arch-vega: Implement buffer_atomic_cmpswap (#439)

This is a standard compare and swap but implemented on vector memory
buffer instructions (i.e., it is the same as FLAT_ATOMIC_CMPSWAP with
MUBUF's special address calculation).

This was tested using a Tensile kernel, a backend for rocBLAS, which is
used by PyTorch and Tensorflow. Prior to this patch both ML frameworks
crashed. With this patch they both make forward progress.

Change-Id: Ie76447a72d210f81624e01e1fa374e41c2c21e06
This commit is contained in:
Matthew Poremba
2023-10-12 07:33:40 -07:00
committed by GitHub
3 changed files with 95 additions and 1 deletions

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@@ -40585,8 +40585,87 @@ namespace VegaISA
void
Inst_MUBUF__BUFFER_ATOMIC_CMPSWAP::execute(GPUDynInstPtr gpuDynInst)
{
panicUnimplemented();
Wavefront *wf = gpuDynInst->wavefront();
if (gpuDynInst->exec_mask.none()) {
wf->decVMemInstsIssued();
return;
}
gpuDynInst->execUnitId = wf->execUnitId;
gpuDynInst->latency.init(gpuDynInst->computeUnit());
gpuDynInst->latency.set(gpuDynInst->computeUnit()->clockPeriod());
ConstVecOperandU32 addr0(gpuDynInst, extData.VADDR);
ConstVecOperandU32 addr1(gpuDynInst, extData.VADDR + 1);
ConstScalarOperandU128 rsrcDesc(gpuDynInst, extData.SRSRC * 4);
ConstScalarOperandU32 offset(gpuDynInst, extData.SOFFSET);
ConstVecOperandU32 src(gpuDynInst, extData.VDATA);
ConstVecOperandU32 cmp(gpuDynInst, extData.VDATA + 1);
rsrcDesc.read();
offset.read();
src.read();
cmp.read();
int inst_offset = instData.OFFSET;
if (!instData.IDXEN && !instData.OFFEN) {
calcAddr<ConstVecOperandU32, ConstVecOperandU32,
ConstScalarOperandU128, ConstScalarOperandU32>(gpuDynInst,
addr0, addr1, rsrcDesc, offset, inst_offset);
} else if (!instData.IDXEN && instData.OFFEN) {
addr0.read();
calcAddr<ConstVecOperandU32, ConstVecOperandU32,
ConstScalarOperandU128, ConstScalarOperandU32>(gpuDynInst,
addr0, addr1, rsrcDesc, offset, inst_offset);
} else if (instData.IDXEN && !instData.OFFEN) {
addr0.read();
calcAddr<ConstVecOperandU32, ConstVecOperandU32,
ConstScalarOperandU128, ConstScalarOperandU32>(gpuDynInst,
addr1, addr0, rsrcDesc, offset, inst_offset);
} else {
addr0.read();
addr1.read();
calcAddr<ConstVecOperandU32, ConstVecOperandU32,
ConstScalarOperandU128, ConstScalarOperandU32>(gpuDynInst,
addr1, addr0, rsrcDesc, offset, inst_offset);
}
for (int lane = 0; lane < NumVecElemPerVecReg; ++lane) {
if (gpuDynInst->exec_mask[lane]) {
(reinterpret_cast<VecElemU32*>(gpuDynInst->x_data))[lane]
= src[lane];
(reinterpret_cast<VecElemU32*>(gpuDynInst->a_data))[lane]
= cmp[lane];
}
}
gpuDynInst->computeUnit()->globalMemoryPipe.issueRequest(gpuDynInst);
} // execute
void
Inst_MUBUF__BUFFER_ATOMIC_CMPSWAP::initiateAcc(GPUDynInstPtr gpuDynInst)
{
initAtomicAccess<VecElemU32>(gpuDynInst);
} // initiateAcc
void
Inst_MUBUF__BUFFER_ATOMIC_CMPSWAP::completeAcc(GPUDynInstPtr gpuDynInst)
{
if (isAtomicRet()) {
VecOperandU32 vdst(gpuDynInst, extData.VDATA);
for (int lane = 0; lane < NumVecElemPerVecReg; ++lane) {
if (gpuDynInst->exec_mask[lane]) {
vdst[lane] = (reinterpret_cast<VecElemU32*>(
gpuDynInst->d_data))[lane];
}
}
vdst.write();
}
} // completeAcc
// --- Inst_MUBUF__BUFFER_ATOMIC_ADD class methods ---
Inst_MUBUF__BUFFER_ATOMIC_ADD

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@@ -37220,6 +37220,8 @@ namespace VegaISA
} // getOperandSize
void execute(GPUDynInstPtr) override;
void initiateAcc(GPUDynInstPtr) override;
void completeAcc(GPUDynInstPtr) override;
}; // Inst_MUBUF__BUFFER_ATOMIC_CMPSWAP
class Inst_MUBUF__BUFFER_ATOMIC_ADD : public Inst_MUBUF

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@@ -713,6 +713,19 @@ namespace VegaISA
gpuDynInst->exec_mask = old_exec_mask;
}
template<typename T>
void
initAtomicAccess(GPUDynInstPtr gpuDynInst)
{
// temporarily modify exec_mask to supress memory accesses to oob
// regions. Only issue memory requests for lanes that have their
// exec_mask set and are not out of bounds.
VectorMask old_exec_mask = gpuDynInst->exec_mask;
gpuDynInst->exec_mask &= ~oobMask;
initMemReqHelper<T, 1>(gpuDynInst, MemCmd::SwapReq, true);
gpuDynInst->exec_mask = old_exec_mask;
}
void
injectGlobalMemFence(GPUDynInstPtr gpuDynInst)
{