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Merged
merged 1 commit into from
Jun 10, 2024
Merged

fixing dmtree.is_nested() and parameterized tree test #19822

merged 1 commit into from
Jun 10, 2024
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@haohuanw
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i encountered an issue where below code snippet doesn't work and later found that the issue is specific to dmtree:

import keras
input1 = keras.layers.Input(shape=(32,))
input2 = keras.layers.Input(shape=(32,))
added = keras.layers.Add()([input1, input2])

throws exception

  File "/home/haohuanw/Documents/keras/keras/src/layers/merging/base_merge.py", line 102, in build
    if not isinstance(input_shape[0], (tuple, list)):
TypeError: 'NoneType' object is not subscriptable

then found the issue was due to a small bug in dmtree::is_nested(). i then also expand to make sure both tree implementation is tested in ci. it also discovered 2 subtle differences between optree and dmtree.

additional help needed: is requirements-common.txt the right place for adding the test deps?

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Codecov Report

Attention: Patch coverage is 66.66667% with 1 line in your changes missing coverage. Please review.

Project coverage is 56.62%. Comparing base (2305fad) to head (ee2e10f).

Files Patch % Lines
keras/src/utils/module_utils.py 50.00% 1 Missing ⚠️
Additional details and impacted files 
@@            Coverage Diff             @@
##           master   #19822      +/-   ##
==========================================
+ Coverage   56.52%   56.62%   +0.10%     
==========================================
  Files         498      498              
  Lines       45801    45803       +2     
  Branches     8440     8440              
==========================================
+ Hits        25890    25938      +48     
+ Misses      18330    18281      -49     
- Partials     1581     1584       +3
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keras 56.62% <66.66%> (+0.10%) ⬆️
keras-numpy 56.62% <66.66%> (+0.10%) ⬆️

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@gbaned gbaned requested a review from fchollet June 10, 2024 06:59
fchollet
fchollet approved these changes Jun 10, 2024
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Nice, thanks for the fix!

@google-ml-butler google-ml-butler bot added kokoro:force-run ready to pull Ready to be merged into the codebase labels Jun 10, 2024
@fchollet fchollet merged commit efaaf85 into keras-team:master Jun 10, 2024
@google-ml-butler google-ml-butler bot removed awaiting review ready to pull Ready to be merged into the codebase kokoro:force-run labels Jun 10, 2024
james77777778 pushed a commit to james77777778/keras that referenced this pull request Jun 15, 2024
@Grvzard @james77777778
Introduce DTypePolicyMap
ae53022 
Fix `LayerNormalization.get_config` (keras-team#19807)

Propagate kwargs through `keras.ops.isclose` (keras-team#19782)

* propagate kwargs through isclose
this allows passing atol and rtol

* switch isclose **kwargs to explicit kwargs

* reduce line lengths

* fix ops.isclose signature

* fix ops.IsClose compute_output_spec signature

* implement isclose rtol atol equal_nan args for all backends

* shorten line lengths again

* revert using tf.experimental.numpy.isclose
tensorflow version now uses code inspired from tf.experimental.numpy.isclose

* fix lint

* add docs for new parameters

Faster in_top_k implementation for Jax backend (keras-team#19814)

* Faster in_top_k implementation.

* Fix bug in rank computation.

Fix CI

Fix TypeError in `Lambda.from_config` (keras-team#19827)

fixing dmtree.is_nested() and parameterized tree test (keras-team#19822)

Fix `keras.ops.repeat` cannot return an expected shape when `x` is a … (keras-team#19826)

* Fix `keras.ops.repeat` cannot return an expected shape when `x` is a `KerasTensor` and the `axis` is `None`

* Test dynamic is still dynamic after repetition

* Improve error messages

`Metric.variables` is now recursive. (keras-team#19830)

This allows it to surface variables from metrics nested at any depth.

Previously, metrics within metrics within metrics would not have their variables tracked in JAX, causing them to not be updated.

Fix `get_file` when the HTTP response has no `Content-Length` header (keras-team#19833)

Add `ops.switch` (keras-team#19834)

* Add `ops.switch`

* Update tests

* Fix out-of-bound issue

* Revert `torch.cond`

Use `absl.testing.parameterized` for `tree_test.py`. (keras-team#19842)

For consistency, use `absl.testing.parameterized` instead of `parameterized` for `tree_test.py` since that is used for all other tests.

It's one less dependency. It also says `optree` or `dmtree` in each test name.

Make batch norm mask shape error more descriptive (keras-team#19829)

* Made batch norm mask shape error more descriptive

* Added shape info in mask error message to help with degugging

Fix code style

doc: `ops.slice` (keras-team#19843)

corrected the example code in unit_normalization.py (keras-team#19845)

Added missing closing bracket and exact output value in example code after replicating the code.

Adjust code example

Add `training` argument to `Model.compute_loss()`. (keras-team#19840)

This allows models to perform different computations during training and evaluation. For instance, some expensive to compute metrics can be skipped during training and only computed during evaluation.

Note that backwards compatibility with overrides that do not have the `training` argument is maintained.

