CUDA: skip masked KV slices for all FA kernels (#14924)

ggml/src/ggml-cuda/fattn-vec-f16.cuh

@@ -16,6 +16,7 @@ static __global__ void flash_attn_vec_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
+        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -177,10 +178,11 @@ static __global__ void flash_attn_vec_ext_f16(
 
     half2 VKQ[ncols] = {{0.0f, 0.0f}};
 
+    const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
     K     += blockIdx.y*D * nb11;
     V     += blockIdx.y*D * nb21;
     maskh += blockIdx.y*D;
-    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*D,
+    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*D,
              // Increment pointers after each loop:
              K += gridDim.y*D*nb11, V += gridDim.y*D*nb21, maskh += gridDim.y*D) {
 
@@ -191,29 +193,7 @@ static __global__ void flash_attn_vec_ext_f16(
             for (int j = 0; j < ncols; ++j) {
                 maskh_shared[j*D + tid] = slopeh*maskh[j*ne11 + tid];
             }
-
             __syncthreads();
-
-            // When using multiple parallel sequences in llama.cpp, some KV slices can be fully masked out.
-            // In such cases, skip the KV slice.
-            // On AMD __all_sync would not work correctly because it assumes a warp size of 64.
-#ifndef GGML_USE_HIP
-            bool skip = true;
-#pragma unroll
-            for (int j = 0; j < ncols; ++j) {
-#pragma unroll
-                for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
-                    const int i = i0 + threadIdx.x;
-
-                    const float2 tmp = __half22float2(((const half2 *) maskh_shared)[j*(D/2) + i]);
-                    skip = skip && isinf(tmp.x) && isinf(tmp.y);
-                }
-            }
-            if (__all_sync(0xFFFFFFFF, skip)) {
-                __syncthreads();
-                continue;
-            }
-#endif // GGML_USE_HIP
         }
 
         // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,