[Bug] [TIRx][CUDA] MergeSharedMemoryAllocations hoists a dynamic allocation above its size binding

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Issue 类型
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技术栈
cpp, python
领域
compilers

调研方向

Start with the reproducer script, which shows the buffer extent size being hoisted above its T.let definition by MergeSharedMemoryAllocations. The use-before-definition check is the assertion at src/tirx/analysis/var_use_def_analysis.cc:127, reached through SplitHostDevice. Done means the merged module keeps size defined before its use and the script runs to passed.

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描述

needs-triage type: bug
Expected behavior

s_tir.transform.MergeSharedMemoryAllocations() should preserve the scope and definition order of variables used in the merged buffer extent. The resulting module should be accepted by tirx.transform.SplitHostDevice().

The example below has two dynamic shared-memory buffers. The second buffer uses a local size binding. The input passes both verify_ssa and verify_well_formed. The intended runtime domain is 1 <= n <= 128.

Actual behavior

The merge pass moves the combined buffer allocation before the definition of size. The transformed IR contains this ordering:

size = T.int64()  # Variable declaration, not a value binding.
buf_dyn_shmem = T.alloc_buffer(
    (T.int64(512) + T.max(T.int64(0), size * T.int64(4)),),
    "uint8",
    scope="shared.dyn",
)
# Buffer declarations omitted.
size = T.min(n, T.int64(128))

SplitHostDevice then fails:

src/tirx/analysis/var_use_def_analysis.cc:127
TVM_FFI_ICHECK(!use_count_.count(v))

tvm.error.InternalError: Check failed: (!use_count_.count(v)) is false:
variable size has been used before definition!

The script below reproduced the same assertion in three fresh processes. Replacing only the second buffer's extent (size,) with the equivalent inline expression (T.min(n, T.int64(128)),) allowed the merge and host/device split to pass in three fresh processes.

This small example does not invoke CSE or Relax compilation. In an FP16 symbolic matmul compiled for CUDA, the same ordering problem was observed between the IR immediately before and after MergeSharedMemoryAllocations, followed by a use-before-definition assertion for cse_v46 in SplitHostDevice.

Environment
  • Apache TVM 0.26.dev0+source.8f328e8 (source snapshot; no .git directory)
  • Ubuntu 22.04.5, Linux 6.8.0-52-generic, x86-64
  • Python 3.11.16, LLVM 15.0.7
  • NVIDIA RTX A6000, driver 550.120
  • CUDA toolkit 12.4, target sm_86, LLVM host target generic

The reproducer only transforms IR; it does not allocate GPU tensors or execute a kernel.

Steps to reproduce

Save the following script as repro_merge_shared.py and run:

python repro_merge_shared.py

For a passing control, change this line:

C = T.alloc_buffer((size,), "float32", scope="shared.dyn")

to:

C = T.alloc_buffer((T.min(n, T.int64(128)),), "float32", scope="shared.dyn")

import tvm
from tvm import s_tir
from tvm.script import ir as I, tirx as T


@I.ir_module
class Module:
    @T.prim_func(s_tir=True)
    def main(A: T.Buffer((128,), "float32"), n: T.int64):
        T.func_attr({
            "target": T.target({
                "kind": "cuda", "arch": "sm_86",
                "host": {"kind": "llvm", "mcpu": "generic"},
            }),
            "tirx.noalias": True,
        })
        bx = T.launch_thread("blockIdx.x", T.int64(1))
        B = T.alloc_buffer((128,), "float32", scope="shared.dyn")
        size: T.let[T.int64] = T.min(n, T.int64(128))
        C = T.alloc_buffer((size,), "float32", scope="shared.dyn")
        tx = T.launch_thread("threadIdx.x", T.int64(128))
        B[tx] = A[tx]
        if tx < size:
            C[tx] = B[tx]
            A[tx] = C[tx] + T.float32(1)


assert tvm.tirx.analysis.verify_ssa(Module["main"])
assert tvm.tirx.analysis.verify_well_formed(Module["main"])
print("input validation passed", flush=True)
merged = s_tir.transform.MergeSharedMemoryAllocations()(Module)
print(merged.script(), flush=True)
tvm.tirx.transform.SplitHostDevice()(merged)
print("passed", flush=True)
Triage
  • needs-triage
  • type: bug
  • backend:cuda
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