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fix compute_buffer estimate: add context GPU usage

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Li, Zonghang 2025-06-24 16:09:59 +04:00
parent c926088d6a
commit 16ba3564ce
2 changed files with 16 additions and 12 deletions
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2
common/common.cpp
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@ -986,7 +986,7 @@ static bool assign_layers_to_device(
if ((is_macos && !dev.gpu_support.metal) || is_linux) { if ((is_macos && !dev.gpu_support.metal) || is_linux) {
mem_budget[m] = dev.memory.available_physical; mem_budget[m] = dev.memory.available_physical;
} else if (is_macos && dev.gpu_support.metal) { } else if (is_macos && dev.gpu_support.metal) {
mem_budget[m] = dev.gpu_props.memory_free; mem_budget[m] = dev.gpu_props.memory_free + 1e-4; // to avoid division by zero
} else if (is_android) { } else if (is_android) {
mem_budget[m] = dev.memory.available_physical + dev.memory.used_can_swap; mem_budget[m] = dev.memory.available_physical + dev.memory.used_can_swap;
} else { } else {

24
src/llama.cpp
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@ -3669,12 +3669,8 @@ void llama_profile_device(
#if defined(GGML_USE_METAL) || defined(GGML_USE_CUDA) #if defined(GGML_USE_METAL) || defined(GGML_USE_CUDA)
dev_info->gpu_props.name = gpu_props.name; dev_info->gpu_props.name = gpu_props.name;
dev_info->gpu_props.description = gpu_props.description; dev_info->gpu_props.description = gpu_props.description;
// reserved/limit memory to avoid potential OOM, default to 300 MiB
dev_info->gpu_props.memory_free = round(gpu_props.memory_free / (double)(1 << 30) * 100) / 100; dev_info->gpu_props.memory_free = round(gpu_props.memory_free / (double)(1 << 30) * 100) / 100;
dev_info->gpu_props.memory_free = std::min((float)gpu_mem, dev_info->gpu_props.memory_free) - 0.3; dev_info->gpu_props.memory_free = std::min((float)gpu_mem, dev_info->gpu_props.memory_free);
dev_info->gpu_props.memory_free = std::max(dev_info->gpu_props.memory_free, 0.0f);
dev_info->gpu_props.memory_total = round(gpu_props.memory_total / (double)(1 << 30) * 100) / 100; dev_info->gpu_props.memory_total = round(gpu_props.memory_total / (double)(1 << 30) * 100) / 100;
dev_info->gpu_props.metal_read_vram_bw = device_metal_read_vram_bw(); dev_info->gpu_props.metal_read_vram_bw = device_metal_read_vram_bw();
dev_info->gpu_props.cuda_read_vram_bw = device_cuda_read_vram_bw(); dev_info->gpu_props.cuda_read_vram_bw = device_cuda_read_vram_bw();
@ -21324,6 +21320,9 @@ void * llama_context_setup_backend(
std::vector<ggml_cgraph *> gf = llama_build_graph(*ctx, ubatch, true); std::vector<ggml_cgraph *> gf = llama_build_graph(*ctx, ubatch, true);
GGML_ASSERT(gf.size() <= MAX_SCHEDULERS && "Number of subgraphs exceeds the maximum number of schedulers\n"); GGML_ASSERT(gf.size() <= MAX_SCHEDULERS && "Number of subgraphs exceeds the maximum number of schedulers\n");
for (size_t i = gf.size(); i < ctx->sched.size(); ++i) {
ggml_backend_sched_free(ctx->sched[i]);
}
ctx->sched.resize(gf.size()); ctx->sched.resize(gf.size());
// initialize scheduler with the worst-case graph // initialize scheduler with the worst-case graph
@ -21993,7 +21992,7 @@ void llama_model_compute_buf_size(
GGML_ASSERT(false && "Unsupported backend type for compute buffer estimation.\n"); GGML_ASSERT(false && "Unsupported backend type for compute buffer estimation.\n");
} }
// CPU compute buffer // CPU compute buffer for NUMA system or Metal with ngl=0
if (*cpu_buf == 0) { if (*cpu_buf == 0) {
*cpu_buf = (n_inp_pos + n_kq_mask + n_bak_embd + n_norm) * type_size_f32; *cpu_buf = (n_inp_pos + n_kq_mask + n_bak_embd + n_norm) * type_size_f32;
if (is_master) { if (is_master) {
@ -22005,10 +22004,15 @@ void llama_model_compute_buf_size(
LLAMA_LOG_INFO("\n"); LLAMA_LOG_INFO("\n");
LLAMA_LOG_INFO("%s: here the compute buffer size is a predicted upper bound, not an exact value\n", __func__); LLAMA_LOG_INFO("%s: here the compute buffer size is a predicted upper bound, not an exact value\n", __func__);
LLAMA_LOG_INFO("%s: (rank %d) compute buffer size = %7.2f MiB (GPU) + %7.2f MiB (GPU-Host)\n", __func__, LLAMA_LOG_INFO("%s: (rank %d) compute buffer size = %7.2f MiB (GPU) + %7.2f MiB (CPU / GPU-host buffer)\n", __func__,
my_rank, *gpu_buf / (1024.0 * 1024.0), gpu_host_buf / (1024.0 * 1024.0)); my_rank, *gpu_buf / (1024.0 * 1024.0), *cpu_buf / (1024.0 * 1024.0));
LLAMA_LOG_INFO("%s: (rank %d) compute buffer size = %7.2f MiB (CPU and GPU-Host buffer)\n", __func__,
my_rank, *cpu_buf / (1024.0 * 1024.0)); if (backend == BACKEND_CUDA) {
// context GPU memory usage, i.e. the initial memory cost of creating a CUDA context,
// even before you launch any kernels or allocate your own buffers.
// this value may vary by GPU and CUDA version, but it's lower than 400 MiB in most cases.
*gpu_buf += 400 * 1024 * 1024;
}
} }
void llama_total_kv_size( void llama_total_kv_size(