diff --git a/.gitignore b/.gitignore index dc89174..3bc74c4 100644 --- a/.gitignore +++ b/.gitignore @@ -116,3 +116,4 @@ data client-sciot.json .python-version uv.lock +sciot-*.json diff --git a/src/server/communication/device_state.py b/src/server/communication/device_state.py new file mode 100644 index 0000000..ece4a31 --- /dev/null +++ b/src/server/communication/device_state.py @@ -0,0 +1,217 @@ +"""Per-device runtime state for the inference/offloading path. + +``RequestHandler`` used to keep every piece of runtime state in class-level +dictionaries mutated from both the ASGI thread and the background I/O thread. +This module owns that state instead, behind a single reentrant lock, and scopes +the variance detector and the adaptive-risk state to one device each. + +The legacy ``device_profiles`` mapping stays the canonical storage for a +device's EMA tables: ``http_server`` and the dashboard read it as a plain dict, +so the manager hands out the live dict rather than a copy. +""" + +import json +import threading + +from server.commons import OffloadingDataFiles +from server.core.variance_detector import VarianceDetector +from server.offloading_algo.adaptive_risk import AdaptiveRiskState + +DEFAULT_MODEL_DIR = "test_model_96x96" +DEFAULT_MODEL_KEY = "fomo_96x96" + +VARIANCE_WINDOW_SIZE = 10 +VARIANCE_THRESHOLD = 0.15 + + +def build_profile( + device_id: str, + *, + model_dir: str = DEFAULT_MODEL_DIR, + model_key: str = DEFAULT_MODEL_KEY, + num_layers: int = 0, + last_offloading_layer: int | None = None, +) -> dict: + """Build a device profile in the legacy ``device_profiles`` shape.""" + profile = { + "id": device_id, + "model_dir": model_dir, + "model_key": model_key, + "device_inference_times": {f"layer_{i}": 1 for i in range(num_layers)}, + "edge_inference_times": {f"layer_{i}": 0.1 for i in range(num_layers)}, + } + if last_offloading_layer is not None: + profile["ultimo_layer"] = last_offloading_layer + return profile + + +class DeviceStateManager: + """Thread-safe registry of per-device runtime state. + + Holds the profile (EMA tables), the variance detector, and the adaptive-risk + state for each device. Every mutation goes through ``_lock``: the EMA updates + run on the ASGI thread while the background I/O worker snapshots the same + dicts. + """ + + def __init__(self): + self._lock = threading.RLock() + # device_id -> legacy profile dict; exposed live via `profiles`. + self._profiles: dict[str, dict] = {} + self._variance_detectors: dict[str, VarianceDetector] = {} + self._adaptive_states: dict[str, AdaptiveRiskState] = {} + self._layer_sizes_cache: dict[str, list] = {} + + @property + def profiles(self) -> dict: + """The live ``device_id -> profile`` mapping. + + Returned by reference for backwards compatibility with direct readers. + """ + return self._profiles + + # ── device lifecycle ──────────────────────────────────────────────── + def register( + self, + device_id: str, + *, + model_dir: str = DEFAULT_MODEL_DIR, + model_key: str = DEFAULT_MODEL_KEY, + num_layers: int = 0, + last_offloading_layer: int | None = None, + ) -> dict: + """Return the device's profile, creating it on first sight.""" + with self._lock: + profile = self._profiles.get(device_id) + if profile is None: + profile = build_profile( + device_id, + model_dir=model_dir, + model_key=model_key, + num_layers=num_layers, + last_offloading_layer=last_offloading_layer, + ) + self._profiles[device_id] = profile + return profile + + def __contains__(self, device_id: str) -> bool: + with self._lock: + return device_id in self._profiles + + def model_dir(self, device_id: str, default: str = DEFAULT_MODEL_DIR) -> str: + with self._lock: + return self._profiles.get(device_id, {}).get("model_dir", default) + + def model_key(self, device_id: str, default: str = DEFAULT_MODEL_KEY) -> str: + with self._lock: + return self._profiles.get(device_id, {}).get("model_key", default) + + def clear(self) -> None: + with self._lock: + self._profiles.clear() + self._variance_detectors.clear() + self._adaptive_states.clear() + self._layer_sizes_cache.clear() + + # ── per-device collaborators ──────────────────────────────────────── + def