# Copyright 2022 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Module for JAX callbacks."""
from __future__ import annotations
from collections.abc import Sequence
import dataclasses
import functools
import logging
from typing import Any, Callable
import jax
from jax._src import core
from jax._src import dispatch
from jax._src import dtypes
from jax._src import effects
from jax._src import sharding_impls
from jax._src import tree_util
from jax._src import util
from jax._src.interpreters import ad
from jax._src.interpreters import batching
from jax._src.interpreters import mlir
from jax._src.lax.control_flow.loops import map as lax_map
from jax._src.lib import xla_client as xc
from jax._src.sharding_impls import SingleDeviceSharding
import numpy as np
logger = logging.getLogger(__name__)
# `pure_callback_p` is the main primitive for staging out Python pure callbacks.
pure_callback_p = core.Primitive("pure_callback")
pure_callback_p.multiple_results = True
dispatch.prim_requires_devices_during_lowering.add(pure_callback_p)
map, unsafe_map = util.safe_map, map
@dataclasses.dataclass(frozen=True)
class _FlatCallback:
"""A Python function callable with flat arguments and results.
An instance of this class is used as a parameter for the callback primitives.
We prefer it to an anonymous flattened function because it produces
equal objects when we call the same Python function with the same argument
structure.
"""
callback_func: Callable[..., Any]
in_tree: tree_util.PyTreeDef # (args, kwargs) pytree for `callback_func`.
def __call__(self, *flat_args: jax.Array) -> Sequence[jax.Array]:
args, kwargs = tree_util.tree_unflatten(self.in_tree, flat_args)
return tree_util.tree_leaves(self.callback_func(*args, **kwargs))
def pure_callback_impl(
*args,
result_avals,
callback: _FlatCallback,
sharding: SingleDeviceSharding | None,
vectorized: bool,
):
del sharding, vectorized, result_avals
cpu_device, *_ = jax.local_devices(backend="cpu")
args = jax.device_put(args, cpu_device)
with jax.default_device(cpu_device):
try:
return tree_util.tree_map(np.asarray, callback(*args))
except BaseException:
logger.exception("jax.pure_callback failed")
raise
pure_callback_p.def_impl(functools.partial(dispatch.apply_primitive,
pure_callback_p))
@pure_callback_p.def_abstract_eval
def pure_callback_abstract_eval(
*avals,
callback: _FlatCallback,
result_avals,
sharding: SingleDeviceSharding | None,
vectorized: bool,
):
del avals, callback, sharding, vectorized
return result_avals
def pure_callback_jvp_rule(*args, **kwargs):
del args, kwargs
raise ValueError(
"Pure callbacks do not support JVP. "
"Please use `jax.custom_jvp` to use callbacks while taking gradients.")
ad.primitive_jvps[pure_callback_p] = pure_callback_jvp_rule
def pure_callback_transpose_rule(*args, **kwargs):
del args, kwargs
raise ValueError(
"Pure callbacks do not support transpose. "
"Please use `jax.custom_vjp` to use callbacks while taking gradients.")
ad.primitive_transposes[pure_callback_p] = pure_callback_transpose_rule
def pure_callback_batching_rule(
args,
dims,
*,
callback: _FlatCallback,
sharding: SingleDeviceSharding | None,
vectorized: bool,
result_avals: Sequence[core.ShapedArray],
):
axis_size = next(a.shape[d] for a, d in zip(args, dims)
if d is not batching.not_mapped)
new_args = [arg if dim is batching.not_mapped else
batching.moveaxis(arg, dim, 0) for arg, dim in zip(args, dims)]
if vectorized:
result_avals = tuple(
core.unmapped_aval(axis_size, core.no_axis_name, 0, aval) # type: ignore
for aval in result_avals)
outvals = pure_callback_p.bind(
*new_args,
callback=callback,
sharding=sharding,
vectorized=vectorized,
result_avals=result_avals,
)
else:
is_batched = [d is not batching.not_mapped for d in dims]
unbatched_args, batched_args = util.partition_list(is_batched, new_args)
def _batch_fun(batched_args):
merged_args = util.merge_lists(is_batched, unbatched_args, batched_args)
return pure_callback_p.bind(
*merged_args,
callback=callback,
sharding=sharding,
result_avals=result_avals,
vectorized=vectorized,
)
outvals = lax_map(_batch_fun, batched_args)
return tuple(outvals), (0,) * len(outvals)
batching.primitive_batchers[pure_callback_p] = pure_callback_batching_rule
def _callback_op_sharding(axis_context, sharding: SingleDeviceSharding | None):
if isinstance(axis_context, sharding_impls.SPMDAxisContext):
# If we have fully manual sharding during lowering, that means the JAX
# program has per-device semantics, so we run the callback on each device.
if axis_context.manual_axes != frozenset(axis_context.mesh.axis_names):
raise NotImplementedError(
"callbacks are only supported in spmd computations when all mesh"
" axes are partitioned manually (no partial automatic sharding)."
