diff options
Diffstat (limited to 'llama.cpp')
| -rw-r--r-- | llama.cpp | 33 |
1 files changed, 9 insertions, 24 deletions
@@ -1991,10 +1991,13 @@ struct llama_model_loader { return tensor; } - struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend) { + struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend_type backend, bool optional = false) { struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str()); if (cur == NULL) { + if (optional) { + return NULL; + } throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str())); } @@ -2812,29 +2815,11 @@ static void llm_load_tensors( layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split); layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split); - try { - layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend); - } catch (const std::runtime_error& e) { - if (std::string(e.what()).find("not found") != std::string::npos) layer.bq = NULL; else throw; - } - - try { - layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend); - } catch (const std::runtime_error& e) { - if (std::string(e.what()).find("not found") != std::string::npos) layer.bk = NULL; else throw; - } - - try { - layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend); - } catch (const std::runtime_error& e) { - if (std::string(e.what()).find("not found") != std::string::npos) layer.bv = NULL; else throw; - } - - try { - layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend); - } catch (const std::runtime_error& e) { - if (std::string(e.what()).find("not found") != std::string::npos) layer.bo = NULL; else throw; - } + // optional bias tensors + layer.bq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, backend, true); + layer.bk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, backend, true); + layer.bv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, backend, true); + layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend, true); layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend); |