Sylvain Jeaugey
920dbe5b35
Optimization for Tree allreduce on A100. Improve aggregation performance. Use shared buffers for inter-node send/recv. Add NVTX profiling hooks. Accelerate alltoall connections by merging communication for all channels. Add support for one hop communication through NVLink, for faster send/recv communication on cubemesh topologies like DGX-1. Improve alltoall scheduling to better balance intra/inter node communication. Increase send/recv parallelism by 8x, each warp sending or receiving to a different peer. Net: move to v4. Net: make flush operation asynchronous to accelerate alltoall. Net: define maximum number of requests. Fix hang when using LL128 protocol after 2^31 steps. Fix #379 : topology injection failing when using less GPUs than described in the XML. Fix #394 : protocol mismatch causing hangs or crashes when using one GPU per node.
657 linhas
27 KiB
C++
657 linhas
27 KiB
C++
/*************************************************************************
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* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
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*
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* See LICENSE.txt for license information
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************************************************************************/
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#include "enqueue.h"
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#include "argcheck.h"
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#include "coll_net.h"
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// Only generate inline kernels for LL
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#define NCCL_FUNC5(func, algo, redop, dtype) \
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(void*)NCCL_KERN_NAME(func, algo, LL, redop, dtype), \
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(void*)NCCL_KERN_NAME(func, algo, LL, redop, dtype), \
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(void*)NCCL_KERN_NAME(func, algo, LL, redop, dtype)
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#define NCCL_FUNC4(func, redop, type) \
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(void*)NCCL_FUNC5(func, TREE, redop, type), \
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(void*)NCCL_FUNC5(func, RING, redop, type), \
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(void*)NCCL_FUNC5(func, COLLNET, redop, type)
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// Must be consistent with ncclDataType_t
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#define NCCL_FUNCS3A(func, redop) \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, uint8_t), \
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(void*)NCCL_FUNC4(func, redop, int32_t), \
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(void*)NCCL_FUNC4(func, redop, uint32_t), \
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(void*)NCCL_FUNC4(func, redop, int64_t), \
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(void*)NCCL_FUNC4(func, redop, uint64_t), \
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(void*)NCCL_FUNC4(func, redop, half), \
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(void*)NCCL_FUNC4(func, redop, float), \
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(void*)NCCL_FUNC4(func, redop, double)
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#define NCCL_FUNCS3B(func, redop) \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t), \
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(void*)NCCL_FUNC4(func, redop, int8_t)
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// Must be consistent with ncclRedOp_t -- but we only generate kernel for sums.
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#define NCCL_FUNCS2A(func) \
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NCCL_FUNCS3A(func, Sum), \
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NCCL_FUNCS3A(func, Sum), \
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NCCL_FUNCS3A(func, Sum), \
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NCCL_FUNCS3A(func, Sum)
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#define NCCL_FUNCS2B(func) \
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NCCL_FUNCS3B(func, Sum), \
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NCCL_FUNCS3B(func, Sum), \
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NCCL_FUNCS3B(func, Sum), \
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NCCL_FUNCS3B(func, Sum)
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// Must be consistent with the ncclFuncSet enum
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static void* const ncclKerns[1+NCCL_NUM_FUNCTIONS*ncclNumOps*ncclNumTypes*NCCL_NUM_ALGORITHMS*NCCL_NUM_PROTOCOLS] = {
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(void*)NCCL_KERN_NAME(SendRecv, RING, SIMPLE, Sum, int8_t),
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NCCL_FUNCS2B(Broadcast),
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NCCL_FUNCS2A(Reduce),
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NCCL_FUNCS2B(AllGather),
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NCCL_FUNCS2A(ReduceScatter),
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NCCL_FUNCS2A(AllReduce)
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};
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/*****************************************************************************/
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/* Launch system : synchronization and CUDA kernel launch */
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/*****************************************************************************/
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ncclResult_t ncclLaunchCooperativeKernelMultiDevice(struct cudaLaunchParams *paramsList, int* cudaDevs, int numDevices, int cgMode) {
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#if CUDART_VERSION >= 9000
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if (cgMode & 0x01) {
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CUDACHECK(cudaLaunchCooperativeKernelMultiDevice(paramsList, numDevices,
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// These flags are to reduce the latency of using this API
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cudaCooperativeLaunchMultiDeviceNoPreSync|cudaCooperativeLaunchMultiDeviceNoPostSync));
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return ncclSuccess;
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}
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#endif
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int savedDev;
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CUDACHECK(cudaGetDevice(&savedDev));
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for (int i = 0; i < numDevices; i++) {
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struct cudaLaunchParams* params = paramsList+i;
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CUDACHECK(cudaSetDevice(cudaDevs[i]));
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CUDACHECK(cudaLaunchKernel(params->func, params->gridDim, params->blockDim, params->args, params->sharedMem, params->stream));
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}
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CUDACHECK(cudaSetDevice(savedDev));
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return ncclSuccess;
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}
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static ncclResult_t getNextOp(struct ncclChannel* channel, struct ncclWork** work, struct ncclWorkElem* base) {
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if (channel->workCount == NCCL_MAX_OPS) {
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WARN("Too many aggregated operations on channel %d (%d max)", channel->id, NCCL_MAX_OPS);
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return ncclInvalidUsage;
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}
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int opIndex = channel->workFifoTail%NCCL_MAX_OPS;
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struct ncclWork* w = channel->workFifo+opIndex;
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struct ncclWorkElem* e = w->elems;
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volatile uint8_t* activePtr = (volatile uint8_t*)&e->active;
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while (activePtr[0] != 0) sched_yield();
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memset(w, 0, sizeof(struct ncclWork));
