==================================================Ascend ============================= test session starts ============================== platform linux -- Python 3.9.19, pytest-6.2.5, py-1.11.0, pluggy-1.5.0 rootdir: /home/jenkins/mindspore/testcases/testcases/tests/st/mint, configfile: ../../../../../../sault/virtual_test/virtualenv_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 2 items test_chunk.py TotalTime = 0.0698686, [21] [bootstrap]: 0.00068878 [type_inference]: 0.0538659 [event_method]: 2.392e-05 [auto_monad]: 0.00019026 [graph_reusing]: 6.89001e-06 [inline]: 3.2e-06 [add_attr]: 0.00678269, [1] [add_attr_with_inline]: 0.00676591, [1] [Cycle 1]: 0.0001106, [2] [tag_attr]: 3.061e-05 [meta_addattr_fg_expand]: 6.54999e-06 [parallel-infer-symbol]: 3.98999e-06 [pre_auto_parallel]: 6.195e-05 [insert-virtual-dataset]: 3.03e-06 [parallel-infer-symbol-second]: 9.70002e-07 [dataset_repeat_opt]: 2.02999e-06 [pipeline_split]: 2.14e-06 [optimize]: 0.00726698, [53] [py_interpret_to_execute]: 8.63001e-06 [rewriter_before_opt_a]: 0.00027594 [opt_a]: 0.00415197, [2] [Cycle 1]: 0.00324336, [45] [expand_dump_flag]: 3.43999e-06 [switch_simplify]: 4.833e-05 [loop_unroll]: 3.376e-05 [a_1]: 0.00072336 [with_stream_mark]: 2.671e-05 [recompute_prepare]: 1.379e-05 [updatestate_depend_eliminate]: 6.62002e-06 [updatestate_assign_eliminate]: 7.15e-06 [updatestate_loads_eliminate]: 5.49998e-06 [parameter_eliminate]: 2.55002e-06 [a_2]: 0.00012865 [accelerated_algorithm]: 1.084e-05 [shard]: 2.33002e-06 [meta_shard_fg_expand]: 2.54001e-06 [shard_inline]: 8.71002e-06 [merge_send_recv]: 2.073e-05 [auto_parallel]: 1.077e-05 [parallel]: 4.968e-05 [flash_sp]: 2.012e-05 [merge_comm]: 7.21999e-06 [allreduce_fusion]: 5.14998e-06 [matmul_add_comm_reduction]: 1.281e-05 [allreduce_slice_to_reducescatter]: 1.04003e-06 [virtual_shard_identity]: 1.41e-05 [virtual_dataset]: 9.18002e-06 [get_grad_eliminate_]: 8.65001e-06 [virtual_output]: 8.38001e-06 [merge_forward]: 6.33998e-06 [cell_reuse_recompute_pass]: 1.87001e-06 [offload_activation]: 1.309e-05 [cell_reuse_handle_not_recompute_node_pass]: 2.096e-05 [merge_recompute_call_nodes]: 1.57001e-06 [before_grad]: 1.541e-05 [set_forward_comm_id_for_comm_node_pass]: 5.61e-06 [meta_fg_expand]: 4.45999e-06 [flash_sp_send_recv_attached]: 3.13998e-06 [receive_attached]: 1.116e-05 [after_resolve]: 1.532e-05 [a_after_grad]: 1.299e-05 [renormalize]: 0.00146082 [add_forward_monad_depend]: 9.24e-06 [auto_monad_grad]: 2.53998e-06 [auto_monad_eliminator]: 2.846e-05 [cse]: 5.507e-05 [a_3]: 7.852e-05 [Cycle 2]: 0.0008947, [45] [expand_dump_flag]: 2.96999e-06 [switch_simplify]: 1.136e-05 [loop_unroll]: 8.07e-06 [a_1]: 0.00017532 [with_stream_mark]: 2.063e-05 [recompute_prepare]: 1.059e-05 [updatestate_depend_eliminate]: 6.58998e-06 [updatestate_assign_eliminate]: 4.85999e-06 [updatestate_loads_eliminate]: 4.88001e-06 [parameter_eliminate]: 1.37e-06 [a_2]: 0.00011836 [accelerated_algorithm]: 8.94998e-06 [shard]: 2.29001e-06 [meta_shard_fg_expand]: 2.14e-06 [shard_inline]: 9.44998e-06 [merge_send_recv]: 1.143e-05 [auto_parallel]: 1.029e-05 [parallel]: 1.036e-05 [flash_sp]: 4.08001e-06 [merge_comm]: 5.22999e-06 [allreduce_fusion]: 5.27999e-06 [matmul_add_comm_reduction]: 1.122e-05 [allreduce_slice_to_reducescatter]: 8.2e-07 [virtual_shard_identity]: 1.099e-05 [virtual_dataset]: 1.551e-05 [get_grad_eliminate_]: 8.64e-06 [virtual_output]: 8.10999e-06 [merge_forward]: 5.83002e-06 [cell_reuse_recompute_pass]: 2.64001e-06 [offload_activation]: 1.211e-05 [cell_reuse_handle_not_recompute_node_pass]: 1.678e-05 [merge_recompute_call_nodes]: 1.62999e-06 [before_grad]: 1.302e-05 [set_forward_comm_id_for_comm_node_pass]: 5.69999e-06 [meta_fg_expand]: 3.71001e-06 [flash_sp_send_recv_attached]: 2.04e-06 [receive_attached]: 2.51e-06 [after_resolve]: 1.154e-05 [a_after_grad]: 1.104e-05 [renormalize]: 8.9989e-08 [add_forward_monad_depend]: 1.80001e-06 [auto_monad_grad]: 2.07001e-06 [auto_monad_eliminator]: 1.33e-05 [cse]: 2.544e-05 [a_3]: 5.028e-05 [py_interpret_to_execute_after_opt_a]: 8.04002e-06 [slice_cell_reuse_recomputed_activation]: 2.07999e-06 [rewriter_after_opt_a]: 3.534e-05 [convert_after_rewriter]: 1.59e-06 [order_py_execute_after_rewriter]: 1.36002e-06 [mutable_eliminate]: 0.0007821 [opt_b]: 0.0003135, [1] [Cycle 1]: 0.00030434, [7] [b_1]: 0.00019747 [b_2]: 1.001e-05 [updatestate_depend_eliminate]: 1.365e-05 [updatestate_assign_eliminate]: 5.04e-06 [updatestate_loads_eliminate]: 4.93001e-06 [renormalize]: 6.40022e-07 [cse]: 3.352e-05 [optimize_parallel_all_gather_comm]: 2.112e-05 [overlap_param_gather]: 4.99998e-06 [cconv]: 3.556e-05 [loop_unroll]: 0.00049658 [opt_after_cconv]: 0.00014918, [1] [Cycle 1]: 0.00014213, [7] [c_1]: 5.107e-05 [parameter_eliminate]: 5.36002e-06 [updatestate_depend_eliminate]: 9.17001e-06 [updatestate_assign_eliminate]: 4.62e-06 [updatestate_loads_eliminate]: 4.23999e-06 [cse]: 2.871e-05 [renormalize]: 5.60016e-07 [remove_dup_value]: 2.045e-05 [tuple_transform]: 8.701e-05, [1] [Cycle 1]: 8.186e-05, [4] [d_1]: 5.096e-05 [none_parameter_eliminate]: 1.56002e-06 [renormalize]: 2.30008e-07 [switch_simplify]: 9.66e-06 [partial_unused_args_eliminate]: 2.12001e-06 [add_recomputation]: 8.183e-05 [cse_after_recomputation]: 3.403e-05, [1] [Cycle 1]: 2.811e-05, [1] [cse]: 2.056e-05 [environ_conv]: 2.338e-05 [swap_dp_allreduce_reducescatter]: 7.08e-06 [bias_add_comm_swap]: 3.12002e-06 [label_micro_interleaved_index]: 4.93001e-06 [label_fine_grained_interleaved_index]: 2.70002e-06 [merge_cast_opt]: 1.40999e-06 [slice_recompute_activation]: 3.25e-06 [micro_interleaved_order_control]: 3.11999e-06 [assign_add_opt]: 1.66e-06 [ForceFp32Comm]: 1.25999e-06 [remove_cast_before_assign_add]: 1.50999e-06 [full_micro_interleaved_order_control]: 2.26e-06 [reorder_send_recv_between_fp_bp]: 3.09999e-06 [comm_op_add_attrs]: 1.17e-06 [add_comm_op_reuse_tag]: 1.05001e-06 [interleave_split_concat_branches]: 1.52999e-06 [interleave_parallel_branches]: 1.09998e-06 [overlap_opt_shard_in_pipeline]: 2.545e-05 [overlap_opt_shard_grad_in_pipeline]: 1.90001e-06 [control_data_broadcast_order]: 2.192e-05 [grouped_pairwise_exchange_alltoall]: 1.56998e-06 [offloading_packed_experts]: 5.99999e-06 [overlap_recompute_and_grad_model_parallel]: 6.66999e-06 [overlap_grad_matmul_and_grad_allreduce]: 1.27999e-06 [overlap_recompute_allgather_and_fa_grad]: 1.50999e-06 [overlap_recompute_comm]: 2.30002e-06 [overlap_grad_ring_attention]: 5.82001e-06 [overlap_grad_flash_sp]: 3.321e-05 [begin_end_overlap_inline]: 5.09986e-07 [split_matmul_comm_elemetwise]: 2.17001e-06 [split_layernorm_comm]: 2.24999e-06 [handle_group_info]: 1.06002e-06 [symbol_engine_optimizer]: 0.00027786, [1] [Cycle 1]: 0.00027042, [6] [build]: 0.00014108 [elim_shapecalc]: 2.045e-05 [elim_not_effective]: 2.998e-05 [opt_reshape]: 9.37999e-06 [fold_const_symbol]: 2.67e-05 [renormalize]: 2.60014e-07 [detach_backward]: 3.10002e-06 [pipeline_parallel_scheduler]: 1.76e-06 [auto_monad_reorder]: 3.632e-05 [get_jit_bprop_graph]: 2.61e-06 [rewriter_after_jit_bprop_graph]: 5.22999e-06 [opt_after_jit_grad]: 0.00064275 [validate]: 8.901e-05 Sums bootstrap : 0.000689s : 1.11% type_inference : 0.053866s : 87.00% event_method : 0.000024s : 0.04% auto_monad : 0.000190s : 0.31% graph_reusing : 0.000007s : 0.01% inline : 0.000003s : 0.01% add_attr.add_attr_with_inline.tag_attr : 0.000031s : 0.05% add_attr.add_attr_with_inline.meta_addattr_fg_expand : 0.000007s : 0.01% parallel-infer-symbol : 0.000004s : 0.01% pre_auto_parallel : 0.000062s : 0.10% 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 : 0.000009s : 0.01% optimize.rewriter_before_opt_a : 0.000276s : 0.45% optimize.opt_a.expand_dump_flag : 0.000006s : 0.01% optimize.opt_a.switch_simplify : 0.000060s : 0.10% optimize.opt_a.loop_unroll : 0.000042s : 0.07% optimize.opt_a.a_1 : 0.000899s : 1.45% optimize.opt_a.with_stream_mark : 0.000047s : 0.08% optimize.opt_a.recompute_prepare : 0.000024s : 0.04% optimize.opt_a.updatestate_depend_eliminate : 0.000013s : 0.02% optimize.opt_a.updatestate_assign_eliminate : 0.000012s : 0.02% optimize.opt_a.updatestate_loads_eliminate : 0.000010s : 0.02% optimize.opt_a.parameter_eliminate : 0.000004s : 0.01% optimize.opt_a.a_2 : 0.000247s : 0.40% optimize.opt_a.accelerated_algorithm : 0.000020s : 0.03% optimize.opt_a.shard : 0.000005s : 0.01% optimize.opt_a.meta_shard_fg_expand : 0.000005s : 0.01% optimize.opt_a.shard_inline : 0.000018s : 0.03% optimize.opt_a.merge_send_recv : 0.000032s : 0.05% optimize.opt_a.auto_parallel : 0.000021s : 0.03% optimize.opt_a.parallel : 0.000060s : 0.10% optimize.opt_a.flash_sp : 0.000024s : 0.04% optimize.opt_a.merge_comm : 0.000012s : 0.02% optimize.opt_a.allreduce_fusion : 0.000010s : 0.02% optimize.opt_a.matmul_add_comm_reduction : 0.000024s : 0.04% optimize.opt_a.allreduce_slice_to_reducescatter : 0.000002s : 0.00% optimize.opt_a.virtual_shard_identity : 0.000025s : 0.04% optimize.opt_a.virtual_dataset : 0.000025s : 0.04% optimize.opt_a.get_grad_eliminate_ : 0.000017s : 0.03% optimize.opt_a.virtual_output : 0.000016s : 0.03% optimize.opt_a.merge_forward : 0.000012s : 0.02% optimize.opt_a.cell_reuse_recompute_pass : 0.000005s : 0.01% optimize.opt_a.offload_activation : 0.000025s : 0.04% optimize.opt_a.cell_reuse_handle_not_recompute_node_pass : 0.000038s : 0.06% optimize.opt_a.merge_recompute_call_nodes : 0.000003s : 0.01% optimize.opt_a.before_grad : 0.000028s : 0.05% optimize.opt_a.set_forward_comm_id_for_comm_node_pass : 0.000011s : 0.02% optimize.opt_a.meta_fg_expand : 0.000008s : 0.01% optimize.opt_a.flash_sp_send_recv_attached : 0.000005s : 0.01% optimize.opt_a.receive_attached : 0.000014s : 0.02% optimize.opt_a.after_resolve : 0.000027s : 0.04% optimize.opt_a.a_after_grad : 0.000024s : 0.04% optimize.opt_a.renormalize : 0.001461s : 2.36% optimize.opt_a.add_forward_monad_depend : 0.000011s : 0.02% optimize.opt_a.auto_monad_grad : 0.000005s : 0.01% optimize.opt_a.auto_monad_eliminator : 0.000042s : 0.07% optimize.opt_a.cse : 0.000081s : 0.13% optimize.opt_a.a_3 : 0.000129s : 0.21% optimize.py_interpret_to_execute_after_opt_a : 0.000008s : 0.01% optimize.slice_cell_reuse_recomputed_activation : 0.000002s : 0.00% optimize.rewriter_after_opt_a : 0.000035s : 0.06% optimize.convert_after_rewriter : 0.000002s : 0.00% optimize.order_py_execute_after_rewriter : 0.000001s : 0.00% optimize.mutable_eliminate : 0.000782s : 1.26% optimize.opt_b.b_1 : 0.000197s : 0.32% optimize.opt_b.b_2 : 0.000010s : 0.02% optimize.opt_b.updatestate_depend_eliminate : 0.000014s : 0.02% optimize.opt_b.updatestate_assign_eliminate : 0.000005s : 0.01% optimize.opt_b.updatestate_loads_eliminate : 0.000005s : 0.01% optimize.opt_b.renormalize : 0.000001s : 0.00% optimize.opt_b.cse : 0.000034s : 0.05% optimize.optimize_parallel_all_gather_comm : 0.000021s : 0.03% optimize.overlap_param_gather : 0.000005s : 0.01% optimize.cconv : 0.000036s : 0.06% optimize.loop_unroll : 0.000497s : 0.80% optimize.opt_after_cconv.c_1 : 0.000051s : 0.08% optimize.opt_after_cconv.parameter_eliminate : 0.000005s : 0.01% optimize.opt_after_cconv.updatestate_depend_eliminate : 0.000009s : 0.01% optimize.opt_after_cconv.updatestate_assign_eliminate : 0.000005s : 0.01% optimize.opt_after_cconv.updatestate_loads_eliminate : 0.000004s : 0.01% optimize.opt_after_cconv.cse : 0.000029s : 0.05% optimize.opt_after_cconv.renormalize : 0.000001s : 0.00% optimize.remove_dup_value : 0.000020s : 0.03% optimize.tuple_transform.d_1 : 0.000051s : 0.08% 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 : 0.02% optimize.partial_unused_args_eliminate : 0.000002s : 0.00% optimize.add_recomputation : 0.000082s : 