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Oh, I overlooked it has to be on the same device. Maybe common tests should also assert it? It should spot the error at least in gpu workflows

Oh, I overlooked it has to be on the same device. Maybe common tests should also assert it? It should spot the error at least in gpu workflows

I think this might be specifically an issue with mps because it seems to work fine with cuda.

FYI I have updated to the latest torch (2.3.1) and it looks like specifying the device here is not required anymore when using mps. 👀

FYI I have updated to the latest torch (2.3.1) and it looks like specifying the device here is not required anymore when using mps. 👀

Nice that they did this. It was verbose to have to specify the device even for scalar arrays.

I am -0 for merging this PR. Since array API is very new, one can expect people who want to try it to use the latest stable versions of libraries and I would rather keep our code based not too verbose if not necessary.

What other people think? We could make that requirement explicit in the _array_api.rst file.

FYI I have updated to the latest torch (2.3.1) and it looks like specifying the device here is not required anymore when using mps. 👀

Nice that they did this. It was verbose to have to specify the device even for scalar arrays.

I am -0 for merging this PR. Since array API is very new, one can expect people who want to try it to use the latest stable versions of libraries and I would rather keep our code based not too verbose if not necessary.

What other people think? We could make that requirement explicit in the _array_api.rst file.

I think it makes sense if it is fixed. No need for the change. @ogrisel should we close this PR?

Let's wait a bit to let others have time to express their opinion.

Let's close, we can reopen if others disagree.

Note: we would have to check with other libraries. For instance I think jax support in array-api-compat is improving so we might include it in our test suite at some point and we might end-up needing this change.