jax.experimental.custom_partitioning module

jax.experimental.custom_partitioning module#

API#

jax.experimental.custom_partitioning.custom_partitioning(fun, static_argnums=())[source]#

Inserts a CustomCallOp into the XLA graph with custom SPMD lowering rules.

@custom_partitioning
def f(*args):
  return ...

def propagate_user_sharding(mesh, user_shape):
  '''Update the sharding of the op from a user's shape.sharding.'''
  user_sharding = jax.tree.map(lambda x: x.sharding, user_shape)

def partition(mesh, arg_shapes, result_shape):
  def lower_fn(*args):
    ... builds computation on per-device shapes ...
  result_shardings = jax.tree.map(lambda x: x.sharding, result_shape)
  arg_shardings = jax.tree.map(lambda x: x.sharding, arg_shapes)
  # result_sharding and arg_shardings may optionally be modified and the
  # partitioner will insert collectives to reshape.
  return mesh, lower_fn, result_sharding, arg_shardings

def infer_sharding_from_operands(mesh, arg_shapes, shape):
  '''Compute the result sharding from the sharding of the operands.'''
  arg_shardings = jax.tree.map(lambda x: x.sharding, arg_shapes)


f.def_partition(partition, propagate_user_sharding, infer_sharding_from_operands)

The args to def_partition are as follows:

  • propagate_user_sharding: Callable which takes the sharding of a user (in the dag) and returns a suggestion for a new NamedSharding. The default implementation is just to return the suggested sharding.

  • partition: Callable which takes the SPMD suggested partition shapes and partition specs and returns the mesh, a per-shard lowering function, and the final input and output sharding specs (the SPMD partitioner will repartition the inputs to match). The mesh is returned to allow configuring axis_names for collectives when no mesh is provided.

  • infer_sharding_from_operands: Callable which computes an output NamedSharding from the NamedSharding chosen for each argument.

  • decode_shardings: When set to True, convert input GSPMDSharding``s to ``NamedSharding if possible. This may not be possible if the user does not provide a contextual mesh.

Positional arguments can be specified as static using static_argnums. JAX uses inspect.signature(fun) to resolve these positional arguments.

Examples

As an example, assume we want to enhance the existing jax.numpy.fft.fft. This function computes the discrete Fourier transform of an N-dimensional input along the last dimension, and is batched along the first N-1 dimensions. By default, however, it will ignore the sharding of the input and gather the input on all devices. However, since jax.numpy.fft.fft is batched along the first N-1 dimensions, this is unnecessary. We will create a new my_fft op that, instead, does not alter the sharding along the first N-1 dimensions, and only gathers the input along the last dimension if needed.

import jax
from jax.sharding import NamedSharding
from jax.experimental.custom_partitioning import custom_partitioning
from jax.experimental.pjit import pjit
from jax.sharding import PartitionSpec as P
from jax.sharding import Mesh
from jax.numpy.fft import fft
import regex as re
import numpy as np

# Pattern to detect all-gather or dynamic-slice in the generated HLO
_PATTERN = '(dynamic-slice|all-gather)'

# For an N-D input, keeps sharding along the first N-1 dimensions
# but replicate along the last dimension
def supported_sharding(sharding, shape):
    rank = len(shape.shape)
    max_shared_dims = min(len(sharding.spec), rank-1)
    names = tuple(sharding.spec[:max_shared_dims]) + tuple(None for _ in range(rank - max_shared_dims))
    return NamedSharding(sharding.mesh, P(*names))

def partition(mesh, arg_shapes, result_shape):
    result_shardings = jax.tree.map(lambda x: x.sharding, result_shape)
    arg_shardings = jax.tree.map(lambda x: x.sharding, arg_shapes)
    return mesh, fft,               supported_sharding(arg_shardings[0], arg_shapes[0]),               (supported_sharding(arg_shardings[0], arg_shapes[0]),)

def infer_sharding_from_operands(mesh, arg_shapes, result_shape):
    arg_shardings = jax.tree.map(lambda x: x.sharding, arg_shapes)
    return supported_sharding(arg_shardings[0], arg_shapes[0])

@custom_partitioning
def my_fft(x):
    return fft(x)

my_fft.def_partition(
    infer_sharding_from_operands=infer_sharding_from_operands,
    partition=partition)

Now create a 2D array sharded along the first axis, pass it through my_fft and notice how it is still sharded as expected, and identical to the output of fft. However, inspecting the HLO (using lower(x).compile().runtime_executable().hlo_modules()) reveals that my_fft does not create any all-gather or dynamic-slice, while fft does.

with Mesh(np.array(jax.devices()), ('x',)):
  x = np.asarray(np.random.randn(32*1024, 1024), dtype=np.complex64)
  y = pjit(lambda x: x, in_shardings=None, out_shardings=P('x'))(x)
  pjit_my_fft = pjit(my_fft, in_shardings=P('x'), out_shardings=P('x'))
  pjit_fft    = pjit(fft,    in_shardings=P('x'), out_shardings=P('x'))
  print(pjit_my_fft(y))
  print(pjit_fft(y))
  # dynamic-slice or all-gather are not present in the HLO for my_fft, because x is a 2D array
  assert(re.search(_PATTERN, pjit_my_fft.lower(x).compile().runtime_executable().hlo_modules()[0].to_string()) is None)
  # dynamic-slice or all-gather are present in the HLO for fft
  assert(re.search(_PATTERN, pjit_fft.lower(x).compile().runtime_executable().hlo_modules()[0].to_string())    is not None)

# my_fft
[[-38.840824   +0.j        -40.649452  +11.845365j
...
  -1.6937828  +0.8402481j  15.999859   -4.0156755j]]

# jax.numpy.fft.fft
[[-38.840824   +0.j        -40.649452  +11.845365j
  ...
  -1.6937828  +0.8402481j  15.999859   -4.0156755j]]

Because of the logic in supported_sharding, my_fft also works on 1-dimensional arrays. However, in this case, the HLO of my_fft does show a dynamic-slice, since the last dimension is the dimension along which FFTs are calculated and needs to be replicated on all devices before the computation can be done.

with Mesh(np.array(jax.devices()), ('x',)):
  x = np.asarray(np.random.randn(32*1024*1024), dtype=np.complex64)
  y = pjit(lambda x: x, in_shardings=None, out_shardings=P('x'))(x)
  pjit_my_fft = pjit(my_fft, in_shardings=P('x'), out_shardings=P('x'))
  pjit_fft    = pjit(fft,    in_shardings=P('x'), out_shardings=P('x'))
  print(pjit_my_fft(y))
  print(pjit_fft(y))
  # dynamic-slice or all-gather are present in the HLO for my_fft, because x is a 1D array
  assert(re.search(_PATTERN, pjit_my_fft.lower(x).compile().runtime_executable().hlo_modules()[0].to_string()) is None)
  # dynamic-slice or all-gather are present in the HLO for fft
  assert(re.search(_PATTERN, pjit_fft.lower(x).compile().runtime_executable().hlo_modules()[0].to_string())    is not None)

# my_fft
[    7.217285   +0.j     -3012.4937  +4287.635j   -405.83594 +3042.984j
...  1422.4502  +7271.4297j  -405.84033 -3042.983j
-3012.4963  -4287.6343j]

# jax.numpy.fft.fft
[    7.217285   +0.j     -3012.4937  +4287.635j   -405.83594 +3042.984j
...  1422.4502  +7271.4297j  -405.84033 -3042.983j
-3012.4963  -4287.6343j]
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