- 12 1月, 2024 1 次提交
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由 Ricardo Vieira 提交于
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- 08 1月, 2024 1 次提交
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由 lucianopaz 提交于
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- 07 1月, 2024 6 次提交
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由 Ricardo Vieira 提交于
The provided output could be silently ignored and replaced by the new output of the vectorized node. The changes also avoid vectorizing multiple-output nodes when none of the unreplaced outputs are needed.
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由 Jesse Grabowski 提交于
* Copy `block_diag` and support functions from `pymc.math` * Evaluate output in sphinx code example Co-authored-by:
Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com> * Test type equivalence with `isinstance` instead of `==` Co-authored-by:
Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com> * Typo in test function * Split `block_diag` into sparse and dense version Closely follow scipy function signature for `block_diag` * Use `as_sparse_or_tensor_variable` in `SparseBlockDiagonalMatrix` to allow sparse matrix inputs to `pytensor.sparse.block_diag` * Test sparse and dense inputs to `pytensor.sparse.block_diag` * Add numba overload for `pytensor.tensor.slinalg.block_diag` * add jax overload for `pytensor.tensor.slinalg.block_diag` * Move stand-alone `block_diag_grad` function into `grad` method * Add `format` prop to `SparseBlockDiagonalMatrix` * Use `compare_numba_and_py` in `numba\test_slinalg.py::test_block_diag` * Add support for Blockwise to `slinalg.block_diag` * Add gradient test Remove `Matrix` from `BlockDiagonal` and `SparseBlockDiagonal` `Op` names Correct errors in docstrings Move input validation to a shared class method * Remove `gufunc_signature` from `__props__` Co-authored-by:
Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com> * Implement correct `__props__` for subclasses of `BaseBlockMatrix` --------- Co-authored-by:
Ricardo Vieira <28983449+ricardoV94@users.noreply.github.com>
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由 Jesse Grabowski 提交于
* Use `solve_triangular` instead of in `psd_solve_with_chol` * Add unittest for `psd_solve_with_chol` * Specify `mode=FAST_RUN` in test * Relax `test_psd_solve_with_chol` `atol` and `rtol` for half-precision tests
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由 Ricardo Vieira 提交于
Also refactors shared logic for other batch axed Ops
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由 Ricardo Vieira 提交于
All types of axis arguments are supported by max_and_argmax
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由 Ricardo Vieira 提交于
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- 02 1月, 2024 3 次提交
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由 Ricardo Vieira 提交于
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由 Adriano M. Yoshino 提交于
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由 Ricardo Vieira 提交于
This reverts commit 0fc2cd8e.
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- 20 12月, 2023 1 次提交
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由 Ricardo Vieira 提交于
This rewrites replaces a batched matmul by a core matmul by raveling the batched dimensions of the batched input, doing a 2D matmul and then unravelling the batched dimensions of the output. This sidesteps the Blockwise Dot operation and allows specialization into BLAS routines. The idea was taken from these two discussions: https://github.com/numpy/numpy/issues/7569 https://github.com/numpy/numpy/issues/8957
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- 18 12月, 2023 1 次提交
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由 dependabot[bot] 提交于
Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 3 to 4. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by:
dependabot[bot] <support@github.com>
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- 17 12月, 2023 1 次提交
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由 Ricardo Vieira 提交于
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- 15 12月, 2023 5 次提交
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由 Rob Zinkov 提交于
Docstring still uses old name of `vectorize`
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由 Ricardo Vieira 提交于
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由 dependabot[bot] 提交于
Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by:
dependabot[bot] <support@github.com>
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由 Ricardo Vieira 提交于
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由 Louis Maddox 提交于
* feat(numpy logical aliases): add aliases for logical function equivalents in NumPy (#462) * fix(logical): bitwise and logical are distinct operators * doc(aliases): Document the new NumPy inequality aliases in the same style as existing bitwise ones. * refactor(numpy-aliases): collect all NumPy aliases in one block at the end of the file
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- 12 12月, 2023 4 次提交
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由 Ricardo Vieira 提交于
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由 Jesse Grabowski 提交于
* Change `import pytensor.tensor as at` to `as pt` everywhere in the docs * Change `import pytensor.tensor as at` to `as pt` everywhere Change `import pytensor.scalar as aes` to `as ps` everywhere Change `import pytensor.tensor.random as aer` to `as ptr` everywhere Change test variables with `_at` suffix or `at_` prefix to `_pt` and `pt_`, respectively * More renaming * Rename remaining instances of `aes` and `aer`
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由 Ricardo Vieira 提交于
When size is not provided, the batch shapes of the parameters were being broadcasted twice, and the second time, wrongly, due to mixing static shape of the original parameters and the potentially larger shape of the just broadcasted parameters.
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由 Ricardo Vieira 提交于
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- 11 12月, 2023 1 次提交
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由 David Horsley 提交于
Fixes #542
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- 10 12月, 2023 15 次提交
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由 Ricardo Vieira 提交于
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由 Ricardo Vieira 提交于
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由 Ricardo Vieira 提交于
Also prevent Alloc from constant_folding when it's used by Elemwise and Blockwise to avoid creating useless large arrays
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由 Ricardo Vieira 提交于
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由 Ricardo Vieira 提交于
Also adds better static shapes
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由 Ricardo Vieira 提交于
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由 Ricardo Vieira 提交于
The Op now always expects rank 3 inputs, and any dimshuffles are added explicitly by the helper function
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由 Ricardo Vieira 提交于
It now supports an arbitrary number of batched dimensions of b, by raveling them together
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由 Ricardo Vieira 提交于
Also return matmul for respective vectorize of dot, to avoid creating redundant Blockwise Ops
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由 Ricardo Vieira 提交于
Also extend eager rewrite to more Ops The Blockwise MatrixInverse grad test became more sensitive in float32, because desired stabilization rewrites (mainly `inv_as_solve`) that target Dot of Blockwise{MatrixInverse} are now triggered in the default blockwise grad but not in the non-default non-blockwise grad -
由 Ricardo Vieira 提交于
Rewrite was already tagged as "shape_unsafe"
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由 Ricardo Vieira 提交于
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由 Ricardo Vieira 提交于
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由 Ricardo Vieira 提交于
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由 Ricardo Vieira 提交于
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- 07 12月, 2023 1 次提交
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由 Ricardo Vieira 提交于
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