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// Initialize with work elem if provided
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if (base) memcpy(e, base, sizeof(struct ncclWorkElem));
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e->active = 1;
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e->index = opIndex;
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channel->workFifoTail++;
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channel->workCount++;
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if (work) *work = w;
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return ncclSuccess;
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}
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static ncclResult_t setupLaunch(struct ncclComm* comm, struct cudaLaunchParams* params) {
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// Only launch blocks where we have work to do.
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for (int c=0; c<comm->p2pnChannels; c++) {
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if (comm->channels[c].workCount) params->gridDim.x = c+1;
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}
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// Set active = 2 for the last operation and add a no-op on empty channels (p2p case).
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for (int c=0; c<params->gridDim.x; c++) {
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struct ncclChannel* channel = comm->channels+c;
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if (channel->workCount == 0) {
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struct ncclWork* w;
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NCCLCHECK(getNextOp(channel, &w, NULL));
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struct ncclWorkElem* e = w->elems;
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e->comm = comm->devComm;
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e->funcIndex = FUNC_INDEX_P2P;
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e->p2p.nThreads = 0;
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}
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channel->workFifo[(channel->workFifoTail-1)%NCCL_MAX_OPS].elems[0].active = 2;
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}
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// Find the first operation, choose the kernel accordingly and pass it
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// as the first argument.
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struct ncclChannel* c0 = comm->channels;
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struct ncclWork* work = c0->workFifo+((c0->workFifoTail-c0->workCount)%NCCL_MAX_OPS);
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struct ncclWorkElem* elem = work->elems;
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memcpy(&comm->args, elem, sizeof(struct ncclWorkElem));
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// As we inline the first coll directly, we can free it immediately.
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if (elem->funcIndex != FUNC_INDEX_P2P) elem->active = 0;
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params->func = ncclKerns[elem->funcIndex];
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return ncclSuccess;
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}
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ncclResult_t ncclCpuBarrierIn(struct ncclComm* comm, int* isLast) {
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volatile int* ptr = (volatile int*)(comm->intraBarrier+comm->intraPhase);
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int val = *ptr;
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bool done = false;
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while (done == false) {
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if (val >= comm->intraRanks) {
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WARN("Trying to launch too many work elements, max is %d", NCCL_MAX_OPS);
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return ncclInvalidUsage;
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}
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if (val+1 == comm->intraRanks) {
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// Reset the barrier.
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comm->intraBarrier[comm->intraPhase^1] = 0;
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*isLast = 1;
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return ncclSuccess;
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}
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done = __sync_bool_compare_and_swap(ptr, val, val+1);
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val++;
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}
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*isLast = 0;
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return ncclSuccess;
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}
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ncclResult_t ncclCpuBarrierLast(struct ncclComm* comm) {
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volatile int* ptr = (volatile int*)(comm->intraBarrier+comm->intraPhase);
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int val = *ptr;
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if (__sync_bool_compare_and_swap(ptr, val, val+1) != true) {
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WARN("Trying to launch too many work elements, max is %d", NCCL_MAX_OPS);
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return ncclInternalError;
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}
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return ncclSuccess;
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}
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ncclResult_t ncclCpuBarrierOut(struct ncclComm* comm) {
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volatile int* ptr = (volatile int*)(comm->intraBarrier+comm->intraPhase);
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while (*ptr < comm->intraRanks) pthread_yield();
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comm->intraPhase ^= 1;
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return ncclSuccess;
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}
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ncclResult_t ncclBarrierEnqueue(struct ncclComm* comm) {
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struct cudaLaunchParams* params = comm->myParams;
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if (params->gridDim.x == 0) return ncclSuccess;
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NCCLCHECK(setupLaunch(comm, params));
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// Use internal NCCL stream for CGMD/GROUP launch if required or if the user stream is NULL
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if (comm->launchMode == ncclComm::GROUP && (comm->groupCudaStream || comm->userStream == NULL)) {
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// Enqueue event in user stream
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CUDACHECK(cudaEventRecord(comm->doneEvent, comm->userStream));
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// Create dependency between user stream and internal NCCL stream
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CUDACHECK(cudaStreamWaitEvent(comm->groupStream, comm->doneEvent, 0));
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params->stream = comm->groupStream;
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} else {
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if (comm->userStream != params->stream) {
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// Stream changed from last call, create dependency against last NCCL kernel launch
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CUDACHECK(cudaStreamWaitEvent(comm->userStream, comm->doneEvent, 0));
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}
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params->stream = comm->userStream;
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}
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if (comm->launchMode == ncclComm::GROUP) {
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int isLast = 0;
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NCCLCHECK(ncclCpuBarrierIn(comm, &isLast));
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if (isLast) {
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// I'm the last. Launch all operations.
