claudevdm commented on code in PR #33364:
URL: https://github.com/apache/beam/pull/33364#discussion_r1884211127
##########
sdks/python/apache_beam/ml/transforms/base.py:
##########
@@ -62,36 +68,31 @@
# Output of the apply() method of BaseOperation.
OperationOutputT = TypeVar('OperationOutputT')
+# Input to the EmbeddingTypeAdapter input_fn
+EmbeddingTypeAdapterInputT = TypeVar(
+ 'EmbeddingTypeAdapterInputT') # e.g., Chunk
+# Output of the EmbeddingTypeAdapter output_fn
+EmbeddingTypeAdapterOutputT = TypeVar(
+ 'EmbeddingTypeAdapterOutputT') # e.g., Embedding
-def _convert_list_of_dicts_to_dict_of_lists(
- list_of_dicts: Sequence[dict[str, Any]]) -> dict[str, list[Any]]:
- keys_to_element_list = collections.defaultdict(list)
- input_keys = list_of_dicts[0].keys()
- for d in list_of_dicts:
- if set(d.keys()) != set(input_keys):
- extra_keys = set(d.keys()) - set(input_keys) if len(
- d.keys()) > len(input_keys) else set(input_keys) - set(d.keys())
- raise RuntimeError(
- f'All the dicts in the input data should have the same keys. '
- f'Got: {extra_keys} instead.')
- for key, value in d.items():
- keys_to_element_list[key].append(value)
- return keys_to_element_list
-
-
-def _convert_dict_of_lists_to_lists_of_dict(
- dict_of_lists: dict[str, list[Any]]) -> list[dict[str, Any]]:
- batch_length = len(next(iter(dict_of_lists.values())))
- result: list[dict[str, Any]] = [{} for _ in range(batch_length)]
- # all the values in the dict_of_lists should have same length
- for key, values in dict_of_lists.items():
- assert len(values) == batch_length, (
- "This function expects all the values "
- "in the dict_of_lists to have same length."
- )
- for i in range(len(values)):
- result[i][key] = values[i]
- return result
+
+@dataclass
+class EmbeddingTypeAdapter(Generic[EmbeddingTypeAdapterInputT,
+ EmbeddingTypeAdapterOutputT]):
+ """Adapts input types to text for embedding and converts output embeddings.
+
+ Args:
+ input_fn: Function to extract text for embedding from input type
+ output_fn: Function to create output type from input and embeddings
+ """
+ input_fn: Callable[[Sequence[EmbeddingTypeAdapterInputT]], List[str]]
+ output_fn: Callable[[Sequence[EmbeddingTypeAdapterInputT], Sequence[Any]],
+ List[EmbeddingTypeAdapterOutputT]]
+
+ def __reduce__(self):
+ """Custom serialization that preserves type information during
+ jsonpickle."""
Review Comment:
The generic type params somehow get lost during the default json
pickling/decoding.
It causes errors when loading transforms from artifacts e.g.
apache_beam/ml/transforms/embeddings/huggingface_test.py::SentenceTransformerEmbeddingsTest::test_mltransform_to_ptransform_with_sentence_transformer
- TypeError: Plain typing.Any is not valid as type argument
```
../../../../.pyenv/versions/3.9.4/lib/python3.9/typing.py:820: in <genexpr>
params = tuple(_type_check(p, msg) for p in params)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _ _ _ _ _
arg = typing.Any, msg = 'Parameters to generic types must be types.',
is_argument = True
def _type_check(arg, msg, is_argument=True):
"""Check that the argument is a type, and return it (internal
helper).
As a special case, accept None and return type(None) instead. Also
wrap strings
into ForwardRef instances. Consider several corner cases, for
example plain
special forms like Union are not valid, while Union[int, str] is OK,
etc.
The msg argument is a human-readable error message, e.g::
"Union[arg, ...]: arg should be a type."
We append the repr() of the actual value (truncated to 100 chars).
"""
invalid_generic_forms = (Generic, Protocol)
if is_argument:
invalid_generic_forms = invalid_generic_forms + (ClassVar, Final)
arg = _type_convert(arg)
if (isinstance(arg, _GenericAlias) and
arg.__origin__ in invalid_generic_forms):
raise TypeError(f"{arg} is not valid as type argument")
if arg in (Any, NoReturn):
return arg
if isinstance(arg, _SpecialForm) or arg in (Generic, Protocol):
> raise TypeError(f"Plain {arg} is not valid as type argument")
E TypeError: Plain typing.Any is not valid as type argument
../../../../.pyenv/versions/3.9.4/lib/python3.9/typing.py:153: TypeError
```
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