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# SPDX-FileCopyrightText: Copyright 2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Common reports module."""
from __future__ import annotations
from dataclasses import dataclass
from mlia.core.data_analysis import Fact
from mlia.core.reporting import Column
from mlia.core.reporting import Format
from mlia.core.reporting import Report
from mlia.core.reporting import Table
from mlia.nn.tensorflow.tflite_compat import TFLiteCompatibilityInfo
@dataclass
class ModelIsNotTFLiteCompatible(Fact):
"""Model could not be converted into TensorFlow Lite format."""
custom_ops: list[str] | None = None
flex_ops: list[str] | None = None
@dataclass
class TFLiteCompatibilityCheckFailed(Fact):
"""TensorFlow Lite compatibility check failed by unknown reason."""
@dataclass
class ModelHasCustomOperators(Fact):
"""Model could not be loaded because it contains custom ops."""
def report_tflite_compatiblity(compat_info: TFLiteCompatibilityInfo) -> Report:
"""Generate report for the TensorFlow Lite compatibility information."""
if compat_info.conversion_errors:
return Table(
[
Column("#", only_for=["plain_text"]),
Column("Operator", alias="operator"),
Column(
"Operator location",
alias="operator_location",
fmt=Format(wrap_width=25),
),
Column("Error code", alias="error_code"),
Column(
"Error message", alias="error_message", fmt=Format(wrap_width=25)
),
],
[
(
index + 1,
err.operator,
", ".join(err.location),
err.code.name,
err.message,
)
for index, err in enumerate(compat_info.conversion_errors)
],
name="TensorFlow Lite conversion errors",
alias="tensorflow_lite_conversion_errors",
)
return Table(
columns=[
Column("Reason", alias="reason"),
Column(
"Exception details",
alias="exception_details",
fmt=Format(wrap_width=40),
),
],
rows=[
(
"TensorFlow Lite compatibility check failed with exception",
str(compat_info.conversion_exception),
),
],
name="TensorFlow Lite compatibility errors",
alias="tflite_compatibility",
)
def handle_model_is_not_tflite_compatible_common( # type: ignore
self, data_item: ModelIsNotTFLiteCompatible
) -> None:
"""Advice for TensorFlow Lite compatibility."""
if data_item.flex_ops:
self.add_advice(
[
"The following operators are not natively "
"supported by TensorFlow Lite: "
f"{', '.join(data_item.flex_ops)}.",
"Using select TensorFlow operators in TensorFlow Lite model "
"requires special initialization of TFLiteConverter and "
"TensorFlow Lite run-time.",
"Please refer to the TensorFlow documentation for more "
"details: https://www.tensorflow.org/lite/guide/ops_select",
"Note, such models are not supported by the ML Inference Advisor.",
]
)
if data_item.custom_ops:
self.add_advice(
[
"The following operators appear to be custom and not natively "
"supported by TensorFlow Lite: "
f"{', '.join(data_item.custom_ops)}.",
"Using custom operators in TensorFlow Lite model "
"requires special initialization of TFLiteConverter and "
"TensorFlow Lite run-time.",
"Please refer to the TensorFlow documentation for more "
"details: https://www.tensorflow.org/lite/guide/ops_custom",
"Note, such models are not supported by the ML Inference Advisor.",
]
)
if not data_item.flex_ops and not data_item.custom_ops:
self.add_advice(
[
"Model could not be converted into TensorFlow Lite format.",
"Please refer to the table for more details.",
]
)
def handle_tflite_check_failed_common( # type: ignore
self, _data_item: TFLiteCompatibilityCheckFailed
) -> None:
"""Advice for the failed TensorFlow Lite compatibility checks."""
self.add_advice(
[
"Model could not be converted into TensorFlow Lite format.",
"Please refer to the table for more details.",
]
)
def analyze_tflite_compatibility_common( # type: ignore
self, data_item: TFLiteCompatibilityInfo
) -> None:
"""Analyze TensorFlow Lite compatibility information."""
if data_item.compatible:
return
if data_item.conversion_failed_with_errors:
self.add_fact(
ModelIsNotTFLiteCompatible(
custom_ops=data_item.required_custom_ops,
flex_ops=data_item.required_flex_ops,
)
)
if data_item.check_failed_with_unknown_error:
self.add_fact(TFLiteCompatibilityCheckFailed())
if data_item.conversion_failed_for_model_with_custom_ops:
self.add_fact(ModelHasCustomOperators())
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