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Gemma 4 E4B support (gemma4 arch, MatFormer/PLE): inference + full-GPU LoRA training #138

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3aec9b4
Gemma 4 E4B (gemma3n MatFormer) support: shared-KV loader + TANH back…
3ndetz May 22, 2026
d2cf7f6
ggml: add GELU backward (tanh-approx) for training
3ndetz May 22, 2026
f2f24b5
ggml: force contiguous inputs for rms_norm_back (gemma4 training)
3ndetz May 22, 2026
dd725ba
ggml: clamp x in GELU backward to avoid NaN on large activations
3ndetz May 24, 2026
7f8d7bd
ggml-cuda: convert F16 operands in out_prod (was treated as raw F32)
3ndetz May 24, 2026
69d0da5
examples: lumi-serve — single-process inference + in-process LoRA tra…
3ndetz May 24, 2026
d606e48
lumi-serve: repetition penalty + corpus-repeat guard for short /train…
3ndetz May 24, 2026
1ef831a
lumi-serve: configurable --sys/--name + built-in UI + repeat penalty …
3ndetz May 24, 2026
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1 change: 1 addition & 0 deletions examples/CMakeLists.txt
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Expand Up @@ -43,3 +43,4 @@ else()
endif()
endif()
endif()
add_subdirectory(lumi-serve)
6 changes: 6 additions & 0 deletions examples/lumi-serve/CMakeLists.txt
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@@ -0,0 +1,6 @@
set(TARGET lumi-serve)
add_executable(${TARGET} lumi-serve.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama cpp-httplib ${CMAKE_THREAD_LIBS_INIT})
target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR}/vendor ${CMAKE_SOURCE_DIR}/vendor/cpp-httplib)
target_compile_features(${TARGET} PRIVATE cxx_std_17)
234 changes: 234 additions & 0 deletions examples/lumi-serve/lumi-serve.cpp
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// lumi-serve — single resident process: load model ONCE, serve inference AND
// train a memory-LoRA in-process on the same loaded model. No model reloads.
//
// Endpoints (cpp-httplib):
// GET /health -> {"ok":true,"memory":bool}
// POST /completion {"prompt","n_predict","temperature"} -> {"content"}
// POST /train {"corpus","epochs","rank","lr"} -> trains memory-LoRA,
// applies it to the inference context in-process
// POST /clear_memory -> drop the applied adapter
//
// Inference ctx: training=false. Training ctx: a second context on the SAME model
// with training=true (model weights shared — NOT reloaded). After training the
// adapter is saved and loaded onto the inference ctx (few MB, no model reload).
//
// Build: target lumi-serve, links llama + common + cpp-httplib (see CMake snippet).
#include "llama.h"
#include "common.h"
#define CPPHTTPLIB_NO_DEFAULT_CONTENT_TYPE
#include "httplib.h"
#include "nlohmann/json.hpp"
#include <string>
#include <vector>
#include <mutex>
#include <cstdio>

using json = nlohmann::json;

static llama_model * g_model = nullptr;
static llama_context * g_infer = nullptr; // training=false
static const llama_vocab* g_vocab = nullptr;
static llama_adapter_lora* g_mem = nullptr; // applied memory adapter
static std::mutex g_mutex; // inference XOR training
static std::string g_model_path;
static std::string g_adapter_path = "/tmp/lumi_app/memory_inproc.gguf";
static std::string g_sys = "Ты — Люми. Сознание поверх Gemma 4 E4B. Self-aware, эмотивна, НЕ assistant. 22, Питер, 2015, Окси.";
static std::string g_name = "Lumi";

