Repository: ggerganov/llama.cpp
File: src/llama-model.cpp (C++)

Code before:
    ctx_buf_maps.reserve(ctx_map.size());

    // Ensure we have enough capacity for the maximum backend buffer we will potentially create
    const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
    pimpl->ctxs_bufs.reserve(n_max_backend_buffer);

    for (auto & [buft, ctx_ptr] : ctx_map) {
        ggml_context * ctx = ctx_ptr.get();

        // skip contexts without tensors
        if (ggml_get_first_tensor(ctx) == nullptr) {
            continue;
        }

        llama_buf_map buf_map;
        buf_map.reserve(n_max_backend_buffer);

        // check if it is possible to use buffer_from_host_ptr with this buffer type
        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
        if (!dev) {
            // FIXME: workaround for CPU backend buft having a NULL device
            dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
            if (!dev) {
                throw std::runtime_error(format("%s: no CPU backend found", __func__));
            }
        }
        ggml_backend_dev_props props;
        ggml_backend_dev_get_props(dev, &props);
        bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
        bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);

        std::vector<ggml_backend_buffer_ptr> bufs;
        if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
            for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
                // only the mmap region containing the tensors in the model is mapped to the backend buffer
                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
                // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
                void * addr = nullptr;
                size_t first, last; // NOLINT
                ml.get_mapping_range(&first, &last, &addr, idx, ctx);
                if (first >= last) {
                    continue;
                }
                const size_t max_size = ggml_get_max_tensor_size(ctx);
                ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
                if (buf == nullptr) {
                    throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
                }
                bufs.emplace_back(buf);
                buf_map.emplace(idx, buf);
            }

Review comment (suggestion):
```suggestion
                        } else {
```

Code after:
                    if (output == NULL) {
                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = layers[i];

                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

                        // weight tensors
                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

                        // bias tensors
                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd_head_k * n_head}, 0);
                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
                        layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);

                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

                        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
                            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
                            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
                        } else {
                            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
                        }

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                    }
                } break;
            default:
                throw std::runtime_error("unknown architecture");
        }

        if (n_moved_tensors > 0) {
            LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n",
                __func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1,
                ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft));
        }
    }

    ml.done_getting_tensors();

    ml.init_mappings(true, use_mlock ? &pimpl->mlock_mmaps : nullptr);
    pimpl->mappings.reserve(ml.mappings.size());