[FEAT] Add input batching for UDFs

daft/daft.pyi

@@ -1188,6 +1188,7 @@ def stateless_udf(
     expressions: list[PyExpr],
     return_dtype: PyDataType,
     resource_request: ResourceRequest | None,
+    batch_size: int | None,
 ) -> PyExpr: ...
 def stateful_udf(
     name: str,
@@ -1196,6 +1197,7 @@ def stateful_udf(
     return_dtype: PyDataType,
     resource_request: ResourceRequest | None,
     init_args: tuple[tuple[Any, ...], dict[str, Any]] | None,
+    batch_size: int | None,
 ) -> PyExpr: ...
 def resolve_expr(expr: PyExpr, schema: PySchema) -> tuple[PyExpr, PyField]: ...
 def hash(expr: PyExpr, seed: Any | None = None) -> PyExpr: ...

daft/expressions/expressions.py

@@ -237,9 +237,12 @@ def stateless_udf(
         expressions: builtins.list[Expression],
         return_dtype: DataType,
         resource_request: ResourceRequest | None,
+        batch_size: int | None,
     ) -> Expression:
         return Expression._from_pyexpr(
-            _stateless_udf(name, partial, [e._expr for e in expressions], return_dtype._dtype, resource_request)
+            _stateless_udf(
+                name, partial, [e._expr for e in expressions], return_dtype._dtype, resource_request, batch_size
+            )
         )
 
     @staticmethod
@@ -250,10 +253,17 @@ def stateful_udf(
         return_dtype: DataType,
         resource_request: ResourceRequest | None,
         init_args: tuple[tuple[Any, ...], dict[builtins.str, Any]] | None,
+        batch_size: int | None,
     ) -> Expression:
         return Expression._from_pyexpr(
             _stateful_udf(
-                name, partial, [e._expr for e in expressions], return_dtype._dtype, resource_request, init_args
+                name,
+                partial,
+                [e._expr for e in expressions],
+                return_dtype._dtype,
+                resource_request,
+                init_args,
+                batch_size,
             )
         )
 
@@ -819,7 +829,13 @@ def batch_func(self_series):
             name = name + "."
         name = name + getattr(func, "__qualname__")  # type: ignore[call-overload]
 
-        return StatelessUDF(name=name, func=batch_func, return_dtype=return_dtype, resource_request=None)(self)
+        return StatelessUDF(
+            name=name,
+            func=batch_func,
+            return_dtype=return_dtype,
+            resource_request=None,
+            batch_size=None,
+        )(self)
 
     def is_null(self) -> Expression:
         """Checks if values in the Expression are Null (a special value indicating missing data)

daft/udf.py

@@ -76,8 +76,13 @@
         return hash(frozenset(self.bound_args.arguments.items()))
 
 
+# Assumes there is at least one evaluated expression
 def run_udf(
-    func: Callable, bound_args: BoundUDFArgs, evaluated_expressions: list[Series], py_return_dtype: PyDataType
+    func: Callable,
+    bound_args: BoundUDFArgs,
+    evaluated_expressions: list[Series],
+    py_return_dtype: PyDataType,
+    batch_size: int | None,
 ) -> PySeries:
     """API to call from Rust code that will call an UDF (initialized, in the case of stateful UDFs) on the inputs"""
     return_dtype = DataType._from_pydatatype(py_return_dtype)
@@ -90,52 +95,87 @@
     ), "Computed series must map 1:1 to the expressions that were evaluated"
     function_parameter_name_to_index = {name: i for i, name in enumerate(expressions)}
 
