ggml : sync (ggml-alloc, GPU, eps, etc.) (#1220)
* ggml : sync (ggml-alloc, GPU, eps, etc.) * ggml : fix build * wasm : fix buildpull/1247/head
Georgi Gerganov
GitHub
parent
6780c98e19
commit
59a3d0cb57
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@ -1 +1 @@
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"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:function(f){(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f)},postMessage:function(msg){parentPort.postMessage(msg)},performance:global.performance||{now:function(){return Date.now()}}})}var initializedJS=false;var pendingNotifiedProxyingQueues=[];function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"\n");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var instance=new WebAssembly.Instance(Module["wasmModule"],info);receiveInstance(instance);Module["wasmModule"]=null;return instance.exports};self.onunhandledrejection=e=>{throw e.reason??e};self.onmessage=e=>{try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=function(){postMessage({cmd:"callHandler",handler:handler,args:[...arguments]})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}whisper_factory(Module).then(function(instance){Module=instance})}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){Module["__embind_initialize_bindings"]();pendingNotifiedProxyingQueues.forEach(queue=>{Module["executeNotifiedProxyingQueue"](queue)});pendingNotifiedProxyingQueues=[];initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["keepRuntimeAlive"]()){}else{Module["__emscripten_thread_exit"](ex.status)}}else{throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processProxyingQueue"){if(initializedJS){Module["executeNotifiedProxyingQueue"](e.data.queue)}else{pendingNotifiedProxyingQueues.push(e.data.queue)}}else if(e.data.cmd){err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}};
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"use strict";var Module={};var ENVIRONMENT_IS_NODE=typeof process=="object"&&typeof process.versions=="object"&&typeof process.versions.node=="string";if(ENVIRONMENT_IS_NODE){var nodeWorkerThreads=require("worker_threads");var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",data=>onmessage({data:data}));var fs=require("fs");Object.assign(global,{self:global,require:require,Module:Module,location:{href:__filename},Worker:nodeWorkerThreads.Worker,importScripts:f=>(0,eval)(fs.readFileSync(f,"utf8")+"//# sourceURL="+f),postMessage:msg=>parentPort.postMessage(msg),performance:global.performance||{now:Date.now}})}var initializedJS=false;function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");if(ENVIRONMENT_IS_NODE){fs.writeSync(2,text+"\n");return}console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;self.alert=threadAlert;Module["instantiateWasm"]=(info,receiveInstance)=>{var module=Module["wasmModule"];Module["wasmModule"]=null;var instance=new WebAssembly.Instance(module,info);return receiveInstance(instance)};self.onunhandledrejection=e=>{throw e.reason||e};function handleMessage(e){try{if(e.data.cmd==="load"){let messageQueue=[];self.onmessage=e=>messageQueue.push(e);self.startWorker=instance=>{Module=instance;postMessage({"cmd":"loaded"});for(let msg of messageQueue){handleMessage(msg)}self.onmessage=handleMessage};Module["wasmModule"]=e.data.wasmModule;for(const handler of e.data.handlers){Module[handler]=(...args)=>{postMessage({cmd:"callHandler",handler:handler,args:args})}}Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob=="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}whisper_factory(Module)}else if(e.data.cmd==="run"){Module["__emscripten_thread_init"](e.data.pthread_ptr,0,0,1);Module["__emscripten_thread_mailbox_await"](e.data.pthread_ptr);Module["establishStackSpace"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].threadInitTLS();if(!initializedJS){Module["__embind_initialize_bindings"]();initializedJS=true}try{Module["invokeEntryPoint"](e.data.start_routine,e.data.arg)}catch(ex){if(ex!="unwind"){throw ex}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["__emscripten_thread_exit"](-1)}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="checkMailbox"){if(initializedJS){Module["checkMailbox"]()}}else if(e.data.cmd){err(`worker.js received unknown command ${e.data.cmd}`);err(e.data)}}catch(ex){if(Module["__emscripten_thread_crashed"]){Module["__emscripten_thread_crashed"]()}throw ex}}self.onmessage=handleMessage;
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File diff suppressed because one or more lines are too long
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@ -1,3 +1,5 @@
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#define _USE_MATH_DEFINES // for M_PI
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#include "common.h"
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// third-party utilities
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@ -13,53 +15,59 @@
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#include <codecvt>
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#include <sstream>
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#ifndef M_PI
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#define M_PI 3.14159265358979323846
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#endif
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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// Function to check if the next argument exists
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std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) {
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if (i + 1 < argc && argv[i + 1][0] != '-') {
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return argv[++i];
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} else {
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fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
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gpt_print_usage(argc, argv, params);
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exit(0);
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}
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}
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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if (arg == "-s" || arg == "--seed") {
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params.seed = std::stoi(argv[++i]);
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params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-t" || arg == "--threads") {
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params.n_threads = std::stoi(argv[++i]);
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params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
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params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-p" || arg == "--prompt") {
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params.prompt = argv[++i];
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params.prompt = get_next_arg(i, argc, argv, arg, params);
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} else if (arg == "-n" || arg == "--n_predict") {
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params.n_predict = std::stoi(argv[++i]);
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params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--top_k") {
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params.top_k = std::max(1, std::stoi(argv[++i]));
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params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--top_p") {
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params.top_p = std::stof(argv[++i]);
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params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--temp") {
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params.temp = std::stof(argv[++i]);
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params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--repeat-last-n") {
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params.repeat_last_n = std::stof(argv[++i]);
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params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--repeat-penalty") {
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params.repeat_penalty = std::stof(argv[++i]);
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params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-b" || arg == "--batch_size") {
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params.n_batch = std::stoi(argv[++i]);
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params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-m" || arg == "--model") {
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params.model = argv[++i];
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params.model = get_next_arg(i, argc, argv, arg, params);
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} else if (arg == "-i" || arg == "--interactive") {
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params.interactive = true;
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} else if (arg == "-ip" || arg == "--interactive-port") {
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params.interactive = true;
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params.interactive_port = std::stoi(argv[++i]);
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params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-h" || arg == "--help") {
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gpt_print_usage(argc, argv, params);
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exit(0);
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} else if (arg == "-f" || arg == "--file") {
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if (++i > argc) {
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fprintf(stderr, "Invalid file param");
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break;
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}
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get_next_arg(i, argc, argv, arg, params);
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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@ -70,7 +78,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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params.prompt.pop_back();
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}
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} else if (arg == "-tt" || arg == "--token_test") {
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params.token_test = argv[++i];
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params.token_test = get_next_arg(i, argc, argv, arg, params);
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}
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else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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@ -89,6 +97,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
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fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers);
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fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
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fprintf(stderr, " prompt to start generation with (default: random)\n");
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fprintf(stderr, " -f FNAME, --file FNAME\n");
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@ -755,3 +764,46 @@ float similarity(const std::string & s0, const std::string & s1) {
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return 1.0f - (dist / std::max(s0.size(), s1.size()));
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}
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bool sam_params_parse(int argc, char ** argv, sam_params & params) {
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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if (arg == "-s" || arg == "--seed") {
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params.seed = std::stoi(argv[++i]);
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} else if (arg == "-t" || arg == "--threads") {
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params.n_threads = std::stoi(argv[++i]);
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} else if (arg == "-m" || arg == "--model") {