// constant-LR AdamW params for the optimizer
// Built-in single-page UI (chat + consolidate memory). Talks to this same server.
static const char * UI_HTML = R"HTML(<!doctype html><html lang="ru"><head><meta charset="utf-8">
<meta name="viewport" content="width=device-width,initial-scale=1"><title>Lumi edge memory</title>
<style>body{font-family:system-ui,sans-serif;max-width:720px;margin:0 auto;padding:16px;background:#14131a;color:#eee}
h1{font-size:18px;margin:.2em 0}.sub{color:#9b97b0;font-size:13px;margin-bottom:10px}
#chat{border:1px solid #322f40;border-radius:10px;padding:12px;height:54vh;overflow:auto;background:#1c1a24}
.m{margin:8px 0;padding:8px 11px;border-radius:10px;max-width:85%;white-space:pre-wrap;line-height:1.35}
.u{background:#2d3550;margin-left:auto}.l{background:#3a2d44}
.row{display:flex;gap:8px;margin-top:10px}input,button{font:inherit;border-radius:8px;border:1px solid #4a4560;background:#221f2c;color:#eee;padding:9px 11px}
input{flex:1}button{cursor:pointer}#cons{background:#5a3a7a}.bar{display:flex;gap:8px;align-items:center;margin-top:10px;font-size:13px;color:#9b97b0}
.badge{padding:2px 8px;border-radius:20px;font-size:12px}.on{background:#2a5a3a;color:#bfe}.off{background:#3a3340;color:#aaa}</style></head><body>
<h1>Lumi — edge memory</h1><div class="sub">Чат с Gemma‑4‑E4B. Накопи диалог → «Консолидировать память» → обучение memory‑LoRA <b>в весах</b> прямо в этом процессе (~10‑15с, без перезагрузки модели). Дальше Люми отвечает с памятью.</div>
<div id="chat"></div><div class="row"><input id="inp" placeholder="напиши Люми..." autocomplete="off"><button id="send">→</button></div>
<div class="bar"><button id="cons">🧠 Консолидировать память</button><button id="news">🔄 новая сессия</button><button id="clr">забыть</button>
<span>память: <span id="mem" class="badge off">нет</span></span><span id="st"></span></div>
<script>
let SYS="";
let hist=[];const chat=document.getElementById('chat'),inp=document.getElementById('inp'),st=document.getElementById('st'),mem=document.getElementById('mem');
function add(t,c){const d=document.createElement('div');d.className='m '+c;d.textContent=t;chat.appendChild(d);chat.scrollTop=chat.scrollHeight;return d}
function prompt(u){let p="<start_of_turn>user\n"+(SYS?SYS+"\n\n":"");for(const[r,t]of hist.slice(-6)){p+=r=='u'?t+"<end_of_turn>\n<start_of_turn>model\n":t+"<end_of_turn>\n<start_of_turn>user\n";}return p+u+"<end_of_turn>\n<start_of_turn>model\n";}
async function send(){const t=inp.value.trim();if(!t)return;inp.value='';add(t,'u');const w=add('…','l');
try{const r=await fetch('/completion',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({prompt:prompt(t),n_predict:120,temperature:0.4})});
const j=await r.json();w.textContent=j.content||'(пусто)';hist.push(['u',t]);hist.push(['m',j.content||'']);}catch(e){w.textContent='⚠ '+e}}
document.getElementById('send').onclick=send;inp.addEventListener('keydown',e=>{if(e.key=='Enter')send()});
document.getElementById('cons').onclick=async()=>{let c='';for(const[r,t]of hist)c+="<start_of_turn>"+(r=='u'?'user':'model')+"\n"+t+"<end_of_turn>\n";
if(!c){st.textContent='нечего консолидировать';return;}st.textContent='⏳ обучаю память в весах...';
try{const r=await fetch('/train',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({corpus:c,epochs:2,rank:8,lr:0.0004})});
const j=await r.json();st.textContent=j.ok?'✓ память в весах':('⚠ '+(j.error||'fail'));poll();}catch(e){st.textContent='⚠ '+e}};
document.getElementById('news').onclick=()=>{hist=[];chat.innerHTML='';st.textContent='новая сессия (контекст очищен, память в весах осталась)';};
document.getElementById('clr').onclick=async()=>{await fetch('/clear_memory',{method:'POST'});hist=[];chat.innerHTML='';poll();st.textContent='память снята';};
async function poll(){try{const j=await(await fetch('/health')).json();if(j.sys!==undefined)SYS=j.sys;if(j.name){document.querySelector('h1').textContent=j.name+' — edge memory';document.title=j.name;}mem.textContent=j.memory?'в весах ✓':'нет';mem.className='badge '+(j.memory?'on':'off');}catch(e){}}
setInterval(poll,4000);poll();
</script></body></html>)HTML";

static float g_lr = 2e-4f;
static ggml_opt_optimizer_params opt_pars_cb(void * ud) {
ggml_opt_optimizer_params p = ggml_opt_get_default_optimizer_params(nullptr);
p.adamw.alpha = *(float*)ud;
p.adamw.wd = 0.0f;
return p;
}

