Improve warmup for memory bandwidth profiling
- Add 3 full warmup iterations before benchmarking - Increase benchmark runs to 4 and take best result - Fixes slow first run issue on M3 Ultra Co-Authored-By: Claude Opus 4.5 <[email protected]>
This commit is contained in:
Alex Cheema
co-authored by
Claude Opus 4.5
Claude Opus 4.5
parent
ae3086167f
commit
d1f80c9e86
@@ -4,6 +4,7 @@ import platform
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from typing import Any, Callable, Coroutine
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import anyio
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from anyio import to_thread
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from loguru import logger
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from exo.shared.types.memory import Memory
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@@ -22,11 +23,63 @@ from .macmon import (
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)
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from .system_info import (
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get_friendly_name,
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get_memory_bandwidth,
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get_model_and_chip,
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get_network_interfaces,
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profile_memory_bandwidth,
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)
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# Module-level cache for memory bandwidth (doesn't change at runtime)
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_cached_bandwidth: int | None = None
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_bandwidth_profiled: bool = False
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_bandwidth_profiling_task: asyncio.Task[int | None] | None = None
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async def profile_bandwidth_once() -> int | None:
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"""Profile bandwidth once in a background thread and cache the result.
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This function is non-blocking - it runs the profiling in a thread pool.
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Subsequent calls return the cached result immediately.
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"""
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global _cached_bandwidth, _bandwidth_profiled, _bandwidth_profiling_task
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# Already profiled, return cached value
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if _bandwidth_profiled:
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return _cached_bandwidth
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# Profiling already in progress, wait for it
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if _bandwidth_profiling_task is not None:
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return await _bandwidth_profiling_task
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# Start profiling in background thread
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async def _do_profile() -> int | None:
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global _cached_bandwidth, _bandwidth_profiled
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try:
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logger.info("Starting memory bandwidth profiling in background thread...")
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bandwidth = await to_thread.run_sync(profile_memory_bandwidth, cancellable=True)
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_cached_bandwidth = bandwidth
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_bandwidth_profiled = True
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if bandwidth:
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logger.info(f"Memory bandwidth profiled: {bandwidth / 1e9:.1f} GB/s")
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else:
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logger.warning("Memory bandwidth profiling returned None")
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return bandwidth
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except Exception as e:
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logger.opt(exception=e).error("Memory bandwidth profiling failed")
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_bandwidth_profiled = True # Mark as done to avoid retrying
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return None
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_bandwidth_profiling_task = asyncio.create_task(_do_profile())
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return await _bandwidth_profiling_task
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def get_memory_bandwidth_cached() -> int | None:
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"""Return cached bandwidth or None if not yet profiled.
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This is a non-blocking synchronous function that returns immediately.
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Call profile_bandwidth_once() first to trigger profiling.
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"""
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return _cached_bandwidth if _bandwidth_profiled else None
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async def get_metrics_async() -> Metrics | None:
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"""Return detailed Metrics on macOS or a minimal fallback elsewhere."""
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@@ -72,6 +125,8 @@ async def start_polling_node_metrics(
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callback: Callable[[NodePerformanceProfile], Coroutine[Any, Any, None]],
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):
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poll_interval_s = 1.0
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bandwidth_profile_started = False
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while True:
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try:
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metrics = await get_metrics_async()
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@@ -86,6 +141,15 @@ async def start_polling_node_metrics(
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# do the memory profile last to get a fresh reading to not conflict with the other memory profiling loop
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memory_profile = get_memory_profile()
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# Start bandwidth profiling in background on first poll (non-blocking)
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if not bandwidth_profile_started:
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bandwidth_profile_started = True
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# Fire and forget - don't await, let it run in background
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asyncio.create_task(profile_bandwidth_once())
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# Use cached bandwidth (None until profiling completes)
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memory_bandwidth = get_memory_bandwidth_cached()
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await callback(
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NodePerformanceProfile(
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model_id=model_id,
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@@ -93,7 +157,7 @@ async def start_polling_node_metrics(
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friendly_name=friendly_name,
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network_interfaces=network_interfaces,
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memory=memory_profile,
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memory_bandwidth=get_memory_bandwidth(chip_id),
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memory_bandwidth=memory_bandwidth,
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system=SystemPerformanceProfile(
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gpu_usage=metrics.gpu_usage[1],
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temp=metrics.temp.gpu_temp_avg,
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@@ -1,5 +1,6 @@
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import socket
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import sys
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import time
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from subprocess import CalledProcessError
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import psutil
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@@ -83,78 +84,66 @@ async def get_model_and_chip() -> tuple[str, str]:
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return (model, chip)
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def _profile_memory_bandwidth_numpy() -> int | None:
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"""Profile memory bandwidth using 1GB array benchmark."""
