BUG: [jax2tf] None of the tests in shape_poly_test.py are ran for enable_xla=False #11929
Assignees
Labels
bug
Something isn't working
Comments
|
@gnecula FYI |
copybara-service bot
pushed a commit
that referenced
this issue
Aug 16, 2022
* Fixes #11804: we only supported `lax.reduce_window` without batch and channel dimensions, which is wrong. This is supported, and in fact something that most users use (this case is actually not explained in the [operational semantics for XLA::ReduceWindow](https://www.tensorflow.org/xla/operation_semantics#reducewindow)). I have fixed this and clarified a number of test cases with batch and channel dimensions. * Also, @sdenton4 gave a failing example in a Colab using polymorphic dimensions. I've added this as a test case to make sure it works now. * Adds support for explicit padding using the existing padding logic from convolutions. * Fixes #11874: we were not handling SAME padding for `lax.add` correctly, since we used `tf.nn.avg_pool`, which does not include non-padding tokens (see issue for more details). I resolved it by adding manual padding and added some additional tests for this. * Ensures we call eval_shape on a shape containing polynomials before calling a TF op. * Fixes #11929 (comment): we weren't running any of the shape_poly_test.py tests for `enable_xla=False`. PiperOrigin-RevId: 467532166
copybara-service bot
pushed a commit
that referenced
this issue
Aug 16, 2022
* Fixes #11804: we only supported `lax.reduce_window` without batch and channel dimensions, which is wrong. This is supported, and in fact something that most users use (this case is actually not explained in the [operational semantics for XLA::ReduceWindow](https://www.tensorflow.org/xla/operation_semantics#reducewindow)). I have fixed this and clarified a number of test cases with batch and channel dimensions. * Also, @sdenton4 gave a failing example in a Colab using polymorphic dimensions. I've added this as a test case to make sure it works now. * Adds support for explicit padding using the existing padding logic from convolutions. * Fixes #11874: we were not handling SAME padding for `lax.add` correctly, since we used `tf.nn.avg_pool`, which does not include non-padding tokens (see issue for more details). I resolved it by adding manual padding and added some additional tests for this. * Ensures we call eval_shape on a shape containing polynomials before calling a TF op. * Fixes #11929 (comment): we weren't running any of the shape_poly_test.py tests for `enable_xla=False`. PiperOrigin-RevId: 467532166
copybara-service bot
pushed a commit
that referenced
this issue
Aug 16, 2022
* Fixes #11804: we only supported `lax.reduce_window` without batch and channel dimensions, which is wrong. This is supported, and in fact something that most users use (this case is actually not explained in the [operational semantics for XLA::ReduceWindow](https://www.tensorflow.org/xla/operation_semantics#reducewindow)). I have fixed this and clarified a number of test cases with batch and channel dimensions. * Also, @sdenton4 gave a failing example in a Colab using polymorphic dimensions. I've added this as a test case to make sure it works now. * Adds support for explicit padding using the existing padding logic from convolutions. * Fixes #11874: we were not handling SAME padding for `lax.add` correctly, since we used `tf.nn.avg_pool`, which does not include non-padding tokens (see issue for more details). I resolved it by adding manual padding and added some additional tests for this. * Ensures we call eval_shape on a shape containing polynomials before calling a TF op. * Fixes #11929 (comment): we weren't running any of the shape_poly_test.py tests for `enable_xla=False`. PiperOrigin-RevId: 467532166
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Description
In
shape_poly_test.py, we use the function _make_harness to create a test harness. If flagenable_and_disable_xla=True, we create two harnesses recursively, one with argumentenable_xla=Trueand one withenable_xla=Falsehere. This will be passed on to_make_harnessin the kwargs argument**params. However, this argument is never forwarded when constructing the Harness here. As a result, we currently create two harnesses, both withenable_xla=True.What jax/jaxlib version are you using?
No response
Which accelerator(s) are you using?
No response
Additional System Info
No response
The text was updated successfully, but these errors were encountered: