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Diffstat (limited to 'src/mlia/cli/commands.py')
| -rw-r--r-- | src/mlia/cli/commands.py | 276 |
1 files changed, 276 insertions, 0 deletions
diff --git a/src/mlia/cli/commands.py b/src/mlia/cli/commands.py new file mode 100644 index 0000000..45c7c32 --- /dev/null +++ b/src/mlia/cli/commands.py @@ -0,0 +1,276 @@ +# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates. +# SPDX-License-Identifier: Apache-2.0 +"""CLI commands module. + +This module contains functions which implement main app +functionality. + +Before running them from scripts 'logging' module should +be configured. Function 'setup_logging' from module +'mli.cli.logging' could be used for that, e.g. + +>>> from mlia.api import ExecutionContext +>>> from mlia.cli.logging import setup_logging +>>> setup_logging(verbose=True) +>>> import mlia.cli.commands as mlia +>>> mlia.all_tests(ExecutionContext(working_dir="mlia_output"), "ethos-u55-256", + "path/to/model") +""" +import logging +from pathlib import Path +from typing import cast +from typing import List +from typing import Optional + +from mlia.api import ExecutionContext +from mlia.api import get_advice +from mlia.api import PathOrFileLike +from mlia.cli.config import get_installation_manager +from mlia.cli.options import parse_optimization_parameters +from mlia.devices.ethosu.operators import generate_supported_operators_report +from mlia.utils.console import create_section_header +from mlia.utils.types import only_one_selected + +logger = logging.getLogger(__name__) + +CONFIG = create_section_header("ML Inference Advisor configuration") + + +def all_tests( + ctx: ExecutionContext, + target_profile: str, + model: str, + optimization_type: str = "pruning,clustering", + optimization_target: str = "0.5,32", + output: Optional[PathOrFileLike] = None, + evaluate_on: Optional[List[str]] = None, +) -> None: + """Generate a full report on the input model. + + This command runs a series of tests in order to generate a + comprehensive report/advice: + + - converts the input Keras model into TFLite format + - checks the model for operator compatibility on the specified device + - applies optimizations to the model and estimates the resulting performance + on both the original and the optimized models + - generates a final report on the steps above + - provides advice on how to (possibly) improve the inference performance + + :param ctx: execution context + :param target_profile: target profile identifier. Will load appropriate parameters + from the profile.json file based on this argument. + :param model: path to the Keras model + :param optimization_type: list of the optimization techniques separated + by comma, e.g. 'pruning,clustering' + :param optimization_target: list of the corresponding targets for + the provided optimization techniques, e.g. '0.5,32' + :param output: path to the file where the report will be saved + :param evaluate_on: list of the backends to use for evaluation + + Example: + Run command for the target profile ethos-u55-256 with two model optimizations + and save report in json format locally in the file report.json + + >>> from mlia.api import ExecutionContext + >>> from mlia.cli.logging import setup_logging + >>> setup_logging() + >>> from mlia.cli.commands import all_tests + >>> all_tests(ExecutionContext(working_dir="mlia_output"), "ethos-u55-256", + "model.h5", "pruning,clustering", "0.5,32", + output="report.json") + """ + opt_params = parse_optimization_parameters( + optimization_type, + optimization_target, + ) + + get_advice( + target_profile, + model, + "all", + optimization_targets=opt_params, + output=output, + context=ctx, + backends=evaluate_on, + ) + + +def operators( + ctx: ExecutionContext, + target_profile: str, + model: Optional[str] = None, + output: Optional[PathOrFileLike] = None, + supported_ops_report: bool = False, +) -> None: + """Print the model's operator list. + + This command checks the operator compatibility of the input model with + the specific target profile. Generates a report of the operator placement + (NPU or CPU fallback) and advice on how to improve it (if necessary). + + :param ctx: execution context + :param target_profile: target profile identifier. Will load appropriate parameters + from the profile.json file based on this argument. + :param model: path to the model, which can be TFLite or Keras + :param output: path to the file where the report will be saved + :param supported_ops_report: if True then generates supported operators + report in current directory and exits + + Example: + Run command for the target profile ethos-u55-256 and the