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# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Module for various helper classes."""
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
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: Optional[str]) -> 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,
"For example: mlia optimization --optimization-type "
f"pruning,clustering --optimization-target 0.5,32 {model_path}",
"For more info: mlia optimization --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 = ",".join(str(opt.optimization_target) for opt in opt_settings)
return [
"For more info: mlia optimization --help",
"Optimization command: "
f"mlia optimization --optimization-type {opt_types} "
f"--optimization-target {opt_targs}{device_opts} {model_path}",
]
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 supported_operators_info(self) -> List[str]:
"""Return command details for generating supported ops report."""
return [
"For guidance on supported operators, run: mlia operators "
"--supported-ops-report",
]
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 performance{device_opts} {model_path}",
]
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 operators{device_opts} {model_path}",
]
def operator_compatibility_details(self) -> List[str]:
"""Return command details for op compatibility."""
return ["For more details, run: mlia operators --help"]
def optimization_details(self) -> List[str]:
"""Return command details for optimization."""
return ["For more info, see: mlia optimization --help"]
def _get_model_and_device_opts(
self, separate_device_opts: bool = True
) -> Tuple[Optional[str], 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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