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#include "caffe2/contrib/gloo/allreduce_ops.h"
#include "caffe2/core/context_gpu.h"
#include "caffe2/core/logging.h"
#include <gloo/cuda_allreduce_bcube.h>
#include <gloo/cuda_allreduce_halving_doubling.h>
#include <gloo/cuda_allreduce_ring.h>
#include <gloo/cuda_allreduce_ring_chunked.h>
#include <gloo/types.h>
namespace caffe2 {
namespace gloo {
namespace {
// Decides on using GPUDirect based on device support.
template <template <typename T, typename W> class A, typename T>
std::unique_ptr<::gloo::Algorithm> initializeAlgorithm(
bool gpu_direct_,
std::shared_ptr<::gloo::Context> context,
std::vector<T*> ptrs,
size_t size) {
if (gpu_direct_) {
if (context->getDevice()->hasGPUDirect()) {
return std::unique_ptr<::gloo::Algorithm>(
new A<T, ::gloo::CudaDeviceWorkspace<T>>(context, ptrs, size));
} else {
LOG(WARNING)
<< "GPUDirect not available; "
<< "Gloo communication will go through system memory instead.";
}
}
return std::unique_ptr<::gloo::Algorithm>(
new A<T, ::gloo::CudaHostWorkspace<T>>(context, ptrs, size));
}
/**
* This is a helper function which attempts to get a base value depending on the
* # of nodes. Larger the base the better performance (up to 4) is what we have
* observed in gloo benchmarks. At the moment bcube works only if # nodes = base
* ^ x. Where x is some constant. So, if # node don't match our expectation
* simply return -1. This will indicate caller to switch to another algorithm
* like halving-doubling.
*/
static int getAllrduceBcubeBase(int nodes) {
auto getExponent = [](int n, int b) -> int {
float lg2n = log2(n);
float lg2b = log2(b);
return ceil(lg2n / lg2b);
};
auto baseCheck = [&](int n, int b) -> bool {
int e = getExponent(n, b);
return n == pow(b, e);
};
for (const auto base : {6, 5, 4, 3, 2}) {
if (baseCheck(nodes, base)) {
return base;
}
/*
* Base could work if # nodes is multiple of the base yet smaller than
* base^2
*/
if (nodes < base * base && 0 == nodes % base) {
return base;
}
}
return -1;
}
} // namespace
template <class Context>
void AllreduceOp<Context>::initializeBcube() {
int base = getAllrduceBcubeBase(init_.size);
if (-1 == base) {
return initializeHalvingDoubling();
}
init_.context->base = base;
if (init_.template IsType<float>()) {
algorithm_ = initializeAlgorithm<::gloo::CudaAllreduceBcube, float>(
gpu_direct_,
init_.context,
init_.template getOutputs<float>(),
init_.size);
} else if (init_.template IsType<at::Half>()) {
algorithm_ =
initializeAlgorithm<::gloo::CudaAllreduceBcube, ::gloo::float16>(
gpu_direct_,
init_.context,
init_.template getOutputs<::gloo::float16>(),
init_.size);
} else {
CAFFE_ENFORCE(false, "Unhandled type: ", init_.meta.name());
}
}
template <class Context>
void AllreduceOp<Context>::initializeHalvingDoubling() {
if (init_.template IsType<float>()) {
algorithm_ =
initializeAlgorithm<::gloo::CudaAllreduceHalvingDoubling, float>(
gpu_direct_,
init_.context,
init_.template getOutputs<float>(),
init_.size);
} else if (init_.template IsType<at::Half>()) {
algorithm_ =
initializeAlgorithm<::gloo::CudaAllreduceHalvingDoubling, ::gloo::float16>(
gpu_direct_,
init_.context,
init_.template getOutputs<::gloo::float16>(),
init_.size);
} else {
CAFFE_ENFORCE(false, "Unhandled type: ", init_.meta.name());
}
}
template <class Context>
void AllreduceOp<Context>::initializeRingFull() {
if (init_.template IsType<float>()) {
algorithm_ =
initializeAlgorithm<::gloo::CudaAllreduceRing, float>(
gpu_direct_,
init_.context,
init_.template getOutputs<float>(),
init_.size);
} else if (init_.template IsType<at::Half>()) {
algorithm_ =
initializeAlgorithm<::gloo::CudaAllreduceRing, ::gloo::float16>(
gpu_direct_,
init_.context,
init_.template getOutputs<::gloo::float16>(),
init_.size);
} else {
CAFFE_ENFORCE(false, "Unhandled type: ", init_.meta.name());
}
}
template <class Context>
void AllreduceOp<Context>::initializeRingChunked() {
if (init_.template IsType<float>()) {
algorithm_ =
initializeAlgorithm<::gloo::CudaAllreduceRingChunked, float>(
gpu_direct_,
init_.context,
init_.template getOutputs<float>(),
init_.size);
} else if (init_.template IsType<at::Half>()) {
algorithm_ =
initializeAlgorithm<::gloo::CudaAllreduceRingChunked, ::gloo::float16>(
gpu_direct_,
init_.context,
init_.template getOutputs<::gloo::float16>(),
init_.size);
} else {
CAFFE_ENFORCE(false, "Unhandled type: ", init_.meta.name());
}
}
namespace {
REGISTER_CUDA_OPERATOR_WITH_ENGINE(Allreduce, GLOO, AllreduceOp<CUDAContext>);
} // namespace
} // namespace gloo
} // namespace caffe2
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