Open smohan10 opened 2 years ago
@Xhark could you take a look at this one?
Would you please provide more details? Did you try to use TF1 QAT on TF1 model but it raise that error?
Sure. Let's take the case of TF1 QAT. The moving_averages.assign_moving_average() method in quant_ops.py in TF1 QAT has zero_debias flag set as True by default. QAT works as expected.
Now, switch to TF2 QAT. The zero_debias flag in TFMOT in the moving_averages.assign_moving_average() method in quant_ops.py in TF2 QAT is set to False.
This behavior is different than TF1 QAT. To reproduce the same behavior between TF1 and TF2, I set zero_debias in TFMOT to be True and I hit this error: Tensor.op is meaningless when eager execution is enabled.
Can the TFMOT library for TF2 QAT support zero_debias=True ?
Let me know if you need additional information!
Hi, I see that TF version 1's QAT library uses zero_debias as True. However, the TFMOT's quantization library calls the following in the quant_ops.py:
_assign_min = moving_averages.assign_moving_average( min_var, range_min, ema_decay, zero_debias=False, name='AssignMinEma') assign_max = moving_averages.assign_moving_average( max_var, range_max, ema_decay, zero_debias=False, name='AssignMaxEma')_
When I set the zero_debias to be True, I hit this error:
Tensor.op is meaningless when eager execution is enabled.
Do you have plans in the future to support zero_debias=True for QAT ?