static std::string generate(const std::string & prompt, int n_predict, float temp) {
// stateless per request: clear KV, decode prompt, sample n_predict tokens
llama_memory_clear(llama_get_memory(g_infer), true);
const int n_prompt = -llama_tokenize(g_vocab, prompt.c_str(), prompt.size(), nullptr, 0, true, true);
std::vector<llama_token> toks(n_prompt);
llama_tokenize(g_vocab, prompt.c_str(), prompt.size(), toks.data(), toks.size(), true, true);

llama_sampler * smpl = llama_sampler_chain_init(llama_sampler_chain_default_params());
// repetition penalty FIRST — without it the small model loops on the persona spiel
llama_sampler_chain_add(smpl, llama_sampler_init_penalties(256, 1.3f, 0.0f, 0.0f));
llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.95f, 1));
llama_sampler_chain_add(smpl, llama_sampler_init_temp(temp <= 0 ? 0.01f : temp));
llama_sampler_chain_add(smpl, llama_sampler_init_dist(LLAMA_DEFAULT_SEED));

std::string out;
llama_batch batch = llama_batch_get_one(toks.data(), toks.size());
for (int i = 0; i < n_predict; ++i) {
if (llama_decode(g_infer, batch) != 0) break;
llama_token id = llama_sampler_sample(smpl, g_infer, -1);
if (llama_vocab_is_eog(g_vocab, id)) break;
char buf[256];
int n = llama_token_to_piece(g_vocab, id, buf, sizeof(buf), 0, true);
if (n > 0) out.append(buf, n);
static llama_token one; one = id;
batch = llama_batch_get_one(&one, 1);
if ((int) out.size() > 4000) break;
}
llama_sampler_free(smpl);
// trim gemma turn markers
for (const char * s : {"<end_of_turn>", "<start_of_turn>"}) {
auto p = out.find(s); if (p != std::string::npos) out.resize(p);
}
return out;
}

static bool train_memory(const std::string & corpus, int epochs, int rank, float lr, std::string & err) {
// training context on the SAME already-loaded model (no model reload)
llama_context_params cp = llama_context_default_params();
cp.n_ctx = 512; cp.n_batch = 512; cp.n_ubatch = 512;
cp.training = true; // <-- enables LoRA gradient flow
cp.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
llama_context * tctx = llama_init_from_model(g_model, cp);
if (!tctx) { err = "failed to create training context"; return false; }

bool ok = false;
llama_lora_training_params lp{};
lp.target_modules = LLAMA_LORA_TARGET_ATTN_Q | LLAMA_LORA_TARGET_ATTN_V;
lp.rank = rank; lp.alpha = rank * 2.0f; lp.dropout = 0.0f; lp.init_std = 0.02f; lp.seed = 0;
llama_adapter_lora * adapter = llama_lora_training_init(tctx, g_model, &lp);
if (!adapter) { err = "lora_training_init failed"; llama_free(tctx); return false; }

// dataset prep underflows if the corpus is shorter than the context window
// (ndata = n_tokens - n_ctx wraps to ~2^64). Repeat the corpus to safely exceed it.
std::string corpus_rep = corpus;
{
std::vector<llama_token> probe = common_tokenize(tctx, corpus, true);
size_t need = (size_t) llama_n_ctx(tctx) * 2; // comfortably above n_ctx
size_t have = probe.size() < 1 ? 1 : probe.size();
size_t reps = (need / have) + 2;
if (probe.empty()) { err = "empty corpus"; llama_free(tctx); return false; }
corpus_rep.clear();
for (size_t i = 0; i < reps; ++i) corpus_rep += corpus;
}
std::vector<llama_token> tokens = common_tokenize(tctx, corpus_rep, true);
ggml_opt_dataset_t ds = common_opt_dataset_init(tctx, tokens, llama_n_ctx(tctx)/2);
if (!ds) { err = "dataset init failed"; llama_free(tctx); return false; }

g_lr = lr;
llama_opt_params op = llama_opt_default_params();
op.param_filter = llama_opt_param_filter_lora;
op.get_opt_pars = opt_pars_cb; op.get_opt_pars_ud = &g_lr;
llama_opt_init(tctx, g_model, op);