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params.model = argv[++i];
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} else if (arg == "-i" || arg == "--inp") {
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params.fname_inp = argv[++i];
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} else if (arg == "-o" || arg == "--out") {
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params.fname_out = argv[++i];
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} else if (arg == "-h" || arg == "--help") {
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sam_print_usage(argc, argv, params);
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exit(0);
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} else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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sam_print_usage(argc, argv, params);
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exit(0);
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}
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}
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return true;
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}
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void sam_print_usage(int argc, char ** argv, const sam_params & params) {
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
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fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stderr, " -m FNAME, --model FNAME\n");
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fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
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fprintf(stderr, " -i FNAME, --inp FNAME\n");
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fprintf(stderr, " input file (default: %s)\n", params.fname_inp.c_str());
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fprintf(stderr, " -o FNAME, --out FNAME\n");
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fprintf(stderr, " output file (default: %s)\n", params.fname_out.c_str());
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fprintf(stderr, "\n");
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}
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@ -11,7 +11,7 @@
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#define COMMON_SAMPLE_RATE 16000
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//
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// CLI argument parsing
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// GPT CLI argument parsing
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//
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struct gpt_params {
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@ -33,6 +33,8 @@ struct gpt_params {
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bool interactive = false;
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int32_t interactive_port = -1;
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int32_t n_gpu_layers = 0;
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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@ -155,3 +157,20 @@ bool vad_simple(
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// compute similarity between two strings using Levenshtein distance
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float similarity(const std::string & s0, const std::string & s1);
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//
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// SAM argument parsing
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//
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struct sam_params {
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int32_t seed = -1; // RNG seed
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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std::string model = "models/sam-vit-b/ggml-model-f16.bin"; // model path
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std::string fname_inp = "img.jpg";
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std::string fname_out = "img.out";
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};
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bool sam_params_parse(int argc, char ** argv, sam_params & params);
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void sam_print_usage(int argc, char ** argv, const sam_params & params);
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@ -191,9 +191,9 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
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// create the ggml context
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{
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struct ggml_init_params params = {
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.mem_size = ctx_size,
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.mem_buffer = NULL,
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.no_alloc = false,
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ false,
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};
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model.ctx = ggml_init(params);
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@ -420,7 +420,6 @@ bool gpt2_eval(
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
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@ -442,7 +441,7 @@ bool gpt2_eval(
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// norm
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{
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// [ 768, N]
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cur = ggml_norm(ctx0, inpL);
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cur = ggml_norm(ctx0, inpL, 1e-5f);
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// cur = ln_1_g*cur + ln_1_b
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// [ 768, N]
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@ -589,7 +588,7 @@ bool gpt2_eval(
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{
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// norm
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{
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cur = ggml_norm(ctx0, inpFF);
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cur = ggml_norm(ctx0, inpFF, 1e-5f);
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// cur = ln_2_g*cur + ln_2_b
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// [ 768, N]
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@ -644,7 +643,7 @@ bool gpt2_eval(
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// norm
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{
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// [ 768, N]
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inpL = ggml_norm(ctx0, inpL);
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inpL = ggml_norm(ctx0, inpL, 1e-5f);
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// inpL = ln_f_g*inpL + ln_f_b
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// [ 768, N]
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@ -664,8 +663,8 @@ bool gpt2_eval(
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//inpL = ggml_soft_max(ctx0, inpL);
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// run the computation
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ggml_build_forward_expand(&gf, inpL);
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ggml_graph_compute (ctx0, &gf);
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ggml_build_forward_expand (&gf, inpL);
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ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
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//if (n_past%100 == 0) {
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// ggml_graph_print (&gf);
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@ -379,6 +379,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
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// - embd_inp: the embeddings of the tokens in the context
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// - embd_w: the predicted logits for the next token
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//
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// TODO: sync latest version from ggml repo
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bool gpt2_eval(
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const gpt2_model & model,
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const int n_threads,
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@ -420,7 +421,6 @@ bool gpt2_eval(
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struct ggml_context * ctx0 = ggml_init(params);
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struct ggml_cgraph gf = {};
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gf.n_threads = n_threads;
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struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
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memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
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@ -442,7 +442,7 @@ bool gpt2_eval(
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// norm
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{
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// [ 768, N]
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cur = ggml_norm(ctx0, inpL);
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cur = ggml_norm(ctx0, inpL, 1e-5f);
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// cur = ln_1_g*cur + ln_1_b
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// [ 768, N]
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@ -589,7 +589,7 @@ bool gpt2_eval(
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{
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// norm
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{
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cur = ggml_norm(ctx0, inpFF);
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cur = ggml_norm(ctx0, inpFF, 1e-5f);
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// cur = ln_2_g*cur + ln_2_b
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// [ 768, N]
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@ -644,7 +644,7 @@ bool gpt2_eval(
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// norm
|
||||
{
|
||||
// [ 768, N]
|
||||
inpL = ggml_norm(ctx0, inpL);
|
||||
inpL = ggml_norm(ctx0, inpL, 1e-5f);
|
||||
|
||||
// inpL = ln_f_g*inpL + ln_f_b
|
||||
// [ 768, N]
|
||||
|
|
@ -664,8 +664,8 @@ bool gpt2_eval(
|
|||
//inpL = ggml_soft_max(ctx0, inpL);
|
||||
|
||||
// run the computation
|
||||
ggml_build_forward_expand(&gf, inpL);
|
||||
ggml_graph_compute (ctx0, &gf);
|
||||
ggml_build_forward_expand (&gf, inpL);
|
||||
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
||||
|
||||
//if (n_past%100 == 0) {
|
||||
// ggml_graph_print (&gf);
|
||||
|
|
|
|||
|
|
@ -0,0 +1,594 @@
|
|||
#include "ggml-alloc.h"
|
||||
#include "ggml.h"
|
||||
#include <assert.h>
|
||||
#include <stdarg.h>
|
||||
#include <stdio.h>
|
||||
#include <stdlib.h>
|
||||
#include <string.h>
|
||||
|
||||
#define UNUSED(x) (void)(x)
|
||||
#define MAX(a, b) ((a) > (b) ? (a) : (b))
|
||||
#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
|
||||
|
||||
//#define GGML_ALLOCATOR_DEBUG
|
||||
|
||||
//#define AT_PRINTF printf
|
||||
#define AT_PRINTF(...) ((void)0)
|
||||
|
||||
struct hash_node {
|
||||
struct ggml_tensor * t;
|
||||
int n_children;
|
||||
int n_views;
|
||||
};
|
||||
|
||||
static size_t hash(void * p) {
|
||||
return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
|
||||
}
|
||||
|
||||
static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
|
||||
size_t h = hash(t);
|
||||
|
||||
// linear probing
|
||||
size_t i = h;
|
||||
while (hash_table[i].t != NULL) {
|
||||
if (hash_table[i].t == t) {
|
||||
return &hash_table[i];
|
||||
}
|
||||
i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
|
||||
if (i == h) {
|
||||
// hash table is full
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
}
|
||||
|
||||
hash_table[i].t = t;
|
||||
return &hash_table[i];
|
||||
}
|
||||
|
||||
// TODO: GGML_PAD ?
|
||||
static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
|
||||
assert(alignment && !(alignment & (alignment - 1))); // power of 2
|
||||
size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
|
||||