-    args = []
-    for name in arg_keys:
-        # special-case to skip `self` since that would be a redundant argument in a method call to a class-UDF
-        if name == "self":
-            continue
-
-        assert name in pyvalues or name in function_parameter_name_to_index
-        if name in pyvalues:
-            args.append(pyvalues[name])
-        else:
-            args.append(evaluated_expressions[function_parameter_name_to_index[name]])
-
-    kwargs = {}
-    for name in kwarg_keys:
-        assert name in pyvalues or name in function_parameter_name_to_index
-        if name in pyvalues:
-            kwargs[name] = pyvalues[name]
-        else:
-            kwargs[name] = evaluated_expressions[function_parameter_name_to_index[name]]
+    def get_args_for_slice(start: int, end: int):
+        args = []
+        must_slice = start > 0 or end < len(evaluated_expressions[0])
+        for name in arg_keys:
+            # special-case to skip `self` since that would be a redundant argument in a method call to a class-UDF
+            if name == "self":
+                continue
+
+            assert name in pyvalues or name in function_parameter_name_to_index
+            if name in pyvalues:
+                args.append(pyvalues[name])
+            else:
+                # we fill in expressions later
+                series = evaluated_expressions[function_parameter_name_to_index[name]]
+                if must_slice:
+                    series = series.slice(start, end)
+                args.append(series)
+
+        kwargs = {}
+        for name in kwarg_keys:
+            assert name in pyvalues or name in function_parameter_name_to_index
+            if name in pyvalues:
+                kwargs[name] = pyvalues[name]
+            else:
+                series = evaluated_expressions[function_parameter_name_to_index[name]]
+                if must_slice:
+                    series = series.slice(start, end)
+                kwargs[name] = series
+
+        return args, kwargs
+
+    if batch_size is None:
+        args, kwargs = get_args_for_slice(0, len(evaluated_expressions[0]))
+        try:
+            results = [func(*args, **kwargs)]
+        except Exception as user_function_exception:
+            raise RuntimeError(
+                f"User-defined function `{func}` failed when executing on inputs with lengths: {tuple(len(series) for series in evaluated_expressions)}"
+            ) from user_function_exception
+    else:
+        # all inputs must have the same lengths for batching
+        # not sure this error can possibly be triggered but it's here
+        if len(set(len(s) for s in evaluated_expressions)) != 1:
+            raise RuntimeError(
+                f"User-defined function `{func}` failed: cannot run in batches when inputs are different lengths: {tuple(len(series) for series in evaluated_expressions)}"
+            )
 
-    try:
-        result = func(*args, **kwargs)
-    except Exception as user_function_exception:
-        raise RuntimeError(
-            f"User-defined function `{func}` failed when executing on inputs with lengths: {tuple(len(series) for series in evaluated_expressions)}"
-        ) from user_function_exception
+        results = []
+        for i in range(0, len(evaluated_expressions[0]), batch_size):
+            cur_batch_size = min(batch_size, len(evaluated_expressions[0]) - i)
+            args, kwargs = get_args_for_slice(i, i + cur_batch_size)
+            try:
+                results.append(func(*args, **kwargs))
+            except Exception as user_function_exception:
+                raise RuntimeError(
+                    f"User-defined function `{func}` failed when executing on inputs with lengths: {tuple(cur_batch_size for _ in evaluated_expressions)}"
+                ) from user_function_exception
 
     # HACK: Series have names and the logic for naming fields/series in a UDF is to take the first
     # Expression's name. Note that this logic is tied to the `to_field` implementation of the Rust PythonUDF
     # and is quite error prone! If our Series naming logic here is wrong, things will break when the UDF is run on a table.
     name = evaluated_expressions[0].name()
 
