diff options
Diffstat (limited to 'lib/loader')
| -rw-r--r-- | lib/loader/__init__.py | 106 | ||||
| -rw-r--r-- | lib/loader/dlog.py | 293 | ||||
| -rw-r--r-- | lib/loader/energytrace.py | 17 |
3 files changed, 386 insertions, 30 deletions
diff --git a/lib/loader/__init__.py b/lib/loader/__init__.py index f0e176d..4ac91b8 100644 --- a/lib/loader/__init__.py +++ b/lib/loader/__init__.py @@ -19,6 +19,7 @@ from .energytrace import ( EnergyTraceWithLogicAnalyzer, EnergyTraceWithTimer, ) +from .dlog import DLog from .keysight import KeysightCSV from .mimosa import MIMOSA @@ -116,6 +117,37 @@ def _preprocess_etlog(measurement): return processed_data +def _preprocess_dlog(measurement): + setup = measurement["setup"] + dlog = DLog( + float(setup["voltage"]), + int(setup["state_duration"]), + with_traces=measurement["with_traces"], + ) + + states_and_transitions = list() + try: + dlog.load_data(measurement["content"]) + states_and_transitions = dlog.analyze_states( + measurement["expected_trace"], measurement["repeat_id"] + ) + except EOFError as e: + dlog.errors.append("DLog file error: {}".format(e)) + except RuntimeError as e: + dlog.errors.append("DLog loader error: {}".format(e)) + + processed_data = { + "fileno": measurement["fileno"], + "repeat_id": measurement["repeat_id"], + "info": measurement["info"], + "energy_trace": states_and_transitions, + "valid": len(dlog.errors) == 0, + "errors": dlog.errors, + } + + return processed_data + + class TimingData: """ Loader for timing model traces measured with on-board timers using `harness.OnboardTimerHarness`. @@ -330,11 +362,13 @@ class RawData: for member in tf.getmembers(): if member.name == "ptalog.json" and self.version == 0: self.version = 1 - # might also be version 2 - # depends on whether *.etlog exists or not + # or greater, if *.etlog / *.dlog exist elif ".etlog" in member.name: self.version = 2 break + elif ".dlog" in member.name: + self.version = 3 + break if self.version >= 1: self.ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json"))) self.pta = self.ptalog["pta"] @@ -390,7 +424,7 @@ class RawData: "ptalog": self.ptalog, "setup_by_fileno": self.setup_by_fileno, } - json.dump(cache_data, f) + json.dump(cache_data, f, cls=NpEncoder) def to_dref(self) -> dict: return { @@ -475,15 +509,15 @@ class RawData: """ if self.preprocessed: return self.traces - if self.version <= 2: - self._preprocess_012(self.version) + if self.version <= 3: + self._preprocess(self.version) else: raise ValueError(f"Unsupported raw data version: {self.version}") self.preprocessed = True self.save_cache() return self.traces - def _preprocess_012(self, version): + def _preprocess(self, version): """Load raw MIMOSA data and turn it into measurements which are ready to be analyzed.""" offline_data = [] for i, filename in enumerate(self.input_filenames): @@ -677,6 +711,55 @@ class RawData: # will not linger in 'offline_aggregates' and confuse the hell # out of other code paths + elif self.version == 3: + with tarfile.open(filename) as tf: + ptalog = json.load(tf.extractfile(tf.getmember("ptalog.json"))) + # generate-dfa-benchmark uses TimingHarness to obtain timing data. + # Data is placed in 'offline_aggregates', which is also + # where we are going to store power/energy data. + # In case of invalid measurements, this can lead to a + # mismatch between duration and power/energy data, e.g. + # where duration = [A, B, C], power = [a, b], B belonging + # to an invalid measurement and thus power[b] corresponding + # to duration[C]. At the moment, this is harmless, but in the + # future it might not be. + if "offline_aggregates" in ptalog["traces"][0][0]["trace"][0]: + for trace_group in ptalog["traces"]: + for trace in trace_group: + for state_or_transition in trace["trace"]: + offline_aggregates = state_or_transition.pop( + "offline_aggregates", None + ) + if offline_aggregates: + state_or_transition[ + "online_aggregates" + ] = offline_aggregates + for j, traces in enumerate(ptalog["traces"]): + self.filenames.append("{}#{}".format(filename, j)) + self.traces_by_fileno.append(traces) + self.setup_by_fileno.append( + { + "voltage": ptalog["configs"][j]["voltage"], + "state_duration": ptalog["opt"]["sleep"], + } + ) + for repeat_id, dlog_file in enumerate(ptalog["files"][j]): + member = tf.getmember(dlog_file) + offline_data.append( + { + "content": tf.extractfile(member).read(), + "fileno": len(self.traces_by_fileno) - 1, + # For debug output and warnings + "info": member, + "setup": self.setup_by_fileno[-1], + # needed to add runtime "return_value.apply_from" parameters to offline_aggregates + "repeat_id": repeat_id, + # only for validation + "expected_trace": traces, + "with_traces": self.with_traces, + } + ) + if self.version == 0 and len(self.input_filenames) > 1: for entry in offline_data: assert_legacy_compatibility( @@ -691,6 +774,8 @@ class RawData: measurements = pool.map(_preprocess_mimosa, offline_data) elif self.version == 2: measurements = pool.map(_preprocess_etlog, offline_data) + elif self.version == 3: + measurements = pool.map(_preprocess_dlog, offline_data) num_valid = 0 for measurement in measurements: @@ -721,7 +806,7 @@ class RawData: e="; ".join(measurement["errors"]), ) ) - elif version == 2: + elif version == 2 or version == 3: if measurement["valid"]: try: EnergyTrace.add_offline_aggregates( @@ -747,12 +832,7 @@ class RawData: num_valid=num_valid, num_total=len(measurements) ) ) - if version == 0: - self.traces = self._concatenate_traces(self.traces_by_fileno) - elif version == 1: - self.traces = self._concatenate_traces(self.traces_by_fileno) - elif version == 2: - self.traces = self._concatenate_traces(self.traces_by_fileno) + self.traces = self._concatenate_traces(self.traces_by_fileno) self.preprocessing_stats = { "num_runs": len(measurements), "num_valid": num_valid, diff --git a/lib/loader/dlog.py b/lib/loader/dlog.py new file mode 100644 index 0000000..b87a6bb --- /dev/null +++ b/lib/loader/dlog.py @@ -0,0 +1,293 @@ +#!/usr/bin/env python3 + +import io +import numpy as np +import struct +import xml.etree.ElementTree as ET + +from bisect import bisect_right + + +class DLogChannel: + def __init__(self, desc_tuple): + self.slot = desc_tuple[0] + self.smu = desc_tuple[1] + self.unit = desc_tuple[2] + self.data = None + + def __repr__(self): + return f"""<DLogChannel(slot={self.slot}, smu="{self.smu}", unit="{self.unit}", data={self.data})>""" + + +class DLog: + def __init__( + self, + voltage: float, + state_duration: int, + with_traces=False, + skip_duration=None, + limit_duration=None, + ): + self.voltage = voltage + self.state_duration = state_duration + self.with_traces = with_traces + self.skip_duration = skip_duration + self.limit_duration = limit_duration + self.errors = list() + + self.sync_min_duration = 0.7 + # TODO auto-detect + self.sync_power = 10e-3 + + def load_data(self, content): + lines = [] + line = "" + with io.BytesIO(content) as f: + while line != "</dlog>\n": + line = f.readline().decode() + lines.append(line) + xml_header = "".join(lines) + raw_header = f.read(8) + data_offset = f.tell() + raw_data = f.read() + + xml_header = xml_header.replace("1ua>", "X1ua>") + xml_header = xml_header.replace("2ua>", "X2ua>") + dlog = ET.fromstring(xml_header) + channels = [] + + for channel in dlog.findall("channel"): + channel_id = int(channel.get("id")) + sense_curr = channel.find("sense_curr").text + sense_volt = channel.find("sense_volt").text + model = channel.find("ident").find("model").text + if sense_volt == "1": + channels.append((channel_id, model, "V")) + if sense_curr == "1": + channels.append((channel_id, model, "A")) + + num_channels = len(channels) + + self.channels = list(map(DLogChannel, channels)) + self.interval = float(dlog.find("frame").find("tint").text) + self.sense_minmax = int(dlog.find("frame").find("sense_minmax").text) + self.planned_duration = int(dlog.find("frame").find("time").text) + self.observed_duration = self.interval * int(len(raw_data) / (4 * num_channels)) + + if self.sense_minmax: + raise RuntimeError( + "DLog files with 'Log Min/Max' enabled are not supported yet" + ) + + self.timestamps = np.linspace( + 0, self.observed_duration, num=int(len(raw_data) / (4 * num_channels)) + ) + + if ( + self.skip_duration is not None + and self.observed_duration >= self.skip_duration + ): + start_offset = 0 + for i, ts in enumerate(self.timestamps): + if ts >= self.skip_duration: + start_offset = i + break + self.timestamps = self.timestamps[start_offset:] + raw_data = raw_data[start_offset * 4 * num_channels :] + + if ( + self.limit_duration is not None + and self.observed_duration > self.limit_duration + ): + stop_offset = len(self.timestamps) - 1 + for i, ts in enumerate(self.timestamps): + if ts > self.limit_duration: + stop_offset = i + break + self.timestamps = self.timestamps[:stop_offset] + self.observed_duration = self.timestamps[-1] + raw_data = raw_data[: stop_offset * 4 * num_channels] + + self.data = np.ndarray( + shape=(num_channels, int(len(raw_data) / (4 * num_channels))), + dtype=np.float32, + ) + + iterator = struct.iter_unpack(">f", raw_data) + channel_offset = 0 + measurement_offset = 0 + for value in iterator: + if value[0] < -1e6 or value[0] > 1e6: + print( + f"Invalid data value {value[0]} at channel {channel_offset}, measurement {measurement_offset}. Replacing with 0." + ) + self.data[channel_offset, measurement_offset] = 0 + else: + self.data[channel_offset, measurement_offset] = value[0] + if channel_offset + 1 == num_channels: + channel_offset = 0 + measurement_offset += 1 + else: + channel_offset += 1 + + # An SMU has four slots + self.slots = [dict(), dict(), dict(), dict()] + + for i, channel in enumerate(self.channels): + channel.data = self.data[i] + self.slots[channel.slot - 1][channel.unit] = channel + + assert "A" in self.slots[0] + self.data = self.slots[0]["A"].data + + def observed_duration_equals_expectation(self): + return int(self.observed_duration) == self.planned_duration + + # very similar to DataProcessor.getStatesdfatool + def analyze_states(self, expected_trace, repeat_id): + sync_start = None + sync_timestamps = list() + above_count = 0 + below_count = 0 + for i, timestamp in enumerate(self.timestamps): + power = self.voltage * self.data[i] + if power > self.sync_power: + above_count += 1 + below_count = 0 + else: + above_count = 0 + below_count += 1 + + if above_count > 2 and sync_start is None: + sync_start = timestamp + elif below_count > 2 and sync_start is not None: + if timestamp - sync_start > self.sync_min_duration: + sync_end = timestamp + sync_timestamps.append((sync_start, sync_end)) + sync_start = None + print(sync_timestamps) + + if len(sync_timestamps) != 3: + self.errors.append( + f"Found {len(sync_timestamps)} synchronization pulses, expected three." + ) + self.errors.append(f"Synchronization pulses == {sync_timestamps}") + return list() + + start_ts = sync_timestamps[0][1] + end_ts = sync_timestamps[1][0] + + # start and end of first state + online_timestamps = [0, expected_trace[0]["start_offset"][repeat_id]] + + # remaining events from the end of the first transition (start of second state) to the end of the last observed state + for trace in expected_trace: + for word in trace["trace"]: + online_timestamps.append( + online_timestamps[-1] + + word["online_aggregates"]["duration"][repeat_id] + ) + + online_timestamps = np.array(online_timestamps) * 1e-6 + online_timestamps = ( + online_timestamps * ((end_ts - start_ts) / online_timestamps[-1]) + start_ts + ) + + trigger_edges = list() + for ts in online_timestamps: + trigger_edges.append(bisect_right(self.timestamps, ts)) + + energy_trace = list() + + for i in range(2, len(online_timestamps), 2): + prev_state_start_index = trigger_edges[i - 2] + prev_state_stop_index = trigger_edges[i - 1] + transition_start_index = trigger_edges[i - 1] + transition_stop_index = trigger_edges[i] + state_start_index = trigger_edges[i] + state_stop_index = trigger_edges[i + 1] + + # If a transition takes less time than the measurement interval, its start and stop index may be the same. + # In this case, self.data[transition_start_index] is the only data point affected by the transition. + # We use the self.data slice [transition_start_index, transition_stop_index) to determine the mean power, so we need + # to increment transition_stop_index by 1 to end at self.data[transition_start_index] + # (self.data[transition_start_index : transition_start_index+1 ] == [self.data[transition_start_index]) + if transition_stop_index == transition_start_index: + transition_stop_index += 1 + + prev_state_duration = online_timestamps[i + 1] - online_timestamps[i] + transition_duration = online_timestamps[i] - online_timestamps[i - 1] + state_duration = online_timestamps[i + 1] - online_timestamps[i] + + # some states are followed by a UART dump of log data. This causes an increase in CPU energy + # consumption and is not part of the peripheral behaviour, so it should not be part of the benchmark results. + # If a case is followed by a UART dump, its duration is longer than the sleep duration between two transitions. + # In this case, we re-calculate the stop index, and calculate the state duration from coarse energytrace data + # instead of high-precision sync data + if ( + self.timestamps[prev_state_stop_index] + - self.timestamps[prev_state_start_index] + > self.state_duration + ): + prev_state_stop_index = bisect_right( + self.timestamps, + self.timestamps[prev_state_start_index] + self.state_duration, + ) + prev_state_duration = ( + self.timestamps[prev_state_stop_index] + - self.timestamps[prev_state_start_index] + ) + + if ( + self.timestamps[state_stop_index] - self.timestamps[state_start_index] + > self.state_duration + ): + state_stop_index = bisect_right( + self.timestamps, + self.timestamps[state_start_index] + self.state_duration, + ) + state_duration = ( + self.timestamps[state_stop_index] + - self.timestamps[state_start_index] + ) + + prev_state_power = self.data[prev_state_start_index:prev_state_stop_index] + + transition_timestamps = self.timestamps[ + transition_start_index:transition_stop_index + ] + transition_power = self.data[transition_start_index:transition_stop_index] + + state_timestamps = self.timestamps[state_start_index:state_stop_index] + state_power = self.data[state_start_index:state_stop_index] + + transition = { + "isa": "transition", + "W_mean": np.mean(transition_power), + "W_std": np.std(transition_power), + "s": transition_duration, + } + if self.with_traces: + transition["plot"] = ( + transition_timestamps - transition_timestamps[0], + transition_power, + ) + + state = { + "isa": "state", + "W_mean": np.mean(state_power), + "W_std": np.std(state_power), + "s": state_duration, + } + if self.with_traces: + state["plot"] = (state_timestamps - state_timestamps[0], state_power) + + transition["W_mean_delta_prev"] = transition["W_mean"] - np.mean( + prev_state_power + ) + transition["W_mean_delta_next"] = transition["W_mean"] - state["W_mean"] + + energy_trace.append(transition) + energy_trace.append(state) + + return energy_trace diff --git a/lib/loader/energytrace.py b/lib/loader/energytrace.py index 8fc2d0e..103a4d6 100644 --- a/lib/loader/energytrace.py +++ b/lib/loader/energytrace.py @@ -721,21 +721,6 @@ class EnergyTraceWithLogicAnalyzer: energy_trace = list() expected_transitions = list() - # Print for debug purposes - # for number, name in enumerate(names): - # if "P15_8MW" in name: - # print(name, energy_trace_new[number]["W_mean"]) - - # st = "" - # for i, x in enumerate(energy_trace_new[-10:]): - # #st += "(%s|%s|%s)" % (energy_trace[i-10]["name"],x['W_mean'],x['s']) - # st += "(%s|%s|%s)\n" % (energy_trace[i-10]["s"], x['s'], x['W_mean']) - - # print(st, "\n_______________________") - # print(len(self.sync_data.timestamps), " - ", len(energy_trace_new), " - ", len(energy_trace), " - ", ",".join([str(x["s"]) for x in energy_trace_new[-6:]]), " - ", ",".join([str(x["s"]) for x in energy_trace[-6:]])) - # if len(energy_trace_new) < len(energy_trace): - # return None - return energy_trace_new @@ -796,8 +781,6 @@ class EnergyTraceWithTimer(EnergyTraceWithLogicAnalyzer): ) return list() - # print(timestamps) - # Stop "Synchronization pulses". The first one has already started. timestamps.extend(np.array([10, 1e6, 1e6 + 10]) + timestamps[-1]) timestamps.extend(np.array([0, 10, 1e6, 1e6 + 10]) + 250e3 + timestamps[-1]) |