[BUG] Failed to use the same buffer in multiple T.Pipelined scope with TMA and WS disabled #1653
Description
Required prerequisites
- I have read the documentation https://tilelang.com.
- I have searched the Issue Tracker that this hasn't already been reported. (comment there if it has.)
What version of TileLang are you using?
0.1.7.post2+cuda.gitce68b51a
System information
>>> tilelang.__version__
'0.1.7.post2+cuda.gitce68b51a'
<frozen runpy>:128: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour
Collecting environment information...
PyTorch version: 2.9.0+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version: Could not collect
CMake version: version 4.1.0
Libc version: glibc-2.35
Python version: 3.12.0 | packaged by Anaconda, Inc. | (main, Oct 2 2023, 17:29:18) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-152-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.9.41
CUDA_MODULE_LOADING set to:
GPU models and configuration:
GPU 0: NVIDIA H200
GPU 1: NVIDIA H200
GPU 2: NVIDIA H200
GPU 3: NVIDIA H200
GPU 4: NVIDIA H200
GPU 5: NVIDIA H200
GPU 6: NVIDIA H200
GPU 7: NVIDIA H200
Nvidia driver version: 575.57.08
cuDNN version: Could not collect
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 240
On-line CPU(s) list: 0-239
Vendor ID: GenuineIntel
Model name: INTEL(R) XEON(R) PLATINUM 8580
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 60
Socket(s): 2
Stepping: 2
BogoMIPS: 4000.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 5.6 MiB (120 instances)
L1i cache: 3.8 MiB (120 instances)
L2 cache: 240 MiB (120 instances)
L3 cache: 600 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,116,118,120,122,124,126,128,130,132,134,136,138,140,142,144,146,148,150,152,154,156,158,160,162,164,166,168,170,172,174,176,178,180,182,184,186,188,190,192,194,196,198,200,202,204,206,208,210,212,214,216,218,220,222,224,226,228,230,232,234,236,238
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127,129,131,133,135,137,139,141,143,145,147,149,151,153,155,157,159,161,163,165,167,169,171,173,175,177,179,181,183,185,187,189,191,193,195,197,199,201,203,205,207,209,211,213,215,217,219,221,223,225,227,229,231,233,235,237,239
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB disabled; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==2.3.4
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] torch==2.9.0
[pip3] torch_c_dlpack_ext==0.1.4
[pip3] triton==3.5.0
[conda] numpy 2.3.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.27.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi
[conda] torch 2.9.0 pypi_0 pypi
[conda] torch-c-dlpack-ext 0.1.4 pypi_0 pypi
[conda] triton 3.5.0 pypi_0 pypi
Problem description
as titiled.
note that disabling tma and aws is required for producing this bug
Reproducible example code
Minimal reproduction:
import tilelang
import tilelang.language as T
@tilelang.jit(
out_idx=[-1],
pass_configs={
tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
}
)
def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
@T.prim_func
def gemm(
A: T.Tensor((M, K), dtype),
B: T.Tensor((K, N), dtype),
C: T.Tensor((M, N), dtype),
):
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
A_shared = T.alloc_shared((block_M, block_K), dtype)
B_shared = T.alloc_shared((block_K, block_N), dtype)
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
T.clear(C_local)
# Pipeline 1
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
T.copy(A[by * block_M, k * block_K], A_shared)
T.copy(B[k * block_K, bx * block_N], B_shared)
T.gemm(A_shared, B_shared, C_local)
# Pipeline 2
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):
T.copy(A[by * block_M, k * block_K], A_shared)
T.copy(B[k * block_K, bx * block_N], B_shared)
T.gemm(A_shared, B_shared, C_local)
T.copy(C_local, C[by * block_M, bx * block_N])
return gemm
if __name__ == "__main__":
kernel = matmul(1024, 1024, 1024, 128, 128, 32)Traceback
Traceback (most recent call last):
File "/home/wt/2pipe_minimal.py", line 43, in <module>
kernel = matmul(1024, 1024, 1024, 128, 128, 32)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wt/tilelang/tilelang/jit/__init__.py", line 414, in __call__
kernel = self.compile(*args, **kwargs, **tune_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wt/tilelang/tilelang/jit/__init__.py", line 349, in compile
kernel_result = compile(
^^^^^^^^
File "/home/wt/tilelang/tilelang/jit/__init__.py", line 98, in compile
return cached(
^^^^^^^
File "/home/wt/tilelang/tilelang/cache/__init__.py", line 74, in cached
