llama_rdma_server.cpp

#define LLAMA_API_INTERNAL
#include "llama.h"

#include "unicode.h"

#include "ggml.h"

#include "ggml-alloc.h"
#include "cuda_runtime.h"

#ifdef GGML_USE_CUBLAS
#  include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#  include "ggml-opencl.h"
#endif

#ifdef GGML_USE_METAL
#  include "ggml-metal.h"
#endif
#ifdef GGML_USE_MPI
#  include "ggml-mpi.h"
#endif
#ifndef QK_K
#  ifdef GGML_QKK_64
#    define QK_K 64
#  else
#    define QK_K 256
#  endif
#endif

#ifdef __has_include
    #if __has_include(<unistd.h>)
        #include <unistd.h>
        #if defined(_POSIX_MAPPED_FILES)
            #include <sys/mman.h>
        #endif
        #if defined(_POSIX_MEMLOCK_RANGE)
            #include <sys/resource.h>
        #endif
    #endif
#endif

#if defined(_WIN32)
    #define WIN32_LEAN_AND_MEAN
    #ifndef NOMINMAX
        #define NOMINMAX
    #endif
    #include <windows.h>
    #include <io.h>
    #include <stdio.h> // for _fseeki64
    #include <direct.h>
    #define F_OK 0
#else
    #include <libgen.h>
#endif

#include <algorithm>
#include <array>
#include <cassert>
#include <cinttypes>
#include <climits>
#include <cmath>
#include <cstdarg>
#include <cstddef>
#include <cstdint>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <forward_list>
#include <fstream>
#include <functional>
#include <initializer_list>
#include <map>
#include <memory>
#include <mutex>
#include <numeric>
#include <queue>
#include <random>
#include <regex>
#include <set>
#include <sstream>
#include <thread>
#include <unordered_map>
#include <vector>
#include "rdma_common.h"
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

#ifdef __GNUC__
#ifdef __MINGW32__
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
#else
#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
#endif
#else
#define LLAMA_ATTRIBUTE_FORMAT(...)
#endif

#define LLAMA_MAX_NODES 4096
void * rdma_idx_vec;
// 
// global variables (should be removed after a better design)
//
size_t vram_budget_bytes = 0;

//
// logging
//

LLAMA_ATTRIBUTE_FORMAT(2, 3)
static void llama_log_internal        (ggml_log_level level, const char* format, ...);
static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);

#define LLAMA_LOG_INFO(...)  llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
#define LLAMA_LOG_WARN(...)  llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)

//
// helpers
//

//===============rdma================
static struct rdma_event_channel *cm_event_channel = NULL;
static struct rdma_cm_id *cm_server_id = NULL, *cm_client_id = NULL;
static struct ibv_pd *pd = NULL;
static struct ibv_comp_channel *io_completion_channel = NULL;
static struct ibv_cq *cq = NULL;
static struct ibv_qp_init_attr qp_init_attr;
static struct ibv_qp *client_qp = NULL;
/* RDMA memory resources */
static struct ibv_mr *client_metadata_mr = NULL, *server_buffer_mr = NULL, *server_metadata_mr = NULL;
static struct rdma_buffer_attr client_metadata_attr, server_metadata_attr;
static struct ibv_recv_wr client_recv_wr, *bad_client_recv_wr = NULL;
static struct ibv_send_wr server_send_wr, *bad_server_send_wr = NULL;
static struct ibv_sge client_recv_sge, server_send_sge;

static struct rdma_buffer_attr_vec server_metadata_attrs;
static struct rdma_buffer_attr_vec client_metadata_attrs;
std::vector<struct ibv_mr *> server_buffer_mrs;
void printf_dim(ggml_tensor * t ,char * str ="") {
    printf("%s:%s %s: %d %d %d %d\n", __func__, t->name, str,t->ne[0], t->ne[1], t->ne[2], t->ne[3]);
}
void printf_value(struct ggml_tensor * tensor,int num =10)
{
    int ne0=tensor->ne[0];
    int ne1=tensor->ne[1];
    int ne2=tensor->ne[2];
    int ne3=tensor->ne[3];
    for(int i=0;i<ne3;i++)
    {
        for(int j=0;j<ne2;j++)
        {
            for(int k=0;k<ne1;k++)
            {
                for(int l=0;l<ne0;l++)
                {   
                    if(--num)
                    {
                        printf("%f ",ggml_get_f32_nd(tensor,l,k,j,i));
                    }
                    else
                    {
                        return;
                    }
                }
            }

        }
    }
}

/* Starts an RDMA server by allocating basic connection resources */
static int start_rdma_server(struct sockaddr_in *server_addr) 
{
	struct rdma_cm_event *cm_event = NULL;
	int ret = -1;
	/*  Open a channel used to report asynchronous communication event */
	cm_event_channel = rdma_create_event_channel(); // 创建一个用于报告异步通信事件的通道
	if (!cm_event_channel) {
		rdma_error("Creating cm event channel failed with errno : (%d)", -errno);
		return -errno;
	}
	debug("RDMA CM event channel is created successfully at %p \n", 
			cm_event_channel);
	/* rdma_cm_id is the connection identifier (like socket) which is used 
	 * to define an RDMA connection. 
	 */
	ret = rdma_create_id(cm_event_channel, &cm_server_id, NULL, RDMA_PS_TCP); //创建一个 RDMA 连接标识符 cm_server_id，用于定义一个 RDMA 连接。rdma_create_id() 函数使用指定的通道和传输协议（这里是 TCP）创建一个 RDMA 连接标识符。
	if (ret) {
		rdma_error("Creating server cm id failed with errno: %d ", -errno);
		return -errno;
	}
	debug("A RDMA connection id for the server is created \n");
	/* Explicit binding of rdma cm id to the socket credentials */
	ret = rdma_bind_addr(cm_server_id, (struct sockaddr*) server_addr); //rdma_bind_addr() 函数将 RDMA 连接标识符绑定到指定的地址。
	if (ret) {
		rdma_error("Failed to bind server address, errno: %d \n", -errno);
		return -errno;
	}
	debug("Server RDMA CM id is successfully binded \n");
	/* Now we start to listen on the passed IP and port. However unlike
	 * normal TCP listen, this is a non-blocking call. When a new client is 
	 * connected, a new connection management (CM) event is generated on the 
	 * RDMA CM event channel from where the listening id was created. Here we
	 * have only one channel, so it is easy. */
	ret = rdma_listen(cm_server_id, 8); /* backlog = 8 clients, same as TCP, see man listen*/ //开始监听传入的 IP 和端口。并指定最大连接数（这里是 8）。
	if (ret) {
		rdma_error("rdma_listen failed to listen on server address, errno: %d ",
				-errno);
		return -errno;
	}
	printf("Server is listening successfully at: %s , port: %d \n",
			inet_ntoa(server_addr->sin_addr),
			ntohs(server_addr->sin_port));
	/* now, we expect a client to connect and generate a RDMA_CM_EVNET_CONNECT_REQUEST 
	 * We wait (block) on the connection management event channel for 
	 * the connect event. 
	 */
	ret = process_rdma_cm_event(cm_event_channel, 
			RDMA_CM_EVENT_CONNECT_REQUEST,
			&cm_event);
	if (ret) {
		rdma_error("Failed to get cm event, ret = %d \n" , ret);
		return ret;
	}
	/* Much like TCP connection, listening returns a new connection identifier 
	 * for newly connected client. In the case of RDMA, this is stored in id 
	 * field. For more details: man rdma_get_cm_event 
	 */
	cm_client_id = cm_event->id; //获取新连接的客户端标识符 
	/* now we acknowledge the event. Acknowledging the event free the resources 
	 * associated with the event structure. Hence any reference to the event 
	 * must be made before acknowledgment. Like, we have already saved the 
	 * client id from "id" field before acknowledging the event. 
	 */
	ret = rdma_ack_cm_event(cm_event); //函数确认接收到的连接管理事件，并释放与事件相关的资源。
	if (ret) {
		rdma_error("Failed to acknowledge the cm event errno: %d \n", -errno);
		return -errno;
	}
	debug("A new RDMA client connection id is stored at %p\n", cm_client_id);
	return ret;
}


static int setup_client_resources() //该函数用于准备客户端连接之前的一些资源设置。
{
	int ret = -1;
	if(!cm_client_id){
		rdma_error("Client id is still NULL \n");
		return -EINVAL;
	}
	/* We have a valid connection identifier, lets start to allocate 
	 * resources. We need: 
	 * 1. Protection Domains (PD) //保护域（Protection Domains，PD）：保护域类似于操作系统中的“进程抽象”，它是一组资源的集合，所有的资源都与特定的保护域相关联。在这里，我们需要为客户端连接创建一个保护域。
	 * 2. Memory Buffers  //为了进行RDMA通信，我们需要为客户端连接分配内存缓冲区，用于存储发送和接收的数据
	 * 3. Completion Queues (CQ) //完成队列用于存储RDMA操作完成的通知。在这里，我们需要为客户端连接创建一个完成队列。
	 * 4. Queue Pair (QP) //队列对是RDMA通信的基本单元，它包含了发送和接收数据的队列。在这里，我们需要为客户端连接创建一个队列对。
	 * Protection Domain (PD) is similar to a "process abstraction" 
	 * in the operating system. All resources are tied to a particular PD. 
	 * And accessing recourses across PD will result in a protection fault.
	 */
	pd = ibv_alloc_pd(cm_client_id->verbs  //这行代码使用ibv_alloc_pd函数为客户端连接分配一个保护域（Protection Domain）
			/* verbs defines a verb's provider, 
			 * i.e an RDMA device where the incoming 
			 * client connection came */);
	if (!pd) {
		rdma_error("Failed to allocate a protection domain errno: %d\n",
				-errno);
		return -errno;
	}
	debug("A new protection domain is allocated at %p \n", pd);
	/* Now we need a completion channel, were the I/O completion 
	 * notifications are sent. Remember, this is different from connection 
	 * management (CM) event notifications. 
	 * A completion channel is also tied to an RDMA device, hence we will 
	 * use cm_client_id->verbs. 
	 */
	io_completion_channel = ibv_create_comp_channel(cm_client_id->verbs);//ibv_create_comp_channel函数创建一个完成通道（Completion Channel），用于接收I/O完成事件的通知。完成通道与RDMA设备相关联，因此我们使用cm_client_id->verbs来指定RDMA设备。
	if (!io_completion_channel) {
		rdma_error("Failed to create an I/O completion event channel, %d\n",
				-errno);
		return -errno;
	}
	debug("An I/O completion event channel is created at %p \n", 
			io_completion_channel);
	/* Now we create a completion queue (CQ) where actual I/O 
	 * completion metadata is placed. The metadata is packed into a structure 
	 * called struct ibv_wc (wc = work completion). ibv_wc has detailed 
	 * information about the work completion. An I/O request in RDMA world 
	 * is called "work" ;) 
	 */
	cq = ibv_create_cq(cm_client_id->verbs /* which device*/,  //ibv_create_cq函数创建一个完成队列（Completion Queue），用于存储RDMA操作完成的通知。完成队列中的元数据被打包到一个叫做struct ibv_wc的结构体中，它包含了有关工作完成的详细信息。在RDMA世界中，一个I/O请求被称为“工作”。
			CQ_CAPACITY /* maximum capacity*/, 
			NULL /* user context, not used here */,
			io_completion_channel /* which IO completion channel */, 
			0 /* signaling vector, not used here*/);
	if (!cq) {
		rdma_error("Failed to create a completion queue (cq), errno: %d\n",
				-errno);
		return -errno;
	}
	debug("Completion queue (CQ) is created at %p with %d elements \n", 
			cq, cq->cqe);
	/* Ask for the event for all activities in the completion queue*/
	ret = ibv_req_notify_cq(cq /* on which CQ */,  //这行代码使用ibv_req_notify_cq函数请求在完成队列上接收所有活动的通知。这里的cq是指定的完成队列，0表示接收所有类型的事件通知，没有过滤。
			0 /* 0 = all event type, no filter*/);
	if (ret) {
		rdma_error("Failed to request notifications on CQ errno: %d \n",
				-errno);
		return -errno;
	}
	/* Now the last step, set up the queue pair (send, recv) queues and their capacity.
	 * The capacity here is define statically but this can be probed from the 
	 * device. We just use a small number as defined in rdma_common.h */ //
       bzero(&qp_init_attr, sizeof qp_init_attr); //这一系列代码用于初始化一个队列对（Queue Pair）的属性。队列对是RDMA通信的基本单元，它包含了发送和接收数据的队列。在这里，我们设置了队列对的最大接收和发送工作请求数量，以及队列对的类型（这里是可靠连接类型）和关联的完成队列。
       qp_init_attr.cap.max_recv_sge = MAX_SGE; /* Maximum SGE per receive posting */
       qp_init_attr.cap.max_recv_wr = MAX_WR; /* Maximum receive posting capacity */
       qp_init_attr.cap.max_send_sge = MAX_SGE; /* Maximum SGE per send posting */
       qp_init_attr.cap.max_send_wr = MAX_WR; /* Maximum send posting capacity */
       qp_init_attr.qp_type = IBV_QPT_RC; /* QP type, RC = Reliable connection */
       /* We use same completion queue, but one can use different queues */
       qp_init_attr.recv_cq = cq; /* Where should I notify for receive completion operations */
       qp_init_attr.send_cq = cq; /* Where should I notify for send completion operations */
       /*Lets create a QP */
       ret = rdma_create_qp(cm_client_id /* which connection id */, //这行代码使用rdma_create_qp函数创建一个队列对（Queue Pair）。它需要指定连接ID、保护域和队列对的初始属性。函数执行成功后，队列对的引用将保存在client_qp变量中。
		       pd /* which protection domain*/,
		       &qp_init_attr /* Initial attributes */);
       if (ret) {
	       rdma_error("Failed to create QP due to errno: %d\n", -errno);
	       return -errno;
       }
       /* Save the reference for handy typing but is not required */
       client_qp = cm_client_id->qp;
       debug("Client QP created at %p\n", client_qp);
       return ret;
}


/* Pre-posts a receive buffer and accepts an RDMA client connection */
static int accept_client_connection()
{
	struct rdma_conn_param conn_param;
	struct rdma_cm_event *cm_event = NULL;
	struct sockaddr_in remote_sockaddr; 
	int ret = -1;
	if(!cm_client_id || !client_qp) {
		rdma_error("Client resources are not properly setup\n");
		return -EINVAL;
	}
	
       memset(&conn_param, 0, sizeof(conn_param)); //我们准备一个连接参数结构体conn_param，并将其初始化为零。这个结构体用于指定连接的一些参数，比如我们可以设置期望的请求深度（initiator_depth）和响应方资源数（responder_resources）
       /* this tell how many outstanding requests can we handle */
       conn_param.initiator_depth = 15; /* For this exercise, we put a small number here */
       /* This tell how many outstanding requests we expect other side to handle */
       conn_param.responder_resources = 15; /* For this exercise, we put a small number */
	   //rdma_accept函数接受客户端的连接请求，并传入连接参数。
	   	   printf("cm_client_id:%p\n",cm_client_id);
       ret = rdma_accept(cm_client_id, &conn_param);
       if (ret) {
	       rdma_error("Failed to accept the connection, errno: %d \n", -errno);
	       return -errno;
       }
       /* We expect an RDMA_CM_EVNET_ESTABLISHED to indicate that the RDMA  
	* connection has been established and everything is fine on both, server 
	* as well as the client sides.
	*/	
        debug("Going to wait for : RDMA_CM_EVENT_ESTABLISHED event \n");
		//使用process_rdma_cm_event函数等待指定类型的RDMA CM事件，并将事件保存在cm_event变量中
       ret = process_rdma_cm_event(cm_event_channel, 
		       RDMA_CM_EVENT_ESTABLISHED,
		       &cm_event);
        if (ret) {
		rdma_error("Failed to get the cm event, errnp: %d \n", -errno);
		return -errno;
	}
	/* We acknowledge the event */
	ret = rdma_ack_cm_event(cm_event);
	if (ret) {
		rdma_error("Failed to acknowledge the cm event %d\n", -errno);
		return -errno;
	}
	//我们使用rdma_get_peer_addr函数获取连接的对端地址，并将其保存在remote_sockaddr变量中。
	/* Just FYI: How to extract connection information */
	memcpy(&remote_sockaddr /* where to save */, 
			rdma_get_peer_addr(cm_client_id) /* gives you remote sockaddr */, 
			sizeof(struct sockaddr_in) /* max size */);
	printf("A new connection is accepted from %s \n", 
			inet_ntoa(remote_sockaddr.sin_addr));
	return ret;
}

/* This function sends server side buffer metadata to the connected client */


static int register_mrs_to_client(std::vector<ggml_tensor*> tensor_dsts )  //该函数用于向连接的客户端发送服务器端缓冲区的元数据。
{
	struct ibv_wc wc; //工作完成（work completion）结构体
	int ret = -1;

		size_t size =  tensor_dsts.size();
		server_buffer_mrs.resize(size);
		for(int i=0;i<size;++i)
		{
			   server_buffer_mrs[i] = rdma_buffer_register(pd /* which protection domain */, 
		       tensor_dsts[i]->data,
			   ggml_nbytes(tensor_dsts[i]) /* what size to allocate */, 
		       (ibv_access_flags)(IBV_ACCESS_LOCAL_WRITE|
		       IBV_ACCESS_REMOTE_READ|
		       IBV_ACCESS_REMOTE_WRITE) /* access permissions */);
			if(!server_buffer_mrs[i]){
				rdma_error("Server failed to create a buffer \n");
				/* we assume that it is due to out of memory error */
				return -ENOMEM;
			}
			server_metadata_attrs.address[i] = (uint64_t) server_buffer_mrs[i]->addr;
			server_metadata_attrs.length[i] = (uint32_t) server_buffer_mrs[i]->length;
			server_metadata_attrs.stags[i].local_stag = (uint32_t) server_buffer_mrs[i]->lkey;
		
		}
		
       /* This buffer is used to transmit information about the above 
	* buffer to the client. So this contains the metadata about the server 
	* buffer. Hence this is called metadata buffer. Since this is already 
	* on allocated, we just register it. 
        * We need to prepare a send I/O operation that will tell the 
	* client the address of the server buffer. 
	*/
	//代码准备一个发送操作，用于告知客户端服务器端缓冲区的地址。代码将服务器端缓冲区的地址、长度和本地标签信息填充到server_metadata_attr 结构体中

       server_metadata_mr = rdma_buffer_register(pd /* which protection domain*/,  //调用 rdma_buffer_register() 函数将其注册到保护域中
		       &server_metadata_attrs /* which memory to register */, 
		       sizeof(server_metadata_attrs) /* what is the size of memory */,
		       IBV_ACCESS_LOCAL_WRITE /* what access permission */);
       if(!server_metadata_mr){
	       rdma_error("Server failed to create to hold server metadata \n");
	       /* we assume that this is due to out of memory error */
	       return -ENOMEM;
       }
       /* We need to transmit this buffer. So we create a send request. 
	* A send request consists of multiple SGE elements. In our case, we only
	* have one 
	*/
		//代码创建一个发送请求，并将 server_metadata_attr 结构体的信息填充到 server_send_sge 结构体中。接着，代码将 server_send_sge 结构体与发送请求关联，并设置发送请求的操作码为 IBV_WR_SEND，表示这是一个发送请求。代码还设置发送请求的标志为 IBV_SEND_SIGNALED，表示希望接收到发送完成的通知。

	   server_send_sge.addr = (uint64_t) &server_metadata_attrs;
       server_send_sge.length = sizeof(server_metadata_attrs);
       server_send_sge.lkey = server_metadata_mr->lkey;
       /* now we link this sge to the send request */
       bzero(&server_send_wr, sizeof(server_send_wr));
       server_send_wr.sg_list = &server_send_sge;
       server_send_wr.num_sge = 1; // only 1 SGE element in the array 
       server_send_wr.opcode = IBV_WR_SEND; // This is a send request 
       server_send_wr.send_flags = IBV_SEND_SIGNALED; // We want to get notification 
       /* This is a fast data path operation. Posting an I/O request */
	   	// sleep(50);
		ret = ibv_post_send(client_qp /* which QP */,   
		&server_send_wr /* Send request that we prepared before */, 
		&bad_server_send_wr /* In case of error, this will contain failed requests */);
		if (ret) {
			rdma_error("Posting of server metdata failed, errno: %d \n",
					-errno);
			return -errno;
		}
	   
	   //代码调用 ibv_post_send() 函数将发送请求提交到客户端的队列对列（QP）中，并检查是否提交成功。

       /* We check for completion notification */
       ret = process_work_completion_events(io_completion_channel, &wc, 1);

	   if (ret != 1) {
	       rdma_error("Failed to send server metadata, ret = %d \n", ret);
	       return ret;
       }
       debug("Local buffer metadata has been sent to the client \n");
       return 0;
}


/* This is server side logic. Server passively waits for the client to call 
 * rdma_disconnect() and then it will clean up its resources */
static int disconnect_and_cleanup_LLM_vec()
{	
	// sleep(1000);
	

	struct rdma_cm_event *cm_event = NULL;
	int ret = -1;
       /* Now we wait for the client to send us disconnect event */
       debug("Waiting for cm event: RDMA_CM_EVENT_DISCONNECTED\n");
       ret = process_rdma_cm_event(cm_event_channel,  //函数等待客户端发送断开连接事件。它调用了 process_rdma_cm_event 函数来处理 RDMA_CM_EVENT_DISCONNECTED 事件，并将返回的事件存储在 cm_event 中。
		       RDMA_CM_EVENT_DISCONNECTED, 
		       &cm_event);
       if (ret) {
	       rdma_error("Failed to get disconnect event, ret = %d \n", ret);
	       return ret;
       }
	/* We acknowledge the event */
	ret = rdma_ack_cm_event(cm_event);
	if (ret) {
		rdma_error("Failed to acknowledge the cm event %d\n", -errno);
		return -errno;
	}
	printf("A disconnect event is received from the client...\n");
	/* We free all the resources */
	/* Destroy QP */
	rdma_destroy_qp(cm_client_id); //首先，它销毁 QP（Queue Pair）。
	/* Destroy client cm id */
	ret = rdma_destroy_id(cm_client_id);//然后，销毁客户端的 cm id（Connection Manager Identifier）
	if (ret) {
		rdma_error("Failed to destroy client id cleanly, %d \n", -errno);
		// we continue anyways;
	}
	/* Destroy CQ */
	ret = ibv_destroy_cq(cq); //函数销毁完成通道（Completion Channel）。
	if (ret) {
		rdma_error("Failed to destroy completion queue cleanly, %d \n", -errno);
		// we continue anyways;
	}
	/* Destroy completion channel */
	ret = ibv_destroy_comp_channel(io_completion_channel);
	if (ret) {
		rdma_error("Failed to destroy completion channel cleanly, %d \n", -errno);
		// we continue anyways;
	}
	/* Destroy memory buffers */
	size_t size = server_buffer_mrs.size();
	for(int i=0;i<size;++i)
	{	
		// tensor_dsts[i]->data = server_buffer_mrs[i]->addr;
		// printf_value(tensor_dsts[i]); //函数释放内存缓冲区。它调用 rdma_buffer_free 函数来释放服务器缓冲区的内存资源，
		printf("\n");
	}

	for(int i=0;i<size;++i)
	{
		rdma_buffer_free(server_buffer_mrs[i]); //函数释放内存缓冲区。它调用 rdma_buffer_free 函数来释放服务器缓冲区的内存资源，
		// free(tensor_dsts[i]->data);
	}
	rdma_buffer_deregister(server_metadata_mr);	 //并调用 rdma_buffer_deregister 函数来注销客户端和服务器的元数据内存资源
	rdma_buffer_deregister(client_metadata_mr);	
	/* Destroy protection domain */
	ret = ibv_dealloc_pd(pd); //最后，函数销毁 rdma 服务器 id（Identifier）。
	if (ret) {
		rdma_error("Failed to destroy client protection domain cleanly, %d \n", -errno);
		// we continue anyways;
	}
	/* Destroy rdma server id */
	ret = rdma_destroy_id(cm_server_id); //最后，函数销毁 rdma 服务器 id（Identifier）。
	if (ret) {
		rdma_error("Failed to destroy server id cleanly, %d \n", -errno);
		// we continue anyways;
	}
	rdma_destroy_event_channel(cm_event_channel);
	printf("Server shut-down is complete \n");
	return 0;
}


/* This is server side logic. Server passively waits for the client to call 
 * rdma_disconnect() and then it will clean up its resources */
sockaddr_in  get_server_sockaddr(char *ip, char * port) {
	struct sockaddr_in server_sockaddr;
	int ret, option;
	bzero(&server_sockaddr, sizeof server_sockaddr);
	server_sockaddr.sin_family = AF_INET;
	server_sockaddr.sin_addr.s_addr = htonl(INADDR_LOOPBACK);
	if(ip)
	{
		ret = get_addr(ip, (struct sockaddr*) &server_sockaddr);
		if(ret) {
			rdma_error("Invalid IP \n");
			exit(1);
		}
	}
	if (!port) {
	  /* no port provided, use the default port */
	  server_sockaddr.sin_port = htons(DEFAULT_RDMA_PORT);
	}
	else
	{	printf("port is %s\n",port);
	  server_sockaddr.sin_port = htons(strtol(port, NULL, 0)); 
	}

	return server_sockaddr;
}

//====================rdma====================

static size_t utf8_len(char src) {
    const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
    uint8_t highbits = static_cast<uint8_t>(src) >> 4;
    return lookup[highbits];
}

static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
    std::string result;
    for (size_t pos = 0; ; pos += search.length()) {
        auto new_pos = s.find(search, pos);
        if (new_pos == std::string::npos) {
            result += s.substr(pos, s.size() - pos);
            break;
        }
        result += s.substr(pos, new_pos - pos) + replace;
        pos = new_pos;
    }
    s = std::move(result);
}

static bool is_float_close(float a, float b, float abs_tol) {
    // Check for non-negative tolerance
    if (abs_tol < 0.0) {
        throw std::invalid_argument("Tolerance must be non-negative");
    }

    // Exact equality check
    if (a == b) {
        return true;
    }

    // Check for infinities
    if (std::isinf(a) || std::isinf(b)) {
        return false;
    }

    // Regular comparison using the provided absolute tolerance
    return std::fabs(b - a) <= abs_tol;
}

#ifdef GGML_USE_CPU_HBM
#include <hbwmalloc.h>
#endif

static void zeros(std::ofstream & file, size_t n) {
    char zero = 0;
    for (size_t i = 0; i < n; ++i) {
        file.write(&zero, 1);
    }
}

LLAMA_ATTRIBUTE_FORMAT(1, 2)
static std::string format(const char * fmt, ...) {
    va_list ap;
    va_list ap2;
    va_start(ap, fmt);
    va_copy(ap2, ap);
    int size = vsnprintf(NULL, 0, fmt, ap);
    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
    std::vector<char> buf(size + 1);
    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
    GGML_ASSERT(size2 == size);
    va_end(ap2);
    va_end(ap);
    return std::string(buf.data(), size);
}

static size_t llama_set_vram_budget(double budget_gb, int gpu_device) {
#if defined(GGML_USE_CUBLAS)
    if (!ggml_cublas_loaded()) {
        throw std::runtime_error("CUDA is not loaded");
    }

    if (budget_gb < 0) {
        // if the user didn't specify a budget, use all available memory
        // and leave 256 MB as a safety margin
        vram_budget_bytes = ggml_cuda_get_free_memory(gpu_device) - 256 * 1024 * 1024;
    } else {
        // otherwise, use the specified budget
        vram_budget_bytes = (size_t) (budget_gb * 1024 * 1024 * 1024);
    }

    return vram_budget_bytes;
#else
    return 0;
#endif
}

static bool llama_reduce_vram_budget(size_t budget_bytes) {
#if not defined(GGML_USE_CUBLAS)
    throw std::runtime_error("CUDA is not enabled");
#endif

    if (vram_budget_bytes >= budget_bytes) {
        vram_budget_bytes -= budget_bytes;
        return true;
    }
    
    return false;
}

//
// gguf constants (sync with gguf.py)
//

enum llm_arch {
    LLM_ARCH_LLAMA,
    LLM_ARCH_FALCON,
    LLM_ARCH_BAICHUAN,
    LLM_ARCH_GPT2,
    LLM_ARCH_GPTJ,
    LLM_ARCH_GPTNEOX,
    LLM_ARCH_MPT,
    LLM_ARCH_STARCODER,
    LLM_ARCH_PERSIMMON,
    LLM_ARCH_REFACT,
    LLM_ARCH_BLOOM,
    LLM_ARCH_STABLELM,
    LLM_ARCH_UNKNOWN,
};

static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
    { LLM_ARCH_LLAMA,           "llama"     },
    { LLM_ARCH_FALCON,          "falcon"    },
    { LLM_ARCH_GPT2,            "gpt2"      },
    { LLM_ARCH_GPTJ,            "gptj"      },
    { LLM_ARCH_GPTNEOX,         "gptneox"   },
    { LLM_ARCH_MPT,             "mpt"       },
    { LLM_ARCH_BAICHUAN,        "baichuan"  },
    { LLM_ARCH_STARCODER,       "starcoder" },
    { LLM_ARCH_PERSIMMON,       "persimmon" },
    { LLM_ARCH_REFACT,          "refact"    },
    { LLM_ARCH_BLOOM,           "bloom"     },
    { LLM_ARCH_STABLELM,        "stablelm"  },

    { LLM_ARCH_UNKNOWN,         "unknown"   },
};

enum llm_kv {
    LLM_KV_GENERAL_ARCHITECTURE,
    LLM_KV_GENERAL_QUANTIZATION_VERSION,
    LLM_KV_GENERAL_ALIGNMENT,
    LLM_KV_GENERAL_NAME,
    LLM_KV_GENERAL_AUTHOR,
    LLM_KV_GENERAL_URL,
    LLM_KV_GENERAL_DESCRIPTION,
    LLM_KV_GENERAL_LICENSE,
    LLM_KV_GENERAL_SOURCE_URL,
    LLM_KV_GENERAL_SOURCE_HF_REPO,

    LLM_KV_CONTEXT_LENGTH,
    LLM_KV_EMBEDDING_LENGTH,
    LLM_KV_BLOCK_COUNT,
    LLM_KV_FEED_FORWARD_LENGTH,
    LLM_KV_USE_PARALLEL_RESIDUAL,
    LLM_KV_TENSOR_DATA_LAYOUT,

    LLM_KV_ATTENTION_HEAD_COUNT,
    LLM_KV_ATTENTION_HEAD_COUNT_KV,
    LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
    LLM_KV_ATTENTION_CLAMP_KQV,
    LLM_KV_ATTENTION_LAYERNORM_EPS,
    LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,

    LLM_KV_ROPE_DIMENSION_COUNT,
    LLM_KV_ROPE_FREQ_BASE,
    LLM_KV_ROPE_SCALE_LINEAR,
    LLM_KV_ROPE_SCALING_TYPE,
    LLM_KV_ROPE_SCALING_FACTOR,
    LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
    LLM_KV_ROPE_SCALING_FINETUNED,

    LLM_KV_TOKENIZER_MODEL,
    LLM_KV_TOKENIZER_LIST,
    LLM_KV_TOKENIZER_TOKEN_TYPE,
    LLM_KV_TOKENIZER_SCORES,
    LLM_KV_TOKENIZER_MERGES,
    LLM_KV_TOKENIZER_BOS_ID,
    LLM_KV_TOKENIZER_EOS_ID,
    LLM_KV_TOKENIZER_UNK_ID,
    LLM_KV_TOKENIZER_SEP_ID,
    LLM_KV_TOKENIZER_PAD_ID,
    LLM_KV_TOKENIZER_HF_JSON,
    LLM_KV_TOKENIZER_RWKV,

    LLM_KV_SPARSE_THRESHOLD,

    LLM_KV_SPLIT_VRAM_CAPACITY,
};

static std::map<llm_kv, std::string> LLM_KV_NAMES = {
    { LLM_KV_GENERAL_ARCHITECTURE,          "general.architecture"                  },
    { LLM_KV_GENERAL_QUANTIZATION_VERSION,  "general.quantization_version"          },
    { LLM_KV_GENERAL_ALIGNMENT,             "general.alignment"                     },
    { LLM_KV_GENERAL_NAME,                  "general.name"                          },
    { LLM_KV_GENERAL_AUTHOR,                "general.author"                        },
    { LLM_KV_GENERAL_URL,                   "general.url"                           },
    { LLM_KV_GENERAL_DESCRIPTION,           "general.description"                   },
    { LLM_KV_GENERAL_LICENSE,               "general.license"                       },
    { LLM_KV_GENERAL_SOURCE_URL,            "general.source.url"                    },
    { LLM_KV_GENERAL_SOURCE_HF_REPO,        "general.source.huggingface.repository" },

    { LLM_KV_CONTEXT_LENGTH,                "%s.context_length"        },
    { LLM_KV_EMBEDDING_LENGTH,              "%s.embedding_length"      },
    { LLM_KV_BLOCK_COUNT,                   "%s.block_count"           },
    { LLM_KV_FEED_FORWARD_LENGTH,           "%s.feed_forward_length"   },
    { LLM_KV_USE_PARALLEL_RESIDUAL,         "%s.use_parallel_residual" },
    { LLM_KV_TENSOR_DATA_LAYOUT,            "%s.tensor_data_layout"    },

    { LLM_KV_ATTENTION_HEAD_COUNT,          "%s.attention.head_count"             },
    { LLM_KV_ATTENTION_HEAD_COUNT_KV,       "%s.attention.head_count_kv"          },
    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,      "%s.attention.max_alibi_bias"         },
    { LLM_KV_ATTENTION_CLAMP_KQV,           "%s.attention.clamp_kqv"              },
    { LLM_KV_ATTENTION_LAYERNORM_EPS,       "%s.attention.layer_norm_epsilon"     },
    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,   "%s.attention.layer_norm_rms_epsilon" },

    { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
    { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
    { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
    { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
    { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
    { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,     "%s.rope.scaling.original_context_length" },
    { LLM_KV_ROPE_SCALING_FINETUNED,        "%s.rope.scaling.finetuned"               },

    { LLM_KV_TOKENIZER_MODEL,               "tokenizer.ggml.model"              },
    { LLM_KV_TOKENIZER_LIST,                "tokenizer.ggml.tokens"             },
    { LLM_KV_TOKENIZER_TOKEN_TYPE,          "tokenizer.ggml.token_type"         },
    { LLM_KV_TOKENIZER_SCORES,              "tokenizer.ggml.scores"             },
    { LLM_KV_TOKENIZER_MERGES,              "tokenizer.ggml.merges"             },
    { LLM_KV_TOKENIZER_BOS_ID,              "tokenizer.ggml.bos_token_id"       },
    { LLM_KV_TOKENIZER_EOS_ID,              "tokenizer.ggml.eos_token_id"       },
    { LLM_KV_TOKENIZER_UNK_ID,              "tokenizer.ggml.unknown_token_id"   },
    { LLM_KV_TOKENIZER_SEP_ID,              "tokenizer.ggml.seperator_token_id" },
    { LLM_KV_TOKENIZER_PAD_ID,              "tokenizer.ggml.padding_token_id"   },
    { LLM_KV_TOKENIZER_HF_JSON,             "tokenizer.huggingface.json"        },
    { LLM_KV_TOKENIZER_RWKV,                "tokenizer.rwkv.world"              },

    { LLM_KV_SPARSE_THRESHOLD,              "powerinfer.sparse_threshold" },

    { LLM_KV_SPLIT_VRAM_CAPACITY,           "split.vram_capacity" },
};

struct LLM_KV {
    LLM_KV(llm_arch arch) : arch(arch) {}

    llm_arch arch;

    std::string operator()(llm_kv kv) const {
        return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
    }
};

enum llm_tensor {
    LLM_TENSOR_TOKEN_EMBD,
    LLM_TENSOR_TOKEN_EMBD_NORM,
    LLM_TENSOR_POS_EMBD,
    LLM_TENSOR_OUTPUT,
    LLM_TENSOR_OUTPUT_NORM,
    LLM_TENSOR_ROPE_FREQS,
    LLM_TENSOR_ATTN_Q,
    LLM_TENSOR_ATTN_K,
    LLM_TENSOR_ATTN_V,
    LLM_TENSOR_ATTN_QKV,
    LLM_TENSOR_ATTN_OUT,
    LLM_TENSOR_ATTN_NORM,
    LLM_TENSOR_ATTN_NORM_2,
    LLM_TENSOR_ATTN_ROT_EMBD,
    LLM_TENSOR_FFN_GATE,
    LLM_TENSOR_FFN_DOWN,
    LLM_TENSOR_FFN_UP,
    LLM_TENSOR_FFN_NORM,
    LLM_TENSOR_ATTN_Q_NORM,
    LLM_TENSOR_ATTN_K_NORM,
    LLM_TENSOR_MLP_PRED_FC1,
    LLM_TENSOR_MLP_PRED_FC2,
    LLM_TENSOR_FFN_DOWN_T,
};

static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
    {
        LLM_ARCH_LLAMA,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_DOWN_T,      "blk.%d.ffn_down_t" },
            { LLM_TENSOR_MLP_PRED_FC1,    "blk.%d.fc1" },
            { LLM_TENSOR_MLP_PRED_FC2,    "blk.%d.fc2" },
        },
    },
    {
        LLM_ARCH_BAICHUAN,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_FALCON,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_DOWN_T,      "blk.%d.ffn_down_t" },
            { LLM_TENSOR_MLP_PRED_FC1,    "blk.%d.fc1" },
            { LLM_TENSOR_MLP_PRED_FC2,    "blk.%d.fc2" },
        },
    },
    {
        LLM_ARCH_GPT2,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
        },
    },
    {
        LLM_ARCH_GPTJ,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
        },
    },
    {
        LLM_ARCH_GPTNEOX,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_PERSIMMON,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd"},
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm"},
            { LLM_TENSOR_OUTPUT,          "output"},
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm"},
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv"},
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output"},
            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm"},
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down"},
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up"},
            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd"},
        },
    },
    {
        LLM_ARCH_MPT,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_STARCODER,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_POS_EMBD,        "position_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
        },
    },
    {
        LLM_ARCH_REFACT,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },
    {
        LLM_ARCH_BLOOM,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
        },
    },
    {
        LLM_ARCH_STABLELM,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
            { LLM_TENSOR_OUTPUT,          "output" },
            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
        },
    },

