metal : matrix-matrix multiplication kernel (#2615)

* metal: matrix-matrix multiplication kernel

This commit removes MPS and uses custom matrix-matrix multiplication
kernels for all quantization types. This commit also adds grouped-query
attention to support llama2 70B.

* metal: fix performance degradation from gqa

Integers are slow on the GPU, and 64-bit divides are extremely slow.
In the context of GQA, we introduce a 64-bit divide that cannot be
optimized out by the compiler, which results in a decrease of ~8% in
inference performance. This commit fixes that issue by calculating a
part of the offset with a 32-bit divide. Naturally, this limits the
size of a single matrix to ~4GB. However, this limitation should
suffice for the near future.

* metal: fix bugs for GQA and perplexity test.

I mixed up ne02 and nb02 in previous commit.

1 files changed, 1 insertions, 17 deletions

diff --git a/llama.cpp b/llama.cpp
index c8ab313d..a161f156 100644
--- a/llama.cpp
+++ b/llama.cpp
@@ -1845,7 +1845,7 @@ static bool llama_eval_internal(
 #endif
 
 #ifdef GGML_USE_METAL
-    if (lctx.ctx_metal && N == 1) {
+    if (lctx.ctx_metal) {
         // TODO: disabled until #2413 is resolved
         //if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
         //    ggml_metal_graph_find_concurrency(lctx.ctx_metal, gf);
@@ -1857,22 +1857,6 @@ static bool llama_eval_internal(
             ggml_metal_get_tensor(lctx.ctx_metal, embeddings);
         }
     } else {
-        // IMPORTANT:
-        // Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
-        // ggml_graph_compute(). It uses Apple's Accelerate CBLAS API which takes advantage of the ANE or the AMX
-        // coprocessor.
-        //
-        // When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
-        // But for now, we have focused only on Matrix x Vector Metal multiplication.
-        //
-        // TODO: avoid these syncs via shared memory (ref #1696)
-        //
-        if (lctx.ctx_metal) {
-            // We need to sync the GPU KV cache with the CPU KV cache
-            ggml_metal_get_tensor(lctx.ctx_metal, kv_self.k);
-            ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
-        }
-
         ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
     }
 #else