Merge pull request #25456 from jakevdp:xla-abstractify

PiperOrigin-RevId: 707175097

Author: Google-ML-Automation

benchmarks/api_benchmark.py

@@ -23,8 +23,8 @@
 from jax import lax
 from jax._src.api_util import shaped_abstractify  # technically not an api fn
 from jax._src.ad_checkpoint import checkpoint  # new jax.remat implementation
+from jax._src import core
 from jax._src.lib import xla_client as xc
-from jax.interpreters import xla
 from jax._src import array
 from jax._src import op_shardings
 from jax._src.pjit import pjit_check_aval_sharding
@@ -427,7 +427,7 @@ def bench_shaped_abstractify(state):
 
 def _run_benchmark_for_xla_abstractify(arg, state):
   while state:
-    xla.abstractify(arg)
+    core.abstractify(arg)
 
 def bench_xla_abstractify():
   _abstractify_args = [

jax/_src/abstract_arrays.py

@@ -50,23 +50,49 @@ def canonical_concrete_aval(val, weak_type=None):
   sharding = core._get_abstract_sharding(val)
   return ShapedArray(np.shape(val), dtype, weak_type=weak_type, sharding=sharding)
 
+
 def masked_array_error(*args, **kwargs):
   raise ValueError("numpy masked arrays are not supported as direct inputs to JAX functions. "
                    "Use arr.filled() to convert the value to a standard numpy array.")
 
 core.pytype_aval_mappings[np.ma.MaskedArray] = masked_array_error
 
-for t in array_types:
+
+def _make_shaped_array_for_numpy_array(x: np.ndarray) -> ShapedArray:
+  dtype = x.dtype
+  dtypes.check_valid_dtype(dtype)
+  return ShapedArray(x.shape, dtypes.canonicalize_dtype(dtype))
+
+core.pytype_aval_mappings[np.ndarray] = canonical_concrete_aval
+core.xla_pytype_aval_mappings[np.ndarray] = _make_shaped_array_for_numpy_array
+
+
+def _make_shaped_array_for_numpy_scalar(x: np.generic) -> ShapedArray:
+  dtype = np.dtype(x)
+  dtypes.check_valid_dtype(dtype)
+  return ShapedArray(np.shape(x), dtypes.canonicalize_dtype(dtype))
+
+for t in numpy_scalar_types:
   core.pytype_aval_mappings[t] = canonical_concrete_aval
+  core.xla_pytype_aval_mappings[t] = _make_shaped_array_for_numpy_scalar
 
 core.literalable_types.update(array_types)
 
+
 def _make_concrete_python_scalar(t, x):
   dtype = dtypes._scalar_type_to_dtype(t, x)
   weak_type = dtypes.is_weakly_typed(x)
   return canonical_concrete_aval(np.array(x, dtype=dtype), weak_type=weak_type)
 
+
+def _make_abstract_python_scalar(typ, val):
+  # Note: all python scalar types are weak except bool, because bool only
+  # comes in a single width.
+  return ShapedArray((), dtypes._scalar_type_to_dtype(typ, val),
+                     weak_type=typ is not bool)
+
 for t in dtypes.python_scalar_dtypes:
   core.pytype_aval_mappings[t] = partial(_make_concrete_python_scalar, t)
+  core.xla_pytype_aval_mappings[t] = partial(_make_abstract_python_scalar, t)
 
 core.literalable_types.update(dtypes.python_scalar_dtypes.keys())

jax/_src/api_util.py

@@ -600,7 +600,7 @@ def _shaped_abstractify_slow(x):
         "does not have a dtype attribute")
   return core.ShapedArray(np.shape(x), dtype, weak_type=weak_type)
 
-# TODO(mattjj,yashkatariya): replace xla.abstractify with this, same behavior
+# TODO(mattjj,yashkatariya): replace core.abstractify with this, same behavior
 def shaped_abstractify(x):
   handler = _shaped_abstractify_handlers.get(type(x), None)
   return handler(x) if handler is not None else _shaped_abstractify_slow(x)

jax/_src/array.py

@@ -1029,7 +1029,7 @@ def make_array_from_single_device_arrays(
 
 
 core.pytype_aval_mappings[ArrayImpl] = abstract_arrays.canonical_concrete_aval
-xla.pytype_aval_mappings[ArrayImpl] = op.attrgetter('aval')
+core.xla_pytype_aval_mappings[ArrayImpl] = op.attrgetter('aval')
 xla.canonicalize_dtype_handlers[ArrayImpl] = pxla.identity
 def _get_aval_array(self):
   if config.sharding_in_types.value and isinstance(self.sharding, NamedSharding):

jax/_src/core.py

@@ -1400,7 +1400,16 @@ def check_valid_jaxtype(x):
       f"Value {x!r} of type {type(x)} is not a valid JAX type")
 
 
+# TODO(jakevdp): merge concrete_aval and abstractify to the extent possible.
+# This is tricky because concrete_aval includes sharding information, and
+# abstractify does not; further, because abstractify is in the dispatch path,
+# performance is important and simply adding sharding there is not an option.
 def concrete_aval(x):
+  # This differs from abstractify below in that the abstract values
+  # include sharding where applicable. Historically (before stackless)
+  # the returned avals were concrete, but after the stackless change
+  # this returns ShapedArray like abstractify.
+  # Rules are registered in pytype_aval_mappings.
   for typ in type(x).__mro__:
     handler = pytype_aval_mappings.get(typ)
     if handler: return handler(x)
@@ -1410,6 +1419,22 @@ def concrete_aval(x):
                    "type")
 
 
+def abstractify(x):
+  # Historically, this was called xla.abstractify. It differs from
+  # concrete_aval in that it excludes sharding information, and
+  # uses a more performant path for accessing avals. Rules are
+  # registered in xla_pytype_aval_mappings.
+  typ = type(x)
+  aval_fn = xla_pytype_aval_mappings.get(typ)
+  if aval_fn: return aval_fn(x)
+  for typ in typ.__mro__:
+    aval_fn = xla_pytype_aval_mappings.get(typ)
+    if aval_fn: return aval_fn(x)
+  if hasattr(x, '__jax_array__'):
+    return abstractify(x.__jax_array__())
+  raise TypeError(f"Argument '{x}' of type '{type(x)}' is not a valid JAX type")
+
+
 def get_aval(x):
   if isinstance(x, Tracer):
     return x.aval
@@ -1810,6 +1835,7 @@ def to_tangent_aval(self):
                         self.weak_type)
 
 pytype_aval_mappings: dict[type, Callable[[Any], AbstractValue]] = {}
+xla_pytype_aval_mappings: dict[type, Callable[[Any], AbstractValue]] = {}
 
 
 class DArray:
@@ -1866,6 +1892,7 @@ def data(self):
 
 pytype_aval_mappings[DArray] = \
     lambda x: DShapedArray(x._aval.shape, x._aval.dtype, x._aval.weak_type)
+xla_pytype_aval_mappings[DArray] = lambda x: x._aval
 
 @dataclass(frozen=True)
 class bint(dtypes.ExtendedDType):
@@ -1898,6 +1925,7 @@ def __getitem__(self, idx): return get_aval(self)._getitem(self, idx)
   def __setitem__(self, idx, x): return get_aval(self)._setitem(self, idx, x)
   def __repr__(self) -> str: return 'Mutable' + repr(self[...])
 pytype_aval_mappings[MutableArray] = lambda x: x._aval
+xla_pytype_aval_mappings[MutableArray] = lambda x: x._aval
 
 def mutable_array(init_val):
   return mutable_array_p.bind(init_val)
@@ -1951,6 +1979,7 @@ def __init__(self, buf):
   def block_until_ready(self):
     self._buf.block_until_ready()
 pytype_aval_mappings[Token] = lambda _: abstract_token
+xla_pytype_aval_mappings[Token] = lambda _: abstract_token
 
 
 # TODO(dougalm): Deprecate these. They're just here for backwards compat.

jax/_src/dispatch.py

@@ -457,7 +457,7 @@ def _device_put_impl(
         " please provide a concrete Sharding with memory_kind.")
 
   try:
-    aval = xla.abstractify(x)
+    aval = core.abstractify(x)
   except TypeError as err:
     raise TypeError(
         f"Argument '{x}' of type {type(x)} is not a valid JAX type") from err

jax/_src/export/shape_poly.py

@@ -35,7 +35,6 @@
 import opt_einsum
 
 import jax
-from jax.interpreters import xla
 
 from jax._src import config
 from jax._src import core
@@ -1206,7 +1205,7 @@ def _geq_decision(e1: DimSize, e2: DimSize, cmp_str: Callable[[], str]) -> bool:
       f"Symbolic dimension comparison {cmp_str()} is inconclusive.{describe_scope}")
 
 core.pytype_aval_mappings[_DimExpr] = _DimExpr._get_aval
-xla.pytype_aval_mappings[_DimExpr] = _DimExpr._get_aval
+core.xla_pytype_aval_mappings[_DimExpr] = _DimExpr._get_aval
 dtypes._weak_types.append(_DimExpr)
 
 def _convertible_to_int(p: DimSize) -> bool:

jax/_src/interpreters/mlir.py

@@ -1825,7 +1825,7 @@ def read(v: core.Atom) -> IrValues:
 
   def aval(v: core.Atom) -> core.AbstractValue:
     if type(v) is core.Literal:
-      return xla.abstractify(v.val)
+      return core.abstractify(v.val)
     else:
       return v.aval
 

jax/_src/interpreters/pxla.py

@@ -349,7 +349,7 @@ def _emap_apply_fn(*args):
                         donated_invars=donated_invars,
                         is_explicit_global_axis_size=is_explicit_global_axis_size)
     return _emap_apply_fn
-  abstract_args = unsafe_map(xla.abstractify, args)
+  abstract_args = unsafe_map(core.abstractify, args)
   compiled_fun, fingerprint = parallel_callable(
       fun, backend, axis_name, axis_size, global_axis_size, devices, name,
       in_axes, out_axes_thunk, donated_invars,
@@ -360,7 +360,7 @@ def _emap_apply_fn(*args):
     distributed_debug_log(("Running pmapped function", name),
                           ("python function", fun.f),
                           ("devices", devices),
-                          ("abstract args", map(xla.abstractify, args)),
+                          ("abstract args", map(core.abstractify, args)),
                           ("fingerprint", fingerprint))
   return compiled_fun
 
@@ -598,7 +598,7 @@ def __init__(self, trace: MapTrace, val, shard_axes: dict[core.AxisName, int]):
 
   @property
   def aval(self):
-    aval = xla.abstractify(self.val)
+    aval = core.abstractify(self.val)
     shard_axes = dict(self.shard_axes)
     for axis_idx in sorted(shard_axes.values())[::-1]:
       aval = core.mapped_aval(aval.shape[axis_idx], axis_idx, aval)
@@ -1145,7 +1145,7 @@ def xla_extension_executable(self):
   @profiler.annotate_function
   def call(self, *args):
     # TODO(frostig): do we need to check sharding and sharded avals?
-    arg_avals = map(xla.abstractify, args)
+    arg_avals = map(core.abstractify, args)
     check_arg_avals_for_call(self.in_avals, arg_avals, self._jaxpr_debug_info)
     return self.unsafe_call(*args)  # pylint: disable=not-callable
 
@@ -3092,7 +3092,7 @@ def call(self, *args):
       ref_avals = self._all_args_info.in_avals
       debug_info = self._all_args_info.debug_info
 
-    all_arg_avals = map(xla.abstractify, kept_args)
+    all_arg_avals = map(core.abstractify, kept_args)
     check_arg_avals_for_call(ref_avals, all_arg_avals, debug_info)
     check_array_xla_sharding_layout_match(
         args_after_dce, self._in_shardings, self._xla_in_layouts, debug_info,

jax/_src/interpreters/xla.py

@@ -146,44 +146,12 @@ def _canonicalize_python_scalar_dtype(typ, x):
 canonicalize_dtype_handlers[core.DArray] = identity
 canonicalize_dtype_handlers[core.MutableArray] = identity
 
+# TODO(jakevdp): deprecate and remove this.
 def abstractify(x) -> Any:
-  typ = type(x)
-  aval_fn = pytype_aval_mappings.get(typ)
-  if aval_fn: return aval_fn(x)
-  for typ in typ.__mro__:
-    aval_fn = pytype_aval_mappings.get(typ)
-    if aval_fn: return aval_fn(x)
-  if hasattr(x, '__jax_array__'):
-    return abstractify(x.__jax_array__())
-  raise TypeError(f"Argument '{x}' of type '{type(x)}' is not a valid JAX type")
-
-def _make_abstract_python_scalar(typ, val):
-  # Note: all python scalar types are weak except bool, because bool only
-  # comes in a single width.
-  return ShapedArray((), dtypes._scalar_type_to_dtype(typ, val),
-                     weak_type=typ is not bool)
-
-def _make_shaped_array_for_numpy_scalar(x: np.generic) -> ShapedArray:
-  dtype = np.dtype(x)
-  dtypes.check_valid_dtype(dtype)
-  return ShapedArray(np.shape(x), dtypes.canonicalize_dtype(dtype))
-
-def _make_shaped_array_for_numpy_array(x: np.ndarray) -> ShapedArray:
-  dtype = x.dtype
-  dtypes.check_valid_dtype(dtype)
-  return ShapedArray(x.shape, dtypes.canonicalize_dtype(dtype))
-
-
-pytype_aval_mappings: dict[Any, Callable[[Any], core.AbstractValue]] = {}
-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)
-pytype_aval_mappings[np.ndarray] = _make_shaped_array_for_numpy_array
-pytype_aval_mappings.update(
-    (t, partial(_make_abstract_python_scalar, t)) for t in _scalar_types)
+  return core.abstractify(x)
 
+# TODO(jakevdp): deprecate and remove this.
+pytype_aval_mappings: dict[Any, Callable[[Any], core.AbstractValue]] = core.xla_pytype_aval_mappings
 
 initial_style_primitives: set[core.Primitive] = set()