.067e-05 [merge_comm]: 8.3e-06 [allreduce_fusion]: 6.66e-06 [matmul_add_comm_reduction]: 1.559e-05 [allreduce_slice_to_reducescatter]: 7.89994e-07 [virtual_shard_identity]: 6.423e-05 [virtual_dataset]: 1.133e-05 [get_grad_eliminate_]: 1.079e-05 [virtual_output]: 1.12e-05 [merge_forward]: 7.23999e-06 [cell_reuse_recompute_pass]: 1.88002e-06 [offload_activation]: 1.509e-05 [cell_reuse_handle_not_recompute_node_pass]: 2.127e-05 [merge_recompute_call_nodes]: 1.86e-06 [before_grad]: 1.889e-05 [set_forward_comm_id_for_comm_node_pass]: 8.18999e-06 [meta_fg_expand]: 4.97e-06 [flash_sp_send_recv_attached]: 4.80001e-06 [receive_attached]: 0.00013963 [after_resolve]: 1.982e-05 [a_after_grad]: 1.683e-05 [renormalize]: 0.00193327 [add_forward_monad_depend]: 7.223e-05 [auto_monad_grad]: 3.46001e-06 [auto_monad_eliminator]: 2.343e-05 [cse]: 0.00033507 [a_3]: 8.872e-05 [Cycle 2]: 0.00108907, [45] [expand_dump_flag]: 2.06998e-06 [switch_simplify]: 1.465e-05 [loop_unroll]: 1.082e-05 [a_1]: 0.00029149 [with_stream_mark]: 1.629e-05 [recompute_prepare]: 1.111e-05 [updatestate_depend_eliminate]: 6.98998e-06 [updatestate_assign_eliminate]: 6.23e-06 [updatestate_loads_eliminate]: 5.39e-06 [parameter_eliminate]: 1.49e-06 [a_2]: 0.00015637 [accelerated_algorithm]: 1.375e-05 [shard]: 1.87999e-06 [meta_shard_fg_expand]: 2.35002e-06 [shard_inline]: 1.127e-05 [merge_send_recv]: 1.031e-05 [auto_parallel]: 1.058e-05 [parallel]: 7.6e-06 [flash_sp]: 4.45999e-06 [merge_comm]: 6.49001e-06 [allreduce_fusion]: 6.77002e-06 [matmul_add_comm_reduction]: 1.1e-05 [allreduce_slice_to_reducescatter]: 8.2e-07 [virtual_shard_identity]: 1.143e-05 [virtual_dataset]: 1.004e-05 [get_grad_eliminate_]: 9.94001e-06 [virtual_output]: 9.84999e-06 [merge_forward]: 6.53998e-06 [cell_reuse_recompute_pass]: 2.39001e-06 [offload_activation]: 1.25e-05 [cell_reuse_handle_not_recompute_node_pass]: 1.941e-05 [merge_recompute_call_nodes]: 1.69998e-06 [before_grad]: 1.626e-05 [set_forward_comm_id_for_comm_node_pass]: 6.94999e-06 [meta_fg_expand]: 4.05e-06 [flash_sp_send_recv_attached]: 1.46002e-06 [receive_attached]: 1.71e-06 [after_resolve]: 1.681e-05 [a_after_grad]: 1.55e-05 [renormalize]: 6.99947e-08 [add_forward_monad_depend]: 1.57999e-06 [auto_monad_grad]: 1.81003e-06 [auto_monad_eliminator]: 1.256e-05 [cse]: 3.692e-05 [a_3]: 6.969e-05 [py_interpret_to_execute_after_opt_a]: 1.944e-05 [slice_cell_reuse_recomputed_activation]: 2.33002e-06 [rewriter_after_opt_a]: 0.00355355 [convert_after_rewriter]: 3.983e-05 [order_py_execute_after_rewriter]: 9.08002e-06 [mutable_eliminate]: 0.00083741 [opt_b]: 0.00042153, [1] [Cycle 1]: 0.00041284, [7] [b_1]: 0.00028333 [b_2]: 1.451e-05 [updatestate_depend_eliminate]: 1.029e-05 [updatestate_assign_eliminate]: 5.30999e-06 [updatestate_loads_eliminate]: 5.50001e-06 [renormalize]: 5.19998e-07 [cse]: 5.28e-05 [optimize_parallel_all_gather_comm]: 2.831e-05 [overlap_param_gather]: 2.32001e-06 [cconv]: 3.832e-05 [loop_unroll]: 0.00055798 [opt_after_cconv]: 0.00017522, [1] [Cycle 1]: 0.00016737, [7] [c_1]: 5.23e-05 [parameter_eliminate]: 4.55001e-06 [updatestate_depend_eliminate]: 1.105e-05 [updatestate_assign_eliminate]: 5.94999e-06 [updatestate_loads_eliminate]: 5.55001e-06 [cse]: 5.035e-05 [renormalize]: 6.30011e-07 [remove_dup_value]: 8.143e-05 [tuple_transform]: 0.00015198, [1] [Cycle 1]: 0.00014493, [4] [d_1]: 0.00010693 [none_parameter_eliminate]: 2.39001e-06 [renormalize]: 2.80008e-07 [switch_simplify]: 1.247e-05 [partial_unused_args_eliminate]: 2.42001e-06 [add_recomputation]: 8.754e-05 [cse_after_recomputation]: 3.935e-05, [1] [Cycle 1]: 3.4e-05, [1] [cse]: 2.785e-05 [environ_conv]: 6.868e-05 [swap_dp_allreduce_reducescatter]: 9.89001e-06 [bias_add_comm_swap]: 3.22002e-06 [label_micro_interleaved_index]: 5.04998e-06 [label_fine_grained_interleaved_index]: 3.05002e-06 [merge_cast_opt]: 1.65001e-06 [slice_recompute_activation]: 2.29999e-06 [micro_interleaved_order_control]: 2.53e-06 [assign_add_opt]: 1.74e-06 [ForceFp32Comm]: 8.30012e-07 [remove_cast_before_assign_add]: 9.70002e-07 [full_micro_interleaved_order_control]: 2.67001e-06 [reorder_send_recv_between_fp_bp]: 2.78e-06 [comm_op_add_attrs]: 1.27e-06 [add_comm_op_reuse_tag]: 1.29e-06 [interleave_split_concat_branches]: 1.27e-06 [interleave_parallel_branches]: 1.19998e-06 [overlap_opt_shard_in_pipeline]: 0.00014083 [overlap_opt_shard_grad_in_pipeline]: 1.95001e-06 [control_data_broadcast_order]: 2.166e-05 [grouped_pairwise_exchange_alltoall]: 1.72999e-06 [offloading_packed_experts]: 7.06999e-06 [overlap_recompute_and_grad_model_parallel]: 7.37002e-06 [overlap_grad_matmul_and_grad_allreduce]: 1.38002e-06 [overlap_recompute_allgather_and_fa_grad]: 1.63002e-06 [overlap_recompute_comm]: 2.89999e-06 [overlap_grad_ring_attention]: 6.51e-06 [overlap_grad_flash_sp]: 0.00023952 [begin_end_overlap_inline]: 5.89993e-07 [split_matmul_comm_elemetwise]: 2.71e-06 [split_layernorm_comm]: 2.01e-06 [handle_group_info]: 1.15999e-06 [symbol_engine_optimizer]: 0.00061995, [1] [Cycle 1]: 0.00061423, [6] [build]: 0.00039451 [elim_shapecalc]: 1.779e-05 [elim_not_effective]: 0.00012814 [opt_reshape]: 1.265e-05 [fold_const_symbol]: 2.392e-05 [renormalize]: 2.19996e-07 [detach_backward]: 2.58e-06 [pipeline_parallel_scheduler]: 1.72999e-06 [auto_monad_reorder]: 3.365e-05 [get_jit_bprop_graph]: 2.09999e-06 [rewriter_after_jit_bprop_graph]: 4e-06 [opt_after_jit_grad]: 0.0005521 [validate]: 0.00027964 Sums bootstrap : 0.000787s : 0.25% type_inference : 0.299720s : 94.71% event_method : 0.000021s : 0.01% auto_monad : 0.000747s : 0.24% graph_reusing : 0.000006s : 0.00% inline : 0.000003s : 0.00% add_attr.add_attr_with_inline.tag_attr : 0.000025s : 0.01% add_attr.add_attr_with_inline.meta_addattr_fg_expand : 0.000005s : 0.00% parallel-infer-symbol : 0.000004s : 