    {
        LLM_ARCH_UNKNOWN,
        {
            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
        },
    },
};

static llm_arch llm_arch_from_string(const std::string & name) {
    for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
        if (kv.second == name) {
            return kv.first;
        }
    }

    return LLM_ARCH_UNKNOWN;
}

enum tensor_offloading_levels {
    TENSOR_NO_OFFLOAD,
    TENSOR_OFFLOAD_FFN,
    TENSOR_OFFLOAD_ATTN,
    TENSOR_OFFLOAD_MLP_PRED,
    TENSOR_OFFLOAD_OUTPUT,
    TENSOR_OFFLOAD_KV_CACHE,
};

tensor_offloading_levels get_offloading_level(llm_tensor tensor) {
    switch (tensor) {
        case LLM_TENSOR_TOKEN_EMBD: case LLM_TENSOR_TOKEN_EMBD_NORM: case LLM_TENSOR_POS_EMBD: 
        case LLM_TENSOR_ROPE_FREQS:
            return TENSOR_NO_OFFLOAD;
        case LLM_TENSOR_OUTPUT: case LLM_TENSOR_OUTPUT_NORM:
            return TENSOR_OFFLOAD_OUTPUT;
        case LLM_TENSOR_ATTN_Q: case LLM_TENSOR_ATTN_K: case LLM_TENSOR_ATTN_V: 
        case LLM_TENSOR_ATTN_QKV: case LLM_TENSOR_ATTN_OUT: case LLM_TENSOR_ATTN_NORM: 
        case LLM_TENSOR_ATTN_NORM_2: case LLM_TENSOR_ATTN_ROT_EMBD:
        case LLM_TENSOR_ATTN_Q_NORM: case LLM_TENSOR_ATTN_K_NORM:
            return TENSOR_OFFLOAD_ATTN;
        case LLM_TENSOR_FFN_GATE: case LLM_TENSOR_FFN_DOWN: case LLM_TENSOR_FFN_UP:
        case LLM_TENSOR_FFN_NORM: case LLM_TENSOR_FFN_DOWN_T: 
            return TENSOR_OFFLOAD_FFN;
        case LLM_TENSOR_MLP_PRED_FC1: case LLM_TENSOR_MLP_PRED_FC2:
            return TENSOR_OFFLOAD_MLP_PRED;
        default:
            throw std::runtime_error("unknown tensor category");
    }
}


// helper to handle gguf constants
// usage:
//
//   const auto tn = LLM_TN(LLM_ARCH_LLAMA);
//
//   std::string name = tn(LLM_TENSOR_OUTPUT);                     -> "output"
//   std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias");         -> "token_embd.bias"
//   std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3);     -> "blk.3.attn_norm.weight"
//
struct LLM_TN {
    LLM_TN(llm_arch arch) : arch(arch) {}

    llm_arch arch;

    std::pair<std::string, llm_tensor> operator()(llm_tensor tensor) const {
        return std::make_pair(LLM_TENSOR_NAMES[arch].at(tensor), tensor);
    }

    std::pair<std::string, llm_tensor> operator()(llm_tensor tensor, const std::string & suffix) const {
        return std::make_pair(LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix, tensor);
    }

    std::pair<std::string, llm_tensor> operator()(llm_tensor tensor, int bid) const {
        return std::make_pair(::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid), tensor);
    }

    std::pair<std::string, llm_tensor> operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
        return std::make_pair(::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix, tensor);
    }
};

//
// gguf helpers
//

#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
do { \
    const std::string skey(key); \
    const int kid = gguf_find_key(ctx, skey.c_str()); \
    if (kid >= 0) { \
        enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
        if (ktype != (type)) { \
            throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \
        } \
        (dst) = func(ctx, kid); \
    } else if (req) { \
        throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \
    } \
} while (0)

static std::map<int8_t, std::string> LLAMA_ROPE_SCALING_TYPES = {
    { LLAMA_ROPE_SCALING_NONE,   "none"   },
    { LLAMA_ROPE_SCALING_LINEAR, "linear" },
    { LLAMA_ROPE_SCALING_YARN,   "yarn"   },
};

static int8_t llama_rope_scaling_type_from_string(const std::string & name) {
    for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
        if (kv.second == name) {
            return kv.first;
        }
    }

    return LLAMA_ROPE_SCALING_UNSPECIFIED;
}

//
// ggml helpers
//

static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
    struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);

    if (plan.work_size > 0) {
        buf.resize(plan.work_size);
        plan.work_data = buf.data();
    }

    ggml_graph_compute(graph, &plan);
}

//
// llama helpers
//

inline void * llama_host_malloc(size_t n) {
#ifdef GGML_USE_CUBLAS
    if (ggml_cublas_loaded()) {
        return ggml_cuda_host_malloc(n);
    } else {
        return malloc(n);
    }
#elif GGML_USE_METAL
    return ggml_metal_host_malloc(n);
#elif GGML_USE_CPU_HBM
    return hbw_malloc(n);
#else
    return malloc(n);
#endif
}

inline void llama_host_free(void * ptr) {
#ifdef GGML_USE_CUBLAS
    if (ggml_cublas_loaded()) {
        return ggml_cuda_host_free(ptr);
    } else {
        return free(ptr);
    }
#elif GGML_USE_METAL
    return ggml_metal_host_free(ptr);
#elif GGML_USE_CPU_HBM
    return hbw_free(ptr);
#else
    return free(ptr);
#endif
}

#if defined(_WIN32)
static std::string llama_format_win_err(DWORD err) {
    LPSTR buf;
    size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
                                 NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
    if (!size) {
        return "FormatMessageA failed";
    }
    std::string ret(buf, size);
    LocalFree(buf);
    return ret;
}
#endif

struct llama_buffer {
    void * data = NULL;
    size_t size = 0;

    // fallback to malloc / free
    // useful in cases where CUDA can try to allocate PINNED memory
    bool fallback = false;

    void resize(size_t n) {
        llama_host_free(data);

        data = llama_host_malloc(n);
        if (!data) {
            fallback = true;
            data = malloc(n);
        } else {
            fallback = false;
        }

        GGML_ASSERT(data);
        size = n;
    }

    ~llama_buffer() {
        if (data) {
            if (fallback) { // NOLINT
                free(data);
            } else {
                llama_host_free(data);
            }
        }

        data = NULL;
    }
};

struct llama_file {
    // use FILE * so we don't have to re-open the file to mmap
    FILE * fp;
    std::string fname;
    size_t size;

    llama_file(const char * fname, const char * mode): fname(fname) {
        fp = std::fopen(fname, mode);
        if (fp == NULL) {
            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
        }
        seek(0, SEEK_END);
        size = tell();
        seek(0, SEEK_SET);
    }

    std::string get_basedir() const {
        const char * model_path = fname.c_str();
#if defined(_WIN32)
        size_t found = fname.find_last_of("/\\");
        return fname.substr(0, found);
#else
        #include <libgen.h>
        const char * base_dir = dirname(const_cast<char *>(model_path));
        return std::string(base_dir);
#endif
    }

    size_t tell() const {
#ifdef _WIN32
        __int64 ret = _ftelli64(fp);
#else
        long ret = std::ftell(fp);
#endif
        GGML_ASSERT(ret != -1); // this really shouldn't fail
        return (size_t) ret;
    }

    void seek(size_t offset, int whence) const {
#ifdef _WIN32
        int ret = _fseeki64(fp, (__int64) offset, whence);
#else
        int ret = std::fseek(fp, (long) offset, whence);
#endif
        GGML_ASSERT(ret == 0); // same
    }

    void read_raw(void * ptr, size_t len) const {
        if (len == 0) {
            return;
        }
        errno = 0;
        std::size_t ret = std::fread(ptr, len, 1, fp);
        if (ferror(fp)) {
            throw std::runtime_error(format("read error: %s", strerror(errno)));
        }
        if (ret != 1) {
            throw std::runtime_error(std::string("unexpectedly reached end of file"));
        }
    }

    uint32_t read_u32() const {
        uint32_t ret;
        read_raw(&ret, sizeof(ret));
        return ret;
    }

    void write_raw(const void * ptr, size_t len) const {
        if (len == 0) {
            return;
        }
        errno = 0;
        size_t ret = std::fwrite(ptr, len, 1, fp);
        if (ret != 1) {
            throw std::runtime_error(format("write error: %s", strerror(errno)));
        }
    }

    void write_u32(std::uint32_t val) const {
        write_raw(&val, sizeof(val));
    }

    ~llama_file() {
        if (fp) {
            std::fclose(fp);
        }
    }
};

struct llama_mmap {
    void * addr;
    size_t size;

    llama_mmap(const llama_mmap &) = delete;

#ifdef _POSIX_MAPPED_FILES
    static constexpr bool SUPPORTED = true;

    llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
        size = file->size;
        int fd = fileno(file->fp);
        int flags = MAP_SHARED;
        // prefetch/readahead impairs performance on NUMA systems
        if (numa) { prefetch = 0; }
#ifdef __linux__
        if (prefetch) { flags |= MAP_POPULATE; }
#endif
        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
        if (addr == MAP_FAILED) {
            throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
        }

        if (prefetch > 0) {
            // Advise the kernel to preload the mapped memory
            if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
                fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
                        strerror(errno));
            }
        }
        if (numa) {
            // advise the kernel not to use readahead
            // (because the next page might not belong on the same node)
            if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
                fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
                        strerror(errno));
            }
        }
    }

    ~llama_mmap() {
        munmap(addr, size);
    }
#elif defined(_WIN32)
    static constexpr bool SUPPORTED = true;

    llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
        (void) numa;

        size = file->size;

        HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));

        HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
        DWORD error = GetLastError();

        if (hMapping == NULL) {
            throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
        }

        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
        error = GetLastError();
        CloseHandle(hMapping);

        if (addr == NULL) {
            throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
        }

        if (prefetch) {
            // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
            BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
            HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");

            // may fail on pre-Windows 8 systems
            pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));

            if (pPrefetchVirtualMemory) {
                // advise the kernel to preload the mapped memory
                WIN32_MEMORY_RANGE_ENTRY range;
                range.VirtualAddress = addr;
                range.NumberOfBytes = (SIZE_T)size;
                if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
                    fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
                            llama_format_win_err(GetLastError()).c_str());
                }
            }
        }
    }

    ~llama_mmap() {
        if (!UnmapViewOfFile(addr)) {
            fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
                    llama_format_win_err(GetLastError()).c_str());
        }
    }
#else
    static constexpr bool SUPPORTED = false;

    llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
        (void) file;
        (void) prefetch;
        (void) numa;

        throw std::runtime_error(std::string("mmap not supported"));
    }
#endif
};

// Represents some region of memory being locked using mlock or VirtualLock;
// will automatically unlock on destruction.
struct llama_mlock {
    void * addr = NULL;
    size_t size = 0;

    bool failed_already = false;

    llama_mlock() {}
    llama_mlock(const llama_mlock &) = delete;

    ~llama_mlock() {
        if (size) {
            raw_unlock(addr, size);
        }
    }

    void init(void * ptr) {
        GGML_ASSERT(addr == NULL && size == 0); // NOLINT
        addr = ptr;
    }

    void grow_to(size_t target_size) {
        GGML_ASSERT(addr);
        if (failed_already) {
            return;
        }
        size_t granularity = lock_granularity();
        target_size = (target_size + granularity - 1) & ~(granularity - 1);
        if (target_size > size) {
            if (raw_lock((uint8_t *) addr + size, target_size - size)) {
                size = target_size;
            } else {
                failed_already = true;
            }
        }
    }

#ifdef _POSIX_MEMLOCK_RANGE
    static constexpr bool SUPPORTED = true;

    static size_t lock_granularity() {
        return (size_t) sysconf(_SC_PAGESIZE);
    }

    #ifdef __APPLE__
        #define MLOCK_SUGGESTION \
            "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
            "decreasing 'vm.global_no_user_wire_amount'.  Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
    #else
        #define MLOCK_SUGGESTION \
            "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
    #endif

    bool raw_lock(const void * addr, size_t size) const {
        if (!mlock(addr, size)) {
            return true;
        }

        char* errmsg = std::strerror(errno);
        bool suggest = (errno == ENOMEM);

        // Check if the resource limit is fine after all
        struct rlimit lock_limit;
        if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
            suggest = false;
        }
        if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
            suggest = false;
        }

        fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
                size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
        return false;
    }

    #undef MLOCK_SUGGESTION

    static void raw_unlock(void * addr, size_t size) {
        if (munlock(addr, size)) {
            fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
        }
    }
#elif defined(_WIN32)
    static constexpr bool SUPPORTED = true;

    static size_t lock_granularity() {
        SYSTEM_INFO si;
        GetSystemInfo(&si);
        return (size_t) si.dwPageSize;
    }

    bool raw_lock(void * ptr, size_t len) const {
        for (int tries = 1; ; tries++) {
            if (VirtualLock(ptr, len)) {
                return true;
            }
            if (tries == 2) {
                fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
                    len, size, llama_format_win_err(GetLastError()).c_str());
                return false;
            }

            // It failed but this was only the first try; increase the working
            // set size and try again.
            SIZE_T min_ws_size, max_ws_size;
            if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
                fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
                        llama_format_win_err(GetLastError()).c_str());
                return false;
            }
            // Per MSDN: "The maximum number of pages that a process can lock
            // is equal to the number of pages in its minimum working set minus
            // a small overhead."
            // Hopefully a megabyte is enough overhead:
            size_t increment = len + 1048576;
            // The minimum must be <= the maximum, so we need to increase both:
            min_ws_size += increment;
            max_ws_size += increment;
            if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
                fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
                        llama_format_win_err(GetLastError()).c_str());
                return false;
            }
        }
    }

    static void raw_unlock(void * ptr, size_t len) {
        if (!VirtualUnlock(ptr, len)) {
            fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
                    llama_format_win_err(GetLastError()).c_str());
        }
    }
#else
    static constexpr bool SUPPORTED = false;

    static size_t lock_granularity() {
        return (size_t) 65536;
    }

    bool raw_lock(const void * addr, size_t len) const {
        fprintf(stderr, "warning: mlock not supported on this system\n");
        return false;
    }

    static void raw_unlock(const void * addr, size_t len) {}
#endif
};

typedef void (*offload_func_t)(struct ggml_tensor * tensor);

static void ggml_offload_nop(struct ggml_tensor * tensor) {
    (void) tensor;
}

static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
    std::vector<char> result(8, 0);
    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
    if (n_tokens < 0) {
        result.resize(-n_tokens);
        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
        GGML_ASSERT(check == -n_tokens);
    }
    else {
        result.resize(n_tokens);
    }

    return std::string(result.data(), result.size());
}

//
// globals
//

struct llama_state {
    // We save the log callback globally
    ggml_log_callback log_callback = llama_log_callback_default;
    void * log_callback_user_data = nullptr;
};

static llama_state g_state;

// available llama models
enum e_model {
    MODEL_UNKNOWN,
    MODEL_1B,
    MODEL_3B,
    MODEL_7B,
    MODEL_8B,
    MODEL_13B,
    MODEL_15B,
    MODEL_30B,
    MODEL_34B,
    MODEL_40B,
    MODEL_65B,
    MODEL_70B,
};

static const size_t kB = 1024;
static const size_t MB = 1024*kB;
static const size_t GB = 1024*MB;

struct llama_hparams {
    bool     vocab_only;
    uint32_t n_vocab;
    uint32_t n_ctx_train; // context size the model was trained on
    uint32_t n_embd;
    uint32_t n_head;
    uint32_t n_head_kv;
    uint32_t n_layer;
    uint32_t n_rot;
    uint32_t n_ff;

    float f_norm_eps;
    float f_norm_rms_eps;

    float    rope_freq_base_train;
    float    rope_freq_scale_train;
    uint32_t n_yarn_orig_ctx;
    int8_t   rope_scaling_type_train : 3;
    bool     rope_finetuned : 1;

    float f_clamp_kqv;
    float f_max_alibi_bias;
    
    // sparse predictor threshold if sparse inference is enabled
    float sparse_pred_threshold = (float)atof(getenv("LLAMA_SPARSE_PRED_THRESHOLD") ? getenv("LLAMA_SPARSE_PRED_THRESHOLD") : "0.0");

    bool operator!=(const llama_hparams & other) const {
        if (this->vocab_only  != other.vocab_only)  return true;
        if (this->n_vocab     != other.n_vocab)     return true;
        if (this->n_ctx_train != other.n_ctx_train) return true;
        if (this->n_embd      != other.n_embd)      return true;
        if (this->n_head      != other.n_head)      return true;
        if (this->n_head_kv   != other.n_head_kv)   return true;
        if (this->n_layer     != other.n_layer)     return true;
        if (this->n_rot       != other.n_rot)       return true;
        if (this->n_ff        != other.n_ff)        return true;
        if (this->rope_finetuned  != other.rope_finetuned)  return true;
        if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;

        const float EPSILON = 1e-9;

        if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
        if (!is_float_close(this->f_norm_rms_eps,        other.f_norm_rms_eps,        EPSILON)) return true;
        if (!is_float_close(this->rope_freq_base_train,  other.rope_freq_base_train,  EPSILON)) return true;
        if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;

        return false;
    }

    uint32_t n_gqa() const {
        return n_head/n_head_kv;
    }

    uint32_t n_embd_head() const {
        return n_embd/n_head;
    }

    uint32_t n_embd_gqa() const {
        return n_embd/n_gqa();
    }
};

struct llama_cparams {
    uint32_t n_ctx;       // context size used during inference
    uint32_t n_batch;
    uint32_t n_threads;       // number of threads to use for generation
    uint32_t n_threads_batch; // number of threads to use for batch processing

    float    rope_freq_base;
    float    rope_freq_scale;

    uint32_t n_yarn_orig_ctx;
    // These hyperparameters are not exposed in GGUF, because all
    // existing YaRN models use the same values for them.
    float yarn_ext_factor;
    float yarn_attn_factor;
    float yarn_beta_fast;
    float yarn_beta_slow;

    bool mul_mat_q;
};

struct llama_layer {
    // normalization
    struct ggml_tensor * attn_norm;
    struct ggml_tensor * attn_norm_b;
    struct ggml_tensor * attn_norm_2;
    struct ggml_tensor * attn_norm_2_b;
    struct ggml_tensor * attn_q_norm;
    struct ggml_tensor * attn_q_norm_b;
    struct ggml_tensor * attn_k_norm;
    struct ggml_tensor * attn_k_norm_b;

    // attention
    struct ggml_tensor * wq;
    struct ggml_tensor * wk;
    struct ggml_tensor * wv;
    struct ggml_tensor * wo;
    struct ggml_tensor * wqkv;

    // attention bias
    struct ggml_tensor * bo;
    struct ggml_tensor * bqkv;

    // normalization
    struct ggml_tensor * ffn_norm;
    struct ggml_tensor * ffn_norm_b;

    // ff
    struct ggml_tensor * ffn_gate; // w1
    struct ggml_tensor * ffn_down; // w2
    struct ggml_tensor * ffn_up;   // w3
    struct ggml_tensor * ffn_down_t;
    
    // ff sliced on gpu
    struct ggml_tensor * ffn_gate_gpu;
    struct ggml_tensor * ffn_down_gpu;
    struct ggml_tensor * ffn_up_gpu;



    // ff bias
    struct ggml_tensor * ffn_down_b; // b2
    struct ggml_tensor * ffn_up_b;   // b3

    // mlp predictor weights
    struct ggml_tensor * mlp_pre_w1;
    struct ggml_tensor * mlp_pre_w2;

    // ffn split
    struct ggml_tensor * gpu_idx; // index of ffn neurons on GPU
    double gpu_offload_ratio; // ratio of ffn split on GPU ([0, 1])
    struct ggml_tensor * gpu_bucket; // double index from GPU split neuron to original neuron

    struct ggml_tensor * rdma_idx; // index of ffn neurons on local
    struct ggml_tensor * rdma_bucket; // index of ffn neurons on local

};

struct llama_kv_cell {
    llama_pos pos   = -1;
    llama_pos delta = 0;

    std::set<llama_seq_id> seq_id;

    bool has_seq_id(const llama_seq_id & id) const {
        return seq_id.find(id) != seq_id.end();
    }
};

// ring-buffer of cached KV data
struct llama_kv_cache {
    bool has_shift = false;

    // Note: The value of head isn't only used to optimize searching
    // for a free KV slot. llama_decode_internal also uses it, so it
    // cannot be freely changed after a slot has been allocated.
    uint32_t head = 0;
    uint32_t size = 0;

    // computed before each graph build
    uint32_t n = 0;

    std::vector<llama_kv_cell> cells;

    struct ggml_tensor * k = NULL;
    struct ggml_tensor * v = NULL;

    struct ggml_context * ctx = NULL;

    llama_buffer buf;

    ~llama_kv_cache() {
        if (ctx) {
            ggml_free(ctx);
        }

#ifdef GGML_USE_CUBLAS
        if (ggml_cublas_loaded()) {
            ggml_cuda_free_data(k);
            ggml_cuda_free_data(v);
        }
#endif
    }
};

struct llama_vocab {
    using id    = int32_t;
    using token = std::string;
    using ttype = llama_token_type;

    struct token_data {
        token text;
        float score;
        ttype type;
    };

    enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;

    std::unordered_map<token, id> token_to_id;
    std::vector<token_data>       id_to_token;

    std::unordered_map<token, id> special_tokens_cache;

    std::map<std::pair<std::string, std::string>, int> bpe_ranks;

    // default LLaMA special tokens
    id special_bos_id = 1;
    id special_eos_id = 2;
    id special_unk_id = 0;
    id special_sep_id = -1;
    id special_pad_id = -1;

    id linefeed_id       = 13;
    id special_prefix_id = 32007;
    id special_middle_id = 32009;
    id special_suffix_id = 32008;
    id special_eot_id    = 32010;

    int find_bpe_rank(std::string token_left, std::string token_right) const {
        GGML_ASSERT(token_left.find(" ") == std::string::npos);
        GGML_ASSERT(token_left.find("\n") == std::string::npos);
        GGML_ASSERT(token_right.find(" ") == std::string::npos);
        GGML_ASSERT(token_right.find("\n") == std::string::npos);

        auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
        if (it == bpe_ranks.end()) {
            return -1;
        }

        return it->second;
    }
};

struct llama_gpu_split_loader;
struct llama_augmentation_model_loader;

struct llama_model {
    e_model     type  = MODEL_UNKNOWN;
    llm_arch    arch  = LLM_ARCH_UNKNOWN;
    llama_ftype ftype = LLAMA_FTYPE_ALL_F32;

    std::string name = "n/a";

    ggml_sparse_deriv sparse_deriv;

    llama_hparams hparams = {};
    llama_vocab   vocab;

    struct ggml_tensor * tok_embd;
    struct ggml_tensor * pos_embd;
    struct ggml_tensor * tok_norm;
    struct ggml_tensor * tok_norm_b;

    struct ggml_tensor * output_norm;
    struct ggml_tensor * output_norm_b;
    struct ggml_tensor * output;

    std::vector<llama_layer> layers;

    int n_gpu_layers;

    // context
    struct ggml_context * ctx = NULL;

    // the model memory buffer
    llama_buffer buf;

    // model memory mapped file
    std::unique_ptr<llama_mmap> mapping;

    // aux model loaders for dynamically loaded/transformed model weights
    std::unique_ptr<struct llama_gpu_split_loader> mlp_model_loader;
    std::unique_ptr<struct llama_augmentation_model_loader> aug_model_loader;

    // objects representing data potentially being locked in memory
    llama_mlock mlock_buf;
    llama_mlock mlock_mmap;

    // for quantize-stats only
    std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;

    int64_t t_load_us = 0;
    int64_t t_start_us = 0;

    // neuron size of spilt and offloaded FFN
    size_t ffn_offloaded_bytes = 0;

    ~llama_model() {
        if (ctx) {
            ggml_free(ctx);
        }

#ifdef GGML_USE_CUBLAS
        if (ggml_cublas_loaded()) {
            for (size_t i = 0; i < tensors_by_name.size(); ++i) {
                ggml_cuda_free_data(tensors_by_name[i].second);
            }
            ggml_cuda_free_scratch();
        }
#endif

#if defined(GGML_USE_CLBLAST)
        for (size_t i = 0; i < tensors_by_name.size(); ++i) {
            ggml_cl_free_data(tensors_by_name[i].second);
        }
#endif
    }
};

struct llama_context {
    llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
    ~llama_context() {
#ifdef GGML_USE_METAL
        if (ctx_metal) {
            ggml_metal_free(ctx_metal);
        }
#endif
        if (alloc) {
            ggml_allocr_free(alloc);
        }
    }

    llama_cparams cparams;

    const llama_model & model;

    // key + value cache for the self attention
    struct llama_kv_cache kv_self;

    std::mt19937 rng;

    bool has_evaluated_once = false;

    int64_t t_start_us;
    int64_t t_load_us;
    int64_t t_sample_us = 0;
    int64_t t_p_eval_us = 0;
    int64_t t_eval_us   = 0;

    int32_t n_sample = 0; // number of tokens sampled
    int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
    int32_t n_eval   = 0; // number of eval calls

    // decode output (2-dimensional array: [n_tokens][n_vocab])
    std::vector<float> logits;
    bool logits_all = false;

    // input embedding (1-dimensional array: [n_embd])
    std::vector<float> embedding;

    // reusable buffer for `struct ggml_graph_plan.work_data`
    std::vector<uint8_t> work_buffer;

    // memory buffers used to evaluate the model
    llama_buffer buf_compute;

    llama_buffer buf_alloc;
    ggml_allocr * alloc = NULL;

#ifdef GGML_USE_METAL
    ggml_metal_context * ctx_metal = NULL;
#endif

#ifdef GGML_USE_MPI
    ggml_mpi_context * ctx_mpi = NULL;
#endif
};

//
// kv cache helpers
//
static bool llama_kv_cache_init(
        const struct llama_hparams & hparams,
             struct llama_kv_cache & cache,
                         ggml_type   wtype,
                          uint32_t   n_ctx,
                               int   n_gpu_layers) {
    const uint32_t n_embd  = hparams.n_embd_gqa();
    const uint32_t n_layer = hparams.n_layer;

    const int64_t n_mem      = n_layer*n_ctx;
    const int64_t n_elements = n_embd*n_mem;

    cache.has_shift = false;

    cache.head = 0;
    cache.size = n_ctx;

    cache.cells.clear();
    cache.cells.resize(n_ctx);

    cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*ggml_tensor_overhead());
    memset(cache.buf.data, 0, cache.buf.size);

    struct ggml_init_params params;
    params.mem_size   = cache.buf.size;
    params.mem_buffer = cache.buf.data;
    params.no_alloc   = false;

    cache.ctx = ggml_init(params);

    if (!cache.ctx) {
        LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
        return false;
    }

    cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
    cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
    ggml_set_name(cache.k, "cache_k");
    ggml_set_name(cache.v, "cache_v");

    (void) n_gpu_layers;

#ifdef GGML_USE_CUBLAS
    if (ggml_cublas_loaded()) {
        size_t vram_kv_cache = 0;

        if (n_gpu_layers > (int)n_layer + 1) {
            ggml_cuda_assign_buffers_no_scratch(cache.v);
            LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
            vram_kv_cache += ggml_nbytes(cache.v);
        }
        if (n_gpu_layers > (int)n_layer + 2) {
            ggml_cuda_assign_buffers_no_scratch(cache.k);
            LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
            vram_kv_cache += ggml_nbytes(cache.k);
        }
        if (vram_kv_cache > 0) {
            LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
        }
    }
#endif

    return true;
}

// find an empty slot of size "n_tokens" in the cache
// updates the cache head
// Note: On success, it's important that cache.head points
// to the first cell of the slot.
static bool llama_kv_cache_find_slot(
           struct llama_kv_cache & cache,
        const struct llama_batch & batch) {
    const uint32_t n_ctx    = cache.size;
    const uint32_t n_tokens = batch.n_tokens;

    if (n_tokens > n_ctx) {
        LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
        return false;
    }

    uint32_t n_tested = 0;

    while (true) {
        if (cache.head + n_tokens > n_ctx) {
            n_tested += n_ctx - cache.head;
            cache.head = 0;
            continue;
        }

        bool found = true;
        for (uint32_t i = 0; i < n_tokens; i++) {
            if (cache.cells[cache.head + i].pos >= 0) {
                found = false;
                cache.head += i + 1;
                n_tested   += i + 1;
                break;
            }
        }

        if (found) {
            break;
        }

        if (n_tested >= n_ctx) {
            //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
            return false;
        }
    }

    for (uint32_t i = 0; i < n_tokens; i++) {
        cache.cells[cache.head + i].pos = batch.pos[i];

        for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
            cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]);
        }
    }

    return true;
}

// find how many cells are currently in use
static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
    for (uint32_t i = cache.size - 1; i > 0; --i) {
        if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
            return i + 1;
        }
    }

    return 0;
}

static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
    for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
        cache.cells[i].pos = -1;
        cache.cells[i].seq_id.clear();
    }
    cache.head = 0;
}

static void llama_kv_cache_seq_rm(
        struct llama_kv_cache & cache,
                 llama_seq_id   seq_id,
                    llama_pos   p0,
                    llama_pos   p1) {
    uint32_t new_head = cache.size;

    if (p0 < 0) p0 = 0;
    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
            if (seq_id < 0) {
                cache.cells[i].seq_id.clear();
            } else if (cache.cells[i].has_seq_id(seq_id)) {
                cache.cells[i].seq_id.erase(seq_id);
            } else {
                continue;
            }
            if (cache.cells[i].seq_id.empty()) {
                cache.cells[i].pos = -1;
                if (new_head == cache.size) new_head = i;
            }
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
    if (new_head != cache.size) cache.head = new_head;
}

static void llama_kv_cache_seq_cp(
        struct llama_kv_cache & cache,
                 llama_seq_id   seq_id_src,
                 llama_seq_id   seq_id_dst,
                    llama_pos   p0,
                    llama_pos   p1) {
    if (p0 < 0) p0 = 0;
    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();

    cache.head = 0;

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
            cache.cells[i].seq_id.insert(seq_id_dst);
        }
    }
}

static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
    uint32_t new_head = cache.size;

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (!cache.cells[i].has_seq_id(seq_id)) {
            cache.cells[i].pos = -1;
            cache.cells[i].seq_id.clear();
            if (new_head == cache.size) new_head = i;
        } else {
            cache.cells[i].seq_id.clear();
            cache.cells[i].seq_id.insert(seq_id);
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
    if (new_head != cache.size) cache.head = new_head;
}

static void llama_kv_cache_seq_shift(
        struct llama_kv_cache & cache,
                 llama_seq_id   seq_id,
                    llama_pos   p0,
                    llama_pos   p1,
                    llama_pos   delta) {
    uint32_t new_head = cache.size;

    if (p0 < 0) p0 = 0;
    if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();

    for (uint32_t i = 0; i < cache.size; ++i) {
        if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
            cache.has_shift = true;
            cache.cells[i].pos   += delta;
            cache.cells[i].delta += delta;

            if (cache.cells[i].pos < 0) {
                cache.cells[i].pos = -1;
                cache.cells[i].seq_id.clear();
                if (new_head == cache.size) new_head = i;
            }
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
    // Otherwise we just start the next search from the beginning.
    cache.head = new_head != cache.size ? new_head : 0;
}

//
// model loading and saving
//

enum llama_fver {
    GGUF_FILE_VERSION_V1 = 1,
    GGUF_FILE_VERSION_V2 = 2,
    GGUF_FILE_VERSION_V3 = 3,
};

static const char * llama_file_version_name(llama_fver version) {
    switch (version) {
        case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
        case GGUF_FILE_VERSION_V2: return "GGUF V2";
        case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
    }

    return "unknown";
}

static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
    char buf[256];
    snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
    for (size_t i = 1; i < ne.size(); i++) {
        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
    }
    return buf;
}

static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
    char buf[256];
    snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
    for (int i = 1; i < GGML_MAX_DIMS; i++) {
        snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
    }
    return buf;
}

struct llama_model_loader {
    int n_kv      = 0;
    int n_tensors = 0;
    int n_created = 0;

    ggml_sparse_deriv sparse_deriv;

    int64_t n_elements = 0;
    size_t  n_bytes    = 0;

    bool use_mmap = false;

    llama_file  file;
    llama_ftype ftype;
    llama_fver  fver;

    std::unique_ptr<llama_mmap> mapping;

    struct gguf_context * ctx_gguf = NULL;
    struct ggml_context * ctx_meta = NULL;

    llama_model_loader(const std::string & fname, bool use_mmap) : file(fname.c_str(), "rb") {
        struct gguf_init_params params = {
            /*.no_alloc = */ true,
            /*.ctx      = */ &ctx_meta,
        };

        ctx_gguf = gguf_init_from_file(fname.c_str(), params);
        if (!ctx_gguf) {
            throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
        }

        n_kv      = gguf_get_n_kv(ctx_gguf);
        n_tensors = gguf_get_n_tensors(ctx_gguf);
        sparse_deriv = gguf_get_sparse_deriv(ctx_gguf);
        fver = (enum llama_fver ) gguf_get_version(ctx_gguf);

        for (int i = 0; i < n_tensors; i++) {
            const char * name = gguf_get_tensor_name(ctx_gguf, i);
            struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
            n_elements += ggml_nelements(t);
            n_bytes    += ggml_nbytes(t);
        }

        LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
                __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));

        // determine file type based on the number of tensors for each quantization and print meta data
        // TODO: make optional
        {
            std::map<enum ggml_type, uint32_t> n_type;

            uint32_t n_type_max = 0;
            enum ggml_type type_max = GGML_TYPE_F32;

            for (int i = 0; i < n_tensors; i++) {
                const char * name = gguf_get_tensor_name(ctx_gguf, i);
                struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, name);

                n_type[meta->type]++;

                if (n_type_max < n_type[meta->type]) {
                    n_type_max = n_type[meta->type];
                    type_max   = meta->type;
                }

                LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).c_str());
            }

            switch (type_max) {
                case GGML_TYPE_F32:  ftype = LLAMA_FTYPE_ALL_F32;       break;
                case GGML_TYPE_F16:  ftype = LLAMA_FTYPE_MOSTLY_F16;    break;
                case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0;   break;
                case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1;   break;
                case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0;   break;
                case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1;   break;
                case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0;   break;
                case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K;   break;
                case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
                case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
                case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
                case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K;   break;
                default:
                     {
                         LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
                         ftype = LLAMA_FTYPE_ALL_F32;
                     } break;
            }

            // this is a way to mark that we have "guessed" the file type
            ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);

            {
                const int kid = gguf_find_key(ctx_gguf, "general.file_type");
                if (kid >= 0) {
                    ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
                }
            }

            for (int i = 0; i < n_kv; i++) {
                const char * name         = gguf_get_key(ctx_gguf, i);
                const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);

                LLAMA_LOG_INFO("%s: - kv %3d: %42s %-8s\n", __func__, i, name, gguf_type_name(type));
            }

            // print type counts
            for (auto & kv : n_type) {
                if (kv.second == 0) {
                    continue;
                }

                LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
            }
        }

        if (!llama_mmap::SUPPORTED) {
            LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
            use_mmap = false;
        }

        this->use_mmap = use_mmap;
    }

    ~llama_model_loader() {
        if (ctx_gguf) {
            gguf_free(ctx_gguf);
        }
        if (ctx_meta) {
            ggml_free(ctx_meta);
        }
    }

    std::string get_arch_name() const {
        const auto kv = LLM_KV(LLM_ARCH_UNKNOWN);

        std::string arch_name;
        GGUF_GET_KEY(ctx_gguf, arch_name, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_GENERAL_ARCHITECTURE));

        return arch_name;
    }

    enum llm_arch get_arch() const {
        const std::string arch_name = get_arch_name();

        return llm_arch_from_string(arch_name);
    }

    const char * get_tensor_name(int i) const {
        return gguf_get_tensor_name(ctx_gguf, i);
    }

    struct ggml_tensor * get_tensor_meta(int i) const {
        return ggml_get_tensor(ctx_meta, get_tensor_name(i));
    }

    void calc_sizes(size_t & ctx_size_p, size_t & mmapped_size_p) const {
        ctx_size_p     = 0;
        mmapped_size_p = 0;

        for (int i = 0; i < n_tensors; i++) {
            struct ggml_tensor * meta = get_tensor_meta(i);
            ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
            (use_mmap ? mmapped_size_p : ctx_size_p) += ggml_nbytes_pad(meta);
        }
    }

    struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend_type backend) {
        if (backend != GGML_BACKEND_CPU) {
            ggml_set_no_alloc(ctx, true);
        }

        struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta);
        tensor->backend = backend; // TODO: ggml_set_backend
        ggml_set_name(tensor, ggml_get_name(meta));

        if (backend != GGML_BACKEND_CPU) {
            ggml_set_no_alloc(ctx, use_mmap);
        }

        n_created++;

        return tensor;
    }

    struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::pair<std::string, llm_tensor> & tn, const std::vector<int64_t> & ne, ggml_backend_type backend) {
        return create_tensor(ctx, tn.first, ne, backend);
    }

    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 * cur = ggml_get_tensor(ctx_meta, name.c_str());

        if (cur == NULL) {
            throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
        }

        if (backend == GGML_BACKEND_GPU_SPLIT) {
            if (ne.size() == 1) {
                throw std::runtime_error(format("%s: 1-dimensional tensor '%s' cannot be split on the GPU", __func__, name.c_str()));
            }
        }

        {
            bool is_ok = true;
            for (size_t i = 0; i < ne.size(); ++i) {
                if (ne[i] != cur->ne[i]) {
                    // allow for -1 in ne for wildcard dimensions
                    is_ok = ne[i] == -1;
                    break;
                }
            }
            if (!is_ok) {
                throw std::runtime_error(
                        format("%s: tensor '%s' has wrong shape; expected %s, got %s",
                            __func__, name.c_str(),
                            llama_format_tensor_shape(ne).c_str(),
                            llama_format_tensor_shape(cur).c_str()));
            }
        }

        return create_tensor_for(ctx, cur, backend);
    }

    void done_getting_tensors() const {
        if (n_created != n_tensors) {
            throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
        }
    }

    size_t file_offset(const char * name) const {
        const int idx = gguf_find_tensor(ctx_gguf, name);

        if (idx < 0) {
            throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name));
        }

        return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx);
    }

    void load_data_for(struct ggml_tensor * cur) const {//给data赋值
        const size_t offs = file_offset(ggml_get_name(cur));

        if (use_mmap) {
            cur->data = (uint8_t *) mapping->addr + offs;
        } else {
            file.seek(offs, SEEK_SET);
            file.read_raw(cur->data, ggml_nbytes(cur));
        }
        // free(cur->data);
        // printf("free_data\n")
        //通过 file.seek 设置文件指针到计算出的偏移量 offs。然后，调用 file.read_raw 方法将数据从文件读取到 cur->data 指向的内存中。读取的字节数量由 ggml_nbytes(cur) 确定，它可能是基于张量的尺寸和数据类型计算出的总字节大小。
    }

    void load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
        size_t size_data = 0;
        size_t size_lock = 0;
        size_t size_pref = 0; // prefetch




