biondizzle
2757bffcb6
- managed_alloc.cu: PyTorch pluggable allocator using cudaMallocManaged - vllm_managed_mem.py: Launcher that patches vLLM for managed memory - Dockerfile: Build and install managed memory components This enables vLLM to use cudaMallocManaged for transparent page-fault access to both HBM (~96 GiB) and LPDDR (EGM, up to 480 GiB additional) on GH200 systems with Extended GPU Memory enabled. Experimental branch: v0.19.0-cmm
49 lines
1.6 KiB
Plaintext
49 lines
1.6 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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#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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// Advise the driver to prefer GPU placement initially.
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// On GH200 with EGM, the hardware will migrate pages as needed.
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cudaMemAdvise(ptr, size, cudaMemAdviseSetPreferredLocation, device);
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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
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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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