95 lines
3.9 KiB
Plaintext
95 lines
3.9 KiB
Plaintext
// managed_alloc.cu - cudaMallocManaged allocator for PyTorch
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// Compile: nvcc -shared -o libmanaged_alloc.so managed_alloc.cu -Xcompiler -fPIC
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// Compatible with CUDA 13+ (uses cudaMemLocation API)
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//
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// Key design decisions for GH200 EGM:
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// 1. cudaMallocManaged → allocations can page-fault across HBM + EGM
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// 2. cudaMemAdviseSetPreferredLocation(GPU) → driver prefers keeping pages on GPU
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// 3. cudaMemAdviseSetAccessedBy(CPU) → CPU can access over C2C NVLink without
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// triggering page migration back to system RAM (critical: prevents OOM)
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// 4. cudaMemPrefetchAsync(GPU) → actively migrates pages to GPU immediately,
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// so subsequent writes go to HBM/EGM, not system RAM (prevents OOM on
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// systems where EGM carved out most of system memory)
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#include <cuda_runtime.h>
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#include <stdio.h>
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extern "C" {
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// PyTorch pluggable allocator signature: void*(size_t, int, cudaStream_t)
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void* managed_malloc(size_t size, int device, cudaStream_t stream) {
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void* ptr = nullptr;
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// Set the device before allocating
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cudaError_t err = cudaSetDevice(device);
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if (err != cudaSuccess) {
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fprintf(stderr, "[managed_alloc] cudaSetDevice(%d) failed: %s\n",
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device, cudaGetErrorString(err));
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return nullptr;
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}
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// Use cudaMallocManaged - this is the key: allocations can page-fault
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// across HBM and LPDDR on GH200 with EGM enabled
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err = cudaMallocManaged(&ptr, size, cudaMemAttachGlobal);
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if (err != cudaSuccess) {
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fprintf(stderr, "[managed_alloc] cudaMallocManaged failed: %s "
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"(size=%zu bytes / %.2f GiB)\n",
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cudaGetErrorString(err), size, (double)size / (1024.0*1024.0*1024.0));
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return nullptr;
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}
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// CUDA 13+ uses cudaMemLocation struct instead of int for device
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cudaMemLocation gpu_loc;
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gpu_loc.type = cudaMemLocationTypeDevice;
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gpu_loc.id = device;
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// Advise: prefer GPU placement. On GH200 with EGM, the hardware will
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// migrate pages as needed, but the driver tries to keep them on GPU.
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cudaMemAdvise(ptr, size, cudaMemAdviseSetPreferredLocation, gpu_loc);
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// Advise: CPU will access this memory too. On GH200, this sets up
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// remote mapping over C2C NVLink so CPU can read/write without
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// triggering page migration back to system RAM. This is CRITICAL
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// to prevent OOM on EGM systems where most system RAM was carved
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// out for the GPU.
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cudaMemLocation cpu_loc;
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cpu_loc.type = cudaMemLocationTypeHost;
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cpu_loc.id = cudaCpuDeviceId;
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cudaMemAdvise(ptr, size, cudaMemAdviseSetAccessedBy, cpu_loc);
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// Prefetch to GPU immediately. This actively migrates the virtual
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// pages to the GPU side so that subsequent writes (e.g., model weight
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// loading) go directly to HBM/EGM instead of pinning system RAM.
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// Without this, the first write to each page faults into system RAM,
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// which causes OOM when the OS only has ~102 GiB after EGM carveout.
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//
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// CUDA 13+ signature: cudaMemPrefetchAsync(ptr, size, cudaMemLocation, flags)
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// Note: no stream parameter in the cudaMemLocation overload.
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// flags=0 means no special flags.
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if (size > 0) {
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err = cudaMemPrefetchAsync(ptr, size, gpu_loc, 0);
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if (err != cudaSuccess) {
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// Non-fatal: prefetch failure shouldn't prevent allocation.
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// Pages will still be migrated on demand.
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fprintf(stderr, "[managed_alloc] cudaMemPrefetchAsync warning: %s "
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"(size=%.2f GiB, will use on-demand migration)\n",
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cudaGetErrorString(err), (double)size / (1024.0*1024.0*1024.0));
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}
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}
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return ptr;
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}
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// PyTorch pluggable allocator signature: void(void*, size_t, int, cudaStream_t)
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void managed_free(void* ptr, size_t size, int device, cudaStream_t stream) {
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if (ptr != nullptr) {
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// Sync the stream before freeing to avoid use-after-free with
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// managed memory (in-flight page faults can race with deallocation).
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if (stream != nullptr) {
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cudaStreamSynchronize(stream);
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}
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cudaFree(ptr);
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}
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}
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} // extern "C"
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