Comments (1)
Which dtype is used is not set within the wrapper at all, but outside the model creation. Hence, there is no need to do any modification with the wrapper.
But to make this clearer for the user, I have added a comment regarding usage of GA with TPUs in the README here:
https://github.com/andreped/GradientAccumulator#mixed-precision
from gradientaccumulator.
Related Issues (20)
- Use tf.function on train_step HOT 11
- 0.5.1, tf 2.11 error for accuoptimizer HOT 8
- Replacing AccumBatchNormalization not working as intended HOT 2
- ConvNeXt not compatible with Model wrapper HOT 1
- No mixed precision support with GradientAccumulateOptimizer? HOT 7
- Replacing BN layer with AccumBN layer results in poorer convergence
- pytest-cov with multiprocessing results in occational deadlock HOT 2
- Add documentations on accumulated batch normalization HOT 1
- `gamma` and `beta` in AccumBatchNormalization never updated HOT 2
- Is mixed precision properly handled for all use cases? HOT 2
- Reduce number of unit tests HOT 2
- Unit tests fail on tf >= 2.10 HOT 1
- AccumBN is not compatible with 3D ops e.g. Conv3D HOT 1
- Mixed precision not working as intended with AccumBatchNormalization HOT 8
- Add linting to improve code style HOT 1
- Unit test for optimizer invariance in distributed trainings HOT 2
- Optimizer wrapper not working as intended HOT 2
- Optimizer wrapper not compatible with tf==2.6 HOT 2
- AttributeError using Optimizer wrapper with tf==2.4 HOT 1
- Optimizer wrapper performance is dependent on tensorflow version HOT 3
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