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NCCLCHECK(ncclLaunchCooperativeKernelMultiDevice(comm->intraParams, comm->intraCudaDevs, comm->intraRanks, *comm->intraCGMode));
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NCCLCHECK(ncclCpuBarrierLast(comm));
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}
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}
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return ncclSuccess;
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}
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ncclResult_t ncclBarrierEnqueueWait(ncclComm_t comm) {
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struct cudaLaunchParams *params = comm->myParams;
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if (params->gridDim.x == 0) return ncclSuccess;
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// We can't print the CG mode before the first barrier happened.
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if (comm->rank == 0 && *comm->intraCGMode & 0x10) {
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*comm->intraCGMode ^= 0x10;
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INFO(NCCL_INIT,"Launch mode %s%s%s",
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comm->launchMode == ncclComm::GROUP ? "Group" : "Parallel",
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*comm->intraCGMode ? "/CGMD" : "",
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(comm->launchMode == ncclComm::GROUP && comm->groupCudaStream) ? "/Stream" : "");
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}
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if (comm->launchMode == ncclComm::PARALLEL) {
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CUDACHECK(cudaLaunchKernel(params->func, params->gridDim, params->blockDim, params->args, params->sharedMem, params->stream));
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} else {
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NCCLCHECK(ncclCpuBarrierOut(comm));
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}
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// Start the network proxies as soon as the kernel has been launched. We can't
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// perform any CUDA call between the two or having a cudaFree between the CUDA
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// launch and the ncclProxyStart call could cause a deadlock.
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// Also, starting the proxies after the CUDA launch seems to be better for
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// performance (latency).
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uint64_t max = 0ULL;
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for (int r=0; r<params->gridDim.x; r++) {
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struct ncclChannel* channel = comm->channels+r;
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max = std::max(max, channel->workFifoTail);
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channel->workCount = 0;
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}
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for (int r=0; r<comm->p2pnChannels; r++) {
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struct ncclChannel* channel = comm->channels+r;
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channel->workFifoTail = max;
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}
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params->gridDim.x = params->blockDim.x = 0;
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comm->lastOpCount = max;
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NCCLCHECK(ncclProxyStart(comm));
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return ncclSuccess;
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}
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ncclResult_t ncclEnqueueEvents(ncclComm_t comm) {
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struct cudaLaunchParams *params = comm->myParams;
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// Enqueue event after NCCL kernel
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CUDACHECK(cudaEventRecord(comm->doneEvent, params->stream));
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// Use internal NCCL stream for CGMD/GROUP launch if required or if the user stream is NULL
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if (comm->launchMode == ncclComm::GROUP && (comm->groupCudaStream || comm->userStream == NULL)) {
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// Create dependency between NCCL internal stream and user stream
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CUDACHECK(cudaStreamWaitEvent(comm->userStream, comm->doneEvent, 0));
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}
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comm->userStreamSet = false;
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return ncclSuccess;
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}
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/*****************************************************************************/
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/* Enqueueing system : computation of kernel and proxy operations parameters */
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/*****************************************************************************/
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static ncclResult_t getAlgoInfo(struct ncclInfo* info) {
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struct ncclComm* comm = info->comm;
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float minTime = 3600000000.0; // Hopefully no operation will take an hour to complete.
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// Find algorithm / protocol.