0.00% pre_auto_parallel : 0.000297s : 0.09% insert-virtual-dataset : 0.000003s : 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 : 0.000140s : 0.04% optimize.rewriter_before_opt_a : 0.000244s : 0.08% optimize.opt_a.expand_dump_flag : 0.000005s : 0.00% optimize.opt_a.switch_simplify : 0.000054s : 0.02% optimize.opt_a.loop_unroll : 0.000036s : 0.01% optimize.opt_a.a_1 : 0.002054s : 0.65% optimize.opt_a.with_stream_mark : 0.000041s : 0.01% optimize.opt_a.recompute_prepare : 0.000096s : 0.03% optimize.opt_a.updatestate_depend_eliminate : 0.000015s : 0.00% optimize.opt_a.updatestate_assign_eliminate : 0.000012s : 0.00% optimize.opt_a.updatestate_loads_eliminate : 0.000020s : 0.01% optimize.opt_a.parameter_eliminate : 0.000004s : 0.00% optimize.opt_a.a_2 : 0.000335s : 0.11% optimize.opt_a.accelerated_algorithm : 0.000044s : 0.01% optimize.opt_a.shard : 0.000004s : 0.00% optimize.opt_a.meta_shard_fg_expand : 0.000006s : 0.00% optimize.opt_a.shard_inline : 0.000023s : 0.01% optimize.opt_a.merge_send_recv : 0.000023s : 0.01% optimize.opt_a.auto_parallel : 0.000022s : 0.01% optimize.opt_a.parallel : 0.000577s : 0.18% optimize.opt_a.flash_sp : 0.000015s : 0.00% optimize.opt_a.merge_comm : 0.000015s : 0.00% optimize.opt_a.allreduce_fusion : 0.000013s : 0.00% optimize.opt_a.matmul_add_comm_reduction : 0.000027s : 0.01% optimize.opt_a.allreduce_slice_to_reducescatter : 0.000002s : 0.00% optimize.opt_a.virtual_shard_identity : 0.000076s : 0.02% optimize.opt_a.virtual_dataset : 0.000021s : 0.01% optimize.opt_a.get_grad_eliminate_ : 0.000021s : 0.01% optimize.opt_a.virtual_output : 0.000021s : 0.01% optimize.opt_a.merge_forward : 0.000014s : 0.00% optimize.opt_a.cell_reuse_recompute_pass : 0.000004s : 0.00% optimize.opt_a.offload_activation : 0.000028s : 0.01% optimize.opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000041s : 0.01% optimize.opt_a.merge_recompute_call_nodes : 0.000004s : 0.00% optimize.opt_a.before_grad : 0.000035s : 0.01% optimize.opt_a.set_forward_comm_id_for_comm_node_pass : 0.000015s : 0.00% optimize.opt_a.meta_fg_expand : 0.000009s : 0.00% optimize.opt_a.flash_sp_send_recv_attached : 0.000006s : 0.00% optimize.opt_a.receive_attached : 0.000141s : 0.04% optimize.opt_a.after_resolve : 0.000037s : 0.01% optimize.opt_a.a_after_grad : 0.000032s : 0.01% optimize.opt_a.renormalize : 0.001933s : 0.61% optimize.opt_a.add_forward_monad_depend : 0.000074s : 0.02% optimize.opt_a.auto_monad_grad : 0.000005s : 0.00% optimize.opt_a.auto_monad_eliminator : 0.000036s : 0.01% optimize.opt_a.cse : 0.000372s : 0.12% optimize.opt_a.a_3 : 0.000158s : 0.05% optimize.py_interpret_to_execute_after_opt_a : 0.000019s : 0.01% optimize.slice_cell_reuse_recomputed_activation : 0.000002s : 0.00% optimize.rewriter_after_opt_a : 0.003554s : 1.12% optimize.convert_after_rewriter : 0.000040s : 0.01% optimize.order_py_execute_after_rewriter : 