[Core feature] Convert List[Any] to a single pickle file

flytekit/core/promise.py

@@ -92,6 +92,12 @@ def extract_value(
                 if len(input_val) == 0:
                     raise
                 sub_type = type(input_val[0])
+            # To maintain consistency between translate_inputs_to_literals and ListTransformer.to_literal for batchable types,
+            # directly call ListTransformer.to_literal to batch process the list items. This is necessary because processing
+            # each list item separately could lead to errors since ListTransformer.to_python_value may treat the literal
+            # as it is batched for batchable types.
+            if ListTransformer.is_batchable(python_type):
+                return TypeEngine.to_literal(ctx, input_val, python_type, lt)
             literal_list = [extract_value(ctx, v, sub_type, lt.collection_type) for v in input_val]
             return _literal_models.Literal(collection=_literal_models.LiteralCollection(literals=literal_list))
         elif isinstance(input_val, dict):

flytekit/core/type_engine.py

@@ -968,22 +968,59 @@ def get_literal_type(self, t: Type[T]) -> Optional[LiteralType]:
         except Exception as e:
             raise ValueError(f"Type of Generic List type is not supported, {e}")
 
+    @staticmethod
+    def is_batchable(t: Type):
+        """
+        This function evaluates whether the provided type is batchable or not.
+        It returns True only if the type is either List or Annotated(List) and the List subtype is FlytePickle.
+        """
+        from flytekit.types.pickle import FlytePickle
+
+        if get_origin(t) is Annotated:
+            return ListTransformer.is_batchable(get_args(t)[0])
+        if get_origin(t) is list:
+            subtype = get_args(t)[0]
+            if subtype == FlytePickle or (hasattr(subtype, "__origin__") and subtype.__origin__ == FlytePickle):
+                return True
+        return False
+
     def to_literal(self, ctx: FlyteContext, python_val: T, python_type: Type[T], expected: LiteralType) -> Literal:
         if type(python_val) != list:
             raise TypeTransformerFailedError("Expected a list")
 
-        t = self.get_sub_type(python_type)
-        lit_list = [TypeEngine.to_literal(ctx, x, t, expected.collection_type) for x in python_val]  # type: ignore
+        if ListTransformer.is_batchable(python_type):
+            from flytekit.types.pickle.pickle import BatchSize, FlytePickle
+
+            batchSize = len(python_val)  # default batch size
+            # parse annotated to get the number of items saved in a pickle file.
+            if get_origin(python_type) is Annotated:
+                for annotation in get_args(python_type)[1:]:
+                    if isinstance(annotation, BatchSize):
+                        batchSize = annotation.val
+                        break
+            lit_list = [TypeEngine.to_literal(ctx, python_val[i : i + batchSize], FlytePickle, expected.collection_type) for i in range(0, len(python_val), batchSize)]  # type: ignore
+        else:
+            t = self.get_sub_type(python_type)
+            lit_list = [TypeEngine.to_literal(ctx, x, t, expected.collection_type) for x in python_val]  # type: ignore
         return Literal(collection=LiteralCollection(literals=lit_list))
 
     def to_python_value(self, ctx: FlyteContext, lv: Literal, expected_python_type: Type[T]) -> typing.List[typing.Any]:  # type: ignore
         try:
             lits = lv.collection.literals
         except AttributeError:
             raise TypeTransformerFailedError()
-
-        st = self.get_sub_type(expected_python_type)
-        return [TypeEngine.to_python_value(ctx, x, st) for x in lits]
+        if self.is_batchable(expected_python_type):
+            from flytekit.types.pickle import FlytePickle
+
+            batch_list = [TypeEngine.to_python_value(ctx, batch, FlytePickle) for batch in lits]
+            if len(batch_list) > 0 and type(batch_list[0]) is list:
+                # Make it have backward compatibility. The upstream task may use old version of Flytekit that
+                # won't merge the elements in the list. Therefore, we should check if the batch_list[0] is the list first.
+                return [item for batch in batch_list for item in batch]
+            return batch_list
+        else:
+            st = self.get_sub_type(expected_python_type)
+            return [TypeEngine.to_python_value(ctx, x, st) for x in lits]
 
     def guess_python_type(self, literal_type: LiteralType) -> list:  # type: ignore
         if literal_type.collection_type:
@@ -1044,7 +1081,7 @@ def _are_types_castable(upstream: LiteralType, downstream: LiteralType) -> bool:
         if len(ucols) != len(dcols):
             return False
 
