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llama : prefer n_ over num_ prefix (#8308)

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Georgi Gerganov 2024-07-05 09:10:03 +03:00 committed by GitHub
parent 6c05752c50
commit aa5898dc53
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1 changed files with 6 additions and 6 deletions
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src/llama.cpp
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@ -4210,7 +4210,7 @@ struct llama_model_loader {
#if defined(GGML_USE_CUDA)
// 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
// NVMe raid configurations might require more / larger buffers.
constexpr size_t num_buffers = 4;
constexpr size_t n_buffers = 4;
constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
std::vector<ggml_backend_buffer_t> host_buffers;
@ -4236,7 +4236,7 @@ struct llama_model_loader {
// If the cuda backend is active create pinned memory buffers and events for synchronisation.
if (cuda_backend) {
for (size_t idx = 0; idx < num_buffers; ++idx) {
for (size_t idx = 0; idx < n_buffers; ++idx) {
host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size));
host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx]));
events.emplace_back(ggml_backend_event_new(cuda_backend));
@ -4317,7 +4317,7 @@ struct llama_model_loader {
bytes_read += read_iteration;
++buffer_idx;
buffer_idx %= num_buffers;
buffer_idx %= n_buffers;
}
}
else
@ -4340,7 +4340,7 @@ struct llama_model_loader {
#if defined(GGML_USE_CUDA)
// free temporary resources used for async cuda uploads
if (cuda_backend) {
for (size_t idx = 0; idx < num_buffers;++idx) {
for (size_t idx = 0; idx < n_buffers;++idx) {
ggml_backend_event_synchronize(events[idx]);
ggml_backend_event_free(events[idx]);
ggml_backend_buffer_free(host_buffers[idx]);
@ -17488,8 +17488,8 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
const llm_arch arch = qs.model.arch;
const auto tn = LLM_TN(arch);
auto use_more_bits = [](int i_layer, int num_layers) -> bool {
return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
auto use_more_bits = [](int i_layer, int n_layers) -> bool {
return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
};
const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {