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# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Ethos-U advice generation."""
from functools import singledispatchmethod
from typing import List
from typing import Union
from mlia.core.advice_generation import Advice
from mlia.core.advice_generation import advice_category
from mlia.core.advice_generation import ContextAwareAdviceProducer
from mlia.core.advice_generation import FactBasedAdviceProducer
from mlia.core.common import AdviceCategory
from mlia.core.common import DataItem
from mlia.devices.ethosu.data_analysis import AllOperatorsSupportedOnNPU
from mlia.devices.ethosu.data_analysis import HasCPUOnlyOperators
from mlia.devices.ethosu.data_analysis import HasUnsupportedOnNPUOperators
from mlia.devices.ethosu.data_analysis import OptimizationResults
from mlia.nn.tensorflow.optimizations.select import OptimizationSettings
class EthosUAdviceProducer(FactBasedAdviceProducer):
"""Ethos-U advice producer."""
@singledispatchmethod
def produce_advice(self, data_item: DataItem) -> None:
"""Produce advice."""
@produce_advice.register
@advice_category(AdviceCategory.OPERATORS, AdviceCategory.ALL)
def handle_cpu_only_ops(self, data_item: HasCPUOnlyOperators) -> None:
"""Advice for CPU only operators."""
cpu_only_ops = ",".join(sorted(set(data_item.cpu_only_ops)))
cpu_only_ops_num = len(data_item.cpu_only_ops)
self.add_advice(
[
f"You have at least {cpu_only_ops_num} "
f"operator{'s' if cpu_only_ops_num > 1 else ''} that is CPU "
f"only: {cpu_only_ops}.",
"Using operators that are supported by the NPU will "
"improve performance.",
]
+ self.context.action_resolver.supported_operators_info()
)
@produce_advice.register
@advice_category(AdviceCategory.OPERATORS, AdviceCategory.ALL)
def handle_unsupported_operators(
self, data_item: HasUnsupportedOnNPUOperators
) -> None:
"""Advice for the unsupported operators."""
self.add_advice(
[
f"You have {data_item.npu_unsupported_ratio*100:.0f}% of operators "
"that cannot be placed on the NPU.",
"For better performance, please review the reasons reported "
"in the table, and adjust the model accordingly "
"where possible.",
]
)
@produce_advice.register
@advice_category(AdviceCategory.OPERATORS, AdviceCategory.ALL)
def handle_all_operators_supported(
self, _data_item: AllOperatorsSupportedOnNPU
) -> None:
"""Advice if all operators supported."""
self.add_advice(
[
"You don't have any unsupported operators, your model will "
"run completely on NPU."
]
+ self.context.action_resolver.check_performance()
)
@produce_advice.register
@advice_category(AdviceCategory.OPTIMIZATION, AdviceCategory.ALL)
def handle_optimization_results(self, data_item: OptimizationResults) -> None:
"""Advice based on optimization results."""
if not data_item.diffs or len(data_item.diffs) != 1:
return
optim_details = data_item.diffs[0]
metrics = [
(metric_name, optim_details.opt_diffs[metric_key])
for (metric_name, metric_key) in (
("DRAM used (KB)", "dram"),
("SRAM used (KB)", "sram"),
("On chip flash used (KB)", "on_chip_flash"),
("Off chip flash used (KB)", "off_chip_flash"),
("NPU total cycles", "npu_total_cycles"),
)
if metric_key in optim_details.opt_diffs
and not optim_details.opt_diffs[metric_key].same
]
improved = [
f"- You have achieved {abs(metric_value.diff):.2f}% performance "
f"improvement in {metric_name}"
for metric_name, metric_value in metrics
if metric_value.improved
]
degraded = [
f"- {metric_name} have degraded by {abs(metric_value.diff):.2f}%"
for metric_name, metric_value in metrics
if metric_value.degraded
]
opts = ", ".join(str(s) for s in optim_details.opt_type)
messages = [f"With the selected optimization ({opts})", *improved, *degraded]
if improved:
if next_optimization_target := self.get_next_optimization_targets(
optim_details.opt_type
):
next_optimization_target_as_str = " and/or ".join(
str(item) for item in next_optimization_target
)
messages.append(
"You can try to push the optimization target higher "
f"(e.g. {next_optimization_target_as_str}) "
"to check if those results can be further improved."
)
messages += self.context.action_resolver.apply_optimizations(
opt_settings=next_optimization_target
)
elif degraded:
messages.append(
"The performance seems to have degraded after "
"applying the selected optimizations, "
"try exploring different optimization types/targets."
)
self.add_advice(messages)
self.add_advice(
[
"The applied tooling techniques have an impact "
"on accuracy. Additional hyperparameter tuning may be required "
"after any optimization."
]
)
@staticmethod
def get_next_optimization_targets(
opt_type: List[OptimizationSettings],
) -> List[OptimizationSettings]:
"""Get next optimization targets."""
next_targets = (item.next_target() for item in opt_type)
# filter out targets that have not been changed
valid_targets = [
next_
for next_, old in zip(next_targets, opt_type)
if (
old.optimization_type == "pruning"
and old.optimization_target < next_.optimization_target
)
or (
old.optimization_type == "clustering"
and old.optimization_target > next_.optimization_target
)
]
return valid_targets
class EthosUStaticAdviceProducer(ContextAwareAdviceProducer):
"""Advice producer that not depends on input data."""
def produce_advice(self, data_item: DataItem) -> None:
"""Do not process passed data items."""
def get_advice(self) -> Union[Advice, List[Advice]]:
"""Return predefined advice based on category."""
if self.context.advice_category is None:
return []
advice_per_category = {
AdviceCategory.PERFORMANCE: [
Advice(
[
"You can improve the inference time by using only operators "
"that are supported by the NPU.",
]
+ self.context.action_resolver.check_operator_compatibility()
),
Advice(
[
"Check if you can improve the performance by applying "
"tooling techniques to your model."
]
+ self.context.action_resolver.apply_optimizations()
),
],
AdviceCategory.OPTIMIZATION: [
Advice(
[
"For better performance, make sure that all the operators "
"of your final TFLite model are supported by the NPU.",
]
+ self.context.action_resolver.operator_compatibility_details()
)
],
}
return advice_per_category.get(self.context.advice_category, [])
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