return offset + align;
|
||||
}
|
||||
|
||||
struct free_block {
|
||||
void * addr;
|
||||
size_t size;
|
||||
};
|
||||
|
||||
#define MAX_FREE_BLOCKS 128
|
||||
|
||||
struct ggml_allocr {
|
||||
void * data;
|
||||
size_t size;
|
||||
size_t alignment;
|
||||
int n_free_blocks;
|
||||
struct free_block free_blocks[MAX_FREE_BLOCKS];
|
||||
struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
|
||||
size_t max_size;
|
||||
bool measure;
|
||||
int parse_seq[GGML_MAX_CONCUR];
|
||||
int parse_seq_len;
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
struct ggml_tensor * allocated_tensors[1024];
|
||||
#endif
|
||||
};
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
for (int i = 0; i < 1024; i++) {
|
||||
if (alloc->allocated_tensors[i] == NULL) {
|
||||
alloc->allocated_tensors[i] = tensor;
|
||||
return;
|
||||
}
|
||||
}
|
||||
GGML_ASSERT(!"out of allocated_tensors");
|
||||
}
|
||||
static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
for (int i = 0; i < 1024; i++) {
|
||||
if (alloc->allocated_tensors[i] == tensor ||
|
||||
(alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
|
||||
alloc->allocated_tensors[i] = NULL;
|
||||
return;
|
||||
}
|
||||
}
|
||||
printf("tried to free tensor %s not found\n", tensor->name);
|
||||
GGML_ASSERT(!"tensor not found");
|
||||
}
|
||||
#endif
|
||||
|
||||
|
||||
static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
return ggml_nbytes(tensor);
|
||||
|
||||
UNUSED(alloc);
|
||||
}
|
||||
|
||||
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
|
||||
size = aligned_offset(NULL, size, alloc->alignment);
|
||||
|
||||
AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
|
||||
|
||||
size_t max_avail = 0;
|
||||
|
||||
// find the best fitting free block besides the last block
|
||||
int best_fit_block = -1;
|
||||
size_t best_fit_size = SIZE_MAX;
|
||||
for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
|
||||
struct free_block * block = &alloc->free_blocks[i];
|
||||
max_avail = MAX(max_avail, block->size);
|
||||
if (block->size >= size && block->size <= best_fit_size) {
|
||||
best_fit_block = i;
|
||||
best_fit_size = block->size;
|
||||
}
|
||||
}
|
||||
|
||||
AT_PRINTF("block %d\n", best_fit_block);
|
||||
|
||||
if (best_fit_block == -1) {
|
||||
// the last block is our last resort
|
||||
struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
|
||||
if (block->size >= size) {
|
||||
best_fit_block = alloc->n_free_blocks - 1;
|
||||
max_avail = MAX(max_avail, block->size);
|
||||
} else {
|
||||
fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
|
||||
__func__, size, max_avail);
|
||||
GGML_ASSERT(!"not enough space in the buffer");
|
||||
return;
|
||||
}
|
||||
}
|
||||
struct free_block * block = &alloc->free_blocks[best_fit_block];
|
||||
void * addr = block->addr;
|
||||
block->addr = (char*)block->addr + size;
|
||||
block->size -= size;
|
||||
if (block->size == 0) {
|
||||
// remove block if empty
|
||||
alloc->n_free_blocks--;
|
||||
for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
|
||||
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
||||
}
|
||||
}
|
||||
|
||||
tensor->data = addr;
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
add_allocated_tensor(alloc, tensor);
|
||||
size_t cur_max = (char*)addr - (char*)alloc->data + size;
|
||||
if (cur_max > alloc->max_size) {
|
||||
printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
|
||||
for (int i = 0; i < 1024; i++) {
|
||||
if (alloc->allocated_tensors[i]) {
|
||||
printf("%s (%.2f MB) ", alloc->allocated_tensors[i]->name, ggml_nbytes(alloc->allocated_tensors[i]) / 1024.0 / 1024.0);
|
||||
}
|
||||
}
|
||||
printf("\n");
|
||||
}
|
||||
#endif
|
||||
|
||||
alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
|
||||
}
|
||||
|
||||
// this is a very naive implementation, but for our case the number of free blocks should be very small
|
||||
static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
|
||||
void * ptr = tensor->data;
|
||||
|
||||
if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) {
|
||||
// the tensor was not allocated in this buffer
|
||||
// this can happen because the graph allocator will try to free weights and other tensors from different buffers
|
||||
// the easiest way to deal with this is just to ignore it
|
||||
return;
|
||||
}
|
||||
|
||||
size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
|
||||
size = aligned_offset(NULL, size, alloc->alignment);
|
||||
AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks);
|
||||
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
remove_allocated_tensor(alloc, tensor);
|
||||
#endif
|
||||
|
||||
// see if we can merge with an existing block
|
||||
for (int i = 0; i < alloc->n_free_blocks; i++) {
|
||||
struct free_block * block = &alloc->free_blocks[i];
|
||||
// check if ptr is at the end of the block
|
||||
if ((char*)block->addr + block->size == ptr) {
|
||||
block->size += size;
|
||||
// check if we can merge with the next block
|
||||
if (i < alloc->n_free_blocks - 1 && (char*)block->addr + block->size == alloc->free_blocks[i+1].addr) {
|
||||
block->size += alloc->free_blocks[i+1].size;
|
||||
alloc->n_free_blocks--;
|
||||
for (int j = i+1; j < alloc->n_free_blocks; j++) {
|
||||
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
// check if ptr is at the beginning of the block
|
||||
if ((char*)ptr + size == block->addr) {
|
||||
block->addr = ptr;
|
||||
block->size += size;
|
||||
// check if we can merge with the previous block
|
||||
if (i > 0 && (char*)alloc->free_blocks[i-1].addr + alloc->free_blocks[i-1].size == block->addr) {
|
||||
alloc->free_blocks[i-1].size += block->size;
|
||||
alloc->n_free_blocks--;
|
||||
for (int j = i; j < alloc->n_free_blocks; j++) {
|
||||
alloc->free_blocks[j] = alloc->free_blocks[j+1];
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
}
|
||||
// otherwise, add a new block
|
||||
GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
|
||||
// insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
|
||||
int insert_pos = 0;
|
||||
while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].addr < ptr) {
|
||||
insert_pos++;
|
||||
}
|
||||
// shift all blocks from insert_pos onward to make room for the new block
|
||||
for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
|
||||
alloc->free_blocks[i] = alloc->free_blocks[i-1];
|
||||
}
|
||||
// insert the new block
|
||||
alloc->free_blocks[insert_pos].addr = ptr;
|
||||
alloc->free_blocks[insert_pos].size = size;
|
||||
alloc->n_free_blocks++;
|
||||
}
|
||||
|
||||
void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n) {
|
||||
for (int i = 0; i < n; i++) {
|
||||
alloc->parse_seq[i] = list[i];
|
||||
}
|
||||
alloc->parse_seq_len = n;
|
||||
}
|
||||
|
||||
void ggml_allocr_reset(struct ggml_allocr * alloc) {
|
||||
alloc->n_free_blocks = 1;
|
||||
size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
|
||||
alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
|
||||
alloc->free_blocks[0].size = alloc->size - align_offset;
|
||||
}
|
||||
|
||||
struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
|
||||
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
|
||||
|
||||
*alloc = (struct ggml_allocr){
|
||||
/*.data = */ data,
|
||||
/*.size = */ size,
|
||||
/*.alignment = */ alignment,
|
||||
/*.n_free_blocks = */ 0,
|
||||
/*.free_blocks = */ {{0}},
|
||||
/*.hash_table = */ {{0}},
|
||||
/*.max_size = */ 0,
|
||||
/*.measure = */ false,
|
||||
/*.parse_seq = */ {0},
|
||||
/*.parse_seq_len = */ 0,
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
/*.allocated_tensors = */ {0},
|
||||
#endif
|
||||
};
|
||||
|
||||
ggml_allocr_reset(alloc);
|
||||
|
||||
return alloc;
|
||||
}
|
||||
|
||||
// address and size of the buffer when measuring
|
||||
// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers
|
||||
static void * const MEASURE_BASE_ADDR = (void *) 0x1000;
|
||||
static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB
|
||||
|
||||
struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
|
||||
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */);
|
||||
|
||||
*alloc = (struct ggml_allocr){
|
||||
/*.data = */ MEASURE_BASE_ADDR,
|
||||
/*.size = */ MEASURE_MAX_SIZE,
|
||||
/*.alignment = */ alignment,
|
||||
/*.n_free_blocks = */ 0,
|
||||
/*.free_blocks = */ {{0}},
|
||||
/*.hash_table = */ {{0}},
|
||||
/*.max_size = */ 0,
|
||||
/*.measure = */ true,
|
||||
/*.parse_seq = */ {0},
|
||||
/*.parse_seq_len = */ 0,
|
||||
#ifdef GGML_ALLOCATOR_DEBUG
|
||||
/*.allocated_tensors = */ {0},
|
||||
#endif
|
||||
};
|
||||
|
||||
ggml_allocr_reset(alloc);
|
||||
|
||||
return alloc;
|
||||
}
|
||||
|
||||
void ggml_allocr_free(struct ggml_allocr * alloc) {
|
||||
free(alloc);
|
||||
}
|
||||
|
||||
bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
|
||||
return alloc->measure;
|
||||
}
|
||||
|
||||
//////////// compute graph allocator
|
||||
|
||||
static bool ggml_is_view(struct ggml_tensor * t) {
|
||||
return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
|
||||
t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
|
||||
}
|
||||
|
||||
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
|
||||
if (a->type != b->type) {
|
||||
return false;
|
||||
}
|
||||
for (int i = 0; i < GGML_MAX_DIMS; i++) {
|
||||
if (a->ne[i] != b->ne[i]) {
|
||||
return false;
|
||||
}
|
||||
if (a->nb[i] != b->nb[i]) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
|
||||
switch (t->op) {
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_RESHAPE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
case GGML_OP_VIEW:
|
||||
return t->src[0];
|
||||
case GGML_OP_CPY:
|
||||
return t->src[1];
|
||||
default:
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
|
||||
static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
|
||||
struct ggml_tensor * parent = t;
|
||||
do {
|
||||
parent = get_view_parent(parent);
|
||||
} while (ggml_is_view(parent));
|
||||
return parent;
|
||||
}
|
||||
|
||||
static bool ggml_op_can_inplace(enum ggml_op op) {
|
||||
switch (op) {
|
||||
case GGML_OP_SCALE:
|
||||
case GGML_OP_DIAG_MASK_ZERO:
|
||||
case GGML_OP_DIAG_MASK_INF:
|
||||
case GGML_OP_ADD:
|
||||
case GGML_OP_ADD1:
|
||||
case GGML_OP_ACC:
|
||||
case GGML_OP_SUB:
|
||||
case GGML_OP_MUL:
|
||||
case GGML_OP_DIV:
|
||||
case GGML_OP_SQR:
|
||||
case GGML_OP_SQRT:
|
||||
case GGML_OP_LOG:
|
||||
case GGML_OP_UNARY:
|
||||
case GGML_OP_ROPE:
|
||||
case GGML_OP_RMS_NORM:
|
||||
case GGML_OP_SET:
|
||||
case GGML_OP_SOFT_MAX:
|
||||
case GGML_OP_CONT:
|
||||
case GGML_OP_ADD_REL_POS:
|
||||
return true;
|
||||
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
|
||||
struct hash_node * ht = alloc->hash_table;
|
||||
if (node->data == NULL) {
|
||||
if (ggml_is_view(node)) {
|
||||
size_t offset;
|
||||
switch(node->op) {
|
||||
case GGML_OP_VIEW:
|
||||
memcpy(&offset, node->op_params, sizeof(size_t));
|
||||
node->data = (char *) node->src[0]->data + offset;
|
||||
break;
|
||||
case GGML_OP_PERMUTE:
|
||||
case GGML_OP_RESHAPE:
|
||||
case GGML_OP_TRANSPOSE:
|
||||
node->data = node->src[0]->data;
|
||||
break;
|
||||
case GGML_OP_CPY:
|
||||
node->data = node->src[1]->data;
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(!"unknown view op");
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
// see if we can reuse a parent's buffer (inplace)
|
||||
if (ggml_op_can_inplace(node->op)) {
|
||||
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
||||
struct ggml_tensor * parent = node->src[i];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
|
||||
// if the node's data is external, then we cannot re-use it
|
||||
if ((char *) parent->data < (char *) alloc->data ||
|
||||
(char *) parent->data >= ((char *) alloc->data + alloc->size)) {
|
||||
AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
|
||||
continue;
|
||||
}
|
||||
|
||||
struct hash_node * p_hn = hash_get(ht, parent);
|
||||
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
|
||||
if (ggml_is_view(parent)) {
|
||||
struct ggml_tensor * view_src = get_view_source(parent);
|
||||
struct hash_node * view_src_hn = hash_get(ht, view_src);
|
||||
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
|
||||
// TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
|
||||
// the parent's data that it will need later (same layout requirement). the problem is that then
|
||||
// we cannot free the tensor because the original address of the allocation is lost.
