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Adding expand_dims for xtensor #1449
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Adding expand_dims for xtensor #1449
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Now that we have this PR based on the right commit, @ricardoV94 it is ready for a first look. One question: my first draft of this was based on a later commit -- this draft goes back to an earlier commit, and it looks like |
That's the new name, it better represents the kind of rewrites it holds |
pytensor/xtensor/shape.py
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def __init__(self, dim, size=1): | ||
self.dims = dim | ||
self.size = size |
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This does not allow symbolic sizes, check UnStack for reference
@ricardoV94 I think this is a step toward handling symbolic sizes, but there are a couple of place where I'm not sure what the right behavior is. See the comments in Do those tests make sense? Are there more cases that should be covered? |
The simplest test for symbolic expand_dims is: size_new_dim = xtensor("size_new_dim", shape=(), dtype=int)
x = xtensor("x", shape=(3,))
y = x.expand_dims(new_dim=size_new_dim)
xr_function = function([x, size_new_dim], y)
x_test = xr_arange_like(x)
size_new_dim_test = DataArray(np.array(5, dtype=int))
result = xr_function(x_test, size_new_dim_test)
expected_result = x_test.expand_dims(new_dim=size_new_dim_test)
xr_assert_allclose(result, expected_result) Yout can parametrize the test to try default and explicit non-default axis as well. Sidenote, what is an implicit |
tests/xtensor/test_shape.py
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# Duplicate dimension creation | ||
y = expand_dims(x, "new") | ||
with pytest.raises(ValueError): |
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match the expected error messages to be sure you are triggering the branch you care about. Sometimes you are testing an earlier error and can't tell because of only checking for ValueError/TypeError
@ricardoV94 I've addressed most of your comments on the previous round, and made a first pass at adding support for multiple dimensions. Please take a look at the Assuming that adding multiple dimensions is rare, what do with think of the loop option, as opposed to making a single Op that adds multiple dimensions? |
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# Test behavior with symbolic size > 1 | ||
# NOTE: This test documents our current behavior where expand_dims broadcasts to the requested size. | ||
# This differs from xarray's behavior where expand_dims always adds a size-1 dimension. |
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This is not true?
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I'm not sure about the general claim in the note, but at least in this case, it seems like we're getting the behavior we want from xtensor, but running the same operation with xarray does something different, causing the test to fail. Here's Cursor's summary
The test failure confirms that our current implementation of expand_dims broadcasts to the requested size (4 in this case), while xarray's behavior is to always add a size-1 dimension. This is evident from the test output, where the left side (our implementation) has a shape of (batch: 4, a: 2, b: 3), and the right side (xarray's behavior) has a shape of (batch: 1, a: 2, b: 3).
I'm inclined to keep this test to note the discrepancy.
That's fine. We used that for other Ops and we can revisit later of we want it to be fused |
@ricardoV94 This is ready for another look. The rewrite was a shambles, but I think I have a clearer idea now. |
Add expand_dims operation for labeled tensors
This PR adds support for the
expand_dims
operation in PyTensor's labeled tensor system, allowing users to add new dimensions to labeled tensors with explicit dimension names.Key Features
ExpandDims
operation that adds a new dimension to an XTensorVariableImplementation Details
The implementation includes:
New
ExpandDims
class inpytensor/xtensor/shape.py
that handles:Rewriting rule in
pytensor/xtensor/rewriting/shape.py
that:Comprehensive test suite in
tests/xtensor/test_shape.py
covering:Usage Example
Testing
The implementation includes extensive tests that verify:
📚 Documentation preview 📚: https://pytensor--1449.org.readthedocs.build/en/1449/