0.13% optimize.cse_after_recomputation.cse : 0.000021s : 0.03% optimize.environ_conv : 0.000023s : 0.04% optimize.swap_dp_allreduce_reducescatter : 0.000007s : 0.01% optimize.bias_add_comm_swap : 0.000003s : 0.01% optimize.label_micro_interleaved_index : 0.000005s : 0.01% optimize.label_fine_grained_interleaved_index : 0.000003s : 0.00% optimize.merge_cast_opt : 0.000001s : 0.00% optimize.slice_recompute_activation : 0.000003s : 0.01% optimize.micro_interleaved_order_control : 0.000003s : 0.01% optimize.assign_add_opt : 0.000002s : 0.00% optimize.ForceFp32Comm : 0.000001s : 0.00% optimize.remove_cast_before_assign_add : 0.000002s : 0.00% optimize.full_micro_interleaved_order_control : 0.000002s : 0.00% optimize.reorder_send_recv_between_fp_bp : 0.000003s : 0.01% optimize.comm_op_add_attrs : 0.000001s : 0.00% optimize.add_comm_op_reuse_tag : 0.000001s : 0.00% optimize.interleave_split_concat_branches : 0.000002s : 0.00% optimize.interleave_parallel_branches : 0.000001s : 0.00% optimize.overlap_opt_shard_in_pipeline : 0.000025s : 0.04% optimize.overlap_opt_shard_grad_in_pipeline : 0.000002s : 0.00% optimize.control_data_broadcast_order : 0.000022s : 0.04% optimize.grouped_pairwise_exchange_alltoall : 0.000002s : 0.00% optimize.offloading_packed_experts : 0.000006s : 0.01% optimize.overlap_recompute_and_grad_model_parallel : 0.000007s : 0.01% 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 : 0.000002s : 0.00% optimize.overlap_grad_ring_attention : 0.000006s : 0.01% optimize.overlap_grad_flash_sp : 0.000033s : 0.05% optimize.begin_end_overlap_inline : 0.000001s : 0.00% optimize.split_matmul_comm_elemetwise : 0.000002s : 0.00% optimize.split_layernorm_comm : 0.000002s : 0.00% optimize.handle_group_info : 0.000001s : 0.00% optimize.symbol_engine_optimizer.build : 0.000141s : 0.23% optimize.symbol_engine_optimizer.elim_shapecalc : 0.000020s : 0.03% optimize.symbol_engine_optimizer.elim_not_effective : 0.000030s : 0.05% optimize.symbol_engine_optimizer.opt_reshape : 0.000009s : 0.02% optimize.symbol_engine_optimizer.fold_const_symbol : 0.000027s : 0.04% optimize.symbol_engine_optimizer.renormalize : 0.000000s : 0.00% detach_backward : 0.000003s : 0.01% pipeline_parallel_scheduler : 0.000002s : 0.00% auto_monad_reorder : 0.000036s : 0.06% get_jit_bprop_graph : 0.000003s : 0.00% rewriter_after_jit_bprop_graph : 0.000005s : 0.01% opt_after_jit_grad : 0.000643s : 1.04% validate : 0.000089s : 0.14% Time group info: ------[substitution.] 0.000322 53 3.04% : 0.000010s : 2: substitution.depend_value_elim 3.64% : 0.000012s : 4: substitution.elim_not_effective 4.43% : 0.000014s : 4: substitution.fold_const_symbol 2.18% : 0.000007s : 5: substitution.graph_param_transform 66.22% : 0.000213s : 4: substitution.inline 1.67% : 0.000005s : 8: substitution.j_node_and_user_rematch 2.50% : 0.000008s : 8: substitution.remove_not_recompute_node 1.80% : 0.000006s : 2: substitution.replace_old_param 3.89% : 0.000013s : 2: substitution.tuple_list_get_item_eliminator 5.66% : 0.000018s : 6: substitution.updatestate_pure_node_eliminater 4.98% : 0.000016s : 8: substitution.updatestate_useless_node_eliminater ------[type_inference.] 0.053773 2 97.11% : 0.052217s : 1: type_inference.infer 2.89% : 0.001556s : 1: type_inference.specialize ------[replace.] 0.000076 6 69.36% : 0.000053s : 4: replace.inline 30.64% : 0.000023s : 2: replace.tuple_list_get_item_eliminator ------[match.] 0.000222 6 94.83% : 0.000210s : 4: match.inline 5.17% : 0.000011s : 2: match.tuple_list_get_item_eliminator ------[predicate.] 