|
||||
// adding a view_src pointer to the tensor would solve this and simplify the code dealing with views
|
||||
// for now, we only reuse the parent's data if the offset is zero (view_src->data == parent->data)
|
||||
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
|
||||
node->data = parent->data;
|
||||
return;
|
||||
}
|
||||
}
|
||||
else {
|
||||
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
|
||||
node->data = parent->data;
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
ggml_allocr_alloc(alloc, node);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static size_t ggml_allocator_alloc_graph_tensors_n(
|
||||
struct ggml_allocr * alloc,
|
||||
struct ggml_cgraph ** graphs, int n_graphs,
|
||||
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
|
||||
|
||||
// reset hash table
|
||||
struct hash_node * ht = alloc->hash_table;
|
||||
memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
|
||||
|
||||
// count number of children and views
|
||||
for (int g = 0; g < n_graphs; g++) {
|
||||
struct ggml_cgraph * gf = graphs[g];
|
||||
for (int i = 0; i < gf->n_nodes; i++) {
|
||||
struct ggml_tensor * node = gf->nodes[i];
|
||||
|
||||
if (ggml_is_view(node)) {
|
||||
struct ggml_tensor * view_src = get_view_source(node);
|
||||
hash_get(ht, view_src)->n_views += 1;
|
||||
}
|
||||
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
hash_get(ht, parent)->n_children += 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// allocate tensors
|
||||
for (int g = 0; g < n_graphs; g++) {
|
||||
struct ggml_cgraph * gf = graphs[g];
|
||||
AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
|
||||
// graph inputs are allocated first to ensure that they are not overwritten by each other
|
||||
if (inputs != NULL && inputs[g] != NULL) {
|
||||
for (int i = 0; inputs[g][i] != NULL; i++) {
|
||||
struct ggml_tensor * input = inputs[g][i];
|
||||
AT_PRINTF("input: %s\n", input->name);
|
||||
allocate_node(alloc, input);
|
||||
}
|
||||
}
|
||||
// if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers
|
||||
int last_barrier_pos = 0;
|
||||
int n_nodes = alloc->parse_seq_len ? alloc->parse_seq_len : gf->n_nodes;
|
||||
|
||||
for (int ind = 0; ind < n_nodes; ind++) {
|
||||
// allocate a node if there is no parse_seq or this is not a barrier
|
||||
if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] != -1) {
|
||||
int i = alloc->parse_seq_len ? alloc->parse_seq[ind] : ind;
|
||||
struct ggml_tensor * node = gf->nodes[i];
|
||||
|
||||
// allocate parents (leafs)
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
allocate_node(alloc, parent);
|
||||
}
|
||||
|
||||
// allocate node
|
||||
allocate_node(alloc, node);
|
||||
|
||||
AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
AT_PRINTF("%s", parent->name);
|
||||
if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
|
||||
AT_PRINTF(", ");
|
||||
}
|
||||
}
|
||||
AT_PRINTF("\n");
|
||||
}
|
||||
|
||||
|
||||
// update parents
|
||||
// update immediately if there is no parse_seq
|
||||
// update only at barriers if there is parse_seq
|
||||
if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] == -1) {
|
||||
int update_start = alloc->parse_seq_len ? last_barrier_pos : ind;
|
||||
int update_end = alloc->parse_seq_len ? ind : ind + 1;
|
||||
for (int i = update_start; i < update_end; i++) {
|
||||
int node_i = alloc->parse_seq_len ? alloc->parse_seq[i] : i;
|
||||
struct ggml_tensor * node = gf->nodes[node_i];
|
||||
|
||||
for (int j = 0; j < GGML_MAX_SRC; j++) {
|
||||
struct ggml_tensor * parent = node->src[j];
|
||||
if (parent == NULL) {
|
||||
break;
|
||||
}
|
||||
struct hash_node * p_hn = hash_get(ht, parent);
|
||||
p_hn->n_children -= 1;
|
||||
|
||||
//AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
|
||||
|
||||
if (p_hn->n_children == 0 && p_hn->n_views == 0) {
|
||||
if (ggml_is_view(parent)) {
|
||||
struct ggml_tensor * view_src = get_view_source(parent);
|
||||
struct hash_node * view_src_hn = hash_get(ht, view_src);
|
||||
view_src_hn->n_views -= 1;
|
||||
AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
|
||||
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
|
||||
ggml_allocator_free_tensor(alloc, view_src);
|
||||
}
|
||||
}
|
||||
else {
|
||||
if (parent->data != node->data) {
|
||||
ggml_allocator_free_tensor(alloc, parent);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
AT_PRINTF("\n");
|
||||
if (alloc->parse_seq_len) {
|
||||
last_barrier_pos = ind + 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
// free graph outputs here that wouldn't be freed otherwise because they have no children
|
||||
if (outputs != NULL && outputs[g] != NULL) {
|
||||
for (int i = 0; outputs[g][i] != NULL; i++) {
|
||||
struct ggml_tensor * output = outputs[g][i];
|
||||
AT_PRINTF("output: %s\n", output->name);
|
||||
ggml_allocator_free_tensor(alloc, output);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return alloc->max_size;
|
||||
}
|
||||
|
||||
size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
|
||||
return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL);
|
||||
}
|
||||
|
|
@ -0,0 +1,26 @@
|
|||
#pragma once
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
|
||||
GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
|
||||
GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
|
||||
|
||||
// tell the allocator to parse nodes following the order described in the list
|
||||
// you should call this if your graph are optimized to execute out-of-order
|
||||
GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);
|
||||
|
||||
GGML_API void ggml_allocr_free(struct ggml_allocr * alloc);
|
||||
GGML_API bool ggml_allocr_is_measure(struct ggml_allocr * alloc);
|
||||
GGML_API void ggml_allocr_reset(struct ggml_allocr * alloc);
|
||||
GGML_API void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
|
||||
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
4336
ggml-cuda.cu
4336
ggml-cuda.cu
File diff suppressed because it is too large
Load Diff
46
ggml-cuda.h
46
ggml-cuda.h
|
|
@ -2,34 +2,44 @@
|
|||
|
||||
#include "ggml.h"
|
||||
|
||||
#ifdef GGML_USE_HIPBLAS
|
||||
#define GGML_CUDA_NAME "ROCm"
|
||||
#define GGML_CUBLAS_NAME "hipBLAS"
|
||||
#else
|
||||
#define GGML_CUDA_NAME "CUDA"
|
||||
#define GGML_CUBLAS_NAME "cuBLAS"
|
||||
#endif
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#define GGML_CUDA_MAX_DEVICES 16
|
||||
|
||||
void ggml_init_cublas(void);
|
||||
void ggml_cuda_set_tensor_split(const float * tensor_split);
|
||||
GGML_API void ggml_init_cublas(void);
|
||||
GGML_API void * ggml_cuda_host_malloc(size_t size);
|
||||
GGML_API void ggml_cuda_host_free(void * ptr);
|
||||
|
||||
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
|
||||
GGML_API bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
|
||||
GGML_API void ggml_cuda_set_tensor_split(const float * tensor_split);
|
||||
GGML_API void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_cuda_free_data(struct ggml_tensor * tensor);
|
||||
|
||||
// TODO: export these with GGML_API
|
||||
void * ggml_cuda_host_malloc(size_t size);
|
||||
void ggml_cuda_host_free(void * ptr);
|
||||
GGML_API void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
|
||||
|
||||
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
|
||||
|
||||
void ggml_cuda_free_data(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
|
||||
void ggml_cuda_set_main_device(int main_device);
|
||||
void ggml_cuda_set_scratch_size(size_t scratch_size);
|
||||
void ggml_cuda_free_scratch(void);
|
||||
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
GGML_API void ggml_cuda_set_main_device(int main_device);
|
||||
GGML_API void ggml_cuda_set_mul_mat_q(bool mul_mat_q);
|
||||
GGML_API void ggml_cuda_set_scratch_size(size_t scratch_size);
|
||||
GGML_API void ggml_cuda_free_scratch(void);
|
||||
GGML_API bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API int ggml_cuda_get_device_count(void);
|
||||
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
20
ggml-metal.h
20
ggml-metal.h
|
|
@ -24,6 +24,7 @@
|
|||
|
||||
// max memory buffers that can be mapped to the device
|
||||
#define GGML_METAL_MAX_BUFFERS 16
|
||||
#define GGML_METAL_MAX_COMMAND_BUFFERS 32
|
||||
|
||||
struct ggml_tensor;
|
||||
struct ggml_cgraph;
|
||||
|
|
@ -34,9 +35,16 @@ extern "C" {
|
|||
|
||||
struct ggml_metal_context;
|
||||
|
||||
struct ggml_metal_context * ggml_metal_init(void);
|
||||
// number of command buffers to use
|
||||
struct ggml_metal_context * ggml_metal_init(int n_cb);
|
||||
void ggml_metal_free(struct ggml_metal_context * ctx);
|
||||
|
||||
void * ggml_metal_host_malloc(size_t n);
