Cookiee235 opened a new issue, #18380:
URL: https://github.com/apache/tvm/issues/18380
The provided TIR module can be successfully compiled using `tvm.build` with
target='llvm'. However, when using `ms.tir_integration.tune_tir` for
Meta-Schedule tuning, the script crashes unexpectedly with a
`tvm.tir.schedule.ScheduleError`.
It seems a bug in the Meta-Schedule component.
### Actual behavior
```
Traceback (most recent call last):
File "/share_container/LLMFuzz/TirFuzz/bug_tp/topi.max_0_M2.py", line 29,
in <module>
database = ms.tir_integration.tune_tir(mod=mod, target='llvm
--num-cores=16', work_dir='./tune_tmp', max_trials_global=1,
num_trials_per_iter=1)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/software/tvm-latest/python/tvm/meta_schedule/tir_integration.py",
line 146, in tune_tir
return tune_tasks(
^^^^^^^^^^^
File "/software/tvm-latest/python/tvm/meta_schedule/tune.py", line 122, in
tune_tasks
task_scheduler.tune(
File
"/software/tvm-latest/python/tvm/meta_schedule/task_scheduler/task_scheduler.py",
line 132, in tune
_ffi_api.TaskSchedulerTune( # type: ignore # pylint: disable=no-member
File "python/tvm_ffi/cython/function.pxi", line 758, in
core.Function.__call__
File "<unknown>", line 0, in
tvm::meta_schedule::GradientBasedNode::Tune(tvm::ffi::Array<tvm::meta_schedule::TuneContext,
void>, tvm::ffi::Array<tvm::FloatImm, void>, int, int, int,
tvm::meta_schedule::Builder, tvm::meta_schedule::Runner,
tvm::ffi::Array<tvm::meta_schedule::MeasureCallback, void>,
tvm::ffi::Optional<tvm::meta_schedule::Database, void>,
tvm::ffi::Optional<tvm::meta_schedule::CostModel, void>)
File "<unknown>", line 0, in
tvm::meta_schedule::TaskSchedulerNode::Tune(tvm::ffi::Array<tvm::meta_schedule::TuneContext,
void>, tvm::ffi::Array<tvm::FloatImm, void>, int, int, int,
tvm::meta_schedule::Builder, tvm::meta_schedule::Runner,
tvm::ffi::Array<tvm::meta_schedule::MeasureCallback, void>,
tvm::ffi::Optional<tvm::meta_schedule::Database, void>,
tvm::ffi::Optional<tvm::meta_schedule::CostModel, void>)
File "<unknown>", line 0, in
tvm::meta_schedule::PostOrderApplyNode::GenerateDesignSpace(tvm::IRModule
const&)
File "<unknown>", line 0, in
tvm::meta_schedule::AutoInlineNode::Apply(tvm::tir::Schedule const&,
tvm::tir::BlockRV const&)
File "<unknown>", line 0, in
tvm::meta_schedule::AutoInlineNode::CheckInline(tvm::tir::Schedule const&,
tvm::tir::BlockRV const&)
File "<unknown>", line 0, in
tvm::tir::GetScopeRoot(tvm::tir::ScheduleState const&, tvm::tir::StmtSRef
const&, bool) [clone .cold]
File "<unknown>", line 0, in tvm::tir::ScheduleError::ScheduleError()
tvm.tir.schedule.schedule.ScheduleError
```
### Environment
tvm-0.22.dev0
### Steps to reproduce
```
import tvm
from tvm import meta_schedule as ms
tir_str = """# from tvm.script import ir as I
# from tvm.script import tir as T
@I.ir_module
class Module:
@T.prim_func
def main(data: T.Buffer((8, 8), "float32"), data_red: T.Buffer((),
"float32")):
T.func_attr({"target": T.target({"keys": ["cpu"], "kind": "llvm",
"mtriple": "x86_64-unknown-linux-gnu", "tag": ""}), "tir.noalias":
T.bool(True)})
with T.block("data_red"):
T.reads(data[0:8, 0:8])
T.writes(data_red[()])
with T.init():
data_red[()] = T.float32(-3400000.0)
for i, j in T.grid(8, 8):
with T.block("update"):
T.reads(data_red[()], data[i, j])
T.writes(data_red[()])
data_red[()] = T.max(data_red[()], data[i, j])
"""
mod = tvm.script.from_source(tir_str)
mod.show()
#with tvm.transform.PassContext(0):
# tvm.build(mod, target='llvm')
database = ms.tir_integration.tune_tir(mod=mod, target='llvm
--num-cores=16', work_dir='./tune_tmp', max_trials_global=1,
num_trials_per_iter=1)
```
### Triage
* needs-triage
* tune:meta_schedule
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