Reduce compiler warnings step 2

ggml/src/ggml-sycl/common.cpp

@@ -11,6 +11,7 @@
 //
 
 #include "common.hpp"
+#include "ggml-impl.h"
 
 int get_current_device_id() {
   return dpct::dev_mgr::instance().current_device_id();
@@ -28,11 +29,7 @@ void* ggml_sycl_host_malloc(size_t size) try {
 
   if (err != 0) {
     // clear the error
-    fprintf(
+    GGML_LOG_ERROR("WARNING: failed to allocate %.2f MB of pinned memory: %s\n", size / 1024.0 / 1024.0,    "syclGetErrorString is not supported");
-        stderr,
-        "WARNING: failed to allocate %.2f MB of pinned memory: %s\n",
-        size / 1024.0 / 1024.0,
-        "syclGetErrorString is not supported");
     return nullptr;
   }
 
@@ -66,17 +63,21 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block
 void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
                                  const ggml_tensor *src1, ggml_tensor *dst,
                                  const ggml_sycl_op_flatten_t op) try {
-    const int64_t nrows0 = ggml_nrows(src0);
+
+    // TODO: What's the use of these?
+    // const int64_t nrows0 = ggml_nrows(src0);
+    // const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
 
     const bool use_src1 = src1 != nullptr;
-    const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1;
 
     GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
     GGML_ASSERT(              dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT);
 
-    ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
+    // TODO: What are these uses of these?
-    ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+
-    ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
+    // ggml_tensor_extra_gpu * src0_extra =            (ggml_tensor_extra_gpu *) src0->extra;
+    // ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr;
+    // ggml_tensor_extra_gpu * dst_extra  =            (ggml_tensor_extra_gpu *)  dst->extra;
 
     // dd = data device
     float * src0_ddf = (float *) src0->data;

ggml/src/ggml-sycl/convert.cpp

@@ -424,7 +424,7 @@ static void convert_unary(const void * __restrict__ vx, dst_t * __restrict__ y,
     const int64_t global_id = item_ct1.get_local_id(2) + work_group_size * item_ct1.get_group(2);
 
     // make each work-item deal with more elements since sycl global range can not exceed max int
-    const src_t * x = (src_t *) vx;
+    const src_t * x = (const src_t *) vx;
     for (int64_t i = global_id; i < k; i += work_group_size * item_ct1.get_group_range(2)) {
         y[i] = x[i];
     }

ggml/src/ggml-sycl/element_wise.cpp

@@ -237,7 +237,7 @@ void upscale_f32(const float  *x, float *dst, const int nb00, const int nb01,
     int i02 = i12 / sf2;
     int i03 = i13 / sf3;
 
-    dst[index] = *(float *)((const char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
+    dst[index] = *(const float *)((const char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00);
 }
 
 void pad_f32(const float  *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02,
@@ -251,8 +251,7 @@ void pad_f32(const float  *x, float *dst, const int ne0, const int ne00, const i
     // operation
     int offset_dst = nidx + item_ct1.get_group(1) * ne0 +
                      item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1);
-    if (nidx < ne00 && item_ct1.get_group(1) < ne01 &&
+    if (nidx < ne00 && item_ct1.get_group(1) < (size_t) ne01 && item_ct1.get_group(0) < (size_t) ne02) {
-        item_ct1.get_group(0) < ne02) {
         int offset_src = nidx + item_ct1.get_group(1) * ne00 +
                          item_ct1.get_group(0) * ne00 * ne01;
             dst[offset_dst] = x[offset_src];

ggml/src/ggml-sycl/gemm.hpp

@@ -23,6 +23,10 @@
 #include "dnnl.hpp"
 #include "dnnl_sycl.hpp"
 
+
+// TODO: Remove this when needed
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wcast-qual"
 class DnnlGemmWrapper {
 public:
     using dt = dnnl::memory::data_type;
@@ -96,6 +100,7 @@ public:
     }
 };
 
+#    pragma clang diagnostic pop
 #endif
 
 #endif // GGML_SYCL_GEMM_HPP

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -47,7 +47,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
 
     info.device_count = dpct::dev_mgr::instance().device_count();
     if (info.device_count == 0) {
-        GGML_LOG_ERROR("%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__);
+        GGML_LOG_ERROR("%s: failed to initialize: %s\n", GGML_SYCL_NAME, __func__);
         return info;
     }
 