|
||||
void ggml_metal_host_free (void * data);
|
||||
|
||||
// set the number of command buffers to use
|
||||
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
|
||||
|
||||
// creates a mapping between a host memory buffer and a device memory buffer
|
||||
// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
|
||||
// - the mapping is used during computation to determine the arguments of the compute kernels
|
||||
|
|
@ -57,6 +65,16 @@ void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
|
|||
// get data from the device into host memory
|
||||
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
|
||||
|
||||
// try to find operations that can be run concurrently in the graph
|
||||
// you should run it again if the topology of your graph changes
|
||||
void ggml_metal_graph_find_concurrency(struct ggml_metal_context * ctx, struct ggml_cgraph * gf, bool check_mem);
|
||||
|
||||
// if the graph has been optimized for concurrently dispatch, return length of the concur_list if optimized
|
||||
int ggml_metal_if_optimized(struct ggml_metal_context * ctx);
|
||||
|
||||
// output the concur_list for ggml_alloc
|
||||
int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx);
|
||||
|
||||
// same as ggml_graph_compute but uses Metal
|
||||
// creates gf->n_threads command buffers in parallel
|
||||
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
|
||||
|
|
|
|||
723
ggml-metal.m
723
ggml-metal.m
File diff suppressed because it is too large
Load Diff
2136
ggml-metal.metal
2136
ggml-metal.metal
File diff suppressed because it is too large
Load Diff
|
|
@ -656,10 +656,14 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
|
|||
\n#if K_QUANTS_PER_ITERATION == 1\n
|
||||
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
|
||||
const int is = 0;
|
||||
|
||||
\n#else\n
|
||||
|
||||
const int l0 = 4 * in; // 0, 4, 8, ..., 28
|
||||
const int is = in / 4;
|
||||
|
||||
\n#endif\n
|
||||
|
||||
const int ql_offset = 64*im + l0;
|
||||
const int qh_offset = 32*im + l0;
|
||||
const int s_offset = 8*im + is;
|
||||
|
|
@ -1376,7 +1380,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
|
|||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
const int64_t ne02 = src0->ne[2];
|
||||
const int64_t ne03 = src0->ne[2];
|
||||
const int64_t ne03 = src0->ne[3];
|
||||
const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
|
||||
const int64_t ne10 = src1->ne[0];
|
||||
const int64_t ne11 = src1->ne[1];
|
||||
|
|
|
|||
620
ggml.h
620
ggml.h
|
|
@ -65,7 +65,7 @@
|
|||
// ggml_set_f32(a, 3.0f);
|
||||
// ggml_set_f32(b, 4.0f);
|
||||
//
|
||||
// ggml_graph_compute(ctx0, &gf);
|
||||
// ggml_graph_compute_with_ctx(ctx, &gf, n_threads);
|
||||
//
|
||||
// printf("f = %f\n", ggml_get_f32_1d(f, 0));
|
||||
//
|
||||
|
|
@ -130,13 +130,16 @@
|
|||
// The data of the tensor is accessed via the "data" pointer. For example:
|
||||
//
|
||||
// {
|
||||
// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3);
|
||||
// const int nx = 2;
|
||||
// const int ny = 3;
|
||||
//
|
||||
// // a[1, 2] = 1.0f;
|
||||
// *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
|
||||
// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
|
||||
//
|
||||
// // a[2, 0] = 2.0f;
|
||||
// *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f;
|
||||
// for (int y = 0; y < ny; y++) {
|
||||
// for (int x = 0; x < nx; x++) {
|
||||
// *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y;
|
||||
// }
|
||||
// }
|
||||
//
|
||||
// ...
|
||||
// }
|
||||
|
|
@ -183,6 +186,15 @@
|
|||
# define GGML_API
|
||||
#endif
|
||||
|
||||
// TODO: support for clang
|
||||
#ifdef __GNUC__
|
||||
# define GGML_DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
|
||||
#elif defined(_MSC_VER)
|
||||
# define GGML_DEPRECATED(func, hint) __declspec(deprecated(hint)) func
|
||||
#else
|
||||
# define GGML_DEPRECATED(func, hint) func
|
||||
#endif
|
||||
|
||||
#include <stdint.h>
|
||||
#include <stddef.h>
|
||||
#include <stdbool.h>
|
||||
|
|
@ -197,12 +209,29 @@
|
|||
#define GGML_MAX_NODES 4096
|
||||
#define GGML_MAX_PARAMS 256
|
||||
#define GGML_MAX_CONTEXTS 64
|
||||
#define GGML_MAX_OPT 4
|
||||
#define GGML_MAX_NAME 48
|
||||
#define GGML_MAX_SRC 6
|
||||
#define GGML_MAX_NAME 64
|
||||
#define GGML_MAX_OP_PARAMS 32
|
||||
#define GGML_DEFAULT_N_THREADS 4
|
||||
|
||||
#if UINTPTR_MAX == 0xFFFFFFFF
|
||||
#define GGML_MEM_ALIGN 4
|
||||
#else
|
||||
#define GGML_MEM_ALIGN 16
|
||||
#endif
|
||||
|
||||
#define GGML_EXIT_SUCCESS 0
|
||||
#define GGML_EXIT_ABORTED 1
|
||||
|
||||
#define GGUF_MAGIC 0x46554747 // "GGUF"
|
||||
#define GGUF_VERSION 2
|
||||
|
||||
#define GGUF_DEFAULT_ALIGNMENT 32
|
||||
|
||||
#define GGML_UNUSED(x) (void)(x)
|
||||
|
||||
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
|
||||
|
||||
#define GGML_ASSERT(x) \
|
||||
do { \
|
||||
if (!(x)) { \
|
||||
|
|
@ -239,8 +268,9 @@
|
|||
extern "C" {
|
||||
#endif
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
// we use the built-in 16-bit float type
|
||||
#if defined(__ARM_NEON) && defined(__CUDACC__)
|
||||
typedef half ggml_fp16_t;
|
||||
#elif defined(__ARM_NEON)
|
||||
typedef __fp16 ggml_fp16_t;
|
||||
#else
|
||||
typedef uint16_t ggml_fp16_t;
|
||||
|
|
@ -250,8 +280,8 @@ extern "C" {
|
|||
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
|
||||
GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
|
||||
|
||||
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
|
||||
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
|
||||
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
|
||||
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
|
||||
|
||||
struct ggml_object;
|
||||
struct ggml_context;
|
||||
|
|
@ -324,20 +354,12 @@ extern "C" {
|
|||
GGML_OP_ARGMAX,
|
||||
GGML_OP_REPEAT,
|
||||
GGML_OP_REPEAT_BACK,
|
||||
GGML_OP_ABS,
|
||||
GGML_OP_SGN,
|
||||
GGML_OP_NEG,
|
||||
GGML_OP_STEP,
|
||||
GGML_OP_TANH,
|
||||
GGML_OP_ELU,
|
||||
GGML_OP_RELU,
|
||||
GGML_OP_GELU,
|
||||
GGML_OP_GELU_QUICK,
|
||||
GGML_OP_SILU,
|
||||
GGML_OP_CONCAT,
|
||||
GGML_OP_SILU_BACK,
|
||||
GGML_OP_NORM, // normalize
|
||||
GGML_OP_RMS_NORM,
|
||||
GGML_OP_RMS_NORM_BACK,
|
||||
GGML_OP_GROUP_NORM,
|
||||
|
||||
GGML_OP_MUL_MAT,
|
||||
GGML_OP_OUT_PROD,
|
||||
|
|
@ -363,16 +385,29 @@ extern "C" {
|
|||
GGML_OP_CLAMP,
|
||||
GGML_OP_CONV_1D,
|
||||
GGML_OP_CONV_2D,
|
||||
GGML_OP_CONV_TRANSPOSE_2D,
|
||||
GGML_OP_POOL_1D,
|
||||
GGML_OP_POOL_2D,
|
||||
|
||||
GGML_OP_UPSCALE, // nearest interpolate
|
||||
|
||||
GGML_OP_FLASH_ATTN,
|
||||
GGML_OP_FLASH_FF,
|
||||
GGML_OP_FLASH_ATTN_BACK,
|
||||
GGML_OP_WIN_PART,
|
||||
GGML_OP_WIN_UNPART,
|
||||
GGML_OP_GET_REL_POS,
|
||||
GGML_OP_ADD_REL_POS,
|
||||
|
||||
GGML_OP_UNARY,
|
||||
|
||||
GGML_OP_MAP_UNARY,
|
||||
GGML_OP_MAP_BINARY,
|
||||
|
||||
GGML_OP_MAP_CUSTOM1_F32,
|
||||
GGML_OP_MAP_CUSTOM2_F32,
|
||||
GGML_OP_MAP_CUSTOM3_F32,
|
||||
|
||||
GGML_OP_MAP_CUSTOM1,
|
||||
GGML_OP_MAP_CUSTOM2,
|
||||
GGML_OP_MAP_CUSTOM3,
|
||||
|
|
@ -383,6 +418,24 @@ extern "C" {
|
|||
GGML_OP_COUNT,
|
||||
};
|
||||
|
||||
enum ggml_unary_op {
|
||||
GGML_UNARY_OP_ABS,
|
||||
GGML_UNARY_OP_SGN,
|
||||
GGML_UNARY_OP_NEG,
|
||||
GGML_UNARY_OP_STEP,
|
||||
GGML_UNARY_OP_TANH,
|
||||
GGML_UNARY_OP_ELU,
|
||||
GGML_UNARY_OP_RELU,
|
||||
GGML_UNARY_OP_GELU,
|
||||
GGML_UNARY_OP_GELU_QUICK,
|
||||
GGML_UNARY_OP_SILU,
|
||||
};
|
||||
|
||||
enum ggml_object_type {
|
||||
GGML_OBJECT_TENSOR,
|
||||
GGML_OBJECT_GRAPH,
|
||||
GGML_OBJECT_WORK_BUFFER
|
||||
};
|
||||
|
||||
// ggml object
|
||||
struct ggml_object {
|
||||
|
|
@ -391,7 +444,9 @@ extern "C" {
|
|||
|
||||
struct ggml_object * next;
|
||||
|
||||
char padding[8];
|
||||
enum ggml_object_type type;
|
||||
|
||||
char padding[4];
|
||||
};
|
||||
|
||||
static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object);
|
||||
|
|
@ -411,15 +466,13 @@ extern "C" {
|
|||
// compute data
|
||||
enum ggml_op op;
|
||||
|
||||
// op params - allocated as int32_t for alignment
|
||||
int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
|
||||
|
||||
bool is_param;
|
||||
|
||||
struct ggml_tensor * grad;
|
||||
struct ggml_tensor * src0;
|
||||
struct ggml_tensor * src1;
|
||||
struct ggml_tensor * opt[GGML_MAX_OPT];
|
||||
|
||||
// thread scheduling
|
||||
int n_tasks;
|
||||
struct ggml_tensor * src[GGML_MAX_SRC];
|
||||
|
||||
// performance
|
||||
int perf_runs;
|
||||
|
|
@ -437,25 +490,46 @@ extern "C" {
|
|||
|
||||
static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
|
||||
|
||||
// the compute plan that needs to be prepared for ggml_graph_compute()
|
||||
// since https://github.com/ggerganov/ggml/issues/287