variance_detector(self, device_id: str) -> VarianceDetector: + """The device's own detector. + + Scoped per device so heterogeneous hardware reporting the same layer + indices cannot pollute each other's variance statistics. + """ + with self._lock: + detector = self._variance_detectors.get(device_id) + if detector is None: + detector = VarianceDetector( + window_size=VARIANCE_WINDOW_SIZE, + variance_threshold=VARIANCE_THRESHOLD, + ) + self._variance_detectors[device_id] = detector + return detector + + def adaptive_state(self, device_id: str) -> AdaptiveRiskState: + with self._lock: + state = self._adaptive_states.get(device_id) + if state is None: + state = AdaptiveRiskState() + self._adaptive_states[device_id] = state + return state + + def variance_stats(self, device_id: str) -> dict: + """Variance statistics for one device, for the offloading context.""" + return self.variance_detector(device_id).get_all_stats() + + # ── EMA updates ───────────────────────────────────────────────────── + def update_device_times(self, device_id: str, layer_times, *, alpha: float) -> None: + """Apply the EMA update for device-measured layer times.""" + profile = self.register(device_id) + detector = self.variance_detector(device_id) + with self._lock: + times = profile["device_inference_times"] + for layer_id, measured in enumerate(layer_times): + self._apply_ema(times, layer_id, float(measured), alpha) + detector.add_device_measurement(layer_id, measured) + + def update_edge_times( + self, device_id: str, layer_times, *, start_layer: int, alpha: float + ) -> None: + """Apply the EMA update for edge-measured layer times.""" + profile = self.register(device_id) + detector = self.variance_detector(device_id) + with self._lock: + times = profile["edge_inference_times"] + for offset, measured in enumerate(layer_times): + layer_id = start_layer + offset + self._apply_ema(times, layer_id, float(measured), alpha) + detector.add_edge_measurement(layer_id, measured) + + @staticmethod + def _apply_ema(times: dict, layer_id: int, measured: float, alpha: float) -> None: + layer_key = f"layer_{layer_id}" + if layer_key in times: + times[layer_key] = alpha * measured + (1 - alpha) * times[layer_key] + else: + times[layer_key] = measured + + # ── reads for the offloading decision ─────────────────────────────── + def snapshot_times(self, device_id: str) -> tuple[list, list]: + """Copy the EMA tables so callers never iterate a dict being mutated.""" + with self._lock: + profile = self._profiles.get(device_id) + if profile is None: + raise ValueError(f"Unknown device_id '{device_id}'") + return ( + list(profile["device_inference_times"].values()), + list(profile["edge_inference_times"].values()), + ) + + def snapshot_debug_times(self, device_id: str) -> tuple[dict, dict]: + """Copy the EMA tables for the background debug-JSON writer.""" + with self._lock: + profile = self._profiles.get(device_id) + if profile is None: + return {}, {} + return ( + dict(profile["device_inference_times"]), + dict(profile["edge_inference_times"]), + ) + + def layer_sizes(self, model_dir: str) -> list: + """Return per-layer output sizes, reading each model's file once.""" + with self._lock: + cached = self._layer_sizes_cache.get(model_dir) + if cached is not None: + return cached + + # Read outside the lock: this touches the disk, and a lookup for another + # model must not block behind it. + with open(OffloadingDataFiles.get_sizes_path(model_dir), "r") as file: + sizes = list(json.load(file).values()) + with self._lock: + return self._layer_sizes_cache.setdefault(model_dir, sizes) + + def load_stats(self, device_id: str) -> tuple[list, list, list]: + """Return (device EMA times, edge EMA times, layer sizes) for a device.""" + device_times, edge_times = self.snapshot_times(device_id) + return device_times, edge_times, self.layer_sizes(self.model_dir(device_id)) diff --git a/src/server/communication/request_handler.py b/src/server/communication/request_handler.py index 352a2c3..7c0c733 100644 --- a/src/server/communication/request_handler.py +++ b/src/server/communication/request_handler.py @@ -13,7 +13,6 @@ from server.commons import ModelFiles from server.edge.edge_initialization import Edge -from server.offloading_algo.adaptive_risk import AdaptiveRiskState from server.offloading_algo.factory import ( OffloadingContext, configured_algorithm_class_name, @@ -35,10 +34,10 @@ from server.communication.message_data import MessageData from server.communication.inference_protocol import decode_inference_payload +from server.communication.device_state import DeviceStateManager from server.models.model_input_converter import ModelInputConverter from server.core.delay_simulator import DelaySimulator -from server.core.variance_detector import VarianceDetector from sciot.config import load_server_config import struct @@ -324,8 +323,6 @@ class RequestHandler: model_registry = {} # hash -> {model_dir, last_offloading_layer, num_layers} device_model_map = {} # device_id -> model_info - # Class-level variance detector (shared across all requests) - variance_detector = VarianceDetector(window_size=10, variance_threshold=0.15) # Class-level CSV file tracking for simulation results csv_file = None csv_writer = None @@ -333,9 +330,11 @@ class RequestHandler: header_printed = False inference_table_rows_printed = 0 - # Dictionary to store device profile data - device_profiles = {} - offloading_states = {} + # Per-device runtime state (profiles, variance detectors, adaptive state). + device_state = DeviceStateManager() + # Live view of `device_state`'s profiles, kept for direct readers such as + # `http_server` and the dashboard. + device_profiles = device_state.profiles num_layers = 0 def __init__(self): @@ -429,12 +428,10 @@ def _cleanup_debug_folder(self): # Debug file saving def _save_debug_files(self, device_id): """Legacy sync method – prefer _save_debug_files_data for bg thread.""" - profile = RequestHandler.device_profiles[device_id] - self._save_debug_files_data( - device_id, - dict(profile["device_inference_times"]), - dict(profile["edge_inference_times"]), + device_times, edge_times = RequestHandler.device_state.snapshot_debug_times( + device_id ) + self._save_debug_files_data(device_id, device_times, edge_times) @staticmethod def _save_debug_files_data(device_id, device_times, edge_times): @@ -563,10 +560,7 @@ def _create_offloading_algorithm( layers_sizes: list, device_cpu_percent: float, ): - adaptive_state = RequestHandler.offloading_states.setdefault( - device_id, - AdaptiveRiskState(), - ) + state = RequestHandler.device_state return create_offloading_algorithm( self.offloading_config, OffloadingContext( @@ -575,12 +569,10 @@ def _create_offloading_algorithm( layers_sizes=list(layers_sizes), inference_time_device=list(device_inference_times), inference_time_edge=list(edge_inference_times), - model_dir=RequestHandler.device_profiles.get(device_id, {}).get( - "model_dir", "test_model_96x96" - ), + model_dir=state.model_dir(device_id), device_cpu_percent=device_cpu_percent, - adaptive_state=adaptive_state, - variance_stats=RequestHandler.variance_detector.get_all_stats(), + adaptive_state=state.adaptive_state(device_id), + variance_stats=state.variance_stats(device_id), ), ) @@ -630,18 +622,13 @@ def handle_registration(self, device_id: str, model_hash: str = ""): model_info = RequestHandler.model_registry[model_hash] RequestHandler.device_model_map[device_id] = model_info - if device_id not in RequestHandler.device_profiles: - num_layers = model_info["num_layers"] - RequestHandler.device_profiles[device_id] = { - "id": device_id, - "model_dir": model_info["model_dir"], - "model_key": model_info.get("model_key", "fomo_96x96"), - "ultimo_layer": model_info["last_offloading_layer"], - "device_inference_times": { - f"layer_{i}": 1 for i in range(num_layers) - }, - "edge_inference_times": {f"layer_{i}": 0.1 for i in range(num_layers)}, - } + RequestHandler.device_state.register( + device_id, + model_dir=model_info["model_dir"], + model_key=model_info.get("model_key", "fomo_96x96"), + num_layers=model_info["num_layers"], + last_offloading_layer=model_info["last_offloading_layer"], + ) return device_id def handle_device_input(self, rgb565_image, height, width): @@ -736,37 +723,22 @@ def handle_device_inference_result(self, body, received_timestamp): # ─── STATO E LOGICA