-        for (u, d) in zip(ucols, dcols):
+        for u, d in zip(ucols, dcols):
             if u.name != d.name:
                 return False
 

flytekit/types/pickle/__init__.py

@@ -9,4 +9,4 @@
    FlytePickle
 """
 
-from .pickle import FlytePickle
+from .pickle import BatchSize, FlytePickle

flytekit/types/pickle/pickle.py

@@ -13,6 +13,19 @@
 T = typing.TypeVar("T")
 
 
+class BatchSize:
+    """
+    Flyte-specific object used to wrap the hash function for a specific type
+    """
+
+    def __init__(self, val: int):
+        self._val = val
+
+    @property
+    def val(self) -> int:
+        return self._val
+
+
 class FlytePickle(typing.Generic[T]):
     """
     This type is only used by flytekit internally. User should not use this type.

tests/flytekit/unit/core/test_flyte_pickle.py

@@ -14,7 +14,7 @@
 from flytekit.models.literals import BlobMetadata
 from flytekit.models.types import LiteralType
 from flytekit.tools.translator import get_serializable
-from flytekit.types.pickle.pickle import FlytePickle, FlytePickleTransformer
+from flytekit.types.pickle.pickle import BatchSize, FlytePickle, FlytePickleTransformer
 
 default_img = Image(name="default", fqn="test", tag="tag")
 serialization_settings = flytekit.configuration.SerializationSettings(
@@ -55,6 +55,11 @@ def test_get_literal_type():
     )
 
 
+def test_batch_size():
+    bs = BatchSize(5)
+    assert bs.val == 5
+
+
 def test_nested():
     class Foo(object):
         def __init__(self, number: int):

tests/flytekit/unit/core/test_promise.py

@@ -3,6 +3,7 @@
 
 import pytest
 from dataclasses_json import dataclass_json
+from typing_extensions import Annotated
 
 from flytekit import LaunchPlan, task, workflow
 from flytekit.core import context_manager
@@ -14,6 +15,8 @@
     translate_inputs_to_literals,
 )
 from flytekit.exceptions.user import FlyteAssertion
+from flytekit.types.pickle import FlytePickle
+from flytekit.types.pickle.pickle import BatchSize
 
 
 def test_create_and_link_node():
@@ -92,7 +95,7 @@ def wf(i: int, j: int):
 
 @pytest.mark.parametrize(
     "input",
-    [2.0, {"i": 1, "a": ["h", "e"]}, [1, 2, 3]],
+    [2.0, {"i": 1, "a": ["h", "e"]}, [1, 2, 3], ["foo"] * 5],
 )
 def test_translate_inputs_to_literals(input):
     @dataclass_json
@@ -102,7 +105,7 @@ class MyDataclass(object):
         a: typing.List[str]
 
     @task
-    def t1(a: typing.Union[float, typing.List[int], MyDataclass]):
+    def t1(a: typing.Union[float, typing.List[int], MyDataclass, Annotated[typing.List[FlytePickle], BatchSize(2)]]):
         print(a)
 
     ctx = context_manager.FlyteContext.current_context()

tests/flytekit/unit/core/test_type_engine.py

@@ -18,7 +18,7 @@
 from marshmallow_enum import LoadDumpOptions
 from marshmallow_jsonschema import JSONSchema
 from pandas._testing import assert_frame_equal
-from typing_extensions import Annotated
+from typing_extensions import Annotated, get_args, get_origin
 