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info->algorithm = -1;
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info->protocol = -1;
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int nAlgos = NCCL_NUM_ALGORITHMS;
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// Check collNet support
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int collNetTypeSupport = 0;
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if (info->comm->collNetSupport)
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NCCLCHECK(collNetReduceSupport(info->datatype, info->op, &collNetTypeSupport));
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if (collNetTypeSupport != 1) nAlgos--;
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for (int a=0; a<nAlgos; a++) {
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for (int p=0; p<NCCL_NUM_PROTOCOLS; p++) {
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float time;
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NCCLCHECK(ncclTopoGetAlgoTime(info, a, p, &time));
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if (time >= 0 && time < minTime) {
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info->algorithm = a;
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info->protocol = p;
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minTime = time;
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}
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}
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}
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if (info->algorithm == -1 || info->protocol == -1) {
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WARN("Error : no algorithm/protocol available");
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return ncclInternalError;
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}
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//if (comm->rank == 0) INFO(NCCL_TUNING, "%ld Bytes -> Algo %d proto %d time %f", info->nBytes, info->algorithm, info->protocol, minTime);
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TRACE(NCCL_COLL, "%ld Bytes -> Algo %d proto %d time %f", info->nBytes, info->algorithm, info->protocol, minTime);
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int nc = (info->nChannels > 0) ? info->nChannels :
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(info->algorithm == NCCL_ALGO_COLLNET) ? comm->nChannels/2 : comm->nChannels; // CollNet uses one channel for up and one channel for down
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int nt = comm->maxThreads[info->algorithm][info->protocol];
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int threadThreshold = comm->threadThresholds[info->algorithm][info->protocol];
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while (info->nBytes < nc*nt*threadThreshold) {
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if (info->algorithm != NCCL_ALGO_COLLNET && nc >= 2) nc--;
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else if ((nt % 128) == 0) nt/=2;
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else break;
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}
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if (info->protocol == NCCL_PROTO_SIMPLE) nt += WARP_SIZE; // Extra warp for sync
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if (info->protocol == NCCL_PROTO_SIMPLE && info->algorithm == NCCL_ALGO_TREE) nt += WARP_SIZE;
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info->nChannels = nc;
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info->nThreads = nt;
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return ncclSuccess;
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}
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static ncclResult_t getPatternInfo(struct ncclInfo* info) {
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switch (info->coll) {
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case ncclFuncBroadcast:
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info->pattern = info->algorithm == NCCL_ALGO_TREE ? ncclPatternTreeDown : ncclPatternPipelineFrom; break;
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case ncclFuncReduce:
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info->pattern = info->algorithm == NCCL_ALGO_TREE ? ncclPatternTreeUp : ncclPatternPipelineTo; break;
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case ncclFuncReduceScatter:
