Add configurable MTP and shared-weight draft memory fitting

STUDIO.md

@@ -157,3 +157,7 @@ Standardwerte (automatische Geräte, keine Aufteilung, Temperature 0.8, Top-p 0.
 Top-k 40). Bestehende Dateien werden beim Lesen nicht umgeschrieben.
 
 Sprachmodelle besitzen jetzt den Reiter **Testen**: interner Textchat mit Streaming, Profilauswahl und Speicherdiagnose. Öffentliche Profilfreigabe ist dafür nicht erforderlich. Details: [ENDPOINT.md](ENDPOINT.md#sprachmodelle--testen).
+
+### MTP bei Sprachmodellprofilen
+
+Im Profileditor kann MTP aktiviert werden, mit 1–8 Draft-Token und Mindestwahrscheinlichkeit 0–1. Medium-Referenz: 2 und 0,05; Draft-KV ist f16. Bestehende Profile bleiben standardmäßig ohne MTP. Das Modell muss eingebettete MTP-Gewichte enthalten. Der CUDA-Build benötigt den Deck-MTP-Fit-Adapter; neue GUI-Builds installieren ihn automatisch. Die Prognose zählt die gemeinsamen Gewichte einmal sowie Haupt- und MTP-Kontext und deren Compute-Puffer. Ein inkompatibles Modell oder ein alter Build wird nicht still ohne MTP gestartet. MTP garantiert keinen Geschwindigkeitsgewinn.

inference.py

@@ -127,6 +127,8 @@ class LlamaWorker:
     def _start(self,profile,generation,cancel=lambda:False):
         directory=self.build();params=profile['parameters'];entry=self.catalog.entry(profile['model_id'])
         model=(self.catalog.root/entry['id']/('model'+Path(entry['file']).suffix)).resolve()
+        mtp=params.get('mtp',False)
+        if mtp and not (directory/'build/bin/deck-mtp-fit-v1').is_file():raise InferenceError('MTP benötigt einen neu gebauten llama.cpp-Build mit Deck-MTP-Speicherprüfung. Unter Laufzeiten erneut bauen.')
         devices=probe();ids=params['gpu_devices']
         if ids:
             selected=[next((g for g in devices if g['uuid']==ident),None) for ident in ids]
@@ -146,10 +148,11 @@ class LlamaWorker:
         common=['--model',str(model),'--ctx-size',str(params['context']),'--parallel',str(params['slots']),'--batch-size',str(params['batch']),'--ubatch-size',str(params['ubatch']),'--cache-type-k','q4_0','--cache-type-v','q4_0','--flash-attn','on','--split-mode',params['split_mode'],'--fit-target',','.join(str(max(1024,round(g['total_mib']*.05))) for g in selected)]
         if params['tensor_split']:common+=['--tensor-split',','.join(map(str,params['tensor_split']))]
         tool=str(directory/'build/bin/llama-fit-params')
+        mtp_fit=['--deck-mtp'] if mtp else []
         margins=[max(1024,round(g['total_mib']*.05)) for g in selected]
         def estimate(layers,extra=()):
             if cancel():raise InferenceError('Modellstart abgebrochen.')
-            output=self._fit_command([tool]+common+['--gpu-layers',str(layers),'--fit-print','on']+list(extra),env,generation,cancel)
+            output=self._fit_command([tool]+mtp_fit+common+['--gpu-layers',str(layers),'--fit-print','on']+list(extra),env,generation,cancel)
             rows={}
             for line in output.splitlines():
                 parts=line.split()
@@ -176,7 +179,7 @@ class LlamaWorker:
                 layers,memory=best
                 estimate(layers)
         else:
-            output=self._fit_command([tool]+common,env,generation,cancel)
+            output=self._fit_command([tool]+mtp_fit+common,env,generation,cancel)
             flags=shlex.split(output.strip());fit={}
             if len(flags)%2:raise InferenceError('Fit-Werkzeug lieferte ungültige Parameter.')
             for i in range(0,len(flags),2):
@@ -193,6 +196,7 @@ class LlamaWorker:
         if cancel():raise InferenceError('Modellstart abgebrochen.')
         with self.lock:self.phase='Modell wird geladen'
         launch=common+extra+['--gpu-layers',str(layers),'--fit','off','--kv-unified','--threads',str(params['threads']),'--load-mode','none','--host','127.0.0.1','--alias',profile['name'],'--no-webui','--log-disable']
+        if mtp:launch+=['--spec-type','draft-mtp','--spec-draft-n-max',str(params.get('mtp_tokens',2)),'--spec-draft-p-min',str(params.get('mtp_min_p',.05)),'--spec-draft-type-k','f16','--spec-draft-type-v','f16']
         self.root.mkdir(parents=True,exist_ok=True,mode=0o700)
         with socket.socket() as sock:sock.bind(('127.0.0.1',0));port=sock.getsockname()[1]
         key=secrets.token_urlsafe(32);keypath=self.root/'worker.key'