)
if sharding is not None:
raise NotImplementedError(
"callbacks do not support specifying sharding inside spmd"
" computations"
)
op_sharding = xc.OpSharding()
op_sharding.type = xc.OpSharding.Type.MANUAL
return op_sharding
if isinstance(axis_context, sharding_impls.ShardingContext):
if sharding is not None:
if not isinstance(sharding, SingleDeviceSharding):
raise NotImplementedError(
"pure_callback only supports SingleDeviceSharding, but got"
f" {type(sharding)}"
)
device = next(iter(sharding.device_set))
device_assignment = axis_context.device_assignment
if device_assignment is None:
raise AssertionError(
"Please file a bug at https://github.com/google/jax/issues")
try:
device_index = device_assignment.index(device)
except IndexError as e:
raise ValueError(
"Sharding provided to pure_callback specifies a device"
f" {device} that is not in the device assignment"
f" ({device_assignment})") from e
else:
device_index = 0
# If we have fully automatic sharding during lowering, that means the JAX
# program has bulk array semantics, so we run the callback with a MAXIMAL
# sharding and hence execute it only once on the full logical value).
op_sharding = xc.OpSharding()
op_sharding.type = xc.OpSharding.Type.MAXIMAL
op_sharding.tile_assignment_dimensions = [1]
op_sharding.tile_assignment_devices = [device_index]
return op_sharding
# When there's no SPMD partitioning going on, don't annotate a sharding.
return None
def pure_callback_lowering(
ctx, *args, callback: _FlatCallback, sharding: SingleDeviceSharding | None, **params
):
def _callback(*flat_args):
return tuple(
pure_callback_impl(
*flat_args,
callback=callback,
sharding=None, # unused.
**params,
)
)
op_sharding = _callback_op_sharding(ctx.module_context.axis_context, sharding)
result, _, _ = mlir.emit_python_callback(
ctx,
_callback,
None,
list(args),
ctx.avals_in,
ctx.avals_out,
False,
sharding=op_sharding,
)
return result
mlir.register_lowering(pure_callback_p, pure_callback_lowering)
def _check_shape_dtype(shape_dtype):
dt = np.dtype(shape_dtype.dtype)
if dtypes.canonicalize_dtype(dt) != dt:
raise ValueError(
"result_shape_dtypes cannot specify 64-bit types when `jax_enable_x64` is disabled")
[docs]
def pure_callback(
callback: Callable[..., Any],
result_shape_dtypes: Any,
*args: Any,
sharding: SingleDeviceSharding | None = None,
vectorized: bool = False,
**kwargs: Any,
):
"""Calls a pure Python callback.
For more explanation, see `External Callbacks`_.
Args:
callback: function to execute on the host. The callback is assumed to be a pure
function (i.e. one without side-effects): if an impure function is passed, it
may behave in unexpected ways, particularly under transformation.
result_shape_dtypes: pytree whose leaves have ``shape`` and ``dtype`` attributes,
whose structure matches the expected output of the callback function at runtime.
:class:`jax.ShapeDtypeStruct` is often used to define leaf values.
*args: arguments to be passed to the callback function
sharding: optional sharding that specifies the device from which the callback should
be invoked.
vectorized: boolean specifying whether the callback function can operate in a
vectorized manner.
**kwargs: keyword arguments to be passed to the callback function
Returns:
result: a pytree of :class:`jax.Array` objects whose structure matches that of
``result_shape_dtypes``.
See Also:
- :func:`jax.experimental.io_callback`: callback designed for impure functions.
- :func:`jax.debug.callback`: callback designed for general-purpose debugging.
- :func:`jax.debug.print`: callback designed for printing.
.. _External Callbacks: https://jax.readthedocs.io/en/latest/notebooks/external_callbacks.html
"""
flat_args, in_tree = tree_util.tree_flatten((args, kwargs))
tree_util.tree_map(_check_shape_dtype, result_shape_dtypes)
result_avals = tree_util.tree_map(
lambda x: core.ShapedArray(x.shape, x.dtype), result_shape_dtypes)
flat_result_avals, out_tree = tree_util.tree_flatten(result_avals)
out_flat = pure_callback_p.bind(
*flat_args,
callback=_FlatCallback(callback, in_tree),
result_avals=tuple(flat_result_avals),
sharding=sharding,
vectorized=vectorized,
)
return tree_util.tree_unflatten(out_tree, out_flat)
def pure_callback_api(
callback: Callable[..., Any],
result_shape_dtypes: Any,
*args: Any,
sharding: SingleDeviceSharding | None = None,
vectorized: bool = False,
**kwargs: Any,
):
"""Applies a functionally pure Python callable. Works under :func:`jit`/:func:`~pmap`/etc.