DEVICE ─── self.profiler.start_phase("server_state_update", trace_id) - if device_id not in RequestHandler.device_profiles: - RequestHandler.device_profiles[device_id] = { - "id": device_id, - "model_dir": "test_model_96x96", - "model_key": "fomo_96x96", - "device_inference_times": { - f"layer_{i}": 1 for i in range(RequestHandler.num_layers) - }, - "edge_inference_times": { - f"layer_{i}": 0.1 for i in range(RequestHandler.num_layers) - }, - } - - device_inference_times = RequestHandler.device_profiles[device_id]["device_inference_times"] - edge_inference_times = RequestHandler.device_profiles[device_id]["edge_inference_times"] + device_state = RequestHandler.device_state + profile = device_state.register(device_id, num_layers=RequestHandler.num_layers) + edge_inference_times = profile["edge_inference_times"] alpha = load_offloading_ema_alpha_config() - for l_id, inference_time in enumerate(message_data.device_layers_inference_time): - layer_key = f"layer_{l_id}" - if layer_key in device_inference_times: - device_inference_times[layer_key] = (alpha * inference_time + (1 - alpha) * device_inference_times[layer_key]) - else: - device_inference_times[layer_key] = inference_time - RequestHandler.variance_detector.add_device_measurement(l_id, inference_time) - + device_state.update_device_times( + device_id, + message_data.device_layers_inference_time, + alpha=alpha, + ) + self.profiler.end_phase("server_state_update", trace_id) # ─── INFERENZA SUL SERVER (SE NECESSARIA) ─── edge_layer_times = [] num_edge_layers = 0 - profile = RequestHandler.device_profiles[device_id] ultimo_layer = profile.get("ultimo_layer", 58) if message_data.offloading_layer_index == -1 or message_data.offloading_layer_index >= ultimo_layer: @@ -792,23 +764,26 @@ def handle_device_inference_result(self, body, received_timestamp): # Aggiornamento tempi Edge (Post-Inference) self.profiler.start_phase("server_state_update", f"{trace_id}_post") - start_layer = message_data.offloading_layer_index + 1 - for i, measured_time in enumerate(edge_layer_times): - layer_key = f"layer_{start_layer + i}" - if layer_key in edge_inference_times: - edge_inference_times[layer_key] = (alpha * measured_time + (1 - alpha) * edge_inference_times[layer_key]) - else: - edge_inference_times[layer_key] = measured_time - RequestHandler.variance_detector.add_edge_measurement(start_layer + i, measured_time) + device_state.update_edge_times( + device_id, + edge_layer_times, + start_layer=message_data.offloading_layer_index + 1, + alpha=alpha, + ) self.profiler.end_phase("server_state_update", f"{trace_id}_post") # ─── LOGICA ALGORITMO E I/O BACKGROUND ─── self.profiler.start_phase("server_algo_and_io", trace_id) - # Offload I/O tasks - _debug_profile = RequestHandler.device_profiles[device_id] + # Offload I/O tasks. The snapshot is taken here, under the state lock, + # so the background thread never serializes a dict mid-update. + _debug_device_times, _debug_edge_times = device_state.snapshot_debug_times( + device_id + ) enqueue_background_io( - lambda did=device_id, dt=dict(_debug_profile["device_inference_times"]), et=dict(_debug_profile["edge_inference_times"]): self._save_debug_files_data(did, dt, et), + lambda did=device_id, dt=_debug_device_times, et=_debug_edge_times: ( + self._save_debug_files_data(did, dt, et) + ), description="debug timing JSON write", ) @@ -841,7 +816,11 @@ def handle_device_inference_result(self, body, received_timestamp): self.network_delay.apply_delay() # Decisione Offloading per la prossima risposta - device_inference_times, edge_inference_times, layers_sizes = RequestHandler._load_stats(device_id) + ( + device_inference_times, + edge_inference_times, + layers_sizes, + ) = device_state.load_stats(device_id) # Usa il pacchetto appena ricevuto per calcolare la velocità reale del Wi-Fi! if getattr(message_data, 'avg_speed', 0) > 0: self.last_avg_speed = message_data.avg_speed @@ -903,9 +882,7 @@ def handle_device_inference_result(self, body, received_timestamp): _eval_outputs = self.evaluation_outputs_config if _eval_outputs["offloading_decisions"]: - model_dir = RequestHandler.device_profiles.get(device_id, {}).get( - "model_dir", "unknown" - ) + model_dir = device_state.model_dir(device_id, "unknown") selected_candidate = next( ( candidate @@ -970,21 +947,14 @@ def handle_device_inference_result(self, body, received_timestamp): # ─── STRUTTURATO INFERENCE CYCLE OUTPUT ⟩⟩⟩ # This is placed after the offloading decision so all data is available. - # Skipping it entirely (build + enqueue) when disabled also avoids the - # `_load_stats` disk/cache access below. if _eval_outputs["inference_cycles"]: - model_key = RequestHandler.device_profiles.get(device_id, {}).get( - "model_key", "fomo_96x96" - ) - _model_dir = RequestHandler.device_profiles.get(device_id, {}).get( - "model_dir", "test_model_96x96" - ) + model_key = device_state.model_key(device_id) + _model_dir = device_state.model_dir(device_id) # `device_layers_inference_time` is a numpy array (see inference_protocol), # so the old `array or []` raised "truth value of an array is ambiguous" # for multi-layer arrays, silently aborting the evaluation write. device_values = _to_float_list(message_data.device_layers_inference_time) - device_ema_list = list(RequestHandler.device_profiles[device_id]["device_inference_times"].values()) if device_id in RequestHandler.device_profiles else [] - edge_ema_list = list(RequestHandler.device_profiles[device_id]["edge_inference_times"].values()) if device_id in RequestHandler.device_profiles else [] + device_ema_list, edge_ema_list = device_state.snapshot_times(device_id) ntp_latency = getattr(message_data, 'latency', 0) or 0 avg_payload_size = getattr(message_data, 'payload_size', 0) or 0 estimated_cost = ( @@ -992,11 +962,7 @@ def handle_device_inference_result(self, body, received_timestamp): if offloading_algo is not None and hasattr(offloading_algo, 'lowest_evaluation') else 0.0 ) - _layer_sizes = ( - list(RequestHandler._load_stats(device_id)[2]) - if hasattr(RequestHandler, '_load_stats') - else [] - ) + _layer_sizes = layers_sizes inference_cycle = build_inference_cycle_event( device_id=str(device_id), @@ -1060,35 +1026,6 @@ def handle_device_inference_result(self, body, received_timestamp): def handle_offloading_layer(self, best_offloading_layer): return best_offloading_layer - # Class-level layer_sizes cache: model_dir -> list of sizes - # Layer sizes never change at runtime, so we read them once per model. - _layer_sizes_cache = {} - - # modificata - @staticmethod - def _load_stats(device_id): - - # Recupero il profilo dal vocabolario in RAM - profile = RequestHandler.device_profiles.get(device_id) - if profile is None: - raise ValueError(f"Unknown device_id '{device_id}'") - - # Prendo solo i valori convertendoli in liste - device_inference_times = list(profile["device_inference_times"].values()) - edge_inference_times = list(profile["edge_inference_times"].values()) - - # Layer sizes: read from disk once, then serve from cache - model_dir = RequestHandler.device_profiles.get(device_id, {}).get( - "model_dir", "test_model_96x96" - ) - if model_dir not in RequestHandler._layer_sizes_cache: - with open(OffloadingDataFiles.get_sizes_path(model_dir), "r") as file: - sizes_data = json.load(file) - RequestHandler._layer_sizes_cache[model_dir] = list(sizes_data.values()) - layers_sizes = RequestHandler._layer_sizes_cache[model_dir] - - return device_inference_times, edge_inference_times, layers_sizes - @staticmethod def _from_raw(topic: str, payload: bytes): """Parse the raw message payload into a MessageData instance.""" diff --git a/tests/unit/test_device_state.py b/tests/unit/test_device_state.py new file mode 100644 index 0000000..d1f0f03 --- /dev/null +++ b/tests/unit/test_device_state.py @@ -0,0 +1,226 @@ +"""Unit tests for the per-device runtime state manager.""" + +import json +import threading + +import pytest + +from server.communication.device_state import ( + DEFAULT_MODEL_DIR, + DEFAULT_MODEL_KEY, + DeviceStateManager, + build_profile, +) + + +@pytest.fixture +def manager(): + return DeviceStateManager() + + +def test_register_builds_legacy_profile_shape(manager): + profile = manager.register( + "dev-1", + model_dir="FOMO_96_CUT", + model_key="fomo_96x96", + num_layers=3, + last_offloading_layer=2, + ) + + assert profile == { + "id": "dev-1", + "model_dir": "FOMO_96_CUT", + "model_key": "fomo_96x96", + "ultimo_layer": 2, + "device_inference_times": {"layer_0": 1, "layer_1": 1, "layer_2": 1}, + "edge_inference_times": {"layer_0": 0.1, "layer_1": 0.1, "layer_2": 0.1}, + } + + +def test_register_is_idempotent_and_preserves_state(manager): + first = manager.register("dev-1", num_layers=2) + first["device_inference_times"]["layer_0"] = 0.42 + + second = manager.register("dev-1", num_layers=2) + + assert second is first + assert second["device_inference_times"]["layer_0"] == 0.42 + + +def test_profiles_is_the_live_mapping(manager): + # `http_server` reads `device_profiles` directly, so it must not be a copy. + profile = manager.register("dev-1", num_layers=1) + + assert manager.profiles["dev-1"] is profile + assert "dev-1" in manager + + +def test_build_profile_omits_ultimo_layer_when_unknown(): + assert "ultimo_layer" not in build_profile("dev-1", num_layers=1) + + +def test_update_device_times_applies_ema_to_known_layers(manager): + manager.register("dev-1", num_layers=2) + + manager.update_device_times("dev-1", [3.0, 5.0], alpha=0.5) + + times = manager.profiles["dev-1"]["device_inference_times"] + # Seeded at 1: 0.5*3 + 0.5*1 = 2.0 ; 0.5*5 + 0.5*1 = 3.0 + assert times == {"layer_0": pytest.approx(2.0), "layer_1": pytest.approx(3.0)} + + +def test_update_device_times_seeds_unknown_layers_with_raw_measurement(manager): + manager.register("dev-1", num_layers=1) + + manager.update_device_times("dev-1", [3.0, 7.0], alpha=0.5) + + times = manager.profiles["dev-1"]["device_inference_times"] + assert times["layer_1"] == pytest.approx(7.0) + + +def test_update_edge_times_offsets_by_start_layer(manager): + manager.register("dev-1", num_layers=4) + + manager.update_edge_times("dev-1", [1.0, 2.0], start_layer=2, alpha=1.0) + + times = manager.profiles["dev-1"]["edge_inference_times"] + assert times["layer_0"] == pytest.approx(0.1) + assert times["layer_1"] == pytest.approx(0.1) + assert times["layer_2"] == pytest.approx(1.0) + assert times["layer_3"] == pytest.approx(2.0) + + +def test_update_registers_unseen_device(manager): + manager.update_device_times("brand-new", [1.5], alpha=0.5) + + assert "brand-new" in manager + assert manager.model_dir("brand-new") == DEFAULT_MODEL_DIR + assert manager.model_key("brand-new") == DEFAULT_MODEL_KEY + + +def test_variance_detectors_are_isolated_per_device(manager): + # Heterogeneous devices report the same layer indices; their measurements + # must not land in a shared histogram. + manager.register("fast-device", num_layers=1) + manager.register("slow-device", num_layers=1) + + manager.update_device_times("fast-device", [0.01], alpha=0.5) + manager.update_device_times("slow-device", [5.0], alpha=0.5) + + fast = manager.variance_stats("fast-device")["device"][0] + slow = manager.variance_stats("slow-device")["device"][0] + assert fast["measurements"] == 1 + assert slow["measurements"] == 1 + assert fast["mean"] == pytest.approx(0.01) + assert slow["mean"] == pytest.approx(5.0) + + +def test_variance_detector_and_adaptive_state_are_stable_per_device(manager): + assert manager.variance_detector("dev-1") is manager.variance_detector("dev-1") + assert manager.adaptive_state("dev-1") is manager.adaptive_state("dev-1") + assert manager.adaptive_state("dev-1") is not manager.adaptive_state("dev-2") + + +def test_variance_stats_for_unknown_device_is_empty(manager): + assert manager.variance_stats("ghost")["device"] == {} + + +def test_snapshot_times_returns_copies(manager): + manager.register("dev-1", num_layers=1) + + device_times, edge_times = manager.snapshot_times("dev-1") + device_times.append(999) + + assert manager.snapshot_times("dev-1")[0] == [1] + assert edge_times == [0.1] + + +def test_snapshot_times_rejects_unknown_device(manager): + with pytest.raises(ValueError, match="Unknown device_id 'ghost'"): + manager.snapshot_times("ghost") + + +def test_snapshot_debug_times_returns_copies_and_tolerates_unknown(manager): + manager.register("dev-1", num_layers=1) + + device_times, edge_times = manager.snapshot_debug_times("dev-1") + device_times["layer_0"] = 999 + + assert