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https://github.com/ggml-org/llama.cpp.git
synced 2026-06-09 07:16:44 +02:00
opencl: fix compiler warnings for non-adreno path (#23922)
* opencl: fix compiler warnings for non-adreno path * opencl: fix const cast warning
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@@ -380,7 +380,7 @@ struct ggml_backend_opencl_device_context {
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ADRENO_GPU_GEN adreno_gen = ADRENO_GPU_GEN::ADRENO_UNKNOWN;
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std::regex *opfilter = nullptr; // regex of ops to not claim
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std::string opfilter_str; // regex string for opfilter
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std::string opfilter_str = ""; // regex string for opfilter
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size_t global_mem_size = 0;
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};
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@@ -6822,9 +6822,6 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
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cl_buffer_region region;
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cl_uchar mask_0F = 0x0F;
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cl_uchar mask_F0 = 0xF0;
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#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
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// Adreno MoE Q6_K kernel needs special transposed layout
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if (use_adreno_moe_kernels(backend_ctx, tensor)) {
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@@ -6858,6 +6855,9 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
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cl_kernel kernel = backend_ctx->kernel_convert_block_q6_k_trans4_ns;
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cl_uchar mask_0F = 0x0F;
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cl_uchar mask_F0 = 0xF0;
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int ne00 = tensor->ne[0];
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int ne01 = tensor->ne[1];
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int ne02 = tensor->ne[2];
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@@ -6994,7 +6994,7 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
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cl_int err;
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cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,
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size, (void *) data, &err);
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size, const_cast<void *>(data), &err);
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CL_CHECK(err);
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cl_kernel kernel = backend_ctx->kernel_convert_bf16_to_f16;
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@@ -7782,9 +7782,6 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
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if (tensor->type == GGML_TYPE_Q6_K) {
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ggml_tensor_extra_cl_q6_K * extra = (ggml_tensor_extra_cl_q6_K *)tensor->extra;
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cl_uchar mask_0F = 0x0F;
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cl_uchar mask_F0 = 0xF0;
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#ifdef GGML_OPENCL_USE_ADRENO_KERNELS
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if (use_adreno_moe_kernels(backend_ctx, tensor)) {
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cl_int err;
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@@ -7794,6 +7791,9 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
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cl_kernel kernel = backend_ctx->kernel_restore_block_q6_k_trans4_ns;
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cl_uchar mask_0F = 0x0F;
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cl_uchar mask_F0 = 0xF0;
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int ne00 = tensor->ne[0];
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int ne01 = tensor->ne[1];
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int ne02 = tensor->ne[2];
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@@ -14888,6 +14888,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
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const int ne1 = dst->ne[1];
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const int ne2 = dst->ne[2];
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GGML_UNUSED(ne2);
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const int r2 = ne12/ne02;
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const int r3 = ne13/ne03;
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const int dst_rows = ne20*ne21; // ne20 = n_used_experts, ne21 = n_rows
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@@ -14902,6 +14904,8 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
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const int n_tile_size = 32;
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const int max_post_router_tile = (ne20 * ne21 / n_tile_size) + ne02;
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GGML_UNUSED(max_post_router_tile);
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cl_kernel kernel;
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// subgroup mat vec
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