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# SPDX-FileCopyrightText: Copyright 2022-2023, Arm Limited and/or its affiliates.
# SPDX-License-Identifier: Apache-2.0
"""Tests for tflite_compat module."""
from __future__ import annotations
from unittest.mock import MagicMock
import pytest
import tensorflow as tf
from tensorflow.lite.python import convert
from mlia.nn.tensorflow.tflite_compat import converter_error_data_pb2
from mlia.nn.tensorflow.tflite_compat import TFLiteChecker
from mlia.nn.tensorflow.tflite_compat import TFLiteCompatibilityInfo
from mlia.nn.tensorflow.tflite_compat import TFLiteCompatibilityStatus
from mlia.nn.tensorflow.tflite_compat import TFLiteConversionError
from mlia.nn.tensorflow.tflite_compat import TFLiteConversionErrorCode
def test_not_fully_compatible_model_flex_ops() -> None:
"""Test models that requires TF_SELECT_OPS."""
model = tf.keras.models.Sequential(
[
tf.keras.layers.Dense(units=1, input_shape=[1], batch_size=1),
tf.keras.layers.Dense(units=16, activation="softsign"),
tf.keras.layers.Dense(units=1),
]
)
checker = TFLiteChecker()
result = checker.check_compatibility(model)
assert result.compatible is False
assert isinstance(result.conversion_exception, convert.ConverterError)
assert result.conversion_errors is not None
assert len(result.conversion_errors) == 1
conv_err = result.conversion_errors[0]
assert isinstance(conv_err, TFLiteConversionError)
assert conv_err.message == "'tf.Softsign' op is neither a custom op nor a flex op"
assert conv_err.code == TFLiteConversionErrorCode.NEEDS_FLEX_OPS
assert conv_err.operator == "tf.Softsign"
assert len(conv_err.location) == 3
def _get_tflite_conversion_error(
error_message: str = "Conversion error",
custom_op: bool = False,
flex_op: bool = False,
unsupported_flow_v1: bool = False,
gpu_not_compatible: bool = False,
unknown_reason: bool = False,
) -> convert.ConverterError:
"""Create TensorFlow Lite conversion error."""
error_data = converter_error_data_pb2.ConverterErrorData
convert_error = convert.ConverterError(error_message)
# pylint: disable=no-member
def _add_error(operator: str, error_code: int) -> None:
convert_error.append_error(
error_data(
operator=error_data.Operator(name=operator),
error_code=error_code,
error_message=error_message,
)
)
if custom_op:
_add_error("custom_op", error_data.ERROR_NEEDS_CUSTOM_OPS)
if flex_op:
_add_error("flex_op", error_data.ERROR_NEEDS_FLEX_OPS)
if unsupported_flow_v1:
_add_error("flow_op", error_data.ERROR_UNSUPPORTED_CONTROL_FLOW_V1)
if gpu_not_compatible:
_add_error("non_gpu_op", error_data.ERROR_GPU_NOT_COMPATIBLE)
if unknown_reason:
_add_error("unknown_op", None) # type: ignore
# pylint: enable=no-member
return convert_error
# pylint: disable=undefined-variable,unused-variable
@pytest.mark.parametrize(
"conversion_error, expected_result",
[
(
None,
TFLiteCompatibilityInfo(status=TFLiteCompatibilityStatus.COMPATIBLE),
),
(
err := _get_tflite_conversion_error(custom_op=True),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.TFLITE_CONVERSION_ERROR,
conversion_exception=err,
conversion_errors=[
TFLiteConversionError(
message="Conversion error",
code=TFLiteConversionErrorCode.NEEDS_CUSTOM_OPS,
operator="custom_op",
location=[],
)
],
),
),
(
err := _get_tflite_conversion_error(flex_op=True),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.TFLITE_CONVERSION_ERROR,
conversion_exception=err,
conversion_errors=[
TFLiteConversionError(
message="Conversion error",
code=TFLiteConversionErrorCode.NEEDS_FLEX_OPS,
operator="flex_op",
location=[],
)
],
),
),
(
err := _get_tflite_conversion_error(unknown_reason=True),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.TFLITE_CONVERSION_ERROR,
conversion_exception=err,
conversion_errors=[
TFLiteConversionError(
message="Conversion error",
code=TFLiteConversionErrorCode.UNKNOWN,
operator="unknown_op",
location=[],
)
],
),
),
(
err := _get_tflite_conversion_error(
flex_op=True,
custom_op=True,
gpu_not_compatible=True,
unsupported_flow_v1=True,
),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.TFLITE_CONVERSION_ERROR,
conversion_exception=err,
conversion_errors=[
TFLiteConversionError(
message="Conversion error",
code=TFLiteConversionErrorCode.NEEDS_CUSTOM_OPS,
operator="custom_op",
location=[],
),
TFLiteConversionError(
message="Conversion error",
code=TFLiteConversionErrorCode.NEEDS_FLEX_OPS,
operator="flex_op",
location=[],
),
TFLiteConversionError(
message="Conversion error",
code=TFLiteConversionErrorCode.UNSUPPORTED_CONTROL_FLOW_V1,
operator="flow_op",
location=[],
),
TFLiteConversionError(
message="Conversion error",
code=TFLiteConversionErrorCode.GPU_NOT_COMPATIBLE,
operator="non_gpu_op",
location=[],
),
],
),
),
(
err := _get_tflite_conversion_error(),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.TFLITE_CONVERSION_ERROR,
conversion_exception=err,
conversion_errors=[],
),
),
(
err := ValueError("Some unknown issue"),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.UNKNOWN_ERROR,
conversion_exception=err,
),
),
(
err := ValueError("Unable to restore custom object"),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.MODEL_WITH_CUSTOM_OP_ERROR,
conversion_exception=err,
),
),
(
err := FileNotFoundError("Op type not registered"),
TFLiteCompatibilityInfo(
status=TFLiteCompatibilityStatus.MODEL_WITH_CUSTOM_OP_ERROR,
conversion_exception=err,
),
),
],
)
# pylint: enable=undefined-variable,unused-variable
def test_tflite_compatibility(
conversion_error: convert.ConverterError | ValueError | None,
expected_result: TFLiteCompatibilityInfo,
monkeypatch: pytest.MonkeyPatch,
) -> None:
"""Test TensorFlow Lite compatibility."""
converter_mock = MagicMock()
if conversion_error is not None:
converter_mock.convert.side_effect = conversion_error
monkeypatch.setattr(
"mlia.nn.tensorflow.tflite_convert.get_tflite_converter",
lambda *args, **kwargs: converter_mock,
)
checker = TFLiteChecker()
result = checker.check_compatibility(MagicMock())
assert result == expected_result
|
