TFLite conversion (w/ int8 quantization) from ConcreteFunction is broken

[gaikwadrahul8]

1. System information

• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Win 10 22H2 (but reproducible elsewhere)
• TensorFlow installation (pip package or built from source): pip package
• TensorFlow library (version, if pip package or github SHA, if built from source): 2.14.0

2. Code

Provide code to help us reproduce your issues using one of the following options:

https://colab.research.google.com/drive/1am-t2AeayTFDpZRcUzdROH1Jf7K03gF_

3. Failure after conversion

N/A

4. (optional) RNN conversion support

N/A

5. (optional) Any other info / logs

TFLite converter fails on calibration step when trying to convert ConcreteFunctions with int8 quantization.
It seems that somewhere during the process it generates a saved model without any signatures, which leads to fail on calibration.
I tried toggling converter.experimental_lower_to_saved_model, it does nothing in this case.
As a workaround, tf.lite.TFLiteConverter.from_keras_model() does work as intended, but I'd like the dedicated CF conversion pipeline to be fixed.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[<ipython-input-4-24b64f09cd68>](https://localhost:8080/#) in <cell line: 6>()
      4 converter.target_spec.supported_types = [tf.int8]
      5 converter.representative_dataset = tflite_loader
----> 6 tflite_model = converter.convert()

14 frames
[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in convert(self)
   2183         Invalid quantization parameters.
   2184     """
-> 2185     return super(TFLiteConverterV2, self).convert()
   2186 
   2187 

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in wrapper(self, *args, **kwargs)
   1137   def wrapper(self, *args, **kwargs):
   1138     # pylint: disable=protected-access
-> 1139     return self._convert_and_export_metrics(convert_func, *args, **kwargs)
   1140     # pylint: enable=protected-access
   1141 

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in _convert_and_export_metrics(self, convert_func, *args, **kwargs)
   1091     self._save_conversion_params_metric()
   1092     start_time = time.process_time()
-> 1093     result = convert_func(self, *args, **kwargs)
   1094     elapsed_time_ms = (time.process_time() - start_time) * 1000
   1095     if result:

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in convert(self)
   1790     )
   1791 
-> 1792     return super(TFLiteFrozenGraphConverterV2, self).convert(
   1793         graph_def, input_tensors, output_tensors
   1794     )

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in convert(self, graph_def, input_tensors, output_tensors)
   1376     )
   1377 
-> 1378     return self._optimize_tflite_model(
   1379         result, self._quant_mode, quant_io=self.experimental_new_quantizer
   1380     )

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
    213       except Exception as error:
    214         report_error_message(str(error))
--> 215         raise error from None  # Re-throws the exception.
    216 
    217     return wrapper

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) 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/lite.py](https://localhost:8080/#) in _optimize_tflite_model(self, model, quant_mode, quant_io)
   1035         q_allow_float = quant_mode.is_allow_float()
   1036         q_variable_quantization = quant_mode.enable_mlir_variable_quantization
-> 1037         model = self._quantize(
   1038             model,
   1039             q_in_type,

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/lite.py](https://localhost:8080/#) in _quantize(self, result, input_type, output_type, activations_type, bias_type, allow_float, enable_variable_quantization)
    733     )
    734     if self._experimental_calibrate_only or self.experimental_new_quantizer:
--> 735       calibrated = calibrate_quantize.calibrate(
    736           self.representative_dataset.input_gen
    737       )

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) in wrapper(*args, **kwargs)
    213       except Exception as error:
    214         report_error_message(str(error))
--> 215         raise error from None  # Re-throws the exception.
    216 
    217     return wrapper

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/convert_phase.py](https://localhost:8080/#) 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/optimize/calibrator.py](https://localhost:8080/#) in calibrate(self, dataset_gen)
    252       dataset_gen: A generator that generates calibration samples.
    253     """
--> 254     self._feed_tensors(dataset_gen, resize_input=True)
    255     return self._calibrator.Calibrate()

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/optimize/calibrator.py](https://localhost:8080/#) in _feed_tensors(self, dataset_gen, resize_input)
    119           self._interpreter = Interpreter(model_content=self._model_content)
    120         signature_key = None
--> 121         input_array = self._create_input_array_from_dict(None, sample)
    122       elif isinstance(sample, list):
    123         signature_key = None

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/optimize/calibrator.py](https://localhost:8080/#) in _create_input_array_from_dict(self, signature_key, inputs)
     86   def _create_input_array_from_dict(self, signature_key, inputs):
     87     input_array = []
---> 88     signature_runner = self._interpreter.get_signature_runner(signature_key)
     89     input_details = sorted(
     90         signature_runner.get_input_details().items(),

[/usr/local/lib/python3.10/dist-packages/tensorflow/lite/python/interpreter.py](https://localhost:8080/#) in get_signature_runner(self, signature_key)
    851     if signature_key is None:
    852       if len(self._signature_defs) != 1:
--> 853         raise ValueError(
    854             'SignatureDef signature_key is None and model has {0} Signatures. '
    855             'None is only allowed when the model has 1 SignatureDef'.format(

ValueError: SignatureDef signature_key is None and model has 0 Signatures. None is only allowed when the model has 1 SignatureDef

[gaikwadrahul8]

This issue originally reported by @DLumi has been moved to this dedicated repository for ai-edge-torch to enhance issue tracking and prioritization. To ensure continuity, we have created this new issue on your behalf.

We appreciate your understanding and look forward to your continued involvement.