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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Module for various helper classes."""
from __future__ import annotations
from typing import Any
from mlia.cli.options import get_target_profile_opts
from mlia.core.helpers import ActionResolver
from mlia.nn.tensorflow.optimizations.select import OptimizationSettings
from mlia.nn.tensorflow.utils import is_keras_model
from mlia.utils.types import is_list_of
class CLIActionResolver(ActionResolver):
"""Helper class for generating cli commands."""
def __init__(self, args: dict[str, Any]) -> None:
"""Init action resolver."""
self.args = args
@staticmethod
def _general_optimization_command(model_path: str | None) -> list[str]:
"""Return general optimization command description."""
keras_note = []
if model_path is None or not is_keras_model(model_path):
model_path = "/path/to/keras_model"
keras_note = ["Note: you will need a Keras model for that."]
return [
*keras_note,
f"For example: mlia optimize {model_path} --pruning --clustering "
"--pruning-target 0.5 --clustering-target 32",
"For more info: mlia optimize --help",
]
@staticmethod
def _specific_optimization_command(
model_path: str,
device_opts: str,
opt_settings: list[OptimizationSettings],
) -> list[str]:
"""Return specific optimization command description."""
opt_types = " ".join("--" + opt.optimization_type for opt in opt_settings)
opt_targs_strings = ["--pruning-target", "--clustering-target"]
opt_targs = ",".join(
f"{opt_targs_strings[i]} {opt.optimization_target}"
for i, opt in enumerate(opt_settings)
)
return [
"For more info: mlia optimize --help",
"Optimization command: "
f"mlia optimize {model_path}{device_opts} {opt_types} {opt_targs}",
]
def apply_optimizations(self, **kwargs: Any) -> list[str]:
"""Return command details for applying optimizations."""
model_path, device_opts = self._get_model_and_device_opts()
if (opt_settings := kwargs.pop("opt_settings", None)) is None:
return self._general_optimization_command(model_path)
if is_list_of(opt_settings, OptimizationSettings) and model_path:
return self._specific_optimization_command(
model_path, device_opts, opt_settings
)
return []
def check_performance(self) -> list[str]:
"""Return command details for checking performance."""
model_path, device_opts = self._get_model_and_device_opts()
if not model_path:
return []
return [
"Check the estimated performance by running the following command: ",
f"mlia check {model_path}{device_opts} --performance",
]
def check_operator_compatibility(self) -> list[str]:
"""Return command details for op compatibility."""
model_path, device_opts = self._get_model_and_device_opts()
if not model_path:
return []
return [
"Try running the following command to verify that:",
f"mlia check {model_path}{device_opts}",
]
def operator_compatibility_details(self) -> list[str]:
"""Return command details for op compatibility."""
return ["For more details, run: mlia check --help"]
def optimization_details(self) -> list[str]:
"""Return command details for optimization."""
return ["For more info, see: mlia optimize --help"]
def _get_model_and_device_opts(
self, separate_device_opts: bool = True
) -> tuple[str | None, str]:
"""Get model and device options."""
device_opts = " ".join(get_target_profile_opts(self.args))
if separate_device_opts and device_opts:
device_opts = f" {device_opts}"
model_path = self.args.get("model")
return model_path, device_opts
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