@@ -380,7 +380,7 @@ struct ggml_backend_opencl_device_context {
380380 ADRENO_GPU_GEN adreno_gen = ADRENO_GPU_GEN::ADRENO_UNKNOWN;
381381
382382 std::regex *opfilter = nullptr; // regex of ops to not claim
383- std::string opfilter_str; // regex string for opfilter
383+ std::string opfilter_str = "" ; // regex string for opfilter
384384 size_t global_mem_size = 0;
385385};
386386
@@ -6822,9 +6822,6 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
68226822
68236823 cl_buffer_region region;
68246824
6825- cl_uchar mask_0F = 0x0F;
6826- cl_uchar mask_F0 = 0xF0;
6827-
68286825#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
68296826 // Adreno MoE Q6_K kernel needs special transposed layout
68306827 if (use_adreno_moe_kernels(backend_ctx, tensor)) {
@@ -6858,6 +6855,9 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
68586855
68596856 cl_kernel kernel = backend_ctx->kernel_convert_block_q6_k_trans4_ns;
68606857
6858+ cl_uchar mask_0F = 0x0F;
6859+ cl_uchar mask_F0 = 0xF0;
6860+
68616861 int ne00 = tensor->ne[0];
68626862 int ne01 = tensor->ne[1];
68636863 int ne02 = tensor->ne[2];
@@ -6994,7 +6994,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
69946994
69956995 cl_int err;
69966996 cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
6997- size, ( void *) data, &err);
6997+ size, const_cast< void *>( data) , &err);
69986998 CL_CHECK(err);
69996999
70007000 cl_kernel kernel = backend_ctx->kernel_convert_bf16_to_f16;
@@ -7782,9 +7782,6 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
77827782 if (tensor->type == GGML_TYPE_Q6_K) {
77837783 ggml_tensor_extra_cl_q6_K * extra = (ggml_tensor_extra_cl_q6_K *)tensor->extra;
77847784
7785- cl_uchar mask_0F = 0x0F;
7786- cl_uchar mask_F0 = 0xF0;
7787-
77887785#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
77897786 if (use_adreno_moe_kernels(backend_ctx, tensor)) {
77907787 cl_int err;
@@ -7794,6 +7791,9 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
77947791
77957792 cl_kernel kernel = backend_ctx->kernel_restore_block_q6_k_trans4_ns;
77967793
7794+ cl_uchar mask_0F = 0x0F;
7795+ cl_uchar mask_F0 = 0xF0;
7796+
77977797 int ne00 = tensor->ne[0];
77987798 int ne01 = tensor->ne[1];
77997799 int ne02 = tensor->ne[2];
@@ -14888,6 +14888,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
1488814888 const int ne1 = dst->ne[1];
1488914889 const int ne2 = dst->ne[2];
1489014890
14891+ GGML_UNUSED(ne2);
14892+
1489114893 const int r2 = ne12/ne02;
1489214894 const int r3 = ne13/ne03;
1489314895 const int dst_rows = ne20*ne21; // ne20 = n_used_experts, ne21 = n_rows
@@ -14902,6 +14904,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
1490214904 const int n_tile_size = 32;
1490314905 const int max_post_router_tile = (ne20 * ne21 / n_tile_size) + ne02;
1490414906
14907+ GGML_UNUSED(max_post_router_tile);
14908+
1490514909 cl_kernel kernel;
1490614910
1490714911 // subgroup mat vec
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