malixian opened a new issue, #15987:
URL: https://github.com/apache/tvm/issues/15987
### Expected behavior
I try to use MetaScheduler to tuning matmul, and the dimensions of the
matrix are m=8192, n=14336, k=8192.
When n=8192, everything is ok, but once m or n is equal to 14336, an error
`RuntimeError: parallel_for_dynamic error with [02:23:57]
/home/malixian/repos/tensorir/tvm/src/ir/expr.cc:88: InternalError: Check
failed: value < 1LL << (dtype.bits() - 1) (8589934591 vs. 2147483648) :
ValueError: Literal value 8589934591 exceeds maximum of int32` will occur. BTW,
it is ok when k equals 14336.
According to the error message, I tried to comment out the` ICHECK` code of
the function IntImm in expr.cc and it worked normally, again.
I think the `DataType` of Tir should be expanded to suit this case.
### Actual behavior
error `RuntimeError: parallel_for_dynamic error with [02:23:57]
/home/malixian/repos/tensorir/tvm/src/ir/expr.cc:88: InternalError: Check
failed: value < 1LL << (dtype.bits() - 1) (8589934591 vs. 2147483648) :
ValueError: Literal value 8589934591 exceeds maximum of int32`
### Environment
TVM version is '0.15.dev0'
### Steps to reproduce
```
def matmul_fp16(M: int, N: int, K: int, in_dtype: str, out_dtype: str):
x = te.placeholder((M, K), name="X", dtype=in_dtype)
y = te.placeholder((K, N), name="Y", dtype=in_dtype)
k = te.reduce_axis((0, K), name="k")
c = te.compute( # pylint: disable=invalid-name
(M, N),
lambda i, j: te.sum(x[i][k].astype(out_dtype) *
y[k][j].astype(out_dtype), axis=[k]),
name="C",
)
return (x, y, c)
def tune(in_dtype, out_dtype):
target = Target("nvidia/nvidia-a100")
M, N, K = 8192, 14336, 8192
func = te.create_prim_func(
matmul_fp16(M=M, N=N, K=K, in_dtype=in_dtype, out_dtype=out_dtype)
).with_attr({"global_symbol": "main"})
space = ms.space_generator.PostOrderApply(
sch_rules="cuda-tensorcore",
postprocs="cuda-tensorcore",
mutator_probs="cuda-tensorcore",
)
mod = tvm.IRModule({"main": func})
with tempfile.TemporaryDirectory() as work_dir:
db = ms.tir_integration.tune_tir(
mod=mod,
target=target,
work_dir=work_dir,
max_trials_global=32,
builder=LocalBuilder(
f_build="meta_schedule.builder.async_build",
initializer=initializer
),
space=space,
)
sch = db.query_schedule(mod, target=target, workload_name="main")
with tvm.transform.PassContext(config={"tir.use_async_copy": 1}):
rt_mod = tvm.build(sch.mod, target=target)
```
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