 from flytekit import kwtypes
 from flytekit.core.annotation import FlyteAnnotation
@@ -51,7 +51,7 @@
 from flytekit.types.file import FileExt, JPEGImageFile
 from flytekit.types.file.file import FlyteFile, FlyteFilePathTransformer, noop
 from flytekit.types.pickle import FlytePickle
-from flytekit.types.pickle.pickle import FlytePickleTransformer
+from flytekit.types.pickle.pickle import BatchSize, FlytePickleTransformer
 from flytekit.types.schema import FlyteSchema
 from flytekit.types.schema.types_pandas import PandasDataFrameTransformer
 from flytekit.types.structured.structured_dataset import StructuredDataset
@@ -1574,3 +1574,65 @@ def test_file_ext_with_flyte_file_wrong_type():
     with pytest.raises(ValueError) as e:
         FlyteFile[WRONG_TYPE]
     assert str(e.value) == "Underlying type of File Extension must be of type <str>"
+
+
+def test_is_batchable():
+    assert ListTransformer.is_batchable(typing.List[int]) is False
+    assert ListTransformer.is_batchable(typing.List[str]) is False
+    assert ListTransformer.is_batchable(typing.List[typing.Dict]) is False
+    assert ListTransformer.is_batchable(typing.List[typing.Dict[str, FlytePickle]]) is False
+    assert ListTransformer.is_batchable(typing.List[typing.List[FlytePickle]]) is False
+
+    assert ListTransformer.is_batchable(typing.List[FlytePickle]) is True
+    assert ListTransformer.is_batchable(Annotated[typing.List[FlytePickle], BatchSize(3)]) is True
+    assert (
+        ListTransformer.is_batchable(Annotated[typing.List[FlytePickle], HashMethod(function=str), BatchSize(3)])
+        is True
+    )
+
+
+@pytest.mark.parametrize(
+    "python_val, python_type, expected_list_length",
+    [
+        # Case 1: List of FlytePickle objects with default batch size.
+        # (By default, the batch_size is set to the length of the whole list.)
+        # After converting to literal, the result will be [batched_FlytePickle(5 items)].
+        # Therefore, the expected list length is [1].
+        ([{"foo"}] * 5, typing.List[FlytePickle], [1]),
+        # Case 2: List of FlytePickle objects with batch size 2.
+        # After converting to literal, the result will be
+        # [batched_FlytePickle(2 items), batched_FlytePickle(2 items), batched_FlytePickle(1 item)].
+        # Therefore, the expected list length is [3].
+        (["foo"] * 5, Annotated[typing.List[FlytePickle], HashMethod(function=str), BatchSize(2)], [3]),
+        # Case 3: Nested list of FlytePickle objects with batch size 2.
+        # After converting to literal, the result will be
+        # [[batched_FlytePickle(3 items)], [batched_FlytePickle(3 items)]]
+        # Therefore, the expected list length is [2, 1] (the length of the outer list remains the same, the inner list is batched).
+        ([["foo", "foo", "foo"]] * 2, typing.List[Annotated[typing.List[FlytePickle], BatchSize(3)]], [2, 1]),
+    ],
+)
+def test_batch_pickle_list(python_val, python_type, expected_list_length):
+    ctx = FlyteContext.current_context()
+    expected = TypeEngine.to_literal_type(python_type)
+    lv = TypeEngine.to_literal(ctx, python_val, python_type, expected)
+
+    tmp_lv = lv
+    for length in expected_list_length:
+        # Check that after converting to literal, the length of the literal list is equal to:
+        # - the length of the original list divided by the batch size if not nested
+        # - the length of the original list if it contains a nested list
+        assert len(tmp_lv.collection.literals) == length
+        tmp_lv = tmp_lv.collection.literals[0]
+
+    pv = TypeEngine.to_python_value(ctx, lv, python_type)
+    # Check that after converting literal to Python value, the result is equal to the original python values.
+    assert pv == python_val
+    if get_origin(python_type) is Annotated:
+        pv = TypeEngine.to_python_value(ctx, lv, get_args(python_type)[0])
+        # Remove the annotation and check that after converting to Python value, the result is equal
+        # to the original input values. This is used to simulate the following case:
+        # @workflow
+        # def wf():
+        #     data = task0()  # task0() -> Annotated[typing.List[FlytePickle], BatchSize(2)]
+        #     task1(data=data)  # task1(data: typing.List[FlytePickle])
+        assert pv == python_val