SavedModel conversion to integer (quantized) TFLite reports error because of fused ops

[jvuillaumier]

Hello,

Firstly, thank you for publishing and maintaining this lib.

Package versions used in my setup are tfjs-graph-converter 1.5.0 and tensorflow 2.8.0.

I have been experimenting the lib with MediaPipe Blazeface TFJS model from TensorFlow Hub: https://tfhub.dev/tensorflow/tfjs-model/blazeface/1/default/1?tfjs-format=compressed TFJS model archive blazeface_1_default_1.tar.gz is extracted to:

$ ls  ./blazeface_js/
group1-shard1of1.bin  model.json"

Conversion to SavedModel is then operated using lib CLI:

$ tfjs_graph_converter ./blazeface_js ./blazeface_savedmodel --output_format tf_saved_model

Resulting saved_model can be converted to float32 TFLite model with:

import tensorflow as tf

def representative_dummy_dataset():
    for i in range(100):
        yield [np.zeros(128*128*3).reshape(1,128,128,3).astype(np.float32)]

converter = tf.lite.TFLiteConverter.from_saved_model("./blazeface_savedmodel")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dummy_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
tflite_quant_model = converter.convert()

Conversion to float32 TFLite succeeds with notice: TFLite interpreter needs to link Flex delegate in order to run the model since it contains the following Select TFop(s): Flex ops: Flex_FusedConv2D

Conversion to integer (quantized) version is done replacing converter supported_ops as below:

converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]

This time conversion fails because _FusedConv2D operator does not exist in TFLITE INT8 operation set:

loc(fused["_FusedConv2D:", "StatefulPartitionedCall/model/activation/Relu"]): error: 'tf._FusedConv2D' op is neither a custom op nor a flex op
loc(fused["_FusedConv2D:", "StatefulPartitionedCall/model/batch_normalization_v1_1/FusedBatchNormV3"]): error: 'tf._FusedConv2D' op is neither a custom op nor a flex op

With this model, 2 fused operators cause issue for quantization and need to be split before conversion: Conv2D + BiasAdd Conv2D + BiasAdd + Relu There is no fused MatMul op in this model, but I presume it would have needed to be split too.

Lib implementation currently supports multiple case of fused op split, for instance: _FusedConv2D( BiasAdd, Prelu) -> Conv2D + BiasAdd + Prelu

Therefore, with a local hack of is_fused_conv2d()' and_split_fused_op()' functions, I unconditonally split the faulty ops: _FusedConv2D( BiasAdd, Relu) -> Conv2D + BiasAdd + Relu _FusedConv2D( BiasAdd) -> Conv2D + BiasAdd It enabled conversion of the saved_model to a functional quantized TFLite model.

Would you consider adding a 'TFLite' compatibility mode to the lib, that would consistently split all fused operators present in the saved_model? Such mode could be every useful for 2 use cases of TFLite conversions: 1) conversion to float TFLite model with only TFLite builtin ops (without need for TF FlexOp delegate) corresponds to: converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] 2) conversion to quantized TFLite model corresponds to: converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]

Thank you for your support

[patlevin]

Thank you for the detailed description👍

I will add the feature over the weekend.

[patlevin]

Fixed in version 1.6.0

[jvuillaumier]

Great addition - it works fine for me. Thank you very much @patlevin for your support.