Fixes #1069, #1070 (#1072)

* Find handling of optimize=True in einsum

* Use einsum path optimizer by default

cuNumeric can only contract two arrays at a time, so the naive
input-order contraction path can easily result in huge intermediates.

* Bump legate.core git hash

cmake/versions.json

@@ -5,7 +5,7 @@
       "git_url" : "https://github.com/nv-legate/legate.core.git",
       "git_shallow": false,
       "always_download": false,
-      "git_tag" : "a4b5430ebb2c52e3f8da8f27534bc0db8826b804"
+      "git_tag" : "6fa0acc9dcfa89be2702f1de6c045bc262f752b1"
     }
   }
 }

cunumeric/module.py

@@ -4587,7 +4587,7 @@ def einsum(
     out: Optional[ndarray] = None,
     dtype: Optional[np.dtype[Any]] = None,
     casting: CastingKind = "safe",
-    optimize: Union[bool, str] = False,
+    optimize: Union[bool, Literal["greedy", "optimal"]] = True,
 ) -> ndarray:
     """
     Evaluates the Einstein summation convention on the operands.
@@ -4628,9 +4628,10 @@ def einsum(
 
         Default is 'safe'.
     optimize : ``{False, True, 'greedy', 'optimal'}``, optional
-        Controls if intermediate optimization should occur. No optimization
-        will occur if False. Uses opt_einsum to find an optimized contraction
-        plan if True.
+        Controls if intermediate optimization should occur. If False then
+        arrays will be contracted in input order, one at a time. True (the
+        default) will use the 'greedy' algorithm. See ``cunumeric.einsum_path``
+        for more information on the available optimization algorithms.
 
     Returns
     -------
@@ -4654,7 +4655,9 @@ def einsum(
     if out is not None:
         out = convert_to_cunumeric_ndarray(out, share=True)
 
-    if not optimize:
+    if optimize is True:
+        optimize = "greedy"
+    elif optimize is False:
         optimize = NullOptimizer()
 
     # This call normalizes the expression (adds the output part if it's

tests/integration/test_einsum.py

@@ -272,7 +272,7 @@ def test_cast(expr, dtype):
         False,
         "optimal",
         "greedy",
-        pytest.param(True, marks=pytest.mark.xfail),
+        True,
     ],
 )
 def test_optimize(optimize):
@@ -282,8 +282,6 @@ def test_optimize(optimize):
     np_res = np.einsum("ik,kj->ij", a, b, optimize=optimize)
     num_res = num.einsum("ik,kj->ij", a, b, optimize=optimize)
     assert allclose(np_res, num_res)
-    # when optimize=True, cunumeric raises
-    # TypeError: 'bool' object is not iterable
 
 
 def test_expr_opposite():