try fix fattn again, porting some older code. the cc detection is not working well, so its hacky

Author: LostRuins

ggml/src/ggml-cuda/fattn.cu

@@ -298,6 +298,12 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
     const int warp_size = ggml_cuda_info().devices[device].warp_size;
     const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
 
+    #if defined(GGML_HIP_ROCWMMA_FATTN)
+    if (GGML_CUDA_CC_IS_AMD(cc) && fp16_mma_available(cc)) { //kcpp: fix for rocwmma
+        return BEST_FATTN_KERNEL_WMMA_F16;
+    }
+    #endif // defined(GGML_HIP_ROCWMMA_FATTN)
+
     switch (K->ne[0]) {
         case  64:
         case 128:
@@ -415,15 +421,21 @@ static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const
         return BEST_FATTN_KERNEL_WMMA_F16;
     }
 
-    //kcpp: always force WMMA for older gpus, fix issues like "FlashAttention without tensor cores only supports head sizes 64 and 128."
-    if (ggml_cuda_highest_compiled_arch(cc) <= GGML_CUDA_CC_TURING || cc == GGML_CUDA_CC_TURING) {
+    //kcpp: always force WMMA for Turing and Volta if above check fails, fix "FlashAttention without tensor cores only supports head sizes 64 and 128."
+    if (cc == GGML_CUDA_CC_TURING || cc == GGML_CUDA_CC_VOLTA) {
         return BEST_FATTN_KERNEL_WMMA_F16;
     }
 
     // If there is no suitable kernel for tensor cores or small batch sizes, use the generic kernel for large batch sizes:
     if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
+        if (Q->ne[1] <= 8 || Q->ne[0] == 256) {
+            return BEST_FATTN_KERNEL_VEC_F16; //kcpp: patch from previous version for my sanity. it worked before, idk it should work now.
+        }
         return BEST_FATTN_KERNEL_TILE_F16;
     }
+    if (Q->ne[1] <= 8 || Q->ne[0] == 256) {
+        return BEST_FATTN_KERNEL_VEC_F32;  //kcpp: patch from previous version for my sanity. it worked before, idk it should work now.
+    }
     return BEST_FATTN_KERNEL_TILE_F32;
 }