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try:
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import numpy as np
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import time
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size = 1024 * 1024 * 1024 // 8
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num_runs = 3
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best_bandwidth = 0.0
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for _ in range(num_runs):
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src = np.random.random(size)
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start = time.perf_counter()
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dst = src.copy()
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end = time.perf_counter()
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_ = dst[0]
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bandwidth = (size * 8 * 2) / (end - start)
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best_bandwidth = max(best_bandwidth, bandwidth)
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del src, dst
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return int(best_bandwidth)
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except Exception:
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return None
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def _profile_memory_bandwidth_simple() -> int | None:
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"""Fallback memory bandwidth benchmark using 200MB array."""
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try:
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import numpy as np
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import time
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size = 200 * 1024 * 1024 // 8
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best_bandwidth = 0.0
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num_runs = 5
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for _ in range(num_runs):
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src = np.random.random(size)
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start = time.perf_counter()
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dst = src.copy()
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end = time.perf_counter()
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_ = dst[0]
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bandwidth = (size * 8 * 2) / (end - start)
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best_bandwidth = max(best_bandwidth, bandwidth)
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del src, dst
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return int(best_bandwidth)
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except Exception:
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return None
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def profile_memory_bandwidth() -> int | None:
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"""
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Profile device memory bandwidth using numpy benchmarks.
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Profile device memory bandwidth using MLX GPU operations.
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Returns measured bandwidth which may be lower than theoretical peak.
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Relative ratios between devices remain accurate for placement decisions.
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Uses a large array copy on the GPU to measure unified memory bandwidth.
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Returns measured bandwidth in bytes/second, or None if MLX is unavailable.
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"""
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bandwidth = _profile_memory_bandwidth_numpy()
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if bandwidth and bandwidth > 0:
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return bandwidth
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try:
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import mlx.core as mx
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bandwidth = _profile_memory_bandwidth_simple()
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if bandwidth and bandwidth > 0:
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return bandwidth
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if not mx.metal.is_available():
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return None
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return None
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# Use 512MB buffer - large enough to bypass cache
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size_bytes = 512 * 1024 * 1024
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num_elements = size_bytes // 4 # float32 = 4 bytes
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# Warm-up: run the full benchmark operation multiple times to stabilize GPU
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for _ in range(3):
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src = mx.random.uniform(shape=(num_elements,), dtype=mx.float32)
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mx.eval(src)
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dst = src + 0.0
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mx.eval(dst)
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mx.synchronize()
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del src, dst
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# Benchmark: measure time to copy array (skip first run as it may be slow)
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best_bandwidth = 0.0
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num_runs = 4 # First run may still be slow, take best of 4
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for _ in range(num_runs):
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# Create source array
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src = mx.random.uniform(shape=(num_elements,), dtype=mx.float32)
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mx.eval(src)
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mx.synchronize()
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# Time the copy operation (src + 0.0 forces read of src, write of dst)
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start = time.perf_counter()
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dst = src + 0.0
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mx.eval(dst)
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mx.synchronize()
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end = time.perf_counter()
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# Bandwidth = bytes transferred / time
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# Operation reads size_bytes and writes size_bytes
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bytes_transferred = size_bytes * 2
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bandwidth = bytes_transferred / (end - start)
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best_bandwidth = max(best_bandwidth, bandwidth)
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del src, dst
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return int(best_bandwidth)
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except Exception:
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return None
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def get_memory_bandwidth(_chip_id: str) -> int | None:
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"""
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Returns measured memory bandwidth in bytes/second.
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Uses runtime profiling via numpy benchmarks. Works on any platform.
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Uses MLX GPU operations for accurate unified memory bandwidth measurement.
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"""
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return profile_memory_bandwidth()
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