@@ -64,7 +64,7 @@ static ggml_sycl_device_info ggml_sycl_init() {
 #else
     GGML_LOG_INFO("%s: SYCL_USE_XMX: no\n", __func__);
 #endif
-    GGML_LOG_INFO("%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count);
+    GGML_LOG_INFO("%s: found %d %s devices:\n", __func__, info.device_count, GGML_SYCL_NAME);
 
     for (int i = 0; i < info.device_count; ++i) {
         info.devices[i].vmm = 0;
@@ -137,7 +137,8 @@ void ggml_backend_sycl_print_sycl_devices() {
 
     for (int id = 0; id < device_count; ++id) {
       sycl::device device = dpct::dev_mgr::instance().get_device(id);
-      sycl::backend backend = device.get_backend();
+      // TODO: backend variable is unused here!
+      // sycl::backend backend = device.get_backend();
       std::string backend_type = get_device_backend_and_type(device);
       int type_id = DeviceNums[backend_type]++;
       std::stringstream device_type;
@@ -420,14 +421,13 @@ ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
         return true;
     }
     return false;
-}
+    // TODO: Buffer is unused
-catch (sycl::exception const &exc) {
+    (void) buffer;
-  std::cerr << exc.what() << "Exception caught at file:" << __FILE__
+} catch (const sycl::exception & exc) {
-            << ", line:" << __LINE__ << std::endl;
+    std::cerr << exc.what() << "Exception caught at file:" << __FILE__ << ", line:" << __LINE__ << std::endl;
     std::exit(1);
 }
 
-
 static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer,
                                            uint8_t value) try {
      ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
@@ -1092,10 +1092,7 @@ struct ggml_sycl_pool_leg : public ggml_sycl_pool {
     ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {};
     size_t pool_size = 0;
 
-    explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) :
+    explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : device(device_), qptr(qptr_) {}
-        qptr(qptr_),
-        device(device_) {
-    }
 
     ~ggml_sycl_pool_leg() {
         for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) {
@@ -1238,7 +1235,7 @@ static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy,
         zeros[i] = 0.f;
         qzeros[i] = 0;
     }
-    const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros;
+    const TC xi = ix < kx ? *(const TC *)&x[iy * kx + ix] : zeros;
     float sum = xi[0];
     float amax = sycl::fabs(xi[0]);
 #pragma unroll
@@ -1799,6 +1796,8 @@ static  void pool2d_nchw_kernel(
         switch (op) {
             case GGML_OP_POOL_AVG: res = 0; break;
             case GGML_OP_POOL_MAX: res = -FLT_MAX; break;
+            default:
+                break;  // TODO: handle this properly
         }
 
         for (int i = bh; i < eh; i += 1) {
@@ -1817,6 +1816,8 @@ static  void pool2d_nchw_kernel(
                 switch (op) {
                     case GGML_OP_POOL_AVG: res += (cur / (kh * kw)); break;
                     case GGML_OP_POOL_MAX: res = sycl::max(res, (To)cur); break;
+                    default:
+                        break;  // TODO: handle this properly
                 }
             }
         }
@@ -1856,6 +1857,7 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
                          });
 
     (void) dst;
+    (void) ctx;
 }
 
 template <typename src0_t>
@@ -1894,9 +1896,9 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens
     }
 
     (void) dst;
+    (void) ctx;
 }
 
-
 static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx,
                                    const int ky, const int kx_padded,
                                    queue_ptr stream) {
@@ -2484,17 +2486,19 @@ inline void ggml_sycl_op_mul_mat_sycl(
     const int64_t ne00 = src0->ne[0];
     const int64_t ne10 = src1->ne[0];
 
-    const int64_t ne0 = dst->ne[0];
+    
 
     const int64_t row_diff = row_high - row_low;
 
     int id;
     SYCL_CHECK(
         CHECK_TRY_ERROR(id = get_current_device_id()));
-
+#if !GGML_SYCL_DNNL
+    const int64_t ne0 = dst->ne[0];
     // the main device has a larger memory buffer to hold the results from all GPUs
     // ldc == nrows of the matrix that cuBLAS writes into
     int ldc = id == ctx.device ? ne0 : row_diff;
+#endif
 
 #ifdef GGML_SYCL_F16
     bool use_fp16 = true;  // TODO(Yu) SYCL capability check
@@ -2531,9 +2535,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
                                          : src1_as_f16.get();
         ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool(), row_diff * src1_ncols);
 
+#if !GGML_SYCL_DNNL
         const sycl::half alpha_f16 = 1.0f;
         const sycl::half beta_f16  = 0.0f;
-#if !GGML_SYCL_DNNL
         SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm(
             *stream, oneapi::mkl::transpose::trans,
             oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10,
@@ -2570,9 +2574,9 @@ inline void ggml_sycl_op_mul_mat_sycl(
         const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get();
         const float * src1_ddf1_i = src1->type == GGML_TYPE_F32 ? (const float *) src1_ddf_i : src1_ddq_as_f32.get();
 
+#if !GGML_SYCL_DNNL
         const float alpha = 1.0f;
         const float beta  = 0.0f;
-#if !GGML_SYCL_DNNL
 #    ifdef GGML_SYCL_NVIDIA
         SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
             oneapi::mkl::backend_selector<oneapi::mkl::backend::cublas>{ *stream }, oneapi::mkl::transpose::trans,
@@ -2870,7 +2874,8 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
 
     ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra;
     ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra;
-    ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
+    // TODO: What's the use of this?
+    // ggml_tensor_extra_gpu *  dst_extra = (ggml_tensor_extra_gpu *)  dst->extra;
 
     const bool src0_is_contiguous = ggml_is_contiguous(src0);
     const bool src1_is_contiguous = ggml_is_contiguous(src1);
@@ -3296,8 +3301,8 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx,
     GGML_ASSERT(src0->type == GGML_TYPE_F16);
 
