[tune] Allow iterators in tune.grid_search (#25220)

`tune.choice` already accepts iterables, the same should be true for `tune.grid_search`.

python/ray/tune/suggest/variant_generator.py

@@ -2,12 +2,11 @@
 import logging
 import re
 from collections.abc import Mapping
-from typing import Any, Dict, Generator, List, Optional, Tuple
+from typing import Any, Dict, Generator, Iterable, List, Optional, Tuple
 
 import numpy
 import random
 
-from ray.tune import TuneError
 from ray.tune.sample import Categorical, Domain, Function, RandomState
 from ray.util.annotations import DeveloperAPI
 
@@ -58,13 +57,12 @@ def generate_variants(
         yield resolved_vars, spec
 
 
-def grid_search(values: List) -> Dict[str, List]:
+def grid_search(values: Iterable) -> Dict[str, List]:
     """Convenience method for specifying grid search over a value.
 
     Arguments:
         values: An iterable whose parameters will be gridded.
     """
-
     return {"grid_search": values}
 
 
@@ -406,10 +404,6 @@ def _try_resolve(v) -> Tuple[bool, Any]:
     elif isinstance(v, dict) and len(v) == 1 and "grid_search" in v:
         # Grid search values
         grid_values = v["grid_search"]
-        if not isinstance(grid_values, list):
-            raise TuneError(
-                "Grid search expected list of values, got: {}".format(grid_values)
-            )
         return False, Categorical(grid_values).grid()
     return True, v
 

python/ray/tune/tests/test_sample.py

@@ -1894,6 +1894,21 @@ def testPointsToEvaluateBasicVariantFixedParam(self):
                 ("Pre-set value `2` is not equal to the value of parameter `a`: 1",),
             )
 
+    def testGridSearchGenerator(self):
+        from ray.tune.suggest.basic_variant import BasicVariantGenerator
+
+        searcher = BasicVariantGenerator(constant_grid_search=False)
+        exp = Experiment(
+            run=_mock_objective,
+            name="test",
+            config={"parameter": tune.grid_search(range(10))},
+            num_samples=1,
+        )
+        searcher.add_configurations(exp)
+
+        trials = [searcher.next_trial() for i in range(10)]
+        assert [t.config["parameter"] for t in trials] == list(range(10))
+
     def testConstantGridSearchBasicVariant(self):
         config = {
             "grid": tune.grid_search([1, 2, 3]),