0.000260 1476 0.88% : 0.000002s : 15: predicate.accumulaten_eliminater 1.36% : 0.000004s : 5: predicate.ad_related_special_op_eliminate 0.65% : 0.000002s : 10: predicate.addn_check_dump 0.84% : 0.000002s : 15: predicate.addn_zero_filter 0.78% : 0.000002s : 15: predicate.adjust_all_reduce_mul_add 2.11% : 0.000005s : 25: predicate.arithmetic_simplify 1.03% : 0.000003s : 15: predicate.cast_eliminate 0.64% : 0.000002s : 10: predicate.check_bprop_eliminate 0.80% : 0.000002s : 10: predicate.compare_switch_simplify 0.16% : 0.000000s : 5: predicate.const_output_eliminate 0.68% : 0.000002s : 10: predicate.depend_value_elim 0.88% : 0.000002s : 15: predicate.dict_get_item_const_eliminator 0.95% : 0.000002s : 15: predicate.dict_get_item_eliminator 0.77% : 0.000002s : 15: predicate.dict_set_item_eliminator 1.56% : 0.000004s : 10: predicate.dumpgradient_eliminate 0.25% : 0.000001s : 5: predicate.elim_not_effective 0.43% : 0.000001s : 5: predicate.elim_shapecalc_of_broadcastargs 1.11% : 0.000003s : 20: predicate.environ_add_const_eliminate 1.05% : 0.000003s : 20: predicate.environ_get_add_eliminate 1.01% : 0.000003s : 20: predicate.environ_get_depend_swap 1.74% : 0.000005s : 30: predicate.environ_get_eliminate 1.04% : 0.000003s : 20: predicate.environ_get_set_eliminate 1.11% : 0.000003s : 21: predicate.exchange_switch_depend_value 1.94% : 0.000005s : 21: predicate.float_depend_g_call 0.59% : 0.000002s : 10: predicate.float_environ_get_switch 1.05% : 0.000003s : 15: predicate.float_tuple_getitem_switch 0.16% : 0.000000s : 5: predicate.fold_const_symbol 0.71% : 0.000002s : 10: predicate.get_grad_eliminate 0.28% : 0.000001s : 5: predicate.graph_param_transform 0.59% : 0.000002s : 10: predicate.incorporate_call 0.50% : 0.000001s : 10: predicate.incorporate_call_switch 5.65% : 0.000015s : 66: predicate.inline 0.80% : 0.000002s : 10: predicate.inline_without_move 0.29% : 0.000001s : 10: predicate.j_node_and_user_rematch 0.87% : 0.000002s : 10: predicate.less_batch_normalization 1.75% : 0.000005s : 27: predicate.list_to_tuple_eliminator_ 2.21% : 0.000006s : 42: predicate.load_eliminater 1.08% : 0.000003s : 5: predicate.loop_unroll_after_grad 2.11% : 0.000005s : 38: predicate.loop_unroll_before_grad 1.71% : 0.000004s : 25: predicate.make_slice_get_slice_eliminator 0.68% : 0.000002s : 10: predicate.merge_addn 0.60% : 0.000002s : 10: predicate.micro_step_allgather_replace 0.66% : 0.000002s : 10: predicate.mini_step_allgather_replace 0.82% : 0.000002s : 15: predicate.minmaximum_grad 1.45% : 0.000004s : 5: predicate.mutable_eliminate 0.43% : 0.000001s : 5: predicate.opt_reshape 0.42% : 0.000001s : 5: predicate.parallel_virtual_node 1.44% : 0.000004s : 21: predicate.partial_defer_inline 1.29% : 0.000003s : 22: predicate.partial_eliminate 0.88% : 0.000002s : 15: predicate.print_const_string_wrapper 0.74% : 0.000002s : 10: predicate.reduce_all_const_elim 1.18% : 0.000003s : 15: predicate.reduce_eliminate 2.52% : 0.000007s : 42: predicate.redundant_stop_gradient_eliminater 0.51% : 0.000001s : 10: predicate.remove_not_recompute_node 1.18% : 0.000003s : 27: predicate.replace_applicator 0.51% : 0.000001s : 10: predicate.replace_old_param 0.32% : 0.000001s : 5: predicate.reset_defer_inline 0.92% : 0.000002s : 15: predicate.reshape_eliminate 0.63% : 0.000002s : 10: predicate.row_tensor_add_zeros_like 0.41% : 0.000001s : 5: predicate.row_tensor_eliminate 0.85% : 0.000002s : 10: predicate.same_eliminate 0.45% : 0.000001s : 10: predicate.set_cell_output_no_recompute 1.05% : 0.000003s : 10: predicate.shard_identity_eliminate 0.90% : 0.000002s : 10: predicate.special_op_eliminate 0.79% : 0.000002s : 10: predicate.specialize_transform 1.03% : 0.000003s : 10: predicate.split_environ_get_set_with_tuple_value 0.86% : 0.000002s : 10: predicate.stack_unstack_eliminate 0.45% : 0.000001s : 5: predicate.switch_call_monad_eliminater 1.21% : 0.000003s : 21: predicate.switch_defer_inline 1.90% : 0.000005s : 31: predicate.switch_layer_defer_inline 4.58% : 0.000012s : 74: predicate.switch_simplify 0.90% : 0.000002s : 15: predicate.tile_eliminate 0.99% : 0.000003s : 15: predicate.transpose_eliminate 1.77% : 0.000005s : 25: predicate.tuple_list_convert_item_index_to_positive 1.49% : 0.000004s : 25: predicate.tuple_list_get_item_const_eliminator 1.54% : 0.000004s : 25: predicate.tuple_list_get_item_depend_reorder 2.97% : 0.000008s : 37: predicate.tuple_list_get_item_eliminator 1.31% : 0.000003s : 25: predicate.tuple_list_get_set_item_eliminator 2.17% : 0.000006s : 35: predicate.tuple_list_set_item_eliminator 1.59% : 0.000004s : 27: predicate.tuple_to_list_eliminator_ 2.28% : 0.000006s : 42: predicate.updatestate_pure_node_eliminater 4.34% : 0.000011s : 52: predicate.updatestate_useless_node_eliminater 0.44% : 0.000001s : 5: predicate.value_based_eliminate 0.73% : 0.000002s : 10: predicate.virtual_dataset_eliminate 0.74% : 0.000002s : 10: predicate.virtual_output_eliminate 0.28% : 0.000001s : 5: predicate.virtual_view_grad_eliminate 0.66% : 0.000002s : 5: predicate.zero_like_fill_zero ------[func_graph_cloner_run.] 0.000993 12 50.82% : 0.000505s : 6: func_graph_cloner_run.FuncGraphClonerGraph 49.18% : 0.000488s : 6: func_graph_cloner_run.FuncGraphSpecializer ------[meta_graph.] 0.000000 0 ------[manager.] 0.000000 0 ------[pynative] 0.000000 0 ------[others.] 0.087158 192 0.00% : 0.000004s : 1: ForceFp32Comm 7.79% : 0.006790s : 1: add_attr 7.77% : 0.006771s : 1: add_attr_with_inline 0.00% : 0.000004s : 1: add_comm_op_reuse_tag 0.10% : 0.000087s : 1: add_recomputation 0.01% : 0.000006s : 1: assign_add_opt 0.23% : 0.000199s : 1: auto_monad 0.05% : 0.000041s : 1: auto_monad_reorder 0.00% : 0.000004s : 1: begin_end_overlap_inline 0.01% : 0.000006s : 1: bias_add_comm_swap 0.83% : 0.000722s : 1: bootstrap 0.05% : 0.000040s : 1: cconv 0.00% : 0.000004s : 1: comm_op_add_attrs 0.03% : 0.000026s : 1: control_data_broadcast_order 0.01% : 0.000005s : 1: convert_after_rewriter 0.04% : 0.000037s : 1: cse_after_recomputation 0.01% : 0.000005s : 1: dataset_repeat_opt 0.01% : 0.000006s : 1: detach_backward 0.03% : 0.000027s : 1: environ_conv 0.04% : 0.000031s : 1: event_method 0.01% : 0.000005s : 1: full_micro_interleaved_order_control 0.01% : 0.000006s : 1: get_jit_bprop_graph 0.01% : 0.000011s : 1: graph_reusing 0.00% : 0.000004s : 1: grouped_pairwise_exchange_alltoall 0.00% : 0.000004s : 1: handle_group_info 0.01% : 0.000007s : 1: inline 0.01% : 0.000007s : 1: insert-virtual-dataset 0.01% : 0.000005s : 1: interleave_parallel_branches 0.01% : 0.000005s : 1: interleave_split_concat_branches 0.01% : 0.000006s : 1: label_fine_grained_interleaved_index 0.01% : 0.000008s : 1: label_micro_interleaved_index 0.58% : 0.000506s : 1: loop_unroll 0.05% : 0.000046s : 1: merge_cast_opt 0.01% : 0.000006s : 1: micro_interleaved_order_control 0.91% : 0.000794s : 1: mutable_eliminate 0.01% : 0.000009s : 1: offloading_packed_experts 0.02% : 0.000020s : 1: opt.transform.loop_unroll_optimizer 0.03% : 0.000026s : 1: opt.transform.mutable_eliminate 1.76% : 0.001538s : 78: opt.transform.opt_a 0.06% : 0.000050s : 1: opt.transform.opt_after_cconv 0.05% : 0.000040s : 1: opt.transform.opt_after_jit_grad 0.20% : 0.000176s : 28: opt.transform.opt_b 0.07% : 0.000058s : 2: opt.transform.opt_trans_graph 0.09% : 0.000081s : 4: opt.transform.symbol_engine_opt 4.77% : 0.004156s : 1: opt_a 0.18% : 0.000153s : 1: opt_after_cconv 0.75% : 0.000655s : 1: opt_after_jit_grad 0.36% : 0.000318s : 1: opt_b 8.34% : 0.007273s : 1: optimize 0.03% : 0.000025s : 1: optimize_parallel_all_gather_comm 0.01% : 0.000004s : 1: order_py_execute_after_rewriter 0.04% : 0.000037s : 1: overlap_grad_flash_sp 0.00% : 0.000004s : 1: overlap_grad_matmul_and_grad_allreduce 0.01% : 0.000009s : 1: overlap_grad_ring_attention 0.01% : 0.000005s : 1: overlap_opt_shard_grad_in_pipeline 0.03% : 0.000029s : 1: overlap_opt_shard_in_pipeline 0.01% : 0.000008s : 1: overlap_param_gather 0.01% : 0.000006s : 1: overlap_recompute_allgather_and_fa_grad 0.01% : 0.000010s : 1: overlap_recompute_and_grad_model_parallel 0.01% : 0.000005s : 1: overlap_recompute_comm 0.01% : 0.000008s : 1: parallel-infer-symbol 0.00% : 0.000004s : 1: parallel-infer-symbol-second 0.01% : 0.000005s : 1: partial_unused_args_eliminate 0.01% : 0.000005s : 1: pipeline_parallel_scheduler 0.01% : 0.000005s : 1: pipeline_split 0.08% : 0.000067s : 1: pre_auto_parallel 0.01% : 0.000012s : 1: py_interpret_to_execute 0.01% : 0.000011s : 1: py_interpret_to_execute_after_opt_a 0.00% : 0.000004s : 1: remove_cast_before_assign_add 0.03% : 0.000024s : 1: remove_dup_value 1.00% : 0.000867s : 1: renormalize.infer 0.66% : 0.000579s : 1: renormalize.specialize 0.01% : 0.000006s : 1: reorder_send_recv_between_fp_bp 0.01% : 0.000009s : 1: rewriter_after_jit_bprop_graph 0.05% : 0.000040s : 1: rewriter_after_opt_a 0.33% : 0.000285s : 1: rewriter_before_opt_a 0.01% : 0.000005s : 1: slice_cell_reuse_recomputed_activation 0.01% : 0.000009s : 1: slice_recompute_activation 0.01% : 0.000005s : 1: split_layernorm_comm 0.01% : 0.000005s : 1: split_matmul_comm_elemetwise 0.01% : 0.000010s : 1: swap_dp_allreduce_reducescatter 0.32% : 0.000281s : 1: symbol_engine_optimizer 0.10% : 0.000090s : 1: tuple_transform 61.83% : 0.053890s : 1: type_inference [ERROR] ANALYZER(171166,ffff8bacbf30,python3.9):2026-01-29-17:38:01.520.111 [mindspore/ccsrc/frontend/jit/ps/static_analysis/evaluator.cc:724] Run] Primitive: infer failed, failed info: For ChunkView, the dimension corresponds to the specified 'dims' is dynamic, which is not supported now. ---------------------------------------------------- - C++ Call Stack: (For framework developers) ---------------------------------------------------- mindspore/ops/infer/ops_func_impl//chunk.cc:104 InferType ---------------------------------------------------- - The Traceback of Net Construct Code: ---------------------------------------------------- # 0 In file /home/jenkins/mindspore/testcases/testcases/tests/st/utils/test_utils.py:42, 15~43 return self.func(*inputs, **kwargs) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~ # 1 In file /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/function/array_func.py:1038, 11~62 return ops.auto_generate.chunk_view_op(input, chunks, dim) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ (See file '/home/jenkins/mindspore/testcases/testcases/tests/st/mint/rank_0/om/analyze_fail.ir' for more details. Get instructions about `analyze_fail.ir` at https://www.mindspore.cn/search?inputValue=analyze_fail.ir) . [hook] pytest_runtest_teardown:test_chunk_forward_dynamic_shape[0] tests/st/mint/test_chunk.py::test_chunk_forward_dynamic_shape[0],max_mem:4.0M . [hook] pytest_runtest_teardown:test_chunk_forward_dynamic_shape[1] tests/st/mint/test_chunk.py::test_chunk_forward_dynamic_shape[1],max_mem:4.0M =============================== warnings summary =============================== ../../../../../../../../usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/classifier/transdata/transdata_classifier.py:222 /usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/classifier/transdata/transdata_classifier.py:222: DeprecationWarning: invalid escape sequence \B """ ../../../../../../../../usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:143 /usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:143: DeprecationWarning: invalid escape sequence \c """ ../../../../../../../../usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:170 /usr/local/Ascend/cann-8.5.0/python/site-packages/tbe/dsl/unify_schedule/vector/transdata/common/graph/transdata_graph_info.py:170: DeprecationWarning: invalid escape sequence \c """ ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero. setattr(self, word, getattr(machar, word).flat[0]) ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero. return self._float_to_str(self.smallest_subnormal) ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for type is zero. setattr(self, word, getattr(machar, word).flat[0]) ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero. return self._float_to_str(self.smallest_subnormal) ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2.py:57 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2.py:57: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("batchnorm_fold2") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad.py:56 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad.py:56: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("batchnorm_fold2_grad") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad_reduce.py:48 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/batchnorm_fold2_grad_reduce.py:48: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("batchnorm_fold2_grad_reduce") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul.py:51 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul.py:51: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("correction_mul") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:51 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:51: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("correction_mul_grad") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:143 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py:143: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("correction_mul_grad_reduce") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perlayer") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad.py:92 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad.py:92: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perlayer_grad_d") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad_reduce.py:49 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perlayer_grad_reduce.py:49: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perlayer_grad_d_reduce") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perchannel") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad.py:91 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad.py:91: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perchannel_grad_d") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad_reduce.py:48 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad_reduce.py:48: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_learned_scale_quant_perchannel_grad_d_reduce") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel.py:52 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel.py:52: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_perchannel") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel_grad.py:81 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perchannel_grad.py:81: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_perchannel_grad") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer.py:54 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer.py:54: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_per_layer") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer_grad.py:81 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/fake_quant_perlayer_grad.py:81: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("fake_quant_per_layer_grad") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perchannel.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perchannel.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("minmax_update_perchannel") ../../../../../../anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perlayer.py:50 /home/jenkins/anaconda3/envs/ci39/lib/python3.9/site-packages/mindspore/ops/_op_impl/_custom_op/minmax_update_perlayer.py:50: DeprecationWarning: te_fusion.fusion_manager.fusion_manager.register is deprecated,please replace it with tbe.common.register.register_op_compute @fusion_manager.register("minmax_update_perlayer") -- Docs: https://docs.pytest.org/en/stable/warnings.html ================== 2 passed, 25 warnings in 65.75s (0:01:05) ===================