|
||||
struct ggml_cplan {
|
||||
size_t work_size; // size of work buffer, calculated by `ggml_graph_plan()`
|
||||
uint8_t * work_data; // work buffer, to be allocated by caller before calling to `ggml_graph_compute()`
|
||||
|
||||
int n_threads;
|
||||
|
||||
// the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
|
||||
int n_tasks[GGML_MAX_NODES];
|
||||
|
||||
// abort ggml_graph_compute when true
|
||||
bool (*abort_callback)(void * data);
|
||||
void * abort_callback_data;
|
||||
};
|
||||
|
||||
// next prime after GGML_MAX_NODES
|
||||
// #define GGML_GRAPH_HASHTABLE_SIZE 4099
|
||||
// next prime after GGML_MAX_NODES * 2 (nodes + leafs)
|
||||
#define GGML_GRAPH_HASHTABLE_SIZE 8273
|
||||
|
||||
// computation graph
|
||||
struct ggml_cgraph {
|
||||
int n_nodes;
|
||||
int n_leafs;
|
||||
int n_threads;
|
||||
|
||||
size_t work_size;
|
||||
struct ggml_tensor * work;
|
||||
|
||||
struct ggml_tensor * nodes[GGML_MAX_NODES];
|
||||
struct ggml_tensor * grads[GGML_MAX_NODES];
|
||||
struct ggml_tensor * leafs[GGML_MAX_NODES];
|
||||
|
||||
void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE];
|
||||
|
||||
// performance
|
||||
int perf_runs;
|
||||
int64_t perf_cycles;
|
||||
int64_t perf_time_us;
|
||||
};
|
||||
|
||||
static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
|
||||
|
||||
// scratch buffer
|
||||
struct ggml_scratch {
|
||||
size_t offs;
|
||||
|
|
@ -509,6 +583,7 @@ extern "C" {
|
|||
GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
|
||||
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
|
||||
GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
|
||||
|
||||
GGML_API int ggml_blck_size (enum ggml_type type);
|
||||
|
|
@ -517,6 +592,7 @@ extern "C" {
|
|||
|
||||
GGML_API const char * ggml_type_name(enum ggml_type type);
|
||||
GGML_API const char * ggml_op_name (enum ggml_op op);
|
||||
GGML_API const char * ggml_op_symbol(enum ggml_op op);
|
||||
|
||||
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
|
||||
|
|
@ -529,6 +605,8 @@ extern "C" {
|
|||
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
|
||||
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
|
||||
|
||||
// use this to compute the memory overhead of a tensor
|
||||
GGML_API size_t ggml_tensor_overhead(void);
|
||||
|
||||
|
|
@ -540,6 +618,7 @@ extern "C" {
|
|||
GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
|
||||
|
||||
GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
|
||||
GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx);
|
||||
GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
|
||||
|
||||
GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
|
||||
|
|
@ -599,9 +678,11 @@ extern "C" {
|
|||
GGML_API void * ggml_get_data (const struct ggml_tensor * tensor);
|
||||
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name);
|
||||
GGML_API struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...);
|
||||
GGML_API enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API const char * ggml_get_name (const struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_tensor * ggml_set_name ( struct ggml_tensor * tensor, const char * name);
|
||||
GGML_API struct ggml_tensor * ggml_format_name( struct ggml_tensor * tensor, const char * fmt, ...);
|
||||
|
||||
//
|
||||
// operations on tensors with backpropagation
|
||||
|
|
@ -611,6 +692,11 @@ extern "C" {
|
|||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// in-place, returns view(a)
|
||||
GGML_API struct ggml_tensor * ggml_dup_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_add(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
@ -735,6 +821,13 @@ extern "C" {
|
|||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// concat a and b on dim 2
|
||||
// used in stable-diffusion
|
||||
GGML_API struct ggml_tensor * ggml_concat(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_abs(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
|
@ -824,25 +917,42 @@ extern "C" {
|
|||
struct ggml_tensor * b);
|
||||
|
||||
// normalize along rows
|
||||
// TODO: eps is hardcoded to 1e-5 for now
|
||||
GGML_API struct ggml_tensor * ggml_norm(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
struct ggml_tensor * a,
|
||||
float eps);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_norm_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
struct ggml_tensor * a,
|
||||
float eps);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_rms_norm(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
struct ggml_tensor * a,
|
||||
float eps);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
struct ggml_tensor * a,
|
||||
float eps);
|
||||
|
||||
// group normalize along ne0*ne1*n_groups
|
||||
// used in stable-diffusion
|
||||
// TODO: eps is hardcoded to 1e-6 for now
|
||||
GGML_API struct ggml_tensor * ggml_group_norm(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_groups);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_group_norm_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_groups);
|
||||
|
||||
// a - x
|
||||
// b - dy
|
||||
// TODO: update with configurable eps
|
||||
GGML_API struct ggml_tensor * ggml_rms_norm_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
@ -934,11 +1044,22 @@ extern "C" {
|
|||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// a -> b, in-place, return view(b)
|
||||
GGML_API struct ggml_tensor * ggml_cpy_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// make contiguous
|
||||
GGML_API struct ggml_tensor * ggml_cont(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// make contiguous, in-place
|
||||
GGML_API struct ggml_tensor * ggml_cont_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// return view(a), b specifies the new shape
|
||||
// TODO: when we start computing gradient, make a copy instead of view
|
||||
GGML_API struct ggml_tensor * ggml_reshape(
|
||||
|
|
@ -1107,6 +1228,37 @@ extern "C" {
|
|||
int mode,
|
||||
int n_ctx);
|
||||
|
||||
// custom RoPE
|
||||
GGML_API struct ggml_tensor * ggml_rope_custom(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_dims,
|
||||
int mode,
|
||||
int n_ctx,
|
||||
float freq_base,
|
||||
float freq_scale);
|
||||
|
||||
// in-place, returns view(a)
|
||||
GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_dims,
|
||||
int mode,
|
||||
int n_ctx,
|
||||
float freq_base,
|
||||
float freq_scale);
|
||||
|
||||
// xPos RoPE, in-place, returns view(a)
|
||||
GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_dims,
|
||||
float base,
|
||||
bool down);
|
||||
|
||||
// rotary position embedding backward, i.e compute dx from dy
|
||||
// a - dy
|
||||
GGML_API struct ggml_tensor * ggml_rope_back(
|
||||
|
|
@ -1114,7 +1266,12 @@ extern "C" {
|
|||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_dims,
|
||||
int mode);
|
||||
int mode,
|
||||
int n_ctx,
|
||||
float freq_base,
|
||||
float freq_scale,
|
||||
float xpos_base,
|
||||
bool xpos_down);
|
||||
|
||||
// alibi position embedding
|
||||
// in-place, returns view(a)
|
||||
|
|
@ -1141,6 +1298,15 @@ extern "C" {
|
|||
int p0, // padding
|
||||
int d0); // dilation
|
||||
|
||||
// conv_1d with padding = half
|
||||
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
|
||||
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
int s,
|
||||
int d);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_conv_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
|
|
@ -1152,14 +1318,70 @@ extern "C" {
|
|||
int d0,
|
||||
int d1);
|
||||
|
||||
// conv_1d with padding = half
|
||||
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
|
||||
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
|
||||
|
||||
// kernel size is a->ne[0] x a->ne[1]
|
||||
// stride is equal to kernel size
|
||||
// padding is zero
|
||||
// example:
|
||||
// a: 16 16 3 768
|
||||
// b: 1024 1024 3 1
|
||||
// res: 64 64 768 1
|
||||
// used in sam
|
||||
GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// kernel size is a->ne[0] x a->ne[1]
|
||||
// stride is 1
|
||||
// padding is half
|
||||
// example:
|
||||
// a: 3 3 256 256
|
||||
// b: 64 64 256 1
|
||||
// res: 64 64 256 1
|
||||
// used in sam
|
||||
GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
int s,
|
||||
int d);
|
||||
int stride);
|
||||
|
||||
enum ggml_op_pool {
|
||||
GGML_OP_POOL_MAX,
|
||||
GGML_OP_POOL_AVG,
|
||||
GGML_OP_POOL_COUNT,
|
||||
};
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_pool_1d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_op_pool op,
|
||||
int k0, // kernel size
|
||||
int s0, // stride
|
||||
int p0); // padding