        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
            size_data += ggml_nbytes(cur);
            if (cur->backend == GGML_BACKEND_CPU) {
                size_pref += ggml_nbytes(cur);
            }
        }

        if (use_mmap) {
            mapping.reset(new llama_mmap(&file, size_pref, ggml_is_numa()));
            if (lmlock) {
                lmlock->init(mapping->addr);
            }
        }

        size_t done_size = 0;
        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
            GGML_ASSERT(cur); // unused tensors should have been caught by load_data already

            if (progress_callback) {
                progress_callback((float) done_size / size_data, progress_callback_user_data);
            }
            if (!use_mmap && cur->data == NULL) {
                GGML_ASSERT(cur->backend != GGML_BACKEND_CPU);
                #ifdef GGML_USE_CPU_HBM
                // printf("hbw_malloc\n");
                cur->data = (uint8_t*)hbw_malloc(ggml_nbytes(cur));
                #else
                // printf("malloc\n"); 1
                cur->data = (uint8_t*)malloc(ggml_nbytes(cur));
                #endif
            }
            // std::free(cur->data);
            // printf("free_data\n");
            
            load_data_for(cur); //给data赋值

            switch (cur->backend) {
                case GGML_BACKEND_CPU:
                    if (use_mmap && lmlock) {
                        size_lock += ggml_nbytes(cur);
                        lmlock->grow_to(size_lock);
                    }
                    break;
#ifdef GGML_USE_CUBLAS
                case GGML_BACKEND_GPU:
                case GGML_BACKEND_GPU_SPLIT:
                    // old code:
                    //ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor);

                    // TODO: test if this works !!
                    ggml_cuda_transform_tensor(cur->data, cur);
                    if (!use_mmap) {
                        free(cur->data); // free the temporary buffer
                    }
                    break;
#elif defined(GGML_USE_CLBLAST)
                case GGML_BACKEND_GPU:
                    ggml_cl_transform_tensor(cur->data, cur);
                    if (!use_mmap) {
                        free(cur->data);
                    }
                    break;
#endif
                default:
                    continue;
            }

            done_size += ggml_nbytes(cur);
        }

    }
    void load_all_data_RDMA(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
        size_t size_data = 0;
        size_t size_lock = 0;
        size_t size_pref = 0; // prefetch
        //====rdma====
        std::vector<struct ggml_tensor *> tensor_dsts;
        char *ip = NULL, *port = NULL;
        struct sockaddr_in server_sockaddr=get_server_sockaddr(ip, port);
        start_rdma_server(&server_sockaddr);
        setup_client_resources();
        accept_client_connection();
        //====rdma====

        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
            size_data += ggml_nbytes(cur);
            if (cur->backend == GGML_BACKEND_CPU) {
                size_pref += ggml_nbytes(cur);
            }

        }

        if (use_mmap) {
            mapping.reset(new llama_mmap(&file, size_pref, ggml_is_numa()));
            if (lmlock) {
                lmlock->init(mapping->addr);
            }
        }

        size_t done_size = 0;
        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
            GGML_ASSERT(cur); // unused tensors should have been caught by load_data already

            if (progress_callback) {
                progress_callback((float) done_size / size_data, progress_callback_user_data);
            }
            if (!use_mmap && cur->data == NULL) {
                GGML_ASSERT(cur->backend != GGML_BACKEND_CPU);
                #ifdef GGML_USE_CPU_HBM
                // printf("hbw_malloc\n");
                cur->data = (uint8_t*)hbw_malloc(ggml_nbytes(cur));
                #else
                // printf("malloc\n"); 1
                cur->data = (uint8_t*)malloc(ggml_nbytes(cur));
                #endif
            }

            tensor_dsts.push_back(cur);

        }
            // std::free(cur->data);
            // printf("free_data\n");
        clock_t start = clock();
        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
            load_data_for(cur); //给data赋值

            if(cur->data==NULL)
            {
                printf("cur->data is NULL\n");
            }

        }
        std::cout << "花费了" << (double)(clock() - start) / CLOCKS_PER_SEC << "秒" << std::endl;
        register_mrs_to_client(tensor_dsts);
        sleep(1000);
        // disconnect_and_cleanup_LLM_vec();
        for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
            struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));

            switch (cur->backend) {
                case GGML_BACKEND_CPU:
                    if (use_mmap && lmlock) {
                        size_lock += ggml_nbytes(cur);
                        lmlock->grow_to(size_lock);
                    }
                    break;
#ifdef GGML_USE_CUBLAS
                case GGML_BACKEND_GPU:
                case GGML_BACKEND_GPU_SPLIT:
                    // old code:
                    //ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor);

                    // TODO: test if this works !!
                    ggml_cuda_transform_tensor(cur->data, cur);
                    if (!use_mmap) {
                        free(cur->data); // free the temporary buffer
                    }
                    break;
#elif defined(GGML_USE_CLBLAST)
                case GGML_BACKEND_GPU:
                    ggml_cl_transform_tensor(cur->data, cur);
                    if (!use_mmap) {
                        free(cur->data);
                    }
                    break;
#endif
                default:
                    continue;
            }

            done_size += ggml_nbytes(cur);
        }

    }
};

//
// load LLaMA models
//

static std::string llama_model_arch_name(llm_arch arch) {
    auto it = LLM_ARCH_NAMES.find(arch);
    if (it == LLM_ARCH_NAMES.end()) {
        return "unknown";
    }
    return it->second;
}

static std::string llama_model_ftype_name(llama_ftype ftype) {
    if (ftype & LLAMA_FTYPE_GUESSED) {
        return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
    }

    switch (ftype) {
        case LLAMA_FTYPE_ALL_F32:     return "all F32";
        case LLAMA_FTYPE_MOSTLY_F16:  return "mostly F16";
        case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
        case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
        case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
                                      return "mostly Q4_1, some F16";
        case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
        case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
        case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";

        // K-quants
        case LLAMA_FTYPE_MOSTLY_Q2_K:   return "mostly Q2_K";
        case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
        case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
        case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
        case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
        case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
        case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
        case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
        case LLAMA_FTYPE_MOSTLY_Q6_K:   return "mostly Q6_K";

        default: return "unknown, may not work";
    }
}

static const char * llama_model_type_name(e_model type) {
    switch (type) {
        case MODEL_1B:  return "1B";
        case MODEL_3B:  return "3B";
        case MODEL_7B:  return "7B";
        case MODEL_8B:  return "8B";
        case MODEL_13B: return "13B";
        case MODEL_15B: return "15B";
        case MODEL_30B: return "30B";
        case MODEL_34B: return "34B";
        case MODEL_40B: return "40B";
        case MODEL_65B: return "65B";
        case MODEL_70B: return "70B";
        default:        return "?B";
    }
}

static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
    model.arch = ml.get_arch();
    if (model.arch == LLM_ARCH_UNKNOWN) {
        throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
    }
}

static void llm_load_hparams(
        llama_model_loader & ml,
        llama_model & model) {
    struct gguf_context * ctx = ml.ctx_gguf;

    const auto kv = LLM_KV(model.arch);

    auto & hparams = model.hparams;

    // get general kv
    GGUF_GET_KEY(ctx, model.name, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_GENERAL_NAME));

    // get hparams kv
    GGUF_GET_KEY(ctx, hparams.n_vocab,        gguf_get_arr_n,   GGUF_TYPE_ARRAY,  true, kv(LLM_KV_TOKENIZER_LIST));
    GGUF_GET_KEY(ctx, hparams.n_ctx_train,    gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_CONTEXT_LENGTH));
    GGUF_GET_KEY(ctx, hparams.n_embd,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
    GGUF_GET_KEY(ctx, hparams.n_ff,           gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
    GGUF_GET_KEY(ctx, hparams.n_head,         gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
    GGUF_GET_KEY(ctx, hparams.n_layer,        gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));

    // n_head_kv is optional, default to n_head
    hparams.n_head_kv = hparams.n_head;
    GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));

    hparams.rope_finetuned = false;
    GGUF_GET_KEY(ctx, hparams.rope_finetuned, gguf_get_val_bool, GGUF_TYPE_BOOL, false,
                 kv(LLM_KV_ROPE_SCALING_FINETUNED));

    hparams.n_yarn_orig_ctx = hparams.n_ctx_train;
    GGUF_GET_KEY(ctx, hparams.n_yarn_orig_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false,
                 kv(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN));

    // rope_freq_base (optional)
    hparams.rope_freq_base_train = 10000.0f;
    GGUF_GET_KEY(ctx, hparams.rope_freq_base_train, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));

    std::string rope_scaling("linear");
    GGUF_GET_KEY(ctx, rope_scaling, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_ROPE_SCALING_TYPE));
    hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
    GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_UNSPECIFIED);

    // rope_freq_scale (inverse of the kv) is optional
    float ropescale = 0.0f;
    GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALING_FACTOR));
    if (ropescale == 0.0f) { // try the old key name
        GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
    }
    hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;

    // sanity check for n_rot (optional)
    {
        hparams.n_rot = hparams.n_embd / hparams.n_head;

        GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));

        if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
            if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
            }
        }
        // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
        // gpt-j n_rot = rotary_dim
    }

    if (gguf_get_sparse_deriv(ctx)) {
        // read sparse threshold override if sparse deriv is enabled
        GGUF_GET_KEY(ctx, hparams.sparse_pred_threshold, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_SPARSE_THRESHOLD));
        if (getenv("LLAMA_SPARSE_PRED_THRESHOLD"))
            hparams.sparse_pred_threshold = (float)atof(getenv("LLAMA_SPARSE_PRED_THRESHOLD"));
    }

    // arch-specific KVs
    switch (model.arch) {
        case LLM_ARCH_LLAMA:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));

                switch (hparams.n_layer) {
                    case 26: model.type = e_model::MODEL_3B; break;
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_13B; break;
                    case 48: model.type = e_model::MODEL_34B; break;
                    case 60: model.type = e_model::MODEL_30B; break;
                    case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_FALCON:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));

                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 60: model.type = e_model::MODEL_40B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_BAICHUAN:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_13B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_STARCODER:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
                switch (hparams.n_layer) {
                    case 24: model.type = e_model::MODEL_1B; break;
                    case 36: model.type = e_model::MODEL_3B; break;
                    case 42: model.type = e_model::MODEL_7B; break;
                    case 40: model.type = e_model::MODEL_15B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_PERSIMMON:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
                switch (hparams.n_layer) {
                    case 36: model.type = e_model::MODEL_8B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_REFACT:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_1B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_BLOOM:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));

                switch (hparams.n_layer) {
                    case 24: model.type = e_model::MODEL_1B; break;
                    case 30:
                        switch (hparams.n_embd) {
                            case 2560: model.type = e_model::MODEL_3B; break;
                            case 4096: model.type = e_model::MODEL_7B; break;
                        } break;
                }
            } break;
        case LLM_ARCH_MPT:
            {
                hparams.f_clamp_kqv = 0.0f;

                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
                GGUF_GET_KEY(ctx, hparams.f_clamp_kqv, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_CLAMP_KQV));
                GGUF_GET_KEY(ctx, hparams.f_max_alibi_bias, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_MAX_ALIBI_BIAS));

                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_7B; break;
                    case 48: model.type = e_model::MODEL_30B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
                }
            } break;
        case LLM_ARCH_STABLELM:
            {
                GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));

                switch (hparams.n_layer) {
                    case 32: model.type = e_model::MODEL_3B; break;
                    default: model.type = e_model::MODEL_UNKNOWN;
               }
            } break;

        default: (void)0;
    }

    model.ftype = ml.ftype;
}

// TODO: This should probably be in llama.h
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false);
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);

static void llm_load_vocab(
        llama_model_loader & ml,
        llama_model & model) {
    auto & vocab = model.vocab;

    struct gguf_context * ctx = ml.ctx_gguf;

    const auto kv = LLM_KV(model.arch);

    const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
    if (token_idx == -1) {
        throw std::runtime_error("cannot find tokenizer vocab in model file\n");
    }

    const float * scores = nullptr;
    const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
    if (score_idx != -1) {
        scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
    }

    const int * toktypes = nullptr;
    const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
    if (toktype_idx != -1) {
        toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
    }

    // determine vocab type
    {
        std::string tokenizer_name;

        GGUF_GET_KEY(ctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL));

        if (tokenizer_name == "llama") {
            vocab.type = LLAMA_VOCAB_TYPE_SPM;

            // default special tokens
            vocab.special_bos_id = 1;
            vocab.special_eos_id = 2;
            vocab.special_unk_id = 0;
            vocab.special_sep_id = -1;
            vocab.special_pad_id = -1;
        } else if (tokenizer_name == "gpt2") {
            vocab.type = LLAMA_VOCAB_TYPE_BPE;

            // read bpe merges and populate bpe ranks
            const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
            if (merges_keyidx == -1) {
                throw std::runtime_error("cannot find tokenizer merges in model file\n");
            }

            const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);

            for (int i = 0; i < n_merges; i++) {
                const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
                GGML_ASSERT(codepoints_from_utf8(word).size() > 0);

                std::string first;
                std::string second;

                const size_t pos = word.find(' ', 1);

                if (pos != std::string::npos) {
                    first  = word.substr(0, pos);
                    second = word.substr(pos + 1);
                }

                vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
            }

            // default special tokens
            vocab.special_bos_id = 11;
            vocab.special_eos_id = 11;
            vocab.special_unk_id = -1;
            vocab.special_sep_id = -1;
            vocab.special_pad_id = -1;
        } else {
            LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
            LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);

            vocab.type = LLAMA_VOCAB_TYPE_SPM;
        }
    }

    const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);

    vocab.id_to_token.resize(n_vocab);

    for (uint32_t i = 0; i < n_vocab; i++) {
        std::string word = gguf_get_arr_str(ctx, token_idx, i);
        GGML_ASSERT(codepoints_from_utf8(word).size() > 0);

        vocab.token_to_id[word] = i;

        auto & token_data = vocab.id_to_token[i];
        token_data.text  = std::move(word);
        token_data.score = scores ? scores[i] : 0.0f;
        token_data.type  = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
    }
    GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());

    // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
    if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
        vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
    } else {
        const std::vector<int> ids = llama_tokenize_internal(vocab, "\u010A", false);
        GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
        vocab.linefeed_id = ids[0];
    }

    // special tokens
    {
        const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
            { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id },
            { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id },
            { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
            { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
            { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
        };
        for (const auto & it : special_token_types) {
            const std::string & key = kv(std::get<0>(it));
            int32_t & id = std::get<1>(it), old_id = id;

            GGUF_GET_KEY(ctx, id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, key);
            // Must be >= -1 and < vocab size. Since the key is unsigned, -1
            // can only come from the default value, so there's no point in
            // validating that.
            if (size_t(id + 1) > vocab.id_to_token.size()) {
                LLAMA_LOG_WARN("%s: bad special token: '%s' = %d, using default id %d\n",
                    __func__, key.c_str(), id, old_id);
                id = old_id;
            }
        }
    }

    // build special tokens cache
    {
        // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
        //  and will always be correctly labeled in 'added_tokens.json' etc.
        // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
        //  to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
        //  are special tokens.
        // From testing, this appears to corelate 1:1 with special tokens.
        //

        // Counting special tokens and verifying in only one direction
        //  is sufficient to detect difference in those two sets.
        //
        uint32_t special_tokens_count_by_type = 0;
        uint32_t special_tokens_count_from_verification = 0;

        bool special_tokens_definition_mismatch = false;

        for (const auto & t : vocab.token_to_id) {
            const auto & token = t.first;
            const auto & id    = t.second;

            // Count all non-normal tokens in the vocab while iterating
            if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
                special_tokens_count_by_type++;
            }

            // Skip single character tokens
            if (token.length() > 1) {
                bool is_tokenizable = false;

                // Split token string representation in two, in all possible ways
                //  and check if both halves can be matched to a valid token
                for (unsigned i = 1; i < token.length();) {
                    const auto left  = token.substr(0, i);
                    const auto right = token.substr(i);

                    // check if we didnt partition in the middle of a utf sequence
                    auto utf = utf8_len(left.at(left.length() - 1));

                    if (utf == 1) {
                        if (vocab.token_to_id.find(left)  != vocab.token_to_id.end() &&
                            vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
                            is_tokenizable = true;
                            break;
                        }
                        i++;
                    } else {
                        // skip over the rest of multibyte utf sequence
                        i += utf - 1;
                    }
                }

                if (!is_tokenizable) {
                    // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
                    //  it's faster to re-filter them here, since there are way less candidates now

                    // Calculate a total "utf" length of a token string representation
                    size_t utf8_str_len = 0;
                    for (unsigned i = 0; i < token.length();) {
                        utf8_str_len++;
                        i += utf8_len(token.at(i));
                    }

                    // And skip the ones which are one character
                    if (utf8_str_len > 1) {
                        // At this point what we have left are special tokens only
                        vocab.special_tokens_cache[token] = id;

                        // Count manually found special tokens
                        special_tokens_count_from_verification++;

                        // If this manually found special token is not marked as such, flag a mismatch
                        if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
                            special_tokens_definition_mismatch = true;
                        }
                    }
                }
            }
        }

        if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
            LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
                __func__,
                special_tokens_count_from_verification, vocab.id_to_token.size(),
                special_tokens_count_by_type, vocab.id_to_token.size()
            );
        } else {
            LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
                __func__,
                special_tokens_count_from_verification, vocab.id_to_token.size()
            );
        }
    }
}

static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
    const auto & hparams = model.hparams;
    const auto & vocab   = model.vocab;

    const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);

    // hparams
    LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
    LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix
    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
    LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
    LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
    LLAMA_LOG_INFO("%s: n_head           = %u\n",     __func__, hparams.n_head);
    LLAMA_LOG_INFO("%s: n_head_kv        = %u\n",     __func__, hparams.n_head_kv);
    LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
    LLAMA_LOG_INFO("%s: n_gqa            = %u\n",     __func__, hparams.n_gqa());
    LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
    LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
    LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
    LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
    LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type.c_str());
    LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
    LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
    LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
    LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
    LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
    LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
    LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
    if (ml.n_bytes < GB) {
        LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
    } else {
        LLAMA_LOG_INFO("%s: model size       = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
    }

    // general kv
    LLAMA_LOG_INFO("%s: general.name   = %s\n",    __func__, model.name.c_str());

    // special tokens
    if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
    if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
    if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
    if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
    if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
    if (vocab.linefeed_id    != -1) { LLAMA_LOG_INFO( "%s: LF token  = %d '%s'\n", __func__, vocab.linefeed_id,    vocab.id_to_token[vocab.linefeed_id].text.c_str() );    }

    // sparse inference
    LLAMA_LOG_INFO("%s: sparse_pred_threshold = %.2f\n", __func__, hparams.sparse_pred_threshold);
}


static int64_t sum_gpu_index(struct ggml_tensor * gpu_index) {
    ggml_context * ctx_aux = ggml_init({
        /* mem_size */ 1 << 10,
    });

    GGML_ASSERT(ctx_aux);

    ggml_cgraph * gf = ggml_new_graph_custom(ctx_aux, 1, false);
    ggml_tensor * sum = ggml_sum(ctx_aux, gpu_index);

    ggml_set_name(sum, "gpu_index_sum");
    ggml_build_forward_expand(gf, sum);

    // TODO: +1 worker for GPU under hybrid inference but no use
    // ggml_graph_compute_helper(work_buffer, gf, 2);
    ggml_graph_compute_with_ctx(ctx_aux, gf, 2);

    int32_t sum_val = ggml_get_i32_1d(sum, 0);

    ggml_free(ctx_aux);

    return sum_val;
}

struct llama_gpu_split_loader {
    int n_tensors = 0;
    size_t n_bytes = 0; // tensor data bytes

    const std::string fname;
    int fver;

    bool use_mmap = false; // only supports mmap yet
    std::unique_ptr<llama_mmap> mapping;
    struct ggml_context * ctx_meta = nullptr;

    llama_model_loader * idx_loader;
    size_t vram_required = 0;
    llama_gpu_split_loader(const std::string & fname, bool use_mmap) : fname(fname), use_mmap(use_mmap)  {
        GGML_ASSERT(use_mmap);

        idx_loader = new llama_model_loader(fname, use_mmap);
        GGUF_GET_KEY(idx_loader->ctx_gguf, vram_required, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_NAMES[LLM_KV_SPLIT_VRAM_CAPACITY]);
        printf("loaded gpu_idx, vram_required: %ld\n", vram_required);

        n_tensors = idx_loader->n_tensors;

        // allocate memadata/data for mlp tensors
        // TODO: support allocating buffer for tensor data (when mmap is not used)
        size_t per_tensor_meta_size = GGML_PAD(sizeof(struct ggml_tensor), GGML_MEM_ALIGN) + GGML_OBJECT_SIZE;
        size_t tensor_meta_size = n_tensors * per_tensor_meta_size;
        struct ggml_init_params params = {
            /*.mem_size   =*/ tensor_meta_size,
            /*.mem_buffer =*/ nullptr,
            /*.no_alloc   =*/ true,
        };
        ctx_meta = ggml_init(params);
    }

    bool check_vram_allocable(size_t vram_budget) {
        return vram_budget >= vram_required;
    }

    int load_gpu_idx_for_model(llama_model * model) {
        int n_layers = model->layers.size();
        // TODO: assert fp is at the end of headers
        if (n_tensors != n_layers * 2) {
           LLAMA_LOG_ERROR("%s: error: the number of gpu splits does not match the layer of model\n", __func__);
            return 1;
        }
        LLAMA_LOG_INFO("%s: applying gpu_idx adapter from '%s' - please wait ...\n", __func__, fname.c_str());
        const int64_t t_start_mlp_us = ggml_time_us();

        for (int il = 0; il < n_layers; il++) {
            llama_layer &model_layer = model->layers[il];
            ggml_tensor * gpu_idx = idx_loader->get_tensor_meta(il*2);
            ggml_tensor * gpu_bucket = idx_loader->get_tensor_meta(il*2+1);
            if (gpu_idx == nullptr || gpu_bucket == nullptr) {
                LLAMA_LOG_ERROR("%s: error: failed to load gpu index or bucket\n", __func__);
                return 1;
            }

                model_layer.gpu_idx = idx_loader->create_tensor_for(ctx_meta, gpu_idx, GGML_BACKEND_CPU);
                // model_layer.rdma_idx = idx_loader->create_tensor_for(ctx_meta, gpu_idx, GGML_BACKEND_CPU); //TODO 暂时用gpu_idx
                model_layer.gpu_bucket = idx_loader->create_tensor_for(ctx_meta, gpu_bucket, GGML_BACKEND_CPU);
            

        }
        llama_progress_callback cb = [](float progress, void *ctx) {
            LLAMA_LOG_INFO(".");
        };
        idx_loader->load_all_data(ctx_meta, cb, nullptr, nullptr);

        for (int il = 0; il < n_layers; il++) {
            llama_layer &model_layer = model->layers[il];
            ggml_tensor * gpu_idx = model_layer.gpu_idx;
            ggml_tensor * gpu_bucket = model_layer.gpu_bucket;
            int64_t gpu_neurons = sum_gpu_index(gpu_idx);
            model_layer.gpu_offload_ratio = (double)gpu_neurons / gpu_idx->ne[0];
            if (gpu_neurons == 0 || gpu_neurons == gpu_idx->ne[0]) {
                // no hybrid inference for this layer, unset gpu_bucket
                model_layer.gpu_bucket = NULL;
                // TODO: maybe can also unset gpu_idx
            } else {
#if defined(GGML_USE_CUBLAS)
                ggml_set_backend(gpu_bucket, GGML_BACKEND_GPU);
                ggml_cuda_transform_tensor(gpu_bucket->data, gpu_bucket);
#else
                GGML_ASSERT(false && "cublas is not enabled");
#endif
            }
        }

        const int64_t t_mlp_us = ggml_time_us() - t_start_mlp_us;
        LLAMA_LOG_INFO(" done (%.2f ms)\n", t_mlp_us / 1000.0);

        return 0;
    }



};
    ggml_tensor * create_striped_mat_to_gpu_rdma(struct ggml_context * aux_ctx,struct ggml_tensor *src, struct ggml_tensor * gpu_bucket) {
#ifdef GGML_USE_CUBLAS
        if (gpu_bucket == NULL) {
            // offload the whole tensor to gpu
            ggml_set_backend(src, GGML_BACKEND_GPU);
            ggml_cuda_transform_tensor(src->data, src);
            return src;
        }

        int64_t row_len = src->ne[0];
        int64_t gpu_rows = gpu_bucket->ne[0];
        GGML_ASSERT(0 < gpu_rows && gpu_rows <= src->ne[1]);

        ggml_set_no_alloc(aux_ctx, true);
        ggml_tensor * gpu_dst = ggml_new_tensor_2d(aux_ctx, src->type, row_len, gpu_rows);
        ggml_set_backend(gpu_dst, GGML_BACKEND_GPU);
        ggml_cuda_alloc_tensor(gpu_dst);

        // init two 1d views on host and device
        ggml_tensor * host_mat_row = ggml_new_tensor_1d(aux_ctx, src->type, row_len);
        static ggml_tensor * device_mat_row = ggml_dup_tensor(aux_ctx, host_mat_row);
        ggml_set_backend(device_mat_row, GGML_BACKEND_GPU);
        ggml_cuda_alloc_tensor(device_mat_row);
        *ggml_cuda_get_data_pp(device_mat_row) = *ggml_cuda_get_data_pp(gpu_dst);

        // read raw data and copy to device depending on gpu_idx
        const enum ggml_type type = src->type;
        const int ne0 = src->ne[0];
        const size_t row_data_size = ne0*ggml_type_size(type)/ggml_blck_size(type);
        for (int i = 0; i < gpu_rows; i++) {
            int32_t host_i = ((int32_t *)gpu_bucket->data)[i];
            host_mat_row -> data = (char *)(src -> data) + host_i * row_data_size;
            char ** gpu_data_pp = reinterpret_cast<char **>(ggml_cuda_get_data_pp(device_mat_row));
            // printf("gpu_data_p: %p\n", *gpu_data_pp);
            ggml_cuda_cpy_1d(device_mat_row, host_mat_row);
            *gpu_data_pp = *gpu_data_pp + row_data_size;
        }
        ggml_set_no_alloc(aux_ctx, false);

        return gpu_dst;
#else
        return NULL;
#endif
    }
// to dynamically load/transform llama model weights
struct llama_augmentation_model_loader {
    struct ggml_context * aux_ctx = nullptr;

    llama_augmentation_model_loader(llama_model *model) {
        // TODO: check precondition - MLP loaded

        // check augmentation fields to load
        // 1. gpu_idx;
        // 2. gpu_bucket;
        // 3. transformed ffn_down;
        // const int64_t ggml_aux_tensor_size = 4 * (100 * 100 + 5120*40*4 * ggml_tensor_overhead() + (int64_t)13824*5120*40*4);
        int model_layer = model->layers.size();
        int ffn_dim = model->layers[0].ffn_up->ne[1];
        const size_t ggml_aux_tensor_size = 4 * (model_layer*ffn_dim*sizeof(float)*2+ model_layer*ffn_dim*sizeof(float) * ggml_tensor_overhead() );

        struct ggml_init_params params = {
            /*.mem_size   =*/ ggml_aux_tensor_size,
            /*.mem_buffer =*/ nullptr,
            /*.no_alloc   =*/ false,
        };
        aux_ctx = ggml_init(params);
    }

        // allocate and copy selected weights to gpu
    ggml_tensor * create_striped_mat_to_gpu(struct ggml_tensor *src, struct ggml_tensor * gpu_bucket) {
#ifdef GGML_USE_CUBLAS
        if (gpu_bucket == NULL) {
            // offload the whole tensor to gpu
            ggml_set_backend(src, GGML_BACKEND_GPU);
            ggml_cuda_transform_tensor(src->data, src);
            return src;
        }

        int64_t row_len = src->ne[0];
        int64_t gpu_rows = gpu_bucket->ne[0];
        GGML_ASSERT(0 < gpu_rows && gpu_rows <= src->ne[1]);

        ggml_set_no_alloc(aux_ctx, true);
        // printf("%s row_len: %ld, gpu_rows: %ld\n", __func__, row_len, gpu_rows);
        ggml_tensor * gpu_dst = ggml_new_tensor_2d(aux_ctx, src->type, row_len, gpu_rows);
        ggml_set_backend(gpu_dst, GGML_BACKEND_GPU);
        ggml_cuda_alloc_tensor(gpu_dst);

        // init two 1d views on host and device
        ggml_tensor * host_mat_row = ggml_new_tensor_1d(aux_ctx, src->type, row_len);
        static ggml_tensor * device_mat_row = ggml_dup_tensor(aux_ctx, host_mat_row);
        ggml_set_backend(device_mat_row, GGML_BACKEND_GPU);
        ggml_cuda_alloc_tensor(device_mat_row);
        *ggml_cuda_get_data_pp(device_mat_row) = *ggml_cuda_get_data_pp(gpu_dst);//设备指针extra->data_device[0]

        // read raw data and copy to device depending on gpu_idx
        const enum ggml_type type = src->type;
        const int ne0 = src->ne[0];
        const size_t row_data_size = ne0*ggml_type_size(type)/ggml_blck_size(type);
        for (int i = 0; i < gpu_rows; i++) {
            int32_t host_i = ((int32_t *)gpu_bucket->data)[i];
            host_mat_row -> data = (char *)(src -> data) + host_i * row_data_size;
            char ** gpu_data_pp = reinterpret_cast<char **>(ggml_cuda_get_data_pp(device_mat_row));
            // printf("gpu_data_p: %p\n", *gpu_data_pp);
            ggml_cuda_cpy_1d(device_mat_row, host_mat_row);
            *gpu_data_pp = *gpu_data_pp + row_data_size;
        }
        ggml_set_no_alloc(aux_ctx, false);

        return gpu_dst;
#else
        return NULL;
#endif
    }

    size_t slice_ffn_mat_to_gpu(llama_layer & layer) {
        std::vector<uint8_t> work_buffer;
        ggml_tensor * gpu_idx = layer.gpu_idx;
        ggml_tensor * gpu_bucket = layer.gpu_bucket;
        size_t offloaded_bytes = 0;

        if (layer.gpu_offload_ratio == 0.) {
            return 0;
        }

        GGML_ASSERT((layer.gpu_bucket != NULL) == (layer.gpu_offload_ratio < 1.0));

        if (layer.ffn_gate) {
            layer.ffn_gate_gpu = create_striped_mat_to_gpu(layer.ffn_gate, gpu_bucket);
            offloaded_bytes += ggml_nbytes(layer.ffn_gate_gpu);
        }
        
        layer.ffn_up_gpu = create_striped_mat_to_gpu(layer.ffn_up, gpu_bucket);
        // struct ggml_tensor * temp  = create_striped_mat_to_gpu(layer.ffn_up, gpu_bucket);
        // printf("create_striped_mat_to_gpu \n")
        // ggml_cuda_free_data(temp);
        // printf("ggml_cuda_free_data\n");
        offloaded_bytes += ggml_nbytes(layer.ffn_up_gpu);
        
        layer.ffn_down_gpu = create_striped_mat_to_gpu(layer.ffn_down_t, gpu_bucket);
        offloaded_bytes += ggml_nbytes(layer.ffn_down_gpu);

        return offloaded_bytes;
    }

    size_t offload_ffn_split(llama_model * model) {
        LLAMA_LOG_INFO("%s: applying augmentation to model - please wait ...\n", __func__);
        const int64_t t_start_aug_us = ggml_time_us();
        std::vector<uint8_t> work_buffer;

        // Set sparsity threshold via global virables
        sparse_pred_threshold = model->hparams.sparse_pred_threshold;
#if defined (GGML_USE_CUBLAS)
        ggml_cuda_set_device_constants(model->hparams.sparse_pred_threshold);
#endif

        // load gpu_idx and slice mat to gpu
        size_t offloaded_bytes = 0;
        for (llama_layer &model_layer : model -> layers) {
            // gpu_idx load
            if (model_layer.gpu_idx == NULL && model_layer.gpu_bucket == NULL) {
                ggml_tensor * gpu_idx = ggml_new_tensor_1d(aux_ctx, GGML_TYPE_I32, model_layer.mlp_pre_w2 -> ne[1]);
                ggml_set_zero(gpu_idx);
                model_layer.gpu_idx = gpu_idx;
                ggml_tensor * gpu_bucket = ggml_new_tensor_1d(aux_ctx, GGML_TYPE_I32, 0);
                model_layer.gpu_bucket = gpu_bucket;
            }
            offloaded_bytes += slice_ffn_mat_to_gpu(model_layer);
            LLAMA_LOG_INFO(".");
        }

        LLAMA_LOG_INFO(" done (%.2f ms)\n", (ggml_time_us() - t_start_aug_us) / 1000.0);
        return offloaded_bytes;
    }
};

struct buffered_tensor_allocator {
    llama_model_loader &ml;
    ggml_context *ctx;
    std::map<tensor_offloading_levels, std::vector<std::tuple<int, llm_tensor, ggml_tensor *>>> alloc_queues;
    const llama_hparams & hparams;
    size_t vram_allocated_bytes = 0;
    int offloaded_layers = 0; // mocks the model's n_gpu_layers
    bool tensor_offload_complete = false;

    buffered_tensor_allocator(llama_model_loader &ml, ggml_context *ctx, const llama_hparams &hparams) : ml(ml), ctx(ctx), hparams(hparams) {}

    ggml_tensor * buffered_alloc(const std::string & name, const llm_tensor tensor_type, const std::vector<int64_t> & ne, const int i_layer) {
#if defined(GGML_USE_CUBLAS)
        tensor_offloading_levels level = get_offloading_level(tensor_type);
        if (level == TENSOR_NO_OFFLOAD || level == TENSOR_OFFLOAD_FFN) { //根据tensor_type判断是否需要offload
            return ml.create_tensor(ctx, name, ne, GGML_BACKEND_CPU);
        }
        // Alloc only metadata for GPU tensors
        bool no_alloc = ctx->no_alloc;
        ggml_set_no_alloc(ctx, true);
        ggml_tensor * meta_tensor = ml.create_tensor(ctx, name, ne, GGML_BACKEND_CPU);
        ggml_set_no_alloc(ctx, no_alloc);
        alloc_queues[level].push_back(std::make_tuple(i_layer, tensor_type, meta_tensor));
        return meta_tensor;
#else
        return ml.create_tensor(ctx, name, ne, GGML_BACKEND_CPU);
#endif
    }

    bool offload_tensor(ggml_tensor * meta_tensor) {
        size_t tensor_data_size = ggml_nbytes(meta_tensor);
        if (!llama_reduce_vram_budget(tensor_data_size)) {
            return false;
        }
        // allocate in VRAM
        ggml_set_backend(meta_tensor, GGML_BACKEND_GPU);
        vram_allocated_bytes += tensor_data_size;
        return true;
    }

    bool reserve(size_t tensor_bytes) {
        return llama_reduce_vram_budget(tensor_bytes);
    }

    // For GPU tensors, we need to allocate them in VRAM as much as possible,
    // and update the tensor data in-place. If the VRAM budget is exceeded,
    // we allocate the tensor in CPU memory.
    // Returns: equivalent of the model's n_gpu_layers
    int flush() {
#if defined(GGML_USE_CUBLAS)
        if (!ggml_cublas_loaded()) {
            return 0;
        }
        
        // iterate over offloading priorities
        for (int enum_i = TENSOR_OFFLOAD_ATTN; enum_i <= TENSOR_OFFLOAD_OUTPUT; enum_i ++) {
            tensor_offloading_levels level = static_cast<tensor_offloading_levels>(enum_i);
            for (auto tensor_tup : alloc_queues[level]) {
                const int i_layer = std::get<0>(tensor_tup);
                const llm_tensor tensor_type = std::get<1>(tensor_tup);
                ggml_tensor * meta_tensor = std::get<2>(tensor_tup);
                if (!offload_tensor(meta_tensor)) {
                    ml.done_getting_tensors();
                    return offloaded_layers;
                }

                if (level == TENSOR_OFFLOAD_ATTN && tensor_type == LLM_TENSOR_ATTN_OUT) {
                    offloaded_layers = i_layer + 1;
                } else if (level == TENSOR_OFFLOAD_OUTPUT) {
                    offloaded_layers = hparams.n_layer + 1; // indicate all layers + output are offloaded
                }
            }
        }
        ml.done_getting_tensors();
        tensor_offload_complete = true;
        return offloaded_layers;
#else // GGML_USE_CUBLAS
        return 0;
#endif
    }
};

static bool load_gpu_split_from_split_file(llama_model & model, std::string split_path, size_t vram_budget) {
    llama_gpu_split_loader loader(split_path,true);
    return loader.check_vram_allocable(vram_budget) 
        && loader.load_gpu_idx_for_model(&model) == 0;
}




static bool llm_load_gpu_split_with_budget(llama_model_loader & ml, llama_model & model, size_t vram_allocatable_bytes, bool no_cache) {
    std::string cached_split_path = ml.file.fname + ".generated.gpuidx";
    std::string model_basedir = ml.file.get_basedir();

    // Load GPU split from previously generated cache
    if (access(cached_split_path.c_str(), F_OK) == 0 && !no_cache) {
        if (load_gpu_split_from_split_file(model, cached_split_path, vram_allocatable_bytes)) {
            return true;
        }
        LLAMA_LOG_ERROR("%s: error: failed to apply previously generated gpu split from '%s'\n", __func__, cached_split_path.c_str());
    }

    // Generate GPU split
    std::string activation_path = std::string(model_basedir);
#if defined (_WIN32)
    activation_path += "\\activation";
#else
    activation_path += "/activation";
#endif
    if (access(activation_path.c_str(), F_OK) != 0) {
        LLAMA_LOG_ERROR("%s: error: activation files under '%s' not found\n", __func__, activation_path.c_str());
        return false;
    }

    // Calculate solver parameters
    ggml_tensor * ffn_up = model.layers[0].ffn_up;
    ggml_tensor * ffn_gate = model.layers[0].ffn_gate;
    int slice_size = ffn_up->ne[1] * ggml_type_size(ffn_up->type) / ggml_blck_size(ffn_up->type);
    // For model arch with FFN gate, the gate is also sliced, otherwise only the up and down matrices are sliced
    int vram_bytes_per_slice = slice_size * (ffn_gate ? 4.5 : 2); // TODO: why 4.5, not 3?
    int neuron_cap = floor((double)vram_allocatable_bytes / vram_bytes_per_slice) * 4;

    LLAMA_LOG_INFO("invoking powerinfer Python module to generate gpu split for %.2f MiB of VRAM\n", vram_allocatable_bytes / 1024.0 / 1024.0);

    std::stringstream command_ss;
#if defined (_WIN32)
    command_ss << "python -m powerinfer"
#else
    command_ss << "python3 -m powerinfer"
#endif
               << " --activation " << activation_path
               << " --layer " << model.hparams.n_layer
               << " --neuron " << ffn_up->ne[1]
               << " --capacity " << neuron_cap
               << " --vram-capacity " << vram_allocatable_bytes
               << " --output " << cached_split_path;
    if (system(command_ss.str().c_str()) != 0 || access(cached_split_path.c_str(), F_OK) != 0) {
        LLAMA_LOG_ERROR("%s: error: failed to generate gpu split\n", __func__);
        return false;
    }

    return load_gpu_split_from_split_file(model, cached_split_path, vram_allocatable_bytes);
}
static bool llm_load_rdma_split_with_budget(llama_model_loader & ml, llama_model & model, size_t vram_allocatable_bytes, bool no_cache) {
    std::string cached_split_path = "./ReluLLaMA-7B/llama-7b-relu.powerinfer.gguf.generated_rdma.gpuidx"; //TODO
    std::string model_basedir ="./ReluLLaMA-7B"; //TODO 不用常量会乱码，可以把llm_load_gpu_split_with_budget_for_rdma放到前面