manager.profiles["dev-1"]["device_inference_times"]["layer_0"] == 1 + assert edge_times == {"layer_0": 0.1} + assert manager.snapshot_debug_times("ghost") == ({}, {}) + + +def test_layer_sizes_reads_each_model_once(manager, tmp_path, monkeypatch): + sizes_file = tmp_path / "sizes.json" + sizes_file.write_text(json.dumps({"layer_0": 10, "layer_1": 20})) + reads = [] + + def fake_path(model_dir): + reads.append(model_dir) + return sizes_file + + monkeypatch.setattr( + "server.communication.device_state.OffloadingDataFiles.get_sizes_path", + fake_path, + ) + + assert manager.layer_sizes("FOMO_96_CUT") == [10, 20] + assert manager.layer_sizes("FOMO_96_CUT") == [10, 20] + assert reads == ["FOMO_96_CUT"] + + +def test_load_stats_uses_the_device_model_dir(manager, tmp_path, monkeypatch): + sizes_file = tmp_path / "sizes.json" + sizes_file.write_text(json.dumps({"layer_0": 10})) + monkeypatch.setattr( + "server.communication.device_state.OffloadingDataFiles.get_sizes_path", + lambda model_dir: sizes_file, + ) + manager.register("dev-1", model_dir="FOMO_96_CUT", num_layers=1) + + device_times, edge_times, layer_sizes = manager.load_stats("dev-1") + + assert device_times == [1] + assert edge_times == [0.1] + assert layer_sizes == [10] + + +def test_clear_drops_all_state(manager): + manager.register("dev-1", num_layers=1) + manager.variance_detector("dev-1") + + manager.clear() + + assert "dev-1" not in manager + assert manager.profiles == {} + + +def test_concurrent_ema_updates_do_not_lose_measurements(manager): + # Regression: `device_profiles` was mutated from the ASGI thread and the + # background I/O thread with no lock. + manager.register("dev-1", num_layers=1) + barrier = threading.Barrier(8) + errors = [] + + def hammer(): + try: + barrier.wait() + for _ in range(200): + manager.update_device_times("dev-1", [2.0], alpha=0.5) + manager.snapshot_debug_times("dev-1") + except Exception as exc: # pragma: no cover - only on a real race + errors.append(exc) + + threads = [threading.Thread(target=hammer) for _ in range(8)] + for thread in threads: + thread.start() + for thread in threads: + thread.join() + + assert errors == [] + stats = manager.variance_stats("dev-1")["device"][0] + assert stats["measurements"] == 10 # window_size cap + assert manager.profiles["dev-1"]["device_inference_times"]["layer_0"] == ( + pytest.approx(2.0) + ) diff --git a/tests/unit/test_request_handler_helpers.py b/tests/unit/test_request_handler_helpers.py index 843be41..87e97e0 100644 --- a/tests/unit/test_request_handler_helpers.py +++ b/tests/unit/test_request_handler_helpers.py @@ -6,6 +6,7 @@ import pytest import server.communication.request_handler as rh +from server.communication.device_state import DeviceStateManager from server.communication.request_handler import ( _sparse_layer_timings, _to_float_list, @@ -233,20 +234,16 @@ def fake_create(config, context): return "algorithm" monkeypatch.setattr(rh, "create_offloading_algorithm", fake_create) - monkeypatch.setattr( - rh.RequestHandler.variance_detector, - "get_all_stats", - lambda: {"needs_retest": False}, - ) handler = object.__new__(rh.RequestHandler) handler.offloading_config = {"algorithm": "adaptive_risk"} + device_state = DeviceStateManager() + monkeypatch.setattr(rh.RequestHandler, "device_state", device_state) monkeypatch.setitem( - rh.RequestHandler.device_profiles, + device_state.profiles, "factory-device", {"model_dir": "FOMO_96_CUT"}, ) - monkeypatch.setattr(rh.RequestHandler, "offloading_states", {}) algorithm = handler._create_offloading_algorithm( device_id="factory-device", @@ -263,9 +260,7 @@ def fake_create(config, context): assert context.model_dir == "FOMO_96_CUT" assert context.avg_speed == 123.0 assert context.device_cpu_percent == 37.5 - assert context.adaptive_state is rh.RequestHandler.offloading_states[ - "factory-device" - ] + assert context.adaptive_state is device_state.adaptive_state("factory-device") def test_append_and_publish_inference_cycle_keeps_local_write(monkeypatch): diff --git a/uv.lock b/uv.lock index 16c8f21..0172fc6 100644 --- a/uv.lock +++ b/uv.lock @@ -2,18 +2,12 @@ version = 1 revision = 3 requires-python = 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