model.save fails with ValueError __inference_conv2d_transpose_layer_call_fn_4530 when Conv2DTranspose is quantization aware

[ypsilon-elle]

Originally I posted this bug #54753 on tensorflow/tensorflow and was advised to repost it here.

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): yes
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
• TensorFlow installed from (source or binary): pip install
• TensorFlow version: tf=2.7.0, tensorflow_model_optimization=0.7.1 tf=2.8.0, tensorflow_model_optimization=0.7.1 tf-nightly=2.9.0dev20211222, tensorflow_model_optimization=0.7.1
• Python version: 3.7.12

Describe the problem We save a quantization-aware keras-model in a .pb model format using model.save(). This operation fails with ValueError: __inference_conv2d_transpose_layer_call_fn_4530 when our model contains a Conv2DTranspose layer.

• The error is reproducible with tf.keras.models.save_model() too
• The error is reproducible when we quantize the entire model using tfmot.quantization.keras.quantize_model()
• The error is also reproducible when we annotate layers using tf.keras.models.clone_model() and apply quantization using tfmot.quantization.keras.quantize_apply(). Our current workaround is to not annotate Conv2DTranspose but this prevents us from having a fully quantization-aware model.
  • The error is reproducible in tf2.7.0, tf2.8.0 and tf-nightly

Saving the same model as .h5 works (unfortunately this workaround is not suitable for us because our technical requirement is to save a .pb-model).

Describe the expected behavior model.save() saves a QAT model with a Conv2DTranspose layer in a .pb-format successfully.

Standalone code to reproduce the issue Here are the collabs to reproduce the issue using a very simple model with a Conv2DTranspose layer and two ways to make a model quantization aware mentioned above:

• Collab with tf2.7.0
• Collab with tf2.8.0

Other info / logs Similar issue #868

Traceback
ValueError                                Traceback (most recent call last)
<ipython-input-7-dc1f93a93afb> in <module>()
      2 annotated_model = tf.keras.models.clone_model(base_model, clone_function=apply_quantization)
      3 q_aware_model = tfmot.quantization.keras.quantize_apply(annotated_model)
----> 4 q_aware_model.save('/output_folder/q_aware_model') # save keras model as .pb, fails

1 frames
/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb

/usr/local/lib/python3.7/dist-packages/tensorflow/python/saved_model/save.py in map_resources(self)
    402           if capture_constant_value is None:
    403             raise ValueError(
--> 404                 f"Unable to save function {concrete_function.name} because it "
    405                 f"captures graph tensor {capture} from a parent function which "
    406                 "cannot be converted to a constant with `tf.get_static_value`.")

ValueError: Unable to save function b'__inference_conv2d_transpose_layer_call_fn_4530' because it captures graph tensor 
Tensor("model/quant_conv2d_transpose/transpose_1:0", shape=(3, 3, 16, 16), dtype=float32) from a parent function which 
cannot be converted to a constant with `tf.get_static_value`.

[sushreebarsa]

@jvishnuvardhan Was able to replicate the issue on colab using TF v2.7.0 and 2.8.0 ,please find the attached gists .Thanks!

[ypsilon-elle]

Hey @jvishnuvardhan, did you have time to look at this issue? We really would appreciate some feedback on this.

[gowthamkpr]

This is very similar to this issue. Please take a look. Thanks!

[ypsilon-elle]

This is very similar to this issue. Conv2Dtranspose is not supported in quantization. Please take a look. Thanks!

@gowthamkpr Thanks for the link, I have checked the issue. The issue might be outdated, because it is from 2020.

To my knowledge Conv2Dtranspose is supported by quantization aware training. A quantization config for Conv2Dtranspose is defined in /default_8bit/default_8bit_quantize_registry.py. In fact, a Conv2Dtranspose layer can be quantized successfully, such a model can be trained with QAT and saved as h5. Please see the attached colabs in the issue description.

However saving a model with q-aware Conv2Dtranspose as .pb fails. So, by the look of it, something goes wrong in model.save().

[fchollet]

Saving the same model as .h5 works (unfortunately this workaround is not suitable for us because our technical requirement is to save a .pb-model).

Is it because you want to export the model for inference?

As a workaround you may want to try to use tf.saved_model.save(model, name) and see if that works.

[ypsilon-elle]

@fchollet I have a technical requirement to save my model in .pb format because this is a format which is used in other parts/modules of the project where I have to pass my model to. Saving my model as .h5 is a temporary workaround and I am hoping for a permanent solution :)

Thanks for you suggestion. Unfortunately the same error is reproducible with tf.keras.models.save_model() too.

[qlzh727]

Also adding @abattery from TFMOT team.

[remicres]

Hi, any news on this?