// llama.cpp (simplified)staticstructggml_cgraph*llm_build_llama(/* ... */){// ...structggml_tensor*inp_tokens=ggml_new_tensor_1d(ctx0,GGML_TYPE_I32,n_tokens);memcpy(inp_tokens->data,tokens,n_tokens*ggml_element_size(inp_tokens));inpL=ggml_get_rows(ctx0,model.tok_embeddings...
// llama.cpp (simplified)staticstructggml_cgraph*llm_build_llama(/* ... */){// ...structggml_tensor*inp_tokens=ggml_new_tensor_1d(ctx0,GGML_TYPE_I32,n_tokens);memcpy(inp_tokens->data,tokens,n_tokens*ggml_element_size(inp_tokens));inpL=ggml_get_rows(ctx0,model.tok_embeddings...
主流的LLM都需要通过CUDA才能高效的运行在本地,但是随着Github上出现了Llama.cpp这个神器,一切都改变了。它通过AVX指令和MPI来实现CPU上并行计算,从而在本地计算机高效地运行各种主流的类Llama模型。同时它也支持metal,使得Apple Silicon的系统也能部署LLM。然而他的架构偏向于编译,安装部署较为复杂,于是衍生了Ollama之类...
memcpy(state_data.data() + nwrite, &token_count, sizeof(size_t)); nwrite += sizeof(size_t); // write the cached tokens (loop) for (size_t i = 0; i < token_count; i++) { const llama_token token = slot->cache_tokens[i]; memcpy(state_data.data() + nwrite, &token, size...
2 changes: 1 addition & 1 deletion 2 ggml/src/ggml-sycl/rope.cpp Original file line numberDiff line numberDiff line change @@ -226,7 +226,7 @@ void ggml_sycl_op_rope( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *...
memcpy(input.data() + sizeof(rpc_tensor) + sizeof(offset), data, size); std::vector<uint8_t> output; bool status = send_rpc_cmd(ctx->sock, SET_TENSOR, input, output); GGML_ASSERT(status); } GGML_CALL static void ggml_backend_rpc_buffer_get_tensor(ggml_backend_buffer_t...
memcpy(t->data, (void *) ((uint8_t *) id_src.contents + offs), ggml_nbytes(t)); } void ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { metal_printf("%s: evaluating graph\n", __func__); ...
在前面的文章 深入理解Llama.cpp (一) 准备模型中, 简要介绍了Llama.cpp这个开源项目。使用llama.cpp主要分为三步。详情参考examples/quantize/README.md。1)准备模型。这一步是把huggingface上的模型转换为ggml…
for (int i = 0; i < (1 << 16); ++i) { uint16_t ui = i; memcpy(&ii, &ui, sizeof(ii)); const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); table_silu_f16[i] = GGML_FP32_TO_...
img->data.resize(n);memcpy(img->data.data(), data, n); return true; }inline int mllama(int x, int lower, int upper) { return std::max(lower, std::min(x, upper)); }void mllama_free(mllama_ctx *ctx) { ggml_free(ctx->ctx_data); gguf_free(ctx->ctx_gguf);gg...