Cannot convert RoBERTa to tflite model

🐛 Bug

Information

Model I am using: RoBERTa (roberta-base)

Language I am using the model on: English

The problem arises when using: Conversion based on https://github.com/huggingface/tflite-android-transformers/blob/master/models_generation/distilbert.py

The tasks I am working on is: It is irrelevant on this step.

To reproduce

1. Build python conversion script.
2. Run it.

Conversion script

import tensorflow as tf
from transformers import TFRobertaForSequenceClassification

model = TFRobertaForSequenceClassification.from_pretrained('roberta-base')
input_spec = tf.TensorSpec([1, 384], tf.int32)
model._set_inputs(input_spec, training=False)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
# For conversion with hybrid quantization:
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
converter.experimental_new_converter = True
tflite_model = converter.convert()
open("test.tflite", "wb").write(tflite_model)

Error: tf.Cumsum op is neither a custom op nor a flex op and needs a custom implementation

Expected behavior

No errors.

Environment info

• Transformers version: 2.8.0
• Platform: Windows 10
• Python version: 3.7.0
• Tensorflow version: 2.1.0

[dshahrokhian]

@Pierrci thanks! It also seems to be have been solved in the latest release of tf-nightly: https://github.com/tensorflow/tensorflow/issues/42382#issuecomment-675000451

[will-rice]

I believe this is because tf.Cumsum is not a supported operation and not an issue relating to this repo. Here is a link to the tensorflow documentation on supported ops. [https://www.tensorflow.org/lite/guide/ops_compatibility] In the past, I’ve been able to get around unsupported ops by reimplementing the operator with supported ops or replacing the unsupported portion with another op. ie. relu in place of gelu.