``pure_callback`` enables calling a Python function in JIT-ed JAX functions.
The input ``callback`` will be passed NumPy arrays in place of JAX arrays and
should also return NumPy arrays. Execution takes place on CPU, like any
Python+NumPy function.
The callback is treated as functionally pure, meaning it has no side-effects
and its output value depends only on its argument values. As a consequence, it
is safe to be called multiple times (e.g. when transformed by :func:`~vmap` or
:func:`~pmap`), or not to be called at all when e.g. the output of a
`jit`-decorated function has no data dependence on its value. Pure callbacks
may also be reordered if data-dependence allows.
When :func:`~pmap`-ed, the pure callback will be called several times (one on each
axis of the map). When `vmap`-ed the behavior will depend on the value of the
``vectorized`` keyword argument. When ``vectorized`` is ``True``, the callback
is assumed to obey
``jax.vmap(callback)(xs) == callback(xs) == jnp.stack([callback(x) for x in xs])``.
Therefore, the callback will be called directly on batched inputs (where the
batch axes are the leading dimensions). Additionally, the callbacks should
return outputs that have corresponding leading batch axes. If not vectorized
``callback`` will be mapped sequentially across the batched axis.
For example, if ``callback = lambda x, y: np.matmul(x, y)``, then we are free
to set ``vectorized=True`` because the ``np.matmul`` function handles
arbitrary leading batch dimensions.
Args:
callback: A Python callable. The callable will be passed PyTrees of NumPy
arrays as arguments, and should return a PyTree of NumPy arrays that
matches ``result_shape_dtypes``.
result_shape_dtypes: A PyTree with leaves that are objects with ``shape``
and ``dtype`` attributes which represent to the shapes and dtypes of the
value of ``callback`` applied to ``args`` and ``kwargs``.
*args: The positional arguments to the callback. Must be PyTrees of JAX
types.
sharding: optional sharding that specifies the device from which the
callback should be invoked.
vectorized: A boolean that indicates whether or not ``callback`` is
vectorized, meaning it can handle arrays with additional leading
dimensions. If ``vectorized`` is `True`, when the callback is mapped
via `jax.vmap`, it will be called directly on inputs with leading batch
dimensions instead of executing ``callback`` on each mapped input
individually. The callback should also return outputs batched across the
leading axis. By default, ``vectorized`` is ``False``.
**kwargs: The keyword arguments to the callback. Must be PyTrees of JAX
types.
Returns:
The value of ``callback(*args, **kwargs)``.
"""
return pure_callback(
callback,
result_shape_dtypes,
*args,
sharding=sharding,
vectorized=vectorized,
**kwargs,
)
# IO Callback
io_callback_p = core.Primitive("io_callback")
io_callback_p.multiple_results = True
dispatch.prim_requires_devices_during_lowering.add(io_callback_p)
class IOEffect(effects.Effect):
__str__ = lambda _: "IO"
class OrderedIOEffect(effects.Effect):
__str__ = lambda _: "OrderedIO"
_IOEffect = IOEffect()
_OrderedIOEffect = OrderedIOEffect()
effects.lowerable_effects.add_type(IOEffect)
effects.lowerable_effects.add_type(OrderedIOEffect)
effects.control_flow_allowed_effects.add_type(IOEffect)
effects.control_flow_allowed_effects.add_type(OrderedIOEffect)
effects.ordered_effects.add_type(OrderedIOEffect)
effects.shardable_ordered_effects.add_type(OrderedIOEffect)
def io_callback_impl(
*args,
result_avals,
callback: _FlatCallback,
sharding: SingleDeviceSharding | None,
ordered: bool,
):
del result_avals, sharding, ordered
cpu_device, *_ = jax.local_devices(backend="cpu")
args = jax.device_put(args, cpu_device)
with jax.default_device(cpu_device):
try:
return tree_util.tree_map(np.asarray, callback(*args))
except BaseException:
logger.exception("jax.io_callback failed")
raise
io_callback_p.def_impl(functools.partial(dispatch.apply_primitive,
io_callback_p))
@io_callback_p.def_effectful_abstract_eval
def io_callback_abstract_eval(
*avals,
callback: _FlatCallback,
result_avals,
sharding: SingleDeviceSharding | None,
ordered: bool,
):
del avals, sharding, callback
effect = _OrderedIOEffect if ordered else _IOEffect
return result_avals, {effect}
def io_callback_jvp_rule(*args, **kwargs):
del args, kwargs
raise ValueError("IO callbacks do not support JVP.")
ad.primitive_jvps[io_callback_p] = io_callback_jvp_rule
def io_callback_transpose_rule(*args, **kwargs):
del args, kwargs
raise ValueError("IO callbacks do not support transpose.")
ad.primitive_transposes[io_callback_p] = io_callback_transpose_rule
def io_callback_batching_rule(
args, dims, callback, result_avals, sharding, ordered
):
if ordered:
raise ValueError("Cannot `vmap` ordered IO callback.")
is_batched = [d is not batching.not_mapped for d in dims]
new_args = [arg if dim is batching.not_mapped else
batching.moveaxis(arg, dim, 0) for arg, dim in zip(args, dims)]
unbatched_args, batched_args = util.partition_list(is_batched, new_args)
def _batch_fun(batched_args):
merged = util.merge_lists(is_batched, unbatched_args, batched_args)
return io_callback_p.bind(*merged, callback=callback, sharding=sharding,
result_avals=result_avals, ordered=False)
out_vals = lax_map(_batch_fun, batched_args)
return out_vals, (0,) * len(out_vals)
batching.primitive_batchers[io_callback_p] = io_callback_batching_rule
def io_callback_lowering(ctx, *args, callback, sharding, ordered, **params):
def _callback(*flat_args):
return tuple(
io_callback_impl(
*flat_args,
callback=callback,
sharding=None, # unused.
ordered=ordered,
**params,
)
)
op_sharding = _callback_op_sharding(ctx.module_context.axis_context, sharding)
if ordered:
token = ctx.tokens_in.get(_OrderedIOEffect)[0]
result, token, _ = mlir.emit_python_callback(
ctx,
_callback,
token,
list(args),
ctx.avals_in,
ctx.avals_out,
True,
sharding=op_sharding,
)
ctx.set_tokens_out(mlir.TokenSet({_OrderedIOEffect: (token,)}))
else:
result, token, _ = mlir.emit_python_callback(
ctx,
_callback,
None,
list(args),
ctx.avals_in,
ctx.avals_out,
True,
sharding=op_sharding,
)
return result
mlir.register_lowering(io_callback_p, io_callback_lowering)
[docs]
def io_callback(
callback: Callable[..., Any],
result_shape_dtypes: Any,
*args: Any,
sharding: SingleDeviceSharding | None = None,
ordered: bool = False,
**kwargs: Any,
):
"""Calls an impure Python callback.
For more explanation, see `External Callbacks`_.
Args:
callback: function to execute on the host. It is assumet to be an impure function.
If ``callback`` is pure, using :func:`jax.pure_callback` instead may lead to
more efficient execution.
result_shape_dtypes: pytree whose leaves have ``shape`` and ``dtype`` attributes,
whose structure matches the expected output of the callback function at runtime.
:class:`jax.ShapeDtypeStruct` is often used to define leaf values.
*args: arguments to be passed to the callback function
sharding: optional sharding that specifies the device from which the callback should
be invoked.
ordered: boolean specifying whether sequential calls to callback must be ordered.
**kwargs: keyword arguments to be passed to the callback function
Returns:
result: a pytree of :class:`jax.Array` objects whose structure matches that of
``result_shape_dtypes``.
See Also:
- :func:`jax.pure_callback`: callback designed for pure functions.
- :func:`jax.debug.callback`: callback designed for general-purpose debugging.
- :func:`jax.debug.print`: callback designed for printing.
.. _External Callbacks: https://jax.readthedocs.io/en/latest/notebooks/external_callbacks.html
"""
flat_args, in_tree = tree_util.tree_flatten((args, kwargs))
tree_util.tree_map(_check_shape_dtype, result_shape_dtypes)
flat_shape_dtypes, out_tree = tree_util.tree_flatten(result_shape_dtypes)
flat_result_avals = map(lambda x: core.ShapedArray(x.shape, x.dtype),
flat_shape_dtypes)
flat_args = map(core.raise_as_much_as_possible, flat_args)
out_flat = io_callback_p.bind(
*flat_args,
callback=_FlatCallback(callback, in_tree),
result_avals=tuple(flat_result_avals),
sharding=sharding,
ordered=ordered,
)
return tree_util.tree_unflatten(out_tree, out_flat)
