Merge pull request jax-ml#25034 from gnecula:poly_state

PiperOrigin-RevId: 698820458

Author: Google-ML-Automation

benchmarks/shape_poly_benchmark.py

@@ -17,7 +17,6 @@
 
 import jax
 from jax import core
-from jax._src.numpy import lax_numpy
 from jax import export
 
 jax.config.parse_flags_with_absl()
@@ -76,7 +75,7 @@ def inequalities_slice(state):
   while state:
     for _ in range(30):
       a.scope._clear_caches()
-      start, _, slice_size = lax_numpy._preprocess_slice(slice(2, a, 4), b)
+      start, _, slice_size = core.canonicalize_slice(slice(2, a, 4), b)
       _ = 0 <= slice_size <= b
       _ = start >= 0
       _ = start + slice_size <= b

jax/_src/core.py

@@ -2063,6 +2063,70 @@ def dimension_as_value(d: DimSize):
   if hasattr(d, "dimension_as_value"): return d.dimension_as_value()
   return operator.index(d)
 
+def canonicalize_slice(
+    s: slice,
+    axis_size: DimSize
+  ) -> tuple[DimSize, DimSize, DimSize]:
+  """Computes the start index, step, and size of the slice `x[s]`.
+
+  This is similar to `s.indices(axis_size)`, except that it returns
+  `(start, step, size)`, and it works when the slice and/or the
+  `axis_size` are symbolic.
+
+  See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
+  """
+  def convert_to_index(d: DimSize) -> DimSize:
+    # Convert np.array and jax.Array to int, leave symbolic dimensions alone
+    try:
+      return operator.index(d)
+    except:
+      return d
+
+  # Must resolve statically if step is {<0, ==0, >0}
+  step = convert_to_index(s.step) if s.step is not None else 1
+  try:
+    if step == 0:
+      raise ValueError("slice step cannot be zero")
+    step_gt_0 = (step > 0)
+  except InconclusiveDimensionOperation as e:
+    raise InconclusiveDimensionOperation(
+        f"In slice with non-constant elements the step ({step}) must " +
+        f"be resolved statically if it is > 0 or < 0.\nDetails: {e}")
+
+  def clamp_index(i: DimSize, which: str):
+    try:
+      i_ge_0 = (i >= 0)
+    except InconclusiveDimensionOperation as e:
+      raise InconclusiveDimensionOperation(
+          f"In slice with non-constant elements the {which} ({i}) must " +
+          f"be resolved statically if it is >= 0.\nDetails: {e}")
+    if i_ge_0:
+      if step_gt_0:
+        return min_dim(axis_size, i)
+      else:
+        return min_dim(axis_size - 1, i)
+    else:
+      if step_gt_0:
+        return max_dim(0, axis_size + i)
+      else:
+        return max_dim(-1, axis_size + i)
+
+  if s.start is None:
+    start = 0 if step_gt_0 else axis_size - 1
+  else:
+    start = clamp_index(convert_to_index(s.start), "start")
+
+  if s.stop is None:
+    stop = axis_size if step_gt_0 else -1
+  else:
+    stop = clamp_index(convert_to_index(s.stop), "stop")
+
+  gap = step if step_gt_0 else - step
+  distance = (stop - start) if step_gt_0 else (start - stop)
+  slice_size = max_dim(0, distance + gap - 1) // gap
+  return start, step, slice_size
+
+
 class SomeTracer:
   __slots__ = ()
   def __repr__(self): return "[dynamic]"

jax/_src/numpy/lax_numpy.py

@@ -12116,7 +12116,7 @@ def _index_to_gather(x_shape: Sequence[int], idx: Sequence[Any],
                "arrays within JIT compiled functions).")
         raise IndexError(msg)
 
-      start, step, slice_size = _preprocess_slice(i, x_shape[x_axis])
+      start, step, slice_size = core.canonicalize_slice(i, x_shape[x_axis])
       slice_shape.append(slice_size)
 
       if core.definitely_equal(step, 1):
@@ -12319,65 +12319,6 @@ def _canonicalize_tuple_index(arr_ndim, idx):
     idx = tuple(idx) + colons
   return idx
 