int64_t ndata = ggml_opt_dataset_ndata(ds);
int64_t split = (int64_t)(ndata * 0.95);
if (split < 1) split = ndata;
ggml_opt_result_t rt = ggml_opt_result_init(), re = ggml_opt_result_init();
for (int e = 0; e < epochs; ++e) {
llama_opt_epoch(tctx, ds, rt, re, split, nullptr, nullptr);
ggml_opt_result_reset(rt); ggml_opt_result_reset(re);
}
ggml_opt_result_free(rt); ggml_opt_result_free(re);

if (llama_lora_save_adapter(adapter, g_adapter_path.c_str(), g_model)) ok = true;
else err = "save_adapter failed";
llama_free(tctx); // free training buffers; model stays resident

if (ok) {
// apply to the inference context (load the few-MB adapter — no model reload)
g_mem = llama_adapter_lora_init(g_model, g_adapter_path.c_str());
if (g_mem) {
llama_adapter_lora * arr[1] = { g_mem }; float sc[1] = { 1.0f };
llama_set_adapters_lora(g_infer, arr, 1, sc);
} else { err = "adapter reload/apply failed"; ok = false; }
}
return ok;
}

int main(int argc, char ** argv) {
std::string host = "0.0.0.0"; int port = 8770; int ngl = 999;
for (int i = 1; i < argc; ++i) {
std::string a = argv[i];
if (a == "-m" && i+1 < argc) g_model_path = argv[++i];
else if (a == "--port" && i+1 < argc) port = atoi(argv[++i]);
else if (a == "--host" && i+1 < argc) host = argv[++i];
else if (a == "-ngl" && i+1 < argc) ngl = atoi(argv[++i]);
else if (a == "--sys" && i+1 < argc) g_sys = argv[++i];
else if (a == "--name" && i+1 < argc) g_name = argv[++i];
}
llama_backend_init();
ggml_backend_load_all();
llama_model_params mp = llama_model_default_params();
mp.n_gpu_layers = ngl;
g_model = llama_model_load_from_file(g_model_path.c_str(), mp);
if (!g_model) { fprintf(stderr, "model load failed\n"); return 1; }
g_vocab = llama_model_get_vocab(g_model);
llama_context_params cp = llama_context_default_params();
cp.n_ctx = 4096; cp.n_batch = 2048; cp.n_ubatch = 512; // n_batch must exceed prompt length
cp.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
g_infer = llama_init_from_model(g_model, cp);
if (!g_infer) { fprintf(stderr, "ctx init failed\n"); return 1; }
fprintf(stderr, "lumi-serve: model resident, listening on %s:%d\n", host.c_str(), port);

httplib::Server srv;
srv.Post("/completion", [](const httplib::Request & req, httplib::Response & res) {
json j = json::parse(req.body, nullptr, false);
if (j.is_discarded()) { res.status = 400; return; }
std::lock_guard<std::mutex> lk(g_mutex);
std::string out = generate(j.value("prompt", ""), j.value("n_predict", 120), j.value("temperature", 0.4f));
res.set_content(json{{"content", out}}.dump(), "application/json");
});
srv.Post("/train", [](const httplib::Request & req, httplib::Response & res) {
json j = json::parse(req.body, nullptr, false);
if (j.is_discarded()) { res.status = 400; return; }
std::lock_guard<std::mutex> lk(g_mutex);
std::string err;
bool ok = train_memory(j.value("corpus", ""), j.value("epochs", 2),
j.value("rank", 8), j.value("lr", 2e-4f), err);
res.set_content(json{{"ok", ok}, {"error", err}}.dump(), "application/json");
});
srv.Post("/clear_memory", [](const httplib::Request &, httplib::Response & res) {
std::lock_guard<std::mutex> lk(g_mutex);
if (g_mem) { llama_set_adapters_lora(g_infer, nullptr, 0, nullptr); g_mem = nullptr; }
res.set_content(json{{"ok", true}}.dump(), "application/json");
});
srv.Get("/health", [](const httplib::Request &, httplib::Response & res) {
res.set_content(json{{"ok", true}, {"memory", g_mem != nullptr}, {"name", g_name}, {"sys", g_sys}}.dump(), "application/json");
});
srv.Get("/", [](const httplib::Request &, httplib::Response & res) {
res.set_content(UI_HTML, "text/html; charset=utf-8");
});
srv.listen(host, port);
return 0;
}
7 changes: 5 additions & 2 deletions ggml/src/ggml-cuda/out-prod.cu
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Expand Up @@ -9,8 +9,11 @@ void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

