75 lines
1.9 KiB
C++
75 lines
1.9 KiB
C++
#include <ATen/record_function.h>
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#include <torch/all.h>
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#include "shm.h"
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// Communication settings
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static int world_rank = -1;
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static int world_size = -1;
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static bool is_initialized = false;
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static bool all_ranks_local_p = false;
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void initialize(int64_t size, int64_t rank) {
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if (is_initialized) {
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return;
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}
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// Check whether all ranks is on the same physical machine.
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// If true, we will use an SHM based low latency allreduce
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auto ls_string = std::getenv("LOCAL_SIZE");
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int ls = 0;
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if (ls_string != NULL) {
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ls = std::stoi(std::getenv("LOCAL_SIZE"));
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}
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if (size >= 1 && size == ls) {
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all_ranks_local_p = true;
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}
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world_size = size;
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world_rank = rank;
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is_initialized = true;
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const char* addr_string = std::getenv("MASTER_ADDR");
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if (addr_string == NULL) {
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addr_string = "";
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}
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const char* port_string = std::getenv("MASTER_PORT");
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if (port_string == NULL) {
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port_string = "";
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}
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if (all_ranks_local_p) {
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shm_initialize(size, rank, addr_string, port_string);
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}
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}
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void shm_allreduce(torch::Tensor& data, int64_t op) {
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RECORD_FUNCTION("sgl-kernel::shm_allreduce", std::vector<c10::IValue>({data}));
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TORCH_CHECK(op == c10d::ReduceOp::SUM, "Only torch.distributed.ReduceOp.SUM is supported");
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auto numel = data.numel();
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int data_size = numel * data.element_size();
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all_reduce_outer_loop(data, numel, data_size);
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return;
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}
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torch::Tensor shm_allgather(torch::Tensor& data, int64_t dim) {
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RECORD_FUNCTION("sgl-kernel::shm_allgather", std::vector<c10::IValue>({data}));
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auto numel = data.numel();
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int data_size = numel * data.element_size();
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if (dim < 0) {
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dim += data.dim();
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}
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std::vector<int64_t> result_shape = data.sizes().vec();
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result_shape[dim] *= world_size;
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torch::Tensor result_tensor = torch::empty(result_shape, data.options());
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return all_gather(result_tensor, data, dim, numel, data_size);
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}
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