return _dispatch_map[execution_backend].cached(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wt/tilelang/tilelang/cache/kernel_cache.py", line 204, in cached
kernel = JITKernel(
^^^^^^^^^^
File "/home/wt/tilelang/tilelang/jit/kernel.py", line 137, in __init__
adapter = self._compile_and_create_adapter(func, out_idx)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wt/tilelang/tilelang/jit/kernel.py", line 242, in _compile_and_create_adapter
artifact = tilelang.lower(
^^^^^^^^^^^^^^^
File "/home/wt/tilelang/tilelang/engine/lower.py", line 251, in lower
mod = OptimizeForTarget(mod, target)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wt/tilelang/tilelang/engine/phase.py", line 227, in OptimizeForTarget
mod = tilelang.transform.InjectSoftwarePipeline()(mod)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/wt/tilelang/3rdparty/tvm/python/tvm/ir/transform.py", line 167, in __call__
return _ffi_transform_api.RunPass(self, mod)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "python/tvm_ffi/cython/function.pxi", line 923, in tvm_ffi.core.Function.__call__
File "<unknown>", line 0, in tvm::transform::Pass::operator()(tvm::IRModule) const
File "<unknown>", line 0, in tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
File "<unknown>", line 0, in tvm::tir::transform::PrimFuncPassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
File "<unknown>", line 0, in std::_Function_handler<tvm::tir::PrimFunc (tvm::tir::PrimFunc, tvm::IRModule, tvm::transform::PassContext), tvm::tl::InjectSoftwarePipeline()::{lambda(tvm::tir::PrimFunc, tvm::IRModule const&, tvm::transform::PassContext const&)#1}>::_M_invoke(std::_Any_data const&, tvm::tir::PrimFunc&&, tvm::IRModule&&, tvm::transform::PassContext&&)
File "<unknown>", line 0, in tvm::tl::software_pipeline::PipelineInjector::Inject(tvm::tir::PrimFunc const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#15}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::BlockRealizeNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#14}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tl::software_pipeline::PipelineInjector::VisitStmt_(tvm::tir::BlockNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::BlockNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#2}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::AttrStmtNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#2}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::AttrStmtNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#2}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::AttrStmtNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#2}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::AttrStmtNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#2}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::AttrStmtNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#15}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::BlockRealizeNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#14}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tl::software_pipeline::PipelineInjector::VisitStmt_(tvm::tir::BlockNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::BlockNode const*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#10}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt_(tvm::tir::SeqStmtNode const*)
File "<unknown>", line 0, in tvm::ffi::ObjectPtr<tvm::ffi::Object> tvm::ffi::Array<tvm::tir::Stmt, void>::MapHelper<tvm::tir::StmtMutator::Internal::Mutate(tvm::tir::StmtMutator*, tvm::ffi::Array<tvm::tir::Stmt, void> const&)::{lambda(tvm::tir::Stmt const&)#1}, tvm::tir::Stmt>(tvm::ffi::ObjectPtr<tvm::ffi::Object>, tvm::tir::StmtMutator::Internal::Mutate(tvm::tir::StmtMutator*, tvm::ffi::Array<tvm::tir::Stmt, void> const&)::{lambda(tvm::tir::Stmt const&)#1})
File "<unknown>", line 0, in tvm::tir::StmtMutator::VisitStmt(tvm::tir::Stmt const&)
File "<unknown>", line 0, in tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>::InitVTable()::{lambda(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)#4}::_FUN(tvm::ffi::ObjectRef const&, tvm::tir::StmtFunctor<tvm::tir::Stmt (tvm::tir::Stmt const&)>*)
File "<unknown>", line 0, in tvm::tl::software_pipeline::PipelineInjector::VisitStmt_(tvm::tir::ForNode const*)
File "<unknown>", line 0, in tvm::runtime::detail::LogFatal::Entry::Finalize()
tvm.error.InternalError: Check failed: (buffers_used_in_pipeline_.count(buffer) == 0) is false: Buffer 'B_shared' is used in multiple software pipeline loops. This is not supported because multi-versioning would conflict.Expected behavior
No response
Additional context
No response