0.000009s : 0.00% optimize.mutable_eliminate : 0.000837s : 0.26% optimize.opt_b.b_1 : 0.000283s : 0.09% optimize.opt_b.b_2 : 0.000015s : 0.00% optimize.opt_b.updatestate_depend_eliminate : 0.000010s : 0.00% optimize.opt_b.updatestate_assign_eliminate : 0.000005s : 0.00% optimize.opt_b.updatestate_loads_eliminate : 0.000006s : 0.00% optimize.opt_b.renormalize : 0.000001s : 0.00% optimize.opt_b.cse : 0.000053s : 0.02% optimize.optimize_parallel_all_gather_comm : 0.000028s : 0.01% optimize.overlap_param_gather : 0.000002s : 0.00% optimize.cconv : 0.000038s : 0.01% optimize.loop_unroll : 0.000558s : 0.18% optimize.opt_after_cconv.c_1 : 0.000052s : 0.02% optimize.opt_after_cconv.parameter_eliminate : 0.000005s : 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 : 0.000006s : 0.00% optimize.opt_after_cconv.cse : 0.000050s : 0.02% optimize.opt_after_cconv.renormalize : 0.000001s : 0.00% optimize.remove_dup_value : 0.000081s : 0.03% optimize.tuple_transform.d_1 : 0.000107s : 0.03% optimize.tuple_transform.none_parameter_eliminate : 0.000002s : 0.00% optimize.tuple_transform.renormalize : 0.000000s : 0.00% optimize.tuple_transform.switch_simplify : 0.000012s : 0.00% optimize.partial_unused_args_eliminate : 0.000002s : 0.00% optimize.add_recomputation : 0.000088s : 0.03% optimize.cse_after_recomputation.cse : 0.000028s : 0.01% optimize.environ_conv : 0.000069s : 0.02% optimize.swap_dp_allreduce_reducescatter : 0.000010s : 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 : 0.000003s : 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 : 0.000003s : 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 : 0.000141s : 0.04% optimize.overlap_opt_shard_grad_in_pipeline : 0.000002s : 0.00% optimize.control_data_broadcast_order : 0.000022s : 0.01% optimize.grouped_pairwise_exchange_alltoall : 0.000002s : 0.00% optimize.offloading_packed_experts : 0.000007s : 0.00% optimize.overlap_recompute_and_grad_model_parallel : 0.000007s : 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 : 0.000007s : 0.00% optimize.overlap_grad_flash_sp : 0.000240s : 0.08% optimize.begin_end_overlap_inline : 0.000001s : 0.00% optimize.split_matmul_comm_elemetwise : 0.000003s : 0.00% optimize.split_layernorm_comm : 0.000002s : 0.00% optimize.handle_group_info : 0.000001s : 0.00% optimize.symbol_engine_optimizer.build : 0.000395s : 0.12% optimize.symbol_engine_optimizer.elim_shapecalc : 0.000018s : 0.01% optimize.symbol_engine_optimizer.elim_not_effective : 0.000128s : 0.04% optimize.symbol_engine_optimizer.opt_reshape : 0.000013s : 0.00% optimize.symbol_engine_optimizer.fold_const_symbol : 0.000024s : 0.01% optimize.symbol_engine_optimizer.renormalize : 0.000000s : 0.00% detach_backward : 0.000003s : 0.00% pipeline_parallel_scheduler : 0.000002s : 0.00% auto_monad_reorder : 0.000034s : 0.01% get_jit_bprop_graph : 0.000002s : 0.00% rewriter_after_jit_bprop_graph : 0.000004s : 0.00% opt_after_jit_grad : 0.000552s : 0.17% validate : 0.000280s : 0.09% Time group info: ------[substitution.] 