-def _preprocess_slice(
-    s: slice,
-    axis_size: core.DimSize
-  ) -> tuple[core.DimSize, core.DimSize, core.DimSize]:
-  """Computes the start index, step, and size of the slice `x[s]`."""
-  # See https://numpy.org/doc/stable/user/basics.indexing.html#slicing-and-striding
-  # "this is harder to get right than you may think"
-  # (from https://github.com/python/cpython/blob/939fc6d6eab9b7ea8c244d513610dbdd556503a7/Objects/sliceobject.c#L275)
-  def convert_to_index(d: DimSize) -> DimSize:
-    # Convert np.array and jax.Array to int, leave symbolic dimensions alone
-    try:
-      return operator.index(d)
-    except:
-      return d
-
-  # Must resolve statically if step is {<0, ==0, >0}
-  step = convert_to_index(s.step) if s.step is not None else 1
-  try:
-    if step == 0:
-      raise ValueError("slice step cannot be zero")
-    step_gt_0 = (step > 0)
-  except core.InconclusiveDimensionOperation as e:
-    raise core.InconclusiveDimensionOperation(
-        f"In slice with non-constant elements the step ({step}) must " +
-        f"be resolved statically if it is > 0 or < 0.\nDetails: {e}")
-
-  def clamp_index(i: DimSize, which: str):
-    try:
-      i_ge_0 = (i >= 0)
-    except core.InconclusiveDimensionOperation as e:
-      raise core.InconclusiveDimensionOperation(
-          f"In slice with non-constant elements the {which} ({i}) must " +
-          f"be resolved statically if it is >= 0.\nDetails: {e}")
-    if i_ge_0:
-      if step_gt_0:
-        return core.min_dim(axis_size, i)
-      else:
-        return core.min_dim(axis_size - 1, i)
-    else:
-      if step_gt_0:
-        return core.max_dim(0, axis_size + i)
-      else:
-        return core.max_dim(-1, axis_size + i)
-
-  if s.start is None:
-    start = 0 if step_gt_0 else axis_size - 1
-  else:
-    start = clamp_index(convert_to_index(s.start), "start")
-
-  if s.stop is None:
-    stop = axis_size if step_gt_0 else -1
-  else:
-    stop = clamp_index(convert_to_index(s.stop), "stop")
-
-  gap = step if step_gt_0 else - step
-  distance = (stop - start) if step_gt_0 else (start - stop)
-  slice_size = core.max_dim(0, distance + gap - 1) // gap
-  return start, step, slice_size
-
 
 @export
 def blackman(M: int) -> Array:

jax/_src/state/indexing.py

@@ -46,11 +46,11 @@ def __post_init__(self):
 
   @property
   def is_dynamic_start(self):
-    return not isinstance(self.start, int)
+    return not core.is_dim(self.start)
 
   @property
   def is_dynamic_size(self):
-    return not isinstance(self.size, int)
+    return not core.is_dim(self.size)
 
   def tree_flatten(self):
     # If `start` is statically known, we treat it as static information
@@ -72,10 +72,10 @@ def tree_unflatten(cls, aux_data, children) -> Slice:
 
   @classmethod
   def from_slice(cls, slc: slice, size: int) -> Slice:
-    start, stop, step = slc.indices(size)
+    start, step, size = core.canonicalize_slice(slc, size)
     if step < 1:
       raise ValueError(f"slice must have a step >= 1 (found: {step})")
-    return cls(start, max((stop - start + step - 1) // step, 0), step)
+    return cls(start, size, step)
 
 
 def dslice(

tests/shape_poly_test.py

@@ -48,6 +48,9 @@
 from jax._src.export import shape_poly_decision
 from jax._src.lax import lax as lax_internal
 from jax._src.lax import control_flow as lax_control_flow
+from jax._src.state import discharge
+from jax._src.state import primitives as ref_primitives
+
 import numpy as np
 
 config.parse_flags_with_absl()
@@ -2062,6 +2065,31 @@ def test_vmap_error(self):
                        polymorphic_shapes=["b, ...", "c, ...", None])
 
 
+  @jtu.parameterized_filterable(
+      kwargs=[
+          dict(slc=slc)
+          for slc in [
+              slice(None, None, None),
+              slice(2, 5),
+          ]
+      ])
+  def test_stateful(self, slc: slice):
+    w, = export.symbolic_shape("w", constraints=["w >= 3"])
+    def f(x_ref):
+      ones = jnp.ones_like(x_ref)[slc]
+      ref_primitives.ref_addupdate(x_ref, slc, ones)
+      x1 = ref_primitives.ref_get(x_ref, slc)
+      x2 = x1 + ones
+      ref_primitives.ref_set(x_ref, slc, x2)
+
+    exp = export.export(jax.jit(discharge.run_state(f)))(
+        jax.ShapeDtypeStruct((w,), dtype=_f32))
+    x = np.ones((32,), dtype=_f32)
+    expected = np.copy(x)
+    expected[slc] = 3.
+    self.assertAllClose(exp.call(x), expected)
+
+
 # List containing either harnesses, or lists of harnesses
 _POLY_SHAPE_TEST_HARNESSES = [
     PolyHarness("add", "",
@@ -3603,7 +3631,7 @@ def test_harness(self, harness: PolyHarness):
         not harness.polymorphic_shapes[0].endswith("...") and
         jtu.test_device_matches(["tpu"])):
       raise unittest.SkipTest(
-          "Shape polymorphsim for Eigh and Svd is only supported for batch dimensions on TPU.")
+          "Shape polymorphism for Eigh and Svd is only supported for batch dimensions on TPU.")
 
     config_flags = harness.override_jax_config_flags
     # Update this here rather than in harness object because vmap_random_gamma is derived

tests/state_test.py

@@ -752,7 +752,7 @@ def f(a_ref, b_ref):
         lu.wrap_init(f), [scalar_ref_1, scalar_ref_2])
 
     discharged_jaxpr, _ = discharge_state(jaxpr, (), should_discharge=[False, True])
-    prim_count = lambda p, jaxpr: sum(eqn.primitive == swap_p for eqn in jaxpr.eqns)
+    prim_count = lambda p, jaxpr: sum(eqn.primitive == p for eqn in jaxpr.eqns)
     self.assertEqual(prim_count(swap_p, jaxpr) // 2, prim_count(swap_p, discharged_jaxpr))
     self.assertEqual(prim_count(get_p, jaxpr) // 2, prim_count(get_p, discharged_jaxpr))