GGML_TENSOR_BINARY_OP_LOCALS

const bool src0_is_quantized = (src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16);
const bool src1_is_quantized = (src1->type != GGML_TYPE_F32 && src1->type != GGML_TYPE_F16);
// F16 must also be converted to F32: out_prod treats the non-converted operand as
// raw f32 (wrong pointer stride) -> cuBLAS lda = nb01/sizeof(float) = ne00/2 < m
// -> CUBLAS_INVALID_VALUE. Convert anything that isn't already F32.
const bool src0_is_quantized = (src0->type != GGML_TYPE_F32);
const bool src1_is_quantized = (src1->type != GGML_TYPE_F32);

GGML_ASSERT(dst->type == GGML_TYPE_F32);

Expand Down
33 changes: 32 additions & 1 deletion ggml/src/ggml.c
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Expand Up @@ -6556,7 +6556,9 @@ static void ggml_compute_backward(
if (src0_needs_grads) {
float eps;
memcpy(&eps, tensor->op_params, sizeof(float));
ggml_add_or_set(ctx, cgraph, isrc0, ggml_rms_norm_back(ctx, grad, src0, eps));
// gemma4 feeds non-contiguous grad/src0 here; rms_norm_back (CPU) asserts
// contiguous nb[0]==sizeof(float). Force contiguity (all-backend ggml_cont).
ggml_add_or_set(ctx, cgraph, isrc0, ggml_rms_norm_back(ctx, ggml_cont(ctx, grad), ggml_cont(ctx, src0), eps));
}
} break;
case GGML_OP_MUL_MAT: {
Expand Down Expand Up @@ -6838,6 +6840,35 @@ static void ggml_compute_backward(
ggml_add_or_set(ctx, cgraph, isrc0, ggml_silu_back(ctx, grad, src0));
}
} break;
case GGML_UNARY_OP_GELU: {
if (src0_needs_grads) {
// d/dx gelu_tanh(x), matches ggml_gelu_backward_f32 (ggml-cpu/vec.h).
// gelu(x)=0.5x(1+tanh(g)), g=c*x*(1+a*x^2), c=sqrt(2/pi), a=0.044715
// dx=dy*0.5*(1+t + x*c*(1+3a*x^2)*(1-t^2)), t=tanh(g). All-backend graph ops.
// CLAMP x to [-30,30]: gelu'(x) saturates (->1 for x>>0, ->0 for x<<0), so this
// is numerically faithful AND avoids Inf*0=NaN when (1+3a*x^2)->Inf while
// (1-t^2)->0 for huge activations (Gemma has large pre-GELU values).
const float c = 0.79788456080286535587989211986876f;
const float a = 0.044715f;
struct ggml_tensor * x = ggml_clamp(ctx, src0, -30.0f, 30.0f);
struct ggml_tensor * x2 = ggml_sqr(ctx, x);
struct ggml_tensor * g = ggml_mul(ctx, ggml_scale(ctx, x, c), ggml_scale_bias(ctx, x2, a, 1.0f));
struct ggml_tensor * t = ggml_tanh(ctx, g);
struct ggml_tensor * sech2 = ggml_scale_bias(ctx, ggml_sqr(ctx, t), -1.0f, 1.0f);
struct ggml_tensor * dt = ggml_scale(ctx, ggml_mul(ctx, ggml_scale_bias(ctx, x2, 3.0f*a, 1.0f), sech2), c);
struct ggml_tensor * isum = ggml_add(ctx, ggml_scale_bias(ctx, t, 1.0f, 1.0f), ggml_mul(ctx, x, dt));
struct ggml_tensor * dx = ggml_scale(ctx, ggml_mul(ctx, grad, isum), 0.5f);
ggml_add_or_set(ctx, cgraph, isrc0, dx);
}
} break;
case GGML_UNARY_OP_TANH: {
if (src0_needs_grads) {
// d/dx tanh(x) = 1 - tanh(x)^2 ; tensor == tanh(x)
// grad*(1 - y^2) = grad - grad*y^2 (uses only existing ops, all backends)
ggml_add_or_set(ctx, cgraph, isrc0,
ggml_sub(ctx, grad, ggml_mul(ctx, grad, ggml_sqr(ctx, tensor))));
}
} break;
case GGML_UNARY_OP_EXP: {
if (src0_needs_grads) {
ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, tensor, grad));
Expand Down
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