Ops listed in 'experimental_select_user_tf_ops' not being recognized by tf lite converter

1. System information

Google Colab Notebook

2. Code

#### Model Definition ---------

class CppTfTest(tf.Module):

    def __init__(self, name=None):
        super().__init__(name=name)

    @tf.function
    def call(self):

        frames = tf.range(600)

        bpm = tf.random.uniform(
            tf.TensorShape([600,]),
            minval=0,
            maxval=90,
            dtype=tf.dtypes.float64
            )

        return bpm, frames

#### Model Saving ------------

cpp_tf_test = CppTfTest()
tf.saved_model.save(
    cpp_tf_test,
    'cpp_tf_test',
    signatures=cpp_tf_test.call.get_concrete_function()
    )

#### Model Conversion -----------

converter = tf.lite.TFLiteConverter.from_saved_model('cpp_tf_test')

converter.target_spec = tf.lite.TargetSpec(
    supported_ops=[tf.lite.OpsSet.TFLITE_BUILTINS],
    experimental_select_user_tf_ops=[
        'RandomUniform', 'Mul'
        ]
)

tflite_model = converter.convert()

with open('cpp_tf_test.tflite', 'wb') as f:
  f.write(tflite_model)

ConverterError Traceback (most recent call last)

in <cell line: 47>() 45 #converter.allow_custom_ops=True 46 ---> 47 tflite_model = converter.convert() 48 49 with open('cpp_tf_test.tflite', 'wb') as f:

7 frames

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py in wrapper(self, *args, kwargs) 960 def wrapper(self, *args, *kwargs): 961 # pylint: disable=protected-access --> 962 return self._convert_and_export_metrics(convert_func, args, kwargs) 963 # pylint: enable=protected-access 964

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py in _convert_and_export_metrics(self, convert_func, *args, *kwargs) 938 self._save_conversion_params_metric() 939 start_time = time.process_time() --> 940 result = convert_func(self, args, *kwargs) 941 elapsed_time_ms = (time.process_time() - start_time) 1000 942 if result:

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py in convert(self) 1245 graph_def) 1246 -> 1247 return self._convert_from_saved_model(graph_def) 1248 1249

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py in _convert_from_saved_model(self, graph_def) 1128 converter_kwargs.update(quant_mode.converter_flags()) 1129 -> 1130 result = _convert_saved_model(**converter_kwargs) 1131 return self._optimize_tflite_model( 1132 result, quant_mode, quant_io=self.experimental_new_quantizer)

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py in wrapper(*args, **kwargs) 210 else: 211 report_error_message(str(converter_error)) --> 212 raise converter_error from None # Re-throws the exception. 213 except Exception as error: 214 report_error_message(str(error))

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py in wrapper(*args, kwargs) 203 def wrapper(*args, *kwargs): 204 try: --> 205 return func(args, kwargs) 206 except ConverterError as converter_error: 207 if converter_error.errors:

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert.py in convert_saved_model(kwargs) 830 model_flags = build_model_flags(kwargs) 831 conversion_flags = build_conversion_flags(**kwargs) --> 832 data = convert( 833 model_flags.SerializeToString(), 834 conversion_flags.SerializeToString(),

/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert.py in convert(model_flags_str, conversion_flags_str, input_data_str, debug_info_str, enable_mlir_converter) 320 for error_data in _metrics_wrapper.retrieve_collected_errors(): 321 converter_error.append_error(error_data) --> 322 raise converter_error 323 324 return _run_deprecated_conversion_binary(model_flags_str,

ConverterError: :0: error: loc(callsite(callsite(fused["RandomUniform:", "random_uniform/RandomUniform@inference_call_11165"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@inference_signature_wrapper_11173"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"])): 'tf.RandomUniform' op is neither a custom op nor a flex op
:0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from :0: note: loc(callsite(callsite(fused["RandomUniform:", "random_uniform/RandomUniform@__inference_call_11165"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper_11173"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"])): Error code: ERROR_NEEDS_FLEX_OPS :0: error: loc(callsite(callsite(fused["Mul:", "random_uniform/Mul@__inference_call_11165"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper_11173"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"])): 'tf.Mul' op is neither a custom op nor a flex op :0: note: loc(fused["StatefulPartitionedCall:", "StatefulPartitionedCall"]): called from :0: note: loc(callsite(callsite(fused["Mul:", "random_uniform/Mul@__inference_call_11165"] at fused["StatefulPartitionedCall:", "StatefulPartitionedCall@__inference_signature_wrapper_11173"]) at fused["StatefulPartitionedCall:", "StatefulPartitionedCall"])): Error code: ERROR_NEEDS_FLEX_OPS :0: error: failed while converting: 'main': Some ops are not supported by the native TFLite runtime, you can enable TF kernels fallback using TF Select. See instructions: https://www.tensorflow.org/lite/guide/ops_select TF Select ops: Mul, RandomUniform Details: tf.Mul(tensor<600xf64>, tensor) -> (tensor<600xf64>) : {device = ""} tf.RandomUniform(tensor<1xi32>) -> (tensor<600xf64>) : {device = "", seed = 0 : i64, seed2 = 0 : i64}

I'm trying to convert this simple model to Tensorflow Lite using the _experimental_select_user_tfops flag to tell the converter what operations from the tf_ops set to include. I need this to run with just a subset of the tf_ops set since I have a bigger model that I need to optimize for a mobile app. I've try many things but the _experimental_select_user_tfops flag just doesn't seem to work.

google-ai-edge / LiteRT

Ops listed in 'experimental_select_user_tf_ops' not being recognized by tf lite converter #155

1. System information

2. Code