Comments (8)
@HAKSOAT yes, you should be able to start a branch already from #764. All the functionality is done, except some tests are failing because they depend non-optionally on GPU. We should make that optional and get it merged soon enough, but you need not wait :)
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Go ahead. We'll link and lock the Op when you open a PR
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Hi @ricardoV94, I have opened a PR for the Softmax Ops. I see it has been grouped with LogSoftmax and Grads, so I can update the PR to include them.
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Hi there @ricardoV94, I'm attending the hackathon at Pydata London.
I'd like to work on Softmax.
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Hi there @ricardoV94, I'm attending the hackathon at Pydata London.
I'd like to work on Softmax.
Sure!
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Thanks @ricardoV94
I have been doing some reading around the docs, I noticed that #764 has the initial setup. I believe that contains ground work for adding other Ops, so I'd be following the PR and the Softmax Ops will likely use some code from it.
Am I thinking about this the right way?
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#764 is merged
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Hello @ricardoV94, I would like to work on Reshape
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Related Issues (20)
- Another test issue
- Get rid of pre-defined inplace Elemwise Operators?
- numba: Incorrect gradient and typing error
- Default typify in JITLinkers expects tensors/arrays, why?
- Torch: Investigate if `set_default_device/with_device` is a deal-breaker HOT 1
- Update APIs to be compatible with NumPy 2.0 or pin NumPy to latest 1.x version HOT 1
- `tensor.where` does not behave like `np.where` when only one input (condition) is passed
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- Allow specifying shape of dtyped tensor constructors
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- Check and Update support for Numba Ops
- More exhausitve list of functions in API section of docs
- `pt.linalg.det` should eagerly fail if known shapes are not square
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