    // Load GPU split from previously generated cache
    if (access(cached_split_path.c_str(), F_OK) == 0 && !no_cache) {
        if (load_gpu_split_from_split_file(model, cached_split_path, vram_allocatable_bytes)) {
            return true;
        }
        LLAMA_LOG_ERROR("%s: error: failed to apply previously generated gpu split from '%s'\n", __func__, cached_split_path.c_str());
    }

    // Generate GPU split
    std::string activation_path = std::string(model_basedir);
#if defined (_WIN32)
    activation_path += "\\activation";
#else
    activation_path += "/activation";
#endif
    if (access(activation_path.c_str(), F_OK) != 0) {
        LLAMA_LOG_ERROR("%s: error: activation files under '%s' not found\n", __func__, activation_path.c_str());
        return false;
    }

    // Calculate solver parameters
    ggml_tensor * ffn_up = model.layers[0].ffn_up;
    ggml_tensor * ffn_gate = model.layers[0].ffn_gate;
    int slice_size = ffn_up->ne[1] * ggml_type_size(ffn_up->type) / ggml_blck_size(ffn_up->type);
    // For model arch with FFN gate, the gate is also sliced, otherwise only the up and down matrices are sliced
    int vram_bytes_per_slice = slice_size * (ffn_gate ? 4.5 : 2); // TODO: why 4.5, not 3?
    int neuron_cap = floor((double)vram_allocatable_bytes / vram_bytes_per_slice) * 4;

    LLAMA_LOG_INFO("invoking powerinfer Python module to generate gpu split for %.2f MiB of VRAM\n", vram_allocatable_bytes / 1024.0 / 1024.0);

    std::stringstream command_ss;
#if defined (_WIN32)
    command_ss << "python -m powerinfer"
#else
    command_ss << "python3 -m powerinfer"
#endif
               << " --activation " << activation_path
               << " --layer " << model.hparams.n_layer
               << " --neuron " << ffn_up->ne[1]
               << " --capacity " << neuron_cap
               << " --vram-capacity " << vram_allocatable_bytes
               << " --output " << cached_split_path;
    if (system(command_ss.str().c_str()) != 0 || access(cached_split_path.c_str(), F_OK) != 0) {
        LLAMA_LOG_ERROR("%s: error: failed to generate gpu split\n", __func__);
        return false;
    }

    return load_gpu_split_from_split_file(model, cached_split_path, vram_allocatable_bytes);
}

static size_t llm_load_gpu_split(llama_model_loader & ml, llama_model & model, bool no_cache, bool no_offload) {
#if defined (GGML_USE_CUBLAS)
    if (!ggml_cublas_loaded()) {
        throw std::runtime_error(format("cannot offload to GPU: " GGML_CUDA_NAME " not loaded"));
    }
    if (!no_offload && !llm_load_gpu_split_with_budget(ml, model, vram_budget_bytes, no_cache)) {
        LLAMA_LOG_ERROR("%s: error: failed to generate gpu split, an empty one will be used\n", __func__);
    }
    // if (!no_offload && !llm_load_rdma_split_with_budget(ml, model, vram_budget_bytes, no_cache)) {
        
    //     LLAMA_LOG_ERROR("%s: error: failed to generate gpu split, an empty one will be used\n", __func__);
    // }
#endif

    // Apply GPU index and split FFNs to GPU
    size_t ffn_offloaded_bytes = llama_model_offload_ffn_split(&model);
    LLAMA_LOG_INFO("%s: offloaded %.2f MiB of FFN weights to GPU\n", __func__, ffn_offloaded_bytes / 1024.0 / 1024.0);

    return ffn_offloaded_bytes;
}

static void llm_load_sparse_model_tensors(
        llama_model_loader & ml,
        llama_model & model,
        const llama_context_params * cparams,
        int main_gpu,
        long int vram_budget_bytes,
        bool reset_gpu_index,
        bool disable_ffn_split,
        bool use_mlock,
        llama_progress_callback progress_callback,
        void * progress_callback_user_data) {
    model.t_start_us = ggml_time_us();
    auto & ctx     = model.ctx;
    auto & hparams = model.hparams;

    size_t ctx_size;
    size_t mmapped_size;
    ml.calc_sizes(ctx_size, mmapped_size);
    LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);

    // create the ggml context
    {
        model.buf.resize(ctx_size);
        if (use_mlock) {
            model.mlock_buf.init   (model.buf.data);
            model.mlock_buf.grow_to(model.buf.size);
        }

        struct ggml_init_params params = {
            /*.mem_size   =*/ model.buf.size,
            /*.mem_buffer =*/ model.buf.data,
            /*.no_alloc   =*/ ml.use_mmap,
        };

        model.ctx = ggml_init(params);
        if (!model.ctx) {
            throw std::runtime_error(format("ggml_init() failed"));
        }
    }

    (void) main_gpu;

    enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU;
    enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU;

#ifdef GGML_USE_CUBLAS
    if (ggml_cublas_loaded()) {
        LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
        ggml_cuda_set_main_device(main_gpu);

        llama_backend_offload = GGML_BACKEND_GPU;
        llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT;
    }
#elif defined(GGML_USE_CLBLAST)
        LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
        llama_backend_offload = GGML_BACKEND_GPU;
        llama_backend_offload_split = GGML_BACKEND_GPU;
#endif

    buffered_tensor_allocator alloc(ml, ctx, hparams);
    uint32_t current_layer = 0;
    auto create_tensor = [&alloc, &current_layer] (
        const std::pair<std::string, llm_tensor> & tn, 
        const std::vector<int64_t> & ne) -> ggml_tensor * {
        return alloc.buffered_alloc(tn.first, tn.second, ne, current_layer);
    };

    {
        const int64_t n_embd     = hparams.n_embd;
        const int64_t n_embd_gqa = hparams.n_embd_gqa();
        const int64_t n_layer    = hparams.n_layer;
        const int64_t n_vocab    = hparams.n_vocab;

        const auto tn = LLM_TN(model.arch);
        switch (model.arch) {
            case LLM_ARCH_LLAMA:
            case LLM_ARCH_REFACT:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    const uint32_t n_ff = hparams.n_ff;
                    model.layers.resize(n_layer);

                    for (uint32_t &i = current_layer; i < n_layer; ++i) {
                       auto & layer = model.layers[i];

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

                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_gqa});
                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});

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

                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                        layer.ffn_down_t = create_tensor(tn(LLM_TENSOR_FFN_DOWN_T, "weight", i), {n_embd, n_ff});
                        layer.mlp_pre_w1 = create_tensor(tn(LLM_TENSOR_MLP_PRED_FC1, "weight", i), {n_embd, GGML_NE_WILDCARD});
                        layer.mlp_pre_w2 = create_tensor(tn(LLM_TENSOR_MLP_PRED_FC2, "weight", i), {GGML_NE_WILDCARD, n_ff});
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            case LLM_ARCH_FALCON:
                {
                    model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

                    // output
                    {
                        model.output_norm   = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                        model.output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
                        model.output        = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    model.layers.resize(n_layer);

                    for (uint32_t &i = current_layer; i < n_layer; ++i) {
                        auto & layer = model.layers[i];

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

                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).first.c_str()) >= 0) {
                            layer.attn_norm_2   = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd});
                            layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd});
                        }

                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
                        layer.wo   = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
                        layer.ffn_down_t = create_tensor(tn(LLM_TENSOR_FFN_DOWN_T, "weight", i), {n_embd, n_ff});
                        layer.mlp_pre_w1 = create_tensor(tn(LLM_TENSOR_MLP_PRED_FC1, "weight", i), {n_embd, GGML_NE_WILDCARD});
                        layer.mlp_pre_w2 = create_tensor(tn(LLM_TENSOR_MLP_PRED_FC2, "weight", i), {GGML_NE_WILDCARD, n_ff});
                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                    }
                } break;
            default:
                throw std::runtime_error("unknown architecture");
        }
    }

    model.n_gpu_layers = alloc.flush();
    LLAMA_LOG_INFO("%s: offloaded layers from VRAM budget(%ld bytes): %d/%d\n", __func__, vram_budget_bytes, model.n_gpu_layers, hparams.n_layer);

    // print memory requirements
    {
        // this is the total memory required to run the inference
        size_t mem_required = ctx_size + mmapped_size;

        LLAMA_LOG_INFO("%s: mem required  = %7.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);

#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
        LLAMA_LOG_INFO("%s: VRAM used: %.2f MB\n", __func__, alloc.vram_allocated_bytes / 1024.0 / 1024.0);
#endif
    }

    // populate `tensors_by_name`
    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i));
        model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
    }

    ml.load_all_data_RDMA(ctx, progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);

    if (progress_callback) {
        progress_callback(1.0f, progress_callback_user_data);
    }

    model.mapping = std::move(ml.mapping);

    // Reserve KV cache in VRAM
    if (cparams != NULL) {
        llama_reserve_model_kv_cache(&model, cparams);
    }
    // Offload FFN segments to GPU if possible
    model.ffn_offloaded_bytes = llm_load_gpu_split(ml, model, reset_gpu_index, disable_ffn_split || !alloc.tensor_offload_complete);

    // loading time will be recalculate after the first eval, so
    // we take page faults deferred by mmap() into consideration
    model.t_load_us = ggml_time_us() - model.t_start_us;
}

void llama_reserve_model_kv_cache(llama_model *model, const llama_context_params *cparams) {
#if defined(GGML_USE_CUBLAS)
    if (!ggml_cublas_loaded()) {
        throw std::runtime_error(format("cannot offload to GPU: " GGML_CUDA_NAME " not loaded"));
    }

    const llama_hparams &hparams = model->hparams;
    if (model->n_gpu_layers < hparams.n_layer + 1) {
        // should only reserve kv cache for models with all layers offloaded
        return;
    }

    const uint32_t n_embd  = hparams.n_embd_gqa();
    const uint32_t n_layer = hparams.n_layer;

    const int64_t n_mem      = n_layer*cparams->n_ctx;
    const int64_t n_elements = n_embd*n_mem;

    const ggml_type wtype = cparams->f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
    const size_t cache_size = n_elements*ggml_type_size(wtype);

    // reserve for k cache and v cache
    for (int i = 0; i < 2; i++) {
        if (!llama_reduce_vram_budget(cache_size)) {
            return;
        }
        model->n_gpu_layers++;
    }
#endif
}
void init_rdma_idx(int num_layer,struct ggml_context *aux_ctx,int dim)
{   
    rdma_idx_vec = malloc(num_layer * sizeof  (struct ggml_tensor*)) ;
    for(int i=0;i<num_layer;i++)
    {   
        struct ggml_tensor * rdma_idx = ggml_new_tensor_1d(aux_ctx, GGML_TYPE_F32, dim);
        ggml_set_zero(rdma_idx);
        ((struct ggml_tensor * *) rdma_idx_vec)[i] = rdma_idx;
    }
    // struct ggml_tensor * tensor = ((struct ggml_tensor **) rdma_idx_vec)[num_layer-1];
}
static void llm_load_tensors(
        llama_model_loader & ml,
        llama_model & model,
        int n_gpu_layers,
        int main_gpu,
        const float * tensor_split,
        bool use_mlock,
        llama_progress_callback progress_callback,
        void * progress_callback_user_data) {
    model.t_start_us = ggml_time_us();

    auto & ctx     = model.ctx;
    auto & hparams = model.hparams;

    model.n_gpu_layers = n_gpu_layers;

    size_t ctx_size;
    size_t mmapped_size;

    ml.calc_sizes(ctx_size, mmapped_size);

    LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);

    // create the ggml context
    {
        model.buf.resize(ctx_size);
        if (use_mlock) {
            model.mlock_buf.init   (model.buf.data);
            model.mlock_buf.grow_to(model.buf.size);
        }

        struct ggml_init_params params = {
            /*.mem_size   =*/ model.buf.size,
            /*.mem_buffer =*/ model.buf.data,
            /*.no_alloc   =*/ ml.use_mmap,
        };

        model.ctx = ggml_init(params);
        if (!model.ctx) {
            throw std::runtime_error(format("ggml_init() failed"));
        }
    }

    (void) main_gpu;

    enum ggml_backend_type llama_backend_offload = GGML_BACKEND_CPU;
    enum ggml_backend_type llama_backend_offload_split = GGML_BACKEND_CPU;

#ifdef GGML_USE_CUBLAS
    if (ggml_cublas_loaded()) {
        LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
        ggml_cuda_set_main_device(main_gpu);

        llama_backend_offload = GGML_BACKEND_GPU;
        llama_backend_offload_split = GGML_BACKEND_GPU_SPLIT;
    }
#elif defined(GGML_USE_CLBLAST)
        LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
        llama_backend_offload = GGML_BACKEND_GPU;
        llama_backend_offload_split = GGML_BACKEND_GPU;
#endif

    // prepare memory for the weights
    size_t vram_weights = 0;
    {
        const int64_t n_embd     = hparams.n_embd;
        const int64_t n_embd_gqa = hparams.n_embd_gqa();
        const int64_t n_layer    = hparams.n_layer;
        const int64_t n_vocab    = hparams.n_vocab;

        const auto tn = LLM_TN(model.arch);
        switch (model.arch) {
            case LLM_ARCH_LLAMA:
            case LLM_ARCH_REFACT:
                {
                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);

                    // output
                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    const int i_gpu_start = n_layer - n_gpu_layers;

                    model.layers.resize(n_layer);
                    init_rdma_idx(n_layer,ctx,model.layers[0].gpu_idx->ne[0]);

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT

                        auto & layer = model.layers[i];

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

                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
                        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);

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

                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);

                        if (backend == GGML_BACKEND_GPU) {
                            vram_weights +=
                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq)       + ggml_nbytes(layer.wk)       +
                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo)       + ggml_nbytes(layer.ffn_norm) +
                                ggml_nbytes(layer.ffn_gate)  + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
                        }
                    }
                } break;
            case LLM_ARCH_BAICHUAN:
                {
                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    const int i_gpu_start = n_layer - n_gpu_layers;

                    model.layers.resize(n_layer);

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT

                        auto & layer = model.layers[i];

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

                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
                        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);

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

                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);

                        if (backend == GGML_BACKEND_GPU) {
                            vram_weights +=
                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq)       + ggml_nbytes(layer.wk)       +
                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo)       + ggml_nbytes(layer.ffn_norm) +
                                ggml_nbytes(layer.ffn_gate)  + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
                        }
                    }
                } break;
            case LLM_ARCH_FALCON:
                {
                    // TODO: CPU-only for now

                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);

                    // output
                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                            vram_weights += ggml_nbytes(model.output_norm_b);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    const int i_gpu_start = n_layer - n_gpu_layers;

                    model.layers.resize(n_layer);

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);

                        if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).first.c_str()) >= 0) {
                            layer.attn_norm_2   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend);
                            layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, backend);

                            if (backend == GGML_BACKEND_GPU) {
                                vram_weights += ggml_nbytes(layer.attn_norm_2);
                                vram_weights += ggml_nbytes(layer.attn_norm_2_b);
                            }
                        }

                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);

                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);

                        if (backend == GGML_BACKEND_GPU) {
                            vram_weights +=
                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
                                ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.wo)          +
                                ggml_nbytes(layer.ffn_down)  + ggml_nbytes(layer.ffn_up);
                        }
                    }
                } break;
            case LLM_ARCH_STARCODER:
                {
                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab},             GGML_BACKEND_CPU);
                    model.pos_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"),   {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);

                    // output
                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                            vram_weights += ggml_nbytes(model.output_norm_b);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    const int i_gpu_start = n_layer - n_gpu_layers;

                    model.layers.resize(n_layer);

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);

                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);

                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},   backend_split);
                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},           backend);

                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);

                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);

                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, backend_split);
                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),           {n_ff}, backend);

                        if (backend == GGML_BACKEND_GPU) {
                            vram_weights +=
                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
                                ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.bqkv)        +
                                ggml_nbytes(layer.wo)        + ggml_nbytes(layer.bo)          +
                                ggml_nbytes(layer.ffn_norm)  + ggml_nbytes(layer.ffn_norm_b)  +
                                ggml_nbytes(layer.ffn_down)  + ggml_nbytes(layer.ffn_down_b)  +
                                ggml_nbytes(layer.ffn_up)    + ggml_nbytes(layer.ffn_up_b);
                        }
                    }
                } break;
            case LLM_ARCH_PERSIMMON:
                {
                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"),  {n_embd, n_vocab}, GGML_BACKEND_CPU);

                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
#ifdef GGML_USE_CUBLAS
                            if (n_gpu_layers > int(n_layer + 1)) {
                                LLAMA_LOG_ERROR("%s: CUDA backend missing Persimmon CUDA ops, can offload at most %ld layers. See: https://github.com/ggerganov/llama.cpp/issues/4038\n",
                                    __func__, n_layer + 1);
                                throw std::runtime_error("Persimmon CUDA offload failed");
                            }
#endif
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm    = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output_norm_b  = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
                        model.output         = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                            vram_weights += ggml_nbytes(model.output_norm_b);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;
                    const int i_gpu_start = n_layer - n_gpu_layers;
                    model.layers.resize(n_layer);
                    for (uint32_t i = 0; i < n_layer; ++i) {
                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload;
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split;
                        auto & layer = model.layers[i];
                        layer.attn_norm     = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
                        layer.attn_norm_b   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias",   i), {n_embd}, backend);
                        layer.wqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
                        layer.bqkv          = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV,    "bias",   i), {n_embd + 2*n_embd_gqa},         backend);
                        layer.wo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "weight", i), {n_embd, n_embd},   backend_split);
                        layer.bo            = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT,    "bias",   i), {n_embd},           backend);
                        layer.ffn_down      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "weight", i), {n_ff, n_embd}, backend_split);
                        layer.ffn_down_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN,    "bias",   i), {n_embd},       backend);
                        layer.ffn_up        = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "weight", i), {n_embd,   n_ff}, backend_split);
                        layer.ffn_up_b      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,      "bias",   i), {n_ff},           backend);
                        layer.ffn_norm      = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "weight", i), {n_embd}, backend);
                        layer.ffn_norm_b    = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM,    "bias",   i), {n_embd}, backend);
                        layer.attn_q_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend);
                        layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias",   i), {64}, backend);
                        layer.attn_k_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend);
                        layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), {64}, backend);
                    }
                } break;
            case LLM_ARCH_BLOOM:
                {
                    // TODO: CPU-only for now

                    model.tok_embd   = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
                    model.tok_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd},          GGML_BACKEND_CPU);
                    model.tok_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"),   {n_embd},          GGML_BACKEND_CPU);

                    // output
                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd},          backend_norm);
                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                            vram_weights += ggml_nbytes(model.output_norm_b);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    const int i_gpu_start = n_layer - n_gpu_layers;

                    model.layers.resize(n_layer);

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        const ggml_backend_type backend       = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT

                        auto & layer = model.layers[i];

                        layer.attn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd}, backend);
                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd}, backend);

                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
                        layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa},         backend);

                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);
                        layer.bo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd},                        backend);

                        layer.ffn_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd}, backend);

                        layer.ffn_down   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
                        layer.ffn_down_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd},       backend);

                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);
                        layer.ffn_up_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff},           backend);

                        if (backend == GGML_BACKEND_GPU) {
                            vram_weights +=
                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
                                ggml_nbytes(layer.wqkv)      + ggml_nbytes(layer.bqkv)        +
                                ggml_nbytes(layer.wo)        + ggml_nbytes(layer.bo)          +
                                ggml_nbytes(layer.ffn_norm)  + ggml_nbytes(layer.ffn_norm_b)  +
                                ggml_nbytes(layer.ffn_up)    + ggml_nbytes(layer.ffn_up_b)    +
                                ggml_nbytes(layer.ffn_down)  + ggml_nbytes(layer.ffn_down_b);
                        }
                    }
                } break;
            case LLM_ARCH_MPT:
                {
                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);

                    // output
                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm   = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output        = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    const int i_gpu_start = n_layer - n_gpu_layers;

                    model.layers.resize(n_layer);

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
                        layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
                        layer.wo   = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd},                backend_split);

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

                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
                        layer.ffn_up   = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);

                        if (backend == GGML_BACKEND_GPU) {
                            vram_weights +=
                                ggml_nbytes(layer.attn_norm) +
                                ggml_nbytes(layer.wqkv)      +
                                ggml_nbytes(layer.wo)        +
                                ggml_nbytes(layer.ffn_norm)  +
                                ggml_nbytes(layer.ffn_down)  +
                                ggml_nbytes(layer.ffn_up);
                        }
                    }
                } break;
            case LLM_ARCH_STABLELM:
                {
                    model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);

                    // output
                    {
                        ggml_backend_type backend_norm;
                        ggml_backend_type backend_output;

                        if (n_gpu_layers > int(n_layer)) {
                            // norm is not performance relevant on its own but keeping it in VRAM reduces data copying
                            // on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
                            backend_norm = llama_backend_offload;
#else
                            backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32

                            backend_output = llama_backend_offload_split;
                        } else {
                            backend_norm   = GGML_BACKEND_CPU;
                            backend_output = GGML_BACKEND_CPU;
                        }

                        model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd},          backend_norm);
                        model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd},          backend_norm);
                        model.output      = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, backend_output);

                        if (backend_norm == GGML_BACKEND_GPU) {
                            vram_weights += ggml_nbytes(model.output_norm);
                        }
                        if (backend_output == GGML_BACKEND_GPU_SPLIT) {
                            vram_weights += ggml_nbytes(model.output);
                        }
                    }

                    const uint32_t n_ff = hparams.n_ff;

                    const int i_gpu_start = n_layer - n_gpu_layers;

                    model.layers.resize(n_layer);

                    for (uint32_t i = 0; i < n_layer; ++i) {
                        /*
                        llama_model_loader: - tensor    4:         blk.0.attn_output.weight f16      [  2560,  2560,     1,     1 ]
                        */
                        const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
                        const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT

                        auto & layer = model.layers[i];

                        layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
                        layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);

                        layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd},     backend_split);
                        layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_gqa}, backend_split);
                        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);

                        layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
                        layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);

                        layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, backend_split);
                        layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, backend_split);
                        layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, backend_split);

                        if (backend == GGML_BACKEND_GPU) {
                            vram_weights +=
                                ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq)       + ggml_nbytes(layer.wk)       +
                                ggml_nbytes(layer.wv)        + ggml_nbytes(layer.wo)       + ggml_nbytes(layer.ffn_norm) +
                                ggml_nbytes(layer.ffn_gate)  + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
                        }
                    }
                } break;

            default:
                throw std::runtime_error("unknown architecture");
        }
    }

    ml.done_getting_tensors();

    // print memory requirements
    {
        // this is the total memory required to run the inference
        size_t mem_required =
            ctx_size +
            mmapped_size - vram_weights; // weights in VRAM not in memory

        LLAMA_LOG_INFO("%s: mem required  = %7.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);

#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
        const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));

        LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
        if (n_gpu_layers > (int) hparams.n_layer) {
            LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
        }

#ifdef GGML_USE_CUBLAS
        const int max_backend_supported_layers = hparams.n_layer + 3;
        const int max_offloadable_layers       = hparams.n_layer + 3;
#elif GGML_USE_CLBLAST
        const int max_backend_supported_layers = hparams.n_layer + 1;
        const int max_offloadable_layers       = hparams.n_layer + 1;
#endif // GGML_USE_CUBLAS

        LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
        LLAMA_LOG_INFO("%s: VRAM used: %.2f MB\n", __func__, vram_weights / 1024.0 / 1024.0);
#else
        (void) n_gpu_layers;
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
    }

    // populate `tensors_by_name`
    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i));
        model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
    }

    (void) tensor_split;
#ifdef GGML_USE_CUBLAS
    {
        ggml_cuda_set_tensor_split(tensor_split);
    }
#endif

    ml.load_all_data(ctx, progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);

    if (progress_callback) {
        progress_callback(1.0f, progress_callback_user_data);
    }

    model.mapping = std::move(ml.mapping);

    // loading time will be recalculate after the first eval, so
    // we take page faults deferred by mmap() into consideration
    model.t_load_us = ggml_time_us() - model.t_start_us;
}

static bool llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params, const llama_context_params * cparams) {
    try {
        llama_model_loader ml(fname, params.use_mmap);

        if (ml.sparse_deriv == GGML_SPARSE_INFERENCE) {
            LLAMA_LOG_INFO("%s: PowerInfer model loaded. Sparse inference will be used.\n", __func__);
        }

        model.hparams.vocab_only = params.vocab_only;
        model.sparse_deriv = ml.sparse_deriv;

        llm_load_arch   (ml, model);
        llm_load_hparams(ml, model);
        llm_load_vocab  (ml, model);

        llm_load_print_meta(ml, model);

        if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
            throw std::runtime_error("vocab size mismatch");
        }

        if (params.vocab_only) {
            LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
            return true;
        }

        if (llama_use_sparse_inference(&model)) {
            if (params.n_gpu_layers > 0) {
                LLAMA_LOG_WARN("%s: sparse inference ignores n_gpu_layers, you can use --vram-budget option instead\n", __func__);
                return false;
            }
#if defined GGML_USE_CUBLAS
            llama_set_vram_budget(params.vram_budget_gb, params.main_gpu);
#endif
            llm_load_sparse_model_tensors(
                ml, model, cparams, params.main_gpu, vram_budget_bytes, params.reset_gpu_index, params.disable_gpu_index,
                params.use_mlock, params.progress_callback, params.progress_callback_user_data
            );
        } else {
            llm_load_tensors(
                ml, model, params.n_gpu_layers, params.main_gpu, params.tensor_split, params.use_mlock,
                params.progress_callback, params.progress_callback_user_data
            );
        }

    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
        return false;
    }

    return true;
}

//
// llm_build
//

using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
using llm_build_cb_short = std::function<void(struct ggml_tensor * cur, const char * name)>;

enum llm_rope_type {
    LLM_ROPE,
    LLM_ROPE_NEOX,
    LLM_ROPE_GLM,
};

enum llm_ffn_op_type {
    LLM_FFN_SILU,
    LLM_FFN_GELU,
    LLM_FFN_RELU,
    LLM_FFN_RELU_SQR,
};

enum llm_ffn_gate_type {
    LLM_FFN_SEQ,
    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
};

enum llm_norm_type {
    LLM_NORM,
    LLM_NORM_RMS,
};

static struct ggml_tensor * llm_build_inp_embd(
        struct ggml_context * ctx,
        const llama_hparams & hparams,
          const llama_batch & batch,
         struct ggml_tensor * tok_embd,
         const llm_build_cb & cb) {
    const int64_t n_embd = hparams.n_embd;

    struct ggml_tensor * inpL;

    if (batch.token) {
        struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
        cb(inp_tokens, "inp_tokens", -1);

        inpL = ggml_get_rows(ctx, tok_embd, inp_tokens);
    } else {
#ifdef GGML_USE_MPI
        GGML_ASSERT(false && "not implemented");
#endif

        inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
    }

    return inpL;
}

// Persimmon: n_rot = n_embd_head/2
// Other:     n_rot = n_embd_head
static void llm_build_k_shift(
      struct ggml_context * ctx,
      const llama_hparams & hparams,
      const llama_cparams & cparams,
     const llama_kv_cache & kv,
       struct ggml_cgraph * graph,
            llm_rope_type   type,
                  int64_t   n_ctx,
                  int64_t   n_rot,
                  float     freq_base,
                  float     freq_scale,
       const llm_build_cb & cb) {
    const int64_t n_layer     = hparams.n_layer;
    const int64_t n_head_kv   = hparams.n_head_kv;
    const int64_t n_embd_gqa  = hparams.n_embd_gqa();
    const int64_t n_embd_head = hparams.n_embd_head();
    const int32_t n_orig_ctx  = cparams.n_yarn_orig_ctx;
    const float   ext_factor  = cparams.yarn_ext_factor;
    const float   attn_factor = cparams.yarn_attn_factor;
    const float   beta_fast   = cparams.yarn_beta_fast;
    const float   beta_slow   = cparams.yarn_beta_slow;

    GGML_ASSERT(n_embd_head % n_rot == 0);

    struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx);
    cb(K_shift, "K_shift", -1);

    int rope_type = 0;

    switch (type) {
        case LLM_ROPE:      rope_type = 0; break;
        case LLM_ROPE_NEOX: rope_type = 2; break;
        case LLM_ROPE_GLM:  rope_type = 4; break;
    }

    for (int il = 0; il < n_layer; ++il) {
        struct ggml_tensor * tmp =
            // we rotate only the first n_rot dimensions
            ggml_rope_custom_inplace(ctx,
                    ggml_view_3d(ctx, kv.k,
                        n_rot, n_head_kv, n_ctx,
                        ggml_element_size(kv.k)*n_embd_head,
                        ggml_element_size(kv.k)*n_embd_gqa,
                        ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il),
                    K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow);
        cb(tmp, "K_shifted", il);
        ggml_build_forward_expand(graph, tmp);
    }
}

static std::pair<ggml_tensor*, ggml_tensor*> llm_build_kv_store(
        struct ggml_context * ctx,
        const llama_hparams & hparams,
       const llama_kv_cache & kv,
         struct ggml_cgraph * graph,
         struct ggml_tensor * k_cur,
         struct ggml_tensor * v_cur,
                    int64_t   n_ctx, //512
                    int32_t   n_tokens,
                    int32_t   kv_head,
         const llm_build_cb & cb,
                    int64_t   il) {
    const int64_t n_embd_gqa = hparams.n_embd_gqa();

    // compute the transposed [n_tokens, n_embd] V matrix
    struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, n_embd_gqa, n_tokens));
    //struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); // TODO: reshape above is likely not needed
    cb(v_cur_t, "v_cur_t", il);

    struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k, n_tokens*n_embd_gqa,
            (ggml_element_size(kv.k)*n_embd_gqa)*(il*n_ctx + kv_head));
    cb(k_cache_view, "k_cache_view", il);
    // printf_dim(kv.k, "kv.k"); //n_ctx*n_head*dim 1 1 1 n_ctx为最大的上下文长度
    struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v, n_tokens, n_embd_gqa,
            (   n_ctx)*ggml_element_size(kv.v),
            (il*n_ctx)*ggml_element_size(kv.v)*n_embd_gqa + kv_head*ggml_element_size(kv.v));
    cb(v_cache_view, "v_cache_view", il);

    // important: storing RoPE-ed version of K in the KV cache!
    ggml_tensor * k_cpy = ggml_cpy(ctx, k_cur,   k_cache_view);
    // printf_dim(k_cur, "k_cur"); //k_dim n_head 1 1
    // printf_dim(k_cache_view, "k_cache_view"); //k_dim*n_head 1 1

    ggml_tensor * v_cpy = ggml_cpy(ctx, v_cur_t, v_cache_view);
    //ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur,   k_cache_view));
    //ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view));
    
    return {k_cpy, v_cpy};
}

static struct ggml_tensor * llm_build_norm(
        struct ggml_context * ctx,
         struct ggml_tensor * cur,
        const llama_hparams & hparams,
         struct ggml_tensor * mw,
         struct ggml_tensor * mb,
              llm_norm_type   type,
         const llm_build_cb & cb,
                        int   il) {
    switch (type) {
        case LLM_NORM:     cur = ggml_norm    (ctx, cur, hparams.f_norm_eps);     break;
        case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
    }

    if (mw || mb) {
        cb(cur, "norm", il);
    }

    if (mw) {
        cur = ggml_mul(ctx, cur, mw);
        if (mb) {
            cb(cur, "norm_w", il);
        }
    }

    if (mb) {
        cur = ggml_add(ctx, cur, mb);
    }

    return cur;
}

static struct ggml_tensor * llm_build_ffn(
        struct ggml_context * ctx,
         struct ggml_tensor * cur,
         struct ggml_tensor * up,
         struct ggml_tensor * up_b,
         struct ggml_tensor * gate,
         struct ggml_tensor * gate_b,
         struct ggml_tensor * down,
         struct ggml_tensor * down_b,
            llm_ffn_op_type   type_op,
          llm_ffn_gate_type   type_gate,
         const llm_build_cb & cb,
                        int   il) {
    struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
    cb(tmp, "ffn_up", il);

    if (up_b) {
        tmp = ggml_add(ctx, tmp, up_b);
        cb(tmp, "ffn_up_b", il);
    }

    if (gate) {
        switch (type_gate) {
            case LLM_FFN_SEQ:
                {
                    cur = ggml_mul_mat(ctx, gate, tmp);
                    cb(cur, "ffn_gate", il);
                } break;
            case LLM_FFN_PAR:
                {
                    cur = ggml_mul_mat(ctx, gate, cur);
                    cb(cur, "ffn_gate", il);
                } break;
        }

        if (gate_b) {
            cur = ggml_add(ctx, cur, gate_b);
            cb(cur, "ffn_gate_b", il);
        }
    } else {
        cur = tmp;
    }

    switch (type_op) {
        case LLM_FFN_SILU:
            {
                cur = ggml_silu(ctx, cur);
                cb(cur, "ffn_silu", il);
            } break;
        case LLM_FFN_GELU:
            {
                cur = ggml_gelu(ctx, cur);
                cb(cur, "ffn_gelu", il);
            } break;
        case LLM_FFN_RELU:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu", il);
            } break;
        case LLM_FFN_RELU_SQR:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu", il);

                cur = ggml_sqr(ctx, cur);
                cb(cur, "ffn_sqr(relu)", il);
            } break;
    }

    if (type_gate == LLM_FFN_PAR) {
        cur = ggml_mul(ctx, cur, tmp);
        cb(cur, "ffn_gate_par", il);
    }

    // cur = ggml_mul_mat(ctx, down, cur);
    cur = ggml_axpy(ctx, down, cur, NULL, NULL);
    if (down_b) {
        cb(cur, "ffn_down", il);
    }

    if (down_b) {
        cur = ggml_add(ctx, cur, down_b);
    }

    return cur;
}

static struct ggml_tensor * llm_build_sparse_mul_mat(
        struct ggml_context * ctx,
         struct ggml_tensor * up,
         struct ggml_tensor * inp,
         struct ggml_tensor * idx,
         struct ggml_tensor * up_gpu,
         struct ggml_tensor * gpu_index,
         struct ggml_tensor * gpu_bucket,
   const llm_build_cb_short & cb,
                 const char * name,
                         bool full_gpu) {
    std::string full_name = "ffn_" + std::string(name) + "_sparse";
    ggml_tensor * out = nullptr;

#ifdef GGML_USE_CUBLAS
    // Full offloading fast path
    if (full_gpu) {
        // printf("%s: full_gpu\n", __func__);
        GGML_ASSERT(up_gpu && "full_gpu but no up_gpu");
        out = ggml_mul_mat_idx(ctx, up_gpu, inp, idx, NULL);
        ggml_cuda_assign_buffers_no_alloc(out);
        cb(out, (full_name).c_str());
        return out;
    }
#endif

    out = ggml_mul_mat_idx(ctx, up, inp, idx, gpu_index);
    cb(out, full_name.c_str());

#ifdef GGML_USE_CUBLAS
    if (up_gpu) {
        // printf_dim(up_gpu);
        // printf_dim(inp);
        ggml_tensor * out_gpu = ggml_mul_mat_idx_upscale(ctx, up_gpu, inp, idx, gpu_bucket, out->ne[0]);
        ggml_cuda_assign_buffers_no_alloc(out_gpu);
        // printf_dim(out_gpu, "out_gpu");
        cb(out_gpu, (full_name + "_gpu").c_str());
        // printf_dim(out_gpu);
        out = ggml_add(ctx, out, out_gpu);
        // We don't need to assign buffers here, as the output will be passed into Axpy,
        // which in this case, is also a hybrid operation.
        cb(out, (full_name + "_merged").c_str());
    }
#endif

    return out;
}

static struct ggml_tensor * llm_build_sparse_axpy(
        struct ggml_context * ctx,
         struct ggml_tensor * w_t,
         struct ggml_tensor * x,
         struct ggml_tensor * sparse_idx,
         struct ggml_tensor * wt_gpu,
         struct ggml_tensor * gpu_index,
         struct ggml_tensor * gpu_bucket,
   const llm_build_cb_short & cb,
                 const char * name,
                         bool full_gpu) {
    std::string full_name = "ffn_" + std::string(name) + "_sparse";
    ggml_tensor * out = nullptr;

#ifdef GGML_USE_CUBLAS
    // Full offloading fast path
    if (full_gpu) {
        GGML_ASSERT(wt_gpu && "full_gpu but no wt_gpu");
        out = ggml_axpy(ctx, wt_gpu, x, sparse_idx, NULL);
        ggml_cuda_assign_buffers_no_alloc(out);
        cb(out, (full_name).c_str());
        return out;
    }
#endif

    // TODO: should pass NULL as hybrid_aux when hybrid_split is false
    out = ggml_axpy(ctx, w_t, x, sparse_idx, gpu_index);
    cb(out, full_name.c_str());

#ifdef GGML_USE_CUBLAS
    if (wt_gpu) {
        ggml_tensor * out_gpu = ggml_axpy(ctx, wt_gpu, x, sparse_idx, gpu_bucket);
        cb(out_gpu, (full_name + "_gpu").c_str());
        ggml_cuda_assign_buffers_no_alloc(out_gpu);
        out = ggml_add(ctx, out, out_gpu);
        ggml_cuda_assign_buffers_no_alloc(out);
        cb(out, (full_name + "_merged").c_str());
    }
#endif

    return out;
}

static struct ggml_tensor * llm_build_ffn_sparse(
        struct ggml_context * ctx,
         struct ggml_tensor * cur,
         struct ggml_tensor * up,
         struct ggml_tensor * up_b,
         struct ggml_tensor * gate,
         struct ggml_tensor * gate_b,
         struct ggml_tensor * down,
         struct ggml_tensor * down_b,
         struct ggml_tensor * down_t,
         struct ggml_tensor * pre_w1,
         struct ggml_tensor * pre_w2,
         struct ggml_tensor * pred_inpl,
         struct ggml_tensor * gpu_index,
         struct ggml_tensor * gpu_bucket,
         struct ggml_tensor * gate_gpu,
         struct ggml_tensor * down_gpu,
         struct ggml_tensor * up_gpu,
            llm_ffn_op_type   type_op,
          llm_ffn_gate_type   type_gate,
                     double   gpu_offload_ratio,
   const llm_build_cb_short & cb_outer) {
    bool full_gpu = gpu_offload_ratio >= 1.0;
    ggml_tensor * ffn_input = cur;
    llm_build_cb_short cb = [&cb_outer](struct ggml_tensor * cur, const char * name) {
        cb_outer(cur, name);
#if defined(GGML_USE_CUBLAS)
        // Determine offloading based on src[0] (weight for both mul and axpy)
        bool operates_on_gpu = cur->src[0]->backend == GGML_BACKEND_GPU;
        if (operates_on_gpu) {
            ggml_cuda_assign_buffers_no_alloc(cur);
        }
#endif
    };

    // prepare sparse idx
    ggml_tensor * idx = ggml_mul_mat(ctx, pre_w1, pred_inpl);
    cb(idx, "mlp_pre_hidden");
    idx = ggml_relu(ctx, idx);
    cb(idx, "mlp_pre_relu");
    idx = ggml_mul_mat(ctx, pre_w2, idx);
    // If the FFN layer is not fully offloaded, we need to transfer the sparsity index
    // back to the CPU to avoid synchronization issues.
    (full_gpu ? cb : cb_outer)(idx, "mlp_pre_out");