profiles-ui.js

@@ -1,6 +1,6 @@
 window.ProfilesUI=(()=>{
  const e=v=>String(v??'').replace(/[&<>"']/g,c=>({'&':'&amp;','<':'&lt;','>':'&gt;','"':'&quot;',"'":'&#39;'}[c]));
- const labels={temperature:'Temperature',top_p:'Top-p',top_k:'Top-k',gpu_devices:'GPUs (in Reihenfolge)',split_mode:'GPU-Split',tensor_split:'GPU-Verteilung',context:'Gesamtes Kontextbudget (Token)',slots:'Parallele Slots',threads:'CPU-Threads',batch:'Batch-Größe',ubatch:'Microbatch-Größe',width:'Breite (Pixel)',height:'Höhe (Pixel)',steps:'Schritte',seed:'Seed (−1 = zufällig)',guidance:'Guidance / CFG',speed:'Sprechgeschwindigkeit',frames:'Bildanzahl',fps:'Bilder pro Sekunde'};
+ const labels={mtp:'MTP',mtp_tokens:'MTP: maximale Draft-Token',mtp_min_p:'MTP: Mindestwahrscheinlichkeit',temperature:'Temperature',top_p:'Top-p',top_k:'Top-k',gpu_devices:'GPUs (in Reihenfolge)',split_mode:'GPU-Split',tensor_split:'GPU-Verteilung',context:'Gesamtes Kontextbudget (Token)',slots:'Parallele Slots',threads:'CPU-Threads',batch:'Batch-Größe',ubatch:'Microbatch-Größe',width:'Breite (Pixel)',height:'Höhe (Pixel)',steps:'Schritte',seed:'Seed (−1 = zufällig)',guidance:'Guidance / CFG',speed:'Sprechgeschwindigkeit',frames:'Bildanzahl',fps:'Bilder pro Sekunde'};
  const html=()=>'<section id="live-profiles"><div class="section-heading"><div><h2>Deine Profile</h2><p>Auf Athena gespeichert. Eine Modelldatei kann mehrere Profile mit unterschiedlichen Parametern haben.</p></div><button id="profile-new">Neues Profil</button></div><p id="profile-message" role="status"></p><div id="profile-list" class="profile-list"></div><div id="profile-editor"></div></section>';
  async function api(path,data){const r=await fetch('/api/v1/'+path,data?{method:'POST',headers:{'Content-Type':'application/json','X-Athena-Deck':'1'},body:JSON.stringify(data)}:{});const v=await r.json();if(!r.ok)throw Error(v.error||'Anfrage fehlgeschlagen');return v;}
  function bind(kind,modelId){
@@ -34,11 +34,11 @@ window.ProfilesUI=(()=>{
   function editor(p=null,id=null){
    panelSequence++;
    if(!models.length)return;const data=p||{id:null,revision:0,name:'',model_id:id||models[0].id,parameters:Object.fromEntries(Object.entries(schema).map(([k,v])=>[k,v[2]]))};
-   el('profile-editor').innerHTML=`<section class="card editor"><h2>${data.id?'Profil bearbeiten':'Profil anlegen'}</h2><form id="profile-form"><div class="form-grid"><label>API-Profilname<input name="name" required minlength="2" maxlength="64" pattern="[a-zA-Z0-9][a-zA-Z0-9_-]{1,63}" placeholder="z. B. qwen-image-test" value="${e(data.name)}"></label><label>Heruntergeladene Modelldatei<select name="model_id">${models.map(m=>`<option value="${m.id}" ${m.id===data.model_id?'selected':''}>${e(m.file)} · ${(m.size/1024**3).toFixed(2)} GiB</option>`).join('')}</select></label>${Object.entries(schema).map(([k,[min,max,defaultValue]])=>`<label>${e(labels[k])}<input name="${k}" type="number" required min="${min}" max="${max}" step="${['guidance','speed','temperature','top_p'].includes(k)?'any':['width','height'].includes(k)?64:1}" value="${e(data.parameters[k]??defaultValue)}"></label>`).join('')}</div>${kind==='chat'?gpuEditor(data.parameters):''}<p class="note">Speichern startet kein Modell. Die Ausführung benötigt eine passende Laufzeit und gegebenenfalls zusätzliche Modelldateien. ${kind==='chat'?'Das Kontextbudget gilt insgesamt für alle Slots.':''}</p><button>Profil auf Athena speichern</button><button class="secondary" type="button" id="profile-close">Schließen</button><p id="profile-error" role="alert"></p></form></section>`;