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_pool_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_op_pool op,
|
||||
int k0,
|
||||
int k1,
|
||||
int s0,
|
||||
int s1,
|
||||
int p0,
|
||||
int p1);
|
||||
|
||||
// nearest interpolate
|
||||
// used in stable-diffusion
|
||||
GGML_API struct ggml_tensor * ggml_upscale(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int scale_factor);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_flash_attn(
|
||||
struct ggml_context * ctx,
|
||||
|
|
@ -1204,6 +1426,37 @@ extern "C" {
|
|||
int h0,
|
||||
int w);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_unary(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_unary_op op);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_unary_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_unary_op op);
|
||||
|
||||
// used in sam
|
||||
GGML_API struct ggml_tensor * ggml_get_rel_pos(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int qh,
|
||||
int kh);
|
||||
|
||||
// used in sam
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_add_rel_pos(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * pw,
|
||||
struct ggml_tensor * ph);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * pw,
|
||||
struct ggml_tensor * ph);
|
||||
|
||||
// custom operators
|
||||
|
||||
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
|
||||
|
|
@ -1213,63 +1466,129 @@ extern "C" {
|
|||
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_unary_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_unary_op_f32_t fun);
|
||||
ggml_unary_op_f32_t fun),
|
||||
"use ggml_map_custom1 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_unary_op_f32_t fun);
|
||||
ggml_unary_op_f32_t fun),
|
||||
"use ggml_map_custom1_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_binary_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_binary_op_f32_t fun);
|
||||
ggml_binary_op_f32_t fun),
|
||||
"use ggml_map_custom2 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_binary_op_f32_t fun);
|
||||
ggml_binary_op_f32_t fun),
|
||||
"use ggml_map_custom2_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_f32_t fun);
|
||||
ggml_custom1_op_f32_t fun),
|
||||
"use ggml_map_custom1 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_f32_t fun);
|
||||
ggml_custom1_op_f32_t fun),
|
||||
"use ggml_map_custom1_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_f32_t fun);
|
||||
ggml_custom2_op_f32_t fun),
|
||||
"use ggml_map_custom2 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_f32_t fun);
|
||||
ggml_custom2_op_f32_t fun),
|
||||
"use ggml_map_custom2_inplace instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_f32_t fun);
|
||||
ggml_custom3_op_f32_t fun),
|
||||
"use ggml_map_custom3 instead");
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
|
||||
GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_f32_t fun);
|
||||
ggml_custom3_op_f32_t fun),
|
||||
"use ggml_map_custom3_inplace instead");
|
||||
|
||||
// custom operators v2
|
||||
|
||||
typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);
|
||||
typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
|
||||
typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
|
||||
|
||||
#define GGML_N_TASKS_MAX -1
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom1_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
ggml_custom1_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom2_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
ggml_custom2_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_map_custom3_inplace(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
struct ggml_tensor * c,
|
||||
ggml_custom3_op_t fun,
|
||||
int n_tasks,
|
||||
void * userdata);
|
||||
|
||||
// loss function
|
||||
|
||||
|
|
@ -1290,15 +1609,28 @@ extern "C" {
|
|||
|
||||
GGML_API void ggml_set_param(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * tensor);
|
||||
struct ggml_tensor * tensor);
|
||||
|
||||
|
||||
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
|
||||
GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
|
||||
|
||||
GGML_API void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
|
||||
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
|
||||
// graph allocation in a context
|
||||
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx);
|
||||
GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
|
||||
GGML_API size_t ggml_graph_overhead(void);
|
||||
|
||||
// ggml_graph_plan() has to be called before ggml_graph_compute()
|
||||
// when plan.work_size > 0, caller must allocate memory for plan.work_data
|
||||
GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
|
||||
GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
|
||||
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
|
||||
|
||||
// same as ggml_graph_compute() but the work data is allocated as a part of the context
|
||||
// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
|
||||
GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
|
||||
|
||||
|
|
@ -1488,6 +1820,127 @@ extern "C" {
|
|||
|
||||
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
|
||||
|
||||
//
|
||||
// gguf
|
||||
//
|
||||
|
||||
enum gguf_type {
|
||||
GGUF_TYPE_UINT8 = 0,
|
||||
GGUF_TYPE_INT8 = 1,
|
||||
GGUF_TYPE_UINT16 = 2,
|
||||
GGUF_TYPE_INT16 = 3,
|
||||
GGUF_TYPE_UINT32 = 4,
|
||||
GGUF_TYPE_INT32 = 5,
|
||||
GGUF_TYPE_FLOAT32 = 6,
|
||||
GGUF_TYPE_BOOL = 7,
|
||||
GGUF_TYPE_STRING = 8,
|
||||
GGUF_TYPE_ARRAY = 9,
|
||||
GGUF_TYPE_UINT64 = 10,
|
||||
GGUF_TYPE_INT64 = 11,
|
||||
GGUF_TYPE_FLOAT64 = 12,
|
||||
GGUF_TYPE_COUNT, // marks the end of the enum
|
||||
};
|
||||
|
||||
struct gguf_context;
|
||||
|
||||
struct gguf_init_params {
|
||||
bool no_alloc;
|
||||
|
||||
// if not NULL, create a ggml_context and allocate the tensor data in it
|
||||
struct ggml_context ** ctx;
|
||||
};
|
||||
|
||||
GGML_API struct gguf_context * gguf_init_empty(void);
|
||||
GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
|
||||
//GGML_API struct gguf_context * gguf_init_from_buffer(..);
|
||||
|
||||
GGML_API void gguf_free(struct gguf_context * ctx);
|
||||
|
||||
GGML_API const char * gguf_type_name(enum gguf_type type);
|
||||
|
||||
GGML_API int gguf_get_version (struct gguf_context * ctx);
|
||||
GGML_API size_t gguf_get_alignment (struct gguf_context * ctx);
|
||||
GGML_API size_t gguf_get_data_offset(struct gguf_context * ctx);
|
||||
GGML_API void * gguf_get_data (struct gguf_context * ctx);
|
||||
|
||||
GGML_API int gguf_get_n_kv(struct gguf_context * ctx);
|
||||
GGML_API int gguf_find_key(struct gguf_context * ctx, const char * key);
|
||||
GGML_API const char * gguf_get_key (struct gguf_context * ctx, int i);
|
||||
|
||||
GGML_API enum gguf_type gguf_get_kv_type (struct gguf_context * ctx, int i);
|
||||
GGML_API enum gguf_type gguf_get_arr_type(struct gguf_context * ctx, int i);
|
||||
|
||||
// results are undefined if the wrong type is used for the key
|
||||
GGML_API uint8_t gguf_get_val_u8 (struct gguf_context * ctx, int i);
|
||||
GGML_API int8_t gguf_get_val_i8 (struct gguf_context * ctx, int i);
|
||||
GGML_API uint16_t gguf_get_val_u16 (struct gguf_context * ctx, int i);
|
||||
GGML_API int16_t gguf_get_val_i16 (struct gguf_context * ctx, int i);
|
||||
GGML_API uint32_t gguf_get_val_u32 (struct gguf_context * ctx, int i);
|
||||
GGML_API int32_t gguf_get_val_i32 (struct gguf_context * ctx, int i);
|
||||
GGML_API float gguf_get_val_f32 (struct gguf_context * ctx, int i);
|
||||
GGML_API uint64_t gguf_get_val_u64 (struct gguf_context * ctx, int i);
|
||||
GGML_API int64_t gguf_get_val_i64 (struct gguf_context * ctx, int i);
|
||||
GGML_API double gguf_get_val_f64 (struct gguf_context * ctx, int i);
|
||||
GGML_API bool gguf_get_val_bool(struct gguf_context * ctx, int i);
|
||||
GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);
|
||||
GGML_API int gguf_get_arr_n (struct gguf_context * ctx, int i);
|
||||
GGML_API const void * gguf_get_arr_data(struct gguf_context * ctx, int i);
|
||||
GGML_API const char * gguf_get_arr_str (struct gguf_context * ctx, int key_id, int i);
|
||||
|
||||
GGML_API int gguf_get_n_tensors (struct gguf_context * ctx);
|
||||
GGML_API int gguf_find_tensor (struct gguf_context * ctx, const char * name);
|
||||
GGML_API size_t gguf_get_tensor_offset(struct gguf_context * ctx, int i);
|
||||
GGML_API char * gguf_get_tensor_name (struct gguf_context * ctx, int i);
|
||||
|
||||
// overrides existing values or adds a new one
|
||||
GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
|
||||
GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);
|
||||
GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
