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llama.cpp
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vulkan: optimized tuning for ARM Mali (stable 4x4) and Qualcomm Adreno

This commit is contained in:
Gong-Mi 2026-01-30 19:23:59 +08:00
parent 322605cb62
commit e6c76f7771
1 changed files with 25 additions and 5 deletions
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30
ggml/src/ggml-vulkan/ggml-vulkan.cpp
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@ -3037,12 +3037,25 @@ static void ggml_vk_load_shaders(vk_device& device) {
// Xe2/Xe3 with coopmat enabled - warptile performance tuning
l_warptile = { 512, 128, 128, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
l_warptile_mmq = { 512, 128, 128, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 };
} else if (device->vendor_id == VK_VENDOR_ID_QUALCOMM) {
uint32_t s = device->subgroup_size;
m_warptile = m_warptile_mmq = m_warptile_mmq_int = m_warptile_mmq_k = m_warptile_mmq_int_k =
m_warptile_id = m_warptile_mmqid = m_warptile_mmqid_int = m_warptile_mmqid_int_k =
{ 64, 64, 64, s, s, 16, 8, 8, 1, 1, s };
s_warptile = s_warptile_mmq = s_warptile_mmq_int = s_warptile_mmq_k = s_warptile_mmq_int_k =
s_warptile_id = s_warptile_mmqid = s_warptile_mmqid_int = s_warptile_mmqid_int_k =
{ 32, 32, 64, s, s, 16, 8, 8, 1, 1, s };
} else if (device->vendor_id == VK_VENDOR_ID_ARM && device->subgroup_size >= 16) {
uint32_t wm_iter = 32 / device->subgroup_size;
uint32_t wm_tile = device->subgroup_size * 2;
m_warptile_mmq = m_warptile_mmq_int = { 64, 64, 64, 16, wm_tile, 32, wm_iter, 2, 2, 1, device->subgroup_size };
m_warptile = { 64, 64, 64, 16, wm_tile, 32, wm_iter, 2, 2, 1, device->subgroup_size };
m_warptile_id = m_warptile_mmqid = { 64, 64, 64, 16, wm_tile, 32, wm_iter, 2, 2, 1, device->subgroup_size };
uint32_t s = device->subgroup_size;
s_warptile = s_warptile_mmq = s_warptile_mmq_int = s_warptile_mmq_k = s_warptile_mmq_int_k =
s_warptile_id = s_warptile_mmqid = s_warptile_mmqid_int = s_warptile_mmqid_int_k =
{ 32, 32, 64, s, s, 16, 4, 4, 1, 1, s };
m_warptile = m_warptile_mmq = m_warptile_mmq_int = m_warptile_mmq_k = m_warptile_mmq_int_k =
m_warptile_id = m_warptile_mmqid = m_warptile_mmqid_int = m_warptile_mmqid_int_k =
{ 64, 64, 64, s, s, 16, 4, 4, 1, 1, s };
l_warptile = l_warptile_mmq = l_warptile_mmq_int = l_warptile_mmq_k = l_warptile_mmq_int_k =
l_warptile_id = l_warptile_mmqid = l_warptile_mmqid_int = l_warptile_mmqid_int_k =
{ 64, 64, 64, s, s, 16, 4, 4, 1, 1, s };
}
l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 };
@ -3052,6 +3065,10 @@ static void ggml_vk_load_shaders(vk_device& device) {
m_align = 64;
s_align = 32;
if (device->vendor_id == VK_VENDOR_ID_QUALCOMM) {
m_align = 128;
}
for (uint32_t i = 0; i < GGML_TYPE_COUNT; ++i) {
ggml_type t = (ggml_type)i;
// Disable medium and large matrix multiplication if not enough shared memory is available
@ -13752,6 +13769,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
// (and scaled down based on model size, so smaller models submit earlier).
// Also submit at least every 100 nodes, in case there are workloads without as much matmul.
int nodes_per_submit = 100;
if (ctx->device->vendor_id == VK_VENDOR_ID_QUALCOMM) {
nodes_per_submit = 1000;
}
int submitted_nodes = 0;
int submit_count = 0;
uint64_t mul_mat_bytes = 0;