GPU MaxPool gradient ops do not yet have a deterministic XLA implementation

[wx0608]

GPU MaxPool gradient ops do not yet have a deterministic XLA implementation

[tilakrayal]

@wx0608,
Could you please share a reproducible code/colab gist that supports your statement so that the issue can be easily understood? Thank you!

[github-actions]

This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you.

[Masumekeshavarzi]

I am using DeepLabV3+ with Resent backbone and I am getting the same error when I want to have reproducible results. I put these lines as well:
SEED=123
tf.keras.utils.set_random_seed(SEED)
os.environ['PYTHONHASHSEED'] = str(SEED)
random.seed(SEED)
tf.random.set_seed(SEED)
np.random.seed(SEED)

os.environ['TF_DETERMINISTIC_OPS'] = '1'
os.environ['TF_CUDNN_DETERMINISTIC'] = '1'
tf.config.experimental.enable_op_determinism()

from here I realied that TF_CUDNN_DETERMINISTIC. When set to 'true' or '1', this selects deterministic gradient algorithms for tf.nn.max_poold and tf.keras.layers.MaxPoolD ,
but still get this error :GPU MaxPool gradient ops do not yet have a deterministic XLA implementation

before I has same problem with Upsampling layers in which we should use bilinear interpolation not Nearestneighbor , but with MaxPooling layers I dont know what should be changed. Please leave any meassage that might be helpful.

[Masumekeshavarzi]

wx0608 any findings?