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gpu-program-swapper/vram_arbitrator.py
drjones 01d2f4cfdd Recalibrate cache-hit thresholds against measured loads; bring MCP to parity 
Calibration. The same 12.87GB model loaded through Ollama on this box:

  3.1% resident (FADV_DONTNEED) -> 34.3s -> 0.38 GB/s
  100% resident (force-warmed)  ->  4.9s -> 2.63 GB/s

The thresholds had been guessed from PCIe bus bandwidth: cache hit at >=5 GB/s. A fully
warm load only reaches 2.63 GB/s, because load_duration covers host-to-device transfer
and model init as well as the file read -- the page cache itself reads at 6.4 GB/s. The
5 GB/s bar was therefore unreachable, and every warm load was being reported as a
partial hit. Now 2.0 / 0.8 GB/s, either side of the measured 6.9x separation.

Warm-skip was also unsafe. A 12.87GB blob was skipped as already resident on the
strength of twelve 2MB probe windows, then loaded at 2.44 GB/s. Skipping now requires
warm_confident: an exact cachestat reading, or a probe finding every one of 32 denser
samples resident. warm_file_to_ram/warm_ollama_blob take force=True, exposed on the
warm-model endpoint, whose Pydantic model was missing the field entirely.

MCP parity: the server had drifted well behind the REST API. Adds tools for measured
residency, warm planning, VRAM requests, per-profile analytics, thermal governor
control, overclock status/apply/restore, and autotune sweeps plus status -- 23 tools
and 6 resources, up from 12 and 3. The telemetry store now starts in __main__ rather
than at import scope, since server.py imports this module for the benchmark tool.

README: replaced the remaining theoretical claims (31.5 GB/s bus rate, sub-1.5s loads,
15ms yields) with the measured numbers.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-28 14:40:05 -07:00

902 lines
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"""VRAM Arbitrator and High-Speed Switch Manager for Ollama and ComfyUI."""
import time
import httpx
import psutil
import logging
from typing import Dict, List, Any, Optional
from collections import deque
import asyncio
import json
import websockets
import overclock_manager
import ram_optimizer
import telemetry_store
try:
import pynvml
pynvml.nvmlInit()
NVML_AVAILABLE = True
except Exception as e:
NVML_AVAILABLE = False
logger = logging.getLogger("vram_arbitrator")
OLLAMA_API_BASE = "http://localhost:11434"
COMFY_API_BASE = "http://127.0.0.1:8188"
# Circular buffer for transition events (the durable log lives in telemetry_store)
SWITCH_HISTORY = deque(maxlen=50)
# Bandwidth thresholds for classifying how a model reached VRAM, calibrated by measuring
# the same 12.87 GB model loaded cold and warm on this box (2026-08-28):
#
# 3.1% resident -> 34.3 s -> 0.38 GB/s
# 100% resident -> 4.9 s -> 2.63 GB/s
#
# The first cut at these numbers assumed a page-cache-fed load would approach the bus
# rate and set the cache-hit bar at 5 GB/s. It does not: Ollama's load_duration covers
# host-to-device transfer and model initialisation as well as the file read, so a fully
# resident model still reports ~2.6 GB/s while the page cache itself reads at 6.4 GB/s.
# A 5 GB/s bar could therefore never be met, and every warm load was being reported as
# a partial hit. Thresholds now sit either side of the measured 6.9x separation.
RAM_HIT_GBPS = 2.0
PARTIAL_HIT_GBPS = 0.8
# How long Ollama's VRAM may take to actually drain before we stop waiting.
# Ollama will not unload a model while a generation is in flight, so a short ceiling
# reports a timeout for what is really just a busy model finishing its request. Observed
# here: two yields hit the old 3 s limit with 8.2 GB still held while ComfyUI was starting.
# Waiting longer is the safer failure mode -- the alternative is diffusion allocating into
# VRAM that is still occupied.
YIELD_CONFIRM_TIMEOUT_S = 10.0
YIELD_CONFIRM_POLL_S = 0.02
YIELD_RESIDUAL_BYTES = 256 * 1024 ** 2 # treat <256 MB as "released"
# Connection-pooled clients. Re-creating an AsyncClient per call meant a fresh TCP
# handshake on every one of the watchdog's polls.
_clients: Dict[str, httpx.AsyncClient] = {}
def _client(base_url: str, timeout: float) -> httpx.AsyncClient:
key = f"{base_url}|{timeout}"
c = _clients.get(key)
if c is None or c.is_closed:
c = httpx.AsyncClient(
base_url=base_url,
timeout=timeout,
limits=httpx.Limits(max_keepalive_connections=4, max_connections=8),
)
_clients[key] = c
return c
async def close_clients() -> None:
for c in list(_clients.values()):
try:
await c.aclose()
except Exception:
pass
_clients.clear()
