Added support for tflite quantized maximum and minimum #6018
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d-smirnov
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@anijain2305 please follow up |
| @@ -250,7 +256,7 @@ def compare_tflite_with_tvm(in_data, in_name, input_tensors, | |||
| # convert to tflite model | |||
| converter = tf.lite.TFLiteConverter.from_session( | |||
| sess, input_tensors, output_tensors) | |||
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| converter.experimental_new_converter=experimental_new_converter | |||
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Why do we need experimental?
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The quantised versions of maximum and minimum cannot be converted using TOCO (TOCO is not supported anymore).
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In that case, I would suggest to have the test but skip it for now. When we upgrade the TFLite to a newer version, we can enable the test.
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MLIR-based (experimental_new_converter=True) converter is already in use in Tensorflow and in TFLite. Why there is a need to postpone?
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ISn't the term experimental suggests that the feature is not mature yet? Typically, I have seen that the experimental features go through code churn and can be deprecated and API may also change before it gets matured. This is the main reason, I am suggesting not to put this.
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I understood that it is not an experimental feature any more. However the name "experimental_new_converter" was preserved. I don't see any harm to use this feature and have this test especially if we plan to migrate to a newer version of TFLite.
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I am still not fully comfortable about this. https://www.tensorflow.org/api_docs/python/tf/lite/TFLiteConverter shows that that API is subject to change. What do @u99127 @siju-samuel think about this?
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@anijain2305 - sorry for some reason github is refusing to send notifications to email when tagged here :( and that's the reason for my delay in responding to this in addition to holidays.
AFAIUI there is no way of freezing a tflite model that contains quantized max or min using the toco converter and thus we need to use the API in that form to get the testsuite coverage. While the API to use this is "subject to change", from my pov it's a use in the testsuite , we aren't using it in the main code base and thus using it is less risky.
Note also that the tflite converter in tensorflow is now defaulting to the mlir based converter as per the latest docs so this use is still a conservative move forward as we are sticking to the default but it gives us additional operator coverage.
Does that help ?
Ramana
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Ok. I understand. If CI upgrade to future TFLite versions causes problem, we can remove the flag.
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@siju-samuel @FrozenGene Please review when you get time |
Bugfix in unit test. Unit test slightly simplified
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Thanks @d-smirnov @u99127 This is merged |
* Added support for tflite quantized maximum and minimum * Unit test simplified Bugfix in unit test. Unit test slightly simplified * re-trigger CI * renamed use_real_qnn to ignore_qnn_params
* Added support for tflite quantized maximum and minimum * Unit test simplified Bugfix in unit test. Unit test slightly simplified * re-trigger CI * renamed use_real_qnn to ignore_qnn_params
* Added support for tflite quantized maximum and minimum * Unit test simplified Bugfix in unit test. Unit test slightly simplified * re-trigger CI * renamed use_real_qnn to ignore_qnn_params
* Added support for tflite quantized maximum and minimum * Unit test simplified Bugfix in unit test. Unit test slightly simplified * re-trigger CI * renamed use_real_qnn to ignore_qnn_params
* Added support for tflite quantized maximum and minimum * Unit test simplified Bugfix in unit test. Unit test slightly simplified * re-trigger CI * renamed use_real_qnn to ignore_qnn_params
Added support for tflite quantized maximum and minimum