Fix the compatibility issues of `Orthogonal` and `GRU` (keras-team#19844)

* Add legacy `Orthogonal` class name

* Add legacy `implementation` arg to `GRU`

Fix inconsistent behavior of `losses.sparse_categorical_crossentropy`… (keras-team#19838)

* Fix inconsistent behavior of `losses.sparse_categorical_crossentropy` with and without `ignore_class`

* Test

* chore(format)

* Fix tests in `losses`

Fix bugs with `Mean`, `Accuracy` and `BinaryAccuracy` metrics. (keras-team#19847)

- `reduce_to_samplewise_values` would not reduce `sample_weights` correctly because the number of dimensions of `values` was checked.
- `reduce_to_samplewise_values` needs to explicitely broadcast `sample_weights`. Before, it was implicitly broadcast in the multiplication with `values`. However, the explicit broadcast is needed for the computation of `num_samples` for the averaging to be correct. This causes a bug when `sample_weights` is of rank 2 or more and a broadcast happens when doing the multiplication. This logic existed in `tf_keras`: https://github.com/keras-team/tf-keras/blob/master/tf_keras/metrics/base_metric.py#L508
- `Accuracy` and `BinaryAccuracy` were doing a mean reduction too early, before multiplying by `sample_weights`. This matters when the rank of `sample_weights` is the same as `y_true` and `y_pred`.

Add tests for `DTypePolicyMap`

Fix test

Update the logic of `default_policy`

Improve serialization of `DTypePolicyMap`

Improve `__repr__` and `__eq__`

Add `custom_gradient` for the numpy backend (keras-team#19849)

fix variable name when add in init function (keras-team#19853)

Address comments
james77777778 pushed a commit to james77777778/keras that referenced this pull request Jun 15, 2024
@Grvzard @james77777778
Introduce DTypePolicyMap
6b19334 
Introduce `DTypePolicyMap`

Fix `LayerNormalization.get_config` (keras-team#19807)

Propagate kwargs through `keras.ops.isclose` (keras-team#19782)

* propagate kwargs through isclose
this allows passing atol and rtol

* switch isclose **kwargs to explicit kwargs

* reduce line lengths

* fix ops.isclose signature

* fix ops.IsClose compute_output_spec signature

* implement isclose rtol atol equal_nan args for all backends

* shorten line lengths again

* revert using tf.experimental.numpy.isclose
tensorflow version now uses code inspired from tf.experimental.numpy.isclose

* fix lint

* add docs for new parameters

Faster in_top_k implementation for Jax backend (keras-team#19814)

* Faster in_top_k implementation.

* Fix bug in rank computation.

Fix CI

Fix TypeError in `Lambda.from_config` (keras-team#19827)

fixing dmtree.is_nested() and parameterized tree test (keras-team#19822)

Fix `keras.ops.repeat` cannot return an expected shape when `x` is a … (keras-team#19826)

* Fix `keras.ops.repeat` cannot return an expected shape when `x` is a `KerasTensor` and the `axis` is `None`

* Test dynamic is still dynamic after repetition

* Improve error messages

`Metric.variables` is now recursive. (keras-team#19830)

This allows it to surface variables from metrics nested at any depth.

Previously, metrics within metrics within metrics would not have their variables tracked in JAX, causing them to not be updated.

Fix `get_file` when the HTTP response has no `Content-Length` header (keras-team#19833)

Add `ops.switch` (keras-team#19834)

* Add `ops.switch`

* Update tests

* Fix out-of-bound issue

* Revert `torch.cond`

Use `absl.testing.parameterized` for `tree_test.py`. (keras-team#19842)

For consistency, use `absl.testing.parameterized` instead of `parameterized` for `tree_test.py` since that is used for all other tests.

It's one less dependency. It also says `optree` or `dmtree` in each test name.

Make batch norm mask shape error more descriptive (keras-team#19829)

* Made batch norm mask shape error more descriptive

* Added shape info in mask error message to help with degugging

Fix code style

doc: `ops.slice` (keras-team#19843)

corrected the example code in unit_normalization.py (keras-team#19845)

Added missing closing bracket and exact output value in example code after replicating the code.

Adjust code example

Add `training` argument to `Model.compute_loss()`. (keras-team#19840)

This allows models to perform different computations during training and evaluation. For instance, some expensive to compute metrics can be skipped during training and only computed during evaluation.

Note that backwards compatibility with overrides that do not have the `training` argument is maintained.

Fix the compatibility issues of `Orthogonal` and `GRU` (keras-team#19844)

* Add legacy `Orthogonal` class name

* Add legacy `implementation` arg to `GRU`

Fix inconsistent behavior of `losses.sparse_categorical_crossentropy`… (keras-team#19838)

* Fix inconsistent behavior of `losses.sparse_categorical_crossentropy` with and without `ignore_class`

* Test

* chore(format)

* Fix tests in `losses`

Fix bugs with `Mean`, `Accuracy` and `BinaryAccuracy` metrics. (keras-team#19847)

- `reduce_to_samplewise_values` would not reduce `sample_weights` correctly because the number of dimensions of `values` was checked.
- `reduce_to_samplewise_values` needs to explicitely broadcast `sample_weights`. Before, it was implicitly broadcast in the multiplication with `values`. However, the explicit broadcast is needed for the computation of `num_samples` for the averaging to be correct. This causes a bug when `sample_weights` is of rank 2 or more and a broadcast happens when doing the multiplication. This logic existed in `tf_keras`: https://github.com/keras-team/tf-keras/blob/master/tf_keras/metrics/base_metric.py#L508
- `Accuracy` and `BinaryAccuracy` were doing a mean reduction too early, before multiplying by `sample_weights`. This matters when the rank of `sample_weights` is the same as `y_true` and `y_pred`.

Add tests for `DTypePolicyMap`

Fix test

Update the logic of `default_policy`

Improve serialization of `DTypePolicyMap`

Improve `__repr__` and `__eq__`

Add `custom_gradient` for the numpy backend (keras-team#19849)

fix variable name when add in init function (keras-team#19853)

Address comments

Update docstrings
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