# Bit flags from nvmlDeviceGetCurrentClocksThrottleReasons, decoded for the governor.
THROTTLE_REASONS = {
0x0000000000000001: "gpu_idle",
0x0000000000000002: "applications_clocks_setting",
0x0000000000000004: "sw_power_cap",
0x0000000000000008: "hw_slowdown",
0x0000000000000010: "sync_boost",
0x0000000000000020: "sw_thermal_slowdown",
0x0000000000000040: "hw_thermal_slowdown",
0x0000000000000080: "hw_power_brake_slowdown",
0x0000000000000100: "display_clock_setting",
}
def decode_throttle_reasons(bits: int) -> List[str]:
return [name for mask, name in THROTTLE_REASONS.items() if bits & mask]
def get_process_vram_bytes() -> Dict[str, int]:
"""Fast NVML-only VRAM attribution, used by the yield barrier's tight poll loop.
Deliberately avoids psutil lookups: this runs every 20 ms while we wait for VRAM
to actually drain.
"""
out = {"ollama_bytes": 0, "comfyui_bytes": 0, "other_bytes": 0, "free_bytes": 0}
if not NVML_AVAILABLE:
return out
try:
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
out["free_bytes"] = pynvml.nvmlDeviceGetMemoryInfo(handle).free
procs = list(pynvml.nvmlDeviceGetComputeRunningProcesses(handle))
try:
procs += list(pynvml.nvmlDeviceGetGraphicsRunningProcesses(handle))
except Exception:
pass
merged: Dict[int, int] = {}
for p in procs:
merged[p.pid] = max(merged.get(p.pid, 0), p.usedGpuMemory or 0)
for pid, used in merged.items():
kind = _PID_KIND_CACHE.get(pid)
if kind is None:
kind = _classify_pid(pid)
_PID_KIND_CACHE[pid] = kind
if kind == "ollama":
out["ollama_bytes"] += used
elif kind == "comfy":
out["comfyui_bytes"] += used
else:
out["other_bytes"] += used
except Exception as e:
logger.debug(f"get_process_vram_bytes failed: {e}")
return out
_PID_KIND_CACHE: Dict[int, str] = {}
def _classify_pid(pid: int) -> str:
try:
proc = psutil.Process(pid)
pname = proc.name().lower()
cmdline = " ".join(proc.cmdline()).lower()
except Exception:
return "other"
if "ollama" in pname or "llama-server" in cmdline:
return "ollama"
if "comfy" in cmdline or "main.py" in cmdline:
return "comfy"
return "other"
def get_gpu_hardware_stats() -> Dict[str, Any]:
"""Retrieve comprehensive GPU hardware and process metrics via NVML."""
if not NVML_AVAILABLE:
return {"available": False, "error": "NVML not initialized"}
try:
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
name = pynvml.nvmlDeviceGetName(handle)
if isinstance(name, bytes):
name = name.decode("utf-8")
mem_info = pynvml.nvmlDeviceGetMemoryInfo(handle)
util_rates = pynvml.nvmlDeviceGetUtilizationRates(handle)
temp_c = pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU)
try:
power_mw = pynvml.nvmlDeviceGetPowerUsage(handle)
power_w = round(power_mw / 1000.0, 1)
except Exception:
power_w = 0.0
fan_pct = 0
fans = []
try:
num_fans = pynvml.nvmlDeviceGetNumFans(handle)
for i in range(num_fans):
try:
fans.append(pynvml.nvmlDeviceGetFanSpeed_v2(handle, i))
except Exception:
pass
if fans:
fan_pct = max(fans)
else:
fan_pct = pynvml.nvmlDeviceGetFanSpeed(handle)
fans = [fan_pct]
except Exception:
try:
fan_pct = pynvml.nvmlDeviceGetFanSpeed(handle)
fans = [fan_pct]
except Exception:
fan_pct = 0
fans = []
try:
clock_graphics = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_GRAPHICS)
clock_mem = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_MEM)
except Exception:
clock_graphics = 0
clock_mem = 0
# PCIe throughput (KB/s) — TX + RX. Key metric for the RAM-cache
# PCIe-speed swap thesis (assimilated from pmady/gpu-mcp-server).
pcie_tx_kbps = 0
pcie_rx_kbps = 0
try:
pcie_tx_kbps = pynvml.nvmlDeviceGetPcieThroughput(handle, pynvml.NVML_PCIE_UTIL_TX_BYTES)
except Exception:
pass
try:
pcie_rx_kbps = pynvml.nvmlDeviceGetPcieThroughput(handle, pynvml.NVML_PCIE_UTIL_RX_BYTES)
except Exception:
pass
# Why the GPU is not running at full clocks — the thermal governor reads this.
throttle_bits = 0
throttle_reasons: List[str] = []
try:
throttle_bits = pynvml.nvmlDeviceGetCurrentClocksThrottleReasons(handle)
throttle_reasons = decode_throttle_reasons(throttle_bits)
except Exception:
pass
# Power management limit (watts) — the OC ceiling.
power_limit_w = 0.0
try:
power_limit_w = round(pynvml.nvmlDeviceGetPowerManagementLimit(handle) / 1000.0, 1)
except Exception:
pass
# Driver + CUDA version (completeness).
driver_version = ""
cuda_version = ""
try:
dv = pynvml.nvmlSystemGetDriverVersion()
driver_version = dv.decode("utf-8") if isinstance(dv, bytes) else str(dv)
except Exception:
pass
try:
cv = pynvml.nvmlSystemGetCudaDriverVersion()
cuda_version = cv # int like 12030 == CUDA 12.3
except Exception:
pass
# Discover processes on GPU
proc_breakdown = {
"ollama_bytes": 0,
"comfyui_bytes": 0,
"system_bytes": 0,
"processes": []
}
try:
procs = pynvml.nvmlDeviceGetComputeRunningProcesses(handle)
graphics_procs = pynvml.nvmlDeviceGetGraphicsRunningProcesses(handle)
all_procs = {p.pid: p.usedGpuMemory for p in procs}
for p in graphics_procs:
all_procs[p.pid] = max(all_procs.get(p.pid, 0), p.usedGpuMemory or 0)
for pid, used_mem in all_procs.items():
pname = "Unknown"
cmdline = ""
try:
proc = psutil.Process(pid)
pname = proc.name()
cmdline = " ".join(proc.cmdline())
except Exception:
pass
is_ollama = "ollama" in pname.lower() or "llama-server" in cmdline.lower()
is_comfy = "comfy" in cmdline.lower() or "main.py" in cmdline.lower()
if is_ollama:
proc_breakdown["ollama_bytes"] += used_mem
elif is_comfy:
proc_breakdown["comfyui_bytes"] += used_mem
else:
proc_breakdown["system_bytes"] += used_mem
proc_breakdown["processes"].append({
"pid": pid,
"name": pname,
"cmdline": cmdline[:60],
"vram_bytes": used_mem,
"vram_mb": round(used_mem / (1024**2), 1),
"is_ollama": is_ollama,
"is_comfy": is_comfy,
})
except Exception as e:
logger.error(f"Error enumerating GPU processes: {e}")
total_vram = mem_info.total
used_vram = mem_info.used
free_vram = mem_info.free
return {
"available": True,
"device_name": name,
"vram_total_bytes": total_vram,
"vram_total_gb": round(total_vram / (1024**3), 2),
"vram_used_bytes": used_vram,
"vram_used_gb": round(used_vram / (1024**3), 2),
"vram_free_bytes": free_vram,
"vram_free_gb": round(free_vram / (1024**3), 2),
"vram_used_pct": round((used_vram / total_vram * 100) if total_vram > 0 else 0, 1),
"gpu_util_pct": util_rates.gpu,
"mem_util_pct": util_rates.memory,
"temperature_c": temp_c,
"power_w": power_w,
"power_limit_w": power_limit_w,
"pcie_tx_kbps": pcie_tx_kbps,
"pcie_rx_kbps": pcie_rx_kbps,
"throttle_bits": throttle_bits,
"throttle_reasons": throttle_reasons,
"driver_version": driver_version,
"cuda_version": cuda_version,
"fan_pct": fan_pct,
"fans": fans,
"num_fans": len(fans),
"clock_graphics_mhz": clock_graphics,
"clock_mem_mhz": clock_mem,
"breakdown": {
"ollama_mb": round(proc_breakdown["ollama_bytes"] / (1024**2), 1),
"ollama_gb": round(proc_breakdown["ollama_bytes"] / (1024**3), 2),
"comfyui_mb": round(proc_breakdown["comfyui_bytes"] / (1024**2), 1),
"comfyui_gb": round(proc_breakdown["comfyui_bytes"] / (1024**3), 2),
"system_mb": round(proc_breakdown["system_bytes"] / (1024**2), 1),
"system_gb": round(proc_breakdown["system_bytes"] / (1024**3), 2),
"free_mb": round(free_vram / (1024**2), 1),
"free_gb": round(free_vram / (1024**3), 2),
"processes": proc_breakdown["processes"],
}
}
except Exception as e:
return {"available": False, "error": str(e)}
async def get_ollama_live_state() -> Dict[str, Any]:
"""Get active models, running status, and VRAM expiration from Ollama."""
state = {
"online": False,
"loaded_models": [],
"active_model_name": None,
"active_model_vram_gb": 0.0,
"active_context": 0,
"expires_at": None,
"installed_models": []
}
try:
client = _client(OLLAMA_API_BASE, 3.0)
# Check running models (ps)
ps_resp = await client.get("/api/ps")
if ps_resp.status_code == 200:
state["online"] = True
models = ps_resp.json().get("models", [])
state["loaded_models"] = models
if models:
first = models[0]
state["active_model_name"] = first.get("name")
vram_bytes = first.get("size_vram", first.get("size", 0))
state["active_model_vram_gb"] = round(vram_bytes / (1024**3), 2)
state["active_context"] = first.get("context_length", 0)
state["expires_at"] = first.get("expires_at")
# Check all tags
tags_resp = await client.get("/api/tags")
if tags_resp.status_code == 200:
state["installed_models"] = tags_resp.json().get("models", [])
except Exception as e:
logger.debug(f"Ollama check error: {e}")
return state
async def get_comfyui_live_state() -> Dict[str, Any]:
"""Get prompt queue, device status, and active execution from ComfyUI."""
state = {
"online": False,
"executing": False,
"queue_remaining": 0,
"queue_running": 0,
"current_node": None,
"current_prompt_id": None,
"vram_free_mb": 0,
"vram_total_mb": 0,
}
try:
client = _client(COMFY_API_BASE, 3.0)
# Check system stats
stats_resp = await client.get("/system_stats")
if stats_resp.status_code == 200:
state["online"] = True
data = stats_resp.json()
devices = data.get("devices", [])
if devices:
dev = devices[0]
state["vram_free_mb"] = round(dev.get("vram_free", 0) / (1024**2), 1)
state["vram_total_mb"] = round(dev.get("vram_total", 0) / (1024**2), 1)
# Check queue
queue_resp = await client.get("/queue")
if queue_resp.status_code == 200:
qdata = queue_resp.json()
running = qdata.get("queue_running", [])
pending = qdata.get("queue_pending", [])
state["queue_running"] = len(running)
state["queue_remaining"] = len(pending)
state["executing"] = len(running) > 0
if running:
state["current_prompt_id"] = running[0][1] if len(running[0]) > 1 else str(running[0])
except Exception as e:
logger.debug(f"ComfyUI check error: {e}")
return state
async def _await_vram_release(baseline_bytes: int,
timeout_s: float = YIELD_CONFIRM_TIMEOUT_S) -> Dict[str, Any]:
"""Block until Ollama's VRAM has actually drained, or we give up.
Posting keep_alive:0 only *asks* Ollama to unload; the driver frees the allocation
some milliseconds later. Returning before that happens is how ComfyUI ends up
allocating into VRAM that is still occupied, which shows up as a CUDA OOM mid-graph.
"""
t0 = time.perf_counter()
last = baseline_bytes
while True:
snap = get_process_vram_bytes()
last = snap["ollama_bytes"]
if last <= YIELD_RESIDUAL_BYTES:
return {
"confirmed": True,
"confirm_ms": round((time.perf_counter() - t0) * 1000, 2),
"residual_bytes": last,
"free_bytes": snap["free_bytes"],
}
if (time.perf_counter() - t0) >= timeout_s:
return {
"confirmed": False,
"confirm_ms": round((time.perf_counter() - t0) * 1000, 2),
"residual_bytes": last,
"free_bytes": snap["free_bytes"],
"error": f"Ollama still holding {round(last / (1024**3), 2)} GB after {timeout_s}s",
}
await asyncio.sleep(YIELD_CONFIRM_POLL_S)
def _record(event: Dict[str, Any]) -> None:
"""Push an event to both the in-memory ring and the durable store."""
event.setdefault("ts", time.time())
event.setdefault("timestamp", time.strftime("%H:%M:%S"))
SWITCH_HISTORY.appendleft(event)
telemetry_store.record_event(event, profile=overclock_manager.ACTIVE_PROFILE)
async def instant_free_ollama_vram(model_name: Optional[str] = None,
confirm: bool = True) -> Dict[str, Any]:
"""Yield Ollama's VRAM and wait for the driver to actually release it.
The returned duration_ms is now the real end-to-end release time, not just how long
the HTTP POST took.
