In https://github.com/jax-ml/jax/pull/23574, we added a new `algorithm` parameter to `lax.dot_general` with the goal of giving users explicit control over the specific algorithm used to control dot product accumulation. When using this feature in real use cases, we have found that the API is both too conservative (it required the user to pass the appropriate input types) and too restrictive for common use cases. In this change, I simplify the API to bring it more in line with user expectations, and generalize it to support a broader range of use cases.
The core change is to update the dot_general lowering rule to add explicit type casts to the inputs, making sure that they always have the appropriate storage types going into the `DotGeneral` StableHLO op. Before this change, some backends would implicitly cast for some algorithms (e.g. f32 -> bf16), but error for others. It seems more user friendly to include automatic casts in all cases where a specific algorithm is requested.
Another change in behavior is to (if needed) cast the result of the `DotGeneral` op (which is defined by the algorithm's `accumulation_type`) to match the input types. This means that, regardless of the algorithm choice, the output type will match the value that a user would expect from past use of `lax.dot_general`. The `preferred_element_type` parameter can now be used to control the output type, even when an algorithm is selected.
To summarize, the updated version of `dot_general` accepts _any_ input dtypes, and the output will always match the inputs (under the existing promotion rules if the LHS and RHS don't match) unless `preferred_element_type` is used to select a specific output type. The specified "algorithm" is now more of an implementation detail, rather than the defining feature of the API, and JAX will do whatever it can to satisfy the user's request. (If an algorithm is not supported on the current device, we will still get a compile time error.)
With the above changes in mind, it's no longer really necessary to have a `transpose_algorithm` parameter, because we can now use the same algorithm for the backwards pass. For users who need to customize the algorithm on the backwards pass, that is still possible using `custom_vjp`.
Given the above changes, @sbodenstein made the excellent point that we don't really need the `algorithm` parameter anymore: just accept `DotAlgorithm` inputs to `precision`. I think this is a really nice suggestion, so I have updated the interface to implement this.
One minor negative of this approach is that `preferred_element_type` isn't a great name for what that parameter does when it is used in conjunction with an algorithm. In the long run, I'd like to rename this parameter, but keeping it as is for now seems like the best short term approach.
PiperOrigin-RevId: 683302687
Historically, we defined Precision to be an enum exported from jaxlib using pybind11, since that was the type the old XLA ComputationBuilder classes expected as input. But we build IR using StableHLO MLIR builders these days, and there's no reason for the JAX-level Precision type to match the XLA-internal one.
In a future change I plan to change the definition of Precision in jaxlib to be defined using nanobind instead of pybind11. Nanobind defines its enum classes to be final by default, which precludes this inheritance, and that's probably a good design decision by nanobind. But as discussed above, there's no good reason to inherit in the first place.
PiperOrigin-RevId: 612575404
This PR is a follow up to #18881.
The changes were generated by adding
from __future__ import annotations
to the files which did not already have them and running
pyupgrade --py39-plus --keep-percent-format {jax,tests,jaxlib,examples,benchmarks}/**/*.py
Notable changes:
* use PEP 585 type names
* use PEP 604 type union syntax where `from __future__ import annotations` is present.
* use f-strings in more places.
* remove redundant arguments to open().