     # Post-processing of results into a Series of the appropriate dtype
-    if isinstance(result, Series):
-        return result.rename(name).cast(return_dtype)._series
-    elif isinstance(result, list):
+    if isinstance(results[0], Series):
+        result_series = Series.concat(results)
+        return result_series.rename(name).cast(return_dtype)._series
+    elif isinstance(results[0], list):
+        result_list = [x for res in results for x in res]
         if return_dtype == DataType.python():
-            return Series.from_pylist(result, name=name, pyobj="force")._series
+            return Series.from_pylist(result_list, name=name, pyobj="force")._series
         else:
-            return Series.from_pylist(result, name=name, pyobj="allow").cast(return_dtype)._series
-    elif _NUMPY_AVAILABLE and isinstance(result, np.ndarray):
-        return Series.from_numpy(result, name=name).cast(return_dtype)._series
-    elif _PYARROW_AVAILABLE and isinstance(result, (pa.Array, pa.ChunkedArray)):
-        return Series.from_arrow(result, name=name).cast(return_dtype)._series
+            return Series.from_pylist(result_list, name=name, pyobj="allow").cast(return_dtype)._series
+    elif _NUMPY_AVAILABLE and isinstance(results[0], np.ndarray):
+        result_np = np.concatenate(results)
+        return Series.from_numpy(result_np, name=name).cast(return_dtype)._series
+    elif _PYARROW_AVAILABLE and isinstance(results[0], (pa.Array, pa.ChunkedArray)):
+        result_pa = pa.concat_arrays(results)
+        return Series.from_arrow(result_pa, name=name).cast(return_dtype)._series
     else:
-        raise NotImplementedError(f"Return type not supported for UDF: {type(result)}")
+        raise NotImplementedError(f"Return type not supported for UDF: {type(results[0])}")
 
 
 # Marker that helps us differentiate whether a user provided the argument or not
@@ -145,6 +185,7 @@
 @dataclasses.dataclass
 class UDF:
     resource_request: ResourceRequest | None
+    batch_size: int | None
 
     @abstractmethod
     def __call__(self, *args, **kwargs) -> Expression: ...
@@ -155,6 +196,7 @@
         num_cpus: float | None = _UnsetMarker,
         num_gpus: float | None = _UnsetMarker,
         memory_bytes: int | None = _UnsetMarker,
+        batch_size: int | None = _UnsetMarker,
     ) -> UDF:
         """Replace the resource requests for running each instance of your stateless UDF.
 
@@ -180,11 +222,18 @@
                 the appropriate GPU to each UDF using `CUDA_VISIBLE_DEVICES`.
             memory_bytes: Amount of memory to allocate each running instance of your UDF in bytes. If your UDF is experiencing out-of-memory errors,
                 this parameter can help hint Daft that each UDF requires a certain amount of heap memory for execution.
+            batch_size: Enables batching of the input into batches of at most this size. Results between batches are concatenated.
         """
         result = self
 
         # Any changes to resource request
-        if not all((num_cpus is _UnsetMarker, num_gpus is _UnsetMarker, memory_bytes is _UnsetMarker)):
+        if not all(
+            (
+                num_cpus is _UnsetMarker,
+                num_gpus is _UnsetMarker,
+                memory_bytes is _UnsetMarker,
+            )
+        ):
             new_resource_request = ResourceRequest() if self.resource_request is None else self.resource_request
             if num_cpus is not _UnsetMarker:
                 new_resource_request = new_resource_request.with_num_cpus(num_cpus)
@@ -194,6 +243,9 @@
                 new_resource_request = new_resource_request.with_memory_bytes(memory_bytes)
             result = dataclasses.replace(result, resource_request=new_resource_request)
 
+        if batch_size is not _UnsetMarker:
+            result.batch_size = batch_size
+
         return result
 
 
@@ -238,6 +290,7 @@
             expressions=expressions,
             return_dtype=self.return_dtype,
             resource_request=self.resource_request,
+            batch_size=self.batch_size,
         )
 
     def bind_func(self, *args, **kwargs) -> inspect.BoundArguments:
@@ -287,6 +340,7 @@
             return_dtype=self.return_dtype,
             resource_request=self.resource_request,
             init_args=self.init_args,
+            batch_size=self.batch_size,
         )
 
     def with_init_args(self, *args, **kwargs) -> StatefulUDF:
@@ -356,6 +410,7 @@
     num_cpus: float | None = None,
     num_gpus: float | None = None,
     memory_bytes: int | None = None,
+    batch_size: int | None = None,
 ) -> Callable[[UserProvidedPythonFunction | type], StatelessUDF | StatefulUDF]:
     """Decorator to convert a Python function into a UDF
 
@@ -463,6 +518,7 @@
             the appropriate GPU to each UDF using `CUDA_VISIBLE_DEVICES`.
         memory_bytes: Amount of memory to allocate each running instance of your UDF in bytes. If your UDF is experiencing out-of-memory errors,
             this parameter can help hint Daft that each UDF requires a certain amount of heap memory for execution.
+        batch_size: Enables batching of the input into batches of at most this size. Results between batches are concatenated.
 