provided + TFLite model and print report on the standard output + + >>> from mlia.api import ExecutionContext + >>> from mlia.cli.logging import setup_logging + >>> setup_logging() + >>> from mlia.cli.commands import operators + >>> operators(ExecutionContext(working_dir="mlia_output"), "ethos-u55-256", + "model.tflite") + """ + if supported_ops_report: + generate_supported_operators_report() + logger.info("Report saved into SUPPORTED_OPS.md") + return + + if not model: + raise Exception("Model is not provided") + + get_advice( + target_profile, + model, + "operators", + output=output, + context=ctx, + ) + + +def performance( + ctx: ExecutionContext, + target_profile: str, + model: str, + output: Optional[PathOrFileLike] = None, + evaluate_on: Optional[List[str]] = None, +) -> None: + """Print the model's performance stats. + + This command estimates the inference performance of the input model + on the specified target profile, and generates a report with advice on how + to improve it. + + :param ctx: execution context + :param target_profile: target profile identifier. Will load appropriate parameters + from the profile.json file based on this argument. + :param model: path to the model, which can be TFLite or Keras + :param output: path to the file where the report will be saved + :param evaluate_on: list of the backends to use for evaluation + + Example: + Run command for the target profile ethos-u55-256 and + the provided TFLite model and print report on the standard output + + >>> from mlia.api import ExecutionContext + >>> from mlia.cli.logging import setup_logging + >>> setup_logging() + >>> from mlia.cli.commands import performance + >>> performance(ExecutionContext(working_dir="mlia_output"), "ethos-u55-256", + "model.tflite") + """ + get_advice( + target_profile, + model, + "performance", + output=output, + context=ctx, + backends=evaluate_on, + ) + + +def optimization( + ctx: ExecutionContext, + target_profile: str, + model: str, + optimization_type: str, + optimization_target: str, + layers_to_optimize: Optional[List[str]] = None, + output: Optional[PathOrFileLike] = None, + evaluate_on: Optional[List[str]] = None, +) -> None: + """Show the performance improvements (if any) after applying the optimizations. + + This command applies the selected optimization techniques (up to the + indicated targets) and generates a report with advice on how to improve + the inference performance (if possible). + + :param ctx: execution context + :param target: target profile identifier. Will load appropriate parameters + from the profile.json file based on this argument. + :param model: path to the TFLite model + :param optimization_type: list of the optimization techniques separated + by comma, e.g. 'pruning,clustering' + :param optimization_target: list of the corresponding targets for + the provided optimization techniques, e.g. '0.5,32' + :param layers_to_optimize: list of the layers of the model which should be + optimized, if None then all layers are used + :param output: path to the file where the report will be saved + :param evaluate_on: list of the backends to use for evaluation + + Example: + Run command for the target profile ethos-u55-256 and + the provided TFLite model and print report on the standard output + + >>> from mlia.cli.logging import setup_logging + >>> setup_logging() + >>> from mlia.cli.commands import optimization + >>> optimization(ExecutionContext(working_dir="mlia_output"), + target="ethos-u55-256", + "model.tflite", "pruning", "0.5") + """ + opt_params = parse_optimization_parameters( + optimization_type, + optimization_target, + layers_to_optimize=layers_to_optimize, + ) + + get_advice( + target_profile, + model, + "optimization", + optimization_targets=opt_params, + output=output, + context=ctx, + backends=evaluate_on, + ) + + +def backend( + backend_action: str, + path: Optional[Path] = None, + download: bool = False, + name: Optional[str] = None, + i_agree_to_the_contained_eula: bool = False, + noninteractive: bool = False, +) -> None: + """Backends configuration.""" + logger.info(CONFIG) + + manager = get_installation_manager(noninteractive) + + if backend_action == "status": + manager.show_env_details() + + if backend_action == "install": + install_from_path = path is not None + + if not only_one_selected(install_from_path, download): + raise Exception( + "Please select only one action: download or " + "provide path to the backend installation" + ) + + if install_from_path: + manager.install_from(cast(Path, path), name) + + if download: + eula_agreement = not i_agree_to_the_contained_eula + manager.download_and_install(name, eula_agreement) |