    // FFN up
    struct ggml_tensor * up_out = llm_build_sparse_mul_mat(ctx, up, ffn_input, idx, up_gpu, gpu_index, gpu_bucket, cb_outer, "up", full_gpu);
    if (up_b) {
        up_out = ggml_add(ctx, up_out, up_b);
        cb(up_out, "ffn_up_b");
    }

    if (gate) {
        // TODO: only support par for now
        GGML_ASSERT(type_gate == LLM_FFN_PAR);
        ggml_tensor * gate_out = llm_build_sparse_mul_mat(ctx, gate, ffn_input, idx, gate_gpu, gpu_index, gpu_bucket, cb_outer, "gate", full_gpu);
        if (gate_b) {
            gate_out = ggml_add(ctx, gate_out, gate_b);
            cb(gate_out, "ffn_gate_b");
        }
        cur = gate_out;
    } else {
        cur = up_out;
    }

    switch (type_op) {
        case LLM_FFN_RELU:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu");
            } break;
        default:
            // only support relu for now
            GGML_ASSERT(type_op == LLM_FFN_RELU);
    }

    if (type_gate == LLM_FFN_PAR) {
        cur = ggml_mul(ctx, cur, up_out);
        cb(cur, "ffn_gate_par");
    }

    cur = llm_build_sparse_axpy(ctx, down_t, cur, idx, down_gpu, gpu_index, gpu_bucket, cb_outer, "down", full_gpu);

    if (down_b) {
        cur = ggml_add(ctx, cur, down_b);
        cb(cur, "ffn_down_b");
    }

    return cur;
}

static struct ggml_tensor * llm_build_ffn_sparse_rdma(
        struct ggml_context * ctx,
         struct ggml_tensor * cur,
         struct ggml_tensor * up,
         struct ggml_tensor * up_b,
         struct ggml_tensor * gate,
         struct ggml_tensor * gate_b,
         struct ggml_tensor * down,
         struct ggml_tensor * down_b,
         struct ggml_tensor * down_t,
         struct ggml_tensor * pre_w1,
         struct ggml_tensor * pre_w2,
         struct ggml_tensor * pred_inpl,
         struct ggml_tensor * gpu_index,
         struct ggml_tensor * gpu_bucket,
         struct ggml_tensor * gate_gpu,
         struct ggml_tensor * down_gpu,
         struct ggml_tensor * up_gpu,
        //  struct ggml_tensor * rdma_idx,
        //  struct ggml_tensor * rdma_bucket,
         const struct llama_layer * layer,
            llm_ffn_op_type   type_op,
          llm_ffn_gate_type   type_gate,
                     double   gpu_offload_ratio,
   const llm_build_cb_short & cb_outer,int &il
   ) {
    // printf("%s",__func__);
    bool full_gpu = gpu_offload_ratio >= 1.0;
    ggml_tensor * ffn_input = cur;
    llm_build_cb_short cb = [&cb_outer](struct ggml_tensor * cur, const char * name) {
        cb_outer(cur, name);
#if defined(GGML_USE_CUBLAS)
        // Determine offloading based on src[0] (weight for both mul and axpy)
        bool operates_on_gpu = cur->src[0]->backend == GGML_BACKEND_GPU;
        if (operates_on_gpu) {
            ggml_cuda_assign_buffers_no_alloc(cur);
        }
#endif
    };

    // prepare sparse idx
    ggml_tensor * idx = ggml_mul_mat(ctx, pre_w1, pred_inpl);
    cb(idx, "mlp_pre_hidden");
    idx = ggml_relu(ctx, idx);
    cb(idx, "mlp_pre_relu");
    idx = ggml_mul_mat(ctx, pre_w2, idx);
    (full_gpu ? cb : cb_outer)(idx, "mlp_pre_out");


    // FFN up
    struct ggml_tensor * up_out = llm_build_sparse_mul_mat(ctx, up, ffn_input, idx, up_gpu, gpu_index, gpu_bucket, cb_outer, "up", full_gpu);
    if (up_b) {
        up_out = ggml_add(ctx, up_out, up_b);
        cb(up_out, "ffn_up_b");
    }
    // printf("llm_build_sparse_mul_mat\n");
    if (gate) {
        // TODO: only support par for now
        GGML_ASSERT(type_gate == LLM_FFN_PAR);
        ggml_tensor * gate_out = llm_build_sparse_mul_mat(ctx, gate, ffn_input, idx, gate_gpu, gpu_index, gpu_bucket, cb_outer, "gate", full_gpu);
        if (gate_b) {
            gate_out = ggml_add(ctx, gate_out, gate_b);
            cb(gate_out, "ffn_gate_b");
        }
        cur = gate_out;
    } else {
        cur = up_out;
    }
    // printf("gate_llm_build_sparse_mul_mat\n");

    switch (type_op) {
        case LLM_FFN_RELU:
            {
                cur = ggml_relu(ctx, cur);
                cb(cur, "ffn_relu");
            } break;
        default:
            // only support relu for now
            GGML_ASSERT(type_op == LLM_FFN_RELU);
    }

    if (type_gate == LLM_FFN_PAR) {
        cur = ggml_mul(ctx, cur, up_out);
        cb(cur, "ffn_gate_par");
    }
    // printf("ggml_mul\n");

    cur = llm_build_sparse_axpy(ctx, down_t, cur, idx, down_gpu, gpu_index, gpu_bucket, cb_outer, "down", full_gpu);

    if (down_b) {
        cur = ggml_add(ctx, cur, down_b);
        cb(cur, "ffn_down_b");
    }

    return cur;
}


static ggml_tensor * k_cpy = nullptr;
static ggml_tensor * v_cpy = nullptr;

// if max_alibi_bias > 0 then apply ALiBi
static struct ggml_tensor * llm_build_kqv(
        struct ggml_context * ctx,
        const llama_hparams & hparams,
       const llama_kv_cache & kv,
         struct ggml_tensor * wo,
         struct ggml_tensor * wo_b,
         struct ggml_tensor * q_cur,
         struct ggml_tensor * kq_scale,
         struct ggml_tensor * kq_mask,
                    int64_t   n_ctx, //512
                    int32_t   n_tokens, //1 
                    int32_t   n_kv, // n
                    float     max_alibi_bias,
         const llm_build_cb & cb,
                    int       il) {
    const int64_t n_embd      = hparams.n_embd; //4096
    const int64_t n_head      = hparams.n_head; //32
    const int64_t n_head_kv   = hparams.n_head_kv; //32
    const int64_t n_embd_head = hparams.n_embd_head(); //128
    const int64_t n_embd_gqa  = hparams.n_embd_gqa(); //4096
    // printf("%s: %d %d %d %d %d %d %d %d %d \n", __func__, n_embd, n_head, n_head_kv, n_embd_head, n_embd_gqa, n_ctx, n_tokens, n_kv, max_alibi_bias);
    struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
    cb(q, "q", il);

    struct ggml_tensor * k =
        ggml_view_3d(ctx, kv.k,
                n_embd_head, n_kv, n_head_kv,
                ggml_element_size(kv.k)*n_embd_gqa,
                ggml_element_size(kv.k)*n_embd_head,
                ggml_element_size(kv.k)*n_embd_gqa*n_ctx*il);
    cb(k, "k", il);
    if (k_cpy != nullptr) {
        k->src[1] = k_cpy;
    }
    // printf_dim(k);//128 token_n 32 1  ps. 128是n_embd_head 32是n_head_kv 
    // printf_dim(q);//128 token_n 32 1

    struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);  // n_kv 1 32 1
    cb(kq, "kq", il);
    // printf_dim(kq);
    kq = ggml_scale(ctx, kq, kq_scale); 
    cb(kq, "kq_scaled", il);

    if (max_alibi_bias > 0.0f) {
        // TODO: n_head or n_head_kv
        // TODO: K-shift is likely not working
        // TODO: change to ggml_add
        kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, max_alibi_bias);
        cb(kq, "kq_scaled_alibi", il);
    }

    kq = ggml_add(ctx, kq, kq_mask);
    cb(kq, "kq_masked", il);
    // printf_dim(kq); // n_kv 1 32 1
    kq = ggml_soft_max(ctx, kq);

    // ggml_tensor * softmax_mask = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_kv);
    // ggml_set_one(softmax_mask);
    // ggml_set_zero_1(softmax_mask,1,n_)
    cb(kq, "kq_soft_max", il);  // n_kv 1 32 1
    // printf_dim(kq);

    // split cached v into n_head heads
    struct ggml_tensor * v =
        ggml_view_3d(ctx, kv.v,
                n_kv, n_embd_head, n_head_kv,
                ggml_element_size(kv.v)*n_ctx,
                ggml_element_size(kv.v)*n_ctx*n_embd_head,
                ggml_element_size(kv.v)*n_ctx*n_embd_gqa*il);
    cb(v, "v", il);
    if (v_cpy != nullptr) {
        v->src[1] = v_cpy;
    }

    struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
    cb(kqv, "kqv", il);

    struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
    cb(kqv_merged, "kqv_merged", il);

    struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd, n_tokens); 
    cb(cur, "kqv_merged_cont", il); //4096 1 1 1
    // printf_dim(cur);
    cur = ggml_mul_mat(ctx, wo, cur);
    if (wo_b) {
        cb(cur, "kqv_wo", il);
    }

    if (wo_b) {
        cur = ggml_add(ctx, cur, wo_b);
    }

    return cur;
}

const llm_build_cb no_offload_cb = [](struct ggml_tensor * cur, const char * name, int nl) {
    ggml_set_name(cur, name);
};
void free_data(void * data) {
    free(data);
}

struct llm_build_context {
    const llama_model    & model;
    const llama_hparams  & hparams;
    const llama_cparams  & cparams;
    const llama_batch    & batch;
    const llama_kv_cache & kv_self;

    const int64_t n_embd;
    const int64_t n_layer;
    const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
    const int64_t n_head;
    const int64_t n_head_kv;
    const int64_t n_embd_head;
    const int64_t n_embd_gqa;

    const float freq_base;
    const float freq_scale;
    const float ext_factor;
    const float attn_factor;
    const float beta_fast;
    const float beta_slow;
    const float norm_eps;
    const float norm_rms_eps;

    const int32_t n_tokens;
    const int32_t n_kv;     // size of KV cache to consider (n_kv <= n_ctx)
    const int32_t kv_head;  // index of where we store new KV data in the cache
    const int32_t n_orig_ctx;

    const bool do_rope_shift;

    llm_build_cb cb;

    llama_buffer & buf_compute;

    struct ggml_context * ctx0 = nullptr;

    // TODO: consider making the entire interface noexcept
    llm_build_context(
        llama_context  & lctx,
    const llama_batch  & batch,
    const llm_build_cb & cb,
                  bool   worst_case) :
        model         (lctx.model),
        hparams       (model.hparams),
        cparams       (lctx.cparams),
        batch         (batch),
        kv_self       (lctx.kv_self),
        n_embd        (hparams.n_embd),
        n_layer       (hparams.n_layer),
        n_ctx         (cparams.n_ctx),
        n_head        (hparams.n_head),
        n_head_kv     (hparams.n_head_kv),
        n_embd_head   (hparams.n_embd_head()),
        n_embd_gqa    (hparams.n_embd_gqa()),
        freq_base     (cparams.rope_freq_base),
        freq_scale    (cparams.rope_freq_scale),
        ext_factor    (cparams.yarn_ext_factor),
        attn_factor   (cparams.yarn_attn_factor),
        beta_fast     (cparams.yarn_beta_fast),
        beta_slow     (cparams.yarn_beta_slow),
        norm_eps      (hparams.f_norm_eps),
        norm_rms_eps  (hparams.f_norm_rms_eps),
        n_tokens      (batch.n_tokens),
        n_kv          (worst_case ? n_ctx            : kv_self.n),
        kv_head       (worst_case ? n_ctx - n_tokens : kv_self.head),
        n_orig_ctx    (cparams.n_yarn_orig_ctx),
        do_rope_shift (worst_case || kv_self.has_shift),
        cb            (cb),
        buf_compute   (lctx.buf_compute) {
            GGML_ASSERT(!!kv_self.ctx);

            // all initializations should be done in init()
        }

    void init() {
        struct ggml_init_params params = {
            /*.mem_size   =*/ buf_compute.size,
            /*.mem_buffer =*/ buf_compute.data,
            /*.no_alloc   =*/ true,
        };
        

        ctx0 = ggml_init(params);
    }

    void free() {
        if (ctx0) {
            ggml_free(ctx0);
            ctx0 = nullptr;
        }
    }

    struct ggml_cgraph * build_llama() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        GGML_ASSERT(n_embd_head == hparams.n_rot);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(inp_pos, "inp_pos", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);//n_kv 1 1 1
        // printf("KQ_mask: %d %d %d %d\n", KQ_mask->ne[0], KQ_mask->ne[1], KQ_mask->ne[2], KQ_mask->ne[3]);
        cb(KQ_mask, "KQ_mask", -1);
        // printf("n_kv: %d",n_kv);
        // shift the entire K-cache if needed
        if (do_rope_shift) {
            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
        }
        // printf_dim(KQ_mask,"KQ_mask_1");

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;
            const llama_layer * layer = il ? &model.layers[il-1] :& model.layers[il];
            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos,
                    n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                    n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
                    ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                std::tie(k_cpy, v_cpy) = llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
                // printf_dim(k_cpy,"k_cpy");
                // printf_dim(v_cpy,"v_cpy");
                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, NULL,
                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                cb(cur, "kqv_out", il);

            }
                // printf_dim(KQ_mask,"KQ_mask_2");

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);

                if (llama_use_sparse_inference(&model)) {
                    llm_build_cb_short cbs = [&](ggml_tensor * cur, const char * name) {
                        std::string name_str = std::string(name) + "-" + std::to_string(il);
                        ggml_set_name(cur, name_str.c_str());
                    };
                    // We only offload the ffn input to GPU if all neurons are offloaded
                    if (model.layers[il].gpu_offload_ratio >= 1.) {
                        cb(cur, "ffn_norm", il);
                    } else {
                        cbs(cur, "ffn_norm");
                    }
                    bool full_gpu = model.layers[il].gpu_offload_ratio >= 1.0;
                    // if(full_gpu)
                    // {   
                    //     if(model.layers[il].ffn_gate->data)
                    //     {
                    //         printf("model.layers[il].ffn_gate->data = %f \n",((float *)(model.layers[il].ffn_gate->data))[0]);
                    //         free_data(model.layers[il].ffn_gate->data);
                    //     }
                    //     if(model.layers[il].ffn_gate_gpu->data)
                    //     {
                    //         printf("model.layers[il].ffn_gate_gpu->data = %f \n",((float *)(model.layers[il].ffn_gate_gpu->data))[0]);
                    //         free_data(model.layers[il].ffn_gate_gpu->data);
                    //     }

                    // }
                    cur = llm_build_ffn_sparse_rdma(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        model.layers[il].ffn_down_t,
                        model.layers[il].mlp_pre_w1,
                        model.layers[il].mlp_pre_w2,
                        ffn_inp, // as for now, llama's pred use the same input as the ffn
                        //  inpSA, // as for now, llama's pred use the last input as the ffn
                        model.layers[il].gpu_idx, 
                        model.layers[il].gpu_bucket, model.layers[il].ffn_gate_gpu, model.layers[il].ffn_down_gpu, model.layers[il].ffn_up_gpu,layer, 
                        LLM_FFN_RELU, LLM_FFN_PAR, model.layers[il].gpu_offload_ratio, cbs,il);
                } else {
                    // fallback to dense
                    cb(cur, "ffn_norm", il);
                    cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down_t, NULL,
                        LLM_FFN_RELU, LLM_FFN_PAR, cb, il);
                }
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_baichuan() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(inp_pos, "inp_pos", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        // shift the entire K-cache if needed
        if (do_rope_shift) {
            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, n_embd_head, freq_base, freq_scale, cb);
        }

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                switch (model.type) {
                    case MODEL_7B:
                        Qcur = ggml_rope_custom(
                            ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
                            n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
                            ext_factor, attn_factor, beta_fast, beta_slow
                        );
                        Kcur = ggml_rope_custom(
                            ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
                            n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,
                            ext_factor, attn_factor, beta_fast, beta_slow
                        );
                        break;
                    case MODEL_13B:
                        Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
                        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
                        break;
                    default:
                        GGML_ASSERT(false);
                }
                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);

                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                // apply ALiBi for 13B model
                const float max_alibi_bias = model.type == MODEL_13B ? 8.0f : -1.0f;

                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, NULL,
                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, max_alibi_bias, cb, il);
                cb(cur, "kqv_out", il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_falcon() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(inp_pos, "inp_pos", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        // shift the entire K-cache if needed
        if (do_rope_shift) {
            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
        }

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * attn_norm;

            attn_norm = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);

            // self-attention
            {
                if (model.layers[il].attn_norm_2) {
                    // Falcon-40B
                    cur = llm_build_norm(ctx0, inpL, hparams,
                            model.layers[il].attn_norm_2,
                            model.layers[il].attn_norm_2_b,
                            LLM_NORM, cb, il);
                    cb(cur, "attn_norm_2", il);
                } else {
                    cur = attn_norm;
                }

                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

                // using mode = 2 for neox mode
                Qcur = ggml_rope_custom(
                    ctx0, Qcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Qcur, "Qcur", il);

                Kcur = ggml_rope_custom(
                    ctx0, Kcur, inp_pos, n_embd_head, 2, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(Kcur, "Kcur", il);

                std::tie(k_cpy, v_cpy) = llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, NULL,
                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                cb(cur, "kqv_out", il);
            }

            struct ggml_tensor * ffn_inp = cur;

            // feed forward
            if (llama_use_sparse_inference(&model)) {
                llm_build_cb_short cbs = [&](ggml_tensor * cur, const char * name) {
                    std::string name_str = std::string(name) + "-" + std::to_string(il);
                    ggml_set_name(cur, name_str.c_str());
                };
                // We only offload the ffn input to GPU if all neurons are offloaded
                if (model.layers[il].gpu_offload_ratio >= 1.) {
                    cb(cur, "attn_norm", il);
                } else {
                    cbs(cur, "attn_norm");
                }
                cur = llm_build_ffn_sparse(ctx0, attn_norm,
                    model.layers[il].ffn_up,   NULL,
                    NULL, NULL,
                    model.layers[il].ffn_down, NULL,
                    model.layers[il].ffn_down_t,
                    model.layers[il].mlp_pre_w1,
                    model.layers[il].mlp_pre_w2,
                    inpL, // Falcon uses the layer's input as the pred input
                    model.layers[il].gpu_idx, 
                    model.layers[il].gpu_bucket, 
                    model.layers[il].ffn_gate_gpu, model.layers[il].ffn_down_gpu, model.layers[il].ffn_up_gpu,
                    LLM_FFN_RELU, LLM_FFN_SEQ, model.layers[il].gpu_offload_ratio, cbs);
            } else {
                cb(attn_norm, "attn_norm", il);
                cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
                        model.layers[il].ffn_up,   NULL,
                        NULL,                      NULL,
                        model.layers[il].ffn_down_t, NULL,
                        LLM_FFN_RELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            cur = ggml_add(ctx0, cur, inpL);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        // norm
        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }





    struct ggml_cgraph * build_starcoder() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        struct ggml_tensor * cur;
        struct ggml_tensor * pos;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(inp_pos, "inp_pos", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
        cb(pos, "pos_embd", -1);

        inpL = ggml_add(ctx0, inpL, pos);
        cb(inpL, "inpL", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, model.layers[il].bo,
                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                cb(cur, "kqv_out", il);
            }

            // add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_persimmon() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        const int64_t n_rot = n_embd_head / 2;

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "imp_embd", -1);

        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(inp_pos, "inp_pos", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        if (do_rope_shift) {
            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, n_embd_head, freq_base, freq_scale, cb);
        }

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * residual = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                // split qkv
                GGML_ASSERT(n_head_kv == n_head);

                struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
                cb(tmpqkv, "tmpqkv", il);

                struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
                cb(tmpqkv_perm, "tmpqkv", il);

                struct ggml_tensor * tmpq = ggml_view_3d(
                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
                        ggml_element_size(tmpqkv_perm) * n_embd_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
                        0
                        );
                cb(tmpq, "tmpq", il);

                struct ggml_tensor * tmpk = ggml_view_3d(
                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
                        ggml_element_size(tmpqkv_perm) * n_embd_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
                        );
                cb(tmpk, "tmpk", il);

                // Q/K Layernorm
                tmpq = llm_build_norm(ctx0, tmpq, hparams,
                        model.layers[il].attn_q_norm,
                        model.layers[il].attn_q_norm_b,
                        LLM_NORM, cb, il);
                cb(tmpq, "tmpq", il);

                tmpk = llm_build_norm(ctx0, tmpk, hparams,
                        model.layers[il].attn_k_norm,
                        model.layers[il].attn_k_norm_b,
                        LLM_NORM, cb, il);
                cb(tmpk, "tmpk", il);

                // RoPE the first n_rot of q/k, pass the other half, and concat.
                struct ggml_tensor * qrot = ggml_view_3d(
                        ctx0, tmpq, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpq) * n_embd_head,
                        ggml_element_size(tmpq) * n_embd_head * n_head,
                        0
                        );
                cb(qrot, "qrot", il);

                struct ggml_tensor * krot = ggml_view_3d(
                        ctx0, tmpk, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpk) * n_embd_head,
                        ggml_element_size(tmpk) * n_embd_head * n_head,
                        0
                        );
                cb(krot, "krot", il);

                // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
                struct ggml_tensor * qpass = ggml_view_3d(
                        ctx0, tmpq, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpq) * n_embd_head,
                        ggml_element_size(tmpq) * n_embd_head * n_head,
                        ggml_element_size(tmpq) * n_rot
                        );
                cb(qpass, "qpass", il);

                struct ggml_tensor * kpass = ggml_view_3d(
                        ctx0, tmpk, n_rot, n_head, n_tokens,
                        ggml_element_size(tmpk) * n_embd_head,
                        ggml_element_size(tmpk) * n_embd_head * n_head,
                        ggml_element_size(tmpk) * n_rot
                        );
                cb(kpass, "kpass", il);

                struct ggml_tensor * qrotated = ggml_rope_custom(
                    ctx0, qrot, inp_pos, n_rot, 2, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(qrotated, "qrotated", il);

                struct ggml_tensor * krotated = ggml_rope_custom(
                    ctx0, krot, inp_pos, n_rot, 2, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(krotated, "krotated", il);

                // ggml currently only supports concatenation on dim=2
                // so we need to permute qrot, qpass, concat, then permute back.
                qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
                cb(qrotated, "qrotated", il);

                krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
                cb(krotated, "krotated", il);

                qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
                cb(qpass, "qpass", il);

                kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
                cb(kpass, "kpass", il);

                struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
                cb(Q, "Q", il);

                Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_view_3d(
                        ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
                        ggml_element_size(tmpqkv_perm) * n_embd_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
                        ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
                        );
                cb(Vcur, "Vcur", il);

                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                // TODO: not tested, could be broken
                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, model.layers[il].bo,
                        Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                cb(cur, "kqv_out", il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_refact() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm, NULL,
                    LLM_NORM_RMS, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
                cb(Kcur, "Kcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
                cb(Qcur, "Qcur", il);

                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, NULL,
                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
                cb(cur, "kqv_out", il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm, NULL,
                        LLM_NORM_RMS, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm, NULL,
                LLM_NORM_RMS, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_bloom() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        inpL = llm_build_norm(ctx0, inpL, hparams,
                model.tok_norm,
                model.tok_norm_b,
                LLM_NORM, cb, -1);
        cb(inpL, "inp_norm", -1);

        for (int il = 0; il < n_layer; ++il) {
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
                cb(cur, "bqkv", il);

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, model.layers[il].bo,
                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, 8.0f, cb, il);
                cb(cur, "kqv_out", il);
            }

            // Add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // FF
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            inpL = ggml_add(ctx0, cur, ffn_inp);
            cb(inpL, "l_out", il);
        }

        cur = llm_build_norm(ctx0, inpL, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_mpt() {
        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * attn_norm;

            attn_norm = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    NULL,
                    LLM_NORM, cb, il);
            cb(attn_norm, "attn_norm", il);

            // self-attention
            {
                cur = attn_norm;

                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
                cb(cur, "wqkv", il);

                if (hparams.f_clamp_kqv > 0.0f) {
                    cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
                    cb(cur, "wqkv_clamped", il);
                }

                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));

                cb(Qcur, "Qcur", il);
                cb(Kcur, "Kcur", il);
                cb(Vcur, "Vcur", il);

                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, NULL,
                        Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, hparams.f_max_alibi_bias, cb, il);
                cb(cur, "kqv_out", il);
            }

            // Add the input
            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
            cb(ffn_inp, "ffn_inp", il);

            // feed forward
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        NULL,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        NULL,                      NULL,
                        model.layers[il].ffn_down, NULL,
                        LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                NULL,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }

    struct ggml_cgraph * build_stablelm() {
        struct ggml_cgraph * gf = ggml_new_graph(ctx0);

        struct ggml_tensor * cur;
        struct ggml_tensor * inpL;

        inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
        cb(inpL, "inp_embd", -1);

        // inp_pos - contains the positions
        struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
        cb(inp_pos, "inp_pos", -1);

        // KQ_scale
        struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
        cb(KQ_scale, "KQ_scale", -1);

        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
        struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
        cb(KQ_mask, "KQ_mask", -1);

        // shift the entire K-cache if needed
        if (do_rope_shift) {
            llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb);
        }

        for (int il = 0; il < n_layer; ++il) {
            struct ggml_tensor * inpSA = inpL;

            // norm
            cur = llm_build_norm(ctx0, inpL, hparams,
                    model.layers[il].attn_norm,
                    model.layers[il].attn_norm_b,
                    LLM_NORM, cb, il);
            cb(cur, "attn_norm", il);

            // self-attention
            {
                // compute Q and K and RoPE them
                struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                cb(tmpq, "tmpq", il);

                struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
                cb(tmpk, "tmpk", il);

                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
                cb(Vcur, "Vcur", il);

                // RoPE the first n_rot of q/k, pass the other half, and concat.
                struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d(
                        ctx0, tmpq, hparams.n_rot, n_head, n_tokens,
                        ggml_element_size(tmpq) * n_embd_head,
                        ggml_element_size(tmpq) * n_embd_head * n_head,
                        0
                        ));
                cb(qrot, "qrot", il);

                struct ggml_tensor * krot = ggml_cont(ctx0, ggml_view_3d(
                        ctx0, tmpk, hparams.n_rot, n_head, n_tokens,
                        ggml_element_size(tmpk) * n_embd_head,
                        ggml_element_size(tmpk) * n_embd_head * n_head_kv,
                        0
                        ));
                cb(krot, "krot", il);

                // get the second half of tmpq, e.g tmpq[n_rot:, :, :]
                struct ggml_tensor * qpass = ggml_view_3d(
                        ctx0, tmpq, (n_embd_head - hparams.n_rot), n_head, n_tokens,
                        ggml_element_size(tmpq) * n_embd_head,
                        ggml_element_size(tmpq) * n_embd_head * n_head,
                        ggml_element_size(tmpq) * hparams.n_rot
                        );
                cb(qpass, "qpass", il);

                struct ggml_tensor * kpass = ggml_view_3d(
                        ctx0, tmpk, (n_embd_head - hparams.n_rot), n_head_kv, n_tokens,
                        ggml_element_size(tmpk) * (n_embd_head),
                        ggml_element_size(tmpk) * (n_embd_head) * n_head_kv,
                        ggml_element_size(tmpk) * hparams.n_rot
                        );
                cb(kpass, "kpass", il);

                struct ggml_tensor * qrotated = ggml_rope_custom(
                    ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(qrotated, "qrotated", il);

                struct ggml_tensor * krotated = ggml_rope_custom(
                    ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                );
                cb(krotated, "krotated", il);

                // ggml currently only supports concatenation on dim=2
                // so we need to permute qrot, qpass, concat, then permute back.
                qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
                cb(qrotated, "qrotated", il);

                krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
                cb(krotated, "krotated", il);

                qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
                cb(qpass, "qpass", il);

                kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
                cb(kpass, "kpass", il);

                struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
                cb(Qcur, "Qcur", il);

                struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
                cb(Kcur, "Kcur", il);

                struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
                cb(Q, "Q", il);

                Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
                cb(Kcur, "Kcur", il);

                llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);

                cur = llm_build_kqv(ctx0, hparams, kv_self,
                        model.layers[il].wo, NULL,
                        Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
                cb(cur, "kqv_out", il);
            }

            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
            cb(ffn_inp, "ffn_inp", il);

            // feed-forward network
            {
                cur = llm_build_norm(ctx0, ffn_inp, hparams,
                        model.layers[il].ffn_norm,
                        model.layers[il].ffn_norm_b,
                        LLM_NORM, cb, il);
                cb(cur, "ffn_norm", il);

                cur = llm_build_ffn(ctx0, cur,
                        model.layers[il].ffn_up,   NULL,
                        model.layers[il].ffn_gate, NULL,
                        model.layers[il].ffn_down, NULL,
                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                cb(cur, "ffn_out", il);
            }

            cur = ggml_add(ctx0, cur, ffn_inp);
            cb(cur, "l_out", il);

            // input for next layer
            inpL = cur;
        }

        cur = inpL;

        cur = llm_build_norm(ctx0, cur, hparams,
                model.output_norm,
                model.output_norm_b,
                LLM_NORM, cb, -1);
        cb(cur, "result_norm", -1);

        // lm_head
        cur = ggml_mul_mat(ctx0, model.output, cur);
        cb(cur, "result_output", -1);

        ggml_build_forward_expand(gf, cur);

        return gf;
    }
};

//
// tensor offloading helpers
//
// TODO: will be removed with backend v2

enum llm_offload_func_e {
    OFFLOAD_FUNC_NOP,
    OFFLOAD_FUNC,
    OFFLOAD_FUNC_KQ,
    OFFLOAD_FUNC_V,
    OFFLOAD_FUNC_NR,
    OFFLOAD_FUNC_EMB,
    OFFLOAD_FUNC_OUT,
};

// TODO: will be removed with backend v2
struct llm_offload_trie {
    struct node {
        ~node() {
            for (int i = 0; i < 256; ++i) {
                if (children[i]) {
                    delete children[i];
                }
            }
        }

        node * children[256] = { nullptr };
        llm_offload_func_e func = OFFLOAD_FUNC_NOP;
    };

    llm_offload_trie() {
        root = new node;
    }

    llm_offload_trie(const std::unordered_map<const char *, llm_offload_func_e> & map) {
        root = new node;

        for (const auto & kv : map) {
            add(kv.first, kv.second);
        }
    }

    ~llm_offload_trie() {
        delete root;
    }

    void add(const char * name, llm_offload_func_e func) {
        node * cur = root;

        for (int i = 0; ; ++i) {
            const uint8_t c = name[i];

            if (!c) {
                break;
            }

            if (!cur->children[c]) {
                cur->children[c] = new node;
            }

            cur = cur->children[c];
        }

        cur->func = func;
    }

    llm_offload_func_e find(const char * name) const {
        const node * cur = root;

        for (int i = 0; ; ++i) {
            const uint8_t c = name[i];

            if (!c) {
                break;
            }

            if (!cur->children[c]) {
                return OFFLOAD_FUNC_NOP;
            }

            cur = cur->children[c];
        }

        return cur->func;
    }

    node * root = nullptr;
};

// TODO: will be removed with backend v2
static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map = {
  //{ "inp_tokens",                 OFFLOAD_FUNC_NR  }, // TODO: missing K-quants get_rows kernel
  //{ "inp_embd",                   OFFLOAD_FUNC_NR  }, // TODO: missing K-quants get_rows kernel
    { "pos_embd",                   OFFLOAD_FUNC_NR  },

    { "inp_pos",                    OFFLOAD_FUNC_KQ  }, // this is often used for KQ ops (e.g. rope)
    { "KQ_scale",                   OFFLOAD_FUNC_KQ  },
    { "KQ_mask",                    OFFLOAD_FUNC_KQ  },
    { "K_shift",                    OFFLOAD_FUNC_KQ  },
    { "K_shifted",                  OFFLOAD_FUNC_KQ  },

    { "inp_norm",                   OFFLOAD_FUNC_NR  },
    { "inp_norm_w",                 OFFLOAD_FUNC_NR  },
    { "inp_norm_wb",                OFFLOAD_FUNC_NR  },

    { "norm",                       OFFLOAD_FUNC     },
    { "norm_w",                     OFFLOAD_FUNC     },
    { "norm_wb",                    OFFLOAD_FUNC     },

    { "attn_norm",                  OFFLOAD_FUNC     },
    { "attn_norm_2",                OFFLOAD_FUNC     },

    { "wqkv",                       OFFLOAD_FUNC_KQ  },
    { "bqkv",                       OFFLOAD_FUNC_KQ  },
    { "wqkv_clamped",               OFFLOAD_FUNC_KQ  },

    { "tmpk",                       OFFLOAD_FUNC_KQ  },
    { "tmpq",                       OFFLOAD_FUNC_KQ  },
    { "tmpv",                       OFFLOAD_FUNC_V   },
    { "Kcur",                       OFFLOAD_FUNC_KQ  },
    { "Qcur",                       OFFLOAD_FUNC_KQ  },
    { "Vcur",                       OFFLOAD_FUNC_V   },

    { "krot",                       OFFLOAD_FUNC_KQ  },
    { "qrot",                       OFFLOAD_FUNC_KQ  },
    { "kpass",                      OFFLOAD_FUNC_KQ  },
    { "qpass",                      OFFLOAD_FUNC_KQ  },
    { "krotated",                   OFFLOAD_FUNC_KQ  },
    { "qrotated",                   OFFLOAD_FUNC_KQ  },

    { "q",                          OFFLOAD_FUNC_KQ  },
    { "k",                          OFFLOAD_FUNC_KQ  },
    { "kq",                         OFFLOAD_FUNC_KQ  },
    { "kq_scaled",                  OFFLOAD_FUNC_KQ  },
    { "kq_scaled_alibi",            OFFLOAD_FUNC_KQ  },
    { "kq_masked",                  OFFLOAD_FUNC_KQ  },
    { "kq_soft_max",                OFFLOAD_FUNC_V   },
    { "v",                          OFFLOAD_FUNC_V   },
    { "kqv",                        OFFLOAD_FUNC_V   },
    { "kqv_merged",                 OFFLOAD_FUNC_V   },
    { "kqv_merged_cont",            OFFLOAD_FUNC_V   },
    { "kqv_wo",                     OFFLOAD_FUNC_V   },
    { "kqv_out",                    OFFLOAD_FUNC_V   },

    { "ffn_inp",                    OFFLOAD_FUNC     },
    { "ffn_norm",                   OFFLOAD_FUNC     },

    { "ffn_up",                     OFFLOAD_FUNC     },
    { "ffn_up_b",                   OFFLOAD_FUNC     },
    { "ffn_gate",                   OFFLOAD_FUNC     },
    { "ffn_gate_b",                 OFFLOAD_FUNC     },
    { "ffn_gate_par",               OFFLOAD_FUNC     },
    { "ffn_down",                   OFFLOAD_FUNC     },
    { "ffn_down_b",                 OFFLOAD_FUNC     },
    { "ffn_out",                    OFFLOAD_FUNC     },

    { "ffn_silu",                   OFFLOAD_FUNC     },
    { "ffn_gelu",                   OFFLOAD_FUNC     },
    { "ffn_relu",                   OFFLOAD_FUNC     },
    { "ffn_sqr(relu)",              OFFLOAD_FUNC     },

    { "l_out",                      OFFLOAD_FUNC     },

    { "result_norm",                OFFLOAD_FUNC_EMB },
    { "result_output",              OFFLOAD_FUNC_OUT },
};

static llm_offload_trie k_offload_func_trie(k_offload_map);

static struct ggml_cgraph * llama_build_graph(
         llama_context & lctx,
     const llama_batch & batch) {
    const auto & model = lctx.model;

    // check if we should build the worst-case graph (for memory measurement)
    const bool worst_case = ggml_allocr_is_measure(lctx.alloc);

    // keep track of the input that has already been allocated
    bool alloc_inp_tokens   = false;
    bool alloc_inp_embd     = false;
    bool alloc_inp_pos      = false;
    bool alloc_inp_KQ_scale = false;
    bool alloc_inp_KQ_mask  = false;
    bool alloc_inp_K_shift  = false;

#ifdef GGML_USE_CUBLAS
    const bool do_offload = true;
#else
    const bool do_offload = true; // TODO: set to false after finishing refactoring
#endif

    int n_non_view = 0; // number of non-view tensors that have been processed by the callback

    // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
    // TODO: will be removed with backend v2

    // For sparse deriv, we offload layers from the starting layer to the end.
    // For dense deriv, we offload layers from the end to the starting layer.
    bool offload_starting_layers = lctx.model.sparse_deriv;

    llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
        if (il >= 0) {
            ggml_format_name(cur, "%s-%d", name, il);
        } else {
            ggml_set_name(cur, name);
        }

        //
        // allocate input tensors and set input data
        //
        // TODO: will be removed with backend v2

        if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) {
            ggml_allocr_alloc(lctx.alloc, cur);

            if (!ggml_allocr_is_measure(lctx.alloc) && batch.token) {
                const int64_t n_tokens = cur->ne[0];

                memcpy(cur->data, batch.token, n_tokens*ggml_element_size(cur));
            }

            alloc_inp_tokens = true;
        }

        if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0) {
            ggml_allocr_alloc(lctx.alloc, cur);

            if (!ggml_allocr_is_measure(lctx.alloc) && batch.embd) {
                const int64_t n_embd   = cur->ne[0];
                const int64_t n_tokens = cur->ne[1];

                memcpy(cur->data, batch.embd, n_tokens*n_embd*ggml_element_size(cur));
            }

            alloc_inp_embd = true;
        }

        if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) {
            ggml_allocr_alloc(lctx.alloc, cur);

            if (!ggml_allocr_is_measure(lctx.alloc) && batch.pos) {
                const int64_t n_tokens = cur->ne[0];

                int32_t * data = (int32_t *) cur->data;

                for (int i = 0; i < n_tokens; ++i) {
                    data[i] = batch.pos[i];
                }
            }

            alloc_inp_pos = true;
        }

        if (!alloc_inp_KQ_scale && strcmp(name, "KQ_scale") == 0) {
            ggml_allocr_alloc(lctx.alloc, cur);

            if (!ggml_allocr_is_measure(lctx.alloc)) {
                const int64_t n_embd_head = model.hparams.n_embd_head();
                ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head)));
            }

            alloc_inp_KQ_scale = true;
        }

        if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) {
            ggml_allocr_alloc(lctx.alloc, cur);

            if (!ggml_allocr_is_measure(lctx.alloc)) {
                const int64_t n_kv     = cur->ne[0];
                const int64_t n_tokens = cur->ne[1];

                float * data = (float *) cur->data;
                memset(data, 0, ggml_nbytes(cur));

                for (int h = 0; h < 1; ++h) {
                    for (int j = 0; j < n_tokens; ++j) {
                        const llama_pos    pos    = batch.pos[j];
                        const llama_seq_id seq_id = batch.seq_id[j][0];

                        for (int i = 0; i < n_kv; ++i) {
                            if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
                                data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
                            }
                        }
                    }
                }
            }

            alloc_inp_KQ_mask = true;
        }

        if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) {
            ggml_allocr_alloc(lctx.alloc, cur);

            if (!ggml_allocr_is_measure(lctx.alloc)) {
                const int64_t n_ctx = cur->ne[0];

                int32_t * data = (int32_t *) cur->data;

                for (int i = 0; i < n_ctx; ++i) {
                    data[i] = lctx.kv_self.cells[i].delta;
                }
            }

            alloc_inp_K_shift = true;
        }

        // view tensors are not processed further
        if (cur->view_src != nullptr) {
            return;
        }

        if (cur->op != GGML_OP_NONE) {
            n_non_view++;
        }

        //
        // offload layers
        //
        // TODO: will be removed with backend v2

//#define LLAMA_OFFLOAD_DEBUG

        if (!do_offload) {
            return;
        }

        const int n_layer = model.hparams.n_layer;

        const int n_gpu_layers = model.n_gpu_layers;
        const int i_gpu_start  = n_layer - n_gpu_layers;
        const int i_gpu_end = n_gpu_layers;

        // should we offload the final norm? yes if we are not computing embeddings
        const bool offload_emb = lctx.embedding.empty();

        static const std::unordered_map<llm_offload_func_e, std::string, std::hash<int>> k_offload_func_name = {
            { OFFLOAD_FUNC_NOP, "CPU" },
            { OFFLOAD_FUNC_OUT, "CPU" },
#ifdef GGML_USE_CUBLAS
            { OFFLOAD_FUNC,     "GPU (CUDA)" },
            { OFFLOAD_FUNC_KQ,  "GPU (CUDA) KQ" },
            { OFFLOAD_FUNC_V,   "GPU (CUDA) V" },
            { OFFLOAD_FUNC_NR,  "GPU (CUDA) NR" },
            { OFFLOAD_FUNC_EMB, "GPU (CUDA) EMB" },
#else
            { OFFLOAD_FUNC,     "CPU" },
            { OFFLOAD_FUNC_KQ,  "CPU" },
            { OFFLOAD_FUNC_V,   "CPU" },
            { OFFLOAD_FUNC_NR,  "CPU" },
            { OFFLOAD_FUNC_EMB, "CPU" },
#endif // GGML_USE_CUBLAS
        };

        // check the global map for what offload function to use for this tensor
        llm_offload_func_e func_e = k_offload_func_trie.find(name);

        if (func_e == OFFLOAD_FUNC_NOP) {
#ifdef LLAMA_OFFLOAD_DEBUG
            // if a tensor hasn't been offloaded, we warn the user
            if (worst_case) {
                LLAMA_LOG_WARN("%s: %32s: not offloaded (ref: %s)\n", __func__,
                        cur->name, "https://github.com/ggerganov/llama.cpp/pull/3837");
            }
#endif

            return;
        }

        // count the number of layers and respect the provided n_gpu_layers
        switch (func_e) {
            case OFFLOAD_FUNC_NOP:
            case OFFLOAD_FUNC_OUT:
                break;
            case OFFLOAD_FUNC:
                if (n_gpu_layers < n_layer) {
                    if ((offload_starting_layers && il >= i_gpu_end)
                        || (!offload_starting_layers && il < i_gpu_start)) {
                        func_e = OFFLOAD_FUNC_NOP;
                    }
                }
                break;
            case OFFLOAD_FUNC_NR:
                if (n_gpu_layers <= n_layer + 0) {
                    func_e = OFFLOAD_FUNC_NOP;
                }
                break;
            case OFFLOAD_FUNC_V:
                if (n_gpu_layers <= n_layer + 1) {
                    func_e = OFFLOAD_FUNC_NOP;
                }
                break;
            case OFFLOAD_FUNC_KQ:
                if (n_gpu_layers <= n_layer + 2) {
                    func_e = OFFLOAD_FUNC_NOP;
                }
                break;
            case OFFLOAD_FUNC_EMB:
                if (!offload_emb || n_gpu_layers < n_layer) {
                    func_e = OFFLOAD_FUNC_NOP;
                }
                break;
            default: GGML_ASSERT(false);
        }

        offload_func_t func = ggml_offload_nop;

        // this is needed for compatibility with Metal for example
#ifdef GGML_USE_CUBLAS
        static offload_func_t ggml_offload_gpu = ggml_cuda_assign_buffers_no_alloc;
#else
        static offload_func_t ggml_offload_gpu = ggml_offload_nop;
#endif

        switch (func_e) {
            case OFFLOAD_FUNC_NOP:
            case OFFLOAD_FUNC_OUT: func = ggml_offload_nop; break;
            case OFFLOAD_FUNC:
            case OFFLOAD_FUNC_KQ:
            case OFFLOAD_FUNC_V:
            case OFFLOAD_FUNC_NR:
            case OFFLOAD_FUNC_EMB: func = ggml_offload_gpu; break;
            default: GGML_ASSERT(false);
        }

        // apply offload function to the tensor
        func(cur);

#ifdef LLAMA_OFFLOAD_DEBUG
        if (worst_case) {
            LLAMA_LOG_INFO("%s: %32s: %s\n", __func__, cur->name, k_offload_func_name.at(func_e).c_str());
        }
#endif
    };

    struct ggml_cgraph * result = NULL;

    struct llm_build_context llm(lctx, batch, cb, worst_case);

    llm.init();
    // sleep(10);
    // printf("%s: %d\n", __func__, model.arch);
    switch (model.arch) {
        case LLM_ARCH_LLAMA:
            {
                result = llm.build_llama();
            } break;
        case LLM_ARCH_BAICHUAN:
            {
                result = llm.build_baichuan();
            } break;
        case LLM_ARCH_FALCON:
            {
                result = llm.build_falcon();
            } break;
        case LLM_ARCH_STARCODER:
            {
                result = llm.build_starcoder();
            } break;
        case LLM_ARCH_PERSIMMON:
            {
                result = llm.build_persimmon();
            } break;
        case LLM_ARCH_REFACT:
            {
                result = llm.build_refact();
            } break;
        case LLM_ARCH_BLOOM:
            {
                result = llm.build_bloom();
            } break;
        case LLM_ARCH_MPT:
            {
                result = llm.build_mpt();
            } break;
         case LLM_ARCH_STABLELM:
            {
                result = llm.build_stablelm();
            } break;
        default:
            GGML_ASSERT(false);
    }

    llm.free();

    if (worst_case) {
        int n_non_view_total = 0;

        for (int i = 0; i < result->n_nodes; ++i) {
            if (result->nodes[i]->view_src == nullptr) {
                n_non_view_total++;
            }
        }

        LLAMA_LOG_INFO("%s: non-view tensors processed: %d/%d\n", __func__, n_non_view, n_non_view_total);

        if (n_non_view != n_non_view_total) {
            LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
            LLAMA_LOG_WARN("%s: not all non-view tensors have been processed with a callback\n",     __func__);
            LLAMA_LOG_WARN("%s: this can indicate an inefficiency in the graph implementation\n",    __func__);
            LLAMA_LOG_WARN("%s: build with LLAMA_OFFLOAD_DEBUG for more info\n",                     __func__);
            LLAMA_LOG_WARN("%s: ref: https://github.com/ggerganov/llama.cpp/pull/3837\n",            __func__);
            LLAMA_LOG_WARN("%s: ****************************************************************\n", __func__);
        }
    }

    return result;
}

// decode a batch of tokens by evaluating the transformer
//
//   - lctx:      llama context
//   - batch:     batch to evaluate
//
// return 0 on success
// return positive int on warning
// return negative int on error
//
static int llama_decode_internal(
         llama_context & lctx,
           llama_batch   batch) {
    const uint32_t n_tokens = batch.n_tokens;

    if (n_tokens == 0) {
        LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
        return -1;
    }

    const auto & model   = lctx.model;
    const auto & hparams = model.hparams;
    const auto & cparams = lctx.cparams;

    const auto n_batch = cparams.n_batch;

    GGML_ASSERT(n_tokens <= n_batch);

    int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT

    const int64_t t_start_us = ggml_time_us();

#ifdef GGML_USE_MPI
    // TODO: needs fix after #3228
    GGML_ASSERT(false && "not implemented");
    //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
#endif

    GGML_ASSERT(n_threads > 0);

    auto & kv_self = lctx.kv_self;

    GGML_ASSERT(!!kv_self.ctx);

    const int64_t n_embd  = hparams.n_embd;
    const int64_t n_vocab = hparams.n_vocab;

    // helpers for smoother batch API transistion
    // after deprecating the llama_eval calls, these will be removed
    std::vector<llama_pos> pos;

    std::vector<int32_t>                   n_seq_id;
    std::vector<llama_seq_id *>            seq_id_arr;
    std::vector<std::vector<llama_seq_id>> seq_id;

    if (batch.pos == nullptr) {
        pos.resize(n_tokens);
        for (uint32_t i = 0; i < n_tokens; i++) {
            pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
        }

        batch.pos = pos.data();
    }

    if (batch.seq_id == nullptr) {
        n_seq_id.resize(n_tokens);
        seq_id.resize(n_tokens);
        seq_id_arr.resize(n_tokens);
        for (uint32_t i = 0; i < n_tokens; i++) {
            n_seq_id[i] = 1;
            seq_id[i].resize(1);
            seq_id[i][0] = batch.all_seq_id;
            seq_id_arr[i] = seq_id[i].data();
        }

        batch.n_seq_id = n_seq_id.data();
        batch.seq_id = seq_id_arr.data();
    }

    if (!llama_kv_cache_find_slot(kv_self, batch)) {
        return 1;
    }

    // a heuristic, to avoid attending the full cache if it is not yet utilized
    // after enough generations, the benefit from this heuristic disappears
    // if we start defragmenting the cache, the benefit from this will be more important
    //kv_self.n = std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32));   // TODO: this might be better for CUDA?
    kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, llama_kv_cache_cell_max(kv_self)));

    //printf("kv_self.n = %d\n", kv_self.n);

    ggml_allocr_reset(lctx.alloc);
    // printf("llama_build_graph(lctx, batch)\n");
    ggml_cgraph * gf = llama_build_graph(lctx, batch);

    ggml_allocr_alloc_graph(lctx.alloc, gf);

    struct ggml_tensor * res        = gf->nodes[gf->n_nodes - 1];
    struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];

    GGML_ASSERT(strcmp(res->name,        "result_output") == 0);
    GGML_ASSERT(strcmp(embeddings->name, "result_norm")   == 0);


#ifdef GGML_USE_CUBLAS
    for (int i = 0; i < gf->n_leafs; i++) {
        ggml_tensor * node = gf->leafs[i];
        if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
            ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
            ggml_cuda_copy_to_device(node);
        }
    }

    for (int i = 0; i < gf->n_nodes; i++) {
        ggml_tensor * node = gf->nodes[i];
        if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
            ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
        }
    }

    // HACK: ggml-alloc may change the tensor backend when reusing a parent, so force output to be on the CPU here if needed
    if (!lctx.embedding.empty()) {
        embeddings->backend = GGML_BACKEND_CPU;
    }
    res->backend = GGML_BACKEND_CPU;
#endif

    // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);

    // for big prompts, if BLAS is enabled, it is better to use only one thread
    // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
    // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
    //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
    //       with the BLAS calls. need a better solution
    if (n_tokens >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
        n_threads = std::min(4, n_threads);
    }

    // If all tensors can be run on the GPU then using more than 1 thread is detrimental.
    const bool full_offload_supported =
        model.arch == LLM_ARCH_LLAMA      ||
        model.arch == LLM_ARCH_BAICHUAN   ||
        model.arch == LLM_ARCH_FALCON     ||
        model.arch == LLM_ARCH_REFACT     ||
        model.arch == LLM_ARCH_MPT        ||
        model.arch == LLM_ARCH_STARCODER  ||
        model.arch == LLM_ARCH_STABLELM;

    // const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
    // if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
    //     n_threads = 8;
    // }

#if GGML_USE_MPI
    const int64_t n_layer = hparams.n_layer;
    ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
#endif

#ifdef GGML_USE_METAL
    if (lctx.ctx_metal) {
        ggml_metal_set_n_cb     (lctx.ctx_metal, n_threads);
        ggml_metal_graph_compute(lctx.ctx_metal, gf);
    } else {
        ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
    }
#else
    ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
#endif

#if GGML_USE_MPI
    ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
#endif

    // update the kv ring buffer
    {
        if (kv_self.has_shift) {
            kv_self.has_shift = false;
            for (uint32_t i = 0; i < kv_self.size; ++i) {
                kv_self.cells[i].delta = 0;
            }
        }

        kv_self.head += n_tokens;

        // Ensure kv cache head points to a valid index.
        if (kv_self.head >= kv_self.size) {
            kv_self.head = 0;
        }
    }

#ifdef GGML_PERF
    // print timing information per ggml operation (for debugging purposes)
    // requires GGML_PERF to be defined
    ggml_graph_print(gf);
#endif

    // plot the computation graph in dot format (for debugging purposes)
    //if (n_past%100 == 0) {
    //    ggml_graph_dump_dot(gf, NULL, "llama.dot");
    //}

    // extract logits
    // TODO: do not compute and extract logits if only embeddings are needed
    //       need to update the graphs to skip "result_output"
    {
        auto & logits_out = lctx.logits;

        if (batch.logits) {
            logits_out.resize(n_vocab * n_tokens);
            for (uint32_t i = 0; i < n_tokens; i++) {
                if (batch.logits[i] == 0) {
                    continue;
                }
                memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab);
            }
        } else if (lctx.logits_all) {
            logits_out.resize(n_vocab * n_tokens);
            memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens);
        } else {
            logits_out.resize(n_vocab);
            memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab);
        }
    }

    // extract embeddings
    if (!lctx.embedding.empty()) {
        auto & embedding_out = lctx.embedding;

        embedding_out.resize(n_embd);
        memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(n_tokens - 1)), sizeof(float)*n_embd);
    }

    // measure the performance only for the single-token evals
    if (n_tokens == 1) {
        lctx.t_eval_us += ggml_time_us() - t_start_us;
        lctx.n_eval++;
    }
    else if (n_tokens > 1) {
        lctx.t_p_eval_us += ggml_time_us() - t_start_us;
        lctx.n_p_eval += n_tokens;
    }

    // get a more accurate load time, upon first eval
    // TODO: fix this
    if (!lctx.has_evaluated_once) {
        lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
        lctx.has_evaluated_once = true;
    }

    return 0;
}

//
// tokenizer
//

static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
    return vocab.type;
}

static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
}

static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
}

static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
}

static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
}

static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
}

static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
    GGML_ASSERT(llama_is_byte_token(vocab, id));
    const auto& token_data = vocab.id_to_token.at(id);
    switch (llama_vocab_get_type(vocab)) {
    case LLAMA_VOCAB_TYPE_SPM: {
        auto buf = token_data.text.substr(3, 2);
        return strtol(buf.c_str(), NULL, 16);
    }
    case LLAMA_VOCAB_TYPE_BPE: {
        GGML_ASSERT(false);
        return unicode_to_bytes_bpe(token_data.text);
    }
    default:
        GGML_ASSERT(false);
    }
}

static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
    static const char * hex = "0123456789ABCDEF";
    switch (llama_vocab_get_type(vocab)) {
    case LLAMA_VOCAB_TYPE_SPM: {
        const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
        return vocab.token_to_id.at(buf);
    }
    case LLAMA_VOCAB_TYPE_BPE: {
        return vocab.token_to_id.at(bytes_to_unicode_bpe(ch));
    }
    default:
        GGML_ASSERT(false);
    }
}

static void llama_escape_whitespace(std::string & text) {
    replace_all(text, " ", "\xe2\x96\x81");
}

static void llama_unescape_whitespace(std::string & word) {
    replace_all(word, "\xe2\x96\x81", " ");
}

struct llm_symbol {
    using index = int;
    index prev;
    index next;
    const char * text;
    size_t n;
};

static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");

// SPM tokenizer
// original implementation:
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4

struct llm_bigram_spm {
    struct comparator {
        bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
            return (l.score < r.score) || (l.score == r.score && l.left > r.left);
        }
    };
    using queue_storage = std::vector<llm_bigram_spm>;
    using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
    llm_symbol::index left;
    llm_symbol::index right;
    float score;
    size_t size;
};

struct llm_tokenizer_spm {
    llm_tokenizer_spm(const llama_vocab & vocab): vocab(vocab) {}

    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
        // split string into utf8 chars
        int index = 0;
        size_t offs = 0;
        while (offs < text.size()) {
            llm_symbol sym;
            size_t len = utf8_len(text[offs]);
            sym.text = text.c_str() + offs;
            sym.n = std::min(len, text.size() - offs);
            offs += sym.n;
            sym.prev = index - 1;
            sym.next = offs == text.size() ? -1 : index + 1;
            index++;
            symbols.emplace_back(sym);
        }

        // seed the work queue with all possible 2-character tokens.
        for (size_t i = 1; i < symbols.size(); ++i) {
            try_add_bigram(i - 1, i);
        }

        // keep substituting the highest frequency pairs for as long as we can.
        while (!work_queue.empty()) {
            auto bigram = work_queue.top();
            work_queue.pop();

            auto & left_sym = symbols[bigram.left];
            auto & right_sym = symbols[bigram.right];

            // if one of the symbols already got merged, skip it.
            if (left_sym.n == 0 || right_sym.n == 0 ||
                left_sym.n + right_sym.n != bigram.size) {
                continue;
            }

            // merge the right sym into the left one
            left_sym.n += right_sym.n;
            right_sym.n = 0;

            //LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);

            // remove the right sym from the chain
            left_sym.next = right_sym.next;
            if (right_sym.next >= 0) {
                symbols[right_sym.next].prev = bigram.left;
            }

            // find more substitutions
            try_add_bigram(left_sym.prev, bigram.left);
            try_add_bigram(bigram.left, left_sym.next);
        }

        for (int i = 0; i != -1; i = symbols[i].next) {
            auto & symbol = symbols[i];
            resegment(symbol, output);
        }
    }

private:
    void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
        auto text = std::string(symbol.text, symbol.n);
        auto token = vocab.token_to_id.find(text);

        // Do we need to support is_unused?
        if (token != vocab.token_to_id.end()) {
            output.push_back((*token).second);
            return;
        }

        const auto p = rev_merge.find(text);

        if (p == rev_merge.end()) {
            // output any symbols that did not form tokens as bytes.
            for (int j = 0; j < (int)symbol.n; ++j) {
                llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
                output.push_back(token_id);
            }
            return;
        }

        resegment(symbols[p->second.first],  output);
        resegment(symbols[p->second.second], output);
    }

    void try_add_bigram(int left, int right) {
        if (left == -1 || right == -1) {
            return;
        }

        const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
        auto token = vocab.token_to_id.find(text);

        if (token == vocab.token_to_id.end()) {
            return;
        }

        if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
            return;
        }

        const auto & tok_data = vocab.id_to_token[(*token).second];

        llm_bigram_spm bigram;
        bigram.left  = left;
        bigram.right = right;
        bigram.score = tok_data.score;
        bigram.size  = text.size();

        work_queue.push(bigram);

        // Do we need to support is_unused?
        rev_merge[text] = std::make_pair(left, right);
    }

    const llama_vocab & vocab;

    std::vector<llm_symbol> symbols;
    llm_bigram_spm::queue work_queue;

    std::map<std::string, std::pair<int, int>> rev_merge;
};

// BPE tokenizer
// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
// tried to simplify unicode stuff, so most likely does not work 100% correctly!

// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused

struct llm_bigram_bpe {
    struct comparator {
        bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
            return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
        }
    };

    using queue_storage = std::vector<llm_bigram_bpe>;
    using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
    llm_symbol::index left;
    llm_symbol::index right;
    std::string text;
    int rank;
    size_t size;
};

struct llm_tokenizer_bpe {
    llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}

    void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
        int final_prev_index = -1;
        auto word_collection = bpe_gpt2_preprocess(text);

        symbols_final.clear();

        for (auto & word : word_collection) {
            work_queue = llm_bigram_bpe::queue();
            symbols.clear();

            int index = 0;
            size_t offset = 0;

            while (offset < word.size()) {
                llm_symbol sym;
                size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
                sym.text = word.c_str() + offset;
                sym.n = char_len;
                offset += sym.n;
                sym.prev = index - 1;
                sym.next = offset == word.size() ? -1 : index + 1;
                index++;
                symbols.emplace_back(sym);
            }
            for (size_t i = 1; i < symbols.size(); ++i) {
                add_new_bigram(i - 1, i);
            }

            // build token(s)
            while (!work_queue.empty()) {
                auto bigram = work_queue.top();
                work_queue.pop();

                auto & left_symbol = symbols[bigram.left];
                auto & right_symbol = symbols[bigram.right];

                if (left_symbol.n == 0 || right_symbol.n == 0) {
                    continue;
                }
                std::string left_token = std::string(left_symbol.text, left_symbol.n);
                std::string right_token = std::string(right_symbol.text, right_symbol.n);
                if (left_token + right_token != bigram.text) {
                    continue;  // Skip this bigram if it's outdated
                }

                // merge the right sym into the left one
                left_symbol.n += right_symbol.n;
                right_symbol.n = 0;

                // remove the right sym from the chain
                left_symbol.next = right_symbol.next;
                if (right_symbol.next >= 0) {
                    symbols[right_symbol.next].prev = bigram.left;
                }

                add_new_bigram(left_symbol.prev, bigram.left);  // left side of current symbol
                add_new_bigram(bigram.left, left_symbol.next);  // right side of current symbol
            }

            // add the fnished tokens to the final list keeping correct order for next and prev
            for (auto & sym : symbols) {
                if (sym.n > 0) {
                    sym.prev = final_prev_index;
                    sym.next = -1;
                    if (final_prev_index != -1) {
                        symbols_final[final_prev_index].next = symbols_final.size();
                    }
                    symbols_final.emplace_back(sym);
                    final_prev_index = symbols_final.size() - 1;
                }
            }
        }

        symbols = symbols_final;

        if (!symbols.empty()) {
            for (int i = 0; i != -1; i = symbols[i].next) {
                auto & symbol = symbols[i];
                if (symbol.n == 0) {
                    continue;
                }

                const std::string str = std::string(symbol.text, symbol.n);
                const auto token = vocab.token_to_id.find(str);

                if (token == vocab.token_to_id.end()) {
                    for (auto j = str.begin(); j != str.end(); ++j) {
                        std::string byte_str(1, *j);
                        auto token_multibyte = vocab.token_to_id.find(byte_str);
                        if (token_multibyte == vocab.token_to_id.end()) {
                            throw std::runtime_error("ERROR: byte not found in vocab");
                        }
                        output.push_back((*token_multibyte).second);
                    }
                } else {
                    output.push_back((*token).second);
                }
            }
        }
    }

private:
    void add_new_bigram(int left, int right) {
        if (left == -1 || right == -1) {
            return;
        }

        std::string left_token  = std::string(symbols[left].text,  symbols[left].n);
        std::string right_token = std::string(symbols[right].text, symbols[right].n);

        int rank_found = -1;

        rank_found = vocab.find_bpe_rank(left_token, right_token);

        if (rank_found < 0) {
            return;
        }

        llm_bigram_bpe bigram;

        bigram.left  = left;
        bigram.right = right;
        bigram.text  = left_token + right_token;
        bigram.size  = left_token.size() + right_token.size();
        bigram.rank  = rank_found;

        work_queue.push(bigram);
    }

    std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
        std::vector<std::string> bpe_words;
        std::vector<std::string> bpe_encoded_words;

        std::string token = "";
        // GPT2 system regex:  's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
        bool collecting_numeric = false;
        bool collecting_letter = false;
        bool collecting_special = false;
        bool collecting_whitespace_lookahead = false;
        bool collecting = false;

        std::vector<std::string> text_utf;
        text_utf.reserve(text.size());
        bpe_words.reserve(text.size());
        bpe_encoded_words.reserve(text.size());

        auto cps = codepoints_from_utf8(text);
        for (size_t i = 0; i < cps.size(); ++i)
            text_utf.emplace_back(codepoint_to_utf8(cps[i]));

        for (int i = 0; i < (int)text_utf.size(); i++) {
            const std::string & utf_char = text_utf[i];
            bool split_condition = false;
            int bytes_remain = text_utf.size() - i;
            // forward backward lookups
            const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : "";
            const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : "";

            // handling contractions
            if (!split_condition && bytes_remain >= 2) {
                // 's|'t|'m|'d
                if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) {
                    split_condition = true;
                }
                if (split_condition) {
                    if (token.size()) {
                        bpe_words.emplace_back(token); // push previous content as token
                    }
                    token = utf_char + utf_char_next;
                    bpe_words.emplace_back(token);
                    token = "";
                    i++;
                    continue;
                }
            }
            if (!split_condition && bytes_remain >= 3) {
                // 're|'ve|'ll
                if (utf_char == "\'" && (
                    (utf_char_next == "r" && utf_char_next_next == "e") ||
                    (utf_char_next == "v" && utf_char_next_next == "e") ||
                    (utf_char_next == "l" && utf_char_next_next == "l"))
                    ) {
                    split_condition = true;
                }
                if (split_condition) {
                    // current token + next token can be defined
                    if (token.size()) {
                        bpe_words.emplace_back(token); // push previous content as token
                    }
                    token = utf_char + utf_char_next + utf_char_next_next;
                    bpe_words.emplace_back(token); // the contraction
                    token = "";
                    i += 2;
                    continue;
                }
            }

            if (!split_condition && !collecting) {
                if (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
                    collecting_letter = true;
                    collecting = true;
                }
                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
                    collecting_numeric = true;
                    collecting = true;
                }
                else if (
                    ((codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (codepoint_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
                    (!token.size() && utf_char == " " && codepoint_type(utf_char_next) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
                    ) {
                    collecting_special = true;
                    collecting = true;
                }
                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && codepoint_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
                    collecting_whitespace_lookahead = true;
                    collecting = true;
                }
                else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
                    split_condition = true;
                }
            }
            else if (!split_condition && collecting) {
                if (collecting_letter && codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER) {
                    split_condition = true;
                }
                else if (collecting_numeric && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
                    split_condition = true;
                }
                else if (collecting_special && (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
                    split_condition = true;
                }
                else if (collecting_whitespace_lookahead && (codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
                    split_condition = true;
                }
            }

            if (utf_char_next == "") {
                split_condition = true; // final
                token += utf_char;
            }

            if (split_condition) {
                if (token.size()) {
                    bpe_words.emplace_back(token);
                }
                token = utf_char;
                collecting = false;
                collecting_letter = false;
                collecting_numeric = false;
                collecting_special = false;
                collecting_whitespace_lookahead = false;
            }
            else {
                token += utf_char;
            }
        }

        for (std::string & word : bpe_words) {
            std::string encoded_token = "";
            for (char & c : word) {
                encoded_token += bytes_to_unicode_bpe(c);
            }
            bpe_encoded_words.emplace_back(encoded_token);
        }

        return bpe_encoded_words;
    }

    const llama_vocab & vocab;

    std::vector<llm_symbol> symbols;
    std::vector<llm_symbol> symbols_final;

    llm_bigram_bpe::queue work_queue;
};

typedef enum FRAGMENT_BUFFER_VARIANT_TYPE{
    FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
    FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
} FRAGMENT_BUFFER_VARIANT_TYPE;

struct fragment_buffer_variant{
    fragment_buffer_variant(llama_vocab::id _token)
    :
        type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
        token(_token),
        raw_text(_dummy),
        offset(0),
        length(0){}
    fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
    :
        type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
        token((llama_vocab::id)-1),
        raw_text(_raw_text),
        offset(_offset),
        length(_length){
            GGML_ASSERT( _offset >= 0 );
            GGML_ASSERT( _length >= 1 );
            GGML_ASSERT( offset + length <= raw_text.length() );
        }

    const FRAGMENT_BUFFER_VARIANT_TYPE type;
    const llama_vocab::id token;
    const std::string _dummy;
    const std::string & raw_text;
    const uint64_t offset;
    const uint64_t length;
};

// #define PRETOKENIZERDEBUG

static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer)
{
    // for each special token
    for (const auto & st: vocab.special_tokens_cache) {
        const auto & special_token = st.first;
        const auto & special_id    = st.second;

        // for each text fragment
        std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
        while (it != buffer.end()) {
            auto & fragment = (*it);

            // if a fragment is text ( not yet processed )
            if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
                auto * raw_text = &(fragment.raw_text);

                auto raw_text_base_offset = fragment.offset;
                auto raw_text_base_length = fragment.length;

                // loop over the text
                while (true) {
                    // find the first occurence of a given special token in this fragment
                    //  passing offset argument only limit the "search area" but match coordinates
                    //  are still relative to the source full raw_text
                    auto match = raw_text->find(special_token, raw_text_base_offset);

                    // no occurences found, stop processing this fragment for a given special token
                    if (match == std::string::npos) break;

                    // check if match is within bounds of offset <-> length
                    if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;

#ifdef PRETOKENIZERDEBUG
                    fprintf(stderr, "FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
#endif
                    auto source = std::distance(buffer.begin(), it);

                    // if match is further than base offset
                    //  then we have some text to the left of it
                    if (match > raw_text_base_offset) {
                        // left
                        const int64_t left_reminder_offset = raw_text_base_offset + 0;
                        const int64_t left_reminder_length = match - raw_text_base_offset;
                        buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);

#ifdef PRETOKENIZERDEBUG
                        fprintf(stderr, "FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
#endif
                        it++;
                    }

                    // special token
                    buffer.emplace_after(it, special_id);
                    it++;

                    // right
                    if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
                        const int64_t right_reminder_offset = match + special_token.length();
                        const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
                        buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);

#ifdef PRETOKENIZERDEBUG
                        fprintf(stderr, "FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
#endif

                        it++;

                        if (source == 0) {
                            buffer.erase_after(buffer.before_begin());
                        } else {
                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
                        }

                        // repeat for the right side
                        raw_text_base_offset = right_reminder_offset;
                        raw_text_base_length = right_reminder_length;

#ifdef PRETOKENIZERDEBUG
                        fprintf(stderr, "RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
#endif
                    } else {
                        if (source == 0) {
                            buffer.erase_after(buffer.before_begin());
                        } else {
                            buffer.erase_after(std::next(buffer.begin(), (source-1)));
                        }
                        break;
                    }
                }
            }
            it++;
        }
    }
}

static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) {
    std::vector<llama_vocab::id> output;

    // OG tokenizer behavior:
    //
    // tokenizer.encode('', add_bos=True)  returns [1]
    // tokenizer.encode('', add_bos=False) returns []

    if (bos && vocab.special_bos_id != -1) {
        output.push_back(vocab.special_bos_id);
    }

    if (raw_text.empty()) {
        return output;
    }

    std::forward_list<fragment_buffer_variant> fragment_buffer;
    fragment_buffer.emplace_front( raw_text, 0, raw_text.length() );

    if (special) tokenizer_st_partition( vocab, fragment_buffer );

    switch (vocab.type) {
        case LLAMA_VOCAB_TYPE_SPM:
            {
                for (const auto & fragment: fragment_buffer)
                {
                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
                    {
                        // without adding this leading whitespace, we do not get the same results as the original tokenizer

                        // TODO: It's likely possible to get rid of this string copy entirely
                        //  by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
                        //  and passing 'add space prefix' as bool argument
                        //
                        auto raw_text = (special ? "" : " ") + fragment.raw_text.substr(fragment.offset, fragment.length);

#ifdef PRETOKENIZERDEBUG
                        fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
                        llm_tokenizer_spm tokenizer(vocab);
                        llama_escape_whitespace(raw_text);
                        tokenizer.tokenize(raw_text, output);
                    }
                    else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                    {
                        output.push_back(fragment.token);
                    }
                }
            } break;
        case LLAMA_VOCAB_TYPE_BPE:
            {
                for (const auto & fragment: fragment_buffer)
                {
                    if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT)
                    {
                        auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);

#ifdef PRETOKENIZERDEBUG
                        fprintf(stderr,"TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
                        llm_tokenizer_bpe tokenizer(vocab);
                        tokenizer.tokenize(raw_text, output);
                    }
                    else // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                    {
                        output.push_back(fragment.token);
                    }
                }
            } break;
    }

    return output;
}

//
// grammar - internal
//

struct llama_partial_utf8 {
    uint32_t value;    // bit value so far (unshifted)
    int      n_remain; // num bytes remaining; -1 indicates invalid sequence
};

struct llama_grammar {
    const std::vector<std::vector<llama_grammar_element>>   rules;
    std::vector<std::vector<const llama_grammar_element *>> stacks;

    // buffer for partially generated UTF-8 sequence from accepted tokens
    llama_partial_utf8                                      partial_utf8;
};

struct llama_grammar_candidate {
    size_t               index;
    const uint32_t     * code_points;
    llama_partial_utf8   partial_utf8;
};

// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
        const char         * src,
        llama_partial_utf8   partial_start) {
    static const int      lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
    const char          * pos      = src;
    std::vector<uint32_t> code_points;
    uint32_t              value    = partial_start.value;
    int                   n_remain = partial_start.n_remain;

    // continue previous decode, if applicable
    while (*pos != 0 && n_remain > 0) {
        uint8_t next_byte = static_cast<uint8_t>(*pos);
        if ((next_byte >> 6) != 2) {
            // invalid sequence, abort
            code_points.push_back(0);
            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
        }
        value = (value << 6) + (next_byte & 0x3F);
        ++pos;
        --n_remain;
    }

    if (partial_start.n_remain > 0 && n_remain == 0) {
        code_points.push_back(value);
    }

    // decode any subsequent utf-8 sequences, which may end in an incomplete one
    while (*pos != 0) {
        uint8_t  first_byte = static_cast<uint8_t>(*pos);
        uint8_t  highbits   = first_byte >> 4;
                 n_remain   = lookup[highbits] - 1;

        if (n_remain < 0) {
            // invalid sequence, abort
            code_points.clear();
            code_points.push_back(0);
            return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
        }

        uint8_t  mask       = (1 << (7 - n_remain)) - 1;
                 value      = first_byte & mask;
        ++pos;
        while (*pos != 0 && n_remain > 0) {
            value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
            ++pos;
            --n_remain;
        }
        if (n_remain == 0) {
            code_points.push_back(value);
        }
    }
    code_points.push_back(0);

    return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
}

// returns true iff pos points to the end of one of the definitions of a rule
static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
    switch (pos->type) {
        case LLAMA_GRETYPE_END: return true;  // NOLINT
        case LLAMA_GRETYPE_ALT: return true;  // NOLINT
        default:                return false;
    }
}

// returns true iff chr satisfies the char range at pos (regular or inverse range)
// asserts that pos is pointing to a char range element
static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
        const llama_grammar_element * pos,
        const uint32_t                chr) {

    bool found            = false;
    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;

    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT

    do {
        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
            // inclusive range, e.g. [a-z]
            found = found || (pos->value <= chr && chr <= pos[1].value);
            pos += 2;
        } else {
            // exact char match, e.g. [a] or "a"
            found = found || pos->value == chr;
            pos += 1;
        }
    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);

    return std::make_pair(found == is_positive_char, pos);
}

// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
// range at pos (regular or inverse range)
// asserts that pos is pointing to a char range element
static bool llama_grammar_match_partial_char(
        const llama_grammar_element * pos,
        const llama_partial_utf8      partial_utf8) {

    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
    GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);

    uint32_t partial_value = partial_utf8.value;
    int      n_remain      = partial_utf8.n_remain;

    // invalid sequence or 7-bit char split across 2 bytes (overlong)
    if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
        return false;
    }

    // range of possible code points this partial UTF-8 sequence could complete to
    uint32_t low  = partial_value << (n_remain * 6);
    uint32_t high = low | ((1 << (n_remain * 6)) - 1);

    if (low == 0) {
        if (n_remain == 2) {
            low = 1 << 11;
        } else if (n_remain == 3) {
            low = 1 << 16;
        }
    }

    do {
        if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
            // inclusive range, e.g. [a-z]
            if (pos->value <= high && low <= pos[1].value) {
                return is_positive_char;
            }
            pos += 2;
        } else {
            // exact char match, e.g. [a] or "a"
            if (low <= pos->value && pos->value <= high) {
                return is_positive_char;
            }
            pos += 1;
        }
    } while (pos->type == LLAMA_GRETYPE_CHAR_ALT);

    return !is_positive_char;
}


// transforms a grammar pushdown stack into N possible stacks, all ending
// at a character range (terminal element)
static void llama_grammar_advance_stack(
        const std::vector<std::vector<llama_grammar_element>>   & rules,
        const std::vector<const llama_grammar_element *>        & stack,
        std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {

    if (stack.empty()) {
        new_stacks.emplace_back(stack);
        return;
    }

    const llama_grammar_element * pos = stack.back();

    switch (pos->type) {
        case LLAMA_GRETYPE_RULE_REF: {
            const size_t                  rule_id = static_cast<size_t>(pos->value);
            const llama_grammar_element * subpos  = rules[rule_id].data();
            do {
                // init new stack without the top (pos)
                std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
                    // if this rule ref is followed by another element, add that to stack
                    new_stack.push_back(pos + 1);
                }
                if (!llama_grammar_is_end_of_sequence(subpos)) {
                    // if alternate is nonempty, add to stack
                    new_stack.push_back(subpos);
                }
                llama_grammar_advance_stack(rules, new_stack, new_stacks);
                while (!llama_grammar_is_end_of_sequence(subpos)) {
                    // scan to end of alternate def
                    subpos++;
                }
                if (subpos->type == LLAMA_GRETYPE_ALT) {
                    // there's another alternate def of this rule to process
                    subpos++;
                } else {
                    break;
                }
            } while (true);
            break;
        }
        case LLAMA_GRETYPE_CHAR:
        case LLAMA_GRETYPE_CHAR_NOT:
            new_stacks.emplace_back(stack);
            break;
        default:
            // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
            // (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
            // those
            GGML_ASSERT(false);
    }
}

// takes a set of possible pushdown stacks on a grammar, which are required to
// be positioned at a character range (see `llama_grammar_advance_stack`), and
// produces the N possible stacks if the given char is accepted at those
// positions
static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
        const std::vector<std::vector<llama_grammar_element>>         & rules,
        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
        const uint32_t                                                  chr) {

    std::vector<std::vector<const llama_grammar_element *>> new_stacks;

    for (const auto & stack : stacks) {
        if (stack.empty()) {
            continue;
        }

        auto match = llama_grammar_match_char(stack.back(), chr);
        if (match.first) {
            const llama_grammar_element * pos = match.second;

            // update top of stack to next element, if any
            std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
            if (!llama_grammar_is_end_of_sequence(pos)) {
                new_stack.push_back(pos);
            }
            llama_grammar_advance_stack(rules, new_stack, new_stacks);
        }
    }

    return new_stacks;
}

static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
        const std::vector<std::vector<llama_grammar_element>>         & rules,
        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
        const std::vector<llama_grammar_candidate>                    & candidates);

static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
        const std::vector<std::vector<llama_grammar_element>> & rules,
        const std::vector<const llama_grammar_element *>      & stack,
        const std::vector<llama_grammar_candidate>            & candidates) {

    std::vector<llama_grammar_candidate> rejects;

    if (stack.empty()) {
        for (const auto & tok : candidates) {
            if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
                rejects.push_back(tok);
            }
        }
        return rejects;
    }

    const llama_grammar_element * stack_pos = stack.back();

    std::vector<llama_grammar_candidate> next_candidates;
    for (const auto & tok : candidates) {
        if (*tok.code_points == 0) {
            // reached end of full codepoints in token, reject iff it ended in a partial sequence
            // that cannot satisfy this position in grammar
            if (tok.partial_utf8.n_remain != 0 &&
                    !llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
                rejects.push_back(tok);
            }
        } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
        } else {
            rejects.push_back(tok);
        }
    }

    const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;

    // update top of stack to next element, if any
    std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
    if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
        stack_after.push_back(stack_pos_after);
    }
    std::vector<std::vector<const llama_grammar_element *>> next_stacks;
    llama_grammar_advance_stack(rules, stack_after, next_stacks);

    auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
    for (const auto & tok : next_rejects) {
        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
    }

    return rejects;
}

static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
        const std::vector<std::vector<llama_grammar_element>>         & rules,
        const std::vector<std::vector<const llama_grammar_element *>> & stacks,
        const std::vector<llama_grammar_candidate>                    & candidates) {
    GGML_ASSERT(!stacks.empty()); // REVIEW

    if (candidates.empty()) {
        return std::vector<llama_grammar_candidate>();
    }

    auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);

    for (size_t i = 1, size = stacks.size(); i < size; ++i) {
        rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
    }
    return rejects;
}

//
// grammar - external
//

struct llama_grammar * llama_grammar_init(
            const llama_grammar_element ** rules,
                                 size_t    n_rules,
                                 size_t    start_rule_index) {
    const llama_grammar_element * pos;

    // copy rule definitions into vectors
    std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
    for (size_t i = 0; i < n_rules; i++) {
        for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
            vec_rules[i].push_back(*pos);
        }
        vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
    }

    // loop over alternates of start rule to build initial stacks
    std::vector<std::vector<const llama_grammar_element *>> stacks;
    pos = rules[start_rule_index];
    do {
        std::vector<const llama_grammar_element *> stack;
        if (!llama_grammar_is_end_of_sequence(pos)) {
            // if alternate is nonempty, add to stack
            stack.push_back(pos);
        }
        llama_grammar_advance_stack(vec_rules, stack, stacks);
        while (!llama_grammar_is_end_of_sequence(pos)) {
            // scan to end of alternate def
            pos++;
        }
        if (pos->type == LLAMA_GRETYPE_ALT) {
            // there's another alternate def of this rule to process
            pos++;
        } else {
            break;
        }
    } while (true);

    return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
}

void llama_grammar_free(struct llama_grammar * grammar) {
    delete grammar;
}

struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
    llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };

    // redirect elements in stacks to point to new rules
    for (size_t is = 0; is < result->stacks.size(); is++) {
        for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
            for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
                for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
                    if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
                         result->stacks[is][ie]  =  &result->rules[ir0][ir1];
                    }
                }
            }
        }
    }

    return result;
}

//
// sampling
//

void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
    if (seed == LLAMA_DEFAULT_SEED) {
        seed = time(NULL);
    }
    ctx->rng.seed(seed);
}

void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
    GGML_ASSERT(candidates->size > 0);

    const int64_t t_start_sample_us = ggml_time_us();

    // Sort the logits in descending order
    if (!candidates->sorted) {
        std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
            return a.logit > b.logit;
        });
        candidates->sorted = true;
    }

    float max_l = candidates->data[0].logit;
    float cum_sum = 0.0f;
    for (size_t i = 0; i < candidates->size; ++i) {
        float p = expf(candidates->data[i].logit - max_l);
        candidates->data[i].p = p;
        cum_sum += p;
    }
    for (size_t i = 0; i < candidates->size; ++i) {
        candidates->data[i].p /= cum_sum;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
    const int64_t t_start_sample_us = ggml_time_us();

    k = std::max(k, (int) min_keep);
    k = std::min(k, (int) candidates->size);

    // Sort scores in descending order
    if (!candidates->sorted) {
        auto comp = [](const llama_token_data & a, const llama_token_data & b) {
            return a.logit > b.logit;
        };
        if (k == (int) candidates->size) {
            std::sort(candidates->data, candidates->data + candidates->size, comp);
        } else {
            std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
        }
        candidates->sorted = true;
    }
    candidates->size = k;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
    if (p >= 1.0f) {
        return;
    }

    llama_sample_softmax(ctx, candidates);

    const int64_t t_start_sample_us = ggml_time_us();

    // Compute the cumulative probabilities
    float cum_sum = 0.0f;
    size_t last_idx = candidates->size;

    for (size_t i = 0; i < candidates->size; ++i) {
        cum_sum += candidates->data[i].p;

        // Check if the running sum is at least p or if we have kept at least min_keep tokens
        // we set the last index to i+1 to indicate that the current iterate should be included in the set
        if (cum_sum >= p && i + 1 >= min_keep) {
            last_idx = i + 1;
            break;
        }
    }

    // Resize the output vector to keep only the top-p tokens
    candidates->size = last_idx;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
    if (p <= 0.0f || !candidates->size) {
        return;
    }

    llama_sample_softmax(ctx, candidates);

    const int64_t t_start_sample_us = ggml_time_us();

    float scale = candidates->data[0].p; // scale by max prob
    size_t i = 1; // first token always matches

    for (; i < candidates->size; ++i) {
        if (candidates->data[i].p < p * scale && i >= min_keep) {
            break; // prob too small
        }
    }

    // Resize the output vector to keep only the matching tokens
    candidates->size = i;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
    if (z >= 1.0f || candidates->size <= 2) {
        return;
    }

    llama_sample_softmax(nullptr, candidates);
    const int64_t t_start_sample_us = ggml_time_us();

    // Compute the first and second derivatives
    std::vector<float> first_derivatives(candidates->size - 1);
    std::vector<float> second_derivatives(candidates->size - 2);

    for (size_t i = 0; i < first_derivatives.size(); ++i) {
        first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
    }
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
    }

    // Calculate absolute value of second derivatives
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        second_derivatives[i] = std::abs(second_derivatives[i]);
    }

    // Normalize the second derivatives
    {
        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);

        if (second_derivatives_sum > 1e-6f) {
            for (float & value : second_derivatives) {
                value /= second_derivatives_sum;
            }
        } else {
            for (float & value : second_derivatives) {
                value = 1.0f / second_derivatives.size();
            }
        }
    }

    float cum_sum = 0.0f;
    size_t last_idx = candidates->size;
    for (size_t i = 0; i < second_derivatives.size(); ++i) {
        cum_sum += second_derivatives[i];

        // Check if the running sum is greater than z or if we have kept at least min_keep tokens
        if (cum_sum > z && i >= min_keep) {
            last_idx = i;
            break;
        }
    }

    // Resize the output vector to keep only the tokens above the tail location
    candidates->size = last_idx;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
    // Reference implementation:
    // https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
    if (p >= 1.0f) {
        return;
    }

    // Compute the softmax of logits and calculate entropy
    llama_sample_softmax(nullptr, candidates);

    const int64_t t_start_sample_us = ggml_time_us();

    float entropy = 0.0f;
    for (size_t i = 0; i < candidates->size; ++i) {
        entropy += -candidates->data[i].p * logf(candidates->data[i].p);
    }

    // Compute the absolute difference between negative log probability and entropy for each candidate
    std::vector<float> shifted_scores;
    for (size_t i = 0; i < candidates->size; ++i) {
        float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
        shifted_scores.push_back(shifted_score);
    }

    // Sort tokens based on the shifted_scores and their corresponding indices
    std::vector<size_t> indices(candidates->size);
    std::iota(indices.begin(), indices.end(), 0);

    std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
        return shifted_scores[a] < shifted_scores[b];
    });

    // Compute the cumulative probabilities
    float cum_sum = 0.0f;
    size_t last_idx = indices.size();

    for (size_t i = 0; i < indices.size(); ++i) {
        size_t idx = indices[i];
        cum_sum += candidates->data[idx].p;

        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
        if (cum_sum > p && i >= min_keep - 1) {
            last_idx = i + 1;
            break;
        }
    }

    // Resize the output vector to keep only the locally typical tokens
    std::vector<llama_token_data> new_candidates;
    for (size_t i = 0; i < last_idx; ++i) {
        size_t idx = indices[i];
        new_candidates.push_back(candidates->data[idx]);
    }

    // Replace the data in candidates with the new_candidates data
    std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
    candidates->size = new_candidates.size();

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
    const int64_t t_start_sample_us = ggml_time_us();

    for (size_t i = 0; i < candidates_p->size; ++i) {
        candidates_p->data[i].logit /= temp;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
    llama_sample_temp(ctx, candidates_p, temp);
}

void llama_sample_repetition_penalties(
            struct llama_context * ctx,
          llama_token_data_array * candidates,
               const llama_token * last_tokens,
                          size_t   penalty_last_n,
                           float   penalty_repeat,
                           float   penalty_freq,
                           float   penalty_present) {
    if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
        return;
    }

    const int64_t t_start_sample_us = ggml_time_us();

    // Create a frequency map to count occurrences of each token in last_tokens
    std::unordered_map<llama_token, int> token_count;
    for (size_t i = 0; i < penalty_last_n; ++i) {
        token_count[last_tokens[i]]++;
    }

    // Apply frequency and presence penalties to the candidates
    for (size_t i = 0; i < candidates->size; ++i) {
        const auto token_iter = token_count.find(candidates->data[i].id);
        if (token_iter == token_count.end()) {
            continue;
        }

        const int count = token_iter->second;

        // The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
        // This is common fix for this problem, which is to multiply by the penalty instead of dividing.
        if (candidates->data[i].logit <= 0) {
            candidates->data[i].logit *= penalty_repeat;
        } else {
            candidates->data[i].logit /= penalty_repeat;
        }

        candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
    }

    candidates->sorted = false;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
    GGML_ASSERT(ctx);
    const int64_t t_start_sample_us = ggml_time_us();

    bool allow_eos = false;
    for (const auto & stack : grammar->stacks) {
        if (stack.empty()) {
            allow_eos = true;
            break;
        }
    }

    const llama_token eos = llama_token_eos(&ctx->model);

    std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
    std::vector<llama_grammar_candidate>                              candidates_grammar;

    for (size_t i = 0; i < candidates->size; ++i) {
        const llama_token id    = candidates->data[i].id;
        const std::string piece = llama_token_to_piece(ctx, id);
        if (id == eos) {
            if (!allow_eos) {
                candidates->data[i].logit = -INFINITY;
            }
        } else if (piece.empty() || piece[0] == 0) {
            candidates->data[i].logit = -INFINITY;
        } else {
            candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8));
            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
        }
    }

    const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
    for (const auto & reject : rejects) {
        candidates->data[reject.index].logit = -INFINITY;
    }

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}

static void llama_log_softmax(float * array, size_t size) {
    float max_l = *std::max_element(array, array + size);
    float sum = 0.f;
    for (size_t i = 0; i < size; ++i) {
        float p = expf(array[i] - max_l);
        sum += p;
        array[i] = p;
    }

    for (size_t i = 0; i < size; ++i) {
        array[i] = logf(array[i] / sum);
    }
}

void llama_sample_classifier_free_guidance(
          struct llama_context * ctx,
        llama_token_data_array * candidates,
          struct llama_context * guidance_ctx,
                         float   scale) {
    int64_t t_start_sample_us = ggml_time_us();

    GGML_ASSERT(ctx);

    auto n_vocab = llama_n_vocab(llama_get_model(ctx));

    GGML_ASSERT(n_vocab == (int)candidates->size);
    GGML_ASSERT(!candidates->sorted);

    std::vector<float> logits_base;
    logits_base.reserve(candidates->size);
    for (size_t i = 0; i < candidates->size; ++i) {
        logits_base.push_back(candidates->data[i].logit);
    }
    llama_log_softmax(logits_base.data(), candidates->size);

    float* logits_guidance = llama_get_logits(guidance_ctx);
    llama_log_softmax(logits_guidance, n_vocab);

    for (int i = 0; i < n_vocab; ++i) {
        float logit_guidance = logits_guidance[i];
        float logit_base = logits_base[i];
        candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
}

llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
    GGML_ASSERT(ctx);

    auto N = float(llama_n_vocab(llama_get_model(ctx)));
    int64_t t_start_sample_us;
    t_start_sample_us = ggml_time_us();

    llama_sample_softmax(nullptr, candidates);

    // Estimate s_hat using the most probable m tokens
    float s_hat = 0.0;
    float sum_ti_bi = 0.0;
    float sum_ti_sq = 0.0;
    for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
        float t_i = logf(float(i + 2) / float(i + 1));
        float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
        sum_ti_bi += t_i * b_i;
        sum_ti_sq += t_i * t_i;
    }
    s_hat = sum_ti_bi / sum_ti_sq;

    // Compute k from the estimated s_hat and target surprise value
    float epsilon_hat = s_hat - 1;
    float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);

    // Sample the next word X using top-k sampling
    llama_sample_top_k(nullptr, candidates, int(k), 1);
    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
    llama_token X = llama_sample_token(ctx, candidates);
    t_start_sample_us = ggml_time_us();

    // Compute error as the difference between observed surprise and target surprise value
    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return candidate.id == X;
    }));
    float observed_surprise = -log2f(candidates->data[X_idx].p);
    float e = observed_surprise - tau;

    // Update mu using the learning rate and error
    *mu = *mu - eta * e;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
    return X;
}

llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
    int64_t t_start_sample_us;
    t_start_sample_us = ggml_time_us();

    llama_sample_softmax(ctx, candidates);

    // Truncate the words with surprise values greater than mu
    candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return -log2f(candidate.p) > *mu;
    }));

    if (candidates->size == 0) {
        candidates->size = 1;
    }

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }

    // Normalize the probabilities of the remaining words
    llama_sample_softmax(ctx, candidates);

    // Sample the next word X from the remaining words
    llama_token X = llama_sample_token(ctx, candidates);
    t_start_sample_us = ggml_time_us();

    // Compute error as the difference between observed surprise and target surprise value
    size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
        return candidate.id == X;
    }));
    float observed_surprise = -log2f(candidates->data[X_idx].p);
    float e = observed_surprise - tau;

    // Update mu using the learning rate and error
    *mu = *mu - eta * e;

    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    }
    return X;
}

llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
    const int64_t t_start_sample_us = ggml_time_us();

    // Find max element
    auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
        return a.logit < b.logit;
    });

    llama_token result = max_iter->id;
    if (ctx) {
        ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
        ctx->n_sample++;
    }
    return result;
}

llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
    GGML_ASSERT(ctx);

    const int64_t t_start_sample_us = ggml_time_us();
    llama_sample_softmax(nullptr, candidates);

    std::vector<float> probs;
    probs.reserve(candidates->size);
    for (size_t i = 0; i < candidates->size; ++i) {
        probs.push_back(candidates->data[i].p);
    }

    std::discrete_distribution<> dist(probs.begin(), probs.end());
    auto & rng = ctx->rng;
    int idx = dist(rng);

    llama_token result = candidates->data[idx].id;

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    ctx->n_sample++;
    return result;
}

void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
    const int64_t t_start_sample_us = ggml_time_us();

    if (token == llama_token_eos(&ctx->model)) {
        for (const auto & stack : grammar->stacks) {
            if (stack.empty()) {
                return;
            }
        }
        GGML_ASSERT(false);
    }

    const std::string piece = llama_token_to_piece(ctx, token);

    // Note terminating 0 in decoded string
    const auto   decoded     = decode_utf8(piece.c_str(), grammar->partial_utf8);
    const auto & code_points = decoded.first;
    for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
        grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
    }
    grammar->partial_utf8 = decoded.second;
    GGML_ASSERT(!grammar->stacks.empty());

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}

//
// Beam search
//

struct llama_beam {
    std::vector<llama_token> tokens;
    float p;  // Cumulative beam probability (renormalized relative to all beams)
    bool eob; // Initialize end-of-beam to false. Callback sets this to true.
    // Sort beams by probability. In case of ties, prefer beams at eob.
    bool operator<(const llama_beam & rhs) const {
        return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
    }
    // Shift off first n tokens and discard them.
    void shift_tokens(const size_t n) {
        if (n) {
            std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
            tokens.resize(tokens.size() - n);
        }
    }
    llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
};

// A struct for calculating logit-related info.
struct llama_logit_info {
    const float * const logits;
    const int n_vocab;
    const float max_l;
    const float normalizer;
    struct sum_exp {
        float max_l;
        float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
    };
    llama_logit_info(llama_context * ctx)
      : logits(llama_get_logits(ctx))
      , n_vocab(llama_n_vocab(llama_get_model(ctx)))
      , max_l(*std::max_element(logits, logits + n_vocab))
      , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
      { }
    llama_token_data get_token_data(const llama_token token_id) const {
        constexpr auto p = std::numeric_limits<float>::quiet_NaN();  // never used
        return {token_id, logits[token_id], p};
    }
    // Return top k token_data by logit.
    std::vector<llama_token_data> top_k(size_t k) {
        std::vector<llama_token_data> min_heap;  // min-heap by logit
        const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
        min_heap.reserve(k_min);
        for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
            min_heap.push_back(get_token_data(token_id));
        }
        auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
        std::make_heap(min_heap.begin(), min_heap.end(), comp);
        for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
            if (min_heap.front().logit < logits[token_id]) {
                std::pop_heap(min_heap.begin(), min_heap.end(), comp);
                min_heap.back().id = token_id;
                min_heap.back().logit = logits[token_id];
                std::push_heap(min_heap.begin(), min_heap.end(), comp);
            }
        }
        return min_heap;
    }
    float probability_from_logit(float logit) const {
        return normalizer * std::exp(logit - max_l);
    }
};

struct llama_beam_search_data {
    llama_context * ctx;
    size_t n_beams;
    int n_past;
    int n_predict;
    std::vector<llama_beam> beams;
    std::vector<llama_beam> next_beams;

    // Re-calculated on each loop iteration
    size_t common_prefix_length;

    // Used to communicate to/from callback on beams state.
    std::vector<llama_beam_view> beam_views;

    llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
      : ctx(ctx)
      , n_beams(n_beams)
      , n_past(n_past)
      , n_predict(n_predict)
      , beam_views(n_beams) {
        beams.reserve(n_beams);
        next_beams.reserve(n_beams);
    }

    // Collapse beams to a single beam given by index.
    void collapse_beams(const size_t beam_idx) {
        if (0u < beam_idx) {
            std::swap(beams[0], beams[beam_idx]);
        }
        beams.resize(1);
    }

    // Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
    // The repetative patterns below reflect the 2 stages of heaps:
    //  * Gather elements until the vector is full, then call std::make_heap() on it.
    //  * If the heap is full and a new element is found that should be included, pop the
    //    least element to the back(), replace it with the new, then push it into the heap.
    void fill_next_beams_by_top_probabilities(llama_beam & beam) {
        // Min-heaps use a greater-than comparator.
        const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
        if (beam.eob) {
            // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
            if (next_beams.size() < n_beams) {
                next_beams.push_back(std::move(beam));
                if (next_beams.size() == n_beams) {
                    std::make_heap(next_beams.begin(), next_beams.end(), comp);
                }
            } else if (next_beams.front().p < beam.p) {
                std::pop_heap(next_beams.begin(), next_beams.end(), comp);
                next_beams.back() = std::move(beam);
                std::push_heap(next_beams.begin(), next_beams.end(), comp);
            }
        } else {
            // beam is not at end-of-sentence, so branch with next top_k tokens.
            if (!beam.tokens.empty()) {
                llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
            }
            llama_logit_info logit_info(ctx);
            std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
            size_t i=0;
            if (next_beams.size() < n_beams) {
                for (; next_beams.size() < n_beams ; ++i) {
                    llama_beam next_beam = beam;
                    next_beam.tokens.push_back(next_tokens[i].id);
                    next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
                    next_beams.push_back(std::move(next_beam));
                }
                std::make_heap(next_beams.begin(), next_beams.end(), comp);
            } else {
                for (; next_beams.front().p == 0.0f ; ++i) {
                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
                    next_beams.back() = beam;
                    next_beams.back().tokens.push_back(next_tokens[i].id);
                    next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
                }
            }
            for (; i < n_beams ; ++i) {
                const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
                if (next_beams.front().p < next_p) {
                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
                    next_beams.back() = beam;
                    next_beams.back().tokens.push_back(next_tokens[i].id);
                    next_beams.back().p = next_p;
                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
                }
            }
        }
    }

    // Find common_prefix_length based on beams.
    // Requires beams is not empty.
    size_t find_common_prefix_length() {
        size_t common_prefix_length = beams[0].tokens.size();
        for (size_t i = 1 ; i < beams.size() ; ++i) {
            common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
            for (size_t j = 0 ; j < common_prefix_length ; ++j) {
                if (beams[0].tokens[j] != beams[i].tokens[j]) {
                    common_prefix_length = j;
                    break;
                }
            }
        }
        return common_prefix_length;
    }

    // Construct beams_state to send back to caller via the callback function.
    // Side effect: set common_prefix_length = find_common_prefix_length();
    llama_beams_state get_beams_state(const bool last_call) {
        for (size_t i = 0 ; i < beams.size() ; ++i) {
            beam_views[i] = beams[i].view();
        }
        common_prefix_length = find_common_prefix_length();
        return {beam_views.data(), beams.size(), common_prefix_length, last_call};
    }

    // Loop:
    //  * while i < n_predict, AND
    //  * any of the beams have not yet reached end-of-beam (eob), AND
    //  * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
    //    (since all other beam probabilities can only decrease)
    void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
        beams.push_back({{}, 1.0f, false});  // Start with one empty beam w/ probability = 1.0 and !eob.
        const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
        for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
                       !beams[top_beam_index()].eob ; ++i) {
            callback(callback_data, get_beams_state(false));  // Sets common_prefix_length
            update_beams_from_beam_views();   // Update values (p,eob) that callback may have changed.
            if (common_prefix_length) {
                llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
                n_past += common_prefix_length;
            }
            // Zero-out next_beam probabilities to place them last in following min-heap.
            std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
            for (llama_beam & beam : beams) {
                beam.shift_tokens(common_prefix_length);
                fill_next_beams_by_top_probabilities(beam);
            }
            // next_beams become the beams of next/final iteration. Swap them to re-use memory.
            beams.swap(next_beams);
            renormalize_beam_probabilities(beams);
        }
        collapse_beams(top_beam_index());
        callback(callback_data, get_beams_state(true));
    }

    // As beams grow, the cumulative probabilities decrease.
    // Renormalize them to avoid floating point underflow.
    static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
        const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
        const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
        std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
    }

    // Assumes beams is non-empty.  Uses llama_beam::operator<() for ordering.
    size_t top_beam_index() {
        return std::max_element(beams.begin(), beams.end()) - beams.begin();
    }

    // Copy (p,eob) for each beam which may have been changed by the callback.
    void update_beams_from_beam_views() {
        for (size_t i = 0 ; i < beams.size() ; ++i) {
            beams[i].p = beam_views[i].p;
            beams[i].eob = beam_views[i].eob;
        }
    }
};

void llama_beam_search(llama_context * ctx,
                       llama_beam_search_callback_fn_t callback, void * callback_data,
                       size_t n_beams, int n_past, int n_predict) {
    assert(ctx);
    const int64_t t_start_sample_us = ggml_time_us();

    llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);

    beam_search_data.loop(callback, callback_data);

    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
    ctx->n_sample++;
}

//
// quantization
//

template <typename T>
struct no_init {
    T value;
    no_init() { /* do nothing */ }
};

struct quantize_state_internal {
    const llama_model                 & model;
    const llama_model_quantize_params * params;

    int n_attention_wv    = 0;
    int n_feed_forward_w2 = 0;
    int i_attention_wv    = 0;
    int i_feed_forward_w2 = 0;

    int n_k_quantized     = 0;
    int n_fallback        = 0;

    quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
        : model(model)
        , params(params)
        {}
};

static void llama_convert_tensor_internal(
    struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
    const size_t nelements, const int nthread
) {
    if (output.size() < nelements) {
        output.resize(nelements);
    }
    float * f32_output = (float *) output.data();

    ggml_type_traits_t qtype;
    if (ggml_is_quantized(tensor->type)) {
        qtype = ggml_internal_get_type_traits(tensor->type);
        if (qtype.to_float == NULL) {
            throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
        }
    } else if (tensor->type != GGML_TYPE_F16) {
        throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
    }

    if (nthread < 2) {
        if (tensor->type == GGML_TYPE_F16) {
            ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
        } else if (ggml_is_quantized(tensor->type)) {
            qtype.to_float(tensor->data, f32_output, nelements);
        } else {
            GGML_ASSERT(false); // unreachable
        }
        return;
    }

    auto block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
    auto block_size_bytes = ggml_type_size(tensor->type);

    GGML_ASSERT(nelements % block_size == 0);
    auto nblocks = nelements / block_size;
    auto blocks_per_thread = nblocks / nthread;
    auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count

    for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
        auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
        auto thr_elems = thr_blocks * block_size; // number of elements for this thread
        auto thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread

        auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
            if (typ == GGML_TYPE_F16) {
                ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
            } else {
                qtype.to_float(inbuf, outbuf, nels);
            }
        };
        workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
        in_buff_offs += thr_block_bytes;
        out_buff_offs += thr_elems;
    }
    for (auto & w : workers) { w.join(); }
    workers.clear();
}

static ggml_type get_k_quant_type(
    quantize_state_internal & qs,
    ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype
) {
    const std::string name = ggml_get_name(tensor);
    // TODO: avoid hardcoded tensor names - use the TN_* constants
    const llm_arch arch = qs.model.arch;
    const auto       tn = LLM_TN(arch);

    auto use_more_bits = [](int i_layer, int num_layers) -> bool {
        return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
    };

    if (name == tn(LLM_TENSOR_OUTPUT, "weight").first) {
        int nx = tensor->ne[0];
        if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
            new_type = GGML_TYPE_Q8_0;
        }
        else if (new_type != GGML_TYPE_Q8_0) {
            new_type = GGML_TYPE_Q6_K;
        }
    } else if (name.find("attn_v.weight") != std::string::npos) {
        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
            new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
        else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
                use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
        else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
                (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
        if (qs.model.type == MODEL_70B) {
            // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
            // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
            // nearly negligible increase in model size by quantizing this tensor with more bits:
            if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
        }
        ++qs.i_attention_wv;
    } else if (name.find("ffn_down.weight") != std::string::npos) {
        if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
            new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
                     : arch != LLM_ARCH_FALCON || use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q4_K
                     : GGML_TYPE_Q3_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
            new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
            if (arch == LLM_ARCH_FALCON) {
                new_type = qs.i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
                           use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
            } else {
                if (use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
            }
        }
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(qs.i_feed_forward_w2, qs.n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && qs.i_feed_forward_w2 < 4) {
            new_type = GGML_TYPE_Q5_K;
        }
        ++qs.i_feed_forward_w2;
    } else if (name.find("attn_output.weight") != std::string::npos) {
        if (arch != LLM_ARCH_FALCON) {
            if      (ftype == LLAMA_FTYPE_MOSTLY_Q2_K  ) new_type = GGML_TYPE_Q3_K;
            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K;
            else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
        } else {
            if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
        }
    }
    else if (name.find("attn_qkv.weight") != std::string::npos) {
        if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
        else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
    }
    else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) {
        if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
    }
    else if (name.find("fc1.weight") != std::string::npos || name.find("fc2.weight") != std::string::npos) {
        if (ftype == LLAMA_FTYPE_MOSTLY_Q4_0) new_type = GGML_TYPE_Q4_0;
        else new_type = GGML_TYPE_Q5_0;
    }
    // This can be used to reduce the size of the Q5_K_S model.
    // The associated PPL increase is fully in line with the size reduction
    //else {
    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
    //}
    bool convert_incompatible_tensor = false;
    if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
        new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
        int nx = tensor->ne[0];
        int ny = tensor->ne[1];
        if (nx % QK_K != 0) {
            LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
            convert_incompatible_tensor = true;
        } else {
            ++qs.n_k_quantized;
        }
    }
    if (convert_incompatible_tensor) {
        switch (new_type) {
            case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break;
            case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break;
            case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
            case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
            case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
            default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
        }
        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
        ++qs.n_fallback;
    }

    return new_type;
}

static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
    ggml_type quantized_type;
    llama_ftype ftype = params->ftype;

    switch (params->ftype) {
        case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
        case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
        case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
        case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
        case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
        case LLAMA_FTYPE_MOSTLY_F16:  quantized_type = GGML_TYPE_F16;  break;
        case LLAMA_FTYPE_ALL_F32:     quantized_type = GGML_TYPE_F32;  break;

        // K-quants
        case LLAMA_FTYPE_MOSTLY_Q2_K:   quantized_type = GGML_TYPE_Q2_K; break;
        case LLAMA_FTYPE_MOSTLY_Q3_K_S:
        case LLAMA_FTYPE_MOSTLY_Q3_K_M:
        case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
        case LLAMA_FTYPE_MOSTLY_Q4_K_S:
        case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
        case LLAMA_FTYPE_MOSTLY_Q5_K_S:
        case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
        case LLAMA_FTYPE_MOSTLY_Q6_K:   quantized_type = GGML_TYPE_Q6_K; break;

        default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
    }

    int nthread = params->nthread;

    if (nthread <= 0) {
        nthread = std::thread::hardware_concurrency();
    }

    // mmap consistently increases speed Linux, and also increases speed on Windows with
    // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
#if defined(__linux__) || defined(_WIN32)
    constexpr bool use_mmap = true;
#else
    constexpr bool use_mmap = false;
#endif

    llama_model_loader ml(fname_inp, use_mmap);
    if (ml.use_mmap) {
        ml.mapping.reset(new llama_mmap(&ml.file, /* prefetch */ 0, ggml_is_numa()));
    }

    llama_model model;
    llm_load_arch(ml, model);
    llm_load_hparams(ml, model);

    struct quantize_state_internal qs(model, params);

    if (params->only_copy) {
        ftype = model.ftype;
    }

    const size_t align = GGUF_DEFAULT_ALIGNMENT;
    struct gguf_context * ctx_out = ml.sparse_deriv == GGML_SPARSE_INFERENCE ? gguf_init_empty_sparse() : gguf_init_empty();

    // copy the KV pairs from the input file
    gguf_set_kv     (ctx_out, ml.ctx_gguf);
    gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
    gguf_set_val_u32(ctx_out, "general.file_type", ftype);

    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * meta = ml.get_tensor_meta(i);

        const std::string name = ggml_get_name(meta);

        // TODO: avoid hardcoded tensor names - use the TN_* constants
        if (name.find("attn_v.weight") != std::string::npos || name.find("attn_qkv.weight") != std::string::npos) {
            ++qs.n_attention_wv;
        }
        else if (name.find("ffn_down.weight") != std::string::npos) {
            ++qs.n_feed_forward_w2;
        }
    }
    if (qs.n_attention_wv != qs.n_feed_forward_w2 || (uint32_t)qs.n_attention_wv != model.hparams.n_layer) {
        LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
                __func__, qs.n_attention_wv, qs.n_feed_forward_w2, model.hparams.n_layer);
    }

    size_t total_size_org = 0;
    size_t total_size_new = 0;
    std::vector<int64_t> hist_all(1 << 4, 0);

    std::vector<std::thread> workers;
    workers.reserve(nthread);
    std::mutex mutex;

    int idx = 0;

    std::vector<no_init<uint8_t>> read_data;
    std::vector<no_init<uint8_t>> work;
    std::vector<no_init<float>> f32_conv_buf;

    // populate the original tensors so we get an initial meta data
    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * meta = ml.get_tensor_meta(i);
        gguf_add_tensor(ctx_out, meta);
    }

    std::ofstream fout(fname_out, std::ios::binary);
    fout.exceptions(std::ofstream::failbit); // fail fast on write errors

    const size_t meta_size = gguf_get_meta_size(ctx_out);

    LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);

    // placeholder for the meta data
    ::zeros(fout, meta_size);

    for (int i = 0; i < ml.n_tensors; ++i) {
        struct ggml_tensor * tensor = ml.get_tensor_meta(i);

        const std::string name = ggml_get_name(tensor);

        if (!ml.use_mmap) {
            if (read_data.size() < ggml_nbytes(tensor)) {
                read_data.resize(ggml_nbytes(tensor));
            }
            tensor->data = read_data.data();
        }
        ml.load_data_for(tensor);

        LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
               ++idx, ml.n_tensors,
               ggml_get_name(tensor),
               llama_format_tensor_shape(tensor).c_str(),
               ggml_type_name(tensor->type));

        // This used to be a regex, but <regex> has an extreme cost to compile times.
        bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?

        // quantize only 2D tensors
        quantize &= (tensor->n_dims == 2);
        quantize &= params->quantize_output_tensor || name != "output.weight";
        quantize &= !params->only_copy;

        enum ggml_type new_type;
        void * new_data;
        size_t new_size;

        if (quantize) {
            new_type = quantized_type;
            if (!params->pure) {
                new_type = get_k_quant_type(qs, new_type, tensor, ftype);
            }

            // If we've decided to quantize to the same type the tensor is already
            // in then there's nothing to do.
            quantize = tensor->type != new_type;
        }
        if (!quantize) {
            new_type = tensor->type;
            new_data = tensor->data;
            new_size = ggml_nbytes(tensor);
            LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
        } else {
            const size_t nelements = ggml_nelements(tensor);

            float * f32_data;

            if (tensor->type == GGML_TYPE_F32) {
                f32_data = (float *) tensor->data;
            } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
                throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
            } else {
                llama_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread);
                f32_data = (float *) f32_conv_buf.data();
            }

            LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
            fflush(stdout);

            if (work.size() < nelements * 4) {
                work.resize(nelements * 4); // upper bound on size
            }
            new_data = work.data();
            std::array<int64_t, 1 << 4> hist_cur = {};

            static const int chunk_size = 32 * 512;
            const int nchunk = (nelements + chunk_size - 1)/chunk_size;
            const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
            if (nthread_use < 2) {
                new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
            } else {
                size_t counter = 0;
                new_size = 0;
                auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() {
                    std::array<int64_t, 1 << 4> local_hist = {};
                    size_t local_size = 0;
                    while (true) {
                        std::unique_lock<std::mutex> lock(mutex);
                        size_t first = counter; counter += chunk_size;
                        if (first >= nelements) {
                            if (local_size > 0) {
                                for (int j=0; j<int(local_hist.size()); ++j) {
                                    hist_cur[j] += local_hist[j];
                                }
                                new_size += local_size;
                            }
                            break;
                        }
                        lock.unlock();
                        size_t last = std::min(nelements, first + chunk_size);
                        local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
                    }
                };
                for (int it = 0; it < nthread_use - 1; ++it) {
                    workers.emplace_back(compute);
                }
                compute();
                for (auto & w : workers) { w.join(); }
                workers.clear();
            }

            LLAMA_LOG_INFO("size = %8.2f MB -> %8.2f MB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
            int64_t tot_count = 0;
            for (size_t i = 0; i < hist_cur.size(); i++) {
                hist_all[i] += hist_cur[i];
                tot_count += hist_cur[i];
            }

            if (tot_count > 0) {
                for (size_t i = 0; i < hist_cur.size(); i++) {
                    LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
                }
            }
            LLAMA_LOG_INFO("\n");
        }
        total_size_org += ggml_nbytes(tensor);
        total_size_new += new_size;

        // update the gguf meta data as we go
        gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
        gguf_set_tensor_data(ctx_out, name.c_str(), new_data, new_size);

        // write tensor data + padding
        fout.write((const char *) new_data, new_size);
        zeros(fout, GGML_PAD(new_size, align) - new_size);
    }

    // go back to beginning of file and write the updated meta data
    {
        fout.seekp(0);
        std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
        gguf_get_meta_data(ctx_out, data.data());
        fout.write((const char *) data.data(), data.size());
    }

    fout.close();

    gguf_free(ctx_out);

    LLAMA_LOG_INFO("%s: model size  = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
    LLAMA_LOG_INFO("%s: quant size  = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);

    // print histogram for all tensors
    {
        int64_t sum_all = 0;
        for (size_t i = 0; i < hist_all.size(); i++) {
            sum_all += hist_all[i];
        }

        if (sum_all > 0) {
            LLAMA_LOG_INFO("%s: hist: ", __func__);
            for (size_t i = 0; i < hist_all.size(); i++) {
                LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
            }
            LLAMA_LOG_INFO("\n");
        }
    }

    if (qs.n_fallback > 0) {
        LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
                __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
    }
}

static int llama_apply_lora_from_file_internal(
    const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
) {
    LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);

    const int64_t t_start_lora_us = ggml_time_us();

    auto fin = std::ifstream(path_lora, std::ios::binary);
    if (!fin) {
        LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
        return 1;
    }

    // verify magic and version
    {
        uint32_t magic;
        fin.read((char *) &magic, sizeof(magic));
        uint32_t format_version;
        fin.read((char *) &format_version, sizeof(format_version));

        if (format_version != 1) {
            LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
            return 1;
        }
    }

    int32_t lora_r;
    int32_t lora_alpha;
    fin.read((char *) &lora_r, sizeof(lora_r));
    fin.read((char *) &lora_alpha, sizeof(lora_alpha));
    float scaling = scale * (float)lora_alpha / (float)lora_r;

    LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);

    // create a temporary ggml context to store the lora tensors
    // todo: calculate size from biggest possible tensor
    std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
    struct ggml_init_params params;
    params.mem_size   = lora_buf.size();
    params.mem_buffer = lora_buf.data();
    params.no_alloc   = false;

    ggml_context * lora_ctx = ggml_init(params);
    std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;

    // create a name -> tensor map of the model to accelerate lookups
    std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
    for (const auto & kv : model.tensors_by_name) {
        model_tensors.insert(kv);
    }

    // load base model
    std::unique_ptr<llama_model_loader> ml;
    ggml_context * base_ctx = NULL;
    std::vector<uint8_t> base_buf;
    if (path_base_model) {
        LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
        ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));

        size_t ctx_size;
        size_t mmapped_size;
        ml->calc_sizes(ctx_size, mmapped_size);
        base_buf.resize(ctx_size);

        ggml_init_params base_params;
        base_params.mem_size   = base_buf.size();
        base_params.mem_buffer = base_buf.data();
        base_params.no_alloc   = ml->use_mmap;

        base_ctx = ggml_init(base_params);

        // maybe this should in llama_model_loader
        if (ml->use_mmap) {
            ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa()));
        }
    }