     Returns:
         Callable[[UserProvidedPythonFunction], UDF]: UDF decorator - converts a user-provided Python function as a UDF that can be called on Expressions
@@ -491,13 +547,15 @@
                 cls=f,
                 return_dtype=return_dtype,
                 resource_request=resource_request,
+                batch_size=batch_size,
             )
         else:
             return StatelessUDF(
                 name=name,
                 func=f,
                 return_dtype=return_dtype,
                 resource_request=resource_request,
+                batch_size=batch_size,
             )
 
     return _udf

src/daft-dsl/src/functions/python/mod.rs

@@ -39,6 +39,7 @@
     num_expressions: usize,
     pub return_dtype: DataType,
     pub resource_request: Option<ResourceRequest>,
+    pub batch_size: Option<usize>,
 }
 
 #[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
@@ -51,6 +52,7 @@
     pub resource_request: Option<ResourceRequest>,
     #[cfg(feature = "python")]
     pub init_args: Option<pyobj_serde::PyObjectWrapper>,
+    pub batch_size: Option<usize>,
 }
 
 #[cfg(feature = "python")]
@@ -60,6 +62,7 @@
     expressions: &[ExprRef],
     return_dtype: DataType,
     resource_request: Option<ResourceRequest>,
+    batch_size: Option<usize>,
 ) -> DaftResult<Expr> {
     Ok(Expr::Function {
         func: super::FunctionExpr::Python(PythonUDF::Stateless(StatelessPythonUDF {
@@ -68,6 +71,7 @@
             num_expressions: expressions.len(),
             return_dtype,
             resource_request,
+            batch_size,
         })),
         inputs: expressions.into(),
     })
@@ -79,13 +83,15 @@
     expressions: &[ExprRef],
     return_dtype: DataType,
     resource_request: Option<ResourceRequest>,
+    batch_size: Option<usize>,
 ) -> DaftResult<Expr> {
     Ok(Expr::Function {
         func: super::FunctionExpr::Python(PythonUDF::Stateless(StatelessPythonUDF {
             name: name.to_string().into(),
             num_expressions: expressions.len(),
             return_dtype,
             resource_request,
+            batch_size,
         })),
         inputs: expressions.into(),
     })
@@ -99,6 +105,7 @@
     return_dtype: DataType,
     resource_request: Option<ResourceRequest>,
     init_args: Option<pyo3::PyObject>,
+    batch_size: Option<usize>,
 ) -> DaftResult<Expr> {
     Ok(Expr::Function {
         func: super::FunctionExpr::Python(PythonUDF::Stateful(StatefulPythonUDF {
@@ -108,6 +115,7 @@
             return_dtype,
             resource_request,
             init_args: init_args.map(pyobj_serde::PyObjectWrapper),
+            batch_size,
         })),
         inputs: expressions.into(),
     })
@@ -119,13 +127,15 @@
     expressions: &[ExprRef],
     return_dtype: DataType,
     resource_request: Option<ResourceRequest>,
+    batch_size: Option<usize>,
 ) -> DaftResult<Expr> {
     Ok(Expr::Function {
         func: super::FunctionExpr::Python(PythonUDF::Stateful(StatefulPythonUDF {
             name: name.to_string().into(),
             num_expressions: expressions.len(),
             return_dtype,
             resource_request,
+            batch_size,
         })),
         inputs: expressions.into(),
     })