"""
t0 = time.perf_counter()
if model_name:
targets = [model_name]
else:
# Unload *every* resident model, not just loaded_models[0]. Ollama will happily
# keep several models in VRAM at once; releasing only the first left the rest
# allocated, which the confirm barrier caught as "still holding 8.2 GB after 3s".
ollama_state = await get_ollama_live_state()
targets = [m.get("name") for m in ollama_state.get("loaded_models", []) if m.get("name")]
if not targets and ollama_state.get("active_model_name"):
targets = [ollama_state["active_model_name"]]
if not targets:
return {"success": True, "message": "No active Ollama model in VRAM",
"duration_ms": 0, "confirmed": True}
model_name = targets[0] if len(targets) == 1 else f"{len(targets)} models"
baseline = get_process_vram_bytes()["ollama_bytes"]
try:
client = _client(OLLAMA_API_BASE, 5.0)
await asyncio.gather(*[
client.post("/api/generate", json={"model": t, "keep_alive": 0})
for t in targets
], return_exceptions=True)
request_ms = round((time.perf_counter() - t0) * 1000, 2)
barrier = {"confirmed": None, "confirm_ms": 0.0, "residual_bytes": baseline}
if confirm:
barrier = await _await_vram_release(baseline)
duration_ms = round((time.perf_counter() - t0) * 1000, 2)
freed_gb = round(max(baseline - barrier.get("residual_bytes", 0), 0) / (1024**3), 2)
_record({
"event_type": "Ollama VRAM Yield",
"source": ", ".join(targets)[:200],
"target": "VRAM 0MB (Kept in RAM)",
"duration_ms": duration_ms,
"yield_confirm_ms": barrier.get("confirm_ms"),
"cache_status": "RAM-Cached" if barrier.get("confirmed") else "Yield Timeout",
"detail": barrier.get("error"),
})
return {
"success": True,
"model": model_name,
"models_unloaded": targets,
"duration_ms": duration_ms,
"request_ms": request_ms,
"confirm_ms": barrier.get("confirm_ms"),
"confirmed": barrier.get("confirmed"),
"freed_gb": freed_gb,
"residual_gb": round(barrier.get("residual_bytes", 0) / (1024**3), 2),
"free_vram_gb": round(barrier.get("free_bytes", 0) / (1024**3), 2),
"error": barrier.get("error"),
}
except Exception as e:
return {"success": False, "error": str(e),
"duration_ms": round((time.perf_counter() - t0) * 1000, 2)}
async def instant_free_comfyui_vram() -> Dict[str, Any]:
"""Tell ComfyUI to purge loaded diffusion models from VRAM."""
t0 = time.perf_counter()
try:
client = _client(COMFY_API_BASE, 5.0)
await client.post("/free", json={"unload_models": True, "free_memory": True})
duration_ms = round((time.perf_counter() - t0) * 1000, 2)
snap = get_process_vram_bytes()
_record({
"event_type": "ComfyUI VRAM Purge",
"source": "ComfyUI Pipeline",
"target": "VRAM Free",
"duration_ms": duration_ms,
"cache_status": "Cleaned",
})
return {"success": True, "duration_ms": duration_ms,
"free_vram_gb": round(snap["free_bytes"] / (1024**3), 2)}
except Exception as e:
return {"success": False, "error": str(e),
"duration_ms": round((time.perf_counter() - t0) * 1000, 2)}
_MODEL_SIZE_CACHE: Dict[str, int] = {}
def _model_size_bytes(model_name: str) -> int:
"""On-disk weight size for an Ollama model, used to turn load time into bandwidth."""
if model_name in _MODEL_SIZE_CACHE:
return _MODEL_SIZE_CACHE[model_name]
try:
for f in ram_optimizer.find_ollama_model_files():
_MODEL_SIZE_CACHE[f["model"]] = f["size_bytes"]
except Exception as e:
logger.debug(f"model size lookup failed: {e}")
return _MODEL_SIZE_CACHE.get(model_name, 0)
def classify_load(size_bytes: int, load_duration_ms: float) -> Dict[str, Any]:
"""Classify how a model reached VRAM, from achieved bandwidth rather than a constant.
The old rule was `load_duration_ms < 2500`, which called a 27B Q2_K read from NVMe a
cache hit and a small model read from RAM a cold load. Bandwidth separates them
cleanly: page cache feeds PCIe at many GB/s, this NVMe does not.
"""
if load_duration_ms <= 1.0:
return {"cache_status": "Already in VRAM", "load_gbps": None, "is_ram_hit": True}
if not size_bytes:
# No size on record — fall back to the old heuristic, but say so.
# Without a size we cannot compute bandwidth at all; this is a guess and is
# labelled as one. 8s roughly splits the measured warm (4.9s) and cold (34.3s)
# loads for a mid-size model, but it is meaningless for very small or large ones.
return {
"cache_status": "RAM Cache Hit ⚡" if load_duration_ms < 8000 else "Cold Disk Load 💾",
"load_gbps": None,
"is_ram_hit": load_duration_ms < 8000,
"detail": "size unknown, fell back to a duration guess",
}
gbps = (size_bytes / (1024**3)) / (load_duration_ms / 1000.0)
if gbps >= RAM_HIT_GBPS:
status = "RAM Cache Hit ⚡"
elif gbps >= PARTIAL_HIT_GBPS:
status = "Partial Cache 🌤"
else:
status = "Cold Disk Load 💾"
return {"cache_status": status, "load_gbps": round(gbps, 2), "is_ram_hit": gbps >= RAM_HIT_GBPS}
async def switch_ollama_model(target_model: str, keep_alive: str = "30m") -> Dict[str, Any]:
"""High-speed hot-swap to target Ollama model, tracking swap metrics."""
t0 = time.perf_counter()
cur_state = await get_ollama_live_state()
prev_model = cur_state.get("active_model_name") or "None"
try:
client = _client(OLLAMA_API_BASE, 180.0)
resp = await client.post(
"/api/generate",
json={"model": target_model, "prompt": "Ready check", "stream": False,
"keep_alive": keep_alive},
)
total_duration_ms = round((time.perf_counter() - t0) * 1000, 2)
if resp.status_code == 200:
data = resp.json()
load_dur_ms = round(data.get("load_duration", 0) / 1e6, 2)
eval_dur_ms = round(data.get("eval_duration", 0) / 1e6, 2)
eval_count = data.get("eval_count", 0)
tokens_per_sec = round((eval_count / (eval_dur_ms / 1000)) if eval_dur_ms > 0 else 0, 1)
size_bytes = _model_size_bytes(target_model)
cls = classify_load(size_bytes, load_dur_ms)
_record({
"event_type": "LLM Model Switch",
"source": prev_model,
"target": target_model,
"duration_ms": total_duration_ms,
"load_duration_ms": load_dur_ms,
"tokens_per_sec": tokens_per_sec,
"bytes_loaded": size_bytes,
"load_gbps": cls["load_gbps"],
"cache_status": cls["cache_status"],
"detail": cls.get("detail"),
})
return {
"success": True,
"prev_model": prev_model,
"target_model": target_model,
"total_duration_ms": total_duration_ms,
"load_duration_ms": load_dur_ms,
"tokens_per_sec": tokens_per_sec,
"model_size_gb": round(size_bytes / (1024**3), 2) if size_bytes else None,
"load_gbps": cls["load_gbps"],
"cache_status": cls["cache_status"],
"is_ram_hit": cls["is_ram_hit"],
"response": data.get("response", ""),
}
return {"success": False, "error": f"HTTP {resp.status_code}: {resp.text}",
"duration_ms": total_duration_ms}
except Exception as e:
return {"success": False, "error": str(e),
"duration_ms": round((time.perf_counter() - t0) * 1000, 2)}
def get_switch_history() -> List[Dict[str, Any]]:
return list(SWITCH_HISTORY)
class AutoArbitrator:
"""Real-time bidirectional background arbitrator for seamless Ollama <-> ComfyUI hot-swapping.
Two behavioural changes worth knowing about:
* ComfyUI's VRAM is no longer purged 1.5 s after every finished prompt. Iterating on
a workflow is the common case, and purging between runs forced a full checkpoint
reload each time. The purge now waits for COMFY_IDLE_PURGE_S of genuinely empty
queue, and happens immediately only when Ollama actually needs the VRAM.
* The watchdog no longer polls two ComfyUI endpoints every 300 ms. The WebSocket is
the primary signal; polling is a fallback that runs at 1 Hz and only hits /queue,
backing off further while the socket is healthy.
"""
COMFY_IDLE_PURGE_S = 30.0
WATCHDOG_INTERVAL_S = 1.0
WATCHDOG_INTERVAL_WS_OK_S = 3.0
def __init__(self):
self.running = False
self.ws_task: Optional[asyncio.Task] = None
self.poll_task: Optional[asyncio.Task] = None
self.idle_task: Optional[asyncio.Task] = None
self.last_yield_time = 0.0
self.last_comfy_free_time = 0.0
self.connected_ws = False
self.last_action = "Idle"
self.comfy_was_active = False
self.comfy_idle_since: Optional[float] = None
self.oc_profile = None
self.pending_purge = False
# While a tuning sweep is running, the arbitrator must not fight it: a ComfyUI
# benchmark would otherwise trip trigger_comfy_priority, which reapplies the whole
# 'comfy' profile and silently overwrites the clock the sweep is measuring.
self.oc_suspended = False
self.stats = {"yields": 0, "purges": 0, "yield_timeouts": 0, "deferred_purges": 0}
async def start(self):
if self.running:
return
self.running = True
self.ws_task = asyncio.create_task(self._ws_listener())
self.poll_task = asyncio.create_task(self._poll_watchdog())
self.idle_task = asyncio.create_task(self._idle_purge_loop())
logger.info("AutoArbitrator background engine started (Bidirectional).")
try:
await asyncio.get_running_loop().run_in_executor(
None, overclock_manager.apply_profile, "balanced")
self.oc_profile = "balanced"
except Exception as e:
logger.warning(f"Startup overclock apply failed: {e}")
async def stop(self):
self.running = False
for task in (self.ws_task, self.poll_task, self.idle_task):
if task:
task.cancel()
await close_clients()
logger.info("AutoArbitrator background engine stopped.")
async def trigger_comfy_priority(self, reason: str = "ComfyUI prompt detected"):
"""Yield Ollama's VRAM — and confirm it is gone — before diffusion allocates."""
self.comfy_was_active = True
self.comfy_idle_since = None
self._apply_oc_profile("comfy")
now = time.time()
if now - self.last_yield_time < 1.0:
return
ollama_state = await get_ollama_live_state()
if ollama_state.get("active_model_name"):
model = ollama_state["active_model_name"]
logger.info(f"⚡ ComfyUI active ({reason}) -> Auto-yielding Ollama model '{model}'...")
self.last_yield_time = time.time()
res = await instant_free_ollama_vram(model, confirm=True)
self.stats["yields"] += 1
if res.get("confirmed"):
self.last_action = (f"Yielded '{model}' for ComfyUI in "
f"{res.get('confirm_ms')}ms (confirmed {res.get('freed_gb')}GB free)")
else:
self.stats["yield_timeouts"] += 1
self.last_action = (f"⚠ Yield of '{model}' NOT confirmed: "
f"{res.get('residual_gb')}GB still held")
logger.warning(f"VRAM yield barrier timed out: {res.get('error')}")
logger.info(f"Ollama auto-yield completed: {res}")
async def trigger_comfy_completed(self, immediate: bool = False):
"""Mark the end of a generation. The actual purge is deferred unless forced."""
self.comfy_was_active = False
if self.comfy_idle_since is None:
self.comfy_idle_since = time.time()
self._apply_oc_profile("ollama")
if immediate:
await self._purge_comfy_now("Ollama needs VRAM")
else:
self.pending_purge = True
self.stats["deferred_purges"] += 1
self.last_action = (f"ComfyUI idle — holding its checkpoints for "
f"{int(self.COMFY_IDLE_PURGE_S)}s in case you iterate")
async def _purge_comfy_now(self, reason: str):
now = time.time()
if now - self.last_comfy_free_time < 3.0:
return
self.last_comfy_free_time = now
self.pending_purge = False
logger.info(f"⚡ Purging ComfyUI VRAM cache ({reason})...")
res = await instant_free_comfyui_vram()
self.stats["purges"] += 1
self.last_action = f"Purged ComfyUI VRAM ({res.get('duration_ms')}ms) — {reason}"
logger.info(f"ComfyUI purge completed: {res}")
async def _idle_purge_loop(self):
"""Purge ComfyUI's VRAM only after a real idle gap, not between iterations."""
while self.running:
try:
if self.pending_purge and self.comfy_idle_since and not self.comfy_was_active:
idle_for = time.time() - self.comfy_idle_since
if idle_for >= self.COMFY_IDLE_PURGE_S:
await self._purge_comfy_now(
f"idle {int(idle_for)}s")
except Exception as e:
logger.debug(f"idle purge loop error: {e}")
await asyncio.sleep(2.0)
async def request_vram_for_ollama(self, needed_gb: float = 0.0) -> Dict[str, Any]:
"""Called when Ollama needs VRAM now: purge ComfyUI immediately rather than waiting."""
snap = get_process_vram_bytes()
free_gb = snap["free_bytes"] / (1024**3)
if needed_gb and free_gb >= needed_gb:
return {"purged": False, "free_gb": round(free_gb, 2), "reason": "enough free VRAM"}
if snap["comfyui_bytes"] > YIELD_RESIDUAL_BYTES:
await self._purge_comfy_now(f"Ollama requested {needed_gb or '?'}GB")
snap = get_process_vram_bytes()
return {"purged": True, "free_gb": round(snap["free_bytes"] / (1024**3), 2)}
return {"purged": False, "free_gb": round(free_gb, 2), "reason": "ComfyUI holds no VRAM"}
async def _ws_listener(self):
client_id = "hyperswap-arbitrator"
ws_url = f"ws://127.0.0.1:8188/ws?clientId={client_id}"
backoff = 2.0
while self.running:
try:
async with websockets.connect(ws_url, ping_interval=10, ping_timeout=10) as ws:
self.connected_ws = True
backoff = 2.0
logger.info("AutoArbitrator connected to ComfyUI WebSocket.")
while self.running:
msg = await ws.recv()
if not isinstance(msg, str):
continue
try:
data = json.loads(msg)
msg_type = data.get("type")
msg_data = data.get("data", {})
if msg_type == "status":
queue_rem = (msg_data.get("status", {})
.get("exec_info", {}).get("queue_remaining", 0))
if queue_rem > 0:
await self.trigger_comfy_priority(f"Queue remaining: {queue_rem}")
elif queue_rem == 0 and self.comfy_was_active:
await self.trigger_comfy_completed()
elif msg_type in ("execution_start", "execution_cached"):
await self.trigger_comfy_priority(f"Event: {msg_type}")
elif msg_type == "executing":
node = msg_data.get("node")
if node is not None:
await self.trigger_comfy_priority(f"Executing node: {node}")
elif self.comfy_was_active:
await self.trigger_comfy_completed()
elif msg_type == "execution_success":
await self.trigger_comfy_completed()
elif msg_type == "execution_error":
logger.warning(f"ComfyUI execution error: {msg_data}")
await self.trigger_comfy_completed()
except Exception as e:
logger.debug(f"WS parse error: {e}")
except (websockets.exceptions.ConnectionClosed, OSError, asyncio.CancelledError):
self.connected_ws = False
except Exception as e:
self.connected_ws = False
logger.debug(f"WS connection error: {e}")
await asyncio.sleep(backoff)
backoff = min(backoff * 1.5, 15.0)
async def _poll_watchdog(self):
"""Fallback for when the WebSocket is down. One cheap /queue call, 1 Hz.
The previous version hit /system_stats and /queue every 300 ms on fresh TCP
connections — roughly 6.6 requests/second against ComfyUI, forever.
"""
while self.running:
interval = self.WATCHDOG_INTERVAL_WS_OK_S if self.connected_ws else self.WATCHDOG_INTERVAL_S
try:
client = _client(COMFY_API_BASE, 3.0)
resp = await client.get("/queue")
if resp.status_code == 200:
q = resp.json()
busy = len(q.get("queue_running", [])) > 0 or len(q.get("queue_pending", [])) > 0
if busy:
await self.trigger_comfy_priority("Watchdog saw an active queue")
elif self.comfy_was_active:
await self.trigger_comfy_completed()
except Exception:
pass
await asyncio.sleep(interval)
def suspend_oc(self, reason: str = "tuning sweep") -> None:
self.oc_suspended = True
logger.info(f"Overclock auto-switching suspended ({reason})")
def resume_oc(self, profile: Optional[str] = None) -> None:
self.oc_suspended = False
# Forget the cached profile so the next transition actually reapplies.
self.oc_profile = profile
logger.info("Overclock auto-switching resumed")
def _apply_oc_profile(self, profile: str):
"""Apply an overclock profile in a background thread; only fire on transition."""
if self.oc_suspended or self.oc_profile == profile:
return
self.oc_profile = profile
try:
loop = asyncio.get_running_loop()
loop.run_in_executor(None, overclock_manager.apply_profile, profile)
logger.info(f"🎛️ Overclock profile switched -> '{profile}'")
except Exception as e:
logger.warning(f"Overclock profile switch failed ({profile}): {e}")
def get_status(self) -> Dict[str, Any]:
idle_for = (time.time() - self.comfy_idle_since) if self.comfy_idle_since else None
return {
"running": self.running,
"connected_ws": self.connected_ws,
"last_action": self.last_action,
"mode": "Bidirectional Hot-Swap (ComfyUI <-> Ollama)",
"comfy_active": self.comfy_was_active,
"pending_purge": self.pending_purge,
"comfy_idle_s": round(idle_for, 1) if idle_for is not None else None,
"idle_purge_after_s": self.COMFY_IDLE_PURGE_S,
"oc_profile": self.oc_profile,
"counters": dict(self.stats),
}
arbitrator = AutoArbitrator()
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