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Fix Tesla K80 VMM pool crash by aligning to granularity

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- Fix CUDA_ERROR_INVALID_VALUE from cuMemAddressReserve by aligning max_pool_size to GPU granularity
- Set dynamic max_pool_size based on 90% of actual GPU memory instead of static 32GB
- Add memory availability check before allocation to prevent OOM
- Tested on Tesla K80 dual GPU setup with successful model loading and chat completions

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Shang Chieh Tseng
2025-08-08 17:48:31 +08:00
parent e4113f080a
commit 46213c5880
1 changed files with 30 additions and 4 deletions
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34
ml/backend/ggml/ggml/src/ggml-cuda/ggml-cuda.cu vendored
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@@ -399,13 +399,14 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool {
// pool with virtual memory
#if defined(GGML_USE_VMM)
struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB
static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB default
int device;
CUdeviceptr pool_addr = 0;
size_t pool_used = 0;
size_t pool_size = 0;
size_t granularity;
size_t max_pool_size;
#if defined(GGML_USE_HIP)
std::vector<std::pair<CUdeviceptr, size_t>> mappings;
#endif
@@ -413,6 +414,16 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
explicit ggml_cuda_pool_vmm(int device) :
device(device),
granularity(ggml_cuda_info().devices[device].vmm_granularity) {
// Get actual GPU memory and set a reasonable max pool size
size_t free_mem, total_mem;
ggml_cuda_set_device(device);
CUDA_CHECK(cudaMemGetInfo(&free_mem, &total_mem));
// Use 90% of total GPU memory as max, or default 32GB, whichever is smaller
max_pool_size = std::min(CUDA_POOL_VMM_MAX_SIZE, (size_t)(total_mem * 0.9));
// CRITICAL: Align max_pool_size to granularity to avoid CUDA_ERROR_INVALID_VALUE
max_pool_size = ((max_pool_size + granularity - 1) / granularity) * granularity;
}
~ggml_cuda_pool_vmm() {
@@ -425,7 +436,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
#else
CU_CHECK(cuMemUnmap(pool_addr, pool_size));
#endif
CU_CHECK(cuMemAddressFree(pool_addr, CUDA_POOL_VMM_MAX_SIZE));
CU_CHECK(cuMemAddressFree(pool_addr, max_pool_size));
}
}
@@ -441,7 +452,22 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
size_t reserve_size = size - avail;
reserve_size = granularity * ((reserve_size + granularity - 1) / granularity);
GGML_ASSERT(pool_size + reserve_size <= CUDA_POOL_VMM_MAX_SIZE);
GGML_ASSERT(pool_size + reserve_size <= max_pool_size);
// Check if we have enough free memory before attempting allocation
size_t free_mem, total_mem;
ggml_cuda_set_device(device);
CUDA_CHECK(cudaMemGetInfo(&free_mem, &total_mem));
if (reserve_size > free_mem) {
// Not enough free memory, reduce reserve_size to what's available
reserve_size = (free_mem / granularity) * granularity; // round down to granularity
if (reserve_size == 0) {
GGML_LOG_WARN("%s: Not enough free GPU memory on device %d (requested: %zu, available: %zu)\n",
__func__, device, size, free_mem);
return nullptr;
}
}
// allocate more physical memory
CUmemAllocationProp prop = {};
@@ -453,7 +479,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
// reserve virtual address space (if not already reserved)
if (pool_addr == 0) {
CU_CHECK(cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0));
CU_CHECK(cuMemAddressReserve(&pool_addr, max_pool_size, 0, 0, 0));
}
// map at the end of the pool