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Native Erlang/OTP wrapper around llama.cpp with token-exact tiered KV cache; cache more warm state than fits in RAM

Retired package: Renamed - Renamed to barrel_inference: https://github.com/barrel-platform/barrel_inference

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erllama c_src llama.cpp ggml src ggml-cuda allreduce.cuh
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c_src/llama.cpp/ggml/src/ggml-cuda/allreduce.cuh

#pragma once
#include "common.cuh"
#include "ggml-backend-impl.h"
#include <cstddef>
// Opaque pipeline context -- owns all pinned buffers, streams, and events.
struct ggml_cuda_ar_pipeline;
// Allocate a pipeline for n_devices GPUs.
// devices[] holds the CUDA device IDs in rank order.
// Returns nullptr on allocation failure.
ggml_cuda_ar_pipeline * ggml_cuda_ar_pipeline_init(
const int * devices, size_t n_devices);
// Release all resources owned by the pipeline.
void ggml_cuda_ar_pipeline_free(ggml_cuda_ar_pipeline * pipeline);
// Execute an in-place AllReduce (sum) across tensors[0..n_devices-1].
// tensors[i] must live on the device managed by backends[i] and be
// contiguous F32, F16, or BF16.
// Preconditions are checked by the CUDA comm dispatcher before calling this.
// Returns true once the reduction work has been enqueued successfully.
bool ggml_cuda_ar_allreduce(
ggml_cuda_ar_pipeline * pipeline,
ggml_backend_t * backends,
ggml_tensor ** tensors);