提交 046bb5c8 编写于 作者: Q Qiyang Min 提交者: Wu Yi

Fix NCCLBcast hang up bug in Parallel Executor (#11377)

* 1. Create buddy allocator in each places before NcclBcast the variables
2. Check the memory usage of ALL gpus rather than the first one

* 1. Make NCCLGroupGuard guards only the ncclBcast part, which avoid ncclGroupEnd blocking the exception throwing
2. NOTE the usage of NCCLGroupGuard

* Remove the memory usage check of gpus

* Fix code style
上级 cbaa24f5
......@@ -145,9 +145,9 @@ void ParallelExecutor::BCastParamsToGPUs(
auto &dims = main_tensor.dims();
if (paddle::platform::is_gpu_place(main_tensor.place())) {
#ifdef PADDLE_WITH_CUDA
std::vector<void *> buffers;
size_t numel = main_tensor.numel();
ncclDataType_t data_type = platform::ToNCCLDataType(main_tensor.type());
platform::NCCLGroupGuard guard;
for (size_t i = 0; i < member_->places_.size(); ++i) {
auto place = member_->places_[i];
void *buffer;
......@@ -159,11 +159,21 @@ void ParallelExecutor::BCastParamsToGPUs(
t->Resize(dims);
buffer = t->mutable_data(place, main_tensor.type());
}
auto &nccl_ctx = member_->nccl_ctxs_->at(place);
platform::dynload::ncclBcast(buffer, numel, data_type, 0,
buffers.push_back(buffer);
}
PADDLE_ENFORCE_EQ(member_->places_.size(), buffers.size(),
"variables' buffer size to bcast NOT equal to places");
{
platform::NCCLGroupGuard guard;
for (size_t i = 0; i < member_->places_.size(); ++i) {
auto &nccl_ctx = member_->nccl_ctxs_->at(member_->places_[i]);
platform::dynload::ncclBcast(buffers[i], numel, data_type, 0,
nccl_ctx.comm_, nccl_ctx.stream());
}
member_->nccl_ctxs_->WaitAll();
}
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
......
......@@ -41,6 +41,11 @@ inline ncclDataType_t ToNCCLDataType(std::type_index type) {
}
}
// NOTE(minqiyang): according to the ncclGroupEnd documentations:
// https://docs.nvidia.com/deeplearning/sdk/nccl-api/ncclapidoc.html,
// ncclGroupEnd will wait for all communicators to be initialized, which will
// cause blocking problem when a runtime_error was thrown, so try only guard
// NCCL actions when use it.
class NCCLGroupGuard {
public:
static std::mutex &NCCLMutex() {
......
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