|
||||
GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val);
|
||||
GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
|
||||
GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);
|
||||
GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);
|
||||
GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
|
||||
GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val);
|
||||
GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val);
|
||||
GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);
|
||||
GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
|
||||
GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
|
||||
GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);
|
||||
|
||||
// set or add KV pairs from another context
|
||||
GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
|
||||
|
||||
// manage tensor info
|
||||
GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
|
||||
GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
|
||||
GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
|
||||
|
||||
// writing gguf files can be done in 2 ways:
|
||||
//
|
||||
// - write the entire gguf_context to a binary file in a single pass:
|
||||
//
|
||||
// gguf_write_to_file(ctx, fname);
|
||||
//
|
||||
// - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
|
||||
//
|
||||
// FILE * f = fopen(fname, "wb");
|
||||
// fseek(f, gguf_get_meta_size(ctx), SEEK_SET);
|
||||
// fwrite(f, ...);
|
||||
// void * data = gguf_meta_get_meta_data(ctx);
|
||||
// fseek(f, 0, SEEK_SET);
|
||||
// fwrite(f, data, gguf_get_meta_size(ctx));
|
||||
// free(data);
|
||||
// fclose(f);
|
||||
//
|
||||
|
||||
// write the entire context to a binary file
|
||||
GGML_API void gguf_write_to_file(struct gguf_context * ctx, const char * fname, bool only_meta);
|
||||
|
||||
// get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
|
||||
GGML_API size_t gguf_get_meta_size(struct gguf_context * ctx);
|
||||
GGML_API void gguf_get_meta_data(struct gguf_context * ctx, void * data);
|
||||
|
||||
//
|
||||
// system info
|
||||
//
|
||||
|
|
@ -1516,25 +1969,28 @@ extern "C" {
|
|||
//
|
||||
|
||||
#ifdef __cplusplus
|
||||
// restrict not standard in C++
|
||||
// restrict not standard in C++
|
||||
#define GGML_RESTRICT
|
||||
#else
|
||||
#define GGML_RESTRICT restrict
|
||||
#endif
|
||||
typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
|
||||
typedef void (*quantize_row_q_t) (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
|
||||
typedef void (*vec_dot_q_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
|
||||
typedef void (*ggml_to_float_t) (const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
|
||||
typedef void (*ggml_from_float_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
|
||||
typedef void (*ggml_vec_dot_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
|
||||
|
||||
typedef struct {
|
||||
dequantize_row_q_t dequantize_row_q;
|
||||
quantize_row_q_t quantize_row_q;
|
||||
quantize_row_q_t quantize_row_q_reference;
|
||||
quantize_row_q_t quantize_row_q_dot;
|
||||
vec_dot_q_t vec_dot_q;
|
||||
enum ggml_type vec_dot_type;
|
||||
} quantize_fns_t;
|
||||
const char * type_name;
|
||||
int blck_size;
|
||||
size_t type_size;
|
||||
bool is_quantized;
|
||||
ggml_to_float_t to_float;
|
||||
ggml_from_float_t from_float;
|
||||
ggml_from_float_t from_float_reference;
|
||||
ggml_vec_dot_t vec_dot;
|
||||
enum ggml_type vec_dot_type;
|
||||
} ggml_type_traits_t;
|
||||
|
||||
quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
|
||||
ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
40
whisper.cpp
40
whisper.cpp
|
|
@ -441,6 +441,7 @@ struct whisper_hparams {
|
|||
int32_t n_text_layer = 4;
|
||||
int32_t n_mels = 80;
|
||||
int32_t ftype = 1;
|
||||
float eps = 1e-5f;
|
||||
};
|
||||
|
||||
// audio encoding layer
|
||||
|
|
@ -1578,7 +1579,7 @@ static bool whisper_encode_internal(
|
|||
{
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_norm(ctx0, inpL);
|
||||
cur = ggml_norm(ctx0, inpL, hparams.eps);
|
||||
|
||||
// cur = ln_0_w*cur + ln_0_b
|
||||
cur = ggml_add(ctx0,
|
||||
|
|
@ -1725,7 +1726,7 @@ static bool whisper_encode_internal(
|
|||
{
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_norm(ctx0, inpFF);
|
||||
cur = ggml_norm(ctx0, inpFF, hparams.eps);
|
||||
|
||||
wstate.use_buf(ctx0, 1);
|
||||
|
||||
|
|
@ -1788,7 +1789,7 @@ static bool whisper_encode_internal(
|
|||
{
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
|
||||
wstate.use_buf(ctx0, 1);
|
||||
|
||||
|
|
@ -1805,10 +1806,9 @@ static bool whisper_encode_internal(
|
|||
// run the computation
|
||||
{
|
||||
struct ggml_cgraph gf = {};
|
||||
gf.n_threads = n_threads;
|
||||
|
||||
ggml_build_forward_expand(&gf, cur);
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
ggml_build_forward_expand (&gf, cur);
|
||||
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
||||
|
||||
//ggml_graph_print(&gf);
|
||||
}
|
||||
|
|
@ -1851,12 +1851,11 @@ static bool whisper_encode_internal(
|
|||
// pre-compute cross-attention memory
|
||||
{
|
||||
struct ggml_cgraph gf = {};
|
||||
gf.n_threads = n_threads;
|
||||
|
||||
// TODO: hack to disconnect the encoded features from the previous graph
|
||||
cur->op = GGML_OP_NONE;
|
||||
cur->src0 = nullptr;
|
||||
cur->src1 = nullptr;
|
||||
cur->src[0] = nullptr;
|
||||
cur->src[1] = nullptr;
|
||||
|
||||
for (int il = 0; il < model.hparams.n_text_layer; ++il) {
|
||||
auto& layer = model.layers_decoder[il];
|
||||
|
|
@ -1894,7 +1893,7 @@ static bool whisper_encode_internal(
|
|||
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcross, v));
|
||||
}
|
||||
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
||||
//ggml_graph_print(&gf);
|
||||
}
|
||||
|
||||
|
|
@ -1965,7 +1964,6 @@ static bool whisper_decode_internal(
|
|||
struct ggml_context * ctx0 = ggml_init(params);
|
||||
|
||||
struct ggml_cgraph gf = {};
|
||||
gf.n_threads = n_threads;
|
||||
|
||||
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
||||
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
||||
|
|
@ -1992,7 +1990,7 @@ static bool whisper_decode_internal(
|
|||
{
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_norm(ctx0, inpL);
|
||||
cur = ggml_norm(ctx0, inpL, hparams.eps);
|
||||
|
||||
// cur = ln_0_w*cur + ln_0_b
|
||||
cur = ggml_add(ctx0,
|
||||
|
|
@ -2119,7 +2117,7 @@ static bool whisper_decode_internal(
|
|||
{
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_norm(ctx0, inpCA); // note: we use inpCA here
|
||||
cur = ggml_norm(ctx0, inpCA, hparams.eps); // note: we use inpCA here
|
||||
|
||||
// cur = ln_0_w*cur + ln_0_b
|
||||
cur = ggml_add(ctx0,
|
||||
|
|
@ -2229,7 +2227,7 @@ static bool whisper_decode_internal(
|
|||
{
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_norm(ctx0, inpFF);
|
||||
cur = ggml_norm(ctx0, inpFF, hparams.eps);
|
||||
|
||||
wstate.use_buf(ctx0, 1);
|
||||
|
||||
|
|
@ -2284,7 +2282,7 @@ static bool whisper_decode_internal(
|
|||
{
|
||||
wstate.use_buf(ctx0, 0);
|
||||
|
||||
cur = ggml_norm(ctx0, cur);
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
|
||||
wstate.use_buf(ctx0, 1);
|
||||
|
||||
|
|
@ -2308,8 +2306,8 @@ static bool whisper_decode_internal(
|
|||
|
||||
// run the computation
|
||||
{
|
||||
ggml_build_forward_expand(&gf, logits);
|
||||
ggml_graph_compute (ctx0, &gf);
|
||||
ggml_build_forward_expand (&gf, logits);
|
||||
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
||||
}
|
||||
|
||||
// extract logits for all N tokens
|
||||
|
|
@ -2358,7 +2356,7 @@ static std::string to_timestamp(int64_t t, bool comma = false) {
|
|||
static float sin_vals[SIN_COS_N_COUNT];
|
||||
static float cos_vals[SIN_COS_N_COUNT];
|
||||
|
||||
// In FFT, we frequently use sine and cosine operations with the same values.
|
||||
// In FFT, we frequently use sine and cosine operations with the same values.
|
||||
// We can use precalculated values to speed up the process.
|
||||
static void fill_sin_cos_table() {
|
||||
static bool is_filled = false;
|
||||
|
|
@ -5165,17 +5163,15 @@ WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
|
|||
|
||||
struct ggml_cgraph gf = ggml_build_forward(c);
|
||||
|
||||
gf.n_threads = n_threads;
|
||||
|
||||
double tsum = 0.0;
|
||||
|
||||
// heat-up
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
||||
|
||||
for (int i = 0; i < n_max; ++i) {
|
||||
const int64_t t0 = ggml_time_us();
|
||||
|
||||
ggml_graph_compute(ctx0, &gf);
|
||||
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
||||
|
||||
const int64_t t1 = ggml_time_us();
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue