Merge pull request #20044 from mattjj:mutable-arrays

PiperOrigin-RevId: 611866507

jax/_src/core.py

@@ -1912,6 +1912,30 @@ def __str__(self) -> str:
 AxisSize = Union[int, DArray, Tracer, Var, DBIdx, InDBIdx, OutDBIdx]
 
 
+class MutableArray:
+  _aval: ShapedArray
+  _buf: Array
+  def __init__(self, aval, buf):
+    self._aval = aval
+    self._buf = buf
+  aval = property(lambda self: self._aval)
+  shape = property(lambda self: self._aval.shape)
+  dtype = property(lambda self: self._aval.dtype)
+  def __getitem__(self, idx): return get_aval(self)._getitem(self, idx)
+  def __setitem__(self, idx, x): return get_aval(self)._setitem(self, idx, x)
+pytype_aval_mappings[MutableArray] = lambda x: x._aval
+
+def mutable_array(init_val):
+  return mutable_array_p.bind(init_val)
+mutable_array_p = Primitive('mutable_array')
+
+@mutable_array_p.def_impl
+def _mutable_array_impl(init_val):
+  from jax._src.state.types import AbstractRef  # type: ignore[import]
+  aval = raise_to_shaped(get_aval(init_val))
+  return MutableArray(AbstractRef(aval), init_val)
+
+
 class AbstractToken(AbstractValue):
   def join(self, other):
     if isinstance(other, AbstractToken):

jax/_src/interpreters/partial_eval.py

@@ -2730,6 +2730,13 @@ def call_padding_rule(prim, in_avals, out_avals, *args, call_jaxpr, **params):
   return prim.bind(*subfuns, *args, **bind_params)
 
 
+def _error_staging_mutable_array_p(trace, x):
+  raise Exception(
+      "mutable_array constructor can't be staged out, and in particular can't "
+      "be used under a jax.jit or jax.lax.scan")
+custom_staging_rules[core.mutable_array_p] = _error_staging_mutable_array_p
+
+
 # TODO(mattjj): the following are deprecated; update callers to _nounits version
 # See https://github.com/google/jax/pull/9498
 @lu.transformation

jax/_src/interpreters/pxla.py

@@ -160,6 +160,10 @@ def _shard_darray(x, sharding):
   return shard_arg(x._data, sharding)
 shard_arg_handlers[core.DArray] = _shard_darray
 
+def _shard_mutable_array(x, sharding):
+  return shard_arg(x._buf, sharding)
+shard_arg_handlers[core.MutableArray] = _shard_mutable_array
+
 def batched_device_put(aval: core.ShapedArray,
                        sharding: jax.sharding.Sharding, xs: Sequence[Any],
                        devices: Sequence[jax.Device], committed: bool = True):
@@ -1778,17 +1782,16 @@ def _dce_jaxpr(closed_jaxpr, global_in_avals, api_name, fun_name,
 @weakref_lru_cache
 def _discharge_refs(
     jaxpr: core.ClosedJaxpr
-) -> tuple[core.ClosedJaxpr, None | Sequence[int | None], None | Sequence[int | None]]:
+) -> tuple[core.ClosedJaxpr, Sequence[int | None], Sequence[int | None]]:
   from jax._src.state.discharge import discharge_state
-  out_mut = [None] * len(jaxpr.out_avals) + [
-      i for i, a in enumerate(jaxpr.in_avals) if isinstance(a, AbstractRef)]
-  count = it.count()
-  inout_aliases = tuple(next(count) if isinstance(a, AbstractRef) else None
-                        for a in jaxpr.in_avals)
-  jaxpr = core.ClosedJaxpr(*discharge_state(jaxpr.jaxpr, jaxpr.consts))
-  assert len(inout_aliases) == len(jaxpr.in_avals)
-  assert       len(out_mut) == len(jaxpr.out_avals)
-  return jaxpr, inout_aliases, out_mut
+  new_jaxpr = core.ClosedJaxpr(*discharge_state(jaxpr.jaxpr, jaxpr.consts))
+  count = it.count(len(jaxpr.out_avals))  # new outputs are appended to the end
+  inout_map = {i: next(count) for i, a in enumerate(jaxpr.in_avals)
+               if isinstance(a, AbstractRef)}
+  outin_map = {j: i for i, j in inout_map.items()}
+  inout_aliases = tuple(map(inout_map.get, range(len(new_jaxpr.in_avals))))
+  out_mut = tuple(map(outin_map.get, range(len(new_jaxpr.out_avals))))
+  return new_jaxpr, inout_aliases, out_mut
 
 
 @dataclasses.dataclass(frozen=True)

jax/_src/interpreters/xla.py

@@ -22,7 +22,6 @@
 import functools
 from functools import partial
 import itertools as it
-import operator
 from typing import Any, Callable, Protocol, Union
 
 import numpy as np
@@ -166,6 +165,7 @@ def _canonicalize_python_scalar_dtype(typ, x):
     (t, partial(_canonicalize_python_scalar_dtype, t)) for t in _scalar_types)
 canonicalize_dtype_handlers[core.Token] = identity
 canonicalize_dtype_handlers[core.DArray] = identity
+canonicalize_dtype_handlers[core.MutableArray] = identity
 
 def abstractify(x) -> Any:
   typ = type(x)
@@ -196,7 +196,8 @@ def _make_shaped_array_for_numpy_array(x: np.ndarray) -> ShapedArray:
 
 
 pytype_aval_mappings: dict[Any, Callable[[Any], core.AbstractValue]] = {}
-pytype_aval_mappings[core.DArray] = operator.attrgetter('_aval')
+pytype_aval_mappings[core.DArray] = lambda x: x._aval
+pytype_aval_mappings[core.MutableArray] = lambda x: x._aval
 pytype_aval_mappings[core.Token] = lambda _: core.abstract_token
 pytype_aval_mappings.update((t, _make_shaped_array_for_numpy_scalar)
                             for t in numpy_scalar_types)

jax/_src/state/primitives.py

@@ -22,6 +22,7 @@
 
 from jax._src import ad_util
 from jax._src import core
+from jax._src import dispatch
 from jax._src import pretty_printer as pp
 from jax._src import tree_util
 from jax._src.interpreters import ad
@@ -53,11 +54,7 @@
 # `Ref((3,), np.dtype('float32'))` leads to a jaxpr eqn printed like
 #   a:f32[3] <- x[]
 get_p = core.Primitive("get")
-
-def _get_impl(ref: AbstractRef, *args: Any, tree):
-  del ref, args, tree
-  raise ValueError("Cannot run stateful primitive.")
-get_p.def_impl(_get_impl)
+get_p.def_impl(partial(dispatch.apply_primitive, get_p))
 
 Indexer = tuple[Union[int, slice, Array], ...]
 # or Ellipsis, but that can't be annotated until Python 3.10? (types.EllipsisType)
@@ -113,11 +110,7 @@ def ref_get(ref_or_view: Any, idx: Indexer | None = None) -> Array:
 # are `ShapedArray((), np.dtype('int32'))` leads to a jaxpr eqn printed like
 #   x:Ref{f32[3]}[i, j] <- a
 swap_p = core.Primitive("swap")
-
-def _swap_impl(ref: AbstractRef, value: Array, *idx: Any, tree):
-  del ref, value, idx, tree
-  raise ValueError("Cannot run stateful primitive.")
-swap_p.def_impl(_swap_impl)
+swap_p.def_impl(partial(dispatch.apply_primitive, swap_p))
 
 def ref_swap(ref_or_view: AbstractRef | RefView, idx: Indexer | None, value: Array,
              _function_name: str = "ref_swap") -> Array:
@@ -143,11 +136,7 @@ def ref_set(ref_or_view: AbstractRef | RefView, idx: Indexer | None, value: Arra
 # ```
 addupdate_p = core.Primitive('addupdate')
 addupdate_p.multiple_results = True
-
-def _addupdate_impl(ref: AbstractRef, value: Array, *args: Any, tree):
-  del ref, value, args, tree
-  raise ValueError("Can't evaluate `addupdate` outside a stateful context.")
-addupdate_p.def_impl(_addupdate_impl)
+addupdate_p.def_impl(partial(dispatch.apply_primitive, addupdate_p))
 
 def ref_addupdate(ref_or_view: AbstractRef, idx: Indexer | None, x: Array) -> None:
   """Mutates a ref with an additive update i.e. `ref[idx] += x`."""

tests/state_test.py

@@ -55,18 +55,6 @@
 
 class StatePrimitivesTest(jtu.JaxTestCase):
 
-  def test_cant_eval_get_primitive(self):
-    with self.assertRaises(ValueError):
-      get_p.bind(jnp.ones(5), tree=None)
-
-  def test_cant_eval_swap_primitive(self):
-    with self.assertRaises(ValueError):
-      swap_p.bind(jnp.ones(5), jnp.zeros(5), tree=None)
-
-  def test_cant_eval_addupdate_primitive(self):
-    with self.assertRaises(ValueError):
-      addupdate_p.bind(jnp.ones(5), jnp.zeros(5), tree=None)
-
   def test_get_abstract_aval_must_take_in_refs(self):
     ref_aval = core.ShapedArray((), jnp.float32)
     def f(x_ref):
@@ -1508,55 +1496,54 @@ def _body(ref):
     jtu.check_grads(f, (0.5,), order=3)
 
 
-class MutableArray:
-  _aval: core.ShapedArray
-  _buf: jax.Array
-  def __init__(self, aval, buf):
-    self._aval = aval
-    self._buf = buf
-  aval = property(lambda self: self._aval)
-  shape = property(lambda self: self._aval.shape)
-  dtype = property(lambda self: self._aval.dtype)
-
-def mutable_array(init_val):
-  return mutable_array_p.bind(init_val)
-mutable_array_p = core.Primitive('mutable_array')
-
-@mutable_array_p.def_impl
-def _mutable_array_impl(init_val):
-  aval = core.raise_to_shaped(core.get_aval(init_val))
-  return MutableArray(AbstractRef(aval), init_val)
-
-def _error_on_staging(trace, x):
-  raise Exception
-pe.custom_staging_rules[mutable_array_p] = _error_on_staging
-
-from jax._src.interpreters import xla
-from jax._src.interpreters import pxla
-xla.canonicalize_dtype_handlers[MutableArray] = lambda x: x
-xla.pytype_aval_mappings[MutableArray] = lambda x: x._aval
-pxla.shard_arg_handlers[MutableArray] = lambda x, s: pxla.shard_arg(x._buf, s)
-core.pytype_aval_mappings[MutableArray] = lambda x: x._aval
-
 class MutableArrayTest(jtu.JaxTestCase):
 
-  def test_basic(self):
-    read = jax.jit(lambda x_ref: x_ref[...])
-
-    @jax.jit
+  @parameterized.parameters([True, False])
+  def test_basic(self, jit):
     def f(x_mut):
       x_mut[...] += 1.
       x_mut[0] += 1
       x_mut[1] += 5
 
-    x_mut = mutable_array(jnp.zeros(3))
+    if jit:
+      f = jax.jit(f)
+
+    x_mut = core.mutable_array(jnp.zeros(3))
     f(x_mut)
 
-    self.assertAllClose(read(x_mut), jnp.array([2., 6., 1.]), check_dtypes=False)
+    self.assertAllClose(x_mut[...], jnp.array([2., 6., 1.]),
+                        check_dtypes=False)
 
     jaxpr = jax.make_jaxpr(f)(x_mut)
     self.assertTrue(any(isinstance(e, RefEffect) for e in jaxpr.effects))
 
+  def test_staging_error(self):
+    x = jnp.zeros(3)
+    with self.assertRaises(Exception):
+      jax.jit(core.mutable_array)(x)
+
+  @parameterized.parameters([True, False])
+  def test_multiple_inputs_and_outputs(self, jit):
+    def f(x_mut, y, z_mut, w):
+      x_mut[...] += 1
+      z_mut[...] += 1
+      return x_mut[...] + y + z_mut[...] + w, y + w
+
+    if jit:
+      f = jax.jit(f)
+
+    x_mut = core.mutable_array(jnp.zeros((1, 3)))
+    y = jnp.ones((2, 3))
+    z_mut = core.mutable_array(jnp.zeros((2, 3)))
+    w = jnp.ones((2, 1))
+
+    out1, out2 = f(x_mut, y, z_mut, w)
+
+    self.assertAllClose(x_mut[...], jnp.ones((1, 3)), check_dtypes=False)
+    self.assertAllClose(z_mut[...], jnp.ones((2, 3)), check_dtypes=False)
+    self.assertAllClose(out1, 4 * jnp.ones((2, 3)), check_dtypes=False)
+    self.assertAllClose(out2, y + w, check_dtypes=False)
+
 
 if CAN_USE_HYPOTHESIS: