Replace `with set_mesh(mesh):` with `with use_mesh(mesh):` context manager
Also expose `AxisTypes` and `use_mesh` into public API via `jax.sharding.AxisTypes` and `jax.sharding.use_mesh`.
PiperOrigin-RevId: 716446406
This will basically drop the gather operation into full auto mode and add a sharding constraint on the output given by the user via `out_spec`.
Co-authored-by: Matthew Johnson <mattjj@google.com>
PiperOrigin-RevId: 716295953
* Split on 1 dimension only and the splitting dimension should be unsharded.
`operand.shape = (4@x, 6@y, 8), new_shape = (4@x, 6@y, 2, 2, 2)`
* Merging into 1 dimension only and all the merging dimensions should be unsharded.
`operand.shape = (4@y, 2, 3, 8), new_shape = (4@y, 6, 8)`
* Split into singleton dimensions i.e. adding extra dims of size 1
`operand.shape = (4@x, 6@y, 8@z), new_shape = (1, 4@x, 1, 6@y, 1, 8@z, 1)`
* Merge singleton dimensions i.e. removing extra dims of size 1
`operand.shape = (1, 4@x, 6, 1, 8, 1), new_shape = (1, 4@x, 6, 8)`
* Identity reshape
`operand.shape = (4@(x,y), 6), new_shape = (4@(x,y), 6)`
These cases are unambiguous to handle. In all other cases, we error out and ask the user to provide the out_sharding.
PiperOrigin-RevId: 716216240
In implementing https://github.com/jax-ml/jax/pull/25787, I realized that while we lower `tridiagonal_solve` to cuSPARSE on GPU, we were using an explicit implementation of the Thomas algorithm on CPU. We should instead lower to LAPACK's `gtsv` on CPU because it should be more numerically stable and faster.
PiperOrigin-RevId: 714069225
Fixes: #25140
Previously, the following code:
```
def f(i, x):
return lax.switch(i, [lambda x: dict(a=x),
lambda x: dict(a=(x, x))], x)
f(0, 42)
```
resulted in the error message:
```
TypeError: branch 0 and 1 outputs must have same type structure, got PyTreeDef({'a': *}) and PyTreeDef({'a': (*, *)}).
```
With this change the error message is more specific where the
difference is in the pytree structure:
```
TypeError: branch 0 output must have same type structure as branch 1 output, but there are differences:
* at output['a'], branch 0 output has pytree leaf and branch 1 output has <class 'tuple'>, so their Python types differ
```
A pivoted QR factorization is possible in `scipy.linalg.qr`, thanks
to the `geqp3` routine of LAPACK. To provide the same functionality
in JAX, we implement a new primitive `geqp3_p` which calls the LAPACK
routine via the FFI on CPU devices.
Both `jax.scipy.linalg.qr` and `jax.lax.linalg.qr` now support the
use of column-pivoting on CPU devices.
To provide a GPU implementation of `geqp3` may require using MAGMA,
due to the lack of a `geqp3` implementation in `cuSolver` - see
ccb331707e80b16d89de6e5c9f2f89b87c1682ed (`jax.lax.linalg.eig`) for
an example of using MAGMA in GPU lowerings. Such a GPU implementation
can be considered in the future.
If PartitionSpec is passed, the mesh is read from the context. The primitives though take `NamedSharding` only. The conversion from `PartitionSpec` to `NamedSharding` happens above `.bind`.
We also raise an error if `PartitionSpec` contain mesh axis names that are of type Auto or Collective for the above functions.
PiperOrigin-RevId: 713352542
This CL builds out a simple sketch of constant prop by construction in mosaic - we walk the graph up from cond, collecting the values and either const propping or failing out of const prop. Failure out of const prop is not a bug, but hitting an unimplemented const prop func is for now, in order to drive better coverage.
This then allows us to pick a single branch, and ignore branches which do not have a viable mosaic implementation.
And, finally, for diag, this means we can replace the initial gather-dependent implementation in lax with a mosaic specific one that avoids gather.
PiperOrigin-RevId: 708752566
A composite function can encapsulate an operation made up of other JAX functions. The semantics of the op is implemented by the `decomposition` function. For example, a `tangent` operation can be implemented as `sin(x) / cos(x)`.
This is what the HLO looks like for a tangent composite:
```
module @jit_my_tangent_composite {
func.func public @main(%arg0: tensor<4xf64>) -> (tensor<4xf64>) {
%0 = stablehlo.composite "my.tangent" %arg0 {decomposition = @my.tangent} : (tensor<4xf64>) -> tensor<4xf64>
return %0 : tensor<4xf64>
}
func.func private @my.tangent(%arg0: tensor<4xf64>) -> tensor<4xf64> {
%0 = stablehlo.sine %arg0 : tensor<4xf64>
%1 = stablehlo.cosine %arg0 : tensor<4xf64>
%2 = stablehlo.divide %0, %1 : tensor<4xf64>
return %2 : tensor<4xf64>
}
}
```
Similarly, this can scale to something like Attention. By preserving such an abstraction, it greatly simplifies pattern matching. Instead of matching the set of ops that represent Attention, the matcher can simply look for a uniquely identifying composite op like "MyAttention".
This is useful for preserving high level abstraction that would otherwise be lost during lowering. The hardware-aware compiler can recognize the single composite op and emit efficient code rather than pattern-matching a generic lowering which is then replaced with your own efficient lowering. And then the decomposition function can be DCE'd away. If the hardware does not have an efficient lowering, it can inline the `decomposition` which implements the semantics of the abstraction.
For more details on the API, refer to the documentation.
PiperOrigin-RevId: 707750633
Also allow users to enter into `Auto`/`User` mode inside jit along all or some axes.
Add checks to make sure that avals inside a context match the surrounding context. This check happens inside `abstract_eval` rules but maybe we need a more central place for it which we can create later on.
PiperOrigin-RevId: 707128096
We need to guaranty that the outermost dimension of the output is big enough to fit all received elements, but it's not necessary for input and output outermost dimensions to be exactly equal.
PiperOrigin-RevId: 707011916