+   el('profile-editor').innerHTML=`<section class="card editor"><h2>${data.id?'Profil bearbeiten':'Profil anlegen'}</h2><form id="profile-form"><div class="form-grid"><label>API-Profilname<input name="name" required minlength="2" maxlength="64" pattern="[a-zA-Z0-9][a-zA-Z0-9_-]{1,63}" placeholder="z. B. qwen-image-test" value="${e(data.name)}"></label><label>Heruntergeladene Modelldatei<select name="model_id">${models.map(m=>`<option value="${m.id}" ${m.id===data.model_id?'selected':''}>${e(m.file)} · ${(m.size/1024**3).toFixed(2)} GiB</option>`).join('')}</select></label>${Object.entries(schema).map(([k,[min,max,defaultValue]])=>`<label>${e(labels[k])}<input name="${k}" type="number" required min="${min}" max="${max}" step="${['guidance','speed','temperature','top_p','mtp_min_p'].includes(k)?'any':['width','height'].includes(k)?64:1}" value="${e(data.parameters[k]??defaultValue)}"></label>`).join('')}</div>${kind==='chat'?gpuEditor(data.parameters)+`<label><input name="mtp" type="checkbox" ${data.parameters.mtp?'checked':''}> MTP aktivieren (eingebautes Draft-Modul)</label><p class="small">Nur für GGUF-Modelle mit MTP-Gewichten und kompatible Builds. Zusätzlicher Speicherbedarf; Geschwindigkeit hängt vom Modell ab. Referenz Medium: 2 Draft-Token, Mindestwahrscheinlichkeit 0,05, Draft-KV f16.</p>`:''}<p class="note">Speichern startet kein Modell. Die Ausführung benötigt eine passende Laufzeit und gegebenenfalls zusätzliche Modelldateien. ${kind==='chat'?'Das Kontextbudget gilt insgesamt für alle Slots.':''}</p><button>Profil auf Athena speichern</button><button class="secondary" type="button" id="profile-close">Schließen</button><p id="profile-error" role="alert"></p></form></section>`;
    if(kind==='chat'){const threads=el('profile-form').querySelector('[name=threads]').closest('label');const advanced=document.createElement('details');advanced.innerHTML='<summary>Erweitert: CPU-Threads</summary><p>CPU-Threads steuern CPU-Arbeit und Offloading, nicht die Anzahl der CUDA-Rechenkerne.</p>';advanced.append(threads);el('profile-form').insertBefore(advanced,el('profile-form').querySelector('.note'));}
    el('profile-close').onclick=()=>el('profile-editor').replaceChildren();
    el('profile-form').onsubmit=async event=>{event.preventDefault();const form=event.target,values=new FormData(form),button=form.querySelector('button');button.disabled=true;
-    try{const parameters=Object.fromEntries(Object.keys(schema).map(k=>[k,Number(values.get(k))]));if(kind==='chat'){if(values.get('gpu_second')&&!values.get('gpu_first'))throw Error('Bitte zuerst die erste GPU auswählen.');if((data.parameters.gpu_devices||[]).length>2)throw Error('Dieses Profil enthält mehr als zwei GPUs; der Editor unterstützt derzeit zwei.');parameters.gpu_devices=[values.get('gpu_first'),values.get('gpu_second')].filter(Boolean);parameters.split_mode=values.get('split_mode');const raw=String(values.get('tensor_split')).trim();parameters.tensor_split=raw?raw.split(',').map(x=>x.trim()?Number(x):NaN):[];}await api('profiles/save',{id:data.id,revision:data.revision,name:values.get('name'),kind,model_id:values.get('model_id'),parameters});if(!root.isConnected)return;el('profile-editor').replaceChildren();await load();message('Profil auf Athena gespeichert. Kein Modell gestartet.');}
+    try{const parameters=Object.fromEntries(Object.keys(schema).map(k=>[k,Number(values.get(k))]));if(kind==='chat'){parameters.mtp=values.has('mtp');if(values.get('gpu_second')&&!values.get('gpu_first'))throw Error('Bitte zuerst die erste GPU auswählen.');if((data.parameters.gpu_devices||[]).length>2)throw Error('Dieses Profil enthält mehr als zwei GPUs; der Editor unterstützt derzeit zwei.');parameters.gpu_devices=[values.get('gpu_first'),values.get('gpu_second')].filter(Boolean);parameters.split_mode=values.get('split_mode');const raw=String(values.get('tensor_split')).trim();parameters.tensor_split=raw?raw.split(',').map(x=>x.trim()?Number(x):NaN):[];}await api('profiles/save',{id:data.id,revision:data.revision,name:values.get('name'),kind,model_id:values.get('model_id'),parameters});if(!root.isConnected)return;el('profile-editor').replaceChildren();await load();message('Profil auf Athena gespeichert. Kein Modell gestartet.');}
     catch(error){if(root.isConnected)el('profile-error').textContent=error.message;}finally{button.disabled=false;}
    };
    el('profile-editor').scrollIntoView({behavior:'smooth',block:'start'});

profiles.py

@@ -7,16 +7,17 @@ import uuid
 from pathlib import Path
 
 SCHEMAS={
- 'chat':{'context':(512,2097152,8192),'slots':(1,16,1),'threads':(1,256,6),'batch':(1,8192,512),'ubatch':(1,8192,128),'temperature':(0,5,.8),'top_p':(0,1,.95),'top_k':(0,1000,40)},
+ 'chat':{'context':(512,2097152,8192),'slots':(1,16,1),'threads':(1,256,6),'batch':(1,8192,512),'ubatch':(1,8192,128),'temperature':(0,5,.8),'top_p':(0,1,.95),'top_k':(0,1000,40),'mtp_tokens':(1,8,2),'mtp_min_p':(0,1,.05)},
  'image':{'width':(256,2048,1024),'height':(256,2048,1024),'steps':(1,100,25),'seed':(-1,2147483647,-1),'guidance':(0,30,1)},
  'audio':{'speed':(.25,4,1)},
  'video':{'width':(256,1920,768),'height':(256,1088,512),'frames':(1,241,33),'fps':(1,60,24),'steps':(1,100,20),'seed':(-1,2147483647,-1)}
 }
-CHAT_GPU_DEFAULTS={'gpu_devices':[], 'split_mode':'none', 'tensor_split':[]}
-CHAT_SAMPLING={'temperature':.8,'top_p':.95,'top_k':40}
+CHAT_GPU_DEFAULTS={'gpu_devices':[], 'split_mode':'none', 'tensor_split':[], 'mtp':False}
+CHAT_SAMPLING={'temperature':.8,'top_p':.95,'top_k':40,'mtp_tokens':2,'mtp_min_p':.05}
 