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case ncclFuncAllGather:
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info->pattern = ncclPatternRing; break;
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case ncclFuncAllReduce:
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info->pattern = info->algorithm == NCCL_ALGO_COLLNET ? ncclPatternCollTreeUp : info->algorithm == NCCL_ALGO_TREE ? ncclPatternTreeUpDown : ncclPatternRingTwice; break;
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default:
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WARN("Unknown pattern for collective %d algorithm %d", info->coll, info->algorithm);
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return ncclInternalError;
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}
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return ncclSuccess;
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}
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static ncclResult_t getLoopInfo(struct ncclInfo* info) {
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switch (info->pattern) {
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case ncclPatternTreeUp:
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case ncclPatternTreeDown:
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case ncclPatternTreeUpDown:
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case ncclPatternPipelineFrom:
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case ncclPatternPipelineTo:
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case ncclPatternCollTreeUp:
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case ncclPatternCollTreeDown:
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info->nstepsPerLoop = info-> nchunksPerLoop = 1; break;
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case ncclPatternRing:
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info->nstepsPerLoop = info->comm->nRanks-1; info->nchunksPerLoop = info->comm->nRanks; break;
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case ncclPatternRingTwice:
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info->nstepsPerLoop = 2*(info->comm->nRanks-1); info->nchunksPerLoop = info->comm->nRanks; break;
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default:
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WARN("Unknown pattern %d\n", info->pattern);
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return ncclInternalError;
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}
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return ncclSuccess;
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}
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static ncclResult_t computeColl(struct ncclInfo* info /* input */, struct ncclWorkElem* work, struct ncclProxyArgs* proxyArgs /* output */) {
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work->comm = info->comm->devComm;
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// Set nstepsPerLoop and nchunksPerLoop
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NCCLCHECK(getAlgoInfo(info));
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NCCLCHECK(getPatternInfo(info));
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NCCLCHECK(getLoopInfo(info));
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work->sendbuff = info->sendbuff;
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work->recvbuff = info->recvbuff;
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work->coll.root = info->root;
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work->coll.count = info->count;
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work->coll.nChannels = info->nChannels;
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work->nThreads = info->nThreads;
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work->funcIndex = FUNC_INDEX(info->coll, info->op, info->datatype, info->algorithm, info->protocol);
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int stepSize = info->comm->buffSizes[info->protocol]/NCCL_STEPS;
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int chunkSteps = (info->protocol == NCCL_PROTO_SIMPLE && info->algorithm == NCCL_ALGO_RING) ? info->chunkSteps : 1;
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int sliceSteps = (info->protocol == NCCL_PROTO_SIMPLE && info->algorithm == NCCL_ALGO_RING) ? info->sliceSteps : 1;
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int chunkSize = stepSize*chunkSteps;
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// Compute lastChunkSize
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if (info->algorithm == NCCL_ALGO_TREE && info->protocol == NCCL_PROTO_SIMPLE) {
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if (info->pattern == ncclPatternTreeUpDown) {
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// Optimize chunkSize / nSteps
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while (info->nBytes / (info->nChannels*chunkSize) < info->comm->channels[0].tree.depth*8 && chunkSize > 131072) chunkSize /= 2;
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while (info->nBytes / (info->nChannels*chunkSize) < info->comm->channels[0].tree.depth*4 && chunkSize > 65536) chunkSize /= 2;
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while (info->nBytes / (info->nChannels*chunkSize) < info->comm->channels[0].tree.depth && chunkSize > 32768) chunkSize /= 2;
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|
}
|
|
// Use lastChunkSize as chunkSize
|
|
work->coll.lastChunkSize = chunkSize / ncclTypeSize(info->datatype);
|
|
} else if (info->algorithm == NCCL_ALGO_COLLNET && info->protocol == NCCL_PROTO_SIMPLE) {
|
|
// Optimize chunkSize / nSteps
|
|
while (info->nBytes / (info->nChannels*chunkSize) < info->comm->channels[0].collTree.depth*16 && chunkSize > 131072) chunkSize /= 2;
|
|
while (info->nBytes / (info->nChannels*chunkSize) < info->comm->channels[0].collTree.depth*4 && chunkSize > 65536) chunkSize /= 2;
|
|
while (info->nBytes / (info->nChannels*chunkSize) < info->comm->channels[0].collTree.depth && chunkSize > 32768) chunkSize /= 2;
|
|
// Use lastChunkSize as chunkSize
|
|
work->coll.lastChunkSize = chunkSize / ncclTypeSize(info->datatype);
|
|
} else if (info->protocol == NCCL_PROTO_LL) {
|
|
const ssize_t sliceSize = stepSize*sizeof(uint64_t)/sizeof(union ncclLLFifoLine);
|
|
const ssize_t loopSize = info->nChannels*info->nchunksPerLoop*(ssize_t)sliceSize;
|
|
work->coll.lastChunkSize = DIVUP((info->nBytes-(info->nBytes/loopSize)*loopSize), info->nChannels*info->nchunksPerLoop);
|
|
ALIGN_SIZE(work->coll.lastChunkSize, info->nThreads*sizeof(uint64_t));
|
|
work->coll.lastChunkSize /= ncclTypeSize(info->datatype);
|
|
} else if (info->algorithm == NCCL_ALGO_TREE && info->protocol == NCCL_PROTO_LL128) {
|
|
int nNodes = info->comm->nNodes;
|
|
float ppn = info->comm->nRanks / (float)nNodes;
|
|
float nstepsLL128 = 1+log2i(nNodes) + 0.1*ppn;
|
|
while (info->nBytes / (info->nChannels*chunkSize) < nstepsLL128*64/ppn && chunkSize > 131072) chunkSize /= 2;
|
|
while (info->nBytes / (info->nChannels*chunkSize) < nstepsLL128*16/ppn && chunkSize > 32768) chunkSize /= 2;
|
|
// Use lastChunkSize as chunkSize
|
|
work->coll.lastChunkSize = chunkSize*NCCL_LL128_DATAELEMS/(NCCL_LL128_LINEELEMS*ncclTypeSize(info->datatype));
|
|
}
|
|
|
|
// Compute nSteps for proxies
|
|
int chunkEffectiveSize = chunkSize;
|
|
if (info->protocol == NCCL_PROTO_LL) chunkEffectiveSize /= 2;
|
|
if (info->protocol == NCCL_PROTO_LL128) chunkEffectiveSize = (chunkSize / NCCL_LL128_LINEELEMS) * NCCL_LL128_DATAELEMS;
|
|
//if (info->comm->rank == 0) printf("Coll %d, size %ld -> %dx%d, chunkSize %d (algo %d proto%d)\n", info->coll, info->nBytes, info->nChannels, info->nThreads, chunkSize, info->algorithm, info->protocol);
|
|
int nLoops = (int)(DIVUP(info->nBytes, (((size_t)(info->nChannels))*info->nchunksPerLoop*chunkEffectiveSize)));
|
|
proxyArgs->nsteps = info->nstepsPerLoop * nLoops * chunkSteps;
|
|
proxyArgs->sliceSteps = sliceSteps;
|
|
proxyArgs->chunkSteps = chunkSteps;
|
|
proxyArgs->protocol = info->protocol;
|
|
proxyArgs->dtype = info->datatype;
|
|
proxyArgs->redOp = info->op;
|
|
// This is used by P2P to reduce the receive buffer size. We don't use it in collectives
|
|
// because some protocols need to transmit more than the total size, plus they sometimes
|
|
// round up
|
|
proxyArgs->recvbytes = stepSize*proxyArgs->sliceSteps;
|
|
|
|
TRACE(NCCL_NET,"opCount %lx slicesteps %d spl %d cpl %d nbytes %zi -> protocol %d nchannels %d nthreads %d, nloops %d nsteps %d comm %p",
|
|
proxyArgs->opCount, proxyArgs->sliceSteps, info->nstepsPerLoop, info->nchunksPerLoop, info->nBytes, info->protocol, info->nChannels, info->nThreads,
|
|
nLoops, proxyArgs->nsteps, info->comm);
|
|
return ncclSuccess;
|
|
}
|
|
|
|
static ncclResult_t checkSetStream(struct ncclInfo* info) {
|
|
if (info->comm->userStreamSet == false) {
|
|
info->comm->userStream = info->stream;
|
|
info->comm->userStreamSet = true;
|
|
} else if (info->stream != info->comm->userStream) {
|
|
WARN("Error : mixing different streams within a group call is not supported.");
|
|
return ncclInvalidUsage;
|
|
}
|
|
return ncclSuccess;
|
|
}
|
|
|
|
ncclResult_t ncclSaveKernel(struct ncclInfo* info) {
|
|
if (info->comm->nRanks == 1) {
|
|
if (info->sendbuff != info->recvbuff)
|
|
CUDACHECK(cudaMemcpyAsync(info->recvbuff, info->sendbuff, info->nBytes, cudaMemcpyDeviceToDevice, info->stream));
|
|
return ncclSuccess;
|
|
}
|
|
|
|
struct ncclWorkElem work;
|
|
struct ncclProxyArgs proxyArgs;
|
|
memset(&proxyArgs, 0, sizeof(struct ncclProxyArgs));
|
|
NCCLCHECK(computeColl(info, &work, &proxyArgs));
|
|
|
|
info->comm->myParams->blockDim.x = std::max<unsigned>(info->comm->myParams->blockDim.x, info->nThreads);
|
|
|
|
int nChannels = work.coll.nChannels;
|
|
int nSubChannels = (info->pattern == ncclPatternCollTreeUp || info->pattern == ncclPatternCollTreeDown) ? 2 : 1;
|
|
|
|
for (int bid=0; bid<nChannels*nSubChannels; bid++) {
|
|
int channelId = info->comm->myParams->gridDim.x % info->comm->nChannels;
|
|
struct ncclChannel* channel = info->comm->channels+channelId;
|
|
|
|
// Proxy
|
|
proxyArgs.channel = channel;
|
|
// Adjust pattern for CollNet based on channel index
|
|
if (nSubChannels == 2) {
|
|
info->pattern = (channelId < info->comm->nChannels/nSubChannels) ? ncclPatternCollTreeUp : ncclPatternCollTreeDown;
|
|
}
|
|
|
|
if (proxyArgs.nsteps) NCCLCHECK(ncclProxySaveColl(&proxyArgs, info->pattern, info->root, info->comm->nRanks));
|
|
|
|
info->comm->myParams->gridDim.x++;
|
|
work.coll.bid = bid % nChannels;
|
|
NCCLCHECK(getNextOp(channel, NULL, &work));
|
|
}
|
|
return ncclSuccess;
|
|
}
|
|
|
|
#define NCCL_MIN_CHANNEL_SIZE (NCCL_LL_THREAD_THRESHOLD*64)
|
|
#define NCCL_AGG_CHANNEL_SIZE (1LL << 21) /* 2 MiB, ideal per-channel size to fully utilize bandwidth */
|
|
|
|
ncclResult_t ncclSaveCommKernels(ncclComm_t comm) {
|
|
if (comm->asyncOpCount == 0) {
|
|
return ncclSuccess;
|
|
} else if (comm->asyncOpCount == 1) {
|
|
// No aggregation
|
|
struct ncclInfo* info = comm->asyncOps;
|
|
info->nChannels = 0;
|
|
NCCLCHECK(ncclSaveKernel(info));
|
|
} else {
|
|
// Aggregation
|
|
size_t channelSize = NCCL_AGG_CHANNEL_SIZE * comm->nRanks; // scale channel size based on nranks as latency increases
|
|
// Reduce the per-channel size if we cannot fully utilize the channels
|
|
while (comm->asyncTotalSize < channelSize * comm->nChannels && channelSize > NCCL_MIN_CHANNEL_SIZE) channelSize /= 2;
|
|
for (int c = 0; c < comm->asyncOpCount; c++) {
|
|
struct ncclInfo* info = comm->asyncOps+c;
|
|
info->nChannels = std::min((int)DIVUP(info->nBytes, channelSize), comm->nChannels); // assign number of channels
|
|
NCCLCHECK(ncclSaveKernel(info));
|
|
}
|
|
}
|
|
// Reset counters
|
|
comm->asyncOpCount = 0;
|
|
comm->asyncTotalSize = 0;
|
|
return ncclSuccess;
|
|
}
|
|
|
|
static ncclResult_t ncclSaveAsyncColl(struct ncclInfo* info) {
|
|
ncclComm_t comm = info->comm;
|
|
if (comm->asyncOpCount >= NCCL_MAX_OPS) {
|
|
WARN("Too many async operations in progress, max is %d", NCCL_MAX_OPS);
|
|
return ncclInvalidUsage;
|
|
}
|
|
memcpy(comm->asyncOps+comm->asyncOpCount, info, sizeof(struct ncclInfo));
|
|
comm->asyncOpCount++;
|
|
comm->asyncTotalSize += info->nBytes;
|
|
return ncclSuccess;
|
|
}
|
|
|
|
// Save p2p operations in comm->p2pSends and p2pRecvs. Operations will be posted to channels
|
|
// during ncclGroupEnd()
|
|
static ncclResult_t ncclSaveP2p(struct ncclInfo* info) {
|
|
struct ncclComm* comm = info->comm;
|
|
int peer = info->root;
|
|
ssize_t nBytes = info->count*ncclTypeSize(info->datatype);
|
|
if (info->opName[0] == 'S') { // Send
|
|
if (peer != comm->rank) {
|
|
int delta = (comm->nRanks - (comm->rank-peer)) % comm->nRanks;
|
|
for (int c=0; c<comm->p2pnChannelsPerPeer; c++) {
|
|
int channelId = (delta+comm->p2pChannels[c]) % comm->p2pnChannels;
|
|
if (comm->channels[channelId].peers[peer].send.connected == 0) {
|
|
comm->connectSend[peer] |= (1<<channelId);
|
|
comm->connect = 1;
|
|
}
|
|
}
|
|
}
|
|
NCCLCHECK(enqueueP2pInfo(comm->p2pSends+info->root, (void*)info->sendbuff, nBytes));
|
|
comm->p2pSendCount++;
|
|
} else {
|
|
if (peer != comm->rank) {
|
|
int delta = (comm->nRanks + (comm->rank-peer)) % comm->nRanks;
|
|
for (int c=0; c<comm->p2pnChannelsPerPeer; c++) {
|
|
int channelId = (delta+comm->p2pChannels[c]) % comm->p2pnChannels;
|
|
if (comm->channels[channelId].peers[peer].recv.connected == 0) {
|
|
comm->connectRecv[peer] |= (1<<channelId);
|
|
comm->connect = 1;
|
|
}
|
|
}
|
|
}
|
|
NCCLCHECK(enqueueP2pInfo(comm->p2pRecvs+info->root, info->recvbuff, nBytes));
|
|
comm->p2pRecvCount++;
|
|
}
|
|
return ncclSuccess;
|
|
}
|
|
|
|
static int getSegment(struct ncclInfo* info, struct ncclWork* work) {
|
|
for (int s=0; s<NCCL_MAX_WORK_ELEMENTS && work->elems[s].p2p.delta != info->delta; s++) {
|
|
if (work->elems[s].p2p.nThreads == 0) return s;
|
|
}
|
|
return -1;
|
|
}
|
|
|
|
static ncclResult_t saveP2pOp(struct ncclInfo* info /* input */, struct ncclWork* work, int s) {
|
|
struct ncclWorkElem* elem = work->elems+s;
|
|
elem->comm = info->comm->devComm;
|
|
elem->funcIndex = FUNC_INDEX_P2P;
|
|
elem->nThreads = info->nThreads = NCCL_MAX_NTHREADS;
|
|
elem->sendbuff = info->sendbuff;
|
|
elem->recvbuff = info->recvbuff;
|
|
elem->p2p.sendCount = info->sendbytes;
|
|
elem->p2p.recvCount = info->recvbytes;
|
|
elem->p2p.delta = info->delta;
|
|
const int nsegments = s+1;
|
|
int nThreads = 512;
|
|
while (nsegments*nThreads > 512) nThreads /= 2;
|
|
if (nThreads >= 128) nThreads += WARP_SIZE;
|
|
for (int i=0; i<nsegments; i++) work->elems[i].p2p.nThreads = nThreads;
|
|
return ncclSuccess;
|
|
}
|
|
|
|
ncclResult_t ncclSaveP2pKernel(struct ncclInfo* info) {
|
|
int channelId = info->channelId;
|
|
struct ncclChannel* channel = info->comm->channels+channelId;
|
|
|
|
// Try to reuse last p2p operation if not full yet
|
|
int opIndex = (channel->workFifoTail-1+NCCL_MAX_OPS)%NCCL_MAX_OPS;
|
|
struct ncclWork* w = channel->workFifo+opIndex;
|
|
int segment = -1;
|
|
if (channel->workCount && w->elems[0].funcIndex == FUNC_INDEX_P2P && w->elems[NCCL_MAX_WORK_ELEMENTS-1].p2p.nThreads == 0) {
|
|
// Try to pack more segments into a single operation
|
|
segment = getSegment(info, w);
|
|
}
|
|
if (segment == -1) {
|
|
NCCLCHECK(getNextOp(channel, &w, NULL));
|
|
segment = 0;
|
|
}
|
|
|
|
NCCLCHECK(ncclProxySaveP2p(info, channel, segment));
|
|
NCCLCHECK(saveP2pOp(info, w, segment));
|
|
info->comm->myParams->gridDim.x = std::max<unsigned>(info->comm->myParams->gridDim.x, channelId+1);
|
|
info->comm->myParams->blockDim.x = std::max<unsigned>(info->comm->myParams->blockDim.x, info->nThreads);
|
|
|
|
return ncclSuccess;
|
|
}
|
|
|
|
ncclResult_t ncclEnqueueCheck(struct ncclInfo* info) {
|
|
// Launch asynchronously if needed
|
|
if (ncclAsyncMode()) {
|
|
ncclResult_t ret = ncclSuccess;
|
|
int savedDev = -1;
|
|
// Check arguments
|
|
NCCLCHECK(PtrCheck(info->comm, info->opName, "comm"));
|
|
if (info->comm->checkPointers) {
|
|
CUDACHECKGOTO(cudaGetDevice(&savedDev), ret, end);
|
|
CUDACHECKGOTO(cudaSetDevice(info->comm->cudaDev), ret, end);
|
|
}
|
|
NCCLCHECKGOTO(ArgsCheck(info), ret, end);
|
|
// Always register comm even in case of error to make sure ncclGroupEnd
|
|
// cleans it up.
|
|
NCCLCHECKGOTO(ncclAsyncColl(info->comm), ret, end);
|
|
NCCLCHECKGOTO(checkSetStream(info), ret, end);
|
|
|
|
INFO(NCCL_COLL,"%s: opCount %lx sendbuff %p recvbuff %p count %zi datatype %d op %d root %d comm %p [nranks=%d] stream %p",
|
|
info->opName, info->comm->opCount, info->sendbuff, info->recvbuff, info->count,
|
|
info->datatype, info->op, info->root, info->comm, info->comm->nRanks, info->stream);
|
|
|
|
if (info->coll == ncclFuncSendRecv) { //p2p stored separately
|
|
NCCLCHECKGOTO(ncclSaveP2p(info), ret, end);
|
|
} else {
|
|
NCCLCHECKGOTO(ncclSaveAsyncColl(info), ret, end);
|
|
}
|
|
end:
|
|
if (savedDev != -1) CUDACHECK(cudaSetDevice(savedDev));
|
|
ncclAsyncErrCheck(ret);
|
|
return ret;
|
|
} else {
|
|
NCCLCHECK(PtrCheck(info->comm, info->opName, "comm"));
|
|
NCCLCHECK(ArgsCheck(info));
|
|
NCCLCHECK(checkSetStream(info));
|
|
|
|
INFO(NCCL_COLL,"%s: opCount %lx sendbuff %p recvbuff %p count %zi datatype %d op %d root %d comm %p [nranks=%d] stream %p",
|
|
info->opName, info->comm->opCount, info->sendbuff, info->recvbuff, info->count,
|
|
info->datatype, info->op, info->root, info->comm, info->comm->nRanks, info->stream);
|
|
|
|
NCCLCHECK(ncclSaveKernel(info));
|
|
NCCLCHECK(ncclBarrierEnqueue(info->comm));
|
|
NCCLCHECK(ncclBarrierEnqueueWait(info->comm));
|
|
NCCLCHECK(ncclEnqueueEvents(info->comm));
|
|
return ncclSuccess;
|
|
}
|
|
}
|