Comments (2)
Looks good. Note that we are calling those arcsin, arctan and so on.
Also it seems XLA does not support all of those, so we will need to see how to address that.
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These are implementable in terms of defn
or from the EXLA Ops we have: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/xla/client/lib/math.cc
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Related Issues (20)
- Need to contact julienmarie
- Matrix not rendered properly in the docs HOT 14
- Default to non-stable sorting
- EXLA does not consider the device on EXLA.Backend operations HOT 1
- `top_k` doesn't behave properly with :neg_infinity HOT 7
- Remove take_along_axis and take HOT 5
- CTranslate2 for fast model inference? HOT 1
- Explicitly broadcast in MLIR binary operations HOT 1
- Issue with using vectorized tensor in a function
- Issue with broadcast and vectorized axes on v0.6.4 HOT 1
- Convert Nx.LinAlg.lu to optional callback HOT 2
- EXLA/MLIR: Add missing Nx.LinAlg operations HOT 2
- EXLA/MLIR: run Nx and Nx.LinAlg doctests
- Convert Nx.LinAlg.eigh to optional callback
- Convert Nx.LinAlg.qr to optional callback HOT 1
- Convert Nx.LinAlg.cholesky to optional callback
- Fix sign of zero values in Nx.ceil and Nx.conjugate HOT 1
- Add executable-level caching to Nx.Defn in EXLA HOT 1
- Nx.LinAlg.solve/2 with Nx.BinaryBackend returns tensor with wrong state HOT 6
- Nx.all_close not working with EXLA HOT 13
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