log_mma.txt

[14:23:22] /home/acer/tvm/src/relay/transforms/convert_layout.cc:99: Warning: Desired layout(s) not specified for op: nn.max_pool2d
[14:23:22] /home/acer/tvm/src/relay/transforms/convert_layout.cc:99: Warning: Desired layout(s) not specified for op: nn.global_avg_pool2d
[14:23:31] /home/acer/tvm/src/relay/transforms/to_mixed_precision.cc:491: Warning: Op "layout_transform" not registered FTVMMixedPrecisionConversionType appears 2 times in graph.
2023-07-28 14:23:49 [INFO] Logging directory: /tmp/tmp_vfiw0j9/logs
2023-07-28 14:23:49 [INFO] LocalBuilder: max_workers = 6
2023-07-28 14:23:49 [INFO] LocalRunner: max_workers = 1
2023-07-28 14:23:49 [INFO] [task_scheduler.cc:159] Initializing Task #0: "fused_nn_conv2d_add"
Traceback (most recent call last):
  File "test_meta_scheduler.py", line 72, in <module>
    database = ms.relay_integration.tune_relay(
  File "/home/acer/tvm/python/tvm/meta_schedule/relay_integration.py", line 352, in tune_relay
    return tune_tasks(
  File "/home/acer/tvm/python/tvm/meta_schedule/tune.py", line 118, in tune_tasks
    task_scheduler.tune(
  File "/home/acer/tvm/python/tvm/meta_schedule/task_scheduler/task_scheduler.py", line 132, in tune
    _ffi_api.TaskSchedulerTune(  # type: ignore # pylint: disable=no-member
  File "tvm/_ffi/_cython/./packed_func.pxi", line 331, in tvm._ffi._cy3.core.PackedFuncBase.__call__
  File "tvm/_ffi/_cython/./packed_func.pxi", line 276, in tvm._ffi._cy3.core.FuncCall
  File "tvm/_ffi/_cython/./base.pxi", line 181, in tvm._ffi._cy3.core.CHECK_CALL
tvm.error.InternalError: Traceback (most recent call last):
  8: TVMFuncCall
  7: _ZN3tvm7runtime13PackedFunc
  6: tvm::runtime::TypedPackedFunc<void (tvm::meta_schedule::TaskScheduler, tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>)>::AssignTypedLambda<tvm::runtime::Registry::set_body_method<tvm::meta_schedule::TaskScheduler, tvm::meta_schedule::TaskSchedulerNode, void, tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>, void>(void (tvm::meta_schedule::TaskSchedulerNode::*)(tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>))::{lambda(tvm::meta_schedule::TaskScheduler, tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>)#1}>(tvm::runtime::Registry::set_body_method<tvm::meta_schedule::TaskScheduler, tvm::meta_schedule::TaskSchedulerNode, void, tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>, void>(void (tvm::meta_schedule::TaskSchedulerNode::*)(tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>))::{lambda(tvm::meta_schedule::TaskScheduler, tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>)#1}, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}::operator()(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*) const [clone .isra.0]
  5: tvm::meta_schedule::GradientBasedNode::Tune(tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>)
  4: tvm::meta_schedule::TaskSchedulerNode::Tune(tvm::runtime::Array<tvm::meta_schedule::TuneContext, void>, tvm::runtime::Array<tvm::FloatImm, void>, int, int, int, tvm::meta_schedule::Builder, tvm::meta_schedule::Runner, tvm::runtime::Array<tvm::meta_schedule::MeasureCallback, void>, tvm::runtime::Optional<tvm::meta_schedule::Database>, tvm::runtime::Optional<tvm::meta_schedule::CostModel>)
  3: tvm::meta_schedule::PostOrderApplyNode::GenerateDesignSpace(tvm::IRModule const&)
  2: tvm::meta_schedule::MultiLevelTilingTensorCoreNode::Apply(tvm::tir::Schedule const&, tvm::tir::BlockRV const&)
  1: tvm::meta_schedule::MultiLevelTilingTensorCoreNode::ApplySubRules(std::vector<tvm::meta_schedule::State, std::allocator<tvm::meta_schedule::State> >)
  0: tvm::meta_schedule::MultiLevelTilingTensorCoreNode::MMATileLoopNest(tvm::meta_schedule::TensorCoreState) const
  File "/home/acer/tvm/src/meta_schedule/schedule_rule/multi_level_tiling_tensor_core.cc", line 347
InternalError: Check failed: (loops.size() == 3 || !state->is_mma) is false: The MMA tensor core only supports SSR loops now