     GGML_TENSOR_BINARY_OP_LOCALS
-
+    // TODO: What's the use of this?
-    const int64_t ne_dst = ggml_nelements(dst);
+    //const int64_t ne_dst = ggml_nelements(dst);
 
     SYCL_CHECK(ggml_sycl_set_device(ctx.device));
     queue_ptr main_stream = ctx.stream();;
@@ -3405,6 +3410,7 @@ catch (sycl::exception const &exc) {
 
 inline bool ggml_sycl_supports_mmq(enum ggml_type type) {
     // TODO: accuracy issues in MMQ
+    (void) type;
     return false;
 }
 
@@ -3836,13 +3842,17 @@ static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, const ggml_tensor
 }
 
 static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    // TODO: Why this function even exists?
     (void) src0;
     (void) src1;
     (void) dst;
+    (void) ctx;
 }
 
 void ggml_sycl_set_main_device(const int main_device) try {
-    if (dpct::get_current_device_id() == main_device) return;
+    if (dpct::get_current_device_id() == static_cast<unsigned int> (main_device)) {
+        return;
+    }
     check_allow_gpu_index(main_device);
     dpct::select_device(main_device);
 
@@ -4210,6 +4220,7 @@ try
 {
     ggml_backend_sycl_context *sycl_ctx =
         (ggml_backend_sycl_context *)backend->context;
+        
     sycl::event *sycl_event = static_cast<sycl::event *>(event->context);
 
     const queue_ptr &stream = sycl_ctx->stream(sycl_ctx->device, 0);
@@ -4224,7 +4235,8 @@ catch (sycl::exception const &exc)
 }
 
 static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_event_t event) try {
-    ggml_backend_sycl_context* sycl_ctx = static_cast<ggml_backend_sycl_context*>(backend->context);
+    // TODO: sycl_ctx is unused here
+    // ggml_backend_sycl_context* sycl_ctx = static_cast<ggml_backend_sycl_context*>(backend->context);
     sycl::event* sycl_event = static_cast<sycl::event*>(event->context);
 
     if (ggml_backend_is_sycl(backend)) {
@@ -4632,6 +4644,8 @@ static void *ggml_backend_sycl_reg_get_proc_address(ggml_backend_reg_t reg, cons
     // SYCL doesn't support registering host memory, left here for reference
     // "ggml_backend_register_host_buffer"
     // "ggml_backend_unregister_host_buffer"
+    // doing this to make the compiler happy
+    (void) name; 
     return nullptr;
 }
 

ggml/src/ggml-sycl/mmq.cpp

@@ -13,6 +13,11 @@
 #include "mmq.hpp"
 #include "vecdotq.hpp"
 
+// Just to make the compiler happy
+// TODO: Remove it when needed
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wdivision-by-zero"
+
 typedef void (*allocate_tiles_sycl_t)(
     int** x_ql,
     sycl::half2** x_dm,
@@ -3029,3 +3034,4 @@ catch (sycl::exception const &exc) {
             << ", line:" << __LINE__ << std::endl;
   std::exit(1);
 }
+#pragma clang diagnostic pop

ggml/src/ggml-sycl/mmvq.cpp

@@ -1031,4 +1031,5 @@ void ggml_sycl_op_mul_mat_vec_q(
     (void) src1;
     (void) dst;
     (void) src1_ddf_i;
+    (void) ctx;
 }

ggml/src/ggml-sycl/norm.cpp

@@ -32,7 +32,7 @@ static void norm_f32(const float* x, float* dst, const int ncols, const float ep
         item_ct1.barrier(sycl::access::fence_space::local_space);
         mean_var = 0.f;
         int nreduce = nwarps / WARP_SIZE;
-        for (size_t i = 0; i < nreduce; i += 1)
+        for (size_t i = 0; i < (size_t) nreduce; i += 1)
         {
             mean_var += s_sum[lane_id + i * WARP_SIZE];
         }
@@ -86,7 +86,7 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con
         */
         item_ct1.barrier();
         tmp = 0.f;
-        for (size_t i = 0; i < nreduce; i += 1)
+        for (size_t i = 0; i < (size_t) nreduce; i += 1)
         {
             tmp += s_sum[lane_id + i * WARP_SIZE];
         }
@@ -121,7 +121,7 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con
         */
         item_ct1.barrier();
         tmp = 0.f;
-        for (size_t i = 0; i < nreduce; i += 1)
+        for (size_t i = 0; i < (size_t) nreduce; i += 1)
         {
             tmp += s_sum[lane_id + i * WARP_SIZE];
         }

ggml/src/ggml-sycl/tsembd.cpp

@@ -68,4 +68,5 @@ void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, const ggml
     const int max_period = dst->op_params[1];
 
     timestep_embedding_f32_sycl(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
+    (void) src1;
 }