raise error for optimize='dp' when no possible contractions

opt_einsum/paths.py

@@ -6,6 +6,7 @@
 import heapq
 import itertools
 import random
+import operator
 from collections import Counter, OrderedDict, defaultdict
 
 import numpy as np
@@ -961,6 +962,7 @@ def __call__(self, inputs, output, size_dict, memory_limit=None):
         output = set(symbol2int[c] for c in output)
         size_dict = {symbol2int[c]: v for c, v in size_dict.items() if c in symbol2int}
         size_dict = [size_dict[j] for j in range(len(size_dict))]
+        naive_cost = functools.reduce(operator.mul, size_dict)
 
         inputs, inputs_done, inputs_contractions = _dp_parse_out_single_term_ops(inputs, all_inds, ind_counts)
 
@@ -1033,6 +1035,9 @@ def __call__(self, inputs, output, size_dict, memory_limit=None):
                                                                 xn, g, all_tensors, inputs, i1_cut_i2_wo_output,
                                                                 memory_limit, cntrct1, cntrct2)
 
+                if (cost_cap >= naive_cost) and (len(x[-1]) == 0):
+                    raise RuntimeError("No contraction found for given `memory_limit`.")
+
                 # increase cost cap for next iteration:
                 cost_cap = cost_increment * cost_cap
 

opt_einsum/tests/test_paths.py

@@ -250,6 +250,22 @@ def test_custom_dp_can_set_cost_cap():
     assert info1.opt_cost == info2.opt_cost == info3.opt_cost
 
 
+def test_dp_errors_when_no_contractions_found():
+    eq, shapes, size_dict = oe.helpers.rand_equation(10, 3, seed=42, return_size_dict=True)
+
+    # first get the actual minimum cost
+    opt = oe.DynamicProgramming(minimize='size')
+    path, info = oe.contract_path(eq, *shapes, shapes=True, optimize=opt)
+    mincost = info.largest_intermediate
+
+    # check we can still find it without minimizing size explicitly
+    oe.contract_path(eq, *shapes, shapes=True, memory_limit=mincost, optimize='dp')
+
+    # but check just below this threshold raises
+    with pytest.raises(RuntimeError):
+        oe.contract_path(eq, *shapes, shapes=True, memory_limit=mincost - 1, optimize='dp')
+
+
 @pytest.mark.parametrize("optimize", ['greedy', 'branch-2', 'branch-all', 'optimal', 'dp'])
 def test_can_optimize_outer_products(optimize):
     a, b, c = [np.random.randn(10, 10) for _ in range(3)]