 def chat_parameters(params):
  params={**CHAT_GPU_DEFAULTS,**CHAT_SAMPLING,**params}
+ if type(params['mtp']) is not bool:raise ValueError('MTP muss an oder aus sein.')
  devices=params['gpu_devices'];split=params['tensor_split']
  if not isinstance(devices,list) or len(devices)>16 or any(not isinstance(v,str) or not re.fullmatch(r'GPU-[0-9a-fA-F-]{36}',v) for v in devices) or len(set(devices))!=len(devices):raise ValueError('GPUs müssen als eindeutige, geordnete GPU-UUIDs angegeben werden.')
  if params['split_mode'] not in ('none','layer','row'):raise ValueError('Ungültiger GPU-Split-Modus.')
@@ -68,7 +69,7 @@ class Profiles:
   if not isinstance(params,dict) or set(params)!=(set(SCHEMAS[kind]) | (set(CHAT_GPU_DEFAULTS) if kind=='chat' else set())):raise ValueError('Unvollständige oder unbekannte Profilparameter.')
   for key,(lo,hi,_) in SCHEMAS[kind].items():
    value=params[key]
-   floating=key in ('guidance','speed','temperature','top_p')
+   floating=key in ('guidance','speed','temperature','top_p','mtp_min_p')
    if isinstance(value,bool) or not isinstance(value,(float,int) if floating else int) or not lo<=value<=hi:raise ValueError('Ungültiger Parameter: '+key)
   if kind in ('image','video') and (params['width']%64 or params['height']%64):raise ValueError('Breite und Höhe müssen durch 64 teilbar sein.')
   if kind=='chat' and params['ubatch']>params['batch']:raise ValueError('Microbatch darf nicht größer als Batch sein.')

runtime.py

@@ -31,6 +31,58 @@ def prepare_fit_source(directory):
     if source.count(anchor)!=1:raise ValueError('Fit-Adapter passt nicht zu dieser Version; Build abgebrochen.')
     path.write_text(source.replace(anchor,marker+'\n'+anchor))
 
+def prepare_mtp_source(directory):
+    """Extend the no-allocation fit tool with a shared-weight MTP context."""
+    path=Path(directory)/'tools/fit-params/fit-params.cpp'
+    source=path.read_text()
+    if '// Deck MTP fit v1' in source:
+        if '#include "speculative.h"' not in source:path.write_text(source.replace('#include "fit.h"','#include "fit.h"\n#include "speculative.h"'))
+        return
+    changes={
+     '#include "fit.h"':'#include "fit.h"\n#include "speculative.h"',
+     '    common_init();':'''    // Deck MTP fit v1
+    bool deck_mtp = false;
+    for (int i = 1; i < argc; ++i) {
+        if (std::string(argv[i]) == "--deck-mtp") {
+            deck_mtp = true;
+            for (int j = i; j + 1 < argc; ++j) argv[j] = argv[j + 1];
+            --argc; --i;
+        }
+    }
+    common_init();''',
+     '    auto mparams = common_model_params_to_llama(params);':'''    if (deck_mtp) params.speculative.types = {COMMON_SPECULATIVE_TYPE_DRAFT_MTP};
+    auto mparams = common_model_params_to_llama(params);''',
+     '    auto cparams = common_context_params_to_llama(params);':'''    auto cparams = common_context_params_to_llama(params);
+    auto draft_params = common_base_params_to_speculative(params);
+    auto draft_mparams = common_model_params_to_llama(draft_params);
+    auto draft_cparams = common_context_params_to_llama(draft_params);
+    draft_cparams.ctx_type = LLAMA_CONTEXT_TYPE_MTP;
+    draft_cparams.n_rs_seq = 0;
+    const common_fit_extra_model draft_extra = {
+        params.model.path.c_str(), &draft_mparams, &draft_cparams, true
+    };''',
+     '                nullptr,':'                deck_mtp ? &draft_extra : nullptr,',
+     '        common_fit_print(params.model.path.c_str(), &mparams, &cparams);':'''        if (!deck_mtp) {
+            common_fit_print(params.model.path.c_str(), &mparams, &cparams);
+        } else {
+            std::vector<ggml_backend_dev_t> devs, draft_devs;
+            uint32_t ngl, ctx, expert;
+            auto main_mem = common_get_device_memory_data(params.model.path.c_str(), &mparams, &cparams, devs, ngl, ctx, expert, GGML_LOG_LEVEL_ERROR);
+            auto draft_mem = common_get_device_memory_data(params.model.path.c_str(), &draft_mparams, &draft_cparams, draft_devs, ngl, ctx, expert, GGML_LOG_LEVEL_ERROR);
+            if (main_mem.size() != draft_mem.size() || devs != draft_devs) return 2;
+            for (size_t i = 0; i < main_mem.size(); ++i) {
+                const auto & a = main_mem[i]; const auto & b = draft_mem[i];
+                auto mib = [](size_t x) { return (x + 1048575) / 1048576; };
+                printf("%s %zu %zu %zu\\n", i < devs.size() ? ggml_backend_dev_name(devs[i]) : "Host",
+                    mib(a.model), mib(a.context + b.context), mib(a.compute + b.compute));
+            }
+        }'''
+    }
+    for old,new in changes.items():
+        if source.count(old)!=1:raise ValueError('MTP-Fit-Adapter passt nicht zu dieser Version; Build abgebrochen.')
+        source=source.replace(old,new)
+    path.write_text(source)
+
 class Runtime:
     def __init__(self,root):
         self.root=Path(root);self.lock=threading.RLock();self.process=None;self.cancelled=threading.Event();self.busy=False
@@ -98,15 +150,17 @@ class Runtime:
             self.run(['git','checkout','--detach','FETCH_HEAD'],directory,'Feste Version auswählen',30)
             commit=command(['git','-C',str(directory),'rev-parse','HEAD'])
             prepare_fit_source(directory)
+            prepare_mtp_source(directory)
             arch=sorted(set(g['architecture'] for g in prereq['gpus']))
             args=['cmake','-S','.', '-B','build','-DCMAKE_BUILD_TYPE=Release','-DGGML_NATIVE=OFF','-DLLAMA_BUILD_TESTS=OFF','-DLLAMA_BUILD_EXAMPLES=OFF','-DLLAMA_BUILD_SERVER=ON','-DGGML_CUDA='+('ON' if backend=='CUDA' else 'OFF')]
             if backend=='CUDA':args+=['-DCMAKE_CUDA_ARCHITECTURES='+';'.join(arch)]
             self.run(args,directory,'Build konfigurieren',300)
             self.run(['cmake','--build','build','--target','llama-server','llama-fit-params','-j',str(jobs)],directory,'llama.cpp-Werkzeuge kompilieren')
+            (directory/'build/bin/deck-mtp-fit-v1').write_text('shared weights + main and MTP contexts, f16 draft KV\n')
             binary=directory/'build/bin/llama-server'
             self.run([str(binary),'--version'],directory,'Binärdatei prüfen',30)
             help_text=command([str(binary),'--help'],30)
-            item=dict(id=ident,revision=revision,commit=commit,backend=backend,architectures=arch if backend=='CUDA' else [],created=time.time(),fit_supported='--fit ' in help_text,fit_tool=(directory/'build/bin/llama-fit-params').exists(),fit_adapter='shared-kv-pool-v1')
+            item=dict(id=ident,revision=revision,commit=commit,backend=backend,architectures=arch if backend=='CUDA' else [],created=time.time(),fit_supported='--fit ' in help_text,fit_tool=(directory/'build/bin/llama-fit-params').exists(),fit_adapter='shared-kv-mtp-v1')
             with self.lock:
                 self.state['builds'].append(item);self.state['job'].update(state='complete',phase='Build geprüft; kann als Standard ausgewählt werden.');self.save()
         except Exception as exc:

