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rpc : fix segfault with nkvo (#9389)
* rpc : fix nkvo * rpc : buf_size must not be static ref: #9337 --------- Co-authored-by: slaren <slarengh@gmail.com>
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@ -2552,7 +2552,11 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
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for (int i = 0; i < cgraph->n_nodes; i++) {
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ggml_tensor * node = cgraph->nodes[i];
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if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
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if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
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continue;
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}
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if (node->src[0] && node->src[0]->buffer && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) {
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use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture
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#ifndef NDEBUG
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GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__);
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@ -883,15 +883,17 @@ ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rp
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}
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result->buffer = reinterpret_cast<ggml_backend_buffer_t>(tensor->buffer);
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if (result->buffer && buffers.find(result->buffer) == buffers.end()) {
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return nullptr;
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result->buffer = nullptr;
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}
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// require that the tensor data does not go beyond the buffer end
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uint64_t tensor_size = (uint64_t) ggml_nbytes(result);
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uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(result->buffer);
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uint64_t buffer_size = (uint64_t) ggml_backend_buffer_get_size(result->buffer);
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GGML_ASSERT(tensor->data + tensor_size >= tensor->data); // check for overflow
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GGML_ASSERT(tensor->data >= buffer_start && tensor->data + tensor_size <= buffer_start + buffer_size);
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if (result->buffer) {
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// require that the tensor data does not go beyond the buffer end
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uint64_t tensor_size = (uint64_t) ggml_nbytes(result);
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uint64_t buffer_start = (uint64_t) ggml_backend_buffer_get_base(result->buffer);
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uint64_t buffer_size = (uint64_t) ggml_backend_buffer_get_size(result->buffer);
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GGML_ASSERT(tensor->data + tensor_size >= tensor->data); // check for overflow
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GGML_ASSERT(tensor->data >= buffer_start && tensor->data + tensor_size <= buffer_start + buffer_size);
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}
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result->op = (ggml_op) tensor->op;
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for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) {
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@ -1060,7 +1062,7 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input, std::vector<u
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const rpc_tensor * tensors = (const rpc_tensor *)(input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t) + sizeof(n_tensors));
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GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors);
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static size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false);
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size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false);
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_size,
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/*.mem_buffer =*/ NULL,
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@ -3847,7 +3847,7 @@ static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml
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if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
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GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
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__func__, cur_end + size_needed, ctx->mem_size);
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__func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size);
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assert(false);
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return NULL;
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}
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