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# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Performance estimation."""
import logging
from dataclasses import dataclass
from enum import Enum
from pathlib import Path
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
import mlia.tools.aiet_wrapper as aiet
import mlia.tools.vela_wrapper as vela
from mlia.core.context import Context
from mlia.core.performance import PerformanceEstimator
from mlia.devices.ethosu.config import EthosUConfiguration
from mlia.nn.tensorflow.config import get_tflite_model
from mlia.nn.tensorflow.config import ModelConfiguration
from mlia.nn.tensorflow.optimizations.select import OptimizationSettings
logger = logging.getLogger(__name__)
@dataclass
class NPUCycles:
"""NPU cycles metrics."""
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
BYTES_PER_KILOBYTE = 1024
class MemorySizeType(Enum):
"""Memory size type enumeration."""
BYTES = 0
KILOBYTES = 1
@dataclass
class MemoryUsage:
"""Memory usage metrics."""
sram_memory_area_size: Union[int, float]
dram_memory_area_size: Union[int, float]
unknown_memory_area_size: Union[int, float]
on_chip_flash_memory_area_size: Union[int, float]
off_chip_flash_memory_area_size: Union[int, float]
memory_size_type: MemorySizeType = MemorySizeType.BYTES
_default_columns = [
"SRAM used",
"DRAM used",
"Unknown memory used",
"On chip flash used",
"Off chip flash used",
]
def in_kilobytes(self) -> "MemoryUsage":
"""Return memory usage with values in kilobytes."""
if self.memory_size_type == MemorySizeType.KILOBYTES:
return self
kilobytes = [
value / BYTES_PER_KILOBYTE
for value in [
self.sram_memory_area_size,
self.dram_memory_area_size,
self.unknown_memory_area_size,
self.on_chip_flash_memory_area_size,
self.off_chip_flash_memory_area_size,
]
]
return MemoryUsage(
*kilobytes, # type: ignore
memory_size_type=MemorySizeType.KILOBYTES,
)
@dataclass
class PerformanceMetrics:
"""Performance metrics."""
device: EthosUConfiguration
npu_cycles: Optional[NPUCycles]
memory_usage: Optional[MemoryUsage]
def in_kilobytes(self) -> "PerformanceMetrics":
"""Return metrics with memory usage in KiB."""
if self.memory_usage is None:
return PerformanceMetrics(self.device, self.npu_cycles, self.memory_usage)
return PerformanceMetrics(
self.device, self.npu_cycles, self.memory_usage.in_kilobytes()
)
@dataclass
class OptimizationPerformanceMetrics:
"""Optimization performance metrics."""
original_perf_metrics: PerformanceMetrics
optimizations_perf_metrics: List[
Tuple[List[OptimizationSettings], PerformanceMetrics]
]
class VelaPerformanceEstimator(
PerformanceEstimator[Union[Path, ModelConfiguration], MemoryUsage]
):
"""Vela based performance estimator."""
def __init__(self, context: Context, device: EthosUConfiguration) -> None:
"""Init Vela based performance estimator."""
self.context = context
self.device = device
def estimate(self, model: Union[Path, ModelConfiguration]) -> MemoryUsage:
"""Estimate performance."""
logger.info("Getting the memory usage metrics ...")
model_path = (
Path(model.model_path) if isinstance(model, ModelConfiguration) else model
)
vela_perf_metrics = vela.estimate_performance(
model_path, self.device.compiler_options
)
memory_usage = MemoryUsage(
vela_perf_metrics.sram_memory_area_size,
vela_perf_metrics.dram_memory_area_size,
vela_perf_metrics.unknown_memory_area_size,
vela_perf_metrics.on_chip_flash_memory_area_size,
vela_perf_metrics.off_chip_flash_memory_area_size,
)
logger.info("Done\n")
return memory_usage
class AIETPerformanceEstimator(
PerformanceEstimator[Union[Path, ModelConfiguration], NPUCycles]
):
"""AIET based performance estimator."""
def __init__(
self, context: Context, device: EthosUConfiguration, backend: str
) -> None:
"""Init AIET based performance estimator."""
self.context = context
self.device = device
self.backend = backend
def estimate(self, model: Union[Path, ModelConfiguration]) -> NPUCycles:
"""Estimate performance."""
logger.info("Getting the performance metrics for '%s' ...", self.backend)
logger.info(
"WARNING: This task may require several minutes (press ctrl-c to interrupt)"
)
model_path = (
Path(model.model_path) if isinstance(model, ModelConfiguration) else model
)
optimized_model_path = self.context.get_model_path(
f"{model_path.stem}_vela.tflite"
)
vela.optimize_model(
model_path, self.device.compiler_options, optimized_model_path
)
model_info = aiet.ModelInfo(model_path=optimized_model_path)
device_info = aiet.DeviceInfo(
device_type=self.device.target, # type: ignore
mac=self.device.mac,
memory_mode=self.device.compiler_options.memory_mode, # type: ignore
)
aiet_perf_metrics = aiet.estimate_performance(
model_info, device_info, self.backend
)
npu_cycles = NPUCycles(
aiet_perf_metrics.npu_active_cycles,
aiet_perf_metrics.npu_idle_cycles,
aiet_perf_metrics.npu_total_cycles,
aiet_perf_metrics.npu_axi0_rd_data_beat_received,
aiet_perf_metrics.npu_axi0_wr_data_beat_written,
aiet_perf_metrics.npu_axi1_rd_data_beat_received,
)
logger.info("Done\n")
return npu_cycles
class EthosUPerformanceEstimator(
PerformanceEstimator[Union[Path, ModelConfiguration], PerformanceMetrics]
):
"""Ethos-U performance estimator."""
def __init__(
self,
context: Context,
device: EthosUConfiguration,
backends: Optional[List[str]] = None,
) -> None:
"""Init performance estimator."""
self.context = context
self.device = device
if backends is None:
backends = ["Vela"] # Only Vela is always available as default
for backend in backends:
if backend != "Vela" and not aiet.is_supported(backend):
raise ValueError(
f"Unsupported backend '{backend}'. "
f"Only 'Vela' and {aiet.supported_backends()} are supported."
)
self.backends = set(backends)
def estimate(self, model: Union[Path, ModelConfiguration]) -> PerformanceMetrics:
"""Estimate performance."""
model_path = (
Path(model.model_path) if isinstance(model, ModelConfiguration) else model
)
tflite_model = get_tflite_model(model_path, self.context)
memory_usage = None
npu_cycles = None
for backend in self.backends:
if backend == "Vela":
vela_estimator = VelaPerformanceEstimator(self.context, self.device)
memory_usage = vela_estimator.estimate(tflite_model)
elif backend in aiet.supported_backends():
aiet_estimator = AIETPerformanceEstimator(
self.context, self.device, backend
)
npu_cycles = aiet_estimator.estimate(tflite_model)
else:
logger.warning(
"Backend '%s' is not supported for Ethos-U performance "
"estimation.",
backend,
)
return PerformanceMetrics(self.device, npu_cycles, memory_usage)
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