test_inference.py

@@ -25,6 +25,18 @@ class WorkerTests(unittest.TestCase):
    self.worker.ensure(self.profile)
   args=launch.call_args.args[0];env=launch.call_args.kwargs['env']
   self.assertEqual(env['CUDA_VISIBLE_DEVICES'],'gpu-first,gpu-second');self.assertEqual(args[args.index('--tensor-split')+1],'85,15');self.assertIn('--kv-unified',args);self.assertIn('--fit-print',fit.call_args.args[0]);self.assertEqual(self.worker.status()['state'],'ready')
+ def test_mtp_requires_adapter_before_loading(self):
+  self.profile['parameters']['mtp']=True
+  with patch('inference.subprocess.Popen') as launch:
+   with self.assertRaisesRegex(InferenceError,'MTP'):self.worker.ensure(self.profile)
+   launch.assert_not_called()
+ def test_mtp_is_passed_to_fit_and_server(self):
+  self.profile['parameters'].update(mtp=True,mtp_tokens=2,mtp_min_p=.05)
+  (self.root/'runtime/build-id/build/bin/deck-mtp-fit-v1').touch()
+  process=Mock();process.poll.return_value=None;http=Mock();http.getresponse.return_value.status=200
+  with self.patches(),patch('inference.probe',return_value=self.gpus),patch('inference.cgroup_headroom',return_value=32*1024**3),patch.object(self.worker,'_fit_command',return_value='CUDA0 9000 1000 1000\nCUDA1 2000 1000 1000\nHost 512 0 256\n') as fit,patch('inference.subprocess.Popen',return_value=process) as launch,patch('inference.http.client.HTTPConnection',return_value=http),patch('inference.threading.Thread'):
+   self.worker.ensure(self.profile)
+  args=launch.call_args.args[0];self.assertEqual(args[args.index('--spec-type')+1],'draft-mtp');self.assertEqual(args[args.index('--spec-draft-n-max')+1],'2');self.assertIn('--deck-mtp',fit.call_args.args[0]);self.assertNotIn('--deck-mtp',args)
  def test_busy_gpu_is_rejected_before_fit_or_spawn(self):
   self.gpus[0]['processes']=1
   with patch('inference.probe',return_value=self.gpus),patch('inference.subprocess.Popen') as launch:

test_model_management.py

@@ -26,8 +26,8 @@ class ManagementTests(unittest.TestCase):
   req.update(id=saved['id'],revision=1)
   self.profiles.save(req)
   restarted=Profiles(self.root/'profiles.json',self.catalog)
-  self.assertEqual(restarted.status()['profiles'][0]['parameters'],params)
-  for change in [dict(gpu_devices=devices*2),dict(tensor_split=[85]),dict(split_mode='none'),dict(top_p=1.1),dict(temperature=float('nan')),dict(top_k=True),dict(tensor_split=[0,100]),dict(gpu_devices=['CUDA0','CUDA1'])]:
+  self.assertEqual(restarted.status()['profiles'][0]['parameters'],dict(params,mtp=False,mtp_tokens=2,mtp_min_p=.05))
+  for change in [dict(mtp='yes'),dict(mtp_tokens=0),dict(mtp_tokens=True),dict(mtp_min_p=1.1),dict(gpu_devices=devices*2),dict(tensor_split=[85]),dict(split_mode='none'),dict(top_p=1.1),dict(temperature=float('nan')),dict(top_k=True),dict(tensor_split=[0,100]),dict(gpu_devices=['CUDA0','CUDA1'])]:
    with self.subTest(change=change),self.assertRaises(ValueError):self.profiles.save(dict(req,revision=2,parameters=dict(params,**change)))
 
  def test_profiles_persist_and_update_conflicts(self):