Comments (5)
Well, I would say, it's mostly a bug in TensorFlow because it doesn't support ndim
in compiled functions:
Calling this fails:
@tf.function
def f(a):
return a.ndim
Calling this works:
@tf.function
def f(a):
return len(a.shape)
We run into this problem, because our matmul
implementation does an additional dimensionality check using ndim
.
from eagerpy.
@eserie I filed a bug in the TensorFlow repository. Let's see what they think. If you need a temporary workaround, you can comment out the shape checks that use ndim
or replace them with calls to shape
.
from eagerpy.
Thank you very much to have posted the issue in TensorFlow repository!
Do you think it could be worth to have the shape implementation in eagerpy in order to be compatible with more versions of TensorFlow? We could come back later to the canonical ndim implementation once it’s corrected in TF?
Another remark, if compilation makes sens in eagerpy, we could made it available in a universal way through an argument ‘compile=True’ in ‘eager_function’ proposed in #34. What do you think about that ?
from eagerpy.
I have to say, I haven't really thought enough about compilation and I am not sure it can be abstracted away enough to unify it between TensorFlow, PyTorch, and JAX. I think it could be interesting, but it requires careful testing of all the special cases and limitations.
from eagerpy.
Thanks to #40 this is resolved, but I'll leave this issue open for now, while the TensorFlow project discusses what to do about it.
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Related Issues (20)
- Will it support for SparseTensor (Tensorflow or Pytorch)? HOT 1
- How to Transform a torch tensor to tensorflow tensor HOT 1
- eagerpy not working together with Neural Tangents HOT 3
- Equivalent of `np.diag`? HOT 5
- topk
- Inclusion of probability distributions (scope question) HOT 1
- implementation of `slogdet` in eagerpy HOT 5
- ep.totensor method? HOT 5
- add type conversions [feature request] HOT 4
- Python Scalars Support HOT 3
- Missing support for ep.nonzero() and ep.flatnonzero() HOT 1
- Have a decorator to wrap universal functions ? HOT 6
- Support for @ operator ? HOT 6
- where method do not works with pytorch
- `index_update` seems very slow for tensorflow backend
- Why restrict cross entropy to 2D inputs only? HOT 1
- TensorFlowTensor.index_update fails for int64/float64 tensors and int/float values
- ValueError: Unknown type: <class 'tuple'>
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