0.001611 103 6.76% : 0.000109s : 5: substitution.elim_not_effective 0.71% : 0.000011s : 6: substitution.float_tuple_getitem_switch 0.50% : 0.000008s : 5: substitution.fold_const_symbol 0.61% : 0.000010s : 8: substitution.graph_param_transform 42.05% : 0.000678s : 3: substitution.inline 0.42% : 0.000007s : 10: substitution.j_node_and_user_rematch 1.02% : 0.000016s : 2: substitution.less_batch_normalization 38.95% : 0.000628s : 4: substitution.minmaximum_grad 0.58% : 0.000009s : 10: substitution.remove_not_recompute_node 0.43% : 0.000007s : 6: substitution.replace_old_param 1.96% : 0.000031s : 8: substitution.tuple_list_convert_item_index_to_positive 0.79% : 0.000013s : 8: substitution.tuple_list_get_item_const_eliminator 1.15% : 0.000019s : 8: substitution.tuple_list_get_item_depend_reorder 2.87% : 0.000046s : 12: substitution.tuple_list_get_item_eliminator 1.19% : 0.000019s : 8: substitution.tuple_list_get_set_item_eliminator ------[type_inference.] 0.299434 2 99.05% : 0.296583s : 1: type_inference.infer 0.95% : 0.002851s : 1: type_inference.specialize ------[replace.] 0.000041 3 100.00% : 0.000041s : 3: replace.inline ------[match.] 0.000675 3 100.00% : 0.000675s : 3: match.inline ------[predicate.] 0.000289 1949 0.81% : 0.000002s : 18: predicate.accumulaten_eliminater 0.78% : 0.000002s : 8: predicate.ad_related_special_op_eliminate 0.75% : 0.000002s : 16: predicate.addn_check_dump 1.05% : 0.000003s : 18: predicate.addn_zero_filter 0.72% : 0.000002s : 18: predicate.adjust_all_reduce_mul_add 2.21% : 0.000006s : 34: predicate.arithmetic_simplify 0.79% : 0.000002s : 18: predicate.cast_eliminate 0.74% : 0.000002s : 16: predicate.check_bprop_eliminate 0.70% : 0.000002s : 16: predicate.compare_switch_simplify 0.25% : 0.000001s : 8: predicate.const_output_eliminate 0.70% : 0.000002s : 16: predicate.depend_value_elim 0.80% : 0.000002s : 18: predicate.dict_get_item_const_eliminator 0.84% : 0.000002s : 18: predicate.dict_get_item_eliminator 0.93% : 0.000003s : 18: predicate.dict_set_item_eliminator 1.05% : 0.000003s : 16: predicate.dumpgradient_eliminate 0.25% : 0.000001s : 8: predicate.elim_not_effective 0.44% : 0.000001s : 8: predicate.elim_shapecalc_of_broadcastargs 1.15% : 0.000003s : 26: predicate.environ_add_const_eliminate 1.08% : 0.000003s : 26: predicate.environ_get_add_eliminate 1.10% : 0.000003s : 26: predicate.environ_get_depend_swap 1.85% : 0.000005s : 42: predicate.environ_get_eliminate 1.10% : 0.000003s : 26: predicate.environ_get_set_eliminate 0.89% : 0.000003s : 21: predicate.exchange_switch_depend_value 1.65% : 0.000005s : 21: predicate.float_depend_g_call 0.73% : 0.000002s : 16: predicate.float_environ_get_switch 1.30% : 0.000004s : 24: predicate.float_tuple_getitem_switch 0.26% : 0.000001s : 8: predicate.fold_const_symbol 0.80% : 0.000002s : 16: predicate.get_grad_eliminate 0.25% : 0.000001s : 8: predicate.graph_param_transform 0.84% : 0.000002s : 16: predicate.incorporate_call 0.66% : 0.000002s : 16: predicate.incorporate_call_switch 5.81% : 0.000017s : 87: predicate.inline 0.91% : 0.000003s : 16: predicate.inline_without_move 0.40% : 0.000001s : 16: predicate.j_node_and_user_rematch 1.09% : 0.000003s : 16: predicate.less_batch_normalization 1.72% : 0.000005s : 34: predicate.list_to_tuple_eliminator_ 2.25% : 0.000006s : 52: predicate.load_eliminater 1.15% : 0.000003s : 8: predicate.loop_unroll_after_grad 1.49% : 0.000004s : 30: predicate.loop_unroll_before_grad 1.78% : 0.000005s : 34: predicate.make_slice_get_slice_eliminator 0.79% : 0.000002s : 16: predicate.merge_addn 0.71% : 0.000002s : 16: predicate.micro_step_allgather_replace 0.78% : 0.000002s : 16: predicate.mini_step_allgather_replace 0.81% : 0.000002s : 18: predicate.minmaximum_grad 1.37% : 0.000004s : 8: predicate.mutable_eliminate 0.44% : 0.000001s : 8: predicate.opt_reshape 0.64% : 0.000002s : 8: predicate.parallel_virtual_node 1.24% : 0.000004s : 21: predicate.partial_defer_inline 1.23% : 0.000004s : 26: predicate.partial_eliminate 0.80% : 0.000002s : 18: predicate.print_const_string_wrapper 0.81% : 0.000002s : 16: predicate.reduce_all_const_elim 1.04% : 0.000003s : 18: predicate.reduce_eliminate 2.28% : 0.000007s : 52: predicate.redundant_stop_gradient_eliminater 0.49% : 0.000001s : 16: predicate.remove_not_recompute_node 1.31% : 0.000004s : 34: predicate.replace_applicator 0.89% : 0.000003s : 16: predicate.replace_old_param 0.28% : 0.000001s : 8: predicate.reset_defer_inline 0.79% : 0.000002s : 18: predicate.reshape_eliminate 0.77% : 0.000002s : 16: predicate.row_tensor_add_zeros_like 0.47% : 0.000001s : 8: predicate.row_tensor_eliminate 1.01% : 0.000003s : 16: predicate.same_eliminate 0.56% : 0.000002s : 16: predicate.set_cell_output_no_recompute 0.95% : 0.000003s : 16: predicate.shard_identity_eliminate 0.91% : 0.000003s : 16: predicate.special_op_eliminate 1.05% : 0.000003s : 16: predicate.specialize_transform 1.07% : 0.000003s : 16: predicate.split_environ_get_set_with_tuple_value 0.98% : 0.000003s : 16: predicate.stack_unstack_eliminate 0.41% : 0.000001s : 8: predicate.switch_call_monad_eliminater 0.97% : 0.000003s : 21: predicate.switch_defer_inline 1.70% : 0.000005s : 37: predicate.switch_layer_defer_inline 4.13% : 0.000012s : 75: predicate.switch_simplify 0.79% : 0.000002s : 18: predicate.tile_eliminate 0.81% : 0.000002s : 18: predicate.transpose_eliminate 1.79% : 0.000005s : 34: predicate.tuple_list_convert_item_index_to_positive 1.90% : 0.000005s : 34: predicate.tuple_list_get_item_const_eliminator 1.57% : 0.000005s : 34: predicate.tuple_list_get_item_depend_reorder 3.54% : 0.000010s : 50: predicate.tuple_list_get_item_eliminator 1.62% : 0.000005s : 34: predicate.tuple_list_get_set_item_eliminator 2.36% : 0.000007s : 50: predicate.tuple_list_set_item_eliminator 1.71% : 0.000005s : 34: predicate.tuple_to_list_eliminator_ 2.16% : 0.000006s : 52: predicate.updatestate_pure_node_eliminater 3.10% : 0.000009s : 68: predicate.updatestate_useless_node_eliminater 0.42% : 0.000001s : 8: predicate.value_based_eliminate 0.80% : 0.000002s : 16: predicate.virtual_dataset_eliminate 0.83% : 0.000002s : 16: predicate.virtual_output_eliminate 0.37% : 0.000001s : 8: predicate.virtual_view_grad_eliminate 0.48% : 0.000001s : 8: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.001805 10 29.79% : 0.000538s : 5: func_graph_cloner_run.FuncGraphClonerGraph 70.21% : 0.001267s : 5: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.883649 192 0.00% : 0.000004s : 1: ForceFp32Comm 30.81% : 0.272218s : 1: add_attr 30.79% : 0.272058s : 1: add_attr_with_inline 0.00% : 0.000004s : 1: add_comm_op_reuse_tag 0.01% : 0.000092s : 1: add_recomputation 0.00% : 0.000005s : 1: assign_add_opt 0.09% : 0.000754s : 1: auto_monad 0.00% : 0.000038s : 1: auto_monad_reorder 0.00% : 0.000004s : 1: begin_end_overlap_inline 0.00% : 0.000006s : 1: bias_add_comm_swap 0.09% : 0.000828s : 1: bootstrap 0.02% : 0.000138s : 1: cconv 0.00% : 0.000004s : 1: comm_op_add_attrs 0.00% : 0.000025s : 1: control_data_broadcast_order 0.01% : 0.000046s : 1: convert_after_rewriter 0.00% : 0.000043s : 1: cse_after_recomputation 0.00% : 0.000005s : 1: dataset_repeat_opt 0.00% : 0.000006s : 1: detach_backward 0.01% : 0.000073s : 1: environ_conv 0.00% : 0.000028s : 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% : 0.000005s : 1: grouped_pairwise_exchange_alltoall 0.00% : 0.000004s : 1: handle_group_info 0.00% : 0.000006s : 1: inline 0.00% : 0.000007s : 1: insert-virtual-dataset 0.00% : 0.000004s : 1: interleave_parallel_branches 0.00% : 0.000004s : 1: interleave_split_concat_branches 0.00% : 0.000006s : 1: label_fine_grained_interleaved_index 0.00% : 0.000008s : 1: label_micro_interleaved_index 0.06% : 0.000569s : 1: loop_unroll 0.00% : 0.000005s : 1: merge_cast_opt 0.00% : 0.000006s : 1: micro_interleaved_order_control 0.10% : 0.000850s : 1: mutable_eliminate 0.00% : 0.000010s : 1: offloading_packed_experts 0.00% : 0.000024s : 1: opt.transform.loop_unroll_optimizer 0.00% : 0.000025s : 1: opt.transform.mutable_eliminate 0.34% : 0.002987s : 78: opt.transform.opt_a 0.01% : 0.000051s : 1: opt.transform.opt_after_cconv 0.00% : 0.000040s : 1: opt.transform.opt_after_jit_grad 0.03% : 0.000267s : 28: opt.transform.opt_b 0.01% : 0.000117s : 2: opt.transform.opt_trans_graph 0.02% : 0.000178s : 4: opt.transform.symbol_engine_opt 0.86% : 0.007566s : 1: opt_a 0.02% : 0.000179s : 1: opt_after_cconv 0.06% : 0.000564s : 1: opt_after_jit_grad 0.05% : 0.000425s : 1: opt_b 1.81% : 0.015950s : 1: optimize 0.00% : 0.000032s : 1: optimize_parallel_all_gather_comm 