    // read tensors and apply
    bool warned = false;
    int n_tensors = 0;

    std::vector<uint8_t> work_buffer;

    while (true) {
        int32_t n_dims;
        int32_t length;
        int32_t ftype;

        fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
        fin.read(reinterpret_cast<char *>(&length), sizeof(length));
        fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
        if (fin.eof()) {
            break;
        }

        int32_t ne[2] = { 1, 1 };
        for (int i = 0; i < n_dims; ++i) {
            fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
        }

        std::string name;
        {
            char buf[1024];
            fin.read(buf, length);
            name = std::string(buf, length);
        }

        // check for lora suffix and get the type of tensor
        const std::string lora_suffix = ".lora";
        size_t pos = name.rfind(lora_suffix);
        if (pos == std::string::npos) {
            LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
            return 1;
        }

        std::string lora_type = name.substr(pos + lora_suffix.length());
        std::string base_name = name;
        base_name.erase(pos);
        // LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());

        if (model_tensors.find(base_name) == model_tensors.end()) {
            LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
            return 1;
        }

        // create ggml tensor
        ggml_type wtype;
        switch (ftype) {
            case 0: wtype = GGML_TYPE_F32;  break;
            case 1: wtype = GGML_TYPE_F16;  break;
            default:
                    {
                        LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
                                __func__, ftype);
                        return false;
                    }
        }
        ggml_tensor * lora_tensor;
        if (n_dims == 2) {
            lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
        }
        else {
            LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
            return 1;
        }
        ggml_set_name(lora_tensor, "lora_tensor");

        // load tensor data
        size_t offset = fin.tellg();
        size_t tensor_data_size = ggml_nbytes(lora_tensor);
        offset = (offset + 31) & -32;
        fin.seekg(offset);
        fin.read((char*)lora_tensor->data, tensor_data_size);

        lora_tensors[name] = lora_tensor;

        // check if we have both A and B tensors and apply
        if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
            lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {

            ggml_tensor * dest_t = model_tensors[base_name];

            offload_func_t offload_func               = ggml_offload_nop;
            offload_func_t offload_func_force_inplace = ggml_offload_nop;

#ifdef GGML_USE_CUBLAS
            if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
                if (dest_t->type != GGML_TYPE_F16) {
                    throw std::runtime_error(format(
                        "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models. dest_t->type: %d", __func__, dest_t->type));
                }
                offload_func = ggml_cuda_assign_buffers;
                offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
            }
#endif // GGML_USE_CUBLAS

            ggml_tensor * base_t;
            if (ml) {
                struct gguf_context * ctx_gguf = ml->ctx_gguf;

                // load from base model
                if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
                    // TODO: throw
                    LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
                    return 1;
                }

                // TODO: not tested!! maybe not working!
                base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
                ml->load_data_for(base_t);
            } else {
                base_t = dest_t;
            }

            if (ggml_is_quantized(base_t->type)) {
                if (!warned) {
                    LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
                                   "use a f16 or f32 base model with --lora-base\n", __func__);
                    warned = true;
                }
            }

            ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
            GGML_ASSERT(loraA->type == GGML_TYPE_F32);
            ggml_set_name(loraA, "loraA");

            ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
            GGML_ASSERT(loraB->type == GGML_TYPE_F32);
            ggml_set_name(loraB, "loraB");

            if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
                LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
                                " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
                return 1;
            }

            // w = w + BA*s
            ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
            offload_func(BA);
            ggml_set_name(BA, "BA");

            if (scaling != 1.0f) {
                ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
                ggml_set_name(scale_tensor, "scale_tensor");

                BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
                offload_func(BA);
                ggml_set_name(BA, "BA_scaled");
            }

            ggml_tensor * r;
            if (base_t == dest_t) {
                r = ggml_add_inplace(lora_ctx, dest_t, BA);
                offload_func_force_inplace(r);
                ggml_set_name(r, "r_add_inplace");
            }
            else {
                r = ggml_add(lora_ctx, base_t, BA);
                offload_func(r);
                ggml_set_name(r, "r_add");

                r = ggml_cpy(lora_ctx, r, dest_t);
                offload_func(r);
                ggml_set_name(r, "r_cpy");
            }

            struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
            ggml_build_forward_expand(gf, r);

            ggml_graph_compute_helper(work_buffer, gf, n_threads);

            // we won't need these tensors again, reset the context to save memory
            ggml_free(lora_ctx);
            lora_ctx = ggml_init(params);
            lora_tensors.clear();

            n_tensors++;
            if (n_tensors % 4 == 0) {
                LLAMA_LOG_INFO(".");
            }
        }
    }

    // TODO: this should be in a destructor, it will leak on failure
    ggml_free(lora_ctx);
    if (base_ctx) {
        ggml_free(base_ctx);
    }

    const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
    LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);

    return 0;
}

//
// interface implementation
//
struct llama_model_params llama_model_default_params() {
    struct llama_model_params result = {
        /*.n_gpu_layers                =*/ 0,
        /*.main_gpu                    =*/ 0,
        /*.vram_budget_gb              =*/ -1.0,
        /*.tensor_split                =*/ nullptr,
        /*.progress_callback           =*/ nullptr,
        /*.progress_callback_user_data =*/ nullptr,
        /*.vocab_only                  =*/ false,
        /*.use_mmap                    =*/ true,
        /*.use_mlock                   =*/ false,
    };

#ifdef GGML_USE_METAL
    result.n_gpu_layers = 1;
#endif

    return result;
}

struct llama_context_params llama_context_default_params() {
    struct llama_context_params result = {
        /*.seed                        =*/ LLAMA_DEFAULT_SEED,
        /*.n_ctx                       =*/ 512,
        /*.n_batch                     =*/ 512,
        /*.n_threads                   =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
        /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
        /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_UNSPECIFIED,
        /*.rope_freq_base              =*/ 0.0f,
        /*.rope_freq_scale             =*/ 0.0f,
        /*.yarn_ext_factor             =*/ -1.0f,
        /*.yarn_attn_factor            =*/ 1.0f,
        /*.yarn_beta_fast              =*/ 32.0f,
        /*.yarn_beta_slow              =*/ 1.0f,
        /*.yarn_orig_ctx               =*/ 0,
        /*.mul_mat_q                   =*/ true,
        /*.f16_kv                      =*/ true,
        /*.logits_all                  =*/ false,
        /*.embedding                   =*/ false,
    };

    return result;
}

struct llama_model_quantize_params llama_model_quantize_default_params() {
    struct llama_model_quantize_params result = {
        /*.nthread                     =*/ 0,
        /*.ftype                       =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
        /*.allow_requantize            =*/ false,
        /*.quantize_output_tensor      =*/ true,
        /*.only_copy                   =*/ false,
        /*.pure                        =*/ false,
    };

    return result;
}

int llama_max_devices(void) {
    return LLAMA_MAX_DEVICES;
}

bool llama_mmap_supported(void) {
    return llama_mmap::SUPPORTED;
}

bool llama_mlock_supported(void) {
    return llama_mlock::SUPPORTED;
}

void llama_backend_init(bool numa) {
    ggml_time_init();

    // needed to initialize f16 tables
    {
        struct ggml_init_params params = { 0, NULL, false };
        struct ggml_context * ctx = ggml_init(params);
        ggml_free(ctx);
    }

    if (numa) {
        ggml_numa_init();
    }

#ifdef GGML_USE_MPI
    ggml_mpi_backend_init();
#endif
}

void llama_backend_free(void) {
#ifdef GGML_USE_MPI
    ggml_mpi_backend_free();
#endif
}

int64_t llama_time_us(void) {
    return ggml_time_us();
}

struct llama_model * llama_load_model_from_file_with_context(
    const char * path_model,
    struct llama_model_params   params,
    struct llama_context_params * cparams
) {
    //===========rdma=========


    ggml_time_init();

    llama_model * model = new llama_model;

    unsigned cur_percentage = 0;
    if (params.progress_callback == NULL) {
        params.progress_callback_user_data = &cur_percentage;
        params.progress_callback = [](float progress, void * ctx) {
            unsigned * cur_percentage_p = (unsigned *) ctx;
            unsigned percentage = (unsigned) (100 * progress);
            while (percentage > *cur_percentage_p) {
                *cur_percentage_p = percentage;
                LLAMA_LOG_INFO(".");
                if (percentage >= 100) {
                    LLAMA_LOG_INFO("\n");
                }
            }
        };
    }

    if (!llama_model_load(path_model, *model, params, cparams)) {
        LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
        delete model;
        return nullptr;
    }

    return model;
}

struct llama_model * llama_load_model_from_file(
                             const char * path_model,
              struct llama_model_params   params) {
    return llama_load_model_from_file_with_context(path_model, params, nullptr);
}

void llama_free_model(struct llama_model * model) {
    delete model;
}

struct llama_context * llama_new_context_with_model(
                 struct llama_model * model,
        struct llama_context_params   params) {

    if (!model) {
        return nullptr;
    }

    llama_context * ctx = new llama_context(*model);

    const auto & hparams = model->hparams;
    auto       & cparams = ctx->cparams;

    cparams.n_batch          = params.n_batch;
    cparams.n_threads        = params.n_threads;
    cparams.n_threads_batch  = params.n_threads_batch;
    cparams.yarn_ext_factor  = params.yarn_ext_factor;
    cparams.yarn_attn_factor = params.yarn_attn_factor;
    cparams.yarn_beta_fast   = params.yarn_beta_fast;
    cparams.yarn_beta_slow   = params.yarn_beta_slow;
    cparams.mul_mat_q        = params.mul_mat_q;

    cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
    cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
    cparams.rope_freq_scale  = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;

    cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
                               hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
                                                              hparams.n_ctx_train;

    auto rope_scaling_type = params.rope_scaling_type;
    if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) {
        rope_scaling_type = hparams.rope_scaling_type_train;
    }

    if (rope_scaling_type == LLAMA_ROPE_SCALING_NONE) {
        cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
    }

    if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
        cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_YARN ? 1.0f : 0.0f;
    }

    if (params.seed == LLAMA_DEFAULT_SEED) {
        params.seed = time(NULL);
    }

    LLAMA_LOG_INFO("%s: n_ctx      = %u\n",     __func__, cparams.n_ctx);
    LLAMA_LOG_INFO("%s: freq_base  = %.1f\n",   __func__, cparams.rope_freq_base);
    LLAMA_LOG_INFO("%s: freq_scale = %g\n",     __func__, cparams.rope_freq_scale);

    ctx->rng = std::mt19937(params.seed);
    ctx->logits_all = params.logits_all;

    ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;

    // reserve memory for context buffers
    if (!hparams.vocab_only) {
        if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, cparams.n_ctx, model->n_gpu_layers)) {
            LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
            llama_free(ctx);
            return nullptr;
        }

        {
            const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
            LLAMA_LOG_INFO("%s: kv self size  = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
        }

        // resized during inference
        if (params.logits_all) {
            ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab);
        } else {
            ctx->logits.reserve(hparams.n_vocab);
        }

        if (params.embedding){
            ctx->embedding.resize(hparams.n_embd);
        }

        {
            static const size_t tensor_alignment = 32;
            // the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
            ctx->buf_compute.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead());

            // create measure allocator
            ctx->alloc = ggml_allocr_new_measure(tensor_alignment);

            // build worst-case graph
            int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch);
            int n_past = cparams.n_ctx - n_tokens;
            llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
            ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0));

#ifdef GGML_USE_METAL
            if (model->n_gpu_layers > 0) {
                ggml_metal_log_set_callback(llama_log_callback_default, NULL);

                ctx->ctx_metal = ggml_metal_init(1);
                if (!ctx->ctx_metal) {
                    LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
                    llama_free(ctx);
                    return NULL;
                }
                //ggml_metal_graph_find_concurrency(ctx->ctx_metal, gf, false);
                //ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
            }
#endif
            // measure memory requirements for the graph
            size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;

            LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);

            // recreate allocator with exact memory requirements
            ggml_allocr_free(ctx->alloc);

            ctx->buf_alloc.resize(alloc_size);
            ctx->alloc = ggml_allocr_new(ctx->buf_alloc.data, ctx->buf_alloc.size, tensor_alignment);
#ifdef GGML_USE_METAL
            if (ctx->ctx_metal) {
                //ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
            }
#endif
#ifdef GGML_USE_CUBLAS
            ggml_cuda_set_scratch_size(alloc_size);
            LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MB\n", __func__, alloc_size / 1024.0 / 1024.0);

            // calculate total VRAM usage
            auto add_tensor = [](const ggml_tensor * t, size_t & size) {
                if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) {
                    size += ggml_nbytes(t);
                }
            };
            size_t model_vram_size = 0;
            for (const auto & kv : model->tensors_by_name) {
                add_tensor(kv.second, model_vram_size);
            }

            size_t kv_vram_size = 0;
            add_tensor(ctx->kv_self.k, kv_vram_size);
            add_tensor(ctx->kv_self.v, kv_vram_size);

            size_t ctx_vram_size = alloc_size + kv_vram_size;
            size_t total_vram_size = model_vram_size + ctx_vram_size;

            LLAMA_LOG_INFO("%s: total VRAM used: %.2f MB (model: %.2f MB, context: %.2f MB)\n", __func__,
                    (total_vram_size + model->ffn_offloaded_bytes) / 1024.0 / 1024.0,
                    model_vram_size / 1024.0 / 1024.0,
                    ctx_vram_size / 1024.0 / 1024.0);
#endif
        }

#ifdef GGML_USE_METAL
        if (model->n_gpu_layers > 0) {
            // this allocates all Metal resources and memory buffers

            void * data_ptr  = NULL;
            size_t data_size = 0;

            if (ctx->model.mapping) {
                data_ptr  = ctx->model.mapping->addr;
                data_size = ctx->model.mapping->size;
            } else {
                data_ptr  = ggml_get_mem_buffer(ctx->model.ctx);
                data_size = ggml_get_mem_size  (ctx->model.ctx);
            }

            const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);

            LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);

#define LLAMA_METAL_CHECK_BUF(result)                            \
            if (!(result)) {                                             \
                LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \
                llama_free(ctx);                                         \
                return NULL;                                             \
            }

            LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data",  data_ptr, data_size, max_size));
            LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv",    ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0));
            LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0));
#undef LLAMA_METAL_CHECK_BUF
        }
#endif
    }

#ifdef GGML_USE_MPI
    ctx->ctx_mpi = ggml_mpi_init();

    if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
        // Enter a blocking eval loop with dummy input, letting rank=0 drive the process
        // TODO: needs fix after #3228
        GGML_ASSERT(false && "not implemented");
        //const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx));
        //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
        llama_backend_free();
        exit(1);
    }
#endif

    return ctx;
}

void llama_free(struct llama_context * ctx) {
    delete ctx;
}

const llama_model * llama_get_model(const struct llama_context * ctx) {
    return &ctx->model;
}

int llama_n_ctx(const struct llama_context * ctx) {
    return ctx->cparams.n_ctx;
}

enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
    return model->vocab.type;
}

bool llama_use_sparse_inference(const struct llama_model * model) {
    return model->sparse_deriv == GGML_SPARSE_INFERENCE;
}

int llama_n_vocab(const struct llama_model * model) {
    return model->vocab.id_to_token.size();
}

int llama_n_ctx_train(const struct llama_model * model) {
    return model->hparams.n_ctx_train;
}

int llama_n_embd(const struct llama_model * model) {
    return model->hparams.n_embd;
}

float llama_rope_freq_scale_train(const struct llama_model * model) {
    return model->hparams.rope_freq_scale_train;
}

int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
    return snprintf(buf, buf_size, "%s %s %s",
            llama_model_arch_name(model->arch).c_str(),
            llama_model_type_name(model->type),
            llama_model_ftype_name(model->ftype).c_str());
}

uint64_t llama_model_size(const struct llama_model * model) {
    uint64_t size = 0;
    for (const auto & it : model->tensors_by_name) {
        size += ggml_nbytes(it.second);
    }
    return size;
}

uint64_t llama_model_n_params(const struct llama_model * model) {
    uint64_t nparams = 0;
    for (const auto & it : model->tensors_by_name) {
        nparams += ggml_nelements(it.second);
    }
    return nparams;
}

struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) {
    return ggml_get_tensor(model->ctx, name);
}

int llama_model_quantize(
        const char * fname_inp,
        const char * fname_out,
        const llama_model_quantize_params * params) {
    try {
        llama_model_quantize_internal(fname_inp, fname_out, params);
        return 0;
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
        return 1;
    }
}

int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
    try {
        return llama_apply_lora_from_file_internal(ctx->model, path_lora, scale, path_base_model, n_threads);
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
        return 1;
    }
}

int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
    try {
        return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
        return 1;
    }
}

size_t llama_model_offload_ffn_split(struct llama_model * model) {
    llama_augmentation_model_loader * aug_ml = new llama_augmentation_model_loader(model);    
    size_t offloaded_bytes = aug_ml->offload_ffn_split(model);
    return offloaded_bytes;
}

int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
    return ctx->kv_self.head;
}

void llama_kv_cache_clear(struct llama_context * ctx) {
    llama_kv_cache_clear(ctx->kv_self);
}

void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
    llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
}

void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
    if (seq_id_src == seq_id_dst) {
        return;
    }
    llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1);
}

void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
    llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
}

void llama_kv_cache_seq_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
    llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta);
}

// Returns the *maximum* size of the state
size_t llama_get_state_size(const struct llama_context * ctx) {
    // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
    // for reference, std::mt19937(1337) serializes to 6701 bytes.
    const size_t s_rng_size        = sizeof(size_t);
    const size_t s_rng             = LLAMA_MAX_RNG_STATE;
    const size_t s_logits_capacity = sizeof(size_t);
    const size_t s_logits_size     = sizeof(size_t);
    const size_t s_logits          = ctx->logits.capacity() * sizeof(float);
    const size_t s_embedding_size  = sizeof(size_t);
    const size_t s_embedding       = ctx->embedding.size() * sizeof(float);
    const size_t s_kv_size         = sizeof(size_t);
    const size_t s_kv_ntok         = sizeof(int);
    const size_t s_kv              = ctx->kv_self.buf.size;

    const size_t s_total = (
        + s_rng_size
        + s_rng
        + s_logits_capacity
        + s_logits_size
        + s_logits
        + s_embedding_size
        + s_embedding
        + s_kv_size
        + s_kv_ntok
        + s_kv
    );

    return s_total;
}

// llama_context_data
struct llama_data_context {
    virtual void write(const void * src, size_t size) = 0;
    virtual size_t get_size_written() = 0;
    virtual ~llama_data_context() = default;
};

struct llama_data_buffer_context : llama_data_context {
    uint8_t * ptr;
    size_t size_written = 0;

    llama_data_buffer_context(uint8_t * p) : ptr(p) {}

    void write(const void * src, size_t size) override {
        memcpy(ptr, src, size);
        ptr += size;
        size_written += size;
    }

    size_t get_size_written() override {
        return size_written;
    }
};

struct llama_data_file_context : llama_data_context {
    llama_file * file;
    size_t size_written = 0;

    llama_data_file_context(llama_file * f) : file(f) {}

    void write(const void * src, size_t size) override {
        file->write_raw(src, size);
        size_written += size;
    }

    size_t get_size_written() override {
        return size_written;
    }
};

/** copy state data into either a buffer or file depending on the passed in context
 *
 * file context:
 * llama_file file("/path", "wb");
 * llama_data_file_context data_ctx(&file);
 * llama_copy_state_data(ctx, &data_ctx);
 *
 * buffer context:
 * std::vector<uint8_t> buf(max_size, 0);
 * llama_data_buffer_context data_ctx(&buf.data());
 * llama_copy_state_data(ctx, &data_ctx);
 *
*/
static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
    // copy rng
    {
        std::stringstream rng_ss;
        rng_ss << ctx->rng;

        const size_t rng_size = rng_ss.str().size();
        char rng_buf[LLAMA_MAX_RNG_STATE];

        memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
        memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());

        data_ctx->write(&rng_size,   sizeof(rng_size));
        data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE);
    }

    // copy logits
    {
        const size_t logits_cap  = ctx->logits.capacity();
        const size_t logits_size = ctx->logits.size();

        data_ctx->write(&logits_cap,  sizeof(logits_cap));
        data_ctx->write(&logits_size, sizeof(logits_size));

        if (logits_size) {
            data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
        }

        // If there is a gap between the size and the capacity, write padding
        size_t padding_size = (logits_cap - logits_size) * sizeof(float);
        if (padding_size > 0) {
            std::vector<uint8_t> padding(padding_size, 0); // Create a buffer filled with zeros
            data_ctx->write(padding.data(), padding_size);
        }
    }

    // copy embeddings
    {
        const size_t embedding_size = ctx->embedding.size();

        data_ctx->write(&embedding_size, sizeof(embedding_size));

        if (embedding_size) {
            data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
        }
    }

    // copy kv cache
    {
        const auto & kv_self = ctx->kv_self;
        const auto & hparams = ctx->model.hparams;
        const auto & cparams = ctx->cparams;

        const auto   n_layer = hparams.n_layer;
        const auto   n_embd  = hparams.n_embd_gqa();
        const auto   n_ctx   = cparams.n_ctx;

        const size_t   kv_buf_size = kv_self.buf.size;
        const uint32_t kv_head     = kv_self.head;
        const uint32_t kv_size     = kv_self.size;

        data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
        data_ctx->write(&kv_head,     sizeof(kv_head));
        data_ctx->write(&kv_size,     sizeof(kv_size));

        if (kv_buf_size) {
            const size_t elt_size = ggml_element_size(kv_self.k);

            ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
            ggml_cgraph * gf = ggml_new_graph(cpy_ctx);

            ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
            std::vector<uint8_t> kout3d_data(ggml_nbytes(kout3d), 0);
            kout3d->data = kout3d_data.data();

            ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
            std::vector<uint8_t> vout3d_data(ggml_nbytes(vout3d), 0);
            vout3d->data = vout3d_data.data();

            ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
                n_embd, kv_head, n_layer,
                elt_size*n_embd, elt_size*n_embd*n_ctx, 0);

            ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
                kv_head, n_embd, n_layer,
                elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);

            ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k3d, kout3d));
            ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v3d, vout3d));
            ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1);

            ggml_free(cpy_ctx);

            // our data is now in the kout3d_data and vout3d_data buffers
            // write them to file
            data_ctx->write(kout3d_data.data(), kout3d_data.size());
            data_ctx->write(vout3d_data.data(), vout3d_data.size());
        }

        for (uint32_t i = 0; i < kv_size; ++i) {
            const auto & cell = kv_self.cells[i];

            const llama_pos pos         = cell.pos;
            const size_t    seq_id_size = cell.seq_id.size();

            data_ctx->write(&pos,         sizeof(pos));
            data_ctx->write(&seq_id_size, sizeof(seq_id_size));

            for (auto seq_id : cell.seq_id) {
                data_ctx->write(&seq_id, sizeof(seq_id));
            }
        }
    }
}

size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
    llama_data_buffer_context data_ctx(dst);
    llama_copy_state_data_internal(ctx, &data_ctx);

    return data_ctx.get_size_written();
}

// Sets the state reading from the specified source address
size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
    uint8_t * inp = src;

    // set rng
    {
        size_t rng_size;
        char   rng_buf[LLAMA_MAX_RNG_STATE];

        memcpy(&rng_size,   inp, sizeof(rng_size));    inp += sizeof(rng_size);
        memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;

        std::stringstream rng_ss;
        rng_ss.str(std::string(&rng_buf[0], rng_size));
        rng_ss >> ctx->rng;

        GGML_ASSERT(!rng_ss.fail());
    }

    // set logits
    {
        size_t logits_cap;
        size_t logits_size;

        memcpy(&logits_cap,  inp, sizeof(logits_cap));  inp += sizeof(logits_cap);
        memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);

        GGML_ASSERT(ctx->logits.capacity() == logits_cap);

        if (logits_size) {
            ctx->logits.resize(logits_size);
            memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
        }

        inp += logits_cap * sizeof(float);
    }

    // set embeddings
    {
        size_t embedding_size;

        memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);

        GGML_ASSERT(ctx->embedding.capacity() == embedding_size);

        if (embedding_size) {
            memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
            inp += embedding_size * sizeof(float);
        }
    }

    // set kv cache
    {
        const auto & kv_self = ctx->kv_self;
        const auto & hparams = ctx->model.hparams;
        const auto & cparams = ctx->cparams;

        const int    n_layer = hparams.n_layer;
        const int    n_embd  = hparams.n_embd_gqa();
        const int    n_ctx   = cparams.n_ctx;

        size_t   kv_buf_size;
        uint32_t kv_head;
        uint32_t kv_size;

        memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
        memcpy(&kv_head,     inp, sizeof(kv_head));     inp += sizeof(kv_head);
        memcpy(&kv_size,     inp, sizeof(kv_size));     inp += sizeof(kv_size);

        if (kv_buf_size) {
            GGML_ASSERT(kv_self.buf.size == kv_buf_size);

            const size_t elt_size = ggml_element_size(kv_self.k);

            ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
            ggml_cgraph * gf = ggml_new_graph(cpy_ctx);

            ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
            kin3d->data = (void *) inp;
            inp += ggml_nbytes(kin3d);

            ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
            vin3d->data = (void *) inp;
            inp += ggml_nbytes(vin3d);

            ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
                n_embd, kv_head, n_layer,
                elt_size*n_embd, elt_size*n_embd*n_ctx, 0);

            ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
                kv_head, n_embd, n_layer,
                elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);

            ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin3d, k3d));
            ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin3d, v3d));
            ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1);

            ggml_free(cpy_ctx);
        }

        ctx->kv_self.head = kv_head;
        ctx->kv_self.size = kv_size;

        ctx->kv_self.cells.resize(kv_size);

        for (uint32_t i = 0; i < kv_size; ++i) {
            llama_pos pos;
            size_t    seq_id_size;

            memcpy(&pos,         inp, sizeof(pos));         inp += sizeof(pos);
            memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);

            ctx->kv_self.cells[i].pos = pos;

            llama_seq_id seq_id;

            for (size_t j = 0; j < seq_id_size; ++j) {
                memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
                ctx->kv_self.cells[i].seq_id.insert(seq_id);
            }
        }
    }

    const size_t nread    = inp - src;
    const size_t max_size = llama_get_state_size(ctx);

    GGML_ASSERT(nread <= max_size);

    return nread;
}

static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
    llama_file file(path_session, "rb");

    // sanity checks
    {
        const uint32_t magic   = file.read_u32();
        const uint32_t version = file.read_u32();

        if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
            LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
            return false;
        }

        llama_hparams session_hparams;
        file.read_raw(&session_hparams, sizeof(llama_hparams));

        if (session_hparams != ctx->model.hparams) {
            LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
            return false;
        }
    }

    // load the prompt
    {
        const uint32_t n_token_count = file.read_u32();

        if (n_token_count > n_token_capacity) {
            LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
            return false;
        }

        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
        *n_token_count_out = n_token_count;
    }

    // restore the context state
    {
        const size_t n_state_size_cur = file.size - file.tell();
        const size_t n_state_size_max = llama_get_state_size(ctx);

        if (n_state_size_cur > n_state_size_max) {
            LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
            return false;
        }

        std::vector<uint8_t> state_data(n_state_size_max);
        file.read_raw(state_data.data(), n_state_size_cur);

        llama_set_state_data(ctx, state_data.data());
    }

    return true;
}

bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
    try {
        return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
    } catch (const std::exception & err) {
        LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
        return false;
    }
}

bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
    llama_file file(path_session, "wb");

    file.write_u32(LLAMA_SESSION_MAGIC);
    file.write_u32(LLAMA_SESSION_VERSION);

    file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));

    // save the prompt
    file.write_u32((uint32_t) n_token_count);
    file.write_raw(tokens, sizeof(llama_token) * n_token_count);

    // save the context state using stream saving
    llama_data_file_context data_ctx(&file);
    llama_copy_state_data_internal(ctx, &data_ctx);

    return true;
}

int llama_eval(
        struct llama_context * ctx,
                 llama_token * tokens,
                     int32_t   n_tokens,
                         int   n_past) {
    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);

    const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
    if (ret < 0) {
        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
    }

    return ret;
}

int llama_eval_embd(
            struct llama_context * ctx,
                           float * embd,
                         int32_t   n_tokens,
                             int   n_past) {
    llama_kv_cache_seq_rm(ctx->kv_self, -1, n_past, -1);

    llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, nullptr, n_past, 1, 0, };

    const int ret = llama_decode_internal(*ctx, batch);
    if (ret < 0) {
        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
    }

    return ret;
}

void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
    ctx->cparams.n_threads       = n_threads;
    ctx->cparams.n_threads_batch = n_threads_batch;
}

struct llama_batch llama_batch_get_one(
             llama_token * tokens,
                 int32_t   n_tokens,
               llama_pos   pos_0,
            llama_seq_id   seq_id) {
    return {
        /*n_tokens       =*/ n_tokens,
        /*tokens         =*/ tokens,
        /*embd           =*/ nullptr,
        /*pos            =*/ nullptr,
        /*n_seq_id       =*/ nullptr,
        /*seq_id         =*/ nullptr,
        /*logits         =*/ nullptr,
        /*all_pos_0      =*/ pos_0,
        /*all_pos_1      =*/ 1,
        /*all_seq_id     =*/ seq_id,
    };
}

struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd, int32_t n_seq_max) {
    llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };

    if (embd) {
        batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd);
    } else {
        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
    }

    batch.pos      = (llama_pos *)     malloc(sizeof(llama_pos)      * n_tokens);
    batch.n_seq_id = (int32_t *)       malloc(sizeof(int32_t)        * n_tokens);
    batch.seq_id   = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
    for (int i = 0; i < n_tokens; ++i) {
        batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
    }
    batch.logits   = (int8_t *)        malloc(sizeof(int8_t)         * n_tokens);

    return batch;
}

void llama_batch_free(struct llama_batch batch) {
    if (batch.token)    free(batch.token);
    if (batch.embd)     free(batch.embd);
    if (batch.pos)      free(batch.pos);
    if (batch.n_seq_id) free(batch.n_seq_id);
    if (batch.seq_id) {
        for (int i = 0; i < batch.n_tokens; ++i) {
            free(batch.seq_id[i]);
        }
        free(batch.seq_id);
    }
    if (batch.logits)   free(batch.logits);
}

int llama_decode(
        struct llama_context * ctx,
          struct llama_batch   batch) {
    const int ret = llama_decode_internal(*ctx, batch);
    if (ret < 0) {
        LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
    }

    return ret;
}

float * llama_get_logits(struct llama_context * ctx) {
    return ctx->logits.data();
}

float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
    return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
}

float * llama_get_embeddings(struct llama_context * ctx) {
    return ctx->embedding.data();
}

const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
    return model->vocab.id_to_token[token].text.c_str();
}

float llama_token_get_score(const struct llama_model * model, llama_token token) {
    return model->vocab.id_to_token[token].score;
}

llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
    return model->vocab.id_to_token[token].type;
}

llama_token llama_token_bos(const struct llama_model * model) {
    return model->vocab.special_bos_id;
}

llama_token llama_token_eos(const struct llama_model * model) {
    return model->vocab.special_eos_id;
}

llama_token llama_token_nl(const struct llama_model * model) {
    return model->vocab.linefeed_id;
}

llama_token llama_token_prefix(const struct llama_model * model) {
    return model->vocab.special_prefix_id;
}

llama_token llama_token_middle(const struct llama_model * model) {
    return model->vocab.special_middle_id;
}

llama_token llama_token_suffix(const struct llama_model * model) {
    return model->vocab.special_suffix_id;
}

llama_token llama_token_eot(const struct llama_model * model) {
    return model->vocab.special_eot_id;
}

int llama_tokenize(
    const struct llama_model * model,
                  const char * text,
                         int   text_len,
                 llama_token * tokens,
                         int   n_max_tokens,
                        bool   add_bos,
                        bool   special) {
    auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);

    if (n_max_tokens < (int) res.size()) {
        // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
        return -((int) res.size());
    }

    for (size_t i = 0; i < res.size(); i++) {
        tokens[i] = res[i];
    }

    return res.size();
}

static std::string llama_decode_text(const std::string & text) {
    std::string decoded_text;
    auto unicode_sequences = codepoints_from_utf8(text);
    for (auto& unicode_sequence : unicode_sequences) {
        decoded_text += unicode_to_bytes_bpe(codepoint_to_utf8(unicode_sequence));
    }

    return decoded_text;
}

// does not write null-terminator to buf
int llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int length) {
    if (0 <= token && token < llama_n_vocab(model)) {
        switch (llama_vocab_get_type(model->vocab)) {
        case LLAMA_VOCAB_TYPE_SPM: {
            if (llama_is_normal_token(model->vocab, token)) {
                std::string result = model->vocab.id_to_token[token].text;
                llama_unescape_whitespace(result);
                if (length < (int) result.length()) {
                    return -result.length();
                }
                memcpy(buf, result.c_str(), result.length());
                return result.length();
            } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
                if (length < 3) {
                    return -3;
                }
                memcpy(buf, "\xe2\x96\x85", 3);
                return 3;
            } else if (llama_is_control_token(model->vocab, token)) {
                ;
            } else if (llama_is_byte_token(model->vocab, token)) {
                if (length < 1) {
                    return -1;
                }
                buf[0] = llama_token_to_byte(model->vocab, token);
                return 1;
            } else {
                // TODO: for now we accept all unsupported token types,
                // suppressing them like CONTROL tokens.
                // GGML_ASSERT(false);
            }
            break;
        }
        case LLAMA_VOCAB_TYPE_BPE: {
            if (llama_is_normal_token(model->vocab, token)) {
                std::string result = model->vocab.id_to_token[token].text;
                result = llama_decode_text(result);
                if (length < (int) result.length()) {
                    return -result.length();
                }
                memcpy(buf, result.c_str(), result.length());
                return result.length();
            } else if (llama_is_control_token(model->vocab, token)) {
                ;
            } else {
                // TODO: for now we accept all unsupported token types,
                // suppressing them like CONTROL tokens.
                // GGML_ASSERT(false);
            }
            break;
        }
        default:
            GGML_ASSERT(false);
        }
    }
    return 0;
}

struct llama_timings llama_get_timings(struct llama_context * ctx) {
    struct llama_timings result = {
        /*.t_start_ms  =*/ 1e-3 * ctx->t_start_us,
        /*.t_end_ms    =*/ 1.00 * ggml_time_ms(),
        /*.t_load_ms   =*/ 1e-3 * ctx->t_load_us,
        /*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
        /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
        /*.t_eval_ms   =*/ 1e-3 * ctx->t_eval_us,

        /*.n_sample =*/ std::max(1, ctx->n_sample),
        /*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
        /*.n_eval   =*/ std::max(1, ctx->n_eval),
    };

    return result;
}

void llama_print_timings(struct llama_context * ctx) {
    const llama_timings timings = llama_get_timings(ctx);

    LLAMA_LOG_INFO("\n");
    LLAMA_LOG_INFO("%s:        load time = %10.2f ms\n", __func__, timings.t_load_ms);
    LLAMA_LOG_INFO("%s:      sample time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
    LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
    LLAMA_LOG_INFO("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
    LLAMA_LOG_INFO("%s:       total time = %10.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
}

void llama_reset_timings(struct llama_context * ctx) {
    ctx->t_start_us = ggml_time_us();
    ctx->t_sample_us = ctx->n_sample = 0;
    ctx->t_eval_us   = ctx->n_eval   = 0;
    ctx->t_p_eval_us = ctx->n_p_eval = 0;
}

const char * llama_print_system_info(void) {
    static std::string s;

    s  = "";
    s += "AVX = "         + std::to_string(ggml_cpu_has_avx())         + " | ";
    s += "AVX2 = "        + std::to_string(ggml_cpu_has_avx2())        + " | ";
    s += "AVX512 = "      + std::to_string(ggml_cpu_has_avx512())      + " | ";
    s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
    s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
    s += "FMA = "         + std::to_string(ggml_cpu_has_fma())         + " | ";
    s += "NEON = "        + std::to_string(ggml_cpu_has_neon())        + " | ";
    s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
    s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
    s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
    s += "WASM_SIMD = "   + std::to_string(ggml_cpu_has_wasm_simd())   + " | ";
    s += "BLAS = "        + std::to_string(ggml_cpu_has_blas())        + " | ";
    s += "SSE3 = "        + std::to_string(ggml_cpu_has_sse3())        + " | ";
    s += "SSSE3 = "       + std::to_string(ggml_cpu_has_ssse3())       + " | ";
    s += "VSX = "         + std::to_string(ggml_cpu_has_vsx())         + " | ";

    return s.c_str();
}

void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
    fprintf(stream, "\n");
    fprintf(stream, "###########\n");
    fprintf(stream, "# Timings #\n");
    fprintf(stream, "###########\n");
    fprintf(stream, "\n");

    fprintf(stream, "mst_eval: %.2f  # ms / token during generation\n",
            1.0e-3 * ctx->t_eval_us / ctx->n_eval);
    fprintf(stream, "mst_p_eval: %.2f  # ms / token during prompt processing\n",
            1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
    fprintf(stream, "mst_sample: %.2f  # ms / token during sampling\n",
            1.0e-3 * ctx->t_sample_us / ctx->n_sample);
    fprintf(stream, "n_eval: %d  # number of tokens generated (excluding the first one)\n", ctx->n_eval);
    fprintf(stream, "n_p_eval: %d  # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
    fprintf(stream, "n_sample: %d  # number of sampled tokens\n", ctx->n_sample);
    fprintf(stream, "t_eval_us: %" PRId64 "  # total microseconds spent generating tokens\n", ctx->t_eval_us);
    fprintf(stream, "t_load_us: %" PRId64 "  # total microseconds spent loading the model\n", ctx->t_load_us);
    fprintf(stream, "t_p_eval_us: %" PRId64 "  # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
    fprintf(stream, "t_sample_us: %" PRId64 "  # total microseconds spent sampling\n", ctx->t_sample_us);
    fprintf(stream, "ts_eval: %.2f  # tokens / second during generation\n",
            1.0e6 * ctx->n_eval / ctx->t_eval_us);
    fprintf(stream, "ts_p_eval: %.2f  # tokens / second during prompt processing\n",
            1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
    fprintf(stream, "ts_sample: %.2f  # tokens / second during sampling\n",
            1.0e6 * ctx->n_sample / ctx->t_sample_us);
}

// For internal test use
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
    struct llama_context * ctx
) {
    return ctx->model.tensors_by_name;
}

void llama_log_set(ggml_log_callback log_callback, void * user_data) {
    g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
    g_state.log_callback_user_data = user_data;
}

static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
    va_list args_copy;
    va_copy(args_copy, args);
    char buffer[128];
    int len = vsnprintf(buffer, 128, format, args);
    if (len < 128) {
        g_state.log_callback(level, buffer, g_state.log_callback_user_data);
    } else {
        char* buffer2 = new char[len+1];
        vsnprintf(buffer2, len+1, format, args_copy);
        buffer2[len] = 0;
        g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
        delete[] buffer2;
    }
    va_end(args_copy);
}

static void llama_log_internal(ggml_log_level level, const char * format, ...) {
    va_list args;
    va_start(args, format);
    llama_log_internal_v(level, format, args);
    va_end(args);
}

static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
    (void) level;
    (void) user_data;
    fputs(text, stderr);
    fflush(stderr);
}