AMOOOMA commented on code in PR #37113: URL: https://github.com/apache/beam/pull/37113#discussion_r2747629755
########## sdks/python/apache_beam/ml/inference/model_manager.py: ########## @@ -0,0 +1,730 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +"""Module for managing ML models in Apache Beam pipelines. + +This module provides classes and functions to efficiently manage multiple +machine learning models within Apache Beam pipelines. It includes functionality +for loading, caching, and updating models using multi-process shared memory, +ensuring that models are reused across different workers to optimize resource +usage and performance. +""" + +import gc +import heapq +import itertools +import logging +import subprocess +import threading +import time +from collections import Counter +from collections import OrderedDict +from collections import defaultdict +from collections import deque +from typing import Any +from typing import Callable +from typing import Dict +from typing import Optional +from typing import Tuple + +import numpy as np +import torch +from scipy.optimize import nnls + +from apache_beam.utils import multi_process_shared + +logger = logging.getLogger(__name__) + + +class GPUMonitor: + """Monitors GPU memory usage in a separate thread using nvidia-smi. + + This class continuously polls GPU memory statistics to track current usage + and peak usage over a sliding time window. It serves as the source of truth + for the ModelManager's resource decisions. + + Attributes: + fallback_memory_mb: Default total memory if hardware detection fails. + poll_interval: Seconds between memory checks. + peak_window_seconds: Duration to track peak memory usage. + """ + def __init__( + self, + fallback_memory_mb: float = 16000.0, + poll_interval: float = 0.5, + peak_window_seconds: float = 30.0): + self._current_usage = 0.0 + self._peak_usage = 0.0 + self._total_memory = fallback_memory_mb + self._poll_interval = poll_interval + self._peak_window_seconds = peak_window_seconds + self._memory_history = deque() + self._running = False + self._thread = None + self._lock = threading.Lock() + + def _detect_hardware(self): + try: + cmd = [ + "nvidia-smi", + "--query-gpu=memory.total", + "--format=csv,noheader,nounits" + ] + output = subprocess.check_output(cmd, text=True).strip() + self._total_memory = float(output) + return True + except (FileNotFoundError, subprocess.CalledProcessError): + logger.warning( + "nvidia-smi not found or failed. Defaulting total memory to %s MB", + self._total_memory) + return False + except Exception as e: + logger.warning( + "Error parsing nvidia-smi output: %s. " + "Defaulting total memory to %s MB", + e, + self._total_memory) + return False + + def start(self): + self._gpu_available = self._detect_hardware() + if self._running or not self._gpu_available: + return + self._running = True + self._thread = threading.Thread(target=self._poll_loop, daemon=True) + self._thread.start() + + def stop(self): + self._running = False + if self._thread: + self._thread.join() + + def reset_peak(self): + with self._lock: + now = time.time() + self._memory_history.clear() + self._memory_history.append((now, self._current_usage)) + self._peak_usage = self._current_usage + + def get_stats(self) -> Tuple[float, float, float]: + with self._lock: + return self._current_usage, self._peak_usage, self._total_memory + + def refresh(self): + """Forces an immediate poll of the GPU.""" + usage = self._get_nvidia_smi_used() + now = time.time() + with self._lock: + self._current_usage = usage + self._memory_history.append((now, usage)) + # Recalculate peak immediately + while self._memory_history and (now - self._memory_history[0][0] + > self._peak_window_seconds): + self._memory_history.popleft() + self._peak_usage = ( + max(m for _, m in self._memory_history) + if self._memory_history else usage) + + def _get_nvidia_smi_used(self) -> float: + try: + cmd = "nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits" + output = subprocess.check_output(cmd, shell=True).decode("utf-8").strip() + free_memory = float(output) + return self._total_memory - free_memory + except Exception: + return 0.0 + + def _poll_loop(self): + while self._running: + usage = self._get_nvidia_smi_used() + now = time.time() + with self._lock: + self._current_usage = usage + self._memory_history.append((now, usage)) + while self._memory_history and (now - self._memory_history[0][0] + > self._peak_window_seconds): + self._memory_history.popleft() + self._peak_usage = ( + max(m for _, m in self._memory_history) + if self._memory_history else usage) + time.sleep(self._poll_interval) + + +class ResourceEstimator: + """Estimates individual model memory usage using statistical observation. + + Uses Non-Negative Least Squares (NNLS) to deduce the memory footprint of + individual models based on aggregate system memory readings and the + configuration of active models at that time. + """ + def __init__(self, smoothing_factor: float = 0.2, min_data_points: int = 5): + self.smoothing_factor = smoothing_factor + self.min_data_points = min_data_points + self.estimates: Dict[str, float] = {} + self.history = defaultdict(lambda: deque(maxlen=20)) + self.known_models = set() + self._lock = threading.Lock() + + def is_unknown(self, model_tag: str) -> bool: + with self._lock: + return model_tag not in self.estimates + + def get_estimate(self, model_tag: str, default_mb: float = 4000.0) -> float: + with self._lock: + return self.estimates.get(model_tag, default_mb) + + def set_initial_estimate(self, model_tag: str, cost: float): + with self._lock: + self.estimates[model_tag] = cost + self.known_models.add(model_tag) + logger.info("Initial Profile for %s: %s MB", model_tag, cost) + + def add_observation( + self, active_snapshot: Dict[str, int], peak_memory: float): + if active_snapshot: + model_list = "\n".join( + f"\t- {model}: {count}" + for model, count in sorted(active_snapshot.items())) + else: + model_list = "\t- None" + + logger.info( + "Adding Observation:\n PeakMemory: %.1f MB\n Instances:\n%s", + peak_memory, + model_list) + if not active_snapshot: + return + with self._lock: + config_key = tuple(sorted(active_snapshot.items())) + self.history[config_key].append(peak_memory) + for tag in active_snapshot: + self.known_models.add(tag) + self._solve() + + def _solve(self): + """ + Solves Ax=b using raw readings (no pre-averaging) and NNLS. + This creates a 'tall' matrix A where every memory reading is + a separate equation. + """ + unique = sorted(list(self.known_models)) + + # We need to build the matrix first to know if we have enough data points + A, b = [], [] + + for config_key, mem_values in self.history.items(): + if not mem_values: + continue + + # 1. Create the feature row for this configuration ONCE + # (It represents the model counts + bias) + counts = dict(config_key) + feature_row = [counts.get(model, 0) for model in unique] + feature_row.append(1) # Bias column + + # 2. Add a separate row to the matrix for EVERY individual reading + # Instead of averaging, we flatten the history into the matrix + for reading in mem_values: + A.append(feature_row) # The inputs (models) stay the same + b.append(reading) # The output (memory) varies due to noise + + # Convert to numpy for SciPy + A = np.array(A) + b = np.array(b) + + if len( + self.history.keys()) < len(unique) + 1 or len(A) < self.min_data_points: + # Not enough data to solve yet + return + + logger.info( + "Solving with %s total observations for %s models.", + len(A), + len(unique)) + + try: + # Solve using Non-Negative Least Squares + # x will be >= 0 + x, _ = nnls(A, b) + + weights = x[:-1] + bias = x[-1] + + for i, model in enumerate(unique): + calculated_cost = weights[i] + + if model in self.estimates: + old = self.estimates[model] + new = (old * (1 - self.smoothing_factor)) + ( + calculated_cost * self.smoothing_factor) + self.estimates[model] = new + else: + self.estimates[model] = calculated_cost + + logger.info( + "Updated Estimate for %s: %.1f MB", model, self.estimates[model]) + logger.info("System Bias: %s MB", bias) + + except Exception as e: + logger.error("Solver failed: %s", e) + + +class ModelManager: + """Manages model lifecycles, caching, and resource arbitration. + + This class acts as the central controller for acquiring model instances. + It handles: + 1. LRU Caching of idle models. + 2. Resource estimation and admission control (preventing OOM). + 3. Dynamic eviction of low-priority models, determined by count of + pending requests, when space is needed. + 4. 'Isolation Mode' for safely profiling unknown models. + """ + _lock = threading.Lock() + + def __init__( + self, + monitor: Optional['GPUMonitor'] = None, + slack_percentage: float = 0.10, + poll_interval: float = 0.5, + peak_window_seconds: float = 30.0, + min_data_points: int = 5, + smoothing_factor: float = 0.2, + eviction_cooldown_seconds: float = 10.0, + min_model_copies: int = 1): + + self._estimator = ResourceEstimator( + min_data_points=min_data_points, smoothing_factor=smoothing_factor) + self._monitor = monitor if monitor else GPUMonitor( + poll_interval=poll_interval, peak_window_seconds=peak_window_seconds) + self._slack_percentage = slack_percentage + + self._eviction_cooldown = eviction_cooldown_seconds + self._min_model_copies = min_model_copies + + # Resource State + self._models = defaultdict(list) + # Idle LRU used to track released models that + # can be freed or reused upon request. + self._idle_lru = OrderedDict() + self._active_counts = Counter() + self._total_active_jobs = 0 + self._pending_reservations = 0.0 + + # Isolation state used to profile unknown models, + # ensuring they run alone to get accurate readings. + # isolation_baseline represents the GPU usage before + # loading the unknown model. + self._isolation_mode = False + self._isolation_baseline = 0.0 + + # Waiting Queue and Ticketing to make sure we have fair ordering + # and also priority for unknown models. + self._wait_queue = [] + self._ticket_counter = itertools.count() + # TODO: Consider making the wait to be smarter, i.e. + # splitting read/write etc. to avoid potential contention. + self._cv = threading.Condition() + + self._monitor.start() + + def all_models(self, tag) -> list[Any]: + return self._models[tag] + + def enter_isolation_mode(self, tag: str, ticket_num: int) -> bool: + if self._total_active_jobs > 0: + logger.info( + "Waiting to enter isolation: tag=%s ticket num=%s", tag, ticket_num) + self._cv.wait() + # return False since we have waited and need to re-evaluate + # in caller to make sure our priority is still valid. + return False + + logger.info("Unknown model %s detected. Flushing GPU.", tag) + self._delete_all_models() + + self._isolation_mode = True + self._total_active_jobs += 1 + self._isolation_baseline, _, _ = self._monitor.get_stats() + self._monitor.reset_peak() + return True + + def should_spawn_model(self, tag: str, ticket_num: int) -> bool: + curr, _, total = self._monitor.get_stats() + est_cost = self._estimator.get_estimate(tag) + limit = total * (1 - self._slack_percentage) + + # Use current usage for capacity check (ignore old spikes) + if (curr + self._pending_reservations + est_cost) <= limit: + self._pending_reservations += est_cost + self._total_active_jobs += 1 + self._active_counts[tag] += 1 + return True + + # Evict to make space (passing tag to check demand/existence) + if self._evict_to_make_space(limit, est_cost, requesting_tag=tag): + return True + + # Manually log status for debugging if we are going to wait + idle_count = 0 + other_idle_count = 0 + for item in self._idle_lru.items(): + if item[1][0] == tag: + idle_count += 1 + else: + other_idle_count += 1 + total_model_count = 0 + for _, instances in self._models.items(): + total_model_count += len(instances) + curr, _, _ = self._monitor.get_stats() + logger.info( + "Waiting for resources to free up: " + "tag=%s ticket num%s model count=%s " + "idle count=%s resource usage=%.1f MB " + "total models count=%s other idle=%s", + tag, + ticket_num, + len(self._models[tag]), + idle_count, + curr, + total_model_count, + other_idle_count) + self._cv.wait(timeout=10.0) + return False + + def acquire_model(self, tag: str, loader_func: Callable[[], Any]) -> Any: + current_priority = 0 if self._estimator.is_unknown(tag) else 1 + ticket_num = next(self._ticket_counter) + my_id = object() + + with self._cv: + # FAST PATH: Grab from idle LRU if available + if not self._isolation_mode: + cached_instance = self._try_grab_from_lru(tag) + if cached_instance: + return cached_instance + + # SLOW PATH: Enqueue and wait for turn to acquire model, + # with unknown models having priority and order enforced + # by ticket number as FIFO. + logger.info( + "Acquire Queued: tag=%s, priority=%d " + "total models count=%s ticket num=%s", + tag, + current_priority, + len(self._models[tag]), + ticket_num) + heapq.heappush( + self._wait_queue, (current_priority, ticket_num, my_id, tag)) + + should_spawn = False + est_cost = 0.0 + is_unknown = False + + try: + while True: + if not self._wait_queue or self._wait_queue[0][2] is not my_id: + logger.info( + "Waiting for its turn: tag=%s ticket num=%s", tag, ticket_num) + self._cv.wait() + continue + + # Re-evaluate priority in case model became known during wait + real_is_unknown = self._estimator.is_unknown(tag) + real_priority = 0 if real_is_unknown else 1 + + # If priority changed, reinsert into queue and wait + if current_priority != real_priority: + heapq.heappop(self._wait_queue) + current_priority = real_priority + heapq.heappush( + self._wait_queue, (current_priority, ticket_num, my_id, tag)) + self._cv.notify_all() + continue + + # Try grab from LRU again in case model was released during wait + cached_instance = self._try_grab_from_lru(tag) + if cached_instance: + return cached_instance + + is_unknown = real_is_unknown + + # Path A: Isolation + if is_unknown: + if self.enter_isolation_mode(tag, ticket_num): + should_spawn = True + break + else: + # We waited, need to re-evaluate our turn + # because priority may have changed during the wait + continue + + # Path B: Concurrent + else: + if self._isolation_mode: + logger.info( + "Waiting due to isolation in progress: tag=%s ticket num%s", + tag, + ticket_num) + self._cv.wait() + continue + + if self.should_spawn_model(tag, ticket_num): + should_spawn = True + est_cost = self._estimator.get_estimate(tag) + break + else: + # We waited, need to re-evaluate our turn + # because priority may have changed during the wait + continue + + finally: + # Remove self from wait queue once done + if self._wait_queue and self._wait_queue[0][2] is my_id: + heapq.heappop(self._wait_queue) + else: + for i, item in enumerate(self._wait_queue): + if item[2] is my_id: + self._wait_queue.pop(i) + heapq.heapify(self._wait_queue) Review Comment: This only happens when things are not working so leaving it as is. -- This is an automated message from the Apache Git Service. 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