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# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Module for backend integration."""
from __future__ import annotations
import logging
from abc import ABC
from abc import abstractmethod
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
from mlia.backend.executor.output_consumer import Base64OutputConsumer
from mlia.backend.executor.output_consumer import OutputConsumer
from mlia.backend.executor.runner import BackendRunner
from mlia.backend.executor.runner import ExecutionParams
from mlia.backend.install import get_application_name
from mlia.backend.install import get_system_name
logger = logging.getLogger(__name__)
@dataclass
class DeviceInfo:
"""Device information."""
device_type: Literal["ethos-u55", "ethos-u65"]
mac: int
@dataclass
class ModelInfo:
"""Model info."""
model_path: Path
@dataclass
class PerformanceMetrics:
"""Performance metrics parsed from generic inference output."""
npu_active_cycles: int
npu_idle_cycles: int
npu_total_cycles: int
npu_axi0_rd_data_beat_received: int
npu_axi0_wr_data_beat_written: int
npu_axi1_rd_data_beat_received: int
class LogWriter(OutputConsumer):
"""Redirect output to the logger."""
def feed(self, line: str) -> bool:
"""Process line from the output."""
logger.debug(line.strip())
return False
class GenericInferenceOutputParser(Base64OutputConsumer):
"""Generic inference app output parser."""
def __init__(self) -> None:
"""Init generic inference output parser instance."""
super().__init__()
self._map = {
"NPU ACTIVE": "npu_active_cycles",
"NPU IDLE": "npu_idle_cycles",
"NPU TOTAL": "npu_total_cycles",
"NPU AXI0_RD_DATA_BEAT_RECEIVED": "npu_axi0_rd_data_beat_received",
"NPU AXI0_WR_DATA_BEAT_WRITTEN": "npu_axi0_wr_data_beat_written",
"NPU AXI1_RD_DATA_BEAT_RECEIVED": "npu_axi1_rd_data_beat_received",
}
@property
def result(self) -> dict:
"""Merge the raw results and map the names to the right output names."""
merged_result = {}
for raw_result in self.parsed_output:
for profiling_result in raw_result:
for sample in profiling_result["samples"]:
name, values = (sample["name"], sample["value"])
if name in merged_result:
raise KeyError(
f"Duplicate key '{name}' in base64 output.",
)
new_name = self._map[name]
merged_result[new_name] = values[0]
return merged_result
def is_ready(self) -> bool:
"""Return true if all expected data has been parsed."""
return set(self.result.keys()) == set(self._map.values())
def missed_keys(self) -> set[str]:
"""Return a set of the keys that have not been found in the output."""
return set(self._map.values()) - set(self.result.keys())
class GenericInferenceRunner(ABC):
"""Abstract class for generic inference runner."""
def __init__(self, backend_runner: BackendRunner):
"""Init generic inference runner instance."""
self.backend_runner = backend_runner
def run(
self, model_info: ModelInfo, output_consumers: list[OutputConsumer]
) -> None:
"""Run generic inference for the provided device/model."""
execution_params = self.get_execution_params(model_info)
ctx = self.backend_runner.run_application(execution_params)
if ctx.stdout is not None:
ctx.stdout = self.consume_output(ctx.stdout, output_consumers)
@abstractmethod
def get_execution_params(self, model_info: ModelInfo) -> ExecutionParams:
"""Get execution params for the provided model."""
def check_system_and_application(self, system_name: str, app_name: str) -> None:
"""Check if requested system and application installed."""
if not self.backend_runner.is_system_installed(system_name):
raise Exception(f"System {system_name} is not installed")
if not self.backend_runner.is_application_installed(app_name, system_name):
raise Exception(
f"Application {app_name} for the system {system_name} "
"is not installed"
)
@staticmethod
def consume_output(output: bytearray, consumers: list[OutputConsumer]) -> bytearray:
"""
Pass program's output to the consumers and filter it.
Returns the filtered output.
"""
filtered_output = bytearray()
for line_bytes in output.splitlines():
line = line_bytes.decode("utf-8")
remove_line = False
for consumer in consumers:
if consumer.feed(line):
remove_line = True
if not remove_line:
filtered_output.extend(line_bytes)
return filtered_output
class GenericInferenceRunnerEthosU(GenericInferenceRunner):
"""Generic inference runner on U55/65."""
def __init__(
self, backend_runner: BackendRunner, device_info: DeviceInfo, backend: str
) -> None:
"""Init generic inference runner instance."""
super().__init__(backend_runner)
system_name, app_name = self.resolve_system_and_app(device_info, backend)
self.system_name = system_name
self.app_name = app_name
self.device_info = device_info
@staticmethod
def resolve_system_and_app(
device_info: DeviceInfo, backend: str
) -> tuple[str, str]:
"""Find appropriate system and application for the provided device/backend."""
try:
system_name = get_system_name(backend, device_info.device_type)
except KeyError as ex:
raise RuntimeError(
f"Unsupported device {device_info.device_type} "
f"for backend {backend}"
) from ex
try:
app_name = get_application_name(system_name)
except KeyError as err:
raise RuntimeError(f"System {system_name} is not installed") from err
return system_name, app_name
def get_execution_params(self, model_info: ModelInfo) -> ExecutionParams:
"""Get execution params for Ethos-U55/65."""
self.check_system_and_application(self.system_name, self.app_name)
system_params = [
f"mac={self.device_info.mac}",
f"input_file={model_info.model_path.absolute()}",
]
return ExecutionParams(
self.app_name,
self.system_name,
[],
system_params,
)
def get_generic_runner(device_info: DeviceInfo, backend: str) -> GenericInferenceRunner:
"""Get generic runner for provided device and backend."""
backend_runner = get_backend_runner()
return GenericInferenceRunnerEthosU(backend_runner, device_info, backend)
def estimate_performance(
model_info: ModelInfo, device_info: DeviceInfo, backend: str
) -> PerformanceMetrics:
"""Get performance estimations."""
output_parser = GenericInferenceOutputParser()
output_consumers = [output_parser, LogWriter()]
generic_runner = get_generic_runner(device_info, backend)
generic_runner.run(model_info, output_consumers)
if not output_parser.is_ready():
missed_data = ",".join(output_parser.missed_keys())
logger.debug("Unable to get performance metrics, missed data %s", missed_data)
raise Exception("Unable to get performance metrics, insufficient data")
return PerformanceMetrics(**output_parser.result)
def get_backend_runner() -> BackendRunner:
"""
Return BackendRunner instance.
Note: This is needed for the unit tests.
"""
return BackendRunner()
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