0.00% : 0.000013s : 1: order_py_execute_after_rewriter 0.03% : 0.000243s : 1: overlap_grad_flash_sp 0.00% : 0.000004s : 1: overlap_grad_matmul_and_grad_allreduce 0.00% : 0.000010s : 1: overlap_grad_ring_attention 0.00% : 0.000006s : 1: overlap_opt_shard_grad_in_pipeline 0.02% : 0.000144s : 1: overlap_opt_shard_in_pipeline 0.00% : 0.000005s : 1: overlap_param_gather 0.00% : 0.000004s : 1: overlap_recompute_allgather_and_fa_grad 0.00% : 0.000010s : 1: overlap_recompute_and_grad_model_parallel 0.00% : 0.000006s : 1: overlap_recompute_comm 0.00% : 0.000009s : 1: parallel-infer-symbol 0.00% : 0.000004s : 1: parallel-infer-symbol-second 0.00% : 0.000006s : 1: partial_unused_args_eliminate 0.00% : 0.000005s : 1: pipeline_parallel_scheduler 0.00% : 0.000005s : 1: pipeline_split 0.03% : 0.000304s : 1: pre_auto_parallel 0.02% : 0.000149s : 1: py_interpret_to_execute 0.00% : 0.000023s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000004s : 1: remove_cast_before_assign_add 0.01% : 0.000086s : 1: remove_dup_value 0.11% : 0.000957s : 1: renormalize.infer 0.11% : 0.000963s : 1: renormalize.specialize 0.00% : 0.000006s : 1: reorder_send_recv_between_fp_bp 0.00% : 0.000009s : 1: rewriter_after_jit_bprop_graph 0.40% : 0.003566s : 1: rewriter_after_opt_a 0.03% : 0.000248s : 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% : 0.000005s : 1: split_matmul_comm_elemetwise 0.00% : 0.000013s : 1: swap_dp_allreduce_reducescatter 0.07% : 0.000623s : 1: symbol_engine_optimizer 0.02% : 0.000155s : 1: tuple_transform 33.92% : 0.299754s : 1: type_inference [SUCCESS] err_cnt = 0 / 0 . [hook] pytest_runtest_teardown:test_dequant_swiglu_quant_dynamic_shape[0] tests/st/ops/ascend/test_aclnn_ops/test_dequant_swiglu_quant.py::test_dequant_swiglu_quant_dynamic_shape[0],max_mem:20.0M [WARNING] ME(167844:281473624141616,MainProcess):2026-01-29-17:41:38.553.641 [mindspore/context.py:1334] For 'context.set_context', the parameter 'device_target' will be deprecated and removed in a future version. Please use the api mindspore.set_device() instead. [SUCCESS] err_cnt = 0 / 0 . [hook] pytest_runtest_teardown:test_dequant_swiglu_quant_dynamic_shape[1] tests/st/ops/ascend/test_aclnn_ops/test_dequant_swiglu_quant.py::test_dequant_swiglu_quant_dynamic_shape[1],max_mem:20.0M =============================== 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") test_dequant_swiglu_quant.py::test_dequant_swiglu_quant_dynamic_shape[0] test_dequant_swiglu_quant.py::test_dequant_swiglu_quant_dynamic_shape[1] /home/jenkins/mindspore/testcases/testcases/tests/st/ops/ascend/test_aclnn_ops/test_dequant_swiglu_quant.py:137: RuntimeWarning: invalid value encountered in divide err_cnt = np.sum(np.abs(out_flatten - expect_flatten) / -- Docs: https://docs.pytest.org/en/stable/warnings.html ================== 2 passed, 27 warnings in 291.11s (0:04:51) ==================